The obesity paradox in intracerebral hemorrhage: a systematic review and meta-analysis

BACKGROUNDIntracerebral hemorrhage (ICH) has a high mortality rate, and even if patients survive, they are likely to have severe disability. Body mass index (BMI) is associated with ICH risk, and extremely low and high BMIs are associated with the site of ICH, which affects functional decline. However, few reports exist on ICH-related functional decline and BMI. This study aimed to clarify the relationship between BMI and stroke-related disability of patients with ICH.METHODSPatients with ICH registered in the Japanese Registry Of All Cardiac and Vascular Diseases Diagnosis Procedure Combination (JROAD-DPC) database from April 2016 to March 2020 were included. BMI was defined according to the World Health Organization Asia-Pacific classification. Functional disability was assessed using the modified Rankin Scale (mRS). ICH-related functional decline was defined as an increase in mRS score at discharge compared with that of the pre-stroke assessment.RESULTSThis study included 155,211 patients with ICH whose median age was 72.0 years and mean BMI was 22.3 kg/m2. The ratio of patients with ICH who experience functional decline was 74.9%. The spline curve between BMI and ICH-related functional decline was U-shaped, revealing that the Normal to Obese I BMI groups (BMI: 22.2–30.4 kg/m2) exhibited reduced odds ratios for ICH-related functional decline. Hospitalization cost and BMI showed similar U-shaped patterns, with a BMI of 25.0 kg/m2as the lowest point, regardless of age group.CONCLUSIONIn patients with ICH, those with both extremely low and high BMIs were more likely to experience functional decline after ICH onset, which resulted in increased hospitalization costs. To reduce ICH-related functional decline, patients should be managed at a normal to slightly obese BMI.

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Dyslipidemia versus obesity as predictors of ischemic stroke prognosis: a multi-center study in China

Ruan,

Ran,

et al. 2024

Lipids Health Dis

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Background This multicenter observational study aimed to determine whether dyslipidemia or obesity contributes more significantly to unfavorable clinical outcomes in patients experiencing a first-ever ischemic stroke (IS). Methods The study employed a machine learning predictive model to investigate associations among body mass index (BMI), body fat percentage (BFP), high-density lipoprotein (HDL), triglycerides (TG), and total cholesterol (TC) with adverse outcomes in IS patients. Extensive real-world clinical data was utilized, and risk factors significantly linked to adverse outcomes were identified through multivariate analysis, propensity score matching (PSM), and regression discontinuity design (RDD) techniques. Furthermore, these findings were validated via a nationwide multicenter prospective cohort study. Results In the derived cohort, a total of 45,162 patients diagnosed with IS were assessed, with 522 experiencing adverse outcomes. A multifactorial analysis incorporating PSM and RDD methods identified TG (adjusted odds ratio (OR) = 1.110; 95% confidence interval (CI): 1.041–1.183; P < 0.01) and TC (adjusted OR = 1.139; 95%CI: 1.039–1.248; P < 0.01) as risk factors. However, BMI, BFP, and HDL showed no significant effect. In the validation cohort, 1410 controls and 941 patients were enrolled, confirming that lipid levels are more strongly correlated with the prognosis of IS patients compared to obesity (TC, OR = 1.369; 95%CI: 1.069–1.754; P < 0.05; TG, OR = 1.332; 95%CI: 1.097–1.618; P < 0.01). Conclusion This study suggests that dyslipidemia has a more substantial impact on the prognosis of IS patients compared to obesity. This highlights the importance of prioritizing dyslipidemia management in the treatment and prevention of adverse outcomes in IS patients. Graphical abstract

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The obesity paradox in intracerebral hemorrhage: a systematic review and meta-analysis

Cited by 5 publications

References 34 publications

Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score

Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score

U-shaped Relationship Between Body Mass Index and Intracerebral Hemorrhage-Related Functional Decline

Dyslipidemia versus obesity as predictors of ischemic stroke prognosis: a multi-center study in China

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