Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics

Metabolomic Associations with Serum Bone Turnover Markers

Bone is a dynamic tissue that is in a constant state of remodeling. Bone turnover markers (BTMs), procollagen type I N-terminal propeptide (P1NP) and C-terminal telopeptides of type I collagen (CTX), provide sensitive measures of bone formation and resorption, respectively. This study used ultra-high-resolution metabolomics (HRM) to determine plasma metabolic pathways and targeted metabolites related to the markers of bone resorption and formation in adults. This cross-sectional clinical study included 34 adults (19 females, mean 27.8 years), without reported illnesses, recruited from a US metropolitan area. Serum BTM levels were quantified by an ELISA. Plasma HRM utilized dual-column liquid chromatography and mass spectrometry to identify metabolites and metabolic pathways associated with BTMs. Metabolites significantly associated with P1NP (p < 0.05) were significantly enriched in pathways linked to the TCA cycle, pyruvate metabolism, and metabolism of B vitamins important for energy production (e.g., niacin, thiamin). Other nutrition-related metabolic pathways associated with P1NP were amino acid (proline, arginine, glutamate) and vitamin C metabolism, which are important for collagen formation. Metabolites associated with CTX levels (p < 0.05) were enriched within lipid and fatty acid beta-oxidation metabolic pathways, as well as fat-soluble micronutrient pathways including, vitamin D metabolism, vitamin E metabolism, and bile acid biosynthesis. P1NP and CTX were significantly related to microbiome-related metabolites (p < 0.05). Macronutrient-related pathways including lipid, carbohydrate, and amino acid metabolism, as well as several gut microbiome-derived metabolites were significantly related to BTMs. Future research should compare metabolism BTMs relationships reported here to aging and clinical populations to inform targeted therapeutic interventions.

Oxidative Stress: Concept and Some Practical Aspects

Oxidative stress is defined as “an imbalance between oxidants and antioxidants in favor of the oxidants, leading to a disruption of redox signaling and control and/or molecular damage”. This Commentary presents basic features of this global concept which has attracted interest in biology and medicine. The term “antioxidants” in cellular defense against oxidants predominantly includes antioxidant enzymes with their substrates and coenzymes. Exogenous low-molecular-mass compounds also have a role, but this is more limited. Multiple biomarkers of damage due to oxidative stress have been identified for different molecular classes (protein, lipid, carbohydrate, and DNA), and the current state of practical aspects in health and disease is delineated.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Macronutrient, Energy, and Bile Acid Metabolism Pathways Altered Following a Physiological Meal Challenge, Relative to Fasting, among Guatemalan Adults

BACKGROUND

The healthy human metabolome, including its physiological responses after meal consumption, remains incompletely understood. One major research gap is the limited literature assessing how human metabolomic profiles differ between fasting and postprandial states after physiological challenges.

OBJECTIVES

Our study objective was to evaluate alterations in high-resolution metabolomic profiles following a standardized meal challenge, relative to fasting, in Guatemalan adults.

METHODS

We studied 123 Guatemalan adults without obesity, hypertension, diabetes, metabolic syndrome, or comorbidities. Every participant received a standardized meal challenge (520 kcal, 67.4 g carbohydrates, 24.3 g fat, 8.0 g protein) and provided blood samples while fasting and at 2 h postprandial. Plasma samples were assayed by high-resolution metabolomics with dual-column LC [C18 (negative electrospray ionization), hydrophilic interaction LC (HILIC, positive electrospray ionization)] coupled to ultra-high-resolution MS. Associations between metabolomic features and the meal challenge timepoint were assessed in feature-by-feature multivariable linear mixed regression models. Two algorithms (mummichog, gene set enrichment analysis) were used for pathway analysis, and P values were combined by the Fisher method.

RESULTS

Among participants (62.6% male, median age 43.0 y), 1130 features (C18: 777; HILIC: 353) differed between fasting and postprandial states (all false discovery rate-adjusted q < 0.05). Based on differing C18 features, top pathways included: tricarboxylic acid cycle (TCA), primary bile acid biosynthesis, and linoleic acid metabolism (all Pcombined < 0.05). Mass spectral features included: taurine and cholic acid in primary bile acid biosynthesis; and fumaric acid, malic acid, and citric acid in the TCA. HILIC features that differed in the meal challenge reflected linoleic acid metabolism (Pcombined < 0.05).

CONCLUSIONS

Energy, macronutrient, and bile acid metabolism pathways were responsive to a standardized meal challenge in adults without cardiometabolic diseases. Our findings reflect metabolic flexibility in disease-free individuals.

Hepatic fat is a stronger correlate of key clinical and molecular abnormalities than visceral and abdominal subcutaneous fat in youth

INTRODUCTION

Body fat distribution is strongly associated with cardiometabolic disease (CMD), but the relative importance of hepatic fat as an underlying driver remains unclear. Here, we applied a systems biology approach to compare the clinical and molecular subnetworks that correlate with hepatic fat, visceral fat, and abdominal subcutaneous fat distribution.

RESEARCH DESIGN AND METHODS

This was a cross-sectional sub-study of 283 children/adolescents (7-19 years) from the Yale Pediatric NAFLD Cohort. Untargeted, high-resolution metabolomics (HRM) was performed on plasma and combined with existing clinical variables including hepatic and abdominal fat measured by MRI. Integrative network analysis was coupled with pathway enrichment analysis and multivariable linear regression (MLR) to examine which metabolites and clinical variables associated with each fat depot.

RESULTS

The data divided into four communities of correlated variables (|r|>0.15, p<0.05) after integrative network analysis. In the largest community, hepatic fat was associated with eight clinical biomarkers, including measures of insulin resistance and dyslipidemia, and 878 metabolite features that were enriched predominantly in amino acid (AA) and lipid pathways in pathway enrichment analysis (p<0.05). Key metabolites associated with hepatic fat included branched-chain AAs (valine and isoleucine/leucine), aromatic AAs (tyrosine and tryptophan), serine, glycine, alanine, and pyruvate, as well as several acylcarnitines and glycerophospholipids (all q<0.05 in MLR adjusted for covariates). The other communities detected in integrative network analysis consisted of abdominal visceral, superficial subcutaneous, and deep subcutaneous fats, but no clinical variables, fewer metabolite features (280, 312, and 74, respectively), and limited findings in pathway analysis.

CONCLUSIONS

These data-driven findings show a stronger association of hepatic fat with key CMD risk factors compared with abdominal fats. The molecular network identified using HRM that associated with hepatic fat provides insight into potential mechanisms underlying the hepatic fat-insulin resistance interface in youth.