REVI EW

Wine, beer or spirit drinking in relation to fatal and non-fatal

cardiovascular events: a meta-analysis
Simona Costanzo

Augusto Di Castelnuovo

Maria Benedetta Donati

Licia Iacoviello

Giovanni de Gaetano
Received: 15 July 2011 / Accepted: 31 October 2011
Springer Science+Business Media B.V. 2011
Abstract In previous studies evaluating whether different
alcoholic beverages would protect against cardiovascular
disease, a J-shaped relationship for increasing wine con-
sumption and vascular risk was found; however a similar
association for beer or spirits could not be established. An
updated meta-analysis on the relationship between wine,
beer or spirit consumption and vascular events was per-
formed. Articles were retrieved through March 2011 by
PubMed and EMBASE search and a weighed least-squares
regression analysis pooled data derived from studies that
gave quantitative estimation of the vascular risk associated
with the alcoholic beverages. From 16 studies, evidence
conrms a J-shaped relationship between wine intake and
vascular risk. A signicant maximal protectionaverage
31% (95% condence interval (CI): 1942%) was observed
at 21 g/day of alcohol. Similarly, from 13 studies a
J-shaped relationship was apparent for beer (maximal
protection: 42% (95% CI: 1958%) at 43 g/day of alcohol).
From 12 studies reporting separate data on wine or beer
consumption, two closely overlapping doseresponse
curves were obtained (maximal protection of 33% at 25
g/day of alcohol). This meta-analysis conrms the J-shaped
association between wine consumption and vascular risk
and provides, for the rst time, evidence for a similar
relationship between beer and vascular risk. In the meta-
analysis of 10 studies on spirit consumption and vascular
risk, no J-shaped relationship could be found.
Keywords Meta-analysis Alcohol Cardiovascular
disease Mortality
Introduction
The relationship between alcohol consumption and car-
diovascular events or all-cause mortality in apparently
healthy people or cardiovascular patients has been depic-
ted as a J-shaped curve attributed to a dose-related com-
bination of benecial and harmful effects [17]. Numerous
mechanisms have been proposed that mediate the protec-
tive effect of alcohol (ethanol) in cardiovascular disease
(e.g., increased levels of high-density lipoprotein choles-
terol, decreased levels of low-density cholesterol, reduc-
tion in platelet aggregation, benecial effects on
inammation) [8, 9]. On the other hand, anti-atherogenic
and anti-thrombotic effects and regulation of endothelial
function were mainly ascribed to polyphenolic and phe-
nolic constituents of (red) wine [10] and beer [11, 12],
respectively.
The inuence of separate wine or beer consumption on
health outcomes has been examined in various conditions:
while experimental studies suggest an alcohol-independent
protective role of wine-derived polyphenols on cardiovas-
cular risk [10], epidemiological evidence of a greater effect
of wine versus beer or spirits is lacking. In 2002, some of
us performed a meta-analysis focused on this issue [2] and
observed a J-shaped relationship for increasing wine con-
sumption and vascular risk; however a similar association
for beer could not be established. In the following years,
additional studies have been published (especially on beer
consumption) and a new statistical method tailored for
meta-analytic investigation of non-linear doseresponse
effects became available [13].
S. Costanzo A. Di Castelnuovo M. B. Donati
L. Iacoviello (&) G. de Gaetano
Laboratory of Genetic and Environmental Epidemiology,
Research Laboratories, Fondazione di Ricerca e Cura Giovanni
Paolo II, Largo Gemelli 1, 86100 Campobasso, Italy
e-mail: licia.iacoviello@rm.unicatt.it
1 3
Eur J Epidemiol
DOI 10.1007/s10654-011-9631-0
Therefore, the aim of this study was to update (increased
number of studies), to improve (more rened methodology
of analysis), and to extend (including spirits as a third type
of alcoholic beverage) our previous meta-analysis on the
relationship between alcoholic beverages consumption and
vascular risk and to include (when possible) the clinical
endpoints of cardiovascular and total mortality.
