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CORRECTION article

Front. Endocrinol., 19 December 2025

Sec. Endocrinology of Aging

Volume 16 - 2025 | https://doi.org/10.3389/fendo.2025.1699622

Correction: The interplay between metabolic health factors and stroke incidence in aging populations

This article is a correction to:

  1. The interplay between metabolic health factors and stroke incidence in aging populations

    1. Read original article
Chonghui Zhang&#x;Chonghui Zhang1†Tao Xiong&#x;Tao Xiong1†Kaili RenKaili Ren2Hongyu WuHongyu Wu1Shanshan Cai*Shanshan Cai3*Liqin Wang*Liqin Wang1*
  • 1Department of Blood Transfusion, The Affiliated Hospital of Qingdao University, Qingdao, China
  • 2Nanjing Medical University, Nanjing, China
  • 3Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom

A Correction on
The interplay between metabolic health factors and stroke incidence in aging populations

By Zhang C, Xiong T, Ren K, Wu H, Cai S and Wang L (2025) Front. Endocrinol. 16:1646643. doi: 10.3389/fendo.2025.1646643

The figures were in the wrong order in all versions of this paper. The correct order appears below. The citations of the figures will not be updated as these were correct.

The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Figure 1
Bar chart titled “Impact of Metabolic Health Factors on Stroke Incidence” showing predictors and their estimated coefficients. Total family income, obesity, and net family wealth show strong positive coefficients, indicating higher stroke incidence, while age and cognitive decline show negative coefficients.

Figure 1. Association of metabolic disorders with stroke risk: logistic regression results from the Health and Retirement Study.

Figure 2
Bar chart titled “Impact of Metabolic Health Factors on Stroke Incidence.” It shows estimated coefficients for predictors like total family income, obesity, and net family wealth, arranged from highest to lowest. Negative coefficients are seen for factors like age, cognitive decline, and memory impairment. The chart compares various factors affecting stroke incidence, highlighting income and obesity as having a greater positive impact.

Figure 2. Association of metabolic disorders with stroke risk: logistic regression results from the english Longitudinal Study of Ageing.

Figure 3
Two radar charts compare feature importance in a Random Forest model for HRS and ELSA datasets. Axes represent hypertension, obesity, hyperlipidemia, and diabetes, with percentages from zero to one hundred.

Figure 3. Metabolic risk factors for stroke: Random Forest analysis results from ELSA and HRS.

Figure 4
ROC curve comparing metabolic disorders' predictive accuracy for stroke using HRS and ELSA datasets. It shows true positive rate versus false positive rate for diabetes, hyperlipidemia, hypertension, and obesity across both datasets.

Figure 4. ROC Curve analysis of metabolic disorders as stroke risk predictors in HRS and ELSA cohorts.

Keywords: Keywords: metabolic disorders, stroke risk, diabetes, hypertension, obesity

Citation: Zhang C, Xiong T, Ren K, Wu H, Cai S and Wang L (2025) Correction: The interplay between metabolic health factors and stroke incidence in aging populations. Front. Endocrinol. 16:1699622. doi: 10.3389/fendo.2025.1699622

Received: 05 September 2025; Accepted: 12 December 2025; Revised: 23 September 2025;
Published: 19 December 2025.

Edited and reviewed by:

Phiwayinkosi V. Dludla, University of Zululand, South Africa

Copyright © 2025 Zhang, Xiong, Ren, Wu, Cai and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shanshan Cai, cy5jYWk2QGxhbmNhc3Rlci5hYy51aw==; Liqin Wang, V2FuZ2xpcWluMDczNkAxNjMuY29t

†These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.