Childhood maltreatment and longitudinal trajectories of disordered eating behaviors: sociodemographic moderators and behavior-specific sensitivity analyses.

Lina Modjarrad, Nicole Marlow, Avery Bollinger, Yenan Zhu

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Article in Journal of eating disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Lina ModjarradDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, USA. LModjarrad@phhp.ufl.edu.

Nicole MarlowDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, USA.

Avery BollingerDepartment of Social and Behavioral Sciences, College of Public Health and Health Professions, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, USA.

Yenan ZhuDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, USA.

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6 · The paper itself

Abstract

objectiveDisordered eating behaviors (DEBs) represent a significant public health issue, with childhood maltreatment emerging as a potential contributing factor. However, the longitudinal impact of maltreatment on the progression of disordered eating behaviors from adolescence to adulthood remains inadequately understood. This study aims to explore how childhood maltreatment influences the trajectory of disordered eating behaviors, focusing on potential moderating factors such as sociodemographic variables, race, and sex.

methodsData from Waves I-III of Add Health (ages 11-26) were used to assess DEB trajectories, with retrospective childhood maltreatment data collected in Wave IV. Repeated measures mixed-effects models accounted for intra-individual variability, adjusting for relevant covariates. Interaction effects between maltreatment subtypes and sociodemographic covariates (sex, race/ethnicity, income) were analyzed to examine how these intersections influenced DEB trajectories.

resultsAmong the 15,363,384 participants, 49.2% experienced childhood maltreatment. Adjusted models indicated significant increases in DEBs over time, particularly between Waves II and III (β = 19.9, p < 0.001). Although physical abuse (β = 1.88, p = 0.1330), verbal abuse (β = -0.67, p = 0.4529), and sexual abuse by a parent (β = 1.59, p = 0.4204) were not significant predictors of worsening behaviors, stratified analyses revealed that low-income individuals exposed to non-familial sexual abuse demonstrated the steepest worsening of DEBs (β = 26.5, p = 0.0048). Interaction effects revealed that Asian or Pacific Islander participants experiencing verbal abuse, and low-income individuals exposed to non-familial sexual abuse demonstrated pronounced worsening of DEBs. We observed significant sex differences: males exhibited higher levels of worsening behaviors over time (β = -10.8, p < 0.0001).

conclusionOur findings suggest that sociodemographic characteristics (sex, race, income) may moderate the persistence of DEBs, underscoring the need for trauma-informed and tailored interventions. Addressing childhood adversity through public health, policy, and clinical efforts can help support those at highest risk. Future research should examine these trajectories beyond early adulthood and explore protective factors that may buffer against long-term DEB risk.

Indexed as

Childhood maltreatmentDisordered eating behaviorsEating disordersLongitudinal analysisMental health disparitiesPrecision psychiatrySex differencesSociodemographic factorsSocioeconomic factorsTrauma-informed interventions

Identifiers

PMID40731372

DOI10.1186/s40337-025-01355-2

PMCPMC12309129

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.