Abstract
We investigated the correlation between Triglyceride-glucose (TyG) index and cardiovascular disease (CVD) mortality in type 2 diabetes mellitus (T2DM) population across different obesity classications using a cohort study. We analyzed 7867 T2DM participants from the National Health and Nutrition Examination Survey 1999–2018, categorizing them into obese or non-obese group by body mass index (BMI) and waist circumference (WC). Cox regression models were used to estimate the correlation between TyG index and CVD mortality risk, comparing the results across the two obesity classifications. Over a 9.1-year follow-up, 691 CVD deaths occurred. Among non-obese T2DM participants (BMI-defined), the hazard ration for CVD mortality was 1.73 in the fourth quartile group of TyG index compared with the first quartile group. Conversely, among obese T2DM participants (WC-defined), the fourth quartile group of TyG index held a 1.51-fold risk of CVD mortality compared with the first quartile group. The association between obesity and higher CVD risk was observed in WC-defined obesity but not in BMI-defined obesity. A totally opposite relationship appeared between TyG index and CVD mortality based on how obesity was defined using BMI or WC in the T2DM participants, suggesting a reevaluation of BMI’s accuracy in predicting mortality risk.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-78365-8.
Keywords: Triglyceride-glucose index, Cardiovascular diasease mortality, Obesity, Type 2 diabetes mellitus
Subject terms: Diabetes, Obesity
Introduction
Cardiovascular disease (CVD) is a substantial contributor to death in people with type 2 diabetes mellitus (T2DM). T2DM patients had a twofold greater risk of all-cause mortality and a threefold higher risk of CVD-related mortality than non-diabetics1. Therefore, lowering the risk of CVD in T2DM patients is of critical importance.
Insulin resistance (IR) constitutes one of the primary mechanisms underlying T2DM. More and more evidence suggest that IR and its associated diseases accelerate the development of CVD in T2DM patients2. Homeostatic model assessment of insulin resistance (HOMA-IR) and TyG index are biomarkers of IR and used to evaluate CVD risk. Multiple investigations have indicated that TyG index may serve as a superior predictor of the risk of T2DM and CVD3–5. TyG index is calculated using the formula: Ln(fasting triglycerides (mg/dl)×fasting blood glucose (mg/dl)/2)6. As TyG is a simple and easily calculable index, it has become an important tool to evaluate the risks of cardiometabolic disorders. Obesity is also an important risk factor for adverse cardiovascular outcomes. T2DM patients with obesity have more obvious IR and significantly increased TyG index7–10. However, an increasing body of research has indicated that under different obesity classification criteria, obese people with T2DM do not always maintain a high risk of adverse cardiovascular outcomes. Yang observed a positive linear association between body mass index (BMI) and the incidence of CVD but a non-linear correlation with CVD-related mortality in T2DM patients11. The lowest mortality risk was observed around a BMI of approximately 28.4 kg/m2, with increased risks for mortality at both lower and higher BMI values according to their meta-analysis of previous clinical studies. Xing discovered that waist circumference (WC) is linked to major adverse cardiovascular events in male people with T2DM, but not in female people12. Li found that CVD-related mortality increased monotonically with the increase in a body shape index (ABSI) in T2DM patients13. Therefore, we believe that there is a correlation between T2DM individuals’ obesity levels and death from CVD. Furthermore, distinct classification criteria may provide varying outcomes and possess their own limitations.
However, there is insufficient research on the correlation between TyG index and CVD-related mortality risk in T2DM patients with or without obesity under different obesity classification criterias at present. As a consequence, we categorized the T2DM participants into non-obese and obese group based on different classifications (BMI/WC) to examine the correlation between TyG index and CVD-related mortality in the above groups of participants.
Research design and methods
Study population
In this study conducted using a cross-sectional design, we integrated data from all National Health and Nutrition Examination Surveys (NHANES) conducted between 1999 and 2018. After excluding pregnant females (N = 43), participants without monitoring period or with a monitoring period of 0 month (N = 21) and participants lacking TyG index (N = 812), a total of 7867 T2DM participants (aged ≥ 20 years) were enrolled in the final data analysis. There were 4144 males and 3723 females.
