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Associations between alcohol consumption and selected cytokines in a Swiss population-based sample (CoLaus study)
Atherosclerosis 222 (2012) 245–250
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Associations between alcohol consumption and selected cytokines in a Swiss population-based sample (CoLaus study) Pedro Marques-Vidal a,∗ , Murielle Bochud a , Franc¸ois Bastardot b , Roland von Känel c , Franc¸ois Ferrero d , Jean-Michel Gaspoz e , Fred Paccaud a , Adrian Urwyler f , Thomas Lüscher g , Christoph Hock h , Gérard Waeber b , Martin Preisig i , Peter Vollenweider b a
Institute of Social and Preventive Medicine (IUMSP), CHUV and Faculty of Biology and Medicine, 1066 Epalinges, Switzerland Department of Medicine, Internal Medicine, CHUV and Faculty of Biology and Medicine, Lausanne, Switzerland c Division of Psychosomatic Medicine, Bern University Hospital Inselspital, and University of Bern, Switzerland d Department of Psychiatry, University of Geneva, Geneva, Switzerland e Department of Community Medicine, University of Geneva, Geneva, Switzerland f Cytolab, Dällikon, Switzerland g Department of Medicine, University of Zürich, Zürich, Switzerland h Department of Psychiatry, University Hospital Zürich, Zürich, Switzerland i Department of Psychiatry, CHUV, Lausanne, Switzerland b
a r t i c l e
i n f o
Article history: Received 30 September 2011 Received in revised form 19 December 2011 Accepted 6 February 2012 Available online 18 February 2012 Keywords: IL-1 IL-6 TNF-␣ Alcohol consumption Population study Inflammation Wine Beer
a b s t r a c t Objective: To assess the associations between alcohol consumption and cytokine levels (interleukin-1beta – IL-1; interleukin-6 – IL-6 and tumor necrosis factor-␣ – TNF-␣) in a Caucasian population. Methods: Population sample of 2884 men and 3201 women aged 35–75. Alcohol consumption was categorized as nondrinkers, low (1–6 drinks/week), moderate (7–13/week) and high (14+/week). Results: No difference in IL-1 levels was found between alcohol consumption categories. Low and moderate alcohol consumption led to lower IL-6 levels: median (interquartile range) 1.47 (0.70–3.51), 1.41 (0.70–3.32), 1.42 (0.66–3.19) and 1.70 (0.83–4.39) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p < 0.01, but this association was no longer significant after multivariate adjustment. Compared to nondrinkers, moderate drinkers had the lowest odds (Odds ratio = 0.86 (0.71–1.03)) of being in the highest quartile of IL-6, with a significant (p < 0.05) quadratic trend. Low and moderate alcohol consumption led to lower TNF-␣ levels: 2.92 (1.79–4.63), 2.83 (1.84–4.48), 2.82 (1.76–4.34) and 3.15 (1.91–4.73) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p < 0.02, and this difference remained borderline significant (p = 0.06) after multivariate adjustment. Moderate drinkers had a lower odds (0.81 [0.68–0.98]) of being in the highest quartile of TNF-␣. No specific alcoholic beverage (wine, beer or spirits) effect was found. Conclusions: Moderate alcohol consumption is associated with lower levels of IL-6 and (to a lesser degree) of TNF-␣, irrespective of the type of alcohol consumed. No association was found between IL-1 levels and alcohol consumption. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
∗ Corresponding author at: Institut Universitaire de Médecine Sociale et Préventive, Bâtiment Biopôle 1, Bureau 091, Route de la Corniche, 2, 1066 Epalinges, Switzerland. Tel.: +41 21 314 72 65; fax: +41 21 314 73 73. E-mail addresses: [email protected] (P. Marques-Vidal), [email protected] (M. Bochud), [email protected] (F. Bastardot), [email protected] (R. von Känel), [email protected] (F. Ferrero), [email protected] (J.-M. Gaspoz), [email protected] (F. Paccaud), [email protected] (A. Urwyler), cardiotfl@gmx.ch (T. Lüscher), [email protected] (C. Hock), [email protected] (G. Waeber), [email protected] (M. Preisig), [email protected] (P. Vollenweider). 0021-9150/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2012.02.011
Moderate wine consumption is associated with a decreased risk of cardiovascular disease [1], mainly through its effects on HDL cholesterol, but also on inflammatory markers [2]. Cytokines, such as interleukin 1 (IL-1), interleukin 6 (IL-6) and tumor necrosis factor (TNF-␣) have been associated with an increased risk of developing coronary heart disease [3]. The effects of alcohol consumption on these cytokines are controversial. In epidemiological studies, alcohol consumption increased [4], decreased [5] or had a J-shaped association [6] on IL-6 levels. Finally, no specific effect of wine on TNF-␣ levels was found [6–8].
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In a previous study [9], we showed that CRP levels increase with alcohol consumption in men but not in women. In this study, we further assessed the effects of alcohol consumption during the previous week on additional inflammatory markers (IL-1, IL-6 and TNF-␣).
conducted using a conventional flow cytometer (FC500 MPL, BeckmanCoulter, Nyon, Switzerland). Intra and inter-assay coefficients of variation (CV) were 15% and 16.7% for IL-1, 16.9% and 16.1% for IL-6 and 12.5% and 13.5% for TNF-␣, respectively. Lower detection limits were 0.2 pg/ml and a good reproducibility has been shown [17].
2. Participants and methods 2.4. Lifestyle and clinical data 2.1. Recruitment The sampling procedure of the CoLaus Study has been described previously [10]. Briefly, the complete list of Lausanne inhabitants aged 35–75 years (n = 56,694) was provided by the population registry of the city. A simple, nonstratified random sample of 35% of the overall population was drawn. The following inclusion criteria were applied: (a) written informed consent; (b) age 35–75 years; (c) willingness to take part in the examination and donate blood sample; (d) Caucasian origin. Recruitment began in June 2003 and ended in May 2006. Participation rate was 41%. All participants attended the outpatient clinic of the University Hospital of Lausanne in the morning after an overnight fast. Data were collected by trained field interviewers in a single visit lasting about 60 min. 2.2. Alcohol consumption Alcohol intake was assessed by the self-reported alcohol consumption of the last seven days, expressed as the number of standard drinks. A standard drink was defined as a glass of wine, a bottle of beer or a shot of spirits, approximating 10–12 g ethanol [11]. Subjects were categorized as nondrinkers, low (1–6 drinks/week), moderate (7–13/week) and high (14+/week). Questionnaire-based data on alcohol consumption have been shown to be well correlated [12]. Alcohol consumption was reassessed about one year later in a sub-sample of 1772 women and 1586 men; nonparametric Spearman correlations between the first and the second assessments were 0.64 in women and 0.66 in men (p < 0.001). Alcohol consumption was re-assessed six years later among 3937 participants (2144 women and 1793 men) using the same questionnaire and a food frequency questionnaire validated in the French-speaking part of Switzerland [13,14]. The correlation between the alcohol consumption assessed using the first questionnaire and the validated food frequency questionnaire was 0.82 for all participants (p < 0.001), 0.81 in women (p < 0.001) and 0.80 (p < 0.001) in men. Similarly, the correlations between alcohol consumption at baseline and six years afterwards were 0.71 for all participants (p < 0.001), 0.67 in women (p < 0.001) and 0.66 in men (p < 0.001). In this study, baseline alcohol consumption was used. 2.3. Biological measurements Venous blood samples were drawn after a minimum fasting of 6 h. Participants were asked to refrain from doing any strenuous exercise and to maintain their usual lifestyle habits the day before examination. Glucose measurements were performed on fresh blood samples within 2 h of blood collection in a Modular P apparatus (Roche Diagnostics, Switzerland) using the glucose dehydrogenase method, with maximum inter- and intra-batch CVs of 2.1% and 1.0%, respectively. Serum samples were kept at −80 ◦ C before assessment of cytokines and sent in dry ice to the laboratory. For cytokine measurements, serum was preferred to plasma as it has been shown that different anticoagulants may affect absolute cytokine levels [15]. Cytokine levels were measured using a multiplexed particle-based flow cytometric cytokine assay [16]. Milliplex kits were purchased from Millipore (Zug, Switzerland). The procedures closely followed the manufacturer’s instructions. The analysis was
