Review articleA systematic review of allostatic load in relation to socioeconomic position: Poor fidelity and major inconsistencies in biomarkers employed
Introduction
The social underpinnings of disease have been long acknowledged and an extensive body of literature has linked lower socioeconomic position (SEP) with adverse health outcomes (Marmot et al., 1991, Sapolsky, 2004, Taylor et al., 1997). The underlying mechanism for some diseases is better understood than others. For example, it is well established that in high income countries those of a lower SEP are more likely to smoke, be hypertensive and have increased cholesterol, which in turn results in an increased risk of cardiovascular disease (CVD) events (Fuller et al., 1983, Sterling and Eyer, 1981, Lynch, 2006, Kivimäki et al., 2008). However, the extent to which stress plays a role in the specific mechanisms through which social factors influence disease has remained elusive. Two key areas of research have emerged: one focused on how stress is related to behavioral mechanisms of disease and the other on the biological mechanisms responsible for translating stress into disease (Harbuz, 1999, Friedman et al., 1958, Kornitzer and Kittel, 1986, Shah and Cole, 2010). The latter has emphasized understanding how the body internalizes an external stressor on a physiological level and how well a person can adapt to changes in his or her environment. Allostasis is a concept describing the normal process of how the human body adapts in response to a given stimulus (Sterling and Eyer, 1988). Allostatic load (AL) is defined as the physiological “wear and tear” a person experiences across his or her life, for instance chronically elevated blood pressure resulting from a lifetime of occupational strain (McEwen and Stellar, 1993).
According to the original AL framework, stress hormones controlled by the hypothalamic-pituitary-adrenal (HPA) axis (e.g. cortisol, epinephrine, and norepinephrine) are the “primary mediators” of AL, which in turn mediate “secondary effectors” such as blood pressure, lipid metabolism, and inflammation (McEwen and Stellar, 1993, Smith and Vale, 2006). Poor health conditions resulting from extreme values of primary mediators and secondary effectors are “tertiary outcomes” (e.g. coronary heart disease, decreased physical capacity, obesity or severe cognitive decline) (Karlamangla et al., 2002, Gruenewald et al., 2006, Gruenewald et al., 2015). In the first study to calculate an AL index, measurements of 10 biomarkers were combined from three biological domains (cardiovascular and metabolic systems, and HPA axis) (Seeman et al., 1997). For clarity, in this paper AL index refers to the quantifiable variable, while allostatic load refers to the conceptual framework devised by McEwen & Stellar (McEwen and Stellar, 1993).
Since the term allostatic load was first introduced in 1993, the number of studies on AL have grown considerably. Between 2010 and 2017 the number of papers in PubMed mentioning AL have more than tripled, with 110 studies published in 2016 alone (Corlan, 2004). However, researchers have not taken a consistent approach to the way they have operationalised the concept. If AL is intended to measure the physiological response to stress, then the inclusion of primary mediators, such as HPA axis biomarkers (or equivalent), in an AL index is intrinsic to its definition.
These methodological inconsistencies make comparisons across studies challenging. There is therefore a need to determine how researchers define AL in the literature and to see how different definitions affect associations between stress, AL, and disease. No prior study has quantified the heterogeneity in AL indices. Previous reviews of AL, health disparities and outcomes have been performed, but none had a methodological focus, although some attention has been given to comparing different methods for how levels of constituent biomarkers should be arithmetically combined into a single index (Karlamangla et al., 2002, Seplaki et al., 2005, Seeman et al., 2001, Dowd and Simanek, 2009, Beckie, 2012, Mauss et al., 2015).
In this systematic review we have aimed to provide a comprehensive overview and discussion of the biomarker content and methods used to calculate AL in studies that have looked at its association with SEP. A secondary aim was to describe the associations of AL with SEP.
Section snippets
Search strategy & data extraction
The scope of this review was limited to the biological internalization of SEP and the effects of this stressor on AL, highlighting AL as a mechanism on the causal pathway between SEP and health outcomes (Fig. 1).
The literature review was restricted to peer-reviewed publications of human population studies that calculated an AL index and analysed the association between SEP as the main exposure and AL as the main outcome. Reviews, protocols, conference abstracts, and theoretical discussions were
Findings from the literature search
The search strategy outlined above identified 282 papers; five additional papers were included from cross-referencing previous systematic reviews resulting in 287 articles screened (Fig. 2). Thirty-one full text articles were reviewed after duplicate removal and title and abstract screening. Of these, five articles were excluded due to not reporting a direct measure of the association between AL and SEP, leaving a total of 26 articles. Of these 26, four analysed the National Health and
Discussion
We reviewed the methodologies used to operationalise the concept of allostatic load, a term intended to represent the biological “wear and tear” a person experiences throughout life. Our findings indicate there is no standard method of calculating an AL index in the literature on AL and SEP. Across the 26 studies in the literature review, there were 59 biomarkers combined in 20 different ways. Not only were studies dissimilar to one another, there was no study that used the same biomarkers as
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