The metabolic syndrome

Pharmacological Research 53 (2006) 457–468

The metabolic syndrome Beyond the insulin resistance syndrome Giuseppe Penno, Roberto Miccoli, Laura Pucci, Stefano Del Prato ∗ Department of Endocrinology & Metabolism, Section of Diabetes and Metabolic Diseases, University of Pisa, Italy

Keywords: Metabolic syndrome; Insulin resistance; Inflammation; Visceral adiposity

1. Introduction Glucose intolerance, visceral adiposity, hypertension and dyslipidemia tend to cluster with a frequency that is higher than expected by chance to the point that the existence of a syndrome has been postulated [1–4]. The metabolic syndrome encompasses a constellation of metabolic disturbances, all known cardiovascular risk factors, and is a common, age-related disorder mainly driven by the increasing prevalence of obesity [5]. The definition of the WHO [1], European Group for the Study of Insulin Resistance [2], and American College of Endocrinology [3] all agree in including glucose intolerance or insulin resistance as an essential component of the syndrome. On the contrary, insulin resistance has been neglected by the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP: ATP III) [4]. Other striking differences exist among various definitions. Thus, the American College of Endocrinology [3] omitted obesity as a component of the syndrome, while obesity is a necessary element in the last International Diabetes Federation (IDF) definition [6]. Not only there is some confusion on definitions, but the precise cause of syndrome also remains uncertain. Different factors are probably involved, many brought about by changes in lifestyle [7]. Insulin resistance has been considered a key player in the pathophysiology of the metabolic syndrome and it was postulated to represent its underlying cause [8]. Nonetheless, such a role has been recently questioned [9]. On the contrary, central obesity has gained a crucial role in the diagnosis [8] as well as in the pathogenesis [5,6] of the metabolic syndrome based on the strong correlation found between waist circumference, cardiovascular risk, and other components of the syndrome. Moreover, there is no doubt that accretion of visceral fat

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may play a triggering effect on full development of the syndrome [6]. On the light of these observations, in this review we will discuss to which extent insulin resistance contributes to the syndrome and whether some component may cluster in the syndrome above and beyond impaired insulin sensitivity. Also, we shall explore the hypothesis that expansion of visceral adipose tissue contributes to the metabolic syndrome and to its association with cardiovascular disease. 2. Insulin resistance: does it explain all? Since impaired insulin sensitivity and/or hyperinsulinemia have been documented in the majority of people with metabolic syndrome, insulin resistance has been claimed to play a unifying pathogenetic mechanism accounting for many, if not all, disturbances clustering in the syndrome [10]. On the contrary, the National Heart, Lung, and Blood Institute, in collaboration with the American Heart Association (NHLBI/AHA) has suggested that at least two more etiological factors can be identified together with insulin resistance: “obesity and disorders of adipose tissue” and a “constellation of independent factors that mediate specific components of the syndrome” including proinflammatory state and several fat-related endocrine factors [11]. This view is also supported by the observation that hyperinsulinemia and insulin resistance, often considered one the reflection of the other, may have independent effects on the components of the syndrome [9,12]. In non-diabetic subjects the prevalence of the metabolic syndrome and the number of the clustering components increase as a function of quartiles of fasting plasma insulin levels [13]. Accordingly, insulin sensitivity decreases with the increase of the number of components in a given individual [14]. Though these observations indicate that most people with metabolic syndrome are insulin resistant, many studies have detracted from such a “natural” link. For instance, in a series of non-diabetic overweight subjects, 78% of those

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G. Penno et al. / Pharmacological Research 53 (2006) 457–468

Fig. 1. Prevalence of insulin resistance in individuals with and without metabolic syndrome (ATPIII criteria) and prevalence of the metabolic syndrome in individuals with and without insulin resistance within an Italian cohort (n = 591) of subjects at risk for type 2 diabetes in the GENFIEV study. Insulin resistance was defined as HOMA-S in the top quartile of control individuals. Personal data.

who had the metabolic syndrome also appeared to be insulin resistant but no more that 52% of those with insulin resistance met the criteria for the metabolic syndrome [15]. Similar results have been observed in an Italian cohort by ourselves (Fig. 1). In the study by Liao et al. [16], among 65 non-diabetic overweight/obese caucasians who did not meet ATPIII criteria for the metabolic syndrome, 20 (31%) had insulin resistance. Still, these individuals had worse CVD risk profile, including higher BMI, waist circumference, fasting glucose, triglycerides, increased large VLDL, increased small LDL, and decreased large HDL particle concentrations as compared to insulin-sensitive-individuals [16]. Similar discrepancies have been repeatedly reported [17–19] implying the link between impaired insulin sensitivity and components of the syndrome might not be as strong as initially thought. It should be, however, noticed that all these studies rely on arbitrary definition of “insulin resistance” as cut off figures for insulin sensitivity was arbitrarily chosen. Also, the metabolic syndrome was diagnosed on the basis of the ATPIII criteria with no evidence this should be the reference definition or the one with the best performance [9]. Thus, if any, uncertainty adds to uncertainty. 3. Exploring the structure of the metabolic syndrome In the attempt to overcome some of the above limitations in understanding the role of insulin resistance in the development of the syndrome and in addressing other potential pathogenetic mechanisms, several studies have adopted factorial analysis. This is a multivariate statistical technique aiming at reducing large number of observed inter-correlated variables (i.e. the clinical features of the metabolic syndrome) to a smaller set of underlying, frequently unknown, independent (possibly etiological) factors. By this analysis it is attempted to define whether a cluster of variables may be related to a unique underlying factor, thus favoring a common pathogenetic cause, or whether it may be related to more than one factor, then supporting a more complex etiology of the syndrome. Both explanatory and confirmatory factor analysis studies have been performed. With the former, the number of latent factors is essentially unknown and has to be determined from

the data. The latter is used to assess robustness of results from explanatory factor analysis in independent data sets or to directly test a priori hypotheses from other research sources [20]. 3.1. Exploratory analysis In 281 non-diabetic women of the Kaiser Permanente Women Twins Study [21], factor analysis reduced 10 correlated risk factors to three uncorrelated domains, each reflecting a different aspect of the metabolic syndrome. Factor 1 included increased body weight, waist circumference, fasting insulin, and glucose; factor 2, increased postload and fasting glucose and insulin and systolic blood pressure; and factor 3, larger low-density lipoprotein particles, decreased plasma triglycerides, and increased high-density lipoprotein. Together, these three distinct domains explained nearly 66% of the total variance in the data. Since the 1994 seminal study by Edwards et al. [21], a large number of factor analyses have been published [24–58]. These studies have examined different populations, including men and women, young and old subjects, different racial/ethnic groups, and people with and without risk of type 2 diabetes. The Framingham Offspring Study was the first large scale trial (2458 non-diabetic subjects) subjected to this statistical approach [23]. In this population three domains were identified. Fasting and 2h post-challenge insulin levels, fasting triglyceride and HDL levels, BMI, and waist-to-hip ratio were associated with one factor. Fasting and 2-h levels of glucose and insulin were associated with a second factor. Systolic blood pressure, diastolic blood pressure, and BMI were associated with a third factor. These results were consistent with more than one independent physiological process, namely: a central metabolic syndrome (hyperinsulinemia, dyslipidemia, and obesity), glucose intolerance, and hypertension. Glucose intolerance was linked to the central domain through shared correlations with insulin levels and hypertension with shared association with obesity. Thus, insulin resistance did not appear to underlie all features of the insulin resistance syndrome [23]. Four domains, interpreted as weight/waist, blood pressure, lipids, and insulin/glucose, were identified in 3159 elderly Japanese–American men from the Honululu Heart Program.

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These four uncorrelated factors explained 78.2% and 74.7% of the variance in non-diabetic and diabetic individuals, respectively [24]. Consistently, in the Strong Heart Study, the core metabolic cluster (glucose, BMI, and insulin) did not include blood pressure and a cluster of HDL-cholesterol and triglycerides [26]. In the Heart Disease and Diabetes Risk Indicators in a Screened Cohort (HDDRISC) study a core metabolic domain (OGTT glucose and insulin response, serum uric acid, and BMI) was identified, which was unlikely to include blood pressure and did not include HDL-cholesterol [27]. In the Bogalusa Heart Study, the metabolic syndrome was characterized by the link of a metabolic entity (hyperinsulinemia/insulin resistance, dyslipidemia, and obesity) to a hemodynamic factor (hypertension) through shared correlation with hyperinsulinemia/insulin resistance independently of age, gender and race [28]. In a large study of non-diabetic Chinese subjects, a distinct insulin-resistancerelated metabolic syndrome factor characterized by hyperinsulinemia, dyslipidemia, and obesity was identified for both men and women. However, hypertension was linked to metabolic syndrome only in women [32]. Consistently with the Framingham Offspring Study performed in younger men and women [23], the Rancho Bernardo Study [38], a community-based study of elderly men and women, identified a central metabolic factor, including body size, serum insulin and dyslipidemia, and separate domains for glucose, independent of insulin action, and for blood pressure. The majority of factor analyses have used surrogate measures of insulin sensitivity, more often plasma insulin levels. Although fasting insulin is considered a relatively good proxy of insulin resistance, it explains less than 50% of the variance in insulin resistance. It is also possible that insulin resistance per se has effects in addition to hyperinsulinemia highlighting the importance to direct assessment of insulin sensitivity [22]. With the relevant exception of the Insulin Resistance Atherosclerosis (IRAS) Study [41], there are only two studies that have directly measured insulin sensitivity [22,25], but the sample size of both these was small. In the IRAS, insulin sensitivity was determined by model analysis of the frequently sampled intravenous glucose tolerance test in a multi-ethnic cohort with different degree of glucose tolerance. By factor analysis two domains were identified accounting for 28% and 9% of metabolic syndrome variance, respectively. These factors were labeled as a “metabolic factor”, with positive loadings of BMI, waist, fasting and 2-h glucose, and triglyceride and inverse loading of insulin sensitivity index and HDL, and a “blood pressure” factor, with positive loadings of systolic and diastolic blood pressure and borderline loading for microalbuminuria at least in some subgroups analyses. The results were not affected by stratification by gender, glucose tolerance, and ethnicity. Interestingly, the results did not change when surrogate measures of insulin resistance (HOMA-IR or fasting and 2-h insulin concentrations) were used [41] providing confidence in the interpretation of other studies using proxy of insulin sensitivity (Table 1). This brief synopsis of the available data clearly show several similarities in the factors identified in these studies. As already reviewed by Meigs [57], these findings include: (1) identification, with few exceptions, of two to four factors with the majority

