Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort

Health inequalities have been highlighted between socioeconomic groups within populations worldwide [1, 2]. In particular, the rate of premature mortality, is found higher among people with more disadvantaged socioeconomic positions (SEP) across countries and at all stages of the lifespan [2‐6]. Several epidemiological studies have shown that behaviours and lifestyle factors are important determinants of mortality, but do not fully explain the social gradient [7, 8]. A better understanding of the aetiological pathways through which adverse health outcomes are generated is key to alleviate the effects of social inequalities in health.

Persistent socioeconomic disadvantage, and psychosocial adversities across the life course have been linked to chronic over activation of stress response mechanisms. The neuroendocrine hormones release and the ensuing biochemical cascade build up over time due to these chronic exposures, promoting the initiation, development and progression of disease [9‐11]. The prolonged activation of compensatory physiological mechanisms can lead to a physiological ‘wear-and-tear’, termed allostatic load (AL) [9, 11‐13]. The AL model of chronic stress focuses on glucocorticoid dysregulation as part of a ‘network of allostasis’ involving autonomic, endocrine, metabolic, and immune mediators [14, 15]. A variety of studies using measures of AL have suggested its association with numerous health outcomes and higher all-cause of mortality risk [16].

Embodiment refers to how people incorporate, biologically, the world in which they live, including social and ecological circumstances [17]. The relationship between life course SEP and chronic stress exemplifies the embodiment dynamics through its complexity and the pathways (behavioural, material and psychosocial) through which SEP can affect health in later life [18]. Three broad, complementary and connected pathways across the life course have been outlined as the main routes towards health inequalities. These involve i)“personal” factors such as health behaviours/lifestyle, ii) structural factors including material resources and socio-economic conditions and iii) psychosocial processes involving physiological responses to environmental stimuli through a cascade of information-processing pathways in the central nervous system leading to a series of bio-physiological adjustments [18, 19].

Multiple studies have examined the relationship between the social environment and AL, showing that AL was socially distributed [16]. Gustafsson et al. [20] examined the influence of life course SEP from 16 years of age on AL observing that cumulative socioeconomic disadvantages was related to AL, in a northern Swedish cohort with 27 years of follow-up data. Regarding pathways, they reported that social adversity during adolescence for women and during early adulthood for men was associated with later AL independently of health behaviours and adulthood adversities [21].

Our own previous studies on the 1958 National Child Development Study (NCDS) evaluated the contribution and the relationship between these three broad pathways (“personal”, “structural” and psychosocial pathways) on all-cause mortality and cancer incidence in a longitudinal setting. To better characterise the social-to-biological embodiment processes, we assessed the contribution of the three main pathways over the life course in mediating the association between childhood SEP and AL. Our results highlighted that the relationship between childhood SEP and AL in early adulthood was mediated by an educational, material and health behaviours pathway [22]. We also examined how exposure to adverse childhood experiences (ACEs) was associated with a higher AL score in midlife, a relationship which was largely explained by early adult behavioural and socioeconomic factors [23]. Having a higher AL at 44 years old was also associated with poorer subjective health five years later using a latent health variable derived from sleep patterns, physical and mental health at 50 years old, after adjusting for life course SEP and behaviours [24]. Additional studies are required to gain understanding on the relationships between AL, its components and a variety of health outcomes.

This study aims to investigate the relationship between AL and subsequent mortality risk in a large prospective birth cohort. We use data from 14 blood biomarkers representing four physiological systems (neuroendocrine, immune and inflammatory, metabolic, cardiovascular system) measured in 45 years old adults. We first examine the performance of the AL score in predicting mortality up to 11 years after blood collection, using models correcting for a priori life course confounders. In a second step we also evaluate the relative contribution of each physiological systems, and each individual biomarkers entering in the AL score definition, in the relationship linking AL and death.

We first compared the subsample of 132 deaths with biological data included in our analyses from the 234 deaths without blood collection. Individuals in each group did not differ in terms of adult covariates (Supplementary Table 3). However, participants without blood samples were more likely to come from overcrowded households (24.3% vs 11.11%, p = 0.005) and their mothers were more likely to have left school before 14 years old (81.2% vs 72.4%, p = 0.079, Supplementary Table 3). Table 1 presents the distribution of selected characteristics for NCDS participants according to death status and for the overall sample. During a median follow-up time of 10.5 years (10–90 percentile: 10.0–11.1), 132 participants with blood collected in the cohort died. Compared with participants who did not die (N = 7,981), those who died (N = 132) were more likely to be men; to have suffered from childhood pathologies; their fathers were more likely to have had a ‘manual’ occupation; their mothers to have been smokers during pregnancy; and they were more likely to have a low birthweight. They also had fewer qualifications; were more likely to have a manual occupation as adults and to rent their accommodation. Participants who died were also more inactive and likely to smoke.

