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Strong adherence to a healthy dietary pattern is associated with better semen quality, especially in men with poor semen quality
ORIGINAL ARTICLE: ANDROLOGY
Strong adherence to a healthy dietary pattern is associated with better semen quality, especially in men with poor semen quality gine P. M. Steegers-Theunissen, M.D., Ph.D.,a,b Jeanne H. M. de Vries, M.Sc.,c Elsje C. Oostingh, M.D.,a Re Joop S. E. Laven, M.D., Ph.D.,d and Maria P. H. Koster, M.D., Ph.D.a a
Department of Obstetrics and Gynecology, Division of Obstetrics and Prenatal Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam; b Department of Pediatrics, Division of Neonatology, Erasmus MC, University Medical Centre Rotterdam, Rotterdam; c Department of Human Nutrition, Wageningen University, Wageningen; and d Department of Obstetrics and Gynecology, Division of Reproductive Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, the Netherlands
Objective: To study associations between periconceptional dietary patterns and semen quality parameters. Design: Prospective periconception cohort study. Setting: Tertiary hospital. Patient(s): One hundred and twenty-nine male partners of pregnant women who participated in the Rotterdam Periconception Cohort (Predict study). Intervention(s): None. Main Outcome Measure(s): Semen quality parameters—ejaculate volume, sperm concentration, total sperm count, progressive motility, immotile sperm, and total motile sperm count (TMSC). Result(s): Men included in our study were on average 35 (6 standard deviation) years old and had a body mass index of 26.4 4 kg/ m2. Two dietary patterns were identified using principle component analysis, which were labeled as ‘‘healthy’’ and ‘‘unhealthy.’’ An increase of one factor score (stated as b) represented an increase of 1 standard deviation. Sperm concentration (b ¼ 0.278; 95% CI, 0.112–0.444), total sperm count (b ¼ 1.369; 95% CI, 0.244–2.495), progressive motility (b ¼ 4.305; 95% CI, 0.675–7.936), and TMSC (b ¼ 0.319; 95% CI, 0.113–0.526) were all positively associated with a strong adherence to the healthy dietary pattern. Subgroup analysis showed that these associations were mainly present in men with a TMSC <10 million spermatozoa. Although there was a trend toward a diminution in semen quality, we found no statistically significant associations with strong adherence to the unhealthy dietary pattern. Conclusion(s): The positive associations between strong adherence to a healthy dietary pattern and semen parameters in men with poor semen quality support the importance of preconceptional tailored nutritional counseling and coaching of couples who are trying to conceive. (Fertil SterilÒ 2017;-:-–-. Ó2017 by American Society for Reproductive Medicine.) Key Words: Dietary pattern, male subfertility, semen analysis Discuss: You can discuss this article with its authors and with other ASRM members at https://www.fertstertdialog.com/users/ 16110-fertility-and-sterility/posts/14960-23415
O
ver the past decades, several studies have provided evidence that semen quality in humans might be decreasing, which
might lead to an increase in male subfertility (1, 2). This can be caused by intrinsic factors such as genetic or congenital disorders and cancer, but a
Received November 18, 2016; revised February 10, 2017; accepted February 11, 2017. E.C.O. has nothing to disclose. R.P.M.S.-T. has nothing to disclose. J.H.M.d.V. has nothing to disclose. J.S.E.L. is a consultant for Euroscreen, Roche, outside of the submitted work, and holds a grant from Dutch Heart Association, outside of the submitted work. M.P.H.K. has nothing to disclose. Reprint requests: Maria P. H. Koster, M.D., Ph.D., Department of Obstetrics and Gynecology, Division of Obstetrics and Prenatal Medicine, Erasmus MC, University Medical Centre Rotterdam, P.O. Box 2040, Rotterdam 3000 CA, the Netherlands (E-mail: [email protected]). Fertility and Sterility® Vol. -, No. -, - 2017 0015-0282/$36.00 Copyright ©2017 American Society for Reproductive Medicine, Published by Elsevier Inc. http://dx.doi.org/10.1016/j.fertnstert.2017.02.103 VOL. - NO. - / - 2017
decline in semen quality is also observed in healthy men without any adverse medical history (2, 3). Besides intrinsic factors, semen quality can also be affected by modifiable lifestyle behaviors. For example, studies have suggested that specific nutritional factors can affect semen quality (4–6). Parental nutritional status is a crucial determinant of normal reproductive function (7), and the nutritional environment of the fetus affects the future child's health, as stated by the 1
ORIGINAL ARTICLE: ANDROLOGY Developmental Origin of Health and Disease paradigm (8). Malnutrition is known to disturb several metabolic pathways, and prominent among these is the one-carbon (1-C) metabolism (9, 10). The 1-C metabolism is in particular essential for DNA synthesis and phospholipid and protein biosynthesis. Derangements in this metabolism can lead to excessive oxidative stress, which can detrimentally affect gametogenesis and fertilization (11, 12). Several nutrients serve as substrate or cofactor to support the 1-C metabolism, such as folate and vitamin B12 (11). A low intake of full-fat dairy food, sweets, and processed meat and a high intake of folate-rich food sources such as fruits and vegetables have been positively associated with healthy semen quality (5, 6, 13–15). A high intake of fruits, vegetables, fish, and whole grains is associated with less sperm DNA damage than usual (4). Moreover, the use of folic acid and zinc supplements is known to increase the total normal sperm count in subfertile men (16, 17). Despite these findings, the impact of a healthy or unhealthy dietary pattern remains unclear because most studies have only focused on the association between semen quality and a single nutrient or food group or only a few nutrients or food groups. This single nutrient approach may be limited because people consume meals that consist of a varying combination of foods that contain lots of different nutrients (18, 19). Some other studies have used dietary pattern analysis, a more accurate approach, to identify the effect of the balance between food groups and nutrients on semen quality (19–21). During preconception counseling often little attention is paid to nutrition, especially in men. However, an unhealthy dietary pattern can be modified, so couples planning a pregnancy may benefit from the knowledge of the possible positive effect of nutrition on semen quality. To substantiate this, we identified dietary patterns in men and studied the associations with semen quality parameters.
MATERIALS AND METHODS Study Population This study was part of the Rotterdam Periconceptional Cohort (Predict study), an ongoing prospective, tertiary, hospitalbased cohort study embedded in patient care and conducted at the department of Obstetrics and Gynecology of the Erasmus University Medical Centre, Rotterdam, the Netherlands. The details of this study have previously been described elsewhere (22). For the current study, we selected men from the participating couples who were included in the study before 12 weeks of gestation between November 2010 and November 2014. Men were excluded when a semen sample was not available or when there was no information about their dietary patterns based on a food frequency questionnaire (FFQ). The window between the date of semen analysis and the date on which the FFQ was completed was restricted to a maximum of 1 year because of the accumulating evidence that dietary patterns remain reasonably constant over time except for in periods of dieting and illness (23–25). If there was more than one semen sample available, the parameters 2
of the sample closest to the moment of completing the FFQ were used in this study (Fig. 1). The study protocol has been approved by the medical ethical and institutional review board of the Erasmus MC, University Medical Centre in Rotterdam, the Netherlands, and all male participants provided written informed consent (METC Erasmus MC 2004-277).
Data Collection At study entry, all male participants completed a selfadministered general questionnaire covering details on paternal age, ethnicity, educational level, medical history, previous children and periconceptional lifestyle (smoking, alcohol use, and folic acid or multivitamin supplements use) at enrollment. At the same moment, qualified research nurses obtained anthropometric measurements (height, weight, waist-hip ratio, and blood pressure) (22). A validated semiquantitative FFQ, developed by the division of Human Nutrition, Wageningen University, the Netherlands, was used to estimate habitual food intake over the 4 weeks before study entry (26, 27). The FFQ consists of 196 food items structured according to meal patterns, with questions including consumption frequency, portion size, and preparation method. Intake of food, food groups, and energy and nutrients were determined using the Dutch food composition table (28). At enrollment, the research nurses checked the FFQs in a standardized manner for completeness and consistency.
Semen Analysis Semen samples were collected via masturbation into polypropylene containers. Within 1 hour the samples were liquefied, and the semen parameters—semen volume, sperm concentration, total sperm count, percentage progressive motility, and percentage immotile motility—were assessed according to World Health Organization (WHO) guidelines (29). We defined normospermia as a total motile sperm count (TMSC) of R10 million spermatozoa. All semen analyses were performed by expert laboratory staff at the Erasmus MC, University Medical Center Rotterdam, the Netherlands. Semen samples were not routinely collected as part of the Predict study; they were collected only when there was a medical indication. Semen parameters were retrieved from medical records to obtain all the data required for our study.
