Abstract
Background
Cardiovascular-kidney-metabolic (CKM) syndrome is defined by the interplay of metabolic risk factors, chronic kidney disease, and cardiovascular disease. The C-reactive protein–triglyceride glucose index (CTI) is a composite biomarker that reflects both inflammation and insulin resistance, but whether it is associated with mortality in persons with early-stage CKM syndrome is unknown.
Methods
We analyzed data from the National Health and Nutrition Examination Survey from 1999 to 2010. We used multivariable Cox proportional-hazards models to assess the association between the CTI score and the risk of all-cause mortality and cardiovascular disease (CVD) mortality, with vital status ascertained through Linkage to the National Death Index through December 31, 2019.
Results
Among 10,718 participants, a total of 1783 deaths (491 from CVD) occurred during a mean follow-up of 14.0 years. In fully adjusted models, a higher CTI score was associated with a greater risk of all-cause mortality (HR per unit increase, 1.56; 95% CI, 1.36 to 1.78) and of CVD mortality (HR per unit increase, 2.03; 95% CI, 1.49 to 2.77). The association with all-cause mortality was stronger among participants under the age of 60 than among those over 60 years old (P < 0.001 for interaction).
Conclusion
Our study found that in patients with early-stage CKM syndrome, a higher CTI was independently associated with an increased risk of all-cause mortality and CVD mortality. This association was more significant in younger participants.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-025-01947-7.
Keywords: Insulin resistance, Inflammation, C-reactive protein–triglyceride glucose index, Cardiovascular-kidney-metabolic syndrome, NHANES
Introduction
Cardiovascular-kidney-metabolic (CKM) syndrome results from the pathophysiological interplay among metabolic risk factors, chronic kidney disease (CKD), and cardiovascular disease (CVD) [1–3]. This syndrome leads to multisystem organ damage and is associated with a high risk of adverse cardiovascular events and death. The prevalence of CKM syndrome is substantial; in the United States, an estimated one in four adults has at least one component of the syndrome [4]. Recognition of its public health importance, the American Heart Association recently proposed a staging system for CKM syndrome to improve risk stratification and guide clinical management [5]. This system classifies CKM syndrome into five stages (0 through 4), of which stages 0 through 3 represent the preclinical spectrum of the syndrome, a period that may be optimal for intervention [6].
The pathophysiology of CKM syndrome involves a progressive cascade that is often initiated early in life by biological, social, and environmental exposures that promote the development of excess or dysfunctional adipose tissue. The resulting adipose tissue dysfunction, in turn, drives key pathophysiological mechanisms, including inflammation, oxidative stress, and insulin resistance, which are central to the progression of the syndrome. Crucially, CKM syndrome Stages 0–3 constitute a critical preclinical window. This phase is characterized by metabolic dysfunction, subclinical organ injury, and heightened states of inflammation and insulin resistance, all of which precede the onset of overt cardiovascular events or end-stage kidney disease. Recent large-scale cohort studies demonstrated that triglyceride–glucose related indices were associated with an increased prevalence of advanced CKM syndrome and mortality in individuals with CKM syndrome stages 0–3 [7, 8]. In a previous study, we showed that markers of insulin resistance (as measured by the estimated glucose disposal rate, eGDR) and inflammation (as measured by the systemic inflammation response index, SIRI) were synergistically associated with the risk of adverse outcomes among patients with early-stage CKM syndrome [9]. A composite biomarker that integrates measures of both insulin resistance and inflammation would be of clinical value for risk stratification and for guiding preventive therapy in persons with early-stage CKM syndrome. The C-reactive protein–triglyceride glucose index (CTI) is a composite biomarker that combines C-reactive protein (CRP), a widely used marker of inflammation, with the triglyceride–glucose (TyG) index, a validated surrogate marker for insulin resistance. Although the homeostasis model assessment of insulin resistance (HOMA-IR) is the established gold standard, its clinical application is limited by the need for fasting insulin measurements, which are not routinely performed. In contrast, the components of CTI—CRP and the TyG index—are cost-effective, standardized, and readily accessible in most clinical laboratories. These attributes position CTI as a highly practical and scalable biomarker, which has been consistently associated with an increased risk of adverse clinical outcomes across diverse populations [10–14]. For example, in analyses involving participants in the China Health and Retirement Longitudinal Study (CHARLS), a higher CTI score was associated with the subsequent development of both stroke and type 2 diabetes [15, 16]. However, whether the CTI is associated with the risk of adverse outcomes in persons with early-stage CKM syndrome is not known.
