Elite soccer players are tasked with playing up to 50 matches per season (Nedelec et al., 2012; Strudwick, 2012). This commitment includes periods of fixture congestion where matches are played every 4 days for up to three consecutive weeks. Playing multiple matches within a short timeframe can exacerbate fatigue and result in suboptimal performance due to the insufficient physical and mental recovery (Carling et al., 2015). In response, various recovery strategies are used in professional soccer. Common recovery strategies include coldwater immersion, active recovery, stretching, compression garments, massage, electrical stimulation, and sleep (Nedelec et al., 2012). Although, it is important to note that the efficacy of such methods required further research and results are often heterogeneous (Nedelec et al., 2013).
Appropriate nutrition choices are a potent strategy to support the health and performance of players between matches and over the entirety of a season (Collins et al., 2021). Beyond the primary role of general nutrition for recovery, such as providing sufficient energy, carbohydrate, protein, and fluid, specific dietary supplements may also be considered (Carey et al., 2021; Gao & Chilibeck, 2020; Kyriakidou et al., 2021). Specifically, the ingestion of omega-3 fatty acids may positively impact postexercise inflammation and enhance muscle adaptive responses to exercise (Calder, 2006; Ferrucci et al., 2006; Kyriakidou et al., 2021; Philpott et al., 2019). Despite the evidence (Davinelli, Corbi, Zarrelli, et al., 2018; Walker et al., 2019), athletes diets contain insufficient quantities of omega-3 fatty acid (500 mg/day of eicosapentaenoic acid [EPA] and docosahexaenoic acid) to promote anti-inflammatory responses (Carbuhn & D’Silva, 2023; Essman et al., 2022; Huggins et al., 2019; Kunces et al., 2021; Ritz et al., 2020). Therefore, additional nutritional strategies for players to achieve the favorable fatty acid status throughout the season are of interest.
The consumption of cocoa-derived flavonols represent an alternative strategy to both increase the availability of polyunsaturated fatty acids and modulate inflammatory status, partly by regulating a number of aspects of inflammation, such as leukocyte chemotaxis and adhesion molecule expression (Davinelli, Corbi, Zarrelli, et al., 2018; Lee et al., 2003; Maleki et al., 2019).
One mechanism by which cocoa-derived polyphenols positively influence general health may relate to their ability to interact bidirectionally with gut microbial community and function (Sorrenti et al., 2020). Specifically, the cocoa polyphenols modulate the intestinal microbiota by promoting the growth of commensal bacteria (Maughan et al., 2018) that produce beneficial by-products (sulfates, glucuronide, and methyl metabolites of epicatechin) for host metabolism and cardiovascular health. Microbial-derived metabolites may positively influence cardiovascular and metabolic health by acting on vascular parameters (i.e., atherosclerosis, antioxidant status, inflammation-related markers) and whole-body metabolism (serum biochemistry and lipid profile; Rechner et al., 2004).
Therefore, the aim of the present study was to investigate the impact of ingesting 30 g of dark chocolate a day on gut microbiota composition and circulating markers of fatty acids status (Arachidonic Acid: EPA ratio; Nelson & Raskin, 2019; Simopoulos, 2008), in professional male soccer players.
Material and Methods
Participants
A randomized controlled trial in a cohort of 38 elite male soccer players (27 ± 4 years) was performed during the last month of the competitive season (May 2022). Players were affiliated with the Italian first league (Serie A), Genoa C.F.C. and Fiorentina A.C.F teams. Exclusion criteria were probiotic use, special diets, such as vegan or vegetarian or high fiber, the consumption of both dark chocolate and white chocolate, and the use of specific medications such as nonsteroidal anti-inflammatory drugs or antibiotics during the previous 7 days. The players were engaged in a ∼120-min training, six times/week and 90 min, match/week. The training typically included a 15-min warm-up, 30-min technical/tactical skills, 30 min of strength training, and 15-min cooldown. The training program exercise volume was similar among the two teams and they played the same number of matches (∼36 matches). Players maintained their habitual diet and supplementation (i.e., 30 g of protein, 3 g of creatine, 2.000 or 4.000 UI vitamin D, and minerals) as established at the beginning of the season by the teams’ registered dietitians. The study’s purposes, risk, and benefit were explained to all players, medical staff, and to the head coach, and all players provided written informed consent. All players read and signed the informed consent document with the description of the testing procedures approved by the ethical committee of the Department of Biomedical Sciences, University of Padova, HEC-DSB/03-21 and conformed to standards for the use of human subjects in research as outlined in the Declaration of Helsinki, Clinical registration number NCT06073327.
Randomization and Sample Size
Randomization was conducted using an online computer-generated sequence (https://www.graphpad.com/quickcalcs/randMenu/), matched for body mass index. A number was assigned to each subject. The server code is running in Cold Fusion, and uses its RANDOMIZE function to initialize, and then its RANDRANGE function to generate random numbers and allocate a number to one of the two groups. Sample size calculation was computed (G*Power; Faul et al., 2007) based on a previous study on healthy individuals provided by flavanol-rich cocoa using α = .05 (Davinelli, Corbi, Zarrelli, et al., 2018) and data from our laboratory. To detect a significant difference among groups for AA/EPA ratio with a power of 0.8 and an expected effect size (ES) of 0.95 a numerosity of 18 per group was calculated.
