ABSTRACT NUMBER: ESOC2026A2021 ASSOCIATION BETWEEN LIPID OBESITY INDICES AND STROKE RISK IN DEPRESSED INDIVIDUALS: ANALYSIS BASED ON CHARLS AND ELSA DATABASES

Abstract Background and aims Depression significantly elevates stroke risk, and metabolic dysregulation may further magnify this burden. However, whether lipid–obesity indices that outperform BMI in general populations can improve risk stratification among depressed individuals remains uncertain, particularly given potential differences between Asian and European populations. This study aimed to investigate the associations between various lipid obesity indices and stroke risk among depressed individuals by analyzing data from the CHARLS and ELSA databases. Methods We analyzed data from depressed participants in the CHARLS and ELSA databases. Three lipid–obesity indices (BRI, AIP, and WHtR) were assessed and categorized into quartiles. Multivariable logistic regression models were applied to estimate the odds ratios (ORs) for stroke risk. Additionally, threshold effect analyses were performed to explore potential non-linear relationships.Subgroup analyses were conducted specifically for BRI, given its consistent associations across both cohorts. Results A total of 9,482 participants with depression scores were included (CHARLS: n=4,028; ELSA: n=5,454). BRI demonstrated a consistent and significant association with stroke risk across both cohorts (CHARLS: OR 1.14, 95% CI: 1.03–1.27, P=0.0109; ELSA: OR 1.32, 95% CI: 1.06–1.65, P=0.0143). According to the results of regression analysis, BRI showed a linear relationship with stroke risk in both populations. Subgroup analyses stratified by age, gender, education, marital status, smoking, and alcohol consumption confirmed the robustness of this association, with no significant interactions across all subgroups. Conclusions BRI consistently associates with stroke risk in depressed adults across Asian and European cohorts, suggesting its potential as a simple, cross-culturally robust metabolic marker beyond traditional BMI. Conflict of interest Xuzi Li:nothing to disclose .Anling Luo: nothing to disclose. Yiting Deng: nothing to disclose.Yujia Yang: nothing to disclose.Qin Yang: nothing to disclose. Yujie Chen:nothing to disclose. Li He: nothing to disclose. Muke Zhou: nothing to disclose Figure 1 - belongs to Methods Table 1 - belongs to Results Table 2 - belongs to Results

Authors

Institutions

Publication Details

Journal
European Stroke Journal
Published
2026-05-01
DOI
https://doi.org/10.1093/esj/aakag023.1706
Primary Topic
Bariatric Surgery and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ABSTRACT NUMBER: ESOC2026A2021 ASSOCIATION BETWEEN LIPID OBESITY INDICES AND STROKE RISK IN DEPRESSED INDIVIDUALS: ANALYSIS BASED ON CHARLS AND ELSA DATABASES

Xuzi Li, Muke Zhou, Hui Li, Yujie Chen et al.
European Stroke Journal
Bariatric Surgery and Outcomes
article

ABSTRACT NUMBER: ESOC2026A2021 ASSOCIATION BETWEEN LIPID OBESITY INDICES AND STROKE RISK IN DEPRESSED INDIVIDUALS: ANALYSIS BASED ON CHARLS AND ELSA DATABASES

Xuzi Li, Muke Zhou, Hui Li, Yujie Chen, Yujia Yang, Anling Luo, Yiting Deng, Qin Yang
article en

Abstract

Abstract Background and aims Depression significantly elevates stroke risk, and metabolic dysregulation may further magnify this burden. However, whether lipid–obesity indices that outperform BMI in general populations can improve risk stratification among depressed individuals remains uncertain, particularly given potential differences between Asian and European populations. This study aimed to investigate the associations between various lipid obesity indices and stroke risk among depressed individuals by analyzing data from the CHARLS and ELSA databases. Methods We analyzed data from depressed participants in the CHARLS and ELSA databases. Three lipid–obesity indices (BRI, AIP, and WHtR) were assessed and categorized into quartiles. Multivariable logistic regression models were applied to estimate the odds ratios (ORs) for stroke risk. Additionally, threshold effect analyses were performed to explore potential non-linear relationships.Subgroup analyses were conducted specifically for BRI, given its consistent associations across both cohorts. Results A total of 9,482 participants with depression scores were included (CHARLS: n=4,028; ELSA: n=5,454). BRI demonstrated a consistent and significant association with stroke risk across both cohorts (CHARLS: OR 1.14, 95% CI: 1.03–1.27, P=0.0109; ELSA: OR 1.32, 95% CI: 1.06–1.65, P=0.0143). According to the results of regression analysis, BRI showed a linear relationship with stroke risk in both populations. Subgroup analyses stratified by age, gender, education, marital status, smoking, and alcohol consumption confirmed the robustness of this association, with no significant interactions across all subgroups. Conclusions BRI consistently associates with stroke risk in depressed adults across Asian and European cohorts, suggesting its potential as a simple, cross-culturally robust metabolic marker beyond traditional BMI. Conflict of interest Xuzi Li:nothing to disclose .Anling Luo: nothing to disclose. Yiting Deng: nothing to disclose.Yujia Yang: nothing to disclose.Qin Yang: nothing to disclose. Yujie Chen:nothing to disclose. Li He: nothing to disclose. Muke Zhou: nothing to disclose Figure 1 - belongs to Methods Table 1 - belongs to Results Table 2 - belongs to Results

European Stroke JournalVol. 11(Supplement_1)
Sichuan University (CN), West China Hospital of Sichuan University (CN)
Good health and well-being
Openalex Percentile: Top 8%
Bariatric Surgery and Outcomes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.