Abstract
Background
Advanced cardiovascular-kidney-metabolic (CKM) syndrome (stages 3–4) signifies high-risk multi-organ dysfunction. The triglyceride-glucose-frailty index (TyG-FI), combining the TyG index with a frailty index, may better reflect both metabolic dysregulation and physiological decline than TyG-related indices alone. This study aimed to examine the association between TyG-FI and advanced CKM syndrome.
Methods
This cross-sectional analysis used 2011-March 2020 National Health and Nutrition Examination Survey data from 6,960 adults with CKM syndrome. CKM syndrome stages 3 or 4 were considered advanced. Survey-weighted logistic regression and restricted cubic splines models were used to evaluate the relationship between TyG-FI and advanced CKM syndrome. The discriminatory power of TyG-FI was assessed using receiver operating characteristic (ROC) curves and compared to other TyG-related indices and FI.
Results
The highest TyG-FI quartile had significantly increased odds of advanced CKM syndrome (odds ratio: 10.32, 95% confidence intervals: 6.51–16.34) versus the lowest. A unit increase in TyG-FI corresponded to an odds ratio of 2.78 (95% confidence intervals: 2.39–3.22). Restricted cubic splines indicated a nonlinear dose-response association between TyG-FI and advanced CKM syndrome. Furthermore, ROC analysis demonstrated that TyG-FI had superior discriminatory ability (AUC = 0.796) compared to FI and all other TyG-related indices.
Conclusions
TyG-FI shows a strong, dose-response association with advanced CKM syndrome and superior predictive performance, suggesting that a higher TyG-FI may increase the risk of advanced CKM syndrome.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s13019-026-04412-2.
Keywords: Cardiovascular-kidney-metabolic syndrome, Frailty index, Triglyceride-glucose index, National Health and Nutrition Examination Survey
Introduction
Cardiovascular disease (CVD), chronic kidney disease (CKD), and diabetes often coexist and interact, forming a complex comorbidity that increases morbidity and mortality [1]. In 2023, the American Heart Association (AHA) introduced the cardiovascular-kidney-metabolic (CKM) syndrome staging system in response to this interplay [2]. Advanced CKM syndrome (stages 3–4), marked by subclinical or clinical CVD, indicates a high-risk condition requiring intensive intervention. Recent studies indicate that CKM syndrome is prevalent among U.S. adults, with 90% experiencing stage 1 or higher and 15% in advanced stages [3]. Consequently, the identification of robust and accessible biomarkers for the early detection of individuals progressing toward advanced CKM syndrome is a critical imperative in preventive medicine.
Insulin resistance (IR) is a key pathophysiological factor in the advancement of CKM syndrome [2]. The triglyceride-glucose (TyG) index is recognized as an efficient and economical surrogate marker for IR [4]. Numerous studies have demonstrated significant links between a higher TyG index and a greater risk of CVD, CKD, and diabetes [5–7]. Concurrently, the concept of frailty has gained significant traction in cardiovascular and metabolic research [8, 9]. It is often quantified through a frailty index (FI), which aggregates a range of health deficits, including comorbidities, physical function, cognitive status, and laboratory abnormalities [10]. Notably, frailty and metabolic disorders exhibit a bidirectional relationship [11], where IR can accelerate muscle loss and functional decline, and vice-versa [12, 13].
While the TyG index and frailty are both recognized predictors of cardiovascular and metabolic risk, their evaluations have largely been conducted independently. The progression to advanced CKM syndrome is a multifactorial process driven by the synergy between metabolic dysregulation and the loss of physiological resilience [14]. We proposed that combining the TyG index, a key metabolic marker, with the FI, a broad physiological health measure, will offer a more thorough risk assessment, effectively encapsulating the CKM syndrome compared to using each index separately. Based on nationally representative data from National Health and Nutrition Examination Survey (NHANES), we investigated the combined effect of the TyG index and FI on advanced CKM syndrome stages.
Methods
Study design and participants
NHANES is a continuous survey administered by the Centers for Disease Control and Prevention. The research protocol for NHANES was approved by the National Center for Health Statistics and Ethics Review Board. All participants provided written informed consent. Our analysis employed data from four NHANES cycles (2011–2012, 2013–2014, 2015–2016, and 2017-March 2020) to accommodate race-specific anthropometric criteria for CKM syndrome stages 0 or 1, with a dedicated Non-Hispanic Asian category available only from 2011 to 2012 onward. To form a valid analytical cohort, we excluded participants based on the following criteria: (1) age < 20 years; (2) pregnant at baseline; (3) missing triglyceride or glucose measurements; (4) missing data on height, weight and waist circumference; (5) incomplete data on CKM syndrome indicators; (6) missing demographic, socioeconomic status, or key questionnaire data. Consequently, the final analytical sample comprised 7,955 adults (Fig. 1).
Fig. 1.
