Anthropometric Indices as Predictors of Metabolic Syndrome in Indonesian Adults: A Comparison of BRI, C- Index, and Traditional Anthropometric Measures

Anthropometric Indices as Predictors of Metabolic Syndrome in Indonesian Adults: A Comparison of BRI, C- Index, and Traditional Anthropometric Measures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Anthropometric Indices as Predictors of Metabolic Syndrome in Indonesian Adults: A Comparison of BRI, C- Index, and Traditional Anthropometric Measures Nadhea Alriessyanne Hindarta, Deny Yudi Fitranti, Ahmad Syauqy, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8674977/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Obesity is strongly linked to insulin resistance, and both conditions underlie the pathophysiology of Metabolic Syndrome (MetS). Anthropometric measurements to identify body fat are useful when screening individuals at risk for MetS. Objectives This study aims to compare the diagnostic ability of the body roundness index (BRI), conicity index (C-index), body mass index (BMI), and waist-to-height ratio (WHtR) as MetS predictors among Indonesian adults. Methods This cross-sectional study used secondary data from the Baseline Health Research of 2018 (Riskesdas 2018). A total of 13,792 adults aged ≥ 19 years (4,655 men and 9,137 women) with complete data on anthropometric measurements, blood pressure, lipid profile, and fasting blood glucose were analyzed using descriptive analysis and area under the curve (AUC) comparisons to determine the diagnostic ability of anthropometric parameters as predictors of MetS. Results The four anthropometric parameters demonstrated moderate to good predictive ability to predict MetS (AUC = 0.7–0.9). WHtR and BRI (AUC men = 0.800; AUC female = 0.802) were significantly better predictors than the other anthropometric parameters in women but were not significantly different from BMI (AUC = 0.794) in men. Conversely, the C-index (AUC men = 0.742; AUC female = 0.710) was significantly less predictive than the other parameters. Conclusions BRI and WHtR demonstrated good and comparable performance in detecting MetS. In women, both were superior to BMI and the C-index, while in men, their performance was similar to BMI. The C-index showed the weakest predictive power. The recommended cut-off values for MetS screening are WHtR ≥ 0.51 (men) and ≥ 0.53 (women). anthropometric body roundness index metabolic syndrome predictors waist-to-hip ratio Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Metabolic syndrome (MetS) denotes a constellation of interrelated metabolic abnormalities, most notably central adiposity, glucose regulation, dyslipidemia, and elevated blood pressure [ 1 ]. Accumulating evidence has demonstrated that MetS confers a substantially heightened risk of both cardiovascular disease and type 2 diebetes mellitus [ 2 , 3 ]. Consequently, the timely identification of individuals with an elevated susceptibility to MetS is essensial for mitigating progression toward these cardiometabolic outcomes, as it can impose a significant burden on the nation through increased health care utilization, higher treatment costs, and restrictions on daily activities [ 4 ]. Insulin resistance is closely intertwined with excess adiposity, and together these interdependent processes form the core pathophysiological basis of MetS [ 5 ]. Anthropometric measurements to identify body fat provide a noninvasive and simple approach for screening individuals at risk for MetS. As conventional anthropometric indices, body mass index (BMI) and waist circumference (WC) are extensively applied to evaluate cardiometabolic risk profile. BMI serves as an indicator of total body fat but does not provide information on body fat distribution. Meanwhile, WC reflects central body fat deposition but does not account for racial differences in body height [ 6 , 7 ]. Another widely used basic anthropometric parameter is the waist-to-hip ratio (WHtR), which has been shown to be superior to BMI and WC as it considers central fat deposition and height differences among individuals [ 7 , 8 ] Several composite anthropometric indices have been formulated using conventional measurements such as body weight, stature, and waist circumference to more precisely characterize obesity phenotypes and patterns of body fat distribution. Among these indices are body roundness index (BRI) and conicity index (C-index). Both measure are designed to capture central adoposity, particularly viscreal fat accumulation, which has been implicated in adverse metabolic alterations, including elevated circulating free fatty acids, increased leptin concentrations, and heightened proinflammatory cytokine activity. These metabolic perturbations are recognized contributors to insulin resistance, endothelial dysfunction, and persistent low-grade inflammation, collectively constituting key mechanisms in the pathogenesis of metabolic syndrome. Empirical evidence has demostrated a significant association between BRI and metabolic syndrome as well as insulin resistance [ 9 ]. Likewise, the C-index has been shown to correlate positively with insulin resistance, hypertension, and dyslipidemia [ 10 ]. Evidence from studies conducted in China and India indicates that selected anthropometric indices exhibit measurable predictive capacity for metabolic syndrome, with optimal threshold values varying across populations [ 11 , 12 ]. Such variability is commonly distributed to differences in ethnic background, body composition, and lifestyle factors, which collectively modulate the association between anthropometric indicators and metabolic risk. Accordingly, the establishment of population-specific threshold values is critical for enhancing diagnostic precision in the identification of individuals at elevated risk of metabolic syndrome. Despite this, investigations examining derivative anthropometric indices with Indonesian population remain scarce, particularly with respect to their utility as predictors of metabolic syndrome. In response to this gap, the present study evaluates the diagnostic performance of computationally derived anthropometric indices, namely the body roundness index (BRI) and conicity index (C-index) and systematically contrasts their predictive capability with that of conventional measures, including body mass index (BMI) and waist-to-heigh ration (WHtR). Furthermore, this study determines optimal cut-off values for metabolic syndrome prediction among Indonesian adults. MATERIALS AND METHODS Study Design This cross-sectional analysis uses data from the Baseline Health Research of 2018 (Riskesdas 2018), a national health survey. Riskesdas is conducted once every five years by the Indonesian Ministry of Health. The execution of Riskesdas was approved by the Ethical Committee of Health Research, Agency of Health Research and Development, Ministry of Health of Indonesia, under number LB.02.01/2/KE.024/2018. Subjects and Samples Collection The study population consisted of nationally representative subsamples drawn from 2018 Basic Heallth Research survey (Riskesdas 2018). Eligibility criteria included individuals aged 19 years or older, availability of age and sex information, complete data on blood pressure, lipid profile, and blood glucose, as well as plausible anthropometry measurements. Participants with missing variables were excluded from the analysis, together with those presenting extreme anthropometric values. The sample selection process is illustrated in Fig. 1 , resulting in a final analytic sample of 13,792 samples who satisfied all inclusion criteria. Data Collection Anthropometric indices served as the independent variables in this study and included body mass index (BMI), body roundness index (BRI), conicity index (C-index). Body mass index was computed as body weight in kilograms devided by the square of height in meters and was used as a general indicator of adiposity [ 6 , 7 ]. In contrast, waist circumference and WHtR were employed to characterize central fat accumulation. The body roundness index estimates body fat percentage and visceral adiposity by quantifying the geometric relationship between waist circumference and height, thereby approximating overall body shape using an elliptical model. The conicity index provides an alternative representation of obesity and fat distribution, grounded in the conceptual assumption that individuals with predominant abdominal fat deposition exhibit a body configuration resembling a double cone, whereas those with lower central fat accumulation approximate a cylindrical shape [ 13 , 14 ]. Both BRI and C-index were derived using the corresponding mathematical formulations described below: The dependent variable in this study was a biomarker of MetS. The criteria from the Joint Interim Statement were used to determine the diagnosis of MetS, where participants were classified to have Mets if they meet ≥3 of follow 5 criteria [ 15 ]. The components comprised elevated triglyceride concentration (≥ 150 mg/dL), reduced high-density lipoprotein level (< 40 mg/dL in men and < 50 mg/dL in women), increased blood pressure derifed as systolic values ≥ 130 mmHg and/or diastolic blood pressure values ≥ 85 mmHg, elevated fasting plasma glucose (level ≥ 100 mg/dL), and increased waist circumference (≥ 90 cm in men and ≥ 80 cm in women). All data in this research were secondary data collected by the Riskesdas team via interview (to obtain age, gender, and smoking habit data), blood tests, blood pressure measurements with a digital sphygmomanometer, and anthropometric measurements. Anthropometric measurements were performed using a digital weight scale (accuracy of 0.1 kg), a stadiometer (accuracy of 1 mm), and a non-elastic tape measure (accuracy of 1 mm) to measure waist circumference [ 16 ]. Data Analysis Continuous variables were summarized using medians and interquartile ranges, whereas