Can cartilage loss be detected in knee osteoarthritis (OA) patients with 3–6 months' observation using advanced image analysis of 3T MRI?

SummaryPurposePrior investigations of magnetic resonance imaging (MRI) biomarkers of cartilage loss in knee osteoarthritis (OA) suggest that trials of interventions which affect this biomarker with adequate statistical power would require large clinical studies of 1–2 years duration. We hypothesized that smaller, shorter duration, “Proof of Concept” (PoC) studies might be achievable by: (1) selecting a population at high risk of rapid medial tibio-femoral (TF) progression, in conjunction with; (2) high-field MRI (3T), and; (3) using advanced image analysis. The primary outcome was the cartilage thickness in the central medial femur.MethodsMulti-centre, non-randomized, observational cohort study at four sites in the US. Eligible participants were females with knee pain, a body mass index (BMI)≥25kg/m2, symptomatic radiographic evidence of medial TF OA, and varus mal-alignment. The 29 participants had a mean age of 62 years, mean BMI of 36kg/m2, with eight index knees graded as Kellgren–Lawrence (K&L)=2 and 21 as K&L=3. Eligible participants had four MRI scans of one knee: two MRIs (1 week apart) were acquired as a baseline with follow-up MRI at 3 and 6 months. A trained operator, blind to time-point but not subject, manually segmented the cartilage from the Dual Echo Steady State water excitation MR images. Anatomically corresponding regions of interest were identified on each image by using a three-dimensional statistical shape model of the endosteal bone surface, and the cartilage thickness (with areas denuded of cartilage included as having zero thickness – ThCtAB) within each region was calculated. The percentage change from baseline at 3 and 6 months was assessed using a log-scale analysis of variance (ANOVA) model including baseline as a covariate. The primary outcome was the change in cartilage thickness within the aspect of central medial femoral condyle exposed within the meniscal window (w) during articulation, neglecting cartilage edges [nuclear (n)] (nwcMF·ThCtAB), with changes in other regions considered as secondary endpoints.ResultsAnatomical mal-alignment ranged from −1.9° to 6.3°, with mean 0.9°. With one exception, no changes in ThCtAB were detected at the 5% level for any of the regions of interest on the TF joint at 3 or 6 months of follow-up. The change in the primary variable (nwcMF·ThCtAB) from (mean) baseline at 3 months from the log-scale ANOVA model was −2.1% [95% confidence interval (CI) (−4.4%, +0.2%)]. The change over 6 months was 0.0% [95% CI (−2.7%, +2.8%)]. The 95% CI for the change from baseline did not include zero for the cartilage thickness within the meniscal window of the lateral tibia (wLT·ThCtAB) at 6 month follow-up (−1.5%, 95% CI [−2.9, −0.2]), but was not significant at the 5% level after correction for multiple comparisons.ConclusionsThe small inconsistent compartment changes, and the relatively high variabilities in cartilage thickness changes seen over time in this study, provide no additional confidence for a 3- or 6-month PoC study using a patient population selected on the basis of risk for rapid progression with the MRI acquisition and analyses employed

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease