Clustering Of Metabolic Factors Research Articles

Metabolic Disease and Risk of Thyroid Disease: Evidence from the National Nurse Health Cohort in China.

Previous studies have shown that metabolic diseases are risk factors for thyroid disease; most studies are cross-sectional design. We aimed to evaluate the causal relationship between thyroid and metabolic diseases in a cohort of Chinese nurses. We conducted an ambispective cohort study of the National Nurses' Health Study. Thyroid disease data based on ultrasonography from 2017 to 2021 were collected. We described thyroid disease incidence and the risk factors associated with a cluster of metabolic factors. We used the Mann‒Whitney U test, repeated-measures ANOVA and multivariable Cox proportional hazard regression to analyze the data. A total of 1529 female nurses without thyroid disease were enrolled in 2017, of which, complete data were available for 1269 nurses. In 2018-2020, thyroid nodule incidence ranged from 32.8%-46.3%, thyroiditis incidence was 13.4%-14.3%, and goiter incidence was 4.1%-29.1%, thyroid adenoma and thyroid tumors incidence were 0.1%-0.5% and 1%-1.5%. We also found that NAFLAD was an independent risk factor for thyroid adenoma (p = .003). The age at diagnosis was an independent risk factor for goiter (p <. 001) and thyroid nodules (p < .001). Fasting blood glucose was an independent risk factor for thyroid tumors (p = .004). The age at diagnosis (p = .003), Body Mass Index (p = .006) and menopause (p = .031) were risk factors for thyroiditis. Thyroid disease incidence among nurses is increasing. Age at diagnosis, BMI, fasting blood glucose, and nonalcoholic fatty liver disease are independent risk factors for different types of thyroid disease. This study provides evidence for future studies to further explore the pathogenesis and prevention of thyroid diseases.

A sex-related mediating effect of uric acid in the association between body composition and blood pressure in children and adolescents.

The increase in blood pressure (BP) during somatic growth might have direct determinants but also mediating factors. We investigated whether uric acid (UA) and other metabolic factors would mediate the association between body composition components and BP. A cross-sectional study was conducted in 928 children and adolescents (aged 6-18years), in which body composition and blood biochemistry were evaluated. Structural equation modeling was performed to test the direct and indirect pathways between systolic blood pressure (SBP) and body composition parameters. Muscle mass (MM) showed a strong direct effect on BP, regardless of sex. In girls, a mediating pathway through UA was not significant, but the association between fat mass (FM) and MM with SBP was mediated by the cluster of metabolic factors. In boys, both MM and FM were associated with SBP through a mediating pathway via UA, but not via the cluster of metabolic factors. The association between body composition and BP in children and adolescents has a complex design and also has a sex-specific mediating component. The increase in the UA levels may affect BP levels early in boys. Also, metabolic changes elicited by FM contribute to the increase in BP at an early age in girls. Novelty: MM showed a strong direct effect on BP, regardless of sex. In girls, the association between FM and MM with SBP was mediated by the cluster of metabolic factors. In boys, both MM and FM were associated with SBP through a mediating pathway via UA.

The metabolic syndrome - What is it and how should it be managed?

A cluster of metabolic factors have been merged into an entity named the metabolic syndrome. Although the characteristics of this syndrome have varied over time the presently used definition was established in 2009. The presence of three abnormal findings out of five components qualifies a person for the metabolic syndrome: elevated waist circumference, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure and elevated fasting plasma glucose. Cut points have been defined for all components apart from waist circumference, for which national or regional values are used. The metabolic syndrome predicts cardiovascular disease and type 2 diabetes. This associated risk does not exceed its components whereof elevated blood pressure is the most frequent. A successful management should, however, address all factors involved. The management is always based on healthy lifestyle choices but has not infrequently to be supported by pharmacological treatment, especially blood pressure lowering drugs. The metabolic syndrome is a useful example of the importance of multiple targets for preventive interventions. To be successful management has to be individualized not the least when it comes to pharmacological therapy. Frail elderly people should not be over-treated. Knowledge transfer of how risk factors act should be accompanied by continuous trust building and motivation. In complex situations with a mix of biological risk factors, adverse social conditions and unhealthy lifestyle, everything cannot be changed at once. It is better to aim for small steps that are lasting than large, unsustainable steps with relapses to unhealthy behaviours. A person with the metabolic syndrome will always be afflicted by its components, which is the reason that management has to be sustained over a very long time. This review summarizes the knowledge on the metabolic syndrome and its management according to present state of the art.