categorical variables were reported as frequencies and proportions. Comparisons between male and female participants were performed using the Mann-Whitney U-test for continuous data and the chi-square test for categorical data. Diagnostic performance was evaluated through receiver operating characteristic (ROC) curve analysis to estimate optimal cut-off values and corresponding areas under the curve (AUC). In addition, 2x2 contingency table analyses were conducted to derive predictive values and likelihood rations [ 17 ]. The discriminative capacity of each anthropometric index for predicting metabolic syndrome was quantified using AUC value, which were interpreted according to predefined thresholds as very weak (0.5–0.6), weak (0.6–0.7), moderate (0.7–0.8), good (0.8–0.9), and very good (0.9-1). Beyond descriptive evaluation, AUC estimates were compared across anthropometric measures, and optimal cut-off points were identified using the Youden index (J), calculated as sensitivity plus specificity minus one (Jmax = sensitivity + specificity – 1) [ 17 ]. Statistical analyses were conducted using SPSS and STATA software, with p-values below 0.05 considered indicative of statistical significance. RESULTS Prevalence of Metabolic Syndrome by Age and Nutritional Status This study involved 13,792 participants (4,655 men and 9,137 women) aged 19-50 years. The prevalence of MetS in the samples was 31.25% and increased with age (Figure 2) . Moreover, the prevalence of MetS increased with greater adiposity, with the obese group showing the highest incidence of metabolic syndrome compared to other nutritional status groups (Figure 3) . Baseline Characteristics by Gender Sample characteristics by gender are presented in Table 1. Compared to men, women showed higher levels of LDL, systolic and diastolic blood pressure, FPG levels, and anthropometric values (BMI, WC, WHtR, BRI, and C-index). Meanwhile, men had significantly higher triglyceride levels than women ( 0,001). Predictive Ability of Anthropometric Parameters for Metabolic Syndrome Figure 4 and Table 2 present the results of the diagnostic tests for anthropometric parameters as predictors of MetS. Overall, the four anthropometric parameters demonstrated moderate-to-good predictive ability for MetS (AUC = 0.7-0.9). In women, WHtR and BRI (AUC men = 0.800; AUC female = 0.802) were significantly better predictors than the other parameters (p<0.001). In men, however, WHtR and BRI were not significantly different from BMI (AUC = 0.794; p=0.277). Conversely, the C-index (AUC men = 0.742; AUC female = 0.710) was significantly lower than the other anthropometric parameters (p<0.001). DISCUSSION Using national health survey data (Riskesdas 2018), this study compared the diagnostic accuracy and determined the optimal cut-off values for both derivative and basic anthropometric measures as MetS predictors in Indonesian adults. This finding indicated that BRI and WHtR demonstrated strong discriminative capacity for MetS, whereas BMI and C-index had moderate predictive ability. Additionally, MetS prevalence increased in tandem with body adiposity, occurring more frequently in the obese group than in other nutritional status categories. Overall prevalence of metabolic syndrome in the study population was 31.25%, a magnitude comparable to estimates reported in India (30%) and the United States (34.7%) [ 19 , 20 ]. Notably, this prevalence exceeded that documented in earlier Indonesian studies, which reported a rate of 21.66% [ 21 ]. Age-related patterns were also evident, with metabolic syndrome prevalence rising with advancing age. Consistent with this observation, previous research has shown a higher burden of metabolic syndrome among older age groups, particularly those aged 20–39 years through ≥ 60 years [ 20 ]. This trend might be attributed to decreased physical activity and immunosenescence, which reduce lean body mass and increasing adiposity, particularly visceral fat. These physiological changes contribute to chronic inflammation associated with metabolic syndrome, diabetes, and cardiovascular diseases [ 22 , 23 ]. The observed pattern in this study is consistent with finding from Philippines, where metabolic syndrome prevalence was reported to be highest among individual with obesity (62.4%), followed by these classified as pre-obese (56.9%), overweight (38.9%), and normal weight (29.6%) [ 24 ]. Furthermore, the present findings are consistent with a study conducted in China, indicating that WHtR and BRI had good predictive power (AUC = 0.8–0.9), while BMI had moderate predictive ability (AUC = 0.7–0.8) [ 11 ]. Moreover, BRI and WHtR exhibited comparable predictive performance and were superior to BMI and C-index. Similar findings were reported in a study on the Chinese population [ 11 ]. The comparable performance of BRI and WHtR may be explained by their shared ability to capture central fat accumulation. BRI as geometrically derived index, estimates body fat percentage and visceral adipose tissue and has been shown to correlate strongly with WHtR [ 13 ]. In support of this, the Spearman Rank correlation analysis in the current study revealed a strong positive association between BRI and WHtR (r = 1; p < 0.001), comsistent with observations reported by by Chang Y et al . [ 25 ]. This strong correlation explains their equivalent predictive ability for MetS. In contrast, the C-index was the weakest MetS predictor among WHtR, BRI, and BMI. This finding aligns with reports from studies performed in China and Vietnam population [ 26 , 27 ]. However, in contrast to these studies, research in India found that the C-index was a more accurate predictor of MetS than BMI [ 12 ]. Similarly, a cohort study in Iran reported that the C-index was the best predictor of fatal cardiovascular events, outperforming hip-to-waist ratio, waist circumference, WHtR, BMI, and the abdominal volume index (AVI) [ 28 ]. These discrepancies are likely due to differences in body composition and fat distribution across racial groups. Although BMI is widely used to classify body weight, accumulating evidence, including the findings of the present study, suggest that indices reflecting central adiposity provide greater utility for MetS screening [ 11 , 29 , 30 ]. Visceral adipose tissue, which exhibits greater metabolic activity than subcutaneous adipose tissue, may explain why central obesity cintribytes to MetS development. Visceral fat secretes non-esterified free fatty acids, leptin, chemerin, tumor necrosis factor (TNF-α), interleukin (IL)-6, C-reactive protein (CRP), fibrinogen, and angiotensin II, all of which induce insulin resistance, chronic inflammation, and hormonal activation, mechanisms that contribute to MetS progression [ 31 , 32 ]. It is well established that women exhibit higher cut-off values than men, a distinction largely attributable to divergent patterns of adipose tissue distribution. In women, fat is preferentially deposited within subcutaneous compartments, particularly in the gluteofemoral region, which functions as a relatively stable long-ter, energy reservoir. By contrast, men tend to accumulate adipose tissue predominantly within visceral compartment [ 33 ]. Notably, evidence indicates that visceral adiposity is more strongly linked to cardiometabolic risk among women than men [ 34 ]. This observation implies that, despite generally lower absolute visceral fat volume, even modest visceral fat accumulation in women may confer a disproportionately elevated risk of cardiometabolic disorder [ 34 ]. Interestingly, female subjects in this study exhibited higher optimal cut-off values for central obesity indicators than men subjects. A possible explanation is that a significant proportion of the men subjects were active smokers. Nicotine, the primary bioactive compound in cigarettes, stimulates the sympathetic nervous activity, thereby enhancing lipolysis within white adipose tissue. The resulting increase in circulating free fatty acids has been implicated in the development of insulin resistance and may also contribute to reductions in body weight [ 35 ]. In addition, the sex composition of the study population, characterized by a substantially greater proportion of women, may have influenced these results. This effect is particularly relevant in light of established sex-related differences in adipose tissue distributin, hormonal regulation, and cardiometablic risk profiles. The anthropometric parameters with the most favorable optimal thresholds for predicting MetS were WHtR (≥ 0.51 in men and ≥ 0.53 in women) and BRI (≥ 4.12 in men and ≥ 4.48 in women). The WHtR cut-off values in this study were comparable to those observed in China (0.52 in men and 0.51 in women) yet lower than corresponding values documented in India (0.58 in men and 0.59 in women) and Poland (0.56 in men and 0.54 in women). Meanwhile, BRI cut-off values varied across studies, with ranges between 3.61 and 4.82 in men and 3.83 and 5.21 in women [ 11 , 12 , 29 ]. These variations are likely attributed to heterogeneity in population characteristics and sample compositions. Specifically, the relatively lower cut-off value in this study may reflect the typical anthropometric and metabolic profile of the Indonesian population, which tends to have a smaller body size and lower average body mass compared to populations such as those in India or Poland [ 12 , 29 ]. Additionally, methodological differences such as the use of different diagnostic criteria for MetS, including variations in cut-off values for each component that may be lower depending on the criteria used, as well as differences in WC thresholds across populations and the use of a nationally representative dataset of substantial size may have facilitated more precise and context-specific determination of cut-off thresholds within the Indonesian population, even though thet appear lower cimpared to several global studies. Based on the predictive value scores, male subject with WHtR and BRI exceeding the defined cut-off values had a 48% and 50% probability of developing MetS, respectively. Among female subjects, the probability of developing MetS when exceeding the WHtR and BRI thresholds was 56% and 57%, respectively. Conversely, both women and men subject with WHtR and BRI below the defined cut-off values had a probability of not developing MetS as high as 88% and 89%, respectively. A diagnostic test is generally considered effective when the aggregate of sensitivity and specificity reaches or exceeds 1.5 [ 36 ]. However, in this study, the defined cut-off values for WHtR and BRI were limited benefit for practical application since their sensitivity and specificity combination scores were below 1.5 (1.47 in men and 1.49 in women). Similarly, based on the likelihood ratio scores, exceeding the defined cut-off values for WHtR and BRI only slightly increased the likelihood of MetS, and vice versa. The positive likelihood ratio values ranged from 2 to 5, while the negative likelihood ratio values ranged from 0.2 to 0.5. The robustness of this study is supported using a large, nationally representative, community-based dataset from Riskesdas. Moreover, the standardized data collection procedures implemented by trained fields teams helped mitigate potential sources of bias. However, this study also had several limitations. First, the cross-sectional design limits causal inference and precludes assessment of underlying biological mechanisms. Second, information on medication uses and post-diagnosis lifestyle modifications was not available, which may have influenced anthropometric measurements and attenuated observed associations. Third, the predominance of female participants may limit the generalizability of the findings, particularly to male populations. Finally, this study did not examine the associations between individual anthropometric indices and specific MetS components, which could have provided more granular clinical insights. The adaption of longitudinal analytical frameowrks in subsequent studies is warranted to more clearly delineate causal relationships and mechanism linking anthropometric indiices to MetS. Collecting data on medication use and lifestyle factors is also important to reduce confounding. Additionally, analyzing the association between each index and individual MetS components may provide more specific and clinically relebant insights. CONCLUSIONS This study demonstrates that the body roundness index and waist-to-height ratio are the most effective anthropometric parameters for predicting metabolic syndrome among Indonesian adults, outperforming the conicity index and body mass index. Optimal cut-off values identified for metabolic syndrome screening were waist-to-height ratio ≥ 0.51 in men and ≥ 0.53 in women, and body roundness index ≥ 4.12 in men and ≥ 4.48 in women. These findings highlight the important of population-specific anthropometric thresholds and support the use of simple, central-obesity-based indices as practical tools for early identification of individuals at increased risk of metabolic syndrome in Indonesia. Future longitudinal studies are needed to confirm causal relationship and to further evaluate the clinical and public health applicability of these indices. Abbreviations The following abbreviations are used in this manuscript: AUC Area Under the Curve BMI Body Mass Index BRI Body Roundness Index C-index Conicity Index FPG Fasting Plasma Glucose HDL High-Density Lipoprotein LR+ Positive Likelihood Ratio LR− Negative Likelihood Ratio MetS Metabolic Syndrome NPV Negative Predictive Value PPV Positive Predictive Value PUSDATIN Pusat Data dan Teknologi Informasi ROC Receiver Operating Characteristic SPSS Statistical Package for the Social Sciences STATA Stata Statistical Software WC Waist Circumference WHtR Waist-to-Height Ratio Declarations Institutional Review Board Statement: This study received ethical approval from the Ethical Committee of Health Research, Agency of Health Research and Development, Ministry of Health of Indonesia, under number LB.02.01/2/KE.024/2018. Informed Consent Statement: Patient consent was waived due to the use of anonymized secondary data obtained from a national health survey, with no identifying information collected. Data Availability Statement: The data used in this study were obtained from Pusat Data dan Teknologi Informasi (PUSDATIN) of the Ministry of Health of Indonesia through a structured data request and with ethical approval. Acknowledgments: We would like to express our gratitude to the Health Development Policy Agency of the Ministry of Health of Indonesia for granting us permission to use the Riskesdas data for this study. Furthermore, we would like to thank you for LPPM Undip (RPIBT 225-18/UN7.D2/PP/IV/2023). Conflicts of Interest: No potential conflict of interest relevant to this article were reported References Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis [Internet]. 2017;11(8):215–25. https://doi.org/10.1177/1753944717711379 . Abdoljalal Marjani. Metabolic Syndrome and Diabetes: A Review. J Clin Basic Res. 2017;1(2):36–43. Guembe MJ, Fernandez-Lazaro CI, Sayon-Orea C, Toledo E, Moreno-Iribas C. Risk for Cardiovascular Disease Associated with Metabolic Syndrome and Its Components: a 13-Year Prospective Study in the RIVANA Cohort. Cardiovasc Diabetol. 2020;19(1):195. https://doi.org/10.1186/s12933-020-01166-6 . Zhao Y, Zhang P, Lee JT, Oldenburg B, van Heusden A, Haregu TN, et al. The Prevalence of Metabolic Disease Multimorbidity and Its Associations With Spending and Health Outcomes in Middle-Aged and Elderly Chinese Adults. Front Public Heal. 2021;9:1–8. https://doi.org/10.3389/fpubh.2021.658706 . James M, Varghese TP, Vijayan A, Anagha PP, Muhas C, Hyder M. Link between Metabolic Syndrome and Diabetes Mellitus: A Pathophysiological Implication. Int J Sci Technol Res. 2019;8(11):1935–8. Buss J. Limitations of Body Mass Index to Assess Body Fat. Workplace Health Saf. 2014;62(6):264. https://doi.org/10.1177/216507991406200608 . Ashwell M, Gunn P, Gibson S. Waist-to-Height Ratio is a Better Screening Tool than Waist Circumference and BMI for Adult Cardiometabolic Risk Factors: Systematic Review and Meta-Analysis. Obes Rev. 2012;13(3):275–86. https://doi.org/10.1111/j.1467-789X.2011.00952.x . Yoo Eun-Gyong. Waist-to-Height Ratio as a Screening Tool for Obesity and Cardiometabolic Risk. Korean J Pediatr. 2016;59(11):426. https://doi.org/10.3345/kjp.2016.59.11.425 . Li G, Wu H, kun, Wu X, wei, Cao Z, Tu Y, chao, Ma Y, et al. The Feasibility of Two Anthropometric Indices to Identify Metabolic Syndrome, Insulin Resistance and Inflammatory Factors in Obese and Overweight Adults. Nutrition. 2019;57:194–201. https://doi.org/10.1016/j.nut.2018.05.004 . 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 African Rural Young Adults. Int J Environ Res Public Health [Internet]. 2021;18(1):281. https://doi.org/10.3390/ijerph18010281 . Tian T, Zhang J, Zhu Q, Xie W, Wang Y, Dai Y. Predicting value of five anthropometric measures in metabolic syndrome among Jiangsu Province, China. BMC Public Health [Internet]. 2020;20(1):1317. https://doi.org/10.1186/s12889-020-09423-9 . Endukuru CK, Gaur GS, Dhanalakshmi Y, Sahoo J, Vairappan B. Cut-off values and clinical efficacy of body roundness index and other novel anthropometric indices in identifying metabolic syndrome and its components among Southern-Indian adults. Diabetol Int [Internet]. 2022;13(1):188–200. https://doi.org/10.1007/s13340-021-00522-5 . Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes [Internet]. 2013;21(11):2264–71. https://doi.org/10.1002/oby.20408 . Valdez R. A Simple Model-Based Index of Abdominal Adiposity. J Clin Epidemiol. 1991;44(9):955–6. https://doi.org/10.1016/0895-4356(91)90059-I . Alberti KGM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al. Harmonizing the Metabolic Syndrome: A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International. Circulation. 2009;120(16):1640–5. https://doi.org/10.1161/CIRCULATIONAHA.109.192644 Badan Penelitian dan Pengembangan Kesehatan. Laporan Nasional Riskesdas 2018. Jakarta: Kementerian Kesehatan RI; 2019. Dahlan MS. Penelitian Diagnostik, Validitas & Reliabilitas. Edisi 2. Jakarta: Epidemiologi Indonesia; 2018. Jaringan Dokumen dan Informasi Hukum Badan Pemeriksa Keuangan Republik Indonesia. Peraturan Menteri Kesehatan Republik Indonesia Nomor 41 Tahun 2014 tentang Pedoman Gizi Seimbang. [Internet]. Available from: https://peraturan.bpk.go.id/Home/Details/119080/permenkes-no-41-tahun-2014 Krishnamoorthy Y, Rajaa S, Murali S, Rehman T, Sahoo J, Kar SS. Prevalence of metabolic syndrome among adult population in India: A systematic review and meta-analysis. Hirst JA, editor. PLoS One [Internet]. 2020;15(10):e0240971. https://doi.org/10.1371/journal.pone.0240971 Hirode G, Wong RJ. Trends in the Prevalence of Metabolic Syndrome in the United States, 2011–2016. JAMA [Internet]. 2020;323(24):2526. https://doi.org/10.1001/jama.2020.4501 . Herningtyas EH, Ng TS. Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health [Internet]. 2019;19(1):377. https://doi.org/10.1186/s12889-019-6711-7 . Jura M, Kozak LP. Obesity and related consequences to ageing. Age (Omaha) [Internet]. 2016;38(1):23. https://doi.org/10.1007/s11357-016-9884-3 . Guarner V, Rubio-Ruiz ME. In. Low-Grade Systemic Inflammation Connects Aging, Metabolic Syndrome and Cardiovascular Disease. 2015. pp. 99–106. https://doi.org/10.1159/000364934 Mata A, Jasul G Jr. Prevalence of Metabolic Syndrome and its Individual Features Across Different (Normal, Overweight, Pre-Obese and Obese) Body Mass Index (BMI) Categories in a Tertiary Hospital in the Philippines. J ASEAN Fed Endocr Soc [Internet]. 