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Published ahead of print on August 5, 2007
Clin J Am Soc Nephrol 2: 984-991, 2007
© 2007 American Society of Nephrology
doi: 10.2215/CJN.01190307
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Epidemiology and Outcomes

Identifying Patients at Risk for Microalbuminuria via Interaction of the Components of the Metabolic Syndrome: A Cross-Sectional Analytic Study

Monica Franciosi, Fabio Pellegrini, Michele Sacco, Giorgia De Berardis, Maria C.E. Rossi, Giovanni F.M. Strippoli, Maurizio Belfiglio, Gianni Tognoni, Miriam Valentini, Antonio Nicolucci; on behalf of the IGLOO (Impaired Glucose tolerance, and Long-term Outcomes Observational Study) Study Group

Department of Clinical Pharmacology and Epidemiology, Consorzio Mario Negri Sud, S. Maria Imbaro, Italy

Address correspondence to: Dr. Antonio Nicolucci, Department of Clinical Pharmacology and Epidemiology, Consorzio Mario Negri Sud, Via Nazionale, 66030 S. Maria Imbaro (CH), Italy. Phone: +39-0872-570260; Fax: +39-0872-570263; E-mail: nicolucci{at}negrisud.it


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
 Disclosures
 References
 
Background and objectives: The objective of this study was to investigate correlates of risk for having microalbuminuria in individuals with one or more cardiovascular risk factors.

Design, setting, participants, & measurements: The study involved 1919 individuals who attended general practice settings, were aged 55 to 75 yr, and did not have a history of cardiovascular events or diabetes but had one or more cardiovascular risk factors. A tree-based regression technique and multivariate analysis were used to identify distinct, homogeneous subgroups of patients with different likelihood of having microalbuminuria; interaction between correlates of microalbuminuria and risk for microalbuminuria was also investigated.

Results: The prevalence of microalbuminuria was 5.9%. Patients who did not have hypertension and had postload glycemia <140 mg/dl showed the lowest prevalence of microalbuminuria (1.9%) and represented the reference class. The likelihood of microalbuminuria was seven times higher in men with hypertension and homeostatic model assessment levels in the upper tertile and four times higher in women with the same characteristics. Individuals with hypertension and lower homeostatic model assessment levels and normotensive individuals with postload glycemia ≥140 mg/dl had a more than three-fold increased likelihood of having microalbuminuria. Treatment with statins was associated with a 54% reduction in the likelihood of having microalbuminuria, whereas levels of triglycerides >150 mg/dl and fibrinogen levels in the upper tertile were associated with a significantly higher risk for microalbuminuria.

Conclusions: The likelihood of having microalbuminuria in a population-based study of elderly individuals is strongly related to the interaction between the components of the metabolic syndrome, particularly hypertension, insulin resistance, and impaired glucose tolerance.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusions
 Disclosures
 References
 
Microalbuminuria (MA), defined as the increase in urine albumin excretion >30 to 299 µg/mg, reflects widespread vascular damage and generalized endothelial dysfunction (1,2) and has been found to be a strong, consistent, and independent predictor of all-cause and cardiovascular mortality and morbidity in individuals with and without diabetes (3,4). A role for MA has been suggested in both micro- and macrovascular damage, including retinopathy, neuropathy, and nephropathy, with the risk for doubling of serum creatinine and end-stage kidney disease increasing by two- to four-fold in the presence of MA (5–7).

Because studies support the hypothesis of an association between insulin resistance and the development of increased urinary albumin excretion (8,9) and given the association among MA, cardiovascular risk factors, and insulin resistance, it has been proposed that MA be included among the components of the metabolic syndrome (10). However, it has not been sufficiently explored whether patients with and without metabolic syndrome show equal risk for developing MA. In addition, the independent role of the different components of the metabolic syndrome and their interaction in predicting the risk for MA have not been fully explored. In other words, it is not clear whether individuals with the metabolic syndrome represent a homogeneous population in terms of risk for having MA or whether the magnitude of such a risk may be influenced by different features of the metabolic syndrome. In this analysis, in the context of the Impaired Glucose tolerance and Long-term Outcomes Observational (IGLOO) Study, we investigated these open questions.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusions
 Disclosures
 References
 
Study Design
The IGLOO Study is a multicenter, prospective cohort study aimed at estimating the prevalence of impaired glucose tolerance (IGT) and unknown diabetes in individuals with one or more cardiovascular (CV) risk factors and assessing the 5-yr incidence of type 2 diabetes and CV events, according to baseline CV and metabolic risk profile (11).

