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1. Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

Author

Zhang, Yuezhou, Folarin, Amos A., Ranjan, Yatharth, Cummins, Nicholas, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Sun, Shaoxiong, Vairavan, Srinivasan, Matcham, Faith, Oetzmann, Carolin, Siddi, Sara, Lamers, Femke, Simblett, Sara, Wykes, Til, Mohr, David C., Haro, Josep Maria, Penninx, Brenda W. J. H., Narayan, Vaibhav A., Hotopf, Matthew, Dobson, Richard J. B., Pratap, Abhishek, and consortium, RADAR-CNS

Subjects
Statistics - Applications
Abstract

Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the interplay between weather, behavior, and depression has not been fully quantified. This study analyzed real-world data from 428 participants (a subset; 68.7% of the cohort) in the RADAR-MDD longitudinal mobile health study to investigate seasonal variations in depression (measured through a remote validated assessment - PHQ-8) and examine the potential interplay between dynamic weather changes, physical activity (monitored via wearables), and depression severity. The clustering of PHQ-8 scores identified four distinct seasonal variations in depression severity: one stable trend and three varying patterns where depression peaks in different seasons. Among these patterns, participants within the stable trend had the oldest average age (p=0.002) and the lowest baseline PHQ-8 score (p=0.003). Mediation analysis assessing the indirect effect of weather on physical activity and depression showed significant differences among participants with different affective responses to weather. These findings illustrate the heterogeneity in individuals' seasonal depression variations and responses to weather, underscoring the necessity for personalized approaches to help understand the impact of environmental factors on the real-world effectiveness of behavioral treatments.

Published
2024

2. Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

Author

Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Bendayan, Rebecca, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Laiou, Petroula, Matcham, Faith, White, Katie M, Lamers, Femke, Siddi, Sara, Simblett, Sara, Myin-Germeys, Inez, Rintala, Aki, Wykes, Til, Haro, Josep Maria, Penninx, Brenda WJH, Narayan, Vaibhav A, Hotopf, Matthew, and Dobson, Richard JB

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundSleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. ObjectiveThe main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). MethodsDaily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. ResultsWe tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P

Published
2021
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3. Sociodemographic, Health and Lifestyle, Sampling, and Mental Health Determinants of 24-Hour Motor Activity Patterns: Observational Study

Author

Difrancesco, Sonia, Riese, Harriëtte, Merikangas, Kathleen R, Shou, Haochang, Zipunnikov, Vadim, Antypa, Niki, van Hemert, Albert M, Schoevers, Robert A, Penninx, Brenda W J H, and Lamers, Femke

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundAnalyzing actigraphy data using standard circadian parametric models and aggregated nonparametric indices may obscure temporal information that may be a hallmark of the circadian impairment in psychiatric disorders. Functional data analysis (FDA) may overcome such limitations by fully exploiting the richness of actigraphy data and revealing important relationships with mental health outcomes. To our knowledge, no studies have extensively used FDA to study the relationship between sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics and daily motor activity patterns assessed with actigraphy in a sample of individuals with and without depression/anxiety. ObjectiveWe aimed to study the association between daily motor activity patterns assessed via actigraphy and (1) sociodemographic, health and lifestyle, and sampling factors, and (2) psychiatric clinical characteristics (ie, presence and severity of depression/anxiety disorders). MethodsWe obtained 14-day continuous actigraphy data from 359 participants from the Netherlands Study of Depression and Anxiety with current (n=93), remitted (n=176), or no (n=90) depression/anxiety diagnosis, based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition. Associations between patterns of daily motor activity, quantified via functional principal component analysis (fPCA), and sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics were assessed using generalized estimating equation regressions. For exploratory purposes, function-on-scalar regression (FoSR) was applied to quantify the time-varying association of sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics on daily motor activity. ResultsFour components of daily activity patterns captured 77.4% of the variability in the data: overall daily activity level (fPCA1, 34.3% variability), early versus late morning activity (fPCA2, 16.5% variability), biphasic versus monophasic activity (fPCA3, 14.8% variability), and early versus late biphasic activity (fPCA4, 11.8% variability). A low overall daily activity level was associated with a number of sociodemographic, health and lifestyle, and psychopathology variables: older age (P

Published
2021
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4. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Author

García-Marín, Luis M., Campos, Adrian I., Diaz-Torres, Santiago, Rabinowitz, Jill A., Ceja, Zuriel, Mitchell, Brittany L., Grasby, Katrina L., Thorp, Jackson G., Agartz, Ingrid, Alhusaini, Saud, Ames, David, Amouyel, Philippe, Andreassen, Ole A., Arfanakis, Konstantinos, Arias-Vasquez, Alejandro, Armstrong, Nicola J., Athanasiu, Lavinia, Bastin, Mark E., Beiser, Alexa S., Bennett, David A., Bis, Joshua C., Boks, Marco P. M., Boomsma, Dorret I., Brodaty, Henry, Brouwer, Rachel M., Buitelaar, Jan K., Burkhardt, Ralph, Cahn, Wiepke, Calhoun, Vince D., Carmichael, Owen T., Chakravarty, Mallar, Chen, Qiang, Ching, Christopher R. K., Cichon, Sven, Crespo-Facorro, Benedicto, Crivello, Fabrice, Dale, Anders M., Smith, George Davey, de Geus, Eco J. C., De Jager, Philip L., de Zubicaray, Greig I., Debette, Stéphanie, DeCarli, Charles, Depondt, Chantal, Desrivières, Sylvane, Djurovic, Srdjan, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fernández, Guillén, Filippi, Irina, Fisher, Simon E., Fleischman, Debra A., Fletcher, Evan, Fornage, Myriam, Forstner, Andreas J., Francks, Clyde, Franke, Barbara, Ge, Tian, Goldman, Aaron L., Grabe, Hans J., Green, Robert C., Grimm, Oliver, Groenewold, Nynke A., Gruber, Oliver, Gudnason, Vilmundur, Håberg, Asta K., Haukvik, Unn K., Heinz, Andreas, Hibar, Derrek P., Hilal, Saima, Himali, Jayandra J., Ho, Beng-Choon, Hoehn, David F., Hoekstra, Pieter J., Hofer, Edith, Hoffmann, Wolfgang, Holmes, Avram J., Homuth, Georg, Hosten, Norbert, Ikram, M. Kamran, Ipser, Jonathan C., Jack Jr, Clifford R., Jahanshad, Neda, Jönsson, Erik G., Kahn, Rene S., Kanai, Ryota, Klein, Marieke, Knol, Maria J., Launer, Lenore J., Lawrie, Stephen M., Hellard, Stephanie Le, Lee, Phil H., Lemaître, Hervé, Li, Shuo, Liewald, David C. M., Lin, Honghuang, Longstreth, Jr, W. T., Lopez, Oscar L., Luciano, Michelle, Maillard, Pauline, Marquand, Andre F., Martin, Nicholas G., Martinot, Jean-Luc, Mather, Karen A., Mattay, Venkata S., McMahon, Katie L., Mecocci, Patrizia, Melle, Ingrid, Meyer-Lindenberg, Andreas, Mirza-Schreiber, Nazanin, Milaneschi, Yuri, Mosley, Thomas H., Mühleisen, Thomas W., Müller-Myhsok, Bertram, Maniega, Susana Muñoz, Nauck, Matthias, Nho, Kwangsik, Niessen, Wiro J., Nöthen, Markus M., Nyquist, Paul A., Oosterlaan, Jaap, Pandolfo, Massimo, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W. J. H., Pike, G. Bruce, Psaty, Bruce M., Pütz, Benno, Reppermund, Simone, Rietschel, Marcella D., Risacher, Shannon L., Romanczuk-Seiferth, Nina, Romero-Garcia, Rafael, Roshchupkin, Gennady V., Rotter, Jerome I., Sachdev, Perminder S., Sämann, Philipp G., Saremi, Arvin, Sargurupremraj, Muralidharan, Saykin, Andrew J., Schmaal, Lianne, Schmidt, Helena, Schmidt, Reinhold, Schofield, Peter R., Scholz, Markus, Schumann, Gunter, Schwarz, Emanuel, Shen, Li, Shin, Jean, Sisodiya, Sanjay M., Smith, Albert V., Smoller, Jordan W., Soininen, Hilkka S., Steen, Vidar M., Stein, Dan J., Stein, Jason L., Thomopoulos, Sophia I., Toga, Arthur W., Tordesillas-Gutiérrez, Diana, Trollor, Julian N., Valdes-Hernandez, Maria C., van ′t Ent, Dennis, van Bokhoven, Hans, van der Meer, Dennis, van der Wee, Nic J. A., Vázquez-Bourgon, Javier, Veltman, Dick J., Vernooij, Meike W., Villringer, Arno, Vinke, Louis N., Völzke, Henry, Walter, Henrik, Wardlaw, Joanna M., Weinberger, Daniel R., Weiner, Michael W., Wen, Wei, Westlye, Lars T., Westman, Eric, White, Tonya, Witte, A. Veronica, Wolf, Christiane, Yang, Jingyun, Zwiers, Marcel P., Ikram, M. Arfan, Seshadri, Sudha, Thompson, Paul M., Satizabal, Claudia L., Medland, Sarah E., and Rentería, Miguel E.

