Population aging has emerged as a critical demographic challenge confronting nations worldwide, including China. From 2025 to 2,100, the proportion of the elderly population is projected to continue increasing, with more than 30% of the population aged 65 years and over by 2,100. During this period, the dependency ratio of the elderly population is expected to reach 4:1 (1). The exploding elderly population has led to a surge in the prevalence and incidence of age-related diseases, posing significant public health challenges (2). Moreover, age-related health problems are likely to result in disability in activities of daily living (ADL) (3). ADL refers to activities oriented toward taking care of one’s own body, which are fundamental to living in a social world. These activities enable basic survival and well-being, including bathing, toileting, dressing, and eating, among others (4, 5). It is estimated that more than 1 billion people worldwide suffer from one or more forms of disability (6). Reduced ADL capacity negatively affects the physical and mental health of middle-aged and older adults, increases the risk of unintentional injuries, and may even be associated with a greater risk of mortality (7). Recent studies in China have shown that the prevalence of ADL disability among older adults is 41.0%, and this number is expected to continue to increase as the population ages (8). This trend places a heavy burden on individuals, caregivers, and the healthcare system. Therefore, early identification of people at risk for ADL disability is critical for both prevention and intervention efforts.

Moreover, the high prevalence of obesity has become a major global health problem, increasing at an alarming rate in recent decades (9). Obesity has been identified as a significant risk factor for dyslipidemia, with the underlying mechanism potentially related to insulin resistance (IR) (10). In the context of IR, the clearance of triglyceride (TG)-rich lipoproteins from the blood is impaired, leading to elevated TG levels and ultimately lower high-density lipoprotein cholesterol (HDL-C) levels (11, 12). In addition, obesity is thought to significantly increase the risk of adverse health outcomes in middle-aged and older populations, including a variety of comorbidities such as hypertension and diabetes, as well as functional impairments, such as mobility problems (13, 14). Annemarie Koster et al. demonstrated that obese older adults are at greater risk for activity limitations than nonobese individuals are and that the effects of obesity appear to partially overwhelm the effects of other lifestyle factors (15). Another study in a Chinese middle-aged and elderly population showed that power-reduced abdominal obesity is associated with functional disability, i.e., difficulty performing basic activities of daily living (BADL) and instrumental activities of daily living (IADL) (16).

Waist circumference (WC) and body mass index (BMI) are the most commonly used measures of obesity in previous studies. Although they have been widely used in many studies to explore the associations between obesity and certain diseases (diabetes, metabolic syndrome (MS), and depressive symptoms), these metrics have certain shortcomings (17, 18). In muscular individuals, BMI fails to differentiate between muscle mass and fat mass, leading to erroneous assessment results (9, 19). Similarly, the strong correlation between WC and BMI complicates the ability to distinguish their respective contributions as independent epidemiologic risk factors (20). Given these limitations, the representativeness of the results could be enhanced by introducing additional anthropometric indices to compare the predictive power of obesity and lipid-related indices comprehensively.

In view of this, the current study employed 13 obesity- and lipid-related indices to assess body fat accumulation and lipid metabolite status, utilizing data from the China Health and Retirement Longitudinal Study (CHARLS). These indices included WC, BMI, the waist–height ratio (WHtR), the visceral adiposity index (VAI), the body shape index (ABSI), the body roundness index (BRI), the lipid accumulation product (LAP), the conicity index (CI), the Chinese visceral adiposity index (CVAI), the triglyceride–glucose (TyG) index and its correlation index (TyG–BMI, TyG–WC, TyG–WHtR). The primary objectives were to investigate the screening and predictive efficacy of these obesity- and lipid-related indices for the risk of ADL disability in middle-aged and older adults and to determine the optimal predictive cutoff values to provide a basis for the prevention and treatment of ADL disability.

