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Article

Body Roundness Index (BRI) Predicts Metabolic Syndrome in Postmenopausal Women with Obesity Better than Insulin Resistance

by
Daniel de Luis
1,2,3,*,
Marife Muñoz
2,3,
Olatz Izaola
1,2,3,
Juan José Lopez Gomez
1,2,3,
Daniel Rico
2,3 and
David Primo
1,2,3
1
Endocrinology and Nutrition Department, Hospital Clínico Universitario de Valladolid, Investigation, 47003 Valladolid, Spain
2
Centre on Endocrinology and Nutrition (IEN), University of Valladolid, 47002 Valladolid, Spain
3
Valladolid Health Research Institute (IBioVALL), C. Rondilla Sta. Teresa, 47010 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Diabetology 2025, 6(7), 60; https://doi.org/10.3390/diabetology6070060
Submission received: 3 May 2025 / Revised: 6 June 2025 / Accepted: 23 June 2025 / Published: 1 July 2025
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Abstract

Background/Objective: The body roundness index (BRI) has emerged as a novel anthropometric parameter with potential utility in the assessment of obesity and its associated metabolic complications. This study aimed to identify the optimal BRI cut-off point for the diagnostic process of metabolic syndrome (MetS) in a cohort of postmenopausal women with obesity and to compare its predictive capacity with that of the homeostasis model assessment of insulin resistance (HOMA-IR). Methods: A cross-sectional analysis was conducted in 468 Caucasian postmenopausal women with obesity. Clinical and biochemical assessments included anthropometric measurements, blood pressure, fasting plasma glucose, insulin levels, the HOMA-IR, lipid profile, C-reactive protein, and adipokines. MetS was diagnosed according to the Adult Treatment Panel III (ATP III) criteria. Results: MetS was identified in 270 patients (57.5%). Stratification by the median BRI revealed that individuals in the higher-BRI group had a significantly increased odds of MetS (OR 2.65; 95% CI: 1.99–3.53; p = 0.03). A Receiver Operating Characteristic (ROC) curve analysis showed that the HOMA-IR had an area under the curve (AUC) of 0.72 (95% CI: 0.67–0.77; p = 0.01), with a cut-off value of 2.64 (sensitivity: 64.9%; specificity: 69.7%). In contrast, the BRI exhibited a higher AUC of 0.75 (95% CI: 0.71–0.80; p = 0.001), with an optimal cut-off of 8.15, demonstrating superior sensitivity (85.6%) and specificity (72.5%). Conclusions: The BRI is a promising and practical alternative anthropometric index for identifying MetS in Caucasian postmenopausal women with obesity. Its strong association with markers of adiposity and metabolic dysregulation underscores its potential value in clinical and epidemiological settings.

