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Gao L, Tang J, Odden MC, Wu C. The Influence of Frailty: How the Associations Between Modifiable Risk Factors and Dementia Vary. Ann Epidemiol 2024; 100:S1047-2797(24)00245-X. [PMID: 39419130 DOI: 10.1016/j.annepidem.2024.10.003] [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: 07/03/2024] [Revised: 10/09/2024] [Accepted: 10/14/2024] [Indexed: 10/19/2024]
Abstract
BACKGROUND The Lancet Commission has highlighted 12 modifiable risk factors for dementia. However, the effects of addressing these risk factors among the heterogeneous older adult population is not fully understood. We compared the association between risk factors and dementia by frailty, conceptualized as an explanation for the underlying health heterogeneity in old age. METHODS Data were from the UK Biobank, a cohort study with over 500,000 participants aged 37-73 between 2006 and 2010. Frailty was measured by five criteria: slowness, weakness, exhaustion, inactivity, and shrinking. Participants meeting 0, 1-2, and 3-5 criteria were considered non-frail, prefrail, and frail, respectively. We included 13 modifiable risk factors. We used logistic regression to determine the associations of risk factors with 10-year dementia among non-frail, pre-frail, and frail individuals, respectively. Additionally, we adopted a g-computation method to estimate the individual and combined population intervention effects (PIE) of the risk factors for dementia. RESULTS Of 381,419 participants, 58.4%, 38.2%, and 3.4% were classified as non-frail, pre-frail, and frail, respectively. Except for smoking, depression, and excessive alcohol use, the other 10 risk factors had a stronger association with dementia among frailer individuals. We observed the highest PIEs among frail individuals when considering hypothetical interventions targeting low education, physical inactivity, central obesity, social isolation, hearing impairment, hypertension, diabetes, high nitrogen dioxide (NO2) exposure, high exposure of particulate matter with a diameter less than or equal to 2.5 micrometers (PM2.5), and traumatic brain injury individually. For the hypothetical interventions targeting all 13 risk factors together, we found a graded increase in the PIE across frailty status. CONCLUSION The associations between modifiable risk factors and dementia were stronger among the frail. We advocate for incorporating frailty assessments to pinpoint those most likely to benefit from targeted risk factor interventions.
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Affiliation(s)
- Lingyuan Gao
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Junhan Tang
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Michelle C Odden
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China.
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Kang S, Lee J, Ali DN, Choi S, Nesbitt J, Min PH, Trushina E, Choi DS. Low to moderate ethanol exposure reduces astrocyte-induced neuroinflammatory signaling and cognitive decline in presymptomatic APP/PS1 mice. Sci Rep 2024; 14:23989. [PMID: 39402264 PMCID: PMC11473946 DOI: 10.1038/s41598-024-75202-w] [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: 12/12/2023] [Accepted: 10/03/2024] [Indexed: 10/19/2024] Open
Abstract
Alcohol use disorder has been associated with the development of neurodegenerative diseases, including Alzheimer's disease (AD). However, recent studies demonstrate that moderate alcohol consumption may be protective against dementia and cognitive decline. We examined astrocyte function, low-density lipoprotein (LDL) receptor-related protein 1 (LRP1), and the NF-κB p65 and IKK-α/β signaling pathways in modulating neuroinflammation and amyloid beta (Aβ) deposition. We assessed apolipoprotein E (ApoE) in the brain of APP/PS1 mice using IHC and ELISA in response to low to moderate ethanol exposure (MEE). First, to confirm the intracerebral distribution of ApoE, we co-stained with GFAP, a marker for astrocytes that biosynthesize ApoE. We sought to investigate whether the ethanol-induced upregulation of LRP1 could potentially inhibit the activity of IL-1β and TNF-α induced IKK-α/β towards NF-κB p65, resulting in a reduction of pro-inflammatory cytokines. To evaluate the actual Aβ load in the brains of APP/PS1 mice, we performed with a specific antibody Aβ (Thioflavin S) on both air- and ethanol-exposed groups, subsequently analyzing Aβ levels. We also measured glucose uptake using 18F- fluorodeoxyglucose (FDG)-positron emission tomography (PET). Finally, we investigated whether MEE induced cognitive and memory changes using the Y maze, noble object recognition test, and Morris water maze. Our findings demonstrate that MEE reduced astrocytic glial fibrillary acidic protein (GFAP) and ApoE levels in the cortex and hippocampus in presymptomatic APP/PS1 mice. Interestingly, increased LRP1 protein expression was accompanied by dampening the IKK-α/β-NF-κB p65 pathway, resulting in decreased IL-1β and TNF-α levels in male mice. Notably, female mice show reduced levels of anti-inflammatory cytokines IL-4, and IL-10 without altering IL-1β and TNF-α concentrations. In both males and females, Aβ plaques, a hallmark of AD, were reduced in the cortex and hippocampus of APP/PS1 mice exposed to ethanol starting at pre-symptomatic stage. Consistently, MEE increased FDG-PET-based brain activities and normalized cognitive and memory deficits in the APP/PS1 mice. Our findings suggest that MEE may benefit AD pathology via modulating LRP1 expression, potentially reducing neuroinflammation and attenuating Aβ deposition. Our study implies that reduced astrocyte-derived ApoE and LDL cholesterol levels are critical for attenuating AD pathology.
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Affiliation(s)
- Shinwoo Kang
- Department of Molecular Pharmacology and Experimental Therapeutics, Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Pharmacology College of Medicine, Soonchunhyang University, 22 Soonchunhyango-ro, Ansan, Chungcheongnam-do, 31508, South Korea
| | - Jeyeon Lee
- Department of Radiology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN, 55905, USA
| | - Dina N Ali
- Department of Molecular Pharmacology and Experimental Therapeutics, Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - Sun Choi
- Department of Molecular Pharmacology and Experimental Therapeutics, Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - Jarred Nesbitt
- Department of Neurology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN, 55905, USA
| | - Paul H Min
- Department of Radiology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN, 55905, USA
| | - Eugenia Trushina
- Department of Molecular Pharmacology and Experimental Therapeutics, Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Neurology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN, 55905, USA
| | - Doo-Sup Choi
- Department of Molecular Pharmacology and Experimental Therapeutics, Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA.
