Metabolomics and Communication Skills Development in Children; Evidence from the Ages and Stages Questionnaire
1Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; hprke@channing.harvard.edu (R.S.K.); aboulin@fenwayhealth.org (A.B.); renml@channing.harvard.edu (N.L.); klee-sarwar@bwh.harvard.edu (K.L.-S.); rechu@channing.harvard.edu (S.H.C.); nhaya@channing.harvard.edu (A.P.Y.); stvjc@channing.harvard.edu (V.C.); rejas@channing.harvard.edu (J.L.-S.)
2The Fenway Institute, Fenway Health, Boston, MA 02215, USA
3Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital, Boston, MA 02115, USA
4Division of Pediatric Pulmonary Medicine, Golisano Children’s Hospital at Strong, University of Rochester Medical Center, Rochester, NY 14642, USA; Augusto_Litonjua@urmc.rochester.edu
*Correspondence: restw@channing.harvard.edu; Tel.: +1-617-525-2278Abstract
We hypothesized metabolomic profiling could be utilized to identify children who scored poorly on the communication component of the Ages and Stages Questionnaire (ASQ); which assesses development in childhood, and to provide candidate biomarkers for autism spectrum disorders (ASD). In a population of three-year-old children, 15 plasma metabolites, were significantly (p < 0.05) different between children who were categorized as having communication skills that were “on schedule” (n = 365 (90.6%)) as compared to those “requiring further monitoring/evaluation” (n = 38 (9.4%)) according to multivariable regression models. Five of these metabolites, including three endocannabinoids, were also dysregulated at age one (n = 204 “on schedule”, n = 24 “further monitoring/evaluation”) in the same children. Stool metabolomic profiling identified 11 significant metabolites. Both the plasma and stool results implicated a role for tryptophan and tyrosine metabolism; in particular, higher levels of N-formylanthranilic acid were associated with an improved communication score in both biosample types. A model based on the significant plasma metabolites demonstrated high sensitivity (88.9%) and specificity (84.5%) for the prediction of autism by age 8. These results provide evidence that ASQ communication score and metabolomic profiling of plasma and/or stool may provide alternative approaches for early diagnosis of ASD, as well as insights into the pathobiology of these conditions.
1. Introduction
Autism spectrum disorders (ASD) are a collection of heterogeneous neurodevelopmental disorders characterized by persistent deficits in social communication and interaction across multiple contexts, including social reciprocity, nonverbal communicative behaviors, and skills in developing, maintaining, and understanding relationships [1]. The Centers for Disease Control and Prevention recently released their bi-annual update of the prevalence of autism in the United States of America. With an estimated 15% increase in cases since 2016, they predicted the prevalence would be 1 in every 59 children in 2018 [2]. Current diagnostics involve behavioral observation and assessments of speech, language, and intellectual abilities. The average age of diagnosis in the United States is four years [2], although parents often report signs of developmental delay as early as 18 months [3]. Earlier detection could allow for more effective treatment in improving social, communicative, adaptive and cognitive outcomes [4].
A common assessment used to measure a child’s development between birth and the age of six is the Ages and Stages Questionnaire (ASQ). This tool assesses five developmental skills domains: communication, gross motor, fine motor, problem solving, and personal-social [5]. Evidence suggests that the ASQ can provide an effective screening tool to identify children at high risk of ASD. Specifically, it has been reported that utilizing a binary cut-off based on ASQ Communication score can identify 95% of at-risk children, as defined by the Modified Checklist for Autism in Toddlers (M-CHAT) [6]. Furthermore, it has been shown that the ASQ is less sensitive to demographic factors than other commonly used development assessment approaches [7].
Nevertheless, in addition to standardized screening, there is a need for a more sensitive means of ASD detection [8]. A growing body of research demonstrates differing metabolic patterns in ASD patients as compared to controls, in particular dysregulated levels of fatty acids, sterols, intermediary metabolites, phospholipids, and molecules associated with oxidative stress [9]. Similarly, an increased level of blood serotonin has been consistently linked with autism [10]. The development of metabolomics now offers the opportunity to characterize efficiently levels of a broad range of metabolites in a large number of subjects. Taken together, this suggests the potential for the development of metabolomic based biomarkers of ASD that could help close the gap in the identification and/or prediction of these disorders.
