Dietary flavonoid intake and cardiovascular risk: A population-based cohort study

Abstract

The cardio-protective effects of flavonoids are still controversial; many studies referred to the benefits of specific foods, such as soy, cocoa, tea. A population-based cohort of middle-aged adults, coming from a semi-rural area where the consumption of those foods is almost negligible, was studied.
The primary objective was establishing if flavonoid intake was inversely associated with the cardiovascular (CV) risk evaluated after 12-year follow-up; the associations between flavonoid intake and CV incidence and mortality and all-cause mortality were also evaluated.
In 2001-2003, a cohort of 1,658 individuals completed a validated food-frequency questionnaire. Anthropometric, laboratory measurements, medical history and the vital status were collected at baseline and during 2014. The CV risk was estimated with the Framingham risk score.
Individuals with the lowest tertile of flavonoid intake showed a worse metabolic pattern and less healthy lifestyle habits. The 2014 CV risk score and the increase in the risk score from baseline were significantly higher with the lowest intake of total and all subclasses of flavonoids, but isoflavones, in a multiple regression model. During follow-up, 125 CV events and 220 deaths (84 of which due to CV causes) occurred. CV non-fatal events were less frequent in individuals with higher flavonoid intake (HR = 0.64; 95%CI 0.42-1.00 and HR = 0.46; 95%CI 0.28-0.75 for the second and third tertiles, respectively) in Cox-regression models, after multiple adjustments. All subclasses of flavonoids, but flavones and isoflavones, were inversely correlated with incident CV events, with HRs ranging from 0.42 (flavan-3-ols) to 0.56 (anthocyanidins). Being in the third tertile of flavan-3-ols (HR = 0.68; 95% CI 0.48-0.96), anthocyanidins (HR = 0.66; 95% CI 0.46-0.95) and flavanones (HR = 0.59; 95% CI 0.40-0.85) was inversely associated with all-cause mortality. Total and subclasses of flavonoids were not significantly associated with the risk of CV mortality.
Flavonoid intake was inversely associated with CV risk, CV non-fatal events and all-cause mortality in a cohort with a low consumption of soy, tea and cocoa, which are typically viewed as the foods responsible for flavonoid-related benefits.

Full text

Ponzo et al. J Transl Med (2015) 13:218
DOI 10.1186/s12967-015-0573-2
RESEARCH
Dietary avonoid intake
andcardiovascular risk: a population-based
cohort study
Valentina Ponzo1, Ilaria Goitre1, Maurizio Fadda2, Roberto Gambino1, Antonella De Francesco2, Laura Soldati3,
Luigi Gentile4, Paola Magistroni5, Maurizio Cassader1 and Simona Bo1*
Abstract
Background: The cardio-protective effects of flavonoids are still controversial; many studies referred to the benefits
of specific foods, such as soy, cocoa, tea. A population-based cohort of middle-aged adults, coming from a semi-rural
area where the consumption of those foods is almost negligible, was studied.
Aims: The primary objective was establishing if flavonoid intake was inversely associated with the cardiovascular (CV)
risk evaluated after 12-year follow-up; the associations between flavonoid intake and CV incidence and mortality and
all-cause mortality were also evaluated.
Methods: In 2001–2003, a cohort of 1,658 individuals completed a validated food-frequency questionnaire. Anthro-
pometric, laboratory measurements, medical history and the vital status were collected at baseline and during 2014.
The CV risk was estimated with the Framingham risk score.
Results: Individuals with the lowest tertile of flavonoid intake showed a worse metabolic pattern and less healthy
lifestyle habits. The 2014 CV risk score and the increase in the risk score from baseline were significantly higher with
the lowest intake of total and all subclasses of flavonoids, but isoflavones, in a multiple regression model. During
follow-up, 125 CV events and 220 deaths (84 of which due to CV causes) occurred. CV non-fatal events were less
frequent in individuals with higher flavonoid intake (HR = 0.64; 95%CI 0.42–1.00 and HR = 0.46; 95%CI 0.28–0.75 for
the second and third tertiles, respectively) in Cox-regression models, after multiple adjustments. All subclasses of fla-
vonoids, but flavones and isoflavones, were inversely correlated with incident CV events, with HRs ranging from 0.42
(flavan-3-ols) to 0.56 (anthocyanidins). Being in the third tertile of flavan-3-ols (HR = 0.68; 95% CI 0.48–0.96), antho-
cyanidins (HR = 0.66; 95% CI 0.46–0.95) and flavanones (HR = 0.59; 95% CI 0.40–0.85) was inversely associated with
all-cause mortality. Total and subclasses of flavonoids were not significantly associated with the risk of CV mortality.
Conclusions: Flavonoid intake was inversely associated with CV risk, CV non-fatal events and all-cause mortality in a
cohort with a low consumption of soy, tea and cocoa, which are typically viewed as the foods responsible for flavo-
noid-related benefits.
Keywords: All-cause mortality, Cardiovascular risk, Cardiovascular mortality, Flavonoids
© 2015 Ponzo et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
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Background
Flavonoids are a group of plant metabolites widely
distributed in the plant kingdom with antioxidant
properties, which can be classified into seven subgroups
based on their chemical structure: flavanones, flavones,
flavonols, flavan-3-ols, anthocyanidins, isoflavones and
proanthocyanidins [1]. ese compounds are present
in small quantities in fruits, vegetables, tea, wine, nuts,
seeds, herbs, spices, cocoa, soybean [2–4].
A wide spectrum of health benefits, such as antioxi-
dant, anti-inflammatory, antibacterial, antithrombotic,
Open Access
*Correspondence: simona.bo@unito.it
1 Department of Medical Sciences, University of Turin, Corso Dogliotti 14,
10126 Turin, Italy
Full list of author information is available at the end of the article

