Abstract
Background
Diabetes represents a growing global public health burden. Traditional risk assessments based on static metabolic markers may not adequately reflect underlying mechanisms such as insulin resistance. As the primary site of glucose disposal, skeletal muscle may offer additional insight through functional assessments. However, the predictive value of muscle function indicators, including grip strength and the five-times chair stand test (5-CST), remains uncertain.
Methods
This prospective cohort study comprised 6,604 participants drawn from the China Health and Retirement Longitudinal Study (CHARLS), with follow-up data collected from 2011 to 2020. Muscle strength and physical performance were assessed at baseline using grip strength and 5-CST, respectively. New-onset diabetes was identified through self-report of physician diagnosis. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between muscle function indicators and new-onset diabetes, adjusting for potential confounders. Subgroup and interaction analyses were performed by age, gender, and body mass index (BMI). Nomograms and Sankey diagrams were used for data visualization.
Results
Among the 6,604 participants, 3,020 were male (45.7%) and 3,584 were female (54.3%), with a median age of 58 years (IQR: 51–65). Low muscle strength was identified in 605 participants (9.2%), and low physical performance in 2,025 (30.7%). Low physical performance was associated with an elevated risk of new-onset diabetes (HR 1.354, 95% CI 1.174–1.561; p < 0.001), which persisted after multivariable adjustment (HR 1.219, 95% CI 1.052–1.414; p = 0.008). This association was stronger in participants aged 40–49 years (HR 1.835, 95% CI 1.249–2.696; p = 0.002), and those with BMI < 25 kg/m² (HR 1.528, 95% CI 1.266–1.845; p < 0.001). Although each 1-second longer 5-CST time was linked to a 2.6% higher risk before adjustment (HR 1.026, p < 0.001), this continuous association was not significant after full adjustment. Neither absolute nor relative grip strength showed independent associations with diabetes risk.
Conclusions
Physical performance surpasses grip strength as a predictor of new-onset diabetes, particularly in middle-aged and non-obese individuals. As a brief, low-resource assessment of lower-limb performance, it provides a practical and scalable tool for identifying high-risk individuals, especially young lean adults who are often missed by traditional risk models.
Keywords: Physical performance, Muscle strength, Diabetes mellitus, Risk factor, Insulin resistance
Graphical Abstract
Introduction
Diabetes has emerged as a significant public health challenge. According to the International Diabetes Federation (IDF), the global prevalence of diabetes among individuals aged 20–79 years was estimated at 10.5% (536.6 million) in 2021, with projections indicating an increase to 12.2% (783.2 million) by 2045 [1]. China bears the greatest burden of diabetes, with a standardized prevalence of diabetes and prediabetes among adults aged 18 years and older at 12.8% and 35.2%, respectively, based on the diagnostic criteria of the American Diabetes Association (ADA) [2]. This concerning trend underscores the urgent need for effective screening and intervention strategies targeting high-risk populations.
Traditional methods for assessing diabetes risk predominantly emphasize factors such as age, gender, body mass index (BMI), and various metabolic indicators [3]. However, these factors may not comprehensively reflect the underlying pathophysiological characteristics. Skeletal muscle, recognized as a critical metabolic organ due to its significant role in insulin-mediated glucose uptake and storage, has emerged as a potential novel biomarker for diabetes risk [4]. Grip strength and the five-repetition chair stand test (5-CST) are practical measures of upper and lower limb muscle strength, respectively, recommended by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) [5]. However, studies on their link to diabetes risk are inconsistent: the UK Biobank study found a negative correlation between grip strength and diabetes risk [6, 7], while the PURE study did not find a significant association after adjusting for confounders [8]. Considering that the lower limbs constitute the largest glycogen reservoir in the human body, their functional status may provide a more accurate reflection of overall glucose metabolism compared to the muscles of the upper limbs. The 5-CST assesses the speed of five sit-to-stand repetitions with arms crossed, reflecting lower limb strength, balance, and neuromuscular coordination [9]. Research has demonstrated that prolonged completion times are significantly associated with an increased risk of impaired physical performance [9, 10]. Recent research, albeit limited, suggests that within the China Health and Retirement Longitudinal Study (CHARLS) cohort, individuals with 5-CST times in the middle range (8.65–11.45 s) and the highest range (≥ 11.45 s) exhibit a 1.306-fold (95% CI 1.091–1.562) and 1.329-fold (95% CI 1.106–1.596) increased risk of developing diabetes, respectively [11]. However, this study did not account for variations across different age groups and adiposity. In parallel, findings from the Asian Women’s Health Study indicate that women with 5-CST times of ≥ 12 s have a 2.3-fold increased risk of diabetes [12]. These inconsistencies may be attributed to differences in skeletal muscle assessment methodologies, population heterogeneity, or insufficient control of confounding variables.
Consequently, this study proposes a 9-year prospective cohort analysis utilizing the CHARLS database to address three primary scientific inquiries: (1) Assess the concurrent impact of muscle strength and physical performance on the longitudinal development of diabetes; (2) Analyze the influence of population heterogeneity, such as age, gender and adiposity on these associations; (3) Investigate the underlying mechanisms. This study aims to enhance the understanding of the relationship between skeletal muscle function and glucose metabolism, providing new methods for risk assessment in high-risk groups and supporting customized exercise interventions. Unlike previous CHARLS studies, it includes longer follow-up, detailed subgroup analyses, direct comparisons of muscle function indicators, and advanced visualization tools for better clinical interpretation [11, 13, 14].
