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   key: cord-333487-zem2d4y6 authors: Thomaz Ugliara Barone, Mark; Bega Harnik, Simone; Vieira de Luca, Patrícia; Letícia de Souza Lima, Bruna; José Pineda Wieselberg, Ronaldo; Ngongo, Belinda; Cordeiro Pedrosa, Hermelinda; Pimazoni-Netto, Augusto; Reis Franco, Denise; de Fatima Marinho de Souza, Maria; Carvalho Malta, Deborah; Giampaoli, Viviana title: The Impact of COVID-19 on People with Diabetes in Brazil date: 2020-07-03 journal: Diabetes Res Clin Pract DOI: 10.1016/j.diabres.2020.108304 sha: doc_id: 333487 cord_uid: zem2d4y6 Abstract The present study aims atidentifying main barriers faced by people living with diabetes in Brazil during the COVID-19 pandemic. Methods In a convenience sampling study, data were collected from 1701 individuals, aged 18 or above; 75.54% female participants; 60.73% T1D and 30.75% T2D, between April 22nd and May 4th, using an anonymous and untraceable survey containing 20 multiple choice questions (socio-demographic; health status and habits of life during COVID-19 pandemic). Relationship between variables was established using the multiple correspondence analysis technique. Results 95.1% of respondents reduced their frequency of going outside of their homes; among those who monitored blood glucose at home during the pandemic (91.5%), the majority (59.4%) experienced an increase, a decrease or a higher variability in glucose levels; 38.4% postponed their medical appointments and/or routine examinations; and 59.5% reduced their physical activity. T1D, the youngest group, was more susceptible to presenting COVID-19 symptoms despite not being testing; whilst the TD2 group had higher frequency of comorbidities that are additional risk factors for COVID-19 severity. Conclusions This study provides a firsthand revelation of the severity of COVID-19 on individuals with diabetes in Brazil, altering their habits, which impacted their glycemia, potentially increasing their risk of poor outcomes if infected by SARS-CoV-2. The outbreak COVID-19 has led to an unprecedented global health crisis which is testing health systems' preparedness and ability to cope with a pandemic response [1] [2] [3] [4] . . Brazilexperienced a similar situation. Thefirst SARS-Cov-2 case was officially reported in December 2019 in Wuhan,China, and rapidly spread in the entire country and beyond within 30 days [5] [6] [7] . The first suspected case in Latin America was reported on January 27th in Brazil and the first case confirmed in Brazil on February 26th of 2020 [2, 3, 7] . A series of measures were put in place p to prepare Brazil for the anticipated health crisis prior to the declaration of COVID-19 as being Public Health Emergency of International Concern, on January 30th [2] , and then a pandemic, on March 11th [8] . Following these international alerts,less than a month after the first confirmed case, on March 20th, the Brazilian Ministry of Health recognized that community transmission was already happening in Brazil [2] . Infection by SARS-Cov-2 causes major disruptions and threats ,in addition to the loss of human lives, with case fatality rate ranging from 0.2% in Germany, to 7.7%in Italy [9] , The impact of COVID-19 is beyond imagination and is sparkling a global mourning,booming afeeling of unsafety and uncertainty. On June 9th, according to the World Health Organization (WHO), 7,127,753 cases and 407,159 deaths duetoCOVID-19 were confirmed in 216 countries [10] . FCases and mortality in Brazil reached 739,503 and 38,406 respectively,with a case fatality rate of 5.2% [11] . While this is the highest number of cases and one of the highest case fatality rate in Latin America [12] , scientists argue that cases and mortality are extremely underscored due to the low number of tests performed among other reasons [1, 4, 12, 13] by possible 14-20 times the official reported figures [14] . Authors of the first Chinese and Italian reports alerted for the worse prognosis of individuals with diabetes and other noncommunicable disease (NCDs), in comparison with populations of the same age group without those chronic conditions [5, [15] [16] [17] [18] . Further retrospective studies have revealed that, within this group of patients, hyperglycemia was associated with higher severity and mortality rates [15, 19, 20, 21] , for reasons under investigation [22, 23] . Additionally, usual comorbidities such as hypertension, cardiovascular disease, older age, and gender (male) also complement increased severity and mortality risks of individuals with diabetes hospitalized due to COVID-19 [24] . , With a prevalence as high as 16.8 million people or 11.4% of the population between 20 and 79 years old, Brazil is among the top 5 countries with high prevalence of diabetes. Diabetes leads g to more than 135,000 deaths yearly [25] . Brazil struggles with the continuous treatment routine of impacted populations; only 28.8% are considered to be in target, having a glycated hemoglobin A1C below 7.0% (53mmol/mol) [26] . Consequently, 71.2% of Brazilians with diabetes belong to a subgroup more exposed to hyperglycemia , which makes them vulnerable to even higher risk of poor outcomes when infected by the SARS-CoV-2 virus. This state of affairs calls fordeep commitments from the public health in all its forms and a concrete alignment with the WHO. Unfortunately, in the midst of the fight against this novel virus, Brazil experienced a troublesome atmosphere of political disputes, blinding authorities to make swift decisions aligned with the global technical recommendations [1, 4, 12] . This also harmed adjustments to continue the proper follow-up and management of other diseases, including both communicable and NCDs. For these reasons, the present study aims to investigate challenges encountered by people living with diabetes in Brazil during the COVID-19 pandemic. Data from individuals with diabetes were collected through an anonymous and untraceable survey containing 20 multiple choice questions in portuguese. The authors shared this survey on social media including Facebook, through their affiliated institutions, co-ligated or partner entities, and diabetes social media groups. Hence, the convenience sampling nature of this study. In addition to being a person living with diabetes, individuals needed to be legally adults (above 18 years old) and accept the terms to share anonymous and untraceable answers. Survey responses were collected from April 22nd through May 4th. The first question provided a complete overview of the questionnaire and required confirmation of the heath conditon (diabetes) by the respondent. . This was followed by socio-demographic questions related to age, gender, state of residence, capital/country/coast, education level, number of people living in the household, and nature of the health system which could be public, private or both. Subsequent questions served to identify the health status of the participants, asking about diabetes type, and presence and type of comorbidity. All remaining questions covered aspects attributed to the COVID-19 pandemic: frequency of going outside, strategies to protect from infection when outside, noted changes in the glycemic levels, access to medical care, changes in eating habits and physical activity, changes in smoking and drinking habits, and suspected or confirmed COVID-19 infection by the respondent or cohabitant family member. At the end of the survey, the respondent could leave a comment. Several questions and their answers were regarding subjective perceptions, such as "my blood glucose increased", "I am eating more" or "I am exercising much less" than before the pandemic. Therefore, answers should not be interpreted as objective measurements, but as subjective comparisons with the prepandemic states. A multivariate analysis of dimensions of COVID-19 and types of diabetes (type 1 diabetes -T1D; type 2 diabetes -T2D; others: LADA, MODY, gestational; and unknown) was performed with multiple correspondence analysis (MCA) in R (version 3.6.1) and RStudio (version 1.1.453) [27] . The survey was answered by 1701 individuals, 75.54% were female respondents, 70.78% between 18 and 50 years old, 64.96% of respondents were from Southeast of Brazil, and the main diabetes types were type 1 (60.73%) and type 2 (30.75%) (full socio-demographic profile in table 1). Among the main reported consequences of the COVID-19 pandemic were: 95.1% reduced frequency of going outside home (among them, 26.9% never went outside since the beginning of the pandemic); among the ones who monitored their blood glucose at home (91.5%), the majority (59.4%) experienced a deterioration, which included: 31.2% are reporting greater variability than before the pandemic, 20% higher glycemia and 8.2% lower glycemic levels. Moreover, 38.4% postponed medical appointments and/or routine exams, and 40.2% did not schedule a medical appointment since the onset of the pandemic. Among recommended habits, slated for diabetes treatment, physical activity was most impacted, with a reduction reported by 59.5% of the respondents (14.7% with a slight reduction and 44.8% with a great reduction). See table 2 for habits and behaviors. Regarding co-morbidities, for the current study and analysis, we opted to group them into the following: 1) "Mental Health" which includes the following conditions: depression, anxiety, bulimia, anorexia and diabulimia; and 2) "Cardiovascular Diseases" comprising : stroke, infarction, hypertension and dyslipidemia. Two other groups were already established in the survey as a single answer, which had a parenthesis to facilitate comprehension, the "Diabetes Related Complications" with: retinopathy, neuropathy and diabetic renal disease; and the "Respiratory": asthma and COPD (table 1) . The analysis revealed the association between age and the occurrence of symptoms of COVID-19, type of diabetes and some of the co-morbidities evaluated (figure 1). People with T1D were among the youngest and appeared in proximity to mental health diseases category, but also close to the no-cardiovascular conditions, no-obesity and no-respiratory disease categories. They were alarmingly close to the category that, despite having symptoms of COVID-19, had no testing for SARS-CoV-2. Not measuring the blood glucose is close to T2D and unknown diabetes type. T2D, not surprisingly, was more associated than other types with older age, cardiovascular diseases and obesity. It is also worth noting that T2D and older group were the closest to test positive for SARS-CoV-2. In opposition, individuals who had symptoms of COVID-19 and were not tested (125 people or 7.35%) presented higher proximity to increased blood glucose (Figure 1 ). Measuring blood glucose was related with food consumption and working out, as depicted on figure 2. Individuals that experienced decrease in blood glucose levels, increased physical activity and decreased food consumption. Meanwhile, higher food consumption and reduced physical activity were associated with higher blood glucose levels. While it seems that the pandemic had a higher impact on habits and glycemic levels of T1D individuals (right side of figure 2), with significant perceived changes, T2D respondents seem to have maintained most of their habits during this period. Individuals who exclusively use the Brazilian public health system (Unified Health System -SUS), which serves more than 70% of the Brazilian population [28] (in our case, 28.16% of the respondents), were who kept venturing out during the pandemic and experienced an increase of blood glucose levels, as shown in figure 3 . T1D individuals were the closest to the use of both health systems (SUS and private) and also experienced higher glycemic variability (ups and downs). They had the ability to have friends or relatives to buy or collect their medicines and medical supplies. Users of the private health system were closer to experience no glycemic changes during the pandemic, using their own stocks of medicine and medical supplies or ordering for home delivery from private pharmacies (Figure 3) . The results above unveil short, mid and long-term risks for individuals with diabetes in Brazil. While the federal government and some states made initial commitments and plans [2] , knowing the risks for poor prognosis among individuals living with diabetes and/or other NCDs if infected by SARS-CoV-2. Our data revealed that implemented measures did not cover most of this population. The strategy to distribute medicines and medical supplies for 90 days, as recommended by different organizations [29, 30] , in order to avoid monthly trips were effective for only 21% of the 64.5% who received their medication and supplies from SUS (or 13.5% of the total respondents). This provision of medicines and supplies for 3 months, avoiding the monthly rounds to public primary healthcare facilities or pharmacies, seemed to be the one of the only specific policies to protect individuals with diabetes and others living with NCDs in Brazil [31, 32] . Although limited, this would at least protect them from encountering infected people seeking care. . Meanwhile, at municipality level, certain locations successfully implemented alternative strategies, as hailed by one participant from an inland town: "my town adopted a delivery of medicine method for people with chronic diseases". According to our data, especially individuals w exclusively depended on SUS were at higher risk, venturing out of home more often and experiencing an increase in blood glucose levels ( figure 3 ). While differences would not be expected, since Brazil prides itself for its Universal Health Coverage, and most people, including the ones with private insurance, rely on SUS to receive part or all their medicines and supplies. We hypothesise that this finding highlights the socioeconomic inequalities in the country, which obliges the less privileged to continue going to work during the pandemic and/or to access affordable food. . Even though 44.3% of the individuals reported having a family member or friend going monthly to public pharmacies on their behalf to collect medicines and supplies, 49.9% went themselves, perhaps due to lack of direct support.. It is important to highlight that family member's or friend's support may reduce but not eliminate the risk of infection, especially if residing in the same location, acting as a potential vehicle of indirect transmission. Another uncomfortable answer came from 5.8% of the individuals who reported halting collection of their medicines and medical supplies and relying on personal stocks, which might be limited in quantity or expected to expire soon. This situation may place those who avoided leaving their homes during the pandemic in a risky untreated situation, a globally shared concern [15, 17, 33, 34, 35] . While it is hard to predict social, economic and health impact of a pandemic, specialists alert to the consequences of implementing only light measures [4, 12, 13, 36, 37, 38] , but, at the same time, also the harmful consequences of lockdowns, mobility restrictions and social distancing on the prevention and control of diabetes and other NCDs [17, 34, 39] . Barone et al. [39] reported an association between stricter measures against the pandemic and perceived challenges and fear in the diabetes community in South and Central American, which, when disconnected from information and educational strategies, would increase the pressure on the health systems [37, 39] . Although decentralized (defined by states and municipalities) and delayed [1, 4, 12, 13, 40] , the measures to contain the SARS-CoV-2 spread in Brazil, and reflected on mobility reduction, was experienced by 95.1% of this study's respondents in different degrees (with 26.9% never going outside). Differently from Kluge et al.'s prediction [17] , in our case it was not associated with increased consumption of alcohol, tobacco and unhealthy foods. Notwithstanding, physical activity was affected, being reduced for 59.5% of the respondents. Other researchers have also reported physical activity reductions during the pandemic, and alerted for the potential negative consequences on metabolic, cardiovascular and musculoskeletal health [41, 42] . These effects were observed even after short periods of inactivity, are hard to recover and, not only increase premature mortality, but also favors the development of diabetes chronic complications [41, 42] . A strategy to minimize those consequences would be developing good channels and partnerships for informing these populations through the internet and TV broadcasting in a structured manner, focusing on preventive measures and maintenance or improvement of habits and behaviors, such as regular physical activity [17, 34, 41, 42, 43] . This recommendation is compatible with the present findings where 87.4% of the individuals maintained or increased their time watching TV and 90.7% maintained or increased their time on the internet. Regarding treatment, the COVID-19 pandemic, as predicted and observed by different authors, is impacting availability and access to healthcare professionals (HCPs) for the routine care of people with diabetes, since they relocated to emergency rooms, testing sites, ICUs and other services to attend infected individuals [15, 17, 33, 34, 39] . COVID-19 transmission was found to be high among HCPs. . We investigated 38.4% of the individuals who postponed their medical appointments and/or examinations. We attributed these results to shortage of HCPs, as cited by 3 individuals, reporting appointment cancelation or absence of s physician, or personal decision because of the fear of getting infected at a hospital or clinic. Also identified, in other studies as one of the greatest fears of the population [39, 35] . The potential consequence of this phenomenon of fear during the pandemic was reported in a pediatric emergency department, in Italy, with reduction and delay in children's arrival, which included cases of severe ketoacidosis and hypoglycemia [35] . Although online consultation and other telemedicine strategies were not investigated profoundly in the present study, one of the respondents commented that he/she was having online medical appointments. Brazilian laws and regulations became more flexible allowing this type of approach during the COVID-19 pandemic [44] . In alignment with other authors and international organizations, we highly recommend it [15, 33, 36, 45, 46, 47, 48] , reinforcing Primary Healthcare's role during the pandemic through ensuring close and timely monitoring of people with diabetes and other NCDs [37, 44, 49] The results show thatall types of diabetes were close to additional risk factors for poor prognosis of individuals infected by SARS-CoV-2. While T2D and unknown diabetes type (figure 1) appear near other NCDs (especially cardiovascular and obesity) [18, 24, 47, 50, 51] , T1D and other types (LADA, MODY and gestational grouped) presented close proximity to glycemic variability (figure 2) [5, 15, 52] . Therefore, we suggest that none of the types should feel safe. Although, not surprising, T2D was associated with "50 years and older" (figure 1), and it is known that the COVID-19 mortality and severity increase with age [5, 24, 53] .It was unfortunate the proximity of individuals reporting increased blood glucose levels and the presence of COVID-19 symptoms without testing (figure 1). The global shortage of tests for SARS-CoV-2 poses a grand challenge [2] [3] [4] , however, cognizant that high blood glucose increases the risk for infection severity and death [15, 19, 20, 21, 22, 23] , early testing efforts are recommended in this group. In addition, supporting them in intensifying their diabetes self-care practices to quickly bring their glycemia back on track must be prioritized [45, 51, 54] . Figure 2 depicts the importance of balancing food and exercise, since individuals who increased or decreased any of them experienced changes in their blood glucose levels, whereas those able to maintain exercise and food consumption avoided glycemic changes. While the present study focused only on diabetes, WHO's data show that other NCDs serviced in similar health systems and pandemic environment, are facing comparable challenges [55] . In terms of unattended needs of individuals with diabetes during this COVID-19 crisis, Brazil is not the only country with unaddressed demands. In a collection of testimonials from people with diabetes in different countries, the International Diabetes Federation exposed some similar issues whilst others different from the Brazilian context [56] . While the absence of specific measures to protect those with diabetes was reported in Greece and Spain, access to medicines was a challenge in Zimbabwe, Iran and India [56]. However, countries like Argentina, Italy, Portugal, and South Korea seemed to have adopted measures that made people with diabetes feel safer [48, 56] . It is urgent for Brazil to follow international best practices and recommendations, since, as stated by the WHO Regional Office for Europe, "the prevention and control of NCDs have a crucial role in the COVID-19 response and an adaptive response is required to account for the needs of people with NCDs" [17] . imitations of the present study, although efforts were made to disseminate the survey as much as possible, this study's sample neither reflects the proportion of Brazilian population nor the fraction with diabetes. Thus, it is a convenience sample, without post-stratification weights. Questions about behaviors, habits and glycemic changes were subjective comparisons with the pre-pandemic state, therefore, we did not have access to objective data to validate the subjective responses. As an online survey, it is biased by individuals who have internet access. While 17% of the Brazilian population have a college degree [57] and 27.9% have private health insurance [28] , among the respondents of the present survey, 51.8% had at least one college degree and only 28.2% exclusively access care services through SUS. Other demographic limitations related to the age groups distribution, younger than the general population with the great majority of the respondents from Sao Paulo state, which would be about 20% of the country, not 42.6% as in our group of respondents. We understand that one of the main factors leading to this bias is the network in which the authors are engaged, the geographic location of diabetes associations and the profile of their active members, tendency to have more individuals with T1D than T2D, mostly young and highly educated. Ann additional limitation is the number of choices for each question and the aggregation made for analysis purposes. This includes the fact that if separated, diseases aggregated in the Cardiovascular Disease group, for example, could behave differently in terms of proximity to other factors; moreover, increase or decrease of the blood glucose, interpreted as distancing from the target, would in fact have led the glycemic to target, while the ones who successfully maintained the blood glucose in pre-pandemic levels, may have kept it out of target. As revealed for the first time in the current study, individuals with diabetes were not spared by the COVID-19 pandemic in Brazil. Measures and adjustments lacked or were insufficient, paving the way to unhealthy and unsafe behaviors such as postponing medical appointments, reducing physical activity and refraining from collecting medication and supplies, which led to a high percentage of glycemic worsen reports. Additionally, announced measures for prevention and mitigation of COVID-19 consequences on this population, such as supply of medicines for 3 months, worked just for a minority. In conclusion, we see a pressing need from the Brazilian federal, states and municipal authorities to broaden the already implemented measures in order to reach more people, and partner with civil society, private sector and media channels to quickly improve the response and, this way, prevent a surge of individuals with diabetes infected by SARS-CoV-2 and of acute and chronic diabetes complications. Among the priorities measures to keep individuals healthy is by ensuring their glycemia is on target and they are at home as much as possible. We recommend that the public and the private health systems maintain and strengthen programs for the continuity of care of individuals with diabetes and other NCDs, and develop information and educational campaigns on how to access those measures. In addition to focusing on reducing the risk of infection and continuity of their healthcare, these measures should facilitate access to healthcare professionals' counseling for therapeutic adjustment. Finally, as key measures we foresee are a) activemonitoring and response to communities' needs at primary heathcare level; b) high quality telehealth for consultations to monitoring and examining; c) distribution of medications and supplies for three or more months, ideally with home delivery; d) lab exams scheduled through home sampling, using mobile point-ofcare strategies or appointment in specific facilities with well established disinfection protocols. Consequently, we believe that the pandemic's challenges can be lessened if appropriate measures protecting individuals with diabetes and other chronic conditions are adopted, and investments are not solely directed to purchasing mechanical ventilators and equipping tertiary care. Epidemiology, public policies and Covid-19 pandemics in Brazil: what can we expect? Revista de Enfermagem Uerj COVID-19 in Brazil: advantages of a socialized unified health system and preparation to contain cases. 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The Lancet Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes Admission hyperglycemia and radiological findings of SARS-CoV2 in patients with and without diabetes Glycemic Characteristics and Clinical Outcomes of COVID-19 Patients Hospitalized in the United States Elevated Glucose Levels Favor Sars-Cov-2 Infection and Monocyte Response Through a Hif-1α/Glycolysis Dependent Axis Hyperglycemia and the worse prognosis of COVID-19. Why a fast blood glucose control should be mandatory Clinical Characteristics and Risk Factors for Mortality of COVID-19 Patients With Diabetes in Wuhan, China: A Two-Center International Diabetes Federation Prevalence of diabetes mellitus as determined by glycated hemoglobin in the Brazilian adult population, National Health Survey An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis and other methods for quantifying categorical multivariate data Private Health Care Coverage in the Brazilian Population, according to the 2013 Brazilian National Health Survey. Ciência & Saúde Coletiva PAHO. If I have diabetes, what do I need to know about COVID-19? Securing the Safety Net and Protecting Health During a Pandemic: Medicaid's Response to COVID-19 Alterações no Programa Farmácia Popular devido à situação de emergência de saúde pública decorrente do coronavírus (COVID19 Saúde amplia entrega de remédios nas farmácias de alto custo para prevenção do coronavírus COVID-19 and diabetes Prevention and management of COVID-19 among patients with diabetes: an appraisal of the literature Delayed access or provision of care in Italy resulting from fear of COVID-19 COVID-19 and endocrine diseases. A statement from the Enfermedad por coronavirus 2019 (COVID-19) en América Latina: papel de la atención primaria en la preparación y respuesta Public Health Measures and the Reproduction Number of SARS-CoV-2 COVID-19 Impact on People with Diabetes in the South and Central America Deep impact of COVID-19 in the healthcare of Latin America: the case of Brazil Social isolation during the COVID-19 pandemic can increase physical inactivity and the global burden of cardiovascular disease Risk of Increased Physical Inactivity During COVID-19 Outbreak in Older People: A Call for Actions Managing COVID-19 in Low-and Middle-Income Countries What is the role of Primary Health Care in the COVID-19 pandemic? Practical recommendations for the management of diabetes in patients with COVID-19. The Lancet The Silver Lining to COVID-19: Avoiding Diabetic Ketoacidosis Admissions with Telehealth Coronavirus Infections and Type 2 Diabetes -Shared Pathways with Therapeutic Implications Ensuring people-centred diabetes care during the COVID-19 pandemic Experiences from Portugal Beyond the virus: Ensuring continuity of care for people with diabetes during COVID-19. Primary Care Diabetes Obesity and COVID-19 Severity in a Designated Hospital in Shenzhen, China. Diabetes Care COVID-19 and diabetes: Knowledge in progress Type 1 Diabetes and COVID-19: Preliminary Findings From a Multicenter Surveillance Study in the U.S. Diabetes Care Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy Clinical considerations for patients with diabetes in times of COVID-19 epidemic World Health Organization, Rapid assessment of service delivery for NCDs during the COVID-19 pandemic COVID-19: Perspectives from people with diabetes Education at a Glance 2019. Country Note Brazil The authors acknowledge Vanessa Pirolo's support and assistance sharing the survey through her network and the non-governmental organizations ADJ Diabetes Brasil (ADJ), Institute for Children with Diabetes (ICD), Brazilian Diabetes Society (SBD), Brazilian Hypercholesterolemia Association (AHF), FórumDCNTs and their member and partner entities for assisting in sharing the survey and providing general support. 


