1. Allostatic load: conceptualisation and measurement in the English Longitudinal Study of Ageing Sanna Read and Emily Grundy  http://pathways.lshtm.ac.uk  pathways@lshtm.ac.uk @PathwaysNCRM

2. 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

3. Allostatic load Environmental 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 AllostasisAdaptation Adapted from McEwen, 1998 Brain’s evaluation of threat -> activates Sympathetic-adrenal- medullary (SAM) axis -> catecholamines Hypothalamic-pituitary-adrenal (HPA) axis - > glucocorticoids

4. 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 AllostasisAdaptation

5. Measures of allostatic load MeasureDescription Group allostatic load indexthe number of biomarkers falling within a high risk percentile (e.g. upper or lower 25 th percentile) based on the sample distribution of biomarkers values Z-score allostatic load indexSummary measure of individual’s obtained z- scores for each biomarker based on the sample distribution of biomarker values. 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, multivariate distance.

6. Examples of biomarkers used in measuring allostatic load TypeBiomarker NeuroendocrineEpinepherine, norepinephrerine, dopamine, cortisol, dehydroepiandrosterone (DHEAS), aldosterone ImmuneInterleukin-6, tumor necrosis factor-alpha, c- reactive protein (CRP), insulin-like growth factor-1 (IGF-1) MetabolicHDL and LDL cholesterol, triglycerides, glucosylated hemoglobin, glucose insulin, albumin, creatinine, homocysteine Cardiovascular and respiratory Systolic blood pressure, diastolic blood pressure, peak expiratory flow, heart rate/pulse AnthropometricWaist-to-hip ratio, body mass index (BMI)

7. 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

8. Sample English Longitudinal Study of Ageing (ELSA) waves 1-6 (2002-2012) men and women (n = 11223*) aged 50+ in 2002 Measures: – Biomarkers available in waves 2, 4 and 6 – Health and functioning: self reported health, limitating long-term illness, walking speed – 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 and intermediate factors: age, marital history, childhood health, qualification, net wealth quintile, smoking, physical activity, social support and social strain * Core members who where interviewed in-person in 2002 (wave 1).

9. Selected biomarkers to measure allostatic load in ELSA Neuro- endocrine ImmuneCardiovascularRespiratoryMetabolicBody fat DHEAS a (dehydroepia ndrostorone sulphate) C-reactive protein Systolic blood pressure Peak expiratory flow Total blood cholesterol/ HDL cholesterol ratio Waist-to-hip ratio FibrinogenDiastolic blood pressure Triglycerides IGF-1 b (insulin-like growth hormone) Glycated HgB a only in wave 4 b only in waves 4 and 6

10. Availability of valid measures in ELSA Measure% valid measure cross- sectionally % valid measure longitudinally among those who participated in wave 1 Wave 2Wave 4Wave 2Wave 4 Blood pressure70725846 Waist-to-hip ratio78766548 Lung function75706244 Blood measures*63585237 * CRP, Fibrinogen, cholesterol, triglycerides, glycated HgB, IGF-1, DHEAS

11. Allostatic load scores in ELSA Group allostatic load index: biomakers indicating high risk (25th percentile) and mean calculated for each five subsystems, range 0 - 5 SubsystemUpper 25 th percentileLower 25 th percentile CardiovascularSystolic blood pressureDiastolic blood pressure ImmuneFibrinogen C-reactive protein MetabolicTriglycerides Glycated HgB Total/HDL cholesterol ratio Body fatWaist-to-hip ratio RespiratoryPeak expiratory flow

12. Allostatic load scores in ELSA Challanges in creating composite scores: Extreme values Medication Fasting Age and ageing Non-linearity and skewness Missing values

13. Extreme values in CRP

14. Medication and AL score Prescribed medication was taken into account so that an individual was given the value 1 (indicating health risk): for diastolic and systolic blood pressure if they used blood pressure lowering medication for fibrinogen if they used anticoagulants for triglycerides and HDL cholesterol ratio if they used lipid lowering medication for glycosylated haemoglobin if they used diabetes medication for peak expiratory flow if they used lung function medication Moreover, because the literature suggests that diabetic, cholesterol and blood pressure lowering medication reduced the values of C-reactive protein between 25-30%, the values in the second highest 25 percentile were given value 1 to indicate health risk.

