Increased insulin resistance due to Long COVID is associated with depressive symptoms and partly predicted by the inflammatory response during acute infection

Increased galanin-galanin receptor 1 signaling, inflammation, and insulin resistance are associated with affective symptoms and chronic fatigue syndrome due to long COVID

BACKGROUND

Patients with Long COVID (LC) often experience neuropsychiatric symptoms such as depression, anxiety, and chronic fatigue syndrome (CFS), collectively referred to as the physio-affective phenome of LC. Activated immune-inflammatory pathways and insulin resistance significantly contribute to the physio-affective phenome associated with LC.

METHODS

In a cohort of 90 individuals, categorized into those with and without LC, we evaluated, 3-6 months following acute SARS-CoV-2 infection, the correlations between the Hamilton Depression (HAMD), Hamilton Anxiety (HAMA), and Fibro-Fatigue (FF) Rating Scale scores, and serum C-reactive protein (CRP), prostaglandin E2 (PGE2), galanin-galanin receptor 1 (GAL-GALR1) signaling, insulin resistance, insulin-like growth factor (IGF-1), plasminogen activator inhibitor-1 (PAI1), S100B and neuron-specific enolase (NSE).

RESULTS

HAMD, HAMA, FF scores, CRP, PGE2, GAL-GALR1 signaling, insulin resistance, PAI1, NSE, and S100B are all higher in people with LC compared to those without LC. The HAMD/HAMA/FF scores were significantly correlated with PGE, CRP, GAL, GALR1, insulin resistance, and PAI1 levels, and a composite score based on peak body temperature (PBT) - oxygen saturation (SpO2) (PBT/SpO2 index) during the acute infectious phase. A combination of biomarkers explained a large part of the variance in CFS and affective scores (33.6%-42.0%), with GAL-GALR1 signaling, PGE2, and CRP being the top 3 most important biomarkers. The inclusion of the PBT/SpO2 index increased the prediction (55.3%-67.1%). The PBT/SpO2 index predicted the increases in GAL-GALR1 signaling.

CONCLUSION

These results indicate that the CFS and affective symptoms that are linked to LC are the consequence of metabolic aberrations, activated immune-inflammatory pathways, and the severity of inflammation during the acute phase of SARS-CoV-2 infection.

Post-COVID-19 Condition and Pulmonary Embolism

Purpose

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes post-acute sequelae of coronavirus disease 2019 (COVID-19), including pulmonary vasculopathy, increasing thrombotic risk. Screening and treating survivors are essential to reduce associated disabilities. We aim to investigate the clinical characteristics of patients with post-COVID-19 condition and pulmonary embolism, as well as their health-related quality of life one year after COVID-19 diagnosis.

Patients and Methods

In our study, we analyzed nine cases of post-COVID-19 condition and pulmonary embolism in a tertiary hospital in Taiwan. Patient characteristics, including age, sex, symptoms, and outcomes, were recorded. One year post-diagnosis, patients underwent follow-up with lab tests, chest X-rays, electrocardiograms, and health-related quality of life (HRQOL) assessments using the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) tool.

Results

Post-COVID-19 condition with pulmonary embolism predominantly affects females. Common symptoms include breathlessness and chest pain, and a lung perfusion scan may be useful for diagnosis. The one-year follow-up of five patients mainly showed normal lab results and no active lung lesions. The mean EQ-5D score was 0.928 ± 0.119, and the EQ visual analogue scale (EQ-VAS) was 85.00 ± 11.18. Pain/discomfort was the most reported issue.

Conclusion

This research reveals that post-COVID-19 condition with pulmonary embolism affects a significant number of women and those with preexisting conditions like cancer or diabetes. Symptoms overlap also complicates diagnosis. Although many lab results were normal, the elevated risk of pulmonary embolism persists. Lower utility scores and increased anxiety highlight the need for targeted interventions and mental health support. Our study underscores the importance of monitoring pulmonary embolism in patients after COVID-19 infection, given the significant impact on post-COVID-19 condition and overall health outcomes.

The tryptophan catabolite or kynurenine pathway in long COVID disease: A systematic review and meta-analysis

BACKGROUND

Recent studies confirm the involvement of activated immune-inflammatory responses and increased oxidative and nitrosative stress in Long COVID (LC) disease. However, the influence of these pathways on the metabolism of tryptophan (TRP) through the TRP catabolite (TRYCAT) pathway and their mediating effects on LC pathophysiology, has not been fully explored.

OBJECTIVE

This meta-analysis investigates peripheral TRP and TRYCAT levels and the TRYCAT pathway in patients with LC disease.

METHOD

This review utilized systematic searches of PubMed, Google Scholar, SCOPUS and SciFinder, including 14 full-text articles and 1,167 participants, consisting of 480 patients with LC and 687 normal controls.

