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

Abstract 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.

[1]  T. Moore,et al.  Estimating the Association Between Exposome and Psychosis as Well as General Psychopathology: Results From the ABCD Study , 2022, Biological psychiatry global open science.

[2]  R. Gur,et al.  Exposome and Trans-syndromal Developmental Trajectories Toward Psychosis , 2022, Biological psychiatry global open science.

[3]  T. Moore,et al.  Association between racial/ethnic discrimination and pubertal development in early adolescence , 2022, Psychoneuroendocrinology.

[4]  N. Volkow,et al.  Associations of family income with cognition and brain structure in USA children: prevention implications , 2021, Molecular Psychiatry.

[5]  Kevin F. Dowling,et al.  Association of adverse prenatal exposure burden with child psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study , 2021, PloS one.

[6]  John J. Foxe,et al.  Breastfeeding Duration Is Associated With Domain-Specific Improvements in Cognitive Performance in 9–10-Year-Old Children , 2021, Frontiers in Public Health.

[7]  C. Nievergelt,et al.  Gene–environment correlations and causal effects of childhood maltreatment on physical and mental health: a genetically informed approach , 2021, The lancet. Psychiatry.

[8]  R. Gur,et al.  Deconstructing the role of the exposome in youth suicidal ideation: Trauma, neighborhood environment, developmental and gender effects , 2021, Neurobiology of Stress.

[9]  S. Assari,et al.  Parental Education, Household Income, Race, and Children’s Working Memory: Complexity of the Effects , 2020, Brain sciences.

[10]  E. Storch,et al.  The p Factor Consistently Predicts Long-Term Psychiatric and Functional Outcomes in Anxiety-Disordered Youth. , 2020, Journal of the American Academy of Child and Adolescent Psychiatry.

[11]  G. Fava,et al.  Allostatic Load and Its Impact on Health: A Systematic Review , 2020, Psychotherapy and Psychosomatics.

[12]  M. O’Donovan,et al.  Association of Recent Stressful Life Events With Mental and Physical Health in the Context of Genomic and Exposomic Liability for Schizophrenia. , 2020, JAMA psychiatry.

[13]  L. S. Haug,et al.  Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach , 2020, Environmental health perspectives.

[14]  Mark C. Pachucki,et al.  Timing of puberty in boys and girls: Implications for population health , 2020, SSM - population health.

[15]  Emma L. Schymanski,et al.  The exposome and health: Where chemistry meets biology , 2020, Science.

[16]  D. Barch,et al.  Prenatal cannabis exposure and childhood outcomes: Results from the ABCD study , 2019 .

[17]  E. Sowell,et al.  Association of lead-exposure risk and family income with childhood brain outcomes , 2019, Nature Medicine.

[18]  M. Ungar,et al.  Resilience and mental health: how multisystemic processes contribute to positive outcomes. , 2019, The lancet. Psychiatry.

[19]  David G. Weissman,et al.  Childhood Adversity and Neural Development: A Systematic Review. , 2019, Annual review of developmental psychology.

[20]  Brendan P. Zietsch,et al.  Genetic correlates of social stratification in Great Britain , 2019, Nature Human Behaviour.

[21]  Adon F. G. Rosen,et al.  Development of a computerized adaptive screening tool for overall psychopathology ("p"). , 2019, Journal of psychiatric research.

[22]  R. Plomin,et al.  The p factor: genetic analyses support a general dimension of psychopathology in childhood and adolescence , 2019, bioRxiv.

[23]  J. Hudziak,et al.  Stress exposures, neurodevelopment and health measures in the ABCD study , 2019, Neurobiology of Stress.

[24]  Sinan Guloksuz,et al.  The Exposome Paradigm and the Complexities of Environmental Research in Psychiatry. , 2018, JAMA psychiatry.

[25]  Florence Demenais,et al.  Integration of the human exposome with the human genome to advance medicine. , 2018, Biochimie.

[26]  H. Garavan,et al.  Recruiting the ABCD sample: Design considerations and procedures , 2018, Developmental Cognitive Neuroscience.

[27]  Bjarni V. Halldórsson,et al.  The nature of nurture: Effects of parental genotypes , 2017, Science.

