The streamlined allostatic load index is associated with perceived stress in life - findings from the MIDUS study

Affect regulation and allostatic load over time

OBJECTIVE

Emerging work suggests that affect regulation strategies (e.g., active coping, anger expression) predict disease and mortality risk, with sometimes divergent estimates by sex or education levels. However, few studies have examined potential underlying biological mechanisms. This study assessed the longitudinal association of affect regulation with future allostatic load.

METHOD

In 2004-2006, 574 participants from the Midlife in the United States study completed validated scales assessing use of nine general and emotion-specific regulatory strategies (e.g., denial, anger expression). As a proxy for how flexibly participants regulate their affect, variability in the use of regulatory strategies was operationalized using a standard deviation-based algorithm and considered categorically (i.e., lower, moderate, greater variability) to assess non-linear effects. Participants also provided data on relevant covariates and 24 allostatic load biomarkers (e.g., cortisol, blood pressure). In 2017-2021, these biomarkers were again collected. Linear regressions modeled betas (β) and 95 % confidence intervals (CI) examining associations of affect regulatory constructs with future allostatic load.

RESULTS

In fully-adjusted models including initial allostatic load, general regulatory strategies were unrelated to future allostatic load. Yet, greater versus moderate affect regulation variability levels predicted lower allostatic load (β=-0.14; 95 %CI: -0.27, -0.01). Only among more educated participants, greater use of anger expression predicted lower allostatic load, while the reverse was noted with anger control (βexpression=-0.12; 95 %CI: -0.20, -0.05; βcontrol=0.14; 95 %CI: 0.05, 0.24).

CONCLUSIONS

While general regulatory strategies appeared unrelated to allostatic load, greater variability in their use and anger-related strategies showed predictive value. Subsequent studies should examine these associations in larger, more diverse samples.

An evaluation of the All of Us Research Program database to examine cumulative stress

OBJECTIVES

To evaluate the NIH All of Us Research Program database as a potential data source for studying allostatic load and stress among adults in the United States (US).

MATERIALS AND METHODS

We evaluated the All of Us database to determine sample size significance for original-10 allostatic load biomarkers, Allostatic Load Index-5 (ALI-5), Allostatic Load Five, and Cohen's Perceived Stress Scale (PSS). We conducted a priori, post hoc, and sensitivity power analyses to determine sample sizes for conducting null hypothesis significance tests.

RESULTS

The maximum number of responses available for each measure is 21 participants for the original-10 allostatic load biomarkers, 150 for the ALI-5, 22 476 for Allostatic Load Five, and n = 90 583 for the PSS.

DISCUSSION

The NIH All of Us Research Program is well-suited for studying allostatic load using the Allostatic Load Five and psychological stress using PSS.

CONCLUSION

Improving biomarker data collection in All of Us will facilitate more nuanced examinations of allostatic load among US adults.

Human immune and metabolic biomarker levels, and stress-biomarker associations, differ by season: Implications for biomedical health research

Although seasonal changes in physiology are well documented, little is known about how human immune and metabolic markers vary across seasons, and no studies have examined how stress → health biomarker associations differ across the year. To investigate these issues, we analyzed data from 2118 participants of the Midlife in the United States (MIDUS) study to determine whether there were differences in (a) levels of 19 immune and metabolic markers, and (b) the association between perceived stress and each biomarker across the year. Results of component-wide boosted generalized additive models revealed seasonal patterning for most biomarkers, with immune proteins generally peaking when days were shorter. Moreover, whereas levels of hemoglobin A1C rose from late fall to spring, triglycerides were elevated in the summer and fall, and high-density lipoprotein decreased steadily from January to December. Urinary cortisol and cortisone exhibited opposite patterns, peaking at the beginning and end of the year, respectively. Most critically, we found that the effects of perceived stress on 18 of the 19 health biomarkers assessed varied by month of measurement. In some cases, these differences involved the magnitude of the stress → biomarker association but, in other cases, it was the direction of the effect that changed. Studies that do not account for month of biomarker assessment may thus yield misleading or unreproducible results.

The allostatic load model: a framework to understand the cumulative multi-system impact of work-related psychosocial stress exposure among firefighters

Firefighting is recognised as a profession where health and well-being can be affected by a variety of occupational factors, such as physical, thermal, and chemical stressors. Along with the risks intuitively associated with the fire service, however, psychosocial stress has begun to attract attention as another variable deserving of consideration. Indeed, long-term exposure to work-related psychosocial stress has been linked with poor health outcomes in many workers; however, despite this association, very little has been done to examine how such stressors become biologically embedded in firefighters. To help facilitate research into how psychosocial stress can affect health-related outcomes in the fire service, we propose a framework centered on the notion of allostatic load. First, we reviewed the occupational characteristics that may generate psychosocial stress within firefighters before introducing allostatic load (that is, dysregulation across various physiological systems caused by the need to manage ongoing stressors). Next, we provided a summary of how allostatic load can be measured and touched on the framework's utility for studying the cumulative effects of work-related stress on firefighter health. After this, factors that may influence the steps leading from stress exposure to health outcomes were discussed; in particular, we commented upon how research in this area should consider specific non-modifiable (age, sex, and ethnicity) and modifiable (psychosocial resources and behavioural habits) factors. Finally, we presented methodological barriers and opportunities that may arise when using the allostatic load framework with this professional group. By introducing the framework, we hope to provide a tool that may be used by those interested in stress-health research in firefighters to build the evidence needed to inform primary prevention measures.

