Score: 18.7, Published: 2024-02-15
DOI: 10.1101/2024.02.14.24302768
IntroductionUnderstanding the potential adverse effects of the Covid-19-pandemic remains a challenge for public mental health. In this regard, the differentiation between potential consequences of actual infection with SARS-CoV-2 and the subjective burden of the pandemic due to measures and restrictions to daily life remains elusive. MethodsHere we investigated the differential association between infection with SARS-Cov-2 and subjective burden of the pandemic in a study cohort of 7601 participants from the German population-based cohort for digital health research (DigiHero), who were recruited between March 4th and April 25th 2022. Data was collected using the online survey tool LimeSurvey(R) between March and October 2022 in consecutive surveys, which included questionnaires on infection status and symptoms following COVID-19 as well as retrospective assessment of the subjective burden of the pandemic. ResultsWe observed an association of a past SARS-CoV-2 infection on deteriorated mental health related symptoms, whereas no association or interaction with burden of the pandemic occurred. The association was driven by participants with persistent symptoms 12 weeks after acute infection. On a symptom specific level, neuropsychiatric symptoms such as exhaustion and fatigue, concentration deficits as well as problems with memory function were the primary drivers of the association. ConclusionOur findings underscore the impact of SARS-CoV-2 infections on mental health in patients suffering from ongoing symptoms 12 weeks after infection. As the association between SARS-CoV-2 infection and mental health appeared more pronounced in populations with higher vulnerability for mental disorders, increased attention should be dedicated towards these subgroups regarding the prevention of infection.
Score: 28.6, Published: 2024-02-05
DOI: 10.1101/2024.02.04.24301363
ObjectiveTo systematically evaluate timely reporting of clinical trial results at medical universities and university hospitals in the Nordic countries. Study Design and SettingIn this cross-sectional study, we included trials (regardless of intervention) registered in the EU Clinical Trials Registry and/or ClinicalTrials.gov, completed 2016-2019, and led by a university with medical faculty or university hospital in Denmark, Finland, Iceland, Norway, or Sweden. We identified summary results posted at the trial registries, and conducted systematic manual searches for results publications (e.g., journal articles, preprints). We present proportions with 95% confidence intervals (CI), and medians with interquartile range (IQR). Protocol: https://osf.io/wua3r ResultsAmong 2,113 included clinical trials, 1,638 (77.5%, 95%CI 75.9-79.2%) reported any results during our follow-up; 1,092 (51.7%, 95%CI 49.5-53.8%) reported any results within 2 years of the global completion date; and 42 (2%, 95%CI 1.5-2.7%) posted summary results in the registry within 1 year. Median time from global completion date to results reporting was 698 days (IQR 1,123). 856/1,681 (50.9%) of ClinicalTrials.gov-registrations were prospective. Denmark contributed approximately half of all trials. Reporting performance varied widely between institutions. ConclusionMissing and delayed results reporting of academically-led clinical trials is a pervasive problem in the Nordic countries. We relied on trial registry information, which can be incomplete. Institutions, funders, and policy makers need to support trial teams, ensure regulation adherence, and secure trial reporting before results are permanently lost. What is new?- Many Nordic registered clinical trials were reported late or not at all. - Almost one in four trials remained unreported at the end of our search period. - About half of registered trials had reported results two years after completion. - Only 2% of trials posted summary results in the registry one year after completion. - Concerted action is needed to improve reporting of Nordic clinical trials.
Score: 17.4, Published: 2024-02-08
DOI: 10.1101/2024.02.08.24302486
BackgroundStudies into the bidirectional relationship between sleep and long COVID have been limited by retrospective pre-infection sleep data and infrequent post-infection follow-up. We therefore used prospectively collected monthly data to evaluate how pre-infection sleep characteristics affect risk of long COVID, and to track changes in sleep duration during the year after SARS-CoV-2 infection. MethodsCOVIDENCE UK is a prospective, population-based UK study of COVID-19 in adults. We included non-hospitalised participants with evidence of SARS-CoV-2 infection, and estimated odds ratios (ORs) for the association between pre-infection sleep characteristics and long COVID using logistic regression, adjusting for potential confounders. We assessed changes in sleep duration after infection using multilevel mixed models. We defined long COVID as unresolved symptoms at least 12 weeks after infection. We defined sleep quality according to age-dependent combinations of sleep duration and efficiency. COVIDENCE UK is registered with ClinicalTrials.gov, NCT04330599. FindingsWe included 3994 participants in our long COVID risk analysis, of whom 327 (8.2%) reported long COVID. We found an inverse relationship between pre-infection sleep quality and risk of long COVID (medium vs good quality: OR 1.37 [95% CI 1.04-1.81]; medium-low vs good: 1.55 [1.12-2.16]; low vs good: 1.94 [1.11-3.38]). Greater variability in pre-infection sleep efficiency was also associated with long COVID (OR per percentage-point increase 1.06 [1.01-1.11]). We assessed post-infection sleep duration in 6860 participants, observing a 0.11 h (95% CI 0.08-0.13) increase in the first month after infection compared with pre-infection, with larger increases for more severe infections. After 1 month, sleep duration largely returned to pre-infection levels, although fluctuations in duration lasted up to 6 months after infection among people reporting long COVID. InterpretationOur findings highlight the bidirectional relationship between sleep and long COVID. While poor-quality sleep before SARS-CoV-2 infection associates with increased risk of long COVID thereafter, changes in sleep duration after infection in these non-hospitalised cases were modest and generally quick to resolve. FundingBarts Charity.
