The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer’s Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51–72 years. Biometrical genetic analyses “twin models” revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.

[1]  A. Dale,et al.  Long-term Associations of Cigarette Smoking in Early Midlife with Predicted Brain Aging from Midlife to Late Life. , 2021, Addiction.

[2]  A. Dale,et al.  Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age , 2021, Neurobiology of Aging.

[3]  H. H. Hulshoff Pol,et al.  The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined , 2020, Cerebral cortex.

[4]  J. Cole,et al.  Commentary: Correction procedures in brain-age prediction , 2020, NeuroImage: Clinical.

[5]  Benjamin S Aribisala,et al.  Genetic architecture of subcortical brain structures in 38,851 individuals , 2019, Nature Genetics.

[6]  Lloyd T. Elliott,et al.  Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations , 2019, bioRxiv.

[7]  Wiro J Niessen,et al.  Gray Matter Age Prediction as a Biomarker for Risk of Dementia , 2019, Proceedings of the National Academy of Sciences.

[8]  Annchen R. Knodt,et al.  Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort , 2019, bioRxiv.

[9]  E. Ferrer,et al.  Studying developmental processes in accelerated cohort-sequential designs with discrete- and continuous-time latent change score models. , 2019, Psychological methods.

[10]  Michael C Neale,et al.  umx: Twin and Path-Based Structural Equation Modeling in R , 2019, Twin Research and Human Genetics.

[11]  P. Visscher,et al.  Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances , 2019, eLife.

[12]  A. Dale,et al.  Negative fateful life events in midlife and advanced predicted brain aging , 2018, Neurobiology of Aging.

[13]  R. Marioni,et al.  Brain age and other bodily ‘ages’: implications for neuropsychiatry , 2018, Molecular Psychiatry.

[14]  A. Dale,et al.  Genetic relatedness of axial and radial diffusivity indices of cerebral white matter microstructure in late middle age , 2018, Human brain mapping.

[15]  J. Cole,et al.  Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers , 2017, Trends in Neurosciences.

[16]  Michael W. Weiner,et al.  Genetic Architecture of Subcortical Brain Structures in Over 40,000 Individuals Worldwide , 2017, bioRxiv.

[17]  A. Dale,et al.  Genetic and environmental influences on mean diffusivity and volume in subcortical brain regions , 2017, Human brain mapping.

[18]  Anders M. Dale,et al.  Genetic and environmental influences on cortical mean diffusivity , 2017, NeuroImage.

[19]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[20]  Daniel S. Margulies,et al.  Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016, NeuroImage.

[21]  Christian Gaser,et al.  The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2016, PloS one.

[22]  N. Timpson,et al.  The role of common genetic variation in educational attainment and income: evidence from the National Child Development Study , 2015, Scientific Reports.

[23]  A. Dale,et al.  The Genetic Association Between Neocortical Volume and General Cognitive Ability Is Driven by Global Surface Area Rather Than Thickness. , 2015, Cerebral cortex.

[24]  Lachlan T. Strike,et al.  Genetic architecture of subcortical brain regions: common and region‐specific genetic contributions , 2014, Genes, brain, and behavior.

[25]  Dorret I. Boomsma,et al.  Heritability of brain volume change and its relation to intelligence , 2014, NeuroImage.

[26]  A. Dale,et al.  Accelerating cortical thinning: unique to dementia or universal in aging? , 2014, Cerebral cortex.

[27]  Chi-Hua Chen,et al.  Genetics of brain structure: Contributions from the vietnam era twin study of aging , 2013, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[28]  Stefan Klöppel,et al.  BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.

[29]  Christian Gieger,et al.  The Molecular Genetic Architecture of Self-Employment , 2013, PloS one.

[30]  Bernadette A. Thomas,et al.  Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.

[31]  Carol E. Franz,et al.  VETSA: The Vietnam Era Twin Study of Aging , 2012, Twin Research and Human Genetics.

[32]  P. Corso,et al.  National Institutes of Health State-of-the-Science Conference , 2012 .

[33]  Clifford R Jack,et al.  Testing the Right Target and Right Drug at the Right Stage , 2011, Science Translational Medicine.

[34]  Bruce Fischl,et al.  Genetic and environmental contributions to regional cortical surface area in humans: a magnetic resonance imaging twin study. , 2011, Cerebral cortex.

[35]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[36]  Nick C Fox,et al.  The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[37]  John Fox,et al.  OpenMx: An Open Source Extended Structural Equation Modeling Framework , 2011, Psychometrika.