The study population was represented by men and women who were aged 55 to 75 yr, did not have a history of diabetes and CV events (angina, myocardial infarction, percutaneous transluminal balloon coronary angioplasty/coronary artery bypass graft, heart failure, transient ischemic attack, stroke, peripheral vascular disease or limb bypass surgery, or percutaneous angioplasty), and had one or more of the following CV risk factors: family history of premature CV events (definite myocardial infarction or sudden death before 55 yr of age in father or other male first-degree relative or before 65 yr of age in mother or other female first-degree relative), hypertension (>140 mmHg systolic or >90 mmHg diastolic or on antihypertensive medication), dyslipidemia (total cholesterol ≥220 mg/dl or LDL cholesterol ≥160 mg/dl or HDL cholesterol <40 mg/dl or on lipid-lowering therapy), left ventricular hypertrophy with strain pattern defined per electrocardiogram (Sokolow and Lyon criteria or Cornell criteria), or smoking (current cigarette smoking or an individual who has quit smoking <12 mo before inclusion).

Consecutive patients up to a maximum of 30 individuals who attended general practice offices between October 2002 and February 2004 for a routine visit and met these eligibility criteria were identified. All patients were referred to a diabetes outpatient clinic to collect blood and urine samples and to perform an oral glucose tolerance test, with determination of venous plasma glucose, fasting and 2 h after the ingestion of 75 g of glucose. All biochemistry was evaluated centrally (Servizio di Patologia Clinica, Ospedale di Desio, Milano). At study entry, general practitioners collected clinical information for all patients enrolled. Local ethics committees approved the protocol, and all patients gave written informed consent before their participation in the study. This analysis was based on cross-sectional data collected at the time of patients’ recruitment in the IGLOO Study.

MA Assessment
Urinary albumin and creatinine concentrations were determined on a morning spot-urine sample. Urine albumin was determined by the immune turbidimetric method (albumin tina-quant; Roche Diagnostics, Rotkreuz, Switzerland), whereas urinary creatinine was assessed by the kinetic Jaffé method with the Autoanalyzer Modular (Hitachi-Roche Diagnostics, Manneheim, Germany). The urinary albumin-to-creatinine ratio (ACR) was then calculated. MA was defined as an ACR of 30 to 299 µg/mg. Patients with macroalbuminuria (defined as ACR ≥300 µg/mg; n = 12) were excluded from the analyses.

Metabolic Syndrome and Biochemical Parameters
Metabolic syndrome was defined according to the Adult Treatment Panel III criteria (12), whereas alterations of glucose metabolism were defined according to the World Health Organization 1999 criteria (10).

Serum concentrations of C-reactive protein (CRP) were determined by a high-sensitivity immunonephelometric assay (NLatex CRP mono; Dade Behring, Newark, DE). Plasma levels of fibrinogen were measured by derived fibrinogen method (Thromborel S; Dade Behring) on Dade Behring photometric coagulation analyzers, whereas homocysteine levels were determined by an HPLC method and fluorescence detection.

Insulin resistance was evaluated from fasting plasma glucose and insulin, using the homeostasis model assessment of insulin resistance (HOMA-IR), with the following equation: Fasting insulin (mU) x fasting glucose (mmol/L)/2.5 (13). High HOMA-IR values indicate high insulin resistance.

