Dietary polyphenol intake is associated with HDL-cholesterol and a better profile of other components of the metabolic syndrome: A PREDIMED-plus sub-study

nutrients Article Dietary Polyphenol Intake is Associated with HDL-Cholesterol and A Better Profile of Other Components of the Metabolic Syndrome: A PREDIMED-Plus Sub-Study Sara Castro-Barquero 1,2,†, Anna Tresserra-Rimbau 2,3,4,5,†, Facundo Vitelli-Storelli 6, Mónica Doménech 1,2, Jordi Salas-Salvadó2,3,4,5 , Vicente Martín-Sánchez 6,7, María Rubín-García6, Pilar Buil-Cosiales 2,8,9 , Dolores Corella 2,10 , Montserrat Fitó2,11, Dora Romaguera 2,12 , Jesús Vioque 7,13 ,Ángel María Alonso-Gómez 2,14 , Julia Wärnberg 2,15 , JoséAlfredo Martínez 2,16,17 , Luís Serra-Majem 2,18 , Francisco JoséTinahones 2,19, JoséLapetra 2,20, Xavier Pintó2,21, Josep Antonio Tur 2,12,22 , Antonio Garcia-Rios 23, Laura García-Molina 7,24, Miguel Delgado-Rodriguez 13,25, Pilar Matía-Martín26, Lidia Daimiel 17 , Josep Vidal 27,28, Clotilde Vázquez 2,29, Montserrat Cofán2,30 , Andrea Romanos-Nanclares 8, Nerea Becerra-Tomas 2,3,4,5, Rocio Barragan 2,10 , Olga Castañer 2,11 , Jadwiga Konieczna 2,12, Sandra González-Palacios 7,13 , Carolina Sorto-Sánchez 2,14, Jessica Pérez-López 2,15 , María Angeles Zulet 2,16,17 , Inmaculada Bautista-Castaño 2,18, Rosa Casas 1,2 , Ana María Gómez-Perez 2,19 , JoséManuel Santos-Lozano 2,20, MaríaÁngeles Rodríguez-Sanchez 21, Alicia Julibert 2,12,22, Nerea Martín-Calvo 2,8 , Pablo Hernández-Alonso 2,3,4,5,31 , JoséV Sorlí2,10 , Albert Sanllorente 2,11, Aina María Galmés-Panadés2,12 , Eugenio Cases-Pérez 32, Leire Goicolea-Güemez 2,14, Miguel Ruiz-Canela 2,8 , Nancy Babio 2,3,4,5 ,Álvaro Hernáez 1,2 , Rosa María Lamuela-Raventós2,33 and Ramon Estruch 1,2,34,* 1Department of Medicine, Faculty of Medicine and Life Sciences, University of Barcelona, Barcelona, Spain. Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain; [email protected] (S.C.-B.); [email protected] (M.D.); r[email protected] (R.C.); [email protected] (Á.H.) 2Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain; [email protected] (A.T.-R.); [email protected] (J.S.-S.); [email protected] (P.B.-C.); [email protected] (D.C.); [email protected] (M.F.); [email protected] (D.R.); [email protected] (Á.M.A.-G.); [email protected] (J.W.); [email protected] (J.A.M.); [email protected] (L.S.-M.); [email protected] (F.J.T.); [email protected] (J.L.); [email protected] (X.P.); [email protected] (J.A.T.); [email protected] (C.V.); [email protected] (M.C.); [email protected] (N.B.-T.); [email protected] (R.B.); [email protected] (O.C.); [email protected] (J.K.); [email protected] (C.S.-S.); [email protected] (J.P.-L.); [email protected] (M.A.Z.); [email protected] (I.B.-C.); [email protected] (A.M.G.-P.); [email protected] (J.M.S.-L.); [email protected] (A.J.); [email protected] (N.M.-C.); [email protected] (P.H.-A.); [email protected] (J.V.S.); [email protected] (A.S.); [email protected] (A.M.G.-P.); [email protected] (L.G.-G.); [email protected] (M.R.-C.); [email protected] (N.B.); [email protected] (R.M.L.-R.) 3Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició, 43204 Reus, Spain 4University Hospital of Sant Joan de Reus, Nutrition Unit, 43201 Reus, Spain 5Institut d’InvestigacióSanitària Pere Virgili (IISPV), 43201 Reus, Spain 6Institute of Biomedicine (IBIOMED), University of León, 24071 León, Spain; [email protected] (F.V.-S.); [email protected] (V.M.-S.); [email protected] (M.R.-G.) Nutrients 2020,12, 689; doi:10.3390/nu12030689 www.mdpi.com/journal/nutrients Nutrients 2020,12, 689 2 of 17 7CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, 28029 Madrid, Spain; [email protected] (J.V.); [email protected] (L.G.-M.); [email protected] (S.G.-P.) 8University of Navarra, Department of Preventive Medicine and Public Health, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Pamplona, Spain; [email protected] 9Servicio Navarro de Salud-Osasunbidea-Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Pamplona, Spain 10 Department of Preventive Medicine, University of Valencia, 46010 Valencia, Spain 11 Cardiovascular Risk and Nutrition Research group, Institut Hospital del Mar de Investigaciones Médicas (IMIM), 08007 Barcelona, Spain 12 Health Research Institute of the Balearic Islands (IdISBa), University Hospital Son Espases (Research Unit), 07120 Palma de Mallorca, Spain 13 Miguel Hernandez University, ISABIAL-FISABIO, 03010 Alicante, Spain; [email protected] 14 Bioaraba Health Research Institute; Osakidetza Basque Health Service, Araba University Hospital; University