Flowchart of the inclusion and exclusion of participants. CKM, cardiovascular-kidney-metabolic; NHANES, National Health and Nutrition Examination Survey
Calculation of TyG-FI
The TyG index was calculated using the formula: Ln [fasting triglyceride (mg/dl)×fasting glucose (mg/dl)/2]. Concurrently, the FI was constructed according to Searle et al.’s deficit accumulation framework [10]. This index integrated 49 standardized items spanning 7 health domains (Table S1) [15]. Each item was converted to a dichotomous or ordinal scale and normalized to a range of 0 to 1, where higher values represent increased deficit severity. The FI was calculated as the proportion of observed deficits relative to the total number of items evaluated. Building on these metrics, we calculated the novel composite TyG-frailty index (TyG-FI) as proposed by Zhao et al.: TyG-FI = TyG×FI [16]. This integrated indicator concurrently captures metabolic dysregulation and physiological frailty burden.
CKM syndrome staging
CKM syndrome staging was defined following the AHA Presidential Advisory [2] and adapted for NHANES by Aggarwal et al. [3]. Stage 0 necessitates normal body mass index (BMI) and waist circumference, along with normal blood pressure, blood glucose levels, lipid profiles, and no presence of CKD or CVD. Stage 1 is defined by excess weight (BMI ≥ 25 kg/m² [≥ 23 kg/m² for Asians]) or abdominal obesity (waist circumference ≥ 88/102 cm in women/men [≥ 80/90 for Asians]), or prediabetes. Stage 2 encompasses individuals with metabolic dysregulation (hypertriglyceridemia, hypertension, metabolic syndrome, or diabetes) or moderate-to-high-risk CKD. Stage 3 comprises those with very high-risk CKD or a 10-year CVD risk of 20% or greater. Stage 4 denotes established clinical CVD (coronary heart disease, angina, myocardial infarction, heart failure, or stroke). Detailed criteria are provided in Table S2. According to the prevalence of CKM syndrome stages in U.S. adults [3], CKM syndrome was defined as stage 1 or higher, with advanced stages being stages 3 and 4, as these indicated participants with or at high risk for CVD.
Covariate assessments
In this study, the covariates taken into consideration included age, sex (female or male), ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, Hispanic and other race or ethnicity), education level (less than high school, high school or equivalent, and college or above), ratio of family income to poverty (less than 1, 1 to 3, and more than 3), marital status (coupled, and single or separated), smoking status (never smokers, former smokers, and current smokers), alcohol status (non-drinker, current heavy drinkers, and current low to moderate drinkers), physical activity level, and overall diet quality. Physical activity level was defined according to the frequency and intensity of participant’s self-reported leisure-time physical activity in the past month, and classified into inactive, insufficient and active, based on metabolic equivalent intensity levels [17]. Diet quality was quantified using the Healthy Eating Index-2015 (HEI-2015) based on 24-hour dietary recalls, with higher scores indicating better adherence to dietary guidelines [18].
Statistical analysis
All analyses incorporated NHANES sampling weights to generate nationally representative estimates. Categorical variables were described using frequencies and weighted proportions, while continuous variables were represented by weighted means and weighted standard errors (SE). Patients with CKM syndrome were categorized into four groups according to the quartiles of TyG-FI. Quartile categorization was chosen to present a clear dose-response pattern, enhance clinical interpretability, and align with previous studies on TyG-related indices [16, 19, 20]. In addition, TyG-FI was analyzed as a continuous variable to avoid arbitrary cutoffs and to provide more stable risk estimates. Differences among the Q1-Q4 groups for categorical and continuous variables were evaluated using the chi-squared test and generalized linear models, respectively. Survey-weighted logistic regression models were employed to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between TyG-FI levels and advanced CKM syndrome. We fitted three statistical models. The crude model included no covariate adjustments. Model 1 was adjusted for sociodemographic variables, including age, sex, ethnicity, educational level, family income, and marital status. Model 2 was adjusted for the variables in Model 1 and additional confounders including smoking status, alcohol status, physical activity, and HEI-2015. Moreover, we used survey-weighted restricted cubic spline (RCS) regressions to examine the relationship between continuous TyG-FI and advanced CKM syndrome. The Akaike information criterion (AIC) was used to select the RCS with 3 to 8 knots, selecting the model that exhibited the lowest AIC value.
In addition, subgroup analyses were performed according to age, sex, ethnicity, educational level, family income, marital status, smoking status, alcohol status, and physical activity. We also implemented several sensitivity analyses to verify the robustness of our conclusions. First, we excluded individuals with a cancer history. Second, omitting participants with incomplete covariate data could lead to selection bias. To address this issue, the R package “missRanger” was employed to iteratively impute missing covariates using a machine learning algorithm. Third, considering the possible link between medication and advanced CKM syndrome, we conducted a sensitivity analysis excluding participants treated with sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide 1 receptor agonists [2]. Fourth, ordinal logistic regression models were developed to assess the association between TyG-FI and CKM syndrome stages. Fifth, to address potential overlap between the FI components and CKM syndrome definitions, we conducted a sensitivity analysis by removing FI items that directly correspond to CKM diagnostic criteria (including congestive heart failure, coronary heart disease, angina, heart attack, stroke, blood pressure, diabetes, weak/failing kidneys, BMI, and glycohemoglobin). A revised FI was calculated from the remaining 39 items, and a revised TyG-FI was computed.
