Dataset Information

Serum metabolomic profile of incident diabetes.

ABSTRACT: AIMS/HYPOTHESIS:Metabolomic profiling offers the potential to reveal metabolic pathways relevant to the pathophysiology of diabetes and improve diabetes risk prediction. METHODS:We prospectively analysed known metabolites using an untargeted approach in serum specimens from baseline (1987-1989) and incident diabetes through to 31 December 2015 in a subset of 2939 Atherosclerosis Risk in Communities (ARIC) study participants with metabolomics data and without prevalent diabetes. RESULTS:Among the 245 named compounds identified, seven metabolites were significantly associated with incident diabetes after Bonferroni correction and covariate adjustment; these included a food additive (erythritol) and compounds involved in amino acid metabolism [isoleucine, leucine, valine, asparagine, 3-(4-hydoxyphenyl)lactate] and glucose metabolism (trehalose). Higher levels of metabolites were associated with increased risk of incident diabetes (HR per 1 SD increase in isoleucine 2.96, 95% CI 2.02, 4.35, p?=?3.18?×?10-8; HR per 1 SD increase in trehalose 1.16, 95% CI 1.09, 1.25, p?=?1.87?×?10-5), with the exception of asparagine, which was associated with a lower risk of diabetes (HR per 1 SD increase in asparagine 0.78, 95% CI 0.71, 0.85, p?=?4.19?×?10-8). The seven metabolites modestly improved prediction of incident diabetes beyond fasting glucose and established risk factors (C statistics 0.744 vs 0.735, p?=?0.001 for the difference in C statistics). CONCLUSIONS/INTERPRETATION:Branched chain amino acids may play a role in diabetes development. Our study is the first to report asparagine as a protective biomarker of diabetes risk. The serum metabolome reflects known and novel metabolic disturbances that improve prediction of diabetes.

SUBMITTER: Rebholz CM

PROVIDER: S-EPMC5878141 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Serum metabolomic profile of incident diabetes.

Rebholz Casey M CM Yu Bing B Zheng Zihe Z Chang Patrick P Tin Adrienne A Köttgen Anna A Wagenknecht Lynne E LE Coresh Josef J Boerwinkle Eric E Selvin Elizabeth E

Diabetologia 20180320 5

<h4>Aims/hypothesis</h4>Metabolomic profiling offers the potential to reveal metabolic pathways relevant to the pathophysiology of diabetes and improve diabetes risk prediction.<h4>Methods</h4>We prospectively analysed known metabolites using an untargeted approach in serum specimens from baseline (1987-1989) and incident diabetes through to 31 December 2015 in a subset of 2939 Atherosclerosis Risk in Communities (ARIC) study participants with metabolomics data and without prevalent diabetes.<h4 ...[more]

PMID: 29556673

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Project description:Novel biomarkers that more accurately reflect kidney function and predict future CKD are needed. The human metabolome is the product of multiple physiologic or pathophysiologic processes and may provide novel insight into disease etiology and progression. This study investigated whether estimated kidney function would be associated with multiple metabolites and whether selected metabolomic factors would be independent risk factors for incident CKD.In total, 1921 African Americans free of CKD with a median of 19.6 years follow-up among the Atherosclerosis Risk in Communities Study were included. A total of 204 serum metabolites quantified by untargeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry was analyzed by both linear regression for the cross-sectional associations with eGFR (specified by the Chronic Kidney Disease Epidemiology Collaboration equation) and Cox proportional hazards model for the longitudinal associations with incident CKD.Forty named and 34 unnamed metabolites were found to be associated with eGFR specified by the Chronic Kidney Disease Epidemiology Collaboration equation with creatine and 3-indoxyl sulfate showing the strongest positive (2.8 ml/min per 1.73 m(2) per +1 SD; 95% confidence interval, 2.1 to 3.5) and negative association (-14.2 ml/min per 1.73 m(2) per +1 SD; 95% confidence interval, -17.0 to -11.3), respectively. Two hundred four incident CKD events with a median follow-up time of 19.6 years were included in the survival analyses. Higher levels of 5-oxoproline (hazard ratio, 0.70; 95% confidence interval, 0.60 to 0.82) and 1,5-anhydroglucitol (hazard ratio, 0.68; 95% confidence interval, 0.58 to 0.80) were significantly related to lower risk of incident CKD, and the associations did not appreciably change when mutually adjusted.These data identify a large number of metabolites associated with kidney function as well as two metabolites that are candidate risk factors for CKD and may provide new insights into CKD biomarker identification.