Table 1 presents the baseline characteristics of the participants according to the presence or absence of ADL disability. The mean age of the participants was 60.2 ± 9.5 years, with 83.0% being female, 85.5% being married, and 83.2% residing in rural areas. The prevalence of smoking was 8.0%, alcohol consumption was 13.4%, obesity was 53.0%, hypertension was 30.7%, hyperlipidemia was 24.7%, and hyperglycemia was 15.4%. Additionally, 90.2% of the participants reported being satisfied with their life, 12.4% had frailty, and 9.7% had IADL disability. Significant differences were observed between participants with and without ADL disability in terms of age, marital status, place of residence, hypertension, hyperlipidemia, life satisfaction, IADL disability, frailty, night sleep duration, triglyceride (TG) level, and height.

Table 2 shows the AUC, 95% CIs, optimal cutoff values and test statistic p values for obesity and lipid-related indices for the prediction of ADL disability by sex. Figures 2, 3 show the ROC curves for each index for predicting the risk of ADL disability in males and females, respectively. The ROC curves for each index for predicting the risk of ADL disability in males and females. As illustrated in Table 2 and Figure 2, in males, the LAP index was the best predictor of ADL disability (AUC = 0.793, 95% CI: 0.704–0.882; optimal cutoff = 35.669). Additionally, the TYG-BMI (AUC = 0.790, 95% CI: 0.692–0.887, and optimal cutoff = 239.694) had a similar predictive value. Furthermore, in women, BMI was the most accurate predictor of ADL disability (AUC = 0.721, 95% CI: 0.678–0.765, and optimal cutoff = 26.142), followed by TYG-BMI (AUC = 0.720, 95% CI: 0.676–0.764, and optimal cutoff = 224.475). All of the above indicators were statistically significant (p < 0.05). Overall, the AUC values of the above 13 indicators were greater than 0.7 (Table 2 and Figure 3), indicating that they have predictive value for ADLs in middle-aged and elderly people in China.

Table 3 shows the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between obesity and lipid-related indices and ADL disability. The 13 obesity and lipid-related indices were transformed into two types of variables for this survey on the basis of the optimal cutoffs in Table 2. Table 3 is based on the transformed variables. In general, a larger OR indicates a greater risk factor. After adjusting for age, education, marital status, and current residence (Model 2), the odds of risk for ADL disability increased progressively with increasing units of the obesity- and lipid-related indices for both males and females. After adjusting for all covariates (Model 3), for example, in the male population, each unit increase in the LAP index was associated with a 6.62-fold (95% CI: 1.75–25.09) increase in the odds of ADL disability in men, and in the female population, each unit increase in the BMI index was associated with a 2.92-fold (95% CI: 1.94–4.40) increase in women. After adjusting for confounders, all indices were statistically significant (p < 0.05).

On the basis of a large sample of CHARLS datasets, this study systematically evaluated the relationships between 13 obesity and lipid-related indicators and ADL disability in Chinese middle-aged and elderly individuals and compared the predictive efficacy of these indicators for the risk of ADL disability. The results of the present study revealed that, after basic population information such as age, education, and marital status was combined, all the indicators were significant predictors of ADL disability in Chinese middle-aged and elderly people. After adjusting for confounders, the odds of developing ADL disability increased gradually with increasing obesity and lipid-related index units. We also found that the LAP index was the best predictor of ADL disability in men (AUC = 0.793), followed by the TYG-BMI (AUC = 0.790), and that BMI was the most accurate predictor in women (AUC = 0.721), followed by the TYG-BMI (AUC = 0.720), according to the ROC analysis.

ADL disability refers to an individual’s inability to perform basic activities of daily living independently, requiring assistance from others in at least one activity (21). Our study revealed that the risk of ADL disability was positively associated with increased obesity and lipid-related indices in patients, which is consistent with previous findings. A longitudinal study of community-dwelling older adults suggested that underweight or obese individuals are more likely to have ADL disability than normal-weight individuals are (22). In another study of individuals aged 50–90 years, the risk of ADL disability was greater in the obese group than in the normal-weight group at any age and became more prevalent with age in obese individuals (23).

