Purpose of review-This manuscript reviews the epidemiological data linking psychosocial stress to cardiovascular disease (CVD), describes recent advances in understanding the biological pathway between them, discusses potential therapies against stress-related CVD, and identifies future research directions. Recent findings-Metabolic activity of the amygdala (a neural center that is critically involved in the response to stress) can be measured on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (18 F-FDG-PET/CT) yielding a neurobiological signal that independently predicts subsequent CVD events. Furthermore, a serial pathway from ↑amygdalar activity → ↑hematopoietic tissue activity → ↑arterial inflammation → ↑CVD events has been elucidated, providing new insights into the mechanism linking stress to CVD. Summary-Psychosocial stress and stress conditions are independently associated with CVD in a manner that depends on the degree and duration of stress as well as the individual response to a stressor. Nevertheless, the fundamental biology remains incompletely defined, and stress is often confounded by adverse health behaviors. Thus, most clinical guidelines do not yet recognize psychosocial stress as an independent CVD risk factor or advocate for its treatment in CVD

Aims Chronic noise exposure associates with increased cardiovascular disease (CVD) risk; however, the role of confounders and the underlying mechanism remain incompletely defined. The amygdala, a limbic centre involved in stress perception, participates in the response to noise. Higher amygdalar metabolic activity (AmygA) associates with increased CVD risk through a mechanism involving heightened arterial inflammation (ArtI). Accordingly, in this retrospective study, we tested whether greater noise exposure associates with higher: (i) AmygA, (ii) ArtI, and (iii) risk for major adverse cardiovascular disease events (MACE). Methods and results Adults (N = 498) without CVD or active cancer underwent clinical 18F-fluorodeoxyglucose positron emission tomography/computed tomography imaging. Amygdalar metabolic activity and ArtI were measured, and MACE within 5 years was adjudicated. Average 24-h transportation noise and potential confounders were estimated at each individual’s home address. Over a median 4.06 years, 40 individuals experienced MACE. Higher noise exposure (per 5 dBA increase) predicted MACE [hazard ratio (95% confidence interval, CI) 1.341 (1.147–1.567), P < 0.001] and remained robust to multivariable adjustments. Higher noise exposure associated with increased AmygA [standardized β (95% CI) 0.112 (0.051–0.174), P < 0.001] and ArtI [0.045 (0.001–0.090), P = 0.047]. Mediation analysis suggested that higher noise exposure associates with MACE via a serial mechanism involving heightened AmygA and ArtI that accounts for 12–26% of this relationship. Conclusion Our findings suggest that noise exposure associates with MACE via a mechanism that begins with increased stress-associated limbic (amygdalar) activity and includes heightened arterial inflammation. This potential neurobiological mechanism linking noise to CVD merits further evaluation in a prospective population.

Aims Activity in the amygdala, a brain centre involved in the perception of and response to stressors, associates with: (i) heightened sympathetic nervous system and inflammatory output and (ii) risk of cardiovascular disease. We hypothesized that the amygdalar activity (AmygA) ratio is heightened among individuals who develop Takotsubo syndrome (TTS), a heart failure syndrome often triggered by acute stress. We tested the hypotheses that (i) heightened AmygA precedes development of TTS and (ii) those with the highest AmygA develop the syndrome earliest. Methods and results Individuals (N=104, median age 67.5 years, 72% female, 86% with malignancy) who underwent clinical 18 F-FDG-PET/CT imaging were retrospectively identified: 41 who subsequently developed TTS and 63 matched controls (median follow-up 2.5 years after imaging). AmygA was measured using validated methods. Individuals with (vs. without) subsequent TTS had higher baseline AmygA (P=0.038) after adjusting for TTS risk factors. Further, AmygA associated with the risk for subsequent TTS after adjustment for risk factors [standardized hazard ratio (95% confidence interval): 1.643 (1.189, 2.270), P=0.003]. Among the subset of individuals who developed TTS, those with the highest AmygA (>mean + 1 SD) developed TTS ∼2 years earlier after imaging vs. those with lower AmygA (P=0.028). Conclusion Higher AmygA associates with an increased risk for TTS among a retrospective population with a high rate of malignancy. This heightened neurobiological activity is present years before the onset of TTS and may impact the timing of the syndrome. Accordingly, heightened stress-associated neural activity may represent a therapeutic target to reduce stress-related diseases, including TTS.

