The Relationship between Different Obesity Indicators and Frailty among the Elderly in Rural Northwest Regions

ZHANG Zhiwei, HE Panpan, YANG Qianwen, JIN Xueyi, MAO Xueqian, HU Ying, JING Lipeng*

Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China
*Corresponding author: JING Lipeng, Associate Professor; E-mail: jinglp@lzu.edu.cn

Abstract

Background
Frailty is an age-related geriatric syndrome characterized by a decline in physiological function across multiple systems, including muscular, metabolic, and immune function, leading to decreased stress resistance and increased vulnerability. In China, the prevalence of frailty among older adults is notably high and continues to rise annually, with significantly higher rates in rural areas compared to urban settings. Obesity is closely associated with the development of numerous diseases, but its relationship with frailty remains controversial. This uncertainty may be attributable to limitations of conventional obesity indicators in characterizing adipose tissue distribution. Therefore, investigating the associations between multiple adiposity metrics and frailty is important for advancing our understanding of frailty pathogenesis and developing preventive interventions.

Objective
This study investigates the relationship between various obesity indicators and frailty, providing a scientific basis for the early prevention and control of frailty in older adults.

Methods
From March to May 2023, we surveyed 1,429 elderly individuals aged 60 years and above across six rural villages in Jingyuan County, Gansu Province. After applying exclusion criteria, a final sample of 1,153 participants was included in the analysis. The FRAIL scale was utilized to assess frailty status. Based on Chinese obesity criteria, waist circumference and BMI were categorized, while waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), body roundness index (BRI), and Chinese visceral adiposity index (CVAI) were grouped by quartiles. Multivariate logistic regression, restricted cubic splines (RCS), and receiver operating characteristic (ROC) curve analysis were employed to explore the relationships between different obesity indicators and frailty.

Results
The study included 1,153 participants aged ≥60 years (474 males [41.11%] and 679 females [58.89%]), with a mean age of 70.86±4.76 years. Based on FRAIL scale assessments, 226 participants were identified as frail and 927 as non-frail, resulting in a frailty prevalence of 19.60%. Adjusted multivariate logistic regression analysis revealed that central obesity, moderate to severe obesity (reference: normal BMI), Q3 and Q4 levels of WHR, and Q4 levels of WHtR, BRI, and CVAI (all referenced to Q1) were significant risk factors for frailty (P<0.05). Moreover, the risk of frailty increased progressively with elevated levels of waist circumference, BMI, WHR, WHtR, BRI, and CVAI (Ptrend<0.05). RCS analysis demonstrated linear positive associations between waist circumference, BMI, WHtR, BRI, CVAI and frailty risk (Plinear<0.05). ROC curve analysis showed that waist circumference, BMI, WHR, WHtR, BRI, and CVAI all had predictive value for frailty risk, with area under the curve (AUC) values of 0.557 (95%CI=0.515-0.598), 0.570 (95%CI=0.528-0.612), 0.558 (95%CI=0.515-0.600), 0.610 (95%CI=0.568-0.652), 0.610 (95%CI=0.568-0.652), and 0.586 (95%CI=0.546-0.626), respectively (P<0.05). Notably, WHtR, BRI, and CVAI demonstrated superior predictive ability compared to waist circumference (Z=-5.443, P<0.001; Z=-5.443, P<0.001; Z=-2.595, P=0.009), while WHtR and BRI showed better predictive performance than BMI (Z=-2.885, P=0.004; Z=-2.884, P=0.004).

Conclusion
Among elderly individuals aged 60 and above in rural Northwest China, obesity indicators including waist circumference, BMI, WHR, WHtR, BRI, and CVAI were positively correlated with frailty risk. Among these, WHtR and BRI exhibited superior predictive capacity for frailty compared to traditional measures.

Keywords
Frailty; Obesity; Aged; Waist circumference; Body mass index; Waist-to-height ratio; Body roundness index; Chinese visceral adiposity index; Northwest rural area

Introduction

Frailty is an age-related geriatric syndrome characterized by declines in physiological function across multiple systems, including muscular, metabolic, and immune function, leading to decreased stress resistance and increased vulnerability. It also elevates the risk of falls, disability, hospitalization, and mortality among older adults. Currently, the prevalence of frailty among Chinese adults over 60 years old is 16.0% (95%CI=12.0%-20.0%) and continues to rise annually, with significantly higher rates in rural compared to urban areas.

