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Multicenter Study
. 2021 Sep;26(9):5124-5139.
doi: 10.1038/s41380-020-0754-0. Epub 2020 May 18.

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Laura K M Han  1 Richard Dinga  2   3 Tim Hahn  4 Christopher R K Ching  5 Lisa T Eyler  6   7 Lyubomir Aftanas  8   9 Moji Aghajani  2 André Aleman  10   11 Bernhard T Baune  4   12   13 Klaus Berger  14 Ivan Brak  8   15 Geraldo Busatto Filho  16 Angela Carballedo  17   18 Colm G Connolly  19 Baptiste Couvy-Duchesne  20 Kathryn R Cullen  21 Udo Dannlowski  4 Christopher G Davey  22   23 Danai Dima  24   25 Fabio L S Duran  16 Verena Enneking  4 Elena Filimonova  8 Stefan Frenzel  26 Thomas Frodl  17   27   28 Cynthia H Y Fu  29   30 Beata R Godlewska  31 Ian H Gotlib  32 Hans J Grabe  26   33 Nynke A Groenewold  34   35 Dominik Grotegerd  4 Oliver Gruber  36 Geoffrey B Hall  37 Ben J Harrison  38 Sean N Hatton  39   40 Marco Hermesdorf  14 Ian B Hickie  39 Tiffany C Ho  32   41 Norbert Hosten  42 Andreas Jansen  43 Claas Kähler  4 Tilo Kircher  43 Bonnie Klimes-Dougan  44 Bernd Krämer  36 Axel Krug  43   45 Jim Lagopoulos  39   46 Ramona Leenings  4 Frank P MacMaster  47   48 Glenda MacQueen  49 Andrew McIntosh  50 Quinn McLellan  47   51 Katie L McMahon  52   53 Sarah E Medland  54 Bryon A Mueller  21 Benson Mwangi  55 Evgeny Osipov  15 Maria J Portella  56   57 Elena Pozzi  21   38 Liesbeth Reneman  58 Jonathan Repple  4 Pedro G P Rosa  16 Matthew D Sacchet  59 Philipp G Sämann  60 Knut Schnell  61   62 Anouk Schrantee  58 Egle Simulionyte  37 Jair C Soares  55 Jens Sommer  44 Dan J Stein  35   63 Olaf Steinsträter  42 Lachlan T Strike  64 Sophia I Thomopoulos  5 Marie-José van Tol  65 Ilya M Veer  66 Robert R J M Vermeiren  67   68 Henrik Walter  66 Nic J A van der Wee  68   69 Steven J A van der Werff  68   69 Heather Whalley  50 Nils R Winter  4 Katharina Wittfeld  26   33 Margaret J Wright  64   70 Mon-Ju Wu  55 Henry Völzke  71 Tony T Yang  72 Vasileios Zannias  50 Greig I de Zubicaray  53   73 Giovana B Zunta-Soares  55 Christoph Abé  74 Martin Alda  75 Ole A Andreassen  76   77 Erlend Bøen  78 Caterina M Bonnin  79 Erick J Canales-Rodriguez  80 Dara Cannon  81 Xavier Caseras  82 Tiffany M Chaim-Avancini  16 Torbjørn Elvsåshagen  83   84 Pauline Favre  85   86 Sonya F Foley  87 Janice M Fullerton  88   89 Jose M Goikolea  79 Bartholomeus C M Haarman  90 Tomas Hajek  75 Chantal Henry  91 Josselin Houenou  85   86 Fleur M Howells  35   92 Martin Ingvar  74 Rayus Kuplicki  93 Beny Lafer  94 Mikael Landén  74   95   96 Rodrigo Machado-Vieira  94 Ulrik F Malt  97   98 Colm McDonald  81 Philip B Mitchell  99   100 Leila Nabulsi  81 Maria Concepcion Garcia Otaduy  101 Bronwyn J Overs  88 Mircea Polosan  102   103 Edith Pomarol-Clotet  80 Joaquim Radua  79 Maria M Rive  104 Gloria Roberts  99   100 Henricus G Ruhe  3   104   105 Raymond Salvador  80 Salvador Sarró  80 Theodore D Satterthwaite  106 Jonathan Savitz  93   107 Aart H Schene  3   105 Peter R Schofield  88   89 Mauricio H Serpa  16 Kang Sim  108   109 Marcio Gerhardt Soeiro-de-Souza  94 Ashley N Sutherland  7 Henk S Temmingh  36   110 Garrett M Timmons  7 Anne Uhlmann  35 Eduard Vieta  79 Daniel H Wolf  106 Marcus V Zanetti  16   111 Neda Jahanshad  5 Paul M Thompson  5 Dick J Veltman  2 Brenda W J H Penninx #  2 Andre F Marquand #  3   112 James H Cole #  25   113   114 Lianne Schmaal #  22   23
Affiliations
Multicenter Study

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Laura K M Han et al. Mol Psychiatry. 2021 Sep.