Methods
Data and study sample
Nationally representative data from the CHARLS, a large-scale, interdisciplinary survey project led by the Peking University was used in our research. The CHARLS national baseline survey was conducted in 2011, covering 450 villages, 150 counties, and 28 provinces, with five survey waves released to date (Wave 1, 2011; Wave 2, 2013; Wave 3, 2015; Wave 4, 2018; Wave 5, 2020) [15]. Our study includes 13,974 participants who participated in the physical examination in 2011. We filtered out those with fractures or other injuries that could affect the results of physical examination tests (n = 598), as well as those with abnormal or missing data (n = 5,481) and patients with diabetes (n = 1,291). Ultimately, we enrolled 6,604 participants who were longitudinally analyzed and followed up (Fig. 1). The CHARLS survey was conducted in accordance with the Declaration of Helsinki [16] and ethically approved by the institutional review board at Peking University. All participants provided written informed consent prior to participation.
Fig. 1.
Flowchart of study sample from the China Health and Retirement Longitudinal Study (CHARLS)
Anthropometric measurement
Muscle strength is evaluated by grip strength, and low muscle strength is defined as handgrip strength < 28 kg for males and < 18 kg for females. Relative grip strength was calculated as the ratio of absolute grip strength to body weight or to BMI [17]. Physical performance is evaluated by the 5-CST, and low physical performance is defined as the 5-CST taking more than 12 s [18]. Waist circumference is defined as the horizontal girth measurement taken around the waist at the level of the navel.
The appendicular skeletal muscle mass (ASM) was estimated using a previously validated equation for Chinese populations [19]. The skeletal muscle index (SMI) was derived as ASM divided by height in meters squared. Consistent with prior studies [20], low muscle mass was defined as the lowest 20th percentile of SMI in the study population, corresponding to SMI < 7.00 kg/m² in male and < 5.28 kg/m² in female.
 |
Study outcome
Participants with prevalent diabetes at baseline (Wave 1) were excluded from our analysis. The primary outcome for this study was new-onset diabetes, which was primarily ascertained through self-report. Participants were considered to have new-onset diabetes if they either: (1) self-reported physician diagnosis (Waves 2–5) or (2) use of glucose-lowering medications (Waves 2–4), This assessment was complemented in wave 3 by objective biochemical measurements, according to the American Diabetes Association criteria [21, 22]. The date of new-onset diabetes diagnosis was determined using either the self-reported diagnosis date or the abnormal blood glucose measurements.
Parameters of anthropometric indices related to insulin resistance (IR)
BMI was calculated as the body weight (kg) divided by the square of body height (m2) [23]. The triglycerides-glucose (TyG) index was calculated as LN [triglycerides (TG [mg/dl]) × FPG (mg/dl)/2] [24]. The triglycerides-glucose - waist circumference (TyG-WC, TyG index × WC [cm]) have been reported as a surrogate marker of IR [25].
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The weight adjusted waist index (WWI) was calculated as WC (cm) divided by the square root of weight (kg) [26].
Physical activity assessment
Physical activity data were derived from the CHARLS database, which documents the weekly frequency (days per week) of engaging in at least 10 min of activity and categorizes daily duration into four levels: More than 10 min and less than 30 min, more than 30 min and less than 2 h, more than 2 h and less than 4 h, and more than 4 h. As the questionnaire did not provide exact times, daily duration was quantified using midpoint values: 20, 75, 180, and 240 min for the respective categories [27]. The total physical activity volume = MET value for each activity (3.3 for walking, 4.0 for moderate activity, and 8.0 for vigorous activity) × duration (in minutes) × frequency per week [28]. Finally, PA was categorized into two levels (low vs. high) according to the median MET-minutes/week value [29].
Statistical analysis
All data were analyzed using IBM SPSS software (version 20.0). The normality of variables was tested using the Kolmogorov-Smirnov (KS) test and Shapiro-Wilk (SW) test. Continuous variables were expressed as medians and interquartile ranges (IQRs) when they did not coincide with normal distribution. The baseline demographic and clinical data of the groups were compared using the Mann-Whitney test for continuous variables and a chi-square test for categorical variables. The Kaplan-Meier analysis was used to compare the time to new-onset diabetes between two groups. Age, BMI, and other relevant factors were included in our analytic models as covariates. Kaplan-Meier curves were performed using R Programming Language (version 4.2.1) and the log-rank test was utilized to compare the differences between the groups. The Cox proportional hazards model was used to investigate the predicting factors for the study outcome. The proportional hazards assumption was verified using Schoenfeld residuals. All variables that showed a statistically significant association in the univariable analysis were selected as candidates for the multivariable model. * p value < 0.05 indicates statistical significances.
