34 Original ISSN 2413-0516 J Contemp Med Sci | Vol. 7, No. 1, January-February 2021: 34 – 39 Introduction Diabetes mellitus (DM) and chronic kidney disease (CKD) (as a part of non-communicable diseases) have replaced infection as a main cause of death in last century. DM is the seventh leading cause of death and responsible for 14.5% of global all-cause mortality. DM is an out of control pandemic, each year 10 million new cases are added globally. In Iraq, it affects 517,080 adults and expected to be 20,09,000 by year 2030, 46.5% of them are unaware of their status.1–9 DM may be subclinical for years; 25–50% of patients have complications at the time of diagnosis like diabetic nephrop- athy which is the leading cause of renal failure and increases death rate 20–40 times, rise in DM morbidity, mortality, and cost; is parallel to that of CKD.4,10–15 CKD is defined as drop in glomerular filtration rate (GFR) below 60 ml/min/1.73 m2 for 3 months or more. Initial assess- ment should include GFR and not by serum creatinine (sCr) alone.16–18. Staging based on GFR is a good way to assess CKD severity; stage 1 (GFR ≥ 90), stage 2 (60–89), stage 3 (30–59), stage 4 (15–29), and stage 5 (<15).17,19 Most of the diabetics with CKD will die from cardiovascular complication; death is two times more at stage 3 and three times at stage 4 than individuals with normal kidney function.4,20,21 Many GFR estimating formulae are available like; Cockcroft Gault (CG), and CKD–epidemiology collaboration (CKD-EPI) (the best for diverse populations).22,23 In primary health care (PHC), most patients with CKD stages 1, 2 are undetected as CKD is silent and reversible. GFR would be half normal before sCr level would be abnormal. Kidney Disease Improving Global Outcome (KDIGO) guidelines consider annual CKD initial screening by sCr-based estimated-GFR (eGFR) in diabetics as cost-effective; starting 5 years after diagnosis in type 1 DM, and at time of diagnosis in type 2 DM. Early detection, slow progression, decrease complications, and need for dialysis. Primary care providers are well positioned to manage CKD patients as early referral to nephrologist improve prognosis but they are only detecting 10.8% of them at stage 3.4,11,16,17,19,24–26 This study aims; to assess diabetic adults attending diabetes centers; socio-demographic profile, CKD prevalence, associ- ated risk factors; and to increase family physicians awareness that GFR estimated by CKD-EPI equation is better than sCr alone to assess renal function and detect CKD among diabetic adults in PHC. Methods An outpatient-based cross-sectional study with some analytic elements conducted on registered diabetic adults attending diabetes and endocrine-related diseases tertiary centers in Baghdad (Rusafa & Karkh) during the period from December 15, 2019 to August 15, 2020. Study project was reviewed and approved by Scientific and Ethics Committees of; Training & Human Development National Center/ IMOH, and Baghdad/ Rusafa and Karkh Health Directorates, and informed consents were obtained from them. After satisfied study explanation, a verbal consent was obtained from all registered diabetic adults who attended study place during study period, met eligibility, and inclusion criteria, who consciously agreed to participate in study. Exclusion criteria were (age <18 or >70 years, dia- betes for <5 years, body mass index (BMI) >30, (Cimetidine, Trimethoprim, Cephalosporin, Aspirin) medications in last 5 days, high meat meal last night, urinary tract structural anomaly, sickle cell anemia, spinal cord injury, or pregnancy). A convenience non-selective sampling method was used; researcher made regular study place visits for data collection in system of 5 h a day/1 day a week for 9 months. Based on eligibility criteria, 