Bradley E. Aouizerat

Abstract

Background: Depression affects 33% of women with type 2 diabetes (T2D) and leads to increased risks of premature mortality. Fluctuation and variation of depressive presentations can hinder clinical identification. Purpose: We aimed to identify and examine subgroups characterized by distinct depressive symptom trajectories among women with T2D. Methods: This retrospective analysis leveraged the Women’s Interagency HIV Study data to identify depressive symptom trajectories based on the Center for Epidemiological Studies Depression scores (2014-2019) among women with and without HIV. Descriptive statistics characterized sample demographics (eg, age, race, income), clinical indices (eg, hemoglobin A1C [HbA1c], BMI, HIV status), and psychosocial experiences (eg, discrimination, social support, anxiety, pain). We used growth mixture modeling to identify groups defined by distinct depressive symptom trajectories and parametric and non-parametric tests to examine demographic, clinical, and psychosocial differences across subgroups. Results: Among the 630 women included, the mean age was 50.4 (SD = 8.3) years, 72.4% identified as Black and non-Hispanic, and 68.2% were living with HIV. Five subgroups were identified and distinguished by severity and symptom type. Participants with lower incomes (P = .01), lower employment (P < .0001), lower social support (P = .0001), and experiences of discrimination (P < .0001) showed greater membership in threshold, moderate, and severe depressive subgroups. Subgroup membership was not associated with metabolic indices (BMI, HbA1c) or HIV status. Anxiety, pain, and loneliness (all P = .0001) were worse in subgroups with higher depressive symptoms. Conclusions: Among women with T2D, depressive symptom trajectories differ across clinical and social contexts. This study advances precision by delineating subgroups within a broad clinical category.

Abstract

Hypermutated proviruses, which arise in a single Human Immunodeficiency Virus (HIV) replication cycle when host antiviral APOBEC3 proteins introduce extensive guanine to adenine mutations throughout the viral genome, persist in all people living with HIV receiving antiretroviral therapy (ART). However, hypermutated sequences are routinely excluded from phylogenetic trees because their extensive mutations complicate phylogenetic inference, and as a result, we know relatively little about their within-host evolutionary origins and dynamics. Using >1400 longitudinal single-genome-amplified HIV env-gp120 sequences isolated from six women over a median of 18 years of follow-up—including plasma HIV RNA sequences collected over a median of 9 years between seroconversion and ART initiation, and >500 proviruses isolated over a median of 9 years on ART—we evaluated three approaches for masking hypermutation in nucleotide alignments. Our goals were to (i) reconstruct phylogenies that can be used for molecular dating and (ii) phylogenetically infer the integration dates of hypermutated proviruses persisting during ART. Two of the approaches (stripping all positions containing putative APOBEC3 mutations from the alignment or replacing individual putative APOBEC3 mutations in hypermutated sequences with the ambiguous base R) consistently normalized tree topologies, eliminated erroneous clustering of hypermutated proviruses, and brought env-intact and hypermutated proviruses into comparable ranges with respect to multiple tree-based metrics. Importantly, these corrected trees produced integration date estimates for env-intact proviruses that were highly concordant with those from benchmark trees that excluded hypermutated sequences, supporting the use of these corrected trees for molecular dating. Subsequent molecular dating of hypermutated proviruses revealed that these sequences spanned a wide within-host age range, with the oldest ones dating to shortly after infection. This indicates that hypermutated proviruses, like other provirus types, begin to be seeded into the proviral pool immediately following infection and can persist for decades. In two of the six participants, hypermutated proviruses differed from env-intact ones in terms of their age distributions, suggesting that different provirus types decay at heterogeneous rates in some hosts. These simple approaches to reconstruct hypermutated provirus’ evolutionary histories reveal insights into their in vivo origins and longevity toward a more comprehensive understanding of HIV persistence during ART.

Abstract

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple “hidden” layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.

Abstract

Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.

Abstract

Substance use disparities among sexual and gender minority (SGM) people are attributed to minority stress, but few studies have examined minority stress and cannabis use over time or investigated differences in cannabis use trajectories by less-studied gender subgroups. We examined if longitudinal cannabis use trajectories are related to baseline minority stressors and if gender differences persisted after accounting for minority stress. Cannabis use risk was measured annually over four years (2017–2021) within a longitudinal cohort study of SGM adults in the United States (N = 11,813). Discrimination and victimization, internalized stigma, disclosure and concealment, and safety and acceptance comprised minority stress (n = 5,673). Latent class growth curve mixture models identified five cannabis use trajectories: ‘low or no risk’, ‘low moderate risk’, ‘high moderate risk’, ‘steep risk increase’, and ‘highest risk’. Participants who reported past-year discrimination and/or victimization at baseline had greater odds of membership in any cannabis risk category compared to the ‘low risk’ category (odds ratios [OR] 1.17–1.33). Internalized stigma was related to ‘high moderate’ and ‘highest risk’ cannabis use (ORs 1.27–1.38). After accounting for minority stress, compared to cisgender men, gender expansive people and transgender men had higher odds of ‘low moderate risk’ (ORs 1.61, 1.67) or ‘high moderate risk’ (ORs 2.09, 1.99), and transgender men had higher odds of ‘highest risk’ (OR 2.36) cannabis use. This study indicates minority stress is related to prospective cannabis use risk trajectories among SGM people, and transgender men and gender expansive people have greater odds of trajectories reflecting cannabis use risk.

Abstract

Background Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. Methods Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. Results The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. Conclusion Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.