Statistical Analysis First, we compared the baseline characteristics between the men who were included and excluded in our study to investigate whether our study sample was a representative reflection of the entire Predict cohort (selection bias). These characteristics were expressed as medians with interquartile ranges or absolute numbers with percentages and were compared using either Mann-Whitney U or chi-square tests. A total of 196 food items from the FFQs were reduced to 23 predefined food groups based on origin and similar nutrient content (18). Next, we performed a principal VOL. - NO. - / - 2017
Fertility and Sterility®
FIGURE 1
Flow chart of the source and included study population. FFQ ¼ Food Frequency Questionnaire. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
component analysis (PCA) to identify dietary patterns based on the correlation among food groups (19). Practically, PCA is a standard multivariate statistical technique that aggregates specific food groups on the basis of the degree to which food items are reciprocally correlated (19, 30). When PCA is performed, a factor loading is automatically calculated for each food group, showing the extent to which each food group is correlated with the specific dietary pattern. Finally, each person automatically receives a score to represent their adherence to every dietary pattern. Our PCA resulted in the identification of 23 dietary patterns (principal components). To reduce the bias of multiple testing and to identify the most common used dietary patterns in the study population, we selected two dietary patterns with eigenvalues of >2.0 VOL. - NO. - / - 2017
for investigation of the associations with the semen parameters. Additionally, the nutrient intake in men with strong (positive correlation) or weak (negative correlation) adherence to these patterns was calculated and compared using Mann-Whitney U tests. Continuous semen parameters (volume, count, and concentration) were log transformed to obtain normal distributions. Linear regression analysis was performed to establish associations between the factor score of each dietary pattern as independent variables and the semen parameters as dependent variables. An increase of one factor score (stated as b) is equal to an increase of 1 standard deviation and can thus be interpreted as such. First, we performed a crude analysis that was only adjusted for energy intake. Next, we constructed an 3
ORIGINAL ARTICLE: ANDROLOGY adjusted model in which we additionally corrected for potential confounders that were based on study population and literature (i.e., paternal age, body mass index, ethnicity, educational level, smoking, and alcohol use). Finally, we performed a similar multivariable linear regression analysis among subgroups of men with a TMSC below or above 10 million spermatozoa. P< .05 was considered statistically significant. All analyses were performed using the Statistical Package for the Social Sciences software for Windows (IBM SPSS, Statistics for Windows, version 21.0).
RESULTS A total of 1,435 men participated in the Predict study. After excluding the men who did not match our inclusion criteria, 129 men were further analyzed (Fig. 1). The baseline characteristics of the included and excluded men are shown in Table 1. Men included in our study were on average older (36 vs. 34 years; P¼ .001), a higher percentage used folic acid supplements (21.7% vs. 6.9%; P< .001), and a higher percentage conceived by in vitro fertilization/intracytoplasmic sperm injection (IVF-ICSI) treatment (59.7% vs. 13.0%; P< .001). Also, these men seemed to have slightly lower TMSC compared with the men who were excluded. All other
characteristics did not statistically significantly differ between the groups. The semen parameters of our study group, stratified by TMSC below and above 10 million spermatozoa, are shown in Supplemental Table 1. The two dietary patterns (selected from a total of 23 patterns) identified by PCA explained 23.1% of the variance of the overall dietary intake of the total study population. The first pattern was labeled ‘‘unhealthy’’ because of high intakes of dairy, mayonnaise, margarines, sauces, snacks, and sweets, which explained 12.2% of the total variance. The second pattern was labeled ‘‘healthy’’ because of high intakes of cereals, fruits, legumes, vegetables, and olive oil, which explained 10.9% of the total variance (Table 2). Supplemental Table 2 shows the macronutrient and micronutrient intakes of men with strong or weak adherence to the healthy or unhealthy dietary pattern. Men with a strong adherence to the healthy dietary pattern had a higher vitamin intake and slightly higher intake of healthy fats (polyunsaturated fatty acids, eicosapentaenoic acid, docosahexaenoic acid) compared with the men who had a weak adherence to this pattern or a strong adherence to the unhealthy dietary pattern. After adjustment for potential confounders, a strong adherence to the healthy dietary pattern was positively
TABLE 1 Baseline characteristics of participants of the Predict cohort that were included and excluded in the current study. Participants Characteristics Age (y) BMI (kg/m2) Geographic origin, n (%) Western Non-Western Missing Educational level, n (%) High Intermediate Low Missing Lifestyle, n (%) Smoking (yes) Missing Alcohol (yes) Missing Folic acid supplement use (yes) Missing Mode of Conception Spontaneous Hormone treatment/IUI IVF–ICSI Missing Semen parameters Ejaculate volume (mL) Sperm concentration (106/mL) Total sperm count (106/mL) Total motile sperm count (106/mL) Progressive motility (A þ B) (%) Immotile sperm (C þ D) (%) Missing
Included (n [ 129)
Excluded (n [ 1,306)
36 (32–40) 25.1 (22.9–27.3)
34 (30–37) 25.1 (23.3–27.7)
109 (87.9) 15 (12.1) 5
748 (87.3) 109 (12.7) 578
60 (49.2) 56 (45.9) 6 (4.9) 7
399 (47.4) 370 (43.9) 73 (8.7) 593
31 (26.2) 11 89 (74.8) 10 26 (21.7) 9
252 (36.8) 622 614 (89.8) 622 46 (6.9) 643
34 (26.4) 18 (13.9) 77 (59.7) 0
745 (80.1) 64 (6.9) 121 (13.0) 376
P value .001 .548 .846
.369
2.50 (1.70–3.90) 28.0 (8.00–63.0) 70.5 (14.4–156) 28.9 (3.08–78.7) 40 (25–51) 57 (46–72) 0
2.60 (2.00–4.00) 37.0 (15.0–61.0) 103 (29.0–206) 40.3 (10.1–88.9) 42 (29–52) 56 (46–66) 1,095
.568 .973 < .001 < .001
.322 .222 .029 .058 .557 .708
Note: Values are expressed as median (interquartile range) or as number (%). BMI ¼ body mass index; ICSI ¼ intracytoplasmic sperm injection; IUI ¼ intrauterine insemination; IVF ¼ in vitro fertilization; RBC ¼ red blood cell. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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TABLE 2 Factor scores of 23 food groups within two dietary patterns identified through principle component analysis. Food groups Variance explained (%) Alcohol Cereals Dairy Eggs Fish Fruits Grains (refined) Grains (whole wheat) Legumes Liquid fat Meat Mayonnaise Margarines Nuts Nonalcohol Olive oil Potatoes Soup Snacks Sauces Sweets Solid fat Vegetables
Unhealthy pattern
Healthy pattern
12.2 0.103 0.126 0.742 0.099 0.110 0.140 0.119 0.296 0.150 0.163 0.067 0.438 0.266 0.006 0.084 0.019 0.225 0.117 0.324 0.662 0.648 0.102 0.061
10.9 0.147 0.659 0.007 0.111 0.071 0.206 0.009 0.077 0.807 0.114 0.122 0.061 0.056 0.185 0.149 0.291 0.085 0.007 0.060 0.042 0.117 0.138 0.708
Note: Factor scores are presented as correlation coefficients. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
associated with TMSC (b ¼ 0.319; 95% CI, 0.113–0.526), sperm concentration (b ¼ 0.278; 95% CI, 0.112–0.444), sperm count (b ¼ 1.369; 95% CI, 0.244–2.495), and progressive motility (b ¼ 4.305; 95% CI, 0.675–7.936). In a subgroup analysis, these positive associations remained but were only statistically significant in men with TMSC <10 million spermatozoa (Table 3). Strong adherence to the unhealthy dietary pattern showed a trend toward diminution in TMSC, sperm concentration, sperm count, progressive motility, and immotile sperm although these results were not statistically significant (Table 3).
