Plasma Protein Profile of Incident Myocardial Infarction, Ischemic Stroke, and Heart Failure in 2 Cohorts

Prognostic effect of osteoprotegerin in patients with ischemic stroke: A systematic review and meta-analysis

BACKGROUND

Osteoprotegerin (OPG) is supposed to participate in the development of atherosclerosis and cardio-cerebrovascular disease. However, the results of research on relationship between OPG and ischemic stroke (IS) are controversial. Therefore, we carried out the first systematic review and meta-analysis to evaluate prognostic effect of osteoprotegerin in patients with IS.

METHODS

We comprehensively searched databases of PubMed, Embase, and the Cochrane Library through 21 August 2023 to identify observational studies that evaluated effect of OPG on poor functional outcome (modified Rankin Scale [mRS] Score of 3-6) and mortality in patients with IS. Adjusted odds ratios (aOR) with a 95% confidence interval (CI) of each included study were used as much as possible to assess the pooled effect.

RESULTS

Five studies that enrolled 4,506 patients in total fulfilled our inclusion criteria. Three studies were included in the pooled analysis for each endpoint since one of the included studies had provided data on poor functional outcome as well as mortality. OPG was neither associated with poor functional outcome (aOR 1.29, 95% CI 0.90-1.85) nor with mortality (aOR 1.57, 95% CI 0.90-2.74) in patients with IS.

CONCLUSIONS

There is insufficient evidence to demonstrate the correlation between OPG and mortality or poor functional outcome in IS patients. OPG cannot be applied to predict worse neurological function in IS patients based on the current evidence.

Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study

BACKGROUND

Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D.

METHODS

The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI).

RESULTS

We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465).

CONCLUSIONS

This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.

Large scale plasma proteomics identifies novel proteins and protein networks associated with heart failure development

Heart failure (HF) causes substantial morbidity and mortality but its pathobiology is incompletely understood. The proteome is a promising intermediate phenotype for discovery of novel mechanisms. We measured 4877 plasma proteins in 13,900 HF-free individuals across three analysis sets with diverse age, geography, and HF ascertainment to identify circulating proteins and protein networks associated with HF development. Parallel analyses in Atherosclerosis Risk in Communities study participants in mid-life and late-life and in Trøndelag Health Study participants identified 37 proteins consistently associated with incident HF independent of traditional risk factors. Mendelian randomization supported causal effects of 10 on HF, HF risk factors, or left ventricular size and function, including matricellular (e.g. SPON1, MFAP4), senescence-associated (FSTL3, IGFBP7), and inflammatory (SVEP1, CCL15, ITIH3) proteins. Protein co-regulation network analyses identified 5 modules associated with HF risk, two of which were influenced by genetic variants that implicated trans hotspots within the VTN and CFH genes.

HFpEF: New biomarkers and their diagnostic and prognostic value

Heart failure characterized by preserved ejection fraction (HFpEF) poses a substantial challenge to healthcare systems worldwide and the diagnostic algorithms used currently mirror those utilized for reduced Ejection Fraction (HFrEF). This literature review aims to explore the diagnostic and prognostic credibility of numerous emerging biomarkers associated with HFpEF. We conducted a thorough analysis of the available medical literature and selected the biomarkers which yielded the maximum amount of published information. After reviewing the current literature we conclude that there are no biomarkers at present which are superior to natriuretic peptides in terms of diagnosis and prognosis of HFpEF. However biomarkers like Suppression of tumorigenicity2, Galectin3 and microRNAs are promising and can be researched further for future use. Although newer individual biomarkers may not be useful in diagnosing and prognosis of HFpEF, we believe that a specific biomarker profile may be identified in each phenotype,which can be used in future.

Plasma Protein Profiling of Incident Cardiovascular Diseases: A Multisample Evaluation

BACKGROUND

Proteomic profiling could potentially disclose new pathophysiological pathways for cardiovascular diseases (CVD) and improve prediction at the individual level. We therefore aimed to study the plasma protein profile associated with the incidence of different CVDs.