Score: 402.0, Published: 2024-02-04
DOI: 10.1101/2024.02.02.24302123
Two papers authored by the same research group were published in academic journals in October 2023, both of which simulate counterfactual COVID-19 cases and deaths using transmission models. One paper estimates that the COVID-19 cases and deaths from Feb 17 to Nov 30, 2021 in Japan would have been as many as 63.3 million and 364 thousand respectively had the vaccination not been implemented, where the 95% confidence interval is claimed to be less than 1% of the estimated value. It also claims that the cases and deaths could have been reduced by 54% and 48% respectively had the vaccination been implemented 14 days earlier. The other paper estimates that the number of cases in early 2022, Tokyo would have been larger than the number of populations in the age group under 49 in the absence of the vaccination program. In this paper, we reexamine the results given by these papers to find that the simulation results do not explain the real-world data in Japan including prefectures with early/late vaccination schedules. The cause of discrepancy is identified as low reliability of model parameters that immensely affect the simulation results of case and death counts. Leaders of public healthcare are required to discern the reliability and credibility of simulation studies and to prepare for variety of possible scenarios when reliable predictions are not available.
Score: 7.8, Published: 2024-02-11
DOI: 10.1101/2024.02.10.24302617
BackgroundMolecular ageing clocks estimate an individuals biological age. Our aim was to compare multiple machine learning algorithms for developing ageing clocks from nuclear magnetic resonance (NMR) spectroscopy metabolomics data. To validate how well each ageing clock predicted age-related morbidity and lifespan, we assessed their associations with multiple health indicators (e.g., telomere length and frailty) and all-cause mortality. MethodsThe UK Biobank is a multicentre observational health study of middle-aged and older adults. The Nightingale Health platform was used to quantify 168 circulating plasma metabolites at the baseline assessment from 2006 to 2010. We trained and internally validated 17 machine learning algorithms including regularised regression, kernel-based methods and ensembles. Metabolomic age (MileAge) delta was defined as the difference between predicted and chronological age. ResultsThe sample included 101,359 participants (mean age = 56.53 years, SD = 8.10). Most metabolite levels varied by chronological age. The nested cross-validation mean absolute error (MAE) ranged from 5.31 to 6.36 years. 31.76% of participants had an age-bias adjusted MileAge more than one standard deviation (3.75 years) above or below the mean. A Cubist rule-based regression model overall performed best at predicting health outcomes. The all-cause mortality hazard ratio (HR) comparing individuals with a MileAge delta more than one standard deviation above and below the mean was HR = 1.52 (95% CI 1.41-1.64, p < 0.001) over a median follow-up of 13.87 years. Individuals with an older MileAge were frailer, had shorter telomeres, were more likely to have a chronic illness and rated their health worse. ConclusionsMetabolomic ageing clocks derived from multiple machine learning algorithms were robustly associated with health indicators and mortality. Our metabolomic ageing clock (MileAge) derived from a Cubist rule-based regression model can be incorporated in research, and may find applications in health assessments, risk stratification and proactive health tracking.
Score: 6.5, Published: 2024-02-17
DOI: 10.1101/2024.02.16.24302885
Immune dysfunction is implicated in the aetiology of psychiatric, neurodevelopmental, and neurodegenerative conditions, but the issue of causality remains unclear impeding attempts to develop new interventions. We have tested evidence for causality for 735 immune response-related biomarkers on 7 neuropsychiatric conditions, using cutting-edge genomic causal inference methods (Mendelian randomization and genetic colocalization) applied to genomic data on protein and gene expression across blood and brain. We provide robust evidence of causality for 21 biomarkers, including two previously unreported (LATS1, and FCN1), confirming a role of both brain specific and systemic immune response in the pathogenesis of several neuropsychiatric conditions especially schizophrenia, Alzheimers disease, depression, and bipolar disorder. Furthermore, 18 of the identified biomarkers are therapeutically tractable, including ACE, TNFRSF17, and CD40, with drugs approved or in advanced clinical trials, offering a potential opportunity for drug repurposing.