In this study, we investigate blood and stool metabolomic profiles associated with the ASQ derived communication score, as a proxy for ASD risk, in children from the Vitamin D Antenatal Asthma Reduction Trial (VDAART, clinicaltrials.gov Identifier: NCT00920621), a clinical trial of prenatal vitamin D supplementation and outcomes in pregnant women and their offspring [11]. We hypothesize that the metabolome of children with poor communication scores differs from that of children exhibiting “normal” communication scores, and that these differences involve biological pathways that are implicated in ASD.
2. Results
2.1. Study Population
In total, 403 children from VDAART had metabolomic profiling of plasma and an ASQ communication score at age three. The ASQ-3 communication score is based on six questions administered to parents regarding their child’s ability. Standardized scores are then compared to the expected mean score obtained from a reference distribution of scores, and the children are categorized as: (i) “On Schedule for developing normally” (above the mean); (ii) “Requires Monitoring” (1–2 standard deviations below the mean); (iii) “Needs further evaluation” (>2 standard deviations below the mean). For these analyses we focused on communication skill using a binary cut off comparing those “On schedule for developing normally” versus those either requiring “further evaluation” or “monitoring”, based on the recommendations of Hardy et al. [6]. The majority (n = 365, 90.6%) of VDAART children were categorized as “on schedule for developing normally” based on their ASQ communication score (Table 1). Of the 403 children, 215 also had plasma metabolomic profiling at age 1 (53.3%), and 228 (56.6%) had metabolomic profiling on a stool sample collected at age three (Supplementary Table S1).
Children in the lowest communication score category (“Requires further evaluation”) were significantly less likely to be from the San Diego VDAART site than from the Boston or St. Louis sites (p = 0.002), and there were borderline significant associations between score category with gender (p = 0.076) and with maternal educational level (p = 0.057). There was no significant association with either prenatal vitamin D supplementation or asthma/wheeze which were the primary exposure and endpoint of interest in the original VDAART study. Communication score was also assessed at the age one and age two visits, and while there appeared to be a strong relationship between communication category when considering the age three and the age two visit (p = 8.0 × 10−9), this was not apparent with the age one visit (Table 1; Supplementary Figure S1). In addition to communication skills, the ASQ also assess four other domains using the same six question approach; gross motor skills, fine motor skills, personal social skills and problem solving skills. Communication score at age three was significantly correlated with the scores in the other four ASQ assessed categories, with the strongest relationships for personal-social skill score (r = 0.45, p < 2.2 × 10−16) and problem solving score (r = 0.46, p ≤ 2.2 × 10−16) (Supplementary Table S2).
2.4. Correlation between the Plasma and Stool Metabolomic Results
Both analyses identified metabolites of tryptophan metabolism and tyrosine metabolism as significantly dysregulated between children with an ASQ communication score indicating “on schedule” and those requiring “further monitoring/evaluation” (Figure 1).
Higher levels of N-formylanthranilic acid, a metabolite of tryptophan metabolism, were associated with a higher ASQ communication score in both plasma (odds ratio (OR): 0.05, 95% confidence interval (CI) 2.6 × 10−3, 0.75, p = 0.043) and stool (OR: 0.38, 95%CI 0.15, 0.93, p = 0.039). In order to explore this further, we looked at the correlation between plasma stool levels of N-formylanthranilic acid and stool levels of N-formylanthranilic acid among the children with measures of both at age three, to determine whether there was a relationship between levels of this metabolite in different biosamples. However we found that levels were not strongly correlated; Spearman r = 0.07, p = 0.3. We further sought to determine of there was any correlation between any of the 15 significant plasma metabolites with the 13 stool metabolites; but again we found little evidence of correlation (Figure 2). The strongest negative correlations were between plasma sphingomyelin and stool suberate (r = −0.26, p = 6.7 × 10−5) and between plasma 5-hydroxyindolacetate and stool 2-hydroxyphenylacetate (r = −0.24, p = 3.3 × 10−4). The strongest positive correlations were between plasma sphingomyelin and stool N-acetylserotonin (r = 0.24, p = 2.8 × 10−4) and between plasma cinnamoylglycine and stool suberate (r = 0.22, p = 6.9 × 10−4). However, it should be noted that although significant the correlation coefficients were not high for any of these plasma–stool metabolite pairs.
2.5. Autism
The VDAART children are now between 6 and 8.5 years old. Beginning at the year six VDAART follow up visit, we asked parents if their child “had ever received a physician diagnosis of autism”. To date, 18 children in VDAART have been diagnosed with autism according to parental report, including 9 (2.2%) of the 403 children with age three ASQ assessment and plasma metabolomic profiling. The rate of diagnosis among the children in the “requiring monitoring or further evaluation” category was 5/38 (13.2%) which was significantly higher (p = 5.5 × 10−4) than in the “on schedule” category; 4/365 (1.1%). This was also evident when considering the age two ASQ communication scores; with rates of 10.4% versus 0.9% (p = 0.001) in the “requiring monitoring or further evaluation” and the “on schedule” categories, respectively (Supplementary Table S3).
