A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring.

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 HbA1c 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 HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.

Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes.

Digital phenotyping refers to characterizing human bio-behavior through wearables, personal devices, and digital health technologies. Digital phenotyping in populations facing a disproportionate burden of type 2 diabetes (T2D) and health disparities continues to lag compared to other populations. Here, we report our study demonstrating the application of multimodal digital phenotyping, i.e., the simultaneous use of CGM, physical activity monitors, and meal tracking in Hispanic/Latino individuals with or at risk of T2D. For 14 days, 36 Hispanic/Latino adults (28 female, 14 with non-insulin treated T2D) wore a continuous glucose monitor (CGM) and a physical activity monitor (Actigraph) while simultaneously logging meals using the MyFitnessPal app. We model meal events and daily digital biomarkers representing diet, physical activity choices, and corresponding glycemic response. We develop a digital biomarker for meal events that differentiates meal events into normal and elevated categories. We examine the contribution of daily digital biomarkers of elevated meal event count and step count on daily time-in-range 54-140 mg/dL (TIR54-140) and average glucose. After adjusting for step count, a change in elevated meal event count from zero to two decreases TIR54-140 by 4.0% (p = 0.003). An increase in 1000 steps in post-meal step count also reduces the meal event glucose response by 641 min mg/dL (p = 0.0006) and reduces the odds of an elevated meal event by 55% (p < 0.0001). The proposed meal event digital biomarkers may provide an opportunity for non-pharmacologic interventions for Hispanic/Latino adults facing a disproportionate burden of T2D.

Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity.

BACKGROUND: 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: 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: Participants (n = 39) were 59 ± 11 years old, mostly female (62%) and White (51%) with a body mass index (BMI) of 34.2 ± 4.7 kg/m2. 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: The continuous glucose monitor and WM detected overlapping but not identical meals and may provide complementary information to self-reported EO.

Temporal changes in bio-behavioral and glycemic outcomes following a produce prescription program among predominantly Hispanic/Latino adults with or at risk of type 2 diabetes.

In the United States (U.S.), consumption of fresh vegetables and fruits is below recommended levels. Enhancing access to nutritious food through food prescriptions has been recognized as a promising approach to combat diet-related illnesses. However, the effectiveness of this strategy at a large scale remains untested, particularly in marginalized communities where food insecurity rates and the prevalence of health conditions such as type 2 diabetes (T2D) are higher compared to the background population. This study evaluated the impact of a produce prescription program for predominantly Hispanic/Latino adults living with or at risk of T2D. A total of 303 participants enrolled in a 3-month observational cohort received 21 medically prescribed portions/week of fresh produce. A subgroup of 189 participants used continuous glucose monitoring (CGM) to assess the relationship between CGM profile changes and HbA1c level changes. For 247 participants completing the study (76% female, 84% Hispanic/Latino, 32% with T2D, age 56·6 ± 11·9 years), there was a reduction in weight (-1·1 [-1·6 to -0·6] lbs., p < 0.001), waist circumference (-0·4 [-1·0 to 0·6] cm, p = 0·007) and systolic blood pressure (SBP) for participants with baseline SBP >120 mmHg (-4·2 [-6·8 to -1·8] mmHg, p = 0·001). For participants with an HbA1c ≥ 7·0% at baseline, HbA1c fell significantly (-0·5 [-0·9 to -0·1] %, p = 0·01). There were also improvements in food security (p < 0·0001), self-reported ratings of sleep, mood, pain (all p < 0·001), and measures of depression (p < 0·0001), anxiety (p = 0·045), and stress (p = 0·002) (DASS-21). There was significant correlation (r = 0·8, p = 0·001) between HbA1c change and the change in average glucose for participants with worsening HbA1c, but not for participants with an improvement in HbA1c. In conclusion, medical prescription of fresh produce is associated with significant improvements in cardio-metabolic and psycho-social risk factors for Hispanic/Latino adults with or at risk of T2D.

Hypoglycemia risk with physical activity in type 1 diabetes: a data-driven approach.

