Author: "Penninx, BWJH" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Penninx, BWJH"' showing total 561 results

Start Over Author "Penninx, BWJH"

561 results on '"Penninx, BWJH"'

1. 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

Author: Schmaal, L, Hibar, DP, Sämann, PG, Hall, GB, Baune, BT, Jahanshad, N, Cheung, JW, van Erp, TGM, Bos, D, Ikram, MA, Vernooij, MW, Niessen, WJ, Tiemeier, H, Hofman, A, Wittfeld, K, Grabe, HJ, Janowitz, D, Bülow, R, Selonke, M, Völzke, H, Grotegerd, D, Dannlowski, U, Arolt, V, Opel, N, Heindel, W, Kugel, H, Hoehn, D, Czisch, M, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Goya-Maldonado, R, Gruber, O, Krämer, B, Hatton, SN, Lagopoulos, J, Hickie, IB, Frodl, T, Carballedo, A, Frey, EM, van Velzen, LS, Penninx, BWJH, van Tol, M-J, van der Wee, NJ, Davey, CG, Harrison, BJ, Mwangi, B, Cao, B, Soares, JC, Veer, IM, Walter, H, Schoepf, D, Zurowski, B, Konrad, C, Schramm, E, Normann, C, Schnell, K, Sacchet, MD, Gotlib, IH, MacQueen, GM, Godlewska, BR, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Hall, J, Sussmann, JE, Li, M, Walter, M, Aftanas, L, Brack, I, Bokhan, NA, Thompson, PM, and Veltman, DJ
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Major Depressive Disorder, Serious Mental Illness, Neurosciences, Biomedical Imaging, Depression, Basic Behavioral and Social Science, Mental Health, Brain Disorders, Clinical Research, Pediatric, Behavioral and Social Science, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Adolescent, Adult, Brain, Cerebral Cortex, Depressive Disorder, Major, Female, Frontal Lobe, Gray Matter, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Neuroimaging, Prefrontal Cortex, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.
Published: 2017

2. Subcortical brain structure and suicidal behaviour in major depressive disorder: a meta-analysis from the ENIGMA-MDD working group.

Author: Rentería, ME, Schmaal, L, Hibar, DP, Couvy-Duchesne, B, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hatton, SN, Lagopoulos, J, Veltman, DJ, van der Wee, N, van Erp, TGM, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Godlewska, BR, Cowen, PJ, Penninx, BWJH, Jahanshad, N, Thompson, PM, Wright, MJ, Martin, NG, Christensen, H, and Hickie, IB
Subjects: Brain, Humans, Magnetic Resonance Imaging, Prevalence, Risk Factors, Suicide, Depressive Disorder, Major, Adult, Aged, Middle Aged, Female, Male, Young Adult, Suicidal Ideation, Neuroimaging, Depressive Disorder, Major, Psychology, Clinical Sciences, Public Health and Health Services
Abstract: The aetiology of suicidal behaviour is complex, and knowledge about its neurobiological mechanisms is limited. Neuroimaging methods provide a noninvasive approach to explore the neural correlates of suicide vulnerability in vivo. The ENIGMA-MDD Working Group is an international collaboration evaluating neuroimaging and clinical data from thousands of individuals collected by research groups from around the world. Here we present analyses in a subset sample (n=3097) for whom suicidality data were available. Prevalence of suicidal symptoms among major depressive disorder (MDD) cases ranged between 29 and 69% across cohorts. We compared mean subcortical grey matter volumes, lateral ventricle volumes and total intracranial volume (ICV) in MDD patients with suicidal symptoms (N=451) vs healthy controls (N=1996) or MDD patients with no suicidal symptoms (N=650). MDD patients reporting suicidal plans or attempts showed a smaller ICV (P=4.12 × 10-3) or a 2.87% smaller volume compared with controls (Cohen's d=-0.284). In addition, we observed a nonsignificant trend in which MDD cases with suicidal symptoms had smaller subcortical volumes and larger ventricular volumes compared with controls. Finally, no significant differences (P=0.28-0.97) were found between MDD patients with and those without suicidal symptoms for any of the brain volume measures. This is by far the largest neuroimaging meta-analysis of suicidal behaviour in MDD to date. Our results did not replicate previous reports of association between subcortical brain structure and suicidality and highlight the need for collecting better-powered imaging samples and using improved suicidality assessment instruments.
Published: 2017

3. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Author: Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, ENIGMA Major Depressive Disorder working group, Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, and ENIGMA Major Depressive Disorder working group
Abstract: Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
Published: 2024

4. Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

Author: Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Vernooij, MW, Niessen, WJ, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hall, GB, van Velzen, LS, van Tol, M-J, van der Wee, NJ, Veer, IM, Walter, H, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, McIntosh, AM, Whalley, HC, Sussmann, JE, Godlewska, BR, Fischer, FH, Penninx, BWJH, Thompson, PM, and Hibar, DP
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Published: 2016

5. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group.

Author: Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Tiemeier, H, Hofman, A, Niessen, WJ, Vernooij, MW, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Wright, MJ, Hall, GB, MacQueen, GM, Frey, EM, Carballedo, A, van Velzen, LS, van Tol, MJ, van der Wee, NJ, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Sussmann, JE, Godlewska, BR, Cowen, PJ, Fischer, FH, Rose, M, Penninx, BWJH, Thompson, PM, and Hibar, DP
Subjects: Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Case-Control Studies, Depressive Disorder, Major, Adult, Middle Aged, Female, Male, Neuroimaging, major depressive disorder, structural volumes, ENIGMA consortium, meta-analysis, hippocampus, Depressive Disorder, Major, Psychiatry, Medical and Health Sciences, Biological Sciences, Psychology and Cognitive Sciences
Abstract: The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen's d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen's d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen's d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen's d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen's d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.
Published: 2016

6. Oxidative stress, anti-oxidants and the cross-sectional and longitudinal association with depressive symptoms: results from the CARDIA study.

Author: Black, CN, Penninx, BWJH, Bot, M, Odegaard, AO, Gross, MD, Matthews, KA, and Jacobs, DR
Subjects: Humans, Antioxidants, Risk, Longitudinal Studies, Cross-Sectional Studies, Depressive Disorder, Oxidative Stress, Socioeconomic Factors, Adult, Female, Male, Coronary Artery Disease, Clinical Sciences, Public Health and Health Services, Psychology
Abstract: Depression may be accompanied by increased oxidative stress and decreased circulating anti-oxidants. This study examines the association between depressive symptoms, F2-isoprostanes and carotenoids in a US community sample. The study includes 3009 participants (mean age 40.3, 54.2% female) from CARDIA (Coronary Artery Risk Development in Young Adults). Cross-sectional analyses were performed on data from the year 15 examination (2000-2001) including subjects whose depressive symptoms were assessed with the Center for Epidemiologic Studies Depression Scale (CES-D) and had measurements of plasma F2-isoprostanes (gas chromatography/mass spectrometry) or serum carotenoids (high-performance liquid chromatography). Carotenoids zeaxanthin/lutein, β-cryptoxanthin, lycopene, α-carotene, β-carotene were standardized and summed. Longitudinal analyses were conducted using the data from other examinations at 5-year intervals. Cross-lagged analyses investigated whether CES-D predicted F2-isoprostanes or carotenoids at the following exam, and vice versa. Regression analyses were controlled for sociodemographics, health and lifestyle factors. F2-isoprostanes were higher in subjects with depressive symptoms (CES-D ⩾ 16) after adjustment for sociodemographics (55.7 vs 52.0 pg ml(-1); Cohen's d = 0.14, P < 0.001). There was no difference in F2-isoprostanes after further adjustment for health and lifestyle factors. Carotenoids were lower in those with CES-D scores ⩾ 16, even after adjustment for health and lifestyle factors (standardized sum 238.7 vs 244.0, Cohen's d = -0.16, P < 0.001). Longitudinal analyses confirmed that depression predicts subsequent F2-isoprostane and carotenoid levels. Neither F2-isoprostanes nor carotenoids predicted subsequent depression. In conclusion, depressive symptoms were cross-sectionally and longitudinally associated with increased F2-isoprostanes and decreased carotenoids. The association with F2-isoprostanes can largely be explained by lifestyle factors, but lower carotenoids were independently associated with depressive symptoms.
Published: 2016

7. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol

Author: F. Matcham, C. Barattieri di San Pietro, V. Bulgari, G. de Girolamo, R. Dobson, H. Eriksson, A. A. Folarin, J. M. Haro, M. Kerz, F. Lamers, Q. Li, N. V. Manyakov, D. C. Mohr, I. Myin-Germeys, V. Narayan, Penninx BWJH, Y. Ranjan, Z. Rashid, A. Rintala, S. Siddi, S. K. Simblett, T. Wykes, M. Hotopf, and on behalf of the RADAR-CNS consortium
Subjects: Major depressive disorder, Remote measurement technology, Passive sensing, M-health, Prospective study, Observational cohort, Psychiatry, RC435-571
Abstract: Abstract Background There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. Methods RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants’ sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation’s self-reported Composite International Diagnostic Interview (CIDI-SF). Discussion This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.
Published: 2019
Full Text: View/download PDF

8. Lifestyle-related risk factors and their cumulative associations with hippocampal and total grey matter volume across the adult lifespan: a pooled analysis in the European Lifebrain consortium

Author: Binnewies, J, Nawijn, L, Brandmaier, AM, Baaré, WFC, Boraxbekk, C-J, Demnitz, N, Drevon, CA, Fjell, AM, Lindenberger, U, Madsen, KS, Nyberg, L, Topiwala, A, Walhovd, KB, Ebmeier, KP, and Penninx, BWJH
Abstract: Background: Lifestyle-related risk factors, such as obesity, physical inactivity, short sleep, smoking and alcohol use, have been associated with low hippocampal and total grey matter volumes (GMV). However, these risk factors have mostly been assessed as separate factors, leaving it unknown if variance explained by these factors is overlapping or additive. We investigated associations of five lifestyle-related factors separately and cumulatively with hippocampal and total GMV, pooled across eight European cohorts. Methods: We included 3838 participants aged 18-90 years from eight cohorts of the European Lifebrain consortium. Using individual person data, we performed cross-sectional meta-analyses on associations of presence of lifestyle-related risk factors separately (overweight/obesity, physical inactivity, short sleep, smoking, high alcohol use) as well as a cumulative unhealthy lifestyle score (counting the number of present lifestyle-related risk factors) with FreeSurfer-derived hippocampal volume and total GMV. Lifestyle-related risk factors were defined according to public health guidelines. Results: High alcohol use was associated with lower hippocampal volume (r=-0.10, p=0.021), and overweight/obesity with lower total GMV (r=-0.09, p=0.001). Other lifestyle-related risk factors were not significantly associated with hippocampal volume or GMV. The cumulative unhealthy lifestyle score was negatively associated with total GMV (r=-0.08, p=0.001), but not hippocampal volume (r=-0.01, p=0.625). Conclusions: This large pooled study confirmed the negative association of some lifestyle- related risk factors with hippocampal volume and GMV, although with small effect sizes. Lifestyle factors should not be seen in isolation as there is evidence that having multiple unhealthy lifestyle factors is associated with a linear reduction in overall brain volume.
Published: 2023

9. Comorbidity between major depressive disorder and physical diseases: a comprehensive review of epidemiology, mechanisms and management

Author: Berk, M, Kohler-Forsberg, O, Turner, M, Penninx, BWJH, Wrobel, A, Firth, J, Loughman, A, Reavley, NJ, Mcgrath, JJ, Momen, NC, Plana-Ripoll, O, O'Neil, A, Siskind, D, Williams, LJ, Carvalho, AF, Schmaal, L, Walker, AJ, Dean, O, Walder, K, Berk, L, Dodd, S, Yung, AR, Marx, W, Berk, M, Kohler-Forsberg, O, Turner, M, Penninx, BWJH, Wrobel, A, Firth, J, Loughman, A, Reavley, NJ, Mcgrath, JJ, Momen, NC, Plana-Ripoll, O, O'Neil, A, Siskind, D, Williams, LJ, Carvalho, AF, Schmaal, L, Walker, AJ, Dean, O, Walder, K, Berk, L, Dodd, S, Yung, AR, and Marx, W
Abstract: Populations with common physical diseases - such as cardiovascular diseases, cancer and neurodegenerative disorders - experience substantially higher rates of major depressive disorder (MDD) than the general population. On the other hand, people living with MDD have a greater risk for many physical diseases. This high level of comorbidity is associated with worse outcomes, reduced adherence to treatment, increased mortality, and greater health care utilization and costs. Comorbidity can also result in a range of clinical challenges, such as a more complicated therapeutic alliance, issues pertaining to adaptive health behaviors, drug-drug interactions and adverse events induced by medications used for physical and mental disorders. Potential explanations for the high prevalence of the above comorbidity involve shared genetic and biological pathways. These latter include inflammation, the gut microbiome, mitochondrial function and energy metabolism, hypothalamic-pituitary-adrenal axis dysregulation, and brain structure and function. Furthermore, MDD and physical diseases have in common several antecedents related to social factors (e.g., socioeconomic status), lifestyle variables (e.g., physical activity, diet, sleep), and stressful live events (e.g., childhood trauma). Pharmacotherapies and psychotherapies are effective treatments for comorbid MDD, and the introduction of lifestyle interventions as well as collaborative care models and digital technologies provide promising strategies for improving management. This paper aims to provide a detailed overview of the epidemiology of the comorbidity of MDD and specific physical diseases, including prevalence and bidirectional risk; of shared biological pathways potentially implicated in the pathogenesis of MDD and common physical diseases; of socio-environmental factors that serve as both shared risk and protective factors; and of management of MDD and physical diseases, including prevention and treatment. We conclude wit
Published: 2023

10. Do neighborhood socioeconomic characteristics contribute to depression and anxiety?: Irina Motoc

Author: Motoc, I, Timmermans, EJ, Thomese, GCF, Penninx, BWJH, Deeg, DJH, and Huisman, M
Published: 2017
Full Text: View/download PDF

11. Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Author: Mullins, N, Kang, J, Campos, A, Coleman, JR, Edwards, AC, Galfalvy, H, Levey, DF, Lori, A, Shabalin, A, Starnawska, A, Su, M-H, Watson, HJ, Adams, M, Awasthi, S, Ganda, M, Hafferty, JD, Hishimoto, A, Kim, M, Okazaki, S, Otsuka, I, Ripke, S, Ware, EB, Bergen, AW, Berrettini, WH, Bohus, M, Brandt, H, Chang, X, Chen, WJ, Chen, H-C, Crawford, S, Crow, S, DiBlasi, E, Duriez, P, Fernandez-Aranda, F, Fichter, MM, Gallinger, S, Glatt, SJ, Gorwood, P, Guo, Y, Hakonarson, H, Halmi, KA, Hwu, H-G, Jain, S, Jamain, S, Jimenez-Murcia, S, Johnson, C, Kaplan, AS, Kaye, WH, Keel, PK, Kennedy, JL, Klump, KL, Li, D, Liao, S-C, Lieb, K, Lilenfeld, L, Liu, C-M, Magistretti, PJ, Marshall, CR, Mitchell, JE, Monson, ET, Myers, RM, Pinto, D, Powers, A, Ramoz, N, Roepke, S, Rozanov, V, Scherer, SW, Schmahl, C, Sokolowski, M, Strober, M, Thornton, LM, Treasure, J, Tsuang, MT, Witt, SH, Woodside, DB, Yilmaz, Z, Zillich, L, Adolfsson, R, Agartz, I, Air, TM, Alda, M, Alfredsson, L, Andreassen, OA, Anjorin, A, Appadurai, V, Artigas, MS, Van der Auwera, S, Azevedo, MH, Bass, N, Bau, CHD, Baune, BT, Bellivier, F, Berger, K, Biernacka, JM, Bigdeli, TB, Binder, EB, Boehnke, M, Boks, MP, Bosch, R, Braff, DL, Bryant, R, Budde, M, Byrne, EM, Cahn, W, Casas, M, Castelao, E, Cervilla, JA, Chaumette, B, Cichon, S, Corvin, A, Craddock, N, Craig, D, Degenhardt, F, Djurovic, S, Edenberg, HJ, Fanous, AH, Foo, JC, Forstner, AJ, Frye, M, Fullerton, JM, Gatt, JM, Gejman, P, Giegling, I, Grabe, HJ, Green, MJ, Grevet, EH, Grigoroiu-Serbanescu, M, Gutierrez, B, Guzman-Parra, J, Hamilton, SP, Hamshere, ML, Hartmann, A, Hauser, J, Heilmann-Heimbach, S, Hoffmann, P, Ising, M, Jones, I, Jones, LA, Jonsson, L, Kahn, RS, Kelsoe, JR, Kendler, KS, Kloiber, S, Koenen, KC, Kogevinas, M, Konte, B, Krebs, M-O, Lander, M, Lawrence, J, Leboyer, M, Lee, PH, Levinson, DF, Liao, C, Lissowska, J, Lucae, S, Mayoral, F, McElroy, SL, McGrath, P, McGuffin, P, McQuillin, A, Medland, SE, Mehta, D, Melle, I, Milaneschi, Y, Mitchell, PB, Molina, E, Morken, G, Mortensen, PB, Mueller-Myhsok, B, Nievergelt, C, Nimgaonkar, V, Noethen, MM, O'Donovan, MC, Ophoff, RA, Owen, MJ, Pato, C, Pato, MT, Penninx, BWJH, Pimm, J, Pistis, G, Potash, JB, Power, RA, Preisig, M, Quested, D, Ramos-Quiroga, JA, Reif, A, Ribases, M, Richarte, V, Rietschel, M, Rivera, M, Roberts, A, Roberts, G, Rouleau, GA, Rovaris, DL, Rujescu, D, Sanchez-Mora, C, Sanders, AR, Schofield, PR, Schulze, TG, Scott, LJ, Serretti, A, Shi, J, Shyn, S, Sirignano, L, Sklar, P, Smeland, OB, Smoller, JW, Sonuga-Barke, EJS, Spalletta, G, Strauss, JS, Swiatkowska, B, Trzaskowski, M, Turecki, G, Vilar-Ribo, L, Vincent, JB, Voelzke, H, Walters, JTR, Weickert, CS, Weickert, TW, Weissman, MM, Williams, LM, Wray, NR, Zai, CC, Ashley-Koch, AE, Beckham, JC, Hauser, ER, Hauser, MA, Kimbrel, NA, Lindquist, JH, McMahon, B, Oslin, DW, Qin, X, Agerbo, E, Borglum, AD, Breen, G, Erlangsen, A, Esko, T, Gelernter, J, Hougaard, DM, Kessler, RC, Kranzler, HR, Li, QS, Martin, NG, McIntosh, AM, Mors, O, Nordentoft, M, Olsen, CM, Porteous, D, Ursano, RJ, Wasserman, D, Werge, T, Whiteman, DC, Bulik, CM, Coon, H, Demontis, D, Docherty, AR, Kuo, P-H, Lewis, CM, Mann, JJ, Renteria, ME, Smith, DJ, Stahl, EA, Stein, MB, Streit, F, Willour, V, Ruderfer, DM, Mullins, N, Kang, J, Campos, A, Coleman, JR, Edwards, AC, Galfalvy, H, Levey, DF, Lori, A, Shabalin, A, Starnawska, A, Su, M-H, Watson, HJ, Adams, M, Awasthi, S, Ganda, M, Hafferty, JD, Hishimoto, A, Kim, M, Okazaki, S, Otsuka, I, Ripke, S, Ware, EB, Bergen, AW, Berrettini, WH, Bohus, M, Brandt, H, Chang, X, Chen, WJ, Chen, H-C, Crawford, S, Crow, S, DiBlasi, E, Duriez, P, Fernandez-Aranda, F, Fichter, MM, Gallinger, S, Glatt, SJ, Gorwood, P, Guo, Y, Hakonarson, H, Halmi, KA, Hwu, H-G, Jain, S, Jamain, S, Jimenez-Murcia, S, Johnson, C, Kaplan, AS, Kaye, WH, Keel, PK, Kennedy, JL, Klump, KL, Li, D, Liao, S-C, Lieb, K, Lilenfeld, L, Liu, C-M, Magistretti, PJ, Marshall, CR, Mitchell, JE, Monson, ET, Myers, RM, Pinto, D, Powers, A, Ramoz, N, Roepke, S, Rozanov, V, Scherer, SW, Schmahl, C, Sokolowski, M, Strober, M, Thornton, LM, Treasure, J, Tsuang, MT, Witt, SH, Woodside, DB, Yilmaz, Z, Zillich, L, Adolfsson, R, Agartz, I, Air, TM, Alda, M, Alfredsson, L, Andreassen, OA, Anjorin, A, Appadurai, V, Artigas, MS, Van der Auwera, S, Azevedo, MH, Bass, N, Bau, CHD, Baune, BT, Bellivier, F, Berger, K, Biernacka, JM, Bigdeli, TB, Binder, EB, Boehnke, M, Boks, MP, Bosch, R, Braff, DL, Bryant, R, Budde, M, Byrne, EM, Cahn, W, Casas, M, Castelao, E, Cervilla, JA, Chaumette, B, Cichon, S, Corvin, A, Craddock, N, Craig, D, Degenhardt, F, Djurovic, S, Edenberg, HJ, Fanous, AH, Foo, JC, Forstner, AJ, Frye, M, Fullerton, JM, Gatt, JM, Gejman, P, Giegling, I, Grabe, HJ, Green, MJ, Grevet, EH, Grigoroiu-Serbanescu, M, Gutierrez, B, Guzman-Parra, J, Hamilton, SP, Hamshere, ML, Hartmann, A, Hauser, J, Heilmann-Heimbach, S, Hoffmann, P, Ising, M, Jones, I, Jones, LA, Jonsson, L, Kahn, RS, Kelsoe, JR, Kendler, KS, Kloiber, S, Koenen, KC, Kogevinas, M, Konte, B, Krebs, M-O, Lander, M, Lawrence, J, Leboyer, M, Lee, PH, Levinson, DF, Liao, C, Lissowska, J, Lucae, S, Mayoral, F, McElroy, SL, McGrath, P, McGuffin, P, McQuillin, A, Medland, SE, Mehta, D, Melle, I, Milaneschi, Y, Mitchell, PB, Molina, E, Morken, G, Mortensen, PB, Mueller-Myhsok, B, Nievergelt, C, Nimgaonkar, V, Noethen, MM, O'Donovan, MC, Ophoff, RA, Owen, MJ, Pato, C, Pato, MT, Penninx, BWJH, Pimm, J, Pistis, G, Potash, JB, Power, RA, Preisig, M, Quested, D, Ramos-Quiroga, JA, Reif, A, Ribases, M, Richarte, V, Rietschel, M, Rivera, M, Roberts, A, Roberts, G, Rouleau, GA, Rovaris, DL, Rujescu, D, Sanchez-Mora, C, Sanders, AR, Schofield, PR, Schulze, TG, Scott, LJ, Serretti, A, Shi, J, Shyn, S, Sirignano, L, Sklar, P, Smeland, OB, Smoller, JW, Sonuga-Barke, EJS, Spalletta, G, Strauss, JS, Swiatkowska, B, Trzaskowski, M, Turecki, G, Vilar-Ribo, L, Vincent, JB, Voelzke, H, Walters, JTR, Weickert, CS, Weickert, TW, Weissman, MM, Williams, LM, Wray, NR, Zai, CC, Ashley-Koch, AE, Beckham, JC, Hauser, ER, Hauser, MA, Kimbrel, NA, Lindquist, JH, McMahon, B, Oslin, DW, Qin, X, Agerbo, E, Borglum, AD, Breen, G, Erlangsen, A, Esko, T, Gelernter, J, Hougaard, DM, Kessler, RC, Kranzler, HR, Li, QS, Martin, NG, McIntosh, AM, Mors, O, Nordentoft, M, Olsen, CM, Porteous, D, Ursano, RJ, Wasserman, D, Werge, T, Whiteman, DC, Bulik, CM, Coon, H, Demontis, D, Docherty, AR, Kuo, P-H, Lewis, CM, Mann, JJ, Renteria, ME, Smith, DJ, Stahl, EA, Stein, MB, Streit, F, Willour, V, and Ruderfer, DM
Abstract: BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.
Published: 2022

12. A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials

Author: Sforzini, L, Worrell, C, Kose, M, Anderson, IM, Aouizerate, B, Arolt, V, Bauer, M, Baune, BT, Blier, P, Cleare, AJ, Cowen, PJ, Dinan, TG, Fagiolini, A, Ferrier, IN, Hegerl, U, Krystal, AD, Leboyer, M, McAllister-Williams, RH, McIntyre, RS, Meyer-Lindenberg, A, Miller, AH, Nemeroff, CB, Normann, C, Nutt, D, Pallanti, S, Pani, L, Penninx, BWJH, Schatzberg, AF, Shelton, RC, Yatham, LN, Young, AH, Zahn, R, Aislaitner, G, Butlen-Ducuing, F, Fletcher, C, Haberkamp, M, Laughren, T, Mantyla, F-L, Schruers, K, Thomson, A, Arteaga-Henriquez, G, Benedetti, F, Cash-Gibson, L, Chae, WR, De Smedt, H, Gold, SM, Hoogendijk, WJG, Mondragon, VJ, Maron, E, Martynowicz, J, Melloni, E, Otte, C, Perez-Fuentes, G, Poletti, S, Schmidt, ME, van de Ketterij, E, Woo, K, Flossbach, Y, Ramos-Quiroga, JA, Savitz, AJ, Pariante, CM, Sforzini, L, Worrell, C, Kose, M, Anderson, IM, Aouizerate, B, Arolt, V, Bauer, M, Baune, BT, Blier, P, Cleare, AJ, Cowen, PJ, Dinan, TG, Fagiolini, A, Ferrier, IN, Hegerl, U, Krystal, AD, Leboyer, M, McAllister-Williams, RH, McIntyre, RS, Meyer-Lindenberg, A, Miller, AH, Nemeroff, CB, Normann, C, Nutt, D, Pallanti, S, Pani, L, Penninx, BWJH, Schatzberg, AF, Shelton, RC, Yatham, LN, Young, AH, Zahn, R, Aislaitner, G, Butlen-Ducuing, F, Fletcher, C, Haberkamp, M, Laughren, T, Mantyla, F-L, Schruers, K, Thomson, A, Arteaga-Henriquez, G, Benedetti, F, Cash-Gibson, L, Chae, WR, De Smedt, H, Gold, SM, Hoogendijk, WJG, Mondragon, VJ, Maron, E, Martynowicz, J, Melloni, E, Otte, C, Perez-Fuentes, G, Poletti, S, Schmidt, ME, van de Ketterij, E, Woo, K, Flossbach, Y, Ramos-Quiroga, JA, Savitz, AJ, and Pariante, CM
Abstract: Criteria for treatment-resistant depression (TRD) and partially responsive depression (PRD) as subtypes of major depressive disorder (MDD) are not unequivocally defined. In the present document we used a Delphi-method-based consensus approach to define TRD and PRD and to serve as operational criteria for future clinical studies, especially if conducted for regulatory purposes. We reviewed the literature and brought together a group of international experts (including clinicians, academics, researchers, employees of pharmaceutical companies, regulatory bodies representatives, and one person with lived experience) to evaluate the state-of-the-art and main controversies regarding the current classification. We then provided recommendations on how to design clinical trials, and on how to guide research in unmet needs and knowledge gaps. This report will feed into one of the main objectives of the EUropean Patient-cEntric clinicAl tRial pLatforms, Innovative Medicines Initiative (EU-PEARL, IMI) MDD project, to design a protocol for platform trials of new medications for TRD/PRD.
Published: 2022

13. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author: Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, Hulshoff Pol, HE, Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, and Hulshoff Pol, HE
Abstract: Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.
Published: 2022

14. Corrigendum: Potential genetic overlap between insomnia and sleep symptoms in major depressive disorder: A polygenic risk score analysis.

Author: Melhuish Beaupre, LM, Tiwari, AK, Gonçalves, VF, Zai, CC, Marshe, VS, Lewis, CM, Martin, NG, McIntosh, AM, Adams, MJ, Baune, BT, Levinson, DF, Boomsma, DI, Penninx, BWJH, Breen, G, Hamilton, S, Awasthi, S, Ripke, S, Jones, L, Jones, I, Byrne, EM, Hickie, IB, Potash, JP, Shi, J, Weissman, MM, Milaneschi, Y, Shyn, SI, de Geus, EJC, Willemsen, G, Brown, GM, Kennedy, JL, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Melhuish Beaupre, LM, Tiwari, AK, Gonçalves, VF, Zai, CC, Marshe, VS, Lewis, CM, Martin, NG, McIntosh, AM, Adams, MJ, Baune, BT, Levinson, DF, Boomsma, DI, Penninx, BWJH, Breen, G, Hamilton, S, Awasthi, S, Ripke, S, Jones, L, Jones, I, Byrne, EM, Hickie, IB, Potash, JP, Shi, J, Weissman, MM, Milaneschi, Y, Shyn, SI, de Geus, EJC, Willemsen, G, Brown, GM, Kennedy, JL, and Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
Abstract: [This corrects the article DOI: 10.3389/fpsyt.2021.734077.].
Published: 2022

15. The association between clinical and biological characteristics of depression and structural brain alterations

Author: Toenders, YJ, Schmaal, L, Nawijn, L, Han, LKM, Binnewies, J, van der Wee, NJA, van Tol, M-J, Veltman, DJ, Milaneschi, Y, Lamers, F, Penninx, BWJH, Toenders, YJ, Schmaal, L, Nawijn, L, Han, LKM, Binnewies, J, van der Wee, NJA, van Tol, M-J, Veltman, DJ, Milaneschi, Y, Lamers, F, and Penninx, BWJH
Abstract: BACKGROUND: Structural brain alterations are observed in major depressive disorder (MDD). However, MDD is a highly heterogeneous disorder and specific clinical or biological characteristics of depression might relate to specific structural brain alterations. Clinical symptom subtypes of depression, as well as immuno-metabolic dysregulation associated with subtypes of depression, have been associated with brain alterations. Therefore, we examined if specific clinical and biological characteristics of depression show different brain alterations compared to overall depression. METHOD: Individuals with and without depressive and/or anxiety disorders from the Netherlands Study of Depression and Anxiety (NESDA) (328 participants from three timepoints leading to 541 observations) and the Mood Treatment with Antidepressants or Running (MOTAR) study (123 baseline participants) were included. Symptom profiles (atypical energy-related profile, melancholic profile and depression severity) and biological indices (inflammatory, metabolic syndrome, and immuno-metabolic indices) were created. The associations of the clinical and biological profiles with depression-related structural brain measures (anterior cingulate cortex [ACC], orbitofrontal cortex, insula, and nucleus accumbens) were examined dimensionally in both studies and meta-analysed. RESULTS: Depression severity was negatively associated with rostral ACC thickness (B = -0.55, pFDR = 0.03), and melancholic symptoms were negatively associated with caudal ACC thickness (B = -0.42, pFDR = 0.03). The atypical energy-related symptom profile and immuno-metabolic indices did not show a consistent association with structural brain measures across studies. CONCLUSION: Overall depression- and melancholic symptom severity showed a dose-response relationship with reduced ACC thickness. No associations between immuno-metabolic dysregulation and structural brain alterations were found, suggesting that although both are associated with
Published: 2022

16. Associations of depression and regional brain structure across the adult lifespan: Pooled analyses of six population-based and two clinical cohort studies in the European Lifebrain consortium

Author: Binnewies, J, Nawijn, L, Brandmaier, AM, Baare, WFC, Bartres-Faz, D, Drevon, CA, Duezel, S, Fjell, AM, Han, LKM, Knights, E, Lindenberger, U, Milaneschi, Y, Mowinckel, AM, Nyberg, L, Plachti, A, Madsen, KS, Sole-Padulles, C, Suri, S, Walhovd, KB, Zsoldos, E, Ebmeier, KP, Penninx, BWJH, Binnewies, J, Nawijn, L, Brandmaier, AM, Baare, WFC, Bartres-Faz, D, Drevon, CA, Duezel, S, Fjell, AM, Han, LKM, Knights, E, Lindenberger, U, Milaneschi, Y, Mowinckel, AM, Nyberg, L, Plachti, A, Madsen, KS, Sole-Padulles, C, Suri, S, Walhovd, KB, Zsoldos, E, Ebmeier, KP, and Penninx, BWJH
Abstract: OBJECTIVE: Major depressive disorder has been associated with lower prefrontal thickness and hippocampal volume, but it is unknown whether this association also holds for depressive symptoms in the general population. We investigated associations of depressive symptoms and depression status with brain structures across population-based and patient-control cohorts, and explored whether these associations are similar over the lifespan and across sexes. METHODS: We included 3,447 participants aged 18-89 years from six population-based and two clinical patient-control cohorts of the European Lifebrain consortium. Cross-sectional meta-analyses using individual person data were performed for associations of depressive symptoms and depression status with FreeSurfer-derived thickness of bilateral rostral anterior cingulate cortex (rACC) and medial orbitofrontal cortex (mOFC), and hippocampal and total grey matter volume (GMV), separately for population-based and clinical cohorts. RESULTS: Across patient-control cohorts, depressive symptoms and presence of mild-to-severe depression were associated with lower mOFC thickness (rsymptoms = -0.15/ rstatus = -0.22), rACC thickness (rsymptoms = -0.20/ rstatus = -0.25), hippocampal volume (rsymptoms = -0.13/ rstatus = 0.13) and total GMV (rsymptoms = -0.21/ rstatus = -0.25). Effect sizes were slightly larger for presence of moderate-to-severe depression. Associations were similar across age groups and sex. Across population-based cohorts, no associations between depression and brain structures were observed. CONCLUSIONS: Fitting with previous meta-analyses, depressive symptoms and depression status were associated with lower mOFC, rACC thickness, and hippocampal and total grey matter volume in clinical patient-control cohorts, although effect sizes were small. The absence of consistent associations in population-based cohorts with mostly mild depressive symptoms, suggests that significantly lower thickness and volume of the studied
Published: 2022

17. A large-scale ENIGMA multisite replication study of brain age in depression

Author: Han, LKM, Dinga, R, Leenings, R, Hahn, T, Cole, JH, Aftanas, LI, Amod, AR, Besteher, B, Colle, R, Corruble, E, Couvy-Duchesne, B, Danilenko, KV, Fuentes-Claramonte, P, Gonul, AS, Gotlib, IH, Goya-Maldonado, R, Groenewold, NA, Hamilton, P, Ichikawa, N, Ipser, JC, Itai, E, Koopowitz, S-M, Li, M, Okada, G, Okamoto, Y, Churikova, OS, Osipov, EA, Penninx, BWJH, Pomarol-Clotet, E, Rodríguez-Cano, E, Sacchet, MD, Shinzato, H, Sim, K, Stein, DJ, Uyar-Demir, A, Veltman, DJ, Schmaal, L, Han, LKM, Dinga, R, Leenings, R, Hahn, T, Cole, JH, Aftanas, LI, Amod, AR, Besteher, B, Colle, R, Corruble, E, Couvy-Duchesne, B, Danilenko, KV, Fuentes-Claramonte, P, Gonul, AS, Gotlib, IH, Goya-Maldonado, R, Groenewold, NA, Hamilton, P, Ichikawa, N, Ipser, JC, Itai, E, Koopowitz, S-M, Li, M, Okada, G, Okamoto, Y, Churikova, OS, Osipov, EA, Penninx, BWJH, Pomarol-Clotet, E, Rodríguez-Cano, E, Sacchet, MD, Shinzato, H, Sim, K, Stein, DJ, Uyar-Demir, A, Veltman, DJ, and Schmaal, L
Abstract: Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases; +1.08 years [SE 0.22], Cohen’s d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen’s d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale indep
Published: 2022

