A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu

The relationship between glycaemic variability and cardiovascular autonomic dysfunction in patients with type 1 diabetes : a systematic review

Rigorous glycaemic control-reflected by low HbA1c goals-is of the utmost importance in the prevention and management of complications in patients with type 1 diabetes mellitus (T1DM). However, previous studies suggested that short-term glycaemic variability (GV) is also important to consider as excessive glucose fluctuations may have an additional impact on the development of diabetic complications. The potential relationship between GV and the risk of cardiovascular autonomic neuropathy (CAN), a clinical expression of cardiovascular autonomic dysfunction, is of increasing interest. This systematic review aimed to summarize existing evidence concerning the relationship between GV and cardiovascular autonomic dysfunction in T1DM. An electronic database search of Medline (PubMed), Web of Science and Embase was performed up to October 2019. There were no limits concerning year of publication. Methodological quality was evaluated using the Newcastle Ottawa Scale for observational studies. Six studies (four cross-sectional and two prospective cohorts) were included. Methodological quality of the studies varied from level C to A2. Two studies examined the association between GV and heart rate variability (HRV), and both found significant negative correlations. Regarding cardiovascular autonomic reflex tests (CARTs), two studies did not, while two other studies did find significant associations between GV parameters and CART scores. However, associations were attenuated after adjusting for covariates such as HbA1c, age and disease duration. In conclusion, this systematic review found some preliminary evidence supporting an association between GV and cardiovascular autonomic dysfunction in T1DM. Hence, uncertainty remains whether high GV can independently contribute to the onset or progression of CAN. The heterogeneity in the methodological approach made it difficult to compare different studies. Future studies should therefore use uniformly evaluated continuous glucose monitoring-derived parameters of GV, while standardized assessment of HRV, CARTs and other potential cardiac autonomic function parameters is needed for an unambiguous definition of CAN

Risk models and scores for type 2 diabetes: Systematic review

This article is published under a Creative Commons Attribution Non Commercial (CC BY-NC 3.0) licence that allows reuse subject only to the use being non-commercial and to the article being fully attributed (http://creativecommons.org/licenses/by-nc/3.0).Objective - To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design - Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion - criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources - Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction - Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results - 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion - Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.Tower Hamlets, Newham, and City and Hackney primary care trusts and National Institute of Health Research

Personalized glucose forecasting for type 2 diabetes using data assimilation

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges

The Roles of Glycated Albumin as Intermediate Glycation Index and Pathogenic Protein

The conventional glycemic indices used in management of diabetic patients includes A1c, fructosamine, 1,5-anhydroglucitol, and glycated albumin (GA). Among these indices, A1c is currently used as the gold standard. However, A1c cannot reflect the glycemic change over a relatively short period of time, and its accuracy is known to decrease when abnormalities in hemoglobin metabolism, such as anemia, coexist. When considering these weaknesses, there have been needs for finding a novel glycemic index for diagnosing and managing diabetes, as well as for predicting diabetic complications properly. Recently, several studies have suggested the potential of GA as an intermediate-term glycation index in covering the short-term effect of treatment. Furthermore, its role as a pathogenic protein affecting the worsening of diabetes and occurrence of diabetic complications is receiving attention as well. Therefore, in this article, we wanted to review the recent status of GA as a glycemic index and as a pathogenic protein

Clinical Application of the Food Insulin Index to Diabetes Mellitus

The Food Insulin Index (FII) is a novel system of ranking foods based on the insulin response in healthy subjects relative to an isoenergetic reference food. The goal of this thesis was to explore the clinical application of the FII to mealtime insulin dosing in type 1 diabetes. Carbohydrate counting is the current gold standard method for determining prandial insulin dose in type 1 diabetes, however only 7 studies assessing the efficacy of carbohydrate counting for glycaemic control in people with type 1 diabetes could be identified. Meta-analysis revealed there was no significant improvement in HbA1c with carbohydrate counting over general dietary advice and/or usual care (-0.35%, p = 0.096). This study highlighted the need for research into alternative strategies to improve the accuracy of the mealtime insulin dose. The FII reveals a notable insulin demand for foods high in protein and fat, nutrients that would normally be disregarded for mealtime insulin dosing with traditional carbohydrate counting. Compared with carbohydrate counting, the FII algorithm was able to reduce mean blood glucose level for six foods high in protein (5.7 +/- 0.2 mmol/L vs 6.5 +/- 0.2 mmol/L, p = 0.003), without significantly increasing the risk of hypoglycaemia (p = 0.155). In the first randomised, controlled trial of the real-world application of the FII, the FII was at least as good as carbohydrate counting for glycaemic control in 26 adults with type 1 diabetes (HbA1c FII: -0.1 ± 0.1% vs CC: -0.3 ± 0.2%, p = 0.855), with a trend towards reduced risk of hypoglycaemia in the FID counters after 12 weeks. Collectively, these studies offer exciting insights into the potential of the FII for optimising glycaemic control in type 1 diabetes. Until a cure can be found, the potential for clinically significant enhancements in glycaemic control offer people with diabetes greater wellbeing through a reduced burden of disease and decreased risk of long-term diabetes complications

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces