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Influence of increasing body mass index on semen and reproductive hormonal parameters in a multi-institutional cohort of subfertile men
ORIGINAL ARTICLE: ANDROLOGY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Q3 Jared M. Bieniek, M.D.,a James A. Kashanian, M.D.,b Christopher M. Deibert, M.D.,c Ethan D. Grober, M.D.,d Kirk C. Lo, M.D.,d Robert E. Brannigan, M.D.,e Jay I. Sandlow, M.D.,f and Keith A. Jarvi, M.D.d 17 a 18 Department of Urology, Hartford Hospital, Hartford, Connecticut; b Department of Urology, Weill Cornell Medical 19 College, New York, New York; c Division of Urology, University of Nebraska Medical Center, Omaha, Nebraska; e Department of Urology, Northwestern University, Chicago, Illinois; f Department of Urology, Medical College of 20 Wisconsin, Milwaukee, Wisconsin; and d Division of Urology, Mount Sinai Hospital, University of Toronto, Toronto, 21 Ontario, Canada 22 23 24 Objective: To determine whether obesity affects serum and seminal measures of male reproductive potential among a multi25 institutional cohort. 26 Design: Retrospective multi-institutional cohort study. 27 Setting: Infertility clinics. 28 Patient(s): All men referred for male infertility evaluation from 2002 to 2014 (n ¼ 4,440). 29 Intervention(s): None. Main Outcome Measure(s): Collected reproductive parameters included hormonal (gonadotropins, T, E2, PRL) and semen analysis 30 (ejaculate volume, sperm concentration, motility, normal morphology) data. Body mass index (BMI) was calculated for all patients 31 with comparisons to reproductive parameters using univariate and multiparametric models. 32 Result(s): Based on World Health Organization definitions, 30.9% of the cohort was normal weight (BMI 18.5–24.9), 45.1% overweight 33 (25–29.9), and 23.3% obese (>30). Neither FSH nor LH demonstrated significant correlations with BMI on multivariate analysis. Total T 34 (r ¼ 0.27) and the T:E2 ratio (r ¼ 0.29) inversely varied with BMI, whereas E2 (r ¼ 0.13) had a direct correlation. On univariate 35 analyses, BMI had weak but significant negative correlations with ejaculate volume (r ¼ 0.04), sperm concentration (r ¼ 0.08), motility (r ¼ 0.07), and morphology (r ¼ 0.04). All parameters remained significant on multivariate modeling with the exception 36 of sperm motility. Rates of azoospermia and oligospermia were also more prevalent among obese (12.7% and 31.7%, respectively) 37 compared with normal weight men (9.8% and 24.5%). 38 Conclusion(s): In one of the largest cohorts of male fertility and obesity, serum hormone and semen parameters demonstrated mild but 39 significant relationships with BMI, possibly contributing to subfertility in this population. (Fertil SterilÒ 2016;-:-–-. Ó2016 by 40 American Society for Reproductive Medicine.) 41 Key Words: Body mass index, obesity, semen analysis, endocrinology, male infertility 42 Discuss: You can discuss this article with its authors and with other ASRM members at 43 44 45 46 47 or the past several decades, the and obese individuals currently make secondary to associated metabolic and 48 Centers for Disease Control have up more than two-thirds of the Ameranatomic pathophysiologic changes. 49 raised awareness on the obesity ican adult population (4). With Disease processes known to correlate 50 epidemic that is now spreading to increasing waistlines comes an with obesity include diabetes, hyper51 global proportions (1–3). Overweight increased rate of comorbid conditions tension, hyperlipidemia, and sleep 52 53 Received February 29, 2016; revised June 26, 2016; accepted June 27, 2016. 54 J.M.B. has nothing to disclose. J.A.K. has nothing to disclose. C.M.D. has nothing to disclose. E.D.G. has nothing to disclose. K.C.L. has nothing to disclose. 55 R.E.B. has nothing to disclose. J.I.S. has nothing to disclose. K.A.J. has nothing to disclose. 56 Reprint requests: Jared M. Bieniek, M.D., 85 Seymour Street, #416, Hartford, Connecticut 06106 (E-mail: [email protected]). 57 Fertility and Sterility® Vol. -, No. -, - 2016 0015-0282/$36.00 58 Copyright ©2016 American Society for Reproductive Medicine, Published by Elsevier Inc. http://dx.doi.org/10.1016/j.fertnstert.2016.06.041 59
Influence of increasing body mass index on semen and reproductive hormonal parameters in a multi-institutional cohort of subfertile men
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apnea and collectively contribute to the diagnosis of the metabolic syndrome. These obesity-associated comorbidities have ultimately been linked to increased mortality and a reduction in life expectancy (5–7). The metabolic changes of obesity can also affect reproductive function, thus altering the fecundity of much of our population as evidenced by the recent Centers for Disease Control statistics. In women, obesity is clearly linked to subfertility, as manifested by ovulatory disorders and idiopathic infertility (8). For men, there has been a concerning trend of declining sperm quality noted that mirrors the increasing rates of obesity (9). Spermatogenesis normally requires a controlled testicular environment and intact endocrine signaling through the hypothalamic-pituitary-gonadal axis. Increasing adiposity may alter the testicular milieu in such a way to alter normal sperm production and health. Cohort studies of specific relationships between semen parameters and obesity in the literature, however, have been inconsistent. Although some data have shown correlations with abnormal sperm number, motility, or morphology, others have suggested that no adverse effects were noted with increasing measures of obesity. Gathering data from individual studies, two meta-analyses completed on the topic came to contradictory conclusions with Sermondade et al. (10) reporting that detrimental changes may occur with increasing body mass index (BMI) and a smaller review by MacDonald et al. (11) countering that no significant relationships were found in their review of the data. In the latter metaanalysis, MacDonald et al. (11) also reported on reproductive hormone changes with obesity with a majority of trials finding negative correlations with T and a few observing positive relationships with estrogen (E) levels. Although changes in male reproductive hormones with increasing adiposity have been more evident, the effects on sperm number and health remain unclear. An understanding of the relationship between obesity and male fertility will allow physicians to better counsel men planning a family about their body habitus. This study correlates measures of obesity with hormonal and semen parameters among men presenting for fertility evaluation at multiple centers, together representing one of the largest cohorts ever published.
