Persistent symptoms are frequently reported many months after SARS-CoV-2 infection. However, evidence of the association between long COVID features and initial Omicron infection severity is lacking. A prospective observational cohort study of n = 40 SARS-CoV-2 (Omicron B.1.1.529) infected individuals was performed, comprising n = 20 non-hospitalised and n = 20 hospitalised cases. ISARIC symptomology was recorded at baseline, 3-, 6- and 12-months post infection. Total and neutralising IgG antibody and inflammatory proteome concentrations were determined in plasma at baseline and 3 months. Pairwise comparisons were made between non-hospitalised and hospitalised subgroup biological and symptom data to investigate temporal changes. The non-hospitalised subgroup had higher median levels of Omicron variant specific ACE2 neutralising antibodies by 3 months, in particular females, though the difference was not statistically significant (BA.1 + R346K p = 0·06; BA.1 + L452R p = 0.08; BA.2 p = 0·09). Inflammatory proteins type 1 keratin, prostasin and leukocyte associated immunoglobulin receptor 1 were significantly elevated in hospitalised subgroup plasma 3 months post infection (padj < 0·05). Fatigue, muscle pain, headache, and cough symptoms subsided after 12 months in non-hospitalised, whilst significantly more hospitalised individuals reported continued muscle pain and shortness of breath (p < 0·05). This study provides the first evidence of the link between Omicron hospitalisation and long-term humoral immune response, inflammatory plasma proteome, and symptom persistence. ClinicalTrials.gov NCT05548829.

BackgroundVarious studies have reported altered expression of metalloproteinases in Alzheimer's disease (AD); however, expression profiles of each metalloproteinase during cognitive decline have not yet been fully characterized.ObjectiveThe purpose of this systematic review was to generate a comprehensive overview of metalloproteinases and their cognate inhibitors expression in AD and mild cognitive impairment (MCI), across sample matrices, to determine whether metalloproteinases are dysregulated in AD and may have predictive power in individuals with cognitive decline.MethodsAn electronic literature search was conducted in PubMed, EMBASE, Scopus and MEDLINE from inception to December 2024. Sixty-one publications reporting metalloproteinase and inhibitor levels in 8576 patients with AD or MCI, and 7333 controls were included in the systematic review, twenty-one of which were extracted for meta-analysis. Standardized mean difference (SMD) was used to illustrate comparisons, and the Newcastle-Ottawa scale to assess bias.ResultsHigher levels of cerebrospinal fluid (CSF) tissue inhibitor of metalloproteinase-2 (TIMP-2; p = 0.0003) were observed in the AD group and in patients with MCI (p = 0.0009) compared to cognitively healthy controls. Following sensitivity analysis, significantly higher levels of CSF MMP-10 (p = 0.0005) and lower plasma TIMP-2 (p = 0.004) were also noted in patients with AD. TIMP-3, across all sample matrices, was decreased in patients with MCI versus controls (p = 0.01).ConclusionsSignificantly altered levels of metalloproteinases and their inhibitors were verified between patients with AD and MCI, representing potential biomarkers and prospective therapeutic targets for cognitive decline. This study was registered with PROSPERO, CRD42024628202.

Depression is characterised by a low mood, loss of interest or pleasure, pessimism, impaired concentration, decreased energy, and fatigue. A strong relationship between inflammatory processes and the pathophysiology of depression had been identified. Studies have suggested that the NLRP3 inflammasome is a key contributor to the pathogenesis of depression. This systematic review aimed to synthesise evidence from animal models of depression to evaluate both priming and activation of the inflammasome, with a specific focus on clarifying the role in depressive pathology. PubMed, Scopus, Web of Science, EMBASE and Medline were searched up until November 2024. Studies involving animal models of depression, measuring NLRP3 inflammasome components (NLRP3, ASC, Caspase-1 and IL-1β) were eligible for inclusion. Risk of bias was assessed using SYRCLEs Risk of Bias tool, and random-effects meta-analysis was conducted for each inflammasome component using RevMan. A total of 3345 studies were identified, with 23 accepted after full text screening, and 16 included in the meta-analysis. Across 170 animals (85 depression model, 85 controls), protein and mRNA levels of NLRP3, ASC, Caspase-1, and IL-1β were significantly upregulated in depression models compared to controls. Analysed brain regions included the hippocampus and prefrontal cortex. Moderate heterogeneity was observed between studies (I2 = 0-68 %). This systematic review and meta-analysis demonstrates consistent upregulation of NLRP3 inflammasome components in animal models of depression, therefore suggesting an association with depression. Further research should investigate the therapeutic potential of targeting the NLRP3 inflammasome to reduce neuroinflammation and alleviate depression symptoms in both animal and human studies.

