Dietary patterns are positively associated with semen quality

ORIGINAL ARTICLE: ANDROLOGY

Dietary patterns are positively associated with semen quality Michal Efrat, M.Sc.,a,b Anat Stein, Ph.D.,c,d,e Haim Pinkas, M.D.,c,d,e Ron Unger, Ph.D.,b and Ruth Birk, Ph.D.a a Department of Nutrition, Faculty of Health Sciences, Ariel University, Ariel, West Bank; b Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan; c Department of Obstetrics and Gynecology, Beilinson Medical Center, Infertility and In Vitro Fertilization Unit, Petah Tikva; d Beilinson Medical Center, Petah Tikva; and e Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

Objective: To study association of semen quality with a priori whole dietary pattern indexes, which reflect real-world dietary practices and the numerous combinations by which foods are consumed: Healthy Eating Index (HEI), Dietary Approaches to Stop Hypertension (DASH), alternate Mediterranean Diet score (aMED), and Alternative Healthy Eating Index (AHEI). Design: A cross-sectional single-center study. Setting: Hospital fertility center and university. Patient(s): A total of 280 men attending fertility center from 2012 to 2015. Intervention(s): Food frequency questionnaire (FFQ) and semen and sperm analysis. Main Outcome Measure(s): Food consumption with the use of FFQ and HEI, AHEI, aMED, DASH nutritional individual scoring indexes. Semen parameters, including semen volume, sperm concentration, motility, total count, and morphology. Result(s): Comparing the highest and lowest quartiles of the nutritional indexes, men in the highest quartiles of HEI, AHEI, aMed, and DASH indexes had significantly higher adjusted means of sperm concentration (by 10%, 45%, and 24% for HEI, AHEI, and DASH, respectively), normal sperm morphology (by 21% and 8% for AHEI and DASH, respectively), total sperm count (by 29% for AHEI), and sperm motility (by 6% and 11% for aMed and HEI, respectively). Conclusion(s): Adherence to any of the four dietary indexes is associated with better overall sperm quality, with AHEI best associated. Following our novel findings, we recommend using AHEI as a clinical and practical tool for public whole nutritional recommendation for semen quality. (Fertil SterilÒ 2018;109:809–16. Ó2018 by American Society for Reproductive Medicine.) Key Words: Semen, sperm, nutrition, dietary indexes Discuss: You can discuss this article with its authors and other readers at https://www.fertstertdialog.com/users/16110-fertilityand-sterility/posts/29128-24936

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emen quality and male fertility have been declining over the past few decades (1–5). For instance, a recent comprehensive metaregression analysis reported significant decline (50%–60%) in sperm counts (concentration and total sperm count) from 1973 to 2011 among men from North America, Europe, Australia, and New Zealand (1). This phenomenon has been attributed mainly to environmental, nutritional, and lifestyle habits. The relative new concept of the role of nutrition in male fertility along with the complexity of nutrition gave rise to

scientific research that focused mainly on the effect of specific nutrients and/ or nutritional supplements on male fertility. Nutrients that were found to have a positive effect on reproduction outcome include, among others vitamin A (6), zinc (7), folate (8), vitamin D (9), vitamin C (10), and omega-3 fatty acids (11). Nutrients that were found to have a negative effect on reproduction outcome include saturated fat (12), trans fat (13), and alcohol (14). Consequently, relatively small number of studies have focused on food groups or dietary patterns in relation to fertility (15–17).

Received August 31, 2017; revised December 28, 2017; accepted January 9, 2018. M.E. has nothing to disclose. A.S. has nothing to disclose. H.P. has nothing to disclose. R.B. has nothing to disclose. Reprint requests: Ruth Birk, Ph.D., Head, Nutrigenetics and Nutrigenomics Laboratory, Nutrition Department, School of Health Sciences, Ariel University, 40700, Petah Tikva, Israel (E-mail: [email protected]). Fertility and Sterility® Vol. 109, No. 5, May 2018 0015-0282/$36.00 Copyright ©2018 American Society for Reproductive Medicine, Published by Elsevier Inc. https://doi.org/10.1016/j.fertnstert.2018.01.010 VOL. 109 NO. 5 / MAY 2018

Unlike some risk factors, diet poses an opportunity for intervention, thus making it important to consider as a tool in recommendations for subfertile men. Nutrition is consumed as a whole dietary pattern. Recently, nutrition research is using whole dietary patterns that reflect real-world dietary practices and their numerous combinations (18–21) to analyze association with disease and health. In the present study we used a scientific approach to analyze a priori nutritional indexes that are based on known/acceptable dietary indexes in relation to health and disease. The commonly used nutritional indexes representing and reflecting major nutritional patterns are: the Healthy Eating Index (HEI), the alternate Mediterranean Diet score (aMED), the Alternative Healthy Eating Index (AHEI), and the Dietary Approaches to Stop Hypertension (DASH) (22–24). These 809

ORIGINAL ARTICLE: ANDROLOGY indexes were studied previously in relation to various morbidities (25–29) and use different approaches to assess and score diet quality; the HEI uses a global approach to assess diet quality and includes nearly all foods in the calculation of the total score (30–32). AHEI and the aMED (33, 34) assess foods and chronic disease outcomes (35), and the DASH index (36, 37) reflects adherence to a particular dietary pattern (38–40). The aim of the present study was to study, for the first time, the association between four established dietary indexes reflecting real-life healthy nutritional recommendations and male fertility as indicated by semen parameters.