Study Design
This study was conducted in May 2022. Elite male soccer players (n = 38) were randomly divided into two groups. The dark chocolate group (DC, n = 19) was provided with 30 g of dark chocolate 88% (Novi Nero Nero 88% cacao, Elah Dufour S.p.A.; 2,5 mg/g of polyphenol). The white chocolate group (WC, n = 19) was provided with 30 g of white chocolate (Bianco Novi, Elah Dufour S.p.A.; 0 mg/g of polyphenol). Each group ingested the chocolate intervention as a “solid bar” in the morning (before 9:00 a.m.) every day for 4 weeks. The participants were instructed to maintain their normal diet but avoid excess consumption of high flavanol-containing products such as blueberry, strawberry, blackberry, green tea, red wine, and pomegranate juice starting 1 week before the beginning of the study and throughout the intervention period, to prevent confounding effects. Compliance to indications was also assessed by the dietitian through direct observation considering that players consumed their meals in the club. The dark chocolate and white chocolate were provided every morning to each subject by the nutritionist of each team, while, during off days, the chocolate was portioned into individual serving sizes and provided to each player.
For each assessment, participants arrived at the training center in the morning, after an overnight fast. The dietitians were asked to monitor the athletes’ daily diet to ensure the maintenance of the dietary intakes throughout the study, including both quality and quantity of food, a 3-day food record was obtained, consisting of records for two weekdays and one weekend day, before the beginning and at the end of the study. Blood was collected between 08:00 a.m. and 09:00 a.m., fecal sample was delivered within the end of the morning, and anthropometry measures were also determined before and after the intervention.
Extraction and Quantification of Total Polyphenol Content in Chocolate
Chromatography analysis of samples was performed to define precisely the chocolate’s composition. Briefly, a chocolate sample was finely powdered with mortar and pestle. 50 ml of methanol was added to 2 g of chocolate powder and sonicated in ultrasound both for 30 min. Supernatant was centrifuged in Eppendorf tube for 10 min 13,000 rpm, and the solution was used for a liquid-chromatography-diode array (DAD)-MSn analysis.
Quali-quantitative analysis of phenolic metabolites was conducted using a LC system, an Agilent 1,260 chromatograph equipped with 1,260 DAD and Agilent/Varian MS-500 ion trap (Agilent) as detectors. An Eclipse XDB C-18 4.6 × 250 mm 5 μm (Agilent) column was used as stationary phase and 1% formic acid in water (A) and acetonitrile (B) was used as mobile phases. The elution gradient was set as follows: linear gradient from 5% B to 10% B, 0–3 min; linear gradient from 10% B to 40% B, 3–20 min; linear gradient from 40% B to 60% B, 20–35 min; linear gradient from 60% B to 90% B, 35–40 min. The flow rate was 0.75 ml/min, and the injection volume was 10 μl. At the end of the column a T connector split the flow in equal amount to DAD and MS detector. MS spectra were recorded in negative and positive ion mode in 50–2,000 Da range, using an electrospray ionization source. The turbo data depending on scanning function allowed to obtain the fragmentation of the main ionic species. Identification of compounds was based on the fragmentation spectra, as well as the comparison of the fragmentation pattern with the literature and injection of reference compounds, when available. The DAD chromatograms were monitored at λ = 350, 330, 280, and 254 nm and were elaborated for the compound’s quantification. Catechin, epicatechin, theobromine, and caffeine were quantified using the external standard method. These quantities were used to determine total polyphenol content. Calibration curves of the standards were prepared by diluting stock standard solutions in methanol to yield final concentrations in the range of 0.7–70 μg/ml for epicatechin and catechin, and 0.5–50 μg/ml for theobromine and caffeine. Linear regressions were as follows: epicatechin y = 53.516x (R2 = .999); catechin y = 81.290x (R2 = .999); theobromine y = 61.669x (R2 = .999); and caffeine y = 61.669x (R2 = .999).
The gross nutritional composition of dark chocolate is shown in Table 1.
Nutritional Profile of Chocolates per 30 g Serving
| Dark | White | |
|---|---|---|
| Energy (kcal) | 189.6 | 177 |
| Carbohydrate (g) | 6.3 | 15.3 |
| Fat (g) | 9.6 | 7.2 |
| Protein (g) | 2.7 | 1.9 |
| Sugar (g) | 3 | 15.3 |
| Polyphenol (mg) | 75 | 0 |
| Catechin | 0.0324 | 0 |
| Epicatechin | 0.156 | 0 |
Anthropometry
Participants underwent anthropometric assessments early in the morning, in an overnight fasted state, and at least 12-hr postexercise, with no long trips during the previous day. Anthropometric measurements were taken following the protocol of The International Society for the Advancement of Kinanthropometry by the same researcher (an International Society for the Advancement of Kinanthropometry Level 3 anthropometrist), whose technical error was 5% and 1.5% for skinfolds and all other measurements, respectively. Eight skinfold thicknesses (biceps, triceps, subscapular, suprailiac, supraspinal, abdominal, front thigh, and medial calf) were measured. Fat mass were estimated for descriptive purposes only. Fat mass was determined using the equation developed by Peterson et al. and then converted to adipose tissue by multiplying it by 1.18 (Heymsfield et al., 1991; Peterson et al., 2003).
