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Gustavson DE, Elman JA, Reynolds CA, Eyler LT, Fennema-Notestine C, Puckett OK, Panizzon MS, Gillespie NA, Neale MC, Lyons MJ, Franz CE, Kremen WS. Brain reserve in midlife is associated with executive function changes across 12 years. Neurobiol Aging 2024; 141:113-120. [PMID: 38852544 PMCID: PMC11246793 DOI: 10.1016/j.neurobiolaging.2024.05.001] [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: 08/01/2023] [Revised: 04/17/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024]
Abstract
We examined how brain reserve in midlife, measured by brain-predicted age difference scores (Brain-PADs), predicted executive function concurrently and longitudinally into early old age, and whether these associations were moderated by young adult cognitive reserve or APOE genotype. 508 men in the Vietnam Era Twin Study of Aging (VETSA) completed neuroimaging assessments at mean age 56 and six executive function tasks at mean ages 56, 62, and 68 years. Results indicated that greater brain reserve at age 56 was associated with better concurrent executive function (r=.10, p=.040) and less decline in executive function over 12 years (r=.34, p=.001). These associations were not moderated by cognitive reserve or APOE genotype. Twin analysis suggested associations with executive function slopes were driven by genetic influences. Our findings suggest that greater brain reserve allowed for better cognitive maintenance from middle- to old age, driven by a genetic association. The results are consistent with differential preservation of executive function based on brain reserve that is independent of young adult cognitive reserve or APOE genotype.
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Affiliation(s)
- Daniel E Gustavson
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Chandra A Reynolds
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Olivia K Puckett
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
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Zheng A, Friedman NP, Gustavson DE, Corley RP, Wadsworth SJ, Reynolds CA. Lifestyle and psychosocial associations with cognition at the cusp of midlife using twins and siblings. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12609. [PMID: 39040465 PMCID: PMC11262029 DOI: 10.1002/dad2.12609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/26/2024] [Accepted: 05/09/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION This study investigates the relationship between cognitive functioning and 59 modifiable and intrinsic factors at the cusp of midlife. METHODS We analyzed data from 1221 participants in the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife; Mage = 33.20, %Female = 52.74). We assessed the impact of 59 factors on cognitive functioning using regularized regression and co-twin control models, controlling for earlier-life cognitive functioning and gray matter volume. RESULTS Eight robust factors were identified, including education attainment, cognitive complexity, purpose-in-life, and smoking status. Twins reporting higher levels of cognitive complexity and purpose-in-life showed better cognitive performance than their cotwin, while smoking was negatively associated. Using meta-analytically derived effect size threshold, we additionally identified that twins experiencing more financial difficulty tend to perform less well compared with their cotwin. DISCUSSION The findings highlight the early midlife link between cognitive functioning and lifestyle/psychological factors, beyond prior cognitive performance, brain status, genetic and familial confounders. Our results further highlight the potential of established adulthood as a crucial window for dementia prevention interventions targeting lifestyle and psychosocial factors. Highlights Cog complexity(+), purpose-in-life(+) were associated with cognition in early midlife.Smoking(-) was also associated with cognition in early midlife.Results were consistent controlling for genetic and environmental confounds.Association between EA and cognition might be mostly genetic and familial confounded.
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Affiliation(s)
- Anqing Zheng
- Institute for Behavioral GeneticsUniversity of Colorado BoulderBoulderColoradoUSA
- Department of PsychologyThe University of CaliforniaRiversideCaliforniaUSA
| | - Naomi P. Friedman
- Institute for Behavioral GeneticsUniversity of Colorado BoulderBoulderColoradoUSA
- Department of Psychology and NeuroscienceUniversity of Colorado BoulderBoulderColoradoUSA
| | - Daniel E. Gustavson
- Institute for Behavioral GeneticsUniversity of Colorado BoulderBoulderColoradoUSA
- Department of Psychology and NeuroscienceUniversity of Colorado BoulderBoulderColoradoUSA
| | - Robin P. Corley
- Institute for Behavioral GeneticsUniversity of Colorado BoulderBoulderColoradoUSA
| | - Sally J. Wadsworth
- Institute for Behavioral GeneticsUniversity of Colorado BoulderBoulderColoradoUSA
| | - Chandra A. Reynolds
- Institute for Behavioral GeneticsUniversity of Colorado BoulderBoulderColoradoUSA
- Department of PsychologyThe University of CaliforniaRiversideCaliforniaUSA
- Department of Psychology and NeuroscienceUniversity of Colorado BoulderBoulderColoradoUSA
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Seitz-Holland J, Haas SS, Penzel N, Reichenberg A, Pasternak O. BrainAGE, brain health, and mental disorders: A systematic review. Neurosci Biobehav Rev 2024; 159:105581. [PMID: 38354871 PMCID: PMC11119273 DOI: 10.1016/j.neubiorev.2024.105581] [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: 11/09/2023] [Revised: 02/05/2024] [Accepted: 02/09/2024] [Indexed: 02/16/2024]
Abstract
The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.
