Inflammatory plasma proteins predict short-term mortality in patients with an acute myocardial infarction

Immune cell signatures and inflammatory mediators: unraveling their genetic impact on chronic kidney disease through Mendelian randomization

Prior research has established associations between immune cells, inflammatory proteins, and chronic kidney disease (CKD). Our Mendelian randomization study aims to elucidate the genetic causal relationships among these factors and CKD. We applied Mendelian randomization using genetic variants associated with CKD from a large genome-wide association study (GWAS) and inflammatory markers from a comprehensive GWAS summary. The causal links between exposures (immune cell subtypes and inflammatory proteins) and CKD were primarily analyzed using the inverse variance-weighted, supplemented by sensitivity analyses, including MR-Egger, weighted median, weighted mode, and MR-PRESSO. Our analysis identified both absolute and relative counts of CD28 + CD45RA + CD8 + T cell (OR = 1.01; 95% CI = 1.01-1.02; p < 0.001, FDR = 0.018) (OR = 1.01; 95% CI = 1.00-1.01; p < 0.001, FDR = 0.002), CD28 on CD39 + CD8 + T cell(OR = 0.97; 95% CI = 0.96-0.99; p < 0.001, FDR = 0.006), CD16 on CD14-CD16 + monocyte (OR = 1.02; 95% CI = 1.01-1.03; p < 0.001, FDR = 0.004) and cytokines, such as IL-17A(OR = 1.11, 95% CI = 1.06-1.16, p < 0.001, FDR = 0.001), and LIF-R(OR = 1.06, 95% CI = 1.02-1.10, p = 0.005, FDR = 0.043) that are genetically predisposed to influence the risk of CKD. Moreover, the study discovered that CKD itself may causatively lead to alterations in certain proteins, including CST5(OR = 1.16, 95% CI = 1.09-1.24, p < 0.001, FDR = 0.001). No evidence of reverse causality was found for any single biomarker and CKD. This comprehensive MR investigation supports a genetic causal nexus between certain immune cell subtypes, inflammatory proteins, and CKD. These findings enhance the understanding of CKD's immunological underpinnings and open avenues for targeted treatments.

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction

The ST-elevation Myocardial Infarction (STEMI) and Non-ST-elevation Myocardial Infarction (NSTEMI) might occur because of coronary artery stenosis. The gene biomarkers apply to the clinical diagnosis and therapeutic decisions in Myocardial Infarction. The aim of this study was to introduce, enrich and estimate timely the blood gene profiles based on the high-throughput data for the molecular distinction of STEMI and NSTEMI. The text mining data (50 genes) annotated with DisGeNET data (144 genes) were merged with the GEO gene expression data (5 datasets) using R software. Then, the STEMI and NSTEMI networks were primarily created using the STRING server, and improved using the Cytoscape software. The high-score genes were enriched using the KEGG signaling pathways and Gene Ontology (GO). Furthermore, the genes were categorized to determine the NSTEMI and STEMI gene profiles. The time cut-off points were identified statistically by monitoring the gene profiles up to 30 days after Myocardial Infarction (MI). The gene heatmaps were clearly created for the STEMI (high-fold genes 69, low-fold genes 45) and NSTEMI (high-fold genes 68, low-fold genes 36). The STEMI and NSTEMI networks suggested the high-score gene profiles. Furthermore, the gene enrichment suggested the different biological conditions for STEMI and NSTEMI. The time cut-off points for the NSTEMI (4 genes) and STEMI (13 genes) gene profiles were established up to three days after Myocardial Infarction. The study showed the different pathophysiologic conditions for STEMI and NSTEMI. Furthermore, the high-score gene profiles are suggested to measure up to 3 days after MI to distinguish the STEMI and NSTEMI.