of the Basque Country UPV/EHU, 01009 Vitoria-Gasteiz, Spain 15 Department of Nursing. University of Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain 16 Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain 17 Precision Nutrition Program, IMDEA Food, CEI UAM +CSIC, 28049 Madrid, Spain; [email protected]g 18 Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria & Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service, 35016 Las Palmas de Gran Canaria, Spain 19 Virgen de la Victoria Hospital, Department of Endocrinology, Instituto de Investigación Biomédica de Málaga (IBIMA). University of Málaga, 29010 Málaga, Spain 20 Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41010 Sevilla, Spain 21 Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, IDIBELL, Hospitalet de Llobregat, 08908 Barcelona, Spain; mr[email protected] 22 Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain 23 Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Cordoba, Spain; [email protected] 24 Department of Preventive Medicine and Public Health, University of Granada, 18016 Granada, Spain 25 Division of Preventive Medicine, Faculty of Medicine, University of Jaén, 23071 Jaén, Spain 26 Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain; [email protected] 27 CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; [email protected] 28 Department of Endocrinology, Institut d’Investigacions Biom é diques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain 29 Department of Endocrinology and Nutrition, Hospital Fundaci ó n Jimenez D í az, Instituto de Investigaciones Biomédicas IISFJD. University Autonoma, 28040 Madrid, Spain 30 Lipid Clinic, Department of Endocrinology and Nutrition, Institut d’Investigacions Biom è diques August Pi Sunyer (IDIBAPS), Hospital Clínic, 08036 Barcelona, Spain 31 Unidad de Gestión Clínica de Endocrinología y Nutrición del Hospital Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain 32 Centro de Salud Raval, 03203 Alicante, Spain; [email protected] 33 Department of Nutrition, Food Science and Gastronomy, XaRTA, INSA, School of Pharmacy and Food Sciences, University of Barcelona, 08028 Barcelona, Spain 34 Department of Internal Medicine, Hospital Clinic de Barcelona, 08036 Barcelona, Spain *Correspondence: restr[email protected]; Tel.: +34-932-279-935 †These authors contributed equally to this work. Received: 7 February 2020; Accepted: 29 February 2020; Published: 4 March 2020   Nutrients 2020,12, 689 3 of 17 Abstract: Dietary polyphenol intake is associated with improvement of metabolic disturbances. The aims of the present study are to describe dietary polyphenol intake in a population with metabolic syndrome (MetS) and to examine the association between polyphenol intake and the components of MetS. This cross-sectional analysis involved 6633 men and women included in the PREDIMED (PREvenci ó n con DIeta MEDiterranea-Plus) study. The polyphenol content of foods was estimated from the Phenol-Explorer 3.6 database. The mean of total polyphenol intake was 846 ± 318 mg/day. Except for stilbenes, women had higher polyphenol intake than men. Total polyphenol intake was higher in older participants (>70 years of age) compared to their younger counterparts. Participants with body mass index (BMI) >35 kg/m 2 reported lower total polyphenol, flavonoid, and stilbene intake than those with lower BMI. Total polyphenol intake was not associated with a better profile concerning MetS components, except for high-density lipoprotein cholesterol (HDL-c), although stilbenes, lignans, and other polyphenols showed an inverse association with blood pressure, fasting plasma glucose, and triglycerides. A direct association with HDL-c was found for all subclasses except lignans and phenolic acids. To conclude, in participants with MetS, higher intake of several polyphenol subclasses was associated with a better profile of MetS components, especially HDL-c. Keywords: polyphenols; metabolic syndrome; Mediterranean diet; glignans; stilbenes; HDL-cholesterol 1. Introduction Polyphenols are plant-derived molecules characterized by the presence of one or more aromatic rings and attached hydroxyl groups [ 1 ]. They are classified into five subclasses according to their chemical structure, including flavonoids and nonflavonoids subclasses defined as phenolic acids, stilbenes, lignans, and other polyphenols. These bioactive compounds are responsible for some health and sensory properties of foods, such as bitterness, astringency, and antioxidant capacity. The intake of phenolic compounds and