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN, 55905, USA.
- Neuroscience Program, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN, 55905, USA.
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3
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Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TLS, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga AW, O'Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Nick Bryan R, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Brain aging patterns in a large and diverse cohort of 49,482 individuals. Nat Med 2024; 30:3015-3026. [PMID: 39147830 PMCID: PMC11483219 DOI: 10.1038/s41591-024-03144-x] [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: 12/31/2023] [Accepted: 06/20/2024] [Indexed: 08/17/2024]
Abstract
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- GE Healthcare, Bellevue, WA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- Site Rostock/Greifswald, German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Data Science & Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Mark A Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sid O'Bryant
- Institute for Translational Research University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Mallar M Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Dept of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, Baltimore, MD, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Thornton V, Chang Y, Chaloemtoem A, Anokhin AP, Bijsterbosch J, Foraker R, Hancock DB, Johnson EO, White JD, Hartz SM, Bierut LJ. Alcohol, smoking, and brain structure: common or substance specific associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.25.24313371. [PMID: 39399056 PMCID: PMC11469368 DOI: 10.1101/2024.09.25.24313371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
Alcohol use and smoking are common substance-use behaviors with well-established negative health effects, including decreased brain health. We examined whether alcohol use and smoking were associated with the same neuroimaging-derived brain measures. We further explored whether the effects of alcohol use and smoking on the brain were additive or interactive. We leveraged a cohort of 36,309 participants with neuroimaging data from the UK Biobank. We used linear regression to determine the association between 354 neuroimaging-derived brain measures and alcohol use defined as drinks per week, pack years of smoking, and drinks per week × pack years smoking interaction. To assess whether the brain associations with alcohol are broadly similar or different from the associations with smoking, we calculated the correlation between z-scores of association for drinks per week and pack years smoking. Results indicated overall moderate positive correlation in the associations across measures representing brain structure, magnetic susceptibility, and white matter tract microstructure, indicating greater similarity than difference in the brain measures associated with alcohol use and smoking. The only evidence of an interaction between drinks per week and pack years smoking was seen in measures representing magnetic susceptibility in subcortical structures. The effects of alcohol use and smoking on brain health appeared to be additive rather than multiplicative for all other brain measures studied. 97% (224/230) of associations with alcohol and 100% (167/167) of the associations with smoking that surpassed a p value threshold are in a direction that can be interpreted to reflect reduced brain health. Our results underscore the similarity of the adverse associations between use of these substances and neuroimaging derived brain measures.
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Affiliation(s)
- Vera Thornton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Yoonhoo Chang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ariya Chaloemtoem
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Andrey P. Anokhin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Randi Foraker
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Dana B. Hancock
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, North Carolina, USA
| | - Eric O. Johnson
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, North Carolina, USA
- Fellow Program, RTI International, Research Triangle Park, North Carolina, USA
| | - Julie D. White
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, North Carolina, USA
| | - Sarah M. Hartz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Laura J. Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
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Karoly HC, Kirk‐Provencher KT, Schacht JP, Gowin JL. Alcohol and brain structure across the lifespan: A systematic review of large-scale neuroimaging studies. Addict Biol 2024; 29:e13439. [PMID: 39317645 PMCID: PMC11421948 DOI: 10.1111/adb.13439] [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: 02/22/2024] [Revised: 08/29/2024] [Accepted: 09/01/2024] [Indexed: 09/26/2024]
Abstract
Alcohol exposure affects brain structure, but the extent to which its effects differ across development remains unclear. Several countries are considering changes to recommended guidelines for alcohol consumption, so high-quality evidence is needed. Many studies have been conducted among small samples, but recent efforts have been made to acquire large samples to characterize alcohol's effects on the brain on a population level. Several large-scale consortia have acquired such samples, but this evidence has not been synthesized across the lifespan. We conducted a systematic review of large-scale neuroimaging studies examining effects of alcohol exposure on brain structure at multiple developmental stages. We included studies with an alcohol-exposed sample of at least N = 100 from the following consortia: ABCD, ENIGMA, NCANDA, IMAGEN, Framingham Offspring Study, HCP and UK BioBank. Twenty-seven studies were included, examining prenatal (N = 1), adolescent (N = 9), low-to-moderate-level adult (N = 11) and heavy adult (N = 7) exposure. Prenatal exposure was associated with greater brain volume at ages 9-10, but contemporaneous alcohol consumption during adolescence and adulthood was associated with smaller volume/thickness. Both low-to-moderate consumption and heavy consumption were characterized by smaller volume and thickness in frontal, temporal and parietal regions, and reductions in insula, cingulate and subcortical structures. Adolescent consumption had similar effects, with less consistent evidence for smaller cingulate, insula and subcortical volume. In sum, prenatal exposure was associated with larger volume, while adolescent and adult alcohol exposure was associated with smaller volume and thickness, suggesting that regional patterns of effects of alcohol are similar in adolescence and adulthood.
