Monday, 22 January 2018

Impact of Flavonols on Cardiometabolic Biomarkers: A Meta-Analysis of Randomized Controlled Human Trials to Explore the Role of Inter-Individual Variability

Nutrients. 2017 Feb; 9(2): 117. Published online 2017 Feb 9. doi: 10.3390/nu9020117 PMCID: PMC5331548 Regina Menezes,1 Ana Rodriguez-Mateos,2 Antonia Kaltsatou,3 Antonio González-Sarrías,4 Arno Greyling,5 Christoforos Giannaki,6 Cristina Andres-Lacueva,7 Dragan Milenkovic,8 Eileen R. Gibney,9 Julie Dumont,10 Manuel Schär,11 Mar Garcia-Aloy,7 Susana Alejandra Palma-Duran,12 Tatjana Ruskovska,13 Viktorija Maksimova,13 Emilie Combet,12 and Paula Pinto1,14,* 1iBET/ITQB, Molecular Nutrition & Health Laboratory, 2780-157 Oeiras, Portugal; tp.tebi@sezenemr 2Division of Diabetes and Nutritional Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9NH, UK; ku.ca.lck@soetam-zeugirdor.ana 3FAME Laboratory, School of Exercise Science, University of Thessaly, 42100 Volos, Greece; moc.liamg@tastlaka 4CEBAS-CSIC, E-30100 Murcia, Spain; se.cisc.sabec@sairrasga 5Unilever R&D, 3133AT Vlaardingen, The Netherlands; moc.revelinu@gnilyerG.onrA 6University of Nicosia, CY1700 Engomi, Cyprus; yc.ca.cinu@c.ikannaig 7Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Campus Torribera, Faculty of Pharmacy and Food Sciences, University of Barcelona, CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 08028 Barcelona, Spain; ude.bu@serdnac (C.A.-L.); ude.bu@aicragram (M.G.-A.) 8INRA, UMR 1019, UNH, CRNH Auvergne, F-63000 Clermont-Ferrand, Clermont Université, Université d’Auvergne, Unité de Nutrition Humaine, BP 10448, F-63000 Clermont-Ferrand, France; rf.arni.tnomrelc@civoknelim.nagard 9University College Dublin, D4 Dublin, Ireland; ei.dcu@yenbig.neelie 10Université Lille, INSERM, Institut Pasteur de Lille, U1167—RID-AGE—Facteurs de Risque et Déterminants Moléculaires des Maladies Liées au Vieillissement, F-59000 Lille, France; rf.ellil-ruetsap@tnomud.eiluj 11Department of Food and Nutritional Sciences, University of Reading, Reading RG6 6AP, UK; ku.ca.gnidaer@rahcs.y.m 12Human Nutrition, University of Glasgow, Glasgow G31 2ER, UK; ku.ca.alg.hcraeser@1.narud-amlap.s (S.A.P.-D.); ku.ca.wogsalg@yarpsAtebmoC.eilimE (E.C.) 13Goce Delcev University, 2000 Stip, Macedonia; km.ude.dgu@aksvoksur.anajtat (T.R.); km.ude.dgu@avomiskam.ajirotkiv (V.M.) 14Polytechnic Institute of Santarem, ESA, Department of Food Technology, Biotechnology and Nutrition, 2001-904 Santarém, Portugal *Correspondence: tp.meratnaspi.ase@otnip.aluap or tp.lnu.bqti@otnipaluap; Tel.: +351-243-307-300 or +351-963-056-556 Abstract Several epidemiological studies have linked flavonols with decreased risk of cardiovascular disease (CVD). However, some heterogeneity in the individual physiological responses to the consumption of these compounds has been identified. This meta-analysis aimed to study the effect of flavonol supplementation on biomarkers of CVD risk such as, blood lipids, blood pressure and plasma glucose, as well as factors affecting their inter-individual variability. Data from 18 human randomized controlled trials were pooled and the effect was estimated using fixed or random effects meta-analysis model and reported as difference in means (DM). Variability in the response of blood lipids to supplementation with flavonols was assessed by stratifying various population subgroups: age, sex, country, and health status. Results showed significant reductions in total cholesterol (DM = −0.10 mmol/L; 95% CI: −0.20, −0.01), LDL cholesterol (DM = −0.14 mmol/L; 95% CI: −0.21, 0.07), and triacylglycerol (DM = −0.10 mmol/L; 95% CI: −0.18, 0.03), and a significant increase in HDL cholesterol (DM = 0.05 mmol/L; 95% CI: 0.02, 0.07). A significant reduction was also observed in fasting plasma glucose (DM = −0.18 mmol/L; 95% CI: −0.29, −0.08), and in blood pressure (SBP: DM = −4.84 mmHg; 95% CI: −5.64, −4.04; DBP: DM = −3.32 mmHg; 95% CI: −4.09, −2.55). Subgroup analysis showed a more pronounced effect of flavonol intake in participants from Asian countries and in participants with diagnosed disease or dyslipidemia, compared to healthy and normal baseline values. In conclusion, flavonol consumption improved biomarkers of CVD risk, however, country of origin and health status may influence the effect of flavonol intake on blood lipid levels. Keywords: flavonols, quercetin, cardiovascular disease, meta-analysis, systematic review, Interindividual variability, blood lipids, blood pressure, glucose Go to: 1. Introduction Despite the reported decline in the mortality rates of cardiovascular diseases (CVD) in the last decades, they continue to be the leading cause of morbidity and mortality in men and women in most developed countries [1,2]. Many well-recognized risk factors including hypertension, hypercholesterolemia, diabetes and obesity, as well as some lifestyle factors such as smoking, are potentially modifiable [3,4]. Thus, apart from medication, lifestyle changes and dietary modifications are crucial for preventing the incidence of CVD. Many studies have associated a high consumption of fruits and vegetables, rich in (poly)phenolic compounds, with a lower risk of chronic diseases and more specifically, CVD, although sometimes the results are inconsistent [5,6]. The mechanisms of this association are not entirely clear, but beyond nutrients, plant (poly)phenols are considered to play an important role in the maintenance of optimal cardiovascular health. Due to their effect in cell signaling pathways related to oxidative stress and inflammation, physiological conditions might be improved, including lipid metabolism, vascular function, blood pressure and glucose metabolism [7,8,9]. Despite many years of research, the evidence regarding the role of (poly)phenols in cardiovascular health is not entirely consistent. This is in part due to the heterogeneity in the design of studies, the intervention periods, and population types and size [7,10]. In addition, a high heterogeneity in individual physiological response to consumption of these bioactive compounds has been identified and has perhaps obscured associations between dietary intakes and cardioprotective effects. Factors such as the individual’s genetic background (i.e., polymorphisms), lifestyle, sex, age and gut microbiome are potential factors contributing to individual differences in the absorption, distribution, metabolism and excretion of (poly)phenols [11]. Flavonols are one of the flavonoid subclasses most common in our diet and the main food sources are vegetables such as onions, spinach, asparagus and some types of berries [12]. Within this subclass, quercetin conjugates are the most consumed type of flavonols (about 22 mg per day in Europe) [13]. Quercetin has been associated with the modulation of several cellular signaling pathways implicated in chronic diseases including cardiovascular diseases, as indicated by in vitro and animal studies [14,15]. On the other hand, epidemiological studies have significantly linked flavonol consumption with decreased risk of stroke [16,17], but not with risk of coronary heart disease [18]. Very few meta-analyses of randomized controlled trials have evaluated the effect of flavonol supplementation on cardiometabolic risk biomarkers, and, of those published, different results were obtained. Serban and collaborators have shown a significant effect in the reduction of blood pressure (mean reduction of 3.04 mm Hg in systolic blood pressure and 2.63 mm Hg in diastolic blood pressure) [19]. On the other hand, Sahebkar did not find a significant effect on triacylglycerol, LDL and HDL plasma levels, but reported a significant increase in total cholesterol (mean increase of 3.13 mg/dL [20]. Thus, to date, the clinical evidence on the beneficial effects of flavonol consumption in humans remains inconclusive, and this could be due to the interindividual variability in the response to flavonol consumption. In the present study, we systematically reviewed all published randomized controlled trials analyzing the impact of flavonol consumption on several biomarkers of cardiometabolic risk, more specifically, blood pressure, blood lipids and glucose. The influences of factors that may be responsible for interindividual variability in the response of participants to flavonol supplementation were also investigated, including age, sex and health status. Go to: 2. Materials and Methods The review was registered in PROSPERO, the international prospective register of systematic reviews (registration number: CRD42016037074). 2.1. Search Strategy A systematic search was conducted in December 2015, by one of the authors in the following databases: Medline [21], SCOPUS [22], ISI Web of Knowledge [23], ClinicalTrials.gov [24] and International Clinical Trials Registry Platform [25]. Search terms in titles and abstracts included a combination with MESH terms: (1) bioactive designation and food rich in flavonol (polyphenol, flavonoid, flavonol, quercetin, kaempferol, galangin, isorhamnetin, jaceidin, kaempferide, morin, myricetin, patuletin, rhamnetin, spinacetin, rutin; spice, caper, saffron, caraway, clove, oregano, onion, shallot, broccoli, spinach, asparagus, bean, chilli pepper, berry, black chokeberry, american cranberry, and lingonberry); (2) type of study and participant (trial, experiment, study, studies, intervention; human, subjects, men, women, patient, volunteer, and participant); and (3) cardiometabolic outcome (flow mediated dilation (FMD), platelet aggregation, blood pressure (BP), total cholesterol (TC), LDL cholesterol, HDL cholesterol, triacylglycerol (TAG), body mass index (BMI), waist circumference (WC), glucose, insulin, insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c) and exercise capacity). The wild-card term “(*)” was used to increase the sensitivity of the search strategy. No language restriction was used in the literature search. Studies included in the meta-analysis were limited to human randomized controlled trials, which had a control group receiving a placebo and measured one or more of the defined outcomes (platelet aggregation and exercise capacity were considered secondary outcomes and papers with results on only these outcomes were excluded). Additional exclusion criteria were: studies with multifactorial interventions (flavonols given as a part of a multicomponent treatment; dietary or physical activity co-intervention), studies with foods having other polyphenols in higher proportions than flavonols, acute studies and studies with non-European language. 2.2. Data Extraction Using a standardized data extraction form, the following data were extracted: (1) publication details—year of publication, name of first author, name and e-mail of corresponding author, and clinical trial registration number (when available); (2) sample characteristics—country, total number of participants, male and female, age mean and age range, ethnicity, health status (healthy, at risk or diagnosed disease), menopausal status, smoking, medication; baseline BMI, diet (assessment method, baseline diet and diet during study), and physical activity level; (3) study characteristics—study design (cross-over or parallel), arms number and description, washout duration, treatment duration, number of participants receiving the test bioactive or food, number of participants receiving the placebo, number of participants completing the study, composition of test and placebo, and dose and mode of administration; (4) information on reported outcomes—type of sample (fasted <8 h, 8–12 h, <12 h, and fed), equipment used (when important to outcome assessment), central measure (mean or median) and dispersion (standard deviation or standard error of the mean) with corresponding units (values before and after test, before and after placebo, or values for test and placebo changes), p value when available, and number of participants with adverse events. Data extraction was performed in duplicate by two authors independently and cross checked by a third author. Before analysis, outcomes on lipid levels and glucose levels were converted to mmol/L if reported in a different unit. 2.3. Assessment of Bias A systematic assessment of bias in the included studies was based on the Cochrane Collaboration with some modifications [26]. The items used for the assessment of each study were as follows: (1) selection bias—random sequence generation, and allocation concealment (in each item, yes = 1; no = 0, unclear = 0); (2) performance bias—blinding (yes = 1 for each participants, researchers and statisticians, no = 0, unclear = 0), and measurement of compliance (1 for biomarker measure, 0.5 if compliance information was collected by counting non used capsules or recipients, or by self-reporting, 0 if no measurement of compliance was done or the information is insufficient); (3) attrition bias—flow of participants (1 if flow of participants is explained in detail, including number of withdrawals and reasons, 0 if there is no information or insufficient information); (4) other bias—baseline comparability between test and control groups (yes = 1, no = 0, unclear = 0), data report (1 if pre and post data or change is reported in table with central measure and spread for placebo and treatment groups, and number per group, 0 if anything is missing), and industry funding (0 if any commercial source provided some or all monetary funding for the trial, if a company carried out a study “in house”, if any of the authors was employed by a relevant industry or if it was unclear that there was any kind of industry funding, 1 if there was no funding from industry or if the only involvement of a company was to provide any ingredient for the intervention). Studies were rated as low risk of bias when total score was 8 to 10, moderate risk of bias when total score was 5 to 7.5 and high risk of bias when total score was below 5. 