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.
The in utero and early postnatal periods represent a window of developmental plasticity during which environmental exposures may be ‘biologically embedded’, such that they subsequently modulate the risk of common non-communicable diseases.1 This ‘programming’ may be regulated by a range of molecular and genetic mechanisms that influence gene expression and alter physiology and metabolism. Metabolomics is a powerful approach to quantify the collective output of a range of biochemical and physiological processes that are subject to developmental programming. Further, metabolomics has considerable potential for the identification of novel biomarkers for risk stratification across the life course, facilitating targeting of interventions to those most likely to benefit.2
It is becoming increasingly apparent that concentration of specific metabolic measures is influenced by both age3 and sex,4,5 particularly in adults.6,7 Most amino acids appear to be higher in men than in women.7,8 We recently reported similar findings in 11–12-year-old children and their parents, although sex differences were more marked in adults than in children.9 Overall, previously reported findings are consistent with modest sex-specific influences in childhood relative to adulthood,10 though data are limited.
Few studies have examined plasma metabolomic patterns in infancy or early childhood,11,12 though some have linked specific metabolites to early-life phenotypes. For example, elevated plasma levels of branched-chain and aromatic amino acids in childhood have been linked to subsequent development of type 2 diabetes (T2D) in adolescents and adults.11,13,14 Data on sex differences in infant metabolic profile are limited, although findings from heel prick tests in newborns (dried blood spot), analysed using tandem mass spectrometry, suggest that females have higher levels of several amino acids (including alanine, glycine, valine, tyrosine) than males, although sex differences were of small magnitude.15
Nuclear magnetic resonance (NMR)-based metabolomic analysis generates measures of >200 low-molecular-weight metabolites associated with a diverse range of biochemical pathways, including lipid and amino acid metabolism, and biomarkers related to inflammation. In this study, we used NMR-based metabolomics to profile the metabolome in 485 12-month-old infants and investigated the associations of metabolite measures with sex, birth weight z-score, current weight (z-score) and adiposity.
Study design and participants
The Barwon Infant study (BIS) is a birth cohort (n = 1074) assembled using an unselected antenatal sampling frame in the south-east of Australia in order to investigate the early-life origins of a range of non-communicable diseases. Recruitment, eligibility and retention criteria are described elsewhere.16 Infants born before 32 completed weeks’ gestational age were excluded, as were those with a serious illness or major congenital malformation and/or genetically determined disease. Venous peripheral blood was collected from infants at approximately 12 months of age and added to a 10-ml Falcon tube containing 100 IU preservative-free sodium heparin. Bloods were generally processed within 4 h; however, a number of samples were collected late in the afternoon and processed the following day (within 18 h of collection). Aliquots of plasma were stored at −80 °C. We randomly selected a subgroup of infants (n = 485) from those with adequate sample volume and shorter processing time. A 300-μl aliquot was sent on dry ice to Nightingale Health (Finland) for NMR quantification of metabolites in two batches. To determine intra- and inter-assay reproducibility, duplicate samples were sent for a small number of children in batch 1(5) and batch 2 (10). The sample characteristics of children with metabolomic data are summarised in Table 1.
Data on maternal age, gestational diabetes during pregnancy, maternal smoking during pregnancy and parental smoking (by any parent) over the first postnatal year were obtained from questionnaires and hospital records. Pre-pregnancy weight was self-reported, and maternal height was measured at the first visit (28–32 weeks’ gestation). Pre-pregnancy body mass index (BMI) was calculated as pre-pregnancy weight in kilograms divided by maternal height in metres squared. The participant’s residential address at recruitment was used to calculate a measure of socio-economic status (SES) using the Socio-Economic Indexes for Areas.17
Birth weight, mode of birth and gestational age at birth were obtained from hospital records and questionnaires. Birth weight z-scores standardised for gestational age and sex were calculated using zanthro18 according to the British 1990 growth reference, reanalysed 2009. Duration of any breastfeeding was calculated from self-reported breastfeeding status.
