Long COVID in the context of social determinants of health

Post-COVID-19 syndrome, who at risk and why: an insight from Qatar 2022

Background

Despite the recovery from the COVID-19 pandemic, many people experience post-COVID-19 syndrome, which negatively impacts their health and function. This condition has become a significant public health problem that requires immediate attention.

Objective

To study the prevalence, clinical characteristics, and predictors of post-COVID-19 Syndrome in Qatar during 2022.

Methods

An analytic cross-sectional study was conducted among COVID-19 confirmed cases from January 2022 to July 2022 in Qatar. A simple random sample was employed to include (n = 588) participants from the list of cases and invited to participate in a telephone survey. The World Health Organization's standard case definition for PCS was adopted.

Results

Out of 368, the prevalence of post-COVID-19 syndrome was 43.2% (n = 159). Most PCS cases were females (67.9%; n = 108), married (73.6%; n = 117), and university and higher educational level (83.6%; n = 133). However, 78.7% (n = 125) reported poor to moderate levels of social support. Only 30.2% (n = 48) of PCS patients had a history of chronic diseases, and 5.7% (n = 9) required hospital admission during acute illness. Among PCS cases, the most commonly reported symptoms were fatigue (75.5%), followed by anxiety (49.1%), forgetfulness (46.5%), mood alteration (45.3%), and general weakness (39.6%). The logistic regression revealed that female gender (AOR: 2.58 95%CI: 1.58-4.225, p < 0.0001), university and high educational level (AOR: 2.2, 95%CI: 1.256-3.98, p < 0.006), poor level of social support (AOR: 2.45; 95%CI: 1.55-4.13; p < 0.002), were significant predictors for PCS.

Conclusion

Post-COVID-19 syndrome may go under-recognized. More efforts are needed to raise awareness and mobilize the resources to respond to this ongoing public health problem.

Social determinants of health during and after coronavirus: a qualitative study

BACKGROUND

Health has multiple dimensions influenced not only by individual factors but also by broader social, economic, cultural, and political structures. The widespread COVID-19 pandemic has multidimensional effects on people's lives, which can have effects on individuals' lifestyles after the COVID-19. This study aimed to speculate the social determinants of health during and after the COVID-19, which can lead to more effective planning for promoting community health.

METHODS

The present study interviewed 21 experts in social and medical fields during four months. The sampling method was snowball. The interviews were semi-structured and administered in-person or electronic. All interviews were transcribed and analyzed according to the Brown and Clarke's six-stage framework to extract themes.

RESULTS

the participants were 13 males, eight experts in social field, all had PhD, 17 were academic members, and 10 were members of the Social Determinants of Health Research Center. The qualitative content analysis induced seven different social themes that affect the health which included: justice (3 Subcategories), integration (4 Subcategories), acceptance (4 Subcategories), participation (2 Subcategories), adaptation (3 Subcategories), flourishing (4 Subcategories), and cohesion (3 Subcategories).

CONCLUSIONS

According to the present study, a grand plan to cover all positive and negative social effects of COVID-19 should have at least seven different dimensions. However, the present models of effective social determinants in health do not have such comprehensiveness. Future studies may provide a proper model to be used in clinical and research fields.

Single-cell RNA sequencing reveals characteristics of myeloid cells in post-acute sequelae of SARS-CoV-2 patients with persistent respiratory symptoms

Background

Although our understanding of the immunopathology and subsequent risk and severity of COVID-19 disease is evolving, a detailed account of immune responses that contribute to the long-term consequences of pulmonary complications in COVID-19 infection remains unclear. Few studies have detailed the immune and cytokine profiles associated with post-acute sequelae of SARS-CoV-2 infection (PASC) with persistent pulmonary symptoms. The dysregulation of the immune system that drives pulmonary sequelae in COVID-19 survivors and PASC sufferers remains largely unknown.

