Browse the corpus

Atherosclerosis is the result of an excessive proliferative and inflammatory response that includes smooth muscle cell migration and proliferation, inflammatory cell infiltration, neovascularization, production of extracellular matrix, and the accumulation of lipids (1). Monocyte chemoattractant protein-1 (MCP-1/CCL2), a member of the CC chemokine family, is involved in most of these processes (2). In culture systems, oxidized LDL (3) and shear stress (4) upregulated MCP-1/CCL2 synthesis in human endothelial cells. In animal models, the role of MCP-1/CCL2 appears to be more evident; MCP-1/CCL2 knockout mice placed in an LDL receptor–deficient background showed an 80% reduction of atherosclerotic plaque and a reduction in the number of macrophages in the aortic walls (5), and similar results were obtained in mice deficient in the MCP-1/CCL2 receptor (CCR2) crossed with apolipoprotein E–deficient mice (6).

CL2 knockout mice placed in an LDL receptor–deficient background showed an 80% reduction of atherosclerotic plaque and a reduction in the number of macrophages in the aortic walls (5), and similar results were obtained in mice deficient in the MCP-1/CCL2 receptor (CCR2) crossed with apolipoprotein E–deficient mice (6). If in animal models the role of MCP-1/CCL2 in atherosclerosis appeared clear, its role in vivo in humans remains unknown. MCP-1/CCL2 levels were found to be increased in aging (7), hypertension (8), hypercholesterolemia (9), and renal failure (10) and to have a prognostic value in the acute and chronic phases in patients with acute coronary syndromes (11,12). We were the first to report increased MCP-1/CCL2 plasma levels in diabetic individuals (13). In that study we found that in 207 women selected from a population survey carried out in 1990–1991 in Lombardy, Italy (Cremona Study), because a properly stored spared fraction of plasma was available, baseline MCP-1/CCL2 correlated with risk markers of cardiovascular disease (CVD). In addition, we reported that in univariate analysis MCP-1/CCL2 was significantly associated with CVD mortality 7 years later, even if in the multivariate analysis this association did not retain a significant association (13). The present study adds to the previous report because the follow-up was extended at 15 years from baseline and because fasting plasma MCP-1/CCL2 was assessed in 156 men also.

associated with CVD mortality 7 years later, even if in the multivariate analysis this association did not retain a significant association (13). The present study adds to the previous report because the follow-up was extended at 15 years from baseline and because fasting plasma MCP-1/CCL2 was assessed in 156 men also. RESEARCH DESIGN AND METHODS The 363 individuals who entered the study were selected from a population survey carried out in 1990–1991 in the Health District of Cremona (Lombardy, Italy) that was performed to determine the prevalence of diabetes in Italy according to an oral glucose tolerance test (OGTT) and World Health Organization criteria (13). Fasting plasma leptin, α-tumor necrosis factor receptor 2 (α-TNF-R2), and MCP-1/CCL2 concentrations were measured in patients with type 2 diabetes (known to be affected or previously undiagnosed) (n = 99) and patients with impaired glucose tolerance (IGT) (n = 77) for whom an aliquot of frozen plasma sample was available, which was stored at −80°C and had not been previously thawed. From the above-described population, 187 individuals with normal glucose tolerance (NGT) were randomly selected to be comparable to the group of patients with type 2 diabetes and IGT in terms of age and anthropometric parameters. Past medical history and clinical data of subjects were collected through a standard protocol conducted by trained interviewers. A venous blood sample was collected after a 12-h overnight fast; thereafter, a 75-g oral glucose monohydrate was given, and a further venipuncture was performed 2 h later. Anthropometric measures were obtained by the same trained individual using the same instruments for all subjects. Heart rate and systolic and diastolic blood pressures were taken twice, at the beginning and at the end of the visit, in the sitting position and after at least a 10-min rest using a full automatic noninvasive sphygmomanometer. The lowest figure was considered. Further details concerning the study protocol have been reported previously (14). Fifteen years later, vital status and time of death were ascertained through Regional Health Registry files and causes of death were classified using the ICD-8 and ICD-9 codes 401–448 (CVD), 410–414 (coronary heart disease), and 430–438 (stroke). The protocol was approved by the Ethics Committee of the Istituto Scientifico H San Raffaele.

vital status and time of death were ascertained through Regional Health Registry files and causes of death were classified using the ICD-8 and ICD-9 codes 401–448 (CVD), 410–414 (coronary heart disease), and 430–438 (stroke). The protocol was approved by the Ethics Committee of the Istituto Scientifico H San Raffaele. Definition of diabetes, IGT, and metabolic syndrome Diabetes was defined at that time according to previously known diabetes status (patients taking oral hypoglycemic agents) or according to the results of the OGTT and on the basis of World Health Organization criteria (basal plasma glucose >7.8 or >11.1 mmol/l after a 2-h oral glucose load). Patients with known diabetes did not undergo the OGTT. IGT was defined as basal plasma glucose <7.8 mmol/l and plasma glucose >7.8 but <11 mmol/l after a 2-h oral glucose load. Metabolic syndrome was defined according to the definition of the National Cholesterol Education Program Adult Treatment Program III.

se load). Patients with known diabetes did not undergo the OGTT. IGT was defined as basal plasma glucose <7.8 mmol/l and plasma glucose >7.8 but <11 mmol/l after a 2-h oral glucose load. Metabolic syndrome was defined according to the definition of the National Cholesterol Education Program Adult Treatment Program III. Analytical determinations Blood and serum and plasma substrates were assessed as described previously (13). Blood was collected into tubes with glycolytic inhibitor, and the glucose concentration was measured within 3–4 h in the central laboratory through the GOD-PAP glucose oxidase method (Boehringer Mannheim, Milan, Italy) with a Hitachi 705 autoanalyzer. At the same time fibrinogen, aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), and alkaline phosphatase (ALP) were also determined. An additional 20 ml of fasting blood was immediately centrifuged, and plasma was obtained for the assessment in the central laboratory of insulin, triglycerides, and total and HDL cholesterol. The plasma human MCP-1/CCL2 concentration was measured in 2001 using a noncommercial sandwich enzyme-linked immunosorbent assay as described previously (15); the enzyme-linked immunosorbent assay for MCP-1/CCL2 is specific for human MCP-1 and did not detect the closely related human chemokines MCP-2 and MCP-3 (16). Leptin concentration was determined as described previously (13) by a radioimmunoassay with a human kit (Linco Research, St. Charles, MO). Intra-assay and interassay coefficients of variation (CVs) were 1.5 and 1.9%, respectively. Insulin was determined by a radioimmunoassay kit (intra-assay and interassay CVs were 6.0 and 5.3%, respectively) (Technogenetics, Medgenics, Brussels, Belgium). α-TNF-R2 was measured with an enzyme immunoassay following the manufacturer's (Immunotech Beckman Coulter, Marseille, France) recommendations as described previously (13). Total cholesterol and triglycerides were measured by enzymatic methods (Boehringer Mannheim, Mannheim, Germany) with the CIBA Corning 550 Express Autoanalyzer. The HDL fraction was separated from plasma by precipitation with polyethylene glycol using a Colortest kit (Roche, Basel, Switzerland).

scribed previously (13). Total cholesterol and triglycerides were measured by enzymatic methods (Boehringer Mannheim, Mannheim, Germany) with the CIBA Corning 550 Express Autoanalyzer. The HDL fraction was separated from plasma by precipitation with polyethylene glycol using a Colortest kit (Roche, Basel, Switzerland). High-sensitivity C-reactive protein In 2004, C-reactive protein (CRP) levels (N high-sensitivity CRP assay; Dade-Behring, Marburg, Germany) were measured in a subgroup of older individuals (≥65 years old; n = 447) within the entire population of the Cremona Study and were reported in another article (17). With respect to that assessment, we have data for 113 subjects in whom MCP-1/CCL2 and high-sensitivity CRP (hs-CRP) were simultaneously available. Calculation BMI was calculated as weight in kilograms divided by the square of height in meters. Insulin sensitivity was estimated using the quantitative insulin sensitivity check index (QUICKI) obtained from fasting baseline determinations (18) and calculated as the logarithm and the reciprocal of the insulin-glucose product: Alcohol consumption was calculated as units of alcohol (glass of wine = 20 units, glass of aperitif = 30 units, and glass of liquor = 80 units).