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Ponzo et al. J Transl Med (2015) 13:218
anti-carcinogenetic properties have been reported for fla-
vonoids [5].
Many epidemiological studies have reported inverse
associations between the total flavonoid intake or the
intake of specific classes of flavonoids and the incidence
or mortality for cardiovascular (CV) diseases [6–20].
However, not all studies agreed about the cardio-pro-
tective effects of these compounds [21–27]. Many stud-
ies referred to specific foods, which are the main sources
of flavonoids in different populations, such as tea [13, 28,
29], cocoa [28, 30], soy [17].
We have studied a population-based cohort of middle-
aged adults, coming from a semi-rural area, where the
consumption of some flavonoid-rich foods, such as cocoa
and soybean is almost negligible, while the most impor-
tant sources of flavonoids are fruits and red wine.
e primary objective of this study was establishing if
the consumption of flavonoids was inversely associated
with the CV risk evaluated after 12-year follow-up; the
secondary aims were evaluating the associations between
flavonoid intake and CV incidence, CV mortality, and all-
cause mortality in our cohort.
Methods
All the Caucasian patients (n=1,877), aged 45–64years,
of six family physicians were invited to participate in a
metabolic screening in 2001–2003. ese subjects were
representative of the Local Health Units of the province
of Asti (northwestern Italy) [31]. Exclusion criteria were:
inability to go to the office of the family physician and to
give the informed consent.
Of these, 1,658 (88.3%) subjects gave their writ-
ten informed consent to participate and 219 patients
declined. Both the participants and non-participants
were similar to the resident population of a correspond-
ing age range with respect to the percentage of males,
level of education, prevalence of known diabetes, and
residence in a rural area [31]. e study was approved by
the local ethics committee. All procedures conformed to
the principles of the Helsinki Declaration.
Methods
In the morning and after fasting, weight, height, waist
circumference, and blood pressure were measured in
the office of the family physicians. Glucose, insulin, total
cholesterol, HDL-cholesterol, triglyceride, uric acid and
high-sensitivity C-reactive protein (CRP) levels were
determined. If the serum glucose value was ≥110mg/dl,
a second fasting glucose determination was performed.
Two blood pressure measurements were performed with
mercury sphygmomanometers and the appropriate cuff
sizes after a 10-min rest in the sitting position, and the
values reported are the means of the two measurements.
e waist circumference was measured by a plastic tape
meter at the level of the umbilicus. e measurements
were performed by trained physicians holding a grant.
Patients completed the Minnesota Leisure Time Physi-
cal Activity questionnaire [32]. e physical activity level
was calculated as the product of the duration and fre-
quency of each activity (in hours/week), weighted by an
estimate of the metabolic equivalent of the activity and
summed for the activities performed.
From January to November 2014, the patients were
submitted to a blood sample analysis and a follow-up
visit by their family physicians. Information on the vital
status of each patient and the causes of death of those
who died was collected from the demographic files of the
town of residence or death.
e laboratory methods have been described previ-
ously [31, 33]. All samples were run blindly.
Ascertainment ofavonoid intake
e semi-quantitative food-frequency questionnaire used
in the EPIC (European Prospective Investigation into
Cancer and Nutrition) studies was used for all subjects
[34]. It assessed average frequency and portion intake
of 148 foods consumed during the 12months before the
enrolment. For each food item, the participants had to
mark if the food or dish was consumed or not during the
previous year. For all food items consumed, the subjects
should select their typical portion size with the help of
photographs, the consumption frequency and the time
period (day, week, month or year), which suited them
best. Questions about the type of fat for cooking were
also included. is tool has been previously validated
[34]. A dietician, blinded to the study details, checked
all questionnaires for completeness, internal coherence,
and plausibility. In case of uncertain answers, the patients
were interviewed by the dietician.
Each nutrient was adjusted for total energy, using the
residual method [35]. e reliability of the reported
energy intake was assessed by calculating the ratio of esti-
mated energy intake to predicted basal metabolic rate,
using age- and sex-specific formulas derived by Schofield
[36]. Subjects with a ratio <0.88 were classified as under-
reporters [37].
Dietary intake of total and subclasses of flavonoids
were estimated by using the latest detailed food compo-
sition tables published by the US Department of Agri-
culture (USDA) on the seven major classes of flavonoids
[2–4] and extended with information from an European
database [38]. Merging of the databases gave a single
data-file. Flavonoid intake was computed by multiplying
the specific flavonoid content of the serving of each food
item (mg aglicone equivalent/100 g food) by the daily
consumption (g/day) of the selected food item. Estimated

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Ponzo et al. J Transl Med (2015) 13:218
total intake of individual flavonoids was the sum of indi-
vidual flavonoid intakes from all food sources reported
in the questionnaire. Total flavonoid intake was calcu-
lated by summing up the seven subclasses (flavanones,
flavones, flavonols, flavan-3-ols, anthocyanidins, isofla-
vones and proanthocyanidins), and were expressed as
mg/day aglycones.
e contribution of each food to the total intake of sub-
group and total flavonoids was calculated as a percent-
age; single foods were then grouped into large categories.
All flavonoid subgroups that were estimated, their
respective compounds, and the main food sources are
shown in Table1.
Denitions
Alcohol intake was assessed by multiplying the mean
daily consumption of each beverage by its ethanol
content, to give grams of alcohol/day. Moderate and
heavy drinkers were considered in the case of consump-
tion of ≤30 and >30g/day alcohol, respectively, in line
with Italian guidelines [39].
Diabetes mellitus was defined according to published
recommendations [40]. Estrogen use included both con-
traceptive medications or estrogen replacement therapy.
e use of nutritional supplements was infrequent in this
cohort and was limited to multivitamin, iron, calcium or,
less frequently, magnesium.
e CV risk score was estimated with the Framingham
risk score [41]. e diagnosis of CV disease was based
on documented events that were recorded by the fam-
ily physician (i.e. angina, previous myocardial infarction,
coronary artery by-pass graft or another invasive proce-
dure to treat coronary artery disease, transient ischemic
attack, stroke, gangrene, amputation, vascular surgery,
Table 1 Flavonoid classes andcompounds, andrespective dietary intakes andmain food sources inthe whole cohort
Sources contributing to ≥5% of the intake.
Compounds Median intake (mg/day) Sources (%)
Total flavonoids 251.0 Fruits (38)
Red wine (25)
Vegetables (5)
Tea (5)
Proanthocyanids Dimers, Trimers, 4-6mers, 7-10mers,
polymers of flavon-3-ols or flavanols 96.1 Fruits (50)
Red wine (23)
Legumes (6)
Flavan-3-ols (−)-Epicatechin
(−)-Epicatechin 3-gallate
(−)-Epigallocatechin
(−)-Epigallocatechin 3-gallate
(+)-Catechin
(+)-Gallocatechin
Theaflavin
Theaflavin-3, 3′-digallate
Theaflavin-3′-gallate
Theaflavin-3-gallate
Thearubigins
50.4 Fruits (26)
Tea (21)
Red wine (19)
Anthocyanidins Cyanidin
Delphinidin
Malvidin
Pelargonidin
Peonidin
Petunidin
32.9 Red wine (53)
Vegetables (17)
Fruits (11)
Flavanones Eriodictyol
Hesperetin
Naringenin
24.2 Fruits (71)
Red wine (12)
Flavonols Isorhamnetin
Kaempferol
Myricetin
Quercetin
14.4 Vegetables (34)
Red wine (14)
Fruits (13)
Flavones Apigenin
Luteolin 1.2 Vegetables (51)
Red wine (18)
Fruits (8)
Isoflavones Daidzein
Genistein
Glycitein
0.7 Legumes (90)

