Abstract
The cardiovascular kidney metabolic (CKM) syndrome is a dynamic geriatric condition that has received limited research attention regarding its potential associations with the triglyceride glucose (TyG) index. This study aims to explore the potential association between the TyG index and advanced CKM syndrome. Data for this cross-sectional study were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. The association between the TyG index and the risk of advanced CKM syndrome was investigated using multivariable logistic regression models. Additionally, a restricted cubic spline (RCS) analysis was employed to assess the dose-response relationship between the TyG index and the risk of advanced CKM syndrome. A total of 7904 participants were included in this study, with a mean TyG index of 5.04 ± 0.41. The prevalence of advanced CKM syndrome among the participants was 14.85%. Our findings indicated that as the TyG index quartiles increased, the risk of advanced CKM syndrome also increased. The results from the three regression analysis models indicated a positive association between the continuous TyG index and advanced CKM syndrome. Furthermore, the quartiles of the TyG index were significantly associated with an increased prevalence of advanced CKM syndrome in the fully adjusted models (TyG index Q4 vs. Q1, OR = 1.94, 95% CI 1.37–2.75, P < 0.001). The results of the RCS analysis indicated a linear and positive association between the TyG index and advanced CKM syndrome. The results indicated that elevated TyG index is associated with an increased prevalence of advanced CKM syndrome. This suggests that the TyG index may be a useful tool for assessing the risk of advanced CKM syndrome.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-82881-y.
Keywords: Cardiovascular-kidney-metabolic (CKM) syndrome, Triglyceride-glucose (TyG) index
Subject terms: Cardiology, Endocrinology, Nephrology
Introduction
The prevalence of metabolic diseases, particularly diabetes and its associated complications, is increasing globally. According to the 10th edition of the Diabetes Atlas in 2021, approximately 537 million people worldwide have diabetes, with about one-third of them suffering from cardio-renal disease1. In 2023, the American Heart Association introduced the concept of Cardiovascular-Kidney-Metabolic (CKM) syndrome to comprehensively reveal the pathological links between metabolic risk factors such as obesity, diabetes mellitus (DM), cardiovascular disease (CVD), chronic kidney disease (CKD), and other metabolic conditions2. The stages of CKM syndrome and its pathological features have been classified from stage 0 (no risk factors present) to stage 4 (CVD diagnosed) to fully illustrate the dynamic progression of the syndrome, with stages 3 and 4 being labeled as advanced CKM syndrome3. Stage 3 CKM syndrome is marked by significant metabolic dysfunction, including dyslipidemia, hyperglycemia, and hypertension, coupled with declining kidney function and an increased cardiovascular burden. In Stage 4, cardiovascular disease becomes established, signaling further progression and representing the most severe stage of CKM syndrome. Aggarwal et al. found that nearly 90% of U.S. adults suffer from CKM syndrome (stage 1 or higher), with 15% of them in advanced stages3.
In terms of disease mechanisms, Kadowaki et al. concluded that diabetes mellitus impairs the cardio-renal system through a series of complex mechanisms4, including hyperglycemia, the formation of advanced glycation end products, insulin resistance, aberrant activation of the renin-angiotensin-aldosterone system, lipotoxicity, oxidative stress, and chronic inflammatory response. These pathophysiological changes are also observed in the context of CVD and CKD, which accelerate the progression of type 2 diabetes mellitus (T2DM), creating a vicious cycle. The importance of insulin resistance (IR) in various chronic diseases has been increasingly recognized through more in-depth research. IR not only leads to a significant reduction in insulin sensitivity but also induces a series of metabolic abnormalities, including hyperglycemia, hyperlipidemia, and hypertension. These metabolic abnormalities may exacerbate pathological changes such as endothelial cell dysfunction, vascular inflammation, and atherosclerosis, further contributing to the onset and progression of CKM syndrome.
However, traditional methods of assessing IR, such as the euglycemic-hyperinsulinemic clamp, intravenous glucose tolerance testing, and HOMA-IR, have limitations in practical application due to their invasive nature, high cost, and inapplicability to patients receiving insulin therapy or suffering from β-cell insufficiency5,6. Guerrero et al.7 proposed a novel assessment method, the TyG index, designed to be used as a potential index for assessing IR. While both fasting glucose and triglyceride levels require blood sampling, the TyG index has been validated in multiple studies as a cost-effective and accessible metric, offering comparable predictive accuracy for insulin resistance with excellent sensitivity and specificity8–10.
Given the significant efficacy of the TyG index in assessing IR and its potential value in predicting cardio-metabolic diseases, it was initially hypothesized that there might be an association between the TyG index and the risk of CKM syndrome at different stages of the disease. Therefore, the present study was designed to explore in depth the potential association between the TyG index and the risk of advanced CKM syndrome in the general population, aiming to provide new insights and inform future research on the prevention, diagnosis, and management of CKM syndrome.