Measurement and definitions
Definition of T2DM
Diabetes was defined based on the recent American Diabetes Association (ADA) recommendation, where the condition was considered met if any of the following conditions were met14: (1) Prior diagnosis of T2DM by physicians (2) HbA1c levels were at least 48 mmol/mol (6.5%) (3) Fasting blood glucose levels were at least 7.0 mmol/L (126 mg/dL) (4) Any blood glucose levels were at least 200 mg/dL (11.1 mmol/L) (5) Postprandial 2 h plasma glucose levels were at least 11.1 mmol/L (200 mg/dL) after a standard 75-g oral glucose tolerance test (6) Treated with insulin or hypoglycemic medications. People potentially diagnosed with type 1 diabetes mellitus (T1DM) were identified as individual under 20 years of age who exclusively received insulin therapy15.
Measurement of main variables
Height (m) and weight (kg) were measured in the Mobile Examination Center (MEC) by trained health technicians. BMI was computed as weight divided by the square of height (kg/m2)16. Obesity was considered when BMI ≥ 30 kg/m2. WC was measured to the nearest 0.1 cm at the midpoint between the bottom of the rib cage and the uppermost border of the iliac crests, with participants in a standing position and using an inelastic tape measure at the end of exhalation16. Abdominal obesity was defined as WC measurements ≥ 102 cm for males and ≥ 88 cm for females. TyG index is determined by taking the natural logarithm of the product of fasting triglycerides (mg/dl) and fasting blood glucose, divided by two6.
Ascertainment of mortality
We obtained mortality data directly from NHANES-linked mortality files17. Probabilistic matching was performed by the National Centre for Health Statistics to connect NHANES data with death certificate records from the National Death Index (NDI) records to assess mortality status17. CVD-related mortality was defined as mortality attributed to heart diseases or cerebrovascular diseases, as documented in prior studies17.
Definition of other variables
For the demographic variables, race was categorized as Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black and Other Race. Education levels were classified as less than high school, high school, more than high school and not recorded. The poverty to income ratio (PIR) (≤ 1, 1< PIR<4 and ≥ 4) was computed by dividing family (or individual) income by the poverty guidelines applicable to the survey year and state18. Smoking habit was categorized as follows: current smokers (average of ≥ 1 cigarette per day), past smokers (average of < 1 cigarette per day or ≥ 100 cigarettes in their lifetime but currently not smoking), and never smokers ( fewer than 100 cigarattes in their lifetime or never smoked). Alcohol consumption was categorized into three categories based on whether respondents reported consuming at least 12 drinks per year and whether their responses were recorded19. Dietary intake, including total energy, carbonhydrate, protein and fat, was evaluated using two 24-h recalls (one conducted in person and another by telephone 3–10 days later)19. Participants were classified into two groups based on whether they had total physical activity ≥ 500 MET- min/week according to established guidelines20. eGFR was calculated by the CKD-EPI creatinine equation. The presences of hypertension, hypercholesterolemia, CVD and cancer were defined by self-reporting21.
Statistical processing and analyses
Unavailable categorical covariates were defined as a separate category as necessary, while unavailable continuous covariates were imputed with group means to mitigate potential bias from missing data. During the initial data assessment phase, the study cohort was stratified into non-obese group and obese group based on BMI or WC. We utilized means ± SEs to summarize continuous variables, and presented numbers (percentages) to depict categorical variables. A t-test or non-parametric test was used to analyze the differences in continuous variables among groups. The categorical variables were evaluated using the chi-square test. TyG index was considered as a variable with continuous data (natural log-transformed) and stratified into four quartile groups(Q1, Q2, Q3 and Q4). Cox proportional hazards models were employed to determine the hazard ratios (HRs) and 95% confidence intervals (CIs) of the TyG index in relation to the CVD-related mortality, adjusting for gender, age, race, PIR, educational level, smoking status, alcohol consumption, physical activity, diabetes duration, CVD, chronic kidney disease (CKD), cancer, high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), and BMI. We utilized a restricted cubic spline regression model to investigate the possible nonlinear relationship between TyG index and CVD-related mortality. Subsequently, a subgroup analysis of participants with or without CVD was performed. Sensitivity analyses were also performed with additional adjustment for the diet-related index and renal function index. Furthermore, we excluded the participants who died within the first 2 years of the survey (n = 781) to mitigate potential bias. All statistical analyses were conducted using R (The R Foundation; version 4.1.1) and EmpowerStats software (X&Y Solutions, Inc., Boston, MA, United States).
Results
Study population characteristics
Tables 1 and 2 detail the clinical profiles of the 7867 participants suffering from T2DM included in our study. The participant cohort was classified into two groups based on BMI or WC. TyG index remained consistently high in the obese group whatever the method of estimation. In addition, age, sex, race, education level, smoking status, alcohol consumption, uric acid, blood creatinine, diabetes duration, and history of hypertension remained significantly different in either classification. Except that age was lower in the obese-group defined by BMI and higher in the obese-group defined by WC, the above indicators were consistent. The prevalence of CVD did not show significantly differences between the two groups defined by BMI; however, this was notably elevated among participants classified as obese based on WC.
Table 1.
Characteristics of the T2DM participants defined by BMI.
| Characteristic | T2DM participants defined by BMI | p value | |
|---|---|---|---|
| Non-obese (n = 3569) | Obese (n = 4298) | ||
| Age (years) | 60.82 ± 0.36 | 56.50 ± 0.28 | < 0.001 |
| Male (%) | 2128 (59.62) | 1441 (40.38) | < 0.001 |
| Race (%) | < 0.001 | ||
| Mexican American | 735 (20.59) | 871 (20.27) | |
| Other Hispanic | 336 (9.41) | 404 (9.40) | |
| Non-Hispanic White | 1250 (35.02) | 1627 (37.85) | |
| Non-Hispanic Black | 709 (19.87) | 1180 (27.45) | |
| Other Race | 539 (15.11) | 216 (5.03) | |
| Education level (%) | < 0.001 | ||
| Less than high school | 1390 (38.95) | 1493 (34.74) | |
| High school | 793 (22.21) | 982 (22.85) | |
| More than high school | 1379 (38.64) | 1819 (42.32) | |
| Not recorded | 7 (0.20) | 4 (0.09) | |
| PIR (%) | 0.087 | ||
| ≤1 | 732 (20.51) | 891 (20.73) | |
| >1, <4 | 1816 (50.88) | 2201 (51.21) | |
| ≥4 | 633 (17.74) | 809 (18.82) | |
| Not recorded | 388 (10.87) | 397 (9.24) | |
| Smoking status (%) | 0.001 | ||
| Current | 620 (17.37) | 622 (14.47) | |
| Past | 1246 (34.91) | 1500 (34.90) | |
| Never | 1703 (47.72) | 2176 (50.63) | |
| Alcohol consumption (%) | < 0.001 | ||
| Yes | 2029 (56.85) | 2410 (56.07) | |
| No | 1265 (35.44) | 1637 (38.09) | |
| Not recorded | 275 (7.71) | 251 (5.84) | |
| Nutritional intake | |||
| Total energy (kcal/day) | 1893.11 ± 19.19 | 1977.48 ± 18.58 | < 0.001 |
| Carbohydrate (% of energy) | 48.47 ± 0.23 | 46.97 ± 0.20 | < 0.001 |
| Protein (% of energy) | 16.57 ± 0.12 | 16.74 ± 0.09 | 0.232 |
| Fat (% of energy) | 34.35 ± 0.19 | 36.03 ± 0.17 | < 0.001 |
| Physical activity (%) | 0.065 | ||
| < 500 (MET-min/week) | 1884 (52.79) | 2190 (50.95) | |
| ≥ 500 (MET-min/week) | 381 (10.68) | 429 (9.98) | |
| Not recorded | 1304 (36.53) | 1679 (39.07) | |
| TyG index | 9.27 ± 0.02 | 9.43 ± 0.02 | < 0.001 |
| BMI (kg/m2) | 26.07 ± 0.06 | 36.95 ± 0.14 | < 0.001 |
| Waist circumference (cm) | 96.18 ± 0.22 | 119.30 ± 0.29 | < 0.001 |
| FPG (mmol/L) | 8.52 ± 0.09 | 8.48 ± 0.07 | 0.764 |
| HbA1c (%) | 7.01 ± 0.04 | 7.18 ± 0.03 | 0.003 |
| SBP (mmHg) | 130.94 ± 0.50 | 130.06 ± 0.42 | 0.175 |
| TC (mmol/L) | 4.97 ± 0.03 | 4.92 ± 0.02 | 0.250 |
| TG (mmol/L) | 2.20 ± 0.06 | 2.39 ± 0.04 | 0.008 |
| UA (umol/L) | 322.26 ± 2.17 | 349.61 ± 2.24 | < 0.001 |
| Scr (umol/L) | 87.47 ± 1.27 | 82.36 ± 0.74 | < 0.001 |
| eGFR (ml/min/1.73 m2) | 79.52 ± 0.66 | 82.36 ± 0.56 | < 0.001 |
| Diabetes duration ≥ 10 years (%) | 1008 (28.24) | 1174 (27.32) | < 0.001 |
| Take insulin | 566 (15.86%) | 858 (19.96%) | < 0.001 |
| Hypertension (%) | 1982 (55.53) | 2923 (68.01) | < 0.001 |
| Hypercholesterolemia (%) | 1821 (51.02) | 2271 (52.84) | 0.109 |
| CKD (%) | 931 (26.09) | 1021 (23.76) | 0.017 |
| CVD (%) | 757 (21.21) | 977 (22.73) | 0.105 |
| Cancer (%) | 460 (12.89) | 500 (11.63) | 0.090 |
Data were adjusted for survey weights of NHANES. Continuous variables are presented as means ± SEs. Categorical variables are presented as numbers (percentages).
