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Modeling Multisystem Physiological Dysregulation.

Joshua F Wiley1, Tara L Gruenewald, Arun S Karlamangla

  • 1From the Department of Psychology (Wiley) and Division of Geriatrics (Karlamangla, Seeman), David Geffen School of Medicine, University of California, Los Angeles, CA; Mary MacKillop Institute for Health Research (Wiley), Australian Catholic University, Victoria, Australia; and Davis School of Gerontology (Gruenewald), University of Southern California, Los Angeles, California.

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Summary
This summary is machine-generated.

Allostatic load (AL) is best understood as multisystem physiological dysregulation, with a bifactor model accurately capturing shared variance across biomarkers and unique system-specific effects. This model showed invariance across age and sex groups.

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Area of Science:

  • Physiology
  • Biomarker Research
  • Aging Research

Background:

  • Allostatic load (AL) is a key concept in understanding health disparities and aging.
  • Previous models have not fully captured the complex interplay of physiological systems contributing to AL.
  • A robust operationalization of AL is needed to precisely measure its impact on health.

Purpose of the Study:

  • To compare the fit of two factor models for allostatic load (AL).
  • To test the invariance of these models across age and sex.
  • To refine the understanding of AL as multisystem physiological dysregulation.

Main Methods:

  • Utilized data from the Midlife in the United States II Biomarker Project (n=1255).
  • Included 23 biomarkers across seven physiological systems (metabolic, cardiovascular, nervous, endocrine, immune).
  • Employed bifactor modeling and tested for measurement invariance across age and sex categories.

Main Results:

  • A bifactor model, incorporating a common AL factor and seven system-specific factors, demonstrated superior fit to the biomarker data.
  • This model indicated that AL represents shared variance across biomarkers.
  • Results confirmed measurement invariance of the bifactor model across different age groups and sexes.

Conclusions:

  • The bifactor model effectively operationalizes allostatic load as multisystem physiological dysregulation.
  • AL accounts for shared variance among biomarkers, while individual systems contribute unique variance.
  • This approach offers enhanced precision for studying both AL and system-specific physiological effects.