The early years of life are characterized by remarkable growth and neurodevelopment (UNICEF, 2017). Child development encompasses many dimensions of a child’s well-being. It is generally described into specific streams or domains of development, including motor development, speech and language progression, cognitive abilities, and socio-emotional skills (Brown et al., 2020).

Neurogenesis starts in the intrauterine environment and continues to shape brain morphology and plasticity after birth (Doi et al., 2022). The interval from birth to eight years represents a unique and critical period in which the development of a child’s brain can be significantly shaped. This phenomenon encompasses special sensitivity to experiences that promote cognitive, social, emotional, and physical development (UNICEF, 2017). The acquisition of developmental skills results from an interplay between the development of the nervous system and other organ systems (Brown et al., 2020). Optimal brain development requires a stimulating environment, adequate nutrients, and social interaction with attentive caregivers (Britto et al., 2017).

The early childhood development (ECD) impacts long-term individual and population health outcomes, including the ability to learn, achievements in school and later life, citizenship, involvement in community activities, and overall quality of life (van den Heuvel, 2019). An estimated 250 million children under five in low- and middle-income countries are at risk of not attaining their developmental potential, leading to an average deficit of 19.8% in adult annual income (Black et al., 2017). In 2015, the importance of ECD was recognized and incorporated into the ‘United Nations Sustainable Development Goals’.

Studies have demonstrated that early child metabolome disturbances may be implicated in the pathogenesis of non-typical neurodevelopment, including autism spectrum disorder (ASD; Sotelo-Orozco et al., 2023; Zhu et al., 2022), communication skills development (Kelly et al., 2019), and risk of impaired neurocognitive development (Moreau et al., 2019).

Children diagnosed with neurodevelopmental delays tend to experience more favorable treatment outcomes when these conditions are identified and addressed earlier (Clark et al., 2018; Li et al., 2023). Therefore, biomarkers are urgently needed to predict an infant’s potential risk for developmental issues while gaining new insights into underlying disease mechanisms. Although child development has been a focus of research for decades, studies in low- and middle-income countries on the potential role of circulating metabolites in ECD remain limited. The present study aims to identify associations between children’s serum metabolome and ECD. Identifying the relationships between metabolic phenotypes and ECD outcomes can elucidate pathways and targets for potential interventions, such as serum metabolites associated with food consumption in infancy (Bruce et al., 2023).

In total, 14,558 children under five years were evaluated, and 12,598 children aged 6–59 months were eligible for blood collection, of whom 8829 (70%) had the biological material collected. Due to the costs involved in the metabolome analysis, it was necessary to reduce the sample size that is equivalent to 57% of total participants from ENANI-2019 with stored blood specimens. Therefore, the infants were stratified by age groups (6–11, 12–23, and 24–59 months) and health conditions such as anemia and micronutrient deficiencies. The selection process aimed to represent diverse health statuses to the original sample. Ultimately, 5004 children were selected for the final sample through a random sampling process that ensured a balanced representation across these groups (Figure 2).

Figure 2

Download asset Open asset

The mean infant age was 34 months, and 48.9% of the participants were between 36 and 59 months. Almost half of the children evaluated lived in the North (24.8%) and Northeast (22.6%) regions. Most children were of normal weight, and 25% were at risk or had excessive weight. The prevalence of MDD was 59.3%. Most children (72.4%) lived in households with a monthly income greater than USD 248.7, the Brazilian minimum wage, and 51% had a mother/caregiver with at least 11 years of education (Table 1).

The DQ mean (95% CI) was 0.98 (0.97; 0.99). Overall, children had lower DQs if they were male (p=2 x 10^–14), older (p<2 x 10^–16), had lower weight for height (p=4 x 10^–5), <5 food groups than the MDD (p=3 x 10^–4), were from the northern region (p=2 x 10^–14), had a lower monthly family income (r=0.05, p=4 x 10^–4), and a mother/caregiver with fewer years of education (p=10^–15). Mode of delivery was not significantly associated with DQ (p=0.724; Supplementary file 1a).

Given the size of our sample, statistical power is not an issue in our analyses. Considering an alpha of 0.05 for a two-sided test, a sample size of 5000 has 95% power to detect a correlation of r=0.05 and an effect of f2=0.003 in a multiple regression model with 4 predictors. As an initial assessment of the zero-order associations between DQ and serum metabolites, Pearson’s correlations were performed. This revealed 26 negative and 2 positive statistically significant associations (Figure 3—figure supplement 1). Two unknown anions, annotated by their accurate mass, relative migration time, and ionization mode as 117.0552:1.67:N and 135.0293:1.71:N presented positive correlations. These two anions were tentatively identified as 3-hydroxyvaleric acid (r=0.05, 95% CI [0.02; 0.08], adjusted-p=0.001) and erythronic acid (r=0.04, 95% CI [0.01; 0.07], adjusted-p=0.011; Figure 3—figure supplement 1).

The highest correlations were all negative and were for serum phenylacetylglutamine (PAG, r=–0.16, 95% CI [–0.18; –0.13], adjusted-p=10^–26), cresol sulfate (CS, r=–0.15, 95% CI [–0.18; –0.12], adjusted-p=10^–24), hippuric acid (HA, r=–0.14, 95% CI [–0.17; –0.11], adjusted-p=2 x 10^–22), creatinine (Crtn, r=–0.13, 95% CI [–0.16; –0.1], adjusted-p=5 x 10^–19), trimethylamine-N-oxide (TMAO, r=–0.1, 95% CI [–0.13; –0.07], adjusted-p=2 x 10^–11), citrulline (Cit, r=–0.09, 95% CI [–0.12; –0.07], adjusted-p=3 x 10^–10), deoxycarnitine or γ-butyrobetaine (dC0, r=–0.09, 95% CI [–0.12; –0.06], adjusted-p=3 x 10^–9), and 3-methylhistidine (MeHis, r=–0.07, 95% CI [–0.1; –0.05], adjusted-p=10^–6).

The model with three components was used for parsimony and to avoid overfitting. The serum metabolites that had the highest loads on the components were the branched-chain amino acids, including leucine (Leu), isoleucine (Ile), and valine (Val) on component 1, the uremic toxins, CS and PAG on component 2 and betaine and amino acids, mainly glutamine (Gln) and asparagine (Asn) on component 3 (Figure 3—figure supplement 2). The three components accounted for 39.8% of the total metabolite variance Figure 3—figure supplement 3 and 4.3% of the DQ variance (Figure 3—figure supplement 4). Twenty-eight serum metabolites had a VIP ≥1 (Figure 3). These metabolites were then entered into the multiple linear regressions adjusted for the child’s diet quality (MDD), nutritional status (weight for length/height z-scores - w/h z-score), and age (months), which were the minimum adjustments indicated by the DAG as described in the statistical analysis section.

Figure 3 with 5 supplements see all

Download asset Open asset

We found inverse associations of serum concentrations of CS (β=–0.07; adjusted-p <0.001), HA (β=–0.06; adjusted-p <0.001), PAG (β=–0.06; adjusted-p <0.001), and TMAO (β=–0.05; adjusted-p=0.002) with the DQ of children, which were also significant in the models described below (Table 2). Since the child’s diet and metabolism may change as the child ages and as neurodevelopmental disorders occur more frequently in boys than in girls, interactions between the metabolites and child age (in months) and between metabolites and child sex were also tested to evaluate a possible modification of the effects by these variables in the models. Considering the interactions between serum metabolites and child age, we observed associations for Crtn (β-interaction=0.05; adjusted-p=0.003), HA (β-interaction=0.04; adjusted-p=0.041), MeHis (β-interaction=0.04; adjusted-p=0.018), PAG (β-interaction=0.04; adjusted-p=0.018), TMAO (β-interaction=0.05; adjusted-p=0.003), and Val (β-interaction=0.04; adjusted-p=0.039; Table 2).

Comparing children one standard deviation (SD) above the mean child age with those one standard deviation below (49 months vs. 19 months), we observed opposite directions for the association with DQ for serum Crtn (for children aged - 1 SD: β = - 0.05; p=0.01;+1 SD: β=0.05; p=0.02) and for MeHis (- 1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03; Figure 4). For serum TMAO, PAG, Val, and HA, the effect size went from a negative value for younger children to a non-significant value for older children (Figure 4). No associations were found for interactions between child sex and each metabolite on DQ (data not shown).

Figure 4

Download asset Open asset

Mediation analyses identified that serum PAG was a mediator for the relationship between mode of delivery (ACME = 0.003, p=2 x 10^–16), child’s diet quality (ACME = 0.002, p=0.019), and child fiber intake (ACME = - 0.002; p=0.034) and DQ (Figure 3—figure supplement 5). Serum HA (ACME = - 0.004, p<0.001) and TMAO (ACME = - 0.002, p=0.022) were also mediators for the relationship between child fiber intake and DQ (Figure 3—figure supplement 5). According to the mediation analysis, having a vaginal delivery (β=–0.05; p<0.001), not achieving MDD (β=–0.03; p=0.019), and greater total fiber intake (β=0.03; p=0.031) increased the serum PAG concentration, that in turn was inversely associated with DQ. Moreover, a higher dietary fiber intake was directly associated with HA (β=0.06; p<0.001) and TMAO (β=0.03; p=0.018), which also was inversely associated with DQ.

A limited number of investigations have examined the link between blood, urine, or stool metabolites and early stages of child development, with most studies focusing on comparing the metabolic profile of patients with developmental disorders against healthy controls (Brydges et al., 2021; Moreau et al., 2019; Needham et al., 2021; Ruggeri et al., 2014; Orozco et al., 2019). This is the first study to explore the association between child serum metabolome and ECD on a population-based level. According to our results, serum concentrations of PAG, CS, HA and TMAO were inversely associated with child DQ, a validated measure to express ECD. The associations of PAG, HA, TMAO, and Val on DQ were also age-dependent and showed stronger associations for children <34 months. In addition, inverse associations were found for serum levels of MeHis and Crtn with DQ for children <34 months, whereas direct associations were found for children >34 months.

PAG is the glutamine conjugate of phenylacetic acid generated from the gut microbial-dependent metabolism of phenylalanine (Krishnamoorthy et al., 2024; Poesen et al., 2016). As circulating concentrations of the essential amino acid, phenylalanine, were not directly associated with DQ in our study, this suggests that differences in gut microbiome composition impacting PAG formation among children are likely a major determinant of DQ rather than dietary protein intake. Similarly, to our findings, a previous study involving 76 patients with Attention-Deficit/Hyperactivity Disorder (ADHD) and 363 healthy children aged 1–18 years identified an inverse relationship between urinary PAG and ADHD (Tian et al., 2022). While the specific pathways contributing to such disorders remain to be fully elucidated, it is known that PAG is structurally similar to catecholamines and can activate adrenergic receptors (Huynh, 2020). The stimuli of adrenergic receptors may have broader implications on behavioral responses, potentially influencing neurological activities (Connors et al., 2005; Pliszka et al., 1996).

Likewise, in our study, circulating TMAO levels were inversely associated with child DQ. Elevated concentrations of TMAO in plasma and cerebrospinal fluid are also implicated in age-related cognitive dysfunction, neuronal senescence, and synaptic damage in the brain (Praveenraj et al., 2022). In addition, its increased levels have been associated with activation of inflammatory pathways (Seldin et al., 2016) and neurodegenerative diseases (Mudimela et al., 2022). Previous studies reported that TMAO can activate astrocytes and microglia and trigger a cascade of inflammatory responses in the brain, induce oxidative stress, superoxide production, and mitochondrial impairment, and cause inhibition of mTOR signaling in the brain (Mudimela et al., 2022; Praveenraj et al., 2022). In this context, dysregulation of the mTOR signaling pathway may lead to substantial abnormalities in brain development, contributing to a wide array of neurological disorders, including ASD, seizure, learning impairments, and intellectual disabilities (Altomare and Gitto, 2015; Lee, 2015).

HA is a glycine conjugate derived from exposure to benzoic acid (i.e. preservative in processed foods), or generated via intestinal microbial fermentation of dietary polyphenols and phenylalanine (Assem et al., 2018). Like other co-metabolized species, circulating HA concentrations depend on dietary exposures and host metabolism (Jian et al., 2021; Khan et al., 2022), in a case-control study involving 65 children with ASD and 20 children with typical development, reported that urinary HA was significantly higher in the ASD group, corroborating the inverse association found with DQ in our study. However, the effect of HA on metabolic health is still controversial as it has been proposed as a potential dietary biomarker for fruit and vegetable consumption in healthy children and adolescents (Krupp et al., 2012; Pallister et al., 2017). HA also inhibits the Organic Anion Transporter (OAT) 3 function and contributes to the toxic action of other compounds, including indoxyl sulfate (Ticinesi et al., 2023), which may affect cognitive function by disrupting the brain barrier (Lin et al., 2019).

CS is a product of tyrosine fermentation in the gut involving more than 55 p-cresol producing bacteria prior to hepatic sulfate conjugation (Saito et al., 2018). CS was inversely associated with DQ in our study, and it has been studied in the early stages of life, particularly concerning conditions such as ASD (Guzmán-Salas et al., 2022; Persico and Napolioni, 2013). Urinary p-cresol and CS are elevated in ASD-diagnosed children <8 years (Altieri et al., 2011; Persico and Napolioni, 2013). Animal models have shown that CS is a gut-derived neurotoxin that can impact neuronal cell structural remodeling even at low doses via oxidative stress and secretion of brain-derived neurotrophic factor (Tevzadze et al., 2022). Indeed, p-cresol might impact developmental processes since it is related to impaired dendritic development, synaptogenesis, and synapse function in hippocampal neurons, which are crucial for cognitive and neural development in children (Guzmán-Salas et al., 2022).

