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Abstract

Objective

To determine the discriminatory ability of different anthropometric indicators of body fat percentage for diagnosing metabolic syndrome (MetS) in a Peruvian sample.

Methods

This was a cross-sectional, non-experimental, diagnostic accuracy study. Anthropometric and biochemical data for 948 participants were analyzed. Waist circumference (WC), body mass index, relative fat mass (RFM), conicity index, body roundness index (BRI), waist-to-height ratio (WHtR), and A Body Shape Index were assessed for their MetS discriminatory ability. The National Cholesterol Education Program’s Adult Treatment Panel III criteria were used to diagnose MetS. Receiver operating characteristic curves and area under the curve (AUC) were used to determine the predictive power of each anthropometric measurement to diagnose MetS.

Results

In both sexes, RFM, BRI, and WHtR showed the same predictive ability to diagnose MetS. In women, indicators incorporating WC showed high discriminatory ability: RFM, BRI, and WHtR (all AUC: 0.869, 95% confidence interval [CI]: 0.828–0.910). In men, WC had the highest AUC (0.829, 95% CI: 0.793–0.866).

Conclusions

In both sexes, RFM, WC, BRI, and WHtR were the best predictors of MetS diagnosis. This is the first study to identify RFM as a potentially useful clinical predictor of MetS in a Peruvian sample of educational workers.

Keywords

Metabolic syndrome anthropometric measurements obesity office worker waist circumference body fat percentage cross-sectional study

Introduction

Addressing the increased incidence of metabolic syndrome (MetS) is a substantial public health challenge worldwide owing to the scope and associated mortality of this syndrome.¹ This rise in the prevalence of MetS aligns with the increase in obesity, type 2 diabetes mellitus, and other non-communicable chronic diseases.^2,3 There is no consensus regarding the criteria that define MetS, but there is no major variation in the risk factors recognized by different institutions. Some definitions of MetS include three or more diagnostic factors, such as the National Cholesterol Education Program’s Adult Treatment Panel III.⁴ The factors associated with the development of MetS can be categorized as modifiable (e.g., diet, smoking, physical activity) or non-modifiable (e.g., age, sex).⁵

MetS is a common disease that can negatively affect the health of office workers.⁶ Therefore, MetS may represent a substantial economic burden, in terms of high medical costs and loss of productivity, that affects individuals, families and governments.⁷ A Korean study estimated that direct medical expenses were 41.8% higher among people with MetS from 2009 to 2013 compared with those without MetS, even in cases with fewer than three risk factors.⁸ These figures highlight the need for timely diagnosis of vulnerable people at the primary care level.

Body mass index (BMI) is closely related to body fat percentage, and has therefore been considered a key indicator of MetS and cardiovascular diseases. A rapid increase in BMI raises the risk of developing MetS by five.⁹ Although BMI is a useful epidemiological tool, its limitations¹⁰ have prompted the development of new body fat estimators and, consequently, more accurate predictors of metabolic disturbances.

Several new indices and estimators more sensitive than BMI have been developed, including relative fat mass (RFM), body roundness index (BRI), A Body Shape Index (ABSI), the conicity index (C-Index), waist circumference (WC) and waist-to-height ratio (WHtR). These measures are clinically important owing to their relationship with various metabolic disturbances.^11–15 In Peru, research on these estimators and their relationship with metabolic disturbances is limited. Hernández-Vasquez et al. showed that WHtR and the C-Index were the best predictors of hypertension and type 2 diabetes mellitus.¹⁶ However, Knowles et al. pointed out that WC and WHtR correlated more with cardiovascular risk components of MetS, although no adiposity measure was identified as a predictor of MetS.¹⁵ Aparco and Cárdenas-Quintana recommended using more than one diagnostic criterion for population obesity,¹⁷ highlighting the need for more comprehensive research in this context. Therefore, the objective of this study was to determine the discriminatory ability of several different anthropometric markers for diagnosing MetS in a sample of Peruvian workers from the education sector.

Methods

Study design and participants

A secondary analysis was conducted using a cross-sectional design and convenience sampling. The sample consisted of 978 educational sector workers from a private university in Lima, Peru, who underwent occupational health examinations in 2019. The manuscript follows the STROBE guidelines for observational studies.¹⁸

The inclusion criterion was employees who completed all medical exams on the scheduled date. The exclusion criteria were aged <18 years (N = 3) and records with incomplete data (N = 4). Additionally, we excluded individuals aged >65 years (N = 23) owing to the redistribution of adipose tissue during senescence, which makes this age group metabolically distinct.¹⁹ There were no pregnant women in the sample. After excluding data according to these criteria, the final sample for analysis consisted of 948 records.

