Exploring Potential Proteomic Biomarkers for Prognosis of Infective Endocarditis through Profiled Autoantibodies by an Immunomics Protein Array Technique

Novel putative biomarkers for infective endocarditis by serum proteomic analysis: a comprehensive review of literature

Infective endocarditis (IE) is a challenging condition with high mortality. Prompt detection of IE has become essential for early and immediate management. The authors aimed to comprehensively review the existing literature on novel putative biomarkers for IE through serum proteomic analysis. The literature reveals high levels of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) levels in IE with staphylococcal etiology, valvular lesions, and when combined with cardiac troponin I (cTnI), had a more significant value for risk stratification. A higher pro-ADM level, copeptin, NT-proBNP, and the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) all impacted mortality during the hospital stay. The biomarker matrix metalloproteinase-9 was utilized to predict new-onset embolic events in patients, thus serving as a predictive marker. Procalcitonin was an important diagnostic marker in IE complicated with severe infection. Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interferon-γ, cTnI, and NT-proBNP were also discovered to be useful as prognostic indicators. Early diagnosis and appropriate treatment are possible using antiphospholipid antibodies as a diagnostic test for definite IE. It is also concluded that antineutrophilic cytoplasmic antibody positive individuals with IE had a lengthier hospital stay. These noninvasive biomarkers can identify patients at risk and provide appropriate and early clinical management. NT-proBNP, Cystatin C, troponins, IL-6, IL-8, S100A11, and AQP9 are examples of possible markers that appear promising for further research. In conclusion, large-scale validation studies should study these biomarkers further to establish their use in clinical settings.

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.

Preoperative serum albumin: a promising indicator of early mortality after surgery for infective endocarditis

Background

Despite novel improvements in the diagnosis and treatment of infective endocarditis (IE), there has been no significant improvement in the survival rate of IE, which indicates that many details still need to be optimized in the preoperative assessment. We sought to evaluate preoperative serum albumin as a biomarker for predicting early mortality after IE surgery.

Methods

Between October 2013 and June 2019, patients with a definite diagnosis of IE were enrolled in this study. Patients’ albumin levels at admission were used as the preoperative albumin levels. Restricted cubic spline and multivariate logistic regression analyses were performed to evaluate the relationship between albumin and early mortality. Receiver operating characteristic curve analyses were performed to assess the role of albumin in predicting early mortality and compare the predictive capacity of traditional models with models that included albumin.

Results

Of the 276 IE patients, 20 (7.2%) died in hospital or within 30 days of surgery. Hypoalbuminemia (an albumin level <3.5 g/dL) was present in 109 (39.5%) patients. The multivariate logistic regression analysis showed that preoperative albumin was inversely associated with early mortality [adjusted odds ratio (OR) =0.22 per 1 g/dL, 95% confidence interval (CI): 0.07–0.65, P=0.006] after full adjustment. Preoperative albumin had value in predicting early mortality [area under the curve (AUC) =0.72, 95% CI: 0.61–0.84; P<0.01]. After adding albumin to the European System for Cardiac Operative Risk Evaluation (EuroSCORE) and Charlson score, the predictive ability of the model was further improved (EuroSCORE II: AUC =0.55; 95% CI: 0.42–0.67 to AUC =0.72; 95% CI: 0.61–0.84; Charlson score: AUC =0.73; 95% CI: 0.64–0.83 to AUC =0.78; 95% CI: 0.68–0.88).

Conclusions

Preoperative serum albumin is inversely associated with early mortality after IE surgery, and is a promising prognostic indicator in preoperative risk stratification assessments of IE patients.