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ORIGINAL ARTICLE
Food consumption and the actual statistics of cardiovascular
diseases: an epidemiological comparison of 42 European countries
Pavel Grasgruber*, Martin Sebera, Eduard Hrazdira, Sylva Hrebickova and Jan Cacek
Faculty of Sports Studies, Masaryk University, Brno, Czech Republic
Abstract
Background: The aim of this ecological study was to identify the main nutritional factors related to the
prevalence of cardiovascular diseases (CVDs) in Europe, based on a comparison of international statistics.
Design: The mean consumption of 62 food items from the FAOSTAT database (19932008) was compared
with the actual statistics of five CVD indicators in 42 European countries. Several other exogenous factors
(health expenditure, smoking, body mass index) and the historical stability of results were also examined.
Results: We found exceptionally strong relationships between some of the examined factors, the highest being a
correlation between raised cholesterol in men and the combined consumption of animal fat and animal protein
(r0.92, pB0.001). The most significant dietary correlate of low CVD risk was high total fat and animal
protein consumption. Additional statistical analyses further highlighted citrus fruits, high-fat dairy (cheese) and
tree nuts. Among other non-dietary factors, health expenditure showed by far the highest correlation
coefficients. The major correlate of high CVD risk was the proportion of energy from carbohydrates and
alcohol, or from potato and cereal carbohydrates. Similar patterns were observed between food con-
sumption and CVD statistics from the period 19802000, which shows that these relationships are stable over
time. However, wefound striking discrepancies in men’s CVD statistics from 1980 and 1990, which can probably
explain the origin of the ‘saturated fat hypothesis’ that influenced public health policies in the following decades.
Conclusion: Our results do not support the association between CVDs and saturated fat, which is still
contained in official dietary guidelines. Instead, they agree with data accumulated from recent studies that
link CVD risk with the high glycaemic index/load of carbohydrate-based diets. In the absence of any scientific
evidence connecting saturated fat with CVDs, these findings show that current dietary recommendations
regarding CVDs should be seriously reconsidered.
Keywords: prevention;BMI;smoking;food consumption
To access the supplementary material to this article, please see Supplementary files under ‘Article Tools’.
Received: 22 March 2016; Revised: 12 July 2016; Accepted: 9 August 2016; Published: 27 September 2016
The relationship between nutrition and the preva-
lence of diseases is a very controversial and hotly
debated topic, and the research conducted during
the last decades has often produced conflicting results.
For example, recent meta-analyses seriously challenge the
role of saturated fat as the fundamental trigger of car-
diovascular diseases (CVDs) (16), which was a prevailing
hypothesis for several decades. The most complex analysis
of Mente et al. (1) identified ‘Mediterranean’ and ‘high-
quality’ dietary patterns, vegetables, nuts, ‘prudent diets’
(including a lot of vegetables, fruit, legumes, whole grains
and fish), and monounsaturated fatty acids (MUFAs) as the
only dietary components, which are strongly and consis-
tently related to low riskof coronary heart disease (CHD) in
observational or clinical studies. Trans-fatty acids, high
glycaemic index/load, and the ‘Western’diet (including pro-
cessed and red meat, butter, high-fat dairy products,
eggs, and refined cereals) were strongly and consistently
relatedtohighCHDrisk.However,therewasonlyweak
evidence for any connection between CHD and saturated
fat, individual animal products (meat, eggs, milk), and fat as
a whole.
The heterogeneity of results is not very surprising because
long-term observational surveys routinely rely on self-
reported consumption rates of selected food items and may
be distorted by the existence of many hidden confounding
factors. For change, controlled clinical trials are usually
too short. Therefore, the fundamental weak point of all
research
food & nutrition
æ
Food & Nutrition Research 2016. #2016 Pavel Grasgruber et al. This is an Open Accessar ticledistributed under the terms of the Creative Commons Attribution4.0 International License(http://
creativecommons.org/licenses/by/4.0/),allowing third parties to copyand redistribute thematerial in any medium or formatand to remix, transform,and build upon the materialfor any purpose, even
commercially, provided the original work is properly cited and states its license.
1
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694
(page number not for citation purpose)
research related to health and nutrition is the lack of
precise, long-term data on food intake.
One possible way to overcome this problem is an
ecological approach based on the use of official national
statistics of food intake, combined with the statistics of
actual disease prevalence. We successfully used this metho-
dology in our previous work dealing with the determinants
of physical growth, using nutritional statistics from the
FAOSTAT database (7). The results that we obtained
were often impressive and remarkably agreed with the
biological quality of consumed proteins. This positive
experience shows that the data from the FAOSTAT
database are reasonably reliable and could be used in
ecological analyses examining the relationship between
nutrition and human health.
Understandably, similar to other study designs, the
ecological approach has its weaknesses. The most fre-
quently cited objection is that relationships that are valid
at the country level may not be valid at the individual
level. And above all, the results may be distorted by
unknown confounders. However, confounders appear in
virtually all study designs and our experience with eco-
logical analyses shows that their power is deeply under-
estimated. This sceptical view probably stems from the
fact that health ecological studies conducted in the past
routinely used only a few variables tested on small, dis-
parate samples of culturally unrelated countries, which
may be ascribed to the limited number of available
statistics. Such restricted data, taken out of context, can
hardly produce useful results. Indeed, to our knowledge,
ecological studies dealing with CVDs have been limited to
few indicators or few countries, and an extensive compa-
rison of the FAOSTAT statistics with the prevalence of
CVDs has never been carried out.
In contrast, the use of a very large number of meaningful
variables enables a very comprehensive approach to the
problem, and in our own ecological analyses, their list is
virtually exhaustive. At the same time, we routinely
observe that the most persuasive results are almost always
connected with food items with the highest consumption
rates, or with basic nutrients such as protein or fat intake.
This is logical because the health effect of food consump-
tion is usually associated with food ingredients that make
up the largest proportion in the diet. The smaller the
consumption, the higher the likelihood of a spurious
result, and here, the ecological approach reaches its limits.
Therefore, attention should be focused to basic foodstuffs.
Understandably, even strong ecological trends in the
incidence of diseases cannot be regarded as definitive proof
of causal relationships at the individual level. However,
the validity of such observations can be supported by
studies using different methodologies. Furthermore, these
findings could also significantly contribute to the identi-
fication of dietary components, whose true health effects
can subsequently be examined in controlled clinical trials.
Having all possible advantages and limitations in mind,
we decided to examine whether FAOSTAT statistics
could produce some meaningful results in relation to the
prevalence/incidence of the main indicators of cardiovas-
cular health in Europe. The choice of examined countries
was purposely limited to Europe because data from the
developing world could be far less reliable. In accordance
with Bradford Hill criteria (8), we decided to view the
problem in the broadest possible context, examining
even historical changes in the relationship between food
consumption and CVD indicators.
Methods
Sources
The actual statistics of CVD indicators were drawn from
the European Cardiovascular Disease Statistics 2012 by
Nichols et al. (9) (see Supplementary Dataset, Sheet 1),
which were compiled mainly using World Health Orga-
nization databases. The data on the prevalence of raised
blood cholesterol (5 mmol/L), raised fasting blood
glucose (7 mmol/L, or on medication), and raised
blood pressure (systolic ]140 or diastolic ]90 mmHg,
or blood pressure medication use) came from 2008. Mean
body mass index (BMI) values (kg/m
2
) were computed
from the period 19902008 (years 1990, 1995, 2000,
20052008). The mean prevalence of smoking was based
on all available data from the period 19902009 (years
19901994, 19951999, and annual data from 2000
2009). In addition, for another support of the results,
we also included the statistics of mean cholesterol levels,
mean systolic blood pressure, and mean blood glucose
levels from 2008, but they were used only for simple
Pearson linear correlations because their data were largely
duplicate. Understandably, the risk of CVDs also depends
on other factors (genetics, stress, physical activity), but
they were not targeted in the present study because their
role is much more difficult to assess. For example, the
data on physical activity listed by Nichols et al. (9) were
self-reported and available from only 34 countries.
Age-standardised total CVD mortality rates and CHD
mortality rates (for all ages, per 100,000 people) were
obtained from Nichols et al. (10). The data came from the
most recent available year in the period 20042011, but
the overwhelming majority was from 20092011. These
age-standardised statistics were further supplemented by
mortality rates below 65 years.
The information on food consumption was obtained
from the database of FAOSTAT [food balance food
supply (7)]. The FAOSTAT website states that ‘The food
balance sheet shows for each food item i.e. each primary
commodity availability for human consumption which
corresponds to the sources of supply and its utilisation.
The total quantity of foodstuffs produced in a country
added to the total quantity imported and adjusted to
Pavel Grasgruber et al.
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any change in stocks that may have occurred since the
beginning of the reference period gives the supply avail-
able during that period’.
Using this database, we collected daily consumption
rates (grams per capita) of 53 foodstuffs. We included
all main food items and even some subcategories, whose
annual per capita consumption was higher than 5 kg
(13.7 g/day), concentrated sources of fat (olive oil, soy-
bean oil, sunflower oil), and the general indicators of
fat, protein, and energy intake. In addition to that, we
computed nine additional food items (the energy from
carbohydrates and alcohol, potato and cereal carbohy-
drates, and plant food; animal fat and animal protein; total
fat and animal protein; total fat and total protein; pork
and beef fat) in order to reveal more subtle relationships.
The data of daily energy intake from potato and cereal
carbohydrates (PC CARB energy) and carbohydrates
and alcohol (CA energy) were derived from daily protein
and fat intake, assuming 4.1 kcal per gram of protein and
9 kcal per gram of fat. The proportion of energy from
plant food was computed from the total energy intake
from plant products, minus alcoholic beverages. Food
items showing no correlation with CVD indicators (tea,
spices) were excluded. Because the negative influence of
food on human health is a long-term process, an average
for the period 19932008 was calculated for each item (see
Supplementary Dataset, Sheets 2,3). Altogether, 62 food
items were used for the subsequent analysis.
The total sample used in our study consisted of 42
European countries, including Armenia, Azerbaijan, and
Georgia, but excluding Turkey and Montenegro (for which
some statistics were lacking). As a possible confounding
variable, we also included health expenditure (for 2008 and
19952008) (according to the World Bank (11)). Life expec-
tancy at birth (for 2008, according to the World Bank (12))
was included as a supplementary variable as well because
it is very closely associated with cardiovascular health.
Statistical analysis
Using the software SPSS Statistics 24.0, the relationships
between CVD indicators and various independent vari-
ables (food consumption, health expenditure, smoking,
BMI, and raised cholesterol) were first investigated using
simple Pearson linear correlations.
Furthermore, we conducted a factor analysis of all the
examined variables. The factor analysis groups variables
with similar characteristics and because it combines
multiple factors that influence the grouping of variables,
it already solves a lot of problems associated with mul-
ticollinearity, which was the key statistical problem in the
present study. Indeed, it proved to be a very useful tool
because it graphically separated specific food groups ac-
cording to the geographical pattern of their consumption.
Other sophisticated procedures used to analyse a large
number of independent variables are the ridge regression,
LASSO regression, and elastic net regression. In our pre-
sent study, we used these regression analyses in the case of
raised blood pressure and total CVD mortality (in both
sexes). All these three methods are based on the penalisa-
tion (artificial lowering) of beta regression coefficients,
which also helps to solve the problem of multicollinearity
among the examined variables. The changing size of the
penalisation creates different models with different pre-
diction errors, and a model with the lowest prediction error
(ideally using low penalisation) is selected as optimal. The
ridge regression workswith all variables, while the LASSO
regression is more selective and with the increasing pena-
lisation, it shrinks the majority of beta coefficients to zero.
The elastic net regression is basically a combination of these
two methods and overcomes their shortcomings (13). To
improve the quality of the regression models, we used the
bootstrapping method, which works with random combi-
nations of a different number of independent variables for
each penalisation level, creates many additional models, and
then calculates their mean result. Thus, this method takes
into account the variability of results and reduces the impact
of various anomalies. Because the general statistical goal is
to achieve a good model with the lowest possible number of
variables, we did not use the ‘optimal models’ with the
lowest prediction errors, but more selective parsimonious
(‘economical’) models that achieve the best ratio between
the number of variables and the prediction error.
Subsequently, we used an analogy of the fixed effect
models and studied temporal changes of correlations
between CVD indicators and food consumption in single
16 years during the period 19932008. This procedure
helped to identify long-term collinearity among the
key food items, via the comparison of their trend lines.
A standard statistical procedure used for the comparison
of two trend lines is the regression slope test (14).
Aweakness of this method lies in its inability to objectively
compare two trend lines that are markedly non-linear,
which was often the case in this temporal comparison of r
values. To overcome this limitation, we also used the
dependent t-test and calculated a mean difference between
16 pairs of rvalues from the same year. A low standard
deviation of such a mean difference indicates that the
distance between rvalues of two trend lines remained
basically constant in time. In other words, the rvalues of
these two trend lines changed parallel to each other.
Finally, we performed a similar comparison of food
consumption and the available CVD statistics from 1980,
1990, and 2000, again using the data of Nichols et al. (9).
Results
Pearson linear correlations
Detailed results of the Pearson linear correlations are
listed in the Supplementary Dataset, Sheet 5. A simpli-
fied version of these results is presented in Table 1.
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 3
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Tab le 1 . Results of the Pearson linear correlations (selected variables)
Raised blood
cholesterol (%)
Raised blood
cholesterol (%)
Raised blood
pressure (%)
Raised blood
pressure (%)
Raised fasting
blood glucose (%)
Raised fasting
blood glucose (%)
Actual
CHD mortality
(all ages)
Actual
CHD mortality
(all ages)
Actual total
CVD mortality
(all ages)
Actual total
CVD mortality
(all ages)
Men Women Men Women Men Women Men Women Men Women
Fruits total 0.58 0.52 0.68 0.69 0.42 0.64 0.64 0.64 0.77 0.71
Bananas 0.74 0.68 0.42 0.60 0.24 0.58 0.44 0.47 0.68 0.72
Oranges and mandarins 0.66 0.58 0.66 0.75 0.38 0.62 0.55 0.57 0.74 0.76
Alcoholic beverages 0.68 0.65 0.30 0.49 0.44 0.63 0.36 0.37 0.52 0.56
Beer 0.61 0.58 0.19 0.38 0.31 0.50 0.27 0.28 0.41 0.44
Dist. beverages 0.08 0.01 0.40 0.34 0.07 0.18 0.36 0.27 0.40 0.31
Wine 0.42 0.40 0.48 0.52 0.45 0.57 0.45 0.42 0.56 0.54
Coffee 0.70 0.66 0.50 0.67 0.36 0.68 0.49 0.51 0.66 0.69
Ref. sugar and sweeteners 0.67 0.65 0.35 0.56 0.33 0.53 0.04 0.08 0.31 0.44
Refined sugar 0.57 0.54 0.33 0.53 0.39 0.50 0.00 0.06 0.26 0.40
Oil crops total 0.16 0.05 0.50 0.38 0.18 0.27 0.36 0.36 0.37 0.30
Tree nuts 0.27 0.18 0.65 0.58 0.35 0.46 0.50 0.50 0.59 0.54
Plant oils total 0.52 0.45 0.56 0.57 0.48 0.61 0.44 0.46 0.55 0.56
Olive oil 0.12 0.06 0.45 0.32 0.11 0.18 0.28 0.29 0.35 0.31
Sunflower oil 0.35 0.30 0.20 0.29 0.10 0.22 0.20 0.21 0.43 0.46
Cereals total 0.74 0.73 0.42 0.65 0.44 0.70 0.32 0.37 0.54 0.61
Potatoes 0.08 0.12 0.32 0.20 0.15 0.08 0.56 0.45 0.35 0.15
Legumes total 0.03 0.02 0.08 0.05 0.05 0.05 0.29 0.27 0.13 0.02
Vegetables total 0.29 0.35 0.21 0.09 0.09 0.15 0.13 0.11 0.01 0.13
Onions 0.54 0.58 0.20 0.43 0.18 0.47 0.26 0.30 0.42 0.50
Plant protein 0.53 0.54 0.19 0.47 0.32 0.49 0.12 0.14 0.32 0.39
Plant fat 0.58 0.50 0.64 0.65 0.45 0.63 0.50 0.53 0.63 0.63
Meat total 0.83 0.79 0.54 0.73 0.50 0.76 0.43 0.49 0.65 0.73
Meat protein 0.84 0.80 0.57 0.76 0.55 0.79 0.44 0.50 0.66 0.74
Meat fat 0.78 0.81 0.47 0.71 0.49 0.78 0.40 0.46 0.61 0.70
Beef and pork fat 0.70 0.74 0.34 0.59 0.50 0.74 0.32 0.38 0.53 0.62
Dairy total 0.62 0.56 0.42 0.62 0.59 0.75 0.37 0.42 0.58 0.66
Milk 0.19 0.16 0.26 0.22 0.03 0.13 0.19 0.17 0.21 0.19
Cheese 0.71 0.64 0.70 0.79 0.52 0.75 0.51 0.53 0.69 0.72
Dairy protein 0.63 0.57 0.45 0.64 0.51 0.68 0.35 0.40 0.55 0.62
Dairy fat 0.54 0.48 0.50 0.62 0.47 0.63 0.40 0.43 0.54 0.57
Animal protein 0.89 0.83 0.60 0.82 0.53 0.79 0.41 0.48 0.68 0.78
Animal fat 0.89 0.87 0.51 0.76 0.57 0.85 0.40 0.46 0.64 0.73
Animal fat and anim. protein 0.92 0.88 0.57 0.82 0.57 0.85 0.42 0.48 0.68 0.78
Pavel Grasgruber et al.
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(page number not for citation purpose) Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694
Tab le 1 .(Continued )
Raised blood
cholesterol (%)
Raised blood
cholesterol (%)
Raised blood
pressure (%)
Raised blood
pressure (%)
Raised fasting
blood glucose (%)
Raised fasting
blood glucose (%)
Actual
CHD mortality
(all ages)
Actual
CHD mortality
(all ages)
Actual total
CVD mortality
(all ages)
Actual total
CVD mortality
(all ages)
Men Women Men Women Men Women Men Women Men Women
Total protein 0.76 0.70 0.59 0.72 0.45 0.67 0.41 0.47 0.62 0.71
Total fat 0.86 0.81 0.66 0.82 0.59 0.86 0.51 0.56 0.73 0.79
Total fat and anim. protein 0.90 0.84 0.66 0.85 0.59 0.86 0.49 0.55 0.73 0.81
Total fat and total protein 0.88 0.82 0.67 0.83 0.58 0.85 0.51 0.57 0.73 0.81
% CA energy 0.85 0.81 0.62 0.81 0.52 0.84 0.52 0.58 0.72 0.77
% PC carb energy 0.87 0.83 0.58 0.79 0.51 0.83 0.46 0.51 0.68 0.74
% Plant food energy 0.87 0.87 0.39 0.69 0.50 0.79 0.33 0.39 0.57 0.67
Total energy 0.77 0.72 0.56 0.68 0.56 0.74 0.37 0.44 0.59 0.68
Raised blood pressure (men) 0.55 0.37 0.60 0.69
Raised blood pressure (women) 0.70 0.77 0.59 0.80
Smoking (men) 0.62 0.48 0.34 0.53 0.67
Smoking (women) 0.47 0.37 0.56 0.59 0.46
BMI (men) 0.59 0.18 0.09 0.40 0.46
BMI (women) 0.37 0.47 0.68 0.49 0.43
Health expenditure (2008) 0.83 0.75 0.69 0.85 0.50 0.79 0.56 0.58 0.79 0.82
Health expenditure (19952008) 0.83 0.75 0.72 0.87 0.49 0.79 0.57 0.59 0.79 0.82
For more details, see Supplementary Dataset Sheet 5.
