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Deciduous tooth biomarkers reveal atypical fetal inflammatory regulation in autism spectrum disorder.

ABSTRACT: Atypical regulation of inflammation has been proposed in the etiology of autism spectrum disorder (ASD); however, measuring the temporal profile of fetal inflammation associated with future ASD diagnosis has not been possible. Here, we present a method to generate approximately daily profiles of prenatal and early childhood inflammation as measured by developmentally archived C-reactive protein (CRP) in incremental layers of deciduous tooth dentin. In our discovery population, a group of Swedish twins, we found heightened inflammation in the third trimester in children with future ASD diagnosis relative to controls (n = 66; 14 ASD cases; critical window: -90 to -50 days before birth). In our replication study, in the US, we observed a similar increase in CRP in ASD cases during the third trimester (n = 47; 23 ASD cases; -128 to -21 days before birth). Our results indicate that the third trimester is a critical period of atypical fetal inflammatory regulation in ASD.

SUBMITTER: Dumitriu D

PROVIDER: S-EPMC10011823 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Deciduous tooth biomarkers reveal atypical fetal inflammatory regulation in autism spectrum disorder.

Dumitriu Dani D Baldwin Elena E Coenen Roozie J J RJJ Hammond Luke A LA Peterka Darcy S DS Heilbrun Lynne L Frye Richard E RE Palmer Raymond R Norrman Hjalmar Nobel HN Fridell Anna A Remnelius Karl Lundin KL Isaksson Johan J Austin Christine C Curtin Paul P Bölte Sven S Arora Manish M

iScience 20230221 3

Atypical regulation of inflammation has been proposed in the etiology of autism spectrum disorder (ASD); however, measuring the temporal profile of fetal inflammation associated with future ASD diagnosis has not been possible. Here, we present a method to generate approximately daily profiles of prenatal and early childhood inflammation as measured by developmentally archived C-reactive protein (CRP) in incremental layers of deciduous tooth dentin. In our discovery population, a group of Swedish ...[more]

PMID: 36926653

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Project description:ImportanceSleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis.ObjectivesTo investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings.Design setting and participantsSleep polysomnogram data were obtained from the Nationwide Children' Health (NCH) Sleep DataBank. Children (ages: 8-16 yrs) with 149 autism and 197 age-matched controls without neurodevelopmental diagnosis were selected for analysis. An additional independent age-matched control group (n = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5-3 yr.; 38 autism and 75 controls) was used for additional validation.Main outcomes and measuresWe computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance.ResultsIn the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal's spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models.Conclusion and relevanceOur results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

Dataset Information

Deciduous tooth biomarkers reveal atypical fetal inflammatory regulation in autism spectrum disorder.

Publications

Deciduous tooth biomarkers reveal atypical fetal inflammatory regulation in autism spectrum disorder.

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