Anthropometric measures including weight, length, skinfolds (triceps and subscapular), head circumference, middle upper arm circumference and abdominal circumference, were obtained at the 12-month visit. One of the two trained examiners obtained skinfold thickness measurements by using Holtain calipers, and the average of two to three measurements was recorded. As a measure of infant adiposity, the sum of skinfolds was used. Weight z-scores at 12 months incorporating adjustment for postnatal age and sex were calculated using zanthro18 according to the World Health Organisation (WHO) Reference 2007 charts. In addition, weight-for-length z-scores (WFL-z) at 12 months were calculated according to the WHO Child Growth Standards using zanthro.
Details of the Nightingale NMR-based metabolomic platform have been extensively described elsewhere,19 and epidemiological applications were recently reviewed in detail.20 Briefly, the simultaneous quantification (absolute concentration units) of routine lipids, lipoprotein subclass distributions, particle size and composition, fatty acids and other low-molecular-weight metabolites such as amino acids and glycolysis-related metabolites was performed using a high-throughput platform. Extensive validation and quality-control measures are included in this analysis, which is now approved for clinical use within the EU. Metabolite concentrations were quantified according to Nightingale’s 2016 bioinformatics protocol.
In total, data on 228 serum metabolites, some of which are derived ratios with clinical utility, were generated. As has been shown in adults and older children,9 substantial correlations exist between metabolites (Supplementary Fig. S1); hence, analyses were undertaken on 74 of the metabolite measures (summarised in Supplementary Table S1). We excluded 5 ratio measures for each of the 14 lipoprotein subclass particles. In addition, the seven measures within each of the lipoprotein subclasses (esterified cholesterol, free cholesterol, total cholesterol, triglycerides, phospholipids, total lipids and particle concentration) are all highly correlated, and therefore we only report total lipids for each of the lipoproteins. Glucose and lactate were excluded given their high sensitivity to time to processing.
To describe the study population, continuous variables were presented as mean (standard deviation (SD)) for symmetrically distributed variables and median [interquartile range] for skewed variables. For categorical variables, values were presented as number (%). For infants with replicate samples, metabolomic measures were averaged for that infant with coefficients of variation found to be very small (not shown). Summaries of metabolites for all infants and stratified by sex were reported using means and SD or geometric means (and relative SD) for skewed metabolites.
Density plots were used to compare the distributions of metabolites by sex. Skewed metabolite measures were log-transformed where appropriate. To investigate sex differences in metabolite concentrations, linear regression models for each metabolite with sex as the explanatory variable and metabolite measure as the outcome were used. Metabolites were standardised (scaled to SD units) prior to analysis to allow comparison of associations across metabolites. Additional models were used allowing adjustment (a) for postnatal age only and (b) additionally for postnatal age, birth weight, smoking by mother in pregnancy, gestational diabetes during pregnancy, breastfeeding duration and SES. To adjust p values for multiple comparisons, we used the method of Benjamini–Hochberg21 with a false discovery rate of 10%.
Based on previous findings in adults,22,23 we expected metabolite associations with birth weight z-score, infant weight z-score and adiposity to differ by sex, so we stratified by sex to explore the metabolite associations with the anthropometric measures. For birth weight z-score, separate linear models were fitted for each sex with metabolite measure as the outcome and birth weight z-score as the explanatory variable. Metabolites were standardised separately for girls and boys prior to analysis. Additional models were used to explore the association of metabolite measures with 12-month measures (weight z-score, sum of skinfolds, WFL-z) as explanatory variables in separate models. We plotted associations to check whether fitting of linear models was appropriate. Analyses considering birth weight z-scores at 12 months incorporated adjustment for sex and gestational age. For birth weight z-score, variables that were considered for inclusion in models were mode of birth, mother smoking in pregnancy, mother’s pre-pregnancy BMI, gestational diabetes during pregnancy and SES. Analyses considering weight z-scores at 12 months incorporated adjustment for sex and postnatal age. Additional variables that were considered for inclusion in the models for 12-month measures were breastfeeding duration, mother smoking in pregnancy, parental smoking over the first postnatal year, gestational diabetes and SES. Analyses focussing on sum of skinfolds and WFL-z at 12 months also considered adjustments for postnatal age. The associations with each metabolite before and after adjusting for each of the potential confounders above were compared. An assessment was made to determine whether there was a substantial (>10%) discrepancy between the unadjusted and adjusted estimates. If there was little evidence of confounding, the variable was excluded from the model. Association measures were expressed as difference in means in SD units of metabolite concentration per 1-unit increase in anthropometric measure. To check robustness of results, sensitivity analyses were performed restricting the analysis to samples that had a processing time of at most 6 h. Results using the full sample and restricted sample were similar; we therefore present the results from the full sample.
