https://orcid.org/0000-0002-8434-8383
Figures
Abstract
Background
Sarcopenic obesity is characterized by a combination of obesity and sarcopenia. Body round index (BRI) is a novel anthropometric index that can more accurately assess body and visceral fat levels than body mass index or waist circumference. This study used data from the National Health and Nutrition Examination Survey (NHANES) to explore the relationship between BRI and handgrip strength (HGS) and muscle quality index (MQI) in American adults aged 20 and over.
Methods
This study used cross-sectional data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) with complete data on BRI, HGS, and MQI. We used multivariate linear regression models and smooth curve fitting methods to explore the relationship between BRI and HGS and MQI. In addition, subgroup analyses and interaction tests were performed to further analyze the potential association between these variables.
Results
A total of 5466 participants were finally included in this study, of whom 2807 were males and 2659 were females. The results showed that BRI was positively correlated with HGS and negatively correlated with MQI. In the fully adjusted model, the negative correlation between BRI and MQI was (β= −0.08, 95% CI = −0.08, −0.07), while the positive correlation with HGS was (β= 0.3 8, 95% CI = 0.29, 0.46), indicating that for every unit increase in BRI, MQI decreases by 0.08 units and HGS increases by 0.38 units (P < 0.0001). In addition, the relationship between BRI and HGS is an L-shaped curve. An inflection point is determined when BRI reaches 3.42. Before this threshold, for every unit increase in BRI, HGS increases significantly (β = 2.19, 95% CI = 1.66, 2.72).
Conclusion
The results showed that BRI was positively correlated with HGS and negatively correlated with MQI, meaning that higher BRI was associated with higher HGS and lower MQI. This highlights the importance of body fat distribution in muscle health and suggests that BRI can be used as an effective anthropometric indicator to predict grip strength and muscle mass.
Citation: Wei Z, Yu T, Jin X, Ma G, Meng X (2025) The association between body roundness index and handgrip strength and muscle quality index: A cross-sectional study. PLoS One 20(5): e0322928. https://doi.org/10.1371/journal.pone.0322928
Editor: Amirmohammad Khalaji, Tehran University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN
Received: December 20, 2024; Accepted: March 31, 2025; Published: May 20, 2025
Copyright: © 2025 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data supporting this study are publicly available in the National Health and Nutrition Examination Survey (NHANES) database. All datasets can be accessed through the NHANES portal: https://wwwn.cdc.gov/nchs/nhanes/. Specific datasets and their access URLs are listed below: Demographics Data 2011-2012: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Demographics&CycleBeginYear=2011 2013-2014: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Demographics&CycleBeginYear=2013 Body Measures Data 2011-2012: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Examination&CycleBeginYear=2011 2013-2014: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Examination&CycleBeginYear=2013 Dual-Energy X-ray Absorptiometry (DXA) Data 2011-2012: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Examination&CycleBeginYear=2011 2013-2014: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Examination&CycleBeginYear=2013 Muscle Strength Grip Test Data 2011-2012: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Examination&CycleBeginYear=2011 2013-2014: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Examination&CycleBeginYear=2013 Diabetes Questionnaire Data 2011-2012: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Questionnaire&CycleBeginYear=2011 2013-2014: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Questionnaire&CycleBeginYear=2013
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations:: BRI, Body round index; HGS, Handgrip strength; MQI, Muscle quality index; BMI, Body mass index; ASM, Attached skeletal muscle; PIR, Poverty income ratio; ANOVA, Analysis of variance; VEGFA, Vascular endothelial growth factor A; AMPK, Activated protein kinase; SII, Immune inflammatory index.
Introduction
Sarcopenia is a disease of loss of skeletal muscle mass and muscle function commonly seen in older adults [1]. However, recent studies have found that sarcopenia has begun to appear and develop in younger people as well, due to metabolic syndrome, physical inactivity, malnutrition, neuromuscular disorders, and other inflammatory diseases [2]. The Puerto Rican population is the second largest Hispanic subgroup in the United States, with poorer health conditions and higher rates of acute and chronic diseases compared to non-Hispanic whites and other Hispanic subgroups. National data shows that 21% of elderly Puerto Ricans experience restricted mobility. Among young people, the obesity rate of Puerto Rican men was 43% and that of women was 60.5%, higher than that of Mexican Americans of the same age reported by the National Health and Nutrition Examination Survey (NHANES), with the prevalence rate of male obesity of 36.3% and female obesity of 52.2% [3].Exacerbation of sarcopenia not only increases the risk of falls and fractures [4,5], but can also lead to diseases of the medical system and even death [6–8]. This condition is not only a serious health detriment, but also carries a significant personal and socioeconomic burden; among admitted older adults, hospitalization costs for patients with sarcopenia are more than five times higher than for those without sarcopenia [9]. Currently, the treatment of sarcopenia relies on resistance training and aerobic exercise, but no specific drug has been approved for the treatment [10].
