Evaluation of body mass index,waist-to-hip ratio,and waist-to-height ratio as predictors of insulin resistance in Syrian adult women: A cross-sectional study

Abstract

Objective

Body mass index, waist-to-hip ratio, and waist-to-height ratio are widely used anthropometric indices for assessing obesity-related metabolic risk; however, their relative contributions to insulin resistance remain debated and vary across different ethnicities.

Methods

This prospective cross-sectional study evaluated the correlations of body mass index, waist-to-hip ratio, and waist-to-height ratio with insulin resistance, measured by the homeostasis model assessment of insulin resistance, among 70 Syrian adult women aged 17–46 years who were consecutively recruited from the outpatient clinic in Homs governorate.

Results

Scatter plots and correlation analyses were performed indicating that different anthropometric parameters are associated, to varying degrees, with the occurrence and progression of insulin resistance. Among studied anthropometric obesity indicators, body mass index has a weak, non-significant correlation with homeostasis model assessment of insulin resistance (r = 0.18, p = 0.1338), whereas waist-to-height ratio (r = 0.62, p = 0.0001) was found to be the most closely associated with occurrence of insulin resistance. Receiver operating characteristic analysis further demonstrated that waist-to-height ratio had the highest predictive accuracy for insulin resistance (area under the curve = 0.82), outperforming body mass index (area under the curve = 0.78) and waist-to-hip ratio (area under the curve = 0.74).

Conclusion

Therefore, waist-to-height ratio appears to be a simple and effective anthropometric tool for identifying individuals at higher risk of insulin resistance in Syrian adult women. However, as our study was only conducted on women, further investigations in men are warranted to justify its widespread adoption in clinical practice.

Keywords

Body mass index waist-to-hip ratio waist-to-height ratio homeostasis model assessment of insulin resistance Syrian adult women

Introduction

Obesity, as described by the World Health Organization (WHO), is a chronic, relapsing disease arising from multiple interacting factors, including genetics, neurobiology, the broader environment, and eating behaviors.¹ In 2022, 2.5 billion adults aged ≥18 years were overweight, of whom more than 890 million adults were obese. Additionally, more than 390 million children and adolescents were overweight in 2022.¹ This epidemic is closely linked to an increased risk of cardiovascular disease, type 2 diabetes mellitus (T2DM), various cancers, and metabolic syndrome, conditions that collectively result in millions of avoidable deaths each year.² The pathophysiological basis of these complications is largely attributed to the relationship between obesity and insulin resistance (IR).³ This relationship is complex and has been linked to a wide range of molecular processes, including inflammatory, neural, and endocrine pathways, that affect the sensitivity of organs to insulin, leading to decreased insulin-stimulated glucose transport and metabolism in adipocytes and skeletal muscle as well as impaired suppression of hepatic glucose output.⁴ The homeostasis model assessment of insulin resistance (HOMA-IR) was introduced by Matthews et al. in 1985 to estimate insulin sensitivity using fasting plasma glucose and insulin concentrations.⁵ This test is a safe, low-cost method used as an accurate clinical and epidemiological tool to describe the pathophysiology of diabetes.⁶