800 diabetic adults were recruited in study and data were collected from them. For each visit, 20–25 Detecting chronic kidney disease in diabetic adults by estimating glomerular filtration rate and serum creatinine Ahmed Kh. Mohammed Almaawi* Senior Specialist Physician, Studies and Research Department, Commission of Medical Tertiary Centers, Ministry Of Health, Baghdad, Iraq. *Correspondence to: Ahmed Kh. Mohammed Almaawi (E-mail: dr.ahmedaswad@gmail.com) (Submitted: 28 November 2020 – Revised version received: 25 December 2020 – Accepted: 15 January 2021 – Published online: 26 February 2021) Abstract Objective To assess chronic kidney disease (CKD) prevalence and risk factors including socio-demography among diabetics by estimating glomerular filtration rate (GFR) rather than serum creatinine (sCr). Methods A cross-sectional study was conducted in December 15, 2019 through August 15, 2020, among 800 diabetics attending tertiary diabetes centers, Baghdad. Data were collected by self-administered questionnaire. SPSS was used for data analysis by (mean, standard deviation and t-test) for quantitative variables and (frequency, percentage, Chi-square test and Kappa index) for qualitative variables. p-Value less than 0.05 was considered significant. Results 800 diabetics for last 5–40 years, 95.6% with type 2. Aged 52.1±13.2 years, with male:female ratio of 1.03:1, 63.6% were with no income, sCr level was 0.86±0.3 mg/dl, and eGFR by Cockcroft Gault (CG) and CKD-EPI equations was 100.4 ± 36.5 & 92.2 ± 25.5 ml/min/1.73 m2, respectively. CKD prevalence based on sCr, and eGFR assessed by above equations was 13.3%, 20% and 15.9%, respectively (p<0.001). Those with CKD were hypertensive, females, and living in peripheries. Conclusions Diabetic patients, mainly those with risk factors are more likely to develop CKD. It is better to detect CKD initially by estimating the GFR, rather than sCr level alone. Furthermore, using CKD-EPI equation might be better than the CG formula to estimate the GFR. Keywords Diabetes, Kidney, Creatinine 35 Original Detecting chronic kidney disease in diabetic adultsAhmed Kh. Mohammed Almaawi J Contemp Med Sci | Vol. 7, No. 1, January-February 2021: 34 – 39 patients were privately interviewed with their medical records checking. For data collection, a self-administered structured questionnaire-form paper was developed, and a pilot study had been done on a randomly selected 10 patients to figure out unclear questions and assess time needed to fill question- naire. According to pilot study questionnaire, adjustment was done to be more acceptable, and patients who were subjected to pilot study weren’t included in study. Questionnaire-form collect data; (age, gender, residence, occupation, income, mar- ital status, smoking, diabetes type and duration, hypertension, heart disease, weight, height, BMI (weight(kg)/height(m2)), exclusion criteria, eGFR, and sCr), sCr and eGFR values were used to detect CKD; sCr ≥ 1.2 mg/dl for female, and ≥1.4 mg/ dl for male and eGFR (below 60 ml/min/1.73 m2 estimated by standard CG and CKD-EPI equations) were considered as CKD. In order to compare results from both equations; val- ues estimated by original CG equation ((140-age)*weight/ sCr*72(*0.85 if female)) in (ml/min), were normalized to 1.73 m2 body surface area by multiplying GFR by (1.73/(weight0.4 25*height0.725)*0.007184) according to Du Bois equation.22,23,27 For data entry, storage, and analysis, computerized software SPSS 20 IBM (Statistical Package for Social Science version 20) was used. Data were stratified according to age, eGFR, and CKD. Mean, standard deviation, and t-test were used to pres- ent continuous variables and to check association significance. Frequency and percentage, Chi-square test, and kappa index cross-tabulation were used to present discrete variables and to check association significance. Probability (p-values) of less 0.05 was considered as statistically significant. Results Study included 800 adults with DM for the last 11.7±6.9 (5–40) years (yr), majority 95.6% (765) with type 2 DM, and only 4.4% (35) with type 1, aged 52.1±13.2 (18–70) yr; (53.75%(430) aged 41–60 yr, 28.38%(227) aged 61–70 yr and 17.87% (143) aged 18–40 yr). Study included 405 (50.6%) males and 395 (49.4%) females, with male:female ratio was 1.03:1. 