Longitudinal metabolome profiling from pregnancy through childhood and risk of neurodevelopmental disorders at age 10

Domain: biology
Published: 2026-01-08
Editorial outcome: major_revision

Abstract
Neurodevelopmental disorders (NDD) are characterized by impairments in brain development that affect motor and cognitive systems. Early identification of NDD risk factors is crucial for prevention. Metabolomic profiling holds promise for early detection but remains underexplored in longitudinal studies. Here, we map the longitudinal metabolomic profiles in 3212 samples covering 683 metabolites from 581 mother-child pairs at six time points in the Danish COPSAC cohort to assess childhood NDD risk over a 10-years follow-up period. Cross-validated sPLS models identify metabolic biomarkers and patterns associated with NDD, with maternal gestational week 24 metabolic profile showing the best prediction. Metabolite trajectories prior 18-month of age are linked to NDD risk. Quinolinate is consistently associated with NDD across time points and mediated the effect of maternal inflammation on NDD. In the single metabolite analysis, no metabolites remained significant after multiple testing correction. In this work, we provide valuable insights into the role of the longitudinal metabolome in neurodevelopment.

Introduction
Neurodevelopmental disorders (NDD) are characterized by impairments in brain development that affect motor and cognitive systems. These difficulties may in turn compromise social abilities, learning abilities, and behavior later in life1,2. Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are the two most prevalent NDD3. Approximately 15–20% of children are affected by NDD, with reported prevalence rates of 5–11% for ADHD, and 0.70–3% for ASD4. NDD manifests in early childhood, and the fetal environment may play a critical role5. The negative effects of NDD often persist throughout life, posing a significant public health concern and highlighting the necessity to better understand and support healthy neurodevelopment, especially in early life4,6.

Both ADHD and ASD are highly heritable disorders (up to 88% for ADHD and up to 91% for ASD) involving complex and heterogeneous neurodevelopmental, cognitive, and behavioral processes7–11. Metabolites are both intermediate and end products of cellular processes, acting as pivotal connections from the genome to the proteome, metabolome, and the clinical phenotype. Studies suggest that individuals with NDD, autism or ADHD symptoms show differences in their metabolome compared to neurotypical participants12,13, particularly in amino acids, sphingolipids, and vitamin profiles13–15. Increased plasma levels of sphingosine-1-phosphate and sphinganine-1-phosphate have been reported in ADHD patients15, while reduced levels of glutathione and tryptophan have been associated with autism and autism severity14. Additionally, lower plasma levels of cortisol, triacylglycerols and phospholipids and elevated levels of γ-tocopherol have been observed in children aged 6 to 14 with ADHD16,17. Alterations in the kynurenine pathway—such as increased kynurenic acid and 3-hydroxyanthranilic acid—have also been associated with ADHD-like behaviors in mouse models of ADHD and ASD18. In children with ASD, lower levels of linolenic acid and long-chain polyunsaturated fatty acids, higher levels of perfluorodecanoic acid, and alteration of a range of triglycerides have been reported19,20. Exposures before onset of disease are more likely to be causally involved in the disease than cross-sectional findings21–23. However, previous metabolomics studies predominantly focused on animal models or data from children at specific time points in case-control designs, rather than examining the pregnancy period or tracking metabolic changes longitudinally through childhood. Additionally, limited sample sizes and the absence of robust clinical validations restrict the generalizability of current findings.

The fetal neurodevelopmental period represents a time of significant brain growth and differentiation24. This period of accelerated neurodevelopment thus represents a window of increased vulnerability, with early adversities potentially increasing the risk of developing NDD25. Key neurodevelopmental processes, such as neuronal apoptosis, myelination, synaptogenesis, neurogenesis and proliferation occur during the second and third trimesters25–27, but our understanding of the mechanisms underlying abnormal neurodevelopment and NDD is still limited. Metabolic alterations during pregnancy have been observed in rodent models of NDD, such as ASD28,29. Similarly, maternal metabolome changes during pregnancy have previously been associated with the risk of ASD assessed during childhood30,31. We hypothesize that metabolic perturbations during pregnancy and early childhood may contribute to the risk of developing NDD, and longitudinal studies tracking metabolic alterations across these developmental stages are therefore essential.

In this work, we analyzed >3000 plasma metabolomic profiles consisting of 683 metabolites from 581 mother-child pairs in the Danish COPSAC cohort. Plasma samples were collected from pregnancy through age 10 as part of the cohort’s original design, with a particular focus on early life to capture critical developmental windows. Samples were collected at 6 time points from gestational week 24, week 1 postpartum, child age 6 months, 18 months, 6 years and age 10 years to investigate potential metabolite biomarkers, patterns and trajectories related to the risk of NDD at age 10.

Results
Baseline characteristics
A total of 604 children of the COPSAC 2010 cohort participated in the follow-up at age 10 years. Of the 604 children, 593 underwent psychopathological assessment. The numbers of participants included at each time point are shown in Supplementary Fig. 1. Of the 593 children, 581 (98%) of their mothers had metabolomic profiling at gestational week 24 and 579 (98%) at one week postpartum, 520 (88%), 535 (90%), 477 (80%) and 520 (88%) of the children had metabolomic profiling at 6 months, 18 months, 6 years, and 10 years, respectively. Figure 1a, b presents the distribution of metabolome data across all timepoints, analyzed using principal component analysis (PCA), as well as the study design including participants and classes of studied metabolites.

Of the 581 children with both psychopathological assessments and maternal metabolome data available from gestational week 24, 86 (15%) met criteria for at least one NDD, 63 (11%) had ADHD, and 16 (3%) had autism by age 10. A total of 59 (11%) had other psychiatric disorders than NDD (e.g., anxiety disorders, enuresis, and encopresis), and 454 (78%) had no mental disorders and were considered neurotypical participants. More boys (n=61, 71%) than girls (n=25, 29%) fulfilled criteria for any NDD (P<0.001). Mothers (P<0.001) and fathers (P=0.04) of children with any NDD had lower educational attainment (Table 1). Baseline characteristics of children with and without ADHD or autism are presented in Supplementary Data 1 and 2.

Association between metabolome profile and NDD at age 10
For any NDD, the sparse partial least squares (sPLS) metabolome model showed a cross-validated median repeat area under the curve (AUC) of 0.60 (range of repeats 0.59–0.62) for gestational week 24, 0.57 (0.52–0.60) for one week postpartum, 0.54 (0.50–0.56) for 6 months, 0.61 (0.58–0.63) for 18 months, 0.41 (0.37–0.45) for 6 years, and 0.55 (0.51–0.59) for 10 years. Among these, metabolome data from gestational week 24 and child age 18 months of children demonstrated the highest AUC value compared to the other time points (Fig. 2a–g). The final sPLS model of any NDD selected 200 metabolites. Figure 2h displays the top 50 metabolites with the highest absolute numerical loading scores from the model at gestational week 24 ranked from high to low. Adipoylcarnitine (C6-DC), glutamine conjugate of C6H10O2, and caprate (10:0) from gestational week 24 were the strongest positively associated metabolites with the risk of any NDD, while 4-hydroxy-2-oxoglutaric acid, branched-chain 14:0 dicarboxylic acid, and gentisate were the metabolites most strongly negatively associated, showing protective effects against any NDD. Metabolites associated with any NDD identified by sPLS at one week postpartum and at 18 months of age were shown in Supplementary Fig. 3a, b.

For ADHD, the sPLS model showed an AUC of 0.60 (0.57–0.62) for week 24, 0.57 (0.55–0.61) for one week postpartum, 0.53 (0.49–0.58) for 6 months, 0.60 (0.56–0.64) for 18 months, 0.46 (0.43–0.49) for 6 years, and 0.51 (0.45–0.53) for 10 years (Supplementary Fig. 2). Among these, the metabolome data from pregnancy week 24 and child age 18 months demonstrated equal performance with AUC values of 0.6 and showed higher AUC than the other time points. Adipoylcarnitine (C6-DC) at week 24 of pregnancy was the metabolite most strongly positively associated with the risk of ADHD, while threonate was the strongest negatively associated metabolite. Metabolites associated with ADHD at one week postpartum (e.g., 5,6-dihydrouridine, urate, and 3,4-dihydroxybutyrate) were shown in Supplementary Fig. 3c. At 18 months, only quinolinate was selected by the model and positively associated with the ADHD risk (Supplementary Fig. 3d).

In the nested five-fold CV model, only the gestational week 24 metabolome achieved comparable performance, with an AUC of 0.60 (SD=0.05, 0.57–0.67) for any NDD and 0.59 (SD=0.04, 0.54–0.65) for ADHD (Supplementary Results in Supplementary Information).

Association between metabolome-NDD risk scores and risk factors
The final sPLS model of gestational week 24 was selected based on the highest median AUC value and lowest variance across repeated cross-validation rounds, and the predicted values from the median repeat were used to extract the predicted metabolome-NDD risk scores. Our association analysis identified several potential risk factors linked to the gestational week 24 metabolome-NDD risk score. Specifically, pre-pregnancy BMI, gestational diabetes, an unhealthy Western dietary pattern derived from Food Frequency Questionnaires (FFQs)32, maternal inflammation measured as hs-CRP levels and maternal inflammation PC1 derived from 92 proteins33, antibiotic use during pregnancy, low family income, and low maternal and paternal educational attainment were associated with an increasing gestational week 24 metabolome-NDD risk score at a false discovery rate (FDR) 5% significance level (Supplementary Fig. 4a). Additionally, we examined the association between quinolinate levels at gestational week 24 and the same potential risk factors mentioned above. Consistently, hs-CRP levels, Western dietary pattern, pre-pregnancy BMI, and maternal inflammation PC1 were all positively associated with quinolinate levels (Supplementary Fig. 4b).

Metabolic trajectories from gestational week 24 and childhood NDD risk
We conducted longitudinal analyses using the metabolomic profiles at six time points. Since the metabolome during gestational week 24 provided the strongest association for both any NDD and ADHD using the sPLS full and nested model, we analyzed the differences in predicted risk scores based on the week 24 metabolome for these outcomes between children with vs. without NDD (Fig. 3). As the prediction model was built based on gestational week 24 metabolome, risk scores for the week 24 metabolome were calculated using the best fold from nested out fold CV of the testing set (n=116). The analysis identified difference in metabolic risk scores over time for any NDD and ADHD. Significant differences in these scores emerged early in childhood, particularly before age 18 months, between children diagnosed with and without NDD or ADHD.

There were significant differences between children with vs. without NDD at one week postpartum (p=0.02), whereas this was non-significant at 6 months (P=0.10), and 18 months (P=0.08). For ADHD, there were significant differences at 1 week postpartum (P=0.002), and 6 months (P=0.04), whereas the difference disappeared by 18 months (P=0.12). Additionally, we correlated the predicted metabolome-NDD risk scores for any NDD and ADHD based on the maternal week 24 metabolome with the NDD outcomes (Supplementary Data 3). Using all timepoints of children, the results demonstrated a trend of association for the metabolome-any NDD risk score (aOR=1.04, 95% CI [1.00–1.07], P=0.058) and the metabolome-ADHD risk score (aOR=1.06 [1.01–1.11], P=0.012), whereas there were significant associations using the timepoints until age 18 months for the metabolome-any NDD risk score (aOR=1.06 [1.00–1.13], P=0.046) and the metabolome-ADHD risk score (OR=1.11 [1.02–1.20], P=0.021), indicating that the early metabolic timepoints may be more important. We further adjusted for maternal exposures, including maternal smoking, maternal antibiotic use and alcohol use during pregnancy, which did not substantially modify the results (Supplementary Data 3).

Single metabolite analysis
We thereafter associated each individual metabolite at each time point to the risk of any NDD, ADHD, and autism, including conservative adjustment for potential confounders and multiple testing adjustments. No metabolites reached significance when adjusted for FDR 5%, while some metabolites exhibited nominal significance across the neurodevelopmental outcomes as outlined below.

In the maternal week 24 plasma metabolome (Fig. 4a–c), 54 metabolites were nominally associated with the risk of any NDD, exhibiting ORs ranging from 0.25 to 16.28 after adjusting for covariates. Sulfate, sphingomyelin (d18:1/18:1, d18:2/18:0), C-glycosyltryptophan, O-sulfo-tyrosine, and N-acetylneuraminate had the highest ORs. For ADHD, 39 metabolites achieved nominal significance in analyses adjusted for covariates (aOR range 0.13–25.83), with sulfate, C-glycosyl tryptophan, N-acetylneuraminate, 5-oxoproline, and quinolinate being the top positively associated metabolites with the highest ORs. For autism, 32 metabolites reached nominal significance after covariate adjustment (aOR range 0.01–11.88), with vanillylmandelate, N(1)+N(8)-acetylspermidine, creatine, sphingomyelin (d18:1/21:0, d17:1/22:0, d16:1/23:0), and picolinoyl glycine being the top positively associated metabolites with the highest ORs.

In the child age 18 months, plasma metabolome (Fig. 4d–f), 27 metabolites were associated with risk of any NDD (aOR range 0.05–4.42) after adjusting for covariates. Hydroxyasparagine, N-acetylputrescine, 2’-O-methylcytidine, 3-methoxytyrosine, and quinolinate had the highest ORs, positively associated with any NDD. For ADHD, 30 metabolites achieved nominal significance (aOR range 0.04–8.31) with hydroxyasparagine, glucose, 2’-O-methylcytidine, 3-methoxytyrosine, and 1,5-anhydroglucitol (1,5-AG) being the top positively associated metabolites with the highest ORs. For autism, 56 metabolites were nominally significant (aOR range 0.01–9.96), with citrate, cortisone, and imidazole lactate being the top positively associated metabolites with the highest ORs. Associations between each single metabolite from the other time points and risk of different NDD outcomes are shown in Supplementary Fig. 5 and Supplementary Data 4. We additionally adjusted for the randomized controlled trials (RCTs) of fish oil and vitamin D supplementation during pregnancy, which did not significantly alter the findings (Supplementary Fig. 6).

We identified three metabolites: quinolinate, ferulic acid 4-sulfate, and 8-methoxykynurenate, which were consistently associated with at least one outcome: any NDD, ADHD, or autism, measured during week 24 of pregnancy, as well as at 6 and 18 months of age (Fig. 4g). Interestingly, levels of quinolinate at all the six time points were positively associated with at least one of the outcomes of any NDD, ADHD, or autism across all time points, particularly ADHD. Ferulic acid 4-sulfate levels from gestational week 24 and child age 6 months were negatively associated with the risk of any NDD, and levels from 6 months and 18 months also showed a negative association with the risk of ADHD and autism, respectively. Similarly, 8-methoxykynurenate levels at 6 and 18 months were positively associated with the risk of any NDD and autism, respectively. Further adjusting for breastfeeding status at one week postpartum showed similar associations between the metabolome and NDD risk (Supplementary Fig. 7).

For ADHD and autism dimensional psychopathology, we observed a similar trend to NDD diagnosis for these metabolites, though with fewer significant data points (Supplementary Fig. 8).

To explore the impact of single versus multiple NDD diagnoses, we performed logistic regression analyses associating each individual metabolite from gestational week 24 and child age 6 months with the binary outcome (0=ADHD only, 1=multiple NDD diagnoses) (Supplementary Fig. 9). Notably, metabolites, such as quinolinate, bilirubin and its degradation products, and several sphingomyelins were positively associated with ADHD-only cases and negatively associated with multiple NDD diagnoses.

Enrichment analysis
An enrichment analysis was conducted using the top 100 metabolites identified by the sPLS model at gestational week 24 to investigate their association with any NDD and ADHD. Figure 5 illustrates the Sankey and bubble diagrams of enrichment of sub-pathways for key metabolites associated with any NDD. The analysis revealed several pathways significantly enriched in long-chain saturated fatty acid, fatty acid-dicarboxylate, medium-chain fatty acid, chemicals, lactoyl amino acid, and vitamin A metabolites. Notably, the long-chain saturated fatty acid pathway (fold enrichment=5.65, P_fdr=0.018) was among the most significantly enriched pathways for any NDD. For ADHD, the enriched pathways were similar to those for any NDD (Supplementary Fig. 10), with Vitamin A metabolism showing the highest fold of enrichment and lowest p-value after FDR correction.

