Cargando…
The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study
AIMS: To reveal the impact of eleven risk factors on stroke and provide estimates of the prevention potential. METHODS: We completed a multicenter case-control study in Jiangxi, China, a middle-income area. Neuroimaging examination was performed in all cases. Controls were stroke-free adults recruit...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437343/ https://www.ncbi.nlm.nih.gov/pubmed/36062135 http://dx.doi.org/10.3389/fpubh.2022.815579 |
_version_ | 1784781585582653440 |
---|---|
author | Wu, Yuhang Chen, Xiaoyun Hu, Songbo Zheng, Huilie Chen, Yiying Liu, Jie Xu, Yan Chen, Xiaona Zhu, Liping Yan, Wei |
author_facet | Wu, Yuhang Chen, Xiaoyun Hu, Songbo Zheng, Huilie Chen, Yiying Liu, Jie Xu, Yan Chen, Xiaona Zhu, Liping Yan, Wei |
author_sort | Wu, Yuhang |
collection | PubMed |
description | AIMS: To reveal the impact of eleven risk factors on stroke and provide estimates of the prevention potential. METHODS: We completed a multicenter case-control study in Jiangxi, China, a middle-income area. Neuroimaging examination was performed in all cases. Controls were stroke-free adults recruited from the community in the case concentration area. Conditional logistic regression and unconditional logistic regression were used for subgroup analysis of stroke type, and other groups (sex, age and urban-rural area), respectively. Odds ratios (ORs) and their population attributable risks (PARs) were calculated, with 95% confidence intervals. RESULTS: A total of 43,615 participants (11,735 cases and 31,880 controls) were recruited from February to September 2018, of whom we enrolled 11,729 case-control pairs. Physical inactivity [PAR 69.5% (66.9–71.9%)] and hypertension [53.4% (49.8–56.8%)] were two major risk factors for stroke, followed by high salt intake [23.9% (20.5–27.3%)], dyslipidemia [20.5% (17.1–24.0%)], meat-based diet [17.5% (14.9–20.4%)], diabetes [7.7% (5.9–9.7%)], cardiac causes [5.3% (4.0–6.7%)], alcohol intake [4.7% (0.2–10.0%)], and high homocysteine [4.3% (1.4–7.4%)]. Nine of these factors were associated with ischemic stroke, and five were associated with intracerebral hemorrhage. Collectively, eleven risk factors accounted for 59.9% of the PAR for all stroke (ischemic stroke: 61.0%; intracerebral hemorrhage: 46.5%), and were consistent across sex (men: 65.5%; women: 62.3%), age (≤55: 65.2%; >55: 63.5%), and urban-rural areas (city: 62.2%; county: 65.7%). CONCLUSION: The 11 risk factors associated with stroke identified will provide an important reference for evidence-based planning for stroke prevention in middle-income areas. There is an urgent need to improve awareness, management and control of behavioral and metabolic risk factors, particularly to promote physical activity and reduce blood pressure. |
format | Online Article Text |
id | pubmed-9437343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94373432022-09-03 The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study Wu, Yuhang Chen, Xiaoyun Hu, Songbo Zheng, Huilie Chen, Yiying Liu, Jie Xu, Yan Chen, Xiaona Zhu, Liping Yan, Wei Front Public Health Public Health AIMS: To reveal the impact of eleven risk factors on stroke and provide estimates of the prevention potential. METHODS: We completed a multicenter case-control study in Jiangxi, China, a middle-income area. Neuroimaging examination was performed in all cases. Controls were stroke-free adults recruited from the community in the case concentration area. Conditional logistic regression and unconditional logistic regression were used for subgroup analysis of stroke type, and other groups (sex, age and urban-rural area), respectively. Odds ratios (ORs) and their population attributable risks (PARs) were calculated, with 95% confidence intervals. RESULTS: A total of 43,615 participants (11,735 cases and 31,880 controls) were recruited from February to September 2018, of whom we enrolled 11,729 case-control pairs. Physical inactivity [PAR 69.5% (66.9–71.9%)] and hypertension [53.4% (49.8–56.8%)] were two major risk factors for stroke, followed by high salt intake [23.9% (20.5–27.3%)], dyslipidemia [20.5% (17.1–24.0%)], meat-based diet [17.5% (14.9–20.4%)], diabetes [7.7% (5.9–9.7%)], cardiac causes [5.3% (4.0–6.7%)], alcohol intake [4.7% (0.2–10.0%)], and high homocysteine [4.3% (1.4–7.4%)]. Nine of these factors were associated with ischemic stroke, and five were associated with intracerebral hemorrhage. Collectively, eleven risk factors accounted for 59.9% of the PAR for all stroke (ischemic stroke: 61.0%; intracerebral hemorrhage: 46.5%), and were consistent across sex (men: 65.5%; women: 62.3%), age (≤55: 65.2%; >55: 63.5%), and urban-rural areas (city: 62.2%; county: 65.7%). CONCLUSION: The 11 risk factors associated with stroke identified will provide an important reference for evidence-based planning for stroke prevention in middle-income areas. There is an urgent need to improve awareness, management and control of behavioral and metabolic risk factors, particularly to promote physical activity and reduce blood pressure. Frontiers Media S.A. 2022-08-19 /pmc/articles/PMC9437343/ /pubmed/36062135 http://dx.doi.org/10.3389/fpubh.2022.815579 Text en Copyright © 2022 Wu, Chen, Hu, Zheng, Chen, Liu, Xu, Chen, Zhu and Yan. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Public Health Wu, Yuhang Chen, Xiaoyun Hu, Songbo Zheng, Huilie Chen, Yiying Liu, Jie Xu, Yan Chen, Xiaona Zhu, Liping Yan, Wei The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study |
title | The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study |
title_full | The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study |
title_fullStr | The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study |
title_full_unstemmed | The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study |
title_short | The impact of potentially modifiable risk factors for stroke in a middle-income area of China: A case-control study |
title_sort | impact of potentially modifiable risk factors for stroke in a middle-income area of china: a case-control study |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437343/ https://www.ncbi.nlm.nih.gov/pubmed/36062135 http://dx.doi.org/10.3389/fpubh.2022.815579 |
work_keys_str_mv | AT wuyuhang theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT chenxiaoyun theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT husongbo theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT zhenghuilie theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT chenyiying theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT liujie theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT xuyan theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT chenxiaona theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT zhuliping theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT yanwei theimpactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT wuyuhang impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT chenxiaoyun impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT husongbo impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT zhenghuilie impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT chenyiying impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT liujie impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT xuyan impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT chenxiaona impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT zhuliping impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy AT yanwei impactofpotentiallymodifiableriskfactorsforstrokeinamiddleincomeareaofchinaacasecontrolstudy |