Statistical Analyses
Baseline characteristics were reported as mean and SD for continuous variables and frequencies and percentages for categorical ones. Analyses were initially performed using univariate comparisons. Patients’ characteristics according to the presence of MA were compared using the Mann-Whitney test for continuous variables. The association of MA with categorical variables was expressed as an odds ratio (OR) with its 95% confidence interval (95% CI). In addition, to take into account the possible confounding effect of the variables investigated, we performed a multiple logistic regression analysis with stepwise variable selection with results expressed as adjusted OR (AOR) and their 95% CI. The covariates tested were age; gender; smoking habits; body mass index; abdominal obesity; total cholesterol; triglycerides; pulse pressure; CRP; homocysteine; fibrinogen; white blood cell (WBC) count; postload glycemia; HOMA-IR; hypertension; dyslipidemia; and use of statins, angiotensin-converting enzyme inhibitors (ACE-I), and angiotensin receptor blockers. For the purpose of these analyses, the following continuous variables were categorized in tertiles: pulse pressure, homocysteine, HOMA-IR, fibrinogen, and WBC count.

Interaction among variables and identification of distinct and homogeneous subgroups of patients with different risk for MA were assessed by the Recursive Partitioning and Amalgamation (RECPAM) method (14,15). This method attempts to integrate the advantages of main-effects logistic regression and tree-growing techniques (15). At each partitioning step, the method chooses the variable and the level of splitting to maximize identification of differences in the outcome of interest. In this study, variables tested in the RECPAM analysis were the same as in those in the multiple logistic regression analysis. Patients’ characteristics according to RECPAM classes were compared using the {chi}2 test for categorical variables. To detect additional global correlates (i.e., variables that played their role in the whole sample, irrespective of RECPAM classes), we ran a final logistic analysis with the classes identified by RECPAM forced in the model, testing all other characteristics that did not enter the tree.

All analyses were performed using SAS Language (Release 9.1; SAS Institute, Cary, NC). For the RECPAM analyses, we used a SAS macro routine written by one of the authors (F.P.).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusions
 Disclosures
 References
 
Patients’ Characteristics
In the IGLOO Study, 170 general practitioners recruited a total of 1919 individuals, 986 (51.4%) of whom were male. The mean age of the study population was 62.6 ± 5.3 yr; 1138 (59.3%) patients had hypertension, 1220 (63.6%) had dyslipidemia, 237 (12.3%) had a family history of CV events, 355 (18.6%) were current smokers, and 183 (9.5%) had left ventricular hypertrophy. Overall, 1008 (53.5%) patients showed abnormalities of glucose metabolism: Impaired fasting glucose (265 [14.1%] patients), IGT (397 [21.1%] patients), or unknown diabetes (346 [18.3%] patients). A total of 730 (38.0%) had metabolic syndrome according to the Adult Treatment Panel III definition (12).

The prevalence of MA was significantly associated with presence of hypertension, alterations of glucose metabolism, and metabolic syndrome (Table 1). Prevalence of MA increased with increasing number of metabolic syndrome components, whereas it was significantly lower in individuals treated with statins (Table 1).


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  		Table 1. Prevalence of MA according to characteristics of the study populationa

 
In comparison with normoalbuminuric patients, those with MA showed higher levels of fasting blood glucose (117 ± 25 versus 110 ± 20 mg/dl; P < 0.0010), postload blood glucose (159 ± 70 versus 141 ± 63 mg/dl; P = 0.0070), HOMA-IR (4.1 versus 2.7; P < 0.0001), fasting insulin levels (14 ± 9 versus 10 ± 6 µU/ml; P < 0.0001), body mass index (28.9 ± 4.3 versus 27.6 ± 4.2; P = 0.0010), waist circumference (100.4 ± 13 versus 96.9 ± 13.4 cm; P = 0.0070), systolic BP (143.2 ± 15.6 versus 138.8 ± 14.3 mmHg; P = 0.0300), pulse pressure (59.0 ± 12.1 versus 54.7 ± 11.8 mmHg; P = 0.0020), triglycerides (159 ± 106 versus 133 ± 71 mg/dl; P = 0.0200), CRP (5.3 ± 8.1 versus 3.3 ± 5.3 mg/L; P = 0.0002), and WBC count (7.0 ± 2.0 versus 6.6 ± 1.7 103/µl; P = 0.0200).

