Plasma metabolomic biomarker panel to distinguish patients with amyotrophic lateral sclerosis from disease mimics.

Our objective was to identify plasma biomarkers of ALS that can aid in distinguishing patients with ALS from those with disease mimics. In this multi-center study, plasma samples were collected from 172 patients recently diagnosed with ALS, 50 healthy controls, and 73 neurological disease mimics. Samples were analyzed using metabolomics. Using all identified biochemicals detected in > 50% of all samples in the metabolomics analysis, samples were classified as ALS or mimic with 65% sensitivity and 81% specificity by LASSO analysis (AUC of 0.76). A subset panel of 32 candidate biomarkers classified these diagnosis groups with a specificity of 90%/sensitivity 58% (AUC of 0.81). Creatinine was lower in subjects with lower revised ALS Functional Rating Scale (ALSFRS-R) scores. In conclusion, ALS can be distinguished from neurological disease mimics by global biochemical profiling of plasma samples. Our analysis identified ALS versus mimics with relatively high sensitivity. We identified a subset of 32 metabolites that identify patients with ALS with a high specificity. Interestingly, lower creatinine correlates significantly with a lower ALSFRS-R score. Finally, molecules previously reported to be important in disease pathophysiology, such as urate, are included in our metabolite panel.

Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach.

Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39-1.95], P = 8.46 × 10(-9)) and was moderately heritable (h(2) = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34-2.11], P = 6.52 × 10(-6)) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27-2.75], P = 1 × 10(-3)). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.

Metabolomics as a key integrator for "omic" advancement of personalized medicine and future therapies.

Investigation into biological complexity, whether for a better understanding of disease or drug process, is a monumental task plaguing investigators. The lure of "omic" technologies for circumventing much of these challenges has led to widespread efforts and adoption. It is becoming clearer that a single "omic" approach (e.g., genomics) is often insufficient for completely defining the complexity in these biological systems. Hence, there is an increasing awareness that a "systems" approach will serve to increase resolution and confidence and provide a strong foundation for further hypothesis-driven investigation. Although certain metabolites are already considered clinically important, the profiling of metabolites via metabolomics (the profiling of metabolites to fully characterize metabolic pathways) is the most recent to mature of these "omic" technologies and has been only recently adopted as compared to genomic or proteomic approaches in systems inquiries. Recent reports suggest that this "omic" may well be a key data stream in systems investigations for endeavors in personalized medicine and biomarker identification, as it seems most closely relevant to the phenotype.

Plasma metabolomic profile in nonalcoholic fatty liver disease.

The plasma profile of subjects with nonalcoholic fatty liver disease (NAFLD), steatosis, and steatohepatitis (NASH) was examined using an untargeted global metabolomic analysis to identify specific disease-related patterns and to identify potential noninvasive biomarkers. Plasma samples were obtained after an overnight fast from histologically confirmed nondiabetic subjects with hepatic steatosis (n = 11) or NASH (n = 24) and were compared with healthy, age- and sex-matched controls (n = 25). Subjects with NAFLD were obese, were insulin resistant, and had higher plasma concentrations of homocysteine and total cysteine and lower plasma concentrations of total glutathione. Metabolomic analysis showed markedly higher levels of glycocholate, taurocholate, and glycochenodeoxycholate in subjects with NAFLD. Plasma concentrations of long-chain fatty acids were lower and concentrations of free carnitine, butyrylcarnitine, and methylbutyrylcarnitine were higher in NASH. Several glutamyl dipeptides were higher whereas cysteine-glutathione levels were lower in NASH and steatosis. Other changes included higher branched-chain amino acids, phosphocholine, carbohydrates (glucose, mannose), lactate, pyruvate, and several unknown metabolites. Random forest analysis and recursive partitioning of the metabolomic data could separate healthy subjects from NAFLD with an error rate of approximately 8% and separate NASH from healthy controls with an error rate of 4%. Hepatic steatosis and steatohepatitis could not be separated using the metabolomic profile. Plasma metabolomic analysis revealed marked changes in bile salts and in biochemicals related to glutathione in subjects with NAFLD. Statistical analysis identified a panel of biomarkers that could effectively separate healthy controls from NAFLD and healthy controls from NASH. These biomarkers can potentially be used to follow response to therapeutic interventions.