Methods
Study design and participants
The data for this study were obtained from the NHANES, a nationally representative survey conducted by the Centers for Disease Control and Prevention in the United States. NHANES assesses nutrition and health conditions in the country and provides reliable, authoritative, and precise health statistics. Written informed consent was obtained from all participants involved in NHANES, and the survey received approval from the Ethics Review Board of the National Center for Health Statistics. The NHANES data are publicly accessible and available online at (https://www.cdc.gov/nchs/nhanes/index.htm).
In this study, data were selected from four NHANES survey cycles spanning from 2011 to 2018. The data collection methods included questionnaires, interview transcripts, physical examinations, and laboratory tests. Initially, the study included a total of 39,156 participants from the continuous NHANES dataset. After a rigorous screening process, participants under the age of 20 years and individuals with missing sample weights (WTSAF2YR) and survival status were excluded. Additionally, participants lacking data related to CKM syndrome and TyG index, as well as covariates, were excluded, ultimately identifying 7904 eligible participants. Figure S1 presents a flowchart that illustrates the participant selection process.
Ascertainment of TyG index
The TyG index was calculated using the formula: TyG index = Ln [fasting TG (mg/dL) × fasting glucose (mg/dL)/2]. The measurement of triglycerides and fasting glucose was conducted through enzymatic assays using the Roche Modular P and Roche Cobas 6000 chemistry analyzers, respectively. The hexokinase-mediated reaction was used to assess fasting glucose on the Roche/Hitachi Cobas C 501 chemistry analyzer 11. Participants were categorized into four groups based on the quartiles of the TyG index, with Q1 serving as the reference group for comparative analysis. The quartile cut points were as follows: Q1 (2.76–5.20], Q2 (5.20–5.64], Q3 (5.64–6.10], and Q4 (6.10–9.95].
Ascertainment of CKM syndrome stages
CKM syndrome stages were classified as follows: CKM Stage 1 was characterized by elevated BMI (≥ 23 kg/m² for Asians, > 25 kg/m2 for other populations), increased waist circumference (≥ 80 cm for women and ≥ 90 cm for men in Asians; ≥88 cm for women and ≥ 102 cm for other populations), or prediabetes (HbA1c 5.7-6.4%, fasting glucose 100–125 mg/dL). CKM Stage 2 was categorized by the presence of multiple metabolic risk factors, such as elevated triglycerides, hypertension, diabetes, or metabolic syndrome, or moderate-to-high-risk CKD according to KDIGO guidelines. Stages 3 and 4 were collectively classified as Advanced CKM Syndrome, encompassing individuals diagnosed with or at high risk of developing cardiovascular disease 3. CKM Stage 3 included participants with very-high-risk CKD based on KDIGO criteria or those with a predicted 10-year CVD risk of 20% or greater using AHA PREVENT equations12. CKM Stage 4 comprised participants with self-reported cardiovascular diseases, such as coronary heart disease, angina, myocardial infarction, heart failure, and cerebrovascular accidents. Detailed descriptions of stage definitions are shown in the Supplemental Methods.
Ascertainment of covariates
Demographic information on participants was collected from the NHANES database, including gender, age, race and ethnicity (Asian, Black, Hispanic, White, Other), educational level (less than high school, high school or equivalent, or college or above), and marital status (Married, Single/Separated). Further information on lifestyle habits and comorbidities was also collected, such as smoking status categorized as never smoked, ever smoked, and current smoker, and intensity of physical activity (vigorous/moderate exercise, mild exercise) with reference to the World Health Organization guidelines on physical activity status13,14. Self-reported general health condition was classified into three groups: very good to excellent, good, and poor to fair. Physical and laboratory tests such as body mass index (BMI), systolic and diastolic blood pressure measurements, serum creatinine and uric acid levels, total cholesterol, glycohemoglobin, and albumin/creatinine ratio (ACR) were selected as potential confounders. The details of relevant definitions were shown in the Supplementary Method. Additionally, mortality status was ascertained by probabilistic matching to the National Death Index through December 31, 2019. The primary outcome of this study was mortality from all causes.
Statistical analysis
The statistical analysis was conducted using R 4.2.1 and the Free Statistics software (version 1.9). Complex survey design factors, including sample weights, clustering, and stratification, were considered for all analyses with instructions for using NHANES data. Baseline characteristics were compared by CKM syndrome stages in the two intervals using the Rao-Scott chi-square test for categorical variables and ANOVA and the Kruskal-Wallis test adjusted for sampling weights for continuous variables. Continuous variables were reported as means accompanied by standard errors, while categorical variables were presented as percentages, based on quartiles (Q1–Q4) of the TyG index and CKM syndrome stages. Descriptive analyses were performed using weighted one-way ANOVA for continuous variables and weighted chi-square tests for categorical variables. To explore the association between the TyG index and advanced CKM syndrome, multivariate logistic regression analyses were conducted, and three different statistical inference models were constructed. Crude model was unadjusted, model 1 was adjusted for age, sex, and ethnicity, and model 2 was further adjusted for education level, marital status, BMI, serum creatinine, serum uric acid, total cholesterol, HDL-C, glycohemoglobin, ACR, systolic blood pressure, diastolic blood pressure, smoking status, general health condition, and physical activity based on model 1. RCS analysis was performed to explore the possible relationship between the TyG index and the likelihood of advanced CKM syndrome. P value of less than 0.05 was considered statistically significant in this study.