their food sources is highly variable and depends on dietary patterns, sex, socioeconomic factors, and the native foods of each region [ 2 ]. The Mediterranean diet (MedDiet) is characterized by a high intake of phenolic compounds because MedDiet interventions promote the intake of phenolic rich and plant-based products, such as legumes, vegetables, fruits, nuts and wholegrain cereals, and promote the use of extra virgin olive oil as the main source of fat. It has been suggested that phenolic compounds are partly responsible for the beneficial effects attributed to the MedDiet [3]. The metabolic syndrome (MetS) is defined as a cluster of metabolic disturbances, which include impaired glucose metabolism, elevated blood pressure, and low level of HDL-c, dyslipidemia, and abdominal obesity [ 4 ]. Sedentary lifestyle, smoking, and unbalanced diets are well-known modifiable risk factors for MetS, and lifestyle interventions in those areas, especially dietary interventions based on the MedDiet [ 3 – 6 ], might improve this condition. Considering the chronic low-grade inflammation and oxidative stress observed in MetS, polyphenols are good candidates to improve the condition because of their antioxidant and anti-inflammatory properties [ 7 ]. Moreover, several epidemiological studies have observed a negative association between polyphenol intake and MetS rates [ 8 ]. Regarding MetS components, an adequate intake of phenolic compounds has been shown to improve lipid profile and insulin resistance, and decrease blood pressure levels and body weight [8,9]. Despite the fact that phenol-rich dietary patterns are effective in improving some MetS components, there is no single phenolic compound or extract able to improve all the components of MetS [ 10 ]. Nevertheless, given the complexity of MetS and the heterogeneity of polyphenols, more large randomized trials with MetS patients are needed to evaluate the effect of polyphenol intake in reducing MetS complications, and whether intake of the different polyphenol subclasses could be associated with improvements in MetS components, because each subtype has different absorption and metabolism [ 11 ]. Nutrients 2020,12, 689 4 of 17 Therefore, the aims of our study were firstly to describe polyphenol intake in 6633 participants with MetS from the PREvenci ó n con DIeta MEDiterranea-Plus (PREDIMED-Plus) trial and to identify the main food sources of polyphenols in those participants, and secondly to examine whether higher intakes of some polyphenol sub-classes are associated with MetS components in this population. 2. Materials and Methods 2.1. Design of the Study A cross-sectional analysis of the baseline data of participants included in the PREvenci ó n con DIeta MEDiterranea-Plus (PREDIMED-Plus) study was performed. The profile of the cohort, recruiting methods, and data collection processes have been described elsewhere [ 12 ] and on the website http://predimedplus.com. The study protocol was approved by the 23 recruiting centers Institutional Review Boards and registered in 2014 at the International Standard Randomized Controlled Trial Number registry (http://www.isrctn.com/ISRCTN89898870). All participants provided written informed consent before joining the study. 2.2. Participants A total of 6874 subjects were recruited and randomized in the 23 recruiting centers between September 2013 and December 2016. Primary care medical doctors from primary care centers of the National Health System assessed potential participants for eligibility. Eligible participants were men (aged 55–75 years) and women (aged 60–75 years) with overweight or obesity (body mass index [BMI] ≥ 27 and <40 kg/m 2 ) and at least three components of MetS according to the comprehensive definition of the International Diabetes Federation; National Heart, Lung, and Blood Institute; and American Heart Association (2009) [ 4 ]. Exclusion criteria were documented history of cardiovascular diseases (CVD), having a long-term illness, drug or alcohol use disorder, a BMI of 40 or higher, a history of allergy or intolerance to extra virgin olive oil or nuts, malignant cancer, inability to follow the recommended diet or physical activity program, history of surgical procedures for weight loss, and obesity of known endocrine disease (except for treated hypothyroidism). Of the total sample of 6874 randomized participants, 241 participants were excluded from the current analysis (Figure 1): 53 without food-frequency questionnaire (FFQ) data at baseline, and 188 participants who reported energy intake values