Participants were classified as never, current, or former smokers. A participant was considered as physically active if he/she reported practicing at least 2 h of leisure-time physical activity per week. Time spent walking was categorized into <30 min/day, 30–59 min/day and ≥60 min/day. Personal history of CVD was defined as self-reported diagnosis of angina, myocardial infarction, stroke, peripheral arterial disease, or history of coronary revascularization. Family history of coronary heart disease was considered if the participant reported that any family member had presented myocardial infarction or angina. Body weight and height were measured with participants standing without shoes in light indoor clothes. Overweight was defined for a BMI ≥25 and <30 kg/m2 ; obesity was defined for a BMI ≥ 30 kg/m2 . Waist was measured with a non-stretchable tape over the unclothed abdomen at the narrowest point between the lowest rib and the iliac crest [18]. Two measures were made and the mean (in centimeters) was used for analyses. Abdominal obesity was considered for a waist >102 cm for men and >88 cm for women [18]. drugs, anti-hypertensive drugs, antiAnti-diabetic inflammatory drugs and statins were assessed by systematically checking all medicines taken and brought to the study site by the participants. Statins were included as a confounding variable because they may influence cytokine levels [19]. Diabetes was defined as fasting plasma glucose (FPG) ≥ 7 mmol/L and/or presence of anti-diabetic treatment (oral drugs and/or insulin). Blood pressure (BP) was measured thrice on the left arm after at least 10 min rest in the seated position using a clinically validated automated oscillometric device (Omron® HEM-907, Matsusaka, Japan). The average of the last two BP readings was used. Hypertension was defined as mean systolic BP ≥ 140 mm Hg or mean diastolic BP ≥ 90 mm Hg or anti-hypertensive medication. Metabolic syndrome was defined according to the ATP-III criteria [20]. For women, information regarding hormone replacement therapy was also collected. 2.5. Statistical analysis Statistical analysis was conducted using SAS v.9.2 (SAS Inc., Cary, NC, USA). Quantitative variables (apart from cytokines) were expressed as mean ± standard deviation and qualitative variables as number of participants and (percentage). Comparison of cytokine levels between alcohol consumption categories were performed using Kruskall–Wallis nonparametric test on untransformed values and on log-transformed values using a generalized linear model adjusting for sex, age, body mass index, smoking, leisure-time physical activity, anti-inflammatory drug use (aspirin and other NSAIDs), statin use, hypertension, diabetes and history of cardiovascular disease. Results were presented as median with (interquartile range) of measured values, or as multivariate-adjusted mean ± standard error. The association between cytokines and alcoholic beverages was assessed by Spearman nonparametric correlation and by multivariate regression on log-transformed values adjusting for the same variables as before. For multivariate analysis, both linear and quadratic terms for alcohol were tested. Two analyses were performed: method 1, considering all alcoholic beverage units to
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Table 1 Clinical and biological characteristics of the sample, by alcohol consumption category. Non drinker N (%) Age, year Men, % BMI, kg/m2 BMI categories, % Normal Overweight Obese Waist (cm) Abdominal obesitya , % Smoking status, % Never Former Current Physical activityb , % Walking time per day, % <30 min 30–59 min ≥60 min Reported CVD, % Family history of CHD, % Diabetes, % Hypertension, % Metabolic syndromec , % Statin, % Antiinflammatory drug, %
1649 (27.1) 52.7 ± 10.9 455 (27.6) 26.4 ± 5.1
Low (1–6/wk) 2149 (35.3) 52.5 ± 10.7 866 (40.3) 25.4 ± 4.5
Moderate (7–13/wk)
High (14+/wk)
1186 (19.5) 53.5 ± 10.8 688 (58.0) 25.5 ± 4.0
1101 (18.1) 54.4 ± 10.5 875 (79.5) 26.1 ± 4.2
P, between groups* <0.001 <0.001 <0.001
744 (45.1) 564 (34.2) 341 (20.7) 89 ± 14 559 (33.9)
1137 (52.9) 731 (34.0) 281 (13.1) 87 ± 13 526 (24.5)
583 (49.2) 463 (39.0) 140 (11.8) 90 ± 12 281 (23.7)
461 (41.9) 464 (42.1) 176 (16.0) 94 ± 13 301 (27.3)
<0.001
868 (52.6) 422 (25.6) 359 (21.8) 814 (49.4)
926 (43.1) 735 (34.2) 488 (22.7) 1310 (61.0)
397 (33.5) 448 (37.8) 341 (28.8) 703 (59.3)
254 (23.1) 404 (36.7) 443 (40.2) 551 (50.1)
<0.001
623 (37.8) 517 (31.4) 509 (30.8) 108 (6.6) 463 (28.1) 119 (7.2) 586 (35.5) 405 (24.6) 174 (10.6) 315 (19.1)
969 (45.1) 717 (33.4) 463 (21.5) 123 (5.7) 622 (28.9) 111 (5.2) 679 (31.6) 398 (18.5) 193 (9.0) 372 (17.3)
526 (44.4) 374 (31.5) 286 (24.1) 68 (5.7) 345 (29.1) 71 (6.0) 403 (34.0) 232 (19.6) 129 (10.9) 201 (17.0)
485 (44.0) 297 (27.0) 319 (29.0) 88 (8.0) 311 (28.3) 101 (9.2) 548 (49.8) 307 (27.9) 159 (14.4) 183 (16.6)
<0.001
<0.001 <0.001
<0.001
0.07 0.91 <0.001 <0.001 <0.001 <0.001 0.29
Results are expressed as average ± standard deviation or as number of subjects (percentage). BMI, body mass index; CVD, cardiovascular disease. a Defined as a waist >102 cm for men and >88 cm for women. b Defined as leisure-time physical activity of at least 2 h per week. c According to ATP-III definition. * p-values from one-way ANOVA and chi-square test for continuous and categorical variables, respectively.
contain 11 g of ethanol; method 2, considering wine and beer drinks as containing 11 g of ethanol and spirits as containing 22 g of ethanol. The effect of consuming selected types of alcoholic beverages (wine, beer and spirits) was also assessed in the multivariate models by including an indicator (yes/no) of their consumption. In this setting, it was not possible to separate red from white wine or to separate the spirits rich (whisky, brandy) from those poor in polyphenols (gin, vodka). All types of alcoholic beverages were introduced simultaneously in the multivariate models, i.e. amount of alcohol due to wine, beer and spirits in the linear regression and the consumption indicators (yes/no) for wine, beer and spirits in the logistic regression. Because of cytokine values below the detection limit, we assessed the probability of being in the highest quartile of each cytokine distribution. Values below the detection limit were included in the first quartile. Analysis was conducted using multivariate logistic regression adjusting for the same variables as before. Two models were tested: (a) assessing the likelihood of being in the highest quartile vs. the other three quartiles and (b) assessing the likelihood of being in the highest vs. the lowest quartile. The effect of beverage type was assessed by including an indicator (yes/no) of their consumption in the model. Results were expressed as odds ratio (OR) and 95% confidence interval (CI). Other analyses were conducted after substituting all values of IL-1, IL-6 and TNF-␣ below LOD with a single value (0.1 pg/ml) equivalent to one-half of the non-detectable value as suggested [21]. Statistical significance was considered for p < 0.05. 3. Results 3.1. Participants’ characteristics Of the initial 6184 participants, 6085 (98.4%) had their cytokines assessed and among these, 2289 (37.6%), 451 (7.4%) and 43 (0.7%) had IL-1, IL-6 and TNF-␣ levels below detection limits,
respectively. The clinical and biological characteristics of the sample, by alcohol consumption category are summarized in Table 1. Participants in the highest alcohol consumption categories were older, more frequently men and more frequently current smokers or presenting with hypertension. A U-shaped relationship with alcohol consumption was found for BMI, waist, abdominal obesity, diabetes, metabolic syndrome and statin use, while an inverse Ushaped relationship was found for leisure-time physical activity and walking ≥60 min/day. No relationship was found with reported family history of CHD or personal history of CVD (Table 1). Finally, 35% of women reported being on hormonal replacement therapy. 3.2. Interleukin-1ˇ and alcohol No differences in IL-1 levels were found between alcohol consumption categories on bivariate or multivariate analysis (Table 2). Similar findings were obtained after stratifying for gender (Table 3) or after excluding participants on anti-inflammatory drugs (Table 4). No significant association was found between IL-1 and alcohol consumption: Spearman correlations = −0.012 (p = 0.45); −0.009 (p = 0.57); −0.017 (p = 0.28); −0.018 (p = 0.27) and 0.018 (p = 0.27) for total alcohol consumption (methods 1 and 2), wine, beer and spirits, respectively. Similar findings were obtained when the analysis was restricted to participants not taking antiinflammatory drugs (not shown). On multivariate regression, no linear or quadratic association was found between IL-1 levels and alcohol consumption in the whole sample (Table 2), after stratifying for gender (Table 3) or excluding participants taking anti-inflammatory drugs (Table 4). Also, no specific effect of wine (p = 0.83), beer (p = 0.26) or spirits (p = 0.11) was found. When IL-1 levels were categorized into quartiles, no significant trend was found between being in the highest quartile and alcohol consumption, for the entire sample (Supplementary table 1) after stratifying on gender (Supplementary table 2) or excluding
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Table 2 Cytokine levels, by alcohol consumption category.