Obesity and dyslipidemia are thought to be linked to ADL disability through multiple pathways, the most important of which are thought to be obesity-related chronic diseases, including type 2 diabetes (T2D), coronary heart disease, MS, osteoarthritis of weight-bearing joints, multiple cancers, and cardiovascular disease (CVD) (24, 25). Adipose tissue in obese individuals (especially those with visceral adiposity) may play an active endocrine role in the secretion of proinflammatory cytokines (e.g., TNF-α, IL-6, and MCP-1) and adipokines, leading to the activation of chronic low-grade inflammation. Chronic low-grade inflammation has been shown to be associated with the onset and progression of several chronic noncommunicable diseases, with the strongest empirical evidence for MS, T2D, and CVD, which ultimately increase the risk of ADL disability (26–28). On the other hand, obesity may be associated with frailty syndrome, which may further contribute to the development of ADL disability (29).

Notably, the present study revealed that the LAP index was the best predictor of ADL disability in men compared with BMI in women. LAP is an index used to assess lipid accumulation, particularly that of central lipids. It is calculated from waist circumference (as an indicator of visceral fat content) and fasting circulating triglyceride levels, has the ability to represent lipotoxicity, has been suggested to be a better indicator of MS and CVD risk, and is superior to BMI in identifying T2D risk (30–32). Considering the limitations of BMI in distinguishing between muscle mass and fat mass-related body weight and that men have higher muscle mass and more muscular individuals, there may be limitations in the ability of BMI to predict ADL risk in men (17). In contrast, the LAP index may be more sensitive to abdominal fat accumulation and dyslipidemia in men; thus, the LAP index has a better predictive effect in men. In women, BMI is a relatively simple indicator of body fat distribution and differs from that in men, and BMI may be more reflective of the impact of overall obesity on body function in women. In addition, there are significant differences in the immune systems of adult males and females, which characterize chronic inflammation differently in males and females (33, 34). Previous studies have shown that BMI is an independent risk factor for chronic inflammation and that increased body mass index is more proinflammatory in women than in men; the proinflammatory effect of increased BMI is greater in women when BMI is greater than 33, whereas there is no tendency for chronic inflammation to be exacerbated in men (35). Considering the contribution of chronic inflammation to obesity-related chronic diseases and ultimately to the development of ADL disability, BMI can be considered a more sensitive predictor for women.

Our study also revealed that the TyG-BMI had a better predictive effect on both men and women. TyG-BMI, an index consisting of three easily measurable biochemical parameters (BMI, TG, and fasting glucose), is a readily available and clinically meaningful indicator of IR (36). In addition, it has also been found to have excellent diagnostic value in assessing the risk of various metabolic diseases, such as MS, prediabetes and diabetes, in recent epidemiologic investigations (37). Studies have shown that IR can affect somatic function by promoting disturbances in muscle glycolipid metabolism and impairing mitochondrial function, leading to muscle attenuation, strength loss, and neurodegeneration (38, 39). Furthermore, high TYG-BMI levels are associated with an elevated risk of coronary atherosclerosis via mechanisms that may involve increased release of chronic inflammatory factors (e.g., IL-6, TNF-α) and oxidative stress-mediated endothelial dysfunction, which in turn accelerates vascular sclerosis and limits exercise capacity (40, 41). More studies are needed in the future to further investigate the specific mechanisms, molecular pathways, and sex-specific modifiers of TYG-BMI affecting ADL disability.

In this study, we found that all 13 obesity indices and lipid-related indices were associated with ADL disability and significantly predicted the risk of ADL disability in middle-aged and elderly Chinese individuals. Among the 13 indices, the LAP index was the best predictor of ADL disability in men, and BMI was the best predictor in women. Moreover, the odds of developing ADL disability increased with increasing obesity and lipid-related indices. Notably, these metrics can be used as simple predictors of ADL disability in clinical practice and epidemiological studies, assisting in the early identification and prevention of ADL disability.