Published
2024
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6. Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19

Author

Sun, Shaoxiong, Folarin, Amos A, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Cummins, Nicholas, Matcham, Faith, Dalla Costa, Gloria, Simblett, Sara, Leocani, Letizia, Lamers, Femke, Sørensen, Per Soelberg, Buron, Mathias, Zabalza, Ana, Guerrero Pérez, Ana Isabel, Penninx, Brenda WJH, Siddi, Sara, Haro, Josep Maria, Myin-Germeys, Inez, Rintala, Aki, Wykes, Til, Narayan, Vaibhav A, Comi, Giancarlo, Hotopf, Matthew, and Dobson, Richard JB

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundIn the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. ObjectiveWe aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. MethodsWe analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. ResultsWe were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P

Published
2020
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7. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing

Author

Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Ranjan, Yatharth, Rashid, Zulqarnain, Stewart, Callum, Conde, Pauline, Sankesara, Heet, Laiou, Petroula, Matcham, Faith, White, Katie M, Oetzmann, Carolin, Lamers, Femke, Siddi, Sara, Simblett, Sara, Vairavan, Srinivasan, Myin-Germeys, Inez, Mohr, David C., Wykes, Til, Haro, Josep Maria, Annas, Peter, Penninx, Brenda WJH, Narayan, Vaibhav A, Hotopf, Matthew, Dobson, Richard JB, and consortium, RADAR-CNS

Subjects
Statistics - Applications, Quantitative Biology - Quantitative Methods
Abstract

Objective: This study aimed to explore the associations between depression severity and wearable-measured circadian rhythms, accounting for seasonal impacts and quantifying seasonal changes in circadian rhythms.Materials and Methods: Data used in this study came from a large longitudinal mobile health study. Depression severity (measured biweekly using the 8-item Patient Health Questionnaire [PHQ-8]) and behaviors (monitored by Fitbit) were tracked for up to two years. Twelve features were extracted from Fitbit recordings to approximate circadian rhythms. Three nested linear mixed-effects models were employed for each feature: (1) incorporating the PHQ-8 score as an independent variable; (2) adding the season variable; and (3) adding an interaction term between season and the PHQ-8 score. Results: This study analyzed 10,018 PHQ-8 records with Fitbit data from 543 participants. Upon adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced activity, irregular behaviors, and delayed rhythms. Notably, the negative association with daily step counts was stronger in summer and spring than in winter, and the positive association with the onset of the most active continuous 10-hour period was significant only during summer. Furthermore, participants had shorter and later sleep, more activity, and delayed circadian rhythms in summer compared to winter. Discussion and Conclusions: Our findings underscore the significant seasonal impacts on human circadian rhythms and their associations with depression and indicate that wearable-measured circadian rhythms have the potential to be the digital biomarkers of depression.

Published
2023

9. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Published
2023

10. X-chromosome and kidney function: evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen response elements.

Author

Scholz, Markus, Horn, Katrin, Pott, Janne, Wuttke, Matthias, Kühnapfel, Andreas, Nasr, M, Kirsten, Holger, Li, Yong, Hoppmann, Anselm, Gorski, Mathias, Ghasemi, Sahar, Li, Man, Tin, Adrienne, Chai, Jin-Fang, Cocca, Massimiliano, Wang, Judy, Nutile, Teresa, Akiyama, Masato, Åsvold, Bjørn, Bansal, Nisha, Biggs, Mary, Boutin, Thibaud, Brenner, Hermann, Brumpton, Ben, Burkhardt, Ralph, Cai, Jianwen, Campbell, Archie, Campbell, Harry, Chalmers, John, Chasman, Daniel, Chee, Miao, Chen, Xu, Cheng, Ching-Yu, Cifkova, Renata, Daviglus, Martha, Delgado, Graciela, Dittrich, Katalin, Edwards, Todd, Endlich, Karlhans, Michael Gaziano, J, Giri, Ayush, Giulianini, Franco, Gordon, Scott, Gudbjartsson, Daniel, Hallan, Stein, Hamet, Pavel, Hartman, Catharina, Hayward, Caroline, Heid, Iris, Hellwege, Jacklyn, Holleczek, Bernd, Holm, Hilma, Hutri-Kähönen, Nina, Hveem, Kristian, Isermann, Berend, Jonas, Jost, Joshi, Peter, Kamatani, Yoichiro, Kanai, Masahiro, Kastarinen, Mika, Khor, Chiea, Kiess, Wieland, Kleber, Marcus, Körner, Antje, Kovacs, Peter, Krajcoviechova, Alena, Kramer, Holly, Krämer, Bernhard, Kuokkanen, Mikko, Kähönen, Mika, Lange, Leslie, Lash, James, Lehtimäki, Terho, Li, Hengtong, Lin, Bridget, Liu, Jianjun, Loeffler, Markus, Lyytikäinen, Leo-Pekka, Magnusson, Patrik, Martin, Nicholas, Matsuda, Koichi, Milaneschi, Yuri, Mishra, Pashupati, Mononen, Nina, Montgomery, Grant, Mook-Kanamori, Dennis, Mychaleckyj, Josyf, März, Winfried, Nauck, Matthias, Nikus, Kjell, Nolte, Ilja, Noordam, Raymond, Okada, Yukinori, Olafsson, Isleifur, Oldehinkel, Albertine, Penninx, Brenda, Perola, Markus, Pirastu, Nicola, and Polasek, Ozren

Subjects
Humans, Male, Female, Androgens, Genome-Wide Association Study, Kidney, Chromosomes, Human, X, Response Elements, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Tetraspanins
Abstract

X-chromosomal genetic variants are understudied but can yield valuable insights into sexually dimorphic human traits and diseases. We performed a sex-stratified cross-ancestry X-chromosome-wide association meta-analysis of seven kidney-related traits (n = 908,697), identifying 23 loci genome-wide significantly associated with two of the traits: 7 for uric acid and 16 for estimated glomerular filtration rate (eGFR), including four novel eGFR loci containing the functionally plausible prioritized genes ACSL4, CLDN2, TSPAN6 and the female-specific DRP2. Further, we identified five novel sex-interactions, comprising male-specific effects at FAM9B and AR/EDA2R, and three sex-differential findings with larger genetic effect sizes in males at DCAF12L1 and MST4 and larger effect sizes in females at HPRT1. All prioritized genes in loci showing significant sex-interactions were located next to androgen response elements (ARE). Five ARE genes showed sex-differential expressions. This study contributes new insights into sex-dimorphisms of kidney traits along with new prioritized gene targets for further molecular research.

Published
2024

11. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Author

Zhang, Yuezhou, Folarin, Amos A, Dineley, Judith, Conde, Pauline, de Angel, Valeria, Sun, Shaoxiong, Ranjan, Yatharth, Rashid, Zulqarnain, Stewart, Callum, Laiou, Petroula, Sankesara, Heet, Qian, Linglong, Matcham, Faith, White, Katie M, Oetzmann, Carolin, Lamers, Femke, Siddi, Sara, Simblett, Sara, Schuller, Björn W., Vairavan, Srinivasan, Wykes, Til, Haro, Josep Maria, Penninx, Brenda WJH, Narayan, Vaibhav A, Hotopf, Matthew, Dobson, Richard JB, Cummins, Nicholas, and consortium, RADAR-CNS

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Quantitative Biology - Quantitative Methods
Abstract

Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no context. Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression: No Expectations, Sleep, Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic emergence and associations with depression, we compared behavioral (from wearables) and linguistic characteristics across identified topics. The correlation between topic shifts and changes in depression severity over time was also investigated, indicating the importance of longitudinally monitoring language use. We also tested the BERTopic model on a similar smaller dataset (356 speech recordings from 57 participants), obtaining some consistent results. In summary, our findings demonstrate specific speech topics may indicate depression severity. The presented data-driven workflow provides a practical approach to collecting and analyzing large-scale speech data from real-world settings for digital health research.

