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Corresponding author. Laboratory of Molecular and Nutritional Epidemioloy, Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo Neuromed, Via dell’Elettronica, 86077 Pozzilli (IS), Italy.
Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo – NEUROMED, Pozzilli (IS), ItalyDepartment of Medicine and Surgery, University of Insubria, Varese, Italy
The intake of various classes of polyphenols was evaluated in a general population.
•
Individual phenolic classes were beneficially associated with pulmonary function.
•
The polyphenol content of the diet was summarized in a dietary index the PAC-score.
•
A higher polyphenol content of diet was associated with better pulmonary function.
•
The association might be partially mediated by WBC in men.
Abstract
Background
The association of the polyphenol content of human diet with pulmonary function is not yet fully understood. This study aims at evaluating the association of polyphenol consumption with lung function in a novel holistic approach.
Methods
A cross-sectional analysis of 4551 women and 5108 men (age ≥35 years) from the Moli-sani study was performed. Participants were randomly recruited from the general population. The EPIC-FFQ was used for the dietary assessment. Polyphenol intakes were calculated using Eurofir–eBASIS, and a polyphenol antioxidant content (PAC) score was constructed to assess the total content of the diet in these nutrients. Pulmonary function maneuvers were performed, and the forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1) were measured; FVC% predicted and FEV1% predicted were computed using the European Community of Coal and Steel prediction equations that included height and age.
Results
In both genders, in age, height, and energy intake adjusted models, the majority of classes of polyphenols (mg/day) showed a positive association with FEV1, FVC, FEV1% predicted, and FVC% predicted (β-coef >0, P < .05). Associations remained significant after adjustment for confounding factors in most cases (β-coef >0, P < .05). The PAC score was associated in both genders with an increase in pulmonary function parameters (β-coef >0, P < .05). The inclusion of white blood cell (WBC) counts in the multivariate model reduced the association in men but not in women. .
Conclusions
A higher overall polyphenol content of human diet was associated with better pulmonary function in a general population. The association might be partially mediated by WBC in men.
Among dietary factors, the consumption of antioxidant-rich food and antioxidant vitamins has been associated with better pulmonary function in various settings [
]. This has been confirmed in a previous analysis of the Moli-sani data, which shows that a diet with higher total antioxidant capacity was associated with better lung function parameters, especially in women [
]. The proposed protection of lungs against oxidative stress by the intake of these dietary components may lead to lower incidence of inflammation-related lung disease [
], has been associated with the prevention of chronic diseases such as cardiovascular and neurodegenerative disorders and with the promotion of healthy aging [
Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis of randomized controlled trials.
Am. J. Clin. Nutr.2008; 88 (26 Pounis G, Bonaccio M, Di Castelnuovo A, Costanzo S, de Curtis A, Persichillo M, Sieri S, Donati MB, Cerletti C, de Gaetano G, Iacoviello L (2016) Polyphenol intake is associated with low-grade inflammation, using a novel data analysis from the Moli-sani study. Thromb Haemost 115:344–352): 38-50https://doi.org/10.1160/TH15-06-0487
]. To the best of our knowledge, only few epidemiological studies have provided promising data on the effect of polyphenols on lung function parameters [
The scarce availability of accurate data on the content of a large number of polyphenol molecules in food was a limiting factor of the related studies [
]. However, recently, the Eurofir project published harmonized EU data on the polyphenol content in foods (Bioactive Substances in Food Information Systems [eBASIS]) [
Exploiting the availability of these data, our research group has previously evaluated the intake of flavonoids and lignans in an Italian, Mediterranean population (Moli-sani study) [
] as a measure of the overall polyphenol content of human diet. This index showed a positive association with MeD adherence.
Thus, considering the limited epidemiological evidence so far available on the effect of polyphenols on pulmonary function parameters, this work aims at evaluating, in a large Italian population, the possible association of the intake of various classes and sub-classes of polyphenols and of overall polyphenol dietary content with forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1). A novel approach for the overall assessment of polyphenol content of diet through the PAC score will be elaborated, thus adding originality to the present work.
2. Subjects and methods
2.1 Study population
The Moli-sani participants were randomly recruited in the Molise region (Italy) from city hall registries by a multistage sampling, as previously described [
]. Between March 2005 and April 2010, a total of 24,325 subjects were enrolled. Participants who had incomplete medical (n = 235) or dietary questionnaires (n = 1917) or were not Caucasians or not born in Italy (n = 332) or had a history of cardiovascular disease or cancer (n = 2528) were excluded from the analysis. Furthermore, participants who were under a special diet or a diet for the control of diabetes, hypertension, or hyperlipidemia (n = 6262) were also excluded, as these conditions may lead to changes in their usual diet. Participants with poor-quality spirometry (n = 4370) were also excluded. The final study sample (Fig. 1) included in this analysis consisted of 9659 subjects (4551 women and 5108 men).
Fig. 1Flow chart of selection of the studied population among Moli-sani participants.
The Moli-sani project was approved by the Catholic University Ethical Committee. All participants provided written informed consent.
2.2 Pulmonary function evaluation
Pulmonary function maneuvers were performed by trained operators following the American Thoracic Society/European Respiratory Society recommendations [
Lung volumes and forced ventilatory flows. report working party. standardization of lung function tests, european community for steel and coal. official statement of the european respiratory society.
], with 3 V-Max Encore 22D equipped with plethysmography V62J Autobox and 2 V-Max Encore 20, all with the same Mass Flow Sensor model (Sensormedics® Viasys).
All the tests were performed in the morning after the technical operator's explanation, with subjects in a sitting position and with the use of a nose clip. Daily volume calibration was performed with a 3-L syringe. A volume variation higher than 0.5% from the real value (3 L) was discarded, and the calibration was repeated.
At the end of each test session, the operators evaluated the acceptability (including start, duration, and end of test) and the reproducibility of the maneuvers to identify high-quality measurements [
]. High-quality spirometry was defined as at least three acceptable tests with differences lower than 0.20 L on the best value for FVC and FEV1. Only high-quality tests were used for the analysis (Fig. 1).
Exclusion criteria for spirometric tests were as follows: recent abdominal or ocular surgery, cardiovascular disorders, blood pressure higher than 180/100 mmHg, untreated glaucoma, and ocular lesions or pain during test performance.
The predicted value for pulmonary indexes was computed using the European Community of Coal and Steel prediction equations, which included height and age [
Abnormal FEV1 and FVC were defined as a reduction of more than 20% of the measured value on predicted FEV1 and FVC, respectively, while the percentage of measured FEV1/FVC that was less than 70% of the predicted ratio was also calculated [
Self-reported presence of pulmonary disease was evaluated as existing pulmonary symptoms at the time of recruitment, and it includes the following: asthma, acute bronchitis, tuberculosis, emphysema, chronic obstructive pulmonary disease, lung fibrosis, lung cancer, or the use of pharmacological agents for lung disease treatment.