Blood Biochemical Analysis
Blood samples were collected using ethylenediamine tetraacetic acid tubes and left for 10–15 min at room temperature. To obtain plasma aliquots samples were centrifuged (1,800g, 15 min, 4 °C) and stored at −80 °C until analysis. Total cholesterol, high-density lipoprotein, cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides were measured using colorimetric enzymatic tests (Siemens Healthcare, s.r.l.). The plasma concentration of oxidized LDL was quantified with a specific ELISA kit (Immundiagnostik AG).
Plasma Polyphenol Extraction and Quantification by High-Performance Liquid Chromatography-Fluorescence Detection-UV
Plasma samples were extracted by the method described by Spadafranca et al. (2010). Epicatechin was hydrolyzed enzymatically using beta-glucuronidase and sulfatase and subsequently extracted by addition of 1 ml acetonitrile, and the mixture was centrifuged at 10.000 g for 5 min at 4 °C. After centrifugation, 50 μl supernatant was injected into the high-performance liquid chromatography (HPLC) column for separation, detection, and analysis. The HPLC analysis was performed using an HPLC system (Agilent 1200 Infinity Series HPLC system). Spectroscopy data from all peaks were captured in the range of 210–400 nm, and chromatograms were recorded at 279 nm. Separations were carried out at a flow rate of 1.5 ml/min. The identification of the epicatechin was made by comparison of retention times and spectra with those of commercially available standard compound (−)-epicatechin, E-1753, Sigma).
Plasma Fatty Acid Analyses
Heparinized evacuated tubes were centrifuged at 1.000 × g for 10 min. Total lipids were extracted from plasma with chloroform: methanol 0.90% potassium chloride (2:1:0.2, v/v/v). Plasma fatty acid composition was determined from 2 ml of the lipid extract after transformation into isopropyl esters. Separation of isopropyl esters was done on a gas chromatograph using a capillary column (internal diameter: 0.32 mm). Column conditions were 175 °C for 4 min and then increased by 3 °C/min to 220 °C for 30 min. Helium was used as the carrier gas (flow rate: 2 ml/min). The peaks were identified by comparison with reference fatty acid esters and peak areas were measured with an automatic integrator. The results for each fatty acid were expressed as a percentage of total fatty acids. The AA-to-EPA and AA-to-Docosahexaenoic acid ratios were calculated. We analyzed fatty acids that were greater than 0.01% of peaks detected.
Microbiome Sample Collection
Athletes were asked to collect their fecal material (single defecation) in a tube containing DNA/RNA Shield buffer (Zymo Research) to preserve the genetic integrity and expression profiles of samples at ambient temperatures (no refrigeration or freezing needed) and completely inactivates infectious agents (viruses, bacteria, fungi, and parasites). The nutritionist and/or the doctor of each team immediately collected the fresh stool samples and delivered them within 72 hr to the research hub facility (University of Padova, Department of Biomedical Science). In this way, we facilitated the collection of the samples at each sport facility. Samples were stored at ambient temperature until delivered to the laboratory (IGA Technology Services, c/o Parco Scientifico e Tecnologico Luigi Danieli di Udine) perform the wet analysis. The samples were homogenized, aliquoted, and stored at −80° in QIAGEN Power Beads 1.5 ml tubes. The sample collection procedure was tested and validated internally comparing two different DNA extraction kits (QIAamp Fast DNA Stool Mini Kit and ZymoBIOMICS DNA Miniprep Kit).
DNA Extraction and Sequencing
DNA was isolated by CELERO DNA-Seq kit (NuGEN Technologies Inc.) using DNA/RNA Shield-fixed microbiome samples. Before library preparation and sequencing, the quality and quantity of the samples were assessed using Fragment Analyzer system (Agilent Technologies). Only samples with a high-quality DNA profile were further processed. Both input and final libraries were quantified by Qubit 2.0 fluorometer (Termo Fisher) and quality tested by Agilent 2100 Bioanalyzer High Sensitive DNA assay. Libraries were then prepared for sequencing and sequenced on NovaSeq 6000 in paired ends (150 bp mode).
Metagenome Quality Control and Preprocessing
Illumina raw reads were then preprocessed to keep high-quality reads only using Trim Galore to remove and trim low-quality reads (with parameters: “--stringency 5 --length 75 --quality 20 --max_n 2 --trim-n,” https://github.com/FelixKrueger/TrimGalore). Illumina PhiX adapters were removed from reads and human DNA was discarded using Bowtie2 (Langmead & Salzberg, 2012) by mapping the reads against the reference PhiX 174 genome (NCBI accession ID 10847), and the hg19 Human Genome, respectively. In-house Python scripts were used to process and sort the quality-controlled reads to generate three fastq files for each sample (two for paired-end reads, R1 and R2, and one for unpaired reads). The data that support the findings of this study are available on request from the corresponding author, L.M. The data are not publicly available due to restriction. Data contain information that could compromise the privacy of research participants.
