Differential Effects of ‘Vaping’ on Lipid and Glucose Profiles and Liver Metabolic Markers in Obese Versus Non-obese Mice

Tobacco smoking increases the risk of metabolic disorders due to the combination of harmful chemicals, whereas pure nicotine can improve glucose tolerance. E-cigarette vapour contains nicotine and some of the harmful chemicals found in cigarette smoke at lower levels. To investigate how e-vapour affects metabolic profiles, male Balb/c mice were exposed to a high-fat diet (HFD, 43% fat, 20kJ/g) for 16weeks, and e-vapour in the last 6weeks. HFD alone doubled fat mass and caused dyslipidaemia and glucose intolerance. E-vapour reduced fat mass in HFD-fed mice; only nicotine-containing e-vapour improved glucose tolerance. In chow-fed mice, e-vapour increased lipid content in both blood and liver. Changes in liver metabolic markers may be adaptive responses rather than causal. Future studies can investigate how e-vapour differentially affects metabolic profiles with different diets.

The Effects of Medium-to-Low Dose Glucocorticoids on Glucose Profiles in Patients with Interstitial Pneumonia with Autoimmune Features (IPAF) Evaluated by Continuous Glucose Monitoring System (CGMS): An Observational Study

Background: Few studies focused on the effects of medium-to-low dose glucocorticoids on glucose profiles and glucose metabolism in patients with interstitial pneumonia with autoimmune features (IPAF). Continuous glucose monitoring system (CGMS) can provide more detailed glucose features than fingertip blood samplings.Methods: This was a observational study in a teaching hospital. Outpatients with IPAF on 15mg prednisone per day for at least 3 months were recruited to receive CGMS and blood tests. The primary data of CGMS were analyzed to demonstrate the glucose features. Data were compared between subjects with a glucocorticoid treatment duration of more than 2 years(Group 1) and subjects with that of less than 2 years(Group 2). In subgroup analysis, among all subjects, subjects who tapered the daily dose to 7.5mg for at least 3 months received second tests. Results: 93% (27/29) on daily 15mg prednisone and all subjects on daily 7.5mg prednisone were with glucocorticoid induced hyperglycemia(GIH). Among subjects on daily 15mg prednisone, glucose AUC of 3 hours post-lunch was significantly higher than those post-breakfast and post-dinner, and mean glucose levels of half an hour pre-lunch and pre-dinner were significantly higher than that pre-breakfast. 81.9% of nocturnal glucose nadirs presented after 5AM, leading to relatively low fasting glucose values. There were no significant differences of glucose profiles between Group 1 and Group 2. No significant differences of CGMS and laboratory parameters were found between on daily 15mg prednisone and on daily 7.5mg prednisone.Conclusions: A large proportion of outpatients on medium-to-low-dose glucocorticoids are with GIH. Post-lunch glucose or post-dinner glucose are good indicators, while fasting glucose is not. The similar glycemic effects of daily 15mg and 7.5mg prednisone provide evidence for physicians to choose long-term maintaining glucocorticoid dose.Clinical Research Registration: This research is registered at clinicaltrials.gov(No.NCT02824757) with the registry named The Effects of Glucocorticoids on Glucose Metabolism in Patients With Interstitial Lung Disease. The URL is https://clinicaltrials.gov/ct2/show/NCT02824757?term=NCT02824757&draw=2&rank=1.This research is registered at July 7th,2016, retrospectively.

Utilizing CGM Ambulatory Glucose Profiles to Optimize Diabetes Management

LEARNING OBJECTIVES At the end of the activity, participant will be able to: • Identify patients who could benefit from continuous glucose monitoring (CGM) vs fingerstick blood glucose monitoring. • List the types of information provided by CGM systems. • Interpret CGM data using the ambulatory glucose profile (AGP) to assess if the patient is achieving targets established by the International Consensus on Time in Range. • Modify the treatment plan based on CGM data to improve patient outcomes.

Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies.

Glucodensities: A new representation of glucose profiles using distributional data analysis

Biosensor data have the potential to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state-of-the-art analysis methods. In particular, our findings demonstrate that (i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes; (ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information and; (iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics and provides more in-depth insight into assessing glucose metabolism.