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An integrative model of cardiometabolic traits identifies two types of metabolic syndrome.

ABSTRACT: Human diseases arise in a complex ecosystem composed of disease mechanisms and the whole-body state. However, the precise nature of the whole-body state and its relations with disease remain obscure. Here we map similarities among clinical parameters in normal physiological settings, including a large collection of metabolic, hemodynamic, and immune parameters, and then use the mapping to dissect phenotypic states. We find that the whole-body state is faithfully represented by a quantitative two-dimensional model. One component of the whole-body state represents 'metabolic syndrome' (MetS) - a conventional way to determine the cardiometabolic state. The second component is decoupled from the classical MetS, suggesting a novel 'non-classical MetS' that is characterized by dozens of parameters, including dysregulated lipoprotein parameters (e.g. low free cholesterol in small high-density lipoproteins) and attenuated cytokine responses of immune cells to ex vivo stimulations. Both components are associated with disease, but differ in their particular associations, thus opening new avenues for improved personalized diagnosis and treatment. These results provide a practical paradigm to describe whole-body states and to dissect complex disease within the ecosystem of the human body.

SUBMITTER: Frishberg A

PROVIDER: S-EPMC7906604 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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An integrative model of cardiometabolic traits identifies two types of metabolic syndrome.

Frishberg Amit A van den Munckhof Inge I Ter Horst Rob R Schraa Kiki K Joosten Leo Ab LA Rutten Joost Hw JH Iancu Adrian C AC Dregoesc Ioana M IM Tigu Bogdan A BA Netea Mihai G MG Riksen Niels P NP Gat-Viks Irit I

eLife 20210128

Human diseases arise in a complex ecosystem composed of disease mechanisms and the whole-body state. However, the precise nature of the whole-body state and its relations with disease remain obscure. Here we map similarities among clinical parameters in normal physiological settings, including a large collection of metabolic, hemodynamic, and immune parameters, and then use the mapping to dissect phenotypic states. We find that the whole-body state is faithfully represented by a quantitative two ...[more]

PMID: 33507147

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Project description:Rates of non-communicable diseases (NCDs), such as obesity, diabetes, cardiovascular events and cancer, continue to rise worldwide, which require objective instruments for preventive and management actions. Diverse anthropometric and biochemical markers have been used to qualitatively evaluate degrees of disease, metabolic traits and evolution of nutritional status. The aim of this study was to integrate and assess the interactions between an anthropometric measurement, such as waist circumference (WC), and biochemical data, such as the triglyceride glucose index (TyG), in order to individually characterize metabolic syndrome (MetS) features considering the hypertriglyceridemic waist phenotype as a marker. An ancillary cross-sectional study was conducted using anthropometric measurements, such as weight, height, waist and hip circumferences, as well as fasting biochemical data of 314 participants. Different indices based on WC (WC, WC*TG and WC*TyG) were estimated to compute MetS components and accompanying comorbidities. ROC curves were fitted to define the strength of the analyses and the validity of the relationships. Associations were confirmed between anthropometric, biochemical and combined indices with some chronic disease manifestations, including hyperglycemia, hypertension and dyslipidemia. Both WC*TG and WC*TyG indices showed similar performance in diagnosing MetS (area under the ROC curve = 0.81). Interestingly, when participants were categorized according to a reference value of the WC*TyG index (842.7 cm*mg/dl), our results evidenced that subjects classified over this limit presented statistically higher prevalence of MetS and accompanying individual components with clinical relevance for interventions. These results revealed that WC*TyG mirrors the hypertriglyceridemic phenotype, which suggests may serve as a good indicator to define the metabolic syndrome phenotype and a suitable, sensitive, and simple proxy to complement others. A reference point was proposed with a good clinical performance and maximized sensitivity and specificity values.

Project description:BackgroundMetabolic regulation plays a significant role in energy homeostasis, and adolescence is a crucial life stage for the development of cardiometabolic disease (CMD). This study aims to investigate the genetic determinants of metabolic biomarkers-adiponectin, leptin, ghrelin, and orexin-and their associations with CMD risk factors.MethodsWe characterized the genetic determinants of the biomarkers among Hispanic/Latino adolescents of the Santiago Longitudinal Study (SLS) and identified the cumulative effects of genetic variants on adiponectin and leptin using biomarker polygenic risk scores (PRS). We further investigated the direct and indirect effect of the biomarker PRS on downstream body fat percent (BF%) and glycemic traits using structural equation modeling.ResultsWe identified putatively novel genetic variants associated with the metabolic biomarkers. A substantial amount of biomarker variance was explained by SLS-specific PRS, and the prediction was improved by including the putatively novel loci. Fasting blood insulin and insulin resistance were associated with PRS for adiponectin, leptin, and ghrelin, and BF% was associated with PRS for adiponectin and leptin. We found evidence of substantial mediation of these associations by the biomarker levels.ConclusionsThe genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers.ImpactThis study characterized the genetic underpinnings of four metabolic hormones and investigated their potential influence on adiposity and insulin biology among Hispanic/Latino adolescents. Fasting blood insulin and insulin resistance were associated with polygenic risk score (PRS) for adiponectin, leptin, and ghrelin, with evidence of some degree of mediation by the biomarker levels. Body fat percent (BF%) was also associated with PRS for adiponectin and leptin. This provides important insight on biological mechanisms underlying early metabolic dysfunction and reveals candidates for prevention efforts. Our findings also highlight the importance of ancestrally diverse populations to facilitate valid studies of the genetic architecture of metabolic biomarker levels.

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An integrative model of cardiometabolic traits identifies two types of metabolic syndrome.

Publications

An integrative model of cardiometabolic traits identifies two types of metabolic syndrome.

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