18. A saturated map of common genetic variants associated with human height

Author: Yengo, L, Vedantam, S, Marouli, E, Sidorenko, J, Bartell, E, Sakaue, S, Graff, M, Eliasen, AU, Jiang, Y, Raghavan, S, Miao, J, Arias, JD, Graham, SE, Mukamel, RE, Spracklen, CN, Yin, X, Chen, S-H, Ferreira, T, Highland, HH, Ji, Y, Karaderi, T, Lin, K, Lull, K, Malden, DE, Medina-Gomez, C, Machado, M, Moore, A, Rueger, S, Sim, X, Vrieze, S, Ahluwalia, TS, Akiyama, M, Allison, MA, Alvarez, M, Andersen, MK, Ani, A, Appadurai, V, Arbeeva, L, Bhaskar, S, Bielak, LF, Bollepalli, S, Bonnycastle, LL, Bork-Jensen, J, Bradfield, JP, Bradford, Y, Braund, PS, Brody, JA, Burgdorf, KS, Cade, BE, Cai, H, Cai, Q, Campbell, A, Canadas-Garre, M, Catamo, E, Chai, J-F, Chai, X, Chang, L-C, Chang, Y-C, Chen, C-H, Chesi, A, Choi, SH, Chung, R-H, Cocca, M, Concas, MP, Couture, C, Cuellar-Partida, G, Danning, R, Daw, EW, Degenhard, F, Delgado, GE, Delitala, A, Demirkan, A, Deng, X, Devineni, P, Dietl, A, Dimitriou, M, Dimitrov, L, Dorajoo, R, Ekici, AB, Engmann, JE, Fairhurst-Hunter, Z, Farmaki, A-E, Faul, JD, Fernandez-Lopez, J-C, Forer, L, Francescatto, M, Freitag-Wolf, S, Fuchsberger, C, Galesloot, TE, Gao, Y, Gao, Z, Geller, F, Giannakopoulou, O, Giulianini, F, Gjesing, AP, Goel, A, Gordon, SD, Gorski, M, Grove, J, Guo, X, Gustafsson, S, Haessler, J, Hansen, TF, Havulinna, AS, Haworth, SJ, He, J, Heard-Costa, N, Hebbar, P, Hindy, G, Ho, Y-LA, Hofer, E, Holliday, E, Horn, K, Hornsby, WE, Hottenga, J-J, Huang, H, Huang, J, Huerta-Chagoya, A, Huffman, JE, Hung, Y-J, Huo, S, Hwang, MY, Iha, H, Ikeda, DD, Isono, M, Jackson, AU, Jager, S, Jansen, IE, Johansson, I, Jonas, JB, Jonsson, A, Jorgensen, T, Kalafati, I-P, Kanai, M, Kanoni, S, Karhus, LL, Kasturiratne, A, Katsuya, T, Kawaguchi, T, Kember, RL, Kentistou, KA, Kim, H-N, Kim, YJ, Kleber, ME, Knol, MJ, Kurbasic, A, Lauzon, M, Le, P, Lea, R, Lee, J-Y, Leonard, HL, Li, SA, Li, X, Liang, J, Lin, H, Lin, S-Y, Liu, J, Liu, X, Lo, KS, Long, J, Lores-Motta, L, Luan, J, Lyssenko, V, Lyytikainen, L-P, Mahajan, A, Mamakou, V, Mangino, M, Manichaikul, A, Marten, J, Mattheisen, M, Mavarani, L, McDaid, AF, Meidtner, K, Melendez, TL, Mercader, JM, Milaneschi, Y, Miller, JE, Millwood, IY, Mishra, PP, Mitchell, RE, Mollehave, LT, Morgan, A, Mucha, S, Munz, M, Nakatochi, M, Nelson, CP, Nethander, M, Nho, CW, Nielsen, AA, Nolte, IM, Nongmaithem, SS, Noordam, R, Ntalla, I, Nutile, T, Pandit, A, Christofidou, P, Parna, K, Pauper, M, Petersen, ERB, Petersen, L, Pitkanen, N, Polasek, O, Poveda, A, Preuss, MH, Pyarajan, S, Raffield, LM, Rakugi, H, Ramirez, J, Rasheed, A, Raven, D, Rayner, NW, Riveros, C, Rohde, R, Ruggiero, D, Ruotsalainen, SE, Ryan, KA, Sabater-Lleal, M, Saxena, R, Scholz, M, Sendamarai, A, Shen, B, Shi, J, Shin, JH, Sidore, C, Sitlani, CM, Slieker, RKC, Smit, RAJ, Smith, A, Smith, JA, Smyth, LJ, Southam, LE, Steinthorsdottir, V, Sun, L, Takeuchi, F, Tallapragada, D, Taylor, KD, Tayo, BO, Tcheandjieu, C, Terzikhan, N, Tesolin, P, Teumer, A, Theusch, E, Thompson, DJ, Thorleifsson, G, Timmers, PRHJ, Trompet, S, Turman, C, Vaccargiu, S, van der Laan, SW, van der Most, PJ, van Klinken, JB, van Setten, J, Verma, SS, Verweij, N, Veturi, Y, Wang, CA, Wang, C, Wang, L, Wang, Z, Warren, HR, Wei, WB, Wickremasinghe, AR, Wielscher, M, Wiggins, KL, Winsvold, BS, Wong, A, Wu, Y, Wuttke, M, Xia, R, Xie, T, Yamamoto, K, Yang, J, Yao, J, Young, H, Yousri, NA, Yu, L, Zeng, L, Zhang, W, Zhang, X, Zhao, J-H, Zhao, W, Zhou, W, Zimmermann, ME, Zoledziewska, M, Adair, LS, Adams, HHH, Aguilar-Salinas, CA, Al-Mulla, F, Arnett, DK, Asselbergs, FW, Asvold, BO, Attia, J, Banas, B, Bandinelli, S, Bennett, DA, Bergler, T, Bharadwaj, D, Biino, G, Bisgaard, H, Boerwinkle, E, Boger, CA, Bonnelykke, K, Boomsma, D, Borglum, AD, Borja, JB, Bouchard, C, Bowden, DW, Brandslund, I, Brumpton, B, Buring, JE, Caulfield, MJ, Chambers, JC, Chandak, GR, Chanock, SJ, Chaturvedi, N, Chen, Y-DI, Chen, Z, Cheng, C-Y, Christophersen, IE, Ciullo, M, Cole, JW, Collins, FS, Cooper, RS, Cruz, M, Cucca, F, Cupples, LA, Cutler, MJ, Damrauer, SM, Dantoft, TM, de Borst, GJ, de Groot, LCPGM, De Jager, PL, de Kleijn, DP, de Silva, HJ, Dedoussis, G, den Hollander, A, Du, S, Easton, DF, Elders, PJM, Eliassen, AH, Ellinor, PT, Elmstahl, S, Erdmann, J, Evans, MK, Fatkin, D, Feenstra, B, Feitosa, MF, Ferrucci, L, Ford, I, Fornage, M, Franke, A, Franks, PW, Freedman, B, Gasparini, P, Gieger, C, Girotto, G, Goddard, ME, Golightly, YM, Gonzalez-Villalpando, C, Gordon-Larsen, P, Grallert, H, Grant, SFA, Grarup, N, Griffiths, L, Gudnason, V, Haiman, C, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayward, C, Heckbert, SR, Heng, C-K, Hengstenberg, C, Hewitt, AW, Hishigaki, H, Hoyng, CB, Huang, PL, Huang, W, Hunt, SC, Hveem, K, Hypponen, E, Iacono, WG, Ichihara, S, Ikram, MA, Isasi, CR, Jackson, RD, Jarvelin, M-R, Jin, Z-B, Jockel, K-H, Joshi, PK, Jousilahti, P, Jukema, JW, Kahonen, M, Kamatani, Y, Kang, KD, Kaprio, J, Kardia, SLR, Karpe, F, Kato, N, Kee, F, Kessler, T, Khera, A, Khor, CC, Kiemeney, LALM, Kim, B-J, Kim, EK, Kim, H-L, Kirchhof, P, Kivimaki, M, Koh, W-P, Koistinen, HA, Kolovou, GD, Kooner, JS, Kooperberg, C, Kottgen, A, Kovacs, P, Kraaijeveld, A, Kraft, P, Krauss, RM, Kumari, M, Kutalik, Z, Laakso, M, Lange, LA, Langenberg, C, Launer, LJ, Le Marchand, L, Lee, H, Lee, NR, Lehtimaki, T, Li, H, Li, L, Lieb, W, Lin, X, Lind, L, Linneberg, A, Liu, C-T, Loeffler, M, London, B, Lubitz, SA, Lye, SJ, Mackey, DA, Magi, R, Magnusson, PKE, Marcus, GM, Vidal, PM, Martin, NG, Marz, W, Matsuda, F, McGarrah, RW, McGue, M, McKnight, AJ, Medland, SE, Mellstrom, D, Metspalu, A, Mitchell, BD, Mitchell, P, Mook-Kanamori, DO, Morris, AD, Mucci, LA, Munroe, PB, Nalls, MA, Nazarian, S, Nelson, AE, Neville, MJ, Newton-Cheh, C, Nielsen, CS, Nothen, MM, Ohlsson, C, Oldehinkel, AJ, Orozco, L, Pahkala, K, Pajukanta, P, Palmer, CNA, Parra, EJ, Pattaro, C, Pedersen, O, Pennell, CE, Penninx, BWJH, Perusse, L, Peters, A, Peyser, PA, Porteous, DJ, Posthuma, D, Power, C, Pramstaller, PP, Province, MA, Qi, Q, Qu, J, Rader, DJ, Raitakari, OT, Ralhan, S, Rallidis, LS, Rao, DC, Redline, S, Reilly, DF, Reiner, AP, Rhee, SY, Ridker, PM, Rienstra, M, Ripatti, S, Ritchie, MD, Roden, DM, Rosendaal, FR, Rotter, J, Rudan, I, Rutters, F, Sabanayagam, C, Saleheen, D, Salomaa, V, Samani, NJ, Sanghera, DK, Sattar, N, Schmidt, B, Schmidt, H, Schmidt, R, Schulze, MB, Schunkert, H, Scott, LJ, Scott, RJ, Sever, P, Shiroma, EJ, Shoemaker, MB, Shu, X-O, Simonsick, EM, Sims, M, Singh, JR, Singleton, AB, Sinner, MF, Smith, JG, Snieder, H, Spector, TD, Stampfer, MJ, Stark, KJ, Strachan, DP, t' Hart, LM, Tabara, Y, Tang, H, Tardif, J-C, Thanaraj, TA, Timpson, NJ, Tonjes, A, Tremblay, A, Tuomi, T, Tuomilehto, J, Tusie-Luna, M-T, Uitterlinden, AG, van Dam, RM, van der Harst, P, Van der Velde, N, van Duijn, CM, van Schoor, NM, Vitart, V, Volker, U, Vollenweider, P, Volzke, H, Wacher-Rodarte, NH, Walker, M, Wang, YX, Wareham, NJ, Watanabe, RM, Watkins, H, Weir, DR, Werge, TM, Widen, E, Wilkens, LR, Willemsen, G, Willett, WC, Wilson, JF, Wong, T-Y, Woo, J-T, Wright, AF, Wu, J-Y, Xu, H, Yajnik, CS, Yokota, M, Yuan, J-M, Zeggini, E, Zemel, BS, Zheng, W, Zhu, X, Zmuda, JM, Zonderman, AB, Zwart, J-A, Chasman, D, Cho, YS, Heid, IM, McCarthy, M, Ng, MCY, O'Donnell, CJ, Rivadeneira, F, Thorsteinsdottir, U, Sun, Y, Tai, ES, Boehnke, M, Deloukas, P, Justice, AE, Lindgren, CM, Loos, RJF, Mohlke, KL, North, KE, Stefansson, K, Walters, RG, Winkler, TW, Young, KL, Loh, P-R, Esko, T, Assimes, TL, Auton, A, Abecasis, GR, Willer, CJ, Locke, AE, Berndt, S, Lettre, G, Frayling, TM, Okada, Y, Wood, AR, Visscher, PM, Hirschhorn, JN, Yengo, L, Vedantam, S, Marouli, E, Sidorenko, J, Bartell, E, Sakaue, S, Graff, M, Eliasen, AU, Jiang, Y, Raghavan, S, Miao, J, Arias, JD, Graham, SE, Mukamel, RE, Spracklen, CN, Yin, X, Chen, S-H, Ferreira, T, Highland, HH, Ji, Y, Karaderi, T, Lin, K, Lull, K, Malden, DE, Medina-Gomez, C, Machado, M, Moore, A, Rueger, S, Sim, X, Vrieze, S, Ahluwalia, TS, Akiyama, M, Allison, MA, Alvarez, M, Andersen, MK, Ani, A, Appadurai, V, Arbeeva, L, Bhaskar, S, Bielak, LF, Bollepalli, S, Bonnycastle, LL, Bork-Jensen, J, Bradfield, JP, Bradford, Y, Braund, PS, Brody, JA, Burgdorf, KS, Cade, BE, Cai, H, Cai, Q, Campbell, A, Canadas-Garre, M, Catamo, E, Chai, J-F, Chai, X, Chang, L-C, Chang, Y-C, Chen, C-H, Chesi, A, Choi, SH, Chung, R-H, Cocca, M, Concas, MP, Couture, C, Cuellar-Partida, G, Danning, R, Daw, EW, Degenhard, F, Delgado, GE, Delitala, A, Demirkan, A, Deng, X, Devineni, P, Dietl, A, Dimitriou, M, Dimitrov, L, Dorajoo, R, Ekici, AB, Engmann, JE, Fairhurst-Hunter, Z, Farmaki, A-E, Faul, JD, Fernandez-Lopez, J-C, Forer, L, Francescatto, M, Freitag-Wolf, S, Fuchsberger, C, Galesloot, TE, Gao, Y, Gao, Z, Geller, F, Giannakopoulou, O, Giulianini, F, Gjesing, AP, Goel, A, Gordon, SD, Gorski, M, Grove, J, Guo, X, Gustafsson, S, Haessler, J, Hansen, TF, Havulinna, AS, Haworth, SJ, He, J, Heard-Costa, N, Hebbar, P, Hindy, G, Ho, Y-LA, Hofer, E, Holliday, E, Horn, K, Hornsby, WE, Hottenga, J-J, Huang, H, Huang, J, Huerta-Chagoya, A, Huffman, JE, Hung, Y-J, Huo, S, Hwang, MY, Iha, H, Ikeda, DD, Isono, M, Jackson, AU, Jager, S, Jansen, IE, Johansson, I, Jonas, JB, Jonsson, A, Jorgensen, T, Kalafati, I-P, Kanai, M, Kanoni, S, Karhus, LL, Kasturiratne, A, Katsuya, T, Kawaguchi, T, Kember, RL, Kentistou, KA, Kim, H-N, Kim, YJ, Kleber, ME, Knol, MJ, Kurbasic, A, Lauzon, M, Le, P, Lea, R, Lee, J-Y, Leonard, HL, Li, SA, Li, X, Liang, J, Lin, H, Lin, S-Y, Liu, J, Liu, X, Lo, KS, Long, J, Lores-Motta, L, Luan, J, Lyssenko, V, Lyytikainen, L-P, Mahajan, A, Mamakou, V, Mangino, M, Manichaikul, A, Marten, J, Mattheisen, M, Mavarani, L, McDaid, AF, Meidtner, K, Melendez, TL, Mercader, JM, Milaneschi, Y, Miller, JE, Millwood, IY, Mishra, PP, Mitchell, RE, Mollehave, LT, Morgan, A, Mucha, S, Munz, M, Nakatochi, M, Nelson, CP, Nethander, M, Nho, CW, Nielsen, AA, Nolte, IM, Nongmaithem, SS, Noordam, R, Ntalla, I, Nutile, T, Pandit, A, Christofidou, P, Parna, K, Pauper, M, Petersen, ERB, Petersen, L, Pitkanen, N, Polasek, O, Poveda, A, Preuss, MH, Pyarajan, S, Raffield, LM, Rakugi, H, Ramirez, J, Rasheed, A, Raven, D, Rayner, NW, Riveros, C, Rohde, R, Ruggiero, D, Ruotsalainen, SE, Ryan, KA, Sabater-Lleal, M, Saxena, R, Scholz, M, Sendamarai, A, Shen, B, Shi, J, Shin, JH, Sidore, C, Sitlani, CM, Slieker, RKC, Smit, RAJ, Smith, A, Smith, JA, Smyth, LJ, Southam, LE, Steinthorsdottir, V, Sun, L, Takeuchi, F, Tallapragada, D, Taylor, KD, Tayo, BO, Tcheandjieu, C, Terzikhan, N, Tesolin, P, Teumer, A, Theusch, E, Thompson, DJ, Thorleifsson, G, Timmers, PRHJ, Trompet, S, Turman, C, Vaccargiu, S, van der Laan, SW, van der Most, PJ, van Klinken, JB, van Setten, J, Verma, SS, Verweij, N, Veturi, Y, Wang, CA, Wang, C, Wang, L, Wang, Z, Warren, HR, Wei, WB, Wickremasinghe, AR, Wielscher, M, Wiggins, KL, Winsvold, BS, Wong, A, Wu, Y, Wuttke, M, Xia, R, Xie, T, Yamamoto, K, Yang, J, Yao, J, Young, H, Yousri, NA, Yu, L, Zeng, L, Zhang, W, Zhang, X, Zhao, J-H, Zhao, W, Zhou, W, Zimmermann, ME, Zoledziewska, M, Adair, LS, Adams, HHH, Aguilar-Salinas, CA, Al-Mulla, F, Arnett, DK, Asselbergs, FW, Asvold, BO, Attia, J, Banas, B, Bandinelli, S, Bennett, DA, Bergler, T, Bharadwaj, D, Biino, G, Bisgaard, H, Boerwinkle, E, Boger, CA, Bonnelykke, K, Boomsma, D, Borglum, AD, Borja, JB, Bouchard, C, Bowden, DW, Brandslund, I, Brumpton, B, Buring, JE, Caulfield, MJ, Chambers, JC, Chandak, GR, Chanock, SJ, Chaturvedi, N, Chen, Y-DI, Chen, Z, Cheng, C-Y, Christophersen, IE, Ciullo, M, Cole, JW, Collins, FS, Cooper, RS, Cruz, M, Cucca, F, Cupples, LA, Cutler, MJ, Damrauer, SM, Dantoft, TM, de Borst, GJ, de Groot, LCPGM, De Jager, PL, de Kleijn, DP, de Silva, HJ, Dedoussis, G, den Hollander, A, Du, S, Easton, DF, Elders, PJM, Eliassen, AH, Ellinor, PT, Elmstahl, S, Erdmann, J, Evans, MK, Fatkin, D, Feenstra, B, Feitosa, MF, Ferrucci, L, Ford, I, Fornage, M, Franke, A, Franks, PW, Freedman, B, Gasparini, P, Gieger, C, Girotto, G, Goddard, ME, Golightly, YM, Gonzalez-Villalpando, C, Gordon-Larsen, P, Grallert, H, Grant, SFA, Grarup, N, Griffiths, L, Gudnason, V, Haiman, C, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayward, C, Heckbert, SR, Heng, C-K, Hengstenberg, C, Hewitt, AW, Hishigaki, H, Hoyng, CB, Huang, PL, Huang, W, Hunt, SC, Hveem, K, Hypponen, E, Iacono, WG, Ichihara, S, Ikram, MA, Isasi, CR, Jackson, RD, Jarvelin, M-R, Jin, Z-B, Jockel, K-H, Joshi, PK, Jousilahti, P, Jukema, JW, Kahonen, M, Kamatani, Y, Kang, KD, Kaprio, J, Kardia, SLR, Karpe, F, Kato, N, Kee, F, Kessler, T, Khera, A, Khor, CC, Kiemeney, LALM, Kim, B-J, Kim, EK, Kim, H-L, Kirchhof, P, Kivimaki, M, Koh, W-P, Koistinen, HA, Kolovou, GD, Kooner, JS, Kooperberg, C, Kottgen, A, Kovacs, P, Kraaijeveld, A, Kraft, P, Krauss, RM, Kumari, M, Kutalik, Z, Laakso, M, Lange, LA, Langenberg, C, Launer, LJ, Le Marchand, L, Lee, H, Lee, NR, Lehtimaki, T, Li, H, Li, L, Lieb, W, Lin, X, Lind, L, Linneberg, A, Liu, C-T, Loeffler, M, London, B, Lubitz, SA, Lye, SJ, Mackey, DA, Magi, R, Magnusson, PKE, Marcus, GM, Vidal, PM, Martin, NG, Marz, W, Matsuda, F, McGarrah, RW, McGue, M, McKnight, AJ, Medland, SE, Mellstrom, D, Metspalu, A, Mitchell, BD, Mitchell, P, Mook-Kanamori, DO, Morris, AD, Mucci, LA, Munroe, PB, Nalls, MA, Nazarian, S, Nelson, AE, Neville, MJ, Newton-Cheh, C, Nielsen, CS, Nothen, MM, Ohlsson, C, Oldehinkel, AJ, Orozco, L, Pahkala, K, Pajukanta, P, Palmer, CNA, Parra, EJ, Pattaro, C, Pedersen, O, Pennell, CE, Penninx, BWJH, Perusse, L, Peters, A, Peyser, PA, Porteous, DJ, Posthuma, D, Power, C, Pramstaller, PP, Province, MA, Qi, Q, Qu, J, Rader, DJ, Raitakari, OT, Ralhan, S, Rallidis, LS, Rao, DC, Redline, S, Reilly, DF, Reiner, AP, Rhee, SY, Ridker, PM, Rienstra, M, Ripatti, S, Ritchie, MD, Roden, DM, Rosendaal, FR, Rotter, J, Rudan, I, Rutters, F, Sabanayagam, C, Saleheen, D, Salomaa, V, Samani, NJ, Sanghera, DK, Sattar, N, Schmidt, B, Schmidt, H, Schmidt, R, Schulze, MB, Schunkert, H, Scott, LJ, Scott, RJ, Sever, P, Shiroma, EJ, Shoemaker, MB, Shu, X-O, Simonsick, EM, Sims, M, Singh, JR, Singleton, AB, Sinner, MF, Smith, JG, Snieder, H, Spector, TD, Stampfer, MJ, Stark, KJ, Strachan, DP, t' Hart, LM, Tabara, Y, Tang, H, Tardif, J-C, Thanaraj, TA, Timpson, NJ, Tonjes, A, Tremblay, A, Tuomi, T, Tuomilehto, J, Tusie-Luna, M-T, Uitterlinden, AG, van Dam, RM, van der Harst, P, Van der Velde, N, van Duijn, CM, van Schoor, NM, Vitart, V, Volker, U, Vollenweider, P, Volzke, H, Wacher-Rodarte, NH, Walker, M, Wang, YX, Wareham, NJ, Watanabe, RM, Watkins, H, Weir, DR, Werge, TM, Widen, E, Wilkens, LR, Willemsen, G, Willett, WC, Wilson, JF, Wong, T-Y, Woo, J-T, Wright, AF, Wu, J-Y, Xu, H, Yajnik, CS, Yokota, M, Yuan, J-M, Zeggini, E, Zemel, BS, Zheng, W, Zhu, X, Zmuda, JM, Zonderman, AB, Zwart, J-A, Chasman, D, Cho, YS, Heid, IM, McCarthy, M, Ng, MCY, O'Donnell, CJ, Rivadeneira, F, Thorsteinsdottir, U, Sun, Y, Tai, ES, Boehnke, M, Deloukas, P, Justice, AE, Lindgren, CM, Loos, RJF, Mohlke, KL, North, KE, Stefansson, K, Walters, RG, Winkler, TW, Young, KL, Loh, P-R, Esko, T, Assimes, TL, Auton, A, Abecasis, GR, Willer, CJ, Locke, AE, Berndt, S, Lettre, G, Frayling, TM, Okada, Y, Wood, AR, Visscher, PM, and Hirschhorn, JN
Abstract: Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.
Published: 2022

19. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Author: Matcham F, Leightley D, Sara Siddi, Lamers F, White KM, Annas P, de Girolamo G, Difrancesco S, Haro JM, Horsfall M, Ivan A, Lavelle G, Li Q, Lombardini F, Mohr DC, Narayan VA, Oetzmann C, Penninx BWJH, Bruce S, Nica R, Simblett SK, Wykes T, Brasen JC, Myin-Germeys I, Rintala A, Conde P, Dobson RJB, Folarin AA, Stewart C, Ranjan Y, Rashid Z, Cummins N, Manyakov NV, Vairavan S, Hotopf M, and RADAR-CNS consortium
Subjects: Longitudinal, Remote measurement technologies, Cohort study, Multicentre, Major depressive disorder
Abstract: BACKGROUND: Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. METHODS: Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. RESULTS: Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. CONCLUSIONS: RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.
Published: 2022

20. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles

Author: Tobias Kurth, Nadine Pelzer, Hautakangas H, Artto, O T Raitakari, Julie E. Buring, Penninx Bwjh., Christian Erikstrup, Mikko Kallela, Matti Pirinen, Christian Benner, Daniel I. Chasman, Kristian Hveem, Kristoffersen Es, Torben Hansen, Olafur A. Sveinsson, Mika Kähönen, van den Maagdenberg Amjm., Mona Ameri Chalmer, Aarno Palotie, Elisabeth Widen, Thorgeir E. Thorgeirsson, Hreinn Stefansson, Lighart L, Paul M. Ridker, de Boer I, Gisela M. Terwindt, Garbrielsen Me, Jouke J. Hottenga, Kogelman Lja., Arpo Aromaa, Bendik S. Winsvold, Dale R. Nyholt, Ikram Ma, van Dijk Kw, Harder Ave, Martin Dichgans, Rainer Malik, Mohsen Ghanbari, Marianne Bakke Johnsen, Marjo-Ritta Jarvelin, Samuli Ripatti, Paavo Häppölä, Gyda Bjornsdottir, Martti Färkkilä, Anne Heidi Skogholt, Terho Lehtimäki, Sanni Ruotsalainen, Frits R. Rosendaal, Gudrun R. Sigurdardottir, Headache Ha, Gormley P, Kari Stefansson, J.A. Zwart, Oluf Pedersen, Raymond Noordam, Ben Michael Brumpton, Laurent F. Thomas, Karina Banasik, M.G. Hrafnsdottir, Lisanne S. Vijfhuizen, Sølvsten Burgdorf K, Jes Olesen, Sigurdur H. Magnusson, T. Freilinger, Kristen Hagen, Andrea Carmine Belin, Dorret I. Boomsma, Henrik Ullum, Maija Wessman, Ran C, and Anna Bjornsdottir
Subjects: Genetics, 0303 health sciences, Aura, Genome wide analysis, Specific risk, Genome-wide association study, Biology, medicine.disease, Migraine with aura, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Migraine, medicine, medicine.symptom, Allele, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. This genome-wide association study (GWAS) of 102,084 migraine cases and 771,257 controls identified 123 loci of which 86 are novel. The loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. A stratification of the risk loci using 29,679 cases with subtype information, of which approximately half have never been used in a GWAS before, indicated three risk variants that appear specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that appear specific for migraine without aura (near SPINK2 and near FECH), and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide (CALCA/CALCB) and serotonin 1F receptor (HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology.
Published: 2021

21. Potential Genetic Overlap Between Insomnia and Sleep Symptoms in Major Depressive Disorder: A Polygenic Risk Score Analysis

Author: Melhuish Beaupre, LM, Tiwari, AK, Goncalves, VF, Zai, CC, Marshe, VS, Lewis, CM, Martin, NG, McIntosh, AM, Adams, MJ, Baune, BT, Levinson, DF, Boomsma, DI, Penninx, BWJH, Breen, G, Hamilton, S, Awasthi, S, Ripke, S, Jones, L, Jones, I, Byrne, EM, Hickie, IB, Potash, JP, Shi, J, Weissman, MM, Milaneschi, Y, Shyn, SI, de Geus, EJC, Willemsen, G, Brown, GM, Kennedy, JL, Melhuish Beaupre, LM, Tiwari, AK, Goncalves, VF, Zai, CC, Marshe, VS, Lewis, CM, Martin, NG, McIntosh, AM, Adams, MJ, Baune, BT, Levinson, DF, Boomsma, DI, Penninx, BWJH, Breen, G, Hamilton, S, Awasthi, S, Ripke, S, Jones, L, Jones, I, Byrne, EM, Hickie, IB, Potash, JP, Shi, J, Weissman, MM, Milaneschi, Y, Shyn, SI, de Geus, EJC, Willemsen, G, Brown, GM, and Kennedy, JL
Abstract: Background: The prevalence of insomnia and hypersomnia in depressed individuals is substantially higher than that found in the general population. Unfortunately, these concurrent sleep problems can have profound effects on the disease course. Although the full biology of sleep remains to be elucidated, a recent genome-wide association (GWAS) of insomnia, and other sleep traits in over 1 million individuals was recently published and provides many promising hits for genetics of insomnia in a population-based sample. Methods: Using data from the largest available GWAS of insomnia and other sleep traits, we sought to test if sleep variable PRS scores derived from population-based studies predicted sleep variables in samples of depressed cases [Psychiatric Genomics Consortium - Major Depressive Disorder subjects (PGC MDD)]. A leave-one-out analysis was performed to determine the effects that each individual study had on our results. Results: The only significant finding was for insomnia, where p-value threshold, p = 0.05 was associated with insomnia in our PGC MDD sample (R 2 = 1.75-3, p = 0.006). Conclusion: Our results reveal that <1% of variance is explained by the variants that cover the two significant p-value thresholds, which is in line with the fact that depression and insomnia are both polygenic disorders. To the best of our knowledge, this is the first study to investigate genetic overlap between the general population and a depression sample for insomnia, which has important treatment implications, such as leading to novel drug targets in future research efforts.
Published: 2021

22. Transcriptome-based polygenic score links depression-related corticolimbic gene expression changes to sex-specific brain morphology and depression risk

Author: Miles, AE, Dos Santos, FC, Byrne, EM, Renteria, ME, McIntosh, AM, Adams, MJ, Pistis, G, Castelao, E, Preisig, M, Baune, BT, Schubert, KO, Lewis, CM, Jones, LA, Jones, I, Uher, R, Smoller, JW, Perlis, RH, Levinson, DF, Potash, JB, Weissman, MM, Shi, J, Lewis, G, Penninx, BWJH, Boomsma, D, Hamilton, SP, Sibille, E, Hariri, AR, Nikolova, YS, Miles, AE, Dos Santos, FC, Byrne, EM, Renteria, ME, McIntosh, AM, Adams, MJ, Pistis, G, Castelao, E, Preisig, M, Baune, BT, Schubert, KO, Lewis, CM, Jones, LA, Jones, I, Uher, R, Smoller, JW, Perlis, RH, Levinson, DF, Potash, JB, Weissman, MM, Shi, J, Lewis, G, Penninx, BWJH, Boomsma, D, Hamilton, SP, Sibille, E, Hariri, AR, and Nikolova, YS
Abstract: Studies in post-mortem human brain tissue have associated major depressive disorder (MDD) with cortical transcriptomic changes, whose potential in vivo impact remains unexplored. To address this translational gap, we recently developed a transcriptome-based polygenic risk score (T-PRS) based on common functional variants capturing 'depression-like' shifts in cortical gene expression. Here, we used a non-clinical sample of young adults (n = 482, Duke Neurogenetics Study: 53% women; aged 19.8 ± 1.2 years) to map T-PRS onto brain morphology measures, including Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index, as well as broad MDD risk, indexed by self-reported family history of depression. We conducted side-by-side comparisons with a PRS independently derived from a Psychiatric Genomics Consortium (PGC) MDD GWAS (PGC-PRS), and sought to link T-PRS with diagnosis and symptom severity directly in PGC-MDD participants (n = 29,340, 59% women; 12,923 MDD cases, 16,417 controls). T-PRS was associated with smaller amygdala volume in women (t = -3.478, p = 0.001) and lower prefrontal gyrification across sexes. In men, T-PRS was associated with hypergyrification in temporal and occipital regions. Prefrontal hypogyrification mediated a male-specific indirect link between T-PRS and familial depression (b = 0.005, p = 0.029). PGC-PRS was similarly associated with lower amygdala volume and cortical gyrification; however, both effects were male-specific and hypogyrification emerged in distinct parietal and temporo-occipital regions, unassociated with familial depression. In PGC-MDD, T-PRS did not predict diagnosis (OR = 1.007, 95% CI = [0.997-1.018]) but correlated with symptom severity in men (rho = 0.175, p = 7.957 × 10-4) in one cohort (N = 762, 48% men). Depression-like shifts in cortical gene expression have sex-specific effects on brain morphology and may contribute to broad depression vulnerability in men.
Published: 2021

23. Contributing factors to advanced brain aging in depression and anxiety disorders

Author: Han, LKM, Schnack, HG, Brouwer, RM, Veltman, DJ, van der Wee, NJA, van Tol, M-J, Aghajani, M, Penninx, BWJH, Han, LKM, Schnack, HG, Brouwer, RM, Veltman, DJ, van der Wee, NJA, van Tol, M-J, Aghajani, M, and Penninx, BWJH
Abstract: Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen's d = 0.25, 95% CI -0.10-0.60) and anxiety patients (+2.91 years, Cohen's d = 0.27, 95% CI -0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (-2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.
Published: 2021

24. The association between genetically determined ABO blood types and major depressive disorder

Author: Garvert, L, Baune, BT, Berger, K, Boomsma, D, Breen, G, Greinacher, A, Hamilton, SP, Levinson, DF, Lewis, CM, Lucae, S, Magnusson, PKE, Martin, NG, McIntosh, AM, Mors, O, Mueller-Myhsok, B, Penninx, BWJH, Perlis, RH, Pistis, G, Potash, JB, Preisig, M, Rietschel, M, Shi, J, Smoller, JW, Tiemeier, H, Uher, R, Voelker, U, Voelzke, H, Weissman, MM, Grabe, HJ, Van der Auwera, S, Garvert, L, Baune, BT, Berger, K, Boomsma, D, Breen, G, Greinacher, A, Hamilton, SP, Levinson, DF, Lewis, CM, Lucae, S, Magnusson, PKE, Martin, NG, McIntosh, AM, Mors, O, Mueller-Myhsok, B, Penninx, BWJH, Perlis, RH, Pistis, G, Potash, JB, Preisig, M, Rietschel, M, Shi, J, Smoller, JW, Tiemeier, H, Uher, R, Voelker, U, Voelzke, H, Weissman, MM, Grabe, HJ, and Van der Auwera, S
Abstract: ABO blood types and their corresponding antigens have long been assumed to be related to different human diseases. So far, smaller studies on the relationship between mental disorders and blood types yielded contradicting results. In this study we analyzed the association between ABO blood types and lifetime major depressive disorder (MDD). We performed a pooled analysis with data from 26 cohorts that are part of the MDD working group of the Psychiatric Genomics Consortium (PGC). The dataset included 37,208 individuals of largely European ancestry of which 41.6% were diagnosed with lifetime MDD. ABO blood types were identified using three single nucleotide polymorphisms in the ABO gene: rs505922, rs8176746 and rs8176747. Regression analyses were performed to assess associations between the individual ABO blood types and MDD diagnosis as well as putative interaction effects with sex. The models were adjusted for sex, cohort and the first ten genetic principal components. The percentage of blood type A was slightly lower in cases than controls while blood type O was more prominent in cases. However, these differences were not statistically significant. Our analyses found no evidence of an association between ABO blood types and major depressive disorder.
Published: 2021