We assessed the predictive efficacy of the TyG-FI for advanced CKM syndrome by comparing its area under the curve (AUC) values with those of other TyG-related indices and FI using receiver operating characteristic (ROC) curves. The TyG-related indices included TyG and four modified TyG indices, including TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist-to-height ratio (TyG-WHtR), and TyG-weight adjusted waist index (TyG-WWI) [21–23]. The Delong test was employed to evaluate whether the TyG-FI exhibited superior predictive performance by comparing its AUC with those of five other TyG-related indices and FI [24].
To detect the interactions between TyG and FI on advanced CKM syndrome, we included a cross-product term between TyG and FI in the logistic regression model to investigate the possible interaction on the multiplicative scale. We also examined the additive interaction by calculating the relative excess risk due to interaction (RERI), attributable proportion (AP), synergy index (SI), and their 95% CI [25, 26]. All analyses incorporated survey weights.
All statistical analyses were carried out using R software version 4.4.1. A two-sided p < 0.05 was considered statistically significant.
Results
Characteristics of the CKM syndrome patients
In this study of 7,955 U.S. adults, CKM syndrome stages 0 to 4 were identified in 12.51%, 21.99%, 51.02%, 4.00%, and 10.48% of participants, respectively. After excluding 995 participants in CKM stage 0, the study comprised 6,960 CKM syndrome patients (Fig. 1). Table 1 presented the baseline characteristics of CKM syndrome patients by quantiles of the TyG-FI level. Patients with higher TyG-FI levels were generally older, female, non-Hispanic Black, single or separated, current or former smokers, non-drinkers, and exhibited lower education and household income levels, as well as reduced physical activity and diet quality, compared to those with lower TyG-FI levels (all p < 0.05). Besides, the prevalence of advanced stages was higher among CKM syndrome patients in the highest TyG-FI level group (39.64%) than those in the lowest TyG-FI level group (3.51%) (p < 0.0001).
Table 1.
Baseline characteristics of CKM syndrome patients in NHANES 2011-March 2020
| Characteristic | TyG-frailty index level (N = 6960) | p value | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall | Quartile 1 ≤ 0.74 |
Quartile 2 0.74–1.18 |
Quartile 3 1.18–1.84 |
Quartile 4 > 1.84 |
|||||||||||||
| CKM Syndrome patients, n | 6960 | 1741 | 1738 | 1741 | 1740 | ||||||||||||
| Age (years), mean (SE) | 49.72(0.34) | 42.67(0.54) | 47.59(0.50) | 52.75(0.53) | 58.00(0.50) | < 0.0001 | |||||||||||
| Sex, n (%) | < 0.0001 | ||||||||||||||||
| Male | 3526(50.90) | 1040(61.76) | 890(50.75) | 830(47.46) | 766(39.55) | ||||||||||||
| Female | 3434(49.10) | 701(38.24) | 848(49.25) | 911(52.54) | 974(60.45) | ||||||||||||
| Race or ethnicity, n (%) | < 0.0001 | ||||||||||||||||
| Hispanic | 1695(14.23) | 490(16.61) | 439(14.69) | 384(12.66) | 382(12.01) | ||||||||||||
| Non-Hispanic Black | 1525(9.79) | 247(6.06) | 369(9.31) | 476(12.84) | 433(12.26) | ||||||||||||
| Non-Hispanic White | 2802(68.69) | 700(69.93) | 682(68.52) | 669(67.23) | 751(68.81) | ||||||||||||
| Non-Hispanic Asian | 680(4.08) | 260(5.55) | 182(4.13) | 150(3.69) | 88(2.37) | ||||||||||||
| Other | 258(3.21) | 44(1.84) | 66(3.35) | 62(3.57) | 86(4.55) | ||||||||||||
| Education level, n (%) | < 0.0001 | ||||||||||||||||
| Less than high school | 1410(13.96) | 296(10.46) | 310(12.79) | 348(13.63) | 456(20.89) | ||||||||||||
| High school or equivalent | 1579(22.51) | 351(19.91) | 362(20.54) | 419(24.62) | 447(26.43) | ||||||||||||
| College or above | 3971(63.52) | 1094(69.63) | 1066(66.67) | 974(61.76) | 837(52.68) | ||||||||||||
| Family PIR, n (%) | < 0.0001 | ||||||||||||||||
