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Dimitrios Karayiannis, Meropi D. Kontogianni, Christina Mendorou, Lygeri Douka, Minas Mastrominas, Nikos Yiannakouris, Association between adherence to the Mediterranean diet and semen quality parameters in male partners of couples attempting fertility, Human Reproduction, Volume 32, Issue 1, 1 January 2017, Pages 215–222, https://doi.org/10.1093/humrep/dew288
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Is adherence to the Mediterranean diet (MedDiet) associated with better semen quality in men of subfertile couples attempting fertility?
Greater adherence to the MedDiet, as assessed through the validated Mediterranean diet score (MedDietScore), was significantly associated with higher sperm concentration, total sperm count and sperm motility.
A-posteriori dietary pattern approaches have revealed that dietary patterns characterized by high intakes of fruits, vegetables, whole grains, fish and low intake of meat are associated with better semen quality. Yet, whether adherence to the MedDiet is associated with better semen profile remains largely unexplored.
This was a cross-sectional study of 225 men from couples attending a fertility clinic in Athens, Greece, recruited between November 2013 and May 2016. The study was designed to evaluate the influence of habitual dietary intake and lifestyle on fertility outcomes.
Men aged 26–55 years, 51.1% overweight or obese, 20.9% smokers, with complete dietary data were analyzed. Diet was assessed via a food-frequency questionnaire and adherence to the MedDiet was assessed through the MedDietScore (range: 0–55; higher scores indicating greater adherence to MedDiet). Semen quality was evaluated according to World Health Organization 2010 guidelines. Multiple logistic regression analysis was used to evaluate associations between tertiles of the MedDietScore and the likelihood of having abnormal semen parameters, after adjusting for potential confounders.
Compared to men in the highest tertile of the MedDietScore (≥37, N = 66), a higher percentage of men in the lowest tertile of the score (≤30, N = 76) exhibited below the WHO reference values for sperm concentration (47.4% vs 16.7%, P < 0.001), total sperm count (55.3% vs 22.7%, P < 0.001), total motility (65.8% vs 31.8%, P < 0.001), progressive motility (84.2 vs 62.1%, P = 0.011) and sperm morphology (50.0 vs 28.8%, P = 0.023). In the multivariable adjusted models, men in the lowest tertile of the MedDietScore had ~2.6 times higher likelihood of having abnormal sperm concentration, total sperm count and motility, compared to men in the highest tertile of the score.
The main limitation of the study stems from its cross-sectional nature, limiting our ability to determine causality.
The results suggest that greater compliance to the MedDiet may help improve semen quality. Whether this translates into differences in male fertility remains to be elucidated. Our findings are consistent with previous studies showing that dietary patterns with some of the characteristics of the MedDiet, i.e. rich in fruit, vegetables, legumes and whole grains, are associated with better measures of semen quality.
No funding was obtained. The authors have no competing interests to declare.
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Introduction
In recent years there has been an increase in research on the factors affecting male fertility given the fact that, in almost 40% of infertile couples, the man is the sole or contributing cause in the inability to conceive. Semen analysis is the cornerstone of the laboratory examination for male infertility and a recent comprehensive review has pointed to a tremendous decrease in semen quality parameters among Western populations during the last four decades (Sengupta et al., 2016). Apart from the traditional risk factors (genetic, endocrine, etc.), lifestyle factors, especially diet, have been suggested to play an important role in male fertility (Gabrielsen and Tanrikut, 2016). Unlike other subfertility risk factors that cannot be reversed, diet presents a potential opportunity for intervention, which means that it is an important consideration in the counseling of subfertile men.
Accumulating literature supports the hypothesis that specific nutrients can affect semen quality parameters (Giahi et al., 2016). However, most studies focus on isolated micronutrients or food compounds, while studies on the effects of dietary patterns are scarce. In a study among healthy young men (Gaskins et al., 2012), a ‘prudent’ dietary pattern characterized by a high intake of fruits, vegetables, chicken, fish and whole grains was associated with higher sperm progressive motility, while the consumption of a ‘Western’ dietary pattern was unrelated to conventional semen quality parameters. Similarly, in a study among male university students, a ‘Mediterranean’ dietary pattern derived by principal component analysis (PCA), which was characterized by high intakes of vegetables, fruits and seafood, was positively associated with total sperm count (Cutillas-Tolin et al., 2015). Studies among men of subfertile couples have also revealed that a ‘Mediterranean’ dietary pattern, characterized by high intakes of fruits, vegetables, fish and whole grains, is related to lower DNA fragmentation, higher sperm motility (Vujkovic et al., 2009), higher sperm concentration and higher levels of testosterone (Jurewicz et al., 2016).
The Mediterranean diet (MedDiet) is a dietary pattern with plenty of well-established health benefits, presenting strong anti-inflammatory and anti-oxidative effects (Trichopoulou et al., 2003), yet the reproductive benefits of this dietary pattern are less clear. Previous studies evaluating the role of dietary patterns on semen quality have relied on a-posteriori dietary pattern approaches, which process the collected dietary information through multivariate statistical methods, such as PCA. However, the interpretation of the components derived by PCA is often difficult, subjective and not easily generalized to the reference population. On the other hand, a-priori dietary pattern approaches are based on the use of dietary indexes, such as the Mediterranean diet score (MedDietScore) (Panagiotakos et al., 2007), that aim to capture pre-defined healthy patterns, and have the advantage of relying on the current scientific data concerning nutrition, health and disease.
Therefore, in order to expand the current knowledge, in this study we investigated associations between adherence to the Mediterranean dietary pattern and semen quality parameters in male partners of couples attempting fertility, by employing thea-priori dietary pattern approach and using the validated MedDietScore.
Materials and Methods
Study population
Couples with primary infertility, seeking evaluation and treatment at the Embryogenesis Assisted Conception Unit in Athens, Greece, were invited to participate in an ongoing prospective cohort study focusing on investigating how background diet and lifestyle patterns impact fertility. Male partners aged 20–55 years and without a medical history of systemic diseases, cryptorchidism or varicocele, microorchidism, vasectomy or hormonal treatment in the last six-months were eligible for the present analysis. There were 243 men evaluated from November 2013 until May 2016, all of whom completed a dietary assessment questionnaire. There were 18 men excluded from the analysis due to the presence of azoospermia, leaving 225 men for this analysis. At enrollment, participants completed a detailed questionnaire requesting information on demographic, socioeconomic and lifestyle factors and medical history.
All procedures were in accord with the Helsinki Declaration and all participants provided written informed consent. The study protocol was approved by the Ethical Review Board of Harokopio University, Athens, Greece.
Physical examination and clinical and lifestyle variables
A physical examination of each participant was performed on the day of semen sampling and the presence of varicocele or other abnormalities was assessed. Body weight and height were measured to the nearest 0.5 kg and 0.5 cm, respectively, and BMI (kg/m2) was calculated. In addition, waist and hip circumferences and arterial blood pressure measurements were taken. Blood samples were collected from fasting participants and a biochemical evaluation was carried out in the Assisted Conception Unit Laboratory.
Assessment of physical activity was performed through a validated version of the ‘International Physical Activity Questionnaire’ (iPAQ) (Papathanasiou et al., 2010). The short version of iPAQ provided information on weekly time spent on walking, on vigorous, moderate-intensity, and on sedentary activity. Participants were instructed to refer to all domains of physical activity during a usual week of the past year. Both continuous [sum of weekly Metabolic Equivalent of Task (MET)-minutes/week of walking and moderate- and vigorous-intensity exercise] and categorical indicators of physical activity (inactive/minimally active/highly active) were assessed.
