Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-24T04:42:29.941Z Has data issue: false hasContentIssue false

Disproportionate early fetal growth predicts postnatal thymic size in humans

Published online by Cambridge University Press:  07 March 2013

A. J. C. Fulford*
Affiliation:
MRC Keneba, MRC Unit, Banjul, The Gambia Department of Population Health, MRC International Nutrition Group, London School of Hygiene and Tropical Medicine, London, UK
S. E. Moore
Affiliation:
MRC Keneba, MRC Unit, Banjul, The Gambia
S. E. Arifeen
Affiliation:
International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR, B), Dhaka, Bangladesh
L. Å. Persson
Affiliation:
Department of Women's and Children's Health, International Maternal and Child Health, Uppsala University, Uppsala, Sweden
L. M. Neufeld
Affiliation:
The Micronutrient Initiative, Elgin St. Suite, Ottawa, ON, Canada
Y. Wagatsuma
Affiliation:
Department of Epidemiology, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
A. M. Prentice
Affiliation:
MRC Keneba, MRC Unit, Banjul, The Gambia Department of Population Health, MRC International Nutrition Group, London School of Hygiene and Tropical Medicine, London, UK
*
*Address for correspondence: Dr A. J. C. Fulford, Department Population Health, MRC International Nutrition Group, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. Email tony.fulford@lshtm.ac.uk

Abstract

Prenatal events can affect neonatal thymus size and adult immune function. The causal insults are unknown, although fetal nutrient restriction is suspected. We used ultrasound at three time points during pregnancy (14, 19 and 30 weeks) to measure the growth of six fetal dimensions in rural Bangladeshi women participating in the Maternal and Infant Nutrition Interventions, Matlab study. Postnatal ultrasound was used to calculate thymic index (TI) at birth, 2, 6 and 12 m. Of the 3267 women recruited, 2861 participated by providing data at least at one fetal biometry and one TI time point. Patterns of fetal growth were summarized using principal components calculated from fetal dimension z-scores. Random effects regression, controlling for infant size and season of measurement were used to relate these patterns to TI. We found that smaller leg length relative to head circumference, characteristic of head-sparing growth restriction, was predictive of lower TI. This association was significant at all time points but strongest in earlier pregnancy. Each standard deviation increase in leg–head proportion was associated with an increase in TI of ∼5%. We conclude that growth patterns typical of poor fetal nutrition are associated with poor thymic development. The greater strength of this association in the first trimester is consistent with a period of vulnerability during the early ontogeny of the thymus and suggests that preventative intervention would need to be given in early pregnancy.