Obesity is becoming increasingly prevalent, with the global prevalence of overweight and obesity having doubled since 1980, and about one-third of the world’s population is now classified as overweight or obese [11]. Obesity not only leads to a variety of complications such as diabetes, hypertension, fatty liver and obstructive sleep apnea [12], but is also strongly associated with sarcopenia. Sarcopenic obesity is a condition in which obesity is accompanied by a decrease in lean body mass, defined as the coexistence of obesity with low skeletal muscle mass or function [13]. Traditionally, body mass index (BMI) has been used to define overweight and obesity; however, BMI has low sensitivity and is susceptible to age, gender, and ethnicity, and does not effectively differentiate between muscle and fat mass [14].
The Body Round Index (BRI) is a new geometric index that estimates total and visceral fat by combining height and waist circumference. Compared with traditional BMI, BRI has higher accuracy in predicting body fat and visceral fat percentage [15], providing a new perspective for evaluating body fat percentage and visceral fat ratio. It not only focuses on overall weight, but also more accurately reflects fat distribution patterns [16]. It is now widely used in clinical research and has shown superiority over other human measurement indicators in evaluating various diseases [17–19]. In addition, handgrip strength (HGS) and muscle mass index (MQI) have been widely used to assess the status of sarcopenia [20,21].
To date, no study has determined the relationship between BRI and HGS and MQI. NHANES (National Health and Nutrition Examination Survey) is a nationally representative survey conducted by the Centers for Disease Control and Prevention (CDC) to comprehensively assess the health and nutritional status of the population in the U.S. [22]. The NHANES data, which covers questionnaires, physical examinations, and laboratory tests provides multidimensional health and nutrition indicators and contains information on people of different ages, genders, races, and economic backgrounds, which has high representativeness and application value. Therefore, this study utilized data from the NHANES database with the aim of exploring the association of BRI with HGS and MQI in adults over the age of 20 years.
Materials and Methods
This study utilized data from the NHANES 2011–2014 cycle, with an initial sample size of 19,931 participants. We first excluded 8,602 participants under 20 years of age. Subsequently, we excluded 1,099 participants lacking valid BRI data, 897 participants without HGS measurements, and 3,862 participants missing MQI data. Finally, we excluded participants with missing covariates due to non-response or unknown answers: four participants with undetermined diabetes status and one participant lacking education level information. Ultimately, 5,466 adult participants were included in the analysis. A flowchart illustrating the screening process is presented in Fig 1. The portions of this study involving human participants, human materials, or human data were conducted in accordance with the Declaration of Helsinki and were approved by the NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.
NHANES, National Health and Nutrition Examination Survey. BRI body round index, HGS handgrip strength, MQI muscle quality index.
Exposed variables and outcome variables
BRI is a novel index for assessing the body fat distribution of an individual, which is calculated from anthropometric data such as waist circumference and height [15]. The formula is: .
HGS was measured using a grip strength meter. All participants with no history of hand or wrist surgery within the past 3 months and no hand pain were eligible to take the test. The test was performed in a standing position and participants were asked to hold the dynamometer as tightly as possible. Each hand was tested three times at 60-second intervals, and the maximum measurement for each hand was used as the final grip strength figure [23].
MQI is defined as the ratio (kg/kg) between the maximum grip strength (HGS, in kilograms) and the attached skeletal muscle mass (ASM) of the limbs [24].
Covariate
Potential covariates considered in this study included gender, age, race, education level, household income to poverty ratio (PIR), and diabetes status. Continuous variables were age and PIR; categorical variables included gender (female, male), race (non-Hispanic white, non-Hispanic black, Mexican American, etc.), education level (less than 9th grade, 9th to 11th grade, high school graduation or equivalent, partial college or AA degree, college graduation or higher), and diabetes status (absent/present/threshold). Diabetes status was determined by physician diagnosis, use of insulin or diabetes medications, and HbA1c level (≥6.5%).
Statistical analysis
Continuous variables in this study are expressed as mean ± standard deviation and categorical variables are expressed as frequency (percentage). Between-group comparisons of categorical variables were performed using chi-square tests, and between-group comparisons of continuous variables were performed using analysis of variance (ANOVA) to assess differences in demographic and clinical characteristics between the different BRI groups. Multivariate linear regression models were used to assess the association between BRI and HGS and MQI. Model 1 was an unadjusted model, Model 2 adjusted for age, gender, and race, and Model 3 further adjusted for PIR, education level, and diabetes status based on Model 2. In addition, subgroup analyses were conducted with stratification variables including sex (male/female), age (20–40 years, 40–60 years, > 60 years), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other), PIR tertiles, education level (less than 9th grade, 9th grade through 11th grade, high school graduate or equivalent, partial college or AA degree, college graduation or higher) and diabetes (yes/no/ Borderline, as diagnosed by NHANES physicians). Interaction tests were used to assess the consistency of the relationship between age, gender, race, education level, PIR, and diabetes status subgroups. Multivariate analyses were performed by controlling for confounders and fitting smoothed curves. Analyses were performed using R software and Empower software, with statistical significance defined as a p-value below 0.05.