MATERIALS AND METHODS Patient demographics were captured in institutional review board-approved databases at three North American male infertility clinics. All men referred for clinical infertility evaluation and agreeing to inclusion were entered into respective databases. Two study centers (Study Centers 1 and 2) collected self-reported height and weight at the time of initial patient intake, whereas the remaining center (Study Center 3) measured height and weight at the initial visit. The BMI was calculated for each patient as a ratio of the weight (in kilograms) to height squared (in meters squared). The BMI was then categorized as underweight (<18.5), normal (18.5– 24.9), overweight (25–29.9), or obese (>30) based on World Health Organization classifications (12). In addition, obesity was subcategorized as class I (30–34.9), class II (35–39.9), or class III (>40).
Laboratory data including reproductive hormones and semen analyses were collected for all men with available height and weight data from 2002 to July 2014. Only hormonal blood work and semen analyses performed at the time of initial patient evaluation were selected for inclusion to reduce any confounding from fertility-related treatments. Patients without available laboratory data were excluded including those referred for vasectomy reversal, as we do not routinely check preoperative hormones or semen analyses in these men. The reproductive hormone parameters collected included total T, E2, FSH, LH, and PRL. Units were standardized across the centers for T (in nanomoles per liter) and E2 (in picomoles per liter). Standard units were used for FSH (in IU per milliliter), LH (in IU per milliliter), and PRL (in nanograms per milliliter). The T:E2 ratio was calculated as a simple ratio for each patient with available data. Semen samples were obtained from patients by masturbation with 2–5 days of abstinence before collection. Samples were then prepared in respective CLIA (Clinical Laboratory Improvement Amendments) certified andrology laboratories by one or two technicians specializing in semen analyses. Study Center 1 performed computer-assisted semen analysis quantification, whereas technicians at Centers 2 and 3 performed manual semen evaluation. Ejaculate volume and sperm concentration, motility, and morphology parameters were graded based on World Health Organization 5th edition criteria (13). Morphology data from Center 3 were excluded as they graded based on Kruger strict criteria alone and could not be compared with the other centers. The total motile count was calculated for each patient as the product of ejaculate volume, sperm concentration, and motility. Azoospermia was defined as zero sperm found on initial semen analysis and oligospermia included men with sperm present at concentrations <15 million/mL. Descriptive statistics were calculated for the overall cohort and individual study centers. Analysis of variance and c2 tests were used to compare continuous and categorical variables between study groups with P< .05 reported as significant. Nonparametric univariate analyses with Spearman's rank correlation were used to describe relationships between patient BMI and reproductive variables. Due to the high frequency of genetic or congenital abnormalities, azoospermic men were also excluded from subgroup analyses to ascertain whether any variable effects existed. As multiple tests were being performed on the same data set, false discovery correction was performed using the Benjamini-Hochberg procedure. Multiparametric linear models were additionally created to control for any variability due to patient age or study center. Statistical analyses were performed using the R software platform, version 2.15.2.
RESULTS During the study time period, complete height and weight results with evaluable laboratory data were available for 4,440 men with a mean age (SD) of 36.1 years (7.6 years) (Table 1). Study Center 1 included data from 835 patients, Center 2 included 3,309, and Center 3 included 296 patients. The average age of men evaluated at each institution was significantly different between the various centers VOL. - NO. - / - 2016
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TABLE 1 Baseline patient cohort demographics. Characteristic Patients, n (%) Mean age, y Mean BMI, kg/m2 BMI category, n (%) Underweight Normal weight Overweight Obese (>30) Class I Class II Class III T, nmol/L (%) Semen analysis, n (%)
Q2
All centers
Center 1 (TO)
Center 2 (NW)
Center 3 (WI)
P value
4,440 36.1 27.6
835 (18.8) 38.0 27.3
3,309 (74.) 35.9 27.4
296 (6.7) 33.8 30.8
< .001 < .001
30 (0.7) 1,373 (30.9) 2,004 (45.1) 1,033 (23.3) 687 (66.5) 206 (19.9) 140 (13.6) 2,973 (67.0) 4,236 (95.4)
5 (0.6) 258 (30.9) 397 (47.5) 175 (21.0) 133 (76.0) 29 (16.6) 13 (7.4) 699 (23.5) 650 (15.3)
25 (0.8) 1,059 (32.0) 1,504 (45.5) 721 (21.8) 478 (66.3) 148 (20.5) 95 (13.2) 1,978 (66.5) 3,291 (77.7)
0 (0) 56 (18.9) 103 (34.8) 136 (45.9) 76 (55.9) 29 (21.3) 32 (23.5) 296 (10.0) 295 (7.0)
.30 .001 .02 < .001 < .001 < .001 < .001 – –
Note: Body mass index (BMI) category percentages reflect frequency of each variable among each respective cohort; all other percentages reflect frequency within the specific variable. Bieniek. Obesity and male fertility. Fertil Steril 2016.
(38.0, 35.9, and 33.8 years, respectively; P< .001). Semen analysis data were available for 4,236 men (95.4%) in the cohort. The hormonal parameters available for data collection varied for each patient with T being the most commonly tested and available for 2,973 men (67.0%). Based on the World Health Organization BMI definitions, 30 (0.7%) men in the combined cohort were underweight, 1,373 (30.9%) normal weight, 2,004 (45.1%) overweight, and 1,033 (23.3%) obese. Of the obese individuals, 687 (66.5%) were subcategorized as Class I, 206 (19.9%) Class II, and 140 (13.6%) Class III. Table 1 includes the distribution of BMI groups across the three study centers. There were significant differences in the frequencies of normal weight, overweight, and obese individuals between the study centers. Within the obese subgroups, all classes similarly demonstrated significant variability between the centers. Overall, BMI was noted to correlate directly with patient age (r ¼ 0.18, P< .001).
