The metabolic signatures of cardiometabolic diseases: Does the shared metabotype offer new therapeutic targets?

Amin, Arwa M.

doi:10.1002/lim2.25

Cited by 14 publications

(8 citation statements)

References 250 publications

(405 reference statements)

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“… 48 Likewise, the glyoxylate and dicarboxylate metabolism is another commonly disturbed pathway found in different CVD. 49 Eventually, the metabolism of glycine, serine, and threonine has been linked with benefits in atherosclerosis, 50 being concordant with the found negative associations of glycine, serine, and threonine with incident MI. Of note, these pathways share most of the included metabolites and are characterized for being sensitive to the amino acids availability, 48 suggesting that deregulation of the matched amino acids might lead to different cardiovascular complications, including MI, and emphasizes the importance of a balanced amino acid profile.…”

Section: Discussionmentioning

confidence: 68%

Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies

Nogal,

Alkis,

Lee

et al. 2023

Cardiovascular Research

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Aims Myocardial infarction (MI) is a major cause of death and disability worldwide. Most metabolomics studies investigating metabolites predicting MI are limited by the participant number and/or the demographic diversity. We sought to identify biomarkers of incident MI in the Consortium of Metabolomics Studies (COMETS). Methods and Results We included 7,897 individuals aged on average 66 years from six intercontinental cohorts with blood metabolomic profiling (n = 1,428 metabolites, of which 168 were present in at least 3 cohorts with over 80% prevalence) and MI information (1,373 cases). We performed a two-stage Individual Patients Data meta-analysis. We first assessed the associations between circulating metabolites and incident MI for each cohort adjusting for traditional risk factors, and then performed a fixed effect inverse-variance meta-analysis to pull the results together. Finally, we conducted a pathway enrichment analysis to identify potential pathways linked to MI. On meta-analysis, 56 metabolites including 21 lipids and 17 amino acids were associated with incident MI after adjusting for multiple testing (false discovery rate, FDR < 0.05), and 10 were novel. The largest increased risk was observed for the carbohydrate mannitol/sorbitol (HR [95% CI] = 1.40[1.26-1.56], p-value < 0.001), whereas the largest decrease in risk was found for glutamine (HR [95% CI] = 0.74[0.67-0.82], p-value < 0.001). Moreover, the identified metabolites were significantly enriched (corrected p-value < 0.05) in pathways previously linked with cardiovascular diseases, including aminoacyl-tRNA biosynthesis. Conclusions In the most comprehensive metabolomics study of incident MI to date, 10 novel metabolites were associated with MI. Metabolite profiles might help to identify high-risk individuals before disease onset. Further research is needed to fully understand the mechanisms of action and elaborate pathway findings. Translational Perspective In the largest meta-analyses covering six international cohorts, we identify 10 novel and 46 known metabolites associated with incident MI, that can be used to identify at-risk individuals before disease onset. Our results improve our understanding of the molecular changes that take place in MI development and provide potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.

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Section: Discussionmentioning

confidence: 68%

Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies

Nogal,

Alkis,

Lee

et al. 2023

Cardiovascular Research

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“…CMR scores are also helpful when analyzing cardiometabolic health in children as they accumulate subtle variation in a range of risk factors that could be too little to show risk on their own in pediatric populations (84, 88). The potential application of metabolomics in identifying CMR is well established, as it provides a comprehensive insight on pathophysiologic mechanisms of diseases (30,89). However, to our knowledge, this study is the rst one to assess the effect of a DP on both metabolomics and conventional CMR scores.…”

Section: Discussionmentioning

confidence: 99%

“…Metabolomics provides measurement of all metabolites, which potentially allows more precise identi cation of dietary associations with metabolic traits and later diseases (29,30). Deelen et al, (31), using a metabolomics approach, identi ed a 14-metabolite score mainly from lipid metabolism, lipoprotein and fatty acids, that had stronger associations with mortality than a conventional risk factors score in 44.168 adults from 9 cohorts.…”

Section: Introductionmentioning

confidence: 99%

Prospective association between an obesogenic dietary pattern in early adolescence and metabolomics derived and traditional cardiometabolic risk scores in adolescents and young adults from the ALSPAC cohort