Anxiety was assessed using a validated version of the Spielberger State-Trait Anxiety Inventory (STAI) (Fountoulakis et al., 2006), which is a 40-item self-reported questionnaire, divided into two parts: the first part evaluates state anxiety by inquiring about the current emotional state, and the second part assesses trait anxiety, asking the subjects to describe how they usually feel. The 40 items are rated from 1 to 4 according to frequency of their feelings (i.e. almost never, sometimes, often, and almost always). The range of the score of each part is 20–80. Values of 20–39 suggest low anxiety, values of 40–59 suggest moderate anxiety, while values >59 suggest severe anxiety.
Dietary assessment and evaluation of adherence to the Mediterranean diet
To estimate habitual food and alcohol intake all participants filled out a (validated for the Greek population) food-frequency questionnaire (FFQ), which included 75 items (foods and beverages commonly consumed in Greece and dietary habits) (Bountziouka et al., 2012).
Τo evaluate the level of adherence to the MedDiet, the MedDietScore was calculated for each participant (Panagiotakos et al., 2007), by taking into account the consumption of food items from nine food groups, as well as olive oil and alcoholic beverages. The components and the scoring system for calculating MedDietScore are presented in Table I. The range of the MedDietScore is 0–55, with higher values indicating greater adherence to the MedDiet.
| How often do you consume . | Frequency of consumption (servings/week or otherwise stated) . | |||||
|---|---|---|---|---|---|---|
| Non-refined cereals (whole grain bread, pasta, rice, etc.) | Never | 1–6 | 7–12 | 13–18 | 19–31 | >32 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Potatoes | Never | 1–4 | 5–8 | 9–12 | 13–18 | >18 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fruits | Never | 1–4 | 5–8 | 9–15 | 16–21 | >22 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Vegetables | Never | 1–6 | 7–12 | 13–20 | 21–32 | >33 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Legumes | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fish | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Red meat and products | ≤1 | 2–3 | 4–5 | 6–7 | 8–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Poultry | ≤3 | 4–5 | 5–6 | 7–8 | 9–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Full fat dairy products (cheese, yoghurt, milk) | ≤10 | 11–15 | 16–20 | 21–28 | 29–30 | >30 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Use of olive oil in cooking (times/week) | Never | Rare | <1 | 1–3 | 3–5 | Daily |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Alcoholic beverages (ml/day, 100 ml = 12 g ethanol) | <300 | 300 | 400 | 500 | 600 | >700 or 0 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| How often do you consume . | Frequency of consumption (servings/week or otherwise stated) . | |||||
|---|---|---|---|---|---|---|
| Non-refined cereals (whole grain bread, pasta, rice, etc.) | Never | 1–6 | 7–12 | 13–18 | 19–31 | >32 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Potatoes | Never | 1–4 | 5–8 | 9–12 | 13–18 | >18 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fruits | Never | 1–4 | 5–8 | 9–15 | 16–21 | >22 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Vegetables | Never | 1–6 | 7–12 | 13–20 | 21–32 | >33 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Legumes | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fish | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Red meat and products | ≤1 | 2–3 | 4–5 | 6–7 | 8–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Poultry | ≤3 | 4–5 | 5–6 | 7–8 | 9–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Full fat dairy products (cheese, yoghurt, milk) | ≤10 | 11–15 | 16–20 | 21–28 | 29–30 | >30 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Use of olive oil in cooking (times/week) | Never | Rare | <1 | 1–3 | 3–5 | Daily |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Alcoholic beverages (ml/day, 100 ml = 12 g ethanol) | <300 | 300 | 400 | 500 | 600 | >700 or 0 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
The range of the Mediterranean diet score (MedDietScore) is from 0 to 55, with higher values indicating greater adherence to the Mediterranean diet (Panagiotakos et al., 2007).
| How often do you consume . | Frequency of consumption (servings/week or otherwise stated) . | |||||
|---|---|---|---|---|---|---|
| Non-refined cereals (whole grain bread, pasta, rice, etc.) | Never | 1–6 | 7–12 | 13–18 | 19–31 | >32 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Potatoes | Never | 1–4 | 5–8 | 9–12 | 13–18 | >18 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fruits | Never | 1–4 | 5–8 | 9–15 | 16–21 | >22 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Vegetables | Never | 1–6 | 7–12 | 13–20 | 21–32 | >33 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Legumes | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fish | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Red meat and products | ≤1 | 2–3 | 4–5 | 6–7 | 8–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Poultry | ≤3 | 4–5 | 5–6 | 7–8 | 9–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Full fat dairy products (cheese, yoghurt, milk) | ≤10 | 11–15 | 16–20 | 21–28 | 29–30 | >30 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Use of olive oil in cooking (times/week) | Never | Rare | <1 | 1–3 | 3–5 | Daily |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Alcoholic beverages (ml/day, 100 ml = 12 g ethanol) | <300 | 300 | 400 | 500 | 600 | >700 or 0 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| How often do you consume . | Frequency of consumption (servings/week or otherwise stated) . | |||||
|---|---|---|---|---|---|---|
| Non-refined cereals (whole grain bread, pasta, rice, etc.) | Never | 1–6 | 7–12 | 13–18 | 19–31 | >32 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Potatoes | Never | 1–4 | 5–8 | 9–12 | 13–18 | >18 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fruits | Never | 1–4 | 5–8 | 9–15 | 16–21 | >22 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Vegetables | Never | 1–6 | 7–12 | 13–20 | 21–32 | >33 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Legumes | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Fish | Never | <1 | 1–2 | 3–4 | 5–6 | >6 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Red meat and products | ≤1 | 2–3 | 4–5 | 6–7 | 8–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Poultry | ≤3 | 4–5 | 5–6 | 7–8 | 9–10 | >10 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Full fat dairy products (cheese, yoghurt, milk) | ≤10 | 11–15 | 16–20 | 21–28 | 29–30 | >30 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
| Use of olive oil in cooking (times/week) | Never | Rare | <1 | 1–3 | 3–5 | Daily |
| 0 | 1 | 2 | 3 | 4 | 5 | |
| Alcoholic beverages (ml/day, 100 ml = 12 g ethanol) | <300 | 300 | 400 | 500 | 600 | >700 or 0 |
| 5 | 4 | 3 | 2 | 1 | 0 | |
The range of the Mediterranean diet score (MedDietScore) is from 0 to 55, with higher values indicating greater adherence to the Mediterranean diet (Panagiotakos et al., 2007).
Semen analysis
Participants were asked to abstain from ejaculation for at least 48 h before sample collection. The semen sample was immediately delivered to the laboratory and incubated in a 37°C incubator. Τhe duration of complete liquefaction (<1 h) was documented, until 1 h was reached. One semen sample was assessed for each subject, particularly the one used during the in-vitro fertilization protocol.
Semen analysis was performed with standardized methods. Semen volume was measured by weighing, assuming a semen density of 1.0 g/ml; sperm concentration was evaluated by hemocytometer (Bürker-Türk; Paul Marienfeld GmbH&Co. KG, Lauda-Königshofen, Germany); sperm morphology was identified from semen smears prepared with 10 μl of well-mixed semen, stained with Papanicolaou and assessed using the Tygerberg strict criteria (Menkveld and Kruger, 1995). Sperm motility was graded into total (progressive + non-progressive motility) and progressive motility. Total sperm count (volume × sperm concentration) was also calculated. Reference values from the newest World Health Organization semen analysis manual were used to assess sperm concentration and motility (WHO, 2010).