25. An integrative study of five biological clocks in somatic and mental health

Author: Jansen, R, Han, LKM, Verhoeven, JE, Aberg, KA, van den Oord, ECGJ, Milaneschi, Y, Penninx, BWJH, Jansen, R, Han, LKM, Verhoeven, JE, Aberg, KA, van den Oord, ECGJ, Milaneschi, Y, and Penninx, BWJH
Abstract: Biological clocks have been developed at different molecular levels and were found to be more advanced in the presence of somatic illness and mental disorders. However, it is unclear whether different biological clocks reflect similar aging processes and determinants. In ~3000 subjects, we examined whether five biological clocks (telomere length, epigenetic, transcriptomic, proteomic, and metabolomic clocks) were interrelated and associated to somatic and mental health determinants. Correlations between biological aging indicators were small (all r < 0.2), indicating little overlap. The most consistent associations of advanced biological aging were found for male sex, higher body mass index (BMI), metabolic syndrome, smoking, and depression. As compared to the individual clocks, a composite index of all five clocks showed most pronounced associations with health determinants. The large effect sizes of the composite index and the low correlation between biological aging indicators suggest that one's biological age is best reflected by combining aging measures from multiple cellular levels.
Published: 2021

26. Examining sex differences in neurodevelopmental and psychiatric genetic risk in anxiety and depression

Author: Guloksuz, S, Martin, J, Asjadi, K, Hubbard, L, Kendall, K, Pardinas, AF, Jermy, B, Lewis, CM, Baune, BT, Boomsma, D, Hamilton, SP, Lucae, S, Magnusson, PK, Martin, NG, McIntosh, AM, Mehta, D, Mors, O, Mullins, N, Penninx, BWJH, Preisig, M, Rietschel, M, Jones, I, Walters, JTR, Rice, F, Thapar, A, O'Donovan, M, Guloksuz, S, Martin, J, Asjadi, K, Hubbard, L, Kendall, K, Pardinas, AF, Jermy, B, Lewis, CM, Baune, BT, Boomsma, D, Hamilton, SP, Lucae, S, Magnusson, PK, Martin, NG, McIntosh, AM, Mehta, D, Mors, O, Mullins, N, Penninx, BWJH, Preisig, M, Rietschel, M, Jones, I, Walters, JTR, Rice, F, Thapar, A, and O'Donovan, M
Abstract: Anxiety and depression are common mental health disorders and have a higher prevalence in females. They are modestly heritable, share genetic liability with other psychiatric disorders, and are highly heterogeneous. There is evidence that genetic liability to neurodevelopmental disorders, such as attention deficit hyperactivity disorder (ADHD) is associated with anxiety and depression, particularly in females. We investigated sex differences in family history for neurodevelopmental and psychiatric disorders and neurodevelopmental genetic risk burden (indexed by ADHD polygenic risk scores (PRS) and rare copy number variants; CNVs) in individuals with anxiety and depression, also taking into account age at onset. We used two complementary datasets: 1) participants with a self-reported diagnosis of anxiety or depression (N = 4,178, 65.5% female; mean age = 41.5 years; N = 1,315 with genetic data) from the National Centre for Mental Health (NCMH) cohort and 2) a clinical sample of 13,273 (67.6% female; mean age = 45.2 years) patients with major depressive disorder (MDD) from the Psychiatric Genomics Consortium (PGC). We tested for sex differences in family history of psychiatric problems and presence of rare CNVs (neurodevelopmental and >500kb loci) in NCMH only and for sex differences in ADHD PRS in both datasets. In the NCMH cohort, females were more likely to report family history of neurodevelopmental and psychiatric disorders, but there were no robust sex differences in ADHD PRS or presence of rare CNVs. There was weak evidence of higher ADHD PRS in females compared to males in the PGC MDD sample, particularly in those with an early onset of MDD. These results do not provide strong evidence of sex differences in neurodevelopmental genetic risk burden in adults with anxiety and depression. This indicates that sex may not be a major index of neurodevelopmental genetic heterogeneity, that is captured by ADHD PRS and rare CNV burden, in adults with anxiety and depressi
Published: 2021

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

Author: Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, Schmaal, L, Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, and Schmaal, L
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.
Published: 2021

28. Cortical thickness and resting-state cardiac function across the lifespan: A cross-sectional pooled mega-analysis

Author: Koenig, J, Abler, B, Agartz, I, akerstedt, T, Andreassen, OA, Anthony, M, Baer, K-J, Bertsch, K, Brown, RC, Brunner, R, Carnevali, L, Critchley, HD, Cullen, KR, de Geus, EJC, de la Cruz, F, Dziobek, I, Ferger, MD, Fischer, H, Flor, H, Gaebler, M, Gianaros, PJ, Giummarra, MJ, Greening, SG, Guendelman, S, Heathers, JAJ, Herpertz, SC, Hu, MX, Jentschke, S, Kaess, M, Kaufmann, T, Klimes-Dougan, B, Koelsch, S, Krauch, M, Kumral, D, Lamers, F, Lee, T-H, Lekander, M, Lin, F, Lotze, M, Makovac, E, Mancini, M, Mancke, F, Mansson, KNT, Manuck, SB, Mather, M, Meeten, F, Min, J, Mueller, B, Muench, V, Nees, F, Nga, L, Nilsonne, G, Ordonez Acuna, D, Osnes, B, Ottaviani, C, Penninx, BWJH, Ponzio, A, Poudel, GR, Reinelt, J, Ren, P, Sakaki, M, Schumann, A, Sorensen, L, Specht, K, Straub, J, Tamm, S, Thai, M, Thayer, JF, Ubani, B, van Der Mee, DJ, van Velzen, LS, Ventura-Bort, C, Villringer, A, Watson, DR, Wei, L, Wendt, J, Schreiner, MW, Westlye, LT, Weymar, M, Winkelmann, T, Wu, G-R, Yoo, HJ, Quintana, DS, Koenig, J, Abler, B, Agartz, I, akerstedt, T, Andreassen, OA, Anthony, M, Baer, K-J, Bertsch, K, Brown, RC, Brunner, R, Carnevali, L, Critchley, HD, Cullen, KR, de Geus, EJC, de la Cruz, F, Dziobek, I, Ferger, MD, Fischer, H, Flor, H, Gaebler, M, Gianaros, PJ, Giummarra, MJ, Greening, SG, Guendelman, S, Heathers, JAJ, Herpertz, SC, Hu, MX, Jentschke, S, Kaess, M, Kaufmann, T, Klimes-Dougan, B, Koelsch, S, Krauch, M, Kumral, D, Lamers, F, Lee, T-H, Lekander, M, Lin, F, Lotze, M, Makovac, E, Mancini, M, Mancke, F, Mansson, KNT, Manuck, SB, Mather, M, Meeten, F, Min, J, Mueller, B, Muench, V, Nees, F, Nga, L, Nilsonne, G, Ordonez Acuna, D, Osnes, B, Ottaviani, C, Penninx, BWJH, Ponzio, A, Poudel, GR, Reinelt, J, Ren, P, Sakaki, M, Schumann, A, Sorensen, L, Specht, K, Straub, J, Tamm, S, Thai, M, Thayer, JF, Ubani, B, van Der Mee, DJ, van Velzen, LS, Ventura-Bort, C, Villringer, A, Watson, DR, Wei, L, Wendt, J, Schreiner, MW, Westlye, LT, Weymar, M, Winkelmann, T, Wu, G-R, Yoo, HJ, and Quintana, DS
Abstract: Understanding the association between autonomic nervous system [ANS] function and brain morphology across the lifespan provides important insights into neurovisceral mechanisms underlying health and disease. Resting-state ANS activity, indexed by measures of heart rate [HR] and its variability [HRV] has been associated with brain morphology, particularly cortical thickness [CT]. While findings have been mixed regarding the anatomical distribution and direction of the associations, these inconsistencies may be due to sex and age differences in HR/HRV and CT. Previous studies have been limited by small sample sizes, which impede the assessment of sex differences and aging effects on the association between ANS function and CT. To overcome these limitations, 20 groups worldwide contributed data collected under similar protocols of CT assessment and HR/HRV recording to be pooled in a mega-analysis (N = 1,218 (50.5% female), mean age 36.7 years (range: 12-87)). Findings suggest a decline in HRV as well as CT with increasing age. CT, particularly in the orbitofrontal cortex, explained additional variance in HRV, beyond the effects of aging. This pattern of results may suggest that the decline in HRV with increasing age is related to a decline in orbitofrontal CT. These effects were independent of sex and specific to HRV; with no significant association between CT and HR. Greater CT across the adult lifespan may be vital for the maintenance of healthy cardiac regulation via the ANS-or greater cardiac vagal activity as indirectly reflected in HRV may slow brain atrophy. Findings reveal an important association between CT and cardiac parasympathetic activity with implications for healthy aging and longevity that should be studied further in longitudinal research.
Published: 2021

29. Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies

Author: Munn-Chernoff, MA, Johnson, EC, Chou, YL, Coleman, JRI, Thornton, LM, Walters, RK, Yilmaz, Z, Baker, JH, Hübel, C, Gordon, S, Medland, SE, Watson, HJ, Gaspar, HA, Bryois, J, Hinney, A, Leppä, VM, Mattheisen, M, Ripke, S, Yao, S, Giusti-Rodríguez, P, Hanscombe, KB, Adan, RAH, Alfredsson, L, Ando, T, Andreassen, OA, Berrettini, WH, Boehm, I, Boni, C, Boraska Perica, V, Buehren, K, Burghardt, R, Cassina, M, Cichon, S, Clementi, M, Cone, RD, Courtet, P, Crow, S, Crowley, JJ, Danner, UN, Davis, OS, Zwaan, M, Dedoussis, G, Degortes, D, DeSocio, JE, Dick, DM, Dikeos, D, Dina, C, Dmitrzak-Weglarz, M, Docampo, E, Duncan, LE, Egberts, K, Ehrlich, S, Escaramís, G, Esko, T, Estivill, X, Farmer, A, Favaro, A, Fernández-Aranda, F, Fichter, MM, Fischer, K, Föcker, M, Foretova, L, Forstner, AJ, Forzan, M, Franklin, CS, Gallinger, S, Giegling, I, Giuranna, J, Gonidakis, F, Gorwood, P, Gratacos Mayora, M, Guillaume, S, Guo, Y, Hakonarson, H, Hatzikotoulas, K, Hauser, J, Hebebrand, J, Helder, SG, Herms, S, Herpertz-Dahlmann, B, Herzog, W, Huckins, LM, Hudson, JI, Imgart, H, Inoko, H, Janout, V, Jiménez-Murcia, S, Julià, A, Kalsi, G, Kaminská, D, Karhunen, L, Karwautz, A, Kas, MJH, Kennedy, JL, Keski-Rahkonen, A, Kiezebrink, K, Kim, YR, Klump, KL, Knudsen, GP, La Via, MC, Le Hellard, S, Levitan, RD, Li, D, Lilenfeld, L, Lin, BD, Lissowska, J, Luykx, J, Magistretti, PJ, Maj, M, Mannik, K, Marsal, S, Marshall, CR, Mattingsdal, M, McDevitt, S, McGuffin, P, Metspalu, A, Meulenbelt, I, Micali, N, Mitchell, K, Monteleone, A M, Monteleone, P, Nacmias, B, Navratilova, M, Ntalla, I, O'Toole, JK, Ophoff, Roel, Padyukov, L, Palotie, A, Pantel, J, Papezova, H, Pinto, D, Rabionet, R, Raevuori, A, Ramoz, N, Reichborn-Kjennerud, T, Ricca, V, Ripatti, S, Ritschel, F, Roberts, M, Rotondo, A, Rujescu, D, Rybakowski, F, Santonastaso, P, Scherag, A, Scherer, SW, Schmidt, U, Schork, NJ, Schosser, A, Seitz, J, Slachtova, L, Slagboom, PE, Slof-Op't Landt, MCT, Slopien, A, Sorbi, S, ?wi?tkowska, B, Szatkiewicz, JP, Tachmazidou, I, Tenconi, E, Tortorella, A, Tozzi, F, Treasure, J, Tsitsika, A, Tyszkiewicz-Nwafor, M, Tziouvas, K, van Elburg, AA, van Furth, EF, Wagner, G, Walton, E, Widen, E, Zeggini, E, Zerwas, S, Zipfel, S, Bergen, AW, Boden, JM, Brandt, H, Crawford, S, Halmi, KA, Horwood, LJ, Johnson, C, Kaplan, AS, Kaye, WH, Mitchell, J E, Olsen, CM, Pearson, JF, Pedersen, NL, Strober, M, Werge, T, Whiteman, DC, Woodside, DB, Grove, J, Henders, AK, Larsen, J T, Parker, R, Petersen, LV, Jordan, J, Kennedy, MA, Birgegård, A, Lichtenstein, P, Norring, C, Landén, M, Mortensen, PB, Polimanti, R, McClintick, JN, Adkins, AE, Aliev, F, Bacanu, SA, Batzler, A, Bertelsen, S, Biernacka, JM, Bigdeli, TB, Chen, L S, Clarke, TK, Degenhardt, F, Docherty, AR, Edwards, AC, Foo, JC, Fox, L, Frank, J, Hack, LM, Hartmann, AM, Hartz, SM, Heilmann-Heimbach, S, Hodgkinson, C, Hoffmann, P, Hottenga, JJ, Konte, B, Lahti, J, Lahti-Pulkkinen, M, Lai, D, Ligthart, L, Loukola, A, Maher, BS, Mbarek, H, McIntosh, AM, McQueen, MB, Meyers, JL, Milaneschi, Y, Palviainen, T, Peterson, RE, Ryu, E, Saccone, N L, Salvatore, JE, Sanchez-Roige, S, Schwandt, M, Sherva, R, Streit, F, Strohmaier, J, Thomas, N, Wang, JCY, Webb, BT, Wedow, R, Wetherill, L, Wills, AG, Zhou, H, Boardman, JD, Chen, D, Choi, D S, Copeland, WE, Culverhouse, RC, Dahmen, N, Degenhardt, L, Domingue, BW, Frye, MA, Gäebel, W, Hayward, C, Ising, M, Keyes, M, Kiefer, F, Koller, G, Kramer, J (John), Kuperman, S, Lucae, S, Lynskey, MT, Maier, W, Mann, K, Männistö, S, Müller-Myhsok, B, Murray, AD, Nurnberger, JI, Preuss, U, Räikkönen, K, Reynolds, MD, Ridinger, M, Scherbaum, N, Schuckit, MA, Soyka, M, Treutlein, J, Witt, SH, Wodarz, N, Zill, P, Adkins, DE, Boomsma, DI, Bierut, LJ, Brown, S, Bucholz, KK, Costello, EJ, Wit, HJ, Diazgranados, N, Eriksson, JG, Farrer, LA, Foroud, TM, Gillespie, NA, Goate, AM, Goldman, D, Grucza, RA, Hancock, DB, Harris, KM, Hesselbrock, V, Hewitt, JK, Hopfer, CJ, Iacono, WG, Johnson, E O, Karpyak, VM, Kendler, KS, Kranzler, HR, Krauter, K, Lind, PA, McGue, M, MacKillop, J, Madden, PA, Maes, HH, Magnusson, PKE, Nelson, EC, Nöthen, MM, Palmer, AA, Penninx, BWJH, Porjesz, B, Rice, JP, Rietschel, M, Riley, BP, Rose, RJ, Shen, PH, Silberg, J, Stallings, MC, Tarter, RE, Vanyukov, MM, Vrieze, S, Wall, TL, Whitfield, JB, Zhao, H, Neale, BM, Wade, TD, Heath, AC, Montgomery, GW, Martin, NG, Sullivan, PF, Kaprio, J, Breen, G, Gelernter, J, Edenberg, HJ, Bulik, CM, Agrawal, A, Munn-Chernoff, MA, Johnson, EC, Chou, YL, Coleman, JRI, Thornton, LM, Walters, RK, Yilmaz, Z, Baker, JH, Hübel, C, Gordon, S, Medland, SE, Watson, HJ, Gaspar, HA, Bryois, J, Hinney, A, Leppä, VM, Mattheisen, M, Ripke, S, Yao, S, Giusti-Rodríguez, P, Hanscombe, KB, Adan, RAH, Alfredsson, L, Ando, T, Andreassen, OA, Berrettini, WH, Boehm, I, Boni, C, Boraska Perica, V, Buehren, K, Burghardt, R, Cassina, M, Cichon, S, Clementi, M, Cone, RD, Courtet, P, Crow, S, Crowley, JJ, Danner, UN, Davis, OS, Zwaan, M, Dedoussis, G, Degortes, D, DeSocio, JE, Dick, DM, Dikeos, D, Dina, C, Dmitrzak-Weglarz, M, Docampo, E, Duncan, LE, Egberts, K, Ehrlich, S, Escaramís, G, Esko, T, Estivill, X, Farmer, A, Favaro, A, Fernández-Aranda, F, Fichter, MM, Fischer, K, Föcker, M, Foretova, L, Forstner, AJ, Forzan, M, Franklin, CS, Gallinger, S, Giegling, I, Giuranna, J, Gonidakis, F, Gorwood, P, Gratacos Mayora, M, Guillaume, S, Guo, Y, Hakonarson, H, Hatzikotoulas, K, Hauser, J, Hebebrand, J, Helder, SG, Herms, S, Herpertz-Dahlmann, B, Herzog, W, Huckins, LM, Hudson, JI, Imgart, H, Inoko, H, Janout, V, Jiménez-Murcia, S, Julià, A, Kalsi, G, Kaminská, D, Karhunen, L, Karwautz, A, Kas, MJH, Kennedy, JL, Keski-Rahkonen, A, Kiezebrink, K, Kim, YR, Klump, KL, Knudsen, GP, La Via, MC, Le Hellard, S, Levitan, RD, Li, D, Lilenfeld, L, Lin, BD, Lissowska, J, Luykx, J, Magistretti, PJ, Maj, M, Mannik, K, Marsal, S, Marshall, CR, Mattingsdal, M, McDevitt, S, McGuffin, P, Metspalu, A, Meulenbelt, I, Micali, N, Mitchell, K, Monteleone, A M, Monteleone, P, Nacmias, B, Navratilova, M, Ntalla, I, O'Toole, JK, Ophoff, Roel, Padyukov, L, Palotie, A, Pantel, J, Papezova, H, Pinto, D, Rabionet, R, Raevuori, A, Ramoz, N, Reichborn-Kjennerud, T, Ricca, V, Ripatti, S, Ritschel, F, Roberts, M, Rotondo, A, Rujescu, D, Rybakowski, F, Santonastaso, P, Scherag, A, Scherer, SW, Schmidt, U, Schork, NJ, Schosser, A, Seitz, J, Slachtova, L, Slagboom, PE, Slof-Op't Landt, MCT, Slopien, A, Sorbi, S, ?wi?tkowska, B, Szatkiewicz, JP, Tachmazidou, I, Tenconi, E, Tortorella, A, Tozzi, F, Treasure, J, Tsitsika, A, Tyszkiewicz-Nwafor, M, Tziouvas, K, van Elburg, AA, van Furth, EF, Wagner, G, Walton, E, Widen, E, Zeggini, E, Zerwas, S, Zipfel, S, Bergen, AW, Boden, JM, Brandt, H, Crawford, S, Halmi, KA, Horwood, LJ, Johnson, C, Kaplan, AS, Kaye, WH, Mitchell, J E, Olsen, CM, Pearson, JF, Pedersen, NL, Strober, M, Werge, T, Whiteman, DC, Woodside, DB, Grove, J, Henders, AK, Larsen, J T, Parker, R, Petersen, LV, Jordan, J, Kennedy, MA, Birgegård, A, Lichtenstein, P, Norring, C, Landén, M, Mortensen, PB, Polimanti, R, McClintick, JN, Adkins, AE, Aliev, F, Bacanu, SA, Batzler, A, Bertelsen, S, Biernacka, JM, Bigdeli, TB, Chen, L S, Clarke, TK, Degenhardt, F, Docherty, AR, Edwards, AC, Foo, JC, Fox, L, Frank, J, Hack, LM, Hartmann, AM, Hartz, SM, Heilmann-Heimbach, S, Hodgkinson, C, Hoffmann, P, Hottenga, JJ, Konte, B, Lahti, J, Lahti-Pulkkinen, M, Lai, D, Ligthart, L, Loukola, A, Maher, BS, Mbarek, H, McIntosh, AM, McQueen, MB, Meyers, JL, Milaneschi, Y, Palviainen, T, Peterson, RE, Ryu, E, Saccone, N L, Salvatore, JE, Sanchez-Roige, S, Schwandt, M, Sherva, R, Streit, F, Strohmaier, J, Thomas, N, Wang, JCY, Webb, BT, Wedow, R, Wetherill, L, Wills, AG, Zhou, H, Boardman, JD, Chen, D, Choi, D S, Copeland, WE, Culverhouse, RC, Dahmen, N, Degenhardt, L, Domingue, BW, Frye, MA, Gäebel, W, Hayward, C, Ising, M, Keyes, M, Kiefer, F, Koller, G, Kramer, J (John), Kuperman, S, Lucae, S, Lynskey, MT, Maier, W, Mann, K, Männistö, S, Müller-Myhsok, B, Murray, AD, Nurnberger, JI, Preuss, U, Räikkönen, K, Reynolds, MD, Ridinger, M, Scherbaum, N, Schuckit, MA, Soyka, M, Treutlein, J, Witt, SH, Wodarz, N, Zill, P, Adkins, DE, Boomsma, DI, Bierut, LJ, Brown, S, Bucholz, KK, Costello, EJ, Wit, HJ, Diazgranados, N, Eriksson, JG, Farrer, LA, Foroud, TM, Gillespie, NA, Goate, AM, Goldman, D, Grucza, RA, Hancock, DB, Harris, KM, Hesselbrock, V, Hewitt, JK, Hopfer, CJ, Iacono, WG, Johnson, E O, Karpyak, VM, Kendler, KS, Kranzler, HR, Krauter, K, Lind, PA, McGue, M, MacKillop, J, Madden, PA, Maes, HH, Magnusson, PKE, Nelson, EC, Nöthen, MM, Palmer, AA, Penninx, BWJH, Porjesz, B, Rice, JP, Rietschel, M, Riley, BP, Rose, RJ, Shen, PH, Silberg, J, Stallings, MC, Tarter, RE, Vanyukov, MM, Vrieze, S, Wall, TL, Whitfield, JB, Zhao, H, Neale, BM, Wade, TD, Heath, AC, Montgomery, GW, Martin, NG, Sullivan, PF, Kaprio, J, Breen, G, Gelernter, J, Edenberg, HJ, Bulik, CM, and Agrawal, A
Abstract: Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [rg], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (rg = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (rg = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (rgs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotyp
Published: 2021

