Usefulness of S100A12 as a prognostic biomarker for adverse events in patients with heart failure

The Association Between S100A12 Protein and C-Reactive Protein with Malignant Ventricular Arrhythmias Following Acute Myocardial Infarction in the Elderly

Objective

To investigate the association of S100A12 protein and C-reactive protein (CRP) with the onset of malignant ventricular arrhythmias (MVA) after acute myocardial infarction (AMI) in the elderly.

Methods

A total of 159 elderly AMI patients admitted to Chongming Hospital affiliated to Shanghai University of Medicine & Health Sciences from January 2018 to January 2023 were enrolled in the study. CRP levels were determined using an automatic biochemical analyzer, and S100A12 levels were measured using enzyme-linked immunosorbent assay (ELISA). Patients were categorized based on the Lown classification into groups without MVA and with MVA. Univariate analysis was initially performed to identify independent variables, followed by multivariate logistic regression to determine the risk factors for malignant ventricular arrhythmias post-AMI. The predictive value of S100A12 protein and CRP for malignant ventricular arrhythmias after acute myocardial infarction in the elderly was analyzed using the receiver operating characteristic (ROC) curve.

Results

Among the 159 patients with AMI, 27 (17%) had MVA. Multivariate logistic regression analysis indicated that both S100A12 protein and CRP could be independent risk factors for malignant ventricular arrhythmias following acute myocardial infarction in the elderly (p < 0.05). The area under the ROC curve showed the area under the curve (AUC) for S100A12 protein to be 0.7147, for CRP 0.7356, and for the combined diagnosis 0.8350 (p < 0.05).

Conclusion

S100A12 protein and CRP are independent risk factors for MVA after MI in the elderly. The combined application of S100A12 protein and CRP has higher diagnostic sensitivity and specificity.

Genomic and RNA-Seq profiling of patients with HFrEF unraveled OAS1 mutation and aggressive expression

BACKGROUND

Heart failure (HF) is a complex pathophysiological state characterized by inadequate delivery of blood and nutrients to the cardiac tissues. It is rarely curable and is commonly associated with a poor prognosis. In this study, we aimed to analyse exomic and RNA-Seq data from patients with HF to identify the key altered pathways in HF.

METHODS

Whole blood samples were collected from patients with HF and subjected to whole exome sequencing (WES) and RNA-Seq analysis. The gene expression and RNA-Seq data obtained were verified using gene chip analysis and RT-PCR.

RESULTS

Both exomic and RNA-Seq data confirmed the dysregulation of phosphorylation and immune signalling in patients with HF. Specifically, exomic analysis showed that TITIN, OBSCURIN, NOD2, CDH2, MAP3K5, and SLC17A4 mutations were associated with HF, and RNA-Seq revealed that S100A12, S100A8, S100A9, PFDN5, and TMCC2, were upregulated in patients with HF. Additionally, comparison between RNA-seq and WES data showed that OAS1 mutations are associated with HF.

CONLCUSION

Our findings indicated that patients with HF show an overall disruption of key phosphorylation and immune signalling pathways. Based on RNA-seq and WES, OAS1 mutations may be primarily responsible for these changes.

Association of the inflammation-related proteome with dementia development at older age: results from a large, prospective, population-based cohort study

Background

Chronic inflammation is a central feature of several forms of dementia. However, few details on the associations of blood-based inflammation-related proteins with dementia incidence have been explored yet.

Methods

The Olink Target 96 Inflammation panel was measured in baseline serum samples (collected 07/2000–06/2002) of 1782 older adults from a German, population-based cohort study in a case-cohort design. Logistic regression models were used to assess the associations of biomarkers with all-cause dementia, Alzheimer’s disease, and vascular dementia incidence.

Results

During 17 years of follow-up, 504 participants were diagnosed with dementia, including 163 Alzheimer’s disease and 195 vascular dementia cases. After correction for multiple testing, 58 out of 72 tested (80.6%) biomarkers were statistically significantly associated with all-cause dementia, 22 with Alzheimer’s disease, and 33 with vascular dementia incidence. We identified four biomarker clusters, among which the strongest representatives, CX3CL1, EN-RAGE, LAP TGF-beta-1, and VEGF-A, were significantly associated with dementia endpoints independently from other inflammation-related proteins. CX3CL1 (odds ratio [95% confidence interval] per 1 standard deviation increase: 1.41 [1.24–1.60]) and EN-RAGE (1.41 [1.25–1.60]) were associated with all-cause dementia incidence, EN-RAGE (1.51 [1.25–1.83]) and LAP TGF-beta-1 (1.46 [1.21–1.76]) with Alzheimer’s disease incidence, and VEGF-A (1.43 [1.20–1.70]) with vascular dementia incidence. All named associations were stronger among APOE ε4-negative subjects.

