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Bian Z, Wang L, Fan R, Sun J, Yu L, Xu M, Timmers PRHJ, Shen X, Wilson JF, Theodoratou E, Wu X, Li X. Genetic predisposition, modifiable lifestyles, and their joint effects on human lifespan: evidence from multiple cohort studies. BMJ Evid Based Med 2024; 29:255-263. [PMID: 38684374 DOI: 10.1136/bmjebm-2023-112583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE To investigate the associations across genetic and lifestyle factors with lifespan. DESIGN A longitudinal cohort study. SETTING UK Biobank. PARTICIPANTS 353 742 adults of European ancestry, who were recruited from 2006 to 2010 and were followed up until 2021. EXPOSURES A polygenic risk score for lifespan with long (highest quintile) risk categories and a weighted healthy lifestyle score, including no current smoking, moderate alcohol consumption, regular physical activity, healthy body shape, adequate sleep duration, and a healthy diet, categorised into favourable, intermediate, and unfavourable lifestyles. MAIN OUTCOME MEASURES Lifespan defined as the date of death or the censor date minus the date of birth. RESULTS Of the included 353 742 participants of European ancestry with a median follow-up of 12.86 years, 24 239 death cases were identified. Participants were grouped into three genetically determined lifespan categories including long (20.1%), intermediate (60.1%), and short (19.8%), and into three lifestyle score categories including favourable (23.1%), intermediate (55.6%), and unfavourable (21.3%). The hazard ratio (HR) of death for individuals with a genetic predisposition to a short lifespan was 1.21 (95% CI 1.16 to 1.26) compared to those with a genetic predisposition to a long lifespan. The HR of death for individuals in the unfavourable lifestyle category was 1.78 (95% CI 1.71 to 1.85), compared with those in the favourable lifestyle category. Participants with a genetic predisposition to a short lifespan and an unfavourable lifestyle had 2.04 times (95% CI 1.87 to 2.22) higher rates of death compared with those with a genetic predisposition to a long lifespan and a favourable lifestyle. No multiplicative interaction was detected between the polygenic risk score of lifespan and the weighted healthy lifestyle score (p=0.10). The optimal combination of healthy lifestyles, including never smoking, regular physical activity, adequate sleep duration, and a healthy diet, was derived to decrease risk of premature death (death before 75 years). CONCLUSION Genetic and lifestyle factors were independently associated with lifespan. Adherence to healthy lifestyles could largely attenuate the genetic risk of a shorter lifespan or premature death. The optimal combination of healthy lifestyles could convey better benefits for a longer lifespan, regardless of genetic background.
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Affiliation(s)
- Zilong Bian
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Lijuan Wang
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Rong Fan
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jing Sun
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lili Yu
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Meihong Xu
- Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing, China
| | - Paul R H J Timmers
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Xia Shen
- Department of Biostatistics, School of Public Health and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
| | - James F Wilson
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Xifeng Wu
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
| | - Xue Li
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
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2
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Lau CE, Manou M, Markozannes G, Ala‐Korpela M, Ben‐Shlomo Y, Chaturvedi N, Engmann J, Gentry‐Maharaj A, Herzig K, Hingorani A, Järvelin M, Kähönen M, Kivimäki M, Lehtimäki T, Marttila S, Menon U, Munroe PB, Palaniswamy S, Providencia R, Raitakari O, Schmidt AF, Sebert S, Wong A, Vineis P, Tzoulaki I, Robinson O. NMR metabolomic modeling of age and lifespan: A multicohort analysis. Aging Cell 2024; 23:e14164. [PMID: 38637937 PMCID: PMC11258446 DOI: 10.1111/acel.14164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.
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Affiliation(s)
- Chung‐Ho E. Lau
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
| | - Maria Manou
- Department of Hygiene and EpidemiologyUniversity of Ioannina Medical SchoolIoanninaGreece
| | - Georgios Markozannes
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Department of Hygiene and EpidemiologyUniversity of Ioannina Medical SchoolIoanninaGreece
| | - Mika Ala‐Korpela
- Systems Epidemiology, Faculty of MedicineUniversity of OuluOuluFinland
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
- Biocenter OuluUniversity of OuluOuluFinland
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health SciencesUniversity of Eastern FinlandKuopioFinland
| | | | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCLUniversity College LondonLondonUK
| | - Jorgen Engmann
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational GenomicsLondonUK
| | - Aleksandra Gentry‐Maharaj
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and MethodologyUniversity College LondonLondonUK
- Department of Women's Cancer, Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
| | - Karl‐Heinz Herzig
- Institute of Biomedicine and Internal Medicine, Biocenter of Oulu, Medical Research Center Oulu, Oulu University Hospital, Faculty of MedicineOulu UniversityOuluFinland
- Department of Pediatric Gastroenterology and Metabolic DiseasesPoznan University of Medical SciencesPoznanPoland
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational GenomicsLondonUK
| | - Marjo‐Riitta Järvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
- Department of Life Sciences, College of Health and Life SciencesBrunel University LondonLondonUK
| | - Mika Kähönen
- Department of Clinical PhysiologyTampere University HospitalTampereFinland
- Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | | | - Terho Lehtimäki
- Faculty of Medicine and Health Technology and Finnish Cardiovascular Research Center TampereTampere UniversityTampereFinland
- Department of Clinical Chemistry Fimlab LaboratoriesTampereFinland
| | - Saara Marttila
- Molecular Epidemiology, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
- Gerontology Research Center (GEREC)Tampere UniversityTampereFinland
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and MethodologyUniversity College LondonLondonUK
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and DentistryQueen Mary University of LondonLondonUK
- National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research CentreQueen Mary University of LondonLondonUK
| | - Saranya Palaniswamy
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
| | - Rui Providencia
- Institute of Health Informatics Research, University College LondonLondonUK
- Barts Heart Centre, Barts Health NHS TrustLondonUK
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University HospitalTurkuFinland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of TurkuTurkuFinland
- Department of Clinical Physiology and Nuclear MedicineTurku University HospitalTurkuFinland
| | - Amand Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College LondonLondonUK
- Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
- UCL BHF Research Accelerator CentreLondonUK
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCLUniversity College LondonLondonUK
| | - Paolo Vineis
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Biomedical Research Foundation, Academy of AthensAthensGreece
| | - Oliver Robinson
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Ageing Epidemiology (AGE) Research Unit, School of Public HealthImperial College LondonLondonUK
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3
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Khan M, Ludl AA, Bankier S, Björkegren JLM, Michoel T. Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables. ARXIV 2024:arXiv:2401.06261v2. [PMID: 38259344 PMCID: PMC10802687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.
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Affiliation(s)
- Mariyam Khan
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Adriaan-Alexander Ludl
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Sean Bankier
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Johan LM Björkegren
- Department of Medicine (Huddinge), Karolinska Institutet, Huddinge, Sweden
- Department of Genetics & Genomic Sciences/Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
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4
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Li H, Wu S, Li J, Xiong Z, Yang K, Ye W, Ren J, Wang Q, Xiong M, Zheng Z, Zhang S, Han Z, Yang P, Jiang B, Ping J, Zuo Y, Lu X, Zhai Q, Yan H, Wang S, Ma S, Zhang B, Ye J, Qu J, Yang YG, Zhang F, Liu GH, Bao Y, Zhang W. HALL: a comprehensive database for human aging and longevity studies. Nucleic Acids Res 2024; 52:D909-D918. [PMID: 37870433 PMCID: PMC10767887 DOI: 10.1093/nar/gkad880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/15/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Diverse individuals age at different rates and display variable susceptibilities to tissue aging, functional decline and aging-related diseases. Centenarians, exemplifying extreme longevity, serve as models for healthy aging. The field of human aging and longevity research is rapidly advancing, garnering significant attention and accumulating substantial data in recent years. Omics technologies, encompassing phenomics, genomics, transcriptomics, proteomics, metabolomics and microbiomics, have provided multidimensional insights and revolutionized cohort-based investigations into human aging and longevity. Accumulated data, covering diverse cells, tissues and cohorts across the lifespan necessitates the establishment of an open and integrated database. Addressing this, we established the Human Aging and Longevity Landscape (HALL), a comprehensive multi-omics repository encompassing a diverse spectrum of human cohorts, spanning from young adults to centenarians. The core objective of HALL is to foster healthy aging by offering an extensive repository of information on biomarkers that gauge the trajectory of human aging. Moreover, the database facilitates the development of diagnostic tools for aging-related conditions and empowers targeted interventions to enhance longevity. HALL is publicly available at https://ngdc.cncb.ac.cn/hall/index.
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Affiliation(s)
- Hao Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Song Wu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuang Xiong
- Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou 350002, China
| | - Kuan Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Weidong Ye
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
- The Joint Innovation Center for Engineering in Medicine, Quzhou Affiliated Hospital of Wenzhou Medical University, 324000, China
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Muzhao Xiong
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zikai Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuo Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zichu Han
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Beier Jiang
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
| | - Jiale Ping
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuesheng Zuo
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyong Lu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiaocheng Zhai
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
| | - Haoteng Yan
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Si Wang
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Shuai Ma
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Bing Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jinlin Ye
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Yun-Gui Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Feng Zhang
- The Joint Innovation Center for Engineering in Medicine, Quzhou Affiliated Hospital of Wenzhou Medical University, 324000, China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Yiming Bao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
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5
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Lau CHE, Manou M, Markozannes G, Ala-Korpela M, Ben-Shlomo Y, Chaturvedi N, Engmann J, Gentry-Maharaj A, Herzig KH, Hingorani A, Järvelin MR, Kähönen M, Kivimäki M, Lehtimäki T, Marttila S, Menon U, Munroe PB, Palaniswamy S, Providencia R, Raitakari O, Schmidt F, Sebert S, Wong A, Vineis P, Tzoulaki I, Robinson O. NMR metabolomic modelling of age and lifespan: a multi-cohort analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.07.23298200. [PMID: 37986811 PMCID: PMC10659522 DOI: 10.1101/2023.11.07.23298200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Metabolomic age models have been proposed for the study of biological aging, however they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. 98 metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈ 31,000 individuals, age range 24-86 years). We used non-linear and penalised regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with ageing risk factors and phenotypes. Within the UK Biobank (N≈ 102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, chronic obstructive pulmonary disease) and all-cause mortality. Cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47-0.65 in the training set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with chronological age were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06 / metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.
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Affiliation(s)
- Chung-Ho E. Lau
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Maria Manou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Georgios Markozannes
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Yoav Ben-Shlomo
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, UK
| | - Jorgen Engmann
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational Genomics
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, UCL, London, UK
| | - Karl-Heinz Herzig
- Institute of Biomedicine and Internal Medicine, Medical Research Center Oulu, Oulu University Hospital, Faculty of Medicine, Oulu University; Finland
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poland
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational Genomics
| | - Marjo-Riitta Järvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kivimäki
- Brain Sciences, University College London, London, UK
| | - Terho Lehtimäki
- Faculty of Medicine and Health Technology and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
- Department of Clinical Chemistry Fimlab Laboratories, Tampere, Finland
| | - Saara Marttila
- Molecular Epidemiology, Faculty of Medicine and Health Technology, Tampere University, Finland
- Gerontology Research Center (GEREC), Tampere University, Finland
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, UK
- National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, UK
| | - Saranya Palaniswamy
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Rui Providencia
- Institute of Health Informatics Research, University College London, London, UK
- Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- UCL BHF Research Accelerator Centre, London, UK
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, UK
| | - Paolo Vineis
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Oliver Robinson
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK
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6
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Wang J, Chen C, Zhou J, Ye L, Li Y, Xu L, Xu Z, Li X, Wei Y, Liu J, Lv Y, Shi X. Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study. THE LANCET. HEALTHY LONGEVITY 2023; 4:e535-e543. [PMID: 37804845 DOI: 10.1016/s2666-7568(23)00140-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Lifestyle and longevity genes have different and important roles in the human lifespan; however, the association between a healthy lifestyle in late-life and life expectancy mediated by genetic risk is yet to be elucidated. We aimed to investigate the associations of healthy lifestyle in late-life and genetic risk with life expectancy among older adults. METHODS A weighted healthy lifestyle score was constructed from the following variables: current non-smoking, non-harmful alcohol consumption, regular physical activity, and a healthy diet. Participants were recruited from the Chinese Longitudinal Healthy Longevity Survey, a prospective community-based cohort study that took place between 1998 and 2018. Eligible participants were aged 65 years and older with available information on lifestyle factors at baseline, and then were categorised into unhealthy (bottom tertile of the weighted healthy lifestyle score), intermediate (middle tertile), and healthy (top tertile) lifestyle groups. A genetic risk score was constructed based on 11 lifespan loci among 9633 participants, divided by the median and classified into low and high genetic risk groups. Stratified Cox proportional hazard regression was used to estimate the interaction between genetic and lifestyle factors on all-cause mortality risk. FINDINGS Between Jan 13, 1998, and Dec 31, 2018, 36 164 adults aged 65 years and older were recruited, among whom a total of 27 462 deaths were documented during a median follow-up of 3·12 years (IQR 1·62-5·94) and included in the lifestyle association analysis. Compared with the unhealthy lifestyle category, participants in the healthy lifestyle group had a lower all-cause mortality risk (hazard ratio [HR] 0·56 [95% CI 0·54-0·57]; p<0·0001). The highest mortality risk was observed in individuals in the high genetic risk and unhealthy lifestyle group (HR 1·80 [95% CI 1·63-1·98]; p<0·0001). The absolute risk reduction was greater for participants in the high genetic risk group. A healthy lifestyle was associated with a gain of 3·84 years (95% CI 3·05-4·64) at the age of 65 years in the low genetic risk group, and 4·35 years (3·70-5·06) in the high genetic risk group. INTERPRETATION A healthy lifestyle, even in late-life, was associated with lower mortality risk and longer life expectancy among Chinese older adults, highlighting the importance of a healthy lifestyle in extending the lifespan, especially for individuals with high genetic risk. FUNDING National Natural Science Foundation of China. TRANSLATION For the Mandarin translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Lanjing Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Zinan Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Hygienic Inspection, School of Public Health, Jilin University, Changchun, China
| | - Junxin Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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7
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Sadeqi MB, Ballvora A, Léon J. Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat. Int J Mol Sci 2023; 24:14011. [PMID: 37762314 PMCID: PMC10531084 DOI: 10.3390/ijms241814011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/02/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified.
