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ORIGINAL ARTICLE Infertility
Body mass index and central adiposity
are associated with sperm quality
in men of subfertile couples
Fatima Hammiche1,2, Joop S.E. Laven1,2, John M. Twigt1,2,
Willem P.A. Boellaard3, Eric A.P. Steegers1,
and Re
´gine P. Steegers-Theunissen1,4,5,*
1
Department of Obstetrics and Gynecology, Erasmus MC, University Medical Center, Dr. Molewaterplein 50, 3000 DR Rotterdam, The
Netherlands
2
Department of Obstetrics and Gynecology, Division of Reproductive Medicine, Erasmus MC, University Medical Center, Dr.
Molewaterplein 50, 3000 DR Rotterdam, The Netherlands
3
Department of Urology, Division of Andrology, Erasmus MC, University Medical
Center, Dr. Molewaterplein 50, 3000 DR Rotterdam, The Netherlands
4
Department of Epidemiology, Erasmus MC, University Medical
Center, Dr. Molewaterplein 50, 3000 DR Rotterdam, The Netherlands
5
Clinical Genetics, Erasmus MC, University Medical Center, Dr.
Molewaterplein 50, 3000 DR Rotterdam, The Netherlands
*Correspondence address. Erasmus MC, University Medical Center, Department of Obstetrics and Gynecology, Dr. Molewaterplein 50,
3015 GD Rotterdam, The Netherlands. Tel: +31-10-7032609; Fax: +31-10-7036815; E-mail: r.steegers@erasmusmc.nl
Submitted on November 14, 2011; resubmitted on April 10, 2012; accepted on April 24, 2012
background: The incidence of overweight and obesity in men of reproductive ages is rising, which may affect fertility. Therefore, this
study aims to assess the associations between BMI, central adiposity and sperm parameters in men of subfertile couples.
methods: Ejaculate volume (ml), sperm concentration (millions per ml), percentage of progressive motile and immotile spermatozoa and
total motile sperm count (millions) were measured in 450 men of subfertile couples visiting a tertiary outpatient clinic for reproductive treat-
ment and preconception counseling.
results: Overweight was negatively associated with the percentage of progressive motility type A [b20.32 (SE 0.2), P¼0.036] and
positively associated with the percentage of immotility type C [b0.21 (SE 0.07), P¼0.002]. Obesity was negatively associated with ejaculate
volume [b20.23 (SE 0.1), P¼0.02], sperm concentration [b20.77 (SE 0.3), P¼0.006] and total motile sperm count [b20.91 (SE 0.3),
P¼0.007]. Waist circumference ≥102 cm, a measure for central adiposity, was inversely associated with sperm concentration [b20.69 (SE
0.2), P¼0.001] and total motile sperm count [b20.62 (SE 0.3), P¼0.02]. All associations remained significant after adjustment for age,
ethnicity, active and passive smoking, alcohol and medication use and folate status.
conclusions: This study shows that in particular, sperm concentration and total motile sperm count in men of subfertile couples are
detrimentally affected by a high BMI and central adiposity. The effect of weight loss on sperm quality and fertility needs further investigation.
Key words: body mass index / overweight / obesity / lifestyle and sperm quality
Introduction
In western countries, subfertility is a serious health problem affecting
10–15% of all couples trying to conceive (Evers, 2002). Male factor
subfertility accounts for 25– 30% of all cases, of which in the majority
no apparent cause can be found (Wong et al., 2000;Taylor, 2003,).
This has drawn attention to the impact of poor lifestyles, reflected
in a high BMI, on sperm quality (Jensen et al., 2004). In recent
decades, the prevalence of overweight and obesity in men of repro-
ductive age has increased dramatically in the Netherlands with
similar trends in other countries (World Health Organization, 2000;
Statistics the Netherlands, 2010,). Overweight is defined as a BMI
≥25 and ,30 kg/m
2
and obesity as a BMI ≥30 kg/m
2
(World
Health Organization, 2000).
The adverse effects of a high BMI on female fertility, such as an
increased time to conception and menstrual irregularities, are well
known (Balen et al., 2007;Zain and Norman, 2008). Additionally,
central adiposity, expressed by waist circumference (WC) or waist-hip
ratio, has been shown to independently influence the reproductive po-
tential in women (Zaadstra et al., 1993). The recent systematic review
with meta-analysis has not found evidence on the disadvantages of a
high BMI on male fertility parameters (MacDonald et al., 2010). This
&The Author 2012. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved.
For Permissions, please email: journals.permissions@oup.com
Human Reproduction, Vol.27, No.8 pp. 2365– 2372, 2012
Advanced Access publication on June 12, 2012 doi:10.1093/humrep/des177
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is partially due to studies in which no adjustments are made for con-
founding by lifestyle factors. It has been shown that poor nutrition,
smoking and alcohol use impair sperm function (Vujkovic et al.,
2009;Gaur et al., 2010). These lifestyles are associated with excessive
oxidative stress, which has been related to male subfertility due to its
damaging effects on spermatozoa (Ebisch et al., 2006;Tremellen,
2008).
Because the incidence of overweight and obesity in men of repro-
ductive ages is rising, this study aims to assess the association
between BMI, central adiposity and sperm quality in men visiting the
preconception outpatient clinic at the Erasmus University Medical
Center in Rotterdam, the Netherlands.
Materials and Methods
Study design
Between October 2007 and October 2010, couples planning pregnancy
and visiting the outpatient clinic for reproductive treatment or specialized
medical preconception care of the Department of Obstetrics and Gyne-
cology at the Erasmus University Medical Center Rotterdam, were
offered preconception counseling at the clinic ‘Achieving a Healthy Preg-
nancy’ (Hammiche et al., 2011). The couples filled out questionnaires
from which the following data were extracted as descriptive or potential
confounders: age, ethnicity, educational level, smoking, alcohol consump-
tion and the use of medication, folic acid and multivitamins. Ethnicity was
defined according to the country of birth of the man and his parents:
Dutch: Netherlands; non-western: Africa, South- and Central America
and Asia, Turkey, Morocco, Antilles, Suriname; other western: all other
countries. (Statistics Netherlands, 2008). Educational level was divided
into three categories: low (primary/lower vocational/intermediate sec-
ondary), intermediate (intermediate vocational/higher secondary) and
high (higher vocational/university) (Statistics Netherlands, 2008). At the
preconception counseling visit, the questionnaires were checked by the
counselor in detail. Height (m) and weight (kg) were standardized mea-
sured to calculate the BMI (weight in kg divided by squared height in cen-
timetres). Weight was measured on an electronic scale without shoes and
jacket and empty pockets. Height was measured without shoes. WC was
measured at the narrowest point between the lower border of the rib
cage and the iliac crest. Subsequently, venous blood samples were
drawn to measure serum and red blood cell (RBC) folate concentrations
evaluated as a potential confounder. All study participants signed an
informed consent form before participation. From the total group of
1248 men who visited the outpatient clinic ‘Achieving a Healthy Preg-
nancy’, we selected only men with a sperm analysis performed within
0–70 days prior to the visit as part of the fertility treatment for further
investigation, n¼455. The Medical Ethical and Institutional Review
Board of the Erasmus University Medical Center in the Netherlands
approved the study.
Sperm collection and analysis
Sperm specimens were produced via masturbation after a required abstin-
ence period of 3–5 days. After liquefaction, ejaculate volume, sperm con-
centration, percentage progressive (type A +B) and immotile
spermatozoa (type C +D) were assessed by a Zeiss microscope (Carl
Zeiss, Oberkochen, Germany) according to World Health Organization
guidelines (WHO, 2010). Sperm concentration was determined with an
improved Neubauer Hemocytometer
w
counting chamber. Total sperm
count was calculated as the product between ejaculate volume and
sperm concentration. Total motile sperm count was calculated as the
product between ejaculate volume, sperm concentration and progressive
motile spermatozoa (type A +B). From a clinical point of view, we studied
the percentage progressive (type A +B) and immotile (type C +D)
spermatozoa. Because of our scientific interest, we also evaluated the in-
dividual sperm motility parameters; type A (rapid progressive motility),
type B (slow or sluggish progressive motility), type C (move locally) and
type D (immotility). The sperm analyses were performed in one single
centre and in one laboratory, which participates in the external quality
control scheme of the Dutch Foundation for Quality Assessment in Clin-
ical Laboratories (SKML).
