Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood

Effect of Body Weight on Glycaemic Indices in People with Type 1 Diabetes Using Continuous Glucose Monitoring

Background/Objectives: Obesity and overweight have become increasingly prevalent in different populations of people with type 1 diabetes (PwT1D). This study aimed to assess the effect of body weight on glycaemic indices in PwT1D. Methods: Adult PwT1D using continuous glucose monitoring (CGM) and followed up at a regional academic diabetes centre were included. Body weight, body mass index (BMI), waist circumference, glycated haemoglobin (HbA1c), and standard CGM glycaemic indices were recorded. Glycaemic indices were compared according to BMI, and correlation and linear regression analysis were performed to estimate the association between measures of adiposity and glycaemic indices. Results: A total of 73 PwT1D were included (48% normal weight, 33% overweight, and 19% obese). HbA1c was 7.2% (5.6-10), glucose management indicator (GMI) 6.9% (5.7-8.9), coefficient of variation (CV) for glucose 39.5% ± 6.4, mean glucose 148 (101-235) mg/dL, TIR (time in range, glucose 70-180 mg/dL) 66% (25-94), TBR70 (time below range, 54-69 mg/dL) 4% (0-16), TBR54 (<54 mg/dL) 1% (0-11), TAR180 (time above range, 181-250 mg/dL) 20% ± 7, and TAR250 (>250 mg/dL) 6% (0-40). Glycaemic indices and achievement (%) of optimal glycaemic targets were similar between normal weight, overweight, and obese patients. BMI was associated negatively with GMI, mean glucose, TAR180, and TAR250 and positively with TIR; waist circumference was negatively associated with TAR250. Conclusions: CGM-derived glycaemic indices were similar in overweight/obese and normal weight PwT1D. Body weight and BMI were positively associated with better glycaemic control. PwT1D should receive appropriate ongoing support to achieve optimal glycaemic targets whilst maintaining a healthy body weight.

Cluster analysis of adult individuals with type 1 diabetes: Treatment pathways and complications over a five-year follow-up period

AIMS

To identify subgroups of adults with type 1 diabetes and analyse their treatment pathways and risk of diabetes-related complications over a 5-year follow-up.

METHODS

We performed a k-means cluster analysis using the T1DExchange Registry (n = 6,302) to identify subgroups based on demographic and clinical characteristics. Annual reassessments linked treatment trajectories with these clusters, considering drug and technology use. Complication risks were analysed using Cox regression.

RESULTS

Five clusters were identified: 1) A favourable combination of all variables (31.67 %); 2) Longer diabetes duration (22.63 %); 3) Higher HbA1c levels (13.28 %); 4) Higher BMI (15.25 %); 5) Older age at diagnosis (17.17 %). Two-thirds of patients remained in their initial cluster annually. Technology adoption showed improved glycaemic control over time. Cox proportional hazards showed different risk patterns: Cluster 1 had low complication risk; Cluster 2 had the highest risk for retinopathy, coronary artery disease and autonomic neuropathy; Cluster 3 had the highest risk for albuminuria, depression and diabetic ketoacidosis; Cluster 4 had increased risk for multiple complications; Cluster 5 had the highest risk for hypertension and severe hypoglycaemia, with elevated coronary artery disease risk.

CONCLUSIONS

Clinical characteristics can identify subgroups of patients with T1DM showing differences in treatment and complications during follow-up.

Endocrine Health and Health Care Disparities in the Pediatric and Sexual and Gender Minority Populations: An Endocrine Society Scientific Statement

Endocrine care of pediatric and adult patients continues to be plagued by health and health care disparities that are perpetuated by the basic structures of our health systems and research modalities, as well as policies that impact access to care and social determinants of health. This scientific statement expands the Society's 2012 statement by focusing on endocrine disease disparities in the pediatric population and sexual and gender minority populations. These include pediatric and adult lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA) persons. The writing group focused on highly prevalent conditions-growth disorders, puberty, metabolic bone disease, type 1 (T1D) and type 2 (T2D) diabetes mellitus, prediabetes, and obesity. Several important findings emerged. Compared with females and non-White children, non-Hispanic White males are more likely to come to medical attention for short stature. Racially and ethnically diverse populations and males are underrepresented in studies of pubertal development and attainment of peak bone mass, with current norms based on European populations. Like adults, racial and ethnic minority youth suffer a higher burden of disease from obesity, T1D and T2D, and have less access to diabetes treatment technologies and bariatric surgery. LGBTQIA youth and adults also face discrimination and multiple barriers to endocrine care due to pathologizing sexual orientation and gender identity, lack of culturally competent care providers, and policies. Multilevel interventions to address these disparities are required. Inclusion of racial, ethnic, and LGBTQIA populations in longitudinal life course studies is needed to assess growth, puberty, and attainment of peak bone mass. Growth and development charts may need to be adapted to non-European populations. In addition, extension of these studies will be required to understand the clinical and physiologic consequences of interventions to address abnormal development in these populations. Health policies should be recrafted to remove barriers in care for children with obesity and/or diabetes and for LGBTQIA children and adults to facilitate comprehensive access to care, therapeutics, and technological advances. Public health interventions encompassing collection of accurate demographic and social needs data, including the intersection of social determinants of health with health outcomes, and enactment of population health level interventions will be essential tools.