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   key: cord-334582-ccg27nmf authors: Bonora, Benedetta Maria; Boscari, Federico; Avogaro, Angelo; Bruttomesso, Daniela; Fadini, Gian Paolo title: Glycaemic Control Among People with Type 1 Diabetes During Lockdown for the SARS-CoV-2 Outbreak in Italy date: 2020-05-11 journal: Diabetes Ther DOI: 10.1007/s13300-020-00829-7 sha: doc_id: 334582 cord_uid: ccg27nmf INTRODUCTION: In late February 2020, due to the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the Italian Government closed down all educational and sport activities. In March, it introduced further measures to stop the spread of coronavirus disease (COVID-19), placing the country in a state of almost complete lockdown. We report the impact of these restrictions on glucose control among people with type 1 diabetes (T1D). METHODS: Data were collected on 33 individuals with T1D who were monitoring their glucose levels using a flash glucose monitoring device and remotely connected to the diabetes clinic on a cloud platform. We retrieved information on average glucose, standard deviation and percentage time in hypoglycaemia (< 70 mg/dl), glucose range (70–180 mg/dl) and hyperglycaemia (> 180 mg/dl). We compared glycaemic measures collected during lockdown to those collected before the SARS-CoV-2 epidemic and to the periods immediately before lockdown. RESULTS: In 20 patients who had stopped working and were at home as a result of the lockdown, overall glycaemic control improved during the first 7 days of the lockdown as compared to the weeks before the spread of SARS-CoV-2. Average glucose declined from 177 ± 45 mg/dl (week before lockdown) to 160 ± 40 mg/dl (lockdown; p = 0.005) and the standard deviation improved significantly. Time in range increased from 54.4 to 65.2% (p = 0.010), and time in hyperglycaemia decreased from 42.3 to 31.6% (p = 0.016). The number of scans per day remained unchanged. In 13 patients who continued working, none of the measures of glycaemic control changed during lockdown. CONCLUSION: Despite the limited possibility to exercise and the incumbent psychologic stress, glycaemic control improved in patients with T1D who stopped working during the lockdown, suggesting that slowing down routine daily activities can have beneficial effects on T1D management, at least in the short term. Why carry out this study? In March 2020, Italy was placed under lockdown due to the outbreak of the new coronavirus disease. Diabetes management during lockdown was particularly challenging. Using data collected by remote monitoring of glucose sensors, we investigated whether glycaemic control in people with type 1 diabetes (T1D) during lockdown improved or worsened. What was learned from the study? Individuals with T1D who stopped working during lockdown significantly improved their glucose control while those who continued working (essential services) showed no change in glucose control. These results suggest that slowing down routine daily activities can achieve beneficial effects on the short-term management of T1D. The long-term effects of lockdown and the factors that affect glucose control in this particular situation deserve future investigation. Achieving glycaemic control is a complex task for people with type 1 diabetes (T1D) as it involves multiple domains of daily functioning [1, 2] . To maintain glucose levels that are as much as possible within range, patients need to pay attention to meals, insulin doses, exercise regimens, working activities, social relations and psychological stress, as well as exercise selfcontrol [3] . This continuous challenge results in people with T1D occasionally deprioritizing glycaemic control in favour of other activities [4] . Glucose control may improve during holidays, but this improvement is highly variable [5] [6] [7] . In December 2019, a new coronavirus (CoV) strain that causes severe acute respiratory syndrome emerged in Wuhan (China), ultimately referred to as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and rapidly spread throughout the world [8] . In February and March 2020, Italy was the second most affected country worldwide [9] . To reduce the spread of infection, in late February 2020, the Italian Government issued a series of restrictions that, in March 2020, culminated in an almost complete lockdown of the country [10] . This lockdown intially involved all sport and educational activities but was then extended to commercial activities and most non-essential services. During lockdown, all citizens were requested to ''stay at home'' [11] . Outpatient clinics were closed while hospitals were coping with thousands of patients infected with SARS-CoV-2. The SARS-CoV-2 pandemic not only caused morbidity and mortality among people with CoV disease but also imposed a heavy burden on societal and population health [12] . It is expected that individuals with chronic disease, such as diabetes, will suffer the most from the prolonged lockdown due to limitations in access to outpatient clinics and services. Remote glycaemic monitoring through cloud platforms has enabled diabetes specialists to interact with individuals with T1D during the SARS-CoV-2 pandemic lockdown. The widespread use of the flash glucose monitoring system (FGM) among patients with T1D [13] has allowed healthcare professionals (HCPs) to monitor these patients through a web-based interface that records real-time sensor readings. Of note, telemedicine has been shown to improve psychosocial outcomes in young adults with diabetes [14] . According to a recent metaanalysis, FGM has the potential to improve overall glucose control [15] and to reduce hypoglycaemia in individuals with T1D [16, 17] . In this study, we examined glycaemic control during the first week of lockdown against the spread of SARS-CoV-2 in people with T1D using FGM in Italy in comparison to the prelockdown period. The study was approved by the Ethical Committee of the University Hospital of Padova. All procedures were performed in accordance with the Helsinki Declaration of 1964, and its later amendments, and was in agreement with national regulations. The study was conceived as a retrospective data collection, and all patients provided written informed consent to the reuse of clinical data for research purposes. Patients with T1D who were eligible for inclusion in the study met the following criteria: attended the diabetes outpatient clinic of the University Hospital of Padova; lived in the area; were using the FreeStyle Libre FGM system (Abbott Diabetes Care, Rome, Italy) for at least 3 months; were sharing sensor data with the diabetes outpatient clinic on a web-based cloud system (LibreView; Abbott Diabetes Care); and had [ 90% coverage of sensor data. The Libre-View platform is intended to assist both people with diabetes and HCPs in reviewing, analysing and evaluating sensor data to support diabetes management. In order to record only spontaneous changes in glycaemic control, patients who had already sought therapeutic advice were excluded from enrolment. Patients had provided online informed consent to be remotely connected to the diabetes clinic. Diagnosis of T1D was based on the American Diabetes Association (ADA) criteria and was confirmed by positive autoantibody testing [18] . For all patients, we retrieved the following data: age, sex, diabetes duration, body mass index, history of hypertension and smoking habit, most recent glycated haemoglobin (HbA1c) values, lipid profile, urinary albumin excretion rate (UAER), estimated glomerular filtration rate (eGFR; CKD-EPI equation [19] ). The presence of chronic complications was recorded as follows. Nephropathy was defined as a UAER [ 30 mg/g or eGFR \ 60 ml/min/1.73 m 2 . Somatic neuropathy was defined based on an assessment using the Michigan Neuropathy Screening Instrument, and eventually confirmed by nerve conduction velocity testing. Autonomic neuropathy was defined based on the results of cardiac autonomic tests performed with the Neurotester instrument (Meteda Srl, San Benedetto del Tronto, Italy), including lying-to-standing response, Valsalva manoeuvre, deep breathing and orthostatic hypotension. Retinopathy (any stage) was defined based on the analysis of digital retinography, as scored by expert ophthalmologists. Coronary artery disease was defined as a history of myocardial infarction or coronary revascularization, or evidence of myocardial ischemia upon stress test (when available). Peripheral arterial disease was defined as a history of claudication or rest pain, peripheral artery revascularization or an ankle-brachial index of \ 0.9. Cerebrovascular disease was defined as a history of stroke or transient ischemic attack or carotid atherosclerosis (symptomatic or asymptomatic). We also collected information on concomitant medications, other than insulin, including metformin, sodium glucose cotransporter-2 inhibitors and drugs for the management of concomitant risk factors. Raw data on sensor glucose readings were retrospectively retrieved from all patients at 15-min intervals. To avoid an impact of scan frequency on average glucose levels, we did not consider glucose readings from sensor scans in our analysis. Raw data were imported on a dedicated spreadsheet, and the following metrics were computed: average glucose with standard deviation (SD); coefficient of variation (CV%; expressed as the percentage of average glucose); time in hypoglycaemia (\ 70 mg/dl); time in glucose range (70-180 mg/dl); time in hyperglycaemia ([ 180 mg/dl); and number of scans per day. As per convention, we set a cutoff of 150 mg/dl to define high/low average glucose and a SD cutoff of 50 mg/dl to define stable/ unstable control. The primary endpoint was average glucose. These variables were calculated for each of the following periods ( Fig. 1a ): 3 months before the SARS-CoV-2 outbreak in Italy; 1 week before the SARS-CoV-2 outbreak in Italy; from the closure of sport and educational activities to lockdown of the Padova area, when commercial activities and non-essential services were closed (period 1); and the first week after lockdown (period 2). Patients were divided into groups based on whether they stopped their working activities during lockdown or whether they continued working during lockdown because of being involved in essential services (e.g. workers in the healthcare system or food supply chain). Patients who continued working served as negative controls for those who stopped working during the lockdown period. Continuous variables are presented as the mean ± SD if normally distributed or as the median A total of 33 patients with T1D were enrolled in the study, then categorized into one of two groups based on whether they stopped working (n = 20) or continued working (n = 13) during the lockdown. Clinical characteristics of the two groups are reported in Table 1 . None of the patients were known to be (have been) infected with SARS-CoV-2 nor to be (have been) quarantined for close contact with infected people. The 20 patients who stopped working due to the lockdown had an average age of 36.9 years, 60% were male and average diabetes duration was 15 years. The mean latest available HbA1c value was 7.6%, and the prevalence of complications was low. When data collected during the week before the SARS-CoV-2 outbreak in Italy were compared to those of the first week of lockdown (period 2), average glucose had decreased from 177.7 ± 45.6 mg/dl (9.9 ± 2.5 mmol/l) to 161.0 ± 40.3 mg/dl (8.9 ± 2.2 mmol/l; p = 0.005; Fig. 1b) , which is equal to a reduction of 16.7 ± 24.5 mg/dl (0.9 ± 1.4 mmol/l). The standard deviation of sensor readings decreased from 58.9 ± 19.6 mg/ dl (3.3 ± 1.1 mmol/l) to 53.2 ± 19.9 mg/dl (3.0 ± 1.1 mmol/l; p = 0.004). As a result of the simultaneous improvement in average glucose and the standard deviation, patients moved from a high-unstable profile towards a lowstable profile (Fig. 1c) . The number of patients in the low-stable profile area increased from three to eight (p = 0.02). However, the CV%, which is a better measure of glycaemic stability according to international consensus and recommendations [20, 21] , did not change substantially across the periods considered. Time in hypoglycaemia was not significantly changed, whereas time in range increased (from 54.4 ± 4.2 to 65.2 ± 4.2%; p = 0.010) and time in hyperglycaemia decreased (from 42.3 ± 4.8 to 31.6 ± 4.4%; p = 0.016; Fig. 1d ). The number of scans per day did not change significantly (from 12.6 ± 2.4 to 13.4 ± 2.9; p = 0.479). Reduction of average glucose was directly correlated with baseline average glucose (r = 0.47; p = 0.016), but not with the number of scans per day. When data collected during period 2 were compared to those collected during the 3 months before SARS-CoV-2 spread, the same significant improvements were noted ( Table 2) . Data collected during period 1 were not significantly different from those collected 1 week or 3 months before SARS-CoV-2 outbreak. Data are expressed as mean ± standard deviation or as the number (of patients) with the percentage in parenthesis. The urinary albumin creatinine ratio is expressed as the median with the interquartile range in square brackets *Significant different between patient groups at p \ 0.05 HDL High-density lipoprotein, LDL low-density lipoprotein, MDI multidose insulin, CSII continuous subcutaneous insulin infusion, SGLT-2i sodium glucose cotransporter-2 inhibitor, ACEi angiotensin converting enzyme inhibitor, ARB angiotensin receptor blockers Period 1: from the closure of sport and educational activities to lockdown of the Padova area, when commercial activities and non-essential services were closed; Period 2: the first week after lockdown Patients who continued working during the lockdown (n = 13) had an average age of 45 years, 53.8% were male and average diabetes duration was 24.6 years (and thus longer than that of the group not working). Eight patients were on insulin pump therapy. None of the patients who continued to work showed improvement in any of the measures of glucose control during the lockdown period (period 2) compared to the 3 months or the week before the SARS-CoV-2 outbreak: average glucose, standard deviation, CV%, time in hypoglycaemia, time in range and time in hyperglycaemia remained unchanged (Table 2) , as did the number of scans per day. No difference was observed regarding any change in glucose control metrics among those who were on the insulin pump and those who were on a regimen of multiple daily insulin injections. Our data show that, during the first week of lockdown due to the SARS-CoV-2 outbreak in North-East Italy, patients with T1D who stayed at home achieved a significant improvement in glucose control. Among patients with T1D followed at the same clinic who continued working in the same period, many of whom were on insulin pump therapy, no deterioration of glucose control was observed. The SARS-CoV-2 pandemic represents a huge challenge to public health worldwide [22] , and healthcare services have faced severe challenges during outbreaks of the coronavirus disease (COVID-19), resulting in major cut backs in the care provided to people with chronic diseases, including diabetes [23, 24] . Many outpatient clinics have had to change their routine interactions with patient and use telemedicine to monitor patients at home [25] . In Italy, the widespread use of FGM among people with T1D allowed these patients to be remotely connected to the clinic through the cloud. Diabetes professionals were concerned that glucose control could worsen during lockdown because of the limited possibility to exercise and the severe psychologic stress imposed by social distancing in a cultural environment heavily reliant on direct inter-personal relationships. During the outbreak, most non-essential activities were shut down, and most citizens stopped their usual working routine or turned to working at home. However, some people, such as those involved in healthcare or the food supply chain, continued to work during the lockdown. Our observation that glucose control improved during the first week of lockdown in people with T1D who stayed at home is reassuring and suggests that a slowing down of routine activities can have favourable effects on glucose control in the short term. Remarkably, the reduction in average glucose and the increase in time in range were not paralleled by an increased time in hypoglycaemia, which remained stable. We speculate that such an improvement occurred because patients had more time to concentrate on diabetes control and had a more regular lifestyle, including the timing and composition of meals, while not being exposed to workplace stress [26] . In addition, the knowledge that diabetes worsens the outcomes of COVID-19 [27, 28] may have improved patients' awareness and compliance to diabetes management. To evaluate whether glucose control changed in all patients with T1D during lockdown, irrespectively of whether they stayed at home or not, we included a group of patients who continued working. The observation that glucose control did not improve in this subgroup of T1D patients supports our interpretation. However, the groups of patients who stopped working and those who continued their usual working activities were not comparable because the latter had a better baseline glucose control than those who stayed at home, and [ 60% were on insulin pump therapy. Users of pump therapy represent approximately 30% of patients with T1D at our clinic, and 13% of T1D patients nationwide [29] . The frequent use of insulin pump therapy among this group of patients is probably related to the fact that most were healthcare workers and/or shift workers who often need pump therapy to cope with their lifestyle and working schedules. Patients who continued working had a higher time in range at baseline, such that further improvements were more difficult to obtain. Yet, it is reasonable to assume that these individuals were subjected to stronger challenges during lockdown, including exercise limitations, social distancing, workplace stress, and fear of infection. Therefore, that these patients maintained good glycaemic control is reassuring in terms of their effective self-management of diabetes. We speculate that the use of FGM combined with insulin pump therapy contributed to this effect [30] ; for example by limiting diabetes-related distress [31] . We acknowledge that detailed information to interpret the drivers of glucose control during lockdown, such as changes in insulin doses, consumption of snacks and exercise, were not available and should be investigated in future studies. In addition, we included patients with T1D having relatively good glycaemic control and showing frequent sensor use. Therefore, it remains unclear whether the same results would apply to patients with worse glucose glycaemic control or less frequent sensor scans. Finally, we did not analyse glycaemic control in the subsequent weeks of lockdown because most patients were remotely contacted by the physicians from the clinic with advice on how to manage diabetes, thereby introducing bias. Hence, the medium-and long-term impact of lockdown on glucose control remains unknown. In summary, we report that glucose control improved in people with T1D who stayed at home during the first week of lockdown due to COVID-19. This observation suggests that slowing down routine activities can have beneficial effects on T1D control in the short term. However, the long-term effects of lockdown and the factors that affect glucose control in this particular situation deserve future investigation. Boscari, Daniela Bruttomesso and Gian Paolo Fadini have received lecture fees or other support from Abbott, the manufacturer of the flash glucose monitoring system and cloud platform described in this study. Benedetta Maria Bonora and Angelo Avogaro declare no conflict of interest in connection with the submitted material. Compliance with Ethics Guidelines. The study was approved by the Ethical Committee of the University Hospital of Padova. All procedures were performed in accordance with with the Helsinki Declaration of 1964, and its later amendments, and was in agreement with national regulations. The study was conceived as a retrospective data collection, and all patients provided written informed consent to the reuse of clinical data for research purposes. Data Availability. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/bync/4.0/. 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No funding or sponsorship was received for this study or publication of this article.Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteriafor authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.Authorship Contributions. Benedetta Maria Bonora and Federico Boscari contributed equally. 


id: cord-334773-yw2qgv13
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   key: cord-334773-yw2qgv13 authors: Lisco, Giuseppe; De Tullio, Anna; Giagulli, Vito Angelo; Guastamacchia, Edoardo; De Pergola, Giovanni; Triggiani, Vincenzo title: Hypothesized mechanisms explaining poor prognosis in type 2 diabetes patients with COVID-19: a review date: 2020-08-10 journal: Endocrine DOI: 10.1007/s12020-020-02444-9 sha: doc_id: 334773 cord_uid: yw2qgv13 PURPOSE: Epidemiological data suggest that comorbid patients, mostly those with type 2 diabetes (T2D), are predisposed to poor prognosis in Coronavirus disease 2019 (COVID-19), leading to serious healthcare concerns. The aim of the present manuscript is to review the main relevant mechanisms possibly contributing to worsen the clinical course of COVID-19 in T2D. RESULTS: Poor glucose control, high glycaemic variability and diabetes-related comorbidities at baseline, particularly cardiovascular diseases and obesity, contribute in worsening the prognosis in the above-mentioned cluster of patients. Moreover, both a lower efficient innate immune system response and cytokine dysregulation predispose patients with T2D to impaired viral clearance and more serious pulmonary and systemic inflammation once the SARS-CoV-2 infection occurred. Inconclusive data are currently available for specifically indicate or contraindicate concurrent medications for managing T2D and its comorbidities in infected patients. CONCLUSIONS: T2D individuals should be considered as more vulnerable to COVID-19 than general population, and thus require adequate advices about hygienic tips to protect themselves during the pandemic. A careful management of glucose levels and diabetes-related comorbidities remains essential for avoiding further complications, and patient monitoring during the pandemic should be performed also at distance by means of telemedicine. Further studies are needed to clarify whether medications normally used for managing T2D and its associated comorbidities could have a protective or detrimental effect on COVID-19 clinical course. Background Firstly identified and characterized as 2019-nCOV [1] , human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been reported as a novel infective agent arisen at the end of the 2019 [2] . SARS-CoV-2 is a positivesense, single strand, enveloped RNA virus belonging to the family of Coronaviridae, and is the 7th beta-coronavirus recognized to infect humans [3] . First metagenomic RNA sequencing of SARS-CoV-2 showed the single-strand RNA consisted of 29,906 nucleotides, and was closely related to a group of bat SARS-like coronaviruses (89,1%) [4] . Further observations confirmed that SARS-CoV-2 was closely related (88%) with two bat SARS-like coronavirus (bat-SL-CoVZC45 and bat-SL-CoVZXC21), but was distant from other two human coronaviruses responsible for severe infective pneumonia: SARS-CoV (79%) and Middle East respiratory syndrome coronavirus (MERS-CoV) (50%) [5] . A high grade of homology between genomic sequences of SARS-CoV-2 form different patients (99.98%) has also been reported, thus confirming a human-to-human transmission of the novel infective agent [5] . Phylogenetic analysis suggested that SARS-CoV-2 progenitors circulated in animal host including bats [6] , snakes, Malayan pangolins, civets, mouse [7, 8] , and underwent to a naturally occurred selection before the zoonotic spillover finally adapting to persistently infect the new host [9] . Official epidemiological report declared that the early cases of infections were detected in December 2019, and involved people who worked or visited the Hua Nan South China Seafood Market [10] , in Wuhan capital city (Hubei province; People's Republic of China). However, it remains still debated whether the zoonotic spillover might have been occurred in other places [11] , particularly in Southeast Asia [12] , and consequently SARS-CoV-2 infection might have been imported to Wuhan. Epidemiological analysis showed a marked widespread of the infection within community places due to a large human-to-human transmission [13] and Wuhan rapidly became the hub of a new pneumonia outbreak [14] . Due to a consistent widespread of detected cases among several countries, the World Health Organization declared the state of pandemic on March 11, 2020 when confirmed cases raised up to 118,000, and SARS-CoV-2 spread into 114 countries [15] . Clinical manifestations of the novel coronavirus disease 2019 (COVID- 19) include fever (87%), cough (58%), dyspnoea (38%), muscle soreness (35%), chest distress (31%) in a context of bilateral pneumonia (76%) with ground glass opacification (70%) at CT scan due to lung interstitial involvement [16] . Autoptic studies described macroscopic features of pleuro-pericarditis, lung consolidation, oedema with overall increased pulmonary weight; while microscopic hallmarks are characterized by pneumocyte hyperplasia, lymphocytic and multinucleated giant cells infiltration, hyaline membranes [17] [18] [19] . Other signs and symptoms of COVID-19 include acute conjunctivitis [20, 21] ; diarrhoea, abdominal pain/discomfort and vomiting [22] [23] [24] ; convulsion, headache, muscle soreness [25] ; diffuse erythematous rush and widespread urticaria [26] ; acute kidney injury [27, 28] ; pharyngodynia, nasal congestion with rhinorrhoea and smell/taste impairment [29] . SARS-CoV-2 may directly affect myocardial tissue, and significantly complicate the prognosis of underlying cardiovascular diseases [30, 31] . SARS-CoV-2 infection usually occurs asymptomatically or mildly symptomatic form of the disease but in predis posed patients with specific clinical conditions, a serious clinical course could be observed thus leading to worse prognosis or death [12] . Worldwide reported case-fatality rate for COVID-19 differs considerably among geographical areas [32] , and could be attributable to several variables, such as testing strategies for screen suspected cases and identification of infectious patients (statistical); accessibility to intensive care according to restricted national healthcare system capacities (organization); baseline patient age and comorbidities (medical) [33, 34] . From the latter point of view, a poor prognosis is usually observed in elderly patients [35] and worldwide age-specific case-fatality rate occurred very high among patients with one or more underlying chronic diseases including cardiocirculatory, renal, pulmonary, central nervous system and mental illness, diabetes mellitus (DM) and malignancies [36, 37] . According to the data shared by the Italian National Institute of Health, patients who died while tested positive for SARS-CoV-2 exhibited an elevated mean age (78.5 years old), mostly men (70%) and with one or more pre-existent chronic diseases (2.7 in mean) [38] . Of these, blood arterial hypertension (78%) and DM (34%) were the most commonly reported clinical comorbidities, followed by ischaemic heart disease (30%) and atrial fibrillation (22%). The leading cause of deaths was attributable to acute respiratory distress (97%) [38] . The global estimated prevalence of T2D accounts for more than 450 millions affected patients corresponding to 9.3% of the worldwide population [39] . Therefore, the number of patients with T2D who will contract SARS-CoV-2 infection is expected to be considerable, and should increase over time. T2D per se does not increase the risk of contracting SARS-CoV-2 infection but could exacerbate the clinical course of COVID-19 leading to a detrimental prognosis [40] . Indeed, the frequency of diabetes in patients with COVID-19 has been reported to 9-12% [41] [42] [43] , raising up to 16-20% in hospitalized patients including those who required intensive care for severe disease [44, 45] . More recently, data collected from nine hospitals from Seattlearea in the United States demonstrated that 58% of patients who required hospitalization for respiratory symptoms attributable to COVID-19 had T2D [46] . In severely ill patients with COVID-19 a pre-existent T2D was observed in about 35% of the cases and, according to the results of an univariate analysis, the presence of T2D resulted a significant risk factor for poor prognosis in this clinical setting (OR 8.14; p < 0.0001) [47] . DM has also been reported as the main clinical condition observed in non-survived patients with COVID-19 (22%) [48] , thus resulting one of the most frequently associated comorbidity in COVID-19 deceased patients [33] . This concern has been further confirmed by the results of a cohort study among 85 fatal cases of COVID-19 in Wuhan, hence defining DM as a potentially harmful comorbidity predisposing to worse clinical course or death once SARS-CoV-2 infection occurred [49] . Different hypothesis should be considered for explaining this clinical phenomenon, including glucose control at baseline and during the infection course, pathophysiology and immune system response in SARS-CoV-2 infected patients with T2D, diabetes-related comorbidities and concomitant medications. Herein, a point-to-point discussion about these putative mechanisms has been carried out. Epidemiological data showed that T2D represents a risk factor for infectious diseases, mostly with bacterial aetiology, particularly at the level of skin and soft tissue, genitourinary, gastrointestinal and respiratory systems [50] . Moreover, life expectancy in individuals with T2D may be affected due to infectious diseases and in certain clusters of patients, such as in elderly with T2D, the leading cause of mortality is attributable to severe pulmonary infections rather than other highly prevalent comorbidities, including cardiovascular diseases and malignancies [51] . On the other hand, DM increases the cumulative risk of medical consultation, hospital admission, intensive care requirement and poor prognosis because of pandemic influenza [52] . Further data reported that airways and pulmonary infections with different aetiologies, including SARS and MERS, were more frequently diagnosed in T2D patients, also showing a severe clinical course [53] [54] [55] [56] . In DM, hyperglycaemia is considered one of the most important factor in determining this burden [57] . Indeed, osteomyelitis, soft tissues infections, endocarditis, tuberculosis and sepsis are most commonly observed in diabetic patients with poor glycaemic control compared to those who achieve better glucose management, and a worse glucose control contributes to increase the rate of hospitalization and mortality, too [57] . Both hyperglycaemia and high glucose variability may consistently complicate the clinical course also in case of influenza A [58] . More recently, a retrospective observational study recruiting more than 7000 cases of COVID-19 from Hubei province (China) and including 952 patients with a pre-existent T2D displayed a higher mortality rate (HR 1.49), more prevalence of multiorgan damage and a greater requirement of medications (antibiotics, systemic corticosteroids, vasoactive substances, oxygen inhalation and either non-invasive or invasive mechanical ventilation) in patients with DM than in nondiabetics [59] . Interestingly, authors also found that, among T2D patients, those with better glucose control (glucose levels between 70 and 180 mg/dl) respective to those with worse glucose control (>180 mg/dl) during hospitalization exhibited a significantly lower rate of mortality (HR 0.14; p < 0.008), and a fewer risk of progression to acute respiratory distress syndrome (HR 0.47; p < 0.009), acute kidney (HR 0.12; p < 0.046) and myocardial (HR 0.24; p < 0.01) injury [59] . Similar results were found by another observation in which worse glucose control (glucose levels >110 mg/dl) at the admission and during hospitalization was found to be an independent risk factor for progression to critical ill or death among T2D patients with confirmed COVID-19 [60] . Therefore, hyperglycaemia represents a relevant matter in patients with COVID-19 fostering poor prognosis once the infection occurred. In addition, recent evidences suggest that SARS-CoV-2 may induce beta-cells damage thus leading to insulin secretion impairment. This phenomenon, in addition to a pre-existent hyperglycaemia and considering that systemic inflammation due to the infection exacerbates the insulin-resistance, is thought to play a significative role to further worsen glucose control and complicate the clinical course of COVID-19 [61] . In conclusion, epidemiological data suggested that DM, particularly T2D, is a frequently observed comorbidity in patients with SARS-CoV-2 infection who require hospitalization, more intensive treatment and exhibit poor prognosis or death. Poor baseline and ongoing glucose control in hospitalized patients rather than the presence of T2D per se seems to facilitate COVID-19 progression [62] . Hence, an optimal and timely blood glucose management during pandemic should be considered as an effective strategy to reduce the probability of hospitalization requirement of infected patients, and for improving the clinical course of those hospitalized for receiving either non-intensive or intensive care. Innate and adaptive immune responses play a crucial role against viral infections [63] . Immune response against coronaviruses has been reviewed elsewhere, highlighting the role of both innate and adaptive systems to promptly contrast virus replication, facilitate virus clearance, stimulate tissue repair and develop persistent defence [64] . Immune response in COVID-19 is not still completely understood making necessary further investigation to better control the pandemic evolution [65] . However, seriously ill COVID-19 patients exhibit an exaggerate response of neutrophils and alveolar macrophages, and a relevant peripheral lymphocytes dysfunction [66] , which lead to an uncontrolled viral shedding, consequent viremia and further systemic immune-mediated damage, thus triggering a harmful vicious circle [66] . Glucose levels may significantly influence immune response as observed in patients with DM. Natural killer (NK) cells activity is weakened in case of hyperglycaemia, and is inversely related with fasting plasma glucose, 2-h postprandial glycaemia and HbA1c levels [67] . Macrophage activation and phagocytosis are both decreased in patients with poor glucose control, but should be restored after an adequate optimization of metabolic control [68] . Neutrophil activation and phagocytosis are both impaired by hyperglycaemia as demonstrated in animal models and humans, thus suggesting a relevant impairment of innate immune response in patients with chronic hyperglycaemia [69] . In an animal model, obese and hyperglycaemic mice experienced higher rate of respiratory infection due to influenza and bacterial pneumonia and that was related to a lower efficient alveolar macrophage response against infections. In addition, a defective Toll-like receptor 4 signalling has been recognized in neutrophils exposed to the gram-negative lipopolysaccharide, leading to a blunted release of chemokines and cytokine, and decreased myeloperoxidase activity [69]. Moreover, T-cells function is significantly dysregulated in T2D and CD4 + lymphocytes preferentially differentiate in Thelper 1 and T-helper 17 instead of T-helper 2 with a consequent imbalance between pro-inflammatory and antiinflammatory activities [70] . On the other hand, the levels of interferon gamma-which normally stimulates CD4 + T cells maturation in sense of T-helper 1 rather than T-helper 2-were found to be lower in T2D patients sera, and can contribute in a blunted T-cell response in T2D [71] . Nevertheless, lower levels of interleukin (IL)-10 have also been described in T2D patients. Considering that IL-10 is capable to suppress the release of pro-inflammatory cytokines, lower levels of IL-10 could be related with higher IL-6-to-IFN gamma and TNF-alpha-to-IFN gamma ratios hence suggesting an enhanced activation of circulating monocytes [71] . High levels of IL-6 have been detected in diabetic patients respective to those with euglycemia, suggesting that hyperglycaemia play a crucial role in determining this immunological effect [72, 73] . In animal models, hyperglycaemia and insulin-resistance increase the level of circulating proinflammatory cytokines and oxidative stress at baseline [74] . Since this pro-inflammatory background usually results reversible after an effective treatment of hyperglycaemia, a low dose endotoxemia consistently enhances systemic inflammation in a animal model [75] . Therefore, hyperglycaemia may predispose to an exaggerate immune response even in case of a mild-to-moderate viral load. As known, a hyperinflammatory syndrome with cytokine dysregulation has been well recognized in seriously ill patients [76] , thus highlighting its crucial role in serious manifestations of COVID-19 [77] . Specific interleukins and chemokines (IL-2, IL-7, IL-6, TNF-alpha, interferon gamma induced protein 10, granulocyte-colony stimulating factor) are upregulated in patients who exhibited a worsen prognosis [78] and particularly high levels of IL-6 have been detected in case of serious pulmonary involvement or in patients requiring intensive care [79] . These findings are also more evident in elderly patients who display less vigorous immune response against viral shedding, greater susceptibility to more serious pulmonary and systemic involvement, and are finally predisposed to COVID-19 progression [80] . Both the number and function of T cells (both CD4 + and CD8 + ) [81, 82] , B cells depletion, and hypercoagulability have also been observed in seriously ill cases and the greater the magnitude of these haematological and biochemical alterations then the greater the severity of the prognosis [83, 84] . In conclusion, diabetic patients especially elderly individuals and those with worse baseline glucose control may exhibit immune system dysregulation that predispose them to a less effective response against SARS-CoV-2 and to a dysfunctional inflammation that requires to be carefully monitored in confirmed cases of COVID-19, for preventing or avoiding a harmful progression of the disease. Angiotensin-converting enzyme 2 (ACE 2 ) is a carboxypeptidase normally involved in the cleavage of angiotensin I and angiotensin II, and is the main receptor for