Neurodevelopment and Metabolism in the Maternal-Placental-Fetal Unit

Key Points Question Are metabolomes in the maternal-placental-fetal unit associated with each other and subsequent neurodevelopmental outcomes? Findings In this cohort study of 100 maternal serum samples, 141 placental samples, and 124 umbilical cord serum samples from 152 pregnancies of younger siblings of children with autism spectrum disorder (ASD), multivariate analysis revealed that the placental and cord serum metabolomes were significantly correlated. Placental and cord serum latent variates were significantly associated with reduced risk of nontypical development but not ASD. Meaning These findings in a cohort with high familial ASD risk, placental and umbilical cord metabolism at birth was associated with neurodevelopmental outcomes.


Introduction
4][5] In a targeted analysis of maternal plasma methylation cycle and transsulfuration pathway metabolites, prenatal metabolic profiles differed significantly between participants with low and high familial risk of having a subsequent child with autism spectrum disorder (ASD). 4In a case-control study, untargeted midpregnancy serum metabolomics identified pathways associated with ASD, including metabolism of glycosphingolipids, phosphatidylinositol phosphate, and steroid hormones. 5These disturbances may reflect altered nutrient availability during sensitive developmental periods that support rapid brain growth during the third trimester. 6st as changes in maternal circulating metabolites are associated with fetal development, so too are changes in placental metabolism.All fetal nutrition must pass through the placenta, and alterations in placental function are likewise associated with fetal neurodevelopmental outcomes. 7,8bilical cord blood has also been used to investigate fetal metabolism at birth. 9However, we are not aware of any analysis of the associations among maternal, placental, and fetal metabolism.
Therefore, we aimed to investigate the associations among metabolism in maternal serum, placental tissue, and umbilical cord serum using quantitative metabolomics and whether these measures were associated with neurodevelopmental outcomes in a prospective pregnancy cohort with high familial risk of ASD.

Methods
The University of California, Davis, institutional review board and the California Committee for the Protection of Human Subjects approved this study and the Markers of Autism Risk in Babies, Learning Early Signs (MARBLES) study protocols.All participants provided written informed consent for collection of data and specimens.We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.Data analysis was conducted from March 1, 2023, to March 15, 2024.

Study Participants, Sample Preparation, and Quantitative Nuclear Magnetic Resonance Metabolomics Analysis
Samples analyzed in this study were collected through the MARBLES study, a prospective birth cohort following younger siblings of children with ASD. 10 We selected samples from MARBLES pregnancies with at least 2 of the following samples available: maternal third trimester serum, placenta collected at delivery, or umbilical cord serum collected at delivery.Some mothers were enrolled in MARBLES through multiple pregnancies, so we limited our analysis to the first available pregnancy that met these criteria to ensure the pregnancies studied were independent within each analysis.A total of 100 maternal third-trimester serum samples, 141 placental samples, and 124 cord serum samples were included in this analysis (eFigure 1 in Supplement 1).There were 89 motherinfant dyads for whom a maternal serum sample and placental sample were available, 72 motherinfant dyads for whom a maternal serum sample and a cord blood sample were available, and 113 mother infant-dyads for whom a placental sample and a cord serum sample were available.However, only 111 mother-infant dyads were included in the placenta-cord serum analysis, since 2 infants were excluded for having an older sibling included in the analysis.Of these, there were 61 mother-infant dyads with all sample types available.
At approximately age 36 months, neurodevelopment was evaluated by MARBLES clinicians using the Mullen's Scales of Early Learning and ASD Diagnostic Observation Schedule (ADOS). 11,12urodevelopment was classified as described elsewhere 13  diagnosed when criteria for ASD or non-TD were not met.
Whole blood was collected during the third trimester and umbilical cord blood was collected at delivery.After collection, blood was centrifuged, and the resulting serum was collected and stored at −80 °C until preparation for metabolomics analysis.Samples were prepared for metabolomics analysis by thawing on ice and subsequently subjected to ultrafiltration centrifugation to remove protein using Amicon Ultra-0.5 mL 3000 MW centrifugal filters (Millipore), as described elsewhere. 14r maternal serum, filtrate volume was adjusted to 207 μL with ultrapure water if an insufficient sample was collected, and an internal standard, containing 5.0 mmol/L 3-(trimethylsilyl)-1propanesulfonic acid-d6 (DSS-d6), 0.2% sodium azide, and 99.8% deuterium oxide (Chenomx) was added.For umbilical cord serum, filtrate volumes were measured, and filtrates were frozen, dried using a miVac concentrator system (Genevac), and reconstituted using 240 μL of 10 mmol/L potassium phosphate buffer prepared in deuterium oxide to improve signal-to-noise.The pH of each sample was adjusted to 6.8 (±0.1) and 180 μL was loaded into 3-mm nuclear magnetic resonance tubes (Bruker) and stored at 4 °C until spectral acquisition.
Placentas were processed at delivery, with full-thickness sections of tissue collected and stored at −80 °C.For metabolomics analysis, samples were partially thawed and 6-mm biopsy punches were collected for metabolic analyses, as described elsewhere. 14Placenta tissue was cryoground using liquid nitrogen and approximately 80 mg of tissue was weighed and extracted using a 2-step chloroform methanol water extraction. 15The upper layer was collected, measured, frozen, dried using a miVac concentrator system, and subsequently stored at −80 °C until preparation for proton nuclear magnetic resonance spectroscopy.Dried samples were reconstituted in 10 mmol/L potassium phosphate buffer, the pH was adjusted to 6.8 (±0.1), and an internal standard was added as described.
An Avance 600-MHz spectrometer (Bruker) equipped with a SampleJet was used to acquire proton nuclear magnetic resonance spectra as previously described. 16Spectra were manually phaseand baseline-corrected, and metabolite concentrations were quantified using Chenomx software version 8.1 (Chenomx).This process relies on a library of spectral signatures for small molecules and the internal standard, DSS-d6.The library allows for identification of metabolites through matching the spectral signature, while the internal standard allows for determination of the concentration of each metabolite within the spectrum. 17This method has been shown to be both accurate and reproducible. 18,19Metabolites that might have been introduced during sample preparation or that were identified in less than 80% of samples were excluded from statistical analysis, as described elsewhere. 14