Laboratory determinations
Venous blood samples were drawn into dry vacutainer tubes and allowed
to clot. After centrifugation at 2000g, serum was collected before being
assayed for the concentration of serum folate. For the determination of
RBC folate venous blood samples were drawn into EDTA–containing
vacutainer tubes. Serum samples from each patient were analysed
during routine laboratory procedures for folate using an immunoelectro-
chemoluminescence assay (E170; Roche Diagnostics GmbH, Mannheim,
Germany). Directly after blood sampling, 0.1 ml EDTA blood was hemo-
lyzed with 0.9 ml of freshly prepared 1% ascorbic acid. Subsequently, the
hematocrit of the EDTA-blood was determined on an ADVIA 120 Hema-
tology Analyzer (Bayer Diagnostics, Leverkusen, Germany). The hemoly-
sate was centrifuged for 5 min at 1000gafter which the folate
concentration was measured in the hemolysate. RBC folate was calculated
using the following formula: (nM hemolysate folate ×10/hematocrit)2
[nM serum folate ×(12hematocrit)/hematocrit] ¼nM RBC folate. Inter-
assay coefficients of variation for serum folate were 4.5% at 13 nmol/l and
5.7% at 23 nmol/l and the detection limit was 1.36 nmol/l.
Statistical analysis
Men were categorized into three BMI groups: (i) ,25 kg/m
2
, (ii) ≥25 and
,30 kg/m
2
and (iii) ≥30 kg/m
2
(World Health Organization, 2000). Eight
underweight (BMI ,18.5 kg/m
2
) men from the entire cohort (n¼1248)
were excluded from the analyses. We dichotomized WC into high- and
low-risk groups on the basis of the gender-specific cut off point of
≥102 cm for the risk of cardiovascular disease according to the National
Institutes of Health (NIH), 2000. Additionally, we categorized men into
quintiles of WC to explore the association between WC and semen para-
meters in more detail. The Kruskal – Wallis test was applied to test differ-
ences between the three BMI strata and the various sperm parameters.
Spearman’s correlation coefficient was calculated between BMI and WC.
The relationships between BMI categories, WC and sperm parameters
were studied using a linear multivariable regression analysis with adjust-
ment for potential confounders. For ease of interpretation, linear regres-
sion was done using log-transformed dependent variables and
non-transformed independent variables, i.e. a log-level linear regression.
The interpretation of the (unstandardized) bis that b×100 equals the
% change in the dependent variable for each 1 unit change in b. Potential
confounders were selected based on significant differences of the covari-
ate between the BMI categories and/or when the literature revealed a
strong relation of the variable with sperm quality and/or BMI. This
resulted in the evaluation of the following potential effect modifiers or con-
founders: age, ethnicity, active and passive smoking, alcohol and medica-
tion use and folate status based on RBC folate. We applied the linear
multivariable regression analysis on the total study sample as well as sep-
arately after stratification in three BMI and two WC categories. All statis-
tics were performed by using the SPSS 17 software package (SPSS, Inc.,
Chicago, IL, USA). A two-tailed P,0.05 was considered statistically
significant.
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Results
To evaluate the selection bias, the general characteristics of the 450
participants and 790 non-participants are depicted in Table I. Age,
BMI, WC, educational level, most lifestyle factors and folate status
were not significantly different between the two groups. However,
in the group of non-participants, there were more men with a non-
western ethnicity and active smokers.
In 61.5% of participants primary subfertility was diagnosed.
In Table II, the general characteristics of the participants are
stratified according to the three BMI categories of which 153
(34.0%) had a BMI ,25 kg/m
2
, 225 (50.0%) a BMI ≥25 and
,30 kg/m
2
and 72 (16.0%) a BMI ≥30 kg/m
2
. Men with over-
weight or obesity were significantly older compared with normal
weight men (P,0.05). Overweight was significantly more
present in Dutch men (75.1%) and obesity in non-western men
(37.5%), (P,0.05). While not statistically significant, the distribu-
tion of the educational level suggests an inverse association with
BMI. Obese men consumed significantly less alcohol compared
with normal weight and overweight men (P,0.001). Correlation
analysis revealed that WC is significantly correlated with BMI
(r¼0.85; P,0.01).
Association between BMI and sperm
parameters
Table II shows that overweight and obese men have a significantly
lower ejaculate volume and sperm count (P,0.05). While not statis-
tically significant, the distribution of the sperm concentrations suggests
a negative association with BMI. Although total motile sperm count
was not significantly different between the groups, overweight and
obese men showed a significantly lower percentage progressive motil-
ity type A (P¼0.02). Furthermore, overweight men showed a signifi-
cantly higher percentage type C motility (P¼0.002).
We further analysed these associations between BMI and sperm
parameters in a multivariable linear regression analysis with adjustment
for the potential confounders age, ethnicity, active and passive
smoking, alcohol and medication use and folate status (Table III).