Published
2023

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

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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14. Genome-wide characterization of circulating metabolic biomarkers

Author

Karjalainen, Minna K., Karthikeyan, Savita, Oliver-Williams, Clare, Sliz, Eeva, Allara, Elias, Fung, Wing Tung, Surendran, Praveen, Zhang, Weihua, Jousilahti, Pekka, Kristiansson, Kati, Salomaa, Veikko, Goodwin, Matt, Hughes, David A., Boehnke, Michael, Fernandes Silva, Lilian, Yin, Xianyong, Mahajan, Anubha, Neville, Matt J., van Zuydam, Natalie R., de Mutsert, Renée, Li-Gao, Ruifang, Mook-Kanamori, Dennis O., Demirkan, Ayse, Liu, Jun, Noordam, Raymond, Trompet, Stella, Chen, Zhengming, Kartsonaki, Christiana, Li, Liming, Lin, Kuang, Hagenbeek, Fiona A., Hottenga, Jouke Jan, Pool, René, Ikram, M. Arfan, van Meurs, Joyce, Haller, Toomas, Milaneschi, Yuri, Kähönen, Mika, Mishra, Pashupati P., Joshi, Peter K., Macdonald-Dunlop, Erin, Mangino, Massimo, Zierer, Jonas, Acar, Ilhan E., Hoyng, Carel B., Lechanteur, Yara T. E., Franke, Lude, Kurilshikov, Alexander, Zhernakova, Alexandra, Beekman, Marian, van den Akker, Erik B., Kolcic, Ivana, Polasek, Ozren, Rudan, Igor, Gieger, Christian, Waldenberger, Melanie, Asselbergs, Folkert W., Hayward, Caroline, Fu, Jingyuan, den Hollander, Anneke I., Menni, Cristina, Spector, Tim D., Wilson, James F., Lehtimäki, Terho, Raitakari, Olli T., Penninx, Brenda W. J. H., Esko, Tonu, Walters, Robin G., Jukema, J. Wouter, Sattar, Naveed, Ghanbari, Mohsen, Willems van Dijk, Ko, Karpe, Fredrik, McCarthy, Mark I., Laakso, Markku, Järvelin, Marjo-Riitta, Timpson, Nicholas J., Perola, Markus, Kooner, Jaspal S., Chambers, John C., van Duijn, Cornelia, Slagboom, P. Eline, Boomsma, Dorret I., Danesh, John, Ala-Korpela, Mika, Butterworth, Adam S., and Kettunen, Johannes

Published
2024
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15. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Author

Sun, Shaoxiong, Folarin, Amos A., Zhang, Yuezhou, Cummins, Nicholas, Garcia-Dias, Rafael, Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Laiou, Petroula, Sankesara, Heet, Matcham, Faith, Leightley, Daniel, White, Katie M., Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Simblett, Sara, Nica, Raluca, Rintala, Aki, Mohr, David C., Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda W. J. H., Vairavan, Srinivasan, Narayan, Vaibhav A., Annas, Peter, Hotopf, Matthew, and Dobson, Richard J. B.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. From 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression.

Published
2022
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16. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods
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=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were 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 achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages

Published
2022

18. Social dysfunction relates to shifts within socioaffective brain systems among Schizophrenia and Alzheimer's disease patients

Author

Braak, Simon, Penninx, Brenda WJH, Su, Tanja, Pijnenburg, Yolande, Nijland, Daphne, Campos, Alba Vieira, de la Torre-Luque, Alejandro, Saris, Ilja M.J., Reus, Lianne M., Beckenstrom, Amy Christine, Malik, Asad, Dawson, Gerard R., Marston, Hugh, Alvarez-Linera, Juan, Ayuso-Mateos, Jose-Luis L., Arango, Celso, van der Wee, Nic, Kas, Martien J., and Aghajani, Moji

Published
2024
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19. Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

Author

Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Vairavan, Srinivasan, Qian, Linglong, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Laiou, Petroula, Sankesara, Heet, Matcham, Faith, White, Katie M, Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Simblett, Sara, Rintala, Aki, Mohr, David C, Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda WJH, Narayan, Vaibhav A, Annas, Peter, Hotopf, Matthew, Dobson, Richard JB, and consortium, RADAR-CNS

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of daily walking in real-world scenarios and their relationships with depression are yet to be fully explored. This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. In this study, we used two ambulatory datasets: a public dataset with 71 elder adults' 3-day acceleration signals collected by a wearable device, and a subset of an EU longitudinal depression study with 215 participants and their phone-collected acceleration signals (average 463 hours per participant). We detected participants' gait cycles and force from acceleration signals and extracted 20 statistics-based daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period corresponding to the self-reported depression score. The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets. Daily-life gait features could significantly improve the goodness of fit of evaluating depression severity relative to laboratory gait patterns and demographics, which was assessed by likelihood-ratio tests in both datasets. This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The gait cadence of faster steps in daily-life walking has the potential to be a biomarker for evaluating depression severity, which may contribute to clinical tools to remotely monitor mental health in real-world settings.

Published
2022
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20. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Author

Zhang, Yuezhou, Folarin, Amos A., Dineley, Judith, Conde, Pauline, de Angel, Valeria, Sun, Shaoxiong, Ranjan, Yatharth, Rashid, Zulqarnain, Stewart, Callum, Laiou, Petroula, Sankesara, Heet, Qian, Linglong, Matcham, Faith, White, Katie, Oetzmann, Carolin, Lamers, Femke, Siddi, Sara, Simblett, Sara, Schuller, Björn W., Vairavan, Srinivasan, Wykes, Til, Haro, Josep Maria, Penninx, Brenda W.J.H., Narayan, Vaibhav A., Hotopf, Matthew, Dobson, Richard J.B., and Cummins, Nicholas

Published
2024
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22. Prioritization of Kidney Cell Types Highlights Myofibroblast Cells in Regulating Human Blood Pressure

Author

Evangelou, Evangelos, Warren, Helen R., Gao, He, Ntritsos, Georgios, Dimou, Niki, Esko, Tonu, Mägi, Reedik, Milani, Lili, Almgren, Peter, Boutin, Thibaud, Debette, Stéphanie, Ding, Jun, Giulianini, Franco, Holliday, Elizabeth G., Jackson, Anne U., Li -Gao, Ruifang, Lin, Wei -Yu, Luan, Jian'an, Mangino, Massimo, Oldmeadow, Christopher, Prins, Bram Peter, Qian, Yong, Sargurupremraj, Muralidharan, Shah, Nabi, Surendran, Praveen, Thériault, Sébastien, Verweij, Niek, Willems, Sara M., Zhao, Jing -Hua, Amouyel, Philippe, Connell, John, de Mutsert, Renée, Doney, Alex S.F., Farrall, Martin, Menni, Cristina, Morris, Andrew D., Noordam, Raymond, Paré, Guillaume, Poulter, Neil R., Shields, Denis C., Stanton, Alice, Thom, Simon, Abecasis, Gonçalo, Amin, Najaf, Arking, Dan E., Ayers, Kristin L., Barbieri, Caterina M., Batini, Chiara, Bis, Joshua C., Blake, Tineka, Bochud, Murielle, Boehnke, Michael, Boerwinkle, Eric, Boomsma, Dorret I., Bottinger, Erwin P., Braund, Peter S., Brumat, Marco, Campbell, Archie, Campbell, Harry, Chakravarti, Aravinda, Chambers, John C., Chauhan, Ganesh, Ciullo, Marina, Cocca, Massimiliano, Collins, Francis, Cordell, Heather J., Davies, Gail, de Borst, Martin H., de Geus, Eco J., Deary, Ian J., Deelen, Joris, Del Greco M, Fabiola, Demirkale, Cumhur Yusuf, Dörr, Marcus, Ehret, Georg B., Elosua, Roberto, Enroth, Stefan, Erzurumluoglu, A. Mesut, Ferreira, Teresa, Frånberg, Mattias, Franco, Oscar H., Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Gieger, Christian, Girotto, Giorgia, Goel, Anuj, Gow, Alan J., Gudnason, Vilmundur, Guo, Xiuqing, Gyllensten, Ulf, Hamsten, Anders, Harris, Tamara B., Harris, Sarah E., Hartman, Catharina A., Havulinna, Aki S., Hicks, Andrew A., Hofer, Edith, Hofman, Albert, Hottenga, Jouke-Jan, Huffman, Jennifer E., Hwang, Shih-Jen, Ingelsson, Erik, James, Alan, Jansen, Rick, Jarvelin, Marjo -Riitta, Joehanes, Roby, Johansson, Åsa, Johnson, Andrew D., Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Jula, Antti, Kähönen, Mika, Kathiresan, Sekar, Keavney, Bernard D., Khaw, Kay-Tee, Knekt, Paul, Knight, Joanne, Kolcic, Ivana, Kooner, Jaspal S., Koskinen, Seppo, Kristiansson, Kati, Kutalik, Zoltan, Laan, Maris, Larson, Marty, Launer, Lenore J., Lehne, Benjamin, Lehtimäki, Terho, Liewald, David C.M., Lin, Li, Lind, Lars, Lindgren, Cecilia M., Liu, YongMei, Loos, Ruth J.F., Lopez, Lorna M., Lu, Yingchang, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamasoula, Chrysovalanto, Marrugat, Jaume, Marten, Jonathan, Milaneschi, Yuri, Morgan, Anna, Morris, Andrew P., Morrison, Alanna C., Munson, Peter J., Nalls, Mike A., Nandakumar, Priyanka, Nelson, Christopher P., Niiranen, Teemu, Nolte, Ilja M., Nutile, Teresa, Oldehinkel, Albertine J., Oostra, Ben A., O'Reilly, Paul F., Org, Elin, Padmanabhan, Sandosh, Palmas, Walter, Palotie, Aarno, Pattie, Alison, Penninx, Brenda W.J.H., Perola, Markus, Peters, Annette, Polasek, Ozren, Pramstaller, Peter P., Nguyen, Quang Tri, Raitakari, Olli T., Rettig, Rainer, Rice, Kenneth, Ridker, Paul M., Ried, Janina S., Riese, Harriëtte, Ripatti, Samuli, Robino, Antonietta, Rose, Lynda M., Rotter, Jerome I., Rudan, Igor, Ruggiero, Daniela, Saba, Yasaman, Sala, Cinzia F., Salomaa, Veikko, Samani, Nilesh J., Sarin, Antti-Pekka, Schmidt, Reinhold, Schmidt, Helena, Shrine, Nick, Siscovick, David, Smith, Albert V., Snieder, Harold, Sõber, Siim, Sorice, Rossella, Starr, John M., Stott, David J., Strachan, David P., Strawbridge, Rona J., Sundström, Johan, Swertz, Morris A., Taylor, Kent D., Teumer, Alexander, Tobin, Martin D., Tomaszewski, Maciej, Toniolo, Daniela, Traglia, Michela, Trompet, Stella, Tuomilehto, Jaakko, Tzourio, Christophe, Uitterlinden, André G., Vaez, Ahmad, van der Most, Peter J., van Duijn, Cornelia M., Verwoert, Germaine C., Vitart, Veronique, Völker, Uwe, Vollenweider, Peter, Vuckovic, Dragana, Watkins, Hugh, Wild, Sarah H., Willemsen, Gonneke, Wilson, James F., Wright, Alan F., Yao, Jie, Zemunik, Tatijana, Zhang, Weihua, Attia, John R., Butterworth, Adam S., Chasman, Daniel I., Conen, David, Cucca, Francesco, Danesh, John, Hayward, Caroline, Howson, Joanna M.M., Laakso, Markku, Lakatta, Edward G., Langenberg, Claudia, Melander, Olle, Mook-Kanamori, Dennis O., Palmer, Colin N.A., Risch, Lorenz, Scott, Robert A., Scott, Rodney J., Sever, Peter, Spector, Tim D., van der Harst, Pim, Wareham, Nicholas J., Zeggini, Eleftheria, Levy, Daniel, Munroe, Patricia B., Newton-Cheh, Christopher, Brown, Morris J., Metspalu, Andres, Psaty, Bruce M., Wain, Louise V., Elliott, Paul, Caulfield, Mark J., Ganji-Arjenaki, Mahboube, Kamali, Zoha, Sardari, Soroush, and de Borst, Martin

Published
2024
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24. Major depressive disorder

Author

Marx, Wolfgang, Penninx, Brenda W. J. H., Solmi, Marco, Furukawa, Toshi A., Firth, Joseph, Carvalho, Andre F., and Berk, Michael

Published
2023
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25. Genetic insights into resting heart rate and its role in cardiovascular disease

Author

van de Vegte, Yordi J., Eppinga, Ruben N., van der Ende, M. Yldau, Hagemeijer, Yanick P., Mahendran, Yuvaraj, Salfati, Elias, Smith, Albert V., Tan, Vanessa Y., Arking, Dan E., Ntalla, Ioanna, Appel, Emil V., Schurmann, Claudia, Brody, Jennifer A., Rueedi, Rico, Polasek, Ozren, Sveinbjornsson, Gardar, Lecoeur, Cecile, Ladenvall, Claes, Zhao, Jing Hua, Isaacs, Aaron, Wang, Lihua, Luan, Jian’an, Hwang, Shih-Jen, Mononen, Nina, Auro, Kirsi, Jackson, Anne U., Bielak, Lawrence F., Zeng, Linyao, Shah, Nabi, Nethander, Maria, Campbell, Archie, Rankinen, Tuomo, Pechlivanis, Sonali, Qi, Lu, Zhao, Wei, Rizzi, Federica, Tanaka, Toshiko, Robino, Antonietta, Cocca, Massimiliano, Lange, Leslie, Müller-Nurasyid, Martina, Roselli, Carolina, Zhang, Weihua, Kleber, Marcus E., Guo, Xiuqing, Lin, Henry J., Pavani, Francesca, Galesloot, Tessel E., Noordam, Raymond, Milaneschi, Yuri, Schraut, Katharina E., den Hoed, Marcel, Degenhardt, Frauke, Trompet, Stella, van den Berg, Marten E., Pistis, Giorgio, Tham, Yih-Chung, Weiss, Stefan, Sim, Xueling S., Li, Hengtong L., van der Most, Peter J., Nolte, Ilja M., Lyytikäinen, Leo-Pekka, Said, M. Abdullah, Witte, Daniel R., Iribarren, Carlos, Launer, Lenore, Ring, Susan M., de Vries, Paul S., Sever, Peter, Linneberg, Allan, Bottinger, Erwin P., Padmanabhan, Sandosh, Psaty, Bruce M., Sotoodehnia, Nona, Kolcic, Ivana, Arnar, David O., Gudbjartsson, Daniel F., Holm, Hilma, Balkau, Beverley, Silva, Claudia T., Newton-Cheh, Christopher H., Nikus, Kjell, Salo, Perttu, Mohlke, Karen L., Peyser, Patricia A., Schunkert, Heribert, Lorentzon, Mattias, Lahti, Jari, Rao, Dabeeru C., Cornelis, Marilyn C., Faul, Jessica D., Smith, Jennifer A., Stolarz-Skrzypek, Katarzyna, Bandinelli, Stefania, Concas, Maria Pina, Sinagra, Gianfranco, Meitinger, Thomas, Waldenberger, Melanie, Sinner, Moritz F., Strauch, Konstantin, Delgado, Graciela E., Taylor, Kent D., Yao, Jie, Foco, Luisa, Melander, Olle, de Graaf, Jacqueline, de Mutsert, Renée, de Geus, Eco J. C., Johansson, Åsa, Joshi, Peter K., Lind, Lars, Franke, Andre, Macfarlane, Peter W., Tarasov, Kirill V., Tan, Nicholas, Felix, Stephan B., Tai, E-Shyong, Quek, Debra Q., Snieder, Harold, Ormel, Johan, Ingelsson, Martin, Lindgren, Cecilia, Morris, Andrew P., Raitakari, Olli T., Hansen, Torben, Assimes, Themistocles, Gudnason, Vilmundur, Timpson, Nicholas J., Morrison, Alanna C., Munroe, Patricia B., Strachan, David P., Grarup, Niels, Loos, Ruth J. F., Heckbert, Susan R., Vollenweider, Peter, Hayward, Caroline, Stefansson, Kari, Froguel, Philippe, Groop, Leif, Wareham, Nicholas J., van Duijn, Cornelia M., Feitosa, Mary F., O’Donnell, Christopher J., Kähönen, Mika, Perola, Markus, Boehnke, Michael, Kardia, Sharon L. R., Erdmann, Jeanette, Palmer, Colin N. A., Ohlsson, Claes, Porteous, David J., Eriksson, Johan G., Bouchard, Claude, Moebus, Susanne, Kraft, Peter, Weir, David R., Cusi, Daniele, Ferrucci, Luigi, Ulivi, Sheila, Girotto, Giorgia, Correa, Adolfo, Kääb, Stefan, Peters, Annette, Chambers, John C., Kooner, Jaspal S., März, Winfried, Rotter, Jerome I., Hicks, Andrew A., Smith, J. Gustav, Kiemeney, Lambertus A. L. M., Mook-Kanamori, Dennis O., Penninx, Brenda W. J. H., Gyllensten, Ulf, Wilson, James F., Burgess, Stephen, Sundström, Johan, Lieb, Wolfgang, Jukema, J. Wouter, Eijgelsheim, Mark, Lakatta, Edward L. M., Cheng, Ching-Yu, Dörr, Marcus, Wong, Tien-Yin, Sabanayagam, Charumathi, Oldehinkel, Albertine J., Riese, Harriette, Lehtimäki, Terho, Verweij, Niek, and van der Harst, Pim