Statistical analysis
Continuous variables are presented as median (25th, 75th percentiles), and categorical variables as absolute and relative frequencies. The normality of the data was assessed graphically using histograms and with the Shapiro–Wilk test. All seminal parameters showed non-normal distributions. Correlations between dietary, anthropometric and semen analysis parameters were assessed using Spearman's or Pearson's method whenever appropriate. Associations between categorical variables were tested by Fisher's exact test, while differences between categorical and several clinical and nutritional variables were tested by the use of Student's t-test and Mann–Whitney test (for the normally distributed and skewed variables, respectively). Comparisons between various variables and tertiles of the MedDietScore were performed using Kruskal–Wallis test, and the Bonferroni correction was used to account for the increase in Type-I error. Analysis of covariance was also used to calculate multivariable adjusted semen quality parameters for each tertile by relevant covariates.
Multiple logistic regression was applied to assess associations between adherence to the MedDiet and the likelihood of having abnormal semen parameters, after adjusting for the following confounders: age (years), BMI (kg/m2), smoking (current/never/ex-smokers), physical activity (MET-min/week), state/trait-anxiety (score value), total energy intake (kcal/day), educational level (primary–secondary school/Bachelor's degree/Master's–Doctoral degree), income level (low/moderate/high), and family subfertility history (yes/no). Collinearity between independent variables was evaluated through the condition index, while the model's goodness-of-fit was graphically evaluated (standardized residuals against fitted values). Standardized residuals were used to test the model's goodness-of-fit. Tests of linear trend across categories were conducted by modeling the median values of each category as a single continuous variable and assessing significance using Wald test. Statistical Package for Social Sciences software (SPSS, version 21.0, Chicago, Illinois, USA) was used for all the statistical calculations. All reported P-values are based on two-sided tests and compared to a significance level of 5%.
Results
The median age of the 225 study participants was 38 (range: 26–55) years. Most men (51.1%) were overweight or obese (BMI ≥ 25 kg/m2), 20.9% were current smokers, and 93.8% were inactive or minimally active. The median (25th, 75th percentiles) values for semen analysis parameters were: 30 × 106/ml (10, 55) for sperm concentration, 60 × 106/ml (20, 111) for total sperm count, 20% (10, 35) for percent progressively motile sperm, and 4% (3, 5) for percent morphologically normal sperm. The median MedDietScore was 34.0 (range: 23–46).
Overall, 50 men (22.2%) had a normal semen analysis, while most (77.8%) had at least one semen analysis parameter below the lower benchmarks according to the WHO 2010 criteria. Compared to men with normal semen analysis, those with abnormal semen analysis had higher median BMI (25.6 vs 24.0 kg/m2, P = 0.021) and exhibited lower median values in the MedDietScore (33.0 vs 35.0, P = 0.002). As was expected, BMI and waist circumference were both negatively correlated with semen quality measures, except for semen volume, and a negative correlation was also observed between semen parameters and anxiety levels (all P < 0.05, data not shown). In contrast, MedDietScore was positively correlated with sperm concentration (Spearman's rho = 0.29, P < 0.001), total sperm count (rho = 0.26, P < 0.001), total and progressive motility (rho = 0.31 and rho = 0.30, P < 0.001, respectively), and with percent morphologically normal sperm (rho = 0.15, P = 0.025).
In Table II, the distribution of various characteristics of the participants according to tertiles of the MedDietScore is presented. Compared to men in the lowest tertile of the MedDietScore (≤30, N = 76), men in the highest tertile (≥37, N = 66) had statistically significant higher median values for sperm concentration (40.0 vs 16.0 × 106/ml, P < 0.001), total sperm count (73.5 vs 31.0 × 106/ml, P < 0.001), percent total and progressive motility (45.0% vs 25.0%, P < 0.001 and 25.0% vs 15.0%, P = 0.016, respectively) and percent morphologically normal sperm (5.0% vs 3.5%, P = 0.018), and less frequently exhibited abnormal values for all semen analysis parameters, except for volume (Table II). Participants with higher adherence to the MedDiet also had lower BMI values (P < 0.001), were less likely to be smokers (P < 0.001) and had higher levels of physical activity (P < 0.001). In addition, they exhibited lower levels of current and long-term anxiety (P ≤ 0.001), and had a more favorable blood lipid profile. Adjusted means (95% CI) for the semen quality parameters for each tertile of the MedDietScore by several potential confounders are presented in Table II.
Characteristics of men attending a fertility clinic by tertiles of the MedDietScore.
| . | MedDietScore tertile . | P-value . | ||
|---|---|---|---|---|
| 1st (≤30) . | 2nd (31–36) . | 3rd (≥37) . | . | |
| Ν | 76 | 83 | 66 | |
| Age, y | 37 (34–42) | 38 (36–42) | 38 (35–41) | 0.314 |
| Educational level, n (%) | 0.124 | |||
| Primary/secondary school | 22 (29.2) | 21 (25.3) | 12 (18.2) | |
| Bachelor's degree | 32 (41.6) | 27 (32.5) | 21 (31.8) | |
| Master's/Doctoral degree | 22 (29.2) | 35 (42.2) | 33 (50.0) | |
| Individual income level, n (%) | 0.208 | |||
| Low ( <10.000 euros/annually) | 2 (2.6) | 1 (1.2) | 1 (1.5) | |
| Moderate (10–30.000 euros) | 53 (69.7) | 52 (62.7) | 35 (53.0) | |
| High ( >30.000 euros) | 21 (27.7) | 30 (36.2) | 30 (45.6) | |
| Total energy intake, kcal/day | 2590.4 (2173.8–3054.8)a | 2092.7 (1982.7–2414.8)b | 2176.7 (1979.3–2642.7)b | <0.001 |
| BMI, kg/m2 | 26.7 (24.3–28.8)a | 24.8 (23.4–26.5)b | 23.8 (22.8–26.2)b | <0.001 |
| BMI category, n (%) | <0.001 | |||
| Normal weight (<25 kg/m2) | 23 (30.3) | 43 (51.8) | 44 (66.7) | |
| Overweight (>25– <30 kg/m2) | 41 (53.9) | 36 (43.4) | 20 (30.3) | |
| Obese (>30 kg/m2) | 12 (15.8) | 4 (4.8) | 2 (3.0) | |
| Smoking status, n (%) | <0.001 | |||
| Current smokers | 25 (32.9) | 18 (21.7) | 4 (6.1) | |
| Never smokers | 43 (56.6) | 60 (72.3) | 56 (84.8) | |
| Ex-smokers | 8 (10.5) | 5 (6.0) | 6 (9.1) | |
| Physical activity, MET-min/week | 646.5 (330.0–1345.0)a | 891.0 (447.5–1596.0)b | 1380.7 (761.7–1887.3)c | <0.001 |
| Physical activity level, n (%) | 0.002 | |||
| Inactive | 36 (47.4) | 28 (33.7) | 11 (16.7) | |
| Minimally active | 37 (48.7) | 51 (61.4) | 48 (72.7) | |
| Highly active | 3 (3.9) | 4 (4.8) | 7 (10.6) | |
| S-Anxiety (score range 20–80)a | 43.0 (38.0–47.0)a | 40.0 (33.0–43.0)b | 40.5 (33.0–44.0)b | 0.001 |
| T-Anxiety (score range 20–80)a | 41.5 (39.0–44.7)a | 36.0 (31.0–42.0)b | 36.0 (31.0–41.2)b | <0.001 |
| Fasting glucose (mg/dl)b | 90.0 (83.5–95.5) | 83.5 (79.0–95.0) | 86.0 (81.0–92.0) | 0.089 |
| Triglycerides (mg/dl)b | 110.0 (81.0–133.0)a | 103.5 (79.5–125.7)a | 82.5 (67.2–103.0)b | 0.008 |
| Total cholesterol (mg/dl)b | 195.0 (180.2–201.7)a | 176.0 (159.7–192.0)b | 175.0 (155.2–187.0)b | <0.001 |
| LDL cholesterol (mg/dl)b | 121.0 (98.0–132.0)a | 101.5 (92.2–124.7)b | 99.0 (84.0–121.0)b | 0.012 |
| ΗDL cholesterol (mg/dl)b | 49.0 (42.0–57.0) | 47.0 (41.2–53.0) | 49.0 (44.0–58.0) | 0.282 |
| Family subfertility history, n (%) | 14 (18.4) | 17 (20.5) | 16 (24.2) | 0.702 |
| Semen analysis | ||||
| Volume (ml)c | 2.0 (1.7, 2.2) | 2.2 (2.0–2.3) | 2.1 (1.9–2.3) | |
| Volume <1.5 ml, n (%) | 15 (19.7) | 11 (13.3) | 8 (12.1) | 0.403 |
| Concentration (×106/ml)c | 30.5 (24.3–36.7) | 37.1 (31.5–42.6) | 37.9 (31.4–44.4) | |
| Concentration <15 × 106/ml, n (%) | 36 (47.4)a | 18 (21.7)b | 11 (16.7)b | <0.001 |
| Total sperm count (×106/ml)c | 63.8 (47.2–80.4) | 86.6 (71.8–101.4) | 82.1 (64.7–99.5) | |
| Total sperm count <39 × 106/ml, n (%) | 42 (55.3)a | 27 (32.5)b | 15 (22.7)b | <0.001 |
| Motility (%)c | 32.1 (27.4–36.8) | 40.7 (36.5–44.9) | 42.6 (37.6–47.5) | |
| Motility <40% motile, n (%) | 50 (65.8)a | 30 (36.1)b | 21 (31.8)b | <0.001 |
| Progressive motility (%)c | 20.0 (16.2–23.8) | 24.1 (20.7–27.5) | 26.0 (22.0–30.0) | |
| Progressive motility <32% motile, n (%) | 64 (84.2)a | 62 (74.7)a | 41 (62.1)b | 0.011 |
| Morphology (%)c | 4.3 (3.7–4.9) | 4.3 (3.8–4.8) | 4.6 (4.0–5.1) | |
| Morphology <4% normal, n (%) | 38 (50.0)a | 28 (33.7)b | 19 (28.8)b | 0.023 |
| Abnormal semen analysis, n (%)d | 66 (86.8) | 62 (74.7) | 47 (71.2) | 0.051 |
| . | MedDietScore tertile . | P-value . | ||
|---|---|---|---|---|
| 1st (≤30) . | 2nd (31–36) . | 3rd (≥37) . | . | |
| Ν | 76 | 83 | 66 | |
| Age, y | 37 (34–42) | 38 (36–42) | 38 (35–41) | 0.314 |
| Educational level, n (%) | 0.124 | |||
| Primary/secondary school | 22 (29.2) | 21 (25.3) | 12 (18.2) | |
| Bachelor's degree | 32 (41.6) | 27 (32.5) | 21 (31.8) | |
| Master's/Doctoral degree | 22 (29.2) | 35 (42.2) | 33 (50.0) | |
| Individual income level, n (%) | 0.208 | |||
| Low ( <10.000 euros/annually) | 2 (2.6) | 1 (1.2) | 1 (1.5) | |
| Moderate (10–30.000 euros) | 53 (69.7) | 52 (62.7) | 35 (53.0) | |
| High ( >30.000 euros) | 21 (27.7) | 30 (36.2) | 30 (45.6) | |
| Total energy intake, kcal/day | 2590.4 (2173.8–3054.8)a | 2092.7 (1982.7–2414.8)b | 2176.7 (1979.3–2642.7)b | <0.001 |
| BMI, kg/m2 | 26.7 (24.3–28.8)a | 24.8 (23.4–26.5)b | 23.8 (22.8–26.2)b | <0.001 |
| BMI category, n (%) | <0.001 | |||
| Normal weight (<25 kg/m2) | 23 (30.3) | 43 (51.8) | 44 (66.7) | |
| Overweight (>25– <30 kg/m2) | 41 (53.9) | 36 (43.4) | 20 (30.3) | |
| Obese (>30 kg/m2) | 12 (15.8) | 4 (4.8) | 2 (3.0) | |
| Smoking status, n (%) | <0.001 | |||
| Current smokers | 25 (32.9) | 18 (21.7) | 4 (6.1) | |
| Never smokers | 43 (56.6) | 60 (72.3) | 56 (84.8) | |
| Ex-smokers | 8 (10.5) | 5 (6.0) | 6 (9.1) | |
| Physical activity, MET-min/week | 646.5 (330.0–1345.0)a | 891.0 (447.5–1596.0)b | 1380.7 (761.7–1887.3)c | <0.001 |
| Physical activity level, n (%) | 0.002 | |||
| Inactive | 36 (47.4) | 28 (33.7) | 11 (16.7) | |
| Minimally active | 37 (48.7) | 51 (61.4) | 48 (72.7) | |
| Highly active | 3 (3.9) | 4 (4.8) | 7 (10.6) | |
| S-Anxiety (score range 20–80)a | 43.0 (38.0–47.0)a | 40.0 (33.0–43.0)b | 40.5 (33.0–44.0)b | 0.001 |
| T-Anxiety (score range 20–80)a | 41.5 (39.0–44.7)a | 36.0 (31.0–42.0)b | 36.0 (31.0–41.2)b | <0.001 |
| Fasting glucose (mg/dl)b | 90.0 (83.5–95.5) | 83.5 (79.0–95.0) | 86.0 (81.0–92.0) | 0.089 |
| Triglycerides (mg/dl)b | 110.0 (81.0–133.0)a | 103.5 (79.5–125.7)a | 82.5 (67.2–103.0)b | 0.008 |
| Total cholesterol (mg/dl)b | 195.0 (180.2–201.7)a | 176.0 (159.7–192.0)b | 175.0 (155.2–187.0)b | <0.001 |
| LDL cholesterol (mg/dl)b | 121.0 (98.0–132.0)a | 101.5 (92.2–124.7)b | 99.0 (84.0–121.0)b | 0.012 |
| ΗDL cholesterol (mg/dl)b | 49.0 (42.0–57.0) | 47.0 (41.2–53.0) | 49.0 (44.0–58.0) | 0.282 |
| Family subfertility history, n (%) | 14 (18.4) | 17 (20.5) | 16 (24.2) | 0.702 |
| Semen analysis | ||||
| Volume (ml)c | 2.0 (1.7, 2.2) | 2.2 (2.0–2.3) | 2.1 (1.9–2.3) | |
| Volume <1.5 ml, n (%) | 15 (19.7) | 11 (13.3) | 8 (12.1) | 0.403 |
| Concentration (×106/ml)c | 30.5 (24.3–36.7) | 37.1 (31.5–42.6) | 37.9 (31.4–44.4) | |
| Concentration <15 × 106/ml, n (%) | 36 (47.4)a | 18 (21.7)b | 11 (16.7)b | <0.001 |
| Total sperm count (×106/ml)c | 63.8 (47.2–80.4) | 86.6 (71.8–101.4) | 82.1 (64.7–99.5) | |
| Total sperm count <39 × 106/ml, n (%) | 42 (55.3)a | 27 (32.5)b | 15 (22.7)b | <0.001 |
| Motility (%)c | 32.1 (27.4–36.8) | 40.7 (36.5–44.9) | 42.6 (37.6–47.5) | |
| Motility <40% motile, n (%) | 50 (65.8)a | 30 (36.1)b | 21 (31.8)b | <0.001 |
| Progressive motility (%)c | 20.0 (16.2–23.8) | 24.1 (20.7–27.5) | 26.0 (22.0–30.0) | |
| Progressive motility <32% motile, n (%) | 64 (84.2)a | 62 (74.7)a | 41 (62.1)b | 0.011 |
| Morphology (%)c | 4.3 (3.7–4.9) | 4.3 (3.8–4.8) | 4.6 (4.0–5.1) | |
| Morphology <4% normal, n (%) | 38 (50.0)a | 28 (33.7)b | 19 (28.8)b | 0.023 |
| Abnormal semen analysis, n (%)d | 66 (86.8) | 62 (74.7) | 47 (71.2) | 0.051 |
Values represent median (25th, 75th percentiles) or number of subjects (%). For semen analysis parameters, adjusted means (95% CI) are presented. Differences in variables across tertiles of the MedDietScore were tested using Kruskal–Wallis test for continuous variables and Fisher's exact test for categorical variables. Values with different superscript letter are statistically significant different (Bonferroni correction, P < 0.015).
aEvaluated by the State-Trait Anxiety Inventory (STAI) Y, with higher values suggesting higher levels of anxiety (Fountoulakis et al., 2006).
bValues available for 177 study participants.
cMeans adjusted for age, BMI, smoking status, physical activity, state and trait anxiety, total energy intake, educational level, individual income level and family subfertility history.
dBelow World Healh Organization reference values for volume, concentration, total sperm count, total motility, progressive motility or morphology (WHO, 2010).
