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

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.

Erick Jorge Canales-Rodríguez | Dan J Stein | O. Andreassen | B. Mueller | M. Alda | I. Hickie | N. Jahanshad | P. Thompson | H. Walter | H. Völzke | A. Aleman | H. Whalley | R. Salvador | M. Ingvar | J. Fullerton | A. Jansen | K. Cullen | A. McIntosh | P. Mitchell | P. Schofield | J. Lagopoulos | K. Schnell | B. Penninx | I. Gotlib | U. Malt | M. Sacchet | T. Kircher | A. Marquand | B. Mwangi | Mon-Ju Wu | G. Zunta-Soares | J. Soares | U. Dannlowski | A. Schene | B. Harrison | I. Veer | M. van Tol | D. Veltman | N. V. D. Van der Wee | D. Dima | B. Baune | J. Houenou | G. MacQueen | G. D. de Zubicaray | M. Portella | O. Gruber | B. Klimes-Dougan | L. Eyler | C. Mcdonald | C. H. Fu | L. Aftanas | D. Grotegerd | D. Wolf | H. Grabe | S. Medland | K. Mcmahon | M. Wright | J. Cole | K. Wittfeld | N. Hosten | T. Satterthwaite | T. Frodl | A. Carballedo | K. Berger | C. Henry | G. Hall | T. Hajek | J. Raduà | C. Davey | M. Polosan | K. Sim | P. Favre | G. Roberts | N. Groenewold | L. Schmaal | B. Krämer | B. Couvy-Duchesne | P. Sämann | C. Ching | L. Strike | D. Cannon | E. Vieta | M. Zanetti | A. Uhlmann | H. Ruhé | M. Serpa | R. Machado-Vieira | T. Hahn | L. Reneman | E. Bøen | X. Caseras | T. Elvsåshagen | B. Godlewska | S. Hatton | M. Landén | M. Aghajani | Pedro G. P. Rosa | E. Pomarol-Clotet | O. Steinsträter | C. Abé | S. Foley | J. Repple | S. Thomopoulos | A. Krug | T. Chaim-Avancini | A. Schrantee | Laura K. M. Han | T. Ho | E. Pozzi | Tony T. Yang | R. Vermeiren | F. Howells | J. Sommer | C. Bonnín | J. Goikolea | L. Nabulsi | B. Overs | H. Temmingh | M. Otaduy | B. Lafer | S. Sarró | M. M. Rive | V. Enneking | C. G. Connolly | S. V. D. van der Werff | R. Dinga | R. Kuplicki | G. B. Filho | F. Macmaster | F. Duran | B. Haarman | E. Filimonova | Q. McLellan | E. Osipov | E. Simulionyte | S. Frenzel | N. Winter | R. Leenings | Ashley N. Sutherland | J. Savitz | M. Hermesdorf | G. Timmons | I. Brak | Claas Kähler | Vasileios Zannias | M. Soeiro-de-Souza

[1]  P A Turski,et al.  Six‐month test–retest reliability of MRI‐defined PET measures of regional cerebral glucose metabolic rate in selected subcortical structures , 2000, Human brain mapping.

[2]  C. Franceschi,et al.  Inflamm‐aging: An Evolutionary Perspective on Immunosenescence , 2000 .

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  C. Lyketsos,et al.  Mood Disorders in the Medically Ill: Scientific Review and Recommendations , 2005, Biological Psychiatry.

[5]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[6]  J. Paruelo,et al.  How to evaluate models : Observed vs. predicted or predicted vs. observed? , 2008 .

[7]  A. Dale,et al.  Distinct genetic influences on cortical surface area and cortical thickness. , 2009, Cerebral cortex.

[8]  N. Sampson,et al.  Mental Disorders, Comorbidity and Suicidal Behavior: Results from the National Comorbidity Survey Replication , 2009, Molecular Psychiatry.

[9]  Anderson M. Winkler,et al.  Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies , 2010, NeuroImage.

[10]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[11]  A. Meyer-Lindenberg,et al.  Normal age-related brain morphometric changes: nonuniformity across cortical thickness, surface area and gray matter volume? , 2012, Neurobiology of Aging.

[12]  N. Martin,et al.  Common heritable contributions to low-risk trauma, high-risk trauma, posttraumatic stress disorder, and major depression. , 2012, Archives of general psychiatry.

[13]  Christian Gaser,et al.  Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2012 .

[14]  Jing Hua,et al.  Age estimation using cortical surface pattern combining thickness with curvatures , 2013, Medical & Biological Engineering & Computing.

[15]  P. Sienaert,et al.  Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables , 2013, Psychological Medicine.

[16]  Femke Lamers,et al.  Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile , 2013, BMC Medicine.

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

[18]  O. Wolkowitz,et al.  Major depressive disorder and accelerated cellular aging: results from a large psychiatric cohort study , 2014, Molecular Psychiatry.

[19]  Christos Davatzikos,et al.  Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. , 2014, Schizophrenia bulletin.

[20]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[21]  R. Duman,et al.  Integrating neuroimmune systems in the neurobiology of depression , 2016, Nature Reviews Neuroscience.

[22]  R. Kahn,et al.  Detecting Neuroimaging Biomarkers for Psychiatric Disorders: Sample Size Matters , 2016, Front. Psychiatry.

[23]  O. Andreassen,et al.  Subcortical volumetric abnormalities in bipolar disorder , 2016, Molecular Psychiatry.

[24]  O. Wolkowitz,et al.  The Association Between Psychiatric Disorders and Telomere Length: A Meta-Analysis Involving 14,827 Persons , 2016, Psychosomatic medicine.

[25]  Lachlan T. Strike,et al.  Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group , 2015, Molecular Psychiatry.

