TyG and RC combined with CVD risk in CKM syndrome: A national cohort study

Summary

Although the triglyceride-glucose index (TyG, insulin resistance) and remnant cholesterol (RC, lipid metabolism) are cardiovascular disease (CVD) risk factors in cardiovascular-kidney-metabolic (CKM) syndrome, their joint predictive power warrants further investigation. We utilized data from the China Health and Retirement Longitudinal Study (2020). Using participants with low TyG (<8.3) and low RC (<13.9) as the reference, individuals with both high TyG and high RC showed significantly elevated CVD risk. Over a median follow-up of 9.0 years, 1744 participants (23.2%) in CKM Stages 0–3 developed CVD. Participants with both high TyG and high RC had the highest risk (HR = 1.35). The TyG-RC index was created by multiplying TyG and RC. Each 1-SD increase in TyG-RC was associated with higher CVD risk, exhibiting an inverse J-shaped relationship. Time-independent ROC analysis demonstrated TyG-RC has superior predictive value compared to TyG-BMI, TyG-WC, TyG-WHtR, eGDR and METSIR. For enhanced CVD risk assessment, the joint assessment TyG-RC index offers a more effective tool than using TyG or RC individually.

Graphical abstract

Highlights

• •TyG-RC integrates insulin resistance and dyslipidemia for CKM CVD risk
• •High TyG plus high RC gave the greatest CVD risk vs. low/low
• •Time-independent ROC: TyG-RC outperformed TyG-BMI/WC/WHtR, eGDR, and METSIR

Health sciences

Introduction

Cardiovascular diseases (CVDs) are the major cause of death and premature mortality in China.¹ The burden of CVD continues to increase annually, with approximately 330 million patients with CVD in China. CVD is attributable to 2 out of every 5 deaths in China.² The Global Burden of Disease (GBD) Study reports that the total prevalence of CVD worldwide has increased from 271 million in 1990 to 523 million in 2019. Additionally, the number of deaths has increased from 12.1 million to 18.6 million, and this trend is continuing.³

A substantial body of research has elucidated the intricate and strong interconnections linking CVD, chronic kidney disease (CKD), and metabolic disorders.⁴ In a presidential advisory released in October 2023, the American Heart Association (AHA) characterized cardiovascular-kidney-metabolic (CKM) syndrome as a systemic disorder driven by pathophysiological interplay between cardiovascular diseases, CKD, and metabolic risk factors.⁵ The convergence of these conditions markedly increases the risk of adverse cardiovascular outcomes and multi-organ dysfunction.⁵ With the increasing prevalence of obesity and metabolic disorders worldwide, the incidence of CKM has been rising annually, posing a significant challenge to global public health.⁶ As academic attention to CKM continues to grow, the staging framework for CKM has become more clearly defined, ranging from stage 0 (absence of risk factors) to stage 4 (confirmed CVD).⁵ The AHA underscores the critical importance of preclinical risk prediction and recommends that investigations focusing on individuals with CKM syndrome in stages 0–3 should emphasize the prevention of cardiovascular events.⁷ Given the substantial clinical burden imposed by CKM syndrome on cardiovascular health, integrated prevention and management of these interconnected conditions may mitigate the rapid progression from stages 0–3.⁸

Simple, non-invasive methods such as the homeostatic model assessment of insulin resistance (IR)⁹ and the triglyceride-glucose (TyG) index¹⁰ are frequently employed to assess insulin sensitivity. Among them, the TyG index, unaffected by insulin treatment, is more widely utilized and has been shown to be associated with cardiovascular diseases and outcomes.^11,12

Remnant cholesterol (RC) refers to the cholesterol content within triglyceride-rich lipoproteins (TRLs), which includes very low-density lipoprotein cholesterol (VLDL-C) and intermediate-density lipoprotein cholesterol (IDL) in the fasting state, as well as chylomicron remnants in the non-fasting state.¹³ Increasing evidence indicates that elevated RC serves as an emerging residual risk factor for type 2 diabetes mellitus (T2D), myocardial infarction, atherosclerotic CVD, stroke, and CKD, beyond the well-established role of conventional lipid indicators such as low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TGs).^14,15 Other research founded that excess adiposity and diabetes status may partially mediate the RC-CVD pathway.¹⁶ However, the association between RC and CKM syndrome progression is still unclear.

Notably, dyslipidemia and IR are intricately linked, with obesity promoting IR through mechanisms such as endoplasmic reticulum stress and inflammatory responses, while IR exacerbates obesity through metabolic dysregulation.¹⁷ Since the triglyceride-glucose (TyG) index offers enhanced cardiovascular risk stratification when combined with additional biomarkers,¹⁸ investigating the joint assessment of the TyG index and remnant cholesterol RC is justified. We emphasize that while existing TyG-derived indices largely combine TyG with adiposity measures, TyG-RC is distinct because it integrates insulin resistance (TyG) with atherogenic lipoprotein particle concentration (RC). This combination aims to capture the core pathophysiological interplay between metabolic dysfunction and lipid abnormalities in the CKM syndrome more directly than indices based on anthropometrics. However, evidence on the joint impact of these two indices on cardiovascular outcomes is still lacking.