2.4. Data Analysis Data for each outcome were analyzed using Comprehensive Meta-Analysis Software, version 3.0 (Biostat, Englewood, NJ, USA). Fixed or random effect meta-analysis were conducted to assess test/placebo differences across studies, with effect size measured as difference in means (DM) with 95% confidence intervals (random effect meta-analysis was used when high heterogeneity across studies was present). Heterogeneity of studies was assessed by the Cochran’s Q statistic (a chi-squared test with n − 1 degrees of freedom) and the inconsistency index I2, with a value higher than 75% being considered substantial heterogeneity [27]. Funnel plots were used to assess for evidence of publication bias. For outcomes with sufficient number of studies, the interindividual variability was assessed by exploring effects in subgroups: sex, age, country of origin and health status (BMI, healthy, with disease, and baseline values of measured outcomes and risk of cardiovascular disease). Additionally, the influence of flavonol dose, study duration and composition of test were also assessed. Sensitivity analyses were also performed by excluding studies with high risk of bias and industry funding. Quality of evidence was assessed based on the GRADE system [28]. Level of evidence was downgraded from high to moderate in the presence of either serious risk of bias across studies or serious risk of reporting bias, and downgraded to low if both were present. Go to: 3. Results 3.1. Description of the Included Studies A total of 671 studies were retrieved from database search and two others from additional sources. After screening and application of exclusion criteria, 32 trials were selected for data extraction. Fifteen of these studies were rejected after detailed analysis of the full text. Many of the rejected studies in this phase were studies where the test food had a significant content in flavonols but was richer in other flavonoids or phenolic acids (for example, apple, cocoa and tea have a higher content of flavanols than flavonols; grape and berries have a higher content of anthocyanins or phenolic acids and, thus, the observed results in the outcomes cannot be imputed to the flavonols, which is the objective of this meta-analysis). Finally, 18 RCTs were included in the systematic review and meta-analysis. The study selection process is shown in Figure 1. Figure 1 Figure 1 Flow diagram of study selection. Characteristics of included studies are displayed in Table 1. The 18 RCTs included 530 participants from European countries: Germany [29,30,31], Ireland [32], Netherlands [33], Finland [34,35] and United Kingdom [36]; 41 participants from USA [37]; 27 participants from Canada [38]; and 388 participants from Asian countries: China [39,40], Korea [41,42,43,44] and Iran [45,46]. Nine trials followed a parallel design and nine followed a crossover design, with a duration of two to twelve weeks (Table 1). Most crossover studies were of short duration (six trials with two to four weeks of intervention) and most parallel studies had longer duration (six trials with 10 to 12 weeks of intervention) (Table 1). Interventions included pure flavonol supplements (eight trials with pure quercetin and one with myricetin) or enriched mixtures of flavonols (five extracted from onion peel, one onion juice, two extracted from sea buckthorn and one from a commercial source) (Table 1). Intervention doses ranged between 16 mg and 1200 mg of flavonol. Four trials had doses below 100 mg, seven trials had doses between 100 and 200 mg and seven trials had doses above 400mg (Table 1). These were compared to placebo versions of capsules, drinks or meals; ten of them had the same matrix as the intervention [29,32,34,35,36,37,39,41,42], five were placebo capsules containing either mannitol [31], rice flour [38], cellulose [33] or lactose [45,46] and three studies did not describe composition of placebo capsules [30,43,44]. Five of the included studies had low risk of bias, two had a high risk of bias and eleven had a moderate risk of bias (Table 1 and Supplementary Materials Table S1 for risk of bias assessment). The main measured cardiometabolic biomarkers were blood pressure (16 trials), total cholesterol, HDL and LDL (16 trials), triacylglycerol (15 trials) and glucose (10 trials). Other outcomes included BMI (seven trials), WC (four trials), insulin and HOMA-IR (four trials), HbA1c (two trials) and FMD (two trials) (Table 1). Table 1 Table 1 Characteristics of selected RCTs examining the effect of flavonol supplementation on cardiometabolic biomarkers. 3.2. Characteristics of Participants Table 2 describes the main characteristics of the participants. From 987 subjects included in the analysis, 679 were supplemented with flavonols and 672 subjects received the placebo (325 subjects received flavonol only, 318 subjects received placebo only, and 354 received both, as enrolled in cross-over RCT). Mean age of most participants was between 40 and 50 years (eleven trials), two studies included older adults (mean ages above 59), three studies included adults with a mean age below 40 and two studies did not report mean age (Table 2). Nine studies included male and female participants, five studies included only male subjects and four studies included only female subjects (Table 2). Six studies included participants with diagnosed diseases: one with rheumatoid arthritis, one with hypertension and four with metabolic diseases—two trials with metabolic syndrome (MS), one trial with diabetes mellitus type 2 (DM2) and one trial with non-alcoholic fatty liver disease (NAFLD). The other studies described the participants as healthy subjects (thirteen trials). However, many of these studies included subjects with mild dyslipidemia (eight trials), impaired glucose tolerance (five trials), pre-hypertension or hypertension (three trials) (Table 2). Most studies had a high percentage of overweight participants (ten trials with BMI means between 25 and 29.9 kg/m2). The two trials with metabolic syndrome patients had a high percentage of obese subjects (BMI means above 30 kg/m2). Only four studies reported normal BMI means (below 25 kg/m2) and two studies did not report BMI values (Table 2). Smoking, an important risk factor of cardiometabolic diseases, was not reported in five studies; two studies had a mixed population of smokers and non-smokers, one study only assessed smokers and ten studies included only non-smokers (Table 2). Other lifestyle factors that may influence the effect of flavonol supplementation on the measured outcomes, such as baseline diet and physical activity level, were reported in very few trials. One trial reported energy intake at baseline [39], three reported energy and macronutrient [29,38,41,46] and one reported consumption of vegetables and fruits [35]. Only one trial reported baseline physical activity level [46]. Most of the studies had no dietary restrictions either before or during the intervention period (fourteen trials [29,30,31,34,35,36,37,38,39,40,43,44,45,46]); two trials restricted quercetin rich foods before the study [41,42], one trial restricted flavonoid rich foods during the intervention [33] and one trial restricted the intake of wine during the intervention [32]. Table 2 Table 2 Characteristics of participants. 3.3. Effect of Flavonol Supplementation on Biomarkers of Cardiometabolic Risk 3.3.1. Blood Lipids Chronic supplementation with flavonols, with quercetin as the main flavonol used in the interventions, was associated with a beneficial effect on blood lipid levels (Figure 2). Fixed-effect meta-analyses showed a significant reduction in TAGs levels (DM = −0.10 mmol/L; CI: −0.18, −0.03; p = 0.007; 17 studies, 467 supplemented participants and 456 control participants, Figure 2a), TC (DM = −0.11 mmol/L; CI: −0.20, −0.02; p = 0.021; 18 studies, 473 supplemented participants and 462 control participants; Figure 2b) and LDL (DM = −0.14 mmol/L; CI: −0.21, −0.07; p = 0.000; 18 studies, 473 supplemented participants and 462 control participants; Figure 2c). A significant increase was observed in HDL (DM = 0.05 mmol/L; CI: 0.02, 0.07; p = 0.000; 18 studies, 473 supplemented participants and 462 control participants; Figure 2d). There was no suggestion of heterogeneity among studies (I2 = 0% and p value for Q test > 0.1). Funnel plots and Egger’s weighted regression statistic did not suggest evidence of publication bias for TC (p = 0.089), HDL (p = 0.461), and LDL (p = 0.494). However, asymmetry was detected for TAG (p = 0.048) (Supplementary Materials Figure S1). Figure 2Figure 2 Figure 2 Effect of flavonol supplementation on measures of blood lipids (mmol/L): (a) Triacylglycerols (TAG); (b) Total Cholesterol (TC); (c) LDL Cholesterol (LDL); and (d) HDL Cholesterol (HDL). All studies were used for fixed effect model meta-analysis. The ... Sensitivity analyses were performed removing studies with high risk of bias [32,41] and industry funding [33]. In LDL and HDL, a small study (12 participants), which had a high relative weight due to a very small variance compared to the other studies was also removed [42]. The intervention effect remained significant for all outcomes (TAG: DM = −0.11 mmol/L; CI: −0.19, −0.03; p = 0.007; 14 studies, 390 supplemented participants and 386 control participants; TC: DM = −0.10 mmol/L; CI: −0.20, −0.00; p = 0.042; 15 studies, 386 supplemented participants and 392 control participants; LDL: DM = −0.09 mmol/L; CI: −0.18, −0.00; p = 0.043; 14 studies, 390 supplemented participants and 386 control participants; HDL: DM = 0.05 mmol/L; CI: 0.01, 0.08; p = 0.013; 14 studies, 390 supplemented participants and 386 control participants). 3.3.2. Blood Pressure A beneficial effect in blood pressure was also observed after chronic supplementation with flavonols (Figure 3). Fixed-effect meta-analyses showed a significant reduction for DBP and SBP (DBP: DM = −2.62 mmHg; CI: −3.83, −1.42; p = 0.000; SBP: DM= −3.05 mmHg; CI: −4.83, −1.27; p = 0.001; 15 studies, 336 supplemented participants and 334 control participants). No heterogeneity was found in any of the outcomes (I2 = 0% and p value for Q test > 0.1). Analysis of funnel plots and Egger’s statistics (Supplementary Materials Figure S1) suggested the existence of asymmetry for SBP (p = 0.018), suggesting the existence of publication bias. Figure 3 Figure 3 Effect of flavonol supplementation on measures of blood pressure (mmHg): (a) Dyastolic blood pressure (DBP); and (b) Systolic blood pressure (SBP). All studies were used for fixed effect model meta-analysis. The Edwards study consisted of two substudies: ... In DBP and SBP, sensitivity analysis was performed by removing the study with high risk of bias [41] and the industry funded study [33].The intervention effect was maintained after removal (DBP: DM = −2.95 mmHg; CI: −4.25, −1.65, p = 0.000; SBP: DM = −3.46 mmHg; CI: −5.39, −1.52, p = 0.000; 13 studies, 269 supplemented participants, 274 control participants). 3.3.3. Fasting Glucose Fixed-effect meta-analysis of glucose data showed a significant reduction in fasting glucose levels after supplementation with flavonols (DM = −0.18 mmol/L; CI: −0.29, −0.08; p = 0.001; 12 studies, 276 supplemented participants and 270 control participants; Figure 4). No heterogeneity was found (I2 = 0% and p value for Q test > 0.1). Sensitivity analysis was performed by removing one study with high risk of bias [41] and one industry funded study [33]. The intervention effect and the significance level were maintained (DM = −0.19 mmol/L; CI: −0.30, −0.07, p = 0.001; 10 studies, 209 supplemented participants and 205 control participants). Analysis of funnel plots and Egger’s statistics (Supplementary Materials Figure S1) showed no evidence of publication bias for glucose (p = 0.441). Figure 4 Figure 4 Effect of flavonol supplementation on measures of plasma glucose (mmol/L). All studies were used for fixed effect model meta-analysis. The Edwards study consisted of two substudies: hypertensive participants (Edwards 2007a) and pre-hypertensive participants ... 