Analysis was performed using Stata version 15.1 and R version 188.8.131.52 Owing to the largely descriptive aims of the paper and to avoid arbitrary dichotomisation of evidence using statistical thresholds,25 we present point estimates and confidence intervals (CIs) in figures and we describe general patterns, allowing the reader to draw their own conclusions.
Metabolomic data from 485 12-month-old infants were available for analysis and the characteristics of the infants are presented in Table 1. Summary statistics for the metabolite measures are presented in Supplementary Table S1.
Differences in metabolite concentrations by sex (unadjusted) are shown in Fig. 1 and are expressed as differences in means in SD units for girls compared to boys. Of note, no metabolites had higher mean concentrations in boys than in girls at 12 months of age. Several cholesterol measures were clearly higher in girls, including total serum, and remnant, esterified and free cholesterol. Similar patterns were observed for fatty acids, including total, docosahexaenoic acid, omega 3, omega-6, and monounsaturated fatty acids, which were all higher in girls than in boys, although overall differences were modest (0.2–0.3 SD). As total fatty acids were also higher in girls, there were no sex differences in the ratio/proportion of specific fatty acids to total fatty acids. In general, girls had higher concentrations of intermediate density lipoproteins (IDL) and low-density lipoprotein (LDL)-related measures and of the inflammation marker glycoprotein acetyls (GlycA). Total serum albumin and glycerol were also higher in girls. Of the amino acids measured, only glycine differed according to sex, with higher levels in girls.
Absolute differences in mean concentration levels are presented in Supplementary Table S2. In general, additional adjustment for postnatal age, birth weight, smoking in pregnancy, gestational diabetes, breastfeeding duration and SES had minimal effect on the pattern of associations (Supplementary Fig. S2).
Associations between anthropometric measures and metabolites
Birth weight z-score
Sex-specific associations between birth weight z-score and 12-month metabolite measures are shown in Fig. 2. No convincing associations were observed for either sex with birth weight z-score after adjustment for multiple comparisons. Association measures in absolute concentration units are presented in Supplementary Table S3.
12-month weight z-score
Sex-specific associations between 12-month weight z-score and metabolite measures are shown in Fig. 3. In general, these associations were more pronounced for girls than for boys. Differences in the pattern of associations observed in boys and girls were most striking for the amino acids histidine and alanine; increasing weight z-score was associated with increased levels of histidine in boys and lower levels in girls. In girls, increasing weight z-score was associated with lower alanine levels, with the opposite pattern in boys. In girls, weight z-score at 12 months had a negative association with very-low-density lipoprotein (VLDL) particle size and total triglycerides, while positive associations were observed for the majority of fatty acid ratio measures. No convincing associations were observed between 12-month weight z-score and apolipoproteins, glycolysis-related ketone body, creatinine or albumin for boys or girls.
Association measures in absolute concentration units are presented in Supplementary Table S4.
Sex-specific associations between sum of skinfolds at 12 months and metabolite measures are shown in Fig. 4. Associations in absolute concentration units are presented in Supplementary Table S5. With the exception of a negative association with glycine for girls, no convincing associations were observed for boys or girls with sum of skinfolds at 12 months after adjustment for multiple comparisons.