Data collection and procedures

Anthropometric measurements and indices

The evaluation included the following anthropometric variables: weight, height, hip circumference, and WC. Weight was recorded in kilograms and measured with a portable digital scale with a capacity of 150 kg (SECA 878 model). Height was measured with a portable stadiometer (SECA 217). WC was measured with a flexible and inextensible tape measure at the waist, at the midpoint between the last rib and the iliac crest. Hip circumference was measured at the most prominent part of the buttocks, aligned with the pubic symphysis. Systolic blood pressure and diastolic blood pressure were measured using an automatic device while participants were seated with their feet resting on the ground.

Six anthropometric indices were constructed from the anthropometric measurements: BMI,²⁰ RFM,²¹ BRI,²² ABSI,²³ WHtR,²⁴ and the C-Index.²⁵

BMI was calculated as the ratio of body mass in kilograms to height squared in meters (kg/m²).

RFM was calculated using the following equation:

RFM = 64 - (20 \times (height / waist circumference (in meters))) + (12 \times sex (0 for men, 1 for women))

BRI was calculated using the following formula:

$BRI = 364.2 - 365.5 \times \sqrt{1 - (\frac{({W C / (2 π))}^{2}}{{(0.5 Height)}^{2}})}$ , and ABSI as: $ABSI = \frac{W C}{{BMI}^{2 / 3} {Height}^{1 / 2}}$ .

To obtain the WHtR, the WC was divided by height. To obtain the C-Index, the formula proposed by Valdez²⁵ was used:

C index = \frac{Waist circumference}{0.109 \sqrt{w eight / height}}

Laboratory measurements

A fasting blood sample was collected from all employees following an 8-hour fasting period. The samples were collected by trained personnel at the educational center and then transported to the same laboratory responsible for sample processing. To determine levels of glucose, triglycerides and total cholesterol, colorimetric analysis of enzymatic reaction methods was used. Direct enzymatic analysis was used to obtain high-density lipoprotein (HDL) cholesterol levels. The results for lipids, lipoproteins, and glucose are reported in mg/dL.

Definitions

The diagnostic criteria for MetS established by the Adult Treatment Panel III were used: a WC >102 cm in men or >88 cm in women, blood pressure >130/85 mmHg, fasting triglyceride levels >150 mg/dL, fasting HDL cholesterol levels <40 mg/dL in men or <50 mg/dL in women, and fasting blood glucose levels >100 mg/dL. MetS was considered present when three or more of the five Adult Treatment Panel III criteria were met.⁴

Data anonymization

The health department shared a de-identified dataset with the university repository; the dataset only included data for employees who gave written informed consent for research purposes. The data provided to the researchers contained no information that could identify the participants.

Statistical analysis

For descriptive analysis, measures of central tendency (e.g., means, medians) with standard deviations and interquartile ranges (IQR) were used for continuous variables, and absolute frequencies with percentages for categorical variables. Stratification by sex was performed for all analyses. After determining the normality of continuous variables using the Kolmogorov–Smirnov test, the Mann–Whitney test was conducted to identify differences between groups (men vs. women). For non-normally distributed data, Spearman’s rank correlation test was used to identify correlations between the anthropometric measurements and MetS components. Receiver operating characteristic analysis and the area under the curve (AUC) with 95% confidence intervals (95% CI) were used to determine the predictive abilities and discriminatory power of the anthropometric indicators of MetS. AUC values were categorized as poor (≥0.5 to <0.6), acceptable (≥0.6 to <0.7), excellent (≥0.7 to <0.8), and outstanding (≥0.8).²⁶ A level of p < 0.05 was assumed to indicate significance. All data were analyzed using IBM SPSS Statistics for Windows, Version 26 (IBM Corp., Armonk, NY, USA).

Ethical aspects

The study did not require ethical committee approval as this was a secondary data analysis in which participants could not be identified by the researchers. However, the study adhered to the ethical principles of the latest Declaration of Helsinki.²⁷ After the institution approved the use of the database, an identification variable was assigned to each participant’s information to ensure the confidentiality and anonymity of the collected data. All data collected for the study were obtained from an annual occupational medical examination and data contained only biochemical and anthropometric information. Participants signed an institutional agreement for their data to be used for research purposes.