Meditation analysis
We lastly performed mediation analyses to investigate whether quinolinate explained the association between NDD risk factors, such as maternal inflammatory proteins, unhealthy Western diet and maternal BMI, and NDD. Causal mediation analyses indicated that maternal inflammatory proteins (Supplementary Fig. 11), such as CCL3 (proportion mediated: 21.9%, P=0.048), were mediated through maternal quinolinate levels during pregnancy. The inflammatory protein profile captured in PC1 of a PCA and individual proteins, including VEGFA, CD5, and IL-12B, showed non-significant results for mediation. Maternal BMI (proportion mediated: 17.6%, P=0.042) and unhealthy Western diet patterns (proportion mediated: 10.0%, P=0.004) were also found to be mediated through maternal quinolinate levels.

Methods
Study population
The COpenhagen Prospective Studies on Neuro-PSYCHiatric Development (COPSYCH) enrolled 10-year-old children from the population-based COPSAC 2010 cohort81,82, including 700 mother-child pairs. The children were assessed clinically for neuropsychiatric outcomes from January 2019 to December 2021. Exclusion criteria for the present study included offspring with a birth weight <1500 grams or gestational age below 28 weeks, as well as twin B in pairs due to lack of independence. The biological sex was defined by the sex assigned at birth. The study was conducted in accordance with the Declaration of Helsinki and approved by the Danish Ethics Committee (H-B-2008-093) and the Danish Data Protection Agency (2015-41-3696). Written informed consent was obtained from all participants prior to any study-related procedures. Study design details of the COPSAC 2010 cohort are available in Supplementary Methods (Supplementary information).

Psychopathological outcomes of NDD
We focused on the most prevalent categorical NDDs outcomes in this cohort. Primary outcomes were any NDD, ADHD, and autism. The prevalence of all research diagnoses in this study is shown in Supplementary Table 1. Research diagnoses followed the International Classification of Disorders 10th revision of mental and behavioral disorders (ICD-10) criteria83,84, based on clinical information from the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime version (K-SADS-PL) interviews and additional clinical information85, as detailed in the COPSYCH study protocol82. Secondary outcomes were dimensional psychopathology using K-SADS-PL. Dimensional psychopathology of NDDs includes autistic and ADHD symptoms component scores, the ADHD rating scale (ADHD-RS) and the social responsiveness scale 2 (SRS-2). Parents completed the ADHD-RS and the SRS-2 questionnaires to assess ADHD symptoms and autistic traits86–88 (detailed Supplementary Methods in Supplementary information).

Covariates
Thirteen covariates were included in the analyses using maternal week 24 metabolome: pre-pregnancy maternal body mass index (BMI), maternal age, smoking, antibiotic use, pre-eclampsia, maternal diabetes, and alcohol intake during pregnancy, household income level, maternal and paternal educational attainment at birth, gestational age, child sex, and birth weight. For analyses using the maternal one week postpartum metabolome, we included: pre-pregnancy BMI, maternal age, maternal diabetes, mother smoking during pregnancy, household income level, maternal and paternal educational attainment, gestational age, child sex, and birth mode (cesarean or vaginal birth). Sex, maternal and paternal educational attainment, household income (at birth), and WHO BMI Z-score of children were included as covariates in analyses using childhood metabolomes. We additionally adjusted for the RCTs of fish oil and vitamin D interventions at each time point89,90.

Metabolomic and proteomics profiling
Metabolomic profiling was run on blood plasma samples from pregnancy week 24 and one week postpartum of mothers, and at 6 months, 18 months, 6 years, and 10 years for children. Metabolome data from one week postpartum were analyzed in this study under the hypothesis that they may reflect both the prenatal and postnatal environment. Blood samples (n=3212) were collected in EDTA tubes at the research clinic, centrifuged for 10 min at 4000 rpm, and plasma was stored at −80 °C until analysis. Untargeted metabolomics of plasma samples was performed by Metabolon, Inc., in Durham, NC, USA, using their HD4 platform. Sample preparation used the automated Hamilton MicroLab STAR system. Recovery standards were added for quality control (QC), and metabolites were extracted with methanol following vigorous shaking (2 min, GenoGrinder 2000) and centrifugation to precipitate proteins. Extracts were divided into four aliquots and analysed across four LC-MS/MS platforms. After drying with TurboVap and storage under nitrogen overnight, samples were analysed using a Waters ACQUITY UPLC system coupled to a Thermo Fisher Q-Exactive Quadrupole-Orbitrap mass spectrometer with HESI-II source. Four chromatographic methods were applied: two reverse-phase UPLC-ESI(+) for hydrophilic and hydrophobic compounds, one reverse-phase UPLC-ESI(−), and one HILIC UPLC-ESI(−). Data were acquired using alternating full-scan MS and data-dependent MSn with dynamic exclusion (70–1000 m/z). Metabolites were identified and semi-quantified using the area under the curve following established QC procedures. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities.

All samples were normalized using anchor samples with metabolites present in every data set. Only bridging metabolites were included in this study, resulting in a total of 683 metabolites common for all timepoints. All Batch-norm datasets were normalized to the reference set using median ratios of non-missing anchor sample pairs (≥6 per metabolite). Metabolites with >30% missingness were excluded, missing values were imputed with the minimum observed value, and data were log-transformed before analysis. A comprehensive description of the metabolomics protocol was outlined previously91, and more details of data extraction, data preprocessing, and annotation are described in the Supplementary Methods. Xenobiotics, as exogenous compounds distinct from endogenous metabolites, were also included in the metabolomics dataset due to their potential relevance to metabolic function and early-life exposures.

Plasma proteins at week 24 of pregnancy were measured using the Olink Target 96 inflammation panels (Olink). Blood plasma samples were thawed, randomized across nine 96-well plates, refrozen, and transferred to Olink on dry ice before being screened with the Olink target 96 Inflammation assay. A total of 92 inflammatory proteins were quantified from our samples. Each sample was tested once, and all samples were fully randomized, without stratification, across nine plates. All samples were analyzed in nine different plates. The results were presented as log2-transformed normalized protein expression (NPX) values. Four internal controls were added to each sample to monitor the quality of assay performance, as well as the quality of individual samples. All plates and samples passed QC thresholds, defined as an internal control SD of <0.2 NPX from the median between plates and <0.3 NPX between samples within plates, and the samples with QC warnings were excluded from the analyses33. A PCA was performed on the 92 proteins, with PC1 explaining 30.7% of the variance. Inflammatory protein PC1 and key proteins associated with NDD risk, identified in our previous study33—VEGFA, CD5, IL-12B, CCL3, FGF-23, and MCP-1—were included in the mediation analysis in this study.

Statistics
All statistical analyses were conducted using R version 4.2.1. R package “caret” and “mixOmics (6.20.0)” were used in sPLS92,93. We applied a FDR of 5% to account for multiple testing. Two-sided P<0.05 was considered significant. Children without ADHD, autism or any NDD were used as controls, depending on the specific outcome being modeled. ADHD, autism, and any NDD outcomes were examined separately. The number of cases for each diagnosis is shown in Fig. 1b.

In this study, we applied univariable (logistic and linear regression) and multivariable (sPLS) analyses. Univariable models assessed associations between each metabolite and any NDD, ADHD, and autism. Autism was analyzed only in univariable models because the small sample size (n=16) precluded reliable multivariable sPLS modeling and subsequent pathway-level analyses. sPLS models focused on ADHD and NDD outcomes, as the low prevalence of autism prevented robust 10-fold cross-validation and nested validation essential for model reliability. The emphasis on ADHD and NDD outcomes was guided by statistical feasibility rather than biological rationale.

First, the sPLS model with 10 times repeated and 10 times cross-validations were performed to predict NDD outcomes from the metabolome data at the six time points. For each time point, the metabolites loading scores were shown when the area under the receiver operating characteristic curve (AUC) was higher than 0.55. We aimed to identify the timepoint that best predicted NDD and determine the most strongly associated metabolites. This derived a metabolome-NDD risk score for each individual based on the best performed model with highest median AUC and lowest variance across repeated cross-validation rounds (Supplementary Methods in Supplementary Information). Due to the limited number of autism cases (N=16), we were unable to perform proper 10-fold cross-validation with 10 repetitions and nested cross-validation. Therefore, only ADHD and any NDD outcomes were included in the sPLS modeling and the following analyses involving the sPLS model.

Second, logistic and linear regression analyses were performed to investigate the association between each single metabolite and the NDD outcomes. These analyses were performed unadjusted and adjusted for covariates. As a further exploration of whether single versus multiple NDD diagnoses are associated with metabolomic profiles, we extracted all NDD cases and categorized them into two groups: children diagnosed with only ADHD (n=48) and those with multiple NDD diagnoses (n=38). In the logistic regression analysis, individuals with only ADHD were labeled as 0, and those with multiple NDD diagnoses were labeled as 1.

Third, pathway enrichment analyses were conducted by initially converting a list of the top 100 significant metabolites associated with NDD in the sPLS model into a data frame. The list of the top 100 metabolites was then merged with the corresponding counts of metabolites associated with each pathway. Enrichment ratios were calculated as the ratio of selected to total metabolites. Statistical significance was assessed using the hypergeometric test, with p-values adjusted for multiple testing using 5% FDR to identify significantly enriched metabolic pathways among the selected metabolites.

We performed mediation analysis to assess whether the effect of NDD-related risk factors, such as inflammatory proteins and maternal BMI, on ADHD is mediated by the highlighted metabolite. The mediate function from the “mediation (4.5.0)” R package was applied with 1000 bootstrap simulations to estimate the average causal mediation effect, the average direct effect, and the proportion of the total effect mediated. Statistical significance was assessed using bootstrap-derived p-values, and the mediation pathways were visualized with annotated effect estimates, including the proportion mediated and corresponding p-values. We focused on six inflammatory proteins—VEGFA, CCL3, CD5, IL-12B, FGF-23, and MCP-1, together with a maternal protein inflammatory profile PC1 that we previously identified as being associated with NDD risk33. Given the limited number of mediation tests and the hypothesis-driven nature of the analyses, we did not apply FDR correction here.

