Pathophysiology of the metabolic syndrome

McCracken, E.; Monaghan, Monica; Sreenivasan, Shiva

doi:10.1016/j.clindermatol.2017.09.004

Cited by 595 publications

(500 citation statements)

References 52 publications

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“…A previous study identified that RHR is associated with a microinflammatory response, and that inflammatory marker (eg, interleukin‐6 and C‐reactive protein) levels were associated with the autonomic nervous system . Inflammation and oxidative stress are the major features of metabolic and cardiovascular abnormalities and are fundamentally inseparable from the pathological processes of MetS . The PROSPER (PROspective Study of Pravastatin in the Elderly at Risk) study further observed that elevated heart rate is associated with increased systemic inflammation and endothelial dysfunction, which may lead to cardiometabolic disorders .…”

Section: Discussionmentioning

confidence: 98%

“…46 Inflammation and oxidative stress are the major features of metabolic and cardiovascular abnormalities and are fundamentally inseparable from the pathological processes of MetS. 47,48 The PROSPER (PROspective Study of Pravastatin in the Elderly at Risk) study further observed that elevated heart rate is associated with increased systemic inflammation and endothelial dysfunction, which may lead to cardiometabolic disorders. 49 In addition, some researchers have suggested that increased sympathetic tone induces insulin resistance, 50,51 which plays an important role in the early development of hyperglycemia, hyperlipidemia, and central adiposity, 52 but not in insulin-sensitive obesity.…”

Section: Discussionmentioning

confidence: 99%

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Resting heart rate is associated with metabolic syndrome and predicted 10‐year risk of cardiovascular disease: a cross‐sectional study

et al. 2019

Journal of Diabetes

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Background This study examined whether resting heart rate (RHR) was associated with metabolic syndrome (MetS) and the 10‐year predicted risk of cardiovascular disease in a Chinese population. Methods The associations of RHR with MetS and 10‐year risk of atherosclerotic cardiovascular diseases (ASCVD) was examined in a cross‐sectional study conducted in Shanghai, China (n = 9486). Results Compared with individuals in the lowest RHR quintile (≤71 b.p.m.), those in the highest quintile (≥91 b.p.m.) had a higher prevalence of MetS (21.2% vs 32.6%, respectively; P < 0.001). Logistic regression analyses showed that the multivariate‐adjusted odds ratio (OR) and 95% confidence interval (CI) for MetS was 1.13 (1.08‐1.18) for each 10‐b.p.m. increment of RHR (P < 0.0001). Furthermore, RHR was strongly associated with the prevalence of hypertension, high blood glucose, and dyslipidemia, but not with central obesity. A stronger association of RHR with MetS was observed among individuals aged <65 years, male, with a body mass index <24 kg/m2, without diabetes, hypertension, abnormal lipids, and insulin resistance than among their counterparts (P < 0.05 for all). A significantly higher 10‐year risk for ASCVD was observed with each 10‐b.p.m. increment in RHR in both men and women (ORs [95% CIs] 1.20 [1.07‐1.33] and 1.28 [1.17‐1.39], respectively; Ptrend = 0.002 and < 0.0001, respectively). Conclusions In this study, RHR was associated with a higher prevalence of MetS and elevated 10‐year predicted risk of ASCVD in both Chinese men and women. Whether RHR may serve as an indicator for MetS among relatively healthy individuals requires further investigation.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Resting heart rate is associated with metabolic syndrome and predicted 10‐year risk of cardiovascular disease: a cross‐sectional study

et al. 2019

Journal of Diabetes

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“…Metabolic and genetic susceptibility are also potential key risk factors for MetS [5,6]. Abdominal fat accumulation, upregulation of serum triglycerides and glucose, hypertension, and a dysregulated ratio of low-to-high-density lipoprotein levels are the most common features of MetS [7]. Patients exhibiting three or more conditions among obesity, atherosclerotic dyslipidemia, hypertension, hyperglycemia, and aggravated inflammation can be clinically diagnosed as having MetS [8].…”