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Affiliation(s)
- Hollis C. Karoly
- Department of PsychologyColorado State UniversityFort CollinsColoradoUSA
| | - Katelyn T. Kirk‐Provencher
- Department of Radiology, School of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Joseph P. Schacht
- Department of Psychiatry, School of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Joshua L. Gowin
- Department of Radiology, School of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
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Pho C, Yu FF, Palka JM, Brown ES. The relationship between alcohol consumption and amygdala volume in a community-based sample. Brain Imaging Behav 2024; 18:884-891. [PMID: 38568283 DOI: 10.1007/s11682-024-00879-6] [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] [Accepted: 03/15/2024] [Indexed: 08/31/2024]
Abstract
Most prior studies have reported decreased amygdala volume in those with a history of alcohol use disorder. Decreased amygdala volume associated with alcohol use disorder may be related to an increased risk of addiction and relapse. However, the relationship between amygdala volume and a broad range of alcohol consumption is largely unexplored. The present cross-sectional analysis investigates the relationship between amygdala volume and self-reported alcohol consumption in participants of the Dallas Heart Study, a community-based study of Dallas County, Texas residents. Brain imaging and survey data from participants (n = 2023) were obtained, and multiple linear regressions were performed with the average amygdala volume as the dependent variable and drinking status, drinking risk, drinks per week, and binge drinking as independent variables. Drinking risk was categorized such that low-risk constituted ≤ 14 drinks per week in men and ≤ 7 drinks per week in women, while > 14 drinks per week in men and > 7 drinks per week in women constituted high-risk. Age, sex, intracranial volume, body mass index, education, and Quick Inventory of Depressive Symptomatology-Self Report score were included in all models as covariates. No statistically significant (p ≤ .05) associations were observed between self-reported alcohol consumption and amygdala volume. The present study suggests non-significant relationships between self-reported alcohol consumption and amygdala volume when controlling for relevant demographic factors in a large, community-based sample.
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Affiliation(s)
- Christine Pho
- Department of Psychiatry, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8849, Dallas, Texas, 75390, United States
| | - Fang F Yu
- Department of Radiology, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Texas, 75390, United States
| | - Jayme M Palka
- Department of Psychiatry, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8849, Dallas, Texas, 75390, United States
| | - E Sherwood Brown
- Department of Psychiatry, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8849, Dallas, Texas, 75390, United States.
- The Altshuler Center for Education and Research at Metrocare Services, 1345 River Bend Dr, Suite 200, Dallas, Texas, 75247, United States.
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Huang H, Wang J, Dunk MM, Guo J, Dove A, Ma J, Bennett DA, Xu W. Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults. Neurology 2024; 103:e209530. [PMID: 38889383 PMCID: PMC11226327 DOI: 10.1212/wnl.0000000000209530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/05/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential 8 (LE8), with neuroimaging-based brain age and brain-predicted age difference (brain-PAD). METHODS This longitudinal community-based study was based on UK Biobank participants aged 40-69 years who were free from dementia and other neurologic diseases at baseline. LE8 score at baseline was assessed with 8 measures and tertiled as low, moderate, and high CVH. Structural and functional brain MRI scans were performed approximately 9 years after baseline, and 1,079 measures from 6 neuroimaging modalities were used to model brain age. A Least Absolute Shrinkage and Selection Operator regression model was trained in 4,355 healthy participants and then used to calculate brain age and brain-PAD in the whole population. Data were analyzed using linear regression models. RESULTS The study included 32,646 participants (mean age at baseline 54.74 years; 53.44% female; mean LE8 score: 71.90). In multivariable-adjusted linear regression, higher LE8 score was associated with younger brain age (β [95% CI] -0.037 [-0.043 to -0.031]) and more negative brain-PAD (β [95% CI] -0.043 [-0.048 to -0.038]) (brain looks younger for chronological age). Compared with high CVH, low/moderate CVH was associated with older brain age (β [95% CI] 1.030 [0.852-1.208]/0.475 [0.303-0.647]) and increased brain-PAD (β [95% CI] 1.193 [1.029-1.357]/0.528 [0.370-0.686]). The associations between low CVH and older brain age/brain-PAD remained similar and significant in both middle-aged (β [95% CI] 1.199 [0.992-1.405]/1.351 [1.159-1.542]) and older adults (β [95% CI] 0.764 [0.417-1.110]/0.948 [0.632-1.263]). DISCUSSION Low CVH is associated with older brain age and greater brain-PAD, even among middle-aged adults. Our findings suggest that optimizing CVH could support brain health. The main limitation of our study is that the study sample was healthier than the general population, thus caution is required when generalizing our findings to other populations.
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Affiliation(s)
- Huijie Huang
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jiao Wang
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Michelle M Dunk
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jie Guo
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Abigail Dove
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jun Ma
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - David A Bennett
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Weili Xu
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
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8
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Giannone F, Ebrahimi C, Endrass T, Hansson AC, Schlagenhauf F, Sommer WH. Bad habits-good goals? Meta-analysis and translation of the habit construct to alcoholism. Transl Psychiatry 2024; 14:298. [PMID: 39030169 PMCID: PMC11271507 DOI: 10.1038/s41398-024-02965-1] [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: 10/08/2023] [Revised: 05/19/2024] [Accepted: 05/24/2024] [Indexed: 07/21/2024] Open
Abstract
Excessive alcohol consumption remains a global public health crisis, with millions suffering from alcohol use disorder (AUD, or simply "alcoholism"), leading to significantly reduced life expectancy. This review examines the interplay between habitual and goal-directed behaviors and the associated neurobiological changes induced by chronic alcohol exposure. Contrary to a strict habit-goal dichotomy, our meta-analysis of the published animal experiments combined with a review of human studies reveals a nuanced transition between these behavioral control systems, emphasizing the need for refined terminology to capture the probabilistic nature of decision biases in individuals with a history of chronic alcohol exposure. Furthermore, we distinguish habitual responding from compulsivity, viewing them as separate entities with diverse roles throughout the stages of the addiction cycle. By addressing species-specific differences and translational challenges in habit research, we provide insights to enhance future investigations and inform strategies for combatting AUD.
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Affiliation(s)
- F Giannone
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - C Ebrahimi
- Faculty of Psychology, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, 01062, Dresden, Germany
| | - T Endrass
- Faculty of Psychology, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, 01062, Dresden, Germany
| | - A C Hansson
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - F Schlagenhauf
- Department of Psychotherapy, Campus Charité Mitte, Charité Universitätsmedizin Berlin & St. Hedwig Hospital, 10117, Berlin, Germany
| | - W H Sommer
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.
- Bethania Hospital for Psychiatry, Psychosomatics and Psychotherapy, Greifswald, Germany.
- German Center for Mental Health (DZPG), Partner Site Mannheim-Heidelberg-Ulm, 68159, Mannheim, Germany.
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9
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Li X, Ramos-Rolón AP, Kass G, Pereira-Rufino LS, Shifman N, Shi Z, Volkow ND, Wiers CE. Imaging neuroinflammation in individuals with substance use disorders. J Clin Invest 2024; 134:e172884. [PMID: 38828729 PMCID: PMC11142750 DOI: 10.1172/jci172884] [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] [Indexed: 06/05/2024] Open
Abstract
Increasing evidence suggests a role of neuroinflammation in substance use disorders (SUDs). This Review presents findings from neuroimaging studies assessing brain markers of inflammation in vivo in individuals with SUDs. Most studies investigated the translocator protein 18 kDa (TSPO) using PET; neuroimmune markers myo-inositol, choline-containing compounds, and N-acetyl aspartate using magnetic resonance spectroscopy; and fractional anisotropy using MRI. Study findings have contributed to a greater understanding of neuroimmune function in the pathophysiology of SUDs, including its temporal dynamics (i.e., acute versus chronic substance use) and new targets for SUD treatment.
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Affiliation(s)
- Xinyi Li
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Astrid P. Ramos-Rolón
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Gabriel Kass
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Lais S. Pereira-Rufino
- Departamento de Morfologia e Genética, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Naomi Shifman
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Zhenhao Shi
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Nora D. Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, NIH, Bethesda, Maryland, USA
| | - Corinde E. Wiers
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
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10
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Wei Y, Wang W, Kang Y, Niu X, Zhang Z, Li S, Han S, Cheng J, Zhang Y. Global, interhemispheric and intrahemispheric functional connection patterns in male adults with alcohol use disorder. Addict Biol 2024; 29:e13398. [PMID: 38899438 PMCID: PMC11187543 DOI: 10.1111/adb.13398] [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: 09/07/2023] [Revised: 03/25/2024] [Accepted: 04/12/2024] [Indexed: 06/21/2024]
Abstract
A growing body of evidence indicates the existence of abnormal local and long-range functional connection patterns in patients with alcohol use disorder (AUD). However, it has yet to be established whether AUD is associated with abnormal interhemispheric and intrahemispheric functional connection patterns. In the present study, we analysed resting-state functional magnetic resonance imaging data from 55 individuals with AUD and 32 healthy nonalcohol users. For each subject, whole-brain functional connectivity density (FCD) was decomposed into ipsilateral and contralateral parts. Correlation analysis was performed between abnormal FCD and a range of clinical measurements in the AUD group. Compared with healthy controls, the AUD group exhibited a reduced global FCD in the anterior and middle cingulate gyri, prefrontal cortex and thalamus, along with an enhanced global FCD in the temporal, parietal and occipital cortices. Abnormal interhemispheric and intrahemispheric FCD patterns were also detected in the AUD group. Furthermore, abnormal global, contralateral and ipsilateral FCD data were correlated with the mean amount of pure alcohol and the severity of alcohol addiction in the AUD group. Collectively, our findings indicate that global, interhemispheric and intrahemispheric FCD may represent a robust method to detect abnormal functional connection patterns in AUD; this may help us to identify the neural substrates and therapeutic targets of AUD.
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Affiliation(s)
- Yarui Wei
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Weijian Wang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yimeng Kang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Xiaoyu Niu
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Zanxia Zhang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Shujian Li
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Shaoqiang Han
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Jingliang Cheng
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yong Zhang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
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11
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Fjell AM. Aging Brain from a Lifespan Perspective. Curr Top Behav Neurosci 2024. [PMID: 38797799 DOI: 10.1007/7854_2024_476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Research during the last two decades has shown that the brain undergoes continuous changes throughout life, with substantial heterogeneity in age trajectories between regions. Especially, temporal and prefrontal cortices show large changes, and these correlate modestly with changes in the corresponding cognitive abilities such as episodic memory and executive function. Changes seen in normal aging overlap with changes seen in neurodegenerative conditions such as Alzheimer's disease; differences between what reflects normal aging vs. a disease-related change are often blurry. This calls for a dimensional view on cognitive decline in aging, where clear-cut distinctions between normality and pathology cannot be always drawn. Although much progress has been made in describing typical patterns of age-related changes in the brain, identifying risk and protective factors, and mapping cognitive correlates, there are still limits to our knowledge that should be addressed by future research. We need more longitudinal studies following the same participants over longer time intervals with cognitive testing and brain imaging, and an increased focus on the representativeness vs. selection bias in neuroimaging research of aging.
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Affiliation(s)
- Anders Martin Fjell
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.
