Faculty Bibliography

The incidence of esophageal adenocarcinoma (EA) has drastically increased in the United States since 1970s for unclear reasons. We hypothesized that the widespread usage of antibiotics has increased the procarcinogenic potential of the orodigestive microbiota along the sequence of gastroesophageal reflux (GR), Barrett's esophagus (BE) and EA phenotypes. This case control study included normal controls (NC) and three disease phenotypes GR, BE and EA. Microbiota in the mouth, esophagus, and stomach, and rectum were analyzed using 16S rRNA gene sequencing. Overall, we discovered 44 significant pairwise differences in abundance of microbial taxa between the four phenotypes, with 12 differences in the mouth, 21 in the esophagus, two in the stomach, and nine in the rectum. Along the GR→BE→EA sequence, oral and esophageal microbiota were more diversified, the dominant genus Streptococcus was progressively depleted while six other genera Atopobium, Actinomyces, Veillonella, Ralstonia, Burkholderia and Lautropia progressively enriched. In NC, Streptococcus appeared to control populations of other genera in the foregut via numerous negative and positive connections, while in disease states, the rich network was markedly simplified. Inferred gene functional content showed a progressive enrichment through the stages of EA development in genes encoding antibiotic resistance, ligands of Toll-like and NOD-like receptors, nitrate-nitrite-nitric oxide pathway and acetaldehyde metabolism. The orodigestive microbiota is in a progressive dysbiotic state along the GR-BE-EA sequence. The increasing dysbiosis and antibiotic and procarcinogenic genes in the disease states warrants further study to define their roles in EA pathogenesis.

BACKGROUND:In 2021, Delta became the predominant SARS-CoV-2 variant worldwide. While vaccines have effectively prevented COVID-19 hospitalization and death, vaccine breakthrough infections increasingly occurred. The precise role of clinical and genomic determinants in Delta infections is not known, and whether they contributed to increased rates of breakthrough infections compared to unvaccinated controls. METHODS:We studied SARS-CoV-2 variant distribution, dynamics, and adaptive selection over time in relation to vaccine status, phylogenetic relatedness of viruses, full genome mutation profiles, and associated clinical and demographic parameters. FINDINGS/RESULTS:We show a steep and near-complete replacement of circulating variants with Delta between May and August 2021 in metropolitan New York. We observed an increase of the Delta sublineage AY.25 (14% in vaccinated, 7% in unvaccinated), its spike mutation S112L, and AY.44 (8% in vaccinated, 2% in unvaccinated) with its nsp12 mutation F192V in breakthroughs. Delta infections were associated with younger age and lower hospitalization rates than Alpha. Delta breakthrough infections increased significantly with time since vaccination, and, after adjusting for confounders, they rose at similar rates as in unvaccinated individuals. INTERPRETATION/CONCLUSIONS:We observed a modest adaptation of Delta genomes in breakthrough infections in New York, suggesting an improved genomic framework to support Delta's epidemic growth in times of waning vaccine protection despite limited impact on vaccine escape. FUNDING/BACKGROUND:The study was supported by NYU institutional funds. The NYULH Genome Technology Center is partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

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.

Within weeks of New York State's first confirmed case of COVID-19, New York City became the epicenter of the nation's COVID-19 pandemic. With more than 80,000 COVID-19 hospitalizations during the first wave alone, hospitals in downstate New York were forced to adapt existing procedures to manage the surge and care for patients facing a novel disease. Given the unprecedented surge, effective patient load balancing-moving patients from a hospital with diminishing capacity to another hospital within the same health system with relatively greater capacity-became chief among the capabilities required of New York health systems. The Greater New York Hospital Association invited members of downstate New York's 6 largest health systems to talk about how each of their systems evolved their patient load balancing procedures throughout the pandemic. Informed by their insights, experiences, lessons learned, and collaboration, we collectively present a set of consensus recommendations and best practices for patient load balancing at the facility and health system level, which may inform regional approaches to patient load balancing.

The incidence of kidney stones is increasing in the US population. Oxalate, a major factor for stone formation, is degraded by gut bacteria reducing its intestinal absorption. Intestinal O. formigenes colonization has been associated with a lower risk for recurrent kidney stones in humans. In the current study, we used a clinical trial of the eradication of Helicobacter pylori to assess the effects of an antibiotic course on O. formigenes colonization, urine electrolytes, and the composition of the intestinal microbiome. Of 69 healthy adult subjects recruited, 19 received antibiotics for H. pylori eradication, while 46 were followed as controls. Serial fecal samples were examined for O. formigenes presence and microbiota characteristics. Urine, collected serially fasting and following a standard meal, was tested for oxalate and electrolyte concentrations. O. formigenes prevalence was 50%. Colonization was significantly and persistently suppressed in antibiotic-exposed subjects but remained stable in controls. Urinary pH increased after antibiotics, but urinary oxalate did not differ between the control and treatment groups. In subjects not on antibiotics, the O. formigenes-positive samples had higher alpha-diversity and significantly differed in Beta-diversity from the O. formigenes-negative samples. Specific taxa varied in abundance in relation to urinary oxalate levels. These studies identified significant antibiotic effects on O. formigenes colonization and urinary electrolytes and showed that overall microbiome structure differed in subjects according to O. formigenes presence. Identifying a consortium of bacterial taxa associated with urinary oxalate may provide clues for the primary prevention of kidney stones in healthy adults.

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients was needed due to a sudden and massive increase in patients during the COVID-19 pandemic as healthcare providers incurred an exponential increase in workload,which created a strain on the staff and limited resources. Further, methods to better understand and characterise the predictors of morbidity and mortality was needed. METHODS: We developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3395 hospital admissions with COVID-19. Multiple methods were applied, and final model was selected based on performance. A variable importance algorithm was used for interpretability, and understanding of performance and predictors was applied to the best model. We built a model with an area under the receiver operating characteristic curve of 83-97 to identify predictors and patients with high risk of mortality due to COVID-19. Oximetry, respirations, blood urea nitrogen, lymphocyte per cent, calcium, troponin and neutrophil percentage were important features, and key ranges were identified that contributed to a 50% increase in patients' mortality prediction score. With an increasing negative predictive value starting 0.90 after the second day of admission suggests we might be able to more confidently identify likely survivors DISCUSSION: This study serves as a use case of a machine learning methods with visualisations to aide clinicians with a better understanding of the model and predictors of mortality. CONCLUSION: As we continue to understand COVID-19, computer assisted algorithms might be able to improve the care of patients.

Early reports showed high mortality from coronavirus disease 2019 (COVID-19). Mortality rates have recently been lower, raising hope that treatments have improved. However, patients are also now younger, with fewer comorbidities. We explored whether hospital mortality was associated with changing demographics at a 3-hospital academic health system in New York. We examined in-hospital mortality or discharge to hospice from March through August 2020, adjusted for demographic and clinical factors, including comorbidities, admission vital signs, and laboratory results. Among 5,121 hospitalizations, adjusted mortality dropped from 25.6% (95% CI, 23.2-28.1) in March to 7.6% (95% CI, 2.5-17.8) in August. The standardized mortality ratio dropped from 1.26 (95% CI, 1.15-1.39) in March to 0.38 (95% CI, 0.12-0.88) in August, at which time the average probability of death (average marginal effect) was 18.2 percentage points lower than in March. Data from one health system suggest that mortality from COVID-19 is decreasing even after accounting for patient characteristics.