Novel Metabolic Markers for the Risk of Diabetes Development in American Indians

OBJECTIVETo identify novel metabolic markers for diabetes development in American Indians.RESEARCH DESIGN AND METHODSUsing an untargeted high-resolution liquid chromatography–mass spectrometry, we conducted metabolomics analysis of study participants who developed incident diabetes (n = 133) and those who did not (n = 298) from 2,117 normoglycemic American Indians followed for an average of 5.5 years in the Strong Heart Family Study. Relative abundances of metabolites were quantified in baseline fasting plasma of all 431 participants. Prospective association of each metabolite with risk of developing type 2 diabetes (T2D) was examined using logistic regression adjusting for established diabetes risk factors.RESULTSSeven metabolites (five known and two unknown) significantly predict the risk of T2D. Notably, one metabolite matching 2-hydroxybiphenyl was significantly associated with an increased risk of diabetes, whereas four metabolites matching PC (22:6/20:4), (3S)-7-hydroxy-2′,3′,4′,5′,8-pentamethoxyisoflavan, or tetrapeptides were significantly associated with decreased risk of diabetes. A multimarker score comprising all seven metabolites significantly improved risk prediction beyond established diabetes risk factors including BMI, fasting glucose, and insulin resistance.CONCLUSIONSThe findings suggest that these newly detected metabolites may represent novel prognostic markers of T2D in American Indians, a group suffering from a disproportionately high rate of T2D

Metabolic Profiles of Obesity in American Indians: The Strong Heart Family Study

The authors would like to thank the Strong Heart Study participants, Indian Health Service facilities, and participating tribal communities for their extraordinary cooperation and involvement, which has contributed to the success of the Strong Heart Study. The views expressed in this article are those of the authors and do not necessarily reflect those of the Indian Health Service.Obesity is a typical metabolic disorder resulting from the imbalance between energy intake and expenditure. American Indians suffer disproportionately high rates of obesity and diabetes. The goal of this study is to identify metabolic profiles of obesity in 431 normoglycemic American Indians participating in the Strong Heart Family Study. Using an untargeted liquid chromatography–mass spectrometry, we detected 1,364 distinct m/z features matched to known compounds in the current metabolomics databases. We conducted multivariate analysis to identify metabolic profiles for obesity, adjusting for standard obesity indicators. After adjusting for covariates and multiple testing, five metabolites were associated with body mass index and seven were associated with waist circumference. Of them, three were associated with both. Majority of the obesity-related metabolites belongs to lipids, e.g., fatty amides, sphingolipids, prenol lipids, and steroid derivatives. Other identified metabolites are amino acids or peptides. Of the nine identified metabolites, five metabolites (oleoylethanolamide, mannosyl-diinositol-phosphorylceramide, pristanic acid, glutamate, and kynurenine) have been previously implicated in obesity or its related pathways. Future studies are warranted to replicate these findings in larger populations or other ethnic groups.Yeshttp://www.plosone.org/static/editorial#pee

Plasma Metabolomics in Human Pulmonary Tuberculosis Disease: A Pilot Study

We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q<0.05. Two-way hierarchical cluster analysis (HCA) was used to identify clusters of ions contributing to separation of cases and controls, and metabolomics databases were used to match these ions to known metabolites. Identity of specific D-series resolvins, glutamate and Mycobacterium tuberculosis (Mtb)-derived trehalose-6-mycolate was confirmed using LC-MS/MS analysis. Over 23,000 metabolites were detected in untargeted metabolomic analysis and 61 metabolites were significantly different between the two groups. HCA revealed 8 metabolite clusters containing metabolites largely upregulated in patients with TB disease, including anti-TB drugs, glutamate, choline derivatives, Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol) and pro-resolving lipid mediators of inflammation, known to stimulate resolution, efferocytosis and microbial killing. The resolvins were confirmed to be RvD1, aspirin-triggered RvD1, and RvD2. This study shows that high-resolution metabolomic analysis can differentiate patients with active TB disease from their asymptomatic household contacts. Specific metabolites upregulated in the plasma of patients with active TB disease, including Mtb-derived glycolipids and resolvins, have potential as biomarkers and may reveal pathways involved in TB disease pathogenesis and resolution

