A Novel Metabolic Risk Classification Incorporating Body Fat, Waist Circumference, and Muscle Strength in Young Mexican Adults: A Cross-Sectional Study

doi:10.21203/rs.3.rs-4993530/v1

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A Novel Metabolic Risk Classification Incorporating Body Fat, Waist Circumference, and Muscle Strength in Young Mexican Adults: A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-4993530/v1

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Background: The rising prevalence of metabolic diseases requires new risk assessment tools beyond BMI and waist circumference (WC). A phenotyping system combining body fat percentage (%BF) and WC has been proposed to better stratify cardiometabolic risk, but muscle strength, a key metabolic health predictor, remains underexplored. This study introduces a novel risk classification system integrating %BF, WC, and grip strength (GS) to enhance risk identification in young adults in Mexico.

Methods:In this cross-sectional study, 300 young adults (18-22 years) from Mexico City were assessed for %BF, WC, and GS using standardized protocols. Participants were classified into risk categories using both traditional and new criteria, including the novel phenotyping system.

Results:The existing %BF-WC system categorized 29.8% of males and 22.2% of females as "no risk." Slightly increased risk included 19% of males and 25% of females, while increased risk comprised 34.5% of males and 24.5% of females. High and very high risks included 16.6% of males and 28.2% of females. The new system, including GS, reduced "no risk" to 15.5% of males and 11.6% of females and increased those at higher risk (70.2% of males, 69% of females). The system also identified a protective condition group. Significant reclassification was observed (p < 0.001).

Conclusions: Integrating muscle strength into metabolic risk assessment alongside %BF and WC provides a more refined understanding of metabolic health, effectively identifying at-risk individuals who may be overlooked by existing methods.

Health sciences/Risk factors

Health sciences/Health care/Disease prevention/Preventive medicine

Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity

The increasing prevalence of metabolic diseases, such as type 2 diabetes mellitus and cardiovascular disorders, highlights the urgent need for more comprehensive and precise risk assessment methodologies (ref. 1). Traditionally, assessments have predominantly relied on body mass index (BMI) and waist circumference (WC), which are widely used due to their simplicity and correlation with metabolic risk factors (ref. 2). However, these conventional indicators often fail to capture the nuanced complexity of metabolic phenotypes, particularly in populations with distinctive characteristics, such as young Mexican adults, who may present unique risk factors (ref. 3–5).

Recent studies have proposed a novel phenotyping system for people with obesity that combines body fat percentage (%BF) and WC to create a matrix of nine phenotypes, classified into five cardiometabolic risk categories ranging from "no risk" to "very high risk" (ref. 6). This system addresses the limitations of BMI by providing a more accurate assessment of both adiposity and its distribution, which are critical in predicting metabolic complications (ref. 7). The utility of this system has been validated in large cohorts, demonstrating its ability to reclassify a significant portion of individuals into higher risk categories that were previously undetected by existing methods, thus offering a more precise tool for clinical practice and research (ref. 8).

In recent years, muscle strength has gained attention as a significant factor in metabolic regulation and the prevention of sarcopenia—a condition marked by the loss of muscle mass and function (ref. 9, 10). Sarcopenia is traditionally associated with aging, but it is increasingly recognized as relevant in younger populations, where reduced muscle strength is linked to heightened risks of metabolic diseases and increased mortality (ref. 11, 12). Despite this growing recognition, the potential of muscle strength as an integral part of metabolic risk assessment remains underexplored, particularly in younger demographics.

Incorporating muscle strength into metabolic risk assessments, in addition to %BF and WC, offers the potential for a more comprehensive understanding of metabolic phenotypes (ref. 13). Reduced muscle strength, even in the presence of normal %BF and WC, may indicate a higher metabolic risk, which is often overlooked by current methods (ref. 14). These findings underscore the value of a combined assessment of %BF, WC, and muscle strength to more accurately identify at-risk individuals and guide the development of targeted, effective interventions (ref. 15).

To address these challenges, the present study aims to establish a novel phenotyping approach that integrates %BF, WC, and grip strength (GS) in a sample of young Mexican adults. This multidimensional strategy is designed to uncover metabolic risk phenotypes that may remain undetected by traditional methods, thereby providing a more refined tool for the prevention and management of metabolic conditions. By addressing this critical gap in current risk assessment strategies, particularly in populations with unique demographic and epidemiological profiles, this study seeks to contribute meaningfully to the advancement of more accurate and individualized metabolic health assessments.