The Genetic Background of Metabolic Trait Clusters in Children and Adolescents.

It is well known that metabolic risk factors of cardiovascular diseases are correlated, but the background of this clustering in children is more poorly known than in adults. Thus, we studied the contribution of genetic and environmental factors to the clustering of metabolic traits in childhood and adolescence. Nine metabolic traits were measured in 214 complete twin pairs aged 3-18 years in the Autonomous Region of Madeira, Portugal, in 2007 and 2008. The variation of and covariations between the traits were decomposed into genetic and environmental components by using classical genetic twin modeling. A model, including additive genetic and environmental factors unique for each twin individual, explained the variation of metabolic factors well. Under this model, the heritability estimates varied from 0.47 (systolic blood pressure in children under 12 years of age) to 0.91 (high-density lipoprotein [HDL] cholesterol in adolescents 12 years of age or older). The most systematic correlations were found between adiposity (body mass index and waist circumference) and blood lipids (HDL cholesterol, low-density lipoprotein cholesterol, and triglycerides), as well as blood pressure. These correlations were mainly explained by common genetic factors. Our results suggest that obesity, in particular, is behind the clustering of metabolic factors in children and adolescents. Both general and abdominal obesity partly share the same genetic background as blood lipids and blood pressure. Obesity prevention early in childhood is important in reducing the risk of metabolic diseases in adulthood.

Global burden of stroke and risk factors in 188 countries, during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

The contribution of modifiable risk factors to the increasing global and regional burden of stroke is unclear, but knowledge about this contribution is crucial for informing stroke prevention strategies. We used data from the Global Burden of Disease Study 2013 (GBD 2013) to estimate the population-attributable fraction (PAF) of stroke-related disability-adjusted life-years (DALYs) associated with potentially modifiable environmental, occupational, behavioural, physiological, and metabolic risk factors in different age and sex groups worldwide and in high-income countries and low-income and middle-income countries, from 1990 to 2013. We used data on stroke-related DALYs, risk factors, and PAF from the GBD 2013 Study to estimate the burden of stroke by age and sex (with corresponding 95% uncertainty intervals [UI]) in 188 countries, as measured with stroke-related DALYs in 1990 and 2013. We evaluated attributable DALYs for 17 risk factors (air pollution and environmental, dietary, physical activity, tobacco smoke, and physiological) and six clusters of risk factors by use of three inputs: risk factor exposure, relative risks, and the theoretical minimum risk exposure level. For most risk factors, we synthesised data for exposure with a Bayesian meta-regression method (DisMod-MR) or spatial-temporal Gaussian process regression. We based relative risks on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks, such as high body-mass index (BMI), through other risks, such as high systolic blood pressure (SBP) and high total cholesterol. Globally, 90·5% (95% UI 88·5-92·2) of the stroke burden (as measured in DALYs) was attributable to the modifiable risk factors analysed, including 74·2% (95% UI 70·7-76·7) due to behavioural factors (smoking, poor diet, and low physical activity). Clusters of metabolic factors (high SBP, high BMI, high fasting plasma glucose, high total cholesterol, and low glomerular filtration rate; 72·4%, 95% UI 70·2-73·5) and environmental factors (air pollution and lead exposure; 33·4%, 95% UI 32·4-34·3) were the second and third largest contributors to DALYs. Globally, 29·2% (95% UI 28·2-29·6) of the burden of stroke was attributed to air pollution. Although globally there were no significant differences between sexes in the proportion of stroke burden due to behavioural, environmental, and metabolic risk clusters, in the low-income and middle-income countries, the PAF of behavioural risk clusters in males was greater than in females. The PAF of all risk factors increased from 1990 to 2013 (except for second-hand smoking and household air pollution from solid fuels) and varied significantly between countries. Our results suggest that more than 90% of the stroke burden is attributable to modifiable risk factors, and achieving control of behavioural and metabolic risk factors could avert more than three-quarters of the global stroke burden. Air pollution has emerged as a significant contributor to global stroke burden, especially in low-income and middle-income countries, and therefore reducing exposure to air pollution should be one of the main priorities to reduce stroke burden in these countries. Bill & Melinda Gates Foundation, American Heart Association, US National Heart, Lung, and Blood Institute, Columbia University, Health Research Council of New Zealand, Brain Research New Zealand Centre of Research Excellence, and National Science Challenge, Ministry of Business, Innovation and Employment of New Zealand.