Multivariate Analysis
Multiple logistic regression showed that several variables were independently associated with the likelihood of having MA (Table 2). In particular, the presence of hypertension and a higher degree of insulin resistance (upper tertile of HOMA-IR: >2.87) were the strongest correlates of MA. Values of triglycerides >150 mg/dl were also independently associated with an increased risk for having MA. Conversely, the use of statins was associated with a markedly lower risk for MA (Table 2).


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  		Table 2. Correlates of MA: Results of logistic regression analysisa

 
RECPAM Analysis
RECPAM algorithm led to the identification of five classes, characterized by a marked variability in the prevalence of MA, ranging from 12.4 to 1.9% (Figure 1). OR were estimated with respect to the referent category (class 5, with the lowest prevalence of MA).


Figure 1
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  		Figure 1. Identification of subgroups of patients showing different risk for microalbuminuria (MA): Results of Recursive Partitioning and Amalgamation (RECPAM) analysis. RECPAM tree-growing algorithm models the odds for MA as outcome following a multivariate logistic. Splitting variables are written in bold between branches; the condition sending patients to the left or right sibling is placed on the relative branch. Subgroups of patients are denoted by circles or squares; the latter indicate RECPAM classes. Numbers inside circles and squares represent the number of patients with MA (in italics) and without MA (plain text), respectively. Adjusted odds ratio (AOR) with the corresponding 95% confidence interval (in parentheses) is attached to each class. The class with the lowest likelihood to present MA, placed at the utmost right (class 5), is set as reference category (OR 1.0).

 
The most important variable in differentiating the risk for MA was hypertension; in particular, normotensive patients with postload glycemia <140 mg/dl showed the lowest prevalence of MA (1.9%). The likelihood of MA was seven times higher in men with hypertension and HOMA-IR levels in the upper tertile (AOR 7.2; 95% CI 3.5 to 14.8) and four times higher in women with the same characteristics (AOR 4.1; 95% CI 1.9 to 9.2). A more than three-fold increase in the likelihood of MA was also present among individuals with hypertension and lower HOMA-IR levels (≤2.87; AOR 3.1; 95% CI 1.5 to 6.2), as well as in normotensive individuals with postload glycemia ≥140 mg/dl (AOR 3.5; 95% CI 1.5 to 8.3). Compared with the reference category (RECPAM class 5), all RECPAM classes that were associated with an increased risk for MA also showed a markedly higher prevalence of the components of the metabolic syndrome (Figure 1).

A final stepwise logistic regression with the RECPAM classes forced in was performed to highlight the role of additional variables that play a role as global correlates (Table 3). Plasma triglycerides and fibrinogen levels and the use of statins were retained in the final model as globally predictive variables. Treatment with statins was associated with a markedly lower likelihood of MA, whereas higher levels of triglycerides (>150 mg/dl) and values of fibrinogen in the upper tertile (>8.2 µmol/L) were correlated with a higher risk for MA (OR 1.86 [95% CI 1.22 to 2.84] and OR 1.62 [95% CI 1.01 to 2.60]).


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  		Table 3. Logistic regression results with RECPAM classes forced ina

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusions
 Disclosures
 References
 
We found that patients with MA display significant increases in the levels of factors of the metabolic syndrome and markers of inflammation (CRP, WBC count). This suggests that the presence of MA in the setting of metabolic syndrome is a marker of clinical severity.

The association between MA and the metabolic syndrome has been previously described. In a large representative sample of US adults who participated in the Third National Health and Nutrition Examination Survey (NHANES III) (16), MA was significantly associated with the metabolic syndrome and each of its components, and the risk for MA increased progressively as the number of components of the metabolic syndrome increased from 0 or 1 to 5.

