|
1
|
Posobiec LM, Cox EM, Solomon HM, Lewis EM,
Wang KF and Stanislaus D: A probability analysis of historical
pregnancy and fetal data from Dutch Belted and New Zealand White
Rabbit Strains from embryo-fetal development studies. Birth Defects
Res B Dev Reprod Toxicol. 107:76–84. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Falk D, Zollikofer CPE, Morimoto N and de
León MS Ponce: Metopic suture of Taung (Australopithecus africanus)
and its implications for hominin brain evolution. Proc Natl Acad
Sci USA. 109:pp. 8467–8470. 2012; View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Niakan KK, Han J, Pedersen RA, Simon C and
Pera RA: Human pre-implantation embryo development. Development.
139:829–841. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Khan KS, Wojdyla D, Say L, Gülmezoglu AM
and Van Look PF: WHO analysis of causes of maternal death: a
systematic review. Lancet. 367:1066–1074. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Moore LG, Charles SM and Julian CG: Humans
at high altitude: hypoxia and fetal growth. Respir Physiol
Neurobiol. 178:181–190. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Wilcox AJ, Weinberg CR, O'Connor JF, Baird
DD, Schlatterer JP, Canfield RE, Armstrong EG and Nisula BC:
Incidence of early loss of pregnancy. N Engl J Med. 319:189–194.
1988. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
McCowan L and Horgan RP: Risk factors for
small for gestational age infants. Best Pract Res Clin Obstet
Gynaecol. 23:779–793. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
McCowan LM, Harding JE and Stewart AW:
Customized birthweight centiles predict SGA pregnancies with
perinatal morbidity. BJOG. 112:1026–1033. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Chang TC, Robson SC, Boys RJ and Spencer
JA: Prediction of the small for gestational age infant: which
ultrasonic measurement is best? Obstet Gynecol. 80:1030–1038.
1992.PubMed/NCBI
|
|
10
|
Slama R, Ballester F, Casas M, Cordier S,
Eggesbø M, Iniguez C, Nieuwenhuijsen M, Philippat C, Rey S,
Vandentorren S, et al: Epidemiologic tools to study the influence
of environmental factors on fecundity and pregnancy-related
outcomes. Epidemiol Rev. 36:148–164. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Zhang X, Decker A, Platt RW and Kramer MS:
How big is too big? The perinatal consequences of fetal macrosomia.
Am J Obstet Gynecol. 198:517.e1–6. 2008. View Article : Google Scholar
|
|
12
|
Conde-Agudelo A, Papageorghiou AT, Kennedy
SH and Villar J: Novel biomarkers for predicting intrauterine
growth restriction: a systematic review and meta-analysis. BJOG.
120:681–694. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Wollmann HA: Intrauterine growth
restriction: definition and etiology. Horm Res. 49 Suppl 2:1–6.
1998. View Article : Google Scholar
|
|
14
|
Ananth CV and Vintzileos AM:
Maternal-fetal conditions necessitating a medical intervention
resulting in preterm birth. Am J Obstet Gynecol. 195:1557–1563.
2006. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Mitchell EA, Robinson E, Clark PM, Becroft
DM, Glavish N, Pattison NS, Pryor JE, Thompson JM and Wild CJ:
Maternal nutritional risk factors for small for gestational age
babies in a developed country: a case-control study. Arch Dis Child
Fetal Neonatal Ed. 89:F431–F435. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Chatzi L, Mendez M, Garcia R, Roumeliotaki
T, Ibarluzea J, Tardón A, Amiano P, Lertxundi A, Iñiguez C, Vioque
J, Kogevinas M and Sunyer J; INMA and RHEA study groups, :
Mediterranean diet adherence during pregnancy and fetal growth:
INMA (Spain) and RHEA (Greece) mother-child cohort studies. Br J
Nutr. 107:135–145. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Catalano PM, Tyzbir ED, Allen SR, McBean
JH and McAuliffe TL: Evaluation of fetal growth by estimation of
neonatal body composition. Obstet Gynecol. 79:46–50.
1992.PubMed/NCBI
|
|
18
|
Catalano PM and Kirwan JP: Maternal
factors that determine neonatal size and body fat. Curr Diab Rep.
1:71–77. 2001. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Locke AE, Kahali B, Berndt SI, Justice AE,
Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, et al
LifeLines Cohort Study; ADIPOGen Consortium; AGEN-BMI Working
Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP;
MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE
Consortium; ReproGen Consortium; GENIE Consortium; International
Endogene Consortium, : Genetic studies of body mass index yield new
insights for obesity biology. Nature. 518:197–206. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Vineis P and Perera F: Molecular
epidemiology and biomarkers in etiologic cancer research: the new
in light of the old. Cancer Epidemiol Biomarkers Prev.
16:1954–1965. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Chadeau-Hyam M, Athersuch TJ, Keun HC, De
Iorio M, Ebbels TM, Jenab M, Sacerdote C, Bruce SJ, Holmes E and
Vineis P: Meeting-in-the-middle using metabolic profiling - a
strategy for the identification of intermediate biomarkers in
cohort studies. Biomarkers. 16:83–88. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Horgan RP, Broadhurst DI, Walsh SK, Dunn
WB, Brown M, Roberts CT, North RA, McCowan LM, Kell DB, Baker PN,
et al: Metabolic profiling uncovers a phenotypic signature of small
for gestational age in early pregnancy. J Proteome Res.