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reporting a three to four-domain solution with factors representing obesity, insulin–glucose metabolism, lipid metabolism, and blood pressure; (2) loading of insulin on more than one factor, including those that have been interpreted as “glycemia”, “obesity”, and in many cases “dyslipidemia”; (3) consistent reporting of a major factor reflecting obesity and hyperinsulinemia/insulin resistance; (4) a separate factor for blood pressure; (5) the possibility that a substantial amount of information may be lost in the attempt to combine different domains in a single entity. Factor analysis limitations should also be recognized [58]. Most notably, results can be dependent on a number of somewhat arbitrary criteria, i.e. the threshold chosen for selection of number of factors retained, the method of rotation, the minimum factor loading chosen to designate a variable as a primary constituent of a domain. Nonetheless, robustness of results can be assessed based on consistency across different groups of individuals and different analytic procedures. From this standpoint the Framingham Offspring cohort [23], the Bogalusa cohort [31,37], the Honululu Heart Program population [24], the Strong Heart Study [26], the Kinmen County of Taiwan [32], the Rancho Bernardo Study cohort [38], the National Heath and Nutrition Examination Survey III population [46] have all lead to identification of relatively stable and reproducible factor pattern across different age [24,54], demographic [43,47,52,53,55], metabolic (i.e. diabetic and non-diabetic cohorts) [33,53], and lifestyle risk groups. Therefore, these studies all strongly support the contention that the metabolic syndrome comprises two to three distinct dimensions (hyperglycemia, dyslipidaemia, and hypertension) somehow connected with a central components most often represented by obesity and insulin resistance/hyperinsulinemia. 3.2. Confirmatory analysis Only a few studies have employed confirmatory factor analysis to examine the structure of the metabolic syndrome [45,48,56]. This approach provides some advantage and is complementary to exploratory factor analysis. With confirmatory factor analysis, a pre-specified model is tested. In the Framingham Offspring Study [23], Meigs et al. reported that an analytic solution yielding more than one factor provided a significantly better fit to the data than a solution yielding only one factor. Shen et al. [45], and Novak et al. [48] proposed a correlated four-factor model. In the study by Shen et al. [45], the proposed model remained solid across younger and older participants and across individuals with and without cardiovascular disease. On the contrary, a more recent comparison of the goodness-of-fit of one- versus four-factor models was tested in three different datasets showing superiority of the one-factor model structure. These findings were taken as an evidence in favor of current clinical definition of the metabolic syndrome as well as the existence of a single factors linking all the core components of the syndrome [56]. However, as reviewed above, the vast majority of the studies have identified more than one factor supporting the etiologic heterogeneity and complexity of the metabolic syndrome.

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Table 1 Summary of main results of studies employing factor analysis in metabolic syndrome Author

Population

No. of domains

Description of factors

Explained variance

Methods for estimating insulin resistance

Edwards et al. [21]

281 non-diabetic women

3

• Body weight, waist, fating insulin, glucose • Postload and fasting glucose and insulin, sBP • Larger LDL, decreased triglyceride, increased HDL

66%

Fasting and postload insulin

Donahue et al. [22]

50 non-diabetic persons

2

• Uric acid, sBP and dBP, TG, waist, increased HDL, rate of insulin-mediated glucose disposal • Fasting insulin, glucose, dBP, waist, rate of insulin-mediated glucose disposal

54.5%

Euglycemic hyperinsulinemic clamp

Meigs et al. [23]

The Framingham Offspring Study; 2458 non-diabetic subjects

3

• Fasting and 2-h insulin, triglyceride and HDL, BMI, WHR • Fasting and 2-h glucose and insulin • sBP, dBP, BMI

63%

Fasting and postload insulin

Edwards et al. [24]

The Honolulu Heart Program; 3159 elderly Japanese–Americam men

4

• Weight, waist • Blood pressure • Lipids • Insulin, glucose

78.2% Non-diabetics 74.7% diabetics

Fasting insulin

Leyva et al. [25]

74 men

3

• Leptin, insulin • Glucose, central obesity • Triglyceride, HDL

55.9%

IVGTT

Gray et al. [26]