Covariate Selection
We included a minimal model (model 1) and a fully adjusted model (model 2) for each analysis (eTable 1 in Supplement 1).Model 1 covariates were selected a priori as factors related to sample collection and storage, including birth year, gestational age at sample collection, and fasted time at sample collection (for maternal serum samples).We used a directed acyclic graph to identify sufficient covariate adjustment sets for our analysis of the associations among the maternal serum, placental, and cord serum metabolomes (eFigure 2 in Supplement 1) using the R package dagitty. 20riables used as proxies for social and economic inequities, such as maternal race and ethnicity, education, and home ownership, were collected by self-report shortly after enrollment using a standardized questionnaire.Race and ethnicity were categorized as White or historically marginalized group, including Asian; Black or African American; Hispanic, non-White; Hispanic, White; Pacific Islander; and multiracial.We also used directed acyclic graphs to identify covariate adjustment sets to evaluate the associations between metabolism and neurodevelopment (eFigure 3 in Supplement 1).In these models, we collapsed maternal metabolic condition into a dichotomous variable, defined as prepregnancy body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) less than 25 and no metabolic conditions (reference group) or prepregnancy BMI 25 or greater or any diabetes or hypertensive disorder, to reduce the number of small cells.

Statistical Analysis
Correlations between metabolites within each tissue (maternal serum, placenta, and cord serum) were evaluated with Spearman correlations, and P values were corrected for false discovery rate (FDR) using the Benjamini-Hochberg procedure (eFigures 4-6 in Supplement 1). 21Bipartite Spearman correlations were used to evaluate the association between each pair of metabolomes and P values were FDR corrected (eFigures 7-9 in Supplement 1).To adjust for covariates, we used rank-based linear regression.We report FDR q values and considered 2-sided q < .10significant.
When metabolites in 1 metabolome (matrix X) were significantly associated with metabolites in a second metabolome (matrix Y), we used partial least squares (PLS) for multivariate analysis and dimension reduction after partialling out the associations from covariates in model 2. When the number of mother-infant dyads was less than the sum of the metabolites in the 2 metabolomes, sparse PLS was used and tuned using the R package mixOmics. 22Since maternal serum, placenta, and cord serum can affect each other, we used canonical PLS, which seeks to model bidirectional associations between X and Y. 23 The number of latent variate pairs retained in the model was selected using the coefficient of prediction (Q 2 ) in leave-1-out cross-validation.We then determined if the covariance between the latent variate pairs was greater than by chance alone using permutation testing with 9999 permutations.When the models were better than chance, the results were visualized using relevance networks showing bipartite associations between the 2 metabolomes. 23We used multinomial logistic regression to evaluate the association between metabolism and neurodevelopmental outcomes.
All statistical analyses were conducted in RStudio version 2022.12.0 using the R statistical language version 4.3.1 (R Project for Statistical Computing).Rank-based linear regressions were implemented using Rfit. 24Multinomial logistic regression models were implemented using nnet and estimated probabilities were simulated using MNLpred. 25,26[29]