Therefore, in this study we used data from the National Health and Nutrition Examination Survey (NHANES) to determine the association between the CTI and the risk of all-cause mortality and CVD mortality among a nationally representative sample of U.S. adults with early-stage (stage 0 to 3) CKM syndrome. We hypothesized that higher CTI values, reflecting the combined burden of systemic inflammation and insulin resistance, would be independently associated with increased long-term risks of all-cause and cardiovascular mortality in this population.
Methods
Study design and population
This study analyzed data from NHANES, a recurring, cross-sectional survey conducted by the US Centers for Disease Control and Prevention (CDC). NHANES employs a complex, stratified, multistage sampling design to obtain a nationally representative sample. Data on health and nutrition are collected in two-year cycles via household interviews and standardized clinical examinations. The National Center for Health Statistics Institutional Review Board approved the primary survey protocol, and all participants provided written informed consent. As this work is a secondary analysis of publicly available, de-identified data, it was exempt from further institutional review.
We analyzed data from NHANES from 1999 to 2010. This period was selected to ensure consistent use of conventional CRP measurements, as post-2010 surveys used high-sensitivity CRP (hs-CRP). From an initial cohort of 62,160 participants, we excluded those with insufficient data to stage CKM syndrome (n = 47,324) or who were already at CKM stage 4 (n = 1,910). Further exclusions included pregnant participants (n = 606) and individuals missing data required for calculating the TyG or CRP levels (n = 715), or those with missing weight (n = 875) or mortality data (n = 12). The final cohort for analysis comprised 10,718 participants (Fig. 1).
Fig. 1.
Flow diagram of participant selection
Definition of CKM syndrome
We defined CKM syndrome stages (0–4) based on established criteria [9], with full definitions provided in Table S1. Stage 0 was defined as the absence of risk factors; stage 1 as the presence of excess body fat or obesity; stage 2 by metabolic risk factors—such as hypertension, type 2 diabetes, or hypertriglyceridemia—or moderate- to high-risk CKD; stage 3 by subclinical CVD or high- to very-high-risk CKD; and stage 4 by established clinical CVD.
Calculation of CTI
The CTI was calculated by combining log-transformed CRP with the TyG using the formula: CTI = 0.412 × ln[CRP (mg/L)] + TyG index, where the TyG is defined as ln[triglycerides (mg/dL) × fasting plasma glucose (mg/dL)]/2 [15].
Ascertainment of mortality
We ascertained mortality status through 31 December 2019 by linkage to the National Death Index (NDI). The primary outcome was all-cause mortality; the secondary outcome was CVD mortality, defined as death from cardiovascular or cerebrovascular diseases (International Classification of Diseases, 10th Revision [ICD-10] codes: I00–I09, I11, I13, I20–I51, and I60–I69). Participant follow-up was calculated from the baseline interview date to the date of death or the end of the observation period (31 December 2019), whichever occurred first.
Assessment of covariates
We adjusted for potential confounding variables selected across demographic, socioeconomic, and clinical domains. Covariates included: age, sex, and ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, or other); socioeconomic status (marital status, educational attainment [< high school, high school graduate, or > high school], and family poverty-to-income ratio); lifestyle factors (smoking status [never, former, or current], alcohol consumption, body mass index [BMI], and Healthy Eating Index [HEI] score); key clinical biomarkers (HbA1c, serum uric acid, lipid profile [total, high-density lipoprotein [HDL], and low-density lipoprotein [LDL] cholesterol], and estimated glomerular filtration rate [eGFR]); and the use of antihypertensive, lipid-lowering, or glucose-lowering medications.