Results

Participants’ anthropometric and biochemical characteristics by sex are shown in Table 1. Of participants in the final sample, 51% were men. The median age was 38 years for women (IQR 30–45 years) and 40 years for men (IQR 32–49 years) (p < 0.001). There were statistically significant sex differences in all anthropometric measurements (all p < 0.001). Men showed a higher median WHtR (0.55) than women (0.52). Men had significantly higher values of systolic and diastolic blood pressure (both p < 0.001), total cholesterol (p = 0.01), fasting glucose (p < 0.001), and triglycerides (p < 0.001) (Table 1). However, women had significantly higher values of HDL cholesterol (p < 0.001).

Table 1.

Sociodemographic, anthropometric and clinical characteristics of the study population (N = 948).

	Women (N = 467)		Men (N = 481)
	Median	IQR	Median	IQR	p value^#
Age	38	30–45	40	32–49	<0.001
Weight (kg)	63.4	55.8–72.7	78	70.8–87	<0.001
Height (m)	1.57*	0.05**	1.68	1.64–1.73	<0.001
WC (cm)	83	76–91	94	87.5–101	<0.001
HC (cm)	100	95–107	102	98–107	<0.001
BMI (kg/m²)	25.85	23.19–28.97	27.58	25.28–30.44	<0.001
RFM	38.02*	4.82**	28.59	25.68–30.59	<0.001
C-Index	1.19	1.15–1.24	1.26	1.22–1.31	<0.001
BRI	3.87	3.02–4.93	4.54	3.78–5.39	<0.001
WHtR	0.52	0.48–0.57	0.55	0.52–0.59	<0.001
ABSI	0.07	0.07–0.07	0.07	0.07–0.08	<0.001
Systolic BP (mmHg)	100	90–110	110	100–120	<0.001
Diastolic BP (mmHg)	60	60–70	70	60–80	<0.001
HDL cholesterol (mg/dL)	52	44–60	42	35–47	<0.001
Total cholesterol (mg/dL)	176	154–200	182	159–208	0.01
Fasting glucose (mg/dL)	85	80–91	89	83–95	<0.001
Triglycerides (mg/dL)	99	74–140	150	99–212	<0.001

IQR, interquartile range; WC, waist circumference; HC, hip circumference; BMI, body mass index; RFM, relative fat mass; C-Index, conicity index; BRI, body roundness index; WHtR, waist-to-height ratio; ABSI, A Body Shape Index; BP, blood pressure; HDL, high-density lipoprotein. *mean, **standard deviation, ^#p value obtained using the Mann–Whitney test.

Analysis of the correlations between anthropometric measurements and MetS components showed that in women, WC showed the highest correlation with all components (all p < 0.01) except HDL, which showed a larger correlation with BMI (−0.350, p < 0.01). ABSI demonstrated very low correlations with all components, and was not significantly correlated with three components. In men, WC was correlated most strongly with systolic blood pressure (0.344, p < 0.01) and diastolic blood pressure (0.378, p < 0.01). RFM, BRI, and WHtR showed the highest correlations with the other components (all p < 0.01). As for women, ABSI showed the lowest correlations across all components. For both sexes, RFM, BRI, and WHtR showed identical correlations with each MetS component (p < 0.01). For example, in men, the correlations between the three anthropometric indicators and systolic blood pressure were all 0.328, the correlations between the three anthropometric indicators and diastolic blood pressure were all 0.359, and so on. The same pattern was observed for women. For the whole sample, WC consistently displayed the highest correlations with all components (p < 0.01) (Table 2).

Table 2.

Spearman’s rank correlation coefficients for anthropometric measures and metabolic syndrome components.

	Systolic BP (mmHg)	Diastolic BP (mmHg)	HDL cholesterol (mg/dL)	Fasting glucose (mg/dL)	Triglycerides (mg/dL)
Women (N = 467)
WC (cm)	0.379	0.358	−0.344	0.339	0.404
BMI (kg/m²)	0.362	0.338	−0.350	0.293	0.383
RFM	0.353	0.348	−0.346	0.338	0.396
C-Index	0.246	0.253	−0.234	0.279	0.299
BRI	0.353	0.348	−0.346	0.338	0.396
WHtR	0.353	0.348	−0.346	0.338	0.396
ABSI	0.064^#	0.088^#	−0.055^#	0.125	0.123
Men (N = 481)
WC (cm)	0.344	0.378	−0.336	0.237	0.380
BMI (kg/m²)	0.337	0.360	−0.343	0.240	0.391
RFM	0.328	0.359	−0.358	0.245	0.426
C-Index	0.231	0.266	−0.211	0.196	0.272
BRI	0.328	0.359	−0.358	0.245	0.426
WHtR	0.328	0.359	−0.358	0.245	0.426
ABSI	0.081^#	0.112	−0.068^#	0.114	0.116
Total (N = 948)
WC (cm)	0.468	0.457	−0.479	0.340	0.484
BMI (kg/m²)	0.400	0.397	−0.397	0.299	0.429
RFM	0.080	−0.016^#	0.160	0.046^#	0.006^#
C-Index	0.359	0.357	−0.389	0.295	0.407
BRI	0.397	0.406	−0.406	0.329	0.463
WHtR	0.397	0.406	−0.406	0.329	0.463
ABSI	0.213	0.210	−0.253	0.188	0.260