Increasing BMI Negatively Impacts Reproductive Hormones Among the reproductive hormones collected, T, E2, and LH were found to have significant relationships with BMI on adjusted univariate analyses, whereas only T and E2 remained significant on multiparametric testing (Table 2). Testosterone (r ¼ 0.27, P< .001) had a significant negative correlation, whereas E2 (r ¼ 0.13, P< .001) conversely demonstrated a positive relationship. The T:E2 ratio (r ¼ 0.29, P< .001) similarly had a negative correlation with BMI for patients with available data. On univariate adjusted testing, LH (r ¼ 0.06, P¼ .007) showed a weak direct relationship with BMI, whereas FSH (r ¼ 0.04, P¼ .06) demonstrated a weak trend that did not reach statistical significance. Prolactin levels were not found to be related to BMI values.
Obesity Associated with Declining Semen Parameters Ejaculate volume, sperm concentration, motility, and morphology were similarly noted to have significant negative
correlations with BMI on univariate testing (Table 2). The strongest correlations with increasing BMIs were seen with sperm concentration (r ¼ 0.08, P< .001) and motility (r ¼ 0.07, P< .001). The relationship between sperm concentration and BMI did not change significantly when azoospermic men were excluded (data not shown). When applying multiparametric models, all semen parameter relationships with BMI remained significant with the exception of sperm motility (P¼ .07). The negative correlation between BMI and calculated total motile count (r ¼ 0.10, P< .001) was even more pronounced than the individual sperm parameter measures. The prevalence of decreased sperm concentrations were found more commonly in obese men as well (Table 3). Although overweight men had a similar rate of azoospermia compared with the normal weight reference group (9.3% vs. 9.8%), oligospermia was more prevalent than the normal weight reference group (27.7% vs. 24.5%). Including all obese men, prevalence
TABLE 2 Body mass index correlations with hormonal and semen analysis parameters. Characteristic Hormonal parameter Total T E2 T:E ratio FSH LH PRL Semen parameter Ejaculate volume Sperm concentration Motility Morphology Total motile count
n
Correlation (r)
Uni-adjust (P value)
Multi (P value)
2,973 2,057 2,021 2,728 2,401 1,804
0.27 0.13 0.29 0.04 0.06 0.04
< .001 < .001 < .001 .062 .007 .067
< .001 < .001 < .001 .605 .072 .072
4,234 4,087
0.04 0.08
.009 < .001
.048 < .001
3,653 3,225 3,829
0.07 0.04 0.10
< .001 .025 < .001
.074 .015 < .001
Note: Multi ¼ multivariate model adjusting for age and study center; Uni-adjust ¼ univariate adjusted model. Bieniek. Obesity and male fertility. Fertil Steril 2016.
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ORIGINAL ARTICLE: ANDROLOGY 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
TABLE 3 Rates of sperm concentration abnormalities between study and body mass index (BMI) groups. Azoospermia Characteristic BMI category Underweight Normal weight Overweight Obese Class I Class II Class III All patients
Oligospermia
Normospermia
N (%)
RR (95% CI)
N (%)
RR (95% CI)
N (%)
P value
6 (22.2) 124 (9.8) 173 (9.3) 120 (12.7) 65 (10.4) 27 (14.8) 28 (21.5) 423 (10.4)
2.31 (1.16–4.61) – 0.99 (0.80–1.24) 1.44 (1.14–1.81) 1.15 (0.87–1.52) 1.71 (1.18–2.47) 2.67 (1.89–3.75) –
7 (25.9) 310 (24.5) 514 (27.7) 299 (31.7) 191 (30.5) 60 (32.8) 48 (36.9) 1,130 (27.6)
1.23 (0.67–2.27) – 1.13 (1.00–1.27) 1.34 (1.18–1.53) 1.25 (1.08–1.45) 1.43 (1.14–1.78) 1.75 (1.40–2.19) –
14 (51.9) 832 (65.7) 1,166 (62.9) 522 (55.4) 371 (59.2) 95 (51.9) 53 (40.8) 2,534 (62.0)
.079 – .005 < .001 .001 .001 < .001 –
Note: % reflects frequency of each sperm concentration in each body mass index (BMI) category; relative risk (RR) ratios and P values reflect comparison between each category and normal weight reference group. CI ¼ confidence interval. Bieniek. Obesity and male fertility. Fertil Steril 2016.
of azoospermia and oligospermia were 12.7% and 31.7%, respectively, both more than rates of decreased sperm concentration among the normal weight group (P< .001). Relative risks of oligospermia and azoospermia among the obese male cohort were 1.34 (95% confidence interval 1.18–1.53) and 1.44 (95% confidence interval 1.14–1.81), respectively. Within the obesity subclasses, significantly increased rates of azoospermia and oligospermia were similarly observed.