The NLRP3 inflammasome contributes to the inflammatory process in atherosclerosis by producing IL-1β. Components of the intracellular NLRP3 inflammasome have been shown to be expressed by macrophages in the atherosclerotic plaque and are a potential therapeutic target. We aimed to determine the efficacy of the novel bispecific antibody InflamAb, designed to target the interleukin-1 receptor type 1 and the NLRP3 inflammasome, in inhibiting atherosclerosis. InflamAb effectively inhibited IL-1β secretion from bone marrow-derived macrophages and reduced circulating IL-1β levels in vivo. Furthermore, InflamAb treatment significantly inhibited atherosclerotic plaque development, accompanied by a reduction in relative macrophage and necrotic core content. InflamAb treatment did not affect the size of established atherosclerotic lesions; however, InflamAb significantly reduced relative macrophage and necrotic core content in these plaques. To conclude, inhibition of the NLRP3 inflammasome by the bispecific antibody InflamAb shows promising efficacy in inhibiting atherosclerotic plaque development and destabilization in Apoe-/- mice.

SARS-CoV-2 has claimed more than 7 million lives worldwide and has been associated with prolonged inflammation, immune dysregulation and persistence of symptoms following severe infection. Understanding the T cell mediated immune response and factors impacting development and continuity of SARS-CoV-2 specific memory T cells is pivotal for developing better therapeutic and monitoring strategies for those most at risk from COVID-19. Here we present a comprehensive analysis of memory T cells in a convalescent cohort (n=20), three months post Omicron infection. Utilising flow cytometry to investigate CD4+CD45RO+ and CD8+CD45RO+ memory T cell IL-2 expression following Omicron (B.1.1.529/BA.1) peptide pool stimulation, alongside T cell receptor repertoire profiling and RNA-Seq analysis, we have identified several immunological features associated with hospitalised status. We observed that while there was no significant difference in median CD4+CD45RO+ IL-2+ and CD8+ CD45RO+ IL-2+ memory T cell count between subgroups, the hospitalised subgroup expressed significantly more IL-2 per cell following Omicron peptide pool exposure in the CD8+CD45RO+ population (p <0.03) and trended towards significance in CD4+CD45RO+ cells (p <0.06). T cell receptor repertoire analysis found that the non-hospitalised subgroup had a much higher number of circulating clonotypes, targeting a wider range of predominantly MHC-I epitopes across the SARS-CoV-2 genome. Several immunodominant epitopes, conserved between both subgroups, were observed, however hospitalised individuals were less likely to express putative HLA alleles responsible for pMHC presentation which may impact TCR affinity. We observed a bias towards shorter CDR3 segments in TCRβ repertoire analysis within the hospitalised subgroup, alongside lower rates of repertoire overlap in CDR3 sequences compared to the non-hospitalised subgroup. We found a significant proportion of TCRs targeted epitopes along the SARS-CoV-2 genome including non-structural proteins, responsible for viral replication and immune evasion. These findings highlight how the continuity of T cell based protective immunity is impacted by both the viral replication cycle of SARS-CoV-2 upon intracellular and innate immune responses, and HLA-type upon TCR affinity and clonotype formation. Our novel Epitope Target Analysis Pipeline (Epi-TAP) could prove beneficial in development of new therapeutic strategies through rapid identification of shared immunodominant epitopes across non-hospitalised and hospitalised subgroups.

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2 has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19 positive patients (n=400 for proteomics, n=483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients, involved in various inflammatory and immunological pathways. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001), were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing correlation with hospitalisation status. Conclusion: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.

Abstract Background The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. Methods Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. Results HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. Conclusions In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. Trial registration https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

Introduction: Cellular senescence is the irreversible growth arrest subsequent to oncogenic mutations, DNA damage, or metabolic insult. Senescence is associated with ageing and chronic age associated diseases such as cardiovascular disease and diabetes. The involvement of cellular senescence in acute kidney injury (AKI) and chronic kidney disease (CKD) is not fully understood. However, recent studies suggest that such patients have a higher-than-normal level of cellular senescence and accelerated ageing. Methods: This study aimed to discover key biomarkers of senescence in AKI and CKD patients compared to other chronic ageing diseases in controls using OLINK proteomics. Results: We show that senescence proteins CKAP4 (p-value < 0.0001) and PTX3 (p-value < 0.0001) are upregulated in AKI and CKD patients compared with controls with chronic diseases, suggesting the proteins may play a role in overall kidney disease development. Conclusions: CKAP4 was found to be differentially expressed in both AKI and CKD when compared to UHCs; hence, this biomarker could be a prognostic senescence biomarker of both AKI and CKD.