WC, waist circumference; BMI, body mass index; RFM, relative fat mass; C-Index, conicity index; BRI, body roundness index; WHtR, waist-to-height ratio; ABSI, A Body Shape Index; BP, blood pressure; HDL, high-density lipoprotein. ^#Not significant (all other coefficients had a p value <0.01). Bold values represent the highest correlation coefficients in each column.

Table 3 presents the AUCs of anthropometric measurements for the diagnosis of MetS. In women, all measurements except for the C-Index and ABSI demonstrated outstanding AUCs, with RFM, BRI, and WHtR showing slight and similar superiority over the others (all AUC: 0.869, 95% CI: 0.828–0.910). In men, BMI (AUC: 0.792, 95% CI: 0.750–0.834) and the C-Index (AUC: 0.760, 95% CI: 0.717–0.804) showed excellent AUCs. WC, RFM, BRI, and WHtR demonstrated outstanding AUCs, with WC showing a slight superiority over the others (AUC: 0.829, 95% CI: 0.793–0.866). For the overall sample, WC, BMI, BRI, and WHtR showed outstanding AUCs, with BRI and WHtR having superior AUCs (both AUC: 0.854, 95% CI: 0.826–0.883). In all stratifications, ABSI consistently showed the lowest AUC.

Table 3.

Area under the curve and confidence intervals of different anthropometric indicators to predict metabolic syndrome (N = 948).

	Women (N = 467)		Men (N = 481)		Total (N = 948)
	AUC	95% CI	AUC	95% CI	AUC	95% CI
WC (cm)	0.866	0.827–0.906	0.829	0.793–0.866	0.845	0.815–0.875
BMI (kg/m²)	0.835	0.784–0.887	0.792	0.750–0.834	0.830	0.797–0.864
RFM	0.869	0.828–0.910	0.826	0.788–0.863	0.648	0.604–0.692
C-Index	0.786	0.732–0.840	0.760	0.717–0.804	0.771	0.734–0.807
BRI	0.869	0.828–0.910	0.826	0.788–0.863	0.854	0.826–0.883
WHtR	0.869	0.828–0.910	0.826	0.788–0.863	0.854	0.826–0.883
ABSI	0.609	0.532–0.687	0.640	0.589–0.692	0.642	0.596–0.688

WC, waist circumference; BMI, body mass index; RFM, relative fat mass; C-Index, conicity index BRI, body roundness index; WHtR, waist-to-height ratio; ABSI, A Body Shape Index; AUC, area under the curve; CI, confidence interval. The bolded values represent the highest AUC in each column.

Discussion

In this study, we evaluated biological and anthropometric markers in 948 Peruvian employees of an educational institution to examine the ability of several anthropometric indices, such as WC, BMI, RFM, and WHtR, to predict MetS. We found that WC, RFM, BRI, and WHtR had the highest predictive ability for the diagnosis of MetS, with WC slightly superior in men. Compared with anthropometric indicators that include WC, BMI showed a lower predictive performance in both sexes, which may be because of its limited ability to differentiate between lean and fat mass.¹⁰ A previous study on a Peruvian adult population found similar results for the predictive ability of WC, BRI, and WHtR for MetS diagnosis. However, that study found that BMI showed strong predictive power in men (AUC: 0.810), though this was lower than the visceral adiposity index (AUC: 0.870).²⁸ Previous research has demonstrated that WC is better than BMI and WHtR in predicting MetS.²⁹ These discrepancies between studies could be attributed to the low concordance between BMI and WC in the Peruvian population.¹⁷

The measurements with the highest predictive ability all incorporate measures of WC. The higher performance of this indicator may be attributed to its association with visceral fat, as intra-abdominal fat compared with peripheral fat is associated with more severe inflammatory effects.³⁰ Thus, WC is considered a more precise indicator of risk than other anthropometric indices. Consistent with this, our results demonstrated a greater correlation between WC and all MetS components in the overall sample, which may explain the slight predictive superiority of anthropometric measurements that incorporate WC.

