Background: Stroke remains a leading cause of morbidity and mortality in aging populations, and its risk is closely linked to metabolic disorders such as diabetes, hypertension, hyperlipidemia, and obesity. To better understand these relationships, this study aimed to quantify the associations between key metabolic health factors and both stroke incidence and cognitive outcomes in older adults, and to evaluate the predictive value of these metabolic factors for stroke risk through cross-sectional secondary analyses of two population-based cohort datasets. Methods: We analyzed data from the Health and Retirement Study (HRS; n = 7,322) and the English Longitudinal Study of Ageing (ELSA; n = 6,630). Associations with stroke incidence were assessed using multivariable logistic regression, and associations with cognitive outcomes were examined using multivariable linear regression. Random forest models evaluated the relative importance of metabolic factors for stroke prediction, with discrimination quantified by ROC curves (AUC). Mediation analyses explored whether stroke mediated the link between metabolic disorders and cognition. Results: Across both cohorts, diabetes and hypertension were consistently associated with higher odds of stroke, while obesity and hyperlipidemia showed smaller and cohort-dependent associations. Random forest analyses identified hypertension and diabetes as the strongest predictors of stroke. ROC analyses showed moderate discriminative performance for single metabolic factors (AUCs generally 0.70 – 0.80), with obesity performing weakest. Metabolic disorders—particularly diabetes and hypertension—were associated with worse cognitive performance, whereas hyperlipidemia showed small positive associations with certain cognitive measures in some models. Mediation analyses suggested that part of the adverse impact of metabolic disorders on cognition operates through stroke. Conclusions: Metabolic health is strongly linked to stroke risk and cognitive outcomes in older adults. Targeted detection and management of hypertension and diabetes should be prioritized to reduce stroke burden and cognitive decline. Given only moderate predictive performance of single metabolic markers, comprehensive risk models integrating socioeconomic, clinical, and lifestyle factors are warranted for improved stroke risk stratification.

Frontiers' correction template for authors A correction refers to a change to their article that the author wishes to publish after publication. The publication of this article is subject to Frontiers' editorial approval. Instructions: ● Please read through all the templates before choosing ● Pick the most relevant text template(s) from the following page and delete all others. ● Edit the text as necessary, ensuring that the original incorrect text is included for the record, please see the below. ● Please do not use any extra formatting when editing the templates, and only modify the red text unless absolutely necessary ● Submit to Frontiers following the instructions on this page. When the original text contained incorrect information, to preserve the scientific record, please include that text when editing the below templates. For example: There was a mistake in the Funding statement, an incorrect number was used. The correct number is "2015C03Bd051.". The publisher apologizes for this mistake. The original version of this article has been updated. In the published article, there was a mistake in the Funding statement. The funding statement for the Key Development Project of the Department of Science and Technology was displayed as "2015CBd051". The correct statement is "Key Development Project of Department of Science and Technology (2015C03Bd051).'' Template continues on the next page Correction: The Interplay between Metabolic Health Factors and Stroke Incidence in Aging Populations Chonghui Zhang1#, Tao Xiong1#, Kaili Ren2, Hongyu Wu1, Shanshan Cai3*, Liqin Wang1* 1Department of Blood Transfusion, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China, 266000 2Nanjing Medical University, Nanjing, China , 210000 3Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, UK #Chonghui Zhang and Tao Xiong have made the same contribution to this article. *Correspondence: Liqin Wang:Wangliqin0736@163.com Shanshan Cai: s.cai6@lancaster.ac.uk Keywords: Metabolic disorders; Stroke risk; Diabetes; Hypertension; Obesity Correction on: Figure order The figures were in the wrong order in ALL versions of this paper, the correct order is the image upload order.

Existing research is limited, and evidence suggests that urinary incontinence may be induced by certain medications, highlighting the urgent need for systematic studies. This study uses the reporting odds ratio (ROR) to evaluate the reports of drug-induced urinary incontinence in the FAERS database from the first quarter of 2004 to the fourth quarter of 2024.Univariate analysis, LASSO regression, and multivariate regression analysis were conducted to further explore the risk factors for drug-induced urinary incontinence. Bonferroni correction was applied to the results of the multiple comparisons. Additionally, the Weibull distribution test was used to assess the temporal characteristics of drug-induced urinary incontinence. Multivariate regression analysis ultimately identified 19 medications as independent risk factors for drug-induced urinary incontinence, including neuropsychiatric drugs (13/19), gastrointestinal and metabolic drugs (2/19), musculoskeletal system drugs (2/19), cardiovascular drugs (1/19), and urogenital and sex hormone drugs (1/19). Over half (57.12%) of the cases of drug-induced urinary incontinence occurred within 30 days after the initiation of medication. This study provides important insights for clinicians in preventing drug-induced urinary incontinence. However, future mechanistic studies and randomized controlled trials are needed to further elucidate and validate these findings.