Hrev_master Healthcare in Low-resource Settings 2025; volume 13:12865 Obesity indices may affect and reflect the blood glucose and lipid profile values Islam Al-Shami,1 Anfal AL-Dalaeen,2 Lana M. Agraib,3 Buthaina Alkhatib4 1Department of Clinical Nutrition and Dietetics, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa; 2Department of Clinical Nutrition and Dietetics, Faculty of Applied Medical Sciences, Applied Science Private University, Amman; 3Department of Nutrition and Food Science, Faculty of Applied Medical Sciences, Al-Balqa Applied University, Al-Salt; 4Department of Clinical Nutrition and Dietetics, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan Abstract The aim of the present study was to observe the association between obesity indices, blood glucose, and lipid profiles as indi- cators for its levels. In a cross-sectional study, 491 Jordanian adults were included. Socio-demographic and anthropometric data were measured. Blood samples were collected and tested for fast- ing plasma glucose (FPG), insulin, and lipid profile. Obesity indices [Conicity Index (CI), Body adiposity index (BAI), Abdominal volume index (AVI), A Body Shape Index (ABSI), Body Roundness Index (BRI), and Weight-adjusted-waist index (WWI)] were calculated using standard formulas. AVI, BRI, and WWI had a higher impact on FPG and lipid profile. They explain 6.2%, 6.6%, and 4.1% of changes observed in FPG and explained 3.1%, 4.1%, and 3.5% of changes observed in total cholesterol (TC), respectively. In addition, they explain 9.9%, 9.7%, and 7.9% of changes in triglyceride (TG), 9.6%, 8.4%, and 6.0% of the variability observed in the high-density lipoprotein cholesterol (HDL), as well as 1%, 1.6%, and 1.5 of change in low-density lipoprotein cholesterol (LDL), and 7.0%, 8.6%, and 6.6% in LDL/HDL ratio; respectively (p<0.001). AVI, BRI, and WWI among obesity indices had the highest impact on blood glucose and lipid profile. The most affected tests were TG, HDL, and LDL / HDL ratio. These indices may be used as noninvasive rapid indi- cators for high glucose and lipid profiles. Introduction Over the past decade, obesity has become a major concern for health experts because of its association with chronic diseases such as Cardiovascular Disease (CVD) and Type 2 Diabetes (T2D).1According to the World Health Organization’s definition of overweight and obesity, approximately two billion adults are affected by these conditions. Furthermore, the annual global death rate from CVD, a well-known consequence of obesity, is estima- ted to reach 23.6 million by 2030.2 This concerning trend has prompted health professionals to focus on early detection of over- weight and obesity, as well as strategies to prevent adverse outco- mes in the general population.3 The most straightforward obesity indices are used widely for screening Metabolic Syndromes (MetS), such a hypertension, blood glucose, and hyperlipidemia, including Waist Circumference (WC), Body Mass Index (BMI), Waist-To-Hip Ratio (WHR), and Waist-To-Height Ratio (WHtR).4,5 recent novel obesity indices have drawn attention in numerous research as pre- dictors for different noncommunicable diseases, as they are sim- ple, accurate, affordable, reliable, and easy techniques without intervention.4,6 These obesity indices include the Abdominal Volume Index (AVI), A Body Shape Index (ABSI), Body Adiposity Index (BAI), Conicity Index (CI), Body Roundness Index (BRI), and Weight-Adjusted-Waist-Index (WWI).7 The BRI was correlated with MetS and was an effective indi- cator for its screening.4 Also, CI was positively associated with T2D, hypertension, and dyslipidemia.5 Many studies have confir- Correspondence: Anfal AL-Dalaeen, Department of Clinical Nutrition and Dietetics, Faculty of Applied Medical Sciences, Applied Science Private University, Amman, 11931, Jordan. E-mail: a_dalaeen@asu.edu.jo Key words: blood glucose; insulin; lipid profile; conicity index; obesity indices; obesity. Contribution: IAS, and BA contributed to the conceptualization, design, data gathering, and data management. LMA and BA analyzed and inter- preted the data and prepared the manuscript. IAS, AAD, LMA, and BA contributed to the initial draft of the manuscript. All authors reviewed and approved the final manuscript. Conflicts of interest: the authors declare no conflict of interest. Ethics approval: the study was approved by The Institutional Board Review Committee reviewed and approved the survey protocol at The Hashemite University (No.7/13/2020/2021). Consent to participate: all participants were informed about the study objectives at the beginning of the questionnaire, then they expressed their informed consent to participate, and their data were anonymous. Funding: none. Availability of data and materials: all data used in the study are available upon the corresponding author's request. The lead author has full access to the data reported in the manuscript. Acknowledgments: the authors thank the participants for their patience and great help. Received: 28 July 2024. Accepted: 19 September 2024. Early access: 4 December 2024. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2024 Licensee PAGEPress, Italy Healthcare in Low-resource Settings 2025; 13:12865 doi:10.4081/hls.2024.12865 Publisher's note: all claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organi- zations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. [page 52] [Healthcare in Low-resource Settings 2025;13:12865] med the associations between ABSI, BRI, WC, BMI, and WHR and the risk of T2D and MetS.4,5 However, there were discrepan- cies amongst research in the correlations between several anthro- pometric/obesity indices and the lipid profile, insulin, and fasting plasma glucose (FPG). Therefore, this study aimed to investigate the relationship between obesity indices, FPG, and lipid profiles and as indications of their levels. Material and Methodology Study population and ethical approval in a cross-sectional study, four hundred and ninety-one adult males and females aged more than 18 were randomly selected and agreed to participate. Pregnant or lactating women, subjects with mental disorders, and subjects with incomplete anthropometric measurements and/or biochemical parameters were excluded. The Ethics Committee of Hashemite University approved the study, and all procedures were performed following the ethical standards of the Committee and with the Helsinki Declaration and its later amendments (The Institutional Board Review (IRB) committee reviewed and approved the survey protocol at The Hashemite University (No.7/13/2020/2021)). Participants who were willing and eligible provided written informed consent after a detailed explanation before participation. Data collection, anthropometry, and biochemical measurements An interview was conducted to collect data using a pre-desi- gned questionnaire. The information included typical demographic data like age and gender, as well as social and lifestyle factors. Questions regarding chronic diseases and other health difficulties were also included, in addition to general health concerns. Anthropometric measurements were taken. The body weight and height were recorded to the nearest 0.1kg and 0.1cm, respectively, where they wore minimal clothing and bare feet. Anthropometric tape measuring the participant’s WC while standing was used, using the RIEDER Body Measure tape (Inct. Bonus Kit, REIDHK, China). This measurement was performed on the horizontal plane midway between the lowest rib and the iliac crest. At the place in which the buttocks are at their most comprehensive, the Hip Circumference (HC) was measured over thin clothing. The measu- rements of both circumferences were adjusted to the nearest 0.1 cm. The BMI was calculated according to Quetelet’s formula, which is weight (kg) divided by the square of the height (m2). WHR was calculated as WC (cm) divided by HC (cm). Based on WC, participants were divided into high or enlarged WC if their WC was ≥ 102 cm in men and ≥ 88 cm in women, and they were categorized as having average or acceptable WC if their WC was <102 cm and <88 cm, in men and women; respectively. WHR was recognized as high if WHR > 0.9 for men and WHR > 0.8 for women, which means the risk for chronic diseases is high. After conducting face-to-face interviews, the researchers scheduled appointments to collect blood samples from participants for biochemical tests. Before drawing blood, participants were instructed to fast for 12 hours. The collected blood samples were centrifuged, separated, and stored at four °C to prepare them for analysis. Serum samples were subsequently analyzed using a com- pact clinical chemistry analyzer, specifically the Hitachi 902 auto- analyzer by Roche in Germany. The Roche/Hitachi 902 system employs colorimetry and absorbance measurement via the ion- selective electrode method to analyze serum samples. Additionally, the researchers calculated each participant’s LDL/HDL ratio by dividing their LDL value by their HDL value. Furthermore, in this study, six obesity indices were considered and