A questionnaire for the assessment of pulmonary symptoms, lung disorders, and risk exposure in the working environment was administered by trained monitors. Risk exposure in the working environment was considered if during the working time the subjects were exposed to cement, stone, carbon, paper, cotton, paint, flour, aluminum, iron, asbestos, kaolin, eternit, brakes, or shield.
2.3 Dietary assessment
The European Prospective Investigation into Cancer and Nutrition–food frequency questionnaire (EPIC-FFQ) specifically adapted for Italian population was used to determine the usual nutritional intakes consumed in the past year [
], was developed by the Epidemiology and Prevention Unit, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, to convert the questionnaire dietary data into frequencies of consumption and average daily quantities of foods (g/day) and energy intake (Kcal/day). NAF was linked to the Italian food composition tables (FTC) for energy assessment [
2.4 Polyphenol evaluation and PAC score development
A total of 164 food items included in the EPIC-FFQ were studied as potential food sources of polyphenols. Four major classes of polyphenols were considered: phenolic acids (sub-classes: derivatives of benzoic acid and cinnamic acid), flavonoids (sub-classes: flavonols, flavones, flavanones, flavanols, anthocyanins, isoflavones), stilbenes, and lignans [
] was used as the main available and accurate harmonized EU FCT data source for the polyphenol content of foods, and when data were missing, the United States Department Agriculture (USDA) FCTs were used (Database for the Flavonoid Content of Selected Foods-Release 3, 2013, Database for the Isoflavone Content of Selected Foods - Release 2.0, 2008, Database for the Proanthocyanidin Content of Selected Foods - 2004) [
White blood cell count, sex and age are major determinants of heterogeneity of platelet indices in an adult general population: results from the MOLI-SANI project.
For each food, the mean content in different classes and sub-classes of polyphenols was calculated according to the availability of FCT data. Using this information and the daily consumption of each food source, the total intakes of 7 classes and sub-classes of polyphenols were calculated in all Moli-sani participants as follows: flavonols (mg/day), flavones (mg/day), flavanones (mg/day), flavanols (mg/day), anthocyanidins (mg/day), isoflavones (mg/day), and lignans (mg/day). The choice of units presented for polyphenols was made according to the units used in the original source (eBASIS).
To assess the overall PAC of the diet in the test sample, a dietary index PAC score was constructed. The development of such score has been previously described [
]. Nutrition data are strongly autocorrelated, and multicollinearity may arise when these data are simultaneously studied in a regression model. This source of biased estimations is limited by the use of PAC score.
In summary, 10 tiles of total intakes of each of the 7 polyphenol classes and sub-classes were generated. Seven components of the PAC score were derived by scoring the 7 different 10 tiles of polyphenol intake as presented. For all polyphenol components, higher intakes (i.e., >Q6) were assigned an increasingly positive score while lower intakes (i.e., <Q5) were assigned a negative score. The PAC score ranged between −28 and 28 and resulted as the sum of the 7 components. An increase in score represented an increase in the total content of polyphenols in the diet.
2.5 Measurements and definition of factors
Blood samples were obtained between 07:00 and 09:00 from participants who had fasted overnight and had refrained from smoking for at least 6 h. Biochemical analyses were performed in the centralized Moli-sani laboratory [
]. Hemochromocytometric analyses were performed using a cell counter (Coulter HMX; Beckman Coulter, Milan, Italy) within 3 h (h) from venipuncture. Glucose was assayed by enzymatic reaction methods using an automatic analyzer (IL 350 Instrumentation Laboratory). High-sensitivity C-reactive protein was measured in fresh serum by a latex particle-enhanced immunoturbidimetric assay (IL 350 Instrumentation Laboratory). Inter- and intra-day coefficient of variabilities were 5.5% and 4.2%, respectively.
The smoking habits of the participants were evaluated as the number of cigarettes per day for current smokers and number of cigarettes per day for ex-smokers. The years of smoking in both current and ex-smokers were also assessed [
Waist circumference (cm) was measured in the middle between the twelfth rib and the iliac crest, and hip circumference, in cm, was measured around the buttocks [
]. The waist-to-hip ratio was then calculated. Physical activity was assessed by a structured questionnaire and expressed as the daily energy expenditure in MET-h [
The normality of continuous variables was assessed by the Shapiro–Wilk test and confirmed graphically. Normally distributed data are presented as mean (standard deviation), skewed as median (first and third quartiles), and categorical variables as frequencies. Comparisons of normally distributed and skewed continuous variables between two groups were performed using the Student t-test and Mann-Whitney test, respectively. Associations betweeen categorical data were derived through the Pearson X2 test.
Linear regression modeling in five levels of adjustment and stratified by gender was used to evaluate the associations of the polyphenol content of diet with pulmonary function parameters.
Model 1 was first generated with the main outcome as one of FEV1 (liters), FEV1 (% predicted), FVC (liters), and FVC (% predicted) and the main independent factor as each of the polyphenol classes and sub-classes intake or PAC score. Model 1 with the main outcome as the FEV1 (liters) or the FVC (liters) was adjusted for age and height, and model 1 with the main outcome as the FEV1 (% predicted) or FVC (% predicted) was not adjusted for these factors because both these models were obtained by using age and height in their calculation formula.
Model 2 was additionally adjusted to model 1 for energy intake (Kcal/day). Further adjustments to model 2 for number of cigarettes/day for current smokers, number of cigarettes/day for ex-smokers, years of smoking, social status, waist-to-hip ratio, physical activity, and fasting blood glucose levels were perfomed to obtain model 3. These confounders have been selected among others as they were associated with both pulmonary function parameters and polyphenol intake.
Models 4 and 5 were generated by further adjustment of model 3 for either white blood cell (WBC) counts or CRP, respectively; the latter models were generated to evaluate any possible mediating effect of these circulating low-grade inflammation biomarkers in the association of polyphenol intake with lung function parameters.
Possible interactions of smoking habits or other population characteristics in the observed associations were also tested.
Results are presented as β-coef and related P value. The normality of residuals, homoscedasticity, and multiple collinearity were evaluated by plotting standardized residuals against the predicted values.
Two-sided P value < .05 was considered as statistically significant. STATA version 9 software was used for all calculations (STATA Corp., College Station, TX, USA).
3. Results
The distribution of polyphenol intake and pulmonary function parameters of Moli-sani participants are shown in Table 1, according to gender. Men participants had higher FEV1 and FVC as measured in liters and lower FEV1% predicted and FVC% predicted compared to the women population (all P values in both groups are <0.001). In addition, the consumption of various classes and sub-classes of polyphenols and the overall polyphenol content of the diet measured through the PAC score was higher in men than in women (Table 1) (P < .05). The percentage of participants with a FEV1-to-FVC ratio <0.7 was 9.3% in women and 17.6% in men (P < .001).
Table 1Distribution of polyphenol intake and pulmonary function parameters in women and men from Moli-sani population.
