Insulin resistance–related indices, sarcopenia, and cardiovascular risk in CKM stages 1–3: a prospective cohort study from CHARLS

Abstract

Background

Sarcopenia often coexists with metabolic and renal dysfunction and may increase cardiovascular disease (CVD) risk. Insulin resistance (IR) is a key mechanism linking sarcopenia and cardiometabolic disorders. However, evidence on IR–related indices and CVD risk among individuals with sarcopenia across cardiovascular–kidney–metabolic (CKM) stages remains limited. This study aimed to examine associations between multiple IR–related indices and incident CVD, and to evaluate their predictive and mediating roles across CKM stages 1–3 and sarcopenia status.

Methods

We analysed 5,965 adults with CKM stages 1–3 from the China Health and Retirement Longitudinal Study (CHARLS), categorised as no sarcopenia, possible sarcopenia, or sarcopenia. We assessed associations between sarcopenia, eight IR–related indices—including triglyceride–glucose index–body mass index (TyG–BMI) and estimated glucose disposal rate (eGDR)—and incident CVD using Cox and the Fine–Gray competing risks model. Missing data were handled by multiple imputation. Dose–response relationships were evaluated with restricted cubic splines, and predictive performance was assessed with the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Mediation and sensitivity analyses explored potential pathways and the robustness of the results.

Results

63.8% of participants had possible sarcopenia or sarcopenia and were at higher risk of incident CVD and all-cause mortality. Mean follow-up was approximately 9 years. TyG–BMI was positively associated with CVD risk (HR = 3.58, 95% CI 2.50–5.14), whereas eGDR was inversely associated (HR = 0.36, 95% CI 0.28–0.47). The combined TyG–BMI and eGDR model improved CVD risk discrimination (AUC = 0.614, 95% CI 0.595–0.634), with gains in NRI (0.117, 95% CI 0.058–0.161) and IDI (0.013, 95% CI 0.007–0.023). IR–related indices accounted for 20%–46% of the association between CKM stage 3 (vs. stage 1) and CVD.

Conclusion

Possible sarcopenia and sarcopenia were associated with increased CVD risk, particularly in CKM stage 3. IR–related indices, especially TyG–BMI and eGDR, were strongly associated with CVD risk and improved prediction, suggesting that IR may partially mediate the impact of CKM progression and sarcopenia on CVD outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12902-026-02305-y.

Introduction

With global population ageing, the coexistence of multiple chronic conditions has emerged as a major public health challenge [1–3]. The cardiovascular–kidney–metabolic (CKM) syndrome refers to a pathological state characterised by concurrent cardiovascular abnormalities, renal dysfunction, and metabolic derangements [4]. This syndrome is highly prevalent among middle-aged and older adults [5], and progression across CKM stages (stages 0–3) is strongly associated not only with accelerated renal decline but also with a markedly increased risk of cardiovascular disease (CVD) [6, 7]. These observations highlight the urgent need to identify and manage high-risk individuals at an early stage. The American Heart Association has also emphasised that individuals with CKM stages 0–3 represent a key target population for the prevention of CVD events [4].

Sarcopenia, defined as the progressive loss of muscle mass, strength, and physical performance, is a common geriatric condition and a hallmark of frailty [8, 9]. In individuals with CKM syndrome, its association with CVD risk may be more pronounced, potentially due to the combined effects of metabolic disturbances and impaired renal function [10]. However, the impact of CKM stages on CVD risk across different sarcopenia statuses has not been comprehensively quantified, and the mechanisms underlying their joint association with CVD remain incompletely understood.

Insulin resistance (IR)—a core metabolic abnormality characterised by impaired glucose uptake and disrupted insulin signalling—is a key driver of cardiometabolic diseases [11, 12]. Within the CKM framework, IR is increasingly recognised as a central mechanism linking metabolic dysregulation, renal impairment, and CVD risk [13]. Although the hyperinsulinaemic–euglycaemic clamp is considered the gold standard for assessing IR, its clinical use is limited by high cost and operational complexity [14, 15]. Several surrogate indices, including the triglyceride–glucose index (TyG), the TyG–waist circumference (TyG–WC), the TyG–body mass index (TyG–BMI) [16, 17], and the estimated glucose disposal rate (eGDR) [18], have been validated as practical alternatives that reliably reflect IR levels and predict CVD and metabolic outcomes [19]. Nevertheless, in the context of CKM staging and coexisting sarcopenia, the independent and combined associations of these IR–related indices with CVD risk, as well as their potential mediating roles and predictive value, remain largely unexplored.

Therefore, using data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS), this study aimed to evaluate the impact of possible sarcopenia and sarcopenia on CVD risk among middle-aged and older adults with CKM stages 1–3, and to investigate the associations, mediating effects, and predictive performance of multiple IR indices within the joint framework of CKM progression and sarcopenia. In particular, we utilized competing risks analysis to account for mortality as a competing risk. Additionally, by integrating multiple IR indices, this study aimed to offer a more comprehensive understanding of their combined role in CKM-related CVD risk. This approach provides valuable insights into the interplay between CKM stages, sarcopenia, and metabolic dysfunction, with potential implications for improving early detection and prevention strategies for high-risk individuals within the CKM spectrum.

Methods

Data source and study population

Data for this study were obtained from the CHARLS, a nationally representative, community-based prospective cohort initiated in 2011. CHARLS targets Chinese adults aged ≥ 45 years and employs a multistage sampling design. The study ultimately enrolled approximately 17,700 participants [20]. The baseline survey (2011–2012) collected detailed information on sociodemographic characteristics, lifestyle behaviours, health status, laboratory measurements, and functional assessments. Follow-up interviews were conducted biennially. Five waves of nationwide data are currently available (2011, 2013, 2015, 2018, and 2020). These data include questionnaire responses, physical examinations, and venous blood samples. The study protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent. Reporting of this study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [21]. We included all baseline participants aged ≥ 45 years with complete data from the 2011 survey. The exclusion criteria were: (1) missing data for sarcopenia assessment, (2) incomplete information for CKM staging, (3) pre-existing CVD at baseline, (4) loss to follow-up, and (5) missing data on TyG and related indices.

