Adipose tissue is considered as an endocrine organ that releases bioactive factors known as adipokines which contribute to the pathogenesis of rotundity-linked metabolic and cardiovascular complications. Rotundity is a major predisposer for the development and progression of coronary artery disease (CAD).

The literature survey from various databases such as Pubmed/Medline, DOAJ, Scopus, Clarivate analytics/Web of Science and Google Scholar were used to prepare this article. The epidemic of rotundity has gained significant attention to understand the biology of adipocytes and the metabolism of adipose tissue in obese individuals. In CAD, visfatin/NAMPT was primarily indicated as a clinical marker of atherosclerosis, endothelial dysfunction and vascular injury having a prognostic significance. Visfatin/NAMPT is a factor that promotes vascular inflammation and atherosclerosis. Omentin is an anti-inflammatory and anti-atherogenic adipokine regulating cardiovascular functions.

This review highlights and summarizes the scientific information pertaining to the role of the adipokines - omentin and visfatin in CAD.

Metabolic syndrome is the constellation of cardiovascular disease risk factors and a growing public health issue affecting more than 20% of world population. Factor analysis is a powerful mathematical tool in exploring the underlying factors of any chronic diseases. Although it is most often criticized for its contrasting results for a common expression differently interpreted by the researchers yet fit the original data equally well.

The present study aims to find out the underlying physiological domains for the phenotypic attribution of metabolic syndrome as documented in several studies.

Literature search was done using Google Scholar, PUBMED, Research Gate and manual searching to identify relevant studies of the selected topic.

More than one physiological domain has been explored for the expression of metabolic syndrome explored in different studies. A reason for this disparity may be because most of explored factors are just mathematically significant but not biologically. Another reason may be the varied factor load concern. Therefore, a fixed factor load value is needed to be restricted for all studies across world.

We investigated whether serum leptin can be a predictor for incident cases of MetS in a population-based study.

This is a prospective cohort study of 1590 adults aged between 40 and 70 years, who did not have MetS in 2005-2008 (at baseline) and 2008-2011 (follow-up). The baseline serum leptin concentrations were measured by radioimmunoassay.

During an average of 2.8 years of follow-up, 113 men (17.1%) and 148 women (15.9%) developed MetS. In multivariable adjusted models, the odds ratio of incident MetS when comparing the lowest to the highest quartiles of leptin levels was 3.17 in men and 2.79 in women; nevertheless, the significance disappeared after adjusting for the body mass index (BMI). In subsidiary analyses by BMI, logistic regression analysis showed that subjects with the highest tertile of serum leptin level were 3.04 and 2.12 times more likely to have MetS than those with the lowest tertile in lean subjects (OR 3.04; 95% CI 1.44-6.41; p = .004 in men vs. OR 2.12; 95% CI 1.06-4.25; p = .036 in women, respectively).

Obesity is an effect regulator, which can predict an association between increased serum leptin level and the incidence of MetS in lean subjects.

Cardiovascular diseases are currently the commonest cause of death worldwide. Different strategies for their primary prevention have been planned, taking into account the main known risk factors, which include an atherogenic lipid profile and visceral fat excess.

The study was designed as a randomized, parallel, single-center study with a nutritional intervention duration of 12 weeks. Whole soy foods corresponding to 30 g/day soy protein were given in substitution of animal foods containing the same protein amount.

Soy nutritional intervention resulted in a reduction in the number of MetS features in 13/26 subjects. Moreover, in the soy group we observed a significant improvement of median percentage changes for body weight (-1.5 %) and BMI (-1.5 %), as well as for atherogenic lipid markers, namely TC (-4.85 %), LDL-C (-5.25 %), non-HDL-C (-7.14 %) and apoB (-14.8 %). Since the majority of the studied variables were strongly correlated, three factors were identified which explained the majority (52 %) of the total variance in the whole data set. Among them, factor 1, which loaded lipid and adipose variables, explained the 22 % of total variance, showing a statistically significant difference between treatment arms (p = 0.002).

The inclusion of whole soy foods (corresponding to 30 g/day protein) in a lipid-lowering diet significantly improved a relevant set of biomarkers associated with cardiovascular risk.

The Cardiometabolic Think Tank was convened on June 20, 2014, in Washington, DC, as a "call to action" activity focused on defining new patient care models and approaches to address contemporary issues of cardiometabolic risk and disease. Individual experts representing >20 professional organizations participated in this roundtable discussion. The Think Tank consensus was that the metabolic syndrome (MetS) is a complex pathophysiological state comprised of a cluster of clinically measured and typically unmeasured risk factors, is progressive in its course, and is associated with serious and extensive comorbidity, but tends to be clinically under-recognized. The ideal patient care model for MetS must accurately identify those at risk before MetS develops and must recognize subtypes and stages of MetS to more effectively direct prevention and therapies. This new MetS care model introduces both affirmed and emerging concepts that will require consensus development, validation, and optimization in the future.

To examine whether serum uric acid (SUA) is associated with 2-hour postload glucose (2-h PG) in Chinese with impaired fasting plasma glucose (IFG) and/or HbA1c (IA1C).

Anthropometric and biochemical examinations, such as SUA concentration, were performed in 3763 individuals from all the villages in Baqiao County, China. A 75-g oral glucose tolerance test (OGTT) was conducted in 1197 Chinese with prediabetes as having IFG (110 ≤ fasting plasma glucose [FPG] <126 mg/dl and HbA1c <6.5%), IA1C (5.7% ≤ HbA1c <6.5% and FPG <126 mg/dl), or both.

The present study included 1197 participants with IFG and/or IA1C (mean age 56.5 ± 10.3 years; 50.6% men). In multivariate linear regression, after adjustment for gender, age, smoking and drinking, body mass index (BMI), systolic and diastolic blood pressure (SBP, DBP), lipid profiles, logarithmic transformed C-reactive protein (log-CRP), estimated glomerular filtration rate (e-GFR), FPG and HbA1c, with a 1-mg/dl increment of SUA, 2-h PG increased by 5.04 ± 0.72 (P<0.001), 3.06 ± 1.08 (P = 0.001), 5.40 ± 1.26 (P<0.001), and 2.34 ± 2.16 mg/dl (P = 0.056) in all participants, in participants with normal glucose tolerance (NGT), with impaired glucose tolerance (IGT), and with 2-h newly diagnosed diabetes (2-h NDM, with 2-h PG ≥ 200 mg/dl), respectively. In both men and women, 2-h PG increased progressively and significantly from the lower to the upper SUA tertiles (P<0.001). Moreover, in multivariate logistic regression, 1-standard deviation (SD; 1.53 mg/dl) increment of SUA was significantly associated with a 36% higher risk for 2-h NDM (Odds ratio [CI 95%]: 1.36 [1.09-1.99]; P = 0.03).