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12
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Mewton L, Visontay R, Hughes G, Browning C, Wen W, Topiwala A, Draper B, Crawford JD, Brodaty H, Sachdev PS. Longitudinal alcohol-related brain changes in older adults: The Sydney Memory and Ageing Study. Addict Biol 2024; 29:e13402. [PMID: 38797559 PMCID: PMC11128337 DOI: 10.1111/adb.13402] [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: 03/25/2024] [Accepted: 04/22/2024] [Indexed: 05/29/2024]
Abstract
Increases in harmful drinking among older adults indicate the need for a more thorough understanding of the relationship between later-life alcohol use and brain health. The current study investigated the relationships between alcohol use and progressive grey and white matter changes in older adults using longitudinal data. A total of 530 participants (aged 70 to 90 years; 46.0% male) were included. Brain outcomes assessed over 6 years included total grey and white matter volume, as well as volume of the hippocampus, thalamus, amygdala, corpus callosum, orbitofrontal cortex and insula. White matter integrity was also investigated. Average alcohol use across the study period was the main exposure of interest. Past-year binge drinking and reduction in drinking from pre-baseline were additional exposures of interest. Within the context of low-level average drinking (averaging 11.7 g per day), higher average amount of alcohol consumed was associated with less atrophy in the left (B = 7.50, pFDR = 0.010) and right (B = 5.98, pFDR = 0.004) thalamus. Past-year binge-drinking was associated with poorer white matter integrity (B = -0.013, pFDR = 0.024). Consuming alcohol more heavily in the past was associated with greater atrophy in anterior (B = -12.73, pFDR = 0.048) and posterior (B = -17.88, pFDR = 0.004) callosal volumes over time. Across alcohol exposures and neuroimaging markers, no other relationships were statistically significant. Within the context of low-level drinking, very few relationships between alcohol use and brain macrostructure were identified. Meanwhile, heavier drinking was negatively associated with white matter integrity.
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Affiliation(s)
- Louise Mewton
- The Matilda Centre for Mental Health and Substance Use, Faculty of Medicine and HealthUniversity of SydneySydneyAustralia
| | - Rachel Visontay
- The Matilda Centre for Mental Health and Substance Use, Faculty of Medicine and HealthUniversity of SydneySydneyAustralia
| | - Gerard Hughes
- Centre for Healthy Brain Ageing, Faculty of Medicine and HealthUniversity of New South WalesSydneyAustralia
| | - Catherine Browning
- Centre for Healthy Brain Ageing, Faculty of Medicine and HealthUniversity of New South WalesSydneyAustralia
| | - Wei Wen
- Centre for Healthy Brain Ageing, Faculty of Medicine and HealthUniversity of New South WalesSydneyAustralia
| | - Anya Topiwala
- Nuffield Department Population Health, Big Data InstituteUniversity of OxfordOxfordUK
| | - Brian Draper
- Centre for Healthy Brain Ageing, Faculty of Medicine and HealthUniversity of New South WalesSydneyAustralia
| | - John D. Crawford
- Centre for Healthy Brain Ageing, Faculty of Medicine and HealthUniversity of New South WalesSydneyAustralia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, Faculty of Medicine and HealthUniversity of New South WalesSydneyAustralia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, Faculty of Medicine and HealthUniversity of New South WalesSydneyAustralia
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13
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Tang J, Ma Y, Hoogendijk EO, Chen J, Yue J, Wu C. Associations between healthy lifestyle and mortality across different social environments: a study among adults with frailty from the UK Biobank. Eur J Public Health 2024; 34:218-224. [PMID: 38288504 PMCID: PMC10990525 DOI: 10.1093/eurpub/ckae003] [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] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Among people living with frailty, adherence to a healthy lifestyle may be a low-cost and effective strategy to decrease frailty-induced health risks across different social environments. METHODS We included 15 594 frail participants at baseline from the UK Biobank study. We used four lifestyle factors to create a composite healthy lifestyle score and 17 social factors to construct a polysocial score. We classified the lifestyle score into two levels (unhealthy and healthy) and the polysocial score into three levels (low, intermediate and high). We used Cox regression to determine the association of each lifestyle factor and lifestyle score with all-cause mortality, respectively. We also examined the associations across polysocial score categories. We evaluated the joint association of the lifestyle score and the categorical polysocial score with all-cause mortality. RESULTS During up to 14.41 follow-up years, we documented 3098 all-cause deaths. After multivariable adjustment, we found a significant association between not smoking and adequate physical activity with all-cause mortality across polysocial score categories, respectively. We also found a significant association between a healthy diet and all-cause mortality among frail participants living in an intermediate social environment. A healthy lifestyle was associated with a lower all-cause mortality risk across polysocial score categories, especially among those with a low polysocial score. CONCLUSIONS Adherence to a healthy lifestyle, particularly not smoking, adequate physical activity and a healthy diet, may provide a feasible solution to decreasing mortality risk among frail adults across different social environments, especially for those in the socially disadvantaged group.
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Affiliation(s)
- Junhan Tang
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Yanan Ma
- Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Emiel O Hoogendijk
- Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC—location VU University Medical Center, Amsterdam, The Netherlands
| | - Jie Chen
- Center for Global Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jirong Yue
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China
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14
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Schindler LS, Subramaniapillai S, Ambikairajah A, Barth C, Crestol A, Voldsbekk I, Beck D, Gurholt TP, Topiwala A, Suri S, Ebmeier KP, Andreassen OA, Draganski B, Westlye LT, de Lange AMG. Cardiometabolic health across menopausal years is linked to white matter hyperintensities up to a decade later. Front Glob Womens Health 2023; 4:1320640. [PMID: 38213741 PMCID: PMC10783171 DOI: 10.3389/fgwh.2023.1320640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/06/2023] [Indexed: 01/13/2024] Open
Abstract
Introduction The menopause transition is associated with several cardiometabolic risk factors. Poor cardiometabolic health is further linked to microvascular brain lesions, which can be detected as white matter hyperintensities (WMHs) using T2-FLAIR magnetic resonance imaging (MRI) scans. Females show higher risk for WMHs post-menopause, but it remains unclear whether changes in cardiometabolic risk factors underlie menopause-related increase in brain pathology. Methods In this study, we assessed whether cross-sectional measures of cardiometabolic health, including body mass index (BMI) and waist-to-hip ratio (WHR), blood lipids, blood pressure, and long-term blood glucose (HbA1c), as well as longitudinal changes in BMI and WHR, differed according to menopausal status at baseline in 9,882 UK Biobank females (age range 40-70 years, n premenopausal = 3,529, n postmenopausal = 6,353). Furthermore, we examined whether these cardiometabolic factors were associated with WMH outcomes at the follow-up assessment, on average 8.78 years after baseline. Results Postmenopausal females showed higher levels of baseline blood lipids (HDL β = 0.14, p < 0.001, LDL β = 0.20, p < 0.001, triglycerides β = 0.12, p < 0.001) and HbA1c (β = 0.24, p < 0.001) compared to premenopausal women, beyond the effects of age. Over time, BMI increased more in the premenopausal compared to the postmenopausal group (β = -0.08, p < 0.001), while WHR increased to a similar extent in both groups (β = -0.03, p = 0.102). The change in WHR was however driven by increased waist circumference only in the premenopausal group. While the group level changes in BMI and WHR were in general small, these findings point to distinct anthropometric changes in pre- and postmenopausal females over time. Higher baseline measures of BMI, WHR, triglycerides, blood pressure, and HbA1c, as well as longitudinal increases in BMI and WHR, were associated with larger WMH volumes (β range = 0.03-0.13, p ≤ 0.002). HDL showed a significant inverse relationship with WMH volume (β = -0.27, p < 0.001). Discussion Our findings emphasise the importance of monitoring cardiometabolic risk factors in females from midlife through the menopause transition and into the postmenopausal phase, to ensure improved cerebrovascular outcomes in later years.
