Bradley E. Aouizerat

Abstract

Background: Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy. Objective: The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D. Methods: The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss. Results: Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]). Conclusions: Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity.

Abstract

MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/

Abstract

Background Depression affects one in three women with Type 2 diabetes, and this concurrence significantly increases the risks of diabetes complications, disability, and early mortality. Depression is underrecognized because of wide variation in presentation and the lack of diagnostic biomarkers. Converging evidence suggests inflammation is a shared biological pathway in diabetes and depression. Overlapping epigenetic associations and social determinants of diabetes and depression implicate inflammatory pathways as a common thread. Objectives This article describes the protocol and methods for a pilot study aimed to examine associations between depressive symptoms, inflammation, and social determinants of health among women with Type 2 diabetes. Methods This is an observational correlational study that leverages existing longitudinal data from the Women's Interagency HIV Study (WIHS), a multicenter cohort of HIV seropositive (66%) and HIV seronegative (33%) women, to inform purposive sampling of members from latent subgroups emergent from a prior retrospective cohort-wide analysis. Local active cohort participants from the Bronx study site are then selected for the study. The WIHS recently merged with the Multicenter Aids Cohort Study (MACS) to form the MACS/WIHS Combined Cohort Study. Latent subgroups represent distinct symptom trajectories resultant from a growth mixture model analysis of biannually collected depressive symptom data. Participants complete surveys (symptom and social determinants) and provide blood samples to analyze plasma levels and DNA methylation of genes that encode for inflammatory markers (CRP, IL-6, TNF-). Correlation and regression analysis will be used to estimate the effect sizes between depressive symptoms and inflammatory markers, clinical indices (body mass index, hemoglobin A1C, comorbidities), and social determinants of health. Results The study began in January 2022, and completed data collection is estimated by early 2023. We hypothesize that depressive symptom severity will associate with higher levels of inflammation, clinical indices (e.g., higher hemoglobin A1C), and exposure to specific social determinants of health (e.g., lower income, nutritional insecurity). Discussion Study findings will provide the basis for future studies aimed at improving outcomes for women with Type 2 diabetes by informing the development and testing of precision health strategies to address and prevent depression in populations most at risk.

Abstract

Purpose: Gestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM. Design: The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Methods: PubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM. Results: Sixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person. Conclusions: The mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.

Abstract

Background: Cervical cancer oncogenesis starts with human papillomavirus (HPV) cell entry after binding to host cell surface receptors; however, the mechanism is not fully known. We examined polymorphisms in receptor genes hypothesized to be necessary for HPV cell entry and assessed their associations with clinical progression to precancer. Methods: African American women (N =1,728) from the MACS/WIHS Combined Cohort Study were included. Two case-control study designs were used-cases with histologybased precancer (CIN3 ) and controls without; and cases with cytology-based precancer [high-grade squamous intraepithelial lesions (HSIL)] and controls without. SNPs in candidate genes (SDC1, SDC2, SDC3, SDC4, GPC1, GPC2, GPC3, GPC4, GPC5, GPC6, and ITGA6) were genotyped using an Illumina Omni2.5-quad beadchip. Logistic regression was used to assess the associations in all participants and by HPV genotypes, after adjusting for age, human immunodeficiency virus serostatus, CD4 T cells, and three principal components for ancestry. Results: Minor alleles in SNPs rs77122854 (SDC3), rs73971695, rs79336862 (ITGA6), rs57528020, rs201337456, rs11987725 (SDC2), rs115880588, rs115738853, and rs9301825 (GPC5) were associated with increased odds of both CIN3 and HSIL, whereas, rs35927186 (GPC5) was found to decrease the odds for both outcomes (P value ≤ 0.01). Among those infected with Alpha-9 HPV types, rs722377 (SDC3), rs16860468, rs2356798 (ITGA6), rs11987725 (SDC2), and rs3848051 (GPC5) were associated with increased odds of both precancer outcomes. Conclusions: Polymorphisms in genes that encode binding receptors for HPV cell entry may play a role in cervical precancer progression.

Abstract

Objectives:This study of people with HIV (PWH) and those without HIV conducted during the COVID-19 pandemic in the United States in 2020 examines the impact of posttraumatic stress disorder (PTSD) on COVID-19 burden, defined as pandemic-related disruptions.Methods:Data consisted of survey responses on PTSD among participants (N = 2434) enrolled in the Multicenter AIDS Cohort Study (MACS) and the Women's Interagency HIV (WIHS) cohorts. Unadjusted and adjusted regression models were used to examine the association of PTSD with COVID-19 burden (overall and domain-specific burdens). Quasi-Poisson regression models were used to assess associations with the COVID-19 burden score and 2 domain-specific burdens: (1) changes in resources and (2) interruptions in health care. Analyses was adjusted for age, race/ethnicity, HIV serostatus, current smoking status, number of comorbidities, education, and study regions.Results:Study participants were a median age of 58 (interquartile range, 52-65) years. In both bivariate and multivariable models, PTSD severity was associated with greater overall COVID-19 burden. PTSD severity was associated with the number of resource changes and number of interruptions in medical care. These findings were also consistent across cohorts (MACS/WIHS) and across HIV serostatus, suggesting a greater risk for COVID-19 burden with greater PTSD severity, which remained significant after controlling for covariates.Conclusions:This study builds on emerging literature demonstrating the impact of mental health on the burden and disruption associated with the COVID-19 pandemic, providing context specific to PWH. The ongoing pandemic requires structural and social interventions to decrease disruption to resources and health resource needs among these vulnerable populations.