The associations for WFL-z at 12 months showed more evidence of a separation between profiles for girls and boys than sum of skinfolds (Fig. 5, Supplementary Table S6), with similar patterns to weight z-score at 12 months. There was suggestion of greater WFL-z at 12 months being negatively associated with total triglycerides, triglycerides in VLDL and VLDL particle diameter and positively associated with total lipids in LDL, some fatty acid ratios and citrate in girls.
There is increasing consensus that early childhood is an opportune intervention period for the prevention of non-communicable diseases later in life, particularly those of cardiometabolic origin.26 Although challenging, accurate and early identification of individuals at highest risk prior to the onset of potentially irreversible pathogenic changes or disease trajectories is necessary to prevent disease. Increased availability and understanding of ‘omics data, such as NMR-based metabolomics, in early childhood may be beneficial for risk stratification and for assessing the impact of primordial prevention. However, such data are currently generally limited, particularly around the time of infancy, and the association with sex and anthropometric measures remains largely unclear.
In this study of the NMR-based metabolic profiles in plasma from 485 12-month-old infants, we identified several differences in the concentration of metabolite measures by sex, consistent with previous evidence.4,5 This adds to other data showing a clear impact of age on metabolic profile,3 though this was not directly tested here. We identified several cross-sectional associations with infant weight and adiposity measures (although modest), some of which also showed marked sex differences. Our findings extend previous studies in adults and older children and are consistent with more modest sex-specific influences in early life relative to adulthood.10
The magnitude of sex differences in infancy (±0.2 SD) was similar to those we recently described in 11-year-old children using the same platform, though much smaller than in adults (±0.8 SD).9 As with 11-year-olds, we found higher average levels of lipids in small and very-small VLDL particles, triglycerides in IDL and LDL particles, apolipoprotein B, mono-unsaturated fatty acids and glycerol in female compared to male infants, as well as evidence of higher inflammation in females. It is possible that this reflects sex differences in immune cells observed in infants and children.27,28 In keeping with adult data,7 we found that 12-month-old female infants had higher levels of glycine compared to males. In infancy, we did not observe higher levels of any metabolite in boys compared to girls, in contrast to observations in older children and adults.9 Sex differences in infancy were suggestive of an emerging pattern consistent with those that were observed at 11 years of age, where several metabolites, including total triglycerides, citrate, alanine and glutamine, showed higher levels in 11-year-old girls compared to boys and the opposite pattern in adults. In addition, in these infants there was no compelling evidence of associations between infant adiposity or weight and the inflammatory marker GlycA, in contrast to previous findings in both adults and adolescents.29
Few studies have examined the metabolomic profile of infants, particularly at 12 months of age. However, several have investigated the relationship between the metabolomic profile at birth, or in older children, with cardiometabolic measures, albeit using different mass spectrometry-based approaches. Of particular note is the reproducible association of branched chain amino acid (BCAA) profile with a range of cardiometabolic measures in children of various ages. Analysis of cord blood (n = 126) from Project Viva found a BCAA pattern positively associated with birth weight adjusted for gestational age, while maternal exposures were not associated with either metabolic pattern.30 Analyses were adjusted for sex, and no sex-specific associations were reported. In the same cohort of children (aged 6–10 years at baseline and 11–15 years at follow-up; n = 213), BCAA profile was associated with lower fasting glucose in boys and increased triglycerides in girls,31 and associations did not differ by baseline BMI percentile. In addition, the BCAA pattern differed between obese and lean children (n = 262 total), with some evidence of a stronger association in males relative to females.32 A similar finding was also reported in a group of 353 non-obese and 450 obese Hispanic children, age 4–19 years.33 BCAAs and their catabolites, propionylcarnitine and butyrylcarnitine, were significantly elevated in obese children. BCAAs, aromatic AAs, asparagine, glycine, and serine made the largest contributions to BMI, while acylcarnitines made the largest contributions to adiposity. Analyses were adjusted for sex, and no sex-specific effects were reported. A cross-sectional, multiethnic cohort of newborns (n = 1600 mother–newborn pairs) from the Hyperglycemia and Adverse Pregnancy Outcome Study examining cord blood found that BCAAs were negatively associated with C-peptide but positively associated with birth weight and/or sum of skinfolds.34 Individual BCAAS were associated with birth weight and network analyses found associations with newborn adiposity. We previously found higher leucine and valine (two BCAAs) in both 11-year-old boys and older males relative to age-matched females9; however, these sex differences were not evident in infancy, where BCAA levels have previously been associated with birth weight but not with other anthropometric measures.30