Results are presented as mean (standard deviation) for normally distributed data and median (1st, 3rd quartile) for skewed. Categorical data are presented as frequencies.
P-value derived through comparisons of continuous characteristics between genders using Student's t-test for normally distributed data and Mann-Whitney's test for skewed. Differences between genders for categorical variables were tested using Pearson's X2-test.
Polyphenol intake, lifestyle and clinical characteristics
Flavonols (mg/day)
15.4 (11.1, 21.2)
19.1 (14.1, 26.0)
<.001
Isoflavones (mg/day)
23.3 (17.9, 31.0)
23.7 (18.1, 31.1)
.09
Lignans (mg/day)
82.7 (61.1, 109.8)
81.2 (61.1, 107.2)
.09
Flavones (mg/day)
0.77 (0.53, 1.10)
0.65 (0.44, 0.95)
<.001
Flavanones (mg/day)
31.1 (22.9, 42.1)
35.0 (26.1, 45.9)
<.001
Flavanols (mg/day)
41.6 (24.4, 73.0)
66.1 (36.3, 108.8)
<.001
Anthocyanidins (mg/day)
145.3 (99.8, 209.3)
148.0 (101.9, 216.3)
.02
PAC-score (−28, 28)
−1 (−11, 10)
2 (−8, 12)
<.001
Energy intake (Kcal/day)
2062 (542)
2446 (656)
<.001
Smoking (%)
<.001
Ex-smokers
15.4
36.0
Current smokers
25.1
31.3
Social status (0–6)
3.64 (1.39)
3.61 (1.38)
.42
Physical activity (METs-h)
42.9 (8.0)
44.3 (10.2)
<.001
Waist to hip ratio
0.87 (0.08)
0.94 (0.06)
<.001
Fasting blood glucose (mg/dL)
93 (13)
101 (17)
<.001
White blood cell counts (103/μL)
5.95 (1.53)
6.49 (1.67)
<.001
C-reactive protein (mg/L)
1.39 (0.66, 2.77)
1.35 (0.72, 2.46)
.62
Exposure to factors of pollution in working environment (%)
9.8
28.3
<.001
Pulmonary function parameters
FEV1 (liters)
2.668 (0.514)
3.574 (0.720)
<.001
FEV1 (% predicted)
114.0 (16.1)
106.8 (15.4)
<.001
FVC (liters)
3.440 (0.622)
4.713 (0.840)
<.001
FVC (% predicted)
124.7 (16.3)
113.8 (14.8)
<.001
FEV1to FVC ratio < 0.7 (%)
9.3
17.6
<.001
FEV1<80% predicted (%)
1.6
3.8
<.001
FEV1<80% predicted (%)
0.2
0.9
<.001
Presence of pulmonary symptoms (%)
22.1
21.7
.64
a Results are presented as mean (standard deviation) for normally distributed data and median (1st, 3rd quartile) for skewed. Categorical data are presented as frequencies.
b P-value derived through comparisons of continuous characteristics between genders using Student's t-test for normally distributed data and Mann-Whitney's test for skewed. Differences between genders for categorical variables were tested using Pearson's X2-test.
Furthermore, women with a FEV1-to-FVC ratio <0.7 presented a lower polyphenol content of the diet (P < .05); this was not found in men (P > .05).
To fully explore the possible associations of various classes and sub-classes of polyphenols and PAC score with pulmonary function parameters, further regression analyses were performed. Fig. 2 illustrates the crude association of PAC score with FEV1% predicted and FVC% predicted. In both genders, the increase in polyphenol content of diet measured with the PAC score was associated with an increase in these pulmonary function parameters, which, by definition, are adjusted for age and height (all P values <0.05).
Fig. 2Polyphenol score (PAC score) in association with % predicted values of pulmonary function parameters.
Results of linear regression modeling of the associations of polyphenol intake with pulmonary parameters along with descriptive data about the quintiles of intake in men and women are detailed in Table 2, Table 3, respectively. In both genders, the majority of the various classes and subclasses of polyphenols showed a positive association with FEV1, FVC, FEV1% predicted, and FVC% predicted (models 1 and 2,Table 2, Table 3; β-coef >0, P < .05). These associations remained significant after adjustment for possible confounding variables (models 3, Table 2, Table 3; β-coef >0, P < .05), except for flavanols and flavones in women (models 3, Table 2; P > .05) and isoflavones, anthocyanidins, and lignans in men (models 3, Table 3; P > .05).
Table 2Multivariate analysis of pulmonary function parameters according to quintile of polyphenol intake and PAC-score in women Moli-sani population.
Models 1 derived through linear regression analyses with main outcome either one of FEV1 (liters), FEV1 (% predicted), FVC (liters), FVC (% predicted) and main independent factor the polyphenol intake or PAC-score. The models with main outcome the FEV1 (liters) and FVC (liters) have been adjusted for age and height and the models with main outcome the FEV1 (% predicted) and FVC (% predicted) are not adjusted for these factors since both of these parameters are derived by using age and height in their calculation formula.
Models 3 have been additionally to models 2 adjusted for number of cigarettes/day for current smokers, number of cigarettes/day for ex-smokers, years of smoking, social status, waist to hip ratio, physical activity level, fasting blood glucose levels.
Models 5 have been additionally to models 3 adjusted for C-Reactive Protein (CRP).
