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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 08366naa a2201849 4500 | |
| 001 | oai:gup.ub.gu.se/315255 | |
| 003 | SwePub | |
| 008 | 240528s2022 | |||||||||||000 ||eng| | |
| 009 | oai:prod.swepub.kib.ki.se:148992940 | |
| 024 | 7 | a https://gup.ub.gu.se/publication/3152552 URI |
| 024 | 7 | a https://doi.org/10.1192/bjp.2022.282 DOI |
| 024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:1489929402 URI |
| 040 | a (SwePub)gud (SwePub)ki | |
| 041 | a eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Cearns, M.4 aut |
| 245 | 1 0 | a Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach |
| 264 | c 2022-02-28 | |
| 264 | 1 | b Royal College of Psychiatrists,c 2022 |
| 520 | a 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. | |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaperx Neurovetenskaper0 (SwePub)301052 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Basic Medicinex Neurosciences0 (SwePub)301052 hsv//eng |
| 653 | a Mood stabilisers | |
| 653 | a bipolar affective disorders | |
| 653 | a genetics | |
| 653 | a outcome | |
| 653 | a studies | |
| 653 | a depressive disorders | |
| 653 | a psychiatric-disorders | |
| 653 | a comparative efficacy | |
| 653 | a schizophrenia | |
| 653 | a network | |
| 653 | a mania | |
| 653 | a Psychiatry | |
| 700 | 1 | a Amare, A. T.4 aut |
| 700 | 1 | a Schubert, K. O.4 aut |
| 700 | 1 | a Thalamuthu, A.4 aut |
| 700 | 1 | a Frank, J.4 aut |
| 700 | 1 | a Streit, F.4 aut |
| 700 | 1 | a Adli, M.4 aut |
| 700 | 1 | a Akula, N.4 aut |
| 700 | 1 | a Akiyama, K.4 aut |
| 700 | 1 | a Ardau, R.4 aut |
| 700 | 1 | a Arias, B.4 aut |
| 700 | 1 | a Aubry, J. M.4 aut |
| 700 | 1 | a Backlund, L.u Karolinska Institutet4 aut |
| 700 | 1 | a Bhattacharjee, A. K.4 aut |
| 700 | 1 | a Bellivier, F.4 aut |
| 700 | 1 | a Benabarre, A.4 aut |
| 700 | 1 | a Bengesser, S.4 aut |
| 700 | 1 | a Biernacka, J. M.4 aut |
| 700 | 1 | a Birner, A.4 aut |
| 700 | 1 | a Brichant-Petitjean, C.4 aut |
| 700 | 1 | a Cervantes, P.4 aut |
| 700 | 1 | a Chen, H. C.4 aut |
| 700 | 1 | a Chillotti, C.u Karolinska Institutet4 aut |
| 700 | 1 | a Cichon, S.4 aut |
| 700 | 1 | a Cruceanu, C.4 aut |
| 700 | 1 | a Czerski, P. M.4 aut |
| 700 | 1 | a Dalkner, N.4 aut |
| 700 | 1 | a Dayer, A.4 aut |
| 700 | 1 | a Degenhardt, F.4 aut |
| 700 | 1 | a Del Zompo, M.4 aut |
| 700 | 1 | a DePaulo, J. R.4 aut |
| 700 | 1 | a Etain, B.4 aut |
| 700 | 1 | a Falkai, P.4 aut |
| 700 | 1 | a Forstner, A. J.4 aut |
| 700 | 1 | a Frisen, L.u Karolinska Institutet4 aut |
| 700 | 1 | a Frye, M. A.4 aut |
| 700 | 1 | a Fullerton, J. M.4 aut |
| 700 | 1 | a Gard, S.4 aut |
| 700 | 1 | a Garnham, J. S.4 aut |
| 700 | 1 | a Goes, F. S.4 aut |
| 700 | 1 | a Grigoroiu-Serbanescu, M.4 aut |
| 700 | 1 | a Grof, P.4 aut |
| 700 | 1 | a Hashimoto, R.4 aut |
| 700 | 1 | a Hauser, J.4 aut |
| 700 | 1 | a Heilbronner, U.4 aut |
| 700 | 1 | a Herms, S.4 aut |
| 700 | 1 | a Hoffmann, P.4 aut |
| 700 | 1 | a Hofmann, A.4 aut |
| 700 | 1 | a Hou, L.4 aut |
| 700 | 1 | a Hsu, Y. H.4 aut |
| 700 | 1 | a Jamain, S.4 aut |
| 700 | 1 | a Jimenez, E.4 aut |
| 700 | 1 | a Kahn, J. P.4 aut |
