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Contains fulltext : 235311.pdf (Publisher’s version ) (Open Access), BACKGROUND: Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. METHODS: We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). RESULTS: Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). CONCLUSIONS: Machine learning-based models are able to accurately and safely predic

Familial combined hyperlipidemia (FCHL) shows many features of the metabolic syndrome. The strong genetic component makes it an excellent model to study the genetic background of metabolic syndrome and insulin resistance. Adipose tissue is believed to contribute to, or even underlie, the FCHL phenotype and is an interesting target tissue for gene expression studies. However, interpretation of adipose tissue gene expression experiments is complex since expression differences cannot only arise as a direct consequence of a genetic trait, but may also reflect an adaptation to metabolic influences at the cellular level. In the present study, we measured gene expression levels in cultured primary human preadipocytes from FCHL and control subjects. Since isolated preadipocytes were allowed to replicate for weeks under standardized conditions, the contribution of previous metabolic influences is rather small whereas genetic defects are preserved and expressed in vitro. The main finding was up-regulation of CD36/FAT in FCHL preadipocytes, confirmed in two independent groups of subjects, and a concomitant increase in CD36/FAT-mediated fatty acid uptake. CD36/FAT overexpression has previously been shown to be associated with other insulin-resistant states. The present data suggest that CD36/FAT overexpression in FCHL occurs very early in adipocyte differentiation and may be of genetic origin.