Sensitivity analysis of sPLS
To address potential overfitting from traditional tenfold cross-validation for sPLS, we additionally conducted a sensitivity analysis using a nested five-fold cross-validation approach, as outlined in our previous study (detailed in Supplementary Methods in Supplementary Information)94.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review summary:\nThe current study by Wang et al. leverages metabolomics data from maternal and child samples in the Danish\nCOPSAC2010 birth cohort study from multiple timepoints, ranging from gestational week 24 to 10 years postnatal, with the\naim to predict neurodevelopmental (NDD) diagnostic outcomes. The research question is clearly articulated and very\nimportant. The scale of the study, especially the longitudinal collection of prospective serum samples and\nmetabolomics at multiple timepoints, is a very notable strength over existing studies. The data collection and study design do not have any fundamental or conceptual flaws that could not be\naddressed with revisions. There are three main comments that relate to the manuscript rather than the study design:\n1. The manuscript has a number of formatting issues with the figures that make them unclear at some points, and these are\nlisted below. These should be easy to address.\n2. The authors need to clearly define the outcome of “any NDD”, why multiple and heterogenous conditions are pooled\ntogether, and why only results for ADHD are reported separately in most instances even though they define any NDD,\nADHD or autism as their primary outcomes.\n3. The statistics seem to be robust, appropriate, and well-adjusted. However, the language in the article frames the results as\nlargely positive and gives the impression of major and distinct metabolic differences in children who receive NDD\ndiagnoses, but this is not supported by the findings. The classification models performed poorly, with AUC ranging from 0.41\nto 0.61 (compared to an AUC of 0.50 for random chance classification); in the single-analyte models, no metabolites from\nany timepoint were significantly altered in any outcome after adjustment for FDR, the actual p-values are not provided so it is\ndifficult to make inferences based on these, and in all cases except week 24 vs any NDD, the number of nominally\nsignificant p-values is equivalent to the number expected by chance alone (roughly 5%); and in the mediation analyses, it is\nnot reported how many models were performed or whether these were also adjusted for FDR. Therefore, while the study and\nstatistics seem to be well-conducted, the findings suggest that there are only small metabolic differences in these children,\nthat the metabolome cannot accurately predict diagnostic cases with sPLS, and that if conclusions are going to be made\nbased on p-value thresholds (which does not necessarily need to be the case), then most of the findings should be reported\nas null. The reported results and manuscript article should remain the same, but the language surrounding them should\nchange to reflect them better.\nFigures:\n•Figure 1: a and b are not labelled as a and b.\n•Figure 1a: PC1 and PC2 are labelled twice.\n•Figure 1b: The font in the Venn diagram is too small to read without zooming to 200%.\n•Figure 1: font size for ADHD and autism is too small to be legible.\n•Figure 2: a-f correctly labelled; g not labelled as g. It is correctly labelled in the corresponding extended data figure 2 but\nwith a different font size to a-f.\n•Figure 2a: group colouring is reversed.\n•Figure 3: ADHD mother 1 week, please print the p-value as decimal, the use of scientific notation does not save space here\n(0.002), unless this is absolutely required by the journal.\n•Figure 5: stars are too small to be clearly discerned without zooming beyond 100%. The stars are also misaligned relative\nto the datapoints.\n•Figure 5: legend shows colouring by text with an “a”, even though there is no “text” aesthetic in any of the three plots, so this\nlikely refers to the stars. However, this obscures the other points in the legend and is not necessary, and it can be removed\nby including the “show.legend = FALSE” argument in the text geometric layer of the plot.\n•Figure 5: Each sub-plot y-axis is on a different range, these should all be set to the same range to facilitate comparison of\neach metabolite.\n•Figure 6: The legend needs much more detail. There is no y-axis label on the enrichment analysis plot. Sankey diagrams\ncan be used to project many different kinds of data; it is unclear (1) whether the node colouring of metabolites has some\nsignificance; (2) whether the edge thickness is the same for all connections (it seems to be) or if it represents any test\nstatistic, which is common. If each metabolite shown here belongs to only one of these pathways, and the purpose is to\nillustrate which pathway they belong to, there may be a clearer way of demonstrating this to the reader other than a Sankey\ndiagram. This comment applies equally to extended data figure 8.\n•Lines 745-746, in the figure legend for Figure 5, states “Each plot featured smoothed 746 loess curves to depict trends over\ntime, with color-coded points and lines distinguishing between groups”. Is this supposed to be in reference to extended data\nfigure 7? Figure 5 contains no loess curves, just points and error bars, but there are loess curves in extended data figure 7.\n•Random parts of the extended data figures are duplicated at the end of the manuscript document, comprising the last 7\npages, so these should be removed (p48-54). This of course is not a problem with the written manuscript but most likely with\nthe automatic compilation of the PDF, for the editor’s attention.\nAbstract and Introduction:\n•Abstract is clear and well-written. The authors should clarify what “machine learning models” means in this case (cross-\nvalidated sPLS).\n•Lines 53-54, the authors cite research that suggest the heritability of autism and ADHD to be 50% and 24-41%,\nrespectively. However, this is likely to be a great underestimation. Please see the following examples:\nhttps://doi.org/10.1001/jama.2017.12141; https://doi.org/10.1111/jcpp.12499; https://doi.org/10.1038/ng.3931;\nhttps://doi.org/10.1038/s41380-018-0070-0; https://doi.org/10.1017/S0033291713002493.\n•Lines 56-58. The sentence “Consequently, understanding the association between metabolic profiles and risk of NDD is\nclinically relevant for identifying risk factors of NDD and unraveling underlying mechanisms” is difficult to read and probably\nredundant. The rest of the introduction is clear and follows a nice, logical order.\n•Lines 77-79. The authors state that “maternal metabolome changes during pregnancy have previously been associated\nwith risk of ASD assessed during childhood (refs 28-29)”. Please elaborate on this and place your own research in the\ncontext of this pre-existing research, as this seems very close to the aims of the current study.\nMethods, Results, and scientific considerations:\n•There is no mention in the Methods section about data pre-treatment, only that “samples were normalized 427 using anchor\nsamples with metabolites present in every data set.” (line 426-427). Was any transformation or scaling applied; how were\nmissing and <LOD values dealt with, and were any imputations used?\n•It is unclear whether the p-values reported for the covariate-adjusted logistic and linear regression models are adjusted full-\nmodel or partial p-values (the authors are using R for their analysis, which prints both the adjusted p-value of the whole\nmodel, and the individual p-values of each covariate including your metabolite of interest when adjusting for covariates).\n•The listed covariates in the Methods section seem very appropriate, and the authors have done a good job to adjust for all\nof these.\n•At least in Figure 2g and possibly elsewhere, it can be seen that exogenous compounds (PFOS and PFOA) are included in\nthe panel. The authors should highlight this and distinguish between these and the other compounds, which are collectively\nreferred to within the manuscript as “metabolites”, although PFAS are not metabolites and are not well metabolized.\n•The authors need to clearly define their outcome of “any NDD” in the manuscript. Based on lines 97-99, it is more than the\nsum of ADHD and autism diagnoses so it must include some other diagnoses as well; does this include children with\ncerebral palsy, intellectual disability, or other conditions? More detail needs to be reported about this group, including in\nTable 1 where the number of each diagnosis within this group needs to be reported. Also please note that the term “any\nNDD” implies that the group includes a wide variety of different NDD diagnoses, so these should be provided in the main\nmanuscript so that the reader can understand the composition of this group.\n•It is unclear why the authors state “primary outcomes were any NDD, ADHD, and autism” (line 399), but do not include\nautism in most of their analyses. Only in the logistic regressions does autism appear as an outcome, but there is no data\nreported for sPLS, metabolic trajectories, or pathway enrichment analysis.\n•“Mothers (P<0.001) and fathers (P=0.04) of children with any NDD had lower educational attainment”. Then was this\nincluded in all models, and if not, it should be. Similar for the role of sex.\n•Line 130: “The final sPLS model of the maternal week 24 was selected based on the highest median AUC 131 value and\nlowest variance across repeated cross-validation rounds, and the predicted values from 132 the median repeat were used to\nextract the predicted metabolome-NDD risk scores.” This is repeated in line 142 among other places in the manuscript. But\nthe results demonstrate that 18-month time point had slightly higher median AUC (0.61 vs 0.60), and the difference in\nvariance between timepoints seems negligible. The overall effect from gestational week 24 metabolites and 18 months\nmetabolites is essentially identical, so why did the authors choose to only study gestational week 24 samples after the\nsPLS?\n•The AUC values for the sPLS models are quite low, indicating poor classification performance not much better than an\nAUC of 0.50 (random allocation to groups by chance).\n•Lines 190-198 and figure 5. Are the data (and stars indicating nominal p < 0.05) based on the same logistic regressions as\nin lines 168-189 and figure 4? If so, it is quite misleading as it gives the impression that new statistics have been done on\ndifferent data which are statistically significant, but this is re-plotting the findings from tests shown in figure 4, which were\nalready reported to have pFDR > 0.05.\n•Lines 213-221. The mediation analysis results are very interesting and should perhaps be highlighted more. However, the\nauthors mention they performed several mediation analyses for different independent variables, including individual immune\nproteins. How many mediation analyses did the authors perform? If conclusions are being made based on a p-value\nthreshold, the p-values should be corrected for multiple testing with FDR the same way the authors have done in some other\nanalyses. An additional point is that while the authors have used Sankey diagrams to illustrate the metabolic pathways that\neach top-100 metabolite belongs to, it might be a more appropriate tool for summarizing the multiple mediation analysis\nresults.\nDiscussion:\n•Lines 275-277: please clarify whether these effects are in vitro.\n•Lines 277-280: please clarify whether these studies are in humans or animal models.\n•Lines 333-337: there was significant mediation of the association with CCL3, and to a lesser degree with VEGFA, CD5, IL-\n12B. While these are interesting, it is equally important for the reader to know which other proteins were tested (if any), and\ntherefore which were associated with NDDs but not mediated by quinolinate (some immune proteins may act via their effects\non maternal quinolinate and other metabolite levels, while other immune proteins may act\nindependently of that).\n•The authors have given almost 3 pages (lines 268-551) to discussing the specific metabolites and metabolic pathways.\nThis is useful information, but should probably be condensed somewhat.\n•The one major topic that appears to be lacking in the discussion is to place the results within\nthe context of human neurodevelopmental trajectories. One of the main strengths of this study\nis the collection of metabolomics data at multiple timepoints, and the authors state multiple\ntimes that the gestational week 24 metabolome has a notably stronger association with NDD\noutcomes than the metabolome at other timepoints? Different cellular and molecular\nneurodevelopmental processes are occurring at each of these stages, and brain tissues are\ngrowing at different rates. For instance, the gestational week 24 timepoint is very shortly after the lifetime peak in neurogenesis;\nthe lifetime peak in grey matter volume occurs at 6 years old; and between birth and 6 years, the brain has reached its lifetime peak in cortical thickness which is\nalready beginning to decline, synaptogenesis and myelination are occurring at rapid rates, which is no longer true to the\nsame extent between 6 and 10 years old. Thus, the same metabolites would affect different processes at different stages,\nand this should be discussed. Any recent reviews would suffice to guide this discussion, as well as the Brain Charts for the\nHuman Lifespan article published in Nature in 2022.\n•Strengths: the authors list “detailed neuropsychiatric assessment, which includes interviews, performance-based measures,\n368 and questionnaires”, but this more so a strength of the cohort in general rather than the current study, as these data were\nnot used/reported much.\n•Strengths: The authors should highlight the longitudinal metabolomics, with a large number of common metabolites present\nat all timepoints, as this seems to be the biggest strength and novelty of the current study design.\n•Conclusion, lines 376-388. The results are framed as primarily positive or strong associations, which does not seem to be\nsupported by the results. This comment applies equally to the first paragraph of the Discussion, lines 230-235.", "reviewer_2": "This is an interesting study mapping the longitudinal metabolomic profiles of 3,212 samples of 581 mother-child pairs in a\npopulation-based Danish cohort to assess the risk of neurodevelopmental disorders (NDD) in offspring across six time\npoints within a 10-year 36 follow-up, using metabolomic profiling (683 metabolites). Machine learning models identified\nmetabolite biomarkers and patterns associated with NDD, with maternal gestational week 24 showing the best prediction.\nDistinct metabolite trajectories before 18 months, involving fatty acids, vitamin A metabolites, lactoyl amino acid, and\nxenobiotics, were linked to NDD risk. Quinolinate was consistently associated with NDD across all timepoints and mediated\nthe association between maternal inflammation and NDD.\nThe findings are novel and informative. Overall, the study’s design, sample size, and longitudinal design are robust, and the\ninvestigation of maternal metabolomic profiles in relation to neurodevelopmental disorder (NDD) risk contributes valuable\ninsights to the field. Among the most striking findings is the identification of metabolic trajectories over different gestational\ntime points, suggesting that there may be specific risk windows for NDD. The study’s large sample size and longitudinal\nnature are strong points of the study. The study’s AUC values were modest, yet the authors appropriately discuss these\nlimitations in the manuscript. The fact that even a modest predictive power was observed across multiple time points—\nespecially gestational week 24—reinforces the value of continued exploration into maternal and early-life metabolic profiles\nas potential early biomarkers for NDD risk.\nPossible refinements that could strengthen the manuscript:\nGeneral:\n1- While the data largely support the manuscript’s main claims, the absence of breastfeeding information is a significant gap.\nMaternal metabolomes at one-week postpartum could be more influential on the child if breastfeeding occurs. If this\ninformation is available, incorporating it into the one-week postpartum metabolome analyses (similar to how other\nconfounders are managed) would be valuable. Otherwise, acknowledging the lack of these data as a limitation\nwould strengthen the transparency of the study.\n2- The results linking metabolic signatures to NDD risk are compelling. However, a secondary exploration of whether single\nvs. multiple NDD diagnoses affect metabolite profiles would strengthen the paper’s contribution.\n3- Autism is presented as a primary outcome, yet there is no direct mention of autism-specific results (either diagnosis or\ntraits) in the manuscript or Appendix. This needs clarifying.\nIntroduction:\nThe introduction is concise, well-referenced, and provides a clear overview of the study’s rationale.\n1- In lines 54-56 the authors state ‘Metabolites serve as the end products of cellular processes’. However, this could be more\nprecise. Metabolites can function as both intermediate and end products, sometimes acting as buffers through ion exchange.\nA rephrasing such as “Metabolites are both intermediate and end products of cellular processes” would capture this more\naccurately.\n2- Have the previous findings highlighted in lines 60-64 been associated with clinical symptoms? It would also be helpful to\nnote which metabolic pathways are implicated or what abnormal levels might imply.\n3- In line 74, it needs to be clarified if the authors mean neuronal apoptosis.\nMethods\nOverall, the study’s design and sensitivity analyses are robust. The following points may further clarify and enhance\nreproducibility:\n1- If the acronym PRS from Table 1 stands for Polygenic Risk Scores, the authors should include an explanation of how\nthese scores were calculated, either in the main text or the Appendix.\n2- Ensure the Methods section explicitly clarifies that “biological sex” was defined by the sex assigned at birth.\nResults:\nThe results are well-organized and effectively present high-dimensional data. The authors have done an excellent job\nsynthesizing complex findings.\n1- In Table 1 (and elsewhere), it is preferable to use “ethnicity” rather than “race,” and to avoid terms such as “Caucasian.”\nInstead, please report the distribution of smaller ethnic groups explicitly whenever possible. If data limitations preclude this,\nconsider using “Other ethnic categories.”\n2- Figure 1b: The term “Energy” can be confusing. If these are energy metabolism nucleotides (or related compounds),\nconsider specifying them more precisely.\n3- Figure 2: Readers may expect to see ROC curves to illustrate the AUC values.\n4- Extended data Figure 4-b: Be sure to reference Extended Data Figure 4-b appropriately within the text.\n5- A figure illustrating the findings discussed in lines 150–153 would be helpful, given that this is a key result in the study.\nMaybe, the authors could add a non-NDD group line to Figure 3 for additional clarity.\nDiscussion:\n1- Paragraph 5: perhaps the finding of western diet in mothers to be associated with NDD risk can strengthen their\ndiscussion of LC-SFAs pathway enrichment analysis finding.\nOther potential improvements in grammar or format:\nAbstract:\nNDD needs defining\nline 36: change ‘metabolite biomarkers’ for ‘metabolic biomarkers’\nline 37: change ‘with maternal gestational week 24 showing the best prediction’ for ‘maternal gestational week 24 metabolic\nprofile showing the best prediction’\nline 38: change ‘before 18 months’ to ‘before/prior to 18 months of age’\nIntroduction:\nline 45: change ‘impairments in brain development that affect motor, cognitive systems, as well as emotional regulation’ for\n‘impairments in brain development that affect motor and cognitive systems’\nlines 58-60: change ‘Studies suggest that individuals with NDD show differences in their metabolome compared to healthy\nindividuals, where amino acids, sphingolipids, and vitamins have been highlighted’ to ‘Studies suggest that individuals with\nNDD show differences in their metabolome compared to healthy individuals, particularly in amino acid, sphingolipid, and\nvitamin profiles.’\nResults:\nTable 1: define PRS abbreviation in the footnote, and in the manuscript in general.\nline 105: Define sPLS abbreviation in the results section, since it precedes the Methods section.\nIn all Figures in general it would improve clarity if the same colour coding was applied to metabolite groups throughout the\nmanuscript.\nFigure 3: rename y axis label and legend to enhance clarity.\nDiscussion:\nLine 303: I would change the adverb ‘Further’ for a different one that better suits the paragraph meaning.\nRelated manuscript figure, if this figure is intended as a graphical abstract I would consider the following:\nReference exclusively to the metabolites participating in the kynurenine pathway in the figure might be misleading given that\nmultiple pathways have been referred to in the manuscript.\nSome metabolites shown in the figure aren’t mentioned in the text; consider aligning them more closely with the\nmanuscript’s content.\nI would avoid the use of gender-stereotypes in the mother figure, likewise I would avoid the breastfeeding figure unless all\nparticipants were breastfed, here too I would avoid the use of long hair. I would also ensure consistency in the way the\noffspring are depicted, in terms of design, and I would change the offspring figures to something more abstract in terms of\nphysical appearance to increase the representativeness of the illustration.\n(Remarks on code availability)\nCode:\nCode for the enrichment analyses was included and appears functional. Although I was unable to run it myself, the output\nprovided matches that of the publication, despite the figures being somewhat difficult to read in the R Markdown format. If the\nauthors consider it relevant, sharing the complete set of analyses could further enhance both reproducibility and educational\nvalue.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\n(Remarks on code availability)\nCode for the enrichment analyses was included and appears functional. Although I was unable to run it myself, the output\nprovided matches that of the publication, despite the figures being somewhat difficult to read in the R Markdown format. If the\nauthors consider it relevant, sharing the complete set of analyses could further enhance both reproducibility and educational\nvalue.", "reviewer_4": "The authors conducted the longitudinal metabolomic profiling from maternal-child cohort to assess the potential risk\nfactors/metabolites associated with NDD risk. Overall, the study is quite novel and well-written. However, several\nmethodological and reporting aspects require further clarification or justification:\n1. In the introduction, the rationale for sampling at the selected developmental timepoints should be more clearly justified, is\nit biologically relevant?(e.g., critical windows of neurodevelopment)\n2. In result section- “Association between metabolome NDD risk scores and potential risk factors”. Correspondingly.\nmaternal inflammation factors were not mentioned and this type of association analysis was not mentioned in the method.\n3. The identification of xenobiotic metabolites warrants further discussion. These markers likely reflect environmental\nexposures during pregnancy and may provide insight into the exposome's role in NDD risk.\n4. Psychopathological outcomes of NDD:\n1). Please clarify the comparison group used for binary outcomes. Were children without NDD used as controls, or were only\nchildren with no psychiatric diagnoses included?\n2). In each analysis, were ADHD and ASD examined separately, or was the composite \"any NDD\" outcome used as the\nprimary endpoint? This should be made clearer in the methods section.\n3). Please clarify how outcomes are defined in the models? Did authors account for skewness or zero-inflation for\ncontinuous scores?\n5. Covariates:\n1). Please describe how missing values on covariates were handled.\n2). Parity was not included among the covariates, although it is an established factor associated with neurodevelopment and\nmetabolome. Was this variable considered?\n3). In week 24 maternal metabolome model, birth weight was included as a covariate. Since birth weight is likely on the\ncausal pathway, it may function more as a mediator than a confounder. The rationale for its inclusion should be justified\n4). In the child metabolome models, why was maternal substance use during pregnancy not included? This could influence\nboth metabolite profiles and neurodevelopment.\n6. Statistics:\n1). Why was sparse Partial Least Squares (sPLS) selected over other interpretable or robust machine learning approaches,\nsuch as penalized regression (e.g., glmnet) or random forest? Please provide justification for this choice.\n2). With only 86 NDD and 63 ADHD cases out of 581 participants, the class imbalance could affect model performance.\nConsider reporting stratified metrics and clarify whether class balancing or stratified cross-validation was applied.\n3). The authors used the logistic and linear regression for each single metabolite and outcomes. This repeated-measures\nstructure introduces within-subject correlation that violates the independence assumption of standard regression models.\nAny consideration on mixed effect model?\n(Remarks on code availability)", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\n(Remarks on code availability)"}, "author_response": "RESPONSE: Thanks a lot for your positive feedback and for recognizing the significance of\nour research. We are very happy that you found our research question is clearly articulated,\nimportant and that the design has notable strength over existing studies. We appreciate your\nthoughtful review and find that the proposed revisions have significantly enhanced the quality\nof the manuscript.\n1. The manuscript has a number of formatting issues with the figures that make them unclear\nat some points, and these are listed below. These should be easy to address.\nRESPONSE: We have now addressed the mentioned formatting issues with all the figures,\ntables, and text, which made them more clear.\n2. The authors need to clearly define the outcome of “any NDD”, why multiple and\nheterogenous conditions are pooled together, and why only results for ADHD are reported\nseparately in most instances even though they define any NDD, ADHD or autism as their\nprimary outcomes.\nRESPONSE: We have now clarified the definition of “any NDD” and the rationale for\noutcome grouping. In our study, “any NDD” was defined as the presence of at least one of the\nfollowing clinically assessed diagnoses based on ICD10 codes described in the\nSupplementary Information: line 10-13: “The group of children with neurodevelopmental\ndisorders (NDD) was established based on the following ICD-10 codes: Pervasive\nDevelopmental Disorders (DF84.0, DF84.5, DF84.8), Other Disorders of Psychological\nDevelopment (DF88), Unspecified Disorders of Psychological Development (DF89),\nHyperkinetic Disorders (DF90.0, DF90.8)”.\nWe chose to include this composite outcome to improve statistical power, given the relatively\nlow prevalence of individual NDDs in our cohort and the known comorbidity and shared risk\nfactors among these neurodevelopmental conditions\n(https://doi.org/10.1586/14737175.2016.1146591;\n(https://doi.org/10.1016/j.rasd.2016.07.003). NDDs often share overlapping genetic risk\nfactors and pathogenic mechanisms. Common genetic variants and recurrent copy number\nvariations (CNVs) are implicated across multiple NDDs such as ADHD and autism. We\napplied the same diagnoses groups, namely, any NDD, ADHD, and autism in our recent study\nthat was published this year (Wang, et al., 2025, JAMA Psychiatry). To be consistent, we used\nthe same diagnoses groups in this study as well. For clarity, we have now added the prevalence\nof each psychiatric disorder in Supplementary Table 1.\nanalyses-sPLS models. In single variable analysis, we included all three diagnoses outcomes:\nany NDD, ADHD, and autism using linear/logistic regression, and autism-related findings are\nnow also included in the supplementary Table 2 for completeness, as shown in Figure 4 and\nSupplementary Table 2 (adjusted and unadjusted). All results for each individual metabolite\nacross all six time points and for all three NDD outcomes are provided there.\nIn our sPLS modeling, we included only ADHD and any NDD outcomes due to limitations in\nstatistical power. Specifically, there were only 16 autism cases in our cohort, which made it\ninfeasible to conduct robust 10-fold cross-validation with 10 repetitions or nested cross-\nvalidation, both of which are essential for model reliability. In our recent study (Wang et al.,\nJAMA Psychiatry, 2025), we performed 1,000 permutation tests for an sPLS model with autism\nas the outcome and observed a non-significant p-value, indicating limited predictive ability.\nTherefore, we did not include autism in the sPLS models in this study.\nWe acknowledge the heterogeneity within the NDD spectrum and recognize that pooling\nconditions under the “any NDD” category may obscure condition-specific effects. However,\nthe primary aim of this study was to identify early metabolic signatures associated with general\nneurodevelopmental vulnerability. To address potential heterogeneity, we performed separate\nsingle-metabolite analyses for ADHD and autism—the two most prevalent and well-defined\ndiagnostic categories in our dataset. ADHD results were presented more prominently because\nthey demonstrated the most robust and consistent associations across models.\n3. The statistics seem to be robust, appropriate, and well-adjusted. However, the language in\nthe article frames the results as largely positive and gives the impression of major and distinct\nmetabolic differences in children who receive NDD diagnoses, but this is not supported by the\nfindings. The classification models performed poorly, with AUC ranging from 0.41\nto 0.61 (compared to an AUC of 0.50 for random chance classification); in the single-analyte models,\nno metabolites from any timepoint were significantly altered in any outcome after adjustment\nfor FDR, the actual p-values are not provided so it is difficult to make inferences based on\nthese, and in all cases except week 24 vs any NDD, the number of nominally significant p-\nvalues is equivalent to the number expected by chance alone (roughly 5%); and in the mediation\nanalyses, it is not reported how many models were performed or whether these were also\nadjusted for FDR. Therefore, while the study and statistics seem to be well-conducted, the\nfindings suggest that there are only small metabolic differences in these children,\nthat the metabolome cannot accurately predict diagnostic cases with sPLS, and that if conclusions are\ngoing to be made based on p-value thresholds (which does not necessarily need to be the case),\nthen most of the findings should be reported as null. The reported results and manuscript article\nshould remain the same, but the language surrounding them should change to reflect them\nbetter.\nRESPONSE: We thank the reviewer for this thoughtful comment. We agree that the\ninterpretation and framing of the findings should be aligned with the statistical strength and\neffect sizes observed in the study.\nAs noted, the actual p-values for all single-analyte models are provided in Supplementary\nTable 2, including both unadjusted and FDR-adjusted p values across all three NDD diagnoses\nand six time points.\nIt is correct that all associations remained only nominally significant after FDR correction,\nwhich is expected given the large number of comparisons—nearly 700 metabolites were tested\nat each time point. Due to the conservative nature of multiple testing correction, it becomes\nincreasingly challenging to retain statistical significance after adjustment.\nacross multiple time points, as these may indicate biologically relevant signals that warrant\nfurther investigation, even if they do not survive strict multiple testing correction and covariates\nadjustment. This rationale has now been clarified through the manuscript.\nRegarding the mediation analyses, each model was conducted using 1,000 bootstrap\nsimulations to estimate the significance of the average causal mediation effect (ACME), as\nwell as the average direct effect (ADE) and proportion mediated. These details are now clearly\ndescribed in the Methods section (lines 508–518). “We performed mediation analysis to assess\nwhether the effect of NDD-related risk factors such as inflammatory proteins, maternal BMI\non ADHD is mediated by the highlighted metabolite. The mediate function from the “mediation”\nR package was applied with 1,000 bootstrap simulations to estimate the average causal\nmediation effect (ACME), the average direct effect (ADE), and the proportion of the total effect\nmediated. Statistical significance was assessed using bootstrap-derived p-values, and the\nmediation pathways were visualized with annotated effect estimates, including the proportion\nmediated and corresponding p-values……” While FDR correction was not applied to the\nmediation models due to their targeted and hypothesis-driven nature, we acknowledge this as\na limitation and have stated it clearly in the revised manuscript.\nIn terms of model performance, as described in the Sensitivity Analysis section and\nSupplementary Results, we performed five-fold nested cross-validation for the sPLS models,\nfollowing the approach used in Leal Rodríguez et al., (Nature Med, 2023), to assess robustness.\nWe also acknowledge the limitations in model predictiveness in lines 273–274, stating:\n“However, modest AUC values of 0.6 indicate that factors beyond maternal metabolic profiles\nare involved in development of NDD in the offspring. This warrants further research to validate\nand expand our findings and potentially incorporate both environmental factors and possibly\nother omics data layers in a multi-omics integrative approach. The lower AUC values at\nsubsequent timepoints during childhood compared to the AUC at week 24 of pregnancy suggest\nthat the predictive capability of metabolic profiles for NDD decreases, particularly after age\n18 months, possibly due to the increasing influence of genetic and environmental factors”\nFinally, in response to the reviewer’s recommendation, we have carefully revised the language\nthroughout the abstract, results, discussion, and conclusion sections to more accurately reflect\nthe modest effect sizes and model performance. The updated text now emphasizes that only\nnon-FDR significant metabolic differences were observed between diagnostic groups, and that\nthe predictive value of metabolomic profiles remains limited in this context. We rephrased\nrelevant sentences to avoid using terms such as “distinct” or “significant” when describing the\nassociations, and discussed the limitations of non-FDR significance and modest AUC. For\nexample:\nAbstract: “However, no metabolites remained significant after multiple testing correction”\nWe have revised the conclusion and discussion sections,\nIn discussion:\nLine 254-257: “However, none of the metabolites remained significant after FDR correction,\nindicating that metabolic perturbations may be subtle. The modest AUC values from the sPLS\nmodel further suggest that factors beyond maternal metabolic profiles likely play a role in the\ndevelopment of NDDs in children..”\nIn conclusion:\nLine 411-413: We observed metabolic trajectories during pregnancy and early life up to 18\nmonths that showed potential associations with NDD risk.\nsuggest that other factors beyond maternal metabolism contribute to NDD risk”\nFigures:\n•Figure 1: a and b are not labelled as a and b.\nRESPONSE:We have now labelled a and b in Figure 1.\n•Figure 1a: PC1 and PC2 are labelled twice.\nRESPONSE:We have now removed the duplicated labelling in Figure 1a.\n•Figure 1b: The font in the Venn diagram is too small to read without zooming to 200%.\nRESPONSE:We have now adjusted the size of the font in Figure 1b and zoomed in the text in\nthe Venn diagram more than two times.\n•Figure 1: font size for ADHD and autism is too small to be legible.\nRESPONSE: We have now adjusted the size of the font for ADHD and autism in Figure 1.\n•Figure 2: a-f correctly labelled; g not labelled as g. It is correctly labelled in the corresponding\nextended data figure 2 but with a different font size to a-f.\nRESPONSE: We have now labelled Figure 2g as g, and adjusted font size of a-g in the\nextended data Figure 2 (Supplementary Figure 2) as well.\n•Figure 2a: group colouring is reversed.\nRESPONSE: We have now reversed the colour of the groups and made them uniformed for\nboxplot in Figure 2a-f, and corrected the mistakes in the previous plots.\n•Figure 3: ADHD mother 1 week, please print the p-value as decimal, the use of scientific\nnotation does not save space here (0.002), unless this is absolutely required by the journal.\nRESPONSE: We have now corrected the p values in the plot, and displayed all the p values\nas decimal.\n•Figure 5: stars are too small to be clearly discerned without zooming beyond 100%. The\nstars are also misaligned relative to the datapoints.\nRESPONSE: We have now adjusted the size of the stars and align the stars with data points\n(now Figure 5 is merged into Figure 4g as they are all outputs from the logistic regression)\n•Figure 5: legend shows colouring by text with an “a”, even though there is no “text” aesthetic\nin any of the three plots, so this likely refers to the stars. However, this obscures the other points\nin the legend and is not necessary, and it can be removed by including the “show.legend =\nFALSE” argument in the text geometric layer of the plot.\nRESPONSE: We have now removed the “a” in the legend.\n•Figure 5: Each sub-plot y-axis is on a different range, these should all be set to the same range\nto facilitate comparison of each metabolite.\nRESPONSE: We have adjusted the Y-axis across all subplots in Figure 5 (Figure 4g) to use a\nconsistent scale to facilitate direct comparison between metabolites. To improve the visibility\nof smaller odds ratios within the same range, we have applied a logarithmic scale to the Y-axis.\nThe original, untransformed odds ratios, 95% CI, and p-values are available in Supplementary\nTable 2 for reference.\n4\n•Figure 6: The legend needs much more detail. There is no y-axis label on the enrichment\nanalysis plot. Sankey diagrams can be used to project many different kinds of data; it is unclear\n(1) whether the node colouring of metabolites has some significance; (2) whether the edge\nthickness is the same for all connections (it seems to be) or if it represents any test statistic,\nwhich is common. If each metabolite shown here belongs to only one of these pathways, and\nthe purpose is to illustrate which pathway they belong to, there may be a clearer way of\ndemonstrating this to the reader other than a Sankey diagram. This comment applies equally to\nextended data figure 8.