Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota.
Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Biol Psychiatry. 2022 Oct 15;92(8):654-662. doi: 10.1016/j.biopsych.2022.05.027. Epub 2022 Jun 7.
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 n = 1254) with infant siblings of children with ASD (n = 377) and a lower ASD-familial-likelihood comparison group (n = 168; n = 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.
神经发育障碍在自闭症谱系障碍（ASD）中的患病率存在明显的性别差异。症状表现的异质性以及测量偏差的可能性阻碍了女性早期 ASD 的检测，这可能导致患病率估计的差异。我们在 ASD 患儿的婴儿兄弟姐妹样本中检查了社交沟通（SC）和受限及重复行为（RRBs）的轨迹，调整了基于年龄和性别的测量偏差。我们假设，利用前瞻性的家族易感性升高的样本，得出数据驱动的行为结构，并考虑测量偏差，将揭示出比 ASD 中通常所见的性别比例差异更小。
我们在 6 至 9、12 至 15、24 和 36 至 60 个月（总 n=1254）的年龄对 ASD 症状进行了直接评估，对象包括 ASD 患儿的婴儿兄弟姐妹（n=377）和 ASD 家族易感性较低的对照组（n=168；n=527）。我们在 SC 和 RRB 的单独模型中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，并评估了轨迹成员中的性别差异。
我们在 SC 和 RRB 模型中确定了 2 个潜在类别，在 SC 和 RRB 的高关注类别中，性别比例相等。在 SC 的高关注类别中也观察到了性别差异，表明被归类为具有升高的社交关注的女孩在该组中表现出比男孩更温和的症状。
这种用于描述 ASD 症状进展的新方法强调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。