Results
Baseline characteristics
Among the 6,604 participants, 3,020 were male (45.7%) and 3,584 were female (54.3%), with a median age of 58 years (IQR 51–65). Of these, 605 (9.2%) had low muscle strength and 2,025 (30.7%) had low physical performance. Over the 9-year follow-up period, a total of 823 new-onset diabetes cases were identified. Table 1 detailed the participant characteristics. Compared to those with normal physical performance, individuals with low physical performance were significantly older and showed elevated levels of C-reactive protein (CRP), weight-adjusted waist index (WWI) and TyG-WC (all p < 0.05), alongside lower sleep duration, creatinine, uric acid, grip strength, relative grip strength, ASM, SMI and reduced smoking and alcohol using rates. However, no significant differences were observed between groups for BMI, FPG, HbA1c, white blood cell (WBC) count, lipid profiles (total cholesterol, low-density lipoprotein cholesterol (LDLc), HDLc, TG), or blood urea nitrogen. Compared to those with normal muscle strength, individuals with low muscle strength were significantly older and showed elevated levels of CRP, WWI and the time of 5-CST (all p < 0.05), alongside lower sleep duration, BMI, TC, LDLc, ASM, SMI and reduced alcohol using rates.
Table 1.
Baseline characteristics of participants
| Physical performance | Muscle strength | ||||||
|---|---|---|---|---|---|---|---|
| Characteristics | Over all (n = 6604) |
Normal physical performance (n = 4579) | Low physical performance (n = 2025) | p value | Normal muscle strength (n = 5999) |
Low muscle strength (n = 605) |
p value |
| Age, median (IQR), y | 58 (51–65) | 56 (49–63) | 61 (55–70) | < 0.001 | 57 (50–64) | 67 (60–74) | < 0.001 |
| Gender (Male), No. (%) | 3020 (45.7) | 2236 (48.8) | 784 (38.7) | < 0.05 | 2736 (45.6) | 284 (46.9) | 0.279 |
| Smoking, No. (%) | 2525 (38.2) | 1828 (39.9) | 697 (34.4) | < 0.001 | 2286 (38.1) | 239 (39.5) | 0.264 |
| Alcohol use, No. (%) | 2173 (32.9) | 1643 (35.9) | 530 (26.2) | < 0.001 | 2017 (33.6) | 156 (25.8) | < 0.001 |
| Sleep duration, median (IQR), hour | 7.0 (5.0–8.0) | 7.0 (5.0–8.0) | 6.0 (5.0–8.0) | 0.029 | 7.0 (5.0–8.0) | 6.0 (5.0–8.0) | 0.007 |
| Physical activity level (High), No. (%) | 370 (49.5) | 295 (50.8) | 75 (44.9) | 0.106 | 345 (50.2) | 25 (41) | 0.106 |
| BMI, median (IQR), kg/m2 | 23.0 (20.7–25.5) | 23.0 (20.8–25.5) | 23.0 (20.5–25.5) | 0.138 | 23.1 (20.9–25.6) | 21.7 (19.7–24.2) | < 0.001 |
| FPG, median (IQR), mg/dL | 100.3 (93.4-107.3) | 100.3 (93.2-107.3) | 99.7 (93.4-107.5) | 0.607 | 100.3 (93.4-107.3) | 99.7 (92.7-108.2) | 0.610 |
| HbA1c, median (IQR), % | 5.1 (4.9–5.3) | 5.1 (4.9–5.4) | 5.1 (4.9–5.3) | 0.441 | 5.1 (4.9–5.4) | 5.1 (4.9–5.3) | 0.900 |
| WBC, median (IQR), mg/dL | 5.9 (4.9–7.2) | 5.9 (4.9–7.2) | 5.9 (4.9–7.2) | 0.391 | 5.9 (4.9–7.2) | 5.8 (4.9–7.1) | 0.331 |
| CRP, median (IQR), mg/dL | 0.9 (0.5-2.0) | 0.9 (0.5–1.9) | 1.0 (0.5–2.2) | < 0.001 | 0.9 (0.5–1.9) | 1.1 (0.6–2.7) | < 0.001 |
| TC, median (IQR), mg/dL | 189.8 (167.0-213.8) | 190.6 (167.4-213.8) | 189.4 (166.6-214.2) | 0.620 | 190.6 (167.4-214.2) | 184.8 (161.2-209.9) | 0.001 |
| LDLc, median (IQR), mg/dL | 114.8 (94.3-136.9) | 115.2 (94.7-136.9) | 114.4 (93.9-137.8) | 0.620 | 115.2 (94.7-137.6) | 111.3 (90.1-133.4) | 0.001 |
| HDLc, median (IQR), mg/dL | 50.3 (41.4–60.7) | 50.3 (41.4–61.1) | 50.3 (41.8–60.7) | 0.967 | 50.3 (41.4–60.7) | 51.8 (42.9–61.5) | 0.224 |
| TG, median (IQR), mg/dL | 100.0 (72.6-143.4) | 100.0 (71.7-143.4) | 101.8 (72.6-144.3) | 0.675 | 100.9 (72.6-144.3) | 97.3 (71.7-139.8) | 0.241 |
| Creatinine, median (IQR), mg/dL | 0.7 (0.6–0.9) | 0.8 (0.7–0.9) | 0.7 (0.6–0.8) | < 0.001 | 0.8 (0.6–0.9) | 0.7 (0.6–0.8) | 0.058 |
| Uric acid, median (IQR), mg/dL | 4.2 (3.5–5.1) | 4.3 (3.6–5.1) | 4.1 (3.4–4.9) | < 0.001 | 4.3 (3.6–5.1) | 4.2 (3.5–5.1) | 0.292 |
| WWI, median (IQR) | 11.1 (10.5–11.7) | 11.0 (10.5–11.5) | 11.3 (10.7–11.9) | < 0.001 | 11.0 (10.5–11.6) | 11.4 (10.8–12.1) | < 0.001 |
| TyG-WC, median (IQR) | 713.8 (645.1-796.5) | 711.5 (642.4-793.4) | 721.2 (651.9-802.5) | 0.001 | 714.8 (646.7-798.6) | 699.1 (627.1-778.1) | < 0.001 |
| ASM, median (IQR), kg | 16.8 (13.7–20) | 17.2 (14.0-20.4) | 15.8 (13.0-19.2) | < 0.001 | 16.9 (13.8–20.2) | 15.2 (12.1–18.0) | < 0.001 |
| SMI, median (IQR), kg/m2 | 6.7 (5.9–7.5) | 6.9 (6.0-7.6) | 6.5 (5.6–7.3) | < 0.001 | 6.8 (5.9–7.5) | 6.5 (5.4–7.1) | < 0.001 |
| Grip strength, median (IQR), kg | 32.0 (25.5–40.0) | 33.8 (27.5–41.0) | 28.0 (21.2–35.0) | < 0.001 | 33.0 (27.5–40.0) | 16.5 (14.0–23.0) | < 0.001 |
| Grip strength/body weight, median (IQR), kg/kg | 0.6 (0.5–0.7) | 0.6 (0.5–0.7) | 0.5 (0.4–0.6) | < 0.001 | 0.6 (0.5–0.7) | 0.3 (0.2–0.4) | < 0.001 |
| Grip strength/BMI, median (IQR), kg/ (kg/m2) | 1.4 (1.1–1.8) | 1.5 (1.2–1.8) | 1.2 (0.9–1.6) | < 0.001 | 1.4 (1.1–1.8) | 0.8 (0.6–1.1) | < 0.001 |
| 5-CST, median (IQR), s | 10.0 (8.0-12.5) | 8.9 (7.4–10.2) | 14.3 (12.9–16.4) | < 0.001 | 9.8 (7.9–12.2) | 12.6 (10.1–15.9) | < 0.001 |
Abbreviation: IQR, interquartile ranges; BMI, body mass index; FPG, fasting plasma glucose; HbA1c, glycated haemoglobin A1c; WBC, white blood cell; CRP, C-reactive protein; TC, total cholesterol; LDLc, low-density lipoprotein cholesterol; HDLc, high-density lipoprotein cholesterol; TG, triglycerides; TyG, triglycerides-glucose; WWI, weight-adjusted waist index; WC, waist circumference; ASM, appendicular skeletal muscle mass; SMI, skeletal muscle index; 5-CST, the five-times chair stand test