BMI analysed as linear variable (BMI linear) and the overweight
and obese categories negatively associated with ejaculate volume (all
P,0.05). The association was most pronounced in men with a
BMI ≥30 [adjusted b20.23 (SE 0.10), P¼0.02]. A BMI ≥30 was in-
versely associated with sperm concentration [adjusted b20.77 (SE
0.3), P¼0.006]. Negative associations were estimated between
BMI linear, BMI ≥30 and total sperm count, [adjusted b20.069
.............................................................................................................................................................................................
Table I Characteristics of men of subfertile couples (n51240).
Study population with sperm analysis
(n5450)
Non-participants without sperm analysis
(n5790)
P-value
Age (years) 35 (22–60) 35 (19– 72) 0.18
BMI (kg/m
2
) 26.3 (19.1– 49.0) 26.3 (18.5–50.1) 0.58
Waist circumference (cm) 94 (65–135) 95 (60–119) 0.42
Ethnicity n(%) 448 (100) 744 (100) ,0.001
Dutch 314 (70.1) 433 (58.2)
Other—western 29 (6.5) 47 (6.3)
Non-western 105 (23.4) 264 (35.5)
Educational level n(%) 450 (100) 743 (100) 0.20
High 169 (37.6) 262 (35.3)
Intermediate 187 (41.6) 292 (39.3)
Low 94 (20.9) 189 (25.4)
Lifestyles (yes), n(%)
Smoking, active 123 (27.3) 249 (31.5) 0.049
Smoking, passive 95 (21.1) 165 (20.9) 0.93
Alcohol 121 (26.9) 231 (30.7) 0.16
Folic acid supplement use 15 (3.3) 14 (1.9) 0.27
Multivitamin supplement use 112 (24.9) 157 (20.8) 0.22
Medication use (prescription and over the
counter)
116 (25.8) 175 (22.2) 0.55
Biochemical parameters
Folate (nmol/l) 17.3 (7–64) 17.3 (5.6–128.0) 0.95
Folate RBC (nmol/l) 905 (64–2247) 890 (252–2361) 0.31
P,0.05 was considered statistically significant. Values are expressed as median (range) or number ( percentages).
Body weight, lifestyle and sperm quality 2367
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(SE 0.02), P¼0.003; adjusted b20.98 (SE 0.3), P¼0.001], respect-
ively. In a similar manner, BMI linear and BMI ≥30 were negatively
associated with total motile sperm count [adjusted b20.058 (SE
0.03), P¼0.04 and adjusted b20.91 (SE 0.3), P¼0.007], respect-
ively. Furthermore, an inverse association was observed between a
25 ≤BMI ,30 and the percentage progressive motility type A
[adjusted b20.32 (SE 0.2), P¼0.036]. BMI linear and the 25 ≤
BMI ,30 category were positively associated with the percentage
of type C motility [adjusted b0.02 (SE 0.01), P¼0.009 and adjusted
b0.21 (SE 0.07), P¼0.002, respectively].
Association between WC and sperm
parameters
Men with a WC ≥102 cm had a significantly lower sperm concentra-
tion compared with men with a WC ,102 cm, respectively 27 ×
10
6
/ml (0– 661 ×10
6
/ml) and 17 ×10
6
/ml (0– 350 ×10
6
/ml);
P≤0.05. Table IV shows the multivariable linear regression analysis
with adjustment for confounders. After adjustment, a WC ≥102 cm
remained negatively associated with sperm concentration [adjusted
b20.69 (SE 0.2), P¼0.001]. Furthermore, a WC ≥102 cm was
.............................................................................................................................................................................................
Table II Characteristics of men of subfertile couples (n5450).