What our study adds is that the risk for having MA is related not only to the number of metabolic syndrome components but also even more to the interaction of specific components of the metabolic syndrome. The concomitant presence of hypertension and insulin resistance represented the major factor affecting the risk for MA, which increased by four- to seven-fold. In absolute values, per every 10 patients who presented hypertension and insulin resistance, one had MA. As for normotensive individuals, the discriminating factor was represented by a postload glycemia ≥140 mg/dl, which was associated with a 3.5-fold increase in the risk for having MA. High levels of triglycerides and fibrinogen further increased the risk for MA in all categories by 60 to 90%.

The notion that hypertension and insulin resistance are strongly, independently, and consistently associated with the risk for MA is not new. The relationship between insulin resistance and MA was previously documented in individuals without diabetes and patients with type 2 diabetes (16–20). The Insulin Resistance Atherosclerosis Study (IRAS) found that the association between MA and insulin resistance was partially dependent on BP and obesity (21). Fujikawa et al. (9) also showed that insulin resistance precedes the onset of MA and hypertension in individuals without diabetes. Similarly, for hypertension, a large body of evidence shows its role as a predictor of MA (22). The additional information that derives from our data is the striking increase in risk for MA that is conferred by the concomitant presence of these two components of the metabolic syndrome.

In our study, postload hyperglycemia represented the strongest correlate of MA in patients without hypertension, confirming the crucial role of glucose metabolism on renal function. Furthermore, two thirds of the normotensive patients with postload glycemia ≥140 mg/dl had IGT, thus confirming the role of even mild glucose metabolism abnormalities in increasing the risk for early renal impairment (MA) (23).

We also identify a close relationship between the risk for MA and lipid alterations. The adjusted risk for having MA is nearly doubled in presence of high levels of triglycerides (≥150 mg/dl), a typical component of the metabolic syndrome. Conversely, the likelihood of having MA was significantly lower among individuals who were treated with statins. Clinical trials have shown that in normotensive patients with type 2 diabetes, simvastatin decreased urinary albumin excretion rate independent of the decrease in plasma LDL cholesterol (24–26). Pravastatin significantly reduced proteinuria in well-controlled hypertensive patients without hyperlipidemia. Similar data have been found with atorvastatin in patients who have chronic kidney disease and receive ACE-I or angiotensin receptor blockers. These interventions reduced proteinuria and the rate of progression of kidney disease (27). Taken together, these data suggest that statins may reduce glomerular injury through their cholesterol-dependent and/or pleiotropic effects (28–32).

In our study, the risk for having MA was also associated with high levels of fibrinogen (i.e., ≥280 mg/dl). The relationship between a procoagulant status and the risk for renal damage has already been underlined in previous studies (33,34). In particular, Bruno et al. (33) documented that high levels of fibrinogen predicted progression to overt nephropathy, independent of the presence of MA or hypertension. Furthermore, Klein et al. (34) showed that hyperfibrinogenemia is independently associated with urinary albumin excretion in patients with type 1 diabetes.

The relevance of our findings should not be overinterpreted, in light of a number of limitations. First, the cross-sectional study design hinders the ability to draw inferences regarding causality among insulin resistance, other components of the metabolic syndrome, and MA. We are therefore unable to determine whether insulin resistance and concomitant hyperinsulinemia lie on the same causal pathway that leads to MA later in the chain of events. For the same reason, the putative protective effects of statins on renal function need confirmation in large-scale, longitudinal studies. Second, we measured MA only once with potential for nondifferential misclassification; this might have occurred and, at most, biased our results toward the null. Furthermore, a single determination of MA is usually considered in epidemiologic studies as a reasonable proxy of renal function (16). Another limitation is represented by the impossibility to exclude other possible causes of MA, such as urinary tract infections, exercise status, or other conditions that may have caused false-positive urine albumin specimens.


