Eat, stress, inflame: examining the link between chronic stress, coronary inflammation and plaque
Bibliographic record
Abstract
This editorial refers to ‘Association between inflammatory biomarkers, chronic stress, and pericoronary adipose tissue attenuation obtained with coronary CT’, by T. Albertini et al., https://doi.org/10.1093/ehjci/jeaf217. Stress is ubiquitous in modern life, triggered by a milieu of sources from the workplace to finance and home life. In our early evolution, the stress response was a critical physiological reaction to external risks, eliciting a cascade of changes within the body that best positioned it to respond to these threats. In modernity, chronic activation of the stress response has turned this once physiological survival mechanism into a pathological state that increases risk of cardiovascular disease on par with more traditional risk factors.1 Chronic stress results in increased amygdala activity, bone marrow activation and vascular inflammation.2 In addition, elevated stress-related inflammatory cytokines are associated with atherosclerosis and increased risk of myocardial infarction (MI).3 While the relationship of stress with systemic inflammatory markers and large vessel inflammation has been well documented, the interaction of this with coronary inflammation is less well explored. Pericoronary adipose tissue (PCAT) attenuation is emerging as a promising imaging biomarker of coronary inflammation and cardiovascular risk. PCAT is biologically active, and its proximity to coronary vessels enables it to influence coronary metabolism, inflammation and atherosclerosis through paracrine signalling.4 In pro-inflammatory or pathological states, PCAT may be involved in promoting atherosclerosis. Conversely, pro-inflammatory signals from the vascular wall in areas of plaque may also modulate the phenotype of the adjacent of PCAT, resulting in an increase of the attenuation of this on CCTA.4 This PCAT attenuatiation has been associated with adverse plaque features and is predictive of MI.5 As a result, it is well positioned to help study interactions between local and regional inflammation and stress. In this issue, Albertini et al.6 examine this interplay between markers of stress, systemic inflammation and pericoronary inflammation and plaque.6 They prospectively measured chronic stress using hair cortisol concentration (HCC) alongside a psychological stress evaluation questionnaire [vital exhaustion (VE) test] in 98 patients referred for clinically indicated CCTA. Systemic inflammation was captured using circulating inflammatory biomarkers interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α), while the CCTA datasets were used for quantifying pericoronary inflammation [using PCAT attenuation (PCATa)] as well as total plaque volume (TPV) and vulnerable plaque features. The authors found that PCATa correlated with levels of systemic inflammation [IL-6 (mean difference 1.05, 95% CI: 0.21–1.89, P = 0.014) and TNF-α (mean difference 0.60, 95% CI: 0.06–1.13, P = 0.027)], with the groups with the highest levels of these cytokines having significantly higher PCATa values. PCATa also correlated with TPV but not with high-risk plaque features or coronary stenosis. Interestingly, despite the association between systemic inflammation and pericoronary inflammation, a direct association was not observed between chronic stress metrics and PCAT, TPV or coronary stenosis. This is in contrast to work by Dai et al.7 who found that stress (as measured by amygdala activity on PET/CT) correlated with PCATa, as well as the presence of high-risk plaque.7 While a direct effect was not observed, chronic stress did appear to modulate the association of IL-6 and PCATa. Moderation analyses showed that chronic stress, whether measured through HCC (interaction term −0.12, 95% CI: −0.22 to 0.02, P = 0.019) or VE (interaction term 0.13, 95% CI: 0.01–0.25, P = 0.024), moderated the association between IL-6 and PCAT. Chronic stress did not moderate the interaction of TNF-α and PCAT nor the association between PCAT and TPV. This suggests that the effect of chronic stress may mediate IL-6 linked inflammation in the coronaries. As elevated PCATa is associated with higher rates of plaque progression,8 this would in turn provide a mechanism through which chronic stress increases the risk of myocardial infarcts in the long term. The contrast between prior observations of amygdala activity being directly associated with vessel inflammation and plaque characteristics2,7 and the current study showing no direct relationship between these factors and hair cortisol is interesting and requires further examination. HCC is a measure of the chronic cumulative stress over several months as the hair grows, while amygdala neural activity is a single timepoint measurement. So too are measures of PCATa and bone marrow activation, which fluctuate with time. Thus, there may be a disconnect between these temporally distinct processes with each providing unique information. However, it must also be considered whether this is an artefact of type II statistical error. The current study size is smaller than prior cohorts, with this compounded by the low prevalence of coronary artery disease with only 45% of the cohort showing evidence of coronary plaque. This is lower than many other contemporary chest pain populations, which typically show evidence of plaque in 55–65% of patients, and will reduce power for detecting associations between stress, inflammatory markers and plaque. A similar consideration must be made as to the absence of modulation of stress on the interaction between TNF-alpha and PCAT. This too may be a product of the sample size and certainly larger scale validation would be beneficial. However, another potential explanation is divergent cytokine-specific responses to the type of chronic stress measured in this study by HCC and VE testing.9 IL-6 is robustly associated with psychological stress,10 and this study perhaps highlights the utility of IL-6 specifically as the key marker to use for evaluation of chronic psychological stress and CAD. The findings of the study by Albertini et al.6 highlight a potential role for a combination of IL-6 and PCAT measurement to inform risk stratification in those with chronic stress. In the future, this could guide the targeted use of early stress interventions, motivate lifestyle change or even individualized anti-inflammatory interventions to reduce downstream CAD in patients with elevated stress and PCAT attenuation. If so, this could be incredibly powerful and widen the utility of CCTA within primary prevention and lifestyle modification. With this comes added challenges in defining which patients would most benefit from PCAT analysis in this context, determining when and how frequently to measure PCAT, and clarifying the incremental benefit of PCAT analysis over IL-6 alone. A combined measurement may allow specific identification of those for whom stress is measurably impacting CAD risk. These are important questions that will need to be navigated should such testing become more widespread and as it is adopted into clinical workflows. Nilanka N Mannakkara (MBBS), and Jonathan Weir-McCall. J.R.W.-M. is supported by a Royal Brompton & Harefield Hospitals Charity Fellowship. No new data were generated or analysed in support of this editorial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".