Impact of glucocorticoid receptor gene (NR3C1) polymorphisms in Turkish patients with metabolic syndrome

The metabolic syndrome (MetS) is characterized by a cluster of metabolic factors, including insulin resistance and type-2 diabetes, abdominal obesity, dyslipidemia, hypertension and microalbuminuria. Impaired glucocorticoid receptor (GR) activity also plays an important role in the etiology of MetS. The objective of our study is to evaluate the effects of GR gene polymorphisms (BclI, N363S, TthIII1 and ER22/23EK) in Turkish patients with MetS. Seventy subjects with MetS and 185 healthy controls were enrolled in the study. PCR-RFLP analysis was used for genotyping. Results for each polymorphism have been verified by allele-specific oligonucleotide analysis. BclI GG genotype was significantly associated with an increased risk of MetS (p = 0.02). Also, only in women, the G allele carriers were significantly associated with higher C-peptide. T allele carriers of TthIII1 polymorphism were significantly associated with higher C-peptide, triglyceride, insulin and C-reactive protein (CRP, p value 0.048, 0.022, 0.005 and 0.022, respectively), and lower fasting blood glucose (FBG, p = 0.02). The combined carriers of BclI polymorphism G allele and TthIII1 polymorphism T allele were significantly associated with higher diastolic blood pressure in all patients, and lower FBG and postprandial blood glucose in only men. All the ER22/23EK polymorphisms coexisted with polymorphic variant of TthIII1 (p = 0.0058). The presence of homozygote polymorphic variant of BclI might be good predictive markers for the disease susceptibility. The BclI and the TthIII1 polymorphism are associated with sex-specific clinical parameters. Our findings also suggest that the combination of BclI and TthIII1 polymorphisms may play a protective role in blood glucose.

Risk of chronic kidney disease in non-obese individuals with clustering of metabolic factors: a longitudinal study.

The impact of the clustering of metabolic factors on chronic kidney disease (CKD) in non-obese individuals remains unclear. We conducted a follow-up study of 23,894 Japanese adults (age, 18-69 years) who continuously received annual health examinations between 2000 and 2011. Obesity, high blood pressure, high triglycerides, low high-density lipoprotein (HDL) cholesterol and high fasting blood sugar were defined as metabolic factors, and CKD was defined as renal dysfunction (estimated glomerular filtration rate: <60 mL/min/1.73 m(2)) or proteinuria (dipstick test: ≥1+). The association between the clustering of metabolic factors and CKD was assessed based on the presence or absence of obesity using a Cox proportional hazard model. Of 2,867 subjects with ≥3 metabolic factors, 650 (22.7%) were non-obese. These individuals were older and had higher metabolic risks than their obese counterparts at baseline. Among the entire cohort of 23,894 subjects, 1,764 developed renal dysfunction and 904 developed proteinuria during an average follow-up period of 7.8 years. The cumulative incidence of renal dysfunction was higher (22.1% vs. 16.1%), whereas that of proteinuria was lower (10.5% vs. 14.4%), among the non-obese subjects with ≥3 metabolic factors than the obese subjects with ≥3 metabolic factors after 11 years. The adjusted relative risk (RR) (95% confidence interval) of renal dysfunction was 1.54 (1.34-1.77) and 1.67 (1.35-2.07) for the obese and non-obese subjects with ≥3 metabolic factors, respectively. Non-obese subjects with ≥3 metabolic factors, who are missed based on the essential criterion of obesity for metabolic syndrome, may have an equal or slightly higher risk of renal dysfunction than obese subjects with ≥3 metabolic factors.

Application of Lipidomics to Assess Lipogenesis in Drug Development and Pre-Clinical Trials

The rising incidence of cardiovascular and metabolic diseases in industrialized countries has led the pharmaceutical industry to make them key areas of drug development. These diseases imply a clustering of metabolic factors where lipid metabolites play a relevant role. Measurement of pharmacodynamic endpoints of drugs on lipid metabolism pathways and downstream biological processes appear crucial for a rational drug discovery/development. Fortunately, recent mass spectrometers with an enhanced sensitivity and resolution in combination with multivariate statistical analysis provide the practical possibility to analyze and measure wide portions of the lipidome. The final goal is to identify lipid signatures which fit with specific pharmacologic responses to therapeutic intervention. Focusing on applications of lipidomics for drug development this review outlines the methodological steps, from analytical measurements to data processing and to graphical representation, for an efficient implementation of informative lipid signatures.