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Affiliation(s)
- Louise S. Schindler
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ananthan Ambikairajah
- Discipline of Psychology, Faculty of Health, University of Canberra, Canberra, Australia
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia
| | - Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Arielle Crestol
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anya Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Klaus P. Ebmeier
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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15
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Schwartzenburg JB, Cruise SC, Reed RE, Hutchinson CM, Mirzalieva OS, Edwards KN, Edwards S, Gilpin NW, Molina PE, Desai SD. Neuropathological Outcomes of Traumatic Brain Injury and Alcohol Use in Males and Females: Studies Using Pre-Clinical Rodent and Clinical Human Specimens. J Neurotrauma 2023; 40:2410-2426. [PMID: 37279290 PMCID: PMC10649185 DOI: 10.1089/neu.2023.0074] [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: 06/08/2023] Open
Abstract
Traumatic brain injury (TBI) and alcohol misuse are inextricably linked and can increase the risk for development of neurodegenerative diseases, particularly in military veterans and contact sport athletes. Proteinopathy (defects in protein degradation) is considered an underlying factor in neurodegenerative diseases. Whether it contributes to TBI/alcohol-mediated neurodegeneration is unexplored, however. Our recent studies have identified ISGylation, a conjugated form of ISG15 (Interferon-Stimulated Gene 15) and inducer of proteinopathy, as a potential mechanistic link underlying TBI-mediated neurodegeneration and proteinopathy in veterans. In the current study, a rat model of combined TBI and alcohol use was utilized to investigate the same relationship. Here, we report sustained induction of Interferon β (IFNβ), changes in TAR DNA Binding 43 (TDP-43) ISGylation levels, TDP-43 proteinopathy (C-terminal fragmentation [CTF]), and neurodegeneration in the ventral horns of the lumbar spinal cords (LSCs) and/or motor cortices (MCs) of female rats post-TBI in a time-dependent manner. In males, these findings mostly remained non-significant, although moderate alcohol use appears to decrease neurodegeneration in males (but not females) post-TBI. We, however, do not claim that moderate alcohol consumption is beneficial for preventing TBI-mediated neurodegeneration. We have previously demonstrated that ISGylation is increased in the LSCs of veterans with TBI/ALS (amyotrophic lateral sclerosis). Here, we show increased ISGylation of TDP-43 in the LSCs of TBI/ALS-afflicted female veterans compared with male veterans. Knowing that ISGylation induces proteinopathy, we suggest targeting ISGylation may prevent proteinopathy-mediated neurodegeneration post-TBI, particularly in women; however, causal studies are required to confirm this claim.
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Affiliation(s)
- Joshua B. Schwartzenburg
- Department of Biochemistry and Molecular Biology and LSUHSC-School of Medicine, New Orleans, Louisiana
| | - Shealan C. Cruise
- Department of Physiology, LSUHSC-School of Medicine, New Orleans, Louisiana
| | - Ryan E. Reed
- Department of Biochemistry and Molecular Biology and LSUHSC-School of Medicine, New Orleans, Louisiana
| | - Corrine M. Hutchinson
- Department of Biochemistry and Molecular Biology and LSUHSC-School of Medicine, New Orleans, Louisiana
| | - Oygul S. Mirzalieva
- Department of Biochemistry and Molecular Biology and LSUHSC-School of Medicine, New Orleans, Louisiana
| | | | - Scott Edwards
- Department of Physiology, LSUHSC-School of Medicine, New Orleans, Louisiana
| | - Nicholas W. Gilpin
- Department of Physiology, LSUHSC-School of Medicine, New Orleans, Louisiana
| | - Patricia E. Molina
- Department of Physiology, LSUHSC-School of Medicine, New Orleans, Louisiana
| | - Shyamal D. Desai
- Department of Biochemistry and Molecular Biology and LSUHSC-School of Medicine, New Orleans, Louisiana
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Lin W, Zhu L, Lu Y. Association of smoking with brain gray and white matter volume: a Mendelian randomization study. Neurol Sci 2023; 44:4049-4055. [PMID: 37289285 DOI: 10.1007/s10072-023-06854-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 05/12/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND Observational studies have found a significant association between smoking and smaller gray matter volume, but this finding was limited by the reverse causality bias and possible confounding factors. Therefore, we conducted a Mendelian randomization (MR) study to explore the causal association of smoking with brain gray and white matter volume from a genetic perspective, and to investigate the possible mediators influencing the association. METHODS Smoking initiation (ever being a regular smoker) was used as the primary exposure from the GWAS & Sequencing Consortium of Alcohol and Nicotine use in up to 1,232,091 individuals of European descent. Their associations with brain volume were acquired from a recent genome-wide association study of brain imaging phenotypes conducted among 34,298 individuals of the UK Biobank. The random-effects inverse-variance weighted method was applied as the main analysis. Multivariable MR analysis was performed to assess the potential interference of confounding factors on causal effect. RESULTS Genetic liability to smoking initiation was significantly associated with lower gray matter volume (beta, -0.100; 95% CI, -0.156 to -0.043; P=5.23×10-4) but not with white matter volume. Multivariable MR results suggested that the association with lower gray matter volume might be mediated by alcohol drinking. Regarding localized gray matter volume, genetic liability to smoking initiation was associated with lower gray matter volume in left superior temporal gyrus, anterior division and right superior temporal gyrus, posterior division. CONCLUSIONS This MR study supports the association between smoking and lower gray matter volume, and highlights the importance of never smoking.