2017;32(2):117–22. https://doi.org/10.15605/jafes.032.02.04 . Chang Y, Guo X, Chen Y, Guo L, Li Z, Yu S, et al. A body shape index and body roundness index: two new body indices to identify diabetes mellitus among rural populations in northeast China. BMC Public Health [Internet]. 2015;15(1):794. https://doi.org/10.1186/s12889-015-2150-2 . Zhang J, Zhu W, Qiu L, Huang L, Fang L. Sex- and Age-Specific Optimal Anthropometric Indices as Screening Tools for Metabolic Syndrome in Chinese Adults. Int J Endocrinol. 2018;2018:1067603. https://doi.org/10.1155/2018/1067603 . Dang AK, Truong MT, Le HT, Nguyen KC, Le MB, Nguyen LT, et al. Anthropometric Cut-Off Values for Detecting the Presence of Metabolic Syndrome and Its Multiple Components among Adults in Vietnam: The Role of Novel Indices. Nutrients. 2022;14(19):4024. https://doi.org/10.3390/nu14194024 . Motamed N, Roozafzai F, Khoonsari MR, Malek M, Mahdavi A, Maadi M, et al. Predictive ability of six obesity measures to identify 7-year fatal and non-fatal cardiovascular events: A population-based cohort study. Int J Cardiol Cardiovasc Risk Prev [Internet]. 2022;14:200142. https://doi.org/10.1016/j.ijcrp.2022.200142 . Suliga E, Ciesla E, Głuszek-Osuch M, Rogula T, Głuszek S, Kozieł D. The Usefulness of Anthropometric Indices to Identify the Risk of Metabolic Syndrome. Nutrients [Internet]. 2019;11(11):2598. https://doi.org/10.3390/nu11112598 . Stefanescu A, Revilla L, Lopez T, Sanchez SE, Williams MA, Gelaye B, Using. A Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res [Internet]. 2020;48(1). https://doi.org/10.1177/0300060519848854 . Lee JJ, Pedley A, Hoffmann U, Massaro JM, Levy D, Long MT. Visceral and Intrahepatic Fat are Associated with Cardiometabolic Risk Factors above Other Ectopic Fat Depots: The Framingham Heart Study. Am J Med. 2018;131(6):684–92. https://doi.org/10.1016/j.amjmed.2018.02.002 . Fahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, Bouferraa Y, et al. Metabolic Syndrome: Updates on Pathophysiology and Management in 2021. Int J Mol Sci [Internet]. 2022;23(2):786. https://doi.org/10.3390/ijms23020786 . Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia [Internet]. 2020;63(3):453–61. https://doi.org/10.1007/s00125-019-05040-3 . Schorr M, Dichtel LE, Gerweck AV, Valera RD, Torriani M, Miller KK, et al. Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ [Internet]. 2018;9(1):28. https://doi.org/10.1186/s13293-018-0189-3 . Yu XY, Song P, Zou MH. Obesity Paradox and Smoking Gun: A Mystery of Statistical Confounding? Circ Res. 2018;122(12):1642–4. https://doi.org/10.1161/CIRCRESAHA.118.312897 . Power M, Fell G, Wright M. Principles for High-Quality, High-Value Testing. Evid Based Med. 2013;18(1):5–10. https://doi.org/10.1136/eb-2012-100645 . Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8674977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585110216,"identity":"f1bbbafb-b209-4b28-9011-1dc89532e88f","order_by":0,"name":"Nadhea Alriessyanne Hindarta","email":"","orcid":"","institution":"Diponegoro University","correspondingAuthor":false,"prefix":"","firstName":"Nadhea","middleName":"Alriessyanne","lastName":"Hindarta","suffix":""},{"id":585110217,"identity":"90502e9c-5c2a-4fa2-ae94-3ae7741a6d37","order_by":1,"name":"Deny Yudi Fitranti","email":"","orcid":"","institution":"Diponegoro 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04:39:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8674977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8674977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102296035,"identity":"9f6a25d3-6ba3-471a-951c-21dce8ff35ec","added_by":"auto","created_at":"2026-02-10 10:16:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16979,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of sample selection\u003c/p\u003e","description":"","filename":"Figure1.Flowchartofsampleselection.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8674977/v1/d8990cf7b16e3398d3895c0e.jpg"},{"id":102087411,"identity":"9db72507-6cca-493e-8c3d-2bd7b9ebe705","added_by":"auto","created_at":"2026-02-07 04:45:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203367,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of MetS among study participants by age group\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes: young adult = 19-29 years old, adult = 30-49 years old, elderly = 50-64 years old\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.TheprevalenceofMetSinthestudysamplesbasedonage.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8674977/v1/b05eb7fe3069f550da389abe.jpg"},{"id":102087413,"identity":"68f7b108-45cc-4df4-b99f-e79933e8e86b","added_by":"auto","created_at":"2026-02-07 04:45:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":181452,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of MetS among study participants by nutritional status\u003c/p\u003e\n\u003cp\u003eNotes: categorization of BMI according to the Ministry of Health of Indonesia\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"Figure3.TheprevalenceofMetSinthestudysamplesbasedonnutritionalstatus.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8674977/v1/c150ae4234b62b761b1bf98c.jpg"},{"id":102087412,"identity":"a3504810-7d94-40af-b621-b07167396dbd","added_by":"auto","created_at":"2026-02-07 04:45:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45292,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves of anthropometric parameters for predicting MetS by sex\u003c/p\u003e","description":"","filename":"Figure4.TheROCcurveofanthropometricparametersasaMetSpredictorbasedongender.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8674977/v1/c7ae65ffc8a6e71a35d63e37.jpg"},{"id":102299004,"identity":"a261f22e-59b1-4461-9d98-3c9a037af794","added_by":"auto","created_at":"2026-02-10 11:02:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1077980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8674977/v1/d4d58f92-9a47-4c8c-bc20-4315c7dcf62b.pdf"},{"id":102087410,"identity":"399f65f8-ee06-451d-b16d-397ce46f6c2f","added_by":"auto","created_at":"2026-02-07 04:45:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21586,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8674977/v1/711e76da831ef02f2d914f52.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anthropometric Indices as Predictors of Metabolic Syndrome in Indonesian Adults: A Comparison of BRI, C- Index, and Traditional Anthropometric Measures","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMetabolic syndrome (MetS) denotes a constellation of interrelated metabolic abnormalities, most notably central adiposity, glucose regulation, dyslipidemia, and elevated blood pressure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Accumulating evidence has demonstrated that MetS confers a substantially heightened risk of both cardiovascular disease and type 2 diebetes mellitus [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, the timely identification of individuals with an elevated susceptibility to MetS is essensial for mitigating progression toward these cardiometabolic outcomes, as it can impose a significant burden on the nation through increased health care utilization, higher treatment costs, and restrictions on daily activities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInsulin resistance is closely intertwined with excess adiposity, and together these interdependent processes form the core pathophysiological basis of MetS [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Anthropometric measurements to identify body fat provide a noninvasive and simple approach for screening individuals at risk for MetS. As conventional anthropometric indices, body mass index (BMI) and waist circumference (WC) are extensively applied to evaluate cardiometabolic risk profile. BMI serves as an indicator of total body fat but does not provide information on body fat distribution. Meanwhile, WC reflects central body fat deposition but does not account for racial differences in body height [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Another widely used basic anthropometric parameter is the waist-to-hip ratio (WHtR), which has been shown to be superior to BMI and WC as it considers central fat deposition and height differences among individuals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSeveral composite anthropometric indices have been formulated using conventional measurements such as body weight, stature, and waist circumference to more precisely characterize obesity phenotypes and patterns of body fat distribution. Among these indices are body roundness index (BRI) and conicity index (C-index). Both measure are designed to capture central adoposity, particularly viscreal fat accumulation, which has been implicated in adverse metabolic alterations, including elevated circulating free fatty acids, increased leptin concentrations, and heightened proinflammatory cytokine activity. These metabolic perturbations are recognized contributors to insulin resistance, endothelial dysfunction, and persistent low-grade inflammation, collectively constituting key mechanisms in the pathogenesis of metabolic syndrome. Empirical evidence has demostrated a significant association between BRI and metabolic syndrome as well as insulin resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Likewise, the C-index has been shown to correlate positively with insulin resistance, hypertension, and dyslipidemia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence from studies conducted in China and India indicates that selected anthropometric indices exhibit measurable predictive capacity for metabolic syndrome, with optimal threshold values varying across populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Such variability is commonly distributed to differences in ethnic background, body composition, and lifestyle factors, which collectively modulate the association between anthropometric indicators and metabolic risk. Accordingly, the establishment of population-specific threshold values is critical for enhancing diagnostic precision in the identification of individuals at elevated risk of metabolic syndrome. Despite this, investigations examining derivative anthropometric indices with Indonesian population remain scarce, particularly with respect to their utility as predictors of metabolic syndrome. In response to this gap, the present study evaluates the diagnostic performance of computationally derived anthropometric indices, namely the body roundness index (BRI) and conicity index (C-index) and systematically contrasts their predictive capability with that of conventional measures, including body mass index (BMI) and waist-to-heigh ration (WHtR). Furthermore, this study determines optimal cut-off values for metabolic syndrome prediction among Indonesian adults.