SwePub - search: WFRF:(Ahmed Sarah)

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1.	Reid, Sarah, et al. (author) From genetic predisposition to clinical outcome in systemic lupus erythematosus : construction and validation of sub-phenotype-specific genetic risk scores Other publication (other academic/artistic)abstract ObjectiveThis study aimed to link known genetic risk factors for systemic lupus erythematosus (SLE) with specific clinical manifestations of the disease, in both a large biobank population and in a clinical cohort of patients with SLE.MethodsScandinavian patients with SLE (n=1487) who fulfilled ≥4 ACR-82/ACR-97/SLICC-2012 classification criteria, were genotyped using the Immunochip or Global Screening array (Illumina). Clinical data was collected from medical records. Summary statistics for 57 established SLE risk SNPs (p<5×10-8 in the European population) with a validated cumulative effect on disease severity in SLE, was retrieved for 30 FinnGen datasets covering manifestations relevant for SLE. Mendelian randomization (MR) analysis was performed using the inverse variance weighed method. Nine datasets were selected for construction of standardized genetic risk scores (GRSs), which were validated in the clinical cohort using gender- and disease duration-adjusted logistic regression.ResultsIn the FinnGen biobank, the cumulative effect of the 57 SLE risk SNPs was associated with an increased risk of rosacea, OR 1.09 (1.03–1.16), polyarthropathies, OR 1.10 (1.06–1.14), pleural effusions, OR 1.09 (1.04–1.14)) and hemolytic anemia, OR 1.32 (1.10–1.58). In the clinical cohort, the GRSs generated from the FinnGen datasets were associated with their corresponding manifestation for arthritis, OR 1.15 (1.02– 1.31), renal disorder, OR 1.15 (1.04–1.29), neurologic disorder, OR 1.24 (1.04–1.47), hematologic disorder, OR 1.12 (1.00–1.24), and immunologic disorder, OR 1.37 (1.22–1.56).ConclusionBy considering associations of SLE risk SNPs with SLE-like manifestations in the FinnGen biobank, construction and validation of GRSs for five of the eleven ACR-82 criteria was possible. The findings may facilitate personalized risk prediction and targeted intervention strategies for patients with SLE.
2.	Williams, Michael J., et al. (author) The statin target Hmgcr regulates energy metabolism and food intake through central mechanisms in Drosophila Other publication (other academic/artistic)

Reid, Sarah, et al. (author)
From genetic predisposition to clinical outcome in systemic lupus erythematosus : construction and validation of sub-phenotype-specific genetic risk scores
Other publication (other academic/artistic)abstract
- ObjectiveThis study aimed to link known genetic risk factors for systemic lupus erythematosus (SLE) with specific clinical manifestations of the disease, in both a large biobank population and in a clinical cohort of patients with SLE.MethodsScandinavian patients with SLE (n=1487) who fulfilled ≥4 ACR-82/ACR-97/SLICC-2012 classification criteria, were genotyped using the Immunochip or Global Screening array (Illumina). Clinical data was collected from medical records. Summary statistics for 57 established SLE risk SNPs (p<5×10-8 in the European population) with a validated cumulative effect on disease severity in SLE, was retrieved for 30 FinnGen datasets covering manifestations relevant for SLE. Mendelian randomization (MR) analysis was performed using the inverse variance weighed method. Nine datasets were selected for construction of standardized genetic risk scores (GRSs), which were validated in the clinical cohort using gender- and disease duration-adjusted logistic regression.ResultsIn the FinnGen biobank, the cumulative effect of the 57 SLE risk SNPs was associated with an increased risk of rosacea, OR 1.09 (1.03–1.16), polyarthropathies, OR 1.10 (1.06–1.14), pleural effusions, OR 1.09 (1.04–1.14)) and hemolytic anemia, OR 1.32 (1.10–1.58). In the clinical cohort, the GRSs generated from the FinnGen datasets were associated with their corresponding manifestation for arthritis, OR 1.15 (1.02– 1.31), renal disorder, OR 1.15 (1.04–1.29), neurologic disorder, OR 1.24 (1.04–1.47), hematologic disorder, OR 1.12 (1.00–1.24), and immunologic disorder, OR 1.37 (1.22–1.56).ConclusionBy considering associations of SLE risk SNPs with SLE-like manifestations in the FinnGen biobank, construction and validation of GRSs for five of the eleven ACR-82 criteria was possible. The findings may facilitate personalized risk prediction and targeted intervention strategies for patients with SLE.

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