Low SES and a conflict-ridden, neglectful, or harsh family environment in childhood have been linked to a high rate of physical health disorders in adulthood. The objective of the present investigation was to evaluate a model of the pathways that may help to explain these links and to relate them to metabolic functioning (MF) in the Coronary Artery Risk Development In Young Adults (CARDIA) dataset.
Participants (n = 3225) in the year 15 assessment of CARDIA, age 33 to 45 years, completed measures of childhood socioeconomic status (SES), risky early family environment (RF), adult psychosocial functioning (PsyF, a latent factor measured by depression, hostility, positive and negative social contacts), and adult SES. Indicators of the latent factor MF were assessed, specifically, cholesterol, insulin, glucose, triglycerides, and waist circumference.
The overall prevalence of metabolic syndrome was 9.7%. Structural equation modeling indicated that childhood SES and RF are associated with MF via their association with PsyF (standardized path coefficients: childhood SES to RF −0.13, RF to PsyF 0.44, PsyF to MF 0.09, all p < .05), but also directly (coefficient from childhood SES to MF −0.12, p < .05), with good overall model fit. When this model was tested separately for race-sex subgroups, it fit best for white women, fit well for African-American women and white men, but did not fit well for African-American men.
These results indicate that childhood SES and early family environment contribute to metabolic functioning through pathways of depression, hostility, and poor quality of social contacts.
SES = socioeconomic status; RF = risky family environment; MF = metabolic functioning; PsyF = psychosocial functioning; CHD = coronary heart disease; HPA = hypothalamic pituitary axis; LDL = low-density lipoprotein; HDL = high-density lipoprotein; CARDIA = Coronary Artery Risk Development In Young Adults; RMSEA = root mean-square error of approximation; NFI = normed fit index; CFI = confirmatory fit index; SEM = structural equation modeling.
From the Department of Psychology (S.E.T.) and Department of Geriatrics (T.E.S.), University of California, Los Angeles, Los Angeles, California; and the Division of Preventive Medicine (C.I.K), University of Alabama, Birmingham and Birmingham Veterans Affairs Medical Center, Birmingham, Alabama; Department of Psychology, Western Washington University, Bellingham, WA (B.J.L.).
Address correspondence and reprint requests to Shelley E. Taylor, PhD, UCLA Department of Psychology, 1282A Franz Hall, Los Angeles, CA 90095. E-mail: email@example.com
Received for publication January 19, 2005; revision received June 13, 2005.
The first author was supported by NIMH training grant MH15750. Work on this manuscript was supported by contracts University of Alabama at Birmingham, Coordinating Center, N01-HC-95095; University of Alabama at Birmingham, Field Center, N01-HC-48047; University of Minnesota, Field Center, N01-HC-48048; Northwestern University, Field Center, N01-HC-48049; Kaiser Foundation Research Institute, N01-HC-48050; University of California, Irvine, Echocardiography Reading Center, N01-HC-45134; Harbor-UCLA Research Education Institute, Computed Tomography Reading Center, N01-HC-05187; from the National Heart, Lung and Blood Institute and through a grant from the MacArthur Research Network on SES and Health.