Section: Introductionmentioning

confidence: 99%

Trace Elements, PPARs, and Metabolic Syndrome

Shi

Zou

Shen

et al. 2020

IJMS

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Metabolic syndrome (MetS) is a constellation of metabolic derangements, including central obesity, insulin resistance, hypertension, glucose intolerance, and dyslipidemia. The pathogenesis of MetS has been intensively studied, and now many factors are recognized to contribute to the development of MetS. Among these, trace elements influence the structure of proteins, enzymes, and complex carbohydrates, and thus an imbalance in trace elements is an independent risk factor for MetS. The molecular link between trace elements and metabolic homeostasis has been established, and peroxisome proliferator-activated receptors (PPARs) have appeared as key regulators bridging these two elements. This is because on one hand, PPARs are actively involved in various metabolic processes, such as abdominal adiposity and insulin sensitivity, and on the other hand, PPARs sensitively respond to changes in trace elements. For example, an iron overload attenuates hepatic mRNA expression of Ppar-α; zinc supplementation is considered to recover the DNA-binding activity of PPAR-α, which is impaired in steatotic mouse liver; selenium administration downregulates mRNA expression of Ppar-γ, thereby improving lipid metabolism and oxidative status in the liver of high-fat diet (HFD)-fed mice. More importantly, PPARs' expression and activity are under the control of the circadian clock and show a robust 24 h rhythmicity, which might be the reasons for the side effects and the clinical limitations of trace elements targeting PPARs. Taken together, understanding the casual relationships among trace elements, PPARs' actions, and the pathogenesis of MetS is of great importance. Further studies are required to explore the chronopharmacological effects of trace elements on the diurnal oscillation of PPARs and the consequent development of MetS.

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“…Other potential mechanisms include low-grade chronic inflammation and oxidative stress. 2 Diagnosis of Met-S could be made once a person has three or more of the following criteria: 3 -Abdominal obesity (Waist circumference (WC) > 40 inches in men, and > 35 inches in women), -Triglyceride (TG) level ≥150 milligrams per deciliter of blood (mg/dL). -High-density lipoprotein (HDL) cholesterol < 40 mg/dL in men or < 50 mg/dL in women, -Systolic blood pressure (SBP) ≥ 130 millimeters of mercury (mmHg), or diastolic blood pressure (DBP) ≥ 85 mmHg.…”

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confidence: 99%

Diagnosis of Metabolic Syndrome Using Radar Chart

2019

bkkmedj

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OBJECTIVE: This cross-sectional study aimed to test a method using the Radar Chart technique to yield continuous data of Areal Similarity Degree (ASD) to indicate the probability of having metabolic syndrome (Met-S). MATERIAL AND METHODS: This study was conducted using anonymous data from a health checkup database. From 1,473 subjects who had five-year consecutive annual health checkups in a private hospital, we used The National Cholesterol Education Program Revised (NCEP-R) criteria version 2004 as the gold standard and applied the Radar Chart method as a screening test. All five criteria for NCEP-R were converted into angles and position of threshold on each radius of a radar chart. A polygon derived from these threshold values, which served as reference, was compared with each polygon arising from each subject's test results. The overlapping area between the reference polygon and the subject's polygon was defined as ASD, which indicates the possibility of having metabolic syndrome. A simple linear regression model was employed to identify ASD values that are comparable to one, two or three criteria of the five criteria of NCEP-R. The ASD value of three of the five criteria was used to label each subject and results were compared with the gold standard of NCEP-R to calculate sensitivity, specificity, positive and negative predictive values of the ASD method. RESULT: From 1,473 subjects, 94 (6.4%) were diagnosed as Met-S using NCEP-R criteria. Applying the Radar Chart -ASD method, these 94 subjects had an ASD value of between 51.29 and 100. The simple linear regression model revealed that an ASD value of 86.76 was comparable to three of the five criteria of NCEP-R. Using this 86.76 cut off point, the Radar Chart -ASD method could identify 23 additional subjects who had not previously been diagnosed with Met-S by NCEP-R in the first year. Among them, 17 (73.9%) progressed to have Met-S in four years, with a cumulative progression of 7 cases in one year, 12 cases in two years, and 15 cases in three years. From this data set, the 86.76 ASD cut off point had 66.0% sensitivity, 97.8% specificity, 67.4% positive and 97.7% negative predictive values. CONCLUSIONS: The Radar Chart -ASD method was able to identify subjects who were firstly diagnosed as not having metabolic syndrome but were then to progress to Met-S in subsequent years. As a result, we recommend using Radar Chart -ASD method to supplement current sets of standard criteria for the diagnosis of Metabolic Syndrome. Medical practitioners could use information from this method to improve the motivation of those who are at an increased risk e.g. having a near threshold ASD value, thus making them able to reverse the course of the disease and to maintain healthy status. Further models using Machine Learning are being explored for improved accuracy.

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Pathophysiology of the metabolic syndrome

Cited by 595 publications

References 52 publications

Resting heart rate is associated with metabolic syndrome and predicted 10‐year risk of cardiovascular disease: a cross‐sectional study

Resting heart rate is associated with metabolic syndrome and predicted 10‐year risk of cardiovascular disease: a cross‐sectional study

Trace Elements, PPARs, and Metabolic Syndrome

Diagnosis of Metabolic Syndrome Using Radar Chart

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