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.

Social status and biological dysregulation: The “status syndrome” and allostatic load

“…Second, selected parameters reflect those for which information could be collected within the logistical and financial constraints of the MIDUS project itself. Selection of subscale components was confirmed by results of factor analyses (Buckwalter et al, in preparation). Measures of (1) cardiovascular functioning included resting systolic blood pressure, pulse pressure, and resting pulse rate.…”

Cases, clusters, densities: Modeling the nonlinear dynamics of complex health trajectories

“…As a second example, in a recent study we conducted on the negative impact of stress, we treated allostatic load as our primary case [1]. We did this by conceptualizing it as a complex clinical construct, comprised of a large number of interdependent biological subsystems, which are represented by an even larger number of interconnected biomarkers.…”

“…Hence, we come to the purpose of the current study: we seek to formalize our case-based density approach, as employed through the SAC Toolkit, in application to several of our recent health studies, including a study on allostatic load [1], public health [6], and international health [18], along with a forthcoming study on depression and wellbeing [19]. While the last three studies are all longitudinal, the first is discrete; nonetheless, we will refer to it here, as it was crucial to developing several of our steps.…”

System under large stress: Prediction and management of catastrophic failures

“…Emergency evacuations of hospitals after hurricane Katharina and Sandy, and the May 22, 2011 tornado in Joplin illustrate the urgent need for modeling the adaptive capacity of hospitals during an extended loss of infrastructure [1]. Presidential Policy Directive 21 [2] and the U.S. Department of Homeland Security National Infrastructure Protection Plan (NIPP) [3] call for increasing resilience of the nation's critical infrastructure.The resilience is the ability to withstand, adapt to, and rapidly recover from a disruptive change [4][5][6][7][8][9]. Complex systems researchers use a holistic interdisciplinary approach to predict emergent behaviors in systems under large stress with the intent to better manage them.…”

“…The resilience is the ability to withstand, adapt to, and rapidly recover from a disruptive change [4][5][6][7][8][9]. Complex systems researchers use a holistic interdisciplinary approach to predict emergent behaviors in systems under large stress with the intent to better manage them.…”