RESULTS

The results indicated a significant increase in the kynurenine (KYN)/TRP ratio, with a large effect size (standardized mean difference, SMD = 0.755; confidence intervals, CI: 0.119;1.392), in LC patients compared to normal controls. Additionally, LC patients exhibited a significant decrease in TRP levels (SMD = -0.520, CI: -0.793; -0.246) and an increase in KYN levels after imputing missing studies (SMD = 1.176, CI: 0.474; 1.877), suggesting activation of the indoleamine 2,3-dioxygenase (IDO) enzyme and upregulation of the TRYCAT pathway. No significant elevation in TRYCAT-related neurotoxicity, kynurenic acid (KA)/KYN and 3-hydroxykynurenine (3-HK)/KYN ratios were observed in LC patients compared to normal controls.

CONCLUSION

The current findings suggest that an activated TRYCAT pathway, characterized by decreased TRP levels and maybe elevated KYN levels, plays a significant role in the pathophysiology of LC.

Brain-targeted autoimmunity is strongly associated with Long COVID and its chronic fatigue syndrome as well as its affective symptoms

INTRODUCTION

Autoimmune responses contribute to the pathophysiology of Long COVID, affective symptoms and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

OBJECTIVES

To examine whether Long COVID, and its accompanying affective symptoms and CFS are associated with immunoglobulin (Ig)A/IgM/IgG directed at neuronal proteins including myelin basic protein (MBP), myelin oligodendrocyte glycoprotein (MOG), synapsin, α + β-tubulin, neurofilament protein (NFP), cerebellar protein-2 (CP2), and the blood-brain-barrier-brain-damage (BBD) proteins claudin-5 and S100B.

METHODS

IgA/IgM/IgG to the above neuronal proteins, human herpes virus-6 (HHV-6) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were measured in 90 Long COVID patients and 90 healthy controls, while C-reactive protein (CRP), and advanced oxidation protein products (AOPP) in association with affective and CFS ratings were additionally assessed in a subgroup thereof.

RESULTS

Long COVID is associated with significant increases in IgG directed at tubulin (IgG-tubulin), MBP, MOG and synapsin; IgM-MBP, MOG, CP2, synapsin and BBD; and IgA-CP2 and synapsin. IgM-SARS-CoV-2 and IgM-HHV-6 antibody titers were significantly correlated with IgA/IgG/IgM-tubulin and -CP2, IgG/IgM-BBD, IgM-MOG, IgA/IgM-NFP, and IgG/IgM-synapsin. Binary logistic regression analysis shows that IgM-MBP and IgG-MBP are the best predictors of Long COVID. Multiple regression analysis shows that IgG-MOG, CRP and AOPP explain together 41.7 % of the variance in the severity of CFS. Neural network analysis shows that IgM-synapsin, IgA-MBP, IgG-MOG, IgA-synapsin, IgA-CP2, IgG-MBP and CRP are the most important predictors of affective symptoms due to Long COVID with a predictive accuracy of r = 0.801.

CONCLUSION

Brain-targeted autoimmunity contributes significantly to the pathogenesis of Long COVID and the severity of its physio-affective phenome.

The prognostic significance of insulin resistance in COVID-19: a review

Objectives

Emerging publications indicate that diabetes predisposes patients with COVID-19 to more severe complications, which is partly attributed to inflammatory condition. In the current review, we reviewed recent published literature to provide evidence on the role of insulin resistance (IR) in diabetes, the association between diabetes and COVID-19 severity and mortality, the impact of COVID-19 infection on incident new-onset diabetes, mechanisms responsible for IR in COVID-19 patients, and the predictive value of different surrogates of IR in COVID-19.

Method

The literature search performs to find out studies that have assessed the association between IR surrogates and morbidity and mortality in patients with COVID-19.

Results

We showed that there is a bulk of evidence in support of the fact that diabetes is a potent risk factor for enhanced morbidity and mortality in COVID-19 patients. COVID-19 patients with diabetes are more prone to remarkable dysglycemia compared to those without diabetes, which is associated with an unfavourable prognosis. Furthermore, SARS-COV2 can make patients predispose to IR and diabetes via activating ISR, affecting RAAS signaling pathway, provoking inflammation, and changing the expression of PPARɣ and SREBP-1. Additionally, higher IR is associated with increased morbidity and mortality in COVID-19 patients and different surrogates of IR can be utilized as a prognostic biomarker for COVID-19 patients.

Conclusion

Different surrogates of IR can be utilized as predictors of COVID-19 complications and death.

Cannabis use associated with lower mortality among hospitalized Covid-19 patients using the national inpatient sample: an epidemiological study

BACKGROUND

Prior reports indicate that modulation of the endocannabinoid system (ECS) may have a protective benefit for Covid-19 patients. However, associations between cannabis use (CU) or CU not in remission (active cannabis use (ACU)), and Covid-19-related outcomes among hospitalized patients is unknown.

METHODS

In this multicenter retrospective observational cohort analysis of adults (≥ 18 years-old) identified from 2020 National Inpatient Sample database, we utilize multivariable regression analyses and propensity score matching analysis (PSM) to analyze trends and outcomes among Covid-19-related hospitalizations with CU and without CU (N-CU) for primary outcome of interest: Covid-19-related mortality; and secondary outcomes: Covid-19-related hospitalization, mechanical ventilation (MV), and acute pulmonary embolism (PE) compared to all-cause admissions; for CU vs N-CU; and for ACU vs N-ACU.

