Abstract

BACKGROUND

This study investigates whether dietary patterns, substantiated by biomarkers, are associated with semen quality.

METHODS

In 161 men of subfertile couples undergoing in vitro fertilization treatment in a tertiary referral clinic in Rotterdam, the Netherlands, we assessed nutrient intakes and performed principal component factor analysis to identify dietary patterns. Total homocysteine (tHcy), folate, vitamin B12 and B6 were measured in blood and seminal plasma. Semen quality was assessed by sperm volume, concentration, motility, morphology and DNA fragmentation index (DFI). Linear regression models analyzed associations between dietary patterns, biomarkers and sperm parameters, adjusted for age, body mass index (BMI), smoking, vitamins and varicocele.

RESULTS

The ‘Health Conscious’ dietary pattern shows high intakes of fruits, vegetables, fish and whole grains. The ‘Traditional Dutch’ dietary pattern is characterized by high intakes of meat, potatoes and whole grains and low intakes of beverages and sweets. The ‘Health Conscious’ diet was inversely correlated with tHcy in blood (β = −0.07, P = 0.02) and seminal plasma (β = −1.34, P = 0.02) and positively with vitamin B6 in blood (β = 0.217, P = 0.01). An inverse association was demonstrated between the ‘Health Conscious’ diet and DFI (β = −2.81, P = 0.05). The ‘Traditional Dutch’ diet was positively correlated with red blood cell folate (β = 0.06, P = 0.04) and sperm concentration (β = 13.25, P = 0.01).

CONCLUSIONS

The ‘Health Conscious’ and ‘Traditional Dutch’ dietary pattern seem to be associated with semen quality in men of subfertile couples.

Introduction

Over the past decades, human fertility rates have declined dramatically in industrialized countries (The World Bank Group, 2005). As a result of abnormal semen characteristics, ∼30% of all subfertile couples today require artificial fertility treatment to address failed reproductive attempts (Wong et al., 2000). The impact of risk factors like smoking and alcohol use is undisputed, and also manifest malnutrition is known to affect semen (Homan et al., 2007).

As a reflection of global changes in dietary behavior, the prevalence of unhealthy diets, characterized by low intakes of fruits and vegetables and high intakes of foods rich in saturated fats, has increased in women and men within the reproductive age range (Vujkovic et al., 2007). However, so far nutritional studies on semen quality in men have rather focused on investigating the role of zinc and the B-vitamin folate. Zinc is a trace element and acts as essential cofactor in metalloenzymes involved in many processes of spermatogenesis and a shortage can lead to oligospermia (Favier, 1992). A folate deficiency results in homocysteine abundance and induces oxidative stress and apoptosis. Recent findings show associations between folate deficiency-dependent and sperm aneuploidy (Young et al., 2008), sperm DNA damage (Boxmeer et al., 2008) and low sperm count (Wallock et al., 2001). As expected, total homocysteine (tHcy) concentrations in seminal plasma appear to be associated with male subfertility and low embryo quality (Ebisch et al., 2006a, b).

Comhaire et al. (2000) hypothesized that folate counterbalances the production of reactive oxygen species (ROS) via its antioxidative properties. Although ROS production is part of normal sperm maturation and capacitation, excessive quantities of ROS may cause damage to DNA and cell membranes (Ebisch et al., 2006a; Agarwal et al., 2005). Intervention studies in fertile and subfertile men with therapeutic dosages of zinc, folic acid and antioxidants show significant increase in sperm motility, sperm concentration and sperm count (Bentivoglio et al., 1993; Moilanen et al., 1993; Kessopoulou et al., 1995; Rolf et al., 1999; Wong et al., 2002; Ebisch et al., 2006a, b, 2007).

The human diet contains a wide range of nutrients that act in concert on many biological pathways. Because nutrition is subject to imposed environmental or, in humans, deliberate change, effects on reproductive health can be expected. Our aim of the present study was to: (i) define and identify dietary patterns in men of subfertile couples undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) treatment; (ii) relate dietary patterns to biomarker concentrations of the homocysteine pathway in blood and seminal plasma and (iii) relate dietary patterns as defined in (ii) to semen quality, controlling for the risk factors of age, body mass index (BMI), smoking, vitamin supplement use and presence of varicocele. In our paper, alcohol is conceptually regarded as a nutrient.

Materials and Methods

Subjects

Between September 2004 and January 2007, subfertile couples undergoing IVF with or without ICSI treatment at the Erasmus MC, University Medical Center in Rotterdam, the Netherlands, were included in the prospective FOod, Lifestyle and Fertility Outcome-study (FOLFO-study). This study was designed to study the influence of preconception nutrition and lifestyle on fertility and pregnancy outcome. Fertile and subfertile men were eligible for enrollment unless semen was cryopreserved or obtained by microsurgical or percutaneous epididymal sperm aspiration. Of the eligible IVF/ICSI population, 66% of the fertile and subfertile men participated in the FOLFO Study (n = 251). Because of the influence of ethnicity on dietary habits and lifestyle, we included in the current analysis male participants of European origin only. This resulted in the evaluation of 161 participants. The study protocol was approved by the Dutch Central Committee for Human Research in and the Medical Ethical and Institutional Review Board of Erasmus MC, University Medical Center in Rotterdam, the Netherlands. All participants gave their written informed consent.

Study design

At the IVF intake visit, the couples were invited to participate in the study, and only the men were included in the current analysis. All participants had filled out a general questionnaires at home comprising information on current lifestyle and demographic factors and returned it on the next hospital appointment. The extracted data comprised of age, weight, height, medical history, education, use of medication and lifestyle factors, such as smoking and the use of vitamin supplements. Between 2 weeks before and 2 weeks after oocyte retrieval, men visited the andrology outpatient clinic for fertility evaluation comprising semen analysis, blood sampling and physical examination including varicocele detection. During physical examination, scrotal ultrasonography was performed using a Toshiba Nemio 20 with a 12 Hz transducer. A varicocele was diagnosed if at least two venous vessels with a diameter of 3 mm or more were present, in addition to reflux or diameter increase during Valsalva's maneuver.

All laboratory analyses were performed by personnel blind to the clinical diagnosis. Subfertility was defined by a sperm concentration of <20 × 106 cells/ml.