Results
A total of 5466 participants were finally included in this study, of whom 2807 were males (51.4%) and 2659 were females (48.6%) (Table 1). Participants were divided into three groups based on their BRI levels, with low, medium, and high BRI groups ranging from (1.04–3.83), (3.83–5.56), and (5.56–18.4), respectively. The analysis showed that there were significant differences between the different BRI groups in terms of gender, age, ethnicity, education level, diabetes status, PIR, MQI, and HGS (all p < 0.01). Compared to the low BRI group, the high BRI group had significantly lower mean values for HGS and MQI, and participants were older and had lower PIR (p < 0.01). In addition, the prevalence of diabetes mellitus was 7.3% (n = 400), demonstrating a statistically significant difference in distribution between comparative groups (p < 0.01). Female participants comprised 48.6% (n = 2659) of the population, with similarly significant between-group disparities in gender stratification (p < 0.01).
Table 2 shows that in the fully adjusted model, BRI is positively correlated with HGS (β=0.38, 95% CI = 0.29, 0.46) and negatively correlated with MQI (β=-0.08, 95% CI = -0.08, -0.07).In the model without adjustment for covariates (Model 1), higher BRI was significantly associated with lower HGS (β = -0.37, 95% CI = -0.50, -0.24) and MQI (β = -0.08, 95% CI = -0.08, -0.08). The model adjusted for all covariates (Model 3) showed that the negative correlation between BRI and MQI remained significant (β = -0.08, 95% CI = -0.08, -0.07), whereas the relationship with HGS shifted from a negative to a positive correlation (β = 0.38, 95% CI = 0.29, 0.46) indicating that each one-unit increase in BRI was associated with a decrease of 0.08 units in MQI and an increase of 0.38 units in HGS. units and a 0.38 unit increase in HGS was associated. When further comparing between-group differences across BRI tertiles, it was found that the high BRI group had significantly higher HGS and significantly lower MQI than the low BRI group; specifically, the highest BRI tertile group had a 1.94-unit higher HGS than the lowest group (β = 1.94, 95% CI = 1.45, 2.42), whereas the MQI was 0.38 units lower (β = - 0.38, 95% CI = -0.40, -0.36).
In the subgroup analysis (Tables 3 and 4), variables including gender, age, education level, race, and BRI were assessed. Analyses in Tables 3 and 4 revealed significant interactions between BRI and HGS for age (p interaction = 0.0001), education level (p interaction = 0.0111), and race (p interaction = 0.0018). Similarly, BRI and MQI showed significant interactions regarding diabetes status (p interaction = 0.0084), education level (p interaction = 0.0012), and race (p interaction = 0.0338).Specifically, each 1-unit increase in BRI was associated with a 0.5-unit increase in HGS among participants aged <40 years (β = 0.50, 95% CI: 0.38 to 0.62), whereas a 0.17-unit increase was observed in those aged ≥40 years (β = 0.17, 95% CI: 0.05 to 0.30). In patients with diabetes, MQI decreased by 0.06 units per 1-unit increase in BRI (β = -0.06, 95% CI: -0.07 to -0.05), compared to a decrease of 0.08 units in the non-diabetic population (β = -0.08, 95% CI: -0.08 to -0.08). Education-stratified analyses indicated that BRI had the smallest negative effect on MQI and the largest positive effect on HGS in the highly educated group. Race-stratified results demonstrated that a 1-unit BRI increase had the minimal effect on MQI in Non-Hispanic Black individuals and on HGS in Non-Hispanic White individuals. No significant interactions were observed between BRI and HGS (interaction all p = 0.44) or MQI (interaction all p = 0.98) by gender. For other stratified variables, associations of BRI with HGS and MQI were non-significant (all interaction p > 0.05).
In order to explore the nonlinear relationship between BRI and HGS and MQI more comprehensively, this study was analyzed using smooth curve fitting. Figs 2 and 3 showed that BRI was negatively correlated with MQI, whereas it had an L-shaped curve relationship with HGS, with an inflection point of 3.42 (Table 5). Before the inflection point, BRI was significantly positively correlated with HGS (β = 2.19, 95% CI = 1.66, 2.72), and after the inflection point, the two remained positively correlated, but the increase weakened (β = 0.20, 95% CI = 0.10, 0.30).
The solid red line represents the smooth curve fit between variables and the blue bands represent the 95% confidence interval from the fit. HGS handgrip strength, BRI body round index.
The solid red line represents the smooth curve fit between variables and the blue bands represent the 95% confidence interval from the fit. MQI muscle quality index, BRI body round index.
Discussion
This study examined the relationship of BRI with HGS and MQI in adults over 20 years of age and found that BRI was positively associated with HGS and negatively associated with MQI. Age, education level, and race showed significant interactions for the relationship between BRI and HGS; whereas the relationship between BRI and MQI showed significant interactions for diabetes status, education level, and race. There was a negative correlation between BRI and MQI, and an L-shaped curvilinear relationship with HGS, with slower growth after the inflection point. The results suggest that increased BRI may be associated with lower MQI and higher HGS.
