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

Abstract

BackgroundInsulin 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.MethodsUsing 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).ResultsRandom 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.Conclusionsα–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.

A Novel Fasting Blood Test for Insulin Resistance and Prediabetes

“…23 A number of metabolites were identified whose plasma levels correlated with IR as measured by M ffm (M normalized by fat-free mass; M wbm gave similar correlations) obtained from the hyperinsulinemic euglycemic clamp. The highest correlating metabolite was α-HB.…”

“…Metabolomic studies in metabolic diseases 31 have been used to identify novel biomarkers that associate with IR 23,32,33 and predict T2DM. [34][35][36] The three metabolites used in the test have a number of potential roles in metabolic pathways relevant to insulin action, insulin secretion, or β-cell function.…”

“…α-Hydroxybutyric acid is a shunt metabolite derived from α-ketobutyrate. 23,38 High dihydronicotinamide adenine dinucleotide/ nicotinamide adenine dinucleotide (NADH/NAD+) levels, which may occur with increased fatty acid oxidation in IR, may steer the metabolic fate of α-ketobutyrate from Krebs cycle oxidation toward conversion to α-HB. α-Ketobutyrate is a product of either threonine catabolism or of methionine metabolism via the cystathionine route, which concomitantly produces cysteine as a precursor for the antioxidant molecule glutathione.…”

“…21,22 We reported a nontargeted metabolomics study using fasting plasma samples obtained from a healthy, nondiabetic population [Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) Study], searching for novel biomarkers of insulin sensitivity. 23 A number of small molecule metabolites were identified that correlated with the M values derived from the hyperinsulinemic euglycemic clamp. This work details subsequent efforts to utilize these findings to create a model for predicting the clamp M value using only data derived from a single fasting blood sample.…”

Urine Metabolite Profiles Predictive of Human Kidney Allograft Status

“…As both intermediate and end point markers of diverse biologic processes in the human body, observations of altered metabolite concentrations provide access to functionally relevant read-outs of perturbed disease-associated pathways in human metabolism. [9][10][11][12] In this investigation, we used cell-free urine supernatants collected from the kidney transplant recipients enrolled in the CTOT-04 study to conduct a large-scale study of the urine metabolome to investigate whether urine metabolite profiles are diagnostic and prognostic of ACR. We further examined the diagnostic and prognostic performance of a combination of metabolites and the previously identified urinary cell mRNA signature.…”