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PATHWAYS
Allostatic load measures in the English
Longitudinal Study of Ageing (ELSA)
Sanna Read & Emily Grundy
Website http://pathways.lshtm.ac.uk
Email
[email protected]
Twitter @pathwaysNCRM
Allostatic load
• a multisystem dysregulation state resulting from
accumulated physiological ‘wear and tear’
• Allostasis = a process whereby organism maintains
physiological stability by adapting itself to
environmental demands
- > health is a state of responsiveness and optimal
predictive fluctuation to adapt to the demands of
the environment -> dynamic biological process
interacting with context
Allostatic load
Environmental stressors
(work, home, neighbourhood)
Major life events
Trauma,
abuse
Perceived stress
Brain’s evaluation of threat
Individual
differences
(genes,
development,
experience)
Behavioural
responses
(fight or flight, healthrelated behaviour –
smoking, alcohol use,
diet, exercise)
Physiological responses
Allostasis
Adaptation
Allostatic load
Adapted from McEwen, 1998
Disease
Allostasis
Adaptation
Multiple mediators of adaptation:
1) Primary effects: stress hormones (e.g. epinephrine, norepinephrine
and cortisol), anti-inflammatory cytokines (e.g. Interleukin-6)
2) Secondary outcomes: metabolic (e.g. insulin, glucose, total cholesterol,
triglycerides, visceral fat depositing), cardiovascular (e.g. systolic and
diastolic blood pressure) and immune system (e.g. C-reactive protein,
fibrinogen).
3) Tertiary outcomes: poor health, disease, death
Mediators interconnected and reciprocal, non-linear effects on many organ
systems in body - > should be measured as multisystem concept,
challenging to develop measures
Allostatic load accumulates throughout the life -> study processes in
longitudinal settings
Measures of allostatic
load
Measure
Description
Group allostatic load index
the number of biomarkers falling within a high risk
percentile (e.g. upper or lower 25th percentile)
based on the sample distribution of biomarkers
values
Z-score allostatic load index
Summary measure of individual’s obtained zscores for each biomarker based on the sample
distribution of biomarker values.
Change score
Measure change between two or more
measurement occasions. This can be a simple
difference score or dynamic measure of variability
over time.
A number of other methods also used for calculating composite measures:
bootstrapping, canonical correlations, recursive partitioning, grade of
membership, k-means cluster analysis, genetic programming.
Examples of biomarkers used
in measuring allostatic load
Type
Biomarker
Neuroendocrine
Epinepherine, norepinephrerine, dopamine,
cortisol, dehydroepiandrosterone (DHEAS),
aldosterone
Immune
Interleukin-6, tumor necrosis factor-alpha, creactive protein (CRP), insulin-like growth factor-1
(IGF-1)
Metabolic
HDL and LDL cholesterole, triglycerides,
glucosylated hemoglobin, glucose insulin,
albumin, creatinine, homocysteine
Cardiovascular and
respiratory
Systolic blood pressure, diastolic blood pressure,
peak expiratory flow, heart rate/pulse
Anthropometric
Waist-to-hip ratio, body mass index (BMI)
Factors associated with
allostatic load in previous studies
Socioeconomics:
education, income,
occupational status,
downward mobility,
homelessness
Individual: type
A/hostility, locus of
control, a polymorphism
of ACE gene
Neigbourhoods:
crowding, noise, lack
of housing,
rural/urban
Ethnicity: Nonwhites (U.S.)
Allostatic load
Social
networks:
emotional
support, social
position
Spirituality: religious
attendance, sense of
meaning/purpose
Family: attachment,
violence, single
parent, separation,
care-giving,
demands/criticism,
spouse
Work: control,
demands,
decisions, career
instability, effortreward imbalance
Sample
• English Longitudinal Study of Ageing (ELSA) waves 1 4 (2002-2008)
• Men and women (n = 5279) aged 50+ in 2002
• Measures:
– Biomarkers available in waves 2 and 4
– Health: self reported health, limitation in health, ADL and
IADL limitation
– Fertility history: number of children, birth before age 20
(women) or age 23 (men), birth after age 34 (women) and
39 (men), coresidence with child
– Background factors: age, marital status, qualification,
tenure status, net wealth quintile (non-pension wealth
indicating financial, physical and housing wealth net of
debts)
Selected biomarkers to measure
allostatic load in ELSA
Neuroendocrine
Immune
Cardiovascular
Respiratory
Metabolic
Body fat
DHEAS*
(dehydroepia
ndrostorone
sulphate)
C-reactive
protein
Systolic blood
pressure
Peak
expiratory
flow
Total blood
cholesterol/
HDL
cholesterol
ratio
Waist-hip
ratio
Fibrinogen
Diastolic blood
pressure
IGF-1*
(insulin-like
growth
hormone)
* only in wave 4
Triglycerides
Glycated HgB
Availability of valid measures in
ELSA
Measure
% valid measure crosssectionally
% valid measure longitudinally among
those who participated in wave 1
Wave 2
Wave 4
Wave 2
Wave 4
Blood pressure
70
72
58
46
Waist-hip ratio
78
76
65
48
Lung function
75
70
62
44
Blood measures*
63
58
52
37
* CRP, Fibrinogen, cholesterole, triglycerides, glycated HgB, IGF-1, DHEAS
Allostatic load scores
in ELSA
• Group allostatic load index: number of biomarkers
indicating high risk (25th percentile) calculated
separately for men and women, range 0 - 9
Upper 25th percentile
Lower 25th percentile
Systolic blood pressure
Diastolic blood pressure
Fibrinogen
Peak expiratory flow
Triglycerides
C-reactive protein
Glycated HgB
Waist-hip ratio
Total/HDL cholesterol ratio
Allostatic load scores
in ELSA
Challenges in creating composite scores:
• Extreme values
• Medication
• Non-linearity
• Missing values
Allostatic load measures in 2004
predicting ADL problems in 2006 in
men in ELSA
25
20
ADL problem %
15
10
5
0
1
Lowest
2 25%
3
4
Highest
25%
Allostatic load change
in ELSA
Comparison between wave 2 (2004) and wave 4 (2008):
• Low allostatic score (score 0-1) and high allostatic score
(2+)
2004
2008
High
High
Low
Low
Allostatic load change
between 2004 and 2008 in
ELSA
100%
80%
High -> High
60%
High -> Low
Low -> High
40%
Low -> Low
20%
0%
Women
Men
Allostatic load change
in ELSA
Is change associated with any of the following factors?