88.4% (707) of patients live in nearby places. Table 1 shows that 90.5% (724) of patients are married while 9.5% (76) were either single, divorced, or widow. 63.6%(509) with no income, and only 31.9%(255) had fixed monthly income salary (from a job 16.5%(132), retirement 13.3%(106), or governmental allow- ance 2.1%(17)), and 4.5%(36) had no fixed income. 90.9% (727) had secondary school educational level or less and only 9.1% (73) had a college level or above. 14.8% (128) of them were smokers. The mean BMI of study group was (25.6±2.6 (16.7- 30) kg/m2). Table 2 shows that mean sCr level was 0.86±0.3 mg/dl, mean eGFR detected by CG and CKD-EPI equations was 100.4±36.5 & 92.2±25.5 ml/min/1.73m2, respectively (p<0.001). CKD prevalence detected by abnormal sCr was 13.3% (106/800) (57/800 (7.13%) females and 49/800 (6.13%) males, p=0.045). This is significantly different from CKD prev- alence by using CG equation 20% (160/800) (p<0.001), and using CKD-EPI equation 15.9%(127/800), results by CG was higher than that by CKD-EPI equation (p<0.001). According to CKD-EPI, 61.4%(78/127) of those with CKD aged 61–70 yr. CKD prevalence among males aged between 61 and 70 yr was higher by using CG than CKD-EPI equation (p<0.001). Table 1. Socio-demographic profile of study sample in different age groups (N: total sample size = 800). Age (yr) Gender Married n. % Income n. % Education n. % BMI kg/m2 Mean ±SD (Range) Fixed income Not fixed No incom e Job Retired Allow ance salary 2ndary College 18-40 Male No.=66 39 59.1% 23 34.8% 0 0% 1 1.5 % 11 16.7% 31 47% 54 81.8% 12 18.2% 24.4±2.8 (17.3-29.8) Female No.=77 50 64.9% 7 9.1% 0 0% 1 1.3% 2 2.6% 67 87% 65 84.4% 12 15.6% 25.2±2.3 (19.5-30) 41-60 Male No=209 208 99.5% 65 31.1% 28 13.4% 8 3.8% 18 8.6% 90 43.1% 188 90% 21 10% 25.4±2.6 (19.2-29.98) Female No=221 209 94.6% 18 8.1% 6 2.7% 0 0% 1 0.5% 196 88.7% 209 94.6% 12 5.4% 26.1±2.6 (18.3-30) 61-70 Male No=130 127 97.7% 16 12.3% 65 50% 4 3.1% 4 3.1% 41 31.5% 115 88.5% 15 11.5% 25.2±2.5 (16.7-30) Female No.=97 91 93.8% 3 3.1% 7 7.2% 3 3.1% 0 0% 84 86.6% 96 99% 1 1% 26.3±2.8 (19.5-30) 18-70 Male No=405 374 92.3% 104 25.7% 93 23% 13 3.2% 33 8.1% 162 40% 357 88.1% 48 11.9% 25.2±2.6 (16.7-29.98) Female No=395 350 88.6% 28 7.1% 13 3.3% 4 1% 3 0.8% 347 87.8% 370 93.7% 25 6.3% 26±2.6 (18.3-30) Total N=800 724 90.5% 132 16.5% 106 13.3% 17 2.1% 36 4.5% 509 63.6% 727 90.9% 73 9.1% 25.6±2.6 (16.7-30) n.: number of subjects with specific characteristic; %: Percentage= (n. /No); yr: year; SD: standard deviation; BMI: body mass index. 36 Original Detecting chronic kidney disease in diabetic adults Ahmed Kh. Mohammed Almaawi J Contemp Med Sci | Vol. 7, No. 1, January-February 2021: 34 – 39 To study different associated risk factors, sample was divided into two groups; with or without CKD (defined by eGFR level of less than 60 ml/min/1.73 m2 detected by CKD-EPI). As shown in Table 3, 265/800 (33.1%) patients were hypertensive for the last 1–32 years, and 48/800 (6%) gave a history of heart disease for the last 1–25 years. Those with CKD were more likely to be hypertensive (p<0.001), female (p=0.029), and lived in periphery (p<0.001), and more likely to be older, less educated with heart disease and no income. To compare CG and CKD-EPI equations, sample