DISCUSSION We identified two dietary patterns among men of couples visiting a tertiary hospital during early pregnancy and observed that high adherence to a healthy dietary pattern is positively associated with better semen quality, in particular in men with a TMSC <10 million spermatozoa. Furthermore, a trend of diminution of semen quality was seen in men with a high adherence to an unhealthy dietary pattern. These results confirm our previous findings in which only male partners of women undergoing IVF or ICSI treatment were included. In that study it was shown that a high adherence to a health-conscious pattern resulted in a lower DNA fragmentation index as well as a higher percentage of progressive motility and thus better semen quality (4). The study of Jurewicz et al. (6) showed that high adherence to a prudent pattern, representing high intake of fish, chicken, fruits, cruciferous vegetables, tomatoes, leafy green vegetables, VOL. - NO. - / - 2017
and whole grains, increases semen quality, reflected by a higher sperm concentration and lower DNA fragmentation index, and the testosterone level. The same investigators also observed a positive effect of strong adherence to the prudent pattern in men and a decrease in chromosomal disorders in offspring (31). These studies have in common that in particular high intakes of fruits, vegetables, and legumes are associated with a higher sperm concentration, and we also demonstrated positive effects on sperm count, TMSC, and percentage progressive motility. The positive effects of high adherence to a prudent pattern are further substantiated by Gaskins et al. (32) who showed a positive and statistically significant association with an increased percentage of progressive sperm motility. In that study the investigators only examined healthy adolescent men whereas our study included a greater variety in ages, making it more applicable to the general reproductive male population. Other studies of the same group also showed that a higher total fat intake was negatively associated with total sperm count, sperm concentration, and sperm morphology and was positively associated with asthenozoospermia (33–35). These associations appeared to be driven primarily by the intake of saturated fats derived from dairy (34). This is substantiated by our results showing that the intake of dairy, but also of other saturated fat sources such as mayonnaise, snacks, and sauces, was low in men with strong adherence to the healthy dietary pattern. On the other hand, the intake of healthy fats such as polyunsaturated fatty acids, eicosapentaenoic acid, and docosahexaenoic acid was higher in men who strongly adhered to the healthy dietary pattern. The positive effect of these compounds of the healthy dietary pattern on semen quality can be explained by the high contents in spermatozoa, which play a crucial role in fertilization (33). In addition, the intake of trans fatty acids from largely the same sources (e.g., snacks, mayonnaise, and other sauces) is also known to negatively influence semen quality (34, 35). We also observed that the contribution of sugars was high in the unhealthy dietary pattern, which is supported by the inverse association shown between sugar intake and sperm motility among young healthy men (36). These findings all support the trends of positive and negative associations between healthy and unhealthy dietary patterns, respectively, and the semen parameters of men in our study sample. Micronutrients such as vitamins and trace metals also play an important role in normal testicular development, spermatogenesis, and sperm motility. For example, seminal plasma zinc concentrations statistically significantly differ between fertile and subfertile men and total normal sperm count increases after supplementation of folic acid and zinc sulfate (15, 17, 37). Both groups of micronutrients are present in cereals, legumes, and fruits in particular, the food groups that were highly represented in our healthy dietary pattern. Vitamins C and E also play a role in male fertility (38–40). These vitamins can be found in fruits and vegetables, the same food groups that strongly contribute to our healthy dietary pattern. These findings are supported by the negative associations between low intake of fruits and 5
Associations between the two identified dietary patterns and semen parameters for the total sample as well as stratified by total motile sperm count below or above 10 million spermatozoa. TMSC <10
Total sample Unhealthy Semen parameters
Crude
Ejaculate volume b 0.041 95% CI 0.049 to 0.131 Sperm concentration b 0.044 95% CI 0.115 to 0.203 Total sperm count b 0.040 95% CI 1.064 to 0.985 Total motile sperm count b 0.039 95% CI 0.155 to 0.234 Progressive motility (A þ B) b 0.327 95% CI 3.045 to 3.700 Immotile sperm (C þ D) b 0.716 95% CI 4.506 to 3.073
Healthy
TMSC ‡10
Unhealthy
Healthy
Unhealthy
Healthy
Adjusted
Crude
Adjusted
Crude
Adjusted
Crude
Adjusted
Crude
Adjusted
Crude
Adjusted
0.003 0.116 to 0.121
0.005 0.085–0.096
0.003 0.100 to 0.093
0.121 0.040 to 0.282
0.267 0.069–0.466
0.012 0.258 to 0.234
0.025 0.286 to 0.237
0.008 0.114 to 0.099
0.121 0.260 to 0.017
0.023 0.121 to 0.074
0.018 0.128 to 0.092
0.007 0.221 to 0.207
0.229 0.075–0.383
0.278 0.112–0.444
0.094 0.129 to 0.318
0.055 0.229 to 0.339
0.384 0.069–0.699
0.474 0.174–0.773
0.052 0.169 to 0.064
0.030 0.190 to 0.131
0.037 0.070 to 0.144
0.049 0.076 to 0.173
0.794 2.176 to 0.588
1.262 0.267–2.257
1.369 0.244–2.495
0.090 0.541to 0.720
0.041 0.800 to 0.882
1.214 0.362–2.065
1.549 0.687–2.411
0.461 1.404 to 0.481
1.160 2.455 to 0.135
0.289 0.579 to 1.157
0.479 0.538 to 1.496
0.124 0.387 to 0.139
0.282 0.094–0.471
0.319 0.113–0.526
0.062 0.135 to 0.259
0.036 0.218 to 0.289
0.375 0.104–0.645
0.476 0.219–0.733
0.071 0.192 to 0.050
0.202 0.360 to 0.043
0.038 0.073–0.150
0.069 0.058–0.196
2.081 6.628 to 2.465
3.683 0.372–6.993
4.305 0.675–7.936
0.524 6.036 to 4.989
0.208 6.249 to 5.833
4.659 3.413 to 12.732
6.763 0.103 to 13.629
0.751 3.111 to 1.609
3.101 6.346 to 0.145
0.156 2.012 to 2.324
0.864 1.7015 to 3.443
0.044 5.260 to 5.172
1.154 4.940 to 2.632
0.566 4.815 to 3.682
2.219 12.249 to 7.811
5.941 19.810 to 7.928
3.871 11.028 to 18.770
4.554 12.191 to 21.298
0.751 1.609 to 3.111
3.101 0.145 to 6.346
0.156 2.324 to 2.012
0.864 3.443 to 1.715
Note: Adjusted for total energy intake, body mass index, age, ethnicity, and smoking. CI ¼ confidence interval; TMSC ¼ total motile sperm count. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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TABLE 3
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Fertility and Sterility® vegetables and the risk of oligoasthenoteratospermia (15), and high intake among the same food groups and a decreased risk of oligoasthenoteratospermia (14). The positive association between a healthy diet and semen quality was more pronounced in men with poor semen quality (i.e., TMSC <10 million spermatozoa). It is not likely that the influence of nutrition on semen quality differs between fertile and subfertile men. However, in men with poor semen quality the effect sizes may be larger because the range of TMSC is much smaller; therefore, it is very likely that there is more room for improvement compared to men with a TMSC R10 million spermatozoa (17). Moreover, the identified not statistically significant positive associations between dietary patterns and semen quality in men with TMSC R10 million spermatozoa may have been hampered by our sample size. The strengths of our study are, first, the availability of validated FFQs as this is a comprehensive method to obtain detailed information about the habitual intake of foods and, second, the use of PCA, a state-of-the-art method to identify dietary patterns (19, 30, 41). Besides, all semen analyses were performed in the same laboratory, which increases the internal validity of the data. However, our study also had some limitations. Both semen analyses and FFQ data were available for only a small proportion of men. Semen analyses were not routinely obtained as part of the Predict study and were only performed when there was a medical indication, so they had to be retrospectively matched to our cohort. Therefore, the time window between completing the FFQ and the semen analysis was relatively large for some men. Because dietary patterns have been shown to remain largely the same over a period of time, we believe that this still provides us with accurate information on the association between periconceptional nutritional status and semen quality (23–25). An expected consequence of the fact that the semen analyses were performed by indication is that our study sample consists of a higher proportion of subfertile men (see Table 1). Nevertheless, 26.4% of all pregnancies were spontaneously conceived, and for the remaining pregnancies subfertility could also have been due to female factors. Knowledge about the associations found in our study can be used in routine preconception counseling. This counseling can be a valuable tool to identify and reduce modifiable health risks in couples who are trying to conceive (42). Because only a small proportion of couples make use of preconception care, more public awareness is necessary. In our modern digital era, mobile health (mHealth) or eHealth tools, such as the online coaching platform Smarter Pregnancy, may contribute to this awareness and stimulate couples to change and maintain more healthy dietary patterns and lifestyle choices (43).
of the considerable effect of nutrition also on male fertility. To create this awareness, we recommend that counseling on dietary patterns, for both women and men, should be implemented in preconception care worldwide. Acknowledgments: The authors thank S. P. Willemsen for his assistance with the statistical analysis and the Predict research team for their contribution to the data collection.