Statistical Analysis
Lipid Profile and AA:EPA Ratio
Statistical analyses were performed using SPSS Statistic software (version 22, IBM Corp.). To each outcome, change from baseline was calculated for each time point and results are presented as the mean ± SD. The two-way repeated-measure analysis of variance was performed with two levels by time (pre and post treatment at Week 4) to identify differences between baseline and endpoint and considering treatments (dark chocolate, white chocolate) as intersubjects’ factors, in order to assess differences between groups over the course of the study. Post hoc analyses were performed using Bonferroni test. In addition, ES calculation was undertaken for all between-group effects with Cohen’s d (d = 0.2 considered a small affect, d = 0.5 as medium effect, and d = 0.8 a large effect; d = 0.8 was used as a guide for substantive significance). The assumption of normally distributed residuals by visual inspection of Q–Q plots was performed, if in doubt a Shapiro–Wilk test, and a Skewness and Kurtosis test for normality were applied.
Microbiome Taxonomic Profiling
The metagenomic analysis was performed following the general guidelines, which relies on the bioBakery computational environment. Relative abundance profiles of species-level microbial communities were obtained with MetaPhlAn4 (version 4.0.3; default parameters, marker database vJan21; Blanco-Miguez et al., 2023) for each sample (see Supplementary Table S1 [available online]). Relative abundances profiles were corrected for data compositionality by imputing the zero values using R package zCompositions (Javier Palarea-Albaladejo, 2015). Beta diversity (differences in the overall taxonomic composition between samples) of the samples was assessed by computing a dissimilarity matrix of pairwise Aitchison distances between relative abundance profiles using vegan and compositions R packages. Principal coordinates analysis ordination was used to visualize the pairwise distances matrix by reducing its dimensionality. Principal coordinates analysis method is used to explore and visualize dissimilarities of data by performing a rotation of the intersample distance matrix in order to represent those distances as accurately as possible in a small number of dimensions. Differentially abundant species analysis between pre-and posttreatment was performed on species present in at least 10% of the samples (see Supplementary Table S2 [available online]). in the DC and WC groups and were then selected by performing Wilcoxon signed rank test on the species-level relative abundance profiles of DC and WC, followed by Benjamini–Hochberg false discovery rate (FDR) correction, and selected species whose relative abundance variation was statistically significant only in the WC or DC group.
Results
Characteristic of Study Population
Of the 42 players enrolled into the study, 38 were eligible and randomly assigned to one of the two groups. All 38 players completed the intervention period and were included in the analysis for plasma lipid profile. However, only 32 stool samples were considered for microbiome analysis. N = 6 stool samples were excluded from the analysis (specifically, n = 1 from DC group and n = 5 from WC group) because of mislabeling errors during the sample collection processing (Figure 1).
—Participant selection flow diagram.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 34, 6; 10.1123/ijsnem.2024-0012
The general baseline characteristic of the participants and key measures are shown in Table 2. No significant differences were noted over the intervention period for anthropometric parameters (body mass, body mass index, and fat mass percentage) and macronutrients intakes across the groups (Table 3). Differently, while plasma total polyphenols unchanged in the WC group (from 150.4 ± 23.2 μg gallic acid equivalents [GAE]/ml to 147.1 ± 34.4 μg GAE/ml, Δ pre vs. post = −3.29 ± 2.32 μg GAE/ml, ES: Cohen’s d: 0.10), total polyphenols content increased in the DC group (from 154.7 ± 18.6 μg GAE/ml to 185.11 ± 57.6 μg GAE/ml, Δ pre vs. post = +30.41 ± 21.50, ES: Cohen’s d: 1.01), compared to WC group (Time × Treatment interaction, p = .003). These data indicate a good compliance of treatment for DC and the polyphenols restriction in WC.
Baseline Physical and Dietary Characteristic of Elite Soccer Players
| DC (n = 19) | WC (n = 19) | p | Cohen’s d | |
|---|---|---|---|---|
| Age (years) | 26 ± 4 | 28 ± 5 | .150 | 0.01 |
| Anthropometric parameters | ||||
| Body mass (kg) | 79.8 ± 8.8 | 80.3 ± 6.2 | .149 | 0.11 |
| BMI (kg/m2) | 23.9 ± 1.4 | 23.7 ± 1.2 | .125 | 0.05 |
| Fat mass (%) | 11.7 ± 1.8 | 11.3 ± 1.6 | .153 | 0.10 |
| Blood markers | ||||
| Total cholesterol (mg/dl) | 180.47 ± 30.78 | 177.53 ± 27.87 | .152 | 0.23 |
| LDL (mg/dl) | 125.32 ± 28.25 | 114.84 ± 28.67 | .222 | 0.21 |
| HDL (mg/dl) | 52.68 ± 11.51 | 57.73 ± 11.70 | .115 | 0.23 |
| Triglycerides (mg/dl) | 78.16 ± 19.13 | 63.11 ± 16.41 | .090 | 0.16 |
| AA:EPA ratio | 10.35 ± 5.82 | 6.83 ± 4.62 | 0.060 | 0.21 |
| Dietary intake | ||||
| Energy (kcal) | 2,389 ± 380 | 2,591 ± 278 | .150 | 0.23 |
| Carbohydrate (g) | 345 ± 51 | 361 ± 400 | .116 | 0.21 |
| Protein (g) | 138 ± 34 | 140 ± 32 | .147 | 0.14 |
| Fat (g) | 90 ± 14.67 | 93 ± 18 | .258 | 0.17 |
Note. Values are mean ± SD. There were no differences in anthropometric measures, blood markers, and dietary nutrient intakes between groups at baseline. AA:EPA = arachidonic acid:eicosapentaenoic acid; BMI = body mass index; LDL = low-density lipoprotein; HDL = high-density lipoprotein; DC = dark chocolate group; WC = white chocolate group.