BMI <25 (n5153) 25
≤
BMI <30 (n5225) BMI
≥
30 (n572) P
Age (years), median 34 (23– 60) 35 (24–57) 35 (22–52) 0.048
Waist circumference (cm) 85 (65–106) 95 (81 –110) 113 (95–135) ,0.001
Ethnicity n(%) 152 (100) 224 (100) 72 (100) 0.02
Dutch 105 (69.1) 169 (75.1) 40 (55.6)
Other—western 12 (7.9) 12 (5.3) 5 (6.9)
Non-western 35 (23.0) 43 (19.1) 27 (37.5)
Educational level n(%) 153 (100) 225 (100) 72 (100) 0.08
High 66 (43.1) 85 (37.8) 18 (25.0)
Intermediate 62 (40.5) 89 (39.6) 36 (50.0)
Low 25 (16.3) 51 (22.7) 18 (25.0)
Subfertility n(%) 137 (100) 196 (100) 64 (100) 0.90
Primary 98 (71.5) 138 (70.4) 47 (73.4)
Secondary 39 (28.5) 58 (29.6) 17 (26.6)
Lifestyles (yes), n(%)
Smoking, active 37 (24.5) 64 (28.8) 22 (30.6) 0.55
Smoking, passive 30 (19.3) 52 (23.1) 13 (18.3) 0.60
Alcohol 120 (78.4) 170 (75.6) 39 (54.2) ,0.001
Folic acid supplement use 7 (4.8) 5 (2.2) 3 (4.2) 0.56
Multivitamin supplement use 43 (28.5) 53 (24.2) 16 (22.5) 0.54
Medication use (prescribed and over the counter) 39 (25.8) 53 (23.7) 24 (33.3) 0.27
Biochemical parameters
Folate (nmol/l) 18.5 (7–64) 17.3 (7– 45) 16.3 (8– 33) 0.21
RBC folate (nmol/l) 879 (64 –2247) 948 (153– 2194) 861 (474– 1622) 0.11
Sperm parameters (p25 –p75)
Ejaculate volume (ml) 3.0 (1.9– 4.0) 2.7 (1.5–3.5) 2.4 (1.6– 3.4) 0.02
Sperm concentration (10
6
/ml) 34.0 (8.9– 62.3) 23.0 (6.8– 51.5) 18.0 (1.1– 60.3) 0.99
Sperm count 68.6 (19.8 –183.2) 49.6 (14–124.8) 45.9 (2.8 –147.5) 0.03
Total motile sperm count (10
6
/ml) 27.3 (4.0–85.2) 17.2 (2.8– 50.0) 14.4 (5.6– 73.2) 0.07
Progressive motility (A +B) (%) 39.0 (22.0– 48.5) 37.0 (21.0– 47.0) 39.0 (23.0– 49.0) 0.48
Immotile sperm (C +D) (%) 61.0 (51.5– 78.0) 63.0 (53.0 –79.0) 61.0 (51.0–77.0) 0.48
Subtypes of motility
Type A motility (%) 8.0 (2.0– 23.0) 5.0 (1.0– 14.0) 6.0 (1.0 –16.0) 0.02
Type B motility (%) 21.0 (13.0 –31.0) 25.5 (15.0– 33.0) 28.0 (14.0– 36.0) 0.20
Type C motility (%) 8.0 (5.0– 10.0) 9.0 (6.0– 13.0) 8.0 (6.0 –10.0) 0.002
Type D motility (%) 52.0 (43.5 – 68.5) 53.0 (43.0– 68.0) 53.0 (44.0– 66.0) 0.91
P,0.05 was considered statistically significant. Values are expressed as median (range), median (p25– p75) or as number (%) per BMI stratum. Totalsperm count ¼ejaculate volume
×sperm concentration. Total motile sperm count ¼ejaculate volume ×sperm concentration ×progressive motile spermatozoa (type A +B).
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Table III Associations between BMI and sperm parameters.
BMI linear (total group n5450) BMI <25
(n5153)
25
≤
BMI <30 (n5225) BMI
≥
30 (n572)
Unadjusted Adjusted
a
Unadjusted Adjusted
a
Unadjusted Adjusted
a
Sperm parameters REF
Ejaculate volume (ml), b(SE), P20.029 (0.01), 0.00 20.027 (0.01), 0.001 20.17 (0.07), 0.01 20.14 (0.07), 0.048 20.24 (0.09), 0.01 20.23 (0.1), 0.02
Sperm concentration (10
6
/ml), b(SE), P20.034 (0.02), 0.12 20.042 (0.02), 0.07 20.10 (0.2), 0.59 20.046 (0.2), 0.86 20.65 (0.3), 0.01 20.77 (0.3), 0.006
Total sperm count (10
6
/ml), b(SE),P20.063 (0.02), 0.004 20.069 (0.02), 0.003 20.26 (0.2), 0.19 0.17 (0.2), 0.42 20.88 (0.3), 0.001 20.98 (0.3), 0.001
Total motile sperm count (10
6
/ml), b(SE), P20.055 (0.03), 0.03 20.058 (0.03), 0.04 20.22 (0.2), 0.33 20.079 (0.2), 0.74 20.84 (0.3), 0.007 20.91 (0.3), 0.007
Progressive motility (A +B) (%), b(SE), P20.001 (0.01), 0.95 0.001 (0.01), 0.96 20.10 (0.08), 0.22 20.066 (0.09), 0.47 20.001 (0.1), 0.99 0.014 (0.1), 0.91
Immotile sperm (C +D) (%), b(SE), P0 (0.003), 0.92 20.001 (0.004), 0.85 0.037 (0.04), 0.30 0.026 (0.04), 0.50 20.059 (0.05), 0.23 20.078 (0.1), 0.15
Subtypes of motility
Type A motility (%), b(SE), P20.028 (0.02), 0.08 20.025 (0.02), 0.14 20.38 (0.1), 0.008 20.32 (0.2), 0.036 20.21 (0.2), 0.27 20.18 (0.2), 0.38
Type B motility (%), b(SE), P0.008 (0.01), 0.42 0.009 (0.1), 0.41 0.051 (0.09), 0.55 0.059 (0.09), 0.53 0.10 (0.1), 0.39 0.12 (0.1), 0.34
Type C motility (%), b(SE), P0.017 (0.01), 0.02 0.02 (0.01), 0.009 0.23 (0.06), ,0.001 0.21 (0.07), 0.002 0.12 (0.09), 0.16 0.16 (0.09), 0.095
Type D motility (%), b(SE), P20.005 (0.004), 0.25 20.006 (0.004), 0.14 0.002 (0.04), 0.96 20.01 (0.04), 0.79 20.028 (0.05), 0.56 20.051 (0.05), 0.34