Graphical Abstract

1. Introduction

Metabolic syndrome (MetS), which encompasses a group of cardiovascular factors, is a significant predictor of overall mortality and early death from cardiovascular events [1]. The prevalence of MetS ranges from 10% to 47% [2]. Due to the substantial individual and societal impact of cardiovascular disease (CVD), identifying individuals with MetS is essential. The early detection of those at risk enables the development of targeted interventions to modify potential risk factors and prevent the development or progression of MetS in the future. Increasing epidemiological evidence suggests that simple and cost-effective anthropometric measurements can be used to predict MetS. These include traditional metrics like the body mass index (BMI) and waist circumference (WC), which have been utilized in clinical settings for many years, alongside emerging indices such as the body roundness index (BRI) [3]. In women, the prevalence of metabolic syndrome (MetS) appears to be influenced by menopausal status, with postmenopausal stage identified as an independent risk factor for MetS [4]. Postmenopausal women tend to accumulate more intra-abdominal fat than premenopausal women, which increases their susceptibility to obesity-related metabolic complications [5]. While obesity is generally defined by excessive body fat, individuals with obesity may differ in their fat mass distribution and associated disease risk. An increase in visceral fat, rather than subcutaneous fat, is particularly linked to a higher risk of metabolic disorders and cardiovascular disease (CVD) [6]. Consequently, fat distribution, rather than total adiposity, is now recognized as a key determinant of metabolic abnormalities [7]. Although radiological techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) are considered the gold standards for assessing visceral fat volume, these methods are impractical for large-scale epidemiological research due to their high cost and logistical limitations [8]. As a result, body fat is often estimated through various anthropometric indices in most studies. One of the most frequently used indices for assessing obesity is the body mass index (BMI), which is widely accepted and commonly employed in epidemiological designs [9,10]. However, the BMI does not differentiate between muscle mass and fat accumulation [11,12], and evidence suggests that, while a higher fat mass is related to an elevated risk of premature death, a greater muscle mass is associated with a reduced risk [13]. Additionally, the BMI does not account for fat distribution, particularly central or abdominal fat, which is considered especially hazardous [14]. Waist circumference (WC), on the other hand, is a widely used anthropometric measure for evaluating central obesity and is a diagnostic criterion for MetS. WC has a strong association with cardiovascular disease (CVD), and numerous investigations have reported that it is a better predictor of mortality than the BMI [15,16].
Recently, Thomas et al. reported the body roundness index (BRI) as a new tool to evaluate adiposity [3]; it is derived from waist circumference (WC) and body height, and it serves as a predictor of body fat mass percentage and visceral adipose tissue. BRI values range from 1 to 16. Several studies have demonstrated that the BRI can be used as an adiposity indicator for identifying conditions such as eccentric left ventricular hypertrophy, hyperuricemia, cardiovascular disease (CVD), and diabetes mellitus [17,18,19,20]. Some studies found that the BRI shows an equal or superior predictive ability in detecting these conditions [17,18], while others did not observe such superiority [19,20]. Therefore, BRI values typically range from 1 to 16 [3], and it is also important to note that an increase in the BRI is associated with a higher cardiovascular risk due to greater visceral and total body fat accumulation [3], supporting its clinical utility as an indicator of cardiometabolic health. However, there is currently limited evidence to determine whether the BRI can be effectively used as an obesity parameter to indicate the presence of MetS in postmenopausal women with obesity. In the literature, there is only one study to date, and it was carried out in a Chinese population of postmenopausal women, with obesity not necessarily present in all evaluated subjects [21]. Finally, insulin resistance is an early factor present in individuals with MetS or type 2 diabetes mellitus, typically occurring 5 to 10 years before the diagnosis of either condition. Individuals with insulin resistance are at a significantly increased risk of developing cardiovascular diseases, lipid abnormalities, glucose intolerance, and hypertension [22,23]. While the hyperinsulinemic–euglycemic clamp technique is taken as the gold standard for assessing insulin resistance, the homeostatic model assessment of insulin resistance (HOMA-IR) and other indirect parameters are more commonly used in clinical practice [24]. An HOMA-IR value of 2.5 is often employed as a threshold for identifying insulin resistance [24].
The objective of our investigation was to report the cut-off point of the BRI for the diagnosis of MetS according to Adult Treatment Panel III (ATPIII) criteria in postmenopausal women with obesity and to compare it with that of the HOMA-IR for detecting MetS.

2. Materials and Methods

2.1. Study Design

A cross-sectional study was conducted in a health area of Spain. A non-probabilistic sampling method was employed to enroll 468 women, all of whom were postmenopausal women with obesity referred from Primary Care Health Departments serving both rural and urban populations to a tertiary hospital. These Caucasian postmenopausal women with obesity were referred to the hospital’s outpatient clinic for health assessments aimed at evaluating their obesity (body mass index >30 kg/m2). All participants provided informed consent and agreed to take part in the study. This study was carried out in compliance with the principles outlined in the Declaration of Helsinki, and the research protocol received approval from the Ethics Committee (approval code 06/2018).
Eligible participants for this study were women with obesity, defined by a body mass index (BMI) exceeding 30 kg/m2, who were in natural postmenopause—characterized by the absence of menstruation for at least one year following their final menstrual period, without any pathological cause of amenorrhea. Exclusion criteria comprised the presence of severe comorbidities, such as chronic kidney disease, chronic liver disease, heart failure, a history of cardiovascular events, or malignancy. Participants were also excluded if they reported alcohol consumption exceeding 10 ethanol units per day (with 1 ethanol unit being equivalent to 14 g of ethanol). Additional exclusion criteria included adherence to a hypocaloric diet within the six months prior to enrollment or ongoing treatment with statins, fibrates, or antidiabetic agents known to influence insulin resistance.
Data collection occurred from December 2019 to December 2022, conducted by a team of graduates in human nutrition and dietetics with extensive experience in clinical research studies. The study variables included sociodemographic data, adiposity-related metrics (such as body weight, body mass index [BMI], fat mass measured by a bioelectrical impedance analysis, and waist circumference), blood pressure, and biochemical parameters. At the same visit, a 5 mL sample of venous blood was collected after a 10 h overnight fast and distributed into ethylenediaminetetraacetic acid (EDTA)-coated tubes for subsequent biochemical analyses.