Olink proteomics profiling platform reveals non-invasive inflammatory related protein biomarkers in autism spectrum disorder

Background

Owing to the lack of valid biomarkers, the diagnosis of autism spectrum disorder (ASD) diagnosis relies solely on the behavioral phenotypes of children. Several researchers have suggested an association between ASD and inflammation; however, the complex relationship between the two is unelucidated to date. Therefore, the current study aims to comprehensively identify novel circulating ASD inflammatory biomarkers.

Methods

Olink proteomics was applied to compare the plasma inflammation-related protein changes in a group of the healthy children (HC, n = 33) and another with ASD (n = 31). The areas under the receiver operating characteristic curves (AUCs) of the differentially expressed proteins (DEPs) were calculated. The functional analysis of the DEPs was performed using Gene Ontology and Kyoto Encyclopedia Genes and Genomes. Pearson correlation tests were used employed to analyze the correlation between the DEPs and clinical features.

Results

A total of 13 DEPs were significantly up-regulated in the ASD group compared with the HC group. The four proteins, namely, STAMBP, ST1A1, SIRT2, and MMP-10 demonstrated good diagnostic accuracy with the corresponding AUCs (95% confidence interval, CI) of 0.7218 (0.5946-0.8489), 0.7107 (0.5827-0.8387), 0.7016 (0.5713-0.8319), and 0.7006 (0.568-0.8332). Each panel of STAMBP and any other differential protein demonstrated a better classification performance [AUC values from 0.7147 (0.5858-0.8436, STAMBP/AXIN1) to 0.7681 (0.6496-0.8867, STAMBP/MMP-10)]. These DEP profiles were enriched in immune and inflammatory response pathways, including TNF and NOD-like receptor signaling pathways. The interaction between STAMBP and SIRT2 (R = 0.97, p = 8.52 × 10^-39) was found to be the most significant. In addition, several DEPs related to clinical features in patients with ASD, particularly AXIN1 (R = 0.36, p = 0.006), SIRT2 (R = 0.34, p = 0.010) and STAMBP (R = 0.34, p = 0.010), were positively correlated with age and parity, indicating that older age and higher parity may be the inflammation-related clinical factors in ASD.

Conclusion

Inflammation plays a crucial role in ASD, and the up-regulated inflammatory proteins may serve as potential early diagnostic biomarkers for ASD.

The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results

INTRODUCTION

Obstructive sleep apnea (OSA) is frequently associated with a chronic inflammatory state and cardiovascular/metabolic complications. The aim of this study was to evaluate the influence of certain comorbidities on a panel of 45 chemokines and cytokines in OSA patients with special regard to their possible association with cardiovascular diseases.

MATERIAL AND METHODS

This cross-sectional study was performed on 61 newly diagnosed OSA patients. For the measurement of the plasma concentration of chemokines and cytokines, the magnetic bead-based multiplex assay for the Luminex^® platform was used.

RESULTS

In the patients with concomitant COPD, there were increased levels of pro-inflammatory cytokines (CCL11, CD-40 ligand) and decreased anti-inflammatory cytokine (IL-10), while in diabetes, there were increased levels of pro-inflammatory cytokines (IL-6, TRIAL). Obesity was associated with increased levels of both pro-inflammatory (IL-13) and anti-inflammatory (IL-1RA) cytokines. Hypertension was associated with increased levels of both pro-inflammatory (CCL3) and anti-inflammatory (IL-10) cytokines. Increased daytime pCO₂, low mean nocturnal SaO₂, and the oxygen desaturation index were associated with increased levels of pro-inflammatory cytokines (CXCL1, PDGF-AB, TNF-α, and IL-15).

CONCLUSIONS

In OSA patients with concomitant diabetes and COPD, elevated levels of certain pro-inflammatory and decreased levels of certain anti-inflammatory cytokines may favor the persistence of a chronic inflammatory state with further consequences. Nocturnal hypoxemia, frequent episodes of desaturation, and increased daytime pCO₂ are factors contributing to the chronic inflammatory state in OSA patients.