DISCUSSION This series represents one of the largest ever published comparing measures of obesity to male reproductive hormonal and semen parameters. Our results demonstrate significant alterations in the hormonal milieu of men pursuing fertility based on their body habitus. We likewise demonstrated correlations with increasing measures of obesity and worsening semen parameters, although the relationships were mild in severity. Abnormal sperm concentration, including azoospermia and oligospermia, was more frequently found among obese men and even more pronounced with increasing subclasses of obesity. Similar cohort studies comparing body habitus to reproductive parameters in men have reported variable results. In one of the largest cross-sectional series to date, consisting of 1,989 normal European men, Aggerholm et al. (14) found no significant relationships between semen parameters and BMI measures. Another large series from Duits et al. (15) likewise concluded that there were no adverse effects on sperm quality seen with obesity among 1,401 subfertile patients. Several contradictory series (16–19), however, have noted variable declines in select semen parameters such as ejaculate volume, sperm concentration, motility, and morphology, although the results are inconsistent between individual studies. Paasch et al. (20), although finding no significant relationship in their overall cohort of 2,157 men, interestingly saw a significant relationship between BMI and total sperm count in the subcohort of men aged 20– 30 years. Discrepant findings between many of these various cohort studies may be related to the review of varying populations of normal and infertile men. Individual
cohorts have typically consisted of approximately 2,000 or fewer patients until recently when Belloc et al. (21) reviewed data from 10,665 French men undergoing evaluation at a single fertility center. They observed a decreased ejaculate volume, sperm concentration, motility, and total count with extremely obese individuals (BMI >40), whereas sperm morphology was unaffected. Results from our multi-institutional cohort suggest similar effects with an additional weak but significant negative correlation between BMI and normal sperm morphology. As mentioned, many of the prior studies in the literature have been relatively small with few including North American patients. The largest North American series to date, in fact, included only 520 men (17). Worldwide obesity rates are known to vary greatly between countries, with the highest prevalence in North America according to 2008 data (22). The European and Asian populations constituting most of the previously reported cohorts tend to have much lower obesity rates. For example, only 13.4% of the nearly 2,000 British men reviewed by Shayeb et al. (18) were obese compared with 23.3% of our cohort. This discrepancy has contributed to smaller obese populations in published studies and suggests that correlations between male adiposity and reduced fecundity may be underappreciated. The current North American series includes a large obese population, further confirming negative correlations between increasing male BMI and nearly all semen parameters. MacDonald et al. (11) published the results of the initial meta-analyses on the topic of obesity and male subfertility in 2010, concluding that no significant relationship existed between BMI and semen parameters based on pooled data from five studies including 4,853 men. Sermondade et al. (10) published an updated meta-analysis in 2013, expanding the results to include 13,077 patients from 21 individual studies. Results from the grouped data in this 2013 review demonstrated increasing risks of oligoospermia or azoospermia in overweight, obese, and morbidly obese men, consistent with our cohort results. The LIFE study (23) represents the first prospective cohort of men captured in the immediate preconception period, recording individual demographic and laboratory data. VOL. - NO. - / - 2016
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Among the 468 men with available data, semen volume was found to be inversely related to BMI and waist circumference, whereas total count demonstrated an inverse correlation with waist circumference alone (24). Overall, the frequency of men with abnormal semen volume, sperm concentration, and total count parameters increased with increasing body size measures; however, no relationships were noted with sperm motility or morphology. Although the LIFE study reduces several possible biases with measured body size measures and a general population of men, the study group size may be too small to detect subtle relationships with the other semen parameters as seen in our series of >4,000 patients. Our cohort further confirms the findings of decreasing T and increasing E2 as body size increases, as demonstrated in several prior series (14, 16, 25–27). Aromatization of systemic T to E2 commonly occurs in adipose cells; therefore, some obese men demonstrate increasing E2 levels. Estradiol also provides a negative feedback to the pituitary in the hypothalamic-pituitary-gonadal axis and may result in decreased T levels secondary to lower LH secretion (28). Spermatogenesis is reliant on intratesticular T levels and stimulation from FSH from the hypothalamic-pituitarygonadal axis, which may be affected by these hormonal changes found in obese men. With increasing adiposity, there is more tissue engulfing and surrounding the genitals, which may additionally lead to increased intrascrotal temperatures and negatively impact spermatogenesis and semen quality (29). A combination of these pathophysiologic processes may be at play in obese men with variable individual effects, as demonstrated by the subtle correlations between BMI and semen parameters in the current study. Male infertility remains a difficult condition to treat, with %30% of cases deemed to be secondary to idiopathic sperm abnormalities (30). Our results show that, although the relationships were mild, obesity may be a contributing factor to decreased semen quality in some men. Studies of exercise and diet-base weight loss regimens have revealed promising improvements in sperm health (31). Conversely, the literature contains several case reports (32, 33) of severe decrements in semen parameters after surgical weight loss procedures. Although a series of men followed after gastric bypass demonstrated no significant changes in semen quality, the group included only 10 surgical patients followed for 20 months postoperatively (34). Counseling regarding weight loss may be a consideration for obese men presenting for fertility evaluation with the understanding that effects of surgical weight loss procedures on sperm health remains uncertain. Further research is needed regarding the etiologies of impaired semen parameters and therapeutic options in obese men with subfertility. The current study has several limitations. Height and weight data from two of the centers were self-reported and may not accurately reflect their actual BMI. Self-reported height and weight data, although, has been previously shown to accurately reflect measured anthropometric data, especially among men (35, 36). The BMI was the only measure of obesity studied in the current review. We did not include alternative measures such as waist circumference or waistto-hip ratio. Intertechnician variability could result in semen
analysis discrepancies; although this was relatively controlled by the use only one or two experienced technicians or computer-assisted quantification. Men in our series were all referred for fertility evaluation and although this does not indicate a male factor in all patients, there is certainly a higher frequency of fertility issues within this population as evidenced by the azoospermia rates. Many prior series included data from a single center but our cohort includes data from three centers in North America, which may make the results more generalizable than prior cohorts. Possible confounders, such as individual comorbidities and lifestyle factors, have been linked to altered semen parameters but were not collected in the present study and may have some effect on the results reported. Future multi-institutional research efforts should aim to further clarify the complex interplay between BMI, other obesity measures, health and lifestyle factors, and their ultimate effects on male fertility. In conclusion, this series is one of the largest ever reported comparing BMI and reproductive parameters in men. With increasing body size, as measured by BMI, men were found to have decreasing T and T:E2 ratios, whereas E2 levels increased. In addition, all semen parameters (volume, sperm concentration, motility, total motile count, and morphology) declined with increasing BMI. This study supports the potential role of increasing body size on reproductive hormones and semen parameters. Acknowledgments: The authors thank Susan Lau for her help with data management throughout the study and Apostolos Dimitromanolakis for his statistical assistance.
