Modelling intestinal glucose absorption in premature infants using continuous glucose monitoring data

Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review

INTRODUCTION

Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components.

METHOD

A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy.

RESULTS

The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%).

CONCLUSION

The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.

Attempts to limit sporulation in the probiotic strain Bacillus subtilis BG01-4TM through mutation accumulation and selection

The use of bacterial spores in probiotics over viable loads of bacteria has many advantages, including the durability of spores, which allows spore-based probiotics to effectively traverse the various biochemical barriers present in the gastrointestinal tract. However, the majority of spore-based probiotics developed currently aim to treat adults, and there is a litany of differences between the adult and infant intestinal systems, including the immaturity and low microbial species diversity observed within the intestines of infants. These differences are only further exacerbated in premature infants with necrotizing enterocolitis (NEC) and indicates that what may be appropriate for an adult or even a healthy full-term infant may not be suited for an unhealthy premature infant. Complications from using spore-based probiotics for premature infants with NEC may involve the spores remaining dormant and adhering to the intestinal epithelia, the out-competing of commensal bacteria by spores, and most importantly the innate antibiotic resistance of spores. Also, the ability of Bacillus subtilis to produce spores under duress may result in less B. subtilis perishing within the intestines and releasing membrane branched-chain fatty acids. The isolate B. subtilis BG01-4^TM is a proprietary strain developed by Vernx Biotechnology through accumulating mutations within the BG01-4^TM genome in a serial batch culture. Strain BG01-4^TM was provided as a non-spore-forming B. subtilis , but a positive sporulation status for BG01-4^TM was confirmed through in vitro testing and suggested that selection for the sporulation defective genes could occur within an environment that would select against sporulation. The durability of key sporulation genes was ratified in this study, as the ability of BG01-4^TM to produce spores was not eliminated by the attempts to select against sporulation genes in BG01-4^TM by the epigenetic factors of high glucose and low pH. However, a variation in the genes in isolate BG01-4-8 involved in the regulation of sporulation is believed to have occurred during the mutation selection from the parent strain BG01-4^TM. An alteration in selected sporulation regulation genes is expected to have occurred from BG01-4^TM to BG01-4-8, with BG01-4-8 producing spores within 24 h, ~48 h quicker than BG01-4^TM.

Model-based subcutaneous insulin for glycemic control of pre-term infants in the neonatal intensive care unit

Hyperglycaemia is a common problem in neonatal intensive care units (NICUs). Achieving good control can result in better outcomes for patients. However, good control is difficult, where poor control and resulting hypoglycaemia reduces outcomes and confounds results. Clinically validated models can provide good control, and subcutaneous insulin delivery can provide more options for insulin therapy for clinicians. However, this combination has only been significantly utilised in adult outpatient diabetes, but could hold benefit for treating NICU infants. This research combines a well-validated NICU metabolic model with subcutaneous insulin kinetics models to assess the feasibility of a model-based approach. Clinical data from 12 very/extremely pre-mature infants was collected for an average study duration of 10.1 days. Blood glucose, interstitial and plasma insulin, as well as subcutaneous and local insulin were modelled, and patient-specific insulin sensitivity profiles were identified for each patient. Modelling error was low, where the cohort median [IQR] mean percentage error was 0.8 [0.3 3.4] %. For external validation, insulin sensitivity was compared to previous NICU cohorts using the same metabolic model, where overall levels of insulin sensitivity were similar. Overall, the combined system model accurately captured observed glucose and insulin dynamics, showing the potential for a model-based approach to glycaemic control using subcutaneous insulin in this cohort. The results justify further model validation and clinical trial research to explore a model-based protocol.

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min.