Positive relationship Negative relationship
r
]0.70
p
50.001
p
B0.01
p
B0.05
p
B0.05
p
B0.01
p
50.001
r
]0.70
CA energy energy from carbohydrates and alcohol; PC CARB energyenergy from potato and cereal carbohydrates.
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 5
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Our comparison shows that the strength of linear correla-
tions in men’s raised blood pressure, men’s raised blood
glucose, and CHD mortality in both sexes is moderate
at best, but in other cases, it is exceptionally high. A par-
ticularly impressive finding is the relationship between
raised cholesterol and animal fat (r0.89 in men, r0.87
in women; pB0.001). Interestingly, the rvalues further
slightly increase, when animal fat is combined with animal
protein: 0.92 in men (Fig. 1) and 0.88 in women. Evidently,
a food most strongly contributing to these results is meat
(r0.83 in men, r0.79 in women; pB0.001).
Low cholesterol levels correlate most strongly with the
proportion of plant food energy in the diet (r0.87,
pB0.001 in both sexes) and with sources of plant car-
bohydrates represented by items such as % PC CARB
energy (r0.87 in men, r0.83 in women; pB0.001)
(Fig. 2), % CA energy (r0.85 in men, r0.81 in
women; pB0.001), and cereals (r0.74 in men,
r0.73 in women; pB0.001). Smoking correlates quite
strongly with lower cholesterol as well, but in men only
(r0.62, pB0.001). Remarkably, the relationship of
raised cholesterol with CVD risk is always negative,
especially in the case of total CVD mortality (r0.69
in men, r0.71 in women; pB0.001) (Figs. 3 and 4,
Supplementary Figs. 1 and 2).
The indicators of CVD prevalence (raised blood pres-
sure, raised fasting blood glucose) and CVD incidence
(CHD mortality, total mortality) generally have the
highest negative correlation with health expenditure and
with total fat and animal protein (or total fat and total
protein). Total fat and animal protein correlates par-
ticularly strongly with raised blood pressure (r0.85)
(Fig. 5), raised blood glucose (r0.86) (Fig. 6), and
total CVD mortality (r0.81, pB0.001) (Fig. 7) in
women. Health expenditure (19932008) reaches similarly
high correlations with raised blood pressure (r0.87,
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat Lith
Lux
Mac
Malta
Mold
NethNor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= 0.92; p<0.001
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
30 35 40 45 50 55 60 65 70 75
Anim. fat & Anim. protein (g/day per capita, 1993–2008)
Prevalence of raised cholesterol in men (%) (2008)
Fig. 1. Correlation between the mean daily consumption of
animal fat and animal protein and the prevalence of raised
cholesterol levels in men (r0.92; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.87; p<0.001
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
55.0
60.0
30 35 40 45 50 55 60 65 70 75
% PC CARB energy (1993–2008)
Prevalence of raised cholesterol in men (%) (2008)
Fig. 2. Correlation between the mean proportion of energy
from potato and cereal carbohydrates (% PC CARB energy)
and the prevalence of raised cholesterol levels in men
(r0.87; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.70; p<0.001
24
26
28
30
32
34
36
38
40
42
44
46
48
50
30 35 40 45 50 55 60 65 70
Prevalence of raised blood pressure in women (%) (2008)
Prevalence of raised cholesterol in women (%) (2008)
Fig. 3. Correlation between the prevalence of raised blood
pressure and the prevalence of raised cholesterol levels in
women (r0.70; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.71; p<0.001
50
100
150
200
250
300
350
400
450
500
550
600
30 35 40 45 50 55 60 65 70
Actual total CVD mortality in women (per 100,000 people)
Prevalence of raised cholesterol in women (%) (2008)
Fig. 4. Correlation between the actual CVD mortality and
the prevalence of raised cholesterol levels in women
(r0.71; pB0.001).
Pavel Grasgruber et al.
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Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.85; p<0.001
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
26 28 30 32 34 36 38 40 42 44 46 48
Total fat & Animal protein (g/day per capita, 1993–2008)
Prevalence of raised blood pressure in women (%) (2008)
Fig. 5. Correlation between the mean daily consumption of
total fat and animal protein and the prevalence of raised
blood pressure in women (r0.85; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.86; p<0.001
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
3 4 5 6 7 8 9 10 11 12 13
Total fat & Animal protein (g/dayr per capita, 1993–2008)
Prevalence of raised blood
g
lucose in women (%) (2008)
Fig. 6. Correlation between the mean daily consumption of
total fat and animal protein and the prevalence of raised
blood glucose in women (r0.86; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.81; p<0.001
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
50 100 150 200 250 300 350 400 450 500 550 600 650
Total fat & Animal protein (g/day per capita, 1993–2008)
Actual total CVD mortalit
y
in women (per 100,000 people)
Fig. 7. Correlation between the mean daily consumption of
total fat and animal protein and the actual total CVD
mortality in women (r0.81; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= 0.81; p<0.001
40.0
42.0
44.0
46.0
48.0
50.0
52.0
54.0
56.0
58.0
60.0
62.0
64.0
66.0
68.0
70.0
72.0
74.0
76.0
26 28 30 32 34 36 38 40 42 44 46 48
% CA energy (1993–2008)
Prevalence of raised blood pressure in women (%) (2008)
Fig. 8. Correlation between the mean proportion of energy
from carbohydrates and alcohol (% CA energy) and the pre-
valence of raised blood pressure in women (r0.81; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger Gre Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= 0.84; p<0.001
40.0
42.0
44.0
46.0
48.0
50.0
52.0
54.0
56.0
58.0
60.0
62.0
64.0
66.0
68.0
70.0
72.0
74.0
76.0
3 4 5 6 7 8 9 10111213
% CA energy (1993–2008)
Prevalence of raised blood
g
lucose in women (%) (2008)
Fig. 9. Correlation between the mean proportion of energy
from carbohydrates and alcohol (% CA energy) and the pre-
valence of raised blood glucose in women (r0.84; pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= 0.77; p<0.001
40.0
42.0
44.0
46.0
48.0
50.0
52.0
54.0
56.0
58.0
60.0
62.0
64.0
66.0
68.0
70.0
72.0
74.0
76.0
50 100 15 0 200 250 300 350 40 0 450 500 550 600 65 0
% CA energy (1993–2008)
Actual total CVD mortalit
y
in women (per 100,000 people)
Fig. 10. Correlation between the mean proportion of energy
from carbohydrates and alcohol (% CA energy) and the
actual total CVD mortality in women (r0.77; pB0.001).
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 7
(page number not for citation purpose)
pB0.001 in women) and total CVD mortality (r0.82,
pB0.001 in women), but not with raised blood glucose
(r0.79, pB0.001 in women). These variables are also
the most significant correlates of high life expectancy:
total fat and animal/total protein in women (r0.85,
pB0.001) and health expenditure (19932008) in men
(r0.81, pB0.001). The list of individual food items with
the highest negative rvalues includes meat (especially
meat protein), dairy (cheese), and fruits (oranges and
mandarins) (Supplementary Figs. 312). Fruits are the
main negative correlate of CHD mortality.
The most significant positive correlate of CVD risk is
the proportion of CA energy, especially in the case of
raised blood pressure (r0.62 in men, r0.81 in women;
pB0.001) (Fig. 8), raised blood glucose (r0.52 in men,
r0.84 in women; pB0.001) (Fig. 9), and total CVD
mortality (r0.72 in men, r0.77 in women; pB0.001)
(Fig. 10). This factor also strongly correlates with low
life expectancy (r0.76 in men, r0.83 in women;
pB0.001). The highest life expectancy is tied with 4550%
CA energy intake. The proportion of PC CARB energy
and plant food energy has a high positive correlation with
CVD risk as well. The number of individual foodstuffs
positively correlating with CVD risk is remarkably
limited and consists of cereals, potatoes, distilled beverages,
sunflower oil and onions (Supplementary Figs. 1522).
The most consistent relationship with CVD risk can be
found in cereals, especially with raised blood glucose
(r0.44, p0.004 in men; r0.70, pB0.001 in women)
and with total CVD mortality (r0.54 in men, r0.61
in women, pB0.001). On the other hand, sunflower
oil correlates only with total CVD mortality and potatoes
come to the foreground as a predictor of CHD mortality.
A paradoxical relationship with CVD risk was found
in smoking and BMI. Although smoking correlates posi-
tively with CVD risk in men (r0.67, pB0.001 with
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl Irl
Ita
Lat Lith
Lux
Mac
Malta
Mold
Neth Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.46; p=0.002
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
850
900
950
1000
23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0
Actual total CVD mortality in men (per 100,000 people)
Mean BMI in men, k
g
/m2 (1990–2008)
Fig. 13. Correlation between the mean BMI and the actual
CVD mortality in men (r0.46; p0.002).