calculated: CI, BAI, AVI, ABSI, BRI, and WWI. Following are the lists of mathemati- cal formulas that were used to calculate these obesity indices: Statistical analysis Frequency descriptive statistical tests were used to obtain the means and standard deviations for continuous variables and the frequencies and percentages for categorical variables to describe the sample. The association between obesity indices and biochemi- cal markers was examined using Pearson correlation. The relation- ship between the biochemical tests and obesity indices was evalua- ted using linear regression coefficient analysis. Set to p < 0.05, the statistical significance was maintained. The Statistical Package for the Social Sciences (SPSS) version 25 (IBM, Chicago, IL, USA) was used for all statistical data analyses. Results A total of 491 adults were included in this study. As shown in Table 1, 70.1% of the participants were males, and 29.9% were females. Based on age, 40.3% of participants ranged from 20 to 34 years, 34.4% aged between 35 and 44, and 25.3% aged 45-65 years. Around half of the participants were of university education level (52.1%) and physically active (57.2%). Of the participants, 71.9% were married, and 40.7% were smokers. Almost all the par- ticipants were free of T2D, dyslipidemia, hypertension, obesity, and CVD. Table 2 illustrates the anthropometric measurements of the par- ticipants. Participants’ mean weight was 78.53±16.04 kg, while the mean BMI was 27.62±5.40 kg/m2. In addition, the participants had a mean of 96.18±14.94 cm WC, 105.44±10.66 cm HC, and 0.91±0.11 WHR. Based on BMI classification, almost two-thirds of the participants were overweight or obese (36.9% and 27.9%, respectively). On the other hand, based on WC and WHR classifi- cation, 66.2% and 85.5% of participants were in high-risk classifi- cation, respectively. The Pearson correlations between biochemical parameters and obesity indices are shown in Table 3. There were significantly low correlations between FPG levels and obesity indices, with R- values ranging from 0.097 (p=0.032) for BAI to 0.317 (p <0.001) Article [Healthcare in Low-resource Settings 2025;13:12865] [page 53] for AVI. TC levels had significant weak correlations with obesity indices ranging from 0.099 (for ABSI) to 0.201 (for BRI). Regarding blood TG and obesity indices, there was a significant moderate correlation between TG levels and BRI (r=0.311, p<0.001) and AVI (r=0.423, p<0.001). While there were weak cor- relations with CI (r=0.283, p<0.001), ABSI (r=0.201, p<0.001), WWI (r=0.282, p<0.001) and BAI (r=0.090, p=0.046). There was a significant negative correlation between obesity indices and blood HDL levels. HDL level had a moderate inverse correlation with AVI r= -0.371 (p<0.001), while it had a negative, weak corre- lation with the rest of the obesity indices. On the other hand, LDL levels and LDL/HDL ratio had a significant positive weak correla- tion with the obesity indices (p<0.001). The linear regression ana- lysis results are shown in Table 4. The CI explains 3.7% of the variability observed in FPG levels, where one unit increase in it results in a 54.48±12.59 mg/dl increase in FPG level (p<0.001). While AVI explains 6.2% and BRI explains 6.6% of the change in FPG. However, an increase in AVI and BRI by one unit results in a 1.38±0.24 mg/dl and an 8.39±1.43 mg/dl increase in FPG level, respectively (p<0.001). Also, WWI increasing by one unit signifi- cantly causes a 7.05±1.53 mg/dl increase in FPG level and explains 4.1% of changes observed. For TC levels, CI, BAI, and ABSI col- lectively explain 4.8% of the change observed at the TC level. Conversely, AVI alone explains 3.1% of the variation in TC levels, where one unit increase in AVI causes a 1.17±0.30 mg/dl increase in TC level (p<0.001). For BRI, it increased by one unit, leading to a 7.92±1.74 mg/dl increase in TC level, which explains 4.1% of the observed change (p<0.001). While WWI explains 3.5% of the change in TC levels, its increase by one unit caused the TC level to increase significantly by 7.85±1.86 mg/dl. When setting the TG level as independent, CI was found to explain significantly 8.0% of observed change, and its increase by one unit led to a 278.66±42.64 mg/dl significant increase in TG levels. The AVI was found to explain 9.9% of the changes observed in the TG level, while its increase by one unit caused an increase of 6.06±0.83 mg/dl in the TG level. Regarding BRI and WWI, it was found that 9.7% and 7.9% of the change observed in TG levels can be explained by