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CONCLUSION Here we demonstrate that a strong adherence to a healthy dietary pattern is positively associated with semen parameters, especially in men with low TMSC. Herewith we support findings from other studies and emphasize the importance of making sure that every couple planning a pregnancy is aware VOL. - NO. - / - 2017
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Jorgensen N, Joensen UN, Jensen TK, Jensen MB, Almstrup K, Olesen IA, et al. Human semen quality in the new millennium: a prospective crosssectional population-based study of 4867 men. BMJ Open 2012;2: e000990. Rolland M, Le Moal J, Wagner V, Royere D, De Mouzon J. Decline in semen concentration and morphology in a sample of 26,609 men close to general population between 1989 and 2005 in France. Hum Reprod 2013;28:462–70. Wong WY, Thomas CM, Merkus JM, Zielhuis GA, Steegers-Theunissen RP. Male factor subfertility: possible causes and the impact of nutritional factors. Fertil Steril 2000;73:435–42. 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. Eslamian G, Amirjannati N, Rashidkhani B, Sadeghi MR, Hekmatdoost A. Nutrient patterns and asthenozoospermia: a case-control study. Andrologia 2016 Jun 1; http://dx.doi.org/10.1111/and.12624. Jurewicz J, Radwan M, Sobala W, Radwan P, Bochenek M, Hanke W. Dietary patterns and their relationship with semen quality. Am J Mens Health 2016, Jan 27. pii: 1557988315627139. Chadio S, Kotsampasi B. The role of early life nutrition in programming of reproductive function. J Dev Orig Health Dis 2014;5:2–15. Barker DJ. The origins of the developmental origins theory. J Intern Med 2007;261:412–7. Stabler SP, Allen RH. Vitamin B12 deficiency as a worldwide problem. Annu Rev Nutr 2004;24:299–326. Thuesen BH, Husemoen LL, Ovesen L, Jorgensen T, Fenger M, Linneberg A. Lifestyle and genetic determinants of folate and vitamin B12 levels in a general adult population. Br J Nutr 2010;103:1195–204. Steegers-Theunissen RP, Twigt J, Pestinger V, Sinclair KD. The periconceptional period, reproduction and long-term health of offspring: the importance of one-carbon metabolism. Hum Reprod Update 2013;19:640–55. Singh K, Jaiswal D. One-carbon metabolism, spermatogenesis, and male infertility. Reprod Sci 2013;20:622–30. 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. 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. Mendiola J, Torres-Cantero AM, Moreno-Grau JM, Ten J, Roca M, MorenoGrau S, et al. Food intake and its relationship with semen quality: a casecontrol study. Fertil Steril 2009;91:812–8. Mora-Esteves C, Shin D. Nutrient supplementation: improving male fertility fourfold. Semin Reprod Med 2013;31:293–300. Wong WY, Merkus HM, Thomas CM, Menkveld R, Zielhuis GA, SteegersTheunissen RP. Effects of folic acid and zinc sulfate on male factor subfertility: a double-blind, randomized, placebo-controlled trial. Fertil Steril 2002; 77:491–8. Slimani N, Fahey M, Welch AA, Wirfalt E, Stripp C, Bergstrom E, et al. Diversity of dietary patterns observed in the European Prospective Investigation into Cancer and Nutrition (EPIC) project. Public Health Nutr 2002;5:1311–28. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002;13:3–9. 7
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Liu CY, Chou YC, Chao JC, Hsu CY, Cha TL, Tsao CW. The association between dietary patterns and semen quality in a general Asian population of 7,282 males. PLoS One 2015;10:e0134224. 21. Cutillas-Tolin A, Minguez-Alarcon L, Mendiola J, Lopez-Espin JJ, Jorgensen N, Navarrete-Munoz EM, et al. Mediterranean and western dietary patterns are related to markers of testicular function among healthy men. Hum Reprod 2015;30:2945–55. 22. Steegers-Theunissen RP, Verheijden-Paulissen JJ, van Uitert EM, Wildhagen MF, Exalto N, Koning AH, et al. Cohort profile: the Rotterdam periconceptional cohort (Predict Study). Int J Epidemiol 2016;45: 374–81. 23. van Driel LMJW, de Vries JHM, Boxmeer JC, Lindemans J, Steegers EAP, Steegers-Theunissen RPM. The preconception nutritional status of women undergoing fertility treatment: use of a one-year post-delivery assessment. E Spen Eur E J Clin Nutr Metab 2010;5:e284–91. 24. Willett W. Nutritional epidemiology. 2nd ed. New York: Oxford University Press; 1998. 25. Borland SE, Robinson SM, Crozier SR, Inskip HM, Group SWSS. Stability of dietary patterns in young women over a 2-year period. Eur J Clin Nutr 2008;62:119–26. 26. Siebelink E, Geelen A, de Vries JH. Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults. Br J Nutr 2011;106:274–81. 27. Verkleij-Hagoort AC, de Vries JH, Stegers MP, Lindemans J, Ursem NT, Steegers-Theunissen RP. Validation of the assessment of folate and vitamin B12 intake in women of reproductive age: the method of triads. Eur J Clin Nutr 2007;61:610–5. 28. Netherlands Nutrition Centre. Dutch Food Composition Database (NEVO). 2011. Available at: www.rivm.nl/en/Topics/D/Dutch_Food_Composition_Database. Accessed October 21, 2016. 29. World Health Organization. Laboratory manual for the examination and processing of human semen. 5th ed. Geneva: WHO; 2010. 30. Hoffmann K, Schulze MB, Schienkiewitz A, Nothlings U, Boeing H. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004;159:935–44.
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Jurewicz J, Radwan M, Sobala W, Gromadzinska J, Jablonska E, Radwan P, et al. Dietary patterns and the frequency of disomy in human sperm. Urology 2016;93:86–91. Gaskins AJ, Colaci DS, Mendiola J, Swan SH, Chavarro JE. Dietary patterns and semen quality in young men. Hum Reprod 2012;27:2899–907. Esmaeili V, Shahverdi AH, Moghadasian MH, Alizadeh AR. Dietary fatty acids affect semen quality: a review. Andrology 2015;3:450–61. Eslamian G, Amirjannati N, Rashidkhani B, Sadeghi MR, Baghestani AR, Hekmatdoost A. Dietary fatty acid intakes and asthenozoospermia: a casecontrol study. Fertil Steril 2015;103:190–8. Chavarro JE, Minguez-Alarcon L, Mendiola J, Cutillas-Tolin A, Lopez-Espin JJ, Torres-Cantero AM. Trans fatty acid intake is inversely related to total sperm count in young healthy men. Hum Reprod 2014;29:429–40. Chiu YH, Afeiche MC, Gaskins AJ, Williams PL, Mendiola J, Jorgensen N, et al. Sugar-sweetened beverage intake in relation to semen quality and reproductive hormone levels in young men. Hum Reprod 2014;29:1575–84. Chia SE, Ong CN, Chua LH, Ho LM, Tay SK. Comparison of zinc concentrations in blood and seminal plasma and the various sperm parameters between fertile and infertile men. J Androl 2000;21:53–7. Thiele JJ, Friesleben HJ, Fuchs J, Ochsendorf FR. Ascorbic acid and urate in human seminal plasma: determination and interrelationships with chemiluminescence in washed semen. Hum Reprod 1995;10:110–5. Suleiman SA, Ali ME, Zaki ZM, el-Malik EM, Nasr MA. Lipid peroxidation and human sperm motility: protective role of vitamin E. J Androl 1996;17:530–7. Zareba P, Colaci DS, Afeiche M, Gaskins AJ, Jorgensen N, Mendiola J, et al. Semen quality in relation to antioxidant intake in a healthy male population. Fertil Steril 2013;100:1572–9. Crozier SR, Robinson SM, Borland SE, Inskip HM, Group SWSS. Dietary patterns in the Southampton Women’s Survey. Eur J Clin Nutr 2006;60:1391–9. de Weerd S, van der Bij AK, Cikot RJ, Braspenning JC, Braat DD, Steegers EA. Preconception care: a screening tool for health assessment and risk detection. Prev Med 2002;34:505–11. Van Dijk MR, Huijgen NA, Willemsen SP, Laven JS, Steegers EA, SteegersTheunissen RP. Impact of an mHealth platform for pregnancy on nutrition and lifestyle of the reproductive population: a survey. JMIR Mhealth Uhealth 2016;4:e53.
