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Current methods for identifying temporal windows of effect for time-varying exposures in omics settings can control false discovery rates at the biomarker-level but cannot efficiently screen for timing-specific effects in high dimensions. Current approaches leverage separate models for site screening and identification of susceptible time windows, which miss associations that vary over time. We introduce the epigenome-wide distributed lag model (EWDLM), a novel approach that combines traditional false discovery rate methods with the distributed lag model (DLM) to screen for timing-specific effects in high dimensional settings. This is accomplished by marginalizing DLM effect estimates over time and correcting for multiple comparisons. In a simulation investigating timing-specific effects of ambient air pollution during pregnancy on DNA methylation across the epigenome at age 12 years, EWDLM achieved an increased sensitivity for associations limited to specific periods of time compared to traditional two-stage approaches. In a real-world EWDLM analysis, 353 CpG sites at which DNAm measured at age 12 was significantly associated with PM2.5 exposure during pregnancy were identified. EWDLM is a novel method that provides an efficient and sensitive way to screen epigenomic datasets for associations with exposures localized to specific time periods., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Exposure to particulate matter with an aerodynamic diameter smaller than 2.5 microns (PM 2.5 ) can affect birth outcomes through physiological pathways such as inflammation. One potential way PM 2.5 affects physiology could be through altering DNA methylation (DNAm). Considering that exposures during specific windows of gestation may have unique effects on DNAm, we hypothesized a timing-specific association between PM 2.5 exposure during pregnancy and DNAm in the neonatal epithelial-cell epigenome., Methods: After collecting salivary samples from a cohort of 91 neonates, DNAm was assessed at over 850,000 cytosine-guanine dinucleotide (CpG) methylation sites on the epigenome using the MethylationEPIC array. Daily ambient PM 2.5 concentrations were estimated based on the mother's address of primary residence during pregnancy. PM 2.5 was averaged over the first two trimesters, separately and combined, and tested for association with DNAm through an epigenome-wide association (EWA) analysis. For each EWA, false discovery rate (FDR)-corrected P < 0.05 constituted a significant finding and every CpG site with uncorrected P < 0.0001 was selected to undergo pathway and network analysis to identify molecular functions enriched by them., Results: Our analysis showed that cg18705808 was associated with the combined average of PM 2.5 . Pathway and network analysis revealed little similarity between the first two trimesters. Previous studies reported that TMEM184A , the gene regulated by cg18705808, has a putative role in inflammatory pathways., Conclusions: The differences in pathway and network analyses could potentially indicate trimester-specific effects of PM 2.5 on DNAm. Further analysis with greater temporal resolution would be valuable to fully characterize the effect of PM 2.5 on DNAm and child development., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report. The results reported herein correspond to specific aims of grant R56MD013006 to investigator A.T.F. and K.B. from the National Institute of Minority Health and Health Disparities. T.B.M. was supported by the National Institutes of Health (NIH) R01 HL132344 and R01 HG011411 grants. This work was also supported by grants from the Cincinnati Children’s Hospital Medical Center including Academic Research Committee and Trustee awards to K.B. and A.T.F. Sponsorships or competing interests that may be relevant to content are disclosed at the end of the article., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

Background and Purpose: Microstructural white matter abnormalities on DTI using Tract-Based Spatial Statistics at term-equivalent age are associated with cognitive and motor outcomes at 2 years of age or younger. However, neurodevelopmental tests administered at such early time points are insufficiently predictive of mild-moderate motor and cognitive impairment at school age. Our objective was to evaluate the microstructural antecedents of cognitive and motor outcomes at 3 years' corrected age in a cohort of very preterm infants., Materials and Methods: We prospectively recruited 101 very preterm infants (<32 weeks' gestational age) and performed DTI at term-equivalent age. The Differential Ability Scales, 2nd ed, Verbal and Nonverbal subtests, and the Bayley Scales of Infant and Toddler Development, 3rd ed, Motor subtest, were administered at 3 years of age. We correlated DTI metrics from Tract-Based Spatial Statistics with the Bayley Scales of Infant and Toddler Development , 3rd ed, and the Differential Ability Scales, 2nd ed, scores with correction for multiple comparisons., Results: Of the 101 subjects, 84 had high-quality DTI data, and of these, 69 returned for developmental testing (82%). Their mean (SD) gestational age was 28.4 (2.5) weeks, and birth weight was 1121.4 (394.1) g. DTI metrics were significantly associated with Nonverbal Ability in the corpus callosum, posterior thalamic radiations, fornix, and inferior longitudinal fasciculus and with Motor scores in the corpus callosum, internal and external capsules, posterior thalamic radiations, superior and inferior longitudinal fasciculi, cerebral peduncles, and corticospinal tracts., Conclusions: We identified widespread microstructural white matter abnormalities in very preterm infants at term that were significantly associated with cognitive and motor development at 3 years' corrected age., (© 2021 by American Journal of Neuroradiology.)

Autism spectrum disorder (ASD) is a developmental disorder, affecting about 1% of the global population. Currently, the only clinical method for diagnosing ASD are standardized ASD tests which require prolonged diagnostic time and increased medical costs. Our objective was to explore the predictive power of personal characteristic data (PCD) from a large well-characterized dataset to improve upon prior diagnostic models of ASD. We extracted six personal characteristics (age, sex, handedness, and three individual measures of IQ) from 851 subjects in the Autism Brain Imaging Data Exchange (ABIDE) database. ABIDE is an international collaborative project that collected data from a large number of ASD patients and typical non-ASD controls from 17 research and clinical institutes. We employed this publicly available database to test nine supervised machine learning models. We implemented a cross-validation strategy to train and test those machine learning models for classification between typical non-ASD controls and ASD patients. We assessed classification performance using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Of the nine models we tested using six personal characteristics, the neural network model performed the best with a mean AUC (SD) of 0.646 (0.005), followed by k-nearest neighbor with a mean AUC (SD) of 0.641 (0.004). This study established an optimal ASD classification performance with PCD as features. With additional discriminative features (e.g., neuroimaging), machine learning models may ultimately enable automated clinical diagnosis of autism.

Objective: The comparison of the effect of antidepressants on psychomotor functions in patients with endogenous depression., Materials and Methods: This prospective interventional study was carried out at a tertiary care teaching hospital on 95 literate patients with newly diagnosed endogenous depression matching inclusion and exclusion criteria. Patients were prescribed either desvenlafaxine (50 mg) or fluoxetine (40 mg) or sertraline (50 mg). Psychomotor functions were assessed by digit letter substitution, six letter cancellation, choice reaction time, hand steadiness and flicker fusion test at the baseline 1 st month and 3 rd month. Efficacy of drugs was also measured by Hamilton rating scale for depression. Data were analyzed by using ANOVA and P < 0.05 was considered as statistically significant., Results: A total of 95 patients were enrolled. Fluoxetine, desvenlafaxine, and sertraline were prescribed in 32, 32, and 31 patients, respectively. At the end of 3 months, a significant improvement in psychomotor functions was observed in patients treated with sertraline ( P < 0.05), while desvenlafaxine-treated patients did not show any significant change in any of the tests. Surprisingly, fluoxetine-treated patients showed deterioration in all psychomotor tests ( P < 0.05). Hamilton rating score improved at the end of 3 months treatment as compared to baseline. Most commonly observed adverse reactions in all three drug groups were nausea ( n = 20), dizziness ( n = 3), headache ( n = 20), and diarrhea ( n = 3)., Conclusion: Sertraline significantly improves psychomotor function as compared to desvenlafaxine while fluoxetine impairs., Competing Interests: There are no conflicts of interest.