The finding of higher glycine in girls compared to boys and a negative association of sum of skinfolds with glycine for girls in infancy is intriguing. A cross-sectional study (n = 118) of full-term infants found a positive association between cord blood glycine and adiposity measures in newborns.35 The Diabetes Prevention Program trial (n = 757) in adults found that glycine betaine (an amino acid derived from glycine) at baseline was associated with incident diabetes (hazard ratio 0.84 per 1 SD log metabolite level, p = 0.02) with increases of betaine associated with lower incidence of diabetes 2 years later (p = 0.01).36 A population-based study from the Framingham Heart Study Offspring cohort (n = 1150, age 40–65 years) found via enrichment analyses that increased nitrogen pathway metabolites, including glycine, were associated with T2D risk.37 In the same study, consistent results were obtained via a Mendelian randomisation approach (odds ratio 0.89 per 1 SD of genetically increased glycine, 95% CI (0.80, 0.99)).
The strengths of the study include availability of detailed metabolomic data that capture multiple metabolic pathways and, to the best of our knowledge, the first reporting of sex differences in the metabolite measures and associations with body size in infancy using the Nightingale NMR platform. We have included WFL-z in addition to weight and sum of skinfolds in order to more fully capture body composition. One limitation is that we did not have dual X-ray absorptiometric or other absorptiometric measures available to additionally assess body composition. In addition, we only measured one inflammatory biomarker (GlycA), which is part of the NMR output; quantification of additional inflammatory markers was beyond the scope of this study. As it is both inappropriate to fast infants and difficult to align feeding patterns with pre-booked study visits, we were unable to account for time of feeding or fasting time. Further, we note the cross-sectional nature of this study; further work considering the longitudinal analysis of metabolite measures across the life course are required to extend and gain a greater understanding of sex differences and explore the mechanisms underpinning differences over the life course. Replication within other infant studies is required to strengthen and further explore these associations.
There are increasing reports of sexually dimorphic traits in humans, reflected in differing prevalence, age of onset, course and severity of many common diseases, particularly those related to the metabolic, cardiovascular and immune systems.38,39,40 In light of this, the US Endocrine Society recently highlighted the importance of studying both sexes in cardiometabolic studies across the life course. Sex differences in the pathways that contribute to disease pathogenesis are reflected in metabolic profiles in adolescence and adulthood. It is possible that endocrine variation in addition to sex-specific genetic architecture (influencing phenotype independently of endocrine processes) underpin these differences. The striking age-specific sex differences in the metabolome of older children and adults relative to infants are potentially reflective of an increasing hormonal divergence between the sexes associated with the onset of puberty, a hypothesis best investigated in a longitudinal analysis of both hormone levels and metabolic profile from infancy to adulthood in the same individuals. Further longitudinal studies from birth are warranted to better understand the timing and trajectories of the blood metabolome in humans.
The findings from the current study provide a cross-sectional ‘snapshot’ of the infant metabolome, highlighting the level of variation within a population-based cohort and how specific measures vary with sex and anthropometry. The interindividual variation seen in infancy provides confidence that such data may be useful in the future for developing risk profiles and algorithms to accurately identify those most likely to transition to poor cardiometabolic health as older children and adults. This further highlights the importance of detailed longitudinal phenotyping and collection of biospecimens within a life-course framework.
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).
Yu, Z. et al. Human serum metabolic profiles are age dependent. Aging Cell 11, 960–967 (2012).
Saito, K. et al. Gender- and age-associated differences in serum metabolite profiles among Japanese populations. Biol. Pharm. Bull. 39, 1179–1186 (2016).