METHODS

Plasma levels of 245 proteins suspected to be linked to CVD or metabolism were measured in 4 Swedish prospective population-based cohorts (SIMPLER [Swedish Infrastructure for Medical Population-Based Life-Course and Environmental Research], ULSAM (Uppsala Longitudinal Study of Adult Men), EpiHealth, and POEM [Prospective Investigation of Obesity, Energy Production, and Metabolism]) comprising 11 869 individuals, free of CVD diagnoses at baseline. Our primary CVD outcome was defined by a combined end point that included either incident myocardial infarction, stroke, or heart failure.

RESULTS

Using a discovery/validation approach, 42 proteins were associated with our primary composite end point occurring in 1163 subjects. In separate meta-analyses for each of the 3 CVD outcomes, 49 proteins were related to myocardial infarction, 34 to ischemic stroke, and 109 to heart failure. Thirteen proteins were related to all 3 outcomes. Of those, urokinase plasminogen activator surface receptor, adrenomedullin, and KIM-1 (kidney injury molecule 1) were also related to several markers of subclinical CVD in Prospective Investigation of Obesity, Energy production and Metabolism, reflecting myocardial or arterial pathologies. In prediction analysis, a lasso selection of 11 proteins in ULSAM improved the discrimination of CVD by 3.3% (P<0.0001) in SIMPLER when added to traditional risk factors.

CONCLUSIONS

Protein profiling in multiple samples disclosed several new proteins to be associated with subsequent myocardial infarction, stroke, and heart failure, suggesting common pathophysiological pathways for these diseases. KIM-1, urokinase plasminogen activator surface receptor, and adrenomedullin were novel early markers of CVD. A selection of 11 proteins improved the discrimination of CVD.

Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures

OBJECTIVE

Patients with rheumatoid arthritis (RA) have different presentations and prognoses. Cluster analysis based on proteomic signatures creates independent phenogroups of patients with different pathophysiological backgrounds. We aimed to identify distinct pathophysiological clusters of RA patients based on circulating proteomic biomarkers.

METHOD

This was a cohort study including 399 RA patients. Clustering was performed on 94 circulating proteins (92 CVDII Olink®, high-sensitivity troponin T, and C-reactive protein). Unsupervised clustering was performed using a partitioning cluster algorithm.

RESULTS

The clustering algorithm identified two distinct clusters: cluster 1 (n = 223) and cluster 2 (n = 176). Compared with cluster 1, cluster 2 included older patients with a higher burden of comorbidities (cardiovascular and RA related), more erosive and longer RA duration, more dyspnoea and fatigue, walking a shorter distance in the Six-Minute Walk Test, with more severe diastolic dysfunction, and a 4.5-fold higher risk of death or hospitalization for cardiovascular reasons. Tumour necrosis factor (TNF) receptor superfamily-related pathways were mainly responsible for the model's discriminative ability.

CONCLUSION

Using unsupervised cluster analysis based on proteomic phenotypes, we identified two clusters of RA patients with distinct biomarkers profiles, clinical characteristics, and different outcomes that could reflect different pathophysiological backgrounds. TNF receptor superfamily-related proteins may be used to distinguish subgroups.

Plasma Proteomic Associations With Incident Ischemic Stroke in Older Adults: The Cardiovascular Health Study

BACKGROUND AND OBJECTIVES

Plasma proteomics may elucidate novel insights into the pathophysiology of ischemic stroke (IS), identify biomarkers of IS risk, and guide development of nascent prevention strategies. We evaluated the relationship between the plasma proteome and IS risk in the population-based Cardiovascular Health Study (CHS).

METHODS

Eligible CHS participants were free of prevalent stroke and underwent quantification of 1,298 plasma proteins using the aptamer-based SOMAScan assay platform from the 1992-1993 study visit. Multivariable Cox proportional hazards regression was used to evaluate associations between a 1-SD increase in the log2-transformed estimated plasma protein concentrations and incident IS, adjusting for demographics, IS risk factors, and estimated glomerular filtration rate. For proteins independently associated with incident IS, a secondary stratified analysis evaluated associations in subgroups defined by sex and race. Exploratory analyses evaluated plasma proteomic associations with cardioembolic and noncardioembolic IS and proteins associated with IS risk in participants with left atrial dysfunction but without atrial fibrillation.