Food frequency questionnaire

All participants filled out an FFQ to estimate habitual food and alcohol intake of the previous 4 weeks. This semi-quantitative FFQ was originally developed at the Division of Human Nutrition, Wageningen University, Wageningen, and validated for intake of energy, fatty acids and B-vitamins (Feunekes et al., 1993; Verkleij-Hagoort et al., 2006). The FFQs were provided on the day of sperm sample collection and were returned on the day of embryo transfer. A checklist was used to verify and check the completeness of the FFQ. Additional questions were asked by telephone interview. The FFQ consists of 195 food items and is structured according to meal pattern. Questions in the FFQ included frequency of consumption, portion size and preparation methods. Portion sizes were estimated according to Dutch household measures (Donders-Engelen et al., 2003). Nutritional values were determined with the use of the Dutch food composition table (NEVO) of 2001 (Netherlands Nutrition Centre, 2001). From 248 questionnaires filled out by the men, 2 out of 23 (8.7%) with no embryo transfer did not respond. Five of 225 men (2.2%) with embryo transfer did not respond. Owing to these very small numbers, it is very unlikely that selection bias has occurred. To evaluate the existence of under-reporting the ratio of energy intake divided by basal metabolic rate (BMR) was calculated. This value is an estimation of the physical activity level (PAL) of a sedentary lifestyle. The equations of Schofield were used for estimating BMR (Schofield, 1985). According to Goldberg et al. (1991), a cut-off point for underreporting for a sedentary lifestyle is a ratio of ≤1.35.

Semen analysis

Semen samples were produced via masturbation into polypropylene containers. Within half an hour, the samples were liquefied, and the semen parameters of volume, sperm concentration, sperm count, percentage progressive motility and percentage normal morphology were assessed according to World Health Organization (WHO) guidelines (World Health Organization, 1999). The 1999 World Health Organization reference values for normal sperm are fulfilled when sperm concentration exceeds 20 × 106/ml, >50% of spermatozoa have forward progression and >30% have normal morphology. Subsequently an aliquot of semen was centrifuged at 2500g for 10 min. The supernatant seminal plasma was frozen without preservatives and stored at –20°C until assayed.

DNA fragmentation index

The principles and procedures of measuring sperm DNA damage by a FACScan flow cytometry SCSA have been described previously (Smit et al., 2007). In short, semen samples were diluted with TNE buffer [0.01 M Tris–HCl, 0.15 M NaCl, 1 mM ethylenediamine tetraacetate (EDTA), pH 7.4] to a concentration of 1–2 × 106 sperm cells/ml in a volume of 0.20 ml. This cell suspension was mixed with 0.40 ml of acid detergent solution (0.08 N HCl, 0.15 M NaCl, 0.1% Triton-X 100, pH 1.2) and then stained with 1.2 ml Acridine Orange (AO) staining solution (0.1 M citric acid, 0.2 M Na2PO4, 1 mM EDTA, 0.15 M NaCl, pH 6.0, containing 0.6 μl/l AO). A reference sample treated in the same way was run prior to the actual measurements and used to adjust the voltage gains of the flow cytometer FL3 and FL1 photomultipliers that detected red and green fluorescence, respectively. An aliquot of reference sample was stained and run again after every 5–10 samples. Data collection of the fluorescent pattern in 5000 cells was performed at 3 min after acid treatment. Each sperm sample was analyzed twice.

The extent of DNA damage was expressed as the DNA fragmentation index (DFI), reflecting the ratio for red fluorescence to total fluorescence. Cell Quest Pro and WinList software (Becton Dickinson, San Jose, CA, USA) was used to calculate the DFI of each sample.

Blood

Venous blood samples were drawn into dry vacutainer tubes and allowed to clot. After centrifugation at 2000g, the blood serum was collected before being assayed for the concentrations of folate, vitamin B12 and testosterone. For the determination of red blood cell (RBC) folate and tHcy, venous blood samples were drawn into EDTA containing vacutainer tubes. The EDTA-blood samples were kept on ice, and plasma was separated by centrifugation within 1 h for determination of tHcy. For the determination of vitamin B6, blood was drawn into lithium-heparin containing vacutainers.

Blood serum and seminal plasma samples from each patient were analyzed during routine laboratory procedures for folate and vitamin B12 using an immunoelectrochemoluminescence assay (E170; Roche Diagnostics GmbH, Mannheim, Germany). Directly after blood sampling, 0.1 ml of blood out of an EDTA tube was hemolyzed with 0.9 ml of freshly prepared 1.0% ascorbic acid. Subsequently, the hematocrit of the EDTA-blood was determined on an ADVIA 120 Hematology Analyzer (Bayer Diagnostics, Leverkusen, Germany). The hemolysate was centrifuged for 5 min at 1000g shortly before the folate measurement. The folate concentration in the hemolysate was recalculated in RBC folate using the following formula: (nM hemolysate folate × 10/hematocrit) − (nM serum folate × [1 − hematocrit]/hematocrit) = nM RBC folate. Vitamin B6 levels in whole blood and seminal plasma and tHcy levels in EDTA plasma and seminal plasma were determined during routine laboratory procedures using high-performance liquid chromatography with reversed-phase separation and fluorescence detection (Schrijver et al., 1981; Pfeiffer et al., 1999). We determined whole blood vitamin B6 with pyridoxal-5'-phosphate (PLP) being the most common form. Testosterone was measured using the Coat-a-Count radioimmunoassay (Diagnostic Products Corp, Los Angeles, CA, USA). Sex hormone binding globulin (SHBG) was determined, using an immunometric technique on an Immulite Analyzer obtained from the same supplier. Serum inhibin B was measured by immunoenzymometric assay (OBI, Oxford Bio-Innovation, UK).

The between-run coefficient of variation for serum vitamin B12 was 5.1% at 125 pmol/l and 2.9% at 753 pmol/l; the coefficients of variation for serum folate were 9.5% at 8.3 nmol/l and 3.2% at 20.2 nmol/l; 3.3% at 14.55 µmol/l and 2.3% at 34.23 µmol/l for tHcy; 1.8% at 40 nmol/l and 1.3% at 115 nmol/l for PLP; for testosterone, these coefficients of variation were <7.5%, 6.1% at 11.6 nmol/l and 6.9% at 93 nmol/l for SHBG. For inhibin B, these coefficients of variation were <15%. The detection limit was 1.36 nmol/l for folate, 22 pmol/l for vitamin B12, 5 nmol/l for PLP, 4 µmol/l for tHcy, 0.1 nmol/l for testosterone, 5 nmol/l for SHBG and 10 ng/l for inhibin B.