SUA is significantly associated with 2-h PG in Chinese with IFG and/or IA1C.

Although attention to metabolic syndrome (MetS) in children has increased, there is still no universally accepted definition and its pathogenesis remains unclear. Our aim was to compare the current definitions of childhood MetS in a Chinese cohort and to examine the clustering pattern of MetS risk factors, particularly inclusion of leptin and adiponectin as additional components.

3373 schoolchildren aged 6 to 18 years were recruited. Anthropometric and biochemical parameters and adipokines were measured. MetS was identified using both the International Diabetes Federation (IDF) and a modified Adult Treatment Panel III (ATP III) definitions. Exploratory factor analysis was performed to establish grouping of metabolic characteristics.

For children ≥ 10 years, the prevalence of MetS was 14.3% in the obese group and 3.7% in the overweight group according to the new IDF definition, and 32.3% in the obese group and 8.4% in the overweight group according to the modified ATPIII definition. Frequency of hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), impaired fasting glucose, elevated blood pressure, and central obesity according to the new IDF definition was 16.7%, 20.7%, 15.8%, 25.5% and 75.5% in obese boys and 14.7%, 24.0%, 12.0%, 11.0% and 89.0% in obese girls, respectively. Metabolic abnormalities in children under 10 years of age were also noted. Using factor analysis on eight conventional variables led to the extraction of 3 factors. Waist circumference (WC) provided a connection between two factors in boys and all three factors in girls, suggesting its central role in the clustering of metabolic risk factors. Addition of leptin and adiponectin also led to the extraction of 3 factors, with leptin providing a connection between two factors in girls. When using WC, mean arterial pressure, triglyceride/HDL-C ratio, HOMA-IR and leptin/adiponectin ratio as variables, a single-factor model was extracted. WC had the biggest factor loading, followed by leptin/adiponectin ratio.

MetS was highly prevalent amongst obese children and adolescents in this cohort, regardless of the definition used. Central obesity is the key player in the clustering of metabolic risk factors in children, supporting the new IDF definition. Moreover, our findings suggest that a common factor may underlie MetS. Leptin/adiponectin ratio as a possible component of MetS deserves further consideration.

The aim of this research was to assess the impact of treatment with Orlistat 120 mg three times daily on serum leptin levels, weight loss, glycemic control, and cardiovascular risk factors involved in the metabolic syndrome.

A 3-month open-labeled prospective study was conducted on 40 patients with the clinical features of the metabolic syndrome divided into two groups-with and without type 2 diabetes mellitus. Twenty type 2 diabetic obese patients (group A) were studied, with BMI of 35.4 +/- 0.9 kg/m(2), as were 20 obese patients without diabetes (group B), with BMI of 36.2 +/- 0.7 kg/m(2). Weight, serum leptin levels, insulin resistance, and cardiovascular risk factors were measured at baseline and at the end of each month.

Patients reduced weight at 8.5 +/- 2.3 kg for men and 5.7 +/- 2.6 kg for women in group A against 7.9 +/- 1.9 kg for men and 5.6 +/- 2.0 kg for women in group B. Plasma leptin levels decreased at 4.5 +/- 1.9 ng/mL for men and 1.9 +/- 0.9 ng/mL for women in group A against 3.8 +/- 2.0 ng/mL for men and 2.8 +/- 1.4 ng/mL for women in group B. The level of insulin resistance measured with HOMA-IR decreased from 4.54 +/- 2.35 to 2.69 +/- 0.86 in group A against 3.98 +/- 1.89 to 2.87 +/- 0.93 in group B. In the lipid parameters, the highest decrease was found in triglycerides levels: 6.1 +/- 2.3 mmol/L for men and 3.5 +/- 2.6 mmol/L for women in group A against 2.1 +/- 1.9 mmol/L for men and 1.8 +/- 0.7 mmol/L for women in group B (all p < 0.05).

Orlistat beneficially enhances weight loss, contributing to a decrease of serum leptin, insulin resistance level, and cardiovascular risk factors in both groups. An additional beneficial pleotropic effect of Orlistat could be proposed through a reduction of plasma leptin and lipid levels.

The aim of the current study was to elucidate the clustering pattern of metabolic syndrome components along with apolipoproteins (Apo) A-I and B in diabetic and nondiabetic subjects.

Factor analysis of conventional variables of metabolic syndrome [i.e., waist circumference, homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides (TG), high-density lipoprotein-cholesterol (HDL-C), and systolic blood pressure (SBP)] with or without addition of Apo A-I and B was performed on 567 and 327 diabetic and nondiabetic subjects, respectively. Thereafter, analyses were repeated after substitution of TG and HDL-C by the TG-to-HDL-C ratio (TG/HDL-C).

Regarding conventional variables of metabolic syndrome, one or two underlying factors were identified, depending on whether lipid measures were entered as two distinct variables or as a composite measure. Apolipoproteins were consistent with a one-factor structure model of metabolic syndrome and did not change the loading pattern remarkably in nondiabetics. TG and HDL-C tended to cluster with Apo B and A-I, respectively, in different models.

The current study confirms that addition of Apo A-I and B is consistent with the one-factor model of metabolic syndrome and does not modify the loading pattern remarkably in nondiabetic subjects.

Leptin is strongly contributed to the clustering of metabolic syndrome (MetS) components and potentially can be regarded as a single predictor of MetS. This population-based study, for the first time, reports the diagnostic accuracy of different leptin cut-points for determining MetS. Further, the current study compares the predictive ability of the appropriate threshold of leptin with insulin resistance. Data of the individuals without history of known diabetes mellitus, aged 25-64 years, from the third national surveillance of risk factors of non-communicable diseases (SuRFNCD-2007) were analyzed. MetS was defined due to either adult treatment panel III (ATPIII) or the modified international diabetes federation (IDF) criteria. Receiver-operating characteristic (ROC) curves were depicted to define cut-off of serum leptin, using the maximum Youden index and the shortest distance methods. Further, the values of leptin cut-offs in prediction of MetS were compared with those of insulin resistance (defined as homeostasis model assessment of insulin resistance >1.775). In men, the optimal cut-offs of leptin for IDF- and ATPIII-defined MetS were 3.6 ng/ml (positive predictive value, PPV: 56.5%; negative predictive value, NPV: 72.7%) and 4.1 ng/ml (PPV: 49.6%; NPV: 78.1%), respectively. In women, the optimal threshold was equal to 11.0 ng/ml (PPV: 53.8%; NPV: 73.0% for IDF criteria and PPV: 60.1%; NPV: 64.9% for ATPIII criteria). The diagnostic accuracy of these values in identifying MetS was similar to that of insulin resistance. Therefore, leptin is comparable to insulin resistance in identifying MetS and can be used as single predictor of MetS.

