Background: Circulating biomarkers are associated with the development of coronary heart disease (CHD) and its complications by reflecting pathophysiological pathways and/or organ dysfunction. We explored the associations between 157 cardiovascular (CV) and inflammatory biomarkers and CV death using proximity extension assays (PEA) in patients with chronic CHD. Methods and findings: The derivation cohort consisted of 605 cases with CV death and 2,788 randomly selected non-cases during 3–5 years follow-up included in the STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY (STABILITY) trial between 2008 and 2010. The replication cohort consisted of 245 cases and 1,042 non-cases during 12 years follow-up included in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study between 1997 and 2000. Biomarker levels were measured with conventional immunoassays and/or with the OLINK PEA panels CVD I and Inflammation. Associations with CV death were evaluated by Random Survival Forest (RF) and Cox regression analyses. Both cohorts had the same median age (65 years) and 20% smokers, while there were slight differences in male sex (82% and 76%), hypertension (70% and 78%), and diabetes (39% and 30%) in the respective STABILITY and LURIC cohorts. The analyses identified 18 biomarkers with confirmed independent association with CV death by Boruta analyses and statistical significance (all p < 0.0001) by Cox regression when adjusted for clinical characteristics in both cohorts. Most prognostic information was carried by N-terminal prohormone of brain natriuretic peptide (NTproBNP), hazard ratio (HR for 1 standard deviation [SD] increase of the log scale of the distribution of the biomarker in the replication cohort) 2.079 (95% confidence interval [CI] 1.799–2.402), and high-sensitivity troponin T (cTnT-hs) HR 1.715 (95% CI 1.491–1.973). The other proteins with independent associations were growth differentiation factor 15 (GDF-15) HR 1.728 (95% CI 1.527–1.955), transmembrane immunoglobulin and mucin domain protein (TIM-1) HR 1.555 (95% CI 1.362–1.775), renin HR 1.501 (95% CI 1.305–1.727), osteoprotegerin (OPG) HR 1.488 (95% CI 1.297–1.708), soluble suppression of tumorigenesis 2 protein (sST2) HR 1.478 (95% CI 1.307–1.672), cystatin-C (Cys-C) HR 1.370 (95% CI 1.243–1.510), tumor necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2) HR 1.205 (95% CI 1.131–1.285), carbohydrate antigen 125 (CA-125) HR 1.347 (95% CI 1.226–1.479), brain natriuretic peptide (BNP) HR 1.399 (95% CI 1.255–1.561), interleukin 6 (IL-6) HR 1.478 (95% CI 1.316–1.659), hepatocyte growth factor (HGF) HR 1.259 (95% CI 1.134–1.396), spondin-1 HR 1.295 (95% CI 1.156–1.450), fibroblast growth factor 23 (FGF-23) HR 1.349 (95% CI 1.237–1.472), chitinase-3 like protein 1 (CHI3L1) HR 1.284 (95% CI 1.129–1.461), tumor necrosis factor receptor 1 (TNF-R1) HR 1.486 (95% CI 1.307–1.689), and adrenomedullin (AM) HR 1.750 (95% CI 1.490–2.056). The study is limited by the differences in design, size, and length of follow-up of the 2 studies and the lack of results from coronary angiograms and follow-up of nonfatal events. Conclusions: Profiles of levels of multiple plasma proteins might be useful for the identification of different pathophysiological pathways associated with an increased risk of CV death in patients with chronic CHD. Trial registration: ClinicalTrials.gov NCT00799903.

A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Background: Emerging data support detectable immune responses for months after severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection and vaccination, but it is not yet established to what degree and for how long protection against reinfection lasts.Methods: We investigated SARS‐CoV‐2‐specific humoral and cellular immune responses more than 8 months post‐asymptomatic, mild and severe infection in a cohort of 1884 healthcare workers (HCW) and 51 hospitalized COVID‐19 patients. Possible protection against SARS‐CoV‐2 reinfection was analyzed by a weekly 3‐month polymerase chain reaction (PCR) screening of 252 HCW that had seroconverted 7 months prior to start of screening and 48 HCW that had remained seronegative at multiple time points.Results: All COVID‐19 patients and 96% (355/370) of HCW who were anti‐spike IgG positive at inclusion remained anti‐spike IgG positive at the 8‐month follow‐up. Circulating SARS‐CoV‐2‐specific memory T cell responses were detected in 88% (45/51) of COVID‐19 patients and in 63% (233/370) of seropositive HCW. The cumulative incidence of PCR‐confirmed SARS‐CoV‐2 infection was 1% (3/252) among anti‐spike IgG positive HCW (0.13 cases per 100 weeks at risk) compared to 23% (11/48) among anti‐spike IgG negative HCW (2.78 cases per 100 weeks at risk), resulting in a protective effect of 95.2% (95% CI 81.9%–99.1%).Conclusions: The vast majority of anti‐spike IgG positive individuals remain anti‐spike IgG positive for at least 8 months regardless of initial COVID‐19 disease severity. The presence of anti‐spike IgG antibodies is associated with a substantially reduced risk of reinfection up to 9 months following asymptomatic to mild COVID‐19.

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.

Summary: Monocytes and monocyte-derived microvesicles (MVs) are the main source of circulating tissue factor (TF). Increased monocyte TF expression and increased circulating levels of procoagulant MVs contribute to the formation of a prothrombotic state in patients with cardiovascular disease. Interleukin (IL)-33 is a pro-inflammatory cytokine involved in atherosclerosis and other inflammatory diseases, but its role in regulating thrombosis is still unclear. The aim of the present study was to investigate the effects of IL-33 on the procoagulant properties of human monocytes and monocyte-derived MVs. IL-33 induced a time- and concentration-dependent increase of monocyte TF mRNA and protein levels via binding to the ST2-receptor and activation of the NFκB-pathway. The IL-33 treated monocytes also released CD14+TF+ MVs and IL-33 was found to increase the TF activity of both the isolated monocytes and monocyte-derived MVs. The monocytes were classified into subsets according to their CD14 and CD16 expression. Intermediate monocytes (IM) showed the highest ST2 receptor expression, followed by non-classical monocytes (NCM), and classical monocytes (CM). IL-33 induced a significant increase of TF only in the IM (p<0.01), with a tendency in NCM (p=0.06), but no increase was observed in CM. Finally, plasma levels of IL–33 were positively correlated with CD14+TF+ MVs in patients undergoing carotid endarterectomy (r=0.480; p=0.032; n=20). We hereby provide novel evidence that the proinflammatory cytokine IL-33 induces differential TF expression and activity in monocyte subsets, as well as the release of procoagulant MVs. In this manner, IL-33 may contribute to the formation of a prothrombotic state characteristic for cardiovascular disease.Supplementary Material to this article is available online at www.thrombosis-online.com.