Accordingly, the binary ASQ score (Model 1) had moderate predictive ability for age 8 asthma in a receiver operator characteristic (ROC) curve analysis (area under the curve (AUC): 0.736 (95% CI: 0.563, 0.909)) (Supplementary Figure S2). Although this classifier had high sensitivity, (91.6%), its specificity was only 55.6% (Table 4). A summary score based on the 15 plasma significant metabolites (Model 2) demonstrated marginal, and non-significant, improvement in the AUC (0.759 (95% CI: 0.603, 0.915)) over the ASQ score model. Although specificity was higher (66.7%), sensitivity was lower (78.7%). However, when levels of all 15 metabolites (Model 3) were included the resulting model had a significantly higher AUC: 0.924 (95% CI: 0.867, 0.980), p = 0.034, than Model 1, and Model 3 demonstrated both high sensitivity (88.9%) and specificity (84.5%) for the prediction of autism by age 8.
3. Discussion
Impaired communication is a defining characteristic of autism spectrum disorders, which are now recognized as one of the most serious health problems in the world next to Acquired Immuno-Deficiency Syndrome (AIDS), cancer, and diabetes [14]. Despite their high prevalence and the public health burden they impart, there are still no tests that offer a reliable confirmation of a clinical diagnosis of ASD; that provide efficient screening of individuals presenting with behavioral features suggestive of ASD; or that aid in early detection of ASD [15]. Consequently, novel screening approaches are critical, particularly those that could be applied to young children. Metabolomics offers a particularly compelling avenue, given the demonstrated metabolic changes that accompany ASD, and the ability of metabolites and metabolomic profiles to act as biomarkers whilst also informing on underlying biological mechanisms.
In this study, we explored the hypothesis that metabolomic biomarkers could help to identify children with poor communication skills, as assessed by the ASQ. We hypothesized that children with a low ASQ communication score at age three were more likely to be diagnosed with autism in the following years. Indeed, more than 10% of the children in the lowest two categories of ASQ communication score at age two years, and more than thirteen percent at age three, went on to be diagnosed with autism, compared to ~ 1% of children who were categorized as “on schedule” at these ages. Furthermore, although we did not identify a global difference in the metabolomes of children “on schedule” versus those requiring “further monitoring/evaluation”, we did identify dysregulation of metabolism in both the plasma and stool of children with low ASQ communication scores in a number of metabolomic pathways that have previously been associated with autism and ASD. In particular tryptophan biosynthesis, tyrosine metabolism and endocannabinoid metabolism.
Tryptophan serves as a precursor for a wide range of bioactive compounds, including major neurotransmitters and neuromodulators, and it is involved in both protein synthesis and bacterial degradation [15]. These crucial functionalities of tryptophan and its associated pathways underlie much of the evidence linking dysregulated tryptophan biosynthesis and metabolism to ASD [15]. The two main pathways of tryptophan metabolism lead to the synthesis of kynurenine and of serotonin [16]. The majority of bioavailable tryptophan, which comes mainly from the diet, enters the kynurenine pathway leading to the production of kynurenic acid, kynuramines, picolinic acid, quinolinic acid, Nicotinamide adenine dinucleotide (NAD), and Adenosine triphosphate (ATP). These downstream products are involved in the regulation of the central nervous system and of the immune system [17], both of which have been shown to be dysregulated among individuals with autism. Less than 2% of the bioavailable tryptophan is metabolized into serotonin [16]. Serotonin is a neurotransmitter involved in multiple aspects of brain function that may influence ASD both dynamically and across development, ranging from the regulation of mood, appetite and social interactions to a critical role in neuronal morphology and circuitry [16]. This is important, as disordered organization of the fronto-temporal lobes is one of the most consistent neuroanatomical findings in ASD patients [18]. Furthermore, serotonin is implicated in regulation of the circadian rhythm, which is often shown to be disrupted in individuals with ASD [19], through its downstream product, melatonin.
