Sex differences in infant blood metabolite profile in association with weight and adiposity measures.
Adiposity
Adolescent
Anthropometry
Australia
Birth Weight
Body Mass Index
Body Weight
Cholesterol
/ blood
Cross-Sectional Studies
Fatty Acids
/ blood
Female
Humans
Infant, Newborn
Linear Models
Magnetic Resonance Spectroscopy
/ methods
Male
Metabolome
Metabolomics
/ methods
Obesity
Sex Characteristics
Sex Factors
Skinfold Thickness
Waist Circumference
Journal
Pediatric research
ISSN: 1530-0447
Titre abrégé: Pediatr Res
Pays: United States
ID NLM: 0100714
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
01
06
2019
accepted:
25
12
2019
revised:
06
12
2019
pubmed:
18
1
2020
medline:
24
8
2021
entrez:
18
1
2020
Statut:
ppublish
Résumé
Nuclear magnetic resonance (NMR) metabolic profiling quantifies a large number of metabolites. From adolescence, specific metabolites are influenced by age, sex and body mass index; data on early-life metabolic profiles are limited. We investigated associations between sex, birth weight, weight and adiposity with NMR metabolic profile at age 12 months. The plasma NMR metabolic profile was quantified in infants (n = 485) from the Barwon Infant Study. Associations between 74 metabolites and sex, birth weight z-score and 12-month measures (weight z-score, skinfold thickness, weight-for-length z-score) were examined using linear regression models. Several cholesterol and fatty acid measures were higher (0.2-0.3 SD) in girls than in boys; we observed modest sex-specific associations of birth weight z-scores and 12-month sum of skinfold thicknesses with metabolites. The pattern of associations between weight z-score and weight-for-length z-score with metabolites at 12 months was more pronounced in girls, particularly for fatty acid ratios. We identified sex differences in the infant metabolic profile. Sex-specific patterns observed differ from those reported in older children and adults. We also identified modest cross-sectional associations between anthropometric and adiposity measures and metabolites, some of which were sex specific.
Sections du résumé
BACKGROUND
Nuclear magnetic resonance (NMR) metabolic profiling quantifies a large number of metabolites. From adolescence, specific metabolites are influenced by age, sex and body mass index; data on early-life metabolic profiles are limited. We investigated associations between sex, birth weight, weight and adiposity with NMR metabolic profile at age 12 months.
METHODS
The plasma NMR metabolic profile was quantified in infants (n = 485) from the Barwon Infant Study. Associations between 74 metabolites and sex, birth weight z-score and 12-month measures (weight z-score, skinfold thickness, weight-for-length z-score) were examined using linear regression models.
RESULTS
Several cholesterol and fatty acid measures were higher (0.2-0.3 SD) in girls than in boys; we observed modest sex-specific associations of birth weight z-scores and 12-month sum of skinfold thicknesses with metabolites. The pattern of associations between weight z-score and weight-for-length z-score with metabolites at 12 months was more pronounced in girls, particularly for fatty acid ratios.
CONCLUSIONS
We identified sex differences in the infant metabolic profile. Sex-specific patterns observed differ from those reported in older children and adults. We also identified modest cross-sectional associations between anthropometric and adiposity measures and metabolites, some of which were sex specific.
Identifiants
pubmed: 31952075
doi: 10.1038/s41390-020-0762-4
pii: 10.1038/s41390-020-0762-4
doi:
Substances chimiques
Fatty Acids
0
Cholesterol
97C5T2UQ7J
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
473-483Investigateurs
Peter Vuillermin
(P)
Anne-Louise Ponsonby
(AL)
John Carlin
(J)
Katie Allen
(K)
Mimi Tang
(M)
Richard Saffery
(R)
Sarath Ranganathan
(S)
David Burgner
(D)
Terry Dwyer
(T)
Peter Sly
(P)
Références
Baird, J. et al. Developmental origins of health and disease: a lifecourse approach to the prevention of non-communicable diseases. Healthcare (Basel) 5, E14 (2017).
Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
doi: 10.1038/nrm.2016.25
Yu, Z. et al. Human serum metabolic profiles are age dependent. Aging Cell 11, 960–967 (2012).
doi: 10.1111/j.1474-9726.2012.00865.x
Saito, K. et al. Gender- and age-associated differences in serum metabolite profiles among Japanese populations. Biol. Pharm. Bull. 39, 1179–1186 (2016).
doi: 10.1248/bpb.b16-00226
Kettunen, J. et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat. Commun. 7, 11122 (2016).
doi: 10.1038/ncomms11122
Dunn, W. B. et al. Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics 11, 9–26 (2015).
doi: 10.1007/s11306-014-0707-1
Mittelstrass, K. et al. Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet. 7, e1002215 (2011).
doi: 10.1371/journal.pgen.1002215
Krumsiek, J. et al. Gender-specific pathway differences in the human serum metabolome. Metabolomics 11, 1815–1833 (2015).
doi: 10.1007/s11306-015-0829-0
Ellul, S. et al. Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents. BMJ Open 9, 106 (2019).
doi: 10.1136/bmjopen-2017-020900
Davis, C. E. et al. Sex difference in high density lipoprotein cholesterol in six countries. Am. J. Epidemiol. 143, 1100–1106 (1996).