Changes in Anthropometric and Dietary Parameters, Dietary Intakes, and Plasma Polyphenol
| DC (n = 19) | WC (n = 19) | p Two-way RM ANOVA Time × Treatment | |
|---|---|---|---|
| Δ (after − before) | Δ (after − before) | ||
| Anthropometric parameters | |||
| Body weight (kg) | 0.11 ± 0.59 | −0.06 ± 1.06 | .120 |
| BMI (kg/m2) | 0.18 ± 0.62 | 0.04 ± 1.08 | .113 |
| Percent body fat (%) | 0.18 ± 1.36 | −0.08 ± 1.20 | .080 |
| Dietary intake | |||
| Energy (kcal) | −119.37 ± 690.2 | −130.28 ± 400.82 | .154 |
| Carbohydrate (g) | −10.02 ± 70.18 | −21.08 ± 69.72 | .112 |
| Fat (g) | −2.01 ± 40 | −4.09 ± 15.20 | .090 |
| Protein (g) | 1.02 ± 30.02 | −2.02 ± 10.02 | .118 |
| Total plasma polyphenol content GAE/ml | 30.41 ± 21.50 | −3.29 ± 2.32 | .003 |
Note. Values are mean ± SD. p value two-way RM ANOVA. BMI = body mass index; RM ANOVA = repeated-measure analysis of variance; DC = dark chocolate group; WC = white chocolate group.
Effects of Dark Chocolate on Plasma Lipid Profile
After 4 weeks of intervention, the reduction in total cholesterol (−32.47 ± 17.18 mg/dl DC, ES: Cohen’s d: 1.02 vs. −2.84 ± 6.25 mg/dl WC, ES: Cohen’s d: 0.10, Time × Treatment interaction p < .001), triglycerides (−6.32 ± 4.96 mg/dl DC, ES: Cohen’s d: 0.35 vs. −0.42 ± 6.47 mg/dl WC, ES: Cohen’s d: 0.07, Time × Treatment interaction p < .001) and LDL cholesterol (−18.42 ± 17.13 mg/dl DC, ES: Cohen’s d: 0.71 vs. −2.05 ± 5.19 mg/dl WC, ES: Cohen’s d: 0.07, Time × Treatment interaction p < .001) were significantly greater in DC group than in WC group.
In addition, 4 weeks of intervention showed a significant increase in high-density lipoprotein concentration in DC group (DC: Δ pre vs. post = +3.26 ± 4.49 mg/dl, ES: Cohen’s d: 0.27, simple main effect DC, Pbonf < 0.001; WC: Δ pre vs. post = −0.79 ± 5.12 mg/dl, ES: Cohen’s d: 0.01, simple main effect WC, Pbonf < 0.001), even though without significant differences between groups (Time × Treatment interaction, p = .44; Table 4).
Effect of 4 Weeks of Chocolate Supplementation on Plasma Lipid Profile and AA:EPA Ratio
| DC pre | DC post | Δ (post − pre) | % change | WC pre | WC post | Δ (post − pre) | % change | p Two-way RM ANOVA Time × Treatment | |
|---|---|---|---|---|---|---|---|---|---|
| Total cholesterol (mg/dl) | 180.47 ± 30.78 | 148 ± 32.57 | −32.47 ± 17.18 | −18.15 | 177.53 ± 27.87 | 174.68 ± 26.40 | −2.84 ± 6.25 | −1.52 | <.001* |
| HDL (mg/dl) | 52.68 ± 11.51 | 55.95 ± 11.94 | 3.26 ± 4.49 | 6.63 | 57.74 ± 11.70 | 56.95 ± 10.31 | −0.79 ± 5.12 | −0.56 | .440 |
| LDL (mg/dl) | 125.32 ± 28.25 | 106.89 ± 23.01 | −18.42 ± 17.13 | −13.67 | 114.84 ± 28.67 | 112.79 ± 28.62 | −2.05 ± 5.19 | −1.78 | <.001* |
| Triglyceride (mg/dl) | 78.16 ± 19.13 | 71.84 ± 16.41 | −6.32 ± 4.96 | −7.62 | 63.11 ± 16.41 | 63.53 ± 16.07 | 0.42 ± 6.47 | +1.24 | <.001* |
| AA:EPA ratio | 10.35 ± 5.82 | 5.08 ± 4.26 | −5.26 ± 2.35 | −54.17 | 6.83 ± 4.62 | 4.62 ± 5.09 | 0.47 ± 0.73 | −6.41 | <.001* |
Note. Values are mean ± SD. p value by two-way RM ANOVA. The percentage of change was calculate through the following formula ([initial value/final value]/initial value) × 100. DC = dark chocolate group; WC = white chocolate group; AA:EPA = arachidonic acid: eicosapentaenoic acid; LDL = low-density lipoprotein; RM ANOVA = repeated-measure analysis of variance; HDL = high-density lipoprotein.