Published
2023
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27. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

Author

Zhang, Yuezhou, Pratap, Abhishek, Folarin, Amos A., Sun, Shaoxiong, Cummins, Nicholas, Matcham, Faith, Vairavan, Srinivasan, Dineley, Judith, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, White, Katie M., Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Rambla, Carla Hernández, Simblett, Sara, Nica, Raluca, Mohr, David C., Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda W. J. H., Annas, Peter, Narayan, Vaibhav A., Hotopf, Matthew, and Dobson, Richard J. B.

Published
2023
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29. Depression, cardiometabolic disease, and their co-occurrence after childhood maltreatment: an individual participant data meta-analysis including over 200,000 participants

Author

Souama, Camille, Lamers, Femke, Milaneschi, Yuri, Vinkers, Christiaan H., Defina, Serena, Garvert, Linda, Stein, Frederike, Woofenden, Tom, Brosch, Katharina, Dannlowski, Udo, Galenkamp, Henrike, de Graaf, Ron, Jaddoe, Vincent W. V., Lok, Anja, van Rijn, Bas B., Völzke, Henry, Cecil, Charlotte A. M., Felix, Janine F., Grabe, Hans J., Kircher, Tilo, Lekadir, Karim, Have, Margreet ten, Walton, Esther, and Penninx, Brenda W. J. H.

Published
2023
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30. Design and rationale of the REStoring mood after early life trauma with psychotherapy (RESET-psychotherapy) study: a multicenter randomized controlled trial on the efficacy of adjunctive trauma-focused therapy (TFT) versus treatment as usual (TAU) for adult patients with major depressive disorder (MDD) and childhood trauma

Author

Gathier, Anouk W., Verhoeven, Josine E., van Oppen, Patricia C., Penninx, Brenda W. J. H., Merkx, Maarten J. M., Dingemanse, Pieter, Stehouwer, Kim M. K. S., van den Bulck, Carmen M. M., and Vinkers, Christiaan H.

Published
2023
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31. Metabolomic and inflammatory signatures of symptom dimensions in major depression

Author

Brydges, Christopher R, Bhattacharyya, Sudeepa, Dehkordi, Siamak Mahmoudian, Milaneschi, Yuri, Penninx, Brenda, Jansen, Rick, Kristal, Bruce S, Han, Xianlin, Arnold, Matthias, Kastenmüller, Gabi, Bekhbat, Mandakh, Mayberg, Helen S, Craighead, W Edward, Rush, A John, Fiehn, Oliver, Dunlop, Boadie W, Kaddurah-Daouk, Rima, and Consortium, for the Mood Disorders Precision Medicine

Subjects
Depression, Mental Health, Brain Disorders, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Amino Acids, Depressive Disorder, Major, Fatty Acids, Nonesterified, Humans, Metabolomics, Anxiety, Inflammation, Mood Disorders Precision Medicine Consortium, Immunology, Neurosciences, Psychology, Neurology & Neurosurgery
Abstract

BackgroundMajor depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.MethodsUsing three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.ResultsThe IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.ConclusionThe IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Published
2022

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

Author

Sforzini, Luca, Worrell, Courtney, Kose, Melisa, Anderson, Ian M, Aouizerate, Bruno, Arolt, Volker, Bauer, Michael, Baune, Bernhard T, Blier, Pierre, Cleare, Anthony J, Cowen, Philip J, Dinan, Timothy G, Fagiolini, Andrea, Ferrier, I Nicol, Hegerl, Ulrich, Krystal, Andrew D, Leboyer, Marion, McAllister-Williams, R Hamish, McIntyre, Roger S, Meyer-Lindenberg, Andreas, Miller, Andrew H, Nemeroff, Charles B, Normann, Claus, Nutt, David, Pallanti, Stefano, Pani, Luca, Penninx, Brenda WJH, Schatzberg, Alan F, Shelton, Richard C, Yatham, Lakshmi N, Young, Allan H, Zahn, Roland, Aislaitner, Georgios, Butlen-Ducuing, Florence, Fletcher, Christine, Haberkamp, Marion, Laughren, Thomas, Mäntylä, Fanni-Laura, Schruers, Koen, Thomson, Andrew, Arteaga-Henríquez, Gara, Benedetti, Francesco, Cash-Gibson, Lucinda, Chae, Woo Ri, De Smedt, Heidi, Gold, Stefan M, Hoogendijk, Witte JG, Mondragón, Valeria Jordán, Maron, Eduard, Martynowicz, Jadwiga, Melloni, Elisa, Otte, Christian, Perez-Fuentes, Gabriela, Poletti, Sara, Schmidt, Mark E, van de Ketterij, Edwin, Woo, Katherine, Flossbach, Yanina, Ramos-Quiroga, J Antoni, Savitz, Adam J, and Pariante, Carmine M

Subjects
Clinical Trials and Supportive Activities, Depression, Clinical Research, Mental Health, Brain Disorders, Serious Mental Illness, Major Depressive Disorder, Good Health and Well Being, Depressive Disorder, Major, Depressive Disorder, Treatment-Resistant, Humans, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
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