Anthropometric measures such as body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) are often used in clinical practice due to their low cost and convenience. BMI, calculated as weight divided by height squared, is used to screen for macronutritional status. This anthropometric index is widely used for classifying underweight, overweight, and obesity.⁷ Its simplicity, affordability, and reproducibility have made it a cornerstone of epidemiological studies and clinical practice. However, BMI has notable limitations, as it is unable to distinguish between lean and fat mass and does not inform practitioners of either the origin or the heterogeneity of obesity and its outcomes in specific individuals or populations.⁸ WHR, derived from waist circumference divided by hip circumference, is more strongly related to visceral fat than to subcutaneous fat.⁹ Visceral fat is metabolically active and secretes pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α); interleukin (IL)-6 (IL-6), IL-13, and IL-18; and adipokines such as leptin and resistin, which impair insulin signaling.¹⁰ The prevalence of WHR-defined obesity was approximately three times that of BMI-defined obesity. Individuals with WHR-defined obesity also had significantly higher metabolic complications, including hypertension (57%), dyslipidemia (62%), and IR (14%).¹¹ Several studies have reported significant positive correlations between BMI and HOMA-IR, reinforcing the role of overall adiposity in metabolic impairment. Raju et al. reported a significant positive correlation (r = 0.64, p < 0.001) between BMI and the HOMA-IR index in prediabetic individuals.¹² Similarly, Raj et al. reported a positive correlation between BMI and the HOMA-IR index in 100 apparently normal participants.¹³ However, other investigations highlight the superior predictive value of WHR and related indices. Liu et al. showed that visceral fat accumulation, rather than subcutaneous fat, was more strongly associated with IR in 107 Chinese participants with prediabetes.¹⁴ Zhu et al. and Benites-Zapata et al. further highlighted WHR as a sensitive marker of IR, particularly in women and high-risk populations.^15,16 WHtR, a newer index for abdominal fat assessment, has the advantage of a unisex cutoff value of 0.5.¹⁷ A recent meta-analysis on studies evaluating different indices of adiposity showed that WHtR was a better predictor of central obesity and cardiovascular risk in the general population and in people with type 2 diabetes, in both men and women.¹⁸

Despite extensive research on the relationship between different anthropometric measurements and IR, the relative contributions of BMI, WHR, and WHtR to IR remain debated. Additionally, to the best of our knowledge no information exists on the application of these anthropometric indicators for the detection of IR in Syrian women. Taking into account that ethnicity may influence body composition, this study was therefore designed to evaluate correlations among BMI, WHR, WHtR, and HOMA-IR in a cohort of Syrian adult women.

Materials and methods

Study design and population

This prospective cross-sectional study was conducted to evaluate the associations among BMI, WHR, WHtR, and IR measured by HOMA-IR. A total of 70 healthy adult women from Homs governorate in Syria (aged 17–46 years) were recruited from outpatient clinics. Recruitment followed a consecutive sampling approach, and the final sample size reflected the number of eligible volunteers available. Inclusion criteria required the availability of anthropometric and fasting biochemical data. Individuals with known endocrine disorders, those with chronic inflammatory diseases, or those receiving medications affecting glucose metabolism were excluded, consistent with established epidemiological practices.

Anthropometric measurements

BMI. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²). Weight was measured using a calibrated digital scale, and height was measured with a stadiometer to the nearest 0.1 cm. All measurements were taken twice by trained staff, with the average used for analysis.

WHR. Waist circumference was measured at the midpoint between the lower rib and iliac crest and hip circumference at the widest part of the buttocks. WHR was calculated as waist circumference divided by hip circumference. All measurements were taken twice by trained staff, with the average used for analysis.

WHtR. WHtR was calculated by dividing waist circumference by height.

Biochemical assessment

Fasting blood samples were collected after an overnight fast of at least 8 h. Plasma glucose and fasting insulin concentrations were determined using standard enzymatic and immunoassay methods. HOMA-IR was calculated using the following formula:¹⁹

$HOMA - IR = \frac{fasting insulin (µ \frac{U}{mL}) * fasting glucose (mmol / L)}{22.5}$

Statistical analysis

Data were analyzed using Statistical Package for the Social Sciences (SPSS). Continuous variables were expressed as mean ± SD. The normality of continuous variables was assessed using the Shapiro–Wilk test. Outliers were examined using boxplots and standardized z-scores; no physiologically implausible values required removal. Pearson correlation coefficients (r) were calculated to assess associations between BMI, WHR, WHtR, and HOMA-IR. Statistical significance was set at p <0.05. Receiver operating characteristic (ROC) analysis was performed to estimate the discriminatory ability of anthropometric indices for IR, and area under the curve (AUC) values were reported.