Alb Arm
Aut Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r=0.67; p<0.001
0
5
10
15
20
25
30
35
40
45
50
55
60
65
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 9501000
Mean prevalence of smoking in men (%) (1990–2008)
Actual total CVD mortalit
y
in men
(p
er 100,000
p
eo
p
le
)
Fig. 11. Correlation between the mean prevalence of smok-
ing and the actual total CVD mortality in men (r0.67;
pB0.001).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r= –0.46; p=0.002
0
5
10
15
20
25
30
35
40
45
50
55
60
65
50 100 150 200 250 300 350 400 450 500 550 600 650
Mean prevalence of smoking in women (%) (1990–2008)
Actual total CVD mortalit
y
in women (per 100,000 people)
Fig. 12. Correlation between the mean prevalence of smoking
and the actual total CVD mortality in women (r0.46;
p0.002).
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
NethNor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
r=0.43; p=0.004
50
100
150
200
250
300
350
400
450
500
550
600
650
23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0
Actual total CVD mortality in women (per 100,000 people)
Mean BMI in women, k
g
/m
2
(1990–2008)
Fig. 14. Correlation between the mean BMI and the actual
CVD mortality in women (r0.43; p0.004).
Pavel Grasgruber et al.
8
(page number not for citation purpose) Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694
total CVD mortality), it correlates negatively in women
(Figs. 11 and 12). In contrast, BMI correlates positively
with CVD risk in women and has the opposite relation-
ship in men (Figs. 13 and 14).
Factor analysis
A factor analysis of the examined variables is displayed
in Figs. 1518. The first two factors explain 36.1 and
8.2% variability, respectively (44.3% of total variability).
The first factor separates variables that have the highest
negative correlation with CVD risk (fat and protein con-
sumption, health expenditure, animal products, alcoholic
beverages, coffee, fruits) from those that are most closely
associated with CVD risk (% CA energy, % PC CARB
energy). This radical division corresponds with the dra-
matic differences between the living style and diet of
Western and Northern Europe on the one hand (espe-
cially in the Netherlands, Luxembourg, and Iceland), and
Eastern and Southeastern Europe on the other hand
(Moldova, Armenia, Azerbaijan). Milk is the only animal
ALCOH. BEV. TOTAL
Beer
Distilled beverages
Wine
Cocoa
Coffe e
Refined sugar
REF. SUGAR &
SWEETENERS TOTAL
PLANT PROT EIN
TOTAL
PROTEIN
TOTAL FAT
TOTAL FAT &
TOTAL PROTEIN
TOTAL FAT&
ANIM. PROTEIN
CA ENERGY
% CA ENERGY
% PC CARB ENERGY
TOTAL
ENERGY
% PLANT FOOD ENERGY
Raised chol. -W
Raised blood pressure - M
Raised blood pressure - W
Raised blood glucose - M
Raised blood glucose - W
CHD mortality - M
CHD mortality - W
TOTAL CVD MORTALIT Y - M
TOTAL CVD MORTALITY - W
Health exp. ( 2008)
Smoking - M
Smoking - W
BMI - M
BMI - W
Beef
Pork
Poultry
MEAT TOTAL
Meat protein
Meat fat
ANIMAL FAT
Beef & Pork fat
DAIRY
TOTAL Milk
Cheese
Dairy prot.
Dairy fat
Bu tter & Gh ee
Edible o ffals
Fish & Seafood
Eg gs to tal
Lard
Honey
ANIMAL PROTEIN
CEREALS TOTAL
Maize
Rye
Wheat
Potatoes
Oilcrop s
Treenuts
Pla nt oils total
Olive oil
Soyb. oil
Sunflower oil
PLANT FAT
LEGUMES TO TAL
VEGETABL ES TOTAL
Onions
Tomatoe s
FRUITS TOTAL
Apples
Bananas
Grapes
Oranges & mandarins
Men
Women
–1.00
–0.90
–0.80
–0.70
–0.60
–0.50
–0.40
–0.30
–0.20
–0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
–1.00–0.90–0.80–0.70–0.60–0.50–0.40–0.30–0.20–0.10
0.000.100.200.300.400.500.600.700.800.901.00
Factor 2 (8.2%)
Factor 1 (36.1%)
Unsorted food items CVD indicators Other factors
Animal products and ho ney Cereals and potatoes Plant fat and oily seeds
Ve
g
etables and le
g
umes Fruits Life expectancy
Fig. 15. A plot of two principal components (Factor 1 and Factor 2) explaining 44.3% variability in the correlation between
food consumption, BMI, smoking, health expenditure, and CVD indicators. For better clarity, some less important or too
repetitive variables (fish and seafood fat, and potato and cereal energy) were omitted. CA energyenergy from carbohydrates
and alcohol (kcal), PC CARB energyenergy from potato and cereal carbohydrates (kcal), BMIbody mass index,
CVDcardiovascular disease, CHD coronary heart disease, Health exp. (2008)health expenditure per capita for 2008,
Raised chol.raised cholesterol, M men, Wwomen.
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 9
(page number not for citation purpose)
product that deviates from this trend because in recent
decades, it has become the main source of animal pro-
teins in less developed countries, and on its own, it makes
up only 8.7% of total fat and total protein intake.
The second factor separates the diets typical for the
Mediterranean (mainly in Greece, Italy, and Albania)
from the diets of Eastern Europe (Belarus, Ukraine,
Russia). It is probably even more revealing because it
highlights variables in the northwestern section of the
plot that are in the strongest opposition against CVDs:
tree nuts, fruits total, plant fat, plant oils, wine, oranges
and mandarins, cheese, and dairy fat. This food composi-
tion also correlates with longevity and largely accords
with the ‘Mediterranean’ dietary style (15). However,
nowadays it can find close parallels even in many
Western European countries, particularly in France and
Luxembourg (Fig. 16).
The statistical power of Factor 3 is weak (4.6% varia-
tion), but it highlights the polarity between two other types
of diet. Dairy products, coffee, fruits (bananas, oranges
and mandarins), soybean oil, and fish and seafood are
consumed in the wealthy countries of Northern and
Northwestern Europe with high health expenses (Norway,
Luxembourg, Switzerland, the Netherlands), whereas
cereals, vegetables, potatoes, onions, distilled beverages,
and sunflower oil are generally typical for the eastern
half of Europe (Belarus, Ukraine, Armenia, Moldova)
(Fig. 18). In this division, longevity and low CVD mor-
tality are more tightly associated with health expenditure.
Nevertheless, some of the variables that are highlighted
by Factor 2 (fruits total, oranges and mandarins, cheese,
dairy fat) appear in the strongest opposition against CVD
risk again, which means that in a three-dimensional
model, they would be the most distant from the indicators
of CVDs.
The position of the potential CVD risk factors remains
basically stable in both factor analyses, but onions and
sunflower oil are notable exceptions. The consumption of
onions is high in Mediterranean countries with low CVD
mortality (Spain, Greece, Portugal) (Factor 2), but it is
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat Lith
Lux Mac
Malta
Mold
Neth Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
–
0.13
–
0.12
–
0.11
–
0.10
–
0.09
–
0.08
–
0.07
–
0.06
–
0.05
–
0.04
–
0.03
–
0.02
–
0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
–0.20–
0.15
–
0.10
–
0.050.000.050.100.150.20
Factor 2 (r-values)
Factor 1 (r-values)
Fig. 16. A summary correlation of Factor 1 and Factor 2 with the data of food consumption, BMI, smoking, health expenditure
and CVD indicators in individual 42 European countries.
Pavel Grasgruber et al.
10
(page number not for citation purpose) Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694
also high in Southeastern Europe (Romania, Armenia,
Macedonia etc.) (Factor 3), where we find high CVD
mortality. Similarly, the consumption of sunflower oil
is high in Spain and France, but even in Bulgaria,
Macedonia, and Romania.
Regression analyses
Regression analyses were performed in raised blood
pressure and total CVD mortality, in both sexes. The
results of the best parsimonious models computed via the
bootstrapping method are displayed in Supplementary
Tables 3a, 4a, 5a, and 6a. Among top 10 variables with
the highest beta coefficients in the ridge regression, health
expenditure (both for 2008 and 19952008), cheese, and
oranges and mandarins are selected three times, followed
by tree nuts, which are selected twice. The LASSO regres-
sion highlighted only a few variables with non-zero beta
coefficients. However, oranges and mandarins appeared
three times in the best models. Total fat and animal
protein and health expenditure (both for 2008 and 1995
2008) emerged in two models. In the elastic net regression,
fruits, oranges and mandarins, and total fat appeared
ALCOH. BEV. TOTAL
Beer
Distilled beverages
Wine
Cocoa
Coffe e
Refined sugar
REF. SUGAR &
SWEETENERS TOTAL
PLANT PROTEIN
TOTAL
PROTEIN
TOTAL FAT
TOTAL FAT &
ANIMAL PROT EIN
TOTAL FAT&TOTAL PR OTEIN
CA ENERGY
% CA ENERGY
% PC CARB ENERGY
TOTAL
ENERGY
% PLANT FOOD ENERGY
Raised chol. - W
Raised blood pressure - M
Raised blood pressure - W
Raised blood glucose - M
Raised blood glucose - W
CHD mortality - M
CHD mortality - W
TOTAL CVD MORTALITY - M
TOTAL CVD MORTALITY - W
Health exp. ( 2008)
Smoking - M
Smoking - W
BMI - M
BMI - W
MEAT TOTAL
Beef
Pork
Poultry
Meat protein
Meat fat
Beef &
Pork fat
DAIRY
TOTAL
Milk
Cheese
Dairy protein
Dairy fat
Butter & Ghee
Edible o ffals
Fish & Seafood
Eggs total
Lard
Honey
ANIMAL PROT.
