Insulin-induced lipodystrophy and predisposing factors in children and adolescents with type 1 diabetes mellitus (T1DM) in a tertiary care Egyptian center

BACKGROUND

Lipodystrophy (LH) is one of the most common complications of subcutaneous insulin injection. Many factors are incriminated in the evolution of LH in children with diabetes type 1 (T1DM). LH may affect insulin absorption in the skin areas involved, resulting in a negative impact on blood glucose levels and glycemic variability.

PATIENTS AND METHODS

We calculated and evaluated the prevalence of LH in relation to possible clinical factors associated with the development of LH in a cohort of children (n =115) with T1DM using insulin pens or syringes and we studied possible predisposing factors including their age, duration of T1DM, injection technique, insulin dose/kg, degree of pain perception, and HbA1c level.

RESULTS

In our cross-sectional study, 84% of patients were using pens for insulin injection and 52.2 % of them were rotating the site of injection on daily basis. 27 % did not experience pain during an injection while 6 % had the worst hurt. 49.5 % had clinically detectable LH. Those with LH had higher HbA1c levels and more unexplained hypoglycemic events compared to those without LH (P: 0.058). The hypertrophied site was related to the preferred site of injection which was the arms in 71.9 % of the cases. Children who had LH were older with a longer duration of T1DM, rotating sites of injection less frequently, and were more frequently reusing needles compared to children without LH (P: < 0.05).

CONCLUSION

Improper insulin injection technique, older age, and longer duration of T1DM were associated with LH. Proper education of patients and their parents must include correct injection techniques, rotating injection sites, and minimal reuse of needles.

The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.