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SUPPLEMENTAL TABLE 1 Semen parameters of included men for the total sample as well as stratified by total motile sperm count below or above 10 million spermatozoa. Semen parameter Ejaculate volume (mL) Semen concentration (106/mL) Total sperm count (106/mL) Total motile sperm count (106/mL) Progressive motility (A+B) (%) Immotile sperm (C+D) (%)
Total sample (n [ 129)
TMSC < 10 (n [ 45)
TMSC ‡ 10 (n [ 84)
2.50 (1.70-3.90) 28.0 (8.00-63.0) 70.5 (14.4-156) 28.9 (3.08-78.7) 40 (25-51) 57 (46-72)
2.30 (1.30-2.90) 3.55 (0.60-9.05) 7.04 (0.57-16.8) 0.94 (0.00-3.20) 17 (2-30) 79 (60-93)
3.00 (1.80-4.20) 49.0 (28.0-79.0) 135 (72.0-198) 68.7 (30.0-96.5) 48 (39-55) 53 (46-61)
Note: Values are expressed as median (interquartile range). TMSC ¼ total motile sperm count. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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SUPPLEMENTAL TABLE 2 Dietary intake on nutrient levels in men with a strong and weak adherence to the (un)healthy dietary patterns. Unhealthy dietary pattern Healthy dietary pattern Nutrient Total fat Saturated fatty acids MUFA PUFA Linoleic Alanine Eicosapentaenoic acid Docosahexaenoic acid Cholesterol Proteins Carbohydrates Fiber Pyridoxine (vitamin B6) Cobalamin (vitamin B12) Folate Zinc Retinoic acid (vitamin A) Thiamin (vitamin B1) Riboflavin (vitamin B2) Niacin (vitamin B3) Vitamin C Vitamin E Alcohol
Weak
Strong
P value
Weak
Strong
P value
76.3 (61.6–96.0) 25.9 (20.6–30.6) 27.2 (22.5–35.1) 17.1 (13.8–22.7) 14.6 (11.6–19.4) 2.01 (1.61–2.51) 0.05 (0.03–0.09) 0.06 (0.02–0.11) 173 (133–232) 76.6 (61.7–91.0) 250 (200–308) 24.0 (18.0–29.1) 1.68 (1.40–2.07) 3.28 (2.37–4.46) 281 (220–380) 9.78 (7.81–11.1) 454 (275–635) 0.99 (0.82–1.25) 1.14 (0.93–1.45) 18.6 (15.2–24.1) 105 (72.9–146) 12.9 (10.8–17.6) 4.14 (0.50–14.0)
100 (84.9–123) 36.2 (27.5–41.7) 36.0 (28.9–42.3) 20.7 (16.3–26.4) 17.2 (13.4–22.2) 2.42 (1.84–2.93) 0.06 (0.04–0.12) 0.07 (0.04–0.16) 222 (169–296) 89.9 (78.6–108) 320 (278–373) 26.9 (21.4–33.2) 1.88 (1.62–2.66) 4.46 (3.74–5.57) 325 (263–443) 11.6 (10.1–13.9) 579 (413–766) 1.18 (0.99–1.45) 1.61 (1.32–1.96) 21.6 (17.5–26.7) 107 (83.2–130) 15.4 (12.5–22.6) 6.64 (2.47–18.5)
< .001 < .001 < .001 .028 .049 .022 .165 .162 < .001 < .001 < .001 .034 .007 < .001 .019 < .001 .012 .001 < .001 .022 .776 .005 .074
87.7 (66.4–104) 27.2 (21.2–37.2) 29.0 (23.4–37.2) 17.9 (14.7–23.8) 15.1 (12.2–20.2) 2.08 (1.57–2.60) 0.05 (0.03–0.08) 0.06 (0.03–0.11) 182 (141–256) 78.0 (64.6–94.0) 281 (212–342) 21.7 (17.8–28.1) 1.67 (1.40–2.15) 3.80 (2.72–4.75) 275 (220–380) 9.64 (7.88–12.4) 494 (352–677) 0.98 (0.84–1.24) 1.24 (0.97–1.65) 18.3 (15.3–24.1) 100 (76.8–125) 13.7 (11.2–17.7) 5.20 (1.32–16.3)
92.7 (73.6–122) 28.9 (25.7–40.4) 33.8 (25.9–43.0) 21.0 (15.1–25.1) 17.3 (12.6–21.5) 2.18 (1.73–2.97) 0.07 (0.04–0.13) 0.08 (0.04–0.17) 216 (161–253) 88.4 (78.5–108) 284 (247–353) 29.1 (24.6–35.2) 1.88 (1.67–2.44) 4.31 (3.31–5.58) 338 (282–465) 11.1 (10.1–13.9) 502 (326–854) 1.25 (1.04–1.41) 1.41 (1.22–1.70) 21.7 (18.3–26.6) 119 (87.3–156) 16.3 (11.3–21.0) 4.14 (1.59–17.4)
.159 .212 .100 .184 .216 .134 .030 .089 .164 .003 .271 < .001 .018 .043 .001 .002 .535 .001 .016 .023 .056 .109 .650
Note: Values are expressed as median (interquartile range). MUFA ¼ monounsaturated fatty acids; PUFA ¼ polyunsaturated fatty acids. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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Strong adherence to a healthy dietary pattern is associated with better semen quality, especially in men with poor semen quality gine P. M. Steegers-Theunissen, M.D., Ph.D.,a,b Jeanne H. M. de Vries, M.Sc.,c Elsje C. Oostingh, M.D.,a Re Joop S. E. Laven, M.D., Ph.D.,d and Maria P. H. Koster, M.D., Ph.D.a a
Department of Obstetrics and Gynecology, Division of Obstetrics and Prenatal Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam; b Department of Pediatrics, Division of Neonatology, Erasmus MC, University Medical Centre Rotterdam, Rotterdam; c Department of Human Nutrition, Wageningen University, Wageningen; and d Department of Obstetrics and Gynecology, Division of Reproductive Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, the Netherlands
Objective: To study associations between periconceptional dietary patterns and semen quality parameters. Design: Prospective periconception cohort study. Setting: Tertiary hospital. Patient(s): One hundred and twenty-nine male partners of pregnant women who participated in the Rotterdam Periconception Cohort (Predict study). Intervention(s): None. Main Outcome Measure(s): Semen quality parameters—ejaculate volume, sperm concentration, total sperm count, progressive motility, immotile sperm, and total motile sperm count (TMSC). Result(s): Men included in our study were on average 35 (6 standard deviation) years old and had a body mass index of 26.4 4 kg/ m2. Two dietary patterns were identified using principle component analysis, which were labeled as ‘‘healthy’’ and ‘‘unhealthy.’’ An increase of one factor score (stated as b) represented an increase of 1 standard deviation. Sperm concentration (b ¼ 0.278; 95% CI, 0.112–0.444), total sperm count (b ¼ 1.369; 95% CI, 0.244–2.495), progressive motility (b ¼ 4.305; 95% CI, 0.675–7.936), and TMSC (b ¼ 0.319; 95% CI, 0.113–0.526) were all positively associated with a strong adherence to the healthy dietary pattern. Subgroup analysis showed that these associations were mainly present in men with a TMSC <10 million spermatozoa. Although there was a trend toward a diminution in semen quality, we found no statistically significant associations with strong adherence to the unhealthy dietary pattern. Conclusion(s): The positive associations between strong adherence to a healthy dietary pattern and semen parameters in men with poor semen quality support the importance of preconceptional tailored nutritional counseling and coaching of couples who are trying to conceive. (Fertil SterilÒ 2017;-:-–-. Ó2017 by American Society for Reproductive Medicine.) Key Words: Dietary pattern, male subfertility, semen analysis Discuss: You can discuss this article with its authors and with other ASRM members at https://www.fertstertdialog.com/users/ 16110-fertility-and-sterility/posts/14960-23415
O
ver the past decades, several studies have provided evidence that semen quality in humans might be decreasing, which
might lead to an increase in male subfertility (1, 2). This can be caused by intrinsic factors such as genetic or congenital disorders and cancer, but a
Received November 18, 2016; revised February 10, 2017; accepted February 11, 2017. E.C.O. has nothing to disclose. R.P.M.S.-T. has nothing to disclose. J.H.M.d.V. has nothing to disclose. J.S.E.L. is a consultant for Euroscreen, Roche, outside of the submitted work, and holds a grant from Dutch Heart Association, outside of the submitted work. M.P.H.K. has nothing to disclose. Reprint requests: Maria P. H. Koster, M.D., Ph.D., Department of Obstetrics and Gynecology, Division of Obstetrics and Prenatal Medicine, Erasmus MC, University Medical Centre Rotterdam, P.O. Box 2040, Rotterdam 3000 CA, the Netherlands (E-mail: [email protected]). Fertility and Sterility® Vol. -, No. -, - 2017 0015-0282/$36.00 Copyright ©2017 American Society for Reproductive Medicine, Published by Elsevier Inc. http://dx.doi.org/10.1016/j.fertnstert.2017.02.103 VOL. - NO. - / - 2017
decline in semen quality is also observed in healthy men without any adverse medical history (2, 3). Besides intrinsic factors, semen quality can also be affected by modifiable lifestyle behaviors. For example, studies have suggested that specific nutritional factors can affect semen quality (4–6). Parental nutritional status is a crucial determinant of normal reproductive function (7), and the nutritional environment of the fetus affects the future child's health, as stated by the 1
ORIGINAL ARTICLE: ANDROLOGY Developmental Origin of Health and Disease paradigm (8). Malnutrition is known to disturb several metabolic pathways, and prominent among these is the one-carbon (1-C) metabolism (9, 10). The 1-C metabolism is in particular essential for DNA synthesis and phospholipid and protein biosynthesis. Derangements in this metabolism can lead to excessive oxidative stress, which can detrimentally affect gametogenesis and fertilization (11, 12). Several nutrients serve as substrate or cofactor to support the 1-C metabolism, such as folate and vitamin B12 (11). A low intake of full-fat dairy food, sweets, and processed meat and a high intake of folate-rich food sources such as fruits and vegetables have been positively associated with healthy semen quality (5, 6, 13–15). A high intake of fruits, vegetables, fish, and whole grains is associated with less sperm DNA damage than usual (4). Moreover, the use of folic acid and zinc supplements is known to increase the total normal sperm count in subfertile men (16, 17). Despite these findings, the impact of a healthy or unhealthy dietary pattern remains unclear because most studies have only focused on the association between semen quality and a single nutrient or food group or only a few nutrients or food groups. This single nutrient approach may be limited because people consume meals that consist of a varying combination of foods that contain lots of different nutrients (18, 19). Some other studies have used dietary pattern analysis, a more accurate approach, to identify the effect of the balance between food groups and nutrients on semen quality (19–21). During preconception counseling often little attention is paid to nutrition, especially in men. However, an unhealthy dietary pattern can be modified, so couples planning a pregnancy may benefit from the knowledge of the possible positive effect of nutrition on semen quality. To substantiate this, we identified dietary patterns in men and studied the associations with semen quality parameters.
