期刊
TRANSLATIONAL PSYCHIATRY
卷 11, 期 1, 页码 -出版社
SPRINGERNATURE
DOI: 10.1038/s41398-021-01632-z
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资金
- Harry C. and Debra A. Stonecipher Predoctoral Fellowship at the Mayo Clinic Graduate School of Biomedical Science, National Science Foundation (NSF) [2041339]
- National Institutes of Health (NIH) [U19 GM61388, R01 GM028157, R01 AA027486, R01 GM28157, R01 MH108348, RC2 GM092729, R24 GM078233, U19 AG063744, N01 MH90003, R01 AG04617, U01 AG061359, RF1 AG051550, R01 MH113700, R01 MH124655]
- Hersh Foundation
- Duke Psychiatry Pharmacometabolomics Center
- Mayo Clinic Center for Individualized Medicine
The study successfully predicted the effectiveness of combination pharmacotherapy in treating depression by combining multi-omics measures and machine learning approaches, with a particular emphasis on the importance of plasma hydroxylated sphingomyelins. The integration of metabolomics with SNPs was found to enhance predictive ability, providing effective models for predicting treatment response across classes of antidepressants.
Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS' and CO-MED's escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS' escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.
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