Abstract
Objective
Both triglyceride glucose index(TyG) and weight-adjusted waist circumference index (WWI) are independent predictors of diabetes onset, but the relationship between TyG index and WWI index and diabetes risk is not well understood. The aim of this study was to investigate the interaction between TyG and WWI indices on diabetes risk and to assess whether the combination of TyG and WWI indices is a better predictor of diabetes incidence.
Methods
A total of 5555 participants aged 45 years and older without diabetes were enrolled and followed up for a duration of up to 9 years according to the China Health and Retirement Longitudinal Study (CHARLS). After identifying key predictors using the least absolute shrinkage with selection operator (LASSO) technique, COX proportional risk model assessments were performed to evaluate the association between TyG index and WWI index and diabetes risk, Kaplan-Meier analyses were used for cumulative risk and mediation analyses were used to assess the mediated relationship between TyG index, WWI index and diabetes risk. In addition, subgroup analyses and sensitivity analyses were performed to investigate the predictive performance and robustness of the results in different populations.
Results
A total of 880 participants developed diabetes over the course of 9 years of follow-up.COX proportional risk regression modelling revealed that participants with high TyG and high WWI had the highest risk (HR = 1.59, 95% CI: 1.28–1.97), followed by participants with high TyG and low WWI (HR = 1.26, 95% CI: 1.01–1.57), and participants with low TyG and low WWI participants (HR = 1.22, 95% CI: 0.97–1.52). Subgroup analyses and sensitivity analyses further confirmed these findings.
Conclusion
There is a complex interaction between TyG index, WWI and diabetes risk, and the combined prediction of TyG index and WWI has higher clinical value. These findings highlight the importance of considering both insulin resistance(IR) and visceral adiposity in diabetes risk assessment to improve the identification of at-risk individuals, who can be monitored and intervened on to reduce the overall burden of diabetes.
Clinical trial number
Not applicable.
Keywords: Triglyceride glucose index, Weight-adjusted waist circumference index, Interaction, The risk of diabetes, Cohort study
Introduction
Diabetes, as a global public health problem, affects approximately 537 million adults worldwide and accounts for 10% of global healthcare expenditure [1–3]. Recent studies have shown that the incidence of diabetes over the past 30 years has been concentrated in areas with higher levels of socioeconomic development; in addition, rapid population growth, aging societies, and the COVID-19 pandemic have led to an increasing burden of diabetes[4–6]. In China, the incidence of diabetes is increasing year by year due to population growth, urbanisation, ageing, rising obesity rates, and poor lifestyles [7–14]. As of 2021, China will account for about 10% of diabetes and has become the first country with diabetes, which brings a heavy economic burden to China [15, 16].
Insulin resistance(IR) serves as an important factor in the pathophysiology of diabetes and prediabetes, however, it is difficult to assess diabetes risk by directly measuring the level of IR due to its difficult and costly measurement [17–22]. Triglyceride glucose(TyG) index has been widely used as an alternative marker of IR due to its simplicity, convenience and low cost [23, 24]. Previous studies have shown [25–30] that TyG index may be involved in the association between IR and diabetes risk and is important in predicting the risk of diabetes and its complications.
It is well known that obesity is a risk factor for the development of diabetes, and body mass index (BMI) is the most commonly used indicator for assessing obesity [31–34]. However, BMI is inherently incapable of providing information on body composition, fat distribution, and body metabolism, leading to the “obesity paradox” [35, 36]. Therefore, there is a need for more comprehensive indicators to provide more information about diabetes.The weight-adjusted waist circumference index (WWI), as a new obesity index that incorporates the benefits of waist circumference while being able to attenuate its effect with BMI, is a reliable indicator of central obesity [37, 38].
Recently, researchers have found that when TyG index is combined with obesity factors such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR), these combined associations are better predictors of diabetes than TyG index alone [39]. body information, it has been shown to be a valuable predictor of diabetes incidence [35, 40]. However, few studies have addressed the combined effect of TyG index and WWI on the incidence of diabetes. The aim of this study was to assess the interaction between TyG index, WWI, and diabetes incidence based on the China Health and Retirement Longitudinal Study (CHARLS) database, and to observe whether the combination of TyG index and WWI could better predict the occurrence of diabetes.
Methods
Study design
This is a prospective cohort study, and all participants were from CHARLS, a national cohort study that began in 2011. The study focuses specifically on the middle-aged and elderly groups in China, and assesses the economic, social, and health status of people aged 45 years and older in order to promote interdisciplinary research on population aging in China. Up to now, data surveys have been completed for 2011, 2013, 2015, 2018 and 2020. Initially, 17,708 participants from 10,257 households in 450 communities in 150 counties in 28 provinces were recruited for the first wave of CHARLS using multi-stage probability-proportional-to-scale sampling. Thereafter, those who were younger than 45 years old (n = 648), those with incomplete information on height, weight, WC, fasting blood glucose (FBG), and triglycerides (TG) (n = 8513), those who already had diabetes at the time of baseline data (n = 1372), those whose diabetes status was unknown during follow-up (n = 1290), and those who had extreme values of BMI (BMI < 15 or BMI > 55) (n = 20) and other participants were excluded.
Finally, we included gender (112, 1.91%), hypertension status (17, 0.29%), dyslipidaemia status (51, 0.87%), cardiac status (22, 0.38%), smoking status (9, 0.15%), alcohol consumption status (3, 0.05%), education level (2, 0.03%), and diastolic blood pressure (DBP) (42, 0.72%), systolic blood pressure (SBP) (41, 0.70%), cognitive leisure activity (CLAI) (25, 0.43%), low-density lipoprotein (HDL-C) (1, 0.02%), and glycated haemoglobin (GHb) (42, 0.72%) were excluded from the data with less than 10% missing values, and a total of 5,555 participants were enrolled in the current study (Figure. 1).CHARLS study received ethical approval from the Biomedical Ethics Committee of Peking University, China (IRB00001052-11015), all participants signed an informed consent form, and datasets related to this study are publicly accessible on the official website of the CHARLS project.
