Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes.

Traditional risk scores for the prediction of type 2 diabetes (T2D) are typically designed for a general population and, thus, may underperform for people with prediabetes. Here, we developed machine learning (ML) models predicting the risk of T2D that are specifically tailored to people with prediabetes. We analyzed data of 13,943 individuals with prediabetes, and built a ML model to predict the risk of transition from prediabetes to T2D, integrating information about demographics, biomarkers, medications, and comorbidities defined by disease codes. Additionally, we developed a simplified ML model with only eight predictors, which can be easily integrated into clinical practice. For a forecast horizon of five years, the area under the receiver operating characteristic curve (AUROC) was 0.753 for our full ML model (79 predictors) and 0.752 for the simplified model. Our ML models allow for an early identification of people with prediabetes who are at risk of developing T2D

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.

BACKGROUND Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. OBJECTIVE This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. METHODS In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. RESULTS Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. CONCLUSIONS Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks

Pathway Analysis Hints Towards Beneficial Effects of Long-Term Vibration on Human Chondrocytes

Background/Aims: Spaceflight negatively influences the function of cartilage tissue in vivo. In vitro human chondrocytes exhibit an altered gene expression of inflammation markers after a two-hour exposure to vibration. Little is known about the impact of long-term vibration on chondrocytes. Methods: Human cartilage cells were exposed for up to 24 h (VIB) on a specialised vibration platform (Vibraplex) simulating the vibration profile which occurs during parabolic flights and compared to static control conditions (CON). Afterwards, they were investigated by phase-contrast microscopy, rhodamine phalloidin staining, microarray analysis, qPCR and western blot analysis. Results: Morphological investigations revealed no changes between CON and VIB chondrocytes. F-Actin staining showed no alterations of the cytoskeleton in VIB compared with CON cells. DAPI and TUNEL staining did not identify apoptotic cells. ICAM-1 was elevated and vimentin, beta-tubulin and osteopontin proteins were significantly reduced in VIB compared to CON cells. qPCR of cytoskeletal genes, ITGB1, SOX3, SOX5, SOX9 did not reveal differential regulations. Microarray analysis detected 13 differentially expressed genes, mostly indicating unspecific stimulations. Pathway analyses demonstrated interactions of PSMD4 and CNOT7 with ICAM. Conclusions: Long-term vibration did not damage human chondrocytes in vitro. The reduction of osteopontin protein and the down-regulation of PSMD4 and TBX15 gene expression suggest that in vitro long-term vibration might even positively influence cultured chondrocytes. (C) 2018 The Author(s) Published by S. Karger AG, Base

Pathway Analysis Hints Towards Benefcial Effects of Long-Term Vibration on Human Chondrocytes

Background/Aims: Spaceflight negatively influences the function of cartilage tissue in vivo. In vitro human chondrocytes exhibit an altered gene expression of inflammation markers after a two-hour exposure to vibration. Little is known about the impact of long-term vibration on chondrocytes. Methods: Human cartilage cells were exposed for up to 24 h (VIB) on a specialised vibration platform (Vibraplex) simulating the vibration profle which occurs during parabolic flights and compared to static control conditions (CON). Afterwards, they were investigated by phase-contrast microscopy, rhodamine phalloidin staining, microarray analysis, qPCR and western blot analysis. Results: Morphological investigations revealed no changes between CON and VIB chondrocytes. F-Actin staining showed no alterations of the cytoskeleton in VIB compared with CON cells. DAPI and TUNEL staining did not identify apoptotic cells. ICAM-1 was elevated and vimentin, beta-tubulin and osteopontin proteins were signifcantly reduced in VIB compared to CON cells. qPCR of cytoskeletal genes, ITGB1, SOX3, SOX5, SOX9 did not reveal differential regulations. Microarray analysis detected 13 differentially expressed genes, mostly indicating unspecifc stimulations. Pathway analyses demonstrated interactions of PSMD4 and CNOT7 with ICAM. Conclusions: Long-term vibration did not damage human chondrocytes in vitro. The reduction of osteopontin protein and the down-regulation of PSMD4 and TBX15 gene expression suggest that in vitro long-term vibration might even positively influence cultured chondrocytes