MATERIALS AND METHODS Men attending the Rabin Medical Center fertility clinic (Petah Tikva, Israel) from 2012 to 2015 were recruited. Informed consent was obtained from each participant before enrollment. The initial recruited study population included 593 men (with poor semen or normospermic) aged 18–55 years. Each recruit was requested to fill medical history, food frequency (FFQ), and lifestyle questionnaires. Exclusion criteria were in accordance with standard nutritional methodologies. Exclusion criteria included technical and nutritional issues such as inadequate filling of FFQ (n ¼ 169), including unreasonable caloric consumption, empty questionnaire pages, questionnaires that were not completed, lack of information about physiologic or medical state, not returning questionnaire, and analysis demonstrating unreasonable values. Excluded subjects were not significantly different from the final cohort (e.g., age, body mass index [BMI], percentage with BMI >25 kg/m2, smoking). In addition, we excluded men with medical conditions or treatments that might jeopardize testis function and sperm quality (n ¼ 172), including genetic disorders, cryptorchidism, azoospermia, varicocele, microorchidism, vasectomy, hormonal disorders and hormonal treatment, medical conditions treated with drugs that may have an effect on semen quality (such as immunosuppressive drugs, steroids, finasteride against baldness, etc.), or any other medical condition that might have a systemic effect (such as medical history of gonadotoxic treatment, diseases accompanied by fever for long period, hormonal impairment due to pituitary gland surgery, and diabetes [41]). After applying all exclusion criteria, the final study population included 280 men.

Ethical Approval The study protocol was approved by the Helsinki Committee at Rabin Medical Center (research code 6580).

Food Frequency Questionnaire The FFQ is a commonly used tool to obtain frequency and portion size information about food consumption over a specified period of time (15–17). All participants filled out a 111food-item FFQ validated for the Israeli population (42, 43). The nutrient components of each food item were taken from the Israeli National Nutrient Database (‘‘Tzameret’’). Statistical analysis of adherence to four different dietary 810

indexes was conducted for each participant. Dietary quality scoring methods for all indexes are presented in Table 1. The use of the FFQ in relation to sperm parameters has an additional benefit; spermatozoa mature within 3 months, so using the FFQ based on nutrient intake over the past 6 months is an effective tool in studying the association between semen quality and nutrition.

Semen Analysis After the required 3-day sexual intercourse abstinence period, semen samples were generated via masturbation into polypropylene containers. Within an hour, the samples were liquefied and the semen parameters of volume, semen concentration, total sperm count, percentage total motility, and percentage normal morphology assessed according to World Health Organization (WHO) guidelines published in 2010 (44). The WHO reference values for normal semen are: semen volume >1.5 mL; semen concentration >15  106 cells/mL; total sperm >39x106; motility >40%; and normal semen morphology >4%. The percentage of normal sperm forms was determined with the use of WHO criteria. Semen morphology was assessed with the use of automated sperm quality analyzers (SQA-V gold; Medical Electronic Systems). This technology is based on the principle of electrooptical signal processing in combination with built-in computer algorithms (45–48) and is approved for use in routine semen analysis (46). To assess subjects' sperm concentration and motility, a 10-mL aliquot was added to a Makler chamber (Sefi-Medical Instruments) (49, 50) and semen visualized with the use of a phase-contrast microscope (Olympus CX21) at 200 magnification. In addition, total sperm counts were calculated with the use of a volume  concentration equation.

Statistical Analysis The normality of the data was assessed graphically with the use of histograms and Q-Q plots as well as the Shapiro-Wilk test. For each participant, his adherence to each dietary index was calculated from his FFQ according to the index's criteria (22). Then the participants were ranked and for each diet index assigned to quartiles. To characterize our data, correlations between different parameters, such as anthropometric parameters and semen analysis parameters, as well as the level of adherence to the different dietary indexes were calculated with the use of either Spearman or Pearson correlations (depending on the normality of parameters). All seminal parameterss except for motility showed nonnormal distribution. There are several relevant lifestyle confounders that may have an effect on semen quality, including: age, BMI, smoking, physical activity, total energy intake, socioeconomic level, ethnicity, and abstinence time (17). For these possible confounders, Kruskal-Wallis or analysis of variance tests were used to compare differences in continuous measures across quartiles whenever appropriate, and chi-square test was used for categoric variables. All covariates were included in the final model whether or not statistical difference between quartiles was observed. After the VOL. 109 NO. 5 / MAY 2018

Fertility and Sterility® general characteristic step, nonnormal distributions of semen parameters were log-transformed to attain an approximately normal distribution. Then linear models were used to examine the relationship between dietary indexes and semen parameters while adjusting for potential confounders for each quartile of the four dietary indexes. Afterward, predicted means were estimated using a linear model (lm function in R) reflecting averages adjusted for the covariates. P values were calculated via adjusted semen quality parameters for men in the highest quartile (Q4) compared with those of men in the lowest quartile (Q1). At the end of the computation, results for these parameters were back-transformed to allow presentation of results in the original scale. In addition, tests for linear trends (51) were conducted with the use of the median values of each quartile score as a continuous variable and semen quality parameters as the outcome of interest, as done previously (12). We also calculated (for the data provided in Supplemental Table 1; available online at www.fertstert.org) P values between quartiles Q3, Q2, and Q1 (which was used as a reference). The R statistical package (version 3.2.3) was used for the statistical calculations. All reported P values are based on two-sided tests and compared with a significance level of 5%.

RESULTS The participants (Table 1), of mean age 33.5  6.3 years, were primarily white Jews (97%) and nonsmokers (74%). The majority (59%) were of normal weight (BMI %25 kg/m2). Thirty-nine percent of the men had one or more semen analysis parameters below the 2010 WHO reference values: 21% had <15 million sperm/mL, 40% had <40% motile sperm, 18% had <4% morphologically normal sperm, 15% had <1.5 mL ejaculate volume, and 28% had a total sperm count <39 million. As expected, positive correlation was observed between age and BMI. In addition, BMI and age were both negatively correlated with semen concentration and motility (P< .01; data not presented). As mentioned in the Materials and Methods section, all the above-mentioned covariants were included in the final regression model. There were appreciable differences in socioeconomic status (for HEI), abstained days (for HEI and AHEI), and daily energy consumption (for aMED and DASH) of patients across quartiles (Table 1). Those findings were taken into consideration and suitable adjustments conducted as described in the Materials and Methods section.

