Increased oxidative metabolism in the Li-Fraumeni syndrome.

There is growing evidence that alterations in metabolism may contribute to tumorigenesis. Here, we report on members of families with the Li-Fraumeni syndrome who carry germline mutations in TP53, the gene encoding the tumor-suppressor protein p53. As compared with family members who are not carriers and with healthy volunteers, family members with these mutations have increased oxidative phosphorylation of skeletal muscle. Basic experimental studies of tissue samples from patients with the Li-Fraumeni syndrome and a mouse model of the syndrome support this in vivo finding of increased mitochondrial function. These results suggest that p53 regulates bioenergetic homeostasis in humans. (Funded by the National Heart, Lung, and Blood Institute and the National Institutes of Health; ClinicalTrials.gov number, NCT00406445.).

p53 improves aerobic exercise capacity and augments skeletal muscle mitochondrial DNA content.

RATIONALE: Exercise capacity is a physiological characteristic associated with protection from both cardiovascular and all-cause mortality. p53 regulates mitochondrial function and its deletion markedly diminishes exercise capacity, but the underlying genetic mechanism orchestrating this is unclear. Understanding the biology of how p53 improves exercise capacity may provide useful insights for improving both cardiovascular as well as general health. OBJECTIVE: The purpose of this study was to understand the genetic mechanism by which p53 regulates aerobic exercise capacity. METHODS AND RESULTS: Using a variety of physiological, metabolic, and molecular techniques, we further characterized maximum exercise capacity and the effects of training, measured various nonmitochondrial and mitochondrial determinants of exercise capacity, and examined putative regulators of mitochondrial biogenesis. As p53 did not affect baseline cardiac function or inotropic reserve, we focused on the involvement of skeletal muscle and now report a wider role for p53 in modulating skeletal muscle mitochondrial function. p53 interacts with Mitochondrial Transcription Factor A (TFAM), a nuclear-encoded gene important for mitochondrial DNA (mtDNA) transcription and maintenance, and regulates mtDNA content. The increased mtDNA in p53(+/+) compared to p53(-/-) mice was more marked in aerobic versus glycolytic skeletal muscle groups with no significant changes in cardiac tissue. These in vivo observations were further supported by in vitro studies showing overexpression of p53 in mouse myoblasts increases both TFAM and mtDNA levels whereas depletion of TFAM by shRNA decreases mtDNA content. CONCLUSIONS: Our current findings indicate that p53 promotes aerobic metabolism and exercise capacity by using different mitochondrial genes and mechanisms in a tissue-specific manner.

The Journal of Cardiovascular Computed Tomography year in review - 2019.

The purpose of this review is to summarize the work published by the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2019, highlighting original research and new guidelines.

A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA.

OBJECTIVES: This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics. BACKGROUND: Coronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known. METHODS: Utilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control study of 468 patients with baseline coronary CTA, the study included ACS patients with invasive coronary angiography-adjudicated CLs that could be aligned to CL precursors on baseline coronary CTA. Separate blinded core laboratories adjudicated CLs and performed atherosclerotic plaque evaluation. Thereafter, the study used a boosted ensemble algorithm (XGBoost) to develop a predictive model of CLs. Data were randomly split into a training set (80%) and a test set (20%). The area under the receiver-operating characteristic curve of this model was compared with that of diameter stenosis (model 1), high-risk plaque features (model 2), and lesion-level features of CL precursors from the ICONIC study (model 3). Thereafter, the machine learning (ML) model was applied to 234 non-ACS patients with 864 lesions to determine model performance for CL exclusion. RESULTS: CL precursors were identified by both coronary angiography and baseline coronary CTA in 124 of 234 (53.0%) patients, with a total of 582 lesions (containing 124 CLs) included in the analysis. The ML model demonstrated significantly higher area under the receiver-operating characteristic curve for discriminating CL precursors (0.774; 95% confidence interval [CI]: 0.758 to 0.790) compared with model 1 (0.599; 95% CI: 0.599 to 0.599; p < 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p < 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p < 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs. CONCLUSIONS: In a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA.

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS: Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS: The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS: An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.