Background: While growing evidence suggests a link between periodontal disease (PD) and cardiovascular disease (CVD), the independence of this association and the pathway remain unclear. Herein, we tested the hypotheses that: (1) inflammation of the periodontium (PD inflammation) predicts future CVD independently of disease risk factors shared between CVD and PD, and (2) the mechanism linking the two diseases involves heightened arterial inflammation. Methods: 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (18 F-FDG-PET/CT) imaging was performed in 304 individuals (median age 54 years; 42.4% male) largely for cancer screening; individuals without active cancer were included. PD inflammation and arterial inflammation were quantified using validated 18 F-FDG-PET/CT methods. Additionally, we evaluated the relationship between PD inflammation and subsequent major adverse cardiovascular events (MACE) using Cox models and log-rank tests. Results: Thirteen individuals developed MACE during follow-up (median 4.1 years). PD inflammation associated with arterial inflammation, remaining significant after adjusting for PD and CVD risk factors (standardized β [95% CI]: 0.30 [0.20-0.40], P < 0.001). PD inflammation predicted subsequent MACE (standardized HR [95% CI]: 2.25 [1.47 to 3.44], P <0.001, remaining significant in multivariable models), while periodontal bone loss did not. Furthermore, mediation analysis suggested that arterial inflammation accounts for 80% of the relationship between PD inflammation and MACE (standardized log odds ratio [95% CI]: 0.438 [0.019-0.880], P = 0.022). Conclusion: PD inflammation is independently associated with MACE via a mechanism that may involve increased arterial inflammation. These findings provide important support for an independent relationship between PD inflammation and CVD.

Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.

Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that ‘radiomics’, or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual meeting.These public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set.

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Background: Chronic exposure to socioeconomic or environmental stressors associates with greater stress-related neurobiological activity (ie, higher amygdalar activity [AmygA]) and higher risk of major adverse cardiovascular events (MACE). However, among individuals exposed to such stressors, it is unknown whether neurobiological resilience (NBResilience, defined as lower AmygA despite stress exposure) lowers MACE risk. We tested the hypotheses that NBResilience protects against MACE, and that it does so through decreased bone marrow activity and arterial inflammation. Methods: Individuals underwent 18 F-fluorodeoxyglucose positron emission tomography/computed tomography; AmygA, bone marrow activity, and arterial inflammation were quantified. Chronic socioeconomic and environmental stressors known to associate with AmygA and MACE (ie, transportation noise exposure, neighborhood median household income, and crime rate) were quantified. Heightened stress exposure was defined as exposure to at least one chronic stressor (ie, the highest tertile of noise exposure or crime or lowest tertile of income). MACE within 5 years of imaging was adjudicated. Relationships were evaluated using linear and Cox regression, Kaplan-Meier survival, and mediation analyses. Results: Of 254 individuals studied (median age [interquartile range]: 57 years [46–67], 36.7% male), 166 were exposed to at least one chronic stressor. Among stress-exposed individuals, 12 experienced MACE over a median follow-up of 3.75 years. Among this group, higher AmygA (ie, lower resilience) associated with higher bone marrow activity (standardized β [95% CI]: 0.192 [0.030–0.353], P =0.020), arterial inflammation (0.203 [0.055–0.351], P =0.007), and MACE risk (standardized hazard ratio [95% CI]: 1.927 [1.370–2.711], P =0.001). The effect of NBResilience on MACE risk was significantly mediated by lower arterial inflammation ( P <0.05). Conclusions: Among individuals who are chronically exposed to socioeconomic or environmental stressors, NBResilience (AmygA <1 SD above the mean) associates with a >50% reduction in MACE risk, potentially via reduced arterial inflammation. These data raise the possibility that enhancing NBResilience may decrease the burden of cardiovascular disease.