Leptin is associated with cardiovascular disease (CVD); however, few studies have assessed its relationship with metabolic syndrome, especially in an Asian population. Therefore, the aim of the present study was to assess leptin levels and evaluate its association with CVD and metabolic syndrome.

In 2009, 957 subjects, who underwent a routine physical examination and choose leptin examination, were selected to participate. Participants (269 females and 688 males) were stratified according to leptin level quartiles. Metabolic syndrome was defined by NCEP ATP III using waist circumference cutoffs modified for Asian populations, and CVD risk was determined using the Framingham Heart Study profile.

Leptin levels were correlated with CVD risk in men and women. With the exception of fasting plasma glucose, increased leptin levels were observed as factors associated with metabolic syndrome increased in both males and females. After adjusting for age, an association between leptin levels and metabolic syndrome was observed. After adjusting for age alone or with tobacco use, subjects in the highest leptin quartile had a higher risk of having metabolic syndrome than those in the lowest quartile (OR=6.14 and 2.94 for men and women, respectively). After further adjustment for BMI, metabolic syndrome risk remained significantly increased with increasing leptin quartiles in men. Finally, increased leptin levels were a predictor of metabolic syndrome in men and women.

Serum leptin levels are correlated with CVD risk and metabolic syndrome. Analysis of leptin as part of routine physical examinations may prove beneficial for early diagnosis of metabolic syndrome.

Metabolic syndrome (MetS), manifested by insulin resistance, dyslipidemia, central obesity, and hypertension, is conceived to be associated with hyperleptinemia and physical activity. The aim of this study was to elucidate the factors underlying components of MetS and also to test the suitability of leptin and physical activity as additional components of this syndrome. Data of the individuals without history of diabetes mellitus, aged 25-64 years, from third national surveillance of risk factors of non-communicable diseases (SuRFNCD-2007), were analyzed. Performing factor analysis on waist circumference, homeostasis model assessment of insulin resistance, systolic blood pressure, triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) led to extraction of two factors which explained around 59.0% of the total variance in both genders. When TG and HDL-C were replaced by TG to HDL-C ratio, a single factor was obtained. In contrast to physical activity, addition of leptin was consistent with one-factor structure of MetS and improved the ability of suggested models to identify obesity (BMI≥30 kg/m2, P<0.01), using receiver-operator characteristics curve analysis. In general, physical activity loaded on the first identified factor. Our study shows that one underlying factor structure of MetS is also plausible and the inclusion of leptin does not interfere with this structure. Further, this study suggests that physical activity influences MetS components via modulation of the main underlying pathophysiologic pathway of this syndrome.

Metabolic syndrome (MetS) encompasses a cluster of coronary heart disease and diabetes mellitus risk factors. In this study, we aimed to elucidate the factors underlying the clustering of MetS components in diabetic and non-diabetic individuals.

Factor analysis was performed on 2978 (1652 non-diabetic and 1326 diabetic) participants. Entering waist circumference, homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides, high-density lipoprotein-cholesterol (HDL-C) and systolic blood pressure (SBP), we performed exploratory factor analysis in diabetic and non-diabetic individuals separately. The analysis was repeated after replacing triglycerides and HDL-C with triglycerides to HDL-C ratio (triglycerides/HDL-C). MetS was defined by either adult treatment panel III (ATPIII), international diabetes federation (IDF) criteria, or by the modified form of IDF using waist circumference cut-off points for Iranian population.

The selection of triglycerides and HDL-C as two distinct variables led to identifying two factors explaining 61.3% and 55.4% of the total variance in non-diabetic and diabetic participants, respectively. In both diabetic and non-diabetic subjects, waist circumference, HOMA-IR and SBP loaded on factor 1. Factor 2 was mainly determined by triglycerides and HDL-C. Factor 1 and 2 were directly and inversely associated with MetS, respectively. When triglycerides and HDL-C were replaced by triglycerides/HDL-C, one factor was extracted, which explained 47.6% and 38.8% of the total variance in non-diabetic and diabetic participants, respectively.

This study confirms that in both diabetic and non-diabetic participants the concept of a single underlying factor representing MetS is plausible.

Several studies hinted about the clustering of risk variables of the metabolic syndrome (MS) and suggested that the underlying genetic polymorphisms could be responsible for the increasing incidence of coronary heart disease (CHD) in people of Indian origin. Therefore, identification of the components of the MS along with the genetic factors could be one of the aspects to make an attempt to prevent the increasing incidence of CHD.

Principal component factor analysis (PCFA) was undertaken to identify the components or factors of the MS among the adult (≥30 years) Asian Indians living in and around Calcutta, India. The study comprised 350 adult Asian Indians. Anthropometric measurements were taken, and lipid profiles, blood pressure and fasting blood glucose were measured for each participant. Two genetic polymorphisms, namely, angiotensin converting enzyme (ACE) gene polymorphism (insertion/deletion [I/D]) or ACE (I/D) and apolipoproteinE (Hha I) were also studied.

PCFA revealed 3 factors that cumulatively explained 65.39% of the observed variance of the MS by measured variables. The 3 factors identified were lipids and lipoprotein (Factor 1), centripetal fat and blood pressure (Factor 2), and ACE (I/D) polymorphism with blood pressure (Factor 3). Moreover, the first 2 factors, that is, lipids, lipoprotein, centripetal fat, and blood pressures cumulatively explained ~46% (45.94%) of the observed variance of MS in this population.

Since more than 1 factor was identified for the MS phenotype, more than 1 physiogenetic mechanism could be accounted for MS in the Asian Indian population.

The objective of this study was to investigate the association of serum UA with either FPG or 2hPG levels among Chinese adults in Qingdao, China. A population-based survey for diabetes was performed in 2006 in Qingdao. The survey included 1,490 men and 2,325 women aged 35-74 years who had data stored as required for the current data analysis. The association of mean UA with plasma glucose was tested using a linear regression model. Serum UA concentration raised with increasing FPG levels up to FPG of 7.0 mmol/l, but significantly decreased thereafter with further increase in FPG levels. The multivariate adjusted beta coefficient between FPG and UA in individuals with newly diagnosed diabetes was -0.20 for men and -0.27 for women (p<0.01 for both). A declining trend for the UA concentration was also observed at 2hPG of 10 mmol/l or higher in both genders. Fasting serum UA levels was higher in the pre-diabetic population but lower in people with diabetes than in normoglycaemic people. UA may serve as a potential biomarker of deterioration in glucose metabolism, but its clinical implication need to be further studied.

Leptin is correlated with several features of metabolic syndrome; however, possible confounders (eg, obesity) of this association are not known. This study evaluated the relationship between leptin, metabolic syndrome, and insulin resistance in an Iranian population and further investigated whether this relationship is confounded by obesity or central obesity.

