Metabolite Profile of Treatment-Naive Metabolic Syndrome Subjects in Relation to Cardiovascular Disease Risk

Warmbrunn, Moritz V.; Koopen, Annefleur M.; Clercq, Nicolien C. de; Groot, Pieter F. de; Kootte, Ruud S.; Bouter, Kristien E.; Horst, Kasper W. ter; Hartstra, Annick V.; Serlie, Mireille J.; Ackermans, Mariëtte T.; Soeters, Maarten R.; Raalte, Daniël H. van; Davids, Mark; Nieuwdorp, Max; Groen, Albert K.

doi:10.3390/metabo11040236

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…Insulin resistance was defined as an insulin-stimulated glucose disappearance rate (Rd) < 37.3 μmol kg −1 min −1 , as previously described [ 19 ]. As there is currently no standard definition for insulin resistance defined by clamping, we used this cutoff, which may be arbitrary but has recently been used in similar studies [ 17 , 20 , 21 ]. No clamp data were available in the replication cohort, and therefore we defined IFG as fasting glucose ≥ 5.6 mmol/L, which is a frequently described measure for IFG [ 13 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

Novel Proteome Targets Marking Insulin Resistance in Metabolic Syndrome

Warmbrunn,

Bahrar,

de Clercq

et al. 2024

Nutrients

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Context/Objective: In order to better understand which metabolic differences are related to insulin resistance in metabolic syndrome (MetSyn), we used hyperinsulinemic–euglycemic (HE) clamps in individuals with MetSyn and related peripheral insulin resistance to circulating biomarkers. Design/Methods: In this cross-sectional study, HE-clamps were performed in treatment-naive men (n = 97) with MetSyn. Subjects were defined as insulin-resistant based on the rate of disappearance (Rd). Machine learning models and conventional statistics were used to identify biomarkers of insulin resistance. Findings were replicated in a cohort with n = 282 obese men and women with (n = 156) and without (n = 126) MetSyn. In addition to this, the relation between biomarkers and adipose tissue was assessed by nuclear magnetic resonance imaging. Results: Peripheral insulin resistance is marked by changes in proteins related to inflammatory processes such as IL-1 and TNF-receptor and superfamily members. These proteins can distinguish between insulin-resistant and insulin-sensitive individuals (AUC = 0.72 ± 0.10) with MetSyn. These proteins were also associated with IFG, liver fat (rho 0.36, p = 1.79 × 10−9) and visceral adipose tissue (rho = 0.35, p = 6.80 × 10−9). Interestingly, these proteins had the strongest association in the MetSyn subgroup compared to individuals without MetSyn. Conclusions: MetSyn associated with insulin resistance is characterized by protein changes related to body fat content, insulin signaling and pro-inflammatory processes. These findings provide novel targets for intervention studies and should be the focus of future in vitro and in vivo studies.

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Section: Methodsmentioning

confidence: 99%

Novel Proteome Targets Marking Insulin Resistance in Metabolic Syndrome

Warmbrunn,

Bahrar,

de Clercq

et al. 2024

Nutrients

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“…Mainly, these molecules were intertwined with the metabolism of glucose, amino acid, and lipid [49]. A distinct metabolite profile was further linked to cardiovascular risk, including androgen, fatty acid, phosphatidylethanolamine, and phophatidylcholine [39]. Polyphenol was proposed in recent decades to be both effective in antioxidation and essential in the maintenance of gut microbiota homeostasis [50].…”

Section: Metabolomicsmentioning

confidence: 99%

“…In recent years, a view that has been gaining attention is that gut disarray is a potential cause of MetS. Although there is argument that the metabolites related to insulin resistance and those associated with cardiovascular prognosis bore little resemblance [39], the effect exerted by altered intestinal microbiomes in response to exogenous stimuli still impacts the host [66]. Emerging evidence also suggests a role in the pathogenesis of MetS.…”

Section: Microbiota and Inflammationmentioning

confidence: 99%

Recent Progress in Metabolic Syndrome Research and Therapeutics

Kao

Huang

2021

IJMS

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Metabolic syndrome (MetS) is a well-defined yet difficult-to-manage disease entity. Both the precipitous rise in its incidence due to contemporary lifestyles and the growing heterogeneity among affected populations present unprecedented challenges. Moreover, the predisposed risk for developing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in populations with MetS, and the viral impacts on host metabolic parameters, underscores the need to investigate this mechanism thoroughly. Recent investigations of metabolomics and proteomics have revealed not only differentially expressed substances in MetS, but also the consequences of diet consumption and physical activity on energy metabolism. These variations in metabolites, as well as protein products, also influence a wide spectrum of host characteristics, from cellular behavior to phenotype. Research on the dysregulation of gut microbiota and the resultant inflammatory status has also contributed to our understanding of the underlying pathogenic mechanisms. As for state-of-the-art therapies, advancing depictions of the bio-molecular landscape of MetS have emerged and now play a key role in individualized precision medicine. Fecal microbiota transplantation, aiming to restore the host’s homeostasis, and targeting of the bile acid signaling pathway are two approaches to combatting MetS. Comprehensive molecular inquiries about MetS by omics measures are mandatory to facilitate the development of novel therapeutic modalities.

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“…A few metabolomic studies on MetS based on untargeted, targeted metabolomic and lipidomic approaches have been reported [12][13][14][15]. With the exception of one recent study analyzing about 200 cases with MetS, previous studies investigated relative smaller sample sizes (e.g., 30 patients), or only 100 men in a study that used a targeted metabolomic approach [12,14,15].…”

Section: Introductionmentioning

confidence: 99%

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts

Shi

Han

Klier

et al. 2023

Preprint

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Background Metabolic syndrome (MetS) consists of risk factors (abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL–C), hypertension, hyperglycemia) for cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its risk factors to better understand the complex interplay of underlying signaling pathways. Methods We quantified serum samples of the KORA F4 study participants (N = 2,815) and analyzed 121 metabolites. Using multiple regression models adjusted for clinical and lifestyle covariates, we examined metabolites that have a Bonferroni significant MetS association, and replicated them in the SHIP-TREND-0 study (N = 988), and further analyzed for each of the five components of MetS. Database-based networks of the identified metabolites with interacting enzymes were also constructed. Results We identified and replicated 56 MetS-specific metabolites: 13 positively associated (e.g., Val, Leu/Ile, Phe and Tyr, sum of hexoses, 2 carnitines, and 6 lipids), and 43 negatively associated (e.g., Gly, Ser, and 40 lipids). Furthermore, most (89%) and least (23%) of the MetS-specific metabolites were separately associated with low HDL–C and hypertension among the components. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of the five components, indicating patients with MetS and each of the risk factors had lowered concentrations of lysoPC a C18:2 compared to corresponding healthy controls. Our metabolic networks clarified our observations by revealing impaired catabolisms of branched-chain and aromatic amino acids, as well as higher rates of Gly catabolism. Conclusion Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors and could help develop therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For example, higher levels of lysoPC a C18:2 may provide protection against MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

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Metabolite Profile of Treatment-Naive Metabolic Syndrome Subjects in Relation to Cardiovascular Disease Risk

Cited by 7 publications

References 43 publications

Novel Proteome Targets Marking Insulin Resistance in Metabolic Syndrome

Novel Proteome Targets Marking Insulin Resistance in Metabolic Syndrome

Recent Progress in Metabolic Syndrome Research and Therapeutics

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts

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