was classified into five stages by eGFR level estimated by using two equations as shown in Table 4; comparing result from each stage showed significant difference (p<0.001). According to CKD-EPI, 122/127 (96.1%) of patients with CKD were in stage 3. CKD (stage 3) prevalence was higher by using CG while stage 4 prevalence was higher by CKD-EPI. Table 5 shows that cross-tabulating results of both equations showed a strong agreement; 684 (85.5%) and by using kappa index (kappa value: 0.73, 95% confidence interval (CI): 0.68–0.77, p < 0.001). CKD-EPI: Chronic Kidney Disease Epidemiology equation; CG: Cockcroft Gault equation; eGFR: estimated Glomerular Filtration Rate; n: Number of subjects with spe- cific character; %: Percent= (n /N). Discussion This study aims to assess CKD prevalence in diabetic adults and to check whether estimating eGFR using CKD-EPI for- mula can detect CKD better than CG formula or sCr level. CKD screening is cost-effective as earlier intervention can slow renal damage progression and serves oriented family physician to use modest PHC resources judgmentally where sCr is feasible, albuminuria is not; but CKD is underestimated whether by PHC providers or by International Classification of Diseases-9(ICD-9).4,24,28,29 To achieve this aim, 800 patients’ data were analyzed regarding socio-demography, risk factors, and CKD prevalence in different age groups and stages. The study diabetic population was with 1.03:1 male:female ratio and was middle aged, overweighed, modestly educated, with no job or fixed income, and lived in nearby city areas. This in agreement with Narenpitak et al where 760 DM patients aged 58.7 ± 9.8 yr. with BMI 25.6 ± 4.2 kg/m2.30 Both genders are somewhat equally affected by DM, but obese people are 80 times more likely to be affected. In Iraq, genetics, socio-de- mographic changes, urbanization, increasing sedentary life- style, and overweight had led to emerging DM epidemics mainly in age above 40.4,6,8,10,12 CKD prevalence in this study was significantly higher by using CG equation (20%) than with CKD-EPI (15.9%), or abnormal sCr level (13.3%). This is in agreement with Zaman et al; CKD prevalence by using CG (31.4%), by CKD-EPI (21.9%) and by sCr (18.6%) though prevalence was higher as the last is a hospital-based and ours is outpatient-based study.31 And in agreement with Bouzid et al; prevalence using CG: 19.8%32 and Fiseha et al; prevalence using CG: 23.8%.33 All this resembles a worldwide over/under- estimation of CKD with a wide variation depending on the used approach, obesity, high age, economic problems, and DM prevalence.15,19,34 In PHC sCr isn’t enough for renal function Table 2. Serum creatinine, eGFR and CKD prevalence of study sample in different age groups (N: total sample size = 800). Age (yr) Gender Serum Creatinine CKD-EPI equation CG equation P values.Cr Mean±SD Range CKD Prevalence n. % eGFR Mean±SD Range CKD Prevalence n. % eGFR Mean±SD Range CKD Prevalence n. % 18-40 Male No.=66 0.81±0.28 0.5-2.15 5 7.6% 119.2±24.3 41.6-161 1 1.5% 137.4±37.6 54-237.8 1 1.5% 0.076 Female No.=77 0.71±0.26 0.3-1.42 10 13% 111.3±27 47.6-165.4 5 6.5% 137.1±45.9 59.7-323.1 2 2.6% 0.015 41-60 Male No=209 0.9±0.3 0.5-3.9 15 7.2% 96.4±18.3 15.9-128.2 14 6.7% 100.9±25 21.04-167.4 19 9.1% <0.001 Female No=221 0.74±0.23 0.34-1.6 23 10.4% 92.6±20 37.3-124.1 29 13.1% 104.6±29.3 40.8-189.7 29 13.1% <0.001 61-70 Male No=130 1.07±0.43 0.54-4.-8 29 22.3% 76.8±21.5 14.1-111.9 38 29.2% 73.9±22.5 14.8-134.6 57 43.8% <0.001 Female No.