Machine learning for non‐invasive sensing of hypoglycaemia while driving in people with diabetes

Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. Materials and Methods: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced ypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1). Results: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). Conclusions: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia

The effect of a single 2h bout of aerobic exercise on ectopic lipids in skeletal muscle, liver and the myocardium

Aims/hypothesis: Ectopic lipids are fuel stores in non-adipose tissues (skeletal muscle [intramyocellular lipids; IMCL], liver [intrahepatocellular lipids; IHCL] and heart [intracardiomyocellular lipids; ICCL]). IMCL can be depleted by physical activity. Preliminary data suggest that aerobic exercise increases IHCL. Data on exercise-induced changes on ICCL is scarce. Increased IMCL and IHCL have been related to insulin resistance in skeletal muscles and liver, whereas this has not been documented in the heart. The aim of this study was to assess the acute effect of aerobic exercise on the flexibility of IMCL, IHCL and ICCL in insulin-sensitive participants in relation to fat availability, insulin sensitivity and exercise capacity. Methods: Healthy physically active men were included. V ⋅ O 2 max $$\overset{\cdot }{V}{\mathrm{O}}_{2 \max }$$ was assessed by spiroergometry and insulin sensitivity was calculated using the HOMA index. Visceral and subcutaneous fat were separately quantified by MRI. Following a standardised dietary fat load over 3days, IMCL, IHCL and ICCL were measured using MR spectroscopy before and after a 2h exercise session at 50-60% of V ⋅ O 2 max $$\overset{\cdot }{V}{\mathrm{O}}_{2 \max }$$ . Metabolites were measured during exercise. Results: Ten men (age 28.9 ± 6.4years, mean ± SD; V ⋅ O 2 max $$\overset{\cdot }{V}{\mathrm{O}}_{2 \max }$$ 56.3 ± 6.4mlkg−1min−1; BMI 22.75 ± 1.4kg/m2) were recruited. A 2h exercise session resulted in a significant decrease in IMCL (−17 ± 22%, p = 0.008) and ICCL (−17 ± 14%, p = 0.002) and increase in IHCL (42 ± 29%, p = 0.004). No significant correlations were found between the relative changes in ectopic lipids, fat availability, insulin sensitivity, exercise capacity or changes of metabolites during exercise. Conclusions/interpretation: In this group, physical exercise decreased ICCL and IMCL but increased IHCL. Fat availability, insulin sensitivity, exercise capacity and metabolites during exercise are not the only factors affecting ectopic lipids during exercise

Differences in Physiological Responses to Cardiopulmonary Exercise Testing in Adults With and Without Type 1 Diabetes: A Pooled Analysis

OBJECTIVE To investigate physiological responses to cardiopulmonary exercise (CPX) testing in adults with type 1 diabetes compared with age-, sex-, and BMI-matched control participants without type 1 diabetes.RESEARCH DESIGN AND METHODS We compared results from CPX tests on a cycle ergometer in individuals with type 1 diabetes and control participants without type 1 diabetes. Parameters were peak and threshold variables of VO2, heart rate, and power output. Differences between groups were investigated through restricted maximum likelihood modeling and post hoc tests. Differences between groups were explained by stepwise linear regressions (P < 0.05).RESULTS Among 303 individuals with type 1 diabetes (age 33 [interquartile range 22; 43] years, 93 females, BMI 23.6 [22; 26] kg/m2, HbA1c 6.9% [6.2; 7.7%] [52 (44; 61) mmol/mol]), VO2peak (32.55 [26.49; 38.72] vs. 42.67 ± 10.44 mL/kg/min), peak heart rate (179 [170; 187] vs. 184 [175; 191] beats/min), and peak power (216 [171; 253] vs. 245 [200; 300] W) were lower compared with 308 control participants without type 1 diabetes (all P < 0.001). Individuals with type 1 diabetes displayed an impaired degree and direction of the heart rate-to-performance curve compared with control participants without type 1 diabetes (0.07 [−0.75; 1.09] vs. 0.66 [−0.28; 1.45]; P < 0.001). None of the exercise physiological responses were associated with HbA1c in individuals with type 1 diabetes.CONCLUSIONS Individuals with type 1 diabetes show altered responses to CPX testing, which cannot be explained by HbA1c. Intriguingly, the participants in our cohort were people with recent-onset type 1 diabetes; heart rate dynamics were altered during CPX testing