Magnesium and the Cardiometabolic Syndrome

The cardiometabolic syndrome is a cluster of metabolic factors that increase an individual’s risk of developing cardiovascular disease and type 2 diabetes mellitus. The metabolic risk factors that cluster in the syndrome include insulin resistance, hypertension, impaired glucose tolerance, central obesity, and dyslipidemia. Other abnormalities, such as chronic proinflammatory and prothrombotic states and oxidative stress, have been added to the syndrome. Magnesium (Mg) plays a key role in regulating insulin action, glucose uptake, and vascular tone. Many experimental, clinical, and epidemiologic studies have shown a clear link between Mg status and any component included in the constellation. Increasing evidence suggests a role for Mg deficiency as a possible unifying mechanism underlying the coincidence among those apparently disparate clinical conditions clustering in the syndrome. Although the use of Mg supplements has been suggested as a potential tool in the prevention of the cardiometabolic syndrome, this needs to be demonstrated by future prospective studies.

Even low level of physical activity is associated with reduced mortality among people with metabolic syndrome, a population based study (the HUNT 2 study, Norway)

BackgroundLow levels of physical activity may increase the risk of developing metabolic syndrome, a cluster of metabolic factors that are associated with the risk of premature death. It has been suggested that physical activity may reduce the impact of factors associated with metabolic syndrome, but it is not known whether physical activity may reduce mortality in people with metabolic syndrome.MethodsIn a prospective study of 50,339 people, 13,449 had metabolic syndrome at baseline and were followed up for ten years to assess cause-specific mortality. The population was divided into two age groups: those younger than 65 years of age and those older than age 65. Information on their physical activity levels was collected at baseline.ResultsMetabolic syndrome was associated with higher mortality from all causes (hazard ratio (HR) 1.35, 95% confidence interval (95% CI) 1.20 to 1.52) and from cardiovascular causes (HR 1.78, 95% CI 1.39 to 2.29) in people younger than 65 years old than among other populations. In older people, there was no overall association of metabolic syndrome with mortality. People with metabolic syndrome who reported high levels of physical activity at baseline were at a reduced risk of death from all causes compared to those who reported no physical activity, both in the younger age group (HR 0.52, 95% CI 0.37 to 0.73) and in the older age group (HR 0.59, 95% CI 0.47 to 0.74).ConclusionAmong people with metabolic syndrome, physical activity was associated with reduced mortality from all causes and from cardiovascular causes. Compared to inactivity, even low levels of physical activity were associated with reduced mortality.

A retrospective review of X-linked Charcot-Marie-Tooth disease in childhood

X-linked Charcot-Marie-Tooth disease (CMTX) is infrequently diagnosed in childhood, and its clinical and neurophysiologic features are not well-described. We reviewed clinical, neurophysiologic, and pathologic findings in 17 children with CMTX. This was a retrospective review of children with CMTX from 2 tertiary pediatric hospitals. The diagnosis of CMTX was based on an identifiable connexin 32 mutation (CMTX1) or a consistent pedigree and neurophysiologic features in children without a connexin 32 mutation (CMTX-other). Six boys and 2 girls from 8 kindreds had CMTX1, and 8 boys and 1 girl from 5 kindreds had other forms of CMTX (CMTX-other). Fifteen children, including males and carrier females, were symptomatic from infancy or early childhood (younger than 5 years). In addition to the typical Charcot-Marie-Tooth disease clinical phenotype, some patients had delayed motor development, sensorineural hearing loss, tremor, pathologic fractures, or transient CNS disturbances. Eleven children underwent nerve conduction studies. Median nerve motor nerve conduction velocities were in the intermediate to normal range (30-54 m/s) in all children older than 2 years. Axon loss, reflected by low-amplitude compound muscle action potentials, was present in all patients. A pattern of X-linked dominant inheritance, with carrier females showing an abnormal neurologic or neurophysiologic examination, correlated with the presence of a connexin 32 mutation in all but 2 pedigrees. The clinical phenotype of CMTX is broader than previously reported. Onset in males and carrier females is most often in early childhood. Families with an X-linked dominant inheritance pattern are likely to have CMTX1.