SARS-CoV-2 playing a determinant role in viral entry into the host, and clearly explaining both the transmissibility and severity of COVID-19 among humans [85] . ACE 2 is expressed at the level of several tissues (transmembrane and soluble forms), such as lung [86] , oral mucosa [87] , intestine [88] , brain [89] , pancreatic islets [90] , testis [91] and kidney [92] . Differentiated type 2 pneumocytes normally express ACE 2 , which is essential to regulate pulmonary homoeostasis and protects against pulmonary injury [93] . Indeed, low levels of ACE 2 have been described in severe acute and chronic pulmonary diseases thus predisposing to poor prognosis [94] . However, ACE 2 is overexpressed in chronic diseases, including T2D, and this phenomenon could be also related with chronical exposure to several medications [94, 95] . This biochemical condition is believed to facilitate the internalization of SARS-CoV-2 into pneumocytes, thus contributing to worse prognosis in COVID-19 [94, 95] . However, the role of ACE 2 overexpression in worsening the prognosis is an emergent issue, and remains currently debated [96] . Cardiovascular diseases, including coronary and cerebrovascular artery disease and heart failure, are frequently observed in T2D patients and it has been estimated that about a third of them displayed these kind of complications over time [97] . Cardiovascular system is the main extrapulmonary compartment extensively involved in COVID-19, as suggested by a frequent myocardial involvement in affected patients especially in those having hypertension, T2D and cardiovascular diseases at baseline [98] . Vascular inflammation and endothelial dysfunction [99] , myocardial injury and cardiac arrhythmias are not-infrequently observed in COVID-19 confirmed cases, significantly influencing the risk of poor prognosis or death in this cluster of patients [100] [101] [102] . Overweight-obesity syndrome is a multifactorial disease which significantly predispose to cardiometabolic risk, and is strictly associated with insulin-resistance, glucose metabolism impairment and T2D [103] [104] [105] . Despite obesity is usually associated with decreased risk of death in patients with severe acute respiratory distress (obesity paradox), currently available data suggest that an elevated body mass index should be considered as an independent risk factor predisposing to poor prognosis [106] and death in COVID-19 [107] [108] [109] [110] . Indeed, the prevalence rate of obesity in this cluster of patients has been reported in 42% of the cases [111] and a BMI greater than 35 kg/m 2 has been usually observed in patients requiring hospitalization and invasive mechanical ventilation [112] also in younger patients (<60 years) [113] . Pathophysiological mechanisms possibly related with poor prognosis in obese patients are not completely understood but may be attributable to a greater inflammatory background as similarly found in DM due to hyperglycaemia and insulin resistance [70] . Obesity is usually associated to other severe comorbidities with high impact on cardio-metabolic health, such as fatty liver disease, vascular inflammation, cardiovascular atherosclerotic diseases and heart failure [114] that might be related with a higher burden of lethal complications, especially in hospitalized patients with COVID-19 [115] . In addition to cardiometabolic risk factors, obese patients are more prone to have a decreased pulmonary ventilation or obstructive sleep apnoea, which predispose them to low levels of blood oxygenation at baseline and consequently to worse respiratory outcomes in case of acute infective respiratory diseases [116, 117] . Nutritional patterns usually exhibited by obese patients are frequently characterized by an elevated dietary consumption of processed food rich in saturated fat, cholesterol, sugar and a low consumption of fibres and micronutrients, such as vitamin D [118] . These dietary patterns may affect physiological microbiome composition, weaken the immune response against microbial agent and foster immune system dysfunction [115] . In addition, vitamin D deficiency/insufficiency and sedentary lifestyle, which are highly prevalent among obese patients, should be considered as predisposing factors for worse prognosis in response to acute infections, including COVID-19 [119] . Abdominal obesity usually leads to a low cardiorespiratory reserve and systemic inflammatory dysfunction which predispose to a worse prognosis in COVID-19 [120] . Conversely, regular physical exercise, which is normally lacking in an obesogenic lifestyle, is associated to higher levels of cardiorespiratory fitness, and is believed to improve the innate immune response and attenuate cytokine dysregulation often experienced by high risk patients with the so called cytokine storm [121] . Visceral obesity is also a risk factor for both the development and progression of cardiovascular diseases [114] considering that it fosters higher level of pro-thrombotic circulating factors and predisposes to thrombotic events [122] . Furthermore, visceral obesity is more prevalent in men than women and this biological phenomenon has been hypothesized to have a putative role in driven poor prognosis of COVID-19 especially in men. From this point of view, male obesity is usually associated with a functional hypogonadism and these clinical conditions are both associated with one other [123] , according to a complex and multifactorial pathogenesis [124] that fosters higher baseline levels of inflammation and endothelial dysfunction [125] . Aromatase gene expression is enhanced by prostaglandin E 2 , whose levels were found to be higher in visceral adipose tissue of obese men [126] . An enhanced aromatase activity at the level of adipose tissue increases local levels of estradiol that is thought to be a defensive mechanism against local inflammation and insulin resistance [127] . Whether augmented aromatase levels (and activity) at the level of adipose tissue in men may be responsible for a systemic testosterone-tooestrogen imbalance remains questionable. However, obese men are usually affected by this biochemical condition and body mass index is inversely correlated to testosterone-to-oestrogen ratio [128] . A low testosterone-to-estradiol ratio contributes to increase the cardiovascular risk particularly in elderly [129] and in those with previous cardiovascular diseases [130] [131] [132] . In addition, hormonal pattern associated with visceral obesity may play a relevant role in reducing the efficacy of immune response against infectious agents, predisposing to cytokine dysregulation, endothelial dysfunction, thrombosis, finally driving patients, particularly men, to poor prognosis or death [133] . Moreover, ACE 2 is also expressed on adipose cells [134] and a larger extension of adipose tissue in obese patients may significantly increase the number of available receptors for SARS-CoV-2, thus leading to a much greater viral shedding once the infection occurs [135] . In conclusion, multiple diabetes-associated chronic comorbidities, particularly obesity, have been found to worsen the prognosis in COVID-19 affected patients by acting as independent risk factors. Careful management and prompt interventions are thus required for improve clinical outcome predominantly in type 2 diabetic patients. Pathogenetic mechanisms basically involved in SARS-CoV-2 infection may be exacerbated by the use of concomitant medications for the management of T2D. Of note, an exaggerate pulmonary and systemic expression of ACE 2 facilitates SARS-CoV-2 replication and may be responsible for worse clinical course and poor prognosis [94, 95] . In animal model, pioglitazone has demonstrated to increase ACE 2 expression particularly at the level of hepatic and adipose tissue [136, 137] . On the other hand, the analysis of potential therapeutic targets for SARS-CoV-2 assessed by a computational model found pioglitazone to have a potential for inhibiting 3CLpro, an essential target for viral replication [138] . These findings did not provide conclusive assumption and diabetologists can safely prescribe pioglitazone taking into account general concerns including fluid retention and heart failure [139] . Gliflozins prescription is increasing over time, also considering a long-term beneficial cardiovascular and renal protection [140, 141] . Many mechanisms of cardiovascular and renal protection have been proposed, including an enhanced induction of ACE 2 expression at the level of heart and kidney but it is unclear whether this effect may negatively influence clinical course in COVID-19 [142] . Moreover, despite gliflozins reduce inflammatory injury and endothelial dysfunction, none conclusive data are currently available to confirm their potential beneficial effect in diabetics with COVID-19 [143] . Concerns about gliflozins use in COVID-19 are attributable to volume contraction, renal insufficiency and increased risk of ketoacidosis that may be supposed to occur particularly in hospitalized patients, including those severely ills. Dipeptidyl peptidase IV (DPP-IV or CD26) is a transmembrane and soluble ectopeptidase largely expressed in human tissues, including airways, lung and leucocytes [144] . Particularly, DPP-IV is specifically involved in MERS pathogenesis given that it mediate MERS-CoV-2 internalization in host cells [145] . No data actually support the role of DPP-IV in the internalization of SARS-CoV-2 and further study are needed to verify this aspect, and for demonstrating clear protective effects [146] . However, DPP-IV inhibitors have shown to potentiate immune response by increasing T-cells survival, consequently enhance immune response [147] , and possibly playing a relevant role in reducing the onset or progression of acute pulmonary manifestations in COVID-19 [145] . Glucagon-like peptide 1 receptor agonists (GLP-1RAs) belong to an effective class of medications approved for the treatment of T2D and for preventing diabetes-related cardiovascular and renal complications over time [148] [149] [150] [151] . Immune response and systemic inflammation play a crucial role in SARS-CoV-2 infection, particularly in case of severe clinical course of the disease. In this clinical scenario, it is speculated that the use of GLP-1RAs could provide both a better glucose control and anti-inflammatory effects with consequent improvement of outcomes in COVID-19 affected patients [143] . In patients with pulmonary inflammation, mononuclear circulating cells express lower level of GLP-1 receptors than controls and this condition is associated with a blunted production of interferon gamma by T cells and NK cells. In addition, the expression of programmed death cell protein 1, which mediates T-cell apoptosis, is enhanced on T-cell surface and consequently the efficiency of cell-mediated immune response is weaken. Liraglutide, a once-daily administered long-acting GLP-1RA, demonstrated to restore the expression of GLP-1 receptors on macrophage surface, and potentiate immunemediated response [152] . In addition, both DPP-IV pharmacological inhibition (linagliptin) and GLP-1RA (liraglutide) were found to contrast pulmonary injury, reduce platelet activation, microvascular thrombosis, and oxidative stress in mice model of endotoxemia [153] . Moreover, GLP-1RAs have been found to reduce the expression of pulmonary IL-33, thus playing an interesting role in contrasting a IL-33 mediated damage in immune-allergic diseases and viral infections [154] . Considering that pancreatic islets express ACE 2 , glucose impairment in COVID-19 diabetic patients could be attributable to a partial insulin deficiency [56] . However, glucose impairment could also be attributable to SARS-CoV-2 infection-induced stress or as the consequence of some medications or treatment protocols, including high-dose glucocorticoids, particularly used in hospitalized patients [155] . Insulin therapy is recommended in hospitalized patients [156] , including those with COVID-19, and a basal-bolus respective to a sliding scale regimen should be preferred in infected patients for avoiding glycaemic excursion and greater glucose variability [157, 158] . Hypertension is frequently observed in T2D patients leading to a consistent increase in the risk of atherosclerotic cardiovascular and renal diseases [159] . ACE inhibitors and angiotensin receptor blockers compared to other antihypertensive medications demonstrated to be more effective to prevent or delay the onset of cardiovascular and renal complications in people with T2D, and are currently used as the first-line treatment in these patients [160, 161] . Evidence from animal models suggested that ACE inhibitors are able to increase the expression of ACE 2 at the level of lung, thus leading to initial clinical concerns about the management of arterial hypertension in diabetic patients [162] . First suggestions advised to replace these medications with calciumchannel blockers in order to avoid undesirable detrimental effects of COVID-19 clinical course [95] . Nevertheless, no sufficient data supported the hypothesis that the use of renin-angiotensin system blockers may interfere with the internalization of SARS-CoV-2 in respiratory cells hence increasing its virulence [163] . In addition, ACE inhibitors and angiotensin receptor blockers protect lung, heart and kidney against inflammation, thus playing a potential beneficial role also in T2D patients with COVID-19 [164] . Statins are usually prescribed especially in T2D patients to protect against cardiovascular complications [165] . The role of statins in COVID-19 is not clearly established, however, some observation should be done. First, COVID-19 is associated with higher cardiovascular mortality specifically in patients with more risk factors, including hypertension and T2D, and statins could be useful to maintain or optimize lipid management and improve endothelial dysfunction in this clinical setting [166] . In animal models, statins inhibit the myeloid differentiation primary response 88 (MYD88) [167] . MYD88 is a protein adaptor for inflammatory signalling pathways downstream of members of Toll-like receptors and IL-1 receptor families, and plays a critical function in the activation and amplification of innate immune response [168] . The inhibition of MYD88 seems to reduce pulmonary inflammatory damage, and to improve survival in SARS-CoV and MERS-CoV infected mice [167] . Patients with established cardiovascular disease, and those at higher risk of atherosclerotic cardiovascular disease including T2D (also in primary prevention) should be advised to maintain current statin treatment also in case of confirmed COVID-19. In patient with active COVID-19, an increased risk of muscular damage has been described and statin therapy could be interrupted for a short period of time in order to favour muscular recovery [169] . T2D should be considered as a risk factor for poor prognosis in COVID-19 due to several reasons, including poor baseline glucose control and high glycaemic variability, diabetes-related immune dysfunction and concurrent comorbidities, also proven to act as independent risk factors for poor prognosis in this clinical setting. Considering that respiratory system remains essentially the leading way for SARS-CoV-2 entering in host, and given that immune barriers are weaken in T2D patients, including resident pulmonary macrophages, neutrophils and T cells [70] , diabetologists could improve clinical outcomes in COVID-19 simply by the use of such a medication with potential for positive immune-modulation and anti-inflammatory effects. Unfortunately, inconclusive deductions are currently available for considering concomitant medications favourable (as well as detrimental) factors in COVID-19 patients with T2D. Despite some putative mechanisms have been identified and some speculative hypothesis have also been formulated indicating that some anti-diabetes medications may improve clinical course in COVID-19 (pioglitazone, gliflozins, DPP-IV inhibitors, GLP-1RA), further studies are needed to clarify the issue. A timely glucose control should be obtained in diabetic patients during pandemic for the purpose to avoid potentially harmful outcomes in case of SARS-CoV-2 infection [170] . Anti-diabetic medications should be used with caution, also considering their impact on patient safety in case of complications related with COVID-19 infection (renal failure, cardio-respiratory burden, ketoacidosis, etc). Oral and non-insulin injectable medications (GLP-1RAs) should be considered to maintain or intensify household glucose control, while insulin treatment is currently known to be safer in case of hospitalized and seriously ill patients [171] . Given this consideration, in seriously ill patients, immune response should be adequately monitored in order to carefully predict or precociously diagnose a severe disease [172] and cytokines, including IL-6, may be considered as a goal for novel and more specific target therapies particularly in T2D individuals [173] [174] [175] . Beyond the risks strictly related with COVID-19 infections in diabetic patients, further concerns should be taken into account. Social distancing and lockdown have been proposed as the most effective action to prevent and reduce spread of SARS-CoV-2 infection [176] . Consequently, more time has been spent in household and access to public and private services has been consistently reduced. Thus, several barriers exist for diabetic patients to maintain an adequate fitness status and weight management as well as access to ambulatory care for obtaining adequate treatment adjustments [177] . In the last few months, home confinement led to physical inactivity and low exposure to sunlight, with possible detrimental effects particularly in diabetic and obese patients [119] . Physical exercise should be encouraged also at home or outdoor, even respecting social distancing rules, since it promotes weight loss, and improves cardiorespiratory fitness [121] ; reduces levels of cortisol thus reinforcing immune response against infections; induces T-cell differentiation and maturation [178] . Importantly, patients should be advised about risks and supported regarding pandemic-related concerns. Particularly, they should be elucidated about general hygienic tips to protect themselves and others from SARS-CoV-2 spread; promptly recognize COVID-19 signs and symptoms; access orderly to approved testing if exposed; furthermore, it is important to avoid unnecessary routine appointment in person especially for elderly patients [179] . Closely monitoring of glucose control should be recommended at home and glucose reports should be periodically sent to the diabetologist for proper therapy adjustments [180] . The hard lesson form Lombardy disaster probably teaches to change prospective in the conception of patients care during the present and in case of further pandemic. Implementation of community-centred care system (home care and mobile clinics) may result in a prompt detection and better management of infected patients particularly for frail individuals. Moreover, it might contain an unnecessary hospital workload, letting hospital activities to focus mainly on acute cares and limiting contagion risks among inpatients and healthcare personnel. Finally, it could provide at home special cares for elderly and comorbid patients [181] . In this historical moment, the use of technologies appears certainly appropriated to better manage chronic clinical conditions, including DM [182] [183] [184] . 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   key: cord-335894-6m0nnufu authors: Ceriello, Antonio; Standl, Eberhard; Catrinoiu, Doina; Itzhak, Baruch; Lalic, Nebojsa M.; Rahelic, Dario; Schnell, Oliver; Škrha, Jan; Valensi, Paul title: Issues for the management of people with diabetes and COVID-19 in ICU date: 2020-07-20 journal: Cardiovasc Diabetol DOI: 10.1186/s12933-020-01089-2 sha: doc_id: 335894 cord_uid: 6m0nnufu In the pandemic “Corona Virus Disease 2019” (COVID-19) people with diabetes have a high risk to require ICU admission. The management of diabetes in Intensive Care Unit is always challenging, however, when diabetes is present in COVID-19 the situation seems even more complicated. An optimal glycemic control, avoiding acute hyperglycemia, hypoglycemia and glycemic variability may significantly improve the outcome. In this case, intravenous insulin infusion with continuous glucose monitoring should be the choice. No evidence suggests stopping angiotensin-converting-enzyme inhibitors, angiotensin-renin-blockers or statins, even it has been suggested that they may increase the expression of Angiotensin-Converting-Enzyme-2 (ACE2) receptor, which is used by “Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to penetrate into the cells. A real issue is the usefulness of several biomarkers, which have been suggested to be measured during the COVID-19. N-Terminal-pro-Brain Natriuretic-Peptide, D-dimer and hs-Troponin are often increased in diabetes. Their meaning in the case of diabetes and COVID-19 should be therefore very carefully evaluated. Even though we understand that in such a critical situation some of these requests are not so easy to implement, we believe that the best possible action to prevent a worse outcome is essential in any medical act. In the pandemic "Corona Virus Disease 2019" (COVID- 19) people with diabetes have a high risk to require ICU admission. The management of diabetes in Intensive Care Unit is always challenging, however, when diabetes is present in COVID-19 the situation seems even more complicated. This article discusses the specific problems of managing people with diabetes and COVID-19 in ICU. In the recent Corona Virus Disease 2019 (COVID-19) pandemic people with diabetes are paying a very high price. Probably they are not exposed to higher risk of being infected, however, in the case, particularly when the metabolic control is not sufficient, they are more prone to serious complications and to die [1] [2] [3] [4] [5] . The rates of severe disease are significantly higher in patients with diabetes compared with non-diabetes (34.6% vs. 14.2) [5] . Similarly, type 2 diabetic patients have higher rates of need for Intensive Care Unit (ICU), (37.0% vs. 26.7%) [5] . It is well recognized that the management of people with diabetes in an ICU is particularly challenging [6] . To this situation we have to add that diabetes is very often accompanied by co-morbidities, such as cardiovascular disease, hypertension and obesity, which by themselves worsen the prognosis of people with COVID-19 [1] [2] [3] [4] [5] . Moreover, there are also several other conditions (described in the course of the article), commonly present in diabetes, which can expose people with diabetes Open Access Cardiovascular Diabetology *Correspondence: antonio.ceriello@hotmail.it 1 IRCCS MultiMedica, Via Gaudenzio Fantoli, 16/15, 20138 Milan, Italy Full list of author information is available at the end of the article and COVID-19 at high risk for complications. It seems, therefore, quite clear that people with diabetes may have a particular profile/needs when hospitalized in ICU for the COVID-19. In this article we seek to discuss the specific issues to which people with diabetes can be exposed in ICU when having the COVID-19. It is well recognized that an optimal glycemic control during the stay in ICU can improve the prognosis [7] . However, the optimal glycemic control, particularly in ICU involves today several aspects. Unfortunately, it is not surprising that patients suffering from COVID-19 with hyperglycemia may have a higher risk and a poorer outcome compared with those with euglycemia [8] . In particular, very recent reports from the USA have shown that uncontrolled glycemia is exposing people with diabetes and COVID-19 at a very high risk to develop serious complications or to die [9, 10] . Evidence shows that in ICU the more time the patients spend in the normal range of glycemia, the better is their prognosis [11, 12] . This is also the case of COVID-19. It has been reported that a tight glycemic control with insulin infusion had a lower risk of severe disease than patients without insulin infusion [13] . This aspect might be of immediate understanding, however, it also implies to recognize why acute peaks of glycemia, episodes of hypoglycemia or, even worse, the exposure of the patients to huge glucose variability during the stay in ICU are all rather deleterious. Acute hyperglycemia produces oxidative stress followed by an enormous production of inflammatory cytokines [14] , a situation that obviously must be avoided during any stay in ICU, but particularly during the stay for COVID-19. It is well known that during this disease a massive cytokines storm can occur, with severely damaging effects [15] . Hypoglycemia can produce the same effects as acute hyperglycemia and can expose directly people to the risk of dying [16, 17] . Furthermore, how hypoglycemia is recovered might be dangerous: hyperglycemia post-hypoglycemia is also an issue, leading to an enhancement of inflammation [18] . Finally, there are plenty of reports in the literature that glucose variability is producing a worsening of the prognosis in ICU [16, [19] [20] [21] [22] even when glucose is kept in normal range [22] . So it seems advisable that glucose variability should be avoided. Glucose variability also induces the generation of the oxidative stress and the release of inflammatory cytokines [23] . Is the issue of glycemic control also of importance for people with diabetes and COVID-19? It seems, unfortunately, the case, according to reports on how glycemia was managed in several situations during this pandemic [24, 25] . When facing high glucose levels due to severe infection per se, it is often required that patients are switched to insulin, with some concerns that insulin treatment might not always be safely managed in such situations, unless insulin is administered intravenously via an exactly dosing perfusion device to avoid subcutaneous absorption irregularities in critically ill patients [26, 27] . Hyperglycemia is common in the Intensive Care Unit (ICU) both in patients with and without a previous diagnosis of diabetes [28] . The optimal glucose range in the ICU population is still a matter of debate. Given the risk of hypoglycemia associated with intensive insulin therapy, current recommendations include treating hyperglycemia after two consecutive glucose > 180 mg/ dL with target levels of 140-180 mg/dL for most patients [28] . The optimal method of sampling glucose and delivery of insulin in critically ill patients remains elusive. While point of care glucose meters are not consistently accurate and have to be used with caution, continuous glucose monitoring (CGM) is not standard of care and is not yet generally recommended for inpatient use. The advent of new technologies, such as electronic glucose management, CGM, and closed-loop systems, promises to improve inpatient glycemic control in the critically ill with lower rates of hypoglycemia [28] . The issue of optimal glycemic control is certainly even more complicated during the management of COVID-19 because high doses of glucocorticoids are often used [29] . Glucocorticoids improve the prognosis of COVID-19 but, of course, induce or worsen hyperglycemia [30] . In the case keeping normal glycemia may be very challenging [30] . Another challenge in managing glycemia during the stay in ICU, particularly during the early phase, is the background anti-hyperglycemia therapy. While for several therapies, such as dipeptidyl-peptidase 4 inhibitors (DPP4 inhibitors), sodium-glucose-transporter-2 inhibitors (SGLT-2 inhibitors), pioglitazone, alpha-glucosidase inhibitors, metformin and short-acting Glucagon-Like-Peptide-1 Receptor Agonists (GLP-1RA) (exenatide and lixisenatide) their action is only shortly enduring after they are stopped, this cannot be the case for long-acting insulins but particularly for the long-acting GLP-1RA (dulaglutide, exenatide LA, liraglutide and semaglutide) [31] . Their action will add to that of insulin used during the treatment in ICU and must be considered in choosing the insulin dose. On the other hand, many of them show an anti-inflammatory activity, which could be quite helpful during COVID-19 [32] (Fig. 1) . Another rising problem might be the concomitant use of hydroxychloroquine that, even it is controversial [33] , sometimes is used for preventing the effect of the "Severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2) [34] . Hydroxychloroquine has a proven hypoglycemic effect, therefore also in this case the insulin treatment must be very carefully managed in order to avoid episodes of hypoglycemia [35] . Intriguingly, there is also evidence that optimal Covid-19 infection management with Tocilizumab is not achieved during hyperglycemia in both diabetic and non-diabetic patients [36] . Increased attention is needed regarding the proper hydration of the diabetic patient with COVID-19 in the ICU [37] . Hyperhydration can induce the onset of life-threatening pulmonary oedema due to the severity of lung damage during corona infection. Serum K levels are equally important, with a major risk of hypokalaemia, frequently associated with COVID-19, possibly due to hyperaldosteronism caused by elevated angiotensin 2 [37] . Insulin treatment may worsen hypokalaemia if not corrected in time [38] . Diabetes is very often accompanied by hypertension [39] . Fortunately, the issue of the possible role of angiotensin-converting-enzyme inhibitors and angiotensin-receptor-blockers in favoring the penetration of the SARS-CoV-2, due to the increasing receptor for the virus "Angiotensin-Converting-Enzyme-2" (ACE2), seems to be over [40] . New specific data coming from people with the COVID-19, are certainly reassuring on this point [41, 42] . In the CORONADO study, which included 1317 patients with diabetes, neither hypertension nor treatment by renin-angiotensin-aldosterone system blockers were associated with a worse prognosis [43] . Anyhow, the control of blood pressure remains an important point in the management of people in ICU and of course this particularly applies to people with diabetes. Fig. 1 Possible issues in the management of people with diabetes and COVID-19 in ICU. Several issues are present during the management of people with diabetes and COVID-19 in ICU. Tight glycemic control, avoiding hypoglycemia and glucose variability improves the prognosis. This goal, to be achieved, needs insulin infusion and continuous glucose monitoring (CGM). *Moreover, the achievement of tight glucose control may be influenced by the background anti-hyperglycemic therapy and by the concomitant therapy with hydroxychloroquine (risk of hypoglycemia) or corticosteroids (inducing hyperglycemia). **The evaluation of the meaning of several biomarkers related to the risk of cardiovascular complications, thrombosis and inflammation must be careful, because many of them are already altered by diabetes itself. There is no contraindication, however, to the use of ACEi or ARBS to control blood pressure. ACEi angiotensin-converting-enzyme inhibitors, ARBs angiotensin-receptor-blockers, CV cardio-vascular, CGM continuous glucose monitoring, CRP C-reactive protein, Hs-Troponin High-sensitive troponin, ICU Intensive Care Unit, IL-6 interleukin-6, NT-proBNP N-Terminal-pro-Brain Natriuretic-Peptide Several biomarkers have been suggested to be helpful in stratification of the risk during the COVID-19. Their value as helpful tools in the case of COVID-19 in people with diabetes needs a careful evaluation. COVID-19 can cause serious acute cardiovascular events [44] . Moreover, people with a previous cardiovascular disease are more prone to a worse prognosis if affected by SARS-CoV-2 [1] [2] [3] [4] [5] . A pre-existing cardiovascular disease very often accompanies diabetes, therefore, people with diabetes also for this reason may be exposed to a more serious complication when having the COVID-19. Furthermore, a large proportion of people with diabetes has asymptomatic coronary artery disease [45, 46] which can increase the risk of acute coronary syndrome, heart failure and arrhythmia during the COVID-19 due to proinflammatory process, hypercoagulability and sympathetic stimulation. QT interval is also often increased in people with diabetes, as a consequence of cardiac autonomic neuropathy [47, 48] . QT interval may be further lengthened by hypoglycemia and by drugs used during the COVID-19 and by hypokalemia and needs to be carefully monitored. Hs-troponin has been suggested for monitoring the risk of myocardial infarction during the COVID-19 [49, 50] . However, in the case of diabetes, increased levels of hs-troponin have been reported [47] , as signal of an existing chronic heart damage, therefore its use for monitoring heart risk in diabetes during COVID-19 deserves some caution. The situation is not different for heart failure, which again, has been described as a serious complication of COVID-19 [49, 50] . The measurement of the "N-Terminal-pro-Brain Natriuretic-Peptide" (NT-proBNP) to define the risk for heart failure in COVID-19 has been suggested [48] [49] [50] . However, plasma NT-proBNP level in a patient with COVID-19 must be seen as a marker of both the presence and extent of pre-existing cardiac disease and the acute haemodynamic stress related to COVID-19. As for hs-troponin, increased levels of NT-proBNP have been often reported in diabetes [51, 52] . According to various studies, an asymptomatic heart failure can be present in up to more than 50% of people with diabetes [51] [52] [53] . This condition is characterized by an increase of plasma NT-proBNP. So, again, the usefulness of a marker, in this case the NT-proBNP, needs a careful evaluation in the presence of diabetes and COVID-19. Thrombosis has been found to occur very often during the COVID-19 and it is one of the most serious complications [54] . The measurement of D-dimer is very useful in the prediction of the risk for a thrombotic event and its evaluation has been suggested regarding the management of COVID-19 patients [55, 56] . The situation in diabetes looks more complicated. D-dimer is often elevated in diabetes [57, 58] . It is just the index of increased thrombophilia, which is highly prevalent in diabetes [59] . The thrombophilia in diabetes is related to an imbalance between thrombosis and fibrinolysis [59] . This status suggests in one side that the evaluation of the thrombotic status in diabetes during the admission in ICU using the D-dimer should be very careful, but at the same time that an anticoagulation could be very helpful. In this context the role of hyperglycemia also deserves attention. An acute increase of glycemia may activate thrombin formation [60] , convincingly through the glycation of the antithrombin III, a phenomenon that can be reversed by a fast control of hyperglycemia or by heparin [61] . Heparin administration is largely suggested in the case of COVID-19, so it seems true that there is further reason for its use in the presence of diabetes [50] . Finally, it has been recently reported that glucose variability may increase the platelet reactivity [62] . Therefore, regarding the risk of thrombosis, there are good reasons to keep glycemia under a strict control, in association with anticoagulation. Markers of inflammation, particularly C-reactive-protein and interleukin-6 have also been suggested as tools for monitoring the severity of the COVID-19 [50, 55] . Again diabetes has a particular situation. Low-grade inflammation is present in this disease [63] , therefore, as for thrombosis and heart failure, the significance of altered values of these markers needs a careful evaluation in people with diabetes and COVID-19. Furthermore, as reported above, an acute increase of glycemia as well as glucose variability are accompanied by an increased production of cytokines [64, 65] , an effect that must be avoided in the COVID-19. Most of diabetic patients are routinely on lipid-lowering treatment, in particular on statins in accordance with the current diabetes and cardiovascular disease guidelines [66] . Statins have well-known anti-inflammatory effects and improve endothelial function, which may be protective against cardiovascular complications during COVID-19. However, through various mechanisms statins may enhance compensatory immune signals [67] . In addition, similar to renin-angiotensin-aldosterone system blockers, experimental studies showed that statins also augment the ACE2 receptor expression [68] and might thus facilitate the penetration of SARS-CoV-2 into the cells. Whether statins may be beneficial or harmful during virus-induced acute respiratory distress syndrome is controversial [69, 70] . Further investigations are urgently required to clarify the interplay of these complex mechanisms with the new coronaviruses. In addition statins may cause myotoxicity. A markedly increase in creatine kinase may be observed in some patients with COVID-19 [1] . The benefit of statins in cardiovascular prevention is well established in people with diabetes. In this context there is no evidence for withdrawing statins during COVID-19. However creatin kinase should be carefully monitored and, if increased, statin therapy should be temporarily withheld in order to avoid rhabdomyolysis. Probably it is not well recognized that all we have reported above, which is true for type 2 diabetes, applies also and particularly and more seriously to type 1 diabetes [71, 72] . Type 1 diabetes has the same, albeit not more than type 2 diabetes, risk for cardiovascular events [71] . Moreover, it is important to note that many old people may have today type 1 diabetes. Data are reassuring, showing that people with type 1 diabetes are not more exposed to SARS-COV-2 infection [73] nor to more severe outcomes compared to patients with type 2 diabetes [43] . However, type 1 diabetes is more complicated to manage and probably deserves a special attention when admitted to ICU for COVID-19. Managing people with diabetes in any acute setting is always very difficult. COVID-19 has a very severe prognosis for people with diabetes and evidence shows that a tight glucose control could be very helpful. In the case of COVID-19 people with diabetes are more exposed to cardiovascular complications, which may be more challenging to manage [74, 75] . The usefulness of several biomarkers suggested for evaluating/monitoring the severity of the disease might be less evident and their use needs a careful evaluation in the case of diabetes. Even though we understand that in such a critical situation some of these requests are not so easy to implement, we believe that the best possible action to prevent a worse outcome is essential in any medical act. China Medical Treatment Expert Group for Covid-19), et al. 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Issues of cardiovascular risk management in people with diabetes in the COVID-19 era Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Not applicable. 


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   key: cord-336201-fl606l3b authors: Daryabor, Gholamreza; Atashzar, Mohamad Reza; Kabelitz, Dieter; Meri, Seppo; Kalantar, Kurosh title: The Effects of Type 2 Diabetes Mellitus on Organ Metabolism and the Immune System date: 2020-07-22 journal: Front Immunol DOI: 10.3389/fimmu.2020.01582 sha: doc_id: 336201 cord_uid: fl606l3b Metabolic abnormalities such as dyslipidemia, hyperinsulinemia, or insulin resistance and obesity play key roles in the induction and progression of type 2 diabetes mellitus (T2DM). The field of immunometabolism implies a bidirectional link between the immune system and metabolism, in which inflammation plays an essential role in the promotion of metabolic abnormalities (e.g., obesity and T2DM), and metabolic factors, in turn, regulate immune cell functions. Obesity as the main inducer of a systemic low-level inflammation is a main susceptibility factor for T2DM. Obesity-related immune cell infiltration, inflammation, and increased oxidative stress promote metabolic impairments in the insulin-sensitive tissues and finally, insulin resistance, organ failure, and premature aging occur. Hyperglycemia and the subsequent inflammation are the main causes of micro- and macroangiopathies in the circulatory system. They also promote the gut microbiota dysbiosis, increased intestinal permeability, and fatty liver disease. The impaired immune system together with metabolic imbalance also increases the susceptibility of patients to several pathogenic agents such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Thus, the need for a proper immunization protocol among such patients is granted. The focus of the current review is to explore metabolic and immunological abnormalities affecting several organs of T2DM patients and explain the mechanisms, whereby diabetic patients become more susceptible to infectious diseases. The metabolic syndrome is defined by the presence of metabolic abnormalities such as obesity, dyslipidemia, insulin resistance, and subsequent hyperinsulinemia in an individual (1) . Dyslipidemia, the main characteristic of metabolic syndrome, is defined by decreased serum levels of high-density lipoproteins (HDLs) but increased levels of cholesterol, free fatty acids (FFAs), triglycerides (TG), VLDL, small dense LDL (sdLDL), and oxidized LDL (ox-LDL) ( Table 1 ) (2) . Individuals with the metabolic syndrome are much more likely to develop type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVDs), and fatty liver disease (2) (3) (4) . T2DM, the most common form of diabetes (∼90%), is characterized by a systemic inflammatory disease accompanied by insulin resistance (IR) or decreased metabolic response to insulin in several tissues, including the adipose tissue, liver, and skeletal muscle, as well as by reduced insulin synthesis by pancreatic beta cells (4, 5) . Studies on immunometabolism have indicated that the metabolic states and immunological processes are inherently interconnected (6) . In this scenario, metabolites derived from the host or microbiota regulate immunological responses during health and disease (6) . Accordingly, in obese individuals, expanded adipose tissue at different locations, by initiating and perpetuating the inflammation, induces a chronic low-level inflammatory state that promotes IR (4). Every organ system in human body can be affected by diabetes, but the extent of organ involvement depends largely on the severity and duration of the disease (Figure 1 and Table 1 ). During the progression of diabetes, hyperglycemia promotes mitochondrial dysfunction and induces the formation of reactive oxygen species (ROS) that cause oxidative stress in several tissues such as blood vessels and pancreatic beta cells (7) (8) (9) . Accumulating damage to the mitochondria, as well as several macromolecules, including proteins, lipids, and nucleic acids by ROS promotes the process of aging (10) . As a result, pancreatic β cells that require functional mitochondria to maintain insulin synthesis fail to generate high enough levels of insulin (11, 12) . In the absence of compensatory mechanisms, stress-responsive intracellular signaling molecules are activated and cellular damage occurs. Elevated intracellular levels of ROS and subsequent oxidative stress play an important role in the pro-atherosclerotic consequences of diabetes and the development vascular complications (9, 13) . Moreover, the non-enzymatic covalent attachment of glucose and its toxic derivatives [e.g., glyoxal, methylglyoxal (MGO), and 3deoxyglucosone] to the biological macromolecules such as nucleic acids, lipids, and proteins leads to the formation of advanced glycation end products (AGEs) (14, 15) . Accumulated AGEs block the insulin signaling pathway and promote inflammation (16, 17) . In addition, the attachment of AGEs to their receptors [e.g., CD36, galectin-3, scavenger receptors types I (SR-A1), and II (SR-A2)] on the surfaces of immune cells in the circulation and tissues activates the expression of pro-inflammatory cytokines and increases free radical generation (18) . Furthermore, due to the chronic exposure of cells to high glucose levels in untreated T2DM patients, glucose toxicity might occur in several organs. This will FIGURE 1 | Effects of T2DM on body organs. T2DM is an inflammatory state that affects circulatory system, gastrointestinal tract, pancreatic beta cells, liver, and skeletal muscles and makes them dysfunctional. NFALD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; ER, endoplasmic reticulum. eventually lead to nephropathy, cardiomyopathy, neuropathy, and retinopathy. Gut microbiome dysbiosis is another important factor that can facilitate the induction and progression of metabolic diseases such as T2DM (19) . The gut microbiome dysbiosis, by altering the barrier functions of intestine and the host metabolic status, promotes the insulin resistance in diabetic patients (19) . Diabetes also impairs the immune system and increases the susceptibility of patients to serious and prolonged infections (20) . This is likely to be the case with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as well (21, 22) . In the current paper we will review recent research to explore the impairment of body organs in T2DM patients and explain how diabetic patients become more susceptible to certain infectious diseases. Vascular homeostasis is an important function of the endothelium. Under homeostatic conditions, the ECs maintain the integrity of blood vessels, modulate blood flow, deliver nutrients to the underlying tissues, regulate fibrinolysis and coagulation, control platelet adherence and patrol the trafficking of leukocytes (Figure 2A ) (23) . Normal ECs also internalize high-density lipoproteins (HDLs) and its main protein part apolipoprotein A-I (apoA-I) in a receptor-mediated manner to activate endothelial cell nitric oxide (eNOS) synthase and promote anti-inflammatory and antiapoptotic mechanisms ( Figure 2B ) (24) . HDL receptors on the surfaces of ECs include: the ATP-binding cassette (ABC) transporters A1 and G1, the scavenger receptor (SR)-B1 and the ecto-F1-ATPase (24) . According to the epidemiological studies, diabetes mellitus is considered as one of the main risk factors for CVD (Figure 1 ) (25) . From the beginning of T2DM, the functions of ECs are impaired, which is the main cause of disease-related side-effects (26) . ECs can initiate and perpetuate the inflammatory milieu during the pathogenesis of diabetes. Due to the negative impacts of hyperglycemia and subsequent oxidative stress, CVDs are more common among diabetic patients (27) . It has been observed that incubation of human aortal endothelial cells (HAECs) with a medium containing high glucose concentrations (HG, 20 mM) increases the intracellular levels of MGO and glycated proteins that in turn activate the unfolded protein response (UPR) and trigger inflammatory and prothrombotic pathways (28) . Glycated apoA-I, which is formed during hyperglycemia, modifies its structure, decreases its lipid-binding ability, prevents cholesterol efflux from macrophages and impairs its anti-inflammatory function (29, 30) . Vaisar et al. have shown that HDLs from diabetic patients have a reduced capacity to trigger eNOS production and suppress tumor necrosis factor-α (TNF-α)mediated inflammatory responses within ECs (31) . Diseases such as T2DM that induce high levels of vascular injury are accompanied by an elevated number of circulating endothelial cells (CECs) (32) . T2DM-related risk factors such as dyslipidemia, hyperglycemia, and hyperinsulinemia as well as other conditions (e.g., inadequate physical activity, smoking, and high blood pressure) facilitate the formation of atherosclerotic plaques/lesions (33) . Dyslipidemia, due to the elevated flux of FFA from insulin-resistant tissues and spillover from entry (C) Blood vessels in T2DM patients. During the progression of the disease, red blood cells become glycated, while activated ECs synthesize elevated levels of adhesion molecules and chemokines that facilitate monocytes recruitment, adhesion, and transmigration across the endothelium toward the subendothelial region. Monocytes are then differentiated into macrophages and eventually, by excess lipid uptake, generate foam cells. Subsequently, further immune cell infiltration into the atherosclerotic lesion occurs, where their inflammatory cytokines promote platelet activation, EC apoptosis, and increased generation of ROS and Ox-LDL. (D) interactions between oxLDL and its receptor aggravate ROS generation, NF-κB activation and inflammation. EC, endothelial cell; RBC, red blood cell; PLT, platelet; HDL, high-density lipoprotein; Ox-LDL, Oxidized low-density lipoprotein; ROS, reactive oxygen species; eNOS, endothelial nitric oxide synthase; NO, Nitric oxide; LOX-1, lectin-type oxidized LDL receptor 1. into adipocytes, is considered as an important risk factor for developing CVD among diabetic patients. This is because dyslipidemia promotes inflammation, endothelial dysfunction, and platelet hyperactivation (34, 35) . During the progression of atherosclerosis, lipids, immune cells, and extracellular matrix accumulate in the arterial intima or subendothelial regions ( Figure 2C ) (33) . Advanced plaques can impede blood flow and cause tissue ischemia or might become disrupted and generate a thrombus that stops the blood flow of important organs. Vascular complications of diabetes engage either tiny or large blood vessels (micro-and macroangiopathy, respectively). Microangiopathies, which can be seen in the kidneys, vasa nervorum and eye tissues, cause nephropathy, neuropathy, and retinopathy. Macroangiopathies, by inducing atherosclerosis in the coronary, carotid, and peripheral arteries, increase the risk of myocardial infarction (MI), stroke and peripheral artery disease (PAD). Macrovascular complications due to EC dysfunction are considered as an important cause of mortality and morbidity among diabetic patients (36) . Oxidative stress has an essential role in the induction of vascular complications during the course of diabetes (8) . EC dysfunction (e.g., delayed replication, dysregulated cell cycling, and apoptosis), as well as enhanced ox-LDL formation are some consequences of oxidative stress. It has been well-established that sdLDL and ox-LDL have an enhanced atherogenic ability and are more useful biomarkers than total LDL for predicting CVD (37, 38) . sdLDL particles have a smaller size than other LDL particles. Thus, sdLDL particles are more easily oxidized, and their atherogenic potential is enhanced. During oxidative stress, levels of ox-LDL increase by the excess action of reactive oxygen species (ROS) (13) . Subsequently, ox-LDL interaction with scavenger receptors, including CD36, SR-A1/CD204, SR-B1, and lectin-like ox-LDL receptor-1 (LOX-1) on the surface of ECs activates the NADPH oxidase that in turn increases the expression of ROS and activates the transcription factor NF-αB (39) . Afterwards, the expression of LOX-1, adhesion molecules (e.g., selectins and integrins) and the secretion of pro-inflammatory cytokines and chemokines are increased, while NO synthesis is decreased in ECs ( Figure 2D ) (39) (40) (41) . EC-derived chemokines bind to their cognate receptors on the surfaces of monocytes and recruit them toward the inflamed endothelium. Following this, selectin-based rolling and integrin-based attachment of monocytes to the ECs cause their migration toward the subendothelial region, where they develop into lipid-laden macrophages or foam cells later on (42) . The scavenger receptor LOX-1 plays an important role in the uptake of ox-LDL during atherogenesis. It is strongly expressed on the surfaces of ECs, but has an inducible pattern of expression on the surface of macrophages and smooth muscle cells (43) . The accelerated uptake of ox-LDL by macrophages accounts for their transformation into foam cells, the initial hallmark of atherosclerosis (41, 43) . Besides, diabetes leads to both quantitative and qualitative defects in circulating angiogenic progenitor cells (CAPCs) that take part in the repair of injured endothelium (44) . It has been shown that humans or mice with decreased numbers of CD31 + CD34 + CD133 + CD45 dim Sca-1 + Flk-1 + CAPCs have an increased prevalence of T2DM, elevated HbA1c levels and aggravated CVD risk scores (44, 45) . In diabetic patients, despite elevated serum levels of proangiogenic molecules, like angiopoietin-1/2, EPO, and VEGF-A, angiogenesis is impaired. This is mainly due to the decreased expression levels of VEGFR2 and CXCR4 on the surfaces of CAPCs, which makes them unresponsive to the angiogenic factors (44, 46) . It has also been shown that circulating proangiogenic granulocytes composed of eosinophils and neutrophils are also impaired in diabetic patients (47) . Besides, elevated levels of AGEs in T2DM cause EC dysfunction and vascular inflammation (48) . Ren et al. have shown that incubation of human coronary artery endothelial cells (HCAECs) with AGEs causes decreased expression (at both mRNA and protein levels) and enzymatic activity of eNOS, increased levels of ROS, diminished mitochondrial membrane potential and declined activity of catalase and superoxide dismutase in treated cells (49) . Another study by Lan et al. has shown that AGEs in the pancreas decrease EC viability and induce their apoptosis in an NFκB signaling-related manner (50) . However, apigenin (4 ′ ,5,7trihydroxyflavone) can protect ECs against oxidative stress and subsequent inflammatory reactions mediated by AGEs (51) . Apigenin binds to methylglyoxal (MGO) and forms a complex that inhibits AGE formation. Chettab et al. have shown that the expression of ICAM-1 as well as the production of IL-8, are significantly increased in HUVECs cultured in HG medium compared to cells cultured in normal glucose (NG, 5.5 mM) conditions (52). Bammert et al. found out that incubation of HUVECs with HG media promotes the generation of endothelial microparticles (EMPs) that, when added to normally cultured HUVECs, downregulate the expression of anti-apoptotic microRNA miR-Let7a, but enhance the synthesis of active caspase-3 and cause cell apoptosis (53) . Several microRNAs, including miR-21, miR-26a, miR-30, miR-92a, miR-126, miR-139, miR-199a, miR-222, and miR-let7d, regulate vascular homeostasis. It has been shown that the expressions of miR-26a and miR-126 are significantly reduced in circulating MPs isolated from diabetic patients compared with normal individuals. This could be involved in making diabetic individuals more susceptible to coronary heart disease (54) . Moreover, HG media upregulate the expression of NADPH oxidase that will induce the generation of ROS. This leads to subsequent apoptosis of the HUVECs through a ROS-dependent caspase-3 pathway (55). Su et al. have demonstrated that argirein medication, by inactivating NADPH oxidase, can prevent endothelial cell apoptosis in a rat model of T2DM and hence attenuate vascular dysfunction (56) . HG further increases the permeability of the HUVECs in a protein kinase C (PKC)-dependent manner (57, 58) . Hassanpour et al. showed that incubation of endothelial progenitor cells with the serum of T2DM patients inhibits their migration toward bFGF, increases their expression of VEGFR-2, but reduces their expression of VEGFR-1 and induces their apoptosis (59) . However, humanin (HN), a mitochondriumderived peptide, is cytoprotective against apoptosis during pathological conditions, such as diabetes mellitus (60) . It has been demonstrated that simultaneous incubation of H9C2 cells, a line of rat cardiac myoblasts, with H 2 O 2 and HN decreases the intracellular levels of ROS, preserve mitochondrial function/structure and decline cellular apoptosis (61) . Wang et al. have indicated that the treatment of HUVECs with HN before their incubation with HG medium increases the expression of eNOS, while decreasing the expression of endothelin 1 (ET-1), VCAM-1, TNF-α, IL-1β, and E-selectin in a krüppellike factor 2 (KLF2)-dependent manner. Such changes in the expression of integrins prevent the attachment of monocytes to HUVECs (62) . Accordingly, HN might be used to prevent the development of hyperglycemia-associated EC dysfunction in T2DM. EC activation and expression of adhesion molecules also facilitate activation and adhesion of platelets. This will increase the risk of thrombosis and promote the development of thrombotic angiopathy, typical for diabetic patients. Platelets are tiny anucleated cellular fragments generated from megakaryocytes in the bone marrow. They circulate in the blood for ∼5-9 days and play essential roles in hemostasis and in controlling vascular integrity (63) . Circulating inactive platelets move in the proximity of vessel walls (Figure 2A ) and rapidly get activated in response to vascular injury. At the end of their life, platelets are cleared from circulation with the action of the liver and spleen-resident macrophages. Platelets have an essential role in the initiation and progression of inflammation. Platelet hyperactivation that occurs during inflammatory states (e.g., T2DM) facilitates the pathogenesis of CVDs ( Figure 2C ) (64, 65) . It has been shown that elevated levels of resistin, an adipokine, in diabetic patients enhances oxidative stress, promotes endothelial dysfunction and facilitates platelet activation (66) . Activated platelets with an increased mean volume [mean platelet volume (MPV)] secrete microparticles (MPs) and soluble adhesion molecules (e.g., sP-selectin and sCD40L) that in turn activate endothelial and immune cells (67) (68) (69) . Higher levels of platelet-derived MPs, which correlate positively with fasting blood sugar and glycated hemoglobin, have been shown in newly diagnosed T2DM patients compared to healthy individuals (70) . In T2DM patients thrombotic microangiopathies can lead to the development of CVDs (71) . Platelets in the patients adhere to ECs and aggregate more rapidly than in healthy individuals thereby increasing the risk of thrombosis. In a mouse model of T2DM, Zhu et al. have shown that AGEs interact with CD36, a member of the type 2 scavenger receptor family, on the surfaces of murine platelets to activate them and induce a prothrombotic state (72) . Elevated levels of the P2Y12 receptor on the surface of platelets in T2DM expose diabetic patients to a prothrombotic condition. This receptor has an essential role in platelet activation (73) . Zhou et al. have shown that long non-coding RNA (lncRNA) metallothionein 1 pseudogene 3 (MT1P3), which is markedly upregulated in megakaryocytes of T2DM patients, enhances the expression of p2y12 receptor in platelets (74) . They indicated that this is due to the inhibitory action of MT1P3 on miR-126. Virtually all parts of the human digestive system, including the gastrointestinal tract, pancreas, and the liver are affected by diabetes. The GIT is populated with a myriad of microorganisms, including principally bacteria but also archaea, viruses, fungi, and protozoans that dynamically influence the health status and homeostasis of the host. The physiological functions of the GIT resident microbes improve gut integrity, protect against microbial pathogens and regulate immune responses (75) . Mucosal barriers, such as intestinal epithelial cells (IECs) and the mucus layer, spatially isolate the host immune system and gut microbiota to prevent unnecessary immune activation and intestinal inflammation. They also facilitate the uptake of nutrients through receptors and transporters. However, hyperglycemia, in a GLUT2-dependent manner, can influence the mucus and alter the integrity of adherence and tight junctions between intestinal epithelial cells of diabetic mice. This will enhance the permeability of the intestinal barrier leading to so called "leaky gut." Subsequently, hyperglycemia may facilitate the dispersal of an enteric infection into a systemic infection (Figure 1 ) (76) . Interestingly, the reversal of hyperglycemia, conditional deletion of GLUT2 from the IECs and inhibition of glucose metabolism will fix the barrier dysfunction and prevent the spread of bacteria (76). Xu et al. have shown that Faecalibacterium prausnitzii, one of the most frequent commensal bacteria in normal individuals with essential roles in gut homeostasis, generates anti-inflammatory molecules that enhance the expression of tight junctions and improve intestinal integrity during diabetes (77) . However, in some cases, gut microbiota dysbiosis or altered microbial composition of the intestines could induce T2DM and lead to its progression (78) . Of interest, the widely used antidiabetic drug metformin can improve barrier integrity and restore the healthy microbiota composition of the gut in diabetic patients (79) . The intestinal commensal bacterium Akkermansia muciniphila can also act as a sentinel to reduce microbial translocation across the gut and prevent the subsequent inflammation in patients with T2DM (80) . Hyperglycemia can further decrease the intracellular levels of glutathione (GSH) but increase iNOS activity and NO production in the IECs (81). Zhao et al. have found out that hyperglycemia in a PKCα-dependent manner inhibits the ubiquitination, internalization and degradation of the divalent metal transporter 1 (DMT1) present on the microvillar membranes of IECs. Subsequently, intestinal iron uptake is enhanced and accumulated iron ions aggravate diabetes-related complications and increase mortality (82, 83) . The pancreas consists of the exocrine and endocrine compartments. The endocrine part is made of different cell types, including α, β, δ, and ε cells that secrete glucagon, insulin, somatostatin, and ghrelin hormones, respectively. These cells are aggregated into specialized structures called islets of Langerhans, which play an important role in controlling blood glucose levels through the secretion of insulin and glucagon. In T2DM, despite normal levels of β-cell replication and islet formation, β-cell apoptosis is increased so that the number of cells declines by ∼50% (Figure 1 ) (84) . During the progression of T2DM, the insulin-resistant state forces β-cells to compensate for the lack of insulin by elevating its synthesis to restore the normal blood glucose level. However, in severe diabetic patients, β-cell exhaustion, and subsequent persistent hyperglycemia occur (7) . Furthermore, chronic elevated serum levels of free fatty acids, seen in obesity and T2DM, induce lipotoxicity in beta-cells and suppress their insulin secretion ability (85) . To alleviate chronic inflammation, overcome insulin resistance (IR) and to prevent β-cell apoptosis, stem cells or stem cell derivatives such as insulin-producing cells (IPCs) and exosomes have been suggested (86) (87) (88) (89) . Their effects are believed to be mainly due to their anti-inflammatory activities. Secretagogin (SCGN) is predominantly expressed by pancreatic β-cells protecting their normal functions. SCGN also acts as an insulin binding protein to make it more stable, avoid its aggregation, improve its functions and enhance its secretion (90, 91) . In T2DM patients, due to the islet cell dysfunction and endoplasmic reticulum (ER) stress, serum levels of SCGN are elevated reflecting stress and dysfunctional islet cells (92) . Moreover, in patients with T2DM, islet amyloid polypeptide (IAPP or amylin), a peptide hormone and one of the main secretory products of pancreatic β-cells, tends to deposit in the islets of Langerhans, form insoluble fibrils and impair secretory functions of β-cells (93) . IAPP is costored with insulin in the secretory granules of pancreatic β cells. In steady-state conditions it regulates food intake, insulin secretion, and glucose metabolism (94). Ribeiro et al. have noted that pancreatic extracellular vesicles (EVs) from healthy individuals, but not from T2DM patients, directly bind to IAPPs and prevent amyloid formation within the pancreatic islets (95) . The authors showed that the altered protein-lipid composition of the EVs is the main reason for this discrepancy (95) . However, Chatterjee et al. have shown that β-cells from T2DM patients have a dysfunctional proteasome complex that fails to degrade pancreatic IAPP, whereby amyloid formation is induced (96) . Furthermore, in T2DM patients, lipids accelerate the formation of fibrillary IAPP, which aggravates islet cell damage (97) . Dhar et al. have demonstrated that chronic use of MGO in Sprague-Dawley rats increases the expression of NF-αB, MGO-derived AGEs and their receptors in pancreatic β cells. MGO can also induce apoptosis of islet β cells, increase fasting plasma glucose levels and impair glucose tolerance (98) . In T2DM patients the plasma level of MGO directly correlates with fasting blood sugar and HbA1c levels (99) . Bo et al. further showed that MGO in a dose-based manner impairs insulin secretion of pancreatic β-cell lines MIN6 and INS-1 through increased generation of ROS and by induction of mitochondrial dysfunction (100). Robertson et al. have found out that elevated levels of ROS in pancreatic β-cells inhibit the pancreas duodenum homeobox-1 (PDX-1) transcription factor that is needed for insulin synthesis (7) . It has been shown that chronic use of MGO in animals could induce T2DM, while simultaneous use of alagebrium, which breaks AGE compounds, attenuates the disease (98) . It has also been reported that during the course of diabetes dedifferentiation and conversion of β-cells into αand δ-"like" cells occurs (101) . In conclusion, the pancreatic β cell function is progressively reduced during the progression of T2DM. The liver is by far the most important metabolic organ with essential roles in regulating homeostasis and mediating glucose and lipid metabolism. Metabolic activities of the tissue are precisely controlled by the actions of metabolic substrates, including free fatty acids (FFAs) and hormones (102) . T2DM patients usually suffer from a chronic liver condition called non-alcoholic fatty liver disease (NAFLD). It is characterized by steatosis that means ectopic fat storage in hepatocytes and subsequent insulin resistance (Figure 1 ) (103) . Lipid accumulation in hepatocytes leads to impaired biogenesis of miR-206 that facilitates insulin signaling and prevents lipogenesis (104) . Several factors such as obesity, increased serum levels of fatty acids, and insulin resistance can increase the risk of fatty liver disease. P2Y2 receptor, through the induction of the c-Jun N-terminal kinase (JNK) and prevention of insulin signaling, can promote insulin resistance in hepatocytes in T2DM (105) . In some cases, NAFLD may progress into an aggressive form of inflammatory fatty liver disease called non-alcoholic steatohepatitis (NASH), which might cause liver cirrhosis and organ failure (106). Dang et al. have indicated that exosomes released from the adipose tissues of obese mice due to the smaller miR-141-3p content can promote insulin resistance in the murine hepatocyte cell line AML12 (alpha mouse liver 12) (107) . The adipokine visfatin that is released from the adipose tissue of obese individuals has also been shown to activate the pro-inflammatory STAT3 signaling pathway and NF-κB in the human liver cell line HepG2 and promote their insulin resistant state (108) . Nevertheless, the hepatocyte growth factor (HGF) can alleviate the insulin resistance of hepatocytes and control their triglyceride and cholesterol contents (109) . Skeletal muscle (SM) is the main tissue that releases glucose after insulin stimulation. Hence, insulin resistance in SM has a pivotal role in the metabolic dysregulation of T2DM. Insulin resistance in SM is the primary defect of T2DM that facilitates the progression of fatty liver disease, deposition of fat