It has been shown that in individuals with ASD, the tryptophan biosynthesis pathway in the brain is preferentially biased toward the production of xanthurenic acid and quinolinic acid along the kynurenine pathway, at the expense of the serotonin pathway [20], and consequently serotonin levels have been demonstrated to be lower in the brains of individuals with ASD [21]. However, the opposite is true outside the blood–brain barrier. In fact, elevated whole blood serotonin, or hyperserotonemia, was identified as the first biomarker of ASD more than fifty years ago, and is apparent in roughly 30% of affected individuals [10]. This is in agreement with our findings of higher levels of plasma serotonin, and lower levels of N-formylanthranilic acid, a downstream product of the kynurenine pathway, in the plasma of children with poor ASQ communication skills, relative to those with “normal” communication development. The remaining ~4% of tryptophan that is not metabolized down either the kynurenine or serotonin pathway undergoes bacterial degradation prior to gut absorption [20], which likely explains why we also saw altered levels of tryptophan metabolites in the stool. What is particularly compelling in the literature to date is that dysregulated tryptophan metabolism appears to be specific to ASD, as it has not been observed in other cognitively impaired individuals [22].
In this population both the plasma and the stool metabolome also indicated disruption of tyrosine metabolism across communication score categories. Tyrosine metabolism has previously been implicated in autism in a study of urinary metabolomics of Italian children [20]. Interestingly, one of the replicated risk genes for ASD, MET, encodes a receptor tyrosine kinase, MET, which modifies a large number of neurodevelopmental events [23]. Three endocannabinoids, which are arachidonic acid-derived compounds, were identified among the significant plasma metabolites, and all three endocannabinoids were also associated with age three ASQ communication score when measured in the age one plasma samples. The endocannabinoid system plays a crucial neuromodulatory role in the regulation of emotional responses, behavioral reactivity and social interaction, and is also implicated in a number of common phenotypic characteristics of ASD including seizures, anxiety and poor memory processing [24]. The endocannabinoid system has, therefore, been suggested as a therapeutic target for these disorders. Our current findings in which endocannabinoids were shown to already be disturbed in affected children at age one year, suggest such therapeutics may be of particular use for early intervention or possible prevention. Despite a large body of evidence from animal models linking endocannabinoids to ASD, this represents the first evidence for such an association in a human population.
This study, therefore, provides compelling evidence to link ASQ communication to dysregulated metabolic processes, which may indicate risk of ASD. Nevertheless, there were a number of limitations. One of the biggest was sample size, the prevalence of ASDs is approximately 1% in the US population aged <8 years [16]. Within this subset of the VDAART population, the prevalence of parent-reported autism diagnosis was 2%, which may reflect the higher percentage of males in the population, nevertheless, we were underpowered to explore ASD as an outcome in VDAART. For this reason, we chose to study the ASQ communication score as a proxy for autism risk based on the findings of Hardy et al. [6], and we demonstrated good power for this endpoint in our population. We noted differences in the prevalence of children in the different ASQ score categories between the three sites; specifically in San Diego, no children were in the lowest communication score category. The San Diego site also had the highest socioeconomic status of the three cohorts, and this discrepancy in score may reflect unmeasured biased relating to the parents of the children that we cannot control for. However, we did adjust for maternal education level and maternal marital status, and stratification of the plasma logistic regression models by site indicated that overall the site differences in ASQ scores were not influencing our results and conclusions. We additionally adjusted for important factors such as sex and race, as well as asthma status and vitamin D supplementation to account for the nature of the VDAART study. Finally, we were limited in our ability to study all the relevant metabolites in the pathways of interest, there are currently no profiling methods that provide coverage of the entire metabolome and consequently there may be important ASD related pathways that we were not able to analyze at all. Many of our associations were not robust to correction for multiple testing, however given the biological relevance of our significant metabolites this may purely be a reflection of the limitations discussed above.
We observed a significant association between ASQ communication score and autism diagnosis in this population, however this should be interpreted with caution due to the limited number of cases. Furthermore, although this classifier demonstrates impressive specificity, sensitivity was poor. This finding supports the argument that more objective and quantifiable markers are crucial. A summary score based on the significant plasma metabolites, moderately improved the AUC and the sensitivity of classification, but at the expense of specificity. In contrast, a classifier based on the relative measures of all 15 metabolites, significantly improved the AUC and maintained impressive sensitivity and specificity. Again, this should be interpreted with caution due to sample size, the likelihood of overfitting and the fact the metabolites were identified using the ASQ proxy. Furthermore, as our biomarker panel was comprised of 15 metabolites, further study will be necessary to identify a potential subset of these, or related metabolites that may be more parsimonious and practical in clinical settings. Nevertheless, these findings provide early evidence to suggest that the development of metabolite based biomarkers that can outperform existing measures of autism prediction and diagnosis may be feasible.
