Boulder Allostatic load June 2007 - CU Population Center · (eds.) Allostasis, Homeostasis, and the Costs of Physiological Adaptation. Cambridge, U.K.: Cambridge University Press

Allostatic Load

Measurement Issues & Future Directions

Noreen GoldmanThe University of Colorado Population Center &

Institute of Behavioral ScienceSummer Course in Biodemography

June 11-13, 2007

Abbreviated History of Allostatic Load

Hans Selye (1907-1982)General Adaptation Syndrome (GAS): the manner in which the body copes with “noxious agents” (“stress”)

Stress is the nonspecific response of the body to a demand: hormonal and neurotransmitter mediators that set in motion responses of cells and tissues throughout the body. Importance of the HPA-axisDemands could be positive or negative (pathogen, lack of sleep, exercise)*Effects of stress on the body could be beneficial or damaging Stages include “fight or flight,” subsequent adaptation, eventual exhaustion/disease if stress is sufficiently long (although Selye thought it was due to hormone depletion)

Thus, stress can make people sick

Allostasis/ Allostatic loadAllostasis: term coined by Sterling & Eyer (1988)

A dynamic regulatory processHow body maintains stability through change in various physiological systems (autonomic nervous system, HPA, cardiovascular, metabolic, immune) in response to internal and external demands (e.g., noise, hunger, extreme temperatures). These systems are designed to operate within broad ranges, by constantly modify “set points.”Vs. homeostasis: maintaining constant internal state

Allostatic load: McEwen and Stellar (1993)The cost or price of allostasis (being forced to adapt to adverse situations)Wear and tear on the body from chronic over- or underactivityof allostatic systemsUltimate effects are a broad range of chronic conditions

Allostatic Load and a Seesaw

Source: McEwen B with EN Lasley.2002. The End of Stress As We Know It. Joseph Henry Press: Washington, D.C.

The Stress Response and Allostatic Load

Source: McEwen, BS. 1998. Protective and damaging effects of stress mediators. NEJM, 171-179

Types of Allostatic Load

Source: McEwen, BS. 1998. Protective and damaging effects of stress mediators. NEJM, 171-179

These Patterns Really do Occur

Source: Kirschbaum et al. 1995. Persistent high cortisol responses to repeated psychological stress in a subpopulation of healthy men. Psychosomatic Medicine, 468-74.

Cortisol responses to public speaking & mental arithmetic task

HPA-Axis & Other Components of Hormonal Stress Response


Autonomic Nervous System, especiallySympathetic Nervous System


Protective & Damaging EffectsRecall the ‘protection-versus-damage paradox’ stressed by McEwen and others. For example,

Cortisol and epinephrine help mobilize energy in acute stress, help immune cells move to sites in the body to combat infection.

But, if their secretion is not turned off, they can promote fat deposition,insulin resistance, hypertension, and immunosuppression (just theopposite of effect above), damage to nerve cells.

Chronic Conditions Arising from Allostatic Load

Research has shown that long-term out of normal range values of certain biological markers (e.g., blood pressure, cortisol) lead to many possible chronic illnesses & conditions –not just physical morbidity, but cognitive & mental health:

Atherosclerosis, hypertension, diabetes, myocardial infarction, obesity Autoimmune disordersMemory loss (hippocampal atrophy), depression

This is a very abbreviated list

Disorders Linked to Cortisol


Primary Mediators, Secondary & Tertiary Outcomes

Primary mediatorsHormonal factors, including markers of SNS activity, HPA axis activity, inflammation. These mediators regulate events at the cellular level (primary effects) such as the action of enzymes and receptors. These effects ultimately lead to secondary outcome.

Secondary outcomesThese are manifested at the level of tissues and organs. These outcomes include abnormal metabolism and cardiovascular disease, such as obesity, hypertension, high cholesterol

Tertiary outcomesDisease endpoints resulting from secondary outcomes

This entire process is likely to be a very long one.

