Early metabolic markers identify potential targets for the prevention of type 2 diabetes.

AIMS/HYPOTHESIS: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. METHODS: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. RESULTS: Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors. CONCLUSIONS/INTERPRETATION: This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.

Insulin resistance and normal thyroid hormone levels: prospective study and metabolomic analysis.

While hyperthyroidism and hypothyroidism cause dysglycemia, the relationship between thyroid hormone levels within the normal range and insulin resistance (IR) is unclear. In 940 participants with strictly normal serum concentrations of free triiodothyronine (fT3), free thyroxine (fT4), and thyroid-stimulating hormone (TSH) followed up for 3 yr, we measured insulin sensitivity (by the insulin clamp technique) and 35 circulating metabolites. At baseline, across quartiles of increasing fT3 levels (or fT3/fT4 ratio) most features of IR emerged [i.e., male sex, greater body mass index (BMI), waist circumference, heart rate, blood pressure, fatty liver index, free fatty acids, and triglycerides; reduced insulin-mediated glucose disposal; and ß-cell glucose sensitivity). In multiadjusted analyses, fT3 was reciprocally related to insulin sensitivity and, in a subset of 303 subjects, directly related to endogenous glucose production. In multiple regression models adjusting for sex, age, BMI, and baseline value of insulin sensitivity, higher baseline fT3 levels were significant predictors of decreases in insulin sensitivity. Moreover, baseline fT3 predicted follow-up increases in glycemia independently of sex, age, BMI, insulin sensitivity, ß-cell glucose sensitivity, and baseline glycemia. Serum tyrosine levels were higher with IR and were directly associated with fT3; higher α-hydroxybutyrate levels signaled enhanced oxidative stress, thereby impairing tyrosine degradation. In 25 patients with morbid obesity, surgery-induced weight loss improved IR and consensually lowered fT3 levels. High-normal fT3 levels are associated with IR both cross-sectionally and longitudinally, and predict deterioration of glucose tolerance. This association is supported by a metabolite pattern that points at increased oxidative stress as part of the IR syndrome.

Early-onset and classical forms of type 2 diabetes show impaired expression of genes involved in muscle branched-chain amino acids metabolism.

The molecular mechanisms responsible for the pathophysiological traits of type 2 diabetes are incompletely understood. Here we have performed transcriptomic analysis in skeletal muscle, and plasma metabolomics from subjects with classical and early-onset forms of type 2 diabetes (T2D). Focused studies were also performed in tissues from ob/ob and db/db mice. We document that T2D, both early and late onset, are characterized by reduced muscle expression of genes involved in branched-chain amino acids (BCAA) metabolism. Weighted Co-expression Networks Analysis provided support to idea that the BCAA genes are relevant in the pathophysiology of type 2 diabetes, and that mitochondrial BCAA management is impaired in skeletal muscle from T2D patients. In diabetic mice model we detected alterations in skeletal muscle proteins involved in BCAA metabolism but not in obese mice. Metabolomic analysis revealed increased levels of branched-chain keto acids (BCKA), and BCAA in plasma of T2D patients, which may result from the disruption of muscle BCAA management. Our data support the view that inhibition of genes involved in BCAA handling in skeletal muscle takes place as part of the pathophysiology of type 2 diabetes, and this occurs both in early-onset and in classical type 2 diabetes.

Prediction of Declining Renal Function and Albuminuria in Patients With Type 2 Diabetes by Metabolomics.

CONTEXT: Renal disease in type 2 diabetes mellitus (T2DM) is associated with excess morbidity/mortality. Although estimated glomerular filtration rate (eGFR) and albuminuria are routine for assessing renal impairment, novel biomarkers could improve risk stratification and prediction. OBJECTIVE: To identify specific biomarkers of progression of renal dysfunction. DESIGN: Prospective observational. SETTING: Academic diabetes clinics. PATIENTS: A total of 286 T2DM patients (age, 62 ± 8 y; glycosylated hemoglobin, 7.2 ± 0.9%; eGFR, 85 ± 20 mL · min(-1) · 1.73 m(2)). INTERVENTIONS: None. MAIN OUTCOME MEASURES: Progression of eGFR and albuminuria. RESULTS: We performed screening metabolomics in serum and urine samples by gas chromatography/mass spectroscopy (MS) and ultra-high performance liquid chromatography/MS/MS. Biomarker identification was performed by random forest using an eGFR cutoff of < 60 mL · min(-1) · 1.73 m(2) or an albumin/creatinine ratio (ACR) cutoff ≥ 30 mg/g as response variables. At follow-up, eGFR had declined by 16 [9] (median [interquartile ratio]) mL · min(-1) · 1.73 m(2), and ACR had increased by 41 [135] mg/g in patients in the respective top quartile of changes from baseline. Clinical parameters (gender, age, fasting glucose, and baseline eGFR) predicted outcome, with receiver operator characteristics curve (ROC) = 0.671. The five serum metabolites best correlated with either eGFR < 60 or ACR ≥ 30 at baseline were tested for their ability to improve clinical prediction. The sum of C-glycosyl tryptophan, pseudouridine, and N-acetylthreonine (MetIndex) raised the ROC to 0.739 (P < .0001). eGFR decline was predicted by the top MetIndex quartile (odds ratio = 5.48 [95% confidence interval, 2.23-14.47]). MetIndex also predicted an ACR increase with an odds ratio of 2.82 [1.20-7.03] and a ROC of 0.750. Top urine metabolites did not add significant predictivity. CONCLUSIONS: A limited number of circulating intermediates of amino acid and nucleotide pathways carry clinically significant predictivity for deterioration of renal function in well-controlled T2DM.

α-Hydroxybutyric Acid Is a Selective Metabolite Biomarker of Impaired Glucose Tolerance.

OBJECTIVE: Plasma metabolites that distinguish isolated impaired glucose tolerance (iIGT) from isolated impaired fasting glucose (iIFG) may be useful biomarkers to predict IGT, a high-risk state for the development of type 2 diabetes. RESEARCH DESIGN AND METHODS: Targeted metabolomics with 23 metabolites previously associated with dysglycemia was performed with fasting plasma samples from subjects without diabetes at time 0 of an oral glucose tolerance test (OGTT) in two observational cohorts: RISC (Relationship Between Insulin Sensitivity and Cardiovascular Disease) and DMVhi (Diabetes Mellitus and Vascular Health Initiative). Odds ratios (ORs) for a one-SD change in the metabolite level were calculated using multiple logistic regression models controlling for age, sex, and BMI to test for associations with iIGT or iIFG versus normal. Selective biomarkers of iIGT were further validated in the Botnia study. RESULTS: α-Hydroxybutyric acid (α-HB) was most strongly associated with iIGT in RISC (OR 2.54 [95% CI 1.86-3.48], P value 5E-9) and DMVhi (2.75 [1.81-4.19], 4E-5) while having no significant association with iIFG. In Botnia, α-HB was selectively associated with iIGT (2.03 [1.65-2.49], 3E-11) and had no significant association with iIFG. Linoleoyl-glycerophosphocholine (L-GPC) and oleic acid were also found to be selective biomarkers of iIGT. In multivariate IGT prediction models, addition of α-HB, L-GPC, and oleic acid to age, sex, BMI, and fasting glucose significantly improved area under the curve in all three cohorts. CONCLUSIONS: α-HB, L-GPC, and oleic acid were shown to be selective biomarkers of iIGT, independent of age, sex, BMI, and fasting glucose, in 4,053 subjects without diabetes from three European cohorts. These biomarkers can be used in predictive models to identify subjects with IGT without performing an OGTT.

A novel test for IGT utilizing metabolite markers of glucose tolerance.

The oral glucose tolerance test (OGTT) is the only method to diagnose patients having impaired glucose tolerance (IGT), but its use has diminished considerably in recent years. Metabolomic profiling studies have identified a number of metabolites whose fasting levels are associated with dysglycemia and type 2 diabetes. These metabolites may serve as the basis of an alternative test for IGT. Using the stable isotope dilution technique, quantitative assays were developed for 23 candidate biomarker metabolites. These metabolites were measured in fasting plasma samples taken just prior to an OGTT from 1623 nondiabetic subjects: 955 from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study (RISC Study; 11.7% IGT) and 668 subjects from the Diabetes Mellitus and Vascular Health Initiative (DMVhi) cohort from the DEXLIFE project (11.8% IGT). The associations between metabolites, anthropometric, and metabolic parameters and 2hPG values were assessed by Pearson correlation coefficients and Random Forest classification analysis to rank variables for their ability to distinguish IGT from normal glucose tolerance (NGT). Multivariate logistic regression models for estimating risk of IGT were developed and evaluated using AUCs calculated from the corresponding ROC curves. A model based on the fasting plasma levels of glucose, α-hydroxybutyric acid, ß-hydroxybutyric acid, 4-methyl-2-oxopentanoic acid, linoleoylglycerophosphocholine, oleic acid, serine and vitamin B5 was optimized in the RISC cohort (AUC = 0.82) and validated in the DMVhi cohort (AUC = 0.83). A novel, all-metabolite-based test is shown to be a discriminate marker of IGT. It requires only a single fasted blood draw and may serve as a more convenient surrogate for the OGTT or as a means of identifying subjects likely to be IGT.

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.

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, M(Q), 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: M(Q) 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(TM), 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.

Functional consequences of cysteine modification in the ligand binding sites of peroxisome proliferator activated receptors by GW9662.

In the course of a high throughput screen to search for ligands of peroxisome proliferator activated receptor-gamma (PPARgamma), we identified GW9662 using a competition binding assay against the human ligand binding domain. GW9662 had nanomolar IC(50) versus PPARgamma and was 10- and 600-fold less potent in binding experiments using PPARalpha and PPARdelta, respectively. Pretreatment of all three PPARs with GW9662 resulted in the irreversible loss of ligand binding as assessed by scintillation proximity assay. Incubation of PPAR with GW9662 resulted in a change in the absorbance spectra of the receptors consistent with covalent modification. Mass spectrometric analysis of the PPARgamma ligand binding domain treated with GW9662 established Cys(285) as the site of covalent modification. This cysteine is conserved among all three PPARs. In cell-based reporter assays, GW9662 was a potent and selective antagonist of full-length PPARgamma. The functional activity of GW9662 as an antagonist of PPARgamma was confirmed in an assay of adipocyte differentiation. GW9662 showed essentially no effect on transcription when tested using both full-length PPARdelta and PPARalpha. Time-resolved fluorescence assays of ligand-modulated receptor heterodimerization, coactivator binding, and corepressor binding were consistent with the effects observed in the reporter gene assays. Control activators increased PPAR:RXR heterodimer formation and coactivator binding to both PPARgamma and PPARdelta. Corepressor binding was decreased. In the case of PPARalpha, GW9662 treatment did not significantly increase heterodimerization and coactivator binding or decrease corepressor binding. The experimental data indicate that GW9662 modification of each of the three PPARs results in different functional consequences. The selective and irreversible nature of GW9662 treatment, and the observation that activity is maintained in cell culture experiments, suggests that this compound may be a useful tool for elucidation of the role of PPARgamma in biological processes.