Abstract

Telomere length (TL) is an important indicator of cellular aging. Shorter TL is associated with several age-related diseases including coronary heart disease, heart failure, diabetes, osteoporosis, and cancer. Recently, a DNA methylation-based TL (DNAmTL) estimator has been developed as an alternative method for directly measuring TL. In this study, we examined the association of DNAmTL with cancer prevalence and mortality risk among people with and without HIV in the Veterans Aging Cohort Study Biomarker Cohort (VACS, N = 1917) and Women's Interagency HIV Study Cohort (WIHS, N = 481). We profiled DNAm in whole blood (VACS) or in peripheral blood mononuclear cells (WIHS) using an array-based method. Cancer prevalence was estimated from electronic medical records and cancer registry data. The VACS Index was used as a measure of physiologic frailty. Models were adjusted for self-reported race and ethnicity, batch, smoking status, alcohol consumption, and five cell types (CD4, CD8, NK, B cell, and monocyte). We found that people with HIV had shorter average DNAmTL than those without HIV infection [beta = −0.25, 95% confidence interval (−0.32, −0.18), p = 1.48E-12]. Greater value of VACS Index [beta = −0.002 (−0.003, −0.001), p = 2.82E-05] and higher cancer prevalence [beta = −0.07 (−0.10, −0.03), p = 1.37E-04 without adjusting age] were associated with shortened DNAmTL. In addition, one kilobase decrease in DNAmTL was associated with a 40% increase in mortality risk [hazard ratio: 0.60 (0.44, 0.82), p = 1.42E-03]. In summary, HIV infection, physiologic frailty, and cancer are associated with shortening DNAmTL, contributing to an increased risk of all-cause mortality.

Abstract

Aims: The purpose of this study was to assess the levels of cardiovascular health (CVH) of Black and Latino adults with type 2 diabetes (T2D) and examine the association of individual and microsystem level factors with their CVH score. Methods: This was a cross-sectional design in 60 Black and Latino Adults aged 18–40 with T2D. Data were collected on sociodemographic, individual (sociodemographic, diabetes self-management, sleep disturbance, depressive symptoms, quality of life, and the inflammatory biomarkers IL-6 and hs-CRP) and microsystem factors (family functioning), and American Heart Association’s Life’s Simple 7 metrics of CVH. Factors significantly associated with the CVH score in the bivariate analyses were entered into a linear regression model. Results: The sample had a mean age 34 ± 5 years and was primarily female (75%) with a mean CVH score was 8.6 ± 2.2 (possible range of 0–14). The sample achieved these CVH factors at ideal levels: body mass index <25 kg/m2 (8%); blood pressure <120/80 (42%); hemoglobin A1c < 7% (57%); total cholesterol <200 mg/dL (83%); healthy diet (18%); never or former smoker > one year (95%); and physical activity (150 moderate-to-vigorous minutes/week; 45%). In the multivariable model, two factors were significantly associated with cardiovascular health: hs-CRP (B = −0.11621, p <.0001) and the general health scale (B = 0.45127, p =.0013). Conclusions: This sample had an intermediate level of CVH, with inflammation and general health associated with overall CVH score.

Abstract

Background: Cocaine-one of the most frequently abused illicit drugs among persons living with HIV [people living with HIV (PLWH)]-slows the decline of viral production after antiretroviral therapy and is associated with higher HIV viral load, more rapid HIV progression, and increased mortality. Setting: We examined the impact of cocaine use on the CD4+ T-cell HIV latent reservoir (HLR) in virally suppressed PLWH participating in a national, longitudinal cohort study of the natural and treated history of HIV in the United States. Methods: CD4+ T-cell genomic DNA from 434 women of diverse ancestry (ie, 75% Black, 14% Hispanic, 12% White) who self-reported cocaine use (ie, 160 cocaine users, 59 prior users, 215 non-users) was analyzed using the Intact Proviral HIV DNA Assay, measuring intact provirus per 106 CD4+ T cells. Findings: HIV latent reservoir size differed by cocaine use (ie, median [interquartile range]: 72 [14-193] for never users, 165 [63-387] for prior users, 184 [28-502] for current users), which was statistically significantly larger in both prior (P = 0.023) and current (P = 0.001) cocaine users compared with never users. Conclusions: Cocaine use may contribute to a larger replication competent HLR in CD4+ T cells among virologically suppressed women living with HIV. Our findings are important because women are underrepresented in HIV reservoir studies and in studies of the impact of cocaine use on outcomes among PLWH.

Abstract

Problem: Bacterial vaginosis (BV) disproportionally impacts Black and Hispanic women, placing them at risk for HIV, sexually transmitted infections and preterm birth. It is unknown whether there are differences by genetic ancestry in BV risk or whether polymorphisms associated with BV risk differ by ancestry. Methods: Women's Interagency HIV Study (WIHS) participants with longitudinal Nugent scores were dichotomized as having (n = 319, Nugent 7–10) or not having BV (n = 367, Nugent 0–3). Genetic ancestry was defined by clustering of principal components from ancestry informative markers and further stratified by BV status. 627 single nucleotide polymorphisms (SNPs) across 41 genes important in mucosal defense were identified in the WIHS GWAS. A logistic regression analysis was adjusted for nongenetic predictors of BV and self-reported race/ethnicity to assess associations between genetic ancestry and genotype. Results: Self-reported race and genetic ancestry were associated with BV risk after adjustment for behavioral factors. Polymorphisms in mucosal defense genes including syndecans, cytokines and toll-like receptors (TLRs) were associated with BV in all ancestral groups. Conclusions: The common association of syndecan, cytokine and TLR genes and the importance of immune function and inflammatory pathways in BV, suggests these should be targeted for further research on BV pathogenesis and therapeutics.