Solsona

Johnson

Northstone

et al. 2023

Preprint

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Background Dietary intake during early life may be a modifying factor for cardiometabolic risk (CMR). Metabolomic profiling may enable more precise identification of CMR in adolescence than traditional CMR scores. We aim to assess and compare the prospective associations between an obesogenic dietary pattern (DP) score at age 13 years with a novel vs. traditional CMR score in adolescence and young adulthood in the Avon Longitudinal Study of Parents and Children (ALSPAC). Methods Study participants were ALSPAC children with diet diary data at age 13. The obesogenic DP z-score, characterized by high energy-density, high % of energy from total fat and free sugars, and low in fibre density, was previously derived using reduced rank regression. CMR scores were calculated by combining novel metabolites or traditional risk factors (fat mass index, insulin resistance, mean arterial blood pressure, triacylglycerol, HDL and LDL cholesterol) at age 15 (n = 1808), 17 (n = 1629), and 24 years (n = 1760). Multivariable linear regression models estimated associations of DP z-score with log-transformed CMR z-scores. Results Compared to the lowest tertile, the highest DP z-score tertile at age 13 was associated with an increase in the metabolomics CMR z-score at age 15 (β = 0.20, 95% CI 0.09, 0.32, p trend < 0.001) and at age 17 (β = 0.22, 95% CI 0.10, 0.34, p trend < 0.001), and with the traditional CMR z-score at age 15 (β = 0.15, 95% CI 0.05, 0.24, p trend 0.020). No evidence of an association was found at age 17 for the traditional CMR z-score (β = 0.07, 95% CI -0.03, 0.16, p trend 0.137) and for both scores at age 24. Conclusions An obesogenic DP was associated with greater CMR in adolescents. Stronger associations were observed with a novel metabolite CMR score compared to traditional risk factors. There may be benefits from modifying diet during adolescence for CMR health, which should be prioritized for further research in trials.

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“…This has been employed widely for identifying biomarkers of inflammatory diseases, drug discovery, and in the study of cellular metabolic profiles. 21 − 24 Despite recent advances in determining the metabolic phenotype of various pathologies using conventional LC-MS-based techniques, 25 − 27 characterizing the metabolome of different cells that are thought to play an important role in the development of such pathologies (e.g., macrophage subsets) is still in its formative stages partly due to the need for a large number of cells (>1 million cells) to obtain adequate signal. 28 , 29 Furthermore, LC-MS-based techniques require complex and extensive sample preparation procedures including metabolic quenching and extraction of intracellular metabolites from cells using organic solvents.…”

Section: Introductionmentioning

confidence: 99%

“…Liquid chromatography–mass spectrometry (LC-MS) is a powerful tool for quantifying a range of small biomolecules and identifying known and unknown metabolites present in biological samples. This has been employed widely for identifying biomarkers of inflammatory diseases, drug discovery, and in the study of cellular metabolic profiles. − Despite recent advances in determining the metabolic phenotype of various pathologies using conventional LC-MS-based techniques, − characterizing the metabolome of different cells that are thought to play an important role in the development of such pathologies (e.g., macrophage subsets) is still in its formative stages partly due to the need for a large number of cells (>1 million cells) to obtain adequate signal. , Furthermore, LC-MS-based techniques require complex and extensive sample preparation procedures including metabolic quenching and extraction of intracellular metabolites from cells using organic solvents. These procedures that target one metabolite type can lead to incomplete sampling and biased quantification of small molecules in biological samples due to rapid turnover and loss of metabolites. ,, Moreover, cell samples are homogenized (metabolite extraction) during the sample preparation, thus losing the architecturally intact and physiologically relevant structure of the tissue and cells and eliminating the potential to correlate the spatial distribution of metabolites to morphology.…”

Section: Introductionmentioning

confidence: 99%

Single-Cell Metabolic Profiling of Macrophages Using 3D OrbiSIMS: Correlations with Phenotype

et al. 2022

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Macrophages are important immune cells that respond to environmental cues acquiring a range of activation statuses represented by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at each end of their spectrum. Characterizing the metabolic signature (metabolic profiling) of different macrophage subsets is a powerful tool to understand the response of the human immune system to different stimuli. Here, the recently developed 3D OrbiSIMS instrument is applied to yield useful insight into the metabolome from individual cells after in vitro differentiation of macrophages into naïve, M1, and M2 phenotypes using different cytokines. This analysis strategy not only requires more than 6 orders of magnitude less sample than traditional mass spectrometry approaches but also allows the study of cell-to-cell variance. Characteristic metabolites in macrophage subsets are identified using a targeted lipid and data-driven multivariate approach highlighting amino acids and other small molecules. The diamino acids alanylasparagine and lipid sphingomyelin SM(d18/16:0) are uniquely found in M1 macrophages, while pyridine and pyrimidine are observed at increased intensity in M2 macrophages, findings which link to known biological pathways. The first demonstration of this capability illustrates the great potential of direct cell analysis for in situ metabolite profiling with the 3D OrbiSIMS to probe functional phenotype at the single-cell level using molecular signatures and to understand the response of the human body to implanted devices and immune diseases.

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The metabolic signatures of cardiometabolic diseases: Does the shared metabotype offer new therapeutic targets?

Cited by 14 publications

References 250 publications

Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies

Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies

Prospective association between an obesogenic dietary pattern in early adolescence and metabolomics derived and traditional cardiometabolic risk scores in adolescents and young adults from the ALSPAC cohort

Single-Cell Metabolic Profiling of Macrophages Using 3D OrbiSIMS: Correlations with Phenotype

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