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World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894:i–xii, 1–253. World Health Organization. WHO laboratory manual for the examination and processing of human semen. 5th ed. Geneva: World Health Organization; 2010. Aggerholm AS, Thulstrup AM, Toft G, Ramlau-Hansen CH, Bonde JP. Is overweight a risk factor for reduced semen quality and altered serum sex hormone profile? Fertil Steril 2008;90:619–26. Duits FH, van Wely M, van der Veen F, Gianotten J. Healthy overweight male partners of subfertile couples should not worry about their semen quality. Fertil Steril 2010;94:1356–9. Jensen TK, Andersson AM, Jorgensen N, Andersen AG, Carlsen E, Petersen JH, et al. Body mass index in relation to semen quality and reproductive hormones among 1,558 Danish men. Fertil Steril 2004;82:863–70. Kort HI, Massey JB, Elsner CW, Mitchell-Leef D, Shapiro DB, Witt MA, et al. Impact of body mass index values on sperm quantity and quality. J Androl 2006;27:450–2. Shayeb AG, Harrild K, Mathers E, Bhattacharya S. An exploration of the association between male body mass index and semen quality. Reprod Biomed Online 2011;23:717–23. Martini AC, Tissera A, Estofan D, Molina RI, Mangeaud A, de Cuneo MF, et al. Overweight and seminal quality: a study of 794 patients. Fertil Steril 2010;94:1739–43. Paasch U, Grunewald S, Kratzsch J, Glander HJ. Obesity and age affect male fertility potential. Fertil Steril 2010;94:2898–901. Belloc S, Cohen-Bacrie M, Amar E, Izard V, Benkhalifa M, Dalleac A, et al. High body mass index has a deleterious effect on semen parameters except morphology: results from a large cohort study. Fertil Steril 2014;102: 1268–73. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011;377:557–67. Buck Louis GM, Schisterman EF, Sweeney AM, Wilcosky TC, Gore-Langton RE, Lynch CD, et al. Designing prospective cohort studies for assessing reproductive and developmental toxicity during sensitive windows of human reproduction and development–the LIFE Study. Paediatr Perinat Epidemiol 2011;25:413–24.
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Eisenberg ML, Kim S, Chen Z, Sundaram R, Schisterman EF, Buck Louis GM. The relationship between male BMI and waist circumference on semen quality: data from the LIFE study. Hum Reprod 2014;29:193–200. Mohr BA, Guay AT, O'Donnell AB, McKinlay JB. Normal, bound and nonbound testosterone levels in normally ageing men: results from the Massachusetts Male Ageing Study. Clin Endocrinol 2005;62:64–73. Svartberg J, von Muhlen D, Sundsfjord J, Jorde R. Waist circumference and testosterone levels in community dwelling men. The Tromso study. Eur J Epidemiol 2004;19:657–63. Wu FC, Tajar A, Pye SR, Silman AJ, Finn JD, O'Neill TW, et al. Hypothalamicpituitary-testicular axis disruptions in older men are differentially linked to age and modifiable risk factors: the European Male Aging Study. J Clin Endocrinol Metab 2008;93:2737–45. Pavlovich CP, King P, Goldstein M, Schlegel PN. Evidence of a treatable endocrinopathy in infertile men. J Urol 2001;165:837–41. Shafik A, Olfat S. Lipectomy in the treatment of scrotal lipomatosis. Br J Urol 1981;53:55–61. Cavallini G. Male idiopathic oligoasthenoteratozoospermia. Asian J Androl 2006;8:143–57. Hakonsen LB, Thulstrup AM, Aggerholm AS, Olsen J, Bonde JP, Andersen CY, et al. Does weight loss improve semen quality and reproductive hormones? Results from a cohort of severely obese men. Reprod Health 2011;8:24. Di Frega AS, Dale B, di Matteo L, Wilding M. Secondary male factor infertility after Roux-en-Y gastric bypass for morbid obesity: case report. Hum Reprod 2005;20:997–8. Lazaros L, Hatzi E, Markoula S, Takenaka A, Sofikitis N, Zikopoulos K, et al. Dramatic reduction in sperm parameters following bariatric surgery: report of two cases. Andrologia 2012;44:428–32. Reis LO, Zani EL, Saad RD, Chaim EA, de Oliveira LC, Fregonesi A. Bariatric surgery does not interfere with sperm quality–a preliminary long-term study. Reprod Sci 2012;19:1057–62. Clarke P, Sastry N, Duffy D, Ailshire J. Accuracy of self-reported versus measured weight over adolescence and young adulthood: findings from the national longitudinal study of adolescent health, 1996–2008. Am J Epidemiol 2014;180:153–9. Pursey K, Burrows TL, Stanwell P, Collins CE. How accurate is web-based self-reported height, weight, and body mass index in young adults? J Med Internet Res 2014;16:e4.
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Influence of increasing body mass index on semen and reproductive hormonal parameters in a multi-institutional cohort of subfertile men
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apnea and collectively contribute to the diagnosis of the metabolic syndrome. These obesity-associated comorbidities have ultimately been linked to increased mortality and a reduction in life expectancy (5–7). The metabolic changes of obesity can also affect reproductive function, thus altering the fecundity of much of our population as evidenced by the recent Centers for Disease Control statistics. In women, obesity is clearly linked to subfertility, as manifested by ovulatory disorders and idiopathic infertility (8). For men, there has been a concerning trend of declining sperm quality noted that mirrors the increasing rates of obesity (9). Spermatogenesis normally requires a controlled testicular environment and intact endocrine signaling through the hypothalamic-pituitary-gonadal axis. Increasing adiposity may alter the testicular milieu in such a way to alter normal sperm production and health. Cohort studies of specific relationships between semen parameters and obesity in the literature, however, have been inconsistent. Although some data have shown correlations with abnormal sperm number, motility, or morphology, others have suggested that no adverse effects were noted with increasing measures of obesity. Gathering data from individual studies, two meta-analyses completed on the topic came to contradictory conclusions with Sermondade et al. (10) reporting that detrimental changes may occur with increasing body mass index (BMI) and a smaller review by MacDonald et al. (11) countering that no significant relationships were found in their review of the data. In the latter metaanalysis, MacDonald et al. (11) also reported on reproductive hormone changes with obesity with a majority of trials finding negative correlations with T and a few observing positive relationships with estrogen (E) levels. Although changes in male reproductive hormones with increasing adiposity have been more evident, the effects on sperm number and health remain unclear. An understanding of the relationship between obesity and male fertility will allow physicians to better counsel men planning a family about their body habitus. This study correlates measures of obesity with hormonal and semen parameters among men presenting for fertility evaluation at multiple centers, together representing one of the largest cohorts ever published.
