Frontiers in public health · 2026
Association of novel obesity-related lipid markers with diabetes prevalence in high-risk middle-aged and older adults with cardiovascular diseases.
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Chen Mingsi, Zhou Xue, Yang Keying, Li Qiaojun, Zhao Liying, Wang Xingren, Lu Ying
Аннотация
OBJECTIVE: This study investigated the association between obesity and lipid markers including the Chinese Visceral Adiposity Index (CVAI), triglyceride-glucose waist-to-height ratio (TyG-WHtR), triglyceride-glucose waist circumference (TyG-WC), triglyceride-glucose body mass index (BMI; TyG-BMI), and conicity index (C-index) with the presence of diabetes among individuals at high cardiovascular disease (CVD) risk in Hainan Province, China, and providing evidence to support early identification of individuals with elevated metabolic burden.
METHODS: Between February 2023 and January 2024, a multistage stratified cluster sampling methodology was used for an initial screening of 6,148 individuals across eight cities and counties in Hainan Province. This screening involved physical measurements, face-to-face questionnaires, and laboratory tests, ultimately identifying 1,603 individuals at high risk for cardiovascular disease. For these high-risk individuals, various indicators were calculated based on their lipid profiles, waist circumference, height, and weight, including the CVAI, TyG-WHtR, TyG-WC, TyG-BMI, and the C-index. Logistic regression and restricted cubic spline (RCS) models were used to evaluate the associations and linear and non-linear associations between these indicators and the presence of diabetes. Additionally, receiver operating characteristic (ROC) curves were generated to evaluate the discriminatory ability of these indicators in distinguishing individuals with and without diabetes within the high-risk cardiovascular disease populations.
RESULTS: This study included 1,603 individuals at high risk for cardiovascular disease, of whom 330 (20.59%) had diabetes. After adjusting for confounding factors using logistic regression, the odds of having diabetes were higher in the Q4 group than in the Q1 group for the CVAI, TyG-WHtR, TyG-WC, and TyG-BMI indicators, but not for the C-index. The corresponding odds ratios (OR) and 95% confidence intervals (CI) were as follows: CVAI was 2.43 (95% CI: 1.41-4.17), TyG-WHtR was 12.80 (95% CI: 7.46-21.96), TyG-WC was 16.41 (95% CI: 9.21-29.24), and TyG-BMI was 5.28 (95% CI: 3.23-8.64). The RCS analyses showed that CVAI, TyG-WHtR, TyG-BMI, and the C-index were positively linearly associated with the presence of diabetes, whereas TyG-WC exhibited a positive nonlinear association with diabetes presence (P < 0.05). ROC curve analysis indicated that TyG-WC had the strongest discriminatory ability, with an area under the curve (AUC) of 0.735 (95% CI: 0.705-0.766), followed by the TyG-WHtR index, which had an AUC of 0.716 (95% CI: 0.685-0.747). The optimal cutoff values determined based on the maximum Youden index were TyG-WC at 797.89 and TyG-WHtR at 5.08, respectively.
CONCLUSION: CVAI, TyG-WHtR, TyG-WC, TyG-BMI, and the C-index were positively associated with diabetes in middle-aged and older adults at high cardiovascular risk. CVAI, TyG-WHtR, TyG-WC, and TyG-BMI showed good discriminatory performance for identifying diabetes, with TyG-WC performing best. These TyG-based and obesity-related composite indicators provide feasible options for diabetes identification and risk stratification in high cardiovascular-risk populations and may support population-level screening and prevention efforts in aging societies.
METHODS: Between February 2023 and January 2024, a multistage stratified cluster sampling methodology was used for an initial screening of 6,148 individuals across eight cities and counties in Hainan Province. This screening involved physical measurements, face-to-face questionnaires, and laboratory tests, ultimately identifying 1,603 individuals at high risk for cardiovascular disease. For these high-risk individuals, various indicators were calculated based on their lipid profiles, waist circumference, height, and weight, including the CVAI, TyG-WHtR, TyG-WC, TyG-BMI, and the C-index. Logistic regression and restricted cubic spline (RCS) models were used to evaluate the associations and linear and non-linear associations between these indicators and the presence of diabetes. Additionally, receiver operating characteristic (ROC) curves were generated to evaluate the discriminatory ability of these indicators in distinguishing individuals with and without diabetes within the high-risk cardiovascular disease populations.
RESULTS: This study included 1,603 individuals at high risk for cardiovascular disease, of whom 330 (20.59%) had diabetes. After adjusting for confounding factors using logistic regression, the odds of having diabetes were higher in the Q4 group than in the Q1 group for the CVAI, TyG-WHtR, TyG-WC, and TyG-BMI indicators, but not for the C-index. The corresponding odds ratios (OR) and 95% confidence intervals (CI) were as follows: CVAI was 2.43 (95% CI: 1.41-4.17), TyG-WHtR was 12.80 (95% CI: 7.46-21.96), TyG-WC was 16.41 (95% CI: 9.21-29.24), and TyG-BMI was 5.28 (95% CI: 3.23-8.64). The RCS analyses showed that CVAI, TyG-WHtR, TyG-BMI, and the C-index were positively linearly associated with the presence of diabetes, whereas TyG-WC exhibited a positive nonlinear association with diabetes presence (P < 0.05). ROC curve analysis indicated that TyG-WC had the strongest discriminatory ability, with an area under the curve (AUC) of 0.735 (95% CI: 0.705-0.766), followed by the TyG-WHtR index, which had an AUC of 0.716 (95% CI: 0.685-0.747). The optimal cutoff values determined based on the maximum Youden index were TyG-WC at 797.89 and TyG-WHtR at 5.08, respectively.
CONCLUSION: CVAI, TyG-WHtR, TyG-WC, TyG-BMI, and the C-index were positively associated with diabetes in middle-aged and older adults at high cardiovascular risk. CVAI, TyG-WHtR, TyG-WC, and TyG-BMI showed good discriminatory performance for identifying diabetes, with TyG-WC performing best. These TyG-based and obesity-related composite indicators provide feasible options for diabetes identification and risk stratification in high cardiovascular-risk populations and may support population-level screening and prevention efforts in aging societies.