2.2. Study Variables

2.2.1. Metabolic Syndrome, Adiposity Parameters, and Blood Pressure

Metabolic syndrome (MetS) was identified based on the Adult Treatment Panel III (ATPIII) guidelines [25]. To establish a diagnosis, at least three of the following criteria needed to be met: high fasting glucose or ongoing treatment for diabetes, elevated triglyceride levels (>150 mg/dL) or treatment for hyperlipidemia, low HDL cholesterol (<50 mg/dL in women), increased blood pressure (>130/85 mmHg or antihypertensive therapy), and an expanded waist circumference (WC) (>88 cm for women). All obesity-related parameters were assessed by the same researcher to minimize interobserver variability. Waist circumference was measured using a flexible standard tape (Omron, LA, CA, USA), positioned between the upper edge of the iliac crest and the lower rib. Two separate measurements were taken, and the average was used as the final value. Height was recorded in centimeters using a standard height measurement scale (Omron, LA, CA, USA). Body weight was measured while subjects wore minimal clothing and no shoes, using digital scales (Omron, LA, CA, USA). The body mass index (BMI) was calculated by dividing body weight in kilograms by height in meters squared. The BRI was obtained using the following formula [3]: BRI = 364.2 − 365.5 × (1 − ((WC/2π)/(0.5 × height))2)0.5. Waist circumference and height were recorded in meters. The total fat mass was assessed using a bioelectrical impedance analysis with a precision of 5 g (EFG BIA 101 Anniversary, Akern, Italy) [26]. Blood pressure was measured using a sphygmomanometer (Omron, LA, CA, USA) after participants had been seated for 10 min during the interview. Four readings were obtained per subject; the first was excluded, and the mean of the remaining three measurements was used for analysis. All measurement devices used in this study were calibrated according to the manufacturer’s specifications. The potential deviations in measurements reported by the manufacturers were as follows: ±0.1 kg for the digital scales, ±0.5 cm for the stadiometers and measuring tapes, ±1 mmHg for the sphygmomanometer, and ±5 g for the bioelectrical impedance analyzer.

2.2.2. Biochemical Procedures

Serum concentrations of glucose, insulin, C-reactive protein (CRP), total cholesterol, HDL cholesterol, and triglycerides were quantified using a COBAS INTEGRA 400 automated analyzer (Roche Diagnostics, Basel, Switzerland). The laboratory reference values used in this study for glucose, lipid profiles, insulin, and adipokines were based on the standard reference ranges provided by the clinical biochemistry laboratory of the Hospital Clínico Universitario de Valladolid, following internationally accepted guidelines and assay manufacturer specifications. LDL cholesterol was calculated using the Friedewald equation (LDL cholesterol = total cholesterol − HDL cholesterol − triglycerides/5) [27]. Insulin resistance was estimated via the homeostasis model assessment of insulin resistance (HOMA-IR), calculated as glucose (mg/dL) × insulin (UI/L)/22.5 [28]. Adipokine concentrations were determined by an enzyme-linked immunosorbent assay (ELISA). Serum adiponectin was assessed using commercial kits from R&D Systems, Inc. (Minneapolis, MN, USA), with a reference range of 8.65–21.43 µg/mL; resistin levels were measured using kits from Biovendor Laboratory, Inc. (Brno, Czech Republic), with a reference range of 4–12 ng/mL; and leptin was quantified using kits from Diagnostic Systems Laboratories, Inc. (Webster, TX, USA), with a reference range of 10–100 ng/mL.

2.3. Data Analysis

A statistical analysis was conducted using SPSS version 23.0 (IBM, SPSS Inc., Chicago, IL, USA). A priori power analysis was performed based on expected differences in BRI values between individuals with and without metabolic syndrome, assuming a medium effect size (Cohen’s d = 0.5), a significance level of 0.05, and a power of 0.90. The analysis indicated that a minimum sample size of 400 participants was required to detect statistically significant differences with adequate power. Data normality was evaluated using the Kolmogorov–Smirnov test. Continuous variables are summarized using measures of central tendency (median) and dispersion (standard deviation), while categorical variables are expressed as absolute frequencies and percentages. Comparisons between categorical variables were performed using Pearson’s chi-square test. For continuous variables, Student’s t-test was applied when a normal distribution was assumed, whereas the Mann–Whitney U test was used for non-normally distributed data. Spearman’s correlation test was used to evaluate the relationship between two dependent variables. A Receiver Operating Characteristic (ROC) curve was generated to determine the optimal cut-off values for the BRI and HOMA-IR in relation to MetS. Cut-off points were established using two methods: the area under the curve (AUC), which provided the best balance of specificity and sensitivity, and the Youden Index, calculated as (sensitivity + specificity) − 1. A p-value of less than 0.05 was considered statistically significant.