T2DM type 2 diabetes mellitus, BMI body mass index, PIR poverty-income ratio, TyG index triglyceride-glucose index, FPG fasting plasma glucose, HbA1c hemoglobin A1c, SBP systolic blood pressure, TC total cholesterol, TG triglycerides, UA uric acid, Scr serum creatinine, eGFR estimate glomerular filtration rate, CKD chronic kidney disease, CVD cardiovascular disease.
Table 2.
Characteristics of the T2DM participants defined by WC.
| Characteristic | T2DM participants defined by WC | p value | |
|---|---|---|---|
| Non-obese (n = 1833) | Obese (n = 6034) | ||
| Age (years) | 57.28 ± 0.52 | 58.54 ± 0.26 | 0.024 |
| Male (%) | 1428 (77.91) | 2716 (45.01) | < 0.001 |
| Race (%) | < 0.001 | ||
| Mexican American | 391 (21.33) | 1215 (20.14) | |
| Other Hispanic | 181 (9.87) | 559 (9.26) | |
| Non-Hispanic White | 525 (28.64) | 2352 (38.98) | |
| Non-Hispanic Black | 376 (20.51) | 1513 (25.07) | |
| Other Race | 360 (19.65) | 395 (6.55) | |
| Education level (%) | 0.011 | ||
| Less than high school | 717 (39.12) | 2166 (35.90) | |
| High school | 379 (20.68) | 1396 (23.14) | |
| More than high school | 732 (39.93) | 2466 (40.86) | |
| Not recorded | 5 (0.27) | 6 (0.10) | |
| PIR (%) | 0.180 | ||
| ≤ 1 | 367 (20.02) | 1256 (20.82) | |
| > 1, <4 | 937 (51.12) | 3080 (51.04) | |
| ≥ 4 | 323 (17.62) | 1119 (18.54) | |
| Not recorded | 206 (11.24) | 579 (9.60) | |
| Smoking status (%) | < 0.001 | ||
| Current | 371 (20.24) | 871 (14.43) | |
| Past | 634 (34.59) | 2112 (35.01) | |
| Never | 828 (45.17) | 3051 (50.56) | |
| Alcohol consumption (%) | < 0.001 | ||
| Yes | 1112 (60.67) | 3327 (55.14) | |
| No | 571 (31.15) | 2331 (38.63) | |
| Not recorded | 150 (8.18) | 376 (6.23) | |
| Nutritional intake | |||
| Total energy (kcal/day) | 2021.44 ± 26.40 | 1922.43 ± 16.50 | 0.002 |
| Carbohydrate (% of energy) | 48.26 ± 0.30 | 47.42 ± 0.18 | 0.020 |
| Protein (% of energy) | 16.78 ± 0.16 | 16.64 ± 0.08 | 0.403 |
| Fat (% of energy) | 33.91 ± 0.25 | 35.70 ± 0.15 | < 0.001 |
| Physical activity (%) | < 0.001 | ||
| < 500 (MET-min/week) | 1031 (56.25) | 3043 (50.43) | |
| ≥ 500 (MET-min/week) | 226 (12.33) | 584 (9.68) | |
| Not recorded | 576 (31.42) | 2407 (39.89) | |
| TyG index | 9.20 ± 0.03 | 9.40 ± 0.02 | < 0.001 |
| BMI (kg/m2) | 24.72 ± 0.10 | 34.42 ± 0.13 | < 0.001 |
| Waist circumference (cm) | 90.30 ± 0.27 | 114.70 ± 0.27 | < 0.001 |
| FPG (mmol/L) | 8.64 ± 0.12 | 8.46 ± 0.06 | 0.152 |
| HbA1c (%) | 7.02 ± 0.06 | 7.13 ± 0.03 | 0.060 |
| SBP (mmHg) | 129.52 ± 0.62 | 130.65 ± 0.38 | 0.118 |
| TC (mmol/L) | 4.96 ± 0.05 | 4.94 ± 0.02 | 0.743 |
| TG (mmol/L) | 2.12 ± 0.10 | 2.36 ± 0.04 | 0.027 |
| UA (umol/L) | 318.28 ± 3.15 | 343.41 ± 1.88 | < 0.001 |
| Scr (umol/L) | 89.17 ± 1.91 | 83.27 ± 0.70 | 0.003 |
| eGFR (ml/min/1.73 m2) | 82.04 ± 1.12 | 80.96 ± 0.48 | 0.376 |
| Diabetes duration ≥ 10 years (%) | 489 (26.68) | 1693 (28.06) | < 0.001 |