Correlation Between Dietary Indexes All indexes showed significant correlation with each other (Table 2). The highest correlation was observed between the DASH and HEI indexes (r ¼ 0.63). Additional correlations values were: 0.53, 0.62, 0.42, 0.44, and 0.43 for HEI versus AHEI, HEI versus aMED, HEI versus DASH, AHEI versus aMED, and aMED versus DASH, respectively. However, none of the indexes were perfectly correlated, which confirms that each index represents a unique combination of dietary constituents. VOL. 109 NO. 5 / MAY 2018

Semen Parameters and Dietary Indexes The next step was to compare, for each dietary index, sperm parameters of men in Q4 and Q1, taking into account relevant cofounders, including age, BMI, smoking, physical activity, total energy intake, educational level, socioeconomic level, ethnicity, and abstinence time. For the four dietary indexes, in most cases men in Q4 demonstrated significant better semen quality parameters than men Q1 (Fig. 1). Semen concentration and motility parameters among men in the highest quartile of the HEI index scored were significantly higher than among men in lowest quartile. Mean semen concentration among men in the HEI Q4 were significantly higher by 10% compared with men in HEI Q1 (33  106 vs. 30  106; P¼ .04). Mean semen motility of men in HEI Q4 was significantly higher by 11% compared with HEI Q1 (53% vs. 48%; P¼ .04). Most semen parameters among men in the highest quartile of the AHEI index were significantly higher compared with the lowest quartile of the index. Mean semen concentration of men in the Q4 of AHEI Q4 was significantly higher by 45% compared to men in Q1 of AHEI Q1 (45  106 vs. 31  106; P< .01). Mean total sperm count in men in the highest quartile the AHEI index was significantly higher by 29% compared with men in the lowest quartile (101  106 vs. 78  106; P< .01). The mean normal morphology percentage in men in AHEI Q4 was significantly higher by 21% compared with men in AHEI Q1 (8.1% vs. 6.7%; P< .01). Mean semen motility in men in the highest quartile of the aMED index was significantly higher by 6% compared with men in the lowest quartile (51% vs. 48%; P¼ .03). Most of the semen parameters among men in the highest quartile of the DASH index were higher compared with the lowest quartile of the index: Mean semen concentration in men in DASH Q4 was significantly higher by 24% compared with men in DASH Q1 (31  106 vs. 25  106; P¼ .010). Mean semen motility in men in DASH Q4 was higher by 6% compared with men in DASH Q1 (51% vs. 48%; P¼ .06). The mean normal morphology percentage in men in DASH Q4 was significantly higher by 8% compared with men in DASH Q1 (6.6 vs. 6.1, P¼ .02). Note that the differences between quartiles are statistically significant mainly between the two extreme quartiles, Q1 and Q4. Supplemental Table 1 presents the data regarding adherence to the various diets for all quartiles. In general, comparison of Q2 and Q3 with Q1 did not yield significant results (excluding one case of concentration vs. DASH). Similarly, P trend estimations along all quartiles did not show statistically significant results (data not presented). Higher adherence to all four indexes is associated with improvement in semen quality. However, in three out of the four parameters (total sperm count, concentration, and morphology), in the AHEI index the difference between participants in Q4 and Q1 were statistically significant with 21%–45% increments (Fig. 1). Therefore, we suggest that evaluating adherence to the AHEI index may be most suitable regarding semen health. 811

ORIGINAL ARTICLE: ANDROLOGY

TABLE 1 Participants' characteristics according to quartiles of dietary indexes scores. HEI Characteristic Score n Mean score Age, y Ethnicity Ashkenazic Sephardic Mixed Other Missing Socioeconomic score Total energy intake, kcal/d Body mass index, kg/m2 BMI category Normal weight (<25 kg/m2) Overweight (R25 to <30 kg/m2) Obese (R30 kg/m2) Cigarettes, n/d Smoking status Nonsmoker Smoker Moderate to vigorous activity, min/wk Abstinence time, d

Q1

Q2

Q3

Q4

%50 98 33.7  6.5 33 (31–37)

55–60 65 33.1  6.1 33 (30–36)

65–70 51 33.8  6.4 33 (30–39)

R75 66 33.4  6.1 34 (29–39)

41 (42%) 31 (32%) 14 (14%) 2 (2%) 10 (10%) 7 (7–8)

20 (31%) 29 (45%) 7 (11%) 2 (3%) 7 (11%) 7 (6–7)

21 (41%) 18 (35%) 7 (14%) 2 (4%) 3 (6%) 7 (7–7)

36 (55%) 16 (24%) 10 (20%) 1 (2%) 3 (5%) 7 (7–8)

2568 2454 2382 2536 (1928, 3712) (1646–3225) (1918–3105) (1980, 3488) 24.0 24.7 24.7 23.7 (23.0–26.8) (22.8–27.1) (23.5–27.1) (21.6–27.3)

AHEI P valuea

Q1

Q2

Q3

Q4

< .01 .81

%20 73 18.4  3.7 33 (30–36)

30 45 30.0  0 35 (31–53)

40 81 40.0  0 33 (30–38)

R60 81 57.2  3.7 33 (30–38)

29 (40%) 31 (42%) 10 (14%) 1 (1%) 2 (3%) 7 (6–7)

13 (29%) 16 (36%) 8 (18%) 0 (0%) 8 (18%) 7 (7–8)

41 (51%) 20 (25%) 7 (9%) 4 (5%) 9 (11%) 7 (7–8)