3.4. Subgroup Analyses for Identification of Factors Affecting Inter-Individual Variability To explore the factors that could influence the individual response to flavonol intake on the studied outcomes, subgroup analysis was performed on blood lipids. Studies with high risk of bias were excluded from the subgroup analysis [32,41]. As in the sensitivity analysis (Section 3.3.1), another study was also excluded in LDL and HDL subgroup analyses [42]. Subgroup analysis on blood pressure was not performed due to a lower number of studies and evidence of publication bias. Subgroup analysis on fasting glucose was also not undertaken due to the low number of studies available. Table 3 presents a summary of the results obtained for subgroup analysis performed on participant characteristics such as age, sex, country of origin, and health status (BMI, diagnosed disease, baseline lipid levels and cardiovascular risk) (the complete data of the subgroup analyses are presented on Supplementary Materials Table S2). Table 3 Table 3 Subgroup analysis on blood lipid biomarkers: stratification by participants’ characteristics. 3.4.1. Stratification by Age, Sex, and Country Based on participants age range, two subgroups of age were defined: eleven studies were categorized as “mixed ages” (700 participants with ages from 19 to 70 years) [29,31,34,35,37,39,40,43,44,45], and four as “age above 40 years” (111 participants, ages from 40 to 79 years) [30,33,38]. Only one study included younger adults, with ages below 25 (12 participants) [42]. A significant intervention effect was found in the mixed ages subgroup for TAG, LDL, and HDL, which was lost in the older age group, possibly due to the lower sample size (Table 3). No heterogeneity was observed between subgroups of mixed ages and ages above 40 years (Supplementary Materials Table S2). Most studies included both sexes but did not differentiate results for men and women (mixed sex subgroup, 10 studies, 649 participants) [29,31,33,35,37,38,39,40,43]. Comparison of studies performed with only men or women (two studies with 99 females [42,44,45]; three studies with 63 males [30,34]) was inconclusive due to the low number of subjects in either subgroup (Table 3). Ethnicity of participants was poorly described in the selected studies, and country of recruitment was used for stratification in two subgroups: Asian countries (China [39,40], Korea [43,44] and Iran [45]; five studies, 255 participants) and countries from Europe and North America (Europe [29,30,31,33,34,35], USA [37] and Canada [38]; 10 studies, 556 participants). Reductions of TAG, TC and LDL after intervention are more pronounced and significant in studies undertaken in Asia (Table 3). The effect of flavonol intake on HDL was not observed when considering the subgroups only (Table 3). Evidence of heterogeneity between the two subgroups was found for LDL (p value for Q test = 0.024), emphasizing the potential role of the participants’ characteristics in the observed difference in the intervention effect (Supplementary Materials Table S2). 3.4.2. Stratification by Health Status Most of the studies included participants with BMI values from normal weight to overweight (mixed BMI subgroup, eight studies, 246 participants) [30,33,34,38,39,40,44], showing significant effects of flavonol intake on all blood lipids (Table 3). A significant effect was not observed for TC, LDL and HDL, in the overweight subgroup (five studies, 274 participants) [29,31,37,43] and in the normal weight subgroup [35] (one study, 229, low risk of bias) (Table 3). A more pronounced effect of flavonol intake was observed for TAG in the overweight subgroup (Table 3), with a p-value near the significance cutoff (Supplementary Materials Table S2). No heterogeneity was found between BMI subgroups in all outcomes, except for LDL (Supplementary Materials Table S2). Regarding disease status, participants were divided in two subgroups: participants with no diagnosed disease (eight studies, 399 participants) [30,34,35,37,38,40,44] and participants with diagnosed cardiometabolic diseases (five studies, 305 participants) [29,31,37,39,45]. Two studies with mixed health status, including both healthy and hypertensive participants, were excluded from this subgroup analysis [33,43]. Results suggest a tendency for a more pronounced intervention effect in the disease subgroup than in the no disease subgroup (Table 3). However, a significant intervention effect was only observed in the disease subgroup for LDL (Table 3). There was no suggestion of heterogeneity between subgroups (Supplementary Materials Table S2). When studies were stratified according to baseline lipid levels, two subgroups were defined: normal and dyslipidemic (see Supplementary Materials Table S2 for data). Studies that included both normal and dyslipidemic participants were excluded (HDL: [33]; LDL: [43], TC: [37,39]). A significant impact of flavonol intake was only detected for TAG and HDL, in the dyslipidemic subgroup (Table 3). No significant intervention effect was observed in the normal baseline subgroup. No evidence of heterogeneity across subgroups was observed (Supplementary Materials Table S2). 3.5. Influence of the Type and Dose of Flavonol Studies were stratified according to the type and dose of flavonol used in the intervention. Studies with high risk of bias were removed. Regarding the type of flavonol (pure or mixture), a more pronounced intervention effect was observed on TAG and LDL only (Table 4), after administration of a pure compound, compared to a mixture of flavonols. When a study with pure myricetin was removed from the subgroup of pure compounds [39] (all other studies used quercetin as the pure flavonol), the intervention effect with pure flavonol was no longer significant on LDL (pure subgroup: DM = −0.06, CI: −0.25, 0.12; p = 0.514; mixture subgroup: DM = −0.06; CI: −0.16, 0.04; p = 0.281). On TAG, the effect was reversed, with the mixture subgroup showing a significant intervention effect and the pure subgroup a non-significant intervention effect (pure subgroup: DM = −0.16; CI: −0.36, 0.04; p = 0.114; mixture subgroup: DM = −0.09; CI: −0.18, −0.00; p = 0.039). Table 4 Table 4 Subgroup analysis: stratification by type of flavonol and dose of intervention. Studies were stratified in two dose groups: (1) below 200 mg of flavonol per day (low dose studies); and (2) above 200 mg per day (high dose studies). No significant effect was observed for TC in either subgroup, with an effect remaining for TAG and HDL in the low dose studies, and for LDL in high dose studies (Table 4). No evidence of heterogeneity between subgroups was observed (p value for Q test > 0.1, I2 = 0%) for all blood lipids, except for LDL (p value for Q test = 0.072, I2 = 6.21%). Go to: 4. Discussion 4.1. Quality of Evidence and Clinical Importance of the Observed Effects The present meta-analysis of RCTs assesses the effect of flavonol supplementation on several cardiometabolic risk factors. Results showed that medium-term flavonol intake exerted a beneficial effect on blood pressure, blood lipids profile, and fasting glucose levels (Table 5). A previously published meta-analysis of RCTs assessing the effects of quercetin on blood pressure, Serban et al., reported a BP-lowering effect of 3.04 mmHg for SBP and 2.63 mmHg for DBP [19]. The present meta-analysis pooled data from the same studies as Serban et al., and five more studies published in 2015, showing a higher lowering effect (Table 5). As shown for quercetin, the possible mechanisms behind the observed blood pressure decrease may be associated with an improvement of endothelial function by increasing NO (nitric oxide) production and/or bioavailability, by modulation of oxidative stress, and by interference with the renin-angiotensin-aldosterone system (RAAS). Direct renal effects might also play some role in the antihypertensive effect, such as the downregulation of epithelial Na+ channel (ENaC) in the kidney [50,51,52]. Table 5 Table 5 Summary for effect of flavonol supplementation on the measured biomarkers and quality of evidence. The present meta-analysis also showed a beneficial effect on blood lipids profile (Table 5) contrary to the one published by Sahebkar [20], who reported no effect on TAG, LDL and HDL, and a small increase in TC (0.08 mmol/L). This may result from the fact that Sahebkar only assessed RCTs with pure quercetin aglycone and quercetin dehydrate. The present meta-analysis pooled results from the six studies used by Sahebkar and ten more studies that also included mixtures of flavonols. To our knowledge, this is the first meta-analysis showing a lowering effect of flavonol intake on fasting glucose levels. Previously published meta-analyses of RCTs have evaluated the effect of other (poly)phenols, or foods rich in other (poly)phenols, on cardiometabolic risk factors. Similar lowering effects on BP were reported with pomegranate juice (−4.96/−2.01 mmHg, SBP/DBP) [53]. On the other hand, grape (poly)phenols, green tea and cocoa had smaller effects on blood pressure than the present meta-analysis (−1.54/non-significant mmHg for grape seed extract [54]; −1.98/−1.92 mm Hg for green tea [55]; and non-significant/−1.60 mmHg for cocoa [56], SBP/DBP), and anthocyanins had no significant effects on blood pressure [57]. On blood lipids, no significant effects were reported for pomegranate juice and grape seed extract meta-analyses [54,58]. Meta-analyses of soy products [59] and black tea [60] reported lowering effects on LDL (−0.12 mmol/L and −0.14 mmol/L), similar to the present meta-analysis, but smaller or non-significant effects in the other lipid levels. Cocoa products and flavan-3-ols also seem to have a smaller effect on lipid levels than the reported for flavonols in the present meta-analysis (non-significant for TAG and TC, −0.07 mmol/L for LDL and 0.03 mmol/L for HDL), and no significant effect on fasting glucose levels [56]. Most of the randomized controlled trials presented here had a moderate to low risk of bias (Supplementary Materials Table S1). Based on the GRADE system [28] the quality of evidence for the intervention effect was evaluated as moderate for TC, LDL, HDL and glucose and low for TAG and blood pressure (Table 5). No high-quality evidence was achieved due to many studies with unclear reporting of allocation concealment (60% for glucose, 69% for blood lipids and 71% for blood pressure), unclear or no blinding of researchers (30% for glucose, 43% for blood pressure and 44% for blood lipids), incomplete description of participants’ flow (28% for blood pressure, 30% for glucose and 44% for blood lipids) and serious evidence of reporting bias for TAG and blood pressure (Table 5). Strengths included the absence of heterogeneity across studies and the fact that the observed intervention effect was stable to sensitivity analysis in all the analyzed biomarkers. The observed intervention effects have clinical relevance in patients with stage 1 hypertension, mild dyslipidemia, and impaired fasting glucose. For example, for blood pressure, the observed mean reduction (4.84/3.32 mmHg, SBP/DBP) would lower stage 1 hypertensive values (140/90 mmHg, SBP/DBP), to pre-hypertensive values (135.2/86.7 mmHg, SBP/DBP) [47], corresponding to a mean reduction of 3% to 4% (SBP/DBP). Furthermore, it has been estimated that a reduction of blood pressure by 2 to 5 mm Hg may reduce risk of stroke by 6% to 14% and risk of coronary heart disease by 4% to 9% [61]. For glucose levels, the observed lowering effect of 0.18 mmol/L would lower values of mild impaired fasting glucose to normal values (below 5.6 mmol/L [49]), corresponding to a mean reduction of 3%. The lowering effect observed for blood lipids is less pronounced (0.1 mmol/L for TAG and TC, 0.14 mmol/L for LDL and 0.05 for HDL). Nevertheless, it may lower mild dyslipidemic values to normal values (below 1.7/5.2/2.6 mmol/L for TAG/TC/LDL [48]), representing a mean reduction of 6% for TAG, 5% for LDL and 2% for TC. In patients with mild dyslipidemia, it has been reported that different approaches, such as introduction of non-drug lowering substances to a healthy diet, physical activity and weight management, should be expected to reduce LDL by 3% to 7% each, and when combined, reach a lowering effect of about 10% to 20% [62]. Thus, supplementation with flavonols, may be used as another potential adjunct to lower global risk of cardiovascular diseases by controlling blood lipid levels, as well as blood pressure and glucose levels, either in single or multiple risk factors patients. 