Faculty Bibliography

BACKGROUND:levels. However, it remains unclear whether the CARE program also improves diabetes self-efficacy and psychosocial outcomes in the same study sample. OBJECTIVE:This is a secondary analysis to examine the potential efficacy of the CARE program on secondary outcomes, including diabetes self-efficacy, self-care activities, beliefs in diabetes self-care activities, and diabetes distress among Chinese Americans with T2D. METHODS:level of 7% or higher. Participants were recruited from various health care settings in New York City, including community health centers, private primary care providers, and NYU Langone Health and its affiliates, and were randomly assigned to either the CARE intervention group (n=30) or a waitlist control group (n=30). The intervention consisted of 2 culturally and linguistically tailored educational videos per week for 12 weeks, covering diabetes self-care topics such as healthy eating, physical activity, and medication adherence. These videos were delivered via the WeChat app. In addition, community health workers provided support calls to assist them in setting goals, problem-solving, and addressing social determinants of health barriers every 2 weeks. Secondary outcomes included patient self-reported diabetes self-efficacy, self-care activities, beliefs in diabetes self-care activities, and diabetes distress. Outcomes were assessed at baseline, 3 months, and 6 months. RESULTS:Participants had a mean age of 54.3 (SD 11.5) years and 62% (37/60) were male, 78% (47/60) were married, 58% (35/60) were employed, 70% (42/60) had a high school education or lower, and 88% (53/60) reported limited English proficiency. Intervention participants demonstrated statistically significant improvements in self-efficacy at 3 months (estimated difference in change: 8.47; 95% CI 2.44-14.5; adjusted P=.02), diabetes distress at 6 months (estimated difference in change: -0.43; 95% CI -0.71 to -0.15; adjusted P=.009), and adherence to a healthy diet at both 3 months (estimated difference in change: 1.61; 95% CI 0.46-2.75; adjusted P=.02) and 6 months (estimated difference in change: 1.64; 95% CI 0.48-2.81; adjusted P=.02). CONCLUSIONS:The culturally and linguistically tailored intervention showed promise in improving self-efficacy and diabetes self-care activities among Chinese Americans with T2D, warranting validation through a large-scale randomized controlled trial. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT03557697; https://clinicaltrials.gov/study/NCT03557697.

IMPORTANCE/UNASSIGNED:The oral microbiota may be involved in the development of pancreatic cancer, yet current evidence is largely limited to bacterial 16S amplicon sequencing and small retrospective case-control studies. OBJECTIVE/UNASSIGNED:To test whether the oral bacterial and fungal microbiome is associated with the subsequent development of pancreatic cancer. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This cohort study used data from 2 epidemiological cohorts: the American Cancer Society Cancer Prevention Study-II Nutrition Cohort and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Among cohort participants who provided oral samples, those who prospectively developed pancreatic cancer were identified during follow-up. Control participants who remained free of cancer were selected by 1:1 frequency matching on cohort, 5-year age band, sex, race and ethnicity, and time since oral sample collection. Data were collected from August 2023 to September 2024, and data were analyzed from August 2023 to January 2025. EXPOSURES/UNASSIGNED:The oral bacterial and fungal microbiome were characterized via whole-genome shotgun sequencing and internal transcribed spacer (ITS) sequencing, respectively. The association of periodontal pathogens of the red complex (Treponema denticola, Porphyromonas gingivalis, and Tannerella forsythia) and orange complex (Fusobacterium nucleatum, F periodonticum, Prevotella intermedia, P nigrescens, Parvimonas micra, Eubacterium nodatum, Campylobacter shower, and C gracilis) with pancreatic cancer was tested via logistic regression. The association of the microbiome-wide bacterial and fungal taxa with pancreatic cancer was assessed by Analysis of Compositions of Microbiomes With Bias Correction 2 (ANCOM-BC2). Microbial risk scores (MRS) for pancreatic cancer were calculated from the risk-associated bacterial and fungal species. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Pancreatic cancer incidence. RESULTS/UNASSIGNED:Of 122 000 cohort participants who provided samples, 445 developed pancreatic cancer over a median (IQR) follow-up of 8.8 (4.9-13.4) years and were matched with 445 controls. Of these 890 participants, 474 (53.3%) were male, and the mean (SD) age was 67.2 (7.5) years. Three oral bacterial periodontal pathogens-P gingivalis, E nodatum, and P micra-were associated with increased risk of pancreatic cancer. A bacteriome-wide scan revealed 8 oral bacteria associated with decreased and 13 oral bacteria associated with increased risk of pancreatic cancer (false discovery rate-adjusted Q statistic less than .05). Of the fungi, genus Candida was associated with increased risk of pancreatic cancer. The MRS, based on 27 oral species, was associated with an increase in pancreatic cancer risk (multivariate odds ratio per 1-SD increase in MRS, 3.44; 95% CI, 2.63-4.51). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this cohort study, oral bacteria and fungi were significant risk factors for pancreatic cancer development. Oral microbiota hold promise as biomarkers to identify individuals at high risk of pancreatic cancer, potentially contributing to personalized prevention.