[38]  Todd E. Golde,et al.  Anti-Aβ Therapeutics in Alzheimer's Disease: The Need for a Paradigm Shift , 2011, Neuron.

[39]  Donald Silberberg,et al.  National Institutes of Health State-of-the-Science Conference Statement: Preventing Alzheimer Disease* and Cognitive Decline , 2010, Annals of Internal Medicine.

[40]  T. Salthouse.  Selective review of cognitive aging , 2010, Journal of the International Neuropsychological Society.

[41]  Anders M. Dale,et al.  Genetic and environmental influences on the size of specific brain regions in midlife: The VETSA MRI study , 2010, NeuroImage.

[42]  R. Marioni,et al.  Age-associated cognitive decline. , 2009, British medical bulletin.

[43]  K. Heyman,et al.  Health characteristics of adults aged 55 years and over: United States, 2004-2007. , 2009, National health statistics reports.

[44]  A. Dale,et al.  Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. , 2009, Radiology.

[45]  R. Kahn,et al.  Genetic influences on human brain structure: A review of brain imaging studies in twins , 2007, Human brain mapping.

[46]  Corwin Boake,et al.  Genes, Environment, and Time: The Vietnam Era Twin Study of Aging (VETSA) , 2006, Twin Research and Human Genetics.

[47]  Michael C Neale,et al.  People are variables too: multilevel structural equations modeling. , 2005, Psychological methods.

[48]  T. Kirkwood,et al.  Understanding the Odd Science of Aging , 2005, Cell.

[49]  E. T. Bullmore,et al.  Genetic Contributions to Regional Variability in Human Brain Structure: Methods and Preliminary Results , 2002, NeuroImage.

[50]  Tyrone D. Cannon,et al.  Genetic influences on brain structure , 2001, Nature Neuroscience.

[51]  R. Kahn,et al.  Quantitative genetic modeling of variation in human brain morphology. , 2001, Cerebral cortex.

[52]  M. Jensen.  Genetic and environmental contributions. , 2000, Nutrition reviews.

[53]  A. Morley.  Somatic Mutation and Aging , 1998, Annals of the New York Academy of Sciences.

[54]  T. Kirkwood,et al.  A network theory of ageing: the interactions of defective mitochondria, aberrant proteins, free radicals and scavengers in the ageing process. , 1996, Mutation research.

[55]  Terry E. Duncan,et al.  The effects of family cohesiveness and peer encouragement on the development of adolescent alcohol use: a cohort-sequential approach to the analysis of longitudinal data. , 1994, Journal of studies on alcohol.

[56]  Michael C. Neale,et al.  Methodology for Genetic Studies of Twins and Families , 1992 .

[57]  Terry E. Duncan,et al.  A latent growth curve approach to investigating developmental dynamics and correlates of change in children's perceptions of physical competence. , 1991, Research quarterly for exercise and sport.

[58]  D I Boomsma,et al.  The genetic analysis of repeated measures. I. Simplex models , 1987, Behavior genetics.

[59]  J J McArdle,et al.  Latent growth curves within developmental structural equation models. , 1987, Child development.

[60]  T. Kirkwood.  Evolution of ageing , 1977, Nature.

[61]  Karl Pearson,et al.  ON POLYCHORIC COEFFICIENTS OF CORRELATION , 1922 .

[62]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[63]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging and the Alzheimer's Association workgroup , 2011 .

[64]  C. Bell,et al.  National Institutes of Health State-of-the-Science Conference Statement: Preventing Alzheimer Disease* and Cognitive Decline , 2010, Annals of Internal Medicine.

[65]  Takashi Asada,et al.  [Diagnosis of mild cognitive impairment]. , 2004, Seishin shinkeigaku zasshi = Psychiatria et neurologia Japonica.

[66]  Martin N. Rossor,et al.  Alzheimer's disease and neuroimaging , 2001 .

[67]  D I Boomsma,et al.  Factor and simplex models for repeated Measures: Application to two psychomotor measures of alcohol sensitivity in twins , 1989, Behavior genetics.

[68]  J. Mcardle,et al.  Latent variable growth within behavior genetic models , 1986, Behavior genetics.

[69]  A C Heath,et al.  A theory of developmental change in quantitative phenotypes applied to cognitive development , 1986, Behavior genetics.

[70]  J R Nesselroade,et al.  Adolescent personality development and historical change: 1970-1972. , 1974, Monographs of the Society for Research in Child Development.

[71]  Karl Pearson,et al.  Mathematical contributions to the theory of evolution. VIII. On the correlation of characters not quantitatively measurable , 1900, Proceedings of the Royal Society of London.