| Take insulin | 278 (14.76%) | 1146 (18.99%) | < 0.001 |
| Hypertension (%) | 877 (47.85) | 4028 (66.76) | < 0.001 |
| Hypercholesterolemia (%) | 863 (47.08) | 3229 (53.51) | < 0.001 |
| CKD (%) | 434 (23.68) | 1518 (25.16) | 0.199 |
| CVD (%) | 355 (19.37) | 1379 (22.85) | 0.002 |
| Cancer (%) | 191 (10.42) | 769 (12.74) | 0.008 |
Data were adjusted for survey weights of NHANES. Continuous variables are presented as means ± SEs. Categorical variables are presented as numbers (percentages).
T2DM type 2 diabetes mellitus, BMI body mass index, PIR poverty-income ratio, TyG index triglyceride-glucose index, FPG fasting plasma glucose, HbA1c hemoglobin A1c, SBP systolic blood pressure, TC total cholesterol, TG triglycerides, UA uric acid, Scr serum creatinine, eGFR estimate glomerular filtration rate, CKD chronic kidney disease, CVD cardiovascular disease.
Association of TyG Index with CVD-related mortality
The average follow-up duration for the entire cohort was 9.1 years. By the end of the monitoring period, 691 participants had died from CVD. Among 3,569 obese and 4,298 non-obese T2DM participants defined by BMI, 316 and 375 participants died due to CVD respectively. Among obese and non-obese T2DM participants defined by WC, 524 and 167 participants died due to CVD respectively.
Cox regression analyses were performed to investigate the correlation between TyG index and mortality related to CVD in participants with T2DM. The results showed that in the non-obese group of T2DM participants defined by BMI, the HR and 95%CI of CVD-related mortality across the quartiles of TyG index was 1.0 (reference), 1.05 (0.77, 1.43), 1.05 (0.77, 1.43) and 1.73 (1.26, 2.40) ( p < 0.001). When examining TyG index as a variable with continuous values, each 1 unit increase in TyG index was associated with a 36% higer risk of mortality related to CVD. Nevertheless, in the obese T2DM group defined by BMI, TyG index, either as continuous or categorical variables, showed no association with CVD-related mortality (Table 3).
Table 3.
Multivariate Cox regression analysis of TyG index with CVD-related mortality among 7867 T2DM participants defined by BMI.
| Non-obese participants defined by BMI | Obese participants defined by BMI | |||
|---|---|---|---|---|
| HR (95%CI)a | p value | HR (95%CI)a | p value | |
| CVD-related mortality | ||||
| Q1 | 1.00 | 1.00 | ||
| Q2 | 1.05 (0.77, 1.43) | 0.751 | 0.91 (0.65, 1.26) | 0.568 |
| Q3 | 1.05 (0.77, 1.43) | 0.755 | 0.99 (0.71, 1.37) | 0.947 |
| Q4 | 1.73 (1.26, 2.40) | < 0.001 | 1.18 (0.83, 1.67) | 0.358 |
| Per 1 unit increment | 1.36 (1.17, 1.58) | < 0.001 | 1.13 (0.95, 1.34) | 0.166 |
BMI body mass index, HR hazard ratio, CI confidence interval, CVD cardiovascular disease.
aAdjusted for gender, age, race, PIR, educational level, smoking status, alcohol consumption, physical activity, diabetes duration, CVD, CKD, cancer, HDL-C, SBP and BMI.