Flavonols
β-coef (P-value) for 10 mg/day increase in flavonols intake
FEV1 (liters)
2.662 (0.521)
2.669 (0.511)
2.665 (0.524)
2.689 (0.500)
2.658 (0.509)
0.022 (<0.001)
0.002 (<0.001)
0.021 (0.001)
0.020 (0.001)
0.019 (0.002)
FEV1 (% predicted)
112.8 (16.0)
114.1 (16.2)
113.8 (16.1)
115.3 (15.4)
114.7 (16.7)
0.884 (0.001)
1.220 (<0.001)
1.046 (<0.001)
1.007 (<0.001)
1.009 (<0.001)
FVC (liters)
3.433 (0.628)
3.428 (0.611)
3.446 (0.630)
3.468 (0.614)
3.435 (0.621)
0.023 (0.001)
0.023(0.002)
0.021 (0.005)
0.024 (0.002)
0.019 (0.01)
FVC (% predicted)
123.6 (16.1)
124.4 (16.6)
124.8 (16.3)
126.1 (15.7)
125.6 (16.8)
0.928 (<0.001)
1.279 (<0.001)
1.115 (<0.001)
1.115 (<0.001)
1.070 (<0.001)
Isoflavones
β-coef (P-value) for 10 mg/day increase in isoflavones intake
FEV1 (liters)
2.635 (0.517)
2.656 (0.525)
2.657 (0.509)
2.674 (0.516)
2.722 (0.498)
0.022(<0.001)
0.025(<0.001)
0.022 (<0.001)
0.022 (<0.001)
0.022 (<0.001)
FEV1 (% predicted)
112.6 (16.5)
113.8 (16.3)
114.4 (15.9)
114.0 (15.9)
115.2 (15.7)
0.829 (<0.001)
1.240 (<0.001)
1.067 (<0.001)
1.105 (<0.001)
1.068 (<0.001)
FVC (liters)
3.406 (0.615)
3.436 (0.640)
3.422 (0.613)
3.445 (0.634)
3.497 (0.603)
0.020 (0.001)
0.021(0.001)
0.021 (0.002)
0.022 (0.001)
0.021 (0.002)
FVC (% predicted)
123.6 (16.3)
124.9 (16.8)
124.9 (16.1)
124.6 (16.2)
125.7 (16.0)
0.644 (0.004)
1.019 (<0.001)
0.967 (<0.001)
0.967 (<0.001)
0.969 (<0.001)
Flavones
β-coef (P-value) for 1 mg/day increase in flavones intake
FEV1 (liters)
2.631 (0.530)
2.648 (0.524)
2.643 (0.505)
2.672 (0.521)
2.726 (0.492)
0.022(0.02)
0.021(0.02)
0.019 (0.05)
0.017 (0.08)
0.016 (0.09)
FEV1 (% predicted)
113.3 (16.9)
113.7 (16.1)
113.9 (15.4)
114.2 (16.3)
114.4 (15.8)
0.473 (0.27)
0.624(0.15)
0.588 (0.19)
0.476 (0.30)
0.479 (0.23)
FVC (liters)
3.406 (0.627)
3.417 (0.640)
3.402 (0.616)
3.440 (0.624)
3.515 (0.600)
0.035(0.002)
0.033(0.004)
0.029 (0.02)
0.027 (0.03)
0.025 (0.03)
FVC (% predicted)
124.5 (16.8)
124.5 (16.5)
124.3 (15.6)
124.9 (16.6)
125.4 (16.0)
0.806 (0.07)
0.973 (0.03)
0.892 (0.05)
0.762 (0.10)
0.763 (0.09)
Flavanones
β-coef (P-value) for 10 mg/day increase in flavanones intake
FEV1 (liters)
2.641 (0.509)
2.639 (0.534)
2.681 (0.505)
2.688 (0.521)
2.708 (0.493)
0.013(<0.001)
0.014(<0.001)
0.012 (0.001)
0.011 (0.002)
0.012 (0.001)
FEV1 (% predicted)
112.7 (16.1)
113.9 (16.6)
114.8 (16.2)
113.9 (15.6)
115.1 (15.5)
0.498 (0.001)
0.737 (<0.001)
0.608 (<0.001)
0.564 (0.001)
0.060 (<0.001)
FVC (liters)
3.417 (0.616)
3.418 (0.643)
3.445 (0.617)
3.457 (0.629)
3.482 (0.596)
0.009 (0.011)
0.009(0.03)
0.010 (0.03)
0.009 (0.04)
0.009 (0.04)
FVC (% predicted)
123.8 (16.2)
125.0 (16.7)
125.2 (16.9)
124.4 (15.8)
125.6 (15.7)
0.327 (0.03)
0.535(0.001)
0.487 (0.004)
0.453 (0.008)
0.477 (0.004)
Flavanols
β-coef (P-value) for 10 mg/day increase in flavanols intake
FEV1 (liters)
2.626 (0.535)
2.654 (0.517)
2.713 (0.497)
2.659 (0.499)
2.716 (0.500)
0.0002(0.74)
0.0001(0.85)
−0.0005 (0.45)
−0.0007 (0.27)
−0.001 (0.27)
FEV1 (% predicted)
113.5 (16.6)
113.7 (16.4)
115.4 (15.2)
113.6 (15.9)
113.6 (15.7)
−0.002 (0.48)
−0.012 (0.69)
−0.031 (0.35)
−0.043 (0.20)
−0.039 (0.23)
FVC (liters)
3.390 (0.642)
3.423 (0.632)
3.487 (0.602)
3.430 (0.594)
3.513 (0.609)
0.0007(0.36)
0.0006 (0.48)
−0.0002 (0.80)
−0.0005 (0.56)
−0.001 (0.52)
FVC (% predicted)
124.3 (16.7)
124.4 (16.9)
126.0 (15.9)
124.4 (15.6)
124.8 (15.8)
0.004 (0.89)
0.014 (0.65)
−0.006 (0.86)
−0.019 (0.58)
−0.017 (0.62)
Anthocyanidins
β-coef (P-value) for 10 mg/day increase in anthocyanidins intake
FEV1 (liters)
2.649 (0.514)
2.651 (0.522)
2.652 (0.514)
2.688 (0.510)
2.704 (0.507)
0.003(<0.001)
0.003 (<0.001)
0.003 (<0.001)
0.003 (<0.001)
0.003 (<0.001)
FEV1 (% predicted)
112.9 (16.6)
113.7 (16.0)
113.4 (15.7)
114.6 (15.8)
115.4 (16.0)
0.103 (<0.001)
0.139 (<0.001)
0.129 (<0.001)
0.130 (<0.001)
0.126 (<0.001)
FVC (liters)
3.425 (0.621)
3.418 (0.618)
3.426 (0.626)
3.456 (0.626)
3.483 (0.614)
0.002(<0.001)
0.002 (0.001)
0.002 (0.001)
0.003 (0.001)
0.002 (0.003)
FVC (% predicted)
123.9 (16.5)
124.4 (16.2)
124.4 (16.4)
125.0 (16.1)
126.1 (16.2)
0.086 (0.001)
0.121 (<0.001)
0.117 (<0.001)
0.117 (<0.001)
0.114 (<0.001)
Lignans
β-coef (P-value) for 10 mg/day increase in lignans intake
FEV1 (liters)
2.637 (0.523)
2.656 (0.522)
2.646 (0.512)
2.710 (0.503)
2.689 (0.508)
0.006 (<0.001)
0.007(<0.001)
0.006 (<0.001)
0.006 (<0.001)
0.006 (<0.001)
FEV1 (% predicted)
112.7 (17.1)
113.6 (15.7)
113.3 (15.5)
115.2 (15.5)
114.9 (16.3)
0.249 (<0.001)
0.345 (<0.001)
0.300 (<0.001)
0.292 (<0.001)
0.292 (<0.001)
FVC (liters)
3.418 (0.630)
3.436 (0.624)
3.403 (0.614)
3.477 (0.611)
3.469 (0.626)
0.006 (<0.001)
0.006 (<0.001)
0.006 (<0.001)
0.006 (<0.001)
0.006 (0.001)
FVC (% predicted)
124.0 (17.0)
124.8 (16.1)
123.7 (15.7)
125.5 (16.0)
125.7 (16.6)
0.216 (<0.001)
0.308(<0.001)
0.283 (<0.001)
0.282 (<0.001)
0.273 (<0.001)
PAC-score
β-coef (P-value) for 1 unit increase in PAC-score
FEV1 (liters)
2.646 (0.532)
2.635 (0.514)
2.665 (0.494)
2.703 (0.510)
2.707 (0.511)
0.002(<0.001)
0.002 (<0.001)
0.002 (<0.001)
0.002 (<0.001)
0.002 (<0.001)
FEV1 (% predicted)
112.8 (16.5)
114.2 (16.2)
113.5 (15.6)
115.1 (15.5)
114.9 (16.3)
0.065 (<0.001)
0.097(<0.001)
0.081 (<0.001)
0.075 (<0.001)
0.077 (<0.001)
FVC (liters)
3.417 (0.636)
3.400 (0.614)
3.431 (0.605)
3.482 (0.622)
3.491 (0.622)
0.002 (<0.001)
0.002(<0.001)
0.002 (0.002)
0.002 (0.003)
0.001 (0.006)
FVC (% predicted)
123.7 (16.5)
125.0 (16.5)
124.0 (16.0)
125.8 (16.0)
125.7 (16.3)
0.056 (0.002)
0.087(<0.001)
0.077 (<0.001)
0.073 (<0.001)
0.072 (<0.001)
a Pulmonary function parameters are presented as mean (standard deviation) for quintiles of polyphenol intake and PAC-score.