Prior investigations have identified PAG, CS, HA, and TMAO as products of gut microbiota metabolism (Pallister et al., 2017; Reichard et al., 2022). Specifically, dietary aromatic amino acids are metabolized by gut microbiota in the large intestine, converting phenylalanine into PAG and HA and tyrosine into CS (Reichard et al., 2022; Ticinesi et al., 2023). In contrast, TMAO originates from trimethylamine (TMA), which is produced from betaine compounds, including γ-butyrobetaine (dC0), choline, and carnitine via gut microbiota co-metabolism that is subsequently oxidized to TMAO in the liver (Reichard et al., 2022).

These metabolic compounds in the bloodstream may elicit physiological responses influencing the central nervous system through direct passage across the blood-brain barrier or indirectly through vagus nerve stimulation (Morais et al., 2021). Such dynamics underscore the complex interactions between environmental exposures early in life, such as mode of birth and the child’s diet, and brain development in early childhood (Azad et al., 2018).

Overall, serum PAG, HA, and TMAO showed a significant average causal mediation effect with dietary fiber intake that was inversely associated with DQ. However, the interpretation of mediation effects is limited by the observational nature of the data, and third variables may explain unexpected relationships between the variables in the analysis. Nevertheless, the results of our mediation analysis are an important step that future studies can build to further investigate the causal pathways leading to optimal DQ levels.

The age-dependent associations observed in our study are consistent with the age-related changes in metabolic profile reported in previous studies (Chiu et al., 2016; Gu et al., 2009; Psihogios et al., 2008; Tian et al., 2022). Increased urinary TMAO and betaine levels were found in children aged six months, whereas creatine and Crtn levels increased significantly after six months (Chiu et al., 2016). Similar findings were reported by Gu et al. in a study that included children from newborn to 12 years of age. The urinary Crtn increased with age, whereas glycine, betaine/TMAO, citrate, succinate, and acetone decreased (Gu et al., 2009). These changes may reflect a physiological age-dependent process related to the rapid growth occurring in early life (Chiu et al., 2016).

Interestingly, we observed that the child’s age changed the direction of the association between Crtn and MeHis with DQ. Crtn is generated non-enzymatically from creatine and is related to energy production within skeletal muscle tissue, whereas MeHis is related to protein turnover and has been evaluated as a biomarker for the rate of skeletal muscle breakdown (Kreider and Stout, 2021; Wang et al., 2012). For example, plasma and urinary MeHis are temporally associated with changes to a health-promoting Prudent diet in contrast to a Western diet, whose concentrations are positively correlated with greater self-reported daily protein intake (Wellington et al., 2019). We hypothesize that higher serum concentrations of Crtn and MeHis in older children (>49 months) may be due to the greater physical activity/mobility needed through the first years (Chiu et al., 2016).

Our study provided valuable insights into the potential role of serum metabolome on ECD for children aged 6–59 months. One of the strengths of this study is the large sample size, which allows for a more comprehensive representation of the population on a national level. Using a subset of 5004 due to cost restrictions did not compromise the representativeness. The sample was randomly selected to constitute the final sample that aimed to represent the total number of children with blood drawn (8829 children). Hence, our efforts were to preserve the original characteristics of the sample and the representativeness of the original sample. Furthermore, our study employed a quantitative targeted and exploratory untargeted metabolomics method. This high-throughput metabolomics platform is strengthened by implementing rigorous quality control measures and batch-correction algorithms, ensuring the high accuracy and reproducibility needed for large-scale epidemiological studies. We used the DQ as a variable for evaluating ECD, which consists of a continuous parameter that integrates developmental milestones attained with the child’s chronological age at its achievement. DQ has been previously used (Sheldrick et al., 2019 and Freitas-Costa et al., 2023) has advantages such as enabling the assessment of each item rather than just the final score, as the item set might be biased—meaning there could be an imbalance in the number of activities more commonly achieved among the specified items. Consequently, reaching the maximum score on the scale may be easier for certain age groups. Some limitations are worth mentioning. First, this study did not include the analysis of hydrophobic/water-insoluble lipids, limiting overall metabolome coverage. Also, the inherent limitations of a cross-sectional study prevent us from making causal inferences concerning the temporal relationship between serum metabolic phenotypes and ECD trajectories. Moreover, birth weight and breastfeeding practices were available only for a limited number of participants and were not included in the regression adjustments. Concerning the child’s diet assessment, we estimated dietary diversity and fiber intake based on one1-day food intake reports, with the MDD specifically measuring dietary diversity within diet quality. Lastly, stool microbiome data was not collected from children in ENANI-2019 as it was not a study objective in this large population-based nutritional survey. However, the lack of microbiome data does not reduce the importance/relevance since there is no evidence that microbiome and factors affecting microbiome composition are confounders in the association between serum metabolome and child development.

In conclusion, this study represents a pioneering effort in Brazil, a population-based survey targeting children from 6 to 59 months of age that incorporated serum metabolome and ECD analysis. We found that serum PAG, HA, CS, and TMAO were inversely associated with ECD and that age can modify the effect of PAG, HA, TMAO, Crtn, and MeHis on development. Some circulating metabolites associated with DQ are relevant to microbiome health, it is possible that those circulating metabolites may be biomarkers of a beneficial effect of the microbiome on development. These results suggest that a panel of circulating metabolites might offer a preliminary warning of developmental risk and potentially be used as a screening tool to help identify children at risk for developmental delays at early stages of life.

We believe the results can contribute to the literature, which should be used to accumulate evidence to overcome knowledge gaps and support the formulation and redirection of public policies aimed at full child growth and development, the promotion of adequate and healthy nutrition and food security; the encouragement, support, and protection of breastfeeding; and the prevention and control of micronutrient deficiencies. Further prospective longitudinal studies, including stool-based microbiome analysis, are warranted to validate our findings and establish targeted intervention biomarkers besides providing further insights into possible mechanistic pathways.

Data described in the manuscript, code book, and analytic code is available at: https://dataverse.nutricao.ufrj.br/dataverse/padilha-metab-dev.

