The therapeutic modulation of atherogenic dyslipidemia and inflammatory markers in the metabolic syndrome: What is the clinical relevance?

Department of Clinical Medicine and Emerging Diseases, University of Palermo, Palermo, Italy.
Acta Diabetologica (Impact Factor: 2.4). 11/2008; 46(1):1-11. DOI: 10.1007/s00592-008-0057-4
Source: PubMed


The metabolic syndrome consists of a constellation of clinical and biochemical risk factors that cluster together and heighten the risk for atherogenesis, cardiovascular diseases, and diabetes. Established risk cardiovascular factors like hypertension, atherogenic dyslipidaemia, and glucose intolerance occur in the setting of insulin resistance and central adiposity, with genetic and environmental influences modulating the ultimate risk. Chronic insults to the endothelium take its toll in the form of silent as well as clinically evident cardiovascular events. The cellular and vascular accompaniments have shed some light into the underlying pathophysiology. Heightened, low-grade inflammatory processes as well as a continuum of vascular insults ranging from early endothelial derangements to advanced atherosclerosis have been examined. In recent years there has been an explosion of basic and clinical knowledge related to the metabolic syndrome. Although dyslipidaemia is considered a traditional risk component for the syndrome, its qualitative aspects, genetically determined subfractions, and variation in proatherogenic tendency have generated renewed interest and debate. New targets within the dyslipidaemic spectrum that have differing clinical relevance are being evaluated. The effect of heredity, lifestyle changes, pharmacotherapeutic agents, and supplements is being investigated. Further research into the impact of dyslipidemia and inflammation as both pathophysiologic risk factors and objects for targeted therapy in the metabolic syndrome should deepen our understanding and unravel answers to the underlying dynamics in this global epidemic.

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    • "This economi‐ cally important disease is an outcome of both genetic and environmental risk factors and their interactions [2]. Inflammatory and metabolic derangements and their synergy are the main causes in the etiopathogenesis of atherosclerosis [3]. However, all aspects of atherosclerosis are not known at the present time, and more human and animal studies are requested to decode the black box of atherosclerosis. "
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    ABSTRACT: Dietary cholesterol has been suggested as a cause of dyslipidemic atherosclerosis with scarce convincing evidence. A systematic review and a meta-analysis were conducted in MEDLINE (2004–2015) to screen randomized controlled trials (RCTs) that used cho‐ lesterol-fed rabbits as a model of atherosclerosis. A total of 32 RCTs (n = 1104 New Zealand rabbits; 4.37 ± 2.52 months old) reported lipid and lipoprotein outcomes fol‐ lowing cholesterol intake (0.98 ± 0.67%) for a duration of 8.90 ± 7.26 weeks. Cholester‐ ol intakes significantly raised combined lipid and lipoprotein outcomes (standardized mean difference) in a random-effect model by 5.618 (95% CI: 4.592, 6.644; P = 0.0001). The value of I 2 , heterogeneity, was 89.387%, indicating real variation. A subgroup analysis based on the duration and amount of cholesterol feeding in a mixed-effects analysis showed combined heterogeneous effects of 2.788 (95% CI: 2.333, 3.244; P = 0.000; Q = 112.206; df = 14) and 5.538 (95% CI: 4.613, 6.463; P = 0.000; Q = 31.622; df = 6), respectively. Random-effect meta-regression conducted using cholesterol moderator did not support causal effects of dietary cholesterol in inducing atherosclerosis, which may be due to significant publication bias. These high levels of heterogeneity among studies may decline fidelity of this animal model for translation of dyslipidemic athe‐ rosclerosis.
    Full-text · Chapter · Jan 2015
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    • "Although dyslipidemia is considered as a traditional risk component for the metabolic syndrome, its qualitative aspects, genetically determined subfractions and variation in proatherogenic tendency have generated renewed interest and debate [1]. Different cholesterol concentrations were reported in diabetes caused by single-gene mutations in children and young adults [2, 3]. "
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    ABSTRACT: Patients with diabetes caused by single-gene mutations generally exhibit an altered course of diabetes. Those with mutations of the glucokinase gene (GCK-MODY) show good metabolic control and low risk of cardiovascular complications despite paradoxically lowered high-density lipoprotein (HDL) cholesterol levels. In order to investigate the matter, we analyzed the composition of low-density lipoprotein (LDL) and HDL subpopulations in such individuals. The LipoPrint(©) system (Quantimetrix, USA) based on non-denaturing, linear polyacrylamide gel electrophoresis was used to separate and measure LDL and HDL subclasses in fresh-frozen serum samples from patients with mutations of glucokinase or HNF1A, type 1 diabetes (T1DM) and healthy controls. Fresh serum samples from a total of 37 monogenic diabetes patients (21 from GCK-MODY and 16 from HNF1A-MODY), 22 T1DM patients and 15 healthy individuals were measured in this study. Concentrations of the small, highly atherogenic LDL subpopulation were similar among the compared groups. Large HDL percentage was significantly higher in GCK-MODY than in control (p = 0.0003), T1DM (p = 0.0006) and HNF1A-MODY groups (p = 0.0246). Patients with GCK-MODY were characterized by significantly lower intermediate HDL levels than controls (p = 0.0003) and T1DM (p = 0.0005). Small, potentially atherogenic HDL content differed significantly with the GCK-MODY group showing concentrations of that subfraction from control (p = 0.0096), T1DM (p = 0.0193) and HNF1A-MODY (p = 0.0057) groups. Within-group heterogeneity suggested the existence of potential gene-gene or gene-environment interactions. GCK-MODY is characterized by a strongly protective profile of HDL cholesterol subpopulations. A degree of heterogeneity within the groups suggests the existence of interactions with other genetic or clinical factors.
    Full-text · Article · Feb 2014 · Acta Diabetologica
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    ABSTRACT: A general problem of supervised remotely. sensed image classification assumes prior knowledge to be available for all thematic classes that are present in the considered data set. However, the ground truth map representing this prior knowledge usually does not really, describe all the land cover typologies in the image and the generation of a complete training set represents a time-consuming, difficult and expensive task. This problem may play a relevant role in remote sensing data analysis, since it affects the classification performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is proposed, which allows the identification of samples drawn from unknown classes, through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density, functions and on a recursive procedure to generate prior probabilities estimates for both known and unknown classes. For experimental purposes, both a synthetic data set and two real data sets are employed.
    No preview · Conference Paper · Nov 2003
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