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Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication.

Publication ,  Journal Article
Joyce, JB; Grant, CW; Liu, D; MahmoudianDehkordi, S; Kaddurah-Daouk, R; Skime, M; Biernacka, J; Frye, MA; Mayes, T; Carmody, T; Croarkin, PE ...
Published in: Transl Psychiatry
October 7, 2021

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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Published In

Transl Psychiatry

DOI

EISSN

2158-3188

Publication Date

October 7, 2021

Volume

11

Issue

1

Start / End Page

513

Location

United States

Related Subject Headings

  • Treatment Outcome
  • Machine Learning
  • Humans
  • Depressive Disorder, Major
  • Citalopram
  • Antidepressive Agents
  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1701 Psychology
 

Citation

APA
Chicago
ICMJE
MLA
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Joyce, J. B., Grant, C. W., Liu, D., MahmoudianDehkordi, S., Kaddurah-Daouk, R., Skime, M., … Athreya, A. P. (2021). Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Transl Psychiatry, 11(1), 513. https://doi.org/10.1038/s41398-021-01632-z
Joyce, Jeremiah B., Caroline W. Grant, Duan Liu, Siamak MahmoudianDehkordi, Rima Kaddurah-Daouk, Michelle Skime, Joanna Biernacka, et al. “Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication.Transl Psychiatry 11, no. 1 (October 7, 2021): 513. https://doi.org/10.1038/s41398-021-01632-z.
Joyce JB, Grant CW, Liu D, MahmoudianDehkordi S, Kaddurah-Daouk R, Skime M, et al. Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Transl Psychiatry. 2021 Oct 7;11(1):513.
Joyce, Jeremiah B., et al. “Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication.Transl Psychiatry, vol. 11, no. 1, Oct. 2021, p. 513. Pubmed, doi:10.1038/s41398-021-01632-z.
Joyce JB, Grant CW, Liu D, MahmoudianDehkordi S, Kaddurah-Daouk R, Skime M, Biernacka J, Frye MA, Mayes T, Carmody T, Croarkin PE, Wang L, Weinshilboum R, Bobo WV, Trivedi MH, Athreya AP. Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Transl Psychiatry. 2021 Oct 7;11(1):513.

Published In

Transl Psychiatry

DOI

EISSN

2158-3188

Publication Date

October 7, 2021

Volume

11

Issue

1

Start / End Page

513

Location

United States

Related Subject Headings

  • Treatment Outcome
  • Machine Learning
  • Humans
  • Depressive Disorder, Major
  • Citalopram
  • Antidepressive Agents
  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1701 Psychology