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An interaction between metabolic glucose impairment and coronavirus disease 2019 is reported. The development of a severe multisystem inflammatory syndrome in children (MIS-C) related to SARS-CoV-2 infection has been described. We evaluated the impact of MIS-C on glycemic patterns in pediatric patients. A group of 30 children and adolescents affected by MIS-C were considered; all patients were normal weight. Clinical and biochemical assessments, including surrogate markers of insulin resistance (IR) such as homeostasis model analysis-IR (HOMA-IR) and triglyceride–glucose (TyG) indexes, were recorded. Patients were also invited to undergo an intermittently scanned continuous glucose monitoring (isCGM). HOMA-IR index was calculated in 18 patients (60%), of which 17 (94%) revealed a pathological value. TyG index was computed for all patients and pathological values were detected in all cases. In 15 patients, isCGM data were recorded on average for 9 days (±3 days). Overall, average glucose was 105 mg/dL (±16 mg/dL) and average time spent in the 70–180 mg/dL range (TIR) was 93.76%, with nearly 10% of glucose readings in the 141–180 mg/dL range; glycemic fluctuations over the hyperglycemic threshold were detected in four patients. Regular glucose monitoring may be useful to prevent metabolic imbalance and obtain a better outcome.

A combination of information from blood glucose (BG) and heart rate (HR) measurements has been proposed to investigate the HR changes related to nocturnal hypoglycemia (NH) episodes in pediatric subjects with type 1 diabetes (T1D), examining whether they could improve hypoglycemia prediction. We enrolled seventeen children and adolescents with T1D, monitored on average for 194 days. BG was detected by flash glucose monitoring devices, and HR was measured by wrist-worn fitness trackers. For each subject, we compared HR values recorded in the hour before NH episodes (before-hypoglycemia) with HR values recorded during sleep intervals without hypoglycemia (no-hypoglycemia). Furthermore, we investigated the behavior after the end of NH. Nine participants (53%) experienced at least three NH. Among these nine subjects, six (67%) showed a statistically significant difference between the before-hypoglycemia HR distribution and the no-hypoglycemia HR distribution. In all these six cases, the before-hypoglycemia HR median value was higher than the no-hypoglycemia HR median value. In almost all cases, HR values after the end of hypoglycemia remained higher compared to no-hypoglycemia sleep intervals. This exploratory study support that HR modifications occur during NH in T1D subjects. The identification of specific HR patterns can be helpful to improve NH detection and prevent fatal events.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant.

Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.