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Ponzo et al. J Transl Med (2015) 13:218
intermittent claudication, absent foot pulses and abnor-
mal brachial and posterior tibial blood pressure using
Doppler techniques).
e underlying cause of death was available for all the
deceased patients and was derived from the death cer-
tificate and coded according to the ICD-9 (International
Classification of Diseases, Ninth Revision). Deaths due to
CV diseases corresponded to ICD codes 410–414 (coro-
nary artery diseases), 430–438 (strokes), 440 (peripheral
artery diseases) and other ICD codes between 390–459
and 798.1 (other CV diseases).
Statistical analyses
Considering a type I error of 0.05 and a type II error of
0.90, a minimum of 83 subjects were needed for each ter-
tile to detect a 10% difference in the CV scores between
the tertiles of total flavonoid intakes.
Dietary total flavonoid intakes of the cohort were
divided into tertiles, separately per sex. Cut-off points
were 191.5, 401.2 and 138.3, 322.3mg/day, respectively
for men and women.
e distributions of flavonoid intake, fasting insulin,
triglycerides, CRP values were skewed. e character-
istics of the cohort according to the tertiles of flavonoid
intakes were analyzed by ANOVA, Kruskal–Wallis tests
(for not-normally distributed variables) or the χ2 test, as
appropriate.
A multiple regression was performed to assess the
association between the 2014 CV score and the varia-
tions from baseline to follow-up (values at 2014 minus
values at baseline) in the CV risk score, and the tertiles of
flavonoid intakes, after adjusting for BMI, education (pri-
mary/secondary/university), living in a rural area, METs
(hour/week), alcohol intake (g/day), history of CV dis-
eases, values of fasting glucose, log-CRP, fiber, and satu-
rated fatty acid intakes. We did not include age, sex, total
and HDL-cholesterol, smoking habits and blood pressure
values, because these variables were included in the CV
score calculation, to avoid over-controlling. However,
when we controlled for these variables, results were not
significantly different.
e relationships between tertiles of flavonoid intakes
and all-cause mortality and CV mortality and incidence
were assessed by estimating the hazard ratio (HR) and its
95% confidence intervals (CI) in Cox regression models,
adjusted for age, sex, BMI, education, living in a rural
area, METs (hour/week), alcohol, fiber, and saturated
fatty acid intakes, smoking, values of systolic and dias-
tolic blood pressure, total and HDL-cholesterol, fasting
glucose, CRP, statin and aspirin use.
In all these analyses, individuals in the first (lower) ter-
tile of flavonoid intakes were considered as the reference
group, and the other groups were introduced as dummy
variables (IBM SPSS Statistical Software Version 22).
Results
Out of 1,658 subjects, 138 (8.3%) resulted under-report-
ers. Among the tertiles of flavonoid intake, the percent-
age of under-reporters did not differ (8.2, 8.5 and 8.3% in
the first, second and third tertiles, respectively).
Mean and median intake of total flavonoids were 320
and 251mg/day, respectively (Table1).Pearson correla-
tions between flavonoids ranged from weak (r=0.04 for
flavan-3-ols with isoflavones) to high (r=0.80 for flavan-
3-ols with proanthocyanids).
Descriptive characteristics of the cohort by tertiles of
flavonoid intakes are shown in Table2.
In the lowest tertile, there was a higher percentage of
smokers, alcohol abstainers, less educated individu-
als, hypertensive and diabetic patients (Table2). On the
other hand, subjects with the highest flavonoid intake
were more frequently heavy drinkers living in a rural
area, were more physically active, ate more calories, fiber
and antioxidant vitamins, and less total fat and saturated
fat. In individuals within the lowest tertile, the metabolic
pattern was significantly worse, CRP values increased,
and the CV risk score higher.
e 2014 CV risk score was significantly increased in
the individuals with the lowest intake of total flavonoids
and their subclasses, with the exception of isoflavones
(Table3). Similarly, after a mean 12-year follow-up, the
difference in the scores (2014 score minus baseline score)
was higher in those subjects. In a multiple regression
models, being in the third (higher) tertile of flavonoid
intake was inversely associated with the 2014 CV score
and with change in score values from baseline to follow-
up, after adjusting for BMI, education, living in a rural
area, METs (hour/week), history of CV diseases, values
of fasting glucose, log-CRP, alcohol, fiber, and saturated
fatty acid intakes.
During follow-up, 125 incident CV events were diag-
nosed and 220 deaths occurred, 84 of which due to CV
causes (Table4). e incidence of CV events was signifi-
cantly lower in individuals with the higher intake of total
flavonoids and with higher intake of all subclasses of fla-
vonoids, but flavones and isoflavones, in Cox-regression
models after adjustments for age, sex, BMI, education,
living in a rural area, METs (hour/week), alcohol, fiber,
and saturated fatty acid intakes, smoking, values of sys-
tolic and diastolic blood pressure, total and HDL-cho-
lesterol, fasting glucose, CRP, statin and aspirin use. HRs
ranged from 0.42 for the higher tertile of flavan-3-ols to
0.56 for the higher tertile of anthocyanidins in the Cox
model after multiple adjustments.

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Table 2 Baseline characteristics bytertiles ofavonoid intake (the rst the lower; the third, the higher)
CHO carbohydrates, CRP C-reactive protein, CV cardiovascular.
*Kruskall–Wallis test for not-normally distributed variables.
First tertile Second tertile Third tertile P
Number 552 551 555
Median intake (mg/day) 89.0 251.4 532.3
Current smoking (%) 28.4 25.7 17.5 <0.001
Males (%) 47.1 46.8 47.2 0.99
Living in a rural area (%) 36.8 36.8 47.4 <0.001
Alcohol
Alcohol abstainers (%) 54.9 41.0 35.7
Moderate alcohol drinking (%) 31.5 41.4 38.6
Heavy alcohol drinking (%) 13.6 17.6 25.8 <0.001
Education
Primary school (%) 78.6 69.9 75.3
Secondary school (%) 14.5 21.8 17.5
University (%) 6.9 8.4 7.2 0.02
History of hypertension (%) 56.5 47.0 50.5 0.006
History of diabetes mellitus (%) 8.5 4.0 4.5 0.002
History of CV disease (%) 6.5 5.3 5.1 0.52
Estrogen use (%) 4.2 5.1 5.1 0.72
Supplements use (%) 3.3 3.3 3.4 0.98
Statin use (%) 3.8 3.8 4.7 0.70
Aspirin use (%) 6.0 6.0 4.1 0.29
Mean SD Mean SD Mean SD
METS (h/week) 20.5 9.4 21.7 9.5 21.9 9.6 0.04
Age (years) 54.8 5.8 54.3 5.5 54.6 5.5 0.37
BMI (kg/m2) 27.2 5.3 26.3 4.3 26.3 4.3 <0.001
Waist circumference (cm) 92.9 13.0 90.2 12.4 90.9 13.4 0.002
Total caloric intake (kcal/day) 1,917.0 722.7 2,142.7 583.2 2,149.3 667.1 <0.001
CHO intake (% total kcal) 47.6 7.6 48.4 6.8 49.5 6.8 <0.001
Total fat intake (kcal/day) 35.6 6.1 35.2 5.9 34.7 5.7 0.03
Saturated fat (% total kcal) 12.3 3.4 12.1 2.8 11.6 3.0 0.001
Polyunsaturated fat (% total kcal) 4.3 1.3 4.3 1.6 4.3 1.4 0.95
Fiber intake (g/day) 16.5 7.3 22.4 8.3 23.3 10.2 <0.001
Beta-carotene (µg/day) 2,768.7 1,658.9 3,571.5 1,840.2 3,914.8 2,292.4 <0.001
Vitamin C (mg/day) 134.9 44.5 142.4 55.0 142.8 49.4 0.01
Vitamin E (mg/day) 8.1 3.0 8.1 2.3 8.2 2.6 0.62
Systolic blood pressure (mmHg) 135.4 16.6 132.3 15.0 133.3 16.0 0.007
Diastolic blood pressure (mmHg) 84.3 9.1 82.6 9.2 82.9 9.6 0.005
Fasting glucose (mg/dl) 109.0 38.7 102.2 24.3 103.5 26.6 <0.001
Fasting insulin 9.3 6.1 8.3 3.9 8.2 4.4 <0.001*
Total cholesterol 217.9 39.5 215.2 40.0 217.7 42.2 0.46
HDL cholesterol 57.9 12.5 60.9 13.1 62.4 14.1 <0.001
Triglycerides 149.1 82.9 131.7 99.9 137.0 92.4 <0.001*
CRP (mg/l) 3.3 7.0 2.4 4.7 2.3 5.2 <0.001*
Uric acid 3.4 1.0 3.3 1.1 3.3 1.0 0.14
CV risk score 12.6 8.3 10.6 6.8 10.5 7.1 <0.001

