Bradley E. Aouizerat

Abstract

Co-occurrence of injection drug use (IDU) and hepatitis C virus infection (HCV) is common in people living with HIV (PLWH) and leads to significantly increased mortality. Epigenetic clocks derived from DNA methylation (DNAm) are associated with disease progression and all-cause mortality. In this study, we hypothesized that epigenetic age mediates the relationships between the co-occurrence of IDU and HCV with mortality risk among PLWH. We tested this hypothesis in the Veterans Aging Cohort Study (n = 927) by using four established epigenetic clocks of DNAm age (i.e., Horvath, Hannum, Pheno, Grim). Compared to individuals without IDU and HCV (IDU-HCV-), participants with IDU and HCV (IDU+HCV+) showed a 2.23-fold greater risk of mortality estimated using a Cox proportional hazards model (hazard ratio: 2.23; 95% confidence interval: 1.62–3.09; p = 1.09E–06). IDU+HCV+ was associated with a significantly increased epigenetic age acceleration (EAA) measured by 3 out of 4 epigenetic clocks, adjusting for demographic and clinical variables (Hannum: p = 8.90E–04, Pheno: p = 2.34E–03, Grim: p = 3.33E–11). Furthermore, we found that epigenetic age partially mediated the relationship between IDU+HCV+ and all-cause mortality, up to a 13.67% mediation proportion. Our results suggest that comorbid IDU with HCV increases EAA in PLWH that partially mediates the increased mortality risk.

Abstract

Purpose: To examine if gut microbial taxa abundances and predicted functional pathways correlate with Bristol Stool Form Scale (BSFS) classification at the end of neoadjuvant chemotherapy and radiation therapy (CRT) for rectal cancer. Methods: Rectal cancer patients (n = 39) provided stool samples for 16S rRNA gene sequencing. Stool consistency was evaluated using the BSFS. Gut microbiome data were analyzed using QIIME2. Correlation analysis were performed in R. Results: At the genus level, Staphylococcus positively correlates (Spearman’s rho = 0.26), while Anaerofustis, Roseburia, Peptostreptococcaceae unclassified, Ruminococcaceae UBA1819, Shuttleworthia, Ca. Soleaferrea, Anaerostignum, Oscillibacter, and Akkermansia negatively correlate with BSFS scores (Spearman’s rho −0.20 to −0.42). Predicted pathways, including mycothiol biosynthesis and sucrose degradation III (sucrose invertase), were positively correlated with BSFS (Spearman’s rho = 0.03–0.21). Conclusion: The data support that in rectal cancer patients, stool consistency is an important factor to include in microbiome studies. Loose/liquid stools may be linked to Staphylococcus abundance and to mycothiol biosynthesis and sucrose degradation pathways.

Abstract

Aims/Hypothesis: Our prior analysis of the Diabetes Prevention Program study identified a subset of five miRNAs that predict incident type 2 diabetes. The purpose of this study was to identify mRNAs and biological pathways targeted by these five miRNAs to elucidate potential mechanisms of risk and responses to the tested interventions. Methods: Using experimentally validated data from miRTarBase version 8.0 and R (2021), we identified mRNAs with strong evidence to be regulated by individual or combinations of the five predictor miRNAs. Overrepresentation of the mRNA targets was assessed in pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation database. Results: The five miRNAs targeted 167 pathways and 122 mRNAs. Nine of the pathways have known associations with type 2 diabetes: Insulin signaling, Insulin resistance, Diabetic cardiomyopathy, Type 2 diabetes, AGE-RAGE signaling in diabetic complications, HIF-1 signaling, TGF-beta signaling, PI3K/Akt signaling, and Adipocytokine signaling pathways. Vascular endothelial growth factor A (VEGFA) has prior genetic associations with risk for type 2 diabetes and was the most commonly targeted mRNA for this set of miRNAs. Conclusions/Interpretation: These findings show that miRNA predictors of incident type 2 diabetes target mRNAs and pathways known to underlie risk for type 2 diabetes. Future studies should evaluate miRNAs as potential therapeutic targets for preventing and treating type 2 diabetes.

Abstract

CONTEXT: MicroRNAs (miRs) are short (ie, 18-26 nucleotide) regulatory elements of messenger RNA translation to amino acids. OBJECTIVE: The purpose of this study was to assess whether miRs are predictive of incident type 2 diabetes (T2D) in the Diabetes Prevention Program (DPP) trial. METHODS: This was a secondary analysis (n = 1000) of a subset of the DPP cohort that leveraged banked biospecimens to measure miRs. We used random survival forest and Lasso methods to identify the optimal miR predictors and the Cox proportional hazards to model time to T2D overall and within intervention arms. RESULTS: We identified 5 miRs (miR-144, miR-186, miR-203a, miR-205, miR-206) that constituted the optimal predictors of incident T2D after adjustment for covariates (hazard ratio [HR] 2.81, 95% CI 2.05, 3.87; P < .001). Predictive risk scores following cross-validation showed the HR for the highest quartile risk group compared with the lowest quartile risk group was 5.91 (95% CI 2.02, 17.3; P < .001). There was significant interaction between the intensive lifestyle (HR 3.60, 95% CI 2.50, 5.18; P < .001) and the metformin (HR 2.72; 95% CI 1.47, 5.00; P = .001) groups compared with placebo. Of the 5 miRs identified, 1 targets a gene with prior known associations with risk for T2D. CONCLUSION: We identified 5 miRs that are optimal predictors of incident T2D in the DPP cohort. Future directions include validation of this finding in an independent sample in order to determine whether this risk score may have potential clinical utility for risk stratification of individuals with prediabetes, and functional analysis of the potential genes and pathways targeted by the miRs that were included in the risk score.

Abstract

Background: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases. Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics. Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov–Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types. Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible.

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

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-α). 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

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.