3. Results

Sample Description

The clinical characteristics of the study population, with and without MetS, are presented in Table 1. A total of 468 participants were included, with a mean age of 60.1 ± 7.5 years (range: 50–82). Of the total participants, 270 (57.5%) were diagnosed with MetS, while 198 (42.5%) did not present MetS. The MetS group had a significantly higher mean age than the non-MetS group (62.3 ± 7.9 years vs. 58.1 ± 6.2 years; p = 0.01).
Table 1 highlights the significant associations between MetS and various adiposity parameters. Statistically significant differences (Delta; p-values) were observed between the MetS and non-MetS groups in terms of the body mass index (BMI) (2.1 ± 0.2 kg/m2; p = 0.01), body weight (10.3 ± 2.9 kg; p = 0.02), total body fat mass (8.4 ± 1.1 kg; p = 0.02), and waist circumference (11.3 ± 1.8 cm; p = 0.02), with all these parameters being higher in the MetS group than in the non-MetS group. Additionally, systolic blood pressure (11.8 ± 3.8 mmHg; p = 0.01) and diastolic blood pressure (5.3 ± 1.3 mmHg; p = 0.02) were also significantly elevated in the MetS group.
The biochemical characteristics of the participants based on the presence of MetS are detailed in Table 1. The MetS group exhibited significantly higher levels of the following parameters compared to the non-MetS group (Delta; p-values): glucose (7.2 ± 1.9 mg/dL; p = 0.01), triglycerides (28.8 ± 6.9 mg/dL; p = 0.02), insulin (4.3 ± 0.9 UI/L; p = 0.02), the HOMA-IR (1.8 ± 0.4 units; p = 0.03), leptin (22.9 ± 4.8 mg/dL; p = 0.02), and the BRI (2.1 ± 0.2 mg/dL; p = 0.01). In contrast, adiponectin levels (23.1 ± 4.1 mg/dL; p = 0.01) and HDL cholesterol (−3.1 ± 0.2 mg/dL; p = 0.04) were significantly lower in the MetS group. No statistically significant differences were observed between the groups in terms of the total cholesterol, LDL cholesterol, C-reactive protein (CRP), or resistin concentrations.
Table 2 provides an overview of the percentage of participants meeting each criterion in the MetS group versus the noMetS group. The prevalence of all MetS criteria was higher in the MetS group than in the noMetS group. We show the values of the BRI according to the number of MetS criteria (0 criteria—6.61 ± 0.29 units, 1 criterion—8.4 ± 0.2 units, 2 criteria—9.7 ± 0.3 units, 3 criteria—10.8 ± 0.3 units, 4 criteria—11.4 ± 1.1 units, and 5 criteria—11.9 ± 0.3 units; p = 0.01). The average BRI value increased as the components of MetS were aggregated. The same results were observed with the HOMA-IR (0 criteria—1.56 ± 0.2 units, 1 criterion—2.30 ± 0.1 units, 2 criteria—2.19 ± 0.1 units, 3 criteria—4.4 ± 0.2 units, 4 criteria—6.1 ± 0.3 units, and 5 criteria—6.2 ± 0.2 units; p = 0.001).
The diagnostic thresholds applied were as follows: central obesity was defined as a waist circumference >88 cm; hypertension was defined as systolic blood pressure >130 mmHg, diastolic blood pressure >85 mmHg, or the use of antihypertensive medication; hypertriglyceridemia was defined as triglyceride levels >150 mg/dL or specific lipid-lowering treatment; and hyperglycemia was defined as fasting plasma glucose >110 mg/dL or pharmacological treatment for elevated glucose. Statistically significant differences between groups were identified at p < 0.05.
Table 3 presents the correlations between various parameters and the BRI. A positive correlation was observed between the BRI and adiposity parameters, glucose, insulin, the HOMA-IR, CRP, LDL cholesterol, and triglycerides, while a negative correlation was found between the BRI and HDL cholesterol. When dividing the sample by the median BRI of 9.21, the risk of developing MetS in patients belonging to the high-BRI group had an odds ratio (OR) of 2.65 (IC95% 1.99–3.53; p = 0.03).
Figure 1 illustrates the ROC of the HOMA-IR for MetS. The area under the curve (AUC) according to ATPIII criteria was 0.72 (0.67–0.77; p = 0.01), with a cut-off value of 2.64 based on the Youden index, yielding a sensitivity of 64.9% and a specificity of 69.7%, a positive likelihood ratio of 2.15, and a negative likelihood ratio of 0.50 (Table 4). For the BRI (Figure 1), the AUC was 0.75 (0.71–0.80; p = 0.001). The Youden index determined a cut-off value of 8.15, with a sensitivity of 85.6% and a specificity of 72.5%, along with a positive likelihood ratio of 1.28 and a negative likelihood ratio of 0.28 (Table 4).