30. Physical Exercise Interventions Targeting Cognitive Functioning and the Cognitive Domains in Nondementia Samples: A Systematic Review of Meta-Analyses

Author: Turner, DT, Hu, MX, Generaal, E, Bos, Daniel, Ikram, Kamran, Heshmatollah, Alis, Fani, Lana, Ikram, Arfan, Penninx, BWJH, Cuijpers, P, Turner, DT, Hu, MX, Generaal, E, Bos, Daniel, Ikram, Kamran, Heshmatollah, Alis, Fani, Lana, Ikram, Arfan, Penninx, BWJH, and Cuijpers, P
Abstract: Objective: We investigated whether physical exercise interventions improve cognitive functioning in nondementia populations. Methods: We conducted a systematic review of meta-analyses including only randomized controlled trials (RCTs). Two reviewers completed a systematic search of PubMed, Embase, PsychInfo, and Cochrane Controlled Register of Trials. Study characteristics, effect size data, and heterogeneity estimates were extracted and presented in tabular form. Methodological quality was assessed by 2 reviewers using the AMSTAR-2 checklist. The validity of results was considered based on AMSTAR-2 scores and study characteristics. Results: We included 11 meta-analyses: 6 focused on disease-free older adults and 5 on mild cognitive impairment (MCI) excluding dementia. These meta-analyses summarized 97 unique RCTs. Methodological quality ranged from critically low to high. For overall cognitive functioning, which was the outcome of 6 meta-analyses, 1 showed improvement due to exercise interventions in disease-free older adults (g = 0.29, P <.01), while 2 reported nonsignificant effects. In patients with MCI, 3 meta-analyses reported significant benefits of exercise interventions on overall cognitive functioning (g = 0.25-0.57, P <.01). For cognitive domains such as attention and memory, there was limited evidence of beneficial effects of exercise demonstrated in either disease-free or MCI samples. Conclusions: Exercise may improve overall cognitive functioning in disease-free older adults, but there is too little high-quality evidence to conclude whether this is achieved through improvement in any of the specific cognitive domains assessed. There is clearer evidence that exercise may improve cognitive functioning in MCI, but again there is limited evidence across most cognitive domains.
Published: 2021

31. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Author: Lagou, V, Mägi, R, Hottenga, JJ, Grallert, H, Perry, JR, Bouatia-Naji, N, Marullo, L, Rybin, D, Jansen, R, Min, JL, Dimas, AS, Ulrich, A, Zudina, L, Gådin, JR, Jiang, L, Faggian, A, Bonnefond, A, Fadista, J, Stathopoulou, MG, Isaacs, Aaron, Willems, SM, Navarro, P, Tanaka, T, Jackson, AU, Montasser, ME, O’Connell, JR, Bielak, LF, Webster, RJ, Saxena, R, Stafford, JM, Pourcain, BS, Timpson, NJ, Salo, P, Shin, SY, Amin, Najaf, Smith, AV, Li, G, Verweij, N, Goel, A, Ford, I, Johnson, PCD, Johnson, T, Kapur, K, Thorleifsson, G, Strawbridge, RJ, Rasmussen-Torvik, LJ, Esko, T, Mihailov, E, Fall, T, Fraser, RM, Mahajan, A, Kanoni, S, Giedraitis, V, Kleber, ME, Silbernagel, G, Meyer, J, Müller-Nurasyid, M, Ganna, A, Sarin, AP, Yengo, L, Shungin, D, Luan, J, Horikoshi, M, An, P, Sanna, S, Boettcher, Y, Rayner, NW, Nolte, IM, Zemunik, T, van Iperen, E, Kovacs, P, Hastie, ND, Wild, SH, McLachlan, S, Campbell, S, Polasek, O, Carlson, O, Egan, J, Kiess, W, Willemsen, G, Kuusisto, J, Laakso, M, Dimitriou, M, Hicks, AA, Rauramaa, R, Bandinelli, S, Thorand, B, Liu, Y, Miljkovic, I, Lind, L, Doney, A, Perola, M, Hingorani, A, Kivimaki, M, Kumari, M, Bennett, AJ, Groves, CJ, Herder, C, Koistinen, HA, Kinnunen, L, de Faire, U, Bakker, SJ, Uusitupa, M, Palmer, CN, Jukema, JW, Sattar, N, Pouta, A, Snieder, H, Boerwinkle, E, Pankow, JS, Magnusson, PKE, Krus, U, Scapoli, C, de Geus, EJCN, Blüher, M, Wolffenbuttel, BH, Province, MA, Abecasis, GR, Meigs, JB, Hovingh, GK, Lindström, J, Wilson, JF, Wright, AF, Dedoussis, GVZ, Bornstein, SR, Schwarz, PEH, Tönjes, A, Winkelmann, BR, Boehm, BO, März, W, Metspalu, A, Price, JF, Deloukas, P, Körner, A, Lakka, TA, Keinanen-Kiukaanniemi, SM, Saaristo, TE, Bergman, RN, Tuomilehto, J, Wareham, NJ, Langenberg, C, Männistö, S, Franks, PW, Hayward, C, Vitart, V, Kaprio, J, Visvikis-Siest, S, Balkau, B, Altshuler, D, Rudan, I, Stumvoll, M, Campbell, H, Duijn, Cornelia, Gieger, C, Illig, T, Ferrucci, L, Pedersen, NL, Pramstaller, PP, Boehnke, M, Frayling, TM, Shuldiner, AR, Peyser, PA, Kardia, SL, Palmer, LJ, Penninx, BWJH, Meneton, P, Harris, TB, Navis, G, van der Harst, P, Smith, GD, Forouhi, NG, Loos, RJ, Salomaa, V, Soranzo, N, Boomsma, DI, Groop, L, Tuomi, T, Hofman, Bert, Munroe, PB, Gudnason, V, Siscovick, DS, Watkins, H, Lecoeur, C, Vollenweider, P, Franco-Cereceda, A, Eriksson, P, Jarvelin, M R R, Stefansson, K, Hamsten, A, Nicholson, G, Karpe, F, Dermitzakis, ET, Lindgren, CM, McCarthy, MI, Froguel, P, Kaakinen, MA, Lyssenko, V, Watanabe, RM, Ingelsson, E, Florez, JC, Dupuis, J, Barroso, I, Morris, AP, Prokopenko, I, Lagou, V, Mägi, R, Hottenga, JJ, Grallert, H, Perry, JR, Bouatia-Naji, N, Marullo, L, Rybin, D, Jansen, R, Min, JL, Dimas, AS, Ulrich, A, Zudina, L, Gådin, JR, Jiang, L, Faggian, A, Bonnefond, A, Fadista, J, Stathopoulou, MG, Isaacs, Aaron, Willems, SM, Navarro, P, Tanaka, T, Jackson, AU, Montasser, ME, O’Connell, JR, Bielak, LF, Webster, RJ, Saxena, R, Stafford, JM, Pourcain, BS, Timpson, NJ, Salo, P, Shin, SY, Amin, Najaf, Smith, AV, Li, G, Verweij, N, Goel, A, Ford, I, Johnson, PCD, Johnson, T, Kapur, K, Thorleifsson, G, Strawbridge, RJ, Rasmussen-Torvik, LJ, Esko, T, Mihailov, E, Fall, T, Fraser, RM, Mahajan, A, Kanoni, S, Giedraitis, V, Kleber, ME, Silbernagel, G, Meyer, J, Müller-Nurasyid, M, Ganna, A, Sarin, AP, Yengo, L, Shungin, D, Luan, J, Horikoshi, M, An, P, Sanna, S, Boettcher, Y, Rayner, NW, Nolte, IM, Zemunik, T, van Iperen, E, Kovacs, P, Hastie, ND, Wild, SH, McLachlan, S, Campbell, S, Polasek, O, Carlson, O, Egan, J, Kiess, W, Willemsen, G, Kuusisto, J, Laakso, M, Dimitriou, M, Hicks, AA, Rauramaa, R, Bandinelli, S, Thorand, B, Liu, Y, Miljkovic, I, Lind, L, Doney, A, Perola, M, Hingorani, A, Kivimaki, M, Kumari, M, Bennett, AJ, Groves, CJ, Herder, C, Koistinen, HA, Kinnunen, L, de Faire, U, Bakker, SJ, Uusitupa, M, Palmer, CN, Jukema, JW, Sattar, N, Pouta, A, Snieder, H, Boerwinkle, E, Pankow, JS, Magnusson, PKE, Krus, U, Scapoli, C, de Geus, EJCN, Blüher, M, Wolffenbuttel, BH, Province, MA, Abecasis, GR, Meigs, JB, Hovingh, GK, Lindström, J, Wilson, JF, Wright, AF, Dedoussis, GVZ, Bornstein, SR, Schwarz, PEH, Tönjes, A, Winkelmann, BR, Boehm, BO, März, W, Metspalu, A, Price, JF, Deloukas, P, Körner, A, Lakka, TA, Keinanen-Kiukaanniemi, SM, Saaristo, TE, Bergman, RN, Tuomilehto, J, Wareham, NJ, Langenberg, C, Männistö, S, Franks, PW, Hayward, C, Vitart, V, Kaprio, J, Visvikis-Siest, S, Balkau, B, Altshuler, D, Rudan, I, Stumvoll, M, Campbell, H, Duijn, Cornelia, Gieger, C, Illig, T, Ferrucci, L, Pedersen, NL, Pramstaller, PP, Boehnke, M, Frayling, TM, Shuldiner, AR, Peyser, PA, Kardia, SL, Palmer, LJ, Penninx, BWJH, Meneton, P, Harris, TB, Navis, G, van der Harst, P, Smith, GD, Forouhi, NG, Loos, RJ, Salomaa, V, Soranzo, N, Boomsma, DI, Groop, L, Tuomi, T, Hofman, Bert, Munroe, PB, Gudnason, V, Siscovick, DS, Watkins, H, Lecoeur, C, Vollenweider, P, Franco-Cereceda, A, Eriksson, P, Jarvelin, M R R, Stefansson, K, Hamsten, A, Nicholson, G, Karpe, F, Dermitzakis, ET, Lindgren, CM, McCarthy, MI, Froguel, P, Kaakinen, MA, Lyssenko, V, Watanabe, RM, Ingelsson, E, Florez, JC, Dupuis, J, Barroso, I, Morris, AP, and Prokopenko, I
Abstract: Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
Published: 2021

32. Non-alcoholic fatty liver disease (NAFLD) as a neglected metabolic companion of psychiatric disorders: common pathways and future approaches

Author: Soto-Angona, Ó, Anmella, G, Valdés-Florido, MJ, De Uribe-Viloria, N, Carvalho, AF, Penninx, BWJH, Berk, Michael, Soto-Angona, Ó, Anmella, G, Valdés-Florido, MJ, De Uribe-Viloria, N, Carvalho, AF, Penninx, BWJH, and Berk, Michael
Abstract: Background: Non-alcoholic fatty liver disease (NAFLD) is characterized by hepatic steatosis in over 5% of the parenchyma in the absence of excessive alcohol consumption. It is more prevalent in patients with diverse mental disorders, being part of the comorbidity driving loss of life expectancy and quality of life, yet remains a neglected entity. NAFLD can progress to non-alcoholic steatohepatitis (NASH) and increases the risk for cirrhosis and hepatic carcinoma. Both NAFLD and mental disorders share pathophysiological pathways, and also present a complex, bidirectional relationship with the metabolic syndrome (MetS) and related cardiometabolic diseases. Main text: This review compares the demographic data on NAFLD and NASH among the global population and the psychiatric population, finding differences that suggest a higher incidence of this disease among the latter. It also analyzes the link between NAFLD and psychiatric disorders, looking into common pathophysiological pathways, such as metabolic, genetic, and lifestyle factors. Finally, possible treatments, tailored approaches, and future research directions are suggested. Conclusion: NAFLD is part of a complex system of mental and non-communicable somatic disorders with a common pathogenesis, based on shared lifestyle and environmental risks, mediated by dysregulation of inflammation, oxidative stress pathways, and mitochondrial function. The recognition of the prevalent comorbidity between NAFLD and mental disorders is required to inform clinical practice and develop novel interventions to prevent and treat these complex and interacting disorders.
Published: 2020