1. 1. Keller VN

   (2025) Observatório de Epidemiologia Nutricional

   ID 20.500.12783/186. All data and code.

   https://hdl.handle.net/20.500.12783/186

1. 1. Altieri L
   2. Neri C
   3. Sacco R
   4. Curatolo P
   5. Benvenuto A
   6. Muratori F
   7. Santocchi E
   8. Bravaccio C
   9. Lenti C
   10. Saccani M
   11. Rigardetto R
   12. Gandione M
   13. Urbani A
   14. Persico AM

   (2011) Urinary p-cresol is elevated in small children with severe autism spectrum disorder

   Biomarkers 16:252–260.

   https://doi.org/10.3109/1354750X.2010.548010

   • PubMed
   • Google Scholar
2. 1. Altomare D
   2. Gitto S

   (2015) Recent insights into the pathophysiology of mTOR pathway dysregulation

   Research and Reports in Biology 6:1.

   https://doi.org/10.2147/RRB.S57088

   • Google Scholar
3. 1. Alves-Santos NH
   2. de Castro I
   3. Anjos LAD
   4. de A Lacerda E
   5. Normando P
   6. de Freitas M
   7. Farias DR
   8. Boccolini CS
   9. de Vasconcellos M
   10. do N Silva P
   11. Kac G

   (2021) General methodological aspects in the Brazilian National Survey on Child Nutrition (ENANI-2019): a population-based household survey

   Cadernos de Saude Publica 37:e00300020.

   https://doi.org/10.1590/0102-311X00300020

   • PubMed
   • Google Scholar
4. 1. Assem M
   2. Lando M
   3. Grissi M
   4. Kamel S
   5. Massy ZA
   6. Chillon JM
   7. Hénaut L

   (2018) The impact of uremic toxins on cerebrovascular and cognitive disorders

   Toxins 10:303.

   https://doi.org/10.3390/toxins10070303

   • PubMed
   • Google Scholar
5. 1. Azad MB
   2. Robertson B
   3. Atakora F
   4. Becker AB
   5. Subbarao P
   6. Moraes TJ
   7. Mandhane PJ
   8. Turvey SE
   9. Lefebvre DL
   10. Sears MR
   11. Bode L

   (2018) Human milk oligosaccharide concentrations are associated with multiple fixed and modifiable maternal characteristics, environmental factors, and feeding practices

   The Journal of Nutrition 148:1733–1742.

   https://doi.org/10.1093/jn/nxy175

   • PubMed
   • Google Scholar
6. 1. Baldeon AD
   2. McDonald D
   3. Gonzalez A
   4. Knight R
   5. Holscher HD

   (2023) Diet quality and the fecal microbiota in adults in the american gut project

   The Journal of Nutrition 153:2004–2015.

   https://doi.org/10.1016/j.tjnut.2023.02.018

   • PubMed
   • Google Scholar
7. 1. Black MM
   2. Walker SP
   3. Fernald LCH
   4. Andersen CT
   5. DiGirolamo AM
   6. Lu C
   7. McCoy DC
   8. Fink G
   9. Shawar YR
   10. Shiffman J
   11. Devercelli AE
   12. Wodon QT
   13. Vargas-Barón E
   14. Grantham-McGregor S

   (2017) Early childhood development coming of age: science through the life course

   The Lancet 389:77–90.

   https://doi.org/10.1016/S0140-6736(16)31389-7

   • Google Scholar
8. 1. Blekherman G
   2. Laubenbacher R
   3. Cortes DF
   4. Mendes P
   5. Torti FM
   6. Akman S
   7. Torti SV
   8. Shulaev V

   (2011) Bioinformatics tools for cancer metabolomics

   Metabolomics 7:329–343.

   https://doi.org/10.1007/s11306-010-0270-3

   • PubMed
   • Google Scholar
9. 1. Breitling LP
   2. Duan C
   3. Dragomir AD
   4. Luta G

   (2022) Using dagR to identify minimal sufficient adjustment sets and to simulate data based on directed acyclic graphs

   International Journal of Epidemiology 50:1772–1777.

   https://doi.org/10.1093/ije/dyab167

   • Google Scholar
10. 1. Britto PR
    2. Lye SJ
    3. Proulx K
    4. Yousafzai AK
    5. Matthews SG
    6. Vaivada T
    7. Perez-Escamilla R
    8. Rao N
    9. Ip P
    10. Fernald LCH
    11. MacMillan H
    12. Hanson M
    13. Wachs TD
    14. Yao H
    15. Yoshikawa H
    16. Cerezo A
    17. Leckman JF
    18. Bhutta ZA

    (2017) Nurturing care: promoting early childhood development

    The Lancet 389:91–102.

    https://doi.org/10.1016/S0140-6736(16)31390-3

    • Google Scholar
11. 1. Brown KA
    2. Parikh S
    3. Patel DR

    (2020) Understanding basic concepts of developmental diagnosis in children

    Translational Pediatrics 9:S9–S22.

    https://doi.org/10.21037/tp.2019.11.04

    • PubMed
    • Google Scholar
12. 1. Bruce CY
    2. Shanmuganathan M
    3. Azab SM
    4. Simons E
    5. Mandhane P
    6. Turvey SE
    7. Subbarao P
    8. Azad MB
    9. Britz-McKibbin P
    10. Anand SS
    11. de Souza RJ
    12. Stearns JC

    (2023) The relationship between diet, gut microbiota, and serum metabolome of south asian infants at 1 year

    The Journal of Nutrition 153:470–482.

    https://doi.org/10.1016/j.tjnut.2022.12.016

    • PubMed
    • Google Scholar
13. 1. Brydges CR
    2. Fiehn O
    3. Mayberg HS
    4. Schreiber H
    5. Dehkordi SM
    6. Bhattacharyya S
    7. Cha J
    8. Choi KS
    9. Craighead WE
    10. Krishnan RR
    11. Rush AJ
    12. Dunlop BW
    13. Kaddurah-Daouk R
    14. Penninx B
    15. Binder E
    16. Kastenmüller G
    17. Arnold M
    18. Nevado-Helgado A
    19. Blach C
    20. Rinaldo P

    (2021) Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature

    Scientific Reports 11:21011.

    https://doi.org/10.1038/s41598-021-99845-1

    • PubMed
    • Google Scholar
14. 1. Chiu CY
    2. Yeh KW
    3. Lin G
    4. Chiang MH
    5. Yang SC
    6. Chao WJ
    7. Yao TC
    8. Tsai MH
    9. Hua MC
    10. Liao SL
    11. Lai SH
    12. Cheng ML
    13. Huang JL

    (2016) Metabolomics reveals dynamic metabolic changes associated with age in early childhood

    PLOS ONE 11:e0149823.

    https://doi.org/10.1371/journal.pone.0149823

    • PubMed
    • Google Scholar
15. 1. Clark MLE
    2. Vinen Z
    3. Barbaro J
    4. Dissanayake C

    (2018) School age outcomes of children diagnosed early and later with autism spectrum disorder

    Journal of Autism and Developmental Disorders 48:92–102.

    https://doi.org/10.1007/s10803-017-3279-x

    • PubMed
    • Google Scholar
16. 1. Connors SL
    2. Crowell DE
    3. Eberhart CG
    4. Copeland J
    5. Newschaffer CJ
    6. Spence SJ
    7. Zimmerman AW

    (2005) beta2-adrenergic receptor activation and genetic polymorphisms in autism: data from dizygotic twins

    Journal of Child Neurology 20:876–884.