Statistical analysis
All analyses incorporated NHANES survey design parameters, including weights, clustering, and stratification, with sampling weights recalculated for the six combined survey cycles (1999–2010). Baseline participant characteristics were compared across CTI tertiles using the Kruskal-Wallis test for continuous variables (reported as mean ± s.e.) and the χ² test for categorical variables (reported as n and weighted %). We used multivariable Cox proportional hazards models to assess the association between CTI and mortality (primary outcome: all-cause; secondary outcome: cardiovascular) among individuals with CKM stages 0–3. Three nested models were constructed: Model 1 adjusted for age, sex, and ethnicity; Model 2 additionally adjusted for socioeconomic and lifestyle factors (marital status, education, poverty-to-income ratio, smoking, alcohol use, BMI, and HEI score); and Model 3, the fully adjusted model, further incorporated clinical covariates (HbA1c, serum uric acid, lipid profile, eGFR) and the use of antihypertensive, Lipid-lowering, or glucose-lowering medications. Potential non-linear associations were assessed using restricted cubic splines, with knots positioned at the 5th, 35th, 65th, and 95th percentiles of the CTI distribution. Survival distributions were estimated using the Kaplan-Meier method and compared with the log-rank test. We conducted subgroup analyses to assess whether the association between CTI and mortality was consistent across strata defined by age, sex, race/ethnicity, marital status, educational attainment, and poverty-to-income ratio. All analyses were performed in R (v.4.5.1), and statistical significance was set at a two-sided P < 0.01.
Results
Baseline characteristics
The final cohort included 10,718 participants with a mean age of 44.84 (0.28). Of the participants in the cohort, 51.27% were female, 70.65% were Non-Hispanic White, 10.71% were Non-Hispanic Black, and 8.09% were Mexican American (Table 1). Median values for CTI were 8.026. Compared with individuals in the lowest CTI tertile, those in the highest tertile were older (mean age, 48.83 versus 39.09 years), less likely to be female (52.44% versus 55.54%), and had lower educational attainment and family income. Participants in the highest tertile also exhibited a greater burden of cardiometabolic risk factors, including higher body mass index (32.38 versus 24.39 kg/m²), lower rates of never smoking (45.59% versus 59.07%), and substantially elevated biomarkers of glycaemia (HbA1c, 5.94% versus 5.21%), dyslipidemia (total cholesterol, 5.59 versus 4.75 mmol/L). Accordingly, the use of antihypertensive, lipid-lowering, and glucose-lowering medications was highest in the top tertile.
Table 1.
Baseline characteristics across CTI categories
| Total | Q1 (≤ 7.384) | Q2 (7.384–8.026) | Q3 (8.026–8.627) | Q4 (> 8.627) | P-value | |
|---|---|---|---|---|---|---|
| n | 10,718 | 2,678 | 2,684 | 2,678 | 2,678 | |
| Age, years, mean (SE) | 44.84 (0.28) | 39.09 (0.40) | 44.93 (0.41) | 47.62 (0.43) | 48.83 (0.37) | < 0.001 |