36. The Human Affectome

Author

Schiller, Daniela, Yu, Alessandra N.C., Alia-Klein, Nelly, Becker, Susanne, Cromwell, Howard C., Dolcos, Florin, Eslinger, Paul J., Frewen, Paul, Kemp, Andrew H., Pace-Schott, Edward F., Raber, Jacob, Silton, Rebecca L., Stefanova, Elka, Williams, Justin H.G., Abe, Nobuhito, Aghajani, Moji, Albrecht, Franziska, Alexander, Rebecca, Anders, Silke, Aragón, Oriana R., Arias, Juan A., Arzy, Shahar, Aue, Tatjana, Baez, Sandra, Balconi, Michela, Ballarini, Tommaso, Bannister, Scott, Banta, Marlissa C., Barrett, Karen Caplovitz, Belzung, Catherine, Bensafi, Moustafa, Booij, Linda, Bookwala, Jamila, Boulanger-Bertolus, Julie, Boutros, Sydney Weber, Bräscher, Anne-Kathrin, Bruno, Antonio, Busatto, Geraldo, Bylsma, Lauren M., Caldwell-Harris, Catherine, Chan, Raymond C.K., Cherbuin, Nicolas, Chiarella, Julian, Cipresso, Pietro, Critchley, Hugo, Croote, Denise E., Demaree, Heath A., Denson, Thomas F., Depue, Brendan, Derntl, Birgit, Dickson, Joanne M., Dolcos, Sanda, Drach-Zahavy, Anat, Dubljević, Olga, Eerola, Tuomas, Ellingsen, Dan-Mikael, Fairfield, Beth, Ferdenzi, Camille, Friedman, Bruce H., Fu, Cynthia H.Y., Gatt, Justine M., de Gelder, Beatrice, Gendolla, Guido H.E., Gilam, Gadi, Goldblatt, Hadass, Gooding, Anne Elizabeth Kotynski, Gosseries, Olivia, Hamm, Alfons O., Hanson, Jamie L., Hendler, Talma, Herbert, Cornelia, Hofmann, Stefan G., Ibanez, Agustin, Joffily, Mateus, Jovanovic, Tanja, Kahrilas, Ian J., Kangas, Maria, Katsumi, Yuta, Kensinger, Elizabeth, Kirby, Lauren A.J., Koncz, Rebecca, Koster, Ernst H.W., Kozlowska, Kasia, Krach, Sören, Kret, Mariska E., Krippl, Martin, Kusi-Mensah, Kwabena, Ladouceur, Cecile D., Laureys, Steven, Lawrence, Alistair, Li, Chiang-shan R., Liddell, Belinda J., Lidhar, Navdeep K., Lowry, Christopher A., Magee, Kelsey, Marin, Marie-France, Mariotti, Veronica, Martin, Loren J., Marusak, Hilary A., Mayer, Annalina V., Merner, Amanda R., Minnier, Jessica, Moll, Jorge, Morrison, Robert G., Moore, Matthew, Mouly, Anne-Marie, Mueller, Sven C., Mühlberger, Andreas, Murphy, Nora A., Muscatello, Maria Rosaria Anna, Musser, Erica D., Newton, Tamara L., Noll-Hussong, Michael, Norrholm, Seth Davin, Northoff, Georg, Nusslock, Robin, Okon-Singer, Hadas, Olino, Thomas M., Ortner, Catherine, Owolabi, Mayowa, Padulo, Caterina, Palermo, Romina, Palumbo, Rocco, Palumbo, Sara, Papadelis, Christos, Pegna, Alan J., Pellegrini, Silvia, Peltonen, Kirsi, Penninx, Brenda W.J.H., Pietrini, Pietro, Pinna, Graziano, Lobo, Rosario Pintos, Polnaszek, Kelly L., Polyakova, Maryna, Rabinak, Christine, Helene Richter, S., Richter, Thalia, Riva, Giuseppe, Rizzo, Amelia, Robinson, Jennifer L., Rosa, Pedro, Sachdev, Perminder S., Sato, Wataru, Schroeter, Matthias L., Schweizer, Susanne, Shiban, Youssef, Siddharthan, Advaith, Siedlecka, Ewa, Smith, Robert C., Soreq, Hermona, Spangler, Derek P., Stern, Emily R., Styliadis, Charis, Sullivan, Gavin B., Swain, James E., Urben, Sébastien, Van den Stock, Jan, vander Kooij, Michael A., van Overveld, Mark, Van Rheenen, Tamsyn E., VanElzakker, Michael B., Ventura-Bort, Carlos, Verona, Edelyn, Volk, Tyler, Wang, Yi, Weingast, Leah T., Weymar, Mathias, Williams, Claire, Willis, Megan L., Yamashita, Paula, Zahn, Roland, Zupan, Barbra, and Lowe, Leroy

Published
2024
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37. Charting brain growth and aging at high spatial precision

Author

Rutherford, Saige, Fraza, Charlotte, Dinga, Richard, Kia, Seyed Mostafa, Wolfers, Thomas, Zabihi, Mariam, Berthet, Pierre, Worker, Amanda, Verdi, Serena, Andrews, Derek, Han, Laura KM, Bayer, Johanna MM, Dazzan, Paola, McGuire, Phillip, Mocking, Roel T, Schene, Aart, Sripada, Chandra, Tso, Ivy F, Duval, Elizabeth R, Chang, Soo-Eun, Penninx, Brenda WJH, Heitzeg, Mary M, Burt, S Alexandra, Hyde, Luke W, Amaral, David, Nordahl, Christine Wu, Andreasssen, Ole A, Westlye, Lars T, Zahn, Roland, Ruhe, Henricus G, Beckmann, Christian, and Marquand, Andre F

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Health Sciences, Biomedical Imaging, Neurosciences, Neurological, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Aging, Big Data, Brain, Child, Child, Preschool, Cohort Studies, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Statistical, Neuroimaging, Young Adult, normative model, lifespan, growth chart, brain chart, big data, individual prediction, Human, human, neuroscience, Biochemistry and Cell Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.

Published
2022

39. Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus

Author

Mathieson, Iain, Day, Felix R., Barban, Nicola, Tropf, Felix C., Brazel, David M., Vaez, Ahmad, van Zuydam, Natalie, Bitarello, Bárbara D., Gardner, Eugene J., Akimova, Evelina T., Azad, Ajuna, Bergmann, Sven, Bielak, Lawrence F., Boomsma, Dorret I., Bosak, Kristina, Brumat, Marco, Buring, Julie E., Cesarini, David, Chasman, Daniel I., Chavarro, Jorge E., Cocca, Massimiliano, Concas, Maria Pina, Davey Smith, George, Davies, Gail, Deary, Ian J., Esko, Tõnu, Faul, Jessica D., Franco, Oscar, Ganna, Andrea, Gaskins, Audrey J., Gelemanovic, Andrea, de Geus, Eco J. C., Gieger, Christian, Girotto, Giorgia, Gopinath, Bamini, Grabe, Hans Jörgen, Gunderson, Erica P., Hayward, Caroline, He, Chunyan, van Heemst, Diana, Hill, W. David, Hoffmann, Eva R., Homuth, Georg, Hottenga, Jouke Jan, Huang, Hongyang, Hyppӧnen, Elina, Ikram, M. Arfan, Jansen, Rick, Johannesson, Magnus, Kamali, Zoha, Kardia, Sharon L. R., Kavousi, Maryam, Kifley, Annette, Kiiskinen, Tuomo, Kraft, Peter, Kühnel, Brigitte, Langenberg, Claudia, Liew, Gerald, Lind, Penelope A., Luan, Jian’an, Mägi, Reedik, Magnusson, Patrik K. E., Mahajan, Anubha, Martin, Nicholas G., Mbarek, Hamdi, McCarthy, Mark I., McMahon, George, Medland, Sarah E., Meitinger, Thomas, Metspalu, Andres, Mihailov, Evelin, Milani, Lili, Missmer, Stacey A., Mitchell, Paul, Møllegaard, Stine, Mook-Kanamori, Dennis O., Morgan, Anna, van der Most, Peter J., de Mutsert, Renée, Nauck, Matthias, Nolte, Ilja M., Noordam, Raymond, Penninx, Brenda W. J. H., Peters, Annette, Peyser, Patricia A., Polašek, Ozren, Power, Chris, Pribisalic, Ajka, Redmond, Paul, Rich-Edwards, Janet W., Ridker, Paul M., Rietveld, Cornelius A., Ring, Susan M., Rose, Lynda M., Rueedi, Rico, Shukla, Vallari, Smith, Jennifer A., Stankovic, Stasa, Stefánsson, Kári, Stöckl, Doris, Strauch, Konstantin, Swertz, Morris A., Teumer, Alexander, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, Thurik, A. Roy, Timpson, Nicholas J., Turman, Constance, Uitterlinden, André G., Waldenberger, Melanie, Wareham, Nicholas J., Weir, David R., Willemsen, Gonneke, Zhao, Jing Hau, Zhao, Wei, Zhao, Yajie, Snieder, Harold, den Hoed, Marcel, Ong, Ken K., Mills, Melinda C., and Perry, John R. B.