MATERIALS AND METHODS Patient demographics were captured in institutional review board-approved databases at three North American male infertility clinics. All men referred for clinical infertility evaluation and agreeing to inclusion were entered into respective databases. Two study centers (Study Centers 1 and 2) collected self-reported height and weight at the time of initial patient intake, whereas the remaining center (Study Center 3) measured height and weight at the initial visit. The BMI was calculated for each patient as a ratio of the weight (in kilograms) to height squared (in meters squared). The BMI was then categorized as underweight (<18.5), normal (18.5– 24.9), overweight (25–29.9), or obese (>30) based on World Health Organization classifications (12). In addition, obesity was subcategorized as class I (30–34.9), class II (35–39.9), or class III (>40).
Laboratory data including reproductive hormones and semen analyses were collected for all men with available height and weight data from 2002 to July 2014. Only hormonal blood work and semen analyses performed at the time of initial patient evaluation were selected for inclusion to reduce any confounding from fertility-related treatments. Patients without available laboratory data were excluded including those referred for vasectomy reversal, as we do not routinely check preoperative hormones or semen analyses in these men. The reproductive hormone parameters collected included total T, E2, FSH, LH, and PRL. Units were standardized across the centers for T (in nanomoles per liter) and E2 (in picomoles per liter). Standard units were used for FSH (in IU per milliliter), LH (in IU per milliliter), and PRL (in nanograms per milliliter). The T:E2 ratio was calculated as a simple ratio for each patient with available data. Semen samples were obtained from patients by masturbation with 2–5 days of abstinence before collection. Samples were then prepared in respective CLIA (Clinical Laboratory Improvement Amendments) certified andrology laboratories by one or two technicians specializing in semen analyses. Study Center 1 performed computer-assisted semen analysis quantification, whereas technicians at Centers 2 and 3 performed manual semen evaluation. Ejaculate volume and sperm concentration, motility, and morphology parameters were graded based on World Health Organization 5th edition criteria (13). Morphology data from Center 3 were excluded as they graded based on Kruger strict criteria alone and could not be compared with the other centers. The total motile count was calculated for each patient as the product of ejaculate volume, sperm concentration, and motility. Azoospermia was defined as zero sperm found on initial semen analysis and oligospermia included men with sperm present at concentrations <15 million/mL. Descriptive statistics were calculated for the overall cohort and individual study centers. Analysis of variance and c2 tests were used to compare continuous and categorical variables between study groups with P< .05 reported as significant. Nonparametric univariate analyses with Spearman's rank correlation were used to describe relationships between patient BMI and reproductive variables. Due to the high frequency of genetic or congenital abnormalities, azoospermic men were also excluded from subgroup analyses to ascertain whether any variable effects existed. As multiple tests were being performed on the same data set, false discovery correction was performed using the Benjamini-Hochberg procedure. Multiparametric linear models were additionally created to control for any variability due to patient age or study center. Statistical analyses were performed using the R software platform, version 2.15.2.
RESULTS During the study time period, complete height and weight results with evaluable laboratory data were available for 4,440 men with a mean age (SD) of 36.1 years (7.6 years) (Table 1). Study Center 1 included data from 835 patients, Center 2 included 3,309, and Center 3 included 296 patients. The average age of men evaluated at each institution was significantly different between the various centers VOL. - NO. - / - 2016
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TABLE 1 Baseline patient cohort demographics. Characteristic Patients, n (%) Mean age, y Mean BMI, kg/m2 BMI category, n (%) Underweight Normal weight Overweight Obese (>30) Class I Class II Class III T, nmol/L (%) Semen analysis, n (%)
Q2
All centers
Center 1 (TO)
Center 2 (NW)
Center 3 (WI)
P value
4,440 36.1 27.6
835 (18.8) 38.0 27.3
3,309 (74.) 35.9 27.4
296 (6.7) 33.8 30.8
< .001 < .001
30 (0.7) 1,373 (30.9) 2,004 (45.1) 1,033 (23.3) 687 (66.5) 206 (19.9) 140 (13.6) 2,973 (67.0) 4,236 (95.4)
5 (0.6) 258 (30.9) 397 (47.5) 175 (21.0) 133 (76.0) 29 (16.6) 13 (7.4) 699 (23.5) 650 (15.3)
25 (0.8) 1,059 (32.0) 1,504 (45.5) 721 (21.8) 478 (66.3) 148 (20.5) 95 (13.2) 1,978 (66.5) 3,291 (77.7)
0 (0) 56 (18.9) 103 (34.8) 136 (45.9) 76 (55.9) 29 (21.3) 32 (23.5) 296 (10.0) 295 (7.0)
.30 .001 .02 < .001 < .001 < .001 < .001 – –
Note: Body mass index (BMI) category percentages reflect frequency of each variable among each respective cohort; all other percentages reflect frequency within the specific variable. Bieniek. Obesity and male fertility. Fertil Steril 2016.
(38.0, 35.9, and 33.8 years, respectively; P< .001). Semen analysis data were available for 4,236 men (95.4%) in the cohort. The hormonal parameters available for data collection varied for each patient with T being the most commonly tested and available for 2,973 men (67.0%). Based on the World Health Organization BMI definitions, 30 (0.7%) men in the combined cohort were underweight, 1,373 (30.9%) normal weight, 2,004 (45.1%) overweight, and 1,033 (23.3%) obese. Of the obese individuals, 687 (66.5%) were subcategorized as Class I, 206 (19.9%) Class II, and 140 (13.6%) Class III. Table 1 includes the distribution of BMI groups across the three study centers. There were significant differences in the frequencies of normal weight, overweight, and obese individuals between the study centers. Within the obese subgroups, all classes similarly demonstrated significant variability between the centers. Overall, BMI was noted to correlate directly with patient age (r ¼ 0.18, P< .001).