When grouped by WC, the outcome was strikingly opposite. In obese group of T2DM participants defined by WC, the HR of CVD-related mortality across the quartiles of TyG index was 1.0 (reference), 1.12 (0.86, 1.45), 1.23 (0.95, 1.59) and 1.51 (1.14, 1.99) (p = 0.004). When examining TyG index as a variable with continuous values, every 1 unit increase in TyG index was associated with a 28% increase in CVD-related mortality. No significant correlations were observed in non-obese T2DM participants defined by WC (Table 4).
Table 4.
Multivariate Cox regression analysis of TyG index with CVD-related mortality among 7867 T2DM participants defined by WC.
| Non-obese participants defined by WC | Obese participants defined by WC | |||
|---|---|---|---|---|
| HR (95%CI)a | p value | HR (95%CI)a | p value | |
| CVD-related mortality | ||||
| Q1 | 1.00 | 1.00 | ||
| Q2 | 0.83 (0.51, 1.32) | 0.426 | 1.12 (0.86, 1.45) | 0.408 |
| Q3 | 0.71 (0.44, 1.13) | 0.149 | 1.23 (0.95, 1.59) | 0.110 |
| Q4 | 1.40 (0.88, 2.22) | 0.154 | 1.51 (1.14, 1.99) | 0.004 |
| Per 1 unit increment | 1.22 (0.98, 1.52) | 0.081 | 1.28 (1.12, 1.46) | < 0.001 |
WC waist circumference, HR hazard ratio, CI confidence interval, CVD cardiovascular disease.
aAdjusted for gender, age, race, PIR, educational level, smoking status, alcohol consumption, physical activity, diabetes duration, CVD, CKD, cancer, HDL-C, SBP and BMI.
Non-linearity of TyG Index with CVD-related mortality
Non-linear associations were noted between the log-transformed TyG index and CVD-related mortality in both non-obese T2DM participants categorized by BMI and obese participants categorized by WC after covariate adjustments (log likelihood ratio test, both p < 0.05) (Fig. 1). Based on the results of the two-piecewise Cox proportional hazards regression models, we obtained the inflection points for CVD-related mortality as 8.5 and 8.2, respectively. When TyG index < 8.5, CVD-related mortality decreased with the increase of TyG index among non-obese T2DM participants defined by BMI. When TyG index < 8.2, no apparent correlation observed among obese T2DM participants defined by WC. When TyG index ≥ 8.5/8.2, the increase of TyG index was significantly positively associated with CVD-related mortality (Table 5). With regard to subgroup analysis, the results obtained remained consistent with the above study. The associations of TyG index with CVD-related mortality were not modified by CVD comorbidities (Table 6).
Fig. 1.
The restricted cubic regression between TyG index with CVD-related mortality among the non-obese T2DM participants defined by BMI (A) or among the obese T2DM participants defined by WC (B). Models are adjusted for gender, age, race, PIR, educational level, smoking status, alcohol consumption, physical activity, diabetes duration, CVD, CKD, cancer, HDL-C, SBP and BMI.
Table 5.
Threshold effect analysis of TyG index on CVD-related mortality in non-obese T2DM participants defined by BMI and obese T2DM participants defined by WC.
| The non-obese paticipants defined by BMI | The obese participants defined by WC | |
|---|---|---|
| CVD-related mortality | ||
| Fitting by the two-piecewise linear model | ||
| Inflection point | 8.5 | 8.2 |
| TyG index < 8.5/8.2 | 0.41 (0.24, 0.71) 0.002 | 0.37 (0.13, 1.07) 0.067 |
| TyG index ≥ 8.5/8.2 | 1.58 (1.34, 1.85) <0.001 | 1.32 (1.15, 1.51) <0.001 |
| p value for Log-likelihood ratio | < 0.001 | 0.04 |
Adjusted for gender, age, race, PIR, educational level, smoking status, alcohol consumption, physical activity, diabetes duration, CVD, CKD, cancer, HDL-C, SBP and BMI.