Results

To characterize the immunological features of pulmonary PASC (PPASC), we performed droplet-based single-cell RNA sequencing (scRNA-seq) to study the transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) from a participant naïve to SARS-CoV-2 (Control) (n=1) and infected with SARS-CoV-2 with chronic pulmonary symptoms (PPASC) (n=2). After integrating scRNA-seq data with a naïve participant from a published dataset, 11 distinct cell populations were identified based on the expression of canonical markers. The proportion of myeloid-lineage cells ([MLCs]; CD14+/CD16+monocytes, and dendritic cells) was increased in PPASC (n=2) compared to controls (n=2). MLCs from PPASC displayed up-regulation of genes associated with pulmonary symptoms/fibrosis, while glycolysis metabolism-related genes were downregulated. Similarly, pathway analysis showed that fibrosis-related (VEGF, WNT, and SMAD) and cell death pathways were up-regulated, but immune pathways were down-regulated in PPASC. Further comparison of PPASC with scRNA-seq data with Severe COVID-19 (n=4) data demonstrated enrichment of fibrotic transcriptional signatures. In PPASC, we observed interactive VEGF ligand-receptor pairs among MLCs, and network modules in CD14+ (cluster 4) and CD16+ (Cluster 5) monocytes displayed a significant enrichment for biological pathways linked to adverse COVID-19 outcomes, fibrosis, and angiogenesis. Further analysis revealed a distinct metabolic alteration in MLCs with a down-regulation of glycolysis/gluconeogenesis in PPASC compared to SARS-CoV-2 naïve samples.

Conclusion

Analysis of a small scRNA-seq dataset demonstrated alterations in the immune response and cellular landscape in PPASC. The presence of elevated MLC levels and their corresponding gene signatures associated with fibrosis, immune response suppression, and altered metabolic states suggests a potential role in PPASC development.

Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID

Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.

SGLT2 Inhibitors in Long COVID Syndrome: Is There a Potential Role?

The coronavirus disease (COVID)-19 has turned into a pandemic causing a global public health crisis. While acute COVID-19 mainly affects the respiratory system and can cause acute respiratory distress syndrome, an association with persistent inflammatory stress affecting different organ systems has been elucidated in long COVID syndrome (LCS). Increased severity and mortality rates have been reported due to cardiophysiological and metabolic systemic disorders as well as multiorgan failure in COVID-19, additionally accompanied by chronic dyspnea and fatigue in LCS. Hence, novel therapies have been tested to improve the outcomes of LCS of which one potential candidate might be sodium-glucose cotransporter 2 (SGLT2) inhibitors. The aim of this narrative review was to discuss rationales for investigating SGLT2 inhibitor therapy in people suffering from LCS. In this regard, we discuss their potential positive effects-next to the well described "cardio-renal-metabolic" conditions-with a focus on potential anti-inflammatory and beneficial systemic effects in LCS. However, potential beneficial as well as potential disadvantageous effects of SGLT2 inhibitors on the prevalence and long-term outcomes of COVID-19 will need to be established in ongoing research.

Single-cell RNA sequencing reveals characteristics of myeloid cells in pulmonary post-acute sequelae of SARS-CoV-2

Background

Although our understanding of the immunopathology and subsequent risk and severity of COVID-19 disease is evolving, a detailed account of immune responses that contribute to the long-term consequences of pulmonary complication in COVID-19 infection remain unclear. Few studies have detailed the immune and cytokine profiles associated with post-acute sequalae of SARS-CoV-2 infection with persistent pulmonary symptoms (PPASC). However, the dysregulation of the immune system that drives pulmonary sequelae in COVID-19 survivors and PASC sufferers remains largely unknown.

Results

To characterize the immunological features of pulmonary PASC (PPASC), we performed droplet-based single-cell RNA sequencing to study the transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) from participants naïve to SARS-CoV-2 (Control) and infected with SARS-CoV-2 with chronic pulmonary symptoms (PPASC). We analyzed more than 34,139 PBMCs by integrating our dataset with previously reported control datasets (GSM4509024) cell distribution. In total, 11 distinct cell populations were identified based on the expression of canonical markers. The proportion of myeloid-lineage cells ([MLCs]; CD14 + /CD16 + monocytes and dendritic cells) was increased in PPASC compared to controls. MLCs from PPASC displayed up-regulation of genes associated with pulmonary symptoms/fibrosis, while glycolysis metabolism-related genes were downregulated. Similarly, pathway analysis showed that fibrosis- related ( VEGF , WNT , and SMAD ) and cell death pathways were up-regulated, but immune pathways were down-regulated in PPASC. In PPASC, we observed interactive VEGF ligand- receptor pairs among MLCs, and network modules in CD14 + (cluster 4) and CD16 + (Cluster 5) monocytes displayed a significant enrichment for biological pathways linked to adverse COVID- 19 outcomes, fibrosis, and angiogenesis. Further analysis revealed a distinct metabolic alteration in MLCs with a down-regulation of glycolysis/gluconeogenesis in PPASC compared to SARS- CoV-2 naïve samples.

Conclusion

This study offers valuable insights into the immune response and cellular landscape in PPASC. The presence of elevated MLC levels and their corresponding gene signatures associated with fibrosis, immune response suppression, and altered metabolic states suggests their potential role as a driver of PPASC.