To address this gap, we employed data from the China Health and Retirement Longitudinal Study (CHARLS)—a nationally representative cohort—to examine the individual and combined relationships of the TyG index and RC with CVD. Additionally, we evaluate the potential interaction between these two indices. We hypothesized that the combination of TyG and RC enhanced the predictive capability for CVD compared to either biomarker alone.

Results

Baseline characteristic of participants

A total of 9108 participants (46.9% male) were included in this study, with an average age of 52.00 ± 7.00 years. The ROC analysis revealed optimal cut-off values of 8.3 for TyG and 13.9 for RC, respectively. The detailed results of the ROC curve analyses, including AUC values, optimal cutoff points, sensitivity, specificity, and 95% confidence intervals, are provided in Table S1. However, the TyG index demonstrated limited predictive power for CVD incidence in patients with CKM stages 0–3 (Table S2, Figure S1). Similarly, RC showed poor predictive ability for CVD occurrence in this patient population with CKM stages 0–3 (Table S3, Figure S1). Participants were categorized into four subgroups based on optimal cut-off values of TyG and RC, respectively: low TyG and low RC (n = 1976), low TyG and high RC (n = 815), high TyG and low TyG (n = 1173), and high TyG and high TyG (n = 5144). The demographic characteristics of the combined groups stratified by TyG and RC are outlined in Table 1. Compared to the lowest level group, participants with both high TyG and high RC were more likely to be females with less education, lower eGFR, and higher rates of smoking, along with elevated TG, CRP, TC, UA levels, and greater prevalence of hypertension, diabetes, dyslipidemia, advanced CKM, and use of antihypertensive and lipid-lowering medications.

Table 1.

Baseline characteristics according to TyG-RC in the entire population

Characteristic	Overall	Low TyG + Low RC	Low TyG + High RC	High TyG + Low RC	High TyG + High RC	p
N	9108	1976	815	1173	5144	–
Age (years)	59.00 (52.00, 66.00)	58.00 (51.00, 66.00)	59.00 (52.00, 67.00)	59.00 (53.00, 67.00)	59.00 (52.00, 65.00)	0.063
Sex, n (%)						<0.001
Male	4,271.00 (46.90%)	1,053.00 (53.29%)	460.00 (56.44%)	499.00 (42.54%)	2,259.00 (43.92%)	–
Female	4,836.00 (53.10%)	923.00 (46.71%)	355.00 (43.56%)	674.00 (57.46%)	2,884.00 (56.08%)	–
Education, n (%)
Below high school	8,418.00 (92.42%)	1,819.00 (92.05%)	768.00 (94.23%)	1,089.00 (92.84%)	4,742.00 (92.19%)	–
High school	641.00 (7.04%)	148.00 (7.49%)	44.00 (5.40%)	78.00 (6.65%)	371.00 (7.21%)	–
Above high school	49.00 (0.54%)	9.00 (0.46%)	3.00 (0.37%)	6.00 (0.51%)	31.00 (0.60%)	–
Smoking, n (%)	3,570.00 (39.21%)	822.00 (41.62%)	401.00 (49.20%)	410.00 (34.95%)	1,937.00 (37.68%)	<0.001
Drinking, n (%)	1,338.00 (14.70%)	288.00 (14.57%)	132.00 (16.20%)	157.00 (13.40%)	761.00 (14.80%)	0.4
Urban, n (%)	3,373.00 (37.03%)	619.00 (31.33%)	258.00 (31.66%)	398.00 (33.93%)	2,098.00 (40.79%)	<0.001
TG (mg/dL)	101.78 (74.34, 143.37)	62.83 (53.10, 71.68)	70.80 (60.18, 78.76)	92.04 (82.31, 104.43)	135.40 (108.86, 172.57)	<0.001
TC (mg/dL)	190.21 (167.40, 214.56)	177.84 (157.73, 201.03)	183.25 (161.21, 205.28)	192.91 (169.72, 219.98)	195.62 (172.42, 220.36)	<0.001
CRP (mg/L)	1.03 (0.55, 2.18)	0.83 (0.47, 1.81)	0.89 (0.47, 2.16)	0.88 (0.52, 1.80)	1.15 (0.61, 2.38)	<0.001
UA (mg/dL)	4.29 (3.56, 5.13)	4.10 (3.45, 4.84)	4.35 (3.50, 5.11)	4.10 (3.42, 4.91)	4.40 (3.66, 5.29)	<0.001
eGFR	97.83 (87.43, 104.63)	99.43 (90.29, 106.05)	98.23 (89.06, 105.29)	97.73 (87.37, 104.45)	97.11 (85.98, 104.19)	<0.001
Hypertension, n (%)	3,700.00 (40.65%)	634.00 (32.09%)	269.00 (33.01%)	446.00 (38.02%)	2,351.00 (45.76%)	<0.001
Diabetes, n (%)	1,332.00 (14.62%)	93.00 (4.71%)	36.00 (4.42%)	221.00 (18.84%)	982.00 (19.09%)	<0.001
Dyslipidemia, n (%)	5,831.00 (64.02%)	784.00 (39.68%)	358.00 (43.93%)	699.00 (59.59%)	3,990.00 (77.57%)	<0.001
Antihypertensive medication, n (%)	1,885.00 (20.80%)	285.00 (14.53%)	108.00 (13.25%)	219.00 (18.81%)	1,273.00 (24.86%)	<0.001
Lipid lowering medication, n (%)	475.00 (5.33%)	60.00 (3.10%)	20.00 (2.48%)	61.00 (5.34%)	334.00 (6.65%)	<0.001
Hypoglycemic medication, n (%)	360.00 (3.99%)	28.00 (1.43%)	13.00 (1.60%)	61.00 (5.25%)	258.00 (5.07%)	<0.001
CKM, n (%)						<0.001
Stage 0	668.00 (7.33%)	312.00 (15.79%)	139.00 (17.06%)	65.00 (5.54%)	152.00 (2.95%)	–
Stage 1	1,531.00 (16.81%)	524.00 (26.52%)	205.00 (25.15%)	290.00 (24.72%)	512.00 (9.95%)	–
Stage 2	3,004.00 (32.98%)	519.00 (26.27%)	195.00 (23.93%)	318.00 (27.11%)	1,972.00 (38.34%)	–
Stage 3	2,624.00 (28.81%)	400.00 (20.24%)	195.00 (23.93%)	323.00 (27.54%)	1,706.00 (33.16%)	–
Stage 4	1,281.00 (14.06%)	221.00 (11.18%)	81.00 (9.94%)	177.00 (15.09%)	802.00 (15.59%)	–
Advanced CKM, n (%)	3,905.00 (42.87%)	621.00 (31.43%)	276.00 (33.87%)	500.00 (42.63%)	2,508.00 (48.76%)	<0.001