doi: 10.1093/oxfordjournals.aje.a008686
Michaliszyn, S. F. et al. Metabolomic profiling of amino acids and beta-cell function relative to insulin sensitivity in youth. J. Clin. Endocrinol. Metab. 97, E2119–E2124 (2012).
doi: 10.1210/jc.2012-2170
Mihalik, S. J. et al. Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced mitochondrial oxidation. Diabetes Care 35, 605–611 (2012).
doi: 10.2337/DC11-1577
Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).
doi: 10.1038/nm.2307
McCormack, S. E. et al. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatr. Obes. 8, 52–61 (2013).
doi: 10.1111/j.2047-6310.2012.00087.x
Ruoppolo, M. et al. Female and male human babies have distinct blood metabolomic patterns. Mol. Biosyst. 11, 2483–2492 (2015).
doi: 10.1039/C5MB00297D
Vuillermin, P. et al. Cohort profile: The Barwon Infant Study. Int. J. Epidemiol. 44, 1148–1160 (2015).
doi: 10.1093/ije/dyv026
Pink, B. Socio-economic Indexes for Areas (SEIFA). Technical Paper (Australian Bureau of Statistics, 2011).
Vidmar, S. I., Cole, T. J. & Pan, H. Standardizing anthropometric measures in children and adolescents with functions for egen: update. Stata J. 13, 366–378 (2013).
doi: 10.1177/1536867X1301300211
Soininen, P. et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134, 1781–1785 (2009).
doi: 10.1039/b910205a
Wurtz, P. et al. Quantitative serum NMR metabolomics in large-scale epidemiology: a primer on -omic technology. Am. J. Epidemiol. 186, 1084–1096 (2017).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).
Lawlor, D. A. et al. Sex differences in the association between birth weight and total cholesterol. A meta-analysis. Ann. Epidemiol. 16, 19–25 (2006).
Thorand, B. et al. Sex differences in the relation of body composition to markers of inflammation. Atherosclerosis 184, 216–224 (2006).
Team RC. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018).
Wasserstein, R. L., Schirm, A. L. & Lazar, N. A. Moving to a world beyond “p < 0.05”. Am. Stat. 73, 1–19 (2019).
doi: 10.1080/00031305.2019.1583913
Gillman Matthew, W. Primordial prevention of cardiovascular disease. Circulation 131, 599–601 (2015).
doi: 10.1161/CIRCULATIONAHA.115.014849
Uekert, S. J. et al. Sex-related differences in immune development and the expression of atopy in early childhood. J. Allergy Clin. Immunol. 118, 1375–1381 (2006).
doi: 10.1016/j.jaci.2006.09.008
Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626 (2016).
doi: 10.1038/nri.2016.90
Würtz, P. et al. Metabolic signatures of adiposity in young adults: mendelian randomization analysis and effects of weight change. PLoS Med. 11, e1001765 (2014).
doi: 10.1371/journal.pmed.1001765
Perng, W. et al. Associations of cord blood metabolites with perinatal characteristics, newborn anthropometry, and cord blood hormones in project viva. Metab. Clin. Exp. 76, 11–22 (2017).
doi: 10.1016/j.metabol.2017.07.001
Perng, W., Rifas-Shiman, S. L., Hivert, M.-F., Chavarro, J. E. & Oken, E. Branched chain amino acids, androgen hormones, and metabolic risk across early adolescence: a prospective study in Project Viva. Obesity (Silver Spring) 26, 916–926 (2018).
doi: 10.1002/oby.22164
Perng, W. et al. Metabolomic profiles and childhood obesity. Obesity (Silver Spring) 22, 2570–2578 (2014).
doi: 10.1002/oby.20901
Butte, N. F. et al. Global metabolomic profiling targeting childhood obesity in the Hispanic population. Am. J. Clin. Nutr. 102, 256–267 (2015).
doi: 10.3945/ajcn.115.111872
Kadakia, R. et al. Cord blood metabolomics: association with newborn anthropometrics and c-peptide across ancestries. J. Clin. Endocrinol. Metab. 104, 4459–4472 (2019).
doi: 10.1210/jc.2019-00238
Kadakia, R. et al. Cord blood metabolites associated with newborn adiposity and hyperinsulinemia. J. Pediatr. 203, 144–149.e141 (2018).
doi: 10.1016/j.jpeds.2018.07.056
Walford, G. A. et al. Metabolite profiles of diabetes incidence and intervention response in the Diabetes Prevention Program. Diabetes 65, 1424–1433 (2016).
doi: 10.2337/db15-1063
Merino, J. et al. Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose. Diabetologia 61, 1315–1324 (2018).
doi: 10.1007/s00125-018-4599-x
Reusch, J. E. B., Kumar, T. R., Regensteiner, J. G., Zeitler, P. S. & Conference, P. Identifying the critical gaps in research on sex differences in metabolism across the life span. Endocrinology 159, 9–19 (2018).
doi: 10.1210/en.2017-03019
Lau, A., West, L. & Tullius, S. G. The impact of sex on alloimmunity. Trends Immunol. 39, 407–418 (2018).
doi: 10.1016/j.it.2018.01.008
Colafella, K. M. M. & Denton, K. M. Sex-specific differences in hypertension and associated cardiovascular disease. Nat. Rev. Nephrol. 14, 185–201 (2018).
doi: 10.1038/nrneph.2017.189