Calculation BMI was calculated as weight in kilograms divided by the square of height in meters. Insulin sensitivity was estimated using the quantitative insulin sensitivity check index (QUICKI) obtained from fasting baseline determinations (18) and calculated as the logarithm and the reciprocal of the insulin-glucose product: Alcohol consumption was calculated as units of alcohol (glass of wine = 20 units, glass of aperitif = 30 units, and glass of liquor = 80 units). Statistical analysis Analyses were performed using SAS software. Concentrations are presented as averages ± SD unless otherwise stated. ANOVA and χ2 analysis were used for comparison between groups, and the Bonferroni adjustment was used for post hoc comparisons. Because of the skewed distribution of serum leptin, insulin, triglycerides, fibrinogen, glucose, AST, ALT, GGT, and ALP, log-transformed values were used in the analysis. Pearson correlation analysis was used for correlations. The association of the risk factors with CVD mortality after the 15-year observational period was estimated by the Cox univariate proportional hazards model. Hazard ratios (HRs) and 95% CIs are presented. A multivariate Cox proportional model (stepwise), including parameters with P < 0.1 on univariate analysis, was used to investigate the independent association of the risk factors with CVD mortality.

al period was estimated by the Cox univariate proportional hazards model. Hazard ratios (HRs) and 95% CIs are presented. A multivariate Cox proportional model (stepwise), including parameters with P < 0.1 on univariate analysis, was used to investigate the independent association of the risk factors with CVD mortality. RESULTS Anthropometric and laboratory characteristics of study subjects Subjects with type 2 diabetes and IGT were slightly older, but post hoc testing did not reach statistical difference among groups (Table 1). BMI and waist circumference were not statistically different among groups. Waist-to-thigh ratio was significantly increased in individuals with type 2 diabetes with respect to that in individuals with NGT; this feature was more evident in women (P = 0.0001) than in men (P = 0.274). The prevalence of smokers was not different in the three groups. Systolic and diastolic blood pressures showed an increasing trend according to glucose tolerance. Systolic (P = 0.008) and diastolic (P = 0.087) blood pressures were higher in women with type 2 diabetes with respect to those in women with NGT, whereas this feature was less evident in men (P = 0.172 and P = 0.130 for systolic and diastolic pressure, respectively). As expected, fasting and 2-h serum glucose concentrations were increased in patients with type 2 diabetes and IGT in comparison with those in subjects with NGT. Total cholesterol and LDL cholesterol were not different in the groups. However, HDL cholesterol and triglycerides differed significantly in patients with type 2 diabetes, which showed unfavorable alterations of the lipid profile. The lipid profiles of subjects with IGT, on the contrary, were not different from those of individuals with NGT. Serum ALT, AST, GGT, ALP, and fibrinogen were not different in any groups. Patients with type 2 diabetes and IGT were characterized by fasting hyperinsulinemia and by insulin resistance expressed as a significantly lower QUICKI value. Serum leptin were not different in the study groups when leptin concentration was normalized to the BMI units.

and fibrinogen were not different in any groups. Patients with type 2 diabetes and IGT were characterized by fasting hyperinsulinemia and by insulin resistance expressed as a significantly lower QUICKI value. Serum leptin were not different in the study groups when leptin concentration was normalized to the BMI units. Table 1 Anthropometric and laboratory parameters of study subjects

and fibrinogen were not different in any groups. Patients with type 2 diabetes and IGT were characterized by fasting hyperinsulinemia and by insulin resistance expressed as a significantly lower QUICKI value. Serum leptin were not different in the study groups when leptin concentration was normalized to the BMI units. Table 1 Anthropometric and laboratory parameters of study subjects Type 2 diabetes IGT NGT n (female/male) 99 (53/46) 77 (42/35) 187 (112/75) Anthropometric parameters Age (years) 63 ± 11 63 ± 12 60 ± 13 Body weight (kg) 77 ± 17 71 ± 16 77 ± 17 Height (cm) 161 ± 9 158 ± 10 160 ± 10 BMI (kg/m2) 30.0 ± 6.3 28.3 ± 5.0 29.9 ± 6.0 Waist (cm) 98 ± 14 94 ± 14 95 ± 15 Waist-to-thigh ratio 1.84 ± 0.19* 1.81 ± 0.23 1.74 ± 0.24 Smoking habit n (%) 20 (20%) 15 (19%) 46 (25%) Alcohol consumption (alcohol units) 3 ± 11 2 ± 10 2 ± 8 Systolic blood pressure (mmHg) 161 ± 24* 155 ± 20 151 ± 21 Diastolic blood pressure (mmHg) 86 ± 14* 83 ± 13 82 ± 12 Biochemical laboratory parameters Glucose (mmol/l) 7.27 ± 1.67*† 5.55 ± 0.83* 5.11 ± 0.50 2-h glucose (mmol/l) 12.71 ± 3.83*† 9.10 ± 0.83* 4.83 ± 1.22 Cholesterol (mmol/l) 6.00 ± 1.03 6.03 ± 1.01 6.15 ± 1.24 HDL cholesterol (mmol/l) 1.14 ± 0.34*† 1.34 ± 0.49 1.32 ± 0.39 Triglycerides (mmol/l) 1.63 ± 0.92*† 1.37 ± 0.57 1.38 ± 0.51 ALT (units/liter) 27 ± 10 28 ± 11 26 ± 8 AST (units/liter) 24 ± 11 23 ± 15 22 ± 15 GGT (units/liter) 36 ± 30 35 ± 31 27 ± 30 ALP (units/liter) 174 ± 51 176 ± 45 172 ± 45 Fibrinogen (mg/dl) 286 ± 79 297 ± 66 275 ± 61 Hormones Insulin (pmol/l) 131 ± 108*† 103 ± 52 95 ± 41 Leptin (ng/ml) 10.7 ± 4.5 10.0 ± 5.8 10.4 ± 5.0 MCP-1/CCL2 (pg/ml) 224 ± 275* 195 ± 171 159 ± 113 Insulin sensitivity QUICKI 0.127 ± 0.014*† 0.136 ± 0.014 0.138 ± 0.011 Data are means ± SD or n (%). Leptin, insulin, triglycerides, glucose, AST, ALT, GGT, and ALP are expressed as geometric means ±SD. Subjects with a previously proved diagnosis of diabetes did not undergo an OGTT.

g/ml) 224 ± 275* 195 ± 171 159 ± 113 Insulin sensitivity QUICKI 0.127 ± 0.014*† 0.136 ± 0.014 0.138 ± 0.011 Data are means ± SD or n (%). Leptin, insulin, triglycerides, glucose, AST, ALT, GGT, and ALP are expressed as geometric means ±SD. Subjects with a previously proved diagnosis of diabetes did not undergo an OGTT. *P < 0.05 vs. NGT. †P < 0.05 vs. IGT ANOVA and Bonferroni post hoc analysis. Cross-sectional analysis of baseline MCP-1/CCL2 The plasma MCP-1/CCL2 concentration was increased in type 2 diabetic women but not in those with IGT with respect to those with NGT (Table 1). MCP-1/CCL2 was not associated with any anthropometric parameters but showed a significant association with biochemical markers of atherosclerotic disease. In fact, MCP-1/CCL2 was associated with fasting (R = 0.15; P = 0.007) and 2-h plasma glucose after the glucose challenge (R = 0.14; P = 0.04), HDL cholesterol (R = −0.21; P = 0.0003), and plasma triglycerides (R = 0.15; P = 0.01). These parameters are typical markers of the insulin resistance syndrome; confirming this observation, QUICKI was also found to be significantly associated (R = −0.19; P = 0.009). In addition, MCP-1/CCL2 showed a significant association with α-TNF-R2 (R = 0.14; P = 0.01) but no association with leptin. Even if MCP-1/CCL2 was not significantly associated with BMI, we tested whether the above-described association of MCP-1/CCL2 was independent of BMI. The results of this analysis demonstrated that all the correlations remained significant regardless of the parameter of body adiposity.