BACKGROUND:Minoritized populations face many barriers to accessing evidence-based diabetes intervention. OBJECTIVES/OBJECTIVE:To evaluate the feasibility, acceptability, and potential efficacy of a social media-based intervention to improve glycemic control among Chinese Americans with type 2 diabetes. DESIGN/METHODS:A pilot randomized controlled trial (RCT) with 3-month and 6-month follow-ups. PARTICIPANTS/METHODS:Chinese Americans (n = 60, mean age 54.3 years old) with limited education (70.0% with high school or less) and low income (50.0% with annual household income < $25,000), and 88.3% have limited English proficiency. INTERVENTION/METHODS:Culturally and linguistically tailored diabetes videos (two videos/week for 12 weeks) delivered via social media and support calls from community health workers. MAIN MEASURES/METHODS:Primary outcomes include feasibility (video watch rate, biweekly call completion rate, and retention rates), acceptability (patient satisfaction), and HbA1c. Secondary health-related outcomes include body weight, BMI, physical activity, and dietary intake. Video watch rate and biweekly call completion rate were assessed at baseline and 3 months, while others were measured at baseline, 3 months, and 6 months. RESULTS:We observed high feasibility and acceptability of the intervention, with retention rates over 87%, an 89% video watch rate, 80% biweekly phone call completion, and a satisfaction rating of 9 out of 10. The intervention group showed a significantly greater increase in fruit intake compared to the control group (0.15 cups vs. - 0.44 cups, adj_p = 0.023) at 3 months. While no significant differences in other outcomes were observed between the groups, the intervention group showed significant improvements in key outcomes, including reduced HbA1c levels (- 1.08%, adj_p < 0.001), weight loss (- 5.15 lbs, adj_p = 0.004), lower BMI (- 0.83, adj_p = 0.023), and reduced starchy food intake (- 0.33 cups, adj_p = 0.033) at 6 months. CONCLUSIONS:The observed high feasibility and acceptability suggest the intervention's feasibility. However, due to the limited sample size, a larger-scale RCT is warranted to test the efficacy of the intervention. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT03557697; https://clinicaltrials.gov/ct2/show/NCT03557697.

The pancreas tumor microbiota may influence tumor microenvironment and influence survival in early-stage pancreatic ductal adenocarcinoma (PDAC); however, current studies are limited and small. We investigated the relationship of tumor microbiota to survival in 201 surgically resected patients with localized PDAC (Stages I-II), from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts. We characterized the tumor microbiome using RNA-sequencing data. We examined the association of the tumor microbiome with overall survival (OS), via meta-analysis with the Cox PH model. A microbial risk score (MRS) was calculated from the OS-associated microbiota. We further explored whether the OS-associated microbiota is related to host tumor immune infiltration. PDAC tumor microbiome α- and β-diversities were not associated with OS; however, 11 bacterial species, including species of Gammaproteobacteria, confirmed by extensive resampling, were significantly associated with OS (all Q < 0.05). The MRS summarizing these bacteria was related to a threefold change in OS (hazard ratio = 2.96 per standard deviation change in the MRS, 95% confidence interval = 2.26-3.86). This result was consistent across the two cohorts and in stratified analyses by adjuvant therapy (chemotherapy/radiation). Identified microbiota and the MRS also exhibited association with memory B cells and naïve CD4⁺ T cells, which may be related to the immune landscape through BCR and TCR signaling pathways. Our study shows that a unique tumor microbiome structure, potentially affecting the tumor immune microenvironment, is associated with poorer survival in resected early-stage PDAC. These findings suggest that microbial mechanisms may be involved in PDAC survival, potentially informing PDAC prognosis and guiding personalized treatment strategies.IMPORTANCEMuch of the available data on the PDAC tumor microbiome and survival are derived from relatively small and heterogeneous studies, including those involving patients with advanced stages of pancreatic cancer. There is a critical knowledge gap in terms of the tumor microbiome and survival in early-stage patients treated by surgical resection; we expect that advancements in survival may initially be best achieved in these patients who are treated with curative intent.