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Table 3 CV risk score bytertiles of avonoid intake (the rst the lower; the third, the higher) ina multiple regression
model
First tertile Second tertile Third tertile
Total avonoids Mean SD Mean SD Mean SD P
2014 CV risk score 28.8 15.4 25.3 12.6 23.8 10.7 <0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −2.58 −4.07 −1.09 <0.001 −4.36 −5.85 −2.87 <0.001
Model 2 Reference −1.27 −2.76 0.22 0.10 −2.69 −4.22 −1.16 <0.001
Mean SD Mean SD Mean SD P
Changes in CV risk score 16.2 10.1 14.7 8.1 13.4 7.4 <0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −0.98 −1.96 0.00 0.05 −2.64 −3.62 −1.66 <0.001
Model 2 Reference −0.46 −1.50 0.58 0.38 −1.92 −2.98 −0.86 <0.001
Proanthocyanids Mean SD Mean SD Mean SD P
2014 CV risk score 28.8 15.4 25.3 12.6 23.8 10.7 <0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −2.39 −3.88 −0.90 0.002 −4.29 −5.78 −2.80 <0.001
Model 2 Reference −1.07 −2.56 0.42 0.16 −2.60 −4.13 −1.07 <0.001
Mean SD Mean SD Mean SD P
Changes in CV risk score 16.1 10.2 14.7 8.0 13.3 7.4 <0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −0.96 −1.94 0.02 0.06 −2.65 −3.65 −1.65 <0.001
Model 2 Reference −0.43 −1.47 0.61 0.41 −1.93 −2.99 −0.87 <0.001
Flavan-3-ols Mean SD Mean SD Mean SD P
2014 CV risk score 28.1 15.1 25.6 12.7 24.2 11.3 <0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −1.80 −3.29 −0.31 0.02 −3.20 −4.69 −1.71 <0.001
Model 2 Reference −0.70 −2.17 0.77 0.35 −1.92 −3.41 −0.43 0.01
Mean SD Mean SD Mean SD P
Changes in CV risk score 15.8 9.8 14.9 8.4 13.6 7.5 <0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −0.58 −1.58 0.42 0.25 −1.94 −2.94 −0.94 <0.001
Model 2 Reference −0.07 −1.09 0.95 0.89 −1.39 −2.41 −0.37 0.007
Anthocyanidins Mean SD Mean SD Mean SD P
2014 CV risk score 27.9 14.7 25.5 12.6 24.5 12.0 <0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −1.60 −3.09 −0.11 0.04 −2.73 −4.22 −1.24 <0.001
Model 2 Reference −0.81 −2.26 0.64 0.28 −1.05 −2.11 0.00 0.05
Mean SD Mean SD Mean SD P
Changes in CV risk score 15.7 9.4 14.7 8.5 13.8 8.0 0.001
Ββ95% CI P Β95% CI P
Model 1 Reference −0.68 −1.68 0.32 0.18 −1.65 −2.65 −0.65 0.001
Model 2 Reference −0.27 −1.29 0.75 0.60 −0.90 −1.74 −0.06 0.03
Flavanones Mean SD Mean SD Mean SD P
2014 CV risk score 28.2 14.8 25.8 12.5 23.9 11.9 <0.001
Β Β 95% CI P Β95% CI P

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Total and subclasses of flavonoids were not significantly
associated with the risk of CV mortality in the same Cox
model (Table4).
Being in the third tertile of flavan-3-ols (HR=0.68;
95% CI 0.48–0.96), anthocyanidins (HR = 0.66; 95%
CI 0.46–0.95) and flavanones (HR = 0.59; 95% CI
0.40–0.85) was inversely associated with all-cause
mortality.
Data did not change after excluding the 138 under-
reporters, the 79 women on estrogen therapy, the 55
individuals on nutritional supplements, and after adjust-
ing for antioxidant vitamin intakes.
Table 3 continued
Flavanones Mean SD Mean SD Mean SD P
Model 1 Reference −2.38 −3.87 −0.89 0.002 −3.90 −5.39 −2.41 <0.001
Model 2 Reference −1.90 −3.35 −0.45 0.01 −2.70 −4.19 −1.21 <0.001
Mean SD Mean SD Mean SD P
Changes in CV risk score 15.8 9.3 14.7 8.9 13.7 7.7 <0.001
Β Β 95% CI P Β95% CI P
Model 1 Reference −1.13 −2.11 −0.15 0.02 −1.97 −2.95 −0.99 <0.001
Model 2 Reference −0.98 −1.98 0.02 0.06 −1.51 −2.55 −0.47 0.004
Flavonols Mean SD Mean SD Mean SD P
2014 CV risk score 27.5 15.0 25.2 12.2 25.2 12.1 0.004
Β Β 95% CI P Β95% CI P
Model 1 Reference −2.10 −3.59 −0.61 0.006 −2.33 −3.82 −0.84 0.002
Model 2 Reference −1.13 −2.60 0.34 0.13 −1.21 −2.40 −0.02 0.04
Mean SD Mean SD Mean SD P
Changes in CV risk score 15.4 9.5 14.5 7.9 14.3 8.6 <0.001
Β Β 95% CI P Β95% CI P
Model 1 Reference −0.84 −1.82 0.14 0.10 −1.29 −2.29 −0.29 0.01
Model 2 Reference −0.44 −1.46 0.58 0.39 −0.72 −1.78 0.34 0.18
Flavones Mean SD Mean SD Mean SD P
2014 CV risk score 28.2 15.2 25.4 12.3 24.3 11.6 <0.001
Β Β 95% CI P Β95% CI P
Model 1 Reference −2.59 −4.08 −1.10 <0.001 −3.43 −4.92 −1.94 <0.001
Model 2 Reference −1.74 −3.21 −0.27 0.02 −2.12 −3.65 −0.59 0.007
Mean SD Mean SD Mean SD P
Changes in CV risk score 15.8 10.1 14.5 8.3 13.9 7.3 0.001
Β Β 95% CI P Β95% CI P
Model 1 Reference −1.37 −2.37 −0.37 0.007 −1.75 −2.75 −0.75 <0.001
Model 2 Reference −1.02 −2.02 −0.02 0.04 −1.28 −2.32 −0.24 0.02
Isoavones Mean SD Mean SD Mean SD P
2014 CV risk score 26.5 13.2 27.1 14.1 24.4 12.2 0.002
Β Β 95% CI P Β95% CI P
Model 1 Reference 0.72 −0.77 2.21 0.34 −1.54 −3.03 −0.05 0.04
Model 2 Reference 0.97 −0.46 2.40 0.19 −0.35 −1.82 1.12 0.64
Mean SD Mean SD Mean SD P
Changes in CV risk score 14.8 8.4 15.4 9.6 14.1 7.9 0.03
Β Β 95% CI P Β95% CI P
Model 1 Reference 0.74 −0.25 1.73 0.15 −0.45 −1.45 0.55 0.38
Model 2 Reference 0.96 −0.06 1.98 0.06 0.03 −0.99 1.05 0.96
Model 1 adjusted for BMI, education, living in a rural area, Model 2 adjusted for BMI, education, living in a rural area, METS (h/week), alcohol intake, history of CV
diseases, values of fasting glucose, log-CRP, ber, and saturated fatty acid intakes.

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Table 4 Cardiovascular events andall-cause and cardiovascular mortality bytertiles of avonoid intake (the rst the
lower; the third, the higher)
First tertile Second tertile Third tertile
Total avonoids
Incident CV events 54 40 31
HR HR 95% CI P HR 95% CI P
Model 1 1 0.65 0.42–0.99 0.05 0.45 0.28–0.73 0.001
Model 2 1 0.64 0.42–1.00 0.05 0.46 0.28–0.75 0.002
CV mortality 34 26 24
HR HR 95% CI P HR 95% CI P
Model 1 1 0.97 0.56–1.67 0.90 0.81 0.45–1.44 0.47
Model 2 1 0.95 0.54–1.66 0.85 0.83 0.46–1.51 0.55
All-cause mortality 89 69 62
HR HR 95% CI P HR 95% CI P
Model 1 1 0.86 0.62–1.21 0.38 0.73 0.51–1.04 0.08
Model 2 1 0.90 0.65–1.26 0.52 0.78 0.55–1.13 0.19
Proanthocyanids
Incident CV events 57 37 31
HR HR 95% CI P HR 95% CI P
Model 1 1 0.56 0.36–0.86 0.01 0.42 0.26–0.68 <0.001
Model 2 1 0.56 0.36–0.87 0.009 0.43 0.27–0.70 0.001
CV mortality 34 27 23
HR HR 95% CI P HR 95% CI P
Model 1 1 0.99 0.58–1.70 0.97 0.77 0.43–1.39 0.39
Model 2 1 0.98 0.56–1.69 0.93 0.80 0.44–1.46 0.46
All-cause mortality 90 70 60
HR HR 95% CI P HR 95% CI P
Model 1 1 0.85 0.61–1.19 0.35 0.69 0.48–0.99 0.05
Model 2 1 0.88 0.63–1.24 0.46 0.75 0.52–1.08 0.12
Flavan-3-ols
Incident CV events 57 42 26
HR HR 95% CI P HR 95% CI P
Model 1 1 0.69 0.46–1.05 0.08 0.40 0.25–0.65 <0.001
Model 2 1 0.71 0.47–1.08 0.11 0.42 0.26–0.68 <0.001
CV mortality 37 23 24
HR HR 95% CI P HR 95% CI P
Model 1 1 0.75 0.44–1.29 0.30 0.70 0.40–1.20 0.19
Model 2 1 0.79 0.46–1.37 0.40 0.72 0.41–1.26 0.25
All-cause mortality 92 71 57
HR HR 95% CI P HR 95% CI P
Model 1 1 0.84 0.61–1.15 0.27 0.63 0.44–0.89 0.009
Model 2 1 0.86 0.62–1.19 0.36 0.68 0.48–0.96 0.03
Anthocyanidins
Incident CV events 53 35 37
HR HR 95% CI P HR 95% CI P
Model 1 1 0.59 0.38–0.92 0.02 0.58 0.37–0.92 0.02
Model 2 1 0.58 0.37–0.91 0.02 0.56 0.36–0.89 0.02
CV mortality 40 20 24
HR HR 95% CI P HR 95% CI P
Model 1 1 0.58 0.33–1.01 0.05 0.65 0.37–1.15 0.14
Model 2 1 0.56 0.32–0.98 0.04 0.67 0.38–1.18 0.16