b Models 1 derived through linear regression analyses with main outcome either one of FEV1 (liters), FEV1 (% predicted), FVC (liters), FVC (% predicted) and main independent factor the polyphenol intake or PAC-score. The models with main outcome the FEV1 (liters) and FVC (liters) have been adjusted for age and height and the models with main outcome the FEV1 (% predicted) and FVC (% predicted) are not adjusted for these factors since both of these parameters are derived by using age and height in their calculation formula.
c Models 2 have been additionally to models 1 adjusted for energy intake (Kcal/day).
d Models 3 have been additionally to models 2 adjusted for number of cigarettes/day for current smokers, number of cigarettes/day for ex-smokers, years of smoking, social status, waist to hip ratio, physical activity level, fasting blood glucose levels.
e Models 4 have been additionally to models 3 adjusted for white blood cell counts (WBC).
f Models 5 have been additionally to models 3 adjusted for C-Reactive Protein (CRP).
Models 1 derived through linear regression analyses with main outcome either one of FEV1 (liters), FEV1 (% predicted), FVC (liters), FVC (% predicted) and main independent factor the polyphenol intake or PAC-score. The models with main outcome the FEV1 (liters) and FVC (liters) have been adjusted for age and height and the models with main outcome the FEV1 (% predicted) and FVC (% predicted) are not adjusted for these factors since both of these parameters are derived by using age and height in their calculation formula.
Models 3 have been additionally to models 2 adjusted for number of cigarettes/day for current smokers, number of cigarettes/day for ex-smokers, years of smoking, social status, waist to hip ratio, physical activity level, fasting blood glucose levels.
Models 5 have been additionally to models 3 adjusted for C-Reactive Protein (CRP).
Flavonols
β-coef (P-value) for 10 mg/day increase in flavonols intake
FEV1 (liters)
3.655 (0.686)
3.606 (0.699)
3.603 (0.704)
3.539 (0.741)
3.517 (0.740)
0.022 (0.001)
0.026 (0.001)
0.019 (0.02)
0.016 (0.04)
0.017 (0.03)
FEV1 (% predicted)
106.3 (14.8)
106.5 (15.1)
106.7 (14.8)
106.3 (15.3)
107.7 (16.3)
0.613 (0.003)
0.651 (0.005)
0.698 (0.003)
0.606 (0.01)
0.672 (0.004)
FVC (liters)
4.768 (0.814)
4.728 (0.807)
4.729 (0.837)
4.687 (0.852)
4.683 (0.868)
0.037(<0.001)
0.035(<0.001)
0.027 (0.006)
0.026 (0.008)
0.025 (0.009)
FVC (% predicted)
112.6 (14.3)
113.1 (14.3)
113.3 (14.7)
113.8 (14.6)
115.4 (15.6)
1.021 (<0.001)
1.104 (<0.001)
0.997 (<0.001)
0.956 (<0.001)
0.974 (<0.001)
Isoflavones
β-coef (P-value) for 10 mg/day increase in isoflavones intake
FEV1 (liters)
3.611 (0.700)
3.569 (0.718)
3.548 (0.724)
3.560 (0.730)
3.584 (0.725)
0.022 (<0.001)
0.024(<0.001)
0.010 (0.14)
0.010 (0.14)
0.010 (0.15)
FEV1 (% predicted)
105.9 (14.8)
107.1 (15.3)
106.1 (15.5)
107.1 (15.6)
107.4 (15.4)
0.596 (0.001)
0.604(0.003)
0.327 (0.11)
0.317 (0.12)
0.329 (0.10)
FVC (liters)
4.741 (0.798)
4.699 (0.848)
4.680 (0.847)
4.710 (0.841)
4.736 (0.864)
0.025 (0.001)
0.021(0.009)
0.010 (0.21)
0.010 (0.24)
0.010 (0.21)
FVC (% predicted)
112.9 (14.0)
114.0 (15.1)
113.0 (14.6)
114.5 (15.2)
114.5 (15.0)
0.617 (0.001)
0.582 (0.003)
0.417 (0.04)
0.389 (0.05)
0.419 (0.04)
Flavones
β-coef (P-value) for 1 mg/day increase in flavones intake
FEV1 (liters)
3.546 (0.727)
3.567 (0.718)
3.555 (0.715)
3.554 (0.754)
3.670 (0.674)
0.038(0.005)
0.038(0.008)
0.019 (0.18)
0.017 (0.23)
0.019 (0.17)
FEV1 (% predicted)
105.6 (15.7)
107.0 (15.5)
107.2 (15.5)
106.5 (15.4)
107.8 (14.3)
1.104 (0.01)
1.052(0.02)
0.442 (0.32)
0.374 (0.40)
0.453 (0.30)
FVC (liters)
4.689 (0.850)
4.693 (0.818)
4.702 (0.834)
4.687 (0.883)
4.815 (0.807)
0.047(0.005)
0.039(0.02)
0.032 (0.07)
0.026 (0.15)
0.033 (0.06)
FVC (% predicted)
112.9 (14.8)
113.8 (14.6)
114.5 (15.2)
113.6 (14.8)
114.5 (14.5)
0.940 (0.02)
0.823(0.05)
0.655 (0.13)
0.478 (0.28)
0.664 (0.13)
Flavanones
β-coef (P-value) for 10 mg/day increase in flavanones intake
FEV1 (liters)
3.654 (0.705)
3.561 (0.719)
3.571 (0.719)
3.546 (0.716)
3.555 (0.733)
0.015(<0.001)
0.017 (<0.001)
0.009 (0.05)
0.008 (0.06)
0.008 (0.05)
FEV1 (% predicted)
106.0 (14.9)
106.0 (15.3)
107.1 (15.2)
106.6 (15.3)
107.8 (15.9)
0.441 (<0.001)
0.461(0.001)
0.347 (0.01)
0.332 (0.02)
0.351 (0.01)
FVC (liters)
4.789 (0.820)
4.690 (0.844)
4.702 (0.825)
4.693 (0.842)
4.704 (0.861)
0.018(<0.001)
0.015(0.005)
0.009 (0.12)
0.008 (0.15)
0.009 (0.12)
FVC (% predicted)
112.9 (14.6)
112.9 (14.7)
114.0 (14.4)