Subjects:

Participants were young adults aged 18-22 years, recruited from the metropolitan area of Mexico City. The study population comprised students enrolled in the Medicine program at the Faculty of Higher Studies Iztacala (FESI), National Autonomous University of Mexico (UNAM). A non-random sampling method was employed to achieve broad representation within the student population. Recruitment was conducted via in-class announcements and extracurricular activities, resulting in a participation rate of approximately 90%. Data collection occurred between April and May 2024.

Study Design:

This cross-sectional study received approval from the Ethics Committee of FESI, UNAM (CE/FESI/032024/1698). All participants provided written informed consent prior to participation. To ensure confidentiality, all data were anonymized and securely stored. Each participant underwent a comprehensive medical history review, and a detailed physical examination to assess metabolic risk factors.

Anthropometric Measurements:

Height was measured using a SECA mechanical stadiometer (SECA GmbH & Co. KG, Hamburg, Germany). Body weight and composition were assessed using a multifrequency bioelectrical impedance analysis (BIA) scale (InBody 120, Biospace Co., Seoul, South Korea), with participants measured while barefoot and minimally clothed. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Waist circumference (WC) was measured at the highest point of the iliac crest using a SECA ergonomic measuring tape (SECA GmbH & Co. KG, Hamburg, Germany) with a precision of 0.1 cm. All measurements were conducted by trained personnel following standardized protocols to ensure consistency.

Grip Strength Measurement

Grip strength (GS) was evaluated using a Jamar digital hand dynamometer (JLW Instruments, Chicago, IL, USA). Each participant performed three maximum grip attempts with both hands, with the arm fully extended at the side while standing. The highest value from each hand was averaged and used for subsequent analysis (16).

Metabolic Risk Scoring System:

A metabolic risk scoring system was developed by integrating body fat percentage (%BF), waist circumference (WC), and grip strength (GS). WC cut-off points were based on WHO guidelines: 80 cm and 88 cm for females, and 94 cm and 102 cm for males. %BF cut-offs were set at 30.0% and 35.0% for females, and 20.0% and 25.0% for males.

Grip strength thresholds were derived from Dodds et al. (ref. 17), categorizing weak strength below the 10th percentile and strong strength above the 90th percentile.

Participants were classified into four phenotypic risk groups based on their scores for %BF, WC, and GS:

%BF: <30% (males) or <20% (females) = 1 point, ≥30% <35% (males) or ≥20% <25% (females) = 2 points, ≥35% (males) or ≥25% (females) = 3 points.
WC (cm): <80 cm (females) or <94 cm (males) = 1 point, ≥80 cm <88 cm (females) or ≥94 cm <102 cm (males) = 2 points, ≥88 cm (females) or ≥102 cm (males) = 3 points.
GS (kg): >52 kg (males) or >36 kg (females) = 1 point, ≥30 kg <52 kg (males) or ≥21 kg <36 kg (females) = 2 points, <30 kg (males) or <21 kg (females) = 3 points.

The total metabolic risk score was calculated by summing the scores for %BF, WC, and GS. Individuals were classified into four risk categories: protective state (score of 3), no risk (score of 4), increased risk (score of 5-7), and high risk (score of 8-9).

Statistical Analyses

Statistical analyses were conducted using SPSS version 27 (IBM Corp., Armonk, NY, USA), and GraphPad Prism 8 (GraphPad Software, La Jolla, CA, USA). Data are presented as mean ± standard deviation (SD). Group differences were evaluated using unpaired Student's t-tests for normally distributed variables, and the Kruskal-Wallis test for non-normally distributed variables, followed by Tukey's post hoc test where applicable. Relationships between variables were assessed using Spearman's rank correlation. The Chi-square test and the Chi-square test for trend were used to compare categorical variables and assess associations between ordinal variables. A p-value of < 0.05 was considered statistically significant.

Anthropometric Characteristics

The study sample included 300 participants with an average age of 20.4 ± 1.6 years, 72% of whom were female. Detailed anthropometric data are provided in Table 1. Participants were categorized based on BMI: 3.7% were underweight, 55% had normal weight, 28.7% were people with overweight, and 12.7% were people with obesity. There were no significant differences in age or BMI between male and female participants (p = 0.112 and p = 0.052, respectively). The prevalence of people with overweight and obesity was higher in men (51.2%) than in women (37.5%). WC measurements indicated that 69.9% of females and 82.1% of males had WC values within the normal range per WHO guidelines. However, 50% of participants exceeded the threshold for high body fat percentage (> 35% for females and > 30% for males).