\nRESPONSE: We thank the reviewer for the thoughtful suggestions regarding Figure 6 and\nExtended Data Figure 8. The primary purpose of the Sankey diagram is to provide an intuitive\nvisual summary of how the top-ranked metabolites (identified from sPLS models) map onto\ntheir respective metabolic subpathways, as defined by Metabolon annotations.\nEach metabolite in the diagram is uniquely assigned to a single subpathway, and our intention\nwas to simultaneously convey both the breadth of subpathway representation and highlight\nspecific metabolite groups of interest, such as long- and medium-chain fatty acids and\nxenobiotics—pathways that were discussed in detail in the manuscript. Given the complexity\nof representing nearly 100 metabolites along with pathway-level enrichment results, the\nSankey diagram allowed for a compact and integrated visualization.\nTo address the reviewer’s points and improve clarity, we have revised the figure legend to\ninclude the following details: “Each left-side node represents a metabolite, and each right-side\nnode represents a sub pathway. All metabolites are uniquely assigned to one pathway. Node\ncolors are arbitrarily assigned for visual clarity and do not reflect statistical significance or\neffect direction. All edges (links) have equal thickness and indicate membership relationships\nbetween metabolites and their respective pathways. Bubble plot displaying pathway-level\nenrichment statistics. Each point corresponds to one pathway. X-axis representing the\nenrichment magnitude of each pathway relative to metabolites. Y-axis: pathways with larger\nfold enrichment are plotted lower. Dot color with darker red indicating higher statistical\nsignificance. Dot size: Encodes p-value tier”\nWhile we acknowledge that other formats (e.g., bar plots or tables) could be used, we believe\nthis integrated visual layout provides a more intuitive overview of both metabolite-pathway\nmapping and enrichment results. The full data, including metabolite-pathway mappings and\nstatistical details, are provided in Source data and available codes to ensure transparency and\nreproducibility.\n•Lines 745-746, in the figure legend for Figure 5, states “Each plot featured smoothed 746 loess\ncurves to depict trends over time, with color-coded points and lines distinguishing between\ngroups”. Is this supposed to be in reference to extended data figure 7? Figure 5 contains no\nloess curves, just points and error bars, but there are loess curves in extended data figure 7.\nRESPONSE: Sorry for the confusion. This has been corrected as there are no loess curves in\nFigure 5.\n•Random parts of the extended data figures are duplicated at the end of the manuscript\ndocument, comprising the last 7 pages, so these should be removed (p48-54). This of course is\nnot a problem with the written manuscript but most likely with the automatic compilation of\nthe PDF, for the editor’s attention.\nRESPONSE:We have now removed the duplicated extended figures.\nAbstract and Introduction:\n5\n•Abstract is clear and well-written. The authors should clarify what “machine learning models”\nmeans in this case (cross-validated sPLS).\nRESPONSE:We have replaced the machine learning models with Cross validated sPLS\nmodels in Line 37: “Cross validated sPLS models identified metabolite biomarkers and\npatterns associated with NDD, with maternal gestational week 24 showing the best prediction”.\n•Lines 53-54, the authors cite research that suggest the heritability of autism and ADHD to be\n50% and 24-41%, respectively. However, this is likely to be a great underestimation. Please\nsee the following\nexamples: https://doi.org/10.1001/jama.2017.12141; https://doi.org/10.1111/jcpp.12499; http\ns://doi.org/10.1038/ng.3931;https://doi.org/10.1038/s41380-018-0070-\n0; https://doi.org/10.1017/S0033291713002493.\nRESPONSE: We thank the reviewer for the valuable clarification and for providing key\nreferences. We acknowledge that our previous estimates of heritability for ADHD and ASD\nwere underestimates based on limited sources. In response, we have updated the manuscript to\nreflect more accurate estimates from recent literature.\nRevised text (Lines 54–56):\n“Both ADHD and ASD are highly heritable disorders (up to 88% for ADHD and up to 91%\nfor ASD), involving complex, heterogeneous neurodevelopmental, cognitive, and behavioral\nprocesses.7-11”\nWe have also cited the relevant references provided by the reviewer in the revised manuscript.\n•Lines 56-58. The sentence “Consequently, understanding the association between metabolic\nprofiles and risk of NDD is clinically relevant for identifying risk factors of NDD and\nunraveling underlying mechanisms” is difficult to read and probably redundant. The rest of the\nintroduction is clear and follows a nice, logical order.\nRESPONSE: We have now deleted the sentence in Lines 56-58 to avoid redundancy and\ndifficult reading.\n•Lines 77-79. The authors state that “maternal metabolome changes during pregnancy have\npreviously been associated with risk of ASD assessed during childhood (refs 28-29)”. Please\nelaborate on this and place your own research in the context of this pre-existing research, as\nthis seems very close to the aims of the current study.\nRESPONSE: We thank the reviewer for this insightful comment. We have now expanded the\ndiscussion to better contextualize our findings in relation to previous studies (Refs 28–29).\nThese prior studies examined the association between maternal metabolomic profiles and ASD\nrisk but differed from ours in important ways. For example, Ritz, Che et al. (2023) analyzed\nsamples from 52 mothers and identified only seven metabolites with confirmed chemical\nidentity, two of which (ornithine and 1-methylhistidine) were detected in our dataset, though\nneither was significant in our analysis. The study by Che et al. (2023) used a broader profiling\napproach, covering 1,208 metabolites, and identified erythritol and arachidonic acid as FDR-\nsignificant, but neither was significant in our cohort.\nNotably, pseudouridine (P value: 0.003, FDR: 0.138, 95% CI 1.8 [1.23-2.66])— the metabolite\nshowing a consistent signal—was positively associated with autism in cord blood in their study\nand also showed a positive association with autism in 6-month blood samples in our cohort (P\nvalue: 0.029, 95% CI 6.96 [1.26-41.56]). The differences in findings may be attributed to\n6\nvariations in metabolomics platforms, sample sizes, and timing of sample collection across\nstudies.\nWe have added this comparison to the discussion section to clarify how our work extends and\ncontrasts with previous findings in this area:\nLine 341-346: “For autism outcome, previous studies have linked maternal metabolomic\nprofiles to autism risk, though findings vary by metabolomics platforms, sample sizes, and\ntiming of sample collection. 30,31While most metabolites identified by Ritz, et al. (2020) and Che\net al. (2023) were not significant in our cohort, we observed a consistent association of\npseudouridine with autism at 6 months, aligning with their cord blood findings and supporting\nits potential relevance.”\nMethods, Results, and scientific considerations:\n•There is no mention in the Methods section about data pre-treatment, only that “samples were\nnormalized using anchor samples with metabolites present in every data set.” (line 426-427).\nWas any transformation or scaling applied; how were missing and <LOD values dealt with,\nand were any imputations used?\nRESPONSE: We thank the reviewer for highlighting this important point. The metabolomics\ndata were generated using the Metabolon platform with an untargeted approach; therefore,\nLOD values were not available. To ensure data quality and consistency, we applied the\nfollowing pre-treatment steps:\nMetabolites with more than 30% missingness were excluded.\nFor the remaining metabolites, missing values were imputed using the minimum observed\nvalue for each respective metabolite.\nAll metabolite values were natural log-transformed prior to statistical analysis to reduce\nskewness and approximate normality.\nThese procedures are described in the Supplementary Information (lines 42-52) following the\ndescription of anchor sample normalization:\n“……These computations were performed for metabolites with at least six non-missing values;\notherwise, the metabolite in the reference set was designated as 'BATCH_NOT_BRIDGED'.\nMetabolites with more than 30% missingness were excluded. For the remaining data, missing\nvalues were imputed using the minimum observed value per metabolite. All values were then\nnatural log-transformed prior to statistical analysis.\n•It is unclear whether the p-values reported for the covariate-adjusted logistic and linear\nregression models are adjusted full-model or partial p-values (the authors are using R for their\nanalysis, which prints both the adjusted p-value of the whole model, and the individual p-values\nof each covariate including your metabolite of interest when adjusting for covariates).\nRESPONSE: We thank the reviewer for this clarification request. The p-values reported for\nthe covariate-adjusted logistic and linear regression models represent p-values for each\nindividual metabolite, calculated while adjusting for all specified covariates in an adjusted full\nmodel. Each metabolite was modeled separately in relation to each NDD outcome, with all\ncovariates included in the model to control for potential confounding.\nTo account for multiple testing, we applied FDR correction across all metabolites at each time\npoint. The complete results—including unadjusted p-values, covariates -adjusted p-values, and\nTable 2.\n•The listed covariates in the Methods section seem very appropriate, and the authors have done\na good job to adjust for all of these.\nRESPONSE: We thank the reviewer for the positive feedback. As noted, we included a\ncomprehensive set of covariates consistent with those used in our previous studies (Wang et\nal., JAMA Psychiatry, 2025; Horner et al., Nature Metabolism, 2025), where similar modeling\napproaches were applied. This allowed us to ensure consistency and robustness in the\nadjustment for potential confounders across studies.\n•At least in Figure 2g and possibly elsewhere, it can be seen that exogenous compounds (PFOS\nand PFOA) are included in the panel. The authors should highlight this and distinguish between\nthese and the other compounds, which are collectively referred to within the manuscript as\n“metabolites”, although PFAS are not metabolites and are not well metabolized.\nRESPONSE: We thank the reviewer for this important observation. Indeed, our metabolomics\ndataset includes xenobiotics, such as PFOS and PFOA, which are exogenous compounds and\nnot true endogenous metabolites. While these compounds are not metabolized in the same way\nas typical metabolites, they are included in the dataset generated by the Metabolon HD4\nplatform and have been retained in our analyses due to their potential biological relevance.\nAs demonstrated in our previous work (Horner et al., Nature Metabolism, 2025), xenobiotics—\nmay derive from diet, environment, or other exposures—can influence human metabolic\nprofiles and may contribute to disease risk. We have now clarified this distinction in the\nMethods section of the manuscript with the following statement:\nLine 467-469“Xenobiotics, as exogenous compounds distinct from endogenous metabolites,\nwere also included in the metabolomics dataset due to their potential relevance to metabolic\nfunction and early-life exposures.”\n•The authors need to clearly define their outcome of “Any NDD” in the manuscript. Based on\nlines 97-99, it is more than the sum of ADHD and autism diagnoses so it must include some\nother diagnoses as well; does this include children with cerebral palsy, intellectual disability,\nor other conditions? More detail needs to be reported about this group, including in Table 1.\nwhere the number of each diagnosis within this group needs to be reported. Also please note\nthat the term “any NDD” implies that the group includes a wide variety of different NDD\ndiagnoses, so these should be provided in the main manuscript so that the reader can understand\nthe composition of this group.\nRESPONSE: We have now provided further details about the NDDs in Supplementary\nInformation: line 10-24: “The group of children with neurodevelopmental disorders (NDD)\nwas established based on the following ICD-10 codes: Pervasive Developmental Disorders\n(DF84.0, DF84.5, DF84.8), Other Disorders of Psychological Development (DF88),\nUnspecified Disorders of Psychological Development (DF89), Hyperkinetic Disorders\n(DF90.0, DF90.8), Attention Deficit Disorder without Hyperactivity (DF98.8), Chronic Motor\nor Vocal Tics (DF95.1), and Combined Vocal and Multiple Tic Disorder (Tourette Syndrome)\n(DF95.2). Children diagnosed with one or more NDDs were included in the \"any NDD\"\ngroup…” We also have other NDD and non-NDD diagnoses, which were detailed in the\nsupporting information in our recent study (Wang, et al., JAMA Psychiatry, 2025).\nnumber of cases in this study.\n•It is unclear why the authors state “primary outcomes were any NDD, ADHD, and autism”\n(line 399), but do not include autism in most of their analyses. Only in the logistic regressions\ndoes autism appear as an outcome, but there is no data reported for sPLS, metabolic trajectories,\nor pathway enrichment analysis.\nRESPONSE: We thank the reviewer for this important observation. As shown in\nSupplementary Table 1, Any NDD, ADHD, and autism were among the most prevalent\ndiagnoses in our cohort. However, the number of autism cases was limited (only 16 cases),\nwhich posed challenges for certain analyses. Specifically, when attempting to build sPLS\nmodels for autism, the models failed to run nested five fold cross validation due to insufficient\ncase numbers. We have previously explored this and found that the limited sample size for\nautism precluded robust model performance (Wang, et al., JAMA Psychiatry, 2025).\nAs a result, sPLS models were constructed only for any NDD and ADHD outcomes. The\nmetabolites included in the pathway enrichment analysis were derived from the top 100\nmetabolites identified by these sPLS models of any NDD and ADHD, and the metabolic\ntrajectories were based on the NDD metabolomic risk scores generated from the same sPLS\nmodels. For this reason, autism was not included in the sPLS, metabolic trajectory, or pathway\nenrichment analyses.\nWe have now clarified this limitation in the revised manuscript. Line 1177-1180: “Due to the\nlimited number of autism cases (N=16), we were unable to perform proper 10-fold cross-\nvalidation with 10 repetitions and nested cross validation. Therefore, only ADHD and any\nNDD outcomes were included in the sPLS modeling”\n•“Mothers (P<0.001) and fathers (P=0.04) of children with any NDD had lower educational\nattainment”. Then was this included in all models, and if not, it should be. Similar for the role\nof sex.\nRESPONSE: Yes, they were all included in the models as covariates, as stated in line 447-455.\n“Thirteen covariates were included in the analyses using maternal week 24 metabolome: pre-\npregnancy maternal body mass index (BMI), maternal age, smoking, antibiotic use, pre-\neclampsia, maternal diabetes, and alcohol intake during pregnancy, household income level,\nmaternal and paternal education level at birth, gestational age, child sex, and birth weight…\nSex, maternal and paternal education level, household income (at birth), and WHO BMI Z-\nscore of children were included as covariates in analyses using childhood metabolomes.”\n•Line 130: “The final sPLS model of the maternal week 24 was selected based on the highest median AUC 131 value and\nlowest variance across repeated cross-validation rounds, and the predicted values from 132 the median repeat were used to\nextract the predicted metabolome-NDD risk scores.” This is repeated in line 142 among other places in the manuscript. But\nthe results demonstrate that 18-month time point had slightly higher median AUC (0.61 vs 0.60), and the difference in\nvariance between timepoints seems negligible. The overall effect from gestational week 24 metabolites and 18 months\nmetabolites is essentially identical, so why did the authors choose to only study gestational week 24 samples after the\nsPLS?\nRESPONSE: We did both 10-times repeated 10 times cross validation and also nested fivefold\ncross-validation model additionally for any NDD and ADHD outcomes due to the lack of an\nexternal validation cohort and to test the robustness of sPLS prediction. We evaluated the\nmodel accuracy based on both validation ways.\nMedicine, 2023; Wang et al., JAMA Psychiatry, 2025). As reported in supplementary\nInformation (lines 95–102), the nested CV model for week 24 yielded the most stable and\nconsistently stronger performance with: AUC of 0.60 (SD = 0.05, range 0.57–0.68) for any\nNDD, and AUC of 0.59 (SD = 0.04, range 0.54–0.65) for ADHD.\nIn contrast, the 18-month metabolome produced an AUC of only 0.55 (SD = 0.08, range 0.43–\n0.60) for any NDD, indicating greater variability and lower overall predictive performance in\nthe nested CV framework.\nTherefore, although the 18-month time point had a slightly higher median AUC in one\nvalidation method, the gestational week 24 metabolome consistently showed superior\nperformance across both validation strategies. For this reason, we selected week 24 for\ndownstream analyses.\nThe results of nested fivefold cross-validation were included in line 95–102 in Supplementary\ninformation: “For the week 24 metabolome, the nested CV model yielded a comparable\nperformance with an AUC of 0.60 (SD=0.05, 0.57-0.68) for any NDD and an AUC of 0.59\n(SD=0.04, 0.54-0.65) for ADHD. However, for any NDD, the AUC of nested CV model was\n0.56 (SD=0.02, 0.54–0.60) for week 1, 0.50 (SD=0.05, 0.44–0.54) for 6 months, 0.55 (SD=0.08,\n0.43–0.60) for 18 months…”\nThis is now also specified in the manuscript line 132-134: “In the nested five-fold CV model,\nonly the week 24 metabolome achieved comparable performance, with an AUC of 0.60\n(SD=0.05, 0.57–0.67) for any NDD and 0.59 (SD=0.04, 0.54–0.65) for ADHD (Supplementary\nResults in Supplementary information).”\n•The AUC values for the sPLS models are quite low, indicating poor classification performance\nnot much better than an AUC of 0.50 (random allocation to groups by chance).\nRESPONSE: We agree with the reviewer that the AUC values for the sPLS models are modest,\nwith the best-performing models reaching an AUC of approximately 0.6. This reflects limited\ndiscriminative ability—only slightly better than random classification (AUC = 0.5). However,\nwe emphasize that our model evaluation was rigorously conducted using both 10-times\nrepeated 10-fold cross-validation and nested five-fold cross-validation, which are designed to\nminimize overfitting and enhance generalizability.\nImportantly, this level of performance is consistent with previous studies using high-\ndimensional omics data from the COPSAC2010 cohort. For example, Rodríguez et al. (Nature\nMedicine, 2023) reported an AUC of 0.59, and similar AUCs (~0.60 ± 0.05) were reported in\nWang et al. (JAMA Psychiatry, 2025) and Stokholm et al. (Allergy, 2024). Given the\ncomplexity and multifactorial nature of neurodevelopmental disorders (NDD), it is expected\nthat metabolomic profiles alone may not fully capture the diagnostic variability.