Red wine polyphenols modulate fecal microbiota and reduce markers of the metabolic syndrome in obese patients Moreno-Indias Isabel1,2, Sánchez-Alcoholado Lidia1, Andrés-Lacueva Cristina3,4, Tinahones Francisco1,2, Cardona Fernando1,2, Queipo-Ortuño María Isabel1,2 1 Unidad de Gestión Clínica de Endocrinología y Nutrición, Laboratorio del Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario de Málaga (Virgen de la Victoria), Málaga, Spain. 2 Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición, Madrid, Spain. Introduction Recent studies have demonstrated that obesity and the metabolic syndrome may be associated with substantial changes in the composition and metabolic function of the gut microbiota. The use of red wine polyphenols may be a potential mechanism for prevention of cardiovascular and metabolic alterations associated with obesity. Thus, our aim was to examine the prebiotic effect of a moderate intake of red wine polyphenols on the modulation of the gut microbiota composition and the improvement in the MetS risk factors in obese patients. Material and Methods Results Table 1. Anthropometric and biochemical variables during the study of MetS patients and healthy subjects. Table 2. Results of energy and dietary intakes in MetS patients and healthy subjects at baseline and after the red wine and de-alcoholized red wine intake periods. MetS patients Healthy subjects Baseline (washout period) Red wine period De-alcoholized red wine period Baseline (washout period) Weight (kg) 113.30 ± 16.54a, * 110.86 ± 16.13 a, § 110.02 ± 14.86 a, ¥ 82.38 ± 12.37 a 81.88 ± 13.10 a 81.68 ± 11.34 a Waist (cm) 116.30 ± 10.90 a, * 116.00 ± 9.82 a, § 115.26 ± 10.82 a, ¥ 96.2 ± 4.70 a 95.6 ± 5.75 a 95.01 ± 5.32 a Hip (cm) 117.70 ± 8.45 a, * 117.0 ± 9.24 a, § 116.80 ± 8.98 a, ¥ 104.4 ± 7.70 a 102.60 ± 6.84 a 101.9 ± 6.98 a DBP (mmHg) 94.20 ± 9.83 a, * 85.20 ± 9.15 b 84.0 ± 8.52 b 82.60 ± 8.76 a 81.80 ± 8.12 a 81.12 ± 7.98 a SBP (mmHg) 134.13 ± 10.52 a, * 123.6 ± 9.23 b 121.06 ± 8.45 b 118.45 ± 9.12 a 116.23 ± 9.91 a 115.93 ± 8.58 a BMI (kg/m2) 35.24 ± 4.21 a, * 34.49 ± 4.17 a, § 34.53 ± 4.23 a, ¥ 27.52 ± 2.10 a 27.34 ± 2.31 a 27.27 ± 2.19 a Glucose (mg/dL) 137.00 ± 16.78 a, * 107.30 ± 15.12 b 102.8 ± 12.7 b 100.6 ± 8.26 a 99.60 ± 8.90 a 97.2 ± 8.14 a Uric acid (mg/dL) 5.02 ± 1.26 a 5.01 ± 1.03 a 4.97 ± 1.15 a 5.48 ± 0.99 a 5.01 ± 0.53 a 5.10 ± 0.67 a GOT (mg/dL) 21.80 ± 8.16 a 20.5 ± 6.45 a 18.27 ± 4.67 a 22.20 ± 7.19 a 18.40 ± 3.25 a 17.76 ± 3.84 a GPT (mg/dL) 47.0 ± 10.83 a 42.0 ± 9.30 a 43.4 ± 7.40 a, 46.80 ± 5.28 a 41.5 ± 3.95 b 39.09 ± 3.39 a GGT (mg/dL) 43.20 ± 7.29 a, * 38.20 ± 7.22 a, § 39.0 ± 7.40 a, ¥ 30.60 ± 5.81 a 27.00 ± 4.26 a 26.89 ± 5.01 a Triglycerides (mg/dL) 365.80 ± 24.18 a, * 257.6 ± 22.55 b, § 248.92 ± 26.35 b, ¥ 125.00 ± 18.09 a 111.2 ± 19.33 a 119.6 ± 19.62 a Cholesterol (mg/dL) 289.40 ± 18.03 a, * 184.66 ± 13.08 b 179.96 ± 17.98 b 191.40 ±10.15 a 180.40 ± 9.21 b 178.90 ± 8.57 b LDL cholesterol (mg/dL) 138.60 ± 22.64 a 133.80 ± 26.73 a 131.6 ± 25.6 a 120.60 ± 23.88 a 117.60 ± 34.51 a 115.0 ± 31.24 a HDLcholesterol (mg/dL) 41.60 ± 9.60 a, * 51.0 ± 9.84 b 52.80 ± 8.84 b 66.0 ±10.8 a 57.80 ± 11.20 a 58.66 ± 8.96 a CRP (mg/L) 8.20 ± 2.57 a, * 5.37 ± 2.23 b 5.01 ± 2.06 b 4.46 ± 1.84 a 3.80 ± 1.56 a 3.59 ± 1.10 a LPS (EU/mL) 0.28 ± 0.05 a, * 0.11 ± 0.03 b 0.11 ± 0.02 b 0.14 ± 0.07 a 0.12 ± 0.03 a 0.11 ± 0.01 a MetS patients Healthy subjects Baseline (washout period) De-alcoholized red wine intervention Red wine intervention *P Red wine intervention Energy (kcal/d) 2007.7 ± 340.5 a 1919.8 ± 376.2 a 1915.3 ± 332.0 a 0.392 1978.5± 368.0 a 1930.7 ± 313.1 a 1985.3 ± 360.2 a 0.772 Total protein (g/d) 71.2 ± 22.2 a 70.8 ± 28.4 a 69.5 ± 30.0 a 0.126 70.7 ± 27.2 a 69.7 ± 20.8 a 68.6 ±28.8 a 0.888 Carbohydrates (g/d) 170.8 ± 66.0 a 165.2 ± 64.5 a 163.9 ± 57.1 a 0.345 167.0 ± 63.2 a 165.2 ± 67.7 a 166.8 ± 70.1 a 0.633 Dietary fiber (g/d) 17.0 ± 7.1 a 17.3 ± 5.6 a 16.9 ± 6.5 a 0.786 16.7 ± 5.5 a 17.1 ± 6.5 a 16.9 ± 5.8 a 0.176 Sugars (g/d) 68.5 ± 26.4 a 67.5 ± 30.7 a 68.1 ± 33.0 a 0.567 66.5 ± 30.3 a 66.1 ± 34.7 a 65.3 ± 37.9 a 0.165 Total lipids (g/d) 81.5 ± 33.6 a 80.7± 32.1 a 79.9 ± 36.2 a 0.661 77.8 ± 37.6 a 76.7 ± 40.3 a 77.6 ± 42.8 a 0.942 * Differences between MetS patients and healthy subjects at baseline P<0.05 § Differences between MetS patients and healthy subjects after red wine intake P<0.05 ¥ Differences between MetS patients and healthy subjects after de-alcoholized red wine intake P<0.05 Table 4. Real-time quantitative PCR of microbiota phyla, genera, groups and species in both study groups. Table 3. Coefficient of similarity index of the DGGE band profiles for the two participant groups after the two intake periods. MetS patients Healthy subjects Baseline (washout period) Red wine period De-alcoholized red wine period De-alcoholized red wine period Proteobacteria 8.84 ± 1.78 a, * 7.58 ± 1.08 a 7.64 ± 0.75 a 6.83 ± 1.89 a 6.53 ± 2.17 a 6.48 ± 2.15 a Escherichia coli 9.29 ± 2.69 a, * 7.41 ± 1.35 b 7.27 ± 1.99 b 7.31 ± 1.41 a 7.32 ± 2.97 a 7.28 ± 1.78 a Enterobacter cloacae 8.99± 1.84 a, * 7.01 ± 1.43 b 6.89 ± 1.39 b 6.89± 1.32 a 6.78 ± 1.28 a 6.63 ± 1.32 a Fusobacteria 6.56 ± 1.19 a 7.85 ± 0.96 b 7.63 ± 0.99 b 6.39 ± 2.08 a 7.82 ± 1.63 a 7.75 ± 1.23 a Actinobacteria 7.87 ± 3.04 a 8.57 ± 2.49 a 8.69 ± 2.18 a 8.76 ± 2.77 a 9.53 ± 2.24 a 9.67 ± 1.97 a Bifidobacterium 6.37 ± 1.54 a, * 10.03 ± 0.77 b 9.73 ± 2.07 b 8.54 ± 1.95 a 10.65 ± 2.08 b 10.33 ± 1.74 b Egghertella lenta 8.00 ± 0.38 a, * 9.92 ± 0.95 b 9.74 ± 0.84 b 9.05 ± 0.86 a 10.02 ± 1.03 b 9.94 ± 0.84 b Bacteroidetes 8.95 ± 0.5 a 9.78 ± 0.65 b 9.85 ± 0.89 b 8.98 ± 0.63 a 10.18 ± 0.49 b 10.33 ± 0.54 b Bacteroides 9.28 ± 0.81 a, * 7.64 ± 2.59 b 7.47 ± 1.25 b 8.34 ± 0.92 a 7.58 ± 2.14 a 7.48 ± 1.68 a Bacteroides uniformis 9.71 ± 0.69 a 8.74 ± 1.41 a 9.46 ± 0.94 a 10.25 ± 0.95 a 8.30 ± 1.00 b 9.19 ± 1.17 b Parabacteroides distasonis 9.26 ± 0.73 a, * 9.62 ± 0.40 a 10.09 ± 1.12 a 7.20 ± 2.40 a 8.92 ± 1.40 a 9.32 ± 1.98 a Prevotella 6.92 ± 0.69 a, * 8.74 ± 0.77b 8.93 ± 0.99 b 8.93 ± 0.72 a 9.40 ± 0.81 a 9.36 ± 0.78 a Firmicutes 9.92 ± 0.35 a,* 8.42 ± 0.63 b 8.31 ± 0.75 b 8.38 ± 0.52 a 8.09 ± 0.91 a 7.97 ± 0.42 a Blautia coccoides- Eubacterium rectale group 4. 09 ± 0.60 a, * 6.69 ± 0.89 b 6.79 ± 0.62 b 6.82 ± 0.68 a 7.27 ± 0.65 a 6.99 ± 0.34 a Enteroccocus 5.71 ± 1.42 a 5.90 ± 0.76 a 5.74 ± 1.08 a 4.66 ± 0.81 a 4.71 ± 1.15 a 4.75 ± 1.38 a Clostridium 5.43 ± 1.69 a, * 3.13 ± 0.90 b 3.09 ± 0.92 b 3.97 ± 1.42 a 3.56 ± 1.52 a 3.47 ± 1.03 a Clostridium histolyticum group 4.08 ± 1.07 a, * 2.88 ± 0.55 b 3.10 ± 0.50 b 3.16 ± 0.92 a 2.50 ± 0.96 a 2.59 ± 0.77 a Lactobacillus 4.30 ± 1.61 a, * 6.83 ± 0.56 b 6.63 ± 0.87 b 5.78 ± 1.43 a 6.34 ± 1.14 a 6.46 ± 1.21 a Red wine period De-alcoholized red wine period MetS patients 29.6 ± 7.13 % 30.1 ± 7.42% Healthy subjects 27.1 ± 8.07% 28.1 ± 7.75% P 0.469 0.563 Figure 1. Relative abundance of bacterial phyla sequenced from DGGE bands. * Differences between MetS patients and Healthy subjects at baseline P<0.05 § Differences between MetS patients and Healthy subjects after red wine intake P<0.05 ¥ Differences between MetS patients and Healthy subjects after de-alcoholized red wine intake P<0.05 Figure 2. Relative abundance of bacterial genera sequenced from DGGE bands. Table 5. Relationship between gut microbiota composition and blood pressure, plasma lipid profile and inflammation markers in MetS patients. Triglycerides (mg/dL) Cholesterol (mg/dL) LPS (EU/mL) Actinobacterias β= 1.11 R²= 0.99 P= 0.005   Lactobacillus β= 0.224 P< 0.001 Clostridium histolyticum β= -0.19 P= 0.029 Bifidobacterium β= 1.004 P= 0.001 β= 0.342 R²= 0.75 P= 0.015 Conclusion In the metabolic syndrome patients, red wine polyphenols significantly increased the number of fecal bifidobacteria and Lactobacillus (intestinal barrier protectors) at the expense of less desirable groups of bacteria such as LPS producers (Escherichia coli and Enterobacter cloacae). The changes in gut microbiota in these patients could be responsible for the improvement in the metabolic syndrome markers. This study has been supported by the grants from Instituto de Salud Carlos III; CP13/00065; CPII13/00023; CD12/000530 and co-founded by Fondo Europeo de Desarrollo Regional–FEDER. The research group belongs to the “Centros de Investigación en Red” [CIBER, CB06/03/0018] of the “Instituto de Salud Carlos III”, Madrid, Spain. Contact: maribel.queipo@ibima.eu