MET = metabolic equivalent of task; S-anxiety = state anxiety, T-anxiety = trait anxiety; LDL = low density lipoprotein, HDL = high density lipoprotein.
Characteristics of men attending a fertility clinic by tertiles of the MedDietScore.
| . | MedDietScore tertile . | P-value . | ||
|---|---|---|---|---|
| 1st (≤30) . | 2nd (31–36) . | 3rd (≥37) . | . | |
| Ν | 76 | 83 | 66 | |
| Age, y | 37 (34–42) | 38 (36–42) | 38 (35–41) | 0.314 |
| Educational level, n (%) | 0.124 | |||
| Primary/secondary school | 22 (29.2) | 21 (25.3) | 12 (18.2) | |
| Bachelor's degree | 32 (41.6) | 27 (32.5) | 21 (31.8) | |
| Master's/Doctoral degree | 22 (29.2) | 35 (42.2) | 33 (50.0) | |
| Individual income level, n (%) | 0.208 | |||
| Low ( <10.000 euros/annually) | 2 (2.6) | 1 (1.2) | 1 (1.5) | |
| Moderate (10–30.000 euros) | 53 (69.7) | 52 (62.7) | 35 (53.0) | |
| High ( >30.000 euros) | 21 (27.7) | 30 (36.2) | 30 (45.6) | |
| Total energy intake, kcal/day | 2590.4 (2173.8–3054.8)a | 2092.7 (1982.7–2414.8)b | 2176.7 (1979.3–2642.7)b | <0.001 |
| BMI, kg/m2 | 26.7 (24.3–28.8)a | 24.8 (23.4–26.5)b | 23.8 (22.8–26.2)b | <0.001 |
| BMI category, n (%) | <0.001 | |||
| Normal weight (<25 kg/m2) | 23 (30.3) | 43 (51.8) | 44 (66.7) | |
| Overweight (>25– <30 kg/m2) | 41 (53.9) | 36 (43.4) | 20 (30.3) | |
| Obese (>30 kg/m2) | 12 (15.8) | 4 (4.8) | 2 (3.0) | |
| Smoking status, n (%) | <0.001 | |||
| Current smokers | 25 (32.9) | 18 (21.7) | 4 (6.1) | |
| Never smokers | 43 (56.6) | 60 (72.3) | 56 (84.8) | |
| Ex-smokers | 8 (10.5) | 5 (6.0) | 6 (9.1) | |
| Physical activity, MET-min/week | 646.5 (330.0–1345.0)a | 891.0 (447.5–1596.0)b | 1380.7 (761.7–1887.3)c | <0.001 |
| Physical activity level, n (%) | 0.002 | |||
| Inactive | 36 (47.4) | 28 (33.7) | 11 (16.7) | |
| Minimally active | 37 (48.7) | 51 (61.4) | 48 (72.7) | |
| Highly active | 3 (3.9) | 4 (4.8) | 7 (10.6) | |
| S-Anxiety (score range 20–80)a | 43.0 (38.0–47.0)a | 40.0 (33.0–43.0)b | 40.5 (33.0–44.0)b | 0.001 |
| T-Anxiety (score range 20–80)a | 41.5 (39.0–44.7)a | 36.0 (31.0–42.0)b | 36.0 (31.0–41.2)b | <0.001 |
| Fasting glucose (mg/dl)b | 90.0 (83.5–95.5) | 83.5 (79.0–95.0) | 86.0 (81.0–92.0) | 0.089 |
| Triglycerides (mg/dl)b | 110.0 (81.0–133.0)a | 103.5 (79.5–125.7)a | 82.5 (67.2–103.0)b | 0.008 |
| Total cholesterol (mg/dl)b | 195.0 (180.2–201.7)a | 176.0 (159.7–192.0)b | 175.0 (155.2–187.0)b | <0.001 |
| LDL cholesterol (mg/dl)b | 121.0 (98.0–132.0)a | 101.5 (92.2–124.7)b | 99.0 (84.0–121.0)b | 0.012 |
| ΗDL cholesterol (mg/dl)b | 49.0 (42.0–57.0) | 47.0 (41.2–53.0) | 49.0 (44.0–58.0) | 0.282 |
| Family subfertility history, n (%) | 14 (18.4) | 17 (20.5) | 16 (24.2) | 0.702 |
| Semen analysis | ||||
| Volume (ml)c | 2.0 (1.7, 2.2) | 2.2 (2.0–2.3) | 2.1 (1.9–2.3) | |
| Volume <1.5 ml, n (%) | 15 (19.7) | 11 (13.3) | 8 (12.1) | 0.403 |
| Concentration (×106/ml)c | 30.5 (24.3–36.7) | 37.1 (31.5–42.6) | 37.9 (31.4–44.4) | |
| Concentration <15 × 106/ml, n (%) | 36 (47.4)a | 18 (21.7)b | 11 (16.7)b | <0.001 |
| Total sperm count (×106/ml)c | 63.8 (47.2–80.4) | 86.6 (71.8–101.4) | 82.1 (64.7–99.5) | |
| Total sperm count <39 × 106/ml, n (%) | 42 (55.3)a | 27 (32.5)b | 15 (22.7)b | <0.001 |
| Motility (%)c | 32.1 (27.4–36.8) | 40.7 (36.5–44.9) | 42.6 (37.6–47.5) | |
| Motility <40% motile, n (%) | 50 (65.8)a | 30 (36.1)b | 21 (31.8)b | <0.001 |
| Progressive motility (%)c | 20.0 (16.2–23.8) | 24.1 (20.7–27.5) | 26.0 (22.0–30.0) | |
| Progressive motility <32% motile, n (%) | 64 (84.2)a | 62 (74.7)a | 41 (62.1)b | 0.011 |
| Morphology (%)c | 4.3 (3.7–4.9) | 4.3 (3.8–4.8) | 4.6 (4.0–5.1) | |
| Morphology <4% normal, n (%) | 38 (50.0)a | 28 (33.7)b | 19 (28.8)b | 0.023 |
| Abnormal semen analysis, n (%)d | 66 (86.8) | 62 (74.7) | 47 (71.2) | 0.051 |
| . | MedDietScore tertile . | P-value . | ||
|---|---|---|---|---|
| 1st (≤30) . | 2nd (31–36) . | 3rd (≥37) . | . | |
| Ν | 76 | 83 | 66 | |
| Age, y | 37 (34–42) | 38 (36–42) | 38 (35–41) | 0.314 |
| Educational level, n (%) | 0.124 | |||
| Primary/secondary school | 22 (29.2) | 21 (25.3) | 12 (18.2) | |
| Bachelor's degree | 32 (41.6) | 27 (32.5) | 21 (31.8) | |
| Master's/Doctoral degree | 22 (29.2) | 35 (42.2) | 33 (50.0) | |
| Individual income level, n (%) | 0.208 | |||
| Low ( <10.000 euros/annually) | 2 (2.6) | 1 (1.2) | 1 (1.5) | |
| Moderate (10–30.000 euros) | 53 (69.7) | 52 (62.7) | 35 (53.0) | |
| High ( >30.000 euros) | 21 (27.7) | 30 (36.2) | 30 (45.6) | |
| Total energy intake, kcal/day | 2590.4 (2173.8–3054.8)a | 2092.7 (1982.7–2414.8)b | 2176.7 (1979.3–2642.7)b | <0.001 |
| BMI, kg/m2 | 26.7 (24.3–28.8)a | 24.8 (23.4–26.5)b | 23.8 (22.8–26.2)b | <0.001 |
| BMI category, n (%) | <0.001 | |||
| Normal weight (<25 kg/m2) | 23 (30.3) | 43 (51.8) | 44 (66.7) | |
| Overweight (>25– <30 kg/m2) | 41 (53.9) | 36 (43.4) | 20 (30.3) | |
| Obese (>30 kg/m2) | 12 (15.8) | 4 (4.8) | 2 (3.0) | |
| Smoking status, n (%) | <0.001 | |||
| Current smokers | 25 (32.9) | 18 (21.7) | 4 (6.1) | |
| Never smokers | 43 (56.6) | 60 (72.3) | 56 (84.8) | |
| Ex-smokers | 8 (10.5) | 5 (6.0) | 6 (9.1) | |
| Physical activity, MET-min/week | 646.5 (330.0–1345.0)a | 891.0 (447.5–1596.0)b | 1380.7 (761.7–1887.3)c | <0.001 |
| Physical activity level, n (%) | 0.002 | |||
| Inactive | 36 (47.4) | 28 (33.7) | 11 (16.7) | |
| Minimally active | 37 (48.7) | 51 (61.4) | 48 (72.7) | |
| Highly active | 3 (3.9) | 4 (4.8) | 7 (10.6) | |
| S-Anxiety (score range 20–80)a | 43.0 (38.0–47.0)a | 40.0 (33.0–43.0)b | 40.5 (33.0–44.0)b | 0.001 |
| T-Anxiety (score range 20–80)a | 41.5 (39.0–44.7)a | 36.0 (31.0–42.0)b | 36.0 (31.0–41.2)b | <0.001 |
| Fasting glucose (mg/dl)b | 90.0 (83.5–95.5) | 83.5 (79.0–95.0) | 86.0 (81.0–92.0) | 0.089 |
| Triglycerides (mg/dl)b | 110.0 (81.0–133.0)a | 103.5 (79.5–125.7)a | 82.5 (67.2–103.0)b | 0.008 |
| Total cholesterol (mg/dl)b | 195.0 (180.2–201.7)a | 176.0 (159.7–192.0)b | 175.0 (155.2–187.0)b | <0.001 |
| LDL cholesterol (mg/dl)b | 121.0 (98.0–132.0)a | 101.5 (92.2–124.7)b | 99.0 (84.0–121.0)b | 0.012 |
| ΗDL cholesterol (mg/dl)b | 49.0 (42.0–57.0) | 47.0 (41.2–53.0) | 49.0 (44.0–58.0) | 0.282 |
| Family subfertility history, n (%) | 14 (18.4) | 17 (20.5) | 16 (24.2) | 0.702 |
| Semen analysis | ||||
| Volume (ml)c | 2.0 (1.7, 2.2) | 2.2 (2.0–2.3) | 2.1 (1.9–2.3) | |
| Volume <1.5 ml, n (%) | 15 (19.7) | 11 (13.3) | 8 (12.1) | 0.403 |
| Concentration (×106/ml)c | 30.5 (24.3–36.7) | 37.1 (31.5–42.6) | 37.9 (31.4–44.4) | |
| Concentration <15 × 106/ml, n (%) | 36 (47.4)a | 18 (21.7)b | 11 (16.7)b | <0.001 |
| Total sperm count (×106/ml)c | 63.8 (47.2–80.4) | 86.6 (71.8–101.4) | 82.1 (64.7–99.5) | |
| Total sperm count <39 × 106/ml, n (%) | 42 (55.3)a | 27 (32.5)b | 15 (22.7)b | <0.001 |
| Motility (%)c | 32.1 (27.4–36.8) | 40.7 (36.5–44.9) | 42.6 (37.6–47.5) | |
| Motility <40% motile, n (%) | 50 (65.8)a | 30 (36.1)b | 21 (31.8)b | <0.001 |
| Progressive motility (%)c | 20.0 (16.2–23.8) | 24.1 (20.7–27.5) | 26.0 (22.0–30.0) | |
| Progressive motility <32% motile, n (%) | 64 (84.2)a | 62 (74.7)a | 41 (62.1)b | 0.011 |
| Morphology (%)c | 4.3 (3.7–4.9) | 4.3 (3.8–4.8) | 4.6 (4.0–5.1) | |
| Morphology <4% normal, n (%) | 38 (50.0)a | 28 (33.7)b | 19 (28.8)b | 0.023 |
| Abnormal semen analysis, n (%)d | 66 (86.8) | 62 (74.7) | 47 (71.2) | 0.051 |
Values represent median (25th, 75th percentiles) or number of subjects (%). For semen analysis parameters, adjusted means (95% CI) are presented. Differences in variables across tertiles of the MedDietScore were tested using Kruskal–Wallis test for continuous variables and Fisher's exact test for categorical variables. Values with different superscript letter are statistically significant different (Bonferroni correction, P < 0.015).
aEvaluated by the State-Trait Anxiety Inventory (STAI) Y, with higher values suggesting higher levels of anxiety (Fountoulakis et al., 2006).
bValues available for 177 study participants.
cMeans adjusted for age, BMI, smoking status, physical activity, state and trait anxiety, total energy intake, educational level, individual income level and family subfertility history.
dBelow World Healh Organization reference values for volume, concentration, total sperm count, total motility, progressive motility or morphology (WHO, 2010).
MET = metabolic equivalent of task; S-anxiety = state anxiety, T-anxiety = trait anxiety; LDL = low density lipoprotein, HDL = high density lipoprotein.
Table III shows the results of the multiadjusted logistic regression analysis exploring the association between tertiles of the MedDietScore and the likelihood of having abnormal semen parameters, after adjusting for the relevant confounders in model 1 (age, smoking status, physical activity, state and trait anxiety, total energy intake, educational level, individual income level and family subfertility history). Men with lower MedDietScores had a higher likelihood of having abnormal sperm concentration [Odds Ratio (OR) for tertile 1 vs tertile 3: 2.65; 95% CI: 1.05–6.71], total sperm count (OR: 2.55; 95% CI: 1.08–6.02), and total motility (OR: 2.88; 95% CI: 1.26–6.62) and progressive motility (OR: 2.74; 95% CI: 1.08–6.92). Further adjustment for BMI (model 2) did not modify the observed associations.