15. Fasting Fasting was controlled using the time when last eaten (varies between the waves how it was asked).

16. Age, ageing and AL score AL score calculated in two age groups: under 65 and 65+ Age (continuous) adjusted in the final models Cut-offs in very old age? Change over time? Papers on biochemical values in old-old population (Swedish twins aged 82+): Nilsson, S., Read, S., & Berg, S. (2009). Heritabilities for fifteen routine biochemical values: Findings in 215 Swedish twin pairs 82 years of age or older. Scandinavian Journal of Clinical Laboratory Investigation, 69, 562 – 569. Nilsson, S., Takkinen, S., Tryding, N., Evrin, P.-E., Berg, S., McClearn, G., & Johansson, B. (2003). Association of biochemical values with morbidity in the elderly: a population-based Swedish study of persons aged 82 or more years. Scandinavian Journal of Clinical Laboratory Investigation, 63, 457-466. Nilsson, S.E., Evrin, P.E., Tryding, N., Berg, S., McClearn, G., & Johansson, B. (2003). Biochemical values in persons older than 82 years of age: report from a population-based study of twins. Scandinavian Journal of Clinical Laboratory Investigation, 63, 1-14.

17. Cut-offs for AL indicators in ELSA W2

18. Non-linearity Allostatic load measures in 2004 predicting ADL problems in 2006 in men in ELSA ADL problem % Lowest 25% Highest 25%

19. Skewness Allostatic load score distribution in men age 60+ in wave 2

20. Missing values Values missing because of medical contraindication or refusal to participate in nurse examination. Taking into account the use of medication made it possible to recover some of missing information Information on at least four out of five subsystems had to be available to calculate the allostatic load score Full maximum likelihood (use of incomplete data) in the analysis

21. Examples of the use of allostatic load in studying the pathways to health in older age Read, S. & Grundy, E. (2014). Allostatic load and health in the older population of England: A crossed-lagged analysis. Psychosomatic Medicine, 76, 490-496. Grundy, E & Read, S. (2015). Pathways from fertility history to later life health: Results from analyses of the English Longitudinal Study of Ageing. Demographic Research, 32, 107−146. Read, S. & Grundy, E. (2012). Allostatic load - a challenge to measure multisystem physiological dysregulation. National Centre for Research Methods Working Paper, 04/12.

22. Allostatic load and health: study direction of sequences Disablement process and accumulation of allostatic load assume a causal path between the factors. An effective method to detect direction of sequences of effects in longitudinal settings is to apply cross-lagged models.

23. Aim To investigate the reciprocal association between allostatic load, self-rated health and walking speed as a measure of functional limitation. – allostatic load would predict functional limitation – the association between self-rated health and allostatic load may be reciprocal or self-rated health may even precede allostatic load.

24. Cross-lagged model http://pathways.lshtm.ac.uk Self-rated health Functional limitation Self-rated health Functional limitation Time 1 Time 2 Allostatic load

25. Cross-lagged model http://pathways.lshtm.ac.uk Self-rated health Functional limitation Self-rated health Functional limitation Time 1 Time 2 Allostatic load

26. Cross-lagged model http://pathways.lshtm.ac.uk Self-rated health Functional limitation Self-rated health Functional limitation Time 1 Time 2 Allostatic load

27. Self-rated health Walking speed Self-rated health Wave 2Wave 4 0.51 (0.015) 0.42 (0.016) 0.05 (0.015) Allostatic load 0.54 (0.016) -0.09 (0.020) -0.07 (0.013) 0.11 (0.014) -0.04 (0.015) Results: Cross-lagged model Adjusted for age, gender, education, marital status, wealth, smoking, physical activity, and social support.

28. Self-rated health Walking speed Self-rated health Wave 2Wave 4 0.51 (0.015) 0.42 (0.016) 0.05 (0.015) Allostatic load 0.54 (0.016) -0.09 (0.020) -0.07 (0.013) 0.11 (0.014) -0.04 (0.015) Results: Cross-lagged model Adjusted for age, gender, education, marital status, wealth, smoking, physical activity, and social support.

29. Self-rated health Walking speed Self-rated health Wave 2Wave 4 0.51 (0.015) 0.42 (0.016) 0.05 (0.015) Allostatic load 0.54 (0.016) -0.09 (0.020) -0.07 (0.013) 0.11 (0.014) -0.04 (0.015) Results: Cross-lagged model Adjusted for age, gender, education, marital status, wealth, smoking, physical activity, and social support.