The Strong Heart Study; 4228 American Indians

3

• Glucose, BMI, insulin • sBP, dBP • Dyslipidemia

70%

Fasting and postload insulin

Leyva et al. [27]

The HDDRISC Study; 742 men

3

• Postload insulin and glucose, uric acid, BMI, • HDL, triglyceride • Blood pressure

Not reported

Fasting and postload insulin

Chen et al. [28]

The Bogalusa Heart Study; 4522 biracial children, adolescents and young adults

2

• Fasting insulin, triglyceride, HDL, glucose, obesity • Fasting insulin, blood pressure

54.6%

Fasting insulin

Lempiainen et al. [29]

1069 non-diabetic subjects from eastern Finland

4

• Fasting insulin, fasting glucose, BMI (and waist-to-hip ratio in women), triglyceride • HDL, triglyceride • sBP, urinary albumin/creatinine ratio, LVH, age • Total cholesterol, triglyceride

Not reported

Fasting and postload insulin

Kek¨al¨ainen et al. [30]

309 sibling of diabetic or non-diabetic probands

4

• BMI, hypertension, glucose area, insulin area, HDL, triglyceride • Sex, HDL • Total cholesterol triglyceride • Urinary protein

Not reported

Fasting and postload insulin

Snehalatha et al. [31]

654 non-diabetic subjects

3 (men)

• 2 h glucose, 2 h insulin, HOMA IR, BMI • sBP, dBP, BMI • Men—BMI, WHR, cholesterol, triglyceride • Women—BMI, WHR, HOMA IR • Cholesterol, triglyceride

64.6% (men)

HOMA model

4 (women)

73.7% (women)

G. Penno et al. / Pharmacological Research 53 (2006) 457–468

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Table 1 (Continued ) Author

Population

No. of domains

Description of factors

Explained variance

Methods for estimating insulin resistance

Chen et al. [32]

3659 men

2 (men)

47.8% (men)

Fasting insulin

4778 women

3 (women)

• Men—insulin, triglyceride, HDL, WHR, BMI • sBP, glucose • Women—insulin, BMI, WHR, triglyceride, sBP • sBP, glucose, triglyceride, • Triglyceride, HDL

Lehto et al. [33]

902 Finnish patients with type 2 diabetes

4

• Sex, BMI, central obsity • BMI, triglyceride, fasting insulin, HDL • Urinary protein, total cholesterol, triglyceride, glucose • Age, hypertension

Not reported

Fasting insulin

Sakkinen et al. [34]

2681 non-diabetic elderly of the Cardiovascular Health Study; a subset of 322 subjects with additional measurements

4

• Body weight, waist, fasting insulin and glucose • Fasting and postload insulin and glucose • Triglyceride, HDL • dBP, sBP, SUBSET: 3 additional factor (inflammation, procoagulant activity, Vitamin K-dependent proteins)

70%

Fasting and postload insulin

Pyorala et al. [35]

Helsinki Policemen Study; 970 healthy men

3

• Insulin resistance factor: BMI, subscapular skinfold, AUC insulin, AUC glucose, mean BP, triglyceride, maximal O2 uptake • Lipid factor: cholesterol, triglyceride • Lifestyle factor: physical activity, smoking

54.4%

Fasting and postload insulin

Shmulewitz et al. [36]

Pacific Island of Kosrae, Micronesia; entire adult population

4

• Weight, waist, leptin, insulin, triglyceride • Total cholesterol, triglyceride, apoB, apoA1, insulin • sBP, dBP, fasting glucose, waist, weight • apoA1, triglyceride, leptin, weight

73%

Fasting insulin

Chen et al. [37]

Bogalusa Heart Study; 264 children

3

• Percent body fat, sBP, dBP • Percent body fat, HDL, triglyceride • Percent body fat, insulin, renin activity (in White, but not in African American)

70.8% (white) 60.7% (African Americans

Fasting insulin

Lindblad et al. [38]

Rancho Bernardo Study; 2274 subjects

3

• BMI, fasting and 2-h insulin, HDL, triglyceride •Fasting and 2-h glucose •sBP, dBP

56%

Fasting and postload insulin

Maison et al. [39]

The Isle of Ely Diabetes Project; 937 individuals

3 (men)

• Men—sBP, dBP • Fasting and 2-h glucose, fasting insulin, BMI, WHR • HDL, triglyceride, fasting insulin, BMI, WHR • Women—sBP, dBP • fasting and 2-h glucose, BMI • HDL triglyceride, fasting insulin, 2-h glucose • WHR, BMI, triglyceride, fasting insulin

58.3% (men)

Fasting and postload insulin

• WHR, BMI, leptin, fasting and 2-h insulin, triglyceride, HDL • sBP, dBP, uric acid (men only), fasting glucose (women only) • Fasting and 2-h glucose and insulin

54–55%

4 (women)

Hodge et al. [40]

Non-diabetic residents of Mauritius; 3068 subjects

3

59.6% (women)

63.4% (women)

Fasting and postload insulin

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Table 1 (Continued ) Author

Population

No. of domains

Description of factors

Explained variance

Methods for estimating insulin resistance

Hanley et al. [41]

