| 4. |
- Sanchez, E, et al.
(författare)
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Effects of Amerindian Genetic Ancestry on Clinical Variables and Therapy in Patients with Rheumatoid Arthritis
- 2017
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Ingår i: The Journal of rheumatology. - : The Journal of Rheumatology. - 0315-162X .- 1499-2752. ; 44:12, s. 1804-1812
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Tidskriftsartikel (refereegranskat)abstract
- To define whether Amerindian genetic ancestry correlates with clinical and therapeutic variables in admixed individuals with rheumatoid arthritis (RA) from Latin America.Methods.Patients with RA (n = 1347) and healthy controls (n = 1012) from Argentina, Mexico, Chile, and Peru were included. Samples were genotyped for the Immunochip v1 using the Illumina platform. Clinical data were obtained through interviews or the clinical history.Results.Percentage of Amerindian ancestry was comparable between cases and controls. Morning stiffness (p < 0.0001, OR 0.05), rheumatoid factor (RF; p < 0.0001, OR 0.22), radiographic changes (p < 0.0001, OR 0.05), and higher number of criteria were associated with lower Amerindian ancestry after Bonferroni correction. Higher Amerindian ancestry correlated only with weight loss (pBonferroni < 0.0001, OR 2.85). Increased Amerindian ancestry correlated with higher doses of azathioprine (p < 0.0001, OR 163.6) and sulfasalazine (p < 0.0001, OR 48.6), and inversely with methotrexate (p = 0.001, OR 0.35), leflunomide (p = 0.001, OR 0.16), and nonsteroidal antiinflammatory drugs (pBonferroni = 0.001, OR 0.37). Only the presence of RF and weight loss were modified after confounders adjustment.Conclusion.Amerindian ancestry protects against most major clinical criteria of RA, but regarding the association of RF with increased European ancestry, age, sex, and smoking are modifiers. Ancestry also correlates with the therapeutic profiles.
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| 5. |
- Toro-Dominguez, D, et al.
(författare)
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Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 15502-
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Tidskriftsartikel (refereegranskat)abstract
- Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.
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