*Statistically significant.
Effects of Dark and White Chocolate on AA/EPA Ratio
The reduction of AA/EPA ratio was significantly greater in DC group (−5.26 ± 2.35; −54.1% DC, ES: Cohen’s d: 1.03 vs. −0.47 ± 0.73; −6.41% WC, ES: Cohen’s d: 0.09, Time × Treatment interaction p < .001) compared with WC group (Table 4).
Dark Chocolate Intake Slightly Maintains a Higher Microbiome Stability
To assess whether chocolate intake is associated with compositional shifts in the overall gut microbiome of athletes, beta diversity of the whole cohort was measured by calculating Aitchison distances between relative abundance species profiles (Figure 2). Aitchison distance is useful to measures the distance between two compositional data. Compositional data are quantitative descriptions of the parts of some whole, conveying specific, relative information. Mathematically, compositional data are represented by points on a simplex. Microbial communities of athletes enrolled in the dark chocolate group showed slightly higher stability over time exhibiting lower within-subject community dissimilarity than subjects of the control group, even though the difference between the two groups was not statistically significant (Wilcoxon nonpaired, two-sided, p = .1159; Figure 3).
—2D ordination plot displaying beta diversity of all 32 pairs of microbial profiles, highlighting the timepoint of sample collection (circles: treatment baseline, triangles: 4 weeks posttreatment), team membership of the athlete (black: Genoa, gray: Fiorentina), and treatment group (dotted line: dark chocolate, straight line: white chocolate). Baseline and posttreatment same-subject samples are connected by a line, whose length is proportional to the magnitude of the compositional shift of the microbial community after the 4-week treatment. The distance between points and triangles represents the overall compositional shift in the microbial profiles of a subject at the baseline and after 4 weeks of intervention.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 34, 6; 10.1123/ijsnem.2024-0012
—Comparison of intrasubject distributions of Aitchison pairwise distances before and after treatment (dotted line: dark chocolate, solid line: white chocolate) in the two groups. The player’s team is color-coded in gray (Fiorentina) and black (Genoa) (Wilcoxon nonpaired, two-sided, p = .1159).
Citation: International Journal of Sport Nutrition and Exercise Metabolism 34, 6; 10.1123/ijsnem.2024-0012
Taxonomic Features of the Microbiota Associated With Dark Chocolate Intake
To investigate the specific bacterial taxa that were significantly affected by dark chocolate consumption, we compared the different microbial composition (different number of sequencing reads) between dark and white groups, in the post treatment timepoint (Wilcoxon, p = .02). Although the dark chocolate group maintained an overall slightly greater stability compared with the control group, the relative abundances of 20 species significantly changed (ntreatment = 18, ncontrol = 14, FDR correction of paired-end Wilcoxon, significance threshold: q value < 0.2) after 4 weeks of dark chocolate intake (see Supplementary Table S3 [available online]). The q value represent an analog of the p value, however, it incorporates multiple testing correction. We considered as significant the q values < 0.2, (see Supplementary Table S4 [available online]).
The relative abundances of Ruthenibacterium lactatiformans (q value = 0.1982), Flavonifractor plautii (q value = 0.1660), and Faecalibacterium prausnitzii (q value = 0.1955) were significantly increased in the dark chocolate group compared with the control, while relative abundances of Blautia wexlerae (q value = 0.0168) Blautia faecis (q value = 0.1955), Blautia massiliensis (q value = 0.0916), Coprococcus catus (q value = 0.0916), Coprococcus comes (q value = 0.0916), Anaerobutyricum hallii (q value = 0.0916), and Dorea longicatena (q value = 0.128) appear to be decreased in both groups, although with statistical significance only in the dark chocolate group (Figure 4). Notably, after FDR correction, no species were found to be differentially abundant before and after this study in the control group. These results identify the effects of dark chocolate consumption on compositional changes in gut microbiota.
—The most significant compositional changes in microbial community after 4 weeks of intervention. Paired boxplots showing differentially abundant species whose differences in relative abundances between pre- and posttreatment were found to be statistically significative after FDR (Benjamini–Hochberg correction, q value < 0.2) only in one of the two groups (Wilcoxon-paired test; n.s: p value ≥ 0.1, *: p value < .1, **: p value < .05, ***: p value < .001). The style of the line connecting the players before and after the treatment indicates whether the relative abundance of a species increased (solid) or decreased (dotted).
Citation: International Journal of Sport Nutrition and Exercise Metabolism 34, 6; 10.1123/ijsnem.2024-0012
Discussion
This is the first study investigating the effect of daily ingestion of 30 g of dark chocolate on blood AA:EPA ratio and lipid profiles in elite male soccer players. The main finding was that daily ingestion of 30 g of dark chocolate for 4 weeks significantly improved blood lipid profiles, by reducing the total cholesterol, LDL cholesterol, tryglicerides, and by improving AA:EPA ratio. High-density lipoprotein cholesterol after 4 weeks of cocoa supplementation improved slightly in DC group but without a significant difference compared with controls (no significant Time × Treatment interaction).