Nc IL-1a IL-1b Nc IL-6a IL-6b Nc TNF-␣a TNF-␣b
Non drinker
Low (1–6/wk)
Moderate (7–13/wk)
High (14+/wk)
1038 1.17 (0.49–3.80) 1.54 ± 1.09 1531 1.47 (0.70–3.51) 2.08 ± 1.06 1635 2.92 (1.79–4.63) 3.17 ± 1.04
1360 1.26 (0.49–4.09) 1.62 ± 1.09 1970 1.41 (0.70–3.32) 2.06 ± 1.06 2136 2.83 (1.84–4.48) 3.19 ± 1.04
755 1.06 (0.48–3.57) 1.50 ± 1.10 1098 1.42 (0.66–3.19) 1.95 ± 1.07 1175 2.82 (1.76–4.34) 2.94 ± 1.04
643 1.13 (0.46–3.96) 1.55 ± 1.1 1035 1.70 (0.83–4.39) 2.18 ± 1.07 1096 3.15 (1.91–4.73) 3.11 ± 1.04
P, between groups
P, linear trend
P, quadratic trend
0.57 0.67
– 0.82
– 0.36
<0.001 0.31
– 0.77
– 0.71
0.02 0.06
– 0.21
– 0.31
IL, interleukin; TNF-␣, Tumor Necrosis Factor-alpha. a Data presented as median and (interquartile range). Statistical analysis by Kruskall–Wallis test. b Data presented as exponentiated adjusted mean ± standard error. Statistical analysis comparing alcohol consumption groups on log-transformed values by a general linear model adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. Linear and quadratic terms assessed by multivariate regression adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. c Number of participants with values above detection limit.
Table 3 Cytokine levels, by alcohol consumption category, stratified by gender.
Men Nc IL-1a IL-1b Nc IL-6a IL-6b Nc TNF-␣a TNF-␣b Women Nc IL-1a IL-1b Nc IL-6a IL-6b Nc TNF-␣a TNF-␣b
Non drinker
Low (1–6/wk)
Moderate (7–13/wk)
High (14+/wk)
284 0.97 (0.45–4.08) 1.49 ± 1.15 432 1.68 (0.75–3.84) 2.27 ± 1.09 450 3.21 (2.07–4.84) 3.32 ± 1.06
526 1.25 (0.46–4.53) 1.60 ± 1.13 798 1.51 (0.76–3.77) 2.09 ± 1.08 861 3.08 (1.94–4.81) 3.22 ± 1.05
426 0.97 (0.42–3.47) 1.37 ± 1.14 636 1.48 (0.70–3.42) 2.04 ± 1.09 684 2.82 (1.76–4.33) 2.79 ± 1.05
510 1.06 (0.45–3.71) 1.43 ± 1.13 831 1.73 (0.83–4.39) 2.22 ± 1.08 870 3.19 (1.95–4.81) 3.10 ± 1.05
754 1.27 (0.52–3.58) 1.69 ± 1.13 1099 1.38 (0.68–3.31) 2.07 ± 1.09 1185 2.80 (1.71–4.60) 3.33 ± 1.06
834 1.29 (0.51–3.68) 1.73 ± 1.13 1172 1.36 (0.66–3.19) 2.13 ± 1.10 1275 2.69 (1.75–4.23) 3.39 ± 1.06
329 1.17 (0.53–3.57) 1.73 ± 1.15 462 1.29 (0.63–2.92) 1.99 ± 1.11 491 2.86 (1.73–4.35) 3.37 ± 1.07
133 1.68 (0.58–5.80) 2.00 ± 1.19 204 1.57 (0.79–4.49) 2.36 ± 1.14 226 2.91 (1.73–4.50) 3.44 ± 1.08
P, between groups
P, linear trend
P, quadratic trend
0.40 0.43
– 0.47
– 0.98
0.05 0.45
– 0.80
– 0.89
0.002 0.002
– 0.07
– 0.17
0.54 0.70
– 0.17
– 0.22
0.23 0.50
– 0.56
– 0.16
0.50 0.95
– 0.82
– 0.31
IL, interleukin; TNF-␣, Tumor Necrosis Factor-alpha. a Data presented as median and (interquartile range). Statistical analysis by Kruskall–Wallis test. b Data presented as exponentiated adjusted mean ± standard error. Statistical analysis comparing alcohol consumption groups on log-transformed values by a general linear model adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. Linear and quadratic terms assessed by multivariate regression adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. For women, a further adjustment on hormone replacement therapy was performed. c Number of participants with values above detection limit.
Table 4 Cytokine levels, by alcohol consumption category, subjects on anti-inflammatory drugs excluded.
Nc IL-1 a IL-1 b Nc IL-6 a IL-6 b Nc TNF-␣ a TNF-␣ b
Non drinker
Low (1–6/wk)
Moderate (7–13/wk)
High (14+/wk)
851 1.11 (0.48–3.51) 1.47 ± 1.09 1236 1.46 (0.69–3.34) 2.03 ± 1.06 1325 2.91 (1.79–4.54) 3.15 ± 1.04
1133 1.26 (0.50–4.09) 1.62 ± 1.09 1633 1.39 (0.70–3.30) 2.05 ± 1.06 1766 2.80 (1.82–4.42) 3.15 ± 1.04
629 1.15 (0.51–3.79) 1.54 ± 1.10 913 1.43 (0.69–3.16) 1.96 ± 1.07 975 2.82 (1.70–4.34) 2.92 ± 1.04
526 1.12 (0.46–3.93) 1.54 ± 1.11 864 1.68 (0.82–4.15) 2.13 ± 1.07 914 3.11 (1.92–4.70) 3.06 ± 1.04
P, between groups
P, linear trend
P, quadratic trend
0.53 0.53
– 0.41
– 0.13
0.004 0.63
– 0.67
– 0.99
0.02 0.14
– 0.22
– 0.26
IL, interleukin; TNF-␣, Tumor Necrosis Factor-alpha. a Data presented as median and (interquartile range). Statistical analysis by Kruskall–Wallis test. b Data presented as exponentiated adjusted mean ± standard error. Statistical analysis comparing alcohol consumption groups on log-transformed values by a general linear model adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. Linear and quadratic terms assessed by multivariate regression adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. c Number of participants with values above detection limit.
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participants on anti-inflammatory drugs. No specific effect of wine, beer and spirits was found.
3.3. Interleukin 6 and alcohol High consumers (≥14 units/week) had higher IL-6 levels, but these differences were no longer significant after multivariate adjustment (Table 2), and similar results were found when the analysis was stratified by gender (Table 3) or after excluding participants on anti-inflammatory drugs (Table 4). On bivariate analysis, a small but significant positive association was found between IL-6 and alcohol consumption: Spearman correlations 0.035 (p < 0.01); 0.036 (p = 0.007); 0.023 (p = 0.08); 0.033 (p < 0.05) and 0.025 (p = 0.06) for total alcohol consumption (methods 1 and 2), wine, beer and spirits, respectively, and similar findings were obtained when restricting the analysis to participants not on anti-inflammatory drugs (not shown). When the analysis was restricted to drinkers, positive relationships were found with all types of alcoholic beverages (not shown). No linear or quadratic association was found between IL-6 levels and alcohol consumption after multivariate adjustment in the whole sample (Table 2), after stratifying on gender (Table 3) or after excluding participants taking anti-inflammatory drugs (Table 4). No specific effect of wine (p = 0.67), beer (p = 0.47) or spirits (p = 0.75) was found. When IL-6 levels were categorized into quartiles, a significant quadratic trend between alcohol consumption and IL-6 categories was found (Supplementary table 1). After stratifying on gender, no clear findings were obtained, although a trend for lower odds for being in the highest vs. the lowest IL-6 quartile among moderate consumers (7–13 units/week) was noted (Supplementary table 2). After excluding participants on anti-inflammatory drugs, the quadratic trend remained but was no longer significant (not shown). No specific effect of wine, beer and spirits was found.
3.4. Tumor necrosis factor-˛ and alcohol Moderate alcohol consumers showed lower TNF-␣ levels on bivariate or multivariate analysis (Table 2). When the analysis was stratified on gender, similar findings were observed in men but not in women (Table 3). On bivariate analysis, no significant associations were found between TNF-␣ and alcohol consumption: Spearman correlations 0.010 (p = 0.42); 0.012 (p = 0.37); 0.010 (p = 0.44); 0.005 (p = 0.70) and 0.023 (p = 0.06) for total alcohol consumption (methods 1 and 2), wine, beer and spirits, respectively. Similar findings were obtained when the analysis was restricted to participants not taking anti-inflammatory drugs (not shown) with a small but significant association with spirits (Spearman correlation 0.030, p < 0.05). No linear or quadratic association was found between TNF-␣ levels and alcohol consumption after multivariate adjustment in the whole sample (Table 2), after stratifying on gender (Table 3) or after excluding participants taking anti-inflammatory drugs (Table 4). No specific effect of wine (p = 0.93), beer (p = 0.33) or spirits (p = 0.17) was found. When TNF-␣ levels were categorized into quartiles, moderate drinkers had lower odds 0.81 (0.68–0.98) of being in the highest quartile vs. the others and also of being in the highest quartile vs. the lowest: 0.75 (0.60–0.94) (Supplementary table 1). Similar findings were obtained in men but not in women (Supplementary table 2), and neither a linear nor a quadratic trend was found (Supplementary table 2). No specific effect of wine and beer was found, while a borderline association was found with spirits consumption: 1.21 (1.03–1.43).