Data assessment and definitions

Definitions of IR–related indices

As described in previous studies [19, 22, 23], eight IR–related indices were included to evaluate CVD risk across CKM stages and within the context of possible sarcopenia and sarcopenia. These indices comprised the TyG, calculated as ln(TG × FPG / 2), where triglycerides (TG, mg/dL) and fasting plasma glucose (FPG, mg/dL) were measured under fasting conditions; the TyG–BMI, defined as TyG × BMI, with BMI (kg/m²) calculated as weight (kg) divided by height squared (m²), reflecting overall adiposity; the TyG–WC, defined as TyG × WC (cm), capturing the influence of central obesity; and the TyG–waist-to-height ratio (TyG–WHtR), defined as TyG × WHtR, with WHtR calculated as WC divided by height (cm), reflecting abdominal fat distribution relative to body size. Additionally, the Metabolic Score for IR (METS–IR), calculated as ln[2 × FPG (mg/dL) + TG (mg/dL)] × BMI (kg/m²) / ln[high-density lipoprotein cholesterol (HDL-C, mg/dL)], served as a composite surrogate for IR; the eGDR, calculated as 21.158 − 0.09 × WC (cm) − 3.407 × hypertension (yes = 1 / no = 0) − 0.551 × glycated haemoglobin (HbA1c, %), assessed systemic insulin sensitivity; the cardiometabolic index (CMI) [24], defined as WHtR × (TG / HDL-C), integrating central adiposity and lipid profile; and the atherogenic index of plasma (AIP), calculated as log(TG / HDL-C), reflecting atherogenic lipid abnormalities. All eight indices were used to evaluate CVD risk across CKM stages in the context of possible sarcopenia or sarcopenia and were further incorporated into predictive modelling and mediation analyses. In this study, IR-related indices were analysed within two complementary frameworks: as independent predictors in regression models for risk stratification, and as potential mediators to explore pathways linking sarcopenia and CVD.

Definition of CKM

According to the American Heart Association Presidential Advisory on CKM [4], CKM status in this study was classified into four stages (0–3). Stage 0 indicates the absence of any CKM-related risk factors. Stage 1 refers to the presence of obesity or mild metabolic dysfunction without evidence of target-organ injury. Stage 2 is characterised by metabolic risk factors such as diabetes, hypertension, or dyslipidaemia, or by chronic kidney disease (CKD) with an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m². Stage 3 represents subclinical CVD. In this study, we additionally classified individuals with very high-risk CKD (G4–G5) [4] or elevated 10-year CVD risk based on the Framingham score as having subclinical CVD [25]. CKD stages were determined according to established estimated glomerular filtration rate (eGFR)–based classifications [26].

Definition of Sarcopenia

Sarcopenia in this study was defined according to the 2019 consensus of the Asian Working Group for Sarcopenia (AWGS) [9]. The diagnosis incorporated three domains: skeletal muscle strength, muscle mass, and physical performance. Participants with normal muscle strength, normal physical performance, and normal muscle mass, were classified as having no sarcopenia. Those with low muscle strength or reduced physical performance, but without low muscle mass, were categorised as having possible sarcopenia. Sarcopenia was confirmed when low muscle mass was present together with either low muscle strength or impaired physical performance. Muscle strength was assessed by trained personnel using a hand dynamometer, and the mean of maximal grip strength from both hands was used. In accordance with AWGS 2019, low muscle strength was defined as < 28 kg in men and < 18 kg in women [9]. Physical performance was evaluated by gait speed, the five-time chair stand test, and the Short Physical Performance Battery (SPPB). Impaired physical performance was defined as a gait speed < 1.0 m/s, a five-time chair stand time ≥ 12 s, or an SPPB score ≤ 9 [9]. Muscle mass was estimated using a previously validated anthropometric equation for the Chinese population: appendicular skeletal muscle mass (ASM) [27] = 0.193 × weight (kg) + 0.107 × height (cm) − 4.157 × sex (male = 1, female = 2) − 0.037 × age (years) − 2.631. Height-adjusted skeletal muscle mass index (SMI) was then calculated as ASM / height² (kg/m²). Low muscle mass was defined using sex-specific 20th percentile cut-off values: <7.00 kg/m² for men and < 5.25 kg/m² for women.

CVD diagnosis and follow-up

Incident CVD in this study was defined by excluding participants who reported existing CVD at the baseline survey. During follow-up (2013–2020), incident CVD cases were identified based on self-reported physician diagnoses, specifically by asking participants: “Have you ever been diagnosed by a doctor with heart attack, coronary heart disease, angina, congestive heart failure, stroke, or other heart problems?” For mediation analyses, CVD events occurring after the 2015 measurements of IR–related indices were included to ensure temporal ordering. All-cause mortality follow-up extended until the last wave of the survey in 2020.

Covariates

Covariates collected in this study included demographic and lifestyle factors: sex, age, educational attainment, marital status, alcohol consumption, smoking, and social activity. Laboratory and physical measurements encompassed blood urea nitrogen (BUN), uric acid (UA), creatinine (CREA), total cholesterol (TC), TG, HDL-C, low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), FPG, HbA1c, eGFR, BMI, mean systolic blood pressure, and mean diastolic blood pressure. Disease history included hypertension, dyslipidaemia [28], CKD, diabetes mellitus, and metabolic syndrome [29, 30]. Hypertension was defined as an average systolic blood pressure ≥ 140 mmHg, an average diastolic blood pressure ≥ 90 mmHg, self-reported physician-diagnosed hypertension, or use of antihypertensive medication. Diabetes mellitus was defined by meeting any of the following criteria: FPG ≥ 126 mg/dL, HbA1c ≥ 6.5%, self-reported physician-diagnosed diabetes, or use of glucose-lowering medication.

Conceptual framework

A directed acyclic graph (DAG) was constructed to define the assumed causal relationships, with sarcopenia and CKM stage considered as co-primary exposures and IR–related indices as shared mediators (Figure S1).

Statistical analysis

All statistical analyses were performed using R software (version 4.4.3). Categorical variables were presented as counts (percentages), while continuous variables were expressed as means (standard deviations [SD]) or medians (interquartile ranges [IQR]). Between-group comparisons were conducted using one-way analysis of variance (ANOVA) or the Kruskal–Wallis rank-sum test for continuous variables and the chi-square test for categorical variables. To compare CKM participants with and without sarcopenia, possible sarcopenia and sarcopenia were combined, and Kaplan–Meier curves, log-rank tests, and Fine–Gray competing risks model were used to estimate CVD incidence. Missing values were imputed using multiple imputation via chained equations (MICE), and results were pooled across imputed datasets for subsequent analyses. Selected IR-related indices (TyG–WC, TyG–BMI, METS–IR, eGDR, and CMI) were log-transformed to reduce right-skewed distributions. All eight IR indices were categorised into quartiles for analysis, while continuous values were used for trend tests and spline analyses. Three sequential models were applied: Model 1 adjusted for age and sex; Model 2 additionally adjusted for marital status, educational level, smoking status, and alcohol consumption; Model 3 further included CRP, BUN, UA, HbA1c, and eGFR. The proportional hazards assumption for Cox models was assessed using Schoenfeld residual tests. Most covariates satisfied this assumption, and detailed results are provided in Supplementary Table S6. Restricted cubic spline (RCS) analyses were conducted to examine dose–response relationships between continuous IR-related indices and CVD risk. Subgroup and interaction analyses were performed to evaluate whether associations varied according to factors such as social activity, hypertension, dyslipidaemia, or CKD. Sensitivity analyses included exclusion of imputed samples, competing risk analysis treating death as a competing event, and exclusion of participants with incident CVD within 2 years. Predictive performance of IR–related indices was assessed using the concordance index (C-index), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and the area under the receiver operating characteristic curve (AUC). Two-sided P-values < 0.05 were considered statistically significant. The analytical workflow is illustrated in Fig. 1.