Survival analysis of physical performance or muscle strength and diabetes
To assess the impact of physical performance versus muscle strength on diabetes development, participants were divided into four groups: normal muscle strength and physical performance, low muscle strength only, low physical performance only, and both of low muscle strength and low physical performance. The unadjusted Kaplan-Meier curve demonstrated significant differences in diabetes incidence among the above four groups (p = 0.00017) (Fig. 2a). Pairwise comparisons revealed that individuals with low physical performance only exhibited a significantly elevated new-onset diabetes risk compared to the normal muscle strength and physical performance group (p = 0.000015), while there were no significant differences between any other pairwise comparisons (Fig. 2b). Based on these findings, the analysis subsequently concentrated on physical performance as the primary determinant. When participants were dichotomized into low versus normal physical performance groups, the Kaplan-Meier curve confirmed a markedly higher incidence of new-onset diabetes in the low physical performance group (p < 0.0001) (Fig. 2c).
Fig. 2.
Survival analysis of the time to new-onset diabetes. (a) Kaplan-Meier analyses compared the incidence of new-onset diabetes among 6,604 participants stratified into four groups based on muscle strength and physical performance: Group A (normal strength and normal performance; 496 incident events), Group B (low strength with normal performance; 32 events), Group C (normal strength with low performance; 250 events), and Group D (low strength and low performance; 45 events).Tabular data beneath Kaplan-Meier curves indicate the number at risk at each follow-up interval. (b) The heatmap showed pairwise comparison of four groups A, B, C and D. Note: This analysis involves multiple comparisons and these comparisons are unadjusted for multiple testing. The resulting p-values should be interpreted with caution as they are intended for hypothesis-generating rather than confirmatory purposes. *** is denoted as p < 0.001. NA indicates not applicable. (c) Kaplan-Meier analyses showed that participants with low physical performance (the total number of events: 295) demonstrated a significantly higher incidence of new-onset diabetes compared to those with normal physical performance (the total number of events: 528). Kaplan–Meier analyses showed that participants with low physical performance (Groups A and B; 295 incident cases) had a significantly higher risk of developing new-onset diabetes compared to those with normal physical performance (Groups C and D; 528 cases)
Cox proportional hazards analysis of muscle strength or physical performance and diabetes
A Cox proportional hazards analysis was performed to investigate the determinants of new-onset diabetes (Table 2). The analysis revealed that low physical performance was significantly associated with an increased risk of new-onset diabetes (HR 1.354, 95% CI 1.174–1.561; p < 0.001), whereas muscle strength showed no significant relationship (HR 1.151, 95% CI 0.910–1.455; p = 0.241). All variables that showed a statistically significant association (p < 0.05) in the univariable analysis (age, muscle mass, physical performance, BMI, CRP, TC, LDLc, uric acid, WWI, TyG-WC, ASM, SMI, grip strength/body weight and grip strength/BMI in this case) were selected as candidates for the multivariable model (fully adjusted). A conditional forward stepwise Cox proportional hazards regression was employed, using the likelihood ratio (LR) as the selection criterion, to construct the final model while mitigating potential multicollinearity. This procedure yielded a final multivariable model in which age, CRP, LDLc, TyG-WC, and physical performance remained independently and statistically significantly associated with the outcome (stepwise selection). Low physical performance remained an independent predictor (HR 1.219, 95% CI 1.052–1.414; p = 0.008) after adjusting for confounders. To facilitate the translation of our findings into a practical tool, a nomogram (Fig. 3a) was developed to graphically represent the predictive contribution of each covariate to the risk of new-onset diabetes, enabling individualized risk estimation based on multiple factors simultaneously. This allows clinicians to estimate an individual’s 5-years and 9-years new-onset diabetes risk by summing points assigned to their specific characteristics, such as age, CRP, LDLc, TyG-WC and physical performance. Furthermore, Sankey diagrams (Fig. 3b) were utilized to illustrate the dynamic relationships and transitions among key variables, particularly in relation to physical performance levels (low vs. normal), age subgroups, TyG-WC categories (TyG-WC categorized into quartiles from the lowest (Q1) to the highest (Q4), Q1-Q4), CRP categories (Q1-Q4) and new-onset diabetes. These visualizations provide an intuitive means of understanding complex associations within the model and support clinical decision-making.