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4. Discussion To our knowledge, this is currently the largest ever populationbased sample in which the associations between alcohol consumption and IL-1, IL-6, and TNF-␣ levels have been examined. Antioxidant phenols present in wine have been shown to decrease IL-1 production [22,23], while higher IL-1 levels have been found among chronic alcoholics [24]. To our knowledge, no study has ever reported on the association between moderatehigh alcohol consumption and IL-1 levels at the population level. Our results showed no consistent association between alcohol consumption or any alcoholic beverage (with the exception of spirits in men) and IL-1 levels. Hence, it is unlikely that alcohol (ethanol) or any other component of alcoholic beverages exerts a clinically significant effect on IL-1 levels, although further studies are needed to confirm our findings. Alcohol consumption has been shown to increase [4,25], decrease [5], exert a U- or J-shaped effect [6,26] or even no effect at all [27] on IL-6 levels. A possible explanation for these discrepant results might be the differing alcohol consumption levels: some studies included chronic alcoholics [25] while others included subjects with a low alcohol consumption [26]. In this study, a Ushape association between alcohol consumption and IL-6 levels was found, a finding in agreement with others [6,26]. It is possible that the association might depend on the range of the U-curve captured by the alcohol consumption, studies focusing on a low consumption reporting an inverse association, while studies focusing on a high consumption reported a positive association. Overall, our results suggest that moderate alcohol consumption (<2 units/day) might exert a beneficial effect by decreasing IL-6 levels. Contrary to one study [28] but in agreement with another [5], no specific effect was found between IL-6 levels and wine consumption. Although it was not possible to separate white from red wine in this study, our results strongly suggest that the association between IL-6 and alcoholic beverages is mainly mediated by ethanol rather than by any non-alcoholic specific component. Ethanol decreases IL-6 release from cultured subcutaneous human adipose tissue [25], but the precise mechanism by which moderate alcohol consumption reduces IL-6 levels awaits further investigations. The mechanisms by which alcohol intake modulate TNF-␣ levels are poorly understood; no direct effect of alcohol consumption on TNF-␣ levels has been shown [7,29]. Based on this study’s findings, moderate alcohol consumption (<2 units/day) might exert a beneficial effect by decreasing TNF-␣ levels, but it would be of interest to see if these findings can be replicated in other population-based studies. In agreement with a previous study [8], no specific effect of wine or other alcoholic beverages on TNF-␣ levels was found, although the borderline effect of spirits consumption should be further investigated. Based on the current findings, the association between TNF-␣ and alcoholic beverages appears to be mainly mediated by ethanol rather than by non-alcoholic specific components. This study has some limitations worth acknowledging. Although the participation rate was similar to other epidemiological studies [30], it was rather low, which might limit the generalization of findings. The fact that all participants were living in a city might also preclude generalization to rural settings. Categorization of alcohol consumption is debatable and might lead to differing conclusions [31]; still, changing the alcohol categories by creating a supplementary “very high” (35+/week) category or by splitting alcohol consumption in quartiles did not change notably the results (not shown). It is also possible that high consumption might be underreported, although the percentage of high or very high consumers is higher than reported in other studies. The very high cytokine values of some participants might have biased the results; still, restricting the analysis to cytokine values below the 95th percentile led to comparable results (not shown). No data regarding diet was
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available, so it was not possible to adjust for it. Similarly, only data for leisure time physical activity and walking time was available, and it was not possible to adjust for other types of physical activity. Finally, the fact that moderate alcohol consumption reduces IL-6 and TNF-␣ levels should not prompt teetotallers to start drinking to reduce their inflammatory profile; a more reasonable recommendation would be that moderate drinkers can still enjoy their favorite drink without unfavorably impacting their inflammatory profile. In summary, our results suggest that IL-1 levels do not appear be associated with alcohol consumption. Moderate alcohol consumption is associated with lower levels of IL-6 and TNF-␣, and this association appears to be gender dependent. No consistent specific effect of the type of alcohol consumed on cytokine levels could be demonstrated. Acknowledgements The authors also express their gratitude to the participants in the Lausanne CoLaus study and to the investigators who have contributed to the recruitment, in particular Yolande Barreau, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey and Sylvie Mermoud for data collection. The CoLaus study was supported by research grants from the Swiss National Science Foundation (grant no: 33CSCO-122661) and the Faculty of Biology and Medicine of Lausanne, Switzerland. M Bochud is supported by the Swiss School of Public Health Plus (SSPH+). GW, MP and PV designed the experiment; FB and AU collected data; PM-V analyzed the data; PM-V, MB and RvK wrote the manuscript; TL, FF, J-MG, FP and CH provided significant advice and consultation. PV and GW received an unrestricted grant for GSK to build the CoLaus study. GSK had no influence in the study design, implementation, analysis and interpretation of the data. The authors report no conflict of interest. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atherosclerosis.2012.02.011. References [1] Marques-Vidal P, Montaye M, Arveiler D, et al. Alcohol consumption and cardiovascular disease: differential effects in France and Northern Ireland. The PRIME study. Eur J Cardiovasc Prev Rehabil 2004;11:336–43. [2] Imhof A, Woodward M, Doering A, et al. Overall alcohol intake, beer, wine, and systemic markers of inflammation in western Europe: results from three MONICA samples (Augsburg, Glasgow, Lille). Eur Heart J 2004;25:2092–100. [3] Danesh J, Kaptoge S, Mann AG, et al. Long-term interleukin-6 levels and subsequent risk of coronary heart disease: two new prospective studies and a systematic review. PLoS Med 2008;5:e78. [4] Hoffmeister A, Imhof A, Rothenbacher D, et al. Moderater Alkoholkonsum und Plasmakonzentration sensitiver Entzundungsmarker. Hinweise auf einen atheroprotektiven Zusammenhang [Moderate alcohol consumption and plasma concentration of sensitive markers of inflammation. Comment on an atheroprotective relationship]. Dtsch Med Wochenschr 2003;128:2237–41. [5] Sacanella E, Vázquez-Agell M, Mena MP, et al. Down-regulation of adhesion molecules and other inflammatory biomarkers after moderate wine consumption in healthy women: a randomized trial. Am J Clin Nutr 2007;86:1463–9.
[6] Volpato S, Pahor M, Ferrucci L, et al. Relationship of alcohol intake with inflammatory markers and plasminogen activator inhibitor-1 in well-functioning older adults: the Health, Aging, and Body Composition study. Circulation 2004;109:607–12. [7] González-Quintela A, Campos J, Loidi L, et al. Serum TNF-alpha levels in relation to alcohol consumption and common TNF gene polymorphisms. Alcohol 2008;42:513–8. [8] Watzl B, Bub A, Pretzer G, et al. Daily moderate amounts of red wine or alcohol have no effect on the immune system of healthy men. Eur J Clin Nutr 2004;58:40–5. [9] Foerster M, Marques-Vidal P, Gmel G, et al. Alcohol drinking and cardiovascular risk in a population with high mean alcohol consumption. Am J Cardiol 2009;103:361–8. [10] Firmann M, Mayor V, Vidal PM, et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord 2008;8:6. [11] Dufour MC. What is moderate drinking? Defining drinks and drinking levels. Alcohol Res Health 1999;23:5–14. [12] Giovannucci E, Colditz G, Stampfer MJ, et al. The assessment of alcohol consumption by a simple self-administered questionnaire. Am J Epidemiol 1991;133:810–7. [13] Bernstein M, Morabia A, Costanza MC, et al. Equilibre nutritionnel de l’alimentation de la population adulte résidant a Genève [Nutritional balance of the diet of the adult residents of Geneva]. Soz Praventivmed 1994;39:333–44. [14] Morabia A, Bernstein M, Kumanyika S, et al. Développement et validation d’un questionnaire alimentaire semi-quantitatif à partir d’une enquête de population [Development and validation of a semi-quantitative food questionnaire based on a population survey]. Soz Praventivmed 1994;39:345–69. [15] Skeppholm M, Wallén NH, Blombäck M, Kallner A. Can both EDTA and citrate plasma samples be used in measurements of fibrinogen and C-reactive protein concentrations? Clin Chem Lab Med 2008;46:1175–9. [16] Vignali DA. Multiplexed particle-based flow cytometric assays. J Immunol Methods 2000;243:243–55. [17] Marques-Vidal P, Bochud M, Bastardot F, et al. Levels and determinants of inflammatory biomarkers in a Swiss population-based sample (CoLaus study). PLoS One 2011;6:e21002. [18] Lean ME, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ 1995;311:158–61. [19] Oka H, Ikeda S, Koga S, Miyahara Y, Kohno S. Atorvastatin induces associated reductions in platelet P-selectin, oxidized low-density lipoprotein, and interleukin-6 in patients with coronary artery diseases. Heart Vessels 2008;23:249–56. [20] Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005;112:2735–52. [21] Hornung R, Reed L. Estimation of average concentration in the presence of nondetectable values. Appl Occup Environ Hyg 1990;5:46–51. [22] Olholm J, Paulsen SK, Cullberg KB, Richelsen B, Pedersen SB. Anti-inflammatory effect of resveratrol on adipokine expression and secretion in human adipose tissue explants. Int J Obes (Lond) 2010. [23] Bertelli A, Migliori M, Bertelli AA, et al. Effect of some white wine phenols in preventing inflammatory cytokine release. Drugs Exp Clin Res 2002;28(1):11–5. [24] Laso FJ, Vaquero JM, Almeida J, Marcos M, Orfao A. Production of inflammatory cytokines by peripheral blood monocytes in chronic alcoholism: relationship with ethanol intake and liver disease. Cytometry B Clin Cytom 2007;72:408–15. [25] Nicolaou C, Chatzipanagiotou S, Tzivos D, et al. Serum cytokine concentrations in alcohol-dependent individuals without liver disease. Alcohol 2004;32:243–7. [26] Pai JK, Hankinson SE, Thadhani R, et al. Moderate alcohol consumption and lower levels of inflammatory markers in US men and women. Atherosclerosis 2006;186:113–20. [27] Tousoulis D, Ntarladimas I, Antoniades C, et al. Acute effects of different alcoholic beverages on vascular endothelium, inflammatory markers and thrombosis fibrinolysis system. Clin Nutr 2008;27:594–600. [28] Vázquez-Agell M, Sacanella E, Tobias E, et al. Inflammatory markers of atherosclerosis are decreased after moderate consumption of cava (sparkling wine) in men with low cardiovascular risk. J Nutr 2007;137:2279–84. [29] Sierksma A, Patel H, Ouchi N, et al. Effect of moderate alcohol consumption on adiponectin, tumor necrosis factor-alpha, and insulin sensitivity. Diabetes Care 2004;27:184–9. [30] Tolonen H, Koponen P, Aromaa A, et al. Review of health examination surveys in Europe. Helsinki, Finland: National Public Health Institute (KTL); 2008. Report No.: B18/2008. [31] Costanzo S, Di Castelnuovo A, Donati MB, Iacoviello L, de Gaetano G. Cardiovascular and overall mortality risk in relation to alcohol consumption in patients with cardiovascular disease. Circulation 2010;121:1951–9.
