Oral minimal model-based estimates of insulin sensitivity in obese youth depend on oral glucose tolerance test protocol duration

Metabolic-associated fatty liver disease is characterized by a post-oral glucose load hyperinsulinemia in individuals with mild metabolic alterations

Metabolic-associated fatty liver disease (MAFLD) has been identified as risk factor of incident type 2 diabetes (T2D), but the underlying postprandial mechanisms remain unclear. We compared the glucose metabolism, insulin resistance, insulin secretion, and insulin clearance post-oral glucose tolerance test (OGTT) between individuals with and without MAFLD. We included 50 individuals with a body mass index (BMI) between 25 and 40 kg/m2 and ≥1 metabolic alteration: increased fasting triglycerides or insulin, plasma glucose 5.5-6.9 mmol/L, or glycated hemoglobin 5.7-5.9%. Participants were grouped according to MAFLD status, defined as hepatic fat fraction (HFF) ≥5% on MRI. We used oral minimal model on a frequently sampled 3 h 75 g-OGTT to estimate insulin sensitivity, insulin secretion, and pancreatic β-cell function. Fifty percent of participants had MAFLD. Median age (IQR) [57 (45-65) vs. 57 (44-63) yr] and sex (60% vs. 56% female) were comparable between groups. Post-OGTT glucose concentrations did not differ between groups, whereas post-OGTT insulin concentrations were higher in the MAFLD group (P < 0.03). Individuals with MAFLD exhibited lower insulin clearance, insulin sensitivity, and first-phase pancreatic β-cell function. In all individuals, increased insulin incremental area under the curve and decreased insulin clearance were associated with HFF after adjusting for age, sex, and BMI (P < 0.02). Among individuals with metabolic alterations, the presence of MAFLD was characterized mainly by post-OGTT hyperinsulinemia and reduced insulin clearance while exhibiting lower first phase β-cell function and insulin sensitivity. This suggests that MAFLD is linked with impaired insulin metabolism that may precede T2D.NEW & NOTEWORTHY Using an oral glucose tolerance test, we found hyperinsulinemia, lower insulin sensitivity, lower insulin clearance, and lower first-phase pancreatic β-cell function in individuals with MAFLD. This may explain part of the increased risk of incident type 2 diabetes in this population. These data also highlight implications of hyperinsulinemia and impaired insulin clearance in the progression of MAFLD to type 2 diabetes.

Metabolic effects of an essential amino acid supplement in adolescents with PCOS and obesity

OBJECTIVE

Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenism, insulin resistance, and hepatic steatosis (HS). Because dietary essential amino acid (EAA) supplementation has been shown to decrease HS in various populations, this study's objective was to determine whether supplementation would decrease HS in PCOS.

METHODS

A randomized, double-blind, crossover, placebo-controlled trial was conducted in 21 adolescents with PCOS (BMI 37.3 ± 6.5 kg/m2, age 15.6 ± 1.3 years). Liver fat, very low-density lipoprotein (VLDL) lipogenesis, and triacylglycerol (TG) metabolism were measured following each 28-day phase of placebo or EAA.

RESULTS

Compared to placebo, EAA was associated with no difference in body weight (p = 0.673). Two markers of liver health improved: HS was lower (-0.8% absolute, -7.5% relative reduction, p = 0.013), as was plasma aspartate aminotransferase (AST) (-8%, p = 0.004). Plasma TG (-9%, p = 0.015) and VLDL-TG (-21%, p = 0.031) were reduced as well. VLDL-TG palmitate derived from lipogenesis was not different between the phases, nor was insulin sensitivity (p > 0.400 for both). Surprisingly, during the EAA phase, participants reported consuming fewer carbohydrates (p = 0.038) and total sugars (p = 0.046).

CONCLUSIONS

Similar to studies in older adults, short-term EAA supplementation in adolescents resulted in significantly lower liver fat, AST, and plasma lipids and thus may prove to be an effective treatment in this population. Additional research is needed to elucidate the mechanisms for these effects.

Estimating insulin sensitivity and β-cell function from the oral glucose tolerance test: validation of a new insulin sensitivity and secretion (ISS) model

Efficient and accurate methods to estimate insulin sensitivity (SI) and β-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes (T2D). Existing methods range in sensitivity, input data, and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological than intravenous methods. However, current analytical methods for OGTT-derived SI and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic surrogate indices (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new insulin secretion and sensitivity (ISS) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. This model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. This model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including postchallenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts across the lifespan. The new model had a strong correlation with gold-standard estimates from intravenous glucose tolerance tests and insulin clamps. The ISS model has broad applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk.NEW & NOTEWORTHY The pathogenesis of type 2 diabetes (T2D) is determined by a balance between insulin sensitivity (SI) and β-cell function (BCF), which can be determined by gold standard direct measurements or estimated by fitting differential equation models to oral glucose tolerance tests (OGTTs). We propose and validate a new differential equation model that is simpler to use than current models and requires less data while maintaining good correlation and agreement with gold standards. Matlab and Python code is freely available.

Estimating Insulin Sensitivity and Beta-Cell Function from the Oral Glucose Tolerance Test: Validation of a new Insulin Sensitivity and Secretion (ISS) Model

Efficient and accurate methods to estimate insulin sensitivity (SI) and beta-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes. Many methods exist, ranging in input data and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological. However, current analytical methods for OGTT-derived SI and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic models (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new ISS (Insulin Secretion and Sensitivity) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. The model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. The model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including post-challenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts including individuals at high risk of prediabetes (adult women with a wide range of BMI and adolescents with obesity). The new model had strong correlation with gold-standard estimates from intravenous glucose tolerance tests and hyperinsulinemic-euglycemic clamp. The ISS model has broad clinical applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk.

Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery

Type 2 diabetes mellitus is a complex and under-treated disorder closely intertwined with obesity. Adolescents with severe obesity and type 2 diabetes have a more aggressive disease compared to adults, with a rapid decline in pancreatic β cell function and increased incidence of comorbidities. Given the relative paucity of pharmacotherapies, bariatric surgery has become increasingly used as a therapeutic option. However, subsets of this population have sub-optimal outcomes with either inadequate weight loss or little improvement in disease. Predicting which patients will benefit from surgery is a difficult task and detailed physiological characteristics of patients who do not respond to treatment are generally unknown. Identifying physiological predictors of surgical response therefore has the potential to reveal both novel phenotypes of disease as well as therapeutic targets. We leverage data assimilation paired with mechanistic models of glucose metabolism to estimate pre-operative physiological states of bariatric surgery patients, thereby identifying latent phenotypes of impaired glucose metabolism. Specifically, maximal insulin secretion capacity, σ, and insulin sensitivity, S_I, differentiate aberrations in glucose metabolism underlying an individual's disease. Using multivariable logistic regression, we combine clinical data with data assimilation to predict post-operative glycemic outcomes at 12 months. Models using data assimilation sans insulin had comparable performance to models using oral glucose tolerance test glucose and insulin. Our best performing models used data assimilation and had an area under the receiver operating characteristic curve of 0.77 (95% confidence interval 0.7665, 0.7734) and mean average precision of 0.6258 (0.6206, 0.6311). We show that data assimilation extracts knowledge from mechanistic models of glucose metabolism to infer future glycemic states from limited clinical data. This method can provide a pathway to predict long-term, post-surgical glycemic states by estimating the contributions of insulin resistance and limitations of insulin secretion to pre-operative glucose metabolism.