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Affiliation(s)
| | - Agim Ballvora
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
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8
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Rosoff DB, Mavromatis LA, Bell AS, Wagner J, Jung J, Marioni RE, Davey Smith G, Horvath S, Lohoff FW. Multivariate genome-wide analysis of aging-related traits identifies novel loci and new drug targets for healthy aging. NATURE AGING 2023; 3:1020-1035. [PMID: 37550455 PMCID: PMC10432278 DOI: 10.1038/s43587-023-00455-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 06/07/2023] [Indexed: 08/09/2023]
Abstract
The concept of aging is complex, including many related phenotypes such as healthspan, lifespan, extreme longevity, frailty and epigenetic aging, suggesting shared biological underpinnings; however, aging-related endpoints have been primarily assessed individually. Using data from these traits and multivariate genome-wide association study methods, we modeled their underlying genetic factor ('mvAge'). mvAge (effective n = ~1.9 million participants of European ancestry) identified 52 independent variants in 38 genomic loci. Twenty variants were novel (not reported in input genome-wide association studies). Transcriptomic imputation identified age-relevant genes, including VEGFA and PHB1. Drug-target Mendelian randomization with metformin target genes showed a beneficial impact on mvAge (P value = 8.41 × 10-5). Similarly, genetically proxied thiazolidinediones (P value = 3.50 × 10-10), proprotein convertase subtilisin/kexin 9 inhibition (P value = 1.62 × 10-6), angiopoietin-like protein 4, beta blockers and calcium channel blockers also had beneficial Mendelian randomization estimates. Extending the drug-target Mendelian randomization framework to 3,947 protein-coding genes prioritized 122 targets. Together, these findings will inform future studies aimed at improving healthy aging.
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Affiliation(s)
- Daniel B Rosoff
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
- NIH-Oxford-Cambridge Scholars Program; Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Lucas A Mavromatis
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Andrew S Bell
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Josephin Wagner
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Jeesun Jung
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Steve Horvath
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- San Diego Institute of Science, Alto Labs, San Diego, CA, USA
| | - Falk W Lohoff
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
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9
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Zhang W, Huang Q, Kang Y, Li H, Tan G. Which Factors Influence Healthy Aging? A Lesson from the Longevity Village of Bama in China. Aging Dis 2023; 14:825-839. [PMID: 37191421 PMCID: PMC10187713 DOI: 10.14336/ad.2022.1108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022] Open
Abstract
A growing aging population is associated with increasing incidences of aging-related diseases and socioeconomic burdens. Hence, research into healthy longevity and aging is urgently needed. Longevity is an important phenomenon in healthy aging. The present review summarizes the characteristics of longevity in the elderly population in Bama, China, where the proportion of centenarians is 5.7-fold greater than the international standard. We examined the impact of genetic and environmental factors on longevity from multiple perspectives. We proposed that the phenomenon of longevity in this region is of high value for future investigations in healthy aging and aging-related disease and may provide guidance for fostering the establishment and maintenance of a healthy aging society.
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Affiliation(s)
- Wei Zhang
- Department of Human Anatomy, Institute of Neuroscience and Guangxi Key Laboratory of Brain Science, Guangxi Health Commission Key Laboratory of Basic Research on Brain Function and Disease, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
- Key Laboratory of Longevity and Aging-related Diseases of Chinese Ministry of Education, Nanning, Guangxi, China.
- China-ASEAN Research Center for Innovation and Development in Brain Science, Nanning, Guangxi, China.
| | - Qingyun Huang
- Department of Human Anatomy, Institute of Neuroscience and Guangxi Key Laboratory of Brain Science, Guangxi Health Commission Key Laboratory of Basic Research on Brain Function and Disease, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
- Key Laboratory of Longevity and Aging-related Diseases of Chinese Ministry of Education, Nanning, Guangxi, China.
- China-ASEAN Research Center for Innovation and Development in Brain Science, Nanning, Guangxi, China.
| | - Yongxin Kang
- Department of Human Anatomy, Institute of Neuroscience and Guangxi Key Laboratory of Brain Science, Guangxi Health Commission Key Laboratory of Basic Research on Brain Function and Disease, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Key Laboratory of Regenerative Medicine, Nanning, Guangxi, China.
- China-ASEAN Research Center for Innovation and Development in Brain Science, Nanning, Guangxi, China.
| | - Hao Li
- Department of Human Anatomy, Institute of Neuroscience and Guangxi Key Laboratory of Brain Science, Guangxi Health Commission Key Laboratory of Basic Research on Brain Function and Disease, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Key Laboratory of Regenerative Medicine, Nanning, Guangxi, China.
- China-ASEAN Research Center for Innovation and Development in Brain Science, Nanning, Guangxi, China.
| | - Guohe Tan
- Department of Human Anatomy, Institute of Neuroscience and Guangxi Key Laboratory of Brain Science, Guangxi Health Commission Key Laboratory of Basic Research on Brain Function and Disease, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
- Key Laboratory of Longevity and Aging-related Diseases of Chinese Ministry of Education, Nanning, Guangxi, China.
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Key Laboratory of Regenerative Medicine, Nanning, Guangxi, China.
- China-ASEAN Research Center for Innovation and Development in Brain Science, Nanning, Guangxi, China.
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10
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Akiyama M, Sakaue S, Takahashi A, Ishigaki K, Hirata M, Matsuda K, Momozawa Y, Okada Y, Ninomiya T, Terao C, Murakami Y, Kubo M, Kamatani Y. Genome-wide association study reveals BET1L associated with survival time in the 137,693 Japanese individuals. Commun Biol 2023; 6:143. [PMID: 36737517 PMCID: PMC9898503 DOI: 10.1038/s42003-023-04491-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
Human lifespan is reported to be heritable. Although previous genome-wide association studies (GWASs) have identified several loci, a limited number of studies have assessed the genetic associations with the real survival information on the participants. We conducted a GWAS to identify loci associated with survival time in the Japanese individuals participated in the BioBank Japan Project by carrying out sex-stratified GWASs involving 78,029 males and 59,664 females. Of them, 31,324 (22.7%) died during the mean follow-up period of 7.44 years. We found a novel locus associated with survival (BET1L; P = 5.89 × 10-9). By integrating with eQTL data, we detected a significant overlap with eQTL of BET1L in skeletal muscle. A gene-set enrichment analysis showed that genes related to the BCAR1 protein-protein interaction subnetwork influence survival time (P = 1.54 × 10-7). These findings offer the candidate genes and biological mechanisms associated with human lifespan.
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Affiliation(s)
- Masato Akiyama
- grid.509459.40000 0004 0472 0267Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.509459.40000 0004 0472 0267Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.177174.30000 0001 2242 4849Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582 Japan
| | - Saori Sakaue
- grid.509459.40000 0004 0472 0267Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.509459.40000 0004 0472 0267Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.136593.b0000 0004 0373 3971Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871 Japan
| | - Atsushi Takahashi
- grid.509459.40000 0004 0472 0267Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.509459.40000 0004 0472 0267Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.410796.d0000 0004 0378 8307Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Osaka, 564-8565 Japan
| | - Kazuyoshi Ishigaki
- grid.509459.40000 0004 0472 0267Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.509459.40000 0004 0472 0267Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan
| | - Makoto Hirata
- grid.26999.3d0000 0001 2151 536XLaboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639 Japan
| | - Koichi Matsuda
- grid.26999.3d0000 0001 2151 536XLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639 Japan
| | - Yukihide Momozawa
- grid.509459.40000 0004 0472 0267Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan
| | - Yukinori Okada
- grid.509459.40000 0004 0472 0267Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.509459.40000 0004 0472 0267Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.136593.b0000 0004 0373 3971Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871 Japan ,grid.509459.40000 0004 0472 0267Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan
| | - Toshiharu Ninomiya
- grid.177174.30000 0001 2242 4849Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Fukuoka, 812-8582 Japan
| | | | - Chikashi Terao
- grid.509459.40000 0004 0472 0267Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan ,grid.509459.40000 0004 0472 0267Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan
| | - Yoshinori Murakami
- grid.26999.3d0000 0001 2151 536XDivision of Molecular Pathology, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639 Japan
| | - Michiaki Kubo
- grid.509459.40000 0004 0472 0267RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. .,Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. .,Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639, Japan.
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11
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Genetic scores for predicting longevity in the Croatian oldest-old population. PLoS One 2023; 18:e0279971. [PMID: 36735720 PMCID: PMC9897585 DOI: 10.1371/journal.pone.0279971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/19/2022] [Indexed: 02/04/2023] Open
Abstract
Longevity is a hallmark of successful ageing and a complex trait with a significant genetic component. In this study, 43 single nucleotide polymorphisms (SNPs) were chosen from the literature and genotyped in a Croatian oldest-old sample (85+ years, sample size (N) = 314), in order to determine whether any of these SNPs have a significant effect on reaching the age thresholds for longevity (90+ years, N = 212) and extreme longevity (95+ years, N = 84). The best models were selected for both survival ages using multivariate logistic regression. In the model for reaching age 90, nine SNPs explained 20% of variance for survival to that age, while the 95-year model included five SNPs accounting for 9.3% of variance. The two SNPs that showed the most significant association (p ≤ 0.01) with longevity were TERC rs16847897 and GHRHR rs2267723. Unweighted and weighted Genetic Longevity Scores (uGLS and wGLS) were calculated and their predictive power was tested. All four scores showed significant correlation with age at death (p ≤ 0.01). They also passed the ROC curve test with at least 50% predictive ability, but wGLS90 stood out as the most accurate score, with a 69% chance of accurately predicting survival to the age of 90.
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12
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Fu CHY, Erus G, Fan Y, Antoniades M, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Garcia J, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Woodham RD, Zahn R, Anderson IM, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale. BMC Psychiatry 2023; 23:59. [PMID: 36690972 PMCID: PMC9869598 DOI: 10.1186/s12888-022-04509-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.
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Affiliation(s)
- Cynthia H Y Fu
- Department of Psychological Sciences, University of East London, London, UK.
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Psychiatry and Behavioral Science, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St Joseph's Healthcare Hamilton, Hamilton, Canada
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jose Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Beata R Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Canada
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rachel D Woodham
- Department of Psychological Sciences, University of East London, London, UK
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Ian M Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - J F William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, USA
| | | | - Sidney H Kennedy
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
- Unity Health Toronto, Toronto, Canada
| | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Heather C Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Allan H Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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13
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Torres GG, Dose J, Hasenbein TP, Nygaard M, Krause-Kyora B, Mengel-From J, Christensen K, Andersen-Ranberg K, Kolbe D, Lieb W, Laudes M, Görg S, Schreiber S, Franke A, Caliebe A, Kuhlenbäumer G, Nebel A. Long-Lived Individuals Show a Lower Burden of Variants Predisposing to Age-Related Diseases and a Higher Polygenic Longevity Score. Int J Mol Sci 2022; 23:10949. [PMID: 36142858 PMCID: PMC9504529 DOI: 10.3390/ijms231810949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 11/22/2022] Open
Abstract
Longevity is a complex phenotype influenced by both environmental and genetic factors. The genetic contribution is estimated at about 25%. Despite extensive research efforts, only a few longevity genes have been validated across populations. Long-lived individuals (LLI) reach extreme ages with a relative low prevalence of chronic disability and major age-related diseases (ARDs). We tested whether the protection from ARDs in LLI can partly be attributed to genetic factors by calculating polygenic risk scores (PRSs) for seven common late-life diseases (Alzheimer's disease (AD), atrial fibrillation (AF), coronary artery disease (CAD), colorectal cancer (CRC), ischemic stroke (ISS), Parkinson's disease (PD) and type 2 diabetes (T2D)). The examined sample comprised 1351 German LLI (≥94 years, including 643 centenarians) and 4680 German younger controls. For all ARD-PRSs tested, the LLI had significantly lower scores than the younger control individuals (areas under the curve (AUCs): ISS = 0.59, p = 2.84 × 10-35; AD = 0.59, p = 3.16 × 10-25; AF = 0.57, p = 1.07 × 10-16; CAD = 0.56, p = 1.88 × 10-12; CRC = 0.52, p = 5.85 × 10-3; PD = 0.52, p = 1.91 × 10-3; T2D = 0.51, p = 2.61 × 10-3). We combined the individual ARD-PRSs into a meta-PRS (AUC = 0.64, p = 6.45 × 10-15). We also generated two genome-wide polygenic scores for longevity, one with and one without the TOMM40/APOE/APOC1 gene region (AUC (incl. TOMM40/APOE/APOC1) = 0.56, p = 1.45 × 10-5, seven variants; AUC (excl. TOMM40/APOE/APOC1) = 0.55, p = 9.85 × 10-3, 10,361 variants). Furthermore, the inclusion of nine markers from the excluded region (not in LD with each other) plus the APOE haplotype into the model raised the AUC from 0.55 to 0.61. Thus, our results highlight the importance of TOMM40/APOE/APOC1 as a longevity hub.