Fig. 1.

Flowchart of participant selection

Results

Baseline characteristics

Among 5,965 participants with CKM stages 1–3, 2,157 individuals (36.16%) had no sarcopenia, while 3,808 participants (63.84%) were classified as having possible sarcopenia or sarcopenia. Significant differences were observed between the groups across most baseline characteristics. Overall, participants with possible sarcopenia or sarcopenia were older, had more advanced CKM stages, and exhibited higher risk of CVD and all-cause mortality. Moreover, the prevalence of hypertension, dyslipidaemia, CKD, and metabolic syndrome differed significantly between the groups.

Notably, all eight IR–related indices differed significantly between the two groups, indicating that the presence of sarcopenia is closely associated with more pronounced metabolic abnormalities. In contrast, the prevalence of diabetes did not differ significantly between the groups, representing a feature of this cohort. Detailed data are presented in Table 1.

Table 1.

Baseline characteristics of participants with CKM stages 1–3 stratified by sarcopenia status

Baseline characteristics	No sarcopenia N = 2,1571	With sarcopenia N = 3,8081	P value2
Gender, n (%)			< 0.001
Male	1,080 (50%)	1,733 (46%)
Female	1,077 (50%)	2,075 (54%)
Age (years)	52 [48–56]	64 [59–70]	< 0.001
Education level, n (%)			< 0.001
Below school	689 (32%)	2,223 (58%)
Primary school	347 (16%)	212 (5.6%)
Middle school	668 (31%)	502 (13%)
High school or above	453 (21%)	871 (23%)
Marital status, n (%)			< 0.001
Married or living with a partner	2,047 (95%)	3,167 (83%)
Others: never married, separated, divorced and widowed	110 (5.1%)	641 (17%)
Drinking status, n (%)			< 0.001
Drink more than once a month	214 (9.9%)	259 (6.8%)
Drink but less than once a month	640 (30%)	923 (24%)
None of these	1,303 (60%)	2,626 (69%)
Smoking status, n (%)			0.148
Current	697 (32%)	1,146 (30%)
Former	168 (7.8%)	329 (8.6%)
Never	1,292 (60%)	2,333 (61%)
Social activity, n (%)	1,230 (57%)	1,746 (46%)	< 0.001
Blood urea nitrogen (mg/dL)	14.65 [12.16–17.62]	15.32 [12.74–18.42]	< 0.001
Creatinine (mg/dL)	0.76 [0.66–0.88]	0.76 [0.66–0.88]	0.297
Uric acid (mg/dL)	4.37 [3.60–5.25]	4.28 [3.58–5.13]	0.037
Total cholesterol (mg/dL)	190.98 [167.78-216.88]	193.30 [169.91-218.43]	0.149
Triglycerides (mg/dL)	112.39 [78.76-169.92]	103.54 [74.34-149.57]	< 0.001
HDL-C (mg/dL)	47.17 [38.66–57.22]	49.87 [40.59–60.70]	< 0.001
LDL-C (mg/dL)	114.82 [93.17–138.40]	116.75 [96.26-140.34]	0.017
CRP (mg/L)	1.0 [0.5-2.0]	1.1 [0.6–2.4]	< 0.001
Glucose (mg/dL)	103.68 [95.76-113.58]	103.50 [95.76-114.48]	0.604
HbA1c (%)	5.10 [4.90–5.40]	5.20 [4.90–5.50]	< 0.001
eGFR	100 [91–106]	91 [80–98]	< 0.001
BMI (kg/m²)	24.48 [22.72–26.77]	22.97 [20.56–25.54]	< 0.001
TyG	8.69 [8.29–9.13]	8.61 [8.24–9.02]	< 0.001
TyG–WC	758 [693–838]	730 [656–817]	< 0.001
TyG–BMI	213.48 [194.05-239.91]	198.08 [174.40-225.61]	< 0.001
TyG–WHtR	4.74 [4.32–5.23]	4.69 [4.16–5.23]	< 0.001
METS–IR	37.17 [33.00-42.58]	33.96 [29.46–39.68]	< 0.001
eGDR	9.97 [7.24–10.83]	9.08 [7.05–10.89]	0.003
CMI	0.57 [0.34–0.98]	0.49 [0.29–0.83]	< 0.001
AIP	0.01 [-0.19-0.25]	-0.04 [-0.25-0.18]	< 0.001
Systolic blood pressure (mm Hg)	126 [115–138]	131 [118–147]	< 0.001
Diastolic blood pressure (mm Hg)	77 [69–85]	75 [68–83]	< 0.001
Hypertension, n (%)	774 (36%)	1,840 (48%)	< 0.001
Dyslipidemia, n (%)	1,058 (49%)	1,624 (43%)	< 0.001
CKD	133 (6.2%)	424 (11%)	< 0.001
Diabetes, n (%)	335 (16%)	661 (17%)	0.075
MetS	682 (32%)	1,075 (28%)	0.006
Framingham 10-year CVD risk ≥ 20%, n (%)	451 (21%)	1,637 (43%)	< 0.001
CKM stage			< 0.001
1	520 (24%)	639 (17%)
2	1,186 (55%)	1,530 (40%)
3	451 (21%)	1,639 (43%)
Incident CVD, n (%)	471 (22%)	1,106 (29%)	< 0.001
All-cause death during follow-up, n (%)	87 (4.0%)	656 (17%)	< 0.001
Follow-up duration, months	105 ± 12	102 ± 18	< 0.001

¹n (%); Median [Q1-Q3]; Mean ± SD

²Statistical tests: Chi-square test for categorical variables; Kruskal–Wallis test for continuous variable

To investigate the incidence of CVD events across different sarcopenia statuses and CKM stages 1–3, we plotted cumulative incidence curves for participants with and without sarcopenia, accounting for all-cause mortality as a competing event. Participants with possible sarcopenia or sarcopenia exhibited a higher incidence of CVD events. We further generated cumulative incidence curves stratified by sarcopenia status (possible and confirmed) across CKM stages 1–3 (Fig. 2C) and within each CKM stage (Fig. 2D). Across all comparisons, participants at CKM stage 3 consistently demonstrated significantly elevated CVD event rates.

Fig. 2.