Published
2023
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41. Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

Author

Gorski, Mathias, Jung, Bettina, Li, Yong, Matias-Garcia, Pamela R, Wuttke, Matthias, Coassin, Stefan, Thio, Chris HL, Kleber, Marcus E, Winkler, Thomas W, Wanner, Veronika, Chai, Jin-Fang, Chu, Audrey Y, Cocca, Massimiliano, Feitosa, Mary F, Ghasemi, Sahar, Hoppmann, Anselm, Horn, Katrin, Li, Man, Nutile, Teresa, Scholz, Markus, Sieber, Karsten B, Teumer, Alexander, Tin, Adrienne, Wang, Judy, Tayo, Bamidele O, Ahluwalia, Tarunveer S, Almgren, Peter, Bakker, Stephan JL, Banas, Bernhard, Bansal, Nisha, Biggs, Mary L, Boerwinkle, Eric, Bottinger, Erwin P, Brenner, Hermann, Carroll, Robert J, Chalmers, John, Chee, Miao-Li, Chee, Miao-Ling, Cheng, Ching-Yu, Coresh, Josef, de Borst, Martin H, Degenhardt, Frauke, Eckardt, Kai-Uwe, Endlich, Karlhans, Franke, Andre, Freitag-Wolf, Sandra, Gampawar, Piyush, Gansevoort, Ron T, Ghanbari, Mohsen, Gieger, Christian, Hamet, Pavel, Ho, Kevin, Hofer, Edith, Holleczek, Bernd, Foo, Valencia Hui Xian, Hutri-Kähönen, Nina, Hwang, Shih-Jen, Ikram, M Arfan, Josyula, Navya Shilpa, Kähönen, Mika, Khor, Chiea-Chuen, Koenig, Wolfgang, Kramer, Holly, Krämer, Bernhard K, Kühnel, Brigitte, Lange, Leslie A, Lehtimäki, Terho, Lieb, Wolfgang, Loos, Ruth JF, Lukas, Mary Ann, Lyytikäinen, Leo-Pekka, Meisinger, Christa, Meitinger, Thomas, Melander, Olle, Milaneschi, Yuri, Mishra, Pashupati P, Mononen, Nina, Mychaleckyj, Josyf C, Nadkarni, Girish N, Nauck, Matthias, Nikus, Kjell, Ning, Boting, Nolte, Ilja M, O’Donoghue, Michelle L, Orho-Melander, Marju, Pendergrass, Sarah A, Penninx, Brenda WJH, Preuss, Michael H, Psaty, Bruce M, Raffield, Laura M, Raitakari, Olli T, Rettig, Rainer, Rheinberger, Myriam, Rice, Kenneth M, Rosenkranz, Alexander R, Rossing, Peter, Rotter, Jerome I, Sabanayagam, Charumathi, Schmidt, Helena, and Schmidt, Reinhold

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Human Genome, Kidney Disease, Rare Diseases, Genetics, Aetiology, 2.1 Biological and endogenous factors, Renal and urogenital, AMP-Activated Protein Kinases, Creatinine, Genome-Wide Association Study, Glomerular Filtration Rate, Humans, Kidney, Protein Disulfide-Isomerases, United Kingdom, acute kidney injury, end-stage kidney disease, genome-wide association study, rapid eGFRcrea decline, Lifelines Cohort Study, Regeneron Genetics Center, Urology & Nephrology, Clinical sciences
Abstract

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

Published
2021

42. Multilingual markers of depression in remotely collected speech samples: A preliminary analysis

Author

Cummins, Nicholas, Dineley, Judith, Conde, Pauline, Matcham, Faith, Siddi, Sara, Lamers, Femke, Carr, Ewan, Lavelle, Grace, Leightley, Daniel, White, Katie M., Oetzmann, Carolin, Campbell, Edward L., Simblett, Sara, Bruce, Stuart, Haro, Josep Maria, Penninx, Brenda W.J.H., Ranjan, Yatharth, Rashid, Zulqarnain, Stewart, Callum, Folarin, Amos A., Bailón, Raquel, Schuller, Björn W., Wykes, Til, Vairavan, Srinivasan, Dobson, Richard J.B., Narayan, Vaibhav A., and Hotopf, Matthew

Published
2023
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44. Genome-wide association study identifies 48 common genetic variants associated with handedness

Author

Cuellar-Partida, Gabriel, Tung, Joyce Y, Eriksson, Nicholas, Albrecht, Eva, Aliev, Fazil, Andreassen, Ole A, Barroso, Inês, Beckmann, Jacques S, Boks, Marco P, Boomsma, Dorret I, Boyd, Heather A, Breteler, Monique MB, Campbell, Harry, Chasman, Daniel I, Cherkas, Lynn F, Davies, Gail, de Geus, Eco JC, Deary, Ian J, Deloukas, Panos, Dick, Danielle M, Duffy, David L, Eriksson, Johan G, Esko, Tõnu, Feenstra, Bjarke, Geller, Frank, Gieger, Christian, Giegling, Ina, Gordon, Scott D, Han, Jiali, Hansen, Thomas F, Hartmann, Annette M, Hayward, Caroline, Heikkilä, Kauko, Hicks, Andrew A, Hirschhorn, Joel N, Hottenga, Jouke-Jan, Huffman, Jennifer E, Hwang, Liang-Dar, Ikram, M Arfan, Kaprio, Jaakko, Kemp, John P, Khaw, Kay-Tee, Klopp, Norman, Konte, Bettina, Kutalik, Zoltan, Lahti, Jari, Li, Xin, Loos, Ruth JF, Luciano, Michelle, Magnusson, Sigurdur H, Mangino, Massimo, Marques-Vidal, Pedro, Martin, Nicholas G, McArdle, Wendy L, McCarthy, Mark I, Medina-Gomez, Carolina, Melbye, Mads, Melville, Scott A, Metspalu, Andres, Milani, Lili, Mooser, Vincent, Nelis, Mari, Nyholt, Dale R, O’Connell, Kevin S, Ophoff, Roel A, Palmer, Cameron, Palotie, Aarno, Palviainen, Teemu, Pare, Guillaume, Paternoster, Lavinia, Peltonen, Leena, Penninx, Brenda WJH, Polasek, Ozren, Pramstaller, Peter P, Prokopenko, Inga, Raikkonen, Katri, Ripatti, Samuli, Rivadeneira, Fernando, Rudan, Igor, Rujescu, Dan, Smit, Johannes H, Smith, George Davey, Smoller, Jordan W, Soranzo, Nicole, Spector, Tim D, Pourcain, Beate St, Starr, John M, Stefánsson, Hreinn, Steinberg, Stacy, Teder-Laving, Maris, Thorleifsson, Gudmar, Stefánsson, Kári, Timpson, Nicholas J, Uitterlinden, André G, van Duijn, Cornelia M, van Rooij, Frank JA, Vink, Jaqueline M, Vollenweider, Peter, Vuoksimaa, Eero, and Waeber, Gérard

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Human Genome, Mental Health, Brain Disorders, Neurosciences, Schizophrenia, Genetics, 2.1 Biological and endogenous factors, Aetiology, Mental health, Adult, Aged, Female, Functional Laterality, Gene Frequency, Genetic Loci, Genetic Variation, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Male, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Sex Factors, Biomedical and clinical sciences, Health sciences
Abstract

Handedness has been extensively studied because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and the International Handedness Consortium, we conducted a genome-wide association meta-analysis of handedness (N = 1,766,671). We found 41 loci associated (P

Published
2021

45. 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, Julia, Nawijn, Laura, Brandmaier, Andreas M., Baaré, William F.C., Boraxbekk, Carl-Johan, Demnitz, Naiara, Drevon, Christian A., Fjell, Anders M., Lindenberger, Ulman, Madsen, Kathrine Skak, Nyberg, Lars, Topiwala, Anya, Walhovd, Kristine B., Ebmeier, Klaus P., and Penninx, Brenda W.J.H.

Published
2023
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47. Minimal phenotyping yields genome-wide association signals of low specificity for major depression.