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Affiliation(s)
- Guillermo G. Torres
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
| | - Janina Dose
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
| | - Tim P. Hasenbein
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
- Department of Neurology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, 24105 Kiel, Germany
- Institute of Pharmacology and Toxicology, Technical University Munich, Biedersteiner Str. 29, 80802 Munich, Germany
| | - Marianne Nygaard
- Department of Public Health, Epidemiology, Biostatistics and Biodemography, University of Southern, Denmark, J.B. Winsloews Vej 9B, 5000 Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, J.B. Winsloews Vej 4, 5000 Odense, Denmark
| | - Ben Krause-Kyora
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
| | - Jonas Mengel-From
- Department of Public Health, Epidemiology, Biostatistics and Biodemography, University of Southern, Denmark, J.B. Winsloews Vej 9B, 5000 Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, J.B. Winsloews Vej 4, 5000 Odense, Denmark
| | - Kaare Christensen
- Department of Public Health, Epidemiology, Biostatistics and Biodemography, University of Southern, Denmark, J.B. Winsloews Vej 9B, 5000 Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, J.B. Winsloews Vej 4, 5000 Odense, Denmark
- Department of Clinical Biochemistry, Odense University Hospital, Kløvervænget 47, 5000 Odense, Denmark
| | - Karen Andersen-Ranberg
- Department of Public Health, Epidemiology, Biostatistics and Biodemography, University of Southern, Denmark, J.B. Winsloews Vej 9B, 5000 Odense, Denmark
- Department of Geriatric Medicine, Odense University Hospital, Kløvervænget 23, 5000 Odense, Denmark
| | - Daniel Kolbe
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank Popgen, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Niemannsweg 11, 24105 Kiel, Germany
| | - Matthias Laudes
- Clinic for Internal Medicine I, Division of Endocrinology, Diabetes and Clinical Nutrition, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Siegfried Görg
- Institute of Transfusion Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
| | - Amke Caliebe
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Brunswiker Str. 10, 24105 Kiel, Germany
| | - Gregor Kuhlenbäumer
- Department of Neurology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, 24105 Kiel, Germany
| | - Almut Nebel
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany
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14
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Wang C, Shen Y, Ni J, Hu W, Yang Y. Effect of chronic stress on tumorigenesis and development. Cell Mol Life Sci 2022; 79:485. [PMID: 35974132 PMCID: PMC11071880 DOI: 10.1007/s00018-022-04455-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 11/03/2022]
Abstract
Chronic stress activates the sympathetic nervous system (SNS) and hypothalamic-pituitary-adrenal (HPA) axis to aggravates tumorigenesis and development. Although the importance of SNS and HPA in maintaining homeostasis has already attracted much attention, there is still a lot remained unknown about the molecular mechanisms by which chronic stress influence the occurrence and development of tumor. While some researches have already concluded the mechanisms underlying the effect of chronic stress on tumor, complicated processes of tumor progression resulted in effects of chronic stress on various stages of tumor remains elusive. In this reviews we concluded recent research progresses of chronic stress and its effects on premalignancy, tumorigenesis and tumor development, we comprehensively summarized the molecular mechanisms in between. And we highlight the available treatments and potential therapies for stressed patients with tumor.
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Affiliation(s)
- Chen Wang
- State Key Laboratory of Natural Medicines, Center for New Drug Safety Evaluation and Research, China Pharmaceutical University, No. 639 Long Mian Avenue, Jiangning District, Nanjing, 211198, Jiangsu, People's Republic of China
| | - Yumeng Shen
- State Key Laboratory of Natural Medicines, Center for New Drug Safety Evaluation and Research, China Pharmaceutical University, No. 639 Long Mian Avenue, Jiangning District, Nanjing, 211198, Jiangsu, People's Republic of China
| | - Jiaping Ni
- State Key Laboratory of Natural Medicines, Center for New Drug Safety Evaluation and Research, China Pharmaceutical University, No. 639 Long Mian Avenue, Jiangning District, Nanjing, 211198, Jiangsu, People's Republic of China
| | - Weiwei Hu
- State Key Laboratory of Natural Medicines, Center for New Drug Safety Evaluation and Research, China Pharmaceutical University, No. 639 Long Mian Avenue, Jiangning District, Nanjing, 211198, Jiangsu, People's Republic of China.
- Lingang Laboratory, Shanghai, 200032, People's Republic of China.
| | - Yong Yang
- State Key Laboratory of Natural Medicines, Center for New Drug Safety Evaluation and Research, China Pharmaceutical University, No. 639 Long Mian Avenue, Jiangning District, Nanjing, 211198, Jiangsu, People's Republic of China.
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15
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Hu D, Li Y, Zhang D, Ding J, Song Z, Min J, Zeng Y, Nie C. Genetic trade-offs between complex diseases and longevity. Aging Cell 2022; 21:e13654. [PMID: 35754110 PMCID: PMC9282840 DOI: 10.1111/acel.13654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 02/28/2022] [Accepted: 05/26/2022] [Indexed: 11/30/2022] Open
Abstract
Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle‐aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long‐lived individuals. We further stratified PRSs into cell‐type groups and significance‐level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p‐values (p ≤ 1x10‐5) for SCZ and T2D were negatively correlated with longevity. While for the less significant SNPs (1x10‐5 < p ≤ 0.05), their effects on disease and longevity were positively correlated. Overall, we identified genetically informed positive and negative factors for human longevity, gained more insights on the accumulation of disease risk alleles during evolution, and provided evidence for the theory of genetic trade‐offs between complex diseases and longevity.
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Affiliation(s)
- Dingxue Hu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.,BGI-Shenzhen, Shenzhen, China
| | - Yan Li
- BGI-Shenzhen, Shenzhen, China
| | | | | | - Zijun Song
- The First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China
| | - Junxia Min
- The First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Zeng
- Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, China.,Center for the Study of Aging and Human Development and Geriatrics Division, Medical School of Duke University, Durham, North Carolina, USA
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16
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Auwerx C, Lepamets M, Sadler MC, Patxot M, Stojanov M, Baud D, Mägi R, Porcu E, Reymond A, Kutalik Z, Metspalu A, Milani L, Mägi R, Nelis M. The individual and global impact of copy-number variants on complex human traits. Am J Hum Genet 2022; 109:647-668. [PMID: 35240056 PMCID: PMC9069145 DOI: 10.1016/j.ajhg.2022.02.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/09/2022] [Indexed: 12/25/2022] Open
Abstract
The impact of copy-number variations (CNVs) on complex human traits remains understudied. We called CNVs in 331,522 UK Biobank participants and performed genome-wide association studies (GWASs) between the copy number of CNV-proxy probes and 57 continuous traits, revealing 131 signals spanning 47 phenotypes. Our analysis recapitulated well-known associations (e.g., 1q21 and height), revealed the pleiotropy of recurrent CNVs (e.g., 26 and 16 traits for 16p11.2-BP4-BP5 and 22q11.21, respectively), and suggested gene functionalities (e.g., MARF1 in female reproduction). Forty-eight CNV signals (38%) overlapped with single-nucleotide polymorphism (SNP)-GWASs signals for the same trait. For instance, deletion of PDZK1, which encodes a urate transporter scaffold protein, decreased serum urate levels, while deletion of RHD, which encodes the Rhesus blood group D antigen, associated with hematological traits. Other signals overlapped Mendelian disorder regions, suggesting variable expressivity and broad impact of these loci, as illustrated by signals mapping to Rotor syndrome (SLCO1B1/3), renal cysts and diabetes syndrome (HNF1B), or Charcot-Marie-Tooth (PMP22) loci. Total CNV burden negatively impacted 35 traits, leading to increased adiposity, liver/kidney damage, and decreased intelligence and physical capacity. Thirty traits remained burden associated after correcting for CNV-GWAS signals, pointing to a polygenic CNV architecture. The burden negatively correlated with socio-economic indicators, parental lifespan, and age (survivorship proxy), suggesting a contribution to decreased longevity. Together, our results showcase how studying CNVs can expand biological insights, emphasizing the critical role of this mutational class in shaping human traits and arguing in favor of a continuum between Mendelian and complex diseases.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland; Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Maarja Lepamets
- Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Marie C Sadler
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Marion Patxot
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
| | - Miloš Stojanov
- Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, CHUV, Lausanne 1011, Switzerland
| | - David Baud
- Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, CHUV, Lausanne 1011, Switzerland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
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- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland.
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland.
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17
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Bin-Jumah MN, Nadeem MS, Gilani SJ, Al-Abbasi FA, Ullah I, Alzarea SI, Ghoneim MM, Alshehri S, Uddin A, Murtaza BN, Kazmi I. Genes and Longevity of Lifespan. Int J Mol Sci 2022; 23:ijms23031499. [PMID: 35163422 PMCID: PMC8836117 DOI: 10.3390/ijms23031499] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/04/2022] [Accepted: 01/26/2022] [Indexed: 12/12/2022] Open
Abstract
Aging is a complex process indicated by low energy levels, declined physiological activity, stress induced loss of homeostasis leading to the risk of diseases and mortality. Recent developments in medical sciences and an increased availability of nutritional requirements has significantly increased the average human lifespan worldwide. Several environmental and physiological factors contribute to the aging process. However, about 40% human life expectancy is inherited among generations, many lifespan associated genes, genetic mechanisms and pathways have been demonstrated during last decades. In the present review, we have evaluated many human genes and their non-human orthologs established for their role in the regulation of lifespan. The study has included more than fifty genes reported in the literature for their contributions to the longevity of life. Intact genomic DNA is essential for the life activities at the level of cell, tissue, and organ. Nucleic acids are vulnerable to oxidative stress, chemotherapies, and exposure to radiations. Efficient DNA repair mechanisms are essential for the maintenance of genomic integrity, damaged DNA is not replicated and transferred to next generations rather the presence of deleterious DNA initiates signaling cascades leading to the cell cycle arrest or apoptosis. DNA modifications, DNA methylation, histone methylation, histone acetylation and DNA damage can eventually lead towards apoptosis. The importance of calorie restriction therapy in the extension of lifespan has also been discussed. The role of pathways involved in the regulation of lifespan such as DAF-16/FOXO (forkhead box protein O1), TOR and JNK pathways has also been particularized. The study provides an updated account of genetic factors associated with the extended lifespan and their interactive contributory role with cellular pathways.
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Affiliation(s)
- May Nasser Bin-Jumah
- Biology Department, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
- Environment and Biomaterial Unit, Health Sciences Research Center, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Muhammad Shahid Nadeem
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Correspondence: (M.S.N.); (I.K.)
| | - Sadaf Jamal Gilani
- Department of Basic Health Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Fahad A. Al-Abbasi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Inam Ullah
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore 54000, Pakistan;
| | - Sami I. Alzarea
- Department of Pharmacology, College of Pharmacy, Jouf University, Sakaka 72341, Saudi Arabia;
| | - Mohammed M. Ghoneim
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa University, Ad Diriyah 13713, Saudi Arabia;
| | - Sultan Alshehri
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Aziz Uddin
- Department of Biotechnology and Genetic Engineering, Hazara University, Mansehra 21300, Pakistan;
| | - Bibi Nazia Murtaza
- Department of Zoology, Abbottabad University of Science and Technology (AUST), Abbottabad 22310, Pakistan;
| | - Imran Kazmi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Correspondence: (M.S.N.); (I.K.)
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18
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Karlsson Linnér R, Koellinger PD. Genetic risk scores in life insurance underwriting. JOURNAL OF HEALTH ECONOMICS 2022; 81:102556. [PMID: 34847443 DOI: 10.1016/j.jhealeco.2021.102556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 11/03/2021] [Accepted: 11/12/2021] [Indexed: 06/13/2023]
Abstract
Genetic tests that predict the lifetime risk of common medical conditions are fast becoming more accurate and affordable. The life insurance industry is interested in using predictive genetic tests in the underwriting process, but more research is needed to establish whether this nascent form of genetic testing can refine the process over conventional underwriting factors. Here, we perform Cox regression of survival on a battery of genetic risk scores for common medical conditions and mortality risks in the Health and Retirement Study, without returning results to participants. Adjusted for covariates in a relevant insurance scenario, the scores could improve mortality risk classification by identifying 2.6 years shorter median lifespan in the highest decile of total genetic liability. We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.
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Affiliation(s)
- Richard Karlsson Linnér
- School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam 1081HV, the Netherlands; Department of Economics, Leiden University, Steenschuur 25, Leiden 2531ES, the Netherlands.
| | - Philipp D Koellinger
- School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam 1081HV, the Netherlands; La Follette School of Public Affairs, University of Wisconsin-Madison, 1225 Observatory Dr., Madison, WI 53706, USA..
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19
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Kronfol MM, Abudahab S, Dozmorov MG, Jahr FM, Halquist MS, McRae M, Wijesinghe DS, Price ET, Slattum PW, McClay JL. Histone acetylation at the sulfotransferase 1a1 gene is associated with its hepatic expression in normal aging. Pharmacogenet Genomics 2021; 31:207-214. [PMID: 34320608 PMCID: PMC8490294 DOI: 10.1097/fpc.0000000000000443] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Phase II drug metabolism is poorly studied in advanced age and older adults may exhibit significant variability in their expression of phase II enzymes. We hypothesized that age-related changes to epigenetic regulation of genes involved in phase II drug metabolism may contribute to these effects. METHODS We examined published epigenome-wide studies of human blood and identified the SULT1A1 and UGT1A6 genes as the top loci showing epigenetic changes with age. To assess possible functional alterations with age in the liver, we assayed DNA methylation (5mC) and histone acetylation changes around the mouse homologs Sult1a1 and Ugt1a6 in liver tissue from mice aged 4-32 months. RESULTS Our sample shows a significant loss of 5mC at Sult1a1 (β = -1.08, 95% CI [-1.8, -0.2], SE = 0.38, P = 0.011), mirroring the loss of 5mC with age observed in human blood DNA at the same locus. We also detected increased histone 3 lysine 9 acetylation (H3K9ac) with age at Sult1a1 (β = 0.11, 95% CI [0.002, 0.22], SE = 0.05, P = 0.04), but no change to histone 3 lysine 27 acetylation (H3K27ac). Sult1a1 gene expression is significantly positively associated with H3K9ac levels, accounting for 23% of the variation in expression. We did not detect any significant effects at Ugt1a6. CONCLUSIONS Sult1a1 expression is under epigenetic influence in normal aging and this influence is more pronounced for H3K9ac than DNA methylation or H3K27ac in this study. More generally, our findings support the relevance of epigenetics in regulating key drug-metabolizing pathways. In the future, epigenetic biomarkers could prove useful to inform dosing in older adults.
