Long COVID exhibits clinically distinct phenotypes at 3-6 months post-SARS-CoV-2 infection: results from the P4O2 consortium

BACKGROUND

Four months after SARS-CoV-2 infection, 22%-50% of COVID-19 patients still experience complaints. Long COVID is a heterogeneous disease and finding subtypes could aid in optimising and developing treatment for the individual patient.

METHODS

Data were collected from 95 patients in the P4O2 COVID-19 cohort at 3-6 months after infection. Unsupervised hierarchical clustering was performed on patient characteristics, characteristics from acute SARS-CoV-2 infection, long COVID symptom data, lung function and questionnaires describing the impact and severity of long COVID. To assess robustness, partitioning around medoids was used as alternative clustering.

RESULTS

Three distinct clusters of patients with long COVID were revealed. Cluster 1 (44%) represented predominantly female patients (93%) with pre-existing asthma and suffered from a median of four symptom categories, including fatigue and respiratory and neurological symptoms. They showed a milder SARS-CoV-2 infection. Cluster 2 (38%) consisted of predominantly male patients (83%) with cardiovascular disease (CVD) and suffered from a median of three symptom categories, most commonly respiratory and neurological symptoms. This cluster also showed a significantly lower forced expiratory volume within 1 s and diffusion capacity of the lung for carbon monoxide. Cluster 3 (18%) was predominantly male (88%) with pre-existing CVD and diabetes. This cluster showed the mildest long COVID, and suffered from symptoms in a median of one symptom category.

CONCLUSIONS

Long COVID patients can be clustered into three distinct phenotypes based on their clinical presentation and easily obtainable information. These clusters show distinction in patient characteristics, lung function, long COVID severity and acute SARS-CoV-2 infection severity. This clustering can help in selecting the most beneficial monitoring and/or treatment strategies for patients suffering from long COVID. Follow-up research is needed to reveal the underlying molecular mechanisms implicated in the different phenotypes and determine the efficacy of treatment.

Fatigue and symptom-based clusters in post COVID-19 patients: a multicentre, prospective, observational cohort study

BACKGROUND

In the Netherlands, the prevalence of post COVID-19 condition is estimated at 12.7% at 90-150 days after SARS-CoV-2 infection. This study aimed to determine the occurrence of fatigue and other symptoms, to assess how many patients meet the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) criteria, to identify symptom-based clusters within the P4O2 COVID-19 cohort and to compare these clusters with clusters in a ME/CFS cohort.

METHODS

In this multicentre, prospective, observational cohort in the Netherlands, 95 post COVID-19 patients aged 40-65 years were included. Data collection at 3-6 months after infection included demographics, medical history, questionnaires, and a medical examination. Follow-up assessments occurred 9-12 months later, where the same data were collected. Fatigue was determined with the Fatigue Severity Scale (FSS), a score of ≥ 4 means moderate to high fatigue. The frequency and severity of other symptoms and the percentage of patients that meet the ME/CFS criteria were assessed using the DePaul Symptom Questionnaire-2 (DSQ-2). A self-organizing map was used to visualize the clustering of patients based on severity and frequency of 79 symptoms. In a previous study, 337 Dutch ME/CFS patients were clustered based on their symptom scores. The symptom scores of post COVID-19 patients were applied to these clusters to examine whether the same or different clusters were found.

RESULTS

According to the FSS, fatigue was reported by 75.9% of the patients at 3-6 months after infection and by 57.1% of the patients 9-12 months later. Post-exertional malaise, sleep disturbances, pain, and neurocognitive symptoms were also frequently reported, according to the DSQ-2. Over half of the patients (52.7%) met the Fukuda criteria for ME/CFS, while fewer patients met other ME/CFS definitions. Clustering revealed specific symptom patterns and showed that post COVID-19 patients occurred in 11 of the clusters that have been observed in the ME/CFS cohort, where 2 clusters had > 10 patients.

CONCLUSIONS

This study shows persistent fatigue and diverse symptomatology in post COVID-19 patients, up to 12-18 months after SARS-CoV-2 infection. Clustering showed that post COVID-19 patients occurred in 11 of the clusters that have been observed in the ME/CFS cohort.

The effects of the COVID-19 pandemic on PICU admissions for severe asthma exacerbations: A single-center experience

BACKGROUND

The incidence of severe asthma exacerbations (SAE) requiring a pediatric intensive care unit (PICU) admission during the coronavirus disease 2019 (COVID-19) pandemic (and its association with public restrictions) is largely unknown. We examined the trend of SAE requiring PICU admission before, during, and after COVID-19 restrictions in Amsterdam, the Netherlands, and its relationship with features such as environmental triggers and changes in COVID-19 restriction measures.