4.2. Inter-Individual Variability To study the inter-individual variability in response to flavonol supplementation, the present meta-analysis stratified the participants in different subgroups of age, sex, BMI, disease status and baseline blood lipid levels. However, the low number of studies was a serious limitation to the power of meta-analysis in the subgroups, particularly in age, sex and BMI subgroups, leading to inconclusive results. A higher number of studies would allow for robust meta-regression analyses, which could give more insight into the impact of inter-individual variability. On the other hand, meta-analysis results obtained after stratification by country, disease status and baseline lipid levels have shown for the first-time evidence that ethnicity and health status may influence the response of participants to flavonol supplementation. Participants from Asian countries showed higher reductions in TAG, TC and LDL, and higher increases in HDL after flavonol supplementation, compared to European and North American participants. The influence of health status on the results obtained for country subgroup analysis were tested by performing the analysis after removal of participants with diagnosed diseases and stratification of remaining studies between Asian and Europe and North American countries. Higher and significant reductions were maintained for Asian participants on TAG and TC, compared to the subgroup of Europe and North America (Supplementary Materials Table S3), suggesting that flavonol supplementation may be more effective in lowering TAG and TC values in Asian individuals, than in individuals from Europe and North American countries. Regarding health status, significant intervention effects were observed for LDL in participants with diagnosed diseases, for HDL in healthy individuals, and for TAG, and HDL in participants with dyslipidemia. To remove the potential influence of ethnicity, analyses of disease status subgroups and baseline levels subgroups were repeated only with EU and North America countries. Significant intervention effects were maintained only for LDL in the disease subgroup and HDL in the dyslipidemia subgroup; no significant effects were observed in healthy participants, or participants with normal baseline levels for any of the blood lipids (Supplementary Materials Table S3). A meta-analysis on the effect of cocoa consumption on blood lipids, has also observed a significant reduction in TC and LDL in participants with cardiovascular risk, but no effect on healthy participants [63]. Similarly, a meta-analysis on the effect of soy products has reported a higher lowering effect for LDL on hypercholesterolemic participants [59]. On the contrary, results from a meta-analysis on the effect of black tea suggest a significant lowering effect for LDL on healthy participants, but not on patients with coronary artery disease [60]. This meta-analysis has several strengths and limitations. Strengths included the absence of heterogeneity in effect size for all the biomarkers analyzed, the direction of the intervention effect was stable to sensitivity analysis and there was a low number of studies with high risk of bias. Another strength is that all RCTs used a pure or enriched mixture of flavonols, instead of food, which has a more complex matrix, with other phytochemicals and nutrients that may also influence the intervention effect. One limitation was the low number of studies, which hindered the definition of well-defined subgroups for analysis, particularly age, sex, and BMI subgroups. Another limitation was the lack of information on some lifestyle habits in most of the published RCTs, which are known to influence cardiometabolic biomarkers [3], namely, out of the 18 trials, 6 did not report smoking habits, 11 did not report baseline diet, and 17 did not report physical activity level. Another important aspect, particularly in lipid profiles and cardiovascular risk assessment, is apolipoprotein E (APOE) genotype, with APOE4 having a more atherogenic profile than APOE3 and thus being considered as non-traditional CVD risk [48]. Only two studies have differentiated between participants with APOE3 and APOE4 genotype [29,30], but results from these trials had different results and were inconclusive regarding the effect of the APOE genotype on the response to flavonol supplementation. Finally, no conclusions on the real effect of long term supplementation with flavonol can be drawn, because only one study was conducted for three months [30] and many studies were short-term [32,33,34,36,37,38,42]. Two meta-analyses of observational large cohort studies have shown that a long term high intake of flavonols, compared with a low intake was inversely associated with nonfatal and fatal stroke [16]. Moreover, an increase of 20 mg of flavonol intake was associated with a 14% decrease risk for developing stroke [17]. Go to: 5. Conclusions The co-existence of risk factors such as dyslipidemia, hypertension and diabetes adds to the risk of cardiovascular disease. The results of this meta-analysis suggest that intake of flavonols, particularly quercetin, has a beneficial effect on blood lipids, glucose and blood pressure, contributing to lower global risk of cardiovascular disease. In the future, large-scale studies with direct clinical endpoints should be conducted to further corroborate the findings of this meta-analysis and establish a causal link between a diet rich in flavonols and cardiovascular health. Our data also show, for the first time, that some individual characteristics may influence this beneficial effect, particularly, ethnicity, disease, and baseline levels. This is an important subject for further research if we want to translate the observed responses to clinical practice and adequate diet and lifestyle recommendations for different populations: men and women, young and older adults, healthy, diseased or at risk. More RCTs need to be performed, assuring a low risk of bias and reporting all the characteristics that may influence the response, preferably differentiated for men and women: mean and range of age and BMI, presence of genetic polymorphisms, number and percentage of different ethnicities, health status (including disease, medication, baseline levels of cardiometabolic markers) and lifestyle factors such as smoking habits, baseline diet and baseline physical activity level. Finally, to allow a pooled analysis of a high number of studies and a higher power of systematic reviews and meta-analysis, a database of pre and post data for each analyzed outcome should be created and made available for the scientific community. Go to: Acknowledgments This article is based upon work from COST Action FA1403—POSITIVe “Interindividual variation in response to consumption of plant food bioactives and determinants involved” supported by COST (European Cooperation in Science and Technology, http://www.cost.eu/). The authors thank the financial support of the COST Action FA1403 “POSITIVe” to conduct two short-term scientific missions to P.P. and A.G.-S. at the University of Glasgow (E.C.) during which the data analysis was performed, and to M.G.-A at the University College Dublin (E.G.) during which the protocol was developed. A.G.-S. is holder of a “Juan de la Cierva” contract from MINECO (Spain). S.P.-D. is in receipt of a CONACyT scholarship. T.R. and V.M. are supported by the Fund for scientific research of Goce Delcev University, Stip. RM acknowledges iNOVA4Health Research Unit (LISBOA-01-0145-FEDER-007344), which is cofunded by Fundação para a Ciência e Tecnologia/Ministério da Ciência e do Ensino Superior, through national funds, and by FEDER under the PT2020 Partnership Agreement. C.A.-L. thanks the grant PCIN-2014-133 (FoodBAll project) from the Spanish Ministry of Economy and Competitiveness (MINECO) within the Joint Programming Initiative Healthy Diet for Healthy Life (JPI-HDHL). Go to: Supplementary Materials The following are available online at http://www.mdpi.com/2072-6643/9/2/117/s1, Figure S1: Funnel plots and Eggers statistics for: lipids (a); blood pressure (b); and glucose (c), Table S1: Quality assessment of selected studies, Table S2: Subgroup analysis, Table S3: Sensitivity analysis in country, disease status and baseline levels. Click here for additional data file.(168K, docx) Go to: Conflicts of Interest The authors declare no conflict of interest. Go to: References 1. 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A Systematic Review and Meta-Analysis of the Effects of Flavanol-Containing Tea, Cocoa and Apple Products on Body Composition and Blood Lipids: Exploring the Factors Responsible for Variability in Their Efficacy

Nutrients. 2017 Jul; 9(7): 746. Published online 2017 Jul 13. doi: 10.3390/nu9070746 PMCID: PMC5537860 Antonio González-Sarrías,1,* Emilie Combet,2 Paula Pinto,3 Pedro Mena,4 Margherita Dall’Asta,4 Mar Garcia-Aloy,5,6 Ana Rodríguez-Mateos,7 Eileen R. Gibney,8 Julie Dumont,9 Marika Massaro,10 Julio Sánchez-Meca,11 Christine Morand,12 and María-Teresa García-Conesa1,* 1Research Group on Quality, Safety and Bioactivity of Plant Foods, Campus de Espinardo, Centro de Edafologia y Biologia Aplicada del Segura-Consejo Superior de Investigaciones Científicas (CEBAS-CSIC), P.O. Box 164, 30100 Murcia, Spain 2Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G31 2ER, UK; ku.ca.wogsalg@yarpsAtebmoC.eilimE 3Polytechnic Institute of Santarem, Escola Superior Agrária (ESA), Department of Food Technology, Biotechnology and Nutrition, 2001-904 Santarém, Portugal; tp.meratnaspi.ase@otnip.aluap 4Human Nutrition Unit, Department of Food & Drug, University of Parma, 43125 Parma, Italy; ti.rpinu@onerrapanem.leugimordep (P.M.); ti.rpinu@atsallad.atirehgram (M.D.) 5Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, University of Barcelona, 08028 Barcelona, Spain; ude.bu@aicragram 6CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 08028 Barcelona, Spain 7Division of Diabetes and Nutritional Sciences, King’s College London, London SE1 9NH, UK; ku.ca.lck@soetam-zeugirdor.ana 8Institute of Food and Health, School of Agriculture and Food Science, University College Dublin (UCD), Belfield, Dublin 4, Ireland; ei.dcu@yenbig.neelie 9U1167-RID-AGE-Facteurs de risque et Déterminants Moléculaires des Maladies Liées au Vieillissement, University Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire (CHU) Lille, Institut Pasteur de Lille, F-59000 Lille, France; rf.ellil-ruetsap@tnomud.eiluj 10National Research Council (CNR), Institute of Clinical Physiology, 73100 Lecce, Italy; ti.rnc.cfi@akiram 11Department of Basic Psychology & Methodology, Faculty of Psychology, University of Murcia, 30100 Murcia, Spain; se.mu@acemsj 12Institut National de la Recherche Agronomique (INRA), Human Nutrition Unit, Université Clermont Auvergne (UCA), Centre de Recherches en Nutrition Humaine (CRNH) Auvergne, F-63000 Clermont-Ferrand, France; rf.arni@dnarom.enitsirhc *Correspondence: se.cisc.sabec@sairrasga (A.G.-S.); se.cisc.sabec@asenoctm (M.-T.G.-C.); Tel.: +34-968-396276 (A.G.-S. & M.-T.G.-C.); Fax: +34-968-396213(A.G.-S. & M.-T.G.-C.) Author information ▼ Article notes ► Copyright and License information ► Go to: Abstract Several randomized controlled trials (RCTs) and meta-analyses support the benefits of flavanols on cardiometabolic health, but the factors affecting variability in the responses to these compounds have not been properly assessed. The objectives of this meta-analysis were to systematically collect the RCTs-based-evidence of the effects of flavanol-containing tea, cocoa and apple products on selected biomarkers of cardiometabolic risk and to explore the influence of various factors on the variability in the responses to the consumption of these products. A total of 120 RCTs were selected. Despite a high heterogeneity, the intake of the flavanol-containing products was associated using a random model with changes (reported as standardized difference in means (SDM)) in body mass index (−0.15, p < 0.001), waist circumference (−0.29, p < 0.001), total-cholesterol (−0.21, p < 0.001), LDL-cholesterol (−0.23, p < 0.001), and triacylglycerides (−0.11, p = 0.027), and with an increase of HDL-cholesterol (0.15, p = 0.005). Through subgroup analyses, we showed the influence of baseline-BMI, sex, source/form of administration, medication and country of investigation on some of the outcome measures and suggest that flavanols may be more effective in specific subgroups such as those with a BMI ≥ 25.0 kg/m2, non-medicated individuals or by specifically using tea products. This meta-analysis provides the first robust evidence of the effects induced by the consumption of flavanol-containing tea, cocoa and apple products on weight and lipid biomarkers and shows the influence of various factors that can affect their bioefficacy in humans. Of note, some of these effects are quantitatively comparable to those produced by drugs, life-style changes or other natural products. Further, RCTs in well-characterized populations are required to fully comprehend the factors affecting inter-individual responses to flavanol and thereby improve flavanols efficacy in the prevention of cardiometabolic disorders. Keywords: flavanols, tea, cocoa, apple, cardiometabolic disorders, meta-analysis, interindividual variability, blood lipids, body mass index, waist circumference Go to: 1. Introduction Metabolic disorders, principally, abdominal obesity, dyslipidemia (high levels of triacylglycerides (TAGs) and low levels of high-density lipoprotein (HDL)), and insulin resistance have been associated to an increased risk of Type-2 diabetes mellitus (DM) and cardiovascular diseases (CVDs). CVDs remain the number one cause of death in developed countries and their prevalence is increasing rapidly in developing nations and in adolescents [1]. It is now well established from population studies that some aspects of CVDs risk can be modulated by various dietary interventions including an increased consumption of plant foods [2], as part of a healthy balanced diet. In addition to other protective compounds (i.e., fiber and vitamins), plant foods are an exclusive and abundant source of phytochemicals, a large and diverse group of compounds which exhibit an array of biological activities. The intake of these bioactive compounds are thought to contribute to the health benefits associated with the consumption of such foods [3]. Polyphenols are some of the most abundant phytochemicals in plant foods and increasing evidence from cohort studies indicate that the intake of some of these compounds such as diverse flavonoids and/or, importantly, some of their derived microbial metabolites (e.g., enterolactone) may help to reduce the development of CVDs and CVDs mortality risk [4,5,6,7]. This evidence is supported by animal and clinical studies reporting beneficial effects of the consumption of some polyphenol-rich foods or pure compounds on CVDs risk factors such as blood cholesterol, blood pressure, endothelial function and arterial stiffness [8]. Polyphenols encompass several families of compounds, the most represented in plant foods being phenolic acids and flavonoids [9]. A major group of flavonoids is constituted by flavanols that are abundant in green tea, red wine, cocoa and various fruits such as apples [10]. A summary of the major flavonoids present in tea (green and black), cocoa powder and apple is shown in Table S1. The assessment of daily intakes of flavanols across Europe revealed a large variation between countries (from 200 to 800 mg/day) depending on their dietary habits and the intake of tea [11]. The flavanol group is composed primarily of the epicatechin and catechin monomers, and of their oligomeric and polymeric forms, the procyanidins. Flavanol monomers and dimeric procyanidins are bioavailable. They undergo extensive phase II conjugation and are found in the blood circulation mostly as O-methylated, sulfated and glucuronidated conjugates (nM to μM range) [12,13]. In contrast, the procyanidins polymers are not absorbed and do not contribute to the systemic pool of flavanols in humans [14]. Human studies remain essential to understanding the effects of the plant bioactive compounds on health and thus, an increasing number of randomized controlled trials (RCTs) with flavanol-containing products have been carried out over the past two decades. Meta-analyses constitute a useful tool to integrate the accumulated RCTs and review the evidence in humans. Some of the main problems affecting the results of meta-analyses are the usually limited number of studies included as well as a range of factors that introduce heterogeneity in the findings. Identifying the factors underlying variability, as well as developing new and innovative methodologies to account for such variability constitute an overarching goal to ultimately optimize the beneficial health effects of plant food bioactives. Among the potential factors involved in such heterogeneity are: (i) factors inherent to the individuals: (epi) genetic factors, gut microbiota, baseline conditions (BMI, medication), sex, health status, ethnicity, and age; and (ii) factors intrinsic to the type of study (design, duration, dose, and type of product) [15]. The main goals of the present study were: (i) to systematically review and appraise, through meta-analysis, the impact of flavanol-containing tea, cocoa and apple products, three main sources of flavanols, on selected biomarkers of cardiometabolic risk, i.e., BMI, WC and blood lipid levels (total-, LDL-, HDL-cholesterol and TAGs); and (ii) to further explore some of the factors that may be implicated in the inter-individual variability in the response to the consumption of these flavanol-containing products. Go to: 2. Materials and Methods This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guidelines [16], the Cochrane Handbook for Systematic Reviews of Interventions [17], and the Centre for Reviews and Dissemination’s guidance for undertaking reviews in health care [18]. The protocol for this review was registered in the International Prospective Register of Systematic Reviews (PROSPERO, www.crd.york.ac.uk/prospero/index.asp) with the registration number CRD42016033878. 2.1. Search Strategy A comprehensive search on PubMed and Web of Science databases was conducted in July 2015. Search terms included a combination of keywords referring to: (1) bioactive (polyphenols, flavonoids, flavanols, flavan-3-ol, (epi)catechin, (epi)gallocatechin gallate, theaflavins, thearubigin, and procyanidin); (2) food source (apple, tea, and cocoa); (3) type of study and participants (trial, experiment, study, intervention; human, subjects, men, women, patients, volunteers, and participants); and (4) cardiometabolic outcomes (BMI, WC, total cholesterol, LDL cholesterol, HDL cholesterol, and TAGs). No type of restriction was applied during the electronic searches. 2.2. Study Selection and Data Extraction Two authors independently assessed all papers and in the case of disagreement, discussed findings to reach a consensus, or in the absence of resolution, a third author was contacted. Studies included in the meta-analysis were limited to human RCTs testing the effect of flavanol-containing tea, cocoa or apple products, which had a control group receiving a placebo and measured one or more of the defined outcomes (BMI, WC, total cholesterol, LDL cholesterol, HDL cholesterol, or TAGs). Manuscripts written in any European language were included, whereas other manuscripts were excluded. Additionally, the studies with the following characteristics were excluded: studies with flavanol-rich food sources other than tea, cocoa or apples; and studies with multifactorial interventions (i.e., flavanols given as a part of a multicomponent treatment; dietary or physical activity co-intervention). Data extraction was performed in duplicate by two authors, independently, and cross-checked by a third author using a standardized data extraction form. Extracted data included publication details (year of publication, contact details, clinical trial and registration number); participant characteristics (geographical origin, total number of participants included in the study and in the analysis, sex distribution, age, ethnicity, health status, menopausal status, smoking habits, and use of medication); study setting and design (cross-over or parallel design, duration of the intervention, number of arms and description, number of participants located in each arm and completing the study, composition of test and placebo, and dose and mode of administration); and outcomes (type of sample, changes in the outcome, values before and after intervention, and p-value). 2.3. Assessment of Quality and Data Analysis The quality of the studies was assessed based on the Cochrane Collaboration measurement with some modifications [19]. The specific items used for the assessments are detailed in a previous meta-analysis following the same protocol [7]. Data for each outcome were analyzed using the Comprehensive Meta-Analysis Software, version 3.0 (Biostat, Englewood, NJ, USA) [20]. The free scale index standardized difference in means (SDM) was used to combine data from the highest number of collected valid studies, increasing the pool of studies and the power to detect significant differences. SDM, standard error (SE) and the corresponding 95% confidence intervals (CI) were calculated and pooled using random effects models to determine test/placebo differences across studies. Statistical heterogeneity between studies was assessed by using the Cochran Q test, the between-studies variance (T2) and I2 (an estimate of the proportion of variance across studies caused by heterogeneity rather than by random errors) where I2 values equal to 25%, 50% and 75% were considered as low, moderate and high heterogeneity, respectively. Publication bias was assessed visually with funnel plots and statistically by applying the Egger’s regression test. Further assessment of the possible associations between the overall changes attributed to the supplementation with the flavanols and the duration of the intervention was examined using random-effects meta-regression analysis. Using the random model, we have additionally estimated the overall effect size as the difference in means (DM) and 95% CI. Subgroup analyses were conducted to explore potential factors that may introduce heterogeneity into the studies and influence the inter-individual variability in the response to supplementation with the flavanol-containing products. We selected those factors that were more clearly described throughout articles (Table 1). We included factors that might be attributed to some of the individuals’ characteristics, such as baseline BMI, sex, smoking habits and medication/health status. Age or ethnicity could not be assessed due to unclear reporting. We also included stratification by the country in which the study was carried out, the source and form of administration of the flavanols, as well as the type of diet reported to be followed during the intervention. For each subgroup, the pooled effects (SDM) and the significance of this value were estimated. Additionally, statistical comparisons between subgroups were performed by applying a random-effects analysis and calculation of the between-categories Q statistic, the p-value and the R2 index (proportion of between-studies variance explained by each factor or covariate). Using some of the factors that partially explained some of the between-studies variance for a particular variable, we applied a multiple meta-regression analysis with a random-effects model to search for a potential combination of factors that best explained the between-study variance for this variable. Statistical significance of the findings was as follows: p-value < 0.05 was considered significant, while p-value ≥ 0.05 and < 0.1 was considered marginally significant. Table 1 Table 1 Potential factors influencing the heterogeneity in the responses to the supplementation with flavanols-containing products investigated in this meta-analysis. Go to: 3. Results 3.1. Description of the Included Studies A total of 1409 articles were initially identified through the search on the electronic databases. After removal of duplicates and screening, 188 trials were selected for data extraction. After detailed analysis of the full text, 71 articles were excluded, due to lack of relevant outcomes, aspects of study design or publication language. The final number of articles selected for meta-analysis was selected from a total of 117 articles published between 1997 and July 2015 (included) [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137]. The detailed study selection flow diagram is shown in Figure 1. Figure 1 Figure 1 Flow diagram showing the study selection process. 3.2. Quality and Characteristics of the Selected Studies Most of the studies (70%) were classified as studies with a moderate to low risk of bias (quality score ≥5.0 and <8.0 or ≥8.0 and ≤10.0, respectively) while 30% of the studies obtained a low quality score (<5.0) and were considered as a high risk of bias. The studies were carried out in countries distributed over five continents: Asia (Japan, Korea, China, Taiwan, Thailand, Saudi Arabia, and Iran), North America (USA and Canada) and Latin America (Brazil and Mexico), Europe (Denmark, Finland, The Netherlands, Germany, Poland, UK, Switzerland, Italy, Spain, Portugal, and Greece), Africa (Mauritania, South Africa, and Republic of Mauritius), and Australia. Thus, they were considered representative of a global population. The participants in these studies also represent a mixed population of men and women ranging from young adults to elderly participants, and with a higher prevalence of individuals with a BMI ≥ 25.0 kg/m2 (overweight and/or obese volunteers). The quality and depth of reporting of the factors potentially contributing toward inter-individual variability of the effect of flavanols varied among studies. The smoking habits were not reported in most studies but for those studies that did, the participants were typically non-smokers or a mixed sample population. Only two studies [34,57] were carried out specifically with smokers. The total sample population included healthy individuals, overweight and/or obese individuals as well as individuals with an incipient or with a reported chronic risk factor or metabolic disease, comprising principally hypertension, hyperlipidemias, type-2 diabetes, metabolic syndrome, atherosclerosis, coronary artery disease and heart failure. Among these, some participants were taking medication, others were not medicated or medication use was not reported. Studies were selected if the source of flavanols was tea, cocoa, or apple provided as liquid (tea drinks, cocoa beverages, and apple juice) or solid (powder or extracts in capsules, snacks, tablets, and foods) forms. Interventions ranged typically 1–6 months, during which participants followed either a controlled diet or their habitual diets. 3.3. Overall Impact of the Supplementation with Flavanol-Containing Tea, Cocoa or Apple Products on Blood Lipids, BMI and WC The number of RCTs varied in function of the outcome measure studied, from 46 to 120 trials, recruiting a total high number of participants ranging from 2875 to 5931 individuals. Forest plots detailing weighted SDM, SE, 95% confidence intervals and relative weight for the impact of supplementation with flavanol-containing tea, cocoa or apple products on BMI, WC, and blood lipid levels are shown in Figures S1–S6. Visual inspection of the Funnel plots (Figures S7–S12) evidenced symmetrical shapes and absence of publication bias in the case of WC, total cholesterol, LDL, HDL and TAGs. Some asymmetry was however detected for BMI. These results were further confirmed by Egger’s regression. A summary of the random overall effects for each lipid and obesity-related variable, heterogeneity and bias analyses is presented in Table 2. Table 2 Table 2 Overall changes (SDM), heterogeneity and publication bias analyses for the impact of flavanol-containing products on BMI, WC and blood lipids levels. Despite a high heterogeneity across the studies (I2 = 70–77% for most variables except for BMI which was more moderate, I2 = 26.7%), the overall pooled analysis (shown as SDM) significantly confirmed a reduction of BMI (−0.153, p-value < 0.001), WC (−0.293, p-value < 0.001), blood total cholesterol (−0.214, p-value < 0.001), LDL (−0.235, p-value < 0.001), and TAGs (−0.114, p-value = 0.027). HDL levels were also significantly increased (0.152, p-value = 0.005). Sensitivity analyses were carried out using the leave-one-out approach where the meta-analysis was performed with each study removed in turn. The pooled estimates consistently showed a similar effect and significance emphasizing the robustness of these results and that the effect was not driven by any particular study (data not shown). Further support of these results was found by a significant relationship between the duration of the supplementation with the flavanol products and the reduction of WC; total-, LDL- and HDL-cholesterol; and TAGs using random-effects meta-regression analysis. Regression coefficients and p-values for each variable can be seen in Table S2. 3.4. Analysis of the Potential Factors Influencing Inter-Individual Responses to Flavanols Consumption 3.4.1. Stratification by the Individuals’ Baseline BMI, Sex, Smoking, and Country Following stratification by the baseline BMI (Table 3), the effects of the flavanol-containing products on BMI, WC, total- and LDL-cholesterol remained significant only in those studies carried out in overweight/obese volunteers (BMI ≥ 25.0 kg/m2). HDL-cholesterol levels were also increased in this subgroup (p = 0.063) whereas the reducing effects on TAGs levels were not significant in any of the two subgroups. Statistical comparison between ≥25.0 vs. <25.0 kg/m2 subgroups did not reach significance for any of the variables investigated. Of note, 61% of the total between-study variance in BMI could be explained by the respective baseline BMI values (total between Q = 2.53, p-value = 0.112, R2 index = 0.61). Table 3 Table 3 Stratification analysis of the influence of baseline BMI, sex, smoking and country where the study was carried out on the effects (SDM) on BMI, WC, and blood lipids levels following supplementation with flavanol-containing products. Regarding stratification by sex, the reduction of WC was significant in both men and women after intervention with flavanol-containing products. However, total- and LDL-cholesterol were significantly reduced only in female whereas BMI was significantly lowered only in male. The effects on HDL and TAG levels were no longer significant after stratification by sex (Table 3). Between groups comparison indicated a difference between sexes (total between Q = 2.833, p-value = 0.092) and a considerable contribution of the sex to the between-study variance for BMI (R2 index = 1.0). The reduction of BMI, WC, and total- and LDL-cholesterol in response to the flavanol-containing products was significant in studies carried out in non-smoker volunteers. It was not possible, however, to establish a comparison with habitual smokers due to the very low number of studies carried out with this type of volunteers (n = 2 studies). Comparison between studies carried out in East Asian countries (assuming Asian ethnicity) against those carried out elsewhere evidenced similar results for BMI, WC, total and LDL cholesterol in both subgroups although the results were slightly less significant in the East Asian subgroup. A small proportion (7%) of the BMI between-groups variance was explained by the study location (East Asian vs. others) (total between Q = 0.963, p-value = 0.327, R2 index = 0.07). In addition, we found a significant difference in TAG levels in response to flavanol-containing products between the East Asian subgroup and the others with a more pronounced effect in the East Asian studies (total between Q = 7.419, p-value = 0.024, R2 index < 0.01). When grouping in North America and European countries, we detected a significant reduction of BMI and WC in the Europe group but not in the American one, whereas the LDL-cholesterol reduction resulted significant in the American group only. Statistical comparison between North America and Europe groups showed a difference in the BMI response (total between Q = 3.143, p-value = 0.076) and a 28% of the between-group variance explained by this factor (R2 index = 0.28). Within Europe, the studies carried out in countries of the Mediterranean area resulted in significant reductions of BMI, WC, total- and LDL-cholesterol and in an increase of HDL (p = 0.078). In the non-Mediterranean countries, we only detected a significant reduction of total-cholesterol. Comparison between the two subgroups indicated a significant difference in the WC reduction (Total between Q = 5.228, p-value = 0.022, R2 index < 0.01). 3.4.2. Stratification by the Individuals’ Medication and Health/Disease Status The influence of medication on the response to the consumption of the flavanol-containing products was also explored (Table 4). The subgroup including participants without any reported medication showed significant reductions of BMI, WC, total- and LDL-cholesterol as well as a reduction of TAGs (p = 0.063). In contrast, in the subgroup of studies including participants under medication the effects did not reach statistical significance. Further comparison between the two subgroups (Yes vs. No medication) revealed no significant differences between them (total between Q = 2.59, p-value = 0.107) but 34% of the between-groups variance for the BMI response was explained by this factor (R2 index = 0.34). Table 4 Table 4 Analysis of the influence of medication and health status on the effects (SDM) of the supplementation with flavanols on BMI, WC, and blood lipids levels. Regarding health/disease status, participants were stratified as healthy, at risk or with a reported disease. Both in healthy subjects and in participants with a disease, the total- and LDL-cholesterol levels were significantly reduced. Studies conducted with volunteers categorized as at a risk exhibited the most significant reduction of BMI and WC in response to the flavanols. Stratification of the studies by the type of disorder showed a significant reduction of BMI and WC in overweight and/or obese individuals, a significant increase of HDL-cholesterol levels in patients with a dyslipidemia and a significant reduction of LDL-cholesterol in patients with diabetes or hypertension (Table 4). Comparison between each of the subgroups against the healthy subgroup was not significant for any of the variables investigated. 3.4.3. Stratification by the Source/Administration Form of the Flavanols and the Diet during the Intervention Among the sources of flavanols investigated, our meta-analysis confirmed that supplementation with tea derived products significantly impacts on all the investigated variables except for TAGs (Table 5). Studies carried out with cocoa as the source of flavanols exhibited a significant effect on total-, LDL-cholesterol and TAGs levels whereas intervention with the apple-derived products appears to only modulate total- and LDL-cholesterol levels. Statistical comparison between the sources of flavanols highlighted a significant difference in the effect on BMI between tea and cocoa products (p-value = 0.012) with a 29% of the between-groups variance explained by this factor (R2 index = 0.29). The apple group resulted also significantly more efficient than the cocoa or tea groups in the reduction of total-cholesterol. In addition, the apple products showed a greater effect on LDL-cholesterol than the tea derived products. Table 5 Table 5 Analysis of the influence of the original source of flavanols and of the diet (during the intervention) on the effects (SDM) of the supplementation on BMI, WC, and blood lipids levels. Regarding the supplementation form, the results showed that the administration of tea as solid extracts caused a significant and efficient modulation of all the variables investigated except for HDL and TAGs, whereas the tea beverages were significant at reducing only BMI and LDL-cholesterol (Table 5). Statistical comparison between liquid and solid tea-flavanols administration pointed out at a difference between the two subgroups at reducing LDL cholesterol (p-value = 0.096) with 11% of the between-group variance explained by this factor (R2 index = 0.11). A very limited number of studies have reported so far the effects of tea purified epigallocatechin gallate (EGCG), one of the main flavanols present in tea. Overall, these studies only support a significant reduction of BMI by this compound. Of note, and as opposed to tea products, the purified EGCG appears to reduce the levels of HDL (results not significant) and increase those of TAGs (p = 0.077) (Table 5). Comparison between the EGCG subgroup and the tea drink or the tea extract subgroups indicated that the form of administration (as a purified compound or as a mixture) partially contributed to explaining the between groups variances for HDL (total between Q = 5.211, p-value = 0.022, R2 index = 0.10, EGCG vs. tea drink; total between Q = 3.835, p-value = 0.050, R2 index = 0.12, EGCG vs. tea extract) and for TAGs (total between Q = 3.282, p-value = 0.070, R2 index = 0.06, EGCG vs. tea drink; total between Q = 3.765, p-value = 0.052, R2 index = 0.09, EGCG vs. tea extract). Regarding the type of diet (controlled vs. usual) during supplementation with the flavanol-containing products, the reducing effects on BMI, WC, total- and LDL-cholesterol remained significant or marginally significant in both subgroups. The levels of TAGs were also reduced although not significantly. We detected, however, a significant increase in the HDL-cholesterol levels only in the subgroup that followed a controlled diet (Table 5). Further, statistical comparison of the two subgroups highlighted a significant difference on the reduction of WC between them (total between Q = 4.761, p-value = 0.029) and a 7% explanation of the between-groups variance by this factor (R2 index = 0.07). 3.4.4. Multiple Meta-Regression Analysis of BMI Modulators of the Response to Flavanol-Containing Products Consumption Multiple meta-regression analysis was performed (Table 6) to derive the independent effect of some of the covariates previously found to partially explain some of the between-groups variance for BMI, i.e., baseline BMI (64%), country where the study was carried out (East-Asian vs. all other countries) (7%), medication use (34%) and source of flavanols (tea vs. cocoa products) (29%). Table 6 Table 6 Main results of the multiple random-effects meta-regression model for the contribution of the covariates, medication and source of flavanols, on BMI response (Standardized difference in means). Although sex also appeared to contribute greatly to the BMI between-groups variance (R2 = 1), it was not included in the multiple regression due to the limited number of studies clearly reporting sex and used in the analysis. The full model reached statistical significance, with a large proportion of variance (94%) accounted for and a considerable number of studies included (n = 40 studies). Both medication and source of flavanols were significantly correlated with the reducing effect on BMI, once controlled the influence of the other predictor. In particular, higher effects of the flavanols-products on BMI were found in the absence of the medication and with the consumption of tea products. Go to: 4. Discussion The consumption of flavanols may contribute to improve cardiometabolic health via the moderation of a range of associated risk factors. Recent meta-analyses (Table S3) [138,139,140,141,142,143,144,145,146,147,148,149,150,151] suggest that the consumption of flavanol-containing tea and tea products could reduce total- and LDL-cholesterol as well as body mass index (BMI) and waist circumference (WC), while chocolate and cocoa flavanols also appear to regulate blood lipid levels. Nonetheless, the results of these analyses are inconsistent, partly due to the large heterogeneity of the clinical trials included. In addition, some of the anthropometric indicators of obesity such as BMI and WC have not yet been systematically investigated. We herein present the largest meta-analysis investigating the impact of flavanol-containing tea, cocoa and apple products, three major dietary sources of these bioactive compounds [152] on several biomarkers of lipid metabolism and anthropometric variables, such as BMI and WC. Our analysis confirms that the intake of these products is significantly associated with: (1) reduced BMI and WC; and (2) a more favorable lipid profile with a decrease in total- and LDL-cholesterol, and TAG plasma levels, and an increase in HDL-cholesterol levels. In addition, our analyses show that the changes in these biomarkers following consumption of the flavanol-containing products can be influenced by a number of factors and thus, the benefits of these products can significantly vary between specific population subgroups. It is of utmost interest to clarify the impact of these factors in order to discern which population subgroups could most benefit of the intake of these bioactive compounds. 4.1. Baseline BMI There is evidence that baseline BMI may be a potential factor with an impact on the individuals’ response to supplementation with different natural products. For instance, treatment with natural probiotics has been shown to significantly increase HDL only in patients with a baseline BMI ≥ 29 kg/m2 [153] or significantly reduce BMI only in participants with a baseline BMI ≥ 25 kg/m2 [154]. Regarding flavanol-containing products, a previous meta-analysis of the effects of black tea on blood cholesterol failed to detect differences in the modulation of cholesterol levels between individuals with normal weight or overweight and obese phenotype, but the results of this meta-analysis were estimated using a very small number of trials per subgroup (4 and 5, respectively) [141]. Our stratification approach by baseline BMI provides some evidence that the changes following consumption of flavanol-containing products on BMI, WC and cholesterol levels are more pronounced in individuals with a baseline BMI ≥ 25 kg/m2 and supports the fact that supplementation with these products may have a better impact on these risk factors in overweight and/or obese people. Nevertheless, it is not yet clear whether there is a general better efficacy of natural treatments in overweight and/or obese people, or if the effects may vary depending on the biomarkers or the products investigated. More trials in individuals with a normal BMI < 25.0 kg/m2 are still needed to further compare and demonstrate significant differences in the benefits of flavanol-containing products in relation to body weight, since most are conducted in populations of greater cardiometabolic risk, who are often obese in nature. 4.2. Sex Understanding the differing responses by sex is becoming increasingly important. Previous work has shown that the reducing effects of green tea on total and LDL-cholesterol were significantly greater in men than in women, giving preliminary evidence of that supplementation with flavanol-containing green tea could have a different effect depending on the sex of the individuals [142]. Our results also support differences between women and men in their capacity to regulate the levels of total and LDL cholesterol in response to the consumption of flavanol-containing products with women exhibiting a more efficient reduction than men. A recent meta-analysis looking at the effects of flavonols (another flavonoids class) on lipids levels, failed to detect a difference between men and women, possibly due to the very low number of trials and participants in the two subgroups [7]. Comparing the regulation of cardiometabolic risk factors between women and men is complex because of the hormonal protection in premenopausal women [155]. We were not able to stratify our analyses based on the age or menopausal status of the women, as these factors were not sufficiently well characterized in the trials selected for the meta-analyses. Nevertheless, our results point out to a different response to flavanols consumption between sexes and reinforce the need to further investigate this factor in future trials specifically designed for this purpose. 4.3. Country Where the Study Was Carried Out Ethno-cultural differences are associated with the risk of development of cardiometabolic disorders [156] and thus, it is important to explore and clarify whether different ethnic groups differ in their responses to consumption of plant bioactives as effective treatment against these diseases. Unfortunately, most of the clinical trials included in the present meta-analysis have not clearly identified the ethnicity of the participants. In the absence of this information, we have explored the potential influence of the country where the studies were carried out. A common comparison is that between studies undertaken in Asian countries vs. non-Asian ones. It has been reported that Asians showed a more marked decrease in the levels of TAGs in response to ϖ-3 fatty acids supplementation as compared to subjects within a USA/European group but, no significant differences were found for total cholesterol or BMI [157]. Flavonols have also been shown to significantly reduce TAGs, total and LDL-cholesterol in studies conducted in Asian countries as compared to those in the EU/European subgroup [7]. Regarding flavanol-containing products, previous meta-analyses have suggested that tea and tea extracts reduce BMI and WC both in Asian and non-Asian trials [140] and that, cocoa products significantly reduce LDL-cholesterol in European countries as compared to USA [149]. Nevertheless, these analyses were all underpowered. Our stratification analysis by country included, in general, a big number of studies per subgroup and showed no apparent differences in the responses to the consumption of tea, cocoa and apple products between East Asian countries and all other countries except for TAGs which were significantly reduced only in the Asian subgroup. We also found some different responses between North American (USA/Canada) and European subgroups, as well as between European Mediterranean and non-Mediterranean ones. This may be partially related to features such as the ethnicity of the participants but also to other factors associated with the life-style of the country. More studies are needed in order to understand the influence of this factor in the response to interventions with plant natural compounds. 4.4. Health and Medication Status Previous meta-analyses had suggested that the consumption of green tea [146,147], black tea [139], and cocoa products [149] had moderating effects on lipid levels both in healthy subjects and in patients with hyperlipidemia or at a higher cross-over or cross-over or s risk. Other bioactive compounds such as flavonols also had a more pronounced effect in the disease subgroup than in the healthy subgroup as significantly evidenced for LDL-cholesterol [7]. Our results show and corroborate a significant reduction of total- and LDL-cholesterol by the flavanol-containing tea, cocoa and apple products both in healthy participants and in individuals with a disease. On the other hand, BMI and WC were reduced and HDL increased in the three subgroups of healthy, “at risk” and individuals with a disease but the results reached statistical significance in the “at risk” group only. As a whole, these results support a metabolic benefit of the consumption of plant bioactive compounds, and in particular of flavanols, regardless of the health status of the individuals. An important consideration regarding the use of plant bioactive compounds as modulators of cardiometabolic risk biomarkers is their potential use as treatment on their own or as coadjuvants in combination with pharmacological drugs [158]. Our results show that the use of flavanol-containing products in the absence of medication was significantly associated with the reduction of BMI, WC, total and LDL cholesterol, as well as TAGs giving some evidence of their efficacy as therapeutics. The number of clinical trials in which the flavanols were supplemented in combination with other drugs was in general smaller than studies carried out in the absence of medication (see Table 4) thus the pooled results did not reach significance. Nonetheless, these data point to a modulatory effect of the flavanol-containing products in medicated individuals. Whether the combined therapy is more efficient and safe than individual treatment with drugs or with natural plant bioactives warrants further investigation. 4.5. Source and Form of Administration of the Flavanols Our results confirm that the flavanol-containing tea products are effective regulators of blood cholesterol (total, LDL and HDL) as well as of BMI and WC. The cocoa or apple products were effective at reducing total- and LDL-cholesterol and the cocoa products were also able to significantly decrease the levels of TAGs. These results might suggest that the metabolic regulatory efficacy of these three flavanol-containing products could be ranked as tea > cocoa > apple but caution should be taken with this interpretation due to the differences in the number of studies carried out with each source of flavanols as well as the differences in the doses and the composition of the products. Further studies are needed to corroborate this comparison. Our analysis also suggests that the administration of tea as a solid extract might be more efficient than tea beverages at reducing WC and total cholesterol. Earlier meta-analyses had suggested that the type of administration of green or black tea either in solid form (extracts and, capsules) or as a drink did not differ at reducing total- and LDL-cholesterol [139,146,147]. Unlike those previous analyses, where the number of studies per subgroup was very small, our stratification between tea drinks and tea extracts included a considerable number of studies per subgroup (>15) and gives preliminary evidence of a potentially higher efficacy of the tea when administered as a solid powder. We may hypothesize that this could be partially related to the presence of higher doses of the bioactive flavanols in such extracts. 