Marotta Justin, Aggarwal Shambhavi, Osayande Nicole, Saltoun Karin, Kopal Jakub, Holmes Avram J, Yip Sarah W, Bzdok Danilo
McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada.
Mila - Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada.
PLoS One. 2025 Aug 13;20(8):e0327729. doi: 10.1371/journal.pone.0327729. eCollection 2025.
As an increasing realization, many behavioral relationships are interwoven with inherent variations in human populations. Presently, there is no clarity in the biomedical community on which sources of population variation are most dominant. The recent advent of population-scale cohorts like the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) are now offering unprecedented depth and width of phenotype profiling that potentially explains interfamily differences. Here, we leveraged a deep learning framework (conditional variational autoencoder) on the totality of the ABCD Study® phenome (8,902 candidate phenotypes in 11,875 participants) to identify and characterize major sources of population stratification. 80% of the top 5 sources of explanatory stratifications were driven by distinct combinations of 202 available socioeconomic status (SES) measures; each in conjunction with a unique set of non-overlapping social and environmental factors. Several sources of variation across this cohort flagged geographies marked by material poverty interlocked with mental health and behavioral correlates. Deprivation emerged in another top stratification in relation to urbanicity and its ties to immigrant and racial and ethnic minoritized groups. Conversely, two other major sources of population variation were both driven by indicators of privilege: one highlighted measures of access to educational opportunity and income tied to healthy home environments and good behavior, the other profiled individuals of European ancestry leading advantaged lifestyles in desirable neighborhoods in terms of location and air quality. Overall, the disclosed social stratifications underscore the importance of treating SES as a multidimensional construct and recognizing its ties into social determinants of health.
随着认识的不断加深,许多行为关系与人类群体固有的差异相互交织。目前,生物医学界对于哪些群体差异来源最为显著尚无明确认识。像青少年大脑认知发展研究(ABCD研究®)这样的大规模群体研究的出现,为表型分析提供了前所未有的深度和广度,有可能解释家庭间的差异。在此,我们利用深度学习框架(条件变分自编码器)对ABCD研究®的全部表型组(11,875名参与者中的8,902个候选表型)进行分析,以识别和描述群体分层的主要来源。前5个解释性分层来源中有80%是由202种可用的社会经济地位(SES)指标的不同组合驱动的;每种组合都与一组独特的、不重叠的社会和环境因素相关。该队列中的几个差异来源表明,物质贫困地区与心理健康和行为相关因素相互关联。在与城市化及其与移民以及少数族裔群体的关系相关的另一个顶级分层中出现了贫困问题。相反,另外两个主要的群体差异来源均由特权指标驱动:一个突出了获得教育机会的措施以及与健康家庭环境和良好行为相关的收入,另一个则描述了欧洲血统的个体在理想社区中过着优越生活方式,这些社区在地理位置和空气质量方面具有优势。总体而言,所揭示的社会分层凸显了将社会经济地位视为一个多维结构并认识其与健康社会决定因素之间联系的重要性。
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