RESULTS

There were 1,698,560 Covid-19-related hospitalizations which were associated with higher mortality (13.44% vs 2.53%, p ≤ 0.001) and worse secondary outcomes generally. Among all-cause hospitalizations, 1.56% of CU and 6.29% of N-CU were hospitalized with Covid-19 (p ≤ 0.001). ACU was associated with lower odds of MV, PE, and death among the Covid-19 population. On PSM, ACU(N(unweighted) = 2,382) was associated with 83.97% lower odds of death compared to others(N(unweighted) = 282,085) (2.77% vs 3.95%, respectively; aOR:0.16, [0.10-0.25], p ≤ 0.001).

CONCLUSIONS

These findings suggest that the ECS may represent a viable target for modulation of Covid-19. Additional studies are needed to further explore these findings.

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

BACKGROUND

Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms.

METHODS

We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics' Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms.

RESULTS

Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases. Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3. Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank. Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS.

CONCLUSION

This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

The plasma metabolome of long COVID patients two years after infection

One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as "long COVID") which has emerged as a consequence of the SARS-CoV-2 epidemic. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. In this study, our goal was to assess the plasma metabolome in a total of 100 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC-MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin were measured in long COVID patients by immunoenzymatic assay. The comparison of paired COVID-19/long COVID-19 samples revealed 53 metabolites that were statistically different. Compared to controls, 27 metabolites remained dysregulated even after two years. Post-COVID-19 patients displayed a heterogeneous metabolic profile. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

Long COVID, the Brain, Nerves, and Cognitive Function

SARS-CoV-2, a single-stranded RNA coronavirus, causes an illness known as coronavirus disease 2019 (COVID-19). Long-term complications are an increasing issue in patients who have been infected with COVID-19 and may be a result of viral-associated systemic and central nervous system inflammation or may arise from a virus-induced hypercoagulable state. COVID-19 may incite changes in brain function with a wide range of lingering symptoms. Patients often experience fatigue and may note brain fog, sensorimotor symptoms, and sleep disturbances. Prolonged neurological and neuropsychiatric symptoms are prevalent and can interfere substantially in everyday life, leading to a massive public health concern. The mechanistic pathways by which SARS-CoV-2 infection causes neurological sequelae are an important subject of ongoing research. Inflammation- induced blood-brain barrier permeability or viral neuro-invasion and direct nerve damage may be involved. Though the mechanisms are uncertain, the resulting symptoms have been documented from numerous patient reports and studies. This review examines the constellation and spectrum of nervous system symptoms seen in long COVID and incorporates information on the prevalence of these symptoms, contributing factors, and typical course. Although treatment options are generally lacking, potential therapeutic approaches for alleviating symptoms and improving quality of life are explored.

Laboratory Findings and Biomarkers in Long COVID: What Do We Know So Far? Insights into Epidemiology, Pathogenesis, Therapeutic Perspectives and Challenges

Long COVID (LC) encompasses a constellation of long-term symptoms experienced by at least 10% of people after the initial SARS-CoV-2 infection, and so far it has affected about 65 million people. The etiology of LC remains unclear; however, many pathophysiological pathways may be involved, including viral persistence; a chronic, low-grade inflammatory response; immune dysregulation and a defective immune response; the reactivation of latent viruses; autoimmunity; persistent endothelial dysfunction and coagulopathy; gut dysbiosis; hormonal and metabolic dysregulation; mitochondrial dysfunction; and autonomic nervous system dysfunction. There are no specific tests for the diagnosis of LC, and clinical features including laboratory findings and biomarkers may not specifically relate to LC. Therefore, it is of paramount importance to develop and validate biomarkers that can be employed for the prediction, diagnosis and prognosis of LC and its therapeutic response, although this effort may be hampered by challenges pertaining to the non-specific nature of the majority of clinical manifestations in the LC spectrum, small sample sizes of relevant studies and other methodological issues. Promising candidate biomarkers that are found in some patients are markers of systemic inflammation, including acute phase proteins, cytokines and chemokines; biomarkers reflecting SARS-CoV-2 persistence, the reactivation of herpesviruses and immune dysregulation; biomarkers of endotheliopathy, coagulation and fibrinolysis; microbiota alterations; diverse proteins and metabolites; hormonal and metabolic biomarkers; and cerebrospinal fluid biomarkers. At present, there are only two reviews summarizing relevant biomarkers; however, they do not cover the entire umbrella of current biomarkers, their link to etiopathogenetic mechanisms or the diagnostic work-up in a comprehensive manner. Herein, we aim to appraise and synopsize the available evidence on the typical laboratory manifestations and candidate biomarkers of LC, their classification based on pathogenetic mechanisms and the main LC symptomatology in the frame of the epidemiological and clinical aspects of the syndrome and furthermore assess limitations and challenges as well as potential implications in candidate therapeutic interventions.