Neuroendocrine biomarkers, allostatic load, and the challenge of measurement: A commentary on Gersten

Loucks, Eric B.; Juster, Robert Paul; Pruessner, Jens C.

doi:10.1016/j.socscimed.2007.09.006

Cited by 40 publications

(49 citation statements)

References 31 publications

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“…In a 2008 commentary by Loucks et al , the authors listed one of the main challenges of employing an AL framework as understanding if AL is affected primarily by psychological stress or if there are other factors that are as or more important such as sedentary lifestyle, smoking or diet 13. We addressed this challenge in the current research.…”

Section: Discussionmentioning

confidence: 99%

“…Among the challenges of using AL is gaining a better understanding of the types of stress that can increase it and whether lifestyle factors can affect AL in the way that psychological stress does 13. Making health behaviours the focus of the analysis rather than just including them as confounders may allow us to better understand how these modifiable risk factors are associated with AL as a construct.…”

Section: Introductionmentioning

confidence: 99%

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Association between allostatic load and health behaviours: a latent class approach

Forrester

Leoutsakos

Gallo

et al. 2019

J Epidemiol Community Health

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BackgroundAllostatic load (AL) has been characterised in many ways throughout the literature; however, its relationship to health behaviours has only been studied in limited populations. We aimed to uncover qualitative patterns of biological indicators in AL and determine if those patterns were associated with certain health behaviours.MethodsWe conducted latent class analysis using biological indicators from a multiethnic population. We fit latent class regression of class on health behaviours (smoking, poor diet, physical activity and alcohol use) to measure the association between each latent class of AL and each health behaviour.ResultsFour classes, ‘Metabolic+Cholesterol, ‘Blood Pressure’, ‘Metabolic+Blood Pressure’ and ‘Low’, were found in the sample. Latent class regression showed that physical activity and alcohol use were significantly associated with the ‘Metabolic+Blood Pressure’ class.ConclusionLess physical activity was required to improve AL than was previously found. Low to moderate alcohol use was beneficial for lower AL. Implications of the amount of physical activity necessary to lower AL is discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association between allostatic load and health behaviours: a latent class approach

Forrester

Leoutsakos

Gallo

et al. 2019

J Epidemiol Community Health

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“…Given its potential as an interdisciplinary index of personal and public health, researchers have developed a variety of ways to measure AL [7,17–19]. The challenge, however, is built into the very nature of what makes AL unique: it is a complex, dynamic, evolving network of intersecting allostatic systems, which are situated within a wider web of intersecting socioecological systems [7,17–21].…”

Section: Introductionmentioning

confidence: 99%

Allostatic load as a complex clinical construct: A case‐based computational modeling approach

et al. 2015

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Allostatic load (AL) is a complex clinical construct, providing a unique window into the cumulative impact of stress. However, due to its inherent complexity, AL presents two major measurement challenges to conventional statistical modeling (the field’s dominant methodology): it is comprised of a complex causal network of bioallostatic systems, represented by an even larger set of dynamic biomarkers; and, it is situated within a web of antecedent socioecological systems, linking AL to differences in health outcomes and disparities. To address these challenges, we employed case-based computational modeling (CBM), which allowed us to make four advances: (1) we developed a multisystem, 7-factor (20 biomarker) model of AL’s network of allostatic systems; (2) used it to create a catalog of nine different clinical AL profiles (causal pathways); (3) linked each clinical profile to a typology of 23 health outcomes; and (4) explored our results (post hoc) as a function of gender, a key socioecological factor. In terms of highlights, (a) the Healthy clinical profile had few health risks; (b) the pro-inflammatory profile linked to high blood pressure and diabetes; (c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems; and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%); while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles.

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“…The approach to conceptualizing allostatic load in large epidemiological studies is often determined pragmatically according to the available information, and most of these trials (for example, the National Health and Nutrition Surveys) do not include neuroendrocrine assessments. Moreover, the primary biomarkers are difficult to measure consistently, as they may be collected from varied biological samples (e.g., blood, saliva, urine) and are prone to circadian fluctuations as well as rapid responses from external stimuli (8,19). Thus, a single point-in-time assessment may not adequately capture true dysregulation.…”

mentioning

confidence: 99%

“…The absence of a “gold standard” representation also limits the comparisons that can be made across applications. Such variability is common in the early stages of biomarker panel development (19), but to further advance the field, agreement is needed regarding the biological indicators and systems that should optimally be used to operationalize allostatic load (5,19). Moreover, additional observational studies in humans and targeted experimental investigations are warranted to evaluate the health implications of allostatic load, especially in long-term prospective cohorts with data on interacting factors (genetic influences, health behaviors).…”

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confidence: 99%

Allostatic Load and the Assessment of Cumulative Biological Risk in Biobehavioral Medicine

Gallo

Fortmann

Mattei

2014

Psychosomatic Medicine

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Allostatic load provides a useful framework for conceptualizing the multi-system physiological impact of sustained stress and its effects on health and well-being. Research across two decades shows that allostatic load indices predict health outcomes including all cause mortality, and vary with stress and related psychosocial constructs. The study by Slopen and colleagues in this issue provides an example both of the utility of the allostatic load framework, and of limitations in related literature, such as inconsistencies in conceptualization and measurement across studies, and the frequent application of cross-sectional designs. The current article describes these limitations and provides suggestions for further research to enhance the value and utility of the allostatic load framework in biobehavioral medicine research.

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Neuroendocrine biomarkers, allostatic load, and the challenge of measurement: A commentary on Gersten

Cited by 40 publications

References 31 publications

Association between allostatic load and health behaviours: a latent class approach

Association between allostatic load and health behaviours: a latent class approach

Allostatic load as a complex clinical construct: A case‐based computational modeling approach

Allostatic Load and the Assessment of Cumulative Biological Risk in Biobehavioral Medicine

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