MATERIALS AND METHODS Study Population This study was part of the Rotterdam Periconceptional Cohort (Predict study), an ongoing prospective, tertiary, hospitalbased cohort study embedded in patient care and conducted at the department of Obstetrics and Gynecology of the Erasmus University Medical Centre, Rotterdam, the Netherlands. The details of this study have previously been described elsewhere (22). For the current study, we selected men from the participating couples who were included in the study before 12 weeks of gestation between November 2010 and November 2014. Men were excluded when a semen sample was not available or when there was no information about their dietary patterns based on a food frequency questionnaire (FFQ). The window between the date of semen analysis and the date on which the FFQ was completed was restricted to a maximum of 1 year because of the accumulating evidence that dietary patterns remain reasonably constant over time except for in periods of dieting and illness (23–25). If there was more than one semen sample available, the parameters 2
of the sample closest to the moment of completing the FFQ were used in this study (Fig. 1). The study protocol has been approved by the medical ethical and institutional review board of the Erasmus MC, University Medical Centre in Rotterdam, the Netherlands, and all male participants provided written informed consent (METC Erasmus MC 2004-277).
Data Collection At study entry, all male participants completed a selfadministered general questionnaire covering details on paternal age, ethnicity, educational level, medical history, previous children and periconceptional lifestyle (smoking, alcohol use, and folic acid or multivitamin supplements use) at enrollment. At the same moment, qualified research nurses obtained anthropometric measurements (height, weight, waist-hip ratio, and blood pressure) (22). A validated semiquantitative FFQ, developed by the division of Human Nutrition, Wageningen University, the Netherlands, was used to estimate habitual food intake over the 4 weeks before study entry (26, 27). The FFQ consists of 196 food items structured according to meal patterns, with questions including consumption frequency, portion size, and preparation method. Intake of food, food groups, and energy and nutrients were determined using the Dutch food composition table (28). At enrollment, the research nurses checked the FFQs in a standardized manner for completeness and consistency.
Semen Analysis Semen samples were collected via masturbation into polypropylene containers. Within 1 hour the samples were liquefied, and the semen parameters—semen volume, sperm concentration, total sperm count, percentage progressive motility, and percentage immotile motility—were assessed according to World Health Organization (WHO) guidelines (29). We defined normospermia as a total motile sperm count (TMSC) of R10 million spermatozoa. All semen analyses were performed by expert laboratory staff at the Erasmus MC, University Medical Center Rotterdam, the Netherlands. Semen samples were not routinely collected as part of the Predict study; they were collected only when there was a medical indication. Semen parameters were retrieved from medical records to obtain all the data required for our study.
Statistical Analysis First, we compared the baseline characteristics between the men who were included and excluded in our study to investigate whether our study sample was a representative reflection of the entire Predict cohort (selection bias). These characteristics were expressed as medians with interquartile ranges or absolute numbers with percentages and were compared using either Mann-Whitney U or chi-square tests. A total of 196 food items from the FFQs were reduced to 23 predefined food groups based on origin and similar nutrient content (18). Next, we performed a principal VOL. - NO. - / - 2017
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FIGURE 1
Flow chart of the source and included study population. FFQ ¼ Food Frequency Questionnaire. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
component analysis (PCA) to identify dietary patterns based on the correlation among food groups (19). Practically, PCA is a standard multivariate statistical technique that aggregates specific food groups on the basis of the degree to which food items are reciprocally correlated (19, 30). When PCA is performed, a factor loading is automatically calculated for each food group, showing the extent to which each food group is correlated with the specific dietary pattern. Finally, each person automatically receives a score to represent their adherence to every dietary pattern. Our PCA resulted in the identification of 23 dietary patterns (principal components). To reduce the bias of multiple testing and to identify the most common used dietary patterns in the study population, we selected two dietary patterns with eigenvalues of >2.0 VOL. - NO. - / - 2017
for investigation of the associations with the semen parameters. Additionally, the nutrient intake in men with strong (positive correlation) or weak (negative correlation) adherence to these patterns was calculated and compared using Mann-Whitney U tests. Continuous semen parameters (volume, count, and concentration) were log transformed to obtain normal distributions. Linear regression analysis was performed to establish associations between the factor score of each dietary pattern as independent variables and the semen parameters as dependent variables. An increase of one factor score (stated as b) is equal to an increase of 1 standard deviation and can thus be interpreted as such. First, we performed a crude analysis that was only adjusted for energy intake. Next, we constructed an 3
ORIGINAL ARTICLE: ANDROLOGY adjusted model in which we additionally corrected for potential confounders that were based on study population and literature (i.e., paternal age, body mass index, ethnicity, educational level, smoking, and alcohol use). Finally, we performed a similar multivariable linear regression analysis among subgroups of men with a TMSC below or above 10 million spermatozoa. P< .05 was considered statistically significant. All analyses were performed using the Statistical Package for the Social Sciences software for Windows (IBM SPSS, Statistics for Windows, version 21.0).