PARAMETER ESTIMATION OF A SIMPLE, REALISTIC STOCHASTIC MODEL OF GASTRIC EMPTYING OF PELLETS UNDER FASTING CONDITIONS

This study is aimed to develop a parameter estimation procedure for a stochastic model of gastric emptying of pellets under fasting conditions of healthy subjects that represent the irregular decrements of gastric contents after administration of pellets. An algorithm for the identification of experimental subjects into subgroups based on their gastric emptying rates has been proposed. The parameter estimation procedure was performed with observational data of gastric emptying profiles of 19 subjects from the existing literature. The identification algorithm was validated through comparison against the original results, showing that seven subjects indicated slow gastric emptying and 12 subjects indicated fast emptying. After excluding one subject with gastric stasis, the optimal results obtained from this study classified subjects into three subgroups: two subjects with slow, five subjects with intermediate and 11 subjects with fast gastric emptying. The proposed parameter estimation procedure of the stochastic model is relatively easy to implement and can be used to analyze real patients’ data. The algorithm for identification of the gastric emptying dynamics in experimental subjects could provide useful information for further investigations of long-term energy balance forecasting.

Blood glucose concentration prediction based on kernel canonical correlation analysis with particle swarm optimization and error compensation

BACKGROUND AND OBJECTIVE

Blood glucose levels in humans change over time. Continuous glucose monitoring system (CGMS), can constantly monitor the change of blood glucose concentration. Given the historical data of blood glucose, predicting the trend of blood glucose in a short term is important for diabetes. Appropriate behaviors can be adopted to prevent hypoglycemia or hyperglycemia.

METHODS

The method proposed in this paper only uses historical blood glucose data as input, rather than complex multi-dimensional input. Previous articles have demonstrated that canonical correlation analysis (CCA) can effectively predict blood glucose. The linear relationship between historical blood glucose values and predicted values was only considered regrettably. To compensate for this, this paper adds a kernel function to find out the non-linear relationship between blood glucose. In the introduced kernel function, some parameters need to be adjusted. To reduce the deviation caused by manual parameter adjustment, this paper discusses the role of particle swarm optimization (PSO). Besides, this article puts forward an error compensation for CCA to enhance the precision. Finally based on the prediction results of PSO-KCCA, a personalized hypoglycemic warning threshold is proposed.

RESULTS

The proposed method is validated using clinical data by the root mean square error (RMSE) and differential coefficient (R²). The average RMSE result in PSO-KCCA was 8.01, 11.98, 12.45, 13.23, 14.53, 16.40 mg/dL in prediction horizon (PH) =5, 10, 15, 20, 25, 30 min. The average R² was 0.95, 0.95, 0.98, 0.97, 0.98, and 0.97, respectively. The CCA with error compensation (EC-CCA) reduced RMSE by 33.45% compared with CCA. For the hypoglycemic warning, the average sensitivity obtained at 6 different PH values was 94.37%, and the specificity was 92.25%.

CONCLUSIONS

The experimental results confirm the effectiveness of PSO-KCCA in blood glucose prediction. The proposed EC-CCA successfully reduces the delay in the time series prediction. The personalized hypoglycemic warning threshold consider the influence of the model accuracy on the prediction results. This method guarantees the rate of underreporting during monitoring and ensures patient safety.

Pathophysiology and management of diabetic gastroenteropathy

Polyneuropathy is a common complication to diabetes. Neuropathies within the enteric nervous system are associated with gastroenteropathy and marked symptoms that severely reduce quality of life. Symptoms are pleomorphic but include nausea, vomiting, dysphagia, dyspepsia, pain, bloating, diarrhoea, constipation and faecal incontinence. The aims of this review are fourfold. First, to provide a summary of the pathophysiology underlying diabetic gastroenteropathy. Secondly to give an overview of the diagnostic methods. Thirdly, to provide clinicians with a focussed overview of current and future methods for pharmacological and nonpharmacological treatment modalities. Pharmacological management is categorised according to symptoms arising from the upper or lower gut as well as sensory dysfunctions. Dietary management is central to improvement of symptoms and is discussed in detail, and neuromodulatory treatment modalities and other emerging management strategies for diabetic gastroenteropathy are discussed. Finally, we propose a diagnostic/investigation algorithm that can be used to support multidisciplinary management.