Faculty Bibliography

BACKGROUND:Type 2 diabetes (T2D) in Chinese Americans is a rising public health concern for the US health care system. The majority of Chinese Americans with T2D are foreign-born older immigrants and report limited English proficiency and health literacy. Multiple social determinants of health limit access to evidence-based diabetes interventions for underserved Chinese immigrants. A social media-based diabetes intervention may be feasible to reach this community. OBJECTIVE:The purpose of the Chinese American Research and Education (CARE) study was to examine the potential efficacy of a social media-based intervention on glycemic control in Chinese Americans with T2D. Additionally, the study aimed to explore the potential effects of the intervention on psychosocial and behavioral factors involved in successful T2D management. In this report, we describe the design and protocol of the CARE trial. METHODS:and psychosocial and behavioral outcomes. RESULTS:This pilot RCT study was approved by the Institutional Review Board at NYU Grossman School of Medicine in March 2021. The first participant was enrolled in March 2021, and the recruitment goal (n=60) was met in March 2022. All data collection is expected to conclude by November 2022, with data analysis and study results ready for reporting by December 2023. Findings from this pilot RCT will further guide the team in planning a future large-scale study. CONCLUSIONS:This study will serve as an important first step in exploring scalable interventions to increase access to evidence-based diabetes interventions among underserved, low-income, immigrant populations. This has significant implications for chronic care in other high-risk immigrant groups, such as low-income Hispanic immigrants, who also bear a high T2D burden, face similar barriers to accessing diabetes programs, and report frequent social media use (eg, WhatsApp). TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT03557697; https://clinicaltrials.gov/ct2/show/NCT03557697. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/42554.

Importance:Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss. Objective:To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity. Design, Setting, and Participants:The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes. Interventions:Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app. Main Outcomes and Measures:The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling. Results:Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26% (95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05). Conclusions and Relevance:A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure. Trial Registration:ClinicalTrials.gov Identifier: NCT03336411.

BACKGROUND:With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction. METHODS:Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: (1) identifying a sub-community consisting of the signature microbial taxa associated with disease and (2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction. RESULTS:Through three comprehensive real-data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa, respectively, are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 (0.85-0.91) and 0.86 (0.78-0.95) in the discovery and validation cohorts, respectively. CONCLUSIONS:The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration and provides a great potential in understanding the microbiome's role in disease diagnosis and prognosis. Video Abstract.

BACKGROUND:Bariatric procedures are safe and effective treatments for obesity, inducing rapid and sustained loss of excess body weight. Laparoscopic adjustable gastric banding (LAGB) is unique among bariatric interventions in that it is a reversible procedure in which normal gastrointestinal anatomy is maintained. Knowledge regarding how LAGB effects change at the metabolite level is limited. OBJECTIVES/OBJECTIVE:To delineate the impact of LAGB on fasting and postprandial metabolite responses using targeted metabolomics. SETTING/METHODS:Individuals undergoing LAGB at NYU Langone Medical Center were recruited for a prospective cohort study. METHODS:We prospectively analyzed serum samples from 18 subjects at baseline and 2 months after LAGB under fasting conditions and after a 1-hour mixed meal challenge. Plasma samples were analyzed on a reverse-phase liquid chromatography time-of-flight mass spectrometry metabolomics platform. The main outcome measure was their serum metabolite profile. RESULTS:We quantitatively detected over 4,000 metabolites and lipids. Metabolite levels were altered in response to surgical and prandial stimuli, and metabolites within the same biochemical class tended to behave similarly in response to either stimulus. Plasma levels of lipid species and ketone bodies were statistically decreased after surgery whereas amino acid levels were affected more by prandial status than surgical condition. CONCLUSIONS:Changes in lipid species and ketone bodies postoperatively suggest improvements in the rate and efficiency of fatty acid oxidation and glucose handling after LAGB. Further investigation is necessary to understand how these findings relate to surgical response, including long term weight maintenance, and obesity-related comorbidities such as dysglycemia and cardiovascular disease.

BACKGROUND/UNASSIGNED:With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction. METHODS/UNASSIGNED:Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: 1) identifying a sub-community consisting of the signature microbial taxa associated with disease, and 2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction. RESULTS/UNASSIGNED:Through three comprehensive real data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa respectively are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 ([0.85-0.91]) and 0.86 ([0.78-0.95]) in the discovery and validation cohorts, respectively. CONCLUSIONS/UNASSIGNED:The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration, and provides great potential in understanding the microbiome's role in disease diagnosis and prognosis.

BACKGROUND:Chinese immigrants bear a high diabetes burden and face significant barriers to accessing diabetes self-management education (DSME) and counseling programs. OBJECTIVE:The goal of this study was to examine the feasibility and acceptability and to pilot test the potential efficacy of a social media-based DSME intervention among low-income Chinese immigrants with type 2 diabetes (T2D) in New York City. METHODS:), self-efficacy, dietary intake, and physical activity, were measured at baseline, 3 months, and 6 months. Descriptive statistics and paired 2-sided t tests were used to summarize the baseline characteristics and changes before and after the intervention. RESULTS:level was 7.3% (SD 1.3%), and this level declined by 0.5% (95% CI -0.8% to -0.2%; P=.003) at 6 months. The mean satisfaction score was 9.9 (SD 0.6) out of 10, indicating a high level of satisfaction with the program. All strongly agreed or agreed that they preferred this video-based DSME over face-to-face visits. Compared to baseline, there were significant improvements in self-efficacy, dietary, and physical activity behaviors at 6 months. CONCLUSIONS:This pilot study demonstrated that a social media-based DSME intervention is feasible, acceptable, and potentially efficacious in a low-income Chinese immigrant population with T2D. Future studies need to examine the efficacy in an adequately powered clinical trial.