RESULTS A total of 1,435 men participated in the Predict study. After excluding the men who did not match our inclusion criteria, 129 men were further analyzed (Fig. 1). The baseline characteristics of the included and excluded men are shown in Table 1. Men included in our study were on average older (36 vs. 34 years; P¼ .001), a higher percentage used folic acid supplements (21.7% vs. 6.9%; P< .001), and a higher percentage conceived by in vitro fertilization/intracytoplasmic sperm injection (IVF-ICSI) treatment (59.7% vs. 13.0%; P< .001). Also, these men seemed to have slightly lower TMSC compared with the men who were excluded. All other
characteristics did not statistically significantly differ between the groups. The semen parameters of our study group, stratified by TMSC below and above 10 million spermatozoa, are shown in Supplemental Table 1. The two dietary patterns (selected from a total of 23 patterns) identified by PCA explained 23.1% of the variance of the overall dietary intake of the total study population. The first pattern was labeled ‘‘unhealthy’’ because of high intakes of dairy, mayonnaise, margarines, sauces, snacks, and sweets, which explained 12.2% of the total variance. The second pattern was labeled ‘‘healthy’’ because of high intakes of cereals, fruits, legumes, vegetables, and olive oil, which explained 10.9% of the total variance (Table 2). Supplemental Table 2 shows the macronutrient and micronutrient intakes of men with strong or weak adherence to the healthy or unhealthy dietary pattern. Men with a strong adherence to the healthy dietary pattern had a higher vitamin intake and slightly higher intake of healthy fats (polyunsaturated fatty acids, eicosapentaenoic acid, docosahexaenoic acid) compared with the men who had a weak adherence to this pattern or a strong adherence to the unhealthy dietary pattern. After adjustment for potential confounders, a strong adherence to the healthy dietary pattern was positively
TABLE 1 Baseline characteristics of participants of the Predict cohort that were included and excluded in the current study. Participants Characteristics Age (y) BMI (kg/m2) Geographic origin, n (%) Western Non-Western Missing Educational level, n (%) High Intermediate Low Missing Lifestyle, n (%) Smoking (yes) Missing Alcohol (yes) Missing Folic acid supplement use (yes) Missing Mode of Conception Spontaneous Hormone treatment/IUI IVF–ICSI Missing Semen parameters Ejaculate volume (mL) Sperm concentration (106/mL) Total sperm count (106/mL) Total motile sperm count (106/mL) Progressive motility (A þ B) (%) Immotile sperm (C þ D) (%) Missing
Included (n [ 129)
Excluded (n [ 1,306)
36 (32–40) 25.1 (22.9–27.3)
34 (30–37) 25.1 (23.3–27.7)
109 (87.9) 15 (12.1) 5
748 (87.3) 109 (12.7) 578
60 (49.2) 56 (45.9) 6 (4.9) 7
399 (47.4) 370 (43.9) 73 (8.7) 593
31 (26.2) 11 89 (74.8) 10 26 (21.7) 9
252 (36.8) 622 614 (89.8) 622 46 (6.9) 643
34 (26.4) 18 (13.9) 77 (59.7) 0
745 (80.1) 64 (6.9) 121 (13.0) 376
P value .001 .548 .846
.369
2.50 (1.70–3.90) 28.0 (8.00–63.0) 70.5 (14.4–156) 28.9 (3.08–78.7) 40 (25–51) 57 (46–72) 0
2.60 (2.00–4.00) 37.0 (15.0–61.0) 103 (29.0–206) 40.3 (10.1–88.9) 42 (29–52) 56 (46–66) 1,095
.568 .973 < .001 < .001
.322 .222 .029 .058 .557 .708
Note: Values are expressed as median (interquartile range) or as number (%). BMI ¼ body mass index; ICSI ¼ intracytoplasmic sperm injection; IUI ¼ intrauterine insemination; IVF ¼ in vitro fertilization; RBC ¼ red blood cell. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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TABLE 2 Factor scores of 23 food groups within two dietary patterns identified through principle component analysis. Food groups Variance explained (%) Alcohol Cereals Dairy Eggs Fish Fruits Grains (refined) Grains (whole wheat) Legumes Liquid fat Meat Mayonnaise Margarines Nuts Nonalcohol Olive oil Potatoes Soup Snacks Sauces Sweets Solid fat Vegetables
Unhealthy pattern
Healthy pattern
12.2 0.103 0.126 0.742 0.099 0.110 0.140 0.119 0.296 0.150 0.163 0.067 0.438 0.266 0.006 0.084 0.019 0.225 0.117 0.324 0.662 0.648 0.102 0.061
10.9 0.147 0.659 0.007 0.111 0.071 0.206 0.009 0.077 0.807 0.114 0.122 0.061 0.056 0.185 0.149 0.291 0.085 0.007 0.060 0.042 0.117 0.138 0.708
Note: Factor scores are presented as correlation coefficients. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
associated with TMSC (b ¼ 0.319; 95% CI, 0.113–0.526), sperm concentration (b ¼ 0.278; 95% CI, 0.112–0.444), sperm count (b ¼ 1.369; 95% CI, 0.244–2.495), and progressive motility (b ¼ 4.305; 95% CI, 0.675–7.936). In a subgroup analysis, these positive associations remained but were only statistically significant in men with TMSC <10 million spermatozoa (Table 3). Strong adherence to the unhealthy dietary pattern showed a trend toward diminution in TMSC, sperm concentration, sperm count, progressive motility, and immotile sperm although these results were not statistically significant (Table 3).
DISCUSSION We identified two dietary patterns among men of couples visiting a tertiary hospital during early pregnancy and observed that high adherence to a healthy dietary pattern is positively associated with better semen quality, in particular in men with a TMSC <10 million spermatozoa. Furthermore, a trend of diminution of semen quality was seen in men with a high adherence to an unhealthy dietary pattern. These results confirm our previous findings in which only male partners of women undergoing IVF or ICSI treatment were included. In that study it was shown that a high adherence to a health-conscious pattern resulted in a lower DNA fragmentation index as well as a higher percentage of progressive motility and thus better semen quality (4). The study of Jurewicz et al. (6) showed that high adherence to a prudent pattern, representing high intake of fish, chicken, fruits, cruciferous vegetables, tomatoes, leafy green vegetables, VOL. - NO. - / - 2017
and whole grains, increases semen quality, reflected by a higher sperm concentration and lower DNA fragmentation index, and the testosterone level. The same investigators also observed a positive effect of strong adherence to the prudent pattern in men and a decrease in chromosomal disorders in offspring (31). These studies have in common that in particular high intakes of fruits, vegetables, and legumes are associated with a higher sperm concentration, and we also demonstrated positive effects on sperm count, TMSC, and percentage progressive motility. The positive effects of high adherence to a prudent pattern are further substantiated by Gaskins et al. (32) who showed a positive and statistically significant association with an increased percentage of progressive sperm motility. In that study the investigators only examined healthy adolescent men whereas our study included a greater variety in ages, making it more applicable to the general reproductive male population. Other studies of the same group also showed that a higher total fat intake was negatively associated with total sperm count, sperm concentration, and sperm morphology and was positively associated with asthenozoospermia (33–35). These associations appeared to be driven primarily by the intake of saturated fats derived from dairy (34). This is substantiated by our results showing that the intake of dairy, but also of other saturated fat sources such as mayonnaise, snacks, and sauces, was low in men with strong adherence to the healthy dietary pattern. On the other hand, the intake of healthy fats such as polyunsaturated fatty acids, eicosapentaenoic acid, and docosahexaenoic acid was higher in men who strongly adhered to the healthy dietary pattern. The positive effect of these compounds of the healthy dietary pattern on semen quality can be explained by the high contents in spermatozoa, which play a crucial role in fertilization (33). In addition, the intake of trans fatty acids from largely the same sources (e.g., snacks, mayonnaise, and other sauces) is also known to negatively influence semen quality (34, 35). We also observed that the contribution of sugars was high in the unhealthy dietary pattern, which is supported by the inverse association shown between sugar intake and sperm motility among young healthy men (36). These findings all support the trends of positive and negative associations between healthy and unhealthy dietary patterns, respectively, and the semen parameters of men in our study sample. Micronutrients such as vitamins and trace metals also play an important role in normal testicular development, spermatogenesis, and sperm motility. For example, seminal plasma zinc concentrations statistically significantly differ between fertile and subfertile men and total normal sperm count increases after supplementation of folic acid and zinc sulfate (15, 17, 37). Both groups of micronutrients are present in cereals, legumes, and fruits in particular, the food groups that were highly represented in our healthy dietary pattern. Vitamins C and E also play a role in male fertility (38–40). These vitamins can be found in fruits and vegetables, the same food groups that strongly contribute to our healthy dietary pattern. These findings are supported by the negative associations between low intake of fruits and 5
Associations between the two identified dietary patterns and semen parameters for the total sample as well as stratified by total motile sperm count below or above 10 million spermatozoa. TMSC <10