outside the predefined limits (<3347 kJ [800 kcal]/day or >17,573 kJ [4000 kcal]/day for men; <2510 kJ [500 kcal]/day or >14,644 kJ [3500 kcal]/day for women) [13]. Nutrients 2020, 12, x FOR PEER REVIEW 7 of 17 The associations between dietary polyphenol intake and MetS components after full adjustment are shown in Figure 2. High flavonoid and low phenolic acid intake were associated with lower waist circumference (p = 0.02 and p < 0.001, respectively). The highest intake of other polyphenols was significantly and inversely associated with systolic (p = 0.001) and diastolic blood pressure levels (p = 0.002). An inverse association was found between fasting plasma glucose levels and lignans (p = 0.04). Positive associations were found between HDL-c levels and all polyphenol classes except for phenolic acid and lignan intake. Lastly, triglyceride concentration was inversely associated with lignans and stilbenes (p = 0.006 and p = 0.004, respectively). Changes in the linear regression models after adjustment are shown in the Supplementary Table (Supplementary Table S1). Figure 1. Flowchart of the participants. Figure 1. Flowchart of the participants. Nutrients 2020,12, 689 5 of 17 2.3. Estimation of Dietary Polyphenol Intake The total dietary polyphenol intake and polyphenol subclasses were obtained at baseline by the 143-item FFQs used in the PREDIMED-Plus study. As described elsewhere [ 14 ], dietary polyphenol intake was estimated following these steps: (1) All foods from the FFQ with no polyphenol content, or only traces, were excluded; (2) recipes were calculated according to their ingredients and portions using traditional MedDiet recipes; (3) when an item from the FFQ included several foods (e.g., oranges and tangerines), the proportion of intake was calculated according to data available in the national survey; (4) no retention or yield factors were used to correct weight changes during cooking because this was already taken into account in the FFQ; (5) the polyphenol content in 100 g of each food item was obtained from the Phenol-Explorer database (version 3.6) [ 15 ]; (6) finally, the individual polyphenol intake from each food was calculated by multiplying the content of each polyphenol by the daily consumption of each food. Total polyphenol intake was calculated as the sum of all individual polyphenol intakes from the food sources reported in the FFQ. The data used to calculate polyphenol intake was obtained by chromatography of all the phenolic compounds, except proanthocyanidins, the content of which was obtained by normal-phase high-performance liquid chromatography. In the case of lignans and phenolic acids in certain foods (i.e., swiss chard, chickpeas, plums, and strawberry jam), data corresponding to chromatography after hydrolysis was also collected, since these treatments are needed to release phenolic compounds that could otherwise not be analyzed. Total and polyphenol subclass intakes were adjusted for energy intake (kcal/day) using the residual method [13]. 2.4. Measurements and Outcome Assessment Data on age, sex, educational levels, anthropometric measurements, dietary habits and lifestyle were collected at baseline. Anthropometric measurements were measured according to the PREDIMED-Plus protocol. Weight was recorded with participants in light clothing without shoes or accessories using a high-quality calibrated scale. Height was measured with a wall-mounted stadiometer. Waist circumference was measured midway between the lowest rib and the iliac crest. The BMI was calculated as weight (kg) divided by the square of height (m2). Physical activity and sedentary behaviors were evaluated using the validated Regicor Short Physical Activity Questionnaire [ 16 ] and the validated Spanish version of the Nurses’ Health Study questionnaire [17], respectively. Information related to sociodemographic and lifestyle habits, individual and family medical history, smoking status, medical conditions, and medication use was evaluated using self-reported questionnaires. Sociodemographic and lifestyle variables were categorized as follows: age (three categories: <65, 65–70, or >70 years), educational level (three categories: primary, secondary, or high school), physical activity level (three categories: low, moderate, or high), BMI (three categories: 27.0–29.9, 30.0–34.9, or ≥ 35 kg/m 2 ), and smoking status (three categories: never, former, or current smoker). Blood samples were collected after overnight fasting. Biochemical analyses were performed to determine plasma glucose (mg/dL), glycated hemoglobin (%), HDL-c (mg/dL), and triglyceride (mg/dL) levels using standard laboratory enzymatic methods. Low-density lipoprotein cholesterol (LDL-c; mg/dL) was calculated using the Friedewald formula whenever triglyceride levels were less than 300 mg/dL. Blood pressure measurements were obtained after the participant had rested for five minutes. Each measurement was obtained with a validated semiautomatic oscillometer (Omron HEM-705CP), ensuring the use of the proper cuffsize for each participant. 2.5. Statistical Analysis Descriptive statistics were used to define the baseline characteristics of the participants. The database used was the PREDIMED-Plus baseline database generated in September 2018. Nutrients 2020,12, 689 6 of 17 Continuous variables are expressed as mean ± SD. Categorical variables are expressed as number (n) and percentage (%). Comparisons among quartiles of dietary polyphenol intake used the Pearson chi square test ( χ2 ) for categorical variables or one-way ANOVA for continuous variables. The associations between dietary polyphenol intake and MetS components were analyzed by linear regression models to determine differences between quartiles of polyphenol subclass intake. The results of the regression models are expressed as unstandardized β -coefficients. For regression models, polyphenol and polyphenol subclasses are expressed as quartiles of energy-adjusted dietary intake. We used robust variance estimators to account for intra-cluster correlation in all linear models, considering members of the same household as a cluster. All regression models were adjusted for potential confounders. Model 1 was adjusted for sex, age, recruiting center, and members of the same household. Model 2 was additionally adjusted for physical activity level, BMI (except for waist circumference criteria), smoking status, and educational level. We additionally adjusted for anti-diabetic treatment when assessing glycemia and antihypertensive treatments when assessing blood pressure. Lastly, model 3 was additionally adjusted for total energy intake (continuous, kcal/day), saturated fatty acids (g/day), and distilled drinks alcohol intake (g/day). In model 3, the analysis of glycemia was additionally adjusted for dietary simple sugar intake (g/day), whereas the analysis of systolic and diastolic blood pressures was also adjusted for dietary sodium intake (mg/day). The normality of the continuous outcomes and standardized residuals was assessed with the Shapiro–Wilk test. Values are shown as 95% confidence interval (CI) and significance for all statistical tests was based on bilateral contrast set at p<0.05. The P value for linear trends was computed by fitting a continuous variable that assigned the median value for each quartile in regression models. The descriptive analyses shown in Tables 1–3 were performed using SPSS software version 22.0 (Chicago, IL, USA) and the regression analysis was performed using Stata software version 16 (StataCorp LP, College Station, TX, USA). Nutrients 2020,12, 689 7 of 17 Table 1. Baseline characteristic of participants by quartiles of total polyphenol intake. Q1 (<623, 3 mg/d) Q2 (623.4–799.4 mg/d) Q3 (799.5–1019.2 mg/d) Q4 (>1019.3 mg/d) pp for Linear Trend n1658 1658 1660 1657 Age, years 65.2 ±4.90 64.8 ±4.87 65.0 ±4.87 64.9 ±4.98 0.10 0.19 Women, n (%) 894 (53.9) 845 (51.0) 785 (47.3) 685 (41.3) <0.001 <0.001 Family history of CVD 1,n (%) 659 (39.7) 698 (42.1) 662 (39.9) 678 (40.9) 0.48 0.81 Current smokers, n (%) 197 (11.9) 205 (12.4) 203 (12.2) 216 (13.0) 0.78 0.36 Former smokers, n (%) 647 (39.0) 695 (41.9) 728 (43.9) 800 (48.3) <0.001 <0.001 BMI, kg/m232.6 ±3.46 32.6 ±3.49 32.6 ±3.51 32.3 ±3.31 0.03 0.02 Waist circumference, cm 107.0 ±9.76 107.4 ±9.70 107.8 ±9.75 107.8 ±9.36 0.06 0.01 Body weight, kg 85.2 ±12.8 86.2 ±12.8 87.3 ±13.3 87.5 ±12.8 <0.001 <0.001 Glucose, mg/dL 113.4 ±28.9 113.9 ±31.0 113.9 ±29.0 113.0 ±27.6 0.78 0.71 Glycated hemoglobin, % 6.10 ±0.88 6.22 ±2.58 6.25 ±3.53 6.10 ±0.88 0.15 0.85 Total-cholesterol, mg/dL 196 ±38.4 197 ±37.7 196 ±37.0 198 ±42.8 0.59 0.57 HDL-cholesterol, mg/dL 47.6 ±11.5 48.2 ±11.7 48.7 ±12.2 47.9 ±11.9 0.06 0.32 Medications, n (%) Antihypertensive agents 1272 (76.7) 1285 (77.5) 1294 (77.9) 1304 (78.7) 0.48 0.46 Colesterol-lowering agents 862 (52.0) 846 (51.0) 858 (51.7) 842 (50.8) 0.97 0.52 Insulin 84 (5.07) 98 (5.91) 67 (4.04) 63 (3.80) 0.01 0.01 Metformin 380 (22.9) 404 (24.4) 383 (23.1) 347 (20.9) 0.13 0.12 Other hypoglycemic drugs 324 (19.5) 331 (20.0) 327 (19.7) 303 (18.3) 0.62 0.35 Aspirin or antiplatelet