35 (43%) 27 (33%) 13 (16%) 2 (2%) 4 (5%) 7 (7–8)

.41

.02 .36

2133 2382 2573 2711 (1655, 2818) (2027, 3244) (2089–3414) (2032–3810) .31 24.2 24.8 24.7 23.8 (22.7–26.4) (22.7–27.0) (22.3–27.0) (22.4–27.0)

61 (62%)

35 (54%)

27 (53%)

41 (62%)

26 (27%)

23 (35%)

19 (37%)

19 (29%)

11 (11%) 0 (0–5)

7 (11%) 0 (0–5)

5 (10%) 0 (0–4)

6 (9%) 0 (0–4)

40 (62%) 23 (35%) 10 (0–240)

35 (69%) 16 (31%) 10 (0–200)

49 (74%) 17 (26%) 10 (0–200)

.56

3 (3–3)

3 (3–3)

62 (63%) 33 (34%) 10 (0–200) 3 (3–3)

3.0 (3–3)

.80

47 (64%)

23 (51%)

43 (53%)

51 (63%)

21 (29%)

17 (38%)

28 (35%)

21 (26%)

5 (7%) 0 (0–5)

5 (11%) 0 (0–3)

10 (12%) 0 (0–6)

9 (11%) 0 (0–3)

.66

45 (62%) 28 (38%) 10 (0–200)

31 (69%) 12 (27%) 10 (0–200)

48 (59%) 31 (38%) 10 (0–240)

62 (77%) 18 (22%) 10 (0–200)

.02

3 (3–3)

3 (3–3)

3 (3–3)

3 (3–3)

.59

Note: Values are presented as mean  SD, median (interquartile range), or n (%). Differences in variables across all four quartiles in the indexes were tested with the use of analysis of variance or Kruskal–Wallis test for continuous variables and chi-square test for categoric variables. AHEI ¼ Alternative Healthy Eating Index–2010; aMED ¼ alternate Mediterranean Diet score; DASH ¼ Dietary Approaches to Stop Hypertension; HEI ¼ Healthy Eating Index–2010.a Statistically significant difference (P<.05). Efrat. Dietary indexes and sperm quality. Fertil Steril 2018.

DISCUSSION In this study, we investigated the association between predefined dietary indexes and semen parameters, demonstrating, for the first time, associations between four different dietary indexes—HEI, AHEI, aMED, and DASH—and semen quality. Using defined known dietary indexes to analyze association with male fertility, as expressed by semen parameters, is novel, because most related studies to date investigated either single food components or specific food groups. A relatively small number of studies have used dietary patterns to investigate semen quality, mainly using a principle component analysis (PCA) approach (15–17, 52). For example, in cross-sectional studies, healthier dietary patterns have been related to lower sperm DNA damage among subfertile couples in the Netherlands (52) and to higher semen motility among healthy men in the United States (16). In an observational prospective study, a Mediterranean dietary pattern was associated with higher pregnancy rates among infertility patients in the Netherlands (15). Cutillas-Tolı et al. (17) found an inverse association between adherence to the Western diet pattern and semen concentration among overweight or obese men. Excluding one study that used the Mediterranean Diet scale (53), all previous 812

studies evaluating the role of dietary patterns on semen quality have used PCA methodology, analyzing correlation of participants' semen quality with two dietary patterns created based on the participants’ collective dietary habits data. Thus, no study has previously evaluated the association between semen quality and various known dietary indexes or furthermore compared indexes. PCA classification is based on the studied population data to build the nutritional reference groups rather than using predefined dietary patterns based on a priori defined dietary patterns. The major advantage of using an a priori nutritional index as a reference is that information is obtained regarding the adherence of the population to healthy dietary habits, which are set according to acceptable standards; furthermore, this approach also allows potential use of an optimal index identified through the study as a nutritional tool. Moreover, no study has ever compared the above four dietary indexes (HEI, AHEI, DASH, and aMED) in relation to sperm parameters. The correlations between the scores across all four nutrition indexes suggest a high level of shared scoring methods among the indexes. However, it should be noted that the correlations between scores across the indexes are in the middle VOL. 109 NO. 5 / MAY 2018

Fertility and Sterility®

TABLE 1 Continued. AHEI

aMED

DASH

P value

Q1

Q2

Q3

Q4

< .01 .78

%4 114 3.1  1.0 34 (30–38)

5 56 5.0  0 33 (30–36)

6 53 6.0  0 32 (30–38)

R7 57 7.4  0.6 33 (30–37)

50 (44%) 34 (29%) 19 (17%) 2 (2%) 9 (8%) 7 (7–8)

22 (39%) 26 (46%) 2 (3%) 0 (0%) 6 (10%) 7 (7–8)

25 (47%) 19 (36%) 5 (9%) 2 (4%) 2 (4%) 7 (7–8)

21 (37%) 15 (26%) 12 (21%) 3 (5%) 6 (11%) 7 (2–8)

.09

2117 (1586–2568) 24.5 (22.7–26.4)

2255 (1888–3301) 23.9 (22.7–26.1)

2857 (2312–3023) 24.8 (23.3–27.5)

2996 (2587–3525) 24.0 (21.9–27.7)

.01

66 (58%)

38 (50%)

27 (51%)

33 (58%)

.45

36 (32%)

13 (23%)

22 (42%)

16 (28%)

12 (11%) 0 (0–3)

5 (9%) 0 (0–4)

4 (8%) 0 (0–3)

8 (14%) 0 (0–6)

35 (63%) 19 (34%) 10 (0–200)

38 (72%) 15 (28%) 10 (0–200)

40 (70%) 15 (26%) 10 (0–220)

.10

.60

73 (64%) 40 (35%) 10 (0–240)

.02

3 (3–3)

3 (3–3)

3 (3–3)

3 (3–3)