Total sample Unhealthy Semen parameters
Crude
Ejaculate volume b 0.041 95% CI 0.049 to 0.131 Sperm concentration b 0.044 95% CI 0.115 to 0.203 Total sperm count b 0.040 95% CI 1.064 to 0.985 Total motile sperm count b 0.039 95% CI 0.155 to 0.234 Progressive motility (A þ B) b 0.327 95% CI 3.045 to 3.700 Immotile sperm (C þ D) b 0.716 95% CI 4.506 to 3.073
Healthy
TMSC ‡10
Unhealthy
Healthy
Unhealthy
Healthy
Adjusted
Crude
Adjusted
Crude
Adjusted
Crude
Adjusted
Crude
Adjusted
Crude
Adjusted
0.003 0.116 to 0.121
0.005 0.085–0.096
0.003 0.100 to 0.093
0.121 0.040 to 0.282
0.267 0.069–0.466
0.012 0.258 to 0.234
0.025 0.286 to 0.237
0.008 0.114 to 0.099
0.121 0.260 to 0.017
0.023 0.121 to 0.074
0.018 0.128 to 0.092
0.007 0.221 to 0.207
0.229 0.075–0.383
0.278 0.112–0.444
0.094 0.129 to 0.318
0.055 0.229 to 0.339
0.384 0.069–0.699
0.474 0.174–0.773
0.052 0.169 to 0.064
0.030 0.190 to 0.131
0.037 0.070 to 0.144
0.049 0.076 to 0.173
0.794 2.176 to 0.588
1.262 0.267–2.257
1.369 0.244–2.495
0.090 0.541to 0.720
0.041 0.800 to 0.882
1.214 0.362–2.065
1.549 0.687–2.411
0.461 1.404 to 0.481
1.160 2.455 to 0.135
0.289 0.579 to 1.157
0.479 0.538 to 1.496
0.124 0.387 to 0.139
0.282 0.094–0.471
0.319 0.113–0.526
0.062 0.135 to 0.259
0.036 0.218 to 0.289
0.375 0.104–0.645
0.476 0.219–0.733
0.071 0.192 to 0.050
0.202 0.360 to 0.043
0.038 0.073–0.150
0.069 0.058–0.196
2.081 6.628 to 2.465
3.683 0.372–6.993
4.305 0.675–7.936
0.524 6.036 to 4.989
0.208 6.249 to 5.833
4.659 3.413 to 12.732
6.763 0.103 to 13.629
0.751 3.111 to 1.609
3.101 6.346 to 0.145
0.156 2.012 to 2.324
0.864 1.7015 to 3.443
0.044 5.260 to 5.172
1.154 4.940 to 2.632
0.566 4.815 to 3.682
2.219 12.249 to 7.811
5.941 19.810 to 7.928
3.871 11.028 to 18.770
4.554 12.191 to 21.298
0.751 1.609 to 3.111
3.101 0.145 to 6.346
0.156 2.324 to 2.012
0.864 3.443 to 1.715
Note: Adjusted for total energy intake, body mass index, age, ethnicity, and smoking. CI ¼ confidence interval; TMSC ¼ total motile sperm count. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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TABLE 3
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Fertility and Sterility® vegetables and the risk of oligoasthenoteratospermia (15), and high intake among the same food groups and a decreased risk of oligoasthenoteratospermia (14). The positive association between a healthy diet and semen quality was more pronounced in men with poor semen quality (i.e., TMSC <10 million spermatozoa). It is not likely that the influence of nutrition on semen quality differs between fertile and subfertile men. However, in men with poor semen quality the effect sizes may be larger because the range of TMSC is much smaller; therefore, it is very likely that there is more room for improvement compared to men with a TMSC R10 million spermatozoa (17). Moreover, the identified not statistically significant positive associations between dietary patterns and semen quality in men with TMSC R10 million spermatozoa may have been hampered by our sample size. The strengths of our study are, first, the availability of validated FFQs as this is a comprehensive method to obtain detailed information about the habitual intake of foods and, second, the use of PCA, a state-of-the-art method to identify dietary patterns (19, 30, 41). Besides, all semen analyses were performed in the same laboratory, which increases the internal validity of the data. However, our study also had some limitations. Both semen analyses and FFQ data were available for only a small proportion of men. Semen analyses were not routinely obtained as part of the Predict study and were only performed when there was a medical indication, so they had to be retrospectively matched to our cohort. Therefore, the time window between completing the FFQ and the semen analysis was relatively large for some men. Because dietary patterns have been shown to remain largely the same over a period of time, we believe that this still provides us with accurate information on the association between periconceptional nutritional status and semen quality (23–25). An expected consequence of the fact that the semen analyses were performed by indication is that our study sample consists of a higher proportion of subfertile men (see Table 1). Nevertheless, 26.4% of all pregnancies were spontaneously conceived, and for the remaining pregnancies subfertility could also have been due to female factors. Knowledge about the associations found in our study can be used in routine preconception counseling. This counseling can be a valuable tool to identify and reduce modifiable health risks in couples who are trying to conceive (42). Because only a small proportion of couples make use of preconception care, more public awareness is necessary. In our modern digital era, mobile health (mHealth) or eHealth tools, such as the online coaching platform Smarter Pregnancy, may contribute to this awareness and stimulate couples to change and maintain more healthy dietary patterns and lifestyle choices (43).
of the considerable effect of nutrition also on male fertility. To create this awareness, we recommend that counseling on dietary patterns, for both women and men, should be implemented in preconception care worldwide. Acknowledgments: The authors thank S. P. Willemsen for his assistance with the statistical analysis and the Predict research team for their contribution to the data collection.
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CONCLUSION Here we demonstrate that a strong adherence to a healthy dietary pattern is positively associated with semen parameters, especially in men with low TMSC. Herewith we support findings from other studies and emphasize the importance of making sure that every couple planning a pregnancy is aware VOL. - NO. - / - 2017
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SUPPLEMENTAL TABLE 1 Semen parameters of included men for the total sample as well as stratified by total motile sperm count below or above 10 million spermatozoa. Semen parameter Ejaculate volume (mL) Semen concentration (106/mL) Total sperm count (106/mL) Total motile sperm count (106/mL) Progressive motility (A+B) (%) Immotile sperm (C+D) (%)
Total sample (n [ 129)
TMSC < 10 (n [ 45)
TMSC ‡ 10 (n [ 84)
2.50 (1.70-3.90) 28.0 (8.00-63.0) 70.5 (14.4-156) 28.9 (3.08-78.7) 40 (25-51) 57 (46-72)
2.30 (1.30-2.90) 3.55 (0.60-9.05) 7.04 (0.57-16.8) 0.94 (0.00-3.20) 17 (2-30) 79 (60-93)
3.00 (1.80-4.20) 49.0 (28.0-79.0) 135 (72.0-198) 68.7 (30.0-96.5) 48 (39-55) 53 (46-61)
Note: Values are expressed as median (interquartile range). TMSC ¼ total motile sperm count. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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SUPPLEMENTAL TABLE 2 Dietary intake on nutrient levels in men with a strong and weak adherence to the (un)healthy dietary patterns. Unhealthy dietary pattern Healthy dietary pattern Nutrient Total fat Saturated fatty acids MUFA PUFA Linoleic Alanine Eicosapentaenoic acid Docosahexaenoic acid Cholesterol Proteins Carbohydrates Fiber Pyridoxine (vitamin B6) Cobalamin (vitamin B12) Folate Zinc Retinoic acid (vitamin A) Thiamin (vitamin B1) Riboflavin (vitamin B2) Niacin (vitamin B3) Vitamin C Vitamin E Alcohol
Weak
Strong
P value
Weak
Strong
P value
76.3 (61.6–96.0) 25.9 (20.6–30.6) 27.2 (22.5–35.1) 17.1 (13.8–22.7) 14.6 (11.6–19.4) 2.01 (1.61–2.51) 0.05 (0.03–0.09) 0.06 (0.02–0.11) 173 (133–232) 76.6 (61.7–91.0) 250 (200–308) 24.0 (18.0–29.1) 1.68 (1.40–2.07) 3.28 (2.37–4.46) 281 (220–380) 9.78 (7.81–11.1) 454 (275–635) 0.99 (0.82–1.25) 1.14 (0.93–1.45) 18.6 (15.2–24.1) 105 (72.9–146) 12.9 (10.8–17.6) 4.14 (0.50–14.0)
100 (84.9–123) 36.2 (27.5–41.7) 36.0 (28.9–42.3) 20.7 (16.3–26.4) 17.2 (13.4–22.2) 2.42 (1.84–2.93) 0.06 (0.04–0.12) 0.07 (0.04–0.16) 222 (169–296) 89.9 (78.6–108) 320 (278–373) 26.9 (21.4–33.2) 1.88 (1.62–2.66) 4.46 (3.74–5.57) 325 (263–443) 11.6 (10.1–13.9) 579 (413–766) 1.18 (0.99–1.45) 1.61 (1.32–1.96) 21.6 (17.5–26.7) 107 (83.2–130) 15.4 (12.5–22.6) 6.64 (2.47–18.5)
< .001 < .001 < .001 .028 .049 .022 .165 .162 < .001 < .001 < .001 .034 .007 < .001 .019 < .001 .012 .001 < .001 .022 .776 .005 .074
87.7 (66.4–104) 27.2 (21.2–37.2) 29.0 (23.4–37.2) 17.9 (14.7–23.8) 15.1 (12.2–20.2) 2.08 (1.57–2.60) 0.05 (0.03–0.08) 0.06 (0.03–0.11) 182 (141–256) 78.0 (64.6–94.0) 281 (212–342) 21.7 (17.8–28.1) 1.67 (1.40–2.15) 3.80 (2.72–4.75) 275 (220–380) 9.64 (7.88–12.4) 494 (352–677) 0.98 (0.84–1.24) 1.24 (0.97–1.65) 18.3 (15.3–24.1) 100 (76.8–125) 13.7 (11.2–17.7) 5.20 (1.32–16.3)
92.7 (73.6–122) 28.9 (25.7–40.4) 33.8 (25.9–43.0) 21.0 (15.1–25.1) 17.3 (12.6–21.5) 2.18 (1.73–2.97) 0.07 (0.04–0.13) 0.08 (0.04–0.17) 216 (161–253) 88.4 (78.5–108) 284 (247–353) 29.1 (24.6–35.2) 1.88 (1.67–2.44) 4.31 (3.31–5.58) 338 (282–465) 11.1 (10.1–13.9) 502 (326–854) 1.25 (1.04–1.41) 1.41 (1.22–1.70) 21.7 (18.3–26.6) 119 (87.3–156) 16.3 (11.3–21.0) 4.14 (1.59–17.4)
.159 .212 .100 .184 .216 .134 .030 .089 .164 .003 .271 < .001 .018 .043 .001 .002 .535 .001 .016 .023 .056 .109 .650
Note: Values are expressed as median (interquartile range). MUFA ¼ monounsaturated fatty acids; PUFA ¼ polyunsaturated fatty acids. Oostingh. Dietary patterns and semen quality. Fertil Steril 2017.
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