Data are presented as mean ± SD, median (interquartile range), or n (%). TG, triglycerides; TC, total cholesterol; CRP, c-reactive protein; UA, uric acid; eGFR, estimated glomerular filtration rate; CKM, cardiovascular-kidney-metabolic.

Prevalence of cardiovascular-kidney-metabolic stages stratified by triglyceride-glucose index-remnant cholesterol

The prevalence percentage of CKM stages 0 through 4 was found to be 7.33, 16.81, 32.98, 28.81, and 14.06%, respectively (Table 1). Notably, there were significant differences in the prevalence of CKM stages within the different TyG-RC groups. In contrast to individuals with lower TyG-RC, those exhibiting higher TyG-RC levels had a significantly low er prevalence across CKM Stages 0–1, but a higher prevalence across CKM Stages 2–4 (Figure 1A). Significantly, the incidence of advanced CKM stages progressively increased with the rise in the high TyG-RC group (Figure 1B).

Figure 1.

The association between cardiovascular-kidney-metabolic stages and TyG-RC

Prevalence of cardiovascular-kidney-metabolic stages (A) and advanced cardiovascular-kidney-metabolic stages (B) by different TyG-RC

Association between triglyceride-glucose index-remnant cholesterol and advanced cardiovascular-kidney-metabolic stages at baseline

As shown in Table 2, the logistic regression model, adjusted for multiple covariates, showed that each 1-SD increase in the TyG-RC index was associated with higher odds of advanced CKM stages (OR 1.53; 95% CI: 1.45–1.61, p < 0.001). Compared to the reference group (low TyG and low RC), the groups with high TyG only and both high TyG and high RC had significantly higher odds of advanced CKM stages (OR 1.56, 95% CI: 1.29–1.89, p < 0.001; and OR 2.57, 95% CI: 2.23–2.96, p < 0.001, respectively), while the high RC only group did not (OR 1.02, 95% CI: 0.82–1.27, p=0.80).

Table 2.

Association between TyG-RC and advanced cardiovascular-kidney-metabolic stages at baseline

Characteristic	Model 1		Model 2		Model 3
	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
TyG-RC per SD	1.32 (1.27, 1.38)	<0.001	1.55 (1.47, 1.63)	<0.001	1.53 (1.45, 1.61)	<0.001
Low TyG + Low RC	Ref.	–	Ref.	–	Ref.	–
Low TyG + High RC	1.12 (0.94, 1.33)	0.20	0.99 (0.80, 1.22)	0.90	1.02 (0.82, 1.27)	0.80
High TyG + Low RC	1.62 (1.40, 1.88)	<0.001	2.10 (1.76, 2.52)	<0.001	1.56 (1.29, 1.89)	<0.001
High TyG+ High RC	2.08 (1.86, 2.32)	<0.001	3.07 (2.68, 3.52)	<0.001	2.57 (2.23, 2.96)	<0.001

Model 1: unadjusted; Model 2: adjusted for age, sex, education, residence, smoking, and drinking; Model 3: adjusted for age, sex, education, residence, smoking, drinking, CRP, LDL-C, UA, eGFR, lipid-lowering medication use, and hypoglycemic medication use.