    https://doi.org/10.1177/08830738050200110401

    • PubMed
    • Google Scholar
17. 1. Cronin P
    2. Joyce SA
    3. O’Toole PW
    4. O’Connor EM

    (2021) Dietary fibre modulates the gut microbiota

    Nutrients 13:1655.

    https://doi.org/10.3390/nu13051655

    • PubMed
    • Google Scholar
18. 1. de A Lacerda E
    2. Boccolini CS
    3. Alves-Santos NH
    4. de Castro I
    5. dos Anjos L
    6. Crispim SP
    7. da Costa T
    8. de M Ferreira M
    9. Farias DR
    10. Carneiro LBV
    11. Berti TL
    12. Kac G

    (2021) Methodological aspects of the assessment of dietary intake in the Brazilian National Survey on Child Nutrition (ENANI-2019): a population-based household survey

    Cadernos de Saúde Pública 37:e00301420.

    https://doi.org/10.1590/0102-311x00301420

    • Google Scholar
19. 1. de Castro IRR
    2. Normando P
    3. Alves-Santos NH
    4. Bezerra FF
    5. Citelli M
    6. de FC Pedrosa L
    7. Jordão Junior AA
    8. de Lira PIC
    9. Kurscheidt FA
    10. da Silva PRP
    11. Salvatte K
    12. de A Lacerda EM
    13. Anjos LAD
    14. Boccolini CS
    15. Kac G

    (2021) Methodological aspects of the micronutrient assessment in the brazilian national survey on child nutrition (enani-2019): a population-based household survey

    Cadernos de Saude Publica 3737:e00301120.

    https://doi.org/10.1590/0102-311X00301120

    • PubMed
    • Google Scholar
20. 1. de L Drachler M
    2. Marshall T
    3. de Carvalho Leite JC

    (2007) A continuous-scale measure of child development for population-based epidemiological surveys: a preliminary study using item response theory for the denver test

    Paediatric and Perinatal Epidemiology 21:138–153.

    https://doi.org/10.1111/j.1365-3016.2007.00787.x

    • PubMed
    • Google Scholar
21. 1. de Vasconcellos MTL
    2. do N Silva PL
    3. de Castro IRR
    4. Boccolini CS
    5. Alves-Santos NH
    6. Kac G

    (2021) Sampling plan of the brazilian national survey on child nutrition (enani-2019): a population-based household survey

    Cadernos de Saude Publica 37:e00037221.

    https://doi.org/10.1590/0102-311X00037221

    • PubMed
    • Google Scholar
22. 1. Doi M
    2. Usui N
    3. Shimada S

    (2022) Prenatal environment and neurodevelopmental disorders

    Frontiers in Endocrinology 13:860110.

    https://doi.org/10.3389/fendo.2022.860110

    • PubMed
    • Google Scholar
23. 1. Freitas-Costa NC
    2. Andrade PG
    3. Normando P
    4. Nunes KSS
    5. Raymundo CE
    6. de Castro I
    7. de A Lacerda E
    8. Farias DR
    9. Kac G

    (2023) Association of development quotient with nutritional status of vitamins b6, b12, and folate in 6-59-month-old children: results from the brazilian national survey on child nutrition (ENANI-2019)

    The American Journal of Clinical Nutrition 118:162–173.

    https://doi.org/10.1016/j.ajcnut.2023.04.026

    • PubMed
    • Google Scholar
24. 1. Gould JF
    2. Fuss BG
    3. Roberts RM
    4. Collins CT
    5. Makrides M

    (2021) Consequences of using chronological age versus corrected age when testing cognitive and motor development in infancy and intelligence quotient at school age for children born preterm

    PLOS ONE 16:e0256824.

    https://doi.org/10.1371/journal.pone.0256824

    • PubMed
    • Google Scholar
25. 1. Gromski PS
    2. Muhamadali H
    3. Ellis DI
    4. Xu Y
    5. Correa E
    6. Turner ML
    7. Goodacre R

    (2015) A tutorial review: metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding

    Analytica Chimica Acta 879:10–23.

    https://doi.org/10.1016/j.aca.2015.02.012

    • PubMed
    • Google Scholar
26. 1. Gu H
    2. Pan Z
    3. Xi B
    4. Hainline BE
    5. Shanaiah N
    6. Asiago V
    7. Gowda GAN
    8. Raftery D

    (2009) 1H NMR metabolomics study of age profiling in children

    NMR in Biomedicine 22:826–833.

    https://doi.org/10.1002/nbm.1395

    • PubMed
    • Google Scholar
27. 1. Guzmán-Salas S
    2. Weber A
    3. Malci A
    4. Lin X
    5. Herrera-Molina R
    6. Cerpa W
    7. Dorador C
    8. Signorelli J
    9. Zamorano P

    (2022) The metabolite p-cresol impairs dendritic development, synaptogenesis, and synapse function in hippocampal neurons: implications for autism spectrum disorder

    Journal of Neurochemistry 161:335–349.

    https://doi.org/10.1111/jnc.15604

    • PubMed
    • Google Scholar
28. 1. Huynh K

    (2020) Novel gut microbiota-derived metabolite promotes platelet thrombosis via adrenergic receptor signalling

    Nature Reviews Cardiology 17:265.

    https://doi.org/10.1038/s41569-020-0367-y

    • Google Scholar
29. 1. Jian C
    2. Carpén N
    3. Helve O
    4. de Vos WM
    5. Korpela K
    6. Salonen A

    (2021) Early-life gut microbiota and its connection to metabolic health in children: perspective on ecological drivers and need for quantitative approach

    EBioMedicine 69:103475.

    https://doi.org/10.1016/j.ebiom.2021.103475

    • PubMed
    • Google Scholar
30. 1. Kelly RS
    2. Boulin A
    3. Laranjo N
    4. Lee-Sarwar K
    5. Chu SH
    6. Yadama AP
    7. Carey V
    8. Litonjua AA
    9. Lasky-Su J
    10. Weiss ST

    (2019) Metabolomics and communication skills development in children; evidence from the ages and stages questionnaire

    Metabolites 9:42.

    https://doi.org/10.3390/metabo9030042

    • PubMed
    • Google Scholar
31. 1. Khan ZUN
    2. Chand P
    3. Majid H
    4. Ahmed S
    5. Khan AH
    6. Jamil A
    7. Ejaz S
    8. Wasim A
    9. Khan KA
    10. Jafri L

    (2022) Urinary metabolomics using gas chromatography-mass spectrometry: potential biomarkers for autism spectrum disorder

    BMC Neurology 22:101.

    https://doi.org/10.1186/s12883-022-02630-4

    • PubMed
    • Google Scholar
32. 1. Kreider RB
    2. Stout JR

    (2021) Creatine in health and disease

    Nutrients 13:447.