Project description:BackgroundGreater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and enhance understanding of underlying mechanisms.ObjectivesUsing untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDIs) (overall PDI, provegetarian diet, healthful PDI, and unhealthful PDI) and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities study.MethodsWe calculated 4 PDIs based on participants' responses on an FFQ. We used multivariable linear regression to examine the association between 4 PDIs and 374 individual metabolites, adjusting for confounders. We used Cox proportional hazards regression to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups (n1 = 1762; n2 = 1960). We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile compared to the lower 4 quintiles of PDIs, and whether PDI- and CKD-related metabolites predicted incident CKD beyond the CKD prediction model.ResultsWe identified 82 significant PDI-metabolite associations (overall PDI = 27; provegetarian = 17; healthful PDI = 20; unhealthful PDI = 18); 11 metabolites overlapped across the overall PDI, provegetarian diet, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile as opposed to the lower 4 quintiles of PDIs compared with participant characteristics alone (range of differences in C-statistics = 0.026-0.104; P value ≤ 0.001 for all tests). Six PDI-related metabolites (glycerate, 1,5-anhydroglucitol, γ-glutamylalanine, γ-glutamylglutamate, γ-glutamylleucine, γ-glutamylvaline), involved in glycolysis, gluconeogenesis, pyruvate metabolism, and γ-glutamyl peptide metabolism, were significantly associated with incident CKD and improved prediction of incident CKD beyond the CKD prediction model (difference in C-statistics for 6 metabolites = 0.005; P value = 0.006).ConclusionsIn a community-based study of US adults, we identified metabolites that were related to plant-based diets and predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.

Project description:BACKGROUND: Presently, colorectal cancer (CRC) is staged preoperatively by radiographic tests, and postoperatively by pathological evaluation of available surgical specimens. However, present staging methods do not accurately identify occult metastases. This has a direct effect on clinical management. Early identification of metastases isolated to the liver may enable surgical resection, whereas more disseminated disease may be best treated with palliative chemotherapy. METHODS: Sera from 103 patients with colorectal adenocarcinoma treated at the same tertiary cancer center were analyzed by proton nuclear magnetic resonance (1H NMR) spectroscopy and gas chromatography-mass spectroscopy (GC-MS). Metabolic profiling was done using both supervised pattern recognition and orthogonal partial least squares-discriminant analysis (O-PLS-DA) of the most significant metabolites, which enables comparison of the whole sample spectrum between groups. The metabolomic profiles generated from each platform were compared between the following groups: locoregional CRC (N = 42); liver-only metastases (N = 45); and extrahepatic metastases (N = 25). RESULTS: The serum metabolomic profile associated with locoregional CRC was distinct from that associated with liver-only metastases, based on 1H NMR spectroscopy (P = 5.10 × 10-7) and GC-MS (P = 1.79 × 10-7). Similarly, the serum metabolomic profile differed significantly between patients with liver-only metastases and with extrahepatic metastases. The change in metabolomic profile was most markedly demonstrated on GC-MS (P = 4.75 × 10-5). CONCLUSIONS: In CRC, the serum metabolomic profile changes markedly with metastasis, and site of disease also appears to affect the pattern of circulating metabolites. This novel observation may have clinical utility in enhancing staging accuracy and selecting patients for surgical or medical management. Additional studies are required to determine the sensitivity of this approach to detect subtle or occult metastatic disease.

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Serum metabolomic profile of incident diabetes.

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Serum metabolomic profile of incident diabetes.

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