| Gender, female, n (%) | 5469 (51.27) | 1437 (55.54) | 1282 (47.03) | 1338 (49.76) | 1412 (52.44) | < 0.001 |
| Race, n (%) | < 0.001 | |||||
| Non-Hispanic White | 5221 (70.65) | 1283 (69.00) | 1346 (71.32) | 1287 (69.93) | 1305 (72.65) | |
| Non-Hispanic Black | 1975 (10.71) | 668 (13.83) | 510 (10.79) | 450 (9.75) | 347 (7.82) | |
| Mexican American | 2315 (8.09) | 411 (6.32) | 522 (7.54) | 640 (9.35) | 742 (9.53) | |
| Other | 1207 (10.55) | 316 (10.86) | 306 (10.35) | 301 (10.97) | 284 (9.99) | |
| Marital status, n (%) | 0.02 | |||||
| Not married or living with a partner | 3937 (33.63) | 1066 (36.67) | 985 (34.39) | 931 (31.87) | 955 (33.70) | |
| Married or living with a partner | 6618 (64.49) | 1579 (63.33) | 1657 (65.61) | 1712 (68.13) | 1670 (66.30) | |
| Education level, n (%) | < 0.001 | |||||
| Less than high school | 3073 (18.08) | 559 (13.48) | 736 (17.89) | 833 (19.18) | 945 (22.87) | |
| High school or equivalent | 2531 (25.04) | 578 (21.36) | 634 (24.30) | 659 (27.68) | 660 (27.76) | |
| College or above | 5099 (56.74) | 1540 (65.17) | 1310 (57.81) | 1183 (53.13) | 1066 (49.37) | |
| Family poverty-income ratio, mean (SE) | 3.08 (0.03) | 3.19 (0.05) | 3.16 (0.04) | 3.04 (0.04) | 2.89 (0.05) | < 0.001 |
| BMI, kg/m2, mean (SE) | 28.26 (0.09) | 24.39 (0.10) | 27.33 (0.13) | 29.92 (0.14) | 32.38 (0.16) | < 0.001 |
| HEI-2015, mean (SE) | 49.69 (0.26) | 50.99 (0.43) | 49.77 (0.42) | 49.51 (0.34) | 48.23 (0.37) | < 0.001 |
| Physical activity, MET-min/week | 2333.00 (79.96) | 2358.01 (123.69) | 2587.48 (140.50) | 2229.11 (107.20) | 2097.52 (112.54) | 0.02 |
| Smoking status, n (%) | < 0.001 | |||||
| Never | 5781 (52.79) | 1627 (59.07) | 1461 (54.11) | 1406 (51.15) | 1287 (45.59) | |
| Current | 2323 (23.15) | 536 (21.04) | 584 (23.57) | 558 (22.05) | 645 (26.50) | |
| Former | 2603 (23.96) | 512 (19.89) | 636 (22.32) | 713 (26.80) | 742 (27.91) | |
| Alcohol use, n (%) | < 0.001 | |||||
| Never | 1367 (10.65) | 315 (10.91) | 307 (10.14) | 347 (10.88) | 398 (13.23) | |
| Current | 6864 (69.47) | 1864 (78.60) | 1791 (74.96) | 1666 (71.70) | 1543 (66.52) | |
| Former | 1841 (14.69) | 319 (10.49) | 422 (14.90) | 505 (17.42) | 595 (20.25) | |
|
Glucose, mmol/L, mean (SE) |
5.66 (0.02) | 5.17 (0.02) | 5.42 (0.02) | 5.63 (0.02) | 6.57 (0.06) | < 0.001 |
| HbA1c, %, mean (SE) | 5.47 (0.01) | 5.21 (0.01) | 5.33 (0.01) | 5.47 (0.01) | 5.94 (0.03) | < 0.001 |
|
Serum uric acid, mg/dl, mean (SE) |
5.44 (0.02) | 4.86 (0.03) | 5.38 (0.03) | 5.70 (0.04) | 5.95 (0.04) | < 0.001 |
|
Triglyceride, mmol/L, mean (SE) |
1.56 (0.02) | 0.82 (0.01) | 1.19 (0.01) | 1.62 (0.01) | 2.83 (0.06) | < 0.001 |
| Total cholesterol, mmol/L, mean (SE) | 5.15 (0.02) | 4.75 (0.02) | 5.06 (0.02) | 5.29 (0.03) | 5.59 (0.03) | < 0.001 |
| HDL, mmol/L, mean (SE) | 1.38 (0.01) | 1.59 (0.01) | 1.41 (0.01) | 1.30 (0.01) | 1.16 (0.01) | < 0.001 |
| LDL, mmol/L, mean (SE) | 3.08 (0.01) | 2.79 (0.02) | 3.10 (0.02) | 3.26 (0.02) | 3.23 (0.03) | < 0.001 |
| eGFR, mL/min/1.73 m², mean (SE) | 96.68 (0.38) | |||||
| CRP, mg/dl, mean (SE) | 0.40 (0.01) | 0.07 (0.00) | 0.22 (0.01) | 0.45 (0.01) | 0.93 (0.03) | < 0.001 |
|
Antihypertensive drugs, n (%) |
