Long-COVID fatigue is not predicted by pre-pandemic plasma IL-6 levels in mild COVID-19.

OBJECTIVE AND DESIGN: Fatigue is a prominent symptom in the general population and may follow viral infection, including SARS-CoV2 infection which causes COVID-19. Chronic fatigue lasting more than three months is the major symptom of the post-COVID syndrome (known colloquially as long-COVID). The mechanisms underlying long-COVID fatigue are unknown. We hypothesized that the development of long-COVID chronic fatigue is driven by the pro-inflammatory immune status of an individual prior to COVID-19. SUBJECTS AND METHODS: We analyzed pre-pandemic plasma levels of IL-6, which plays a key role in persistent fatigue, in N = 1274 community dwelling adults from TwinsUK. Subsequent COVID-19-positive and -negative participants were categorized based on SARS-CoV-2 antigen and antibody testing. Chronic fatigue was assessed using the Chalder Fatigue Scale. RESULTS: COVID-19-positive participants exhibited mild disease. Chronic fatigue was a prevalent symptom among this population and significantly higher in positive vs. negative participants (17% vs 11%, respectively; p = 0.001). The qualitative nature of chronic fatigue as determined by individual questionnaire responses was similar in positive and negative participants. Pre-pandemic plasma IL-6 levels were positively associated with chronic fatigue in negative, but not positive individuals. Raised BMI was associated with chronic fatigue in positive participants. CONCLUSIONS: Pre-existing increased IL-6 levels may contribute to chronic fatigue symptoms, but there was no increased risk in individuals with mild COVID-19 compared with uninfected individuals. Elevated BMI also increased the risk of chronic fatigue in mild COVID-19, consistent with previous reports.

Cutaneous manifestations of SARS-CoV-2 infection during the Delta and Omicron waves in 348 691 UK users of the UK ZOE COVID Study app.

BACKGROUND: Symptoms of SARS-CoV-2 infection have differed during the different waves of the pandemic but little is known about how cutaneous manifestations have changed. OBJECTIVES: To investigate the diagnostic value, frequency and duration of cutaneous manifestations of SARS-CoV-2 infection and to explore their variations between the Delta and Omicron waves of the pandemic. METHODS: In this retrospective study, we used self-reported data from 348 691 UK users of the ZOE COVID Study app, matched 1 : 1 for age, sex, vaccination status and self-reported eczema diagnosis between the Delta and Omicron waves, to assess the diagnostic value, frequency and duration of five cutaneous manifestations of SARS-CoV-2 infection (acral, burning, erythematopapular and urticarial rash, and unusual hair loss), and how these changed between waves. We also investigated whether vaccination had any effect on symptom frequency. RESULTS: We show a significant association between any cutaneous manifestations and a positive SARS-CoV-2 test result, with a diagnostic value higher in the Delta compared with the Omicron wave (odds ratio 2·29, 95% confidence interval 2·22-2·36, P < 0·001; and odds ratio 1·29, 95% confidence interval 1·26-1·33, P < 0·001, respectively). Cutaneous manifestations were also more common with Delta vs. Omicron (17·6% vs. 11·4%, respectively) and had a longer duration. During both waves, cutaneous symptoms clustered with other frequent symptoms and rarely (in < 2% of the users) as first or only clinical sign of SARS-CoV-2 infection. Finally, we observed that vaccinated and unvaccinated users showed similar odds of presenting with a cutaneous manifestation, apart from burning rash, where the odds were lower in vaccinated users. CONCLUSIONS: Cutaneous manifestations are predictive of SARS-CoV-2 infection, and their frequency and duration have changed with different variants. Therefore, we advocate for their inclusion in the list of clinically relevant COVID-19 symptoms and suggest that their monitoring could help identify new variants. What is already known about this topic? Several studies during the wildtype COVID-19 wave reported that patients presented with common skin-related symptoms. It has been observed that COVID-19 symptoms differ among variants. No study has focused on how skin-related symptoms have changed across different variants. What does this study add? We showed, in a community-based retrospective study including over 348 000 individuals, that the presence of cutaneous symptoms is predictive of SARS-CoV-2 infection during the Delta and Omicron waves and that this diagnostic value, along with symptom frequency and duration, differs between variants. We showed that infected vaccinated and unvaccinated individuals reported similar skin-related symptoms during the Delta and Omicron waves, with only burning rashes being less common after vaccination.

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19.

BACKGROUND: COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain. SUBJECTS/METHODS: In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry. RESULTS: Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 × 10-6), obesity status (P = 4.81 × 10-5), higher serum fasting insulin (P = 5.32 × 10-4), BMI (P = 3.94 × 10-4), and lower serum HDL levels (P = 1.92 × 10-7). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 × 10-4) and higher proportion of macrophages (P = 2.74 × 10-5). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression. CONCLUSIONS: Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.

Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic - area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

Self-Reported Symptoms of COVID-19, Including Symptoms Most Predictive of SARS-CoV-2 Infection, Are Heritable.

Susceptibility to infection such as SARS-CoV-2 may be influenced by host genotype. TwinsUK volunteers (n = 3261) completing the C-19 COVID-19 symptom tracker app allowed classical twin studies of COVID-19 symptoms, including predicted COVID-19, a symptom-based algorithm to predict true infection, derived from app users tested for SARS-CoV-2. We found heritability of 49% (32-64%) for delirium; 34% (20-47%) for diarrhea; 31% (8-52%) for fatigue; 19% (0-38%) for anosmia; 46% (31-60%) for skipped meals and 31% (11-48%) for predicted COVID-19. Heritability estimates were not affected by cohabiting or by social deprivation. The results suggest the importance of host genetics in the risk of clinical manifestations of COVID-19 and provide grounds for planning genome-wide association studies to establish specific genes involved in viral infectivity and the host immune response.

Current smoking and COVID-19 risk: results from a population symptom app in over 2.4 million people.

BACKGROUND: The association between current tobacco smoking, the risk of developing symptomatic COVID-19 and the severity of illness is an important information gap. METHODS: UK users of the Zoe COVID-19 Symptom Study app provided baseline data including demographics, anthropometrics, smoking status and medical conditions, and were asked to log their condition daily. Participants who reported that they did not feel physically normal were then asked by the app to complete a series of questions, including 14 potential COVID-19 symptoms and about hospital attendance. The main study outcome was the development of 'classic' symptoms of COVID-19 during the pandemic defined as fever, new persistent cough and breathlessness and their association with current smoking. The number of concurrent COVID-19 symptoms was used as a proxy for severity and the pattern of association between symptoms was also compared between smokers and non-smokers. RESULTS: Between 24 March 2020 and 23 April 2020, data were available on 2 401 982 participants, mean (SD) age 43.6 (15.1) years, 63.3% female, overall smoking prevalence 11.0%. 834 437 (35%) participants reported being unwell and entered one or more symptoms. Current smokers were more likely to report symptoms suggesting a diagnosis of COVID-19; classic symptoms adjusted OR (95% CI) 1.14 (1.10 to 1.18); >5 symptoms 1.29 (1.26 to 1.31); >10 symptoms 1.50 (1.42 to 1.58). The pattern of association between reported symptoms did not vary between smokers and non-smokers. INTERPRETATION: These data are consistent with people who smoke being at an increased risk of developing symptomatic COVID-19.

Real-time tracking of self-reported symptoms to predict potential COVID-19.

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31-7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.