| <1.0 | 1445(13.97) | 294(11.38) | 309(11.88) | 370(14.10) | 472(20.31) | ||||||||||||
| 1.0–3.0 | 2890(35.81) | 693(32.89) | 673(32.83) | 711(35.11) | 813(44.70) | ||||||||||||
| >3.0 | 2625(50.22) | 754(55.74) | 756(55.32) | 660(50.79) | 455(34.99) | ||||||||||||
| Marital, n (%) | < 0.0001 | ||||||||||||||||
| Coupled | 4232(65.27) | 1136(68.15) | 1096(67.70) | 1065(64.83) | 935(58.48) | ||||||||||||
| Single or separated | 2728(34.73) | 605(31.85) | 642(32.30) | 676(35.17) | 805(41.52) | ||||||||||||
| Smoking status, n (%) | < 0.0001 | ||||||||||||||||
| Never | 3807(54.02) | 1090(62.55) | 1010(56.42) | 941(51.56) | 766(41.52) | ||||||||||||
| Former | 1769(27.29) | 365(23.16) | 400(25.84) | 470(30.94) | 534(30.92) | ||||||||||||
| Now | 1384(18.69) | 286(14.38) | 328(17.74) | 330(17.49) | 440(27.56) | ||||||||||||
| Alcohol status, n (%) | < 0.0001 | ||||||||||||||||
| Non-drinker | 1767(21.77) | 351(15.54) | 396(18.96) | 463(24.31) | 557(31.46) | ||||||||||||
| Low to moderate drinker | 3733(57.21) | 975(60.54) | 950(59.02) | 940(55.78) | 868(51.69) | ||||||||||||
| Heavy drinker | 1460(21.02) | 415(23.92) | 392(22.02) | 338(19.90) | 315(16.85) | ||||||||||||
| Physical activity, n (%) | < 0.0001 | ||||||||||||||||
| Inactive | 3573(47.11) | 698(36.35) | 793(42.63) | 923(49.55) | 1159(65.59) | ||||||||||||
| Insufficient | 1881(29.27) | 443(27.47) | 504(32.00) | 524(32.68) | 410(24.37) | ||||||||||||
| Active | 1506(23.61) | 600(36.18) | 441(25.37) | 294(17.77) | 171(10.04) | ||||||||||||
| HEI-2015, mean (SE) | 50.12(0.26) | 49.93(0.42) | 51.28(0.50) | 50.20(0.45) | 48.79(0.51) | 0.02 | |||||||||||
| TyG | 8.65(0.01) | 8.47(0.02) | 8.53(0.02) | 8.73(0.02) | 8.96(0.03) | < 0.0001 | |||||||||||
| TyG-BMI | 265.23(1.33) | 249.22(1.78) | 254.88(2.04) | 272.82(2.54) | 293.09(3.28) | < 0.0001 | |||||||||||
| TyG-WC | 898.81(3.44) | 850.87(4.83) | 869.50(5.41) | 919.65(6.44) | 982.15(7.97) | < 0.0001 | |||||||||||
| TyG-WHtR | 5.33(0.02) | 4.98(0.03) | 5.14(0.03) | 5.47(0.04) | 5.91(0.05) | < 0.0001 | |||||||||||
| TyG-WWI | 96.45(0.24) | 91.95(0.33) | 94.25(0.28) | 98.16(0.38) | 103.81(0.50) | < 0.0001 | |||||||||||
| FI | 0.15(0.00) | 0.06(0.00) | 0.11(0.00) | 0.17(0.00) | 0.30(0.00) | < 0.0001 | |||||||||||
| TyG-FI | 1.27(0.02) | 0.49(0.01) | 0.96(0.00) | 1.50(0.01) | 2.65(0.03) | < 0.0001 | |||||||||||
| CKM syndrome stage, n (%) | < 0.0001 | ||||||||||||||||
| Stage 1 | 1749(27.56) | 800(47.14) | 583(33.80) | 270(14.67) | 96(6.07) | ||||||||||||
| Stage 2 | 4059(56.56) | 887(49.35) | 1037(57.61) | 1179(66.40) | 956(54.29) | ||||||||||||
| Stage 3 | 318(5.40) | 37(2.38) | 49(4.03) | 101(7.62) | 131(8.96) | ||||||||||||
| Stage 4 | 834(10.48) | 17(1.13) | 69(4.56) | 191(11.31) | 557(30.68) | ||||||||||||
| Advanced CKM syndrome, n (%) | < 0.0001 | ||||||||||||||||
| No | 5808(84.12) | 1687(96.49) | 1620(91.41) | 1449(81.07) | 1052(60.36) | ||||||||||||
| Yes | 1152(15.88) | 54(3.51) | 118(8.59) | 292(18.93) | 688(39.64) | ||||||||||||
Values are weighted mean (weighted standard errors) for continuous variables or numbers (weighted %) for categorical variables
CKM, Cardiovascular-Kidney-Metabolic; FI, frailty index; HEI-2015, Healthy Eating Index-2015; PIR, poverty income ratio; TyG, triglyceride glucose index; TyG-BMI, TyG-body mass index; TyG-FI, TyG-frailty index; TyG-WC, TyG-waist circumference; TyG-WHtR, TyG-waist-to-height ratio; TyG-WWI, TyG-weight adjusted waist index
Association of TyG-FI level with advanced CKM syndrome
Table 2 showed the association of TyG-FI with advanced CKM syndrome. After adjustment for all covariates, CKM syndrome patients in the top quartile had a OR of 10.32 (95% CI: 6.51–16.34; p < 0.0001) for advanced stages when compared to those in the lowest quartile. A unit increase in TyG-FI level was associated with a OR of 2.78 for advanced CKM syndrome (95% CI: 2.39–3.22, p < 0.0001).
