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The association of body roundness index and body mass index with frailty and all-cause mortality: a study from the population aged 40 and above in the United States

Lipids in Health and Disease volume 24, Article number: 30 (2025) Cite this article

Abstract

Background

The relationship between body roundness index (BRI), a new obesity index, and frailty has not been established. This study aims to compare the associations of traditional obesity index body mass index (BMI) and BRI with frailty and the risk of all-cause mortality.

Methods

The clinical data of 15,157 participants over 40 years old from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018 were analyzed. Based on weighted logistic regression, COX regression and restricted cubic spline, the associations of BRI and BMI with the odds of frailty and the risk of all-cause mortality were estimated. The receiver operating characteristic curve (ROC) and concordance index were used to evaluate the ability of BRI and BMI to predict frailty and survival.

Results

Weighted logistic regression showed that the odds of frailty showed a trend of increasing with the increase of BRI and BMI (P for trend < 0.0001, respectively). After adjusting for all confounding factors, the association between BRI and frailty was stronger (OR: 1.20, 95% CI: 1.13–1.27, P < 0.0001), and the association between BMI and frailty was slightly weaker (OR: 1.14, 95% CI: 1.08–1.21, P < 0.0001). ROC showed that the area under the curve (AUC) of BRI for predicting frailty was 0.628, while the AUC of BMI was 0.603, and the difference between the two was significant (PDeLong < 0.001). In addition, in survival analysis, BRI and BMI showed a significant U-shaped association with the risk of all-cause mortality. Piecewise regression based on the inflection point shows that when BRI < 7.33, an increase in BRI reduces the risk of all-cause mortality (HR: 0.85, 95% CI: 0.78–0.92, P < 0.0001), while when BRI ≥ 7.33, an increase in BRI increases the risk of all-cause mortality (HR: 1.19, 95% CI: 1.03–1.38, P = 0.02); when BMI < 33.57, an increase in BMI reduces the risk of all-cause mortality (HR: 0.84, 95% CI: 0.78–0.91, P < 0.0001), and when BMI ≥ 33.57, an increase in BRI increases the risk of all-cause mortality (HR: 1.18, 95% CI: 1.04–1.34, P = 0.01). Moreover, the time-dependent c-index curve showed that the ability of BRI to predict the risk of all-cause mortality in frail people was comparable to that of BMI.

Conclusion

In the American population over 40 years old, both BRI and BMI are independently and positively associated with frailty. Moreover, BRI has a stronger ability to predict frailty than BMI. In addition, both BRI and BMI have a U-shaped association with the risk of all-cause mortality in frail individuals, and the two have comparable abilities to predict the risk of all-cause mortality.

Introduction

With the advent of the era of population aging, the prevalence of frailty continues to rise. Frailty is characterized by a comprehensive decline in the physiological functions of the body and an increased susceptibility to stressors [1]. Globally, frailty poses a huge challenge to the current medical environment [2]. Individuals with physical frailty have an increased risk of adverse health outcomes, including but not limited to death, disability, hospitalization rates, and dementia [3]. Studies have shown that frailty can be prevented, and strategies to prevent and slow the progression of frailty are crucial [4]. Therefore, in today’s context of aging, early identification of frailty can enable multidisciplinary teams to formulate potential mitigation plans as early as possible and ultimately greatly improve the quality of life of patients.

As of now, the assessment and measurement of frailty are quite complex and require the evaluation of dozens of physical or psychological indicators, which makes the assessment of frailty in the overall population difficult [5]. In addition, although the elderly population is a high-incidence group of frailty, in recent years, studies have shown that frailty also exists in a considerable proportion in young people, especially in economically underdeveloped areas [6]. Not only that, frailty is in a dynamic state and may fluctuate over time, which increases the difficulty and cost of identifying frailty in clinical practice [7].

Obesity and frailty is an important topic. Both of them show a higher incidence with the arrival of the aging era [8, 9]. The research around obesity and frailty is also relatively insufficient and contradictory. In a multicenter prospective cohort study, overweight and obesity reduced the risk of clinical adverse events in frail community - dwelling older women [10]. A longitudinal cohort study from Beijing, China showed that abdominal obesity was more closely related to the incidence of frailty, and waist circumference (WC) might be a better indicator for detecting frailty than body mass index (BMI) [11]. Jayanama et al. [12] found that overweight and obesity defined by BMI were related to a higher degree of frailty; however, grade 1 obesity had a protective effect on the mortality of people with mild frailty. Crow et al. [13] suggested that in the elderly population, frailty and pre - frailty were related to a larger WC. In 2013, Thomas et al. [14] modeled the human body shape as an ellipse, used the eccentricity theory to derive a geometric model of body shape, and constructed the body roundness index (BRI). Compared with BMI, BRI has more advantages in predicting the percentage of body fat and the percentage of visceral fat. Since its proposal, several studies have shown that BRI is superior to BMI in predicting cardiometabolic abnormalities [15], gallstones [16], hyperuricemia and gout [17], non - alcoholic fatty liver disease (NAFLD) [18], kidney stones [19], and urinary incontinence [20]. However, it remains unclear whether BRI is related to frailty and whether this association is stronger than that of BMI.

This study investigated 15,157 people over 40 years old in the United States, aiming to compare the associations of BRI and BMI with the odds of frailty and their prognosis.

Methods

Study participants and sample size

This study initially included NHANES participants from eight cycles from 2003 to 2018. A total of 80,312 participants in eight cycles. In turn, 52,731 participants under 40 years old were excluded, 12,622 participants with less than 80% (< 40 items) of the 49 items of the Frailty Index (FI), participants with unavailable BRI (N = 1029) and BMI (N = 26), and 747 participants with missing covariables. Finally, 15,157 participants were included (Supplementary Fig. 1).

Definition of body roundness index and body mass index

Physical indicators were measured and obtained by professional health technicians from the Mobile Inspection Center (MEC). The height of participants of all ages was recorded, while the standing height was only recorded for participants older than 2 years. The Body roundness index (BRI) is a new body shape assessment index. This index is estimated based on height (centimeter, cm) and waist circumference (WC) (cm), which can assess the content of visceral fat. The higher the value, the more obvious the accumulation of visceral fat [14]. Its calculation formula is as follows:

$$\:\text{B}\text{R}\text{I}=364.2-365.5\text{*}\sqrt{1-\frac{{\left(\begin{array}{c}WC\\\:\stackrel{-}{2\pi\:}\end{array}\right)}^{2}}{{\left(0.5\text{*}Height\right)}^{2}}}$$

Body mass index (BMI) is currently a commonly used standard in the international community to calculate the degree of fatness and thinness of the human body and whether it is healthy. BMI is calculated as the weight in kilograms divided by the square of the height in meters. The weight is in kilograms and the height is in meters.

Diagnosis of frailty

In this study, frailty is defined according to the diagnostic criteria proposed by Hakeem et al. [21, 22]. Specifically, frailty is assessed according to the 49-item Frailty Index (FI); this index reflects 49 items in multiple dimensions and comprehensively considers cognitive level, physical skills, daily activity level, depressive symptoms, physical health status, chronic disease conditions, laboratory test indicators and healthcare status. See Supplementary Table 1 for a detailed assessment scale. Sum up the scores of the above indicators and standardize them to obtain a score range from 0 to 1. According to previous studies, the cut-off value of FI is 0.21. Values greater than or equal to 0.21 are defined as frailty, while values less than 0.21 are non-frailty [23].

Survival outcome

In this study, the survival outcome of interest is all-cause mortality. The NHANES database is matched with the death registration information of the Centers for Disease Control and Prevention through a unique subject identification symbol. Follow-up until death or December 31, 2019, whichever comes first [24]. In this study, causes of death are defined according to the International Classification of Diseases 10th Revision (ICD-10).

Covariables

According to existing publications and clinical practice, this study included demographic factors, lifestyle habits and comorbidities. These factors may affect both the prevalence of frailty and physical indicators [13, 25]. Among demographic factors, sex, age, race/ethnicity, education level, marital status and household economic level in the NHANES database were included. Lifestyle habits mainly considered smoking, drinking, weekly exercise intensity and daily dietary energy. As for chronic comorbidities, arthritis, thyroiditis, liver disease, cancer, chronic kidney disease (CKD), hypertension, diabetes mellitus (DM), cardiovascular disease (CVD) and hyperlipidemia were considered.