Physical activity (PA) provides numerous health benefits for individuals with type 1 diabetes (T1D). However, the threat of exercise-induced hypoglycemia may impede the desire for regular PA. Therefore, we aimed to study the association between three common types of PA (walking, running, and cycling) and hypoglycemia risk in 50 individuals with T1D. Real-world data, including PA duration and intensity, continuous glucose monitor (CGM) values, and insulin doses, were available from the Tidepool Big Data Donation Project. Participants' mean (SD) age was 38.0 (13.1) years with a mean (SD) diabetes duration of 21.4 (12.9) years and an average of 26.2 weeks of CGM data available. We developed a linear regression model for each of the three PA types to predict the average glucose deviation from 70 mg/dl for the 2 h after the start of PA. This is essentially a measure of hypoglycemia risk, for which we used the following predictors: PA duration (mins) and intensity (calories burned), 2-hour pre-exercise area under the glucose curve (adjusted AUC), the glucose value at the beginning of PA, and total bolus insulin (units) within 2 h before PA. Our models indicated that glucose value at the start of exercise and pre-exercise glucose adjusted AUC (p < 0.001 for all three activities) were the most significant predictors of hypoglycemia. In addition, the duration and intensity of PA and 2-hour bolus insulin were weakly associated with hypoglycemia for walking, running, and cycling. These findings may provide individuals with T1D with a data-driven approach to preparing for PA that minimizes hypoglycemia risk.

A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c.

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.

Analysis of Immune Intratumor Heterogeneity Highlights Immunoregulatory and Coinhibitory Lymphocytes as Hallmarks of Recurrence in Stage I Non-Small Cell Lung Cancer.

Our understanding of the molecular mechanisms underlying postsurgical recurrence of non-small cell lung cancer (NSCLC) is rudimentary. Molecular and T cell repertoire intratumor heterogeneity (ITH) have been reported to be associated with postsurgical relapse; however, how ITH at the cellular level impacts survival is largely unknown. Here we report the analysis of 2880 multispectral images representing 14.2% to 27% of tumor areas from 33 patients with stage I NSCLC, including 17 cases (relapsed within 3 years after surgery) and 16 controls (without recurrence ≥5 years after surgery) using multiplex immunofluorescence. Spatial analysis was conducted to quantify the minimum distance between different cell types and immune cell infiltration around malignant cells. Immune ITH was defined as the variance of immune cells from 3 intratumor regions. We found that tumors from patients having relapsed display different immune biology compared with nonrecurrent tumors, with a higher percentage of tumor cells and macrophages expressing PD-L1 (P =.031 and P =.024, respectively), along with an increase in regulatory T cells (Treg) (P =.018), antigen-experienced T cells (P =.025), and effector-memory T cells (P =.041). Spatial analysis revealed that a higher level of infiltration of PD-L1+ macrophages (CD68+PD-L1+) or antigen-experienced cytotoxic T cells (CD3+CD8+PD-1+) in the tumor was associated with poor overall survival (P =.021 and P =.006, respectively). A higher degree of Treg ITH was associated with inferior recurrence-free survival regardless of tumor mutational burden (P =.022), neoantigen burden (P =.021), genomic ITH (P =.012) and T cell repertoire ITH (P =.001). Using multiregion multiplex immunofluorescence, we characterized ITH at the immune cell level along with whole exome and T cell repertoire sequencing from the same tumor regions. This approach highlights the role of immunoregulatory and coinhibitory signals as well as their spatial distribution and ITH that define the hallmarks of tumor relapse of stage I NSCLC.

Towards the characterization of the tumor microenvironment through dictionary learning-based interpretable classification of multiplexed immunofluorescence images.

Objective.Histology image analysis is a crucial diagnostic step in staging and treatment planning, especially for cancerous lesions. With the increasing adoption of computational methods for image analysis, significant strides are being made to improve the performance metrics of image segmentation and classification frameworks. However, many developed frameworks effectively function as black boxes, granting minimal context to the decision-making process. Thus, there is a need to develop methods that offer reasonable discriminatory power and a biologically-informed intuition to the decision-making process.Approach.In this study, we utilized and modified a discriminative feature-based dictionary learning (DFDL) paradigm to generate a classification framework that allows for discrimination between two distinct clinical histologies. This framework allows us (i) to discriminate between 2 clinically distinct diseases or histologies and (ii) provides interpretable group-specific representative dictionary image patches, or 'atoms', generated during classifier training. This implementation is performed on multiplexed immunofluorescence images from two separate patient cohorts- a pancreatic cohort consisting of cancerous and non-cancerous tissues and a metastatic non-small cell lung cancer (mNSCLC) cohort of responders and non-responders to an immunotherapeutic treatment regimen. The analysis was done at both the image-level and subject-level. Five cell types were selected, namely, epithelial cells, cytotoxic lymphocytes, antigen presenting cells, HelperT cells, and T-regulatory cells, as our phenotypes of interest.Results.We showed that DFDL had significant discriminant capabilities for both the pancreatic pathologies cohort (subject-level AUC-0.8878) and the mNSCLC immunotherapy response cohort (subject-level AUC-0.7221). The secondary analysis also showed that more than 50% of the obtained dictionary atoms from the classifier contained biologically relevant information.Significance.Our method shows that the generated dictionary features can help distinguish patients presenting two different histologies with strong sensitivity and specificity metrics. These features allow for an additional layer of model interpretability, a highly desirable element in clinical applications for identifying novel biological phenomena.