Table 2.
Screening of independent prognostic factors in diabetes using univariate and multivariate cox regression analysis
| Univariate analysis | Multivariate analysis (fully adjusted) | Multivariate analysis (stepwise selection) | ||||
|---|---|---|---|---|---|---|
| Characteristics | HR (95% CI) | p value | HR (95% CI) | p value | HR (95% CI) | p value |
| Age | 1.015 (1.008–1.022) | < 0.001 | 1.013 (1.004–1.022) | 0.003 | 1.012 (1.004–1.020) | 0.002 |
| Gender | ||||||
| Male | Reference | |||||
| Female | 1.044 (0.909–1.198) | 0.543 | ||||
| Smoking | ||||||
| No | Reference | |||||
| Yes | 0.977 (0.910–1.049) | 0.529 | ||||
| Alcohol use | ||||||
| No | Reference | |||||
| Yes | 0.928 (0.801–1.076) | 0.323 | ||||
| Sleep duration | 0.978 (0.943–1.015) | 0.241 | ||||
| Physical activity level | ||||||
| Low | Reference | |||||
| High | 0.772 (0.500-1.192) | 0.243 | ||||
| Muscle mass | ||||||
| Normal | Reference | |||||
| Low | 0.679 (0.558–0.827) | < 0.001 | 1.110 (0.867–1.422) | 0.409 | ||
| Muscle strength | ||||||
| Normal | Reference | |||||
| Low | 1.151 (0.910–1.455) | 0.241 | ||||
| Physical performance | ||||||
| Normal | Reference | |||||
| Low | 1.354 (1.174–1.561) | < 0.001 | 1.244 (1.068–1.448) | 0.005 | 1.219 (1.052–1.414) | 0.008 |
| BMI | 1.075 (1.061–1.089) | < 0.001 | 0.961 (0.920–1.004) | 0.073 | ||
| CRP | 1.010 (1.003–1.016) | 0.005 | 1.010 (1.002–1.017) | 0.014 | 1.010 (1.002–1.017) | 0.012 |
| TC | 1.004 (1.002–1.005) | < 0.001 | 0.995 (0.990–0.999) | 0.015 | ||
| LDLc | 1.004 (1.002–1.006) | < 0.001 | 1.007 (1.003–1.012) | 0.002 | 1.002 (1.000-1.004) | 0.019 |
| Creatinine | 0.887 (0.608–1.295) | 0.535 | ||||
| Uric acid | 1.098 (1.039–1.159) | 0.001 | 0.998 (0.937–1.064) | 0.962 | ||
| WWI | 1.322 (1.231–1.421) | < 0.001 | 0.804 (0.688–0.939) | 0.006 | ||
| TyG-WC | 1.004 (1.003–1.005) | < 0.001 | 1.006 (1.004–1.008) | < 0.001 | 1.004 (1.003–1.005) | < 0.001 |
| ASM | 1.029 (1.012–1.046) | 0.001 | 0.894 (0.784–1.019) | 0.092 | ||
| SMI | 1.195 (1.126–1.269) | < 0.001 | 1.499 (1.033–2.175) | 0.033 | ||
| Grip strength | 0.996 (0.989–1.003) | 0.225 | ||||
| Grip strength/body weight | 0.276 (0.181–0.423) | < 0.001 | 2.320 (0.051-105.197) | 0.665 | ||
| Grip strength/BMI | 0.665 (0.574–0.772) | < 0.001 | 0.719 (0.159–3.248) | 0.668 | ||
Abbreviation: HR: hazard ratio; CI: confidence interval; BMI, body mass index; CRP, C-reactive protein; TC, total cholesterol; LDLc, low-density lipoprotein cholesterol; WWI, weight-adjusted waist index; TyG-WC, the triglycerides-glucose - waist circumference; ASM, appendicular skeletal muscle mass; SMI, skeletal muscle index
Fig. 3.