Materials and methods
Search strategy and data extraction
Our meta-analysis was performed in accordance with the
meta-analysis of Observational Studies in epidemiology
reporting guidelines [14]. Articles were retrieved until
March 2011, by search in PUBMED and EMBASE using
at least one of the following terms alcohol, wine, beer,
liquor, spirits in combination with, cardiovascular disease
mortality, morbidity, survival, and death, supplemented by
references of the retrieved articles and reviews. Studies
were excluded if they were not in English, or only con-
sidered one category of risk (i.e., drinkers versus not-
drinkers), or mortality for specic causes (except vascular
mortality) or when the reference category was not the one
with the lowest intake or when relative risks or numbers of
cases and person-years were not available. In case of
multiple reports, data from the longer follow-up were
considered.
A check of abstracts identied by electronic searches
using the aforementioned keywords and consequently a full
text revision of selected articles considering the inclusion
and exclusion criteria was performed. Ninety-seven pub-
lications were identied (Fig. 1); two of us independently
reviewed articles and agreed to select 18 studies [1532].
Events for vascular mortality included cardiovascular dis-
ease (CVD), coronary heart disease (CHD) and ischemic
heart disease (IHD), whereas non-fatal vascular events
comprised acute myocardial infarction (AMI), stroke and
CHD. Four studies reported results separately for all-cause
and vascular mortality [19, 22, 31, 32], two for fatal and
non-fatal vascular disease [15, 16], 10 for non-fatal vas-
cular disease (AMI, stroke, CHD, CVD) [17, 18, 20, 2325,
27, 29, 30], one for all-cause [26] and two for CHD mor-
tality as unique endpoint [21, 28] (Tables 1 and 2). In
relation to beverage type, 11 studies investigated the
effects of wine, beer and spirits, 2 studies investigated the
effects of wine and beer, four studies wine and one study
beer only (Table 2).
In seven studies, former drinkers had been excluded
from reference group, whereas in other 7 studies, either the
inclusion or the exclusion of former drinkers from
97 publications identified by electronic searches using key-words
46 excluded, as considered only alcohol intake
2 excluded, as considered as outcomes not relevant
2 excluded, as no observational studies
9 excluded, as the reference category was not the one with lowest intake
or the relative risks or number of cases and person years were not available
3 excluded, as multiple reports
17 excluded, as considered only one category of risk (drinkers vs not-drinkers)
18 publications included in the analysis
Fig. 1 Flow chart of the
selected studies
S. Costanzo et al.
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S. Costanzo et al.
1 3
reference group was mentioned (Table 1); in 3 studies the
reference group included occasional but not former drink-
ers [19, 20, 22].
The amount of alcohol (grams) in a drink was taken
as quantied by each article. Adjusted relative risks (RR)
for each categories of alcohol consumption were extracted
(four studies reported RR adjusted for age only [15, 16, 29,
31] (Table 2)).
Data analysis
Data collected were: (a) the value x of alcohol intake
(g/day) assigned as the midpoint of the reported ranges;
x was dened as 1.2 times the lower boundary for the open-
ended upper categories [33]; (b) frequency counts, adjusted
RR, and 95% CI for each x level; (c) covariates describing
the characteristics of the study. Inverse-variance-weighed
methods, taking into account the correlation between esti-
mates within each study, were used [33]. The models to be
tted were selected among fractional polynomial curves of
the second order [33]. Fractional polynomials are a family
of models considering power transformations of a contin-
uous exposure variable, restricted to a predened set of
integer and non integer exponents [34]. The regression
models were log(RR|x) = b
1
x
p
? b
2
x
q
and the exponents p
and q were selected among the set: (-2, -1, -0.5, 0, 0.5, 1,
2). When p = 0, x
p
is replaced by log(x), and when p = q
the model becomes log(RR|x) = b
1
x
p
? b
2
x
q
log(x) [13].
The best t was dened as that with the highest likelihood.
To consider differences among studies as a further source of
random variability, an additional component of the variance
was added in weighing each observation (random-effects
model). In sensitivity analysis, comparison of two hierar-
chical models was tested by the likelihood ratio test
including or not in the models the interaction terms between
the covariates (design of study, country setting, duration of
follow-up) and alcohol intake (amount) [35]. To make some
allowance for multiple comparisons, 95% CIs were used in
subgroup analyses, and pairwise contrasts were adjusted
following the Sidak method, as outlined by Ludbrook [36].
