Elevated symptoms of depression and anxiety among family members and friends of critically ill COVID-19 patients - An observational study of five cohorts across four countries (preprint)

Background. Little is known regarding the mental health impact of having a significant person (family member and/or close friend) with COVID-19 of different severity. Methods. The study included five prospective cohorts from four countries (Iceland, Norway, Sweden, and the UK) with self-reported data on COVID-19 and symptoms of depression and anxiety during March 2020-March 2022. We calculated the prevalence ratio (PR) of depression and anxiety in relation to having a significant person with COVID-19 and performed a longitudinal analysis in the Swedish cohort to describe the temporal patterns of the results. Results. 162,237 and 168,783 individuals were included in the analysis of depression and anxiety, respectively, of whom 24,718 and 27,003 reported a significant person with COVID- 19. Overall, the PR was 1.07 (95% CI: 1.05-1.10) for depression and 1.08 (95% CI: 1.03-1.13) for anxiety among significant others of COVID-19 patients. The respective PRs for depression and anxiety were 1.04 (95% CI: 1.01-1.07) and 1.03 (95% CI: 0.98-1.07) if the significant person was never hospitalized, 1.15 (95% CI: 1.08-1.23) and 1.24 (95% CI: 1.14-1.34) if the patient was hospitalized, 1.42 (95% CI: 1.27-1.57) and 1.45 (95% CI: 1.31-1.60) if admitted to the ICU, and 1.34 (95% CI: 1.22-1.46) and 1.36 (95% CI: 1.22-1.51) if the significant person died. Individuals of hospitalized, ICU admitted, or deceased patients showed higher prevalence of depression and anxiety during the entire 12 months after the COVID-19 diagnosis of the significant person. Conclusions. Close friends and family members of critically ill COVID-19 patients show elevated prevalence of depression and anxiety throughout the first year after the diagnosis.

Acute COVID-19 severity and 16-month mental morbidity trajectories in patient populations of six nations (preprint)

BACKGROUND The aim of this multinational study was to assess the development of adverse mental health symptoms among individuals diagnosed with COVID-19 in the general population by acute infection severity up to 16 months after diagnosis. METHODS Participants consisted of 247 249 individuals from seven cohorts across six countries (Denmark, Estonia, Iceland, Norway, Scotland, and Sweden) recruited from April 2020 through August 2021. We used multivariable Poisson regression to contrast symptom-prevalence of depression, anxiety, COVID-19 related distress, and poor sleep quality among individuals with and without a diagnosis of COVID-19 at entry to respective cohorts by time (0-16 months) from diagnosis. We also applied generalised estimating equations (GEE) analysis to test differences in repeated measures of mental health symptoms before and after COVID-19 diagnosis among individuals ever diagnosed with COVID-19 over time. FINDINGS A total of 9979 individuals (4%) were diagnosed with COVID-19 during the study period and presented overall with a higher symptom burden of depression (prevalence ratio [PR] 1.18, 95% confidence interval [95% CI] 1.03-1.36) and poorer sleep quality (1.13, 1.03-1.24) but not with higher levels of symptoms of anxiety or COVID-19 related distress compared with individuals without a COVID-19 diagnosis. While the prevalence of depression and COVID-19 related distress attenuated with time, the trajectories varied significantly by COVID-19 acute infection severity. Individuals diagnosed with COVID-19 but never bedridden due to their illness were consistently at lower risks of depression and anxiety (PR 0.83, 95% CI 0.75-0.91 and 0.77, 0.63-0.94, respectively), while patients bedridden for more than 7 days were persistently at higher risks of symptoms of depression and anxiety (PR 1.61, 95% CI 1.27-2.05 and 1.43, 1.26-1.63, respectively) throughout the 16-month study period. CONCLUSION Acute infection severity is a key determinant of long-term mental morbidity among COVID-19 patients.

A proteomic survival predictor for COVID-19 patients in intensive care (preprint)

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.