[28]  Terry L. Jernigan,et al.  Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description , 2017, Developmental Cognitive Neuroscience.

[29]  Peter R. Giacobbi,et al.  Childhood obesity and adult cardiovascular disease risk factors: a systematic review with meta-analysis , 2017, BMC Public Health.

[30]  M. Bellis,et al.  The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. , 2017, The Lancet. Public health.

[31]  Yuxia Cui,et al.  Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology. , 2017, Annual review of public health.

[32]  W. Revelle psych: Procedures for Personality and Psychological Research , 2017 .

[33]  Mark D. Hoover,et al.  Use of the "Exposome" in the Practice of Epidemiology: A Primer on -Omic Technologies. , 2016, American journal of epidemiology.

[34]  S. Reise,et al.  Evaluating bifactor models: Calculating and interpreting statistical indices. , 2016, Psychological methods.

[35]  Samuel B Green,et al.  The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree , 2016, Multivariate behavioral research.

[36]  Kosha Ruparel,et al.  The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. , 2015, Journal of child psychology and psychiatry, and allied disciplines.

[37]  R. Gur,et al.  Characterizing social environment's association with neurocognition using census and crime data linked to the Philadelphia Neurodevelopmental Cohort , 2015, Psychological Medicine.

[38]  Sarah Depaoli,et al.  Iteration of Partially Specified Target Matrices: Applications in Exploratory and Bayesian Confirmatory Factor Analysis , 2015, Multivariate behavioral research.

[39]  Thomas A. Green,et al.  Inoculation stress hypothesis of environmental enrichment , 2015, Neuroscience & Biobehavioral Reviews.

[40]  Tyler Maxwell Moore,et al.  Iteration of Target Matrices in Exploratory Factor Analysis , 2013 .

[41]  S. Reise The Rediscovery of Bifactor Measurement Models , 2012 .

[42]  G. Flores,et al.  Racial/ethnic disparities in health and health care among U.S. adolescents. , 2012, Health services research.

[43]  Moritz Heene,et al.  Masking misfit in confirmatory factor analysis by increasing unique variances: a cautionary note on the usefulness of cutoff values of fit indices. , 2011, Psychological methods.

[44]  S. Rappaport Implications of the exposome for exposure science , 2011, Journal of Exposure Science and Environmental Epidemiology.

[45]  N. Fox,et al.  Sensitive Periods. , 2011, Monographs of the Society for Research in Child Development.

[46]  Stephen M Rappaport,et al.  Environment and Disease Risks , 2010, Science.

[47]  S. Reise,et al.  Bifactor Models and Rotations: Exploring the Extent to Which Multidimensional Data Yield Univocal Scale Scores , 2010, Journal of personality assessment.

[48]  Katherine M Flegal,et al.  Changes in terminology for childhood overweight and obesity. , 2010, National health statistics reports.

[49]  B. Chaix,et al.  Neighborhood-level confounding in epidemiologic studies: unavoidable challenges, uncertain solutions. , 2010, Epidemiology.

[50]  Claudia Buss,et al.  Developmental Origins of Health and Disease: Brief History of the Approach and Current Focus on Epigenetic Mechanisms , 2009, Seminars in reproductive medicine.

[51]  R. Plomin,et al.  Rethinking environmental contributions to child and adolescent psychopathology: a meta-analysis of shared environmental influences. , 2009, Psychological bulletin.

[52]  J. Kaprio,et al.  Genetic and environmental influences on pubertal timing assessed by height growth , 2008, American journal of human biology : the official journal of the Human Biology Council.

[53]  Kristopher J Preacher,et al.  Item factor analysis: current approaches and future directions. , 2007, Psychological methods.

[54]  C. Wild Complementing the Genome with an “Exposome”: The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology , 2005, Cancer Epidemiology Biomarkers & Prevention.

[55]  N. Ryan,et al.  Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. , 1997, Journal of the American Academy of Child and Adolescent Psychiatry.

[56]  D. Sörbom Model modification , 1989 .

[57]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[58]  A Wolman,et al.  The environment and disease. , 1976, Bulletin of the Pan American Health Organization.