Abstract

Neurologic complications of the human immunodeficiency virus (HIV) are common in treated individuals, and toxicity of certain antiretroviral therapies (ART) may contribute to cognitive impairment. We investigated exposures to specific ART and cognition among women living with HIV (WLWH). Virologically suppressed (viral load <200 copies/mL during at least two semi-annual visits) WLWH and age/race matched HIV-seronegative controls enrolled in the Women's Interagency HIV Study who completed at least two biennial cognitive assessments were included. Analysis of WLWH was restricted to those with exposure to the drug class of interest and a nucleoside reverse transcriptase inhibitor (NRTI) backbone. Generalized estimating equations were used to evaluate repeated measures of cognition over time in association with ART class exposure. Among 1,242 eligible WLWH, 20% (n = 247) had isolated drug exposure to non-nucleoside reverse transcriptase inhibitors (NNRTI), 18% (n = 219) to protease inhibitors (PIs), and 6% (n = 79) to integrase inhibitors with a NRTI backbone. Cognitive assessments were performed at a median of 3 biennial visits {IQR 2-4 visits}. At the index assessment, 21% of WLWH demonstrated global cognitive impairment versus 29% at their last cognitive assessment. In multivariable analyses adjusted for hypertension, depression, diabetes mellitus, history of AIDS-defining illness, alcohol use, number of medications, and time on ART, WLWH exposed to NNRTIs demonstrated verbal learning improvements (mean T-score change 1.3, p = .020) compared to other treated women. Compared to HIV-seronegative women, WLWH exposed to PIs had worse verbal learning (mean T-score difference -2.62, p = .002) and verbal memory performance (mean T-score difference -1.74, p = .032) at baseline. Compared to HIV-seronegative women, WLWH exposed to PIs had improvements in verbal learning (mean T-score slope difference 0.36, p = .025) and verbal memory (mean T-score slope difference 0.32, p = .042). The index T-score and slope of change in the T-score were similar among other treated groups and the HIV-seronegative group. We noted emerging trends in cognition in WLWH exposed to specific drug classes. Ongoing study of this relatively young group is important to characterize long-term cognitive outcomes and effect of antiretrovirals as treatment guidelines evolve.

Abstract

Micrornas (mirnas) may be considered impor- tant regulators of risk for type 2 diabetes (T2d). The aim of the present study was to identify novel sets of mirnas associ- ated with T2d risk, as well as their gene and pathway targets. circulating mirnas (n=59) were measured in plasma from participants in a previously completed clinical trial (n=82). an agnostic statistical approach was applied to identify novel sets of mirnas with optimal co-expression patterns. In silico analyses were used to identify the messenger rna and biolog- ical pathway targets of the mirnas within each factor. a total of three factors of miRNAs were identified, containing 18, seven and two mirnas each. eight biological pathways were revealed to contain genes targeted by the mirnas in all three factors, 38 pathways contained genes targeted by the mirnas in two factors, and 55, 18 and two pathways were targeted by the mirnas in a single factor, respectively (all q<0.05). The pathways containing genes targeted by mirnas in the largest factor shared a common theme of biological processes related to metabolism and inflammation. By contrast, the pathways containing genes targeted by mirnas in the second largest factor were related to endocrine function and hormone activity. The present study focused on the pathways uniquely targeted by each factor of mirnas in order to identify unique mecha- nisms that may be associated with a subset of individuals. Further exploration of the genes and pathways related to these biological themes may provide insights about the subtypes of T2D and lead to the identification of novel therapeutic targets.

Abstract

Background: Alterations in gut microbiota and blood metabolomic profiles have been implicated in HIV infection and cardiovascular disease. However, it remains unclear whether alterations in gut microbiota may contribute to disrupted host blood metabolomic profiles in relation to atherosclerosis, especially in the context of HIV infection. Methods: We analyzed cross-sectional associations between gut microbiota features and carotid artery plaque in 361 women with or at high risk of HIV (67% HIV+), and further integrated plaque-associated microbial features with plasma lipidomic/metabolomic profiles. Furthermore, in 737 women and men, we examined prospective associations of baseline gut bacteria-associated lipidomic and metabolomic profiles with incident carotid artery plaque over 7-year follow-up. Results: We found 2 potentially pathogenic bacteria, Fusobacterium and Proteus, were associated with carotid artery plaque; while the beneficial butyrate producer Odoribacter was inversely associated with plaque. Fusobacterium and Proteus were associated with multiple lipids/metabolites which were clustered into 8 modules in network. A module comprised of 9 lysophosphatidylcholines and lysophosphatidylethanolamines and a module comprised of 9 diglycerides were associated with increased risk of carotid artery plaque (risk ratio [95% CI], 1.34 [1.09-1.64] and 1.24 [1.02-1.51] per SD increment, respectively). Functional analyses identified bacterial enzymes in lipid metabolism associated with these plasma lipids. In particular, phospholipase A1 and A2 are the key enzymes in the reactions producing lysophosphatidylcholines and lysophosphatidylethanolamines. Conclusions: Among individuals with or at high risk of HIV infection, we identified altered gut microbiota and related functional capacities in the lipid metabolism associated with disrupted plasma lipidomic profiles and carotid artery atherosclerosis.