BMI body mass index, WC waist circumference, CVD cardiovascular disease, TyG index triglyceride-glucose index.
Table 6.
TyG index and risk for CVD-related mortality among 7867 T2DM participants, stratified by obesity and CVD.
| Groups | N | HR (95%CI) | p value |
|---|---|---|---|
| Obese T2DM patients defined by BMI | |||
| Without CVD | 3321 | 1.11 (0.87, 1.41) | 0.405 |
| With CVD | 977 | 1.16 (0.91, 1.49) | 0.239 |
| Non-obese T2DM patients defined by BMI | |||
| Without CVD | 2812 | 1.31 (1.08, 1.60) | 0.007 |
| With CVD | 757 | 1.39 (1.11, 1.75) | 0.004 |
| Obese T2DM patients defined by WC | |||
| Without CVD | 4655 | 1.26 (1.06, 1.50) | 0.010 |
| With CVD | 1379 | 1.28 (1.04, 1.56) | 0.018 |
| Non-obese T2DM patients defined by WC | |||
| Without CVD | 1478 | 1.25 (0.91, 1.72) | 0.166 |
| With CVD | 355 | 1.25 (0.91, 1.72) | 0.162 |
HR hazard ratio, CI confidence interval, T2DM Type 2 diabetes mellitus, BMI body mass index, CVD cardiovascular disease, WC waist circumference.
aAdjusted for gender, age, race, PIR, educational level, smoking status, alcohol consumption, physical activity, diabetes duration, CKD, cancer, HDL-C and SBP.
Sensitivity analyses
We performed additional adjustments for total energy intake (kcal), carbohydrate percentage (Car%), and fat percentage (Fat%) to account for potential confounding effects of dietary habits. Despite these adjustments, the relationship between TyG index and CVD-related mortality remained largely unchanged, indicating that dietary factors did not significantly influence the observed association (as detailed in Supplementary Tables 1 and Table 2). We also adjusted for renal function markers such as uric acid (UA), serum creatinine (Scr), and estimated glomerular filtration rate (eGFR) to control for the impact of renal health on CVD mortality risk. The results showed consistency, further validating the robustness of our findings. After further excluding paiticipants who died within two years of follow-up, the overall results were not significantly changed (Supplementary Table 3).
Discussion
T2DM is a metabolic disorder with a high prevalence, which has become a global health problem. Reducing the exponential increase in CVD-related mortality remains challenging. IR is a critical factor in the onset of CVD among people with T2DM2. TyG index, derived from TG-FPG, is currently being studied as a reliable alternative to IR. Considering the relationship between obesity and IR, we aim to elucidate the predictive value of TyG index for CVD-related mortality in T2DM people with different types of obesity.
We obtained the opposite conclusion regarding the correlation between TyG index and CVD-related mortality under different obesity diagnostic criterias. In BMI-defined non-obesity, TyG index was positively associated with CVD-related mortality, whereas in WC-defined obesity, TyG index was significantly associated with CVD-related mortality. An overview map of the results is presented in Fig. 2. Based on the findings from this study, the following points need to be considered. First, the limitations of BMI as the obesity classification index. BMI cannot scientifically represent obesity status. In fact, it cannot distinguish between body fat and lean mass, as well as central and peripheral fat22. That is the so-called obesity paradox we often discuss nowadays. For instance, athletes with increased muscle mass may be misclassified as obese when using BMI alone to diagnose obesity22. Similar with our results, a recent retrospective study showed the increased cardiometabolic risk in children and adolescents without obesity defined by BMI but with obesity defined by BF%23. Another study also demonstrated that BMI-classified obesity conferred a false mortality risk in people with CKD when compared with different indicators defining obesity24. Beside of this, we speculated that the non-obese participants defined by BMI might be metabolically obese but had normal weight (MONW). In the baseline characteristics of our study, BMI-defined non-obese T2DM participants were more likely to have a worse metabolic status such as higher prevalence of current smokers, CKD, and were older than obese T2DM participants. Second, WC might serve as a better indicator for diagnosing abdominal obesity in diabetes and predicting CVD risk25,26. It can detect individuals with lower body weight but increased ectopic fat accumulation27,28. Compared with non-obese T2DM participants, which includes a significant number of MONW individuals, non-obese T2DM participants classified based on WC should have less abdominal fat and mild IR. Numerous studies have proved that WC is an independent risk factor for obesity-related CVD in both European, American and Asian populations29–32. A recent meta-analysis from the British Medical Journal (BMJ) also confirmed the notion that central fatness index were positively associated with higher mortality risk, independent of overall adiposity defined by BMI33. And finally, TyG index is a more precise indicator of IR in people with central obesity than general obesity34,35. TyG index is proposed as an alternative to IR assessment and calculated from triglycerides and fasting glucose levels. In central obesity populations, the abnormalities in lipid and glucose metabolism may be more pronounced, potentially linked to exacerbated visceral fat inflammation, abnormal adipokine secretion, and hepatic fat accumulation36,37. Higher levels of IR are closely associated with an increased risk of CVD38,39.