[26]  I. Melle,et al.  Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium , 2016, Molecular Psychiatry.

[27]  Wiepke Cahn,et al.  Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study. , 2016, The American journal of psychiatry.

[28]  N. Jahanshad,et al.  Multimodal neuroimaging of male and female brain structure in health and disease across the life span , 2016, Journal of neuroscience research.

[29]  R. Marioni,et al.  Accelerated Epigenetic Ageing in Major Depressive Disorder , 2017, bioRxiv.

[30]  Ronald C. Kessler,et al.  The burden of depressive illness , 2017 .

[31]  Erick Jorge Canales-Rodríguez,et al.  Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group , 2017, Molecular Psychiatry.

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

[33]  Lachlan T. Strike,et al.  Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group , 2016, Molecular Psychiatry.

[34]  B. Penninx.  Depression and cardiovascular disease: Epidemiological evidence on their linking mechanisms , 2017, Neuroscience & Biobehavioral Reviews.

[35]  E. Castrén,et al.  Brain-derived neurotrophic factor in mood disorders and antidepressant treatments , 2017, Neurobiology of Disease.

[36]  B. Penninx,et al.  Six‐year trajectory of objective physical function in persons with depressive and anxiety disorders , 2017, Depression and anxiety.

[37]  Nancy L. Pedersen,et al.  Biological Age Predictors , 2017, EBioMedicine.

[38]  Luke J. Chang,et al.  Building better biomarkers: brain models in translational neuroimaging , 2017, Nature Neuroscience.

[39]  K. Matthews,et al.  Depression, telomeres and mitochondrial DNA: between- and within-person associations from a 10-year longitudinal study , 2018, Molecular Psychiatry.

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

[41]  J. Cole,et al.  Quantification of the Biological Age of the Brain Using Neuroimaging , 2018, Healthy Ageing and Longevity.

[42]  E. J. van den Oord,et al.  Epigenetic Aging in Major Depressive Disorder. , 2018, The American journal of psychiatry.

[43]  Paul M. Thompson,et al.  Smaller Hippocampal Volume in Posttraumatic Stress Disorder: A Multisite ENIGMA-PGC Study: Subcortical Volumetry Results From Posttraumatic Stress Disorder Consortia , 2018, Biological Psychiatry.

[44]  Stuart J. Ritchie,et al.  Brain age predicts mortality , 2017, Molecular Psychiatry.

[45]  Adrian Preda,et al.  Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium , 2018, Biological Psychiatry.

[46]  B. Miller,et al.  Meta-analysis of Cerebrospinal Fluid Cytokine and Tryptophan Catabolite Alterations in Psychiatric Patients: Comparisons Between Schizophrenia, Bipolar Disorder, and Depression , 2018, Schizophrenia bulletin.

[47]  E. Epel,et al.  Aerobic exercise lengthens telomeres and reduces stress in family caregivers: A randomized controlled trial - Curt Richter Award Paper 2018 , 2018, Psychoneuroendocrinology.

[48]  Karolinska Schizophrenia.  Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium , 2018 .

[49]  O. Abe,et al.  Relationship between interleukin (IL)-6 and brain morphology in drug-naïve, first-episode major depressive disorder using surface-based morphometry , 2018, Scientific Reports.

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

[51]  Markus H. Sneve,et al.  Continuity and discontinuity in human cortical development and change from embryonic stages to old age , 2018, bioRxiv.

[52]  Wesley K. Thompson,et al.  A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE , 2018, bioRxiv.

[53]  O. Wolkowitz,et al.  Circulating cell-free mitochondrial DNA, but not leukocyte mitochondrial DNA copy number, is elevated in major depressive disorder , 2018, Neuropsychopharmacology.

[54]  Trang T. Le,et al.  Effect of Ibuprofen on BrainAGE: A Randomized, Placebo-Controlled, Dose-Response Exploratory Study. , 2018, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[55]  J. Rusted,et al.  Affective problems and decline in cognitive state in older adults: a systematic review and meta-analysis , 2018, Psychological Medicine.

[56]  E. J. van den Oord,et al.  Methylome-wide association findings for major depressive disorder overlap in blood and brain and replicate in independent brain samples , 2018, Molecular Psychiatry.

[57]  Thomas E. Nichols,et al.  Estimation of Brain Age Delta from Brain Imaging , 2019 .

[58]  Christian Gaser,et al.  Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging , 2019, Psychiatry Research: Neuroimaging.

[59]  Karolinska Schizophrenia,et al.  Common brain disorders are associated with heritable patterns of apparent aging of the brain , 2019 .

[60]  Knut K. Kolskår,et al.  Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain , 2019, Nature Neuroscience.

[61]  Nicolas Cherbuin,et al.  Quantification of the Biological Age of the Brain Using Neuroimaging , 2019, Healthy Ageing and Longevity.

[62]  Christian Gaser,et al.  Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained? , 2019, Front. Neurol..

[63]  Hualou Liang,et al.  Investigating systematic bias in brain age estimation with application to post‐traumatic stress disorders , 2019, Human brain mapping.

[64]  David C. Funder,et al.  Evaluating Effect Size in Psychological Research: Sense and Nonsense , 2019, Advances in Methods and Practices in Psychological Science.

[65]  Anders M. Dale,et al.  Common brain disorders are associated with heritable patterns of apparent aging of the brain , 2019, Nature Neuroscience.

[66]  O. Wolkowitz,et al.  Accelerating research on biological aging and mental health: Current challenges and future directions , 2019, Psychoneuroendocrinology.

[67]  Anders M. Dale,et al.  The genetic architecture of the human cerebral cortex , 2020, Science.