We do not have detailed information on the physician diagnosed cases, as autism was assessed by parental report of a physician diagnosis. It is also possible that there is observation bias in that all low ASQ communication score subjects were not specifically evaluated for autism. ASD, by definition, represents a heterogeneous spectrum of neurodevelopmental conditions rather than a single disease, and it is feasible they do not all share the same metabolomic profile. It has been noted that even among subjects with ASD, the level of tryptophan can vary considerably in patients with autistic disorder as compared to Asperger’s syndrome [15].
In the comparison of the plasma and stool metabolomes, it was of interest to note that while similar pathways were highlighted, in particular tryptophan and tyrosine metabolism, the constituent metabolites differed. The only exception was N-formylanthranilic acid, and here we could identify no direct correlation between the levels of this metabolite in the stool and the plasma samples. This may reflect the timing of sample collection; the stool samples were collected up to 48 h before the plasma. However, it is likely also a reflection of the different stages of metabolism represented by the stool versus the plasma metabolome. It is the ability of different bio samples to capture differing aspects of dysregulated metabolism that has led to the argument that integrating multiple bio samples may provide “better clues to biological and pathological pathways” underlying disease [25]. However, the statistical methods for doing so remain underdeveloped, and in particular there is little literature directly comparing stool versus plasma metabolites. Further work is required to better understand these relationships.
4. Materials and Methods
4.1. Study Population
This study was nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART); which aimed to assess the potential of vitamin D supplementation in pregnant women to prevent asthma in their offspring. The study has been described in detail previously [11]. Briefly, pregnant non-smoking women between 10 to 18 weeks of gestation who had a history of asthma, eczema, or allergic rhinitis, or who conceived the child with a man with a history of such diseases were recruited from three sites across the USA; Boston, San Diego and St Louis, between 2009 and 2011. Women were randomized 1:1 to a daily dose of 4000 International Units (IU) vitamin D3 or a placebo tablet until delivery. All women additionally received a daily multivitamin containing 400 IU vitamin D3. VDAART was approved by the Institutional Review Boards (IRB) of the participating Clinical Centers and the Data Coordinating Center, with pregnant women signing informed consent at the enrollment visit covering both primary and secondary analyses of data.
4.3. Ages and Stages Questionnaire (ASQ) Assessment
The Ages and Stages Questionnaire, 3rd Edition (ASQ-3) (https://agesandstages.com/; Paul H. Brookes Publishing Co., Inc.) [5] was administered to primary caregivers of the VDAART offspring at ages one, two and three years in person at the annual visit. The ASQ-3 is a validated and widely used tool that assesses five developmental domains: gross motor skills, fine motor skills, problem solving ability, personal/social skills and communication, with six questions in each category resulting in a domain specific score. Standardized domain specific scores are then compared to the expected mean score obtained from a reference distribution of scores within age-groups, and categorized as: (i) “On Schedule for developing normally” (above the mean); (ii) “Requires Monitoring” (1–2 standard deviations from the mean); (iii) “Needs further evaluation” (>2 standard deviations from the mean). For these analyses we focused on communication skill using a binary cut off comparing those “On schedule for developing normally” versus those either requiring “further evaluation” or “monitoring”, based on the recommendations of Hardy et al. [6].
4.4. Statistical Analysis
4.4.2. Correction for Multiple Testing
There are currently no consensus standards for multiple testing correction in metabolomics; methods applied to other ‘omic’ datatypes such as the Bonferroni correction, and even more liberal corrections are considered too stringent for metabolomics data due to the high correlation of metabolites that are closely linked together through biological pathways. Therefore, we report a nominal p-value significance of p < 0.05 throughout. However, we also explore an additional multiple-testing correction which partially takes into account the presence of highly correlated metabolites mapping to the same biological pathway, using a principal components analysis (PCA) approach to identify the number of effective tests [12,13]. We applied PCA to the metabolites that passed quality control (QC) and processing and determined the number of components required to explain 50% of the variance in the data (i.e., the number of effective tests). The adjusted p-value threshold was then calculated as α/m where α denotes the nominal p-value threshold of 0.05, and m denotes the number of effective (i.e., independent) tests. This was applied to both the plasma and the stool metabolites separately.