Statistical analysis

Because of the prior knowledge of high correlations among several food constituents, we used principal components analysis (PCA) to summarize dietary patterns from food consumption data (Hu, 2002; Vujkovic et al., 2007). In summary, all 195 food items from the FFQ data of men undergoing IVF/ICSI treatment were first reduced to 22 predefined food groups based on a nutrient content grouping which conformed to the schemes of other reports (Slimani et al., 2002). Hereafter food groups were adjusted for the total intake of energy (Willett et al., 1997), and subsequently PCA was performed. To keep rotated factors uncorrelated, the solution was rotated with the varimax method by maximizing the sum of the variance of the loading vectors (Kaiser, 1958). The first two factors were extracted, and each represents a distinct dietary profile. A factor consists of a selection of the initial variables, each with its own coefficient (here called ‘loading’), which defines the observed correlation of that variable with the ‘latent’ constructed factor. As a weighted ‘mix’ of the initial variables, a factor explains a substantial amount of variation in the data set under study. In the preferable case, the statistically derived factor represents a recognizable pattern in the observable world. The eigenvalue was used as indicator of the amount of variation explained by each factor. Each participant was assigned two personalized scores for the two factors, i.e. which represents a quantification of the similarity of the individual's diet with each of the two extracted factors.

The factor loadings, i.e. the association of a factor with all measured food components, are presented for each factor separately; the association is calculated by Pearson's r correlation coefficient (Table I). After computation of the personalized scores, all 161 men were classified into tertiles according to their personal score for the respective dietary pattern.

Table I

Food group factor loadings for two identified dietary patterns from food-frequency questionnaire data of 160 men undergoing IVF/ICSI treatment

 ‘Heart healthy’ ‘Traditional Dutch’ 
Alcoholic drinks −0.10 −0.191 
Breakfast cereals −0.09 −0.282 
Butter 0.00 0.03 
Dairy products −0.04 0.04 
Eggs 0.00 0.12 
Fish and other seafood 0.562 −0.14 
Fruits 0.802 −0.161 
Legumes 0.161 0.05 
Margarine −0.13 0.181 
Mayonnaise and fatty sauces −0.201 0.222 
Meat products −0.171 0.472 
Non-alcoholic drinks 0.06 −0.712 
Nuts 0.12 0.00 
Pasta, rice and refined grains −0.161 −0.13 
Potatoes −0.01 0.562 
Sauces and condiments 0.11 0.05 
Snacks −0.08 −0.03 
Soup 0.08 −0.232 
Sugar and confectionary −0.171 −0.462 
Vegetable oil 0.09 0.03 
Vegetables 0.742 0.12 
Whole grains 0.432 0.482 
Variance explained 11.68% 9.53% 
 ‘Heart healthy’ ‘Traditional Dutch’ 
Alcoholic drinks −0.10 −0.191 
Breakfast cereals −0.09 −0.282 
Butter 0.00 0.03 
Dairy products −0.04 0.04 
Eggs 0.00 0.12 
Fish and other seafood 0.562 −0.14 
Fruits 0.802 −0.161 
Legumes 0.161 0.05 
Margarine −0.13 0.181 
Mayonnaise and fatty sauces −0.201 0.222 
Meat products −0.171 0.472 
Non-alcoholic drinks 0.06 −0.712 
Nuts 0.12 0.00 
Pasta, rice and refined grains −0.161 −0.13 
Potatoes −0.01 0.562 
Sauces and condiments 0.11 0.05 
Snacks −0.08 −0.03 
Soup 0.08 −0.232 
Sugar and confectionary −0.171 −0.462 
Vegetable oil 0.09 0.03 
Vegetables 0.742 0.12 
Whole grains 0.432 0.482 
Variance explained 11.68% 9.53% 

PCA was used as an extraction method in which the factor loading of a food group represents the contribution of that food group to the factor identified.

1P ≤ 0.05.

2P ≤ 0.01.

Additional explanatory variables were conventionally described. However, as age, BMI (kg/m2) and energy intake showed skewed distributions even if log-transformed, these variables are described by the median. For the same reason, we displayed medians and inter-quartile ranges for the biomarkers.

Differences in general characteristics between tertiles were evaluated in an unconditional linear regression model. The prevalence of subfertility causes, ethnic background, smoking and vitamin use were related to each tertile of the respective dietary pattern score, and the dietary associations with these prevalence were tested with χ2 test for linear association.

Next, the diet was used to predict the logarithmically transformed biomarkers of the homocysteine pathway and semen quality parameters in a multivariable linear regression model, additionally adjusted for the potential confounding variables age, BMI, smoking, intake of multivitamin and/or folic acid supplements and the presence of a varicocele. Individual food groups were also analyzed in a linear regression model to predict semen parameters and investigate which food groups of the dietary pattern contribute most to the observed associations with semen parameters. Covariates were age, BMI, smoking, intake of multivitamin and/or folic acid supplements. The food groups with all β-estimates and 95% CI described. All analysis was performed with SPSS software, release 15.0.0 for Windows.

Results

The characteristics of the dietary intake of 161 male participants, in terms of the two selected factors (components), are shown in Table I. The first factor explained 11.7% of the total variance. After inspection of the associative pattern, it was labeled the ‘Health Conscious’ dietary pattern; it comprises high intakes of fruit, vegetables, fish and other seafood, whole grains and legumes and low intakes of mayonnaise and other fatty sauces, meat products, refined grains, sugar and confectionary. The second factor explained 9.5% of the total variance of dietary intake, and in our interpretation represented the ‘Traditional Dutch’ diet; it is characterized by high intakes of potatoes, meat products, whole grains, margarine, mayonnaise and other fatty sauces and low intakes of non-alcoholic and alcoholic drinks, breakfast cereals, fruit, soup, sugar and confectionary. Neither subgroups of the ‘Health Conscious’ nor the ‘Traditional Dutch’ diet showed differences in general characteristics or endocrine parameters (Table II).

