Population-level management of Type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health

OBJECTIVE: To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review. RESEARCH DESIGN AND METHODS: We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review. RESULTS: The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 min per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6–16.9 pp) greater time-in-range (70–180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 months after T1D onset. CONCLUSIONS: An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range

Role of Sox-9, ER81 and VE-Cadherin in Retinoic Acid-Mediated Trans-Differentiation of Breast Cancer Cells

Many aspects of development, tumor growth and metastasis depend upon the provision of an adequate vasculature. This can be a result of regulated angiogenesis, recruitment of circulating endothelial progenitors and/or vascular trans-differentiation. The present study demonstrates that treatment of SKBR-3 breast cancer cells with retinoic acid (RA), an important regulator of embryogenesis, cancer and other diseases, stimulates the formation of networks in Matrigel. RA-treatment of SKBR-3 cells co-cultured with human umbilical vein endothelial cells resulted in the formation of mixed structures. RA induces expression of many endothelial genes including vascular endothelial (VE) cadherin. VE-cadherin was also induced by RA in a number of other breast cancer cells. We show that RA-induced VE-cadherin is responsible for the RA-induced morphological changes. RA rapidly induced the expression of Sox-9 and ER81, which in turn form a complex on the VE-cadherin promoter and are required to mediate the transcriptional regulation of VE-cadherin by RA. These data indicate that RA may promote the expression of endothelial genes resulting in endothelial-like differentiation, or provide a mechanism whereby circulating endothelial progenitor cells could be incorporated into a growing organ or tumor

Differences in COVID-19 Outcomes Among Patients With Type 1 Diabetes: First vs Later Surges

Background Outcomes of the novel coronavirus SARS-CoV-2 (COVID-19) have improved throughout the pandemic. However, whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time is unknown. Therefore, we aim to investigate differences in COVID-19 outcomes for patients with T1D in the US. Method We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. First, we grouped cases into First Surge (04/09/2020 - 07/31/2020, n=188) and Late Surge (08/01/2020 - 01/31/2021, n=410). Then, we compared outcomes between both groups using descriptive statistics and logistic regression models. Results Adverse outcomes were more frequent during the first surge including Diabetic Ketoacidosis (32% versus 15%, p<0.001), severe hypoglycemia (4% versus 1%, p=0.04) and hospitalization (52% versus 22%, p<0.001). The First surge cases were older (28 +/- 18.8 years versus 18.8 +/- 11.1 years, p<0.001), had higher hemoglobin A1c (HbA1c) levels (Median (IQR): 9.3 (4.0) versus 8.4(2.8), <0.001) and use public insurance (n(%): 107 (57) versus 154 (38), p <0.001). There were five times increased odds of hospitalization for adults (OR 5.01 (2.11,12.63) in the first surge compared to the late surge. Conclusion COVID-19 cases among patients with T1D reported during the first surge had a higher proportion of adverse outcomes than those presented in a later surge

Retinoic Acid Mediates Regulation of Network Formation by COUP-TFII and VE-Cadherin Expression by TGFβ Receptor Kinase in Breast Cancer Cells

Tumor development, growth, and metastasis depend on the provision of an adequate vascular supply. This can be due to regulated angiogenesis, recruitment of circulating endothelial progenitors, and/or vascular transdifferentiation. Our previous studies showed that retinoic acid (RA) treatment converts a subset of breast cancer cells into cells with significant endothelial genotypic and phenotypic elements including marked induction of VE-cadherin, which was responsible for some but not all morphological changes. The present study demonstrates that of the endothelial-related genes induced by RA treatment, only a few were affected by knockdown of VE-cadherin, ruling it out as a regulator of the RA-induced endothelial genotypic switch. In contrast, knockdown of the RA-induced gene COUP-TFII prevented the formation of networks in Matrigel but had no effect on VE-cadherin induction or cell fusion. Two pan-kinase inhibitors markedly blocked RA-induced VE-cadherin expression and cell fusion. However, RA treatment resulted in a marked and broad reduction in tyrosine kinase activity. Several genes in the TGFβ signaling pathway were induced by RA, and specific inhibition of the TGFβ type I receptor blocked both RA-induced VE-cadherin expression and cell fusion. Together these data indicate a role for the TGFβ pathway and COUP-TFII in mediating the endothelial transdifferentiating properties of RA

Multimethod, multidataset analysis reveals paradoxical relationships between sociodemographic factors, Hispanic ethnicity and diabetes.