4.4.3. Diagnosing and Predicting Autism
We compared the rates of autism diagnosis between the communication score categories to determine whether children “requiring further monitoring/evaluation” at ages 2 and 3 were more likely to go on to develop autism by age eight than those “on schedule for developing normally”. We used the Fishers exact test to account for small cell counts. Next, we aimed to determine whether the plasma metabolites identified as being associated with ASQ at age three had any predictive ability for autism by age eight. We compared three different predictive models using ROC cures and the corresponding AUC; Model 1: binary ASQ score at age three; Model 2: a summary score, generated by taking the first five principal components of the 15 plasma metabolites that were significantly associated with communication score at age three. Five PCs were chosen, as this was the number required to explain 50% of the variance in the data; and Model 3: Levels of all 15 significant metabolites. For the Models 2 and 3, the sensitivity and specificity were computed based on the optimal cut-off to maximize sensitivity and specificity weighting both equally, as determined using the ‘ROCR’ package in R [27].
All analyses were conducted in R version 3.5.0 and all statistical tests were two-sided.
5. Conclusions
Despite the relatively limited sample size, this study still represents one of the largest to consider metabolomics and neurodevelopment. It is unique in the inclusion of both blood and stool metabolites and is, to our knowledge, the first to explore ASQ scores and metabolomics. We identified a number of metabolomic pathways and metabolites with biologically plausible relationships with impaired development of communication skills and with autism risk. Finally, we demonstrated the predictive ability of these metabolites, providing evidence that ASQ communication score and metabolomic profiling may provide alternative approaches for early diagnosis of ASDs.
Acknowledgments
The authors wish to thank the study participants of the VDAART trial and all those involved in the collection and management of data.
Appendix Group
Supplementary Materials
The following are available online at https://www.mdpi.com/2218-1989/9/3/42/s1: Supplementary Figure S1: Trajectory of Communication Score Category at ages 1,2 and 3 in 403 children with plasma metabolomics profiling; Supplementary Figure S2: (A) Scores plot for the PLS-DA Model Based on all 481 Plasma Metabolites Comparing Children with ASQ Assessed Communication Skills on Schedule for Developing Normally (n = 365) versus those Requiring Further Monitoring/Follow up (n-38); R2 = 0.13; Q2 = −0.21; permutation p-value = 0.994; (B) Plasma Metabolites with a VIP score >2, Indicating the Greatest Ability to Discriminate Between the Two Groups; Supplementary Figure S3: (A) Scores plot for the PLS-DA Model Based on all 737 Stool Metabolites Comparing Children with ASQ Assessed Communication Skills on Schedule for Developing Normally (n = 204) versus those Requiring Further Monitoring/Follow up (n = 24); R2 = 0.10; Q2 = −1.13; permutation p-value = 0.985; (B) Plasma Metabolites with a VIP score >2, Indicating the Greatest Ability to Discriminate Between the Two Groups; Supplementary Figure S4: Receiver Operator Characteristic Curves and Corresponding AUCS for the prediction of autism by age 8 according to three models; Table S1: Baseline characteristics of 228 children from VDAART with plasma and stool metabolomic profiling and an ASQ score at age 3; Table S2: Correlation between Communication Score and Scores in the three other ASQ domains; Table S3: 481 Plasma Metabolites that Passed QC with information on Profiling Platform, Superpathway, Subpathway and HMDB ID; Table S4: Association Between 15 Significant Plasma Metabolites and Binary ASQ score, Stratified by Study Site; Table S5: 737 Stool Metabolites that Passed QC with information on Profiling Platform, Superpathway, Subpathway and HMDB ID; Table S6: Metabolites Associated with Binary ASQ Communication Score at age three in blood plasma samples from ages 1 and 3; Table S7: Subsequent Diagnoses of Autism by age eight stratified by ASQ communication score category at age 2 and at age 3 among 403 children with ASQ communication score and metabolomics profiling; Table S8: Power Analysis for the Plasma and Stool Logistic Regression Models; assuming an alpha of 0.05 and an event rate of 0.09 and a sample size of 403 for plasma and an event rate of 0.11 and a sample size of 228 for stool
Funding
VDAART was supported by U01HL091528 and 1R01HL123915-01 from the National Heart, Lung, and Blood Institute (NHLBI); and U54TR001012 from the National Centers for Advancing Translational Sciences. Metabolomic analyses and R.S.K were supported by 5R01HL123915-05, 1R01HL141826-0 and W81XWH-17-1-0533. The funding bodies played no role in the study design; the collection, analysis and interpretation of data; the writing of the report; or the decision to submit the article for publication.