Fig. 1.
Participant selection flow chart
Data acquisition
Every two years, professionally trained interviewers collected demographic information (e.g., age, gender, and marriage), health status (e.g., smoking, alcohol consumption), diagnosed chronic diseases, and health-related behaviours from participants and their families through standardised questionnaires administered through personal interviews. Notably in 2011 and 2015, after obtaining information on participants’ height, weight, and blood pressure, each subject underwent venous blood sampling and biochemical analyses under the guidance of medically trained staff. Unfortunately, not all participants reported that they strictly fasted 8 h prior to blood collection.
Definition of new-onset diabetes
The diagnosis of new-onset diabetes was based on self-reported data and laboratory tests. When the interviewer asked, “Have you been diagnosed with diabetes by your doctor?”, respondents answered “yes” and were classified as diabetic. Subjects were also classified as diabetic if they had a fasting blood glucose level of ≥ 126 mg/dl and/or an HbAlc level of ≥ 6.5% on a blood test, and subjects with diabetes in 2011 were excluded if they were diagnosed with diabetes after this period until the 2020 follow-up period and were included in the study according to our definition of a diabetic patient. The interval between assessment and final between diabetes onset and baseline assessment was calculated to determine the time to diabetes. For patients who did not report diabetes during follow-up, we determined the duration of follow-up based on the interval between the baseline assessment and the final investigation. In addition, the incidence of diabetes was coded as binary (diabetes = 1, no diabetes = 0) as the outcome of interest during this study.
TyG and WWI assessment
The specific procedure for defining the TyG index in this study was as follows:
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The specific procedure for defining the WWI is as follows:
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Covariates
Various potential predictors that may influence diabetes were selected based on socio-demographic characteristics, lifestyle behaviours, current health status, and clinical expertise, including gender (male, female), age (< 60, ≥ 60), place of residence (urban, rural), marital status (married, other), level of education (less than primary school, primary school, secondary school and above), smoking and drinking status (never, past, present), hypertension, dyslipidaemia, cardiovascular disease, cognitive leisure activities (0, 1, 2–7). Laboratory data: DBP, SBP, WC, WHtR, BMI, FBG, GHb, total cholesterol (TC), TG, high-density lipoprotein (HDL-C), LDL-C, TyG index, WWI.
In addition, BMI was classified into the following groups according to Chinese adult weight standards: normal weight (< 24), overweight (24−27.9), and obese (> 28). Hypertension was defined as SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, taking into account the current use of antihypertensive medication or self-reported history of hypertension, according to reference standards established by the World Health Organisation. Dyslipidaemia was defined as TC level ≥ 240 mg/dL, TG level ≥ 150 mg/dL, LDL-C level ≥ 160 mg/dL, HDL-C level b < 40 mg/dL, and current use of lipid-lowering drugs or self-reported history of dyslipidaemia.
Statistical analyses
Data analysis was performed using IBM SPSS 26.0 and R software. A two-sided P value < 0.05 was considered statistically significant. Continuous variables were described using mean and standard deviation. Categorical variables were described using frequency and percentage. Variance analysis or rank-sum tests were performed on baseline data within each group.
To assess the association between TyG and WWI indices and the incidence of new-onset diabetes, we calculated the incidence rate per 1,000 person-years for each outcome. Kaplan-Meier survival analysis was used to estimate the cumulative incidence of new-onset diabetes in the four combined TyG and WWI index groups, and cumulative risk curves were generated. Log-rank tests were employed to assess group differences.
To assess the association between TyG and WWI indices and the risk of new-onset diabetes, we first divided participants into ‘low’ and ‘high’ groups based on the median values of TyG and WWI indices. We then used Cox proportional hazards regression models to independently analyse the association between TyG and WWI indices and the risk of new-onset diabetes, and assessed the risk assumptions using Schoenfeld residuals. No major violations of the proportional hazards assumption were observed (P > 0.05), indicating that the model assumptions were met.
After confirming the independent association between TyG and WWI indices and the risk of new-onset diabetes, we further investigated the potential interaction between TyG and WWI indices. To assess multiplicative interaction, we introduced an interaction term (TyG*WWI) into the Cox proportional hazards regression model and established four models to evaluate the significance of this interaction term for each outcome. Model 1: Unadjusted; Model 2: Adjusted for age and gender; Model 3: Adjusted for age, gender, marital status, residence status, smoking and drinking status, education level, and BMI; Model 4: Model 3 plus hypertension, dyslipidaemia, and heart disease. Additionally, we conducted a mediation analysis between the TyG index and WWI.
To further validate the robustness of the interaction between TyG and WWI indices on the risk of new-onset diabetes, we conducted subgroup analyses by age group (< 60, ≥ 60), sex (male, female), marital status (married, other), residence (urban, rural), education level (primary school or below, primary school, secondary school or above), smoking and drinking status (never, past, current), BMI (< 24, 24–27.9, > 28), hypertension, dyslipidaemia, and cardiovascular disease. Finally, in sensitivity analyses, we conducted different types of evaluations. First, we adjusted various health factors by focusing on specific subgroups to validate the strength and consistency of the association between TyG and WWI indices and the risk of new-onset diabetes; second, to examine the impact of different cutoff points, we grouped TyG and WWI indices using quantiles and reanalysed the data; finally, we conducted sensitivity analyses on the original data retaining BMI extreme values and missing data.
Results
Baseline characteristics of participants
The study included 5555 participants from the CHARLS dataset, collected between 2011 and 2020, with a mean age of 58.06 ± 8.51 years, of which 2465 were men and 3090 were women. Based on the median TyG and WWI indices, the participants were categorised into four groups: 1579 low TyG and low WWI, 1198 low TyG and high WWI, 1200 high TyG and low WWI, and 1578 high TyG and high WWI.The risk factors were initially screened using LASSO regression, and the optimal λ value was determined through 10-fold cross-validation. The results showed that age, gender, marital status, place of residence, educational level, smoking and drinking habits, BMI, hypertension, dyslipidemia and heart disease might be predictive factors (Table 1, Fig. 2).