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Table 4 continued
First tertile Second tertile Third tertile
All-cause mortality 95 62 63
HR HR 95% CI P HR 95% CI P
Model 1 1 0.69 0.50–0.96 0.03 0.66 0.47–0.94 0.02
Model 2 1 0.66 0.47–0.94 0.02 0.66 0.46–0.95 0.02
Flavanones
Incident CV events 54 42 29
HR HR 95% CI P HR 95% CI P
Model 1 1 0.71 0.47–1.07 0.11 0.45 0.28–0.73 0.001
Model 2 1 0.73 0.48–1.10 0.13 0.48 0.29–0.77 0.003
CV mortality 39 24 21
HR HR 95% CI P HR 95% CI P
Model 1 1 0.67 0.40–1.13 0.14 0.56 0.32–0.99 0.05
Model 2 1 0.71 0.42–1.20 0.20 0.66 0.37–1.17 0.15
All-cause mortality 91 80 49
HR HR 95% CI P HR 95% CI P
Model 1 1 0.91 0.67–1.24 0.54 0.54 0.37–0.78 0.001
Model 2 1 0.94 0.68–1.29 0.69 0.59 0.40–0.85 0.005
Flavonols
Incident CV events 56 31 38
HR HR 95% CI P HR 95% CI P
Model 1 1 0.49 0.31–0.76 0.002 0.53 0.34–0.83 0.006
Model 2 1 0.51 0.32–0.80 0.003 0.53 0.34–0.83 0.005
CV mortality 36 22 26
HR HR 95% CI P HR 95% CI P
Model 1 1 0.63 0.36–1.09 0.10 0.68 0.39–1.19 0.18
Model 2 1 0.69 0.40–1.20 0.19 0.72 0.41–1.27 0.26
All-cause mortality 91 64 65
HR HR 95% CI P HR 95% CI P
Model 1 1 0.72 0.51–1.00 0.05 0.70 0.50–0.99 0.05
Model 2 1 0.78 0.55–1.08 0.14 0.72 0.51–1.02 0.06
Flavones
Incident CV events 42 51 32
HR HR 95% CI P HR 95% CI P
Model 1 1 1.13 0.74–1.72 0.56 0.68 0.41–1.10 0.11
Model 2 1 1.14 0.75–1.75 0.54 0.66 0.40–1.09 0.10
CV mortality 30 31 23
HR HR 95% CI P HR 95% CI P
Model 1 1 1.08 0.64–1.82 0.77 0.87 0.48–1.56 0.63
Model 2 1 1.10 0.65–1.87 0.72 0.83 0.45–1.52 0.55
All-cause mortality 88 71 61
HR HR 95% CI P HR 95% CI P
Model 1 1 0.79 0.57–1.09 0.16 0.71 0.50–1.01 0.06
Model 2 1 0.83 0.60–1.16 0.28 0.73 0.51–1.05 0.09
Isoavones
Incident CV events 48 38 39
HR HR 95% CI P HR 95% CI P
Model 1 1 0.78 0.51–1.20 0.26 0.77 0.49–1.19 0.23
Model 2 1 0.81 0.53–1.25 0.35 0.77 0.49–1.21 0.26
CV mortality 30 34 20
HR HR 95% CI P HR 95% CI P

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Ponzo et al. J Transl Med (2015) 13:218
Discussion
e results of this population-based cohort study suggest
that higher dietary intakes of flavonoids may be associ-
ated with a reduced CV risk score and a 40–50% lower
risk of non-fatal CV events.
is is intriguing since in our cohort the consumption
of some flavonoid-rich foods inversely associated with
CV risk such as cocoa, soybean and tea [13, 17, 28, 29], is
infrequent, being fruits and red wine the main sources of
flavonoids. Epidemiological studies have suggested that a
Mediterranean diet reduces the CV risk [42] and a high
concentration of flavonoids has been found in fruits, veg-
etables, red wine and other elements of the Mediterra-
nean diet. However, there is inconsistent evidence on the
role of flavonoids derived from these foods and CV risk,
since previous studies reported either a decreased CV
incidence and mortality with increased intake of apples,
pears, and red wine [8, 12, 16, 24, 25], or no significant
effect [6, 10, 21, 26, 27].
We have found both a lower CV risk score at baseline
and at follow-up in the higher tertile of flavonoid intake.
Intriguingly, the increase in the score from enrolment
to the end of follow-up was higher in those individuals.
Accordingly, the consumption of flavonoid-rich food has
been associated with lower systolic blood pressure [15,
43, 44], lower total cholesterol [44], higher HDL choles-
terol values [44–46].
Benefits of flavonoids on blood pressure, lipid values,
insulin resistance, and flow-mediated dilatation seem to
derive above all from soy, cocoa and tea, as suggested by
systematic reviews [47, 48]. However, more recently, fla-
vonoids from fruits and vegetables have been reported
to reduce the risk of diabetes mellitus and to improve
microvascular reactivity and inflammatory status [49–
51]. Accordingly, although a small number of incident
CV events occurred in our cohort, the risk of non-fatal
CV events was significantly lower in individuals with the
higher intake of total and all subclasses of flavonoids,
but flavones and isoflavones, which were consumed at
negligible concentrations in our cohort. erefore, the
dietary intakes of flavonoids seems relevant for healthy
CV outcomes at relatively low concentrations, since most
inverse associations with CV risk score and non-fatal CV
events appeared with intermediate or low intakes of spe-
cific subclasses, suggesting that even small amounts may
be beneficial. However, a threshold of intake is probably
needed, under which these compounds are unlike to be
active.
Flavonoids can inhibit or induce a large variety of
enzyme systems, involved in pathways regulating platelet
aggregation, inflammatory and immune responses [1, 52,
53]. Furthermore, by their antioxidant properties, flavo-
noids may protect tissues against oxygen free radicals and
lipid peroxidation, thus contributing to the prevention of
atherosclerosis, chronic inflammation and cancer [1, 52,
53]. Because of their antioxidant and chelating proper-
ties, flavonoids may inactivate reactive oxygen species
(ROS) and counteract the oxidation of circulating LDL
particles [52–54]. Other anti-atherogenic actions pro-
posed for these compounds are: reduction of the activ-
ity of enzymes increasing ROS production; inhibition of
HMG-CoA reductase, cholesteryl ester transfer protein
(CEPT), angiotensin-converting enzyme, signal trans-
ducers and activators of transcription (STAT), and glu-
cose transporters; synthesis of nitric oxide; inhibition of
platelet activation and function; anti-angiogenetic effects;
improvement in endothelial function, vascular fragil-
ity, cellular permeability [54–56]. e anti-inflammatory
properties of flavonoids may be due to the inhibition of
NF-κB activation and adhesion molecule expression;
suppression of the activity and secretion of inflamma-
tory cells; reduction of the concentrations of CRP and
cytokines [57, 58].
e associations with fatal events were controversial
in our cohort. No significant association was found with
CV mortality, probably because the number of fatal CV
events was low. Otherwise, many flavonoid subclasses,
such as flavan-3-ols, anthocyanidins and flavanones
were inversely associated with all-cause mortality. Pre-
vious studies have reported a reduced total and/or CV
Table 4 continued
First tertile Second tertile Third tertile
Model 1 1 1.23 0.74–2.03 0.42 0.78 0.44–1.41 0.42
Model 2 1 1.21 0.73–2.02 0.48 0.74 0.41–1.36 0.34
All-cause mortality 95 63 62
HR HR 95% CI P HR 95% CI P
Model 1 1 0.68 0.49–0.94 0.02 0.70 0.50–0.98 0.04
Model 2 1 1.45 1.05–2.00 0.03 1.39 1.00–1.95 0.05
Model 1 adjusted for age, sex, BMI, education, living in a rural area, METs (h/week), ber and saturated fatty acid intakes, Model 2 adjusted for age, sex, BMI, education,
living in a rural area, METs (hour/week), ber, and saturated fatty acid intakes, alcohol intake, smoking, values of systolic and diastolic blood pressure, total and HDL-
cholesterol, fasting glucose, CRP, statin and aspirin use.

