Searching for predictors of male fecundity

Searching for semen phenotypes in impaired male fecundity

Chirag J Patel Raji Sundaram, Germaine Buck Louis

Epi Congress, Miami06/22/16

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, 2013Buck 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

volumeconcentration straw distance

hypo-osmotic swollen

motility average path

straight & curvilinear velocityamplitude head displacement

beat cross frequency% motility% straight

% linear movement

% normal (2 criteria) % amorphous

roundpyriform

bichephalictapered

megalo/micro headneck/midpiece abnorm.coiled/other tail abnorm.

immature spermcytoplasmic droplet

morphology head lengthwidth

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 variationEffective number of variables: 38 (vs. 40)

WHO normal and strict criteria

Impaired fecundity and “traditional” risk factorsTime-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 + agePi 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 + cotinineversus

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

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Sperm phenotypes offer modest predictive boost over “traditional” factors (age, smoking, fathered)

p < 0.05

Nagelkerke R2: 0.2 vs 0.1

OR PAge* 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 Patelchirag@hms.harvard.edu

@chiragjpwww.chiragjpgroup.org

NIH Common FundBig Data to Knowledge

Acknowledgements

RagGroup Chirag Lakhani Adam Brown Danielle RasoolyArjun ManraiErik CoronaNam Pho

Germaine Buck Louis Rajeshwari Sundaram