Multiple logistic regression models exploring the association between tertiles of the MedDietScore and the likelihood of having abnormal semen parameters.a
| Semen parameter . | MedDietScore tertile . | P for trend . | ||
|---|---|---|---|---|
| 1st . | 2nd . | 3rd . | ||
| Concentration (<15 × 106/ml vs >15 × 106/ml) | ||||
| Model 1 | 2.65 (1.05–6.71)* | 1.19 (0.48–2.91) | 1(ref) | 0.04 |
| Model 2 | 2.69 (1.05–6.90)* | 1.03 (0.41–2.58) | 1 | 0.04 |
| Total sperm count (<39 × 106/ml vs >39 × 106/ml) | ||||
| Model 1 | 2.55 (1.08–6.02)* | 1.36 (0.62–2.99) | 1(ref) | 0.03 |
| Model 2 | 2.61 (1.09–6.27)* | 1.20 (0.53–2.70) | 1 | 0.04 |
| Total motility (<40% vs > 40%) | ||||
| Model 1 | 2.88 (1.26–6.62)* | 1.14 (0.54–2.39) | 1(ref) | 0.02 |
| Model 2 | 2.88 (1.23–6.78)* | 0.98 (0.46–2.12) | 1 | 0.03 |
| Progressive motility (<32% vs >32%) | ||||
| Model 1: adj. for age | 2.74 (1.08–6.92)* | 1.77 (0.84–3.73) | 1(ref) | 0.02 |
| Model 2 | 2.67 (1.03–6.90)* | 1.57 (0.73–3.37) | 1 | 0.04 |
| Morphology (<4% normal spermatozoa vs > 4%) | ||||
| Model 1 | 1.53 (0.66–3.53) | 1.22 (0.57–2.62) | 1(ref) | 0.32 |
| Model 2 | 1.56 (0.64–3.50) | 1.07 (0.49–2.34) | 1 | 0.34 |
| Semen parameter . | MedDietScore tertile . | P for trend . | ||
|---|---|---|---|---|
| 1st . | 2nd . | 3rd . | ||
| Concentration (<15 × 106/ml vs >15 × 106/ml) | ||||
| Model 1 | 2.65 (1.05–6.71)* | 1.19 (0.48–2.91) | 1(ref) | 0.04 |
| Model 2 | 2.69 (1.05–6.90)* | 1.03 (0.41–2.58) | 1 | 0.04 |
| Total sperm count (<39 × 106/ml vs >39 × 106/ml) | ||||
| Model 1 | 2.55 (1.08–6.02)* | 1.36 (0.62–2.99) | 1(ref) | 0.03 |
| Model 2 | 2.61 (1.09–6.27)* | 1.20 (0.53–2.70) | 1 | 0.04 |
| Total motility (<40% vs > 40%) | ||||
| Model 1 | 2.88 (1.26–6.62)* | 1.14 (0.54–2.39) | 1(ref) | 0.02 |
| Model 2 | 2.88 (1.23–6.78)* | 0.98 (0.46–2.12) | 1 | 0.03 |
| Progressive motility (<32% vs >32%) | ||||
| Model 1: adj. for age | 2.74 (1.08–6.92)* | 1.77 (0.84–3.73) | 1(ref) | 0.02 |
| Model 2 | 2.67 (1.03–6.90)* | 1.57 (0.73–3.37) | 1 | 0.04 |
| Morphology (<4% normal spermatozoa vs > 4%) | ||||
| Model 1 | 1.53 (0.66–3.53) | 1.22 (0.57–2.62) | 1(ref) | 0.32 |
| Model 2 | 1.56 (0.64–3.50) | 1.07 (0.49–2.34) | 1 | 0.34 |
Data represent odds ratios (95% CI). Model 1: Odds ratios adjusted for age, smoking status, physical activity, state & trait anxiety, total energy intake, educational level, individual income level and family subfertility history. Model 2: Model 1 + adjustment for BMI.
*P < 0.05.
aBased on World Health Organization reference values for semen characteristics (WHO, 2010).
Multiple logistic regression models exploring the association between tertiles of the MedDietScore and the likelihood of having abnormal semen parameters.a
| Semen parameter . | MedDietScore tertile . | P for trend . | ||
|---|---|---|---|---|
| 1st . | 2nd . | 3rd . | ||
| Concentration (<15 × 106/ml vs >15 × 106/ml) | ||||
| Model 1 | 2.65 (1.05–6.71)* | 1.19 (0.48–2.91) | 1(ref) | 0.04 |
| Model 2 | 2.69 (1.05–6.90)* | 1.03 (0.41–2.58) | 1 | 0.04 |
| Total sperm count (<39 × 106/ml vs >39 × 106/ml) | ||||
| Model 1 | 2.55 (1.08–6.02)* | 1.36 (0.62–2.99) | 1(ref) | 0.03 |
| Model 2 | 2.61 (1.09–6.27)* | 1.20 (0.53–2.70) | 1 | 0.04 |
| Total motility (<40% vs > 40%) | ||||
| Model 1 | 2.88 (1.26–6.62)* | 1.14 (0.54–2.39) | 1(ref) | 0.02 |
| Model 2 | 2.88 (1.23–6.78)* | 0.98 (0.46–2.12) | 1 | 0.03 |
| Progressive motility (<32% vs >32%) | ||||
| Model 1: adj. for age | 2.74 (1.08–6.92)* | 1.77 (0.84–3.73) | 1(ref) | 0.02 |
| Model 2 | 2.67 (1.03–6.90)* | 1.57 (0.73–3.37) | 1 | 0.04 |
| Morphology (<4% normal spermatozoa vs > 4%) | ||||
| Model 1 | 1.53 (0.66–3.53) | 1.22 (0.57–2.62) | 1(ref) | 0.32 |
| Model 2 | 1.56 (0.64–3.50) | 1.07 (0.49–2.34) | 1 | 0.34 |
| Semen parameter . | MedDietScore tertile . | P for trend . | ||
|---|---|---|---|---|
| 1st . | 2nd . | 3rd . | ||
| Concentration (<15 × 106/ml vs >15 × 106/ml) | ||||
| Model 1 | 2.65 (1.05–6.71)* | 1.19 (0.48–2.91) | 1(ref) | 0.04 |
| Model 2 | 2.69 (1.05–6.90)* | 1.03 (0.41–2.58) | 1 | 0.04 |
| Total sperm count (<39 × 106/ml vs >39 × 106/ml) | ||||
| Model 1 | 2.55 (1.08–6.02)* | 1.36 (0.62–2.99) | 1(ref) | 0.03 |
| Model 2 | 2.61 (1.09–6.27)* | 1.20 (0.53–2.70) | 1 | 0.04 |
| Total motility (<40% vs > 40%) | ||||
| Model 1 | 2.88 (1.26–6.62)* | 1.14 (0.54–2.39) | 1(ref) | 0.02 |
| Model 2 | 2.88 (1.23–6.78)* | 0.98 (0.46–2.12) | 1 | 0.03 |
| Progressive motility (<32% vs >32%) | ||||
| Model 1: adj. for age | 2.74 (1.08–6.92)* | 1.77 (0.84–3.73) | 1(ref) | 0.02 |
| Model 2 | 2.67 (1.03–6.90)* | 1.57 (0.73–3.37) | 1 | 0.04 |
| Morphology (<4% normal spermatozoa vs > 4%) | ||||
| Model 1 | 1.53 (0.66–3.53) | 1.22 (0.57–2.62) | 1(ref) | 0.32 |
| Model 2 | 1.56 (0.64–3.50) | 1.07 (0.49–2.34) | 1 | 0.34 |
Data represent odds ratios (95% CI). Model 1: Odds ratios adjusted for age, smoking status, physical activity, state & trait anxiety, total energy intake, educational level, individual income level and family subfertility history. Model 2: Model 1 + adjustment for BMI.
*P < 0.05.
aBased on World Health Organization reference values for semen characteristics (WHO, 2010).
Discussion
In this cross-sectional study among male partners of subfertile couples attempting fertility, we implemented an a-priori dietary pattern approach to investigate the relationship between adherence to the MedDiet and semen quality parameters. We found that greater compliance to the MedDiet, as assessed through the validated MedDietScore, was associated with better semen quality measures (i.e. sperm concentration, total sperm count, total and progressive motility). This association was independent of a large number of potential confounders. Moreover, we found that men in the lowest tertile of the MedDietScore had ~2.6 times higher likelihood of having abnormal sperm concentration, total sperm count and motility.