1) but no association with leptin. Even if MCP-1/CCL2 was not significantly associated with BMI, we tested whether the above-described association of MCP-1/CCL2 was independent of BMI. The results of this analysis demonstrated that all the correlations remained significant regardless of the parameter of body adiposity. Univariate analysis after the 15-year observational period After 15 years, among the 363 subjects, 82 deaths occurred due to CVD (22.6%) (Table 2). The CVD mortality rate was 30% in patients with type 2 diabetes (30 of 99), 19.5% in individuals with IGT (15 of 77), and 19.8% in individuals with NGT (37 of 187). In univariate analysis, age (HR 1.127 [95% CI 1.098–1.157]; P < 0.0001) was associated with CVD mortality, and female sex was less prone to be associated (0.429 [0.275–0.671]; P < 0.0002). Age- and sex-adjusted univariate analysis showed that fasting plasma glucose concentration, MCP-1/CCL2, fibrinogen, cigarette smoking, and diabetes were significantly associated with CVD mortality (Table 2). In addition, fasting insulin showed a trend to be associated with CVD mortality (P = 0.064). When the analysis was performed separately by sex, age, fasting plasma glucose, and MCP-1/CCL2 were significantly associated in both men and women, whereas fasting plasma insulin and smoking were significantly associated with CVD mortality only in men and serum fibrinogen was significantly associated with CVD mortality only in women. Table 2 Age- and sex-adjusted risk ratio associated with 15 years cardiovascular mortality by univariate analysis

Univariate analysis after the 15-year observational period After 15 years, among the 363 subjects, 82 deaths occurred due to CVD (22.6%) (Table 2). The CVD mortality rate was 30% in patients with type 2 diabetes (30 of 99), 19.5% in individuals with IGT (15 of 77), and 19.8% in individuals with NGT (37 of 187). In univariate analysis, age (HR 1.127 [95% CI 1.098–1.157]; P < 0.0001) was associated with CVD mortality, and female sex was less prone to be associated (0.429 [0.275–0.671]; P < 0.0002). Age- and sex-adjusted univariate analysis showed that fasting plasma glucose concentration, MCP-1/CCL2, fibrinogen, cigarette smoking, and diabetes were significantly associated with CVD mortality (Table 2). In addition, fasting insulin showed a trend to be associated with CVD mortality (P = 0.064). When the analysis was performed separately by sex, age, fasting plasma glucose, and MCP-1/CCL2 were significantly associated in both men and women, whereas fasting plasma insulin and smoking were significantly associated with CVD mortality only in men and serum fibrinogen was significantly associated with CVD mortality only in women. Table 2 Age- and sex-adjusted risk ratio associated with 15 years cardiovascular mortality by univariate analysis Variable HR (95% CI) P BMI 1.037 (0.992–1.083) 0.109 LDL cholesterol 0.999 (0.993–1.005) 0.769 HDL cholesterol 0.994 (0.98–1.009) 0.448 Triglycerides 1.001 (0.997–1.004) 0.723 Fasting glucose 1.010 (1.004–1.016) 0.0015 2-h glucose 1.002 (0.997–1.007) 0.367 Fasting insulin 1.011 (0.999–1.023) 0.064 Systolic blood pressure 1.005 (0.995–1.015) 0.296 Diastolic blood pressure 1.004 (0.987–1.022) 0.642 Leptin 0.998 (0.972–1.026) 0.910 α-TNF-R2 1.000 (1.000–1.000) 0.987 MCP-1/CCL2 1.001 (1.000–1.002) 0.019 Fibrinogen 1.003 (1.000–1.006) 0.021 Smoking 2.383 (1.211–4.691) 0.012 Metabolic syndrome 1.117 (0.701–1.779) 0.641 Diabetes 1.716 (1.092–2.697) 0.019 Data are HR (95% CI).

c blood pressure 1.004 (0.987–1.022) 0.642 Leptin 0.998 (0.972–1.026) 0.910 α-TNF-R2 1.000 (1.000–1.000) 0.987 MCP-1/CCL2 1.001 (1.000–1.002) 0.019 Fibrinogen 1.003 (1.000–1.006) 0.021 Smoking 2.383 (1.211–4.691) 0.012 Metabolic syndrome 1.117 (0.701–1.779) 0.641 Diabetes 1.716 (1.092–2.697) 0.019 Data are HR (95% CI). Multivariate analysis after the 15-year observational period Multivariate analysis was performed using only variables significant at P < 0.1 in univariate analysis. The analysis in the entire population (Table 3) showed that age, sex, fasting glucose, MCP-1/CCL2, and smoking were independent predictive variables of CVD mortality (Table 3). Table 3 Cox proportional hazards model of the predictors of 15 years CVD mortality by multivariate analysis Variable HR (95% CI) P Age 1.153 (1.114–1.193) <0.0001 Sex 0.434 (0.259–0.728) 0.0006 Fasting glucose 1.009 (1.001–1.016) 0.0253 MCP-1/CCL2 1.001 (1.000–1.002) 0.0089 Cigarette smoking 2.432 (1.110–5.328) 0.035 Data are HR (95% CI). Only variables significant at P < 0.1 at univariate analysis were tested in the models. Only variables that remained significantly associated are shown.

(0.259–0.728) 0.0006 Fasting glucose 1.009 (1.001–1.016) 0.0253 MCP-1/CCL2 1.001 (1.000–1.002) 0.0089 Cigarette smoking 2.432 (1.110–5.328) 0.035 Data are HR (95% CI). Only variables significant at P < 0.1 at univariate analysis were tested in the models. Only variables that remained significantly associated are shown. hs-CRP and MCP-1/CCL2 For hs-CRP, data on MCP-1/CCL2 and hs-CRP were simultaneously available for only 113 subjects. These subjects were older (71 ± 4 years) than those reported in the present article, and the number of cardiovascular events was 46. No correlation between plasma MCP-1/CCL2 and hs-CRP (Pearson r = 0.019; P = 0.85) was detected. In univariate analysis hs-CRP was not related to CVD (HR 1.029 [95% CI 0.984–1.076]; P = 0.22), and in this smaller cohort MCP-1/CCL2 but not hs-CRP (1.030 [0.986–1.077]; P = 0.19) showed a trend to be significantly associated with CVD mortality in age- and sex-adjusted multivariate analysis (1.001 [1.000–1.002]; P = 0.0593). CONCLUSIONS In the present study, we have shown that in middle-aged overweight/obese individuals, the fasting plasma MCP-1/CCL2 concentration was related to biochemical risk markers of atherosclerosis and that this concentration was higher in individuals with type 2 diabetes and IGT with respect to that in nondiabetic patients. Most importantly, when CVD mortality was assessed retrospectively 15 years after the baseline assessment, we report that MCP-1/CCL2 was independently associated with CVD mortality.