The Insulin Resistance Atherosclerosis Study; 1087 non-diabetics

2

• BMI, waist, fasting and 2-h glucose, triglyceride, insulin sensitivity (or HOMA-IR), HDL • sBP, dBP

37%

IVGTT

Lakka et al. [42]

The Kuopio Ischaemic Heart Disease Risk Factor Study; 1209 men

4

• BMI, waist–hip ratio, fasting insulin and glucose, triglyceride, HDL, sBP • Smoking, fibrinogen, white blood cells • Fasting glucose, triglyceride, HDL, sBP, alcohol, LDL cholesterol • LDL cholesterol and family history of IHD

54%

Fasting insulin

Hanson et al. [43]

Pima Indians; 1918 subjects

4

• Fasting and 2-h insulin and glucose • Body weight, waist, fasting insulin and glucose •sBP, dBP, • HDL, triglyceride

79%

Fasting and postload insulin

Arya et al. [44]

261 non-diabetic Mexican–American subjects

3

• BMI, fasting insulin, leptin •sBP, dBP • HDL, triglyceride

68%

Fasting insulin

Shen et al. [45]

The Normative Aging Study; 847 men

4

• Fasting and postload insulin, fasting and 2-h glucose • BMI, WHR • HDL, triglyceride • sBP, dBP

Not reported

Fasting and postload insulin (CFA)

Ford [46]

National Health and Nutrition

3

Men • waist, fasting insulin, triglyceride, HDL • sBP, dBP • Glucose, albuminuria Women • waist, fasting insulin • sBP, dBP • Glucose, HDL, triglyceride

60%

Fasting insulin

4

• BMI, WHR, HDL, triglyceride, (fasting and 2-h insulin only in men) • sBP, dBP • 2-h insulin, 2-h glucose, (fasting glucose only in women) • Fasting insulin, fasting glucose

67.9% (men)

Fasting and postload insulin

Examination Survey III; 3410 men, 3458 women

Choi et al. [47]

The South-west Seoul Study; 1314 non-diabetic elderly subjects

64.6% (women)

Novak et al. [48]

284 middle-aged men from Sweden

4

• Fasting insulin, fasting glucose • BMI, WHR • Triglyceride, HDL • sBP, dBP

Not reported

Fasting insulin (CFA)

Howard et al. [49]

Women’s Health Initiative; 3083, 50-to 79-year-old, women (1635 white)

4

• BMI, hip, waist, HOMA-IR, glucose • HOMA-IR, insulin, triglyceride, HDL and HDL2 distribution • Total cholesterol, LDL • sBP, dBP

84.2%

HOMA-IR

Zitzmann et al. [50]

106 healthy men

5

• Body fat mass, CAG repeat in the androgen receptor gene, insulin, leptin, physical activity • HDL, LDL, triglyceride, smoking, CAG repeat • sBP, dBP • Age, physical activity, LDL, smoking, alcohol • Sexual hormones (estradiol, testosterone, SHBG)

Not reported

Fasting insulin

G. Penno et al. / Pharmacological Research 53 (2006) 457–468

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Table 1 (Continued ) Author

Population

No. of domains

Description of factors

Explained variance

Methods for estimating insulin resistance

Tang et al. [51]

The National Heart, Lung, and Blood Institute Family Heart Study; 2831 subjects

4

• BMI, WHR, suscapular skinfold, triglyceride, HDL, HOMA-IR, PAI-1, serum uric acid • Triglyceride, LDL, total cholesterol • BMI, subscapular skinfold • Triglyceride, LDL cholesterol

47.4%

HOMA-IR

Oh et al. [52]

Urban Korean population: 206 men, 449 women

4 (men)

Men • BMI, waist, fasting insulin • Fasting and 2-h glucose, fasting insulin • sBP, dBP • Fasting insulin, triglyceride, HDL Women • BMI, waist, sBP, dBP • Fasting and 2-h glucose, fasting insulin • BMI, waist, fasting insulin, triglyceride, HDL

75.8% (men)

Fasting insulin

3 (women)

Wang et al. [53]

• BMI, WHR, fasting insulin, (fasting glucose only in males) • sBP, dBP • 2-h insulin, fasting glucose, 2-h glucose • Total cholesterol, triglyceride

66.8% (women)

Non-diabetic and diabetic Chinese; 934 non-diabetics 305 diabetics

4

59% (men)

Fasting and postload insulin

Weiss et al. [54]

470 obese and overweight children and adolescents

3

• BMI, HOMA-IR, fasting and 2-h glucose • HOMA-IR, triglyceride, HDL • sBP, dBP

58%

HOMA-IR

Ang et al. [55]

Chinese, Malays and Asian Indians; 1957 men, 2308 women

3

• HOMA-IR, BMI, WHR, HDL, triglyceride • Fasting glucose, 2-h glucose, HOMA-IR • BMI, WHR, sBP, dBP