.04

.07 .11 .53 .50

.13 .07

P value

Q1

Q2

Q3

Q4

P value

< .01 .83

%20 80 17.5  2.1 33 (29–37)

21–23 60 22.0  0.8 32 (30–38)

24–27 45 25.3  1.0 31 (29, 37)

R28 95 30.1  2.1 34 (32–39)

< .01 .12

31 (39%) 30 (38%) 10 (13%) 1 (1%) 8 (10%) 7 (7–8)

31 (52%) 23 (38%) 7 (12%) 1 (2%) 4 (7%) 7 (6–8)

16 (36%) 16 (36%) 6 (13%) 3 (7%) 4 (9%) 7 (6–8)

46 (48%) 25 (26%) 15 (16%) 2 (2%) 7 (7%) 7 (7–8)

.71

2102 (1669–2816) 24.5 (22.7–27.3)

2411 (1891–3210) 24.2 (22.8, 26.5)

2382 (1910, 3248) 24.9 (23.1–27.0)

2930 (2397–3637) 23.9 (22.1–27.1)

.04

46 (58%)

36 (60%)

23 (51%)

59 (62%)

24 (30%)

20 (33%)

18 (40%)

25 (26%)

10 (13%) 0 (0–5)

4 (7%) 0 (0–5)

4 (9%) 0 (0–6)

11 (12%) 0 (0–5)

40 (67%) 19 (32%) 10 (0–240)

30 (67%) 15 (33%) 10 (0–200)

71 (75%) 23 (24%) 10 (0–240)

.13

.45

45 (56%) 32 (40%) 10 (0–240)

.34

3 (3–3)

3 (3–3)

3 (3–3)

3 (3–3)

.13

.06

.58

.88

.07

.84 .67

.13

.15

Efrat. Dietary indexes and sperm quality. Fertil Steril 2018.

range (0.42–0.63). This confirms that although the nutrition indexes are based on similar nutrition values, each index represents a unique combination of dietary components and differs in scoring and classification methodology. For example, the HEI-2010 and the AHEI-2010 indexes primarily use absolute measures for all components. On the other hand, the aMED and DASH indexes are based on individual scoring compared with the study population: The DASH scoring is

TABLE 2 Spearman correlation coefficients of total summary dietary index scores for men (n [ 280). Index

HEI

AHEI

aMED

DASH

HEI AHEI aMED DASH

1.00

0.53 1.00

0.62 0.44 1.00

0.63 0.42 0.43 1.00

Note: All coefficients were significant (P<.01). AHEI ¼ Alternative Healthy Eating Index– 2010; aMED ¼ alternate Mediterranean Diet score; DASH ¼ Dietary Approaches to Stop Hypertension; HEI ¼ Healthy Eating Index–2010. Efrat. Dietary indexes and sperm quality. Fertil Steril 2018.

VOL. 109 NO. 5 / MAY 2018

based on adding points to highest and lowest quintiles, and aMED scoring is based on population medians. Our results indicate that the strongest correlation was between the HEI2010 and the DASH indexes (0.63; P< .01). Similarly, a previous study that compared these four indexes in relation to risk of mortality from cardiovascular disease (CVD) and cancer also demonstrated the strongest correlation between the HEI-2010 and DASH indexes (0.72 in men and 0.70 in women; P< .01) (22). Our study investigated the associations between HEI, AHEI, DASH, and aMED and semen parameters. Significant increments were demonstrated in semen concentration and motility. The HEI measures compatibility of individual diets to the Dietary Guidelines for Americans, which serves as the basis of nutritional policy for the United States government and the foundation of all federal nutrition guidance. The HEI guidelines include a diet rich in vegetables, fruits, whole grains, and low-fat dairy products and recommend a relatively low intake of refined grains, saturated fatty acids, and added sugars (31). Our results show that men in the highest quartile of adherence to AHEI displayed significantly better results for concentration, total sperm count, and percentage of normal 813

ORIGINAL ARTICLE: ANDROLOGY

FIGURE 1

Adjusted semen quality parameters (mean and 95% confidence interval)) according to lowest (Q1) and highest (Q4) quartiles of dietary index scores. Models are adjusted for total calorie intake, age, ethnicity, body mass index, smoking, physical activity, and abstinence time. *P<.05; **P<.01. AHEI ¼ Alternative Healthy Eating Index–2010; aMED, alternate Mediterranean Diet score; DASH, Dietary Approaches to Stop Hypertension; HEI, Healthy Eating Index–2010. Efrat. Dietary indexes and sperm quality. Fertil Steril 2018.

semen morphology compared with men in the lowest AHEI adherence quartile. It is noteworthy that higher AHEI scores were previously found to be strongly associated with lower risks of major chronic diseases such as CVD, diabetes, heart failure, colorectal, and estrogen-receptor-negative breast cancer, as well as with total and cardiovascular mortality (33). The DASH diet is high in fruits, vegetables, and wholegrain products, moderate in low-fat dairy products, low in sugar-sweetened beverages, sodium, and animal protein, and contains a substantial amount of plant protein from legumes and nuts. This diet is known to reduce both systolic and diastolic blood pressure among hypertensive and normotensive individuals and is strongly associated with lower risks of CVD and diabetes (36, 37). The present study studied the association between adherence to the DASH diet and semen parameters: Men with the highest DASH scores had significantly higher semen parameters (concentration and percentage of sperm with normal morphology) compared with men with the lowest DASH scores. The present study has also investigated adherence to aMED and its relation to semen parameters. The aMED score is based on the Mediterranean Diet scale (54). The score was based on the intake of nine food items: vegetables, legumes, fruit and nuts, dairy, cereals, meat and meat products, fish, alcohol, and the ratio of monounsaturated to saturated fat. 814

