Allostatic load as a complex clinical construct: A case‐based computational modeling approach |
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| Authors: | J. Galen Buckwalter Brian Castellani Bruce Mcewen Arun S. Karlamangla Albert A. Rizzo Bruce John Kyle O'donnell Teresa Seeman |
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| Affiliation: | 1. Institute for Creative Technologies, University of Southern California, Los Angeles, California;2. Department of Sociology, Kent State University, Ohio;3. Laboratory of Neuroendocrinology, Rockefeller University, New York, New York;4. Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, United States |
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| 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. © 2015 Wiley Periodicals, Inc. Complexity 21: 291–306, 2016 |
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| Keywords: | allostatic load health risk outcomes complexity theory artificial neural nets computational modeling case‐based modeling |
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