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Affiliation(s)
- Mohamad M. Kronfol
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Sara Abudahab
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Mikhail G. Dozmorov
- Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, Virginia
| | - Fay M. Jahr
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Matthew S. Halquist
- Department of Pharmaceutics, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - MaryPeace McRae
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Dayanjan S. Wijesinghe
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Elvin T. Price
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
- Geriatric Pharmacotherapy Program, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
- Institute for Inclusion, Inquiry and Innovation: Health and Wellness in Aging Populations Core, Virginia Commonwealth University, Richmond, Virginia
| | - Patricia W. Slattum
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
- Geriatric Pharmacotherapy Program, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
- Institute for Inclusion, Inquiry and Innovation: Health and Wellness in Aging Populations Core, Virginia Commonwealth University, Richmond, Virginia
| | - Joseph L. McClay
- Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
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20
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Roy S, Sleiman MB, Jha P, Ingels JF, Chapman CJ, McCarty MS, Ziebarth JD, Hook M, Sun A, Zhao W, Huang J, Neuner SM, Wilmott LA, Shapaker TM, Centeno AG, Ashbrook DG, Mulligan MK, Kaczorowski CC, Makowski L, Cui Y, Read RW, Miller RA, Mozhui K, Williams EG, Sen S, Lu L, Auwerx J, Williams RW. Gene-by-environment modulation of lifespan and weight gain in the murine BXD family. Nat Metab 2021; 3:1217-1227. [PMID: 34552269 PMCID: PMC8478125 DOI: 10.1038/s42255-021-00449-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 08/06/2021] [Indexed: 02/07/2023]
Abstract
How lifespan and body weight vary as a function of diet and genetic differences is not well understood. Here we quantify the impact of differences in diet on lifespan in a genetically diverse family of female mice, split into matched isogenic cohorts fed a low-fat chow diet (CD, n = 663) or a high-fat diet (HFD, n = 685). We further generate key metabolic data in a parallel cohort euthanized at four time points. HFD feeding shortens lifespan by 12%: equivalent to a decade in humans. Initial body weight and early weight gains account for longevity differences of roughly 4-6 days per gram. At 500 days, animals on a HFD typically gain four times as much weight as control, but variation in weight gain does not correlate with lifespan. Classic serum metabolites, often regarded as health biomarkers, are not necessarily strong predictors of longevity. Our data indicate that responses to a HFD are substantially modulated by gene-by-environment interactions, highlighting the importance of genetic variation in making accurate individualized dietary recommendations.
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Affiliation(s)
- Suheeta Roy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Maroun Bou Sleiman
- Laboratory of Integrative Systems Physiology, Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pooja Jha
- Laboratory of Integrative Systems Physiology, Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jesse F Ingels
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Casey J Chapman
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Melinda S McCarty
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Jesse D Ziebarth
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Michael Hook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Anna Sun
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Wenyuan Zhao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Jinsong Huang
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Sarah M Neuner
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Lynda A Wilmott
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Thomas M Shapaker
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Arthur G Centeno
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - David G Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Megan K Mulligan
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | | | - Liza Makowski
- Department of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Robert W Read
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Richard A Miller
- Department of Pathology, University of Michigan Geriatrics Center, Ann Arbor, MI, USA
| | - Khyobeni Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Evan G Williams
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Saunak Sen
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA.
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21
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Yegla B, Foster TC. Operationally defining cognitive reserve genes. Neurobiol Aging 2021; 110:96-105. [PMID: 34565615 DOI: 10.1016/j.neurobiolaging.2021.08.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/05/2021] [Accepted: 08/24/2021] [Indexed: 01/22/2023]
Abstract
Variability in cognitive decline is related to the environment, lifestyle factors, and individual differences in biological aging, including cognitive reserve, plastic properties of the brain, which account for better-than-expected cognition for a given level of brain aging or pathology. Cognitive reserve has not been thoroughly investigated in aged rodents. To address this gap, cognitive reserve was examined using Gene Expression Omnibus data for the CA1 region of the hippocampus of young and aged behaviorally characterized male rats. Statistical filtering identified brain aging and potential cognitive reserve genes, and multiple regression was employed to confirm cognitive reserve genes as genes that predicted better-than-expected cognition for a given level of brain aging. In general, cognitive reserve genes, in which increased expression was associated with better cognition, were not different with age or directly correlated with measures of cognition and appear to act as negative regulators of aging processes, including neuroinflammation and oxidative stress. The results suggest that, for some animals, resilience mechanisms are activated to counteract aging stressors that impair cognition. In contrast, cognitive reserve genes, in which decreased expression was associated with better cognition, were linked to nervous system development and cation transport, suggesting adaptive changes in the circuit to preserve cognition.
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Affiliation(s)
- Brittney Yegla
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Thomas C Foster
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Genetics and Genomics Program University of Florida, Gainesville, FL, USA.
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22
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Treaster S, Karasik D, Harris MP. Footprints in the Sand: Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity. Front Genet 2021; 12:678073. [PMID: 34163529 PMCID: PMC8215702 DOI: 10.3389/fgene.2021.678073] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/12/2021] [Indexed: 01/09/2023] Open
Abstract
With the modern quality, quantity, and availability of genomic sequencing across species, as well as across the expanse of human populations, we can screen for shared signatures underlying longevity and lifespan. Knowledge of these mechanisms would be medically invaluable in combating aging and age-related diseases. The diversity of longevities across vertebrates is an opportunity to look for patterns of genetic variation that may signal how this life history property is regulated, and ultimately how it can be modulated. Variation in human longevity provides a unique window to look for cases of extreme lifespan within a population, as well as associations across populations for factors that influence capacity to live longer. Current large cohort studies support the use of population level analyses to identify key factors associating with human lifespan. These studies are powerful in concept, but have demonstrated limited ability to resolve signals from background variation. In parallel, the expanding catalog of sequencing and annotation from diverse species, some of which have evolved longevities well past a human lifespan, provides independent cases to look at the genomic signatures of longevity. Recent comparative genomic work has shown promise in finding shared mechanisms associating with longevity among distantly related vertebrate groups. Given the genetic constraints between vertebrates, we posit that a combination of approaches, of parallel meta-analysis of human longevity along with refined analysis of other vertebrate clades having exceptional longevity, will aid in resolving key regulators of enhanced lifespan that have proven to be elusive when analyzed in isolation.
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Affiliation(s)
- Stephen Treaster
- Department of Orthopaedics, Boston Children's Hospital, Boston, MA, United States.,Department of Genetics, Harvard Medical School, Boston, MA, United States
| | - David Karasik
- Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan, Israel.,Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, United States
| | - Matthew P Harris
- Department of Orthopaedics, Boston Children's Hospital, Boston, MA, United States.,Department of Genetics, Harvard Medical School, Boston, MA, United States
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23
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Porcu E, Sjaarda J, Lepik K, Carmeli C, Darrous L, Sulc J, Mounier N, Kutalik Z. Causal Inference Methods to Integrate Omics and Complex Traits. Cold Spring Harb Perspect Med 2021; 11:a040493. [PMID: 32816877 PMCID: PMC8091955 DOI: 10.1101/cshperspect.a040493] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.
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Affiliation(s)
- Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Jennifer Sjaarda
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Kaido Lepik
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
- Institute of Computer Science, University of Tartu, Tartu 50409, Estonia
| | - Cristian Carmeli
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Liza Darrous
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Jonathan Sulc
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Ninon Mounier
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX2 5AX, United Kingdom
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24
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Shadrina AS, Zlobin AS, Zaytseva OO, Klarić L, Sharapov SZ, D Pakhomov E, Perola M, Esko T, Hayward C, Wilson JF, Lauc G, Aulchenko YS, Tsepilov YA. Multivariate genome-wide analysis of immunoglobulin G N-glycosylation identifies new loci pleiotropic with immune function. Hum Mol Genet 2021; 30:1259-1270. [PMID: 33710309 DOI: 10.1093/hmg/ddab072] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 01/19/2021] [Accepted: 03/05/2021] [Indexed: 12/15/2022] Open
Abstract
The N-glycosylation of immunoglobulin G (IgG) affects its structure and function. It has been demonstrated that IgG N-glycosylation patterns are inherited as complex quantitative traits. Genome-wide association studies identified loci harboring genes encoding enzymes directly involved in protein glycosylation as well as loci likely to be involved in regulation of glycosylation biochemical pathways. Many of these loci could be linked to immune functions and risk of inflammatory and autoimmune diseases. The aim of the present study was to discover and replicate new loci associated with IgG N-glycosylation and to investigate possible pleiotropic effects of these loci onto immune function and the risk of inflammatory and autoimmune diseases. We conducted a multivariate genome-wide association analysis of 23 IgG N-glycosylation traits measured in 8090 individuals of European ancestry. The discovery stage was followed up by replication in 3147 people and in silico functional analysis. Our study increased the total number of replicated loci from 22 to 29. For the discovered loci, we suggest a number of genes potentially involved in the control of IgG N-glycosylation. Among the new loci, two (near RNF168 and TNFRSF13B) were previously implicated in rare immune deficiencies and were associated with levels of circulating immunoglobulins. For one new locus (near AP5B1/OVOL1), we demonstrated a potential pleiotropic effect on the risk of asthma. Our findings underline an important link between IgG N-glycosylation and immune function and provide new clues to understanding their interplay.
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Affiliation(s)
- Alexandra S Shadrina
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Alexander S Zlobin
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Olga O Zaytseva
- Genos Glycoscience Research Laboratory, Zagreb 10000, Croatia
| | - Lucija Klarić
- Genos Glycoscience Research Laboratory, Zagreb 10000, Croatia.,MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Sodbo Z Sharapov
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Eugene D Pakhomov
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Marcus Perola
- Genomics and Biomarkers Unit, Department of Health, National Institute for Health and Welfare (THL), Helsinki, Finland
| | - Tonu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - James F Wilson
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK.,Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, Scotland
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb 10000, Croatia
| | - Yurii S Aulchenko
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk 630090, Russia.,PolyOmica, 's-Hertogenbosch 5237 PA, The Netherlands
| | - Yakov A Tsepilov
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk 630090, Russia.,Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk 630090, Russia
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25
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Liu X, Song Z, Li Y, Yao Y, Fang M, Bai C, An P, Chen H, Chen Z, Tang B, Shen J, Gao X, Zhang M, Chen P, Zhang T, Jia H, Liu X, Hou Y, Yang H, Wang J, Wang F, Xu X, Min J, Nie C, Zeng Y. Integrated genetic analyses revealed novel human longevity loci and reduced risks of multiple diseases in a cohort study of 15,651 Chinese individuals. Aging Cell 2021; 20:e13323. [PMID: 33657282 PMCID: PMC7963337 DOI: 10.1111/acel.13323] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 01/16/2021] [Accepted: 01/23/2021] [Indexed: 12/14/2022] Open
Abstract
There is growing interest in studying the genetic contributions to longevity, but limited relevant genes have been identified. In this study, we performed a genetic association study of longevity in a total of 15,651 Chinese individuals. Novel longevity loci, BMPER (rs17169634; p = 7.91 × 10-15 ) and TMEM43/XPC (rs1043943; p = 3.59 × 10-8 ), were identified in a case-control analysis of 11,045 individuals. BRAF (rs1267601; p = 8.33 × 10-15 ) and BMPER (rs17169634; p = 1.45 × 10-10 ) were significantly associated with life expectancy in 12,664 individuals who had survival status records. Additional sex-stratified analyses identified sex-specific longevity genes. Notably, sex-differential associations were identified in two linkage disequilibrium blocks in the TOMM40/APOE region, indicating potential differences during meiosis between males and females. Moreover, polygenic risk scores and Mendelian randomization analyses revealed that longevity was genetically causally correlated with reduced risks of multiple diseases, such as type 2 diabetes, cardiovascular diseases, and arthritis. Finally, we incorporated genetic markers, disease status, and lifestyles to classify longevity or not-longevity groups and predict life span. Our predictive models showed good performance (AUC = 0.86 for longevity classification and explained 19.8% variance of life span) and presented a greater predictive efficiency in females than in males. Taken together, our findings not only shed light on the genetic contributions to longevity but also elucidate correlations between diseases and longevity.
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Affiliation(s)
- Xiaomin Liu
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
- BGI Education Center University of Chinese Academy of Sciences Shenzhen China
| | - Zijun Song
- The First Affiliated Hospital Institute of Translational Medicine School of Medicine, Zhejiang University Hangzhou China
| | - Yan Li
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Yao Yao
- Center for the Study of Aging and Human Development Medical School of Duke University Durham USA
- Center for Healthy Aging and Development Studies National School of Development, Raissun Institute for Advanced Studies, Peking University Beijing China
| | - Mingyan Fang
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Chen Bai
- Center for Healthy Aging and Development Studies National School of Development, Raissun Institute for Advanced Studies, Peking University Beijing China
- School of Labor and Human Resources Renmin University Beijing China
| | - Peng An
- Beijing Advanced Innovation Center for Food Nutrition and Human Health China Agricultural University Beijing China
| | - Huashuai Chen
- Business School of Xiangtan University Xiangtan China
| | - Zhihua Chen
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Biyao Tang
- The First Affiliated Hospital Institute of Translational Medicine School of Medicine, Zhejiang University Hangzhou China
| | - Juan Shen
- BGI Genomics BGI‐Shenzhen Shenzhen China
| | - Xiaotong Gao
- The First Affiliated Hospital Institute of Translational Medicine School of Medicine, Zhejiang University Hangzhou China
| | | | - Pengyu Chen
- The First Affiliated Hospital Institute of Translational Medicine School of Medicine, Zhejiang University Hangzhou China
| | - Tao Zhang
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Huijue Jia
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Xiao Liu
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Yong Hou
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Huanming Yang
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Jian Wang
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Fudi Wang
- The First Affiliated Hospital Institute of Translational Medicine School of Medicine, Zhejiang University Hangzhou China
- Beijing Advanced Innovation Center for Food Nutrition and Human Health China Agricultural University Beijing China
| | - Xun Xu
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
- Guangdong Provincial Key Laboratory of Genome Read and Write Shenzhen China
| | - Junxia Min
- The First Affiliated Hospital Institute of Translational Medicine School of Medicine, Zhejiang University Hangzhou China
| | - Chao Nie
- BGI‐Shenzhen Shenzhen China
- China National Genebank Shenzhen China
| | - Yi Zeng
- Center for the Study of Aging and Human Development Medical School of Duke University Durham USA
- Center for Healthy Aging and Development Studies National School of Development, Raissun Institute for Advanced Studies, Peking University Beijing China
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26
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Zhao JV, Schooling CM. Sex-specific Associations of Sex Hormone Binding Globulin with CKD and Kidney Function: A Univariable and Multivariable Mendelian Randomization Study in the UK Biobank. J Am Soc Nephrol 2021; 32:686-694. [PMID: 33318152 PMCID: PMC7920164 DOI: 10.1681/asn.2020050659] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 10/15/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Kidney function declines faster in men. Testosterone levels may mediate the sex disparity. Correspondingly, levels of sex hormone binding globulin (SHBG), which modulates sex hormones, might also be relevant to the lower kidney function in men. The sex-specific role of SHBG is unclear. METHODS A sex-specific, Mendelian randomization (MR) study provided unconfounded estimates of SHBG levels among the United Kingdom Biobank population. Univariable MR applied 357 single nucleotide polymorphisms (SNPs) in men and 359 SNPs in women. These published SNPs strongly (P<5×10-8) predict SHBG level. They were profiled in 179,916 white British men (6016 patients with CKD) and 212,079 white British women (5958 patients with CKD), to obtain the effect of SHBG on CKD, albuminuria, and eGFR. Multivariable MR controlling for testosterone was used to assess the effect of SHBG on CKD and kidney function independent of testosterone in men. RESULTS Genetically predicted higher SHBG was associated with a lower risk of CKD in men (odds ratio [OR], 0.78 per SD; 95% confidence interval [95% CI], 0.65 to 0.93) but had no benefit in women. The effect in men remained in multivariable MR, allowing for testosterone (OR, 0.61; 95% CI, 0.45 to 0.82). CONCLUSIONS Genetically predicted higher SHBG was associated with a lower risk of CKD and better kidney function in men, but not in women, suggesting that SHBG may play a role in CKD specifically in men. Identifying drivers of SHBG and the underlying pathways could provide new insights into CKD prevention and treatment.
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Affiliation(s)
- Jie V. Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - C. Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,City University of New York, School of Public Health and Health Policy, New York, New York
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27
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Bou Sleiman M, Jha P, Houtkooper R, Williams RW, Wang X, Auwerx J. The Gene-Regulatory Footprint of Aging Highlights Conserved Central Regulators. Cell Rep 2020; 32:108203. [PMID: 32997995 PMCID: PMC7527782 DOI: 10.1016/j.celrep.2020.108203] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 07/31/2020] [Accepted: 09/04/2020] [Indexed: 12/13/2022] Open
Abstract
Many genes and pathways have been linked to aging, yet our understanding of underlying molecular mechanisms is still lacking. Here, we measure changes in the transcriptome, histone modifications, and DNA methylome in three metabolic tissues of adult and aged mice. Transcriptome and methylome changes dominate the liver aging footprint, whereas heart and muscle globally increase chromatin accessibility, especially in aging pathways. In mouse and human data from multiple tissues and regulatory layers, age-related transcription factor expression changes and binding site enrichment converge on putative aging modulators, including ZIC1, CXXC1, HMGA1, MECP2, SREBF1, SREBF2, ETS2, ZBTB7A, and ZNF518B. Using Mendelian randomization, we establish possible epidemiological links between expression of some of these transcription factors or their targets, including CXXC1, ZNF518B, and BBC3, and longevity. We conclude that conserved modulators are at the core of the molecular footprint of aging, and variation in tissue-specific expression of some may affect human longevity.
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Affiliation(s)
- Maroun Bou Sleiman
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Pooja Jha
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Riekelt Houtkooper
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee, Memphis, TN 38163, USA
| | - Xu Wang
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.
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28
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Zhang ZD, Milman S, Lin JR, Wierbowski S, Yu H, Barzilai N, Gorbunova V, Ladiges WC, Niedernhofer LJ, Suh Y, Robbins PD, Vijg J. Genetics of extreme human longevity to guide drug discovery for healthy ageing. Nat Metab 2020; 2:663-672. [PMID: 32719537 PMCID: PMC7912776 DOI: 10.1038/s42255-020-0247-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 06/22/2020] [Indexed: 02/07/2023]
Abstract
Ageing is the greatest risk factor for most common chronic human diseases, and it therefore is a logical target for developing interventions to prevent, mitigate or reverse multiple age-related morbidities. Over the past two decades, genetic and pharmacologic interventions targeting conserved pathways of growth and metabolism have consistently led to substantial extension of the lifespan and healthspan in model organisms as diverse as nematodes, flies and mice. Recent genetic analysis of long-lived individuals is revealing common and rare variants enriched in these same conserved pathways that significantly correlate with longevity. In this Perspective, we summarize recent insights into the genetics of extreme human longevity and propose the use of this rare phenotype to identify genetic variants as molecular targets for gaining insight into the physiology of healthy ageing and the development of new therapies to extend the human healthspan.
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Affiliation(s)
- Zhengdong D Zhang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA.
| | - Sofiya Milman
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Jhih-Rong Lin
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Shayne Wierbowski
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, New York, NY, USA
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, New York, NY, USA
| | - Nir Barzilai
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Vera Gorbunova
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - Warren C Ladiges
- Department of Comparative Medicine, School of Medicine, University of Washington, Seattle, WA, USA
| | - Laura J Niedernhofer
- Institute on the Biology of Aging and Metabolism and Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Yousin Suh
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Departments of Obstetrics and Gynecology, Genetics and Development, Columbia University, New York, NY, USA
| | - Paul D Robbins
- Institute on the Biology of Aging and Metabolism and Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Center for Single-Cell Omics in Aging and Disease, School of Public Health, Shanghai, Jiao Tong University School of Medicine, Shanghai, China
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29
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Mounier N, Kutalik Z. bGWAS: an R package to perform Bayesian genome wide association studies. Bioinformatics 2020; 36:4374-4376. [PMID: 32470106 PMCID: PMC7520046 DOI: 10.1093/bioinformatics/btaa549] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 05/18/2020] [Accepted: 05/25/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. AVAILABILITY AND IMPLEMENTATION bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ninon Mounier
- Department of Training, Research and Innovation, University Center for Primary Care and Public Health, Lausanne 1010, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Zoltán Kutalik
- Department of Training, Research and Innovation, University Center for Primary Care and Public Health, Lausanne 1010, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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30
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Robinson O, Chadeau Hyam M, Karaman I, Climaco Pinto R, Ala-Korpela M, Handakas E, Fiorito G, Gao H, Heard A, Jarvelin M, Lewis M, Pazoki R, Polidoro S, Tzoulaki I, Wielscher M, Elliott P, Vineis P. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 2020; 19:e13149. [PMID: 32363781 PMCID: PMC7294785 DOI: 10.1111/acel.13149] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 12/19/2019] [Accepted: 03/02/2020] [Indexed: 01/08/2023] Open
Abstract
Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.
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Affiliation(s)
- Oliver Robinson
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Marc Chadeau Hyam
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Ibrahim Karaman
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Rui Climaco Pinto
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of MedicineUniversity of Oulu and Biocenter OuluOuluFinland
- NMR Metabolomics LaboratorySchool of Pharmacy, University of Eastern FinlandKuopioFinland
| | - Evangelos Handakas
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Giovanni Fiorito
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
- Laboratory of BiostatisticsDepartment of Biomedical SciencesUniversity of SassariSassariItaly
- Italian Institute for Genomic Medicine (IIGM, former HuGeF)CandioloItaly
| | - He Gao
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Andy Heard
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Marjo‐Riitta Jarvelin
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
- Center for Life Course Health ResearchFaculty of MedicineUniversity of Oulu and Unit of Primary Health CareOulu University HospitalOuluFinland
- Department of Life SciencesCollege of Health and Life SciencesBrunel University LondonUxbridgeUK
| | - Matthew Lewis
- National Phenome CentreDepartment of MetabolismDigestion and ReproductionImperial College LondonLondonUK
| | - Raha Pazoki
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
- Department of Life SciencesCollege of Health and Life SciencesBrunel University LondonUxbridgeUK
| | - Silvia Polidoro
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
- Italian Institute for Genomic Medicine (IIGM, former HuGeF)CandioloItaly
| | - Ioanna Tzoulaki
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Matthias Wielscher
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Paul Elliott
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
| | - Paolo Vineis
- MRC Centre for Environment and HealthDepartment of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
- Italian Institute for Genomic Medicine (IIGM, former HuGeF)CandioloItaly
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31
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Geisert EE, Williams RW. Using BXD mouse strains in vision research: A systems genetics approach. Mol Vis 2020; 26:173-187. [PMID: 32180682 PMCID: PMC7058434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 03/04/2020] [Indexed: 11/06/2022] Open
Abstract
We illustrate the growing power of the BXD family of mice (recombinant inbred strains from a cross of C57BL/6J and DBA/2J mice) and companion bioinformatic tools to study complex genome-phenome relations related to glaucoma. Over the past 16 years, our group has integrated powerful murine resources and web-accessible tools to identify networks modulating visual system traits-from photoreceptors to the visual cortex. Recent studies focused on retinal ganglion cells and glaucoma risk factors, including intraocular pressure (IOP), central corneal thickness (CCT), and susceptibility of cellular stress. The BXD family was exploited to define key gene variants and then establish linkage to glaucoma in human cohorts. The power of this experimental approach to precision medicine is highlighted by recent studies that defined cadherin 11 (Cdh11) and a calcium channel (Cacna2d1) as genes modulating IOP, Pou6f2 as a genetic link between CCT and retinal ganglion cell (RGC) death, and Aldh7a1 as a gene that modulates the susceptibility of RGCs to death after elevated IOP. The role of three of these gene variants in glaucoma is discussed, along with the pathways activated in the disease process.
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Affiliation(s)
- Eldon E. Geisert
- Department of Ophthalmology, Emory University, 1365B Clifton Road NE Atlanta GA, 30322
| | - Robert W. Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, 71 S Manassas St, Memphis TN 38163
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32
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Huang W, Campbell T, Carbone MA, Jones WE, Unselt D, Anholt RRH, Mackay TFC. Context-dependent genetic architecture of Drosophila life span. PLoS Biol 2020; 18:e3000645. [PMID: 32134916 PMCID: PMC7077879 DOI: 10.1371/journal.pbio.3000645] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 03/17/2020] [Accepted: 02/14/2020] [Indexed: 12/13/2022] Open
Abstract
Understanding the genetic basis of variation in life span is a major challenge that is difficult to address in human populations. Evolutionary theory predicts that alleles affecting natural variation in life span will have properties that enable them to persist in populations at intermediate frequencies, such as late-life-specific deleterious effects, antagonistic pleiotropic effects on early and late-age fitness components, and/or sex- and environment-specific or antagonistic effects. Here, we quantified variation in life span in males and females reared in 3 thermal environments for the sequenced, inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and an advanced intercross outbred population derived from a subset of DGRP lines. Quantitative genetic analyses of life span and the micro-environmental variance of life span in the DGRP revealed significant genetic variance for both traits within each sex and environment, as well as significant genotype-by-sex interaction (GSI) and genotype-by-environment interaction (GEI). Genome-wide association (GWA) mapping in both populations implicates over 2,000 candidate genes with sex- and environment-specific or antagonistic pleiotropic allelic effects. Over 1,000 of these genes are associated with variation in life span in other D. melanogaster populations. We functionally assessed the effects of 15 candidate genes using RNA interference (RNAi): all affected life span and/or micro-environmental variance of life span in at least one sex and environment and exhibited sex-and environment-specific effects. Our results implicate novel candidate genes affecting life span and suggest that variation for life span may be maintained by variable allelic effects in heterogeneous environments.
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Affiliation(s)
- Wen Huang
- Program in Genetics, W. M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Terry Campbell
- Program in Genetics, W. M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Mary Anna Carbone
- Program in Genetics, W. M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - W. Elizabeth Jones
- Program in Genetics, W. M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Desiree Unselt
- Program in Genetics, W. M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Robert R. H. Anholt
- Program in Genetics, W. M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Trudy F. C. Mackay
- Program in Genetics, W. M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail:
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33
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Qin L, Xu Q, Li Z, Chen L, Li Y, Yang N, Liu Z, Guo J, Shen L, Allen EG, Chen C, Ma C, Wu H, Zhu X, Jin P, Tang B. Ethnicity-specific and overlapping alterations of brain hydroxymethylome in Alzheimer's disease. Hum Mol Genet 2020; 29:149-158. [PMID: 31814020 PMCID: PMC7001720 DOI: 10.1093/hmg/ddz273] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 10/30/2019] [Accepted: 11/04/2019] [Indexed: 01/06/2023] Open
Abstract
5-Methylcytosine (5mC), generated through the covalent addition of a methyl group to the fifth carbon of cytosine, is the most prevalent DNA modification in humans and functions as a critical player in the regulation of tissue and cell-specific gene expression. 5mC can be oxidized to 5-hydroxymethylcytosine (5hmC) by ten-eleven translocation (TET) enzymes, which is enriched in brain. Alzheimer's disease (AD) is the most common neurodegenerative disorder, and several studies using the samples collected from Caucasian cohorts have found that epigenetics, particularly cytosine methylation, could play a role in the etiological process of AD. However, little research has been conducted using the samples of other ethnic groups. Here we generated genome-wide profiles of both 5mC and 5hmC in human frontal cortex tissues from late-onset Chinese AD patients and cognitively normal controls. We identified both Chinese-specific and overlapping differentially hydroxymethylated regions (DhMRs) with Caucasian cohorts. Pathway analyses revealed specific pathways enriched among Chinese-specific DhMRs, as well as the shared DhMRs with Caucasian cohorts. Furthermore, two important transcription factor-binding motifs, hypoxia-inducible factor 2α (HIF2α) and hypoxia-inducible factor 1α (HIF1α), were enriched in the DhMRs. Our analyses provide the first genome-wide profiling of DNA hydroxymethylation of the frontal cortex of AD patients from China, emphasizing an important role of 5hmC in AD pathogenesis and highlighting both ethnicity-specific and overlapping changes of brain hydroxymethylome in AD.
