PMID- 34658363
OWN - NLM
STAT- MEDLINE
DCOM- 20211228
LR  - 20211228
IS  - 1479-6805 (Electronic)
IS  - 0022-0795 (Linking)
VI  - 252
IP  - 1
DP  - 2021 Nov 24
TI  - Algorithms predicting gestational stage from the maternal steroid metabolome of 
      mares.
PG  - 45-57
LID - 10.1530/JOE-21-0249 [doi]
AB  - Hormone secretion by the maternal ovaries, trophoblast/placenta and fetus occurs 
      sequentially, creating distinct steroid metabolomic 'signatures' in systemic 
      blood of pregnant mares that vary with gestational stage. Algorithms were 
      developed to predict the gestational day (GD) from the maternal steroid 
      metabolome (nine steroids; pregnenolone (P5), progesterone (P4), 
      5alpha-dihydroprogesterone (DHP), 17alpha-hydroxyprogesterone, allopregnanolone, 
      20alpha-hydroxy-DHP, 3beta,20alpha-dihydroxy-DHP, DHEA and androstenedione) determined by 
      liquid chromatography-tandem mass spectrometry (LC-MS/MS) of eight thoroughbred 
      mares sampled longitudinally throughout pregnancy. A physiologically based model 
      was developed to infer rates of steroid secretion during chorionic gonadotropin 
      secretion, the luteo-placental shift and by the equine feto-placenta unit, 
      demonstrating more variability in P5 and DHP than P4. The average of four 
      empirical models, using nine steroids to predict GD, was calibrated (five mares, 
      R2 = 0.94, RMSE = 20 days) and validated (three mares, R2 = 0.84, RMSE = 32 
      days). Validation performance was improved using paired samples taken 14 or 30 
      days apart (RMSE = 29 and 19 days, respectively). A second validation used an 
      independent dataset (single serum samples from 56 mixed breed mares, RMSE = 79 
      days) and an additional longitudinal subset from the same population sampled 
      monthly throughout gestation (seven mares, RMSE = 42 days). Again, using paired 
      samples improved model performance (RMSE = 32.5 days). Despite less predictive 
      performance of the mixed breed than the thoroughbred datasets, these models 
      demonstrate the feasibility and potential for using maternal steroid metabolomic 
      algorithms to estimate the stage of gestation in pregnant mares and perhaps 
      monitor fetal development.
FAU - Shorten, Paul R
AU  - Shorten PR
AD  - AgResearch Ltd., Ruakura Research Centre, Hamilton, New Zealand.
FAU - Legacki, Erin L
AU  - Legacki EL
AD  - Department of Population Health & Reproduction, School of Veterinary Medicine, 
      University of California, Davis, California, USA.
FAU - Chavatte-Palmer, Pascale
AU  - Chavatte-Palmer P
AD  - Universite Paris-Saclay, UVSQ, INRAE, BREED, Jouy-en-Josas, France.
AD  - Ecole Nationale Veterinaire d'Alfort, BREED, Maisons-Alfort, France.
FAU - Conley, Alan J
AU  - Conley AJ
AD  - Department of Population Health & Reproduction, School of Veterinary Medicine, 
      University of California, Davis, California, USA.
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20211124
PL  - England
TA  - J Endocrinol
JT  - The Journal of endocrinology
JID - 0375363
RN  - 0 (Steroids)
SB  - IM
MH  - *Algorithms
MH  - Animals
MH  - Chromatography, Liquid/veterinary
MH  - Datasets as Topic
MH  - Feasibility Studies
MH  - Female
MH  - Gestational Age
MH  - Horses
MH  - Metabolome
MH  - Models, Theoretical
MH  - Pregnancy
MH  - Pregnancy Tests/methods/veterinary
MH  - *Pregnancy, Animal
MH  - *Prenatal Diagnosis/methods/veterinary
MH  - Steroids/analysis/*metabolism
MH  - Tandem Mass Spectrometry/methods/veterinary
OTO - NOTNLM
OT  - algorithms
OT  - equine
OT  - fetal development
OT  - gestation
OT  - maternal blood
OT  - mathematical modeling
OT  - metabolome
OT  - steroid
EDAT- 2021/10/19 06:00
MHDA- 2021/12/29 06:00
CRDT- 2021/10/18 08:45
PHST- 2021/10/06 00:00 [received]
PHST- 2021/10/14 00:00 [accepted]
PHST- 2021/10/19 06:00 [pubmed]
PHST- 2021/12/29 06:00 [medline]
PHST- 2021/10/18 08:45 [entrez]
AID - JOE-21-0249.R1 [pii]
AID - 10.1530/JOE-21-0249 [doi]
PST - epublish
SO  - J Endocrinol. 2021 Nov 24;252(1):45-57. doi: 10.1530/JOE-21-0249.