them, respectively, where one unit increase in BRI causes a 35.20±4.86 mg/dl increase in TG level, and one unit increase in WWI causes a 33.84±5.21 mg/dl increase in TG level (p<0.001). For HDL, the CI, AVI, BRI, and WWI, their increasing by one unit resulted in a reduction in HDL level by -26.07±4.23 mg/dl, -0.59±0.08 mg/dl, -3.23±0.48 mg/dl, and -2.90±0.52 mg/dl respectively (p<0.001). In addition, the CI, AVI, BRI, and WWI were found to explain significantly 7.2%, 9.6%, 8.4%, and 6.0% of the variability observed in the HDL level, respectively (p<0.05). The obesity indices were found to have little impact on LDL levels, whereas BRI and WWI had the highest r-square values. BRI explains 1.6% of the changes in LDL level, and its increase by one unit led to a 4.15±1.46 mg/dl increase in LDL level. While WWI explains 1.5% of the changes in LDL level, its increase by one unit led to a 4.16±1.55 mg/dl increase in LDL level. LDL/HDL ratio increased by 0.39±0.06, 0.06±0.01, 0.36±0.06, 2.67±0.51, and 3.20±0.68 as a result of one unit increase in BRI, AVI, WWI, CI and BAI; respectively (p<0.001). They explain 8.6%, 7.0%, 6.6%, 5.4%, and 4.3% of the variability observed in the LDL/HDL ratio. There is no significant data about insulin. Article Table 1. General characteristics (n=491). Variables n (%) Gender Male 344 (70.1) Female 147 (29.9) Age (years) 20-34 198 (40.3) 35-44 169 (34.4) 45-65 124 (25.3) Education level School education level 235 (47.9) University education level 256 (52.1) Marital status Married 353 (71.9) Single 132 (26.9) Divorced 4 (0.8) Widow 2 (0.4) Total physical activity classification Physically active 281 (57.2) Physically inactive 210 (42.8) Smoking Yes 200 (40.7) No 162 (33.0) Ex-smoker 43 (8.8) Passive smoking 86 (17.5) Diseases history Diabetes mellitus Yes 28 (5.7) No 463 (94.3) Dyslipidemia Yes 28 (5.7) No 463 (94.3) Hypertension Yes 27 (5.5) No 464 (94.5) Obesity Yes 41 (8.4) No 450 (91.6) Cardiovascular diseases Yes 12 (2.4) No 479 (97.6) Table 2. Anthropometric measurements (n=491). Variables Mean ± SD Height (m) 1.69 ± 0.09 Weight (kg) 78.53 ± 16.04 Waist circumference (cm) 96.18 ± 14.94 Hip circumference (cm) 105.44 ± 10.66 Body mass index (kg/m2) 27.62 ± 5.40 Waist to Hip ratio 0.91 ± 0.11 n (%) Body mass index categories Underweight 9 (1.8) Normal weight 164 (33.4) Overweight 181 (36.9) Obese 137 (27.9) Waist Circumference categories High 325 (66.2) Acceptable 166 (33.8) Body Waist to Hip Ratio categories High 420 (85.5) Acceptable 71 (14.5) [page 54] [Healthcare in Low-resource Settings 2025;13:12865] Discussion Obesity induces several biochemical parameters such as lipids profile, blood pressure, inflammatory indices, and blood glucose levels, which increase the risk of non-communicable diseases such as MetS, ischemic stroke, CVD, and T2D Recently, studies have tried to find which obesity indices most closely correlate with the earlier parameters to aid in preventing chronic diseases. In the cur- rent study, the BRI was highly positively correlated with the FPG level, and the regression model of BRI explained 6.6% of the chan- geability detected in the FPG level.13 Moreover, BRI was positive- ly correlated with TG level and negatively correlated with HDL level.14 These findings were consistent with the findings of pre- vious studies, such as Nkwana and colleagues, who found that BRI was significantly associated with increased TG and blood glucose levels. 15 However, the BRI is a novel central obesity index estima- ted using height and WC.5 Stefanescu and his team indicated that measuring central obesity, especially the BRI, could help identify who is at high risk for T2D and CVD among disease-free obese adults.16 Moreover, they found that a unit increase in BRI was asso- ciated with a 2.43-fold increase in odds of MetS in males and a 1.89-fold increased odds in females.16 However, Zaid and collea- gues found that when identifying hypertriglyceridemia, several anthropometric/metabolic indicators are more predictive than any other type of plasma lipid.17 While ABSI could not identify dysli- pidemia, BRI’s ability to predict the condition was on a level with, if not better, that of the traditional obesity indicators. Nosrati- Oskouie and colleagues found that TC showed a statistically signi- ficant (p<0.05) positive correlation with BMI (r=0.207), WC (r=0.214), and BRI (r=0.237).18 In contrast, TG showed a statisti- cally significant positive correlation with BRI (r=0.242) and a highly significant positive correlation with BMI (r=0.311).14 On the other hand, LDL showed a