Faculty Bibliography

BACKGROUND:There has been growing interest in studying postprandial glucose responses using continuous glucose monitoring (CGM) in nondiabetic individuals. Accurate measurement of glucose responses to meals can facilitate applications such as precision nutrition and early detection of diabetes. OBJECTIVES/OBJECTIVE:We aimed to quantify the discordance between simultaneous postprandial glucose measurements made using plasma and CGM. METHODS:We studied 10 nondiabetic older adults who randomly consumed 9 predefined meals of varying macronutrient compositions. Glucose was measured for 8 h after the meal by the CGM, blood samples for plasma collection were taken regularly, and plasma glucose was quantified using gold-standard laboratory measurement and a fingerstick blood glucose meter. The primary outcome measured was the mean absolute relative difference (MARD) of CGM and fingerstick measurements relative to the gold standard. Secondary outcomes were Bland-Altman statistics, Clarke Error Grid, and time in range metrics. Additional subgroup analyses were performed by stratifying the postprandial glucose measurements based on the macronutrient composition of the meals. RESULTS:When compared against the gold-standard postprandial glucose measurements, the fingerstick meter was more accurate (MARD: 8.0%; 95% CI: 7.6%, 8.6%) than the CGM (MARD: 13.7%; 95% CI: 13.1%, 14.3%; P < 0.0001). After the meals, Bland-Altman analysis demonstrated that the CGM underestimated the 8-h gold-standard glucose rise by 12.8 mg/dL on average (P < 0.0001), whereas the fingerstick meter did so by 5.5 mg/dL on average (P < 0.0001). The CGM underestimated the time spent in the 70-180 mg/dL range (P = 0.002) and overestimated the time spent <70 mg/dL (P < 0.0001) compared with the other 2 methods. CONCLUSIONS:We discovered discordance between gold standard, fingerstick, and CGM in measuring plasma glucose concentrations after a meal. Consequently, emerging applications of CGM in healthy individuals, such as precision nutrition and diabetes onset prediction, may need to account for these discordances.This trial was registered at clinicaltrials.gov as NCT04928872.

BACKGROUND:There is minimal experience in continuous glucose monitoring (CGM) among underserved racial/ethnic minority populations with or at risk of type 2 diabetes (T2D), and therefore a lack of CGM-driven insight for these individuals. We analyzed breakfast-related CGM profiles of free-living, predominantly Hispanic/Latino individuals at-risk of T2D, with pre-T2D, or with non-insulin treated T2D. METHODS:levels into (i) at-risk of T2D, (ii) with pre-T2D, and (iii) with non-insulin treated T2D, wore blinded CGMs for two weeks. We compared valid CGM profiles from 106 of these participants representing glucose response to breakfast using four parameters. FINDINGS/RESULTS: INTERPRETATION/CONCLUSIONS:levels and monitor diabetes progression. FUNDING/BACKGROUND:US Department of Agriculture (Grant #2018-33800-28404), a seed grant from the industry board fees of the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (Award #1648451), and the Elsevier foundation.

BACKGROUND:Continuous glucose monitoring (CGM) has demonstrable benefits for people living with diabetes, but the supporting evidence is almost exclusively from White individuals with type 1 diabetes. Here, we have quantified CGM profiles in Hispanic/Latino adults with or at-risk of non-insulin treated type 2 diabetes (T2D). METHODS:100 participants (79 female, 86% Hispanic/Latino [predominantly Mexican], age 54·6 [±12·0] years) stratified into (i) at risk of T2D, (ii) with pre-diabetes (pre-T2D), and (iii) with non-insulin treated T2D, wore blinded CGMs for 2 weeks. Beyond standardized CGM measures (average glucose, glucose variability, time in 70-140 mg/dL and 70-180 mg/dL ranges), we also examined additional CGM measures based on the time of day. FINDINGS/RESULTS:<0·0001). INTERPRETATION/CONCLUSIONS:Standardized CGM measures show a progression of dysglycemia from at-risk of T2D, to pre-T2D, and to T2D. Stratifying CGM readings by time of day and the range 140-180 mg/dL provides additional metrics to differentiate between the groups. FUNDING/BACKGROUND:US Department of Agriculture (Grant #2018-33800-28404) and NSF PATHS-UP ERC (Award #1648451).

