Author: Cathal McCrory1, Sinead McLoughlin2, Richard Layte3, Cliona NiCheallaigh4, Aisling M O'Halloran2, Henrique Barros5, Lisa F Berkman6, Murielle Bochud7, Eileen M Crimmins8, Meagan T Farrell6, Silvia Fraga5, Emily Grundy9, Michelle Kelly-Irving10, Dusan Petrovic7, Teresa Seeman11, Silvia Stringhini12, Peter Vollenveider13, Rose Anne Kenny2
Affiliation: <sup>1</sup> The Irish Longitudinal Study on Ageing, Department of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland. Electronic address: mccrorc@tcd.ie.
<sup>2</sup> The Irish Longitudinal Study on Ageing, Department of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland.
<sup>3</sup> Department of Sociology, Trinity College Dublin, Ireland.
<sup>4</sup> Department of Clinical Medicine, Trinity College Dublin and St James's Hospital, Ireland.
<sup>5</sup> EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal, Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal.
<sup>6</sup> Harvard Center for Population and Development Studies, Harvard. T.H. Chan School of Public Health, 9 Bow Street, Cambridge, MA 02138, USA.
<sup>7</sup> Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Route de la Corniche 10, 1010 Lausanne, Switzerland.
<sup>8</sup> Davis School of Gerontology, University of Southern California, USA.
<sup>9</sup> Institute for Social & Economic Research, University of Essex, UK and Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
<sup>10</sup> Centre for Epidemiology and Research in Population Health (CERPOP), Université de Toulouse, Inserm-Université Paul Sabatier, Toulouse, France.
<sup>11</sup> David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
<sup>12</sup> Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Route de la Corniche 10, 1010 Lausanne, Switzerland; Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospital, Faculty of Medicine, University of Geneva, Switzerland.
<sup>13</sup> Department of Medicine, Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland, University of Lausanne, Lausanne, Switzerland.
Conference/Journal: Psychoneuroendocrinology
Date published: 2023 Apr 19
Other:
Volume ID: 153 , Pages: 106117 , Special Notes: doi: 10.1016/j.psyneuen.2023.106117. , Word Count: 290
Background:
Allostatic load (AL) is a multi-system composite index for quantifying physiological dysregulation caused by life course stressors. For over 30 years, an extensive body of research has drawn on the AL framework but has been hampered by the lack of a consistent definition.
Methods:
This study analyses data for 67,126 individuals aged 40-111 years participating in 13 different cohort studies and 40 biomarkers across 12 physiological systems: hypothalamic-pituitary-adrenal (HPA) axis, sympathetic-adrenal-medullary (SAM) axis, parasympathetic nervous system functioning, oxidative stress, immunological/inflammatory, cardiovascular, respiratory, lipidemia, anthropometric, glucose metabolism, kidney, and liver. We use individual-participant-data meta-analysis and exploit natural heterogeneity in the number and type of biomarkers that have been used across studies, but a common set of health outcomes (grip strength, walking speed, and self-rated health), to determine the optimal configuration of parameters to define the concept.
Results:
There was at least one biomarker within 9/12 physiological systems that was reliably and consistently associated in the hypothesised direction with the three health outcomes in the meta-analysis of these cohorts: dehydroepiandrosterone sulfate (DHEAS), low frequency-heart rate variability (LF-HRV), C-reactive protein (CRP), resting heart rate (RHR), peak expiratory flow (PEF), high density lipoprotein cholesterol (HDL-C), waist-to-height ratio (WtHR), HbA1c, and cystatin C. An index based on five biomarkers (CRP, RHR, HDL-C, WtHR and HbA1c) available in every study was found to predict an independent outcome - mortality - as well or better than more elaborate sets of biomarkers.
Discussion:
This study has identified a brief 5-item measure of AL that arguably represents a universal and efficient set of biomarkers for capturing physiological 'wear and tear' and a further biomarker (PEF) that could usefully be included in future data collection.
Keywords: Allostatic load; Biomarker; Cohort study; Cumulative physiological dysregulation; Individual participant data meta-analysis.
PMID: 37100008 DOI: 10.1016/j.psyneuen.2023.106117