Some Biomarkers Thought to be Involved in Allostatic Load

Cardiovascular & Metabolic SystemsDiastolic & systolic blood pressureObesity: waist to hip ratio, BMIGlycosylated hemoglobin, fasting glucoseCholesterol measurementsC-reactive protein

NeuroendocrineCortisolCatecholamines (epinephrine, norepinephrine)DHEAS

Immune/ InflammatoryLymphocytes, natural killer cells, macrophagesTumor necrosis factor alphaInterleukins (IL-6)Insulin-like growth factorImmoglobulin levelsCoagulation (fibrinogen)C-Reactive ProteinAlbuminFibrinogen

OtherHomocysteine

Brain (not easily measurable)

Operationalizing Allostatic Load:The Original FormulationFor now, we focus on how researchers calculate AL scores rather than how they use them analyticallyBased on MacArthur Successful Aging Study, high functioning men & women 70-79*Idea is to summarize levels of physiological activity across range of regulatory systems related to stress responseThis formulation focused on 10 markers:

CV risk factors/metabolic syndrome (syndrome X)HPA-axis activitySNS activity

Biomarkers obtained from fasting blood, 12-hour urine and anthropometric measurements

* Seeman et al. 1997. Arch Intern Med

Calculation of Risk Score

Define “risk zones” (distinct from clinical cut-offs)Highest risk quartile for each of 10 biomarkers based on high-functioning MacArthur cohort

Highest quartile for all but DHEA-S and HDL (good) cholesterol, where lowest quartile usedSome debate about whether cut-off should be sex (or age) specific

Score calculated by summing number (out of 10) markers with high-risk values

Authors note that equal weighting of different biomarkers was consistent with factor analysisFinal score is a count of number of biomarkers ‘outside of normal operating ranges’

Defining Risk Zones for Allostatic Load Score

Source: Singer B et al. 2004. “Operationalizing Allostatic Load.” Pp.113-149 in Jay Schulkin(eds.) Allostasis, Homeostasis, and the Costs of Physiological Adaptation. Cambridge, U.K.: Cambridge University Press

.

Number/Types of Biomarkers Vary by Study

Source: Szanton, SL et al. 2005. Allostatic Load: A Mechanism of Socioeconomic Health Disparities?

Biological Research for Nursing, 7-15.

Brief Description of Biomarkers

Source: Turra CM et al. 2005 Determinants of Mortality at Older Ages: The Role of Biological Markers of Chronic Disease. Pop Dev Rev, 675-698.

Extensions of Method

Seplaki et al. (2005) examined alternative scores, most of which are counts

Use of 16 rather than 10 markers (including immune system markers)Use of deciles rather than quartiles to define risk zonesUse of two-tails of risk when appropriate

Cortisol, BMI, diastolic blood pressure are clear candidates10/90 and 25/75 were chosen although need not be symmetric

Use of z-score in lieu of a dummy variable to identify high risk (think about outliers)Grade of Membership (GoM) score

Measures similarity of each individual’s set of biomarkers to four profiles that represent high risk (high or low on primary mediators and secondary outcomes)

Which Type of Score is Best?

How should we assess preferences for one measure over another?Seplaki et al. conclude that:

additional markers and use of two-tails (with 10/90) cutoffs improves performanceZ-score works as well as countsG0M has nice features, but it is very complicated.

Prediction of Various Health Outcomes Using Different Allostatic Load Scores

Source: Seplaki, CL et al. 2005. A comparative analysis of measurement approaches for physiological dysregulation in an older population. Experimental Gerontology, 438-449

Other Modifications

These days, investigators frequently add new biomarkers but sometimes at the expense of the notion of allostatic load

Seeman et al. (2004) consider low peak flow, low creatinine clearance as well as other biomarkersMarkers of ‘performance’ may have stronger associations with health and survival than the ‘typical biomarker’

Should we consider renaming measures of allostatic load as cumulative physiological dysregulation (or biological risk)?

Expanded Set of Biological Risk Factors

Source: Seeman, TE et al. 2004. Cumulative biological risk and socio-economic differences in mortality: MacArthur Studies of Successful Aging. Soc Sci Med, 1985-1997

ORs Vary Substantially

Source: Seeman, TE et al. 2004. Cumulative biological risk and socio-economic differencesin mortality: MacArthur Studies of Successful Aging. Soc Sci Med, 1985-1997

ORs associated with individual biomarkers in predicting 7.5 year mortality

Drawbacks of Allostatic Load ScoresMeasures used now are largely atheoreticalWhich biomarkers should we include?