Association between baseline triglyceride-glucose index-remnant cholesterol and incident cardiovascular disease in individuals with cardiovascular-kidney-metabolic stages 0–3

During a median follow-up of 9.0 years (interquartile range: 7.0–9.0), 1744 (23.2%) participants progressed to CVD. Kaplan-Meier curves showed differences in cumulative CVD incidence among groups defined by TyG and RC levels (Figure S2; all log rank p < 0.05). In Cox proportional hazards models adjusted for potential confounders (Model 3, Table 3), the hazard ratio for CVD was 1.20 (95% CI: 0.99–1.46, p = 0.062) for the high RC group compared to the low RC group, and 1.24 (95% CI: 1.04–1.47, p = 0.015) for the high TyG group compared to the low TyG group (Tables S4 and S5). Using the group with low TyG and low RC as the reference, the hazard ratio for the group with both high TyG and high RC was 1.35 (95% CI: 1.19–1.54, p < 0.001). We used the Schoenfeld residual test to examine the proportional hazards assumption. The results showed that the p-values for all variables were greater than 0.05, indicating that the proportional hazards assumption was satisfied. Restricted cubic spline analyses revealed a linear association between TyG and CVD risk (P for nonlinearity >0.05), while an inverse J-shaped associations were observed for RC and the TyG-RC index (both P for nonlinearity <0.05; Figure 2).

Table 3.

Association between TyG - RC and the risk of incident cardiovascular disease in participants with cardiovascular-kidney-metabolic stages 0–3

Characteristic	Model 1		Model 2		Model 3
	HR (95% CI)	P	HR (95% CI)	P	HR (95% CI)	P
TyG-RC per SD	1.10 (1.05, 1.15)	<0.001	1.10 (1.05, 1.15)	<0.001	1.08 (1.03, 1.14)	<0.001
Low TyG + Low RC	Ref.	–	Ref.	–	Ref.	–
Low TyG + High RC	1.22 (1.00, 1.48)	0.045	1.20 (0.99, 1.45)	0.067	1.20 (0.99, 1.46)	0.062
High TyG + Low RC	1.33 (1.13, 1.58)	<0.001	1.29 (1.09, 1.53)	0.003	1.24 (1.04, 1.47)	0.015
High TyG+ High RC	1.42 (1.25, 1.61)	<0.001	1.41 (1.24, 1.60)	<0.001	1.35 (1.19, 1.54)	<0.001

Figure 2.

CVD incidence exhibits nonlinear associations with TyG, RC, and TyG–RC

Nonlinear associations of TyG (A), RC (B), and TyG – RC (C) with CVD incidence. Graphs show HRs for CVD incidence based on Cox hazards regression model 3 (adjusted for age, sex, education, residence, smoking, drinking, CRP, LDL-C, UA, eGFR, lipid-lowering medication use, and hypoglycemic medication use). Solid lines indicate HRs. Shadow shapes indicate 95% CIs

Interaction between triglyceride-glucose index and remnant cholesterol

The p-value for the interaction term (TyG × RC) in our regression model was 0.42, indicating no statistically significant evidence of multiplicative interaction on the log odds scale. The measures of additive interaction, along with their 95% confidence intervals, were as follows: RERI (−0.09, 95% CI: −0.38, 0.20); AP (−0.07, 95% CI: −0.28, 0.15); SI (0.79, 95% CI: 0.71, 1.15). As all confidence intervals included the null value (0 for RERI and AP, 1 for SI), we did not find statistically significant evidence of additive interaction. (Table S6, all p > 0.05).

Subgroup analyses

We performed stratified analyses to assess the associations of TyG and RC with CVD events across various subgroups. The associations between TyG or RC and the risk of CVD in most subgroups were consistent with the main results. No significant interaction was observed (Tables S7 and S8). Similar results were observed when stratified by TyG-RC (Table 4). Moreover, the associations remained directionally consistent within these subgroups, with the TyG-RC consistently linked to a higher risk of cardiovascular mortality across various demographic, socioeconomic, and clinical strata. These findings indicate that the association between the TyG-RC and cardiovascular mortality is generally consistent across most subgroups.

Table 4.

Subgroup analysis for the TyG-RC on CVD risk

	Low TyG and Low RC	Low TyG and High RC	High TyG and Low RC	High TyG and High RC	P for interaction
Age (years)					0.432
<65	Ref.	1.1 (0.86, 1.41)	1.27 (1.03, 1.57)	1.37 (1.17, 1.6)	–
≥65	Ref.	1.43 (1.03, 1.98)	1.16 (0.85, 1.58)	1.28 (1.01, 1.63)	–
Sex					0.762
Male	Ref.	1.13 (0.86, 1.48)	1.18 (0.91, 1.52)	1.37 (1.14, 1.65)	–
Female	Ref.	1.30 (0.98, 1.72)	1.30 (1.03, 1.65)	1.36 (1.13, 1.63)	–
Residence					0.339
Rural	Ref.	1.32 (1.05, 1.66)	1.26 (1.02, 1.55)	1.44 (1.23, 1.69)	–
City	Ref.	0.94 (0.65, 1.36)	1.19 (0.88, 1.62)	1.19 (0.95, 1.49)	–
Smoking					0.629
No	Ref.	1.35 (1.04, 1.75)	1.31 (1.05, 1.64)	1.41 (1.19, 1.68)	–
Yes	Ref.	1.04 (0.78, 1.39)	1.16 (0.88, 1.54)	1.28 (1.05, 1.57)	–
Drinking					0.895
No	Ref.	1.17 (0.95, 1.45)	1.26 (1.05, 1.52)	1.36 (1.18, 1.57)	–
Yes	Ref.	1.41 (0.87, 2.30)	1.11 (0.70, 1.77)	1.36 (0.97, 1.91)	–

Based on Cox hazards regression Model 3 (adjusted for age, sex, education, residence, smoking, drinking, CRP, LDL-C, UA, eGFR, lipid-lowering medication use, and hypoglycemic medication use).