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Affiliation(s)
- Lixia Qin
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Qian Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- National Clinical Research Center for Geriatric Disorders (XIANGYA), Changsha, Hunan 410078, China
| | - Ziyi Li
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA 30322, USA
| | - Li Chen
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yujing Li
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Nannan Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhenhua Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jifeng Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- National Clinical Research Center for Geriatric Disorders (XIANGYA), Changsha, Hunan 410078, China
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- National Clinical Research Center for Geriatric Disorders (XIANGYA), Changsha, Hunan 410078, China
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan, China
| | - Emily G Allen
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Chao Chen
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Chao Ma
- Department of Human Anatomy, Histology and Embryology, Institute of Basic Medical Sciences, Neuroscience Center, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100000, China
| | - Hao Wu
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA 30322, USA
| | - Xiongwei Zhu
- Department of Pathology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Peng Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- National Clinical Research Center for Geriatric Disorders (XIANGYA), Changsha, Hunan 410078, China
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan, China
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34
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Giuliani C, Garagnani P, Franceschi C. Genetics of Human Longevity Within an Eco-Evolutionary Nature-Nurture Framework. Circ Res 2019; 123:745-772. [PMID: 30355083 DOI: 10.1161/circresaha.118.312562] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Human longevity is a complex trait, and to disentangle its basis has a great theoretical and practical consequences for biomedicine. The genetics of human longevity is still poorly understood despite several investigations that used different strategies and protocols. Here, we argue that such rather disappointing harvest is largely because of the extraordinary complexity of the longevity phenotype in humans. The capability to reach the extreme decades of human lifespan seems to be the result of an intriguing mixture of gene-environment interactions. Accordingly, the genetics of human longevity is here described as a highly context-dependent phenomenon, within a new integrated, ecological, and evolutionary perspective, and is presented as a dynamic process, both historically and individually. The available literature has been scrutinized within this perspective, paying particular attention to factors (sex, individual biography, family, population ancestry, social structure, economic status, and education, among others) that have been relatively neglected. The strength and limitations of the most powerful and used tools, such as genome-wide association study and whole-genome sequencing, have been discussed, focusing on prominently emerged genes and regions, such as apolipoprotein E, Forkhead box O3, interleukin 6, insulin-like growth factor-1, chromosome 9p21, 5q33.3, and somatic mutations among others. The major results of this approach suggest that (1) the genetics of longevity is highly population specific; (2) small-effect alleles, pleiotropy, and the complex allele timing likely play a major role; (3) genetic risk factors are age specific and need to be integrated in the light of the geroscience perspective; (4) a close relationship between genetics of longevity and genetics of age-related diseases (especially cardiovascular diseases) do exist. Finally, the urgent need of a global approach to the largely unexplored interactions between the 3 genetics of human body, that is, nuclear, mitochondrial, and microbiomes, is stressed. We surmise that the comprehensive approach here presented will help in increasing the above-mentioned harvest.
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Affiliation(s)
- Cristina Giuliani
- From the Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology (C.G.), University of Bologna, Italy.,School of Anthropology and Museum Ethnography, University of Oxford, United Kingdom (C.G.).,Interdepartmental Centre 'L. Galvani' (CIG), University of Bologna, Italy (C.G.)
| | - Paolo Garagnani
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES) (P.G.), University of Bologna, Italy.,Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet at Huddinge University Hospital, Stockholm, Sweden (P.G.)
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35
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A Prospective Analysis of Genetic Variants Associated with Human Lifespan. G3-GENES GENOMES GENETICS 2019; 9:2863-2878. [PMID: 31484785 PMCID: PMC6723124 DOI: 10.1534/g3.119.400448] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
We present a massive investigation into the genetic basis of human lifespan. Beginning with a genome-wide association (GWA) study using a de-identified snapshot of the unique AncestryDNA database – more than 300,000 genotyped individuals linked to pedigrees of over 400,000,000 people – we mapped six genome-wide significant loci associated with parental lifespan. We compared these results to a GWA analysis of the traditional lifespan proxy trait, age, and found only one locus, APOE, to be associated with both age and lifespan. By combining the AncestryDNA results with those of an independent UK Biobank dataset, we conducted a meta-analysis of more than 650,000 individuals and identified fifteen parental lifespan-associated loci. Beyond just those significant loci, our genome-wide set of polymorphisms accounts for up to 8% of the variance in human lifespan; this value represents a large fraction of the heritability estimated from phenotypic correlations between relatives.
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36
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Deelen J, Evans DS, Arking DE, Tesi N, Nygaard M, Liu X, Wojczynski MK, Biggs ML, van der Spek A, Atzmon G, Ware EB, Sarnowski C, Smith AV, Seppälä I, Cordell HJ, Dose J, Amin N, Arnold AM, Ayers KL, Barzilai N, Becker EJ, Beekman M, Blanché H, Christensen K, Christiansen L, Collerton JC, Cubaynes S, Cummings SR, Davies K, Debrabant B, Deleuze JF, Duncan R, Faul JD, Franceschi C, Galan P, Gudnason V, Harris TB, Huisman M, Hurme MA, Jagger C, Jansen I, Jylhä M, Kähönen M, Karasik D, Kardia SLR, Kingston A, Kirkwood TBL, Launer LJ, Lehtimäki T, Lieb W, Lyytikäinen LP, Martin-Ruiz C, Min J, Nebel A, Newman AB, Nie C, Nohr EA, Orwoll ES, Perls TT, Province MA, Psaty BM, Raitakari OT, Reinders MJT, Robine JM, Rotter JI, Sebastiani P, Smith J, Sørensen TIA, Taylor KD, Uitterlinden AG, van der Flier W, van der Lee SJ, van Duijn CM, van Heemst D, Vaupel JW, Weir D, Ye K, Zeng Y, Zheng W, Holstege H, Kiel DP, Lunetta KL, Slagboom PE, Murabito JM. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nat Commun 2019; 10:3669. [PMID: 31413261 PMCID: PMC6694136 DOI: 10.1038/s41467-019-11558-2] [Citation(s) in RCA: 168] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 07/17/2019] [Indexed: 12/16/2022] Open
Abstract
Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.
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Affiliation(s)
- Joris Deelen
- Max Planck Institute for Biology of Ageing, 50866, Cologne, Germany.
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands.
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, 94158, USA.
| | - Dan E Arking
- McKusick-Nathans Institute of Genetic Medicine, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Niccolò Tesi
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, 2600 GA, Delft, The Netherlands
| | - Marianne Nygaard
- The Danish Aging Research Center, Department of Public Health, University of Southern Denmark, 5000, Odense C, Denmark
| | - Xiaomin Liu
- BGI-Shenzhen, Shenzhen, 518083, China
- China National Genebank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Mary L Biggs
- Department of Biostatistics, University of Washington, Seattle, WA, 98115, USA
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | | | - Gil Atzmon
- Department of Biology, Faculty of Natural Science, University of Haifa, Haifa, 3498838, Israel
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Erin B Ware
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Albert V Smith
- School of Public Health, Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Icelandic Heart Association, 201, Kópavogur, Iceland
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland
| | - Heather J Cordell
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, NE1 3BZ, UK
| | - Janina Dose
- Institute of Clinical Molecular Biology, Kiel University, 24105, Kiel, Germany
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, 3000 CA, Rotterdam, The Netherlands
| | - Alice M Arnold
- Department of Biostatistics, University of Washington, Seattle, WA, 98115, USA
| | | | - Nir Barzilai
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | | | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | | | - Kaare Christensen
- The Danish Aging Research Center, Department of Public Health, University of Southern Denmark, 5000, Odense C, Denmark
- Clinical Biochemistry and Pharmacology, Odense University Hospital, 5000, Odense C, Denmark
- Department of Clinical Genetics, Odense University Hospital, 5000, Odense C, Denmark
| | - Lene Christiansen
- The Danish Aging Research Center, Department of Public Health, University of Southern Denmark, 5000, Odense C, Denmark
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Joanna C Collerton
- Institute of Health & Society, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Sarah Cubaynes
- MMDN, Univ. Montpellier, EPHE, Unité Inserm 1198, PSL Research University, 34095, Montpellier, France
| | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, 94158, USA
| | - Karen Davies
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Birgit Debrabant
- Department of Public Health, University of Southern Denmark, 5000, Odense C, Denmark
| | - Jean-François Deleuze
- Fondation Jean Dausset-CEPH, 75010, Paris, France
- Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie François Jacob, 91000, Evry, France
| | - Rachel Duncan
- Institute of Health & Society, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
- Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Jessica D Faul
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Claudio Franceschi
- Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, 603022, Russia
- IRCCS Institute of Neurological Sciences of Bologna (ISNB), 40124, Bologna, Italy
| | - Pilar Galan
- EREN, UMR U1153 Inserm/U1125 Inra/Cnam/Paris 13, Université Paris 13, CRESS, 93017, Bobigny, France
| | - Vilmundur Gudnason
- Icelandic Heart Association, 201, Kópavogur, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Bethesda, MD, 20892, USA
| | - Martijn Huisman
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1007 MB, Amsterdam, The Netherlands
| | - Mikko A Hurme
- Department of Microbiology and Immunology, Faculty of Medicine and Health Technology, Tampere University, 33014, Tampere, Finland
| | - Carol Jagger
- Institute of Health & Society, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
- Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Iris Jansen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Marja Jylhä
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), Tampere University, 33104, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, 33521, Tampere, Finland
| | - David Karasik
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, 13010, Israel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, 02131, USA
| | - Sharon L R Kardia
- School of Public Health, Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Andrew Kingston
- Institute of Health & Society, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
- Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Thomas B L Kirkwood
- Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Bethesda, MD, 20892, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank PopGen, Kiel University, 24105, Kiel, Germany
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland
| | - Carmen Martin-Ruiz
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Junxia Min
- Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, 311058, China
| | - Almut Nebel
- Institute of Clinical Molecular Biology, Kiel University, 24105, Kiel, Germany
| | - Anne B Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Chao Nie
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Ellen A Nohr
- Research Unit of Gynecology and Obstetrics, Department of Clinical Research, University of Southern Denmark, 5000, Odense C, Denmark
| | - Eric S Orwoll
- Bone and Mineral Unit, Oregon Health Sciences University, Portland, OR, 97239, USA
| | - Thomas T Perls
- Department of Medicine, Geriatrics Section, Boston Medical Center, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
- Department of Epidemiology, University of Washington, Seattle, WA, 98101, USA
- Department of Health Services, University of Washington, Seattle, WA, 98101, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, 20521, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20014, Turku, Finland
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, 2600 GA, Delft, The Netherlands
| | - Jean-Marie Robine
- MMDN, Univ. Montpellier, EPHE, Unité Inserm 1198, PSL Research University, 34095, Montpellier, France
- CERMES3, UMR CNRS 8211-Unité Inserm 988-EHESS-Université Paris Descartes, 94801, Paris, France
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Division of Genetic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Jennifer Smith
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, 48104, USA
- School of Public Health, Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, and Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen N, Denmark
- MRC Integrative Epidemiology Unit, Bristol University, BS8 2BN, Bristol, UK
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC, 3000 CA, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, 3000 CA, Rotterdam, The Netherlands
| | - Wiesje van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
| | - Sven J van der Lee
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, 3000 CA, Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Diana van Heemst
- Department of Gerontology and Geriatrics, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - James W Vaupel
- Max Planck Institute for Demographic Research, 18057, Rostock, Germany
| | - David Weir
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Kenny Ye
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Yi Zeng
- Center for Healthy Aging and Development Studies, National School of Development and Raissun Institute for Advanced Studies, Peking University, 100871, Beijing, China
- Center for the Study of Aging and Human Development and Geriatrics Division, Medical School of Duke University, Durham, NC, 27710, USA
| | - Wanlin Zheng
- California Pacific Medical Center Research Institute, San Francisco, CA, 94158, USA
| | - Henne Holstege
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, 1007 MB, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, 2600 GA, Delft, The Netherlands
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, 02131, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
- Broad Institute of MIT & Harvard, Cambridge, MA, 02142, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands.
| | - Joanne M Murabito
- NHLBI's and Boston University's Framingham Heart Study, Framingham, MA, 01702, USA.
- Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, 02118, USA.
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van der Lee SJ, Conway OJ, Jansen I, Carrasquillo MM, Kleineidam L, van den Akker E, Hernández I, van Eijk KR, Stringa N, Chen JA, Zettergren A, Andlauer TFM, Diez-Fairen M, Simon-Sanchez J, Lleó A, Zetterberg H, Nygaard M, Blauwendraat C, Savage JE, Mengel-From J, Moreno-Grau S, Wagner M, Fortea J, Keogh MJ, Blennow K, Skoog I, Friese MA, Pletnikova O, Zulaica M, Lage C, de Rojas I, Riedel-Heller S, Illán-Gala I, Wei W, Jeune B, Orellana A, Then Bergh F, Wang X, Hulsman M, Beker N, Tesi N, Morris CM, Indakoetxea B, Collij LE, Scherer M, Morenas-Rodríguez E, Ironside JW, van Berckel BNM, Alcolea D, Wiendl H, Strickland SL, Pastor P, Rodríguez Rodríguez E, Boeve BF, Petersen RC, Ferman TJ, van Gerpen JA, Reinders MJT, Uitti RJ, Tárraga L, Maier W, Dols-Icardo O, Kawalia A, Dalmasso MC, Boada M, Zettl UK, van Schoor NM, Beekman M, Allen M, Masliah E, de Munain AL, Pantelyat A, Wszolek ZK, Ross OA, Dickson DW, Graff-Radford NR, Knopman D, Rademakers R, Lemstra AW, Pijnenburg YAL, Scheltens P, Gasser T, Chinnery PF, Hemmer B, Huisman MA, Troncoso J, Moreno F, Nohr EA, Sørensen TIA, Heutink P, Sánchez-Juan P, Posthuma D, Clarimón J, Christensen K, Ertekin-Taner N, Scholz SW, Ramirez A, Ruiz A, Slagboom E, van der Flier WM, Holstege H. A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity. Acta Neuropathol 2019; 138:237-250. [PMID: 31131421 PMCID: PMC6660501 DOI: 10.1007/s00401-019-02026-8] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 05/03/2019] [Accepted: 05/05/2019] [Indexed: 12/12/2022]
Abstract
The genetic variant rs72824905-G (minor allele) in the PLCG2 gene was previously associated with a reduced Alzheimer's disease risk (AD). The role of PLCG2 in immune system signaling suggests it may also protect against other neurodegenerative diseases and possibly associates with longevity. We studied the effect of the rs72824905-G on seven neurodegenerative diseases and longevity, using 53,627 patients, 3,516 long-lived individuals and 149,290 study-matched controls. We replicated the association of rs72824905-G with reduced AD risk and we found an association with reduced risk of dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). We did not find evidence for an effect on Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) risks, despite adequate sample sizes. Conversely, the rs72824905-G allele was associated with increased likelihood of longevity. By-proxy analyses in the UK Biobank supported the associations with both dementia and longevity. Concluding, rs72824905-G has a protective effect against multiple neurodegenerative diseases indicating shared aspects of disease etiology. Our findings merit studying the PLCγ2 pathway as drug-target.