Score: 76.7, Published: 2024-01-22
DOI: 10.1101/2024.01.20.24301525
Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,461 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. Our primary outcome measure was a PASC computable phenotype. Secondary outcomes were the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.99, 95% confidence interval [CI] 0.96-1.01). However, its effect varied across the cognitive (RR = 0.85, 95% CI 0.79-0.90), fatigue (RR = 0.93, 95% CI 0.89-0.96), and respiratory (RR = 0.99, 95% CI 0.95-1.02) symptom clusters, suggesting that Paxlovid treatment may help prevent post-acute cognitive and fatigue symptoms more than others.
Score: 8.1, Published: 2024-02-06
DOI: 10.1101/2024.02.05.24302352
BackgroundLong COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication. MethodsIn an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and pre-existing major depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use at the time of COVID-19 infection and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before COVID-19 infection and not ending before COVID-19 infection. To minimize bias, we estimated the causal associations of interest using a nonparametric approach, targeted maximum likelihood estimation, to aggressively adjust for high-dimensional covariates. ResultsWe analyzed a sample (n = 506,903) of patients with a diagnosis of major depressive disorder before COVID-19 diagnosis, where 124,928 (25%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.90, 95% CI (0.86, 0.94)). ConclusionThese findings suggest that SSRI use during COVID-19 infection may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID.
Score: 8.5, Published: 2024-01-31
DOI: 10.1101/2024.01.30.24302040
BackgroundPost-COVID complications are emerging as a global public health crisis. Effective prevention strategies are needed to inform patients, clinicians and policy makers, and to reduce their cumulative burden. We aimed to investigate whether a habitual healthy lifestyle predated pandemic is associated with lower risks of multisystem sequelae and other adverse outcomes of COVID-19, and whether the potential protective effects are independent of pre-existing comorbidities. MethodsThe prospective population-based cohort study enrolled participants with SARS-CoV-2 infection confirmed by a positive polymerase chain reaction test result between March 1, 2020, and March 1, 2022. Participants with no history of the related outcome one year before infection were included and followed up for 210 days. Exposures included ten modifiable healthy lifestyle factors including past or never smoking, moderate alcohol intake ([≤]4 times week), body mass index <30 kg/m2, at least 150 minutes of moderate or 75 minutes of vigorous physical activity per week, less sedentary time (<4 hours per day), healthy sleep duration (7-9 hours per day), adequate intake of fruit and vegetables ([≥]400 g/day), adequate oily fish intake ([≥]1 portion/week), moderate intake of red meat ([≤]4 portions week) and processed meat ([≤]4 portions week). Outcomes included multisystem COVID-19 sequelae (consisting of 75 diseases/symptoms in 10 organ systems), death, and hospital admission following SARS-CoV-2 infection, confirmed by hospital inpatient and death records. Risk was reported in relative scale (hazard ratio [HR]) and absolute scale (absolute risk reduction [ARR]) during both the acute (the first 30 days) and post-acute (30-210 days) phases of infection using Cox models. FindingsA total of 68,896 participants (mean [SD] age, 66.6 [8.4]; 32,098 women [46.6%]) with COVID-19 were included. A favorable lifestyle (6-10 healthy lifestyle factors; 46.4%) was associated with a 36% lower risk of multisystem sequelae of COVID-19 (HR, 0.64; 95% CI, 0.58-0.69; ARR, 7.08%; 95% CI, 5.98-8.09), compared with unfavorable lifestyle (0-4 factors; 12.3%). Risk reductions were observed across all 10 prespecified organ systems including cardiovascular, coagulation, metabolic and endocrine, gastrointestinal, kidney, mental health, musculoskeletal, neurologic, and respiratory disorders, and general symptoms of fatigue and malaise. This beneficial effect was largely attributable to direct effects of healthy lifestyle, with mediation proportion ranging from 44% to 93% across organ systems. A favorable lifestyle was also associated with lower risk of post-COVID death (HR, 0.59; 95% CI, 0.52-0.66; ARR, 1.99%; 95% CI, 1.61-2.32) and hospitalization (HR, 0.78; 95% CI, 0.73-0.84; ARR, 6.14%; 95% CI, 4.48-7.68). These associations were observed after accounting for potential misclassification of lifestyle factors, and during acute and post-acute infection, in those tested positive in the hospital and community setting, and independent of vaccination status or SARS-CoV-2 variant. InterpretationAdherence to a healthy lifestyle predated pandemic was associated with substantially lower risk of complications across organ systems, death, and hospitalization following COVID-19, regardless of phases of infection, vaccination status, test setting, and SARS-CoV-2 variants, and independent of comorbidities. These findings illustrate the benefits of adhering to a healthy lifestyle to reduce the long-term adverse health consequences following SARS-CoV-2 infection. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and MEDLINE for articles published between March 1, 2020, and December 1, 2023, using the search terms "healthy lifestyle", "risk factor", "post-COVID condition", "long COVID", "post-acute sequelae", "prevention", "management", and "treatment", with no language restrictions. Previous evidence on the prevention and management of long COVID has mainly focused on vaccination and pharmaceutical approaches, including antivirals (e.g., molnupiravir and nirmatrelvir) and other drugs (e.g., metformin). Vaccination before infection or use of antivirals in selected high-risk patients during acute infection only partially mediates the risk of COVID-19 sequelae. Evidence for the non-pharmaceutical prevention strategies are lacking. We identified only two publications on the association between healthy lifestyle and post-COVID condition, and one meta-analysis of the risk factors for long COVID symptoms. A cross-sectional study of 1981 women suggested an inverse association between healthy lifestyle factors and self-reported symptoms following infection of non-Omicron variants, which was mainly driven by BMI and sleep duration. Another study suggested an inverse prospective association between healthy lifestyle prior to infection and post-COVID cardiovascular events. High BMI and smoking are risk factors for long COVID mainly in hospitalized patients. We did not find any study that assessed the association between a composite healthy lifestyle and subsequent post-COVID complications or sequelae across organ systems, hospitalization, and death. Added value of this studyIn a prospective, population-based cohort of 68,896 participants with COVID-19, adherence to a healthy lifestyle prior to infection was associated with a substantially lower risk of multisystem sequelae (by 20%-36%), death (by 26%-41%), and hospital admission (by 13%-22%) following COVID-19. The reduced risk of sequelae was evident across 10 prespecified organ systems, including cardiovascular, coagulation and hematologic, metabolic and endocrine, gastrointestinal, kidney, mental health, musculoskeletal, neurologic, and respiratory disorders, as well as general symptoms of fatigue and malaise. The reduced risk of multisystem sequelae, hospitalization, and death associated with a healthy lifestyle was consistently observed across participants, regardless of their vaccination status, disease severity, and major SARS-CoV-2 variants, and largely independent of relevant comorbidities. Adherence to a healthy lifestyle prior to infection was consistently and directly associated with reduced risk of sequelae and other adverse health outcomes following COVID-19. Implications of all the available evidenceThe inverse association of healthy lifestyle with multisystem sequelae was even larger than those observed in previous studies of pharmaceutical interventions in non-hospitalized patients. Considering the restricted scope of currently available therapies, such as antivirals (only selected patients at higher risk are qualified during the acute infection) and limited efficacy of vaccination in preventing long COVID, adherence to a healthy lifestyle, in combination with vaccination and, if necessary, potential medications, emerges as practical prevention and care strategies to mitigate the long-term health consequences of SARS-CoV-2 infection. These strategies are of significant clinical and public health importance in reducing the overall burden of post-COVID conditions and improving preparedness for future pandemics.
Score: 1050.2, Published: 2024-01-26
DOI: 10.1101/2023.08.03.23293612
BackgroundA large share of SARS-CoV-2 infections now occur among previously infected individuals. In this study, we sought to determine whether prior infection modifies disease severity relative to no prior infection. MethodsWe used data from first and second COVID-19 episodes in the National COVID Cohort Collaborative, a nationwide collection of de-identified electronic health records. We used nested logistic regressions of monthly cohorts weighted on the inverse probability of prior infection to assess risk of hospitalization, death, and increased severity in the first versus second infection cohorts. ResultsWe included a total of 2,058,274 individuals in the analysis, 147,592 of whom had two recorded infections. The impact of prior infection differed meaningfully between months. Prior infection was largely protective prior to March 2022, with odds ratios (ORs) as low as 0.66 (95% confidence interval: 0.51 to 0.86) in November 2021 for hospitalization. and as low as 0.23 (0.06 to 0.86) in June 2021 for death. However, prior infection was associated with an increased risk of hospitalization and death, mostly after March 2022 when the ORs were as high as 1.87 (1.26 to 2.80) and 2.99 (1.65 to 5.41) in April 2022, respectively. The overall OR for more severe disease was 1.06 (1.03 to 1.10) among previously infected individuals. ConclusionIn the pandemics first two years, previously infected patients generally had less severe disease than people without prior infection. During the Omicron era, however, previously infected patients had the same or worse severity of disease as patients without prior infection.