Maternal Serum and Umbilical Cord Serum Metabolomes
Bipartite Spearman correlations corrected for FDR revealed few significant associations among 48 maternal third trimester serum metabolites and 44 umbilical cord serum metabolites (eFigure 8 in Supplement 1).Rank-based regression adjusted for covariates in models 1 and 2 identified similar patterns after FDR correction (Table 2).The associations between other metabolite pairs with q > .10 are presented in eTable 3 in Supplement 2.
With few significant associations between individual metabolites, we did not expect to find a significant multivariate association between the maternal and cord serum metabolomes.When we used sparse PLS to test this multivariate association (adjusted for model 2 covariates), the model failed to converge under a variety of tuning conditions, indicating poor correlation between the 2 metabolomes.The covariance between the first latent variate pair was not significantly different from chance (covariance, 0.04; P = .27).

Placental and Umbilical Cord Serum Metabolomes
Bipartite Spearman correlations revealed many associations between 54 placental metabolites and 44 cord serum metabolites after FDR correction, particularly among lipid-, energy-, and amino acidrelated metabolites (eFigure 9 in Supplement 1).Rank-based regression adjusted for covariates identified similar patterns after FDR correction (Table 3).The findings between other metabolite pairs with q > .10 are presented in eTable 4 in Supplement 2.
c All P values were adjusted for FDR and both the original P values and FDR quantities, q values, are reported.Only findings with FDR q < .10 are reported here.

3-OHB and Neurodevelopment
Since 3-OHB was the largest driver of the placental and cord variate scores, we tested associations of 3-OHB concentrations with neurodevelopmental outcomes (  Abbreviation: FDR, false discovery rate. a Model 1 was adjusted for birth year and gestational age at sample collection (delivery).
b Model 2 was adjusted for all covariates in model 1 and fetal sex, maternal education, maternal race and ethnicity, home ownership, prenatal vitamin use in the first month of pregnancy, and maternal metabolic conditions.
c All P values were adjusted for FDR and both the original P values and FDR quantities, q values, are reported.Only findings with FDR q < .10 are reported here.