Fig. 2.
Overview map of our research.
Therefore, in the centrally obese T2DM population, TyG index can be a powerful indicator to assess CVD risk due to its special fat distribution characteristics and metabolic abnormalities. Consistent with our findings, research has indicated that TyG index mediated the correlation between general and central obesity and CVD risk, with a stronger predictive value in central obesity populations40. Overall, our study suggested that in clinical practice, relying solely on the BMI definition without considering fat distribution patterns when using the simple TyG index to predict CVD risk related to obesity in T2DM individuals could lead to misleading outcomes.
Our results also showed a non-linear association between TyG index and CVD-related mortality in obese T2DM participants defined by WC. Only when TyG index ≥ 8.2, positive correlation can be observed. The results suggest us that for people with central obesity, controlling the level of TyG index within 8.2 may be valuable for reducing the risks of CVD-related mortality. Although we have also obtained the corresponding TyG index risk cut point in non-obese T2DM participants defined by BMI, considering the non-obese population and the defects of BMI itself, the clinical significance may be less significant .
Innovation points and limitations
In our conventional view, obese people with T2DM should have a greater CVD risk than non-obese people since pronounced IR. However, our study is the first to demonstrate that when the classification criteria of obesity were different, TyG index, an IR indicator, showed significant difference with CVD-related mortality in obese people. Again, this demonstrated that the limitations of BMI as a classification criterion for obesity in research and clinical practice. Further, finding of the threshold of TyG index for predicting the CVD-related death in obese T2DM patients by WC definition might provide guidance for clinical practice. Certainly, there were some limitations in our study. Firstly, obesity was not distinguished by BF%, which may be more accurate than BMI and WC. Secondly, we used the fasting TG level captured at one time to measure TyG index which could not objectively represent the IR status of participants and need further investigations in the future. Additionally, the study did not provide data on weight trajectory during the follow-up period, making it impossible to rule out the potential impact of weight loss due to uncontrolled diabetes on cardiovascular disease risk.
Conclusion
We found that under different obesity classification criterias for T2DM participants (systemic obesity defined by BMI and central obesity defined by WC), the relationship between TyG index and CVD-related mortality showed a completely opposite trend. BMI should be used cautionly when evaluating CVD risk in obese individuals.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the NHANES database for providing the data source for this study.
Author contributions
HL and FG, design and revising it critically for important intellectual content; HH, analysis, interpretation of the data and drafting the work; JT, acquisition and drafting the work; JX, acquisition and revising it critically for important intellectual content; QC and MC, interpretation and revising it critically for important intellectual content. All authors approved the submitted version and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding
FG is supported by the National Natural Science Foundation of China (82104786). HL is supported by the National Natural Science Foundation of China (82074381,81874434), Shanghai Municipal Science and Technology Commission Research Program (22S21900400) and Shanghai Key Laboratory of Traditional Chinese Clinical Medicine (20DZ2272200). MC is supported by Shanghai Municipal Health Commission’s Traditional Chinese Medicine Research Project (2022QN092).
Data availability
The data that support the findings of this study are openly available in NHANES at http://www.cdc.gov/nchs/nhanes/index.htm.
Declarations
Competing interests
The authors declare no competing interests.
Ethical approval
The NCHS Ethics Review Board has approved the implementation of NHANES, and all participants have provided written informed consent.
Informed consent
The written informed consent of all subjects was obtained following the Declaration of Helsinki. Registry and the registration no. of the stydy/trial: Data: Protocol #2011-17.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Hao Lu, Email: luhao403@163.com.
Fan Gong, Email: 372438536@qq.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 that support the findings of this study are openly available in NHANES at http://www.cdc.gov/nchs/nhanes/index.htm.