IMPORTANCE/UNASSIGNED:The oral microbiota may be involved in development of head and neck squamous cell cancer (HNSCC), yet current evidence is largely limited to bacterial 16S amplicon sequencing or small retrospective case-control studies. OBJECTIVE/UNASSIGNED:To test whether oral bacterial and fungal microbiomes are associated with subsequent risk of HNSCC development. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Prospective nested case-control study among participants providing oral samples in 3 epidemiological cohorts, the American Cancer Society Cancer Prevention Study II Nutrition Cohort, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, and the Southern Community Cohort Study. Two hundred thirty-six patients who prospectively developed HNSCC were identified during a mean (SD) of 5.1 (3.6) years of follow-up. Control participants who remained HNSCC free were selected by 2:1 frequency matching on cohort, age, sex, race and ethnicity, and time since oral sample collection. Data analysis was conducted in 2023. EXPOSURES/UNASSIGNED:Characterization of the oral bacterial microbiome using whole-genome shotgun sequencing and the oral fungal microbiome using internal transcribed spacer sequencing. Association of bacterial and fungal taxa with HNSCC was assessed by analysis of compositions of microbiomes with bias correction. Association with red and orange oral pathogen complexes was tested by logistic regression. A microbial risk score for HNSCC risk was calculated from risk-associated microbiota. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was HNSCC incidence. RESULTS/UNASSIGNED:The study included 236 HNSCC case participants with a mean (SD) age of 60.9 (9.5) years and 24.6% women during a mean of 5.1 (3.6) years of follow-up, and 485 matched control participants. Overall microbiome diversity at baseline was not related to subsequent HNSCC risk; however 13 oral bacterial species were found to be differentially associated with development of HNSCC. The species included the newly identified Prevotella salivae, Streptococcus sanguinis, and Leptotrichia species, as well as several species belonging to beta and gamma Proteobacteria. The red/orange periodontal pathogen complex was moderately associated with HNSCC risk (odds ratio, 1.06 per 1 SD; 95% CI, 1.00-1.12). A 1-SD increase in microbial risk score (created based on 22 bacteria) was associated with a 50% increase in HNSCC risk (multivariate odds ratio, 1.50; 95% CI, 1.21-1.85). No fungal taxa associated with HNSCC risk were identified. CONCLUSIONS AND RELEVANCE/UNASSIGNED:This case-control study yielded compelling evidence that oral bacteria are a risk factor for HNSCC development. The identified bacteria and bacterial complexes hold promise, along with other risk factors, to identify high-risk individuals for personalized prevention of HNSCC.

BACKGROUND:Risk of early-stage lung adenocarcinoma (LUAD) recurrence after surgical resection is significant, and post-recurrence median survival is approximately two years. Currently there are no commercially available biomarkers that predict recurrence. Here, we investigated whether microbial and host genomic signatures in the lung can predict recurrence. METHODS:In 91 early-stage (Stage IA/IB) LUAD-patients with extensive follow-up, we used 16s rRNA gene sequencing and host RNA-sequencing to map the microbial and host transcriptomic landscape in tumor and adjacent unaffected lung samples. RESULTS:23 out of 91 subjects had tumor recurrence over 5-year period. In tumor samples, LUAD recurrence was associated with enrichment with Dialister, Prevotella, while in unaffected lung, recurrence was associated with enrichment with Sphyngomonas and Alloiococcus. The strengths of the associations between microbial and host genomic signatures with LUAD recurrence were greater in adjacent unaffected lung samples than in the primary tumor. Among microbial-host features in the unaffected lung samples associated with recurrence, enrichment with Stenotrophomonas geniculata and Chryseobacterium were positively correlated with upregulation of IL-2, IL-3, IL-17, EGFR, HIF-1 signaling pathways among the host transcriptome. In tumor samples, enrichment with Veillonellaceae Dialister, Ruminococcacea, Haemophilus Influenza, and Neisseria were positively correlated with upregulation of IL-1, IL-6, IL17, IFN, and Tryptophan metabolism pathways. CONCLUSIONS:Overall, modeling suggested that a combined microbial/transcriptome approach using unaffected lung samples had the best biomarker performance (AUC=0.83). IMPACT/CONCLUSIONS:This study suggests that LUAD recurrence is associated with distinct pathophysiological mechanisms of microbial-host interactions in the unaffected lung rather than those present in the resected tumor.