Results
Baseline characteristics of study participants
Baseline characteristics of study participants stratified by TyG index quartiles
Table 1 showed the baseline characteristics of the participants stratified by quartiles of the TyG index. The study enrolled a total of 7904 participants with a mean age of 47.67 ± 16.94 years, and 49.45% of them were male. The mean prevalence of advanced CKM syndrome was found to be 14.85%. Notably, the prevalence of advanced CKM syndrome was higher in participants with higher quartiles of the TyG index (Q1: 7.88%; Q2: 12.91%; Q3: 15.94%; Q4: 23.31%). Additionally, there was a higher mortality rate among participants in the highest quartile compared to the lowest quartile. Participants with higher TyG index were more likely to be older, male, of Hispanics or white ethnicity, less educated, former smokers or current smokers, and mildly physically active compared to participants in the lowest TyG index quartile. In terms of biochemical parameters, the highest TyG index group showed a statistically significant increase in several parameters, including serum creatinine, uric acid, total cholesterol, glycohemoglobin, and ACR, along with a significantly lower HDL-C level. Furthermore, physiological parameters such as systolic blood pressure (SBP), diastolic blood pressure (DBP), and BMI tended to increase in the highest TyG index group. Analyses revealed that the prevalence of chronic diseases, such as hypertension, diabetes mellitus, cardiovascular diseases, and renal failure, was significantly higher in the highest TyG index group compared to the lowest group.
Table 1.
Baseline characteristics according to the TyG index quartiles.
| Characteristic | Total (n = 7904) | Quartiles of the CALLY index Prevalence, % (95% CI) | P value | |||
|---|---|---|---|---|---|---|
| Q1 (n = 1976) | Q2 (n = 1976) | Q3 (n = 1976) | Q4 (n = 1976) | |||
| Age group | ||||||
| < 60 | 5219 (72.39) | 1531 (81.16) | 1285 (72.56) | 1209 (68.72) | 1194 (66.54) | < 0.0001 |
| ≥ 60 | 2685 (27.61) | 445 (18.84) | 691 (27.44) | 767(31.28) | 782 (33.46) | |
| Male, n (%) | 3925 (49.45) | 784 (40.43) | 978 (49.03) | 1029 (51.09) | 1134 (57.94) | |
| Race and ethnicity, n (%) | ||||||
| Hispanics | 1968 (14.91) | 336 (12.01) | 461 (14.35) | 571 (16.45) | 600 (17.06) | < 0.0001 |
| White | 3074 (65.91) | 670 (61.25) | 779 (66.58) | 782 (67.20) | 843 (68.93) | |
| Black | 1637 (10.91) | 659 (18.45) | 443 (11.37) | 309 (7.54) | 226 (5.78) | |
| Asian | 947 (4.91) | 242 (5.14) | 223 (4.40) | 245 (5.08) | 237 (5.00) | |
| Other race or ethnicity | 278 (3.36) | 69 (3.15) | 70 (3.31) | 69 (3.74) | 70 (3.23) | |
| Education levels, n (%) | ||||||
| Less than high school | 1686 (14.29) | 296 (10.36) | 413 (13.85) | 473 (16.29) | 504 (16.95) | < 0.0001 |
| High school diploma | 1752 (22.80) | 414 (20.24) | 445 (22.87) | 448 (23.47) | 445 (24.78) | |
| More than high school | 4466 (62.91) | 1266 (69.40) | 1118 (63.28) | 1055 (60.24) | 1027 (58.27) | |
| Marital status, n (%) | ||||||
| Married | 4751 (63.62) | 1085 (60.47) | 1148 (61.92) | 1263 (65.71) | 1255 (66.66) | < 0.0001 |
| Single/separated | 3153 (36.38) | 891 (39.53) | 828 (38.08) | 713 (34.29) | 721 (33.34) | |
| General health condition, n (%) | ||||||
| Excellent | 2720 (41.29) | 892 (52.49) | 731 (46.39) | 613 (37.71) | 484 (27.51) | < 0.0001 |
| Good | 3365 (41.38) | 772 (35.58) | 851 (39.41) | 884 (42.90) | 858 (48.14) | |
| Poor to fair | 1819 (17.34) | 312 (11.93) | 394 (14.20) | 479 (19.40) | 634 (24.35) | |
| Smoking status, n (% ) | ||||||
| Never smoker | 4460 (55.68) | 1246 (62.46) | 1168 (59.12) | 1068 (52.06) | 978 (48.46) | < 0.0001 |