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Affiliation(s)
- Wenjuan Lin
- Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Lisheng Zhu
- Cardiovascular Key Lab of Zhejiang Province, Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Yunlong Lu
- Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China.
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17
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Walhovd KB, Lövden M, Fjell AM. Timing of lifespan influences on brain and cognition. Trends Cogn Sci 2023; 27:901-915. [PMID: 37563042 DOI: 10.1016/j.tics.2023.07.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/04/2023] [Accepted: 07/04/2023] [Indexed: 08/12/2023]
Abstract
Modifiable risk and protective factors for boosting brain and cognitive development and preventing neurodegeneration and cognitive decline are embraced in neuroimaging studies. We call for sobriety regarding the timing and quantity of such influences on brain and cognition. Individual differences in the level of brain and cognition, many of which present already at birth and early in development, appear stable, larger, and more pervasive than differences in change across the lifespan. Incorporating early-life factors, including genetics, and investigating both level and change will reduce the risk of ascribing undue importance and causality to proximate factors in adulthood and older age. This has implications for both mechanistic understanding and prevention.
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Affiliation(s)
- Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | - Martin Lövden
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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18
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Topiwala A, Mankia K, Bell S, Webb A, Ebmeier KP, Howard I, Wang C, Alfaro-Almagro F, Miller K, Burgess S, Smith S, Nichols TE. Association of gout with brain reserve and vulnerability to neurodegenerative disease. Nat Commun 2023; 14:2844. [PMID: 37202397 PMCID: PMC10195870 DOI: 10.1038/s41467-023-38602-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/09/2023] [Indexed: 05/20/2023] Open
Abstract
Studies of neurodegenerative disease risk in gout are contradictory. Relationships with neuroimaging markers of brain structure, which may offer insights, are uncertain. Here we investigated associations between gout, brain structure, and neurodegenerative disease incidence. Gout patients had smaller global and regional brain volumes and markers of higher brain iron, using both observational and genetic approaches. Participants with gout also had higher incidence of all-cause dementia, Parkinson's disease, and probable essential tremor. Risks were strongly time dependent, whereby associations with incident dementia were highest in the first 3 years after gout diagnosis. These findings suggest gout is causally related to several measures of brain structure. Lower brain reserve amongst gout patients may explain their higher vulnerability to multiple neurodegenerative diseases. Motor and cognitive impairments may affect gout patients, particularly in early years after diagnosis.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK.
| | - Kulveer Mankia
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds and NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Chapel Allerton Hospital, Leeds, UK
| | - Steven Bell
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Alastair Webb
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Isobel Howard
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai, China
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Stephen Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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19
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Nallapu BT, Petersen KK, Lipton RB, Grober E, Sperling RA, Ezzati A. Association of Alcohol Consumption with Cognition in Older Population: The A4 Study. J Alzheimers Dis 2023; 93:1381-1393. [PMID: 37182868 PMCID: PMC10392870 DOI: 10.3233/jad-221079] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Alcohol use disorders have been categorized as a 'strongly modifiable' risk factor for dementia. OBJECTIVE To investigate the cross-sectional association between alcohol consumption and cognition in older adults and if it is different across sexes or depends on amyloid-β (Aβ) accumulation in the brain. METHODS Cognitively unimpaired older adults (N = 4387) with objective and subjective cognitive assessments and amyloid positron emission tomography (PET) imaging were classified into four categories based on their average daily alcohol use. Multivariable linear regression was then used to test the main effects and interactions with sex and Aβ levels. RESULTS Individuals who reported no alcohol consumption had lower scores on the Preclinical Alzheimer Cognitive Composite (PACC) compared to those consuming one or two drinks/day. In sex-stratified analysis, the association between alcohol consumption and cognition was more prominent in females. Female participants who consumed two drinks/day had better performance on PACC and Cognitive Function Index (CFI) than those who reported no alcohol consumption. In an Aβ-stratified sample, the association between alcohol consumption and cognition was present only in the Aβ- subgroup. The interaction between Aβ status and alcohol consumption on cognition was not significant. CONCLUSION Low or moderate consumption of alcohol was associated with better objective cognitive performance and better subjective report of daily functioning in cognitively unimpaired individuals. The association was present only in Aβ- individuals, suggesting that the pathophysiologic mechanism underlying the effect of alcohol on cognition is independent of Aβ pathology. Further investigation is required with larger samples consuming three or more drinks/day.