113.9 (14.8)
114.9 (15.4)
0.498 (<0.001)
0.493(<0.001)
0.385 (0.005)
0.359 (0.009)
0.389 (0.004)
Flavanols
β-coef (P-value) for 10 mg/day increase in flavanols intake
FEV1 (liters)
3.578 (0.731)
3.598 (0.689)
3.629 (0.753)
3.570 (0.716)
3.519 (0.710)
0.003(0.001)
0.003(0.002)
0.002 (0.04)
0.002 (0.04)
0.002 (0.04)
FEV1 (% predicted)
105.3 (15.5)
105.9 (15.2)
106.9 (15.4)
107.3 (15.0)
109.4 (15.7)
0.085 (0.001)
0.084(0.003)
0.073 (0.01)
0.068 (0.02)
0.071 (0.02)
FVC (liters)
4.691 (0.854)
4.717 (0.795)
4.777 (0.872)
4.716 (0.837)
4.670 (0.838)
0.004(<0.001)
0.003(0.002)
0.002 (0.05)
0.002 (0.06)
0.002 (0.07)
FVC (% predicted)
111.8 (14.7)
112.6 (14.4)
113.8 (14.4)
114.6 (14.7)
114.9 (15.4)
0.107 (<0.001)
0.103(<0.001)
0.081 (0.006)
0.079 (0.008)
0.079 (0.007)
Anthocyanidins
β-coef (P-value) for 10 mg/day increase in anthocyanidins intake
FEV1 (liters)
3.599 (0.695)
3.569 (0.735)
3.567 (0.700)
3.562 (0.755)
3.573 (0.715)
0.002(0.001)
0.002(0.001)
0.001 (0.12)
0.001 (0.20)
0.001 (0.15)
FEV1 (% predicted)
105.9 (14.9)
106.0 (15.5)
107.4 (15.2)
106.5 (16.2)
107.9 (14.9)
0.055 (0.004)
0.054(0.008)
0.037 (0.07)
0.031 (0.14)
0.036 (0.08)
FVC (liters)
4.739 (0.808)
4.700 (0.862)
4.683 (0.818)
4.729 (0.868)
4.713 (0.844)
0.002(0.002)
0.002(0.02)
0.001 (0.17)
0.001 (0.24)
0.001 (0.20)
FVC (% predicted)
113.1 (14.4)
112.9 (14.8)
113.8 (14.6)
114.4 (15.4)
114.7 (16.7)
0.063 (0.001)
0.058 (0.003)
0.048 (0.02)
0.043 (0.04)
0.047 (0.02)
Lignans
β-coef (P-value) for 10 mg/day increase in lignans intake
FEV1 (liters)
3.607 (0.685)
3.587 (0.727)
3.546 (0.736)
3.560 (0.720)
3.570 (0.730)
0.007(<0.001)
0.007 (<0.001)
0.002 (0.20)
0.002 (0.23)
0.002 (0.24)
FEV1 (% predicted)
106.1 (14.9)
106.8 (15.3)
106.3 (15.6)
106.8 (15.2)
107.8 (15.7)
0.175 (0.001)
0.178 (0.001)
0.080 (0.15)
0.074 (0.19)
0.077 (0.17)
FVC (liters)
4.742 (0.805)
4.720 (0.840)
4.672 (0.859)
4.693 (0.829)
4.737 (0.868)
0.008(<0.001)
0.007 (0.002)
0.004 (0.12)
0.003 (0.15)
0.003 (0.15)
FVC (% predicted)
113.1 (14.4)
113.8 (14.7)
113.1 (14.9)
113.8 (14.5)
115.2 (15.4)
0.200 (<0.001)
0.194(<0.001)
0.139 (0.01)
0.130 (0.02)
0.136 (0.01)
PAC-score
β-coef (P-value) for 1 unit increase in PAC-score
FEV1 (liters)
3.617 (0.692)
3.576 (0.733)
3.572 (0.725)
3.548 (0.720)
3.562(0.725)
0.002(<0.001)
0.002(<0.001)
0.001 (0.10)
0.001 (0.17)
0.001 (0.11)
FEV1 (% predicted)
106.0 (15.0)
106.0 (15.3)
106.9 (15.2)
106.9 (16.0)
107.9 (15.3)
0.060 (<0.001)
0.064(<0.001)
0.040 (0.03)
0.034 (0.07)
0.039 (0.03)
FVC (liters)
4.751 (0.804)
4.705 (0.848)
4.703 (0.845)
4.680 (0.837)
4.728 (0.861)
0.003(<0.001)
0.002(0.001)
0.001 (0.10)
0.001 (0.18)
0.001 (0.10)
FVC (% predicted)
113.0 (14.5)
112.8 (14.3)
113.7 (14.8)
113.9 (15.3)
115.4 (15.0)
0.073 (<0.001)
0.075(<0.001)
0.055 (0.002)
0.048 (0.009)
0.055 (0.002)
a Pulmonary function parameters are presented as mean (standard deviation) for quintiles of polyphenol intake and PAC-score.
b Models 1 derived through linear regression analyses with main outcome either one of FEV1 (liters), FEV1 (% predicted), FVC (liters), FVC (% predicted) and main independent factor the polyphenol intake or PAC-score. The models with main outcome the FEV1 (liters) and FVC (liters) have been adjusted for age and height and the models with main outcome the FEV1 (% predicted) and FVC (% predicted) are not adjusted for these factors since both of these parameters are derived by using age and height in their calculation formula.
c Models 2 have been additionally to models 1 adjusted for energy intake (Kcal/day).
d Models 3 have been additionally to models 2 adjusted for number of cigarettes/day for current smokers, number of cigarettes/day for ex-smokers, years of smoking, social status, waist to hip ratio, physical activity level, fasting blood glucose levels.
e Models 4 have been additionally to models 3 adjusted for white blood cell counts (WBC).
f Models 5 have been additionally to models 3 adjusted for C-Reactive Protein (CRP).
Moreover, in multiadjusted models, an increase in PAC score was associated with an increase in all investigated pulmonary function parameters in women (models 3, Table 2; β-coef >0, P < .05) and with FEV1% predicted and FVC % predicted in men (models 3, Table 3; β-coef >0, P < .05).
The inclusion of WBC counts in the multiadjusted models made some of the associations nonsignificant, especially in men (models 4,Table 3). On the contrary, the inclusion of CRP in the models did not affect the associations in either gender (models 5,Table 2, Table 3).
The assessment of possible interactions of smoking habits or other population characteristics in the observed associations did not yield any significant result (P for interactions >0.05).
4. Discussion
Despite the availability of several epidemiological data on the protective effect of polyphenols and polyphenol-rich foods on chronic disease prevention and progression [
Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis of randomized controlled trials.
Am. J. Clin. Nutr.2008; 88 (26 Pounis G, Bonaccio M, Di Castelnuovo A, Costanzo S, de Curtis A, Persichillo M, Sieri S, Donati MB, Cerletti C, de Gaetano G, Iacoviello L (2016) Polyphenol intake is associated with low-grade inflammation, using a novel data analysis from the Moli-sani study. Thromb Haemost 115:344–352): 38-50https://doi.org/10.1160/TH15-06-0487
]. Our findings indicate a protective effect of the intake of various classes of polyphenols on pulmonary function parameters in a Mediterranean population. Moreover, the overall polyphenol content of the diet assessed by a novel approach, the dietary index PAC score [
Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis of randomized controlled trials.
Am. J. Clin. Nutr.2008; 88 (26 Pounis G, Bonaccio M, Di Castelnuovo A, Costanzo S, de Curtis A, Persichillo M, Sieri S, Donati MB, Cerletti C, de Gaetano G, Iacoviello L (2016) Polyphenol intake is associated with low-grade inflammation, using a novel data analysis from the Moli-sani study. Thromb Haemost 115:344–352): 38-50https://doi.org/10.1160/TH15-06-0487
According to a crude chemical definition, polyphenols are secondary metabolites of plants and are generally involved in the defense against ultraviolet radiation or aggression by pathogens [
]. We have recently shown by an epidemiological approach that dietary polyphenol intake is associated with a reduction in low-grade inflammation status in healthy subjects [
Interestingly, we report here that the increase in the consumption of various classes of polyphenols was associated with higher FEV1 and FVC, FEV1% predicted, and FVC% predicted.
] analyzed data from 13,651 adults from three Dutch cities during the period 1994–1997 and observed that intake of flavonols and flavones showed a positive association with FEV1 and an inverse relation to the presence of chronic cough.
] studied on 839 participants from the Veterans Affairs Normative Aging Study and showed slower rates of FEV1 and FVC decline in the group with the higher quartile of anthocyanidin intake compared with the lower quartile of anthocyanidin intake. In the same population, no significant association of other classes of polyphenols or of the overall polyphenol content of the diet with pulmonary function parameters was described.
These results are somehow in agreement with the present data. More precisely, both Moli-sani and MORGEN studies [
] confirmed the positive association of flavonols with pulmonary parameters in both genders and of flavones only in women. The protective effect of anthocyanidin intake, as previously shown by Mehta et al., was found in women and not in men, according to the Moli-sani data.
It is worth mentioning that our present analysis is extending these protective associations to other classes of dietary polyphenols (i.e., isoflavones, flavanones, and lignans). Moreover, our approach was more comprehensive because all the various classes of polyphenols were tested for their association not only with measured lung function parameters but also with % predicted values.
Part of the originality of the present work was based on the recent availability of accurate and harmonized EU data on the polyphenol content of foods that has been published under the eBASIS platform [
]. This should be considered of crucial importance because in the previous epidemiological studies such as the MORGEN study, the scarce availability of polyphenol data limited the analysis and the conclusions that could be derived.
Furthermore, the present results are strengthened by the elaboration of a “holistic” approach in “a priori” dietary pattern analysis, the calculation of PAC score. In fact, the polyphenol content of the diet evaluated by this index showed a positive association with FEV1, FVC, FEV1% predicted, and FVC% predicted.
Conceptually, methodological limitations in the dietary assessment have been overcome by the use of dietary indexes: as a paradigmatic example, the evaluation of MeD adherence with ad hoc dietary scores [
]. The calculation of the PAC score meets such a need. Using a single score, researchers are able to discriminate populations according to the polyphenol content in their diet. In addition, the conclusions extracted from the analysis performed using this kind of methodology are more easily understandable in public health perspectives.
Apart from the original dietary methodological applications, in this work for the first time, the overall content in the diet of polyphenols, a major class of anti-inflammatory molecules, was associated with lung function parameters sensitive to oxidative stress.
In general, polyphenols have been reported to reduce inflammation by (a) acting as an antioxidant or increasing antioxidant gene or protein expression, (b) attenuating endoplasmic reticulum stress signaling, (c) blocking pro-inflammatory cytokines or endotoxin-mediated kinases and transcription factors involved in metabolic disease, (d) suppressing inflammatory- or inducing metabolic gene expression by increasing histone deacetylase activity, or (e) activating transcription factors that antagonize chronic inflammation [
Consumption of healthy foods at different content of antioxidant vitamins and phytochemicals and metabolic risk factors for cardiovascular disease in men and women of the Moli-sani study.
The analysis of CRP and WBC as possible mediators in the association of polyphenol content of the diet with lung function parameters was significant for WBC in men. This somehow supports the hypothesis of the role of the anti-inflammatory pathway in men. However, further studies are required on biomarkers of inflammation (either circulating or lung specific) in relation to respiratory function and its modulation by polyphenols.
The gender difference observed in this mediating function has previously been discussed in view of differences in the level of oxidative stress between men and women [
]. This mechanism has also been hypothesized as a possible rational basis for differences in the propensity of the two genders in the development of chronic diseases [
Beyond the relevance and novelty of the findings of the present work, some limitations still exist. First, the significance of the present findings is limited by the cross-sectional design of the study. The observed associations cannot express causal effects between dietary exposures and pulmonary function. This remains to be addressed by other study settings with prospective and clinical character. To rule out any bias related to the poor quality of spirometric data, a considerable proportion of the total population was excluded from the analysis. However, the polyphenol content of the diet did not vary between the analyzed and excluded groups. Moreover, despite the adjustment of the linear regression models for the smoking habits of the participants, residual confounding may still exist. In addition, although adequate from a broad epidemiological perspective, a FFQ is less accurate at the individual level than other measurement methods. In addition, dietary information was retrieved only once and, thus, may be prone to recall biases and seasonal variation. Possible errors because of misreporting by the participating subjects should also be acknowledged. However, to rule out the possibility that the associations found were dependent on either changes in lifestyle (particularly in dietary habits) as a consequence of a disease or of the intake of less healthy food in healthy people, we had preliminarily excluded from our analyses all subjects with previous CVD or cancer, participants with unreliable dietary questionnaires, or subjects under a special diet.
Altogether, in this epidemiological study, a higher polyphenol content of the diet was associated with a better pulmonary function. Various classes of polyphenols were beneficially associated with pulmonary function parameters. These data need to be confirmed in different settings of lung function monitoring. In any case, if further supported, the observed associations may be relevant in public health perspectives, for the prevention of inflammation-related lung disease.