Screening of independent prognostic factors of new-onset diabetes. (a) Nomogram of the multivariate Cox proportional hazards regression model. (b) Sankey diagram illustrating the flow and relationships between physical performance levels (low vs. normal), age subgroups, TyG-WC categories (Q1-Q4), and CRP categories (Q1-Q4), in relation to the risk of new-onset diabetes. Abbreviation: CRP, C-reactive protein; LDLc, low-density lipoprotein cholesterol; TyG-WC, the triglycerides-glucose - waist circumference; Q1-Q4, groups from the lowest quartile (Q1) to the highest quartile (Q4)
Cox regression analysis of quantified physical performance and new-onset diabetes
When analyzing 5-CST performance as a continuous variable, we observed a significant positive association between longer 5-CST times (indicating poorer physical performance) and the risk of new-onset diabetes (HR 1.026, 95% CI: 1.013–1.039). This association remained significant after adjusting for age and BMI (adjusted HR 1.013, 95% CI: 1.000-1.027), suggesting that each additional second in 5-CST time increases new-onset diabetes risk by about 1.3%. After adjusting for covariates, including age, BMI, CRP, LDLc, TyG-WC, smoking, alcohol use, sleep duration and physical activity level (covariates from aforementioned established model alongside potential lifestyle risk factors for new-onset diabetes), the trend was not significant (Table 3).
Table 3.
The influence of physical performance on the occurrence of new-onset diabetes
| Total (n = 6604) | Male (n = 3020) | Female (n = 3584) | ||||
|---|---|---|---|---|---|---|
| HR (95% CI) | p value | HR (95% CI) | p value | HR (95% CI) | p value | |
| Model 1 | 1.026 (1.013–1.039) | < 0.001 | 1.046 (1.022–1.070) | < 0.001 | 1.018 (1.001–1.035) | 0.038 |
| Model 2 | 1.013 (1.000-1.027) | 0.049 | 1.041 (1.016–1.066) | 0.001 | 1.002 (0.983–1.021) | 0.846 |
| Model 3 | 0.996 (0.936–1.059) | 0.896 | 1.014 (0.934–1.101) | 0.734 | 0.962 (0.876–1.057) | 0.424 |
Model 1: not adjusted for any covariates; Model 2: adjusted for covariates including age and BMI; Model 3: adjusted for covariates including age, BMI, CRP, LDLc, TyG-WC, smoking, alcohol use, sleep duration and physical activity level
Abbreviations: CI, confidence interval; BMI, body mass index; CRP, C-reactive protein; LDLc, low-density lipoprotein cholesterol; TyG-WC, the triglycerides-glucose - waist circumference
Subgroup analysis of physical performance or muscle strength and diabetes
Figure 4 showed the Cox regression analyses of subgroups according to age, sex and BMI. Low physical performance was significantly associated with a higher risk of new-onset diabetes (p < 0.001). The association between low physical performance and new-onset diabetes was significantly modified by gender (p for interaction < 0.001), BMI (p for interaction < 0.001), and age (p for interaction < 0.001). Subsequent exploratory analyses revealed that significant differences in physical performance were found in the 40–49 age group (p = 0.002), but not in the participants of other age groups (p >0.05). Gender-based analysis showed significant differences in new-onset diabetes for both males (p < 0.001) and females (p = 0.032). In accordance with the World Health Organization’s (WHO) criteria, we perform analyses utilizing a BMI of 25 kg/m2 as the delineating threshold [30]. Low physical performance significantly affected new-onset diabetes onset in participants with lower BMI (BMI < 25 kg/m2, p < 0.001), but not in the participants with higher BMI (BMI >= 25 kg/m2, p = 0.203).
Fig. 4.
Subgroup analysis: forest plot of hazard ratios for new-onset diabetes. Forest plot of the hazard ratios for low physical performance and low muscle strength in relation to the new-onset diabetes in different subgroups. Abbreviation: HR: hazard ratio; CI: confidence interval; BMI, body mass index
In contrast, the association for low muscle strength with new-onset diabetes was not significantly modified by gender (p for interaction = 0.252), BMI (p for interaction = 0.171) or age (p for interaction = 0.203). As mentioned above, the Cox regression analysis showed that low muscle strength had no significant effect on new-onset diabetes (p = 0.241). Subgroup analyses further supported the null association, showing no significant effect across all age groups (40-49y: p = 0.390; 50-59y: p = 0.792; 60-69y: p = 0.892; 70-79y: p = 0.564; 80-89y: p = 0.178) and both genders (males: p = 0.374; females: p = 0.437). Interestingly, muscle strength showed a modest association in lower BMI (BMI < 25 kg/m2, p = 0.011), but not in the participants with higher BMI (BMI > = 25 kg/m2, p = 0.672).
Discussion
In this nationally representative prospective cohort study using data from the CHARLS (2011–2020), we identified low physical performance, assessed by 5-CST, as an independent risk factor for new-onset diabetes, particularly among individuals aged 40–49 years and those with a BMI < 25 kg/m². In contrast, no significant association was observed between absolute grip strength and new-onset diabetes risk. Higher relative grip strength was inversely associated with new-onset diabetes in univariable analysis, suggesting a crude protective effect. However, it was not retained in the final multivariable model. These findings underscore the importance of functional muscle performance in glucose regulation and offer new perspectives for early stratification of high-risk population for new-onset diabetes screening, particularly in younger adults (aged 40–49 years) and non-obese individuals (BMI < 25 kg/m²).