Conclusion

With this large, population-based cohort study, we show for the first time that the majority of inflammation-related proteins measured in blood samples are associated with total dementia incidence. Future studies should concentrate not only on single biomarkers but also on the complex relationships in biomarker clusters.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01063-y.

Increased serum S100A12 levels are associated with higher risk of acute heart failure in patients with type 2 diabetes

AIMS

The hyperglycaemic stress induces the release of inflammatory proteins such as S100A12, one of the endogenous ligands of the receptors for advanced glycation end products (RAGE). Chronic activation of RAGE has multiple deleterious effects in target tissues such as the heart and the vessels by promoting oxidative stress, inflammation by the release of cytokines, macrophages infiltration, and vascular cell migration and proliferation, causing ultimately endothelial cell and cardiomyocyte dysfunction. The aim of our study was to investigate the prognostic value of circulating S100A12 beyond established cardiovascular risk factors (CVRF) for heart failure (HF) and major adverse cardiovascular events (MACE) in a cohort of patients with type 2 diabetes.

METHODS AND RESULTS

Serum S100A12 concentrations were measured at baseline in 1345 type 2 diabetes patients (58% men, 64 ± 11 years) recruited in the SURDIAGENE prospective cohort. Endpoints were the occurrence of acute HF requiring hospitalization (HHF) and MACE. We used a proportional hazard model adjusted for established CVRF (age, sex, duration of diabetes, estimated glomerular filtration rate, albumin/creatinine ratio, history of coronary artery disease) and serum S100A12. During the median follow-up of 84 months, 210 (16%) and 505 (38%) patients developed HHF and MACE, respectively. Baseline serum S100A12 concentrations were associated with an increased risk of HHF [hazard ratio (HR) (95% confidence interval) 1.28 (1.01-1.62)], but not MACE [1.04 (0.90-1.20)]. After adjustment for CVRF, S100A12 concentrations remained significantly associated with an increased risk of HHF [1.29 (1.01-1.65)]. In a sub-analysis, patients with high probability of pre-existing HF [N terminal pro brain natriuretic peptide (NT-proBNP) >1000 pg/mL, n = 87] were excluded. In the remaining 1258 patients, the association of serum S100A12 with the risk of HHF tended to be more pronounced [1.39 (1.06-1.83)]. When including the gold standard HF marker NT-proBNP in the model, the prognostic value of S100A12 for HHF did not reach significance. Youden method performed at 7 years for HHF prediction yielded an optimal cut-off for S100A12 concentration of 49 ng/mL (sensitivity 53.3, specificity 52.2). Compared with those with S100A12 ≤ 49 ng/mL, patients with S100A12 > 49 ng/mL had a significantly increased risk of HHF in the univariate model [HR = 1.58 (1.19-2.09), P = 0.0015] but also in the multivariate model [HR = 1.63 (1.23-2.16), P = 0.0008]. After addition of NT-proBNP to the multivariate model, S100A12 > 49 ng/mL remained associated with an increased risk of HHF [HR = 1.42 (1.07-1.90), P = 0.0160]. However, the addition of S100A12 categories on top of multivariate model enriched by NT-pro BNP did not improve the ability of the model to predict HHF (relative integrated discrimination improvement = 1.9%, P = 0.1500).

CONCLUSIONS

In patients with type 2 diabetes, increased serum S100A12 concentration is independently associated with risk of HHF, but not with risk of MACE. Compared with NT-proBNP, the potential clinical interest of S100A12 for the prediction of HF events remains limited. However, S100A12 could be a candidate for a multimarker approach for HF risk assessment in diabetic patients.