Metabolic Syndrome and All-Cause and Cardiovascular Disease Mortality Japan Public Health Center-Based Prospective (JPHC) Study

Although the metabolic syndrome (MetS) is considered to be caused primarily by visceral fat accumulation, epidemiological evidence is lacking as to whether or not obesity is an essential element in the syndrome. Between 1990 and 2005, the Japan Public Health Center-based Prospective (JPHC) Study conducted baseline measurements of metabolic risk factors in 12,412 men and 21,639 women, aged 40-69 years, with no history of cardiovascular disease (CVD) or cancer. To clarify the role of obesity, which the definition of MetS in Japan has adopted as an essential criterion, clustering of risk factors in data grouped according to overweight condition was examined. During a 12.3-year follow-up there were 2,040 deaths, including 947 from cancers and 304 from CVD. MetS significantly increased the hazard ratios for all-cause mortality in women and CVD mortality in men. Non-overweight with > or = 2 risk factors had a similar impact on all-cause and CVD mortality. Clustering of metabolic factors caused a linear increase in the hazard ratios for mortality. MetS caused moderate increases in all-cause and CVD mortality. However, the MetS definition requiring obesity may not necessarily identify non-overweight individuals who have a high mortality risk and are more prevalent than subjects with MetS.

The Metabolic Syndrome

See related article, pages 1078–1083. The metabolic syndrome is a clustering of metabolic abnormalities, which include insulin resistance or diabetes, obesity, hypertension, and dyslipidemia. Although the mechanisms of the underlying abnormalities remain to be clarified, the pathogenesis includes both genetic predisposition and modifiable risk factors such as sedentary lifestyle and dietary intake. Recent estimates suggest that approximately 24% of US adults have the metabolic syndrome.1 In several studies, the metabolic syndrome has been shown to increase the risk of cardiovascular diseases2; however, the strength of its association with stroke is weaker than that for coronary heart disease.3 In the guidelines for the primary prevention of stroke from the American Heart Association/American Stroke Association, the metabolic syndrome is listed as a less–well-documented risk factor.4 But, given the high prevalence of the metabolic syndrome, studying its association with stroke is certainly relevant for 2 reasons: first, to obtain a better etiologic understanding of the causes of stroke; second, to identify individuals at high risk for stroke. This translates into 2 distinct scientific questions: is the metabolic syndrome by itself a risk factor for stroke?; and can we predict which individuals are at increased risk for stroke based on information about the metabolic syndrome? To answer these questions from observational data, one needs 2 distinct model-building strategies that require differences in assumptions of biological associations and statistical considerations.5,6 In this issue of Stroke ,7 Wang and …

Metabolic syndrome, plasma lipid, lipoprotein and glucose levels, and endometrial cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC)

To clarify the role of metabolic factors in endometrial carcinogenesis, we conducted a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), and examined the relation between prediagnostic plasma lipids, lipoproteins, and glucose, the metabolic syndrome (MetS; a cluster of metabolic factors) and endometrial cancer risk. Among pre- and postmenopausal women, 284 women developed endometrial cancer during follow-up. Using risk set sampling, 546 matched control subjects were selected. From conditional logistic regression models, high-density lipoprotein cholesterol (HDL-C) levels were inversely associated with risk body mass index (BMI)-adjusted relative risk (RR) for top versus bottom quartile 0.61 (95% confidence intervals (CI) 0.38-0.97), P(trend) = 0.02). Glucose levels were positively associated with risk (BMI-adjusted RR top versus bottom quartile 1.69 (95% CI 0.99-2.90), P(trend) = 0.03), which appeared stronger among postmenopausal women (BMI-adjusted RR top versus bottom tertile 2.61 (95% CI 1.46-4.66), P(trend) = 0.0006, P(heterogeneity) = 0.13) and never-users of exogenous hormones (P(heterogeneity) = 0.005 for oral contraceptive (OC) use and 0.05 for hormone replacement therapy-use). The associations of HDL-C and glucose with risk were no longer statistically significant after further adjustment for obesity-related hormones. Plasma total cholesterol, Low-density lipoprotein cholesterol (LDL-C), and triglycerides were not significantly related to overall risk. The presence of MetS was associated with risk (RR 2.12 (95% CI 1.51-2.97)), which increased with the number of MetS factors (P(trend) = 0.02). An increasing number of MetS factors other than waist circumference, however, was marginally significantly associated with risk only in women with waist circumference above the median (P(interaction) = 0.01). None of the associations differed significantly by fasting status. These findings suggest that metabolic abnormalities and obesity may act synergistically to increase endometrial cancer risk.