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Affiliation(s)
- Sven J van der Lee
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Olivia J Conway
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Iris Jansen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | | | - Luca Kleineidam
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
- DZNE, German Center for Neurodegenerative Diseases, Bonn, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Cologne, Cologne, Germany
| | - Erik van den Akker
- Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Isabel Hernández
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Kristel R van Eijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Najada Stringa
- Amsterdam UMC-Vrije Universiteit Amsterdam, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jason A Chen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, USA
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap) at the University of Gothenburg, Gothenburg, Sweden
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- German Competence Network Multiple Sclerosis (KKNMS), Munich, Germany
| | - Monica Diez-Fairen
- Movement Disorders and Memory Unit, Department of Neurology, University Hospital Mutua de Terrassa, Barcelona, Spain
- Fundacio per la Recerca Biomedica I Social Mutua Terrassa, Terrassa, Barcelona, Spain
| | - Javier Simon-Sanchez
- German Center for Neurodegenerative Diseases (DZNE)-Tübingen, Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Alberto Lleó
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK
| | - Marianne Nygaard
- The Danish Aging Research Center, Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Cornelis Blauwendraat
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, 20892-3707, USA
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jonas Mengel-From
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Sonia Moreno-Grau
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Michael Wagner
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
- DZNE, German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Juan Fortea
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Michael J Keogh
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, NE1 3BZ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap) at the University of Gothenburg, Gothenburg, Sweden
| | - Manuel A Friese
- German Competence Network Multiple Sclerosis (KKNMS), Munich, Germany
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Olga Pletnikova
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Miren Zulaica
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Instituto Biodonostia, San Sebastian, Spain
| | - Carmen Lage
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- University Hospital "Marques de Valdecilla", Santander, Spain
- IDIVAL, Santander, Spain
| | - Itziar de Rojas
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Steffi Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health (ISAP), University of Leipzig, Leipzig, Germany
| | - Ignacio Illán-Gala
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Wei Wei
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Bernard Jeune
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Adelina Orellana
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Florian Then Bergh
- German Competence Network Multiple Sclerosis (KKNMS), Munich, Germany
- Department of Neurology, University of Leipzig, Leipzig, Germany
| | - Xue Wang
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Marc Hulsman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Nina Beker
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Niccolo Tesi
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Christopher M Morris
- Newcastle Brain Tissue Resource, Edwardson Building, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Begoña Indakoetxea
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Instituto Biodonostia, San Sebastian, Spain
- Cognitive Disorders Unit, Department of Neurology, Hospital Universitario San Sebastian, San Sebastian, Spain
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martin Scherer
- Department of Primary Medical Care, Center for Psychosocial Medicine, University Medical Center, Hamburg-Eppendorf, Germany
| | - Estrella Morenas-Rodríguez
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - James W Ironside
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Daniel Alcolea
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Heinz Wiendl
- German Competence Network Multiple Sclerosis (KKNMS), Munich, Germany
- Department of Neurology, Klinik für Neurologie mit Institut für Translationale Neurologie, University of Münster, Münster, Germany
| | | | - Pau Pastor
- Movement Disorders and Memory Unit, Department of Neurology, University Hospital Mutua de Terrassa, Barcelona, Spain
- Fundacio per la Recerca Biomedica I Social Mutua Terrassa, Terrassa, Barcelona, Spain
| | - Eloy Rodríguez Rodríguez
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- University Hospital "Marques de Valdecilla", Santander, Spain
- IDIVAL, Santander, Spain
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic Minnesota, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic Minnesota, Rochester, MN, 55905, USA
| | - Tanis J Ferman
- Department of Psychiatry and Psychology, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Jay A van Gerpen
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Ryan J Uitti
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Lluís Tárraga
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Wolfgang Maier
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
- DZNE, German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Oriol Dols-Icardo
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Amit Kawalia
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Cologne, Cologne, Germany
| | - Maria Carolina Dalmasso
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Cologne, Cologne, Germany
- Fundación Instituto Leloir-IIBBA-CONICET, Buenos Aires, Argentina
| | - Mercè Boada
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Uwe K Zettl
- German Competence Network Multiple Sclerosis (KKNMS), Munich, Germany
- Department of Neurology, University of Rostock, Rostock, Germany
| | - Natasja M van Schoor
- Amsterdam UMC-Vrije Universiteit Amsterdam, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Eliezer Masliah
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Adolfo López de Munain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Instituto Biodonostia, San Sebastian, Spain
- Department of Neurology, Hospital Universitario San Sebastian, San Sebastian, Spain
| | - Alexander Pantelyat
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, 21287, USA
| | - Zbigniew K Wszolek
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | | | - David Knopman
- Department of Neurology, Mayo Clinic Minnesota, Rochester, MN, 55905, USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Thomas Gasser
- Center of Neurology, Department of Neurodegenerative diseases, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Bernhard Hemmer
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- German Competence Network Multiple Sclerosis (KKNMS), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Martijn A Huisman
- Amsterdam UMC-Vrije Universiteit Amsterdam, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Department of Sociology, VU University, Amsterdam, The Netherlands
| | - Juan Troncoso
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Fermin Moreno
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Instituto Biodonostia, San Sebastian, Spain
- Cognitive Disorders Unit, Department of Neurology, Hospital Universitario San Sebastian, San Sebastian, Spain
| | - Ellen A Nohr
- Research Unit of Gynecology and Obstetrics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Copenhagen, Denmark
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- MRC Integrative Epidemiology Unit, Bristol University, Bristol, UK
| | - Peter Heutink
- German Center for Neurodegenerative Diseases (DZNE)-Tübingen, Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Pascual Sánchez-Juan
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- University Hospital "Marques de Valdecilla", Santander, Spain
- IDIVAL, Santander, Spain
| | - Danielle Posthuma
- Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jordi Clarimón
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Kaare Christensen
- The Danish Aging Research Center, Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, 20892-3707, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, 21287, USA
| | - Alfredo Ramirez
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Hospital Cologne, Cologne, Germany
| | - Agustín Ruiz
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Eline Slagboom
- Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Dutch Society for Research on Ageing, Leiden, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Henne Holstege
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
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38
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Porcu E, Rüeger S, Lepik K, Santoni FA, Reymond A, Kutalik Z. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun 2019; 10:3300. [PMID: 31341166 PMCID: PMC6656778 DOI: 10.1038/s41467-019-10936-0] [Citation(s) in RCA: 146] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 06/11/2019] [Indexed: 01/21/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene-trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.
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Affiliation(s)
- Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Sina Rüeger
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland
| | - Kaido Lepik
- University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland.,Institute of Computer Science, University of Tartu, Tartu, Estonia
| | | | | | - Federico A Santoni
- Endocrine, Diabetes, and Metabolism Service, CHUV and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland.
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39
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Komljenovic A, Li H, Sorrentino V, Kutalik Z, Auwerx J, Robinson-Rechavi M. Cross-species functional modules link proteostasis to human normal aging. PLoS Comput Biol 2019; 15:e1007162. [PMID: 31269015 PMCID: PMC6634426 DOI: 10.1371/journal.pcbi.1007162] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 07/16/2019] [Accepted: 06/07/2019] [Indexed: 11/23/2022] Open
Abstract
The evolutionarily conserved nature of the few well-known anti-aging interventions that affect lifespan, such as caloric restriction, suggests that aging-related research in model organisms is directly relevant to human aging. Since human lifespan is a complex trait, a systems-level approach will contribute to a more comprehensive understanding of the underlying aging landscape. Here, we integrate evolutionary and functional information of normal aging across human and model organisms at three levels: gene-level, process-level, and network-level. We identify evolutionarily conserved modules of normal aging across diverse taxa, and notably show proteostasis to be conserved in normal aging. Additionally, we find that mechanisms related to protein quality control network are enriched for genes harboring genetic variants associated with 22 age-related human traits and associated to caloric restriction. These results demonstrate that a systems-level approach, combined with evolutionary conservation, allows the detection of candidate aging genes and pathways relevant to human normal aging.
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Affiliation(s)
- Andrea Komljenovic
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Hao Li
- Laboratory of Integrative Systems Physiology, EPFL, Lausanne, Switzerland
| | | | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, EPFL, Lausanne, Switzerland
| | - Marc Robinson-Rechavi
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
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40
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Morris BJ, Willcox BJ, Donlon TA. Genetic and epigenetic regulation of human aging and longevity. Biochim Biophys Acta Mol Basis Dis 2019; 1865:1718-1744. [PMID: 31109447 PMCID: PMC7295568 DOI: 10.1016/j.bbadis.2018.08.039] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 08/02/2018] [Accepted: 08/28/2018] [Indexed: 02/06/2023]
Abstract
Here we summarize the latest data on genetic and epigenetic contributions to human aging and longevity. Whereas environmental and lifestyle factors are important at younger ages, the contribution of genetics appears more important in reaching extreme old age. Genome-wide studies have implicated ~57 gene loci in lifespan. Epigenomic changes during aging profoundly affect cellular function and stress resistance. Dysregulation of transcriptional and chromatin networks is likely a crucial component of aging. Large-scale bioinformatic analyses have revealed involvement of numerous interaction networks. As the young well-differentiated cell replicates into eventual senescence there is drift in the highly regulated chromatin marks towards an entropic middle-ground between repressed and active, such that genes that were previously inactive "leak". There is a breakdown in chromatin connectivity such that topologically associated domains and their insulators weaken, and well-defined blocks of constitutive heterochromatin give way to generalized, senescence-associated heterochromatin, foci. Together, these phenomena contribute to aging.
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Affiliation(s)
- Brian J Morris
- Basic & Clinical Genomics Laboratory, School of Medical Sciences and Bosch Institute, University of Sydney, New South Wales 2006, Australia; Honolulu Heart Program (HHP)/Honolulu-Asia Aging Study (HAAS), Department of Research, Kuakini Medical Center, Honolulu, HI 96817, United States; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Kuakini Medical Center Campus, Honolulu, HI 96813, United States.
| | - Bradley J Willcox
- Honolulu Heart Program (HHP)/Honolulu-Asia Aging Study (HAAS), Department of Research, Kuakini Medical Center, Honolulu, HI 96817, United States; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Kuakini Medical Center Campus, Honolulu, HI 96813, United States.
| | - Timothy A Donlon
- Honolulu Heart Program (HHP)/Honolulu-Asia Aging Study (HAAS), Department of Research, Kuakini Medical Center, Honolulu, HI 96817, United States; Departments of Cell & Molecular Biology and Pathology, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, United States.
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41
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Zenin A, Tsepilov Y, Sharapov S, Getmantsev E, Menshikov LI, Fedichev PO, Aulchenko Y. Identification of 12 genetic loci associated with human healthspan. Commun Biol 2019; 2:41. [PMID: 30729179 PMCID: PMC6353874 DOI: 10.1038/s42003-019-0290-0] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 01/08/2019] [Indexed: 02/06/2023] Open
Abstract
Aging populations face diminishing quality of life due to increased disease and morbidity. These challenges call for longevity research to focus on understanding the pathways controlling healthspan. We use the data from the UK Biobank (UKB) cohort and observe that the risks of major chronic diseases increased exponentially and double every eight years, i.e., at a rate compatible with the Gompertz mortality law. Assuming that aging drives the acceleration in morbidity rates, we build a risk model to predict the age at the end of healthspan depending on age, gender, and genetic background. Using the sub-population of 300,447 British individuals as a discovery cohort, we identify 12 loci associated with healthspan at the whole-genome significance level. We find strong genetic correlations between healthspan and all-cause mortality, life-history, and lifestyle traits. We thereby conclude that the healthspan offers a promising new way to interrogate the genetics of human longevity.