Mathematical modeling reveals differential dynamics of insulin action models on glycerol and glucose in adolescent girls with obesity

Under healthy conditions, the pancreas responds to a glucose challenge by releasing insulin. Insulin suppresses lipolysis in adipose tissue, thereby decreasing plasma glycerol concentration, and it regulates plasma glucose concentration through action in muscle and liver. Insulin resistance (IR) occurs when more insulin is required to achieve the same effects, and IR may be tissue-specific. IR emerges during puberty as a result of high concentrations of growth hormone and is worsened by youth-onset obesity. Adipose, liver, and muscle tissue exhibit distinct dose-dependent responses to insulin in multi-phase hyperinsulinemic-euglycemic (HE) clamps, but the HE clamp protocol does not address potential differences in the dynamics of tissue-specific insulin responses. Changes to the dynamics of insulin responses would alter glycemic control in response to a glucose challenge. To investigate the dynamics of insulin acting on adipose tissue, we developed a novel differential-equations based model that describes the coupled dynamics of glycerol concentrations and insulin action during an oral glucose tolerance test in female adolescents with obesity and IR. We compared these dynamics to the dynamics of insulin acting on muscle and liver as assessed with the oral minimal model applied to glucose and insulin data collected under the same protocol. We found that the action of insulin on glycerol peaks approximately 67 min earlier (p < 0.001) and follows the dynamics of plasma insulin more closely compared to insulin action on glucose as assessed by the parameters representing the time constants for insulin action on glucose and glycerol (p < 0.001). These findings suggest that the dynamics of insulin action show tissue-specific differences in our IR adolescent population, with adipose tissue responding to insulin more quickly compared to muscle and liver. Improved understanding of the tissue-specific dynamics of insulin action may provide novel insights into the progression of metabolic disease in patient populations with diverse metabolic phenotypes.

A parsimonious model of blood glucose homeostasis

The mathematical modelling of biological systems has historically followed one of two approaches: comprehensive and minimal. In comprehensive models, the involved biological pathways are modelled independently, then brought together as an ensemble of equations that represents the system being studied, most often in the form of a large system of coupled differential equations. This approach often contains a very large number of tuneable parameters (> 100) where each describes some physical or biochemical subproperty. As a result, such models scale very poorly when assimilation of real world data is needed. Furthermore, condensing model results into simple indicators is challenging, an important difficulty in scenarios where medical diagnosis is required. In this paper, we develop a minimal model of glucose homeostasis with the potential to yield diagnostics for pre-diabetes. We model glucose homeostasis as a closed control system containing a self-feedback mechanism that describes the collective effects of the physiological components involved. The model is analyzed as a planar dynamical system, then tested and verified using data collected with continuous glucose monitors (CGMs) from healthy individuals in four separate studies. We show that, although the model has only a small number (3) of tunable parameters, their distributions are consistent across subjects and studies both for hyperglycemic and for hypoglycemic episodes.

11-Oxyandrogens in Adolescents With Polycystic Ovary Syndrome

Context

Polycystic ovary syndrome (PCOS) is common and diagnosis requires an elevated testosterone. The clinical importance of adrenal 11-oxyandrogens in PCOS is unclear.

Objective

We sought to determine if 11-oxyandrogens 1) better identify PCOS diagnosis compared to testosterone, 2) predict clinical comorbidities of PCOS, and 3) are altered with an combined oral contraceptive pill (COCP) or metformin therapy.

Methods

Data from 200 adolescent female participants aged 12 to 21 years, most with obesity, enrolled across 6 studies in pediatric endocrinology were included: 70 non-PCOS controls, 115 untreated PCOS, 9 PCOS + obesity treated with COCP, and 6 PCOS + obesity treated with metformin. 11-Hydroxyandrostenedione (11-OHA4), 11-hydroxytestosterone (1-OHT), 11-ketotestosterone (11-KT), and testosterone were measured with liquid chromatography-tandem mass spectrometry. Data between 1) untreated PCOS and controls and 2) untreated PCOS and the 2 treatment groups were compared.

Results

Untreated girls with PCOS had higher 11-OHA4 (P = .003) and 11-OHT (P = .005) compared to controls, but not 11-KT (P = .745). Elevated 11-OHA4 remained statistically significant after controlling for obesity. Testosterone better predicted PCOS status compared to 11-oxyandrogens (receiver operating characteristic curve analysis: 11-OHA4 area under the curve [AUC] = 0.620, 11-OHT AUC = 0.638; testosterone AUC = 0.840). Among untreated PCOS patients, all 3 11-oxyandrogens correlated with hirsutism severity. 11-KT (P = .039) and testosterone (P < .006) were lower in those on COCP treatment compared to untreated PCOS. Metformin treatment had no effect on 11-oxyandrogens, although testosterone was lower (P = .01).

Conclusion

Although 11-oxyandrogens do not aid in the diagnosis of PCOS, they relate to excess hair growth. COCP treatment may related to 11-KT; however, further work is needed to determine causality, relationship with metabolic outcomes, and the clinical utility of measuring these androgens in PCOS.

Pancreatic fat relates to fasting insulin and postprandial lipids but not polycystic ovary syndrome in adolescents with obesity

OBJECTIVE

Adolescents with polycystic ovary syndrome (PCOS) and obesity can have insulin resistance, dysglycemia, and hepatic steatosis. Excess pancreatic fat may disturb insulin secretion and relate to hepatic fat. Associations between pancreatic fat fraction (PFF) and metabolic measures in PCOS were unknown.

METHODS

This secondary analysis included 113 sedentary, nondiabetic adolescent girls (age = 15.4 [1.9] years), with or without PCOS and BMI ≥ 90th percentile. Participants underwent fasting labs, oral glucose tolerance tests, and magnetic resonance imaging for hepatic fat fraction (HFF) and PFF. Groups were categorized by PFF (above or below the median of 2.18%) and compared.

RESULTS

Visceral fat and HFF were elevated in individuals with PCOS versus control individuals, but PFF was similar. PFF did not correlate with serum androgens. Higher and lower PFF groups had similar HFF, with no correlation between PFF and HFF, although hepatic steatosis was more common in those with higher PFF (≥5.0% HFF; 60% vs. 36%; p = 0.014). The higher PFF group had higher fasting insulin (p = 0.026), fasting insulin resistance (homeostatic model assessment of insulin resistance, p = 0.032; 1/fasting insulin, p = 0.028), free fatty acids (p = 0.034), and triglycerides (p = 0.004) compared with those with lower PFF. β-Cell function and insulin sensitivity were similar between groups.

CONCLUSIONS

Neither PCOS status nor androgens related to PFF. However, fasting insulin and postprandial lipids were worse with higher PFF.

Dipeptidyl-Peptidase-IV Inhibitors, Imigliptin and Alogliptin, Improve Beta-Cell Function in Type 2 Diabetes

OBJECTS

Imigliptin is a novel dipeptidyl peptidase-4 inhibitor. In the present study, we aimed to evaluate the effects of imigliptin and alogliptin on insulin resistance and beta-cell function in Chinese patients with type-2 diabetes mellitus (T2DM).

METHODS

A total of 37 Chinese T2DM patients were randomized to receive 25 mg imigliptin, 50 mg imigliptin, placebo, and 25 mg alogliptin (positive drug) for 13 days. Oral glucose tolerance tests were conducted at baseline and on day 13, followed by the oral minimal model (OMM).

RESULTS

Imigliptin or alogliptin treatment, compared with their baseline or placebo, was associated with higher beta-cell function parameters (φ_s and φ_tot) and lower glucose area under the curve (AUC) and postprandial glucose levels. The changes in the AUC for the glucose appearance rate between 0 and 120 min also showed a decrease in imigliptin or alogliptin groups. However, the insulin resistance parameter, fasting glucose, was not changed. For the homeostatic model assessment (HOMA-β and HOMA-IR) parameters or secretory units of islets in transplantation index (SUIT), no statistically significant changes were found both within treatments and between treatments.

CONCLUSIONS

After 13 days of treatment, imigliptin and alogliptin could decrease glycemic levels by improving beta-cell function. By comparing OMM with HOMA or SUIT results, glucose stimulation might be more sensitive for detecting changes in beta-cell function.