Table 1.
Characteristics of participants according to TyG index and WWI
| Characteristic | Total (n = 5555) | Group 1 (n = 1579) | Group 2 (n = 1198) | Group 3 (n = 1200) | Group 4 (n = 1578) | P |
|---|---|---|---|---|---|---|
| Age | 58.06 ± 8.51 | 56.49 ± 8.15 | 60.37 ± 9.06 | 55.84 ± 7.67 | 59.57 ± 8.30 | < 0.001 |
| Age (%) | < 0.001 | |||||
| < 60 | 3327 (59.9) | 1068 (67.6) | 572 (47.7) | 857 (71.4) | 830 (52.6) | |
| ≥ 60 | 2228 (40.1) | 511 (32.4) | 626 (52.3) | 343 (28.6) | 748 (47.4) | |
| BMI | 23.45 ± 3.72 | 21.98 ± 3.18 | 23.23 ± 3.70 | 23.47 ± 3.71 | 25.06 ± 3.59 | < 0.001 |
| BMI (%) | < 0.001 | |||||
| < 24 | 3351 (60.3) | 1268 (80.3) | 719 (60.0) | 733 (61.1) | 631 (40.0) | |
| 24−27.9 | 1617 (29.1) | 270 (17.1) | 364 (30.4) | 353 (29.4) | 630 (39.9) | |
| ≥ 28 | 587 (10.6) | 41 (2.6) | 115 (9.6) | 114 (9.5) | 317 (20.1) | |
| Gender (%) | < 0.001 | |||||
| Male | 2465 (44.4) | 1005 (63.6) | 372 (31.1) | 666 (55.5) | 422 (26.7) | |
| Female | 3090 (55.6) | 574 (36.4) | 826 (68.9) | 534 (44.5) | 1156 (73.3) | |
| Residence status (%) | < 0.001 | |||||
| Rural | 3705 (66.7) | 1109 (70.2) | 837 (69.9) | 773 (64.4) | 986 (62.5) | |
| Urban | 1850 (33.3) | 470 (29.8) | 361 (30.1) | 427 (35.6) | 592 (37.5) | |
| Married status(%) | < 0.001 | |||||
| Married | 4992 (89.9) | 1440 (91.2) | 1041 (86.9) | 1109 (92.4) | 1402 (88.8) | |
| Non-married | 563 (10.1) | 139 (8.8) | 157 (13.1) | 91 (7.6) | 176 (11.2) | |
| Education level(%) | < 0.001 | |||||
| Below elementary school | 2609 (47.0) | 604 (38.3) | 697 (58.2) | 455 (37.9) | 853 (54.1) | |
| Elementary school | 1224 (22.0) | 366 (23.2) | 242 (20.2) | 270 (22.5) | 346 (21.9) | |
| Secondary school and higher | 1722 (31.0) | 609 (38.6) | 259 (21.6) | 475 (39.6) | 379 (24.0) | |
| CLAI | < 0.001 | |||||
| 0 | 2774 (49.9) | 808 (51.2) | 667 (55.7) | 528 (44.0) | 771 (48.9) | |
| 1 | 1889 (34.0) | 511 (32.4) | 372 (31.1) | 437 (36.4) | 569 (36.1) | |
| 2–7 | 892 (16.1) | 260 (16.5) | 159 (13.3) | 235 (19.6) | 238 (15.1) | |
| Smoking status (%) | < 0.001 | |||||
| Never | 3497 (63.0) | 783 (49.6) | 869 (72.5) | 659 (54.9) | 1186 (75.2) | |
| Ever | 412 (7.4) | 140 (8.9) | 66 (5.5) | 111 (9.3) | 95 (6.0) | |
| Current | 1646 (29.6) | 656 (41.5) | 263 (22.0) | 430 (35.8) | 297 (18.8) | |
| Drinking status(%) | < 0.001 | |||||
| Never | 3311 (59.6) | 760 (48.1) | 793 (66.2) | 648 (54.0) | 1110 (70.3) | |
| Ever | 454 (8.2) | 134 (8.5) | 95 (7.9) | 105 (8.8) | 120 (7.6) | |
| Current | 1790 (32.2) | 685 (43.4) | 310 (25.9) | 447 (37.3) | 348 (22.1) | |
| Hypertension (%) | < 0.001 | |||||
| No | 3139 (56.5) | 1077 (68.2) | 669 (55.8) | 708 (59.0) | 685 (43.4) | |
| Yes | 2416 (43.5) | 502 (31.8) | 529 (44.2) | 492 (41.0) | 893 (56.6) | |
| Dyslipidemia (%) | < 0.001 | |||||
| No | 3094 (55.7) | 1282 (81.2) | 925 (77.2) | 419 (34.9) | 468 (29.7) | |
| Yes | 2461 (44.3) | 297 (18.8) | 273 (22.8) | 781 (65.1) | 1110 (70.3) | |
| Heart disease (%) | 0.050 | |||||
| No | 4987 (91.3) | 1460 (92.5) | 1103 (92.1) | 1076 (89.7) | 2348 (91.1) | |
| Yes | 568 (8.7) | 119 (7.5) | 95 (7.9) | 124 (10.3) | 230 (8.9) | |
| Diabetes (%) | < 0.001 | |||||
| No | 4675 (84.2) | 1418 (89.8) | 1029 (85.9) | 1009 (84.1) | 1219 (77.2) | |
| Yes | 880 (15.8) | 161 (10.2) | 169 (14.1) | 191 (15.9) | 359 (22.8) | |
| SBP (mmHg) | 127.17 ± 20.43 | 123.09 ± 19.07 | 126.98 ± 20.44 | 126.36 ± 19.31 | 132.03 ± 21.55 | < 0.001 |
| DBP (mmHg) | 74.67 ± 11.97 | 72.67 ± 11.82 | 73.77 ± 11.22 | 75.39 ± 12.08 | 76.80 ± 12.21 | < 0.001 |
| WC (cm) | 83.77 ± 12.17 | 76.96 ± 10.96 | 87.06 ± 8.55 | 79.43 ± 13.55 | 91.39 ± 8.89 | < 0.001 |
| WWI | 11.00 ± 1.30 | 10.22 ± 1.19 | 11.76 ± 0.61 | 10.21 ± 1.48 | 11.80 ± 0.62 | < 0.001 |
| WHtR | 0.53 ± 0.79 | 0.48 ± 0.06 | 0.57 ± 0.05 | 0.49 ± 0.08 | 0.59 ± 0.05 | < 0.001 |