\nWe have now rephrased the relevant sections of the manuscript to acknowledge this limitation\nmore clearly. For example, line 273–277 of the revised manuscript states:\n“However, modest AUC values of 0.6 indicate that factors beyond maternal metabolic profiles\nare involved in development of NDD in the offspring. This warrants further research to validate\nand expand our findings and potentially incorporate both environmental factors and possibly\nother omics data layers in a multi-omics integrative approach”\n•Lines 190-198 and figure 5. Are the data (and stars indicating nominal p < 0.05) based on the\nsame logistic regressions as in lines 168-189 and figure 4? If so, it is quite misleading as it\ngives the impression that new statistics have been done on different data which are statistically\n10\nsignificant, but this is re-plotting the findings from tests shown in figure 4, which were\nalready reported to have pFDR > 0.05.\nRESPONSE: We thank the reviewer for this important clarification. Yes, the data shown in\nFigures 4 and 5 are derived from the same logistic regression models. In Figure 5, our intent\nwas not to present new statistical tests but to visualize the longitudinal trajectories of selected\nmetabolites that showed consistent nominal significance (p < 0.05) across early time points\n(week 24, 6 months, and 18 months), despite not passing FDR correction due to the large\nnumber of comparisons (~700 metabolites per time point).\nWe agree that separating these figures may create the impression of independent analyses. To\naddress this and avoid potential misinterpretation, we have now merged Figures 4 and 5 into a\nsingle composite figure with clearly labeled panels. This revised presentation makes it explicit\nthat the metabolite trajectories in the new panel reflect re-plotting of the same logistic\nregression results shown earlier.\nWe have also clarified this in the figure legend of the revised manuscript to emphasize that the\ndisplayed p-values reflect nominal significance only and are not adjusted for multiple\ncomparisons. This highlights biological patterns of interest without overstating statistical\ncertainty.\n•Lines 213-221. The mediation analysis results are very interesting and should perhaps be\nhighlighted more. However, the authors mention they performed several mediation analyses\nfor different independent variables, including individual immune proteins. How many\nmediation analyses did the authors perform? If conclusions are being made based on a p-value\nthreshold, the p-values should be corrected for multiple testing with FDR the same way the\nauthors have done in some other analyses. An additional point is that while the authors have\nused Sankey diagrams to illustrate the metabolic pathways that each top-100 metabolite\nbelongs to, it might be a more appropriate tool for summarizing the multiple mediation analysis\nresults.\nRESPONSE: We appreciate the reviewer’s thoughtful comment and suggestions. In this study,\nwe performed mediation analyses based on a targeted set of exposures that have previously\nbeen linked to NDD risk. Specifically, we assessed six key inflammatory protein biomarkers\nand a maternal inflammatory profile PC1 measured at gestational week 24 (as reported in Wang\net al., JAMA Psychiatry, 2025), as well as maternal BMI and unhealthy Western dietary pattern\n(as described in Horner et al., Nature Metabolism, 2025). These variables were selected a priori\nbased on previous findings and biological relevance.\nGiven the limited number of mediation analyses and the hypothesis-driven nature of the\nanalyses, we did not apply FDR correction. However, we acknowledge the importance of\ntransparency and have updated the Methods section to clarify the number of tests conducted\nand the rationale for not applying multiple testing correction in this context.\nRevised Methods (Lines 508–518):\n“We performed mediation analysis to assess whether the effect of NDD-related risk factors\nsuch as inflammatory proteins, maternal BMI on ADHD is mediated by the highlighted\nmetabolite. The mediate function from the “mediation” R package was applied with 1,000\nbootstrap simulations to estimate the average causal mediation effect (ACME), the average\ndirect effect (ADE), and the proportion of the total effect mediated. Statistical significance was\nassessed using bootstrap-derived p-values, and the mediation pathways were visualized with\nannotated effect estimates, including the proportion mediated and corresponding p-values. We\nfocused on six inflammatory proteins—VEGFA, CCL3, CD5, IL-12B, FGF-23, and MCP-1\n11\ntogether with maternal proteins profile PC1 that were previously identified being associated\nwith NDD risk in our recent study (Wang et al., JAMA Psychiatry, 2025). Given the limited\nnumber of mediation tests and the hypothesis-driven nature of the analyses, we did not apply\nFDR correction here.”\nWe also appreciate the suggestion to use Sankey diagrams to visualize the mediation results.\nWhile we only carried out meditation analyses for one highlighted metabolite---quinolinate and\nsix preselected proteins together with a maternal protein inflammatory profile PC1. The\nenrichment analysis for the all 92 inflammatory proteins have been discussed in our recent\nstudy (Wang et al., JAMA Psychiatry, 2025) in the Supporting Information part. The current\nSankey plots are used for pathway enrichment of the metabolome. We agree this is a valuable\nidea and will consider it in future work to summarize complex mediation pathways if we\ninclude more proteins in our future studies.\nDiscussion:\n•Lines 275-277: please clarify whether these effects are in vitro.\nRESPONSE: Both studies mentioned from Line 275-277 are based on in vitro studies, the first\none was based on Clonetics® hCAECs (Human Coronary Artery Endothelial Cells) derived\nfrom a Caucasian donor, and the second one was based on RAW 264.7 cells and HT-29 cells.\nThis is now specified:\nLine 308-310: “However, in vitro studies have shown that LC-SFAs can induce adverse\nmetabolic effects, such as lipotoxicity and apoptotic cell death44, as well as upregulate the\nexpression of cyclooxygenase-2 (COX-2) via activation of Toll-like receptor 4 (TLR4)”\n•Lines 277-280: please clarify whether these studies are in humans or animal models.\nRESPONSE: These are both human and cell models. We have rephrased the text from line\n310 to 311: “TLR4 involved in the maturation of immune responsiveness in infants and adults\nand cortical development in vitro, suggesting its role in neurodevelopmental processes46,47.”\n•Lines 333-337: there was significant mediation of the association with CCL3, and to a lesser\ndegree with VEGFA, CD5, IL-12B. While these are interesting, it is equally important for the\nreader to know which other proteins were tested (if any), and therefore which were associated\nwith NDDs but not mediated by quinolinate (some immune proteins may act via their effects\non maternal quinolinate and other metabolite levels, while other immune proteins may act\nindependently of that).\nRESPONSE: We appreciate the reviewer’s thoughtful comment. In this mediation analysis,\nwe focused on six inflammatory proteins—VEGFA, CCL3, CD5, IL-12B, FGF-23, and MCP-\n1—that were previously identified as key proteins associated with NDD risk in our earlier study\n(Wang et al., JAMA Psychiatry, 2025). These proteins were selected a priori based on their\nbiological relevance and statistical significance. In addition, we included a maternal\ninflammatory profile PC1, which represents the overall inflammatory proteome, in the\nmediation analysis with quinolinate. This inflammatory proteome profile PC1 also showed a\nborderline significant mediating effect (Supplementary Fig.11, Mediated p-values: 0.096),\nsupporting the broader link between maternal inflammation, metabolic dysregulation, and\nNDD risk. We did not find mediation effects for MCP-1 and FGF-23, which is now added in\nthe mediation plots in Supplementary Fig.11.\nAs the primary focus of the current manuscript is the longitudinal metabolome, we limited the\nmediation analysis to quinolinate and its relationship with these key inflammatory proteins.\ninflammatory signals and NDDs could be explained, at least in part, by metabolic pathways\ninvolving quinolinate. Analyses involving the broader set of inflammatory proteins are ongoing\nand will be addressed in a follow-up study.\nWe agree with the reviewer that future work should systematically evaluate whether additional\ninflammatory proteins influence NDD risk through metabolic intermediates or through\nindependent pathways. This is now clarified in the revised manuscript:\nLine 514-516 (Revised): “We focused on six inflammatory proteins—VEGFA, CCL3, CD5, IL-\n12B, FGF-23, and MCP-1 together with a maternal protein inflammatory profile PC1 that we\npreviously identified as being associated with NDD risk33”\nLine 371-372 (Revised): “It would be valuable in future work to explore whether additional\ninflammatory proteins act independently or via metabolic pathways such as the kynurenine\npathway.”\n•The authors have given almost 3 pages (lines 268-551) to discussing the specific metabolites\nand metabolic pathways.\nThis is useful information, but should probably be condensed somewhat.\nRESPONSE: We have now tried to condense the discussion of specific metabolites and\nmetabolic pathways to improve clarity and focus. Key findings have been retained, while\nredundant or overly detailed descriptions have been removed to streamline the narrative.\n•The one major topic that appears to be lacking in the discussion is to place the results within\nthe context of human neurodevelopmental trajectories. One of the main strengths of this study\nis the collection of metabolomics data at multiple timepoints, and the authors state multiple\ntimes that the gestational week 24 metabolome has a notably stronger association with NDD\noutcomes than the metabolome at other timepoints? Different cellular and molecular\nneurodevelopmental processes are occurring at each of these stages, and brain tissues are\ngrowing at different rates. For instance, the gestational week 24 timepoint is very shortly after\nthe lifetime peak in neurogenesis; the lifetime peak in grey matter volume occurs at 6 years old;\nand between birth and 6 years, the brain has reached its lifetime peak in cortical thickness\nwhich is already beginning to decline, synaptogenesis and myelination are occurring at rapid\nrates, which is no longer true to the same extent between 6 and 10 years old. Thus, the same\nmetabolites would affect different processes at different stages, and this should be discussed.\nAny recent reviews would suffice to guide this discussion, as well as the Brain Charts for the\nHuman Lifespan article published in Nature in 2022.\nRESPONSE: We thank the reviewer for this insightful and important comment. We fully agree\nthat placing our findings within the context of human neurodevelopmental trajectories adds\nimportant depth and relevance to the interpretation of our results. One of the key strengths of\nour study is indeed the repeated sampling of metabolomic profiles from gestation through mid-\nchildhood.\nThe following has been added in the discussion line 393-408:\n“Gestational week 24 represents a critical window in fetal brain development, occurring\nshortly after peak neurogenesis, when processes like neuronal proliferation, migration, early\nsynapse formation and cortical layering are highly active and sensitive to maternal metabolic\nand inflammatory cues. 5,25 This can lead to long-term consequences for neurodevelopmental\nprogramming.\nprocesses. Between birth and 6 years, the brain undergoes rapid synaptogenesis, myelination,\nand cortical thickening, with gray matter volume peaking around age 6. From 6 to 10 years,\nthese processes slow and give way to synaptic pruning and cortical refinement. Therefore, the\nsame metabolite may interact with different molecular or cellular pathways depending on the\ndevelopmental stage, potentially explaining the weaker associations observed at later time\npoints. Incorporating a developmental perspective provides important biological context to our\nfindings and highlights the significance of prenatal metabolic exposures—particularly during\nmid-gestation—as early-life determinants of NDD risk, as supported by recent literature\noutlining normative trajectories in brain growth and structure across developmental\nstages40,41.”\n•Strengths: the authors list “detailed neuropsychiatric assessment, which includes interviews,\nperformance-based measures, and questionnaires”, but this more so a strength of the cohort in\ngeneral rather than the current study, as these data were not used/reported much.\nRESPONSE: Actually all the NDD diagnoses, and symptom scores we used in this study were\nbased on clinical interviews, performance-based measures, and questionnaires - see line 434-\n440: “Primary outcomes were any NDD, ADHD, and autism. …Research diagnoses followed\nthe International Classification of Disorders 10th Revision of Mental and Behavioural\nDisorders (ICD-10) criteria, based on clinical information from the Kiddie Schedule for\nAffective Disorders and Schizophrenia Present and Lifetime version (K-SADS-PL) interviews\nand additional clinical information, as detailed in the COPSYCH study protocol.\nSecondary outcomes were dimensional psychopathology using K-SADS-PL. Dimensional\npsychopathology of NDDs includes autistic and ADHD symptoms component scores, ADHD\nRating Scale (ADHD-RS) and the Social Responsiveness Scale 2 (SRS-2). Parents completed\nthe ADHD-RS and the SRS-2 to assess ADHD symptoms and autistic traits”.\nCompared with other studies who only used questionnaires for NDD diagnoses, our clinical\ninterviews and additional clinical information together with questionnaires give better\naccuracy. For example, we showed the results of symptom scores in Supplementory Fig.8,\nwhich was also obtained from interviews, performance-based measures, and questionnaires.\nEven though we did not report much about detailed neuropsychiatric assessment, all the\ndiagnoses and symptom scores we used in this study were derived from those thorough\nassessments.\n•Strengths: The authors should highlight the longitudinal metabolomics, with a large number\nof common metabolites present at all timepoints, as this seems to be the biggest strength and\nnovelty of the current study design.\nRESPONSE: We have added this strength in the strength and limitation part: Line 397-399:\n“The longitudinal metabolomic profiles with a large number of common metabolites present\nat all timepoints is the biggest strength and novelty of the current study design.”\n• Conclusion, lines 376-388. The results are framed as primarily positive or strong associations,\nwhich does not seem to be supported by the results. This comment applies equally to the first\nparagraph of the Discussion, lines 230-235.\nRESPONSE: We have rephrased the conclusion and discussion part and rephrased the\nsentences and also avoid using “distinct” when describing these associations, e.g.,\nLine 411-413: “We observed metabolic trajectories during pregnancy and early life up to 18\nmonths that showed potential associations with NDD risk.”\nDiscussion\nLine 254-257: “However, none of the metabolites remained significant after FDR correction.\nThe modest AUC values from the sPLS model suggest that factors beyond maternal metabolic\nprofiles likely play a role in the development of NDDs in children.”.\nSimilarly, we have also added the following limitation in conclusion.\nLine 417-420: “However, no metabolites passed FDR correction, and modest AUCs from sPLS\nsuggest that other factors beyond maternal metabolism contribute to NDD risk. Further\nresearch is needed to identify molecular pathways for targeted prevention and risk\nidentification to improve neurodevelopment.\nReviewer #2 (Remarks to the Author):\nThis is an interesting study mapping the longitudinal metabolomic profiles of 3,212 samples\nof 581 mother-child pairs in a population-based Danish cohort to assess the risk of\nneurodevelopmental disorders (NDD) in offspring across six time points within a 10-year 36\nfollow-up, using metabolomic profiling (683 metabolites). Machine learning models identified\nmetabolite biomarkers and patterns associated with NDD, with maternal gestational week 24\nshowing the best prediction. Distinct metabolite trajectories before 18 months, involving fatty\nacids, vitamin A metabolites, lactoyl amino acid, and xenobiotics, were linked to NDD risk.\nQuinolinate was consistently associated with NDD across all timepoints and mediated the\nassociation between maternal inflammation and NDD.\nThe findings are novel and informative. Overall, the study’s design, sample size, and\nlongitudinal design are robust, and the investigation of maternal metabolomic profiles in\nrelation to neurodevelopmental disorder (NDD) risk contributes valuable insights to the field.\nAmong the most striking findings is the identification of metabolic trajectories over different\ngestational time points, suggesting that there may be specific risk windows for NDD. The\nstudy’s large sample size and longitudinal nature are strong points of the study. The study’s\nAUC values were modest, yet the authors appropriately discuss these limitations in the\nmanuscript. The fact that even a modest predictive power was observed across multiple time\npoints—especially gestational week 24—reinforces the value of continued exploration into\nmaternal and early-life metabolic profiles as potential early biomarkers for NDD risk.\nRESPONSE: Thank you for your positive feedback and for recognizing the significance and\nnovelty of our research. We are happy that you found that our study’s design, sample size, and\nlongitudinal design are robust, and the investigation of metabolomics in relation to NDD risk\ncontributes valuable insights to the field. We appreciate your thoughtful review and find that\nthe proposed revisions have significantly enhanced the quality of the manuscript.\nPossible refinements that could strengthen the manuscript:\nGeneral:\n1- While the data largely support the manuscript’s main claims, the absence of breastfeeding\ninformation is a significant gap. Maternal metabolomes at one-week postpartum could be more\ninfluential on the child if breastfeeding occurs. If this information is available, incorporating it\ninto the one-week postpartum metabolome analyses (similar to how other confounders are\nmanaged) would be valuable. Otherwise, acknowledging the lack of these data as a limitation\nwould strengthen the transparency of the study.\nwas indeed available in our dataset and was already used to adjust the association between\nmaternal NDD/ADHD risk scores and childhood outcomes in Supplementary Table 3. In\nresponse to the reviewer’s suggestion, we have now incorporated breastfeeding status (yes/no\nat week 1 postpartum) as a covariate in the one-week postpartum metabolome analyses as well.\nThe updated results, adjusted for breastfeeding, are presented in Supplementary Table 2. The\ntop 20 metabolites with the highest absolute odds ratios are visualized in Supplementary\nFigure 7. The findings remained largely consistent with our previous results, indicating that\nadjustment for breastfeeding did not substantially alter the associations.\n2- The results linking metabolic signatures to NDD risk are compelling. However, a secondary\nexploration of whether single vs. multiple NDD diagnoses affect metabolite profiles would\nstrengthen the paper’s contribution.\nRESPONSE: We thank the reviewer for this insightful suggestion. We agree that\ndistinguishing between single and multiple NDD diagnoses could provide additional depth to\nour findings. To explore this, we extracted all NDD cases and categorized them into two groups:\nchildren diagnosed with only ADHD (n = 48) and those with multiple NDD diagnoses (n = 38).