A total of 387 participants (18-65 years old) who referred to a large university general hospital for routine health examinations were categorized into 2 groups with (n = 130) and without (n = 257) metabolic syndrome. Fasting plasma glucose, insulin, lipids, and leptin levels were measured and the homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Metabolic syndrome was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria.

Age- and sex-adjusted leptin levels were significantly higher in patients with than those without metabolic syndrome (29.62 +/- 1.67 vs. 18.50 +/- 1.21 ng/mL, P < 0.001). After adjustment for age, sex, and body mass index (BMI), leptin values were significantly correlated with HOMA-IR (P < 0.001), metabolic syndrome, and its components (P < 0.05). After adjustment for waist circumference, however, these associations were no longer statistically significant.

We demonstrated that high leptin levels are associated with insulin resistance and metabolic syndrome independent of BMI but these associations are significantly mediated through the effects of central obesity.

We previously reported a significant association between plasma leptin (LPT) concentration and blood pressure (BP), which was partly independent of serum insulin levels and insulin resistance. The aims of this study were to detect whether serum LPT levels predict the development of hypertension (HPT) in the 8-yr follow-up investigation of a sample of an adult male population (the Olivetti Heart Study), and to evaluate the role of body mass index (BMI) and insulin resistance in this putative association.

The study population was made up of 489 untreated normotensive subjects examined in 1994-1995 (age: 50.1 +/- 6.7 yr; BMI: 26.3 +/- 2.8 kg/m(2); BP: 120 +/- 10/78 +/- 6 mm Hg; and homeostatic model assessment index: 2.1 +/- 1.6).

The HPT incidence over 8 yr was 35%. The participants with incident HPT had similar age but higher BMI (P < 0.001), serum LPT (P < 0.001), and BP (P < 0.01) at baseline. One sd positive difference in baseline serum LPT log was associated at univariate analysis with a 49% higher rate of HPT [95% confidence interval (CI) 22-83; P < 0.001]). In three different models of multivariable logistical regression analysis, LPT was respectively associated with a 41% greater risk to develop HPT (95% CI 15-74; P < 0.001) upon adjustment for age and baseline BP, with a 48% (95% CI 20-81) greater risk when adding the homeostatic assessment model index to the model, and with 33% greater risk (95% CI 6-67; P < 0.02) upon adjustment for BMI.

In this sample of originally normotensive men, circulating LPT level was a significant predictor of the risk to develop HPT over 8 yr, independently of BMI and insulin resistance.

Metabolic syndrome (MetSyndr), a constellation of abnormalities [obesity, glucose intolerance, insulin resistance (IR), dyslipidemia (low HDL-cholesterol, high LDL-cholesterol and triglycerides (TG)], and elevated blood pressure (BP)], increases the risk of cardiovascular (CV) disease and premature death. From 10% to 30% of the adult population in industrialized countries has MetSyndr, which effectively predicts the development of type 2 diabetes mellitus (T2D) and CV disease. Because of the complex etiology of MetSyndr, a multi-targeted, integrated therapeutic approach is required to simultaneously treat high BP, obesity, lipid disorders and T2D (if present), to fully protect CV, cerebrovascular and renal systems. If lifestyle modification (weight control, diet, exercise, smoking cessation, moderation of alcohol intake) is ineffective, pharmaco-theraphy should be added to treat simultaneously the lipid- and non-lipid CV risk factors. Patients with HTN and MetSyndr should be started on angiotensin-converting enzyme (ACE) inhibitors, unless contraindicated. The ACE inhibitors and angiotensin receptor blockers (ARBs) reduce the odds of developing new onset T2D and also decrease albuminuria. The ACE inhibitors provide cardioprotective and renoprotective benefits beyond their effect on BP; they also improve IR. The ARBs are renoprotective in addition to being cardioprotective. Long-acting calcium channel blockers are also recommended in hypertensive patients with MetSyndr; these drugs also improve IR. Thiazides (at low doses) and selected ss-blockers can be given to patients with HTN and MetSyndr. Celiprolol in combination with diuretics has a favorable effect on glucose tolerance and IR in patients with HTN and MetSyndr, and spironolactone added to ACE inhibitor or ARB therapy provides additional reno- and CV protective benefits in patients with diabetic nephropathy. Carvedilol, a ss-blocker with vasodilating properties, added to ACE inhibitor or ARB therapy, is effective in preventing worsening of microalbuminuria in patients with HTN and MetSyndr; it also improves IR and glycemic control. Most patients eventually require two or more antihypertensive drugs to reach BP goal. It is recommended that therapy in patients whose BP is more than 20/10 mm Hg above target at diagnosis be initiated with a combination of antihypertensive drugs, administered either as individual drugs or as fixed-dose formulations. Treatment with fixed-dose combinations, such as irbesartan + hydrochlorothiazide provides good BP control in more than two-thirds of hypertensive patients with MetSyndr. Lipid and BP targets are reached in a high percent of patients with HTN and CV disease treated with a combination of amlodipine + atorvastatin. In conclusion, hypertensive patients with the MetSyndr be treated aggressively for each component of the syndrome to provide CV, cerebrovascular and renal protection.

The metabolic syndrome (MetS) is an important tool that identifies populations at increased risk for cardiovascular disease (CVD) and type 2 diabetes, targeting them for preventive measures. The criteria for the identification of the MetS were initially constructed from data in Caucasian populations. Recent research suggests that the current criteria for the MetS may not accurately characterize disease risk in non-Caucasian populations, either over or underestimating the risk in certain ethnic groups. Altering the criteria for each population by making ethnic-specific cutoffs as has been done with waist circumference will help in more accurate characterization. Using different combinations of the MetS criteria for different ethnic groups based CVD risk and factor analysis needs consideration. With better characterizations of patient populations, the ultimate goal would be to make MetS more accurate for predicting CVD risk while retaining the ease of screening afforded by the MetS. The proposed alterations of definition and criteria of the MetS would ensure its continued viability and sustainability.

Because high circulating plasma leptin is associated with many features of the metabolic syndrome (MS), such as abdominal obesity, insulin resistance and high blood pressure (BP), we analysed the ability of plasma leptin concentration to predict the risk of developing MS in a prospective investigation of adult male participants of the Olivetti Heart Study (OHS).