Increasing BMI Negatively Impacts Reproductive Hormones Among the reproductive hormones collected, T, E2, and LH were found to have significant relationships with BMI on adjusted univariate analyses, whereas only T and E2 remained significant on multiparametric testing (Table 2). Testosterone (r ¼ 0.27, P< .001) had a significant negative correlation, whereas E2 (r ¼ 0.13, P< .001) conversely demonstrated a positive relationship. The T:E2 ratio (r ¼ 0.29, P< .001) similarly had a negative correlation with BMI for patients with available data. On univariate adjusted testing, LH (r ¼ 0.06, P¼ .007) showed a weak direct relationship with BMI, whereas FSH (r ¼ 0.04, P¼ .06) demonstrated a weak trend that did not reach statistical significance. Prolactin levels were not found to be related to BMI values.
Obesity Associated with Declining Semen Parameters Ejaculate volume, sperm concentration, motility, and morphology were similarly noted to have significant negative
correlations with BMI on univariate testing (Table 2). The strongest correlations with increasing BMIs were seen with sperm concentration (r ¼ 0.08, P< .001) and motility (r ¼ 0.07, P< .001). The relationship between sperm concentration and BMI did not change significantly when azoospermic men were excluded (data not shown). When applying multiparametric models, all semen parameter relationships with BMI remained significant with the exception of sperm motility (P¼ .07). The negative correlation between BMI and calculated total motile count (r ¼ 0.10, P< .001) was even more pronounced than the individual sperm parameter measures. The prevalence of decreased sperm concentrations were found more commonly in obese men as well (Table 3). Although overweight men had a similar rate of azoospermia compared with the normal weight reference group (9.3% vs. 9.8%), oligospermia was more prevalent than the normal weight reference group (27.7% vs. 24.5%). Including all obese men, prevalence
TABLE 2 Body mass index correlations with hormonal and semen analysis parameters. Characteristic Hormonal parameter Total T E2 T:E ratio FSH LH PRL Semen parameter Ejaculate volume Sperm concentration Motility Morphology Total motile count
n
Correlation (r)
Uni-adjust (P value)
Multi (P value)
2,973 2,057 2,021 2,728 2,401 1,804
0.27 0.13 0.29 0.04 0.06 0.04
< .001 < .001 < .001 .062 .007 .067
< .001 < .001 < .001 .605 .072 .072
4,234 4,087
0.04 0.08
.009 < .001
.048 < .001
3,653 3,225 3,829
0.07 0.04 0.10
< .001 .025 < .001
.074 .015 < .001
Note: Multi ¼ multivariate model adjusting for age and study center; Uni-adjust ¼ univariate adjusted model. Bieniek. Obesity and male fertility. Fertil Steril 2016.
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ORIGINAL ARTICLE: ANDROLOGY 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
TABLE 3 Rates of sperm concentration abnormalities between study and body mass index (BMI) groups. Azoospermia Characteristic BMI category Underweight Normal weight Overweight Obese Class I Class II Class III All patients
Oligospermia
Normospermia
N (%)
RR (95% CI)
N (%)
RR (95% CI)
N (%)
P value
6 (22.2) 124 (9.8) 173 (9.3) 120 (12.7) 65 (10.4) 27 (14.8) 28 (21.5) 423 (10.4)
2.31 (1.16–4.61) – 0.99 (0.80–1.24) 1.44 (1.14–1.81) 1.15 (0.87–1.52) 1.71 (1.18–2.47) 2.67 (1.89–3.75) –
7 (25.9) 310 (24.5) 514 (27.7) 299 (31.7) 191 (30.5) 60 (32.8) 48 (36.9) 1,130 (27.6)
1.23 (0.67–2.27) – 1.13 (1.00–1.27) 1.34 (1.18–1.53) 1.25 (1.08–1.45) 1.43 (1.14–1.78) 1.75 (1.40–2.19) –
14 (51.9) 832 (65.7) 1,166 (62.9) 522 (55.4) 371 (59.2) 95 (51.9) 53 (40.8) 2,534 (62.0)
.079 – .005 < .001 .001 .001 < .001 –
Note: % reflects frequency of each sperm concentration in each body mass index (BMI) category; relative risk (RR) ratios and P values reflect comparison between each category and normal weight reference group. CI ¼ confidence interval. Bieniek. Obesity and male fertility. Fertil Steril 2016.
of azoospermia and oligospermia were 12.7% and 31.7%, respectively, both more than rates of decreased sperm concentration among the normal weight group (P< .001). Relative risks of oligospermia and azoospermia among the obese male cohort were 1.34 (95% confidence interval 1.18–1.53) and 1.44 (95% confidence interval 1.14–1.81), respectively. Within the obesity subclasses, significantly increased rates of azoospermia and oligospermia were similarly observed.