    https://doi.org/10.3390/nu13020447

    • PubMed
    • Google Scholar
33. 1. Krishnamoorthy NK
    2. Kalyan M
    3. Hediyal TA
    4. Anand N
    5. Kendaganna PH
    6. Pendyala G
    7. Yelamanchili SV
    8. Yang J
    9. Chidambaram SB
    10. Sakharkar MK
    11. Mahalakshmi AM

    (2024) Role of the gut bacteria-derived metabolite phenylacetylglutamine in health and diseases

    ACS Omega 9:3164–3172.

    https://doi.org/10.1021/acsomega.3c08184

    • PubMed
    • Google Scholar
34. 1. Krupp D
    2. Doberstein N
    3. Shi L
    4. Remer T

    (2012) Hippuric acid in 24-hour urine collections is a potential biomarker for fruit and vegetable consumption in healthy children and adolescents

    The Journal of Nutrition 142:1314–1320.

    https://doi.org/10.3945/jn.112.159319

    • PubMed
    • Google Scholar
35. 1. Lee DY

    (2015) Roles of mtor signaling in brain development

    Experimental Neurobiology 24:177–185.

    https://doi.org/10.5607/en.2015.24.3.177

    • PubMed
    • Google Scholar
36. 1. Li K
    2. Bertrand K
    3. Naviaux JC
    4. Monk JM
    5. Wells A
    6. Wang L
    7. Lingampelly SS
    8. Naviaux RK
    9. Chambers C

    (2023) Metabolomic and exposomic biomarkers of risk of future neurodevelopmental delay in human milk

    Pediatric Research 93:1710–1720.

    https://doi.org/10.1038/s41390-022-02283-6

    • PubMed
    • Google Scholar
37. 1. Lin YT
    2. Wu PH
    3. Liang SS
    4. Mubanga M
    5. Yang YH
    6. Hsu YL
    7. Kuo MC
    8. Hwang SJ
    9. Kuo PL

    (2019) Protein-bound uremic toxins are associated with cognitive function among patients undergoing maintenance hemodialysis

    Scientific Reports 9:20388.

    https://doi.org/10.1038/s41598-019-57004-7

    • PubMed
    • Google Scholar
38. 1. Lipkin PH
    2. Macias MM
    3. Norwood KW Jr
    4. Brei TJ
    5. Davidson LF
    6. Davis BE
    7. Ellerbeck KA
    8. Houtrow AJ
    9. Hyman SL
    10. Kuo DZ
    11. Noritz GH
    12. Yin L
    13. Murphy NA
    14. Levy SE
    15. Weitzman CC
    16. Bauer NS
    17. Childers Jr DO
    18. Levine JM
    19. Peralta-Carcelen AM
    20. Smith PJ
    21. Blum NL
    22. Contompasis SH
    23. Korb DR
    24. McGuinn LJ
    25. Voigt RG
    26. COUNCIL ON CHILDREN WITH DISABILITIES, SECTION ON DEVELOPMENTAL AND BEHAVIORAL PEDIATRICS

    (2020) Promoting optimal development: identifying infants and young children with developmental disorders through developmental surveillance and screening

    Pediatrics 145:e20193449.

    https://doi.org/10.1542/peds.2019-3449

    • PubMed
    • Google Scholar
39. 1. Mevik BH
    2. Wehrens R

    (2007) The pls package: principal component and partial least squares regression in R

    Journal of Statistical Software 18:1–24.

    https://doi.org/10.18637/jss.v018.i02

    • Google Scholar
40. 1. Morais LH
    2. Schreiber HL
    3. Mazmanian SK

    (2021) The gut microbiota-brain axis in behaviour and brain disorders

    Nature Reviews. Microbiology 19:241–255.

    https://doi.org/10.1038/s41579-020-00460-0

    • PubMed
    • Google Scholar
41. 1. Moreau GB
    2. Ramakrishnan G
    3. Cook HL
    4. Fox TE
    5. Nayak U
    6. Ma JZ
    7. Colgate ER
    8. Kirkpatrick BD
    9. Haque R
    10. Petri WA

    (2019) Childhood growth and neurocognition are associated with distinct sets of metabolites

    EBioMedicine 44:597–606.

    https://doi.org/10.1016/j.ebiom.2019.05.043

    • PubMed
    • Google Scholar
42. 1. Moreira RS
    2. Magalhães LDC
    3. Siqueira CM
    4. Alves CRL

    (2019) Adaptação Transcultural do instrumento de vigilância do desenvolvimento infantil “Survey of Wellbeing of Young Children (SWYC)” no contexto brasileiro

    Journal of Human Growth and Development 29:28–38.

    https://doi.org/10.7322/jhgd.145001

    • Google Scholar
43. 1. Mudimela S
    2. Vishwanath NK
    3. Pillai A
    4. Morales R
    5. Marrelli SP
    6. Barichello T
    7. Giridharan VV

    (2022) Clinical significance and potential role of trimethylamine N-oxide in neurological and neuropsychiatric disorders

    Drug Discovery Today 27:103334.

    https://doi.org/10.1016/j.drudis.2022.08.002

    • PubMed
    • Google Scholar
44. Book

    1. Muthén LK
    2. Muthén BO

    (2017)

    Mplus User’s Guide

    Muthén and Muthén.

    • Google Scholar
45. 1. Needham BD
    2. Adame MD
    3. Serena G
    4. Rose DR
    5. Preston GM
    6. Conrad MC
    7. Campbell AS
    8. Donabedian DH
    9. Fasano A
    10. Ashwood P
    11. Mazmanian SK

    (2021) Plasma and fecal metabolite profiles in autism spectrum disorder

    Biological Psychiatry 89:451–462.

    https://doi.org/10.1016/j.biopsych.2020.09.025

    • PubMed
    • Google Scholar
46. 1. Orozco JS
    2. Hertz-Picciotto I
    3. Abbeduto L
    4. Slupsky CM

    (2019) Metabolomics analysis of children with autism, idiopathic-developmental delays, and Down syndrome

    Translational Psychiatry 9:243.

    https://doi.org/10.1038/s41398-019-0578-3

    • PubMed
    • Google Scholar
47. 1. Pallister T
    2. Jackson MA
    3. Martin TC
    4. Zierer J
    5. Jennings A
    6. Mohney RP
    7. MacGregor A
    8. Steves CJ
    9. Cassidy A
    10. Spector TD
    11. Menni C

    (2017) Hippurate as a metabolomic marker of gut microbiome diversity: modulation by diet and relationship to metabolic syndrome

    Scientific Reports 7:13670.

    https://doi.org/10.1038/s41598-017-13722-4

    • PubMed
    • Google Scholar
48. 1. Persico AM
    2. Napolioni V

    (2013) Urinary p-cresol in autism spectrum disorder

    Neurotoxicology and Teratology 36:82–90.

    https://doi.org/10.1016/j.ntt.2012.09.002

    • PubMed
    • Google Scholar
49. 1. Pliszka SR
    2. McCracken JT
    3. Maas JW

