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Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response.
- Source :
-
Scientific reports [Sci Rep] 2021 Feb 12; Vol. 11 (1), pp. 3780. Date of Electronic Publication: 2021 Feb 12. - Publication Year :
- 2021
-
Abstract
- Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.
- Subjects :
- Antidepressive Agents metabolism
Antidepressive Agents therapeutic use
Area Under Curve
Citalopram pharmacology
Databases, Factual
Databases, Genetic
Decision Trees
Depression drug therapy
Depression genetics
Depressive Disorder, Major drug therapy
Drug-Related Side Effects and Adverse Reactions
Genetic Variation genetics
Humans
Logistic Models
Machine Learning
Polymorphism, Single Nucleotide genetics
Prognosis
Treatment Outcome
Biomarkers, Pharmacological analysis
Depressive Disorder, Major genetics
Polymorphism, Single Nucleotide drug effects
Subjects
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 11
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 33580158
- Full Text :
- https://doi.org/10.1038/s41598-021-83338-2