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      Integrating the environmental and genetic architectures of aging and mortality

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          Abstract

          Both environmental exposures and genetics are known to play important roles in shaping human aging. Here we aimed to quantify the relative contributions of environment (referred to as the exposome) and genetics to aging and premature mortality. To systematically identify environmental exposures associated with aging in the UK Biobank, we first conducted an exposome-wide analysis of all-cause mortality ( n = 492,567) and then assessed the associations of these exposures with a proteomic age clock ( n = 45,441), identifying 25 independent exposures associated with mortality and proteomic aging. These exposures were also associated with incident age-related multimorbidity, aging biomarkers and major disease risk factors. Compared with information on age and sex, polygenic risk scores for 22 major diseases explained less than 2 percentage points of additional mortality variation, whereas the exposome explained an additional 17 percentage points. Polygenic risk explained a greater proportion of variation (10.3–26.2%) compared with the exposome for incidence of dementias and breast, prostate and colorectal cancers, whereas the exposome explained a greater proportion of variation (5.5–49.4%) compared with polygenic risk for incidence of diseases of the lung, heart and liver. Our findings provide a comprehensive map of the contributions of environment and genetics to mortality and incidence of common age-related diseases, suggesting that the exposome shapes distinct patterns of disease and mortality risk, irrespective of polygenic disease risk.

          Abstract

          Based on a systematic analysis of environmental exposures associated with aging and mortality in the UK Biobank, the relative contributions of such exposures and genetic risk for mortality and a range of age-related diseases were compared, highlighting the potential beneficial effects of environment-focused interventions.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The UK Biobank resource with deep phenotyping and genomic data

            The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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              Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population

              Abstract The UK Biobank cohort is a population-based cohort of 500,000 participants recruited in the United Kingdom (UK) between 2006 and 2010. Approximately 9.2 million individuals aged 40–69 years who lived within 25 miles (40 km) of one of 22 assessment centers in England, Wales, and Scotland were invited to enter the cohort, and 5.5% participated in the baseline assessment. The representativeness of the UK Biobank cohort was investigated by comparing demographic characteristics between nonresponders and responders. Sociodemographic, physical, lifestyle, and health-related characteristics of the cohort were compared with nationally representative data sources. UK Biobank participants were more likely to be older, to be female, and to live in less socioeconomically deprived areas than nonparticipants. Compared with the general population, participants were less likely to be obese, to smoke, and to drink alcohol on a daily basis and had fewer self-reported health conditions. At age 70–74 years, rates of all-cause mortality and total cancer incidence were 46.2% and 11.8% lower, respectively, in men and 55.5% and 18.1% lower, respectively, in women than in the general population of the same age. UK Biobank is not representative of the sampling population; there is evidence of a “healthy volunteer” selection bias. Nonetheless, valid assessment of exposure-disease relationships may be widely generalizable and does not require participants to be representative of the population at large.
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                Author and article information

                Contributors
                aargentieri@mgh.harvard.edu
                cornelia.vanduijn@dph.ox.ac.uk
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                19 February 2025
                19 February 2025
                2025
                : 31
                : 3
                : 1016-1025
                Affiliations
                [1 ]Nuffield Department of Population Health, University of Oxford, ( https://ror.org/052gg0110) Oxford, UK
                [2 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, ( https://ror.org/002pd6e78) Boston, MA USA
                [3 ]Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, ( https://ror.org/05a0ya142) Boston, MA USA
                [4 ]Department of Psychiatry, University of Oxford, ( https://ror.org/052gg0110) Oxford, UK
                [5 ]Department of Epidemiology and Data Science, Amsterdam UMC, University of Amsterdam, ( https://ror.org/04dkp9463) Amsterdam, The Netherlands
                [6 ]Amsterdam Reproduction and Development, Amsterdam UMC, University of Amsterdam, ( https://ror.org/04dkp9463) Amsterdam, The Netherlands
                [7 ]Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, ( https://ror.org/018906e22) Rotterdam, The Netherlands
                [8 ]Department of Epidemiology, Harvard TH Chan School of Public Health, ( https://ror.org/03vek6s52) Boston, MA, USA
                [9 ]ISEM, University of Montpellier, CNRS, IRD, ( https://ror.org/051escj72) Montpellier, France
                Author information
                http://orcid.org/0000-0003-0242-853X
                http://orcid.org/0000-0002-8944-1771
                http://orcid.org/0000-0001-9276-2720
                http://orcid.org/0000-0002-8010-2503
                http://orcid.org/0000-0001-9608-8112
                http://orcid.org/0000-0002-9476-7143
                http://orcid.org/0000-0002-3151-9919
                http://orcid.org/0000-0002-2374-9204
                Article
                3483
                10.1038/s41591-024-03483-9
                11922759
                39972219
                d2676ac4-13e1-40d9-a180-302f162497a2
                © The Author(s) 2025, corrected publication 2025

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 May 2023
                : 18 December 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004191, Novo Nordisk;
                Funded by: FundRef https://doi.org/10.13039/501100002066, GlaxoSmithKline foundation (GSK);
                Funded by: Ono Pharma
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 223100/Z/21/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100006505, Swiss Re;
                Funded by: FundRef https://doi.org/10.13039/501100000274, British Heart Foundation (BHF);
                Award ID: RE/18/3/34214
                Award Recipient :
                Funded by: Health Data Research UK
                Funded by: common mechanisms and pathways in Stroke and Alzheimer’s disease (CoSTREAM) project (grant agreement No. 667375), and ZonMW Memorabel program (project number 733050814).
                Categories
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                Custom metadata
                © Springer Nature America, Inc. 2025

                Medicine
                risk factors,epidemiology,predictive markers,metabolic disorders,diagnosis
                Medicine
                risk factors, epidemiology, predictive markers, metabolic disorders, diagnosis

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