Three hundred and sixty out of 907 men participating in the 1994-95 and 2002-04 OHS examinations (mean age at baseline 50.4 years, range 25-73 years) were free of MS at first visit according to NCEP-ATP III criteria (modified for the lack of high-density lipoprotein cholesterol measurement at baseline). During an average follow-up period of 8 years, there were 52 incident cases of MS (14.5%) due, in particular, to a rise in the prevalence of high BP (+42.4%), abdominal obesity (+16.4%) and impaired fasting glucose (IFG, +6.1%). In multivariate analyses, a one standard deviation difference in baseline plasma leptin concentration was associated with a 1.58-fold greater risk of developing MS (95% confidence interval = 1.10-2.30, P = 0.016) accounting for age, waist circumference, homeostatic assessment model index, smoking, alcohol consumption and physical activity. In particular, plasma leptin was positively associated with the risk of developing high BP (0.006) and IFG (0.014), after adjustment for confounders.

In this sample of an adult male population free of MS at baseline, circulating plasma leptin was a significant predictor of the risk of MS and, in particular, of its high BP and IFG components, independently of potential confounders.

The diagnostic criteria and the clinical usefulness of the metabolic syndrome (MetSy) are currently questioned. The objective was to describe the structure of MetSy and to evaluate its components for prediction of diabetes type 2 (T2DM).

This was a case-referent study nested within a population-based health survey. Among 33,336 participants, we identified 177 initially non-diabetic individuals who developed T2DM after 0.1 to 10.5 years (mean, 5.4 years), and, for each diabetes case, two referents matched for sex, age, and year of health survey. Baseline variables included oral glucose tolerance test, BMI, blood pressure, blood lipids, adipokines, inflammatory markers, insulin resistance, and beta-cell function. Exploratory and confirmative factor analyses were applied to hypothesize the structure of the MetSy. The prediction of T2DM by the different factors was evaluated by multivariate logistic regression analysis.

A hypothetical five-factor model of intercorrelated composite factors was generated. The inflammation, dyslipidemia, and blood pressure factors were predicitive only in univariate analysis. In multivariable analyses, two factors independently and significantly predicted T2DM: an obesity/insulin resistance factor and a glycemia factor. The composite factors did not improve the prediction of T2DM compared with single variables. Among the original variables, fasting glucose, proinsulin, BMI, and blood pressure values were predictive of T2DM.

Our data support the concept of a MetSy, and we propose five separate clusters of components. The inflammation and dyslipidemia factors were not independently associated with diabetes risk. In contrast, obesity and accompanying insulin resistance and beta-cell decompensation seem to be two core perturbations promoting and predicting progression to T2DM.

The purpose of the present cross-sectional study was to identify components of risk variables associated with metabolic syndrome in Asian Indian adolescents. The sample included 400 adolescents (boys = 200; mean age, 15.0 +/- 4.5 years; girls = 200; mean age, 14.4 +/- 3.8 years) from Calcutta, India. The following variables were considered: body mass index, waist circumference, sum of four skinfolds, subscapular/triceps ratio, total cholesterol, triglycerides, blood glucose, and systolic, diastolic, and mean arterial pressure. Principal component factor analysis revealed four uncorrelated factors for adolescent boys that cumulatively explained 76.3% of the observed variance of metabolic syndrome. Four factors with overlap between factors 1 and 2 were observed for adolescent girls that cumulatively explained 74.3% of the total variation of metabolic syndrome. The four factors identified were central body fat distribution (factor 1), centralized subcutaneous fat (factor 2), lipids-blood glucose (factor 3), and blood pressure (factor 4). Furthermore, the first two factors, i.e., central body fat distribution and centralized subcutaneous fat, cumulatively explained more than 46% (46.5% for boys; 46.4% for girls) of the observed variation of metabolic syndrome. Since more than one factor was identified for metabolic syndrome, more than one physiological mechanism could account for the clustering of risk variables of metabolic syndrome in Asian Indian adolescents. Factor analysis of Asian Indian adults also revealed four uncorrelated factors, similar to the present factors, therefore warranting intervention as early as adolescence.

Hyperuricemia is commonly associated with obesity, glucose intolerance, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. The resemblance of the metabolic syndrome and hyperuricemia has led to the suggestion that hyperuricemia is a part of the metabolic syndrome. The purpose of this study is to examine the contribution of uric acid (UA) as an additional component of the metabolic syndrome in middle-aged men.

In total, 393 male participants, aged 45-60 years, were recruited from a professional health evaluation program. Anthropometric measurements and blood pressure (BP) were taken after an overnight fast. Fasting blood samples were collected for the measurements of glucose, UA, and lipid profile. Logistic regression models were fitted to examine the relationship between UA and the diagnosis of metabolic syndrome. Factor analysis was performed to explore the relationship between UA and the components of the metabolic syndrome.

The diagnosis of the metabolic syndrome was significantly associated with waist circumference (WC), glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), systolic BP, and liver enzyme levels, but not associated with UA levels. The sensitivity of hyperuricemia (serum UA > or = 7.0 mg/dL) for the diagnosis of the metabolic syndrome was 58.0% and the specificity was 55.3%. In factor analysis, UA aggregated with body mass index, WC, glucose, log TG, and HDL-C as a metabolic factor. Systolic and diastolic BP were loaded on a second factor separately. The model loaded with UA explained a similar proportion of the total variance (56.9%), as did the model loaded without UA (62.5%).

Our results suggest that the contribution of UA as an additional component of the syndrome seems to be insignificant. We propose that hyperuricemia might not be an important facet for the understanding of the underlying structure of the metabolic syndrome.

The term metabolic syndrome (MS) describes a cluster of cardiovascular risk factors including dyslipidemia, glucose intolerance, insulin resistance, and hypertension. Obesity increases the risk of MS, but as obesity is neither necessary nor sufficient to cause the syndrome, there is considerable interest in identifying obesity-independent pathways. One such pathway may involve the actions of the adipokine leptin, which is associated cross-sectionally with MS and prospectively with coronary heart disease and stroke, independently of obesity. Our goal was to test the hypothesis that leptin predicts the development of the features of MS independently of obesity.

This study used a prospective population-based cohort of 748 middle-aged whites in whom baseline measures of leptin and repeated measurement of the subcomponents of the MS at 5 and 10 years were available. The features of the MS were characterized as five factors (obesity, dyslipidemia, elevated blood pressure, glucose intolerance, and insulin resistance), which were combined to create an MS summary score.

Baseline leptin significantly predicted the development of obesity (p = 0.001) and, after adjustment for BMI, development of glucose intolerance (p = 0.016) and insulin resistance (p < 0.0001). Leptin levels did not independently predict a change in lipids or blood pressure. Leptin levels significantly predicted the development of the MS (p = 0.036), independently of baseline BMI.

Leptin predicts the development of the MS independently of baseline obesity. This association is specifically related to the development of glucose intolerance and insulin resistance. The extent to which these relationships are explained through residual confounding by obesity remains to be determined.