Table II

Baseline characteristics of men of subfertile couples undergoing IVF/ICSI treatment displayed from the lowest to the highest tertile of the two major dietary patterns

 ‘Health Conscious’
 
‘Traditional Dutch’
 
 Low (n = 53) Intermediate (n = 55) High (n = 53) P-value Low (n = 54) Intermediate (n = 54) High (n = 53) P-value 
Age (year), median (range) 35.3 (29.1–53.9) 35.8 (29.8–46.6) 37.3 (28.6–50.5) 0.13 36.3 (30–50.5) 36.3 (28.6–46.8) 35.6 (28.7–53.9) 0.91 
BMI (kg/m2), median (range) 25 (18.4–37.9) 26 (19.6–37.1) 24.9 (18.8–34.8) 0.38 24.8 (18.4–37.9) 24.7 (18.8–33.8) 26 (19.9–37.1) 0.07 
Cause of subfertility, n (%)    0.69    0.11 
 Male factor 20 (37.7) 20 (36.4) 17 (32.1)  14 (25.9) 27 (50.0) 16 (30.2)  
 Female factor 13 (24.5) 11 (20.0) 7 (13.2)  14 (25.9) 9 (16.7) 8 (15.1)  
 Both 3 (5.7) 4 (7.2) 4 (7.5)  5 (9.2) 2 (3.7) 4 (7.5)  
 Unexplained 17 (32.1) 20 (36.4) 25 (47.2)  21 (38.9) 16 (29.6) 25 (47.2)  
Presence of varicocele* 10 (20.8%) 8 (17.8%) 8 (17.4%) 0.90 12 (26.1%) 4 (8.2%) 10 (22.7%) 0.06 
Dutch Caucasian, n (%) 46 (86.8) 45 (81.8) 44 (83.0) 0.77 44 (81.5) 46 (85.2) 45 (84.9) 0.84 
Smoking, n (%) 14 (26.4) 14 (25.5) 6 (11.3) 0.10 12 (22.2) 9 (16.7) 13 (24.5) 0.59 
Vitamin use, n (%) 12 (22.6) 13 (23.6) 17 (32.1) 0.27 16 (29.6) 14 (25.9) 12 (22.6) 0.41 
Energy intake, median (range) 10 820 (4489–19 392) 9207 (3578–18 132) 10 449 (5342–15 475) 0.86 10 588 (5747–19 392) 9601 (3578–18 132) 10 846 (5802–18 569) 0.36 
Endocrine parameters, mean (95% CI)         
 Testosterone (nmol/l) 16.0 (14.4–17.7) 14.6 (13.3–16.2) 14.9 (13.8–16.1) 0.96 15.2 (13.9–16.5) 14.9 (13.5–16.4) 15.5 (14.1–17.2) 0.99 
 SHBG (nmol/l) 28.2 (25.0–31.8) 23.1 (20.7–25.8) 28.0 (24.6–31.7) 0.35 28.0 (24.8–31.6) 26.2 (23.6–29.2) 24.4 (21.4–27.9) 0.19 
 Inhibin B (ng/l) 172 (150–195) 171 (151–191) 175 (152–199) 0.79 174 (153–194) 174 (151–198) 170 (148–193) 0.86 
 ‘Health Conscious’
 
‘Traditional Dutch’
 
 Low (n = 53) Intermediate (n = 55) High (n = 53) P-value Low (n = 54) Intermediate (n = 54) High (n = 53) P-value 
Age (year), median (range) 35.3 (29.1–53.9) 35.8 (29.8–46.6) 37.3 (28.6–50.5) 0.13 36.3 (30–50.5) 36.3 (28.6–46.8) 35.6 (28.7–53.9) 0.91 
BMI (kg/m2), median (range) 25 (18.4–37.9) 26 (19.6–37.1) 24.9 (18.8–34.8) 0.38 24.8 (18.4–37.9) 24.7 (18.8–33.8) 26 (19.9–37.1) 0.07 
Cause of subfertility, n (%)    0.69    0.11 
 Male factor 20 (37.7) 20 (36.4) 17 (32.1)  14 (25.9) 27 (50.0) 16 (30.2)  
 Female factor 13 (24.5) 11 (20.0) 7 (13.2)  14 (25.9) 9 (16.7) 8 (15.1)  
 Both 3 (5.7) 4 (7.2) 4 (7.5)  5 (9.2) 2 (3.7) 4 (7.5)  
 Unexplained 17 (32.1) 20 (36.4) 25 (47.2)  21 (38.9) 16 (29.6) 25 (47.2)  
Presence of varicocele* 10 (20.8%) 8 (17.8%) 8 (17.4%) 0.90 12 (26.1%) 4 (8.2%) 10 (22.7%) 0.06 
Dutch Caucasian, n (%) 46 (86.8) 45 (81.8) 44 (83.0) 0.77 44 (81.5) 46 (85.2) 45 (84.9) 0.84 
Smoking, n (%) 14 (26.4) 14 (25.5) 6 (11.3) 0.10 12 (22.2) 9 (16.7) 13 (24.5) 0.59 
Vitamin use, n (%) 12 (22.6) 13 (23.6) 17 (32.1) 0.27 16 (29.6) 14 (25.9) 12 (22.6) 0.41 
Energy intake, median (range) 10 820 (4489–19 392) 9207 (3578–18 132) 10 449 (5342–15 475) 0.86 10 588 (5747–19 392) 9601 (3578–18 132) 10 846 (5802–18 569) 0.36 
Endocrine parameters, mean (95% CI)         
 Testosterone (nmol/l) 16.0 (14.4–17.7) 14.6 (13.3–16.2) 14.9 (13.8–16.1) 0.96 15.2 (13.9–16.5) 14.9 (13.5–16.4) 15.5 (14.1–17.2) 0.99 
 SHBG (nmol/l) 28.2 (25.0–31.8) 23.1 (20.7–25.8) 28.0 (24.6–31.7) 0.35 28.0 (24.8–31.6) 26.2 (23.6–29.2) 24.4 (21.4–27.9) 0.19 
 Inhibin B (ng/l) 172 (150–195) 171 (151–191) 175 (152–199) 0.79 174 (153–194) 174 (151–198) 170 (148–193) 0.86 

*Data on varicocele presence were missing in 22 men.

Initial significant correlations were present between the ‘Health Conscious’ diet and serum and RBC folate, tHcy, vitamin B6 and vitamin B12, but after adjusting for confounders significant correlations only remained for tHcy (β = −0.07, P = 0.02) and vitamin B6 (β = 0.22, P = 0.01) as shown in Table III. In seminal plasma, the adjusted linear model revealed an inverse association between tHcy and the ‘Health Conscious’ diet (β = −1.34, P = 0.02).