24-Hour Movement Behaviours (Physical Activity, Sedentary Behaviour and Sleep) Association with Glycaemic Control and Psychosocial Outcomes in Adolescents with Type 1 Diabetes: A Systematic Review of Quantitative and Qualitative Studies

Type 1 Diabetes (T1D) is a condition requiring 24-hour management. The way in which an individual combines their 24-hour movement behaviours (24-h MBs), which is comprised of physical activity (PA), sedentary behaviour (SB), and sleep, throughout the day can have a significant impact on physical and mental health. This mixed methods systematic review aimed to investigate 24-h MBs' relationship with glycaemic control and psychosocial outcomes in adolescents (11-18 years) with T1D. Ten databases were searched for quantitative and qualitative English language articles reporting at least one of the behaviours and their relationship with outcomes. There were no restrictions on article publication dates or study design. Articles were subjected to title and abstract screening, full text screening, data extraction and quality assessment. Data were summarised narratively, and a meta-analysis was conducted where possible. From 9922 studies, 84 were included for data extraction (quantitative (n = 76), qualitative (n = 8)). Meta-analyses revealed a significant favourable association between PA and HbA1c (-0.22 [95% CI: -0.35, -0.08; I² = 92.7%; p = 0.001). SB had an insignificant unfavourable association with HbA1c (0.12 [95% CI: -0.06, 0.28; I² = 86.1%; p = 0.07]) and sleep had an insignificant favourable association (-0.03 [95% CI: -0.21, 0.15; I² = 65.9%; p = 0.34]). Importantly, no study investigated how combinations of behaviours collectively interacted and impacted on outcomes.

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study

Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy in the general population. We used data from ORISCAV-LUX 2, a nationwide, cross-sectional, population-based study. On the 1356 participants, we used a machine learning semi-supervised cluster method guided by body mass index (BMI) and glycated hemoglobin (HbA1c), and a set of 29 cardiometabolic variables, to identify subgroups of interest for cardiometabolic health. Cluster stability was assessed with the Jaccard similarity index. We have observed 4 clusters with a very high stability (ranging between 92 and 100%). Based on distinctive features that deviate from the overall population distribution, we have labeled Cluster 1 (N = 729, 53.76%) as "Healthy", Cluster 2 (N = 508, 37.46%) as "Family history-Overweight-High Cholesterol ", Cluster 3 (N = 91, 6.71%) as "Severe Obesity-Prediabetes-Inflammation" and Cluster 4 (N = 28, 2.06%) as "Diabetes-Hypertension-Poor CV Health". Our work provides an in-depth characterization and thus, a better understanding of cardiometabolic health in the general population. Our data suggest that such a clustering approach could now be used to define more targeted and tailored strategies for the prevention of cardiometabolic diseases at a population level. This study provides a first step towards precision cardiometabolic prevention and should be externally validated in other contexts.

Differential Effects of Empagliflozin on Microvascular Complications in Murine Models of Type 1 and Type 2 Diabetes

Microvascular complications account for the significant morbidity associated with diabetes. Despite tight glycemic control, disease risk remains especially in type 2 diabetes (T2D) patients and no therapy fully prevents nerve, retinal, or renal damage in type 1 diabetes (T1D) or T2D. Therefore, new antidiabetic drug classes are being evaluated for the treatment of microvascular complications. We investigated the effect of empagliflozin (EMPA), an inhibitor of the sodium/glucose cotransporter 2 (SGLT2), on diabetic neuropathy (DPN), retinopathy (DR), and kidney disease (DKD) in streptozotocin-induced T1D and db/db T2D mouse models. EMPA lowered blood glycemia in T1D and T2D models. However, it did not ameliorate any microvascular complications in the T2D model, which was unexpected, given the protective effect of SGLT2 inhibitors on DKD progression in T2D subjects. Although EMPA did not improve DKD in the T1D model, it had a potential modest effect on DR measures and favorably impacted DPN as well as systemic oxidative stress. These results support the concept that glucose-centric treatments are more effective for DPN in T1D versus T2D. This is the first study that provides an evaluation of EMPA treatment on all microvascular complications in a side-by-side comparison in T1D and T2D models.

Functional data analysis and prediction tools for continuous glucose-monitoring studies

Introduction

To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes.

Methods

A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions.

Results

The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions.

Conclusions

By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual's glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.