Abstract

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

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Conflict of interest statement

LA, MA, AA, BTB, KB, IB, GBF, AC, CRKC, JHC, CGC, BC-D, KC, UD, CGD, DD, RD, FLSD, VE, LTE, EF, SF, TF, CHYF, BRG, IHG, NAG, DG, OG, TH, GBH, LKMH, BJH, SNH, MH, TCH, NH, NJ, AJ, CK, TK, BK-D, BK, AK, JL, RL, FPM, GM, AFM, AM, KLM, SEM, PBM, BAM, BM, EO, MJP, EP, LR, JR, PGPR, MDS, PGS, LS, AS, ES, JS, DJS, OS, LTS, SIT, M-JvT, IMV, RRJMV, HW, NJAvdW, SJAvdW, HW, NRW, KW, MJW, M-JW, DJV, HV, TTY, VZ, GIdZ, GBZ-S, CA, MA, OAA, EB, CMB, EJC-R, DC, XC, TMC-A, PF, SFF, JMF, JMG, BCMH, TH, CH, JH, FMH, MI, RK, BL, RM-V, UFM, CM, PBM, LN, MCGO, BJO, MP, EP-C, JR, MMR, GR, HGR, RS, SS, TDS, JS, AHS, PRS, MHS, KS, MGS-d-S, ANS, HST, GMT, AU, DHW, MVZ: these authors received the following funding; however, all unrelated to the current manuscript: BRG has received a (nonrelated) travel grant from Janssen UK. HJG has received travel grants and speakers’ honoraria from Servier, Fresenius Medical Care and Janssen Cilag. He has received research funding from the German Research Foundation (DFG), the German Ministry of Education and Research (BMBF), the DAMP Foundation, Fresenius Medical Care, the EU “Joint Programme Neurodegenerative Disorders (JPND) and the European Social Fund (ESF)”. IBH was an inaugural Commissioner on Australia’s National Mental Health Commission (2012-2018). He is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney. The BMC operates an early-intervention youth services at Camperdown under contract to headspace. IBH has previously led community-based and pharmaceutical industry-supported (Wyeth, Eli Lily, Servier, Pfizer, AstraZeneca) projects focused on the identification and better management of anxiety and depression. He was a member of the Medical Advisory Panel for Medibank Private until October 2017, a Board Member of Psychosis Australia Trust and a member of Veterans Mental Health Clinical Reference group. He is the Chief Scientific Advisor to, and an equity shareholder in, Innowell. Innowell has been formed by the University of Sydney and PwC to deliver the $30 million Australian Government-funded “Project Synergy.” Project Synergy is a 3-year program for the transformation of mental health services through the use of innovative technologies. BWJHP has received (nonrelated) research funding from Boehringer Ingelheim and Jansen Research. KS has consulted for Roche Pharmaceuticals and Servier Pharmaceuticals. JCS has received research support from BMS, Forest, Merck, Elan, Johnson & Johnson and COMPASS in the form of grants and clinical trials. He is a member of the speakers’ bureaus for Pfizer, Abbott and Sonify and he is a consultant for Astellas. TE has served as a speaker for Lundbeck. ML declares that, over the past 36 months, he has received lecture honoraria from Lundbeck pharmaceutical. No other equity ownership, profit-sharing agreements, royalties, or patent. PMT has received (nonrelated) research funding from Biogen, Inc. (Boston). EV has received grants and served as consultant, advisor or CME speaker for the following entities: AB-Biotics, Abbott, Allergan, Angelini, AstraZeneca, Bristol-Myers Squibb, Dainippon Sumitomo Pharma, Farmindustria, Ferrer, Forest Research Institute, Gedeon Richter, Glaxo-Smith-Kline, Janssen, Lundbeck, Otsuka, Pfizer, Roche, SAGE, Sanofi-Aventis, Servier, Shire, Sunovion, and Takeda. Supplementary information is available at MP’s website. CRKC has received (nonrelated) research funding from Biogen, Inc. (Boston).

Figures

Fig. 1
Fig. 1. Data partition approach.
a Schematic illustration of features used and data partition into training and test samples, separately for males and females. A full list of features can be found in the Supplementary Material. b Data from control groups (blue) were partitioned into balanced 50:50 splits within each scanning site following random sampling but preserving the overall chronological age distribution. Major depressive disorder (MDD) groups are shown in red. The top panel illustrates the male (left) and female (right) training samples. The middle and bottom panels show the male (controls: mean [SD] in years, 43.1 [15.3]; MDD: 42.8 [13.1]) and female test samples (controls: 39.4 [15.7]; MDD: 43.2 [14.0]). ICV intracranial volume.
Fig. 2
Fig. 2. Brain age prediction based on 7 FreeSurfer subcortical volumes, lateral ventricles, 34 cortical thickness and 34 surface area measures, and total intracranial volume.
The plots show the correlation between chronological age and predicted brain age in the tenfold cross-validation of the ridge regression in the control train sample, the out-of-sample validation of the test samples (controls and MDD patients) from the ENIGMA MDD working group, and generalizability to completely independent test samples (controls only) from the ENIGMA BD working group (top to bottom). The colors indicate scanning sites and each circle represents an individual subject. Diagonal dashed line reflects the line of identity (x = y).
Fig. 3
Fig. 3. Case–control differences in brain aging.
Brain-PAD (predicted brain age—chronological age) in patients with major depressive disorder (MDD) and controls. Group level analyses showed that MDD patients exhibited significantly higher brain-PAD than controls (b = 1.08, p < 0.0001), although large within-group variation and between-group overlap are observed as visualized in a the density plot and b the Engelmann–Hecker plot. The brain-PAD estimates are adjusted for chronological age, age2, sex, and scanning site.
Fig. 4
Fig. 4. Structure coefficients of predicted brain age and FreeSurfer features across control and major depressive disorder (MDD) groups.
Bivariate correlations are shown for illustrative purposes and to provide a sense of importance of features in the brain age prediction. The figure shows Pearson correlations between predicted brain age and cortical thickness features (top row), cortical surface areas (middle row), and subcortical volumes (bottom row). The negative correlation with ICV was excluded from this figure for display purposes.

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