End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n = 256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. Two hundred and three proteins were associated with clinical severity, including IL6, markers of monocyte recruitment , neutrophil activation (e.g. proteinase-3), and epithelial injury (e.g. KRT19). Machine-learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte–endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets. COVID-19 varies from a mild illness in some people to fatal disease in others. Patients with severe disease tend to be older and have underlying medical problems. People with kidney failure have a particularly high risk of developing severe or fatal COVID-19.Patients with severe COVID-19 have high levels of inflammation, causing damage to tissues around the body. Many drugs that target inflammation have already been developed for other diseases. Therefore, to repurpose existing drugs or design new treatments, it is important to determine which proteins drive inflammation in COVID-19.Here, Gisby, Clarke, Medjeral-Thomas et al. measured 436 proteins in the blood of patients with kidney failure and compared the levels between patients who had COVID-19 to those who did not. This revealed that patients with COVID-19 had increased levels of hundreds of proteins involved in inflammation and tissue injury. Using a combination of statistical and machine learning analyses, Gisby et al. probed the data for proteins that might predict a more severe disease progression. In total, over 200 proteins were linked to disease severity, and 69 with increased risk of death. Tracking how levels of blood proteins changed over time revealed further differences between mild and severe disease. Comparing this data with a similar study of COVID-19 in people without kidney failure showed many similarities. This suggests that the findings may apply to COVID-19 patients more generally.Identifying the proteins that are a cause of severe COVID-19 – rather than just correlated with it – is an important next step that could help to select new drugs for severe COVID-19. Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, displays wide clinical heterogeneity from asymptomatic to fatal disease. Patients with severe disease exhibit marked inflammatory responses and immunopathology. The mechanisms underlying this remain incompletely characterised, and the key molecular mediators are yet to be determined. The first treatment shown to reduce mortality from COVID-19 in randomised trials was dexamethasone , a cortiRisk factors for severe or fatal COVID-19 include age, male sex, non-European ancestry, obesity, diabetes mellitus, cardiovascular disease, and immunosuppression . End-staHere we performed proteomic profiling of serial blood samples of ESKD patients with COVID-19, leveraging the unique opportunity for longitudinal sampling in both the outpatient and inpatient settings afforded by a large multi-ethnic haemodialysis cohort . These dWe recruited 55 ESKD patients with COVID-19  and 11 serum samples from ESKD COVID-19-negative controls (a subset of the controls described above). For the large majority of patients in subcohort B, only a single timepoint was available. A higher proportion of these patients  were hospitalised and had severe disease  than in Principal component analysis (PCA) of proteomic data from subcohort A demonstrated differences between samples from COVID-19-positive cases and controls, although the two groups did not separate into discrete clusters . To exam−5; We observed that a high proportion of the measured proteins were associated with COVID-19. Given the highly targeted nature of the Olink panels that we used (enriched for immune and inflammation-related proteins), this was not surprising. Nevertheless, to ensure that the Benjamini–Hochberg adjustment of p-values was controlling the FDR at the 5% level, we performed two additional analyses . First, we estimated the FDR using an alternative method  of samples, the most recent haemodialysis was between 48 and 72 hr prior to blood draw. This consistency in timing of blood sampling reduces the potential for impact of this issue. Nevertheless, to evaluate whether timing of haemodialysis might have impacted our results, we performed a sensitivity analysis including time from last haemodialysis as a covariate. Our results were not materially affected by this, with −log10 p-values and estimated effect sizes very highly correlated with those obtained without inclusion of this covariate  for validation. We first projected the data from subcohort B into the PCA space of subcohort A to examine how well the separation of cases and controls in the PCA space replicated . This revealed clearer separation of infected and non-infected patients than in subcohort A , perhapsExamination of the principal components plot labelling samples by clinical severity at the time of sampling  demonstrated a gradient of COVID-19 severity, best captured by principal components 1 and 3 . To deteWe next asked how does the COVID-19 severity protein signature relate to the proteins that are differentially abundant between cases and controls? The majority  of severity-associated proteins were also identified as differentially abundant in the COVID-19-positive versus -negative analysis . Log folPCA revealed that some samples from patients who had mild or moderate disease at the time of sampling clustered with samples from patients with severe disease . ExaminaThe Random Forests method achieved 71% accuracy in predicting peak severity. By contrast, using only clinically available predictors , the Random Forests method achieved 66% accuracy in predicting peak severity. Combining clinical parameters plus proteins did not improve accuracy (71%) compared to using proteomic predictors alone, suggesting that the information contained in the clinical predictors is captured at the proteomic level. While we do not believe that proteomic profiling is likely to enter clinical practice for risk stratification during this pandemic, the features selected by the classifier can highlight proteins of biological importance. We therefore interrogated the model to identify key proteins by calculating feature importance metrics  . We therThe immune response to infection is dynamic, and therefore snapshot measurements provide only partial insights. Leveraging the dense serial sampling in our dataset , we modeOne hundred and seventy-eight proteins displayed a significant association with time from first symptoms (5% FDR), demonstrating the temporal variability in plasma proteins across the disease course . MoreoveIn the UK, individuals from ethnic minorities are at higher risk of severe disease and death from COVID-19 . We therOther studies have used a variety of proteomic platforms to investigate COVID-19. We compared our findings to those of three published studies  and a prWe focussed in more detail on the study by 2 had a hazard ratio (HR) for death of 3.56 after adjustment for age and sex.In this study, we performed plasma proteomic profiling of haemodialysis patients with COVID-19. A strength of our study was that we were able to perform serial blood sampling in both the outpatient and inpatient settings, including longitudinal samples from the same individual before and after hospitalisation. This was possible because haemodialysis patients are unable to fully isolate as they must continue to attend for regular dialysis sessions. Moreover, haemodialysis patients represent an important group since ESKD is one of the strongest risk factors for death from COVID-19 . Data frIn part, the high mortality from COVID-19 in ESKD patients likely reflects the fact that these patients are enriched for cardiometabolic traits that predispose to severe COVID-19. However, in multivariable analyses adjusting for these factors, impaired renal function remains an independent risk factor for severe COVID-19 . MoreoveOur comparison of COVID-19-positive and -negative haemodialysis patient plasma samples revealed 221 proteins that were differentially abundant in COVID-19. The majority of these were upregulated, with strong representation of viral response proteins , cytokines/chemokines , and epithelial proteins  . The COVESKD is itself likely to significantly impact the plasma proteome. Previous cross-sectional studies have shown that the levels of many circulating proteins have an inverse relationship with eGFR . A longiAnalysis within COVID-19 cases revealed 203 proteins associated with disease severity, the strongest of which was IL6 . AssociaMembers of the CCL and CXCL chemokine families  were strongly associated with severity. Likewise, higher levels of CCL2, CCL7, CCL20, and CXCL10 were associated with lower blood lymphocyte count and higher inflammatory markers (CRP and ferritin) , which aThe neutrophil proteases PRTN3 (proteinase-3) and MPO (myeloperoxidase)  and the A striking finding of our study was the association of disease severity with upregulation of epithelial proteins (e.g. KRT19) and epithelial tissue repair pathways , most likely reflecting lung and vascular damage. KRT19 was notably prominent in our analyses, as well as the study by Sixty-nine percent of proteins associated with severity were also differentially abundant in the case versus control analysis , and forSurvival analysis identified 44 proteins associated with increased risk of death . As expeThe host immune response to COVID-19 is a dynamic process, and clinical deterioration typically occurs 7–10 days after first