Influence of time point of calibration on accuracy of continuous glucose monitoring in individuals with type 1 diabetes

Abstract Background and Aims: Data on the influence of calibration on accuracy of continuous glucose monitoring (CGM) are scarce. The aim of the present study was to investigate whether the time point of calibration has an influence on sensor accuracy and whether this effect differs according to glycemic level. Subjects and Methods: Two CGM sensors were inserted simultaneously in the abdomen on either side of 20 individuals with type 1 diabetes. One sensor was calibrated predominantly using preprandial glucose (calibration(PRE)). The other sensor was calibrated predominantly using postprandial glucose (calibration(POST)). At minimum three additional glucose values per day were obtained for analysis of accuracy. Sensor readings were divided into four categories according to the glycemic range of the reference values (low, ≤4 mmol/L; euglycemic, 4.1-7 mmol/L; hyperglycemic I, 7.1-14 mmol/L; and hyperglycemic II, >14 mmol/L). Results: The overall mean±SEM absolute relative difference (MARD) between capillary reference values and sensor readings was 18.3±0.8% for calibration(PRE) and 21.9±1.2% for calibration(POST) (P<0.001). MARD according to glycemic range was 47.4±6.5% (low), 17.4±1.3% (euglycemic), 15.0±0.8% (hyperglycemic I), and 17.7±1.9% (hyperglycemic II) for calibration(PRE) and 67.5±9.5% (low), 24.2±1.8% (euglycemic), 15.5±0.9% (hyperglycemic I), and 15.3±1.9% (hyperglycemic II) for calibration(POST). In the low and euglycemic ranges MARD was significantly lower in calibration(PRE) compared with calibration(POST) (P=0.007 and P<0.001, respectively). Conclusions: Sensor calibration predominantly based on preprandial glucose resulted in a significantly higher overall sensor accuracy compared with a predominantly postprandial calibration. The difference was most pronounced in the hypo- and euglycemic reference range, whereas both calibration patterns were comparable in the hyperglycemic range

Exercise-induced Growth Hormone Secretion in the Assessment of Growth Hormone Deficiency in Adult Individuals

The role of exercise testing in the assessment of GH deficiency (GHD) in adult patients is currently unclear. This study aimed at evaluating the diagnostic value of exercise-induced GH levels in the detection of severe GHD in adult patients

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

Lower Daily Carbohydrate Intake Is Associated With Improved Glycemic Control in Adults With Type 1 Diabetes Using a Hybrid Closed-Loop System.

OBJECTIVE To assess the association between daily carbohydrate (CHO) intake and glycemic control in adult hybrid closed-loop (HCL) users with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS Mean individual daily CHO intake (MIDC) and relative deviation from MIDC (≤80% low, 81-120% medium, >120% high CHO consumption) were compared with parameters of glycemic control assessed by continuous glucose monitoring. RESULTS Records from 36 patients (26 male, 10 female; age 36.9 ± 13.5 years; HbA1c 7.1 ± 0.9% [54 ± 10 mmol/mol]) provided 810 days of data (22.5 ± 6.7 days per patient). Time in range (70-180 mg/dL) for low, medium, and high CHO consumption was 77.4 ± 15.4%, 75.2 ± 16.7%, and 70.4 ± 17.8%, respectively (P 180 mg/dL) was 20.1 ± 14.7%, 22.0 ± 16.9%, and 27.2 ± 18.4%, respectively (P < 0.001). There was no between-group difference for time in hypoglycemia (<70 mg/dL; P = 0.50). CONCLUSIONS Daily CHO intake was inversely associated with glycemic control in adults with T1D using an HCL system. Lower CHO intake may be a strategy to optimize glucose control in HCL users

Glucocorticoid Replacement and Mortality in Patients with Nonfunctioning Pituitary Adenoma

Current treatment guidelines generally suggest using lower and weight-adjusted glucocorticoid replacement doses in patients with insufficiency of the hypothalamic-pituitary-adrenal (HPA) axis. Although data in patients with acromegaly revealed a positive association between glucocorticoid dose and mortality, no comparable results exist in patients with nonfunctioning pituitary adenomas (NFPA)