Sensitivity analysis

Several sets of sensitivity analyses were conducted to assess the robustness of the main findings (Figure 3). The associations of TyG and RC with the risk of CVD remained consistent with the main findings after excluding participants with a follow-up duration of less than 2 years. Additionally, including participants with hypertension, diabetes, or hyperlipidemia at baseline did not compromise the stability of the results.

Figure 3.

Sensitivity analysis of the combined effect of the TyG index and RC level on CVD risk

(A) excluding participants with follow-up duration less than two years; (B) including participants with hypertension, diabetes, or hyperlipidemia disease. Based on Cox hazards regression Model 3 (adjusted for age, sex, education, residence, smoking, drinking, CRP, LDL-C, UA, eGFR, lipid-lowering medication use, and hypoglycemic medication use).

Predictive value of triglyceride-glucose index indices, remnant cholesterol indices, and other insulin resistance indices in cardiovascular disease incident

Based on Model 3, the time-independent AUC curves indicated that combined with the TyG index and RC showed a higher predictive value for cardiovascular mortality than the TyG index, RC alone, TyG-derived indices, or other insulin resistance indices (Figure 4).

Figure 4.

The area under the curve for TyG, RC, TyG + RC, various TyG-derived indices (TyG-BMI, TyG-WC, and TyG-WHtR), and other insulin resistance markers (eGDR and METSIR) predicting CVD incidence based on Model 3 (adjusted for age, sex, education, residence, smoking, drinking, CRP, LDL-C, UA, eGFR, lipid-lowering medication use, and hypoglycemic medication use)

Discussion

In this national longitudinal cohort study based on the Chinese health examination population, we comprehensively investigated the associations between the TyG index and modified TyG indices with new-onset cardiovascular disease and compared the time-dependent predictive capacity. The key findings of our study are as follows: (1) In a national cohort study of middle-aged and older adults, baseline analysis demonstrated that participants with concurrently elevated TyG index and RC levels exhibited a markedly higher risk of advanced CKM stages compared to those with elevated TyG alone or elevated RC alone. (2) Over a median follow-up of 9.0 years, simultaneous elevations in TyG index and RC levels were significantly associated with a progressively increased risk of developing CVD among individuals with CKM syndrome who were free of CVD at baseline, a relationship that remained consistent across various factors such as age, gender, residence, smoking status, and drinking status. (3) No significant interaction was observed between TyG and RC regarding CVD risk. (4) The combined TyG-RC index has a higher predictive capacity than other IR indices in predicting CVD risk. In summary, our findings provide additional evidence for the application of insulin resistance markers in future clinical practice. The combination of the TyG index and RC may help improve risk stratification in individuals with and without CKM syndrome.

As expected, logistic regression analysis identified TyG-RC as an independent risk factor for advanced, but not non-advanced, CKM syndrome. Notably, the TyG-RC index showed superior predictive ability for advanced CKM syndrome compared to the TyG or RC indices individually. As outlined in a recent scientific statement, effective management of CKM syndrome aims to establish optimal approaches for preventing adverse cardiovascular events and arresting or reversing disease progression.¹⁹ A recent study indicated that elevated RC is associated with a higher risk of premature cardiovascular disease, even in young adults.²⁰ Furthermore, a nationwide prospective cohort study revealed that the atherogenic index of plasma (AIP)—calculated as the log-transformed ratio of triglycerides to high-density lipoprotein cholesterol—was significantly associated with elevated cardiovascular risk in individuals at CKM syndrome stages 0–3.²¹ As previously noted, the TyG index serves as a reliable biomarker of IR, which is closely linked to chronic inflammation, oxidative stress, and vascular endothelial dysfunction.²² The accumulation of RC in the arterial wall, coupled with elevated TyG index levels, may jointly contribute to the development of cardiovascular disease, especially in individuals with CKM syndrome.²³

Evidence demonstrated that TyG or RC, when combined with other biomarkers, captures the risk of CVD more effectively.¹⁸ However, studies integrating TyG with RC remain limited. A study by Shi et al. indicated that elevated TyG-LDL-C correlates with a 13% increased risk of CVD,²⁴ which showed that the ability of TyG to predict the risk of CVD by combining with LDL-C is limited. In our study, using low TyG and low RC as the reference group, It was observed that elevated TyG alone was associated with a 24% increased risk of CVD. Similarly, elevated RC alone corresponded to a 20% higher risk. Notably, the coexistence of high TyG and high RC was linked to a 35% higher risk of stroke. In contrast to Shi et al.'s study, which was confined to Xinzheng city residents in northeastern China, our research recruited participants from diverse urban and rural areas across the country, yielding a more representative sample and significantly enhancing the generalizability of our findings. Additionally, we compared the predictive power of the combined TyG and RC index relative to that of TyG and RC individually across different survey periods. The combined TyG and RC index significantly enhanced CVD prediction when analyzed as categorical variables, whereas the improvement was relatively modest when analyzed as continuous variables. We further compared the performance of TyG-RC with seven other commonly used IR indices, including TyG, TyG-WC, TyG-BMI, TyG-WHtR, eGDR, and METS-IR, for predicting the incidence of CVD events. Importantly, we found that the TyG-RC index demonstrated superior predictive value compared to the other commonly used IR indices, further underscoring its potential as an effective tool in clinical risk assessment.