osclerosis and that this concentration was higher in individuals with type 2 diabetes and IGT with respect to that in nondiabetic patients. Most importantly, when CVD mortality was assessed retrospectively 15 years after the baseline assessment, we report that MCP-1/CCL2 was independently associated with CVD mortality. Pathophysiological implications Our results showed that diabetes may be a determinant of the circulating levels of MCP-1/CCL2. In fact, at baseline, MCP-1/CCL2 was associated with biochemical markers of the metabolic syndrome such as serum triglycerides, HDL cholesterol, and surrogate markers of insulin sensitivity (QUICKI). With respect to insulin resistance, because obese insulin-resistant subjects are in a proinflammatory state with an increase in intranuclear nuclear factor-κB binding activity (19) and because insulin was shown to suppress the MCP-1/CCL2 plasma concentration in vivo (20), insulin resistance itself may explain why MCP-1/CCL2 was increased in our patients. In addition, the higher amount of plasma MCP-1/CCL2 observed in individuals with type 2 diabetes was correlated with both fasting and 2-h plasma glucose after the glucose challenge, suggesting that hyperglycemia may have an independent contribution in explaining the circulating levels of MCP-1/CCL2. This finding was not unexpected because in vitro, advanced glycation end-products, a high glucose concentration, glycated albumin, and glycoxidized LDL enhanced MCP-1/CCL2 expression in human endothelial cells (21). More importantly, the results support the hypothesis that MCP-1/CCL2 may be involved directly in the pathogenesis of atherosclerosis in humans because its baseline serum concentration was independently associated with CVD mortality assessed 15 years after the baseline determination of its fasting plasma concentration. There are many data in the literature that support a role of MCP-1/CCL2 in the pathogenesis of both the initiation and progression of atherosclerosis. MCP-1/CCL2 was found to be highly expressed within atherosclerotic lesions (22), and its role in atherogenesis is consistent with in vitro and in vivo studies in animal models (23).

ny data in the literature that support a role of MCP-1/CCL2 in the pathogenesis of both the initiation and progression of atherosclerosis. MCP-1/CCL2 was found to be highly expressed within atherosclerotic lesions (22), and its role in atherogenesis is consistent with in vitro and in vivo studies in animal models (23). In humans, elevated MCP-1/CCL2 serum levels were found to be increased not only in individuals with diabetes but also in subjects at risk because of other risk factors for atherosclerosis, such as age, hypertension, hypercholesterolemia, renal failure, and vascular disease or in individuals with overt coronary artery disease as outlined in the introduction. Recently, a genetic variation in the MCP-1/CCL2 gene was associated with a higher serum concentration of the chemokine and a higher prevalence of myocardial infarction (24).

n, hypercholesterolemia, renal failure, and vascular disease or in individuals with overt coronary artery disease as outlined in the introduction. Recently, a genetic variation in the MCP-1/CCL2 gene was associated with a higher serum concentration of the chemokine and a higher prevalence of myocardial infarction (24). Clinical implications The results of the present study are potentially clinically important. With regard to the inflammatory hypothesis of atherosclerosis, these data suggest that elevated MCP-1/CCL2 levels, serving as a direct marker of inflammatory activity, may also be a biomarker of the risk of CVD. Holding this view and taking into account the fact that the fasting MCP-1/CCL2 level predicted CVD mortality when assessed 15 years later, we suggest that this chemokine may be an early independent predictor of atherosclerosis and an early biomarker of its natural development in the history of an at-risk individual. Interestingly, within our population the MCP-1/CCL2 plasma concentration was linked more strongly with CVD than the postchallenge plasma glucose concentration, metabolic syndrome, and serum circulating lipids (Table 2). Another clinical implication is that because treatment of several components of the insulin resistance syndrome (adiposity, dyslipidemia, and hypertension) had beneficial effects in preventing cardiovascular disease, if subclinical inflammation is indeed another facet of the insulin resistance syndrome, anti-inflammatory treatment may also be beneficial. A recent randomized clinical trial tested the administration of 20 mg/day rosuvastatin in nondiabetic individuals with anthropometric features similar to those of the subjects in our study (age, sex, and BMI) who had LDL cholesterol levels in the normal range but higher circulating hs-CRP levels. In this study, a 44% reduction of major cardiovascular events along with a significant reduction of hs-CRP circulating levels was reported (25), suggesting that the effects of this statin may, at least partly, be mediated through anti-inflammatory properties. In addition, it is known that pharmacological treatment with hydroxymethylglutaryl-CoA reductase inhibitors may lower MCP-1/CCL2 levels (9). Thus, elevated MCP-1/CCL2 levels could identify patients more likely to benefit from aggressive statin treatment, although we must emphasize that whether treatments to reduce circulating levels of MCP-1/CCL2 are effective in reducing the onset of CVD is yet to be determined.

inhibitors may lower MCP-1/CCL2 levels (9). Thus, elevated MCP-1/CCL2 levels could identify patients more likely to benefit from aggressive statin treatment, although we must emphasize that whether treatments to reduce circulating levels of MCP-1/CCL2 are effective in reducing the onset of CVD is yet to be determined. Based also on our results, the hypothesis that the development of pharmacological tools able to target anti-inflammatory pathways may be a potential approach to reduce rates of vascular events may be true. More potent prospective studies are clearly needed to address these issues. Study limitations and strengths The population in which MCP-1/CCL2 plasma concentrations were obtained was small, and larger studies are needed to confirm our results. Conversely, our observational period was 15 years, and we had a high number of events (82 CVD deaths in 363 individuals) relative to the entire population. An additional limitation was that the distribution of MCP-1/CCL2 plasma concentrations in the individuals with CVD mortality and those without overlapped considerably, suggesting that plasma MCP-1/CCL2 levels alone will not be helpful in predicting CVD mortality. Therefore, we are exploring circulating levels of MCP-1/CCL2 as a systemic surrogate, trying to test whether these may be linked to a local event (atherosclerosis). However, when MCP-1/CCL2 is considered along with other classic risk factors, it may eventually be useful as a surrogate biomarker in patients with IGT and diabetes.

we are exploring circulating levels of MCP-1/CCL2 as a systemic surrogate, trying to test whether these may be linked to a local event (atherosclerosis). However, when MCP-1/CCL2 is considered along with other classic risk factors, it may eventually be useful as a surrogate biomarker in patients with IGT and diabetes. Another limitation of the study is that we did not obtain serial plasma MCP-1/CCL2 concentrations. The association we report in the present work is between one single assessment of the plasma chemokine concentration and the CVD mortality assessed 15 years later, but we have no data about the circulating levels of the chemokine during the observational period. Finally, we tested MCP-1/CCL2 along with a standard inflammatory marker such as fibrinogen because that biomarker was measured in 1990–1991. hs-CRP is a more reliable marker, but, unfortunately, we only had simultaneous availability of MCP-1/CCL2 and hs-CRP for 113 subjects. Even if MCP-1/CCL2 remained associated with CVD mortality in this subset of individuals, suggesting an independent role of MCP-1/CCL2, a rigorous comparison remains to be done. In summary, in the present study, we report that in middle-aged individuals the plasma MCP-1/CCL2 concentration was related to biochemical risk markers of atherosclerosis and, in particular, to both fasting and post-OGTT plasma glucose concentrations. In the multivariate analysis MCP-1/CCL2 was associated with increased CVD mortality assessed 15 years later.

ort that in middle-aged individuals the plasma MCP-1/CCL2 concentration was related to biochemical risk markers of atherosclerosis and, in particular, to both fasting and post-OGTT plasma glucose concentrations. In the multivariate analysis MCP-1/CCL2 was associated with increased CVD mortality assessed 15 years later. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Acknowledgments This work was supported by grants from Italian Minister of Health (030.5/RF96.305 and 030.5/RF98.49), Ministero dell'Istruzione, Università e Ricerca Cofin (9806409093), and the Italian National Research Council (CNR 97.00485.CT04). The financial support of the European Association for the Study of Diabetes is also gratefully acknowledged. No potential conflicts of interest relevant to this article were reported. Parts of this study were presented in abstract form at the 69th Scientific Sessions of the American Diabetes Association, New Orleans, Louisiana, 5–9 June 2009.