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).
Dunn, W. B. et al. Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics 11, 9–26 (2015).
Mittelstrass, K. et al. Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet. 7, e1002215 (2011).
Krumsiek, J. et al. Gender-specific pathway differences in the human serum metabolome. Metabolomics 11, 1815–1833 (2015).
Ellul, S. et al. Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents. BMJ Open 9, 106 (2019).
Davis, C. E. et al. Sex difference in high density lipoprotein cholesterol in six countries. Am. J. Epidemiol. 143, 1100–1106 (1996).
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).
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).
Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).
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).
Ruoppolo, M. et al. Female and male human babies have distinct blood metabolomic patterns. Mol. Biosyst. 11, 2483–2492 (2015).
Vuillermin, P. et al. Cohort profile: The Barwon Infant Study. Int. J. Epidemiol. 44, 1148–1160 (2015).
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).
Soininen, P. et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134, 1781–1785 (2009).
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).
Gillman Matthew, W. Primordial prevention of cardiovascular disease. Circulation 131, 599–601 (2015).
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).
Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626 (2016).
Würtz, P. et al. Metabolic signatures of adiposity in young adults: mendelian randomization analysis and effects of weight change. PLoS Med. 11, e1001765 (2014).
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).
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).
Perng, W. et al. Metabolomic profiles and childhood obesity. Obesity (Silver Spring) 22, 2570–2578 (2014).
Butte, N. F. et al. Global metabolomic profiling targeting childhood obesity in the Hispanic population. Am. J. Clin. Nutr. 102, 256–267 (2015).
Kadakia, R. et al. Cord blood metabolomics: association with newborn anthropometrics and c-peptide across ancestries. J. Clin. Endocrinol. Metab. 104, 4459–4472 (2019).
Kadakia, R. et al. Cord blood metabolites associated with newborn adiposity and hyperinsulinemia. J. Pediatr. 203, 144–149.e141 (2018).
Walford, G. A. et al. Metabolite profiles of diabetes incidence and intervention response in the Diabetes Prevention Program. Diabetes 65, 1424–1433 (2016).
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).
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).
Lau, A., West, L. & Tullius, S. G. The impact of sex on alloimmunity. Trends Immunol. 39, 407–418 (2018).
Colafella, K. M. M. & Denton, K. M. Sex-specific differences in hypertension and associated cardiovascular disease. Nat. Rev. Nephrol. 14, 185–201 (2018).
We thank the BIS participants for the generous contribution they have made to this project. We also thank current and past staff for their efforts in recruiting and maintaining the cohort and in obtaining and processing the data and biospecimens. The establishment work and infrastructure for the BIS was provided by the Murdoch Children’s Research Institute, Deakin University and Barwon Health. Subsequent funding was secured from the National Health and Medical Research Council of Australia, The Jack Brockhoff Foundation, the Scobie Trust, the Shane O’Brien Memorial Asthma Foundation, the Our Women’s Our Children’s Fund Raising Committee Barwon Health, The Shepherd Foundation, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, GMHBA Limited and the Percy Baxter Charitable Trust, Perpetual Trustees. In-kind support was provided by the Cotton On Foundation and CreativeForce. Research at Murdoch Children’s Research Institute is supported by the Victorian Government’s Operational Infrastructure Support Program. This work was also supported by NHMRC Senior Research Fellowships (APP1008396 to A.-L.P.; APP1064629 to D.B.; APP1045161 to R.S.).
The authors declare no competing interests.
The BIS protocol was approved by the Barwon Health Human Research Ethics Committee (HREC 10/24).
Written informed consent was obtained from all participating families in the study.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Members of the Barwon Infant Study Investigator Team are listed at the end of the paper.
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Ellul, S., Ponsonby, AL., Carlin, J.B. et al. Sex differences in infant blood metabolite profile in association with weight and adiposity measures. Pediatr Res 88, 473–483 (2020). https://doi.org/10.1038/s41390-020-0762-4