We are generally limited to non-invasive easy to measure markers. How invasive can non-invasive be?

Examples from Taiwan

How should we measure the biomarkers (mean vs. variability, reactivity vs. basal levels, type of specimen, etc.)

Heart rate variability is an important measure of PNS regulationbelieved to be related to psychosocial stressors (Sloan et al., 2005); 20-minute EKG or Holter monitor Overnight cortisol or morning rise? Blood, urine or saliva?

What are most appropriate cutpoints?Should cutpoints vary (by age? sex?)Do we redefine cutpoints from each data set?

When should we have two tails of risk?

Drawbacks of Allostatic Load Scores, con’t

How should we weight different biomarkers?The weights are likely to vary as a function of the health outcome

Is a sum type of score a good idea?Are we masking effects of individual markers?One compromise is to consider sub-scores of allostaticload (see Seeman et al., 2004 in earlier slide)What about interactions among biomarkers?

Many persons in the sample use medications which may mask underlying value of biomarkersHow do we know if we have a good measure?

We will return to this very difficult issue

More Complex Measures of Allostatic Load

Canonical CorrelationKarlamangla et al. (2002). “Allostatic load as a predictor of functional decline: MacArthur studies of successful aging.” Journal of Clinical Epidemiology 55: 696-710.

Recursive PartitioningSinger, Ryff and Seeman (2004). Chapter 4 (“Operationalizing Allostatic Load” in Schulkin (ed.))Gruenewald et al. (2006). “Combinations of biomarkers predictive of later life mortality.” PNAS 103: 14158-14163.

Future Directions

Canonical Correlation (CC)Use a set of biomarkers to predict multiple outcomes (components of cognitive and physical functioning).Find the best linear combination of the biomarkers (at baseline) that is maximally correlated with the best linear combination of functional outcomes over the follow-up periodThat is, canonical correlation finds a linear combination of the variables from each set (biomarkers & outcomes) – a canonical variable – such that the correlation (canonical correlation) between the two is maximized. The weights in the best linear combination are known as the canonical weights and are used to score AL.

One typically calculates weights after predictor & outcome variables have been standardized

The contributions of individual predictors and outcomes to the overall association can be determined.

Canonical Weights Based on 10 Biomarkers


.

What Did CC Contribute to the AL Debate?

In contrast to the conventional measure, contributions of markers can vary

Some markers are not predictive (cholesterol), others (epinephrine) matter a lot (Karlamangla et al., 2002)

The Syndrome X markers do not tell the entire story; the hormonal markers are useful predictorsOf course, these findings are likely to vary by type of outcome

Recursive Partitioning (RP)

Identify subclinical levels of biomarkers (“pathways”) that lead to high risk outcomesBegin with a set of biomarkers and a set of outcomes as in CCStep 1: Partition the data into 2 groups

Use the algorithm to search among the biomarkers and possible binary cut points to the identify the best single biomarker (and cut point) that differentiates individuals by outcome (e.g., survival over follow-up period).In the example that follows (Singer et al., 2004), DBP was the best predictor with high risk defined as DBP < 60 mm Hg (hypotension)Note that this could not have been discovered with original formulation which looked only at high BP

First Stage in Recursive Partitioning Tree

- 727 Persons -

Is DBP > 60?

No Yes

#Dead / N = 135/684 = .20#Dead / N = 19/43 = .44


.

Based on mortality in 7-year follow-up period in MacArthur study

Recursive Partitioning (RP), con’t

Step 2: Partition each of 2 subgroups separately, by identifying which biomarker (and cut point) would lead to best survival prediction for that subgroup

One can identify terminal nodes (no further subdivisions) – e.g., group with DBP <60mm Hg was designated to have terminal node with high mortality (44%)

Second Stage in Recursive Partitioning Tree

- 727 Persons -

Is DBP > 60?No Yes

#Dead / N = 73/271 = .27

#Dead / N = 19/43 = .44 N = 684

Is DBP > 80?

#Dead / N = 36/320 = .11#Dead / N = 26/93 = .28

YesNo

N = 413

Is HDL > 36?YesNo


.