symptoms. Temporal information may therefore be important in determining optimum timing of therapeutic intervention (e.g. blockade of a particular cytokine). By taking serial samples and examining their patterns within individuals over time, we were able to model protein trajectories and found that many proteins display temporal variability during COVID-19. Longitudinal measurements also allow molecular comparison of severe versus mild disease trajectories. By modelling the interaction term between time from first symptoms and overall disease course, we found 32 proteins that displayed distinct temporal profiles in severe versus mild disease. These results point to enhanced leucocyte–endothelial cell interactions indicated by upregulation of cell adhesion molecules  in severe disease. This endothelial activation may contribute to COVID-19-associated thrombosis discussed above. Management of thrombosis in COVID-19 currently consists of anticoagulation. Our results suggest that disrupting leucocyte–endothelial interactions may be a complementary therapeutic strategy.Several proteins associated with either risk of death or clinical severity lie in pathways targeted by existing drugs. PARP1 was identified as an important marker of current or future severe COVID-19 and also was associated with risk of death. PARP1 is associated with inflammatory and vascular disease . PARP1 iOther groups have studied the plasma or serum proteome in COVID-19 , using eIn summary, this study reveals proteins associated with COVID-19 infection and severity and demonstrates altered dynamic profiles between patients with severe disease and those with a more indolent course. Our results emphasise the importance of studying and targeting mechanisms that reduce the lung epithelial and endothelial damage to both alleviate the severity of the infection and reduce the chance of long-lasting complications. These data provide a valuable resource for therapeutic target prioritisation.All participants (patients and controls) were recruited from the Imperial College Renal and Transplant Centre and its satellite dialysis units, London, United Kingdom, and provided written informed consent prior to participation. Study ethics were reviewed by the UK National Health Service (NHS) Health Research Authority (HRA) and Health and Care Research Wales (HCRW) Research Ethics Committee . Ethical approval was given.We recruited 55 COVID-19-positive haemodialysis patients, either as outpatients or as inpatients . All patIn addition, we recruited 51 COVID-19-negative haemodialysis controls. COVID-19-negative haemodialysis controls were selected to mirror the cases in terms of demographic features  . These cWe also recruited a separate set of 46 COVID-19-positive haemodialysis patients . These p2) greater than 92% on air, or an oxygen requirement no greater than 4 L/min. ‘Severe’ was defined as SaO2 less than 92% on air, or respiratory rate more than 30 per minute, or oxygen requirement more than 4 L/min. ‘Critical’ was defined as organ dysfunction or shock or need for high dependency or intensive care support (i.e. the need for non-invasive ventilation or intubation). Severity scores were charted throughout a patient’s illness. We defined the overall severity/clinical course for each patient as the peak severity score that occurred during the patient’s illness.Severity scoring was performed based on WHO classifications  adapted for clinical data available from electronic medical records. ‘Mild’ was defined as COVID-19 symptoms but no evidence of pneumonia and no hypoxia. ‘Moderate’ was defined as symptoms of pneumonia or hypoxia with oxygen saturation (SaOhttps://www.olink.com/products/). Five 92-protein multiplex Olink panels were run , resulting in 460 measurements per sample. Since a small number of proteins were measured on more than one panel, we measured a total of 436 unique proteins. The Olink assays were run using 88 samples/plate. All plates were run in a single batch. Plate layouts was carefully designed to avoid confounding of potential plate effects with biological or clinical variables of interest. To achieve this, we used an experimental design that combined ensuring case/control balance across plates with random selection of samples from each category and random ordering of allocation to wells. This is outlined in more detail as follows. We ensured that each plate contained a mixture of control and case samples. Specifically, a fixed proportion of each plate was designated for control samples. The allocation of specific control samples to each plate was performed using randomisation. For the case samples, we again used randomisation for plate assignment, with the constraint that once one sample from a given patient was allocated to a plate, all other longitudinal samples from that patient were assigned to same plate. Finally, once all the samples had been allocated to plates, the layout of samples within each plate was determined through a further randomisation step for well allocation.Plasma and serum proteomic measurements were performed using Olink proximity extension immunoassays . Quality assessment was performed by (1) examination of Olink internal controls and (2) inspection of boxplots, relative log expression plots , and PCAPCA revealed no substantial impact of plate effects . ThirteeFor 11 ESKD controls, we had contemporaneous plasma and serum samples. To assess the comparability of these two matrices, we calculated the Pearson’s correlation coefficient between the assays for each protein . Three hFollowing QC, 0.22% data points were missing for the plasma dataset and 0.35% for the serum dataset. For analyses that required no missing values (PCA and supervised learning), we imputed missing values as follows. The dataset was first scaled and centred, and missing values imputed using caret’s k-nearest neighbours method . The fivSingular value decomposition was used to perform PCA on the proteomic data from subcohort A (plasma samples). We then used the loadings from subcohort A together with the proteomic data from subcohort B to calculate principal component scores. This enabled projection of subcohort B data into the PCA space of subcohort A.Differential protein abundance analyses between COVID-19 positive and negative samples were performed using linear mixed models, to account for the use of serial samples from the same individuals .As sensitivity analyses, we repeated the differential abundance analyses between case and controls for the subcohort A adjusting for additional covariates and comparing this to the basic model . This was performed for each of the following parameters: diabetes status, cause of ESKD, and time to last haemodialysis.For testing the association of plasma proteins with the four-level WHO severity rating  within COVID-19-positive cases from subcohort A (n = 256 samples from 55 patients), we used a similar linear mixed modelling approach to the COVID-19-positive versus -negative differential abundance analysis; for this analysis, the covid_status term was replaced by a severity variable encoded using orthogonal polynomial contrasts to account for ordinal nature of severity levels. As before, age, sex, and ethnicity were included as covariates. As a sensitivity analysis, we repeated the analysis with time to last haemodialysis as an additional covariate.The linear mixed modelling strategy was also employed for testing association of temporal clinical laboratory variables and protein levels, with the value of the clinical variable (as a quantitative trait) used in place of covid_status. Only COVID-19-positive patients were included in this analysis. Contemporaneous lab measurements were not available for all samples. This varied according to the clinical lab parameter. Some  were measured less frequently than full blood count and CRP. Details of the proportion of missing values for each lab parameter are included in We performed testing of protein levels and ethnicity separately in COVID-19-negative ESKD patients and COVID-19-positive ESKD patients. These analyses were limited to individuals who were White, South Asian , or Black as there were too few individuals from other ethnic groups for meaningful interpretation. For COVID-19-negative patients (one sample per patient), we performed linear regression for each protein with ethnicity as the predictor variable and age and sex as covariates. For COVID-19-positive patients, we used a linear mixed model to account for serial samples from the same individual, again with age, and sex as covariates.We used the Benjamini–Hochberg method to control the FDR at 5% for all statistical analyses.To provide additional support that the Benjamini–Hochberg procedure was providing adequate control of the FDR, we also used the plug-in procedure  as an alWe defined R as the number of associations declared significant in the real data.We defined C as the test statistic used as the significance threshold used in the real data .C in 100,000 permutations of the data, divided by the number of permutations.The expected number of proteins that we would find significant under the null hypothesis that no proteins are differentially abundant between COVID-19-positive versus -negative patients  was estimated using a permutation strategy. We randomly permuted each individual’s COVID-19 status label 100,000 times and, in each case, repeated the differential abundance analysis on the permuted data. The estimated the number of false positives .We also applied this method to the association testing of