ANIMAL FAT
CEREALS TOTAL
Maize
Rye
Wheat
Potatoes
Oilcrops
Treenuts
Plant oils total
Olive oil
Soybean oil
Sunflower o il
PLANT FAT
LEGUMES TOTAL
VEGETABL ES TOTAL
Onions
Tomatoe s
FRUITS TOTAL
Apples
Bananas Grapes
Oranges & mandarins
Men
Women
–0.6
–0.5
–0.4
–0.3
–0.2
–0.1
–2E-15
0.1
0.2
0.3
0.4
0.5
0.6
–1.00–0.90–0.80–0.70–0.60–0.50–0.40–0.30–0.20–0.10
0.000.100.200.300.400.500.600.700.800.901.00
Factor 3 (4.6%)
Factor 1 (36.1%)
Unsorted food items CV D indicators Other factors
Animal products and honey Cereals and potatoes Plant fat and oily seeds
Ve
g
etables and le
g
umes Fruits Life ex
p
ectanc
y
Fig. 17. A plot of two principal components (Factor 1 and Factor 3) explaining 40.7% variability in the correlation between
food consumption, BMI, smoking, health expenditure and CVD indicators. For better clarity, some less important or too
repetitive variables (fish and seafood fat, and potato and cereal energy) were omitted. CA energyenergy from carbohydrates
and alcohol (kcal), PC CARB energyenergy from potato and cereal carbohydrates (kcal), BMIbody mass index,
CVDcardiovascular diseases, CHD coronary heart disease, Health exp. (2008)health expenditure per capita for 2008,
Raised chol.raised cholesterol, M men, Wwomen.
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 11
(page number not for citation purpose)
in all four models. Cheese, total fat and animal protein,
and health expenditure (both for 2008 and 19952008)
were identified three times. Total fat and total protein,
tree nuts, total protein, % CA energy, and % PC CARB
energy were identified twice. Therefore, these regression
models produced results that are quite similar to the
factor analysis. Health expenditure and oranges and
mandarins were consistently highlighted by all three
regression methods. Cheese, tree nuts, and the intake
of fat and protein were selected by two regression
methods.
The model of the elastic net regression computed
without bootstrapping, based on the lowest prediction
error (Supplementary Tables 3b, 4b, 5b and 6b), always
included more variables and was less helpful, especially
in the case of total CVD mortality in women, where
53 variables (albeit often with low beta coefficients) were
selected. The only items that appeared in all four models
included % CA energy, health expenditure, total fat and
total protein, and total fat. Cheese, tree nuts, oranges and
mandarins, and % PC CARB energy were selected three
times.
Temporal changes of correlation coefficients
Considering that many examined variables are charac-
terised by a strong degree of multicollinearity, it would
be important to examine their temporal correlation with
CVD indicators. This approach would be particularly
important with regard to the possible influence of the
major confounder health expenditure. At the same
time, it is possible that some of these temporal trends
could reflect a long-term effect of a particular variable on
CVD health (Figs. 1922 and Supplementary Figs. 2326).
Using the dependent t-test and comparing standard
deviations of the mean differences in rvalues (Supplemen-
tary Tables 710), we can clearly see that out of all main
negative correlates, health expenditure is most consistently
related to total fat and animal protein, cheese, and fruits.
It is also partly associated with bananas (in the case of total
CVD mortality) and tree nuts (in the case of raised blood
pressure). The linear slopes of these trends are mostly
similar as well. Because bananas play a much weaker role
among the correlates of raised blood pressure, they seem to
be the most likely spurious correlate in the analysis. In
contrast, tree nuts and cheese have a stronger correlation
with CVD prevalence (raised blood pressure) than with
CVD mortality (Supplementary Figs. 13 and 14), which
suggests a relationship that is independent of healthcare.
The same applies for total fat and animal protein and other
indicators of fat and protein intake, but in women only.
Cheese shows some temporal relationship with tree nuts in
Alb
Arm
Aut
Azerb
Belar
Belg
Bos
Bul
Cro
Cyprus
Cze
Den
Est
Fin
Fra
Geo
Ger
Gre
Hun
Isl
Irl
Ita
Lat
Lith
Lux
Mac
Malta
Mold
Neth
Nor
Pol
Port
Rom
Rus
Serb
Svk
Slo
Spain
Swe
Switz
Ukr
UK
–0.25
–0.20
–0.15
–0.10
–0.05
0.00
0.05
0.10
0.15
0.20
0.25 –0.20–0.15–0.10–0.050.000.050.100.150.20
Factor 3 (r-values)
Factor 1 (r-values)
Fig. 18. A summary correlation of Factor 1 and Factor 3 with the data of food consumption, BMI, smoking, health
expenditure, and CVD indicators in individual 42 European countries.
Pavel Grasgruber et al.
12
(page number not for citation purpose) Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694
–0.9
–0.85
–0.8
–0.75
–0.7
–0.65
–0.6
–0.55
–0.5
–0.45
–0.4
–0.35
–0.3
–0.25
19931994 19951996 19971998 19992000 20012002 20032004 20052006 20072008
Women's raised blood pressure (r-values)
Annual value
Health expenditure Total fat & Animal protein
Fruits Oranges & Mandarins
Wine Coffee
Treenuts Cheese
Fig. 21. Temporal changes in the correlation among
8 negative correlates of raised blood pressure (women).
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
19931994 199519961997 199819992000 200120022003 200420052006 20072008
Women's raised blood pressure (r-values)
Annual value
Cereals Sunflower oil
Distilled beverages Onions
Potatoes % CA energy
Fig. 22. Temporal changes in the correlation among
6 positive correlates of raised blood pressure (women).
–0.86
–0.84
–0.82
–0.8
–0.78
–0.76
–0.74
–0.72
–0.7
–0.68
–0.66
–0.64
–0.62
–0.6
–0.58
–0.56
–0.54
–0.52
–0.5
–0.48
–0.46
–0.44
–0.42
–0.4
Actual women's total CVD mortality (r-values)
Health expenditure Total fat & Animal protein
Fruits Oranges & Mandarins
Wine Coffee
Bananas Cheese
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Fig. 19. Temporal changes in the relationship among
8 negative correlates of total CVD mortality (women).
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
Actual women's total CVD mortality (r-values)
Cereals Sunflower oil
Distilled beverages Onions
Potatoes % CA energy
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Fig. 20. Temporal changes in the relationship among
6 positive correlates of total CVD mortality (women).
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 13
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the comparison of raised blood pressure, but not with
oranges and mandarins.
The relationship of health expenditure to other nega-
tively correlated variables is less likely. Among them, we
can clearly separate the highly independent trend line of
wine. The trends in oranges and mandarins, and coffee
tend to go in the opposite direction. The annual consump-
tion of coffee has been significantly associated with fruits
and oranges and mandarins in all 16 years, and especially
with oranges and mandarins (p50.001). This can again
indicate a spurious relationship to CVD indicators.
The plot of the positive correlates separates % CA
energy and cereals from sunflower oil, onions and pota-
toes. The rvalues of % CA energy decreased throughout
the 90s due to the unstable position of Georgia (compare
Fig. 10 and Supplementary Figs. 17 and 21), but they
otherwise follow those of cereals, which is understandable
because cereals are the main source of carbohydrates in
the diet. The rvalues of % CA energy and cereals generally
rise with increasing time. This picture would accord with
the chronic nature of CVDs, where the consequences of
food intake manifest after several decades.
The trends of sunflower oil, onions, and potatoes are
completely different and appear to be cumulative. They
are also mutually similar, particularly in the plot of total
CVD mortality. Despite that, sunflower oil intake sig-
nificantly correlates only with onions in 13 out of
16 years (pB0.05). It does not correlate with potatoes in
a single year. Potatoes were associated with onions only
weakly in 2 years (2004 and 2008). This suggests that the
origin of this similarity is only spurious. Alternatively,
all of these three variables are associated with some
unknown confounder. Indeed, they cluster in the factor
analysis (Fig. 17), together with distilled beverages, and
are negative dietary correlates of some food items with
potentially preventive effects such as fruits and dairy.
The fact that sunflower oil and onions significantly
correlate with mortality, but much less with raised blood
pressure, also indicates that they may be casually asso-
ciated with some factors related to mortality. Distilled
beverages are significantly positively associated only with
potatoes (in 12 years) and the trend line of rvalues is
markedly U-shaped. In contrast with sunflower oil and
onions, the correlation of distilled alcohol and potatoes
with raised blood pressure and total CVD mortality is
not very different.
Historical stability
The last tool that we used for the assessment of the
validity of our results was a historical comparison of CVD
statistics (mean blood pressure, CHD mortality, stroke
mortality, and total CHD and stroke mortality) from
1980, 1990, and 2000, with the mean food consumption in
the preceding 16 years. (For example, CVD statistics from
1980 were compared with the mean food consumption
from the period 19651980) (see Supplementary Dataset,
Sheet 4). A limiting factor of this analysis was a smaller
number of useable countries. The data of mean blood
pressure were available for all 42 countries, but the less
complete statistics of CHD and stroke mortality, and food
consumption reduced this number to 16 for 1980, 21 for
1990, and 24 for 2000 (Tables 2 and 3).
We found that stroke mortality showed a stable his-
torical relationship with many food items in both sexes,
and these relationships are in accord with the analysis of
the present-day statistics. The common denominators of
high stroke mortality were cereals, sunflower oil, distilled
beverages, and CA energy. The common predictors of low
stroke mortality in all the examined years were bananas,
oranges and mandarins, coffee, dairy, animal protein, and
total fat and animal protein. Stroke mortality correla-
ted highly negatively with life expectancy and GDP per
capita.
