Wellcome Open Research (Sep 2023)

Systematic review and meta-analyses: What has the application of Mendelian randomization told us about the causal effect of adiposity on health outcomes? [version 2; peer review: 1 approved, 2 approved with reservations]

  • Kaitlin H Wade,
  • Si Fang,
  • Nicholas J Timpson,
  • Laura J Corbin,
  • Nancy McBride,
  • Thomas Battram,
  • Matthew A Lee,
  • Charlie Hatcher,
  • Wenxin Wan,
  • Luke A McGuinness

Journal volume & issue
Vol. 7

Abstract

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Mendelian randomization (MR) is increasingly used for generating estimates of the causal impact of exposures on outcomes. Evidence suggests a causal role of excess adipose tissue (adiposity) on many health outcomes. However, this body of work has not been systematically appraised. We systematically reviewed and meta-analysed results from MR studies investigating the association between adiposity and health outcomes prior to the SARS-CoV-2/COVID-19 pandemic (PROSPERO: CRD42018096684). We searched Medline, EMBASE, and bioRxiv up to February 2019 and obtained data on 2,214 MR analyses from 173 included articles. 29 meta-analyses were conducted using data from 34 articles (including 66 MR analyses) and results not able to be meta-analysed were narratively synthesised. Body mass index (BMI) was the predominant exposure used and was primarily associated with an increase in investigated outcomes; the largest effect in the meta-analyses was observed for the association between BMI and polycystic ovary syndrome (estimates reflect odds ratios (OR) per standard deviation change in each adiposity measure): OR = 2.55; 95% confidence interval (CI) = 1.22–5.33. Only colorectal cancer was investigated with two exposures in the meta-analysis: BMI (OR = 1.18; 95% CI = 1.01–1.37) and waist-hip ratio (WHR; OR = 1.48; 95% CI = 1.08–2.03). Broadly, results were consistent across the meta-analyses and narrative synthesis. Consistent with many observational studies, this work highlights the impact of adiposity across a broad spectrum of health outcomes, enabling targeted follow-up analyses. However, missing and incomplete data mean results should be interpreted with caution.

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