We contextualize the performance within the heterogeneous and complex nature of the CKM population, where risk prediction is inherently challenging. We note that TyG-RC’s performance is comparable to other well-established and more complex indices used for comparison. Most importantly, we argue for its clinical utility based on simplicity and practicality. TyG-RC is derived from readily available, low-cost routine blood tests (fasting glucose, triglycerides, standard lipid profile), making it highly suitable for widespread screening and risk stratification in primary care settings. Even with modest discrimination, identifying a higher-risk subgroup within CKM can guide prioritization for more intensive lifestyle counseling and preventive pharmacotherapy.

Our RCS analyses indicated mainly linear associations, but the relationship between RC/TyG-RC and incident stroke was nonlinear, suggesting complexity. The RCS curves reflect the shape of the association, not data dispersion, and are influenced by sample size, disease factors, and modeling choices.²⁵

The underlying mechanisms by which this indicator influences CVD risk remain unclear. The joint effect between these two markers can be explained by the following mechanism: (1) Elevated TyG levels reflected significant metabolic dysfunction characterized by disrupted glucose and lipid metabolism. In the context of metabolic syndrome, this dysregulation could exacerbate inflammatory response, leading to endothelial dysfunction and increased vascular permeability.²² This dysfunction could ultimately increase CVD risk²⁶; (2) RC may induce lipotoxicity and inflammatory responses, thereby exacerbating β-cell dysfunction and insulin resistance²⁷; (3) unlike LDL-C, RC can be directly engulfed by macrophages, promoting foam cell formation²⁸; (4) owing to its larger molecular size and enhanced interactions with extracellular proteoglycans, RC demonstrates greater propensity for arterial intima infiltration and retention compared to LDL-C, thereby promoting local vascular injury, inflammatory responses, and plaque destabilization, ultimately elevating CVD risk.²⁹Consequently, early identification and therapeutic intervention targeting elevated RC levels may play a pivotal role in attenuating CKM progression and decreasing CVD incidence. Comprehensive management strategies incorporating lifestyle modifications - particularly weight reduction to decrease excessive adiposity - in conjunction with pharmacologic interventions including statins, fibrates, and PCSK9 inhibitors, may effectively reduce RC concentrations and ameliorate residual cardiovascular risk.³⁰ Future investigations employing appropriate animal models, such as non-invasive metabolic syndrome paradigms,³¹ are needed to investigate the mechanisms by which RC accelerates CKM progression. Such scientific exploration would not only deepen our comprehension of RC’s pathophysiological role beyond conventional lipid modulation but also yield essential insights for developing more effective prevention and management strategies for CKM syndrome.

Based on the findings of this study, we propose the following directions for future research. First, our analysis revealed that categorizing TyG and RC levels was more effective than using a continuous interaction term in identifying their combined effect on cardiovascular risk. This suggests the existence of a potential threshold effect. To elucidate the underlying mechanism, in vitro and in vivo studies are needed to examine whether the coexistence of elevated TyG and RC acts as a critical trigger, jointly activating detrimental pathways such as inflammation or oxidative stress. Such investigations would provide a mechanistic basis for the “dual-high risk” phenotype observed in our study. Additionally, large-scale, diverse prospective studies are warranted to establish optimal clinical cut-off values for TyG and RC jointly, which would facilitate the identification of high-risk individuals who may benefit most from targeted preventive strategies. Second, future studies should include repeated measurements of RC over time to assess the influence of its dynamic changes on CKM progression. Such longitudinal data would help clarify whether rising RC levels are a more robust predictor of risk than a single baseline measurement, and whether the therapeutic reduction of RC can meaningfully alter the trajectory of CKM syndrome. Further research into the determinants of RC variability may also yield deeper insights into the pathophysiology of CKM syndrome.

In conclusion, our findings demonstrate that elevated TyG and RC are significantly correlated with an increased risk of CVD. The concurrent evaluation of TyG and RC indices enhances the predictive capacity for CVD events, highlighting the importance of IR and dyslipidemia in identifying high-risk individuals.

Limitations of the study

This study has several limitations that should be acknowledged. First, due to the lack of standardized clinical cutoff values for the TyG index and remnant cholesterol (RC), participants were classified using optimal cutoff values derived from ROC curves, which may not fully align with clinically meaningful thresholds. Future studies are warranted to establish more precise and clinically applicable categorization criteria. Second, the TyG index and RC were measured only at baseline. However, the relationships between insulin resistance, dyslipidemia, and cardiovascular disease (CVD) may be dynamic and influenced by various factors over extended follow-up periods. The absence of repeated measurements prevented the assessment of how temporal variations in TyG-RC influence CVD risk. It requires further exploration in future prospective studies. Third, as all CHARLS participants were from China, applying our findings to other countries may be limited. Finally, given that the CHARLS study is observational, we cannot infer causality, and further studies are required to confirm these findings.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Xianming Chu (chuxianming@qdu.edu.cn).

Materials availability

This study did not generate new reagents.