proteins with severity scores within cases . Again, Missing values with the caveat that, during cross-validation, the pre-processing procedure was first applied on the resampled (training) data before the same method was applied without re-calculation to the holdout (test) set. To estimate model accuracy, we used fourfold cross-validation. The cross-validation procedure was repeated 100 times. The model’s parameters were kept constant at 500 trees and an mtry value (number of proteins randomly sampled as candidates at each node) calculated as the square root of the number of features. After parameter estimation, we fitted a final model trained using the entirety of the dataset. This model was used for subsequent feature extraction. Random forest feature extraction was carried out using the R randomForestExplainer package. We made use of the following importance measures: accuracy decrease (the average decrease in prediction accuracy upon swapping out a feature), number of trees (the number of trees with a node corresponding to a feature), and mean minimal depth (the average depth at which a node corresponding to a feature occurs). Three models were generated with different input features: (1) proteomic data alone; (2) clinical parameters alone; and (3) proteomic data and clinical parameters. Clinical parameters included sex, age, ethnicity, cause of ESKD, comorbidities, smoking status, radiological evidence of pulmonary infiltrates, and clinical laboratory tests.Random forest models were fitted using R’s randomForest and caret packages . Data waFollowing scaling and centring, we fitted linear mixed models for each protein to capture the temporal trajectories of each individual. A polynomial spline of degree two was used to model protein concentration with respect to time ; the spline was fitted for samples that were taken between 1 and 28 days from first symptoms, inclusive. Proteomic data after that point was censored. We estimated both random intercepts and random slopes for each individual, as per the following R formula notation:These were joined to a Cox regression model using the jointModel package  in orderWe also used linear mixed models to estimate the temporal profile of each protein. For this longitudinal analysis, we explicitly modelled the time from first symptoms. We set up the model to test for each protein (1) whether the protein significantly change over time and (2) whether the protein changes over time differently in individuals with a mild versus severe disease course. The latter was performed statistically by testing for an interaction effect between time and clinical course. For the purposes of this analysis, we binarised patients into severe or non-severe clinical course according to the peak WHO severity disease of their illness. Patients with a peak WHO score of mild or moderate were considered non-severe and those with a peak score of severe or critical were considered severe.We then used R’s bs function to fit a polynomial spline of degree two to model protein concentration with respect time  . The splTo identify proteins that changed significantly over time, we examined the p-values for the main effect of time. To identify proteins with distinct temporal profiles between severe and non-severe cases, we examined the p-values for the time × severity interaction term. For each of these two research questions, p-values were adjusted for the multiple proteins tested using the Benjamini–Hochberg method and 5% FDR used as the significance threshold.https://github.com/jackgisby/longitudinal_olink_proteomics; swh:1:rev:32f08137859d44707ec4f086eed9af9b9ee91a87.Code is available in the following GitHub repository:  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The authors analyzed blood proteomic profiles from COVID-19 patients with end-stage renal disease (ESRD) and report changes compatible with an aberrant innate immune response. They also identified several novel molecular predictors of death, as well as markers of severe versus non-severe clinical development in high-risk patients with ESRD infected with SARS-CoV2. Overall, the findings are novel and important as they are the first to characterize potential molecular markers that may help to predict the severity of the clinical course of COVID-19 in this high-risk group of patients.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, including Evangelos J Giamarellos-Bourboulis as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Senior Editor.Thank you for submitting your article "Longitudinal proteomic profiling of high-risk COVID-19 patients reveals markers of severity and predictors of death" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at 1) The authors are trying to do generalizations for all COVID-19 patients. They need to focus on ESRD which is their main aim. This should be reflected on the title that needs to be appropriately revised.2) The authors claim that ESRD is a major predisposing factor for COVID-19. This is not yet supported by the existing literature whereas many clinicians feel that patients with ESRD have a milder course than the rest of patients with COVID-19. The authors need to provide support of this (i.e. the selected patients come from how many infected and how was their course compared to the rest of the available cohort) and they need to provide evidence if this is an underlying confounding factor which does not allow the clustering of proteins clearly separating ESRD patients with COVID-19 from ESRD without COVID-19.3a) The authors discuss about longitudinal sampling whereas they analyze all samples together.3b) I suggest that they provide analysis of the samples on the day of peak of symptoms that defined the severity classification and compare with samples collected before and after that day.4) The authors need to provide analysis how the haemodialysis per se has impacted their findings. This can be provided through adjustment with the time since last dialysis.5) The non-ESRD patients with COVID-19 do not seem appropriate since they have lower frequency of diabetes as co-morbidity.6) The validation cohort needs to be described in more detail. Are these just more samples from the same study?7) Figure 5: Individual measured data points should be added to these time-series to show how well the data is modeled.8) Supplementary Figure 4: As this is supposed to be a validation, please add lead loadings from the primary cohort . How well do these replicate?9) Supplementary Figure 8: X-axis units are "Relative Log2 Abundance" – are these identical to NPX values?10) The technical replication using just two samples is not relevant . Better refer to technical replications done by Olink or in the literature with larger sample sizes. Also, the reported Pearson correlation of 0.999 looks overly optimistic and is likely a result from correlating data over a vast range of concentrations. What is the relative error ?11) It appears that some of the controls have been measured twice, in serum and plasma. These data should be compared and substantial differences reported. This information may be important if proteins are taken forward for follow-up and to interpret differences between future studies.12) All predictions and models should be compared to models that use relevant clinical parameters alone as a reference and then proteins + clinical parameters to test how much information is added by the protein(s).13) Some information about the role of OLINK measured proteins in ESKD should be provided (maybe from the literature).14) Ethnicity appears to play a role in Covid-19 severity. The authors included ethnicity into the models. It would be important to highlight and discuss proteins that associate with ethnicity in light of selecting the most suitable and unconfounded biomarkers in future studies.15) The authors do not describe the matching procedure. Moreover, as shown in Supplementary Table 1 controls differ significantly in terms of comorbidities.16) Second, the authors should describe better the validation cohort used. Was from the same site? Was in parallel recruited and if so, why is it independent.17) The Discussion section is confusing. It should be fully re-written in more hierarchical way outscoring the main important findings. Emphasis should be given in ESRD and not in differences with all other papers of proteomics in COVID-19.18) The authors cite a preprint by Huang  which has a similar design. In the meantime, a more complete evaluation of that same dataset has been posted by Filbin et al. . It may be worthwhile to take some of their results into account in the Discussion. 1) The authors are trying to do generalizations for all COVID-19 patients. They need to focus on ESRD which is their main aim. This should be reflected on the title that needs to be appropriately revised.We have revised the title to “Longitudinal proteomic profiling of dialysis patients with COVID-19 patients reveals markers of severity and predictors of death".2) The authors claim that ESRD is a major predisposing factor for COVID-19. This is not yet supported by the existing literature whereas many clinicians feel that patients with ESRD have a milder course than the rest of patients with COVID-19. The authors need to provide support of this (i.e. the selected patients come from how many infected and how was their course compared to the rest of the available cohort) and they need to provide evidence if this is an underlying confounding factor which does not allow the clustering of proteins clearly separating ESRD patients with COVID-19 from ESRD without COVID-19.The reviewer’s comments that end-stage kidney disease is not an established risk factor for severe or fatal COVID-19, and that ESKD patients have a milder disease course, are not correct. Multiple major epidemiological and registry studies provide unequivocal evidence that chronic kidney disease (CKD), particularly ESKD, is a major risk factor for severe or fatal COVID-19.i) Data from the UK Renal Registry shows that 7- and 14-day mortality for COVID-19 infected in-centre haemodialysis (HD) patients was 11% and 19%, respectively.ii) Data from the Scottish Renal Registry estimates 30-day mortality for HD patients following a positive COVID-19 test as 22% . As of 31 May 2020, 28.2% of renal replacement therapy patients who had had a positive COVID-19 test had died.2 had a hazard ratio (HR) for death of 3.56 after adjustment for age and sex . The association of impaired renal function with risk of death from COVID-19 persisted after adjustment for all other demographic and clinical risk factors in multivariable analysis (eGFR <30 conferred HR for death of 2.52 in the fully adjusted model). This suggests that ESKD per se is a risk factor for COVID19 rather than simply a marker of multi-morbidity. ESKD was one of the strongest risk factors for death from COVID-19, with similar risk to organ transplant or haematological malignancy.iii) The OpenSAFELY study  examined ~17 million UK primary care records and linked these to the UK national COVID-19 mortality register. This enabled identification of risk factors for fatal COVID-19. Impaired renal function was a strong risk factor for death from COVID-19, and the risk was proportional to the degree of renal impairment. Patients with estimated glomerular filtration rate (eGFR) <30ml/min/1.73miv) The ISARIC study  examined 20,133 hospitalised patient records and identified CKD as the comorbidity with the strongest association with death . This study analysed CKD as one group, so the HR for ESKD patients specifically is not available.v) In our local cohort of 1,352 in-centre haemodialysis patients, by the end of our study recruitment period (30 May 2020), 315 patients had tested positive for COVID-19 . Excluding the 11 patients who acquired COVID-19 hospital, 160 (53%) required hospitalisation. Of the 315 patients, 85 died , leading to a 27% mortality rate. This figure is in line with the Scottish registry data.Clearly, these multiple sources of data show that mortality rate for ESKD patients far exceeds that of the general population, and that the mortality rate of the patient sample on whom our proteomic study was performed is in line with that observed elsewhere.We understand the second part of the reviewers’ comment to be asking whether any potential selection bias for severe cases has influenced the differential expression analysis of COVID-19 positive vs. negative ESKD patients. As can be seen from the figures above, the subset of patients we sampled is not overrepresented for mortality compared to either our local haemodialysis population or the UK more generally. Moreover, the striking concordance between our results and that the study of Filbin et al. of COVID-19 positive vs. negative patients in a non-renal population  provides reassurance that our findings of proteins differentially expressed in COVID-19 are robust.We have expanded the Discussion to reflect the above points.3a) The authors discuss about longitudinal sampling whereas they analyze all samples together.To clarify, we performed 5 distinct major analyses. Some, but not all of these, use all available samples. This is explained below:1) Differential protein abundance analysis between all case samples and control samples .As the reviewer implies, for this analysis we leveraged the fact that for our cases we had serial samples which enables greater statistical power. To appropriately account for the non-independence of serial samples, we used linear mixed models.2) Analysis of severity within cases.at the time of blood sampling. This analysis is greatly enhanced by the fact that we have serial protein measurements, and sometimes changing clinical severity levels, for a given individual. Again, to appropriately model non-independence of samples from the same individual we used a linear mixed modelling strategy. By leveraging all the data, including intra-individual changes in proteome and severity, we gain power.For this analysis, we analysed associations between protein levels and clinical severity 3) Supervised learning to predict severe disease.only the first sample for each patient to identify a model that could predict whether a patient either had severe disease or would develop it in the future.This analysis was performed using 4) Identifying death associated proteins through modelling with joint models.This analysis uses repeated measurements to identify predictors of death from serial data. A joint model combines a linear mixed model and a Cox proportional hazard model and is the appropriate analytical method when the predictor (i.e. the protein) is itself a dynamic entity that may be impacted by the clinical state.5) Identifying proteins with distinct longitudinal profiles.This analysis is the “longitudinal” model and explicitly models the time from first symptoms. This model tests a) whether proteins significantly change over time and b) examines whether there were proteins that change over time differently in individuals with a mild versus severe disease course. The latter was performed statistically by testing for an interaction effect between time and clinical course. We have realised that we omitted to show the results from a) in the previous manuscript and these are now included (see “Changes to the manuscript in response to 3a) and b)” below).3b) I suggest that they provide analysis of the samples on the day of peak of symptoms that defined the severity classification and compare with samples collected before and after that day.It is our view that the analysis we performed to be preferable to the approach suggested by the reviewer – although we acknowledge that this may not have been clear from our original description of our analyses. Linear mixed modelling appropriately accounts for the relationship between serial samples from the same individuals, and is a widely used, well-established, and principled approach for longitudinal data analysis . Springer, New York, NY).In contrast, the reviewer’s suggested approach would limit longitudinal analysis to just 3 samples . Apart from the practical difficulties that this would present , we do not believe that a reliable longitudinal proteomic trajectory can be constructed through analysis of just 3 data points.Moreover, we note that the very high concordance (81.8%) in the proteins we identified as severity-associated and those reported in the study by Filbin et al. , providing independent support that our approach is robust.Changes to the manuscript in response to 3a) and b:)We appreciate that we did not articulate clearly enough the multiple types of analyses we performed. We thus have made the following changes in response to the reviewer’s comments:i) We have revised the text and introduced new subheadings in the Results section to make the distinction between these analyses clearer. We have also revised the Discussion so that the main findings are discussed in a more structured manner . We have also edited the Materials and methods to improve clarity.ii) We have made a new figure  illustrating how the death-associated proteins detected in Analysis 4 above relate to the associations with clinical markers of disease severity .iii) We have added results showing proteins that significantly change over time (new Supplementary file 1H), in addition to proteins that have different temporal trajectories between severe and non-severe cases. We have amended the text of the Results, Discussion and Materials and methods accordingly.all proteins that display a significantly different longitudinal profile (5% FDR) between patients with a mild versus severe clinical course, since the shape of trajectories was not evident from the p-values alone.iv) We have made plots  showing the temporal trajectories for 4) The authors need to provide analysis how the haemodialysis per se has impacted their findings. This can be provided through adjustment with the time since last dialysis.We agree with the reviewer that it is possible that haemodialysis might impact the plasma proteome. To reduce the potential confounding effects of haemodialysis, we designed the study such that all samples were taken immediately before haemodialysis. For the large majority (86.6%) of samples, last haemodialysis was between 48 and 72 hours prior to blood draw. This consistency in timing of blood sampling reduces the potential for impact of this issue.To evaluate whether time from last haemodialysis might have nevertheless impacted our results, we performed the analysis suggested by the reviewer, including time from last haemodialysis as a covariate along with age, sex and ethnicity in the regression models. Our results were not substantially changed by this. In the differential abundance analysis of COVID-19 positive vs. negative patients, the correlation between log fold changes estimated with and without inclusion of time from last dialysis as a covariate was >0.99 . Minus log10 p-values were also very highly correlated (r >0.99) . Similarly, in the analysis of severity, the correlation of estimated effect sizes and of -log10 