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Affiliation(s)
- Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Funk-White M, Wing D, Eyler LT, Moore AA, Reas ET, McEvoy L. Neuroimaging-Derived Predicted Brain Age and Alcohol Use Among Community-Dwelling Older Adults. Am J Geriatr Psychiatry 2023; 31:669-678. [PMID: 36925380 DOI: 10.1016/j.jagp.2023.02.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVES Observational studies have suggested that moderate alcohol use is associated with reduced risk of dementia. However, the nature of this association is not understood. We investigated whether light to moderate alcohol use may be associated with slower brain aging, among a cohort of older community-dwelling adults using a biomarker of brain age based on structural neuroimaging measures. DESIGN Cross-sectional observational study. PARTICIPANTS Well-characterized members of a longitudinal cohort study who underwent neuroimaging. We categorized the 163 participants (mean age 76.7 ± 7.7, 60% women) into current nondrinkers, light drinkers (1-7 drinks/week) moderate drinkers (>7-14 drinks/week), or heavier drinkers (>14 drinks/week). MEASUREMENTS We calculated brain-predicted age using structural MRIs processed with the BrainAgeR program, and calculated the difference between brain-predicted age and chronological age (brain-predicted age difference, or brain-PAD). We used analysis of variance to determine if brain-PAD differed across alcohol groups, controlling for potential confounders. RESULTS Brain-PAD differed across alcohol groups (F[3, 150] = 4.02; p = 0.009) with heavier drinkers showing older brain-PAD than light drinkers (by about 6 years). Brain-PAD did not differ across light, moderate, and nondrinkers. Similar results were obtained after adjusting for potentially mediating health-related measures, and after excluding individuals with a history of heavier drinking. DISCUSSION Among this sample of healthy older adults, consumption of more than 14 drinks/week was associated with a biomarker of advanced brain aging. Light and moderate drinking was not associated with slower brain aging relative to non-drinking.
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Affiliation(s)
- Makaya Funk-White
- Interdisciplinary Research on Substance Use (MFW), University of California San Diego, La Jolla, CA
| | - David Wing
- Herbert Wertheim School of Public Health and Human Longevity Science (DW, LKM), University of California San Diego, La Jolla, CA
| | - Lisa T Eyler
- Department of Psychiatry (LTE), University of California San Diego, La Jolla, CA; Desert-Pacific Mental Illness Research (LTE), Education, and Clinical Center, VA San Diego Healthcare System, San Diego, CA
| | - Alison A Moore
- Division of Geriatrics, Gerontology, and Palliative Care, Department of Medicine (AAM), University of California San Diego, La Jolla, CA
| | - Emilie T Reas
- Department of Neurosciences (ETR), University of California San Diego, La Jolla, CA
| | - Linda McEvoy
- Herbert Wertheim School of Public Health and Human Longevity Science (DW, LKM), University of California San Diego, La Jolla, CA; Department of Radiology (LKM), University of California San Diego, La Jolla, CA
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Mo C, Wang J, Ye Z, Ke H, Liu S, Hatch K, Gao S, Magidson J, Chen C, Mitchell BD, Kochunov P, Hong LE, Ma T, Chen S. Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank. Addiction 2023; 118:739-749. [PMID: 36401354 PMCID: PMC10443605 DOI: 10.1111/add.16088] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 11/07/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND AIMS Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging. DESIGN Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis. SETTING United Kingdom. PARTICIPANTS The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28]. MEASUREMENTS Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers. FINDINGS The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results. CONCLUSIONS There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.