Ethical considerations

The study protocol was reviewed and approved by the Institutional Review Board of Wadi International University, Homs, Syria (Approval Number: 132-2025, granted on 13 February 2025). All patient details were fully deidentified prior to analysis and reporting. Written informed consent was obtained from all participants prior to enrollment, in accordance with the Declaration of Helsinki (1975), as revised in 2024.²⁰ The reporting of this study conforms to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.²¹

Results and discussion

IR is significantly associated with the occurrence of a wide variety of chronic diseases, including diabetes, cardiovascular diseases, and metabolic syndrome.²² Therefore, early identification and management of IR could be of great significance in preventing related diseases. Currently, biochemical examinations, including HOMA-IR, euglycemic–hyperinsulinemic clamp, Quantitative Insulin Sensitivity Check Index (QUICKI), Matsuda Index, and Insulin Secretion-Sensitivity Index-2 (ISSI-2), are the most commonly used parameters for IR detection.²³ However, all these examinations are complex, expensive, and invasive and require specialized equipment and trained professionals. In recent studies, anthropometric measurements such as BMI, WHR, and WHtR have been shown to be cost-effective, rapid, and safe tools for IR prediction and detection.²⁴

Before examining the associations between anthropometric indices and IR, the baseline characteristics of the study population were summarized. Table 1 presents the distribution of age, height, weight, waist and hip circumferences, BMI, WHR, WHtR, and HOMA-IR among the 70 participating Syrian women, providing essential context for interpreting the subsequent analyses.

Table 1.

Baseline anthropometric and biochemical characteristics of the study population (n = 70). Values are presented as mean ± SD with observed ranges.

Variable	Mean ± SD	Range
Age (years)	26.88 ± 7.4	17–46
Height (cm)	163.97 ± 5.9	150–175
Weight (kg)	81.5 ± 20.0	50–163
Waist circumference (cm)	87.83 ± 17.0	50–163
Hip circumference (cm)	110.16 ± 17.3	61–155
BMI (kg/m²)	30.34 ± 7.1	17.3–72.4
WHR	0.79 ± 0.09	0.53–1.10
WHtR	0.53 ± 0.11	0.34–0.89
HOMA-IR	3.03 ± 1.63	0.8–8.6

BMI: body mass index; WHR: waist-to-hip ratio; WHtR: waist-to-height ratio; HOMA-IR: homeostasis model assessment of insulin resistance.

Although many studies have found a positive relationship between BMI and IR, the scatter plot illustrated in Figure 1 reveals a weak, non-significant correlation between BMI and HOMA-IR (r = 0.18, p = 0.1338).

Figure 1.

Scatter plot showing the weak, non-significant correlation between BMI and HOMA-IR in Syrian adult women (n = 70). Pearson correlation analysis showed a weak, non-significant association (r = 0.18, p = 0.1338). BMI: body mass index; HOMA-IR: homeostasis model assessment of insulin resistance.

Uludağ et al. reported that individuals with higher BMI exhibited elevated HOMA-IR values and increased levels of high-sensitivity C-reactive protein (CRP) (hs-CRP), reinforcing the role of inflammation in obesity-related IR.²⁵ Similarly, Chung et al. found a significant positive correlation between BMI and HOMA-IR among adults with obesity, highlighting BMI’s predictive value for metabolic impairment.²⁶ However, numerous studies have emphasized the limitations of BMI as a sole measure for predicting IR, as it does not distinguish between lean mass and fat mass nor does it account for fat distribution. Results demonstrated by Meah et al. failed to reveal a significant relationship between BMI and HOMA-IR.²⁷ Gastaldelli et al. demonstrated that visceral fat accumulation, rather than subcutaneous fat, was more strongly associated with IR.²⁸ Eboka-Loumingou Sakou et al. suggested that combining BMI with inflammatory markers such as CRP and IL-6 enhances the predictive accuracy for IR.²⁹

Therefore, complementary measures such as WHR may provide more nuanced insights into metabolic risk. As presented in Figure 2, a statistically significant moderate positive correlation between WHR and HOMA-IR (r = 0.44, p = 0.0001) was observed. This finding supports the hypothesis that central adiposity, as reflected by WHR, is a key contributor to IR. Unlike BMI, WHR captures fat distribution, particularly visceral fat accumulation, which is metabolically active, secreting inflammatory cytokines and adipokines, thereby enhancing IR and predisposing individuals to a wide range of chronic diseases.³⁰

Figure 2.

Scatter plot showing the moderate positive correlation between WHR and HOMA-IR in Syrian adult women (n = 70). Pearson correlation revealed a moderate, statistically significant positive correlation (r = 0.44, p = 0.0001). WHR: waist-to-hip ratio; HOMA-IR: homeostasis model assessment of insulin resistance.