4. Discussion

In the present study, a novel anthropometric index, the body roundness index (BRI), was compared with the traditional homeostatic model assessment of insulin resistance (HOMA-IR) to assess their effectiveness in screening Caucasian postmenopausal women at risk of metabolic syndrome (MetS). To the best of our knowledge, this is the first investigation to assess and compare the discriminative capacity of this novel obesity index with that of the HOMA-IR in predicting MetS in this population. Our results showed that the BRI is a reliable tool for detecting MetS in a population of Caucasian postmenopausal women and serves as a more robust surrogate marker for predicting MS than the HOMA-IR. In our analysis, the average BRI values were higher in the MetS group, and we identified a cut-off point of 8.15 for Caucasian postmenopausal women.
Obesity, defined by an elevated accumulation of body fat, is a primary factor contributing to the development of MetS, which is characterized by a cluster of metabolic disorders: hyperlipidemia, hypertension, hyperglycemia, and insulin resistance [25]. As a result, accurately identifying obesity is crucial for estimating the risk of related conditions and adverse cardiovascular events. Several methods have been developed for this purpose, with the body mass index (BMI) being the most commonly used due to its simplicity. Nevertheless, the BMI has notable drawbacks, especially its inability to distinguish between lean and fat mass, and it does not provide an accurate representation of body fat distribution [12,14]. The body roundness index (BRI), derived from waist circumference and height, is a relatively recent anthropometric metric proposed by Thomas et al. [3]. It conceptualizes body shape as an ellipse or oval to assess adiposity. Their findings indicated that the BRI offers more accurate estimations of body fat than conventional obesity indices. However, the BRI has limitations in predicting the fat mass percentage in people without obesity, especially when compared to widely available field methods, such as bioelectrical impedance analysis. To date, few studies have assessed the BRI’s ability to predict metabolic syndrome (MetS) in postmenopausal populations, which is also a population at risk of developing MetS [4,21]. Additionally, various studies have explored the associations between obesity, as assessed by the BRI, and other populations with different diseases. For instance, one study reported that the body roundness index (BRI) exhibited a higher predictive accuracy than the body mass index (BMI) in detecting hyperuricemia, and it demonstrated comparable performance to waist circumference and the waist-to-height ratio (WHtR) in women [18]. Similarly, in a population-based study conducted among rural residents in northeastern China, Chang et al. [20] observed that the BRI showed a predictive ability for diabetes mellitus equivalent to that of the BMI and waist circumference. Consistent findings were also documented by Maessen et al. [19] in a cohort of Caucasian individuals from Nijmegen, the Netherlands.
The ideal BRI cut-off point for predicting metabolic syndrome (MetS) risk was previously identified as 6.2 for women [29]. However, this study was conducted in an Iranian cohort of the general population. In studies conducted in Chinese populations, this cut-off value was again 6.2 in women, whereas in a Colombian population, the optimal point was 4.0 for women [30,31]. Our current cut-off in a Caucasian postmenopausal population was 8.15. Other investigations have demonstrated that the BRI outperforms indices such as the BMI and waist circumference in detecting MetS and its related components [32]. Even women exhibited notably higher AUC values and cut-off points for the BRI in identifying MetS compared to men [33]. The BRI demonstrated a superior predictive ability in detecting hyperglycemia in both sexes [33]. In this last study, the authors observed that the median BRI values [5,20] were lower than those reported by Thomas et al. [3] in women with MetS (6.86). According to Thomas et al. [3], the BRI reflects visceral adipose tissue (VAT) and body fat, suggesting that the higher BRI values in our study indicate greater VAT and body fat among our participants.