Table III

Concentration of biomarkers in blood and seminal plasma according to tertiles of the two major dietary patterns

 ‘Health Conscious’
 
‘Traditional Dutch’
 
 Low (n = 32) Intermediate (n = 21) High (n = 26) P-value1 Low (n = 31) Intermediate (n = 31) High (n = 17) P-value1 
Blood 
Serum folate (nmol/l) 16.1 (5.6) 15.3 (6.5) 18.5 (8.9) 0.15 17.3 (7.7) 16.2 (6.7) 15.7 (9.1) 0.84 
RBC folate (nmol/l) 1006 (350) 1063 (536) 1107 (542) 0.10 990 (554) 986 (382) 1110 (511) 0.04 
Vitamin B6 (nmol/l) 81.5 (36.8) 77.0 (35.6) 94.0 (48.3) 0.01 99.0 (48.0) 78.5 (31.0) 77.0 (36.5) 0.81 
Vitamin B12 (pmol/l) 288 (94) 289 (188) 361 (155) 0.07 329 (172) 293 (148) 282 (158) 0.41 
tHcy (μmol/l) 12.2 (3.2) 11.7 (4.1) 10.8 (4.4) 0.02 11.9 (4.7) 11.2 (3.1) 12.3 (3.7) 0.84 
Seminal plasma         
Folate (nmol/l) 26.5 (11.6) 25.7 (16.4) 25.0 (15.5) 0.70 28.3 (14.8) 24.8 (10.5) 22.6 (15.8) 0.69 
Vitamin B6 (nmol/l) 30.5 (39.0) 30.0 (23.5) 28.5 (30.2) 0.30 33.0 (32.0) 29.0 (21.3) 29.0 (33.5) 0.42 
Vitamin B12 (pmol/l) 467 (608) 424 (450) 493 (339) 0.51 409 (406) 438 (400) 680 (440) 0.44 
tHcy (μmol/l) 4.1 (3.1) 4.4 (3.2) 3.7 (4.5) 0.02 3.7 (3.9) 4.0 (2.5) 4.7 (5.7) 0.62 
 ‘Health Conscious’
 
‘Traditional Dutch’
 
 Low (n = 32) Intermediate (n = 21) High (n = 26) P-value1 Low (n = 31) Intermediate (n = 31) High (n = 17) P-value1 
Blood 
Serum folate (nmol/l) 16.1 (5.6) 15.3 (6.5) 18.5 (8.9) 0.15 17.3 (7.7) 16.2 (6.7) 15.7 (9.1) 0.84 
RBC folate (nmol/l) 1006 (350) 1063 (536) 1107 (542) 0.10 990 (554) 986 (382) 1110 (511) 0.04 
Vitamin B6 (nmol/l) 81.5 (36.8) 77.0 (35.6) 94.0 (48.3) 0.01 99.0 (48.0) 78.5 (31.0) 77.0 (36.5) 0.81 
Vitamin B12 (pmol/l) 288 (94) 289 (188) 361 (155) 0.07 329 (172) 293 (148) 282 (158) 0.41 
tHcy (μmol/l) 12.2 (3.2) 11.7 (4.1) 10.8 (4.4) 0.02 11.9 (4.7) 11.2 (3.1) 12.3 (3.7) 0.84 
Seminal plasma         
Folate (nmol/l) 26.5 (11.6) 25.7 (16.4) 25.0 (15.5) 0.70 28.3 (14.8) 24.8 (10.5) 22.6 (15.8) 0.69 
Vitamin B6 (nmol/l) 30.5 (39.0) 30.0 (23.5) 28.5 (30.2) 0.30 33.0 (32.0) 29.0 (21.3) 29.0 (33.5) 0.42 
Vitamin B12 (pmol/l) 467 (608) 424 (450) 493 (339) 0.51 409 (406) 438 (400) 680 (440) 0.44 
tHcy (μmol/l) 4.1 (3.1) 4.4 (3.2) 3.7 (4.5) 0.02 3.7 (3.9) 4.0 (2.5) 4.7 (5.7) 0.62 

Data are presented as mean with standard deviation.

1P-values are adjusted for age, BMI, smoking, vitamin supplement use and presence of varicocele.

The ‘Traditional Dutch’ pattern was positively correlated with RBC folate (β = 0.06, P = 0.05, Padjusted = 0.04), and no significant associations were observed with biomarkers in seminal plasma.

Table IV reveals that the use of the ‘Health Conscious’ diet is inversely associated with sperm DNA damage (β = −2.81, P = 0.05). This effect seems to be explained by the high intakes of fruits [β = −0.13 (−0.25; −0.02)] and vegetables [β = −0.25 (−0.49; −0.01)] (Table V). The ‘Traditional Dutch’ diet showed an increase in sperm concentration (β = 13.25, P = 0.01) (Table IV). The high intake of potatoes [β = 0.82 (0.24; 1.4)] and low intake of non-alcoholic beverages [β = −0.29 (−0.49; −0.1)] seem to explain this finding (Table V). All significance levels denoted have been adjusted for age, BMI, smoking, vitamin use and presence of varicocele at the study moment of semen collection.

Table IV

Semen parameters according to the tertiles of the two major dietary patterns

 ‘Health Conscious’
 
‘Traditional Dutch’
 
 Low (n = 40) Intermediate (n = 44) High (n = 42) P-value1 Low (n = 44) Intermediate (n = 43) High (n = 39) P-value1 
Semen parameters         
DFI (%) 25.2 (21.1–29.2) 25.0 (20.2–29.8) 20.6 (17.5–23.6) 0.05 23.5 (20.1–26.9) 25.0 (20.0–30.1) 22.0 (18.5–25.6) 0.53 
Volume (ml) 3.4 (2.9–3.8) 2.8 (2.4–3.2) 3.1 (2.6–3.5) 0.41 3.1 (2.6–3.5) 3.1 (2.7–3.5) 3.0 (2.6–3.5) 0.89 
Concentration (×106 cells/ml) 46.2 (30.8–61.5) 39 (27.6–50.4) 48.2 (33.1–63.2) 0.89 36.7 (26.8–46.6) 36.4 (23.1–49.8) 61.7 (44.5–78.9) 0.01 
Motility (%) 35 (30–40) 33 (28–39) 37 (32–42) 0.06 35 (31–39) 34 (29–40) 37 (31–43) 0.98 
Morphology (%) 5 (4–6) 5 (4–6) 5 (4–6) 0.74 5 (4–6) 5 (4–6) 6 (5–7) 0.34 
 ‘Health Conscious’
 
