Clinical Characterization of Data-Driven Diabetes Clusters of Pediatric Type 2 Diabetes.

Background: Pediatric Type 2 diabetes (T2D) is highly heterogeneous. Previous reports on adult-onset diabetes demonstrated the existence of diabetes clusters. Therefore, we set out to identify unique diabetes subgroups with distinct characteristics among youth with T2D using commonly available demographic, clinical, and biochemical data. Methods: We performed data-driven cluster analysis (K-prototypes clustering) to characterize diabetes subtypes in pediatrics using a dataset with 722 children and adolescents with autoantibody-negative T2D. The six variables included in our analysis were sex, race/ethnicity, age, BMI Z-score and hemoglobin A1c at the time of diagnosis, and non-HDL cholesterol within first year of diagnosis. Results: We identified five distinct clusters of pediatric T2D, with different features, treatment regimens and risk of diabetes complications: Cluster 1 was characterized by higher A1c; Cluster 2, by higher non-HDL; Cluster 3, by lower age at diagnosis and lower A1c; Cluster 4, by lower BMI and higher A1c; and Cluster 5, by lower A1c and higher age. Youth in Cluster 1 had the highest rate of diabetic ketoacidosis (DKA) (p = 0.0001) and were most prescribed metformin (p = 0.06). Those in Cluster 2 were most prone to polycystic ovarian syndrome (p = 0.001). Younger individuals with lowest family history of diabetes were least frequently diagnosed with diabetic ketoacidosis (p = 0.001) and microalbuminuria (p = 0.06). Low-BMI individuals with higher A1c had the lowest prevalence of acanthosis nigricans (p = 0.0003) and hypertension (p = 0.03). Conclusions: Utilizing clinical measures gathered at the time of diabetes diagnosis can be used to identify subgroups of pediatric T2D with prognostic value. Consequently, this advancement contributes to the progression and wider implementation of precision medicine in diabetes management.

Evolution of Mood Symptomatology Through the COVID-19 Pandemic: Findings From the CovidSense Longitudinal Study.

Background The severe acute respiratory syndrome coronavirus 2 global pandemic, with its associated coronavirus disease 2019 (COVID-19) illness, has led to significant mental, physical, social, and economic hardships. Physical distancing, isolation, and fear of illness have significantly affected the mental health of people worldwide. Several studies have documented the cross-sectional elevated prevalence of mental anguish, but due to the sudden nature of the pandemic, very few longitudinal studies have been reported, especially covering the first phase of the pandemic. CovidSense, a longitudinal adaptive study, was initiated to answer some key questions: how did the pandemic and related social and economic conditions affect depression, which groups showed more vulnerability, and what protective factors emerged as the pandemic unfolded? Methodology CovidSense was deployed from April to December 2020. The adaptive design enabled adaption to fluctuating demographics/health status. Participants were regularly queried via SMS messages about their mental health, physical health, and life circumstances. The study included 1,190 participants who answered a total of 18,783 survey panels. This was a prospective longitudinal cohort study following adult participants in the general population through the COVID-19 pandemic. The participant cohort reported self-assessed measures ranging from subjective mood ratings and substance use to validated questionnaires, such as the Quick Inventory of Depressive Symptoms (QIDS) and Cognitive and Affective Mindfulness Scale-Revised (CAMS-R). Results Participants with pre-existing physical (especially pulmonary) or mental conditions had overall higher levels of depression, as measured by the QIDS and self-reported mood. Participants with pre-existing conditions also showed increased vulnerability to the stress caused by watching the news and the increase in COVID-19 cases. Younger participants (aged 18-25 years) were more affected than older groups. People with severe levels of depression had the most variation in QIDS scores, whereas individuals with none to low depressive scores had the most variability in self-reported mood fluctuations. Conclusions The effects of pandemic-related chronic stress were predominant in young adults and individuals with pre-existing mental and medical conditions regardless of whether they had acquired COVID-19 or not. These results point to the possibility of allocating preventive as well as treatment resources based on vulnerability.

Patterns of Timing and Intensity of Physical Activity and HbA1c Levels in Hispanic/Latino Adults With or at Risk of Type 2 Diabetes.

Despite the clear benefits of increased physical activity (PA) on glycemic control, little is known about the importance of the timing of exercise among people with diabetes. Our objective was to compare the time of day of PA with concurrent HbA1c levels and body mass index (BMI) among Hispanic/Latino adults with or at risk of type 2 diabetes (T2D).Monitored activity data obtained from Hispanic/Latino adults were summarized as number of steps per day, moderate-to-vigorous-intensity physical activity (MVPA), and energy expenditure (kcals/day). We next examined the association between PA measures and participants' HbA1c. K-means clustering analysis was applied to identify daily PA patterns by time of day and intensity. Thus, three dominant clusters were identified: low-intensity PA, and early and late PA by time of day.The step counts were correlated with HbA1c in the late-active group (P = .01). Furthermore, independently in younger adults (age ≤ 50 years) and in overweight adults (25 ≤ BMI < 30 kg/m2), there was an association between HbA1c and step counts (P < .01 and P < .005, respectively) as well as HbA1c and MVPA (P < .05 and P < .035, respectively).In conclusion, for Hispanic/Latino adults with or at risk of T2D, there appears to be clustering of PA by intensity and time of day which, in turn, may influence achieved HbA1c and BMI. Our findings demonstrate that the amount of activity is more efficacious on HbA1c in participants who are more active later during the day and separately in overweight and younger individuals.This finding may help design more personalized PA recommendations in this population.ClinicalTrials.gov Identifiers: NCT03830840 and NCT03736468.