Statistical analysis

NHANES uses complex designs to obtain a representative sample of the U.S. population. The sampling plan consists of four stages: counties, segments, households, and individuals. This study included participants from eight cycles, so WTMEC2YR/8 was selected as the weight for analysis. The characteristics of the participants were analyzed. Continuous variables were expressed as mean (standard error, SE) or median (Interquartile Range, IQR), while categorical variables were expressed as sample size (N%). Student’s t-test or Mann-Whitney U test was used to analyze continuous variables; for categorical variables, chi-square (χ2) test was used to compare baseline characteristics between frail and non-frail. This study used gradually adjusted multivariable logistic regression or COX regression models to estimate the association between BRI or BMI and frailty and survival. The four models are: (1) without adjusting for confounding factors; (2) adjusting for demographic factors; (3) adjusting for demographic factors and lifestyle; 3) fully adjusting for demographic factors, lifestyle and comorbidities. Since there is no recognized cut-off value for BRI, and in order to maintain the interpretability of BMI and BRI, BRI and BMI were included in the model as continuous variables or categorical variables based on quartiles [24]. Restricted cubic spline (RCS) is a reliable means to analyze nonlinear associations. This study fitted restricted cubic spline (RCS) with three knots according to the principle of minimizing the Akaike information criterion (AIC). Both logistic regression and COX regression models were diagnosed for collinearity, and the Schoenfeld residual method was also used to test whether the COX regression model satisfies the proportional hazards assumption. In addition, the receiver operating characteristic curve (ROC) was used to compare the ability of BRI and BMI to predict frailty; the time-dependent concordance-index (c-index) curve was used to compare the ability of BRI and BMI to predict the risk of all-cause mortality in frail survivors. When fitting the time-dependent c-index curve, bootstrap resampling was performed 1000 times for cross-validation. In addition, subgroup analysis and likelihood ratio test were also performed to find potentially vulnerable populations. Finally, sensitivity analysis was conducted. In sensitivity analysis 1, multiple imputation was performed for the missing values. In sensitivity analysis 2, individuals with BRI or BMI values outside the 5-95% percentile range were excluded to eliminate the impact of extreme values on the results.

All statistical processes were implemented using R language version 4.40. For data cleaning, packages such as “nhanesR”, “tidyverse”, “rio”, and “data.table” were mainly used; for fitting regression models, packages such as “survival”, “survey”, and “rms” were used; for comparing model differences, packages such as “multipleROC” and “pec” were used; for collinearity diagnosis and proportional hazards assumption test, packages such as “car” and “survival” were used. For multiple imputation, “mice” was used. In this study, a two-tailed P value < 0.05 was defined as statistically significant.

Results

Participant characteristics in cross-sectional studies

As shown in Supplementary Fig. 1, a total of 15,157 eligible participants were included in this study. Table 1 presents the characteristics of the participants. The average age of the participants is 64.51 years old, and there are 7,669 women (54.12%). Among the 15,157 participants, 5,301 have frailty. Compared with the lowest quartile group of the Frailty Index (FI), those in the highest quartile group of FI had higher BRI and BMI, were slightly younger, had a higher proportion of females, a lower educational level, a higher proportion of divorced or widowed individuals, and significantly worse economic conditions. The highest quartile group of FI had a larger proportion of people who had ever consumed alcohol and were currently smoking, and had lower dietary energy. In addition, the highest quartile group of FI had a higher prevalence of comorbidities such as hyperlipidemia, liver diseases, arthritis, CKD, thyroid problems, cancer, diabetes, CVD, and hypertension.

Table 1 Weighted participant characteristics based on the quartiles of Frailty Index in cross-sectional study
Full size table

Baseline characteristics in cross-sectional studies

In the survival analysis, the follow-up was terminated on December 31, 2019. During an average follow-up period of 6.67 years, 5 frail participants were lost to follow-up, and finally 5,296 participants were included. Supplementary Table 2 describes the characteristics of 5,296 frail participants. Compared with survivors, the deceased group has lower BRI and BMI, is older, has more men, has a lower educational level, has an increased proportion of divorced or widowed people, is significantly worse off economically, more people have smoked and drunk alcohol in the past, has lower dietary energy, and rarely participates in physical activities; in addition, the deceased group has more comorbidities such as cancer and CKD, CVD and hypertension.

The estimated odds ratio (OR) of BRI and BMI with frailty

Table 2 shows the results of weighted logistic regression of BRI and BMI with frailty. When BRI and BMI are included in the regression model as continuous variables, in all models, BRI and BMI are significantly positively associated with frailty. After fully adjusting for demographic factors, lifestyle and comorbidities (Model 3), BRI increases the odds of frailty by 20% (OR: 1.20, 95% CI: 1.13–1.27, P < 0.0001), and BMI increases the odds of frailty by 14% (OR: 1.14, 95% CI: 1.08–1.21, P < 0.0001). When BRI and BMI are included in the regression model as quartiles, compared with the lowest quartile group, the odds of frailty in the highest quartile group are significantly increased in the full model (Model 0 - Model 3). In the fully adjusted Model 3, the odds of frailty in individuals in the highest BRI quartile group are increased by 55% compared to the lowest quartile group (OR: 1.55, 95% CI: 1.30–1.85, P < 0.0001); the odds of frailty in individuals in the highest BMI quartile group are increased by 41% compared to the lowest quartile group (OR: 1.41, 95% CI: 1.20–1.67, P < 0.0001); trend tests show that BRI and BMI both increase the odds of frailty in a trend manner (P for trend < 0.0001, respectively).

Table 2 OR estimates for the association between the two indicators of obesity with frailty
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Figure 1 shows the results of nonlinear regression of BRI and BMI with frailty. RCS shows that BRI and BMI are almost linearly associated with frailty (Nonlinear P = 0.059, Nonlinear P = 0.051, respectively). The prevalence of frailty gradually increases as BRI and BMI increase. Moreover, when BRI exceeds 6 or BMI exceeds 30, the increase in the prevalence of frailty becomes more pronounced. Figure 2 compares the ability of BRI and BMI to predict frailty. Regardless of whether the complex sampling weight is considered or not, the area under the curve (AUC) of BRI is significantly higher than that of BMI. When unweighted, the AUC of BRI is 0.628 (0.619–0.638), while the AUC of BMI is 0.603 (0.594–0.613), and the difference between the two is significant (PDelong < 0.001) (Fig. 2A); after weighting, the AUC of BRI is 0.630 and the AUC of BMI is 0.600 (Fig. 2B). These results suggest that the ability of BRI to predict frailty is significantly stronger than that of BMI. Supplementary Fig. 2 shows that the association between BRI and BMI and frailty remains stable in all subgroups (all P for interaction > 0.05).

Fig. 1
figure 1

Weighted restricted cubic spline regression of BRI and BMI with frailty. Notes: The adjusted restricted triple spline model was adjusted for age, sex, race, marital status, education, poverty-to-income ratio, drinking, smoking, total energy intake, weekly physical activity level, hyperlipidemia, DM, arthritis, CVD, thyroid problems, liver problem, hypertension, CKD, and cancer. DM, diabetes; CVD, cardiovascular disease; CKD, chronic kidney disease; BRI, body roundness index; BMI, body mass index

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Fig. 2
figure 2

Receiver operating characteristic curves of BRI and BMI for the prediction of frailty. Notes: (A) unweighted Receiver operating characteristic; (B) weighted receiver operating characteristic. ROC, receiver operating characteristic; AUC, area under the receiver operating characteristic curve; BRI, body roundness index; BMI, body mass index

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The estimated hazard ratio (HR) of BRI and BMI with all-cause mortality among frail individuals

Figure 3 shows that both BRI and BMI have a U-shaped association with the risk of all-cause mortality (Nonlinear P < 0.0001, respectively); the inflection points inferred based on the slope method are 7.33 and 33.57 respectively. Before and after the inflection points, the risk of all-cause mortality first decreases and then increases. Piecewise regression based on the inflection point shows that when BRI < 7.33, an increase in BRI reduces the risk of all-cause mortality (HR: 0.85, 95% CI: 0.78–0.92, P < 0.0001), while when BRI ≥ 7.33, an increase in BRI increases the risk of all-cause mortality (HR: 1.19, 95% CI: 1.03–1.38, P = 0.02); when BMI < 33.57, an increase in BMI reduces the risk of all-cause mortality (HR: 0.84, 95% CI: 0.78–0.91, P < 0.0001), and when BMI ≥ 33.57, an increase in BRI increases the risk of all-cause mortality (HR: 1.18, 95% CI: 1.04–1.34, P = 0.01) (Table 3).