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Affiliation(s)
- Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jingtao Wang
- Department of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hongjie Ke
- Department of Mathematics, University of Maryland, College Park, MD, USA
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Kathryn Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica Magidson
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
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McEvoy LK, Bergstrom J, Hagler DJ, Wing D, Reas ET. Elevated Pure Tone Thresholds Are Associated with Altered Microstructure in Cortical Areas Related to Auditory Processing and Attentional Allocation. J Alzheimers Dis 2023; 96:1163-1172. [PMID: 37955091 PMCID: PMC10793660 DOI: 10.3233/jad-230767] [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] [Indexed: 11/14/2023]
Abstract
BACKGROUND Hearing loss is associated with cognitive decline and increased risk for Alzheimer's disease, but the basis of this association is not understood. OBJECTIVE To determine whether hearing impairment is associated with advanced brain aging or altered microstructure in areas involved with auditory and cognitive processing. METHODS 130 participants, (mean 76.4±7.3 years; 65% women) of the Rancho Bernardo Study of Healthy Aging had a screening audiogram in 2003-2005 and brain magnetic resonance imaging in 2014-2016. Hearing ability was defined as the average pure tone threshold (PTA) at 500, 1000, 2000, and 4000 Hz in the better-hearing ear. Brain-predicted age difference (Brain-pad) was calculated as the difference between brain-predicted age based on a validated structural imaging biomarker of brain age, and chronological age. Regional diffusion metrics in temporal and frontal cortex regions were obtained from diffusion-weighted MRIs. Linear regression analyses adjusted for age, gender, education, and health-related measures. RESULTS PTAs were not associated with brain-PAD (β= 0.09; 95% CI: -0.084 to 0.243; p = 0.34). PTAs were associated with reduced restricted diffusion and increased free water diffusion primarily in right hemisphere temporal and frontal areas (restricted diffusion: βs = -0.21 to -0.30; 95% CIs from -0.48 to -0.02; ps < 0.03; free water: βs = 0.18 to 0.26; 95% CIs 0.01 to 0.438; ps < 0.04). CONCLUSIONS Hearing impairment is not associated with advanced brain aging but is associated with differences in brain regions involved with auditory processing and attentional control. It is thus possible that increased dementia risk associated with hearing impairment arises, in part, from compensatory brain changes that may decrease resilience.
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Affiliation(s)
- Linda K McEvoy
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Jaclyn Bergstrom
- Division of Geriatrics, Gerontology, and Palliative Care, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - David Wing
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Emilie T Reas
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
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Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
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Gillespie NA, Hatton SN, Hagler DJ, Dale AM, Elman JA, McEvoy LK, Eyler LT, Fennema-Notestine C, Logue MW, McKenzie RE, Puckett OK, Tu XM, Whitsel N, Xian H, Reynolds CA, Panizzon MS, Lyons MJ, Neale MC, Kremen WS, Franz C. The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years. Front Aging Neurosci 2022; 14:831002. [PMID: 35493948 PMCID: PMC9051484 DOI: 10.3389/fnagi.2022.831002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/15/2022] [Indexed: 01/27/2023] Open
Abstract
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.
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Affiliation(s)
- Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behaviour Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States,QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia,*Correspondence: Nathan A. Gillespie,
| | - Sean N. Hatton
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Donald J. Hagler
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Anders M. Dale
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States,Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States,Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
| | - Jeremy A. Elman
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Linda K. McEvoy
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Lisa T. Eyler
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, United States
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Mark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, United States,Department of Psychiatry and Biomedical Genetics Section, Boston University School of Medicine, Boston, MA, United States,Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ruth E. McKenzie
- Department of Psychology, Boston University, Boston, MA, United States,School of Education and Social Policy, Merrimack College, North Andover, MA, United States
| | - Olivia K. Puckett
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Xin M. Tu
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Nathan Whitsel
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Hong Xian
- Department of Epidemiology and Biostatistics, Saint. Louis University, St. Louis, MO, United States,Research Service, VA St. Louis Healthcare System, St. Louis, MO, United States
| | - Chandra A. Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
| | - Matthew S. Panizzon
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behaviour Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States,Department of Biological Psychology, Free University of Amsterdam, Amsterdam, Netherlands
| | - William S. Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, United States,William S. Kremen,
| | - Carol Franz
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States,Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, United States,Carol Franz,
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Treur JL. Commentary on Whitsel et al.: Smoking, alcohol use and the brain- the challenge of answering causal questions. Addiction 2022; 117:1060-1061. [PMID: 35080072 PMCID: PMC9306711 DOI: 10.1111/add.15802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/04/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Jorien L. Treur
- Department of Psychiatry, Amsterdam UMCUniversity of AmsterdamAmsterdamthe Netherlands
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