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis cross-sectional analysis uses data from the Baseline Health Research of 2018 (Riskesdas 2018), a national health survey. Riskesdas is conducted once every five years by the Indonesian Ministry of Health. The execution of Riskesdas was approved by the Ethical Committee of Health Research, Agency of Health Research and Development, Ministry of Health of Indonesia, under number LB.02.01/2/KE.024/2018.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubjects and Samples Collection\u003c/h3\u003e\n\u003cp\u003eThe study population consisted of nationally representative subsamples drawn from 2018 Basic Heallth Research survey (Riskesdas 2018). Eligibility criteria included individuals aged 19 years or older, availability of age and sex information, complete data on blood pressure, lipid profile, and blood glucose, as well as plausible anthropometry measurements. Participants with missing variables were excluded from the analysis, together with those presenting extreme anthropometric values. The sample selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, resulting in a final analytic sample of 13,792 samples who satisfied all inclusion criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eAnthropometric indices served as the independent variables in this study and included body mass index (BMI), body roundness index (BRI), conicity index (C-index). Body mass index was computed as body weight in kilograms devided by the square of height in meters and was used as a general indicator of adiposity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast, waist circumference and WHtR were employed to characterize central fat accumulation. The body roundness index estimates body fat percentage and visceral adiposity by quantifying the geometric relationship between waist circumference and height, thereby approximating overall body shape using an elliptical model. The conicity index provides an alternative representation of obesity and fat distribution, grounded in the conceptual assumption that individuals with predominant abdominal fat deposition exhibit a body configuration resembling a double cone, whereas those with lower central fat accumulation approximate a cylindrical shape [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Both BRI and C-index were derived using the corresponding mathematical formulations described below:\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"336\" height=\"123\"\u003e\u003c/p\u003e\u003cp\u003eThe dependent variable in this study was a biomarker of MetS. The criteria from the Joint Interim Statement were used to determine the diagnosis of MetS, where participants were classified to have Mets if they meet \u0026ge;3 of follow 5 criteria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The components comprised elevated triglyceride concentration (\u0026ge;\u0026thinsp;150 mg/dL), reduced high-density lipoprotein level (\u0026lt;\u0026thinsp;40 mg/dL in men and \u0026lt;\u0026thinsp;50 mg/dL in women), increased blood pressure derifed as systolic values\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg and/or diastolic blood pressure values\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg, elevated fasting plasma glucose (level\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL), and increased waist circumference (\u0026ge;\u0026thinsp;90 cm in men and \u0026ge;\u0026thinsp;80 cm in women).\u003c/p\u003e \u003cp\u003eAll data in this research were secondary data collected by the Riskesdas team via interview (to obtain age, gender, and smoking habit data), blood tests, blood pressure measurements with a digital sphygmomanometer, and anthropometric measurements. Anthropometric measurements were performed using a digital weight scale (accuracy of 0.1 kg), a stadiometer (accuracy of 1 mm), and a non-elastic tape measure (accuracy of 1 mm) to measure waist circumference [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized using medians and interquartile ranges, whereas categorical variables were reported as frequencies and proportions. Comparisons between male and female participants were performed using the Mann-Whitney U-test for continuous data and the chi-square test for categorical data. Diagnostic performance was evaluated through receiver operating characteristic (ROC) curve analysis to estimate optimal cut-off values and corresponding areas under the curve (AUC). In addition, 2x2 contingency table analyses were conducted to derive predictive values and likelihood rations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe discriminative capacity of each anthropometric index for predicting metabolic syndrome was quantified using AUC value, which were interpreted according to predefined thresholds as very weak (0.5\u0026ndash;0.6), weak (0.6\u0026ndash;0.7), moderate (0.7\u0026ndash;0.8), good (0.8\u0026ndash;0.9), and very good (0.9-1). Beyond descriptive evaluation, AUC estimates were compared across anthropometric measures, and optimal cut-off points were identified using the Youden index (J), calculated as sensitivity plus specificity minus one (Jmax\u0026thinsp;=\u0026thinsp;sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026ndash; 1) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Statistical analyses were conducted using SPSS and STATA software, with p-values below 0.05 considered indicative of statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePrevalence of Metabolic Syndrome by Age and Nutritional Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved 13,792 participants (4,655 men and 9,137 women) aged 19-50 years. The prevalence of MetS in the samples was 31.25% and increased with age \u003cstrong\u003e(Figure 2)\u003c/strong\u003e. Moreover, the prevalence of MetS increased with greater adiposity, with the obese group showing the highest incidence of metabolic syndrome compared to other nutritional status groups \u003cstrong\u003e(Figure 3)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Characteristics by Gender\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample characteristics by gender are presented in \u003cstrong\u003eTable 1.\u003c/strong\u003e Compared to men, women showed higher levels of LDL, systolic and diastolic blood pressure, FPG levels, and anthropometric values (BMI, WC, WHtR, BRI, and C-index). Meanwhile, men had significantly higher triglyceride levels than women (\u003cimg width=\"7\" height=\"17\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAAZCAMAAADQbCSEAAAAAXNSR0IArs4c6QAAADxQTFRFAAAAAAA6ADo6ADpmOgAAOjoAOjpmOma2Zjo6ZrbbkNv/tmYAtmY6ttvb27Zm29u229v/2////9vb///bXuPvDwAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAOklEQVQoU2NgoBMQ5mOD2sTPwcguAGIL8bCw8YIYwnysTJyCEGluZi64i5DFkdVDFMDNAfMQ5tPMQwD/JQGz2qJcqAAAAABJRU5ErkJggg==\" alt=\"image\"\u003e0,001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Ability of Anthropometric Parameters for Metabolic Syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e and \u003cstrong\u003eTable 2\u003c/strong\u003e present the results of the diagnostic tests for anthropometric parameters as predictors of MetS. Overall, the four anthropometric parameters demonstrated moderate-to-good predictive ability for MetS (AUC = 0.7-0.9). In women, WHtR and BRI (AUC\u003csub\u003emen\u003c/sub\u003e = 0.800; AUC\u003csub\u003efemale\u003c/sub\u003e = 0.802) were significantly better predictors than the other parameters (p\u0026lt;0.001). In men, however, WHtR and BRI were not significantly different from BMI (AUC = 0.794; p=0.277). Conversely, the C-index (AUC\u003csub\u003emen\u003c/sub\u003e = 0.742; AUC\u003csub\u003efemale\u003c/sub\u003e = 0.710) was significantly lower than the other anthropometric parameters (p\u0026lt;0.001).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUsing national health survey data (Riskesdas 2018), this study compared the diagnostic accuracy and determined the optimal cut-off values for both derivative and basic anthropometric measures as MetS predictors in Indonesian adults. This finding indicated that BRI and WHtR demonstrated strong discriminative capacity for MetS, whereas BMI and C-index had moderate predictive ability. Additionally, MetS prevalence increased in tandem with body adiposity, occurring more frequently in the obese group than in other nutritional status categories.