Modelling and assessment of glucose-lactate kinetics in youth with overweight, obesity and metabolic dysfunction-associated steatotic liver disease: A pilot study.

AIM: In this study, we investigated glucose and lactate kinetics during a 75 g oral glucose tolerance test (OGTT) in 23 overweight and obese adolescents and assessed putative differences among participants with and without metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS: We enrolled 23 young people (six girls) with obesity [body mass index 33 (29-37)]. Glucose-lactate kinetics parameters (disposal glucose insulin sensitivity, SID; fraction of glucose converted into lactate, fr; fractional lactate clearance rate, kL) and lactate production rate (LPR) were estimated using the oral glucose-lactate minimal model. MASLD presence was assessed using the proton density fat fraction. We analysed glucose, lactate and LPR time to peak, peak values and area under the curve and evaluated differences using the Wilcoxon test. MASLD and no-MASLD participants were compared using the Mann-Whitney test. Correlations between parameters were assessed using the Spearman correlation coefficient (ρ). We also tested the performance of two (4 or 3 h OGTT) protocols in estimating oral glucose-lactate minimal model and LPR parameters. RESULTS: Glucose peaks 30 min earlier than lactate (p = .0019). This pattern was present in the no-MASLD group (p < .001). LPR peaks 30 min later in the MASLD group (p = .02). LPR and kL were higher in MASLD, suggesting higher glycolysis and lactate utilization. SID and fr correlate significantly (ρ = -0.55, p = .008). SID and fr were also correlated with the body mass index, (ρ = -0.45, p = .04; and ρ = 0.45; p = .03). The protocol duration did not influence the estimates of the parameters. DISCUSSION: Youth with MASLD showed a delayed glucose metabolism, possibly because of greater utilization of the underlying substrates. A 3-h OGTT may be used to assess lactate metabolism effectively.

Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study.

BACKGROUND: Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN. METHOD: To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process. RESULTS: The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia. CONCLUSIONS: Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.

Tailoring the Padova Type 2 Diabetes Simulator for Treatment Guidance in Target Populations.

OBJECTIVE: The Padova type 2 diabetes (T2D) simulator (T2DS) has been recently proposed to optimize T2D treatments including novel long-acting insulins. It consists of a physiological model and an in silico population describing glucose dynamics, derived from early-stage T2D subjects studied with sophisticated tracer-based experimental techniques. This limits T2DS domain of validity to this specific sub-population. Conversely, running simulations in insulin-naïve or advanced T2D subjects, would be more valuable. However, it is rarely possible or cost-effective to run complex experiments in such populations. Therefore, we propose a method for tuning the T2DS to any desired T2D sub-population using published clinical data. As case study, we extended the T2DS to insulin-naïve T2D subjects, who need to start insulin therapy to compensate the reduced insulin function. METHODS: T2DS model was identified based on literature data of the target population. The estimated parameters were used to generate a virtual cohort of insulin-naïve T2D subjects (inC1). A model of basal insulin degludec (IDeg) was also incorporated into the T2DS to enable basal insulin therapy. The resulting tailored T2DS was assessed by simulating IDeg therapy initiation and comparing simulated vs. clinical trial outcomes. For further validation, this procedure was reiterated to generate a new cohort of insulin-naïve T2D (inC2) assuming inC1 as target population. RESULTS: No statistically significant differences were found when comparing fasting plasma glucose and IDeg dose, neither in clinical data vs. inC1, nor inC1 vs. inC2. CONCLUSIONS: The tuned T2DS allowed reproducing the main findings of clinical studies in insulin-naïve T2D subjects. SIGNIFICANCE: The proposed methodology makes the Padova T2DS usable for supporting treatment guidance in target T2D populations.

A New Oral Model to Assess Postprandial Lactate Production Rate.

OBJECTIVE: Pediatric obesity predisposes children and adolescents to early onset insulin resistance and dysglycemia. In the last 20 years this has led to a rise in the prevalence of prediabetes, diabetes and fatty liver in youngsters, due to the high degree of insulin resistance experienced by these patients and the consequent high availability of glucose. As glucose accesses the liver, it is partly metabolized through glycolysis, whose main product is pyruvate that is then converted into Acetyl CoA and lactate. Therefore, lactate production rate (LPR) represents the best proxy for the assessment of glycolysis. Since to date there are not methods to estimate postprandial LPR, here we proposed a novel oral glucose-lactate model to estimate LPR during an oral glucose tolerance test and tested it in 24 youth with and without obesity. METHODS: The model is based on the oral glucose minimal model and assumes that LPR is a fraction (fr) of glucose disposal rate, proportional to glucose concentration and controlled by insulin action. RESULTS: The model well fitted the glucose and lactate data, and provided both precise parameter estimates (e.g., fr = 22.5 [12.6-54.1]%, median [IQR]), CV = 18 [13-25]%) and LPR time course. CONCLUSIONS: The proposed model is a valid tool to assess LPR, and thus glycolysis, during OGTT in nondiabetic subjects. SIGNIFICANCE: The proposed methodology will allow to assess postprandial LPR in simple and cost-effective way.