in the liver (Figure 1 ) (110) . Skeletal muscle from diabetic patients expresses less genes related to insulin signaling and metabolic pathways, but more apoptosis and immune-related genes (111) . This inflammatory milieu is mainly due to the proinflammatory actions of obesity-related adipose tissue mediators, which are released into the circulation and promote inflammation within the SM (4). Furthermore, obesity causes intermyocellular and perimuscular adipose tissue expansion that acts like adipose tissue depots to enhance SM inflammation (112) . It has been shown that human skeletal muscle cells (hSMC), isolated from diabetic patients, after a 24-h culture generate significantly more TNF-α, IL-6, IL-8, IL-15, monocyte chemotactic protein (MCP)-1, Growth-Related Oncogene (GRO)-α, and follistatin compared to non-diabetic individuals (113) . This altered secretion of myokines (e.g., cytokines secreted by SMs) is an intrinsic feature of SM during the progression of T2DM. In SM, GLUT-4, which is quickly translocated to the cell surface, facilitates glucose uptake in response to insulin hormone as well as muscle contraction. Pinto-Junior et al. have shown that the use of AGE-albumin in rats increases the expression of the inflammatory molecule NF-κB1 within the SM. NF-κB1 binds to the promoter of the GLUT-4 gene and suppresses its expression (at both mRNA and protein levels) (114) . Accordingly, GLUT-4 levels on the surfaces of SM decrease and subsequently, whole-body IR develops. The immune system is generally classified into two main arms, innate and adaptive (or acquired) immunity. Adaptive immunity is mediated by B cells, which produce antibodies and T cells, which are classified into CD4 + helper cells and cytotoxic CD8 + cells. A considerable literature has discussed the dysfunctional immune responses in diabetic patients ( Table 2 ) (115) (116) (117) (118) (119) (120) . Abnormal immune cell activation and subsequent inflammatory environment has an essential role in the progression of T2DM (121) . In this regard, chronic inflammation due mainly to the activation of the myeloid cell lineage (e.g., macrophages and neutrophils), is directly related to the induction of IR (4, 122). Their numbers are elevated, are larger and more granular, express diminished levels of antioxidant genes but elevated levels of pro-apoptotic and pro-inflammatory genes. Complement system Attachment of C-type lectin proteins to mannose residues is decreased, lectin pathway is impaired, CD59 activity is reduced, MAC deposition in vascular walls is increased. Dendritic cells (DCs) Their numbers and activity are reduced. Their cholesterol efflux is decreased, generate foam cells, have dysfunctional efferocytosis. Are activated, constitutively release NETs, produce high levels of MPO, ROS, and calprotectin (S100A8/A9), are more susceptible to apoptosis, their migration, phagocytosis and microbial killing are impaired. NK cells Their numbers are increased but are usually dysfunctional, express high levels of GLUT4 but decreased levels of NKG2D and NKp46, have reduced degranulation capacity, are more susceptible to apoptosis. NKT cells Their numbers are increased, produce high levels of IFN-γ, IL-4, and IL-17, express high levels of NKp30, NKG2D, and NKp44 but low levels of NKG2A and 158b. Innate lymphoid cells (ILCs) ILC1s are increased and produce high levels of IFN-γ. Humoral immunity (B cells) Germinal centers are reduced, Ab production and isotype switching is defective, Abs become glycated, Abs fail to activate complement. functions of osteoclasts, which are bone-resident innate immune cells (126) . This may affect bone structure and delay bone healing. Defects in the innate, as well as adaptive immunity, are supposed to be the main cause of diabetic individuals' susceptibility to infections (127) . Furthermore, some microorganisms, especially bacteria, in hyperglycemic conditions are better nourished and become more virulent, while also having a better milieu to cause infections. Complement System The complement system is a first-line defense mechanism against invading microorganisms. It acts via different but interconnected classical, alternative, and lectin pathways (128) . Ilyas et al. have shown that under high glucose conditions, the attachment of Ctype lectin proteins to high-mannose containing glycoproteins is substantially decreased in a dose-dependent manner. These carbohydrate-binding proteins include mannose-binding lectin (MBL), surfactant protein D (SP-D), dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN, CD209), and DC-SIGN-related (DC-SIGNR) protein (129) . Reduced binding of MBL in the presence of high levels of sugar causes a significant reduction in the lectin pathway activity, but does not influence classical or alternative pathway activity (129) . Nevertheless, Barkai et al. did not find significant differences in the function of classical or MBL pathways between T2DM and healthy individuals (130) . However, significantly decreased activity of ficolin-3-mediated lectin and alternative pathways, as well as decreased levels of C4d and soluble complement C5b-9 (sC5b-9) were seen in diabetic patients with Escherichia coli-mediated urinary tract infections (130) . This may be linked to a reduced ability of diabetics to protect themselves against bacterial infections. The lipopolysaccharides of certain Gram-negative bacteria, like Salmonella serotype O6,7 as well as the cell walls of fungi, are rich in mannose. Possibly, because of this, in addition to additional provision of nutrients, an increased prevalence of fungal infections is seen in T2DM patients (131, 132) . Patel et al. found a significantly higher prevalence of oral candida carriage in diabetic patients compared to healthy controls (131) . They found that Candida albicans was the most commonly isolated species followed by C. tropicalis, but uncommon species such as C. lusitaniae and C. lipolytica were also isolated (131) . Another study by Jhugroo et al. showed that C. albicans is the predominant yeast isolated from oral mucosal lesions of diabetic patients, followed by. C. tropicalis and C. krusei Dendritic cells (DCs) are a heterogeneous population of specialized and professional antigen-presenting cells (APCs) that create a crucial link between the innate and adaptive immune responses (136, 137) . Some studies have shown that the numbers of DCs are reduced in both type 1 and 2 diabetes (138, 139). Seifarth et al. have found that T2DM patients with poor metabolic control have decreased numbers of both myeloid and plasmacytoid DCs compared with healthy controls. This could make them more susceptible to opportunistic infections (139) . In the case of good blood glucose control, the reduction in DC numbers was less prominent but still significant, especially for myeloid DC1 (mDC1) cells (139) . Another study by Blank et al. demonstrated that women with T2DM and poor glycemic control (HbA1c ≥ 7%) have fewer numbers of circulating plasmacytoid DCs (pDCs) compared to diabetic women with good glycemic control (HbA1c < 7%) or to healthy women (140). Montani et al. have recently shown that hyperglycemic medium and hyperglycemic sera derived from T2DM patients prevent the maturation of monocytes into effective DCs and their activation in vitro (141) . Interestingly, quercetin, a flavonoid with antiinflammatory and antioxidant characteristics, prevented such effects (141) . Macrophages are important immune cells that play critical roles through all stages of the pathogenesis of T2DMrelated atherosclerosis (41). Swirski et al. have shown a significantly elevated number of pro-inflammatory monocytes in the circulation of ApoE −/− mice, an animal model of atherosclerosis, compared to control mice (142) . Modifications of the lipoproteins in the arterial walls of diabetic individuals make them pro-inflammatory and activate the overlying endothelium. In response, monocytes are recruited into the subendothelial region, differentiate into macrophages and internalize the accumulated lipoproteins. Finally, cholesterol-laden foam cells are generated. They promote inflammation and progression of the disease through the synthesis and secretion of cytokines, chemokines, ROS, and matrix metalloproteinases (MMPs) (Figure 2C ) (42) . Foam cells lose their migratory potential, die by apoptosis and generate a necrotic core within the atherosclerotic plaque (143) . It has been demonstrated that the use of mesenchymal stem cells in ApoE −/− mice reduces the numbers of monocytes/macrophages at the site of inflammation, decreases lipid deposition and diminishes plaque size (144). Ma et al. have studied the effects of long-term hyperglycemia in diabetic mice and found out that compared to non-diabetic control mice, the numbers of F4/80 + macrophages isolated from spleen (SPMs), as well as from peritoneal exudates (PEMs) of diabetic mice are significantly decreased (145) . Subsequently, Sun et al. showed that stimulation of PEMs from diabetic mice in vitro with IFN-γ and lipopolysaccharide (LPS) significantly decreased the expression of intercellular adhesion molecule 1 (ICAM-1 or CD54), CD86, TNF-α, and IL-6, while it increased the production of nitric oxide (NO) (146) . They further showed that stimulation of PEMs isolated from diabetic mice with IL-4 caused an enhanced arginase activity (146) . Kousathana et al. have demonstrated that circulating monocytes isolated from diabetic patients produce higher levels IL-6, while having an impaired activation of the NLRP3 inflammasome and subsequently reduced IL-1β production (147) . However, they showed that proper glycemic control would restore such modifications. Poor inflammatory responses in circulating monocytes, as well as in macrophages, are responsible for elevated susceptibility to infections and their severity in patients with T2DM. Macrophages play a critical role in tissue repair. Early in wound healing, they are pro-inflammatory to clear pathogens and debris but later, they resolve inflammation and promote tissue repair. In pathological conditions, failure to transform from pro-inflammatory to the anti-inflammatory proliferative phase can cause chronic inflammation in the affected tissue (148). have shown that an impaired wound healing process in animals with T2DM is due to high levels of NLRP3 inflammasome activity, which promotes the generation of IL-1β and IL-18 in macrophages (149, 150) . Efficient skin wound healing process is mediated by the up-regulation of the peroxisome proliferatoractivated receptor (PPAR)-γ in macrophages that convert their pro-inflammatory phenotype into healing-related. PPARγ suppresses cytokine production by macrophages and hence is upregulated in inflamed tissue-resident macrophages. However, in T2DM, PPARγ expression is down-regulated in skin-resident macrophages that enhance the activity of NLRP-3 inflammasome and cause chronic inflammation. Using myeloid-specific PPARγ −/− mice, it has been shown that the absence of PPAR-γ in macrophages is sufficient to delay the healing process and extend tissue inflammation (150) . In T2DM patients, chronic hyperglycemia and hyperlipidemia trigger the secretion of a damage-associated S100A8 molecule (calgranulin A) from pancreatic islets that in turn increase macrophage infiltration (151) . Westwell-Roper et al. have shown that IAPP aggregates in T2DM patients polarize islet-resident macrophages toward the M1-like F4/80 + CD11b + CD11c + phenotype that produces pro-inflammatory cytokines, including TNF-α, IL-1β, and IL-6. Furthermore, M1 cells promote islet inflammation, cause β-cell malfunction and apoptosis (152) . In T2DM, excess phagocytosis of apoptotic β-cells by macrophages induces their lysosomal permeabilization, generation of ROS, inflammasome activation, and pro-inflammatory cytokines secretion (153) . Collectively, these observations reveal that the functions and plasticity of macrophages are compromised during the progression of T2DM. Neutrophils are the most prevalent circulating leukocytes and one of the main components of innate immunity. They are recruited to the sites of infection through chemotaxis following complement activation, most importantly by C5a. Activated neutrophils bind via their surface receptors to induced ligands on the surfaces of inflamed endothelial cells to migrate to tissues. There they phagocytose and kill invading microbes with lysosomal enzymes, antimicrobial peptides and by the generation of ROS (154). Neutrophils from patients with T2DM, but not from healthy individuals, are activated and produce elevated levels of ROS. So, it could increase the risk of random organ injury (155) . In diabetic patients, the plasma levels of homocysteine are elevated, which is mainly due to its impaired clearance rate (156) . This will induce neutrophils to constitutively release neutrophil extracellular traps (NETs) that can cause vascular damage and delays in wound healing (157, 158) . It has been shown that the circulating level of hydrogen sulfide (H 2 S) is significantly reduced in fasting blood of patients with T2DM compared with healthy individuals as well as in streptozotocin-induced diabetic rats compared with controls (159) . H 2 S is produced from cysteine by the action of several enzymes. It acts as a regulator of cell signaling and homeostasis (160) . It is essential to maintain balanced levels of antioxidants and protect tissues from oxidative stress (160) . The use of H 2 S or the endogenous L-cysteine in vitro blocks the production of IL-8 and monocyte chemoattractant protein-1 (MCP-1) in the human U937 monocyte cell line incubated in high-glucose medium (159) . Yang et al. have shown that H 2 S treatment decreases NETosis and enhances the healing process of diabetic wounds by preventing ROS-dependent ERK1/2 and p38 activation (161) . It has been shown that the levels of NET components, including histones, elastase and proteinase-3, are elevated in the sera from patients with diabetic foot ulcers (162) . Wang et al. have recently indicated that HG dramatically enhances NADPH oxidasedependent NET generation in diabetic rats and humans. It was proposed that this could have a role in the induction of diabetic retinopathy (163) . Indeed, patients with T2DM have elevated plasma levels of MGO, which can induce the production of proinflammatory cytokines like TNF-α, IL-6, and IL-8 by neutrophils and make them more susceptible to apoptosis (99) . Myeloperoxidase (MPO), which is abundantly produced by neutrophils, but only to a small extent by monocytes and macrophages, might be useful as an early biomarker of inflammation in diabetic individuals (164) . Binding of MPO to endothelial cells increases its half-life. Thereby, more proinflammatory oxidant hypochloric acid (HClO) is generated that extends the damage to blood vessels (165) . In T2DM patients, neutrophil activities, including migration, phagocytosis and microbial killing are impaired. This makes diabetic individuals more susceptible to infections (166) . It has been welldocumented that neutrophils isolated in animal models of T2DM have an impaired TLR4 signaling pathway. This is reflected as a diminished cytokine and chemokine production, possibly as a consequence of reduced phosphorylation of NFκB and IκBα (167) . The half-life of these neutrophils as well as their in vivo migration and myeloperoxidase activity are decreased. During hyperglycemia, neutrophils produce calprotectin (S100A8/A9), which interacts with the receptor for advanced glycation end products (RAGE) on the surface of hepatic Kupffer cells and promotes the synthesis of IL-6 (168). Subsequently, IL-6 stimulates hepatocytes to increase the generation of thrombopoietin that in turn attaches to its receptor on the surfaces of bone marrow precursor cells and megakaryocytes to enhance their proliferation and expansion. This results in reticulated thrombocytosis, which means elevated megakaryocyte activity and thrombopoiesis. Interestingly, diabetes-related thrombocytosis and subsequent atherothrombosis can be reduced by lowering blood glucose, depleting Kupffer cells or neutrophils or by preventing the binding of S100A8/A9 to RAGE using paquinimod (168) . Thom et al. have shown that the incubation of human and murine neutrophils with HG medium would cause their cytoskeletal and membrane instability. This will induce the generation of 0.1 to 1 µm diameter microparticles and activate the NLRP3 inflammasome (169) . Microparticles, which are potently pro-inflammatory, are found in the circulation of healthy individuals, but their generation is increased during cell activation in several diseases, including T2DM and cardiovascular diseases (170, 171) . Furthermore, serum levels of soluble FasL (sFasL) are increased in patients with T2DM thereby activating neutrophils and aggravating the inflammatory milieu (172, 173) . The proinflammatory roles of sFasL are mediated through increased amounts or activity of NFκB, IL-1β, caspase-1, CD11b/CD18, and ROS (173) . Caspase-1 activation prevents the sFasL-dependent apoptosis of neutrophils and inhibits their expression of Fas and caspase-3 (173) . Accordingly, hyperglycemia disturbs the normal functions of neutrophils and increases the susceptibility to infections by pathogenic microorganisms. The expression level of NKG2D is negatively correlated with HbA1c levels implying that chronic hyperglycemia would cause NK cell dysfunction (176) . Also, hyperglycemia increases the expression of unfolded protein response (UPR) genes in NK cells and induces their apoptosis (176) . NKT cells express simultaneously markers of both T cells (TCR and CD3) and NK cells [CD16, CD56, CD314 (NKG2D), and CD337 (NKp30)]. NKT cell subsets produce a broad range of cytokines, including GM-CSF, IFN-γ, TNF-α, IL-2, IL-4, IL-5, IL-9, IL-10, IL-13, IL-17, and IL-21 (178) . They recognize lipids and glycolipids presented by CD1d molecules. Phoksawat et al. have shown that the frequency of CD3 + CD4 + CD28 null CD56 + NKG2D hi NKT cells, which produce high levels of IL-17, are increased in diabetic patients and their numbers are directly correlated with HbA1c levels (179, 180) . Lv et al. have recently shown that the numbers of CD3 + CD56 + NKT cells are higher in diabetic patients compared to healthy individuals (181) . They further showed that such cells are mostly CD4 + , produce elevated levels of IFN-γ and IL-4 and express high levels of NKp30, NKG2D, and NKp44 but low levels of inhibitory receptors NKG2A and 158b (181) . The co-culture of these cells with HUVECs significantly decreased their proliferation and migration abilities that were mainly IL-4dependent (181) . Taken together these studies show that diabetic individuals appear to have elevated levels of inflammationpromoting NKT cells. ILCs are critical effectors of innate immunity that produce both regulatory and pro-inflammatory cytokines to promote tissue repair, immunity, and inflammation (182) . Mature ILCs lack the TCRs. Based on their cell surface markers, cytokine production as well as expression of transcription factors the ILCs are classified into types 1, 2, and 3 (183) . These correspond to the different types of CD4+ T helper cells: Th1, Th2, and Th17, respectively. IFN-γ is the cytokine signature of ILC1s, while type 2 cytokines (e.g., IL-5 and IL-13) are mainly produced by ILC2s and the main product of ILC3s are IL-17 and IL-22. Regarding transcription factors, T-bet is mainly expressed by ILC1s, GATA3 and RORα are mostly expressed by ILC2s and RORγt is predominantly expressed by ILC3 (183) . In T2DM, the numbers of circulating as well as adipose tissue-resident ILC1s are increased compared with normal individuals (184, 185) . The frequency of circulating ILC1s is positively correlated with fasting plasma glucose (FPG), HbA1c, homeostasis model assessment for insulin resistance (HOMA-IR), serum-free fatty acids (FFAs) and adipose tissue insulin resistance index (Adipo-IR) (184, 185) . It has also been shown that patients with increased numbers of ILC1 have an elevated risk of developing T2DM (184) . A study by Wang et al. indicated that adipose tissue-resident ILC1s, via the production of IFN-γ, promote tissue fibrosis and induce diabetes in obese individuals (185) . Liu et al. have demonstrated that the numbers of ILC2s as well as serum cytokine levels of IL-4, IL-5, and IL-13 are significantly elevated in diabetic kidney disease patients and have a positive correlation with disease severity (186) . They further demonstrated that ILC2s, through the TGF-β1 signaling pathway, are involved in renal fibrosis seen in diabetic kidney disease (184) . However, Galle-Treger et al. indicated that the engagement of the glucocorticoid-induced tumor necrosis factor receptor (GITR/or TNFRSF18) on the surface of activated ILC2s promotes their secretion of IL-5 and IL-13, ameliorates glucose homeostasis, protects against the onset of and improves established insulin resistance (187) . The protective role of ILC2s during acute metabolic stress has also been well-documented by Dalmas et al. (188) . Humoral Immunity (B Cells) Elevated levels of blood glucose generate covalent sugar adducts with several proteins through non-enzymatic glycation. This can impair humoral immunity in many ways, e.g., by modifying the structure and functions of immunoglobulins (Igs) (189) (190) (191) (192) (193) (194) . Such modifications in the structure of Igs can be determined using matrix-assisted laser desorption ionization (MALDI) mass spectrometry (119, 191) . The molecular mass of Igs in diabetic patients is higher than in normal subjects (189) . This can lead to reduced efficiency of vaccines that stimulate humoral immunity in these patients. It has been shown that immunization with influenza (flu) vaccines in diabetic patients induces normal or even elevated levels of flu-specific antibodies compared with normal individuals (195) (196) (197) (198) . However, the ability of the dysfunctional glycated antibodies to neutralize viruses is impaired, which will increase the susceptibility to infections. Farnsworth et al. have shown that in T2DM, class switch defects in the assembly of antibody genes are also present (199) . In a model system, mice with T2DM have decreased amounts of specific anti-Staphylococcus aureus antibodies (total as well as IgG), which will increase the risk of infection and morbidity of diabetic mice. However, the levels of IgM were elevated, but inefficient in protecting against infection, possibly because of their inability to directly promote phagocytosis. In another study, Farnsworth et al. have demonstrated that defects in humoral immunity, as shown by decreased levels of total IgG and anti-Staphylococcus aureus antibody, aggravate foot infections in a murine model of T2DM (200) . This was due to a reduced germinal center induction and decreased numbers of T and Blymphocytes within the germinal centers. This causes failures in antibody generation and class-switch recombination (200) . Mathews et al. have shown that the protective levels of antibodies against Streptococcus pneumoniae surface protein A are lower in diabetic patients compared to non-diabetic individuals. These antibodies also have a reduced potential to trigger complement activation on the surface of pneumococci, whereby phagocytosis of the bacteria becomes compromised (201) . They showed that hyperglycemia reduces both the antibody titers as well as the ability to deposit complement on the bacteria. The abovementioned changes in the ability to protect against S. aureus and S. pneumoniae are important, because these bacteria belong to the most common infection-causing pathogens in diabetic patients. Another major group is constituted by Gram-negative bacteria that commonly cause e.g., urinary tract infections. Many studies have shown that T-cell functions are impaired in individuals with T2DM (202) (203) (204) (205) . Elevated levels of activated CD4 + CD278 + T helper cells, cytotoxic T-cells, and Th17 cells have been observed in obese diabetic patients compared to nonobese ones (205, 206) . Nevertheless, PBMCs isolated from obese diabetic patients produced smaller amounts of IL-2, IL-6, and TNF-α after stimulation with phytohemagglutinin (PHA) (205) . Martinez et al. indicated that diabetic patients have reduced pathogen-specific memory Th17 responses as well as decreased numbers of CD4+ T cells in response to stimulation with Streptococcus pneumoniae (206) . Th17 cells are critical for the recruitment of neutrophils to the infection site and improve the phagocytosis of invading bacteria and yeast (207) . Moura et al. have shown that diabetic patients, particularly those with foot ulcers, have reduced levels of naive T-cells, but an elevated number of effector T cells and a reduction in the TCR-Vβ repertoire diversity (204) . The observed changes are mainly due to an abnormal amount of inflammatory cytokines (e.g., IFN-γ and TNF-α) produced during infection and to subsequent robust stimulation of T-cells. Leung et al. have reported that ischemic tissues of T2DM patients contain elevated numbers of TNF-α and IFN-γ producing Th1 cells but diminished numbers of regulatory T cells (Tregs), which suppress angiogenesis and decrease vascular density (208) . The high rate of infectious diseases in T2DM patients might also be linked to a reduction in the mitochondrial DNA function that causes downstream lymphocyte dysfunction and subsequently increased susceptibility to infection (209) (210) (211) (212) . In support, we have recently shown that the numbers of IFN-γ producing cells against cytomegalovirus (CMV), Epstein-Barr virus (EBV), and influenza virus are fewer in T2DM patients compared to normal controls (202) . Kumar et al. have also investigated the functions of CD8 + T cells and NK cells in the whole blood of T2DM patients infected with Mycobacterium tuberculosis (M.tb). Compared to controls, the patients exhibited a reduction in cytokine production (IFN-γ, IL-2, IL-17A/F, and TNF-α) and decreased expression of cytotoxic molecules (perforin, granzyme B, and CD107a) (203, 213) . These studies conclude that the functions of both CD4 + and CD8 + T-cell are defective in T2DM patients. T2DM is usually associated with an elevated risk of asymptomatic bacteriuria, urinary tract infections (UTIs), pyelonephritis and non-sexually transmitted genital infections, such as balanitis and vulvovaginal infections (213) (214) (215) . The incidence of infections with a complicated course is significantly higher in diabetic patients compared to healthy controls ( Table 3) . It seems that it is principally defects in the innate immune responses of diabetic individuals that are responsible for the increased susceptibility and prevalence of infections (4, 225 (199, 201) CD8 + Tcells Mycobacterium tuberculosis (203, 213) susceptible to the causative pathogen of Lyme disease, Borrelia burgdorferi (216) . The disease is mainly due to the ability of the bacteria to escape complement opsonization and attack, which leads to an impaired uptake and killing of bacteria by neutrophils (227) . Neutrophil dysfunction also increases the susceptibility of diabetic animals to Staphylococcus aureus (217) (233) . During the progression of T2DM in human subjects, the basal phenotype of macrophages is altered so their capacity to control Mycobacterium tuberculosis is diminished (222) . Martinez et al. have indicated that alveolar macrophages isolated from diabetic mice express decreased levels of macrophage receptor with collagenous structure (MARCO) and CD14 that are engaged in the recognition of trehalose 6,6'-dimycolate, a bacterial cell wall component (223) . Diabetes increases the severity of tuberculosis (TB) and enhances the risk of progression to the active form in latent infections (234, 235) . Diabetic TB patients have elevated frequencies of Th1 and Th17 cells as well as increased serum levels of inflammatory cytokines, including IFN-γ, TNF-α, IL-1β, IL-2, IL-6, IL-17A, and IL-18 but decreased levels of IL-22 compared to non-diabetic TB patients. This can contribute to dysfunctional immune responses and poor immune control of a TB infection (236) . A positive correlation between the serum levels of IFN-γ, TNF-α, IL-2, and IL-17A with Hb-A1c levels was also observed. This indicates an association between impaired control of diabetes and the proinflammatory milieu. Tripathi et al. have demonstrated that serum levels of IL-22 were significantly decreased in TB-infected T2DM mice and humans compared to non-diabetic TB-infected mice and humans (224) . They revealed that the treatment of TB-infected diabetic mice with recombinant IL-22 or ILC3s (cellular source of IL-22) increased the survival of mice, prevented the accumulation of neutrophils near alveoli, diminished the generation of neutrophil