RESULTS

Of 2,983 eligible participants, the mean age was 74.3 (±4.8) years, 61.2% were women, and 15.4% were Black. Over a median follow-up of 12.6 years, 450 participants experienced an incident IS. N-terminal probrain natriuretic peptide (NTproBNP, adjusted HR 1.37, 95% CI 1.23-1.53, p = 2.08 × 10-08) and macrophage metalloelastase (MMP12, adjusted HR 1.30, 95% CI 1.16-1.45, p = 4.55 × 10-06) were independently associated with IS risk. These 2 associations were similar in men and women and in Black and non-Black participants. In exploratory analyses, NTproBNP was independently associated with incident cardioembolic IS, E-selectin with incident noncardioembolic IS, and secreted frizzled-related protein 1 with IS risk in participants with left atrial dysfunction.

DISCUSSION

In a cohort of older adults, NTproBNP and MMP12 were independently associated with IS risk. We identified plasma proteomic determinants of incident cardioembolic and noncardioembolic IS and found a novel protein associated with IS risk in those with left atrial dysfunction.

Large-Scale Metabolomics and the Incidence of Cardiovascular Disease

Background The study aimed to show the relationship between a large number of circulating metabolites and subsequent cardiovascular disease (CVD) and subclinical markers of CVD in the general population. Methods and Results In 2278 individuals free from CVD in the EpiHealth study (aged 45-75 years, mean age 61 years, 50% women), 790 annotated nonxenobiotic metabolites were measured by mass spectroscopy (Metabolon). The same metabolites were measured in the PIVUS (Prospective Investigation of Vasculature in Uppsala Seniors) study (n=603, all aged 80 years, 50% women), in which cardiac and carotid artery pathologies were evaluated by ultrasound. During a median follow-up of 8.6 years, 107 individuals experienced a CVD (fatal or nonfatal myocardial infarction, stroke, or heart failure) in EpiHealth. Using a false discovery rate of 0.05 for age- and sex-adjusted analyses and P<0.05 for adjustment for traditional CVD risk factors, 37 metabolites were significantly related to incident CVD. These metabolites belonged to multiple biochemical classes, such as amino acids, lipids, and nucleotides. Top findings were dimethylglycine and N-acetylmethionine. A lasso selection of 5 metabolites improved discrimination when added on top of traditional CVD risk factors (+4.0%, P=0.0054). Thirty-five of the 37 metabolites were related to subclinical markers of CVD evaluated in the PIVUS study. The metabolite 1-carboxyethyltyrosine was associated with left atrial diameter as well as inversely related to both ejection fraction and the echogenicity of the carotid artery. Conclusions Several metabolites were discovered to be associated with future CVD, as well as with subclinical markers of CVD. A selection of metabolites improved discrimination when added on top of CVD risk factors.

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.

Large-Scale Plasma Protein Profiling of Incident Myocardial Infarction, Ischemic Stroke, and Heart Failure

Background

We recently reported a link between plasma levels of 2 of 84 cardiovascular disease (CVD)–related proteins and the 3 major CVDs, myocardial infarction, ischemic stroke, and heart failure. The present study investigated whether measurement of almost 10 times the number of proteins could lead to discovery of additional risk markers for CVD.

Methods and Results

We measured 742 proteins using the proximity extension assay in 826 male participants of ULSAM (Uppsala Longitudinal Study of Adult Men) who were free from CVD at the age of 70 years. Cox proportional hazards models were adjusted for age only, as well as all traditional risk factors. During a 12.5‐year median follow‐up (maximal, 22.0 years), 283 incident CVDs occurred. Forty‐one proteins were significantly (false discovery rate <0.05) related to the combined end point of incident CVD, with N‐terminal pro–brain natriuretic peptide as the top finding, while 53 proteins were related to incident myocardial infarction. A total of 13 and 16 proteins were significantly related to incident ischemic stroke and heart failure, respectively. Growth differentiation factor 15, 4‐disulfide core domain protein 2, and kidney injury molecule were related to all of the 3 major CVD outcomes. A lasso selection of 11 proteins improved discrimination of incident CVD by 5.0% (P=0.0038).

Conclusions

Large‐scale proteomics seem useful for the discovery of new risk markers for CVD and to improve risk prediction in an elderly population of men. Further studies are needed to replicate the findings in independent samples of both men and women of different ages.