Human Plasma Transcriptome Implicates Dysregulated S100A12 Expression: A Strong, Early-Stage Prognostic Factor in ST-Segment Elevated Myocardial Infarction: Bioinformatics Analysis and Experimental Verification

The ability of blood transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction remains unknown. Two gene expression datasets (GSE60993 and GSE61144) were downloaded from Gene Expression Omnibus (GEO) Datasets to identify altered plasma transcriptomes in patients with ST-segment elevated myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. GEO2R, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotations, protein–protein interaction analysis, etc., were adopted to determine functional roles and regulatory networks of differentially expressed genes (DEGs). Dysregulated expressomes were verified at transcriptional and translational levels by analyzing the GSE49925 dataset and our own samples, respectively. A total of 91 DEGs were identified in the discovery phase, consisting of 15 downregulated genes and 76 upregulated genes. Two hub modules consisting of 12 hub genes were identified. In the verification phase, six of the 12 hub genes exhibited the same variation patterns at the transcriptional level in the GSE49925 dataset. Among them, S100A12 was shown to have the best discriminative performance for predicting in-hospital mortality and to be the only independent predictor of death during follow-up. Validation of 223 samples from our center showed that S100A12 protein level in plasma was significantly lower among patients who survived to discharge, but it was not an independent predictor of survival to discharge or recurrent major adverse cardiovascular events after discharge. In conclusion, the dysregulated expression of plasma S100A12 at the transcriptional level is a robust early prognostic factor in patients with STEMI, while the discrimination power of the protein level in plasma needs to be further verified by large-scale, prospective, international, multicenter studies.

Influence of ejection fraction on biomarker expression and response to spironolactone in people at risk of heart failure: findings from the HOMAGE trial

BACKGROUND

Left ventricular ejection fraction (LVEF) can provide hemodynamic information and may influence the response to spironolactone and other heart failure (HF) therapies.

AIMS

To study the patient characteristics and circulating protein associations with LVEF, and whether LVEF influenced the response to spironolactone.

METHODS

HOMAGE enrolled patients aged >60 years at high risk of developing HF with a LVEF ≥45%. 527 patients were randomized to either spironolactone or standard-of-care for ≈9 months. 276 circulating proteins were measured using Olink® technology.

RESULTS

364 patients had available LVEF determined by the Simpson's bi-plane method. The respective LVEF tertiles were: Tertile1:<60% (N = 122), Tertile2:60%-65% (N = 121), and Tertile3:>65% (N = 121). Patients with a LVEF>65% had smaller LV chamber size and volumes, and lower natriuretic peptide levels. Compared to patients with a LVEF<60%, those with LVEF>65% had higher levels of circulating c-c motif chemokine ligand-23 and interleukin-8, and lower levels of tissue plasminogen activator, BNP, S100 calcium binding protein A12, and collagen type I alpha 1 chain (COL1A1). Spironolactone significantly reduced the circulating levels of BNP and COL1A1 without significant treatment-by-LVEF heterogeneity: BNP change β = -0.36 Log₂ and COL1A1 change β = -0.16 Log₂ (P < 0.0001 for both; interactionP>0.1 for both). Spironolactone increased LVEF from baseline to month 9 by 1.1%, P = 0.007.

CONCLUSION

Patients with higher LVEF had higher circulating levels of chemokines and inflammatory markers and lower levels of stretch, injury, and fibrosis markers. Spironolactone reduced the circulating levels of natriuretic peptides and type 1 collagen, and increased LVEF. This article is protected by copyright. All rights reserved.

Utility of S100A12 as an Early Biomarker in Patients With ST-Segment Elevation Myocardial Infarction

Importance: S100A12 is a calcium binding protein which is involved in inflammation and progression of atherosclerosis.

Objective: We sought to investigate the utility of S100A12 as a biomarker for the early diagnosis and prognostication of patients presenting with ST-segment elevation myocardial infarction (STEMI).

Design, Setting, and Participants: S100A12 was measured in 1023 patients presenting to the emergency department with acute chest pain between June 2012 and November 2015. An independent cohort of 398 patients enrolled at 3 different hospitals served as a validation cohort.

Main Outcomes and Measures: The primary clinical endpoint of interest was major adverse cardiac and cerebral events (MACCE) defined as a composite of all-cause death, MI, stroke, or hospitalization for heart failure.

