Mental illness and COVID-19 vaccination: a multinational investigation of observational & register-based data (preprint)

Background: Individuals with mental illness are at higher risk of severe COVID-19 outcomes. However, previous studies on the uptake of COVID-19 vaccination in this population have reported conflicting results. Therefore, we aimed to investigate the association between mental illness and COVID-19 vaccination uptake, using data from five countries. Methods: Data from seven cohort studies (N=325,298), and the Swedish registers (8,080,234), were used to identify mental illness and COVID-19 vaccination uptake. Multivariable modified Poisson regression models were conducted to calculate the prevalence ratio (PR) and 95% CIs of vaccination uptake among individuals with v.s. without mental illness. Results from the cohort studies were pooled using random effects meta-analyses. Findings: Most of the meta-analyses performed using the COVIDMENT study population showed no significant association between mental illness and vaccination uptake. In the Swedish register study population, we observed a very small reduction in the uptake of both the first (prevalence ratio [PR]: 0.98, 95% CI: 0.98-0.99, p<0.001) and second dose among individuals with mental illness; the reduction was however greater among those not using pyschiatric medication (PR: 0.91, 95% CI: 0.91-0.91, p<0.001). Conclusions: The high uptake of COVID-19 vaccination observed among individuals with most types of mental illness highlights the comprehensiveness of the vaccination campaign , however lower levels of vaccination uptake among subgroups of individuals with unmedicated mental illness warrants attention in future vaccination campaigns.

Mental illness and COVID-19 vaccination: a multinational investigation of observational & register-based data (preprint)

Background: Individuals with mental illness are at higher risk of severe COVID-19 outcomes. However, previous studies on the uptake of COVID-19 vaccination in this population have reported conflicting results. Therefore, we aimed to investigate the association between mental illness and COVID-19 vaccination uptake, using data from five countries. Methods: Data from seven cohort studies (N=325,298), and the Swedish registers (8,080,234), were used to identify mental illness and COVID-19 vaccination uptake. Multivariable modified Poisson regression models were conducted to calculate the prevalence ratio (PR) and 95% CIs of vaccination uptake among individuals with v.s. without mental illness. Results from the cohort studies were pooled using random effects meta-analyses. Findings: Most of the meta-analyses performed using the COVIDMENT study population showed no significant association between mental illness and vaccination uptake. In the Swedish register study population, we observed a very small reduction in the uptake of both the first (prevalence ratio [PR]: 0.98, 95% CI: 0.98-0.99, p<0.001) and second dose among individuals with mental illness; the reduction was however greater among those not using pyschiatric medication (PR: 0.91, 95% CI: 0.91-0.91, p<0.001). Conclusions: The high uptake of COVID-19 vaccination observed among individuals with most types of mental illness highlights the comprehensiveness of the vaccination campaign , however lower levels of vaccination uptake among subgroups of individuals with unmedicated mental illness warrants attention in future vaccination campaigns.

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.

Loneliness in Scottish Adolescents Before, During and After the First National UK Lockdown (preprint)

PurposeWhile lockdowns are essential in fighting the COVID-19 pandemic, school closures may increase risk of loneliness in adolescents. In this paper, we investigate how lockdown affects loneliness in adolescents and potential protective factors. MethodsThis study examines 768 young people in Scotland age 12 to 17, who took part in TeenCovidLife surveys during and after the first national lockdown in 2020. Survey 1 ran from May to July 2020, during the first school closures period. Survey 2 ran from August to October 2020, after schools reopened for most pupils. Participants reported current loneliness and pre-pandemic loneliness. Participants also completed self-report measures of resilience and social support.ResultsLoneliness increased from pre-pandemic levels during lockdown and then decreased when restrictions eased. However, loneliness remained significantly higher post-lockdown compared to pre-pandemic. Predictors of loneliness were assessed with ordinal logistic regression. Greater resilience was associated with lower loneliness at all stages of lockdown, but older teens were more likely to be lonely post-lockdown. Greater peer support was associated with lower loneliness before lockdown. However, during lockdown, family support was associated with lower loneliness. After schools re-opened, participants with greater social support from school staff were 15% less likely to be lonely.ConclusionLoneliness was higher during lockdown than before lockdown. Moreover, loneliness remained higher after lockdown than before lockdown. However, resilience and social support in school may protect against this lingering loneliness. Resilience training and school-based social support programmes may reduce the long-term effects of lockdown on well-being in young people.

Development and validation of DNA Methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes (preprint)

Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of CpGs one-at-a-time, and binary outcomes. We present a flexible approach (via an R package, MethylPipeR ) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set n cases =374, n controls =9,461; test set n cases =252, n controls =4,526) our best-performing model (Area Under the Curve (AUC)=0.872, Precision Recall AUC (PRAUC)=0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC=0.839, PRAUC=0.227). Replication was observed in the German-based KORA study (n=1,451, n cases = 142, p=1.6×10 -5 ).