Obesity is a metabolic disease whose health risks depend not only on total fat mass but also critically on fat distribution patterns. Previous research has demonstrated a positive association between obesity and frailty risk. However, Jayanama et al. found that among older adults with moderate to severe frailty, overweight and mild obesity may actually reduce mortality risk. This paradox likely stems from limitations of conventional obesity measures in accurately characterizing fat distribution. Currently, BMI and waist circumference are commonly used in clinical practice, but these have notable constraints: BMI cannot distinguish between muscle and fat mass and fails to reflect visceral fat distribution, while waist circumference does not account for individual height differences in assessing abdominal adiposity.

In recent years, novel obesity assessment indices have been explored and applied. Research by Zhao Liancheng et al. demonstrated that waist-to-height ratio (WHtR) is superior to waist circumference in identifying central obesity across different body types and shows positive associations with chronic diseases such as cardiovascular disease and diabetes. Additionally, both body roundness index (BRI) and Chinese visceral adiposity index (CVAI) can evaluate visceral fat accumulation. BRI correlates strongly with visceral fat area and metabolic status, while CVAI—which incorporates waist circumference, BMI, blood lipids, and sex—effectively reflects visceral fat metabolic activity. Previous studies have shown significant associations between body fat distribution and frailty, particularly that increased visceral fat correlates with elevated frailty risk. However, direct fat measurement is impractical for large-scale community screening. Therefore, this study employs macro-level indicators including WHtR, BRI, and CVAI that indirectly reflect body fat distribution to assess obesity in older adults and explore their relationships with frailty, providing a more comprehensive evaluation of obesity-frailty associations and new scientific evidence for health management and frailty prevention.

Methods

Study Population

This study was conducted within the framework of the National Basic Public Health Service for Elderly Health Management, primarily through community health service centers in villages of Jingyuan County, Baiyin City, Gansu Province. From March to May 2023, we surveyed 1,429 rural elderly individuals aged 60 and above across six villages. Of these, 1,209 completed the questionnaire (response rate: 84.60%). After further exclusions, 1,153 participants were included in the final analysis.

Inclusion criteria: (1) age ≥60 years; (2) completion of questionnaire, blood biochemical tests, and physical measurements; (3) local residence for ≥5 years; (4) signed informed consent.

Exclusion criteria: (1) history of mental illness (e.g., schizophrenia) or communication disorders (e.g., cognitive, comprehension, or expression impairments); (2) inability to undergo measurements due to long-term bed rest or disability; (3) unwillingness to cooperate with the survey.

This study was approved by the Ethics Committee of Lanzhou University (approval number: IRB21010301).

Data Collection

General Information Survey: A questionnaire collected demographic data including age, sex, ethnicity, marital status, education level, family economic status, daily step count, smoking, alcohol consumption, tea drinking, and self-rated health. Chronic disease status was based on self-report and on-site diagnosis by community health center physicians. Multimorbidity was defined as having ≥2 chronic diseases, and polypharmacy as taking ≥5 medications daily.

Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), with PSQI>7 indicating poor sleep quality. Upper and lower limb muscle strength were evaluated using the Short Physical Performance Battery (SPPB) and grip strength, with higher scores indicating better function.

Fasting venous blood samples (≥8 hours) were collected by professional physicians from Jingyuan County Traditional Chinese Medicine Hospital. Triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using an automatic biochemical analyzer (Beckman AU680).

Frailty Assessment: The FRAIL scale was used to evaluate frailty status. This 5-item scale includes fatigue, resistance (endurance), ambulation (mobility), illness (≥5 chronic conditions such as hypertension, diabetes, acute cardiac events, stroke, malignancy, congestive heart failure, asthma, arthritis, chronic lung disease, kidney disease, and angina), and loss of weight. Each positive item scores 1 point: 0=robust, 1-2=pre-frail, 3-5=frail. Robust and pre-frail categories were combined as "non-frail" for analysis.

Obesity Indicators: Height and weight were measured using a calibrated stadiometer and scale (LK-200). Waist and hip circumferences were measured by trained investigators using a non-elastic tape measure with 1 mm precision. Weight was recorded to 0.01 kg; height, waist, and hip circumferences to 0.01 cm.

Waist circumference and BMI were categorized according to the Chinese Guidelines for the Diagnosis and Treatment of Obesity (2024 Edition): central obesity defined as waist circumference ≥90 cm for men and ≥85 cm for women; BMI categories: underweight (<18.50 kg/m²), normal (18.50-23.99 kg/m²), overweight (24.00-27.99 kg/m²), mild obesity (28.00-32.49 kg/m²), and moderate to severe obesity (≥32.50 kg/m²). WHR, WHtR, BRI, and CVAI were grouped by quartiles.

