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. 2025 Jun 2;20(5):e0323788.
doi: 10.1371/journal.pone.0323788. eCollection 2025.

A novel approach for measuring allostatic load highlights differences in stress burdens due to race, sex and smoking status

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A novel approach for measuring allostatic load highlights differences in stress burdens due to race, sex and smoking status

Aleah Bailey et al. PLoS One. .

Abstract

The psychosocial and environmental stressors resulting from adverse social determinants of health (SDOH) can result in allostatic load, or 'wear and tear' on the body due to prolonged stress. Allostatic load serves as a useful tool to measure stress- related biological responses that can be integrated into risk assessments. In this study a novel allostatic load measure was used to investigate the relationship between acute stress, stress-related physiological dysregulation and allostatic load (AL) and various demographic variables. Using a small volume of serum collected from 63 participants, we assessed stress biomarkers through ELISA assays and developed a one-sample, semi-automated method for calculating AL scores derived from the Toxicological Prioritization Index (ToxPi) framework. Our analysis employed ordinal regression models to identify the contributions of primary mediators to predicting blood pressure classification, revealing that epinephrine was the most significant predictor of blood pressure, followed by cortisol. Primarily, our results highlight racial disparities in stress loads, in which Black participants had greater secondary mediator scores, and higher AL compared to White participants. Black females and Black non-smokers had higher physiological dysregulation and allostatic load than White participants. Overall, our study presents a novel scoring approach that facilitates the integration of allostatic load measures from a single clinical sample into environmental health research. It also demonstrates the utility of allostatic load in capturing race and sex differences in stress burdens, with important implications for understanding health disparities and improving risk assessments.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Biomarkers’ contribution to ordinal regression models.
Three acute stress biomarkers (epinephrine, cortisol, and noradrenaline) were used to predict a participant’s blood pressure category. Blood pressure was categorized into 3 classes: normal, at risk, and hypertensive and (B) 2 classes: normal and at risk and hypertensive participants combined. Each mediator’s contribution to the model was extracted and depicted as a percentage in the pie chart.
Fig 2
Fig 2. Stress outcomes by race, sex and smoking status.
Stress outcomes measured include (A-C) acute stress, (D-F) secondary mediator scores and (G-I) allostatic load. Groups were compared using T-tests. Significant differences are denoted as follows: Represents significance between groups (p < 0.1), * represents significance between groups (p < 0.05), ** represents significance between groups (p < 0.01), *** represents significance between groups (p < 0.001).
Fig 3
Fig 3. Stratified stress outcomes by race, sex and smoking status.
Stress outcomes measured include (A-C) acute stress, (D-F) secondary mediator scores and (G-I) allostatic load. Groups were compared using a two-way ANOVA test to obtain an overall p-value as depicted in the upper left- or right-hand corner of each plot. Pairwise t-tests were then run for post-hoc testing and include the following comparisons: Represents significance between groups (Padj < 0.1), * represents significance between groups (Padj < 0.05), ** represents significance between groups (Padj < 0.01), *** represents significance between groups (Padj < 0.001).

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