Faculty Bibliography

BACKGROUND:Epidemiological studies that investigate alterations in gut microbial composition associated with cognitive dysfunction are limited. OBJECTIVE:To examine the association between the gut microbiota and subjective memory complaints (SMCs), a self-reported, validated indicator of cognitive dysfunction. METHODS:In this cross-sectional study of 95 older women selected from the New York University Women's Health Study (NYUWHS), we characterized the gut microbial composition using 16S rRNA gene sequencing. We estimated odds ratio (OR) from beta regression which approximates the ratio of mean relative abundances of individual bacterial taxon from phylum to genus levels by binary (2+ versus < 2) and continuous SMCs. RESULTS:Women reporting 2 or more SMCs had higher relative abundances of genus Holdemania and family Desulfovibrionaceae compared with those reporting one or no complaint. Compared with women with < 2 SMCs, the relative abundances of Holdemania and family Desulfovibrionaceae were 2.09 times (OR: 2.09, 95% confidence interval [CI]: 1.38-3.17) and 2.10 times (OR: 2.10, 95% CI: 1.43-3.09) higher in women with 2+ SMCs, respectively (false discovery rate (FDR)-adjusted p = 0.038 and 0.010, respectively). A dose-response association was observed for genus Sutterella and family Desulfovibrionaceae. Every one-unit increase in SMCs was associated with 25% and 27% higher relative abundances of Sutterella (OR: 1.25; 95% CI: 1.11-1.40) and Desulfovibrionaceae (OR: 1.27; 95% CI: 1.13-1.42), respectively (FDR-adjusted p = 0.018 and 0.006, respectively). CONCLUSION/CONCLUSIONS:Our findings support an association between alterations in the gut bacterial composition and cognitive dysfunction.

Background/UNASSIGNED:Accruing evidence indicates that accumulation of advanced glycation end products (AGEs) and activation of the receptor for AGEs (RAGE) play a significant role in obesity and type 2 diabetes. The concentrations of circulating RAGE isoforms, such as soluble RAGE (sRAGE), cleaved RAGE (cRAGE), and endogenous secretory RAGE (esRAGE), collectively sRAGE isoforms, may be implicit in weight loss and energy compensation resulting from caloric restriction. Objectives/UNASSIGNED:We aimed to evaluate whether baseline concentrations of sRAGE isoforms predicted changes (∆) in body composition [fat mass (FM), fat-free mass (FFM)], resting energy expenditure (REE), and adaptive thermogenesis (AT) during weight loss. Methods/UNASSIGNED:Data were collected during a behavioral weight loss intervention in adults with obesity. At baseline and 3 mo, participants were assessed for body composition (bioelectrical impedance analysis) and REE (indirect calorimetry), and plasma was assayed for concentrations of sRAGE isoforms (sRAGE, esRAGE, cRAGE). AT was calculated using various mathematical models that included measured and predicted REE. A linear regression model that adjusted for age, sex, glycated hemoglobin (HbA1c), and randomization arm was used to test the associations between sRAGE isoforms and metabolic outcomes. Results/UNASSIGNED:) experienced modest and variable weight loss over 3 mo. Although baseline sRAGE isoforms did not predict changes in ∆FM or ∆FFM, all baseline sRAGE isoforms were positively associated with ∆REE at 3 mo. Baseline esRAGE was positively associated with AT in some, but not all, AT models. The association between sRAGE isoforms and energy expenditure was independent of HbA1c, suggesting that the relation was unrelated to glycemia. Conclusions/UNASSIGNED:This study demonstrates a novel link between RAGE and energy expenditure in human participants undergoing weight loss.This trial was registered at clinicaltrials.gov as NCT03336411.

Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.