Compatible with other indexes results in our study, the aMED analysis also retrieved a significant increment in semen motility associated with adherence to aMED diet. The same trend was observed in a previous study that demonstrated a positive association between adherence to the validated Mediterranean Diet scale and semen quality (53). The idea that better health is positively correlated with one's fertility (or correspondingly, that chronic diseases are negatively correlated with good reproduction performance) has been discussed, suggesting the possibility that difficulties to conceive and chronic diseases may share various physiologic mechanisms. For example, male obesity has been related to impaired reproduction and is known to affect the molecular and physical structure of sperm (55, 56). In addition, different nutrients or dietary habits that have been associated with an increased risk of obesity and diabetes, such as diets rich in calories (57) or trans-fatty acids (13, 58), have been proven to be related to low sperm quality. In addition, consumption of higher amounts of vegetables, fruits, and fish and lower amounts of red and processed meat, refined grains, and sugar-sweetened beverages was found to be positively associated with semen quality (16, 17, 59, 60). Regarding fruits and vegetables, the ‘‘protective’’ effect on sperm quality is probably due to high content of vitamins, antioxidants, and fiber (15,17,61–63). VOL. 109 NO. 5 / MAY 2018

Fertility and Sterility® It should be noted that the present study was not binary classified to fertile or not fertile. Rather, the main focus was to study the variation within a large cohort ranging from infertile to the spectrum within the ‘‘normal values’’ and correlate this range with nutritional parameters. Indeed, many of the participants had sperm parameters within normal ranges, because many of them came before the assisted reproductive technology (ART) procedure and turned out to be within range. In addition, a large proportion of men who demonstrated abnormal sperm parameters were excluded because of medical issues. Therefore, eventually, only a small group of men with abnormal sperm parameters were included in the study. As is inherent to any study, this study has some limitations. The nature of our cross-sectional analysis does not allow the determination of causality of the observed associations. Therefore, only associations can be made, which is the major limitation of the study. It is also extremely difficult to exclude the possibility that a selection bias was introduced as a result of which participants agreed to enroll in the study. This problem is well known in nutritional studies (64) and to a certain degree is unavoidable. However, we would like to note that our study population is varied and included subjects with varied socioeconomic, BMI, and ethnicity characteristics typical of the Israeli population. For example, according to the Israeli National Health Interview Survey INHIS-3, 26% of Israeli men are smokers; in addition, overweight in the Israeli male population is estimated to be 41% and obesity 10%. Our study population characteristics included 41% overweight (BMI >25 kg/m2) and 10% obese (BMI >30 kg/m2) men and 32% smokers, indicating that our study population reflected the general Israeli population characteristics (65, 66). The possibility of residual confounding factors also could not be ruled out, because participants in Q4 may have had better health habits that we did not assess for, or better access to health care, etc. In addition, nonsignificant associations may have been declared to be significant by chance alone; finally, it is not possible to extrapolate in certainty from these findings regarding semen quality as to fertility potential, although the sperm parameters assayed are known to be clearly associated with fertility. The strengths of our study are the large study population, the use of strict medical and nutritional exclusion and inclusion criteria, the use of a previously validated Israeli FFQ, and the use of dietary pattern analysis as opposed to nutrient or whole food analysis, which more closely reflects real-world food consumption and allows for easier applicability of the results for use by both the public and medical staff.

CONCLUSION This is the first study investigating a priori dietary patterns and sperm and semen quality, comparing four different predefined dietary indexes—HEI, AHEI, aMED, and DASH—in the same study population in relation to semen quality. Although adherence to each of the four indexes had some positive association with measures of sperm quality, the best results were with adherence to AHEI, showing the most notable effects and significant association with three of the VOL. 109 NO. 5 / MAY 2018

four sperm parameters. We therefore suggest that the AHEI index is the most suitable for use in nutritional recommendations concerning promoting sperm parameters.

REFERENCES 1.

2.

3.

4.

5. 6.

7.

8. 9.

10.

11. 12.

13.

14. 15.

16. 17.

18. 19.

20. 21.