30. Conclusions & Discussion Allostatic load predicts functional limitation → allostatic load may be a useful early objective indicator of health problems. The drawbacks of using it is that it is a complex composite measure which involves invasive data collection methods and therefore subject to refusal and drop-out. No standardized way of measuring it. The association between self-rated health and allostatic load and functional limitations were reciprocal, although the strength of the associations suggested that self-rated health may be an earlier indicator of health problems → The role of self-rate health in the disablement process seem to be less clear: it predicts better functioning, but it is also an outcome of good functioning. Self-rated health is simple and quick to use with high response rates. The limitations are its subjective content and variation from one population to another.

31. Conclusions & Discussion As hypothesised, allostatic load predicts later functional limitations. In the future, it is important to include earlier indicators of chronic stress (neuroendocrine and inflammatory markers) and study longer time spans from middle adulthood to old age to detect the accumulation of stress.

32. Parenthood history Allostatic load Health Demographic and life history factors Is the association between parenthood history and health mediated by wealth, health-related behaviours, social support and strain, and allostatic load? The model to be tested Wealth, health-related behaviours, social support and strain

33. Parenthood history Allostatic load Health Demographic and life history factors Is the association between parenthood history and health mediated by wealth, health-related behaviours, social support and strain, and allostatic load? The model to be tested Wealth, health-related behaviours, social support and strain

34. Wealth Wave 1Wave 2Wave 3 Allostatic load Limiting long-term illness Children 4 vs. 2 Smoking Social strain -0.16 (0.024) 0.09 (0.021) 0.46 (0.090) 0.37 (0.109) 0.11 (0.021) 0.30 (0.084) Figure 1. Path model for all women in ELSA (n=6123). Model adjusted for age, education, being married, marital disruption and childhood health. Significant paths are shown (unstandardized estimate and standard error). -0.55 (0.054) Physical activity -0.40 (0.040) -0.61 (0.047) -0.10 (0.028) -0.19 (0.053)

35. Wealth Wave 1Wave 2Wave 3 Allostatic load Limiting long-term illness Children 4 vs. 2 -0.59 (0.064) -0.13 (0.030) -0.35 (0.047) 0.12 (0.023) -0.14 (0.043) Figure 2. Path model for all men in ELSA (n=5110). Model adjusted for age, education, being married, marital disruption and childhood health. Significant paths are shown (unstandardized estimate and standard error). Smoking Social strain Physical activity 0.54 (0.122) 0.05 (0.023) 0.62 (0.099) 0.23 (0.085) 0.39 (0.097) -0.62 (0.054) -0.13 (0.027) 0.26 (0.121)

36. Wealth Wave 1Wave 2Wave 3 Allostatic load Limiting long-term illness Early childbirth Physical activity Smoking -0.48 (0.061) -0.14 (0.026) 0.09 (0.023) -0.40 (0.043) 0.33 (0.110) -0.64 (0.051) 0.46 (0.100) Figure 3. Path model for parous women in ELSA (n=5219). Model adjusted for age, education, being married, marital disruption, childhood health, and coresidence with child. Significant paths are shown (unstandardized estimate and standard error). -0.23 (0.040) 0.29 (0.114) -0.10 (0.031) Late childbirth -0.24 (0.121) 0.13 (0.103) NS

37. Wealth Wave 1Wave 2Wave 3 Allostatic load Limiting long-term illness Early childbirth Smoking -0.24 (0.063) -0.13 (0.029) 0.12 (0.025) 0.29 (0.121) -0.13 (0.040) Figure 4. Path model for parous men in ELSA (n=4256). Model adjusted for age, education, being married, marital disruption, childhood health, and coresidence with child. Significant paths are shown (unstandardized estimate and standard error). -0.35 (0.052) 0.30 (0.149) Physical activity 0.33 (0.114) 0.62 (0.109) -0.12 (0.033) -0.65 (0.060) Late childbirth 0.35 (0.108) -0.29 (0.071) -0.11 (0.047) Social strain 0.06 (0.026)

38. Conclusions & Discussion Socio-economic position, health-related behaviors and social strain mediate the association between high parity and later life health. They also partially mediate the association between early childbirth and later life health. Of these socio- economic position was the strongest mediator. So, as hypothesised, biosocial pathways from parenthood history to health involve economic position, social strain and health related behaviours → need now to examine in more detail pathways to particular fertility trajectories- especially childhood SES and broader environmental influences (e.g. support from the state) and other potential mechanisms (e.g. moderation).