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Associations between alcohol consumption and selected cytokines in a Swiss population-based sample (CoLaus study) Pedro Marques-Vidal a,∗ , Murielle Bochud a , Franc¸ois Bastardot b , Roland von Känel c , Franc¸ois Ferrero d , Jean-Michel Gaspoz e , Fred Paccaud a , Adrian Urwyler f , Thomas Lüscher g , Christoph Hock h , Gérard Waeber b , Martin Preisig i , Peter Vollenweider b a
Institute of Social and Preventive Medicine (IUMSP), CHUV and Faculty of Biology and Medicine, 1066 Epalinges, Switzerland Department of Medicine, Internal Medicine, CHUV and Faculty of Biology and Medicine, Lausanne, Switzerland c Division of Psychosomatic Medicine, Bern University Hospital Inselspital, and University of Bern, Switzerland d Department of Psychiatry, University of Geneva, Geneva, Switzerland e Department of Community Medicine, University of Geneva, Geneva, Switzerland f Cytolab, Dällikon, Switzerland g Department of Medicine, University of Zürich, Zürich, Switzerland h Department of Psychiatry, University Hospital Zürich, Zürich, Switzerland i Department of Psychiatry, CHUV, Lausanne, Switzerland b
a r t i c l e
i n f o
Article history: Received 30 September 2011 Received in revised form 19 December 2011 Accepted 6 February 2012 Available online 18 February 2012 Keywords: IL-1 IL-6 TNF-␣ Alcohol consumption Population study Inflammation Wine Beer
a b s t r a c t Objective: To assess the associations between alcohol consumption and cytokine levels (interleukin-1beta – IL-1; interleukin-6 – IL-6 and tumor necrosis factor-␣ – TNF-␣) in a Caucasian population. Methods: Population sample of 2884 men and 3201 women aged 35–75. Alcohol consumption was categorized as nondrinkers, low (1–6 drinks/week), moderate (7–13/week) and high (14+/week). Results: No difference in IL-1 levels was found between alcohol consumption categories. Low and moderate alcohol consumption led to lower IL-6 levels: median (interquartile range) 1.47 (0.70–3.51), 1.41 (0.70–3.32), 1.42 (0.66–3.19) and 1.70 (0.83–4.39) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p < 0.01, but this association was no longer significant after multivariate adjustment. Compared to nondrinkers, moderate drinkers had the lowest odds (Odds ratio = 0.86 (0.71–1.03)) of being in the highest quartile of IL-6, with a significant (p < 0.05) quadratic trend. Low and moderate alcohol consumption led to lower TNF-␣ levels: 2.92 (1.79–4.63), 2.83 (1.84–4.48), 2.82 (1.76–4.34) and 3.15 (1.91–4.73) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p < 0.02, and this difference remained borderline significant (p = 0.06) after multivariate adjustment. Moderate drinkers had a lower odds (0.81 [0.68–0.98]) of being in the highest quartile of TNF-␣. No specific alcoholic beverage (wine, beer or spirits) effect was found. Conclusions: Moderate alcohol consumption is associated with lower levels of IL-6 and (to a lesser degree) of TNF-␣, irrespective of the type of alcohol consumed. No association was found between IL-1 levels and alcohol consumption. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
∗ Corresponding author at: Institut Universitaire de Médecine Sociale et Préventive, Bâtiment Biopôle 1, Bureau 091, Route de la Corniche, 2, 1066 Epalinges, Switzerland. Tel.: +41 21 314 72 65; fax: +41 21 314 73 73. E-mail addresses: [email protected] (P. Marques-Vidal), [email protected] (M. Bochud), [email protected] (F. Bastardot), [email protected] (R. von Känel), [email protected] (F. Ferrero), [email protected] (J.-M. Gaspoz), [email protected] (F. Paccaud), [email protected] (A. Urwyler), cardiotfl@gmx.ch (T. Lüscher), [email protected] (C. Hock), [email protected] (G. Waeber), [email protected] (M. Preisig), [email protected] (P. Vollenweider). 0021-9150/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2012.02.011
Moderate wine consumption is associated with a decreased risk of cardiovascular disease [1], mainly through its effects on HDL cholesterol, but also on inflammatory markers [2]. Cytokines, such as interleukin 1 (IL-1), interleukin 6 (IL-6) and tumor necrosis factor (TNF-␣) have been associated with an increased risk of developing coronary heart disease [3]. The effects of alcohol consumption on these cytokines are controversial. In epidemiological studies, alcohol consumption increased [4], decreased [5] or had a J-shaped association [6] on IL-6 levels. Finally, no specific effect of wine on TNF-␣ levels was found [6–8].
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In a previous study [9], we showed that CRP levels increase with alcohol consumption in men but not in women. In this study, we further assessed the effects of alcohol consumption during the previous week on additional inflammatory markers (IL-1, IL-6 and TNF-␣).
conducted using a conventional flow cytometer (FC500 MPL, BeckmanCoulter, Nyon, Switzerland). Intra and inter-assay coefficients of variation (CV) were 15% and 16.7% for IL-1, 16.9% and 16.1% for IL-6 and 12.5% and 13.5% for TNF-␣, respectively. Lower detection limits were 0.2 pg/ml and a good reproducibility has been shown [17].
2. Participants and methods 2.4. Lifestyle and clinical data 2.1. Recruitment The sampling procedure of the CoLaus Study has been described previously [10]. Briefly, the complete list of Lausanne inhabitants aged 35–75 years (n = 56,694) was provided by the population registry of the city. A simple, nonstratified random sample of 35% of the overall population was drawn. The following inclusion criteria were applied: (a) written informed consent; (b) age 35–75 years; (c) willingness to take part in the examination and donate blood sample; (d) Caucasian origin. Recruitment began in June 2003 and ended in May 2006. Participation rate was 41%. All participants attended the outpatient clinic of the University Hospital of Lausanne in the morning after an overnight fast. Data were collected by trained field interviewers in a single visit lasting about 60 min. 2.2. Alcohol consumption Alcohol intake was assessed by the self-reported alcohol consumption of the last seven days, expressed as the number of standard drinks. A standard drink was defined as a glass of wine, a bottle of beer or a shot of spirits, approximating 10–12 g ethanol [11]. Subjects were categorized as nondrinkers, low (1–6 drinks/week), moderate (7–13/week) and high (14+/week). Questionnaire-based data on alcohol consumption have been shown to be well correlated [12]. Alcohol consumption was reassessed about one year later in a sub-sample of 1772 women and 1586 men; nonparametric Spearman correlations between the first and the second assessments were 0.64 in women and 0.66 in men (p < 0.001). Alcohol consumption was re-assessed six years later among 3937 participants (2144 women and 1793 men) using the same questionnaire and a food frequency questionnaire validated in the French-speaking part of Switzerland [13,14]. The correlation between the alcohol consumption assessed using the first questionnaire and the validated food frequency questionnaire was 0.82 for all participants (p < 0.001), 0.81 in women (p < 0.001) and 0.80 (p < 0.001) in men. Similarly, the correlations between alcohol consumption at baseline and six years afterwards were 0.71 for all participants (p < 0.001), 0.67 in women (p < 0.001) and 0.66 in men (p < 0.001). In this study, baseline alcohol consumption was used. 2.3. Biological measurements Venous blood samples were drawn after a minimum fasting of 6 h. Participants were asked to refrain from doing any strenuous exercise and to maintain their usual lifestyle habits the day before examination. Glucose measurements were performed on fresh blood samples within 2 h of blood collection in a Modular P apparatus (Roche Diagnostics, Switzerland) using the glucose dehydrogenase method, with maximum inter- and intra-batch CVs of 2.1% and 1.0%, respectively. Serum samples were kept at −80 ◦ C before assessment of cytokines and sent in dry ice to the laboratory. For cytokine measurements, serum was preferred to plasma as it has been shown that different anticoagulants may affect absolute cytokine levels [15]. Cytokine levels were measured using a multiplexed particle-based flow cytometric cytokine assay [16]. Milliplex kits were purchased from Millipore (Zug, Switzerland). The procedures closely followed the manufacturer’s instructions. The analysis was
Participants were classified as never, current, or former smokers. A participant was considered as physically active if he/she reported practicing at least 2 h of leisure-time physical activity per week. Time spent walking was categorized into <30 min/day, 30–59 min/day and ≥60 min/day. Personal history of CVD was defined as self-reported diagnosis of angina, myocardial infarction, stroke, peripheral arterial disease, or history of coronary revascularization. Family history of coronary heart disease was considered if the participant reported that any family member had presented myocardial infarction or angina. Body weight and height were measured with participants standing without shoes in light indoor clothes. Overweight was defined for a BMI ≥25 and <30 kg/m2 ; obesity was defined for a BMI ≥ 30 kg/m2 . Waist was measured with a non-stretchable tape over the unclothed abdomen at the narrowest point between the lowest rib and the iliac crest [18]. Two measures were made and the mean (in centimeters) was used for analyses. Abdominal obesity was considered for a waist >102 cm for men and >88 cm for women [18]. drugs, anti-hypertensive drugs, antiAnti-diabetic inflammatory drugs and statins were assessed by systematically checking all medicines taken and brought to the study site by the participants. Statins were included as a confounding variable because they may influence cytokine levels [19]. Diabetes was defined as fasting plasma glucose (FPG) ≥ 7 mmol/L and/or presence of anti-diabetic treatment (oral drugs and/or insulin). Blood pressure (BP) was measured thrice on the left arm after at least 10 min rest in the seated position using a clinically validated automated oscillometric device (Omron® HEM-907, Matsusaka, Japan). The average of the last two BP readings was used. Hypertension was defined as mean systolic BP ≥ 140 mm Hg or mean diastolic BP ≥ 90 mm Hg or anti-hypertensive medication. Metabolic syndrome was defined according to the ATP-III criteria [20]. For women, information regarding hormone replacement therapy was also collected. 2.5. Statistical analysis Statistical analysis was conducted using SAS v.9.2 (SAS Inc., Cary, NC, USA). Quantitative variables (apart from cytokines) were expressed as mean ± standard deviation and qualitative variables as number of participants and (percentage). Comparison of cytokine levels between alcohol consumption categories were performed using Kruskall–Wallis nonparametric test on untransformed values and on log-transformed values using a generalized linear model adjusting for sex, age, body mass index, smoking, leisure-time physical activity, anti-inflammatory drug use (aspirin and other NSAIDs), statin use, hypertension, diabetes and history of cardiovascular disease. Results were presented as median with (interquartile range) of measured values, or as multivariate-adjusted mean ± standard error. The association between cytokines and alcoholic beverages was assessed by Spearman nonparametric correlation and by multivariate regression on log-transformed values adjusting for the same variables as before. For multivariate analysis, both linear and quadratic terms for alcohol were tested. Two analyses were performed: method 1, considering all alcoholic beverage units to
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Table 1 Clinical and biological characteristics of the sample, by alcohol consumption category. Non drinker N (%) Age, year Men, % BMI, kg/m2 BMI categories, % Normal Overweight Obese Waist (cm) Abdominal obesitya , % Smoking status, % Never Former Current Physical activityb , % Walking time per day, % <30 min 30–59 min ≥60 min Reported CVD, % Family history of CHD, % Diabetes, % Hypertension, % Metabolic syndromec , % Statin, % Antiinflammatory drug, %
1649 (27.1) 52.7 ± 10.9 455 (27.6) 26.4 ± 5.1
Low (1–6/wk) 2149 (35.3) 52.5 ± 10.7 866 (40.3) 25.4 ± 4.5
Moderate (7–13/wk)
High (14+/wk)
1186 (19.5) 53.5 ± 10.8 688 (58.0) 25.5 ± 4.0
1101 (18.1) 54.4 ± 10.5 875 (79.5) 26.1 ± 4.2
P, between groups* <0.001 <0.001 <0.001
744 (45.1) 564 (34.2) 341 (20.7) 89 ± 14 559 (33.9)
1137 (52.9) 731 (34.0) 281 (13.1) 87 ± 13 526 (24.5)
583 (49.2) 463 (39.0) 140 (11.8) 90 ± 12 281 (23.7)
461 (41.9) 464 (42.1) 176 (16.0) 94 ± 13 301 (27.3)
<0.001
868 (52.6) 422 (25.6) 359 (21.8) 814 (49.4)
926 (43.1) 735 (34.2) 488 (22.7) 1310 (61.0)
397 (33.5) 448 (37.8) 341 (28.8) 703 (59.3)
254 (23.1) 404 (36.7) 443 (40.2) 551 (50.1)
<0.001
623 (37.8) 517 (31.4) 509 (30.8) 108 (6.6) 463 (28.1) 119 (7.2) 586 (35.5) 405 (24.6) 174 (10.6) 315 (19.1)
969 (45.1) 717 (33.4) 463 (21.5) 123 (5.7) 622 (28.9) 111 (5.2) 679 (31.6) 398 (18.5) 193 (9.0) 372 (17.3)
526 (44.4) 374 (31.5) 286 (24.1) 68 (5.7) 345 (29.1) 71 (6.0) 403 (34.0) 232 (19.6) 129 (10.9) 201 (17.0)
485 (44.0) 297 (27.0) 319 (29.0) 88 (8.0) 311 (28.3) 101 (9.2) 548 (49.8) 307 (27.9) 159 (14.4) 183 (16.6)
<0.001
<0.001 <0.001
<0.001
0.07 0.91 <0.001 <0.001 <0.001 <0.001 0.29
Results are expressed as average ± standard deviation or as number of subjects (percentage). BMI, body mass index; CVD, cardiovascular disease. a Defined as a waist >102 cm for men and >88 cm for women. b Defined as leisure-time physical activity of at least 2 h per week. c According to ATP-III definition. * p-values from one-way ANOVA and chi-square test for continuous and categorical variables, respectively.
contain 11 g of ethanol; method 2, considering wine and beer drinks as containing 11 g of ethanol and spirits as containing 22 g of ethanol. The effect of consuming selected types of alcoholic beverages (wine, beer and spirits) was also assessed in the multivariate models by including an indicator (yes/no) of their consumption. In this setting, it was not possible to separate red from white wine or to separate the spirits rich (whisky, brandy) from those poor in polyphenols (gin, vodka). All types of alcoholic beverages were introduced simultaneously in the multivariate models, i.e. amount of alcohol due to wine, beer and spirits in the linear regression and the consumption indicators (yes/no) for wine, beer and spirits in the logistic regression. Because of cytokine values below the detection limit, we assessed the probability of being in the highest quartile of each cytokine distribution. Values below the detection limit were included in the first quartile. Analysis was conducted using multivariate logistic regression adjusting for the same variables as before. Two models were tested: (a) assessing the likelihood of being in the highest quartile vs. the other three quartiles and (b) assessing the likelihood of being in the highest vs. the lowest quartile. The effect of beverage type was assessed by including an indicator (yes/no) of their consumption in the model. Results were expressed as odds ratio (OR) and 95% confidence interval (CI). Other analyses were conducted after substituting all values of IL-1, IL-6 and TNF-␣ below LOD with a single value (0.1 pg/ml) equivalent to one-half of the non-detectable value as suggested [21]. Statistical significance was considered for p < 0.05. 3. Results 3.1. Participants’ characteristics Of the initial 6184 participants, 6085 (98.4%) had their cytokines assessed and among these, 2289 (37.6%), 451 (7.4%) and 43 (0.7%) had IL-1, IL-6 and TNF-␣ levels below detection limits,
respectively. The clinical and biological characteristics of the sample, by alcohol consumption category are summarized in Table 1. Participants in the highest alcohol consumption categories were older, more frequently men and more frequently current smokers or presenting with hypertension. A U-shaped relationship with alcohol consumption was found for BMI, waist, abdominal obesity, diabetes, metabolic syndrome and statin use, while an inverse Ushaped relationship was found for leisure-time physical activity and walking ≥60 min/day. No relationship was found with reported family history of CHD or personal history of CVD (Table 1). Finally, 35% of women reported being on hormonal replacement therapy. 3.2. Interleukin-1ˇ and alcohol No differences in IL-1 levels were found between alcohol consumption categories on bivariate or multivariate analysis (Table 2). Similar findings were obtained after stratifying for gender (Table 3) or after excluding participants on anti-inflammatory drugs (Table 4). No significant association was found between IL-1 and alcohol consumption: Spearman correlations = −0.012 (p = 0.45); −0.009 (p = 0.57); −0.017 (p = 0.28); −0.018 (p = 0.27) and 0.018 (p = 0.27) for total alcohol consumption (methods 1 and 2), wine, beer and spirits, respectively. Similar findings were obtained when the analysis was restricted to participants not taking antiinflammatory drugs (not shown). On multivariate regression, no linear or quadratic association was found between IL-1 levels and alcohol consumption in the whole sample (Table 2), after stratifying for gender (Table 3) or excluding participants taking anti-inflammatory drugs (Table 4). Also, no specific effect of wine (p = 0.83), beer (p = 0.26) or spirits (p = 0.11) was found. When IL-1 levels were categorized into quartiles, no significant trend was found between being in the highest quartile and alcohol consumption, for the entire sample (Supplementary table 1) after stratifying on gender (Supplementary table 2) or excluding
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Table 2 Cytokine levels, by alcohol consumption category.
Nc IL-1a IL-1b Nc IL-6a IL-6b Nc TNF-␣a TNF-␣b
Non drinker
Low (1–6/wk)
Moderate (7–13/wk)
High (14+/wk)
1038 1.17 (0.49–3.80) 1.54 ± 1.09 1531 1.47 (0.70–3.51) 2.08 ± 1.06 1635 2.92 (1.79–4.63) 3.17 ± 1.04
1360 1.26 (0.49–4.09) 1.62 ± 1.09 1970 1.41 (0.70–3.32) 2.06 ± 1.06 2136 2.83 (1.84–4.48) 3.19 ± 1.04
755 1.06 (0.48–3.57) 1.50 ± 1.10 1098 1.42 (0.66–3.19) 1.95 ± 1.07 1175 2.82 (1.76–4.34) 2.94 ± 1.04
643 1.13 (0.46–3.96) 1.55 ± 1.1 1035 1.70 (0.83–4.39) 2.18 ± 1.07 1096 3.15 (1.91–4.73) 3.11 ± 1.04
P, between groups
P, linear trend
P, quadratic trend
0.57 0.67
– 0.82
– 0.36
<0.001 0.31
– 0.77
– 0.71
0.02 0.06
– 0.21
– 0.31
IL, interleukin; TNF-␣, Tumor Necrosis Factor-alpha. a Data presented as median and (interquartile range). Statistical analysis by Kruskall–Wallis test. b Data presented as exponentiated adjusted mean ± standard error. Statistical analysis comparing alcohol consumption groups on log-transformed values by a general linear model adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. Linear and quadratic terms assessed by multivariate regression adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. c Number of participants with values above detection limit.
Table 3 Cytokine levels, by alcohol consumption category, stratified by gender.
Men Nc IL-1a IL-1b Nc IL-6a IL-6b Nc TNF-␣a TNF-␣b Women Nc IL-1a IL-1b Nc IL-6a IL-6b Nc TNF-␣a TNF-␣b
Non drinker
Low (1–6/wk)
Moderate (7–13/wk)
High (14+/wk)
284 0.97 (0.45–4.08) 1.49 ± 1.15 432 1.68 (0.75–3.84) 2.27 ± 1.09 450 3.21 (2.07–4.84) 3.32 ± 1.06
526 1.25 (0.46–4.53) 1.60 ± 1.13 798 1.51 (0.76–3.77) 2.09 ± 1.08 861 3.08 (1.94–4.81) 3.22 ± 1.05
426 0.97 (0.42–3.47) 1.37 ± 1.14 636 1.48 (0.70–3.42) 2.04 ± 1.09 684 2.82 (1.76–4.33) 2.79 ± 1.05
510 1.06 (0.45–3.71) 1.43 ± 1.13 831 1.73 (0.83–4.39) 2.22 ± 1.08 870 3.19 (1.95–4.81) 3.10 ± 1.05
754 1.27 (0.52–3.58) 1.69 ± 1.13 1099 1.38 (0.68–3.31) 2.07 ± 1.09 1185 2.80 (1.71–4.60) 3.33 ± 1.06
834 1.29 (0.51–3.68) 1.73 ± 1.13 1172 1.36 (0.66–3.19) 2.13 ± 1.10 1275 2.69 (1.75–4.23) 3.39 ± 1.06
329 1.17 (0.53–3.57) 1.73 ± 1.15 462 1.29 (0.63–2.92) 1.99 ± 1.11 491 2.86 (1.73–4.35) 3.37 ± 1.07
133 1.68 (0.58–5.80) 2.00 ± 1.19 204 1.57 (0.79–4.49) 2.36 ± 1.14 226 2.91 (1.73–4.50) 3.44 ± 1.08
P, between groups
P, linear trend
P, quadratic trend
0.40 0.43
– 0.47
– 0.98
0.05 0.45
– 0.80
– 0.89
0.002 0.002
– 0.07
– 0.17
0.54 0.70
– 0.17
– 0.22
0.23 0.50
– 0.56
– 0.16
0.50 0.95
– 0.82
– 0.31
IL, interleukin; TNF-␣, Tumor Necrosis Factor-alpha. a Data presented as median and (interquartile range). Statistical analysis by Kruskall–Wallis test. b Data presented as exponentiated adjusted mean ± standard error. Statistical analysis comparing alcohol consumption groups on log-transformed values by a general linear model adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. Linear and quadratic terms assessed by multivariate regression adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, anti-inflammatory drug use, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. For women, a further adjustment on hormone replacement therapy was performed. c Number of participants with values above detection limit.
Table 4 Cytokine levels, by alcohol consumption category, subjects on anti-inflammatory drugs excluded.
Nc IL-1 a IL-1 b Nc IL-6 a IL-6 b Nc TNF-␣ a TNF-␣ b
Non drinker
Low (1–6/wk)
Moderate (7–13/wk)
High (14+/wk)
851 1.11 (0.48–3.51) 1.47 ± 1.09 1236 1.46 (0.69–3.34) 2.03 ± 1.06 1325 2.91 (1.79–4.54) 3.15 ± 1.04
1133 1.26 (0.50–4.09) 1.62 ± 1.09 1633 1.39 (0.70–3.30) 2.05 ± 1.06 1766 2.80 (1.82–4.42) 3.15 ± 1.04
629 1.15 (0.51–3.79) 1.54 ± 1.10 913 1.43 (0.69–3.16) 1.96 ± 1.07 975 2.82 (1.70–4.34) 2.92 ± 1.04
526 1.12 (0.46–3.93) 1.54 ± 1.11 864 1.68 (0.82–4.15) 2.13 ± 1.07 914 3.11 (1.92–4.70) 3.06 ± 1.04
P, between groups
P, linear trend
P, quadratic trend
0.53 0.53
– 0.41
– 0.13
0.004 0.63
– 0.67
– 0.99
0.02 0.14
– 0.22
– 0.26
IL, interleukin; TNF-␣, Tumor Necrosis Factor-alpha. a Data presented as median and (interquartile range). Statistical analysis by Kruskall–Wallis test. b Data presented as exponentiated adjusted mean ± standard error. Statistical analysis comparing alcohol consumption groups on log-transformed values by a general linear model adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. Linear and quadratic terms assessed by multivariate regression adjusting for sex, age, body mass index, waist, smoking, leisure-time physical activity, walking time, statin use, hypertension, diabetes, family history of coronary heart disease and personal history of cardiovascular disease. c Number of participants with values above detection limit.
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participants on anti-inflammatory drugs. No specific effect of wine, beer and spirits was found.
3.3. Interleukin 6 and alcohol High consumers (≥14 units/week) had higher IL-6 levels, but these differences were no longer significant after multivariate adjustment (Table 2), and similar results were found when the analysis was stratified by gender (Table 3) or after excluding participants on anti-inflammatory drugs (Table 4). On bivariate analysis, a small but significant positive association was found between IL-6 and alcohol consumption: Spearman correlations 0.035 (p < 0.01); 0.036 (p = 0.007); 0.023 (p = 0.08); 0.033 (p < 0.05) and 0.025 (p = 0.06) for total alcohol consumption (methods 1 and 2), wine, beer and spirits, respectively, and similar findings were obtained when restricting the analysis to participants not on anti-inflammatory drugs (not shown). When the analysis was restricted to drinkers, positive relationships were found with all types of alcoholic beverages (not shown). No linear or quadratic association was found between IL-6 levels and alcohol consumption after multivariate adjustment in the whole sample (Table 2), after stratifying on gender (Table 3) or after excluding participants taking anti-inflammatory drugs (Table 4). No specific effect of wine (p = 0.67), beer (p = 0.47) or spirits (p = 0.75) was found. When IL-6 levels were categorized into quartiles, a significant quadratic trend between alcohol consumption and IL-6 categories was found (Supplementary table 1). After stratifying on gender, no clear findings were obtained, although a trend for lower odds for being in the highest vs. the lowest IL-6 quartile among moderate consumers (7–13 units/week) was noted (Supplementary table 2). After excluding participants on anti-inflammatory drugs, the quadratic trend remained but was no longer significant (not shown). No specific effect of wine, beer and spirits was found.
3.4. Tumor necrosis factor-˛ and alcohol Moderate alcohol consumers showed lower TNF-␣ levels on bivariate or multivariate analysis (Table 2). When the analysis was stratified on gender, similar findings were observed in men but not in women (Table 3). On bivariate analysis, no significant associations were found between TNF-␣ and alcohol consumption: Spearman correlations 0.010 (p = 0.42); 0.012 (p = 0.37); 0.010 (p = 0.44); 0.005 (p = 0.70) and 0.023 (p = 0.06) for total alcohol consumption (methods 1 and 2), wine, beer and spirits, respectively. Similar findings were obtained when the analysis was restricted to participants not taking anti-inflammatory drugs (not shown) with a small but significant association with spirits (Spearman correlation 0.030, p < 0.05). No linear or quadratic association was found between TNF-␣ levels and alcohol consumption after multivariate adjustment in the whole sample (Table 2), after stratifying on gender (Table 3) or after excluding participants taking anti-inflammatory drugs (Table 4). No specific effect of wine (p = 0.93), beer (p = 0.33) or spirits (p = 0.17) was found. When TNF-␣ levels were categorized into quartiles, moderate drinkers had lower odds 0.81 (0.68–0.98) of being in the highest quartile vs. the others and also of being in the highest quartile vs. the lowest: 0.75 (0.60–0.94) (Supplementary table 1). Similar findings were obtained in men but not in women (Supplementary table 2), and neither a linear nor a quadratic trend was found (Supplementary table 2). No specific effect of wine and beer was found, while a borderline association was found with spirits consumption: 1.21 (1.03–1.43).
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4. Discussion To our knowledge, this is currently the largest ever populationbased sample in which the associations between alcohol consumption and IL-1, IL-6, and TNF-␣ levels have been examined. Antioxidant phenols present in wine have been shown to decrease IL-1 production [22,23], while higher IL-1 levels have been found among chronic alcoholics [24]. To our knowledge, no study has ever reported on the association between moderatehigh alcohol consumption and IL-1 levels at the population level. Our results showed no consistent association between alcohol consumption or any alcoholic beverage (with the exception of spirits in men) and IL-1 levels. Hence, it is unlikely that alcohol (ethanol) or any other component of alcoholic beverages exerts a clinically significant effect on IL-1 levels, although further studies are needed to confirm our findings. Alcohol consumption has been shown to increase [4,25], decrease [5], exert a U- or J-shaped effect [6,26] or even no effect at all [27] on IL-6 levels. A possible explanation for these discrepant results might be the differing alcohol consumption levels: some studies included chronic alcoholics [25] while others included subjects with a low alcohol consumption [26]. In this study, a Ushape association between alcohol consumption and IL-6 levels was found, a finding in agreement with others [6,26]. It is possible that the association might depend on the range of the U-curve captured by the alcohol consumption, studies focusing on a low consumption reporting an inverse association, while studies focusing on a high consumption reported a positive association. Overall, our results suggest that moderate alcohol consumption (<2 units/day) might exert a beneficial effect by decreasing IL-6 levels. Contrary to one study [28] but in agreement with another [5], no specific effect was found between IL-6 levels and wine consumption. Although it was not possible to separate white from red wine in this study, our results strongly suggest that the association between IL-6 and alcoholic beverages is mainly mediated by ethanol rather than by any non-alcoholic specific component. Ethanol decreases IL-6 release from cultured subcutaneous human adipose tissue [25], but the precise mechanism by which moderate alcohol consumption reduces IL-6 levels awaits further investigations. The mechanisms by which alcohol intake modulate TNF-␣ levels are poorly understood; no direct effect of alcohol consumption on TNF-␣ levels has been shown [7,29]. Based on this study’s findings, moderate alcohol consumption (<2 units/day) might exert a beneficial effect by decreasing TNF-␣ levels, but it would be of interest to see if these findings can be replicated in other population-based studies. In agreement with a previous study [8], no specific effect of wine or other alcoholic beverages on TNF-␣ levels was found, although the borderline effect of spirits consumption should be further investigated. Based on the current findings, the association between TNF-␣ and alcoholic beverages appears to be mainly mediated by ethanol rather than by non-alcoholic specific components. This study has some limitations worth acknowledging. Although the participation rate was similar to other epidemiological studies [30], it was rather low, which might limit the generalization of findings. The fact that all participants were living in a city might also preclude generalization to rural settings. Categorization of alcohol consumption is debatable and might lead to differing conclusions [31]; still, changing the alcohol categories by creating a supplementary “very high” (35+/week) category or by splitting alcohol consumption in quartiles did not change notably the results (not shown). It is also possible that high consumption might be underreported, although the percentage of high or very high consumers is higher than reported in other studies. The very high cytokine values of some participants might have biased the results; still, restricting the analysis to cytokine values below the 95th percentile led to comparable results (not shown). No data regarding diet was
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available, so it was not possible to adjust for it. Similarly, only data for leisure time physical activity and walking time was available, and it was not possible to adjust for other types of physical activity. Finally, the fact that moderate alcohol consumption reduces IL-6 and TNF-␣ levels should not prompt teetotallers to start drinking to reduce their inflammatory profile; a more reasonable recommendation would be that moderate drinkers can still enjoy their favorite drink without unfavorably impacting their inflammatory profile. In summary, our results suggest that IL-1 levels do not appear be associated with alcohol consumption. Moderate alcohol consumption is associated with lower levels of IL-6 and TNF-␣, and this association appears to be gender dependent. No consistent specific effect of the type of alcohol consumed on cytokine levels could be demonstrated. Acknowledgements The authors also express their gratitude to the participants in the Lausanne CoLaus study and to the investigators who have contributed to the recruitment, in particular Yolande Barreau, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey and Sylvie Mermoud for data collection. The CoLaus study was supported by research grants from the Swiss National Science Foundation (grant no: 33CSCO-122661) and the Faculty of Biology and Medicine of Lausanne, Switzerland. M Bochud is supported by the Swiss School of Public Health Plus (SSPH+). GW, MP and PV designed the experiment; FB and AU collected data; PM-V analyzed the data; PM-V, MB and RvK wrote the manuscript; TL, FF, J-MG, FP and CH provided significant advice and consultation. PV and GW received an unrestricted grant for GSK to build the CoLaus study. GSK had no influence in the study design, implementation, analysis and interpretation of the data. The authors report no conflict of interest. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atherosclerosis.2012.02.011. References [1] Marques-Vidal P, Montaye M, Arveiler D, et al. Alcohol consumption and cardiovascular disease: differential effects in France and Northern Ireland. The PRIME study. Eur J Cardiovasc Prev Rehabil 2004;11:336–43. [2] Imhof A, Woodward M, Doering A, et al. Overall alcohol intake, beer, wine, and systemic markers of inflammation in western Europe: results from three MONICA samples (Augsburg, Glasgow, Lille). Eur Heart J 2004;25:2092–100. [3] Danesh J, Kaptoge S, Mann AG, et al. Long-term interleukin-6 levels and subsequent risk of coronary heart disease: two new prospective studies and a systematic review. PLoS Med 2008;5:e78. [4] Hoffmeister A, Imhof A, Rothenbacher D, et al. Moderater Alkoholkonsum und Plasmakonzentration sensitiver Entzundungsmarker. Hinweise auf einen atheroprotektiven Zusammenhang [Moderate alcohol consumption and plasma concentration of sensitive markers of inflammation. Comment on an atheroprotective relationship]. Dtsch Med Wochenschr 2003;128:2237–41. [5] Sacanella E, Vázquez-Agell M, Mena MP, et al. Down-regulation of adhesion molecules and other inflammatory biomarkers after moderate wine consumption in healthy women: a randomized trial. Am J Clin Nutr 2007;86:1463–9.
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