Modeling Multisystem Physiological Dysregulation

Abstract

Objectives: The purposes of this study were to compare the relative fit of two alternative factor models of allostatic load (AL) and physiological systems, and to test factor invariance across age and sex.

Methods: Data were from the Midlife in the United States II Biomarker Project, a large (n = 1255) multisite study of adults aged 34 to 84 years (56.8% women). Specifically, 23 biomarkers were included, representing seven physiological systems: metabolic lipids, metabolic glucose, blood pressure, parasympathetic nervous system, sympathetic nervous system, hypothalamic-pituitary-adrenal axis, and inflammation. For factor invariance tests, age was categorized into three groups (≤45, 45-60, and >60 years).

Results: A bifactor model where biomarkers simultaneously load onto a common AL factor and seven unique system-specific factors provided the best fit to the biomarker data (comparative fit index = 0.967, root mean square error of approximation = 0.043, standardized root mean square residual = 0.028). Results from the bifactor model were consistent with invariance across age groups and sex.

Conclusions: These results support the theory that represents and operationalizes AL as multisystem physiological dysregulation and operationalizing AL as the shared variance across biomarkers. Results also demonstrate that in addition to the variance in biomarkers accounted for by AL, individual physiological systems account for unique variance in system-specific biomarkers. A bifactor model allows researchers greater precision to examine both AL and the unique effects of specific systems.

Figure 1

Continuum of specificity of biomarker outcomes, from the level of individual biomarkers, to individual physiological systems, to composites across multiple systems.

Figure 2

Sample diagrams of the two plausible factor models tested and compared. Adapted from http://score-project.org and reprinted with permission.

Figure 3

Item Parameter Invariance Plot. Standardized loadings with 95% confidence intervals for the overall bi-factor model are shown in black triangles. Results from seven “reduced” models where each system was systematically dropped are shown slightly below the results from the overall bi-factor model. The overlap in points and confidence intervals shows that the standardized loadings for the remaining biomarkers do not change substantially when biomarkers for any one system are dropped.