The trends in mean blood pressure and CHD mortality
were different. Although the results in men from 2000,
and in women from 1990 and 2000, were basically very
similar such as in stroke mortality, the rvalues were
mostly much lower. In other years we observed either
very few significant correlations or very eccentric results,
which went in the opposite direction than in stroke mor-
tality and in the contemporary statistics. More concretely,
men’s mean blood pressure and CHD mortality from
1980 correlated positively with animal fat and animal
protein, and showed no negative relationship with life
expectancy. In addition, lower blood pressure in men was
most strongly predicted by cereals, which again con-
tradicts the contemporary statistics, in which cereals
correlate positively with raised blood pressure. These
tendencies largely persisted even in 1990. At the same
time, the trends in women’s mean blood pressure from
1980 were completely opposite than in men, and in the
case of women’s CHD mortality, only one significant
rvalue (in wine) could be found. No variable correlated
with CHD mortality consistently in both sexes, but
plant fat did so five times, and wine and oranges and
mandarins four times.
Apparently, the men‘s statistics from 1980 and 1990 pose
a fundamental problem of the historical analysis. The key
problem is why the rcoefficients were different from those
of women and why they began to reverse in the following
years. The statistics of total CHD and stroke mortality,
which should better represent total CVD mortality,
remained roughly in between. In 1980, they still corre-
lated weakly positively with animal fat in men (r0.43,
p0.10), but in 2000, the trend reversed in both sexes
and resembled the results of the actual statistics. In both
sexes, total CHD and stroke mortality always correlated
negatively with oranges and mandarins.
Pavel Grasgruber et al.
14
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Discussion
Raised cholesterol correlates negatively with CVD risk
The results of our study show that animal fat (and
especially its combination with animal protein) is a very
strong predictor of raised cholesterol levels. This is in
accordance with the meta-analyses of clinical trials,
which show that saturated animal fat is the major trigger
of raised cholesterol (6, 16). Interestingly, the relationship
between raised cholesterol and CVD indicators in the
present study is always negative. As shown in Figs. 3 and
4 and Supplementary Figs. 1 and 2, this finding is visually
less persuasive in the case of CVD mortality, where
factors such as the quality of healthcare come to the
foreground, but it is quite unambiguous in the case of
women’s raised blood pressure.
The negative relationship between raised cholesterol
and CVD may seem counterintuitive, but it is not at
variance with the available evidence. The largest of the
recent worldwide meta-analyses dealing with cholesterol
and CVD risk (17) observed a positive relationship
between raised cholesterol and CVD mortality at younger
ages, but this association gradually started to reverse in
seniors, where the number of deaths is the highest. In fact,
the relationship between raised cholesterol and stroke
mortality in seniors was slightly negative. Both this study
and other studies dealing with blood profiles of patients
hospitalised with CVD events (1822) demonstrate
that low HDL (high-density lipoprotein associated) cho-
lesterol (around 1.0 mmol/L), or high total cholesterol:
HDL-cholesterol ratio are the best indicators of CVD
risk. Total cholesterol is usually normal or slightly
elevated (4.55.5 mmol/L), and hence it cannot serve as
a predictor of CVD events. Some other authors also point
to high plasma triglycerides (which correlate with low
HDL-cholesterol levels) (23), or to the ratio between
Tab le 2 . Historical comparison: correlations between food consumption (a mean of 16 preceding years) and men’s statistics of cardiovascular
diseases between 1980 and 2000
Mean blood pressure CHD mortality Stroke mortality
CHD and stroke
mortality total
1980 1990 2000 1980 1990 2000 1980 1990 2000 1980 1990 2000
Countries (
n
) 162124162124162124162124
Fruits total 0.20 0.26 0.43 0.40 0.36 0.57 0.06 0.32 0.51 0.39 0.40 0.57
Bananas 0.62 0.05 0.09 0.02 0.31 0.52 0.63 0.70 0.69 0.26 0.52 0.62
Oranges and mandarins 0.03 0.49 0.51 0.41 0.49 0.67 0.61 0.70 0.67 0.62 0.65 0.70
Distilled beverages 0.04 0.47 0.52 0.03 0.46 0.50 0.74 0.69 0.55 0.26 0.62 0.55
Wine 0.10 0.03 0.15 0.70 0.55 0.33 0.12 0.04 0.06 0.60 0.38 0.23
Coffee 0.33 0.05 0.08 0.06 0.06 0.34 0.55 0.46 0.50 0.17 0.24 0.42
Cereals total 0.59 0.03 0.02 0.13 0.13 0.39 0.68 0.64 0.60 0.15 0.36 0.50
Sunflower oil 0.39 0.07 0.03 0.27 0.08 0.40 0.62 0.74 0.70 0.00 0.25 0.55
Potatoes 0.26 0.21 0.30 0.00 0.19 0.13 0.24 0.21 0.08 0.10 0.05 0.05
Vegetables total 0.56 0.41 0.40 0.69 0.29 0.02 0.12 0.19 0.22 0.59 0.13 0.09
Onions 0.50 0.22 0.24 0.50 0.01 0.35 0.20 0.29 0.46 0.38 0.11 0.41
Plant fat 0.39 0.50 0.35 0.60 0.61 0.42 0.19 0.27 0.31 0.63 0.55 0.39
Dairy total 0.52 0.04 0.01 0.44 0.18 0.18 0.67 0.48 0.46 0.14 0.07 0.31
Meat total 0.49 0.23 0.04 0.34 0.16 0.12 0.20 0.03 0.25 0.23 0.10 0.19
Animal protein 0.55 0.07 0.19 0.36 0.04 0.35 0.66 0.49 0.58 0.08 0.18 0.47
Animal fat 0.57 0.45 0.19 0.63 0.44 0.06 0.38 0.22 0.36 0.43 0.23 0.19
Anim. fat and anim. protein 0.59 0.26 0.05 0.56 0.30 0.18 0.51 0.34 0.48 0.31 0.07 0.32
Total protein 0.15 0.20 0.30 0.31 0.08 0.23 0.36 0.18 0.38 0.14 0.02 0.30
Total fat 0.47 0.13 0.09 0.37 0.05 0.34 0.69 0.43 0.52 0.07 0.15 0.44
Total fat and anim. protein 0.54 0.05 0.13 0.39 0.05 0.37 0.73 0.48 0.58 0.08 0.17 0.48
Total fat and total protein 0.43 0.03 0.16 0.41 0.06 0.34 0.68 0.39 0.53 0.11 0.12 0.44
CA energy 0.35 0.14 0.24 0.10 0.26 0.44 0.70 0.69 0.47 0.19 0.48 0.47
% CA energy 0.44 0.01 0.19 0.29 0.05 0.45 0.78 0.60 0.63 0.04 0.29 0.55
Stroke mortality 0.24 0.44 0.26 0.01 0.49 0.81 0.38 0.77 0.93
Mean blood pressure 0.32 0.64 0.41 0.24 0.44 0.26 0.20 0.65 0.36
Life expectancy (men) 0.16 0.53 0.37 0.01 0.57 0.82 0.72 0.86 0.93 0.28 0.77 0.91
GDP per capita by PPP 0.08 0.08 0.29 0.58 0.74 0.80 0.52 0.70
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 15
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triglycerides and HDL-cholesterol (24) as another useful
risk indicators.
In this context it is important to note that saturated fat
is not only the key trigger of high total cholesterol, but
even high HDL-cholesterol and LDL (low-density lipo-
protein associated)-cholesterol (16). Saturated fat also
decreases triglyceride levels, but the total cholesterol:
HDL-cholesterol ratio remains stable. The main sources
of saturated fatty acids are red meat and milk products
(whole fat milk, cheese, butter) (see Supplementary
Table 1). Therefore, in Europe, where the consumption
of animal products is the highest in the world, we can
assume a strong connection between total cholesterol and
HDL-cholesterol. Understandably, this relationship may
not be so strong outside Europe and it may also vary
depending on the individual diet. This could explain
regional and individual differences in the relationship
between total cholesterol and CVD risk.