Fig. 3
figure 3

Weighted restricted cubic spline regression of BRI and BMI with all-cause mortality. Notes: The adjusted restricted cubic spline model was adjusted for age, sex, race, marital status, education, poverty-to-income ratio, drinking, smoking, total energy intake, weekly physical activity level, hyperlipidemia, DM, arthritis, CVD, thyroid problems, liver problem, hypertension, CKD, and cancer. DM, diabetes; CVD, cardiovascular disease; CKD, chronic kidney disease; BRI, body roundness index; BMI, body mass index

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Table 3 HR estimates for the association between the two indicators of obesity with all-cause mortality among frail participants
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Figure 4 shows the ability of BRI and BMI to predict the risk of all-cause mortality in frail individuals. The time-dependent c-index curve shows that the c-index of BRI at each time point is comparable to that of BMI.

Fig. 4
figure 4

The time-dependent c-index curve with 1000 rounds of bootstrap for comparison of model stability between BRI and BMI for predicting all-cause mortality. Notes: The two models were adjusted for age, sex, race, marital status, education, poverty-to-income ratio, drinking, smoking, total energy intake, weekly physical activity level, hyperlipidemia, DM, arthritis, CVD, thyroid problems, liver problem, hypertension, CKD, and cancer. DM, diabetes; CVD, cardiovascular disease; CKD, chronic kidney disease; BRI, body roundness index; BMI, body mass index

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Sensitivity analysis

In sensitivity analysis 1, multiple imputation was performed for the missing values of exposures and covariables. After multiple imputation, 16,959 participants with complete data were obtained. On this basis, the regression results were consistent with the main analysis results (Supplementary Tables 3 and Supplementary Table 4). In sensitivity analysis 2, after the participants with BRI or BMI values outside the 5th − 95th percentiles were excluded, the regression results also supported our findings (Supplementary Tables 5 and Supplementary Table 6).

Discussion

Based on a large sample population, this study analyzed the associations of BRI and BMI with frailty and all-cause mortality in the American population over 40 years old. Both BRI and BMI increase the prevalence of frailty, but they show a U-shaped association with their risk of all-cause mortality. In addition, subgroup analysis shows that the associations of BRI and BMI with frailty are robust in different populations. ROC analysis shows that BRI is significantly better than BMI in predicting frailty, while the time-dependent c-index curve suggests that BRI and BMI have comparable predictive abilities in terms of all-cause mortality. In conclusion, this study combines traditional and new obesity indicators to determine the relationship between obesity and frailty and the risk of all-cause mortality.

