Stesura Seveso 357Archivio Italiano di Urologia e Andrologia 2020; 92, 4 ORIGINAL PAPER No conflict of interest declared. DOI: 10.4081/aiua.2020.4.357 Modeling the contribution of the obesity epidemic to the temporal decline in sperm counts Alex Kasman 1, Francesco Del Giudice 1, 2, Eugene Shkolyar 1, Angelo Porreca 3, Gian Maria Busetto 2, Ying Lu 4, Michael L. Eisenberg 1, 4 1 Department of Urology, Stanford University School of Medicine, Stanford, California; 2 Department of Maternal-Infant and Urological Sciences, “Sapienza” Rome University, Policlinico Umberto I Hospital, Rome, Italy; 3 Department of Urology, Policlinico Abano Terme, Abano Terme (PD), Italy; 4 Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California; 5 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California. over the past 40 years (4). Additionally, several other stud- ies in specific populations/countries have identified simi- lar findings (5-10). However, the underlying cause or causes of the decline remains unknown. Given the com- plexity of spermatogenesis, there are likely multiple mech- anisms behind declining sperm counts (e.g. environmen- tal effects of chemical exposure, endocrine disruption, etc.) (11-14). Over the past four decades, the prevalence of obesity has increased over 50% in the world. As the obesity epidemic continues to worsen, the effect it may have on fertility has been increasingly investigated and several studies have been published on the topic. A sys- tematic review by Guo et al., showed that overall for every five unit increase in BMI there was a 2.4% drop in sperm count (15). Additionally, a recent large observational study of 3,966 sperm donors showed a significant decrease in sperm count for overweight and obese men (16). However, the overall contribution the obesity epi- demic has to falling total sperm counts remains unknown. Given the public health implications of falling sperm counts, understanding the potential contributions of varying etiologies may have remains important. In the current study, we sought to model the potential contri- bution the US obesity epidemic could have to sperm counts over the past four decades. MATERIALS AND METHODS This systematic review was conducted according to the Systematic Review and Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines (17). The research question was established based on the fol- lowing PICO criteria: what is the contribution of the obe- sity epidemic to the temporal decline in sperm counts? Furthermore, our goal was to explore the weighted influ- ence of the US obesity on total sperm counts over the last four decades. Obesity rates across the world were determined for the last four decades starting in 1973 up to 2011 using the World Health Organization’s (WHO) Global Health Observatory (GHO) data (https://www.who.int/gho/ncd/ risk_factors/over- weight/en/). Obesity rates were quantified using body mass index (BMI). The dates, 1973-2011, were selected based Objective: Total sperm count (TSC) has been declining worldwide over the last sev- eral decades due to unknown etiologies. Our aim was to model the contribution that the obesity epidemic may have on declin- ing TSC. Materials and methods: Obesity rates were determined since 1973 using the WHO’s Global Health Observatory data. A lit- erature review was performed to determine the association between TSC and obesity. Using the measured obesity rates and published TSC since 1973, a model was created to evalu- ate the association between temporal trends in obesity/temper- ature and sperm count. Results: Since 1973, obesity prevalence in the United States was increased from 41% to 67.9%. A review of the literature showed that body mass index (BMI) categories 2, 3, and 4 were associated with TSC (millions) of 164.27, 155.71, and 142.29, respectively. The contribution to change over time for obesity from 1974 to 2011 was modeled at 1.8%. When the model was changed to represent the most extreme possible contribution to obesity reported, the modeled change over time rose to 7.2%. When stratified according to fertility status, the contribution that BMI had to falling sperm counts for all comers was 1.7%, while those presenting for fertility evaluation was 2.1%. Conclusions: While the decline in TSC may be partially due to rising obesity rates, these contributions are minimal which highlights the complexity of this problem. KEY WORDS: Obesity; Sperm count; Total sperm count; Semen analysis. Submitted 16 September 2020; Accepted 15 October 2020 INTRODUCTION Infertility remains an important