| Former smoker | 1931 (25.75) | 410 (22.63) | 450 (23.16) | 497 (27.64) | 574 (29.89) | |
| Current smoker | 1513 (18.58) | 320 (14.92) | 358 (17.72) | 411 (20.30) | 424 (21.65) | |
| Physical activity, n (%) | ||||||
| mild | 5283 (63.79) | 1125 (53.15) | 1307 (61.85) | 1372 (67.24) | 1479 (73.78) | < 0.0001 |
| Moderate/vigorous | 2621 (36.21) | 851 (46.85) | 669 (38.15) | 604 (32.76) | 497 (26.22) | |
| BMI(kg/m2) | 29.29 (6.95) | 26.58 (6.33) | 28.487 (6.69) | 30.364 (6.91) | 31.94 (6.68) | < 0.0001 |
| < 25 | 2247 (28.57) | 920 (48.77) | 642 (32.46) | 426 (20.62) | 259 (10.86) | |
| ≥ 25 | 5657 (71.43) | 1056 (51.23) | 1334 (67.54) | 1550 (79.38) | 1717 (89.14) | |
| SBP(mmHg) | 122.13 (16.92) | 116.80 (15.33) | 121.35(16.90) | 123.48 (16.84) | 127.31 (16.92) | < 0.0001 |
| DBP(mmHg) | 70.23 (11.91) | 67.68 (10.77) | 69.66 (11.80) | 71.08 (12.13) | 72.71 (12.38) | < 0.0001 |
| Cholesterol(mg/dL) | 190.47 (41.25) | 173.33 (34.17) | 185.55 (36.11) | 196.64 (40.04) | 207.79 (46.04) | < 0.0001 |
| HDL-C(mg/dL) | 54.19 (16.37) | 64.05 (17.79) | 56.90 (15.26) | 51.65 (13.19) | 43.33 (10.63) | < 0.0001 |
| SCR (mg/dL) | 76.96 (25.68) | 74.18 (21.79) | 77.28 (24.80) | 77.59 (29.44) | 78.98 (26.07) | < 0.0001 |
| Uric acid(umol/L) | 324.84 (83.62) | 292.89 (73.24) | 316.40(76.51) | 335.56 (82.50) | 357.16 (88.35) | < 0.0001 |
| Glycohemoglobin(%) | 5.65 (0.96) | 5.32 (0.43) | 5.46 (0.54) | 5.61 (0.65) | 6.26 (1.53) | < 0.0001 |
| ACR(mg/g) | 31.93 (221.80) | 18.72 (127.02) | 23.29 (186.48) | 24.98 (159.71) | 62.46 (351.28) | 0.0001 |
| Chronic disease, n (%) | ||||||
| Diabetes | 1716 (16.23) | 125 (3.50) | 245 (8.28) | 437 (16.27) | 909 (38.29) | < 0.0001 |
| Hypertension | 3434(38.87) | 587 (24.10) | 783 (33.70) | 948(44.57) | 1116(54.38) | < 0.0001 |
| Chronic kidney failure | 1468 (14.30) | 242 (9.48) | 344 (12.61) | 355 (13.64) | 527 (21.94) | < 0.0001 |
| Cardiovascular disease | 856 (9.06) | 128 (5.15) | 206 (7.98) | 224 (9.86) | 298 (13.58) | < 0.0001 |
| Advanced CKM syndrome | 1564(14.85) | 224 (7.88) | 364(12.91) | 408 (15.94) | 568 (23.31) | < 0.0001 |
| All-cause mortality, n (%) | 404 (3.92) | 68 (2.63) | 99 (3.37) | 110 (4.32) | 127 (5.50) | 0.0019 |
The weighted mean for continuous variables was calculated, and the p-value was determined using the weighted Kruskal- Wallis test. For categorical variables, the numbers (unweighted) and percentages (weighted) were presented, and the p-value was calculated using the weighted chi-square test.
Q quartile, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, SCR serum creatinine, ACR albumin-to-creatinine ratio, HDL-C high-density lipoprotein-cholesterol.
Baseline characteristics of study participants stratified by CKM syndrome stages
Table 2 showed the baseline characteristics of the participants stratified by stages of CKM syndrome. The mean TyG index observed in the study was 5.04 ± 0.41. Compared to participants in the low CKM stage, those in the late CKM syndrome stage tended to exhibit certain distinguishing characteristics. These characteristics included being older, male, having relatively lower education, and engaging in milder intensity of physical activity. Furthermore, participants in the late CKM syndrome stage showed significant changes in physiological parameters. Specifically, they had higher levels of serum creatinine, uric acid, glycohemoglobin, and ACR. They also tended to have higher levels of SBP and BMI, as well as higher values of TyG index and increased mortality rates (Fig. 1).
Fig. 1.
Prevalence and mortality of CKM syndrome stages among U.S. adults (2011–2018) .
Table 2.