Table 2.
Association between TyG-frailty index with advanced CKM syndrome in NHANES 2011-March 2020
| TyG-frailty index | Crude model | Model 1 | Model 2 | ||||
|---|---|---|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | OR (95% CI) | p value | ||
| Quartile 1 (≤ 0.74) | 1 (reference) | 1 (reference) | 1 (reference) | ||||
| Quartile 2 (0.74–1.18) | 2.58(1.66,4.01) | < 0.0001 | 2.08(1.29,3.33) | 0.003 | 1.97(1.22,3.20) | 0.01 | |
| Quartile 3 (1.18–1.84) | 6.42(4.20,9.80) | < 0.0001 | 3.96(2.47,6.37) | < 0.0001 | 3.86(2.39, 6.23) | < 0.0001 | |
| Quartile 4 (> 1.84) | 18.05(11.87,27.44) | < 0.0001 | 10.95(6.92,17.32) | < 0.0001 | 10.32(6.51,16.34) | < 0.0001 | |
| Per 1 unit increment | 3.19(2.84,3.58) | < 0.0001 | 2.80(2.42,3.24) | < 0.0001 | 2.78(2.39,3.22) | < 0.0001 | |
Values are weighted OR (weighted 95% CI)
Crude model: no adjusted
Model 1: adjusted for sociodemographic variables (age, sex, ethnicity, educational level, family income, and marital status)
Model 2: Model 1 + smoking status, alcohol status, physical activity, and HEI-2015 data
Abbreviations: CI, Confidence interval; CKM, Cardiovascular-Kidney-Metabolic; HEI-2015, Healthy Eating Index-2015; OR, Odds ratios; TyG, triglyceride-glucose
In addition, we fitted dose-response curve for the relationship of the TyG-FI level with advanced CKM syndrome risk. The use of AIC statistics suggested that the RCS model with 3 knots showed the best performance (lowest AIC: 3848.49) (Fig. 2A and Table S3). The RCS model revealed that the risk of advanced CKM syndrome increased with the increase of TyG-FI level and showed a non-linear relationship (p for non-linearity = 0.0005) (Fig. 2B).
Fig. 2.
The survey-weighted multivariable adjusted restricted cubic spline model for the association of TyG-FI level with advanced CKM syndrome from NHANES 2011-March 2020. A The restricted cubic spline model with 3 knots showed the lowest Akaike information criterion statistics; B Association of TyG-FI level with advanced CKM syndrome from NHANES 2011-March 2020. CI, Confidence interval; CKM, Cardiovascular-Kidney-Metabolic; NHANES, National Health and Nutrition Examination Survey; OR, Odds ratios; TyG-FI, triglyceride-glucose-frailty index
Subgroup and sensitivity analyses
Table 3 presented the results of the subgroup analysis. The association between TyG-FI and advanced CKM syndrome was consistent across different age groups, sexes, ethnicities, education levels, marital statuses, family incomes, smoking statuses, alcohol statuses, and physical activity levels, with all p values for interaction being > 0.05. Besides, the results were stable after excluding individuals with a history of cancer (Table S4), applying iterative imputation for missing covariate data (Table S5), removing participants who were treated with sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide 1 receptor agonists (Table S6), using ordinal logistic regression models to assess the link between TyG-FI and CKM syndrome stages (Table S7), and removing FI items overlapping with CKM syndrome criteria (Table S8).
Table 3.