Regarding physiological functioning, respondents who died were more likely to have been in the high-risk group for each physiological system with the exception of the neuroendocrine system, and for the following biomarkers: fibrinogen, IGF1, CRP, Triglycerides, HbA1C, heart rate and peak expiratory flow (Table 1). The AL score was higher among participants who died (mean = 4.2,  %low-med-high 23.5–34.1–42.4) than for those who were still alive (mean = 3.1,  %low-med-high 44.9–32.3–22.8).

Our study suggests that having a higher allostatic load score was significantly associated with an increased risk of all-cause premature mortality over an 11 year period. After controlling for early life SEP, other life course factors and health status, participants with a high AL at 44 years old had a risk of dying before 55y almost 3 times higher as those with a low AL providing evidence that the observed association was independent of early life SEP and other life course factors. Adding SEP and health status at 42 slightly attenuated this association [HR = 2.57 (1.59 to 4.15)], with heavy smoking and living in rented accommodation making a significant contribution to mortality risk.

We also analysed the relationship between the components of our AL score and mortality, examining to what extent each system or individual biomarker contributed to the increased risk of death. Among the four physiological systems composing our AL score, only the immune-inflammatory and cardiovascular sub-scores remained significantly related to mortality after adjustment and multiple testing correction suggesting a pivotal role of these systems in our allostatic load index. To evaluate the contribution of each sub-score in the relationship between AL and future risk of death, additional analyses controlling for each physiological system were performed (Supplementary Table 7). Adding either the inflammatory/immune or the cardiovascular sub-score had the greater effect on the AL HR estimate. Nevertheless the association between AL and future risk of death was only slightly weakened HR = 2.17 (1.22 to 3.89) and HR = 2.22 (1.27 to 3.88) after controlling respectively for the inflammatory/immune and cardiovascular sub-score. Participants with a high CRP, fibrinogen, heart rate and peak flow had a greater risk of death compared to those with a low score. Similarly when controlling for CRP, fibrinogen, heart rate and peak flow in the relationship between AL and future risk of death, the association was mostly affected by CRP [HR = 2.27 (1.36 to 3.68)] followed by peak flow [HR = 2.26 (1.38 to 3.68)] and heart rate [HR = 2.27 (1.36 to 3.77)] and to less extent fibrinogen [HR = 2.42 (1.46 to 4.03), Supplementary Table 8]. Our results suggest that the cumulative AL measure consisting of all the biomarkers was a better measure for predicting death compared to each sub-score and biomarker analysed separately, in line with studies in US [28, 32‐35], Taiwan [36] and Scotland [37], and with the assumption of global physiological wear and tear captured by AL.

Our findings should be understood in the light of our previous research which highlighted the pathways between a lower socioeconomic position at birth and having a higher allostatic load at age 45 in the same cohort study [22]. We showed that the most important indirect pathway, explaining up to 31% of the effect, was through the cohort members’ educational attainment. However, the majority of the association (up to 68%) between early life social position and mid-life allostatic load was unexplained by any of the mediating factors. We also previously analysed the pathways between adverse childhood conditions and allostatic load, showing that the association operated largely via education, health behaviours, wealth and BMI in adulthood [23]. Here, our work examines the other end of the life course, focusing on the association between allostatic load and mortality before 55 years of age. Our findings show that the association between allostatic load in mid-life, and mortality is virtually unchanged after taking into account many of the major early life factors, including socioeconomic position at birth and adverse childhood experiences, and after including mediating factors such as health behaviours, socioeconomic position and housing tenure in adulthood. This suggests that the observed relationship between such an indicator of multi-system physiological wear-and-tear in mid-life and death is largely independent in this cohort. We suggest therefore that biological risk, and notably degraded inflammatory (through CRP and fibrinogen) and cardiovascular (through peak flow and heart rate) systems, are driving the association with the specific types of mortality observed at this early stage of the life course. Unfortunately, cause-specific mortality is currently unavailable for the cohort, which would allow us to examine this further. These findings highlight the potential importance of biological risk scores, such as allostatic load, in capturing both pre-disease states and the social embedding.