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Ponzo et al. J Transl Med (2015) 13:218
mortality with proanthocyanids [19], flavan-3-ols, [11,
19, 25], anthocyanidins [16, 19], flavonols [13, 19], fla-
vanones [16, 18], flavones [16, 19], and isoflavones [17,
44–47]. On the other hand, other authors reported no
protective effects of total or specific subclasses of flavo-
noids on mortality [7, 12, 21, 22, 27].
ese highly divergent results among studies might be
due to differences in nutritional, socio-cultural and eth-
nic characteristics.
e median intakes of flavonoids are highly variable
among studies, and values ranging from 50 to 450 mg
have been reported in European studies [54]. In particu-
lar, the following median intakes have been described
for Mediterranean countries: 92mg/day in Greece [59]
and 332.4mg/day in Spain [60]. On the other hand, in
non-Mediterranean countries, the median consump-
tion of flavonoids was much lower, varying from 203mg/
day in US population [19] to 88mg/day in Sweden, and
13 mg/day in Finland [61]. Our values were between
these extreme intakes, in line with other Italian data [62,
63]. e high consumption of red wine and fruits, such
as apples and citrus fruits, in our Italian cohort justify
the higher intake of total flavonoids and proanthocya-
nidins with respect to other non-Mediterranean cohorts
[19, 61]. On the other hand, the low consumption of tea,
justified the lower intakes of flavon-3-ols (in particular
epigallocatechin 3-gallate, epicatechin 3-gallate and epi-
gallocatechin) with respect to UK and Ireland [61], and
the negligible use of soy explain why the intake of isofla-
vones and flavones was much lower in our cohort when
compared with Asian studies [17].
In most studies, the higher consumption of flavonoids
was associated with an overall healthy dietary and meta-
bolic pattern, in line with our results [8, 10–12, 16–21,
25, 26, 49]. Our cohort indeed included individuals with
a low level of education, differently from previous stud-
ies performed in samples where most participants had at
least a high school education [13, 16, 17, 19, 26].
Finally, many compounds tend to be present in the same
foods: for example, in our cohort, individuals with lower
intakes of flavonoids, ate less fiber and antioxidant vita-
mins and more saturated fats. It is therefore difficult to
ascertain the independent effect of dietary components
because of multicollinearity. However, our associations
remained significant after adjusting for these dietary fac-
tors, thus suggesting that a higher flavonoid intake might
not merely be an indicator of a healthier lifestyle.
Limitations
e EPIC questionnaire was not originally designed to
measure flavonoid intake, but it has been extensively
used and validated for this purpose [60, 64, 65].
e flavonoid intake might have been underestimated
because of the limitations of the food composition data-
bases. It should be noted that the presence of particular
flavonoids in vegetables and fruits depends on the crop
variety, location and type of cultivation. e adaptabil-
ity of the USDA database to the Italian diet is question-
able. e absorption and microbial transformation in
the gut of specific subclasses of flavonoids vary consid-
erably, therefore the different flavonoid bioavailability
could have an impact on the associations between the
assumption of these compounds and chronic diseases.
In general, flavonoid subclasses are present simultane-
ously in foods and establishing which of the compound
is responsible for the potential biological effect is diffi-
cult. We relied on dietary intake from the questionnaire
administered at one point in time; thus misclassification
of dietary exposure might have occurred if individuals
have changed their diets during the follow-up. Further-
more, measurement error in collecting self-reported
dietary intake is inevitable and our observational study
was prone to the possibility of unmeasured confounding.
However, the recent versions of the USDA database
includes more cooked foods [2], because in culinary
preparations important losses in flavonoid content occur,
and is the most complete and used database in the esti-
mation of flavonoid intake. Moreover, we have referred
also to a European database, and the USDA has been
already used for the Italian population [62–64]. We have
used a validated instrument and, both at baseline and at
follow-up, the associations between flavonoid intakes and
the CV risk score were consistent. Measurement errors
and misclassification was likely to be random and would
have attenuated the association found. We have took care
to adjust for many potential confounders. Finally, we have
studied a large population-based cohort from a localized
region, with a high level of participation, which could
have limited the number of potential confounders.
Conclusions
Individuals with higher intakes of flavonoids showed
a lower CV risk after a mean 12-year follow-up, and a
reduced risk of non-fatal CV events. If these results will
be confirmed in larger prospective cohorts, it would be
useful to obtain reliable markers of flavonoid intake in
order to define the optimal doses of specific flavonoids
for CV protection.
Abbreviations
BMI: body mass index; CEPT: cholesteryl ester transfer protein; CI: confidence
intervals; CRP: high-sensitivity C-reactive protein; CV: cardiovascular; HR:
hazard ratio; ICD: International Classification of Diseases; ROS: reactive oxygen
species; STAT: signal transducers and activators of transcription.