The aim of the study was to establish the best cut-off value for the homeostatic model assessment (HOMA) index in identifying children and adolescents with the metabolic syndrome. The study included 72 non-obese and 68 obese children aged 7 to 16 years. Obesity is defined using the criteria proposed by Cole et al., being included as metabolic syndrome variables waist circumference, systolic blood pressure, diastolic blood pressure and seric values of glucose, uric acid, fasting insulin, leptin, triglycerides and HDL-cholesterol. Children were considered as having the metabolic syndrome when four or more characteristics showed abnormal values. The HOMA index was calculated as the product of the fasting plasma insulin level (microU/mL) and the fasting plasma glucose level (mmol/L), divided by 22.5. HOMA index cut-offs from the 5th to the 95th percentile were used. A receiver operating characteristic (ROC) curve was generated using the different HOMA cut-offs for the screening of the metabolic syndrome. The areas under the ROC curve, 95% confidence intervals, and the point to the ROC curve closest to 1, were calculated. The area under the ROC curve was 0.863 (95% C.I.: 0.797, 0.930). The point closest to 1 corresponds to the 60th percentile of the HOMA index distribution in our sample. HOMA index value at the 60th percentile was 2.28. Cut-off values corresponding to a range of HOMA index from the 50 to the 75 percentile, showed similar distances to 1. HOMA index values for percentiles 50 to 75 ranged from 2.07 to 2.83. In conclusion, HOMA index could be a useful tool to detect children and adolescents with the metabolic syndrome. HOMA cut-off values need to be defined in the paediatric population; however, values near to 3 seem to be adequate.

Leptin's hematopoietic or proinflammatory role has been experimentally reported. We investigated whether serum leptin concentrations are associated with white blood cell (WBC) counts in humans.

Serum leptin concentrations of Japanese civil servants aged 40 to 59 years (1082 men and 200 women) were analyzed in relation to their WBC count. Serum leptin concentrations and WBC counts were measured by radioimmunoassay and automated particle counter respectively, using samples obtained at the time of the participants' annual health checkups.

The geometric mean (+/-geometric standard deviation) leptin concentrations were 3.25 +/- 1.82 ng/mL and 6.25 +/- 3.99 ng/mL, and the geometric mean WBC counts, 5770 +/- 1269/mm(3) and 5107 +/- 1228/mm(3), in men and women respectively. The WBC count adjusted for age, body mass index (BMI), physical activity, and drinking and smoking habits increased together with the increase in leptin concentration. Multiple linear regression against WBC count by the leptin concentration and those covariates revealed a significant and independent association with serum leptin concentration especially in women (standardized beta = 0.31, p < 0.001), and also in men (standardized beta = 0.17, p < 0.001). BMI was not significantly associated with WBC counts in the multivariate model adjusting for leptin levels in both sexes.

Our results are in line with leptin's hematopoietic or proinflammatory functions. The increased WBC counts often observed in obese people would be mediated by the increased leptin concentration.

The role of leptin has been more clear in the endocrinology area after the discovery of its secretion from the adipose tissue. The aim of the study is to investigate the leptin levels in obese women in whom type 2 diabetes mellitus were present or absent.

Thirty-five obese women with type 2 diabetes mellitus (group 1) and 34 obese women without type 2 diabetes mellitus (group 2) were enrolled in the study. In both groups the body mass index (BMI), waist circumference, and waist-to-hip ratio were measured. Leptin, HbA1c, creatinine and the lipid profile were assessed.

Leptin was found to be statistically significantly lower in group 1 than in group 2 (40.22 +/- 17.77 ng/ml versus 50.12 +/- 15.51 ng/ml, respectively; p = 0.019). It was well correlated with BMI in group 1 (r = 0.60, p = 0.0001). In group 1 also, correlation of leptin was moderate with creatinine and high-density lipoprotein-cholesterol (r = 0.36, p = 0.037 versus r = 0.37, p = 0.027, respectively), whereas triglyceride had a negative correlation (r = -0.34, p = 0.046). In group 2, the only significant correlation with leptin was BMI (r = 0.41, p = 0.02). Leptin was also significantly lower in 17 subjects with poorly controlled diabetes mellitus than in 18 well-controlled diabetics (33.54 +/- 15.82 ng/ml versus 44.61 +/- 17.54 ng/ml, respectively; p = 0.038).

Since leptin is lower in obese women with diabetes than without diabetes and additionally it is even lower in the poorly controlled diabetes subgroup, we think that further studies a rerequired to make clear the issue for lower leptin levels, whether it is a reason or an outcome.

As we enter the twenty-first century, the burden of chronic diseases, such as obesity, type 2 diabetes, and CVDs, is expected to increase dramatically. These diseases are a consequence of several factors that include an aging population,changes in demographic composition, and an excess of contemporary lifestyle. The prevention and control of overweight, obesity, metabolic syndrome, and diabetes pose special challenges for clinical and public heath practice as well as for basic, clinical, and population science research.

The prevalence of coronary heart disease (CHD) is known to be very high in the people of Indian origin. In India, rates are rising and CHD has been predicted to rank first among the causes of death in the Indian population by 2015. The reasons for the increased susceptibility of Indians to CHD are yet to be understood completely. However, studies hinted that clustering of risk variables of the metabolic syndrome (MS) could be responsible for the increasing incidence of CHD in the Indians. Therefore, identification of the components of the MS could be one aspect in the way to curb the increasing incidence of CHD among the Asian Indians.

Principal component factor analysis (PCFA) was undertaken to identify the components or factors of the metabolic syndrome (MS) among the middle-aged Bengalee Hindu men of Calcutta, India, and was compared with the findings from other studies. The present cross-sectional study consisted of 212 Bengalee Hindu men aged 30 years and above. Besides anthropometric measures, lipid profile, blood pressure, and fasting plasma glucose (FPG) were collected from each participant. Waist-hip ratio (WHR), trunk-extremity ratio (TER), and central fat skinfold ratio (CFSR) were computed accordingly. The lipid profile measures that were included were total cholesterol (TC), fasting triglyceride (FTG), high (HDL-C), low (LDL-C), and very low density lipoprotein cholesterol (VLDL-C).

Principal components factor analysis revealed four uncorrelated factors that cumulatively explained 72.37% of the observed variance of the metabolic syndrome by measured variables. These four factors could be identified as central obesity (factor 1), centralized subcutaneous fat (factor 2), lipid profile blood glucose (factor 3), and blood pressure (factor 4). The present factor analysis confirms the general finding from other factor analyses of the metabolic syndrome on different ethnic groups that have identified three to four factors. Furthermore, the first two factors, that is, central obesity and centralized subcutaneous fat cumulatively explained 47% of the observed variance of metabolic syndrome in this population.

Since more than one factor was identified for the metabolic syndrome and as no observed variable loaded on all four factors, therefore, more than one physiological mechanism could be accounted for the clustering of risk variables of the metabolic syndrome among the Bengalee Hindu men.