INTRODUCTION Population-level and individual-level analyses have strengths and limitations as do 'blackbox' machine learning (ML) and traditional, interpretable models. Diabetes mellitus (DM) is a leading cause of morbidity and mortality with complex sociodemographic dynamics that have not been analyzed in a way that leverages population-level and individual-level data as well as traditional epidemiological and ML models. We analyzed complementary individual-level and county-level datasets with both regression and ML methods to study the association between sociodemographic factors and DM. RESEARCH DESIGN AND METHODS County-level DM prevalence, demographics, and socioeconomic status (SES) factors were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data. Analogous individual-level data were extracted from 2007 to 2016 National Health and Nutrition Examination Survey studies and corrected for oversampling with survey weights. We used multivariate linear (logistic) regression and ML regression (classification) models for county (individual) data. Regression and ML models were compared using measures of explained variation (area under the receiver operating characteristic curve (AUC) and R2). RESULTS Among the 3138 counties assessed, the mean DM prevalence was 11.4% (range: 3.0%-21.1%). Among the 12 824 individuals assessed, 1688 met DM criteria (13.2% unweighted; 10.2% weighted). Age, gender, race/ethnicity, income, and education were associated with DM at the county and individual levels. Higher county Hispanic ethnic density was negatively associated with county DM prevalence, while Hispanic ethnicity was positively associated with individual DM. ML outperformed regression in both datasets (mean R2 of 0.679 vs 0.610, respectively (p<0.001) for county-level data; mean AUC of 0.737 vs 0.727 (p<0.0427) for individual-level data). CONCLUSIONS Hispanic individuals are at higher risk of DM, while counties with larger Hispanic populations have lower DM prevalence. Analyses of population-level and individual-level data with multiple methods may afford more confidence in results and identify areas for further study

Lower HbA1c targets are associated with better metabolic control

Van Loocke, Marlies/0000-0003-3443-0482; Davis, Elizabeth/0000-0003-4244-5473WOS:000605862500003PubMed: 33415466Previous studies have suggested that clear HbA1c target setting by the diabetes team is associated with HbA1c outcomes in adolescents. The aim of this study was to evaluate whether this finding is consistent in a larger cohort of children from centers participating in the SWEET international diabetes registry. A questionnaire was sent out to 76 SWEET centers, of which responses from 53 pediatric centers were included (70%). Descriptive outcomes were presented as median with lower and upper quartile. The association between the centers' target HbA1c and mean outcome HbA1c was calculated using linear regression adjusted for age, diabetes duration, sex, and gross domestic product. Median age of the children in the studied centers (n = 35,483) was 13.3 [12.6-14.6] years (49% female). of the 53 centers, 13.2% reported an HbA1c target between 6.0 and 6.5%, 32.1% had a target between >= 6.0 and 7.0%, 18.9% between >= 7.0 and 7.5%, and 3.8% between >= 7.5 and 8.5%. No specific target value was reported by 32.1% of all centers. Median HbA1c across all centers was 7.9 [7.6-8.3] %. Adjusted regression analysis showed a positive association between HbA1c outcome and target HbA1c (p = 0.005). Conclusions: This international study demonstrated that a lower target for HbA1c was associated with better metabolic control. It is unclear whether low target values result in better metabolic control, or lower HbA1c values actually result in more ambitious target values. This target setting could contribute to the differences in HbA1c values between centers and could be an approach for improving metabolic outcomes. What is Known: center dot Target setting of HbA1c is important in children and adolescents with type 1 diabetes. center dot The optimal therapeutic approach of children with type 1 diabetes requires a trained multidisciplinary team. What is New: center dot Lower HbA1c targets are associated with better metabolic control. center dot No associations between the composition of the diabetes teams and metabolic control could be demonstratedAbbott GmbH; Boehringer IngelheimPharmaGmbH Co. KGBoehringer Ingelheim; Dexcom Operating LTD; Insulet International Ltd; Eli Lilly Italia S.p.A.Eli Lilly; MEDTRONIC International Trading Sarl; Sanofi Aventis GroupeThis work was supported by the SWEET corporate members, namely: Abbott GmbH, Boehringer IngelheimPharmaGmbH& Co. KG, Dexcom Operating LTD, Insulet International Ltd, Eli Lilly Italia S.p.A., MEDTRONIC International Trading Sarl, and Sanofi Aventis Groupe. The content is solely the responsibility of the authors and does not necessarily represent the official views of the corporate members

Knee acoustic emissions as a noninvasive biomarker of articular health in patients with juvenile idiopathic arthritis: a clinical validation in an extended study population

Abstract Background Joint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort. Findings A total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different. Conclusion Joint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy

A quantitative model to ensure capacity sufficient for timely access to care in a remote patient monitoring program

Abstract Introduction Algorithm‐enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole‐population RPM‐based care for T1D. Methods Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard. Results The primary population‐level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic‐level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care. Conclusion We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM‐based care programs

An Evaluation of Point-of-Care HbA1c, HbA1c Home Kits, and Glucose Management Indicator: Potential Solutions for Telehealth Glycemic Assessments

During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 &plusmn; 4.6 years). Bland&ndash;Altman analyses and Lin&rsquo;s concordance correlation coefficient (&#120588;c) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (&#120588;c = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (&#120588;c = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual