Latent Class Analysis of Depressive Symptom Phenotypes Among Black/African American Mothers

Author:

Perez Nicole BeaulieuORCID,D'Eramo Melkus GailORCID,Wright FayORCID,Yu GaryORCID,Vorderstrasse Allison A.ORCID,Sun Yan V.ORCID,Crusto Cindy A.ORCID,Taylor Jacquelyn Y.ORCID

Abstract

Background Depression is a growing global problem with significant individual and societal costs. Despite their consequences, depressive symptoms are poorly recognized and undertreated because wide variation in symptom presentation limits clinical identification—particularly among African American (AA) women—an understudied population at an increased risk of health inequity. Objectives The aims of this study were to explore depressive symptom phenotypes among AA women and examine associations with epigenetic, cardiometabolic, and psychosocial factors. Methods This cross-sectional, retrospective analysis included self-reported Black/AA mothers from the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure study (data collected in 2015–2020). Clinical phenotypes were identified using latent class analysis. Bivariate logistic regression examined epigenetic age, cardiometabolic traits (i.e., body mass index ≥ 30 kg/m2, hypertension, or diabetes), and psychosocial variables as predictors of class membership. Results All participants were Black/AA and predominantly non-Hispanic. Over half of the sample had one or more cardiometabolic traits. Two latent classes were identified (low vs. moderate depressive symptoms). Somatic and self-critical symptoms characterized the moderate symptom class. Higher stress overload scores significantly predicted moderate-symptom class membership. Discussion In this sample of AA women with increased cardiometabolic burden, increased stress was associated with depressive symptoms that standard screening tools may not capture. Research examining the effect of specific stressors and the efficacy of tools to identify at-risk AA women are urgently needed to address disparities and mental health burdens.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

General Nursing

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