33. A methylation study of long-term depression risk

Author: Clark, SL, Hattab, MW, Chan, RF, Shabalin, AA, Han, LKM, Zhao, M, Smit, JH, Jansen, R, Milaneschi, Y, Xie, LY, van Grootheest, G, Penninx, BWJH, Aberg, KA, van den Oord, EJCG, Clark, SL, Hattab, MW, Chan, RF, Shabalin, AA, Han, LKM, Zhao, M, Smit, JH, Jansen, R, Milaneschi, Y, Xie, LY, van Grootheest, G, Penninx, BWJH, Aberg, KA, and van den Oord, EJCG
Abstract: Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.
Published: 2020

34. Methylome-wide association findings for major depressive disorder overlap in blood and brain and replicate in independent brain samples

Author: Aberg, KA, Dean, B, Shabalin, AA, Chan, RF, Han, LKM, Zhao, M, van Grootheest, G, Xie, LY, Milaneschi, Y, Clark, SL, Turecki, G, Penninx, BWJH, van den Oord, EJCG, Aberg, KA, Dean, B, Shabalin, AA, Chan, RF, Han, LKM, Zhao, M, van Grootheest, G, Xie, LY, Milaneschi, Y, Clark, SL, Turecki, G, Penninx, BWJH, and van den Oord, EJCG
Abstract: We present the first large-scale methylome-wide association studies (MWAS) for major depressive disorder (MDD) to identify sites of potential importance for MDD etiology. Using a sequencing-based approach that provides near-complete coverage of all 28 million common CpGs in the human genome, we assay methylation in MDD cases and controls from both blood (N = 1132) and postmortem brain tissues (N = 61 samples from Brodmann Area 10, BA10). The MWAS for blood identified several loci with P ranging from 1.91 × 10-8 to 4.39 × 10-8 and a resampling approach showed that the cumulative association was significant (P = 4.03 × 10-10) with the signal coming from the top 25,000 MWAS markers. Furthermore, a permutation-based analysis showed significant overlap (P = 5.4 × 10-3) between the MWAS findings in blood and brain (BA10). This overlap was significantly enriched for a number of features including being in eQTLs in blood and the frontal cortex, CpG islands and shores, and exons. The overlapping sites were also enriched for active chromatin states in brain including genic enhancers and active transcription start sites. Furthermore, three loci located in GABBR2, RUFY3, and in an intergenic region on chromosome 2 replicated with the same direction of effect in the second brain tissue (BA25, N = 60) from the same individuals and in two independent brain collections (BA10, N = 81 and 64). GABBR2 inhibits neuronal activity through G protein-coupled second-messenger systems and RUFY3 is implicated in the establishment of neuronal polarity and axon elongation. In conclusion, we identified and replicated methylated loci associated with MDD that are involved in biological functions of likely importance to MDD etiology.
Published: 2020

35. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Author: Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, Veltman, DJ, Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, and Veltman, DJ
Abstract: A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.
Published: 2020

36. Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression

Author: Glanville, KP, Coleman, JR, Hanscombe, KB, Euesden, J, Choi, SW, Purves, KL, Breen, G, Air, TM, Andlauer, TFM, Baune, BT, Binder, EB, Blackwood, DHR, Boomsma, D, Buttenschon, HN, Colodro-Conde, L, Dannlowski, U, Direk, N, Dunn, EC, Forstner, AJ, de Geus, EJC, Grabe, HJ, Hamilton, SP, Jones, I, Jones, LA, Knowles, JA, Kutalik, Z, Levinson, DF, Lewis, G, Lind, PA, Lucae, S, Magnusson, PK, McGuffin, P, McIntosh, AM, Milaneschi, Y, Mors, O, Mostafavi, S, Mueller-Myhsok, B, Pedersen, NL, Penninx, BWJH, Potash, JB, Preisig, M, Ripke, S, Shi, J, Shyn, S, Smoller, JW, Streit, F, Sullivan, PF, Tiemeier, H, Uher, R, Van der Auwera, S, Weissman, MM, O'Reilly, PF, Lewis, CM, Wray, NR, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Bryois, J, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Dolan, C, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jorgenson, E, Kohane, IS, Kraft, J, Kretzschmar, WW, Li, Y, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milani, L, Mondimore, FM, Montgomery, GW, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Cichon, S, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Li, QS, Madden, PAF, Martin, NG, Metspalu, A, Mortensen, PB, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Perlis, RH, Porteous, DJ, Rietschel, M, Schaefer, C, Schulze, TG, Stefansson, K, Voelzke, H, Werge, T, Borglum, AD, Glanville, KP, Coleman, JR, Hanscombe, KB, Euesden, J, Choi, SW, Purves, KL, Breen, G, Air, TM, Andlauer, TFM, Baune, BT, Binder, EB, Blackwood, DHR, Boomsma, D, Buttenschon, HN, Colodro-Conde, L, Dannlowski, U, Direk, N, Dunn, EC, Forstner, AJ, de Geus, EJC, Grabe, HJ, Hamilton, SP, Jones, I, Jones, LA, Knowles, JA, Kutalik, Z, Levinson, DF, Lewis, G, Lind, PA, Lucae, S, Magnusson, PK, McGuffin, P, McIntosh, AM, Milaneschi, Y, Mors, O, Mostafavi, S, Mueller-Myhsok, B, Pedersen, NL, Penninx, BWJH, Potash, JB, Preisig, M, Ripke, S, Shi, J, Shyn, S, Smoller, JW, Streit, F, Sullivan, PF, Tiemeier, H, Uher, R, Van der Auwera, S, Weissman, MM, O'Reilly, PF, Lewis, CM, Wray, NR, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Bryois, J, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Dolan, C, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jorgenson, E, Kohane, IS, Kraft, J, Kretzschmar, WW, Li, Y, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milani, L, Mondimore, FM, Montgomery, GW, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Cichon, S, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Li, QS, Madden, PAF, Martin, NG, Metspalu, A, Mortensen, PB, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Perlis, RH, Porteous, DJ, Rietschel, M, Schaefer, C, Schulze, TG, Stefansson, K, Voelzke, H, Werge, T, and Borglum, AD
Abstract: BACKGROUND: The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the overlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression. METHODS: We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 human leukocyte antigen (HLA) alleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a region-wide significance threshold (3.9 × 10-6) and a candidate threshold (1.6 × 10-4). RESULTS: No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA genes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97-0.99). CONCLUSIONS: We found no evidence that an increased risk for depression was conferred by HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are rare or have very modest effect sizes.
Published: 2020

37. Genetic comorbidity between major depression and cardio-metabolic traits, stratified by age at onset of major depression

Author: Hagenaars, SP, Coleman, JR, Choi, SW, Gaspar, H, Adams, MJ, Howard, DM, Hodgson, K, Traylor, M, Air, TM, Andlauer, TFM, Arolt, V, Baune, BT, Binder, EB, Blackwood, DHR, Boomsma, D, Campbell, A, Cearns, M, Czamara, D, Dannlowski, U, Domschke, K, de Geus, EJC, Hamilton, SP, Hayward, C, Hickie, IB, Hottenga, JJ, Ising, M, Jones, I, Jones, L, Kutalik, Z, Lucae, S, Martin, NG, Milaneschi, Y, Mueller-Myhsok, B, Owen, MJ, Padmanabhan, S, Penninx, BWJH, Pistis, G, Porteous, DJ, Preisig, M, Ripke, S, Shyn, S, Sullivan, PF, Whitfield, JB, Wray, NR, McIntosh, AM, Deary, IJ, Breen, G, Lewis, CM, Hagenaars, SP, Coleman, JR, Choi, SW, Gaspar, H, Adams, MJ, Howard, DM, Hodgson, K, Traylor, M, Air, TM, Andlauer, TFM, Arolt, V, Baune, BT, Binder, EB, Blackwood, DHR, Boomsma, D, Campbell, A, Cearns, M, Czamara, D, Dannlowski, U, Domschke, K, de Geus, EJC, Hamilton, SP, Hayward, C, Hickie, IB, Hottenga, JJ, Ising, M, Jones, I, Jones, L, Kutalik, Z, Lucae, S, Martin, NG, Milaneschi, Y, Mueller-Myhsok, B, Owen, MJ, Padmanabhan, S, Penninx, BWJH, Pistis, G, Porteous, DJ, Preisig, M, Ripke, S, Shyn, S, Sullivan, PF, Whitfield, JB, Wray, NR, McIntosh, AM, Deary, IJ, Breen, G, and Lewis, CM
Abstract: It is imperative to understand the specific and shared etiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic etiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in 11 data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease, and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic etiology irrespective of the age depression first presents.
Published: 2020

38. Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort

Author: Tozzi, L, Garczarek, L, Janowitz, D, Stein, DJ, Wittfeld, K, Dobrowolny, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Buelow, R, Voelzke, H, Klinger-Koenig, J, Schnell, K, Schoepf, D, Grotegerd, D, Opel, N, Dannlowski, U, Kugel, H, Schramm, E, Konrad, C, Kircher, T, Jueksel, DI, Nenadic, I, Krug, A, Hahn, T, Steinstraeter, O, Redlich, R, Zaremba, D, Zurowski, B, Fu, CHY, Dima, D, Cole, J, Grabe, HJ, Connolly, CG, Yang, TT, Ho, TC, Lewinn, KZ, Li, M, Groenewold, NA, Salminen, LE, Walter, M, Simmons, AN, van Erp, TGM, Jahanshad, N, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Hibar, DP, Thompson, PM, Veltman, DJ, Schmaal, L, Frodl, T, Tozzi, L, Garczarek, L, Janowitz, D, Stein, DJ, Wittfeld, K, Dobrowolny, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Buelow, R, Voelzke, H, Klinger-Koenig, J, Schnell, K, Schoepf, D, Grotegerd, D, Opel, N, Dannlowski, U, Kugel, H, Schramm, E, Konrad, C, Kircher, T, Jueksel, DI, Nenadic, I, Krug, A, Hahn, T, Steinstraeter, O, Redlich, R, Zaremba, D, Zurowski, B, Fu, CHY, Dima, D, Cole, J, Grabe, HJ, Connolly, CG, Yang, TT, Ho, TC, Lewinn, KZ, Li, M, Groenewold, NA, Salminen, LE, Walter, M, Simmons, AN, van Erp, TGM, Jahanshad, N, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Hibar, DP, Thompson, PM, Veltman, DJ, Schmaal, L, and Frodl, T
Abstract: BACKGROUND: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age. METHODS: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer. RESULTS: CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions. CONCLUSIONS: Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.
Published: 2020

39. The genetic architecture of the human cerebral cortex

Author: Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnaes, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bulow, R, Burger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Nhat, TD, Dohm, K, Ehrlich, S, Engelbrecht, H-R, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, J-J, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Kramer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, Milaneschi, Y, Mirza-Schreiber, N, Moreira, JCV, Muhleisen, TW, Mueller-Myhsok, B, Najt, P, Nakahara, S, Nho, K, Loohuis, LMO, Orfanos, DP, Pearson, JF, Pitcher, TL, Putz, B, Quide, Y, Ragothaman, A, Rashid, FM, Reay, WR, Redlich, R, Reinbold, CS, Repple, J, Richard, G, Riedel, BC, Risacher, SL, Rocha, CS, Mota, NR, Salminen, L, Saremi, A, Saykin, AJ, Schlag, F, Schmaal, L, Schofield, PR, Secolin, R, Shapland, CY, Shen, L, Shin, J, Shumskaya, E, Sonderby, IE, Sprooten, E, Tansey, KE, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Turner, JA, Uhlmann, A, Vallerga, CL, van der Meer, D, van Donkelaar, MMJ, van Eijk, L, van Erp, TGM, van Haren, NEM, van Rooij, D, van Tol, M-J, Veldink, JH, Verhoef, E, Walton, E, Wang, M, Wang, Y, Wardlaw, JM, Wen, W, Westlye, LT, Whelan, CD, Witt, SH, Wittfeld, K, Wolf, C, Wolfers, T, Wu, JQ, Yasuda, CL, Zaremba, D, Zhang, Z, Zwiers, MP, Artiges, E, Assareh, AA, Ayesa-Arriola, R, Belger, A, Brandt, CL, Brown, GG, Cichon, S, Curran, JE, Davies, GE, Degenhardt, F, Dennis, MF, Dietsche, B, Djurovic, S, Doherty, CP, Espiritu, R, Garijo, D, Gil, Y, Gowland, PA, Green, RC, Hausler, AN, Heindel, W, Ho, B-C, Hoffmann, WU, Holsboer, F, Homuth, G, Hosten, N, Jack, CR, Jang, M, Jansen, A, Kimbrel, NA, Kolskar, K, Koops, S, Krug, A, Lim, KO, Luykx, JJ, Mathalon, DH, Mather, KA, Mattay, VS, Matthews, S, Van Son, JM, McEwen, SC, Melle, I, Morris, DW, Mueller, BA, Nauck, M, Nordvik, JE, Noethen, MM, O'Leary, DS, Opel, N, Martinot, M-LP, Pike, GB, Preda, A, Quinlan, EB, Rasser, PE, Ratnakar, V, Reppermund, S, Steen, VM, Tooney, PA, Torres, FR, Veltman, DJ, Voyvodic, JT, Whelan, R, White, T, Yamamori, H, Adams, HHH, Bis, JC, Debette, S, Decarli, C, Fornage, M, Gudnason, V, Hofer, E, Ikram, MA, Launer, L, Longstreth, WT, Lopez, OL, Mazoyer, B, Mosley, TH, Roshchupkin, GV, Satizabal, CL, Schmidt, R, Seshadri, S, Yang, Q, Alvim, MKM, Ames, D, Anderson, TJ, Andreassen, OA, Arias-Vasquez, A, Bastin, ME, Baune, BT, Beckham, JC, Blangero, J, Boomsma, DI, Brodaty, H, Brunner, HG, Buckner, RL, Buitelaar, JK, Bustillo, JR, Cahn, W, Cairns, MJ, Calhoun, V, Carr, VJ, Caseras, X, Caspers, S, Cavalleri, GL, Cendes, F, Corvin, A, Crespo-Facorro, B, Dalrymple-Alford, JC, Dannlowski, U, de Geus, EJC, Deary, IJ, Delanty, N, Depondt, C, Desrivieres, S, Donohoe, G, Espeseth, T, Fernandez, G, Fisher, SE, Flor, H, Forstner, AJ, Francks, C, Franke, B, Glahn, DC, Gollub, RL, Grabe, HJ, Gruber, O, Haberg, AK, Hariri, AR, Hartman, CA, Hashimoto, R, Heinz, A, Henskens, FA, Hillegers, MHJ, Hoekstra, PJ, Holmes, AJ, Hong, LE, Hopkins, WD, Pol, HEH, Jernigan, TL, Jonsson, EG, Kahn, RS, Kennedy, MA, Kircher, TTJ, Kochunov, P, Kwok, JBJ, Le Hellard, S, Loughland, CM, Martin, NG, Martinot, J-L, McDonald, C, McMahon, KL, Meyer-Lindenberg, A, Michie, PT, Morey, RA, Mowry, B, Nyberg, L, Oosterlaan, J, Ophoff, RA, Pantelis, C, Paus, T, Pausova, Z, Penninx, BWJH, Polderman, TJC, Posthuma, D, Rietschel, M, Roffman, JL, Rowland, LM, Sachdev, PS, Samann, PG, Schall, U, Schumann, G, Scott, RJ, Sim, K, Sisodiya, SM, Smoller, JW, Sommer, IE, St Pourcain, B, Stein, DJ, Toga, AW, Trollor, JN, Van der Wee, NJA, van't Ent, D, Volzke, H, Walter, H, Weber, B, Weinberger, DR, Wright, MJ, Zhou, J, Stein, JL, Thompson, PM, Medland, SE, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnaes, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bulow, R, Burger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Nhat, TD, Dohm, K, Ehrlich, S, Engelbrecht, H-R, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, J-J, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Kramer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, Milaneschi, Y, Mirza-Schreiber, N, Moreira, JCV, Muhleisen, TW, Mueller-Myhsok, B, Najt, P, Nakahara, S, Nho, K, Loohuis, LMO, Orfanos, DP, Pearson, JF, Pitcher, TL, Putz, B, Quide, Y, Ragothaman, A, Rashid, FM, Reay, WR, Redlich, R, Reinbold, CS, Repple, J, Richard, G, Riedel, BC, Risacher, SL, Rocha, CS, Mota, NR, Salminen, L, Saremi, A, Saykin, AJ, Schlag, F, Schmaal, L, Schofield, PR, Secolin, R, Shapland, CY, Shen, L, Shin, J, Shumskaya, E, Sonderby, IE, Sprooten, E, Tansey, KE, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Turner, JA, Uhlmann, A, Vallerga, CL, van der Meer, D, van Donkelaar, MMJ, van Eijk, L, van Erp, TGM, van Haren, NEM, van Rooij, D, van Tol, M-J, Veldink, JH, Verhoef, E, Walton, E, Wang, M, Wang, Y, Wardlaw, JM, Wen, W, Westlye, LT, Whelan, CD, Witt, SH, Wittfeld, K, Wolf, C, Wolfers, T, Wu, JQ, Yasuda, CL, Zaremba, D, Zhang, Z, Zwiers, MP, Artiges, E, Assareh, AA, Ayesa-Arriola, R, Belger, A, Brandt, CL, Brown, GG, Cichon, S, Curran, JE, Davies, GE, Degenhardt, F, Dennis, MF, Dietsche, B, Djurovic, S, Doherty, CP, Espiritu, R, Garijo, D, Gil, Y, Gowland, PA, Green, RC, Hausler, AN, Heindel, W, Ho, B-C, Hoffmann, WU, Holsboer, F, Homuth, G, Hosten, N, Jack, CR, Jang, M, Jansen, A, Kimbrel, NA, Kolskar, K, Koops, S, Krug, A, Lim, KO, Luykx, JJ, Mathalon, DH, Mather, KA, Mattay, VS, Matthews, S, Van Son, JM, McEwen, SC, Melle, I, Morris, DW, Mueller, BA, Nauck, M, Nordvik, JE, Noethen, MM, O'Leary, DS, Opel, N, Martinot, M-LP, Pike, GB, Preda, A, Quinlan, EB, Rasser, PE, Ratnakar, V, Reppermund, S, Steen, VM, Tooney, PA, Torres, FR, Veltman, DJ, Voyvodic, JT, Whelan, R, White, T, Yamamori, H, Adams, HHH, Bis, JC, Debette, S, Decarli, C, Fornage, M, Gudnason, V, Hofer, E, Ikram, MA, Launer, L, Longstreth, WT, Lopez, OL, Mazoyer, B, Mosley, TH, Roshchupkin, GV, Satizabal, CL, Schmidt, R, Seshadri, S, Yang, Q, Alvim, MKM, Ames, D, Anderson, TJ, Andreassen, OA, Arias-Vasquez, A, Bastin, ME, Baune, BT, Beckham, JC, Blangero, J, Boomsma, DI, Brodaty, H, Brunner, HG, Buckner, RL, Buitelaar, JK, Bustillo, JR, Cahn, W, Cairns, MJ, Calhoun, V, Carr, VJ, Caseras, X, Caspers, S, Cavalleri, GL, Cendes, F, Corvin, A, Crespo-Facorro, B, Dalrymple-Alford, JC, Dannlowski, U, de Geus, EJC, Deary, IJ, Delanty, N, Depondt, C, Desrivieres, S, Donohoe, G, Espeseth, T, Fernandez, G, Fisher, SE, Flor, H, Forstner, AJ, Francks, C, Franke, B, Glahn, DC, Gollub, RL, Grabe, HJ, Gruber, O, Haberg, AK, Hariri, AR, Hartman, CA, Hashimoto, R, Heinz, A, Henskens, FA, Hillegers, MHJ, Hoekstra, PJ, Holmes, AJ, Hong, LE, Hopkins, WD, Pol, HEH, Jernigan, TL, Jonsson, EG, Kahn, RS, Kennedy, MA, Kircher, TTJ, Kochunov, P, Kwok, JBJ, Le Hellard, S, Loughland, CM, Martin, NG, Martinot, J-L, McDonald, C, McMahon, KL, Meyer-Lindenberg, A, Michie, PT, Morey, RA, Mowry, B, Nyberg, L, Oosterlaan, J, Ophoff, RA, Pantelis, C, Paus, T, Pausova, Z, Penninx, BWJH, Polderman, TJC, Posthuma, D, Rietschel, M, Roffman, JL, Rowland, LM, Sachdev, PS, Samann, PG, Schall, U, Schumann, G, Scott, RJ, Sim, K, Sisodiya, SM, Smoller, JW, Sommer, IE, St Pourcain, B, Stein, DJ, Toga, AW, Trollor, JN, Van der Wee, NJA, van't Ent, D, Volzke, H, Walter, H, Weber, B, Weinberger, DR, Wright, MJ, Zhou, J, Stein, JL, Thompson, PM, and Medland, SE
Abstract: The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
Published: 2020