To our knowledge, there are no comparable studies on the direct relationship between BRI and HGS and MQI. Previous studies have focused on the association of obesity with HGS and MQI. In terms of the relationship between muscle strength and obesity, a cross-sectional study in Taiwan found that higher grip strength was associated with a lower risk of diabetes, suggesting the need to focus on muscle health in obese populations [25]. Another cross-sectional study of 304 participants showed that HGS was positively associated with height and BMI, but was not directly affected by weight, which is consistent with the present study showing that BRI was significantly and positively associated with HGS [26]. This result is supported by a large cross-sectional study based on data from the 1970 British Cohort Study (BCS70), showing that higher BMI since childhood is associated with stronger grip strength at age 46 years, suggesting that the anabolic effects of fat may outweigh its catabolic effects, and thus middle age may be a critical time for intervention to prevent sarcopenic obesity [27]. In terms of the relationship between MQI and obesity, a cross-sectional study including 86 severely obese individuals found that MQI was negatively associated with abdominal obesity and markers of the metabolic syndrome, and mediated the association between abdominal obesity and markers of the metabolic syndrome [28]. Another study found that the negative impact of abdominal obesity on health-related quality of life was partially moderated by MQI [29,30]. In recent years, the impact of obesity on sarcopenia has received increasing attention. A cohort study on sarcopenic obesity and mortality noted that sarcopenic obesity is more prevalent in older adults and is significantly associated with all-cause mortality, a risk that is further exacerbated by changes in body composition. The use of low muscle strength as an initial diagnostic approach may help in the early identification of those at risk [31]. Therefore, considering that BRI, calculated by height and waist circumference, reflects individual body fat distribution, especially abdominal fat, avoids the limitation of BMI that makes it difficult to differentiate between fat and muscle due to the influence of body weight, and is a more effective assessment of visceral fat content than BMI alone, making it a valid measure of obesity. Meanwhile, HGS and MQI are important indicators for assessing sarcopenia, and the results of this study suggest that rational management of BRI may help to improve HGS and MQI to a certain extent, thus contributing to the maintenance and improvement of muscle health.
The present study showed a significant positive relationship between BRI and HGS before the inflection point, indicating that moderate amounts of fat contribute to grip strength. A study on fat-free mice found [32] that muscle mass and strength were significantly reduced in fat-free mice, but muscle function was fully restored after restoring to 10% of normal fat mass, and this restoration was fully mediated by leptin, suggesting that leptin plays a key role in the maintenance of muscle mass and strength in adipose tissue. Another animal experiment showed [33] that in leptin receptor-deficient (Leprdb) mice, the number of skeletal muscle capillaries was significantly reduced, indicating that leptin deficiency affects angiogenesis. Perivascular cells expressing platelet-derived growth factor receptor (PDGFR) β in skeletal muscle may act as “nutrient sensors” to increase leptin production under nutrient-sufficient conditions such as a high-fat diet, thereby elevating the mRNA and protein levels of VEGFA, and linking nutritional status to the angiogenic demands of skeletal muscle. angiogenic requirements. Furthermore, several studies have shown that serum leptin deficiency is detrimental to muscle health [34–36]. Thus, the significant increase in HGS prior to the BRI inflection point may be driven by the supportive effects of metabolically active substances secreted by fat on muscle strength, and the body size advantage of moderate amounts of abdominal fat may also enhance grip strength [37]. However, higher leptin concentrations are associated with an increased risk of frailty in older adults [38]. In a longitudinal study of middle-aged women, higher leptin concentrations predicted poorer mobility [39]. In addition, it has been shown that leptin levels are higher in patients with sarcopenic visceral obesity than in patients with sarcopenia alone [40]. Therefore, when BRI increased to the inflection point, the negative effect of fat on muscle strength intensified with further increases in fat and leptin concentrations, causing the increase in HGS to plateau. Our findings demonstrate the clinical utility of BRI as a screening tool for muscle health in public health practice. The identified threshold of 3.42 distinguishes two population subgroups requiring differentiated interventions: individuals with BRI < 3.42 (particularly aging adults and clinically vulnerable groups) may benefit from community programs integrating nutritional support and resistance training to reduce sarcopenia risk, while those with BRI ≥ 3.42 should receive combined aerobic-strength exercise and high-protein diets to concurrently address obesity and muscle preservation. Public health systems could improve sarcopenic obesity detection by incorporating BRI into routine assessments alongside traditional measures like BMI. Future research must validate this threshold across diverse populations and evaluate its clinical workflow integration.