drugs 246 (14.8) 272 (16.4) 249 (15.0) 271 (16.3) 0.26 0.61 NSAIDS 534 (32.2) 469 (28.3) 484 (29.2) 446 (26.9) 0.01 0.01 Vitamins and minerals 210 (12.7) 184 (11.1) 220 (13.3) 183 (11.0) 0.19 0.11 Sedative or tranquilliser agents 417 (25.1) 416 (25.1) 389 (23.4) 392 (23.7) 0.85 0.31 Hormonal treatment (only women) 42 (2.53) 41 (2.47) 33 (1.99) 38 (2.29) 0.924 0.935 Educational level, n (%) <0.001 <0.001 Primary school 887 (53.6) 854 (51.5) 805 (48.5) 719 (43.4) Secondary school 468 (28.3) 467 (28.2) 497 (30.0) 481 (29.0) University and other studies 301 (18.2) 337 (20.3) 356 (21.5) 456 (27.5) 1 Cardiovascular diseases (CVD), body mass index (BMI), high-density lipoprotein-cholesterol (HDL-c) and nonsteroidal anti-inflammatory drugs (NSAIDs). Continue variables are expressed as mean ( ± SD). Categorical variables are expressed as number (n) and percentage (%). Comparisons among quartiles of dietary polyphenol intake with Pearson’s chi square test for categorical variables or one-way ANOVA for continuous variables. For glycated hemoglobine parameter, 9% of participants had no values available. The P value for linear trend was computed by fitting a continuous variable that assigned the median value for each quartile in regression models. Nutrients 2020,12, 689 8 of 17 Table 2. Contribution (%) of polyphenol subclasses to total polyphenol intake and food sources. Polyphenol Subclasses Contribution, Mean (mg/d) ±SD, (%) Polyphenol Contribution as Aglycones, Mean (mg/d) ±SD, (%) Food Sources (% of Contribution) Total polyphenols 846 ±318 620.9 ±273.5 Flavonoids 491 ±253, (58.0) 406.3 ±237.2 (65.44) Anthocyanins 43.5 ±37.8, (5.14) 24.7 ±21.7 (3.98) Cherries (42.2), red wine (24.1), olives (10.5), strawberries (10.1), grape (9.30), other foods (3.8) Chalcones 0.009 ±0.18, (<0.01) 0.006 ±0.01 (<0.01) Beer (100) Dihydrochalcones 1.72 ±1.59, (0.20) 0.98 ±0.91 (0.16) Apples (93.2), fruit juices from concentrate (6.77) Dihydroflavonols 2.62 ±4.92, (0.31) 1.81 ±3.43 (0.29) Red wine (97.6), white wine (1.80), roséwine (0.59) Catechines 28.1 ±22.4, (3.32) 27.1 ±20.7 (4.36) Tea (23.0), red wine (19.2), apples (18.6), chocolate (11.6), peaches (6.0), cocoa powder (3.18), fruit juices from concentrate (2.83), other foods (15.6) Proanthocyanidins 204±185, (24.1) 200.7 ±189.4 (32.32) Chocolate (42.7), apples (20.4), plums (9.53), red wine (7.09), cocoa powder (5.68), strawberries (4.20), other foods (10.4) Theaflavin 0.70 ±1.81, (0.08) 0.57 ±1.46 (0.09) Tea (100) Flavanones 83.2 ±76.6, (9.83) 58.1 ±55.0 (9.35) Oranges (71.3), natural orange juice (23.0), fruit juices from concentrate (3.22), other foods (2.09) Flavones 73.2 ±47.4, (8.65) 54.7 ±32.9 (8.81) Whole-grain bread (30.0), bread (23.6), oranges (21.6), natural orange juice (8.53), artichoke (3.80), other foods (12.5). Flavonols 54.0 ±22.3, (6.40) 35.6 ±15.3 (5.73) Onions (27.8), spinach (26.7), lettuce (11.9), red wine (6.02), olives (5.10), asparagus (4.93), other foods (17.55) Isoflavonoids 0.002 ±0.004, (<0.01) 0.002 ±0.003 (<0.01) Beer (100) Phenolic acids 280 ±131, (33.1) 164.2 ±70.8 (26.44) Hydroxybenzoic acids 15.5 ±10.3, (1.83) 20.5 ±12.4 (3.30) Red wine (21.2), olives (19.9), walnuts (18.1), tea (9.46), swiss chard leaves (6.15), white wine (1.34), other foods (23.8) Hydroxycinnamic acids 264 ±129, (30.9) 141.6 ±66.8 (22.80) Decaffeinated coffee (37.7), coffee (26.1), plums (5.66), potatoes (5.50), olives (4.21), red wine (1.79), other foods (19.0) Hydroxyphenylacetic acids 0.90 ±1.04, (0.10) 1.16 ±1.40 (0.19) Olives (87.2), red wine (6.57), beer (3.86), extra virgin olive oil (1.52), white wine (0.65)  Hydroxyphenylpropanoic acids 0.48 ±0.65, (0.06) 0.91 ±1.23 (0.14) Olives (100) Stilbenes 2.13 ±3.92, (0.25) 1.78 ±3.19 (0.29) Red wine (91.9), white wine (3.94), grapes (1.60), roséwine (1.21), other foods (0.07) Lignans 1.53 ±0.56, (0.18) 1.33 ±0.55 (0.21) Extra virgin olive oil (16.7), seeds (9.84), oranges (9.73), green bean (5.42), pepper (5.32), peaches (4.97), broccoli (4.71), bread (4.48), red wine (4.16), cabbage (2.77), other foods (31.9) Other polyphenols 70.8 ±41.5, (8.37) 45.6 ±27.8 (7.34) Alkylmethoxyphenols 0.93 ±0.87, (0.11) 0.93 ±0.87 (0.15) Decaffeinated coffee (74.1), coffee (16.2), beers (9.77) Alkylphenols 13.7 ±17.8, (1.62) 13.8 ±18.5 (2.23) Whole-grain bread (69.1), whole-grain pastries (14.8), breakfast cereals (8.40), pasta (3.29), other foods (4.41) Furanocoumarins 0.37 ±0.38, (0.04) 0.37 ±0.39 (0.06) Celery stalks (98.3), grapefruit juice (1.7) Hydroxybenzaldehydes 0.42 ±0.65, (0.05) 0.42 ±0.66 (<0.01) Red wine (78.9), walnuts (14.5), beer (2.61), white wine (1.95), other foods (2.04) Hydroxybenzoketones 0.002 ±0.004, (<0.01) 0.002 ±0.003 (<0.01) Beer (100) Hydroxycoumarins 0.10 ±0.19, (0.01) 0.09 ±0.18 (<0.01) Beer (73.6), white wine (26.3), cocoa powder (0.10) Methoxyphenols 0.13 ±0.12, (0.01) 0.11 ±0.12 (0.01) Decaffeinated coffee (81.3), coffee (18.7) Naphtoquinones 0.82 ±1.12, (0.09) 0.84 ±1.14 (0.14) Walnuts (100) Tyrosols 52.4 ±37.8, (6.19) 30.0 ±21.2 (4.83) Olives (50.0), extra virgin olive oil (34.8), refined olive oil (5.17), red wine (3.29), other foods (6.74) Other 1.96 ±2.30, (0.23) 0.66 ±0.54 (0.11) Orange juice (45.4), pears (18.2), coffee (16.0), other fruit juices (9.98), olives (5.86), other foods (4.56) Nutrients 2020,12, 689 9 of 17 Table 3. Energy-adjusted intake of total polyphenol and their main subclasses according to sociodemographic and lifestyle characteristics. n Total Polypenols (mg/d) pFlavonoids (mg/d) p Phenolic Acids (mg/d) pStilbenes (mg/d) pLignans (mg/d) p Other Polyphenols (mg/d) p Total population 6633 846 ±275 1 491 ± 229 290 ± 127 2.13 ±3.81 1.53 ±0.54 70.8 ±38.5 Men 3424 830 ±288 <0.001 469 ± 234 <0.001 285 ± 134 0.003 3.00 ±4.74 <0.001 1.53 ±0.54 0.933 72.1 ±42.5 0.006 Women 3209 863 ±259 515 ± 220 276 ± 118 1.21 ±2.12 1.53 ±0.53 69.5 ±33.7 Age (years) <65 3530 835 ±275 0.002 476 ± 230 <0.001 285 ± 128 0.014 2.15 ±4.03 0.605 1.51 ±0.54 0.006 70.7 ±39.2 0.967 65-70 2122 854 ±271 503 ± 225 276 ± 123 2.07 ±3.62 1.55 ±0.52 71.0 ±38.3 >70 981 866 ±281 517 ± 228 275 ± 127 2.21 ±3.40 1.55 ±0.54 70.8 ±36.4 BMI (Kg/m2) <29.9 1762 847 ±268 0.042 501 ± 225 0.004 272 ± 124 0.006 2.26 ±3.85 <0.001 1.52 ±0.49 0.679 69.9 ±36.8 0.353 30-34.9 3258 852 ±280 493 ± 232 284 ± 129 2.24 ±3.90 1.53 ±0.54 71.5 ±39.7 >35 1613 831 ±270 475 ± 226 282 ± 124 1.77 ±3.57 1.54 ±0.57 70.5 ±37.9 Physical activity level Low 3953 833 ±278 <0.001 480 ± 231 <0.001 280 ± 129 0.884 1.85 ±3.48 <0.001 1.51 ±0.54 <0.001 70.0 ±38.5 0.034 Moderate 1253 861 ±267 503 ± 217 282 ± 123 2.30 ±3.79 1.55 ±0.54 71.7 ±36.6 Active 1408 867 ±271 510 ± 230 280 ± 123 2.76 ±4.55 1.58 ±0.53 72.8 ±40.0 Educational level Primary school 3266 834 ±259 <0.001 482 ± 213 <0.001 278 ± 121 0.070 1.80 ±3.38 <0.001 1.54 ±0.55 0.093 70.9 ±40.2 0.290 Secondary school 1913 840 ±270 487 ± 227 279 ± 125 2.27 ±3.98 1.51 ±0.53 69.9 ±38.1 University 1450 880 ±311 517 ± 260 287 ± 139 2.70 ±4.40 1.55 ±0.52 72.0 ±35.0 Smoking status Current smokers 821 841 ±296 0.581 455 ± 243 <0.001 311 ± 143 <0.001 2.33 ±4.43 0.114 1.47±0.53 <0.001 70.5 ±46.3 0.768 Non-smokers 5812 847 ±272 496 ± 226 276 ± 123 2.10 ±3.72 1.54±0.54 70.9 ±37.3 1 Mean ± Standard deviation. BMI: body mass index. Total and polyphenol subclasses were adjusted for total energy intake using the residual method. Comparison between subcategories was performed using ANOVA. Nutrients 2020,12, 689 16 of 17 24. Ovaskainen, M.L.; Torronen, R.; Koponen, J.M.; Sinkko, H.; Hellstrom, J.; Reinivuo, H.; Mattila, P. Dietary intake and major food sources of polyphenols in Finnish adults. J. Nutr. 2008,138, 562–566. [CrossRef] 25. Vitale, M.; Masulli, M.; Rivellese, A.A.; Bonora, E.; Cappellini, F.; Nicolucci, A.; Squatrito, S.; Antenucci, D.; Barrea, A.; Bianchi, C.; et al. Dietary intake and major food sources of polyphenols in people with type 2 diabetes: The TOSCA.IT study. Eur. J. Nutr. 2018,57, 679–688. [CrossRef] 26. Vetrani, C.; Vitale, M.; Bozzetto, L.; Della Pepa, G.; Cocozza, S.; Costabile, G.; Mangione, A.; Cipriano, P.; Annuzzi, G.; Rivellese, A.A. Association between different dietary polyphenol subclases and the improvement in cardiometabolic risk factors: Evidence from a randomized controlled clinical trial. Acta Diabetol. 2018 ,55, 149–153. [CrossRef] 27. Marseglia, L.; Manti, S.; D’Angelo, G.; Nicotera, A.; Parisi, E.; Di Rosa, G.; Gitto, E.; Arrigo, T. Oxidative stress in obesity: A critical component in human diseases. Int. J. Mol. Sci. 2015 ,16, 378–400. [CrossRef] [PubMed] 28. Wisnuwardani, R.W.; De Henauw, S.; Androutsos, O.; Forsner, M.; Gottrand, F.; Huybrechts, I.; Knaze, V.; Le Donne, C.; Marcos, A.; Moln á r, D.; et al. Estimated dietary intake of polyphenols in European adolescents: The HELENA study. Eur. J. Nutr. 2019,58, 2345–2363. [CrossRef] [PubMed] 29. Hossain, M.K.; Dayem, A.A.; Han, J.; Yin, K.; Kim, K.; Saha, S.K.; Yang, G.M.; Choi, H.Y.; Cho, S.G. Molecular mechanisms of the anti-obesity and anti-diabetic properties of flavonoids. Int. J. Mol. Sci. 2016 ,17, 569. [CrossRef] [PubMed] 30. Grosso, G.; Stepanlak, U.; Micek, A.; Stefler, D.; Bobak, M.; Pajak, A. Dietary