Conflicts of Interest
J.L.-S. is a consultant for Metabolon, Inc. S.T.W. reports grants from the NIH, and speaker fees from Illumina Corp during the conduct of the study. A.A.L. reports author royalites from “UptoDate, Inc” and consultant fees from “AstraZenaca”. Besides the above mentioned, authors declare no potential, perceived, or real conflict of interest regarding the content of this manuscript.
| Baseline Characteristic | Age 3ASQ Communication Score | p-Value | ||||||
|---|---|---|---|---|---|---|---|---|
| On Schedule (n = 365) | Needs Monitoring (n = 23) | Requires Further Evaluation (n = 15) | ||||||
| n | % | n | % | n | % | |||
| Sex | Female | 176 | 48.2% | 9 | 39.1% | 3 | 20.0% | 0.076 |
| Males | 189 | 51.8% | 14 | 60.9% | 12 | 80.0% | ||
| Site | San Diego | 131 | 35.9% | 6 | 26.1% | 0 | 0.0% | 0.002 |
| Boston | 72 | 19.7% | 5 | 21.7% | 9 | 60.0% | ||
| St Louis | 162 | 44.4% | 12 | 52.2% | 6 | 40.0% | ||
| Race | Black | 173 | 47.4% | 13 | 56.5% | 9 | 60.0% | 0.725 |
| White | 122 | 33.4% | 6 | 26.1% | 5 | 33.3% | ||
| Other | 70 | 19.2% | 4 | 17.4% | 1 | 6.7% | ||
| Treatment | Vitamin D | 186 | 51.0% | 12 | 52.2% | 7 | 46.7% | 0.940 |
| Placebo | 179 | 49.0% | 11 | 47.8% | 8 | 53.3% | ||
| Asthma/Wheeze | Yes | 93 | 25.5% | 5 | 21.7% | 6 | 40.0% | 0.414 |
| No | 272 | 74.5% | 18 | 78.3% | 9 | 60.0% | ||
| Maternal Marital Status | Married | 179 | 49.0% | 9 | 39.1% | 4 | 26.7% | 0.449 |
| Not married/not living together | 88 | 24.1% | 8 | 34.8% | 6 | 40.0% | ||
| Not married - living together | 86 | 23.6% | 6 | 26.1% | 5 | 33.3% | ||
| Separated/Divorced | 12 | 3.3% | 0 | 0.0% | 0 | 0.0% | ||
| Maternal Educational Level | Less than high school | 45 | 12.3% | 3 | 13.0% | 3 | 20.0% | 0.057 |
| High school, Technical school | 96 | 26.3% | 10 | 43.5% | 5 | 33.3% | ||
| Some college | 84 | 23.0% | 5 | 21.7% | 6 | 40.0% | ||
| College graduate/Graduate school | 140 | 38.4% | 5 | 21.7% | 1 | 6.7% | ||
| Body Mass Index (BMI) at age 3 | Mean (standard deviation, SD) | 16.7 (1.9) | 16.4 (1.3) | 17.2 (2.3) | 0.528 | |||
| Age 1 Communication Score | On Schedule | 308 | 84.4% | 18 | 78.3% | 14 | 93.3% | 0.200 |
| Needs monitoring | 5 | 1.4% | 1 | 4.3% | 1 | 6.7% | ||
| Requires further evaluation | 2 | 0.5% | 0 | 0.0% | 0 | 0.0% | ||
| Missing | 50 | 13.7% | 4 | 174% | 0 | 0.0% | ||
| Age 2 Communication Score | On Schedule | 315 | 86.3% | 11 | 47.8% | 5 | 33.3% | 8.0 × 10−9 |
| Needs monitoring | 24 | 6.6% | 5 | 21.7% | 5 | 33.3% | ||
| Requires further evaluation | 7 | 1.9% | 3 | 13.0% | 4 | 26.7% | ||
| Missing | 19 | 5.2% | 4 | 17.4% | 1 | 6.7% | ||
| Stool samples available | Yes | 204 | 55.9% | 14 | 60.9% | 10 | 66.7% | - |
| Metabolite | Super Pathway | Sub Pathway | HMDB ID a | OR (95% Confidence Interval, CI) | p-Value |
|---|---|---|---|---|---|
| N-formylphenylalanine * | Amino Acid | Tyrosine Metabolism | - | 4.1 × 10−3 (4.1 × 10−2,0.11) | 0.002 |
| trimethylamine N-oxide * | Lipid | Phospholipid Metabolism | HMDB00925 | 30.13 (2.74,337.23) | 0.005 |
| cinnamoylglycine * | Xenobiotics | Food Component/Plant | HMDB11621 | 4.41 (1.52,12.80) | 0.006 |
