在两个独立的青少年队列中通过一般暴露因素建模环境。

Exposome Pub Date : 2022-01-01 DOI:10.1093/exposome/osac010
Tyler M Moore, Elina Visoki, Stirling T Argabright, Grace E Didomenico, Ingrid Sotelo, Jeremy D Wortzel, Areebah Naeem, Ruben C Gur, Raquel E Gur, Varun Warrier, Sinan Guloksuz, Ran Barzilay
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引用次数: 3

摘要

暴露于围产期、家庭、社会和物理环境刺激对人类发育有重大影响。我们的目标是在两个独立的青少年队列中,使用环境暴露(主要来自社会心理环境)的多层次数据(参与者报告、父母报告和地理编码测量),生成一个单一的测量方法,以捕获环境(即暴露)的复杂网络结构:青少年大脑认知发展研究(ABCD研究,N = 11 235;平均年龄10.9岁;47.7%女性)和来自费城神经发育队列(PNC, N = 4993)的年龄和性别匹配的样本。在ABCD研究中,我们进行了一系列数据驱动的迭代因子分析和双因子建模,将348个与环境相关的变量降维为6个正交暴露子因子和一个一般(不利)暴露因子。一般暴露因素与总体精神病理(B = 0.28, 95% CI, 0.26-0.3)和主要健康相关结局相关:肥胖(优势比[OR], 1.4;95% CI, 1.3-1.5)和青春期发育晚期(OR, 1.3;95% ci, 1.2-1.5)。在PNC中,类似的方法将环境维度从29个变量减少到4个暴露子因子和一个一般暴露因子。PNC分析得出一般暴露因子与精神病理的一致关联(B = 0.15;95% CI, 0.13-0.17),肥胖(OR, 1.4;95% CI, 1.3-1.6)和青春期发育晚期(OR, 1.3;95% ci, 1-1.6)。在这两个队列中,与仅依赖人口统计学和父母教育的模型相比,纳入暴露因素大大增加了总体精神病理学解释的方差(ABCD中为38%;PNC为18.5%)。研究结果表明,可以推导出捕获多层次环境暴露的一般暴露因子,并且可以一致地解释青少年心理和一般健康的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Modeling environment through a general exposome factor in two independent adolescent cohorts.

Modeling environment through a general exposome factor in two independent adolescent cohorts.

Modeling environment through a general exposome factor in two independent adolescent cohorts.

Modeling environment through a general exposome factor in two independent adolescent cohorts.

Exposures to perinatal, familial, social, and physical environmental stimuli can have substantial effects on human development. We aimed to generate a single measure that capture's the complex network structure of the environment (ie, exposome) using multi-level data (participant's report, parent report, and geocoded measures) of environmental exposures (primarily from the psychosocial environment) in two independent adolescent cohorts: The Adolescent Brain Cognitive Development Study (ABCD Study, N = 11 235; mean age, 10.9 years; 47.7% females) and an age- and sex-matched sample from the Philadelphia Neurodevelopmental Cohort (PNC, N = 4993). We conducted a series of data-driven iterative factor analyses and bifactor modeling in the ABCD Study, reducing dimensionality from 348 variables tapping to environment to six orthogonal exposome subfactors and a general (adverse) exposome factor. The general exposome factor was associated with overall psychopathology (B = 0.28, 95% CI, 0.26-0.3) and key health-related outcomes: obesity (odds ratio [OR] , 1.4; 95% CI, 1.3-1.5) and advanced pubertal development (OR, 1.3; 95% CI, 1.2-1.5). A similar approach in PNC reduced dimensionality of environment from 29 variables to 4 exposome subfactors and a general exposome factor. PNC analyses yielded consistent associations of the general exposome factor with psychopathology (B = 0.15; 95% CI, 0.13-0.17), obesity (OR, 1.4; 95% CI, 1.3-1.6), and advanced pubertal development (OR, 1.3; 95% CI, 1-1.6). In both cohorts, inclusion of exposome factors greatly increased variance explained in overall psychopathology compared with models relying solely on demographics and parental education (from <4% to >38% in ABCD; from <4% to >18.5% in PNC). Findings suggest that a general exposome factor capturing multi-level environmental exposures can be derived and can consistently explain variance in youth's mental and general health.

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