10:3660–3673. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Heazell AE, Bernatavicius G, Warrander L,
Brown MC and Dunn WB: A metabolomic approach identifies differences
in maternal serum in third trimester pregnancies that end in poor
perinatal outcome. Reprod Sci. 19:863–875. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Ciborowski M, Zbucka-Kretowska M,
Bomba-Opon D, Wielgos M, Brawura-Biskupski-Samaha R, Pierzynski P,
Szmitkowski M, Wolczynski S, Lipinska D, Citko A, et al: Potential
first trimester metabolomic biomarkers of abnormal birth weight in
healthy pregnancies. Prenat Diagn. 34:870–877. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Diaz SO, Barros AS, Goodfellow BJ, Duarte
IF, Galhano E, Pita C, Almeida MC, Carreira IM and Gil AM: Second
trimester maternal urine for the diagnosis of trisomy 21 and
prediction of poor pregnancy outcomes. J Proteome Res.
12:2946–2957. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Tea I, Le Gall G, Küster A, Guignard N,
Alexandre-Gouabau MC, Darmaun D and Robins RJ: 1H-NMR-based
metabolic profiling of maternal and umbilical cord blood indicates
altered materno-foetal nutrient exchange in preterm infants. PLoS
One. 7:e299472012. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Walsh JM, Wallace M, Brennan L and
McAuliffe FM: Early pregnancy maternal urinary metabolomic profile
and later insulin resistance and fetal adiposity. J Matern Fetal
Neonatal Med. 28:1697–1700. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Fanos V, Atzori L, Makarenko K, Melis GB
and Ferrazzi E: Metabolomics application in maternal-fetal
medicine. BioMed Res Int. 2013:7205142013. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Maitre L, Fthenou E, Athersuch T, Coen M,
Toledano MB, Holmes E, Kogevinas M, Chatzi L and Keun HC: Urinary
metabolic profiles in early pregnancy are associated with preterm
birth and fetal growth restriction in the Rhea mother-child cohort
study. BMC Med. 12:1102014. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Nicholson JK, Holmes E and Elliott P: The
metabolome-wide association study: a new look at human disease risk
factors. J Proteome Res. 7:3637–3638. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Nicholson G, Rantalainen M, Maher AD, Li
JV, Malmodin D, Ahmadi KR, Faber JH, Hallgrímsdóttir IB, Barrett A,
Toft H, et al The Molpage Consortium, : Human metabolic profiles
are stably controlled by genetic and environmental variation. Mol
Syst Biol. 7:525. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Sampson JN, Boca SM, Shu XO,
Stolzenberg-Solomon RZ, Matthews CE, Hsing AW, Tan YT, Ji BT, Chow
WH, Cai Q, et al: Metabolomics in epidemiology: sources of
variability in metabolite measurements and implications. Cancer
Epidemiol Biomarkers Prev. 22:631–640. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Holmes E, Loo RL, Stamler J, Bictash M,
Yap IK, Chan Q, Ebbels T, De Iorio M, Brown IJ, Veselkov KA, et al:
Human metabolic phenotype diversity and its association with diet
and blood pressure. Nature. 453:396–400. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Elliott P, Posma JM, Chan Q, Garcia-Perez
I, Wijeyesekera A, Bictash M, Ebbels TM, Ueshima H, Zhao L, van
Horn L, et al: Urinary metabolic signatures of human adiposity. Sci
Transl Med. 7:285ra622015. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Wang TJ, Larson MG, Vasan RS, Cheng S,
Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, et
al: Metabolite profiles and the risk of developing diabetes. Nat
Med. 17:448–453. 2011. View
Article : Google Scholar : PubMed/NCBI
|
|
36
|
Adams SH: Emerging perspectives on
essential amino acid metabolism in obesity and the
insulin-resistant state. Adv Nutr. 2:445–456. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Guertin KA, Moore SC, Sampson JN, Huang
WY, Xiao Q, Stolzenberg-Solomon RZ, Sinha R and Cross AJ:
Metabolomics in nutritional epidemiology: identifying metabolites
associated with diet and quantifying their potential to uncover
diet-disease relations in populations. Am J Clin Nutr. 100:208–217.
2014. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Floegel A, von Ruesten A, Drogan D,
Schulze MB, Prehn C, Adamski J, Pischon T and Boeing H: Variation
of serum metabolites related to habitual diet: a targeted
metabolomic approach in EPIC-Potsdam. Eur J Clin Nutr.
67:1100–1108. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
O'Keefe SJD, Li JV, Lahti L, Ou J,
Carbonero F, Mohammed K, Posma JM, Kinross J, Wahl E, Ruder E, et
al: Fat, fibre and cancer risk in African Americans and rural
Africans. Nat Commun. 6:63422015. View Article : Google Scholar : PubMed/NCBI
|