| linoleoyl ethanolamide * | Lipid | Endocannabinoid | HMDB12252 | 12.38 (1.67,92.73) | 0.013 |
| palmitoyl ethanolamide | Lipid | Endocannabinoid | HMDB02100 | 141.67 (2.25,9324.32) | 0.019 |
| 5-hydroxyindoleacetate * | Amino Acid | Tryptophan Metabolism | HMDB00763 | 0.08 (0.01,0.64) | 0.023 |
| erythritol | Xenobiotics | Food Component/Plant | HMDB02994 | 21.39 (1.31,300.33) | 0.024 |
| pyrraline * | Xenobiotics | Food Component/Plant | HMDB33143 | 0.11 (0.01,0.68) | 0.024 |
| sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0) * | Lipid | Sphingolipid Metabolism | - | 0.02 (3.3 × 10−4,0.62) | 0.034 |
| docosahexaenoylcarnitine (C22:6) | Lipid | Fatty Acid Metabolism (Acyl Carnitine) | - | 6.28 (1.07,34.75) | 0.037 |
| prolylhydroxyproline | Amino Acid | Urea cycle; Arginine and Proline Metabolism | HMDB06695 | 95.30 (1.29,7378.98) | 0.038 |
| alpha-ketobutyrate | Amino Acid | Methionine, Cysteine, SAM and Taurine Metabolism | HMDB00005 | 3.98 (1.04,14.72) | 0.040 |
| N-formylanthranilic acid * | Amino Acid | Tryptophan Metabolism | HMDB04089 | 0.05 (3.3 × 10−4,0.75) | 0.043 |
| Serotonin * | Amino Acid | Tryptophan Metabolism | HMDB00259 | 5.75 (1.03,32.63) | 0.046 |
| oleoyl ethanolamide | Lipid | Endocannabinoid | HMDB02088 | 12.75 (0.99,161.42) | 0.048 |
| Metabolite | Super Pathway | Sub Pathway | HMDB ID | OR (95% CI) | p-Value |
|---|---|---|---|---|---|
| salicylate * | Xenobiotics | Drug | HMDB01895 | 0.3 (0.13,0.64) | 0.003 |
| R-mevalonate 5-diphosphate * | Lipid | Mevalonate Metabolism | HMDB01981 | 0.38 (0.18,0.69) | 0.004 |
| N-acetylglutamine * | Amino Acid | Glutamate Metabolism | HMDB06029 | 0.25 (0.09,0.65) | 0.005 |
| 2-hydroxyphenylacetate | Amino Acid | Tyrosine Metabolism | HMDB00669 | 0.44 (0.22,0.83) | 0.014 |
| suberate (octanedioate) * | Lipid | Fatty Acid, Dicarboxylate | HMDB00893 | 0.31 (0.12,0.78) | 0.015 |
| 2-aminophenol | Amino Acid | Tryptophan Metabolism | - | 0.43 (0.21,0.85) | 0.016 |
| cadaverine * | Amino Acid | Lysine Metabolism | HMDB02322 | 0.55 (0.33,0.89) | 0.017 |
| N-acetyl-3-methylhistidine | Amino Acid | Histidine Metabolism | - | 0.57 (0.34,0.96) | 0.034 |
| N-formylanthranilic acid | Amino Acid | Tryptophan Metabolism | HMDB04089 | 0.38 (0.15,0.93) | 0.039 |
| cis-4-hydroxycyclohexylacetic acid | Amino Acid | Tyrosine Metabolism | HMDB00451 | 0.54 (0.29,1.00) | 0.043 |
| 2,3-dimethylsuccinate | Amino Acid | Leucine, Isoleucine and Valine Metabolism | - | 0.55 (0.31,1.00) | 0.044 |
| S-1-pyrroline-5-carboxylate | Amino Acid | Glutamate Metabolism | HMDB01301 | 0.55 (0.30,1.00) | 0.048 |
| N-acetylserotonin | Amino Acid | Tryptophan Metabolism | HMDB01238 | 0.50 (0.24,0.97) | 0.050 |
| Classifier. | Area under the Curve (AUC) (95% CI) | Performance Compared to Model 1 | Sensitivity | Specificity |
|---|---|---|---|---|
| Model 1: Binary ASQ Communication Score | 0.736 (0.563, 0.909) | - | 55.6% | 91.6% |
| Model 2: Metabolite Summary Score | 0.759 (0.603, 0.915) | p = 0.635 | 66.7% | 78.7% |
| Model 3: Metabolite Levels | 0.924 (0.867, 0.980) | p = 0.034 | 88.9% | 84.5% |