Cumulative incidence of CVD events across sarcopenia status and CKM stages. (A) Kaplan–Meier curves showing the cumulative event-free probability for CVD among participants with and without sarcopenia. (B) Cumulative incidence function (CIF) curves for CVD accounting for all-cause mortality as a competing risk. (C) Cumulative Incidence of CVD by CKM Stages and Sarcopenia Status. (D) Cumulative incidence of CVD by combined CKM stage and sarcopenia status groups

Associations between IR–related indices and incident CVD

We examined the associations of eight IR–related indices—TyG, TyG–WC, TyG–BMI, TyG–WHtR, METS–IR, eGDR, CMI, AIP—with incident CVD across CKM stages 1–3 and by sarcopenia status. Each index was categorised into quartiles, and baseline characteristics across quartiles were compared; detailed results for TyG–BMI and eGDR are presented in Tables S1–S2, while missing data for key variables are summarised in Table S3. Kaplan–Meier survival curves indicated that participants in the highest quartiles (Q4) of TyG, TyG–WC, TyG–BMI, TyG–WHtR, METS–IR, and CMI, as well as those in the lowest quartile (Q1) of eGDR, exhibited significantly higher cumulative incidence of CVD compared with other quartiles (Fig. 3). In addition, the prevalence of hypertension, diabetes, dyslipidaemia, metabolic syndrome, and all-cause mortality differed significantly across quartiles, whereas CKD did not show significant differences, likely due to the limited number of cases.

Fig. 3.

Kaplan–Meier curves for event-free survival probability of CVD by quartiles of IR–related indices. (A) TyG. (B) TyG–WC. (C) TyG–WHtR. (D)TyG–BMI. (E) eGDR. (F) METS–IR. (G) CMI. (H) AIP

In multivariable Cox proportional hazards analyses among participants with CKM stages 1–3 and possible sarcopenia or sarcopenia, several IR–related metabolic indices were significantly associated with increased CVD risk (Table 2). Analyses of continuous variables per SD, as well as trend analyses, were statistically significant. In the fully adjusted model (Model 3), TyG–BMI, as a continuous variable, showed the strongest positive association with CVD risk (hazard ratio [HR] 3.58, 95% confidence interval [CI] 2.50–5.14) compared with other positively associated indices. In contrast, eGDR was consistently inversely associated with CVD risk, with each SD increase corresponding to a 21%–23% reduction in hazard of CVD (Model 3: HR 0.77, 95% CI 0.72–0.82, P < 0.001). Additionally, Table 2 includes incidence rates per 1,000 person-years for each IR-related index category, providing absolute event rates to complement the reported hazard ratios. To further enhance transparency, potential multicollinearity among covariates was assessed, with results presented in Supplementary Table S4.

Table 2.

Multivariate cox models of insulin resistance–related indices and incident CVD in CKM stages 1–3 with sarcopenia (Including Possible Cases)

IR-related Index	Category	Incidence per 1,000 PY	Events/N	Model 1 h (95% CI)	P value	Model 2 h (95% CI)	P value	Model 3 h (95% CI)	P value
TyG	Continuous			1.18 (1.07–1.30)	< 0.001	1.16 (1.06–1.28)	0.002	1.19 (1.07–1.32)	0.001
	Per SD increase			1.12 (1.06–1.19)	< 0.001	1.12 (1.06–1.18)	< 0.001	1.13 (1.06–1.20)	< 0.001
	Quartile 1	29.60	232/952	Reference		Reference		Reference
	Quartile 2	34.16	266/952	1.15 (0.96–1.37)	0.120	1.14 (0.95–1.36)	0.155	1.14 (0.96–1.36)	0.141
	Quartile 3	38.57	296/952	1.29 (1.08–1.53)	0.004	1.26 (1.06–1.50)	0.009	1.27 (1.07–1.52)	0.007
	Quartile 4	40.75	312/952	1.37 (1.15–1.62)	< 0.001	1.35 (1.13–1.60)	< 0.001	1.37 (1.15–1.65)	< 0.001
	P for trend			1.20 (1.09–1.31)	< 0.001	1.19 (1.09–1.31)	< 0.001	1.22 (1.10–1.34)	< 0.001
TyG–WC	Continuous			1.96 (1.38–2.78)	< 0.001	1.85 (1.31–2.62)	< 0.001	1.98 (1.37–2.85)	< 0.001
	Per SD increase			1.21 (1.14–1.29)	< 0.001	1.20 (1.13–1.28)	< 0.001	1.23 (1.15–1.31)	< 0.001
	Quartile 1	27.14	215/952	Reference		Reference		Reference
	Quartile 2	32.65	254/952	1.21 (1.01–1.45)	0.040	1.21 (1.00–1.45)	0.045	1.22 (1.02–1.47)	0.032
	Quartile 3	36.14	280/952	1.33 (1.11–1.59)	0.002	1.30 (1.09–1.56)	0.004	1.33 (1.11–1.60)	0.002
	Quartile 4	47.56	357/952	1.78 (1.50–2.11)	< 0.001	1.75 (1.47–2.08)	< 0.001	1.84 (1.54–2.20)	< 0.001
	P for trend			1.21 (1.14–1.27)	< 0.001	1.20 (1.13–1.27)	< 0.001	1.22 (1.15–1.29)	< 0.001
TyG–BMI	Continuous			3.29 (2.35–4.60)	< 0.001	3.09 (2.20–4.36)	< 0.001	3.58 (2.50–5.14)	< 0.001
	Per SD increase			1.23 (1.17–1.30)	< 0.001	1.23 (1.16–1.30)	< 0.001	1.25 (1.18–1.33)	< 0.001
	Quartile 1	26.63	211/952	Reference		Reference		Reference
	Quartile 2	32.99	257/952	1.26 (1.05–1.52)	0.012	1.27 (1.06–1.53)	0.011	1.28 (1.07–1.54)	0.008
	Quartile 3	35.49	275/952	1.37 (1.14–1.65)	< 0.001	1.36 (1.13–1.64)	< 0.001	1.41 (1.17–1.70)	< 0.001
	Quartile 4	48.45	363/952	1.92 (1.61–2.29)	< 0.001	1.90 (1.59–2.27)	< 0.001	2.01 (1.67–2.42)	< 0.001
	P for trend			1.23 (1.17–1.30)	< 0.001	1.23 (1.16–1.30)	< 0.001	1.25 (1.18–1.33)	< 0.001
TyG–WHtR	Continuous			1.18 (1.09–1.28)	< 0.001	1.17 (1.08–1.27)	< 0.001	1.20 (1.10–1.30)	< 0.001
	Per SD increase			1.18 (1.11–1.26)	< 0.001	1.18 (1.11–1.26)	< 0.001	1.20 (1.12–1.28)	< 0.001
	Quartile 1	27.59	218/952	Reference		Reference		Reference
	Quartile 2	33.37	260/952	1.21 (1.01–1.45)	0.038	1.19 (0.99–1.43)	0.059	1.21 (1.01–1.45)	0.040
	Quartile 3	35.92	279/952	1.28 (1.06–1.53)	0.009	1.25 (1.04–1.50)	0.016	1.29 (1.07–1.55)	0.008
	Quartile 4	46.58	349/952	1.67 (1.39–1.99)	< 0.001	1.63 (1.37–1.95)	< 0.001	1.71 (1.42–2.06)	< 0.001
	P for trend			1.18 (1.11–1.24)	< 0.001	1.17 (1.10–1.24)	< 0.001	1.19 (1.12–1.26)	< 0.001
METS–IR	Continuous			2.53 (1.89–3.39)	< 0.001	2.37 (1.76–3.19)	< 0.001	2.65 (1.94–3.63)	< 0.001
	Per SD increase			1.21 (1.15–1.28)	< 0.001	1.20 (1.14–1.27)	< 0.001	1.23 (1.16–1.31)	< 0.001
	Quartile 1	27.05	214/952	Reference		Reference		Reference
	Quartile 2	34.33	267/952	1.31 (1.09–1.57)	0.003	1.31 (1.09–1.57)	0.003	1.32 (1.10–1.58)	0.003
	Quartile 3	34.85	271/952	1.32 (1.10–1.59)	0.003	1.31 (1.09–1.57)	0.004	1.35 (1.12–1.62)	0.002
	Quartile 4	47.26	354/952	1.85 (1.55–2.20)	< 0.001	1.81 (1.52–2.16)	< 0.001	1.90 (1.58–2.28)	< 0.001
	P for trend			1.21 (1.14–1.27)	< 0.001	1.20(1.13–1.27)	< 0.001	1.21 (1.15–1.29)	< 0.001
eGDR	Continuous			0.41 (0.33–0.52)	< 0.001	0.43 (0.34–0.53)	< 0.001	0.36 (0.28–0.47)	< 0.001
	Per SD increase			0.78 (0.74–0.83)	< 0.001	0.79 (0.74–0.84)	< 0.001	0.77 (0.72–0.82)	< 0.001
	Quartile 1	51.31	381/953	Reference		Reference		Reference
	Quartile 2	35.07	270/952	0.67 (0.57–0.78)	< 0.001	0.68 (0.58–0.80)	< 0.001	0.66 (0.56–0.77)	< 0.001
	Quartile 3	31.54	249/955	0.60 (0.51–0.70)	< 0.001	0.60 (0.51–0.71)	< 0.001	0.58 (0.49–0.68)	< 0.001
	Quartile 4	25.96	206/948	0.50 (0.42–0.59)	< 0.001	0.51 (0.43–0.60)	< 0.001	0.48 (0.40–0.57)	< 0.001
	P for trend			0.79(0.75–0.84)	< 0.001	0.80(0.75–0.84)	< 0.001	0.78 (0.74–0.83)	< 0.001
CMI	Continuous			1.15 (1.07–1.24)	< 0.001	1.14 (1.05–1.23)	0.001	1.16 (1.06–1.25)	< 0.001
	Per SD increase			1.04 (1.0–1.09)	< 0.001	1.05 (1.0–1.09)	< 0.001	1.05 (1.01–1.10)	< 0.001
	Quartile 1	28.75	227/952	Reference		Reference		Reference
	Quartile 2	33.45	260/952	1.16 (0.97–1.39)	0.1	1.15 (0.96–1.37)	0.128	1.15 (0.96–1.38)	0.116
	Quartile 3	39.07	300/952	1.35 (1.13–1.60)	< 0.001	1.32 (1.11–1.57)	0.002	1.33 (1.11–1.59)	0.002
	Quartile 4	41.93	319/952	1.46 (1.23–1.73)	< 0.001	1.43 (1.20–1.70)	< 0.001	1.47 (1.23–1.76)	< 0.001
	P for trend			1.13 (1.08–1.20)	< 0.001	1.13(1.07–1.19)	< 0.001	1.14 (1.08–1.20)	< 0.001
AIP	Continuous			1.37 (1.14–1.66)	< 0.001	1.34 (1.10–1.62)	0.003	1.38 (1.13–1.68)	0.002
	Per SD increase			1.12 (1.06–1.19)	< 0.001	1.12 (1.05–1.18)	< 0.001	1.13 (1.06–1.20)	< 0.001
	Quartile 1	30.78	242/952	Reference		Reference		Reference
	Quartile 2	31.21	244/952	1.01 (0.85–1.21)	0.913	1.01 (0.84–1.20)	0.936	1.01 (0.85–1.21)	0.902
	Quartile 3	42.47	324/952	1.39 (1.17–1.64)	< 0.001	1.36 (1.15–1.60)	< 0.001	1.36 (1.15–1.61)	< 0.001
	Quartile 4	38.72	296/952	1.26 (1.07–1.50)	0.007	1.25 (1.05–1.49)	0.011	1.27 (1.07–1.52)	0.008
	P for trend			1.11 (1.05–1.17)	< 0.001	1.10(1.04–1.16)	< 0.001	1.11 (1.05–1.17)	< 0.001

Model 1: Adjusted for age and gender

Model 2: Adjusted for age, gender, marital status, educational level, smoking, drinking, and social activity

Model 3: Adjusted for age, gender, marital status, educational level, smoking, drinking, social activity, CRP, eGFR, HbA1c, blood urea nitrogen, and uric acid. Blood urea nitrogen and uric acid (UA) were included as routinely available indicators reflecting renal function and metabolic status, both of which are closely related to IR and CVD risk

In RCS analyses, TyG, TyG–BMI, METS–IR, and AIP exhibited a linear positive association with CVD risk. In contrast, eGDR, TyG–WC, TyG–WHtR, and CMI showed nonlinear relationships. Notably, eGDR was inversely associated with CVD risk, with the overall association reaching statistical significance (overall P < 0.05) and a significant nonlinear trend observed (P for nonlinearity = 0.028) (Fig. 4).

Fig. 4.

RCS analyses of the associations between IR–related indices and CVD risk. (A) TyG. (B) TyG–WC. (C) TyG–WHtR. (D)TyG–BMI. (E) eGDR. (F) METS–IR. (G) CMI. (H) AIP

Predictive performance of IR–related indices for incident CVD

We evaluated the predictive performance of IR-related indices for incident CVD using Harrell’s C-index, NRI, IDI, and AUC (Table S5). The inclusion of each IR-related index improved model discrimination to varying degrees, with the AUC ranging from 0.575 to 0.618. Among single-index models, eGDR demonstrated the greatest improvement in predictive performance, with an AUC of 0.607, the largest NRI (0.142, 95% CI 0.056–0.188), and a significant improvement in IDI (0.011, 95% CI 0.006–0.016). Among TyG-related indices, TyG–BMI demonstrated the strongest predictive performance (AUC 0.604; NRI 0.085, 95% CI 0.048–0.141).

