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et al., Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging., The ENIGMA-Plasticity Working Group is part of the ENIGMA World Aging Center, funded by NIA grants R56 AG058854 and R01 AG058854. The ENIGMA Consortium core funding was supported by NIH Consortium Grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centers of Excellence. 1000BRAINS: 1000BRAINS is a population-based cohort based on the Heinz-Nixdorf Recall Study and is supported, in part, by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support in terms of the Heinz Nixdorf Study. The authors are supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements 785907 (Human Brain Project SGA2; Svenja Caspers, Sven Cichon and Katrin Amunts). This work was further supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e:Med Program (grant 01ZX1314A; Sven Cichon) and by the Swiss National Science Foundation (SNSF, grant 156791; Sven Cichon). The authors acknowledge grants supporting their work from the European Union’s Horizon 2020 Research and Innovation Programme (H2020/2014–2020) under grant agreements 667302 (CoCA), 728018 (Eat2beNICE), 785907 (HBP SGA2) and 772376 (EScORIAL) and the Netherlands ALS Foundation.Additional support is received from the European Community’s Seventh Framework Programme (FP7/2007—2013) under grant agreements n° 602805 (Aggressotype), n° 603016 (MATRICS), n° 602450 (IMAGEMEND) and n° 278948 (TACTICS) and from the European Community’s Horizon 2020 Programme (H2020/2014—2020) under grant agreements n° 643051 (MiND) and n° 667302 (CoCA). FP7 Ideas: the European Research Council (ERC-230374 to D.B.). This work was supported by Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012 and PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds; European Union Seventh Framework Program under grant agreements FP7- HEALTH-2013-2.2.1-2-603196 (Project PSYSCAN) and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement 115916, Project PRISM and grant agreement 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. The Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313). The FP7 project MATRICS (603016). The research leading to these results also received support from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 278948 (TACTICS), 602805 (Aggressotype), 603016 (MATRICS) and 602450 (Imagemend) and the Innovation Medicine Initiative grants 115300 (EU-AIMS) and 777394 (AIMS-2-TRIALS).PAFIP: PAFIP was supported by the Instituto de Salud Carlos III (PI14/00639, PI14/00918 and PI17/01056) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT025KG Jebsen Stiftelsen and H2020 CoMorMent (847776).34363). European Union Marie Curie Research Training Network (MRTN-CT-2006-035987).

SummaryCurrent concepts regarding the biology of aging are based on studies aimed at identifying factors regulating natural lifespan. However, lifespan as a sole proxy measure for aging can be of limited value because it may be restricted by specific sets of pathologies, rather than by general physiological decline. Here, we employed large-scale phenotyping to analyze hundreds of phenotypes and thousands of molecular markers across tissues and organ systems in a single study of aging male C57BL/6J mice. For each phenotype, we established lifetime profiles to determine when age-dependent phenotypic change is first detectable relative to the young adult baseline. We examined central genetic and environmental lifespan regulators (putative anti-aging interventions, PAAIs; the following PAAIs were examined: mTOR loss-of-function, loss-of-function in growth hormone signaling, dietary restriction) for a possible countering of the signs and symptoms of aging. Importantly, in our study design, we included young treated groups of animals, subjected to PAAIs prior to the onset of detectable age-dependent phenotypic change. In parallel to our studies in mice, we assessed genetic variants for their effects on age-sensitive phenotypes in humans. We observed that, surprisingly, many PAAI effects influenced phenotypes long before the onset of detectable age-dependent changes, rather than altering the rate at which these phenotypes developed with age. Accordingly, this subset of PAAI effects does not reflect a targeting of age-dependent phenotypic change. Overall, our findings suggest that comprehensive phenotyping, including the controls built in our study, is critical for the investigation of PAAIs as it facilitates the proper interpretation of the mechanistic mode by which PAAIs influence biological aging.HighlightsPhenotyping at scale defines lifetime trajectories of age-dependent changes in C57BL/6J miceCentral genetic and environmental lifespan regulators (putative anti-aging interventions; PAAIs) influence age-sensitive phenotypes (ASPs) often long before the appearance of age-dependent changes in these ASPsCorresponding genetic variants in humans also have age-independent effectsMany PAAI effects shift the baseline of ASPs rather than slowing their rate of change

Mitochondrial dysfunction can either extend or decrease Caenorhabditis elegans lifespan, depending on whether transcriptionally regulated responses can elicit durable stress adaptation to otherwise detrimental lesions. Here, we test the hypothesis that enhanced metabolic flexibility is sufficient to circumvent bioenergetic abnormalities associated with the phenotypic threshold effect, thereby transforming short-lived mitochondrial mutants into long-lived ones. We find that CEST-2.2, a carboxylesterase mainly localizes in the intestine, may stimulate the survival of mitochondrial deficient animals. We report that genetic manipulation of cest-2.2 expression has a minor lifespan impact on wild-type nematodes, whereas its overexpression markedly extends the lifespan of complex I-deficient gas-1(fc21) mutants. We profile the transcriptome and lipidome of cest-2.2 overexpressing animals and show that CEST-2.2 stimulates lipid metabolism and fatty acid beta-oxidation, thereby enhancing mitochondrial respiratory capacity through complex II and LET-721/ETFDH, despite the inherited genetic lesion of complex I. Together, our findings unveil a metabolic pathway that, through the tissue-specific mobilization of lipid deposits, may influence the longevity of mitochondrial mutant C. elegans.