    (1996) Catecholamines in attention-deficit hyperactivity disorder: current perspectives

    Journal of the American Academy of Child and Adolescent Psychiatry 35:264–272.

    https://doi.org/10.1097/00004583-199603000-00006

    • PubMed
    • Google Scholar
50. 1. Poesen R
    2. Claes K
    3. Evenepoel P
    4. de Loor H
    5. Augustijns P
    6. Kuypers D
    7. Meijers B

    (2016) Microbiota-derived phenylacetylglutamine associates with overall mortality and cardiovascular disease in patients with CKD

    Journal of the American Society of Nephrology 27:3479–3487.

    https://doi.org/10.1681/ASN.2015121302

    • PubMed
    • Google Scholar
51. 1. Praveenraj SS
    2. Sonali S
    3. Anand N
    4. Tousif HA
    5. Vichitra C
    6. Kalyan M
    7. Kanna PV
    8. Chandana KA
    9. Shasthara P
    10. Mahalakshmi AM
    11. Yang J
    12. Pandi-Perumal SR
    13. Sakharkar MK
    14. Chidambaram SB

    (2022) The role of a gut microbial-derived metabolite, trimethylamine n-oxide (tmao), in neurological disorders

    Molecular Neurobiology 59:6684–6700.

    https://doi.org/10.1007/s12035-022-02990-5

    • PubMed
    • Google Scholar
52. 1. Psihogios NG
    2. Gazi IF
    3. Elisaf MS
    4. Seferiadis KI
    5. Bairaktari ET

    (2008) Gender-related and age-related urinalysis of healthy subjects by NMR-based metabonomics

    NMR in Biomedicine 21:195–207.

    https://doi.org/10.1002/nbm.1176

    • PubMed
    • Google Scholar
53. 1. Reichard CA
    2. Naelitz BD
    3. Wang Z
    4. Jia X
    5. Li J
    6. Stampfer MJ
    7. Klein EA
    8. Hazen SL
    9. Sharifi N

    (2022) Gut microbiome-dependent metabolic pathways and risk of lethal prostate cancer: prospective analysis of a PLCO cancer screening trial cohort

    Cancer Epidemiology, Biomarkers & Prevention 31:192–199.

    https://doi.org/10.1158/1055-9965.EPI-21-0766

    • PubMed
    • Google Scholar
54. 1. Reyman M
    2. van Houten MA
    3. van Baarle D
    4. Bosch AATM
    5. Man WH
    6. Chu MLJN
    7. Arp K
    8. Watson RL
    9. Sanders EAM
    10. Fuentes S
    11. Bogaert D

    (2019) Impact of delivery mode-associated gut microbiota dynamics on health in the first year of life

    Nature Communications 10:4997.

    https://doi.org/10.1038/s41467-019-13014-7

    • PubMed
    • Google Scholar
55. 1. Ruggeri B
    2. Sarkans U
    3. Schumann G
    4. Persico AM

    (2014) Biomarkers in autism spectrum disorder: the old and the new

    Psychopharmacology 231:1201–1216.

    https://doi.org/10.1007/s00213-013-3290-7

    • PubMed
    • Google Scholar
56. 1. Saito Y
    2. Sato T
    3. Nomoto K
    4. Tsuji H

    (2018) Identification of phenol- and p-cresol-producing intestinal bacteria by using media supplemented with tyrosine and its metabolites

    FEMS Microbiology Ecology 94:1–11.

    https://doi.org/10.1093/femsec/fiy125

    • PubMed
    • Google Scholar
57. Book

    1. Samejima F

    (1997) Graded response models

    In: Hambleton RK, editors. Handbook of Item Response Theory. Springer. pp. 85–100.

    https://doi.org/10.1007/978-1-4757-2691-6_5

    • Google Scholar
58. 1. Saoi M
    2. Li A
    3. McGlory C
    4. Stokes T
    5. von Allmen MT
    6. Phillips SM
    7. Britz-McKibbin P

    (2019) Metabolic perturbations from step reduction in older persons at risk for sarcopenia: plasma biomarkers of abrupt changes in physical activity

    Metabolites 9:134.

    https://doi.org/10.3390/metabo9070134

    • PubMed
    • Google Scholar
59. 1. Seldin MM
    2. Meng Y
    3. Qi H
    4. Zhu WF
    5. Wang Z
    6. Hazen SL
    7. Lusis AJ
    8. Shih DM

    (2016) Trimethylamine n-oxide promotes vascular inflammation through signaling of mitogen-activated protein kinase and nuclear factor-κB

    Journal of the American Heart Association 5:e002767.

    https://doi.org/10.1161/JAHA.115.002767

    • PubMed
    • Google Scholar
60. 1. Shanmuganathan M
    2. Kroezen Z
    3. Gill B
    4. Azab S
    5. de Souza RJ
    6. Teo KK
    7. Atkinson S
    8. Subbarao P
    9. Desai D
    10. Anand SS
    11. Britz-McKibbin P

    (2021) The maternal serum metabolome by multisegment injection-capillary electrophoresis-mass spectrometry: a high-throughput platform and standardized data workflow for large-scale epidemiological studies

    Nature Protocols 16:1966–1994.

    https://doi.org/10.1038/s41596-020-00475-0

    • PubMed
    • Google Scholar
61. 1. Sheldrick RC
    2. Perrin EC

    (2013) Evidence-based milestones for surveillance of cognitive, language, and motor development

    Academic Pediatrics 13:577–586.

    https://doi.org/10.1016/j.acap.2013.07.001

    • PubMed
    • Google Scholar
62. 1. Sheldrick RC
    2. Schlichting LE
    3. Berger B
    4. Clyne A
    5. Ni P
    6. Perrin EC
    7. Vivier PM

    (2019) Establishing new norms for developmental milestones

    Pediatrics 144:e20190374.

    https://doi.org/10.1542/peds.2019-0374

    • PubMed
    • Google Scholar
63. 1. Sotelo-Orozco J
    2. Schmidt RJ
    3. Slupsky CM
    4. Hertz-Picciotto I

    (2023) Investigating the urinary metabolome in the first year of life and its association with later diagnosis of autism spectrum disorder or non-typical neurodevelopment in the MARBLES study

    International Journal of Molecular Sciences 24:9454.

    https://doi.org/10.3390/ijms24119454

    • PubMed
    • Google Scholar
64. 1. Tevzadze G
    2. Barbakadze T
    3. Kvergelidze E
    4. Zhuravliova E
    5. Shanshiashvili L
    6. Mikeladze D

    (2022) Gut neurotoxin p-cresol induces brain-derived neurotrophic factor secretion and increases the expression of neurofilament subunits in PC-12 cells

    AIMS Neuroscience 9:12–23.