Results: A total of 438/1023 patients (42.8%) in the diagnosis cohort were adjudicated as STEMI, among whom plasma S100A12 levels increased within 30 min and peaked 1–2 h after symptom onset. Compared with high-sensitivity cardiac troponin T and creatine kinase-MB isoenzyme, S100A12 more accurately identified STEMI, especially within the first 2 h after symptom onset (area under the curve 0.963 compared with 0.860 for hscTnT and 0.711 for CK-MB, both P &lt; 0.05). These results were consistent in the 243-patient validation cohort. The 1-year rate of MACCE was greatest in patients in the highest peak S100A12 tertile, intermediate in the middle tertile and least in the lowest tertile (9.3 vs. 5.7 vs. 3.0% respectively, Ptrend = 0.0006). By multivariable analysis the peak plasma concentration of S100A12 was an independent predictor of MACCE within 1 year after STEMI (HR, 1.001, 95%CI, 1.000–1.002; P = 0.0104).

Conclusions and Relevance: S100A12 rapidly identified patients with STEMI, more accurately than other cardiac biomarkers, especially within the first 2 h after symptom onset. The peak plasma S100A12 level was a strong predictor of 1-year prognosis after STEMI.

A Specific Blood Signature Reveals Higher Levels of S100A12: A Potential Bladder Cancer Diagnostic Biomarker Along With Urinary Engrailed-2 Protein Detection

Urothelial carcinoma of the urinary bladder (UCB) or bladder cancer remains a major health problem with high morbidity and mortality rates, especially in the western world. UCB is also associated with the highest cost per patient. In recent years numerous markers have been evaluated for suitability in UCB detection and surveillance. However, to date none of these markers can replace or even reduce the use of routine tools (cytology and cystoscopy). Our current study described UCB's extensive expression profile and highlighted the variations with normal bladder tissue. Our data revealed that JUP, PTGDR, KLRF1, MT-TC, and RNU6-135P are associated with prognosis in patients with UCB. The microarray expression data identified also S100A12, S100A8, and NAMPT as potential UCB biomarkers. Pathway analysis revealed that natural killer cell mediated cytotoxicity is the most involved pathway. Our analysis showed that S100A12 protein may be useful as a biomarker for early UCB detection. Plasma S100A12 has been observed in patients with UCB with an overall sensitivity of 90.5% and a specificity of 75%. S100A12 is highly expressed preferably in high-grade and high-stage UCB. Furthermore, using a panel of more than hundred urine samples, a prototype lateral flow test for the transcription factor Engrailed-2 (EN2) also showed reasonable sensitivity (85%) and specificity (71%). Such findings provide confidence to further improve and refine the EN2 rapid test for use in clinical practice. In conclusion, S100A12 and EN2 have shown potential value as biomarker candidates for UCB patients. These results can speed up the discovery of biomarkers, improving diagnostic accuracy and may help the management of UCB.

Relation of postoperative serum S100A12 levels to delirium and cognitive dysfunction occurring after hip fracture surgery in elderly patients

OBJECTIVE

Brain injury is implicated in pathogenesis of postoperative delirium (POD) and cognitive dysfunction (POCD). S100A12 is involved in inflammatory process and is recently known as a biomarker for brain injury. Herein, we clarified whether serum S100A12 levels are related to POD and POCD after hip fracture surgery in elderly patients.

MATERIALS AND METHODS

In this prospective, observational study, we gauged S100A12 levels in preoperative and postoperative serum from 186 patients and serum from 186 controls. Patients were categorized according to the presence of POD and POCD.

RESULTS

Postoperative, but not preoperative serum S100A12 levels were significantly higher in patients than in controls. There was a positive and independent correlation between postoperative C-reactive protein and S100A12 levels (t = 8.797, p < 0.001). Postoperative S10012 levels and age were independently associated with the risk of developing POD (S100A12 levels: odds ratio [OR] = 1.166, 95% confidence interval [CI] = 1.045-2.087, p = 0.001; age: OR = 1.243, 95% CI = 1.073-1.419, p = 0.012) and POCD (S100A12: OR = 1.157, 95% CI = 1.030-1.986, p = 0.003; age: OR = 1.228, 95% CI = 1.054-1.387, p = 0.014). In terms of area under receiver operating characteristic curve, postoperative S100A12 levels had a higher predictive ability than age and their combination dramatically exceeded that of each one alone.

