Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0011570 (depression)
 172,036 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The hypothesis that chromosomal region Xq27-28 harbours a gene for manic-depression has been a focus of interest in human genetics. X-linked inheritance of manic depressive illness has been re-examined in 3 multigeneration Israeli kindreds. Extension and re-evaluation of pedigree data, including new individuals, diagnostic follow-up, and analysis with DNA markers, shows greatly diminished support for linkage to Xq28. The peak lod scores in two of the pedigrees have dropped several lod units to clearly negative values at the RCP-F8-G6PD gene cluster. On the other hand, positive lod scores (Zmax = 2.09) are sustained in another pedigree at the same map location. None of the pedigrees show linkage to more proximal markers, including the Xq27 locus DXS98. Our analysis underscores the uncertainties in studying complex disorders.
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PMID:Diminished support for linkage between manic depressive illness and X-chromosome markers in three Israeli pedigrees. 849 Jun 52

Pimozide is often coprescribed with serotonin reuptake inhibitor (SSRI) antidepressants to treat depression in patients with Tourette's syndrome. In human liver microsomes (HLMs), the inhibition of the primary route of pimozide metabolism, N-dealkylation to 1,3-dihydro-1-(4-piperidinyl)-2H-benzimidazol-2-one (DHPBI), by four SSRIs (fluoxetine, sertraline, paroxetine, and fluvoxamine) and azithromycin was tested. Inhibition constants (K(i) values) were estimated from Dixon plots (three HLMs for each inhibitor) using the appropriate enzyme inhibition model by nonlinear regression. At 10 microM paroxetine, sertraline, fluoxetine, or fluvoxamine, the formation of DHPBI from pimozide (10 microM) in HLMs was inhibited by an average (three HLMs) of 7%, 7.7%, 8%, and 16%, respectively, whereas this inhibition did not exceed 55% at the maximum concentrations (100 microM) of the SSRIs tested. Azithromycin had negligible effect on pimozide (10 microM) N-dealkylation (19% at 100 microM azithromycin). These inhibition data were compared with ketoconazole, which was included as a positive control of CYP3A inhibition. At 0.1 microM and 0.5 microM ketoconazole, the formation of DHPBI from 10 microM pimozide was inhibited by 32% and 62%, respectively. The K(i) values (+/- SD) of ketoconazole, sertraline, fluvoxamine, azithromycin, fluoxetine, and paroxetine were 0.07 microM, 89 +/- 44 microM, 89 +/- 24 microM, 103 +/- 52 microM, 117 +/- 27 microM, and 129 +/- 33 microM, respectively. These values are least 100-fold higher than the expected plasma concentrations after the usual daily doses of the SSRIs and azithromycin, suggesting that coadministration of SSRIs and azithromycin are unlikely to markedly diminish the elimination of pimozide in patients. However, in vivo predictions from in vitro data are not always perfect. In vivo, the SSRIs or azithromycin may concentrate in the liver relative to plasma. In addition, the possibility that these drugs could alter pimozide disposition through effects on transport proteins or via promoter repression cannot be ruled out.
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PMID:In vitro inhibition of pimozide N-dealkylation by selective serotonin reuptake inhibitors and azithromycin. 1191 Feb 61

Comorbidities, dietary supplement use, and prescription drug use may negatively (or positively) affect mental health in cardiovascular patients. Although the significance of mental illnesses, such as depression, anxiety, and schizophrenia, on cardiovascular disease is well documented, mental illnesses resulting from heart disease are not well studied. In this paper, we introduce the risk factors of mental illnesses as an exploratory study and develop a prediction framework for mental illness that uses comorbidities, dietary supplements, and drug usage in heart disease patients. Particularly, the data used in this study consist of the records of 68,647 patients with heart disease, including the patient's mental illness information and the patient's intake of dietary supplements, antibiotics, and comorbidities. Patients in age groups <61, gender differences, and drug intakes, such as Azithromycin, Clarithromycin, Vitamin B6, and Coenzyme Q10, were associated with mental illness. For predictive modeling, we consider applying various state-of-the-art machine learning techniques with tuned parameters and finally obtain the following: Depression: 78.01% accuracy, 79.13% sensitivity, 72.65% specificity, and 86.26% Area Under the Curve (AUC). Anxiety: 82.93% accuracy, 82.86% sensitivity, 83.35% specificity, and 88.45% AUC. Schizophrenia: 87.59% accuracy, 87.70% sensitivity, 85.14% specificity, and 92.73% AUC. Disease: 86.63% accuracy, 95.50% sensitivity, 77.76% specificity, and 91.59% AUC. From the results, we conclude that using heart disease information, comorbidities, dietary supplement use, and antibiotics enables us to accurately predict the mental health outcome.
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PMID:Prediction of Mental Illness in Heart Disease Patients: Association of Comorbidities, Dietary Supplements, and Antibiotics as Risk Factors. 3318 35