Predicting glucose intolerance with normal fasting plasma glucose by the components of the metabolic syndrome

BACKGROUNDSurprisingly, it is estimated that about half of type 2 diabetics remain undetected. The possible causes may be partly attributable to people with normal fasting plasma glucose (FPG) but abnormal postprandial hyperglycemia. We attempted to develop an effective predictive model by using the metabolic syndrome (MeS) components as parameters to identify such persons.SUBJECTS AND METHODSAll participants received a standard 75-g oral glucose tolerance test, which showed that 106 had normal glucose tolerance, 61 had impaired glucose tolerance, and 6 had diabetes-on-isolated postchallenge hyperglycemia. We tested five models, which included various MeS components. Model 0: FPG; Model 1 (clinical history model): family history (FH), FPG, age and sex; Model 2 (MeS model): Model 1 plus triglycerides, high-density lipoprotein cholesterol, body mass index, systolic blood pressure and diastolic blood pressure; Model 3: Model 2 plus fasting plasma insulin (FPI); Model 4: Model 3 plus homeostasis model assessment of insulin resistance. A receiver-operating characteristic (ROC) curve was used to determine the predictive discrimination of these models.RESULTSThe area under the ROC curve of the Model 0 was significantly larger than the area under the diagonal reference line. All the other 4 models had a larger area under the ROC curve than Model 0. Considering the simplicity and lower cost of Model 2, it would be the best model to use. Nevertheless, Model 3 had the largest area under the ROC curve.CONCLUSIONWe demonstrated that Model 2 and 3 have a significantly better predictive discrimination to identify persons with normal FPG at high risk for glucose intolerance.

Low-density lipoprotein particle number and risk for cardiovascular disease.

The key role played by low-density lipoprotein (LDL) particles in the pathogenesis of coronary heart disease (CHD) is well accepted, as is the benefit of lowering LDL in high-risk patients. What remains controversial is whether we are using the best measure(s) of LDL to identify all individuals who would benefit from therapy. Many studies have shown that, at a given level of LDL cholesterol, individuals with predominantly small LDL particles (pattern B) experience greater CHD risk than those with larger-size LDL. However, it is not clear from this observation that small LDL particles are inherently more atherogenic than large ones because, at a given level of LDL cholesterol, individuals with small LDL have more LDL particles in total. The phenotype of small LDL particle size co-segregates with a cluster of metabolic factors, including elevated triglycerides and reduced HDL cholesterol, and in multivariate analyses has generally been found not to be independently associated with CHD risk. In contrast, LDL particle number measured by nuclear magnetic resonance has consistently been shown to be a strong, independent predictor of CHD.

The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study.

The oral glucose tolerance test identifies high-risk subjects for diabetes, but it is costly and inconvenient. To find better predictors of type 2 diabetes, we evaluated two different definitions of the metabolic syndrome because insulin resistance, which is commonly associated with this clustering of metabolic factors, frequently precedes the onset of type 2 diabetes. We compared the ability of the National Cholesterol Education Program (NCEP) definition, a modified version of the 1999 World Health Organization (WHO) definition that excludes the 2-h glucose requirement, and impaired glucose tolerance (IGT) to predict incident type 2 diabetes. In the San Antonio Heart Study, 1734 participants completed a 7- to 8-year follow-up examination. IGT and the NCEP definition had higher sensitivity than the modified WHO definition (51.9, 52.8, and 42.8%, respectively). IGT had a higher positive predictive value than the NCEP and modified WHO definitions (43.0, 30.8, and 30.4%, respectively). The combination of the IGT and NCEP definitions increased the sensitivity to 70.8% with an acceptable positive predictive value of 29.7%. Risk for incidence of type 2 diabetes using the NCEP definition was independent of other risk factors, including IGT and fasting insulin (odds ratio 3.30, 95% CI 2.27-4.80). The NCEP definition performed better with fasting glucose >or=5.4 mmol/l (sensitivity 62.0% and positive predictive value 30.9%). The metabolic syndrome predicts diabetes independently of other factors. However, the NCEP definition performs better than the modified 1999 WHO definition. Lowering the fasting glucose cutoff to 5.4 mmol/l improves the prediction of diabetes by the metabolic syndrome.