70.5–72.4%

HOMA-IR

Pladewall et al. [56]

Spanish, Mauritian and US males; 410, 3061 and 847 subjects, respectively

1

• Standard model: waist, triglyceride/HDL, HOMA-IR, MAP • Expanded model: waist, leptin, uric acid, triglyceride/HDL, HOMA-IR, MAP

Not reported

HOMA-IR (CFA)

56% (women)

WHR, waist-to-hip ratio; IVGTT, intravenous glucose tolerance test; HDDRISC, Heart Disease and Diabetes Risk Indicators in a Screened Cohort study; AUC, area under curve; IHD, ischaemic heart disease; CFA, confirmatory factor analysis; MAP, mean arterial pressure; SHBG, sex hormone binding globulin; LVH, left ventricular hypertrophy.

4. Extension the metabolic syndrome After its initial description, many other features have been related to the metabolic syndrome, including elevated uric acid, fibrinogen, plasminogen activator inhibitor-1 (PAI-1), urinary albumin excretion, leptin, adipokines, and, more recently, inflammatory markers. Some of these factors have been included in factorial analysis. Thus, Leyva et al. [25] observed that inter-individual variations in plasma leptin concentrations were strongly related to the principal components of the metabolic syndrome but not necessarily associated with the glucose/central obesity and a high triglyceride/low high-density lipoprotein cholesterol factors. A role for leptin has been suggested in analysis performed in the population-based survey from Mauritius [40], in the study of Pacific Island of Kosroe, Federated States of Micronesia [36], failing, however, to prove it could act as an obesity-related unifying factor accounting for the pathogenesis of the syndrome.

A role for leptin was confirmed in the San Antonio Family Diabetes Study [44] where leptin loaded with the BMI and fasting insulin factor 1, but not with blood pressure (factor 2) and lipids (factor 3). To similar results has come the National Health and Nutrition Examination Survey (NAHNES III) [46] confirming that leptin concentration did not provide a unifying explanation for the set of metabolic syndrome factors. Microalbuminuria has been suggested to belong to the metabolic syndrome as well. In a non-diabetic population from eastern Finland, factor analysis identified a four-domain structure loading urinary albumin-to-creatinine ratio and left ventricular hypertrophy in the domain dominated by age and systolic blood pressure [29] while urinary protein excretion was independent of blood pressure, insulin resistance, and dyslipidemia in sibling of diabetic and non-diabetic probands [30]. In type 2 diabetic patients, urinary albumin-to-creatinine ratio emerged as a factor independent of blood pressure, obesity and insulin, but loaded with triglycerides, total cholesterol and glucose [33].

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Finally, in a Chinese population, urinary albumin correlated with fasting plasma glucose and 2-h plasma glucose in diabetic, but not in non-diabetic men and women, implying that microalbuminuria is most likely a complication of hyperglycenmia rather than a marker of the syndrome [53]. Sakkinen et al. [34] employed factor analysis to describe the clustering with metabolic variables of several hemostatic parameters mainly PAI-1, fibrinogen, and factor VIIc as well as inflammatory factors [59]. Inclusion of these variables in the factor analysis yielded three new factors identified as “inflammation”, “Vitamin K-dependent proteins” and “pro-coagulant activity”. PAI-1 clustered with body mass and insulin/glucose factors implying obesity may be related to impaired fibrinolysis in a complex manner [60]. Of the hemostatic and inflammatory variables examined, only PAI-1 remained significantly correlated with metabolic summary domains of the insulin resistance syndrome. This suggests that markers of inflammation and pro-coagulation, often correlated with metabolic variables, may reflect separate underlying processes in spite of many reports linking elevated levels of C-reactive protein with body mass index [61,62]. The lack of association between fibrinogen and phenotypes of the syndrome is consistent with those study where no association was found between fibrinogen and other cardiovascular risk factors [63]. Thus, in the Kuopio Ischemic Heart Disease Risk Factor Study [42], fibrinogen and white blood cell count (WBC) did not load on the main factor “metabolic syndrome”, rather they loaded together smoking to explain only 14% of total variance. Traditional and “non-traditional” risk factors of the metabolic syndrome have been analyzed in a factor analysis based on data from families participating in the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study [51]. Uric acid and PAI-1, but not fibrinogen clustered with the factor with strong loading for obesity and insulin variables [51] suggesting PAI-1, but not WBCs, Willebrand factor, and fibrinogen, could belong to the “traditional” set of alterations of the metabolic syndrome [64]. Several studies have included C-reactive protein in factor analyses of the metabolic syndrome. Adding markers of procoagulation, inflammation, and fibrinolysis to a factor analysis performed within the Cardiovascular Heart Study [34] led to the identification of an independent “inflammation factor” lending support to the hypothesis that the inflammatory phenotype is an independent component of the syndrome. In obese children and adolescents [54], high C-reactive protein and interleukin-6 were related to the degree of obesity, but not to insulin resistance. On the contrary, low adiponectin levels [65] were significantly associated with both obesity and insulin resistance. In a sample of Japanese–American families, using nine phenotypes that included LDL particle size and C-reactive protein, factor analysis has identified lipids, blood pressure, and body fat/insulin/glucose/C-reactive protein as three independent factors explaining 65% of the variance [66]. When nontraditional cardiovascular risk factors and insulin sensitivity were included in factor analysis performed in 1087 non-diabetic participants in the IRAS [67] three factors accounting for 41.8% of total variance were identified, namely (1) a “metabolic” factor, with