Background/UNASSIGNED:Text-messaging interventions can reach many individuals across a range of socioeconomic groups, at a low cost. Few randomized controlled trials (RCTs) of text-messaging weight loss interventions have been conducted in United States. Methods/UNASSIGNED:From September of 2016 to September of 2018, we conducted a two-parallel group, superiority, RCT of a 16-week text-messaging, weight loss intervention in Baltimore, Maryland, in overweight and obese adults younger than 71, who were able to receive text-messages. Our objective was to assess the effect of receiving the message content only (in printed documents distributed at baseline and week 8), versus receiving messages via short messaging service (SMS) on weight loss (primary outcome), body mass index, perceived exercise benefits and barriers, self-efficacy, and physical activity (PA). The random allocation sequence was equally balanced intervention groups by gender and age groups. Participants were randomized after the baseline assessment. Then, participants and most study staff were unblinded. Follow-up assessments were conducted at 8-, 16-, and 42-week post randomization. We performed intention-to-treat analysis using mixed linear regression models. Results/UNASSIGNED:Of the 155 adults randomized (printed messages =77, SMS =78), 87.1% were women, 53.5% were African Americans, and 93.5% non-Hispanic. Participants who completed at least one follow-up assessment were included in regression analyses (n=145, printed messages =74, SMS =71). Compared to baseline, at the 42-week assessment, the average percent weight loss was 1.23 for the SMS group (P=0.006) and 0.86 for the printed messages group (P=0.047). Both groups experienced small reductions in weight (printed messages: -0.96 kg, P=0.022; SMS: -1.19 kg, P=0.006), BMI (printed messages: -0.32, P=0.035; SMS: -0.52, P=0.002), and percent energy from fat consumption (printed messages: -1.43, P=0.021; SMS: -2.14, P≤0.001). No statistically significant between groups differences were detected for any of the study outcomes. SMS response rates were not statistically significantly associated with study outcomes. No adverse events were reported. Conclusions/UNASSIGNED:A semi-tailored SMS weight loss intervention among overweight and obese adults was not statistically superior in efficacy to paper-based messaging. Trial Registration/UNASSIGNED:ClinicalTrials.gov Identifier: NCT04506996.

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.

We aim to describe temporal eating patterns in a population of adults with overweight or obesity. In this cross-sectional analysis, data were combined from two separate pilot studies during which participants entered the timing of all eating occasions (>0 kcals) for 10-14 days. Data were aggregated to determine total eating occasions, local time of the first and last eating occasions, eating window, eating midpoint, and within-person variability of eating patterns. Eating patterns were compared between sexes, as well as between weekday and weekends. Participants (n = 85) had a median age of 56 ± 19 years, were mostly female (>70%), white (56.5%), and had a BMI of 31.8 ± 8.0 kg/m². The median eating window was 14 h 04 min [12 h 57 min-15 h 21 min], which was significantly shorter on the weekend compared to weekdays (p < 0.0001). Only 13.1% of participants had an eating window <12 h/d. Additionally, there was greater irregularity with the first eating occasion during the week when compared to the weekend (p = 0.0002). In conclusion, adults with overweight or obesity have prolonged eating windows (>14 h/d). Future trials should examine the contribution of a prolonged eating window on adiposity independent of energy intake.

BACKGROUND:Studies have demonstrated that Black men may undergo definitive prostate cancer (CaP) treatment less often than men of other races, but it is unclear whether they are avoiding overtreatment of low-risk disease or experiencing a reduction in appropriate care. The authors' aim was to assess the role of race as it relates to treatment benefit in access to CaP treatment in a single-payer population. METHODS:The authors used the Veterans Health Administration (VHA) Corporate Data Warehouse to perform a retrospective cohort study of veterans diagnosed with low- or intermediate-risk CaP between 2011 and 2017. RESULTS:The authors identified 35,427 men with incident low- or intermediate-risk CaP. When they controlled for covariates, Black men had 1.05 times the odds of receiving treatment in comparison with non-Black men (P < .001), and high-treatment-benefit men had 1.4 times the odds of receiving treatment in comparison with those in the low-treatment-benefit group (P < .001). The interaction of race and treatment benefit was significant, with Black men in the high-treatment-benefit category less likely to receive treatment than non-Black men in the same treatment category (odds ratio, 0.89; P < .001). CONCLUSIONS:Although race does appear to influence the receipt of definitive treatment in the VHA, this relationship varies in the context of the patient's treatment benefit, with Black men receiving less definitive treatment in high-benefit situations. The influence of patient race at high treatment benefit levels invites further investigation into the driving forces behind this persistent disparity in this consequential group.