BRI was negatively correlated with MQI, suggesting that the negative impact of obesity on muscle mass may receive multiple potential mechanisms. In obese individuals, hypertrophic adipocytes secrete interleukins (IL-6 and IL-1β), which inhibit the expression of contractile proteins in myotubes, leading to decreased muscle mass. Visceral adipocytes are more likely to induce this effect compared to subcutaneous fat [41]. A review indicated that the proportion of type I fibers was negatively correlated with BMI, while the proportion of type IIX fibers was positively correlated, and the proportion of type IIX fibers was significantly higher in obese individuals than in lean body types, while the proportion of type I fibers was relatively low [42]. Another study showed that obesity interferes with calcium signaling and 5’-adenosine monophosphate-activated protein kinase (AMPK) activity, leading to a shift in muscle fibers from type 1 to type 2 [43]. Although obesity increases the need for muscles to support body weight, thereby boosting absolute strength and power, muscle mass is decreased, which is consistent with the results of the present study. Furthermore, the negative impact of chronic inflammation and fat accumulation on muscle mass cannot be ignored. Clinical evidence suggests that adipose tissue in obese individuals produces chronic low-grade inflammation, which affects the function of other organs and tissues and increases the risk of metabolic disease [44]. Higher levels of inflammatory markers further reduce muscle mass [45]. A cross-sectional study on dietary inflammatory index and muscle mass showed that higher dietary inflammatory index was significantly associated with lower muscle mass and muscle strength and was more pronounced in older adults [46]. Another cross-sectional study in US adults also found that elevated levels of the Systemic Immune Inflammatory Index (SII) were associated with an increased risk of low muscle mass [47]. Rapid accumulation of intramuscular fat may affect muscle mass through metabolic pathways [48]. In a study of stroke patients [49], muscle strength in ambulatory chronic stroke survivors was shown to be strongly associated with muscle mass and intramuscular fat content of the quadriceps. Increasing muscle mass and decreasing intramuscular fat in the quadriceps bilaterally contributed to muscle strength. Similarly, in patients with rheumatoid arthritis, low muscle density is associated with low muscle mass, excessive obesity, decreased strength and increased disability [50]. Thus increased abdominal fat due to elevated BRI is closely associated with decreased MQI and may be caused by a combination of muscle fiber type shifts, chronic inflammation, and fat accumulation. These factors not only influence MQI, but may also explain the suppression of the rate of increase in HGS by obesity after the BRI inflection point.
In subgroup analyses, there was a significant interaction effect of age on the relationship between BRI and HGS. for each unit increase in BRI, the increase in HGS was significantly higher in those under 40 years of age than in those over 40 years of age. The likely reason for this is that muscle in the under 40s is in a rapid developmental phase, when the anabolic effects of fat outweigh the catabolic effects that may lead to muscle loss later in life [27]. In this age group, muscles are more responsive to mechanical stress stimuli, and the increase in BRI not only enhances the mechanical load but also provides adequate nutritional support, and a combination of factors contributes to the significant growth of HGS. In people over 40 years of age, with aging, immune cells in skeletal and cardiac muscle release more pro-inflammatory mediators, leading to disruption of endothelial structure, impaired cellular processes such as mitochondrial activity, mitochondrial autophagy, and a gradual weakening of myocyte contractile properties [51]. Thus, although obesity still provides mechanical stress stimulation to muscles in older people, HGS growth is more limited compared to younger people. In a stratified analysis of diabetic patients, it was found that when BRI increased by one unit, the reduction in MQI was smaller in diabetic patients than in the nondiabetic population. This may be related to fat distribution characteristics. BRI is calculated based on waist circumference and height, and diabetic patients usually show more significant central obesity [52]. Therefore, for the same increase in BRI, diabetic patients may have a lower overall fat gain than the non-diabetic population, resulting in a smaller decrease in their MQI. In addition, a review on obesity and diabetes noted that overweight or obese individuals who are able to increase subcutaneous fat stores, especially in the hip and thigh regions, may have lower health risks than expected in the presence of excess energy intake [53]. BRI showed significant interactions with HGS and MQI with respect to education level and ethnicity. This may be due to the fact that highly educated individuals usually possess greater health awareness and pay more attention to nutrition, exercise, and health management [54]. In addition, the effect of BRI on HGS and MQI varied by race due to differences in physiological and genetic characteristics and socio-cultural background. Two other studies have also found significant differences in the effects of BRI on muscle health among older adults of different races and countries [55,56].
This study has the following strengths. First, the sample data were obtained from NHANES, a database with a large sample size and reliable data that accurately represent the characteristics of the entire national population. Second, we adjusted for confounders and performed subgroup analyses to ensure significant correlations between BRI and HGS and MQI. Finally, this is the first study to address the relationship of BRI with HGS and MQI in a U.S. population. Nonetheless, this study has limitations to acknowledge. First, the cross-sectional design precludes causal inference between BRI and HGS/MQI relationships, necessitating future longitudinal verification. Second, while we adjusted for key demographic and metabolic confounders using NHANES-recommended socioeconomic indices, residual confounding from unmeasured factors persists. Third, physical activity data may be subject to recall bias despite NHANES standardized protocols. Most critically, two database-related constraints require emphasis: (1) restricted generalizability due to excluding institutionalized populations and age truncation (20–59 years), potentially underrepresenting marginalized subgroups and older adults; (2) absence of systematic strength training data – a critical musculoskeletal health determinant that may confound observed associations. Finally, findings are contextually bound to U.S. residents; cross-cultural validation through multinational cohorts remains imperative.