DISCUSSION This series represents one of the largest ever published comparing measures of obesity to male reproductive hormonal and semen parameters. Our results demonstrate significant alterations in the hormonal milieu of men pursuing fertility based on their body habitus. We likewise demonstrated correlations with increasing measures of obesity and worsening semen parameters, although the relationships were mild in severity. Abnormal sperm concentration, including azoospermia and oligospermia, was more frequently found among obese men and even more pronounced with increasing subclasses of obesity. Similar cohort studies comparing body habitus to reproductive parameters in men have reported variable results. In one of the largest cross-sectional series to date, consisting of 1,989 normal European men, Aggerholm et al. (14) found no significant relationships between semen parameters and BMI measures. Another large series from Duits et al. (15) likewise concluded that there were no adverse effects on sperm quality seen with obesity among 1,401 subfertile patients. Several contradictory series (16–19), however, have noted variable declines in select semen parameters such as ejaculate volume, sperm concentration, motility, and morphology, although the results are inconsistent between individual studies. Paasch et al. (20), although finding no significant relationship in their overall cohort of 2,157 men, interestingly saw a significant relationship between BMI and total sperm count in the subcohort of men aged 20– 30 years. Discrepant findings between many of these various cohort studies may be related to the review of varying populations of normal and infertile men. Individual
cohorts have typically consisted of approximately 2,000 or fewer patients until recently when Belloc et al. (21) reviewed data from 10,665 French men undergoing evaluation at a single fertility center. They observed a decreased ejaculate volume, sperm concentration, motility, and total count with extremely obese individuals (BMI >40), whereas sperm morphology was unaffected. Results from our multi-institutional cohort suggest similar effects with an additional weak but significant negative correlation between BMI and normal sperm morphology. As mentioned, many of the prior studies in the literature have been relatively small with few including North American patients. The largest North American series to date, in fact, included only 520 men (17). Worldwide obesity rates are known to vary greatly between countries, with the highest prevalence in North America according to 2008 data (22). The European and Asian populations constituting most of the previously reported cohorts tend to have much lower obesity rates. For example, only 13.4% of the nearly 2,000 British men reviewed by Shayeb et al. (18) were obese compared with 23.3% of our cohort. This discrepancy has contributed to smaller obese populations in published studies and suggests that correlations between male adiposity and reduced fecundity may be underappreciated. The current North American series includes a large obese population, further confirming negative correlations between increasing male BMI and nearly all semen parameters. MacDonald et al. (11) published the results of the initial meta-analyses on the topic of obesity and male subfertility in 2010, concluding that no significant relationship existed between BMI and semen parameters based on pooled data from five studies including 4,853 men. Sermondade et al. (10) published an updated meta-analysis in 2013, expanding the results to include 13,077 patients from 21 individual studies. Results from the grouped data in this 2013 review demonstrated increasing risks of oligoospermia or azoospermia in overweight, obese, and morbidly obese men, consistent with our cohort results. The LIFE study (23) represents the first prospective cohort of men captured in the immediate preconception period, recording individual demographic and laboratory data. VOL. - NO. - / - 2016
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Among the 468 men with available data, semen volume was found to be inversely related to BMI and waist circumference, whereas total count demonstrated an inverse correlation with waist circumference alone (24). Overall, the frequency of men with abnormal semen volume, sperm concentration, and total count parameters increased with increasing body size measures; however, no relationships were noted with sperm motility or morphology. Although the LIFE study reduces several possible biases with measured body size measures and a general population of men, the study group size may be too small to detect subtle relationships with the other semen parameters as seen in our series of >4,000 patients. Our cohort further confirms the findings of decreasing T and increasing E2 as body size increases, as demonstrated in several prior series (14, 16, 25–27). Aromatization of systemic T to E2 commonly occurs in adipose cells; therefore, some obese men demonstrate increasing E2 levels. Estradiol also provides a negative feedback to the pituitary in the hypothalamic-pituitary-gonadal axis and may result in decreased T levels secondary to lower LH secretion (28). Spermatogenesis is reliant on intratesticular T levels and stimulation from FSH from the hypothalamic-pituitarygonadal axis, which may be affected by these hormonal changes found in obese men. With increasing adiposity, there is more tissue engulfing and surrounding the genitals, which may additionally lead to increased intrascrotal temperatures and negatively impact spermatogenesis and semen quality (29). A combination of these pathophysiologic processes may be at play in obese men with variable individual effects, as demonstrated by the subtle correlations between BMI and semen parameters in the current study. Male infertility remains a difficult condition to treat, with %30% of cases deemed to be secondary to idiopathic sperm abnormalities (30). Our results show that, although the relationships were mild, obesity may be a contributing factor to decreased semen quality in some men. Studies of exercise and diet-base weight loss regimens have revealed promising improvements in sperm health (31). Conversely, the literature contains several case reports (32, 33) of severe decrements in semen parameters after surgical weight loss procedures. Although a series of men followed after gastric bypass demonstrated no significant changes in semen quality, the group included only 10 surgical patients followed for 20 months postoperatively (34). Counseling regarding weight loss may be a consideration for obese men presenting for fertility evaluation with the understanding that effects of surgical weight loss procedures on sperm health remains uncertain. Further research is needed regarding the etiologies of impaired semen parameters and therapeutic options in obese men with subfertility. The current study has several limitations. Height and weight data from two of the centers were self-reported and may not accurately reflect their actual BMI. Self-reported height and weight data, although, has been previously shown to accurately reflect measured anthropometric data, especially among men (35, 36). The BMI was the only measure of obesity studied in the current review. We did not include alternative measures such as waist circumference or waistto-hip ratio. Intertechnician variability could result in semen
analysis discrepancies; although this was relatively controlled by the use only one or two experienced technicians or computer-assisted quantification. Men in our series were all referred for fertility evaluation and although this does not indicate a male factor in all patients, there is certainly a higher frequency of fertility issues within this population as evidenced by the azoospermia rates. Many prior series included data from a single center but our cohort includes data from three centers in North America, which may make the results more generalizable than prior cohorts. Possible confounders, such as individual comorbidities and lifestyle factors, have been linked to altered semen parameters but were not collected in the present study and may have some effect on the results reported. Future multi-institutional research efforts should aim to further clarify the complex interplay between BMI, other obesity measures, health and lifestyle factors, and their ultimate effects on male fertility. In conclusion, this series is one of the largest ever reported comparing BMI and reproductive parameters in men. With increasing body size, as measured by BMI, men were found to have decreasing T and T:E2 ratios, whereas E2 levels increased. In addition, all semen parameters (volume, sperm concentration, motility, total motile count, and morphology) declined with increasing BMI. This study supports the potential role of increasing body size on reproductive hormones and semen parameters. Acknowledgments: The authors thank Susan Lau for her help with data management throughout the study and Apostolos Dimitromanolakis for his statistical assistance.
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