Previous studies have yielded inconsistent findings regarding the association between grip strength and new-onset diabetes. Several large-scale cohort studies have demonstrated an inverse association. Boonpor et al. reported that each standard deviation decrease in grip strength was associated with a 12% (95% CI: 1.08–1.16) and 20% (95% CI: 1.15–1.25) increased risk of new-onset diabetes in women and men, respectively, after adjusting for multiple confounders [6]. Other studies by Wander et al. [31] and Momma et al. [32] further support this relationship, while analyses from NHANES linked lower adolescent grip strength to markers of insulin resistance [33]. Conversely, the CoLaus study found no association, likely due to the inclusion of metabolically healthier participants at baseline [34]. These discrepancies may be attributed to differences in population characteristics, grip strength measurement protocols, and the degree of confounder adjustment. Notably, the association appears more pronounced in younger individuals [35], suggesting age as an effect modifier. We expanded our analysis to include relative grip strength (normalized to body weight and BMI). While univariable models indicated a protective association for both absolute and relative grip strength, neither measure was retained in the final multivariable Cox model selected by the forward stepwise procedure. The final model identified age, CRP, LDL-C, TyG-WC, and physical performance as the most essential set of independent predictors. Our finding that grip strength lost significance after adjusting for metabolic and inflammatory factors reinforces the notion that it serves as a proxy, rather than a direct driver, of diabetes risk. In contrast, physical performance remained independently predictive, supporting its use as a more sensitive functional indicator in risk stratification.
Current consensus, including the 2019 EWGSOP2 guidelines, recommends grip strength and 5-CST as proxies for muscle strength [5]. A cohort study found that grip strength and 5-CST independently relate to all-cause mortality, with 5-CST having a stronger connection. Therefore, both tests should be used together, not interchangeably, to assess probable sarcopenia [36]. However, studies directly comparing the predictive value of upper vs. lower limb muscle function in diabetes onset remain limited. A prior 7-year CHARLS follow-up study found that both reduced relative grip strength (handgrip strength to body weight ratio) and prolonged 5-CST times were associated with higher diabetes risk in individuals with obesity, whereas only poor 5-CST performance was predictive among non-obese individuals [11]. Building on this, our longer-term analysis revealed that impaired lower-limb physical performance (5-CST time) was significantly associated with increased diabetes incidence (HR = 1.354, 95% CI: 1.174–1.561). Although longer 5-CST time was initially associated with higher diabetes risk, the association became non-significant after full adjustment, suggesting limited independent predictive value. Moreover, the test excludes the frailest individuals who are potentially at the highest risk. Performance depends not only on muscle strength but also on balance, coordination, and power [37]. In contrast, no significant association was observed with low muscle strength (HR = 1.151, 95% CI: 0.910–1.455). This suggests that lower-extremity function may be a more sensitive and specific indicator of metabolic risk than upper-limb strength. The biological rationale for this association is well supported. Skeletal muscle constitutes approximately 40% of total body mass and is responsible for over 80% of postprandial glucose uptake [38]. Insulin resistance in skeletal muscle is a primary and early event in the pathogenesis of diabetes, often preceding overt hyperglycaemia by years or even decades [38]. Prior studies have also demonstrated selective lower-limb muscle weakness among individuals with diabetes [39], with lower-body resistance, aerobic, and high-intensity interval training shown to improve insulin sensitivity and glucose metabolism [40].
Interestingly, no significant differences in FPG or HbA1c were observed at baseline across performance or strength strata, although key demographic and anthropometric characteristics (age, gender, and BMI) varied significantly. Subgroup analysis revealed that the predictive strength of physical performance was greatest in the 40–49 age group, where individuals with low physical performance had a 1.835-fold increased risk of new-onset diabetes compared to those with normal physical performance. This finding supports the hypothesis that muscle function decline may precede dysglycaemia and reinforces the importance of early intervention. Muscle mass begins to decline at approximately 1% per year starting in the fourth decade of life, with more rapid loss of strength and power [41, 42]. Importantly, skeletal muscle in middle age retains significant plasticity; appropriately prescribed resistance training can increase mitochondrial density, oxidative capacity, and insulin responsiveness [43, 44].
Notably, the association between low physical performance and new-onset diabetes was more prominent among individuals with a BMI < 25 kg/m² (HR = 1.528, 95% CI: 1.266–1.845), but not in those with BMI ≥ 25 kg/m². These results align with the concepts of metabolically unhealthy normal weight and metabolically healthy obesity [45], and challenge the sufficiency of BMI as a screening metric. Lean individuals may still present with ectopic fat deposition, insulin resistance, and β-cell dysfunction phenomena especially prevalent in Asian populations [46]. The emerging role of intermuscular adipose tissue (IMAT), which accumulates between muscle fibers and is associated with impaired glucose metabolism, is particularly relevant. Studies have demonstrated that individuals with new-onset diabetes have elevated intermuscular adipose tissue levels and stronger insulin resistance [47–49]. An unexpected finding was that lower estimated muscle mass appeared associated with reduced risk of new-onset diabetes. This counterintuitive result may stem from the limitation of the prediction equation, which estimates muscle quantity but not muscle quality [50]. In non-obese individuals, high intermuscular adipose tissue (IMAT) may lead to misclassification. This confusion between muscle “quality” and “quantity” could explain the paradoxical association.
