Towards a consensus definition of allostatic load: a multi-cohort, multi-system, multi-biomarker individual participant data (IPD) meta-analysis

Abstract

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.

Study specific associations of being dysregulated in each biomarker with maximal grip strength (kgs)

Study specific associations of being dysregulated in each biomarker with maximal grip strength (kgs)

Study specific associations of being dysregulated in each biomarker with maximal grip strength (kgs)

Study specific associations of being dysregulated in each biomarker with maximal grip strength (kgs)

Study specific associations of being dysregulated in each biomarker with maximal grip strength (kgs)

Appendix B:

Study specific associations of being dysregulated in each biomarker with walking speed (cms/sec)

Appendix B:

Study specific associations of being dysregulated in each biomarker with walking speed (cms/sec)

Appendix B:

Study specific associations of being dysregulated in each biomarker with walking speed (cms/sec)

Appendix B:

Study specific associations of being dysregulated in each biomarker with walking speed (cms/sec)

Appendix B:

Study specific associations of being dysregulated in each biomarker with walking speed (cms/sec)

Appendix C:

Study specific associations of being dysregulated in each biomarker with fair/poor self-rated health

Appendix C:

Study specific associations of being dysregulated in each biomarker with fair/poor self-rated health

Appendix C:

Study specific associations of being dysregulated in each biomarker with fair/poor self-rated health

Appendix C:

Study specific associations of being dysregulated in each biomarker with fair/poor self-rated health

Appendix C:

Study specific associations of being dysregulated in each biomarker with fair/poor self-rated health

Figure 1:. Relationship between scoring in the highest risk quartile for each biomarker with maximal grip strength (kg) in individual participant data (IPD) meta-analysis

Estimates were derived using restricted maximum likelihood (REML) estimation with the Hartung-Knapp-Siduk-Jonkman (HSJK) method while holding age, sex, and height (cms) constant. No adjustment for height was made with respect to body mass index or waist-to-height ratio. *denotes biomarkers which are reverse scored prior to analysis

Figure 2:. Relationship between scoring in the highest risk quartile for each biomarker with average walking speed (cms/sec) in individual participant data (IPD) meta-analysis

Estimates were derived using restricted maximum likelihood (REML) estimation with the Hartung-Knapp-Siduk-Jonkman (HSJK) method while holding age, sex, and height (cms) constant, No adjustment for height was made with respect to body mass index or waist-to-height ratio. *denotes biomarkers which are reverse scored prior to analysis

Figure 3:. Odds ratio of scoring in the highest risk quartile for each biomarker with fair/poor self-rated health in individual participant data (IPD) meta-analysis

Estimates were derived using restricted maximum likelihood (REML) estimation with the Hartung-Knapp-Siduk-Jonkman (HSJK) method while holding age, sex constant, *denotes biomarkers which are reverse scored prior to analysis

Figure 4:. Within-cohort comparison of the performance of the brief 5-item allostatic load index with longer batteries in predicting odds of mortality

All models adjusted for age and sex Five-item = C-Reactive Protein, Resting Heart Rate, High Density Lipoprotein-Cholesterol, Waist-to-Height Ratio, HbA1c DHEAS = dehydroepiandrosterone sulfate; PEF = peak expiratory flow; CYSC = cystatin C; LF-HRV = low frequency heart rate variability