p-values were both > 0.99 .We have amended Supplementary file 1C  and Supplementary file 1D  to show for all proteins the results from a) both the model using only age, sex and ethnicity as covariates, and b) the extended model that also includes time for last dialysis .We have added this description of the analysis and the results in the Results and Materials and methods.5) The non-ESRD patients with COVID-19 do not seem appropriate since they have lower frequency of diabetes as co-morbidity.16) Second, the authors should describe better the validation cohort used. Was from the same site? Was in parallel recruited and if so, why is it independent.We address reviewers’ points 5 and 16 together due to their similarity.We matched COVID-19 positive and negative ESKD patients as closely as possible in terms of age, sex and ethnicity. We believe that this close demographic matching and the fact that our controls were also haemodialysis patients represents an advance on previous studies. Unfortunately, exact matching of all comorbidities was not feasible, and this issue also applies to other published COVID19 proteomic studies . However, we agree with the reviewers that it is important to check whether the lower prevalence of diabetes in our controls might be a potential confounder.To address this, we have repeated the differential abundance analysis between COVID-19 positive and COVID-19 negative patients adjusting for diabetes status . Comparison of effect size estimates and -log10 p-values revealed highly consistent results with the model that used only age, sex and ethnicity . We now show the results from the extended model under the column header “sensitivity analyses with additional covariates” in Supplementary file 1C.In addition to the differences in diabetes prevalence, there were also other differences in the underlying cause of ESKD in cases compared controls. We therefore performed a further sensitivity analysis adjusting for underlying cause of renal failure. This did not make any meaningful difference to our results . ESKD is characterised pathologically by small shrunken kidneys, loss of glomeruli and scarring, irrespective of the original cause of the renal injury. Thus, even though there maybe disparate causes, ESKD is a more clinically, biochemically and pathologically homogeneous entity than the renal diseases that lead to it. This may explain the lack of impact of this covariate on the plasma proteome.We have made changes to the text detailing these sensitivity analyses in the Results, Discussion and Materials and methods.6) The validation cohort needs to be described in more detail. Are these just more samples from the same study?15) The authors do not describe the matching procedure. Moreover, as shown in Supplementary Table 1 controls differ significantly in terms of comorbidities.Given the similarities in comments 6 and 15 we have responded to these points together.The two groups of COVID-19 patients were collected from the same site but there was a small temporal difference in collection and a difference in sample types (plasma versus serum).th March 2020. At this time, we had very limited access to laboratory facilities and were only able to process serum. Once we had the relevant approvals to open our research laboratory facilities, we switched to collecting plasma (from 8th April 2020). The reason for the switch was that a) although the Olink platform can be used for both serum and plasma, it is optimised for the latter, and b) given the high incidence of micro- and macrovascular thrombosis, we hypothesised that it might be important to measure coagulation factors (which can only be measured in plasma).To put the collections into context, patients were recruited during a highly challenging period for both clinical care and research during the first UK national lockdown. We initiated patient recruitment on 30The initial sample collection (serum) was considerably smaller than the subsequent collection (plasma). In addition, for the majority of patients in the initial collection, we only had a single timepoint, thus precluding longitudinal analyses. Therefore, we used the larger subcohort as the discovery cohort, and the smaller cohort to provide validation for the case-control analysis.In terms of clinical characteristics, the validation cohort had a higher proportion of hospitalised and severely ill patients. This was because PCR COVID-19 testing in the UK at that time was largely limited to hospitalised patients. By the time we recruited the discovery cohort, the capacity to perform COVID PCR testing had been expanded and was available for patients attending outpatient haemodialysis. Consequently, we were able to identify and recruit patients with milder disease as well as hospitalised patients. Therefore, the discovery cohort included a broader spread of illness severity.We used the term “independent” to indicate that the validation cohort was a set of non-overlapping patients from the discovery cohort. However, we can now appreciate that this might be misleading and lead a reader to interpret this as patients from a different centre.Actions:– We have removed the term “independent cohort” from the revised manuscript.– To improve clarity, we now refer to the two patient groups as subcohort A  and subcohort B .– We have added a new table (Table 2) with characteristics of subcohort B.7) Figure 5: Individual measured data points should be added to these time-series to show how well the data is modeled.We now provide these in Figure 9—figure supplement 2. Since the large number of data points makes the modelled trajectories difficult to visualise, we show the modelled trajectories without individual data points in the main display item .8) Supplementary Figure 4: As this is supposed to be a validation, please add lead loadings from the primary cohort . How well do these replicate?We thank the reviewer for this excellent suggestion. To best demonstrate how the patterns in the PCA for subcohort A compare to those in subcohort B, we have done the following:We present the PCA on the primary cohort and then project the data from subcohort B onto the same PCA coordinates  . This reveals that the axis along which cases and controls are separated in subcohort A clearly divides cases and controls in subcohort B .We have amended the text as follows:Results:“We used the smaller subcohort B  for validation. […] This revealed clearer separation of infected and non-infected patients than in subcohort A , perhaps reflecting the higher proportion of hospitalised patients (41 of 46 patients) in subcohort B (Table 2).”Materials and methods:“Principal Components AnalysisSingular value decomposition was used to perform PCA on the proteomic data from subcohort A (plasma samples). […] This enabled projection of subcohort B data into the PCA space of subcohort A.”In addition, we have revised the presentation of the PCA analysis to improve clarity as described below:i) We now show plots for both PC1 vs. PC2 and PC1 vs. PC3 for each subcohort .ii) In Figure 2 we show the samples coloured by case control status, and in Figure 2—figure supplement 1 we show samples coloured by severity.9) Supplementary Figure 8: X-axis units are "Relative Log2 Abundance" – are these identical to NPX values?Yes these are NPX values. We were trying to make the plot more accessible to readers not familiar with the Olink platform and used the term “relative” to indicate that the measurements were not in absolute units (e.g. ng/L). However, we can see that this may cause confusion.We have now changed to the axis units to “NPX (log2 protein abundance)”, and added the definition of NPX in the legend. We have also made this change for all relevant figures .10) The technical replication using just two samples is not relevant . Better refer to technical replications done by Olink or in the literature with larger sample sizes. Also, the reported Pearson correlation of 0.999 looks overly optimistic and is likely a result from correlating data over a vast range of concentrations. What is the relative error ?We accept the reviewers’ point that technical replication using just two samples is too small for reliable inference and we have removed this from the manuscript.11) It appears that some of the controls have been measured twice, in serum and plasma. These data should be compared and substantial differences reported. This information may be important if proteins are taken forward for follow-up and to interpret differences between future studies.For the 11 control samples from which we had both serum and plasma, we now present the correlation between plasma and serum levels for each protein in Supplementary file 1K.We have made the following changes to the text to reflect this:“For 11 ESKD controls, we had contemporaneous plasma and serum samples. To assess the comparability of these two matrices, we calculated the Pearson’s correlation coefficient between the assays for each protein (Supplementary file 1K). […] Caution should be made in extrapolating these findings to the context of active infection where protein dynamic ranges may be different.”12) All predictions and models should be compared to models that use relevant clinical parameters alone as a reference and then proteins + clinical parameters to test how much information is added by the protein(s).We have performed the analysis suggested by the reviewer. We also made a minor modification to our method for estimating model accuracy: we now use 4-fold cross-validation and increased the number of iterations to 100 to ensure stability of the accuracy estimate.Using the Random Forests supervised learning approach, the accuracy of the models are as follows:– Using only the clinical data : 66%.