Allostatic Load Measurement: A Systematic Review of Reviews, Database Inventory, and Considerations for Neighborhood Research

BACKGROUND

Neighborhoods are critical to understanding how environments influence health outcomes. Prolonged environmental stressors, such as a lack of green spaces and neighborhood socioeconomic disadvantage, have been associated with higher allostatic load levels. Since allostatic load levels experienced earlier in life have stronger associations with mortality risk, neighborhoods may be uniquely suited to monitor and mitigate the impacts of environmental stressors. Researchers often study allostatic load in neighborhoods by utilizing administrative boundaries within publicly accessible databases as proxies for neighborhoods.

METHODS

This systematic review of reviews aims to identify commonly used biomarkers in the measurement of allostatic load, compare measurement approaches, inventory databases to study allostatic load, and spotlight considerations referenced in the literature where allostatic load is studied in neighborhoods. The review was conducted using the search term "allostatic load" in the MEDLINE, CINAHL, and PsychINFO databases. The search results were filtered to include reviews.

RESULTS

The search returned 499 articles after deduplication. Overall, 18 synthesis reviews met the inclusion criteria and were retained for extraction. The synthesis reviews analyzed represented 238 studies published from 1995 to 2020. The original ten biomarkers were most often used to measure allostatic load. More recently, body mass index and C-reactive protein have additionally been frequently used to measure allostatic load burden.

CONCLUSIONS

The scientific contributions of this study are that we have identified a clear gap in geographic considerations when studying allostatic load. The implication of this study is that we have highlighted geographic concepts when conducting neighborhood-level research using administrative databases as a neighborhood proxy and outlined emerging future trends that can enable future study of allostatic load in the neighborhood context.

Higher Levels of Stress-Related Hair Steroid Hormones Are Associated with the Increased SCORE2 Risk Prediction Algorithm in Apparently Healthy Women

Cardiovascular diseases (CVDs) are the major cause of death worldwide. Although the importance of conventional CVD risk factors, including older age, male gender, hypertension, obesity, dyslipidemia and hyperglycemia, is well-studied, psychosocial stress, which is considered an independent CVD risk factor, requires further investigation. Thus, we aimed to investigate the association between long-term secretion of stress-related steroid hormones, including cortisol, cortisone and dehydroepiandrosterone, and the 10-year fatal and non-fatal CVD risk estimated by the SCORE2 risk prediction algorithm, as well as traditional CVD risk factors in a group of apparently healthy women. A total of 145 women (aged 50–64 years) participating in the national CVD prevention program were enrolled in the study. Sociodemographic, lifestyle, health-related characteristics, stress, anxiety and sleep quality indicators were evaluated using specific questionnaires. Anthropometric and arterial blood pressure measures were assessed by trained personnel, lipid and glucose metabolism biomarkers were measured using routine methods, and hair steroid hormone levels were determined by ultra-high-performance liquid chromatography-tandem mass spectrometry. The results showed that higher levels of hair cortisol and cortisone are associated with increased SCORE2 values. Moreover, significant associations between hair glucocorticoids and individual cardiovascular risk factors, including obesity, hypertension, dyslipidemia and hyperglycemia, were found. These findings indicate that stress-related hair steroid hormones might be valuable biomarkers for CVD prediction and prevention.

Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors

Background

Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior, potentially leading to impaired mental and physical health. Multiple physiological and behavioral lifestyle features can now be recorded unobtrusively in daily-life using wearable sensors. The aim of the current study was to identify a distinct set of physiological and behavioral lifestyle features that are associated with elevated levels of chronic stress across different stress sources.

Methods

For that, 140 healthy female participants completed the Trier inventory for chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven consecutive days while maintaining their daily routine. Physiological and lifestyle features that were extracted from sensor data, alongside demographic features, were used to predict high versus low chronic stress with support vector machine classifiers, applying out-of-sample model testing.

Results

The model achieved 79% classification accuracy for chronic stress from a social tension source. A mixture of physiological (resting heart-rate, heart-rate circadian characteristics), lifestyle (steps count, sleep onset and sleep regularity) and non-sensor demographic features (smoking status) contributed to this classification.

Conclusion

As wearable technologies continue to rapidly evolve, integration of daily-life indicators could improve our understanding of chronic stress and its impact of physiology and behavior.