Association between maternal autistic traits and children’s anxiety among Chinese preschool children in the general population: the chained mediation model of maternal meta-emotion philosophy and children’s emotional instability

Background: Little research has elucidated the effects of maternal autistic traits (MATs) on children's anxiety in normal populations, and their underlying mechanisms. The present study aimed to test this relationship and the mediating role of maternal negative meta-emotional philosophy (MEP) and children's emotional instability among Chinese people. Methods: This study recruited 590 mother-child dyads. These mothers have no other children with autism, and they completed the Autism Spectrum Quotient, Maternal Meta-Emotion Philosophy, Emotion Regulation Checklists, and the Chinese version of the Spence Children's Anxiety Scale. The chain mediation model was tested using SPSS software. Results: (1) There are significantly positive correlations among MATs and children's anxiety. (2) Children's anxiety was affected by MATs through 3 different pathways: the mediating role of maternal MEP (dysfunction and noninvolvement emotional philosophy), the mediating role of children's emotional lability, and the chain mediating role of both MEP and children's emotional lability. Conclusions and limitations: This cross-sectional study demonstrates that MATs predict child anxiety through the sequential mediation of mothers' negative MEP and children's emotional instability. These findings deepen our understanding of the adverse effects of subclinical autistic traits within the general population. Furthermore, they suggest that early interventions for families with mothers exhibiting high autistic traits should focus not solely on the traits themselves, but on improving maternal MEP. Such a focus would help children develop adaptive emotion regulation strategies, thereby reducing the risk of anxiety. A primary limitation of this study is its cross-sectional design, which precludes causal inferences. Future longitudinal research is needed to clarify the temporal dynamics and long-term effects among these variables.