INTRODUCTION/BACKGROUND:Poor diet is the leading cause of poor health in USA, with fresh vegetable consumption below recommended levels. We aimed to assess the impact of medical prescriptions for fresh (defined as picked within 72 hours) vegetables, at no cost to participants on cardiometabolic outcomes among adults (predominantly Mexican-American women) with or at risk of type 2 diabetes (T2D). METHODS:, a measure of long-term blood glucose control); self-reported sleep, mood and pain; vegetable, tortilla and soda consumption. After obtaining devices for this study, 66 of 72 participants asked, agreed to wear blinded continuous glucose monitors (CGM). RESULTS:fell by -0.35 (-0.8 to -0.1), p=0.009. For participants with paired CGM data (n=40), time in range 70-180 mg/dL improved (from 97.4% to 98.9%, p<0.01). Food insecurity (p<0.001), tortilla (p<0.0001) and soda (p=0.013) consumption significantly decreased. Self-reported sleep, mood and pain level scores also improved (all p<0.01). CONCLUSIONS:Medical prescriptions for fresh vegetables were associated with clinically relevant improvements in cardiovascular risk factors and quality of life variables (sleep, mood and pain level) in adults (predominantly Mexican-American and female) with or at risk of T2D. TRIAL REGISTRATION NUMBER/BACKGROUND:ClinicalTrials.gov Identifier: NCT03940300.

Intraductal papillary mucinous neoplasms (IPMNs), critical precursors of the devastating tumor pancreatic ductal adenocarcinoma (PDAC), are poorly understood in the pancreatic cancer community. Researchers have shown that IPMN patients with high-grade dysplasia have a greater risk of subsequent development of PDAC in the remnant pancreas than do patients with low-grade dysplasia. In this study, we built a computational prediction model that encapsulates the spatial cellular interactions in IPMNs that play key roles in the transformation of low-grade IPMN cysts to high-grade cysts en route to PDAC. Using multiplex immunofluorescent images of IPMN cysts, we adopted algorithms from spatial statistics and functional data analysis to create metrics that summarize the spatial interactions in IPMNs. We showed that an ensemble of models learned using these spatial metrics can robustly predict, with high accuracy, (1) the dysplasia grade (low vs high grade) and (2) the risk of a low-grade cyst progressing to a high-grade cyst. We obtained high classification accuracies on both tasks, with areas under the curve of 0.81 (95% confidence interval: 0.71-0.9) for task 1 and 0.81 (95% confidence interval: 0.7-0.94) for task 2. To the best of our knowledge, this is the first application of an ensemble machine learning approach for discovering critical cellular spatial interactions in IPMNs using imaging data. We envision that our work can be used as a risk assessment tool for patients diagnosed with IPMNs and facilitate greater understanding and investigation of the cellular interactions that cause transition of IPMNs to PDAC.

OBJECTIVES:To determine the prognostic significance of spatial proximity of lung cancer cells and specific immune cells in the tumor microenvironment. MATERIALS AND METHODS:We probed formalin-fixed, paraffin-embedded (FFPE) tissue microarrays using a novel tyramide signal amplification multiplexing technique labelling CD8, CD4, Foxp3, and CD68+ cells. Each multiplex stained immunohistochemistry slide was digitally processed by Vectra INFORMS software, and an X- and Y-coordinate assigned to each labeled cell type. The abundance and spatial location of each cell type and their proximity to one another was analyzed using a novel application of the G-cross spatial distance distribution method which computes the probability of finding at least one immune cell of any given type within a rμm radius of a tumor cell. Cox proportional hazards multiple regression was used for multivariate analysis of the influence of proximity of lymphocyte types. RESULTS:Pathologic tumor specimens from 120 NSCLC patients with pathologic tumor stage I-III disease were analyzed. On univariate analysis, age (P = .0007) and number of positive nodes (P = .0014) were associated with overall survival. Greater area under the curve (AUC) of the G-cross function for tumor cell-Treg interactions was significantly associated with worse survival adjusting for age and number of positive nodes (HR 1.52 (1.11-2.07), P = .009). Greater G-cross AUC for T-reg-CD8 was significantly associated with better survival adjusting for age and number of positive lymph nodes (HR 0.96 (0.92-0.99), P = .042). CONCLUSION:Increased infiltration of regulatory T cells into core tumor regions is an independent predictor of worse overall survival in NSCLC. However, increased infiltration of CD8+ cytotoxic T cells among regulatory T cells seems to mitigate this effect and was significantly associated with better survival. Validation of the G-cross method of measuring spatial proximity between tumor and immune cell types and exploration of its use as a prognostic factor in lung cancer treatment is warranted.