Author

Cai, Na, Revez, Joana, Adams, Mark, Andlauer, Till, Breen, Gerome, Byrne, Enda, Clarke, Toni-Kim, Forstner, Andreas, Grabe, Hans, Hamilton, Steven, Levinson, Douglas, Lewis, Cathryn, Lewis, Glyn, Martin, Nicholas, Milaneschi, Yuri, Mors, Ole, Müller-Myhsok, Bertram, Penninx, Brenda, Perlis, Roy, Pistis, Giorgio, Potash, James, Preisig, Martin, Shi, Jianxin, Smoller, Jordan, Streit, Fabien, Tiemeier, Henning, Uher, Rudolf, Van der Auwera, Sandra, Viktorin, Alexander, Weissman, Myrna, Kendler, Kenneth, and Flint, Jonathan

Subjects
Adult, Aged, Bipolar Disorder, Depressive Disorder, Major, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Risk Factors, Sensitivity and Specificity
Abstract

Minimal phenotyping refers to the reliance on the use of a small number of self-reported items for disease case identification, increasingly used in genome-wide association studies (GWAS). Here we report differences in genetic architecture between depression defined by minimal phenotyping and strictly defined major depressive disorder (MDD): the former has a lower genotype-derived heritability that cannot be explained by inclusion of milder cases and a higher proportion of the genome contributing to this shared genetic liability with other conditions than for strictly defined MDD. GWAS based on minimal phenotyping definitions preferentially identifies loci that are not specific to MDD, and, although it generates highly predictive polygenic risk scores, the predictive power can be explained entirely by large sample sizes rather than by specificity for MDD. Our results show that reliance on results from minimal phenotyping may bias views of the genetic architecture of MDD and impede the ability to identify pathways specific to MDD.

Published
2020

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

Author

Tozzi, Leonardo, Garczarek, Lisa, Janowitz, Deborah, Stein, Dan J, Wittfeld, Katharina, Dobrowolny, Henrik, Lagopoulos, Jim, Hatton, Sean N, Hickie, Ian B, Carballedo, Angela, Brooks, Samantha J, Vuletic, Daniella, Uhlmann, Anne, Veer, Ilya M, Walter, Henrik, Bülow, Robin, Völzke, Henry, Klinger-König, Johanna, Schnell, Knut, Schoepf, Dieter, Grotegerd, Dominik, Opel, Nils, Dannlowski, Udo, Kugel, Harald, Schramm, Elisabeth, Konrad, Carsten, Kircher, Tilo, Jüksel, Dilara, Nenadić, Igor, Krug, Axel, Hahn, Tim, Steinsträter, Olaf, Redlich, Ronny, Zaremba, Dario, Zurowski, Bartosz, Fu, Cynthia HY, Dima, Danai, Cole, James, Grabe, Hans J, Connolly, Colm G, Yang, Tony T, Ho, Tiffany C, LeWinn, Kaja Z, Li, Meng, Groenewold, Nynke A, Salminen, Lauren E, Walter, Martin, Simmons, Alan N, van Erp, Theo GM, Jahanshad, Neda, Baune, Bernhard T, van der Wee, Nic JA, van Tol, Marie-Jose, Penninx, Brenda WJH, Hibar, Derrek P, Thompson, Paul M, Veltman, Dick J, Schmaal, Lianne, and Frodl, Thomas

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Pediatric, Neurosciences, Clinical Research, Mental Health, Brain Disorders, Depression, Child Abuse and Neglect Research, Behavioral and Social Science, Violence Research, Aetiology, 2.1 Biological and endogenous factors, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Brain Cortical Thickness, Case-Control Studies, Cerebral Cortex, Child, Child Abuse, Cohort Studies, Depressive Disorder, Major, Female, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Parietal Lobe, Prefrontal Cortex, Temporal Lobe, Young Adult, Childhood maltreatment, cortical thickness, ENIGMA, major depressive disorder, ‘for the ENIGMA-MDD Consortium’, Public Health and Health Services, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

BackgroundChildhood 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.MethodsWithin 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.ResultsCM 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.ConclusionsSeverity 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

49. Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia

Author

Sønderby, Ida E, Gústafsson, Ómar, Doan, Nhat Trung, Hibar, Derrek P, Martin-Brevet, Sandra, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Michael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas, Blangero, John, Boomsma, Dorret I, Bralten, Janita, Brattbak, Hans-Richard, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Calhoun, Vince, Caspers, Svenja, Cavalleri, Gianpiero, Chen, Chi-Hua, Cichon, Sven, Ciufolini, Simone, Corvin, Aiden, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Delanty, Norman, den Braber, Anouk, Desrivières, Sylvane, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Espeseth, Thomas, Fisher, Simon E, Franke, Barbara, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Glahn, David C, Grabe, Hans, Groenewold, Nynke A, Haavik, Jan, Håberg, Asta, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Heinz, Andreas, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Hottenga, Jouke-Jan, Hulshoff, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jernigan, Terry, Jockwitz, Christiane, Johansson, Stefan, Jonsdottir, Gudrun A, Jönsson, Erik G, Kahn, Rene, Kaufmann, Tobias, Kelly, Sinead, Kikuchi, Masataka, Knowles, Emma EM, Kolskår, Knut K, Kwok, John B, Hellard, Stephanie Le, Leu, Costin, Liu, Jingyu, Lundervold, Astri J, Lundervold, Arvid, Martin, Nicholas G, Mather, Karen, Mathias, Samuel R, McCormack, Mark, McMahon, Katie L, McRae, Allan, Milaneschi, Yuri, Moreau, Clara, Morris, Derek, Mothersill, David, Mühleisen, Thomas W, Murray, Robin, Nordvik, Jan E, Nyberg, Lars, Olde Loohuis, Loes M, Ophoff, Roel, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda, Peralta, Juan M, Pike, Bruce, and Prieto, Carlos

Subjects
Intellectual and Developmental Disabilities (IDD), Neurosciences, Brain Disorders, Autism, Biomedical Imaging, Mental Health, Mental health, Adult, Autism Spectrum Disorder, Autistic Disorder, Basal Ganglia, Brain, Chromosome Deletion, Chromosome Disorders, Chromosome Duplication, Chromosomes, Human, Pair 16, DNA Copy Number Variations, Databases, Factual, Female, Globus Pallidus, Humans, Image Processing, Computer-Assisted, Intellectual Disability, Magnetic Resonance Imaging, Male, Middle Aged, Neurodevelopmental Disorders, Organ Size, Putamen, Schizophrenia, 16p11.2 European Consortium, for the ENIGMA-CNV working group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P

Published
2020

50. Correction: Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Author

Sønderby, Ida E, Gústafsson, Ómar, Doan, Nhat Trung, Hibar, Derrek P, Martin-Brevet, Sandra, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Michael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas, Blangero, John, Boomsma, Dorret I, Bralten, Janita, Brattbak, Hans-Richard, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Calhoun, Vince, Caspers, Svenja, Cavalleri, Gianpiero, Chen, Chi-Hua, Cichon, Sven, Ciufolini, Simone, Corvin, Aiden, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Delanty, Norman, den Braber, Anouk, Desrivières, Sylvane, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Espeseth, Thomas, Fisher, Simon E, Franke, Barbara, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Glahn, David C, Grabe, Hans, Groenewold, Nynke A, Haavik, Jan, Håberg, Asta, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Heinz, Andreas, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Hottenga, Jouke-Jan, Hulshoff, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jernigan, Terry, Jockwitz, Christiane, Johansson, Stefan, Jonsdottir, Gudrun A, Jönsson, Erik G, Kahn, Rene, Kaufmann, Tobias, Kelly, Sinead, Kikuchi, Masataka, Knowles, Emma EM, Kolskår, Knut K, Kwok, John B, Hellard, Stephanie Le, Leu, Costin, Liu, Jingyu, Lundervold, Astri J, Lundervold, Arvid, Martin, Nicholas G, Mather, Karen, Mathias, Samuel R, McCormack, Mark, McMahon, Katie L, McRae, Allan, Milaneschi, Yuri, Moreau, Clara, Morris, Derek, Mothersill, David, Mühleisen, Thomas W, Murray, Robin, Nordvik, Jan E, Nyberg, Lars, Olde Loohuis, Loes M, Ophoff, Roel, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda, Peralta, Juan M, Pike, Bruce, and Prieto, Carlos

Subjects
16p11.2 European Consortium, for the ENIGMA-CNV working group, Neurosciences, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Prior to and following the publication of this article the authors noted that the complete list of authors was not included in the main article and was only present in Supplementary Table 1. The author list in the original article has now been updated to include all authors, and Supplementary Table 1 has been removed. All other supplementary files have now been updated accordingly. Furthermore, in Table 1 of this Article, the replication cohort for the row Close relative in data set, n (%) was incorrect. All values have now been corrected to 0(0%). The publishers would like to apologise for this error and the inconvenience it may have caused.

Published
2020

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