Visceral adipose tissue is frequently associated with elevated levels of triglycerides (TG) and low-density lipoprotein (LDL) cholesterol and decreased levels of high-density lipoprotein (HDL) cholesterol, which impair insulin signaling in hepatic and muscular tissues.³¹ Additionally, oxidative stress and mitochondrial dysfunction in visceral fat exacerbate IR by disrupting glucose metabolism,³² whereas subclinical inflammation originating in visceral depots further contributes to insulin signaling impairment and metabolic syndrome development.³³ A cross-sectional study in euthyroid normal-weight nondiabetic women found that high WHR levels were positively correlated with HOMA-IR and serum insulin after oral glucose tolerance test (OGTT) values.³⁴ Elevated WHR has been found to be associated with dyslipidemia and IR in children. BMI and WHR are both associated with IR. This indicator has also been found to play a role in the assessment of cardiometabolic risk in children.³⁵ Liu et al. found that WHR was a superior index in reflecting visceral fat distribution and predicting type 2 diabetes risk in populations with obesity.³⁶ Therefore, interventions targeting visceral fat reduction, including caloric restriction, anti-inflammatory diets, and aerobic exercise, are essential for improving insulin sensitivity.^37,38

One aspect of research on obesity and IR that is currently attracting attention is WHtR. As illustrated in Figure 3, there is a statistically significant strong positive correlation between WHtR and HOMA-IR (r = 0.62, p = 0.0001).

Figure 3.

Scatter plot showing the significant strong positive correlation between WHtR and HOMA-IR in Syrian adult women (n = 70). Pearson correlation indicated a significant association (r = 0.62, p = 0.0001).

ROC curve analysis, as presented in Table 2, demonstrated meaningful differences in the ability of BMI, WHR, and WHtR to discriminate IR (defined as HOMA-IR ≥ 2.5).³⁹ WHtR showed the highest discriminative power with an AUC of 0.82 (95% confidence interval (CI): 0.73–0.90), outperforming both BMI (AUC = 0.78) and WHR (AUC = 0.74). The optimal WHtR cutoff for predicting IR in this cohort was 0.58, yielding a sensitivity of 85% and specificity of 74%. BMI also demonstrated acceptable predictive ability, with an optimal cutoff of 28.4 kg/m². WHR showed the lowest predictive performance among the three indices. Although WHR exhibited a stronger linear correlation with HOMA-IR, BMI achieved better classification accuracy in ROC analysis, reflecting the fact that correlation assesses continuous association and ROC analysis evaluates threshold-based discrimination.⁴⁰ These findings support the use of WHtR as a simple, practical, and superior screening tool for early identification of IR in young Syrian women, consistent with its superior discriminative performance in ROC analysis.

Table 2.

ROC analysis evaluating the ability of BMI, WHR, and WHtR to predict insulin resistance (defined as HOMA-IR ≥ 2.5). AUC values, 95% CIs, optimal cutoffs, sensitivity, and specificity are shown.

Index	AUC	95% CI	Optimal cutoff	Sensitivity	Specificity
BMI	0.78	0.68–0.87	>28.4	82%	71%
WHR	0.74	0.63–0.84	>0.78	76%	66%
WHtR	0.82	0.73–0.90	>0.58	85%	74%

ROC: receiver operating characteristic; BMI: body mass index; WHR: waist-to-hip ratio; WHtR: waist-to-height ratio; HOMA-IR: homeostasis model assessment of insulin resistance; AUC: area under the curve; CI: confidence interval.

