Researchers document a reproducible workflow for estimating allostatic overload

A research team has documented a workflow and linked repository for calculating allostatic overload, an estimated measure of individual cumulative stress over a lifetime. The repository includes an R script for generating datasets from the workflow using secondary data from the All of Us Research Program, the Health and Retirement Study and the National Health and Nutrition Examination Survey.

Allostatic overload is described as the overall “wear and tear” associated with social and environmental stressors. The project treats it as a practical indicator of general health trends in community settings, but its contribution is methodological rather than a report of new biological effects, clinical benefits or health associations. No results comparing stress levels or health outcomes across the three data sources are presented in the supplied material.

The workflow is designed to make the researchers’ overall approach and reasoning explicit when processing secondary data for allostatic overload analysis. Its associated repository provides the processing materials and an R script, creating a means for documenting and sharing a protocol-driven measurement process. This focus on reproducibility is intended to support consistent data analysis and knowledge sharing among researchers working in community environments.

The source material does not specify the variables, scoring thresholds, weighting procedures or participant characteristics used in each calculation. It therefore does not support conclusions about how allostatic overload varies by population or whether the resulting estimate predicts a particular health outcome. Instead, the work records how datasets can be generated through a shared workflow and offers a technical foundation for subsequent analyses of estimated cumulative stress.

Subject of Research: Reproducible measurement of estimated lifetime cumulative stress in community-health datasets

Article Title: Allostatic (over) load measurement: Workflow and repository

Article References: Beese, S., Cross, J., Rice, D., & DeJong, T. L. (2026). Allostatic (over) load measurement: Workflow and repository. PLOS One, 21(10), e0360639. https://doi.org/10.1371/journal.pone.0360639

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360639

Keywords: allostatic overload, cumulative stress, reproducible research, R workflow, secondary data, community health, All of Us, HRS, NHANES, Allostatic, over, load