In line with our findings, recent observations showed that a similar ingestion regime of cocoa supplementation (4 weeks of 1 g for low cocoa group: ∼55 mg flavanols; 2 g for middle cocoa group: ∼110 mg flavanols or 4 g for high cocoa group: ∼220 mg flavanols) positively influenced plasmatic lipid profile in a healthy nonathlete population (Davinelli, Corbi, Zarrelli, et al., 2018). Furthermore, two meta-analyses concluded that ingesting dark chocolate/cocoa products (ranging from 30 to 963 mg/day of polyphenol) for 2–18 weeks can lower LDL cholesterol (Jia et al., 2010; Tokede et al., 2011). Interestingly, Jia et al. demonstrated that low doses of cocoa consumption (<260 mg/day) for short-term periods (<6 weeks) reduced blood LDL cholesterol (Jia et al., 2010).
Our results showed that dark chocolate may influence the metabolism of polyunsaturated fatty acids by decreasing the AA/EPA ratio in elite soccer players. The AA/EPA ratio has been considered a representative marker of polyunsaturated fatty acid status (Nelson & Raskin, 2019; Simopoulos, 2008) and provides an indicator for chronic inflammation, with a lower ratio corresponding to higher levels of inflammation (Ferrucci et al., 2006). The mechanism by which cocoa flavanols elevate plasma EPA level or decrease AA concentration remain unclear. However, given that the DC group exhibited a significant reduction in the AA/EPA ratio, compared with WC group, it is plausible to hypothesize that the observed enhancements after dark chocolate intervention could be attributed to the influence of cocoa-derived polyphenols on host physiology and/or potential interactions between polyphenols and the gut microbiome (Sorrenti et al., 2020).
Dark Chocolate Showed an Overall Greater Stability of Gut Microbiota, Although Not Statistically Significant Compared With White Chocolate, Void of Polyphenols
The 30 days of 30 g dark chocolate did not affect the overall gut microbiota community in terms of microbial alpha diversity (Faith’s phylogenetic diversity) and richness indices (see Supplementary Table S2 [available online]). Nonetheless, higher microbial diversity per se does not represent an absolute requirement for healthy microbial community (Reese & Dunn, 2018; Shade, 2017). Instead, it is important to assess the response of specific bacteria to dietary interventions (i.e., beneficial vs. pathogenic).
Correspondingly, the microbial stability and resilience within each individual player, considered a more appropriate indicator of intestinal health, was completed in the present study (Fassarella et al., 2021). Our analysis revealed that the microbial community of individuals in the DC group was more stable over time exhibiting lower “within-subject community dissimilarity” compared with individuals in the control group. Conversely, players in the WC group exhibited greater microbial turbulence with bigger shifts in the gut communities across the study. Importantly, gut microbial stability and resilience represent essential ecological characteristics of the gut ecosystem and are indicative of host gastrointestinal health (Fassarella et al., 2021). The maintenance of gut microbial resilience after dietary perturbations is associated with greater athletic performance (Furber et al., 2022). Indeed, Furber et al. (2022) recently demonstrated that participants’ run time to exhaustion was consistently extended when microbial communities remained relatively unchanged throughout dietary challenges (high protein or high carbohydrate diets). Thus, athletes undergoing dietary periodization (Stellingwerff et al., 2019) or dietary supplementation protocols (Maughan et al., 2018), with the aim of improving athletic performance, would likely benefit from greater gut microbial stability and resilience.
Although the DC group maintained an overall greater stability compared with the WC control group, we observed some slight compositional changes in the microbial community. Our results reported a slight increase in the species of flavonoid-degrading bacteria Flavonifractor plautii. (Braune & Blaut, 2016; Takagaki & Nanjo, 2015). The increase in F. plautii suggests that the polyphenols’ content in the dark chocolate were able to reach the colonic microbial community and positively influence the growth rate of flavonoid-degrading bacteria. We also observed a slight increase in Faecalibacterium prausnitzii species. F. prausnitzii is a butyrate producers’ bacteria with anti-inflammatory properties and represents a fundamental community for the maintenance of gut homeostasis (Lopez-Siles et al., 2017; Sokol et al., 2008). F. prausnitzii is of interest as it has been found in greater relative abundance in active women (with an increase associated with exercise training (Bressa et al., 2017) and in microbiomes of professional athletes participating in high-intensity sports (O’Donovan et al., 2020). However, in contrast to our results, Shin et al. (2022) reported a decrease in F. prausnitzii after dark chocolate supplementation. Differences in these findings may be attributed to the difference in doses (400 and 250 mg/day of polyphenol vs. the current study 75 mg/day) or duration (3 weeks vs. 4 weeks intervention). Nevertheless, a likely explanation may be the study participants. The study by Shin et al. was completed in healthy sedentary adults, whose microbial profile differs significantly from professional athletes (Cerda et al., 2016). As a matter of fact, athletes’ typical diet prompt changes in the gut microbiome, altering both composition and function of gut microbiome. Indeed, there are remarkable differences among sedentary individuals and athletes, with athletes tending to show higher microbial diversity. However, it is important to consider that there are also significant differences in microbiome profile between athletes of different sports (i.e., endurance vs. strengths; O’Donovan et al., 2020).