   Conclusions
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
Disclosures
 References
 
The presence of MA was found to be associated with a higher risk profile in terms of traditional CV risk factors, components of the metabolic syndrome, and markers of inflammation and prothrombotic state. The concomitant presence of hypertension and insulin resistance dramatically increases the risk for MA, which is also strongly associated with the other components of the metabolic syndrome. Independent of whether MA is considered as a component of the metabolic syndrome, its presence should be systematically investigated in all individuals with one or more CV risk factors. The role of MA and the metabolic syndrome in CV and renal diseases is a matter of debate. If the presence of the metabolic syndrome is causally related to MA, then it would be plausible that lifestyle and/or pharmacologic interventions that address the metabolic syndrome may also result in significant effects on MA, CV, and renal outcomes.


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
 Disclosures
References
 
None.


   Acknowledgments
 
This study was supported by Novartis Farma S.p.A., Origgio (VA).

We thank Dr. Roberto Boero (Divisione di Nefrologia e Dialisi, Ospedale S.G. Bosco, Torino) for useful comments.

Investigators: Diabetologists: L. Gentile, P. Cichero, Asti; P. Di Berardino, C. Di Petta, V. Montani, Atri (TE); M. Poli, Bovolone (VR); J. Grosso, F. De Marco, Castel Di Sangro (AQ); F. Perticone, A. Mattace, M. Vatrano, G. Ventura, Catanzaro; M. Sprovieri, V. Spagnuolo, Cosenza (CS); A. Mastropasqua, P. Marenco, M. Caruso, Garbagnate Milanese (MI); E. D'ugo, M.R. Squadrone, Gissi (CH); M. Pupillo, A. Minnucci, A. De Luca, Lanciano (CH); M. Tagliaferri, C. Vitale, Larino (CB); L. Sciangula, E. Banfi, Mariano Comense (CO); D. Cucinotta, A. Di Benedetto, M. Previti, Messina; A. Tiengo, A. Avogaro, M. Bettio, S. De Kreuzenberg, Padova; A. Galluzzo, C. Camilleri, S. Merlino, D. Sinagra, Palermo; V. Provenzano, M. Fleres, Partinico (PA); M. Carnovali, E. Crespi, M. Sommariva, C. Vecchio, Passirana Di Rho (MI); A. Consoli, E. Ciccarone, E. Devangelio, G. Formoso, M. Taraborrelli, Pescara; G. Seghieri, L. Alviggi, G. Bardini, A. De Bellis, Pistoia; T. Porro, A. Bianchi, R. Dagani, R. Di Battista, A. Ferrario, R. Ottaviano, Rho (MI); S. Gambardella, D. Bracaglia, Roma (ASL Roma B); G. Testa, A. Mancini, D. Giannini, Roma (ASL Roma D); G. Monesi, F. Mollo, M. Osti, Rovigo; D. Di Michele, E. Lattanzi, C. Piersanti, Teramo; E. Ghigo, F. Camanni, S. Destefanis, D. Gaia, V. Gasco, M. Maccario, Torino; R. Carretta, F. Fiammengo, R. Gerloni, L. Macaluso, Trieste; P. Donnini, S. Alvaro, Varese; G.B. Ambrosio, C. Leprotti, E. Moro, M. Pais, S. Pianetti, Venezia. General practitioners: A. Garbin, Albignasego (PD); V. Frascone, Alfedena (AQ); P. Marmo, Ariccia (Room); G.F. Munari, Arquà Polesine (RO); B. Cataldi, M.L. Filiani, Atri (TE); G. Ursini, Basciano (TE); M. Augello, P. Tassan, Bollate (MI); D. Giraldi, Bovolone (VR); G. Berarducci, Bussi sul Tirino (PE); M. Braggion, Cadoneghe (PD); B.F. Novelletto, Cadoneghe (PD); R.L. Dell'Orco, Campomarino (CB); A. Baj, Cantello (VA); S. Pardo, Caramanico Terme (PE); M. Persia, Castel di Sangro (AQ); F. Bellini, F. Nistico, C.L. Rossi, Catanzaro; G. Quinzii, Celenza sul Trigno (CH); A. Ferrigato, M.L. Zaramella, Ceneselli (RO); E. Crisante, Cerratina (PE); G. Biundo, G. Serughetti, D. Sofra, Cinisi (PA); L. Trono, Civitaquana (PE); G. Arduino, Cocconato (AT); S. Chiappetta, Cosenza; I. Novarese, Cossombrato (AT); M. Monari, Costa di Rovigo (RO); I. Cappello, Crespino (RO); L. Lipari, Faro Superiore (ME); G.P. Bersani, G. Marcomini, Ficarolo (RO); M.G. Felici, Frascati (Room); A. Bragiotto, Frassinelle Polesine (RO); L. Felice, Furci (CH); I. Caberletti, Gaiba (RO); P. Vergani, Inverigo (CO); E. Orecchia, Isola D'Asti (AT); F. Ferracin, Lendinara (RO); G. Cavallo, Limena (PD); L. Giardina, Lurago d'Erba (CO); L. Felloni, Luvinate (VA); L. Cesarone, Manoppello Scalo (PE); V. Albanese, L. Bizzozero, C. Cerati, L. Mauri, C. Ratti, Mariano Comense (CO); U. Alecci, A. Alibrando, S. Marino, Messina; G. Forastiere, Monale (AT); G. Petrella, Monteodorisio (CH); Q. Di Mattia, N. Grimaldi, I. Olivieri, Montesilvano (PE); M.C. Cardella, B. Duren, A. Falzone, G. Furlan, N. Nesladek, N. Novel, M. Pasquariello, M. Russo, D. Veglia, Muggia (TS); M. Fusello, Murano (VE); C. Baldi, Nizza Monferrato (AT); R. Seller, Nocciano (PE); G. Tosi, Occhiobello (RO); S. Barberio, R. Tonon, Padova; G. Cardinale, F.P. Lombardo, F. Magliozzo, G. Merlino, N. Merlino, G. Quartetti, Palermo; F. Bolognese, Palmoli (CH); S. Baglieri, P. Giarrusso, S. Speciale, Partinico (PA); M. Buffone, O. Di Domizio, F. Panzieri, G. Perfetto, Pescara; A. Granati, P.R. Lattari, P.G. Potenti, M. Quattrocchi, R. Vannucci, Pistoia; L. Daddi, Quarrata (PT); G. Dallatorre, G.P. Guido, F. Orlando, A. Santoro, R. Zagni, Rho (MI); O. Genova, Rocca di Papa (Room); A. Alaimo, M. Canfora, C. Cappelli, F. Caracciolo, R. Casimirri, A.M. Crestini, G. Daniele, L. De Lucia, A. De Marchis, A. Di Masi, F. Di Rosa, A. Filabozzi, M. Levati, A. Lucente, G. Manzo, M. Marchionne, G.A. Marino, E. Paolini, M. Quaresima, P. Scala, M. Scotto, R. Scotto, A. Simeoni, Roma; G. Chieregato, S. Sparesato, Rovigo; E. Cavallo, Salizzole (VR); G. Ceglia, San Martino in Pensilis (CB); T. Chiarini, San Nicolò (TE); E. Visentini, Sant'Angelo di Piave (PD); R. Sammarone, Sant'Eusanio del Sangro (CH); P. Di Giambattista, Scafa (PE); M.A. Fumagalli, F. Milanese, L. Santoro, Senago (MI); P. Giusti, G. Nafra, G. Romito, Silvi Marina (TE); R. Balsamo, Spoltore (PE); M. Rinaldi, Termoli (CB); F. Biondo, G. Consiglio, Terrasini (PA); G. Arbore, I. Garione, C. Merlini, A. Pizzini, G. Titta, S. Vitali, Torino; G. Rotondo, Torre Faro (ME); A. Berardi, Tufillo (CH); G. Castellani, L.V. Cova, Varese; C. Lamberti, Venetico (ME); S. Granzotto, P.A. Mazzi, Venezia; G. Bergamasco, Venezia Lido (VE); V. Autiero, Vigonza (PD); L. Ghiraldelli, Villanova del Ghebbo (RO).


   Footnotes
 
Published online ahead of print. Publication date available at www.cjasn.org.

Received March 9, 2007. Accepted May 7, 2007.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusions
 Disclosures
 References
 

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