Several limitations of this study should be considered. Since analyses were performed using a birth cohort, an important weakness is related to attrition and selection bias. However, the surviving cohort remains broadly representative of the initial cohort on key childhood and adult characteristics [27]. To allow for uncertainty about the missing data and to ensure distribution, variability, and relationships between variables, multiple imputations were used for confounding variables taking the missing at random assumption. In addition, we ran all analyses on complete cases i.e. individuals with no missing data for any of the selected characteristics. The smaller sample size reduced statistical power, and measures of association were subsequently weakened. Nevertheless, the hazard ratios were all consistent with the imputed results. Given the small number of deaths included in our study (N = 132), and that dead participants who were excluded (no blood collected, N = 234) were most likely to come from overcrowded households and their mothers were more likely to have left school before 14 years old (Supplementary Table 4), and considering the negative association between mother’s education level and AL, our results might be conservative. We cannot exclude the possibility that other factors may contribute to the mechanisms linking AL and future risk of death. Since AL is a latent variable capturing multi-system physiological wear-and-tear and there is no standard measure for capturing it, another limitation is in the measurement of AL, which varies across studies. We used 14 available biological parameters representing four physiological systems. However, we were constrained by variable availability and lack ‘primary’ biomarkers (epinephrine and norepinephrine), which means that the neuroendocrine system was poorly represented compared to the others [38]. There is currently no consensus regarding (i) the choice of the relevant markers to be included (ii) their measurement (iii) their combination and (iv) ad-hoc statistical analyses [15, 16, 39]. Furthermore, the relative importance of each AL component in the stress response cascade remains to be explored in order to better capture physiological wear-and-tear.

Despite the limitations mentioned above, we used a longitudinal population-based birth cohort collecting data prospectively across the life span. Important strengths include: the large sample size for the biomedical survey; the large number of biomarkers available; the great detail and breadth of variables within the cohort which allowed us to control for a number of potential confounding factors.

Multiple studies have addressed the relationship between AL and future risk of death. Higher allostatic load was a significant predictor of functional decline [40] and mortality across different time periods and countries [28, 32‐37], a relationship not attributable to age, sex, ethnicity, education, or income. Seeman et al. used data from the MacArthur Successful Aging Study, a longitudinal study of men and women aged 70-79 living in the United States and reported that higher baseline AL scores were associated with significantly greater risk for mortality within 7 years [28]. Karlamangla et al. further examined the effect of changes in AL on mortality risk using two measures of AL on a smaller sample, they reported that participants whose AL increased had higher risk of all-cause mortality [32]. Using data from the third National Health and Nutrition Examination Survey (NHANES III) (1988–1994), a large, nationally representative study of around 40,000 U.S. children and adults, Borrell et al. found an increased risk of all-cause mortality within 12 years in participants with AL scores of two, three and above compared to those with an AL score of zero or one, independently of ethnicity, income and education [33]. Levine & Crimmins found that participants in the top AL quintile had higher overall mortality risk within 10 years compared to participants in the lowest AL quintile after adjusting for age and sex [34]. Howard and Sparks showed that each 1-point increase in AL was associated with 7% incremental risk of mortality controlling for age, sex, ethnicity, SEP and health behaviours [35]. Hwang et al. used the Taiwanese Social Environment and Biomarkers of Aging Study, a longitudinal survey of adults 54 years and older, reported that a higher AL was significantly associated with 10-year increased risk of death [36]. More recently, Robertson et al. used data from the Scottish Health Survey and identified increased risk of all-cause mortality within 10 years associated with increasing AL [37]. Our study fits into this international literature, with the added strength of being a birth cohort where the relationship between variables over time facilitates our understanding of underlying mechanisms. We also focused on understanding the drivers behind the relationship between AL and subsequent mortality by examining physiological sub-scores. We found that the relationship was notably driven by two major systems: the immune-inflammatory and cardiovascular system. Specifically, four individual biomarkers were identified as driving the association: CRP, fibrinogen, for the immune-inflammatory system; heart rate and peak flow for the cardiovascular system. Our results were consistent with cause of death registered in England and Wales in 2013 (Office for National Statistics (www.​ons.​gov.​uk)) where neoplasms (notably lung cancers) were the first cause of death in both men and women between 50-59 years old followed by diseases of the cardiovascular system. Our study suggests that the relationship between AL, its components and future risk of death at 55 years may be a reflection of both age and cause-specific nature of death at this stage of the life course. These findings may provide evidence that immune-inflammatory and cardiovascular wear-and-tear remain areas for primary prevention at earlier phases of the life course, given their importance in driving premature mortality risk and subsequent ageing patterns.

Since biomarker information was only available at 44 years of age, the AL measurement we used does not provide information on dynamic change over time and does not fully capture the flexibility of stress response mechanisms across the lifespan. Our findings support the conceptual validity of AL as being able to provide insight into cumulative risks to health across multiple physiological system [28].

To take into account the complexity and the dynamic nature of AL as the result of adaptation to environmental challenges, additional studies are required to define a set of representative physiological systems, identify time-specific biomarkers and investigate AL at multiple time points in population longitudinal studies across contexts. Strategies to increase physiological resilience along with targeted prevention policies over the life course to manage exposures and physiological responses to stress are necessary to prevent its detrimental effects on health.