elastase 2 (ELA2) and prevented epithelial cell damage (224) . Tan et al. have shown that B. pseudomallei and M. tuberculosisinfected PBMCs of diabetic patients fail to produce IL-12. This leads to a decreased IFN-γ production, poor bacterial killing and elevated intracellular bacterial loads (237) . An impaired IL-12 production is mainly due to decreased intracellular glutathione (GSH) concentrations within the infected cells of diabetic individuals (237) . Such a combination of an inflammatory microenvironment and dysfunctional immune responses enhances the bacterial load and can subsequently amplify lung injury and fibrosis in diabetic TB patients. Chellan et al. have further shown that infections caused by Enterococcus faecalis, Staphylococcus aureus, and Pseudomonas aeruginosa are more prevalent in the wounds of diabetic patients (238) . T2DM patients are more susceptible to UTIs caused by antibioticresistant Escherichia coli, Proteus spp., Klebsiella spp., coagulasenegative staphylococci, Enterobacter spp., and enterococci (215, 239) . Diabetic patients are also more susceptible to Helicobacter pylori (H. pylori) infections (240). Cui et al. have recently reported that T2DM patients have an increased risk of infection with Kaposi's sarcoma-associated herpesvirus (KSHV or HHV-8) (241) . They further showed that the viral load and antibody titers are positively correlated with blood glucose levels (241) . Diabetic patients also have been shown to have an increased risk of infection with the severe acute respiratory syndrome coronavirus (SARS-CoV) ( (248) . The influenza virus that usually causes self-limiting infections can induce severe forms of the disease in diabetic patients (249, 250) . Following the 2009 H1N1 influenza pandemic, diabetic individuals suffered from more severe infections compared to non-diabetic people (251, 252) . Diabetic patients have also a higher prevalence of chronic cytomegalovirus (CMV), Herpes simplex virus (especially HSV-1), and varicellazoster virus infections (253) (254) (255) . Accordingly, it seems that the immune response against viruses is impaired in diabetics, and these patients need more care during viral infections. Coronavirus virions are enveloped positive-strand RNA spherical viruses with a diameter of ∼125 nm characterized by spike proteins projecting from their surface and with an unusual large RNA genome (256) . The spike (S) protein of the virus binds to its receptor on the surface of cells by which intracellular proteases are induced (257) (258) (259) . Subsequently, the S protein priming and cleavage occurs that allow viral fusion to the plasma membrane and entrance of viral genome into the cells (259) . SARS-CoV and SARS-CoV-2 use angiotensin-converting enzyme 2 (ACE2) as their receptor while MERS-CoV uses dipeptidyl peptidase-4 (DPP4) to enter the cells (260, 261) . ACE2 is strongly expressed in blood vessels, pancreas, intestine, brain, lungs, heart, and testis (262) . Interestingly, nasal epithelial cells, especially goblet, and ciliated cells express the highest levels of ACE2 and the intracellular protease transmembrane serine protease 2 (TMPRSS2) that facilitates the entrance of the SARS-COV-2 (263) . Furthermore, the expression of ACE2 is significantly up-regulated in diabetic patients and those treated with ACE inhibitors (264) . Coronaviruses cause respiratory, enteric and central nervous system (CNS) diseases in various animal species except rats and mice (264) . Most coronavirus infections are mild, but major outbreaks of deadly pneumonia have been caused by SARS-CoV, MERS-CoV, and SARS-CoV-2 in 2002, 2014, and 2019-2020, respectively (265) . On March 11, 2020, The World Health Organization (WHO) announced the pandemic of SARS-CoV-2, the etiologic agent of coronavirus disease-19 (COVID-19) (265) . The novel coronavirus pandemic, which has emanated from Wuhan, China, promotes symptoms similar to those caused by the SARS-CoV outbreak in 2002. The viral pandemic, which has put the world on alert, has caused over 7.9 × 10 6 confirmed human cases and at least 43 × 10 4 deaths throughout the world (https://www.worldometers.info/coronavirus/) by June 14, 2020. Most of the infected people experience only mild to moderate respiratory disease and recover soon without the need for special treatment. However, aged individuals and those with health problems, including diabetes, obesity, cardiovascular disease (CVD), hypertension, immune deficiency, and chronic respiratory disease are more likely to develop serious illness (https://www.who.int/health-topics/coronavirus#tab= tab_1). Patients death is mainly due to the acute respiratory distress syndrome, disseminated intravascular coagulation, hemorrhage, coagulopathy, acute organ (e.g., kidney, heart, liver) injury, multi-organ failure, and secondary bacterial infections (266) . Elevated levels of adipose-tissue derived adipokines, interferon, and TNF-α in diabetic patients may impair immune-responses against SARS-COV-2 (267, 268) . It has been shown that diabetic patients have impaired clearance of SARS-CoV-2 from their circulation (269) . Accordingly, diabetic patients due to the diminished viral clearance, impaired T cell function, and accompanied cardiovascular disease are more susceptible to the coronaviruses infection and subsequent cytokine release syndrome (CRS) (270, 271) . In support, elevated levels of IL-1β, IL-2, IL-6, IL-7, IL-8, IL-10, IFN-γ, interferon gamma-induced protein 10 (IP-10), granulocyte colony-stimulating factor (G-CSF), macrophage inflammatory protein 1α (MIP1α), serum ferritin, fibrinogen, plasminogen, C-reactive protein (CRP), and D-dimer have been observed in patients with COVID-19 (266, 269, 272, 273) . COVID-19 patients, especially those requiring intensive care unit (ICU) have decreased total lymphocytes (lymphopenia), T cells (both CD4+ and CD8+), B cells, and NK cells (274, 275) . It should be noted that most of the surviving T cells in such patients have an exhausted phenotype (274) . Consequently, disease severity is mainly because of the host immune response to viral infection. Current evidence about the relationship between pathophysiological mechanisms of diabetes and COVID-19 are limited and further research is still needed. Patients with T2DM have an elevated risk of infection with Plasmodium falciparum (276) , Toxoplasma gondii (277), Opisthorchis viverrini (278), Strongyloides stercoralis (279), Cryptosporidium parvum (280), Blastocystis hominis (281), Ascaris lumbricoides (280, 282, 283) , and Giardia lamblia (283) . Interestingly, diabetic patients who were treated with metformin had less P. falciparum infections compared to untreated patients (276) . Omaña-Molina et al. have shown that in a mouse model of T2DM the animals have an increased susceptibility to granulomatous amoebic encephalitis (GAE) caused by trophozoites of Acanthamoeba culbertsoni (284) . The possible reasons for the increased risk of diabetics for parasitic infections are metabolic abnormalities and immune dysregulation. Chellan et al. have shown a higher prevalence of fungal infections in the wounds of diabetic patients (238) . The prevalence correlated with the levels of HbA1c. The most widely observed fungal isolates were C. albicans, Candida parapsilosis, C. tropicalis, Trichosporon asahii, and Aspergillus species. Some of them were resistant to antifungal medications (238) . Al Mubarak et al. have also demonstrated that diabetic patients with periodontitis are more susceptible to infection with C. albicans, C. dubliniensis, C. tropicalis, and C. glabrata (285) . The incidence of candidiasis was significantly increased in patients over the age of 40 with HbA1c > 9 (285). It has also been shown that diabetic patients are more susceptible to UTIs caused by C. albicans (239) . Hyperglycemia impairs the normal functions of the circulatory system, gastrointestinal tract, pancreatic beta cells, liver as well as of skeletal muscles to boost systemic insulin resistance. A hyperglycemic environment also leads to immune cells dysfunction. It increases intestinal permeability, which subsequently enhances the risk of infections in T2DM patients. Accordingly, further research is still needed to find missing links between impaired physiological/immunological mechanisms and increased susceptibility to infections in T2DM patients. The information would be important for better therapy and the design of much more effective vaccination strategies in diabetic patients. GD and KK conceived the study and wrote the manuscript. GD contributed to the final revision of the manuscript. MA participated in preparing the first draft. DK and SM were involved in the final revision of the manuscript. All authors contributed to the article and approved the submitted version. 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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. 


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   key: cord-339637-hb6bsb6q authors: Khader, Mohammed Abdul; Jabeen, Talha; Namoju, Ramanachary title: A cross sectional study reveals severe disruption in glycemic control in people with diabetes during and after lockdown in India date: 2020-08-18 journal: Diabetes Metab Syndr DOI: 10.1016/j.dsx.2020.08.011 sha: doc_id: 339637 cord_uid: hb6bsb6q Background and aims: Uncontrolled diabetes has been associated with poorer clinical outcomes in COVID-19. We aimed to evaluate and assess the impact of COVID-19 pandemic on management of diabetes and challenges faced by people with diabetes in India during and after the lockdown phase. Methods: A cross-sectional study based on an online questionnaire survey was designed. The questions collected socio-demographic details, medical and social history, and impact of the pandemic on medical and social life from 1582 participants. Linear regression was employed to evaluate association of different parameters with the change in glycemic levels. Results: The frequency of clinical visits during the COVID-19 pandemic were reduced in 87.28% of participants. 92.45% of participants were able to monitor their blood glucose levels (BGLs) in which 78.42% (49.35%, 20.91%, and 8.16%) participants experienced an increase in BGL (mild, moderate, and severe respectively). Only 47.41% of participants possessed the digital glucometer at home. 69.07% of participants reported a decrease in physical activity while 46.88% reported an increase in food intake. 80.06% of participants were able to buy all medicines and 29.80% were gone for virtual consultations while 87.81% reported that they didn't have access to healthcare services. Overall, 89.47% participants experienced disruption in therapy. A highly significant correlation (r = 0.89, p = 0.0145) was found between increasing age and reporting of higher BGLs. Conclusion: This study provides a firsthand evidence of major disruption in diabetes care activities during and after the lockdown phase in India and increased risk of poorer clinical outcomes, if infected by SARS-CoV-2. The coronavirus disease (COVID- 19) , a pandemic originated from China as a cluster of unknown pneumonia cases is continuing to wreak havoc globally [1] . As of July 24, the World Health Organization (WHO) has documented 15,931,445 positive COVID-19 cases and the death toll stood at 641,885. India, with no exception, is ringing high alarm bells to the outbreak. Currently, India has a load of 13,35,210 COVID-19 cases, making it the worst-hit nation of Asia and third worst-hit nation in the world after the United States and Brazil [2] . The outbreak is frequently testing the preparedness of health services and their ability to cope with a pandemic response [3] . The first confirmed case of SARS-CoV-2 positive in India was reported on 30 th January. Following the drastic growth in daily case counts, the government had drawn up plans to deal with a worsening of the pandemic in the country. A series of lockdowns starting from 24 th March 2020 were imposed on people in the country to break the chain of virus transmission [4] . Further, United Nations and WHO have praised India's response to the pandemic as 'comprehensive' and 'robust'. However, since the start of "unlocking" and easing of restrictions from June 1 st , India is witnessing an exponential rise in COVID-19 cases indicating the possibility of 'community transmission' [2] . Accumulating evidence suggests a high risk of becoming severely ill with the Coronavirus in people with increasing age and pre-existing comorbidities [5, 6] . The mortality rate due to COVID-19 in India is very low (i.e. 2.57%) and interestingly majority (nearly 73%) of deaths are being observed in those having comorbidities [7] . Among all, diabetes found to pose a particular adverse risk for COVID-19 infection [8] . Uncontrolled diabetes compromises innate immunity, the first line of defense against SARS-CoV-2 [9] . This results in disruption of cell-mediated immunity, diminished lymphocyte transformation, impaired leukocyte function, greater viral replication in pulmonary epithelial cells, and consequent pneumonitis [10] . Since people with diabetes are at increased risk of morbidity and mortality associated with SARS-CoV-2 infection, their routine care, and monitoring are highly important [11] . Apart from the risk of COVID-19 infection, a poorly controlled diabetes has the potential to root life-threatening complications like macrovascular diseases (angina pectoris, myocardial infarction, stroke, peripheral artery disease), microvascular diseases (retinopathy, nephropathy, and neuropathy), and immune dysfunction (more susceptible to bacterial and fungal infections) [12] . With medical focus largely centered on COVID-19, the clinical support needed by patients living with non-communicable diseases (NCDs) is getting severely affected [13, 14] . Treatment delays, discontinuation of routine care, services, and uncertainty around medicine availability have left them more at risk than ever. A survey of 155 countries by the WHO reported dramatic curtailment of health services for patients living with NCDs during the COVID-19 pandemic. It was reported that diabetes treatment has been partially or completely disrupted in 49% of the countries surveyed. The lower-income countries and countries moving in to transition towards the community transmission are being majorly affected [15] . A recent study in the Brazilian population revealed the impact of COVID-19 on diabetics [16] . India harbors 77 million diabetes patients, which makes it the second most affected nation in the world, after China. The number is projected to grow 134 million by 2045 as per the International Diabetes Federation [17] . On the other hand, only a few papers have discussed the association between the COVID-19 outbreak and its impact on diabetes care in India. However, these studies had a relatively smaller sample size and were primarily conducted during the period of nation-wide lockdown [18] [19] [20] [21] . By virtue of being with a higher prevalence of diabetes and a hasty upsurge in COVID-19 cases during the unlocking phase, and with their overlapping on morbidity and mortality of people, it is essential to study the impact of COVID-19 on diabetic care in India and helps improvise the preparedness for current and future thereat imposed by COVID-19. Keeping the above points in view, this study was designed to evaluate the impact of COVID-19 pandemic on medical treatment, routine care services, and challenges faced by people living with diabetes in India. A cross-sectional study based on an online questionnaire survey was designed to assess the impact of COVID-19 on the medical treatment of diabetes people. The study was conducted in between 12:00 IST, June 10 th , 2020, and 12:00 IST, July 15 th , 2020. People with diabetes were invited for the study through social networking platforms including Facebook, WhatsApp, LinkedIn, text message, through their affiliated institutions, and diabetes social media groups. The questionnaire consisted of 25 multiple choice questions in English and Telugu. The survey initiation clearly stated the right of the individuals to participate or not to participate in the survey. All the participants enrolling must have to fill the informed consent form. Inclusion criteria -People who are diagnosed with diabetes -Any gender -Age more than 18 years Exclusion criteria -Not able to provide informed consent for the study The questionnaire consisted of mainly 3 domains: A) socio-demographic details, e.g., subject's age, gender, state, city, town, educational qualification, financial income; B) medical and social history, e.g., type of diabetes, comorbidities, smoking, and drinking habits; C) impact of COVID-19 outbreak on their medical and social life, e.g., frequency of clinic visits, changes in glycemic levels, digital glucometer at home, access to health care services, changes in the eating habits and physical activity, changes in drinking and smoking habits, online consultations, the reason for cancellation or postponement. In the end, a blank J o u r n a l P r e -p r o o f space was given to leave a comment. The questions were designated to relate the current situation of participants "during" the pandemic, to "before" the pandemic. Descriptive analysis was computed in terms of mean value ± (standard deviation) for continuous variables and frequency (percentage) for categorical variables. Student's t-test was used to examine the association between continuous variables. Multivariable logistic regression analysis was used to adjust for the effect of cofounders when appropriate to determine the association of binary outcomes. Linear regression was employed to evaluate the association of different parameters with the change in glycemic levels. A p-value of < 0.05 was considered statistically significant. All statistical analyses were done using the SPSS statistic 22.0 (IBM SPSS Statistics, New York, United States) database. Bar diagrams, tables and charts were created using Microsoft Excel 2016 version to depict percentages, and averages. A total of 1582 responses obtained in the study duration and 72 responses excluded due to the incomplete information. A total of 1510 responses then assessed for the study. The majority of participants were male (63.77%) and belonged to the age group of 41-50 (36.29%). The mean age was 41.6 years. The participants were from 9 different states in India -Telangana, Andhra Pradesh, Tamil Nadu, Maharashtra, Karnataka, Kerala, Delhi, Punjab, and Rajasthan., of which, 27.15% comprised from Telangana state (Hyderabad=14.76%). A significantly higher number of participants lived in urban localities (80.33%). The highest levels of education for most of the participants were graduation (42.31%) and secondary (22.11%). 29% of participants had a monthly income ₹ >20,000 and 25.28% had a monthly income in the range of ₹3,000-10,000. People with type-2 diabetes were predominant in the study (85.16%). Comorbidities were present in 80.13% of the participants. The most common comorbidities were hypertension and dyslipidemia. 12.5% and 10.19% of participants had smoking and drinking habits respectively. The complete socio-demographic profile of the participants is shown in Table 1 . Impact of epidemic on medical treatment and social life of people with diabetes: The frequency of clinical visits of participants significantly decreased as compared to prepandemic period (Fig. 1. A) : 135 (8.94%) participants didn't visit the clinic till now i.e., from March 22 nd to June 30 th ; 755 (50.00%) participants visited once in three months; 428 (28.34%) visited once in two months; and 192 (12.71%) visited once monthly. 1396 (92.45%) participants monitored their blood glucose level (BGL), either with digital glucometer in-home or through blood samples in a lab or a clinic (Fig. 1. B) in which 78.42% (49.35%, 20.91%, and 8.16%) participants experienced an increase in BGL (mild, moderate, and severe respectively) (Fig. 2. A) . 716 (47.41%) possessed blood glucose measuring device (digital glucometer) at home (Fig. 1. C) . The COVID-19 pandemic disrupted physical activity and food intake. 1043 (69.07%), 378 (25.03%) and 89 (5.89%) participants showed a decrease, no change, and increase in physical activity respectively than before the pandemic ( Fig. 1. D) . 708 (46.88%), 597 (39.53%), and 205 (13.57%) participants showed an increase, no change, and a decrease in food intake respectively than before (Fig. 1. E) . The epidemic increased virtual consultations. 450(29.80%) participants used the virtual platform to follow up with doctors during the study period, while 116 (7.68%) expressed that they used the virtual platform before the pandemic also (Fig. 2. B) . Further, the COVID-19 pandemic disrupted the purchase of medicines. 1209 (80.06%) participants purchased all the medicine. 398 (26.35%) did not buy due to unavailability of medicines, 614 (40.66%) did not buy due to financial constraints and 944 (62.51%) felt purchase of medicines difficult due to fear of corona ( Fig. 1. F) . Participants allowed to choose more than one reason in case of multiple answers applied. Among 1510 participants, a total of 1326 (87.81%) didn't have access to healthcare services as before the pandemic (Fig. 1. G) . The major reasons for cancellation and postponement of appointments with doctors were a) fear of getting infected with COVID-19 -1365 (90.39%), b) absence of physician from the clinic -1250 (82.78%), c) lack of communication with clinics -1032 (68.34%), and d) financial constraints -997(66.02%). Participants were asked to choose more than one reason in case of multiple answers applied (Fig. 1. H) . A total of 1351 (89.47%) expressed that the COVID-19 pandemic disrupted their therapy [majorly -1011 (66.95%) and slightly -340 (22.51%)] (Fig. 2. C) . Further, Logistic regression analysis showed a significant correlation with regard to increasing age and reporting of an increase in BGLs (r=0.89, p=0.0154), (Fig. 4. C) . This study provides the firsthand evidence of major disruption in diabetes care activities during and after the lockdown phase in India. With India witnessing exponential growth in COVID-19 cases since the start of unlocking [2] , the challenged healthcare resources laid down falling short to meet the needs of the entire population, including individuals with diabetes. In our survey, we found the majority of people irrespective of being in urban or rural localities, were not able to follow up with doctors. However, people living in rural areas were found to have an additional level of difficulties through traveling, making it more inconvenient for them to see a doctor. With global priorities set to contain the spread of the virus, the absence of physicians for people having NCDs and fear of getting infected with COVID-19 are playing key roles in the disruption of diabetes care. Other major factors identified were financial constraints and altered work-timings of clinics due to pandemic. The majority of participants monitoring their BGL either at home/laboratory/clinic said that they observed a spike in their BGL. Our data mainly consisted of people having type-2 diabetes (87.01%). Recent literature has mainly focused on disrupted glycemic control among type-1 diabetes people [19, 21] . Therefore, irrespective of the type of diabetes, there is a need to monitor BGLs in this population. Diabetes people infected with SARS-CoV-2 had a more than triple mortality rate of 7% in comparison to 2% in those without diabetes [22] . Uncontrolled diabetes has the potential to results in long-term complications, if not addressed at the earliest [12] . 53% of participants had digital glucometer at home while 47% didn't have. This was not studied previously. In the times of COVID-19, the use of such devices are a must for diabetes people and they should be educated about its importance. Pharmacists working in clinical settings and dispensing areas can play a great role in patient education about such devices. Concerning authorities should make sure that no shortage of such devices happens. Regardless of the existence of pandemic, such devices help diabetics for selfmonitoring and self-care [23] . With no doubts, lockdown measures and mobility restrictions were found effective to control the spread of COVID-19 [24] , but, at the same time, showing their harmful consequences on prevention and control of diabetes and other NCDs [25] . Barone et al, reported that stricter measures against the pandemic may lead to present and future severe impact on diabetes care in South and Central America [26] . Though mobility restrictions are eased during the postlockdown phase in India, fear of getting infected with COVID-19 and "work from home" scenario is keeping people confined to their homes only. 69.07% of participants experienced a decrease in their physical activity and 46.88% had an increase in their food intake than before. A decrease in physical activity of diabetes people (59.5%) was also reported by Barone et al. The study also reported an increase in food intake (29.71%) among diabetics. In line with recommendations suggested by Barone et al, internet and television broadcasting can be used as an effective medium to create awareness among people of habits and behaviors, such as regular physical activity [16] . However, the use of television by participants was not investigated in the current study. The proportion of people using virtual consultation platforms with doctors has significantly increased. Only 7.68% of participants said of using virtual consultation before while it got increased to 29.80% during the pandemic. A pilot study from Government medical university reported 4880 teleconsultations were given by 38 ophthalmologists during COVID-19 lockdown in India [27] . Overall, 87.81% of participants said not having access to overall healthcare services as before and 89.47% said pandemic has disrupted their therapy. Increased age people were found to have the worst glycemic control (p=0.0154). The presence of comorbidities and behavioral habits predicted increase BGLs, but did not reach statistical significance. Many participants in the comment section provided at the end said that they are more stressed about uncertainty prevailing due to pandemic and waiting for normalcy to return. LE Joesnen et al, reported an increase in COVID-19 specific worry among diabetics in Denmark [28] . The results from our survey revealed flaws in government measures, healthcare policies to protect this vulnerable group of people from public health emergency due to the COVID-19 pandemic. Though a lot of plans are being made by the government and states [2] , they did not cover most of this population. We found people with old age are those having a major disruption in the BGLs. Since the risk of becoming severely ill with SARS-CoV-2 appears to be high in people with increased age and poorly controlled diabetes, disturbed diabetes care is paying the way for the consequences that might be worse than the pandemic itself. This may also result in the diabetes population in India projected to grow 134 million by 2045 much sooner than predicted earlier. As India has entered into the "unlock" phase, an increase in COVID-19 caseload is resulting in an additional disruption in care for diabetes people. Since many other countries have also entered into the "unlocking phase", an immediate call to look for people having diabetes seems eminently appropriate. Self-monitoring of BGLs through devices like digital glucometer is essential. Availability of medicines, diagnostic tools shouldn't get under stocked. Diabetes people should be educated about the symptoms of hyperglycemia, essentially in times when their visit to doctor's in-person appears risky and difficult. Though the use of telemedicine platforms is growing with much attention in India since the beginning of the pandemic, many milestones are still needed for its acceptance among people [29] . Since many people in the survey said fear of getting infected with COVID-19 and the absence of physicians from clinics were the main reasons for therapy disturbance, clear knowledge and understanding of people about telemedicine will provide greater convenience while not having to expose themselves to infection in an in-person clinic. Broader strategies to protect people's jobs and economic crises arising from pandemic are essential. The main limitations of our study includes a) our sample is not reflective of the diabetic population of India. We had more than two-third participants from Southern India (72.51%) therefore the geographical distribution could lead to varying responses. b) All responses have been collected through online-mode without having access to objective data to validate the subjective responses and c) only people having internet facilities were able to participate in survey while India has 58.52% of people who don't have access to the internet. Our study revealed a severe disruption in diabetes care activities during and after the lockdown phase in India. The COVID-19 pandemic has created additional challenges for the healthcare sector and people with diabetes are not spared from this. Though the lockdown measures are eased, the ongoing pandemic continues to devastate the healthcare services for NCDs. Considering more severe symptoms and complications of SARS-CoV-2 in diabetics; disrupted glycemic control, physical inactivity and altered food consumption are paying the way for consequences that may be worse than the epidemic itself. India is having the second largest diabetes population globally and need an additional set of measures to be implemented soon. Teleconsultation, use of digital devices, minimizing sedentary behavior, and home delivery of medicines should be encouraged among people with diabetes and NCDs. The authors received no specific funding for this work. The authors reports no conflict of interest. COVID-19: a review Contextualising geographical vulnerability to COVID-19 in India Virtual health care in the era of COVID-19 The Lancet null. 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Infectious Diseases (except HIV/AIDS) Covid-19: Pandemic is having "severe" impact on non-communicable disease care, WHO survey finds The impact of COVID-19 on people with diabetes in Brazil The current state of diabetes mellitus in India Effects of nationwide lockdown during COVID-19 epidemic on lifestyle and other medical issues of patients with type 2 diabetes in north India Impact of lockdown in COVID 19 on glycemic control in patients with type 1 Diabetes Mellitus Coping with diabetes during the COVID -19 lockdown in India: Results of an online pilot survey Awareness regarding COVID-19 and problems being faced by young adults with type 1 diabetes mellitus amid nationwide lockdown in India: A qualitative interview study Diabetes and COVID-19: evidence, current status and unanswered research questions Beyond the virus: Ensuring continuity of care for people with diabetes during COVID-19 Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications: A simulation model using multivariate regression analysis COVID-19 impact on people with diabetes in South and Central America (SACA region) Teleconsultation at a tertiary care government medical university during COVID-19 Lockdown in India -A pilot study Diabetes and COVID-19: psychosocial consequences of the COVID-19 pandemic in people with diabetes in Denmark-what characterizes people with high levels of COVID-19-related worries Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: Guidelines for physicians Table 1: Socio-demographic profile and health status of the participants (n=1510) We are immensely grateful to all the participants who took part in the survey. We thank our colleagues who greatly assisted the research. We thank Dr. Laeeq Ur Rehman who helped to distribute the anonymous survey to all diabetic groups and target physicians. We thank our friends across the states in helping us to distribute the survey platform to diabetics. All 3 authors (AKM, TJ and RN) contributed equally to the designing of study, acquisition and analysis of data, preparation, revision and final version of approval of the manuscript. All authors have read and consented to the manuscript. J o u r n a l P r e -p r o o f A cross sectional study reveals severe disruption in glycemic control in people with diabetes during and after lockdown in India.Abdul Khader Mohammed 1 , PharmD, Talha Jabeen 1 * , PharmD and Ramanachary Namoju 1,2 MS.Affiliations: 