2540 (20.02) | 324 (8.97) | 546 (16.84) | 735 (24.64) | 935 (32.31) | < 0.001 |
|
Antihyperlipidemic drugs, n (%) |
1258 (10.35) | 160 (4.77) | 292 (9.66) | 381 (13.33) | 425 (14.84) | < 0.001 |
| Antidiabetic drugs, n (%) | 736 (4.95) | 50 (1.21) | 96 (2.52) | 183 (4.70) | 407 (12.49) | < 0.001 |
| All-cause death, n (%) | 1783 (11.97) | 223 (5.47) | 423 (11.04) | 522 (14.38) | 615 (18.41) | < 0.001 |
| CVD death, n (%) | 491 (3.05) | 58 (1.20) | 111 (2.69) | 158 (4.26) | 164 (4.43) | < 0.001 |
Continuous variables are presented as mean ± SE and were compared using the Kruskal–Wallis test. Categorical variables are presented as n (%) and were compared using the χ² test. BMI, Body mass index; HEI, Healthy Eating Index; CVD, Cardiovascular disease; CKD, Chronic kidney disease
Association between CTI with all-cause and CVD mortality
During a mean follow-up period of 167.58 months, 1,783 deaths were recorded, with 491 attributed to CVD. In a Kaplan–Meier analysis, the probability of survival decreased progressively across increasing quartiles of the CTI score, with respect to both all-cause mortality and CVD mortality (Fig. 2; P < 0.001 by the log-rank test). In multivariable Cox proportional-hazards models, a higher CTI was associated with a greater risk of all-cause mortality. In a model adjusted for age, sex, and race (Model 1), the hazard ratio for death per unit increase in the CTI was 1.32 (95% CI, 1.23 to 1.41; P < 0.001). After further adjustment for socioeconomic and lifestyle factors (Model 2), the association was attenuated but remained significant (hazard ratio, 1.24; 95% CI, 1.15 to 1.34; P < 0.001). In the fully adjusted Model 3, which incorporated clinical and laboratory variables, the hazard ratio of 1.56 (95% CI, 1.36 to 1.78; P < 0.001) indicated a 56% increased risk of all-cause mortality. A similar association was observed for CVD mortality (Table 2). The hazard ratio in Model 1 was 1.41 (95% CI, 1.22 to 1.63; P < 0.001). The risk remained elevated in Model 2 (hazard ratio, 1.35; 95% CI, 1.15 to 1.58; P < 0.001) and in the fully adjusted Model 3 (hazard ratio, 2.03; 95% CI, 1.49 to 2.77; P < 0.001). As compared with participants in the lowest quartile of the CTI score, those in the highest quartile had a higher risk of all-cause mortality (P for trend, < 0.001). A similar association was observed for CVD mortality (P for trend, < 0.001). In the fully adjusted model, an analysis with the use of restricted cubic splines showed a linear association between the CTI score and the risk of death from any cause (P = 0.04 for nonlinearity). A linear association was also evident for the risk of death from cardiovascular causes (P = 0.86 for nonlinearity) (Fig. 3).
Fig. 2.
Kaplan–Meier estimates of (A) all-cause mortality and (B) cardiovascular mortality among participants with CKM syndrome (stages 0–3), stratified by quartiles of CTI. Quartile 1: CTI < 7.384; Quartile 2: 7.384–8.026; Quartile 3: 8.026–8.627; Quartile 4: > 8.627. Survival distributions differed significantly across quartiles for both outcomes (log-rank test, P < 0.001)
Table 2.