| FBG (mg/dl) | 100.26 ± 10.86 | 97.11 ± 10.42 | 97.16 ± 10.98 | 102.63 ± 10.18 | 103.95 ± 10.05 | < 0.001 |
| GHb (%) | 5.12 ± 0.39 | 5.06 ± 0.38 | 5.11 ± 0.37 | 5.11 ± 0.39 | 5.18 ± 0.40 | < 0.001 |
| TC (mg/dl) | 193.52 ± 37.13 | 182.21 ± 32.96 | 188.95 ± 34.38 | 197.19 ± 37.73 | 205.52 ± 38.61 | < 0.001 |
| TG (mg/dl) | 119.72 ± 73.19 | 70.82 ± 18.33 | 74.66 ± 17.84 | 162.58 ± 74.03 | 170.27 ± 78.92 | < 0.001 |
| HDL-C (mg/dl) | 52.18 ± 14.84 | 58.61 ± 14.54 | 57.91 ± 14.43 | 46.63 ± 12.70 | 45.60 ± 12.26 | < 0.001 |
| LDL-C (mg/dl) | 118.32 ± 33.62 | 111.85 ± 29.90 | 117.93 ± 30.40 | 118.07 ± 35.01 | 125.29 ± 36.91 | < 0.001 |
| TyG | 8.55 ± 0.54 | 8.10 ± 0.28 | 8.16 ± 0.28 | 8.95 ± 0.37 | 9.01 ± 0.39 | < 0.001 |
Fig. 2.
Ten-fold cross-validation for the best variable selection The tenfold cross-validation filters λ
Regression results
After adjusting for several potential confounders, we found that TyG index and WWI were independently associated with an increased risk of new-onset diabetes in the cox proportional risk regression model. The multifactorial model showed that the risk of developing new-onset diabetes was significantly higher in the high TyG index participant group compared with the low TyG index group (HR = 1.30, 95% CI: 1.11–1.52). Similarly, the risk of developing new-onset diabetes was significantly higher in the high WWI participation group compared to the low WWI group (HR = 1.26, 95% CI: 1.08–1.46) (Table 2, Fig. 3-A and -B).
Table 2.
Independent and combined effects of TyG index and WWI on the risk of new-onset diabetes
| Variables | Incidence rate (1000PY) | Model 1 | P for interaction | Model 2 | P for interaction | Model 3 | P for interaction | Model 4 | P for interaction |
|---|---|---|---|---|---|---|---|---|---|
| Diabetes incidence | |||||||||
| Low TyG | 14.03 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 |
| High TyG | 23.37 | 1.73 (1.51,1.99) | 1.72 (1.50,1.97) | 1.54 (1.34,1.77) | 1.30 (1.11,1.52) | ||||
| Low WWI | 18.24 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 |
| High WWI | 27.39 | 1.57 (1.37,1.79) | 1.52 (1.31,1.75) | 1.34 (1.16, 1.55) | 1.26 (1.08,1.46) | ||||
| Low TyG & low WWI | 12.04 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 |
| Low TyG & high WWI | 16.66 | 1.42 (1.15,1.76) | 1.38 (1.10, 1.73) | 1.24 (0.99,1.56) | 1.22 (0.97,1.52) | ||||
| High TyG & low WWI | 18.79 | 1.60 (1.29,1.97) | 1.60 (1.30,1.98) | 1.46 (1.18,1.80) | 1.26 (1.01,1.57) | ||||
| High TyG & high WWI | 26.86 | 2.40 (1.99, 2.89) | 2.34 (1.93, 2.85) | 1.93 (1.58,2.36) | 1.59 (1.28,1.97) |
Model 1: unadjusted
Model 2: Adjustment for age, gender
Model 3: Adjustment for age, gender, marriage, residence status, smoking and drinking status, education level and BMI
Model 4: plus hypertension, dyslipidaemia and heart disease
Fig. 3.
K-M chart of diabetes incidence in TyG and WWI.A: TyG. B: TyG C: TyG&WWI
Interaction analysis
We then examined the interaction between the TyG index and the WWI for new-onset diabetes events. The results of the long hair interaction analysis showed that the interaction term (TyG*WWI) was significant for new-onset diabetes in the cox proportional risk regression model. Further analyses showed that participants with high TyG and high WWI had the highest risk compared to participants with low TyG and low WWI (HR = 1.59, 95% CI: 1.28–1.97), followed by participants with high TyG and low WWI (HR = 1.26, 95% CI: 1.01–1.57), and participants with low TyG and low WWI (HR = 1.22, 95% CI: 0.97–1.52) (Table 2, Fig. 3-C).
Intermediary analysis
To investigate the mediating relationship between the WWI and the TyG index, a mediation analysis was performed. The mediation analysis showed a mediating effect between both WWI and TyG and diabetes risk.(Table 3).
Table 3.