40. Predicting individual clinical trajectories of depression with generative embedding

Author: Fraessle, S, Marquand, AF, Schmaal, L, Dinga, R, Veltman, DJ, van der Wee, NJA, van Tol, M-J, Schoebi, D, Penninx, BWJH, Stephan, KE, Fraessle, S, Marquand, AF, Schmaal, L, Dinga, R, Veltman, DJ, van der Wee, NJA, van Tol, M-J, Schoebi, D, Penninx, BWJH, and Stephan, KE
Abstract: Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy - generative embedding (GE) - which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of con
Published: 2020

41. Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank

Author: Coleman, JRI, Peyrot, WJ, Purves, KL, Davis, KAS, Rayner, C, Choi, SW, Hubel, C, Gaspar, HA, Kan, C, Van der Auwera, S, Adams, MJ, Lyall, DM, Choi, KW, Dunn, EC, Vassos, E, Danese, A, Maughan, B, Grabe, HJ, Lewis, CM, O'Reilly, PF, McIntosh, AM, Smith, DJ, Wray, NR, Hotopf, M, Eley, TC, Breen, G, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Forstner, AJ, Frank, J, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Volzke, H, Weissman, MM, Werge, T, Levinson, DF, Borglum, AD, Sullivan, PF, Coleman, JRI, Peyrot, WJ, Purves, KL, Davis, KAS, Rayner, C, Choi, SW, Hubel, C, Gaspar, HA, Kan, C, Van der Auwera, S, Adams, MJ, Lyall, DM, Choi, KW, Dunn, EC, Vassos, E, Danese, A, Maughan, B, Grabe, HJ, Lewis, CM, O'Reilly, PF, McIntosh, AM, Smith, DJ, Wray, NR, Hotopf, M, Eley, TC, Breen, G, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Forstner, AJ, Frank, J, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Volzke, H, Weissman, MM, Werge, T, Levinson, DF, Borglum, AD, and Sullivan, PF
Abstract: Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg = 0.24, p = 1.8 × 10-7 versus rg = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10-4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.
Published: 2020

42. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group

Author: van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, Schmaal, L, van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, and Schmaal, L
Abstract: Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.
Published: 2020

43. Genome-wide Association Analysis in Humans Links Nucleotide Metabolism to Leukocyte Telomere Length

Author: Li, C, Stoma, S, Lotta, LA, Warner, S, Albrecht, E, Allione, A, Arp, PP, Broer, L, Buxton, JL, Alves, ADSC, Deelen, J, Fedko, IO, Gordon, SD, Jiang, T, Karlsson, R, Kerrison, N, Loe, TK, Mangino, M, Milaneschi, Y, Miraglio, B, Pervjakova, N, Russo, A, Surakka, I, van der Spek, A, Verhoeven, JE, Amin, N, Beekman, M, Blakemore, A, Canzian, F, Hamby, SE, Hottenga, J-J, Jones, PD, Jousilahti, P, Magi, R, Medland, SE, Montgomery, GW, Nyholt, DR, Perola, M, Pietilainen, KH, Salomaa, V, Sillanpaa, E, Suchiman, HE, van Heemst, D, Willemsen, G, Agudo, A, Boeing, H, Boomsma, D, Chirlaque, M-D, Fagherazzi, G, Ferrari, P, Franks, P, Gieger, C, Eriksson, JG, Gunter, M, Hagg, S, Hovatta, I, Imaz, L, Kaprio, J, Kaaks, R, Key, T, Krogh, V, Martin, NG, Melander, O, Metspalu, A, Moreno, C, Onland-Moret, NC, Nilsson, P, Ong, KK, Overvad, K, Palli, D, Panico, S, Pedersen, NL, Penninx, BWJH, Ramon Quiros, J, Riitta Jarvelin, M, Rodriguez-Barranco, M, Scott, RA, Severi, G, Slagboom, PE, Spector, TD, Tjonneland, A, Trichopoulou, A, Tumino, R, Uitterlinden, AG, van der Schouw, YT, van Duijn, CM, Weiderpass, E, Denchi, EL, Matullo, G, Butterworth, AS, Danesh, J, Samani, NJ, Wareham, NJ, Nelson, CP, Langenberg, C, Codd, V, Li, C, Stoma, S, Lotta, LA, Warner, S, Albrecht, E, Allione, A, Arp, PP, Broer, L, Buxton, JL, Alves, ADSC, Deelen, J, Fedko, IO, Gordon, SD, Jiang, T, Karlsson, R, Kerrison, N, Loe, TK, Mangino, M, Milaneschi, Y, Miraglio, B, Pervjakova, N, Russo, A, Surakka, I, van der Spek, A, Verhoeven, JE, Amin, N, Beekman, M, Blakemore, A, Canzian, F, Hamby, SE, Hottenga, J-J, Jones, PD, Jousilahti, P, Magi, R, Medland, SE, Montgomery, GW, Nyholt, DR, Perola, M, Pietilainen, KH, Salomaa, V, Sillanpaa, E, Suchiman, HE, van Heemst, D, Willemsen, G, Agudo, A, Boeing, H, Boomsma, D, Chirlaque, M-D, Fagherazzi, G, Ferrari, P, Franks, P, Gieger, C, Eriksson, JG, Gunter, M, Hagg, S, Hovatta, I, Imaz, L, Kaprio, J, Kaaks, R, Key, T, Krogh, V, Martin, NG, Melander, O, Metspalu, A, Moreno, C, Onland-Moret, NC, Nilsson, P, Ong, KK, Overvad, K, Palli, D, Panico, S, Pedersen, NL, Penninx, BWJH, Ramon Quiros, J, Riitta Jarvelin, M, Rodriguez-Barranco, M, Scott, RA, Severi, G, Slagboom, PE, Spector, TD, Tjonneland, A, Trichopoulou, A, Tumino, R, Uitterlinden, AG, van der Schouw, YT, van Duijn, CM, Weiderpass, E, Denchi, EL, Matullo, G, Butterworth, AS, Danesh, J, Samani, NJ, Wareham, NJ, Nelson, CP, Langenberg, C, and Codd, V
Abstract: Leukocyte telomere length (LTL) is a heritable biomarker of genomic aging. In this study, we perform a genome-wide meta-analysis of LTL by pooling densely genotyped and imputed association results across large-scale European-descent studies including up to 78,592 individuals. We identify 49 genomic regions at a false dicovery rate (FDR) < 0.05 threshold and prioritize genes at 31, with five highlighting nucleotide metabolism as an important regulator of LTL. We report six genome-wide significant loci in or near SENP7, MOB1B, CARMIL1, PRRC2A, TERF2, and RFWD3, and our results support recently identified PARP1, POT1, ATM, and MPHOSPH6 loci. Phenome-wide analyses in >350,000 UK Biobank participants suggest that genetically shorter telomere length increases the risk of hypothyroidism and decreases the risk of thyroid cancer, lymphoma, and a range of proliferative conditions. Our results replicate previously reported associations with increased risk of coronary artery disease and lower risk for multiple cancer types. Our findings substantially expand current knowledge on genes that regulate LTL and their impact on human health and disease.
Published: 2020

44. Does food intake mediate the association between mindful eating and change in depressive symptoms?

Author: Winkens, Laura, Elstgeest, Liset, van Strien, T, Penninx, Bwjh, Visser, M, Brouwer, I A, Winkens, Laura, Elstgeest, Liset, van Strien, T, Penninx, Bwjh, Visser, M, and Brouwer, I A
Abstract: OBJECTIVE: To examine mediation by (i) diet quality and (ii) diet quantity in the associations of mindful eating domains with 3-year change in depressive symptoms.DESIGN: Depressive symptoms were measured with the Center for Epidemiologic Studies Depression scale at baseline and 3-year follow-up. Four mindful eating domains (Focused Eating; Eating in response to Hunger and Satiety Cues; Eating with Awareness; Eating without Distraction) were measured with the Mindful Eating Behavior Scale. Food intake was measured with a 238-item FFQ. Diet quality was defined as the Mediterranean Diet Score (MDS). Diet quantity was defined as total energy intake (kcal/d; 1 kcal = 4·184 kJ). Mediation analyses with percentile-corrected bootstrap confidence intervals were conducted to calculate indirect effects.SETTING: Longitudinal Aging Study Amsterdam.PARTICIPANTS: Adults aged 55 years or above (n 929).RESULTS: Diet quality (MDS) did not mediate associations of any of the four mindful eating domains with change in depressive symptoms. In contrast, total energy intake did mediate the associations with change in depressive symptoms for the mindful eating domains Eating with Awareness (indirect effects fully adjusted models: B = -0·014, 95 % CI -0·037, -0·002) and Eating without Distraction (B = -0·013, 95 % CI -0·033, -0·001), but not for the other two domains. Post hoc multiple mediation analyses showed similar results.CONCLUSIONS: Higher scores on two mindful eating domains were associated with a decrease in depressive symptoms through lower total energy intake. Diet quantity, but not diet quality, could be a possible underlying mechanism in the associations between mindful eating and change in depressive symptoms.
Published: 2020
Full Text: View/download PDF

45. Performance of methods to conduct mediation analysis with time-to-event outcomes

Author: Burgos Ochoa, Lizbeth, Rijnhart, JJM, Penninx, BWJH, Wardenaar, KJ, Twisk, JW, Heymans, MW, Burgos Ochoa, Lizbeth, Rijnhart, JJM, Penninx, BWJH, Wardenaar, KJ, Twisk, JW, and Heymans, MW
Published: 2020

46. Meta-analysis of genome-wide association studies for personality

Author: de Moor, MHM, Costa, P T, Terracciano, A, Krueger, R F, de Geus, EJC, Toshiko, T, Penninx, BWJH, Esko, T, Madden, PAF, Derringer, J, Amin, N, Willemsen, G, Hottenga, J-J, Distel, M A, Uda, M, Sanna, S, Spinhoven, P, Hartman, C A, Sullivan, P, Realo1, A, Allik, J, Heath, A C, Pergadia, M L, Agrawal, A, Lin, P, Grucza, R, Nutile, T, Ciullo, M, Rujescu, D, Giegling, I, Konte, B, Widen, E, Cousminer, D L, Eriksson, J G, Palotie, A, Peltonen, L, Luciano, M, Tenesa, A, Davies, G, Lopez, L M, Hansell, N K, Medland, S E, Ferrucci, L, Schlessinger, D, Montgomery, G W, Wright, M J, Aulchenko, Y S, Janssens, ACJW, Oostra, B A, Metspalu, A, Abecasis, G R, Deary, I J, Räikkönen, K, Bierut, L J, Martin, N G, van Duijn, C M, and Boomsma, D I
Published: 2012
Full Text: View/download PDF

47. Serum levels of brain-derived neurotrophic factor in major depressive disorder: state-trait issues, clinical features and pharmacological treatment

Author: Molendijk, ML, Bus, BAA, Spinhoven, Ph, Penninx, BWJH, Kenis, G, Prickaerts, J, Voshaar, RC Oude, and Elzinga, BM
Published: 2011
Full Text: View/download PDF

48. Genetic risk profiles for depression and anxiety in adult and elderly cohorts

Author: Demirkan, A, Penninx, BWJH, Hek, K, Wray, NR, Amin, N, Aulchenko, YS, van Dyck, R, de Geus, EJC, Hofman, A, Uitterlinden, AG, Hottenga, J-J, Nolen, WA, Oostra, BA, Sullivan, PF, Willemsen, G, Zitman, FG, Tiemeier, H, Janssens, ACJW, Boomsma, DI, van Duijn, CM, and Middeldorp, CM
Published: 2011
Full Text: View/download PDF

49. Does food intake mediate the association between mindful eating and change in depressive symptoms?

Author: Winkens, LHH, primary, Elstgeest, LEM, additional, van Strien, T, additional, Penninx, BWJH, additional, Visser, M, additional, and Brouwer, IA, additional
Published: 2020
Full Text: View/download PDF

50. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD)

Author: Jose Ferrao, Francesco Nobilia, Ashley Polhemus, Matthew Hotopf, Vaibhav A. Narayan, Sjaak Peelen, Inez Myin-Germeys, Maximilian Kerz, Femke Lamers, Katie M White, Zulqarnain Rashid, Hans Eriksson, Yatharth Ranjan, Z. Rashid, Wolfgang Viechtbauer, Michiel Ringkjøbing-Elema, David C. Mohr, Sara Simblett, Nikolay V. Manyakov, Nick Meyer, Qingqin Li, Faith Matcham, Richard Dobson, Amos Folarin, Sonia Difrancesco, Alina Ivan, Aki Rintala, C Barattieri di San Pietro, G de Girolamo, Josep Maria Haro, Penninx Bwjh, Viola Bulgari, Til Wykes, Janneke Boere, Sara Siddi, Nicholas Cummins, Matcham, F, Barattieri di San Pietro, C, Bulgari, V, de Girolamo, G, Dobson, R, Eriksson, H, Folarin, A, Haro, J, Kerz, M, Lamers, F, Li, Q, Manyakov, N, Mohr, D, Myin-Germeys, I, Narayan, V, Bwjh, P, Ranjan, Y, Rashid, Z, Rintala, A, Siddi, S, Simblett, S, Wykes, T, and Hotopf, M
Subjects: M-health, Major depressive disorder, Observational cohort, Outcome measurement, Passive sensing, Prospective study, Remote measurement technology, Adolescent, Adult, Depressive Disorder, Major, Female, Humans, Male, Mobile Applications, Observational Studies as Topic, Recurrence, Remote Sensing Technology, Smartphone, Surveys and Questionnaires, Telemedicine, Young Adult, Prospective Studies, SYMPTOMS, ALCOHOL, THERAPY, Study Protocol, 0302 clinical medicine, FITBIT, lcsh:Psychiatry, 030212 general & internal medicine, Depressió psíquica, SCALE, Psychiatry, Activity tracker, PRIMARY-CARE, Cognition, 3. Good health, Psychiatry and Mental health, Mental depression, RELIABILITY, Psychology, M-PSI/01 - PSICOLOGIA GENERALE, Life Sciences & Biomedicine, Anxiety disorder, Clinical psychology, Cohort study, L-LIN/01 - GLOTTOLOGIA E LINGUISTICA, Elementary cognitive task, lcsh:RC435-571, ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI, EVENTS, 03 medical and health sciences, Quality of life (healthcare), medicine, Medical technology, VALIDITY, MED/26 - NEUROLOGIA, Depressive Disorder, Science & Technology, Major, medicine.disease, 030227 psychiatry, LIFE, Mood, MED/25 - PSICHIATRIA, Tecnologia mèdica
Abstract: Background There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. Methods RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants’ sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation’s self-reported Composite International Diagnostic Interview (CIDI-SF). Discussion This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed. Electronic supplementary material The online version of this article (10.1186/s12888-019-2049-z) contains supplementary material, which is available to authorized users.
Published: 2019

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Region

Database

Publisher

561 results on '"Penninx, BWJH"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources