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Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from “bottom-up” mechanistic models to “top-down” data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven modeling approach to quantify individuals’ glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.

Objective: This study (1) investigated the effect of weight loss on whole-body and tissue-specific insulin sensitivity and on intrahepatic lipid (IHL) content and composition and (2) investigated the association between weight-loss-induced changes in insulin sensitivity and IHL content in individuals with overweight or obesity. Methods: In this secondary analysis of the European SWEET project, 50 adults (age 18–65 years) with overweight or obesity (BMI ≥ 25 kg/m2) followed a low-energy diet (LED) for 2 months. At baseline and after the LED, body composition (dual-energy x-ray absorptiometry), IHL content and composition (proton magnetic resonance spectroscopy), whole-body insulin sensitivity (Matsuda index), muscle insulin sensitivity index (MISI), and hepatic insulin resistance index (HIRI) were determined (7-point oral glucose tolerance test). Results: The LED reduced body weight (p < 0.001). This was accompanied by increased Matsuda index and reduced HIRI (both p < 0.001) but no change in MISI (p = 0.260). Weight loss decreased IHL content (mean [SEM], 3.9% [0.7%] vs. 1.6% [0.5%], p < 0.001) and the hepatic saturated fatty acid fraction (41.0% [1.5%] vs. 36.6% [1.9%], p = 0.039). The reduced IHL content was associated with an improvement in HIRI (r = 0.402, p = 0.025). Conclusions: Weight loss decreased IHL content and the hepatic saturated fatty acid fraction. The decrease in IHL content was associated with weight-loss-induced improvement in hepatic insulin sensitivity in individuals with overweight or obesity.

Objective: A proinflammatory adipose tissue (AT) microenvironment and systemic low-grade inflammation may differentially affect tissue-specific insulin sensitivity. This study investigated the relationships of abdominal subcutaneous AT (aSAT) and circulating immune cells, aSAT gene expression, and circulating inflammatory markers with liver and skeletal muscle insulin sensitivity in people with overweight and obesity. Methods: Individuals with overweight and obesity from the PERSonalized Glucose Optimization Through Nutritional Intervention (PERSON) Study (n = 219) and the Maastricht Study (replication cohort; n = 1256) underwent a seven-point oral glucose tolerance test to assess liver and muscle insulin sensitivity, and circulating inflammatory markers were determined. In subgroups, flow cytometry was performed to identify circulating and aSAT immune cells, and aSAT gene expression was evaluated. Results: The relative abundances of circulating T cells, nonclassical monocytes, and CD56dim CD16+ natural killer cells were inversely associated with liver, but not muscle, insulin sensitivity in the PERSON Study. The inverse association between circulating (classical) monocytes and liver insulin sensitivity was confirmed in the Maastricht Study. In aSAT, immune cell populations were not related to insulin sensitivity. Furthermore, aSAT gene expression of interleukin 6 and CD14 was positively associated with muscle, but not liver, insulin sensitivity. Conclusions: The present findings demonstrate that circulating immune cell populations and inflammatory gene expression in aSAT show distinct associations with liver and muscle insulin sensitivity.

Precision nutrition based on metabolic phenotype may increase the effectiveness of interventions. In this proof-of-concept study, we investigated the effect of modulating dietary macronutrient composition according to muscle insulin-resistant (MIR) or liver insulin-resistant (LIR) phenotypes on cardiometabolic health. Women and men with MIR or LIR (n = 242, body mass index [BMI] 25–40 kg/m2, 40–75 years) were randomized to phenotype diet (PhenoDiet) group A or B and followed a 12-week high-monounsaturated fatty acid (HMUFA) diet or low-fat, high-protein, and high-fiber diet (LFHP) (PhenoDiet group A, MIR/HMUFA and LIR/LFHP; PhenoDiet group B, MIR/LFHP and LIR/HMUFA). PhenoDiet group B showed no significant improvements in the primary outcome disposition index, but greater improvements in insulin sensitivity, glucose homeostasis, serum triacylglycerol, and C-reactive protein compared with PhenoDiet group A were observed. We demonstrate that modulating macronutrient composition within the dietary guidelines based on tissue-specific insulin resistance (IR) phenotype enhances cardiometabolic health improvements. Clinicaltrials.gov registration: NCT03708419, CCMO registration NL63768.068.17.

Detrimental consequences of antibiotic treatment may include long-lasting disruption of the gut microbiota. Previous studies found no negative effects of antibiotics on metabolic health, although individualized responses were observed. Here, we aimed to investigate the subject-specific response to vancomycin use in tissue-specific insulin sensitivity by stratifying individuals based on the presence of antibiotic resistance genes (ARGs) or opportunistic pathogens (OPs) in the baseline fecal microbiota. Quantitative Polymerase Chain Reaction (qPCR) was used to detect ARGs and OPs in DNA isolated from fecal samples of 56 males with overweight/obesity (Body Mass Index: 25–35 kg/m2) and impaired glucose metabolism (fasting plasma glucose ≥5.6 mmol/L and/or 2-hour glucose 7.8–11.1 mmol/L). A two-step hyperinsulinemic-euglycemic clamp was performed to determine tissue-specific insulin sensitivity. Abdominal subcutaneous adipose tissue (AT) gene expression was assessed using Affymetrix microarray. Gut microbial composition was determined using the Human Intestinal Tract Chip (HITChip) microarray. At baseline, the vancomycin resistance gene vanB was present in 60% of our population. In individuals that were vanB-negative at baseline, AT insulin sensitivity (insulin-mediated suppression of plasma free fatty acids) improved during vancomycin use, while it decreased among vanB-positive individuals (% change post versus baseline: 14.1 ± 5.6 vs. −6.7 ± 7.5% (p = .042)). The vancomycin-induced increase in AT insulin sensitivity was accompanied by downregulation of inflammatory pathways and enrichment of extracellular matrix remodeling pathways in AT. In the vanB-positive group, well-known vanB-carrying bacteria, Enterococcus and Streptococcus, expanded in the gut microbiome. In conclusion, microbiome composition and adipose tissue biology were differentially affected by vancomycin treatment based on fecal vanB carriage.

In the original article, there was a mistake in Supplementary Tables 3 and 4. Some numbers in these tables were incorrect. The corrected tables Supplementary Tables 3 and 4 appear below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.