Meta-analysis: overweight, obesity, and Parkinson's disease.
Jinhu ChenZhenlong GuanLiqin WangGuangyao SongBoqing MaYanqin WangPublished in: International journal of endocrinology (2014)
Objective. Parkinson's disease (PD) is a severe neurological disease and its risk factors remain largely unknown. A meta-analysis was carried out to investigate the relationship of overweight and obesity with PD. Methods. We used PubMed, EMBASE, and the Chinese National Knowledge Infrastructure (CNKI) databases to identify studies of associations between overweight/obesity and PD. Overweight, obesity, and PD were used as keywords, and published works were retrieved until September 30, 2013. The extracted data were classified (BMI ≥ 30, 25 ≤ BMI < 30, and BMI < 25) according to BMI values and analyzed using RevMan5.2 and Stata11.0. Results. Four cohort studies and three case-control studies were used to evaluate the association between overweight/obesity and PD, including 2857 PD patients and 5, 683, 939 cases of non-PD controls. There was a statistically significant difference between 25 ≤ BMI < 30 and BMI < 25 in the cohort study (RR = 1.17, 95% CI, 1.03-1.32, P = 0.03), but there was no difference between BMI ≥ 30 and BMI < 25 or BMI ≥ 30 and 25 ≤ BMI < 30, where the respective RR was 1.16 and 0.84; the respective 95% CI was 0.67-2.01 and 0.61-1.15, respectively, and the P values were 0.60 and 0.28, respectively. Case-control studies showed that there was no statistical difference between any two groups. Conclusion. Meta-analysis showed that overweight might be a potential risk factor of PD. Demonstration of a causal role of overweight/obesity in PD development could have important therapeutic implications.
Keyphrases
- weight gain
- case control
- body mass index
- weight loss
- systematic review
- risk factors
- metabolic syndrome
- insulin resistance
- type diabetes
- physical activity
- healthcare
- end stage renal disease
- climate change
- adipose tissue
- chronic kidney disease
- high fat diet induced
- early onset
- ejection fraction
- deep learning
- newly diagnosed
- subarachnoid hemorrhage
- artificial intelligence
- prognostic factors
- electronic health record
- big data
- cerebral ischemia
- patient reported