| 700 | 1 | a Kassem, L.4 aut |
| 700 | 1 | a Kuo, P. H.4 aut |
| 700 | 1 | a Kato, T.4 aut |
| 700 | 1 | a Kelsoe, J.4 aut |
| 700 | 1 | a Kittel-Schneider, S.4 aut |
| 700 | 1 | a Kliwicki, S.4 aut |
| 700 | 1 | a Konig, B.4 aut |
| 700 | 1 | a Kusumi, I.4 aut |
| 700 | 1 | a Laje, G.4 aut |
| 700 | 1 | a Landén, Mikael,d 1966u Karolinska Institutet,Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi,Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry4 aut0 (Swepub:gu)xlandt |
| 700 | 1 | a Lavebratt, C.4 aut |
| 700 | 1 | a Leboyer, M.u Karolinska Institutet4 aut |
| 700 | 1 | a Leckband, S. G.4 aut |
| 700 | 1 | a Maj, M.u Karolinska Institutet4 aut |
| 700 | 1 | a Manchia, M.4 aut |
| 700 | 1 | a Martinsson, L.4 aut |
| 700 | 1 | a McCarthy, M. J.4 aut |
| 700 | 1 | a McElroy, S.4 aut |
| 700 | 1 | a Colom, F.4 aut |
| 700 | 1 | a Mitjans, M.4 aut |
| 700 | 1 | a Mondimore, F. M.4 aut |
| 700 | 1 | a Monteleone, P.4 aut |
| 700 | 1 | a Nievergelt, C. M.4 aut |
| 700 | 1 | a Nothen, M. M.4 aut |
| 700 | 1 | a Novak, T.4 aut |
| 700 | 1 | a O'Donovan, C.4 aut |
| 700 | 1 | a Ozaki, N.4 aut |
| 700 | 1 | a Millischer, V.u Karolinska Institutet4 aut |
| 700 | 1 | a Papiol, S.4 aut |
| 700 | 1 | a Pfennig, A.4 aut |
| 700 | 1 | a Pisanu, C.4 aut |
| 700 | 1 | a Potash, J. B.4 aut |
| 700 | 1 | a Reif, A.4 aut |
| 700 | 1 | a Reininghaus, E.4 aut |
| 700 | 1 | a Rouleau, G. A.4 aut |
| 700 | 1 | a Rybakowski, J. K.4 aut |
| 700 | 1 | a Schalling, M.4 aut |
| 700 | 1 | a Schofield, P. R.4 aut |
| 700 | 1 | a Schweizer, B. W.4 aut |
| 700 | 1 | a Severino, G.4 aut |
| 700 | 1 | a Shekhtman, T.4 aut |
| 700 | 1 | a Shilling, P. D.4 aut |
| 700 | 1 | a Shimoda, K.4 aut |
| 700 | 1 | a Simhandl, C.4 aut |
| 700 | 1 | a Slaney, C. M.4 aut |
| 700 | 1 | a Squassina, A.4 aut |
| 700 | 1 | a Stamm, T.4 aut |
| 700 | 1 | a Stopkova, P.4 aut |
| 700 | 1 | a Tekola-Ayele, F.4 aut |
| 700 | 1 | a Tortorella, A.4 aut |
| 700 | 1 | a Turecki, G.4 aut |
| 700 | 1 | a Veeh, J.4 aut |
| 700 | 1 | a Vieta, E.4 aut |
| 700 | 1 | a Witt, S. H.4 aut |
| 700 | 1 | a Roberts, G.4 aut |
| 700 | 1 | a Zandi, P. P.4 aut |
| 700 | 1 | a Alda, M.4 aut |
| 700 | 1 | a Bauer, M.4 aut |
| 700 | 1 | a McMahon, F. J.4 aut |
| 700 | 1 | a Mitchell, P. B.4 aut |
| 700 | 1 | a Schulze, T. G.4 aut |
| 700 | 1 | a Rietschel, M.4 aut |
| 700 | 1 | a Clark, S. R.4 aut |
| 700 | 1 | a Baune, B. T.4 aut |
| 710 | 2 | a Karolinska Institutetb Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi4 org |
| 773 | 0 | t British Journal of Psychiatryd : Royal College of Psychiatristsg 220:4, s. 219-228q 220:4<219-228x 0007-1250x 1472-1465 |
| 856 | 4 8 | u https://gup.ub.gu.se/publication/315255 |
| 856 | 4 8 | u https://doi.org/10.1192/bjp.2022.28 |
| 856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:148992940 |
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- Simhandl, C.
- Slaney, C. M.
- Squassina, A.
- Stamm, T.
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- Tortorella, A.
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- Veeh, J.
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- Alda, M.
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