Distribution of Participants by %BF and WC

The distribution of the 300 participants according to the combination of %BF and WC, segregated by sex, is shown in Fig. 1A. The number of subjects in the nine groups is detailed in Fig. 1B. Among females, 48 (22%) were classified in the green group (absence of risk), 54 (25%) in the yellow group (slightly increased risk), 26 (12%) in the orange group (increased risk), 6 (3%) in the dark orange group (high risk), and 34 (16%) in the red group (very high risk). Among males, 25 (30%) were classified in the green group, 16 (19%) in the yellow group, 28 (33%) in the orange group, 1 (1%) in the dark orange group, and 7 (8%) in the red group.

Grip Strength and Correlations with Anthropometric Measures

GS averaged 26.28 ± 8.78 kg across the sample, with males demonstrating significantly greater strength (36.52 ± 8.61 kg) compared to females (22.29 ± 7.54 kg). According to Dodds et al. (2014), 47.3% of participants were categorized as weak, with no significant sex-based differences (p = 0.525). Spearman’s correlation analysis revealed a strong negative correlation between body fat percentage and muscle percentage in both, females (r= -0.97, p < 0.001) and males (r= -0.99, p < 0.001) (Figure S1). No significant correlation was observed between muscle percentage and GS in females (r= -0.02, p = 0.76), while a weak yet significant positive correlation was found in males (r = 0.30, p < 0.001) (Figure S2A, B). The correlation between body fat percentage and GS was positive but non-significant in females (r = 0.10, p = 0.12) and approached significance in a negative direction in males (r= -0.19, p = 0.06) (Figure S2C, D). A significant but weak positive correlation was observed between WC and GS in females (r = 0.30, p < 0.01), but no significant correlation was found in males (r= -0.01, p = 0.98) (Figure S2E, F)

Grip Strength Variation Across Metabolic Risk Categories

Participants were stratified into metabolic risk categories based on %BF, WC, and GS (Fig. 2). This stratification utilized the color-coded risk phenotype classification, which now includes muscle strength as a critical factor. Table 2 provides a detailed breakdown of participants' characteristics across the metabolic risk groups. Among females, significant differences in GS were observed across the risk categories. Specifically, the very high-risk group exhibited significantly greater GS compared to the no risk, slightly increased risk, and increased risk groups (p-values < 0.01). This indicates heterogeneity in muscle strength within these categories.

In contrast, no significant differences in GS were observed among males across the risk categories (p > 0.67), indicating a more uniform distribution of muscle strength across different risk phenotypes. However, within each risk category, there was notable variability in GS, with some individuals falling into the normal strength range while others were classified as weak. To further explore this, a test of homogeneity was conducted to evaluate whether the distribution of muscle strength (weak, normal, strong) within each risk category and gender was homogeneous. The results showed that the differences in muscle strength distribution were highly significant across all gender and risk combinations (p-value < 0.0001), indicating that these groups are not homogeneous in terms of muscle strength. This finding suggests that measuring muscle strength is essential, as its variability within each risk category may provide crucial information for more precise stratification and clinical management of patients. Thus, muscle strength emerges as a mandatory parameter for a more accurate and personalized risk assessment.

Development and Implementation of a New Metabolic Risk Classification

Given the observed variability in muscle strength across the metabolic risk categories, we developed a new classification system that places muscle strength at its core (Fig. 3). This system is simplified into a traffic light scoring model with four distinct categories: protective condition, no risk, increased risk, and high risk. Participants are assigned individual scores based on body fat percentage (BF), waist circumference (WC), and grip strength (GS). The total metabolic risk score is obtained by summing these individual scores, with higher totals indicating greater metabolic risk. The "protective condition," represented in purple, captures individuals with a unique profile of low body fat but high muscle strength. This scoring system provides a clear and intuitive method to classify metabolic risk, with each category distinctly represented by a color for easier interpretation.

