Increased Pericardial Adipose Tissue in Smokers

Zimmermann, G.; Ruether, Tobias; Ziegler, Franz von; Martin, Guillaume; Tittus, Janine; Schenzle, Jan; Becker, Christoph; Becker, Alexander

doi:10.3390/jcm10153382

Cited by 2 publications

(3 citation statements)

References 41 publications

(85 reference statements)

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“… 29 Supporting this hypothesis, PAT is known to be distributed asymmetrically in ‘pockets’ over the myocardium 30 and studies have demonstrated that the distribution of coronary artery disease closely follows the distribution of PAT. 6 , 21 , 31 This supports the hypothesis that this highly metabolically active tissue may exert a degree of proinflammatory paracrine effects that accelerate atherosclerosis and regional inflammation with consequent localised damage. 1 , 30 …”

Section: Discussionsupporting

confidence: 69%

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Pericardial adiposity is independently linked to adverse cardiovascular phenotypes: a CMR study of 42 598 UK Biobank participants

Ardissino

McCracken

Bard

et al. 2022

European Heart Journal - Cardiovascular Imaging

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Aims We evaluated independent associations of cardiovascular magnetic resonance (CMR)-measured pericardial adipose tissue (PAT) with cardiovascular structure and function and considered underlying mechanism in 42 598 UK Biobank participants. Methods and results We extracted PAT and selected CMR metrics using automated pipelines. We estimated associations of PAT with each CMR metric using linear regression adjusting for age, sex, ethnicity, deprivation, smoking, exercise, processed food intake, body mass index, diabetes, hypertension, height cholesterol, waist-to-hip ratio, impedance fat measures, and magnetic resonance imaging abdominal visceral adiposity measures. Higher PAT was independently associated with unhealthy left ventricular (LV) structure (greater wall thickness, higher LV mass, more concentric pattern of LV hypertrophy), poorer LV function (lower LV global function index, lower LV stroke volume), lower left atrial ejection fraction, and lower aortic distensibility. We used multiple mediation analysis to examine the potential mediating effect of cardiometabolic diseases and blood biomarkers (lipid profile, glycaemic control, inflammation) in the PAT-CMR relationships. Higher PAT was associated with cardiometabolic disease (hypertension, diabetes, high cholesterol), adverse serum lipids, poorer glycaemic control, and greater systemic inflammation. We identified potential mediation pathways via hypertension, adverse lipids, and inflammation markers, which overall only partially explained the PAT-CMR relationships. Conclusion We demonstrate association of PAT with unhealthy cardiovascular structure and function, independent of baseline comorbidities, vascular risk factors, inflammatory markers, and multiple non-invasive and imaging measures of obesity. Our findings support an independent role of PAT in adversely impacting cardiovascular health and highlight CMR-measured PAT as a potential novel imaging biomarker of cardiovascular risk.

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Section: Discussionsupporting

confidence: 69%

“…PAT mirrors systemic inflammation, 2 , 6 which is a risk factor for cardiovascular disease. 7 Furthermore, being an adipose tissue compartment, PAT is usually associated with greater general obesity, which similarly associates with multiple vascular risk factors and cardiovascular diseases.…”

Section: Introductionmentioning

confidence: 99%

Pericardial adiposity is independently linked to adverse cardiovascular phenotypes: a CMR study of 42 598 UK Biobank participants

Ardissino

McCracken

Bard

et al. 2022

European Heart Journal - Cardiovascular Imaging

View full text Add to dashboard Cite

show abstract

“…On the other hand, PAT itself is also known to be highly metabolically active, and pre-clinical studies have demonstrated that it secretes multiple cytokines and inflammatory mediators that might contribute to a local, paracrine inflammatory effect [ 36 ]. To support this hypothesis, asymmetric, localised pockets of PAT have been noted to follow the distribution of coronary artery disease (CAD) in previous studies [ 11 , 12 , 37 ]. This suggests that either CAD or the resultant localised inflammation increases PAT deposition, or vice versa.…”

Section: Discussionmentioning

confidence: 92%

Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction

Szabo,

Salih,

Pujadas

et al. 2023

Eur Radiol

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Objectives To use pericardial adipose tissue (PAT) radiomics phenotyping to differentiate existing and predict future heart failure (HF) cases in the UK Biobank. Methods PAT segmentations were derived from cardiovascular magnetic resonance (CMR) studies using an automated quality-controlled model to define the region-of-interest for radiomics analysis. Prevalent (present at time of imaging) and incident (first occurrence after imaging) HF were ascertained using health record linkage. We created balanced cohorts of non-HF individuals for comparison. PyRadiomics was utilised to extract 104 radiomics features, of which 28 were chosen after excluding highly correlated ones (0.8). These features, plus sex and age, served as predictors in binary classification models trained separately to detect (1) prevalent and (2) incident HF. We tested seven modeling methods using tenfold nested cross-validation and examined feature importance with explainability methods. Results We studied 1204 participants in total, 297 participants with prevalent (60 ± 7 years, 21% female) and 305 with incident (61 ± 6 years, 32% female) HF, and an equal number of non-HF comparators. We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. Our radiomics models showed marginally better performance compared to PAT area alone. Increased PAT size (maximum 2D diameter in a given column or slice) and texture heterogeneity (sum entropy) were important features for prevalent and incident HF classification models. Conclusions The amount and character of PAT discriminate individuals with prevalent HF and predict incidence of future HF. Clinical relevance statement This study presents an innovative application of pericardial adipose tissue (PAT) radiomics phenotyping as a predictive tool for heart failure (HF), a major public health concern. By leveraging advanced machine learning methods, the research uncovers that the quantity and characteristics of PAT can be used to identify existing cases of HF and predict future occurrences. The enhanced performance of these radiomics models over PAT area alone supports the potential for better personalised care through earlier detection and prevention of HF. Key Points •PAT radiomics applied to CMR was used for the first time to derive binary machine learning classifiers to develop models for discrimination of prevalence and prediction of incident heart failure. •Models using PAT area provided acceptable discrimination between cases of prevalent or incident heart failure and comparator groups. •An increased PAT volume (increased diameter using shape features) and greater texture heterogeneity captured by radiomics texture features (increased sum entropy) can be used as an additional classifier marker for heart failure.

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Increased Pericardial Adipose Tissue in Smokers

Cited by 2 publications

References 41 publications

Pericardial adiposity is independently linked to adverse cardiovascular phenotypes: a CMR study of 42 598 UK Biobank participants

Pericardial adiposity is independently linked to adverse cardiovascular phenotypes: a CMR study of 42 598 UK Biobank participants

Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction

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