The metabolic syndrome: a practical guide to origins and treatment: Part II.

The metabolic syndrome (MetS), a cluster of metabolic abnormalities with insulin resistance as a major characteristic, has gone by several names over the past two decades. The diagnostic criteria proposed by the Adult Treatment Program III (ATP III) of the National Cholesterol Education Program (NCEP) have led to greater awareness of the components and treatment strategies.1 Five diagnostic traits are listed in the ATP III version of the MetS (Table; referred to as the “metabolic syndrome”), and the presence of any 3 of these factors is considered sufficient for diagnosis. This practical review will consider each in turn, providing advice for cardiologists, internists, and other health care providers who are diagnosing and treating persons with the syndrome in an effort to prevent a variety of clinical outcomes. View this table: Diagnostic Criteria for the Metabolic Syndrome The major adverse consequence of the MetS is cardiovascular disease (CVD). Several of the metabolic abnormalities associated with the syndrome, in fact, are CVD risk factors. One of these abnormalities, insulin resistance, also predisposes to the development of type 2 diabetes mellitus (T2DM). In age-adjusted estimates from the National Health and Nutrition Examination Survey III in 1998 to 1994, approximately 24% of adult Americans had ≥3 of the 5 MetS criteria. Key determinants of greater prevalence were age and ethnicity. Prevalence rates were highest in Mexican Americans and were successively lower in white, African American and other racial groups.2 These published estimates included persons with diabetes mellitus who had met the 1998 fasting glucose criteria (≥126 mg/dL [8.0 mmol/L]) of the American Diabetes Association.3 This article will focus on the features of the MetS in persons without diabetes mellitus, although most persons with T2DM also have the MetS. Moreover, the therapeutic approaches to metabolic risk factors described here also can be applied to …

Clustering of metabolic factors and coronary heart disease.

The degree of clustering for common metabolic coronary disease risk factors is not well known, the antecedents of clustering are not well studied, and the impact of such clusters on coronary risk has not been assessed systematically. Prospective community sample of 2406 men and 2569 women aged 18 to 74 years at baseline. The 6 metabolically linked risk factors considered were the lowest sex-specific quintile of high-density lipoprotein cholesterol and the highest quintiles of body mass index, systolic blood pressure, triglycerides, glucose, and serum total cholesterol. At baseline the risk factor sum, represented as integer values, ranged from 0 to 6, and clusters of 3 or more risk factors occurred at twice the rate predicted by chance. After adjustment for age and obesity level, a 2.25-kg (5-lb) weight increase over 16 years was associated with an increased risk factor sum in men (+20%; P=.002) and women (+37%; P<.001), and a 2.25-kg weight loss was associated with a decreased risk factor sum in men (-48%; P<.001) and women (-40%; P<.001). Clusters of 3 or more risk factors were associated with a 2.39 (95% confidence interval, 1.56-3.36) and 5.90 (95% confidence interval, 2.54-13.73) times greater risk of coronary heart disease in men and women, respectively (both P<.001). Atherogenic risk factor clustering is common in both sexes, worsens with weight gain, and is associated with greatly increased risk of coronary disease risk in both sexes.

Clustering of metabolic factors among the patients with essential hypertension

The prevalence of metabolic syndrome was determined in clinic-based 1,517 hypertensive patients. All traits were present in 1.1% men and 12.8% women. Combination of different three traits were present as follows: hypertension with high triglyceride and low HDL (men 29.4% vs. women 51.8%), hypertensio with high blood glucose and low HDL (men 13.5% vs. women 29.8%), hypertension with high glucose and high triglyceride (men 18.1% vs. women 18.1%), hypertension with high blood glucose and large waist (men 2.7% vs. women 25.7%), hypertension with high triglyceride and large waist (men 3.4% vs. women 39.3%) and hypertension with low HDL and large waist (men 2.5% vs. women 70.6%). The study shows that the metabolic syndrome is highly prevalent among hypertensive patients especially women.