The anthropometric indicators with higher AUCs in the present findings also demonstrate greater predictive ability for other metabolic diseases. For instance, WHtR has been identified as the best predictor of diabetes³¹ and kidney disease.³² BRI has also shown superior predictive ability for hypertension across various populations.³³ Additionally, RFM may be a better predictor of diabetes, hypertension, and arthritis compared with BMI and WC.³⁴ However, findings vary across countries. To obtain more consistent results, factors such as changes in body composition with age, exposure to diet and harmful habits, and population ethnicity must be taken into account.

To the best of our knowledge, this is the first study to use a Peruvian sample to assess RFM as a predictor of MetS. Our results show that RFM exhibits outstanding predictive ability for MetS in women (AUC: 0.869) and men (AUC: 0.826). A Chinese study found that RFM had high predictive ability for MetS diagnosis, with an AUC of 0.918 in women and 0.844 in men.³⁵ Interestingly, our results show the same correlation and AUCs for RFM, WHtR, and BRI. This pattern has also been observed in a Jordanian population, in which WHtR and BRI showed identical AUCs for predicting MetS in both sexes.³⁶ This could indicate the collinearity of these indicators, which incorporate measures of WC and height.

In the present study, the C-Index and ABSI were the weakest predictors of MetS. This finding is consistent with a study conducted on Iranian adults showing that ABSI was not an adequate predictor of MetS.³⁷ Moreover, ABSI demonstrated a low predictive ability for MetS in another Peruvian sample.²⁸ However, some evidence suggests the contrary.³⁸ Additionally, Wu et al.³⁹ reported that although the C-Index, body adiposity index, and ABSI showed positive associations with MetS, they were not equally effective in detecting MetS in both men and women. Our findings suggest that the C-Index and BRI tend to show higher values in women than in men, suggesting a higher overall metabolic risk for women. This sex disparity emphasizes the importance of considering not only body fat distribution, as seen in RFM, but also additional metabolic risk factors when assessing cardiovascular and metabolic health.

There were some study limitations that should be considered when interpreting these results. We identified a confounding effect bias owing to the inability to measure potential confounding variables, such as medication, physical activity, and lifestyle; this may have biased our results. Future research should address these limitations to provide more comprehensive results for this population. Additionally, studies that include a broad range of age and ethnicity groups are needed to assess the applicability of the RFM formula and its potential for use in public health and clinical settings.

The study also had several strengths. Our sample consisted of over 900 individuals with complete clinical and biochemical measurements obtained using standardized protocols and trained staff. Additionally, the use of seven anthropometric indices for the MetS diagnosis provides a broader perspective on their performance. Incorporating indices that complement BMI in clinical practice could offer early diagnosis and treatment of obesity and associated non-communicable chronic diseases, such as MetS. Therefore, we hope that the present findings encourage a re-evaluation of commonly used anthropometric indices in daily clinical practice in Peru.

In conclusion, our results suggest that RFM, WHtR, and BRI are the most suitable predictors of MetS diagnosis in women, whereas WC is more suitable for men. These findings indicate that anthropometric indices that incorporate WC may be more clinically relevant tools for predicting MetS in Peruvian educational workers.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605241300017 - Supplemental material for Predictive ability of anthropometric indices for risk of developing metabolic syndrome: a cross-sectional study

Supplemental material, sj-pdf-1-imr-10.1177_03000605241300017 for Predictive ability of anthropometric indices for risk of developing metabolic syndrome: a cross-sectional study by José A. Chaquila, Gianella Ramirez-Jeri, Fresia Miranda-Torvisco, Luis Baquerizo-Sedano and Juan Pablo Aparco in Journal of International Medical Research

Footnotes

Acknowledgements

We thank all the participants from the educational institution who gave their consent for the analysis of their data for research purposes.

Author contributions

JAC and JPA designed the study. JAC performed the statistical analysis. JAC, GRJ, FMT, JPA, and LBS interpreted the results and wrote the manuscript. JAC, GRJ, and FMT prepared the final manuscript for submission.

Declaration of conflicting interest

The authors declare that there is no conflict of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

ORCID iDs

José A. Chaquila

Gianella Ramirez-Jeri

Fresia Miranda-Torvisco

Luis Baquerizo-Sedano

Juan Pablo Aparco

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