– Using only the protein data: 71%.– Using clinical + protein data 71%.If our primary goal was to develop a clinically useful test based on Olink plasma proteomics to predict severe disease, then developing a predictive model that combined standard clinical parameters and proteins would be most logical. However, we do not believe there is any realistic prospect of Olink proteomics being applied outside the research context in this pandemic. Our goal in performing the supervised learning was not to develop a predictive tool per se, but to use the predictive model to better understand the biology of severe COVID-19 by identifying proteins that the algorithm selected as important predictors. Interrogation of the model to this end is best done for the analysis that used only the proteomic data.We have made changes to the text to reflect these new analyses (Results).13) Some information about the role of OLINK measured proteins in ESKD should be provided (maybe from the literature).We have added this to the Discussion:“ESKD is itself likely to significantly impact the plasma proteome. Previous cross-sectional studies have shown that the levels of many circulating proteins have an inverse relationship with eGFR . […] Regardless, this observation of widespread changes in the blood proteome of kidney disease patients emphasises the importance of using COVID-19 ESKD patients rather than healthy individuals as our control group.”14) Ethnicity appears to play a role in Covid-19 severity. The authors included ethnicity into the models. It would be important to highlight and discuss proteins that associate with ethnicity in light of selecting the most suitable and unconfounded biomarkers in future studies.We have added an analysis of protein associations with ethnicity.Our view is that this is best done separately for COVID-19 negative and positive ESKD patients, as in the latter proteins associated with severity may be confounded with ethnicity.We therefore adopted the following approach:i) In COVID-19 negative ESKD patients (where there was only 1 sample per patient) we fit the following linear regression model (R notation):NPX ~ ethnicity + age + sexThis revealed no proteins associated with ethnicity at 5% FDR.ii) In COVID-19 positive ESKD patients  we included fit the following linear mixed model (again R notation), with a term to adjust for the potential confounding effect of severity:NPX ~ ethnicity + age + sex + severity + This revealed a single protein (LY75) associated with ethnicity at 5% FDR in subcohort A. In subcohort B no proteins were significant at 5% FDR, although LY75 did have nominal P <0.05. In summary, we did not find evidence for substantial ethnicity effects on the proteins we measured. However, we think these results should be interpreted cautiously as the number of individuals in any one ethnic group were modest and consequently power was limited. We do not believe we can make any strong conclusions on this matter from our data and that larger multi-ethnic proteomic studies are needed to properly address this important question.We have revised the text to reflect the above .17) The Discussion section is confusing. It should be fully re-written in more hierarchical way outscoring the main important findings. Emphasis should be given in ESRD and not in differences with all other papers of proteomics in COVID-19.We thank the reviewer for this helpful suggestion. We have fully re-written the Discussion with a structure corresponding to the major analyses.We believe that some comparison with other COVID-19 proteomic studies is important to understand whether our findings are applicable only in the context of ESKD or have more general relevance, but we agree with the reviewer’s suggestion to reduce the emphasis on this. We have revised the text accordingly and we now focus on the paper by Filbin et al. as suggested in reviewer comment 18 below. The interested reader can still find comparisons to other studies in Supplementary file 1I  and in Supplementary file 1J .18) The authors cite a preprint by Huang  which has a similar design. In the meantime, a more complete evaluation of that same dataset has been posted by Filbin et al. . It may be worthwhile to take some of their results into account in the Discussion.We now present a comparison of our data with the analysis of Filbin et al. Despite the clinical differences in the populations studied and the differences in Olink proteomics platform used, we observed strikingly similar results. We compared effect sizes from our differential abundance analysis of COVID-19 +ve and COVID-19 -ve ESKD patients to those of Filbin et al. (COVID-19 +ve respiratory failure vs. COVID-19 -ve respiratory failure). This revealed a strong correlation, r = 0.688, (new figure 4—figure supplement 1). Moreover, we observed very high concordance (81.8%) in proteins associated with clinical severity between the two studies. This indicates that similarities in the COVID-19 plasma proteomic signature across clinical contexts and that our findings have relevance beyond the narrow context of COVID-19 in ESKD patientsWe have restructured the Results so there is a subsection on comparisons with previous studies, focussing on the report of Filbin et al. (subsection “Comparisons to other proteomic studies in COVID-19”).We have also revised the Discussion to reflect this.


Transient ischaemic attacks (TIA) and minor strokes are important risk factors for further vascular events. We explored the role of albumin creatinine ratio (ACR) in improving risk prediction after a first event.Rapid access stroke clinics in the UK.2202 patients attending with TIA or minor stroke diagnosed by the attending stroke physician, able to provide a urine sample to evaluate ACR using a near-patient testing device.Primary outcome was major adverse cardiac events  at 90 days. The key secondary outcome was to determine whether urinary ACR could contribute to a risk prediction tool for use in a clinic setting.151 MACE occurred in 144 participants within 90 days. Participants with MACE had higher ACR than those without. A composite score awarding a point each for age >80 years, previous stroke/TIA and presence of microalbuminuria identified those at low risk and high risk. 90% of patients were at low risk (scoring 0 or 1). Their 90-day risk of MACE was 5.7%. Of the remaining ‘high-risk’ population (scoring 2 or 3) 12.4% experienced MACE over 90 days (p<0.001 compared with the low-risk population). The need for acute admission in the first 7 days was twofold elevated in the high-risk group compared with the low-risk group . These findings were validated in an independent historic sample.A risk score comprising age, previous stroke/TIA and microalbuminuria predicts future MACE while identifying those at low risk of a recurrent event. This tool shows promise in the risk stratification of patients to avoid the admission of low-risk patients. The pragmatic design of this study provides good generalisability to clinical practice.The predictive role of the combination of urinary albumin creatinine ratio (ACR) in combination with very basic demographics would be able to reassure the 90% of the population that there was only approximately a 1 in 20 risk of an event recurring, whereas the higher risk population had a 1 in 8 chance of a major adverse cardiac events (MACE) outcome.The study is limited by the availability of data which were available to the physicians at the time of the stroke appointment. This limits the ability to determine mechanistic association between ACR and MACE outcome.Further work is required to determine whether therapies that reduce ACR can modify the risk of subsequent MACE.Stroke is the second most common cause of death and a leading cause of disability worldwide.Accurate identification of those patients presenting to TIA clinics with TIA or minor stroke who are most at risk for future events is important to (1) intensify treatments such as giving dual antiplatelet therapy,Age, presenting Blood pressure, Clinical features of unilateral weakness or aphasia, Duration of symptoms and Diabetes), California scoreSeveral risk stratification tools are already used in such clinics such as ABCD2Increased urinary albumin excretion rate (AER) has been shown to predict incident stroke and heart failure in people with diabetes. In the general population, AER predicts cardiovascular disease and mortality post stroke independent of conventional cardiovascular risk factors such as hypertension, diabetes and smoking.Patients diagnosed with suspected minor stroke/TIA by a consultant stroke physician, attending a rapid access clinics in one of the 12 UK hospitals were recruited at the end of their clinic consultation. As these clinics are rapid access, often within the first 24 hours of symptoms, it was impossible to accurately differentiate between TIA (with symptoms <24 hours) and minor stroke in all cases. Only individuals with events that had occurred within the previous month were included in the study. Routine clinical care, including urgent revascularisation in those with severe ICA stenosis ,Participants were followed up by telephone on day 7, 30 and 90, and any history of any further vascular events, hospitalisation or death was obtained. If contact with participants was not possible initially, the