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Searching for semen phenotypes in
impaired male fecundity
Chirag J Patel 

Raji Sundaram, Germaine Buck Louis

Epi Congress, Miami

06/22/16
chirag@hms.harvard.edu
@chiragjp
www.chiragjpgroup.org
Sperm count, morphology, and motility have been
hypothesized to be predictive of fecundity…
However, prognostic utility of semen phenotypes under
debate and elusive.
Lack prospective cohorts to examine association
between sperm phenotype and male fecundity.
Semen phenotypes are important for fertility…

but clinical use have been debated.
NEJM, 2001
Semen and the curse of cutoffs.
Niederberger, J Urol 2011
Fertil Steril, 2013
What sperm phenotypes have utility of prediction of
impaired couple fecundity (> 6 cycles to pregnancy)?
Longitudinal Investigation of Fertility and the
Environment (LIFE): a prospective study of couples
desiring to become pregnant
• Reproductive age (18-40 for females; >18 for males)

• N=501 couples (Michigan and Texas) in 2005-2007

• Data collected in couples’ home

• urine, blood, semen (at baseline and at month 1)

• pregnancy and fertility monitors (ClearBlue)

• monitors and journals used to define menstrual cycle
Buck Louis et al, 2013

Buck Louis et al, 2014
EHP, 2012
Fertility and Sterility, 2014
“male and female persistent pollutants and reduced fecundibility…”
“male phthalates and 20% reduction in fecundity…”
Fertility and Sterility, 2014
35 semen phenotypes:

5 general, 8 motility, 6 head measures, 12
morphology, 2 chromatin stability
Buck Louis, 2014
general
count

volume

concentration
straw distance

hypo-osmotic swollen
motility
average path

straight & curvilinear velocity

amplitude head displacement

beat cross frequency

% motility

% straight

% linear movement
% normal (2 criteria)
% amorphous

round

pyriform

bichephalic

tapered
megalo/micro head

neck/midpiece abnorm.

coiled/other tail abnorm.

immature sperm

cytoplasmic droplet
morphology head
length

width

perimeter
% elongation factor
area
% acrosome
chromatin
% fragmentation

high fragmentation sustain.
35 semen + 5 risk factors have modest ρ
motility/morphology
head/morphology
chromatin/morphology
head
34 PCs (out of 40)~ 99% of variation

Effective number of variables: 38 (vs. 40)
WHO normal and strict criteria
Impaired fecundity and “traditional” risk factors

Time-to-pregnancy (TTP) > 6 cycles (N=402)
TTP ≤ 6
mean (SE)
TTP > 6
mean (SE)
p-value
Age (year) 31.2 (0.3) 33.3 (0.5) 0.0005
BMI (kg/m2) 29.8 (0.3) 29.2 (0.6) 0.6
Lipids (ng/g) 726.8 (12) 713.5 (22) 0.6
Cotinine (mg/dL) 40.9 (6.7) 79.9 (17.3) 0.04
Previous
pregnancies (#)
1.3 (0.04) 1.6 (0.13) 0.07
N=302 N=100
LIFE Study
35 semen and 5 non-semen

Prospective TTP
Time-to-pregnancy IQR: 1-6
N=302 and 100
TTP > 6? = Pi + age
Pi
n=40
age-adjusted
logistic regression
False discovery rate < 10%:

Px … Pz
TTP > 6? = age + father + BMI +
cotinine + Px + … + Pz
TTP > 6? = age + father + BMI +
cotinine
versus
OR in time-to-pregnancy > 6 cycles: 

5 morphological phenotypes
Age-adjusted
FDR < 10%
head
f(volume, count, concentration, motility)
f(head, neck, tail)
ROC Curve for TTP > 6, AUC=0.730 [green]/0.673 [red]
False positive rate
Truepositiverate
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0 Sperm phenotypes offer modest predictive boost
over “traditional” factors (age, smoking, fathered)
p < 0.05
Nagelkerke R2: 

0.2 vs 0.1
OR P
Age* 1.66 (1.29, 2.15) 0.0001
Fathered? 0.45 (0.26, 0.75) 0.002
BMI* 0.87 (0.67, 1.11) 0.25
Cotinine* 1.27 (1.01, 1.60) 0.04
% Normal* 1.23 (0.73, 2.12) 0.44
% Coiled Tail* 1.52 (1.09, 2.14) 0.02
% Pyriform* 1.46 (1.08, 2.01) 0.01
% Amorphous* 1.57 (1.07, 2.35) 0.03
* per 1 SD
Conclusions: 

Semen phenotypes modest prognostics for TTP > 6
cycles
Weak to moderate associations between sperm phenotypes
and time to pregnancy greater than 6 cycles.
Little predictive value beyond common risk factors of age,
smoking, and history.
Cannot rule out measurement errors and residual confounding.
Larger sample sizes and more measures to describe variance in fecundity.
Chirag J Patel

chirag@hms.harvard.edu

@chiragjp

www.chiragjpgroup.org
NIH Common Fund

Big Data to Knowledge
Acknowledgements
RagGroup
Chirag Lakhani
Adam Brown
Danielle Rasooly