Levine H, Jørgensen N, Martino-Andrade A, Mendiola J, Weksler-Derri D, Mindlis I, et al. Temporal trends in sperm count: a systematic review and meta-regression analysis. Hum Reprod Update 2017;23:646–59. Auger J, Kunstmann JM, Czyglik F, Jouannet P. Decline in semen quality among fertile men in Paris during the past 20 years. N Engl J Med 1995; 332:281–5. Jorgensen N, Carlsen E, Nermoen I, Punab M, Suominen J, Andersen AG, et al. East-west gradient in semen quality in the Nordic-Baltic area: a study of men from the general population in Denmark, Norway, Estonia and Finland. Hum Reprod 2002;17:2199–208. Swan SH, Brazil C, Drobnis EZ, Liu F, Kruse RL, Hatch M, et al., Study For Future Families Research Group. Geographic differences in semen quality of fertile U.S. males. Environ Health Perspect 2003;111:414–20. Iwamoto T, Nozawa S, Yoshiike M, Hoshino T, Baba K, Matsushita T, et al. Semen quality of 324 fertile Japanese men. Hum Reprod 2006;21:760–5. Al-Azemi M, Omu AE, Fatinikun T, Mannazhath N, Abraham S. Factors contributing to gender differences in serum retinol and alpha-tocopherol in infertile couples. Reprod Biomed 2009;19:583–90. Dissanayake D, Wijesinghe P, Ratnasooriya W, Wimalasena S. Relationship between seminal plasma zinc and semen quality in a subfertile population. J Hum Reprod Sci 2010;3:124–8. Bentivoglio G, Melica F, Cristoforoni P. Folinic acid in the treatment of human male infertility. Fertil Steril 1993;60:698–701. Blomberg Jensen M, Nielsen J, Jorgensen A, Rajpert–de Meyts E, Kristensen DM, Jørgensen N, et al. Vitamin D receptor and vitamin D metabolizing enzymes are expressed in the human male reproductive tract. Hum Reprod 2010;25:1303–11. Eskenazi B, Kidd SA, Marks AR, Sloter E, Block G, Wyrobek AJ. Antioxidant intake is associated with semen quality in healthy men. Hum Reprod 2005; 20:1006–12. Safarinejad MR, Safarinejad S. The roles of omega-3 and omega-6 fatty acids in idiopathic male infertility. Asian J Androl 2012;14:514–5. Attman J, Toth T, Furtado J, Campos H, Hauser R, Chavarro JE. Dietary fat and semen quality among men attending a fertility clinic. Hum Reprod 2012;27:1466–74. Chavarro JE, Furtado J, Thomas L, Toth TL, Ford J, Campos H, et al. Trans fatty acid levels in sperm are associated with sperm concentration among men from an infertility clinic. Fertil Steril 2011;95:1794–7. Muthusami KR, Chinnaswamy P. Effect of chronic alcoholism on male fertility hormones and semen quality. Fertil Steril 2005;84:919–24. Vujkovic M, de Vries JH, Lindemans J, Macklon NS, van der Spek PJ, Steegers EAP, et al. The preconception Mediterranean dietary pattern in couples undergoing in vitro fertilization/intracytoplasmic sperm injection treatment increases the chance of pregnancy. Fertil Steril 2010;94: 2096–101. Gaskins AJ, Colaci DS, Mendiola J, Swan SH, Chavarro JE. Dietary patterns and semen quality in young men. Hum Reprod 2012;27:2899–907.
n L, Mendiola J, Lo
pez-Espín J, Jorgensen N, Cutillas-Tolín A, Mínguez-Alarco ~oz EM, et al. Mediterranean and western dietary patterns are Navarrete-Mun related to markers of testicular function among healthy men. Hum Reprod 2015;30:1–11. Schulze MB, Hu FB. Dietary patterns and risk of hypertension, type 2 diabetes mellitus, and coronary heart disease. Curr Atheroscler Rep 2002;4:462–7. Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio A, et al. Reproducibility and validity of dietary patterns assessed with a foodfrequency questionnaire. Am J Clin Nutr 1999;69:243–9. Jacques PF, Tucker KL. Are dietary patterns useful for understanding the role of diet in chronic disease? Am J Clin Nutr 2001;73:1–2. van Dam RM, Grievink L, Ock
e MC, Feskens EJ. Patterns of food consumption and risk factors for cardiovascular disease in the general Dutch population. Am J Clin Nutr 2003;77:1156–63. 815

ORIGINAL ARTICLE: ANDROLOGY 22.

23.

24.

25.

26.

27.

28.

29.

30. 31.

32. 33.

34.

35.

36.

37.

38.

39.

40.

41. 42.

816

Harmon BE, Boushey CJ, Shvetsov YB, Ettienne R, Reedy J, Wilkens LR, et al. Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. Am J Clin Nutr 2015;101: 587–97. George SM, Ballard-Barbash R, Manson JE, Reedy J, Shikany JM, Subar AF, et al. Comparing indices of diet quality with chronic disease mortality risk in postmenopausal women in the Women’s Health Initiative Observational Study: evidence to inform national dietary guidance. Am J Epidemiol 2014;180:616–25. Reedy J, Krebs-Smith SM, Miller PE, Liese AD, Kahle LL, Park Y, et al. Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J Nutr 2014;144:881–9. Fung TT, Rimm EB, Spiegelman D, Rifai N, Tofler GH, Willett WC, et al. Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr 2001;73:61–7. Arem H, Reedy J, Sampson J, Jiao L, Hollenbeck AR, Risch H, et al. The Healthy Eating Index 2005 and risk for pancreatic cancer in the NIH-AARP study. J Natl Cancer Inst 2013;105:1298–305. Reedy J, Wirf€ alt E, Flood A, Mitrou PN, Krebs-Smith SM, Kipnis V, et al. Comparing 3 dietary pattern methods—cluster analysis, factor analysis, and index analysis—with colorectal cancer risk. Am J Epidemiol 2010;171: 479–87. Fung TT, Rexrode KM, Mantzoros CS, Manson JE, Willett WC, Hu FB. Mediterranean diet and incidence of and mortality from coronary heart disease and stroke in women. Circulation 2009;119:1093–100. Millen BE, Quatromoni PA, Copenhafer DL, Demissie S, O'Horo CE, d'Agostino RB. Validation of a dietary approach for evaluating nutritional risk: the Framingham Nutrition Studies. J Am Diet Assoc 2001;101:187–94. Guenther PM, Reedy J, Krebs-Smith SM. Development of the Healthy Eating Index-2005. J Am Diet Assoc 2008;108:1896–901. Guenther PM, Casavale KO, Reedy J, Kirkpatrick SI, Hiza HA, Kuczynski KJ, et al. Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Diet 2013;113:569–80. Kennedy ET, Ohls J, Carlson S, Fleming K. The Healthy Eating Index: design and applications. J Am Diet Assoc 1995;95:1103–8. Fung TT, McCullough ML, Newby PK, Manson JE, Meigs JB, Rifai N, et al. Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr 2005;82:163–73. Fung TT, Hu FB, Wu K, Chiuve SE, Fuchs CS, Giovannucci E. The Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets and colorectal cancer. Am J Clin Nutr 2010;92:1429–35. Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, et al. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 2012;142:1009–18. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med 2008;168:713–20. €nther AL, Liese AD, Bell RA, Dabelea D, Lawrence JM, Rodriguez BL, et al. Gu Association between the dietary approaches to hypertension diet and hypertension in youth with diabetes mellitus. Hypertension 2009;53:6–12. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, et al., DASH-Sodium Collaborative Research Group. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. N Engl J Med 2001;344:3–10. Appel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM, et al., DASH Collaborative Research Group. A clinical trial of the effects of dietary patterns on blood pressure. N Engl J Med 1997;336:1117–24. Blumenthal JA, Babyak MA, Hinderliter A, Watkins LL, Craighead L, Lin PH, et al. Effects of the DASH diet alone and in combination with exercise and weight loss on blood pressure and cardiovascular biomarkers in men and women with high blood pressure: the ENCORE study. Arch Intern Med 2010;170:126–35. Bhattacharya SM, Ghosh M, Nandi N. Diabetes mellitus and abnormalities in semen analysis. J Obstet Gynaecol Res 2014;40:167–71. Shahar D, Shai I, Vardi H, Brener-Azrad A, Fraser D. Development of a semiquantitative Food Frequency Questionnaire (FFQ) to assess dietary intake of multiethnic populations. Eur J Epidemiol 2003;18:855–61.