Variables-stratified analyses for the association between TyG-frailty index with advanced CKM syndrome in NHANES 2011-March 2020
| TyG-frailty index level | |||||||
|---|---|---|---|---|---|---|---|
| Quartile 1 < 0.74 |
Quartile 2 0.74–1.18 |
Quartile 3 1.18–1.84 |
Quartile 4 > 1.84 |
p interaction | |||
| Age | 0.129 | ||||||
| 20–44 | 1 (reference) | 2.43(0.55,10.83) | 6.75(1.43,31.89) | 20.80(5.66,76.44) | |||
| 45–64 | 1 (reference) | 2.56(0.83,7.91) | 6.04(2.24,16.30) | 14.87(5.71,38.70) | |||
| ≥ 65 | 1 (reference) | 1.96(1.03,3.73) | 3.65(1.86,7.17) | 12.13(5.74,25.61) | |||
| Sex | 0.183 | ||||||
| Male | 1 (reference) | 2.82(1.48,5.38) | 4.39(2.36,8.15) | 12.29(7.23,20.87) | |||
| Female | 1 (reference) | 1.17(0.54,2.50) | 3.07(1.42,6.64) | 8.52(3.73,19.47) | |||
| Race or ethnicity | 0.124 | ||||||
| Hispanic | 1 (reference) | 9.69(2.92,32.22) | 20.51(7.14,58.95) | 54.36(18.26,161.85) | |||
| Non-Hispanic White | 1 (reference) | 1.85(1.07, 3.21) | 3.29(1.89, 5.71) | 9.71(5.69,16.58) | |||
| Non-Hispanic Black | 1 (reference) | 5.82(0.68, 50.03) | 23.27(3.01,179.87) | 82.68(10.16,672.95) | |||
| Non-Hispanic Asian | 1 (reference) | 2.19(0.19, 24.59) | 12.88(1.91, 86.97) | 34.72(5.62,214.41) | |||
| Others | 1 (reference) | 7.25(3.02,17.43) | 19.74(8.82,44.18) | 53.42(22.04,129.47) | |||
| Educational level | 0.736 | ||||||
| Less than high school | 1 (reference) | 2.05(0.78,5.41) | 3.58(1.57,8.15) | 10.15(4.46,23.13) | |||
| High school or equivalent | 1 (reference) | 3.36(1.29,8.74) | 5.23(1.84,14.84) | 17.43(6.58,46.11) | |||
| College or above | 1 (reference) | 1.74(0.99,3.06) | 3.75(2.12,6.63) | 9.47(5.24,17.13) | |||
| Marital status | 0.970 | ||||||
| Married | 1 (reference) | 2.08(1.20,3.64) | 4.11(2.45,6.87) | 12.00(7.06,20.38) | |||
| Single or separated | 1 (reference) | 1.77(0.80,3.91) | 3.35(1.44,7.80) | 8.80(4.07,19.03) | |||
| PIR | 0.399 | ||||||
| < 1 | 1 (reference) | 5.97(1.44,24.66) | 8.36(2.54,27.52) | 24.06(8.40,68.94) | |||
| 1–3 | 1 (reference) | 1.78(0.91,3.48) | 3.13(1.48,6.60) | 10.03(4.61,21.79) | |||
| > 3 | 1 (reference) | 1.77(0.89,3.52) | 4.38(2.30,8.33) | 10.02(4.94,20.33) | |||
| Smoking status | 0.682 | ||||||
| Never | 1 (reference) | 1.71(0.83,3.52) | 3.73(1.85,7.54) | 9.27(4.76,18.05) | |||
| Ever or current | 1 (reference) | 2.36(1.19,4.68) | 3.91(2.01,7.63) | 11.73(6.25,22.01) | |||
| Alcohol status | 0.786 | ||||||
| Non-drinker | 1 (reference) | 1.40(0.63,3.14) | 3.19(1.46,6.97) | 9.52(4.28,21.20) | |||
| Drinker | 1 (reference) | 2.20(1.25,3.90) | 3.96(2.29,6.85) | 10.51(6.35,17.40) | |||
| Physical activity | 0.180 | ||||||
| Inactive | 1 (reference) | 3.55(1.74,7.26) | 6.20(2.82,13.60) | 19.04(9.56,37.92) | |||
| Insufficient | 1 (reference) | 1.12(0.54,2.33) | 2.83(1.45,5.50) | 5.96(2.91,12.19) | |||
| Active | 1 (reference) | 2.91(0.89,9.56) | 3.86(1.40,10.62) | 13.73(4.93,38.27) | |||
Values are weighted OR (weighted 95% CI)
CI, Confidence interval; CKM, Cardiovascular-Kidney-Metabolic; OR, Odds ratios; PIR, poverty income ratio; TyG, triglyceride-glucose
ROC analysis
Figure 3; Table 4 presented the accuracies of the logistic regression models of TyG-FI, FI, TyG, and other TyG-related indices in predicting advanced CKM syndrome. ROC analysis showed that TyG-FI had the highest AUC of 0.796, significantly outperforming FI (AUC: 0.710), TyG-WWI (AUC: 0.656), TyG-WHtR (AUC: 0.604), TyG-WC (AUC: 0.603), TyG (AUC: 0.580), and TyG-BMI (AUC: 0.532), as indicated by Delong test (all p < 0.0001).
Fig. 3.
Receiver operating characteristic curves highlighting the superior predictive performance of TyG-FI for the risk of advanced CKM syndrome. CKM, Cardiovascular-Kidney-Metabolic; FI, frailty index; TyG, triglyceride-glucose; TyG-BMI, triglyceride-glucose-body mass index; TyG-FI, triglyceride-glucose-frailty index; TyG-WC, triglyceride-glucose-waist circumference; TyG-WHtR, triglyceride-glucose-waist-to-height ratio; TyG-WWI, triglyceride-glucose-weight adjusted waist index
Table 4.
