Prenatal phthalate exposure and neurodevelopmental differences in twins at 2 years of age.

BACKGROUND: Previous studies of singletons evaluating prenatal phthalate exposure and early neurodevelopment reported mixed results and the associations could be biased by parental, obstetrical, and genetic factors. METHODS: A co-twin control design was employed to test whether prenatal phthalate exposure was associated with children's neurocognitive development. We collected information from 97 mother-twin pairs enrolled in the Wuhan Twin Birth Cohort between March 2016 and October 2018. Fourteen phthalate metabolites were measured in maternal urine collected at each trimester. Neurodevelopmental differences in twins at the age of two were examined as the outcome of interest. Multiple informant model was used to examine the covariate-adjusted associations of prenatal phthalate exposure with mental development index (MDI) and psychomotor development index (PDI) scores assessed at 2 years of age based on Bayley Scales of Infant Development (Second Edition). This model also helps to identify the exposure window of susceptibility. RESULTS: Maternal urinary levels of mono-2-ethyl-5-oxohexyl phthalate (MEOHP) (ß = 1.91, 95% CI: 0.43, 3.39), mono (2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) (ß = 1.56, 95% CI: 0.33, 2.79), and the sum of di-(2-ethylhexyl) phthalate metabolites (∑DEHP) (ß = 1.85, 95% CI: 0.39, 3.31) during the first trimester showed the strongest and significant positive associations with intra-twin MDI difference. When stratified with twin chorionicity, the positive associations of monoethyl phthalate (MEP), monoisobutyl phthalate (MiBP), mono-n-butyl phthalate (MBP), monobenzyl phthalate (MBzP), individual DEHP metabolites, and ∑DEHP exposure during pregnancy with intra-twin neurodevelopmental differences were more significant in monochorionic diamniotic (MCDA) twins than those in dichorionic diamniotic (DCDA) twins. CONCLUSIONS: Neurodevelopmental differences in MCDA twins were strongly associated with prenatal phthalate exposure. Our findings warrant further confirmation in longitudinal studies with larger sample sizes.

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences.

Objective: Individuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering. Methods: Using the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach. Results: HAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3-12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC. Conclusion: Our study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.