Type
Original Article
Copyright
Copyright © Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Prentice, AM. The thymus: a barometer of malnutrition. Br J Nutr. 1999; 81, 345347.Google Scholar
2.Hasselbalch, H, Jeppesen, DL, Engelmann, MD, Michaelsen, KF, Nielsen, MB. Decreased thymus size in formula-fed infants compared with breastfed infants. Acta Paediatr. 1996; 85, 10291032.Google Scholar
3.Hasselbalch, H, Engelmann, MD, Ersboll, AK, Jeppesen, DL, Fleischer-Michaelsen, K. Breast-feeding influences thymic size in late infancy. Eur J Pediatr. 1999; 158, 964967.Google Scholar
4.Collinson, AC, Moore, SE, Cole, TJ, Prentice, AM. Birth season and environmental influences on patterns of thymic growth in rural Gambian infants. Acta Paediatr. 2003; 92, 10141020.Google Scholar
5.Moore, SE, Prentice, AM, Wagatsuma, Y, et al. Early-life nutritional and environmental determinants of thymic size in infants born in rural Bangladesh. Acta Paediatr. 2009; 98, 11681175.Google Scholar
6.Ngom, PT, Collinson, AC, Pido-Lopez, J, et al. Improved thymic function in exclusively breastfed infants is associated with higher interleukin 7 concentrations in their mothers’ breast milk. Am J Clin Nutr. 2004; 80, 722728.Google Scholar
7.Aaby, P, Marx, C, Trautner, S, et al. Thymus size at birth is associated with infant mortality: a community study from Guinea-Bissau. Acta Paediatr. 2002; 91, 698703.Google Scholar
8.Moore, SE, Cole, TJ, Poskitt, EM, et al. Season of birth predicts mortality in rural Gambia. Nature. 1997; 388, 434.Google Scholar
9.Moore, SE, Cole, TJ, Collinson, AC, et al. Prenatal or early postnatal events predict infectious deaths in young adulthood in rural Africa. Int J Epidemiol. 1999; 28, 10881095.Google Scholar
10.McDade, TW, Beck, MA, Kuzawa, CW, Adair, LS. Prenatal undernutrition and postnatal growth are associated with adolescent thymic function. J Nutr. 2001; 131, 12251231.Google Scholar
11.Moore, SE, Jalil, F, Ashraf, R, et al. Revaccination does not improve an observed deficit in antibody responses in Pakistani adults born of a lower birth weight. Vaccine. 2008; 26, 158165.Google Scholar
12.Barker, DJ. The developmental origins of adult disease. J Am Coll Nutr. 2004; 23, 588S595S.Google Scholar
13.Wilson, JG. Environment and Birth Defects (Environmental Science Series), 1973. Academic Press: London.Google Scholar
14.Palmer, AC. Nutritionally mediated programming of the developing immune system. Adv Nutr. 2011; 2, 377395.Google Scholar
15.Persson, LA, Arifeen, S, Ekström, EC, et al. Effects of prenatal micronutrient and early food supplementation on maternal hemoglobin, birth weight, and infant mortality Among children in Bangladesh; the MINIMat randomized trial. JAMA. 2012; 307, 20502059.Google Scholar
16.Saha, KK, Frongillo, EA, Alam, DS, et al. Appropriate infant feeding practices result in better growth of infants and young children in rural Bangladesh. Am J Clin Nutr. 2008; 87, 18521859.Google Scholar
17.World Health Organization (WHO). Manual of diagnostic ultrasound (ed. Palmer PES), 1995. WHO: Geneva.Google Scholar
18.Pexsters, A, Daemen, A, Bottomley, C, et al. New crown-rump length curve based on over 3500 pregnancies. Ultrasound Obstet Gynecol. 2010; 35, 650655.Google Scholar
19.Joshi, BR. Estimation of gestational age according to crown-rump length in Nepalese population: a comparison with previously published nomograms. Iran J Radiol. 2009; 6, 167170.Google Scholar
20.Neufeld, LM, Wagatsuma, Y, Hussain, R, Begum, M, Frongillo, EA. Measurement error for ultrasound fetal biometry performed by paramedics in rural Bangladesh. Ultrasound Obstet Gynecol. 2009; 34, 387394.Google Scholar
21.Altman, DG, Chitty, LS. Charts of fetal size: 1 Methodology. Br J Obstet Gynaecol. 1994; 101, 2934.Google Scholar
22.Chitty, LS, Altman, DG, Henderson, A, Campbell, S. Charts of fetal size: 2 head measurements. Br J Obstet Gynaecol. 1994; 101, 3543.Google Scholar
23.Chitty, LS, Altman, DG, Henderson, A, Campbell, S. Charts of fetal size: 3 abdominal measurements. Br J Obstet Gynaecol. 1994; 101, 125131.Google Scholar
24.Chitty, LS, Altman, DG, Henderson, A, Campbell, S. Charts of fetal size: 4 femur length. Br J Obstet Gynaecol. 1994; 101, 132135.Google Scholar
25.Chitty, LS, Altman, DG. Charts of fetal size: limb bones. Br J Obstet Gynaecol. 2002; 109, 919929.Google Scholar
26.Hasselbalch, H, Nielsen, MB, Jeppsen, D, Pedersen, JF, Karkov, J. Sonographic measurement of the thymus in infants. Eur Radiol. 1996; 6, 700703.Google Scholar
27.Fulford, AJ, Rayco-Solon, P, Prentice, AM. Statistical modelling of the seasonality of preterm delivery and intrauterine growth restriction in rural Gambia. Paediatr Perinat Epidemiol. 2006; 20, 251259.Google Scholar
28.Liang, KY, Zeger, SL. Longitudinal data analysis using generalised linear models. Biometrika. 1986; 73, 1322.Google Scholar
29.Barker, DJ. Fetal origins of coronary heart disease. BMJ. 1993; 311, 171174.Google Scholar
30.Kramer, MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull WHO. 1987; 65, 663737.Google Scholar
31.Kramer, MS, Victora, CG. Low birth weight and perinatal mortality. In Nutrition and Health in Developing Countries (eds. Semba RD, Bloem MW), 2001; pp. 5769. Humana Press: Totowa, NJ.Google Scholar
32.Bakketeig, LS, Butte, N, de Onis, M, et al. Report of the IDECG working group on definitions, classifications, causes, mechanisms and prevention of IUGR. E J Clin Nutr. 1998; 52(Suppl 1), S94S96.Google Scholar
33.Neufeld, LM, Haas, JD, Grajeda, R, Martorell, R. Ultrasound measurement of fetal size in rural Guatemala. Int J Gynaecol Obstet. 2004; 84, 220228.Google Scholar
34.Rowland, S, Royston, P. Estimated date of delivery from date of last menstrual period and ultrasound scan: which is more accurate? Br J Gen Pracur. 1993; 43, 322325.Google Scholar
35.Bakketeig, LS. Current growth standards, definitions, diagnosis and classification of fetal growth retardation. E J Clin Nutr. 1998; 52(Suppl 1), S1S4.Google Scholar