rence was higher in obese insulin-sensitive than nonobese insulin-resistant subjects but were lower than in obese insulin-resistant subjects. Systolic and diastolic blood pressure, plasma transaminases, γGT, and ALP were higher in the two groups of insulin-resistant subjects than in nonobese insulin-sensitive subjects. Mortality in the cohort During the 15-year observation period, 495 deaths occurred. A total of 221 deaths were CVD related and 180 were cancer related. Age and sex were associated with higher all-cause mortality (age: HR 1.11 [95% CI 1.10–1.12], P < 0.0001; female sex: 0.42 [0.35–0.50], P < 0.0001), mortality for CVD (age: 1.15 [1.13–1.17], P < 0.0001; female sex: 0.40 [0.31–0.53], P < 0.0001), and mortality for cancer (age: 1.07 [1.06–1.09], P < 0.0001; female sex: 0.39 [0.29–0.52], P < 0.0001). All-cause mortality was higher in the obese insulin-resistant subjects (31%) in comparison with the reference group of nonobese insulin-sensitive subjects (20%) (age- and sex-adjusted HR 1.4, P = 0.01) (Table 2) (Fig. 1) but not in the obese insulin-sensitive subjects (12%) (0.99, P = 0.97) (Table 2) (Fig. 1) and in the nonobese insulin-resistant subjects (26%) (1.11, P = 0.35) (Table 2) (Fig. 1). Table 2 Cox proportional hazard model adjusting for age and sex

Mortality in the cohort During the 15-year observation period, 495 deaths occurred. A total of 221 deaths were CVD related and 180 were cancer related. Age and sex were associated with higher all-cause mortality (age: HR 1.11 [95% CI 1.10–1.12], P < 0.0001; female sex: 0.42 [0.35–0.50], P < 0.0001), mortality for CVD (age: 1.15 [1.13–1.17], P < 0.0001; female sex: 0.40 [0.31–0.53], P < 0.0001), and mortality for cancer (age: 1.07 [1.06–1.09], P < 0.0001; female sex: 0.39 [0.29–0.52], P < 0.0001). All-cause mortality was higher in the obese insulin-resistant subjects (31%) in comparison with the reference group of nonobese insulin-sensitive subjects (20%) (age- and sex-adjusted HR 1.4, P = 0.01) (Table 2) (Fig. 1) but not in the obese insulin-sensitive subjects (12%) (0.99, P = 0.97) (Table 2) (Fig. 1) and in the nonobese insulin-resistant subjects (26%) (1.11, P = 0.35) (Table 2) (Fig. 1). Table 2 Cox proportional hazard model adjusting for age and sex n events/n HR (95% CI) P All-cause mortality Nonobese insulin-sensitive subjects 141/708 — — Obese insulin-sensitive subjects 7/43 0.99 (0.46–2.11) 0.97 Nonobese insulin-resistant subjects 241/923 1.11 (0.90–1.36) 0.35 Obese insulin-resistant subjects 106/337 1.40 (1.08–1.81) 0.01 CVD mortality Nonobese insulin-sensitive subjects 58/708 — — Obese insulin-sensitive subjects 2/43 0.73 (0.18–3.00) 0.66 Nonobese insulin-resistant subjects 112/923 1.19 (0.86–1.64) 0.29 Obese insulin-resistant subjects 49/337 1.61 (1.10–2.36) 0.015 Cancer mortality Nonobese insulin-sensitive subjects 51/708 — — Obese insulin-sensitive subjects 3/43 1.04 (0.32–3.30) 0.95 Nonobese insulin-resistant subjects 85/923 1.09 (0.78–1.52) 0.64 Obese insulin-resistant subjects 41/337 1.52 (1.02–2.26) 0.04 Figure 1 Survival by Kaplan-Meier estimates of all-cause mortality. Follow-up period was 15 years (180 months). Subjects were divided according to BMI (nonobese: <30 kg/m2; obese: ≥30 kg/m2) and estimated insulin resistance (insulin sensitive: HOMA-IR <2.5; insulin resistant: ≥2.5). At the bottom are the detailed figures of the number at risk for each subgroup of individuals. NOb-IR, nonobese insulin-resistant subjects; NOb-IS, nonobese insulin-sensitive subjects (the reference); Ob-IR, obese insulin-resistant subjects; Ob-IS, obese insulin-sensitive subjects.

Metabolically healthy obese (MHO) individuals are considered as a subset of obese subjects without metabolic abnormalities (such as insulin resistance, proatherogenic lipoprotein profile, proinflammatory state, or hypertension) and a model for better understanding the pathogenesis of insulin resistance (1–3). The prevalence of the MHO phenotype in the general population, the reasons for not developing metabolic alterations, and the less aggressive therapeutic approach with respect to obese individuals with metabolic abnormalities are currently debated (4,5). In the Framingham Offspring Study, Meigs et al. (6) found that MHO individuals do not have increased risk of incident diabetes and cardiovascular disease (CVD). Conversely, in the Third National Health and Nutrition Examination Survey (NHANES III), Kuk et al. (7) reported increased all-cause mortality associated with the MHO phenotype. Finally, in a Scandinavian study (8), middle-aged overweight/obese subjects without metabolic syndrome also had an increased risk of CVD when compared with normal-weight individuals without metabolic syndrome. The present study shows the prevalence of the MHO phenotype, its metabolic features, and 15-year all-cause, CVD, and cancer mortality rates in the Caucasian population of the Cremona Study (9,10).

tabolic syndrome also had an increased risk of CVD when compared with normal-weight individuals without metabolic syndrome. The present study shows the prevalence of the MHO phenotype, its metabolic features, and 15-year all-cause, CVD, and cancer mortality rates in the Caucasian population of the Cremona Study (9,10). RESEARCH DESIGN AND METHODS Study cohort and follow-up The Cremona Study is a population survey carried out in 1990–1991 in the health district of Cremona (Lombardia, Italy) to determine the prevalence of diabetes according to the oral glucose tolerance test (OGTT) and World Health Organization criteria (8,9). A total of 2,074 individuals were enrolled. Past medical history, anthropometric measures, and clinical data of subjects were collected by trained interviewers using standardized procedures. A venous blood sample was collected after a 12-h overnight fast, and thereafter a 75-g oral glucose monohydrate was given. An additional blood sample was collected 2 h later. Heart rate and blood pressure were recorded twice, at the beginning and at the end of the visit, in the sitting position, and after at least 10 min rest using a full automatic noninvasive sphyngomanometer. The lowest figure was considered. Further details concerning the study protocol were previously described (8,9). Vital status and time of death were acquired from the Regional Health Registry (updated to 31 December 2005), and causes of death were classified using the ICD-9 (death codes for CVD are from 401 to 448 and cancer from 140.0 to 208.9). Median follow-up was 180 months, and median follow-up of those still alive at 182 months (98% of those who were still alive had a minimum follow-up period of 174 months). Data for 2,011 of 2,074 individuals were available.

ssified using the ICD-9 (death codes for CVD are from 401 to 448 and cancer from 140.0 to 208.9). Median follow-up was 180 months, and median follow-up of those still alive at 182 months (98% of those who were still alive had a minimum follow-up period of 174 months). Data for 2,011 of 2,074 individuals were available. Definition of study groups Study subjects were divided in four categories based on BMI (nonobese: <30 kg/m2; obese: ≥30 kg/m2) and estimated insulin resistance (insulin sensitive: homeostasis model assessment of insulin resistance [HOMA-IR] <2.5; insulin resistant ≥2.5). The cutoff of 2.5 for HOMA-IR was chosen to compare our data with those recently published by Kuk et al. (7). Therefore, the four categories were 1) the nonobese subjects with normal insulin sensitivity, 2) the obese but insulin-sensitive subjects, 3) the nonobese but insulin-resistant subjects, and 4) the obese and insulin-resistant subjects. The features of these subgroups are summarized in Table 1. Table 1 Baseline anthropometric, clinical, and laboratory features of study groups