To identify regions on the genome linked to plasma leptin levels.

Full genome scan with 402 microsatellite markers, spaced approximately 10 cM apart. Data were analyzed using the Haseman-Elston regression linkage analysis.

A total of 160 sibling pairs from 59 predominantly African American, obese families recruited to participate in a genetic-epidemiological study of obstructive sleep apnea.

Serum leptin levels adjusted for age, sex, race and body mass index (BMI).

Suggestive linkage peaks were observed on chromosomes 2 (P=0.00170; marker D2S1384), 3 (P=0.00007; marker D3S3034), 4 (P=0.00020; marker D4S1652) and 21 (P=0.00053; marker D21s1411).

The peak on chromosome 3 is near the gene for glycogensynthase kinase 2 beta, an important factor in glucose homeostasis. Linkage was generally stronger after BMI adjustment, suggesting the potential influence of a number of metabolic pathways on leptin levels other than those that directly determine obesity levels. The evidence of linkage for leptin levels is consistent with prior linkage analyses for cholesterol, hypertension and other metabolic phenotypes.

The objectives of this study were to evaluate the relationships of leptin with the metabolic syndrome and to examine leptin's role in clustering of the metabolic components among Korean adolescents.

A cross sectional study was carried out in 68 male and 80 female adolescents aged 13-18 years in an urban area of South Korea. Anthropometric variables were measured and blood pressure, fasting plasma glucose, low-density lipoprotein cholesterol, triglyceride, high-density lipoprotein cholesterol, and serum leptin were obtained.

As more metabolic components were clustered, body mass index, body fat, waist circumference, and serum leptin levels were significantly increased. Principal components factor analysis revealed three factors in males and females that explained 70% and 65%, respectively, of the observed variance of the 10 measured variables. These were obesity-leptin-lipid factor, blood pressure factor, and glucose-cholesterol factor in males and obesity-leptin-glucose factor, blood pressure factor, and cholesterol factor in females. Leptin loaded on only one factor in both genders.

Leptin did not appear to have a major role linking various components of the metabolic syndrome, even though it was strongly associated with obesity indices. Gender difference of linking of leptin with glucose or lipid was observed. There seems to be more than one pathophysiological mechanism which might underlie full expression of the metabolic syndrome among Korean adolescents.

Disturbances of carbohydrate and lipid metabolism in men with spinal cord injury are common, but poorly defined. Clustering of recognized risk factors for obesity and disorders of carbohydrate and lipid metabolism are characteristic of the metabolic syndrome. The purpose of this study was to investigate the presence of metabolic syndrome using modifications of the World Health Organization (WHO) definition and including total physical activity levels (minutes/week), in a group of active males with spinal cord injury who were carefully matched for age, height, and weight with active able-bodied males. Factor analysis is used widely to explore factors of the metabolic syndrome. This technique was used in this study of 20 spinal cord-injured (SCI) men and 20 able-bodied controls, matched for age, height, and weight. Three-factor models, each reflecting a different aspect of the metabolic syndrome, were identified for both study groups. The average communality score for the SCI group was 0.8 and 0.7 for the control group. For the SCI group, factor 1 reflected an interaction between adiposity measures, physical activity, and postload insulin and glucose, factor 2 was reflective of dyslipidemia, while factor 3 revealed an interaction between fasting levels of insulin and glucose. In the control group, factor 1 reflected an association between the adiposity measures and physical activity, factor 2 was reflective of postload glycemic control, with factor 3 reflecting an interaction between fasting insulin and dyslipidemia. By summation of the total variance of each factor, the 3-factor models explained 80% and 69% of the variance in the original 9 variables examined in the SCI and control groups, respectively. In summary, while the WHO definition for the metabolic syndrome appears suitable for use in identifying the incidence of this syndrome in SCI men, some modification of anthropometric and lipid measures may be required.

To compare plasma leptin in Saudi subjects with Type 2 diabetes and coronary heart disease (CHD) with non-diabetic control subjects and to examine the relationship of plasma leptin to other CHD risk factors.

Serum leptin concentrations were measured in 144 Saudi men. Subjects studied included 59 with Type 2 diabetes mellitus [BMI 27.5 (3.7) kg/m2 mean (sd)], 34 with coronary heart disease [BMI 29.6 (1.8) kg/m2], and 51 non-diabetic controls [BMI 28.0 (3.5) kg/m2]. There was no significant difference in BMI between the groups. Fasting serum leptin, lipids, insulin, apolipoproteins and glucose were measured. BMI, blood pressure; smoking habit and age were also recorded. Insulin resistance was assessed using the HOMA model.

Leptin concentrations were significantly higher in diabetic and CHD patients than in controls (P = 0.024 and 0.016, respectively). Multiple regression analysis showed that body weight (P < 0.0006), serum triglyceride concentration (P = 0.046) and systolic blood pressure (P = 0.013) were all significantly related to the logarithm of the serum leptin concentration (R2 = 0.549) in CHD patients. A subgroup analysis, comparing those patients who had the metabolic syndrome, as defined by WHO, with controls, showed higher serum leptin in those with metabolic syndrome (P = 0.05).

Serum leptin is increased in Saudi subjects with diabetes mellitus, metabolic syndrome and CHD. Leptin may be a marker of risk of CHD, at least in men, and contribute to the CHD risk profile in subjects with insulin resistance. Further studies are needed to evaluate this relationship prospectively.

Plasma leptin levels have been shown to be an independent risk factor for cardiovascular disease. Leptin has been shown to have sympathetic and vascular effects, and may increase cardiovascular risk through increased blood pressure, left ventricular hypertrophy, or atherosclerotic mechanisms. This study examines whether leptin levels, independent of body mass and insulin resistance, are a risk factor for hypertension and left ventricular hypertrophy.

A population-based, cross-sectional sample of 410 adults from rural Spain was studied. The correlations between plasma leptin levels and left ventricular mass index, sum of wall thicknesses, and blood pressure were calculated. Multiple linear regression analysis was used to adjust for other cardiovascular risk factors.

After adjusting for age, body mass index, systolic blood pressure, sex, and insulin resistance, leptin was inversely associated with left ventricular mass index (beta = -0.20, P < 0.01). Leptin was also inversely related to the sum of wall thicknesses; however, this association did not reach statistical significance (beta = -0.12, P = 0.063). Leptin was not statistically associated with blood pressure after adjusting for body mass index.

The results do not support the hypothesis that leptin increases cardiovascular risk by increasing left ventricular mass index or blood pressure. Other mechanisms, related to atherosclerosis, could explain the increased risk of cardiovascular diseases observed with high leptin levels.