Indicator calculations:
1. BMI = weight (kg) / height (m)²
2. WHR = waist circumference (cm) / hip circumference (cm)
3. WHtR = waist circumference (cm) / height (cm)
4. BRI = 364.2 - 365.5 × {1 - [waist (cm)/2π]² / [0.5 × height (cm)]²}^(1/2)
5. Male CVAI = -267.93 + 0.68 × age (years) + 0.03 × BMI (kg/m²) + 4.00 × waist (cm) + 22.00 × log₁₀TG (mmol/L) - 16.32 × HDL-C (mmol/L)
6. Female CVAI = -187.32 + 1.71 × age (years) + 4.23 × BMI (kg/m²) + 1.12 × waist (cm) + 39.76 × log₁₀TG (mmol/L) - 11.66 × HDL-C (mmol/L)

Quartile groupings:
- WHR (Q1: ≤0.86, Q2: 0.87-0.90, Q3: 0.91-0.94, Q4: >0.94)
- WHtR (Q1: ≤0.51, Q2: 0.52-0.55, Q3: 0.56-0.59, Q4: >0.59)
- BRI (Q1: ≤3.49, Q2: 3.50-4.34, Q3: 4.35-5.30, Q4: >5.30)
- CVAI (Q1: ≤99.11, Q2: 99.12-124.60, Q3: 124.61-146.35, Q4: >146.35)

Statistical Analysis

Data were analyzed using SPSS 27.0 and R 4.3.1. Continuous variables with normal distribution were expressed as mean±standard deviation and compared between groups using independent samples t-tests. Categorical variables were described using frequencies and percentages, with inter-group comparisons performed using χ² tests. Multivariate logistic regression and restricted cubic splines (RCS) were used to analyze associations and dose-response relationships between obesity indicators and frailty, with trend tests and model adjustments. Three models were constructed: Model 1 unadjusted; Model 2 adjusted for age and sex; Model 3 adjusted for age, sex, ethnicity, marital status, education, family economic status, smoking, alcohol consumption, tea drinking, daily step count, multimorbidity, polypharmacy, falls, self-rated health, PSQI (≤7 vs >7), SPPB score, and grip strength. ROC curves were plotted to compare the predictive value of different obesity indicators, with sex- and age-specific stratified analyses performed for WHR, WHtR, BRI, and CVAI. Two-sided tests were used with P<0.05 considered statistically significant.

Results

Participant Characteristics

The study included 1,153 elderly participants aged ≥60 years (474 males [41.11%] and 679 females [58.89%]), with a mean age of 70.86±4.76 years. Based on FRAIL scale assessments, 226 participants were identified as frail and 927 as non-frail, yielding a frailty prevalence of 19.60%.

Significant differences between frail and non-frail groups were observed for age, sex, marital status, education, family economic status, daily step count, smoking, tea consumption, self-rated health, multimorbidity, polypharmacy, PSQI, falls, grip strength, SPPB score, height, waist circumference, central obesity, BMI, WHR, WHtR, BRI, and CVAI (all P<0.05). No significant differences were found for ethnicity, alcohol consumption, weight, thigh circumference, calf circumference, or hip circumference (P>0.05) [TABLE:1].

Multivariate Logistic Regression Analysis of Obesity Indicators and Frailty

Using frailty status as the dependent variable and obesity indicators (central obesity, BMI, WHR, WHtR, BRI, CVAI) as independent variables, multivariate logistic regression analysis (variable assignments shown in [TABLE:2]) revealed that after adjustment, central obesity, moderate to severe obesity (reference: normal BMI), Q3 and Q4 levels of WHR, and Q4 levels of WHtR, BRI, and CVAI (all referenced to Q1) were significant risk factors for frailty (P<0.05). Furthermore, the risk of frailty increased progressively with higher levels of waist circumference, BMI, WHR, WHtR, BRI, and CVAI (Ptrend<0.05) [TABLE:3].

Stratified Analysis by Sex and Age

Sex- and age-stratified multivariate logistic regression analyses (adjusted for the same covariates) showed that among male elderly, central obesity and Q4 levels of WHR, WHtR, BRI, and CVAI were risk factors for frailty (all P<0.05), with increasing risk across higher quartiles (Ptrend<0.05). Among female elderly, central obesity, mild obesity, moderate to severe obesity (reference: normal BMI), and Q4 levels of WHtR and CVAI were risk factors (P<0.05), with similar dose-response trends (Ptrend<0.05). For participants aged ≥70 years, central obesity, overweight, mild obesity, moderate to severe obesity, and Q3-Q4 levels of WHR, WHtR, BRI, and CVAI were risk factors (P<0.05), with progressive increases in frailty risk across all indicator levels (Ptrend<0.05) [TABLE:4].

Dose-Response Relationships

RCS analysis demonstrated positive linear associations between waist circumference, BMI, WHtR, BRI, CVAI and frailty risk (Plinear<0.05) [FIGURE:1].

ROC Curve Analysis

ROC curve analysis revealed that waist circumference, BMI, WHR, WHtR, BRI, and CVAI all predicted frailty risk with AUC values of 0.557 (95%CI=0.515-0.598), 0.570 (95%CI=0.528-0.612), 0.558 (95%CI=0.515-0.600), 0.610 (95%CI=0.568-0.652), 0.610 (95%CI=0.568-0.652), and 0.586 (95%CI=0.546-0.626), respectively (P<0.05). WHtR, BRI, and CVAI showed significantly higher AUCs than waist circumference (Z=-5.443, P<0.001; Z=-5.443, P<0.001; Z=-2.595, P=0.009), while WHtR and BRI outperformed BMI (Z=-2.885, P=0.004; Z=-2.884, P=0.004) [TABLE:5] [FIGURE:2].

Discussion

This study, consistent with previous research, demonstrates that obesity is a risk factor for frailty. Obesity is associated with 21 diseases across multiple body systems, and more severe obesity increases the risk of chronic multimorbidity, which positively correlates with frailty. Research in very old adults has shown an inverse relationship between BMI and lower extremity physical function—a hallmark of frailty. However, some studies suggest that among those with moderate to severe frailty, overweight and mild obesity may reduce mortality risk. This paradox likely arises because BMI, while reflecting fat mass, cannot distinguish fat distribution and correlates positively with muscle mass, making the relationship between BMI and health in older adults complex.

Our findings indicate that WHtR, BRI, and CVAI have superior predictive value for frailty compared to traditional indicators like BMI and waist circumference. A Korean study showed WHtR mediates the BMI-frailty association, while longitudinal research has identified both high waist circumference and WHtR as frailty risk factors, with WHtR showing stronger associations. BRI constructs an elliptical body model based on roundness and eccentricity to assess visceral fat accumulation. Studies show BRI is more closely associated with metabolically unhealthy normal weight phenotype, which increases frailty risk. CVAI, developed specifically for Chinese populations using anthropometric and physiological measures (BMI, waist circumference, TG, HDL-C), effectively reflects visceral fat metabolic activity. Visceral fat accumulation correlates with higher frailty risk through mechanisms including adipose tissue dysfunction, insulin resistance, and chronic inflammation—key pathophysiological pathways in frailty. Insulin resistance may promote muscle degradation via increased myostatin, while chronic low-grade inflammation, exacerbated by visceral fat, elevates pro-inflammatory cytokines (IL-6, IL-18, TNF-α) that are associated with poor physical function and frailty.

Significant associations between obesity indicators and frailty were primarily observed in participants aged ≥70 years. This may reflect age-related sarcopenia and visceral fat accumulation, which interact through insulin resistance and inflammatory pathways to accelerate physical decline. While weight loss in obese older adults can reduce chronic disease risk, inappropriate approaches may decrease muscle mass and strength, increasing frailty risk. Therefore, scientifically sound strategies combining dietary control with appropriate exercise are essential to preserve muscle mass during weight reduction. A randomized controlled trial in adults over 65 demonstrated that such combined interventions effectively reduce weight while improving physical function.

This study's strengths include exploring multiple obesity indicators and their relationships with frailty, providing new perspectives for prevention and intervention. The investigation of six communities in rural Northwest China offers population-based evidence for low-income regions. However, limitations include the cross-sectional design, which precludes causal inference; the focus on rural elderly, limiting generalizability; lack of muscle mass adjustment (though grip strength and SPPB were included); and reliance on self-reported covariates, which may introduce recall bias.

In conclusion, among elderly adults aged ≥60 in rural Northwest China, obesity—particularly central obesity—is a risk factor for frailty, with waist circumference, BMI, WHR, WHtR, BRI, and CVAI all showing positive associations. WHtR and BRI demonstrate superior predictive value compared to traditional measures, offering valuable tools for early identification of high-risk individuals and informing targeted health interventions in resource-limited settings. These findings have important practical significance for improving health outcomes and promoting healthy aging strategies in economically disadvantaged rural areas.

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Author Contributions: ZHANG Zhiwei contributed to data collection, statistical analysis, and manuscript drafting. HE Panpan and YANG Qianwen participated in data collection and organization. JIN Xueyi, MAO Xueqian, and HU Ying assisted with data collection. JING Lipeng supervised the project, provided quality control, and secured funding.

Conflict of Interest Statement: The authors declare no conflicts of interest.

ORCID IDs:
JING Lipeng: https://orcid.org/0000-0003-1856-0324
ZHANG Zhiwei: https://orcid.org/0009-0002-4000-8210

Received: April 22, 2025
Revised: July 2, 2025
Accepted: [Not provided]
Editor: KANG Yanhui