| Autor: |
Jeremiah B. Joyce, Caroline W. Grant, Duan Liu, Siamak MahmoudianDehkordi, Rima Kaddurah-Daouk, Michelle Skime, Joanna Biernacka, Mark A. Frye, Taryn Mayes, Thomas Carmody, Paul E. Croarkin, Liewei Wang, Richard Weinshilboum, William V. Bobo, Madhukar H. Trivedi, Arjun P. Athreya |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Translational Psychiatry, Vol 11, Iss 1, Pp 1-11 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2158-3188 |
| DOI: |
10.1038/s41398-021-01632-z |
| Popis: |
Abstract 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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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