The exact nature and dynamics of pancreatic ductal adenocarcinoma (PDAC) immune composition remains largely unknown. Desmoplasia is suggested to polarize PDAC immunity. Therefore, a comprehensive evaluation of the composition and distribution of desmoplastic elements and T-cell infiltration is necessary to delineate their roles. Here we develop a novel computational imaging technology for the simultaneous evaluation of eight distinct markers, allowing for spatial analysis of distinct populations within the same section. We report a heterogeneous population of infiltrating T lymphocytes. Spatial distribution of cytotoxic T cells in proximity to cancer cells correlates with increased overall patient survival. Collagen-I and αSMA⁺ fibroblasts do not correlate with paucity in T-cell accumulation, suggesting that PDAC desmoplasia may not be a simple physical barrier. Further exploration of this technology may improve our understanding of how specific stromal composition could impact T-cell activity, with potential impact on the optimization of immune-modulatory therapies.

In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA_1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37-63%)] compared to pre-T2D [36% (95% CI 31-48%), p = 0.01] and at-risk participants [34% (95% CI 27-39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA_1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.

BACKGROUND:Recent studies have demonstrated considerable interindividual variability in postprandial glucose response (PPGR) to the same foods, suggesting the need for more precise methods for predicting and controlling PPGR. In the Personal Nutrition Project, the investigators tested a precision nutrition algorithm for predicting an individual's PPGR. OBJECTIVE:This study aimed to compare changes in glycemic variability (GV) and HbA1c in 2 calorie-restricted weight loss diets in adults with prediabetes or moderately controlled type 2 diabetes (T2D), which were tertiary outcomes of the Personal Diet Study. METHODS:The Personal Diet Study was a randomized clinical trial to compare a 1-size-fits-all low-fat diet (hereafter, standardized) with a personalized diet (hereafter, personalized). Both groups received behavioral weight loss counseling and were instructed to self-monitor diets using a smartphone application. The personalized arm received personalized feedback through the application to reduce their PPGR. Continuous glucose monitoring (CGM) data were collected at baseline, 3 mo and 6 mo. Changes in mean amplitude of glycemic excursions (MAGEs) and HbA1c at 6 mo were assessed. We performed an intention-to-treat analysis using linear mixed regressions. RESULTS:We included 156 participants [66.5% women, 55.7% White, 24.1% Black, mean age 59.1 y (standard deviation (SD) = 10.7 y)] in these analyses (standardized = 75, personalized = 81). MAGE decreased by 0.83 mg/dL per month for standardized (95% CI: 0.21, 1.46 mg/dL; P = 0.009) and 0.79 mg/dL per month for personalized (95% CI: 0.19, 1.39 mg/dL; P = 0.010) diet, with no between-group differences (P = 0.92). Trends were similar for HbA1c values. CONCLUSIONS:Personalized diet did not result in an increased reduction in GV or HbA1c in patients with prediabetes and moderately controlled T2D, compared with a standardized diet. Additional subgroup analyses may help to identify patients who are more likely to benefit from this personalized intervention. This trial was registered at clinicaltrials.gov as NCT03336411.