Walter Gall

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting.

Background Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study. Methodology/Principal Findings 40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid). Conclusions/Significance Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.

α-Hydroxybutyrate Is an Early Biomarker of Insulin Resistance and Glucose Intolerance in a Nondiabetic Population

Background Insulin resistance is a risk factor for type 2 diabetes and cardiovascular disease progression. Current diagnostic tests, such as glycemic indicators, have limitations in the early detection of insulin resistant individuals. We searched for novel biomarkers identifying these at-risk subjects. Methods Using mass spectrometry, non-targeted biochemical profiling was conducted in a cohort of 399 nondiabetic subjects representing a broad spectrum of insulin sensitivity and glucose tolerance (based on the hyperinsulinemic euglycemic clamp and oral glucose tolerance testing, respectively). Results Random forest statistical analysis selected α-hydroxybutyrate (α–HB) as the top-ranked biochemical for separating insulin resistant (lower third of the clamp-derived MFFM = 33 [12] µmol·min−1·kgFFM −1, median [interquartile range], n = 140) from insulin sensitive subjects (MFFM = 66 [23] µmol·min−1·kgFFM −1) with a 76% accuracy. By targeted isotope dilution assay, plasma α–HB concentrations were reciprocally related to MFFM; and by partition analysis, an α–HB value of 5 µg/ml was found to best separate insulin resistant from insulin sensitive subjects. α–HB also separated subjects with normal glucose tolerance from those with impaired fasting glycemia or impaired glucose tolerance independently of, and in an additive fashion to, insulin resistance. These associations were also independent of sex, age and BMI. Other metabolites from this global analysis that significantly correlated to insulin sensitivity included certain organic acid, amino acid, lysophospholipid, acylcarnitine and fatty acid species. Several metabolites are intermediates related to α-HB metabolism and biosynthesis. Conclusions α–hydroxybutyrate is an early marker for both insulin resistance and impaired glucose regulation. The underlying biochemical mechanisms may involve increased lipid oxidation and oxidative stress.

Dietary Leucine - An Environmental Modifier of Insulin Resistance Acting on Multiple Levels of Metabolism

Environmental factors, such as the macronutrient composition of the diet, can have a profound impact on risk of diabetes and metabolic syndrome. In the present study we demonstrate how a single, simple dietary factor—leucine—can modify insulin resistance by acting on multiple tissues and at multiple levels of metabolism. Mice were placed on a normal or high fat diet (HFD). Dietary leucine was doubled by addition to the drinking water. mRNA, protein and complete metabolomic profiles were assessed in the major insulin sensitive tissues and serum, and correlated with changes in glucose homeostasis and insulin signaling. After 8 weeks on HFD, mice developed obesity, fatty liver, inflammatory changes in adipose tissue and insulin resistance at the level of IRS-1 phosphorylation, as well as alterations in metabolomic profile of amino acid metabolites, TCA cycle intermediates, glucose and cholesterol metabolites, and fatty acids in liver, muscle, fat and serum. Doubling dietary leucine reversed many of the metabolite abnormalities and caused a marked improvement in glucose tolerance and insulin signaling without altering food intake or weight gain. Increased dietary leucine was also associated with a decrease in hepatic steatosis and a decrease in inflammation in adipose tissue. These changes occurred despite an increase in insulin-stimulated phosphorylation of p70S6 kinase indicating enhanced activation of mTOR, a phenomenon normally associated with insulin resistance. These data indicate that modest changes in a single environmental/nutrient factor can modify multiple metabolic and signaling pathways and modify HFD induced metabolic syndrome by acting at a systemic level on multiple tissues. These data also suggest that increasing dietary leucine may provide an adjunct in the management of obesity-related insulin resistance.