‘Traditional Dutch’
 
 Low (n = 40) Intermediate (n = 44) High (n = 42) P-value1 Low (n = 44) Intermediate (n = 43) High (n = 39) P-value1 
Semen parameters         
DFI (%) 25.2 (21.1–29.2) 25.0 (20.2–29.8) 20.6 (17.5–23.6) 0.05 23.5 (20.1–26.9) 25.0 (20.0–30.1) 22.0 (18.5–25.6) 0.53 
Volume (ml) 3.4 (2.9–3.8) 2.8 (2.4–3.2) 3.1 (2.6–3.5) 0.41 3.1 (2.6–3.5) 3.1 (2.7–3.5) 3.0 (2.6–3.5) 0.89 
Concentration (×106 cells/ml) 46.2 (30.8–61.5) 39 (27.6–50.4) 48.2 (33.1–63.2) 0.89 36.7 (26.8–46.6) 36.4 (23.1–49.8) 61.7 (44.5–78.9) 0.01 
Motility (%) 35 (30–40) 33 (28–39) 37 (32–42) 0.06 35 (31–39) 34 (29–40) 37 (31–43) 0.98 
Morphology (%) 5 (4–6) 5 (4–6) 5 (4–6) 0.74 5 (4–6) 5 (4–6) 6 (5–7) 0.34 

Data are presented as median with range.

1P-values are adjusted for age, BMI, smoking, vitamin supplement use and presence of varicocele.