=97 0.94±0.34 0.47-2.33 24 24.7% 69.1±21.9 20.6-105.5 40 41.2% 71.2±23.9 26.2-129.6 52 53.6% <0.001 18-70 Male No=405 0.94±0.36 0.5-4.08 49 12.1% 93.8±24.8 14.1-161 53 13.1% 98.2±34 14.8-237.8 77 19% <0.001 Female No=395 0.78±0.28 0.3-2.33 57 14.4% 90.5±26.1 20.6-165.4 74 18.7% 102.8±38.8 26.2-323.1 83 21% <0.001 Total N=800 0.86±0.3 0.3-4.08 106 13.3% 92.2±25.5 14.1-165.4 127 15.9% 100.4±36.5 14.8-323.1 160 20% <0.001 n.: number of subjects with specific characteristic; %: Percent = (n. /No); SD: Standard Deviation; s.Cr: Serum Creatinine; yr: Year; eGFR: estimated Glomerular Filtration Rate in (ml/min/1.73 m2) units; CKD-EPI: Chronic Kidney Disease Epidemiology equation; CG: Cockcroft Gault equation; Abnormal s.Cr: ≥1.4mg/dl in male: ≥1.2 mg/dl in female; Abnormal eGFR: less than 60 ml/min/1.73 m2. 37 Original Detecting chronic kidney disease in diabetic adultsAhmed Kh. Mohammed Almaawi J Contemp Med Sci | Vol. 7, No. 1, January-February 2021: 34 – 39 assessment as its level will keep normal till renal function drop by 50%,19,25 compared to measured GFR; estimating eGFR have systematic bias but it is minimal (10–20%) in CG and CKD-EPI. CG formula is affected by weight while CKD-EPI is the most accurate one for diverse population.18,23 Majority (61.4%) of CKD (by CKD-EPI) were in patients aged 61–70 yr which is in agreement with Nakata.35 This is expected as there is continuous renal function drop after age of 30 yr, reaching a CKD prevalence of 25% by age of 70.16,17 In order to study CKD provoking factors, sample was divided into two groups; with and without CKD, hypertension prevalence was higher in those with CKD, in agreement with Bradshaw et al, Narenpitak et al, Hooi et al, and Jolly et al; DM and hypertension are inde- pendent risk factors and responsible for 15% of CKD.4,30,36–39 Table 3. Risk factors prevalence of study sample in association with CKD (N: total sample size = 800). Associated Risk Factor Presence of CKD p. value With CKD Without CKD Male No.=53 Female No.=74 Total No.=127 Male No.=352 Female No.=321 Total No.=673 Hypertension n. % 29 45.7% 39 52.7% 68 53.5% 87 24.7% 110 34.3% 197 29.3% <0.001 Heart disease n. % 4 7.5% 6 8.1% 10 7.9% 19 5.4% 19 5.9% 38 5.6% 0.332 Female n. % - 74 100% 74 58.3% - 321 100% 673 47.7% 0.029 Periphery living n. % 4 7.5% 24 32.4% 28 22% 28 8% 37 11.5% 65 9.7% <0.001 2ndary School or less n. % 40 75.5% 71 95.9% 111 87.4% 317 90.1% 299 93.1% 616 91.5% 0.138 Smoking n. % 15 28.3% 3 4.1% 18 14.2% 98 27.8% 2 0.6% 100 14.9% 0.842 No income n. % 19 35.8% 63 85.1% 82 64.6% 143 40.6% 284 88.5% 472 63.4% 0.81 n.: number of subjects with specific characteristic; %: Percentage= (n. /No); DM: Diabetes Mellitus, yr: Year; SD: Standard Deviation; CKD: Chronic Kidney Disease; eGFR less than 60 ml/min/1.73 m2 detected by CKD-EPI equation. Table 4. CKD classification into different stages according to eGFR level detected by CKD-EPI & CG equations (N: Total Sample size = 800). CKD stage eGFR (ml/min/1.73m2) Gender CKD Prevalence by CKD-EPI equation N= 800 CKD Prevalence by CG equation N= 800 p. value 1 >90 Male n. (%) 284 (35.5%) 234 (29.3%) <0.001 Female n. (%) 252 (31.5%) 246 (30.8%) 2 60-89 Male n. (%) 68 (8.5%) 94 (11.8%) <0.001 Female n. (%) 69 (8.6%) 66 (8.3%) 3 30-59 Male n. (%) 51 (6.4%) 75 (9.4%) <0.001 Female n. (%) 71 (8.9%) 81 (10.1%) 4 15-29 Male n. (%) 1 (0.1%) 1 (0.1%) <0.001 Female n. (%) 3 (0.4%) 2 (0.3%) 5 <15 Male n. (%) 1 (0.1%) 1 (0.1%) - Female n. (%) 0 (0%) 0 (0%) 3-5 <60 Male n. (%) 53 (6.6%) 77 (9.6%) 0.001 Female n. (%) 74 (9.3%) 83 (10.4%) CKD-EPI: Chronic Kidney Disease Epidemiology equation; CG: Cockcroft Gault equation; eGFR: estimated Glomerular Filtration Rate; n.: number of subjects with characteristic; %: Percent= (n. /N). 