title: Allostatic Load as a Complex Clinical Construct: A Case-Based Computational Modeling Approach.
abstract: Allostatic load (AL) is a complex clinical construct, providing a unique window into the cumulative impact of stress. However, due to its inherent complexity, AL presents two major measurement challenges to conventional statistical modeling (the field's dominant methodology): it is comprised of a complex causal network of bioallostatic systems, represented by an even larger set of dynamic biomarkers; and, it is situated within a web of antecedent socioecological systems, linking AL to differences in health outcomes and disparities. To address these challenges, we employed case-based computational modeling (CBM), which allowed us to make four advances: (1) we developed a multisystem, 7-factor (20 biomarker) model of AL's network of allostatic systems; (2) used it to create a catalog of nine different clinical AL profiles (causal pathways); (3) linked each clinical profile to a typology of 23 health outcomes; and (4) explored our results (post hoc) as a function of gender, a key socioecological factor. In terms of highlights, (a) the Healthy clinical profile had few health risks; (b) the pro-inflammatory profile linked to high blood pressure and diabetes; (c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems; and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%); while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles.
authors.affiliation: Institute for Creative Technologies, University of Southern California, Los Angeles, California 90094.
authors.forename: J Galen
authors.initials: JG
authors.lastname: Buckwalter
authors.affiliation: Department of Sociology, Kent State University, Ohio 44240.
authors.forename: Brian
authors.initials: B
authors.lastname: Castellani
authors.affiliation: Laboratory of Neuroendocrinology, Rockefeller University, New York, New York 10065.
authors.forename: Bruce
authors.initials: B
authors.lastname: McEwen
authors.affiliation: Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
authors.forename: Arun S
authors.initials: AS
authors.lastname: Karlamangla
authors.affiliation: Institute for Creative Technologies, University of Southern California, Los Angeles, California 90094.
authors.forename: Albert A
authors.initials: AA
authors.lastname: Rizzo
authors.affiliation: Institute for Creative Technologies, University of Southern California, Los Angeles, California 90094.
authors.forename: Bruce
authors.initials: B
authors.lastname: John
authors.affiliation: Institute for Creative Technologies, University of Southern California, Los Angeles, California 90094.
authors.forename: Kyle
authors.initials: K
authors.lastname: O'Donnell
authors.affiliation: Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
authors.forename: Teresa
authors.initials: T
authors.lastname: Seeman
keywords: allostatic load; artificial neural nets; case-based modeling; complexity theory; computational modeling; health risk outcomes