Mediation analysis
| Intermediate variable | association | Proportion mediated | |
|---|---|---|---|
| Total HR (95 per cent) | Direct HR (95 per cent) | ||
| Model 1 | |||
| WWI | 0.087(0.070,0.105) | 0.083(0.066,0.101) | 4.472 per cent |
| TyG | 0.020(0.012,0.027) | 0.016(0.009,0.024) | 17.842 per cent |
| Model 2 | |||
| WWI | 0.086(0.069,0.104) | 0.083(0.066,0.101) | 3.070 per cent |
| TyG | 0.017(0.009,0.025) | 0.014(0.006,0.022) | 17.649 per cent |
| Model 3 | |||
| WWI | 0.065(0.047,0.083) | 0.064(0.045,0.082) | 2.232 per cent |
| TyG | 0.012(0.004,0.020) | 0.010(0.003,0.018) | 13.195 per cent |
| Model 4 | |||
| WWI | 0.047(0.026,0.069) | 0.046(0.024,0.068) | 2.602 per cent |
| TyG | 0.011(0.003,0.019) | 0.010(0.002,0.018) | 6.684 per cent |
Model 1: unadjusted
Model 2: Adjustment for age, gender
Model 3: Adjustment for age, gender, married status, residence status, smoking and drinking status, education level and BMI
Model 4: plus hypertension, dyslipidaemia and heart disease
Subgroup analyses
In subgroup analyses stratified by age, sex, marriage, place of residence, level of education, smoking and drinking status, BMI, hypertension, dyslipidaemia and cardiovascular disease, we found no significant interactions observed between these variables and the combined effect of the TyG index and the WWI on the risk of new-onset diabetes (p > 0.05 for all interactions). This suggests that the effects of TyG index and WWI on the risk of new-onset diabetes were consistent across demographic and clinical subgroups. (Table 4)
Table 4.
Subgroup analysis of the combined effects of TyG index and WWI on the risk of new-onset diabetes
| Subgroups | Incidence rate (1000PY) | Variables | HR (95 per cent CI) | P for interaction |
|---|---|---|---|---|
| Overall | 12.04 | Low TyG & low WWI | Ref | |
| 16.66 | Low TyG & high WWI | 1.22 (0.97,1.52) | ||
| 18.79 | High TyG & low WWI | 1.26 (1.01,1.57) | ||
| 26.86 | High TyG & high WWI | 1.59 (1.28,1.97) | ||
| Age | 0.716 | |||
| < 60 | 11.50 | Low TyG & low WWI | Ref | |
| 14.86 | Low TyG & high WWI | 1.14 (0.84,1.55) | ||
| 18.46 | High TyG & low WWI | 1.27 (0.97,1.67) | ||
| 26.17 | High TyG & high WWI | 1.56 (1.18,2.07) | ||
| ≥ 60 | 13.17 | Low TyG & low WWI | Ref | |
| 18.29 | Low TyG & high WWI | 1.27 (0.90,1.78) | ||
| 19.62 | High TyG & low WWI | 1.20 (0.81,1.76) | ||
| 27.62 | High TyG & high WWI | 1.55 (1.10, 2.19) | ||
| Gender | 0.844 | |||
| Male | 13.04 | Low TyG & low WWI | Ref | |
| 15.23 | Low TyG & high WWI | 1.43 (1.02,2.00) | ||
| 17.55 | High TyG & low WWI | 1.58 (1.10, 2.26) | ||
| 28.82 | High TyG & high WWI | 1.73 (1.24, 2.42) | ||
| Female | 10.28 | Low TyG & low WWI | Ref | |
| 17.30 | Low TyG & high WWI | 1.05 (0.74,1.48) | ||
| 20.34 | High TyG & low WWI | 1.07 (0.80,1.43) | ||
| 26.15 | High TyG & high WWI | 1.61 (1.19, 2.19) | ||
| Married status | 0.191 | |||
| Married | 12.38 | Low TyG & low WWI | Ref | |
| 16.44 | Low TyG & high WWI | 1.18 (0.93,1.50) | ||
| 18.52 | High TyG & low WWI | 1.21 (0.96,1.53) | ||
| 25.94 | High TyG & high WWI | 1.46 (1.7, 1.84) | ||
| Non-married | 8.49 | Low TyG & low WWI | Ref | |
| 18.05 | Low TyG & high WWI | 1.93 (0.89,4.16) | ||
| 22.06 | High TyG & low WWI | 2.19 (0.95,5.03) | ||
| 34.21 | High TyG & high WWI | 1.15 (0.92,1.43) | ||
| Residence status | 0.628 | |||
| Rural | 12.78 | Low TyG & low WWI | Ref | |
| 16.93 | Low TyG & high WWI | 1.21 (0.93,1.58) | ||
| 18.79 | High TyG & low WWI | 1.25 (0.95,1.62) | ||
| 29.46 | High TyG & high WWI | 1.78 (1.38, 2.31) | ||
| Urban | 10.30 | Low TyG & low WWI | Ref | |
| 16.03 | Low TyG & high WWI | 1.34 (0.87,2.06) | ||
| 18.80 | High TyG & low WWI | 1.27 (0.84,1.93) | ||
| 22.54 | High TyG & high WWI | 1.28 (0.85,1.92) | ||
| Education level | 0.158 | |||
| Below elementary school | 11.73 | Low TyG & low WWI | Ref | |
| 16.60 | Low TyG & high WWI | 1.25 (0.90,1.75) | ||
| 22.32 | High TyG & low WWI | 1.55 (1.10, 2.20) | ||
| 27.96 | High TyG & high WWI | 1.71 (1.23, 2.38) | ||
| Elementary school | 14.52 | Low TyG & low WWI | Ref | |
| 16.59 | Low TyG & high WWI | 1.17 (0.73,1.85) | ||
| 16.62 | High TyG & low WWI | 0.94 (0.59,1.48) | ||
| 26.96 | High TyG & high WWI | 1.45 (0.94,2.25) | ||
| Secondary school and higher | 10.86 | Low TyG & low WWI | Ref | |