polyphenols are inversely associated with metabolic syndrome in Polish adults of the HAPIEE study. Eur. J. Nutr. 2017 ,56, 1709–1720. [CrossRef] 31. Ellison, R.C.; Zhang, Y.; Qureshi, M.M.; Knox, S.; Arnett, D.K.; Province, M.A.; Investigators of the NHLBI Family Heart Study. Lifestyle determinants of high-density lipoprotein cholesterol: The national heart, lung, and blood institute family heart study. Am. Heart J. 2004,147, 529–535. [CrossRef] [PubMed] 32. Tresserra-Rimbau, A.; Medina-Rem ó n, A.; Lamuela-Ravent ó s, R.M.; Bull ó , M.; Salas-Salvad ó , J.; Corella, D.; Fit ó , M.; Gea, A.; G ó mez-Garcia, E.; Lapetra, J.; et al. Moderate red wine consumption is associated with a lower prevalence of the metabolic syndrome in the PREDIMED population. Br. J. Nutr. 2015 ,113, S121–S130. [CrossRef] [PubMed] 33. Wilsgaard, T.; Jacobsen, B.K. Lifestyle factors and incident metabolic syndrome. The Tromso study 1979–2001. Diabetes Res. Clin. Pract. 2007,78, 217–224. [CrossRef] [PubMed] 34. Wang, S.; Moustaid-Moussa, N.; Chen, L.; Mo, H.; Shastri, A.; Su, R.; Bapat, P.; Kwun, I.; Shen, C.L. Novel insights of dietary polyphenols and obesity. J. Nutr. Biochem. 2014,25, 1–18. [CrossRef] 35. Lee, S.H.; Mantzoros, C.; Kim, Y.B. Resveratrol: Is selectivity opening the key to therapeutic effects? Metabolism 2012,61, 289–290. [CrossRef] 36. Miranda, A.M.; Steluti, J.; Fisberg, R.M.; Marchioni, D.M. Association between polyphenol intake and hypertension in adults and older adults: A population-based study in Brazil. PLoS ONE 2016 ,11, e0165791. [CrossRef] 37. Grosso, G.; Stepaniak, U.; Micek, A.; Kozela, M.; Stefler, D.; Bobak, M.; Pajak, A. Dietary polyphenol intake and risk of hypertension in the Polish arm of the HAPIEE study. Eur. J. Nutr. 2018 ,57, 1535–1544. [CrossRef] 38. Zamora-Ros, R.; Forouhi, N.G.; Sharp, S.J.; Gonz á lez, C.A.; Buijsse, B.; Guevara, M.; van der Schouw, Y.T.; Amiano, P.; Boeing, H.; Bredsdorff, L.; et al. The association between dietary flavonoid and lignan intakes and incident type 2 diabetes in European populations: The EPIC-InterAct study. Diabetes Care 2013 ,36, 3961–3970. [CrossRef] 39. Tresserra-Rimbau, A.; Guasch-Ferr é , M.; Salas-Salvad ó , J.; Toledo, E.; Corella, D.; Castañer, O.; Guo, X.; G ó mez-Garcia, E.; Lapetra, J.; Ar ó s, F.; et al. Intake of total polyphenols and some clases of polyphenols is inversely associated with diabetes in elderly people at high cardiovascular disease risk. J. Nutr. 2016 . [CrossRef] 40. Tresserra-Rimbau, A.; Castro-Barquero, S.; Vitelli-Storelli, F.; Becerra-Tomas, N.; V á zquez-Ruiz, Z.; D í az-L ó pez, A.; Corella, D.; Castañer, O.; Romaguera, D.; Vioque, J.; et al. Associations between dietary polyphenols and type 2 diabetes in a cross-sectional analysis of the PREDIMED-plus trial: Role of the body mass index and sex. Antioxidants 2019,8, 537. [CrossRef] 41. Sun, Q.; Wedick, N.M.; Pan, A.; Townsend, M.K.; Cassidy, A.; Franke, A.A.; Rimm, E.B.; Hu, F.B.; van Dam, R.M. Gut microbiota metabolites of dietary lignans and risk of type 2 diabetes: A prospective investigation in two cohorts of U.S. women. Diabetes Care 2014,37, 1287–1295. [CrossRef] Nutrients 2020,12, 689 17 of 17 42. Guo, X.; Tresserra-Rimbau, A.; Estruch, R.; Mart í nez-Gonz á lez, M.A.; Medina-Rem ó n, A.; Castañer, O.; Corella, D.; Salas-Salvad ó , J.; Lamuela-Ravent ó s, R.M. Effects of polyphenol, measured by a biomarker of total polyphenols in urine, on cardiovascular risk factors after a long-term follow-up in the PREDIMED study. Oxid. Med. Cell. Longev. 2016,2016, 2572606. [CrossRef] [PubMed] 43. Cicero, A.F.G.; Colletti, A. Polyphenols effect on circulating lipids and lipoproteins: From biochemistry to clinical evidence. Curr. Pharm. Des. 2018,24, 178–190. [CrossRef] [PubMed] 44. Medina-Rem ó n, A.; Barrionuevo-Gonz á lez, A.; Zamora-Ros, R.; Andres-Lacueva, C.; Estruch, R.; Mart í nez-Gonz á lez, M.A.; Diez-Espino, J.; Lamuela-Ravent ó s, R.M. Rapid Folin-Ciocalteu method using microtiter 96-well plate cartridges for solid phase extraction to assess urinary total phenolic compounds, as a biomarker of total polyphenols intake. Anal. Chim. Acta 2009,634, 56–60. [CrossRef] [PubMed] 45. Fern á ndez-Ballart, J.D.; Piñol, J.L.; Zazpe, I.; Corella, D.; Carrasco, P.; Toledo, E.; Perez-Bauer, M.; Mart í nez-Gonz á lez, M.A.; Salas-Salvad ó , J.; Mart í n-Moreno, J.M. Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain. Br. J. Nutr. 2010 ,103, 1808–1816. [CrossRef] [PubMed] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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