Discordance between postprandial plasma glucose measurement and continuous glucose monitoring.

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: 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.

The northeast glucose drift: Stratification of post-breakfast dysglycemia among predominantly Hispanic/Latino adults at-risk or with type 2 diabetes.

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: Starting February 2019, 119 participants in Santa Barbara, CA, USA, (93 female, 87% Hispanic/Latino [predominantly Mexican-American], age 54·4 [±12·1] years), stratified by HbA1c 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: A "northeast drift" was observed in breakfast glucose responses comparing at-risk to pre-T2D to T2D participants. T2D participants had a significantly higher pre-breakfast glucose level, glucose rise, glucose incremental area under the curve (all p < 0·0001), and time to glucose peak (p < 0·05) compared to pre-T2D and at-risk participants. After adjusting for demographic and clinical covariates, pre-breakfast glucose and time to peak (p < 0·0001) were significantly associated with HbA1c. The model predicted HbA1c within (0·55 ± 0·67)% of true laboratory HbA1c values. Interpretation: For predominantly Hispanic/Latino adults, the average two-week breakfast glucose response shows a progression of dysglycemia from at-risk of T2D to pre-T2D to T2D. CGM-based breakfast metrics have the potential to predict HbA1c levels and monitor diabetes progression. Funding: 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.

18FDG positron emission tomography mining for metabolic imaging biomarkers of radiation-induced xerostomia in patients with oropharyngeal cancer.

PURPOSE: Head and neck cancers radiotherapy (RT) is associated with inevitable injury to parotid glands and subsequent xerostomia. We investigated the utility of SUV derived from 18FDG-PET to develop metabolic imaging biomarkers (MIBs) of RT-related parotid injury. METHODS: Data for oropharyngeal cancer (OPC) patients treated with RT at our institution between 2005 and 2015 with available planning computed tomography (CT), dose grid, pre- & first post-RT 18FDG-PET-CT scans, and physician-reported xerostomia assessment at 3-6 months post-RT (Xero 3-6 ms) per CTCAE, was retrieved, following an IRB approval. A CT-CT deformable image co-registration followed by voxel-by-voxel resampling of pre & post-RT 18FDG activity and dose grid were performed. Ipsilateral (Ipsi) and contralateral (contra) parotid glands were sub-segmented based on the received dose in 5 Gy increments, i.e. 0-5 Gy, 5-10 Gy sub-volumes, etc. Median and dose-weighted SUV were extracted from whole parotid volumes and sub-volumes on pre- & post-RT PET scans, using in-house code that runs on MATLAB. Wilcoxon signed-rank and Kruskal-Wallis tests were used to test differences pre- and post-RT. RESULTS: 432 parotid glands, belonging to 108 OPC patients treated with RT, were sub-segmented & analyzed. Xero 3-6 ms was reported as: non-severe (78.7%) and severe (21.3%). SUV- median values were significantly reduced post-RT, irrespective of laterality (p = 0.02). A similar pattern was observed in parotid sub-volumes, especially ipsi parotid gland sub-volumes receiving doses 10-50 Gy (p < 0.05). Kruskal-Wallis test showed a significantly higher mean RT dose in the contra parotid in the patients with more severe Xero 3-6mo (p = 0.03). Multiple logistic regression showed a combined clinical-dosimetric-metabolic imaging model could predict the severity of Xero 3-6mo; AUC = 0.78 (95%CI: 0.66-0.85; p < 0.0001). CONCLUSION: We sought to quantify pre- and post-RT 18FDG-PET metrics of parotid glands in patients with OPC. Temporal dynamics of PET-derived metrics can potentially serve as MIBs of RT-related xerostomia in concert with clinical and dosimetric variables.

