TyG-ABSI as a novel dual biomarker for stroke risk prediction: a nationwide cohort study of middle-aged and older chinese adults

Stroke is a leading cause of morbidity and mortality worldwide, particularly in aging populations. The triglyceride-glucose index (TyG index) reflects insulin resistance, whereas the Body Shape Index (ABSI) reflects abdominal adiposity. Their combination, TyG-ABSI, may integrate metabolic and phenotypic risk, but its prospective relevance for incident stroke remains insufficiently studied. This study examined the association between TyG-ABSI and incident stroke risk in middle-aged and older Chinese adults, evaluating its utility as a novel predictive biomarker. This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) conducted between 2011 and 2018. A cohort of 7,358 participants with no prior history of stroke was selected through a rigorous screening process that excluded individuals based on specific criteria such as age, incomplete TyG-ABSI data, and missing stroke information. The predictive capacity of TyG-ABSI for incident stroke was evaluated using multivariable Cox proportional hazards regression models, and hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Three models were constructed: an unadjusted model, a model adjusted for age and sex, and a fully adjusted model that included potential confounders such as education level, place of residence, marital status, smoking status, alcohol consumption, hypertension, diabetes mellitus, heart disease, dyslipidemia, and BMI. Additionally, restricted cubic spline (RCS) analysis was performed to explore the potential non-linear relationship between TyG-ABSI and incident stroke. Receiver operating characteristic (ROC) analysis was used to assess both overall model discrimination across nested models and the incremental predictive value of TyG-ABSI beyond conventional clinical risk factors. Elevated TyG-ABSI was associated with higher incident stroke risk (highest vs. lowest quartile: HR = 1.57, 95% CI = 1.18–2.08, P = 0.0019). Urban residents showed a stronger association (HR = 1.04, 95% CI = 1.03–1.05) than rural counterparts (HR = 1.01, 95% CI = 0.98–1.03; P for interaction = 0.038). In incremental prediction analysis, TyG-ABSI provided modest but statistically significant added value beyond conventional clinical risk factors. TyG-ABSI was significantly associated with incident stroke in middle-aged and older Chinese adults and demonstrated a positively associated relationship. As a composite marker integrating metabolic and obesity-related information, TyG-ABSI may provide modest incremental value for stroke risk stratification. Further external validation is warranted before broader clinical application.

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Publication Details

Journal
BMC Neurology
Published
2026-10-09
DOI
https://doi.org/10.1186/s12883-026-05460-w
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
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article

TyG-ABSI as a novel dual biomarker for stroke risk prediction: a nationwide cohort study of middle-aged and older chinese adults

Mingxiang Ding, XIE Bing, Kai Hu
BMC Neurology
Diabetes, Cardiovascular Risks, and Lipoproteins
article

TyG-ABSI as a novel dual biomarker for stroke risk prediction: a nationwide cohort study of middle-aged and older chinese adults

Mingxiang Ding, XIE Bing, Kai Hu
article en

Abstract

Stroke is a leading cause of morbidity and mortality worldwide, particularly in aging populations. The triglyceride-glucose index (TyG index) reflects insulin resistance, whereas the Body Shape Index (ABSI) reflects abdominal adiposity. Their combination, TyG-ABSI, may integrate metabolic and phenotypic risk, but its prospective relevance for incident stroke remains insufficiently studied. This study examined the association between TyG-ABSI and incident stroke risk in middle-aged and older Chinese adults, evaluating its utility as a novel predictive biomarker. This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) conducted between 2011 and 2018. A cohort of 7,358 participants with no prior history of stroke was selected through a rigorous screening process that excluded individuals based on specific criteria such as age, incomplete TyG-ABSI data, and missing stroke information. The predictive capacity of TyG-ABSI for incident stroke was evaluated using multivariable Cox proportional hazards regression models, and hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Three models were constructed: an unadjusted model, a model adjusted for age and sex, and a fully adjusted model that included potential confounders such as education level, place of residence, marital status, smoking status, alcohol consumption, hypertension, diabetes mellitus, heart disease, dyslipidemia, and BMI. Additionally, restricted cubic spline (RCS) analysis was performed to explore the potential non-linear relationship between TyG-ABSI and incident stroke. Receiver operating characteristic (ROC) analysis was used to assess both overall model discrimination across nested models and the incremental predictive value of TyG-ABSI beyond conventional clinical risk factors. Elevated TyG-ABSI was associated with higher incident stroke risk (highest vs. lowest quartile: HR = 1.57, 95% CI = 1.18–2.08, P = 0.0019). Urban residents showed a stronger association (HR = 1.04, 95% CI = 1.03–1.05) than rural counterparts (HR = 1.01, 95% CI = 0.98–1.03; P for interaction = 0.038). In incremental prediction analysis, TyG-ABSI provided modest but statistically significant added value beyond conventional clinical risk factors. TyG-ABSI was significantly associated with incident stroke in middle-aged and older Chinese adults and demonstrated a positively associated relationship. As a composite marker integrating metabolic and obesity-related information, TyG-ABSI may provide modest incremental value for stroke risk stratification. Further external validation is warranted before broader clinical application.

BMC Neurology
Zhongshan People's Hospital (CN)
Openalex Percentile: Top 11%
Diabetes, Cardiovascular Risks, and Lipoproteins
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