A study shows that combining the TyG index with BMI, WC, and WHtR enhances its potential to assess and predict diabetes risk over time, with TyG-WC being the most effective for short-term risk assessment [51]. To further explore this link, we examined TyG-WC, a validated surrogate of insulin resistance, and observed that in multivariate Cox proportional hazards regression analyses, TyG-WC was included as a covariate to ascertain the precise independent effect of low physical performance on new-onset diabetes risk. This suggests that lower-limb performance may serve as a sensitive early indicator of glucose metabolic dysfunction, particularly among individuals with normal BMI but underlying metabolic derangements. Importantly, gender-stratified analysis revealed that the association between 5-CST and new-onset diabetes was more pronounced in men (HR = 1.551) than in women (HR = 1.228), potentially reflecting hormonal influences. Testosterone deficiency in men is associated with increased visceral adiposity and insulin resistance [52], while estrogen may protect against metabolic dysfunction via modulation of insulin sensitivity, lipid metabolism, and inflammation [53].
However, several limitations should be noted. First, our dataset includes censored values resulting from patient loss to follow-up and the termination of the study. These censored data points signify that we were unable to ascertain the precise timing of event occurrences for certain participants. The presence of censoring, particularly in right-censored cases (diabetes-free at the conclusion of the study), may introduce potential bias into the results. Blood biomarkers at follow-up were measured only in Wave 3, which may result in non-differential misclassification of diabetes status and potentially underestimate the incidence of new-onset diabetes. To mitigate this issue, we employed standard survival analysis techniques, including Kaplan-Meier estimation and Cox proportional hazards models. Importantly, the extended mean follow-up duration of 7.87 years (maximum 9 years) enabled the observation of a greater number of outcome events, thereby contributing to the reduction of potential bias. Second, our findings are based on a single cohort of middle-aged and older Chinese adults, which may limit generalizability to younger or non-Asian populations. Third, the absence of direct body composition measurements by dual-energy X-ray absorptiometry, or bioelectrical impedance analysis. Fourth, while the 5-CST is widely used, standardized protocols are lacking, potentially affecting comparability. Lastly, the observational nature of the study limits causal inference, despite extensive adjustment for confounders.
Conclusions
In this prospective cohort, low physical performance, assessed by slower 5-CST, was independently associated with increased new-onset diabetes risk, especially among individuals aged 40–49 years and those with BMI < 25 kg/m². Grip strength showed no such association. These findings underscore the potential of simple lower-limb function tests, like the 5-CST, in early identification of new-onset diabetes risk-particularly in metabolically unhealthy individuals with normal BMI. Therefore, we suggest that the 5-CST be used alongside grip strength in clinical screening algorithms, providing a more complete assessment of muscle function and early diabetes risk. Incorporating physical performance into routine screening may improve prevention strategies beyond traditional metrics like BMI or muscle strength alone.
Acknowledgements
We are grateful to the China Health and Retirement Longitudinal Study (CHARLS) team for providing the data sets, as well as to all the volunteers and participants involved in the CHARLS research. CHARLS was ethically approved by the Institutional Review Board at Peking University (00001052–11014, 00001052–11015). We are grateful for the guidance provided by Professor Roger A. Fielding, MD, PhD, Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center, Tufts University, Boston, MA, USA (roger.fielding@tufts.edu). His insights profoundly enriched this work. We acknowledge that no financial compensation was provided for these contributions.
Abbreviations
- CHARLS
The China Health and Retirement Longitudinal Study
- 5-CST
The five-times chair stand test
- ASM
Appendicular skeletal muscle mass
- SMI
Skeletal muscle index
- FPG
Fasting plasma glucose
- RPG
Random plasma glucose
- HbA1c
Glycated haemoglobin A1c
- IR
Insulin resistance
- TyG
Triglycerides-glucose
- TG
Triglycerides
- WWI
Weight-adjusted waist index
- WC
Waist circumference
- IQR
Interquartile ranges
- BMI
Body mass index
- FPG
Fasting plasma glucose
- HbA1c
Glycated haemoglobin A1c
- WBC
White blood cell
- CRP
C-reactive protein
- TC
Total cholesterol
- LDLc
Low-density lipoprotein cholesterol
- HDLc
High-density lipoprotein cholesterol
- HR
Hazard ratio
- CI
Confidence interval
Author contributions
Mingyu Zhu, Qin Zhu, Hongxia Liu, Rong Huang and Yaomin Hu contributed to the study design. Rui Du, Jie Cao, Ning Chen, Tingting Han, Yuru Yan, Yurong Weng and Ting Zhang contributed to acquisition of data. Mingyu Zhu, Rui Du, Ting Liu, Mi Xiang, Ping Hu and Rong Huang contributed to analysis and interpretation of data. Mingyu Zhu, Qin Zhu, Rui Du, Hongxia Liu and Rong Huang wrote the manuscript. All coauthors revised the manuscript together.
Funding
National Scientific Foundation of China (No. 81870554;U22A20287). Shanghai Municipal Health Commission project (202240042).
Data availability
The data that support the findings of this study are available from the website of the China Health and Retirement Longitudinal Study (CHARLS) at http://charls.pku.edu.cn/.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Mingyu Zhu, Qin Zhu and Hongxia Liu contributed equally to this work.
Contributor Information
Yuru Yan, Email: yyryyc@126.com.
Rong Huang, Email: renjihuangrong@163.com.
Yaomin Hu, Email: amin99@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the website of the China Health and Retirement Longitudinal Study (CHARLS) at http://charls.pku.edu.cn/.