To further assess incremental predictive value, the Least Absolute Shrinkage and Selection Operator (LASSO) identified TyG–BMI and eGDR as the optimal combination of IR-related indices. When incorporated into the fully adjusted model, the TyG–BMI + eGDR combination improved model performance, with a higher AUC of 0.614 (Table S5 and Figure S2) and significant improvements in NRI (0.117, 95% CI 0.058–0.161) and IDI (0.013, 95% CI 0.007–0.023). Although the model incorporating all eight IR-related indices slightly increased the AUC to 0.618, the NRI and IDI gains did not exceed those of the optimal two-index model, suggesting that further increasing model complexity may not provide additional predictive benefit.

Mediation analysis of IR–related indices

We further evaluated the potential mediating role of IR–related indices in the association between CKM stage (1 vs. 3) and incident CVD (Table 3), as well as between possible sarcopenia versus sarcopenia and incident CVD (Table S7). All models were adjusted for demographic and behavioral covariates. To ensure temporal ordering, mediator variables were derived from measurements obtained in 2015, and only CVD events occurring after 2015 were included in the analyses.

Table 3.

Mediation analysis of insulin resistance–related indices in the association between CKM stage 1 vs. 3 and incident CVD

Mediator	ACME (95% CI)	P value	ADE (95% CI)	P value	Total effect (95% CI)	P value	Proportion mediated (95% CI)	P value
TyG	0.029 (0.0057, 0.0574)	0.012	0.057 (− 0.0207, 0.1350)	0.160	0.092 (0.0174, 0.1624)	0.012	0.319 (0.0539, 1.7051)	0.024
TyG–WC	0.042 (0.0220, 0.0698)	< 0.001	0.044 (− 0.0293, 0.1139)	0.268	0.093 (0.0221, 0.1644)	0.004	0.457 (0.1610, 2.2271)	0.004
TyG–BMI	0.020 (0.0044, 0.0455)	0.004	0.069 (− 0.0008, 0.1408)	0.056	0.093 (0.0229, 0.1656)	0.008	0.211 (0.0364, 0.9680)	0.012
TyG–WHtR	0.033 (0.0111, 0.0580)	0.004	0.054 (− 0.0212, 0.1286)	0.152	0.092 (0.0191, 0.1672)	0.008	0.353 (0.0889, 1.9712)	0.012
METS–IR	0.019 (0.0033, 0.0441)	0.004	0.071 (− 0.0020, 0.1400)	0.060	0.094 (0.0262, 0.1643)	0.016	0.202 (0.0205, 0.9293)	0.020
eGDR	0.026 (0.0061, 0.0655)	< 0.001	0.061 (− 0.0223, 0.1356)	0.168	0.092 (0.0172, 0.1645)	0.008	0.282 (0.0459, 1.9306)	0.008
CMI	0.037 (0.0161, 0.0654)	< 0.001	0.050 (− 0.0210, 0.1237)	0.192	0.093 (0.0156, 0.1666)	0.016	0.399 (0.1351, 1.9145)	0.016
AIP	0.031 (0.0139, 0.0543)	< 0.001	0.057 (− 0.0093, 0.1248)	0.096	0.094 (0.0255, 0.1586)	0.008	0.333 (0.1072, 1.2529)	0.008

Notes: All mediation models were adjusted for age, gender, marital status, education level, drinking status, smoking status, and social activity. All mediators were measured in 2015, with incident CVD occurring during subsequent follow-up

Abbreviations: ACME, average causal mediation effect; ADE, average direct effect

In the analysis of the association between CKM stage and incident CVD, all eight IR–related indices exhibited significant average causal mediation effects (ACME), suggesting that worsening IR may partially explain the link between CKM progression and elevated CVD risk. Among these indices, TyG–WC (ACME = 0.042, 95% CI 0.022–0.070), CMI (ACME = 0.037, 95% CI 0.016–0.065), and AIP (ACME = 0.031, 95% CI 0.014–0.054) showed relatively larger indirect effects, corresponding to mediation proportions of approximately 33%–46%. The eGDR also demonstrated a significant ACME (0.026), indicating that reduced insulin sensitivity may additionally contribute to this pathway.

In the analysis of the impact of progression from possible sarcopenia to sarcopenia on CVD, most IR-related indices exhibited significant negative ACMEs, suggesting that deterioration of IR with increasing severity of sarcopenia may partially contribute to CVD risk. TyG–WC (ACME = − 0.051, P = 0.004; mediation proportion 52.2%), CMI (ACME = − 0.028, P < 0.001; 29.0%), and AIP (ACME = − 0.020, P = 0.008; 20.9%) displayed relatively strong indirect effects. In contrast, METS–IR and TyG–BMI did not show significant ACMEs, suggesting a weaker mediating role in this pathway. The directions of total effects in all models were consistent with the main analyses.

To enhance analytical transparency, the baseline characteristics of participants included in the mediation analyses are presented in Supplementary Tables S8–S9. Sensitivity analyses addressing potential unmeasured confounding were further conducted (Supplementary Tables S10–S11). These analyses indicated that the mediation effects of several indices were somewhat sensitive to unmeasured confounding, suggesting that causal interpretation should be approached with caution, particularly regarding the estimation of exact mediation proportions.

Subgroup and sensitivity analyses

Subgroup analyses for each index (Figure S3) showed that the effect directions were largely consistent across subgroups, suggesting a stable positive association between metabolic-related indices and adverse outcomes. Stratified analyses by age and sex suggested relatively higher risk increases among participants aged < 65 years and males; however, none of the interaction tests reached statistical significance, indicating that demographic factors did not modify the predictive effect of the indices. The associations remained robust across subgroups defined by lifestyle factors and chronic disease status, including alcohol consumption, smoking, hypertension, diabetes, and metabolic syndrome, supporting the generalisability of these indices across diverse population contexts. Notably, indices reflecting overall metabolic burden, such as METS–IR, TyG–WC, and TyG–BMI, exhibited particularly strong predictive performance, with hazard ratios ranging from 2- to 6-fold across multiple subgroups. Additionally, significant interactions were observed between several indices—TyG–WC (P = 0.004), TyG–WHtR (P = 0.039), CMI (P = 0.031), and AIP (P = 0.044)—and social activity, with participants lacking social engagement displaying higher risk, suggesting a potential modifying effect of social participation.

To assess the robustness of our results, multiple sensitivity analyses were conducted. After excluding participants with missing covariate data (Table S12), multivariable Cox regression analyses demonstrated that TyG-related indices, METS–IR, CMI, and AIP remained significantly associated with an increased risk of CVD, whereas eGDR retained its inverse association. When accounting for all-cause mortality as a competing risk using Fine–Gray models, the directions and significance of these associations were largely unchanged (Table S13). Further exclusion of participants who developed incident CVD within the first two years of follow-up yielded consistent results (Table S14). Overall, these sensitivity analyses support the robustness of the observed associations between IR–related indices and CVD risk.