P,0.05 was considered statistically significant. All data in the table are presented as unstandardized adjusted linear regression coefficients (b) [standard error (SE)] which reflect the relative effect per 1 point of BMI on the respective sperm
parameter. Total sperm count ¼ejaculate volume ×sperm concentration. Total motile sperm count ¼ejaculate volume ×sperm concentration ×progressive motile spermatozoa (type A +B).
a
P-values are adjusted for the following covariates: age (in years), ethnicity, active and passive smoking, alcohol, medication use and folate status.
............................................................... ..........................................................
..........................................................................................................................................................................................................................................................
Table IV Associations between WC and sperm parameters.
Waist circumference linear (total group
n5413)
Waist circumference <102 cm
(n5310)
Waist circumference
≥
102 cm (n5103)
Unadjusted Adjusted
a
Unadjusted Adjusted
a
Sperm parameters REF
Ejaculate volume (ml), b(SE), P20.005 (0.003), 0.06 20.006 (0.003), 0.04 20.12 (0.08), 0.14 20.15 (0.08), 0.07
Sperm concentrations (10
6
/ml), b(SE), P20.018 (0.007), 0.02 20.019 (0.008), 0.02 20.64 (0.2), 0.002 20.69 (0.2), 0.001
Total sperm count (10
6
/ml), b(SE), P20.023 (0.008), 0.003 20.025 (0.008), 0.002 20.73 (0.2), 0.001 20.81 (0.2), ,0.001
Total motile sperm count (10
6
/ml), b(SE), P20.021 (0.009), 0.02 20.021 (0.01), 0.03 20.59 (0.2), 0.02 20.62 (0.3), 0.02
Progressive motility (A +B) (%), b(SE), P20.003 (0.003), 0.39 20.002 (0.004), 0.65 20.063 (0.1), 0.49 20.032 (0.1), 0.75
Immotile sperm (C +D) (%), b(SE), P0.001 (0.001), 0.50 0.00 (0.001), 0.94 0.014 (0.03), 0.69 20.004 (0.04), 0.90
P,0.05 was considered statistically significant. All data in the table are presented as unstandardized adjusted linear regression coefficients (b) [standard error (SE)] which reflect the relative effect of WC on the respective sperm parameter.
Total sperm count ¼ejaculate volume ×sperm concentration. Total motile sperm count ¼ejaculate volume ×sperm concentration ×progressive motile spermatozoa (type A +B).
a
P-values are adjusted for the following covariates: age (in years), ethnicity, active and passive smoking, alcohol, medication use and folate status.
Body weight, lifestyle and sperm quality 2369
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also negatively associated with total sperm count [adjusted b20.81
(SE 0.2), P¼0.001] and total motile sperm count [adjusted b
20.62 (SE 0.3), P¼0.02]. To further specify the relation between
WC and sperm parameters, we categorized men into quintiles
based on the observed WC in our cohort. The quintile analyses
show that the NIH cut-off for a high WC ( ≥102 cm) is a sensible
cut-off for the study of adverse effects of WC. In these analyses, we
show that the effects of WC on sperm parameters are only seen in
the highest quintile of WC (≥104 cm). With the lowest quintile
(WC ≤85 cm) as a reference, men in the highest quintile of WC
(≥104 cm) had a lower ejaculate volume [adjusted b20.223 (SE
0.11), P¼0.045], sperm concentration [adjusted b20.642
(SE 0.29), P¼0.03] and total sperm count [adjusted b20.875 (SE
0.30), P¼0.004]. With similar effect estimates, the effect of WC
on total motile sperm count was only significant before adjustments
[adjusted b20.633 (SE 0.36), P¼0.08]. To investigate if we could
disentangle possible independent effects of WC and BMI on sperm
parameters (i.e. does the distribution of fat tissue explain the effect
of WC on sperm parameters), we generated WC residuals, i.e. vari-
ation of WC not explained by variation in BMI. We additionally
adjusted for BMI and WC residuals in the linear multivariable regres-
sion analysis after which all associations between WC and sperm para-
meters disappeared.