ROC analysis comparing the ability of the TyG-FI, TyG-related indices and FI to predict advanced CKM syndrome
| AUC | Optimal cutoff value | Sensitivity | Specificity | Youden’s index | p † | |
|---|---|---|---|---|---|---|
| TyG-FI | 0.796 | 1.431 | 0.759 | 0.687 | 0.446 | / |
| TyG-WWI | 0.656 | 98.424 | 0.606 | 0.616 | 0.222 | < 0.0001 |
| TyG-WHtR | 0.604 | 5.438 | 0.540 | 0.618 | 0.158 | < 0.0001 |
| TyG-WC | 0.603 | 900.701 | 0.561 | 0.596 | 0.157 | < 0.0001 |
| TyG | 0.580 | 8.717 | 0.521 | 0.595 | 0.116 | < 0.0001 |
| TyG-BMI | 0.532 | 263.118 | 0.479 | 0.575 | 0.054 | < 0.0001 |
| FI | 0.710 | 0.163 | 0.665 | 0.657 | 0.322 | < 0.0001 |
CKM, Cardiovascular-Kidney-Metabolic; ROC, receiver operating characteristic; FI, frailty index; TyG, triglyceride glucose; TyG-BMI, TyG-body mass index; TyG-FI, TyG-frailty index; TyG-WC, TyG-waist circumference; TyG-WHtR, TyG-waist-to-height ratio; TyG-WWI, TyG-weight adjusted waist index
†Delong’s test p value comparing each TyG-related parameters and FI with TyG-FI
Interaction between TyG and FI on advanced CKM syndrome
As shown in Table S9, there was not strong evidence in favor of multiplicative interactions between TyG and FI on advanced CKM syndrome (OR, 0.40; 95% CI, 0.09–1.84; p = 0.24). Besides, according to Table S10, based on RERI of -0.14 (95% CI: -0.22 to 0.05), AP of -0.40 (95% CI: -0.86 to 0.12), and SI of 1.08 (95% CI: 0.87 to 1.23), the analyses revealed no interaction on the additive scale between TyG and FI on advanced CKM syndrome. The above results indicated that there were no interactions between TyG and FI on advanced CKM syndrome.
Discussion
In this study, we assessed the relationship between TyG-FI levels and advanced CKM syndrome using a large, nationally representative sample of U.S. adults. Patients with CKM syndrome in the highest TyG-FI quartile exhibited a 932% greater risk of advanced stages compared to those in the lowest quartile, with a 178% risk increase per unit rise in TyG-FI. The RCS curve demonstrated a non-linear relationship, revealing that the risk of advanced CKM syndrome rises with higher TyG-FI levels. TyG-FI exhibited the highest discriminative power for advanced CKM syndrome among FI and all TyG-related indices, achieving an AUC of 0.796. These results positioned TyG-FI as a comprehensive and powerful tool for identifying individuals at the highest risk for complex multi-organ dysfunction.
Non-communicable diseases, including CVD, CKD, cancers, and diabetes, are the leading causes of morbidity and mortality globally. CVD is the primary global cause of mortality, with fatalities rising from 12.1 million in 1990 to an estimated 20.5 million by 2025 [27, 28]. Diabetes is another major contributor to the global burden of non-communicable diseases. In 2022, an estimated 828 million adults were diagnosed with diabetes, leading to 1.4 million deaths [29]. Furthermore, the global prevalence and death rate of CKD are concerning, with 697.3 million cases and 1.4 million fatalities in 2019 [30]. Cardiovascular, kidney, and metabolic functions are closely linked, with dysfunction in one potentially causing problems in the others [1]. Recognizing the complex interactions of the mechanisms in these conditions, the AHA proposed CKM syndrome staging in October 2023 [2]. Advanced CKM syndrome was categorized as stages 3 or 4, as these stages indicate individuals with subclinical or clinical CVD. Analysis of NHANES data from 2011 to 2020 revealed that almost 90% of U.S. adults met the diagnostic criteria for stage 1 or higher of CKM syndrome, with 15% reaching advanced stages [3]. The prevalence of CKM syndrome in the U.S. underscored the urgent necessity for preventive measures against its progression. However, managing traditional risk-enhancing factors for CKM syndrome does not fully arrest disease progression [2], indicating a need for more exploration into additional risk factors.
IR diminishes the body’s sensitivity to insulin, impairing glucose transport into cells and potentially causing metabolic problems such as hyperglycemia [31]. The AHA presidential advisory identifies IR as a key mechanism in the progression of CKM syndrome [2]. The TyG index emphasizes the intricate interaction between triglyceride and glucose metabolism, providing insights into metabolic disturbances linked to various medical conditions. Extensive research has identified the TyG index as an independent risk factor for CVD, CKD, and diabetes [5–7]. Although the TyG index is a recognized and dependable marker for IR, its predictive accuracy for cardiovascular risk is enhanced when combined with obesity indices [19, 32–34]. The standard TyG index primarily reflects metabolic dysfunction at the cellular level. In contrast, composite indices like TyG-WC, TyG-WHtR, and TyG-WWI offer a comprehensive evaluation by combining two key pathophysiological pathways: IR and abdominal obesity. By simultaneously quantifying metabolic derangement and adiposity distribution, these combined indices offer a more holistic view of an individual’s cardiometabolic health [35]. However, our results indicated that these adiposity-focused enhancements offer only marginal improvements over the standard TyG index for predicting a complex endpoint like advanced CKM syndrome. This phenomenon can be attributed to the shared metabolic information they convey. TyG primarily reflects IR, whereas WC, WHtR, and WWI highlight central obesity. Combining TyG with WC, WHtR, and WWI may not significantly improve discriminative ability due to the overlapping relationship between IR and visceral obesity in relation to cardiometabolic risk.
The truly novel aspect of our work was the integration of a FI. There is growing acknowledgment that frailty and metabolic disorders are closely linked in a bidirectional relationship [11]. IR can accelerate muscle protein breakdown, promote sarcopenia, and exacerbate physiological decline, thereby fostering a frail state [12]. Besides, a frail individual is at a higher risk of IR and metabolic dysfunction [13]. The TyG-FI, developed by Zhao et al. [16], indicated a combined effect of metabolic issues and systemic physiological weakening, both of which are key contributors to cardiometabolic susceptibility. The marked superiority of TyG-FI (AUC: 0.796) for advanced CKM syndrome over FI and all other compared TyG-related indices is a central finding of our study. Unlike anthropometric measures like WHtR or BMI, which are essentially snapshots of body size and composition, a FI is a dynamic summary of health status [36]. It aggregates deficits across a range of domains, including functional capacity, cognitive state, mood, comorbidity burden, and laboratory values [37]. This aggregate measure provides a proxy for an individual’s overall biological aging and resilience. Advanced CKM syndrome represents the culmination of cumulative damage across the heart, kidneys, and metabolic systems [38]. The advantage of TyG-FI lies in its ability to quantify both insulin resistance and frailty, which together contribute to the progression of advanced multiorgan disease.
The robust performance of TyG-FI carries significant potential for clinical practice. TyG-FI is an effective tool for risk stratification. In primary care, clinicians can use routine laboratory data and a structured deficit accumulation assessment to calculate TyG-FI for identifying high-risk individuals among large populations [39]. A high TyG-FI score would flag a patient not just as metabolically unhealthy, but as possessing a high level of systemic physiological vulnerability, placing them at extreme risk for progression to advanced CKM syndrome-related events. Additionally, TyG-FI could help guide more personalized and targeted intervention strategies. A patient with a high TyG-FI score would benefit from a dual-pronged management approach that includes both aggressive metabolic parameter management (e.g., using sodium-glucose cotransporter-2 inhibitors or glucagon-like peptide-1 receptor agonists [2]) and targeted interventions (e.g., supervised resistance exercise training to combat sarcopenia and nutritional supplementation [40]) to reduce frailty. By identifying the confluence of metabolic and frailty pathways, TyG-FI can inform a holistic treatment plan that addresses the full spectrum of the patient’s risk.
This study has some limitations. First, the cross-sectional nature of the NHANES prevented any definitive conclusions regarding causality. Second, our analysis relied on single baseline measurements of both the TyG index and the FI. Future research should examine whether the trajectory of TyG-FI offers better prognostic value for advanced CKM syndrome. Third, the definition of advanced CKM syndrome in our study relied on available NHANES data, which lacks specific diagnostic imaging and clinical details. The inaccessibility of data from echocardiography, coronary computed tomography angiography, and invasive coronary catheterization can lead to an underestimation of the true prevalence of advanced CKM syndrome, potentially introducing misclassification bias and influencing the strength of the observed associations. Fourth, the FI used in this study includes several chronic diseases (e.g., hypertension, diabetes, coronary heart disease) that are also part of the CKM syndrome staging criteria. This overlap may introduce some degree of bias. However, our sensitivity analysis removing these overlapping items yielded a persistent and strong association, suggesting that the observed relationship is not solely driven by definitional overlap. Nonetheless, readers should interpret the results with this consideration in mind. Finally, our study population consisted exclusively of U.S. adults, so the generalizability of our findings may be limited.
Conclusion
In conclusion, our study suggested that the TyG-FI offered a notable improvement over current biomarkers for evaluating the risk of advanced CKM syndrome. Future cohort studies and clinical trials are needed to confirm the prognostic significance of TyG-FI and assess whether interventions based on TyG-FI can enhance patient outcomes and reduce the impact of advanced CKM syndrome.
Supplementary Information
Acknowledgements
We thank the National Center for Health Statistics of the Centers for Disease Control and Prevention for sharing the NHANES data. We also thank Home for Researchers (https://www.home-for-researchers.com/) for their linguistic assistance.
Author contributions
**Dingyuan Tu: ** Methodology, Formal analysis, Writing – original draft. **Jinru Li: ** Methodology, Data curation. **Yutang Li: ** Methodology, Software. **Jianming Wang: ** Conceptualization, Writing – review & editing.
Funding
This study was supported by grant from Liaoning Province Union Program - Application Fundamental Research Project (2023JH2/101700138) and Shenyang Joint Logistics Support Force Young Elite Cultivation Program (2025SL0012).
Data availability
The National Health and Nutrition Examination Survey dataset is publicly available at the National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/index.html).
Declarations
Ethics approval and consent to participate
Clinical trial number: not applicable.
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.
Dingyuan Tu, Jinru Li and Yutang Li contributed equally to this work.
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Supplementary Materials
Data Availability Statement
The National Health and Nutrition Examination Survey dataset is publicly available at the National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/index.html).