Genome-wide association study for Chagas Cardiomyopathy identify a new risk locus on chromosome 18 associated with an immune-related protein and transcriptional signature.

BACKGROUND: Chronic Chagas Cardiomyopathy (CCC) usually develops between 10 and 20 years after the first parasitic infection and is one of the leading causes of end-stage heart failure in Latin America. Despite the great inter-individual variability in CCC susceptibility (only 30% of infected individuals ever present CCC), there are no known predictors for disease development in those chronically infected. METHODOLOGY/PRINCIPAL FINDINGS: We describe a new susceptibility locus for CCC through a GWAS analysis in the SaMi-Trop cohort, a population-based study conducted in a Chagas endemic region from Brazil. This locus was also associated with CCC in the REDS II Study. The newly identified locus (rs34238187, OR 0.73, p-value 2.03 x 10-9) spans a haplotype of approximately 30Kb on chromosome 18 (chr18: 5028302-5057621) and is also associated with 80 different traits, most of them blood protein traits significantly enriched for immune-related biological pathways. Hi-C data show that the newly associated locus is able to interact with chromatin sites as far as 10Mb on chromosome 18 in a number of different cell types and tissues. Finally, we were able to confirm, at the tissue transcriptional level, the immune-associated blood protein signature using a multi-tissue differential gene expression and enrichment analysis. CONCLUSIONS/SIGNIFICANCE: We suggest that the newly identified locus impacts CCC risk among T cruzi infected individuals through the modulation of a downstream transcriptional and protein signature associated with host-parasite immune response. Functional characterization of the novel risk locus is warranted.

Two-year death prediction models among patients with Chagas Disease using machine learning-based methods.

Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the high morbidity and mortality rate. Machine Learning (ML) can identify patterns in data that can be used to increase our understanding of a specific problem or make predictions about the future. Thus, the aim of this study was to evaluate different models of ML to predict death in two years of patients with CD. ML models were developed using different techniques and configurations. The techniques used were: Random Forests, Adaptive Boosting, Decision Tree, Support Vector Machine, and Artificial Neural Networks. The adopted settings considered only interview variables, only complementary exam variables, and finally, both mixed. Data from a cohort study with CD patients called SaMi-Trop were analyzed. The predictor variables came from the baseline; and the outcome, which was death, came from the first follow-up. All models were evaluated in terms of Sensitivity, Specificity and G-mean. Among the 1694 individuals with CD considered, 134 (7.9%) died within two years of follow-up. Using only the predictor variables from the interview, the different techniques achieved a maximum G-mean of 0.64 in predicting death. Using only the variables from complementary exams, the G-mean was up to 0.77. In this configuration, the protagonism of NT-proBNP was evident, where it was possible to observe that an ML model using only this single variable reached G-mean of 0.76. The configuration that mixed interview variables and complementary exams achieved G-mean of 0.75. ML can be used as a useful tool with the potential to contribute to the management of patients with CD, by identifying patients with the highest probability of death. Trial Registration: This trial is registered with ClinicalTrials.gov, Trial ID: NCT02646943.

Vertical transmission of SARS-CoV2 during pregnancy: A high-risk cohort.

OBJECTIVE: Identify the potential for and risk factors of SARS-CoV-2 vertical transmission. METHODS: Symptomatic pregnant women with COVID-19 diagnosis in whom PCR for SARS-CoV-2 was performed at delivery using maternal serum and at least one of the biological samples: cord blood (CB), amniotic fluid (AF), colostrum and/or oropharyngeal swab (OPS) of the neonate. The association of parameters with maternal, AF and/or CB positivity and the influence of SARS-CoV-2 positivity in AF and/or CB on neonatal outcomes were investigated. RESULTS: Overall 73.4% (80/109) were admitted in hospital due to COVID-19, 22.9% needed intensive care and there were four maternal deaths. Positive RT-PCR for SARS-CoV-2 was observed in 14.7% of maternal blood, 13.9% of AF, 6.7% of CB, 2.1% of colostrum and 3.7% of OPS samples. The interval between COVID-19 symptoms and delivery was inversely associated with SARS-CoV-2 positivity in the maternal blood (p = 0.002) and in the AF and/or CB (p = 0.049). Maternal viremia was associated with positivity for SARS-CoV-2 in AF and/or CB (p = 0.001). SARS-CoV-2 positivity in the compartments was not associated with neonatal outcomes. CONCLUSION: Vertical transmission is possible in pregnant women with COVID-19 and a shorter interval between maternal symptoms and delivery is an influencing factor.