presence or absence of further events was verified during additional attempts at contact or during future follow-up. If no further contact was possible with the participant , medical history was collected from the next of kin and verified from the primary care physician and hospital records. All reported clinical events were adjudicated according to the standardised diagnostic criteria by a data monitoring committee including two independent stroke physicians, blinded to the ACR, by examination of clinical records if they attended hospital, or their primary care physician, or by verifying their clinical symptoms from the research records if patients did not seek further medical advice. In the event of disagreement, a third independent stroke physician acted as adjudicator. The primary outcome was to determine the utility of microalbuminuria in predicting the time to first major adverse cardiovascular event  within 90 days. Secondary outcomes were time to explore the predictive role of microalbuminuria on recurrent stroke, total mortality (even if these were not the first MACE), the presence of first MACE within 7 days, and the need for hospitalisation within the first 7 days. All events were adjudicated blind to urinary ACR by an independent data monitoring committee.The study protocol was approved by the national research ethics committee . All participants provided written informed consent prior to enrolment and confirmed their willingness to continue participation at each telephone consultation.Patients were involved in the design and conduct of this research. During the feasibility stage, priority of the research question and methods of recruitment were informed by discussions with patients through a focus group session and three structured interviews. During the trial, two patients joined the independent trial steering committee. Once the trial has been published, participants will be informed of the results through a dedicated newsletter suitable for a non-specialist audience, led by one of the patients on the steering committee.2 test and the Student’s t-test for continuous variables. Time to event was measured from the clinic consultation rather than the index event, in keeping with the pragmatic nature of the study. Independence of ACR (as a risk predictor) from diabetes and other measures of the ABCD2 score was assessed using logistic regression. Microalbuminuria was defined using the currently accepted clinical thresholds for microalbuminuria used in people with diabetes 3.5 mg/mol for women and 2.5 mg/mol for men. Identification of independent predictors of recurrent MACE was performed by backwards stepwise logistic regression, commencing with all information available for the complete dataset, representing all information that would routinely be available to the clinician in the clinic setting. Where multiple related measures were available , the variable that accounted for the greatest degree of variance in a univariate analysis was included. Fractional polynomial regression modelling was used to identify sex-specific thresholds of ACR to define ‘microalbuminuria’ in the context of stroke risk stratification. Prediction of events were calculated using a Cox proportional HR with time to first MACE as the primary outcome. In keeping with the recommendations of Cupples et alData were treated as continuous variables wherever possible to maximise power. All normally distributed data are presented as mean±SD. Skewed data were appropriately transformed and presented as geometric mean scores (with 95% CIs). Statistical significance for categorical variables was calculated using the χA total of 2408 patients with a diagnosis of definite or probable minor stroke/TIA were recruited; 149 participants were subsequently excluded after the diagnosis was revised to that of a stroke mimic, 8 withdrew consent and a further 49 (2%) were excluded due to intercurrent urinary tract infection rendering the assessment of ACR invalid. No patient was lost to follow-up. Baseline characteristics are presented in Over 90 days, 151 primary outcome events (MACE) occurred in 144 participants (6.7% of patients) including 8 cardiovascular deaths. All MACE were atherosclerotics with no haemorrhagic events occurring in the 3 months. There were also eight non-cardiovascular deaths. Within 7 days, there were 38 MACE in 36 participants. Those with MACE were more likely to have had previous stroke/TIA, however, with this exception, there were no significant differences in routinely collected clinic data . There wFractional polynomial regression modelling identified the sex-specific threshold of risk for ACR aligned with the currently accepted thresholds for microalbuminuria used in people with diabetes of 2.5 mg/mmol for men and 3.5 mg/mol for women. These thresholds identified 562 participants  with microalbuminuria. These participants were more likely than those with a low ACR to experience a primary outcome event (HR 1.66 (95% CI 1.19 to 2.34); p=0.003) and had a greater than fivefold increase in 90-day mortality (HR 5.44 (1.86–15.90); p=0.0003). Alone, however, microalbuminuria did not have sufficient positive or negative predictive value to justify its clinical utility : 9.1%, specificity: 75.4%).In a stepwise multivariate model, the only independent predictors of MACE were elevated ACR, age >80 years and history of stroke or TIA. Similar variance within the model was explained microalbuminuria  as being older than 80 years of age and history of stroke/TIA, so they were afforded equal weighting. This generated a 4-point scale between 0 and 3 , with the lowest risk participants having a score of 0 . There w10.1136/bmjopen-2020-043253.supp1Supplementary dataThe high-risk group was older, with higher systolic blood pressure and pulse pressure compared with the low-risk group . The 90-One thousand and forty-nine participants were reviewed within 48 hours of their index event. For these individuals, the predictive role of the APA score was numerically superior  than those who were seen with a longer delay. There was no significant interaction between time from event to appointment and predictive role of the APA score.To validate the APA score, we applied the tool to the individuals who participated in the pilot study but did not contribute their data to this dataset. Details of this pilot study have been published elsewhere.In this large UK multicentre prospective study with blinded outcome adjudication, we have demonstrated the utility of an elevated ACR, measured using a simple point-of-care analyser, in combination with simple clinical data (age and history of stroke or TIA) in the prediction of MACE and death. This differentiates between low-risk and high-risk individuals. Specifically, >90% of individuals were identified as low risk with<1.5% risk of a MACE or death over the next 7 days, increasing to 5.7% by 90 days, whereas 9.9% of the population were identified as high risk with a 12.4% event rate over 90 days. Although only a small number, our findings were validated in a separate historic population who participated in the pilot study.The utility of APA scoring may go beyond simply guiding conversations regarding prognosis in a clinic setting. The Platelet-Oriented Inhibition in New TIA and Minor Ischaemic Stroke (POINT) TrialWhen considering the populations identified by the APA score, there are similarities to the ABCD2 score. Indeed, the demographics of the high-risk group were older, with higher blood pressure and a trend towards more diabetes. We would suggest, that microalbuminuria represents vascular susceptibility to the adverse consequences of hypertension and diabetes rather than simply an indication of the presence of hypertension and/or diabetes in general. This would explain why the APA score predicted further MACE and total mortality at 7 and 90 days, whereas in our large sample the ABCD2 score did not.Elevated ACR is a recognised marker of generalised endothelial and microvascular dysfunction. The increased filtration of albumin through the renal glomerular filtration barrier is thought to be due to changes in the chemical and physical properties of this endothelial barrier and its glycocalyx.Although unlikely to be mechanistically involved in the progression of disease, the question as to whether it is a good surrogate of therapeutic effect remains. Improving systemic inflammation, such as through the pleiotropic effect of statins, has been associated with simultaneous improvements in urinary albumin excretion and cardiovascular event rates in patients with elevated high sensitivity C reactive protein.Recurrent event rate was lower than anticipated from our pilot study and contemporaneous clinical trials such as CHANCE (11.7% in the control arm),There is no consensus on the gold standard for diagnosing TIA in a rapid access stroke clinic.Finally, the use of a single urinary ACR sample obtained during the clinic assessment using a near patient testing kit is not as robust as a sample tested in a central laboratory. Although there is significant diurnal variability in urinary AER within individuals, potentially limiting the scientific or mechanistic merit of the study compared with studies using overnight or 24-hour urine collection, the pragmatic design makes this study more applicable to general clinic populations. Furthermore, the relatively low cost of the near patient test kit makes it accessible in most emergency settings, in primary and secondary care.We have demonstrated for the first time the potential utility of a single point-of-care test of urinary ACR in patients presenting with TIA and minor stroke as a prediction tool to assist triaging patients. When used in combination with patient age and history of stroke or TIA, this generated a risk stratification score  that could reliably identify the large proportion of patients with a low risk of recurrent vascular event over the next 90 days. The value of ACR that is most predictive is aligned with the conventional definition of microalbuminuria used in diabetes, suggesting that in clinical practice the existing technology for evaluating microalbuminuria including urine dipsticks may be used. A high APA score was associated with a doubling of risk of MACE outcome and a fivefold increase in mortality. This APA score was validated in an independent population. We believe the APA score may represent a practical tool for clinicians engaged in the acute ambulatory assessment of TIA and minor stroke, to assist in the assessment of risk:benefit ratio for the use dual antiplatelet therapy or to admit for more urgent investigation. Further work is required to determine whether the increase in ACR represents a therapeutic target or solely a prognostic indicator.