Background Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression. Objective The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19. Methods The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk. Results An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes. Conclusions This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants. International Registered Report Identifier (IRRID) DERR1-10.2196/50733

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Dutch Heart Foundation Background/Introduction Atherosclerosis is the underlying pathology of many cardiovascular diseases and is characterized by chronic inflammation in the larger arteries. Activation of the NLRP3 inflammasome is one of the drivers of inflammation during atherosclerosis. Purpose Therefore, we aimed to determine the ability of the novel bispecific anti-NLRP3 inflammasome antibody, called InflamAb, to inhibit atherosclerosis. Methods and Results In vitro treatment of bone marrow derived macrophages with 25 ng/mL InflamAb effectively inhibited the IL-1β release induced by NLRP3 inflammasome activation with LPS and Aluminium hydroxide (P<0.05). In addition, InflamAb administration in western-type diet fed Apoe-/- mice significantly reduced circulating IL-1β at 4 hours post inflammasome activation (P<0.05), thereby showing in vivo efficacy of InflamAb. Subsequently, we assessed the effect of InflamAb on both the development of atherosclerosis and on pre-existing plaques. Treatment of western-type diet fed female Apoe-/- mice with 100 µg InflamAb or isotype control antibody (3x per week i.p) significantly inhibited collar-induced atherosclerotic plaque development in the carotid artery from 59±8*10^3 µm^2 in control mice to 36±5*10^3 µm^2 (P<0.05), which coincided with a reduction in relative macrophage (control: 36±2% versus InflamAb: 28±3%, P<0.05) and necrotic core content (control: 16±2% versus InflamAb: 8±1%, P<0.05). InflamAb treatment of male Apoe-/- with pre-existing atherosclerosis did not affect lesion size, but improved plaque stability parameters as illustrated by a reduced relative macrophage (control: 48±2% versus InflamAb: 42±2%, P<0.05) and necrotic core (control: 21±1% versus InflamAb: 18±1%, P<0.05) content. In addition, we observed a trend towards increased collagen levels upon InflamAb treatment (control: 39±2% versus InflamAb: 44±2%, P=0.08). Conclusion To conclude, NLRP3 inflammasome inhibition by the bispecific antibody InflamAb shows promising efficacy in inhibiting atherosclerotic plaque development and destabilization.

Severe COVID-19 is characterised by an overactive pro-inflammatory response of the immune system which is associated with new persistent symptoms. Differences in the persistence of symptoms, plasma inflammatory proteins and antibodies between hospitalised and non-hospitalised cases were investigated in the current study. n=120 participants were recruited with informed consent, of whom n=60 were hospitalised with severe disease. Blood samples and detailed symptom data were collected 3 months after a positive PCR test for SARS-CoV-2. Plasma samples were analysed by OLINK Explore 384 inflammation panel and ACE2 neutralisation and IgG assays. R software was used to identify statistically significant differences in protein concentration in hospitalised cases, relative to non-hospitalised cases. Differences in anti-SARS-CoV-2 IgG antibody concentration, ACE2 neutralisation and symptoms were also analysed between hospitalisation subgroups. 14 proteins including angiopoietin-like protein 2 and C-X-C motif chemokine 17 were significantly elevated (by greater than +/− 0.20 log2 fold change; p < 0.05) at 3 months in the hospitalised study group, relative to non-hospitalised cases. The concentrations of BA.2 and BA.3 specific anti-SARS-CoV-2 IgG and spike receptor binding domain neutralising antibodies were more frequently detected in non-hospitalised cases. The hospitalised cohort more frequently reported persistence of muscle pain, headache, fatigue and shortness of breath. Characteristic changes in the plasma proteome and symptom clusters that persist 3 months after infection have been observed in hospitalised COVID-19 cases. This data could help inform clinical care requirements for longer term COVID-19 recovery. Supported by grants from UKRI, SFI and HSC R&D Office

Background: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS). Methods: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRS CAD ) was calculated. The prediction was performed using combinations of PRS CAD , proteins, and PMRS as features in models using stability selection and machine learning. Results: Prediction of absence of CAD yielded an area under the curve of PRS CAD -model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, −0.04; P =0.13). Optimal predictive ability was achieved by the full model (PRS CAD +protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone ( P <0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients. Conclusions: For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02264717

