Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables

Raquel Iniesta; Karen Hodgson; Daniel Stahl; Karim Malki; Wolfgang Maier; Marcella Rietschel; Ole Mors; Joanna Hauser; Neven Henigsberg; Mojca Zvezdana Dernovsek; Daniel Souery; Richard Dobson; Katherine J. Aitchison; Anne Farmer; Peter McGuffin; Cathryn M. Lewis; Rudolf Uher

doi:10.1038/s41598-018-23584-z

Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables

Raquel Iniesta, Karen Hodgson, Daniel Stahl, Karim Malki, Wolfgang Maier, Marcella Rietschel, Ole Mors, Joanna Hauser, Neven Henigsberg, Mojca Zvezdana Dernovsek, Daniel Souery, Richard Dobson, Katherine J. Aitchison, Anne Farmer, Peter McGuffin, Cathryn M. Lewis, Rudolf Uher

Medicine

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

52 Citas (Scopus)

Resumen

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.

Idioma original	English
Número de artículo	5530
Publicación	Scientific Reports
Volumen	8
N.º	1
DOI	https://doi.org/10.1038/s41598-018-23584-z
Estado	Published - dic. 1 2018

Nota bibliográfica

Funding Information:
Competing Interests: R.I., K.H., P.M.c.G., A.F., W.M., D.S., M.R., M.Z.D., D.S., J.H., O.M., R.D. and K.M. have no conflicts of interest. C.M.L., R.U. and K.J.A. report grants from European Commission, during the conduct of the study. N.H. reports grant from European Commission (through Institute of Psychiatry, King’s College, London), and participation in clinical trials sponsored by pharmaceutical companies, including Lundbeck outside the submitted work. K.J.A. was previously (more than 48 months ago) a member of various advisory boards, receiving consultancy fees and honoraria (including from Lundbeck), and has received research grants from various companies including Johnson and Johnson Pharmaceuticals Research and Development and Bristol-Myers Squibb Pharmaceuticals Limited. She has also received consultancy fees and research support from Roche Diagnostics and Roche Molecular Systems She currently holds an Alberta Centennial Addiction and Mental Health Research Chair, funded by the Government of Alberta.

Publisher Copyright:
© 2018 The Author(s).

ASJC Scopus Subject Areas

General

PubMed: MeSH publication types

Journal Article
Randomized Controlled Trial

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Iniesta, R., Hodgson, K., Stahl, D., Malki, K., Maier, W., Rietschel, M., Mors, O., Hauser, J., Henigsberg, N., Dernovsek, M. Z., Souery, D., Dobson, R., Aitchison, K. J., Farmer, A., McGuffin, P., Lewis, C. M., & Uher, R. (2018). Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. Scientific Reports, 8(1), Artículo 5530. https://doi.org/10.1038/s41598-018-23584-z

Iniesta, R, Hodgson, K, Stahl, D, Malki, K, Maier, W, Rietschel, M, Mors, O, Hauser, J, Henigsberg, N, Dernovsek, MZ, Souery, D, Dobson, R, Aitchison, KJ, Farmer, A, McGuffin, P, Lewis, CM & Uher, R 2018, 'Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables', Scientific Reports, vol. 8, n.º 1, 5530. https://doi.org/10.1038/s41598-018-23584-z

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abstract = "Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.",

author = "Raquel Iniesta and Karen Hodgson and Daniel Stahl and Karim Malki and Wolfgang Maier and Marcella Rietschel and Ole Mors and Joanna Hauser and Neven Henigsberg and Dernovsek, {Mojca Zvezdana} and Daniel Souery and Richard Dobson and Aitchison, {Katherine J.} and Anne Farmer and Peter McGuffin and Lewis, {Cathryn M.} and Rudolf Uher",

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AU - Maier, Wolfgang

AU - Rietschel, Marcella

AU - Mors, Ole

AU - Hauser, Joanna

AU - Henigsberg, Neven

AU - Dernovsek, Mojca Zvezdana

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AU - Dobson, Richard

AU - Aitchison, Katherine J.

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Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables

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ASJC Scopus Subject Areas

PubMed: MeSH publication types

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