Byrne H, Visontay R, Devine E K, Wade N E, Jacobus J, Squeglia L M, Mewton L
bioRxiv. 2025 May 11:2025.05.08.652983. doi: 10.1101/2025.05.08.652983.
Early alcohol initiation (before age 15) is associated with adverse outcomes. Understanding mechanisms behind early alcohol initiation is essential for informing prevention efforts.
To examine whether structural covariance network properties at ages 9-10 years predict early alcohol initiation.
Case-control, population-based study design.
Data from the Adolescent Brain Cognitive Development study were used. Baseline structural brain imaging data (ages 9-10) were used for generation and comparison of structural covariance networks. Data from baseline to 4-year follow-up (≤age 15) assessments were used to determine alcohol initiation.
Participants were excluded if they reported consuming a full drink of alcohol at baseline, or did not meet imaging inclusion criteria. Controls were excluded if they had not yet been assessed or were missing substance use data at 4-year follow-up. In total, 3,878 participants met study criteria, of which 182 participants initiated alcohol. Structural covariance network properties were compared between the full sample and a 1:1 propensity-matched sample based on age, sex, race, ethnicity, religion, parental education, prenatal alcohol exposure, and baseline alcohol sipping.
Structural covariance networks were estimated using regional cortical thickness and volume measurements. Measures of network segregation (modularity, clustering coefficient), integration (characteristic path length, global efficiency), and resilience (degree assortativity) were compared between groups. Early alcohol initiation was defined as consuming a full drink between baseline and 4-year follow-up.
Alcohol initiators ( =182, median[IQR] age, 10.3[9.9-10.8]; 101 female[55.5%]) demonstrated lower network segregation (modularity: area-under-the-curve[AUC] difference[95%CI]=-0.017[-0.017,-0.007], =0.030; clustering coefficient: AUC[95%CI]=-0.026[-0.027,-0.012], =0.0495) and higher network integration (characteristic path length: AUC[95%CI]=-0.106[-0.099,-0.046], =0.020; global efficiency: AUC[95%CI]=0.011[0.005,0.011], p=0.010), compared to non-initiators ( =3,696, median[IQR] age, 9.9[9.4-10.4]; 1750 female[47.4%]) when controlling for age, sex, and mean cortical thickness. Within the matched sample, only differences in network integration were preserved (characteristic path length: AUC[95%CI]=-0.044[-0.032,0.035], =0.010; global efficiency: AUC[95%CI]=0.003[-0.003,0.003], =0.040). There were no differences between full or matched samples when comparing cortical volume structural covariance networks.
Differences in cortical thickness structural covariance network properties at ages 9-10 predicted alcohol initiation before age 15. These findings suggest cortical thickness network topology may reflect a neuroanatomical risk marker for early alcohol initiation.
Do structural covariance network properties at age 9-10 years predict alcohol initiation prior to age 15? In this case-control study of 3,878 participants, early adolescent alcohol initiators demonstrated differences in cortical thickness network integration and segregation compared to their non-initiating peers. Alcohol-naïve adolescents who initiate alcohol use early in life demonstrate differences in structural brain network organization compared to their abstinent peers, which may reflect a neuroanatomical risk marker for early alcohol use.
过早开始饮酒(15岁之前)与不良后果相关。了解过早开始饮酒背后的机制对于指导预防工作至关重要。
研究9至10岁时的结构协方差网络特性是否能预测过早开始饮酒。
基于人群的病例对照研究设计。
使用了青少年大脑认知发展研究的数据。基线结构脑成像数据(9至10岁)用于生成和比较结构协方差网络。从基线到4年随访(≤15岁)评估的数据用于确定是否开始饮酒。
如果参与者报告在基线时饮用了整杯酒,或不符合成像纳入标准,则被排除。如果对照在4年随访时尚未接受评估或缺少物质使用数据,则被排除。共有3878名参与者符合研究标准,其中182名参与者开始饮酒。基于年龄、性别、种族、民族、宗教、父母教育程度、产前酒精暴露和基线饮酒情况,在全样本和1:1倾向匹配样本之间比较结构协方差网络特性。
使用区域皮质厚度和体积测量来估计结构协方差网络。比较两组之间的网络分离(模块化、聚类系数)、整合(特征路径长度、全局效率)和弹性(度相关性)指标。过早开始饮酒的定义为在基线和4年随访之间饮用整杯酒。
在控制年龄、性别和平均皮质厚度后,与未开始饮酒者(n = 3696,年龄中位数[四分位间距],9.9[9.4 - 10.4];1750名女性[47.4%])相比,开始饮酒者(n = 182,年龄中位数[四分位间距],10.3[9.9 - 10.8];101名女性[55.5%])表现出较低的网络分离(模块化:曲线下面积[AUC]差异[95%置信区间]= -0.017[-0.017, -0.007],p = 0.030;聚类系数:AUC[95%置信区间]= -0.026[-0.027, -0.012],p = 0.0495)和较高的网络整合(特征路径长度:AUC[95%置信区间]= -0.106[-0.099, -0.046],p = 0.020;全局效率:AUC[95%置信区间]= 0.011[0.005, 0.011],p = 0.010)。在匹配样本中,仅保留了网络整合方面的差异(特征路径长度:AUC[95%置信区间]= -0.044[-0.032, 0.035],p = 0.010;全局效率:AUC[