Baseline characteristics according to the cardiovascular-kidney-metabolic (CKM) syndrome stages.
| Characteristic | Stage 0 (n = 674) | Stage 1 (n = 1786) | Stage 2 (n = 3880) | Stage 3 (n = 708) | Stage 4 (n = 856) | Advanced CKM syndrome (n = 1564) | P value | |
|---|---|---|---|---|---|---|---|---|
| Age group, n (%) | 34.93 (13.78) | 40.07 (14.27) | 48.17 (14.42) | 73.07 (8.29) | 64.56 (12.48) | 67.88 (11.79) | < 0.0001 | |
| < 60 | 625 (92.10) | 1556 (87.57) | 2766 (76.11) | 47 (6.85) | 225 (29.01) | 272 (20.36) | ||
| ≥ 60 | 49 (7.90) | 230 (12.43) | 1114 (23.89) | 661 (93.15) | 631 (70.99) | 1292 (79.64) | ||
| Male, n (%) | 255 (38.68) | 856 (49.17) | 1895(49.75) | 446 (59.34) | 473 (54.61) | 919(56.46) | < 0.0001 | |
| Race and ethnicity, n (%) | ||||||||
| Hispanics | 118 (11.07) | 495 (18.35) | 1053 (15.64) | 144 (10.66) | 158 (8.48) | 302 (9.33) | < 0.0001 | |
| White | 303 (70.85) | 631 (62.51) | 1384 (64.95) | 324 (70.03) | 432 (72.35) | 756 (71.45) | ||
| Black | 103 (9.03) | 350 (10.78) | 819 (10.99) | 163 (12.63) | 202 (11.95) | 365 (12.21) | ||
| Asian | 120 (6.13) | 258 (5.34) | 476 (5.09) | 60 (4.21) | 33 (1.73) | 93 (2.7) | ||
| Other race or ethnicity | 30 (2.92) | 52 (3.02) | 148 (3.34) | 17 (2.47) | 31 (5.48) | 48 (4.31) | ||
| Education levels, n (%) | ||||||||
| Less than high school | 72 (8.50) | 304 (11.67) | 851 (14.61) | 208 (20.78) | 251 (22.37) | 459 (21.75) | 0.0001 | |
| High school diploma | 117 (16.23) | 352 (21.16) | 879 (23.66) | 183 (27.56) | 221 (27.14) | 404 (27.31) | ||
| More than high school | 485 (75.27) | 1130 (67.18) | 2150 (61.72) | 317 (51.66) | 384 (50.48) | 701 (50.94) | ||
| Marital status, n (%) | ||||||||
| Married | 327 (53.55) | 1104 (64.81) | 2419 (65.37) | 415 (61.56) | 486 (63.61) | 901 (62.81) | 0.0001 | |
| Single/separated | 347 (46.45) | 682 (35.19) | 1461 (34.63) | 293 (38.44) | 370 (36.39) | 663 (37.19) | ||
| General health condition, No. (% ) | ||||||||
| Excellent | 421 (69.15) | 814 (50.58) | 1121(34.59) | 212 (38.54) | 152 (21.69) | 364 (28.27) | < 0.0001 | |
| Good | 205 (25.74) | 754 (39.20) | 1170 (46.17) | 313 (41.79) | 323 (39.01) | 636 (40.09) | ||
| Poor to fair | 48 (5.11) | 218 (10.23) | 989 (19.25) | 183 (19.67) | 381 (39.30) | 564 (31.64) | ||
| Smoking status, No. (% ) | ||||||||
| Never smoker | 462 (66.72) | 1131 (61.21) | 2220 (55.40) | 308 (42.55) | 339 (37.43) | 647 (39.43) | < 0.0001 | |
| Former smoker | 97 (16.00) | 345 (23.05) | 880 (24.60) | 287 (44.12) | 322 (38.98) | 609 (40.98) | ||
| Current smoker | 115 (17.28) | 310 (15.74) | 780 (20.00) | 113 (13.33) | 195 (23.59) | 308 (19.59) | ||
| Physical activity, n (%) | ||||||||
| Mild | 344 (47.44) | 1032 (56.92) | 2695 (67.34) | 548 (76.76) | 664 (74.12) | 1212 (75.15) | < 0.0001 | |
| Moderate/vigorous | 330 (52.56) | 754 (43.08) | 1185 (32.66) | 160 (23.24) | 192 (25.88) | 352 (24.85) | ||
| BMI(kg/m2) | ||||||||
| < 25 | 674 (100) | 493 (24.86) | 743 (17.73) | 157 (20.22) | 180 (21.46) | 337 (20.98) | < 0.0001 | |
| ≥ 25 | 0 (0) | 1293 (75.14) | 3137 (82.27) | 551 (79.78) | 676 (78.54) | 1227 (79.02) | ||
| SBP(mmHg) | 110.08 (9.95) | 114.00 (9.94) | 125.18(15.84) | 140.51(21.77) | 130.20 (20.44) | 134.23 (21.56) | < 0.0001 | |
| DBP(mmHg) | 65.901 (8.93) | 67.86 (8.37) | 73.22 (12.26) | 66.59 (14.63) | 67.82 (14.91) | 67.34 (14.81) | < 0.0001 | |
| Cholesterol(mg/dL) | 175.97 (33.28) | 185.20 (34.99) | 199.57 (43.42) | 182.59 (40.67) | 177.20 (42.07) | 179.31 (41.60) | < 0.0001 | |
| HDL-C(mg/dL) | 63.43 (15.35) | 57.80 (14.41) | 51.16 (16.30) | 51.39 (15.48) | 51.92 (17.64) | 51.72 (16.83) | < 0.0001 | |
| SCR (mg/dL) | 72.04 (14.29) | 73.93 (15.24) | 74.34 (17.07) | 101.90 (67.38) | 89.43 (35.53) | 94.29 (50.76) | < 0.0001 | |
| Uric acid(umol/L) | 279.55 (66.38) | 306.30 (72.58) | 335.60 (82.84) | 357.32 (95.57) | 349.09 (94.53) | 352.30 (95.00) | < 0.0001 | |
| ACR(mg/g) | 7.90 (5.19) | 6.58 (4.28) | 24.20 (158.76) | 146.60 (578.45) | 99.14 (419.65) | 117.66 (488.20) | < 0.0001 | |
| Glycohemoglobin(%) | 5.147 (0.262) | 5.320 (0.325) | 5.740 (1.034) | 6.381 (1.163) | 6.221 (1.331) | 6.283 (1.270) | < 0.0001 | |
| All-cause mortality | 6 ( 0.8) | 18 (0.72) | 108 (2.71) | 130 (18.08) | 142 (14.04) | 272(15.62) | < 0.0001 | |
| TyG index | 5.04 (0.41) | 5.27 (0.40) | 5.91 (0.66) | 5.96 (0.68) | 5.91 (0.74) | 5.93 (0.71) | < 0.0001 | |
The weighted mean for continuous variables was calculated, and the p-value was determined using the weighted Kruskal–Wallis test. For categorical variables, the numbers (unweighted) and percentages (weighted) were presented, and the p-value was calculated using the weighted chi-square test.