positive correlation with WC (r=0.25), ABSI (r=0.21), and BRI (r=0.25) in healthy middle-aged adults.19 The present findings indicated no correlation between obesity indices and insulin, similar to the findings of Nkwana and collea- gues, while Mameli et al. found a significant association between ABSI and HOMA-IR.20 Also, contrary to the present insulin fin- dings, Peterson and colleagues approved that the indices of insulin resistance were correlated significantly with BMI and visceral adi- posity indices (WC, ABSI, and BRI) but not with the fat mass per- centage. Including MetS when discussing lipid profiles is essential.21 It has been found that BRI, CI, and AVI have a clinical approach to identifying MetS and its components, as well as the efficacy of these indices in identifying MetS affected by gender.22 In the present study, the AVI was positively correlated with FPG level and explained by 6.1% of the detected changeability of FPG levels. In recent studies, the AVI has been used as an essential anthropometric parameter to predict increases in FPG levels. In agreement, Zhang and colleagues concluded that AVI could identi- fy the occurrence of T2D in Chinese women. Moreover, Wang et al. and Wu et al. have found that AVI was positively correlated with elevated blood glucose among adult males and females.23 These prior studies showed that AVI could be a sensitive indicator of abdominal obesity-related metabolic abnormalities, including glucose levels.4 Furthermore, Liu et al. used computed tomography to assess the changes in abdominal fat during a 10-year follow-up database. They proposed that an increase in abdominal fat showed increased glucose levels and the development of T2D BRI and AVI have been used as obesity indices to predict metabolic disorders in many studies.8,24 On the contrary, Gowda et al. found that AVI is not a reliable marker in diabetic patients to predict the degree of glycemic con- trol and microalbuminuria.8 Moreover, Endukuru and colleagues found that BRI showed a superior predictive ability to detect hyperglycemia in both genders.22 The BRI’s ability to detect other MetS components, such as central obesity, high TG, low HDL-C, and raised BP, was equivalent to but not superior to those of the other novel anthropometric indices.25 In the present findings, the CI explains 3.7% of the variability observed in FPG levels, where one unit increase in it results in a 54.48±12.59 mg/dl increase in FPG level.26 In accordance, Donkor and colleagues found that CI was positively and significantly asso- ciated with FPG (r=0.21, p=0.005).27 The findings of the study by Sanchez-Viveros on older Mexican adults concluded that CI had a stronger relationship with T2D than BMI and WC.28 Additionally, Sanchez and colleagues indicated that BRI and CI were associated with dyslipidemia. Furthermore, Quaye and colleagues found that AVI and CI had larger Areas Under the Curves (AUCs) in females, Article [Healthcare in Low-resource Settings 2025;13:12865] [page 55] Table 3. The Pearson correlation between biochemical parameters and obesity indices (n=491). Variables CI ABSI BRI WWI BAI AVI FPG Pearson r 0.192** 0.118** 0.257** 0.203** 0.097* 0.317** p-value <0.001 0.009 <0.001 <0.001 0.032 <0.001 Insulin Pearson r 0.063 0.059 0.010 0.021 -0.013 0.197** p-value 0.374 0.404 0.891 0.768 0.851 0.005 TC Pearson r 0.157** 0.099* 0.201** 0.188** 0.112* 0.186** p-value <0.001 0.029 <0.001 <0.001 0.013 <0.001 TG Pearson r 0.283** 0.201** 0.311** 0.282** 0.090* 0.423** p-value <0.001 <0.001 <0.001 <0.001 0.046 <0.001 HDL Pearson r -0.269** -0.185** -0.289** -0.244** -0.139** -0.371** p-value <0.001 <0.001 <0.001 <0.001 0.002 <0.001 LDL Pearson r 0.091* 0.049 0.128** 0.121** 0.115* 0.117* p-value 0.045 0.279 0.005 0.007 0.011 0.010 LDL/HDL ratio Pearson r 0.232** 0.146** 0.293** 0.256** 0.208** 0.322** p-value <0.001 0.001 <0.001 <0.001 <0.001 <0.001 CI: Conicity Index; ABSI: A Body Shape Index; BRI: Body Roundness Index; WWI: Weight-adjusted-waist index; BAI: Body adiposity index; AVI: Abdominal volume index; HDL: High- density lipoprotein cholesterol; LDL: Low-density lipoprotein cholesterol; FPG: fasting plasma glucose; TC: total cholesterol; TG: triglyceride. **Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Article Table 4. Liner regression association between biochemical parameters and obesity indices. Indices β Std. Error t p* Change statistics R Square change % of change p* Fasting plasma glucose (FPG) Conicity Index (CI) 54.48 12.59 4.33 <0.001 0.037 3.7 <0.001 Body adiposity index (BAI) 36.57 16.98 2.15 0.032 0.009 0.9 0.032 Abdominal volume index (AVI) 1.38 0.24 5.66 <0.001 0.062 6.2 <0.001 A Body Shape Index (ABSI) 567.54 215.29 2.64 0.009 0.014 1.4 0.009 Body Roundness Index (BRI) 8.39 1.43 5.88 <0.001 0.066 6.6 <0.001 Weight-adjusted-waist index (WWI) 7.05 1.53 4.59 <0.001 0.041 4.1 <0.001 Insulin Conicity Index (CI) 20.25 22.71 0.89 0.374 0.004 0.4 0.374 Body adiposity index (BAI) -5.10 27.07 -0.19 0.851 0.000 0.0 0.851 Abdominal volume index (AVI) 0.40 0.44 0.90 0.369 0.004 0.4 0.369 A Body Shape Index (ABSI) 302.91 362.25 0.84 0.404 0.004 0.4 0.404 Body Roundness Index (BRI) 0.34 2.50 0.14 0.891 0.000 0.0 0.891 Weight-adjusted-waist index (WWI) 0.79 2.67 0.30 0.768 0.000 0.0 0.768 Total cholesterol (TC) Conicity Index (CI) 53.86 15.28 3.53 <0.001 0.025 2.5 <0.001 Body adiposity index (BAI) 50.93 20.45 2.49 0.013 0.013 1.3 0.013 Abdominal volume index (AVI) 1.17 0.30 3.94 <0.001 0.031 3.1 <0.001 A Body Shape Index (ABSI) 571.40 260.24 2.20 0.029 0.010 1.0 0.029 Body Roundness Index (BRI) 7.92 1.74 4.54 <0.001 0.041 4.1 <0.001 Weight-adjusted-waist index (WWI) 7.85 1.86 4.23 <0.001 0.035 3.5 <0.001 Triglycerides (TG) Conicity Index (CI) 278.66 42.64 6.53 <0.001 0.080 8.0 <0.001 Body adiposity index (BAI) 118.04 58.91 2.00 0.046 0.008 0.8 0.046 Abdominal volume index (AVI) 6.06 0.83 7.35 <0.001 0.099 9.9 <0.001 A Body Shape Index (ABSI) 3348.43 736.14 4.55 <0.001 0.041 4.1 <0.001 Body Roundness Index (BRI) 35.20 4.86 7.24 <0.001 0.097 9.7 <0.001 Weight-adjusted-waist index (WWI) 33.84 5.21 6.49 <0.001 0.079 7.9 <0.001 High-density lipoprotein cholesterol (HDL) Conicity Index (CI) -26.07 4.23 -6.16 <0.001 0.072 7.2 <0.001 Body adiposity index (BAI) -17.98 5.78 -3.11 0.002 0.019 1.9 0.002 Abdominal volume index (AVI) -0.59 0.08 -7.19 <0.001 0.096 9.6 <0.001 A Body Shape Index (ABSI) -302.87 72.94 -4.15 <0.001 0.034 3.4 <0.001 Body Roundness Index (BRI) -3.23 0.48 -6.68 <0.001 0.084 8.4 <0.001 Weight-adjusted-waist index (WWI) -2.90 0.52 -5.58 <0.001 0.060 6.0 <0.001 Low-density lipoprotein cholesterol (LDL) Conicity Index (CI) 25.59 12.71 2.01 0.045 0.008 0.8 0.045 Body adiposity index (BAI) 42.94 16.85 2.55 0.011 0.013 1.3 0.011 Abdominal volume index (AVI) 0.55 0.25 2.23 0.026 0.010 1.0 0.026 A Body Shape Index (ABSI) 233.65 215.41 1.08 0.279 0.002 0.2 0.279 Body Roundness Index (BRI) 4.15 1.46 2.85 0.005 0.016 1.6 0.005 Weight-adjusted-waist index (WWI) 4.16 1.55 2.69 0.007 0.015 1.5 0.007 LDL/HDL ratio Conicity Index (CI) 2.67 0.51 5.26 <0.001 0.054 5.4 <0.001 Body adiposity index (BAI) 3.20 0.68 4.71 <0.001 0.043 4.3 <0.001 Abdominal volume index (AVI) 0.06 0.01 6.05 <0.001 0.070 7.0 <0.001 A Body Shape Index (ABSI) 28.57 8.73 3.27 0.001 0.021 2.1 0.001 Body Roundness Index (BRI) 0.39 0.06 6.76 <0.001 0.086 8.6 <0.001 Weight-adjusted-waist index (WWI) 0.36 0.06 5.86 <0.001 0.066 6.6 <0.001 *The p<0.05 level (2-tailed) is considered statistically significant. [page 56] [Healthcare in Low-resource Settings 2025;13:12865] while BMI remained the superior index in males.29 While BMI and WC remained functional parameters, they did not help predict MetS and its components in the female population.4 Moreover, CI was significant and positively correlated with obesity, muscle, and diastolic blood pressure, and also correlated with blood glucose and cholesterol level.30 In a large prospective study of an older Chinese population with a 10-year follow-up cohort, they have been suggested that WWI was significantly associated with an increased TG level and decreased blood glucose level, which is consistent with the current study, WWI is highly correlated with TG level. WWI explained 7.9 % of the detected changeability of TG level, as one unit increase in WWI resulted in a TG increase of 33.84±5.21. WWI, which is a novel obesity index developed in recent years, has a good predic- tive ability for cardiometabolic morbidity and mortality in the Korean population.12 Recently, Li et al. reported in a cohort study that a significant association exists between the highest WWI cate- gory and the increased risk of TG and glucose levels.31 Moreover, Yu and colleagues found that increasing WWI was significantly associated with a higher incidence of newly diagnosed T2D among rural Chinese adults.32 In the present findings, although the LDL/HDL ratio had a significant positive weak correlation with the obesity indices (p<0.001), it increased by 0.39±0.06, 0.06±0.01, 0.36±0.06, 2.67±0.51, and 3.20±0.68 as a result of one unit increase in BRI, AVI, WWI, CI, and BAI; respectively (p<0.001). They explain 8.6%, 7.0%, 6.6%, 5.4%, and 4.3% of the variability observed in the LDL/HDL ratio. No research covered the ability of obesity indices to predict the LDL/HDL ratio. Conversely, our research suggests that the LDL/HDL choleste- rol ratio is a more effective predictor of cardiovascular and cere- brovascular metabolic-related disorders than a single lipoprotein (using LDL only or HDL only). It can simultaneously assess LDL and HDL cholesterol levels. Furthermore, the LDL/HDL choleste- rol ratio has a superior predictive value compared to other lipopro- teins. It can independently forecast the onset of non-Alcoholic Fatty Liver Disease (AFLD) in Chinese non-obese individuals with normal lipid profiles.33 Lam and colleagues (2015) studied the relationship between BMI, WC, WHR, WHtR, BAI, and CVD risk factors in the local adult population in Singapore.34 They found that when compared to BMI, WC, and WHtR, BAI consistently had a lower correlation; however, these differences were frequently negligible and had overlapping 95% confidence ranges. In contrast to WC and WHtR, BAI did not raise the probabilities of CVD risk variables after con- trolling for BMI (for all but hypertension and poor HDL).34 The obesity indicators are affordable and easy to use. It can be applied to medical and health institutions at all levels, particularly when medical standards are lacking or large-scale data research is required. Strengths and limitations The current study has some strengths and limitations. To the best of our knowledge, this is the first study to use six different obesity indices to diagnose the metabolic disorder, including lipid profile and blood glucose level. On the other hand, This study had various limitations, such as the cross-sectional nature of the current research, and a causal relationship could not be achieved. The sam- ple size was relatively small. The present study has not taken into consideration several characteristics that are linked to the progres- sion of T2D, including dietary factors, physical activity, and gene- tic factors. To answer these questions, more research needs to be done Conclusions In conclusion, obesity indices may affect blood sugar and lipid profile levels. The highest impact was for AVI, BRI, and WWI indices. TG, HDL, and LDL / HDL ratios were the most affected tests. Studies to examine the useability of these indices as indica- tors for high sugar and lipid profiles are recommended and needed as they are not invasive rapid tools that will help in the early detec- tion of T2D and CVD. References 1. Forse RA, Betancourt-Garcia MM, Kissee MC. Epidemiology and Discrimination in Obesity. In The ASMBS Textbook of Bariatric Surgery; 2020. 2. Kahaer M, Zhang B, Chen W, et al. Triglyceride Glucose Index Is More Closely Related to Hyperuricemia Than Obesity Indices in the Medical Checkup Population in Xinjiang, China. Front Endocrinol (Lausanne) 2022;13:861760. 3. Phelps NH, Singleton RK, Zhou B, et al. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet 2024;403:1027– 1050. 4. Liu Y, Liu X, Guan H, et al. Body roundness index is a superior obesity index in predicting diabetes risk among hypertensive patients: a prospective cohort study in China. Front Cardiovasc Med 2021;8:736073. 5. Tian T, Zhang J, Zhu Q, et al. Predicting value of five anthro- pometric measures in metabolic syndrome among Jiangsu Province, China. BMC Public Health 2020;20:1317. 6. Kumar C, Girwar E, Gaur S, et al. Cut�off values and clinical efficacy of body roundness index and other novel anthropome- tric indices in identifying metabolic syndrome and its compo- nents among Southern�Indian adults. Diabetol Int 2022;13:188–200. 7. Wang H, Liu A, Zhao T, et al. Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in chinese adults: a prospective, longitudinal study. BMJ Open 2017;7:016062. 8. Gowda V, Philip K. Abdominal volume index and conicity index in predicting metabolic abnormalities in young women of different socioeconomic class. Int J Med Sci Public Heal 2016;5:1452. 9. Wang H, Chen Y, Sun G, et al. Validity of cardiometabolic index, lipid accumulation product, and body adiposity index in predicting the risk of hypertension in chinese population. Postgrad Med 2018;130:325-33. 10. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One 2012;7:0039504. 11. Thomas DM, Bredlau C, Bosy-Westphal A, et al. Relationships between body roundness with body fat and visceral adipose tis- sue emerging from a new geometrical model. Obesity 2013;21:2264–71. 