Data and code availability

• •All population research data are open access and available for download from the following repositories: CHARLS: https://charls.pku.edu.cn.
• •The code for epidemiological analysis and the aggregated data have been stored in the GitHub database (GitHub database: https://github.com/WZHsdu/Code), and are made available to the public upon release.
• •Any additional information required to reanalyze the data reported in this article is available from the lead contact upon request.

Acknowledgments

We express our gratitude to all participants in the CHARLS study and the project team. This work was supported by the National Natural Science Foundation of China (No. 82172574), the Natural Science Foundation of Shandong Province (No. ZR2024MH083, No. ZR2025QC1665), and the 2024 Health and Medical Leading Innovation Team Program of Shandong Province (Xianming Chu).

Author contributions

X.M.C. conceived the design and supervised the work. X.M.C. critically revised the article. H.T.L., X.J., Z.Z., R.L.C., B.H.W, and C.A.Q. contributed to the acquisition, analysis, or interpretation of data for the work. H.T.L. and X.J. drafted the article and contributed equally to this article. All gave final approval and agreed to be accountable for all aspects of work, ensuring integrity and accuracy.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Deposited data
The data for 9,108 participants from CHARLS database (2011–2020)	CHARLS database	https://charls.pku.edu.cn
Software and algorithms
R version 4.2.3	R Software Foundation	https://cran.r-project.org/
R code and packages	GitHub	https://github.com/WZHsdu/Code

Experimental model and study participant details

Observational analyses

This study utilized data from the publicly available 2011-2020 wave of the CHARLS. As this study involves the analysis of original clinical data using real-world patient datasets, it therefore adhered to strict ethical and reporting standards rather than employing experimental models.

Ethics approval and consent to participate

The CHARLS study was conducted in line with the principles stated in the Declaration of Helsinki and received approval from the Institutional Review Board of Peking University (IRB00001052-11015). Prior to their involvement in the CHARLS study, all participants gave their written informed consent. The research adhered to the STROBE guidelines for reporting observational studies in epidemiology.

Method details

Source of data and study population

This study utilized data from the publicly available 2011 wave of the CHARLS, comprising a total of 17,708 participants. CHARLS is a nationally representative random sample survey that follows up 450 villages randomly selected from within 28 provincial administrative units across the country and follows up every 2 years.³² Briefly, CHARLS is an ongoing national survey designed to collect comprehensive information on middle-aged and older adults, facilitating health research and evidence-based policy development.³³ A total of 8,600 participants were excluded based on the following criteria: age below 45 years or missing age data, absence of a fasting blood sample, missing or aberrant TyG index and RC values, or incomplete CKM syndrome indicators. After applying these exclusion criteria, 9,108 participants remained eligible for baseline cross-sectional analysis. For longitudinal assessment, we further excluded 1,424 individuals with prevalent CVD at baseline or those lost to follow-up.

Covariates data collection and definitions

This study collected data on a range of covariates. Fasting venous blood samples were obtained and analyzed to measure biochemical indicators.³⁴ Demographic factors, including age, sex, education levels, residence, as well as health status indicators like diabetes, hypertension, dyslipidemia, smoking status, and drinking status, were meticulously gathered by well-trained interviewers. The educational information was categorized into three groups: primary school or lower, junior high school, and high school or higher, based on previous studies.³⁵ Hypertension was defined as self-reported physician diagnosis, current use of antihypertensive medication, or measured systolic/diastolic blood pressure (SBP/DBP) ≥140/90 mmHg.³⁶ Diabetes was diagnosed based on fasting plasma glucose (FPG) ≥126 mg/dL, glycated hemoglobin (HbA1c) ≥6.5%, self-reported physician diagnosis, or use of hypoglycemic medication.³⁷ Dyslipidemia was identified through self-reported physician diagnosis, use of lipid-lowering agents, or meeting any of the following laboratory criteria: total cholesterol (TC) ≥240 mg/dL, TG ≥150 mg/dL, HDL-C <40 mg/dL, or LDL-C ≥160 mg/dL.³⁸ CKD staging was determined using the estimated glomerular filtration rate (eGFR), calculated with the race-free Chronic Kidney Disease Epidemiology Collaboration 2021 creatinine equation,³⁹ and the urinary albumin-to-creatinine ratio.

TyG indices, RC indices, and other insulin resistance indices assessment

During the baseline survey in 2011, CHARLS effectively obtained fasting blood samples from the participants. Before blood sample collection, participants were instructed to observe an overnight fast. These blood samples were initially stored at local hospitals and then transported to Peking University in Beijing, where they were meticulously preserved at −80 °C until further analysis. The levels of blood lipids, including TG, LDL-C, HDL-C, and TC), were meticulously assessed using enzymatic colorimetric tests.⁴⁰ RC was calculated as [TC – HDL-C – LDL-C], utilizing a standardized lipid profiling method widely recognized for its cost-effectiveness as previously described.⁴¹ TG and FPG concentrations were determined using the enzyme colorimetric assay. The TyG = ln[TG (mg/dl) × FPG (mg/dl)/2]⁴²; TyG-BMI index = TyG index × BMI⁴³; TyG-WC index = TyG index × waist circumference (cm)⁴³; TyG-WHtR index = TyG index × waist circumference (cm)/height (cm)⁴³; eGDR = 21.158 - (0.09 × waist circumference [cm]) − (3.407 × hypertension [yes 1 or no 0]) − (0.551 × glycated hemoglobin A1c [HbA1c] [%])⁴⁴; Metabolic score for insulin resistance (METS-IR) = Ln (2 × fasting glucose [mg/dL] + fasting triglyceride [mg/dL]) × BMI/Ln (fasting HDL-C [mg/dL]).⁴⁵