\nInitially, we attempted to build an sPLS model using the gestational week 24 metabolome to\ncompare these two groups. However, the small sample size (n = 86) relative to the high\ndimensionality of the metabolomics data (683 metabolites) posed significant challenges.\nSpecifically, the “p >> n” (more predictors than observations) structure led to: 1) High risk of\noverfitting, where the model captured noise rather than true signal; 2) Low predictive\nperformance (AUC < 0.5); 3) Unstable feature selection, with results highly sensitive to data\nperturbations; and 4) Poor generalizability across resamples.\nGiven these limitations, we adopted a more robust and simpler approach using logistic\nregression to avoid overfitting. In this analysis, individuals with only ADHD were labelled as\n0, and those with multiple NDD diagnoses as 1. Several metabolites showed nominal\nassociations with increased or decreased odds of multiple NDD diagnoses.\nThe results are presented in Supplementary Figure 9. Notably, metabolites such as\nquinolinate, bilirubin (E,E) and its degradation products, C-glycosyltryptophan, and various\nsphingomyelins were positively associated with ADHD-only cases and negatively associated\nwith multiple NDD diagnoses. These preliminary findings suggest the possibility of divergent\nmetabolic profiles between children with isolated vs. comorbid neurodevelopmental conditions,\nalthough further validation in larger cohorts is needed.\nWe have added these results and their implications to the revised manuscript and\nsupplementary materials.\nMethod, Line 496-500: “As a further exploration of whether single versus multiple NDD\ndiagnoses are associated with distinct metabolomic profiles, we extracted all NDD cases and\ncategorized them into two groups: children diagnosed with only ADHD (n = 48) and those\nwith multiple NDD diagnoses (n = 38). In the logistic regression analysis, individuals with\nonly ADHD were labeled as 0, and those with multiple NDD diagnoses were labeled as 1”\nResults: Line 214-219 “To explore the impact of single versus multiple NDD diagnoses, we\nperformed logistic regression analyses associating each individual metabolite from gestational\nweek 24 and child age 6 months with the binary outcome (0 = ADHD only, 1 = multiple NDD\ndiagnoses) (Supplementary Figure 9). Notably, metabolites such as quinolinate, bilirubin\n16\n(E,E), and several sphingomyelins were positively associated with ADHD-only cases and\nnegatively associated with multiple NDD diagnoses.”\nDiscussion: Line 337-341: “As a secondary exploration, preliminary findings of metabolic\nprofile differences between ADHD-only cases and multiple NDD diagnoses suggest the\npossibility of divergent metabolic signatures between children with isolated versus comorbid\nneurodevelopmental conditions. However, these results require validation in larger cohorts.”\n3- Autism is presented as a primary outcome, yet there is no direct mention of autism-specific\nresults (either diagnosis or traits) in the manuscript or Appendix. This needs clarifying.\nRESPONSE: We mentioned autism only in the results of “Single metabolite analyses”, while\nwe did not build an sPLS model for autism due to the very limited autism cases (only 16 cases),\nwhich posed challenges for certain analyses. We show the results of logistic regression for\nautism in the Results section Line 195: “For autism, 56 metabolites were nominally significant\n(aOR range 0.01-9.96) with citrate, cortisone, and imidazole lactate being the top positively\nassociated metabolites ……”\nDiscussion regrarding single metabolite analyis line 341-346 “For autism outcome, previous\nstudies have linked maternal metabolomic profiles to autism risk, though findings vary by\nmetabolomics platforms, sample sizes, and timing of sample collection. …we observed a\nconsistent association of pseudouridine with autism at 6 months, aligning with their cord blood\nfindings and supporting its potential relevance.”\nSpecifically, when attempting to build sPLS models for autism, the models failed to run nested\nfive fold cross validation due to insufficient case numbers. We had previously explored this in\nearlier work and found that the limited sample size for autism precluded robust model\nperformance (Wang, et al., JAMA Psychiatry, 2025). Therefore, in this study we did not use\nsPLS for autism.\nAs a result, sPLS models were constructed only for any NDD and ADHD and not for autism.\nThe metabolites included in the pathway enrichment analysis were derived from the top 100\nmetabolites identified by these sPLS models of any NDD and ADHD, and the metabolic\ntrajectories were based on the “any NDD” and ADHD metabolomic risk scores derived from\nsPLS as well.\nWe have now clarified this limitation in the revised manuscript. Line 490-493: “Due to the\nlimited number of autism cases (N=16), we were unable to perform proper 10-fold cross-\nvalidation with 10 repetitions and nested cross validation. Therefore, only ADHD and any\nNDD outcomes were included in the sPLS modeling”\nIntroduction:\nThe introduction is concise, well-referenced, and provides a clear overview of the study’s\nrationale.\n1- In lines 54-56 the authors state ‘Metabolites serve as the end products of cellular processes’.\nHowever, this could be more precise. Metabolites can function as both intermediate and end\nproducts, sometimes acting as buffers through ion exchange. A rephrasing such as “Metabolites\nare both intermediate and end products of cellular processes” would capture this more\naccurately.\nand end products of cellular processes, acting as pivotal connections from the genome to the\nproteome, metabolome, and the clinical phenotype”\n2- Have the previous findings highlighted in lines 60-64 been associated with clinical\nsymptoms? It would also be helpful to note which metabolic pathways are implicated or what\nabnormal levels might imply.\nRESPONSE: We thank the reviewer for this insightful comment. Yes, several previous studies\nhave linked specific metabolites to clinical symptoms. For example, Wang et al. (2021)\nreported that levels of O-phosphoethanolamine, 4-aminohippuric acid, 5-hydroxylysine, L-\ncystine, tryptophyl-phenylalanine, and gentisic acid were significantly correlated with ADHD\nsymptom severity. Similarly, Adams et al. (2011) found that metabolic profiles in children\nwere associated with both autism diagnosis and symptom severity.\nIn the studies referenced in lines 60–64, the direction of metabolic alterations was not always\nconsistently reported, as some metabolites within the same class were found to be positively\nassociated with NDDs and others negatively. In response to the reviewer’s suggestion, we have\nnow revised the text to specify the direction of changes and the implicated pathways where\nsuch information was available in the literature. We have now revised the text:\nLine 58-68 “Studies suggest that individuals with NDD, autism or ADHD symptoms show\ndifferences in their metabolome compared to healthy individuals12,13, particularly in amino\nacids, sphingolipids, and vitamin profiles13–15. Increased plasma levels of sphingosine-1-\nphosphate and sphinganine-1-phosphate have been reported in ADHD patients15, while\nreduced levels of glutathione and tryptophan have been associated with autism and autism\nseverity14, Additionally, lower plasma levels of cortisol, triacylglycerols and phospholipids and\nelevated levels of γ-tocopherol have been observed in children aged 6 to 14 with ADHD16,17.\nAlterations in the kynurenine pathway—such as increased kynurenic acid and 3-\nhydroxyanthranilic acid—have also been associated with ADHD-like behaviors in mouse\nmodels of ADHD and ASD18. In children with ASD, lower levels of linolenic acid and long-\nchain polyunsaturated fatty acids, higher levels of perfluorodecanoic acid, and alterations of\na range of triglycerides have been reported19,20”\n3- In line 74, it needs to be clarified if the authors mean neuronal apoptosis.\nRESPONSE: We have specified that in Line 77 “Key neurodevelopmental processes such as\nneuronal apoptosis, myelination, synaptogenesis, neurogenesis and proliferation occur during\nthe second and third trimesters”\nMethods\nOverall, the study’s design and sensitivity analyses are robust. The following points may\nfurther clarify and enhance reproducibility:\n1- If the acronym PRS from Table 1 stands for Polygenic Risk Scores, the authors should\ninclude an explanation of how these scores were calculated, either in the main text or the\nAppendix.\nRESPONSE: We have now added a description regarding how we calculated the PRS score\nin line 54-59 in Supplementary information: “The software package Polygenic risk score\ncontinuous shrinkage (CS) was used for PRS construction through regularizing SNP effects\nusing a shrinkage prior7. PRS-CS uses a linkage disequilibrium (LD) reference panel\nconstructed via European samples from the 1000 genomes project. We used the automatic\n18\nestimation of the polygenicity parameter (phi). Subsequent to SNP effect adjustment with PRS-\nCS, we used the software PLINK2 (REF to plink2) to aggregate SNP effects into PRSs for each\nindividual. The scores were scaled to mean 0 and SD 1 for each phenotype.”\n2- Ensure the Methods section explicitly clarifies that “biological sex” was defined by the sex\nassigned at birth.\nRESPONSE: We have added a classification in Line 430. “The biological sex was defined by\nthe sex assigned at birth.”\nResults:\nThe results are well-organized and effectively present high-dimensional data. The authors have\ndone an excellent job synthesizing complex findings.\nRESPONSE: Thank you!\n1- In Table 1 (and elsewhere), it is preferable to use “ethnicity” rather than “race,” and to avoid\nterms such as “Caucasian.” Instead, please report the distribution of smaller ethnic groups\nexplicitly whenever possible. If data limitations preclude this, consider using “Other ethnic\ncategories.”\nRESPONSE: We thank the reviewer for this important suggestion. In response, we have\nreplaced the term “race” with “ethnicity” in Table 1 and throughout the manuscript for\nconsistency and appropriateness. Unfortunately, detailed information on smaller ethnic groups\nwas not collected at the time of participant recruitment. As a result, we are unable to report\nthese distributions explicitly and have instead used the term “Other ethnic categories” where\napplicable. This limitation has been acknowledged in the revised manuscript. Further, we have\nreplaced “Caucasian” with “White”, which also aligns with our previous study (Wang, et\nal., JAMA Psychiatry, 2025).\n2- Figure 1b: The term “Energy” can be confusing. If these are energy metabolism nucleotides\n(or related compounds), consider specifying them more precisely.\nRESPONSE: We appreciate the reviewer’s comment regarding the clarity of the term “Energy”\nin Figure 1b. The label “Energy” was used as the original Super Pathway designation provided\nby the Metabolon platform. However, we agree that this term may be too broad or ambiguous\nfor readers.\nThe eight metabolites in this group include: alpha-ketoglutarate, succinate, fumarate, malate,\nphosphate, citrate, aconitate, and succinylcarnitine (C4-DC). Among these, phosphate is part\nof the oxidative phosphorylation pathway, while the remaining metabolites are components of\nthe TCA cycle.\nTo improve clarity, we have updated the figure and legend and replaced \"Energy\" with \"TCA\nCycle and Oxidative Phosphorylation\", which more accurately reflects the biochemical context\nof these metabolites.\n3- Figure 2: Readers may expect to see ROC curves to illustrate the AUC values.\nRESPONSE: In line with our previous studies (Rodríguez et al., Nature Medicine, 2023; Wang\net al., JAMA Psychiatry, 2025; Brustad et al., Nature Communications, 2025), Figures 2a–f\ninclude box plots showing the range of AUC values.\nHowever, we have now also generated ROC curves for both ADHD and any NDD diagnosis\nmetabolome sPLS models across all timepoints, which is presented in Figure 2h and\nSupplementary Fig.2h. ROC curves were constructed for sPLS models at each timepoint, with\n19\nthe best-performing resample selected based on AUC calculated using the pROC package.\nAUC values are displayed in the legend for comparison across timepoints, alongside the range\nof AUCs from repeated 10×10-fold cross-validation.\n4- Extended data Figure 4-b: Be sure to reference Extended Data Figure 4-b appropriately\nwithin the text.\nRESPONSE: This has been corrected as follows:\nResults: Line 145-148: “Additionally, we examined the association between quinolinate levels\nat gestational week 24 and the same potential risk factors mentioned above. Consistently, hs-\nCRP levels, Western dietary pattern, pre-pregnancy BMI, and maternal inflammatory PC1\nwere all positively associated with quinolinate levels (Supplementary Fig.4b)”.\nDiscussion: line 361-362: “In our study, quinolinate was found to be associated with maternal\ninflammation, Western dietary pattern, and pre-pregnancy BMI.”.\n5- A figure illustrating the findings discussed in lines 150–153 would be helpful, given that\nthis is a key result in the study. Maybe, the authors could add a non-NDD group line to Figure\n3 for additional clarity.\nRESPONSE: We appreciate the reviewer’s suggestion to clarify the findings presented in lines\n150–153. These results are actually already illustrated in Figure 3, which shows the difference\nin mean NDD or ADHD risk scores between diagnosed and non-diagnosed children at each\ntime point. Specifically, we generated a continuous predicted risk score for each child based\non the maternal metabolome at gestational week 24. Each child—regardless of diagnosis\nstatus—was assigned a risk score, where higher values indicate higher predicted risk of NDD\nor ADHD.\nTo compare risk across groups, we calculated the mean risk score separately for diagnosed\n(NDD/ADHD = Yes) and non-diagnosed (NDD/ADHD = No) individuals. The Y-axis in\nFigure 3 represents the difference in mean risk scores between these two groups: Statistical\nsignificance was assessed using the Wilcoxon rank-sum test with 95% confidence intervals.\nMean Difference (Y axis) =Mean Risk Score (NDD-Yes)−Mean Risk Score (NDD-No)\nSince every participant has a risk score, there is no separate “non-NDD risk score” to plot\nindependently—the non-diagnosed individuals are already included in the comparison. To\navoid confusion, we have revised the group labels and figure legend to better explain this point.\nAdditionally, while we previously considered plotting raw risk scores by groups, data\nnormalization across time points limits comparability.\nbetween two groups (NDD: yes/no) at each time point, which more accurately reflects the\npredictive value of the metabolome while maintaining interpretability.\nDiscussion:\nThe discussion is well-structured, and the authors effectively contextualize their findings\nwithin the broader literature.\nRESPONSE: We thank the reviewer for the positive feedback and are pleased to hear that the\n“Discussion” section effectively contextualizes our findings within the broader literature.\n1- Paragraph 5: perhaps the finding of western diet in mothers to be associated with NDD risk\ncan strengthen their discussion of LC-SFAs pathway enrichment analysis finding.\nRESPONSE: We have now added the following in the discussion part from line 315-317: “Our\nfinding that maternal Western dietary patterns during pregnancy are associated with increased\nNDD risk scores, along with the previously reported association with ADHD risk,32 further\nstrengthens the relevance of the LC-SFA pathway enrichment results.”\nOther potential improvements in grammar or format:\nAbstract:\nNDD needs defining\nRESPONSE: We have now defined NDD in Abstract “Neurodevelopmental disorders\n(NDD) are characterized by impairments in brain development that affect motor, cognitive\nsystems, and emotional regulation.”\nline 36: change ‘metabolite biomarkers’ for ‘metabolic biomarkers’\nRESPONSE:We have replaced “metabolite biomarkers” to ‘metabolic biomarkers’\nline 37: change ‘with maternal gestational week 24 showing the best prediction’ for ‘maternal\ngestational week 24 metabolic profile showing the best prediction’\nRESPONSE: We have changed ‘with maternal gestational week 24 showing the best\nprediction’ for ‘maternal gestational week 24 metabolic profile showing the best prediction’\nline 38: change ‘before 18 months’ to ‘before/prior to 18 months of age’\nRESPONSE:We have replaced ‘before 18 months’ to ‘before/prior to 18 months of age’\nIntroduction:\nline 45: change ‘impairments in brain development that affect motor, cognitive systems, as well\nas emotional regulation’ for ‘impairments in brain development that affect motor and cognitive\nsystems’\nRESPONSE: We have replaced ‘impairments in brain development that affect motor,\ncognitive systems, as well as emotional regulation’ with ‘impairments in brain development\nthat affect motor and cognitive systems’\nlines 58-60: change ‘Studies suggest that individuals with NDD show differences in their\nmetabolome compared to healthy individuals, where amino acids, sphingolipids, and vitamins\nhave been highlighted’ to ‘Studies suggest that individuals with NDD show differences in their\nmetabolome compared to healthy individuals, particularly in amino acid, sphingolipid, and\nvitamin profiles.’\nin their metabolome compared to healthy individuals, where amino acids, sphingolipids, and\nvitamins have been highlighted’ to ‘Studies suggest that individuals with NDD show differences\nin their metabolome compared to healthy individuals, particularly in amino acid, sphingolipid,\nand vitamin profiles.’\nResults:\nTable 1: define PRS abbreviation in the footnote, and in the manuscript in general.\nline 105: Define sPLS abbreviation in the results section, since it precedes the Methods section.\nRESPONSE: We have added a footnote in Table 1, “PRS: Polygenetic risk scores”.\nThe sPLS abbreviation is now also been defined in line 111 “Sparse Partial Least Squares”\nIn all Figures in general it would improve clarity if the same colour coding was applied to\nmetabolite groups throughout the manuscript.\nRESPONSE: We appreciate the reviewer’s suggestion. We agree that consistent color coding\nacross figures would improve clarity. We have now applied consistent color coding for each\nsuper pathway group across Figure 1, Figure 2g, Figure 4, Supplementary Figure 3, and\nSupplementary Figure 5 to Supplementary Figure 7, Supplementary Figure 9, All metabolite\ngroups follow the same super pathway color scheme as defined in Figure 1b.\nFigure 3: rename y axis label and legend to enhance clarity.\nRESPONSE: We have renamed the Y-axis label to “Any NDDs/ADHD risk score difference\nbetween groups” to enhance clarity. We have also updated the figure legend to clearly explain\nhow the Y-axis values were calculated, specifying that the difference reflects the mean risk\nscore for diagnosed individuals minus the mean risk score for non-diagnosed individuals at\neach time point\nDiscussion:\nLine 303: I would change the adverb ‘Further’ for a different one that better suits the paragraph\nmeaning.\nRESPONSE: We replaced ‘Further’ with “Notably”\nRelated manuscript figure, if this figure is intended as a graphical abstract I would consider\nthe following:\nReference exclusively to the metabolites participating in the kynurenine pathway in the figure\nmight be misleading given that multiple pathways have been referred to in the manuscript.\nSome metabolites shown in the figure aren’t mentioned in the text; consider aligning them\nmore closely with the manuscript’s content.