Discussion
In this cohort study investigating the associations among maternal, placental, and fetal metabolism, we found that the placental and umbilical cord serum metabolomes were highly correlated.We also found that 3-OHB was an important metabolite across the maternal, placental, and fetal metabolomes and that lower levels of 3-OHB in maternal serum and higher levels in cord serum were associated with increased risk of non-TD.We speculate that these unexpected findings are related to a metabolic switch that occurs during the perinatal transition. 30Labor is a highly coordinated process, associated with changes in maternal and fetal metabolism. 31,32Birth itself is associated with profound metabolic and transcriptomic changes in the newborn to maintain energy supply after losing the steady supply of maternal nutrients. 33,34The placental and cord serum metabolomes were collected at the same postdelivery time point and likely reflect profound metabolic changes in this critical window, whereas maternal serum collected during the third trimester reflects a different developmental window.It is possible that metabolic profiles in maternal serum collected perinatally would more closely align with placental-fetal metabolic profiles.Additionally, the opposite  association between non-TD risk and maternal 3-OHB could be explained by these different developmental windows.
In maternal serum, the negative association between 3-OHB and non-TD risk could relate to lipid metabolism.During the third trimester, maternal insulin resistance and lipolysis ensures nutrient availability for the placenta and developing fetus. 35High free fatty acids in the blood trigger the production of ketones, which readily cross the placenta to be used as fuel or substrate for lipid and cholesterol synthesis. 36Indeed, maternal 3-OHB has been shown to be rapidly incorporated into placental cholesterol and fetal liver and brain tissues in a rat model. 37Lower maternal 3-OHB concentrations in the third trimester might indicate dysregulated maternal lipid metabolism, resulting in reduced availability of important lipid substrates for brain development.While this analysis was limited to the polar metabolome, a 2021 study 38 reporting on the untargeted metabolomic analysis of maternal third trimester plasma showed that higher levels of fatty acids involved in lipid biosynthesis were associated with reduced risk of non-TD in the MARBLES cohort.
Taken together, these findings suggest that disturbed lipid metabolism during late pregnancy could play a role in the etiology of non-TD.
0 However, the study by Rizzo et al 40 reported much higher levels of plasma 3-OHB for healthy women than reported here.This could reflect differences in fasting status between these studies: most MARBLES participants had eaten in the 2 hours before blood collection, while participants in the study by Rizzo et al 40 fasted overnight.However, these findings might also indicate a U-shaped association between altered neurodevelopment with maternal third trimester circulating 3-OHB.
The placental and cord serum metabolomes could reflect the metabolic adaptations to labor and birth. 33Here, we observed and association between cord serum 3-OHB concentrations with metabolites related to lipid, tricarboxylic acid cycle, and amino acid catabolism pathways.This aligns with metabolic changes at birth, when the steady supply of maternal glucose is abruptly cut off and the newborn must rely on gluconeogenesis and ketogenesis until feeding is initiated. 33The newborn also shifts to oxidative metabolism in the new oxygen-rich extrauterine environment. 41Increased oxidative metabolism can lead to increased oxidative stress.Indeed, we observed that cord serum and placental 3-OHB were positively associated with cord serum and placental 2-hydroxybutyrate (2-OHB) and 2-aminobutyrate, biomarkers of glutathione synthesis and status. 42,43Cord serum 3-OHB was also negatively associated with pyroglutamate, a metabolite related to glutathione depletion 44,45 and positively associated with placental glutathione concentrations.3-OHB acts as a signaling molecule to protect against oxidative stress through several proposed mechanisms, including activation of antioxidant response-related transcription factors. 46Additionally, elevated 3-OHB and 2-OHB concentrations are associated with gestational diabetes, signaling elevated lipid metabolism under conditions of insulin resistance and glutathione synthesis to combat the associated oxidants. 47,48In this study, we adjusted for maternal diabetes, so associations of 3-OHB with 2-OHB, 2-aminobutyrate, pyroglutamate, and glutathione may be a sign of increased glutathione synthesis to manage increased oxidative metabolism.
The cord serum and placenta metabolic profiles were associated with risk of non-TD and may reflect altered fetal lipid metabolism.It has been reported that plasma 3-OHB concentrations measured from heel-stick samples collected from neonates are low in the first 12 hours after birth and peak between 48 and 72 hours. 49However, mean cord serum 3-OHB concentrations collected shortly after birth in our study were higher and positively associated with risk of non-TD.Non-TD is associated with various phenotypes, including attention-deficit/hyperactivity disorder (ADHD) and speech or other learning delays. 10In an umbilical cord serum lipidomic analysis, circulating acylcarnitines were positively associated with symptoms of both ASD and ADHD at age 2 years.

Table 1 .
Demographic Characteristics of Mother-Infant Dyads Demographic Characteristics of Mother-Infant Dyads (continued) Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); GED, general educational development; NA, not applicable.a Note that 1 child in the maternal serum-cord serum analysis had a missing neurodevelopmental diagnosis.
test the association among the concentrations of 54 placental metabolites and 44 cord serum metabolites from 107 mother-infant dyads with complete model 2 covariates, retaining 1 latent variate pair after leave-1-out cross-validation.The first latent variate pair explained 74.7% of the variance, and covariance between the first latent variate pair was significantly different from chance (covariance, 0.15; P < .001).The relevance network revealed central roles for placental and cord serum 3-hydroxybutyrate (3-OHB) related to lipid, energy, and amino acid metabolism (Figure, A).Furthermore, both placental and cord serum 3-OHB concentrations were most strongly associated with the placental and cord serum latent variates, respectively (eFigure 10 in Supplement 1).Since the model 2 covariates were also appropriate to evaluate the association between the first latent variate pair and neurodevelopment (eFigure 3 in Supplement 1), multinomial logistic regression was used to evaluate ASD and non-TD risk compared with TD references (Figure, B and C).

