PATHWAYS Allostatic load measures in the English Longitudinal Study of Ageing (ELSA) Sanna Read & Emily Grundy Website http://pathways.lshtm.ac.uk Email
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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.