4.6. Magnitude of the Changes An interesting issue worth discussing here is the magnitude of the changes attributed to the intake of the flavanol-containing products and to the extent these changes can contribute to the regulation of the analyzed biomarkers in comparison with other approaches, i.e., drugs, lifestyle changes, or other natural compounds. Based on the Cohen guidelines [159], the effects of the flavanol-containing products (expressed as SDM) are, in general, small (≤0.2) or medium (between 0.2 and 0.5) although changes in some specific risk markers in some specific subgroups can be considered high (≥0.8). We used the same random effects model to generate the overall size effects by computing the difference in means (Table S4) and compare these values to some of the reported effects of pharmacological, behavioral or dietary interventions on BMI, WC and lipid levels. Some of the most potent reducing effects on BMI can be achieved with restricted energy diet (−2.7 kg/m2) [160], pharmacological interventions (−1.3 kg/m2) [161] or behavioral (diet, exercise) interventions (−0.9 to −1.2 kg/m2) [162,163]. These reductions constitute between 5% and 10% of the WHO established limit values for overweight (BMI = 25.0–29.9 kg/m2) and obesity (BMI ≥ 30.0 kg/m2). Alternatively, intervention with probiotics [154] or nutraceuticals (e.g., lipoic acid) [164] shows a more modest but also significant reduction of BMI (approximately −0.5 kg/m2, ~2% change of the WHO values). On average, the size effect of the flavanol-containing products on BMI was smaller (−0.15 kg/m2, Table S3) but, notably, this effect may be enhanced in specific subpopulations (up to −0.91 kg/m2 in studies conducted in European Mediterranean countries), more similar to other behavioral or dietary interventions. WC can also be significantly and efficiently reduced by brisk walking (−2.83 cm, ~3% of the established 102/88 cm risk values) [162] and, more modestly (−0.53 cm, ~0.5% of the established risk values) by intervention with supplements such as ϖ-3 polyunsaturated fatty acids [165]. Along these lines, intervention with the flavanol-containing products significantly reduces WC by 1.7 cm and can reach reducing values of −4.58 cm in studies conducted in European Mediterranean countries. Regarding the cholesterol lowering effects, statins remain, at present, the first-choice agents. The pooled effects of various statins on total- and LDL-cholesterol were −0.89 mmol/L (~17% of the desirable 5.17 mmol/L limit level) and −0.92 mmol/L (~27% of the near optimal 3.36 mmol/L level), respectively [166]. Intervention with natural products such as red yeast rice or spirulina can be as effective as the statins, whereas other plant dietary bioactive compounds such as soluble fiber, sterols/stanols, probiotics and flavonols also significantly reduce total- and LDL-cholesterol by 0.5–0.1 mmol/L [7,167]. In this context, the flavanol-containing products show a similar efficiency at lowering total cholesterol (−0.13 mmol/L) and LDL cholesterol (−0.17 mmol/L). Again, in specific subgroups (e.g., supplementation with flavanol-containing apple products) the reduction of total cholesterol was much more efficient (−0.44 mmol/L). These results are very relevant considering that the reduction of LDL-cholesterol by 1 mmol/L has been associated with a 23% reduction of CVDs risk [168] and reinforce the interest in understanding the influence of different factors on the regulatory efficiency of plant bioactive compounds, in general, and of flavanols in particular. 4.7. Additional Recent Evidences Since the completion of this meta-analysis, additional RCTs investigating the effects of tea or cocoa products containing flavanols on lipid and anthropometric variables have been added to the existing literature [111,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183]. The heterogeneity of these trials remains high with population samples including mixed sexes and ages, obese, overweight, healthy, hyperlipidemic and/or diabetic subjects, etc. The products were administered in different forms, mostly as green tea extracts/capsules or cocoa drinks and at different doses and intervention periods. Some of these studies further support the reduction of total- and LDL-cholesterol by green tea or cocoa flavanols or the increase of HDL by dark chocolate or cocoa [111,169,170,171]. Others show no significant effects on these variables [175]. Noteworthy, some of these trials included stratification analyses by baseline conditions, medication, disease, age, sex, or even genotype and further point to specific responses in some subgroups [169,172,174,176,179]. Likewise, the intake of a cocoa product caused a greater increased of HDL in normocholesterolemic patients than in dyslipidemic patients [176], green tea capsules caused a significant reduction of total-cholesterol in women with a cholesterol baseline value above 5.17 mmol/L [169] or of LDL-cholesterol in patients not receiving anti-hyperlipidemic drugs [179]. Of note, the interactions between two factors: baseline BMI and catechol-O-methyltransferase (COMT) genotype, was also recently investigated although the COMT genotype did not modify the effect of green tea extract on any of the variables investigated including BMI [172]. In our study, we were able to identify several factors that may contribute to explaining the heterogeneity on the BMI changes in response to the flavanol-containing products. By multiple meta-regression analysis, we also found that supplementation with these products may be most effective at reducing BMI when specifically using tea products in non-medicated patients. These results highlight the importance of understanding not only the factors affecting the variability in the responses but also the interactions between these factors. Go to: 5. Conclusions To the best of our knowledge, the meta-analysis conducted here is the largest one to date that compiles the evidence on the effects on various metabolic risk factors after supplementation with three sources of flavanols, tea, cocoa, and apple products. Our results show consistent and significant modulatory effects on BMI, WC and lipid levels. The size of these effects is modest but similar to that prompted by other natural products. We have also presented evidence of the influence of several factors on these beneficial effects that suggest that flavanols might be very effective in specific subpopulations such as overweight people or non-medicated individuals or when the source of these bioactive compounds is tea. Moreover, a combination of these factors may best explain interindividual variability in the response to the flavanols-containing products. Although the total number of studies included in the meta-analysis was quite large, the number of studies (and of participants) remained small in some of the subgroup analyses. In addition, many of the studies reported limited or unclear information about the potential factors that may influence the treatment. These limitations affect the capability of the meta-analysis to unequivocally detect moderator variables and limit the significance of our findings. More randomized comparison studies with larger number of well-phenotyped volunteers and providing detailed descriptions of the participants and study characteristics are still needed. This research is crucial for a better understanding of the factors most relevantly involved in the variability of the responses to the consumption of these compounds and to achieve maximum efficacy so that flavanols may become an effective non-pharmacological alternative to battle hyperlipidemia, overweight/obesity and associated cardiometabolic disorders in humans. Go to: Acknowledgments This article is based upon work from COST Action FA1403—POSITIVe “Interindividual variation in response to consumption of plant food bioactives and determinants involved” supported by COST (European Cooperation in Science and Technology, http://www.cost.eu/). The authors offer thanks for the financial support of the COST Action FA1403 “POSITIVe” to conduct two short-term scientific missions to A.G.-S. and P.P. at the University of Glasgow (E.C.) during which the data analysis was performed, and to M.G.-A. at the University College Dublin (E.G.) during which the protocol was developed. M.G.-A. also thanks to the Spanish Ministry of Economy and Competitiveness (MINECO) (PCIN-2014-133-MINECO, Spain), and CIBERFES (co-funded by the FEDER Program from EU). Go to: Supplementary Materials The following are available online at www.mdpi.com/2072-6643/9/7/746/s1. Figure S1: Forest plot of the meta-analysis evaluating the effects of supplementation with flavanols-containing tea, cocoa or apple products on human body mass index (BMI). A total of 74 studies (displayed in alphabetical order) were analysed. Pooled results are shown at the bottom using a random-effects model. SDM: Standardized difference in means, SE: standard error, 95% CI: lower and upper confidence limits for the average SDM, RW: relative weight; Figure S2: Forest plot of the meta-analysis evaluating the effects of supplementation with flavanols-containing tea, cocoa or apple products on human waist circumference (WC). A total of 46 studies (displayed in alphabetical order) were analysed. Pooled results are shown at the bottom using a random-effects model. SDM: standardized difference in means, SE: standard error, 95% CI: lower and upper confidence limits for the average SDM, RW: relative weight; Figure S3: Forest plot of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of total cholesterol. A total of 112 studies (displayed in alphabetical order) were analysed. Pooled results are shown at the bottom using a random-effects model. SDM: standardized difference in means, SE: standard error, 95% CI: lower and upper confidence limits for the average SDM, RW: relative weight; Figure S4: Forest plot of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of LDL cholesterol. A total of 105 studies (displayed in alphabetical order) were analysed. Pooled results are shown at the bottom using a random-effects model. SDM: standardized difference in means, SE: standard error, 95% CI: lower and upper confidence limits for the average SDM, RW: relative weight; Figure S5: Forest plot of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of HDL cholesterol. A total of 112 studies (displayed in alphabetical order) were analysed. Pooled results are shown at the bottom using a random-effects model. SDM: standardized difference in means, SE: standard error, 95% CI: lower and upper confidence limits for the average SDM, RW: relative weight; Figure S6: Forest plot of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of triglycerides (TAGs). A total of 120 studies (displayed in alphabetical order) were analysed. Pooled results are shown at the bottom using a random-effects model. SDM: standardized difference in means, SE: standard error, 95% CI: lower and upper confidence limits for the average SDM, RW: relative weight; Figure S7: Funnel plot and Eager statistics (intercept and 2-tailed p-value) of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human BMI; Figure S8: Funnel plot and Eager statistics (intercept and 2-tailed p-value) of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human WC; Figure S9: Funnel plot and Eager statistics (intercept and 2-tailed p-value) of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of total cholesterol; Figure S10: Funnel plot and Eager statistics (intercept and 2-tailed p-value) of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of LDL cholesterol; Figure S11: Funnel plot and Eager statistics (intercept and 2-tailed p-value) of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of HDL cholesterol; Figure S12: Funnel plot and Eager statistics (intercept and 2-tailed p-value) of the meta-analysis evaluating the effects of a prolonged supplementation with flavanols-containing tea, cocoa or apple products on human blood levels of triglycerides (TAGs); Table S1: Mean content (mg/100 fresh weight, FW) in flavonoids of green and black tea infusions, cocoa powder and whole apple illustrative of the composition of the three main sources of flavanols examined in this study: tea, cocoa and apple (data are based on the Phenol-Explorer database); Table S2: Results of the meta-regression of the changes in BMI, WC and blood lipids levels vs. duration of the supplementation with the flavanol-containing tea, cocoa or apple products; Table S3: Summary of most recent meta-analysis looking at the effects of flavanol-containing tea or cocoa products on anthropometric measurements and blood lipids associated with the development of metabolic disorders; Table S4: Overall effect size estimations (DM) for the impact of flavanols containing products on BMI, WC and blood lipids levels. Click here for additional data file.(1.2M, zip) Go to: Author Contributions A.G.-S., A.R.-M., E.R.G., M.G.-A., E.C., P.P., C.M. and M.-T.G.-C. conceived and designed the study; A.G.-S., E.C., P.P., P.M., M.D’A., M.G.-A., A.R.-M., E.R.G., J.D., M.M., J.S.M., C.M., M.-T.G.-C. performed the data extraction; A.G.-S., J.S.M. and M.-T.G.-C. analyzed the data; A.G.-S., E.C., P.P., P.M., M.D’A., M.G.-A., A.R.-M., E.R.G., J.D., M.M., J.S.M., C.M., M.-T.G.-C. contributed to the discussions and preparation of the manuscript; and M.-T.G-C. wrote the article. Go to: Conflicts of Interest The authors declare no conflict of interest. Go to: References 1. Pucci G., Alcidi R., Tap L., Battista F., Mattace-Raso F., Schillaci G. Sex- and gender-related prevalence, cardiovascular risk and therapeutic approach in metabolic syndrome: A review of the literature. Pharmacol. Res. 2017;120:34–42. doi: 10.1016/j.phrs.2017.03.008. [PubMed] [Cross Ref] 2. 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