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An approach to the etiology of metabolic syndrome.

ABSTRACT: Increased prevalence of obesity in the world, especially accumulation of abnormal amounts of visceral fat predisposes to insulin resistance, which is the central role of metabolic syndrome (MS). Obesity can deregulate the intracellular signaling of insulin due to the production of inflammatory substances, chemoattractant proteins, adipokines and molecules that trigger hormonal mediator potentials for destabilization of signal transduction, leading to metabolic disorders such as hyperglycemia, hypertension, and dyslipidemia. The complexity of the MS and of the genetic mechanisms involved in its etiology derives from the combination of variants on genes involved and environmental factors that predispose it. The purpose of this paper is to review the effects of obesity in molecular and biochemical responses that trigger insulin resistance and its relation to some candidate genes and the ancestral component of the population.

SUBMITTER: Munoz Contreras AM

PROVIDER: S-EPMC4002015 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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An approach to the etiology of metabolic syndrome.

Muñoz Contreras Angélica M AM Bedoya Berrío Gabriel G Velásquez R Claudia M CM

Colombia medica (Cali, Colombia) 20130101 1

Increased prevalence of obesity in the world, especially accumulation of abnormal amounts of visceral fat predisposes to insulin resistance, which is the central role of metabolic syndrome (MS). Obesity can deregulate the intracellular signaling of insulin due to the production of inflammatory substances, chemoattractant proteins, adipokines and molecules that trigger hormonal mediator potentials for destabilization of signal transduction, leading to metabolic disorders such as hyperglycemia, hy ...[more]

PMID: 24892324

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Project description:Background:The objective of this study was to utilize a human-centered approach in designing a diet app that would assist in management of patients with metabolic syndrome. Methods:A convenience sample of 10 dietetic interns (DIs) who were attending their informatics rotation at the City University of New York School of Public Health and Health Policy (CUNY GSPHHP) were recruited during October 2017. The study was conducted in two phases. In phase 1, the DIs received a tutorial on the use of human-centered approach in designing mobile health applications. In phase 2, the DIs were provided a case study for which they designed an app for patients to manage metabolic syndrome using a human centered approach. The goal of phase 2 was to identify the features that were believed to be most important when designing this metabolic syndrome app. An initial questionnaire was administered to the DIs to gather information on their socio-demographics, prior training in nutrition, smart phone usage, perceptions about food logs, and calorie intake calculations. Subsequent questionnaires gathered information from the DIs on their preferred diet app components, app features, rankings of the features, and mock representations of the diet app with the selected features. Results:The DIs were enrolled in the supervised practice component of their training to be Registered Dietitian Nutritionists (RDNs). Fifty percent of them had previously worked in a nutrition-related field. One-third of them were currently using a nutrition app. The top five features of the proposed diet app which the DIs identified as very important included (I) personalization of the app based on user preferences (80%, n=8); (II) disease specific education tips (90%, n=9); (III) ability to track progress (80%; n=8); (IV) reminders (70%, n=7) and (V) reinforcement based on user feedback (50%, n=5). In translating the identified features into functional requirements, majority of the DIs felt that the five key features identified should incorporate the following components: (I) personalization of the app should comprise information on medical factors, personal information, personal preferences, and recording weights; (II) disease specific educational tips should comprise information on food selections, low sodium options, and recipes for diabetes; (III) tracking progress should comprise features for storing audio files, viewing previous logs and uploading photos into a library; (IV) reminders should comprise daily messages to the users from the app; and (V) reinforcement should comprise provisions to enter motivational messages. Conclusions:Our study lends support to the need for utilizing human-centered design (HCD) approaches in developing e-health dietary apps as well other non-diet related apps. App developers are encouraged to incorporate user characteristics, needs and preferences using a HCD framework that will allow for reproducibility, tailoring, user satisfaction, and effectiveness assessments.

Project description:IntroductionIndividuals with metabolic syndrome (MetS) are at increasing risk of coronary artery disease (CAD). We investigated the common metabolic perturbations of CAD and MetS via serum metabolomics to provide insight into potential associations.MethodsNon-targeted serum metabolomics analyses were performed using ultra high-performance liquid chromatography coupled with Q Exactive hybrid quadrupole-orbitrap high-resolution accurate mass spectrometry (UHPLC-Q-Orbitrap HRMS) in samples from 492 participants (272 CAD vs. 121 healthy controls (HCs) as cohort 1, 55 MetS vs. 44 HCs as cohort 2). Cross-sectional data were obtained when the participants were recruited from the First Affiliated Hospital of Zhengzhou University. Multivariate statistics and Student's t test were applied to obtain the significant metabolites [with variable importance in the projection (VIP) values >1.0 and p values <0.05] for CAD and MetS. Logistic regression was performed to investigate the association of identified metabolites with clinical cardiac risk factors, and the association of significant metabolic perturbations between CAD and MetS was visualized by Cytoscape software 3.6.1. Finally, the receiver operating characteristic (ROC) analysis was evaluated for the risk prediction values of common changed metabolites.ResultsThirty metabolites were identified for CAD, mainly including amino acids, lipid, fatty acids, pseudouridine, niacinamide; 26 metabolites were identified for MetS, mainly including amino acids, lipid, fatty acids, steroid hormone, and paraxanthine. The logistic regression results showed that all of the 30 metabolites for CAD, and 15 metabolites for MetS remained significant after adjustments of clinical risk factors. In the common metabolic signature association analysis between CAD and MetS, 11 serum metabolites were significant and common to CAD and MetS outcomes. Out of this, nine followed similar trends while two had differing directionalities. The nine common metabolites exhibiting same change trend improved risk prediction for CAD (86.4%) and MetS (90.9%) using the ROC analysis.ConclusionSerum metabolomics analysis might provide a new insight into the potential mechanisms underlying the common metabolic perturbations of CAD and MetS.

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An approach to the etiology of metabolic syndrome.

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An approach to the etiology of metabolic syndrome.

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