Figure
Figure.Placenta-Cord Serum Metabolic Profiles and Neurodevelopment

JAMA Network Open | Obstetrics and Gynecology Neurodevelopment
and Metabolism in the Maternal-Placental-Fetal Unit : ASD was diagnosed when ADOS scores JAMA Network Open.2024;7(5):e2413399.doi:10.1001/jamanetworkopen.2024.13399(Reprinted) May 28, 2024 2/16 Downloaded from jamanetwork.comby guest on 06/05/2024 met or exceeded the ASD cutoff; nontypical development (non-TD) was diagnosed when ADOS scores were within 3 points below the ASD cutoff, or when Mullen's Scales of Early Learning scores and subdomain scores were 1.5 to 2 SDs below the mean; and typical development (TD) was

Table 2 .
Estimates of the Associations of Each Maternal Third Trimester Serum Metabolite With Each Umbilical Cord Serum Metabolite Abbreviation: FDR, false discovery rate.aModel 1 was adjusted for birth year, gestational age at maternal serum collection, fasted time at maternal serum collection, and gestational age at umbilical cord serum collection (delivery).

Table 3 .
Estimates of the Associations Between Each Placental Metabolite and Each Umbilical Cord Serum Metabolite

Table 4 .
Unadjusted and Adjusted Associations of Maternal Serum, Placental, and Umbilical Cord Serum 3-Hydroxybutyrate With Neurodevelopmental OutcomeAdjusted for gestational age at sampling, fasted time at sampling, fetal sex, home ownership, maternal education, maternal race and ethnicity, and maternal metabolic condition as a dichotomous variable.Adjusted for year of birth, gestational age at birth, delivery mode, prenatal vitamin use in the first month of pregnancy, fetal sex, home ownership, maternal race and ethnicity, and maternal education and maternal metabolic condition as a dichotomous variable.Adjusted for year of birth, gestational age at birth, delivery mod, prenatal vitamin use in the first month of pregnancy, fetal sex, home ownership, maternal race and ethnicity, and maternal education and maternal metabolic condition as a dichotomous variable.
a b c Interestingly, negative associations between third trimester maternal circulating 3-OHB concentrations and Bayley Scales of Infant and Toddler Development mental development index scores at age 2 years and mean Stanford-Binet Intelligence Scale scores during preschool age have been reported by Rizzo et al. 50

JAMA Network Open | Obstetrics and Gynecology Neurodevelopment
and Metabolism in the Maternal-Placental-Fetal Unit Diagram of Samples Included in This Analysis eTable 1. Covariates for Each Analysis and Each Model eFigure 2. The Directed Acyclic Graph for the Relationships Between the Maternal Third Trimester Serum Metabolome, Placental Metabolome, and Umbilical Cord Blood Metabolome eFigure 3. The Directed Acyclic Graph for the Relationships Between Neurodevelopmental Outcomes and 3-Hydroxybutyrate Levels in Maternal Third Trimester Serum, Placenta, and Umbilical Cord Serum eFigure 4. Spearman Correlations Between Maternal Third Trimester Serum Metabolites eFigure 5. Spearman Correlations Between Placental Metabolites eFigure 6. Spearman Correlations Between Umbilical Cord Serum Metabolites eFigure 7. Spearman Correlations Between Placental Metabolites and Maternal Third Trimester Serum Metabolites eFigure 8. Spearman Correlations Between Umbilical Cord Serum Metabolites and Maternal Third Trimester Serum Metabolites eFigure 9. Spearman Correlations Between Placental Metabolites and Umbilical Cord Serum Metabolites eFigure 10.Metabolite Loadings for the First Latent Placenta Variate and the First Latent Cord Blood Variate JAMA Network Open | Obstetrics and Gynecology Neurodevelopment and Metabolism in the Maternal-Placental-Fetal Unit Estimates of the Relationship Between Each Maternal Third Trimester Serum Metabolite and Each Placental Metabolite eTable 3. Estimates of the Relationship Between Each Maternal Third Trimester Serum Metabolite and Each Umbilical Cord Serum Metabolite eTable 4. Estimates of the Relationship Between Each Placental Metabolite and Each Umbilical Cord Serum Metabolite JAMA Network Open.2024;7(5):e2413399.doi:10.1001/jamanetworkopen.2024.13399(Reprinted) May 28, 2024 11/16 SUPPLEMENT 1. eFigure 1. JAMA Network Open.2024;7(5):e2413399.doi:10.1001/jamanetworkopen.2024.13399(Reprinted) May 28, 2024 15/16 Downloaded from jamanetwork.comby guest on 06/05/2024