PATHWAYS

Allostatic load measures in the English Longitudinal Study of Ageing (ELSA)

Sanna Read & Emily Grundy

Website http://pathways.lshtm.ac.ukEmail pathways@lshtm.ac.ukTwitter @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 loadEnvironmental stressors Major life events Trauma, (work, home, neighbourhood) abuse

Perceived stress

Behavioural responses (fight or flight, health-related behaviour – smoking, alcohol use, diet, exercise)

Individual differences(genes, development, experience)

Brain’s evaluation of threat

Physiological responses

Allostatic load

Disease

Allostasis Adaptation

Adapted from McEwen, 1998

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

Allostasis Adaptation

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 z-scores 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, c-reactive 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

Family: attachment, violence, single parent, separation, care-giving, demands/criticism, spouse

Individual: type A/hostility, locus of control, a polymorphism of ACE gene

Neigbourhoods: crowding, noise, lack of housing, rural/urban

Allostatic load

Ethnicity: Non-whites (U.S.)

Spirituality: religious attendance, sense of meaning/purpose

Social networks: emotional support, social position

Work: control, demands, decisions, career instability, effort-reward 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

Neuro-endocrine

Immune Cardiovascular Respiratory Metabolic Body fat

DHEAS* (dehydroepiandrostorone sulphate)

C-reactive protein

Systolic blood pressure

Peak expiratory flow

Total blood cholesterol/HDL cholesterol ratio

Waist-hip ratio

Fibrinogen Diastolic blood pressure

Triglycerides

IGF-1* (insulin-like growth hormone)

Glycated HgB

* only in wave 4

Availability of valid measures in ELSA

Measure % valid measure cross-sectionally

% 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

Systo

lic BP

Diasto

lic BP

Fibrin

ogen

C-reac

tive pro

tein

Triglys

erides

Glycate

d HgB

Wais

t-hip ra

tio

HDL choleste

role ra

tio

Peak exp

irato

ry flow

0

5

10

15

20

25

1234AD

L pr

oble

m %

Lowest 25%

Highest25%

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+)

High High

Low Low

2004 2008

Allostatic load change between 2004 and 2008 in ELSA

Women Men0%

20%

40%

60%

80%

100%

High -> HighHigh -> LowLow -> HighLow -> Low

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.

Fertility history Allostatic load

Health

Education

Is the association between fertility history and health mediated by allostatic load?Does SEP influence this association?

The model to be tested

Wealth

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.

Fertility history, allostatic load and health in ELSA

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 1

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.

References 2