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   key: cord-345360-wlzgq7f9 authors: Yan, Alice F.; Sun, Xiaomin; Zheng, Jinge; Mi, Baibing; Zuo, Hui; Ruan, Guorui; Hussain, Akhtar; Wang, Youfa; Shi, Zumin title: Perceived Risk, Behavior Changes and Health-related Outcomes During COVID-19 Pandemic: Findings among Adults with and without Diabetes in China date: 2020-07-22 journal: Diabetes Res Clin Pract DOI: 10.1016/j.diabres.2020.108350 sha: doc_id: 345360 cord_uid: wlzgq7f9 AIMS: To examine perceived infection risk of COVID-19 and the health and related behavior changes among people with diabetes, compared with people without diabetes, and to examine factors associated with self-reported health during the national quarantine period in China. METHODS: The 2020 China COVID-19 Survey is an anonymous 74-item survey administered via social media across China. A national sample of 10,545 adults in all 31 provinces in mainland China provided data on sociodemographic characteristics, awareness, attitudes towards COVID-19, lifestyle factors, and health outcomes during the quarantine. Regression models tested associations among study variables adjusting for covariates. RESULTS: Among the 9,016 total participants (42.6% men and 57.4% women), 585 reported having diagnosed diabetes and 8,431 had no diabetes. Participants with diabetes perceived themselves to be at higher risk and were more worried about being infected with COVID-19 when compared to non-diabetic individuals (p<.001). During the COVID-19 pandemic, participants with diabetes were more likely to experience food and drug shortages and to increase their physical activity, compared to their counterparts. Among diabetic respondents, a high proportion of current smokers (74.1%) and drinkers (68.5%) reported increased amounts of smoking and drinking. People with diabetes were 11% less likely to report excellent or very good health. Having 150 minutes/week physical activity was positively associated with excellent or very good health (prevalence ratio, PR=1.14, 95%CI 1.11-1.16). CONCLUSIONS: A high proportion of people with diabetes perceived risk of COVID-19 infection and increased their smoking and drinking during the pandemic. Since the outbreak of the novel coronavirus disease (COVID- 19) was first reported in Wuhan City in China in December 2019 [1] , it has quickly become a global pandemic. A novel betacoronavirus, known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the pathogen for COVID-19 [2] . China implemented a lockdown of Wuhan in late January 2020 to contain the spread of COVID-19. On March 11, 2020 , the World Health Organization (WHO) declared COVID-19, which triggered severe pneumonia and acute, even lethal, lung failure, a pandemic [3] . COVID-19 infection is a double challenge for people with diabetes. It was reported that among COVID-19 patients admitted to hospitals, diabetes was the second most prevalent comorbidity (with a rate of approximately 10-20%) [4] [5] [6] . A growing body of clinical and population studies shows that patients with diabetes, when contracting SARS-CoV-2, are more likely to develop severe symptoms and complications [7, 8] , and they have much higher admission rates in intensive care units. The risk of dying from COVID-19 was up to 50% higher for those with diabetes, particularly among elderly type 2 diabetes cases [9] than for people without diabetes [10, 11] . In addition to the direct vulnerability to the worst outcomes of COVID-19, people with diabetes can be affected by limited access to healthcare due to massive population lockdowns. Consequently, their health condition would become worse. Second, the prolonged period of quarantine and social distancing, although effective to contain the spread of the virus, is likely to have undesirable effects on the lifestyle behaviors and health and well-being of chronic disease patients, including those with diabetes. Third, during pandemics such as COVID-19, vulnerable populations, including women, the elderly, and those with chronic diseases, become more vulnerable due to their impaired ability to access and understand health information and make sound, informed decisions. China has the largest number of diabetes patients in the world, and the number has been continually rising due to the improvement of living standards and increase of obesity. China has an estimated 116 million adults living with diabetes (10.9% total diabetes and 35.7% prediabetes) [12] [13] [14] . So far, it is unknown how Chinese diabetes patients perceived COVID-19 related risks and how they modified their health-related behaviors accordingly during the pandemic. The objectives of this time-sensitive study are: 1) to examine perceived risk of COVID-19 and levels of worry among people with diabetes, and compare with the perceptions of those without diabetes; 2) to examine the health-related experiences and behaviors of people with diabetes when compared with those without diabetes, and 3) to examine factors associated with self-reported health status during the COVID-19 quarantine period in China. The China COVID-19 Survey is a cross-sectional anonymous survey that was administered via WeChat, China's leading social network. Almost every Chinese adult uses WeChat daily, and many children use it as well. We selected this platform not only because the nation was under quarantine and we could only reach respondents online, but also because WeChat is China's leading social network with more than one billion users, and most Chinese adults use WeChat daily. Based on national usage statistics in 2020, the WeChat's penetration rate in China is 78% among 16-64-year-olds. [15] Survey data collection was administered between April 25 and early May 11, 2020. We used both snowball and convenience sampling approaches to recruit a diverse sample in China. The 2020 China COVID-19 Survey questionnaire has 74 items and provides about 150 study variables. It covers 8 comprehensive topics: 1) Awareness, knowledge, attitudes, and practices toward COVID-19, 2) personal experiences and impacts of COVID-19, 3) attitude toward government responses to COVID-19, 4) healthcare-seeking behaviors, 5) demographics characteristics, 6) lifestyle behaviors, 7) psychological well-being , and 8) health outcomes, including obesity and other non-communicable diseases (NCDs) during the COVID-19 pandemic. The Xian Jiaotong University Institutional Review Board approved study procedures, and participants provided consent online. Data includes a national sample of 10,545 adults aged>=18 years in 31 province-level administrative units. For the present study, the study sample included 9,016 participants. Of the total, 8, 432 have neither diabetes nor other NCDs (hypertension, heart disease, stroke, cancer, asthma, chronic lung disease, etc.) (n=8,432; referred to as "people without diabetes" hereafter) and 585 people have been diagnosed as having diabetes (n=585; referred to as "people with diabetes" hereafter). Those with other NCDs but not diabetes were excluded in the analysis. We focused on the following study variables: demographic characteristics, awareness and attitudes toward COVID-19, lifestyle behaviors, sleep, and quality of life. Data on demographic characteristics, including gender, education, income, residence, and occupation were collected and then included as covariates in analysis models. For this study, we Awareness and attitudes towards the COVID-19 were assessed using items adapted from a recent COVID-19 awareness, attitude, and action questionnaire [17] . Two questions asked participants to rate the likelihood they perceived that they or someone in the family might get We adopted the questionnaire developed by Conway and colleagues [18] . Two items, drug shortage and food shortage due to COVID-19, were used to measure coronavirus experiences. Measures of health-related behaviors, including changes in lifestyle behaviors (diet, physical activity, and sleeping), were assessed using items from the Kadoorie Study of Chronic Disease in China (KSCDC) [19] and the CCDRF Survey [16] . These surveys were validated in the Chinese population. Body mass index (BMI) was calculated using self-reported weight and height. We used BMI>=24 to define overweight/obese [20] . Physical activity during COVID-19 was measured with items adapted from the International Physical Activity Questionnaire (IPAQ) [21] . We then calculated participants' weekly physical activity minutes and whether they meet the 150 min weekly physical activity recommendation. Participants' self-rated health [22] was measured by the most commonly used item from Patient-Reported Outcomes Measurement Information System (PROMIS) item with responses from poor to excellent. To assess chronic disease status, participants completed questions regarding the following questions: "Did they currently have a chronic disease? If so, they were to identify specific diseases or health conditions." Healthy individuals who self-identified of having no existing chronic diseases were defined as "healthy people without diabetes," those who self-identified as having diabetes was defined as "people with diabetes." Those with other chronic diseases but no diabetes was excluded in the analysis. We first described the distribution of sample characteristics including age, gender, education, income (low, medium, high based on tertiles), residence (city, town, and rural), smoking (non-smoker, current smoker, ex-smoker), and alcohol drinking (non-drinker, current drinker, ex-drinker), body mass index (BMI, based on reported weight and height), and weight status (classified based on BMI>=24 for overweight and >=28 for obesity, which are the Chinese national BMI cut points), and analysed these by the subjects' diabetes status. Chi square test was used to compare differences between groups for categorical variables and ANOVA for continuous variables. Second, we graphically presented the distributions of frequency of physical activity, change in smoking and alcohol drinking by perceived COVID-19 infection risk with bar charts. Then we performed a multivariable Poisson regression with robust variance to calculate prevalence ratio (PR) [23] , to assess the association between diabetes status and lifestyle behaviour changes during COVID-19 pandemic with self-reported health (excellent/very good vs. others), while controlling for sociodemographic variables. It is usually preferable to use PRs instead of odds ratios (ORs) in cross-sectional studies when the prevalence of outcome measures is above 10% [24] . All the analyses were performed using STATA 16.1 (Stata Corporation, College Station, TX, USA). Statistical significance was considered when p<0.05 (two-sided). The sample characteristics by diabetes status are summarized in Table 1 . A total of 9,016 participants (3,839 men and 5,177 women) were included in the present study, of which 585 (6.5%) reported having diabetes. Compared to participants without diabetes, more participants with diabetes were men (60.3%) and older. They were more likely to live in cities (68.7%), had a lower level of education, a high level of income (47.4%), and were overweight (23.8%) or obese (10%). Notably, those with diabetes were more likely to be current smokers (43.6% vs. 11.8%, p<0.001) and current alcohol drinkers (40.2% vs. 19.7%, p<0.001), compared to those without diabetes. The prevalence of daily exercise was 12.9% in participants with diabetes vs. 8.2% in participants without diabetes (Figure 1) . When asked about participating in certain types of exercise, people with diabetes (when participated in physical activity) were more likely to do Tai Chi and/or Qigong than the others (38.8% vs 11.8%). Perceived high COVID-19 infection risk was associated with increases in smoking and drinking (Figure 2) . Among those who perceived high infection risk, 63.7% of the smokers increased smoking a lot and 54.8% drinkers increased drinking a lot. The corresponding figures were only 19.3% and 8.2% among those individuals that did not perceive themselves to be at a higher risk of infection. Findings (Table 3 ) from the multivariable Poisson regression model showed associations between diabetes and lifestyle behavior changes during COVID-19 pandemic with self-reported health, controlling for demographic characteristic. The model showed that those with diabetes were 11% less likely to report having excellent/very good health. Obtaining 150 minutes/week physical activity was positively associated with excellent/very good health (PR 1.14, 95%CI 1.11-1.16). Compared with those who reported no change of PA, those individuals who increased PA levels were more likely to report excellent/very good health. Compared with those who obtained 6-8 hours of sleep per day, participants who got less than 6 hours of sleep were 12% less likely to report excellent/very good health. Changes in drinking were not associated with self-reported health outcomes. Surprisingly, those who reported increases in smoking were more likely to have excellent/good health. Risk factor assessments based on pandemic data from multiple countries, although still notably limited [7] [8] [9] [10] [11] 25] , indicate that those at higher risk for severe illness from COVID-19 include older males, and people with obesity and related comorbidities such type 2 diabetes. As China has the highest number of obese and diabetic patients worldwide (about 46% of adults are obese or overweight) [26, 27] , and 116 million adults living with diabetes (10.9% total diabetes and 35.7% prediabetes [12] ), it is critical to understand how COVID-19 impacts Chinese adults, especially those with chronic conditions like diabetes and overweight/obesity, which likely coexist. To our knowledge, this study is the first sizeable nationwide survey to examine Chinese diabetics and healthy non-diabetic residents' attitudes towards COVID-19 risks and the impact of COVID-19 on health-related behavior changes, and self-related health during the home quarantine period in China. The survey respondents were from all the provinces, municipal cities, and special administrative regions of China. Our study shows that Chinese adults with diabetes were more likely to perceive a high risk of COVID-19 infection and high level of worry than those without diabetes. Literature has shown that food insecurity is an important risk factor for type 2 diabetes. [28] People with diabetes are more dependent on the regularity of food and medicine and therefore this may have caused the perceived risk of food and drug shortage. Concurrently, a higher proportion of individuals with diabetes reported an increase in smoking and drinking than those without diabetes in our sample. We attempted to investigate the association between self-reported health and the alcohol drinking and smoking. We found that those who increased smoking were more likely to have excellent/good health. Although this cross-section data does not allow us to draw a causal conclusion, we propose two explanations for future studies to validate. First, increased alcohol consumption and smoking may be related to elevated anxiety and stress due to prolonged isolation and uncertainly during the COVID-19 pandemic. When people's lives are stressful, alcohol consumption and tobacco smoking may become a coping approach for their stress. Second, the controversial, unproven but popular social media claims that [29] tobacco or nicotine could reduce the risks of COVID-19 may play a role in shaping at-risk individuals' behaviors. With around 314 million adult smokers, and accounts for nearly one-third (30%) of smokers worldwide in China [30] , the use of tobacco products is of particular concern during the COVID-19 pandemic. We advocate for more research to examine the role of smoking in the contraction, transmission, and mortality rate of COVID-19. Previous research suggest that alcohol consumption is a potential risk factor for type 2 diabetes [31, 32] . Regardless of the amount of alcohol a person consumes, alcohol consumption can weaken the immune systems [33] . People with problematic drinking behaviors can be among the most vulnerable populations for COVID-19 infection [34] . Given the high rates of smoking and drinking among those with diabetes, countries should allocate resources to health stimulus packages, scientific research, and health promotion campaigns to further reduce alcohol drinking and smoking rates. The COVID-19 pandemic casts new light on some old problems: medication access, drug shortages, and food shortages. Our findings showed that diabetic patients were at higher risk for drug and food shortages. Although some global supply disruptions and shortages cannot be predicted or prevented, government agencies and healthcare providers can provide telemedicine, mail order pharmacy, home delivery, and drive-thru or curbside pick-up of groceries or medications in order to mitigate the shortages while enhancing social distancing. While China is slowly beginning to reopen since lifting the lockdown in mid-May, it is of interest to understand how people coped with stress during the time of social distancing and home quarantine in China. Our findings indicate that having an adequate amount of physical activity (i.e., engaging in weekly physical activity for 150 minutes or more) and increasing physical activity during pandemic were positively associated with self-reported excellent/very good health. Insufficient sleep was more common in people with diabetes than people without diabetes and was related to poor health. These findings further highlight the importance of the promotion of health and wellness during COVID-19. This study has some strengths and limitations. To our knowledge, no studies to date have reported findings related to perceived infection risks and the impacts of COVID-19 on a large sample of people with diabetes on health-related outcomes and lifestyle behavior changes in China, where COVID-19 was first reported, and many vigorous nationwide measures were taken to control its spread. Our findings may shed light on how to target or manage risk factors (i.e., drinking and smoking) for the diabetic population during the pandemic. Given the anonymous design of the online survey, such an approach can work better to collect people's honest answers, especially about sensitive information; and it is more feasible in special situations like COVID-19, where face-to-face interviews are difficult. This study has some limitations. First, potential self-selection bias is common in survey data, including ours due to the use of convenience and snowball sampling method. Our sample may also have bias related to age or education since 45.8% of diabetic adults have a bachelor's degree in comparison to 64% of non-diabetic adults. However, some health indicator rates from our data, such as rates of overweight/obesity and diabetes, were consistent with those from other national survey data [27, 35] . Second, the sample was relatively young (48.9% of participants were 18-29 years old and 17.7% 40 years old and above). Therefore, the observations are limited and cannot be extrapolated to older individuals who likely have different perceptions of the COVID-19 pandemic. Third, the survey questionnaire did not ask participants to identify the specific type of diabetes that they had been diagnosed. Although we did not attempt to distinguish type 1 diabetes from type 2 by self-report, the vast majority of diabetes cases in China are currently type 2 [36] . Fourth, the self-reporting scale used for changes in smoking and drinking was crude and subjective (changed a lot, change a little, et al). We did not collect the baseline data for tobacco or alcohol consumptions prior to COVID-19 and cannot really quantify the changes. In addition to collecting data on the self-reported changes in smoking and alcohol consumption for the current diabetic smokers or drinkers during COVID-19 as this study did, it is also important to know the prevalence of initiation of smoking/drinking during the COVID-19 pandemic. Fifth, the number and/or types of medications taken as well as duration of diabetes were not collected in the survey. This was because stratified analysis by number of medications taken by participants would require a much larger sample size. This was beyond the scope of the current investigation. Lastly, a review by Dryhurst and colleagues [37] pointed out that many rapid-response studies almost exclusively rely on single-item measures of risk perception, selectively tapping into either cognitive or emotional dimensions. This study's two-item measures of risk perception share the above-mentioned limitation. Risk perception is a subjective psychological construct that is influenced by cognitive, emotional, social, cultural, and individual variation both between individuals and between different countries [37] . We encourage future studies to include theory-based measures of risk perception that involve multidimension determinants of risk perception. In conclusion, our results showed that Chinese adults with diabetes perceived themselves at high risk for COVID-19 infection and were more worried about being infected when compared to individuals without diabetes. They reported experiences with shortages of food and medication and exhibited adverse lifestyle behaviors and declined health. Even though we cannot identify precisely what caused the fear, it may be hypothesized that imprecise information and instructions of being at higher risk for COVID-19 and death in people with diabetes by media and government, without necessary follow-up and support structures in place, created the anxiety. This is indeed a wake-up call, a tocsin, to the world on the importance of primary prevention and providing good support to chronic disease patients, including those with diabetes. A novel coronavirus outbreak of global health concern Identification of a novel coronavirus in patients with severe acute respiratory syndrome Clinical considerations for patients with diabetes in times of COVID-19 epidemic Clinical features of patients infected with 2019 novel coronavirus in Wuhan Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? The Clinical Characteristics and Outcomes of Diabetes Mellitus and Secondary Hyperglycaemia Patients with Coronavirus Disease 2019: a Single-center, Retrospective, Observational Study in Wuhan Practical recommendations for the management of diabetes in patients with COVID-19 Clinical characteristics and outcomes of patients with severe covid-19 with diabetes American Diabetes Association. How COVID-19 Impacts People With Diabetes Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China in 2013 Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas WeChat Revenue and Usage Statistics Business of Apps Disease and disparity in China: a view from stroke and MI disease Attitudes, and Actions Related to COVID-19 Among Adults With Chronic Conditions at the Onset of the U.S. Outbreak Social Psychological Measurements of COVID-19: Coronavirus Perceived Threat, Government Response, Impacts, and Experiences Questionnaires. National Institute of Health Cohort Profile: The Kadoorie Study of Chronic Disease in China (KSCDC) Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review General Population Estimate for "Excellent" to "Poor" Self-Rated Health Item Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio A comparison of two methods for estimating prevalence ratios Obesity as a predictor for a poor prognosis of COVID-19: A systematic review New national data show alarming increase in obesity and noncommunicable chronic diseases in China Prevention and control of obesity in China Food insecurity is an important risk factor for type 2 diabetes: a case-control study of new referrals to the University clinics Two important controversial risk factors in SARS-CoV-2 infection: Obesity and smoking Tobacco and the lung cancer epidemic in China Alcohol and type 2 diabetes. A review. Nutrition, Metabolism and Cardiovascular Diseases Alcohol consumption and risk of pre-diabetes and type 2 diabetes development in a Swedish population Alcohol and the Immune System COVID-19 Hangover: A Rising Tide of Alcohol Use Disorder and Alcohol-Associated Liver Disease Understanding the China Blue Paper on Obesity Prevention and Control and policy implications and recommendations for obesity prevention and control in China Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication Risk perceptions of COVID-19 around the world The authors would like to thank the study participants, collaborators, and staff members who contributed to this study. In particular, we would like to thank Professors Shiyong Liu, Peng Ni and Qianli Xue, and Mrs. Lihua Yan.Author contributions: A.F.Y, Y.W, Z.S. contributed to the study design, data collection, and drafting the report. All authors contributed to interpretation of the data, commented on and revised the report, and approved the final version for publication. Board (Grant number: 16-262) and Xi'an Jiaotong University Global Health Institute. 


