Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
I01 BX003431
BLRD VA - United States
I01 CX000363
CSRD VA - United States
Centro de Investigación en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centres de Recerca de Catalunya Programme/Generalitat de Catalunya, Miguel Servet II, Instituto de Salud Carlos III Intramural Research Program of the National Institute of Mental Health Intramural Research Program of the National Institute of Mental Health Investissements d'Avenir National Institute of Drug Abuse Swiss National Foundation NPU I Australian National Health and Medical Research Council INSERM (Institut National de la Santé et de la Recherche Médicale), AP-HP (Assistance Publique des Hôpitaux de Paris), Fondation FondaMental (RTRS Santé Mentale
17-07070S
Grantová Agentura České Republiky
P50CA89392
NIH Clinical Center
FOR2107
Deutsche Forschungsgemeinschaft
RI 908/11-1
Deutsche Forschungsgemeinschaft
RI 908/7-1
Deutsche Forschungsgemeinschaft
64410
CIHR - Canada
PubMed
35225756
DOI
10.1192/bjp.2022.28
PII: S0007125022000289
Knihovny.cz E-zdroje
- Klíčová slova
- Mood stabilisers, bipolar affective disorders, depressive disorders, genetics, outcome studies,
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. AIMS: To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. METHOD: This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. RESULTS: The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. CONCLUSIONS: Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Biometric Psychiatric Genetics Research Unit Alexandru Obregia Clinical Psychiatric Hospital Romania
Bipolar Center Wiener Neustadt Sigmund Freud University Medical Faculty Austria
Centre for Healthy Brain Ageing School of Psychiatry University of New South Wales Australia
Department of Adult Psychiatry Poznan University of Medical Sciences Poland
Department of Biomedical Sciences University of Cagliari Italy
Department of Clinical Neurosciences Karolinska Institutet Sweden
Department of Medicine Surgery and Dentistry 'Scuola Medica Salernitana' University of Salerno Italy
Department of Mental Health Johns Hopkins Bloomberg School of Public Health USA
Department of Psychiatry and Behavioral Sciences Johns Hopkins University USA
Department of Psychiatry and Center of Sleep Disorders National Taiwan University Hospital Taiwan
Department of Psychiatry and Psychology Mayo Clinic USA
Department of Psychiatry and Psychotherapeutic Medicine Landesklinikum Neunkirchen Austria
Department of Psychiatry and Psychotherapy Ludwig Maximilian University Munich Germany
Department of Psychiatry Dalhousie University Canada
Department of Psychiatry Dokkyo Medical University School of Medicine Japan
Department of Psychiatry Hokkaido University Graduate School of Medicine Japan
Department of Psychiatry Lindner Center of Hope University of Cincinnati USA
Department of Psychiatry Mood Disorders Unit HUG Geneva University Hospitals Switzerland
Department of Psychiatry University of California San Diego USA
Department of Psychiatry University of Perugia Italy
Discipline of Psychiatry School of Medicine University of Adelaide Australia
Douglas Mental Health University Institute McGill University Canada
Institute of Psychiatric Phenomics and Genomics Georg August University Göttingen Germany
Montreal Neurological Institute and Hospital McGill University Canada
Mood Disorders Center of Ottawa Canada
National Institute of Mental Health Czech Republic
Office of Mental Health VA San Diego Healthcare System USA
Psychiatric Genetic Unit Poznan University of Medical Sciences Poland
School of Psychiatry University of New South Wales Australia
Service de Psychiatrie Hôpital Charles Perrens France
The Neuromodulation Unit McGill University Health Centre Canada
Unit of Clinical Pharmacology Hospital University Agency of Cagliari Italy
Unitat de Zoologia i Antropologia Biològica University of Barcelona CIBERSAM Spain
Univ Paris Est Créteil INSERM IMRB Translational Neuropsychiatry Fondation FondaMental France
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