public health concern with an estimated 15% of couples unable to conceive after 1 year of trying and therefore are labeled infertile with up to 50% having a male factor etiology (1, 2). A such, semen analysis remains an important component of a couple’s fertility evaluation (3). With this knowledge, the overall decline in sperm count worldwide is worri- some and requires further attention. A large meta-analysis of 185 studies and data from over 42,000 men, demon- strated a 50% decline in sperm concentration and counts Summary Archivio Italiano di Urologia e Andrologia 2020; 92, 4 A. Kasman, F. Del Giudice, E. Shkolyar, A. Porreca, G.M. Busetto, Y. Lu, M.L. Eisenberg 358 on the real world measured sperm count data from the sys- tematic review done by Levine et al. (4). To determine the contribution that obesity has, on aver- age, to total sperm count (TSC) we performed a systematic review of the literature in PubMed, Embase, and Cochrane from 1973-2011, without language restriction, to identify studies that examined infertility and/or male factor infer- tility in relation to the risk of mortality. The reference lists of the included studies were also screened for relevant arti- cles. Original population-based retrospective cohort stud- ies as well as cross-sectional and case-control cohort stud- ies were included and critically evaluated (Level of Evidence: III-2, III-3). Case reports, abstracts and meeting reports were excluded from the analysis. Search terms included but were not limited to: primary field: body mass index or BMI, obesity, overweight AND, infertility, subfer- tility, semen parameters, or sperm parameters, sperm count, semen quality, sperm quality; secondary fields: oligospermia, azoospermia, oligozoospermia. A total of 26 studies were identified that examined obesi- ty’s impact on male fertility. Six of these studies were excluded as they did not report total sperm count. From the remaining 20 studies, BMI was categorized according to healthy weight (BMI 18.5-24.9), overweight (BMI 25- 29.9), and obese (BMI > 30) using the Center for Disease Control’s standard definition (https://www.cdc.gov/obesi- ty/adult/defining.html). After categorization, a further 7 studies were eliminated due to overlapping BMI categories (e.g. TSC reported together for categories 3 and 4). From these remaining 14 studies, data was extracted to obtain the average TSC for each BMI category across studies with larger studies having a higher weight (Table 1). To assess the risk of bias (RoB), all included reports were independently reviewed using the “Quality Assessment Tool for Observational Cohort and Cross- Sectional Studies”, provided by the National Institute of Health (NIH), by assessing the potential risk for selec- tion bias, information bias, measure- ment bias, or confounding bias (con- founding bias includes cointerven- tions, differences at baseline in patient characteristics, and other issues as shown in Supplementary Table 1) (18). Studies were rated as good, fair, and poor quality, where high risk of bias translated to a rating of poor quality (“−”) and low risk of bias translated to a rating of good quality (“+”). No study was considered to be seriously flawed according to the aforementioned crite- ria. Studies’ risk of performance bias was low overall with absence of attri- tion bias due to incomplete outcome data across all the studies. Annual/Decade rates of body mass index categories (i.e. normal, over- weight, obese) were obtained from the WHO for 1973 and 2011. For each year, we used our calculated association between BMI category and sperm count to determine the average sperm count based on annual BMI. BMI category, the TSC was then multiplied by the appropriate obesity rate and a TSC for obesity was obtained for that time peri- od (e.g. 1973 or 2011). The rates between 1973 and 2011 were then compared and a percent change over time was calculated. Over all years, we could then evaluate changes in sperm count based on temporal trends in obesity over time. TSC was then categorized according to obesity, most extreme BMI contribution (e.g. the study reporting the strongest association between BMI and TSC), region, and fertility status (unknown fertility versus those presenting for fertility evaluation). Regional areas (USA, Europe, Asia, Table 1. Studies utilized for obesity effect on sperm count. Category N Studies Obese 11504 Belloc (2014), Paash (2010), Shayeb (2011), Aggerholm (2008), Duits (2010), Xiao (2013), Macdonald (2012), Chavarro (2010), Andersen (2015), Hajshafiha (2013), Vignera (2012), Gutorova (2014), Ma (2019) Extreme obese 297 Hammiche (2012) USA 360 Chavarro (2010) Europe 8643 Belloc (2014), Paasch (2010), Shayeb (2011), Aggerholm (2008), Duits (2010), Anderson (2015), Vignera (2012) Asia 1304 Gutorova (2014), Ma (2019), Xiao (2013), New Zealand 372 Macdonald (2012) All comers 2852 Paasch (2010), Aggerholm (2008), Vignera (2012), Gutorova (2014) Fertility evaluations 8652 Belloc (2014), Shayeb (2011), Duits (2010), Xiao (2013), Macdonald (2012), Chavarro (2010), Andersen (2015), Hajshafiha (2013) Supplementary Table 1. Risk assessment of individual studies according to “Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies”. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Belloc 2014 + + + + - + + + + - + NA NA - Paasch 2010 - + NA + - + + + + - + NA NA - Shayeb 2011 + + NA + - + + + + - + NA NA - Aggerholm 2008 + + + + - + + + + - + NA NA - Duits 2010 + + NA + - + + + + - + NA NA - Xiao 2013 + + NA + - + + + + - + NA NA - Macdonald 2012 + + + + - + + + + - + NA NA - Chavarro 2010 + + + + - + + + + - + NA NA - Hammiche 2012 + + + + - + + + + - + NA NA - Andersen 2015 + + NA + - + + + + - + NA NA - Hajshafiha 2013 + + NA + - + + + + - + NA NA - Vignera 2012 + + + + - + + + + - + NA NA - Gutorova 2014 + + NA + - + + + + - + NA NA - Ma 2019 + + + + - + + + + - + NA NA - NA: not applicable. Criteria 1: Was the research question or objective in this paper clearly stated? Criteria 2: Was the study population clearly specified and defined? Criteria 3: Was the participation rate of eligible persons at least 50%? Criteria 4: Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants? Criteria 5: Were a sample size justification, power description, or variance and effect estimates provided? Criteria 6: For the analyses in this paper, was the exposure(s) of interest measured prior to the outcome(s) being measured? Criteria 7: Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? Criteria 8: For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome? Criteria 9: Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? Criteria 10: Was the exposure(s) assessed more than once over time? Criteria 11: Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? Criteria 12: Were the outcome assessors blinded to the exposure status of participants? Criteria 13: Was loss to follow-up after baseline 20% or less? Criteria 14: Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)? 359Archivio Italiano di Urologia e Andrologia 2020; 92, 4 Obesity and sperm count and New Zealand) were chosen based on those regions sampled in the 14 studies used. P < 0.05 were considered significant. RESULTS The average total sperm count (TSC, millions) for increasing BMI categories 2 (normal), 3 (over- weight), 4 (obese) were 164.3, 155.7, and 142.3. The average TSC (millions) for individuals above nor- mal BMI range (e.g. categories 3 and 4) was 149. There was not enough data present in the literature for a TSC to be calculated for BMI cate- gory 1 (underweight). Obesity has increased in prevalence of the past 40 years. In 1973, 59% of men were normal weight and 41% were obese. In contrast, in 2011 (the most recent year with available data), 32.1% were normal with 67.9% obese. Averaged across all studies, BMI categories 2, 3, and 4 were associated with TSC (millions) of 164.27, 155.71, and 142.29, respectively. The most extreme asso- ciation between BMI and sperm count reported TSC (millions) of 68.6, 49.6, and 45.9 for BMI cate- gories 2, 3, and 4, respectively (19). Overall, the contribution to change over time for obesity from 1973 to 2011 was calculated at 1.8% (Figure 2a). When the model was changed to repre- sent the most extreme possible contribution to obesity reported in any given study, the modeled change over time rose to 7.2% (Figure 2a). When modeled based on region- al BMI, the change for USA was 9.9%, Europe 3.1%, Asia 1.9%, and New Zealand -0.4% (Figure 2b). When strat- ified according to fertility status, the contribution that BMI had to falling sperm counts for men with unknown fertility status was 1. 