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.

Brief Report: Predictors of Adolescent Mental Health and Wellbeing During the COVID-19 Pandemic (preprint)

Purpose: This study explored predictors of COVID-19-related stress and wellbeing of Scottish adolescents during the COVID-19 lockdown to identify potentially malleable risk and protective factors. Methods: 5,548 participants were surveyed regarding stress, loneliness, wellbeing, schoolwork, support from school, and interaction with friends and family. Multiple linear regressions within a structural equation modelling framework were fit to predict COVID-19-related stress and wellbeing during the UK’s first lockdown. Results: Loneliness, variables related to the ability to continue with schoolwork, and perceived support from school were important predictors of greater COVID-19-related stress and wellbeing during the first lockdown. Female adolescents were also more likely to show higher stress and poorer wellbeing. Conclusions: Facilitating meaningful social interaction and ensuring the ability to continue with schoolwork, and providing social support from school should be priority strategies to help protect the mental health and wellbeing of secondary school students during lockdowns and other disruptions to school attendance.

Face covering adherence is positively associated with better mental health and wellbeing: a longitudinal analysis of the CovidLife surveys (preprint)

Face masks or coverings are effective at reducing airborne infection rates, yet pandemic mitigation measures, including wearing face coverings, have been suggested to contribute to reductions in quality of life and poorer mental health. Longitudinal analyses of more than 11,000 participants across the UK found no association between lower adherence to face covering guidelines and poorer mental health. The opposite appears to be true. Even after controlling for behavioral, social, and psychological confounds, including measures of pre-pandemic mental health, individuals who wore face coverings "most of the time" or "always" had better mental health and wellbeing than those who did not. These results suggest that wearing face coverings more often will not negatively impact mental health.

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.

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility (preprint)

Epidemiological and genetic studies on COVID-19 are hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict potential COVID-19 cases using cross-sectional self-reported disease-related symptoms. Using a previously reported COVID-19 prediction model, we show that it is possible to conduct a GWAS on predicted COVID-19 which benefits from a larger sample size in order to gain new insights into the genetic susceptibility of the disease. Furthermore, we find suggestive evidence that genetic variants for other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. Our findings demonstrate the added value of using self-reported symptom assessments to quickly monitor novel endemic viral outbreaks in a scenario of limited testing. Should there be another outbreak of a novel infectious disease, then we recommend repeatedly collecting data of disease-related symptoms.

Mental health during the COVID-19 pandemic in two longitudinal UK population cohorts (preprint)

Background: The impact of COVID-19 on mental health is unclear. Evidence from longitudinal studies with pre pandemic data are needed to address (1) how mental health has changed from pre-pandemic levels to during the COVID-19 pandemic and (2), whether there are groups at greater risk of poorer mental health during the pandemic? Methods: We used data from COVID-19 surveys (completed through April/May 2020), nested within two large longitudinal population cohorts with harmonised measures of mental health: two generations of the Avon Longitudinal Study of Parents and Children (ALPSAC): the index generation ALSPAC-G1 (n= 2850, mean age 28) and the parents generation ALSPAC-G0 (n= 3720, mean age = 59) and Generation Scotland: Scottish Family Health Study (GS, (n= 4233, mean age = 59), both with validated pre-pandemic measures of mental health and baseline factors. To answer question 1, we used ALSPAC-G1, which has identical mental health measures before and during the pandemic. Question 2 was addressed using both studies, using pre-pandemic and COVID-19 specific factors to explore associations with depression and anxiety in COVID-19. Findings: In ALSPAC-G1 there was evidence that anxiety and lower wellbeing, but not depression, had increased in COVID-19 from pre-pandemic assessments. The percentage of individuals with probable anxiety disorder was almost double during COVID-19: 24% (95% CI 23%, 26%) compared to pre-pandemic levels (13%, 95% CI 12%, 14%), with clinically relevant effect sizes. In both ALSPAC and GS, depression and anxiety were greater in younger populations, women, those with pre-existing mental and physical health conditions, those living alone and in socio-economic adversity. We did not detect evidence for elevated risk in key workers or health care workers. Interpretation: These results suggest increases in anxiety and lower wellbeing that may be related to the COVID-19 pandemic and/or its management, particularly in young people. This research highlights that specific groups may be disproportionally at risk of elevated levels of depression and anxiety during COVID-19 and supports recent calls for increasing funds for mental health services. Funding: The UK Medical Research Council (MRC), the Wellcome Trust and University of Bristol.

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.