Early detection of vascular inflammation would allow deployment of targeted strategies for the prevention or treatment of multiple disease states. Because vascular inflammation is not detectable with commonly used imaging modalities, we hypothesized that phenotypic changes in perivascular adipose tissue (PVAT) induced by vascular inflammation could be quantified using a new computerized tomography (CT) angiography methodology. We show that inflamed human vessels release cytokines that prevent lipid accumulation in PVAT-derived preadipocytes in vitro, ex vivo, and in vivo. We developed a three-dimensional PVAT analysis method and studied CT images of human adipose tissue explants from 453 patients undergoing cardiac surgery, relating the ex vivo images with in vivo CT scan information on the biology of the explants. We developed an imaging metric, the CT fat attenuation index (FAI), that describes adipocyte lipid content and size. The FAI has excellent sensitivity and specificity for detecting tissue inflammation as assessed by tissue uptake of 18F-fluorodeoxyglucose in positron emission tomography. In a validation cohort of 273 subjects, the FAI gradient around human coronary arteries identified early subclinical coronary artery disease in vivo, as well as detected dynamic changes of PVAT in response to variations of vascular inflammation, and inflamed, vulnerable atherosclerotic plaques during acute coronary syndromes. Our study revealed that human vessels exert paracrine effects on the surrounding PVAT, affecting local intracellular lipid accumulation in preadipocytes, which can be monitored using a CT imaging approach. This methodology can be implemented in clinical practice to noninvasively detect plaque instability in the human coronary vasculature.

SummaryBackgroundCoronary artery inflammation inhibits adipogenesis in adjacent perivascular fat. A novel imaging biomarker—the perivascular fat attenuation index (FAI)—captures coronary inflammation by mapping spatial changes of perivascular fat attenuation on coronary computed tomography angiography (CTA). However, the ability of the perivascular FAI to predict clinical outcomes is unknown.MethodsIn the Cardiovascular RISk Prediction using Computed Tomography (CRISP-CT) study, we did a post-hoc analysis of outcome data gathered prospectively from two independent cohorts of consecutive patients undergoing coronary CTA in Erlangen, Germany (derivation cohort) and Cleveland, OH, USA (validation cohort). Perivascular fat attenuation mapping was done around the three major coronary arteries—the proximal right coronary artery, the left anterior descending artery, and the left circumflex artery. We assessed the prognostic value of perivascular fat attenuation mapping for all-cause and cardiac mortality in Cox regression models, adjusted for age, sex, cardiovascular risk factors, tube voltage, modified Duke coronary artery disease index, and number of coronary CTA-derived high-risk plaque features.FindingsBetween 2005 and 2009, 1872 participants in the derivation cohort underwent coronary CTA (median age 62 years [range 17–89]). Between 2008 and 2016, 2040 patients in the validation cohort had coronary CTA (median age 53 years [range 19–87]). Median follow-up was 72 months (range 51–109) in the derivation cohort and 54 months (range 4–105) in the validation cohort. In both cohorts, high perivascular FAI values around the proximal right coronary artery and left anterior descending artery (but not around the left circumflex artery) were predictive of all-cause and cardiac mortality and correlated strongly with each other. Therefore, the perivascular FAI measured around the right coronary artery was used as a representative biomarker of global coronary inflammation (for prediction of cardiac mortality, hazard ratio [HR] 2·15, 95% CI 1·33–3·48; p=0·0017 in the derivation cohort, and 2·06, 1·50–2·83; p<0·0001 in the validation cohort). The optimum cutoff for the perivascular FAI, above which there is a steep increase in cardiac mortality, was ascertained as −70·1 Hounsfield units (HU) or higher in the derivation cohort (HR 9·04, 95% CI 3·35–24·40; p<0·0001 for cardiac mortality; 2·55, 1·65–3·92; p<0·0001 for all-cause mortality). This cutoff was confirmed in the validation cohort (HR 5·62, 95% CI 2·90–10·88; p<0·0001 for cardiac mortality; 3·69, 2·26–6·02; p<0·0001 for all-cause mortality). Perivascular FAI improved risk discrimination in both cohorts, leading to significant reclassification for all-cause and cardiac mortality.InterpretationThe perivascular FAI enhances cardiac risk prediction and restratification over and above current state-of-the-art assessment in coronary CTA by providing a quantitative measure of coronary inflammation. High perivascular FAI values (cutoff ≥–70·1 HU) are an indicator of increased cardia...

Accumulating knowledge on the biology and function of the adipose tissue has led to a major shift in our understanding of its role in health and disease. The adipose tissue is now recognized as a crucial regulator of cardiovascular health, through the secretion of several bioactive products, including adipocytokines, microvesicles, and gaseous messengers, with a wide range of endocrine and paracrine effects on the cardiovascular system. The adipose tissue function and secretome are tightly controlled by complex homeostatic mechanisms and local cell-cell interactions, which can become dysregulated in obesity. Systemic or local inflammation and insulin resistance lead to a shift in the adipose tissue secretome from anti-inflammatory and anti-atherogenic towards a pro-inflammatory and pro-atherogenic profile. Moreover, the interplay between the adipose tissue and the cardiovascular system is bidirectional, with vascular-derived and heart-derived signals directly affecting adipose tissue biology. In this Review, we summarize the current knowledge on the biology and regional variability of adipose tissue in humans, deciphering the complex molecular mechanisms controlling the crosstalk between the adipose tissue and the cardiovascular system, and their possible clinical translation. In addition, we highlight the latest developments in adipose tissue imaging for cardiovascular risk stratification, and discuss how therapeutic targeting of the adipose tissue can improve prevention and treatment of cardiovascular disease.

Background Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. Methods and results We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P &lt; 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI. Conclusion The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art.

IMPORTANCE Echocardiographic left ventricular global longitudinal strain (GLS) detects early subclinical ventricular dysfunction and can be used in patients receiving potentially cardiotoxic chemotherapy. A meta-analysis of the prognostic value of GLS for cancer therapy-related cardiac dysfunction (CTRCD) has not been performed, to our knowledge. OBJECTIVE To explore the prognostic value of GLS for the prediction of CTRCD.DATA SOURCES Systematic search of the MEDLINE, Embase, Scopus, and the Cochrane Library databases from database inception to June 1, 2018.STUDY SELECTION Cohort studies assessing the prognostic or discriminatory performance of GLS before or during chemotherapy for subsequent CTRCD.DATA EXTRACTION AND SYNTHESIS Random-effects meta-analysis and hierarchical summary receiver operating characteristic curves (HSROCs) were used to summarize the prognostic and discriminatory performance of different GLS indices. Publication bias was assessed using the Egger test, and meta-regression was performed to assess sources of heterogeneity. MAIN OUTCOMES AND MEASURESThe primary outcome was CTRCD, defined as a clinically significant change in left ventricular ejection fraction with or without new-onset heart failure symptoms.RESULTS Analysis included 21 studies comprising 1782 patients with cancer, including breast cancer, hematologic malignancies, or sarcomas, treated with anthracyclines with or without trastuzumab. The incidence of CTRCD ranged from 9.3% to 43.8% over a mean follow-up of 4.2 to 23.0 months (pooled incidence, 21.0%). For active treatment absolute GLS (9 studies), the high-risk cutoff values ranged from −21.0% to −13.8%, with worse GLS associated with a higher CTRCD risk (odds ratio, 12.27; 95% CI,; area under the HSROC, 0.86; 95% CI, 0.83-0.89). For relative changes vs a baseline value (9 studies), cutoff values ranged from 2.3% to 15.9%, with a greater decrease linked to a 16-fold higher risk of CTRCD (odds ratio, 15.82; 95% CI, 5.84-42.85; area under the HSROC, 0.86; 95% CI, 0.83-0.89). Both indices showed significant publication bias. Meta-regression identified differences in sample size and CTRCD definition but not GLS cutoff value as significant sources of interstudy heterogeneity. CONCLUSIONS AND RELEVANCEIn this meta-analysis, measurement of GLS after initiation of potentially cardiotoxic chemotherapy with anthracyclines with or without trastuzumab had good prognostic performance for subsequent CTRCD. However, risk of bias in the original studies, publication bias, and limited data on the incremental value of GLS and its optimal cutoff values highlight the need for larger prospective multicenter studies.