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.