Association of CTI with all-cause and CVD mortality in CKM syndrome stages 0–3
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| HR (95%CI) | P-value | HR (95%CI) | P-value | HR (95%CI) | P-value | |
| All-cause mortality | ||||||
| Per unit | 1.32(1.23,1.41) | < 0.001 | 1.24(1.15,1.34) | < 0.001 | 1.56(1.36,1.78) | < 0.001 |
| Per SD | 1.30(1.22,1.38) | < 0.001 | 1.22(1.14,1.32) | < 0.001 | 1.51(1.33,1.72) | < 0.001 |
| Q1 | Ref | Ref | Ref | |||
| Q2 | 1.18(0.96,1.46) | 0.12 | 1.19(0.94,1.50) | 0.15 | 1.32(1.05,1.65) | 0.02 |
| Q3 | 1.30(1.06,1.60) | 0.01 | 1.30(1.04,1.62) | 0.02 | 1.52(1.23,1.88) | < 0.001 |
| Q4 | 1.72(1.43,2.08) | < 0.001 | 1.54(1.24,1.90) | < 0.001 | 1.83(1.43,2.34) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| CVD mortality | ||||||
| Per unit | 1.41(1.22,1.63) | < 0.001 | 1.35(1.15,1.58) | < 0.001 | 2.03(1.49,2.77) | < 0.001 |
| Per SD | 1.38(1.20,1.58) | < 0.001 | 1.32(1.14,1.53) | < 0.001 | 1.94(1.45,2.59) | < 0.001 |
| Q1 | Ref | Ref | Ref | |||
| Q2 | 1.22(0.84,1.78) | 0.30 | 1.30(0.83,2.01) | 0.25 | 1.55(0.99,2.43) | 0.06 |
| Q3 | 1.69(1.19,2.40) | 0.003 | 1.79(1.17,2.73) | 0.01 | 2.35(1.45,3.81) | < 0.001 |
| Q4 | 1.86(1.27,2.73) | 0.001 | 1.82(1.17,2.84) | 0.01 | 2.64(1.55,4.47) | < 0.001 |
| P for trend | < 0.001 | 0.004 | < 0.001 | |||
Model 1: adjusted for age, sex, and race
Model 2: further adjusted for marital status, education level, family poverty-income ratio, smoking status, alcohol use, BMI, and HEI score
Model 3: further adjusted for HbA1c, TC, HDL, LDL, serum uric acid, eGFR, drugs for hypertension, hyperlipidemia, diabetes
Fig. 3.
Dose–response association between the CTI and the risk of all-cause mortality (A) and cardiovascular mortality (B) in participants with CKM syndrome (stages 0–3). Analyses were adjusted for age, sex, ethnicity, marital status, education, poverty-to-income ratio, smoking status, alcohol use, body-mass index, Healthy Eating Index score, glycated hemoglobin, serum uric acid, total, HDL, and LDL cholesterol, and the estimated glomerular filtration rate
Subgroup analysis
We performed prespecified subgroup analyses to evaluate whether the association between the CTI score and the risk of all-cause mortality was consistent across key demographic and socioeconomic subgroups. The hazard ratio per unit increase in the CTI score was 1.70 (95% CI, 1.28 to 2.26) among persons younger than 60 years of age, as compared with 1.57 (95% CI, 1.30 to 1.89) among those 60 years of age or older (P < 0.001 for interaction). In contrast, there was no evidence of a significant interaction between the CTI score and sex (P = 0.13 for interaction), marital status (P = 0.21), race or ethnic group (P = 0.17), educational level (P = 0.09), or the family poverty-to-income ratio (P = 0.18) with respect to the risk of death. The hazard ratios and confidence intervals for these subgroups are shown in Fig. 4.
Fig. 4.
Subgroup analysis of the association between the CTI and all-cause mortality among participants with CKM syndrome (stages 0–3)
Discussion
In this large, nationally representative cohort of U.S. adults, we found that a higher CTI was associated with an increased risk of death from any cause and from cardiovascular causes. This association was independent of a wide range of demographic, socioeconomic, and clinical risk factors, and the dose–response association appeared to be Linear. A key finding was the significant interaction with age, in which the association between the CTI score and the risk of death was stronger in persons under the age of 60 than in older persons. In contrast, we found no evidence of significant effect modification by sex, race or ethnic group, marital status, educational level, or income. Taken together, these findings suggested that the CTI, a simple composite marker of inflammation and insulin resistance, might be a useful tool for risk stratification.
The clinical implications of our findings are threefold. First, CTI is derived from routine laboratory tests (CRP, triglycerides, and fasting glucose), making it a practical and cost-effective tool for routine clinical use. Second, its enhanced predictive power in younger participants highlights its value for early risk stratification, enabling timely preventive interventions when they are most effective. Third, the consistency of its associations across diverse demographic and socioeconomic groups underscores its broad applicability and potential as a robust public health instrument.