The aim of this study was to examine how major components of the insulin resistance (IR) syndrome relate to each other and to cardiovascular disease (CVD) in postmenopausal women in 4 ethnic groups. Baseline data from the Women's Health Initiative (WHI) on 3,083 50- to 79-year-old women (1,635 white, 802 black, 390 Hispanic, and 256 Asian/Pacific Islander) were examined. Participants underwent a personal interview and a physical examination, blood samples were drawn, and a detailed cardiovascular history was ascertained. Factor analysis was used to assess the clustering and interdependence of groups of CVD-related IR syndrome variables. Four factors were identified. An obesity factor included IR in all groups and had a significant association with CVD in white (P =.0001) and Hispanic (P =.0024) women. A dyslipidemia factor (high-density lipoprotein [HDL], triglycerides, and HDL2: total HDL ratio) also included insulin and IR and was significantly correlated with CVD in black (P=.0006) and Hispanic (P =.0217) women and had a borderline association in white women (P =.068). Total and low-density lipoprotein (LDL) cholesterol did not relate to CVD in any group. Blood pressure was related weakly to CVD in white women (P =.0434) and strongly in black women (P =.0095). Components of the IR syndrome appear to be associated with CVD in postmenopausal women, although the magnitude of these relationships differed by ethnicity.

The role of leptin in the association between body mass, central adiposity, and blood pressure (BP) is controversial. This study evaluated the relationship between leptin and BP in relation to body mass index (BMI) and fat distribution in a large sample of untreated male adults.

The study population was made up of 457 untreated male employees of the Olivetti factory in Naples. Plasma leptin, complete anthropometry, BP, and relevant biochemical variables were measured.

Log-transformed plasma leptin levels were directly associated with BMI (r = 0.661, p < 0.001) and waist circumference (r = 0.630; p < 0.001). Leptin also correlated with systolic (r = 0.258) and diastolic (r = 0.277) BP (p < 0.001). The association between leptin and BP was maintained after accounting for age, BMI (or waist circumference), log-insulin, and serum creatinine (p < 0.01); this association was stronger than that with BMI. Logistic regression analysis showed that an increased prevalence of hypertension (BP >or= 140 and/or 90 mm Hg) was associated with high plasma leptin levels when controlling for age and waist circumference (odds ratio, 1.99; 95%CI, 1.06 to 3.72) or for age and BMI (odds ratio, 1.92; 95%CI, 1.02 to 3.61).

A graded positive relationship between plasma leptin levels and BP was observed in this sample of untreated male adults. This association was independent of age, BMI, abdominal adiposity, and fasting plasma insulin. Moreover, elevated plasma leptin concentrations were associated with greater probability of hypertension, again independently of potential confounders.

The metabolic syndrome is like an elephant, and any literary review of its importance is shamefully reduced to an examination of tusks, trunk, and tail. Evidence continues to mount that this diminutive approach is an incorrect management strategy for such a large problem. Diet and lifestyle are effective strategies, but they must effectively compete with behaviors that have instant gratification. Our society has turned its focus away from the long-term rewards of good sustainable behaviors and has instead focused on short-term rewards of unsustainable behaviors. To tame the behaviors that promote the metabolic syndrome, simple answers from diet and drug therapy will require support from society to be effective.

Overweight is associated with both higher bone mineral density (BMD) and higher serum leptin concentrations. In humans, little is known about the relationship of leptin concentration and bone density. We studied this relationship in a large, national population-based sample. Participants included 5815 adults in the Third U.S. National Health and Nutrition Examination Survey (NHANES III; 1988-1994) who underwent DXA of the proximal femur and measurement of fasting serum leptin. Mean +/- SE BMD (gm/cm2) of the total hip was 1.01 +/- 0.005 in men, 0.94 +/- 0.004 in premenopausal women, and 0.78 +/- 0.007 in postmenopausal women. Bone density increased with increasing leptin concentration in men (p = 0.003), premenopausal women (p < 0.001), and postmenopausal women (p < 0.001). However, after adjusting for body mass index (BMI) and other bone density-related factors, an inverse association emerged in men (p < 0.001), being most evident among men < 60 years old. There was no association of leptin and BMD in premenopausal women (p = 0.66) or postmenopausal women (p = 0.69) in multivariate analysis. Controlling for leptin had no effect on the strong positive association of BMI and BMD in either men or women. Serum leptin concentration did not appear to affect directly BMD. If present, the association appeared to be limited to younger men who are at lower risk of osteoporosis.

The combination of insulin resistance, dyslipidemia, hypertension, and obesity has been described as a "metabolic syndrome" that is a strong determinant of type 2 diabetes. Factor analysis was used to identify components of this syndrome in 1,918 Pima Indians. Prospective analyses were conducted to evaluate associations of identified factors with incidence of diabetes. Factor analysis identified 4 factors that accounted for 79% of the variance in the original 10 variables. Each of these factors reflected a proposed component of the metabolic syndrome: insulinemia, body size, blood pressure, and lipid metabolism. Among 890 originally nondiabetic participants with follow-up data, 144 developed diabetes in a median follow-up of 4.1 years. The insulinemia factor was strongly associated with diabetes incidence (incidence rate ratio [IRR] for a 1-SD difference in factor scores = 1.81, P < 0.01). The body size and lipids factors also significantly predicted diabetes (IRR 1.52 and 1.37, respectively, P < 0.01 for both), whereas the blood pressure factor did not (IRR 1.11, P = 0.20). Identification of four unique factors with different associations with incidence of diabetes suggests that the correlations among these variables reflect distinct metabolic processes, about which substantial information may be lost in the attempt to combine them into a single entity.

Insulin resistance syndrome (IRS)-related phenotypes, such as hyperinsulinemia, obesity-related traits, impaired glucose tolerance, dyslipidemia, and hypertension, tend to cluster into factors. We attempted to identify loci influencing the factors of IRS-related phenotypes using phenotypic data from 261 nondiabetic subjects distributed across 27 low-income Mexican-American extended families. Principal component factor analyses were performed using eight IRS-related phenotypes: fasting glucose (FG), fasting specific insulin (FSI), BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), HDL cholesterol, ln triglycerides (ln TGs), and leptin (LEP). The factor analysis yielded three factors: factor 1 (BMI, LEP, and FSI), factor 2 (DBP and SBP), and factor 3 (HDL and ln TG). We conducted multipoint variance components linkage analyses on these factors with the program SOLAR using a 10--15 cM map. We found significant evidence for linkage of factor 1 to two regions on chromosome 6 near markers D6S403 (logarithm of odds [LOD] = 4.2) and D6S264 (LOD = 4.9). We also found strong evidence for linkage of factor 3 to a genetic location on chromosome 7 between markers D7S479 and D7S471 (LOD = 3.2). In conclusion, we found substantial evidence for susceptibility loci on chromosomes 6 and 7 that appear to influence the factors representing the IRS-related phenotypes in Mexican-Americans.