| 16.87 | Low TyG & high WWI | 1.35(0.88,2.07) | ||
| 16.65 | High TyG & low WWI | 1.23 (0.84,1.81) | ||
| 24.30 | High TyG & high WWI | 1.57 (1.06,2.32) | ||
| Smoking status | 0.161 | |||
| Never | 10.55 | Low TyG & low WWI | Ref | |
| 16.85 | Low TyG & high WWI | 1.43 (1.05,1.93) | ||
| 22.93 | High TyG & low WWI | 1.78 (1.31, 2.42) | ||
| 26.28 | High TyG & high WWI | 1.78 (1.32, 2.40) | ||
| Ever | 11.81 | Low TyG & low WWI | Ref | |
| 16.10 | Low TyG & high WWI | 1.16 (0.49,2.75) | ||
| 13.83 | High TyG & low WWI | 0.84 (0.37,1.92) | ||
| 38.53 | High TyG & high WWI | 2.49 (1.19, 5.21) | ||
| Never | 13.86 | Low TyG & low WWI | Ref | |
| 16.16 | Low TyG & high WWI | 1.08 (0.72,1.63) | ||
| 13.73 | High TyG & low WWI | 0.81 (0.55,1.18) | ||
| 25.44 | High TyG & high WWI | 1.40 (0.95,2.05) | ||
| Drinking status | 0.147 | |||
| Never | 11.34 | Low TyG & low WWI | Ref | |
| 15.93 | Low TyG & high WWI | 1.23 (0.90,1.67) | ||
| 20.59 | High TyG & low WWI | 1.44 (1.05,1.96) | ||
| 25.63 | High TyG & high WWI | 1.52 (1.13, 2.06) | ||
| Ever | 14.10 | Low TyG & low WWI | Ref | |
| 17.40 | Low TyG & high WWI | 1.02 (0.49,2.14) | ||
| 21.36 | High TyG & low WWI | 1.31 (0.64,2.70) | ||
| 38.37 | High TyG & high WWI | 1.99 (1.03,3.82) | ||
| Never | 12.41 | Low TyG & low WWI | Ref | |
| 18.28 | Low TyG & high WWI | 1.35 (0.92,1.99) | ||
| 15.58 | High TyG & low WWI | 1.03 (0.71,1.48) | ||
| 26.80 | High TyG & high WWI | 1.70 (1.18,2.44) | ||
| BMI | 0.455 | |||
| < 24 | 10.99 | Low TyG & low WWI | Ref | |
| 14.29 | Low TyG & high WWI | 1.22 (0.91,1.63) | ||
| 14.01 | High TyG & low WWI | 1.17 (0.87,1.57) | ||
| 21.52 | High TyG & high WWI | 1.71 (1.26, 2.31) | ||
| 24−27.9 | 14.87 | Low TyG & low WWI | Ref | |
| 18.16 | Low TyG & high WWI | 1.06 (0.68,1.65) | ||
| 22.74 | High TyG & low WWI | 1.31(0.85,2.00) | ||
| 26.61 | High TyG & low WWI | 1.35 (0.89,2.04) | ||
| ≥ 28 | 25.92 | Low TyG & low WWI | Ref | |
| 26.69 | Low TyG & high WWI | 1.38 (0.62,3.07) | ||
| 37.28 | High TyG & low WWI | 1.44 (0.67,3.06) | ||
| 37.99 | High TyG & high WWI | 1.56 (0.74,3.26) | ||
| Hypertension | 0.426 | |||
| No | 9.65 | Low TyG & low WWI | Ref | |
| 14.47 | Low TyG & high WWI | 1.40 (1.02,1.93) | ||
| 17.01 | High TyG & low WWI | 1.54 (1.14, 2.09) | ||
| 23.27 | High TyG & high WWI | 2.01 (1.47, 2.74) | ||
| Yes | 17.17 | Low TyG & low WWI | Ref | |
| 19.42 | Low TyG & high WWI | 1.02 (0.74,1.40) | ||
| 21.36 | High TyG & low WWI | 0.98 (0.71,1.36) | ||
| 29.62 | High TyG & high WWI | 1.22 (0.90,1.65) | ||
| Dyslipidemia | 0.092 | |||
| No | 10.68 | Low TyG & low WWI | Ref | |
| 16.34 | Low TyG & high WWI | 1.37 (1.04,1.79) | ||
| 14.09 | High TyG & low WWI | 1.21 (0.87,1.69) | ||
| 21.70 | High TyG & high WWI | 1.73 (1.28, 2.33) | ||
| Yes | 16.70 | Low TyG & low WWI | Ref | |
| 17.73 | Low TyG & high WWI | 0.94 (0.61,1.44) | ||
| 21.31 | High TyG & low WWI | 1.17 (0.84,1.64) | ||
| 29.04 | High TyG & high WWI | 1.39 (0.99,1.94) | ||
| Heart disease | 0.638 | |||
| No | 11.64 | Low TyG & low WWI | Ref | |
| 16.59 | Low TyG & high WWI | 1.26(1.00,1.60) | ||
| 17.78 | High TyG & low WWI | 1.23 (0.97,1.57) | ||
| 25.57 | High TyG & high WWI | 1.61 (1.28,2.04) | ||
| Yes | 16.87 | Low TyG & low WWI | Ref | |
| 17.40 | Low TyG & high WWI | 0.88 (0.42,1.82) | ||
| 27.61 | High TyG & low WWI | 1.43 (0.75,2.71) | ||
| 34.39 | High TyG & high WWI | 1.40 (0.76,2.58) |
Sensitivity analysis
To verify the robustness of our findings, we performed several sensitivity analyses. First, we adjusted for different special groups as well as health factors, and the results were consistent with the preliminary analyses (Table 5). Second, we grouped the TyG and WWI indices using tertiles in order to see whether different thresholds of the TyG and WWI indices would have an impact on the results (Table 6). Further analysis showed that participants with medium TyG index and high WWI had the highest risk of new-onset diabetes (HR = 1.77, 95% CI: 1.32–2.38). Finally, we conducted multiple imputation for the original data with missing values and extreme BMI values, and retained the extreme BMI values. The results showed stability (Table 7).These studies fully demonstrate the robustness of our findings.
Table 5.