Our results suggest that there are advantages to using WHtR for indicating IR. Recent studies support the predictive value of WHtR. A cross-sectional analysis by Zhu et al. found that WHtR was a strong predictor of IR in women with polycystic ovary syndrome (PCOS) and could be used as a simple, practical, and reliable anthropometric measure to predict the risk of IR in this population.⁴¹ Jamar et al. found that WHtR was most closely associated with occurrences of IR and predicted the onset of diabetes in individuals with obesity.⁴² A recent meta-analysis showed that WHtR was a better predictor of hyperinsulinemia and metabolic syndrome than BMI and WHR in both men and women.⁴³

Limitations and future directions

Because this study used a consecutive sampling approach, the final sample size (n = 70) reflected the number of eligible participants available during the recruitment period, and no a priori sample size calculation was performed. As an exploratory cross-sectional study, the findings provide preliminary evidence rather than definitive predictive thresholds. The relatively small sample size and the exclusive inclusion of Syrian adult women limit the generalizability of our findings. The non-significant correlation observed for BMI may reflect limited statistical power or the narrow age range of participants (17–46 years) rather than a true absence of association. Because the cohort consisted exclusively of women, sex-based comparisons could not be performed, and the results cannot be extrapolated to men. Future studies should therefore include representative samples of both sexes to determine whether WHtR remains superior to BMI and WHR in predicting IR across populations. Additionally, variations in height across different ethnic groups may influence the applicability of WHtR, underscoring the need for larger, multiethnic cohorts to validate these findings and establish population-specific thresholds.⁴⁴ Furthermore, the cross-sectional design and short study duration limit the ability to infer causality or evaluate long-term metabolic outcomes. Longer-term longitudinal studies with larger and more diverse populations are needed to better understand the mechanisms underlying these associations and to confirm the long-term predictive value of WHtR.

Future research should also examine the utility of WHtR in specific endocrine and metabolic conditions such as PCOS. Additionally, novel anthropometric markers, including the second-to-fourth digit ratio (2D:4D), may provide further insight into predisposition to IR and related comorbidities. Longitudinal studies are needed to determine whether targeting these markers improves long-term metabolic outcomes. A lower 2D:4D ratio (<1) has been associated with higher androgen exposure, increased metabolic risk, and susceptibility to depressive disorders, suggesting that it may serve as a simple, noninvasive predictor of high-risk individuals. Longitudinal studies incorporating 2D:4D measurements alongside traditional anthropometric indices could help clarify its predictive value and potential role in early risk stratification.^45,46

Conclusion

This study suggests that simple anthropometric indices may help in identifying IR among Syrian adult women. Although BMI and WHR showed varying degrees of association with HOMA-IR, WHtR demonstrated the strongest relationship and the highest predictive accuracy. ROC analysis confirmed that WHtR provided the greatest discriminative ability for IR (AUC = 0.82), outperforming both BMI and WHR, with an optimal cutoff of 0.58 offering a favorable balance of sensitivity and specificity. Given its simplicity and low cost, WHtR may represent a practical and effective screening tool for identifying individuals at higher risk of IR in this population, supported by its superior performance in ROC analysis. Nonetheless, the findings are limited by the modest sample size and the exclusive inclusion of women, underscoring the need for larger, multiethnic, and mixed-sex studies to validate these results and establish population-specific thresholds. Continued research integrating additional anthropometric and metabolic markers may further enhance early identification and prevention strategies for IR.

Footnotes

Acknowledgment

We gratefully acknowledge Dr. Azzam Khallouf, Dr. Mais Tayar, and Dr. Mariam Haddad for their valuable contribution in providing the data that supported this work. Their efforts were essential to the completion and integrity of this study.

Author contributions

Wissam Zam: study conception, design, statistical analysis, and manuscript drafting.

Haya Farkoh: data collection, biochemical assays, and manuscript editing.

Jana Makhoul: anthropometric measurements, data entry, and literature review.

Jouel Youssef: data interpretation and critical revision of the manuscript.

All authors read and approved the final manuscript.

Data availability

The data are available from the corresponding author upon reasonable request.

Declaration of conflicting interests

The authors declare no potential conflicts of interest.

Funding

This research received no specific funding.

ORCID iD

Wissam Zam

References

World Health Organization. Obesity and overweight, 8 December 2025.

Ward

Bleich

Cradock

, et al. Projected U.S. state-level prevalence of adult obesity and severe obesity. N Engl J Med 2019; 381: 2440–2450.

Ruud

Steculorum

Brüning

JC.

Neuronal control of peripheral insulin sensitivity and glucose metabolism. Nat Commun 2017; 8: 15259.

Tangvarasittichai

Oxidative stress, insulin resistance, dyslipidemia, and type 2 diabetes mellitus. World J. Diabetes 2015; 6: 456–480.

