BackgroundLong COVID is associated with multiple symptoms and impairment in multiple organs. Cross-sectional studies have reported cardiac impairment to varying degrees by varying methodologies. Using cardiac MR (CMR), we investigated a 12-month trajectory of abnormalities in Long COVID.ObjectivesTo investigate cardiac abnormalities 1-year post-SARS-CoV-2 infection.Methods534 individuals with Long COVID underwent CMR (T1/T2 mapping, cardiac mass, volumes, function and strain) and multiorgan MRI at 6 months (IQR 4.3–7.3) since first post-COVID-19 symptoms. 330 were rescanned at 12.6 (IQR 11.4–14.2) months if abnormal baseline findings were reported. Symptoms, questionnaires and blood samples were collected at both time points. CMR abnormalities were defined as ≥1 of low left or right ventricular ejection fraction (LVEF), high left or right ventricular end diastolic volume, low 3D left ventricular global longitudinal strain (GLS), or elevated native T1 in ≥3 cardiac segments. Significant change over time was reported by comparison with 92 healthy controls.ResultsTechnical success of multiorgan and CMR assessment in non-acute settings was 99.1% and 99.6% at baseline, and 98.3% and 98.8% at follow-up. Of individuals with Long COVID, 102/534 (19%) had CMR abnormalities at baseline; 71/102 had complete paired data at 12 months. Of those, 58% presented with ongoing CMR abnormalities at 12 months. High sensitivity cardiac troponin I and B-type natriuretic peptide were not predictive of CMR findings, symptoms or clinical outcomes. At baseline, low LVEF was associated with persistent CMR abnormality, abnormal GLS associated with low quality of life and abnormal T1 in at least three segments was associated with better clinical outcomes at 12 months.ConclusionCMR abnormalities (left entricular or right ventricular dysfunction/dilatation and/or abnormal T1mapping), occurred in one in five individuals with Long COVID at 6 months, persisting in over half of those at 12 months. Cardiac-related blood biomarkers could not identify CMR abnormalities in Long COVID.Trial registration numberNCT04369807.

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Background: Chronic liver disease (CLD) and cardiovascular diseases (CVD) share common risk factors; the former is associated with a two-fold greater incidence of CVD. With most CLD being preventable/modifiable, early identification of at high-risk individuals is crucial. Using data from the UK Biobank imaging sub-study, we tested the hypothesis that early signs of liver disease (measured by iron corrected T1-mapping (cT1)) is associated with an increased risk of major cardiovascular events. Methods: Liver disease activity (cT1) and fat (PDFF) were measured using LiverMultiScan from images acquired between January-2016 and February-2020 in the UK Biobank imaging sub-study. Multivariable Cox regression was used to explore associations between liver cT1 (MRI) and primary CVD outcomes (coronary artery disease, atrial fibrillation, embolism/vascular events, heart failure and stroke), as well as CVD hospitalisation and all-cause mortality. Other liver blood biomarkers (AST, ALT, AST/ALT ratio, FIB4), general metabolism biomarkers (CRP, HbA1c, systolic blood pressure (SBP), total cholesterol), and demographics were also included. Subgroup analysis was conducted in those without metabolic syndrome (MetS= at least 3 of these traits: a large waist, high triglycerides, low HDL cholesterol, increased SBP, or elevated HbA1c) Results: 33,616 participants in the UK Biobank imaging sub-study (65 years, mean BMI 26kg/m2, mean HbA1c 35mmol/mol) had complete MRI liver data with linked clinical outcomes [median time to major CVD event onset: 1.4 years (range:0.002-5.1); follow-up: 2.5 years (range:1.1-5.2)]. Liver disease activity (cT1), but not liver fat (PDFF), was associated with a higher risk of any major CVD event [HR(CI) 1.14(1.03-1.26), p=0.008], AF [1.30 (1.12-1.5), p<0.001]; HF [1.30 (1.08 - 1.58), p=0.004]; CVD hospitalisation [1.27(1.18-1.387, p<0.001] and all-cause mortality [1.19(1.02-1.38), p=0.026]. FIB4 index, was associated with HF [1.06 (1.01 - 1.10)), p=0.007]. The risk of CVD hospitalisation was also independently associated with cT1 in individuals without MetS [1.26(1.13-1.4), p<0.001]. Conclusion: Liver disease activity, as measured with MRI-derived biomarker cT1, was independently associated with a higher risk of new onset CVD events and all-cause mortality. This association occurred even without pre-existing impairment of metabolic health and was independent of FIB4 or liver fat content. cT1 was identified as a major predictor of adverse CVD outcomes.

BackgroundAn estimated 55.5% and 37.3% of people globally with type 2 diabetes (T2D) will have concomitant non-alcoholic fatty liver disease (NAFLD) and the more severe fibroinflammatory stage, non-alcoholic steatohepatitis (NASH). NAFLD and NASH prevalence is projected to increase exponentially over the next 20 years. Bayesian Networks (BNs) offer a powerful tool for modelling uncertainty and visualising complex systems to provide important mechanistic insight.MethodsWe applied BN modelling and probabilistic reasoning to explore the probability of NASH in two extensively phenotyped clinical cohorts: 1) 211 participants with T2D pooled from the MODIFY study &amp; UK Biobank (UKBB) online resource; and 2) 135 participants without T2D from the UKBB. MRI-derived measures of visceral (VAT), subcutaneous (SAT), skeletal muscle (SMI), liver fat (MRI-PDFF), liver fibroinflammatory change (liver cT1) and pancreatic fat (MRI-PDFF) were combined with plasma biomarkers for network construction. NASH was defined according to liver PDFF &gt;5.6% and liver cT1 &gt;800ms. Conditional probability queries were performed to estimate the probability of NASH after fixing the value of specific network variables.ResultsIn the T2D cohort we observed a stepwise increase in the probability of NASH with each obesity classification (normal weight: 13%, overweight: 23%, obese: 36%, severe obesity: 62%). In the T2D and non-T2D cohorts, elevated (vs. normal) VAT conferred a 20% and 1% increase in the probability of NASH, respectively, while elevated SAT caused a 7% increase in NASH risk within the T2D cohort only. In those with T2D, reducing HbA1c from the ‘high’ to ‘low’ value reduced the probability of NASH by 22%.ConclusionUsing BNs and probabilistic reasoning to study the probability of NASH, we highlighted the relative contribution of obesity, ectopic fat (VAT and liver) and glycaemic status to increased NASH risk, namely in people with T2D. Such modelling can provide insights into the efficacy and magnitude of public health and pharmacological interventions to reduce the societal burden of NASH.