Association of biological age with health outcomes and its modifiable factors

• Published in: Aging cell Vol. 22; no. 12; pp. e13995 - n/a
• Main Authors: Liu, Wei‐Shi, You, Jia, Ge, Yi‐Jun, Wu, Bang‐Sheng, Zhang, Yi, Chen, Shi‐Dong, Zhang, Ya‐Ru, Huang, Shu‐Yi, Ma, Ling‐Zhi, Feng, Jian‐Feng, Cheng, Wei, Yu, Jin‐Tai
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.12.2023; John Wiley and Sons Inc
• Subjects: Age differences; Aging; Aging - genetics; Algorithms; Biobanks; biological age; Blood pressure; Body mass; Brain; C-reactive protein; Cardiovascular Diseases; Child, Preschool; Chronic obstructive pulmonary disease; Cognitive ability; disease; Ethnicity; Genetic analysis; Glomerular filtration rate; Growth factors; Hand Strength; Humans; Metabolic rate; modifiable factor; Mortality; Outcome Assessment, Health Care; Physiology; Regression analysis; Therapeutic targets; unmodifiable factor; United Kingdom > UK; Aging; mortality; disease; biological age; modifiable factor; unmodifiable factor
• ISSN: 1474-9718; 1474-9726; 1474-9726
• DOI: 10.1111/acel.13995

Identifying the clinical implications and modifiable and unmodifiable factors of aging requires the measurement of biological age (BA) and age gap. Leveraging the biomedical traits involved with physical measures, biochemical assays, genomic data, and cognitive functions from the healthy participants in the UK Biobank, we establish an integrative BA model consisting of multi‐dimensional indicators. Accelerated aging (age gap >3.2 years) at baseline is associated incident circulatory diseases, related chronic disorders, all‐cause, and cause‐specific mortality. We identify 35 modifiable factors for age gap (p < 4.81 × 10−4), where pulmonary functions, body mass, hand grip strength, basal metabolic rate, estimated glomerular filtration rate, and C‐reactive protein show the most significant associations. Genetic analyses replicate the possible associations between age gap and health‐related outcomes and further identify CST3 as an essential gene for biological aging, which is highly expressed in the brain and is associated with immune and metabolic traits. Our study profiles the landscape of biological aging and provides insights into the preventive strategies and therapeutic targets for aging. The study included 59,316 healthy participants in the UK Biobank and considered 8276 phenotypes for developing biological age model. LightGBM algorithm was conducted to identify the most important predictors for biological age and build the model and the top 20 predictors were selected. We tested the longitudinal associations of age gap with 70 common health‐related outcomes, all‐cause mortality and cause‐specific mortality, and the genetic correlations of age gap with common health‐related outcomes. We identified 34 modifiable factors and 9 genomic risk loci for age gap and profiled the pleiotropy of rs3761280 in the UK Biobank.