Comparison of Phenotyping Systems for People with Obesity Incorporating Muscle Strength

Figure 4 compares the established phenotyping system for people with obesity based on %BF and WC, as described by Gomez-Ambrosi et al. (2023), with the updated system that incorporates GS. The %BF-WC system identified 29.8% of males and 22.2% of females as having "no risk," while 15.7% of females and 8.3% of males were categorized as "very high risk." By incorporating GS into this phenotyping system, a significant redistribution across risk categories was observed. The proportion of participants classified as "no risk" decreased to 15.5% for males and 11.6% for females, demonstrating the added value of GS in risk assessment. The "increased risk" category expanded to include 69.0% of females and 70.2% of males, emphasizing the importance of muscle strength in refining risk classification. Additionally, a "protective condition" group emerged, comprising 2.4% of males, which was not identified by the original %BF-WC system. This redistribution highlights the critical role of muscle strength in metabolic risk assessment, providing a more comprehensive understanding of metabolic health, and enabling the identification of individuals who might otherwise be overlooked.

This study introduces a novel metabolic risk classification system that integrates body fat percentage (%BF), waist circumference (WC), and muscle strength (grip strength, GS), to provide a more comprehensive assessment of metabolic risk in young Mexican adults. Our findings underscore the importance of incorporating multiple dimensions of body composition into metabolic risk assessments, with muscle strength emerging as a critical yet often overlooked factor .

A key finding of this study is the substantial reclassification of participants when muscle strength is included in the risk assessment. Previous phenotyping systems, such as the one proposed by Gomez-Ambrosi et al. (2023), rely solely on %BF and WC, categorizing a larger proportion of individuals as "no risk” (ref. 6). However, our classification system reduces the proportion in the "no risk" category and increases those classified as increased risk. This shift suggests that conventional systems may underestimate metabolic risk, particularly in individuals with lower muscle strength, thereby missing opportunities for early intervention.

The identification of muscle strength as a critical factor in our new metabolic risk classification aligns with growing evidence that handgrip strength (HGS) serves as a reliable biomarker of overall health (ref. 13, 18–20). Just as HGS has been shown to predict various health outcomes, including cardiovascular disease and mortality, our findings suggest that integrating muscle strength into metabolic risk assessments offers a more comprehensive understanding of metabolic health (ref. 21, 22). This is particularly relevant in young adults, where traditional assessments may underestimate risk. By focusing on muscle strength, we can identify individuals at risk who might otherwise be overlooked, emphasizing the importance of early interventions aimed at enhancing muscle strength as a preventive measure against metabolic diseases (ref. 23).

This study also identifies a "protective condition" group—characterized by high muscle strength, normal %BF, and WC introducing a crucial perspective in metabolic risk assessment. The lean and strong phenotype, theoretically offering protection against cardiometabolic diseases, was notably rare in our sample, with no females and only 2.4% of males (two individuals) meeting these criteria. This rarity challenges the assumption that leanness automatically equates to health and highlights the potential risks even among those with normal %BF and WC if muscle strength is inadequate.

The absence of this protective phenotype in most participants underscores a critical public health issue: the need to move beyond conventional metrics of health that focus solely on body fat and waist circumference. Instead, our findings suggest that muscle strength should be emphasized in health interventions, especially in young adults. Strength training, which enhances insulin sensitivity and promotes the secretion of health-protective myokines like exerkines during exercise, should be prioritized in public health strategies (ref. 24–26).

This novel classification system not only exposes hidden risks among those traditionally considered low risk, but also encourages a more nuanced approach to metabolic health. By including muscle strength as a key factor, we move towards a more comprehensive understanding of metabolic well-being, promoting interventions that foster both leanness and strength as a holistic approach to preventing cardiometabolic diseases.

Despite these strengths, several limitations must be acknowledged. The cross-sectional design of our study limits the ability to establish causality or determine the directionality of the observed relationships. Future longitudinal studies are needed to determine whether low muscle strength precedes metabolic risk or is a consequence of underlying metabolic dysfunctions.

Moreover, the use of bioelectrical impedance analysis (BIA) for body composition measurement, while practical, may have limitations in accuracy, especially in individuals with extreme body compositions. Future research could benefit from using more precise methods, such as dual-energy X-ray absorptiometry (DXA), to validate these findings.

Nevertheless, this study offers a well-characterized sample and employs standardized protocols for anthropometric measurements and GS assessment. The integration of %BF, WC, and GS into a single classification system provides a more nuanced understanding of metabolic risk in young adults a group often overlooked in metabolic risk assessments.

Looking forward, there is a need to validate this new classification system in diverse populations, including different age groups, ethnicities, and physical activity levels. Understanding how this system performs across various contexts will be crucial for its broader adoption. Additionally, future research should explore the longitudinal impact of muscle strength on metabolic health outcomes. Investigating whether interventions to increase muscle strength can reduce metabolic risk and improve long-term health outcomes will be essential for establishing muscle strength as a central component of metabolic risk assessment and management.