Early Metabolic Markers of the Development of Dysglycemia and Type 2 Diabetes and Their Physiological Significance

Metabolomic screening of fasting plasma from nondiabetic subjects identified α-hydroxybutyrate (α-HB) and linoleoyl-glycerophosphocholine (L-GPC) as joint markers of insulin resistance (IR) and glucose intolerance. To test the predictivity of α-HB and L-GPC for incident dysglycemia, α-HB and L-GPC measurements were obtained in two observational cohorts, comprising 1,261 nondiabetic participants from the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) study and 2,580 from the Botnia Prospective Study, with 3-year and 9.5-year follow-up data, respectively. In both cohorts, α-HB was a positive correlate and L-GPC a negative correlate of insulin sensitivity, with α-HB reciprocally related to indices of β-cell function derived from the oral glucose tolerance test (OGTT). In follow-up, α-HB was a positive predictor (adjusted odds ratios 1.25 [95% CI 1.00–1.60] and 1.26 [1.07–1.48], respectively, for each standard deviation of predictor), and L-GPC was a negative predictor (0.64 [0.48–0.85] and 0.67 [0.54–0.84]) of dysglycemia (RISC) or type 2 diabetes (Botnia), independent of familial diabetes, sex, age, BMI, and fasting glucose. Corresponding areas under the receiver operating characteristic curve were 0.791 (RISC) and 0.783 (Botnia), similar in accuracy when substituting α-HB and L-GPC with 2-h OGTT glucose concentrations. When their activity was examined, α-HB inhibited and L-GPC stimulated glucose-induced insulin release in INS-1e cells. α-HB and L-GPC are independent predictors of worsening glucose tolerance, physiologically consistent with a joint signature of IR and β-cell dysfunction.

Developmental Programming by Maternal Insulin Resistance: Hyperinsulinemia, Glucose Intolerance, and Dysregulated Lipid Metabolism in Male Offspring of Insulin-Resistant Mice

Maternal obesity and gestational diabetes mellitus (GDM) are associated with obesity and diabetes risk in offspring. We tested whether maternal insulin resistance, which frequently coexists with GDM and obesity, could independently contribute to dysregulation of offspring metabolism. Female mice haploinsufficient for insulin receptor substrate-1 (IRS1-het) are hyperinsulinemic and insulin resistant during pregnancy, despite normal plasma glucose and body weight, and thus serve as a model of isolated maternal insulin resistance. Wild-type (WT) offspring of IRS1-het dams insulin resistance-exposed [IR-exposed] were compared with WT offspring of WT dams. Despite no differences in adiposity, male IR-exposed pups were glucose intolerant (P = 0.04) and hyperinsulinemic (1.3-fold increase, P = 0.02) by 1 month of age and developed progressive fasting hyperglycemia. Moreover, male IR-exposed pups challenged with high-fat diet exhibited insulin resistance. Liver lipidomic analysis of 3-week-old IR-exposed males revealed increases in the 16:1n7 fraction of several lipid classes, suggesting increased Scd1 activity. By 6 months of age, IR-exposed males had increased lipid accumulation in liver as well as increased plasma refed fatty acids, consistent with disrupted lipid metabolism. Our results indicate that isolated maternal insulin resistance, even in the absence of hyperglycemia or obesity, can promote metabolic perturbations in male offspring.

Genetic variants associated with glycine metabolism and their role in insulin sensitivity and type 2 diabetes.

Circulating metabolites associated with insulin sensitivity may represent useful biomarkers, but their causal role in insulin sensitivity and diabetes is less certain. We previously identified novel metabolites correlated with insulin sensitivity measured by the hyperinsulinemic-euglycemic clamp. The top-ranking metabolites were in the glutathione and glycine biosynthesis pathways. We aimed to identify common genetic variants associated with metabolites in these pathways and test their role in insulin sensitivity and type 2 diabetes. With 1,004 nondiabetic individuals from the RISC study, we performed a genome-wide association study (GWAS) of 14 insulin sensitivity–related metabolites and one metabolite ratio. We replicated our results in the Botnia study (n = 342). We assessed the association of these variants with diabetes-related traits in GWAS meta-analyses (GENESIS [including RISC, EUGENE2, and Stanford], MAGIC, and DIAGRAM). We identified four associations with three metabolites—glycine (rs715 at CPS1), serine (rs478093 at PHGDH), and betaine (rs499368 at SLC6A12; rs17823642 at BHMT)—and one association signal with glycine-to-serine ratio (rs1107366 at ALDH1L1). There was no robust evidence for association between these variants and insulin resistance or diabetes. Genetic variants associated with genes in the glycine biosynthesis pathways do not provide consistent evidence for a role of glycine in diabetes-related traits.