Moreover, other studies have confirmed that the BRI can serve as a useful indicator for predicting MetS in individuals with diabetes mellitus [20]. Insulin resistance and chronic inflammation are the primary drivers of MetS, and, in our design, the BRI showed significant correlations with both insulin resistance and inflammatory biomarkers such as CRP, performing similarly to traditional obesity measures. Insulin resistance is a key risk factor for MetS. In the context of insulin resistance within adipose tissues, the insulin-mediated suppression of lipolysis is diminished. This leads to a subsequent rise in circulating free fatty acids (FFAs), which exacerbates insulin resistance by disrupting the insulin-signaling pathway, thereby creating a self-perpetuating cycle. In light of our findings and previous research, the BRI, as an emerging non-invasive anthropometric tool, exhibits sufficient predictive strength for MetS and may offer a viable alternative to conventional indices for assessing MetS risk. Various factors, such as ethnic and racial diversity, which can impact lifestyle behaviors like dietary patterns and physical activity levels, likely contribute to the differences in the cut-off points observed across studies. Moreover, the use of distinct diagnostic criteria for metabolic syndrome (MetS) from organizations such as the IDF (International Diabetes Federation), ATP III (Adult Treatment Panel III), AHA (American Heart Association), and WHO (World Health Organization) may explain some of the inconsistencies in the findings. Additionally, the differing rates of obesity and overweight across populations could be another important factor influencing the variation in the anthropometric index cut-off points between studies. Although the BRI did not outperform traditional obesity indices in identifying the presence of metabolic syndrome (MetS), there were no significant differences in the AUC values between the BRI and the traditional indices for predicting MetS. In our study, the mean BRI value was notably higher in individuals diagnosed with MetS than in those without MetS. Furthermore, the average BRI progressively increased with the accumulation of MS criteria, and a strong correlation was observed between the BRI and various adiposity and metabolic parameters. This indicates that the BRI may have potential as an alternative measure of obesity in the assessment of MetS. In our study, the BRI demonstrated a superior AUC compared to the HOMA-IR in predicting MetS. This superiority can be explained by the hypothesis that the BRI reflects insulin resistance in central adipose tissues, whereas the HOMA-IR primarily represents hepatic insulin resistance. From a pathophysiological perspective, these findings make the BRI a more effective marker for predicting MetS prevalence. Beyond its stronger predictive capacity, the BRI offers practical and economic benefits over the HOMA-IR. Unlike the HOMA-IR, which requires insulin assays that are costlier than triglyceride measurements, the BRI can be determined in a simple anthropometric evaluation. This makes the BRI a highly attractive and cost-effective option for MetS.
The novelty of the body roundness index (BRI) as an anthropometric marker for metabolic syndrome (MetS) lies in its geometric foundation and ability to estimate body fat and visceral adiposity using only waist circumference and height. As originally proposed by Thomas et al. [3], the BRI is calculated using waist circumference and height, which are simple clinical parameters. This formula estimates the degree of “roundness” of the human body and correlates strongly with both total and visceral fat mass. Several studies have demonstrated that, as the BRI increases, so does the proportion of visceral adipose tissue, which is a key driver of insulin resistance, inflammation, and cardiometabolic risk [3,20,30,32]. Our results confirm this relationship in postmenopausal Caucasian women with obesity, as the BRI showed strong correlations with waist circumference, fat mass, the HOMA-IR, and serum triglycerides. Importantly, we identified a cut-off value of 8.15 as optimal in our population, higher than in other ethnic cohorts, likely reflecting both demographic and metabolic differences. Therefore, including the BRI in routine clinical assessment may offer a cost-effective, non-invasive strategy to improve the early detection of individuals at high risk of MetS and cardiovascular disease.
This study presents several limitations. Firstly, its cross-sectional design precludes causal inference, limiting our ability to determine whether the risk factors associated with metabolic syndrome (MetS) in this analysis directly contribute to its development. Secondly, the study population consisted exclusively of Caucasian women recruited from a single medical center, which may limit the generalizability of the findings to other ethnic or demographic groups and underscores the need for validation in more diverse populations. Thirdly, dietary intake and physical activity were not assessed, potentially introducing residual confounding into the observed associations. Among the strengths of this investigation is the inclusion of a representative sample of Caucasian women with obesity, which enhances the internal validity.