Dysglycemia in adults at risk for or living with non-insulin treated type 2 diabetes: Insights from continuous glucose monitoring.

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: Standardized CGM measures were significantly different for participants with T2D compared to at-risk and pre-T2D participants (p<0·0001). In addition, pre-T2D participants spent more time between 140 and 180 mg/dL during the day than at-risk participants (p<0·01). T2D participants spent more time between 140 and 180 mg/dL both during the day and overnight compared to at-risk and pre-T2D participants (both p<0·0001). Time in 70-140 mg/dL range during the day was significantly correlated with HbA1c (r=-0·72, p<0·0001), after adjusting for age, sex, BMI, and waist circumference (p<0·0001). INTERPRETATION: 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: US Department of Agriculture (Grant #2018-33800-28404) and NSF PATHS-UP ERC (Award #1648451).

Stabilized epithelial phenotype of cancer cells in primary tumors leads to increased colonization of liver metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is therapeutically recalcitrant and metastatic. Partial epithelial to mesenchymal transition (EMT) is associated with metastasis; however, a causal connection needs further unraveling. Here, we use single-cell RNA sequencing and genetic mouse models to identify the functional roles of partial EMT and epithelial stabilization in PDAC growth and metastasis. A global EMT expression signature identifies ∼50 cancer cell clusters spanning the epithelial-mesenchymal continuum in both human and murine PDACs. The combined genetic suppression of Snail and Twist results in PDAC epithelial stabilization and increased liver metastasis. Genetic deletion of Zeb1 in PDAC cells also leads to liver metastasis associated with cancer cell epithelial stabilization. We demonstrate that epithelial stabilization leads to the enhanced collective migration of cancer cells and modulation of the immune microenvironment, which likely contribute to efficient liver colonization. Our study provides insights into the diverse mechanisms of metastasis in pancreatic cancer and potential therapeutic targets.

Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients.

Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61-0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.

Farming for life: impact of medical prescriptions for fresh vegetables on cardiometabolic health for adults with or at risk of type 2 diabetes in a predominantly Mexican-American population.

INTRODUCTION: 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: Between February 2019 and March 2020, 159 participants (122 female, 75% of Mexican heritage, 31% with non-insulin treated T2D, age 52.5 (13.2) years) were recruited using community outreach materials in English and Spanish, and received prescriptions for 21 servings/week of fresh vegetable for 10 weeks. Pre-post comparisons were made of weight; waist circumference; blood pressure; Hemoglobin A1c (HbA1c, 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: Paired data were available for 131 participants. Over 3 months, waist circumference fell (-0.77 (95% CI -1.42 to 0.12) cm, p=0.022), as did systolic blood pressure (SBP) (-2.42 (95% CI -4.56 to 0.28) mm Hg, p=0.037), which was greater among individuals with baseline SBP >130 mm Hg (-7.5 (95% CI -12.4 to 2.6) mm Hg, p=0.005). Weight reduced by -0.4 (-0.7 to -0.04) kg, p=0.029 among women. For participants with baseline HbA1c >7.0%, HbA1c 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: ClinicalTrials.gov Identifier: NCT03940300.

Mathematical Modeling of the Metastatic Colorectal Cancer Microenvironment Defines the Importance of Cytotoxic Lymphocyte Infiltration and Presence of PD-L1 on Antigen Presenting Cells.

BACKGROUND: Although immune-based therapy has proven efficacious for some patients with microsatellite instability (MSI) colon cancers, a majority of patients receive limited benefit. Conversely, select patients with microsatellite stable (MSS) tumors respond to checkpoint blockade, necessitating novel ways to study the immune tumor microenvironment (TME). We used phenotypic and spatial data from infiltrating immune and tumor cells to model cellular mixing to predict disease specific outcomes in patients with colorectal liver metastases. METHODS: Formalin fixed paraffin embedded metastatic colon cancer tissue from 195 patients were subjected to multiplex immunohistochemistry (mfIHC). After phenotyping, the G-function was calculated for each patient and cell type. Data was correlated with clinical outcomes and survival. RESULTS: High tumor cell to cytotoxic T lymphocyte (TC-CTL) mixing was associated with both a pro-inflammatory and immunosuppressive TME characterized by increased CTL infiltration and PD-L1+ expression, respectively. Presence and engagement of antigen presenting cells (APC) and helper T cells (Th) were associated with greater TC-CTL mixing and improved 5-year disease specific survival compared to patients with a low degree of mixing (42% vs. 16%, p = 0.0275). Comparison of measured mixing to a calculated theoretical random mixing revealed that PD-L1 expression on APCs resulted in an environment where CTLs were non-randomly less associated with TCs, highlighting their biologic significance. CONCLUSION: Evaluation of immune interactions within the TME of metastatic colon cancer using mfIHC in combination with mathematical modeling characterized cellular mixing of TCs and CTLs, providing a novel strategy to better predict clinical outcomes while identifying potential candidates for immune based therapies.