Conclusion
This study found that BRI and HGS showed an L-shaped curve, while it was negatively correlated with MQI. This finding has important implications for understanding the relationship between abdominal obesity and HGS and MQI, suggesting that BRI can be a practical tool for assessing HGS and MQI in clinical settings. Furthermore, maintaining a moderate BRI had a positive impact on HGS (inflection point of 3.42).
Acknowledgments
We would like to thank all participants in this study.
Clinical trial number
Not applicable.
References
- 1. Sayer AA, Cruz-Jentoft A. Sarcopenia definition, diagnosis and treatment: consensus is growing. Age Ageing. 2022;51(10):afac220. pmid:36273495
- 2. Jung HN, Jung CH, Hwang Y-C. Sarcopenia in youth. Metabolism. 2023;144:155557. pmid:37080353
- 3. Tucker KL, Mattei J, Noel SE, Collado BM, Mendez J, Nelson J, et al. The Boston Puerto Rican Health Study, a longitudinal cohort study on health disparities in Puerto Rican adults: challenges and opportunities. BMC Public Health. 2010;10:107. pmid:20193082
- 4. Qi Y-M, Li H-T, Chang S-M, Hu S-J, Du S-C, Liu C-D, et al. Sarcopenia is a risk factor for postoperative delirium in geriatric hip fracture patients: a retrospective study. Front Med (Lausanne). 2025;11:1526240. pmid:39835112
- 5. Kong MTK, Fang C, Yung CSY, Kwok T, Leung K, Leung F. Evaluation of using grip strength and hand muscle cross-sectional area to predict secondary fractures post distal radius fracture. Arch Osteoporos. 2025;20(1):10. pmid:39821704
- 6. Damluji AA, Alfaraidhy M, AlHajri N, et al. Sarcopenia and Cardiovascular Diseases. Circulation. 2023;147(20):1534–53.
- 7. Sepúlveda-Loyola W, Osadnik C, Phu S, Morita AA, Duque G, Probst VS. Diagnosis, prevalence, and clinical impact of sarcopenia in COPD: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2020;11(5):1164–76. pmid:32862514
- 8. De Buyser SL, Petrovic M, Taes YE, Toye KRC, Kaufman J-M, Lapauw B, et al. Validation of the FNIH sarcopenia criteria and SOF frailty index as predictors of long-term mortality in ambulatory older men. Age Ageing. 2016;45(5):602–8. pmid:27126327
- 9. Antunes AC, Araújo DA, Veríssimo MT, Amaral TF. Sarcopenia and hospitalisation costs in older adults: a cross-sectional study. Nutr Diet. 2017;74(1):46–50. pmid:28731551
- 10. Papadopoulou SK. Sarcopenia: A Contemporary Health Problem among Older Adult Populations. Nutrients. 2020;12(5):1293. pmid:32370051
- 11. Wannamethee SG, Atkins JL. Muscle loss and obesity: the health implications of sarcopenia and sarcopenic obesity. Proc Nutr Soc. 2015;74(4):405–12. pmid:25913270
- 12. Perdomo CM, Cohen RV, Sumithran P, Clément K, Frühbeck G. Contemporary medical, device, and surgical therapies for obesity in adults. Lancet. 2023;401(10382):1116–30. pmid:36774932
- 13. Gortan Cappellari G, Guillet C, Poggiogalle E, Ballesteros Pomar MD, Batsis JA, Boirie Y, et al. Sarcopenic obesity research perspectives outlined by the sarcopenic obesity global leadership initiative (SOGLI) - Proceedings from the SOGLI consortium meeting in rome November 2022. Clin Nutr. 2023;42(5):687–99. pmid:36947988
- 14. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10. pmid:30253139
- 15. Zhang X, Liang J, Luo H, Zhang H, Xiang J, Guo L, et al. The association between body roundness index and osteoporosis in American adults: analysis from NHANES dataset. Front Nutr. 2024;11:1461540. pmid:39430785
- 16. 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. Obesity (Silver Spring). 2013;21(11):2264–71. pmid:23519954
- 17. Zhang Y, Gao W, Ren R, Liu Y, Li B, Wang A, et al. Body roundness index is related to the low estimated glomerular filtration rate in Chinese population: A cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1148662. pmid:37056676
- 18. Cai X, Song S, Hu J, Zhu Q, Yang W, Hong J, et al. Body roundness index improves the predictive value of cardiovascular disease risk in hypertensive patients with obstructive sleep apnea: a cohort study. Clin Exp Hypertens. 2023;45(1):2259132. pmid:37805984
- 19. Rico-Martín S, Calderón-García JF, Sánchez-Rey P, Franco-Antonio C, Martínez Alvarez M, Sánchez Muñoz-Torrero JF. Effectiveness of body roundness index in predicting metabolic syndrome: A systematic review and meta-analysis. Obes Rev. 2020;21(7):e13023. pmid:32267621
- 20. Yoo MY, Song HJ, Park KH, Seo Y-G, An H-J, Paek Y-J, et al. Relationship between Eating Alone and Handgrip Strength in Korean Older Adults. Nutrients. 2024;16(5):654. pmid:38474782
- 21. Weng L, Xu Z, Chen Y, Chen C. Associations between the muscle quality index and adult lung functions from NHANES 2011-2012. Front Public Health. 2023;11:1146456. pmid:37234758
- 22.