Currently an exact definition of what is required for a monoclonal antibody to be classed as a broadly neutralising antibody (bnAb) has not yet been established. This has led to hundreds of antibodies with varying neutralisation breadth being studied and has given insight into antibody maturation pathways and epitopes targeted. However, even with this knowledge, immunisation studies and vaccination trials to date have had limited success in eliciting antibodies with neutralisation breadth. For this reason there is a growing need to identify factors specifically associated with bnAb development, yet to do this a set of criteria is necessary to distinguish bnAbs from non-bnAbs. This review aims to define what it means to be a HIV-1 bnAb by comparing neutralisation breadth, genetic features and epitopes of bnAbs, and in the process highlights the challenges of comparing the array of antibodies that have been isolated over the years.Since their discovery, antibodies capable of broad neutralisation have been at the forefront of HIV-1 research and are of particular interest due to  Initiew cells . High muew cells . After aew cells . Infantsew cells . This haew cells . Individl models –22 and sl models . This suThe first bnAbs were discovered in the early 1990’s, and since then over 300 antibodies described as bnAbs and their lineage members have been isolated . These h50 titre of 300, across four clades  with an ICr clades . The autr clades . Furtherr clades , 47. Curr clades , 48, 49.r clades . An alter clades , 52, ther clades –56. Studr clades , 57–59, This review will focus on a subset of monoclonal antibodies isolated from HIV-1 infected adults that have been previously tested for neutralisation and subsequently referred to as bnAbs. The Los Alamos National Lab (LANL) HIV database  was usedvia single B cell cloning following either single B cell culture or antigen-specific sorting [reviewed in  led to a second generation of more potent bnAbs being isolated iewed in ]. More riewed in . Followiiewed in , 61. HowThe tier of a virus is important because this defines the sensitivity to antibody neutralisation , and hasThe clade of the virus is also an important factor to consider, although there is limited data on clade-based differences in neutralisation sensitivity . HoweverThe establishment of standard PV panels that take the viral tier and clade into account has provided a consistent way to screen for broadly neutralising antibody responses and also offers a way to compare antibody neutralisation breadth. The 6 PV standard panel and 12 PV global panel consist almost entirely of tier 2 viruses from multiple clades that were shown to be representative of larger panels of viruses from the global epidemic , 72, thuEven with these standard virus panels available it is clear that not all reported bnAbs have been tested against them and that the panels used to define neutralisation breadth by HIV researchers continue to vary  and have50 of 0.02-4.3 µg/ml   . Althougd VRC01) . This apd VRC01) . In the d VRC01) , 85. Ongd VRC01) –88] willd VRC01) , althougDuring affinity maturation the introduction of SHM into immunoglobulin genes by activation-induced cytidine deaminase (AID) is random and only B cells with improved antigen binding are selected to receive signals for survival . Thus, rde novo response  with SHM typically accumulating in CDR loops for improved interaction with antigen. Mutations are less tolerated in the FWRs, which support the three CDR loops per antibody chain, as changes in these regions are more likely to adversely affect the overall structure of the antibody. However, unlike most antibodies some HIV-1 bnAbs have a high number of FWR mutations that appear to be essential for their broad and potent neutralisation, by increasing flexibility of binding or through contacts with the antigen , 102. OnThe length of the CDR loops can also affect antigen binding, and the heavy chain CDR3 (CDRH3) in particular is important for determining antigen specificity because it contains the most sequence diversity due to VDJ gene recombination in the pre-B cell . During via their CDRH2  differ depending on the site being targeted on the HIV-1 Env and their mode of binding. Although HIV bnAbs have a high VH mutation and/or a long CDRH3, an increase in VH mutation did not correlate with increased breadth for any bnAbs and only an increase in CDRH3 length correlated with increased neutralisation breadth for those targeting the high mannose patch.The limited number of properly folded, functional Env trimers present on the surface of each virion is thought to be one of the many ways HIV is able to evade the immune system . UnproceHIV initially requires interactions with CD4 on the host cell to gain entry, this is mediated by the CD4 receptor binding site (CD4bs) on the Env trimer and is therefore a functionally conserved region and a site of vulnerability. The CD4bs is situated on the gp120 subunit in a recessed hydrophobic pocket at the interface of the outer and inner domains , and is The majority of CD4bs nAbs and bnAbs contact the highly conserved residue D368 on the CD4 binding loop , with on324GDIR327) linear sequence at the base of the V3 loop is masked by the V1/V2 domain as well as the N-linked glycan supersite  in the closed (pre-fusion) conformation of Env, typical of most circulating tier 2 viruses , which are involved in co-receptor binding, and the more variable stem . However viruses , 143. It viruses , 144. Th viruses . Often m viruses –150. The viruses , 146.Finally, the V1/V2 domain is another site on the gp120 subunit of Env that is targeted by the humoral response, and although this site itself is not directly involved in viral entry the V1/V2 loops are necessary to shield the region involved in co-receptor binding until it is required , 152. NeOverall, although the binding footprint of HIV bnAbs and nAbs are often similar, the angle of approach or specific residues contacted differ to enable bnAbs to neutralise a broad spectrum of viruses. In addition, bnAbs are able to accommodate glycans or even incorporate them into their epitope through the acquisition of beneficial mutations to allow a degree of flexibility in their binding. However even bnAbs against the same region on Env can vary in the residues that they engage, and whilst the epitopes of bnAbs have been well characterised the epitopes of nAbs have been studied less, thus making it challenging to predict the neutralisation breadth of an antibody based on epitope alone.50 ≤3.6 µg/ml. In addition, bnAbs have a wide range of VH mutation (9-43%) and CDRH3 length (10-37AAs) but overall these features did not correlate with an increase in neutralisation breadth. Finally, it is hard to distinguish bnAbs solely on the epitope targeted because their footprints can, to a degree, overlap with those of nAbs.HIV bnAbs are of particular interest in HIV-1 research due to many studies demonstrating their potential to provide protection, and thus have been extensively studied. However, despite the vast amount of knowledge acquired about these antibodies there is no set of criteria to determine or define what it means to be a bnAb. This question has been addressed here by comparing bnAbs based on their neutralisation breadth and potency, their mutational frequencies and CDRH3 lengths and the epitopes targeted. The majority of bnAbs previously tested against the 118 multi-clade PV panel can neutralise ≥7 clades/CRFs and achieve >30% neutralisation breadth with a geomean ICIn conclusion, while some traits are common to many bnAbs  it would not have been possible to identify current HIV-1 bnAbs based on these alone. Without prior knowledge of their neutralisation breadth these bnAbs are not distinctive enough from those of nAbs with limited neutralisation breadth. Regrettably this prevents new bnAbs from being discovered directly from repertoire sequencing  or from SAG performed the literature searches, data collection and wrote the manuscript. LEM revised the final version of the manuscript. All authors contributed to the article and approved the submitted version.LEM is supported by an MRC Career Development Award MR/R008698/1 and receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 757601).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.