METHODS

In this single-center, retrospective cohort study, all PICU admissions of children aged ≥2 years for severe asthma at the Amsterdam UMC between 2018 and 2022 were included. The concentrations of ambient fine particulate matter (PM2.5 ) and pollen were obtained from official monitoring stations.

RESULTS

Between January 2018 and December 2022, 228 children were admitted to the PICU of the Amsterdam UMC for SAE. While we observed a decrease in admissions during periods of more stringent restriction, there was an increase in the PICU admission rate for SAE in some periods following the lifting of restrictions. In particular, following the COVID-19 restrictions in 2021, we observed a peak incidence of admissions from August to November, which was higher than any other peak during the indicated years. No association with air pollution or pollen was observed.

CONCLUSION

We hypothesize that an increase in clinically diagnosed viral infections after lockdown periods was the reason for the altered incidence of SAE at the PICU in late 2021, rather than air pollution and pollen concentrations.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Precision Medicine for More Oxygen (P4O2)-Study Design and First Results of the Long COVID-19 Extension

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has led to the death of almost 7 million people, however, with a cumulative incidence of 0.76 billion, most people survive COVID-19. Several studies indicate that the acute phase of COVID-19 may be followed by persistent symptoms including fatigue, dyspnea, headache, musculoskeletal symptoms, and pulmonary functional-and radiological abnormalities. However, the impact of COVID-19 on long-term health outcomes remains to be elucidated. Aims: The Precision Medicine for more Oxygen (P4O2) consortium COVID-19 extension aims to identify long COVID patients that are at risk for developing chronic lung disease and furthermore, to identify treatable traits and innovative personalized therapeutic strategies for prevention and treatment. This study aims to describe the study design and first results of the P4O2 COVID-19 cohort. Methods: The P4O2 COVID-19 study is a prospective multicenter cohort study that includes nested personalized counseling intervention trial. Patients, aged 40-65 years, were recruited from outpatient post-COVID clinics from five hospitals in The Netherlands. During study visits at 3-6 and 12-18 months post-COVID-19, data from medical records, pulmonary function tests, chest computed tomography scans and biological samples were collected and questionnaires were administered. Furthermore, exposome data was collected at the patient's home and state-of-the-art imaging techniques as well as multi-omics analyses will be performed on collected data. Results: 95 long COVID patients were enrolled between May 2021 and September 2022. The current study showed persistence of clinical symptoms and signs of pulmonary function test/radiological abnormalities in post-COVID patients at 3-6 months post-COVID. The most commonly reported symptoms included respiratory symptoms (78.9%), neurological symptoms (68.4%) and fatigue (67.4%). Female sex and infection with the Delta, compared with the Beta, SARS-CoV-2 variant were significantly associated with more persisting symptom categories. Conclusions: The P4O2 COVID-19 study contributes to our understanding of the long-term health impacts of COVID-19. Furthermore, P4O2 COVID-19 can lead to the identification of different phenotypes of long COVID patients, for example those that are at risk for developing chronic lung disease. Understanding the mechanisms behind the different phenotypes and identifying these patients at an early stage can help to develop and optimize prevention and treatment strategies.

Validity of Body Image Pictogram to Determine Overweight/Obesity in Adults from Less Developed Populations: Results From Pars Cohort Study

BACKGROUND

Despite the evidence for validity of body image pictogram (BIP) to discriminate overweight, obese, and normal individuals, there is little evidence on the probable effect of socio-demographic variables on its validity. To investigate the effects of socioeconomic status (SES), age, ethnicity, and educational level on the validity of BIP to discriminate normal weight, overweight, and obese people.

METHODS

We used the Pars Cohort Study (PCS) data. Stunkard's BIP score was used as test measure. Participants were classified as normal (body mass index [BMI]<25), overweight (BMI=25 to 29.9), and obese (BMI≥29.9) based on their BMI (kg/m2 ). Area under curve (AUC) and its 95% CI were estimated and compared. Optimal cutoff points and their sensitivity, specificity, and likelihood ratio were reported.

RESULTS

A total of 9232 participants with a female/male ratio of 1.03 were included. The prevalence of overweight and obesity was 37.4% and 18.2%, respectively. Regardless of socio-demographic levels, the optimal cut-points to discriminate normal BMI from overweight, and overweight from obese participants were BIP score of four and five, respectively. Estimated AUC correlated with ethnicity (P<0.001) for both genders, and with SES for females (P<0.05).

CONCLUSION

Although BIP may be a valid measure to categorize the general adult population into normal, overweight and obese, its validity depends on SES and ethnicity. BIP may be available as a proxy measure for BMI categories in socio-demographically homogeneous populations but not in heterogeneous populations.