Centers for Disease Control and Prevention National Center for Health Statistics About the National Health and nutrition examination survey 2023 https://www.cdc.gov/nchs/nhanes/about_nhanes.htm
- 23.
National Center for Health Statistics . National Youth Fitness Survey (NYFS): Muscle strength (grip) procedures manual. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. 2024. (Accessed May 2024).
- 24. Lopes LCC, Vaz-Gonçalves L, Schincaglia RM, Gonzalez MC, Prado CM, de Oliveira EP, et al. Sex and population-specific cutoff values of muscle quality index: Results from NHANES 2011-2014. Clin Nutr. 2022;41(6):1328–34. pmid:35576845
- 25. Chang C-Y, Chu N-F, Lin M-H, Wang S-C, Wu D-M, Tsai M-K, et al. Association between Grip Strength, Obesity, and Cardiometabolic Risk Factors among the Community-Dwelling Elderly Population in Taiwan. Int J Environ Res Public Health. 2022;19(18):11359. pmid:36141648
- 26. Agtuahene MA, Quartey J, Kwakye S. Influence of hand dominance, gender, and body mass index on hand grip strength. S Afr J Physiother. 2023;79(1):1923. pmid:37928652
- 27. Musing. 2011:27–27.
- 28. Caamaño-Navarrete F, Jerez-Mayorga D, Alvarez C, Del-Cuerpo I, Cresp-Barría M, Delgado-Floody P. Muscle Quality Index in Morbidly Obesity Patients Related to Metabolic Syndrome Markers and Cardiorespiratory Fitness. Nutrients. 2023;15(11):2458. pmid:37299421
- 29. Barahona-Fuentes G, Huerta Ojeda Á, Romero GL, Delgado-Floody P, Jerez-Mayorga D, Yeomans-Cabrera M-M, et al. Muscle Quality Index is inversely associated with psychosocial variables among Chilean adolescents. BMC Public Health. 2023;23(1):2104. pmid:37884950
- 30. Delgado-Floody P, Gómez-López M, Caamaño-Navarrete F, Valdés-Badilla P, Jerez-Mayorga D. The Mediating Role of the Muscle Quality Index in the Relation of Screen Time and Abdominal Obesity with Health-Related Quality of Life in Chilean Schoolchildren. Nutrients. 2023;15(3):714. pmid:36771420
- 31. Benz E, Pinel A, Guillet C, Capel F, Pereira B, De Antonio M, et al. Sarcopenia and Sarcopenic Obesity and Mortality Among Older People. JAMA Netw Open. 2024;7(3):e243604. pmid:38526491
- 32. Collins KH, Gui C, Ely EV, Lenz KL, Harris CA, Guilak F, et al. Leptin mediates the regulation of muscle mass and strength by adipose tissue. J Physiol. 2022;600(16):3795–817. pmid:35844058
- 33. Nwadozi E, Ng A, Strömberg A, Liu H-Y, Olsson K, Gustafsson T, et al. Leptin is a physiological regulator of skeletal muscle angiogenesis and is locally produced by PDGFRα and PDGFRβ expressing perivascular cells. Angiogenesis. 2019;22(1):103–15. pmid:30121753
- 34. Lu W, Xiao W, Xie W, Fu X, Pan L, Jin H, et al. The Role of Osteokines in Sarcopenia: Therapeutic Directions and Application Prospects. Front Cell Dev Biol. 2021;9:735374. pmid:34650980
- 35. Kao T-W, Peng T-C, Chen W-L, Chi Y-C, Chen C-L, Yang W-S. Higher Serum Leptin Levels are Associated with a Reduced Risk of Sarcopenia but a Higher Risk of Dynapenia Among Older Adults. J Inflamm Res. 2021;14:5817–25. pmid:34764673
- 36. Sáinz N, Rodríguez A, Catalán V, Becerril S, Ramírez B, Gómez-Ambrosi J, et al. Leptin administration downregulates the increased expression levels of genes related to oxidative stress and inflammation in the skeletal muscle of ob/ob mice. Mediators Inflamm. 2010;2010:784343. pmid:20671928
- 37. Nieczuja-Dwojacka J, Marchewka J, Siniarska A, Budnik A, Popielarz K, Tabak I. Influence of body build on hand grip strength, simple reaction time and strength of the abdominal muscles in prepubertal children. Anthropol Anz. 2023;80(2):151–8. pmid:36752666
- 38. Lana A, Valdés-Bécares A, Buño A, Rodríguez-Artalejo F, Lopez-Garcia E. Serum Leptin Concentration is Associated with Incident Frailty in Older Adults. Aging Dis. 2017;8(2):240–9. pmid:28400989
- 39. Karvonen-Gutierrez CA, Zheng H, Mancuso P, Harlow SD. Higher Leptin and Adiponectin Concentrations Predict Poorer Performance-based Physical Functioning in Midlife Women: the Michigan Study of Women’s Health Across the Nation. J Gerontol A Biol Sci Med Sci. 2016;71(4):508–14. pmid:26302979