Table V

Contribution of individual food groups to semen parameters

Food group DFI Volume Concentration Motility Morphology 
Alcoholic drinks 0.00 (−0.05; 0.05) 0.00 (−0.01; 0.01) 0.03 (−0.13; 0.19) −0.01 (−0.06; 0.05) 0.00 (−0.01; 0.01) 
Breakfast cereals −0.36 (−1.1; 0.37) −0.05 (−0.12; 0.02) 0.96 (−1.32; 3.24) 0.52 (−0.24; 1.28) 0.00 (−0.15; 0.16) 
Butter −4.14 (−13.09; 4.81) −0.44 (−1.32; 0.44) 2.76 (−24.73; 30.24) 7.58 (−1.71; 16.87) −1.38 (−3.19; 0.42) 
Dairy products 0.06 (−0.28; 0.41) 0.03 (0; 0.07) 0.47 (−0.62; 1.57) −0.1 (−0.45; 0.25) 0.05 (−0.01; 0.12) 
Eggs −0.28 (−0.87; 0.31) 0.07 (0.01; 0.13)* −0.17 (−2.05; 1.72) −0.11 (−0.7; 0.47) 0.00 (−0.11; 0.12) 
Fish and other seafood −0.73 (−2.04; 0.59) −0.1 (−0.24; 0.03) 0.9 (−3.28; 5.08) 1.31 (−0.03; 2.64)* 0.13 (−0.13; 0.39) 
Fruits −0.13 (−0.25; −0.02)* 0 (−0.01; 0.02) −0.06 (−0.45; 0.32) 0.16 (0.04; 0.28)* −0.01 (−0.03; 0.02) 
Legumes −0.53 (−1.17; 0.1) −0.03 (−0.09; 0.03) 0.18 (−1.72; 2.07) 0.14 (−0.52; 0.8) 0.11 (−0.01; 0.24) 
Margarine 4.39 (−2.32; 11.1) 0.46 (−0.24; 1.17) −3.89 (−26.01; 18.23) −9.1 (−16.68; −1.52)* 0.21 (−1.29; 1.71) 
Mayonnaise and fatty salads 0.14 (−1.33; 1.62) −0.17 (−0.31; −0.04)* −0.03 (−4.31; 4.24) −0.84 (−2.29; 0.61) −0.11 (−0.39; 0.18) 
Meat products 0.96 (−0.12; 2.04) 0.05 (−0.07; 0.16) −2.16 (−5.72; 1.39) −0.97 (−2.15; 0.21) −0.13 (−0.36; 0.1) 
Non-alcoholic drinks 0.01 (−0.05; 0.08) 0.00 (−0.01; 0.01) −0.29 (−0.49; −0.1)* −0.01 (−0.08; 0.05) −0.01 (−0.02; 0) 
Nuts −0.8 (−1.55; −0.06)* −0.05 (−0.12; 0.03) −1.04 (−3.38; 1.3) 0.22 (−0.59; 1.04) 0.08 (−0.08; 0.24) 
Pasta. rice and refined grains −0.11 (−0.45; 0.23) −0.02 (−0.05; 0.02) 0.32 (−0.69; 1.34) −0.14 (−0.46; 0.18) 0.01 (−0.05; 0.07) 
Potatoes −0.08 (−0.27; 0.11) 0.00 (−0.02; 0.02) 0.82 (0.24; 1.4)* 0.1 (−0.11; 0.3) 0.02 (−0.02; 0.06) 
Sauces and condiments −0.08 (−1.79; 1.63) −0.11 (−0.29; 0.06) 0.2 (−5.31; 5.71) −0.83 (−2.63; 0.96) −0.02 (−0.37; 0.33) 
Snacks 0.05 (−0.42; 0.51) 0.00 (−0.05; 0.05) −0.56 (−2.07; 0.95) 0.18 (−0.34; 0.7) −0.08 (−0.18; 0.02) 
Soup 0.01 (−0.04; 0.07) 0.01 (0; 0.01) −0.21 (−0.4; −0.02)* −0.01 (−0.07; 0.04) 0.00 (−0.01; 0.01) 
Sugar and confectionary 0.42 (−0.43; 1.28) −0.03 (−0.12; 0.06) 0.31 (−2.52; 3.14) −0.07 (−0.91; 0.78) −0.03 (−0.2; 0.13) 
Vegetable oil −1.1 (−5.17; 2.98) −0.26 (−0.68; 0.17) −3.27 (−16.66; 10.13) 1.06 (−3.44; 5.56) −0.19 (−1.08; 0.69) 
Vegetables −0.25 (−0.49; −0.01)* −0.01 (−0.03; 0.02) 0.79 (0.01; 1.58)* 0.32 (0.05; 0.6)* 0.03 (−0.03; 0.08) 
Whole grains −0.15 (−0.34; 0.03) 0.00 (−0.02; 0.02) 0.41 (−0.19; 1.01) 0.17 (−0.03; 0.37) 0.03 (−0.01; 0.06) 
Food group DFI Volume Concentration Motility Morphology 
Alcoholic drinks 0.00 (−0.05; 0.05) 0.00 (−0.01; 0.01) 0.03 (−0.13; 0.19) −0.01 (−0.06; 0.05) 0.00 (−0.01; 0.01) 
Breakfast cereals −0.36 (−1.1; 0.37) −0.05 (−0.12; 0.02) 0.96 (−1.32; 3.24) 0.52 (−0.24; 1.28) 0.00 (−0.15; 0.16) 
Butter −4.14 (−13.09; 4.81) −0.44 (−1.32; 0.44) 2.76 (−24.73; 30.24) 7.58 (−1.71; 16.87) −1.38 (−3.19; 0.42) 
Dairy products 0.06 (−0.28; 0.41) 0.03 (0; 0.07) 0.47 (−0.62; 1.57) −0.1 (−0.45; 0.25) 0.05 (−0.01; 0.12) 
Eggs −0.28 (−0.87; 0.31) 0.07 (0.01; 0.13)* −0.17 (−2.05; 1.72) −0.11 (−0.7; 0.47) 0.00 (−0.11; 0.12) 
Fish and other seafood −0.73 (−2.04; 0.59) −0.1 (−0.24; 0.03) 0.9 (−3.28; 5.08) 1.31 (−0.03; 2.64)* 0.13 (−0.13; 0.39) 
Fruits −0.13 (−0.25; −0.02)* 0 (−0.01; 0.02) −0.06 (−0.45; 0.32) 0.16 (0.04; 0.28)* −0.01 (−0.03; 0.02) 
Legumes −0.53 (−1.17; 0.1) −0.03 (−0.09; 0.03) 0.18 (−1.72; 2.07) 0.14 (−0.52; 0.8) 0.11 (−0.01; 0.24) 
Margarine 4.39 (−2.32; 11.1) 0.46 (−0.24; 1.17) −3.89 (−26.01; 18.23) −9.1 (−16.68; −1.52)* 0.21 (−1.29; 1.71) 
Mayonnaise and fatty salads 0.14 (−1.33; 1.62) −0.17 (−0.31; −0.04)* −0.03 (−4.31; 4.24) −0.84 (−2.29; 0.61) −0.11 (−0.39; 0.18) 
Meat products 0.96 (−0.12; 2.04) 0.05 (−0.07; 0.16) −2.16 (−5.72; 1.39) −0.97 (−2.15; 0.21) −0.13 (−0.36; 0.1) 
Non-alcoholic drinks 0.01 (−0.05; 0.08) 0.00 (−0.01; 0.01) −0.29 (−0.49; −0.1)* −0.01 (−0.08; 0.05) −0.01 (−0.02; 0) 
Nuts −0.8 (−1.55; −0.06)* −0.05 (−0.12; 0.03) −1.04 (−3.38; 1.3) 0.22 (−0.59; 1.04) 0.08 (−0.08; 0.24) 
Pasta. rice and refined grains −0.11 (−0.45; 0.23) −0.02 (−0.05; 0.02) 0.32 (−0.69; 1.34) −0.14 (−0.46; 0.18) 0.01 (−0.05; 0.07) 
Potatoes −0.08 (−0.27; 0.11) 0.00 (−0.02; 0.02) 0.82 (0.24; 1.4)* 0.1 (−0.11; 0.3) 0.02 (−0.02; 0.06) 
Sauces and condiments −0.08 (−1.79; 1.63) −0.11 (−0.29; 0.06) 0.2 (−5.31; 5.71) −0.83 (−2.63; 0.96) −0.02 (−0.37; 0.33) 
Snacks 0.05 (−0.42; 0.51) 0.00 (−0.05; 0.05) −0.56 (−2.07; 0.95) 0.18 (−0.34; 0.7) −0.08 (−0.18; 0.02) 
Soup 0.01 (−0.04; 0.07) 0.01 (0; 0.01) −0.21 (−0.4; −0.02)* −0.01 (−0.07; 0.04) 0.00 (−0.01; 0.01) 
Sugar and confectionary 0.42 (−0.43; 1.28) −0.03 (−0.12; 0.06) 0.31 (−2.52; 3.14) −0.07 (−0.91; 0.78) −0.03 (−0.2; 0.13) 
Vegetable oil −1.1 (−5.17; 2.98) −0.26 (−0.68; 0.17) −3.27 (−16.66; 10.13) 1.06 (−3.44; 5.56) −0.19 (−1.08; 0.69) 
Vegetables −0.25 (−0.49; −0.01)* −0.01 (−0.03; 0.02) 0.79 (0.01; 1.58)* 0.32 (0.05; 0.6)* 0.03 (−0.03; 0.08) 
Whole grains −0.15 (−0.34; 0.03) 0.00 (−0.02; 0.02) 0.41 (−0.19; 1.01) 0.17 (−0.03; 0.37) 0.03 (−0.01; 0.06) 

*P < 0.05. The mean estimates are presented with 95% confidence interval and are adjusted for age, BMI, smoking and vitamin supplement use.

Discussion

This study demonstrates that human nutrition affects semen quality in men undergoing IVF/ICSI procedures. We observed that men who consume the ‘Health Conscious’ diet have lower sperm DNA damage. Furthermore, sperm concentrations are much higher in men who strongly adhere to the ‘Traditional Dutch’ dietary pattern. These findings suggest that both dietary patterns can indeed have beneficial effects on semen quality. Our findings also represent important epidemiological information as it provides a specific explanation and empirical evidence on the putative relation between unhealthy diets and the decline of semen quality in industrialized countries.

