Handbook of Clinical Gender Medicine. Группа авторов

      Pathogenesis Temporal Cascade of Allostatic Load Mediators

      Multiple mediators of adaptation are involved and interconnected in nonlinear networks [7]. When chronically stressed, the biphasic effects of numerous biomarkers eventually lead to AL and disease in a tripartite sequence: (1) overactivation of primary mediators such as stress hormones and pro-and anti-inflammatory cytokines induce primary effects on cellular activities; (2) subsequent secondary outcomes, whereby metabolic, cardiovascular, and second-order immune biomarkers become dysregulated, and (3) culmination in tertiary outcomes or clinical endpoints [7].

The AL model proposes that, by measuring the multisystemic, reciprocal interactions among primary mediators, primary effects, and secondary outcomes, individuals at a high risk for tertiary outcomes can be identified [8]. Physicians already routinely incorporate many related biomarkers in typical blood work, although attention is primarily placed on values reaching clinically significant levels. By including additional biomarkers, identifying preclinical values, and triangulating methods with interdisciplinary measures (e.g. genetic assessment, neuropsychological testing, clinical interviews, psychometrics, etc.), advances in diagnostic, treatment, and prevention strategies could be improved.

      Allostatic Load Index

Epidemiological work by Seeman et al. [9] has led to AL algorithms predictive of numerous tertiary/clinical outcomes. Utilization of longitudinal data from the MacArthur studies on the Successful Aging cohort led to a count-based AL index representing the following 10 biomarkers: 12-hour urinary cortisol, epinephrine, and norepinephrine output; serum dehydroepiandrosterone-sulphate, high-density lipoprotein (HDL) and HDL-to-total cholesterol ratio; plasma glycosylated hemoglobin; aggregate systolic and diastolic blood pressures, and finally the waist-to-hip ratio. Individuals’ values falling within high-risk quartiles with respect to the sample’s biomarker distributions are dichotomized as ‘1’ and those within normal ranges as ‘0’. Once tabulated, these values are summed to yield an AL index ranging from a possible 0 to 10 which can then be used to predict health outcomes. Beyond these traditional biomarkers, many others have been incorporated into dozens of studies worldwide (fig. 2) using similar count-based formulations and/or sophisticated statistical analyses.

      Clinical Allostatic Load Index Formula

The AL index is thus far a research index with the promising possibility of becoming a clinical tool. It must therefore become accessible to medical professionals [10]. In this endeavor, the following shall demonstrate a simple formulation to calculate an AL index based on clinical reference ranges used routinely for diagnostic purposes. For each biomarker value included, a subclinical cutoff can be easily calculated based on normative clinical ranges.

Fig. 2. Frequencies of biomarkers repeatedly included in approximately 60 AL studies conducted between 1997 and 2010. TC = Total cholesterol; HDL = high-density lipoprotein; LDL = low-density lipoprotein.

Let us consider, as an example, total cholesterol levels with a normal range of 3.3-5.2 nmol/l. First, to determine the range, subtract the lower limit from the upper limit (5.2-3.3 = 1.9). Next, to determine the quartile, divide the range by four (1.9/4 = 0.475). Finally, to determine the cutoff, either subtract the quartile from the upper limit for the upper cutoff (5.2-0.475 = 4.725) or add the quartile to the lower limit for the lower cutoff (3.3 + 0.475 = 3.775) in the case of biomarkers like HDL cholesterol, DHEA-S, and albumin, whereby lower levels denote danger. Based on this example, a patient with total cholesterol at 4.725 nmol/l or higher would get a score of ‘1’ while values below this cutoff would be scored as ‘0’. A clinical AL index is therefore the sum of subclinically dysregulated biomarkers for each individual. Our previous work found that a clinical AL index was associated with increased subjective reports of chronic stress, frequency of burnout symptoms, and hypocortisolemic profiles characteristic of fatigue states [11]. While this formulation is designed for medical practice, it does not yield cutoffs that are exceedingly different from those based on biomarker distributions based on sample distributions generally used in empirical AL studies summarized in the following section.

      Summary of Allostatic Load Research Findings

      A recent review by Juster et al. [8] of nearly 60 empirical studies suggests that AL indices incorporating subclinical ranges for numerous biomarkers (mean = 10; range = 4-17) predict clinical outcomes better than traditional biomedical methods that address only clinical thresholds for single biomarkers. Importantly, AL inclusion of neuroendocrine and/or immune biomarkers is stronger than metabolic syndrome parameters or systemic clusters.

As summarized in table 1, increased AL indices correspond either cross-sectionally or longitudinally to a plethora of antecedents (e.g. socioeconomic disadvantage, poor social networks, workplace stress, maladaptive personality traits, lifestyle behaviors, genetic polymorphisms, etc.) and consequences (e.g. mortality, cardiovascular disease, psychiatric symptoms, cognitive decline, physical/mobility limitations, neurological atrophy, etc.). Individual differences in unique configurations of these antecedents should be explored further, as they are experienced differently by each sex throughout life and are strong mediators and/or moderators of AL consequences.

      Epidemiology

      Age and Sex Differences

The MacArthur studies provide evidence for age-specific sex differences in AL [8]. In assessing the 12-year mortality risk between the sexes, Gruenewald et al. [12] found that high-risk pathways of AL biomarker clustering for men included adrenalin, noradrenalin, interleukin-6, C-reactive protein, and fibrinogen, while, for women, the pathways included interleukin-6, C-reactive protein, glycosylated hemoglobin, and systolic blood pressure. Interestingly, elevated systolic blood pressure occurred in 100% of female high-risk pathways but only in 17% of male high-risk pathways principally dominated by elevated fibrinogen, noradrenalin, and adrenalin levels, otherwise completely absent for females. In an earlier analysis of the MacArthur cohort, however, cardiovascular biomarkers were more often dysregulated for males, while neuroendocrine biomarkers were more often dysregulated for females [13].

These findings from the same cohort measured at different time points reveal that AL pathways indeed differ between the sexes, although sex-specific biomarker clusters might only emerge with advanced age. Indeed, data from a 15-year follow-up study using the Coronary Artery Risk Development in Young Adults (CARDIA) study found little evidence for sex or ethnicity differences in 40-year-old participants. Nevertheless, it must be noted that aggregate meta-factors including heart rate variability, blood pressure, inflammatory, metabolic, catecholaminergic, and gluco-corticoid biomarkers captured 84% of the statistical variance, suggesting that AL biomarkers indeed form a constellation of shared interrelations consistent with theory [14]. However, subtle physiological dysregulations of intertwined systems may not be easily detected and they do not differ significantly between