Although the concurrent increase of LDL-cholesterol
levels is often taken out of context and used as an
argument against the intake of saturated fats in dietary
recommendations (25), saturated fat is primarily tied to
the less dense, large LDL particles (26), whereas car-
diovascular risk is connected with the denser, small
LDL particles (27), which accompany carbohydrate-based
diets. There is also no evidence that the reduction of
saturated fat intake (on its own) would decrease CVD
risk (28). On the other hand, it is true, that so far, there is
Tab le 3 . Historical comparison: correlations between food consumption (a mean of 16 preceding years) and women’s statistics of cardiovascular
diseases between 1980 and 2000
Mean blood pressure CHD mortality Stroke mortality
CHD and stroke
mortality total
1980 1990 2000 1980 1990 2000 1980 1990 2000 1980 1990 2000
Countries (
n
) 162124162124162124162124
Fruits total 0.00 0.27 0.46 0.36 0.45 0.64 0.16 0.44 0.64 0.34 0.50 0.66
Bananas 0.50 0.38 0.25 0.11 0.35 0.51 0.70 0.76 0.70 0.45 0.63 0.63
Oranges and mandarins 0.35 0.59 0.57 0.48 0.59 0.70 0.67 0.75 0.72 0.70 0.76 0.74
Distilled beverages 0.48 0.51 0.49 0.05 0.38 0.36 0.73 0.62 0.45 0.42 0.56 0.42
Wine 0.20 0.08 0.26 0.56 0.50 0.36 0.02 0.06 0.20 0.41 0.32 0.28
Coffee 0.44 0.27 0.40 0.31 0.25 0.38 0.58 0.55 0.51 0.53 0.45 0.46
Cereals total 0.71 0.41 0.47 0.15 0.21 0.45 0.69 0.70 0.65 0.47 0.51 0.57
Sunflower oil 0.33 0.27 0.26 0.03 0.01 0.42 0.65 0.71 0.62 0.32 0.40 0.54
Potatoes 0.02 0.04 0.16 0.13 0.01 0.05 0.17 0.30 0.13 0.18 0.17 0.09
Vegetables total 0.25 0.03 0.03 0.42 0.22 0.06 0.04 0.20 0.12 0.28 0.01 0.03
Onions 0.19 0.00 0.17 0.36 0.04 0.31 0.25 0.28 0.42 0.12 0.18 0.38
Plant fat 0.06 0.32 0.30 0.44 0.56 0.53 0.24 0.32 0.46 0.44 0.50 0.51
Dairy total 0.41 0.37 0.32 0.04 0.03 0.31 0.56 0.54 0.43 0.27 0.32 0.38
Meat total 0.34 0.19 0.41 0.12 0.13 0.29 0.23 0.16 0.40 0.04 0.02 0.36
Animal protein 0.50 0.59 0.67 0.09 0.04 0.46 0.59 0.51 0.60 0.38 0.31 0.55
Animal fat 0.41 0.08 0.30 0.30 0.28 0.20 0.33 0.32 0.41 0.04 0.02 0.32
Anim. fat and anim. protein 0.47 0.30 0.48 0.16 0.16 0.32 0.45 0.42 0.52 0.12 0.14 0.44
Total protein 0.12 0.54 0.56 0.02 0.05 0.33 0.27 0.17 0.39 0.16 0.07 0.37
Total fat 0.62 0.32 0.47 0.05 0.10 0.54 0.66 0.58 0.67 0.32 0.38 0.63
Total fat and anim. protein 0.61 0.46 0.57 0.01 0.08 0.55 0.68 0.58 0.69 0.37 0.37 0.64
Total fat and total protein 0.53 0.43 0.54 0.03 0.05 0.53 0.63 0.49 0.65 0.31 0.31 0.61
CA energy 0.73 0.46 0.47 0.19 0.33 0.39 0.69 0.72 0.46 0.50 0.59 0.44
% CA energy 0.73 0.47 0.61 0.08 0.17 0.60 0.75 0.70 0.74 0.46 0.49 0.70
Stroke mortality 0.40 0.53 0.55 0.28 0.57 0.87 0.73 0.88 0.97
Mean blood pressure 0.04 0.42 0.55 0.40 0.53 0.55 0.19 0.54 0.57
Life expectancy (women) 0.48 0.64 0.60 0.44 0.63 0.85 0.81 0.89 0.92 0.74 0.86 0.92
GDP per capita by PPP 0.47 0.55 0.38 0.64 0.80 0.82 0.67 0.76
p50.001
p
B0.01
p
B0.05
p
B0.05
p
B0.01
p
50.001
Some missing data of CVD statistics were supplemented by values from the previous or following year. The data of GDP per capita (by purchasing
power parity) and life expectancy were taken from the World Bank (www.worldbank.org/). The value of GDP for Hungary (for 1990) was taken from
the year 1991.
Pavel Grasgruber et al.
16
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no clear evidence that saturated fat would be beneficial
for the prevention of CVD. The only possible exception
among the sources of saturated fat is dairy (2931).
Major correlates of high CVD risk
Carbohydrates
The results of our study show that high-glycaemic car-
bohydrates or a high overall proportion of carbohydrates
in the diet are the key ecological correlates of CVD
risk. These findings strikingly contradict the traditional
‘saturated fat hypothesis’, but in reality, they are compatible
with the evidence accumulated from observational studies
that points to both high glycaemic index and high
glycaemic load (the amount of consumed carbohydrates
their glycaemic index) as important triggers of CVDs
(1, 3234). The highest glycaemic indices (GI) out of all
basic food sources can be found in potatoes and cereal
products (Supplementary Table 2), which also have one
of the highest food insulin indices (FII) that betray their
ability to increase insulin levels.
The role of the high glycaemic index/load can be
explained by the hypothesis linking CVD risk to inflam-
mation resulting from the excessive spikes of blood glu-
cose (‘post-prandial hyperglycaemia’) (35). Furthermore,
multiple clinical trials have demonstrated that when
compared with low-carbohydrate diets, a low-fat diet
increases plasma triglyceride levels and decreases total
cholesterol and HDL-cholesterol, which generally indi-
cates a higher CVD risk (36, 37). Simultaneously,
LDL-cholesterol decreases as well and the number of
dense, small LDL particles increases at the expense of less
dense, large LDL particles, which also indicates increased
CVD risk (27). These findings are mirrored even in
the present study because cereals and carbohydrates in
general emerge as the strongest correlates of low choles-
terol levels.
In light of these findings, the negative correlation of
refined sugar with CVD risk may seem surprising, but the
mean daily consumption of refined sugar in Europe is
quite low (84 g/day), when compared with potato and
cereal carbohydrates (235 g/day), and makes up only
20% of CA energy. Refined sugar is also positively tied
to many animal products such as animal fat (r0.57;
pB0.001) and total fat and animal protein (r0.52;
pB0.001), and negatively to % PC CARB energy
(r0.58; pB0.001) and % CA energy (r0.47;
p0.001). Therefore, a high consumption of refined
sugar is accompanied by a high consumption of animal
products and lower intakes of other carbohydrates.
Furthermore, the glycaemic index of refined sugar
(sucrose) is rather moderate (65) (38).
Distilled beverages
Although alcohol (fermented carbohydrate) has almost
zero values of GI and FII, its highly concentrated sources
(distilled beverages) correlate moderately positively with
raised blood pressure and CVD mortality in the present
study, especially in men, in the absence of any relation-
ship with blood cholesterol and blood glucose. Although
distilled beverages played no important role in the
regression models, they occupy a stable position in both
factor analyses (Figs. 15 and 17) and are very loosely
associated with other variables. They correlate signifi-
cantly positively only with potatoes (r0.52, pB0.001)
and rye (r0.49, pB0.001), which are the usual sub-
strates for their production.
In contrast, the total consumption of alcoholic bev-
erages, as well as the consumption of beer and wine,
generally has a moderately negative relationship with
CVD risk, although it may not necessarily be causal. In
both factor analyses, alcoholic beverages and beer are
never highlighted in the opposition against CVDs. In
fact, the proportion of CA energy (carbohydrates and
alcohol) correlates more strongly with CVD risk than the
proportion of energy from carbohydrates alone, alcoholic
beverages excluded (data not showed). This observation
would suggest that the role of alcoholic beverages and
beer in the Pearson linear correlations may be influenced
by other factors. Indeed, alcoholic beverages correlate
most negatively with % PC CARB energy (r0.68,
pB0.001) and most positively with meat, particularly
pork (both r0.76, pB0.001).
In observational studies, alcohol generally appears as
protective (1), but in reality, its relationship with CVD
risk is J-shaped (39). Although it may be beneficial at
low/moderate daily intake, this does not necessarily apply
for excessive consumption above 510 g alcohol/day.
Recently, Fillmore et al. (40) discussed an interesting
hypothesis that this seemingly protective effect is due to
methodological errors because the category ‘abstainers’
often includes former drinkers, who stopped drinking
because of poor health. Still, our study offers some
support to the protective role of wine, which is explained
by the content of specific flavonoids in red wine (see
below). Therefore, it is possible that the relationship of
alcohol to CVD risk depends on the amount consumed,
or on the content of specific plant extracts. Beer (GI 66,
but a very low FII 20) contains both alcohol (3.9%) and
carbohydrates (3.6%). Wine (GI 0, FII 3) has a much
higher proportion of alcohol (10.4%) than carbohydrates
(2.7%) (41). Distilled beverages (such as gin, rum, vodka,
whiskey) contain zero carbohydrates, but 40% alcohol.
As a result, distilled beverages constitute only 6.1% of
the total consumption of alcoholic beverages, but 26.4%
of the total energy from alcoholic beverages. This fact
can explain their negative health impact, relative to
the small volume consumed. The dramatic increase of
CVD mortality in the former USSR after 1989 (9) could
also be linked with the heavy binge drinking of alcohol
(vodka).
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 17
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Sunflower oil
Sunflower oil belonged to the most consistent correlates
of stroke mortality in the historical comparison. Its linear
correlation with actual total CVD mortality is rather
vague, but it markedly increases in the period 20002008.
Because plant oil is generally associated with low CVD
risk and sunflower oil is consumed mainly in the eastern
half of Europe, where we find the highest intake of the
supposed risk factors such as carbohydrates and distilled
alcohol, its role could be disregarded as purely spurious.
However, sunflower oil is only very loosely correlated
with other variables in our dataset (r0.41, p0.007
with legumes; r0.40, p0.008 with onions; r0.34,
p0.028 with smoking in men; r0.31; p0.045 with
vegetables). Similar to distilled alcohol, sunflower oil was
not highlighted by the penalised regression methods, but
it creates very productive regression models (adj. R
2
) with
some highly significant correlates of total CVD mortality,
especially with % CA energy (63.9% of total variance in
men, 75.9% in women) and total fat and total protein
(62.3% in men, 76.3% in women). In contrast, onions do
not improve these models virtually at all. For example,
the combination of onions with % CA energy explains
only 50.4 and 61.9% of total variability in men’s and
women’s total CVD mortality, respectively.
At present, we do not have any reliable explanation for
the peculiar role of sunflower oil in our analysis, but we
think that there are several possibilities. First, sunflower
oil has been the main component of solidified margarines,
which were industrially produced from hydrogenated
plant oils (trans-fatty acids). Trans-fatty acids (such as
elaidic acid) are already recognised as an important risk of
CVDs (42, 43). A weak point of this explanation is the fact
that sunflower oil is consumed mainly in countries of
Southeastern and Eastern Europe, where it is used in its
unhydrogenated form in the local cuisine.
Second, some authors maintain that highly concen-
trated sources of linoleic acid [n-6 essential polyunsaturated
fatty acid (PUFA)], containing only small amounts of
alpha-linolenic acid (n-3 essential PUFA) (e.g. sunflower
oil, corn oil), may have proinflammatory properties, but
other data indicate the opposite (44). Our present study
cannot illuminate this problem because corn oil correlates
negatively with CVD risk (data not showed) and with
regard to the low mean daily intake (2g/day), its
inclusion did not seem to be meaningful. Therefore, we
must also work with a hypothesis that sunflower oil
expresses some unknown confounder that is related to its
culinary use. Perhaps even more likely, both sunflower oil
and onions symbolise a diet in Southeastern and Eastern
Europe, which is characterised by a low consumption of
fruits, dairy, and animal products in general, and low
health expenses. In any case, the significance (pB0.05) of
sunflower oil as a correlate of total CVD mortality
disappears when controlled for smoking (in men only)
and health expenditure.
Onions
The role of onions as another potential risk factor is
unclear and unexpected because Allium vegetables (onions
and garlic) are often propagated as a prevention of
CVDs (45). All we can say is that the role of onions is
generally the weakest out of all positive correlates of
CVD risk, which might indicate a spurious relationship.
Similar to sunflower oil, onions are used as a food addi-
tive and they show the strongest positive correlation with
vegetables (r0.63; pB0.001) and % plant food energy in
general (r0.56; pB0.001). Onions do not correlate with
men’s raised blood pressure (r0.20; p0.21) and rather
weakly with women’s raised blood pressure (r0.43;
p0.004). They also do not show any notable correlation
with CVD indicators in the historical comparison and
although they do show significant associations with the
actual total CVD mortality, they do not contribute much
to the regression models.
Smoking and BMI
Perhaps the most surprising finding of our study relates
to CVDs and smoking because it differs by sex. Although
smoking is the third strongest correlate of total CVD
mortality in men (r0.67; pB0.001), it has the opposite
relationship in women (r0.46; p0.002). The possi-
bility that there would exist some sex-specific differences
related to CVDs and smoking is very unlikely because the
risky nature of smoking in women has been demonstrated
quite persuasively (46). Therefore, we are apparently deal-
ing with a mere statistical artefact. Furthermore, data
from our study examining the determinants of cancer in
39 European countries (in preparation) demonstrate an
expectable, positive link between smoking and the pre-
valence of lung cancer (r0.41, p0.01 in men; r0.55,
pB0.001 in women) and larynx cancer (r0.49, p0.002
in men; r0.30, p0.07 in women). These results indi-
cate that the used statistics of smoking prevalence cannot
be far from reality.
A closer examination of Figs. 11 and 12 reveals that an
answer to this problem is not difficult. First, the mean
prevalence of smoking in women (18.7%) is much lower
than in men (38.0%). Second, the geographical pattern of
smoking differs by sex (Supplementary Fig. 27). Although
men smoke mainly in the former Soviet republics, the
highest prevalence of smoking in women occurs in the
Balkans and Northern and Central Europe. In general,
smoking in women correlates with variables that appear
as protective: total fat (r0.58), total fat and animal
protein (r0.54), and high health expenditure (r0.54;
pB0.001).
Another problem related to smoking lies in its
effect on BMI values. Smoking emerges as the only
common denominator of low BMI values in both sexes
Pavel Grasgruber et al.
18
(page number not for citation purpose) Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694
(r0.47, p0.002 in men; r0.53, pB0.001 in
women) (Supplementary Figs. 29 and 30). This finding
has been consistently documented even in observational
studies because smoking increases energy expenditure
and reduces appetite (47). As a result, the relationship
between men’s and women’s BMI is curvilinear because
smoking markedly decreases BMI values in men from the
former USSR, when comparedwith women (Supplementary
Fig. 28). This can explain, why the correlation between
BMI and CVD indicators differs by sex (Figs. 13 and 14).
Major correlates of low CVD risk
Fat and protein intake
Our finding that total fat and animal protein (or total fat
and total protein) is the strongest correlate of low CVD
risk is again in accordance with the hypothesis linking
CVD risk to postprandial hyperglycaemia because a high
consumption of fat and protein indicates a low dietary
glycaemic load. Naturally, this observation also raises the
question of whether our study can illuminate the unclear
role of saturated fat. It is true that in clinical trials,
the replacement of saturated fat with PUFAs decreases
CVD risk (28, 48), but this evidence is not necessarily a
proof of the harmful effect of saturated fat because
PUFAs most effectively decrease total cholesterol: HDL
ratio, LDL-cholesterol, and triglyceride levels (16). The
richest natural sources of PUFAs are walnuts (47%
weight) (41) and they also have a very good ratio between
linoleic acid and alpha-linolenic acid (see Supplementary
Table 1), not to mention further possible benefits on
oxidative stress and inflammatory markers (49). This
evidence supports the causal role of tree nuts in our study.
The factor and regression analyses indicate that the
effect of dairy fat (and particularly its main dietary
source cheese) may be beneficial as well. This finding is
in accordance with the growing evidence pointing to the
preventive role of dairy (2931). Although many obser-
vational studies still connect this effect with low-fat dairy
(31), the positive role of dairy fat could be explained by
the observations of clinical trials showing that lauric acid
(12:0) and myristic acid (14:0), which are most abundant
in dairy fat and particularly in coconut oil (Supplementary
Table 1), most strongly increase HDL-cholesterol (16).
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Plant fat (g/day per capita, 1993–2008)
Prevalence of raised blood pressure in women (%) (2008)
Fig. 23. Correlation between the mean daily consumption of
plant fat and the prevalence of raised blood pressure in
women (r0.65; p0.001).
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r= –0.76; p<0.001
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Animal fat (g/day per capita, 1993–2008)
Prevalence of raised blood pressure in women (%) (2008)
Fig. 24. Correlation between the mean daily consumption of
animal fat and the prevalence of raised blood pressure in
women (r0.76; p0.001).
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r= –0.82; p<0.001
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Total fat (g/day per capita, 1993–2008)
Prevalence of raised blood pressure in women (%) (2008)
Fig. 25. Correlation between the mean daily consumption of
total fat and the prevalence of raised blood pressure in
women (r0.82; p0.001).
Food consumption and the actual statistics of cardiovascular diseases
Citation: Food & Nutrition Research 2016, 60: 31694 - http://dx.doi.org/10.3402/fnr.v60.31694 19
(page number not for citation purpose)
Lauric acid also quite strongly decreases the total
cholesterol: HDL-cholesterol ratio, whereas the effect of
other saturated fatty acids is more or less neutral.
However, some other factors unrelated to fat content
may be comparably important. For example, cheese does
not increase total and LDL-cholesterol, when compared
with butter (50).
Fish & seafood (the source of long-chain PUFAs) is
another animal item highlighted by the factor analysis,
although its significance is rather secondary (Factor 3).
This could also be attributed to small consumption rates
constituting only 1.3% of total fat intake. In contrast, the
role of meat fat (and meat in general) in the factor and
regression analyses is rather marginal, which would
indicate that it works rather neutrally and passively, via
the decrease of the glycaemic load. This conclusion is
complicated by the strong collinearity between meat and
alcoholic beverages (r0.76, pB0.001), which usually
applies even at the individual level and it may effectively
blur any health role of these two food items. In any case,
both plant fat and animal fat correlate with low CVD
risk and their combination further increases rvalues (see
Figs. 2325).
Fruits and wine
Both fruits (as a whole) and wine do not figure among the
consistent correlates of CVD indicators in the historical
comparison, but this can be ascribed to the fact that this
comparison did not include the former Soviet republics,
where their consumption is the lowest. Still, the only
fruits that came to the foreground in all analyses were
oranges and mandarins. Wine is remarkably independent
of other food items and health expenditure during the
period 19932008.
The protective effect of fruits and fruit products such
as wine is mostly explained by the content of flavo-
noids (51). Flavonoids specific for citrus fruits are
flavanones (52). Another promising group of flavonoids
are anthocyanins (53) contained in berries, currants, and
red wine (52). The position of ‘fruits total’ in the factor
analysis indeed indicates that some non-tropical fruits
may be both independent of healthcare and simulta-
neously related to low CVD risk.
Vegetables
Vegetables, which are frequently recommended as a pre-
caution against CVDs because of their low glycaemic
index, in the context of the ‘Mediterranean diet’, and
emerge as a consistent protective factor even in observa-
tional studies (1), did not figure among the negative cor-
relates of CVD risk. A closer examination of the graphic
comparisons shows that vegetables have a basically curvi-
linear relationship to CVD risk, and they may work
as a sort of prevention only when very high amounts
(300 g/day) are consumed (Supplementary Figs. 31 and
33), and especially when they substitute cereal and potato
carbohydrates (Supplementary Figs. 32 and 34).
Health expenditure
Health expenditure (both for 2008 and 19952008) is the
most negative correlate of total CVD mortality in both
sexes and health expenditure for 19952008 is even the
strongest negative correlate of raised blood pressure. It is
also the main confounding factor in the whole study. The
mean health expenditure for 19952008 correlates very
strongly positively especially with total fat and animal
protein (r0.86, pB0.001), and even with some indivi-
dual foodstuffs such as coffee (r0.84, pB0.001), oranges
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