Discussion

Using data from the nationally representative CHARLS, this study investigated how sarcopenia status, CKM stage, and eight IR-related indices relate to CVD risk among middle-aged and older Chinese adults. First, we found that individuals with possible sarcopenia or sarcopenia had a higher risk of developing CVD, and this risk increased with advancing CKM stages. Second, within the context of CKM stages 1–3 and sarcopenia status, eight IR-related indices were significantly associated with incident CVD. Among these indices, the eGDR and TyG–BMI showed the greatest improvement in prediction when added to conventional models. Third, mediation analyses indicated that all eight IR-related indices may partially mediate the relationship between CKM progression (stage 1 vs. stage 3) and CVD, suggesting that IR may contribute to this pathway. By integrating multiple IR-related indices into the CKM staging framework and accounting for competing mortality risk, this study provides a more comprehensive evaluation of the sarcopenia–CVD relationship than approaches based on individual indices or conventional survival models. This approach may offer novel insights into the joint impact of metabolic dysfunction and sarcopenia on CVD risk.

Recent studies have consistently linked sarcopenia with elevated CVD risk among individuals with CKM disorders. Using CHARLS, Wei et al. [10] reported that both possible sarcopenia and sarcopenia were associated with higher CVD incidence across CKM stages, while a National Health and Nutrition Examination Survey (NHANES)–based study similarly observed stage-related differences in sarcopenia-associated risk [31]. However, these studies largely characterised stage-specific risk gradients without evaluating whether CKM stage modifies the sarcopenia–CVD association. Our findings extend this evidence by demonstrating a clear stage-dependent pattern in older Chinese adults. Within the CKM stages 1–3 framework, we found that the contribution of sarcopenia to CVD risk increased progressively with CKM progression, with the highest risk concentrated in stage 3. This suggests that the impact of sarcopenia on CVD may be shaped by the underlying metabolic and renal milieu rather than remaining constant across disease stages. To explore potential mechanisms, we incorporated multiple IR-related indices and examined their mediating roles. Indices such as TyG–WC, CMI and AIP accounted for a comparatively larger proportion of the indirect association, pointing to metabolic pathways involving impaired insulin sensitivity [32], chronic low-grade inflammation [33], accumulation of uraemic toxins [34], and reduced physical activity [35]. Overall, by evaluating sarcopenia within the CKM staging framework, our study highlights interactions between metabolic dysfunction and sarcopenia that may be particularly relevant in advanced CKM. These results indicate that early identification and targeted management of sarcopenia—especially among individuals at CKM stage 3—may help improve CVD outcomes.

This study further explored potential metabolic mechanisms underlying the joint contribution of CKM progression and sarcopenia to CVD events. Notably, the eight IR–related indices showed consistent associations with CVD risk across both glucose–lipid metabolism (TyG-related indices) and systemic insulin sensitivity (eGDR, METS–IR, CMI) measures, suggesting that IR may act as a shared metabolic pathway linking CKM progression and sarcopenia. Based on these findings, we propose a putative pathway: metabolic dysregulation in CKM, together with mild-to-moderate renal impairment, may progressively exacerbate IR [36], which suppresses protein synthesis and increases muscle catabolism, accelerating sarcopenia onset and progression [37]. Conversely, sarcopenia may impair glucose utilization and mitochondrial function, further aggravating IR and forming a vicious cycle [38]. In addition, impaired secretion of myokines from skeletal muscle, such as irisin, may further reduce insulin sensitivity [39] and compromise vascular function [40], linking sarcopenia–related muscle decline to cardiometabolic risk. Moreover, in advanced CKM (Stage 3), declining renal function can amplify IR and promote muscle catabolism via the accumulation of uremic toxins [41], reinforcing the sarcopenia–IR–CVD cycle. Ultimately, this continuous chain may contribute to elevated CVD risk. Mediation analyses provided preliminary support: multiple IR–related indices consistently mediated the association between combined CKM–sarcopenia exposure and CVD, although overall mediation proportions were modest. These results suggest that IR may partially explain the joint impact of CKM progression and sarcopenia on CVD risk rather than acting as an independent parallel factor. Given the observational design, unmeasured confounding cannot be excluded, and the proposed pathway remains hypothesis-generating, warranting further validation in independent cohorts and interventional studies.

Among the eight IR–related indices examined in this study, eGDR demonstrated the most consistent association with CVD risk in CKM populations. Although TyG and its derivative indices are widely used due to their accessibility and ease of calculation, eGDR—an integrated measure combining BMI, HbA1c, and systolic blood pressure—may more closely reflect insulin sensitivity, showing higher sensitivity in certain CKM subgroups. In this context, the components of eGDR align with the metabolic characteristics of CKM, which may make it more informative than traditional TyG-based indices for capturing CKM stage–specific IR. Furthermore, previous studies have shown that eGDR is associated with sarcopenia and its key components (muscle mass, strength, and physical function) [42, 43], indicating that it may better reflect the metabolic pathway linking sarcopenia with reduced insulin sensitivity.

Based on these findings, eGDR may serve as a useful surrogate index of insulin sensitivity in CKM populations, particularly those not primarily driven by diabetes. Interestingly, the prevalence of diabetes was slightly higher in the sarcopenia group, although the difference did not reach statistical significance (P = 0.075). This may indicate that diabetes status alone is insufficient to distinguish sarcopenia in CKM populations, and that other metabolic factors may also contribute to muscle-related impairment. In subgroup analyses stratified by social activity, TyG–WHtR and METS–IR showed significant interactions, indicating that low levels of social engagement may amplify the impact of metabolic abnormalities on CVD risk. These results indicate that lifestyle or social-behavioral factors may be relevant for risk stratification, although further validation is needed.

Our predictive analyses showed that the combined TyG–BMI and eGDR model outperformed individual indices and other combinations in incremental predictive performance, as indicated by C-index, NRI, and IDI. Clinically, this dual-indices combination offers several advantages: all required variables are readily obtained from routine physical exams and standard biochemical tests (fasting glucose, lipids, weight, height, waist circumference), making it simple, cost-effective, and suitable for primary care or community screening. Pathophysiologically, TyG–BMI and eGDR capture complementary aspects of metabolic risk—lipid–glucose load and systemic insulin sensitivity—allowing for a more comprehensive assessment [44, 45]. In this context, although some studies have reported slightly higher hazard ratios for TyG–WC than TyG–BMI in predicting incident CVD in CKM populations [46, 47], the difference appears modest. In contrast, evidence from the Chongqing Aging and Sarcopenia Evaluation cohort showed that TyG–BMI outperformed TyG–WC in predicting sarcopenia (AUC: 0.892 vs. 0.814) [48]. Consistent with this, TyG–BMI also showed relatively better performance in our study. This difference may reflect that BMI captures overall body composition and the combined effects of adiposity and sarcopenia-related muscle loss, whereas waist circumference primarily reflects central fat distribution and may not fully represent systemic metabolic alterations. Compared with models including numerous indices, the dual-indices approach achieves near-maximal discrimination while remaining parsimonious and may facilitate future clinical translation. It should be noted that these results come from model development and retrospective validation and are not sufficient to guide changes in clinical management. Future work should involve external validation in independent cohorts, model calibration, determination of clinically actionable cut-points, and assessment of net benefit via decision-curve analysis. Implementation studies and cost-effectiveness evaluations will further clarify its practical utility.