Discussion
This study demonstrates that BMI and WC—independent of other
lifestyle factors—affect sperm quality in men of subfertile couples
attending an outpatient preconception clinic, in which BMI seems a
slightly stronger predictor than WC. Being overweight is associated
with a significantly lower ejaculate volume, a lower percentage of pro-
gressive motility type A and a higher percentage of motility type
C. Furthermore, obesity is associated with a significantly lower ejacu-
late volume, lower sperm concentration, lower total sperm count and
a lower total motile sperm count. A WC ≥102 cm, as a marker for
central adiposity, was associated with a lower sperm concentration,
lower total sperm count and a lower total motile sperm count. Quin-
tile analyses of the effects of WC on sperm parameters confirm that
the NIH cut-off for a high WC is sensible and that the effects are only
seen in the highest quintile with a comparable cut-off as the NIH ref-
erence. Due to the stronger correlations between BMI and sperm
parameters, the associations with WC disappeared after adjustment
for BMI.
Thus, BMI and WC are especially associated with ejaculate volume,
sperm concentration and sperm motility. These associations have also
been investigated and described by MacDonald et al. (2010). Our find-
ings, however, are in contrast to a recent Dutch study (Duits et al.,
2010) that observed no significant association between BMI and
sperm parameters. This lack of an association may be a statistical
power issue, given that a smaller proportion of obese men (10.4%)
compared with the 16% in our study has been investigated. In addition,
it is not clear whether the anthropometric features were standardized
measured or self-reported. The latter could have induced a differential
misclassification of the exposure of interest, which may have led to a
bias towards the null hypothesis resulting in a non-significant estimate.
This is supported by others showing that the prevalence of obesity
based on self-reported data underestimates the true prevalence
(Fear et al., 2011).
In line with the study of Chavarro et al. (2010), we also found dif-
ferent effects of the BMI strata on sperm parameters. They reported a
similar inverse association between BMI and ejaculate volume and
total sperm count. However, they did not find an association
between BMI and sperm concentration, which is the most consistent
finding across studies (Jensen et al., 2004;Koloszar et al., 2005;Mag-
nusdottir et al., 2005;Hammoud et al., 2008a,b). Furthermore, in con-
trast to our results, the Chavarro group showed that overweight men
had a higher percentage of progressive motile sperm.
Our findings of the inverse association between a high BMI and
sperm parameters strengthen a previously reported study in Europe
and the USA (Hammoud et al., 2008a,b). The majority of the
studies have focused only on BMI as the predominant measure of adi-
posity and not on WC. To our knowledge, this study is the second
study to show an association between WC and sperm parameters
(Fejes et al., 2005) and the first to do so after adjusting for various con-
founders and in a large study population. The sensitivity of BMI in es-
timating individuals body fat mass suffers from the inability to
distinguish between variability in body composition and body fat
mass distribution (Akpinar et al., 2007), to which WC offers a
partial solution. Recent studies indicated that abdominal obesity is
more strongly associated with obesity-related health problems than
adiposity measured by BMI (Yusuf et al., 2005). In women, it has
been shown that differences in fat mass distribution exist between
subfertile women and normal controls. The different fat mass patterns
were accompanied by different prognoses of fertility (Kirchengast and
Huber, 2004). We showed that a WC of ≥102 cm is associated with
a lower sperm concentration, total sperm count and total motile
sperm count. However, after additional adjustment for BMI in the
linear multivariable regression analysis the association attenuated,
which may indicate that BMI and WC are markers for the same vari-
able, i.e. physiology associated with adipose tissue/weight gain with
comparable effect estimates.