12. Park Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep 2018;8:1–8. 13. Tian S, Zhang X, Xu Y, Dong H. Feasibility of body roundness index for identifying a clustering of cardiometabolic abnorma- lities compared to BMI, waist circumference and other anthro- Article [Healthcare in Low-resource Settings 2025;13:12865] [page 57] pometric indices: the china health and nutrition survey, 2008 to 2009. Medicine (United States) 2016;95:4642. 14. Nkwana MR, Monyeki KD, Lebelo SL. Body roundness index, a body shape index, conicity index, and their association with nutritional status and cardiovascular risk factors in South Aafrican rural young adults. Int J Environ Res Public Health 2021;18:18010281. 15. Khan SH, Shahid R, Fazal N, Ijaz A. Comparison of various abdominal obesity measures for predicting metabolic syndro- me, diabetes, nephropathy, and dyslipidemia. J Coll Physicians Surg Pakistan 2019;29:1159–64. 16. Stefanescu A, Revilla L, Lopez T, et al. Using A Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res 2020;48:300060519848854. 17. Pardo M, Crujeiras AB, Amil M, et al. Association of irisin with fat mass, resting energy expenditure, and daily activity in conditions of extreme body mass index. Int J Endocrinol 2014;2014:857270. 18. Nosrati-oskouie M, Arefinia S, Eslami S, Abadi H. Evaluation of non-invasive arterial stiffness parameters and their relation- ship with physical activity , anthropometric indices and lipid profiles in healthy middle-aged adults: results of the PERSIAN cohort study. Int J Clin Pract 2021;75:e14275. 19. Wu L, Zhu W, Qiao Q, et al. Novel and traditional anthropo- metric indices for identifying metabolic syndrome in non-over- weight/obese adults. Nutr Metab 2021;18:3. 20. Mameli C, Krakauer NY, Krakauer JC, et al. The association between a body shape index and cardiovascular risk in over- weight and obese children and adolescents. PLoS One 2018;13:0190426. 21. Peterson MD, Al Snih S, Stoddard J, et al. Obesity misclassifi- cation and the metabolic syndrome in adults with functional mobility impairments: nutrition examination survey 2003- 2006. Prev Med 2014;60:71-6. 22. Liu B, Liu B, Wu G, Yin F. Relationship between body-roun- dness index and metabolic syndrome in type 2 diabetes. Diabetes Metab Syndr Obes Targets Ther 2019;12:931–5. 23. Wang H, Chen Y, Sun G, et al. Validity of cardiometabolic index, lipid accumulation product, and body adiposity index in predicting the risk of hypertension in Chinese population. Postgrad Med 2018;130:325–33. 24. Sharafi SE, Garmaroudi G, Ghafouri M, et al. Prevalence of anxiety and depression in patients with overweight and obesi- ty. Obes Med 2020:17:100169. 25. Candan S, Candan OO, Karabag T. The relationship between nocturnal blood pressure drop and body composition indices among hypertensive patients. J Clin Hypertens 2020;22:616– 22. 26. Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta- analysis of prospective studies. Obes Rev 2011;12:e426-37. 27. Donkor N, Farrell K, Ocho O, et al. Correlates of obesity indi- ces and cardiovascular disease risk factors among trinidadian nurses. Int J Africa Nurs Sci 2020;12:100194. 28. Sanchez-Viveros S, Barquera S, Medina-Solis CE, et al. Association between diabetes mellitus and hypertension with anthropometric indicators in older adults: results of the mexi- can health survey, 2000. J Nutr Heal Aging 2008;12:327–33. 29. Quaye L, Owiredu WKBA, Amidu N, et al. Comparative abi- lities of body mass index, waist circumference, abdominal volume index, body adiposity index, and conicity index as pre- dictive screening tools for metabolic syndrome among appa- rently healthy ghanaian adults. J Obes 2019;2019:8143179. 30. Liu X, Wu Q, Yan G, et al. Cardiometabolic index: a new tool for screening the metabolically obese normal weight phenoty- pe. J Endocrinol Invest 2021;44:1253–61. 31. Liu X, Wu W, Mao Z, et al. Prevalence and Influencing Factors of Overweight and Obesity in a Chinese Rural Population: The Henan Rural Cohort Study. Sci Rep 2018;8:1–11. 32. Yu S, Wang B, Guo X, et al. Weight-adjusted-waist index pre- dicts newly diagnosed diabetes in Chinese rural adults. J Clin Med 2023;12:1620. 33. Nyakudya TT, Tshabalala T, Dangarembizi R, et al. The poten- tial therapeutic value of medicinal plants in the management of metabolic disorders. Molecules 2020;25:25112669. 34. Garcia-Arellano A, Ramallal R, Ruiz-Canela M, et al. Dietary Inflammatory Index and Incidence of Cardiovascular Disease in the PREDIMED Study. Nutrients 2015;7:7064124. Article [page 58] [Healthcare in Low-resource Settings 2025;13:12865]