Risk factors for long COVID: analyses of 10 longitudinal studies and electronic health records in the UK (preprint)

The impact of long COVID is increasingly recognised, but risk factors are poorly characterised. We analysed questionnaire data on symptom duration from 10 longitudinal study (LS) samples and electronic healthcare records (EHR) to investigate sociodemographic and health risk factors associated with long COVID, as part of the UK National Core Study for Longitudinal Health and Wellbeing. Methods Analysis was conducted on 6,899 adults self-reporting COVID-19 from 45,096 participants of the UK LS, and on 3,327 cases assigned a long COVID code in primary care EHR out of 1,199,812 adults diagnosed with acute COVID-19. In LS, we derived two outcomes: symptoms lasting 4+ weeks and symptoms lasting 12+ weeks. Associations of potential risk factors (age, sex, ethnicity, socioeconomic factors, smoking, general and mental health, overweight/obesity, diabetes, hypertension, hypercholesterolaemia, and asthma) with these two outcomes were assessed, using logistic regression, with meta-analyses of findings presented alongside equivalent results from EHR analyses. Results Functionally limiting long COVID for 12+ weeks affected between 1.2% (age 20), and 4.8% (age 63) of people reporting COVID-19 in LS. The proportion reporting symptoms overall for 12+ weeks ranged from 7.8 (mean age 28) to 17% (mean age 58) and for 4+ weeks 4.2% (age 20) to 33.1% (age 56). Age was associated with a linear increase in long COVID between age 20-70. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]), poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma also had higher risk (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]), as did those categorised as overweight or obese (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) though associations for symptoms lasting 12+ weeks were less pronounced. Non-white ethnic minority groups had lower 4+ week symptom risk (LS: OR=0.32 [0.22-0.47]), a finding consistent in EHR. Associations were not observed for other risk factors. Few participants in the studies had been admitted to hospital (0.8-5.2%). Conclusions Long COVID is clearly distributed differentially according to several sociodemographic and pre-existing health factors. Establishing which of these risk factors are causal and predisposing is necessary to further inform strategies for preventing and treating long COVID.

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome (preprint)

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.

Genetic mechanisms of critical illness in Covid-19 (preprint)

The subset of patients who develop critical illness in Covid-19 have extensive inflammation affecting the lungs and are strikingly different from other patients: immunosuppressive therapy benefits critically-ill patients, but may harm some non-critical cases. Since susceptibility to life-threatening infections and immune-mediated diseases are both strongly heritable traits, we reasoned that host genetic variation may identify mechanistic targets for therapeutic development in Covid-19. GenOMICC (Genetics Of Mortality In Critical Care, genomicc.org) is a global collaborative study to understand the genetic basis of critical illness. Here we report the results of a genome-wide association study (GWAS) in 2790 critically-ill Covid-19 patients from 208 UK intensive care units (ICUs), representing >95% of all ICU beds. Random controls were drawn from three distinct UK population studies. We identify and replicate several novel genome-wide significant associations including variants chr19p13.3 (rs2109069, P = 3.98 x 10-12), within the gene encoding dipeptidyl peptidase 9 (DPP9), and at chr21q22.1 (rs2236757, P = 4.99 x 10-8) in the interferon receptor IFNAR2. Consistent with our focus on extreme disease in younger patients with less comorbidity, we detect a stronger signal at the known 3p21.31 locus than previous studies (rs73064425, P = 1.2 x 10-27). We identify potential targets for repurposing of existing licensed medications. Using Mendelian randomisation we found evidence in support of a causal link from low expression of IFNAR2, and high expression of TYK2, to life-threatening disease. Transcriptome-wide association in lung tissue revealed that high expression of the monocyte/macrophage chemotactic receptor CCR2 is associated with severe Covid-19. We detected genome-wide significant gene-level associations for genes with central roles in viral restriction (OAS1, OAS2, OAS3). These results identify specific loci associated with life-threatening disease, and potential targets for host-directed therapies. Randomised clinical trials will be necessary before any change to clinical practice.

Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics (preprint)

The COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high-throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks. Highlights- A completely redesigned clinical proteomics platform increases throughput and precision while reducing costs. - 27 biomarkers are differentially expressed between WHO severity grades for COVID-19. - The study highlights potential therapeutic targets that include complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling both upstream and downstream of interleukin 6.