5. Conclusions

The results of this study suggest that the body roundness index (BRI) may represent a valuable alternative anthropometric indicator for evaluating metabolic syndrome (MetS) in Caucasian postmenopausal women, demonstrating significant associations with both adiposity and metabolic biomarkers. A BRI threshold of 8.15 was identified as optimal for discriminating individuals at elevated risk. The early identification of at-risk subjects using this index may facilitate the implementation of preventive lifestyle interventions, potentially mitigating the progression to more advanced cardiometabolic conditions such as type 2 diabetes mellitus or cardiovascular disease [34,35].

Author Contributions

Conceptualization, J.J.L.G. and D.d.L.; Data curation, M.M. and J.J.L.G.; Formal analysis, J.J.L.G.; Funding acquisition, D.d.L.; Investigation, D.R., J.J.L.G., O.I., D.P. and D.d.L.; Methodology, J.J.L.G. and D.d.L.; Project administration, J.J.L.G. and D.d.L.; Resources J.J.L.G. and D.d.L.; Supervision, J.J.L.G. and D.d.L.; Validation, D.R., J.J.L.G. and D.d.L.; Visualization, J.J.L.G. and D.d.L.; Writing—original draft D.R. and J.J.L.G.; Writing—review and editing, D.R., J.J.L.G. and D.d.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in compliance with the principles of the Declaration of Helsinki and received approval from the East Valladolid Ethics Committee (protocol number PI 06-2018; approval date: 13 October 2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data is unavailable due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest in the development of this study.

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Diabetology 06 00060 g001aDiabetology 06 00060 g001b
Figure 1. Receiver Operating Characteristic (ROC) curve used to determine optimal cut-off values for the body roundness index (BRI) and HOMA-IR (the red line is the reference line with the AUC, and the blue line is the line of our AUC).
Figure 1. Receiver Operating Characteristic (ROC) curve used to determine optimal cut-off values for the body roundness index (BRI) and HOMA-IR (the red line is the reference line with the AUC, and the blue line is the line of our AUC).
Diabetology 06 00060 g001aDiabetology 06 00060 g001b
Table 1. Comparative analysis of anthropometric, lipid, and glycemic parameters in individuals with and without metabolic syndrome.
Table 1. Comparative analysis of anthropometric, lipid, and glycemic parameters in individuals with and without metabolic syndrome.
ParametersTotal Group
n = 468		No Metabolic Syndrome
n = 198		Metabolic
Syndrome
n = 270		p Value
BMI (k/m2)37.8 ± 1.236.6 ± 0.938.7 ± 0.70.01
Body weight (kg)88.6 ± 9.182.2 ± 8.992.8 ± 5.90.02
Total fat mass (kg)40.7 ± 11.135.8 ± 3.044.2 ± 4.10.02
WC (cm)110.8 ± 3.0104.5 ± 4.1115.8 ± 3.10.02
SBP (mmHg)132.7 ± 7.1125.6 ± 4.2137.7 ± 3.90.01
DBP (mmHg)83.1 ± 3.280.0 ± 4.185.3 ± 3.20.02
Fasting glucose (mg/dL)106.2 ± 6.296.9 ± 8.0113.5 ± 5.00.01
Total cholesterol (mg/dL)217.4 ± 11.8217.8 ± 9.2218.8 ± 8.20.29
LDL cholesterol (mg/dL)135.6 ± 22.9132.8 ± 6.1138.7 ± 5.90.23
HDL cholesterol (mg/dL)58.3 ± 2.159.3 ± 2.156.0 ± 1.10.04
Triglycerides (mg/dL)121.7 ± 9.2105.1 ± 8.1133.9 ± 6.90.02
Insulin (mUI/L)12.3 ± 1.310.2 ± 1.914.5 ± 0.40.02
HOMA-IR3.3 ± 0.92.3 ± 0.54.1 ± 0.20.03
CRP (mg/dL)5.4 ± 0.65.2 ± 0.95.9 ± 1.10.22
Resistin (ng/mL) 4.9 ± 0.24.5 ± 0.45.0 ± 0.90.38
Adiponectin (ng/mL)24.7 ± 3.432.9 ± 3.819.8 ± 1.10.01
Leptin (ng/mL)70.4 ± 7.156.7 ± 2.378.1 ± 3.80.02
BRI9.6 ± 2.38.4 ± 0.210.5 ± 0.30.01
BMI: body mass index. DBP: diastolic blood pressure. SBP: systolic blood pressure. WC: waist circumference. HOMA-IR: homeostasis model assessment of insulin resistance. CRP: C-reactive protein. BRI: body roundness index. Statistical differences between groups: p < 0.05.
Table 2. Prevalence of metabolic syndrome and distribution of its individual components.