Matthews

Hosker

Rudenski

, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28: 412–419.

Tsai

Yang

Liu

, et al. Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study. EClinicalMedicine 2023; 58: 101934.

Vermund

SH.

Advantages and limitations of the body mass index (BMI) to assess adult obesity. Int J Environ Res Public Health 2024; 21: 757.

Bray

GA.

Beyond BMI. Nutrients 2023; 15: 2254.

Koster

Leitzmann

Schatzkin

, et al. Waist circumference and mortality. Am J Epidemiol 2008; 167: 1465–75.

10.

Nesto

RW.

Obesity: a major component of the metabolic syndrome. Tex Heart Inst J 2005; 32: 387–389.

11.

Wei

Marrachelli

Melgarejo

, et al. Lipoprotein profiles of fat distribution and its association with insulin sensitivity. Front Endocrinol (Lausanne) 2022; 13: 978745.

12.

Raju

Radhika

Srinivas

, et al. Assessing the impact of body mass index on insulin resistance and metabolic risk factors in pre-diabetic individuals: a comprehensive cross-sectional study. Asian J Med Sci 2024; 15: 77–81.

13.

Raj

Vijayakumar

Correlation of HOMA IR with BMI. Asian J Med Res 2021; 10: 5–8.

14.

Liu

Feng

Zhang

, et al. Visceral adipose tissue is more strongly associated with insulin resistance than subcutaneous adipose tissue in Chinese subjects with pre-diabetes. Curr Med Res Opin 2018; 34: 123–129.

15.

Zhu

Wang

Feng

, et al. The waist-to-height ratio is a good predictor for insulin resistance in women with polycystic ovary syndrome. Front Endocrinol (Lausanne) 2024; 15: 1502321.

16.

Benites-Zapata

Toro-Huamanchumo

Urrunaga-Pastor

, et al. High waist-to-hip ratio levels are associated with insulin resistance markers in normal-weight women. Diabetes Metab Syndr 2019; 13: 636–642.

17.

Browning

Hsieh

Ashwell

A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev 2010; 23: 247–269.

18.

Wang

Luo

Yang

, et al. Association of waist-to-height ratio with all-cause and obesity-related mortality in adults: a prospective cohort study. Front Nutr 2025; 12: 1614347.

19.

Minh

Tien

Sinh

, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich) 2021; 23: 529–537.

20.

World Medical Association. Declaration of Helsinki: ethical principles for medical research involving human participants Ferney-Voltaire: WMA; 2024.

21.

Von Elm

Altman

Egger

; STROBE Initiativeet al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007; 335: 806–808.

22.

Duan

Zhao

, et al. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001-2018. Cardiovasc Diabetol 2024; 23: 243.

23.

Placzkowska

Pawlik-Sobecka

Kokot

, et al. Indirect insulin resistance detection: current clinical trends and laboratory limitations. BioMed Pap Med Fac Univ Palacky Olomouc Czech Repub 2019; 163: 187–199.

24.

Alptekin

Çizmecioğlu

Işık

, et al. Predicting gestational diabetes mellitus during the first trimester using anthropometric measurements and HOMA-IR. J Endocrinol Invest 2016; 39: 577–583.

25.

Uludağ

Solmaz

Alihanoğlu

Yİ

, et al. The relationship of body mass index with insulin resistance, hs CRP, and Lp(a) levels in female gender. Int J Cardiovasc Acad 2023; 9: 3–8.

26.

Chung

Cho

Chung

MY.

Associations among body mass index, insulin resistance, and pancreatic β-cell function in Korean patients with new-onset type 2 diabetes. Korean J Intern Med 2012; 27: 66–71.

27.

Meah

DiMeglio

Greenbaum

; Type 1 Diabetes TrialNet Study Groupet al. The relationship between BMI and insulin resistance and progression from single to multiple autoantibody positivity and type 1 diabetes among TrialNet Pathway to Prevention participants. Diabetologia 2016; 59: 1186–1195.

28.

Gastaldelli

Miyazaki

Pettiti

, et al. Metabolic effects of visceral fat accumulation in type 2 diabetes. J Clin Endocrinol Metab 2002; 87: 5098–103.