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   key: cord-353867-617f90wq authors: Ory, Marcia G.; Lee, Shinduk; Towne, Samuel D.; Flores, Starr; Gabriel, Olga; Smith, Matthew Lee title: Implementing a Diabetes Education Program to Reduce Health Disparities in South Texas: Application of the RE-AIM Framework for Planning and Evaluation date: 2020-08-30 journal: Int J Environ Res Public Health DOI: 10.3390/ijerph17176312 sha: doc_id: 353867 cord_uid: 617f90wq Health disparities in diabetes management and control are well-documented. The objective of this study is to describe one diabetes education program delivered in the United States in terms of the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) Planning and Evaluation Framework. Questionnaires, clinical data, and administrative records were analyzed from 8664 adults with diabetes living in South Texas, an area characterized by high health disparities. The Diabetes Education Program delivered was a professionally led 12-month program involving 8 h of in-person workshop education followed by quarterly follow-up sessions. Changes in average blood glucose levels over the past 3 months (e.g., A1c levels) were the primary clinical outcome. Descriptive and multiple generalized linear mixed models were performed. This community-based initiative reached a large and diverse population, and statistically significant reductions in A1c levels (p < 0.01) were observed among participants with Type 2 diabetes at 3 months. These reductions in A1c levels were sustained at 6-, 9-, and 12-month follow-up assessments (p < 0.01). However, considerable attrition over time at follow-up sessions indicate the need for more robust strategies to keep participants engaged. For this diabetes education program, the RE-AIM model was a useful framework to present study processes and outcomes. Health disparities are often geographically bound and occur more frequently in impoverished populations characterized by low socio-economic status and a dearth of available healthcare resources [1] [2] [3] [4] . The U.S.-Mexico border is impacted by extremely high disparities in income, education, and healthcare access, and these social determinants of health make this region among the nation's Figure 1 illustrated the 27 counties formally included in the Healthy South Texas initiative [30] , and the counties in which the Diabetes Education Program was offered were marked with a red dot. Counties along the US-Mexico border were included, as well as areas adjacent to border counties, which were all referred to as South Texas. Including urban, small town, and rural areas, the overall estimated population in these counties in 2015 was approximately 2.8 million, and these areas were among the most impoverished in the nation in terms of socioeconomic status and lack of healthcare services [31, 32] . As a community-driven initiative, inclusionary criteria were broad with the intent of serving those both directly and indirectly involved in a person's diabetes prevention and management. While the focus was on adults with Type 2 diabetes, persons with pre-diabetes and Type 1 diabetes were invited, as well as family members or friends providing care for persons with diabetes. As indicated in Figure 1 , the formal Diabetes Education Program was offered in 14 of the 27 South Texas counties in two primary hubs clustered around Nueces and Hidalgo counties. Given the community-based nature of this initiative and the desire to reach as many participants as possible to show widespread program penetration, participants were recruited from a variety of sources including screenings at health fairs, referrals from healthcare facilities, outreach to community partnerships with flyers and other social media, and self-referrals. Although there was no attempt to standardize referral sources, which differed by organizational sponsorship and location, promotional materials (e.g., flyers) were standardized with a uniform Healthy South Texas brand. The Diabetes Education Program was a recognized American Diabetes Association (ADA) program that was professionally led but also included community health workers for outreach and programming assistance [27] . All ADA-recognized programs provided quality education for people with diabetes and followed the National Standards for Diabetes Self-Management Education and Support (DSMES) guidelines [33] . ADA-recognized diabetes education programs were eligible for reimbursement through many federal and private U.S. insurers [34] . Offered in both Spanish and English, the program consisted of 8 hours of face-to-face educational workshop sessions led by at least one trained health professional (e.g., registered nurse (RN), registered dietician (RD), pharmacist, or certified diabetes educator). Workshop sessions were followed with brief (e.g., 15-30 min) in-person individualized follow-up educational and support sessions offered on a quarterly basis for a year. Focal workshop topics included a discussion of, as well as hands-on experiential learning about what diabetes is, blood glucose monitoring, Given the community-based nature of this initiative and the desire to reach as many participants as possible to show widespread program penetration, participants were recruited from a variety of sources including screenings at health fairs, referrals from healthcare facilities, outreach to community partnerships with flyers and other social media, and self-referrals. Although there was no attempt to standardize referral sources, which differed by organizational sponsorship and location, promotional materials (e.g., flyers) were standardized with a uniform Healthy South Texas brand. The Diabetes Education Program was a recognized American Diabetes Association (ADA) program that was professionally led but also included community health workers for outreach and programming assistance [27] . All ADA-recognized programs provided quality education for people with diabetes and followed the National Standards for Diabetes Self-Management Education and Support (DSMES) guidelines [33] . ADA-recognized diabetes education programs were eligible for reimbursement through many federal and private U.S. insurers [34] . Offered in both Spanish and English, the program consisted of 8 h of face-to-face educational workshop sessions led by at least one trained health professional (e.g., registered nurse (RN), registered dietician (RD), pharmacist, or certified diabetes educator). Workshop sessions were followed with brief (e.g., 15-30 min) in-person individualized follow-up educational and support sessions offered on a quarterly basis for a year. Focal workshop topics included a discussion of, as well as hands-on Descriptive statistics (mean and standard deviation or frequency and percentage) were used to describe the characteristics of participants. Not all implementation sites collected the exact same set of variables; thereby, the descriptive statistics reflected available data (i.e., not all variables had the same number of missing cases). Bivariate analyses (e.g., independent t-tests or Chi-square tests) were performed to compare the characteristics of participants recruited during the first biennium and second biennium. Next, retention rates were estimated for each follow-up session. As a part of retention analysis, characteristics of participants who attended and did not attend each follow-up session were described and were then compared using bivariate analyses. This study used data collected between September 2015 and December 2019. Although having four additional data collection months after the second biennium ended in August 2019 allowed for more follow-ups, not all participants had an opportunity to complete all their follow-up sessions. Intervention delivery dates were used to identify and exclude participants from the retention analyses based on their eligibility to participate in the follow-up. For example, participants who participated in the workshop after June 2019 were excluded from the retention analyses for the 6-month follow-up. Similarly, participants who participated in the workshop after March 2019 were excluded from the retention analyses for the 9-month follow-up; and those who participated in the workshop after December 2018 were excluded from the retention analyses for the 12-month follow-up. Multiple generalized linear mixed models with participant-level random intercepts were fitted to examine changes in A1c level over time among participants with pre-diabetes or Type 2 diabetes. Persons with Type 1 diabetes (n = 221) or gestational diabetes (n = 12) were not included in the regression models due to small sample sizes. Separate models were performed for the first and second biennia. The first set of models examined changes in A1c levels over time in participants with Type 2 diabetes (n = 1922 in the first biennium and n = 2733 in the second biennium). The second set of models examined changes in A1c level over time in participants with pre-diabetes (n = 380 in the first biennium and n = 482 in the second biennium). The third set of models examined any differences in the changes in A1c levels over time based on diabetes type (pre-diabetes or Type 2 diabetes) (n = 2302 in the first biennium and n = 3215 in the second biennium). The next set of models examined changes in A1c level among participants with Type 2 diabetes by their baseline A1c level (i.e., 4.0-5.6%, 5.7-6.4%, 6.5-7.9%, 8.0-8.9%, 9.0-9.9%, 10.0-11.9%, and 12.0% or higher) (n = 1912 in the first biennium and n = 2722 in the second biennium). In addition, a separate regression model was used to examine any racial/ethnic differences in changes in A1c level (n = 2302 in the first biennium and n = 3215 in the second biennium). All regression models controlled for covariates including age, sex, race/ethnicity, education, language, and baseline BMI category. Given that there were only 5 participants reported speaking "Other" as their primary language, they were excluded from the regression analyses. A significance level of 0.01 was used. This study involved retrospective reviews and analyses of limited data, and this study was reviewed and approved by the Institutional Review Board at Texas A&M University (IRB2019-0225D). Results were presented based on the five RE-AIM elements to provide a case study of this applied research about diabetes self-management education [29] . Reach was defined as "the absolute number, proportion, and representativeness of individuals who are willing to participate in a given initiative, intervention, or program, and reasons why or why not" [36] . The number of persons who participated in the program and their general characteristics were tracked (Table 1 ). The majority of program participants were aged between 45 and 64 years (55.3%), female (61.6%), Hispanic (68.6%), and had high school or less education (72.2%). Most participants reported English as their primary language (89.5%) ( Table 1 ). The intervention could be attended by individuals with pre-diabetes or diabetes as well as their family and friends. Among the program participants with a recorded diabetes type, nearly 15% had pre-diabetes and more than 80% had Type 2 diabetes. The mean A1c level was 6.2% among those with pre-diabetes, 8.7% among those with Type 1 diabetes, and 8.6% among those with Type 2 diabetes. * p < 0.01; ** p < 0.001; a . p-values from bivariate analyses (e.g., independent t-tests or Chi-square tests) comparing the characteristics of participants recruited during the first biennium and second biennium; b . Chi-square comparison was performed after excluding "Other" language (n = 8650); c . In total, 21.7% of diabetes type records were missing, and frequency and percentage were calculated based on available data (n = 6788); d . Mean and standard deviation of baseline A1c percentage measures were estimated among those with pre-diabetes and Type 1 and Type 2 diabetes. BMI, body mass index. A1c, average blood glucose level over the past 3 months. There were statistically significant differences in characteristics of the participants recruited during the first and second biennium (Table 1) , indicating changes in program participant profiles and expanded program reach. Compared to participants recruited during the first biennium, those recruited during the second biennium tended to be younger (22.0% vs. 17.1% aged 18-44 years), normal or overweight (32.4% vs. 29.6%), and not knowing their diabetes type (2.0% vs. 0.4%) ( Table 1) . In addition to considering initial recruitment, it was important to assess population representativeness over time. The program consisted of an educational session and four quarterly follow-ups to track behavioral goals and clinical outcomes. However, less than 50% of participants attended the first scheduled quarterly follow-up session at 3 months, and the attendance rate for the subsequent follow-up sessions further decreased to 30.5% at 6 months, 23.0% at 9 months, and 18.4% at 12 months. The attendance rates at 9 and 12-month follow-up sessions were higher during the second biennium than during the first biennium (24.8% vs. 21.0% at 9 months and 21.1% vs. 15.9% at 12 months). Table 2 shows the number and characteristics of overall program participants who attended and did not attend at each follow-up assessment. For all four follow-ups, retention rates were higher among those in the older age group, females, non-Hispanic individuals, those with more than a high school education, and those whose primary language was Spanish ( Table 2) . Retention rates tended to be lowest for those with BMIs classified as being underweight (Table 2) . Among participants with Type 2 diabetes, those not attending a follow-up session at any given time point had significantly higher baseline A1c levels than those who attended the follow-up session ( Table 2) . Among participants with pre-diabetes or Type 1 diabetes, no statistically significant differences were observed based on baseline A1c level attending a follow-up session at any given time point (Table 2 ). At the 3-month follow-up, the retention rate was significantly different based on participants' diabetes type ( Table 2 ). The retention rate was highest among those with pre-diabetes (54.9% at 3 months) and Type 2 diabetes (51.3%), followed by those with Type 1 diabetes (44.3%) and those who were unaware of their diabetes type (35.5%) ( Table 2 ). However, the association between retention rates and diabetes type was not statistically significant at subsequent follow-ups (Table 2) . Effectiveness was defined as "the impact of an intervention on important individual outcomes, including potential negative effects, and broader impact including quality of life and economic outcomes; and variability across subgroups (generalizability or heterogeneity of effect)" [36] . This study evaluated changes in A1c level among participants from baseline to each follow-up time point. In both the first and second biennium, a statistically significant reduction in A1c level was observed among participants with Type 2 diabetes at the 3-month follow-up (b = −0.97, p < 0.001 in the first biennium and b = −1.13, p < 0.001 in the second biennium), and this A1c level reduction was sustained at the 6-month (b = −0.98, p < 0.001 and b = −1.20, p < 0.001), 9-month (b = −1.10, p < 0.001 and b = −1.19, p < 0.001), and 12-month (b = −0.95, p < 0.001 and b = −1.32, p < 0.001) follow-up. For example, for participants with Type 2 diabetes who joined during the second biennium, the average A1c level dropped from 8.6% at baseline to 7.5% at 3 months, and this A1c level reduction was sustained at subsequent follow-ups (7.4% at 6 months, 7.3% at 9 months, and 7.3% at 12 months). On average, participants with pre-diabetes had A1c levels that remained controlled (<6.5%) from baseline to the subsequent follow-ups. For example, in the first biennium, a statistically non-significant reduction in A1c level was observed among participants with pre-diabetes at all follow-ups (b = −0.12 and p = 0.06 at 3 months; b = −0.13 and p = 0.07 at 6 months; b = −0.12 and p = 0.13 at 9 months; and b = −0.12 and p = 0.19 at 12 months). Participants who had Type 2 diabetes showed significantly greater reductions in A1c level than those with pre-diabetes (p < 0.001 for the interaction term between time and diabetes type in both the first and second biennia) ( Figure 2 ). Table 2 . Characteristics of the program participants who attended and did not attend 3-, 6-, 9-, and 12-month follow-up assessments. p-values from bivariate analyses (e.g., independent t-tests or Chi-square tests) comparing the characteristics of participants who attended and did not attend the follow-up session; e . Excluded 5 participants who reported "Other" primary language to prevent possibility of identifying the individuals; f . Excluded 12 participants who reported having gestational diabetes to prevent possibility of identifying the individuals; g . High missing response rates and frequency and percentage were calculated based on available data (n = 6776 at 3 months, 6400 at 6 months, 5834 at 9 months, and 5288 at 12 months); h . Mean and standard deviation of baseline A1c percentage measures were estimated among those with pre-diabetes, Type 1 diabetes, or Type 2 diabetes. BMI, body mass index. A1c, average blood glucose level over the past 3 months. significant reduction in A1c level was observed among participants with pre-diabetes at all followups (b = −0.12 and p = 0.06 at 3 months; b = −0.13 and p = 0.07 at 6 months; b = −0.12 and p = 0.13 at 9 months; and b = −0.12 and p = 0.19 at 12 months). Participants who had Type 2 diabetes showed significantly greater reductions in A1c level than those with pre-diabetes (p < 0.001 for the interaction term between time and diabetes type in both the first and second biennia) (Figure 2 ). (a) (b) Figure 2 . Estimated changes in A1c from baseline to 3, 6, 9, and 12-month follow-up after adjusting for age, gender, ethnicity, education, language, and baseline BMI category, by diabetes type and biennia: (a) first biennium; (b) second biennium. A1c, average blood glucose level over the past 3 months. BMI, body mass index. Changes in A1c values over time among participants with Type 2 diabetes were also examined based on baseline A1c values. Estimated changes in A1c by baseline A1c values show similar trends over time among the participants enrolled during the first biennium ( Figure 3a ) and second biennium (Figure 3b ). For both biennia, there was a statistically significant modification effect of the baseline A1c level on changes in A1c values over time (p < 0.001 for the interaction term between time and the baseline A1c level in both the first and second biennia). Participants with high baseline A1c values (e.g., 8% or higher) achieved a decline in their A1c values at the 3-month follow-up assessments and maintained during the subsequent follow-ups. The estimated A1c level decline was most pronounced for those with highest baseline A1c values (e.g., 12% or higher). On average, participants with controlled diabetes at the baseline remained in control during the subsequent follow-ups. level on changes in A1c values over time (p < 0.001 for the interaction term between time and the baseline A1c level in both the first and second biennia). Participants with high baseline A1c values (e.g., 8% or higher) achieved a decline in their A1c values at the 3-month follow-up assessments and maintained during the subsequent follow-ups. The estimated A1c level decline was most pronounced for those with highest baseline A1c values (e.g., 12% or higher). On average, participants with controlled diabetes at the baseline remained in control during the subsequent follow-ups. In a separate regression model, which included the interaction term between time and race/ethnicity, there was no statistically significant differences in changes in A1c values over time among participants with pre-diabetes or Type 2 diabetes (p = 0.11 in the first biennium and p = 0.25 in the second biennium). Adoption was defined as "the absolute number, proportion, and representativeness of settings and intervention agents (people who deliver the program) who are willing to initiate a program, and why" [36] . In lieu of being able to quantify adoption, the general adoption approach was described. The Coastal Bend Health Education Center (CBHEC) in Corpus Christi served as the Healthy South Texas regional headquarters. The Texas A&M South Texas Center, McAllen Campus, served as a second regional hub. While planning the intervention roll-out, a hub-and-spoke model was determined to be the most effective strategy in which a central "hub" supports multiple "spokes" in communities to provide a range of services. With this approach, CBHEC and the McAllen campus identified other regional partners to help recruit and deliver the program. Due to the staffing requirements (e.g., needing a health professional to lead the educational workshops and sessions), the program was not delivered in all counties; rather, it was more selectively offered around the two hubs-with the spokes representing adjacent service areas. In accordance with ongoing collaborative health promotion activities in their respective local communities, both CBHEC and the McAllen campus were able to call upon their extended healthcare and public health networks for program delivery assistance. Regional partners that helped deliver the program represented diverse community and clinical entities including community-based organizations, federally qualified health centers, hospitals, clinics, pharmacies, school districts, academic institutions of higher education, state agencies and not-for-profit social service organizations, behavioral health organizations, and city and county government offices. Partnerships varied with some organizations assuming a fuller responsibility for delivering courses on their own, some helping with overall recruitment, and some solely offering physical space for classes. Implementation was defined as "the intervention agents' fidelity to the various elements of an intervention's key functions or components, including consistency of delivery as intended and the time and cost of the intervention. Adaptations are also included in this RE-AIM element" [36] . As an ADA-recognized program, there was a need to demonstrate that ADA principles of diabetes education were being followed. This involved an annual review by a designated quality control coordinator to review delivery processes, certify them as compliant, or note aspects to be corrected. Program standardization across sites was facilitated by having a centralized hub for training and data reporting, in coordination with the scientific-administrative oversight functions provided by Healthy South Texas Leadership. CBHEC trained staff in program delivery using a standardized program manual and holding periodic problem-solving feedback sessions with regional program managers and implementers. Adaptations to the program were discussed with the evaluation team to enhance program reach and retention. For example, program staff reported that many participants found it difficult to attend a single-day, 8-h workshop. Therefore, other options were offered such as spreading the workshop over multiple days in two 4-h sessions or four 2-h sessions. Program costs were not tracked in the first biennium, but efforts were made to retrospectively estimate actual program costs based on personnel, supply and space costs toward the end of the second biennium. The program was estimated to cost between USD 800 and 1200 per participant, albeit with substantial variation based on how established the program was delivered at different sites, methods of recruitment, and the number of participants in each class. A major indicator of program implementation was how many participants engaged in all program activities (e.g., the initial educational workshop plus four quarterly follow-up sessions to track behavioral goals and clinical outcomes over time). As seen in Table 2 , only about 50% of participants attended the first quarterly follow-up session at 3 months, and attendance rates decreased for the subsequent follow-up sessions. Given that process evaluation activities were initially built into the program evaluation, program staff were made aware of this issue concerning attendance. Subsequent action was taken in an attempt to bolster follow-up rates. Retention rates at 9 and 12-month follow-up sessions during the second biennium were significantly higher than the rates during the first biennium (i.e., 24.8% vs. 21.0% at 9 months and 21.1% vs. 15.9% at 12 months). Maintenance was defined at the individual level as "the long-term effects of a program on outcomes after a program is completed" and at the setting level as "the extent to which a program or policy becomes institutionalized" [36] . At the individual level, the trajectory of A1c level change over time during the 12-month intervention period has already been reported in the effectiveness section (Figures 2 and 3) . At the setting level, programs included within the Healthy South Texas initiative were intentionally designed to be housed within and delivered by established community partners who could draw upon their existing networks to facilitate programmatic spread and sustainability. For legislative feedback purposes, the amount of actual and in-kind dollars the Health Science Center leveraged during the first two biennia was calculated. From private and public sources, over USD 15,000,000 was identified in direct support and in-kind dollars for the Healthy South Texas initiative (including delivery of the Diabetes Education Program, as well as other disease prevention and health promotion activities) by governmental and nongovernmental entities. The program is still ongoing with a legislative commitment for a third biennium (September 2019-August 2021), and program delivery remains a core function of the two regional hubs. The Healthy South Texas Diabetes Education Program reached and benefitted large numbers of participants in a region with documented health inequities that have perpetuated health disparities [13] . Utilizing the RE-AIM framework for both planning and evaluation enabled the study team to describe this diabetes program in terms of its reach, adoption, implementation, effectiveness and maintenance [37] , and explore the unique challenges faced when applying and assessing RE-AIM elements in community settings [38, 39] . This community-based initiative reached a large and diverse population in this region, thus supporting the external validity of the positive results observed. The Hispanic population accounted for over two-thirds of the total population in the service region [40]; hence, the ability to provide the program in Spanish was a critical element that enabled the program to reach this population. An estimated 65,000 persons were served in the first two biennia across a broader range of diabetes outreach and education activities implemented by the Texas A&M Health Science Center Healthy South Texas initiative, in which the Diabetes Education Program was a single component. This initiative capitalized on its understanding of the local community and organizational context, which has been deemed critical for the implementation and dissemination of other health promotion programs [41] . This enabled the implementation sites to draw upon highly visible stakeholders and their diverse relationships with community and clinical organizations for outreach and delivery. Culture, language, and access to resources are known barriers to access to care in the Hispanic population with Type 2 diabetes [42] , and collaboration with diverse local community and clinical entities enabled recruitment of culturally competent staff and facilitators to reach the Hispanic population, who are likely to be an underserved population in the region. This study has generated several general take-home messages to be considered before implementing future health promotion initiatives. Foremost, program planners should conduct community needs assessments and/or engage stakeholders from the communities they wish to target during initial planning, when assessing feasibility, and when deciding which components to include (e.g., considering potential cultural competency considerations and the need to tailor materials). While research studies typically need to offer incentives for participant recruitment [43] , the Healthy South Texas initiative promoted program adoption by providing sites with necessary materials and/or subcontracted for services, which allowed programs to be offered free to participants who may have otherwise lacked resources to pay for such services. This was a critical component that helped alleviate or limit the participants' financial burden as a major barrier to participation, particularly among the socioeconomically disadvantaged population. When considering the generalizability of this evaluation in future settings and populations, program implementers and other key stakeholders should consider this option, where possible, as it may determine whether or not participants can access similar services in other initiatives. Balancing fidelity to program implementation with the need for adaptation remains challenging in translational research [44] . Over the past two biennia, many of the recommended RE-AIM strategies for improving the implementation processes were implicitly followed and should serve as explicit guideposts in future studies [29, 37] . For example, some sites adapted intervention delivery modes and routinized follow-up reminder contacts. Other sites switched the health professional type needed to lead workshops to accommodate local needs and preferences. For example, one hub determined that in their setting a registered dietician was preferable to a registered nurse, and the ADA offered some latitude when selecting the specific type of health professional used to lead workshops without jeopardizing the ADA-recognized status or program fidelity. Of particular note, the high attrition rates for follow-up visits raises issues about the feasibility and appropriateness of the current Diabetes Education Program structure. The first biennium was a learning curve for the problem facilitators and implementation sites, and additional activities (e.g., reminder calls) were considered and conducted to enhance follow-up rates in the second biennium. Reviewing other successful chronic disease self-management programs [45] , the difficulties of expecting the targeted population to consistently engage in intervention sessions over a 12-month period were recognized. Hence, one major adaptation for future programming is to consider modifications of the current Diabetes Education Program and/or the development of a new iteration of such a program with a shorter active intervention period. Long-term supports are still valuable for participant success and should still be incorporated in some capacity (e.g., by their healthcare provider, virtually, telephonically). The effectiveness of the Diabetes Education Program demonstrated significant decreases in A1c levels over time which were clinically meaningful [46] . However, a closer look provides guidance for future targeting, which is a major concern in intervention research [47] . While reducing A1c levels is a clinical goal for persons with elevated A1c levels, targeting those with A1c values of 8 and above has greater potential to be most cost effective, given these A1c levels are associated with the most diabetes complications and need for costly medical care [19] . Findings from our study indicate that self-management programs adhering to ADA best practices can achieve large decreases in A1c levels among this population. Maintenance of individual and system-level outcomes is often the most challenging RE-AIM element to achieve [36, 48, 49] . It is critical to be aware of how context can influence outcomes at both levels. The Practical Robust Implementation and Sustainability Model (PRISM) framework, which is now an integral part of the RE-AIM framework [50] , helps us understand the contributory role of recipients, implementation and sustainability, infrastructure, and the external environment. One successful maintenance strategy at the individual maintenance level was to pair the Diabetes Education Program with opportunities for class-based exercise programming throughout the year. Based on feedback from program managers, participants seemed to enjoy these sessions and kept them engaged in the Diabetes Education Program. Additionally, the Diabetes Education Program was seen as a core component of the two Healthy South Texas regional hubs, and organizational efforts are underway to build capacity and support for continued delivery through a network of concerned partners. In the current third biennium of state funding (September 2019-August 2021), program staff are fully aware of the importance of context during these unprecedented times. Thus, different options for long-term sustainability are being explored, noting that the current COVID-19 crisis may make future state support less likely due to budgetary considerations [51] . Toward this end, strategies for defining and promoting the value proposition of this and other health education programs are being formulated. For example, fee-for-service options, seeking insurance reimbursement for recognized diabetes management programs, and/or providing the program for a modest charge to community or healthcare organizations as a community benefit are being investigated. To assist in making a value proposition based on the potential return on investments made, economic evaluation studies that can demonstrate objective value in multiple ways (e.g., monetary outcomes, measures of gains in quality of life, reductions in years of potential life lost, reductions in potentially preventable hospitalizations) are recommended. Such studies are important to inform local, state, and national stakeholders about the potential return on investment for these and similar studies and should complement other evaluation activities of similar initiatives to reach a broader audience. While there were many strengths in this community-based study, there were some limitations that must be acknowledged. This is a case study of a single diabetes education program in a geographic area. Hence, the findings, while promising, may not be generalizable to all community diabetes prevention and control programs or to other areas with differing population characteristics, settings, or varying levels of baseline risk (e.g., A1c level). Additionally, in contrast to academic-based research studies, service delivery often has priority in community-driven programs relative to data collection and management processes, which can limit the types and quality of data collected. For example, although there was a record of engagement by partnering organizations, there was no record of the extent to which organizations agreed to participate when asked. Furthermore, while a large sample size was used, lack of randomization in community settings could be impacted by selection bias. The RE-AIM model provided a general framework for reporting the planning and evaluation of this initiative. As such, full measures on all RE-AIM dimensions were not collected. While this is a potential shortcoming, it is also aligned with the recognition that applying the RE-AIM model in "real-world" studies does not depend on the assessment of all five dimensions [39] . Despite the relatively large numbers of community residents served by the Diabetes Education Program, the proportion reached relative to people with diabetes residing in these South Texas counties was still minimal, given the high rates of adults with diabetes in the South Texas region [52] . Further, the large attrition rate for follow-up sessions was considerable, which may highlight recruitment or fidelity issues and introduce self-selection and/or a healthful bias (i.e., participants with Type 2 diabetes with lower A1c levels had better retention rates at all follow-up sessions). Finally, data were not collected longer than 12 months following the Diabetes Education Program workshop; therefore, an assessment of long-term clinical control and management among participants was not possible. Similarly, as the Healthy South Texas Initiative is still ongoing, long-term program delivery and institutionalization post external funding could not be assessed. However, lessons can be learned from this initiative that advance knowledge about research translation. In line with guidelines for implementing evidence-based diabetes prevention and control programs [53] , two successful strategies were employed to enhance reach with the ultimate goal of reducing health disparities in underserved populations. First, community health workers were integral to participant recruitment because they were seen as trusted members in the community [54] . Another successful strategy was establishing and using diabetes health champions [55] (i.e., persons in the program who had successfully lowered their own A1c levels) to serve as program promoters who could engage populations typically characterized as "hard to reach" and who were unaware of, or previously uninterested in, participating in health promotion programs [56] . The Diabetes Education Program provided an example of dedicated effort to meet the overall public health goal of diabetes prevention and management for all Americans, especially among those experiencing a multitude of social determinants of health inequities [57] . Offering the Diabetes Education Program through two regional hubs was a major advantage for program reach and adoption as well as launching the initiative quickly. 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A special thanks is given to the Diabetes Education Program trainers, educators, and staff in Corpus Christi and McAllen who are continually dedicated to improving lives in South Texas. Additionally, the authors would like to thank Carrie L. Byington, former Senior Vice President of the Texas A&M University Health Science Center and Vice Chancellor for Health Services of the Texas A&M University System (currently Executive Vice President and Head of UC Health at the University of California) and Susan Ballabina, Deputy Vice Chancellor for Texas A&M AgriLife for their leadership role in the Healthy South Texas initiative during the study period. The authors declare no conflict of interest. 