The research conclusions on obesity and frailty are inconsistent. In 2018, a longitudinal cohort study on aging in Beijing that included 6,320 people over 65 years old showed that compared with BMI, WC is more closely related to the risk of frailty 11]. In 2023, a population study of community-dwelling adults in Norway showed that general obesity and abdominal obesity, especially over time during adulthood, are associated with an increased risk of pre-frailty/frailty in the following years [26]. A study analyzing NHANES from 1999 to 2004 showed that in the elderly (> 60 years old), frailty and pre-frailty are associated with a larger WC [13]. Jia et al. also analyzed NHANES from 2007 to 2018, and the results showed a strong association between the weight-adjusted waist index (WWI) and an increased odds of frailty [25]. A meta-analysis in 2021 showed that compared with normal BMI, underweight and obese individuals have a higher risk of frailty, while there is no significant change in the risk of frailty in the overweight group; and people in higher waist circumference categories have an overall 57% higher risk of frailty than those with normal waist circumference [27]. A recent meta-analysis of seven prospective cohort studies showed that overweight and obesity in middle age are significantly associated with a higher risk of frailty in the elderly, while obesity and underweight in old age are associated with a relatively higher risk of frailty in the elderly [28]. The current study shows that BRI is significantly positively associated with frailty, and BRI is better than BMI in predicting frailty. This is reasonable because BRI combines height and WC, optimizing the deficiency of BMI in predicting body fat percentage and fat distribution [14]. In addition, due to BRI’s focus on body roundness and abdominal fat, its assessment of the association with health problems such as metabolic syndrome (metS), CVD, etc. is more targeted [29,30,31]. Central obesity is an important risk factor for metS. metS can lead to insulin resistance, which is related to skeletal muscle atrophy, fatigue and slow gait, and may eventually lead to frailty [32, 33]. Mechanistically, High BRI and BMI usually indicate a relatively high amount of body fat. Adipose tissue, especially visceral fat, is an active endocrine organ that can secrete a variety of inflammatory factors, such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), etc. These inflammatory factors can lead to a chronic inflammatory state. When adipocytes become hypertrophic, they will induce macrophage infiltration, which in turn stimulates the production of inflammatory cytokines [34, 35]. Chronic inflammation affects muscle health through multiple pathways, resulting in muscle loss and decreased strength, thus promoting the occurrence and development of frailty. These mechanisms include facilitating muscle protein degradation, inhibiting muscle regeneration, impairing nerve conduction and vascular function, etc [36,37,38]. The inflammatory response related to obesity can also activate inflammatory signaling pathways, such as the NF-κB pathway. The activation of NF-κB will promote the expression of a series of inflammation-related genes, further exacerbating the inflammatory response. Frailty is closely related to chronic inflammation. Inflammatory factors can interfere with the synthesis and metabolism of muscle proteins, leading to muscle loss and decreased strength. For example, IL-6 can activate the ubiquitin-proteasome system in muscles, accelerating the degradation of muscle proteins [37,38,39]. Meanwhile, the inflammatory state can also affect nerve conduction and vascular function, which are important components of frailty [38]. Chronic inflammation can also impair endothelial reactivity and muscle perfusion and interfere with the uptake of essential amino acids that are crucial for muscle energy and protein metabolism [38]. The inflammatory state can also affect nerve conduction and vascular function, which are important components of frailty [38, 40]. Studies have also shown that there is a significant association between chronic inflammation and muscle strength, mass and function, especially more obvious in the frail population [36]. Inflammatory markers such as IL-6 and tumor necrosis factor-β (TNF-β) play important roles in sarcopenia and frailty [36, 37]. On the other hand, high BRI and BMI are often accompanied by metabolic syndrome, including insulin resistance, dyslipidemia, etc [32, 33, 41]. Frail individuals also have metabolic disorders. Muscle metabolism changes during the process of frailty, with reduced energy production and utilization efficiency. For example, mitochondrial dysfunction is relatively common in frail muscles, which will lead to a reduction in adenosine triphosphate (ATP) production and affect the contractile function of muscles [42, 43]. Metabolic disorders can also affect the absorption and utilization of nutrients in the body, resulting in nutrient deficiencies. From this perspective, the metabolic disorders related to obesity and the metabolic changes in frailty are interrelated, which may be the potential metabolic mechanism underlying the association between obesity and frailty.

This study also reports for the first time the relationship between traditional and new obesity indicators and the risk of all-cause mortality in frail participants. Consistent with our study, three previous cohort studies have shown that the association between BRI and all-cause mortality in the general population in the United States presents a U shape [24, 44, 45]. A cohort study from Taiwan shows that in patients with type 2 diabetes mellitus (T2DM), the all-cause mortality risks in the first and fourth quartile groups of BRI are higher than those in the second quartile group, which also implies a potential nonlinear association [46]. In an elderly cohort study in China, a U-shaped relationship between BRI and all-cause mortality was also observed [47]. In this study, both BRI and BMI show a U-shaped association with the risk of all-cause mortality in frail participants, and BRI and BMI have comparable abilities in predicting the risk of death in frail participants. This nonlinear association is reasonable. Obesity indicators are related to nutritional status. A lower BRI and BMI may mean insufficient nutrient intake, frailty, decreased endurance and decreased muscle mass [45, 48]. After exceeding the threshold, obesity indicators increase the risk of death, which may be related to an increased risk of higher cardiovascular and metabolic disorders and even cancer [47, 49,50,51,52,53,54,55]. It should be noted that compared with the general population, the current study shows that in the frail population, the inflection point of the association between BRI and all-cause mortality is further to the right, which may be related to the fact that the frail need more fat. Previously, Kulapong et al. [12] also found that overweight is a protective factor for death in people with moderate/severe frailty, and grade 1 obesity may have a protective effect on the mortality of people with at least mild frailty.