Dark chocolate ingestion also increased the species of Ruthenibacterium lactatiformans, a lactate-producing bacteria, member of the family Ruminococcaceae (Shkoporov et al., 2016). The Ruminococcaceae family is positively associated with a plant-based diet and a dietary intake rich in vegetable fat and bioactive compounds (such as those including nuts, walnuts, and extra virgin olive oil; Muralidharan et al., 2019; Tomova et al., 2019). In our study, the increase in R. lactatiformans abundance was observed following the ingestion of dark chocolate which contained cocoa nibs and cocoa butter (a plant-based fat derived from the cocoa beans; Theobroma cacao tree; Sorrenti et al., 2020). Thus, it is not possible to determine if the changes in R. lactatiformans were due to the polyphenol content or the fat content of the dark chocolate intervention per se.
Conversely, we observed a greater decline in Blautia wexlerae with dark chocolate ingestion in comparison to the ingestion of white chocolate. Blautia genus is associated with a greater release of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (Tuovinen et al., 2013). A similar reduction in Blautia was observed following 2 weeks of aerobic training that also decreased concentrations of tumor necrosis factor-alpha (Pache et al., 2009).
Thus, dark chocolate ingestion had an additional effect on this species compared to exercise alone.
Study Limitations
The main limitation of the current study was that the total quantity of polyphenol content (in milligrams) was not assessed in the daily energy intake. Nonetheless, excluding the study intervention, players were instructed by the team dietitian to avoid dark chocolate and avoid excess consumption of foods high in polyphenols. Compliance to these instructions was also assessed by the dietitian through direct observation during meals provided by the club. Another key limitation is the absence of recovery or performance assessments. The inclusion of players from two different clubs, each with different performance staff, made the qualitative standardization of recovery or performance tests challenging. In order to maintain a high methodological rigor, the decision was made not to include performance analyses, which would have added applicable data in the clinical context. Future research should aim to include function recovery or performance outcomes associated with the intervention.
Practical Implications
Despite evidence to advocate improving AA:EPA ratios (Calder, 2006; Ferrucci et al., 2006), athletes diets commonly contain insufficient quantities of omega-3 fatty acid to increase blood EPA concentrations (Carbuhn & D’Silva, 2023; Essman et al., 2022; Ferrucci et al., 2006; Huggins et al., 2019; Kunces et al., 2021; Ritz et al., 2020). To date, the only effective intervention to increase blood EPA concentrations is the dietary supplementation of omega-3 fatty acid (Calder, 2006; Davinelli, Corbi, Righetti, et al., 2018; Kyriakidou et al., 2021; McGlory et al., 2019; Nishizaki & Daida, 2020). Thus, dark chocolate ingestion provides an alternative nutritional strategy to improve blood lipid profiles in athletic populations. Our results are aligned with the concept that, although “healthy” microbiome states are yet to be established (Lloyd-Price et al., 2016), gut microbiome stability is a key indicator of gut function (Sommer et al., 2017). As such, dark chocolate ingestion may be considered an effective nutritional strategy in elite sport environments during periods of high-intensity training and congested competition. Finally, although beyond the scope of the present study, the ingestion of cocoa flavonoids is linked with reduction of anxiety and depression-like behaviors (Macht & Mueller, 2007; Shin et al., 2022). The role of cocoa flavonoids ingestion on positively influencing these and other mood states via the gut–brain axis requires further investigation.
In conclusion, ingesting 30 g per day of dark chocolate ingestion over 4 weeks improves AA:EPA ratio while maintaining gut microbial stability. Our results suggest that dark chocolate ingestion may be considered an effective alternative nutritional strategy in elite sport environments to modulate polyunsaturated fatty acid metabolism. However, further research is required to elucidate if these changes result in functional recovery/performance related outcomes.
Acknowledgments
Ethical Approval Information: All players read and signed the informed consent document with the description of the testing procedures approved by the ethical committee of the Department of Biomedical Sciences, University of Padova, HEC-DSB/03-21 and conformed to standards for the use of human subjects in research as outlined in the Declaration of Helsinki, Clinical registration number NCT06073327. Equity, Diversity, and Inclusion Statement: Our research and author team included women and men, as well as senior and less-experienced investigators from a variety of disciplines and different nationalities. The study population included a spectrum of ethnicities but did not include women players. In discussing the generalizability of our results and limitations of the findings, we acknowledge that professional soccer players represent a homogeneous group, this cohort may exclude individuals of a lower socioeconomic status or from more marginalized communities. Conflict of Interest: Rollo is an employee of the Gatorade Sports Science Institute, the views expressed in this manuscript are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc. Other authors declare they have no competing interests. Author Contributions: Concept and design: Mancin, Rollo, Paoli. Collection of data: Cassone, Petri, Corsini, Pengue. Statistical analysis: Mancin, Golzato, Segata, Paoli. Chromatography analysis of dark chocolate: Dall’ Acqua. Cowriting the first draft: Mancin, Rollo, Paoli. Revising the original manuscript: Rollo, Vergani, Mota. Mancin completed first draft and all authors contributed. All authors read and approved the final version. Funding: This study was completed as part of a PhD funded by University of Padova and intramural grant.
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