Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study.
ENIGMA
addiction
brain age
machine learning
neuroimaging
predicted brain age difference
substance use disorder
Journal
Addiction (Abingdon, England)
ISSN: 1360-0443
Titre abrégé: Addiction
Pays: England
ID NLM: 9304118
Informations de publication
Date de publication:
21 Aug 2024
21 Aug 2024
Historique:
received:
29
01
2024
accepted:
19
06
2024
medline:
21
8
2024
pubmed:
21
8
2024
entrez:
21
8
2024
Statut:
aheadofprint
Résumé
The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls. This was a cross-sectional study. This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites. This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls. This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls. Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models. People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.
Sections du résumé
BACKGROUND AND AIMS
OBJECTIVE
The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls.
DESIGN
METHODS
This was a cross-sectional study.
SETTING
METHODS
This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites.
PARTICIPANTS
METHODS
This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls.
MEASUREMENTS
METHODS
This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls.
FINDINGS
RESULTS
Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models.
CONCLUSIONS
CONCLUSIONS
People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : CIHR
Pays : Canada
Organisme : NIDA NIH HHS
ID : R01DA014100
Pays : United States
Organisme : CONACYT-FOSISS
ID : 0201493
Organisme : CONACYT-Catedras
ID : 2358948
Organisme : Netherlands Organisation for Health Research and Development
Organisme : NIH HHS
ID : DA051922
Pays : United States
Organisme : AI and Val Rosenstrauss Senior Research Fellowship
Organisme : National Health and Medical Research (NHMRC) Investigator Grant
ID : 2016833
Organisme : Australian Catholic University competitive scheme
Organisme : NIAAA NIH HHS
ID : ZIAAA000125
Pays : United States
Organisme : Division of Intramural Clinical and Biological Research
Organisme : NHMRC Investigator Leadership
ID : 2017962
Organisme : University of Melbourne Dame Kate Campbell fellowship
Organisme : NIH HHS
ID : RO1MH129832
Pays : United States
Organisme : NHMRC Investigator Leadership Grant
ID : 2009464
Investigateurs
A Batalla
(A)
K T Brady
(KT)
J Cousijn
(J)
A Dagher
(A)
F M Filbey
(FM)
J J Foxe
(JJ)
E A Garza-Villarreal
(EA)
A E Goudriaan
(AE)
R H Hester
(RH)
K E Hutchison
(KE)
A M Kaag
(AM)
E Kroon
(E)
C R Li
(CR)
E D London
(ED)
V Lorenzetti
(V)
M Luijten
(M)
R Martin-Santos
(R)
A L McRae
(AL)
R Momenan
(R)
M P Paulus
(MP)
G D Pearlson
(GD)
L Reneman
(L)
R Salas
(R)
L Schmaal
(L)
M L J Schouw
(MLJ)
R Sinha
(R)
N Solowij
(N)
E A Stein
(EA)
R J Van Holst
(RJ)
D J Veltman
(DJ)
A Verdejo-García
(A)
R W Wiers
(RW)
M Yucel
(M)
Informations de copyright
© 2024 The Author(s). Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
Références
Cole JH, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 2017;40(12):681–690. https://doi.org/10.1016/j.tins.2017.10.001
Clausen AN, Fercho KA, Monsour M, Disner S, Salminen L, Haswell CC, et al. Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups. Brain Behav. 2021;1–14. Available from: https://onlinelibrary.wiley.com/doi/10.1002/brb3.2413
Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, et al. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry. 2021;26(9):5124–5139. https://doi.org/10.1038/s41380-020-0754-0
Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ. Karolinska schizophrenia project (KaSP), et al. common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci. 2019;22(10):1617–1623. https://doi.org/10.1038/s41593-019-0471-7
de Lange AMG, Anatürk M, Suri S, Kaufmann T, Cole JH, Griffanti L, et al. Multimodal brain‐age prediction and cardiovascular risk: the Whitehall II MRI sub‐study. Neuroimage. 2020;222:117292. https://doi.org/10.1016/j.neuroimage.2020.117292
Guggenmos M, Schmack K, Sekutowicz M, Garbusow M, Sebold M, Sommer C, et al. Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Transl Psychiatry. 2017;7(12):1279. https://doi.org/10.1038/s41398-017-0037-y
GBD 2016 Alcohol and Drug Use Collaborators. The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry. 2018;5(12):987–1012.