The discovery of genetic factors for the predisposition of longevity is promising but their functional role and clinical relevance remain largely unclear. Based on results from a large genome-wide association study (GWAS) on human longevity (N ≈ 390,000) we identified six phenotype categories belonging to behavioral and psychiatric traits showing significant genetic correlations using LD Hub. We validated these genetic correlations on the phenotype level in a general population sample using a polygenic risk score (PRS) based on the longevity GWAS as proxy for longevity (N ≈ 8190; Study of Health in Pomerania). The behavioral phenotypes education, smoking and body mass index (BMI) were highly associated with the PRS for longevity especially in females (peducation=0.003, psmoking=0.049, pBMI=2.0E−4) with increased rates for higher education, lower smoking rates and decrease in BMI attributed to a higher PRS for longevity. Moreover, the psychiatric phenotypes depression and subjective health complaints showed significant associations (pDEPR=0.032, pSHC=0.002) in females only. Generally, a higher genetic predisposition for longevity had a stronger association with behavioral phenotypes in females than in males. It is unclear what causes the higher ``behavioral heterogeneity'' in males but different biological mechanisms might be involved. Sensitivity analyses showed that the association for the PRS for longevity with BMI and smoking were robust against adjustment with the PRS for BMI and smoking. In conclusion, our analyses demonstrated that genetic information obtained from highly powered GWAS for longevity revealed a clear behavioral signature on the phenotype level in a smaller population based sample.