Q quartile, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, SCR serum creatinine, ACR albumin-to-creatinine ratio, HDL-C high-density lipoprotein-cholesterol.
Association of TyG index with advanced CKM syndrome risk
In our study, a potential association between the TyG index and the likelihood of advanced CKM syndrome was observed (Table 3). In the fully adjusted model (model 2), this positive association remained stable, with an odds ratio (OR) of 1.53 and a 95% confidence interval (CI) of 1.21–1.94 (p < 0.001). To verify the sensitivity of this result, we analyzed the TyG index by converting it from a continuous variable to a categorical variable. The multivariate-adjusted ORs and 95% CIs for the quartiles, from the lowest to the highest TyG index, were 1.00 (reference value), 1.25 (0.95, 1.63), 1.42 (1.06, 1.90), and 1.94 (1.37, 2.75), respectively. Furthermore, our study provided insight into the potential linear relationship between the TyG index and the risk of advanced CKM syndrome using RCS curves (Fig. 2). The results significantly revealed the existence of a linear association (P nonlinear = 0.476) in model 2.
Fig. 2.
Restricted cubic spline analysis of the association between the TyG index and the risk of advanced CKM syndrome.
Table 3.
Multivariate regression analysis of TyG index with advanced CKM syndrome.
| Characteristic | Crude model | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|
| OR(95%Cl) | P-value | OR(95%Cl) | P-value | OR(95%Cl) | P-value | |
| Continous TyG index | 1.90 (1.70,2.12) | < 0.001 | 1.93 (1.66, 2.25) | < 0.001 | 1.53 (1.21,1.94) | < 0.001 |
| TyG index category | ||||||
| Quartile 1 | Ref | Ref | Ref | |||
| Quartile 2 | 1.73 (1.37,2.20) | < 0.001 | 1.43 (1.10, 1.86) | 0.009 | 1.25 (0.95, 1.63) | 0.107 |
| Quartile 3 | 2.22 (1.74,2.82) | < 0.001 | 1.74 (1.33, 2.27) | < 0.001 | 1.42 (1.06, 1.90) | 0.020 |
| Quartile 4 | 3.55 (2.79,4.53) | < 0.001 | 3.07 (2.34, 4.02) | < 0.001 | 1.94 (1.37, 2.75) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
Crude model: Nn-adjusted.
Model 1:Adjusted for sex, age, race and ethnicity.
Model 2:Adjusted for sex, age, race and ethnicity, education level, marital status, BMI, serum creatinine, serum uric acid, total cholesterol, glycohemoglobin, ACR, HDL-C, SBP, DBP, smoking status, general health condition and physical activity.
OR: Odds ratio; CI: Confidence interval.
Subgroup analysis
Subgroup analyses and interaction tests were performed to evaluate the robustness of the association between the TyG index and the risk of advanced CKM syndrome across various population subgroups (Figure S2). Results showed a positive association between the TyG index and advanced CKM syndrome in both men and women. Notably, the association was stronger in females (OR = 1.72, 95% CI 1.23–2.41, p = 0.002) than in males (OR = 1.37, 95% CI 1.01–1.87, p = 0.05). Additionally, participants aged ≥ 60 exhibited a more pronounced association (OR = 1.72, 95% CI 1.31–2.26, p < 0.001) compared to those under 60 (OR = 1.29, 95% CI 0.91–1.82, p = 0.15). Interaction analysis identified a significant interaction between sex and age (p for interaction < 0.005), while no significant interactions were found in other subgroups, suggesting that these factors may not substantially modify the association between the TyG index and advanced CKM syndrome.