29.

Eboka-Loumingou Sakou

Longo-Mbenza

Nkalla-Lambi

, et al. Inflammatory biomarkers and prediction of insulin resistance in Congolese adults. Heliyon 2021; 7: e06139.

30.

Cesaro

De Michele

Fimiani

, et al. Visceral adipose tissue and residual cardiovascular risk: a pathological link and new therapeutic options. Front Cardiovasc Med 2023; 10: 1187735.

31.

Ouchi

Komiya

Taira

, et al. Triglyceride/low-density-lipoprotein cholesterol ratio is the most valuable predictor for increased small, dense LDL in type 2 diabetes patients. Lipids Health Dis 2022; 21: 4.

32.

Veluthakal

Esparza

Hoolachan

, et al. Mitochondrial dysfunction, oxidative stress, and inter-organ miscommunications in T2D progression. Int J Mol Sci 2024; 25: 1504.

33.

Chait

Den Hartigh

LJ.

Adipose tissue distribution, inflammation and its metabolic consequences, including diabetes and cardiovascular disease. Front Cardiovasc Med 2020; 7: 22.

34.

Benites-Zapata

Toro-Huamanchumo

Urrunaga-Pastor

; Insulin Resistance and Metabolic Syndrome Research Groupet al. High waist-to-hip ratio levels are associated with insulin resistance markers in normal-weight women. Diabetes Metab Syndr 2019; 13: 636–642.

35.

Chowdhury

Juarez

Carter

, et al. Waist to hip ratio versus obesity severity in predicting cardiometabolic risk in children. J Am Coll Cardiol 2016; 67: 1991.

36.

Liu

Wang

Zhang

, et al. Waist-to-hip ratio better reflect beta-cell function and predicts diabetes risk in adult with overweight or obesity. Ann Med 2025; 57: 2462447.

37.

Clemente-Suárez

Martín-Rodríguez

Redondo-Flórez

, et al. New insights and potential therapeutic interventions in metabolic diseases. Int J Mol Sci 2023; 24: 10672.

38.

Onu

Trofin

Tutu

, et al. Integrative strategies for preventing and managing metabolic syndrome: the impact of exercise and diet on oxidative stress reduction-a review. Life 2025; 15: 757.

39.

Geloneze

Vasques

Stabe

; BRAMS Investigatorset al. HOMA1-IR and HOMA2-IR indexes in identifying insulin resistance and metabolic syndrome: Brazilian Metabolic Syndrome Study (BRAMS). Arq Bras Endocrinol Metabol 2009; 53: 281–287.

40.

Fouad

Pan

Nafady

, et al. HOMA-IR, an independent predictor of advanced liver fibrosis in metabolic-dysfunction associated fatty liver disease: a cross-sectional study in Egyptian patients. Sci Rep 2025; 15: 31098.

41.

Zhu

Wang

Feng

, et al. The waist-to-height ratio is a good predictor for insulin resistance in women with polycystic ovary syndrome. Front Endocrinol (Lausanne) 2024; 15: 1502321.

42.

Jamar

Almeida

Gagliardi

, et al. Evaluation of waist-to-height ratio as a predictor of insulin resistance in non-diabetic obese individuals. A cross-sectional study. Sao Paulo Med J 2017; 135: 462–468.

43.

Basaran

Aktas

Waist-to-height ratio as a novel marker of metabolic syndrome in patients with type 2 diabetes mellitus. Expl Endocr Metab Dis 2025; 2: 101421.

44.

Aliyu

Toor

Abdalhakam

, et al. Evaluating indices of insulin resistance and estimating the prevalence of insulin resistance in a large biobank cohort. Front Endocrinol 2025; 16: 1591677.

45.

Fusar-Poli

Rodolico

Sturiale

, et al. Second-to-fourth digit ratio (2D:4D) in psychiatric disorders: a systematic review of case-control studies. Clin Psychopharmacol Neurosci 2021; 19: 26–45.

46.

Yurtsever

Vatansever

Aykanat Yurtsever

, et al. 2D: 4D digit ratio, bilateral asymmetry, and insulin resistance in women with obesity. Early Hum Dev 2026; 217: 106518.