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Multicenter Study
. 2010 Jul-Aug;22(4):463-72.
doi: 10.1002/ajhb.21018.

Modeling multisystem biological risk in young adults: The Coronary Artery Risk Development in Young Adults Study

Teresa Seeman  1 , Tara Gruenewald, Arun Karlamangla, Steve Sidney, Kiang Liu, Bruce McEwen, Joseph Schwartz
Affiliations
Multicenter Study

Modeling multisystem biological risk in young adults: The Coronary Artery Risk Development in Young Adults Study

Teresa Seeman et al. Am J Hum Biol. 2010 Jul-Aug.

Abstract

Although much prior research has focused on identifying the roles of major regulatory systems in health risks, the concept of allostatic load (AL) focuses on the importance of a more multisystems view of health risks. How best to operationalize allostatic load, however, remains the subject of some debate. We sought to test a hypothesized metafactor model of allostatic load composed of a number of biological system factors, and to investigate model invariance across sex and ethnicity. Biological data from 782 men and women, aged 32-47, from the Oakland, CA and Chicago, IL sites of the Coronary Artery Risk Development in Young Adults Study (CARDIA) were collected as part of the Year 15exam in 2000. These include measures of blood pressure, metabolic parameters (glucose, insulin, lipid profiles, and waist circumference), markers of inflammation (interleukin-6, C-reactive protein, and fibrinogen), heart rate variability, sympathetic nervous system activity (12-hr urinary norepinephrine and epinephrine) and hypothalamic-pituitary-adrenal axis activity (diurnal salivary free cortisol). A "metafactor" model of AL as an aggregate measure of six underlying latent biological subfactors was found to fit the data, with the metafactor structure capturing 84% of variance of all pairwise associations among biological subsystems. There was little evidence of model variance across sex and/or ethnicity. These analyses extend work operationalizing AL as a multisystems index of biological dysregulation, providing initial support for a model of AL as a metaconstruct of inter-relationships among multiple biological regulatory systems, that varies little across sex or ethnicity.

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Figures

Figure 1
Figure 1
a. Results for final one-factor allostatic load model (model fit statistics: χ2 (135) = 2349.44 χ2/df = 17.40, CFI = .45, RMSEA = .15, Model AIC = 2079.44). All parameter estimates are standardized and significant (p ≤ .05) except for path estimate for epinephrine (EPI). Estimates for error terms are not depicted. b. Results for “correlated 6 factor” allostatic load model (model fit statistics: χ2 (120) = 568.89, χ2/df = 4.7, CFI = .89, RMSEA = .07, Model AIC = 328.89). All parameter estimates are standardized and significant (p ≤ .05) except for path estimate for AM rise, and correlations between blood pressure and hormone factors, metabolic and hormone factors, HRV and salivary cortisol factors, and metabolic and salivary cortisol factors. Estimates for error terms are not depicted. c. Results for meta-factor allostatic load model including epinephrine (model fit statistics: χ2 (130) = 656.09, χ2/df = 5.0, CFI = .87, RMSEA = .07, Model AIC = 396). All parameter estimates are standardized and significant (p ≤ .05). Estimates for error terms are not depicted.
Figure 1
Figure 1
a. Results for final one-factor allostatic load model (model fit statistics: χ2 (135) = 2349.44 χ2/df = 17.40, CFI = .45, RMSEA = .15, Model AIC = 2079.44). All parameter estimates are standardized and significant (p ≤ .05) except for path estimate for epinephrine (EPI). Estimates for error terms are not depicted. b. Results for “correlated 6 factor” allostatic load model (model fit statistics: χ2 (120) = 568.89, χ2/df = 4.7, CFI = .89, RMSEA = .07, Model AIC = 328.89). All parameter estimates are standardized and significant (p ≤ .05) except for path estimate for AM rise, and correlations between blood pressure and hormone factors, metabolic and hormone factors, HRV and salivary cortisol factors, and metabolic and salivary cortisol factors. Estimates for error terms are not depicted. c. Results for meta-factor allostatic load model including epinephrine (model fit statistics: χ2 (130) = 656.09, χ2/df = 5.0, CFI = .87, RMSEA = .07, Model AIC = 396). All parameter estimates are standardized and significant (p ≤ .05). Estimates for error terms are not depicted.
Figure 1
Figure 1
a. Results for final one-factor allostatic load model (model fit statistics: χ2 (135) = 2349.44 χ2/df = 17.40, CFI = .45, RMSEA = .15, Model AIC = 2079.44). All parameter estimates are standardized and significant (p ≤ .05) except for path estimate for epinephrine (EPI). Estimates for error terms are not depicted. b. Results for “correlated 6 factor” allostatic load model (model fit statistics: χ2 (120) = 568.89, χ2/df = 4.7, CFI = .89, RMSEA = .07, Model AIC = 328.89). All parameter estimates are standardized and significant (p ≤ .05) except for path estimate for AM rise, and correlations between blood pressure and hormone factors, metabolic and hormone factors, HRV and salivary cortisol factors, and metabolic and salivary cortisol factors. Estimates for error terms are not depicted. c. Results for meta-factor allostatic load model including epinephrine (model fit statistics: χ2 (130) = 656.09, χ2/df = 5.0, CFI = .87, RMSEA = .07, Model AIC = 396). All parameter estimates are standardized and significant (p ≤ .05). Estimates for error terms are not depicted.
Figure 2
Figure 2
Results for final meta-factor allostatic load model (model fit statistics: χ2 (113) = 459.08, χ2/df = 4.1, CFI = .91, RMSEA = .06, Model AIC = 233.08). All parameter estimates are standardized and significant (p ≤ .05). Estimates for error terms and the correlation between error terms for HDL-C and triglycerides (r = −.36) are not depicted.
Figure 3
Figure 3
Estimated Allostatic Load & Sub-factor Mean Scores for White & Black Men and Women
See this image and copyright information in PMC

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