Evidence suggests that Helicobacter pylori plays a role in gastric cancer (GC) initiation. However, epidemiologic studies on the specific role of other bacteria in the development of GC are lacking. We conducted a case-control study of 89 cases with gastric intestinal metaplasia (IM) and 89 matched controls who underwent upper gastrointestinal endoscopy at three sites affiliated with NYU Langone Health. We performed shotgun metagenomic sequencing using oral wash samples from 89 case-control pairs and antral mucosal brushing samples from 55 case-control pairs. We examined the associations of relative abundances of bacterial taxa and functional pathways with IM using conditional logistic regression with and without elastic-net penalty. Compared with controls, oral species Peptostreptococcus stomatis, Johnsonella ignava, Neisseria elongata and Neisseria flavescens were enriched in cases (odds ratios [ORs] = 1.29-1.50, P = .004-.01) while Lactobacillus gasseri, Streptococcus mutans, S parasanguinis and S sanguinis were under-represented (ORs = 0.66-0.76, P = .006-.042) in cases. Species J ignava and Filifactor alocis in the gastric microbiota were enriched (ORs = 3.27 and 1.43, P = .005 and .035, respectively), while S mutans, S parasanguinis and S sanguinis were under-represented (ORs = 0.61-0.75, P = .024-.046), in cases compared with controls. The lipopolysaccharide and ubiquinol biosynthesis pathways were more abundant in IM, while the sugar degradation pathways were under-represented in IM. The findings suggest potential roles of certain oral and gastric microbiota, which are correlated with regulation of pathways associated with inflammation, in the development of gastric precancerous lesions.

Recent studies have suggested that the temporal dynamics of the human microbiome may have associations with human health and disease. An increasing number of longitudinal microbiome studies, which record time to disease onset, aim to identify candidate microbes as biomarkers for prognosis. Owing to the ultra-skewness and sparsity of microbiome proportion (relative abundance) data, directly applying traditional statistical methods may result in substantial power loss or spurious inferences. We propose a novel joint modeling framework [JointMM], which is comprised of two sub-models: a longitudinal sub-model called zero-inflated scaled-Beta generalized linear mixed-effects regression to depict the temporal structure of microbial proportions among subjects; and a survival sub-model to characterize the occurrence of an event and its relationship with the longitudinal microbiome proportions. JointMM is specifically designed to handle the zero-inflated and highly skewed longitudinal microbial proportion data and examine whether the temporal pattern of microbial presence and/or the non-zero microbial proportions are associated with differences in the time to an event. The longitudinal sub-model of JointMM also provides the capacity to investigate how the (time-varying) covariates are related to the temporal microbial presence/absence patterns and/or the changing trend in non-zero proportions. Comprehensive simulations and real data analyses are used to assess the statistical efficiency and interpretability of JointMM. This article is protected by copyright. All rights reserved.

Antibody responses serve as the primary protection against SARS-CoV-2 infection through neutralization of viral entry into cells. We have developed a two-dimensional multiplex bead binding assay (2D-MBBA) that quantifies multiple antibody isotypes against multiple antigens from a single measurement. Here, we applied our assay to profile IgG, IgM and IgA levels against the spike antigen, its receptor-binding domain and natural and designed mutants. Machine learning algorithms trained on the 2D-MBBA data substantially improve the prediction of neutralization capacity against the authentic SARS-CoV-2 virus of serum samples of convalescent patients. The algorithms also helped identify a set of antibody isotypeâ€"antigen datasets that contributed to the prediction, which included those targeting regions outside the receptor-binding interface of the spike protein. We applied the assay to profile samples from vaccinated, immune-compromised patients, which revealed differences in the antibody profiles between convalescent and vaccinated samples. Our approach can rapidly provide deep antibody profiles and neutralization prediction from essentially a drop of blood without the need of BSL-3 access and provides insights into the nature of neutralizing antibodies. It may be further developed for evaluating neutralizing capacity for new variants and future pathogens.

We propose a randomized controlled trial to evaluate the effectiveness of a community health worker-led decision-coaching program to facilitate shared decision-making for prostate cancer screening decisions by Black men at a primary care federally qualified health center.

[Figure: see text].

High levels of autoimmune antibodies are observed in COVID-19 patients but their specific contribution to disease severity and clinical manifestations remains poorly understood. We performed a retrospective study of 115 COVID-19 hospitalized patients with different degrees of severity to analyze the generation of autoimmune antibodies to common antigens: a lysate of erythrocytes, the lipid phosphatidylserine (PS) and DNA. High levels of IgG autoantibodies against erythrocyte lysates were observed in a large percentage (up to 36%) of patients. Anti-DNA and anti-PS antibodies determined upon hospital admission correlated strongly with later development of severe disease, showing a positive predictive value of 85.7% and 92.8%, respectively. Patients with positive values for at least one of the two autoantibodies accounted for 24% of total severe cases. Statistical analysis identified strong correlations between anti-DNA antibodies and markers of cell injury, coagulation, neutrophil levels and erythrocyte size. Anti-DNA and anti-PS autoantibodies may play an important role in the pathogenesis of COVID-19 and could be developed as predictive biomarkers for disease severity and specific clinical manifestations.