We were surprised by the observation that the ‘Health Conscious’ dietary pattern is inversely related to tHcy in blood and seminal plasma, but positively related to vitamin B6 in blood. This may imply that this dietary pattern prohibits sperm DNA from being damaged by regulating the flow of tHcy concentrations. We also found that the beneficial effects of the ‘Health Conscious’ pattern seem to be determined in particular by vegetable and fruit intake, as has been further substantiated by the single food group analyses. The high content of folates and vitamin B6-rich foods may stimulate tHcy to be remethylated into methionine and trans-sulfurated into cystathionine and cysteine. In males, the regulation of homocysteine is mediated partially through the testosterone-dependent cystathionine-β-synthase pathway (Vitvitsky et al., 2007). Other studies support the importance of the homocysteine pathway on semen parameters. A Spanish study revealed positive associations between the intake of folate-rich food sources, such as fruit and vegetables, and semen quality (Mendiola et al., 2008). Furthermore, Young et al. (2008) observed inverse relationships between folate, zinc and antioxidant intake in healthy non-smoking men in the USA and sperm aneuploidy. Suggested mechanisms by which elevated tHcy may exert its effects are excessive induction of oxidative stress, defective methylation of proteins, lipids and DNA, altered nitric oxide bioavailability, induction of vascular inflammation and activation of apoptosis (Homan et al., 2007). However, these mechanisms in reproductive tissues should be studied in much more detail in future studies, both experimentally in animals and observationally in human cohorts. Unfortunately, no data on seminal PUFA concentrations were collected, which would have enabled us to measure whether the increased fish intake resulted in higher levels of unsaturated fats that might enhance the fluidity of the plasma membrane. A fluid membrane, however, also makes spermatozoa more vulnerable to ROS attack, which may explain why lifestyle factors that induce oxidative stress may have led to reduced human fertility in the past decades.

The second identified dietary pattern being the ‘Traditional Dutch’ comprises high intakes of meat products, potatoes and whole grains. This resembles a typically Dutch dietary tradition since the 19th century when agriculture and domestic education were widely implemented in the Netherlands. This dietary pattern is positively correlated with folate in blood and shows a significant positive association with sperm concentrations. Because of the substantial amount of potatoes eaten in one serving, they provide a rich source of folate. Furthermore, the high intake of meat, a natural source of zinc elements, influences the bioavailability of dietary folate. The zinc-dependent enzyme γ-glutamylhydrolase in the jejunum efficiently converts dietary folate as polyglutamates into monoglutamates, which are the only absorbable form of folate. Furthermore, zinc is a cofactor of methionine synthase involved in the remethylation of tHcy into methionine thereby reducing tHcy. The beneficial effects of zinc are supported by a randomized, placebo-controlled intervention study conducted in the late nineties, in which administration of zinc and/or folic acid to subfertile patients led to a significant increase in semen concentration ranging from 18% to 74% (Wong et al., 2002; Ebisch et al., 2006a, b).

In the same FOLFO Study, but in a much smaller study group of men, we measured the biomarkers of homocysteine metabolism in blood and seminal plasma and investigated the associations with semen parameters. A positive association was observed between seminal plasma vitamin B12 and sperm concentration (Boxmeer et al., 2007). In the current analysis, we demonstrate a significant association between the ‘Traditional Dutch’ dietary pattern and semen concentrations (increase from 36.7 × 106 to 61.7 × 106 cells/ml in the highest tertile, P = 0.01). We also observe positive but not statistically significant associations between the ‘Traditional Dutch’ dietary pattern and seminal vitamin B12 concentrations, which are very likely to be due to the high meat intake. This supports the previous observed association between seminal vitamin B12 and sperm concentration.

In a recent study, Mendiola et al. (2008) suggested that meat intake negatively affects semen quality. They assumed this detrimental effect to be due to natural and synthetic estrogens in meat, hormones that are used in cattle to stimulate growth and development (Henricks et al., 2001). However, their study size was rather small (n = 61) and only single food items were investigated. Furthermore, only reports of frequency were used to assess the dietary intake, and portion sizes were not reported so that there was no possibility of comparing dosage effects. We can only speculate that the favorable effect of the meat-rich ‘Traditional Dutch’ diet on semen quality might be due to the relative lower amounts of meat used in our country, or perhaps a lower exposure of Dutch cattle to hormones and endocrine disruptors.

Some concerns regarding the study design apply. We cannot exclude the possibility that the dietary patterns identified are confounded by lifestyle factors other than the adjusted covariates age, energy intake, BMI, smoking and use of vitamin supplements. We observed little or no association of these factors with the ‘Health Conscious’ and ‘Traditional Dutch’ dietary pattern, but cannot rule out a possible role of other residual confounding variables, e.g. exercise, psychological stress and exposure to environmental pollutants (Homan et al., 2007). We have reduced the possibility of multiple testing by summarizing the overall nutrient intake with PCA into two dietary patterns. With this restriction to two exposure variables and taking all nutrients into account, we have avoided the issue of selective testing. These two exposure variables were associated with biomarkers of the homocysteine metabolism and semen parameters, whereby we have attempted to minimize the probability of chance-finding.

All methods of dietary assessment are prone to error. Nutritional intake was assessed with an FFQ and individually checked for completeness and accuracy. However, self-reported dietary intake tends to habitually underestimate energy intake (Black et al., 1991). Therefore, we evaluated the presence of underreporting by estimating the PAL. The mean PAL for all participants in our study was 1.28, and similar in fertile and subfertile men indicating an underestimation of energy intake in both groups according to Goldberg standard (PAL < 1.35). We judge it therefore to be unlikely that underestimation has distorted the associations between semen parameters and energy.

We recognize that the findings from this Dutch sample and from comparable European subfertile samples may not apply to other populations, particularly if dietary pattern is dissimilar or foods are differently produced and processed. Still our results apply to a substantial part of the globe which has westernized dietary patterns. The strength of the study is its prospective design and the relatively large study sample size of 161 men. This gave us the opportunity to produce reliable dietary patterns which were comparable to studies conducted in the Dutch general population (van Dam et al., 2003).

In conclusion, the ‘Health Conscious’ and the ‘Traditional Dutch’ dietary pattern seem to be associated with semen quality. More studies are needed to show the causation of these associations and their effects on reproduction.

Funding

This study is financially supported by the Department of Obstetrics and Gynaecology, Subdivison of Obstetrics and Prenatal Medicine of the Erasmus MC, University Medical Center, Rotterdam in the Netherlands.

Acknowledgements

The authors gratefully acknowledge Mrs J.C. Boxmeer and the IVF team of the Division of Reproductive Medicine, Department of Obstetrics and Gynecology for the contributions in data collection. Special thanks go to Dr J. Romijn and Mrs M. Smit of the Department of Andrology, and the laboratory technicians and research assistants of the Laboratory of Clinical Chemistry and Endocrinology of the Erasmus MC, University Medical Center in Rotterdam, the Netherlands, for the collection and laboratory determinations.

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