Table 2. Prevalence of metabolic syndrome and distribution of its individual components.
ParametersTotal Group
n = 468		No Metabolic Syndrome
n = 198		Metabolic Syndrome
n = 270		p
Percentage of MetS 57.5%0%100%0.001
Percentage of central obesity83.8%68.7%95.8%0.02
Percentage of hypertriglyceridemia12.5%4.6%18.3%0.01
Low HDL cholesterol16.6%7.5% 23.3% 0.02
Percentage of hypertension 59.5%26.3%82.5%0.001
Percentage of hyperglycemia 30.6%3.6%50.2%0.001
Table 3. Correlation analysis of the BRI and HOMA-IR with biochemical and anthropometric parameters.
Table 3. Correlation analysis of the BRI and HOMA-IR with biochemical and anthropometric parameters.
ParametersTotal Group
n = 468		No Metabolic Syndrome
n = 198		Metabolic
Syndrome
n = 270		Total Group
n = 468		No Metabolic Syndrome
n = 198		Metabolic
Syndrome
n = 270
BRIHOMA-IR
Glucose (mg/dL)r = 0.28, p = 0.01 r = 0.13, p = 0.12 r = 0.36, p = 0.01 r = 0.50, p = 0.001 r = 0.48, p = 0.001r = 0.54, p = 0.001
CRP (mg/dL)r = 0.21, p = 0.01r = 0.14, p = 0.01r = 0.23, p = 0.001r = 0.12, p = 0.01r = 0.11, p = 0.02r = 0.15 p = 0.03
HDL cholesterol (mg/dL)r = −0.17, p = 0.01 r = −0.09, p = 0.12 r = −0.26, p = 0.01 r = −0.17, p = 0.01 r = −0.13, p = 0.02 r = −0.17, p = 0.001
Triglycerides (mg/dL)r = 0.20, p = 0.01r = 0.18, p = 0.01r = 0.33, p = 0.001r = 0.29, p = 0.01r = 0.20, p = 0.02r = 0.31, p = 0.01
Insulin (UI/L)r = 0.35, p = 0.002r = 0.25, p = 0.002r = 0.38, p = 0.001r = 0.95, p = 0.001r = 0.94, p = 0.001r = 0.98, p = 0.001
HOMA-IR r = 0.37, p = 0.001r = 0.29, p = 0.001 r = 0.48, p = 0.001 -- -
Body weight (kg)r = 0.67, p = 0.001r = 0.39, p = 0.02r = 0.70, p = 0.001r = 0.36, p = 0.01r = 0.25, p = 0.01r = 0.46, p = 0.001
Total fat mass (kg)r = 0.54, p = 0.002r = 0.20, p = 0.03r = 0.66, p = 0.001r = 0.34, p = 0.01r = 0.26, p = 0.01r = 0.35, p = 0.001
Waist circumference (cm)r = 0.94, p = 0.001r = 0.93, p = 0.001r = 0.94, p = 0.001r = 0.38, p = 0.01r = 0.30, p = 0.02r = 0.39, p = 0.001
CRP: C-reactive protein. HOMA-IR: homeostasis model assessment. BRI: body roundness index. Statistical differences between groups: p < 0.05.
Table 4. Diagnostic cut-off points for the HOMA-IR and BRI in predicting metabolic syndrome.
Table 4. Diagnostic cut-off points for the HOMA-IR and BRI in predicting metabolic syndrome.
Cut-Off PointPositive Likelihood RatioNegative Likelihood RatioSensitivitySpecificity
HOMA-IR2.642.150.5064.9%69.7%
BRI 8.151.800.2885.6%72.5%
HOMA-IR: homeostasis model assessment. BRI: body roundness index. Statistical differences between groups: p < 0.05.
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de Luis, D.; Muñoz, M.; Izaola, O.; Lopez Gomez, J.J.; Rico, D.; Primo, D. Body Roundness Index (BRI) Predicts Metabolic Syndrome in Postmenopausal Women with Obesity Better than Insulin Resistance. Diabetology 2025, 6, 60. https://doi.org/10.3390/diabetology6070060

AMA Style

de Luis D, Muñoz M, Izaola O, Lopez Gomez JJ, Rico D, Primo D. Body Roundness Index (BRI) Predicts Metabolic Syndrome in Postmenopausal Women with Obesity Better than Insulin Resistance. Diabetology. 2025; 6(7):60. https://doi.org/10.3390/diabetology6070060

Chicago/Turabian Style

de Luis, Daniel, Marife Muñoz, Olatz Izaola, Juan José Lopez Gomez, Daniel Rico, and David Primo. 2025. "Body Roundness Index (BRI) Predicts Metabolic Syndrome in Postmenopausal Women with Obesity Better than Insulin Resistance" Diabetology 6, no. 7: 60. https://doi.org/10.3390/diabetology6070060

APA Style

de Luis, D., Muñoz, M., Izaola, O., Lopez Gomez, J. J., Rico, D., & Primo, D. (2025). Body Roundness Index (BRI) Predicts Metabolic Syndrome in Postmenopausal Women with Obesity Better than Insulin Resistance. Diabetology, 6(7), 60. https://doi.org/10.3390/diabetology6070060

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