Arjun Manrai

Erik Corona

Nam Pho
Germaine Buck Louis
Rajeshwari Sundaram

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Searching for predictors of male fecundity

  • 1. Searching for semen phenotypes in impaired male fecundity Chirag J Patel Raji Sundaram, Germaine Buck Louis Epi Congress, Miami 06/22/16 chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org
  • 2. Sperm count, morphology, and motility have been hypothesized to be predictive of fecundity… However, prognostic utility of semen phenotypes under debate and elusive. Lack prospective cohorts to examine association between sperm phenotype and male fecundity.
  • 3. Semen phenotypes are important for fertility… but clinical use have been debated. NEJM, 2001 Semen and the curse of cutoffs. Niederberger, J Urol 2011 Fertil Steril, 2013
  • 4. What sperm phenotypes have utility of prediction of impaired couple fecundity (> 6 cycles to pregnancy)?
  • 5. Longitudinal Investigation of Fertility and the Environment (LIFE): a prospective study of couples desiring to become pregnant • Reproductive age (18-40 for females; >18 for males) • N=501 couples (Michigan and Texas) in 2005-2007 • Data collected in couples’ home • urine, blood, semen (at baseline and at month 1) • pregnancy and fertility monitors (ClearBlue) • monitors and journals used to dene menstrual cycle Buck Louis et al, 2013 Buck Louis et al, 2014
  • 6. EHP, 2012 Fertility and Sterility, 2014 “male and female persistent pollutants and reduced fecundibility…” “male phthalates and 20% reduction in fecundity…”
  • 8. 35 semen phenotypes: 5 general, 8 motility, 6 head measures, 12 morphology, 2 chromatin stability Buck Louis, 2014 general count volume concentration straw distance hypo-osmotic swollen motility average path straight & curvilinear velocity amplitude head displacement beat cross frequency % motility % straight % linear movement % normal (2 criteria) % amorphous round pyriform bichephalic tapered megalo/micro head neck/midpiece abnorm. coiled/other tail abnorm. immature sperm cytoplasmic droplet morphology head length width perimeter % elongation factor area % acrosome chromatin % fragmentation high fragmentation sustain.
  • 9. 35 semen + 5 risk factors have modest ρ motility/morphology head/morphology chromatin/morphology head 34 PCs (out of 40)~ 99% of variation Effective number of variables: 38 (vs. 40) WHO normal and strict criteria
  • 10. Impaired fecundity and “traditional” risk factors Time-to-pregnancy (TTP) > 6 cycles (N=402) TTP ≤ 6 mean (SE) TTP > 6 mean (SE) p-value Age (year) 31.2 (0.3) 33.3 (0.5) 0.0005 BMI (kg/m2) 29.8 (0.3) 29.2 (0.6) 0.6 Lipids (ng/g) 726.8 (12) 713.5 (22) 0.6 Cotinine (mg/dL) 40.9 (6.7) 79.9 (17.3) 0.04 Previous pregnancies (#) 1.3 (0.04) 1.6 (0.13) 0.07 N=302 N=100
  • 11. LIFE Study 35 semen and 5 non-semen Prospective TTP Time-to-pregnancy IQR: 1-6 N=302 and 100 TTP > 6? = Pi + age Pi n=40 age-adjusted logistic regression False discovery rate < 10%: Px … Pz TTP > 6? = age + father + BMI + cotinine + Px + … + Pz TTP > 6? = age + father + BMI + cotinine versus
  • 12. OR in time-to-pregnancy > 6 cycles: 5 morphological phenotypes Age-adjusted FDR < 10% head f(volume, count, concentration, motility) f(head, neck, tail)
  • 13. ROC Curve for TTP > 6, AUC=0.730 [green]/0.673 [red] False positive rate Truepositiverate 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Sperm phenotypes offer modest predictive boost over “traditional” factors (age, smoking, fathered) p < 0.05 Nagelkerke R2: 0.2 vs 0.1 OR P Age* 1.66 (1.29, 2.15) 0.0001 Fathered? 0.45 (0.26, 0.75) 0.002 BMI* 0.87 (0.67, 1.11) 0.25 Cotinine* 1.27 (1.01, 1.60) 0.04 % Normal* 1.23 (0.73, 2.12) 0.44 % Coiled Tail* 1.52 (1.09, 2.14) 0.02 % Pyriform* 1.46 (1.08, 2.01) 0.01 % Amorphous* 1.57 (1.07, 2.35) 0.03 * per 1 SD
  • 14. Conclusions: Semen phenotypes modest prognostics for TTP > 6 cycles Weak to moderate associations between sperm phenotypes and time to pregnancy greater than 6 cycles. Little predictive value beyond common risk factors of age, smoking, and history. Cannot rule out measurement errors and residual confounding. Larger sample sizes and more measures to describe variance in fecundity.
  • 15. Chirag J Patel chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org NIH Common Fund Big Data to Knowledge Acknowledgements RagGroup Chirag Lakhani Adam Brown Danielle Rasooly Arjun Manrai Erik Corona Nam Pho Germaine Buck Louis Rajeshwari Sundaram