Journal Article

Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort

Bzdok, Danilo, Yip, Sarah W., Marotta, Justin, Holmes, Avram J., Osayande, Nicole, Saltoun, Karin, Kopal, Jakub, Aggarwal, Shambhavi

2025

Overview

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 Development SM 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adolescent / Adult / Air quality / Analysis / Biology and Life Sciences / Biomedical research / Biomedicine / Brain / Brain research / Candidates / Cohort Studies / Computer and Information Sciences / Datasets / Deep Learning / Deprivation / Environmental aspects / Environmental factors / Ethnic groups / Female / Genetic aspects / Genetic variation / Genotype & phenotype / Health behavior / Home environment / Human populations / Humans / Immigrants / Income inequality / Influence / Learning / Machine learning / Male / Measures / Medicine and Health Sciences / Mental health / Middle Aged / Minority groups / Neighborhoods / Phenotype / Phenotypes / Physical Sciences / Population / Population genetics / Poverty / Profiles / Research and Analysis Methods / Social Class / Social classes / Social determinants of health / Social discrimination learning / Social factors / Social interactions / Social Sciences / Social Status / Social stratification / Socioeconomic factors / Socioeconomic status / Socioeconomics / Stratification / United States / Variables / Variation / Width / Young Adult