Sensitivity analysis of the influence of TyG index and WWI on new-onset diabetes under different models
| Variables | Incidence rate (1000PY) | Model 1 | P for interaction | Model 2 | P for interaction | Model 3 | P for interaction | Model 4 | P for interaction | Model 5 | P for interaction |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Diabetes incidence | |||||||||||
| Low TyG & low WWI | 12.04 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 |
| Low TyG & high WWI | 16.66 | 1.32 (1.05,1.65) | 1.24 (0.99,1.55) | 1.23 (0.98,1.54) | 1.21 (0.97,1.52) | 1.37 (1.10, 1.72) | |||||
| High TyG & low WWI | 18.79 | 1.34 (1.07,1.67) | 1.28 (1.02,1.59) | 1.43 (1.16, 1.77) | 1.25 (1.00,1.57) | 1.62 (1.31,2.00) | |||||
| High TyG & high WWI | 26.86 | 1.84 (1.49,2.28) | 1.64 (1.32, 2.04) | 1.83 (1.49,2.24) | 1.59 (1.28,1.97) | 2.37 (1.95,2.89) |
Model 1: Adjustment for age, gender, married status, residence status, smoking and drinking status, education level, hypertension, dyslipidaemia and cardiovascular disease
Model 2: Adjustment for age, gender, married status, residence status,, smoking and drinking status, education level, BMI, dyslipidaemia and cardiovascular disease
Model 3: Adjustment for age, gender, married status, residence status, smoking and drinking status, education level, BMI, hypertension, and cardiovascular disease
Model 4: Adjustment for age, gender, married status, residence status, smoking and drinking status, education level, BMI, hypertension, and dyslipidaemia
Model 5: Adjusted for age, gender, married status, residence status, smoking and alcohol consumption, education level
Table 6.
Relationship between TyG index and WWI and risk of new-onset diabetes in tertiary classification
| Variables | WWI level(HR,95%CI) | P for interaction | ||
|---|---|---|---|---|
| Diabetes incidence | Low WWI | Medium WWI | High WWI | |
| Low TyG | Ref | 0.97 (0.69,1.37) | 1.11 (0.78.1.56) | < 0.001 |
| Medium TyG | 1.05 (0.76,1.46) | 1.13 (0.83,1.55) | 1.77 (1.32, 2.38) | |
| High TyG | 1.23 (0.88,1.71) | 1.51 (1.12, 2.05) | 1.69 (1.24, 2.29) | |
Models: adjusted for age, gender, married status, residence status, smoking and drinking status, education level, BMI, hypertension, dyslipidaemia and cardiovascular disease
Table 7.
Independent and combined effects of TyG index and WWI on the risk of new-onset diabetes
| Variables | Incidence rate (1000PY) | Model 1 | P for interaction | Model 2 | P for interaction | Model 3 | P for interaction | Model 4 | P for interaction |
|---|---|---|---|---|---|---|---|---|---|
| Diabetes incidence | |||||||||
| Low TyG | 24.12 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 |
| High TyG | 41.02 | 1.78 (1.557,2.02) | 1.77 (1.56,2.01) | 1.78 (1.57,2.02) | 1.44 (1.25,1.66) | ||||
| Low WWI | 25.83 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 |
| High WWI | 39.31 | 1.60 (1.41,1.81) | 1.52 (1.33,1.74) | 1.52 (1.33,1.74) | 1.37 (1.20,1.58) | ||||
| Low TyG & low WWI | 20.11 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 | Ref | < 0.001 |
| Low TyG & high WWI | 29.43 | 1.52 (1.24,1.85) | 1.45 (1.18,1.79) | 1.45 (1.17,1.78) | 1.38 (1.12,1.70) | ||||
| High TyG & low WWI | 33.41 | 1.71 (1.41,2.08) | 1.73 (1.42,2.11) | 1.74 (1.43,2.12) | 1.45 (1.18,1.78) | ||||
| High TyG & high WWI | 46.77 | 2.53 (2.12,3.01) | 2.45 (2.04,2.94) | 2.47 (2.05,2.97) | 1.92 (1.57,2.34) |
Model 1: unadjusted
Model 2: Adjustment for age, gender
Model 3: Adjustment for age, gender, married status, residence status, smoking and drinking status, education level and BMI
Model 4: plus hypertension, dyslipidaemia and heart disease
Discussion
In this large national longitudinal cohort study involving 5555 Chinese middle-aged and older adults in China with a follow-up period of 9 years, the prevalence of diabetes was 15.8%. We found significant associations between TyG index, WWI, and risk of new-onset diabetes.Both TyG index and WWI were independently associated with increased risk of diabetes. When stratified by TyG index and WWI, participants with high TyG and high WWI had the highest risk, followed by participants with high TyG and low WWI, and participants with low TyG and low WWI. Second, mediation analyses found a mediating role in the relationship between WWI, TyG index, and diabetes. In addition, we found that the combination of TyG and WWI may better predict the incidence of diabetes. Subgroup analyses of statistical and clinical characteristics for different populations, as well as multiple sensitivity analyses, further support the robustness of our results. Overall, these results emphasise the importance of considering insulin resistance (as reflected by the TyG index) and visceral adiposity (as measured by WWI) while assessing diabetes risk.
The TyG index has clear advantages in terms of validity and homeostatic modelling compared to other more costly and limitedly available metrics for assessing individual insulin resistance, such as the HOMA-IR [41]. Recent studies have shown that people generally believe that insulin resistance plays a crucial role in the occurrence and development of various diseases (diabetes, cardiovascular diseases, hypertension, fatty liver, liver fibrosis, weakness, etc.). Since the TyG index shows a high sensitivity of 96.5% and specificity of 85% in insulin-resistant patients, it can also be applied to identify the predictive performance of glucose status transformation [42–48]. At the same time, TyG index has different manifestations in different populations [49, 50]. For example, in the study of TyG index and risk of coronary heart disease [50], it was shown that non-diabetic Chinese individuals face a disproportionately elevated risk compared to the uniform trend in the UK.
Furthermore, as a simple and reliable measure of insulin resistance, TyG index may influence the risk of developing diabetes through the following pathways: first, insulin resistance is negatively correlated with insulin sensitivity, and higher insulin resistance leads to impaired pancreatic β-cell function, further exacerbating hyperglycaemia [51, 52]. Secondly, higher insulin resistance corresponds to a stronger inflammatory response, which can inhibit insulin signalling pathways and thus reduce insulin sensitivity [53–55]. In addition. mitochondrial dysfunction also affects insulin signalling [56]. In recent years, more and more studies have found that TyG is closely related to the development of diabetes [57–59]. In addition, most researchers have found that combining TyG with obesity markers (BMI, WC, WHtR) yields better diagnostic efficacy [60, 61].It has also been found that TyG index is more efficacious compared to TyG obesity index, especially in young, middle-aged and women [62].
Obesity, as a major health crisis, poses a significant threat to global health progress [63]. The traditional obesity index (BMI), by only reflecting overall obesity, fails to capture the accumulation of visceral fat and differentiate body composition, leading to the “obesity paradox” whereby BMI-defined obesity may be associated with improved clinical outcomes [64–67].The WWI, as a surrogate for centripetal obesity, is a good measure of obesity, weakening the association with BMI while at the same time providing a good indication of its prevalence [63]. As a proxy for centripetal obesity, WWI, while weakening the association with BMI, is a good indicator of body fat mass and muscle mass, thus circumventing the “obesity paradox”; furthermore, only visceral fat is significantly unchanged in diabetes with age and weight change [68–70]. Previous studies [71–80] have shown that WWI, as a body mass index independent of the obesity paradox, not only has high clinical value in predicting the risk of diabetes, but also has a higher predictive value than other obesity indices such as WC, BMI, and body weight. In addition, this study found a nonlinear relationship between WWI index and diabetes occurrence after RCS regression analysis based on model 4, which fills the gap of previous studies.
To the best of our knowledge, this is the first study to combine TyG and WWI to explore the complex interaction between insulin resistance and visceral fat accumulation in influencing the risk of diabetes mellitus in Chinese middle-aged and elderly people.The combination of TyG and WWI indices allows for a comprehensive analysis of insulin resistance and obesity status of the body; secondly, the combination of the two is able to capture the differences between multiple indices for an earlier prediction of diabetes occurrence; and finally, since each individual has different biomarkers, the WWI index has a higher predictive value than the TyG index., due to the differences in the levels of different biomarkers in each individual, it is beneficial to analyse the combined indices on an individual basis. Going forward, a combined assessment combining insulin resistance and visceral adiposity indices will help to more accurately identify individuals at elevated levels and develop more targeted prevention strategies that reflect the subtle relationship between these risk factors and diabetes risk.
This study has several limitations. First, this study was conducted as an observational study, and although we revealed an association between TyG index, WWI, and diabetes, we were unable to determine causality. Second, although we tried to adjust for a variety of known confounders, unmeasured confounders may still have influenced the results. Third, pharmacological interventions as well as the use of specific medications and respondent adherence were not considered. Fourth, without considering the dynamics of the TyG index and WWI, the detailed development of the joint metrics could not be obtained. Fifth, the CHARLS databases used in this study all originated in China and may not be directly applicable to other countries. Sixth, because regular exercise has an effect on diabetes, however, the data in this study were not screened for relevant data, which may have affected the results. Finally, the database for this study was derived from a health questionnaire, so the diagnosis of diabetes relied only on self-reporting by the participants, which may be a limitation.
Conclusion
Our study demonstrates that IR (TyG index) and visceral adiposity (WWI) act as independent risk factors for diabetes risk, that simultaneous consideration of interactions between IR and visceral adiposity may improve diabetes risk assessment, and that the interplay of these two metrics has higher clinical value and helps to more accurately identify at-risk individuals.
Acknowledgements
Inapplicability.
Abbreviations
- CHARLS
China Health and Retirement Longitudinal Study
- TyG
Triglyceride glucose
- FBG
Fasting plasma glucose
- CRP
C-Reactive Protein
- GHb
Glycated hemoglobin
- TC
Total cholesterol
- TG
Triglyceride
- LDL-C
Low-dendity lipoproteins cholesterol
- HDL-C
High-dendity lipoproteins cholesterol
- SBP
Systolic blood pressure
- DBP
Diastolic blood pressure
- BMI
Body mass index
- WC
Waist circumference
- WHtR
Waist-to-height ratio
- WWI
Weight-adjusted waist circumference index
- HR
Hazard ratio
- CI
Confidence interval
- ROC
Receiver Operating Characteristic
- AUC
Area under the curve
- RCS
Restricted cubic spline
Author contributions
S wrote the main manuscript text, and L and G reviewed the text.
Funding
The study was conducted without any financial support; there were no grants, institutions, or other sources of funding obtained.
Data availability
All the data generated during the fire analysis of this study can be in online access at http://www.isss.pku.edu.cn/cfps/. To get the data, you need to register as a user on the website. Once your registration has been reviewed and approved, you can download the dataset by following the instructions provided.
Declarations
Ethics approval and consent to participate
This study was conducted and approved by the Biomedical Ethics Review Committee of Peking University in accordance with the principles of the Declaration of Helsinki. In addition, all participants provided written informed consent to participate in the study (IRB approval number IRB00001052-11015). This study does not disclose any personal privacy of the participants and does not violate data protection laws.The research has been performed in accordance with the Declaration of Helsinki.
Consent for publication
Inapplicability.
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.
Contributor Information
Qingyang Liu, Email: qingyang-tcm@163.com.
Tianshu Gao, Email: gaotianshu67@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Citations
- Hu B, Wang Y, Wang Y, Feng J, Fan Y, Hou L. Association between Triglyceride-Glucose Index and risk of all-cause and cardiovascular mortality in adults with prior cardiovascular disease. BMJ Open. 2024;14(7):e084549. 10.1136/bmjopen-2024-084549. PMID: 38969366; PMCID: PMC11227790. a cohort study using data from the US National Health and Nutrition Examination Survey, 2007–2018. [DOI] [PMC free article] [PubMed]
Data Availability Statement
All the data generated during the fire analysis of this study can be in online access at http://www.isss.pku.edu.cn/cfps/. To get the data, you need to register as a user on the website. Once your registration has been reviewed and approved, you can download the dataset by following the instructions provided.