Lifestyle Factors and Genetic Variants on 2 Biological Age Measures: Evidence From 94 443 Taiwan Biobank Participants. Issue 6 (24th August 2021) - Lin, Wan-Yu
Abstract: Background: Biological age (BA) can be estimated by phenotypes and is useful for predicting life span and health span. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians. Methods: I have estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94 443 Taiwan Biobank (TWB) participants, wherein 25 460 TWB1 participants formed a discovery cohort and 68 983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study. Results: A unit (kg/m 2 ) increase of body mass index was associated with a 0.177-year PhenoAgeAccel (95% confidence interval [CI] = 0.163–0.191, p = 6.0 × 10 –129 ) and 0.171-year BioAgeAccel (95% CI = 0.165–0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% CI = 0.966–1.303, p = 1.3 × 10 –39 ) compared with nonsmokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% CI = 0.433–0.847, p = 1.3 × 10 –9 ) and 0.193-year BioAgeAccel (95% CI = 0.107–0.279, p = 1.1 × 10 –5 ) relative to nondrinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel ( p < 5 × 10 –8 in both TWB1 and TWB2), respectively. Conclusions: A PhenoAgeAccel-associated SNP (rs1260326 in GCKR ) and 2 BioAgeAccel-associated SNPs (rs7412 in APOE ; rs16998073 near FGF5 ) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.