Plasma metabolites and lipids predict insulin sensitivity improvement in obese, nondiabetic individuals after a 2-phase dietary intervention

Abstract

Background: Weight loss in obese individuals aims to reduce the risk of type 2 diabetes by improving glycemic control. Yet, significant intersubject variability is observed and the outcomes remain poorly predictable.

Objective: The aim of the study was to predict whether an individual will show improvements in insulin sensitivity above or below the median population change at 6 mo after a low-calorie-diet (LCD) intervention.

Design: With the use of plasma lipidomics and metabolomics for 433 subjects from the Diet, Obesity, and Genes (DiOGenes) Study, we attempted to predict good or poor Matsuda index improvements 6 mo after an 8-wk LCD intervention (800 kcal/d). Three independent analysis groups were defined: "training" (n = 119) for model construction, "testing" (n = 162) for model comparison, and "validation" (n = 152) to validate the final model.

Results: Initial modeling with baseline clinical variables (body mass index, Matsuda index, total lipid concentrations, sex, age) showed limited performance [area under the curve (AUC) on the "testing dataset" = 0.69; 95% CI: 0.61, 0.77]. Significantly better performance was achieved with an omics model based on 27 variables (AUC = 0.77; 95% CI: 0.70, 0.85; P = 0.0297). This model could be greatly simplified while keeping the same performance. The simplified model relied on baseline Matsuda index, proline, and phosphatidylcholine 0-34:1. It successfully replicated on the validation set (AUC = 0.75; 95% CI: 0.67, 0.83) with the following characteristics: specificity = 0.73, sensitivity = 0.68, negative predictive value = 0.60, and positive predictive value = 0.80. Marginally lower performance was obtained when replacing the Matsuda index with homeostasis model assessment of insulin resistance (AUC = 0.72; 95% CI: 0.64, 0.80; P = 0.08).

Conclusions: Our study proposes a model to predict insulin sensitivity improvements, 6 mo after LCD completion in a large population of overweight or obese nondiabetic subjects. It relies on baseline information from 3 variables, accessible from blood samples. This model may help clinicians assessing the large variability in dietary interventions and predict outcomes before an intervention. This trial was registered at www.clinicaltrials.gov as NCT00390637.

FIGURE 1

Study and analyses workflow. (A) DiOGenes dietary intervention; (B) analysis workflow and definition of the analysis data sets. The 2 leading centers (Netherlands and Denmark) provided most of the food to participants during the weight-maintenance intervention (following recommendations from dietitians and in accordance with the participant's randomly assigned diet). This enabled a better monitoring of patients’ compliance during the WMD intervention. The definition of glycemic responders was based on Matsuda index improvements ≥40.36%, as estimated for all DiOGenes completers (n = 433 subjects with OGTT data). *The discovery data set was composed of subjects with complete data for all clinical variables (BMI, Matsuda index, total lipid concentrations from blood biochemistry, age, sex, fasting glucose and insulin concentrations, HOMA-IR) and all omics variables (125 lipids from liquid chromatography–mass spectrometry and 18 metabolites from nuclear magnetic resonance) and the validation data set was composed of subjects not included in the discovery analyses and who had complete data for the Matsuda index and plasma concentrations of proline and PC O-34:1. CID, clinical intervention day; DiOGenes, Diet, Obesity, and Genes; DK, Denmark; LCD, low-caloric diet; NL, Netherlands; OGTT, oral-glucose-tolerance test; PC O-31:1, phosphatidylcholine 0-34:1; WMD, weight-maintenance diet.

FIGURE 2

Performance of the 169 models. (A) Histogram of all 169 values of ROC AUCs. The dotted blue line indicates an AUC = 0.50, the performance of a random classifier. (B) Performances grouped by the time point of the variables involved in the models. (C) Performances grouped by the statistical approach used to construct the classification models. ADA, adaptive boosting algorithm; BART, Bayesian additive regression trees; CID, clinical intervention day; GBM, Gradiant Boosting Machine; GLM, generalized linear model; LCD, low-calorie diet; ROC, receiver operating characteristic; SVM, Support Vector Machine.

FIGURE 3

Performance of classification models. (A) ROC curves showing classification performance as obtained on the testing data set. The omics model outperforms the clinical model (Delong's P = 0.0297). The diagonal line indicates the performance from a random predictor. (B) Plot of relative variable importance from the top elastic net. Importance is relative to the top predictor (the one with the highest absolute coefficient). (C, D) ROC curves showing the performance on the testing set and validation set for the simplified omics models with the use of either the Matsuda index (C) or HOMA-IR (D), together with baseline proline and PC O-34:1 concentrations. CID, clinical intervention day; PC, phosphatidylcholine; PE, phosphatidylethanolamine; ROC, receiver operating characteristic; SM, sphygingomyelin; TG, triglyceride.

FIGURE 4

Boxplots of baseline values for the top (most predictive) variables from the simplified models, stratified by responders and nonresponders, for baseline Matsuda index (A), HOMA-IR (B), and concentrations of PC O-34:1 (C) and proline (D). PC, phosphatidylcholine.