CONCLUSIONS

Postoperative elevated serum S100A12 levels have a strong relation to inflammation and are associated independently with the development of POD and POCD, substantializing serum S100A12 as a potential biomarker for predicting POD and POCD in elderly patients undergoing hip fracture surgery.

S100A12 as a marker of worse cardiac output and mortality in pulmonary hypertension

BACKGROUND AND OBJECTIVE

Molecular biomarkers are needed to refine prognostication and phenotyping of pulmonary hypertension (PH) patients. S100A12 is an emerging biomarker of various inflammatory diseases. This study aims to determine the prognostic value of S100A12 in PH.

METHODS

Exploratory microarray analysis performed on peripheral blood mononuclear cells (PBMC) collected from idiopathic pulmonary fibrosis (IPF) patients suggested an association between S100A12 and both PH and mortality. So the current study was designed to evaluate for an association between S100A12 in peripheral blood collected from two well-phenotyped PH cohorts in two other centres to derive and validate an association between S100A12 protein serum concentrations and mortality.

RESULTS

The majority of the patients in the discovery and validation cohorts were either World Health Organization (WHO) group 1 (pulmonary arterial hypertension (PAH)) or 3 (lung disease-associated) PH. In the discovery PH cohort, S100A12 was significantly increased in patients with PH (n = 51) compared to controls (n = 22) (29.8 vs 15.7 ng/mL, P < 0.001) and negatively correlated with cardiac output (r = -0.58, P < 0.001) in PH patients. When S100A12 data were pooled from both cohorts, PAH and non-PAH PH patients had higher S100A12 compared to healthy external controls (32.6, 30.9, 15.7 ng/mL; P < 0.001). S100A12 was associated with an increased risk in overall mortality in PH patients in both the discovery (n = 51; P = 0.008) and validation (n = 40; P < 0.001) cohorts.

CONCLUSION

S100A12 levels are increased in PH patients and are associated with increased mortality.

S100A12 in renal and cardiovascular diseases

Expression of S100A12, a small calcium-binding protein, by neutrophils and monocytes/macrophages induces proinflammatory responses via ligation with the receptor for advanced glycation end-products (RAGE) and subsequent activation of intracellular signal transduction pathways such as the nuclear factor (NF)-κB pathway. Although S100A12 has been demonstrated to be a useful biomarker during inflammatory conditions, its precise role in the pathogenesis of renal and cardiovascular diseases has not been fully understood. Recently, several studies have employed S100A12 transgenic mice to investigate its pathological effects. Further studies using these models are required before we can translate these findings to human diseases such as renal and cardiovascular diseases.

The frontline of immune response in peripheral blood

Peripheral blood is an attractive source for the discovery of disease biomarkers. Gene expression profiling of whole blood or its components has been widely conducted for various diseases. However, due to population heterogeneity and the dynamic nature of gene expression, certain biomarkers discovered from blood transcriptome studies could not be replicated in independent studies. In the meantime, it's also important to know whether a reliable biomarker is shared by several diseases or specific to certain health conditions. We hypothesized that common mechanism of immune response in blood may be shared by different diseases. Under this hypothesis, we surveyed publicly available transcriptome data on infectious and autoimmune diseases derived from peripheral blood. We examined to which extent common gene dys-regulation existed in different diseases. We also investigated whether the commonly dys-regulated genes could serve as reliable biomarkers. First, we found that a limited number of genes are frequently dys-regulated in infectious and autoimmune diseases, from which we selected 10 genes co-dysregulated in viral infections and another set of 10 genes co-dysregulated in bacterial infections. In addition to its ability to distinguish viral infections from bacterial infections, these 20 genes could assist in disease classification and monitoring of treatment effect for several infectious and autoimmune diseases. In some cases, a single gene is sufficient to serve this purpose. It was interesting that dys-regulation of these 20 genes were also observed in other types of diseases including cancer and stroke where certain genes could also serve as biomarkers for diagnosis or prognosis. Furthermore, we demonstrated that this set of 20 genes could also be used in continuous monitoring of personal health. The rich information from these commonly dys-regulated genes may find its wide application in clinical practice and personal healthcare. More validation studies and in-depth investigations are warranted in the future.