All analyses were carried out using a SAS macro [13] (SAS,
9.1.3 for Windows, Cary, NC: SAS Institute Inc. 1989). The
hypothesis that publication bias might affect the validity of
the estimates was tested by a funnel-plot-based approach. A
simple test of asymmetry of the funnel plot was used
according to the method proposed by Egger et al. [37]. The
symmetry of funnel plots was measured applying the fol-
lowing linear model: RRj/se(RRj) = a ? c*1/se(RRj),
where RRj/se(RRj) is the standard normal deviate (RR
divided by its standard error); 1/se(RRj) the precision of the
estimate; and a and c are the unknown parameters of the
model. The correction for publication bias was performed
pooling studies after the exclusion of the ones that
determined the asymmetry of the funnel plot. We assessed
the quality of each study using both the Newcastle-Ottawa
Scale [38] and a quality scale that also considered the
assessment of alcohol drinking [39]. Estimations of the
metrics maximal protection and reversion point from
the pooled doseresponse curves were used to help data
interpretation. Imprecision in the evaluation of these met-
rics from tting of data is unavoidable; thus, point estimates
of these parameters should not be emphasized.
Results
From ninety-seven identied publications 79 studies were
excluded with the criteria shown in Fig. 1. More than half
were excluded since they did not distinguish wine, beer or
spirits intake (n. 46) or only compared abstention with a
unique category of alcohol intake (n. 17).
The main characteristics of the 18 studies included in
this analysis are reported in Tables 1 and 2.
Wine consumption and fatal or non-fatal vascular
events
From 16 studies [1519, 2125, 2732] (11 prospective
studies involving 288,363 individuals with 5,554 combined
fatal or non-fatal cardiovascular events and 5 casecontrol
studies involving 2,060 cases and 3,784 controls), 17 dose
response independent relationships were obtained for wine
consumption and vascular risk, since one study reported
results separately for men and women [28]. Symmetric
funnel plots (a = 0) were obtained for 12.525 and
2560 g/day categories of alcohol intake (Fig. 2a); for the
112.5 category a slightly deviation from symmetry was
observed (a = 0, P = 0.009). The best-tting model was
obtained when p = q = 0.5, corresponding to the model:
Log(RR) = b
1
Hx ? b
2
Hx*log(x), for both the xed and
random models. The deviances of xed and random effects
models fell from 131.89 to 30.39 (P\0.001 for differ-
ence), suggesting heterogeneity among studies. In sub-
sequent analyses, using a random effects model with
p = q = 0.5, we explored the possible role of study char-
acteristics in explaining the inter-study heterogeneity. Fit-
ted parameters for the random model were b
1
= -0.20
(SE = 0.059; P\0.001) and b
2
= 0.04 (SE = 0.015;
P = 0.004) (Table 3). The relationship observed has to be
interpreted as a J-shaped curve; the association with a
lower vascular risk was apparent up to 72 g/day and the
lowest risk was seen at 21 g/day, (RR = 0.69; 95% CI:
0.580.82) (Fig. 3). After the exclusion of studies deter-
mining the asymmetry in the funnel plot [16, 28, 29] and
then studies only adjusted for age [15, 16, 29, 31], the
J-shape curve was conrmed (Table 3).
Alcoholic beverages consumption and vascular risk
1 3
T
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S. Costanzo et al.
1 3
In quality analyses, using both scales (The Newcastle-
Ottawa Scale [38] and the score used by Tramacere et al.
[39]), no heterogeneity of results was found according to
the stratication of studies in low and high quality (data
not shown).
When subgroup analyses were performed separately
considering cohort or casecontrol studies, the maximal
protection of about 30% in the range of 2025 g/day of
alcohol was conrmed in both pooled data (Table 3,
P = 0.7). Pooled analyses of 6 studies [19, 22, 23, 25, 27,
28] that formally excluded former drinkers from the ref-
erence category, conrmed the maximal protection (28%;
95% CI: 944%) at moderate wine consumption against
vascular risk (Table 3). In sensitivity analyses separately
investigating the relationship between wine consumption
and vascular events in 5 Mediterranean [1719, 24, 29], 5
Northern European [21, 22, 3032] and 6 Western (USA
and Australia) [15, 16, 23, 25, 27, 28] countries, the
J-shape curves were conrmed and appeared to be very
similar among them (Sidak-adjusted P values for pairwise
comparisons among countries were not statistically sig-
nicant (Table 3)). Among cohort studies, the curves for
short and long duration of follow-up were different
(P\0.001); in particular, b
1
was greater in shorter follow-
up studies analysis, whereas b
2
was equal (Table 3). As a
consequence, the pooled curves for the duration of follow-
up were different for the range at which alcohol remained
protective but comparable regarding the maximum pro-
tection at ligher doses.
Wine consumption and cardiovascular mortality
Six cohort studies [19, 21, 22, 28, 31, 32], involving
105,196 individuals (2,677 cardiovascular deaths), pro-
vided seven doseresponse independent relationships for
alcohol intake and cardiovascular mortality. The best-t-
ting model was obtained when p = q = 0.5 for both the
xed and random models. Deviances of xed and random
effects models fell from 18.0 to 6.2 (P\0.001 for dif-
ference), indicating heterogeneity among studies. An
overall J-shaped curve was obtained from the seven
adjusted doseresponse curves; the maximal protection was
34% at 24 g/day in the random-effects model (Table 3 and
Fig. 4).
Wine consumption and mortality for any cause
Five cohort studies [19, 22, 26, 31, 32] (56,696 individuals
and 11,905 deaths for any-cause) gathered information on
wine consumption and total mortality and provided six
Fig. 2 Funnel plot for different intake categories (112.5 g/day, 12.525 g/day, 2560 g/day) for meta-analyses on wine (a), beer (b) and spirit
(c) consumption and vascular risk
Alcoholic beverages consumption and vascular risk
1 3
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S. Costanzo et al.
1 3
doseresponse independent relationship, since one study
reported results separately for men and women [26]. Ran-
dom effect model was performed, using the model:
Log(RR) = b
1
Hx ? b
2
Hx*log(x) (P\0.001 for differ-
ence random vs. xed models). In these studies, a J-shape
curve was conrmed, with 25% maximal risk reduction at
approximately 10 g/day and signicant protection up to
41 g/day (Table 3 and Fig. 5).
Beer consumption and fatal or non-fatal vascular events
Thirteen studies [15, 16, 18, 20, 21, 2325, 2730, 32] (8
prospective studies involving 224,219 individuals (4,823
events) and ve casecontrol studies involving 1,964 cases
and 3,834 controls) provided 13 doseresponse indepen-
dent relationships for beer consumption and fatal or non
fatal vascular events. Symmetric funnel plots (a = 0) were
obtained for all the categories of beer intake, showing the
absence of publication bias (Fig. 2b). The best-tting
model was obtained when p = q = 1, corresponding to the
model: Log(RR) = b
1
x ? b
2
x*log(x), for both the xed
and random models. The deviances of xed and random
effects models fell from 104.6 to 32.8 (P\0.001 for dif-
ference), suggesting heterogeneity among studies. Using a
random effects model with p = q = 1, the possible role of
study characteristics was explored to explain the inter-
study heterogeneity. Fitted parameters for the random
model were b
1
= -0.06 (SE = 0.02; P = 0.009) and
b
2
= 0.01 (SE = 0.007; P = 0.04) (Table 4). The rela-
tionship observed was interpreted as a J-shaped curve; the
association with a lower vascular risk was apparent up to
55 g/day and the lowest risk was seen at 43 g/day
(RR = 0.58; 95% CI: 0.420.81; Table 4 and Fig. 6).
In quality analyses, using both scales mentioned above,
no heterogeneity of results was found according to the
stratication of studies in low and high quality (data not
shown). A J-shape relationship between beer consumption
and vascular risk was also conrmed by pooling data from
casecontrol studies, with a maximal protection of about
60% at 36 g/day of alcohol (Table 4). However, perform-
ing a sensitivity analysis excluding the three studies only
adjusted for age [15, 16, 29] or a sub-group analysis of 5
studies [20, 23, 25, 27, 28] that formally excluded former
drinkers from the reference category, or by country cate-
gorization (Mediterranean [18, 24, 29], Northern European
[20, 21, 30, 32] and Western [15, 16, 23, 25, 27, 28]
countries), the characteristic J-shape relationship for beer
consumption and vascular risk was no more apparent
(Table 4).
As a small number of studies only investigated the
relationship between beer consumption and cardiovascular
[21, 28, 32] and/or total mortality [26, 32], it was not
possible to perform any meta-analysis related to these
clinical end-points.
Comparison of wine and beer consumption in relation
to vascular risk
From 12 studies [15, 16, 18, 21, 2325, 2730, 32] (8
prospective studies involving 224,219 individuals (4,823
events) and four casecontrol studies involving 1,762 cases
and 3,099 controls) that reported separate data both on
Fig. 3 Pooled curves of relative
risk (95% CI: dotted lines) of
fatal and non-fatal vascular
events and wine intake,
extracted from 17 independent
relationships using random
models
Alcoholic beverages consumption and vascular risk
1 3
wine and beer consumption in relationship with vascular
risk, two similar doseresponse curves were obtained
(P = 0.4, Table 5 and Fig. 7). The two curves were closely
overlapping, especially at light-moderate alcohol con-
sumption and the maximal protection by either beverage
was 33% at 25 g/day (Table 5 and Fig. 7). This similarity
between wine and beers protection persisted even when
the studies that did not simultaneously adjust for different
types of alcoholic beverages or total amount of alcohol,
were excluded (Table 5, P = 0.2).
Spirit consumption and fatal and non-fatal vascular
events
In a meta-analysis of 10 studies [15, 18, 21, 23, 25, 2730,
32] on spirit consumption and vascular risk (seven pro-
spective studies involving 136,693 individuals (4,523
events) and three casecontrol studies involving 1,255 cases
and 2,621 controls), no J-shaped relationship could be found
(Fig. 8). Symmetric funnel plots (a = 0) were obtained for
all the categories of spirit intake, showing the absence of
Fig. 4 Pooled curves of relative
risk (95% CI: dotted lines) of
fatal and cardiovascular
mortality and wine intake,
extracted from seven
independent relationships using
random models
Fig. 5 Pooled curves of relative
risk (95% CI: dotted lines) of
fatal and all-cause mortality and
wine intake, extracted from six
independent relationships using
random models
S. Costanzo et al.
1 3
publication bias (Fig. 2c). Neither the rst nor the second
order terms of the model Log(RR) = b
1
Hx ?
b
2
Hx*log(x) were statistically signicant, the tted param-
eters for the random model being b
1
= -0.01 (SE = 0.10;
P = 0.4) and b
2
= -0.005 (SE = 0.03; P = 0.4).
As a small number of studies only investigated the
relationship between spirits consumption and cardiovas-
cular [21, 28, 32] and/or total mortality [26, 32], it was not
possible to perform any meta-analysis related to these
clinical end-points.
Discussion
A previous meta-analysis had shown a clear inverse dose
effect curve against vascular events for wine but not for
beer intake [2]. Evidence from the current updated and
extended meta-analysis conrms the signicant reduction
of overall vascular risk associated with wine consumption
and shows, apparently for the rst time, a similar J-shaped
relationship between beer intake and cardiovascular risk.
Moreover, the comparison of studies which included a
parallel, separate evaluation of wine and beer consumption,
indicates a similar protecting effect of either beverage
against cardiovascular risk (Fig. 7).
Thus, in relation to health, drinking in moderation is
more important than the content of the bottle, at least when
wine and beer are taken into consideration.
On the contrary, no statistically signicant association
with vascular events was apparent for the intake of spirits
up to 60 g/day, which is the maximum dose investigated in
the 10 studies included in this meta-analysis. In several
studies spirit consumption mostly occurred as binge
drinking (dened as the consumption of three or more
drinks within 12 h) and was restricted to only few days
per week. This may explain the absence of association
between moderate spirit consumption and cardiovascular
disease observed, in contrast, for the other two alcoholic
beverages both in the present meta-analysis and in a pre-
vious study [40]. It is known that drinking out of mealtimes
and binge drinking are associated with increased CHD risk
[4144], both behaviours being preferentially linked to the
type of alcoholic beverage consumed [4547].
Moderate alcohol drinking reportedly induces healthy
changes in lipid prole, vascular, haemostatic and endo-
thelial cell function, platelet aggregation and inammation
[8, 9]. On the other hand, beer and wine contains different
substances that might provide additional cardiovascular
benet to that obtained by alcohol. If liquor -the drink with
the purest concentration of alcohol - does not clearly
decrease vascular events (the negative association between
spirit intake and vascular events was non-signicant in the
present meta-analysis), it should not lead us to the T
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6
Alcoholic beverages consumption and vascular risk
1 3
conclusion that polyphenolic constituents found in wine or
beer are (mainly) responsible for the benecial effect on
vascular events [912, 48, 49]. In fact the proportion of
subjects consuming liquors is much lower than that con-
suming wine or beer and patterns of liquor consumption are
very different. We cannot therefore exclude that the neg-
ative association of wine and beer drinking with cardio-
vascular events could be (mainly) due to ethanol itself.
Effects of different alcoholic beverages on different
clinical outcomes
We tried to dissect the potential benet of wine or beer
consumption on different clinical end-points. Both wine
and beer consumption were comparable as far as the
reduction of the risk of combined fatal and non fatal car-
diovascular events was concerned. Wine drinking was also
effective in reducing both cardiovascular and total
mortality. The maximum intake of wine at which protec-
tion was still apparent decreased from 72 to 66 to 41 g/day
when either combined fatal and non fatal vascular events,
or cardiovascular mortality or total mortality were con-
sidered as endpoint, respectively. At variance, the minimal
doses of wine at which its maximal protection could be
obtained were 21, 24 and 10 g/day, respectively. Thus,
while low-moderate doses are similarly protective against
any clinical endpoint considered, the hardest the endpoint,
the lowest the amount of wine that starts to be associated
with harm. The maximum protection obtained at light-
moderate wine intake gradually vanishes at higher doses
that differ according to different clinical endpoints, possi-
bly because of increase in harmful collateral effects.
Unfortunately, the very limited data available about
either beer or spirit consumption in relation to cardiovas-
cular or total mortality, did not allow us to perform a fully
meta-analytic investigation on the latter two beverages.
Fig. 6 Pooled curves of relative
risk (95% CI: dotted lines) of
fatal and non-fatal vascular
events and beer intake,
extracted from 13 independent
relationships using random
models
Table 5 Results of best tting models of meta-analysis of studies reported data both on wine and beer consumption in relationship with vascular
risk
No of
curves
Maximal protection Reversion
point
Parameters of the best tted model Log(RR) = b
1
Hx ? b
2
Hx*log(x)
% 95% CI g/day g/day b
1
SE P b
2
SE P P for heterogeneity
Wine consumption 12 32 1844 25 70 -0.19 0.07 0.003 0.04 0.02 0.03 \0.001
Beer consumption 12 33 1348 25 43 -0.17 0.10 0.04 0.03 0.03 0.2 \0.001
Adjusted studies for different types of alcohol beverages or total amount of alcohol intake
Wine consumption 7 -0.22 0.15 0.06 0.05 0.05 0.2 \0.001
Beer consumption 7 -0.16 0.16 0.16 0.02 0.05 0.3 \0.001
CI condence interval, RR relative risk
S. Costanzo et al.
1 3
Limitations
All the studies included in our meta-analyses were obser-
vational, and could be themselves associated with a number
of limitations: the alcohol patterns assessed only once at
inclusion into the study, the absence of detailed history of
alcohol consumption behavior and the possible changes in
alcohol habits could have had an effect on the relation of
alcohol consumption with cardiovascular events. Ran-
domized controlled trials would offer a more solid answer
than observational studies to many questions in medicine;
the latter are mainly restricted, however, to the efcacy of
drugs and are difcult and ethically questionable to per-
form to evaluate alcohol effects [4, 9]. On the other hand,
the results of well-designed observational studies (with
either a cohort or a casecontrol design) do not systemat-
ically overestimate the magnitude of the effects as com-
pared with those in randomized, controlled trials on the
same topic [50]. In particular it is believed that self-
reported wine or beer consumption is inaccurate. Under-
reporting on wine or beer drinkers would however result in
a tendency for relative risks to be biased toward the null
Fig. 7 Curves for wine intake
(solid lines) and beer intake
(dotted lines) were extracted
from the same studies
Fig. 8 Pooled curves of relative
risk (95% CI: dotted lines) of
fatal and non-fatal vascular
events and spirit intake,
extracted from 10 independent
relationships using random
models
Alcoholic beverages consumption and vascular risk
1 3
hypothesis, while our meta-analysis showed signicant
associations. The potential confounding effect of combined
drinking of different types of alcoholic beverages in the
same population was excluded by pooling data from
studies that had taken this issue into consideration
(Table 5).
Wine drinkers, at least in some Countries, tend to have a
healthier lifestyle prole than beer drinkers. This uncon-
trolled confounding can be reasonably excluded, as the
great majority of studies were adjusted for these variables
(Table 1). Furthermore, a sensitivity analysis investigated
the relationship between wine or beer consumption and
vascular events in Mediterranean and not-Mediterranean
Countries, but did nd comparable results among them
(Tables 3 and 4).
A weakness of all pooling studies on alcohol con-
sumption is the heterogeneity among the reference groups,
which sometimes have included lifelong teetotallers, for-
mer drinkers and/or occasional drinkers [51]. In few arti-
cles of this meta-analysis, the Authors did not clearly state
if former or occasional drinkers were excluded from the
reference group; however, analysis of ve studies on wine
consumption that formally excluded former drinkers con-
rmed the signicant relation between drinking in moder-
ation and vascular risk.
The results of any meta-analysis, especially in non-
experimental epidemiology, may be invalid due to publi-
cation bias [52]. The funnel plot analysis revealed sym-
metry for all categories of beer, spirit and wine intake,
except for the lowest category of wine intake, suggesting
the presence of a slight publication bias, if any. However,
after the exclusion of the studies which determined the
asymmetry in the funnel plot, the J-shape relationship
between wine consumption and vascular risk was
conrmed.
Alcohol may have different health effects in men and
women [3]. Unfortunately, the paucity of data separately
reported for either sexes, made it impossible to include
men or women in two distinct meta-analyses [2, 4].
Standing from previous data on total alcohol intake [3, 4]
optimal consumption of alcohol should be reduced in
women- 1 unit a day instead of 2 units in men. However, if
this difference holds separately for wine or beer remains to
be investigated.
Signicant protection could be extrapolated from the
curves obtained up to doses of alcoholic beverages of 72 g/
day that must be no doubt considered as a heavy intake.
Besides the fact that the doses of alcohol associated with
maximum protection should be chosen rather than that at
which harm may start, the majority of the studies included
in our meta-analyses did not in fact investigate such a large
range of alcohol intake; the inferences from pooled curves
at larger amounts of alcohol were a mathematical trick
rather than a nding derived from experimental data. On
the contrary, the conclusions obtained on the protection at
light-moderate alcohol intake are quite solid as they are
based on a very large amount of experimental data.
In conclusion, this meta-analysis provides further evi-
dence for a J-shaped signicant inverse association
between wine consumption and vascular risk and shows a
similar relationship for beer consumption. Doseresponse
curves from comparable studies appeared substantially
similar for either alcoholic beverage. No protection was
apparent instead, in association with the consumption of
any spirit amount.
The hazards of excess or binge alcohol drinking should
be always highlighted and heavy or binge drinkers pushed
to cut their consumption to a regular, low-moderate level.
Acknowledgments Supported in part by Cervisia Consulenze and
Istituto Nazionale per la Comunicazione.
Conict of interest This was an investigator-initiated study. The
partial sponsor of the study had no role in the selection of articles or
conduct of the analyses or drafting of the manuscript. We disclaim
any other relationships with industry that might pose a conict of
interest in connection with the submitted article.
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