Ning K, Zhao L, Matloff W, Sun F, Toga AW. Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants. Sci Rep. 2020;10(1):10. https://doi.org/10.1038/s41598-019-56089-4
Smith SM, Elliott LT, Alfaro‐Almagro F, McCarthy P, Nichols TE, Douaud G, et al. Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. Elife. 2020;9:e52677. https://doi.org/10.7554/eLife.52677
Whitsel N, Reynolds CA, Buchholz EJ, Pahlen S, Pearce RC, Hatton SN, et al. Long‐term associations of cigarette smoking in early mid‐life with predicted brain aging from mid‐ to late life. Addiction. 2021;add.15710;117(4):1049–1059. https://doi.org/10.1111/add.15710
Wrigglesworth J, Ward P, Harding IH, Nilaweera D, Wu Z, Woods RL, et al. Factors associated with brain ageing ‐ a systematic review. BMC Neurol. 2021;21(1):312. https://doi.org/10.1186/s12883-021-02331-4
Franke K, Gaser C, Manor B, Novak V. Advanced BrainAGE in older adults with type 2 diabetes mellitus. Front Aging Neurosci. 2013;5:90. https://doi.org/10.3389/fnagi.2013.00090
Linli Z, Feng J, Zhao W, Guo S. Associations between smoking and accelerated brain ageing. Prog Neuropsychopharmacol Biol Psychiatry. 2022;113:110471. https://doi.org/10.1016/j.pnpbp.2021.110471
Mackey S, Allgaier N, Chaarani B, Spechler P, Orr C, Bunn J, et al. Mega‐analysis of gray matter volume in substance dependence: general and substance‐specific regional effects. Am J Psychiatry. 2019;176(2):119–128. https://doi.org/10.1176/appi.ajp.2018.17040415
Dale AM, Fischl B, Sereno MI. Cortical surface‐based analysis. I Segmentation and Surface Reconstruction. Neuroimage. 1999;9(2):179–194. https://doi.org/10.1006/nimg.1998.0395
Monereo‐Sánchez J, de Jong JJA, Drenthen GS, Beran M, Backes WH, Stehouwer CDA, et al. Quality control strategies for brain MRI segmentation and parcellation: practical approaches and recommendations ‐ insights from the Maastricht study. Neuroimage. 2021;237:118174. https://doi.org/10.1016/j.neuroimage.2021.118174
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–355. https://doi.org/10.1016/S0896-6273(02)00569-X
Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, et al. A multi‐modal parcellation of human cerebral cortex. Nature. 2016;536(7615):171–178. https://doi.org/10.1038/nature18933
Ipser JC, Joska J, Sevenoaks T, Gouse H, Freeman C, Kaufmann T, et al. Limited evidence for a moderating effect of HIV status on brain age in heavy episodic drinkers. J Neurovirol. 2022;28(3):383–391. https://doi.org/10.1007/s13365-022-01072-5
Bacas E, Kahhalé I, Raamana PR, Pablo JB, Anand AS, Hanson JL. Probing multiple algorithms to calculate brain age: examining reliability, relations with demographics, and predictive power. Hum Brain Mapp. 2023;44(9):3481–3492. https://doi.org/10.1002/hbm.26292
Jirsaraie RJ, Kaufmann T, Bashyam V, Erus G, Luby JL, Westlye LT, et al. Benchmarking the generalizability of brain age models: challenges posed by scanner variance and prediction bias. Hum Brain Mapp. 2023;44(3):1118–1128. https://doi.org/10.1002/hbm.26144
Immonen S, Launes J, Järvinen I, Virta M, Vanninen R, Schiavone N, et al. Moderate alcohol use is associated with decreased brain volume in early middle age in both sexes. Sci Rep. 2020;10(1):13998. https://doi.org/10.1038/s41598-020-70910-5
Ansari MA, Scheff SW. Oxidative stress in the progression of Alzheimer disease in the frontal cortex. J Neuropathol Exp Neurol. 2010;69(2):155–167. https://doi.org/10.1097/NEN.0b013e3181cb5af4
Shaerzadeh F, Streit WJ, Heysieattalab S, Khoshbouei H. Methamphetamine neurotoxicity, microglia, and neuroinflammation. J Neuroinflammation. 2018;15(1):341. https://doi.org/10.1186/s12974-018-1385-0
Edinoff AN, Kaufman SE, Green KM, Provenzano DA, Lawson J, Cornett EM, et al. Methamphetamine Use: A Narrative Review of Adverse Effects and Related Toxicities. Health Psychol Res. 2022;10(3):1–10. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476235/
More S, Antonopoulos G, Hoffstaedter F, Caspers J, Eickhoff SB, Patil KR. Brain‐age prediction: a systematic comparison of machine learning workflows. Neuroimage. 2023;270:119947. https://doi.org/10.1016/j.neuroimage.2023.119947
Baecker L, Garcia‐Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: introduction to methods and clinical applications. EBioMedicine. 2021;72:103600. https://doi.org/10.1016/j.ebiom.2021.103600
Cole JH. Multimodality neuroimaging brain‐age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging. 2020;92:34–42. https://doi.org/10.1016/j.neurobiolaging.2020.03.014
Dörfel RP, Arenas‐Gomez JM, Fisher PM, Ganz M, Knudsen GM, Svensson J, et al. Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test‐retest reliability of publicly available software packages. bioRxiv. 2023;2023.01.26.525514. Available from: https://www.biorxiv.org/content/10.1101/2023.01.26.525514v1
Hanson JL, Adkins DJ, Bacas E, Zhou P. Examining the reliability of brain age algorithms under varying degrees of participant motion. Brain Inform. 2024;11(1):9. https://doi.org/10.1186/s40708-024-00223-0
Beheshti I, Maikusa N, Matsuda H. The association between “brain‐age score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease. Brain Behav. 2018;8(8):e01020. https://doi.org/10.1002/brb3.1020