Definition of study groups Study subjects were divided in four categories based on BMI (nonobese: <30 kg/m2; obese: ≥30 kg/m2) and estimated insulin resistance (insulin sensitive: homeostasis model assessment of insulin resistance [HOMA-IR] <2.5; insulin resistant ≥2.5). The cutoff of 2.5 for HOMA-IR was chosen to compare our data with those recently published by Kuk et al. (7). Therefore, the four categories were 1) the nonobese subjects with normal insulin sensitivity, 2) the obese but insulin-sensitive subjects, 3) the nonobese but insulin-resistant subjects, and 4) the obese and insulin-resistant subjects. The features of these subgroups are summarized in Table 1. Table 1 Baseline anthropometric, clinical, and laboratory features of study groups Nonobese insulin-sensitive subjects Obese insulin-sensitive subjects Nonobese insulin-resistant subjects Obese insulin-resistant subjects Anthropometric parameters n (female/male) 708 (392/316) 43 (31/12) 923 (512/411) 337 (191/146) Age (years) 55 ± 11*† 55 ± 9 59 ± 11‡§ 59 ± 10‡§ BMI (kg/m2) 23.8 ± 2.8*†§ 32.5 ± 4.3†‡ 25.8 ± 2.3*‡§ 33.3 ± 3.4†‡ Waist circumference (cm) 82 ± 9*†§ 94 ± 4*†‡ 89 ± 10*‡§ 104 ± 11†‡§ Actual smoking 201 (28%)¶ 8 (19%) 177 (19%) 64 (19%) Alcohol intake (g/day) 44 ± 59 39 ± 45 42 ± 59 39 ± 54 Systolic blood pressure (mmHg) 139 ± 20*† 143 ± 23* 147 ± 21*‡ 154 ± 20†‡§ Diastolic blood pressure (mmHg) 77 ± 11*† 79 ± 13* 81 ± 12*‡ 85 ± 12†‡§ Heart rate (beats/min) 73 ± 11*† 72 ± 10 76 ± 13‡ 77 ± 11‡ Biochemical lab parameters Glucose (mmol/l) 4.83 ± 0.50*† 4.83 ± 0.33*† 5.44 ± 1.05†‡§ 6.00 ± 1.67†‡§ Cholesterol (mmol/l) 5.92 ± 1.09† 6.20 ± 1.19 6.20 ± 1.14‡ 6.10 ± 1.14 HDL cholesterol (mmol/l) 1.52 ± 0.36*† 1.50 ± 0.34*† 1.29 ± 0.36 *‡§ 1.21 ± 0.34†‡§ LDL cholesterol (mmol/l) 3.90 ± 1.03*† 4.13 ± 1.00 4.19 ± 1.03‡ 4.11 ± 1.06‡ Triglycerides (mmol/l) 1.15 ± 0.63*† 1.26 ± 0.58*† 1.56 ± 1.04*‡ 1.72 ± 0.94†‡§ Alanine aminotransferase (units/l) 21 ± 14*† 23 ± 12* 27 ± 22*‡ 31 ± 26†‡§ Aspartate aminotransferase (units/l) 26 ± 12* 25 ± 8 28 ± 13 30 ± 19‡ γGT (units/l) 31 ± 38*† 33 ± 41 42 ± 58‡ 50 ± 82‡ ALP (units/l) 169 ± 64*† 159 ± 52* 180 ± 66‡ 187 ± 76‡§ Fibrinogen (mg/dl) 271 ± 66*† 274 ± 48*† 286 ± 74*‡ 302 ± 76‡§ Hormones Insulin (pmol/l) 50 ± 13*† 56 ± 13*† 112 ± 70*‡§ 154 ± 89†‡§ Insulin sensitivity, metabolic syndrome, diabetes status HOMA-IR 1.80 ± 0.45*† 2.00 ± 0.43*† 4.65 ± 3.70*‡§ 7.18 ± 5.57†‡§ Metabolic syndrome 37 (5%) 3 (7%) 218 (24%)‖ 139 (41%)‖ Diabetes 16 (2%) 0 (0%) 98 (11%)‖ 74 (28%)‖ Data are means ± SD or n (%), unless otherwise indicated. One-way ANOVA and Tukey post hoc for continuous variables.

, metabolic syndrome, diabetes status HOMA-IR 1.80 ± 0.45*† 2.00 ± 0.43*† 4.65 ± 3.70*‡§ 7.18 ± 5.57†‡§ Metabolic syndrome 37 (5%) 3 (7%) 218 (24%)‖ 139 (41%)‖ Diabetes 16 (2%) 0 (0%) 98 (11%)‖ 74 (28%)‖ Data are means ± SD or n (%), unless otherwise indicated. One-way ANOVA and Tukey post hoc for continuous variables. *P < 0.05 vs. obese insulin-resistant subjects; †P < 0.05 vs. nonobese insulin-resistant subjects; ‡P < 0.05 vs. nonobese insulin-sensitive subjects; §P < 0.05 vs. obese insulin-sensitive subjects. χ2 for categorical variables ¶P < 0.05 vs. all; ‖P < 0.05 vs. nonobese and obese insulin-sensitive subjects. Definition of diabetes, impaired glucose tolerance, and metabolic syndrome Diabetes was defined according to the use of oral hypoglycemic agents or insulin and according to the World Health Organization diagnostic criteria for the OGTT (basal plasma glucose >7.8 mmol/l or >11.1 mmol/l after a 2-h oral glucose load). Patients with manifest diabetes did not undergo the OGTT. Impaired glucose tolerance was defined as basal plasma glucose <7.8 mmol/l and plasma glucose >7.8 but <11 mmol/l after a 2-h oral glucose load. Metabolic syndrome was defined accordingly to the definition of the National Cholesterol Education Program Adult Treatment Program III. Analytical determinations Blood, serum, and plasma measurements were done as previously described (8,9).

Definition of diabetes, impaired glucose tolerance, and metabolic syndrome Diabetes was defined according to the use of oral hypoglycemic agents or insulin and according to the World Health Organization diagnostic criteria for the OGTT (basal plasma glucose >7.8 mmol/l or >11.1 mmol/l after a 2-h oral glucose load). Patients with manifest diabetes did not undergo the OGTT. Impaired glucose tolerance was defined as basal plasma glucose <7.8 mmol/l and plasma glucose >7.8 but <11 mmol/l after a 2-h oral glucose load. Metabolic syndrome was defined accordingly to the definition of the National Cholesterol Education Program Adult Treatment Program III. Analytical determinations Blood, serum, and plasma measurements were done as previously described (8,9). Calculations BMI was calculated as weight in kilograms divided by the square of height in meters and alcohol consumption as grams of alcohol (glass of wine = 20 g, glass of aperitif = 30 g, and glass of liquor = 80 g). HOMA-IR was calculated as previously described (11), and LDL cholesterol was calculated using the Friedwald formula.