Abstract Objective The Covid Response Study (COVRES, NCT05548829) aims to carry out an integrated multi-omic analysis of factors contributing to host susceptibility to SARS-CoV-2 among a patient cohort of 1000 people from the geographically isolated island of Ireland. Background Health organisations and countries around the world have found it difficult to control the spread of the coronavirus disease 2019. To minimise the impact on the NHS and improve patient care, there is a drive for rapid tests capable of detecting individuals who are at high risk of contracting severe COVID-19. Early work focused on single omic approaches, highlighting a limited amount of information. Study Design The protocol below describes the study to be carried out in Northern Ireland (NI-COVRES) by Ulster University, the Republic of Ireland component will be described separately. All participants (n = 519) were recruited from the Western Health and Social Care Trust, Northern Ireland, forty patients are also being followed up at 1, 3, 6 and 12 months to assess the longitudinal impact of infection on symptoms, general health, and immune response, this is ongoing. Methods Data will be sourced from whole blood, saliva samples, and clinical data from the Northern Ireland Electronic Care Record, general health questionnaire, and the GHQ12 mental health survey. Saliva and blood samples were processed for DNA and RNA prior to whole genomic sequencing, RNA sequencing, DNA methylation, microbiome, 16S, and proteomic analysis. Multi-omics data will be combined with clinical data to produce sensitive and specific prognostic models of severity risk. Results An initial profile of the cohort has been completed: n = 249 hospitalised and n = 270 non-hospitalised patients were recruited, 64% were female, the mean age was 45 years. High levels of comorbidity were evident in the hospitalised cohort, with cardiovascular disease and metabolic and respiratory disorders (P < 0.001) being the most significant. Conclusion This study will provide a comprehensive opportunity to study multi-omic mechanisms of COVID-19 severity in re-contactable participants. Trial Registration - The trial has been registered as an observational study on clinicaltrials.gov as NCT05548829. An outline of the trial protocol is included; SPIRIT checklist (Supplementary Fig. 1).

Abstract Background: Prompt recognition and treatment of occlusion myocardial infarction (OMI) is essential, yet current pathways miss a proportion of patients who have OMI as not all have electrocardiogram changes. This exploratory study aimed to determine if proteomic analysis combined with clinical factors could improve diagnostic accuracy in OMI patients. Methods: In this case-controlled exploratory study 368 proteins were analysed from patients having a myocardial infarction and controls with stable angina. Angiographic and clinical features were recorded. Proteins were analysed using a proximity extension assay. Machine-learning techniques of hybrid and forward feature selection algorithms followed by comparing decision tree and logistical regression analysis were used to indicate the optimal classifier of proteins and clinical factors to increase diagnostic sensitivity in OMI. Results: Plasma samples were obtained from 130 patients, 41 (31.5%) had a non-OMI and 16 (12.3%) had OMI. The other 73 (56.2%) had stable angina with no evidence of myocardial infarction. A combination of 19 clinical features and 87 biomarkers for OMI gave a detection of AUC=0.90 which was higher than identification of OMI by clinical features alone (AUC=0.84) although similar to biomarkers alone (AUC=0.91). The decision tree classifier that included combination of biomarkers and clinical factors reached statistical significance for detection for OMI (p<0.001) compared to the logistical regression tree classifier. Conclusion: In this study we created a classifier for the diagnosis of OMI through a combination of clinical factors and proteins following proteomic analysis. Further refinement with larger cohorts and focused prior feature selection are required for validation.

Abstract Background: Prompt recognition and treatment of occlusion myocardial infarction (OMI) is essential, yet current pathways miss a proportion of patients who have OMI as not all have electrocardiogram changes. This exploratory study aimed to determine if proteomic analysis combined with clinical factors could improve diagnostic accuracy in OMI patients. Methods: In this case-controlled exploratory study 368 proteins were analysed from patients having a myocardial infarction and controls with stable angina. Angiographic and clinical features were recorded. Proteins were analysed using a proximity extension assay. Machine-learning techniques of hybrid and forward feature selection algorithms followed by comparing decision tree and logistical regression analysis were used to indicate the optimal classifier of proteins and clinical factors to increase diagnostic sensitivity in OMI. Results: Plasma samples were obtained from 130 patients, 41 (31.5%) had a non-OMI and 16 (12.3%) had OMI. The other 73 (56.2%) had stable angina with no evidence of myocardial infarction. A combination of 19 clinical features and 87 biomarkers for OMI gave a detection of AUC=0.90 which was higher than identification of OMI by clinical features alone (AUC=0.84) although similar to biomarkers alone (AUC=0.91). The decision tree classifier that included combination of biomarkers and clinical factors reached statistical significance for detection for OMI (p<0.001) compared to the logistical regression tree classifier. Conclusion: In this study we created a classifier for the diagnosis of OMI through a combination of clinical factors and proteins following proteomic analysis. Further refinement with larger cohorts and focused prior feature selection are required for validation.