From a clinical perspective, in patients with CKM Stage 3, particularly those with coexisting sarcopenia, conventional measures such as fasting glucose alone may not adequately reflect underlying IR. In this setting, calculation of TyG–BMI and eGDR may be considered as complementary measures within routine metabolic assessment. Their use may assist in identifying individuals who require closer monitoring or more comprehensive evaluation. However, further validation is required before routine clinical implementation.

This study has several strengths. First, it is the first to systematically evaluate, in middle-aged and older adults with CKM, the associations of sarcopenia and multiple IR–related indices—including eGDR, TyG-related indices, CMI, and METS–IR—with CVD events, providing a comprehensive perspective on the potential interplay between metabolic dysfunction, sarcopenia, and CVD risk. Second, the study is based on a large, nationally representative longitudinal cohort with extended follow-up, ensuring adequate statistical power and robust data quality. Furthermore, through adjustment for multiple confounders and subgroup analyses, the reliability and interpretability of the results were enhanced. In addition, we developed a predictive model based on TyG–BMI and eGDR and evaluated its performance using the C-index, AUC, NRI and IDI, which may serve as a reference for future community- and clinic-based CVD risk screening in older adults with CKM.

Despite these strengths, several limitations should be acknowledged. First, IR-related indices and sarcopenia were assessed at baseline only without longitudinal updates. Given that both IR status and sarcopenia may change over time, this approach may not fully capture dynamic changes and could introduce potential misclassification. Muscle mass was estimated using an anthropometric equation rather than direct measurements such as dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA), which may introduce measurement error and affect the precision of sarcopenia classification. Furthermore, information on CVD events was primarily obtained from self-report or survey records without verification using medical records or objective clinical measures, which may introduce misclassification and potential information bias. Second, the mediation analyses are sensitive to unmeasured confounding, and the reported mediation proportions provide hypothesis-generating rather than definitive evidence, warranting cautious interpretation. Third, the study population was limited to middle-aged and older adults in China, and the generalisability of the findings to other populations requires further validation. In addition, our analysis was restricted to individuals with CKM stages 1–3, as those with pre-existing CVD were excluded to focus on incident events. Therefore, the findings should be interpreted within this CKM spectrum rather than extrapolated to the full CKM population. Although Schoenfeld residual tests indicated that most covariates met the proportional hazards assumption, a few variables showed borderline deviations, which may represent a limitation of the Cox regression analyses. Future studies could incorporate repeated measurements of sarcopenia-related muscle mass and muscle strength, as well as IR–related indices over longitudinal follow-up, along with data on inflammation, genomics, and metabolomics, to further elucidate the CKM–sarcopenia–CVD pathway. In addition, the predictive models should be externally validated in independent or multicentre cohorts, and interventional studies are needed to assess whether improving sarcopenia or IR–related indices can effectively reduce CVD risk, thereby enhancing clinical translational potential.

Conclusion

Within the CKM framework, both possible sarcopenia and sarcopenia were associated with an increased risk of CVD, with the risk increasing across CKM stages and peaking at stage 3. Multiple IR-related indices were significantly associated with CVD, and the combination of eGDR and TyG–BMI showed the best predictive performance. Mediation analyses suggested that several IR-related indices may partly mediate the association between sarcopenia and CVD, suggesting a potential role of IR as a shared pathway linking CKM progression, sarcopenia, and CVD risk. These findings may help improve CVD risk stratification in high-risk CKM populations and inform future prevention strategies.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CVD

Cardiovascular disease

CKM

Cardiovascular–kidney–metabolic

IR

Insulin resistance

CHARLS

China Health and Retirement Longitudinal Study

TyG

Triglyceride–glucose index

TyG–WC

TyG–waist circumference

TyG–BMI

TyG–body mass index

TyG–WHtR

TyG–waist-to-height ratio

METS–IR

Metabolic score for insulin resistance

eGDR

Estimated glucose disposal rate

CMI

Cardiometabolic index

AIP

Atherogenic index of plasma

C-index

Concordance index

NRI

Net reclassification improvement

IDI

Integrated discrimination improvement

STROBE

Strengthening the Reporting of Observational Studies in Epidemiology

TG

Triglycerides

FPG

Fasting plasma glucose

HbA1c

Glycated haemoglobin

HDL-C

High-density lipoprotein cholesterol

CKD

Chronic kidney disease

AWGS

Asian Working Group for Sarcopenia

SPPB

Short Physical Performance Battery

ASM

Appendicular skeletal muscle mass

BUN

Blood urea nitrogen

UA

Uric acid

CREA

Creatinine

TC

Total cholesterol

LDL-C

Low-density lipoprotein cholesterol

CRP

C-reactive protein

eGFR

Estimated glomerular filtration rate

SD

Standard deviation

IQR

Interquartile range

ANOVA

One-way analysis of variance

MICE

Multiple imputation via chained equations

RCS

Restricted cubic spline

HR

Hazard ratio

CI

Confidence interval

ACME

Average causal mediation effect

NHANES

National Health and Nutrition Examination Survey

DAG

Directed acyclic graph

PY

Person-years

Author contributions

H.J.Z. and C.S.W contributed to the study design, statistical analysis, and drafting of the manuscript. Y.H.L., Y.B.L., and M.L. participated in the study design and data collection. J.F.L. and J.X. performed the statistical analysis. C.O. and H.H. contributed to the editing and review of the manuscript. All authors made critical revision of the manuscript for important intellectual content and approved the final manuscript.

Funding

This study was supported in part by grants from the Key R & D program Natural Science Foundation of Guangxi Province under Grant No. AB19110007.

Data availability

The data that support the findings of this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS). Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission from CHARLS (http://charls.pku.edu.cn/en).

Declarations

Ethics approval and consent to participate

This study is a retrospective analysis based on data from the CHARLS. Participant information was anonymized prior to analysis. The original CHARLS survey was approved by the Ethics Review Board of Peking University (IRB00001052-11015), and all participants provided written informed consent at the time of enrollment. The study was conducted in accordance with the principles of the Declaration of Helsinki. Clinical trial number: not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.