ATENCIÓ: la correcció està també al DDD, cal relacionar??? https://ddd.uab.cat/record/226203 Altres ajuts: The following studies and consortia have contributed to this manuscript. Amsterdam dementia Cohort (ADC): Research of the Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. The clinical database structure was developed with funding from Stichting Dioraphte. Genotyping of the Dutch case-control samples was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW projectnumber 733051061). 100-Plus study: We are grateful for the collaborative efforts of all participating centenarians and their family members and/or relations. This work was supported by Stichting Alzheimer Nederland (WE09.2014-03), Stichting Diorapthe, horstingstuit foundation, Memorabel (ZonMW projectnumber 733050814) and Stichting VUmc Fonds. Genotyping of the 100-Plus Study was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW projectnumber 733051061). German Study on Ageing, Cognition and Dementia in Primary Care Patients (AgeCoDe): This study/publication is part of the German Research Network on Dementia (KND), the German Research Network on Degenerative Dementia (KNDD; German Study on Ageing, Cognition and Dementia in Primary Care Patients; AgeCoDe), and the Health Service Research Initiative (Study on Needs, health service use, costs and health-related quality of life in a large sample of oldest-old primary care patients (85+; AgeQualiDe)) and was funded by the German Federal Ministry of Education and Research (grants KND: 01GI0102, 01GI0420, 01GI0422, 01GI0423, 01GI0429, 01GI0431, 01GI0433, 01GI0434; grants KNDD: 01GI0710, 01GI0711, 01GI0712, 01GI0713, 01GI0714, 01GI0715, 01GI0716; grants Health Service Research Initiative: 01GY1322A, 01GY1322B, 01GY1322C, 01GY1322D, 01GY1322E, 01GY1322F, 01GY1322G). Alfredo Ramirez was partly supported by the ADAPTED consortium: Alzheimer's disease Apolipoprotein Pathology for Treatment Elucidation and Development, which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115975. Brain compendium: This work was funded by the UK Medical Research Council (13044). P.F.C. is a Wellcome Trust principal Fellow (212219/Z/18/Z) and a UK NIHR Senior Investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.Clinical AD, Sweden: We would like to thank UCL Genomics for performing the genotyping analyses. Danish data: The studies behind the Danish long-lived cases received funding from The National Program for Research Infrastructure 2007 (grant no. 09-063256), the Danish Agency for Science Technology and Innovation, the Velux Foundation, the US National Institute of Health (P01 AG08761), the Danish Agency for Science, Technology and Innovation/The Danish Council for Independent Research (grant no. 11-107308), the European Union's Seventh Framework Programme (FP7/2007-2011) under grant agreement no. 259679, the INTERREG 4 A programme Syddanmark-Schleswig-K.E.R.N. (by EU funds from the European Regional Development Fund), the CERA Foundation (Lyon), the AXA Research Fund, Paris, and The Health Foundation (Helsefonden), Copenhagen, Denmark. The GOYA study was conducted as part of the activities of the Danish Obesity Research Centre (DanORC, www.danorc.dk) and The MRC centre for Causal Analyses in Translational Epidemiology (MRC CAiTE). The genotyping for GOYA was funded by the Wellcome Trust (WT 084762). GOYA is a nested study within The Danish National Birth Cohort which was established with major funding from the Danish National Research Foundation. Additional support for this cohort has been obtained from the Pharmacy Foundation, the Egmont Foundation, The March of Dimes Birth Defects Foundation, the Augustinus Foundation, and the Health Foundation. Fundació ACE (FACE): We would like to thank patients and controls who participated in this project. We are indebted to Trinitat Port-Carbó and her family for their support of Fundació ACE research programs. Fundació ACE collaborates with the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED, Spain) and is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). Agustín Ruiz has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking ADAPTED Grant No. 115975 and by grants PI13/02434 and PI16/01861. Acción Estratégica en Salud, integrated in the Spanish National R + D + I Plan and financed by ISCIII (Instituto de Salud Carlos III)-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER- "Una manera de Hacer Europa"), by Fundación bancaria "La Caixa" and Grifols SA (GR@ACE project). Genetics of Healthy Ageing Study (GEHA - NL): The work described in this paper was funded mainly by the EU GEHA Project contract no. LSHM-CT-2004-503-270. Gothenburg Birth Cohort (GBC) Studies: We would like to thank UCL Genomics for performing the genotyping analyses. The studies were supported by The Stena Foundation, The Swedish Research Council (2015-02830, 2013-8717), The Swedish Research Council for Health, Working Life and Wellfare (2013-1202, 2005-0762, 2008-1210, 2013-2300, 2013-2496, 2013-0475), The Brain Foundation, Sahlgrenska University Hospital (ALF), The Alzheimer's Association (IIRG-03-6168), The Alzheimer's Association Zenith Award (ZEN-01-3151), Eivind och Elsa K:son Sylvans Stiftelse, The Swedish Alzheimer Foundation. International FTD-Genomics Consortium (IFGC): International FTD-Genomics Consortium (IFGC): The authors thank the IFGC for providing relevant data to support the analyses presented in this manuscript. Further acknowledgments for IFGC (https://ifgcsite.wordpress.com/), e.g. full members list and affiliations, are found in the online supplementary files. IPDGC (​The International Parkinson Disease Genomics Consortium): We also would like to thank all members of the International Parkinson Disease Genomics Consortium (IPDGC). See for a complete overview of members, acknowledgements and funding http://pdgenetics.org/partners. Kompetenznetz Multiple Sklerose (KKNMS): This work was supported by the German Ministry for Education and Research (BMBF) as part of the "German Competence Network Multiple Sclerosis" (KKNMS) (grant nos. 01GI0916 and 01GI0917) and the Munich Cluster for Systems Neurology (SyNergy). TA was supported by the BMBF through the Integrated Network IntegraMent, under the auspices of the e:Med Programme (01ZX1614J). BH was supported by the EU Horizon 2020 project MultipleMS.Longitudinal Aging Study Amsterdam (LASA) is largely supported by a grant from the Netherlands Ministry of Health, Welfare and Sports, Directorate of Long-Term Care. The authors are grateful to all LASA participants, the fieldwork team and all researchers for their ongoing commitment to the study. Leiden Longevity Study: This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology, and the Netherlands Consortium for Healthy Ageing (Grant 050-060-810), all in the framework of the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research (NWO) and by Unilever Colworth.Maria Carolina Dalmasso: Georg Forster Research Award (Alexander von Humboldt Foundation). Mayo Clinic AD, DLB, PD, PSP: We thank the patients and their families for their participation, without whom these studies would not have been possible. Funding for this work was supported by National Institute on Aging [RF AG051504 to NET.; U01 AG046139 to NET]; and National Institute of Neurological Disorders and Stroke [R01 NS080820 to NET; P50 NS072187]. The Mayo Clinic is a Lewy Body Dementia Association (LBDA) Research Center of Excellence, American Parkinson Disease Association (APDA) Information and Referral Center and Center for Advanced Research, NINDS Tau Center without Walls (U54-NS100693) and is supported by Mayo Clinic AD and related dementias genetics program, The Little Family Foundation, the Mangurian Foundation for Lewy body research and NINDS R01 NS078086 (to OAR). The PD program at the Mayo Clinic Florida is also supported by the Mayo Clinic Center for Regenerative Medicine, Mayo Clinic Center for Individualized Medicine, Mayo Clinic Neuroscience Focused Research Team (Cecilia and Dan Carmichael Family Foundation, and the James C. and Sarah K. Kennedy Fund for Neurodegenerative Disease Research at Mayo Clinic in Florida), the gift from Carl Edward Bolch, Jr., and Susan Bass Bolch, and The Sol Goldman Charitable Trust. Samples included in this study are from the brain bank at Mayo Clinic in Jacksonville which is supported by CurePSP The online version of this article (10.1007/s00401-019-02026-8) contains supplementary material, which is available to authorized users.

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’.\ud Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been\ud limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we\ud perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying\ud aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways.\ud Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong\ud overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover\ud that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also\ud tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets\ud with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular\ud evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest\ud systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing\ud new insights on aging-related genes as a whole and their interactions with age-related diseases.

Human longevity and healthy aging show moderate heritability (20%-50%). We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death. No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p < 5 x 10(-8)). We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p < 10(-5)). These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease. In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings. These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity. (C) 2011 Elsevier Inc. All rights reserved.