UNLABELLED:With the development of sequencing technology and analytic tools, studying within-species variations enhances the understanding of microbial biological processes. Nevertheless, most existing methods designed for strain-level analysis lack the capability to concurrently assess both strain proportions and genome-wide single nucleotide variants (SNVs) across longitudinal metagenomic samples. In this study, we introduce LongStrain, an integrated pipeline for the analysis of large-scale metagenomic data from individuals with longitudinal or repeated samples. In LongStrain, we first utilize two efficient tools, Kraken2 and Bowtie2, for the taxonomic classification and alignment of sequencing reads, respectively. Subsequently, we propose to jointly model strain proportions and shared haplotypes across samples within individuals. This approach specifically targets tracking a primary strain and a secondary strain for each subject, providing their respective proportions and SNVs as output. With extensive simulation studies of a microbial community and single species, our results demonstrate that LongStrain is superior to two genotyping methods and two deconvolution methods across a majority of scenarios. Furthermore, we illustrate the potential applications of LongStrain in the real data analysis of The Environmental Determinants of Diabetes in the Young study and a gastric intestinal metaplasia microbiome study. In summary, the proposed analytic pipeline demonstrates marked statistical efficiency over the same type of methods and has great potential in understanding the genomic variants and dynamic changes at strain level. LongStrain and its tutorial are freely available online at https://github.com/BoyanZhou/LongStrain. IMPORTANCE/OBJECTIVE:The advancement in DNA-sequencing technology has enabled the high-resolution identification of microorganisms in microbial communities. Since different microbial strains within species may contain extreme phenotypic variability (e.g., nutrition metabolism, antibiotic resistance, and pathogen virulence), investigating within-species variations holds great scientific promise in understanding the underlying mechanism of microbial biological processes. To fully utilize the shared genomic variants across longitudinal metagenomics samples collected in microbiome studies, we develop an integrated analytic pipeline (LongStrain) for longitudinal metagenomics data. It concurrently leverages the information on proportions of mapped reads for individual strains and genome-wide SNVs to enhance the efficiency and accuracy of strain identification. Our method helps to understand strains' dynamic changes and their association with genome-wide variants. Given the fast-growing longitudinal studies of microbial communities, LongStrain which streamlines analyses of large-scale raw sequencing data should be of great value in microbiome research communities.

BACKGROUND/UNASSIGNED:Accurately identifying eating patterns, specifically the timing, frequency, and distribution of eating occasions (EOs), is important for assessing eating behaviors, especially for preventing and managing obesity and type 2 diabetes (T2D). However, existing methods to study EOs rely on self-report, which may be prone to misreporting and bias and has a high user burden. Therefore, objective methods are needed. METHODS/UNASSIGNED:We aim to compare EO timing using objective and subjective methods. Participants self-reported EO with a smartphone app (self-report [SR]), wore the ActiGraph GT9X on their dominant wrist, and wore a continuous glucose monitor (CGM, Abbott Libre Pro) for 10 days. EOs were detected from wrist motion (WM) using a motion-based classifier and from CGM using a simulation-based system. We described EO timing and explored how timing identified with WM and CGM compares with SR. RESULTS/UNASSIGNED:. All had prediabetes or moderately controlled T2D. The median time-of-day first EO (and interquartile range) for SR, WM, and CGM were 08:24 (07:00-09:59), 9:42 (07:46-12:26), and 06:55 (04:23-10:03), respectively. The median last EO for SR, WM, and CGM were 20:20 (16:50-21:42), 20:12 (18:30-21:41), and 21:43 (20:35-22:16), respectively. The overlap between SR and CGM was 55% to 80% of EO detected with tolerance periods of ±30, 60, and 120 minutes. The overlap between SR and WM was 52% to 65% EO detected with tolerance periods of ±30, 60, and 120 minutes. CONCLUSION/UNASSIGNED:The continuous glucose monitor and WM detected overlapping but not identical meals and may provide complementary information to self-reported EO.

The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our approach is validated through extensive simulation studies, demonstrating its effectiveness in addressing overdispersion and improving inference accuracy. Additionally, we apply our approach to two real datasets collected from the human gut and vagina, respectively, demonstrating the wide applicability of our methods. The results highlight the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis. The corresponding software implementation is publicly available at https://github.com/yimshi/robustestimates.