Adipose tissue dysfunction, inflammation, and insulin resistance alternative pathways to cardiac remodelling in schizophrenia. A multimodal, case-control study

Background Cardiovascular disease is the leading cause of excess mortality in schizophrenia. Patients with schizophrenia show evidence of increased concentric cardiac remodelling (CCR), defined as an increase in left-ventricular mass over end-diastolic volumes. CCR is a predictor of cardiac disease, but the molecular pathways leading to this in schizophrenia are unknown. We aimed to explore the relevance of hypertensive and non-hypertensive pathways to CCR, and their potential molecular underpinnings in schizophrenia. Methods and findings In this multimodal case-control study we collected cardiac and whole-body fat magnetic resonance imaging (MRI), clinical measures, and blood levels of several cardiometabolic biomarkers known to potentially cause CCR from individuals with schizophrenia, alongside healthy controls (HCs) matched for age, sex, ethnicity, and body surface area. Of 50 participants, 34 (68%) were male. Participants with schizophrenia showed increases in cardiac concentricity (d=0.71, 95%CI: 0.12,1.30; p=0.01), indicative of CCR, but showed no differences in overall content or regional distribution of adipose tissue compared to HCs. Despite the cardiac changes, participants with schizophrenia did not demonstrate activation of the hypertensive CCR pathway; however, they showed evidence of adipose dysfunction: adiponectin was reduced (d=-0.69, 95%CI: -1.28,-0.10; p=0.02), with evidence of activation of downstream pathways including hypertriglyceridemia, elevated C-reactive protein, fasting glucose, and alkaline phosphatase. Conclusions People with schizophrenia showed adipose tissue dysfunction compared to BMI-matched HCs. The presence of non-hypertensive CCR and a dysmetabolic phenotype may contribute to excess cardiovascular risk in schizophrenia. If our results are confirmed, acting on this pathway could reduce cardiovascular risk and resultant lifeyears lost in people with schizophrenia

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Assessing exercise cardiac reserve using real-time cardiovascular magnetic resonance

10.1186/s12968-017-0322-1Journal of Cardiovascular Magnetic Resonance1911-10JCMR

Fractal analysis of right ventricular trabeculae in pulmonary hypertension

10.1148/radiol.2018172821Radiology2882386-39

Quality of reporting in AI cardiac MRI segmentation studies – a systematic review and recommendations for future studies

Background: There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation. Methods: MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains. Results: 209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59–73%). Median compliance was highest for the model description domain (100%, IQR 80–100%) and lower for the study (71%, IQR 63–86%), dataset (63%, IQR 50–67%) and performance (60%, IQR 50–70%) description domains. Conclusion: This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing—most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis—that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field. Systematic Review Registration: [www.crd.york.ac.uk/prospero/], identifier [CRD42022279214]

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

10.1038/s41436-020-00972-3Genetics in Medicine23169-7