43.

44. 45. 46.

47.

48.

49. 50.

51. 52.

53.

54.

55.

56.

57. 58.

59.

60.

61.

62.

63.

64. 65. 66.

Shai I, Rosner BA, Shahar DR, Vardi H, Azrad AB, Kanfi A, et al. Dietary evaluation and attenuation of relative risk: multiple comparisons between blood and urinary biomarkers, food frequency, and 24-hour recall questionnaires: the DEARR study. J Nutr 2005;135:573–9. World Health Organization. Manual for the examination and processing of human semen. 5th ed. Cambridge University Press; 2010. Medical Electronic Systems. Automated sperm quality analyzers. Available at: www.mes-global.com/analyzers. Lammers J, Splingart C, Barriere P, Jean M, Fr
eour T. Double-blind prospective study comparing two automated sperm analyzers versus manual semen assessment. J Assist Reprod Genet 2014;31:35–43. Akashi T, Mizuno I, Okumura A, Fuse H. Usefulness of sperm quality analyzer-V (SQA-V) for the assessment of sperm quality in infertile men. Arch Androl 2005;51:437–42. Hirano Y, Shibahara H, Shimada K, Yamanaka S, Suzuki T, Takamizawa S, et al. Accuracy of sperm velocity assessment using the Sperm Quality Analyzer V. Reprod Med Biol 2003;4:151–7. Makler A. The improved 10 micrometer chamber for rapid sperm count and motility evaluation. Fertil Steril 1980;33:337–8. Cardona-Maya W, Berdugo J, Cadavid A. Comparing the sperm concentration determined by the Makler and the Neubauer chambers. Actas Urol Esp 2008;32:443–5. Rosner B. Fundamentals of biostatistics. Pacific Grove, California: Duxbury Press; 2000. Vujkovic M, de Vries JH, Dohle GR, Bonsel GJ, Lindemans J, Macklon NS, et al. Associations between dietary patterns and semen quality in men undergoing IVF/ICSI treatment. Hum Reprod 2009;24:1304–12. Karayiannis D, Kontogianni MD, Mendorou C, Douka L, Mastrominas M, Yiannakouris N. Association between adherence to the Mediterranean diet and semen quality parameters in male partners of couples attempting fertility. Hum Reprod 2017;32:215–22. Trichopoulou A, Costacou T, Bamia C, Trichopoulos D. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med 2003;348: 2599–608. Palmer NO, Bakos HW, Fullston T, Lane M. Impact of obesity on male fertility, sperm function and molecular composition. Spermatogenesis 2012;2:253–63. Palmer NO, Bakos HW, Owens JA, Setchell BP, Lane M. Diet and exercise in an obese mouse fed a high fat diet improves metabolic health and reverses perturbed sperm function. Am J Physiol Endocrinol Metab 2012;3002: 768–80. Rato L, Alves MG, Cavaco JE, Oliveira PF. High-energy diets: a threat for male fertility? Obes Rev 2014;15:996–1007.
n L, Mendiola J, Cutillas-Tolín A, Lo
pez-Espín JJ, Chavarro JE, Mínguez-Alarco Torres-Cantero AM. Trans fatty acid intake is inversely related to total sperm count in young healthy men. Hum Reprod 2014;29:429–40. Eslamian G, Amirjannati N, Rashidkhani B, Sadeghi MR, Hekmatdoost A. Intake of food groups and idiopathic asthenozoospermia: a case-control study. Hum Reprod 2012;27:3328–36. Braga DP, Halpern G, Figueira RC, Setti AS, Iaconelli A Jr, Borges E Jr. Food intake and social habits in male patients and its relationship to intracytoplasmic sperm injection outcomes. Fertil Steril 2012;97:53–9. Mendiola J, Torres-Cantero AM, Vioque J, Moreno-Grau JM, Ten J, Roca M, et al. A low intake of antioxidant nutrients is associated with poor semen quality in patients attending fertility clinics. Fertil Steril 2010;93:1128–33. Pasqualotto FF, Sharma RK, Pasqualotto EB, Agarwal A. Poor semen quality and ROS-TAC scores in patients with idiopathic infertility. Urol Int 2008;81: 263–70. Pasqualotto FF, Sundaram A, Sharma RK, Borges E Jr, Pasqualotto EB, Agarwal A. Semen quality and oxidative stress scores in fertile and infertile patients with varicocele. Fertil Steril 2008;89:602–7. Willet W. Nutritional epidemiology. 2nd ed. Oxford University Press; 1998. Ministry of Health Israel. Israel National Health Interview Survey. Available at: www.health.gov.il/UnitsOffice/ICDC/Health_Surveys/Pages/INHIS.aspx. Ministry of Health Israel. Available at: www.health.gov.il/UnitsOffice/ICDC/ HealthAndLifestyle/Overweight_Obesity/Pages/ObesityInHealthSurvey.aspx.

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