positive loadings for BMI, waist circumference, 2-h glucose, triglyceride, PAI-1 and inverse loading for insulin sensitivity and HDL; (2) an “inflammatory” factor with positive loadings for BMI, waist circumference, fibrinogen and C-reactive protein and an inverse loading for insulin sensitivity; and (3) a “blood pressure” factor, with positive loading for systolic and diastolic blood pressure [67]. Low insulin sensitivity and adiposity loaded on both the “metabolic” and the “inflammation” variable clusters suggesting that adiposity and/or insulin resistance or both may play a role in the clustering of both conventional and nonconventional cardiovascular risk factors. 5. Metabolic syndrome: cause or consequence of inflammation? The association of the metabolic syndrome with low-grade inflammation is well documented [68]. However, to which extent chronic inflammation is a consequence or the cause of metabolic syndrome is an ongoing debate. CRP, TNF-␣, fibrinogen, and IL6 have all been associated with the metabolic syndrome, but there are data indicating that elevated hs-CRP levels predict development of the metabolic syndrome, though this observation was limited to the female gender [69]. On the other hand, many features of the syndrome may sustain low-grade inflammation. Overnutrition, increased macronutrient intake, physical inactivity, and ageing [70,71] are associated with expansion of adipose tissue mass and cytokine, thus favoring the development, in genetically and metabolically predisposed individuals, of insulin resistance, metabolic syndrome and diabetes. Accretion of adipose tissue mass may contribute to insulin resistance via metabolic and a hormonal mechanisms [5]. Free-fatty acid flux increases as a function of adipose tissue mass [72]. In the liver, FFA stimulate gluconeogenesis [73] and synthesis of triglycerides and very low-density lipoproteins (VLDL) favoring reduction of HDL-cholesterol and increase of density of the low-density lipoproteins (LDL) particles [74]. In muscles, FFA inhibit insulin-mediated glucose uptake, reduce glucose partitioning to glycogen and increase intramyocellular lipids [75]. Accumulation of FFA increase in pancreatic islets conributes to impairment of insulin secretion favoring the development of glucose intolerance [75,76]. Beside this metabolic effect, adipocytes may influence insulin sensitivity through the release of several peptides [76]. Adipocytes and monocytederived macrophages resident in the expanded adipose tissue result in generation of pro-inflammatory cytokines such as IL-6, resistin, TNF-␣ [77–79]. In human subcutaneous adipose tissue, there is a strong positive relationship between gene expression of CD68, a macrophage marker, and TNF-␣ production, and plasma IL-6 [78]. Furthermore, a significant inverse relation was observed between CD68 mRNA and insulin sensitivity. In humans, TNF-␣ is not released into the circulation, but acts locally by inhibiting insulin signaling [80]. Human fat cells, unlike mice, do not produce resistin, a hormone associated with obesity-related insulin resistance. Thus, most likely, resistin does not affect insulin sensitivity in men [81]. IL-6, and adipokines such as leptin and adiponectin are released into the circulation in humans. Elevated levels of IL-6 are strongly linked

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Fig. 2. Relationship between adipokines and insulin sensitivity. TNF-␣ is a true adipokine; it is not released into the circulation and acts in a paracrine manner stimulating adipocyte lipolysis and enhancing fatty acids release in the bloodstream. The effects of TNF-␣ on insulin sensitivity, insulin production, and glucose metabolism are mediated by FFA. Resistin is not a true adipokine; it is produced by unidentified cells within the stroma of adipose tissue, but whether circulating resistin is derived from adipose tissue is debated. Its function is unknown and any role in insulin sensitivity is unlikely. Adiponectin, leptin and IL-6 are true adipokines. Adiponectin is produced in adipocytes, released into the circulation and has direct effects on insulin sensitivity and glucose metabolism, but also indirect effects through inhibition of the inflammatory process. Leptin acts indirectly on insulin sensitivity through regulation of appetite and energy expenditure. IL-6 is secreted both by macrophages and adipocytes; it regulates glucose metabolism in the liver and insulin action in muscle directly, but also elicits hepatic synthesis of CRP, serum amyloid A, fibrinogen, and PAI-1.

to insulin resistance. Both macrophages and adipocytes produce IL-6 that acts stimulating hepatic synthesis of CRP, serum amyloid A (SAA), fibrinogen and PAI-1, and enhances hepatic glucose production, synthesis of VLDL in the liver and insulin resistance in muscle [80]. Although leptin is a satiety signal, it also exerts pro-inflammatory and platelet pro-aggregation action [82]. On the contrary, adiponectin exerts an anti-inflammatory and insulin sensitizing effect. The circulating levels of this adipokine are inversely related to insulin sensitivity and its plasma concentration is reduced in subjects with metabolic syndrome [83,84]. Adiponectin enhances insulin sensitivity, increases muscle glucose transport and fatty acid oxidation, decreases hepatic glucose production (inhibiting the expression of hepatic gluconeogenic enzymes), and decreases intracellular triglycerides [85]. Furthermore, adiponectin inhibits many steps of the inflammatory process. The links between adipokines, insulin sensitivity and glucose and lipid metabolism are summarized in Fig. 2. In summary, there is increasing evidence that insulin resistance is not merely associated with an inflammatory state, but also that activation of the inflammatory process may cause and definitely sustain insulin resistance and, in turn, development of the metabolic syndrome [80]. Though fascinating, the hypothesis still requires further elaboration since the concomitant hyperinsulinemia could be expected to counteract this inflammatory condition [61]. The hormone, indeed, has been shown to suppress NF-kB binding activity, inhibit reactive oxygen species

(ROS) generation, increase IkB expression, reduce plasma concentration of adhesion molecules, matrix metalloproteinase9, tissue factor and PAI-1, exhibiting a comprehensive antiinflammatory as well as anti-oxidant, anti-aggregatory and pro-fibrinolytic effect [86]. However, resistance to the antiinflammatory actions of insulin might contribute to the increased levels of circulating inflammatory cytokines favoring the maintenance of low-grade inflammation. 6. Factor analysis and the heritability of the metabolic syndrome Independent phenotypes as identified by factor analysis may help in mapping susceptibility genes for the metabolic syndrome [87]. Studies performed in Japanese–American families have indicated that the influence of genetic predisposition may account for 25–52% of independent factors (lipids, body fat/insulin/glucose/CRP, and blood pressure) [65]. Significant genetic influence (49–58%) has been reported also in Kaiser Permanente Women’s Twin Study [88], and in the San Antonio Family Diabetes Study [44]. In the latter, in particular, linkage was reported between some factors of the metabolic syndrome and some genetic loci [44]. Moreover, in the National Heart, Lung, and Blood Institute Family Heart Study [51], a strong evidence was found for the presence of a genetic locus on chromosome 2 linked to the “metabolic syndrome factor” (BMI, waist-to-hip ratio, subscapular skinfold, triglycerides, HDL, HOMA, PAI-1,

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and uric acid). In addition, several other regions on chromosomes 7, 12, 14, and 15 also were suggestive for contribution to the underlying correlation structure of these traits. Though, further study will be necessary for full understanding of the genetic background of the metabolic syndrome, these initial observation support the existence for a genetic predisposition. Nonetheless, the complexity and multiplicity of the genetic determinants of the syndrome should not be underestimated since each of the main independent domain so far identified by factorial analysis is, to some degree, associated to one or more, genetic trait. In the meantime, these initial studies highlight the importance of the interaction with environmental factors since no more than with 25–50% of the variance can be so far accounted for by. 7. Conclusions From the initial Reaven’s proposal [89], insulin resistance has been repeatedly suggested to be the possible common player responsible for the development of or acceleration of the many disturbances that so often cluster in the same individual [10]. Many possible etiologic mechanisms have been proposed to explain how impaired insulin action and compensatory concomitant hyperinsulinemia may lead to glucose intolerance, dyslipidemia, increased blood pressure, pro-coagulative state, and so on [90]. Moreover, it has been suggested that insulin resistance per se may be an independent CVD risk factor both in diabetic [91] and non-diabetic populations [92]. However, while obesity and visceral adiposity tend to be among the most common features of the metabolic syndrome, insulin resistance seems to be a more likely consequence than a cause for body weight accrual. Interestingly enough, in his initial theorem, Reaven did not include obesity [92] as it was essential to prove that insulin resistance per se could favor some classical CVD risk factors. There is no doubt obesity may act as a potent catalyzing trigger leading to full development of insulin resistance and metabolic syndrome. This view is supported by the many factorial analysis performed in a variety of populations and ethnic groups. Most of these studies have concluded that more than one factor, i.e. more than just insulin resistance, must contribute to the development of the syndrome. The question, however, remain of the forces that keep these factors running together. This is the essential of the syndrome, as the Greek etymology of the word “syndrome” just stands for “running together”. Among these forces, inflammation appears to be more than plausible. It belongs to the syndrome, can cause impaired insulin action, it is strongly associated to adipose tissue deposition in correlation as well as in pathogenic terms. Moreover, inflammation may represent a core pathogenic mechanism in the formation and instability of the atherosclerotic plaque, the final outcome of the syndrome itself. All this can only lead to the conclusion that, at the time being, the syndrome still requires intensive multifactorial intervention because of the many pathogenetic factors beyond insulin resistance and because of their relative independence one from the other. Nonetheless, the importance and efficacy of reducing the body weight and in particular the reduction of visceral obesity

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