Association of biological age with health outcomes and its modifiable factors
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 participant...
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| Published in | Aging cell Vol. 22; no. 12; pp. e13995 - n/a |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
John Wiley & Sons, Inc
01.12.2023
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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.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. 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 ), 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. 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. 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. 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. 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. |
| Author | You, Jia Ge, Yi‐Jun Zhang, Ya‐Ru Yu, Jin‐Tai Liu, Wei‐Shi Chen, Shi‐Dong Cheng, Wei Wu, Bang‐Sheng Ma, Ling‐Zhi Zhang, Yi Feng, Jian‐Feng Huang, Shu‐Yi |
| AuthorAffiliation | 1 Department of Neurology and National Center for Neurological Diseases, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Shanghai Medical College, Fudan University Shanghai China 3 Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University), Ministry of Education Shanghai China 5 Department of Computer Science University of Warwick Coventry UK 4 Department of Neurology, Qingdao Municipal Hospital Qingdao University Qingdao China 6 Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired Intelligence Zhejiang Normal University Jinhua China 2 Institute of Science and Technology for Brain‐Inspired Intelligence, Fudan University Shanghai China 7 Shanghai Medical College and Zhongshan Hosptital Immunotherapy Technology Transfer Center Shanghai China |
| AuthorAffiliation_xml | – name: 2 Institute of Science and Technology for Brain‐Inspired Intelligence, Fudan University Shanghai China – name: 1 Department of Neurology and National Center for Neurological Diseases, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Shanghai Medical College, Fudan University Shanghai China – name: 5 Department of Computer Science University of Warwick Coventry UK – name: 6 Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired Intelligence Zhejiang Normal University Jinhua China – name: 7 Shanghai Medical College and Zhongshan Hosptital Immunotherapy Technology Transfer Center Shanghai China – name: 4 Department of Neurology, Qingdao Municipal Hospital Qingdao University Qingdao China – name: 3 Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University), Ministry of Education Shanghai China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37723992$$D View this record in MEDLINE/PubMed |
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| Keywords | Aging mortality disease biological age modifiable factor unmodifiable factor |
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| SubjectTerms | 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 |
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| Title | Association of biological age with health outcomes and its modifiable factors |
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