•
•
•
•
•
•
Age
Qualification, tenure status, net wealth quintile
Being married
Perceived support and critique received from family and friends
Number of children
Co-residence with child, early child birth, late child birth (among
parents only)
Allostatic load change
in ELSA
Is change associated with poorer health?
• Poorer self-rated health, health limitation, and ADL/IADL
limitation was most frequent among those who stayed in
high allostatic load group in both waves.
• Those men who moved from low to high group rated their
health poorer and those men who moved from high to low
group rated better health. In women the differences in health
were less clear.
• Those staying in low allostatic group rated their health best of
all four groups.
The model to be tested
Is the association between fertility history and health
mediated by allostatic load?
Does SEP influence this association?
Education
Wealth
Fertility history
Allostatic
load
Health
Fertility history, allostatic load
and health in ELSA
Is the association between fertility history and health
mediated by allostatic load?
- Yes, it is in men and to some extent also in women. In
women there are also direct paths to health suggesting
that there are other potential mediators.
Does SEP influence this association?
- In men, and to some extent in women, SEP mediates
the association between fertility history and later
allostatic load and health.
References 1
Crimmins, E.M., Kim, J.K., Seeman, T.E. (2009). Poverty and biological risk: The earlier “aging” of the
poor. Journal of Gerontology: Medical Sciences, 64A, 286-292.
Dowd, J.B., & Goldman, N. (2006). Do biomarkers of stress mediate the relation between
socioeconomic status and health? Journal of Epidemiology and Community Health, 60, 633-639.
Dowd, J.B., Simanek, A.M., & Aiello, A.E. (2009). Socio-economic status, cortisol and allostatic load:
a review of the literature. International Journal of Epidemiology. 38, 1297-1309.
Goldman, N., Turra, C.M., Glei, D.A., Lin, Y.-H., Weinstein, M. (2006). Physiological dysregulation and
changes in health in an older population. Experimental Gerontology, 41, 862 - 870.
Gustafsson, P.E., Janlert, U., Theorell, T., Westerlund, H., & Hammarström, A. (2011). Socioeconomic
status over the life course and allostatic load in adulthood: results from the Northern Swedish
Cohort. Journal of Epidemiology and Community Health, 65, 986-992.
Hu, P., Wagle, N., Goldman, N., Weinstein, M., & Seeman, T.E. (2006). The associations between
socioeconomic status, allostatic load and measures of health on older Taiwanese persons: Taiwan
Social Environment and Biomarkers of Aging Study. Journal of Biosocial science, 39, 545-556.
References 2
Juster, R.-P., McEwen, B.S., & Lupien, S.J. (2010) Allostatic load biomarkers of chronic stress and
impact on health and cognition. Neuroscience and Biobehavioural Reviews, 35, 2 – 16.
Karlamangla, A.S., Singer, B.H., & Seeman, T.E. (2006). Reduction in allostatic load in older adults is
associated with lower all-cause mortality risk: ManArthur Studies of Successful Aging.
Psychosomatic Medicine, 68, 500-507.
McEwen, B.S. (1998). Protective and damaging effects of stress mediators. New England Journal of
Medicine, 338, 171.
Piazza, J.R., Almeida, D.M., Dmitrieva, N.O., & Klein, L.C. (2010). Frontiers in the use of biomarkers of
health in research on stress and aging. Journal of Gerontology: Psychological Sciences, 65B, 513-525.
Seplaki, C.L., Goldman, N., Glei, D., & Weinstein, M.(2005). A comparative analysis of measurement
approaches for physiological dysregulation in an older population. Experimental Gerontology, 40,
438-449.

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