A novel fasting blood test for insulin resistance and prediabetes.

Background: Insulin resistance (IR) can precede the dysglycemic states of prediabetes and type 2 diabetes mellitus (T2DM) by a number of years and is an early marker of risk for metabolic and cardiovascular disease. There is an unmet need for a simple method to measure IR that can be used for routine screening, prospective study, risk assessment, and therapeutic monitoring. We have reported several metabolites whose fasting plasma levels correlated with insulin sensitivity. These metabolites were used in the development of a novel test for IR and prediabetes. Methods: Data from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study were used in an iterative process of algorithm development to define the best combination of metabolites for predicting the M value derived from the hyperinsulinemic euglycemic clamp, the gold standard measure of IR. Subjects were divided into a training set and a test set for algorithm development and validation. The resulting calculated M score, MQ, was utilized to predict IR and the risk of progressing from normal glucose tolerance to impaired glucose tolerance (IGT) over a 3 year period. Results: MQ correlated with actual M values, with an r value of 0.66. In addition, the test detects IR and predicts 3 year IGT progression with areas under the curve of 0.79 and 0.70, respectively, outperforming other simple measures such as fasting insulin, fasting glucose, homeostatic model assessment of IR, or body mass index. Conclusions: The result, Quantose™, is a simple test for IR based on a single fasting blood sample and may have value as an early indicator of risk for the development of prediabetes and T2DM.

A Novel Insulin Resistance Index to Monitor Changes in Insulin Sensitivity and Glucose Tolerance: the ACT NOW Study

OBJECTIVE The objective was to test the clinical utility of Quantose M(Q) to monitor changes in insulin sensitivity after pioglitazone therapy in prediabetic subjects. Quantose M(Q) is derived from fasting measurements of insulin, α-hydroxybutyrate, linoleoyl-glycerophosphocholine, and oleate, three nonglucose metabolites shown to correlate with insulin-stimulated glucose disposal. RESEARCH DESIGN AND METHODS Participants were 428 of the total of 602 ACT NOW impaired glucose tolerance (IGT) subjects randomized to pioglitazone (45 mg/d) or placebo and followed for 2.4 years. At baseline and study end, fasting plasma metabolites required for determination of Quantose, glycated hemoglobin, and oral glucose tolerance test with frequent plasma insulin and glucose measurements to calculate the Matsuda index of insulin sensitivity were obtained. RESULTS Pioglitazone treatment lowered IGT conversion to diabetes (hazard ratio = 0.25; 95% confidence interval = 0.13-0.50; P < .0001). Although glycated hemoglobin did not track with insulin sensitivity, Quantose M(Q) increased in pioglitazone-treated subjects (by 1.45 [3.45] mg·min(-1)·kgwbm(-1)) (median [interquartile range]) (P < .001 vs placebo), as did the Matsuda index (by 3.05 [4.77] units; P < .0001). Quantose M(Q) correlated with the Matsuda index at baseline and change in the Matsuda index from baseline (rho, 0.85 and 0.79, respectively; P < .0001) and was progressively higher across closeout glucose tolerance status (diabetes, IGT, normal glucose tolerance). In logistic models including only anthropometric and fasting measurements, Quantose M(Q) outperformed both Matsuda and fasting insulin in predicting incident diabetes. CONCLUSIONS In IGT subjects, Quantose M(Q) parallels changes in insulin sensitivity and glucose tolerance with pioglitazone therapy. Due to its strong correlation with improved insulin sensitivity and its ease of use, Quantose M(Q) may serve as a useful clinical test to identify and monitor therapy in insulin-resistant patients.