Lack of associations of microRNAs with severe NAFLD in people living with HIV: discovery case-control study.

Background & objective: Nonalcoholic fatty liver disease (NAFLD) is highly prevalent in people living with HIV (PLWH) and the expression of some microRNAs could be useful as biomarkers for the diagnosis of NAFLD. The aim of this study was to identify patterns of differential expression of microRNAs in PLWH and assess their diagnostic value for NALFD. Methods: A discovery case-control study with PLWH was carried out. The expression of miRNAs was determined using HTG EdgeSeq technology. Cases were defined as patients with severe NAFLD and controls as patients without NAFLD, characterized using the controlled attenuation parameter (CAP). Cases and controls were matched 1:1 for age, sex, BMI, CD4+ lymphocyte count, active HCV infection, and ART regimen. Results: Serum 2,083 simultaneous microRNA transcripts were analyzed using HTG technology and compared between cases and controls. Forty-five patients, 23 cases, and 22 controls were included in the study. In the analysis of the expression pattern of the 2,083 microRNAs, no differential expression patterns were found between both groups of patients included in the study. Conclusion: Analysis of the microRNA transcriptome profile of nonobese PLWH with severe NAFLD did not appear to differ from that of patients without NAFLD. Thus, microRNA might not serve as a proper biomarker for predicting severe NALFD in this population.

Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach.

BACKGROUND: The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. OBJECTIVE: The aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. METHODS: We built predictive models using different subsets of factors, selected according to their importance in predicting patient classification. We then evaluated each independent model and also a combination of them, leading to a better predictive model. RESULTS: Our data mining approach identified genetic patterns that escaped detection using conventional statistics. More specifically, the partial decision trees and ensemble models increased the classification accuracy of hepatitis C virus outcome compared with conventional methods. CONCLUSIONS: Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.