\nI would avoid the use of gender-stereotypes in the mother figure, likewise I would avoid the\nbreastfeeding figure unless all participants were breastfed, here too I would avoid the use of\nlong hair. I would also ensure consistency in the way the offspring are depicted, in terms of\ndesign, and I would change the offspring figures to something more abstract in terms of\nphysical appearance to increase the representativeness of the illustration.\nRESPONSE: We have revised the graphical abstract by removing specific references to\nindividual metabolites and metabolic pathways to avoid misleading emphasis on the\nkynurenine pathway. Instead, we now use a general symbol to represent metabolomics. We\n22\nhave also removed gender-stereotypical features from the maternal figure, including long hair\nand breastfeeding imagery, and ensured consistent and more abstract representation of\noffspring to improve inclusivity and clarity.\nReviewer #2 (Remarks on code availability):\nCode:\nCode for the enrichment analyses was included and appears functional. Although I was unable\nto run it myself, the output provided matches that of the publication, despite the figures being\nsomewhat difficult to read in the R Markdown format. If the authors consider it relevant,\nsharing the complete set of analyses could further enhance both reproducibility and educational\nvalue.\nRESPONSE: We appreciate the reviewer’s feedback and are glad to hear the enrichment\nanalysis code appears functional and consistent with the results presented in the manuscript.\nWe agree that the figures may be easier to view when generated directly in the R console, where\ninteractive features (such as zooming) are available. While the R Markdown format was\nintended to facilitate reproducibility.\nWe have now also provided a standalone script version of the code to make it easier to run and\nvisualize the outputs. This should enhance both usability and reproducibility for future users.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks on code availability):\nCode for the enrichment analyses was included and appears functional. Although I was unable\nto run it myself, the output provided matches that of the publication, despite the figures being\nsomewhat difficult to read in the R Markdown format. If the authors consider it relevant,\nsharing the complete set of analyses could further enhance both reproducibility and educational\nvalue.\nare unable to provide the raw metabolomics data. However, for enrichment analysis, we have\nincluded the list of the metabolites selected by the sPLS model to enable reproducibility. The\nfull analysis code has been made publicly available on GitHub, excluding the raw data.\nWe acknowledge that figures may be more readable when generated directly in the R console,\nwhere users can take advantage of interactive features such as zooming. While the R Markdown\nformat was designed to facilitate step-by-step reproducibility, we have now also provided a\nstandalone R script version of the enrichment analysis code to improve usability and\nvisualization. This addition should enhance both educational value and reproducibility for\nfuture users.\nReviewer #4 (Remarks to the Author):\nThe authors conducted the longitudinal metabolomic profiling from maternal-child cohort to\nassess the potential risk factors/metabolites associated with NDD risk. Overall, the study is\nquite novel and well-written. However, several methodological and reporting aspects require\nfurther clarification or justification:\nRESPONSE: Thank you for your positive feedback. We are pleased that you found our work\nnovel and well-written. We greatly appreciate your time and effort in reviewing our manuscript.\n1. In the introduction, the rationale for sampling at the selected developmental timepoints\nshould be more clearly justified, is it biologically relevant?(e.g., critical windows of\nneurodevelopment)\nRESPONSE: The COPSAC2010 cohort is a mother-child birth cohort originally designed to\ninvestigate the early-life origins of asthma, allergy, and other chronic inflammatory diseases.\nAsthma is the primary outcome, whereas NDD is an important secondary outcome.\nBiological samples were collected from gestational week 24 through age 10 as part of the\ncohort’s original design, with a particular focus on early life to capture critical developmental\nwindows. This design is based on the hypothesis that both asthma and neurodevelopmental\noutcomes have roots in early life. As such, more frequent sampling was performed during the\nprenatal and early postnatal periods to align with key stages of immune, metabolic, and\nneurodevelopmental maturation. We have now clarified this rationale in the revised\nIntroduction.\nWe added the rationale in line 87-89: “Plasma samples were collected from pregnancy through\nage 10 as part of the cohort’s original design, with a particular focus on early life to capture\ncritical developmental windows.”\n2. In result section- “Association between metabolome NDD risk scores and potential risk\nfactors”. Correspondingly. maternal inflammation factors were not mentioned and this type\nof association analysis was not mentioned in the method.\nRESPONSE: We have now added the following description in the Method section:\nline 470-475: “Plasma proteins at week 24 of pregnancy were measured using the Olink Target\n96 inflammation panels (Olink). Details on sample preparation, quality control, and hs-CRP\nmeasurement are available in our previous study33. A PCA was performed on the 92 proteins,\nwith PC1 explaining 30.7% of the variance. Inflammatory protein PC1 and key proteins\n24\nassociated with NDD risk, identified in our previous study33—VEGFA, CD5, IL-12B, CCL3,\nFGF-23, and MCP-1—were included in the mediation analysis in this study.”\n3. The identification of xenobiotic metabolites warrants further discussion. These markers\nlikely reflect environmental exposures during pregnancy and may provide insight into the\nexposome's role in NDD risk.\nRESPONSE: We have now added this in discussion:\nLine 381-384: “Xenobiotics such as erythritol, which reflect environmental exposures during\npregnancy, were associated with NDD risk in both single-metabolite and sPLS models,\nsuggesting that the prenatal exposome may play a role in neurodevelopmental outcomes and\nwarrants further investigation.”\n4. Psychopathological outcomes of NDD:\n1). Please clarify the comparison group used for binary outcomes. Were children without NDD\nused as controls, or were only children with no psychiatric diagnoses included?\nRESPONSE: We have now clarified that in line 479-480: “Children without ADHD, autism\nor any NDD were used as controls, depending on the specific outcome being modeled.”\n2). In each analysis, were ADHD and ASD examined separately, or was the composite \"any NDD\"\noutcome used as the primary endpoint? This should be made clearer in the methods section.\nRESPONSE: We have clarified this in line 481-482: “ADHD, autism, and any NDD outcomes\nwere examined separately. The number of cases for each diagnosis is shown in Figure 1b and\nSupplementary Table 1.” The number of cases for each diagnosis is reported in Figure 1b and\nSupplementary Table 1. The composite “any NDD” outcome includes ADHD, autism, and also\nother neurodevelopmental disorders, as described in the Supplementary Methods under\n\"Psychopathological outcomes of NDD.\" Any NDD and ADHD are the primary outcomes used\nin both single metabolite analysis and sPLS models, while autism was only included in single\nmetabolite analysis due to the limited number of cases.\n3). Please clarify how outcomes are defined in the models? Did authors account for skewness\nor zero-inflation for continuous scores?\nRESPONSE: We thank the reviewer for this question. In all of our primary analyses in the\nmain article, including the logistic regression and sPLS models, we used binary outcomes,\nwhere a clinical diagnosis of NDD (or ADHD/autism) was coded as “1” and absence of\ndiagnosis as “0”.\nContinuous outcome scores were only used in Supplementary Figure 8 using simple linear\nregression, which includes SRS-2, ADHD-RS, and ADHD/autistic symptom component scores.\nThese scores were derived using the same validated methods as in our recent studies (Wang,\net al., JAMA psychiatry, 2025). In our dataset, such as the SRS-2 (one zero) and ADHD-RS\nscores (20 zeros) there were very few zero values, and we did not observe issues with zero\ninflation. While some degree of skewness might be expected, these scores were not in other\nmodels requiring strict distributional assumptions.\n5. Covariates:\n25\n1). Please describe how missing values on covariates were handled.\nRESPONSE: We thank the reviewer for this question. Participants with missing values in any\nof the covariates used in a given model were excluded from that specific analysis. However, as\nshown in Table 1, the overall level of missingness across covariates was very low and we did\nnot impute missing values.\nTo support transparency, we also provided the unadjusted associations (odds ratios, confidence\nintervals, and p-values) prior to covariate adjustment in supplementory Table 2.\n2). Parity was not included among the covariates, although it is an established factor associated\nwith neurodevelopment and metabolome. Was this variable considered?\nRESPONSE: We thank the reviewer for this important observation. We did consider parity\nduring the design phase of this study. However, based on prior internal discussions and\nconsistency with our recent publications, we chose not to include parity as a covariate in the\nprimary analyses since it is not associated with NDD outcomes in our cohort.To address this\nconcern, we have now added parity in the baseline Table 1 in the manuscript, and the results\nshow that parity is not significantly associated with NDD outcomes with a p values of 0.2.\nWe have also performed additional analyses including parity as a covariate for gestational week\n24 metabolome profile. The results showed that parity did not alter the associations of interest\n(Supplementory Table 2). These findings are now noted in the revised manuscript.\n3). In week 24 maternal metabolome model, birth weight was included as a covariate. Since\nbirth weight is likely on the causal pathway, it may function more as a mediator than a\nconfounder. The rationale for its inclusion should be justified\nRESPONSE: We thank the reviewer for this insightful comment. We had a long discussion\nregarding whether we should take birth weight as covariates. We acknowledge that birth weight\nmay lie on the causal pathway between maternal metabolomic profiles and child NDD\noutcomes and could therefore act as a mediator rather than a confounder. Our rationale for\nincluding birth weight as a covariate in the week 24 maternal metabolome model was to adjust\nfor its potential influence on the outcome and metabolome and to assess metabolite associations\nthat are independent of birth weight. Further, birth weight was also included as covariate in our\nprevious NDD studies in this cohort as well (Horner, et al., Nature Metabolism, 2025, Wang,\net al., JAMA Psych, 2025).\nHowever, we recognize that adjusting for a potential mediator may lead to overadjustment bias\nand attenuate the total effect of maternal metabolites. To address this concern, we have\nconducted sensitivity analyses excluding birth weight as a covariate in the week 24 metabolome\nanalyses, and the main findings remained consistent (results available in Supplementary\nTable 2). This suggests that the associations observed are robust and not solely driven by\npathways involving birth weight.\nWe agree that mediation analyses could help clarify the role of birth weight in the causal\npathway and appreciate the reviewer’s suggestion. To explore this, we conducted mediation\nanalyses using birth weight as a potential mediator for maternal inflammatory PC1 (previously\nassociated with NDD), as well as for quinolinate, maternal BMI, and maternal dietary pattern,\nwhich showed associations with ADHD. In all cases, birth weight did not exhibit a significant\nmediating effect.\n26\n4). In the child metabolome models, why was maternal substance use during pregnancy not\nincluded? This could influence both metabolite profiles and neurodevelopment.\nRESPONSE: We thank the reviewer for this important point. In the child metabolome models\n(i.e., NDD metabolome risk scores at different time points derived from sPLS models), we\nhave now adjusted for maternal exposures, where data were available in our cohort, including\nparticipation in the Vitamin D and fish oil interventions during pregnancy, maternal smoking,\nmaternal antibiotic use, and maternal alcohol use during pregnancy (Supplementary Table 3).\nIn addition, for the single-metabolite models using logistic regression, we applied the same\nadjustments for maternal exposures for child age 6 months metabolome (Supplementary\nTable 2). The OR, 95% CI, and p-values remained largely unchanged after these adjustments,\nsuggesting that the observed associations are robust to potential confounding by maternal\nexposures.\nLine 170-172: “We further adjusted for maternal exposures including maternal smoking,\nmaternal antibiotic use and alcohol use during pregnancy, which did not substantially alter\nthe results (Supplementary Table 3). ”\n6. Statistics:\n1). Why was sparse Partial Least Squares (sPLS) selected over other interpretable or robust\nmachine learning approaches, such as penalized regression (e.g., glmnet) or random forest?\nPlease provide justification for this choice.\nRESPONSE: We selected sparse Partial Least Squares (sPLS) for our analysis due to its\nsuitability for high-dimensional, multicollinear omics data, where the number of variables (683\nmetabolites) exceeds the number of participants (581). sPLS is specifically designed to perform\nboth variable selection and dimension reduction simultaneously, which is essential in our\ncontext to avoid overfitting and improve model interpretability.\nmodel latent components that capture the covariance structure between predictors and\noutcomes, which is beneficial in uncovering biologically meaningful patterns. While random\nforests offer robust prediction and handle nonlinearity, they tend to be less interpretable and\nless efficient at identifying a parsimonious set of biomarkers in high-dimensional settings.\nTherefore, sPLS provided a balance between interpretability, variable selection, and relevance\nto the multivariate structure inherent in metabolomics data, making it a well-suited method for\nour biomarker discovery goals in this longitudinal study.\nWe also applied sPLS model in a range of our previous studies, e.g, Rodríguez, et al, Nature\nMedicine (https://doi.org/10.1038/s41591-023-02685-x), Horner, et al, Nature Metabolism,\n2025 (https://doi.org/10.1038/s42255-025-01230-z), Wang, et al., JAMA Psychiatry, 2025,\nwhere we have experiences about how to do proper validation to avoid overfitting.\n2). With only 86 NDD and 63 ADHD cases out of 581 participants, the class imbalance could\naffect model performance. Consider reporting stratified metrics and clarify whether class\nbalancing or stratified cross-validation was applied.\nRESPONSE: We did sensitivity analysis to address potential overfitting from traditional\ntenfold cross-validation for sPLS. This is clarified in Supplementary information\nline 84-90: “To address potential overfitting from traditional tenfold cross-validation for sPLS,\nwe additionally conducted a sensitivity analysis using a nested five-fold cross-validation\napproach, as outlined in our previous stu9. The dataset was divided into five outer folds, with\n20% reserved as an independent test set. For each outer fold, the test set was exclusively used\nfor model assessment, while the training set underwent repeated tenfold cross-validation for\nmodel selection. The best model from the inner loop was then applied to the entire outer\ntraining set and used for predictions on the test set.”.\nThis is also the exact same approach we applied in our previous study (Rodríguez, et al, Nature\nMedicine, 2023).\n3). The authors used the logistic and linear regression for each single metabolite and outcomes.\nThis repeated-measures structure introduces within-subject correlation that violates the\nindependence assumption of standard regression models. Any consideration on mixed effect\nmodel?\nRESPONSE: We thank the reviewer for this important point. We acknowledge that the\nrepeated-measures structure introduces within-subject correlation, which can violate the\nindependence assumption of standard regression models. Our primary goal in the single-\nmetabolite analyses was to perform an initial high-dimensional screening to identify candidate\nmetabolites associated with neurodevelopmental outcomes. Given the scale of the data—nearly\n700 metabolites across 581 participants with up to six different time points—logistic and linear\nregressions provided a computationally efficient and interpretable approach, and each\nmetabolite at each time point was separately associated with NDD outcome first in single\nmetabolite analysis, then we applied FDR correction to control for multiple testing and reduce\nthe likelihood of false positives. While mixed-effects models offer advantages in modeling\nwithin-subject correlation, they present substantial computational and interpretability\nchallenges in high-dimensional settings. Moreover, our main multivariate analyses (e.g., sPLS)\nwere specifically designed to capture temporal and multivariate structures more holistically.\nof this study. Nevertheless, we fully agree that future validation work focusing on a smaller set\nof prioritized metabolites would benefit from mixed-effects modeling to rigorously assess\nlongitudinal associations.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\n29"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Previously, there were three main concerns, which were formatting of the figures,\ndefinition of the any-NDD outcome group, and the language surrounding the modest\np-values and effect sizes. The authors have satisfactorily addressed all of these\nconcerns, and the manuscript is now much clearer and the language gives an accurate\ndescription of the findings. The authors have given a clear, reasonable point-by-point\nreply to all of my comments.\nReviewer #1 (Remarks on code availability):\nThank you for uploading all the R code to Github. However, Github pages can be\nedited and removed and therefore do not represent permanent repositories. One can\ncreate a permanent repository of the final version of all codes, by saving a static\nversion of a Github page at any given time using Zenodo or an equivalent. This will\ncreate a DOI as well, which links to the permanent version of the Github page, and\nthis DOI can be given in the data availability section. Instructions are available here:\nhttps://docs.github.com/en/repositories/archiving-a-github-repository/referencing-\nand-citing-content.", "reviewer_2": "Our reviews have been addressed satisfactorily", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The comments were well addressed by authors and I have no further comment.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "The authors have done an excellent job addressing all my comments and concerns. I have\nno further suggestions, and I believe this is an important study that should be accepted for\npublication."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The comments have been addressed satisfactorily."}, "author_response": ""}]

Decision letter