To date, many studies in humans have investigated the relationship between diet and semen quality (Giahi et al., 2016). Most of these studies have focussed on the role of specific nutrients or food compounds, while a few examine overall dietary patterns extracted from multivariate statistical methods, such as PCA (an a-posteriori dietary pattern approach). For example, in a cross-sectional study among 161 men of subfertile couples undergoing IVF in the Netherlands (Vujkovic et al., 2009), two dietary patterns were identified using PCA: a ‘health conscious’ pattern (characterized by a high intake of fruits, vegetables, fish and seafood, whole grains and legumes, and a low intake of mayonnaise, meat products, refined grains and desserts) and a ‘traditional Dutch’ pattern. The ‘health conscious’ dietary pattern showed an association with lower sperm DNA fragmentation while the ‘traditional Dutch’ dietary pattern showed an association with higher sperm concentration. In the study of Jurewicz et al. (2016), among 336 men who were attending a fertility clinic for diagnostic purposes, a similar a-posteriori approach was employed to identify dietary patterns and relate them to semen quality parameters. In this study, a ‘prudent’ dietary pattern, (characterized by a high intake of fruits, vegetables, legumes and whole grains) showed a positive association with increased sperm concentration, higher levels of testosterone and decreased DNA fragmentation index. Although the dietary patterns identified in these two studies are clearly different, there is some consistency in the relation of the dietary patterns high in fruits, vegetables, legumes and whole grains with semen quality.
The so-called MedDiet is also characterized by a high intake of fruits, vegetables, legumes and whole grains, and a low intake of meat and saturated fatty acids. Adherence to the MedDiet has been shown to confer multiple health benefits (Sofi et al., 2010), and a recent study among 215 healthy male university students showed that higher adherence to a ‘Mediterranean’ pattern identified by PCA was positively associated with total sperm count (Cutillas-Tolin et al., 2015), a result in accordance with our findings. Comparable studies from infertile populations have also shown that higher adherence to a ‘Prudent’ dietary pattern, bearing close resemblance to the Mediterranean pattern, is associated with higher total sperm count and motility (Vujkovic et al., 2009; Jurewicz et al., 2016).
The favorable effect(s) of the MedDiet on semen parameters may be mediated by various mechanisms. The MedDiet is naturally high in nutrients with favorable anti-inflammatory properties and low in pro-inflammatory nutrients. Inflammation may affect reproduction through anatomical or functional alteration of the male accessory gland, and/or direct negative effects on the spermatozoa (La Vignera et al., 2013). The association between adherence to the MedDiet and semen quality could also be mediated through an increased intake of omega-3 fatty acids found in fish and seafood. Compared with other cells or tissues, sperm and testicular cellshave a higher concentration of long-chain (LC-) polyunsaturated fatty acids (PUFAs), particularly docosahexaenoic acid (DHA). The increase in DHA levels in the sperm membrane during sperm maturation suggests that testes have high levels ofactive fatty acid metabolism, resulting in the preferential accumulation of LC-PUFAs due to efficient metabolization of PUFAs into the long-chain metabolites. Furthermore, seafood is characterized by a high proportion of fat-soluble vitamins which play a crucial role in fertilization (Blomberg Jensen, 2014). Finally, the MedDiet is a dietary patern low in saturated and trans-fatty acids, which have been shown to adversely affect semen quality (Chavarro et al., 2014).
The MedDiet is also characterized by high intakes of fruits and vegetables rich in antioxidants, such as beta-carotene and vitamins E and C, which have been suggested to improve semen quality. Carotenoid intake, for example, has been associated with higher sperm motility in young healthy males (Zareba et al., 2013), while vitamin A supplementation was recently shown to enhance spermatogenesis progression in vitro (Dumont et al., 2016). Numerous health benefits have been ascribed to antioxidants, mainly because of their protective effect against generation of reactive oxygen species which may negatively affect sperm motility and sperm–oocyte fusion (Ko et al., 2014). Furthermore, antioxidants are thought to protect sperm against endogenous oxidative damage by neutralizing hydroxyl, superoxide and hydrogen peroxide radicals, thus preventing sperm agglutination. With an accent placed on plant food consumption rich in antioxidants as opposed to meat and meat products, the MedDiet could therefore exert favorable effects on semen indicators.
In the present analysis, we have considered the confounding effect of several factors which are known to be critical determinants of normal reproductive function and to affect semen quality (such as age, BMI, smoking and physical activity). Recent reports confirm a negative association between semen quality parameters and overall and central adiposity, cigarette smoking and lower physical activity levels (Eisenberg et al., 2014; Gaskins et al., 2014; Pärn et al., 2015; Sharma et al., 2016). Evidence suggests that increased male age and family subfertility history are also associated with a decline in semen quality (Sharma et al., 2015). Moreover, findings from observational studies suggest that state and trait anxiety (Vellani et al., 2013) and low educational levels (Pärn et al., 2015) may also represent significant factors affecting semen quality. Alcohol consumption has also been associated with a deterioration of sperm parameters (Condorelli et al., 2015). In our analysis, we did not adjust for alcohol intake because alcohol consumption was taken into account in the construction of the MedDietScore. Of note, we also performed a sensitivity analysis by calculating the MedDietScore without considering alcohol consumption and found similar results (data not shown). Other strengths of this study are also the use of a previously validated FFQ for use in the Greek population and the evaluation of compliance to the MedDiet by using an a-priori dietary pattern approach and calculation of the MedDietScore.
Our study has several limitations. First, due to its cross-sectional design causal inference is limited. It is not possible to predict whether the beneficial effect of the MedDiet on semen quality measures translates into a higher probability of successful conception. However, previous work showing that higher adherence to the MedDiet is associated with a lower risk of difficulty conceiving (Toledo et al., 2011), and higher biochemical pregnancy rates among women undergoing IVF (Vujkovic et al., 2010), suggest a greater reproductive success where the study participants have a higher adherence to this dietary pattern. Another limitation is that only one semen sample from each study participant was analyzed. Nonetheless, some studies have shown that one sample is enough to assess semen quality in epidemiological studies (Stokes-Riner et al., 2007). A third limitation is that the study participants were selected from an IVF clinic, were generally unhealthy or overweight and did not exercise, so our finding may not be extrapolated to a healthy population. Finally, men adhering more closely to the MedDiet were generally healthier and although we have adjusted for a large number of known and suspected confounders, it is difficult to know whether diet, or other health related factors, could have an impact on semen quality.
In summary, in this study among Greek men of infertile couples, we employed an a-priori dietary pattern approach to evaluate adherence to the MedDiet and its relation to semen quality. We found that greater adherence to this traditional diet was associated with a lower likelihood of having an abnormal semen profile, which suggests that diet modificatons and compliance to the MedDiet may help improve at least one measure of semen quality. Whether the beneficial effect of the MedDiet on semen parameters translates into a higher probability of successful conception remains to be elucidated. Furthermore, due to the cross-sectional nature of the study, it is not possible to assess whether advice to adhere more closely to the MedDiet would improve semen quality; this needs to be addressed in future intervention studies.
Acknowledgments
The authors would like to thank the Embryogenesis Assisted Conception Unit clinic staff for their assistance in data collection.
Authors’ roles
D.K., M.K. and N.Y. were involved in study concept and design. D.K., C.M., L.D. and M.M. contributed to the acquisition of data. D.K. analyzed data, wrote the manuscript and had a primary responsibility for final content; M.K. and N.Y. supervised the analysis and critically revised the manuscript. All authors read and approved the final manuscript.
Funding
No external funding was obtained for this study.
Conflict of interest
None declared.
