A J-shaped relation of BMI and stroke: Systematic review and dose–response meta-analysis of 4.43 million participants

Liu, X.; Zhang, D.; Liu, Y.; Sun, Xizhuo; Hou, Yachen; Wang, B.; Ren, Yongcheng; Zhao, Yang; Han, Chulhee; Cheng, Ching‐Li; Liu, F.; Shi, Yan; Chen, X.; Liu, L.; Chen, G.; Hong, Shihao; Zhang, Meng; Hu, Dongsheng

doi:10.1016/j.numecd.2018.07.004

Cited by 46 publications

(38 citation statements)

References 46 publications

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“…28 A recent meta-analysis of 44 cohort studies with over 100,000 cases of stroke found a non-linear, slightly J-shaped association, with a moderate increase in risk at very high BMI levels. 29 The Nurses' Health study of largely middle-aged women 30 found increased BMI to be an important risk factor for IS.…”

Section: Discussionmentioning

confidence: 99%

Obesity, overweight and risk for cardiovascular disease and mortality in young women

Dikaiou

Björck

Adiels

et al. 2020

European Journal of Preventive Cardiology

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Aims The aim of this study was to investigate the relation between body mass index (BMI) in young women, using weight early in pregnancy as a proxy for pre-pregnancy weight, and risk for early cardiovascular disease (CVD) and mortality. Methods and results In this prospective, registry-based study, we used weight data in early pregnancy from women, registered in the Swedish Medical Birth Registry, and who gave birth between 1982 and 2014 ( n = 1,495,499; median age 28.3 years). Of the women, 118,212 (7.9%) were obese (BMI ≥ 30 kg/m2) and 29,630 (2.0%) severely obese (BMI ≥ 35 kg/m2). After a follow-up of median 16.3 years, we identified 3295 and 4375 cases of acute myocardial infarction (AMI) and ischemic stroke (IS) corresponding to 13.4 and 17.8 per 100,000 observation years, respectively, occurring at mean ages of 49.8 and 47.3 years. Compared to women with a BMI 20–<22.5 kg/m2, the hazard ratio (HR) of AMI increased with higher BMI from 1.40 (95% confidence interval (CI) 1.27–1.54) among women with BMI 22.5–<25.0 kg/m2 to 4.71 (95% CI 3.88–5.72) among women with severe obesity, with similar findings for IS and CVD death, after adjustment for age, pregnancy year, parity and comorbidities at baseline. Women with BMI 30–<35.0 and ≥35 kg/m2 had increased all-cause mortality with adjusted HR 1.53 (95% CI 1.43–1.63) and 1.83 (95% CI 1.63–2.05), respectively. Conclusion A significant increase in the risk for early AMI, IS and CVD death was noticeable in overweight young women, with a marked increase in obese women.

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Section: Discussionmentioning

confidence: 99%

Obesity, overweight and risk for cardiovascular disease and mortality in young women

Dikaiou

Björck

Adiels

et al. 2020

European Journal of Preventive Cardiology

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“…Obesity has become a major global public health issue (4,5). Obesity with a high body mass index (BMI ≥28.0) is a risk factor for various chronic diseases, including cardiovascular diseases (6), diabetes mellitus (7), stroke (8), chronic kidney disease (9), cancer (10), musculoskeletal disorders (11) and multimorbidities (12)(13)(14). Accumulating evidence suggests that marital status is closely associated with weight changes (15)(16)(17), with married individuals reported to gain more bodyweight than never-married individuals (16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

Marriage contributes to higher obesity risk in China: findings from the China Health and Nutrition Survey

Liu¹,

Garstka²,

Chai³

et al. 2021

Ann Transl Med

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Background: To investigate the association between marriage and the prevalence of overweight and obesity in China.Methods: We conducted cross-sectional and retrospective cohort analyses using a nationwide sample of 36,310 individuals from the China Health and Nutrition Survey [2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015].Results: The prevalence of overweight and obesity increased from 28.7% to 36.7% and from 8.0% to 14.5% between 2004 and 2015, respectively. The cross-sectional analysis showed that married individuals were at a higher risk of being overweight (OR =2.18; 95% CI, 1.90-2.51) or obese (OR =1.95; 1.57-2.43) than never-married individuals. Divorced/widowed individuals were also at a greater risk of being overweight (OR =1.80; 1.51-2.13) or obese (OR =1.67; 1.28-2.17) than never-married individuals. Retrospective cohort analysis showed that individuals who married during the study were 1.55 (1.13-2.11) times more likely to be overweight than those who remained never-married. Compared to those who remained nevermarried, individuals who remained married were 1.71 (1.42-2.07) and 1.45 (1.11-1.89) times more likely to be overweight and obese. Individuals who became divorced or widowed were more likely to be overweight (RR =1.59; 1.18-2.15) or obese (RR =1.63; 1.08-2.46) than those who remained never-married. However, the risk of being overweight or obese among those who became divorced or widowed did not differ significantly from the risk among those who remained married.Conclusions: Marriage contributes to an increased risk of overweight and obesity in China; however, this risk is not significantly reduced by exiting a marriage.

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“…The results of our study are in accordance with the aforementioned studies, suggesting that elevated homocysteine levels may be a significant marker for predicting ischemic stroke in AC-LVO. Regarding the association between BMI and ischemic stroke, a previous meta-analysis revealed a J-shaped dose-response relationship between being overweight or obese and an increased risk of incident ischemic stroke (23). However, few studies have focused on the relationship between BMI and risks of ischemic stroke subtype (24,25).…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the association between BMI and ischemic stroke, a previous meta-analysis revealed a J-shaped dose-response relationship between being overweight or obese and an increased risk of incident ischemic stroke ( 23 ). However, few studies have focused on the relationship between BMI and risks of ischemic stroke subtype ( 24 , 25 ).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion

et al. 2021

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Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission.Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked.Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57–0.74) for LR, 0.66 (95% CI 0.57–0.74) for RLR, 0.55 (95% CI 0.45–0.64) for RF and 0.67 (95% CI 0.58–0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts.Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.

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A J-shaped relation of BMI and stroke: Systematic review and dose–response meta-analysis of 4.43 million participants

Cited by 46 publications

References 46 publications

Obesity, overweight and risk for cardiovascular disease and mortality in young women

Obesity, overweight and risk for cardiovascular disease and mortality in young women

Marriage contributes to higher obesity risk in China: findings from the China Health and Nutrition Survey

Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion

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