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Published on: June 13, 2025
Causal effects of life-course obesity on sleep apnoea: a Mendelian randomization study
Jinyu Zhang1,2, Jie Min3,2, Haili Wang4,2
1Department of General Surgery, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Huzhou, 313000, China.
Abstract:
Sleep apnoea (SA) is a common condition associated with obesity, conferring significant cardiovascular and metabolic risks. This study investigates the causal effects of obesity phenotypes throughout the life course, from birth weight to adult obesity, on SA risk using Mendelian randomization (MR). A two-sample MR approach was utilized, employing genome-wide association study (GWAS) data from individuals of European ancestry. Exposures included birth weight, childhood obesity, adult obesity classifications (overweight and obesity classes 1-3), and fat distribution characteristics (waist/hip circumference and MRI-derived abdominal adipose tissue volumes). Outcome data were sourced from the FinnGen database. Analyses were conducted using inverse variance weighting (IVW), MR-Egger regression, and sensitivity analyses. A replication analysis using another GWAS dataset on SA was also performed. The analysis demonstrated a positive causal relationship between birth weight and SA risk (OR = 1.142, P < 0.001). Features of childhood obesity, including BMI (OR = 1.218, P < 0.001) and early life body size (OR = 1.533, P < 0.001), were associated with increased SA risk. In adulthood, BMI was strongly associated with SA risk (OR = 2.419, P < 0.001). Fat distribution measures, notably MRI-derived subcutaneous adipose tissue volume (OR = 1.119, P = 0.003) and waist circumference (OR = 1.953, P < 0.001), were also predictors of SA risk. Sensitivity analyses affirmed these findings, suggesting minimal horizontal pleiotropy. The replication analysis confirmed a positive correlation between obesity indicators and SA. This study supports the causal relationship of life-course obesity, including birth weight and obesity, in childhood and adulthood, on SA risk, highlighting the importance of fat distribution metrics in understanding SA determinants.
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