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J Allergy Clin Immunol. 2017 Jun;139(6):1923-1934.e17. doi: 10.1016/j.jaci.2016.10.023. Epub 2016 Nov 29.

Effects of nongenetic factors on immune cell dynamics in early childhood: The Generation R Study.

Author information

1
Department of Immunology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
2
Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, The Netherlands; Department of Pediatrics, Erasmus MC-Sophia, Rotterdam, The Netherlands.
3
Department of Biostatistics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
4
Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, The Netherlands; Department of Pediatrics, Erasmus MC-Sophia, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
5
Department of Pediatrics, Erasmus MC-Sophia, Rotterdam, The Netherlands.
6
Department of Immunology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands; Department of Immunology and Pathology, Central Clinical School, Monash University, Melbourne, Australia. Electronic address: menno.vanzelm@monash.edu.

Abstract

BACKGROUND:

Numbers of blood leukocyte subsets are highly dynamic in childhood and differ greatly between subjects. Interindividual variation is only partly accounted for by genetic factors.

OBJECTIVE:

We sought to determine which nongenetic factors affect the dynamics of innate leukocytes and naive and memory lymphocyte subsets.

METHODS:

We performed 6-color flow cytometry and linear mixed-effects modeling to define the dynamics of 62 leukocyte subsets from birth to 6 years of age in 1182 children, with 1 to 5 measurements per subject. Subsequently, we defined the effect of prenatal maternal lifestyle-related or immune-mediated determinants, birth characteristics, and bacterial/viral exposure-related determinants on leukocyte subset dynamics.

RESULTS:

Functionally similar leukocyte populations were grouped by using unbiased hierarchical clustering of patterns of age-related leukocyte dynamics. Innate leukocyte numbers were high at birth and predominantly affected by maternal low education level. Naive lymphocyte counts peaked around 1 year, whereas most memory lymphocyte subsets more gradually increased during the first 4 years of life. Dynamics of CD4+ T cells were predominantly associated with sex, birth characteristics, and persistent infections with cytomegalovirus (CMV) or EBV. CD8+ T cells were predominantly associated with CMV and EBV infections, and T-cell receptor γδ+ T cells were predominantly associated with premature rupture of membranes and CMV infection. B-cell subsets were predominantly associated with sex, breast-feeding, and Helicobacter pylori carriership.

CONCLUSIONS:

Our study identifies specific dynamic patterns of leukocyte subset numbers, as well as nongenetic determinants that affect these patterns, thereby providing new insights into the shaping of the childhood immune system.

KEYWORDS:

B cells; CD4(+) T cells; CD8(+) T cells; Longitudinal leukocyte dynamics; T-cell receptor γδ(+) T cells; innate leukocytes; interindividual immunologic variance; nongenetic determinants

PMID:
27913304
DOI:
10.1016/j.jaci.2016.10.023
[Indexed for MEDLINE]

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