    https://doi.org/10.3934/Neuroscience.2022002

    • PubMed
    • Google Scholar
65. 1. Tian X
    2. Liu X
    3. Wang Y
    4. Liu Y
    5. Ma J
    6. Sun H
    7. Li J
    8. Tang X
    9. Guo Z
    10. Sun W
    11. Zhang J
    12. Song W

    (2022) Urinary metabolomic study in a healthy children population and metabolic biomarker discovery of attention-deficit/hyperactivity disorder (ADHD)

    Frontiers in Psychiatry 13:819498.

    https://doi.org/10.3389/fpsyt.2022.819498

    • PubMed
    • Google Scholar
66. 1. Ticinesi A
    2. Guerra A
    3. Nouvenne A
    4. Meschi T
    5. Maggi S

    (2023) Disentangling the complexity of nutrition, frailty and gut microbial pathways during aging: a focus on hippuric acid

    Nutrients 15:1138.

    https://doi.org/10.3390/nu15051138

    • PubMed
    • Google Scholar
67. 1. Tingley D
    2. Yamamoto T
    3. Hirose K
    4. Keele L
    5. Imai K

    (2014) Mediation: R package for causal mediation analysis

    Journal of Statistical Software 59:1–38.

    https://doi.org/10.18637/jss.v059.i05

    • Google Scholar
68. Report

    1. UNICEF

    (2017)

    Early Moments Matter for every child

    United Nations Children’s Fund (UNICEF).

    • Google Scholar
69. 1. van den Heuvel M

    (2019) Metabolomics, stunting and neurodevelopment

    EBioMedicine 44:10–11.

    https://doi.org/10.1016/j.ebiom.2019.05.067

    • PubMed
    • Google Scholar
70. 1. Wang H
    2. Hu P
    3. Jiang J

    (2012) Measurement of 1- and 3-methylhistidine in human urine by ultra performance liquid chromatography–tandem mass spectrometry

    Clinica Chimica Acta 413:131–138.

    https://doi.org/10.1016/j.cca.2011.09.007

    • Google Scholar
71. 1. Wehrens R
    2. Hageman JA
    3. van Eeuwijk F
    4. Kooke R
    5. Flood PJ
    6. Wijnker E
    7. Keurentjes JJB
    8. Lommen A
    9. van Eekelen HDLM
    10. Hall RD
    11. Mumm R
    12. de Vos RCH

    (2016) Improved batch correction in untargeted MS-based metabolomics

    Metabolomics 12:88.

    https://doi.org/10.1007/s11306-016-1015-8

    • PubMed
    • Google Scholar
72. 1. Wei R
    2. Wang J
    3. Su M
    4. Jia E
    5. Chen S
    6. Chen T
    7. Ni Y

    (2018) Missing value imputation approach for mass spectrometry-based metabolomics data

    Scientific Reports 8:663.

    https://doi.org/10.1038/s41598-017-19120-0

    • PubMed
    • Google Scholar
73. 1. Wellington N
    2. Shanmuganathan M
    3. de Souza RJ
    4. Zulyniak MA
    5. Azab S
    6. Bloomfield J
    7. Mell A
    8. Ly R
    9. Desai D
    10. Anand SS
    11. Britz-McKibbin P

    (2019) Metabolic trajectories following contrasting prudent and western diets from food provisions: identifying robust biomarkers of short-term changes in habitual diet

    Nutrients 11:2407.

    https://doi.org/10.3390/nu11102407

    • PubMed
    • Google Scholar
74. 1. Whitesell NR
    2. Sarche M
    3. Trucksess C
    4. Ayoub CC
    5. Barnes-Najor J
    6. Ferron C
    7. Fitzgerald HE
    8. Lyon K
    9. Lundy C
    10. Meyer AL
    11. Myra Parker JD
    12. Salvador M
    13. Walls M
    14. Williams S

    (2015) The survey of well-being of young children: results of a feasibility study with american indian and alaska native communities

    Infant Mental Health Journal 36:483–505.

    https://doi.org/10.1002/imhj.21526

    • PubMed
    • Google Scholar
75. Report

    1. WHO

    (2006)

    WHO child growth standards: length/height-forage, weight-for-age, weight-for-length, weight -for-height and body mass index-for-age: methods and development

    Developmental Medicine and Child Neurology, World Health Organization.

    • Google Scholar
76. Report

    1. WHO

    (2021)

    Indicators for Assessing Infant and Young Child Feeding Practices: Definitions and Measurement Methods

    World Health Organization.

    • Google Scholar
77. 1. Wold S
    2. Sjöström M
    3. Eriksson L

    (2001) PLS-regression: a basic tool of chemometrics

    Chemometrics and Intelligent Laboratory Systems 58:109–130.

    https://doi.org/10.1016/S0169-7439(01)00155-1

    • Google Scholar
78. 1. Worley B
    2. Powers R

    (2013) Multivariate analysis in metabolomics

    Current Metabolomics 1:92–107.

    https://doi.org/10.2174/2213235X11301010092

    • PubMed
    • Google Scholar
79. 1. Zhu J
    2. Hua X
    3. Yang T
    4. Guo M
    5. Li Q
    6. Xiao L
    7. Li L
    8. Chen J
    9. Li T

    (2022) Alterations in gut vitamin and amino acid metabolism are associated with symptoms and neurodevelopment in children with autism spectrum disorder

    Journal of Autism and Developmental Disorders 52:3116–3128.

    https://doi.org/10.1007/s10803-021-05066-w

    • PubMed
    • Google Scholar

Author details

1. Marina Padilha

   Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1736-0411

2. Victor Nahuel Keller

   Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4444-272X

3. Paula Normando

   Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Conceptualization, Investigation, Methodology, Writing – original draft, Project administration

   Competing interests

   No competing interests declared

4. Raquel M Schincaglia

   Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

5. Nathalia C Freitas-Costa

   Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Data curation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1798-0087

6. Samary SR Freire

   Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Conceptualization, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7326-1058

7. Felipe M Delpino

   Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Formal analysis, Validation, Writing – original draft

   Competing interests

   No competing interests declared

8. Inês RR de Castro

   Department of Social Nutrition, Institute of Nutrition, State University of Rio de Janeiro, Rio de Janeiro, Brazil

   Contribution

   Conceptualization, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Elisa MA Lacerda

   Department of Nutrition and Dietetics, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

   Contribution

   Conceptualization, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Dayana R Farias

    Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

    Contribution

    Conceptualization, Validation, Investigation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Zachary Kroezen

    Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada

    Contribution

    Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9415-7604

12. Meera Shanmuganathan

    Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada

    Contribution

    Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Philip Britz-Mckibbin

    Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada

    Contribution

    Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9296-3223

14. Gilberto Kac

    Department of Social and Applied Nutrition, Federal University of Rio de Janeiro, Josué de Castro Nutrition Institute, Rio de Janeiro, Brazil

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    gilberto.kac@gmail.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8603-9077

• 170

  views

Views, downloads and citations are aggregated across all versions of this paper published by eLife.