PDF(Pubmed)
Abstract：
Sex differences in the prevalence of neurodevelopmental disorders are particularly evident in autism spectrum disorder (ASD). Heterogeneous symptom presentation and the potential of measurement bias hinder early ASD detection in females and may contribute to discrepant prevalence estimates. We examined trajectories of social communication (SC) and restricted and repetitive behaviors (RRBs) in a sample of infant siblings of children with ASD, adjusting for age- and sex-based measurement bias. We hypothesized that leveraging a prospective elevated familial likelihood sample, deriving data-driven behavioral constructs, and accounting for measurement bias would reveal less discrepant sex ratios than are typically seen in ASD.
We conducted direct assessments of ASD symptoms at 6 to 9, 12 to 15, 24, and 36 to 60 months of age (total nobservations = 1254) with infant siblings of children with ASD (n = 377) and a lower ASD-familial-likelihood comparison group (n = 168; nobservations = 527). We established measurement invariance across age and sex for separate models of SC and RRB. We then conducted latent class growth mixture modeling with the longitudinal data and evaluated for sex differences in trajectory membership.
We identified 2 latent classes in the SC and RRB models with equal sex ratios in the high-concern cluster for both SC and RRB. Sex differences were also observed in the SC high-concern cluster, indicating that girls classified as having elevated social concerns demonstrated milder symptoms than boys in this group.
This novel approach for characterizing ASD symptom progression highlights the utility of assessing and adjusting for sex-related measurement bias and identifying sex-specific patterns of symptom emergence.
We conducted direct assessments of ASD symptoms at 6 to 9, 12 to 15, 24, and 36 to 60 months of age (total nobservations = 1254) with infant siblings of children with ASD (n = 377) and a lower ASD-familial-likelihood comparison group (n = 168; nobservations = 527). We established measurement invariance across age and sex for separate models of SC and RRB. We then conducted latent class growth mixture modeling with the longitudinal data and evaluated for sex differences in trajectory membership.
We identified 2 latent classes in the SC and RRB models with equal sex ratios in the high-concern cluster for both SC and RRB. Sex differences were also observed in the SC high-concern cluster, indicating that girls classified as having elevated social concerns demonstrated milder symptoms than boys in this group.
This novel approach for characterizing ASD symptom progression highlights the utility of assessing and adjusting for sex-related measurement bias and identifying sex-specific patterns of symptom emergence.
摘要：
在自闭症谱系障碍（ASD）中，神经发育障碍患病率的性别差异尤其明显。不均匀的症状表现和潜在的测量偏差阻碍了女性的早期ASD检测，并可能导致不同的患病率估计。我们在ASD儿童的婴儿兄弟姐妹样本中检查了社交交流（SC）以及受限和重复行为（RRB）的轨迹，调整基于年龄和性别的测量偏差。我们假设利用预期的家族可能性升高样本，推导数据驱动的行为结构，考虑到测量偏差会显示出与ASD中通常看到的差异较小的性别比。
我们对6至9、12至15、24和36至60个月大的ASD症状进行了直接评估（总数=1254），其中包括ASD儿童的婴儿兄弟姐妹（n=377）和较低的ASD家族可能性比较组（n=168；总数=527）。我们为SC和RRB的单独模型建立了跨年龄和性别的测量不变性。然后，我们使用纵向数据进行了潜在类别增长混合建模，并评估了轨迹成员的性别差异。
我们在SC和RRB模型中确定了2个潜在类别，在SC和RRB的高关注簇中性别比例相等。在SC高度关注集群中也观察到性别差异，这表明，在这一组中，被归类为社会问题加剧的女孩表现出的症状比男孩轻。
这种表征ASD症状进展的新方法突出了评估和调整与性别相关的测量偏差以及识别症状出现的性别特异性模式的效用。
我们对6至9、12至15、24和36至60个月大的ASD症状进行了直接评估（总数=1254），其中包括ASD儿童的婴儿兄弟姐妹（n=377）和较低的ASD家族可能性比较组（n=168；总数=527）。
我们在SC和RRB模型中确定了2个潜在类别，在SC和RRB的高关注簇中性别比例相等。
这种表征ASD症状进展的新方法突出了评估和调整与性别相关的测量偏差以及识别症状出现的性别特异性模式的效用。