Advantages and limitations

This is the first study to compare the associations of BRI and BMI with frailty and the risk of all-cause mortality. This study has a large sample size, a long follow-up time, and strictly follows the design concept of NHANES to weight the samples. This study found that there is a significant association between BRI and frailty, and compared with BMI, BRI has a stronger ability to predict frailty. This result provides clinicians with a new indicator for the early identification of frailty risk. For example, during routine physical examinations, by measuring BRI, individuals at high risk of frailty can be detected earlier, so that timely intervention measures can be taken to delay the progress of frailty. The early identification of frailty is crucial for preventing adverse health outcomes. Frail individuals are more likely to experience adverse events such as falls, disabilities, hospitalizations, and deaths. Through the early screening using BRI, comprehensive treatments such as rehabilitation training, nutritional support, and psychological intervention can be provided targeted at high-risk groups, reducing the incidence of adverse events.

However, some limitations must be acknowledged. First, cross-sectional studies limit the causal exploration of the relationship between BRI and BMI and frailty. Since it is impossible to determine the temporal sequence of high BRI or high BMI and the occurrence of a frail state, it is possible that frailty leads to changes in body fat content and distribution. Second, the included samples are all from the American population. Therefore, the conclusions of the study should be cautiously extended to other populations. Third, in addition to the adjusted covariables, there may still be residual confounding factors. Finally, both BRI and the frail state are dynamic and may change over time. Using only a single measurement may not accurately reflect the true situation of an individual at different time points, thus potentially introducing bias. This single measurement method does increase the risk of misclassification bias. It may misclassify some individuals whose BRI or frail state actually changes over time, thus affecting the accuracy and reliability of the research results.

Conclusion

In the American population over 40 years old, both BRI and BMI are independently and positively associated with frailty, and BRI has a stronger ability to predict frailty than BMI. In addition, both BRI and BMI have a U-shaped association with the risk of all-cause mortality in frail survivors, and their predictive abilities are comparable. In short, this study provides some references for understanding the relationship between visceral fat content indicators and traditional obesity indicators and frailty. In clinical practice, doctors can incorporate the BRI into routine physical examination items, especially for people over 40 years old, so as to better identify individuals at high risk of frailty. Future studies can further explore in depth the underlying mechanisms between BRI and BMI and frailty as well as the risk of all-cause mortality.

Data availability

The website for cross-sectional data is https://wwwn.cdc.gov/nchs/nhanes/; the website for survival data is https://www.cdc.gov/nchs/ndi/index.htm.

Abbreviations

ATP:

Adenosine triphosphate

AUC:

Area under the curve

BMI:

Body mass index

BRI:

Body roundness index

CI:

Confidence interval

c-index:

Concordance-index

CKD:

Chronic kidney disease

CM:

Centimetre

CVD:

Cardiovascular disease

DM:

Diabetes mellitus

FI:

Frailty Index

HR:

Hazaed ratio

ICD-10:

International Classification of Diseases 10th Revision

IL-6:

Interleukin-6

IQR:

Interquartile Range

MEC:

Mobile Examination Center

NAFLD:

Non-alcoholic fatty liver disease

NHANES:

National Health and Nutrition Examination Survey

OR:

Odds ratio

PIR:

Poverty-income ratio

RCS:

Restricted cubic spline

ROC:

Receiver operating characteristic

SD:

Standard deviation

SE:

Standard error

T2DM:

Type 2 diabetes mellitus

TNF-α:

Tumor necrosis factor-α

TNF-β:

Tumor necrosis factor-β

WC:

waist circumference

WWI:

Weight-adjusted Waist Index

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Acknowledgements

Thanks to Chen Lele from Henan Provincial People’s Hospital for providing assistance in data extraction. We also thank Ying Xu from Henan University of Science and Technology for their help in statistical knowledge, writing and some graphing code in R. Thanks to the participants of NHANES.

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This study did not receive any additional funding.

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Authors and Affiliations

  1. Department of Medical Intensive Care Unit, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China

    Jianqiang Zhang

  2. Department of Neurology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China

    Jianqiang Zhang

  3. Department of Cardiovascular, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China

    Huifeng Zhang

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Jianqiang Zhang.: Designing topics, literature search, writing, image portfolio, writing, review, literature search. Huifeng Zhang: data cleansing, writing, statistical analysis, and funding.

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Correspondence to Huifeng Zhang.

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Zhang, J., Zhang, H. The association of body roundness index and body mass index with frailty and all-cause mortality: a study from the population aged 40 and above in the United States. Lipids Health Dis 24, 30 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02450-8

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02450-8

Keywords

Lipids in Health and Disease

ISSN: 1476-511X