38 Original Detecting chronic kidney disease in diabetic adults Ahmed Kh. Mohammed Almaawi J Contemp Med Sci | Vol. 7, No. 1, January-February 2021: 34 – 39 Heart disease prevalence was higher in those with CKD in agreement with Bradshaw et al and (Jolly et al; cardiovascu- lar risk increase as eGFR drop, death related to heart disease was 2–3 times in those with stage 3 and 4 CKD, respectively, than that with normal kidney function.4,21,36,38 Those with CKD are more likely to be unemployed with no income which is in agreement with Bradshaw et al. Females’ percent was higher in those with CKD which is in agreement with Hooi et al37; as age-related diabetic kidney disease progression differ between sexes.40 Those living in periphery are more likely to have CKD; in low income countries where unaffordable costly health service, unawareness,41 unemployment and DM epidemic in Iraq, making CKD a leading cause of death and lately referred CKD.36,42–44 Study sample was categorized to five stages accord- ing to eGFR level19 using two equations; standard CKD-EPI (the most accepted index one23) and CG, comparing between above equations showed strong agreement: 85.5%, kappa: 0.73 (95% CI: 0.68–0.77, p < 0.001), 96.1% of CKD patients were with stage 3 that was higher by depending on CG which is in agreement with Zaman et al that found strong agreement: 70.9%, kappa: 0.56 (95% CI: 0.44–0.67, p < 0.001) and con- sidered high CG prevalence as overestimation compared to CKD-EPI,31 and in agreement with Kitiyakara et al 2012;45 that is related to continuously increasing CKD (stage 3) prevalence among adult in correlation with increasing DM, advanced age, and obesity.19 CG doesn’t take diverse people in consid- eration and requires weight and height which isn’t available in laboratories while CKD-EPI does not; CKD-EPI is better to assess CKD prevalence in diabetics despite strong agreement between them. Conclusion Diabetic adults especially those with risk factors are likely to develop CKD and it is better to detect CKD initially in PHC and in tertiary centers by estimating GFR rather than depend- ing on sCr alone. CKD-EPI formula may be better than CG to achieve that goal. Recommendations – Family physicians, general practitioners, and PHC provid- ers should be aware for CKD among risky patients includ- ing diabetics attending PHC centers. – Initial CKD screening for risky patients should be done by eGFR estimation using a creatinine-based formula rather than sCr alone. – Family physicians are in the best position for CKD detec- tion, initial management, and follow up in coordination with nephrologist. References 1. Omran A. The epidemiologic transition: a theory of the epidemiology of population change. Milbank Q 2005:83(4);731–757. Available at: https:// www.ncbi.nlm.nih.gov/pmc/articles/PMC2690264/#. 2. Stenvinkel P. 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CKD staging by CKD-EPI n (%) Total 1 2 3 4 5 CKD staging by CG n (%) 1 472 (59%) 8 (1%) 0 (0%) 0 (0%) 0 (0%) 480 (60%) 2 64 (8%) 91 (11.4%) 5 (0.6%) 0 (0%) 0 (0%) 160 (20%) 3 0 (0%) 38 (4.8%) 117 (14.6%) 1 (0.1%) 0 (0%) 156 (19.5%) 4 0 (0%) 0 (0%) 0 (0%) 3 (0.4%) 0 (0%) 3 (0.4%) 5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (0.1%) 1 (0.1%) Total 536 (67%) 137 (17.1%) 122 (15.2%) 4 (0.5%) 1 (0.1%) 800 (100%) CKD-EPI: Chronic Kidney Disease Epidemiology equation; CG: Cockcroft Gault equation; eGFR: estimated Glomerular Filtration Rate; n: Number of subjects with specific character; %: Percent= (n /N). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690264/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690264/ https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2796.2010.02269 https://www.who.int/hac/crises/irq/background/Iraq_Health_in_Iraq_second_edition.pdf?ua=1 https://www.who.int/hac/crises/irq/background/Iraq_Health_in_Iraq_second_edition.pdf?ua=1 https://www.nature.com/articles/jhh2008169 https://www.nature.com/articles/jhh2008169 39 Original Detecting chronic kidney disease in diabetic adultsAhmed Kh. 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