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Affiliation(s)
- Aleksandr Zenin
- Gero LLC, Novokuznetskaya street 24/2, Moscow, Russia 119017
| | - Yakov Tsepilov
- Novosibirsk State University, Pirogova 2, Novosibirsk, Russia 630090
- Institute of Cytology and Genetics SB RAS, Lavrentyeva ave. 10, Novosibirsk, Russia 630090
| | - Sodbo Sharapov
- Novosibirsk State University, Pirogova 2, Novosibirsk, Russia 630090
- Institute of Cytology and Genetics SB RAS, Lavrentyeva ave. 10, Novosibirsk, Russia 630090
| | | | - L. I. Menshikov
- Gero LLC, Novokuznetskaya street 24/2, Moscow, Russia 119017
- National Research Center “Kurchatov Institute”, 1, Akademika Kurchatova pl., Moscow, Russia 123182
| | - Peter O. Fedichev
- Gero LLC, Novokuznetskaya street 24/2, Moscow, Russia 119017
- Moscow Institute of Physics and Technology, Institutskii per. 9, Dolgoprudny, Moscow Russia 141700
| | - Yurii Aulchenko
- Novosibirsk State University, Pirogova 2, Novosibirsk, Russia 630090
- Institute of Cytology and Genetics SB RAS, Lavrentyeva ave. 10, Novosibirsk, Russia 630090
- PolyOmica, Het Vlaggeschip 61, 5237PA ‘s-Hertogenbosch, The Netherlands
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, Scotland EH8 9AG UK
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42
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Timmers PR, Mounier N, Lall K, Fischer K, Ning Z, Feng X, Bretherick AD, Clark DW, Shen X, Esko T, Kutalik Z, Wilson JF, Joshi PK. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances. eLife 2019; 8:39856. [PMID: 30642433 PMCID: PMC6333444 DOI: 10.7554/elife.39856] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 11/20/2018] [Indexed: 12/31/2022] Open
Abstract
We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer - but not other cancers - explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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Affiliation(s)
- Paul Rhj Timmers
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Ninon Mounier
- Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Kristi Lall
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Krista Fischer
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Zheng Ning
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Xiao Feng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Andrew D Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - David W Clark
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Xia Shen
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Broad Institute of Harvard and MIT, Cambridge, United States
| | - Zoltán Kutalik
- Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - James F Wilson
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.,Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland
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43
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Cataractogenic load – A concept to study the contribution of ionizing radiation to accelerated aging in the eye lens. MUTATION RESEARCH-REVIEWS IN MUTATION RESEARCH 2019; 779:68-81. [DOI: 10.1016/j.mrrev.2019.02.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Revised: 02/12/2019] [Accepted: 02/14/2019] [Indexed: 12/11/2022]
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44
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Hook M, Roy S, Williams EG, Bou Sleiman M, Mozhui K, Nelson JF, Lu L, Auwerx J, Williams RW. Genetic cartography of longevity in humans and mice: Current landscape and horizons. Biochim Biophys Acta Mol Basis Dis 2018; 1864:2718-2732. [PMID: 29410319 PMCID: PMC6066442 DOI: 10.1016/j.bbadis.2018.01.026] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/24/2018] [Accepted: 01/28/2018] [Indexed: 12/14/2022]
Abstract
Aging is a complex and highly variable process. Heritability of longevity among humans and other species is low, and this finding has given rise to the idea that it may be futile to search for DNA variants that modulate aging. We argue that the problem in mapping longevity genes is mainly one of low power and the genetic and environmental complexity of aging. In this review we highlight progress made in mapping genes and molecular networks associated with longevity, paying special attention to work in mice and humans. We summarize 40 years of linkage studies using murine cohorts and 15 years of studies in human populations that have exploited candidate gene and genome-wide association methods. A small but growing number of gene variants contribute to known longevity mechanisms, but a much larger set have unknown functions. We outline these and other challenges and suggest some possible solutions, including more intense collaboration between research communities that use model organisms and human cohorts. Once hundreds of gene variants have been linked to differences in longevity in mammals, it will become feasible to systematically explore gene-by-environmental interactions, dissect mechanisms with more assurance, and evaluate the roles of epistasis and epigenetics in aging. A deeper understanding of complex networks-genetic, cellular, physiological, and social-should position us well to improve healthspan.
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Affiliation(s)
- Michael Hook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Suheeta Roy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Evan G Williams
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich CH-8093, Switzerland
| | - Maroun Bou Sleiman
- Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Khyobeni Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - James F Nelson
- Department of Cellular and Integrative Physiology and Barshop Institute for Longevity and Aging Studies, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Johan Auwerx
- Interfaculty Institute of Bioengineering, Laboratory of Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
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45
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Halaschek-Wiener J, Tindale LC, Collins JA, Leach S, McManus B, Madden K, Meneilly G, Le ND, Connors JM, Brooks-Wilson AR. The Super-Seniors Study: Phenotypic characterization of a healthy 85+ population. PLoS One 2018; 13:e0197578. [PMID: 29795606 PMCID: PMC5967696 DOI: 10.1371/journal.pone.0197578] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Accepted: 05/06/2018] [Indexed: 11/18/2022] Open
Abstract
Background To understand why some people live to advanced age in good health and others do not, it is important to study not only disease, but also long-term good health. The Super-Seniors Study aims to identify factors associated with healthy aging. Methods 480 healthy oldest-old ‘Super-Seniors’ aged 85 to 105 years and never diagnosed with cancer, cardiovascular disease, diabetes, dementia, or major pulmonary disease, were compared to 545 mid-life controls aged 41–54, who represent a group that is unselected for survival from late-life diseases. Health and lifestyle information, personal and family medical history, and blood samples were collected from all participants. Super-Seniors also underwent four geriatric tests. Results Super-Seniors showed high cognitive (Mini-Mental State Exam mean = 28.3) and functional capacity (Instrumental Activities of Daily Living Scale mean = 21.4), as well as high physical function (Timed Up and Go mean = 12.3 seconds) and low levels of depression (Geriatric Depression Scale mean = 1.5). Super-Seniors were less likely to be current smokers than controls, but the frequency of drinking alcohol was the same in both groups. Super-Seniors were more likely to have 4 or more offspring; controls were more likely to have no children. Female Super-Seniors had a mean age of last fertility 1.9 years older than controls, and were 2.3 times more likely to have had a child at ≥ 40 years. The parents of Super-Seniors had mean ages of deaths of 79.3 years for mothers, and 74.5 years for fathers, each exceeding the life expectancy for their era by a decade. Conclusions Super-Seniors are cognitively and physically high functioning individuals who have evaded major age-related chronic diseases into old age, representing the approximately top 1% for healthspan. The familiality of long lifespan of the parents of Super-Seniors supports the hypothesis that heritable factors contribute to this desirable phenotype.
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Affiliation(s)
- Julius Halaschek-Wiener
- Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency (BCCA), Vancouver, British Columbia, Canada
| | - Lauren C. Tindale
- Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency (BCCA), Vancouver, British Columbia, Canada
- Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Jennifer A. Collins
- Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency (BCCA), Vancouver, British Columbia, Canada
| | - Stephen Leach
- Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency (BCCA), Vancouver, British Columbia, Canada
| | - Bruce McManus
- PROOF Centre of Excellence, University of British Columbia, Providence Health Care, Vancouver, British Columbia, Canada
| | - Kenneth Madden
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Graydon Meneilly
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nhu D. Le
- Cancer Control Research, BCCA, Vancouver, British Columbia, Canada
| | - Joseph M. Connors
- Centre for Lymphoid Cancer, BCCA, Vancouver, British Columbia, Canada
| | - Angela R. Brooks-Wilson
- Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency (BCCA), Vancouver, British Columbia, Canada
- Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
- * E-mail:
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46
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van der Lee SJ, Wolters FJ, Ikram MK, Hofman A, Ikram MA, Amin N, van Duijn CM. The effect of APOE and other common genetic variants on the onset of Alzheimer's disease and dementia: a community-based cohort study. Lancet Neurol 2018; 17:434-444. [DOI: 10.1016/s1474-4422(18)30053-x] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 01/22/2018] [Accepted: 01/31/2018] [Indexed: 12/14/2022]
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47
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Wang X, Malawista A, Qian F, Ramsey C, Allore HG, Montgomery RR. Age-related changes in expression and signaling of TAM receptor inflammatory regulators in monocytes. Oncotarget 2018. [PMID: 29515754 PMCID: PMC5839385 DOI: 10.18632/oncotarget.23851] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
The multifactorial immune deterioration in aging--termed “inflamm-aging”--is comprised of a state of low-grade, chronic inflammation and complex dysregulation of responses to immune stimulation. The TAM family (Tyro 3, Axl, and Mer) of receptor tyrosine kinases are negative regulators of Toll like receptor-mediated immune responses that broadly inhibit cytokine receptor cascades to inhibit inflammation. Here we demonstrate elevated expression of TAM receptors in monocytes of older adults, and an age-dependent difference in signaling mediator AKT resulting in dysregulated responses to signaling though Mer. Our results may be especially significant in tissue, where levels of Mer are highest, and may present avenues for modulation of chronic tissue inflammation noted in aging.
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Affiliation(s)
- Xiaomei Wang
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Anna Malawista
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Feng Qian
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut.,State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Christine Ramsey
- Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, Connecticut
| | - Heather G Allore
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Ruth R Montgomery
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut.,Human Translational Immunology, Yale University School of Medicine, New Haven, Connecticut
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48
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Pilling LC, Kuo CL, Sicinski K, Tamosauskaite J, Kuchel GA, Harries LW, Herd P, Wallace R, Ferrucci L, Melzer D. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. Aging (Albany NY) 2017; 9:2504-2520. [PMID: 29227965 PMCID: PMC5764389 DOI: 10.18632/aging.101334] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 11/26/2017] [Indexed: 12/22/2022]
Abstract
We undertook a genome-wide association study (GWAS) of parental longevity in European descent UK Biobank participants. For combined mothers' and fathers' attained age, 10 loci were associated (p<5*10-8), including 8 previously identified for traits including survival, Alzheimer's and cardiovascular disease. Of these, 4 were also associated with longest 10% survival (mothers age ≥90 years, fathers ≥87 years), with 2 additional associations including MC2R intronic variants (coding for the adrenocorticotropic hormone receptor). Mother's age at death was associated with 3 additional loci (2 linked to autoimmune conditions), and 8 for fathers only. An attained age genetic risk score associated with parental survival in the US Health and Retirement Study and the Wisconsin Longitudinal Study and with having a centenarian parent (n=1,181) in UK Biobank. The results suggest that human longevity is highly polygenic with prominent roles for loci likely involved in cellular senescence and inflammation, plus lipid metabolism and cardiovascular conditions. There may also be gender specific routes to longevity.
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Affiliation(s)
- Luke C. Pilling
- Epidemiology and Public Health Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK
| | - Chia-Ling Kuo
- Department of Community Medicine and Health Care, Connecticut Institute for Clinical and Translational Science, Institute for Systems Genomics, University of Connecticut Health Center, CT 06269 USA
| | - Kamil Sicinski
- Center for Demography of Health and Aging, University of Wisconsin, Madison, WI 53706, USA
| | - Jone Tamosauskaite
- Epidemiology and Public Health Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK
| | - George A. Kuchel
- UConn Center on Aging, University of Connecticut, Farmington, CT 06030, USA
| | - Lorna W. Harries
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
| | - Pamela Herd
- La Follette School of Public Affairs and the Department of Sociology, University of Wisconsin, Madison, WI 53706, USA
| | - Robert Wallace
- College of Public Health, University of Iowa, Iowa City, IA 52242, USA
| | | | - David Melzer
- Epidemiology and Public Health Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK
- UConn Center on Aging, University of Connecticut, Farmington, CT 06030, USA
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49
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Sasaki T. Neural and Molecular Mechanisms Involved in Controlling the Quality of Feeding Behavior: Diet Selection and Feeding Patterns. Nutrients 2017; 9:nu9101151. [PMID: 29053636 PMCID: PMC5691767 DOI: 10.3390/nu9101151] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 10/12/2017] [Accepted: 10/13/2017] [Indexed: 12/20/2022] Open
Abstract
We are what we eat. There are three aspects of feeding: what, when, and how much. These aspects represent the quantity (how much) and quality (what and when) of feeding. The quantitative aspect of feeding has been studied extensively, because weight is primarily determined by the balance between caloric intake and expenditure. In contrast, less is known about the mechanisms that regulate the qualitative aspects of feeding, although they also significantly impact the control of weight and health. However, two aspects of feeding quality relevant to weight loss and weight regain are discussed in this review: macronutrient-based diet selection (what) and feeding pattern (when). This review covers the importance of these two factors in controlling weight and health, and the central mechanisms that regulate them. The relatively limited and fragmented knowledge on these topics indicates that we lack an integrated understanding of the qualitative aspects of feeding behavior. To promote better understanding of weight control, research efforts must focus more on the mechanisms that control the quality and quantity of feeding behavior. This understanding will contribute to improving dietary interventions for achieving weight control and for preventing weight regain following weight loss.
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Affiliation(s)
- Tsutomu Sasaki
- Laboratory for Metabolic Signaling, Institute for Molecular and Cellular Regulation, Gunma University, 3-39-15 Showa-machi, Maebashi, Gunma 371-8512, Japan.
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50
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Molony RD, Malawista A, Montgomery RR. Reduced dynamic range of antiviral innate immune responses in aging. Exp Gerontol 2017; 107:130-135. [PMID: 28822811 PMCID: PMC5815956 DOI: 10.1016/j.exger.2017.08.019] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 08/11/2017] [Accepted: 08/12/2017] [Indexed: 01/04/2023]
Abstract
The worldwide population aged ≥ 65 years is increasing and the average life span is expected to increase another 10 years by 2050. This extended lifespan is associated with a progressive decline in immune function and a paradoxical state of low-grade, chronic inflammation that may contribute to susceptibility to viral infection, and reduced responses to vaccination. Here we review the effects of aging on innate immune responses to viral pathogens including elements of recognition, signaling, and production of inflammatory mediators. We specifically focus on age-related changes in key pattern recognition receptor signaling pathways, converging on altered cytokine responses, including a notable impairment of antiviral interferon responses. We highlight an emergent change in innate immunity that arises during aging – the dampening of the dynamic range of responses to multiple sources of stimulation – which may underlie reduced efficiency of immune responses in aging. We review the effects of aging on innate antiviral immunity, including recognition, signaling, and cytokine responses. Lower Toll-like receptor expression leads to impaired signaling and responses upon activation of these sensors. Effects of aging on cytosolic nucleic acid sensing receptors and inflammasomes remains incompletely characterized. In aging the dynamic range of innate immunity is compressed, with increased basal activation of many signaling pathways. Interferon production is impaired with age, which may lead to the increased viral susceptibility of older persons.
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Affiliation(s)
- Ryan D Molony
- Departments of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, United States
| | - Anna Malawista
- Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, United States
| | - Ruth R Montgomery
- Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, United States; Human Translational Immunology, Yale University School of Medicine, New Haven, CT 06520, United States.
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