ELIZA

return kwic search for patients with out of >500 occurrences
404916 occurrences (No.41 in the rank) during 5 years in the PubMed. [cache]

225) 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.

--- ABSTRACT ---

PMID:34620827 DOI:10.1038/s41398-021-01632-z

2021 Translational psychiatry

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

- 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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(1)14 a	(26)3 PAH	(51)2 IgAn	(76)2 neurological
(2)14 severe	(27)3 PTB	(52)2 MIS-C	(77)2 no
(3)12 COVID-19	(28)3 SB	(53)2 MS,	(78)2 or
(4)10 cancer	(29)3 and	(54)2 PH,	(79)2 pancreatic
(5)10 chronic	(30)3 autonomic	(55)2 TSC	(80)2 pre-existing
(6)8 diabetes	(31)3 clinical	(56)2 UWS	(81)2 prolonged
(7)7 acute	(32)3 dysphagia	(57)2 accuracies	(82)2 psoriasis
(8)6 relapsed	(33)3 epilepsy	(58)2 ankylosing	(83)2 pulmonary
(9)5 AN	(34)3 idiopathic	(59)2 any	(84)2 resected
(10)5 MDD	(35)3 inflammatory	(60)2 cerebellar	(85)2 respiratory
(11)5 T2DM	(36)3 newly	(61)2 community-based	(86)2 rheumatoid
(12)5 advanced	(37)3 recurrent	(62)2 comorbidities	(87)2 schizophrenia
(13)5 lipoedema	(38)3 uncontrolled	(63)2 drug-resistant	(88)2 shared
(14)4 AD	(39)2 16p11.2	(64)2 heart	(89)2 special
(15)4 IBD	(40)2 ALL	(65)2 hemorrhagic	(90)2 spondyloarthritis
(16)4 breast	(41)2 ALS	(66)2 high	(91)2 stage
(17)4 chorea	(42)2 AML	(67)2 high-risk	(92)2 suspected
(18)4 critical	(43)2 ASD	(68)2 known	(93)2 symptomatic
(19)4 low	(44)2 BM	(69)2 locally	(94)2 the
(20)3 ARDS	(45)2 CHB	(70)2 lung	(95)2 these
(21)3 CKD	(46)2 COVID-19,	(71)2 mIDH1	(96)2 treatment-resistant
(22)3 CLL	(47)2 CRC	(72)2 major	(97)2 tuberculosis
(23)3 HGSOC	(48)2 CVDRF	(73)2 metastatic
(24)3 ILD	(49)2 HIV	(74)2 mild
(25)3 MD	(50)2 IIM	(75)2 neurologic

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--- WordNet output for patients --- Overview of noun patient The noun patient has 2 senses (first 1 from tagged texts) 1. (73) patient -- (a person who requires medical care; "the number of emergency patients has grown rapidly") 2. affected role, patient role, patient -- (the semantic role of an entity that is not the agent but is directly involved in or affected by the happening denoted by the verb in the clause) --- WordNet end ---