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Affiliation(s)
- Bhargav T. Nallapu
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Kellen K. Petersen
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Richard B. Lipton
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Ellen Grober
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Reisa A. Sperling
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Ali Ezzati
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
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20
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Topiwala A, Taschler B, Ebmeier KP, Smith S, Zhou H, Levey DF, Codd V, Samani NJ, Gelernter J, Nichols TE, Burgess S. Alcohol consumption and telomere length: Mendelian randomization clarifies alcohol's effects. Mol Psychiatry 2022; 27:4001-4008. [PMID: 35879401 PMCID: PMC9718662 DOI: 10.1038/s41380-022-01690-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 06/23/2022] [Accepted: 06/29/2022] [Indexed: 02/07/2023]
Abstract
Alcohol's impact on telomere length, a proposed marker of biological aging, is unclear. We performed the largest observational study to date (in n = 245,354 UK Biobank participants) and compared findings with Mendelian randomization (MR) estimates. Two-sample MR used data from 472,174 participants in a recent genome-wide association study (GWAS) of telomere length. Genetic variants were selected on the basis of associations with alcohol consumption (n = 941,280) and alcohol use disorder (AUD) (n = 57,564 cases). Non-linear MR employed UK Biobank individual data. MR analyses suggested a causal relationship between alcohol traits, more strongly for AUD, and telomere length. Higher genetically-predicted AUD (inverse variance-weighted (IVW) β = -0.06, 95% confidence interval (CI): -0.10 to -0.02, p = 0.001) was associated with shorter telomere length. There was a weaker association with genetically-predicted alcoholic drinks weekly (IVW β = -0.07, CI: -0.14 to -0.01, p = 0.03). Results were consistent across methods and independent from smoking. Non-linear analyses indicated a potential threshold relationship between alcohol and telomere length. Our findings indicate that alcohol consumption may shorten telomere length. There are implications for age-related diseases.
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Affiliation(s)
- A Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.
| | - B Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - K P Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - S Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - H Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - D F Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - V Codd
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - N J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - J Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - T E Nichols
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - S Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, CB1 8RN, UK
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21
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Topiwala A, Wang C, Ebmeier KP, Burgess S, Bell S, Levey DF, Zhou H, McCracken C, Roca-Fernández A, Petersen SE, Raman B, Husain M, Gelernter J, Miller KL, Smith SM, Nichols TE. Associations between moderate alcohol consumption, brain iron, and cognition in UK Biobank participants: Observational and mendelian randomization analyses. PLoS Med 2022; 19:e1004039. [PMID: 35834561 PMCID: PMC9282660 DOI: 10.1371/journal.pmed.1004039] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/01/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Brain iron deposition has been linked to several neurodegenerative conditions and reported in alcohol dependence. Whether iron accumulation occurs in moderate drinkers is unknown. Our objectives were to investigate evidence in support of causal relationships between alcohol consumption and brain iron levels and to examine whether higher brain iron represents a potential pathway to alcohol-related cognitive deficits. METHODS AND FINDINGS Observational associations between brain iron markers and alcohol consumption (n = 20,729 UK Biobank participants) were compared with associations with genetically predicted alcohol intake and alcohol use disorder from 2-sample mendelian randomization (MR). Alcohol intake was self-reported via a touchscreen questionnaire at baseline (2006 to 2010). Participants with complete data were included. Multiorgan susceptibility-weighted magnetic resonance imaging (9.60 ± 1.10 years after baseline) was used to ascertain iron content of each brain region (quantitative susceptibility mapping (QSM) and T2*) and liver tissues (T2*), a marker of systemic iron. Main outcomes were susceptibility (χ) and T2*, measures used as indices of iron deposition. Brain regions of interest included putamen, caudate, hippocampi, thalami, and substantia nigra. Potential pathways to alcohol-related iron brain accumulation through elevated systemic iron stores (liver) were explored in causal mediation analysis. Cognition was assessed at the scan and in online follow-up (5.82 ± 0.86 years after baseline). Executive function was assessed with the trail-making test, fluid intelligence with puzzle tasks, and reaction time by a task based on the "Snap" card game. Mean age was 54.8 ± 7.4 years and 48.6% were female. Weekly alcohol consumption was 17.7 ± 15.9 units and never drinkers comprised 2.7% of the sample. Alcohol consumption was associated with markers of higher iron (χ) in putamen (β = 0.08 standard deviation (SD) [95% confidence interval (CI) 0.06 to 0.09], p < 0.001), caudate (β = 0.05 [0.04 to 0.07], p < 0.001), and substantia nigra (β = 0.03 [0.02 to 0.05], p < 0.001) and lower iron in the thalami (β = -0.06 [-0.07 to -0.04], p < 0.001). Quintile-based analyses found these associations in those consuming >7 units (56 g) alcohol weekly. MR analyses provided weak evidence these relationships are causal. Genetically predicted alcoholic drinks weekly positively associated with putamen and hippocampus susceptibility; however, these associations did not survive multiple testing corrections. Weak evidence for a causal relationship between genetically predicted alcohol use disorder and higher putamen susceptibility was observed; however, this was not robust to multiple comparisons correction. Genetically predicted alcohol use disorder was associated with serum iron and transferrin saturation. Elevated liver iron was observed at just >11 units (88 g) alcohol weekly c.f. <7 units (56 g). Systemic iron levels partially mediated associations of alcohol intake with brain iron. Markers of higher basal ganglia iron associated with slower executive function, lower fluid intelligence, and slower reaction times. The main limitations of the study include that χ and T2* can reflect changes in myelin as well as iron, alcohol use was self-reported, and MR estimates can be influenced by genetic pleiotropy. CONCLUSIONS To the best of our knowledge, this study represents the largest investigation of moderate alcohol consumption and iron homeostasis to date. Alcohol consumption above 7 units weekly associated with higher brain iron. Iron accumulation represents a potential mechanism for alcohol-related cognitive decline.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Klaus P. Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Steven Bell
- Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Daniel F. Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
| | - Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Masud Husain
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, United Kingdom
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Thomas E. Nichols
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
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