- 40. Kohara K, Ochi M, Tabara Y, Nagai T, Igase M, Miki T. Leptin in sarcopenic visceral obesity: possible link between adipocytes and myocytes. PLoS One. 2011;6(9):e24633. pmid:21931785
- 41. Pellegrinelli V, Rouault C, Rodriguez-Cuenca S, Albert V, Edom-Vovard F, Vidal-Puig A, et al. Human Adipocytes Induce Inflammation and Atrophy in Muscle Cells During Obesity. Diabetes. 2015;64(9):3121–34. pmid:25695947
- 42. Damer A, El Meniawy S, McPherson R, Wells G, Harper M-E, Dent R. Association of muscle fiber type with measures of obesity: A systematic review. Obes Rev. 2022;23(7):e13444. pmid:35293095
- 43. Tallis J, James RS, Seebacher F. The effects of obesity on skeletal muscle contractile function. J Exp Biol. 2018;221(Pt 13):jeb163840. pmid:29980597
- 44. Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375–91. pmid:33356944
- 45. Tuttle CSL, Thang LAN, Maier AB. Markers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis. Ageing Res Rev. 2020;64:101185. pmid:32992047
- 46. Chen L, Ming J, Chen T, Hébert JR, Sun P, Zhang L, et al. Association between dietary inflammatory index score and muscle mass and strength in older adults: a study from National Health and Nutrition Examination Survey (NHANES) 1999-2002. Eur J Nutr. 2022;61(8):4077–89. pmid:35809101
- 47. Shi L, Zhang L, Zhang D, Chen Z. Association between systemic immune-inflammation index and low muscle mass in US adults: a cross-sectional study. BMC Public Health. 2023;23(1):1416. pmid:37488531
- 48. Pieńkowska J, Brzeska B, Kaszubowski M, Kozak O, Jankowska A, Szurowska E. MRI assessment of ectopic fat accumulation in pancreas, liver and skeletal muscle in patients with obesity, overweight and normal BMI in correlation with the presence of central obesity and metabolic syndrome. Diabetes Metab Syndr Obes. 2019;12:623–36. pmid:31118724
- 49. Akazawa N, Harada K, Okawa N, Tamura K, Moriyama H. Muscle mass and intramuscular fat of the quadriceps are related to muscle strength in non-ambulatory chronic stroke survivors: A cross-sectional study. PLoS One. 2018;13(8):e0201789. pmid:30071100
- 50. Baker JF, Mostoufi-Moab S, Long J, Zemel B, Ibrahim S, Taratuta E, et al. Intramuscular Fat Accumulation and Associations With Body Composition, Strength, and Physical Functioning in Patients With Rheumatoid Arthritis. Arthritis Care Res (Hoboken). 2018;70(12):1727–34. pmid:29481721
- 51. Livshits G, Kalinkovich A. Inflammaging as a common ground for the development and maintenance of sarcopenia, obesity, cardiomyopathy and dysbiosis. Ageing Res Rev. 2019;56:100980. pmid:31726228
- 52. Zhong P, Tan S, Zhu Z, et al. Normal-weight central obesity and risk of cardiovascular and microvascular events in adults with prediabetes or diabetes. Chinese and British cohorts. Diabetes Metab Res Rev. 2023;39(8):e3707.
- 53. Piché M-E, Tchernof A, Després J-P. Obesity Phenotypes, Diabetes, and Cardiovascular Diseases. Circ Res. 2020;126(11):1477–500. pmid:32437302
- 54. Hamulka J, Wadolowska L, Hoffmann M, Kowalkowska J, Gutkowska K. Effect of an Education Program on Nutrition Knowledge, Attitudes toward Nutrition, Diet Quality, Lifestyle, and Body Composition in Polish Teenagers. The ABC of Healthy Eating Project: Design, Protocol, and Methodology. Nutrients. 2018;10(10):1439. pmid:30720795
- 55. Germain CM, Vasquez E, Batsis JA, McQuoid DR. Sex, race and age differences in muscle strength and limitations in community dwelling older adults: Data from the Health and Retirement Survey (HRS). Arch Gerontol Geriatr. 2016;65:98–103. pmid:27017414
- 56. Tyrovolas S, Koyanagi A, Olaya B, Ayuso-Mateos JL, Miret M, Chatterji S, et al. Factors associated with skeletal muscle mass, sarcopenia, and sarcopenic obesity in older adults: a multi-continent study. J Cachexia Sarcopenia Muscle. 2016;7(3):312–21. pmid:27239412