Replicated evidence for aminoacylase 3 and nephrin gene variations to predict antihypertensive drug responses

Peer reviewe

Metabolite Profiles of the Serum of Patients with Non–Small Cell Carcinoma

AbstractIntroductionAlterations of serum metabolites may allow us to identify individuals with lung cancer and advance our understanding of the nature and treatment of their cancer. We aimed to identify serum metabolites that differentiate patients with lung cancer from at-risk controls.MethodsSerum samples from patients with biopsy-confirmed untreated stage I through stage III non–small cell lung cancer and at-risk controls were divided into fractions for analysis by ultrahigh-performance liquid chromatography–tandem mass spectrometry and gas chromatography–mass spectrometry. Compounds were identified by comparison with library entries of purified standards. Differences in concentrations of single metabolites and metabolite ratios were identified. Prediction models were developed.ResultsSerum samples from 284 subjects was analyzed. The subjects' mean age was 67 and 48% were female. Ninety-four patients had lung cancer (50 had adenocarcinoma and 44 had squamous cell carcinoma), 44% had stage I disease, 17% had stage II disease, and 39% had stage III disease. The patients with cancer were slightly older than the controls (68.7 versus 66.2 years, p = 0.013). A total of 534 metabolites were identified in eight metabolite superpathways and 73 subpathways. The concentrations of 149 metabolites differed significantly (q values <0.05) between the cancer and control groups (70 were lower in the cancer group and 79 were higher), and 9723 metabolite ratios differed significantly (q values <0.001) between the cancer and control groups. The accuracies of the models (cancer and cancer subtypes versus control) trained on 70% of the subjects and tested on 30% (expressed as C-statistics) ranged from 0.748 to 0.858.ConclusionsDifferences in the serum metabolite profile exist between patients with stage I through stage III non–small cell lung cancer and matched controls

Metabolome Changes during In&nbsp;Vivo Red Cell Aging Reveal Disruption of Key Metabolic Pathways

Understanding the mechanisms for cellular aging is a fundamental question in biology. Normal red blood cells (RBCs) survive for approximately 100&nbsp;days, and their survival is likely limited by functional decline secondary to cumulative damage to cell constituents, which may be reflected in altered metabolic capabilities. To investigate metabolic changes during in&nbsp;vivo RBC aging, labeled cell populations were purified at intervals and assessed for abundance of metabolic intermediates using mass spectrometry. A total of 167 metabolites were profiled and quantified from cell populations of defined ages. Older RBCs maintained ATP and redox charge states at the cost of altered activity of enzymatic pathways. Time-dependent changes were identified in metabolites related to maintenance of the redox state and membrane structure. These findings illuminate the differential metabolic pathway usage associated with normal cellular aging and identify potential biomarkers to determine average RBC age and rates of RBC turnover from a single blood sample

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 (h2 = 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.</jats:p