Novel Metabolic Markers for the Risk of Diabetes Development in American Indians

OBJECTIVE: To identify novel metabolic markers for diabetes development in American Indians. RESEARCH DESIGN AND METHODS: Using an untargeted high-resolution liquid chromatography–mass spectrometry, we conducted metabolomics analysis of study participants who developed incident diabetes (n = 133) and those who did not (n = 298) from 2,117 normoglycemic American Indians followed for an average of 5.5 years in the Strong Heart Family Study. Relative abundances of metabolites were quantified in baseline fasting plasma of all 431 participants. Prospective association of each metabolite with risk of developing type 2 diabetes (T2D) was examined using logistic regression adjusting for established diabetes risk factors. RESULTS: Seven metabolites (five known and two unknown) significantly predict the risk of T2D. Notably, one metabolite matching 2-hydroxybiphenyl was significantly associated with an increased risk of diabetes, whereas four metabolites matching PC (22:6/20:4), (3S)-7-hydroxy-2′,3′,4′,5′,8-pentamethoxyisoflavan, or tetrapeptides were significantly associated with decreased risk of diabetes. A multimarker score comprising all seven metabolites significantly improved risk prediction beyond established diabetes risk factors including BMI, fasting glucose, and insulin resistance. CONCLUSIONS: The findings suggest that these newly detected metabolites may represent novel prognostic markers of T2D in American Indians, a group suffering from a disproportionately high rate of T2D

Clinical characteristics of the study participants according to obesity (N = 431).

<p>Clinical characteristics of the study participants according to obesity (N = 431).</p

Metabolites associated with body mass index in American Indians.

<p>Metabolites associated with body mass index in American Indians.</p

Separation of individuals with normal body weight versus those with abdominal obesity by a multi-metabolites score comprising of all metabolites significantly associated with waist circumference using sparse partial least-squares discriminant analysis.

<p>Separation of individuals with normal body weight versus those with abdominal obesity by a multi-metabolites score comprising of all metabolites significantly associated with waist circumference using sparse partial least-squares discriminant analysis.</p

High-resolution plasma metabolomics analysis to detect Mycobacterium tuberculosis-associated metabolites that distinguish active pulmonary tuberculosis in humans.

INTRODUCTION:Pulmonary tuberculosis (TB) is a major worldwide health problem that lacks robust blood-based biomarkers for detection of active disease. High-resolution metabolomics (HRM) is an innovative method to discover low-abundance metabolites as putative blood biomarkers to detect TB disease, including those known to be produced by the causative organism, Mycobacterium tuberculosis (Mtb). METHODS:We used HRM profiling to measure the plasma metabolome for 17 adults with active pulmonary TB disease and 16 of their household contacts without active TB. We used a suspect screening approach to identify metabolites previously described in cell culture studies of Mtb based on retention time and accurate mass matches. RESULTS:The association of relative metabolite abundance in active TB disease subjects compared to their household contacts predicted three Mtb-associated metabolites that were significantly increased in the active TB patients based on accurate mass matches: phosphatidylglycerol (PG) (16:0_18:1), lysophosphatidylinositol (Lyso-PI) (18:0) and acylphosphatidylinositol mannoside (Ac1PIM1) (56:1) (p<0.001 for all). These three metabolites provided excellent classification accuracy for active TB disease (AUC = 0.97). Ion dissociation spectra (tandem MS/MS) supported the identification of PG (16:0_18:1) and Lyso-PI (18:0) in the plasma of patients with active TB disease, though the identity of Ac1PIM1 could not be definitively confirmed. CONCLUSIONS:Presence of the Mtb-associated lipid metabolites PG (16:0_18:1) and Lyso-PI (18:0) in plasma accurately identified patients with active TB disease. Consistency of in vitro and in vivo data suggests suitability for exploring these in future studies for possible development as TB disease biomarkers

Metabolome-Wide Association Study of Primary Open Angle Glaucoma

PURPOSE. To determine if primary open-angle glaucoma (POAG) patients can be differentiated from controls based on metabolic characteristics. METHODS. We used ultra-high resolution mass spectrometry with C18 liquid chromatography for metabolomic analysis on frozen plasma samples from 72 POAG patients and 72 controls. Metabolome-wide Spearman correlation was performed to select differentially expressed metabolites (DEM) correlated with POAG. We corrected P values for multiple testing using Benjamini and Hochberg false discovery rate (FDR). Hierarchical cluster analysis (HCA) was used to depict the relationship between participants and DEM. Differentially expressed metabolites were matched to the METLIN metabolomics database; both DEM and metabolites significantly correlating with DEM were analyzed using MetaboAnalyst to identify metabolic pathways altered in POAG. RESULTS. Of the 2440 m/z (mass/charge) features recovered after filtering, 41 differed between POAG cases and controls at FDR ¼ 0.05. Hierarchical cluster analysis revealed these DEM to associate into eight clusters; three of these clusters contained the majority of the DEM and included palmitoylcarnitine, hydroxyergocalciferol, and high-resolution METLIN matches to sphingolipids, other vitamin D-related metabolites, and terpenes. MetaboAnalyst also indicated likely alteration in steroid biosynthesis pathways. CONCLUSIONS. Global ultrahigh resolution metabolomics emphasized the importance of altered lipid metabolism in POAG. The results suggest specific metabolic processes, such as those involving palmitoylcarnitine, sphingolipids, vitamin D-related compounds, and steroid precursors, may contribute to POAG status and merit more detailed study with targeted methods