Interactive protective role of HDL-C against TyG index in advanced CKM syndrome
The analysis demonstrated that HDL-C is significantly and independently associated with a lower likelihood of advanced CKM syndrome (coefficient = 0.0084, p < 0.001), with higher HDL-C levels being strongly associated with reduced risk. Additionally, there was a significant negative interaction between HDL-C and the TyG index (interaction coefficient = −0.001658, p < 0.001). Low HDL-C levels intensified the positive association between the TyG index and CKM risk, whereas high HDL-C levels corresponded with a weaker relationship, nearly reaching a plateau in risk reduction. Stratified analysis further confirmed that elevated HDL-C levels effectively buffer the adverse impact of an increased TyG index (Figure S3, Figure S4).
Discussion
Based on data from a cross-sectional study involving 7,904 adults, our research has uncovered an association between the progression stages of CKM syndrome and the TyG index. Specifically, we observed that as the stage of CKM syndrome advanced, both the TyG index exhibited corresponding upward trends. Further analysis revealed a significant and independent association between a higher TyG index and the likelihood of advanced CKM syndrome, with this association demonstrating notable linear characteristics. Additionally, subgroup analyses consistently showed associations between the TyG index and the probability of advanced CKM syndrome onset across various subgroups, including those categorized by hypertension status, diabetes status, smoking habits, and exercise status. Concurrently, we noted that differences in gender, age groups, and BMI influenced this association.
Current research has found that abnormal accumulation and dysfunction of adipose tissue, particularly visceral adipose tissue, serve as key drivers of excessive secretion of pro-inflammatory factors15. Additionally, insulin resistance and abnormal glucose tolerance in peripheral tissues induced by metabolism-related inflammation significantly exacerbate the damage caused by pro-inflammatory factors and oxidative stress products to the cardiovascular system and kidneys16. This inflammatory and oxidative stress response triggered by IR may provide a fundamental basis for explaining the potential association between the TyG index and CKM syndrome. During the pathological process of IR, it is observed that the inhibition of the insulin signaling pathway leads to a significant increase in the synthesis of monocyte chemoattractant protein-1 (MCP-1), which further promotes inflammatory responses in adipose tissue. When adipose tissue is stimulated by inflammation, macrophages are activated and produce key pro-inflammatory cytokines: tumor necrosis factor (TNF)-a and interleukin-6 (IL-6)17,18. Among them, TNF-a significantly promotes the production of MCP-119, thereby initiating a positive feedback loop that intensifies the inflammatory response. Furthermore, IL-6 stimulates the release of free fatty acids (FFA)20, and the accumulation of FFA, in turn, exacerbates the vicious cycle of IR and inflammation by activating signaling pathways such as NF-κB and JNK21. These intricate interactions underscore the complexity of the pathological processes underlying IR and highlight the need for targeted therapeutic interventions.Furthermore, the hyperglycemic state induced by insulin resistance impairs endothelial function, thereby increasing the risk of atherosclerosis and heart failure22,23. Endothelial dysfunction, a key factor in promoting lipid accumulation, also contributes to increased platelet aggregation and inflammatory responses, thereby accelerating the development of cardiovascular diseases24. Cardiovascular diseases often occur concomitantly with kidney diseases and metabolic disorders, including obesity and T2DM. The presence of T2DM or CKD also leads to worse cardiovascular outcomes for patients25,26. The exact mechanisms behind this relationship require further research.
In the field of assessing IR, although the hyperinsulinemic-normoglycemic clamp technique is recognized as the gold standard, researchers have been dedicated to finding simpler and more cost-effective surrogate markers due to its complex, time-consuming, and expensive nature. Multiple studies have confirmed the role of IR in the pathogenesis of T2DM, dyslipidemia, and obesity in the general population, as well as its close association with the development of cardiovascular diseases. As IR intensifies, the risk of clustering metabolic risk factors also gradually increases27,28, The TyG index, based on fasting triglyceride and glucose levels, is a surrogate marker of IR and is closely related to various metabolic diseases. Notably, TyG levels are strongly associated not only with IR but also with adverse lipid profiles and disrupted lipid metabolism29, further enhancing its relevance in metabolic health assessments. A meta-analysis of 15 cohort studies found a significant positive association between the TyG index and the risk of T2DM30. Wang et al. discovered that the TyG index is a valuable predictive indicator for assessing the severity of coronary heart disease, particularly in prediabetic patients31. In non-diabetic populations, participants with higher TyG index values exhibit an increased likelihood of impaired cardiovascular health32. Furthermore, the TyG index has been positively associated with the risk of cardiovascular diseases in several studies, including those focused on metabolic syndrome, hypertension, arterial stiffness, carotid atherosclerosis, coronary artery disease, and coronary artery calcification33–39. The TyG index is an effective predictor of metabolic syndrome components, such as obesity and hypertension, and its strong association with adverse lipid profiles makes it a valuable tool for assessing broader metabolic risks across diverse clinical settings40. Evidence from a meta-analysis involving 49,325 participants from 13 observational studies indicates that the pooled sensitivity and specificity of the TyG index for MetS screening are both above 80%41. Furthermore, a large-scale longitudinal study has demonstrated that the TyG index also possesses high predictive value in determining the risk of death from MetS in the US population 42. Research has also confirmed the value of the TyG index in the study of CKD. In a cross-sectional survey involving 18,078 American adults, the prevalence of CKD and proteinuria was significantly positively correlated with TyG index levels43,44. A Japanese cohort study found a link between higher TyG index levels and increased prevalence of CKD45, Similarly, an Austrian study reported a positive association between the TyG index and end-stage kidney disease (ESKD)46. Patients with CKM syndrome exhibit a complex metabolic profile that includes conditions such as obesity and diabetes. These comorbidities significantly elevate insulin resistance, which in turn increases the TyG index. This vicious cycle exacerbates the progression of CKM syndrome. Our study has identified a significant positive association between the risk of the TyG index and advanced CKM syndrome. This finding offers a novel perspective for disease management, highlighting the importance of managing TyG index levels as part of strategies aiming at reducing the likelihood of advanced CKM syndrome. In the early stages of CKM syndrome, an elevated TyG index can serve as an indicator of underlying metabolic abnormalities and increased insulin resistance, underscoring the importance of early identification and intervention. Timely management of the TyG index may help slow disease progression. In the advanced stages of CKM syndrome, the TyG index may serve as a marker of disease severity and potential irreversibility.