Insulin resistance, a state of impaired cellular response to insulin leading to hyperglycemia and compensatory hyperinsulinemia, is a central pathophysiological link among the components of CKM syndrome. This condition is primarily driven by excess visceral adipose tissue, which secretes proinflammatory cytokines such as tumor necrosis factor-α and interleukin-6 [17, 18]. These mediators impair insulin-signaling pathways, in part through the inhibition of insulin receptor substrate-1, thereby establishing the metabolic syndrome. The progression of the metabolic syndrome to overt type 2 diabetes mellitus because of beta-cell failure markedly amplifies the risk of end-organ damage [6]. In the vasculature, insulin resistance promotes endothelial dysfunction and atherogenesis, which contribute to all forms of CVD, including coronary heart disease, heart failure, and stroke [19]. In the kidneys, insulin resistance contributes to glomerular hyperfiltration and hypertrophy [20]. Consequent hyperglycemia generates advanced glycation end products and reactive oxygen species, which activate proinflammatory and profibrotic pathways that culminate in glomerulosclerosis and fibrosis [21]. Furthermore, insulin resistance is closely linked to metabolic dysfunction–associated steatotic liver disease, which in turn exacerbates systemic inflammation and worsens insulin resistance, thereby accelerating the progression of CKM syndrome [22]. Previous studies also found that TyG is closely associated with disease progression and an increased risk of adverse outcomes in persons with CKM syndrome [7, 8, 23].
In addition to insulin resistance, chronic, low-grade inflammation is a key mediator in the pathogenesis of CKM syndrome. The clinical consequence of this chronic inflammation is the acceleration of pathological processes such as atherosclerosis and fibrosis in the heart and kidneys. Studies involving patients with chronic kidney disease have shown that inflammatory markers are strong predictors of atherosclerotic cardiovascular events and death, even after adjustment for traditional risk factors, underscoring the role of inflammation as a source of residual risk in CKM syndrome [24, 25]. The hs-CRP has also been associated with an increased risk of adverse clinical outcomes in persons with CKM syndrome [26]. Our previous studies have also found that insulin resistance and inflammation have a synergistic effect on adverse prognostic outcomes in patients with CKM [9]. However, prior studies failed to integrate the indices of insulin resistance and inflammation, limiting their practical application in clinical settings. In this study, we used the CTI to represent both insulin resistance and inflammatory status and found that in patients with early-stage CKM, CTI was significantly positively correlated with all-cause and CVD mortality. This association remained stable even after adjusting for relevant confounding factors. Moreover, unlike TyG [23], the associations between CTI and both all-cause and CVD mortality were linear.
Previous research has linked biomarkers such as the TyG index, hs-CRP, and SIRI to adverse outcomes in CKM syndrome. However, these markers are limited as they each reflect a single risk domain: insulin resistance for the TyG index, and systemic inflammation for hs-CRP and SIRI. In contrast, the CTI, which combines the TyG index with CRP, integrates both metabolic and inflammatory pathways to offer a more comprehensive measure of CKM pathophysiology. This integrative nature likely explains the stronger and more linear association with mortality we observed, highlighting the CTI’s potential as a superior prognostic marker in early-stage CKM syndrome.
A key finding was the significant interaction with age, in which the association between the CTI score and the risk of death was stronger in persons under the age of 60 than in older persons. The stronger association between CTI and mortality in younger participants may be explained by several mechanisms. Younger adults typically have a lower baseline burden of traditional cardiovascular risk factors, so the adverse influence of insulin resistance and inflammation may exert a proportionally greater effect on outcomes in this group. In contrast, among older adults, competing risks and the cumulative impact of multiple comorbidities may attenuate the relative contribution of CTI to mortality risk. Moreover, age-related differences in adipose tissue distribution, immune function, and vascular remodeling could further account for the observed interaction [9, 17]. Importantly, given its stronger predictive value in younger adults, CTI may be particularly useful for detecting high-risk individuals who are not yet captured by traditional scoring systems. In practice, clinicians could use CTI as part of routine screening protocols to identify patients warranting closer follow-up, lifestyle counseling, or preventive pharmacotherapy.