Poor Response to Neoadjuvant Chemotherapy Correlates with Mast Cell Infiltration in Inflammatory Breast Cancer.

Our understanding is limited concerning the tumor immune microenvironment of inflammatory breast cancer (IBC), an aggressive form of primary cancer with low rates of pathologic complete response to current neoadjuvant chemotherapy (NAC) regimens. We retrospectively identified pretreatment (N = 86) and matched posttreatment tissue (N = 27) from patients with stage III or de novo stage IV IBC who received NAC followed by a mastectomy. Immune profiling was performed including quantification of lymphoid and myeloid infiltrates by IHC and T-cell repertoire analysis. Thirty-four of 86 cases in this cohort (39.5%) achieved a pathologic complete response. Characterization of the tumor microenvironment revealed that having a lower pretreatment mast cell density was significantly associated with achieving a pathologic complete response to NAC (P = 0.004), with responders also having more stromal tumor-infiltrating lymphocytes (P = 0.035), CD8+ T cells (P = 0.047), and CD20+ B cells (P = 0.054). Spatial analysis showed close proximity of mast cells to CD8+ T cells, CD163+ monocytes/macrophages, and tumor cells when pathologic complete response was not achieved. PD-L1 positivity on tumor cells was found in fewer than 2% of cases and on immune cells in 27% of cases, but with no correlation to response. Our results highlight the strong association of mast cell infiltration with poor response to NAC, suggesting a mechanism of treatment resistance and a potential therapeutic target in IBC. Proximity of mast cells to immune and tumor cells may suggest immunosuppressive or tumor-promoting interactions of these mast cells.

B7-H3 Expression in Merkel Cell Carcinoma-Associated Endothelial Cells Correlates with Locally Aggressive Primary Tumor Features and Increased Vascular Density.

PURPOSE: Merkel cell carcinoma (MCC) is an aggressive cutaneous malignancy whose pathogenesis and prognosis are related to the integrity of the host immune system. Despite promising clinical responses to immune-checkpoint blockade, response and resistance remain unpredictable, underscoring a critical need to delineate novel prognostic biomarkers and/or therapeutic targets for this disease.Experimental Design: Expression of immune-regulatory markers (PD-L2, B7-H3, B7-H4, IDO-1, ICOS, TIM3, LAG3, VISTA, and OX-40) was assessed using singlet chromogenic IHC in 10 primary MCCs. Multiplex immunofluorescence quantified CD31 and B7-H3 expression in 52 primary and 25 metastatic MCCs. B7-H3 and CD31 expressions were tabulated as a series of independent (X,Y) cell centroids. A spatial G-function, calculated based on the distribution of distances of B7-H3+ (X,Y) cell centroids around the CD31+ (X,Y) cell centroids, was used to estimate a colocalization index equivalent to the percentage of CD31-positive cell centroids that overlap with a B7-H3-positive cell centroid. RESULTS: Primary and metastatic MCCs exhibit a dynamic range of colocalized CD31 and B7-H3 expression. Increasing colocalized expression of B7-H3 with CD31 significantly associated with increased tumor size (P = 0.0060), greater depth of invasion (P = 0.0110), presence of lymphovascular invasion (P = 0.0453), and invasion beyond skin (P = 0.0428) in primary MCC. Consistent with these findings, increasing colocalized expression of B7-H3 and CD31 correlated with increasing vascular density in primary MCC, but not metastatic MCC. CONCLUSIONS: Our results demonstrate that colocalized expression of B7-H3/CD31 is a poor prognostic indicator and suggest therapies targeting B7-H3 may represent an effective approach to augmenting immune-activating therapies for MCC.

A Functional Spatial Analysis Platform for Discovery of Immunological Interactions Predictive of Low-Grade to High-Grade Transition of Pancreatic Intraductal Papillary Mucinous Neoplasms.

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.