I was calculated as weight in kilograms divided by the square of height in meters and alcohol consumption as grams of alcohol (glass of wine = 20 g, glass of aperitif = 30 g, and glass of liquor = 80 g). HOMA-IR was calculated as previously described (11), and LDL cholesterol was calculated using the Friedwald formula. Statistical analysis Data are presented as means ± SD, unless otherwise indicated. Serum insulin, triglycerides, fibrinogen, and glucose had a skewed distribution; therefore, log-transformed values were used in the analysis. ANOVA and Tukey post hoc analysis were used for comparison between groups. Differences in proportion between groups were tested by the χ2 test. The associations of each investigated risk factor with all-cause, CVD, and cancer mortality were estimated by the Cox proportional hazard model, with adjustments for age and sex. Multivariate Cox regression analysis was performed to adjust the comparisons of mortality among the different subgroups for possible confounding factors. Hazard ratios (HRs) and 95% CIs are presented. Proportions' 95% CIs were calculated using the normal approximation or the exact method. Kaplan and Meier curves for all-cause mortality were plotted for the four groups, as previously described. A P value <0.05 indicated statistical significance. Analyses were performed using SAS software (version 9.1).

are presented. Proportions' 95% CIs were calculated using the normal approximation or the exact method. Kaplan and Meier curves for all-cause mortality were plotted for the four groups, as previously described. A P value <0.05 indicated statistical significance. Analyses were performed using SAS software (version 9.1). RESULTS Prevalence of the obese insulin-sensitive phenotype Of 2,011 subjects, 708 were nonobese and insulin sensitive, 923 nonobese and insulin resistant, and 337 obese and insulin resistant. There were a total of 43 obese insulin-sensitive individuals, representing 11.0% (95% CI 8.1–14.5) of the obese population and 2.1% (1.6–2.9) of the entire population. Anthropometric and metabolic features of obese insulin-sensitive individuals The features of the four groups are summarized in Table 1. Sex distribution did not differ among all groups, whereas cigarette smoking was more frequent in nonobese insulin-sensitive subjects than all other groups. Systolic and diastolic blood pressure, heart rate, plasma glucose, insulin, total cholesterol, HDL cholesterol, triglycerides, transaminases, γ-glutamyltransferase (γGT), alkaline phosphatase (ALP), and fibrinogen did not differ between the two insulin-sensitive groups.

insulin-sensitive subjects than all other groups. Systolic and diastolic blood pressure, heart rate, plasma glucose, insulin, total cholesterol, HDL cholesterol, triglycerides, transaminases, γ-glutamyltransferase (γGT), alkaline phosphatase (ALP), and fibrinogen did not differ between the two insulin-sensitive groups. Individuals in the insulin-sensitive groups were younger, had lower heart rates, had higher plasma HDL cholesterol, and had lower fibrinogen and triglycerides, as well as had a lower prevalence of diabetes and metabolic syndrome than insulin-resistant groups. Waist circumference was higher in obese insulin-sensitive than nonobese insulin-resistant subjects but were lower than in obese insulin-resistant subjects. Systolic and diastolic blood pressure, plasma transaminases, γGT, and ALP were higher in the two groups of insulin-resistant subjects than in nonobese insulin-sensitive subjects.

MA-IR <2.5; insulin resistant: ≥2.5). At the bottom are the detailed figures of the number at risk for each subgroup of individuals. NOb-IR, nonobese insulin-resistant subjects; NOb-IS, nonobese insulin-sensitive subjects (the reference); Ob-IR, obese insulin-resistant subjects; Ob-IS, obese insulin-sensitive subjects. Also, mortality for CVD (15%, P = 0.015) and cancer (12%, P = 0.04) (Table 2) was higher in the obese insulin-resistant subjects but not in the obese insulin-sensitive subjects (CVD related: 5%, P = 0.66, and cancer related: 7%, P = 0.95) and nonobese insulin-resistant subjects (CVD related: 12%, P = 0.29, and cancer related: 9%, P = 0.64) when compared with nonobese insulin-sensitive subjects (CVD related: 8% and cancer related: 7%). Because the prevalence of cigarette smoking and baseline plasma LDL cholesterol were different among the groups, we performed the analysis adjusting also for these two factors. All-cause mortality remained higher in the obese insulin-resistant subjects (HR 1.66 [95% CI 1.12–2.46], P = 0.011) but not in obese insulin-sensitive subjects (0.79 [0.19–3.28], P = 0.75) and in nonobese insulin-resistant subjects (1.22 [0.88–1.70], P = 0.23) when compared with nonobese and insulin-sensitive subjects.

All-cause mortality remained higher in the obese insulin-resistant subjects (HR 1.66 [95% CI 1.12–2.46], P = 0.011) but not in obese insulin-sensitive subjects (0.79 [0.19–3.28], P = 0.75) and in nonobese insulin-resistant subjects (1.22 [0.88–1.70], P = 0.23) when compared with nonobese and insulin-sensitive subjects. The analysis was also repeated after the exclusion of diabetic patients. When compared with nonobese insulin-sensitive (reference group), all-cause mortality tended to be higher in obese insulin-resistant subjects (HR 1.29 [95% CI 0.96–1.73], P = 0.087) but was again not different in obese insulin-sensitive subjects (1.01 [0.47–2.17], P = 0.97) and nonobese insulin-resistant subjects (1.00 [0.80–1.25], P = 0.98). Similarly, mortality for CVD and cancer tended to be higher in obese insulin-resistant subjects (CVD: HR 1.40 [95% CI 0.94–2.11], P = 0.071; cancer: 1.46 [0.94–2.27], P = 0.097) but was not different in obese insulin-sensitive subjects (CVD: 0.76 [0.197–3.11], P = 0.71; cancer: 1.05 [0.33–3.56], P = 0.94) and nonobese insulin-resistant subjects (CVD: [1.09 [0.77–1.54], P = 0.65; cancer: 1.01 [0.93–2.27], P = 0.95) than nonobese insulin-sensitive subjects. Finally, instead of the preselected HOMA-IR of 2.5, we repeated the analysis using cutoff values (top tertile and top quartile) for HOMA-IR obtained from the present study. Even in this case, the results did not change (see the online appendix, available at http://care.diabetesjournals.org/cgi/content/full/dc10-0665/DC1).

Finally, instead of the preselected HOMA-IR of 2.5, we repeated the analysis using cutoff values (top tertile and top quartile) for HOMA-IR obtained from the present study. Even in this case, the results did not change (see the online appendix, available at http://care.diabetesjournals.org/cgi/content/full/dc10-0665/DC1). CONCLUSIONS The 15-year follow-up of the Cremona Study demonstrates that obese insulin-sensitive individuals, also known as MHO individuals 1) have a prevalence of 11% in the obese population and 2% in the entire population; 2) have less features of the metabolic syndrome, when compared with obese insulin-resistant individuals; and 3) do not have increased all-cause, CVD, and cancer mortality, when compared with nonobese insulin-sensitive (reference group) subjects.

prevalence of 11% in the obese population and 2% in the entire population; 2) have less features of the metabolic syndrome, when compared with obese insulin-resistant individuals; and 3) do not have increased all-cause, CVD, and cancer mortality, when compared with nonobese insulin-sensitive (reference group) subjects. Major findings and comparison with the literature The prevalence of the obese insulin-sensitive phenotype (11%) in our obese cohort was lower than reported by Iacobellis et al. (3) (27.5%) in a cohort of 681 obese individuals living in Rome and the surrounding areas. The discrepancy may be related to the different regional habits of the Italian cohorts but most likely to the different definition of MHO. Iacobellis et al. based their definition mainly on the metabolic syndrome; meanwhile, our definition was centered on HOMA-IR, a surrogate index of insulin resistance, in order to compare our results with those recently published by Kuk and Ardern (7), who analyzed the NHANES III survey in U.S. using HOMA-IR <2.5 as the cutoff. Interestingly, they reported a prevalence of metabolically healthy subjects of 6%. Our finding is in line with this report (7); therefore, we think that the frequency of this phenotype is lower than previously thought.

d by Kuk and Ardern (7), who analyzed the NHANES III survey in U.S. using HOMA-IR <2.5 as the cutoff. Interestingly, they reported a prevalence of metabolically healthy subjects of 6%. Our finding is in line with this report (7); therefore, we think that the frequency of this phenotype is lower than previously thought. The present study has also clearly shown that the obese insulin-sensitive phenotype carries less features of the metabolic syndrome. These subjects were characterized by lower waist circumference, blood pressure, circulating triglycerides, transaminases, γGT (as a surrogate markers of fatty liver), and fibrinogen (as a surrogate marker of low-grade inflammation), when compared with the obese insulin-resistant subjects, in spite of similar BMIs. Not surprisingly, they had a lower prevalence of the metabolic syndrome (7% in comparison to the observed 41% in the obese insulin-resistant subjects) and of diabetes (0 vs. 28% of the obese insulin-resistant subjects). We think, therefore, that the deleterious metabolic features associated with obesity are largely related to the presence of insulin resistance rather than obesity, per se.