CKM stage 0 to 4 definitions

According to the pathophysiological mechanisms, prevention-treatment strategies, and disease risk, CKM syndrome is classified into five clinical stages⁵: stage 0 (no risk factors): all conditions are normal; stage 1 (metabolic precursor phase): simple obesity or prediabetic state; stage 2 (metabolic disorder phase): at least one other metabolic abnormality or CKD present; stage 3 (subclinical cardiovascular phase): metabolic abnormalities or CKD combined with subclinical CVD; stage 4 (clinical cardiovascular phase): clear clinical manifestations of CVD in patients with metabolic abnormalities or CKD.

Due to the lack of relevant indicators for subclinical CVD, we defined it using risk equivalents, including individuals with very high-risk CKD at Stages G4 or G5, as well as those predicted to have a 10-year CVD risk of 20% or higher (utilizing PREVENT equations).⁴⁶

Stages 0–2 were classified as non-advanced CKM, and stages 3–4 as advanced CKM, reflecting increasing severity of metabolic, renal, and cardiovascular impairments.⁵

Assessment of endpoint events

The primary outcome was the incidence of CVD, encompassing heart disease and stroke. Diagnoses of heart disease and stroke were assessed through standardized questions adapted from established methodologies. Participants were inquired: “Has a physician ever diagnosed you with a heart attack, coronary heart disease, angina, congestive heart failure, or any other heart condition?”. Stroke was identified based on the question: “Has a doctor ever diagnosed you with a stroke?”. Participant were followed up in 2013, 2015, 2018, and 2020. If a participant developed CVD before the 2020 follow-up, subsequent assessments were discontinued. Otherwise, follow-up continued until 2020. The time to CVD event was calculated as the interval between the follow-up date when CVD was identified and the baseline year (2011).

Quantification and statistical analysis

The receiver operating characteristic (ROC) curve was employed to identify optimal cutoff points for the TyG index and RC. This approach helps ensure the clinical relevance of the identified thresholds.⁴⁷ Participants were stratified into quartiles according to the optimal cutoff values for the TyG index, RC, and their combination. Based on this, they were categorized into 4 groups (low TyG and low RC, high TyG and low RC, low TyG and high RC, and high TyG and high RC). These two indicators were subsequently multiplied to establish TyG-RC. In addition, linear trends in TyG-RC four group were assessed by the median value within each groupas a continuous variable and further standardization.⁴⁸

Baseline characteristics were stratified according to the optimal cutoff values for the TyG index, RC, and their combination. Categorical variables were detailed in terms of frequencies and percentages and evaluated with the χ2 test. Normally dis-tributed variables were represented as means ± SD and compared utilizing one-way ANOVA. Non-normally distributed variables are expressed as median with interquartile range and analyzed with the Kruskal–Wallis test. Logistic regression was used to examine the association between the combined TyG and RC measures and advanced cardiovascular-kidney-metabolic (CKM) syndrome (Stages 3 and 4). Additionally, adjusted variable selection was based on established clinical relevance, prior literature, and univariate screening (P < 0.05), consistent with methodological standards in cohort studies. Setting new CVD incidence as the primary outcome events,^49,50 Cox proportional hazards regression models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD risk according to TyG, RC, and their combination, each categorized by optimal cutoff values. The crude model and two adjusted model were estimated. Model 1 unadjusted, while model 2 were adjusted for age, sex and socio-demographic information (education levels, residence, smoking and drinking status). Model 3 were adjusted for factors in model 2, laboratory examination laboratory examination (CRP, LDL-C, UA, eGFR), and disease medications (lipid lowering medication use, and hypoglycemic medication use). Restricted cubic splines (RCS) with three knots were applied to evaluate potential nonlinear relationships between the TyG index, RC, and CVD incidence. Kaplan–Meier curves were generated based on TyG and RC strata to visualize cumulative CVD incidence, with between-group differences evaluated by the log-rank test. The predictive power of the indices for CVD risks was evaluating using time-dependent ROC curve analysis. For joint assessment, both the TyG index and RC were treated as continuous variables and included simultaneously in the predictive model. The area under the curve (AUC) was computed to compare the predictive ability of the TyG index, RC, and their combination. The test for multicollinearity showed that the variance inflation factor (VIF) for each covariate was less than 5, indicating the absence of substantial multicollinearity among the covariates.⁵¹

Using Model 3 as the reference, we performed stratified analyses to evaluate potential effect modifications by age (>65 vs ≤65 years), sex (male vs female), residence (rural vs city), smoking status (current vs non-smoker), and alcohol use (drinker vs non-drinker). Sensitivity analyses were performed by excluding those with a follow-up duration of less than two years, including those with hypertension, diabetes, or hyperlipidemia disease at baseline and by including those with non-fasting blood samples. The above assessments were conducted using R 4.2.3 software, and statistical significance was determined using a two-sided p-value threshold of less than 0.05.

Published: February 17, 2026