Several mechanisms might account for the harmful effects of a high
BMI on sperm parameters. Numerous studies have noted that obesity
and several of its causes, such as insulin resistance and dyslipidemia,
are associated with increased oxidative stress (Dandona et al., 2005;
Davi and Falco, 2005). Oxidative stress is an independent marker
for male factor subfertility since it impairs sperm quality (Ebisch
et al., 2008). A mouse study showed that obesity increases oxidative
stress and, as a result, reduced sperm motility and increased DNA
damage (Bakos et al., 2011).
It has also been suggested that the detrimental influence of a high
body weight on sperm quality is partially driven by an altered repro-
ductive hormonal profile (Jensen et al., 2004;Hammoud et al.,
2008a,b). Overweight and obesity, particularly when central, have
been shown to affect the GnRH-LH/FSH pulse, which may impair
Leydig and Sertoli cell functions and thus interfere with the release
of sex hormones and production and maturation of sperm
(Hammoud et al., 2008a,b). Consequently, a high BMI is associated
with lower levels of total testosterone, sex hormone-binding globulin
and inhibin B and higher levels of serum estradiol (Jensen et al., 2004;
Chavarro et al., 2010). Additionally, serum leptin, which is higher in
overweight and obese men, inhibits testosterone synthesis, which is
a cause of impaired sperm quality (Hofny et al., 2010). However,
2370 Hammiche et al.
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the levels across which alterations of these hormones have a deleteri-
ous effect on sperm quality are unknown. In our study, we were not
able to substantiate our findings with changes in male sex hormone
levels. While weight loss normalizes testosterone and inhibin B
levels in obese men, it is unknown whether this also restores sperm
quality (Globerman et al., 2005). A previous study concluded that
associations between male BMI and sperm quality were found to be
statistically significant even after adjustment for reproductive hor-
mones (Qin et al., 2007). This suggests that a hormonal explanation
as the sole mechanism is unlikely. Future studies are needed to inves-
tigate this finding in more detail.
The strengths and weaknesses of this study design have to be
addressed. The strengths of our study are the assessment of standar-
dized anthropometric measures, i.e., BMI, WC, sperm parameters,
biomarkers and potential confounders in a relatively large homogen-
ous group of men in subfertile couples. To prevent selection bias
we included all men of subfertile couples planning pregnancy who
visited a single tertiary centre between October 2007 and October
2010. Sperm parameters and biomarkers were also measured at
one single centre and laboratory. A limitation of our study might be
that only one single sperm analysis was performed. However, we
do not believe that this poses a major threat to the validity of this ana-
lysis, because it has been shown in a population based study that ana-
lyzing multiple sperm samples per subject does not seem superior to a
single sperm sample analysis (Stokes-Riner et al., 2007). A strength of
our study is that we limited measurement errors by selecting men on
whom a sperm analysis was performed in a standardized window of
0–70 days before the preconceptional visit. Moreover, the indication
for the sperm analysis and referral to the preconceptional outpatient
clinic was not influenced by BMI. We are aware, however, that this
study has been performed in men of subfertile couples, which limits
its external validity with the consequence that the results cannot be
extrapolated to the general population. It is also well recognized
that fecundity as assessed by the standard sperm parameters does
not necessarily translate to fertility including live births. There are
also additional sperm quality parameters of importance, in particular
sperm DNA fragmentation.
Conclusion
A high BMI and a high WC are associated with detrimental effects on
sperm quality. Increasing awareness of the target population of men,
gynecologists, urologists, andrologists and general practitioners is
needed to address the importance of this relationship. Future pre-
ventative interventions should be developed and directed at men to
loose weight especially during the window of planning a pregnancy.
However, this emphasizes the need of intervention studies directed
at the effects of loosing weight on sperm quality. Future studies are
also needed to gain insight into the underlying mechanisms and the
effects on fertility outcome.
Authors’ roles
F.H. acquired, analysed and interpreted data, drafted the manuscript
and designed study. J.S.E.L. drafted the manuscript, critically revised
the manuscript for intellectual content and data interpretation.
J.M.T. acquired and analysed data. W.P.A.B. critically revised the
manuscript for intellectual content. E.A.P.S. critically revised the manu-
script for intellectual content. R.P.M.S.T. drafted the manuscript, inter-
preted data, critically revised the manuscript for intellectual content
and study design.
Funding
The authors are financially supported by the Department of Obste-
trics and Gynecology of the Erasmus MC, University Medical
Center, Rotterdam, The Netherlands.
Conflict of interest
J.S.E.L. has received fees and grant support from the following com-
panies (in alphabetic order): Ferring, Genovum, Merck-Serono,
Organon, Schering Plough and Serono. All other authors have
nothing to disclose.
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