Funding
Funders had no role in study design, collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the article for publication. All authors were and are independent from funders.
Author contributions
Conception and design: GP, AA, LI; Analysis and interpretation: GP, AA, SC, ADC, LI; Drafting the manuscript for important intellectual content: GP, AA, SC, ADC, MB, MP, MBD, GDG, LI; Critical review of the manuscript: MBD, GDG, LI.
Conflicts of interest
None declared.
Acknowledgments
The Moli-sani research group would like to thank Drs. Vittorio Krogh and Sabina Sieri from Istituto Nazionale dei Tumori, Milan, Italy, for their contribution to dietary questionnaire analysis and interpretation.
The enrolment phase of the Moli-sani Study was supported by research grants from Pfizer Foundation (Rome, Italy) and the Italian Ministry of University and Research (MIUR, Rome, Italy)–Programma Triennale di Ricerca, Decreto no.1588.
The authors thank Associazione Cuore-Sano (Campobasso, Italy), IL Instrumentation-Laboratory (Milano, Italy), Derby-Blue (San Lazzaro di Savena, Bologna, Italy), Caffè Monforte (Campobasso, Italy), and Sepinia SpaA (Sepino, Italy) for their support to the MOLI-SANI project.
Appendix.
Moli-sani Study Investigators.
The enrolment phase of the Moli-sani study was conducted at the Research Laboratories of the Catholic University in Campobasso (Italy), and the follow-up of the MOLI-SANI cohort is being conducted at the IRCCS Neuromed, Pozzilli, Italy.
Steering Committee: Licia Iacoviello (Neuromed, Pozzilli, Italy), Chairperson, Maria Benedetta Donati and Giovanni de Gaetano (Neuromed, Pozzilli, Italy).
Safety and data monitoring Committee: Jos Vermylen (Catholic Univesity, Leuven, Belgio), Chairman, Ignacio De Paula Carrasco (Accademia Pontificia Pro Vita, Roma, Italy), Simona Giampaoli (Istituto Superiore di Sanità, Roma, Italy), and Antonio Spagnuolo (Catholic University, Roma, Italy).
Event adjudicating Committee: Deodato Assanelli (Brescia, Italy), Vincenzo Centritto (Campobasso, Italy), and Pasquale Spagnuolo and Dante Staniscia (Termoli, Italy).
Scientific and organizing secretariat: Francesco Zito (Coordinator), Americo Bonanni, Chiara Cerletti, Amalia De Curtis, Augusto Di Castelnuovo, Licia Iacoviello, Roberto Lorenzet, Antonio Mascioli, Marco Olivieri, and Domenico Rotilio.
Data management and analysis: Augusto Di Castelnuovo (Coordinator), Marialaura Bonaccio, Simona Costanzo, and Francesco Gianfagna.
Informatics: Marco Olivieri (Coordinator), Maurizio Giacci, Antonella Padulo, and Dario Petraroia.
Biobank and biomedical analyses: Amalia De Curtis (Coordinator), Sara Magnacca, Federico Marracino, Maria Spinelli, Christian Silvestri, Giuseppe dell’Elba, Claudio Grippi.
Communication and Press Office: Americo Bonanni (Coordinator), Marialaura Bonaccio, and Francesca De Lucia.
Moli-family Project: Francesco Gianfagna, Branislav Vohnout.
Recruitment staff: Franco Zito (General Coordinator); Secretariat: Mariarosaria Persichillo (Coordinator), Angelita Verna, Maura Di Lillo, Irene Di Stefano; Blood sample: Agnieszka Pampuch; Branislav Vohnout, Agostino Pannichella, Antonio Rinaldo Vizzarri; Spirometry: Antonella Arcari (Coordinator), Daniela Barbato, Francesca Bracone, Simona Costanzo, Carmine Di Giorgio, Sara Magnacca, Simona Panebianco, Antonello Chiovitti, Federico Marracino, Sergio Caccamo, Vanesa Caruso; Electrocardiograms: Livia Rago (Coordinator), Daniela Cugino, Francesco Zito, Francesco Gianfagna, Alessandra Ferri, Concetta Castaldi, Marcella Mignogna, Tomasz Guszcz; Questionnaires: Romina di Giuseppe (Coordinator), Paola Barisciano, Lorena Buonaccorsi, Floriana Centritto, Antonella Cutrone, Francesca De Lucia, Francesca Fanelli, Iolanda Santimone, Anna Sciarretta, Maura Di Lillo, Isabella Sorella, Irene Di Stefano, Emanuela Plescia, Alessandra Molinaro, and Christiana Cavone.
Call Center: Giovanna Galuppo, Maura Di Lillo, Concetta Castaldi, Dolores D'Angelo and Rosanna Ramacciato.
Follow-up: Simona Costanzo (Coordinator); Data management: Simona Costanzo, Marco Olivieri; Event adjudication: Livia Rago (Coordinator), Simona Costanzo, Amalia de Curtis, Licia Iacoviello, Mariarosaria Persichillo.
Regional Health Institutions: Azienda Sanitaria Regionale del Molise (ASReM, Campobasso, Italy), UOC Servizio Igiene e Sanità Pubblica - Dipartimento di Prevenzione; Offices of vital statistics of the Molise region and Molise Dati Spa (Campobasso, Italy).
Hospitals: Presidi Ospedalieri ASReM (Presidio Ospedaliero A. Cardarelli – Campobasso, Ospedale F. Veneziale – Isernia, Ospedale San Timoteo – Termoli (CB), Ospedale Ss. Rosario – Venafro (IS), Ospedale Vietri – Larino (CB), Ospedale San Francesco Caracciolo – Agnone (IS); Istituto di cura Villa Maria – Campobasso; Fondazione di Ricerca e Cura Giovanni Paolo II – Campobasso; IRCCS Neuromed – Pozzilli (IS).
References
Report of the joint WHO/FAO expert consultation
Diet, Nutrition and the Prevention of Chronic Diseases. WHO Technical Report Series, No. 916.
Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis of randomized controlled trials.
Am. J. Clin. Nutr.2008; 88 (26 Pounis G, Bonaccio M, Di Castelnuovo A, Costanzo S, de Curtis A, Persichillo M, Sieri S, Donati MB, Cerletti C, de Gaetano G, Iacoviello L (2016) Polyphenol intake is associated with low-grade inflammation, using a novel data analysis from the Moli-sani study. Thromb Haemost 115:344–352): 38-50https://doi.org/10.1160/TH15-06-0487
Lung volumes and forced ventilatory flows. report working party. standardization of lung function tests, european community for steel and coal. official statement of the european respiratory society.
White blood cell count, sex and age are major determinants of heterogeneity of platelet indices in an adult general population: results from the MOLI-SANI project.
Consumption of healthy foods at different content of antioxidant vitamins and phytochemicals and metabolic risk factors for cardiovascular disease in men and women of the Moli-sani study.
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