Our subgroup analyses observed a significant interaction effect between sex and age, suggesting a complex interplay of these variables in disease development. This finding underscores the importance of considering participants patient characteristics when assessing the risk of advanced CKM syndrome. In line with our results, a meta-analysis of 87 studies reported that women with metabolic syndrome are more prone to developing cardiovascular disease compared to men47. Furthermore, among participants with impaired glucose tolerance, women exhibit a heightened risk of coronary heart disease relative to men48. These findings partially elucidate the higher prevalence of cardiovascular risk factors among women, which aligns with our current observations49. With advancing age, human metabolic capabilities gradually decline, potentially leading to weight gain, accumulation of adipose tissue, and reduction in muscle mass. This process is associated with an increased risk of developing chronic diseases, thereby exacerbating the progression of CKM syndrome. This study suggests that HDL-C may play a pivotal role in modulating the relationship between the TyG index and CKM syndrome. Individuals with low HDL-C levels appear to face an elevated risk due to the TyG index’s impact on CKM progression, while higher HDL-C levels seem to buffer and stabilize this risk. This aligns with previous findings linking HDL dysfunction to risks of metabolic syndrome, cardiovascular disease, and diabetes, and recognizing HDL-C as an independent predictor of diabetes in those with metabolic syndrome50,51. HDL-C shows potential as a clinical target to reduce CKM risk, particularly for individuals with higher TyG indices. While interactions in other subgroups did not reach statistical significance, certain trends were observed. Given the limitations in sample size, further studies with larger cohorts are needed to clarify these potential associations on CKM syndrome.
This study is based on the NHANES database, utilizing a nationally representative sample of the US population. Rigorous multi-stage probability sampling methods were employed, significantly enhancing the reliability and generalizability of the research results. However, this study also has some limitations. Firstly, although adjustments have been made for multiple potential confounding covariates, the possibility of residual confounding still exists, which may impact the results. Secondly, this study is primarily based on participants from the United States, and geographical constraints may limit the universality and applicability of the research results due to factors such as living environment and dietary habits. Thirdly, only baseline values of the TyG index were collected in the NHANES study, and there is a lack of long-term data on participants’ TyG index status, which may underestimate the assessment of the association between the TyG index and advanced CKM syndrome risk. Additionally, Our CKM staging definitions were adapted from AHA guidelines; however, due to data availability constraints within NHANES, certain staging criteria were adjusted. This study relied on self-reported variables and available biomarkers in NHANES, lacking some subclinical cardiovascular indicators and related disease information, which may affect the consistency and accuracy of CKM staging. Future research should further explore the specific mechanisms linking the TyG index and the risk of advanced CKM syndrome.
Conclusion
In conclusion, there is a significant positive association between the risk of the TyG index and advanced CKM syndrome. Our findings suggest that the TyG index may serve as a practical tool for early risk identification, stratification, and management guidance for CKM syndrome, particularly in primary care settings.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors express gratitude to the participants and staff of the NHANES for their invaluable contributions to this study.
Author contributions
The study design was conceived by Z.H and LL.W, and LL.W organized the data, conducted the analyses, and wrote and edited the manuscript. Z.H supervised the study. All authors have reviewed and approved the final version of the manuscript.
Data availability
The data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. NHANES data is publicly available and can be accessed at the following URL: https://www.cdc.gov/nchs/nhanes/index.htm. The specific datasets used in this analysis include demographic, laboratory, and examination data files from the respective survey years.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
The National Center for Health Statistics and the Ethics Review Board approved the protocol for NHANES, and all participants provided written informed consent. The authors have disclosed no conflicts of interest.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
The data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. NHANES data is publicly available and can be accessed at the following URL: https://www.cdc.gov/nchs/nhanes/index.htm. The specific datasets used in this analysis include demographic, laboratory, and examination data files from the respective survey years.