Step 3: Identify Boolean statements defining High, Intermediate, and Low Levels of AL

Source: Singer B et al. 2004. “Operationalizing Allostatic Load.” Pp.113-149 in Jay Schulkin(eds.) Allostasis, Homeostasis, and the Costs of Physiological Adaptation. Cambridge, U.K.: Cambridge University Press.


Step 4: Continue without overfitting the data (simplifying rules)Gruenewald et al. (2006) use alternative RP procedures that lead to multiple trees (use of suboptimal splits at the top) and subsequent selection of a “forest from the trees” to consider multiple pathways across the trees

Final Recursive Partitioning Tree

N = 727DBP > 60 ?

N = 727DBP > 60 ?

#D/N = 19/43 = .44

No

N = 684DBP > 80 ?

N = 684DBP > 80 ?

#D/N = 73/271 = .27

Yes

Yes

N = 413HDL > 36 ?

N = 413HDL > 36 ?

No

No

#D/N = 26/93 = .28

Yes

N = 320HDL > 38 ?

N = 320HDL > 38 ?

No

#D/N = 0/24 = 0

Yes

N = 296Cortisol > 9.4 ?

N = 296Cortisol > 9.4 ?

No

#D/N = 1/39 = .03

Yes

N = 257HDL > 79 ?

#D/N = 0/18 = 0

YesNo

N = 239SBP > 141 ?No

#D/N = 22/185 = .12

Yes

N = 54Glyc. Hem. > 6.8?

No

#D/N = 0/20 = 0 #D/N = 13/34 = .38

Yes


.

Final Boolean Statements for RP


.

What Does RP Contribute to the AL debate?

Empirical identification of cut points is less arbitrary than predetermined percentilesImplicitly permits interaction among biomarkers (and statements)Recognition of multiple pathways to downstream health outcomesIdentification of “dominant” markers: e.g., predominance of inflammatory biomarkers in high risk pathways

Canonical Correlation & Recursive Partitioning

These 2 procedures differ in a fundamental way from other methods of measuring allostatic load (apart from being more statistically sophisticated)How so?Is this a problem?

Future Directions:A New Approach for Measuring AL

MetabonomicsIdentify and quantify time-related metabolic changes in an integrated biological system – i.e., identify low molecular weight molecules produced by active living cells

Find metabolites (e.g., gene & protein expression) in biofluids (urine, serum), based on 1H-NMR spectroscopy (high resolution, complex spectra)

Metabonomics, con’tIdentify distinct metabolic signatures (e.g., amino acids, lipoproteins) between populations high on stressors/adverse histories vs. low on stressors/positive experiences Analysis requires computer-based data reduction and pattern recognition methods. Most work has been done in animals looking at response to drug toxicity or disease profiles.

Source: Singer B et al. 2004. “Operationalizing AllostaticLoad.” Pp.113-149 in Jay Schulkin (eds.) Allostasis, Homeostasis, and the Costs of Physiological Adaptation.Cambridge, U.K.: Cambridge University Press.

The Million Dollar Question:What is the Evidence to Support “Allostatic Load”?

Some claim that finding that cumulative score has stronger association with health than individual markers provides evidence

“The summary measure of AL was a much better predictor of health outcomes than any individual component, supporting the argument that AL reflects the cumulative burden of a number of modest deviations from a range of systems.” (Clark et al. Psychology, Health & Medicine). Do you buy this?

Is there evidence that chronic stressors/adverse histories are associated with AL?

There are not many such studies

Is there evidence that AL is associated with downstream health and survival?

Many studies suggest that this is the case

This brings us to the next topic: linkages among the social environment, stress, & physiological dysregulation

Final Conundrums

There are obvious weaknesses in AL measures in current useThere are concerns with regard to using any type of cumulative measure given our state of knowledgeSo, how should we improve our measures of cumulative physiological dysregulation?Allostatic load should not be measured at one-point in time. But, how do we begin to deal with multi-system changes in biomarkers over time?

Documents

Boulder Allostatic load June 2007 - CU Population Center · (eds.) Allostasis, Homeostasis, and the Costs of Physiological Adaptation. Cambridge, U.K.: Cambridge University Press