7% while those presenting for fer- tility evaluation was 2.1% (Figure 2c). Figure 1. PRISMA flow diagram. Figure 2. Model of obesity effect on sperm count stratified by overall obesity effect and largest obesity effect: (a) fertility status known versus unknown; (b) and region; (c) Reported decline for all models is based on Levine et al. (Levine, Jørgensen, Martino, et al., 2017). Archivio Italiano di Urologia e Andrologia 2020; 92, 4 A. Kasman, F. Del Giudice, E. Shkolyar, A. Porreca, G.M. Busetto, Y. Lu, M.L. Eisenberg 360 DISCUSSION The current report demonstrates the modest impact increasing rates of obesity may have on reported decline in semen quality. Increasing obesity rates were shown to have a small (1-10%), though measurable contribution to the overall decline with the most measured effect, log- ically, observed at the extreme end of obesity’s contribu- tion. Additionally, the countries with higher obesity rates were shown to have a larger (~10%), though still mod- est, contribution to the reported 50% TSC decline over the past half century. When the obesity group was strat- ified by fertility status, the effect did decrease in observed men with unknown fertility versus those presenting for fertility evaluation. Overall, the contributions of rising obesity rates on declining TSC appear to be individually small and suggest that the etiology for reported declines in semen quality are likely multifactorial. As the obesity epidemic continues to worsen globally, the health effects of each continue to gain importance (20- 23). Additionally, during this time period, global sperm counts have been observed to be declining with unknown mechanisms (4-8). Obesity has been postulat- ed to be one of the mechanisms driving this especially given its implications for overall health (24). Indeed, a number of primary studies have demonstrated that as an individual’s BMI increases that sperm analysis parameters are affected (25-27). However, it should be noted that not all studies have found an impactful reduction in semen parameters in obese men, including a large sys- tematic review by MacDonald, et al. (28, 29). The etiology of this potential relationship is likely multi- factorial which may explain the small effect that was measured in the current model. Increasing obesity has been associated with altered levels of both sex hormone binding globulin and testosterone as well as an increased estradiol to testosterone ratio (30-33). Additionally, there is increased conversion of testosterone to estradiol in the setting of increased adiposity (34). All of these hormonal changes may ultimately lead to a negative effect downstream on spermatogenesis through the hypo- thalamic-pituitary-gonadal axis. While this may be a potential way in which sperm analysis parameters may be affected by obesity, the underlying mechanism through which increased adiposity could lead to impaired sper- matogenesis is unknown. In addition, the additional body mass may insulate the scrotum contributing to rising scro- tal temperature and lower sperm production. The current model has several other limitations. The model itself is based on data from literature review and therefore is prone to both the bias of suitable articles for data extraction as well as the bias of the primary study itself. Additionally, a number of assumptions for the obe- sity model were made including that the measured effect of obesity overtime is constant. While other factors have been postulated to lead to declining sperm counts (e.g. chemical exposures), rigorous longitudinal surveillance did not allow modeling. CONCLUSIONS The current report demonstrates the modest contribu- tion that obesity may have on declining total sperm counts and highlights the complex nature of infertility. Further studies are needed to examine the underlying mechanisms behind declining total sperm counts as this has large public health implications. REFERENCES 1. Thoma M, McLain A, Louis JF, et al. The prevalence of infertility in the United States as estimated by the current duration approach and a traditional constructed approach. Fertil Steril. 2014; 99:1324-1331. 2. Louis J, Thoma M, Sorensen D, et al. The prevalence of couple infertility in the United States from a male perspective : evidence from a nationally representative sample. Andrology. 2013; 1:741-748. 3. Oehninger S, Ombelet W. Limits of current male fertility testing. 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Correspondence Alex Kasman, MD, MS Eugene Shkolyar, MD Angelo Porreca MD Department of Urology, Policlinico Abano Terme, Abano Terme (PD) (Italy) Francesco Del Giudice, MD Gian Maria Busetto MD, PhD Department of Maternal-Infant and Urological Sciences, “Sapienza” Rome University, Policlinico Umberto I Hospital, Rome (Italy) Ying Lu, PhD Department of Biomedical Data Science, Stanford University School of Medicine, Stanford (California) Michael L. Eisenberg, MD (Corresponding Author) eisenberg@stanford.edu Department of Urology, Stanford University School of Medicine, 300 Pasteur Dr., S285, Stanford, California 94305-5118