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Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18–2.01) and European sample: OR = 1.75 (95% CI: 1.30–2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61–4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
Association of polygenic score for major depression with response to lithium in patients with bipolar disorder
Amare A. T.;Schubert K. O.;Hou L.;Clark S. R.;Papiol S.;Cearns M.;Heilbronner U.;Degenhardt F.;Tekola-Ayele F.;Hsu Y. -H.;Shekhtman T.;Adli M.;Akula N.;Akiyama K.;Ardau R.;Arias B.;Aubry J. -M.;Backlund L.;Bhattacharjee A. K.;Bellivier F.;Benabarre A.;Bengesser S.;Biernacka J. M.;Birner A.;Brichant-Petitjean C.;Cervantes P.;Chen H. -C.;Chillotti C.;Cichon S.;Cruceanu C.;Czerski P. M.;Dalkner N.;Dayer A.;Del Zompo M.;DePaulo J. R.;Etain B.;Jamain S.;Falkai P.;Forstner A. J.;Frisen L.;Frye M. A.;Fullerton J. M.;Gard S.;Garnham J. S.;Goes F. S.;Grigoroiu-Serbanescu M.;Grof P.;Hashimoto R.;Hauser J.;Herms S.;Hoffmann P.;Hofmann A.;Jimenez E.;Kahn J. -P.;Kassem L.;Kuo P. -H.;Kato T.;Kelsoe J. R.;Kittel-Schneider S.;Kliwicki S.;Konig B.;Kusumi I.;Laje G.;Landen M.;Lavebratt C.;Leboyer M.;Leckband S. G.;Tortorella A.;Manchia M.;Martinsson L.;McCarthy M. J.;McElroy S. L.;Colom F.;Mitjans M.;Mondimore F. M.;Monteleone P.;Nievergelt C. M.;Nothen M. M.;Novak T.;O'Donovan C.;Ozaki N.;Osby U.;Pfennig A.;Potash J. B.;Reif A.;Wray N. R.;Ripke S.;Mattheisen M.;Trzaskowski M.;Byrne E. M.;Abdellaoui A.;Adams M. J.;Agerbo E.;Air T. M.;Andlauer T. F. M.;Bacanu S. -A.;Baekvad-Hansen M.;Beekman A. T. F.;Bigdeli T. B.;Binder E. B.;Blackwood D. H. R.;Bryois J.;Buttenschon H. N.;Bybjerg-Grauholm J.;Cai N.;Castelao E.;Christensen J.;Clarke T. -K.;Coleman J. R. I.;Colodro-Conde L.;Couvy-Duchesne B.;Craddock N.;Crawford G. E.;Davies G.;Deary I. J.;Degenhardt F.;Derks E. M.;Direk N.;Dolan C. V.;Dunn E. C.;Eley T. C.;Escott-Price V.;Kiadeh F. F. H.;Finucane H. K.;Forstner A. J.;Frank J.;Gaspar H. A.;Gill M.;Goes F. S.;Gordon S. D.;Grove J.;Hall L. S.;Hansen C. S.;Hansen T. F.;Herms S.;Hickie I. B.;Hoffmann P.;Homuth G.;Horn C.;Hottenga J. -J.;Hougaard D. M.;Ising M.;Jansen R.;Jorgenson E.;Knowles J. A.;Kohane I. S.;Kraft J.;Kretzschmar W. W.;Krogh J.;Kutalik Z.;Li Y.;Lind P. A.;MacIntyre D. J.;MacKinnon D. F.;Maier R. M.;Maier W.;Marchini J.;Mbarek H.;McGrath P.;McGuffin P.;Medland S. E.;Mehta D.;Middeldorp C. M.;Mihailov E.;Milaneschi Y.;Milani L.;Mondimore F. M.;Montgomery G. W.;Mostafavi S.;Mullins N.;Nauck M.;Ng B.;Nivard M. G.;Nyholt D. R.;O'Reilly P. F.;Oskarsson H.;Owen M. J.;Painter J. N.;Pedersen C. B.;Pedersen M. G.;Peterson R. E.;Pettersson E.;Peyrot W. J.;Pistis G.;Posthuma D.;Quiroz J. A.;Qvist P.;Rice J. P.;Riley B. P.;Rivera M.;Mirza S. S.;Schoevers R.;Schulte E. C.;Shen L.;Shi J.;Shyn S. I.;Sigurdsson E.;Sinnamon G. C. B.;Smit J. H.;Smith D. J.;Stefansson H.;Steinberg S.;Streit F.;Strohmaier J.;Tansey K. E.;Teismann H.;Teumer A.;Thompson W.;Thomson P. A.;Thorgeirsson T. E.;Traylor M.;Treutlein J.;Trubetskoy V.;Uitterlinden A. G.;Umbricht D.;Van der Auwera S.;van Hemert A. M.;Viktorin A.;Visscher P. M.;Wang Y.;Webb B. T.;Weinsheimer S. M.;Wellmann J.;Willemsen G.;Witt S. H.;Wu Y.;Xi H. S.;Yang J.;Zhang F.;Arolt V.;Baune B. T.;Berger K.;Boomsma D. I.;Cichon S.;Dannlowski U.;de Geus E. J. C.;DePaulo J. R.;Domenici E.;Domschke K.;Esko T.;Grabe H. J.;Hamilton S. P.;Hayward C.;Heath A. C.;Kendler K. S.;Kloiber S.;Lewis G.;Li Q. S.;Lucae S.;Madden P. A. F.;Magnusson P. K.;Martin N. G.;McIntosh A. M.;Metspalu A.;Mors O.;Mortensen P. B.;Muller-Myhsok B.;Nordentoft M.;Nothen M. M.;O'Donovan M. C.;Paciga S. A.;Pedersen N. L.;Penninx B. W. J. H.;Perlis R. H.;Porteous D. J.;Potash J. B.;Preisig M.;Rietschel M.;Schaefer C.;Schulze T. G.;Smoller J. W.;Stefansson K.;Tiemeier H.;Uher R.;Volzke H.;Weissman M. M.;Werge T.;Lewis C. M.;Levinson D. F.;Breen G.;Borglum A. D.;Sullivan P. F.;Reininghaus E.;Rouleau G. A.;Rybakowski J. K.;Schalling M.;Schofield P. R.;Schweizer B. W.;Severino G.;Shilling P. D.;Shimoda K.;Simhandl C.;Slaney C. M.;Squassina A.;Stamm T.;Stopkova P.;Maj M.;Turecki G.;Vieta E.;Veeh J.;Witt S. H.;Wright A.;Zandi P. P.;Mitchell P. B.;Bauer M.;Alda M.;Rietschel M.;McMahon F. J.;Schulze T. G.;Baune B. T.
2020
Abstract
Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18–2.01) and European sample: OR = 1.75 (95% CI: 1.30–2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61–4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1471922
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Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