Page 12 of 13
Ponzo et al. J Transl Med (2015) 13:218
Authors’ contributions
VP participated in the conception and design of the study, supervision of data
collection, data analysis, interpretation of the findings of the study, manuscript
writing and revision. IG participated in the data analysis, interpretation of the
findings, manuscript writing and revision. MF participated in the data analysis,
interpretation of the findings, and manuscript revision. RG participated in the
interpretation of the findings, and manuscript revision. ADF participated in the
data analysis, interpretation of the findings, and manuscript revision. LS par-
ticipated in the data collection, interpretation of the findings of the study and
manuscript revision. LG participated in the data collection, interpretation of
the findings of the study and manuscript revision. PM participated in the data
analysis, interpretation of the findings of the study and manuscript revision.
MC participated in the data analysis, interpretation of the findings of the study
and manuscript revision. SB participated in the conception and design of the
study, interpretation of the findings of the study, manuscript writing and revi-
sion. All authors have read and approved the final manuscript.
Author details
1 Department of Medical Sciences, University of Turin, Corso Dogliotti 14,
10126 Turin, Italy. 2 Unit of Clinical Nutrition, “Città della Salute e della Scienza”
Hospital of Turin, Turin, Italy. 3 Department of Health Sciences, University
of Milan, Milan, Italy. 4 Diabetic Clinic, Hospital of Asti, Asti, Italy. 5 Immuno-
genetics and Transplant Biology, “Città della Salute e della Scienza” Hospital
of Turin, Turin, Italy.
Acknowledgements
This study was supported by a grant from the Ministry of Education, University
and Research of Italy (ex-60% 2014).
Compliance with ethical guidelines
Competing interests
The authors declare that they have no competing interests.
Received: 11 May 2015 Accepted: 12 June 2015
References
1. Hollman PCH, Katan MB (1999) Dietary flavonoids: intake, health effects
and bioavailability. Food Chem Toxicol 37:937–942
2. Agricultural Research Service. US Department of Agriculture (USDA)
(2014) Database for the flavonoid content of selected foods, Beltsville.
http://www.ars.usda.gov/SP2UserFiles/Place/80400525/Data/Flav/
Flav_R03-1.pdf. Accessed 5 Jun 2015
3. Agricultural Research Service. US Department of Agriculture (USDA)
(2008) Database for the isoflavone content of selected foods, Beltsville.
http://www.ars.usda.gov/SP2UserFiles/Place/80400525/Data/isoflav/
Isoflav_R2.pdf. Accessed 9 Jun 2015
4. Agricultural Research Service. US Department of Agriculture (USDA)
(2004) Database for the proanthocyanidin content of selected foods,
Beltsville. http://www.ars.usda.gov/SP2UserFiles/Place/80400525/Data/
PA/PA.pdf. Accessed 7 Jun 2015
5. Middleton E, Kandaswami C, Theoharides TC (2000) The effects of plant
flavonoids on mammalian cells: implications for inflammation, heart
disease, and cancer. Pharmacol Rev 52:673–751
6. Hertog MGL, Feskens EJM, Hollman PCH, Katan MB, Kromhout D (1993)
Dietary antioxidant flavonoids and risk of coronary heart disease: the
Zutphen Elderly Study. Lancet 342:1007–1011
7. Hertog MG, Kromhout D, Aravanis C, Blackburn H, Buzina R, Fidanza F
et al (1995) Flavonoid intake and long-term risk of coronary heart disease
and cancer in the seven countries study. Arch Intern Med 155:381–386
8. Knekt P, Järvinen R, Reunanen A, Maatela J (1996) Flavonoid intake and
coronary mortality in Finland: a cohort study. Br Med J 312:478–481
9. Hertog MG, Feskens EJM, Kromhout D (1997) Antioxidant flavonols and
coronary heart disease risk. Lancet 349:699
10. Yochum L, Kushi LH, Meyer K, Folsom AR (1999) Dietary flavonoid intake
and risk of cardiovascular disease in postmenopausal women. Am J
Epidemiol 149:943–949
11. Arts IC, Hollman PCH, Feskens EJM, de Mesquita HBB, Kromhout D
(2001) Cathechin intake might explain the inverse relation between tea
consumption and ischemic heart disease: the Zutphen Elderly Study. Am
J Clin Nutr 74:227–232
12. Hirvonen T, Pietinen P, Virtanen M, Ovask ainen ML, Häkkinen S, Albanes
D et al (2001) Intake of flavonols and flavones and risk of coronary heart
disease in male smokers. Epidemiology 12:62–67
13. Geleijnse JM, Launer LJ, van der Kuip DAM, Hofman A, Witteman JCM
(2002) Inverse association of tea and flavonoid intakes with incident
myocardial infarction: the Rotterdam Study. Am J Clin Nutr 75:880–886
14. Knekt P, Kumpulainen J, Järvinen R, Rissanen H, Heliövaara M, Reunanen
A et al (2002) Flavonoid intake and risk of chronic diseases. Am J Clin Nutr
76:560–568
15. Mennen LI, Sapinho D, de Bree A, Arnault N, Bertrais S, Galan P et al (2004)
Consumption of foods rich in flavonoids is related to a decreased cardio-
vascular risk in apparently healthy French women. J Nutr 134:923–926
16. Mink PJ, Scrafford CG, Barraj LM, Harnack L, Hong CP, Nettleton JA et al
(2007) Flavonoid intake and cardiovascular disease mortality: a prospec-
tive study in postmenopausal women. Am J Clin Nutr 85:895–909
17. Kokubo Y, Iso H, Ishihara J, Okada K, Inoue M, Tsugane S, for the JPHC
Study Group (2007) Association of dietary intake of soy, beans, and
isoflavones with risk of cerebral and myocardial infarctions in Japanese
populations. Circulation 116:2553–2562
18. Mursu J, Voutilainen S, Nurmi T, Tuomainen TP, Kurl S, Salonen JT (2008)
Flavonoid intake and the risk of ischaemic stroke and CVD mortality in
middle-aged Finnish men: the Kuopio Ischaemic Heart Disease Risk Fac-
tor Study. Br J Nutr 100:890–895
19. McCullough ML, Peterson JJ, Patel R, Jacques PF, Shah R, Dwyer JT (2012)
Flavonoid intake and cardiovascular disease mortality in a prospective
cohort of US adults. Am J Clin Nutr 95:454–464
20. Cassidy A, Mukamal KJ, Liu L, Franz M, Eliassen AH, Rimm EB (2013) High
anthocyanin intake is associated with a reduced risk in myocardial infarc-
tion in young and middle-aged women. Circulation 127:188–196
21. Rimm EB, Katan MB, Ascherio A, Stampfer MJ, Willett WC (1996) Relation
between intake of flavonoids and risk for coronary heart disease in male
health professionals. Ann Intern Med 125:384–389
22. Hertog MGL, Sweetnam PM, Fehily AM, Elwood PC, Kromhout D (1997)
Antioxidant flavonols and ischemic heart disease in a Welsh population
of men: the Caerphilly Study. Am J Clin Nutr 65:1489–1494
23. Hirvonen T, Virtamo J, Korhonen P, Albanes D, Pietinen P (2000) Intake of
flavonoids, carotenoids, vitamins C and E, and risk of stroke in male smok-
ers. Stroke 31:2301–2306
24. Knekt P, Isotupa S, Rissanen H, Heliövaara M, Järvinen R, Häkkinen S et al
(2000) Quercetin intake and the incidence of cerebrovascular disease. Eur
J Clin Nutr 54:415–417
25. Arts ICW, Jacobs DR, Harnack LJ, Gross M, Folsom AR (2001) Dietary
catechins in relation to coronary heart disease death among postmeno-
pausal women. Epidemiology 12:668–675
26. Sesso HD, Gaziano JM, Liu S, Buring JE (2003) Flavonoid intake and the
risk of cardiovascular disease in women. Am J Clin Nutr 77:1400–1408
27. Lin J, Rexrode KM, Hu F, Albert CM, Chae CU, Rimm EB et al (2007) Dietary
intakes of flavonols and flavones and coronary heart disease in US
women. Am J Epidemiol 165:1305–1313
28. Hooper L, Kroon PA, Rimm EB, Cohn JS, Harvey I, Le Cornu CA et al (2008)
Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis
of randomized controlled trails. Am J Clin Nutr 88:38–50
29. Keli SO, Hertog MGL, Feskens EJM, Kromhout D (1996) Dietary flavonoids,
antioxidant vitamins, and incidence of stroke. The Zutphen study. Arch
Intern Med. 154:637–642
30. Hooper L, Kay C, Abdelhamid A, Kroon PA, Cohn JS, Rimm EB et al (2012)
Effects of chocolate, cocoa, and flavan-3-ols on cardiovascular health: a
systematic review and meta-analysis of randomized trials. Am J Clin Nutr
95:740–751
31. Bo S, Gentile L, Ciccone G, Baldi C, Benini L, Dusio F et al (2005) The meta-
bolic syndrome and high C-reactive protein: prevalence and difference
by sex in a southern-European population-based cohort. Diabetes Metab
Research Rev 21:515–524
32. Taylor HL, Jacobs DR Jr, Schucker B, Knudsen J, Leon AS, Debacker G
(1978) Questionnaire for the assessment of leisure time physical activities.
J Chronic Diseases 31:741–755

Page 13 of 13
Ponzo et al. J Transl Med (2015) 13:218
33. Bo S, Durazzo M, Guidi S, Carello M, Sacerdote C, Silli B et al (2006) Dietary
magnesium and fiber intake, inflammatory and metabolic parameters in
middle-aged subjects from a population-based cohort. Am J Clin Nutr
84:1062–1069
34. Kroke A, Klipstein-Grobusch K, Voss S, Moseneder J, Thielecke F, Noack R
et al (1999) Validation of a self-administered food-frequency question-
naire administered in the European Prospective Investigation into Cancer
and Nutrition (EPIC) study: comparison of energy, protein, and macronu-
trient intakes estimated with the doubly labeled water, urinary nitrogen,
and repeated 24-h dietary recall methods. Am J Clin Nutr 70:439–447
35. Willett W, Stampfer MJ (1986) Total energy intake: implications for epide-
miologic analyses. Am J Epidemiol 124:17–27
36. Schofield WN (1985) Predicting basal metabolic rate, new standards and
review of previous work. Hum Nutr Clin Nutr 39:5–41
37. Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA
et al (1991) Critical evaluation of energy intake data using fundamental
principles of energy physiology: 1. derivation of cut-off limits to identify
under-recording. Eur J Clin Nutr 45:569–581
38. Gry J, Black L, Eriksen FD, Pilegaard K, Plumb J, Rhodes M et al (2007)
EuroFIR-BASIS—a combined composition and biological activity data-
base for bioactive compounds in plant-based foods. Trends Food Sci Tech
18:434–444
39. Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione (INRAN)
(2003) Linee guida per una sana alimentazione italiana. http://www.
piramidealimentare.it/files_allegati/guida.pdf. Accessed 5 Jun 2015 (in
Italian)
40. American Diabetes Association (2014) Diagnosis and classification of
diabetes mellitus. Diabetes Care 37:S81–S90
41. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM
et al (2008) General cardiovascular risk profile for use in primari care: the
Framingham Heart Study. Circulation 117:743–753
42. Grosso G, Mistretta A, Frigiola A, Gruttadauria S, Biondi A, Basile F et al
(2014) Mediterranean diet and cardiovascular risk factors: a systematic
review. Crit Rev Food Sci Nutr 54:593–610
43. Cassidy A, O’Reilly EJ, Kay C, Sampson L, Franz M, Forman JP et al (2011)
Habitual intake of flavonoid subclasses and incident hypertension in
adults. Am J Clin Nutr 93:338–347
44. Sagara M, Kanda T, Jelekera MN, Teramoto T, Armitage L, Birt N et al (2003)
Effects of dietary intake of soy protein and isoflavones on cardiovascular
disease risk factors in high risk, middle-aged men in Scotland. J Am Coll
Nutr 23:85–91
45. Kurowska EM, Spence JD, Jordan J, Wetmore S, Freeman DJ, Pinché LA
et al (2000) HDL-cholesterol-raising effect of orange juice in subjects with
hypercholesterolemia. Am J Clin Nutr 72:1095–1100
46. Qin Y, Xia M, Ma J, Hao JT, Liu J, Mou H et al (2009) Anthocyanin sup-
plementation improves serum LDL- and HDL-cholesterol concentrations
associated with the inhibition of cholesteryl ester transfer protein in
dyslipidemic subjects. Am J Clin Nutr 90:485–492
47. Hooper L, Kroom PA, Rimm EB, Cohn JS, Harvey I, Le Cornu KA et al (2008)
Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis
of randomized controlled trials. Am J Clin Nutr 88:38–50
48. Hooper L, Key C, Abdelhamid A, Kroom PA, Cohn JS, Rimm EB et al (2012)
Effects of chocolate, cocoa and flavan-3-ols on cardiovascular health: a
systematic review and meta-analysis of randomized trials. Am J Clin Nutr
95:740–751
49. Wedick NM, Pan A, Cassidy A, Rimm EB, Sampson L, Rosner B et al (2012)
Dietary flavonoids intakes and risk of type 2 diabetes in US men and
women. Am J Clin Nutr 95:925–933
50. Jennings A, Welch AA, Fairweather-Tait SJ, Kay C, Minihane AM, Chowie-
nczyk P et al (2012) Higher anthocyanin intake is associated with lower
arterial stiffness and central blood pressure in women. Am J Clin Nutr
96:781–788
51. Macready AL, George TW, Chong MF, Alimbertov DS, Jin Y, Vidal A et al
(2014) Flavonoid-rich fruit and vegetables improve microvascular reactiv-
ity and inflammatory status in men at risk of cardiovascular disease-
FLAVURS: a randomized controlled trial. Am J Clin Nutr 99:479–489
52. De Pascual-Teresa S, Moreno DA, García-Viguera C (2010) Flavanols and
anthocyanins in cardiovascular health: a review of current evidence. Int J
Mol Sci 11:1679–1703
53. Middleton E, Kandaswami C, Theoharides TC (2000) The effects of plant
flavonoids on mammalian cells: implications for inflammation, heart
disease, and cancer. Pharmacol Rev 52:673–751
54. Kozkowska A, Szostak-Węgierek D (2014) Flavonoids—food sources and
health benefits. Rocz Państw Zakł Hig 65:79–85
55. Rein D, Paglieroni TG, Wun T, Pearson DA, Schmitz HH, Gosselin R et al
(2000) Cocoa inhibits platelet activation and function. Am J Clin Nutr
72:30–35
56. Vita JA (2005) Polyphenols and cardiovascular disease: effects on
endothelial and platelet function. Am J Clin Nutr 81:292S–297S
57. Landberg R, Sun Q, Rimm EB, Cassidy A, Scalbert A, Mantzoros CS et al
(2011) Selected dietary flavonoids are associated with markers of inflam-
mation and endothelial dysfunction in US women. J Nutr 141:618–625
58. Chun OK, Chung SJ, Claycombe KJ, Song WO (2008) Serum C-reactive
protein concentrations are inversely associated with dietary flavonoid
intake in US adults. J Nutr 138:753–760
59. Dilis V, Trichopoulou A (2010) Antioxidant intakes and food sources in
Greek adults. J Nutr 140:1274–1279
60. Zamora-Ros R, Sacerdote C, Ricceri F, Weiderpass E, Roswall N, Buckland
G et al (2014) Flavonoid and lignan intake in relation to bladder cancer
risk in the European Prospective Investigation into Cancer and Nutrition
(EPIC) study. Br J Cancer 111:1870–1880
61. Vogiatzoglou A, Mulligan AA, Lentjes MA, Luben RN, Spencer JP, Schro-
eter H et al (2015) Flavonoid intake in European adults (18 to 64 years).
PLoS One 10:e0128132
62. Rossi M, Negri E, Lagiou P, Talamini R, Del Maso L, Montella M et al (2008)
Flavonoids and ovarian cancer: a case-control study in Italy. Int J Cancer
123:895–898
63. Società Italiana di Nutrizione Umana (2014) Livelli di assunzione di
riferimento di nutrienti ed energia per la popolazione italiana. SICS Ed,
October 2014, 4th Revision
64. Bosetti C, Rossi M, McLaughlin JK, Negri E, Talamini R, Lagiou P et al
(2007) Flavonoids and the risk of renal cell carcinoma. Cancer Epidemiol
Biomarkers Prev 16:98–101
65. Zamora-Ros R, Agudo A, Luján-Barroso L, Romieu I, Ferrari P, Knaze V et al
(2012) Dietary flavonoid and lignin intake and gastric adenocarcinoma
risk in the European Prospective Investigation into Cancer and Nutrition
(EPIC) study. Am J Clin Nutr 96:1398–1408
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