\u003c/p\u003e \u003cp\u003eOverall prevalence of metabolic syndrome in the study population was 31.25%, a magnitude comparable to estimates reported in India (30%) and the United States (34.7%) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Notably, this prevalence exceeded that documented in earlier Indonesian studies, which reported a rate of 21.66% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Age-related patterns were also evident, with metabolic syndrome prevalence rising with advancing age. Consistent with this observation, previous research has shown a higher burden of metabolic syndrome among older age groups, particularly those aged 20\u0026ndash;39 years through \u0026ge;\u0026thinsp;60 years [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This trend might be attributed to decreased physical activity and immunosenescence, which reduce lean body mass and increasing adiposity, particularly visceral fat. These physiological changes contribute to chronic inflammation associated with metabolic syndrome, diabetes, and cardiovascular diseases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed pattern in this study is consistent with finding from Philippines, where metabolic syndrome prevalence was reported to be highest among individual with obesity (62.4%), followed by these classified as pre-obese (56.9%), overweight (38.9%), and normal weight (29.6%) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, the present findings are consistent with a study conducted in China, indicating that WHtR and BRI had good predictive power (AUC\u0026thinsp;=\u0026thinsp;0.8\u0026ndash;0.9), while BMI had moderate predictive ability (AUC\u0026thinsp;=\u0026thinsp;0.7\u0026ndash;0.8) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, BRI and WHtR exhibited comparable predictive performance and were superior to BMI and C-index. Similar findings were reported in a study on the Chinese population [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe comparable performance of BRI and WHtR may be explained by their shared ability to capture central fat accumulation. BRI as geometrically derived index, estimates body fat percentage and visceral adipose tissue and has been shown to correlate strongly with WHtR [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In support of this, the Spearman Rank correlation analysis in the current study revealed a strong positive association between BRI and WHtR (r\u0026thinsp;=\u0026thinsp;1; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), comsistent with observations reported by by Chang Y \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This strong correlation explains their equivalent predictive ability for MetS.\u003c/p\u003e \u003cp\u003eIn contrast, the C-index was the weakest MetS predictor among WHtR, BRI, and BMI. This finding aligns with reports from studies performed in China and Vietnam population [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, in contrast to these studies, research in India found that the C-index was a more accurate predictor of MetS than BMI [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, a cohort study in Iran reported that the C-index was the best predictor of fatal cardiovascular events, outperforming hip-to-waist ratio, waist circumference, WHtR, BMI, and the abdominal volume index (AVI) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These discrepancies are likely due to differences in body composition and fat distribution across racial groups.\u003c/p\u003e \u003cp\u003eAlthough BMI is widely used to classify body weight, accumulating evidence, including the findings of the present study, suggest that indices reflecting central adiposity provide greater utility for MetS screening [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Visceral adipose tissue, which exhibits greater metabolic activity than subcutaneous adipose tissue, may explain why central obesity cintribytes to MetS development. Visceral fat secretes non-esterified free fatty acids, leptin, chemerin, tumor necrosis factor (TNF-α), interleukin (IL)-6, C-reactive protein (CRP), fibrinogen, and angiotensin II, all of which induce insulin resistance, chronic inflammation, and hormonal activation, mechanisms that contribute to MetS progression [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is well established that women exhibit higher cut-off values than men, a distinction largely attributable to divergent patterns of adipose tissue distribution. In women, fat is preferentially deposited within subcutaneous compartments, particularly in the gluteofemoral region, which functions as a relatively stable long-ter, energy reservoir. By contrast, men tend to accumulate adipose tissue predominantly within visceral compartment [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, evidence indicates that visceral adiposity is more strongly linked to cardiometabolic risk among women than men [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This observation implies that, despite generally lower absolute visceral fat volume, even modest visceral fat accumulation in women may confer a disproportionately elevated risk of cardiometabolic disorder [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, female subjects in this study exhibited higher optimal cut-off values for central obesity indicators than men subjects. A possible explanation is that a significant proportion of the men subjects were active smokers. Nicotine, the primary bioactive compound in cigarettes, stimulates the sympathetic nervous activity, thereby enhancing lipolysis within white adipose tissue. The resulting increase in circulating free fatty acids has been implicated in the development of insulin resistance and may also contribute to reductions in body weight [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, the sex composition of the study population, characterized by a substantially greater proportion of women, may have influenced these results. This effect is particularly relevant in light of established sex-related differences in adipose tissue distributin, hormonal regulation, and cardiometablic risk profiles.\u003c/p\u003e \u003cp\u003eThe anthropometric parameters with the most favorable optimal thresholds for predicting MetS were WHtR (\u0026ge;\u0026thinsp;0.51 in men and \u0026ge;\u0026thinsp;0.53 in women) and BRI (\u0026ge;\u0026thinsp;4.12 in men and \u0026ge;\u0026thinsp;4.48 in women). The WHtR cut-off values in this study were comparable to those observed in China (0.52 in men and 0.51 in women) yet lower than corresponding values documented in India (0.58 in men and 0.59 in women) and Poland (0.56 in men and 0.54 in women). Meanwhile, BRI cut-off values varied across studies, with ranges between 3.61 and 4.82 in men and 3.83 and 5.21 in women [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese variations are likely attributed to heterogeneity in population characteristics and sample compositions. Specifically, the relatively lower cut-off value in this study may reflect the typical anthropometric and metabolic profile of the Indonesian population, which tends to have a smaller body size and lower average body mass compared to populations such as those in India or Poland [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, methodological differences such as the use of different diagnostic criteria for MetS, including variations in cut-off values for each component that may be lower depending on the criteria used, as well as differences in WC thresholds across populations and the use of a nationally representative dataset of substantial size may have facilitated more precise and context-specific determination of cut-off thresholds within the Indonesian population, even though thet appear lower cimpared to several global studies.\u003c/p\u003e \u003cp\u003eBased on the predictive value scores, male subject with WHtR and BRI exceeding the defined cut-off values had a 48% and 50% probability of developing MetS, respectively. Among female subjects, the probability of developing MetS when exceeding the WHtR and BRI thresholds was 56% and 57%, respectively. Conversely, both women and men subject with WHtR and BRI below the defined cut-off values had a probability of not developing MetS as high as 88% and 89%, respectively.\u003c/p\u003e \u003cp\u003eA diagnostic test is generally considered effective when the aggregate of sensitivity and specificity reaches or exceeds 1.5 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, in this study, the defined cut-off values for WHtR and BRI were limited benefit for practical application since their sensitivity and specificity combination scores were below 1.5 (1.47 in men and 1.49 in women). Similarly, based on the likelihood ratio scores, exceeding the defined cut-off values for WHtR and BRI only slightly increased the likelihood of MetS, and vice versa. The positive likelihood ratio values ranged from 2 to 5, while the negative likelihood ratio values ranged from 0.2 to 0.5.\u003c/p\u003e \u003cp\u003eThe robustness of this study is supported using a large, nationally representative, community-based dataset from Riskesdas. Moreover, the standardized data collection procedures implemented by trained fields teams helped mitigate potential sources of bias. However, this study also had several limitations. First, the cross-sectional design limits causal inference and precludes assessment of underlying biological mechanisms. Second, information on medication uses and post-diagnosis lifestyle modifications was not available, which may have influenced anthropometric measurements and attenuated observed associations. Third, the predominance of female participants may limit the generalizability of the findings, particularly to male populations. Finally, this study did not examine the associations between individual anthropometric indices and specific MetS components, which could have provided more granular clinical insights.\u003c/p\u003e \u003cp\u003eThe adaption of longitudinal analytical frameowrks in subsequent studies is warranted to more clearly delineate causal relationships and mechanism linking anthropometric indiices to MetS. Collecting data on medication use and lifestyle factors is also important to reduce confounding. Additionally, analyzing the association between each index and individual MetS components may provide more specific and clinically relebant insights.