The strengths of our study include the use of data from a large, prospective, and nationally representative cohort. To our knowledge, this is the first study to assess the prognostic value of the CTI—a composite marker of insulin resistance and inflammation—in persons with early-stage CKM syndrome. The long duration of follow-up allowed for the assessment of long-term mortality, and the size and diversity of the cohort enabled robust subgroup analyses to test for consistency of effect and potential interactions. Finally, the CTI is calculated from laboratory measures that are widely available in clinical practice, which supports its potential for broad application in risk stratification. Our study also has several limitations. First, both the CTI and all covariates were ascertained only at baseline. A single measurement may not fully reflect long-term exposure to inflammation and insulin resistance, and we could not account for changes in risk factors that may have occurred during the extended follow-up period. Second, although the NHANES cohort is representative of the U.S. population, our findings may not be generalizable to other populations, such as persons of Asian descent, who were not well represented in the survey. Third, the potential for residual confounding exists. Although we adjusted for the use of antihypertensive and lipid-lowering drugs, we lacked data on other medications that may influence outcomes, including aspirin and colchicine. Finally, because our study was observational, it shows an association between CTI and mortality and does not establish causality. In the future, interventional studies should evaluate whether strategies that reduce CTI—such as lifestyle modification, insulin-sensitizing agents, or anti-inflammatory therapies—translate into lower risks of cardiovascular and all-cause mortality.
Conclusion
In summary, our study demonstrates that CTI is an independent predictor of long-term all-cause and CVD mortality in patients with early-stage CKM syndrome. This association follows a Linear dose-response pattern and is particularly pronounced in individuals younger than 60. From a clinical perspective, an elevated CTI could serve as a clear indication for intensive lifestyle interventions—such as structured exercise, nutritional counseling, and weight management—which are proven to improve insulin sensitivity and reduce systemic inflammation. Therefore, incorporating CTI into patient management could facilitate a more personalized, mechanism-driven approach to preventing CKM progression.
Supplementary Information
Acknowledgements
We thank the participants and staff of the NHANES for their invaluable work in generating this public dataset.
Abbreviations
- BMI
Body mass index
- CDC
Centers for Disease Control and Prevention
- CKD
Chronic kidney disease
- CKM
Cardiovascular-kidney-metabolic
- CRP
C-reactive protein
- CTI
C-reactive protein-triglyceride glucose index
- CVD
Cardiovascular disease
- CHARLS
China Health and Retirement Longitudinal Study
- eGDR
estimated glucose disposal rate
- eGFR
estimated glomerular filtration rate
- HDL
High-density lipoprotein
- HEI
Healthy Eating Index
- hs
CRP-high-sensitivity C-reactive protein
- ICD
10-International Classification of Diseases, 10th Revision
- LDL
Low-density lipoprotein
- NDI
National Death Index
- NHANES
National Health and Nutrition Examination Survey
- SIRI
Systemic inflammation response index
- TyG
Triglyceride-glucose
Author contributions
Jian Xu and Qucheng Wei designed the study. Fan He and Lunzhe Wu extracted the data. Qianliang Ying and Fan He conducted the data analysis. Qianliang Ying drafted the manuscript, which was revised by Jian Xu and Qucheng Wei. All authors critically reviewed and approved the final version of the manuscript prior to submission.
Funding
This work was supported by the General Research Program of the Zhejiang Provincial Department of Health (grant no. 2025KY483) and the Key Research and Development Program of Zhejiang Province (No. 2025C02144).
Data availability
The data analyzed in this study were obtained from the publicly available NHANES database ([https://www.cdc.gov/nchs/nhanes/](https:/www.cdc.gov/nchs/nhanes)).
Declarations
Ethics approval and consent to participate
The primary NHANES protocol was approved by the National Center for Health Statistics Institutional Review Board, and all participants provided written informed consent. This study, a secondary analysis of publicly available, de-identified data, was exempt from further institutional review.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Qianliang Ying and Fan He contributed equally to this work.
Contributor Information
Qucheng Wei, Email: wqc2060@zju.edu.cn.
Jian Xu, Email: xujian_lishui@outlook.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data analyzed in this study were obtained from the publicly available NHANES database ([https://www.cdc.gov/nchs/nhanes/](https:/www.cdc.gov/nchs/nhanes)).