(7% in comparison to the observed 41% in the obese insulin-resistant subjects) and of diabetes (0 vs. 28% of the obese insulin-resistant subjects). We think, therefore, that the deleterious metabolic features associated with obesity are largely related to the presence of insulin resistance rather than obesity, per se. The third aim was to establish the prognosis of the MHO subjects. The present study has also shown that all-cause mortality is significantly higher in obese insulin-resistant subjects but not obese insulin-sensitive subjects, when compared with nonobese insulin-sensitive individuals (considered as reference group). These findings were confirmed when the analysis was adjusted for LDL cholesterol and cigarette smoking (risk factors not related to metabolic syndrome). However, our findings are in contrast with recent data from a U.S. population that suggest increased all-cause mortality in MHO subjects (defined according to the same BMI and HOMA-IR criteria we use here) (7). The potential explanations for this discrepancy are number of events, reference HR, and different ethnicity. Even though our population was smaller, the number of events was higher (495 vs. 292, or 25 vs. 5%). This was likely because of the longer observational period (15 vs. 8.7 years). It is important to point out that a 10- to 15-year follow-up may be the least to see the effects of metabolic risk factors on mortality (8,12). Regarding reference HRs, we studied nonobese insulin-sensitive individuals, which also includes overweight individuals with BMIs ranging between 25 and 29.9 kg/m2, whereas Kuk et al. (7) studied normal-weight insulin-sensitive subjects with BMI <25 kg/m2.

o see the effects of metabolic risk factors on mortality (8,12). Regarding reference HRs, we studied nonobese insulin-sensitive individuals, which also includes overweight individuals with BMIs ranging between 25 and 29.9 kg/m2, whereas Kuk et al. (7) studied normal-weight insulin-sensitive subjects with BMI <25 kg/m2. Our finding is also in contrast with another report by Arnlov et al. (8) in a Scandinavian population in which overweight and obese individuals without the metabolic syndrome showed a higher mortality when compared with normal-weight and insulin-sensitive individuals. We believe that the reason for this discrepancy could be attributed to sex differences because our study included both male and female subjects, whereas only male subjects were included in the Scandinavian study. This is worth mentioning because male sex was a significant risk factor for all-cause mortality in our study. We used all-cause mortality as primary outcome (because this variable is less affected by errors in reporting), whereas CVD and cancer mortality were considered secondary outcomes. Mortality for CVD and cancer, as for all-cause mortality, were also higher in obese insulin-resistant individuals but not in the MHO subjects.

We used all-cause mortality as primary outcome (because this variable is less affected by errors in reporting), whereas CVD and cancer mortality were considered secondary outcomes. Mortality for CVD and cancer, as for all-cause mortality, were also higher in obese insulin-resistant individuals but not in the MHO subjects. Strengths and limitations The following are major strengths of the present study: 1) this was a population-based study including both male and female subjects, 2) there was careful and homogeneous acquisition of the anthropometric parameter of interest, 3) there was a robust end point (all-cause mortality) whose ascertainment was based on the Regional Health Registry, and 4) there was a long follow-up period (15 years).

ion-based study including both male and female subjects, 2) there was careful and homogeneous acquisition of the anthropometric parameter of interest, 3) there was a robust end point (all-cause mortality) whose ascertainment was based on the Regional Health Registry, and 4) there was a long follow-up period (15 years). The following are the limitations of this study: 1) there was a small sample size of the group of obese insulin-sensitive subjects (n = 43), and their low prevalence in the cohort (2%) could represent a problem because of the consequent small number of events even if it was similar to previously reported data (8); 2) there was a lack of collection of intermediate data points about the parameters of interest during the 15-year observation period; 3) the glucose clamp technique is the gold standard for the assessment of insulin sensitivity and HOMA is inferior; nevertheless, it was suggested that HOMA appeared to be specifically suited to large-scale epidemiologic studies in which only fasting glucose and insulin concentrations were available (13); and 4) there was a lack of collection of the dietary habits and habitual physical activity, known to have a well recognized impact on insulin sensitivity.

ested that HOMA appeared to be specifically suited to large-scale epidemiologic studies in which only fasting glucose and insulin concentrations were available (13); and 4) there was a lack of collection of the dietary habits and habitual physical activity, known to have a well recognized impact on insulin sensitivity. Pathogenic remarks It is currently unclear why these MHO subjects may be protected. It was reported that a lower amount of visceral fat content may contribute to the favorable metabolic profile (1,6). Fitting this view, the waist circumference was lower in MHO subjects than in the obese insulin-resistant subjects; on the other hand, it was higher in comparison to the nonobese and insulin-sensitive group (Table 1), in spite of a similar all-cause mortality. We speculate that visceral fat and insulin resistance may, in combination, explain the difference and the trends observed between groups in our study, and, in addition, an undetectable effect of ectopic fat accumulation in the skeletal muscle (14) and the liver (15) should be considered. In particular, the potential, but yet-to-be-demonstrated, role of the liver (see the profile of surrogate markers of fatty liver) in mediating the increased CVD mortality may be hypothesized based on the proinflammatory and proatherosclerotic profile of individuals with nonalcoholic fatty liver disease but also based on some initial epidemiological data (16).

be-demonstrated, role of the liver (see the profile of surrogate markers of fatty liver) in mediating the increased CVD mortality may be hypothesized based on the proinflammatory and proatherosclerotic profile of individuals with nonalcoholic fatty liver disease but also based on some initial epidemiological data (16). All-cause mortality in obese insulin-resistant subjects but not in MHO subjects is higher when compared with nonobese insulin-sensitive subjects. The effect of obesity on the increasing risk is strongly related with insulin resistance, and we therefore agree with Bonora et al. (17) and McLaughlin et al. (18) that it is important to not limit our risk evaluation to the identification of obesity alone but to put more effort into identifying those at higher risk, insulin-resistant obese individuals. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Acknowledgments This work was supported by grants from the Italian Minister of Health (030.5/RF96.305 and 030.5/RF98.49), Ministero dell' Università e della Ricerca Scientifića e Tecnologica Cofin (9806409093), and the Italian National Research Council (Consiglo Nazionale delle Ricerche 97.00485.CT04). The financial support of the European Foundation for the Study of Diabetes is also gratefully acknowledged. No potential conflicts of interest relevant to this article were reported.

Acknowledgments This work was supported by grants from the Italian Minister of Health (030.5/RF96.305 and 030.5/RF98.49), Ministero dell' Università e della Ricerca Scientifića e Tecnologica Cofin (9806409093), and the Italian National Research Council (Consiglo Nazionale delle Ricerche 97.00485.CT04). The financial support of the European Foundation for the Study of Diabetes is also gratefully acknowledged. No potential conflicts of interest relevant to this article were reported. G.C. researched data, contributed to the discussion, wrote the manuscript, and reviewed/edited the manuscript. G.L. researched data and reviewed/edited the manuscript. L.P. contributed to the discussion and reviewed/edited the manuscript. M.P.G. researched data and reviewed/edited the manuscript. F.R. researched data and reviewed/edited the manuscript. M.V. researched data and reviewed/edited the manuscript. S.M. researched data and reviewed/edited the manuscript. P.C. researched data and reviewed/edited the manuscript. E.B. contributed to the discussion and reviewed/edited the manuscript. L.L. contributed to the discussion and reviewed/edited the manuscript. G.R. contributed to the discussion and reviewed/edited the manuscript. G.P. contributed to the discussion, wrote the manuscript, and reviewed/edited the manuscript. This work was presented in abstract form at the American Diabetes Association's 70th Scientific Sessions, 25–29 June 2010, Orlando, Florida.