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study demonstrates that the body roundness index and waist-to-height ratio are the most effective anthropometric parameters for predicting metabolic syndrome among Indonesian adults, outperforming the conicity index and body mass index. Optimal cut-off values identified for metabolic syndrome screening were waist-to-height ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.51 in men and \u0026ge;\u0026thinsp;0.53 in women, and body roundness index\u0026thinsp;\u0026ge;\u0026thinsp;4.12 in men and \u0026ge;\u0026thinsp;4.48 in women. These findings highlight the important of population-specific anthropometric thresholds and support the use of simple, central-obesity-based indices as practical tools for early identification of individuals at increased risk of metabolic syndrome in Indonesia. Future longitudinal studies are needed to confirm causal relationship and to further evaluate the clinical and public health applicability of these indices.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area Under the Curve\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003eBRI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Roundness Index\u003c/p\u003e\n\u003cp\u003eC-index\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conicity Index\u003c/p\u003e\n\u003cp\u003eFPG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fasting Plasma Glucose\u003c/p\u003e\n\u003cp\u003eHDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eLR+\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Positive Likelihood Ratio\u003c/p\u003e\n\u003cp\u003eLR\u0026minus;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Negative Likelihood Ratio\u003c/p\u003e\n\u003cp\u003eMetS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Metabolic Syndrome\u003c/p\u003e\n\u003cp\u003eNPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative Predictive Value\u003c/p\u003e\n\u003cp\u003ePPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Positive Predictive Value\u003c/p\u003e\n\u003cp\u003ePUSDATIN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Pusat Data dan Teknologi Informasi\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eSPSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eSTATA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Stata Statistical Software\u003c/p\u003e\n\u003cp\u003eWC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Waist Circumference\u003c/p\u003e\n\u003cp\u003eWHtR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Waist-to-Height Ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e This study received ethical approval from the Ethical Committee of Health Research, Agency of Health Research and Development, Ministry of Health of Indonesia, under number LB.02.01/2/KE.024/2018.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003ePatient consent was waived due to the use of anonymized secondary data obtained from a national health survey, with no identifying information collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data used in this study were obtained from Pusat Data dan Teknologi Informasi (PUSDATIN) of the Ministry of Health of Indonesia through a structured data request and with ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We would like to express our gratitude to the Health Development Policy Agency of the Ministry of Health of Indonesia for granting us permission to use the Riskesdas data for this study. Furthermore, we would like to thank you for LPPM Undip (RPIBT 225-18/UN7.D2/PP/IV/2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e No potential conflict of interest relevant to this article were reported\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis [Internet]. 2017;11(8):215\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1753944717711379\u003c/span\u003e\u003cspan address=\"10.1177/1753944717711379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdoljalal Marjani. Metabolic Syndrome and Diabetes: A Review. J Clin Basic Res. 2017;1(2):36\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuembe MJ, Fernandez-Lazaro CI, Sayon-Orea C, Toledo E, Moreno-Iribas C. Risk for Cardiovascular Disease Associated with Metabolic Syndrome and Its Components: a 13-Year Prospective Study in the RIVANA Cohort. Cardiovasc Diabetol. 2020;19(1):195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12933-020-01166-6\u003c/span\u003e\u003cspan address=\"10.1186/s12933-020-01166-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Zhang P, Lee JT, Oldenburg B, van Heusden A, Haregu TN, et al. The Prevalence of Metabolic Disease Multimorbidity and Its Associations With Spending and Health Outcomes in Middle-Aged and Elderly Chinese Adults. Front Public Heal. 2021;9:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2021.658706\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2021.658706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames M, Varghese TP, Vijayan A, Anagha PP, Muhas C, Hyder M. Link between Metabolic Syndrome and Diabetes Mellitus: A Pathophysiological Implication. Int J Sci Technol Res. 2019;8(11):1935\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuss J. Limitations of Body Mass Index to Assess Body Fat. Workplace Health Saf. 2014;62(6):264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/216507991406200608\u003c/span\u003e\u003cspan address=\"10.1177/216507991406200608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshwell M, Gunn P, Gibson S. Waist-to-Height Ratio is a Better Screening Tool than Waist Circumference and BMI for Adult Cardiometabolic Risk Factors: Systematic Review and Meta-Analysis. Obes Rev. 2012;13(3):275\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1467-789X.2011.00952.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-789X.2011.00952.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo Eun-Gyong. Waist-to-Height Ratio as a Screening Tool for Obesity and Cardiometabolic Risk. Korean J Pediatr. 2016;59(11):426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3345/kjp.2016.59.11.425\u003c/span\u003e\u003cspan address=\"10.3345/kjp.2016.59.11.425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Wu H, kun, Wu X, wei, Cao Z, Tu Y, chao, Ma Y, et al. The Feasibility of Two Anthropometric Indices to Identify Metabolic Syndrome, Insulin Resistance and Inflammatory Factors in Obese and Overweight Adults. Nutrition. 2019;57:194\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nut.2018.05.004\u003c/span\u003e\u003cspan address=\"10.1016/j.nut.2018.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNkwana 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 African Rural Young Adults. Int J Environ Res Public Health [Internet]. 2021;18(1):281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18010281\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18010281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian T, Zhang J, Zhu Q, Xie W, Wang Y, Dai Y. Predicting value of five anthropometric measures in metabolic syndrome among Jiangsu Province, China. BMC Public Health [Internet]. 2020;20(1):1317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-020-09423-9\u003c/span\u003e\u003cspan address=\"10.1186/s12889-020-09423-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndukuru CK, Gaur GS, Dhanalakshmi Y, Sahoo J, Vairappan B. Cut-off values and clinical efficacy of body roundness index and other novel anthropometric indices in identifying metabolic syndrome and its components among Southern-Indian adults. Diabetol Int [Internet]. 2022;13(1):188\u0026ndash;200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13340-021-00522-5\u003c/span\u003e\u003cspan address=\"10.1007/s13340-021-00522-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes [Internet]. 2013;21(11):2264\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/oby.20408\u003c/span\u003e\u003cspan address=\"10.1002/oby.20408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValdez R. A Simple Model-Based Index of Abdominal Adiposity. J Clin Epidemiol. 1991;44(9):955\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0895-4356(91)90059-I\u003c/span\u003e\u003cspan address=\"10.1016/0895-4356(91)90059-I\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberti KGM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al. Harmonizing the Metabolic Syndrome: A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International. Circulation. 2009;120(16):1640\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.109.192644\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.109.192644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadan Penelitian dan Pengembangan Kesehatan. Laporan Nasional Riskesdas 2018. Jakarta: Kementerian Kesehatan RI; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDahlan MS. Penelitian Diagnostik, Validitas \u0026amp; Reliabilitas. Edisi 2. Jakarta: Epidemiologi Indonesia; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaringan Dokumen dan Informasi Hukum Badan Pemeriksa Keuangan Republik Indonesia. Peraturan Menteri Kesehatan Republik Indonesia Nomor 41 Tahun 2014 tentang Pedoman Gizi Seimbang. [Internet]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://peraturan.bpk.go.id/Home/Details/119080/permenkes-no-41-tahun-2014\u003c/span\u003e\u003cspan address=\"https://peraturan.bpk.go.id/Home/Details/119080/permenkes-no-41-tahun-2014\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnamoorthy Y, Rajaa S, Murali S, Rehman T, Sahoo J, Kar SS. Prevalence of metabolic syndrome among adult population in India: A systematic review and meta-analysis. Hirst JA, editor. PLoS One [Internet]. 2020;15(10):e0240971. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0240971\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0240971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirode G, Wong RJ. Trends in the Prevalence of Metabolic Syndrome in the United States, 2011\u0026ndash;2016. JAMA [Internet]. 2020;323(24):2526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2020.4501\u003c/span\u003e\u003cspan address=\"10.1001/jama.2020.4501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerningtyas EH, Ng TS. Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health [Internet]. 2019;19(1):377. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-019-6711-7\u003c/span\u003e\u003cspan address=\"10.1186/s12889-019-6711-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJura M, Kozak LP. Obesity and related consequences to ageing. Age (Omaha) [Internet]. 2016;38(1):23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11357-016-9884-3\u003c/span\u003e\u003cspan address=\"10.1007/s11357-016-9884-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuarner V, Rubio-Ruiz ME. In. Low-Grade Systemic Inflammation Connects Aging, Metabolic Syndrome and Cardiovascular Disease. 2015. pp. 99\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000364934\u003c/span\u003e\u003cspan address=\"10.1159/000364934\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMata A, Jasul G Jr. Prevalence of Metabolic Syndrome and its Individual Features Across Different (Normal, Overweight, Pre-Obese and Obese) Body Mass Index (BMI) Categories in a Tertiary Hospital in the Philippines. J ASEAN Fed Endocr Soc [Internet]. 2017;32(2):117\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15605/jafes.032.02.04\u003c/span\u003e\u003cspan address=\"10.15605/jafes.032.02.04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang Y, Guo X, Chen Y, Guo L, Li Z, Yu S, et al. A body shape index and body roundness index: two new body indices to identify diabetes mellitus among rural populations in northeast China. BMC Public Health [Internet]. 2015;15(1):794. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-015-2150-2\u003c/span\u003e\u003cspan address=\"10.1186/s12889-015-2150-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Zhu W, Qiu L, Huang L, Fang L. Sex- and Age-Specific Optimal Anthropometric Indices as Screening Tools for Metabolic Syndrome in Chinese Adults. Int J Endocrinol. 2018;2018:1067603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2018/1067603\u003c/span\u003e\u003cspan address=\"10.1155/2018/1067603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang AK, Truong MT, Le HT, Nguyen KC, Le MB, Nguyen LT, et al. Anthropometric Cut-Off Values for Detecting the Presence of Metabolic Syndrome and Its Multiple Components among Adults in Vietnam: The Role of Novel Indices. Nutrients. 2022;14(19):4024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu14194024\u003c/span\u003e\u003cspan address=\"10.3390/nu14194024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotamed N, Roozafzai F, Khoonsari MR, Malek M, Mahdavi A, Maadi M, et al. Predictive ability of six obesity measures to identify 7-year fatal and non-fatal cardiovascular events: A population-based cohort study. Int J Cardiol Cardiovasc Risk Prev [Internet]. 2022;14:200142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijcrp.2022.200142\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcrp.2022.200142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuliga E, Ciesla E, Głuszek-Osuch M, Rogula T, Głuszek S, Kozieł D. The Usefulness of Anthropometric Indices to Identify the Risk of Metabolic Syndrome. Nutrients [Internet]. 2019;11(11):2598. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu11112598\u003c/span\u003e\u003cspan address=\"10.3390/nu11112598\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefanescu A, Revilla L, Lopez T, Sanchez SE, Williams MA, Gelaye B, Using. A Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res [Internet]. 2020;48(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0300060519848854\u003c/span\u003e\u003cspan address=\"10.1177/0300060519848854\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JJ, Pedley A, Hoffmann U, Massaro JM, Levy D, Long MT. Visceral and Intrahepatic Fat are Associated with Cardiometabolic Risk Factors above Other Ectopic Fat Depots: The Framingham Heart Study. Am J Med. 2018;131(6):684\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amjmed.2018.02.002\u003c/span\u003e\u003cspan address=\"10.1016/j.amjmed.2018.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, Bouferraa Y, et al. Metabolic Syndrome: Updates on Pathophysiology and Management in 2021. Int J Mol Sci [Internet]. 2022;23(2):786. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23020786\u003c/span\u003e\u003cspan address=\"10.3390/ijms23020786\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia [Internet]. 2020;63(3):453\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-019-05040-3\u003c/span\u003e\u003cspan address=\"10.1007/s00125-019-05040-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchorr M, Dichtel LE, Gerweck AV, Valera RD, Torriani M, Miller KK, et al. Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ [Internet]. 2018;9(1):28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13293-018-0189-3\u003c/span\u003e\u003cspan address=\"10.1186/s13293-018-0189-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu XY, Song P, Zou MH. Obesity Paradox and Smoking Gun: A Mystery of Statistical Confounding? Circ Res. 2018;122(12):1642\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCRESAHA.118.312897\u003c/span\u003e\u003cspan address=\"10.1161/CIRCRESAHA.118.312897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePower M, Fell G, Wright M. Principles for High-Quality, High-Value Testing. Evid Based Med. 2013;18(1):5\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/eb-2012-100645\u003c/span\u003e\u003cspan address=\"10.1136/eb-2012-100645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrire","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Nutrire](https://www.springer.com/journal/41110)","snPcode":"41110","submissionUrl":"https://submission.nature.com/new-submission/41110/3","title":"Nutrire","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"anthropometric, body roundness index, metabolic syndrome, predictors, waist-to-hip ratio","lastPublishedDoi":"10.21203/rs.3.rs-8674977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8674977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eObesity is strongly linked to insulin resistance, and both conditions underlie the pathophysiology of Metabolic Syndrome (MetS). Anthropometric measurements to identify body fat are useful when screening individuals at risk for MetS.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aims to compare the diagnostic ability of the body roundness index (BRI), conicity index (C-index), body mass index (BMI), and waist-to-height ratio (WHtR) as MetS predictors among Indonesian adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used secondary data from the Baseline Health Research of 2018 (Riskesdas 2018). A total of 13,792 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;19 years (4,655 men and 9,137 women) with complete data on anthropometric measurements, blood pressure, lipid profile, and fasting blood glucose were analyzed using descriptive analysis and area under the curve (AUC) comparisons to determine the diagnostic ability of anthropometric parameters as predictors of MetS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe four anthropometric parameters demonstrated moderate to good predictive ability to predict MetS (AUC\u0026thinsp;=\u0026thinsp;0.7\u0026ndash;0.9). WHtR and BRI (AUC\u003csub\u003emen\u003c/sub\u003e = 0.800; AUC\u003csub\u003efemale\u003c/sub\u003e = 0.802) were significantly better predictors than the other anthropometric parameters in women but were not significantly different from BMI (AUC\u0026thinsp;=\u0026thinsp;0.794) in men. Conversely, the C-index (AUC\u003csub\u003emen\u003c/sub\u003e = 0.742; AUC\u003csub\u003efemale\u003c/sub\u003e = 0.710) was significantly less predictive than the other parameters.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBRI and WHtR demonstrated good and comparable performance in detecting MetS. In women, both were superior to BMI and the C-index, while in men, their performance was similar to BMI. The C-index showed the weakest predictive power. The recommended cut-off values for MetS screening are WHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.51 (men) and \u0026ge;\u0026thinsp;0.53 (women).\u003c/p\u003e","manuscriptTitle":"Anthropometric Indices as Predictors of Metabolic Syndrome in Indonesian Adults: A Comparison of BRI, C- Index, and Traditional Anthropometric Measures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-07 04:45:15","doi":"10.21203/rs.3.rs-8674977/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-22T22:31:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T20:47:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109395338827515965528461117518931097394","date":"2026-03-07T12:18:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243802578013183501734252486250223830680","date":"2026-03-03T23:38:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T23:51:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28040117435387187326663224795651626907","date":"2026-02-05T01:13:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-03T14:14:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T14:12:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T09:58:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrire","date":"2026-01-23T04:24:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrire","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Nutrire](https://www.springer.com/journal/41110)","snPcode":"41110","submissionUrl":"https://submission.nature.com/new-submission/41110/3","title":"Nutrire","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"843b93bd-a4e9-49f5-9e3c-871b21633671","owner":[],"postedDate":"February 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T18:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-07 04:45:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8674977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8674977","identity":"rs-8674977","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}