This study presents a novel metabolic risk classification system that integrates body fat percentage, waist circumference, and muscle strength, offering a more accurate assessment of metabolic risk in young Mexican adults. Muscle strength proved to be a key factor in refining risk categories, complementing traditional metrics. Although tested in a specific population, this system holds potential for broader validation and application.

CALL FOR ACTION

We encourage further research to validate this classification system in different populations and settings. Incorporating muscle strength into routine risk assessments could enhance the identification of at-risk individuals and support the development of more effective prevention strategies for metabolic diseases.

Funding statement declaration.

This work was supported by grants from Consejo Mexiquense de Ciencia y Tecnología (COMECYT) FICDTEM-2023-131 to HISC.

Conflict of interest:

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

ACKNOWLEDGEMENTS

We would like to extend our gratitude to Dr. Adolfo René Méndez Cruz, Head of the Medical Surgeon Program, and Dr. Rafael Villalobos Molina for their invaluable support in the successful completion of this project.

AUTHOR CONTRIBUTIONS STATEMENT: CRRM collected data, created the database, and reviewed the manuscript. MRDD and AECR assisted in data collection and reviewed the manuscript. HISC conceived and designed the study, conducted data analysis, and wrote the manuscript. HISC had primary responsibility for the final content.

FUNDING

This work was supported by the Consejo Mexiquense de Ciencia y Tecnología (COMECYT) through the grant FICDTEM-2023-131 to HISC.

ETHICAL APPROVAL

This study was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Faculty of Higher Studies Iztacala, National Autonomous University of Mexico (approval number: CE/FESI/032024/1698). All participants provided written informed consent prior to participation in the study.
COMPETING INTERESTS

The authors declare no competing interests

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Tables 1 to 2 are available in the Supplementary Files section

There is NO conflict of interest to disclose.

Table1.xlsx
Table 1. Characteristics of Participants Stratified by Gender. Significant differences between genders were observed in weight, height, body mass index, body fat percentage, body fat mass, muscle mass percentage, muscle mass, visceral fat, waist circumference, hip circumference, and strength (p values <0.05). The data highlight the distinct physiological differences between male and female participants. Statistical significance was determined using the Student's t-test for continuous variables, with a p value <0.05 considered significant.
Table2.xlsx
Table 2. Characteristics of Participants Stratified by Risk Categories. Significant differences across risk categories were observed in weight, body mass index (BMI), body fat percentage, body fat mass, muscle mass percentage, muscle mass, visceral fat, waist circumference, hip circumference, and grip strength (p values <0.05). The data emphasize the physiological differences associated with varying risk levels. Statistical significance was determined using ANOVA for normally distributed variables and the Kruskal-Wallis test for non-normally distributed variables, with a p value <0.05 considered significant.
FIGURES2.pdf
Figure S2: Correlation of GS with Body Composition and WC. Scatter plots show the relationship between grip strength and body muscle percentage (A, B), body fat percentage (C, D), and waist circumference (E, F) in female (A, C, E) and male (B, D, F) participants. Each point represents an individual participant, color-coded by grip strength classification (green for strong, yellow for normal, red for weak). The correlation coefficient (r) and p-value (p) for each relationship are indicated on the plots. In females, no significant correlations were found between grip strength and body muscle percentage (A) or body fat percentage (C), but a significant positive correlation was observed with waist circumference (E). In males, a significant positive correlation was found between grip strength and body muscle percentage (B), while no significant correlations were found with body fat percentage (D) or waist circumference (F). Horizontal dashed lines indicate the thresholds for weak, normal, and strong grip strength categories, while vertical dashed lines denote thresholds for body composition and waist circumference categories.

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A Novel Metabolic Risk Classification Incorporating Body Fat, Waist Circumference, and Muscle Strength in Young Mexican Adults: A Cross-Sectional Study

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Abstract

Figures

INTRODUCTION

MATERIALS/SUBJECTS/AND METHODS

RESULTS

Anthropometric Characteristics

Distribution of Participants by %BF and WC

Grip Strength and Correlations with Anthropometric Measures

Grip Strength Variation Across Metabolic Risk Categories

Development and Implementation of a New Metabolic Risk Classification

Comparison of Phenotyping Systems for People with Obesity Incorporating Muscle Strength

Discussion

CONCLUSION

Declarations

References

Tables

Additional Declarations

Supplementary Files

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