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  1. Article ; Online: Sex-based differences in cardiovascular proteomic profiles and their associations with adverse outcomes in patients with chronic heart failure.

    de Bakker, Marie / Petersen, Teun B / Akkerhuis, K Martijn / Harakalova, Magdalena / Umans, Victor A / Germans, Tjeerd / Caliskan, Kadir / Katsikis, Peter D / van der Spek, Peter J / Suthahar, Navin / de Boer, Rudolf A / Rizopoulos, Dimitris / Asselbergs, Folkert W / Boersma, Eric / Kardys, Isabella

    Biology of sex differences

    2023  Volume 14, Issue 1, Page(s) 29

    Abstract: Background: Studies focusing on sex differences in circulating proteins in patients with heart failure with reduced ejection fraction (HFrEF) are scarce. Insight into sex-specific cardiovascular protein profiles and their associations with the risk of ... ...

    Abstract Background: Studies focusing on sex differences in circulating proteins in patients with heart failure with reduced ejection fraction (HFrEF) are scarce. Insight into sex-specific cardiovascular protein profiles and their associations with the risk of adverse outcomes may contribute to a better understanding of the pathophysiological processes involved in HFrEF. Moreover, it could provide a basis for the use of circulating protein measurements for prognostication in women and men, wherein the most relevant protein measurements are applied in each of the sexes.
    Methods: In 382 patients with HFrEF, we performed tri-monthly blood sampling (median follow-up: 25 [13-31] months). We selected all baseline samples and two samples closest to the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization) or censoring. We then applied an aptamer-based multiplex proteomic assay identifying 1105 proteins previously associated with cardiovascular disease. We used linear regression models and gene-enrichment analysis to study sex-based differences in baseline levels. We used time-dependent Cox models to study differences in the prognostic value of serially measured proteins. All models were adjusted for the MAGGIC HF mortality risk score and p-values for multiple testing.
    Results: In 104 women and 278 men (mean age 62 and 64 years, respectively) cumulative PEP incidence at 30 months was 25% and 35%, respectively. At baseline, 55 (5%) out of the 1105 proteins were significantly different between women and men. The female protein profile was most strongly associated with extracellular matrix organization, while the male profile was dominated by regulation of cell death. The association of endothelin-1 (P
    Conclusion: Baseline cardiovascular protein levels differ between women and men. However, the predictive value of repeatedly measured circulating proteins does not seem to differ except for endothelin-1 and somatostatin.
    MeSH term(s) Humans ; Female ; Male ; Middle Aged ; Heart Failure ; Ventricular Function, Left/physiology ; Stroke Volume/physiology ; Sex Characteristics ; Endothelin-1 ; Proteomics
    Chemical Substances Endothelin-1
    Language English
    Publishing date 2023-05-17
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2587352-0
    ISSN 2042-6410 ; 2042-6410
    ISSN (online) 2042-6410
    ISSN 2042-6410
    DOI 10.1186/s13293-023-00516-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms.

    Petersen, Teun B / de Bakker, Marie / Asselbergs, Folkert W / Harakalova, Magdalena / Akkerhuis, K Martijn / Brugts, Jasper J / van Ramshorst, Jan / Lumbers, R Thomas / Ostroff, Rachel M / Katsikis, Peter D / van der Spek, Peter J / Umans, Victor A / Boersma, Eric / Rizopoulos, Dimitris / Kardys, Isabella

    EBioMedicine

    2023  Volume 93, Page(s) 104655

    Abstract: Background: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith ...

    Abstract Background: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment.
    Methods: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1-2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated.
    Findings: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1-4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76-6.69), and 2.88 (1.37-6.03), respectively).
    Interpretation: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis.
    Clinical trial registration: ClinicalTrials.gov Identifier: NCT01851538https://clinicaltrials.gov/ct2/show/NCT01851538.
    Funding: EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.
    MeSH term(s) Humans ; Infant ; Child, Preschool ; Heart Failure/diagnosis ; Heart Failure/therapy ; Stroke Volume ; Proteomics ; Biomarkers ; Prognosis
    Chemical Substances Biomarkers
    Language English
    Publishing date 2023-06-14
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2851331-9
    ISSN 2352-3964
    ISSN (online) 2352-3964
    DOI 10.1016/j.ebiom.2023.104655
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Machine learning-based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure.

    de Bakker, Marie / Petersen, Teun B / Rueten-Budde, Anja J / Akkerhuis, K Martijn / Umans, Victor A / Brugts, Jasper J / Germans, Tjeerd / Reinders, Marcel J T / Katsikis, Peter D / van der Spek, Peter J / Ostroff, Rachel / She, Ruicong / Lanfear, David / Asselbergs, Folkert W / Boersma, Eric / Rizopoulos, Dimitris / Kardys, Isabella

    European heart journal. Digital health

    2023  Volume 4, Issue 6, Page(s) 444–454

    Abstract: Aims: Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly ... ...

    Abstract Aims: Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF.
    Methods and results: In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated
    Conclusion: Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively 'novel' biomarkers for prognostication.
    Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT01851538?term=nCT01851538&draw=2&rank=1 24.
    Language English
    Publishing date 2023-10-04
    Publishing country England
    Document type Journal Article
    ISSN 2634-3916
    ISSN (online) 2634-3916
    DOI 10.1093/ehjdh/ztad056
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Heart failure subphenotypes based on repeated biomarker measurements are associated with clinical characteristics and adverse events (Bio-SHiFT study).

    de Lange, Iris / Petersen, Teun B / de Bakker, Marie / Akkerhuis, K Martijn / Brugts, Jasper J / Caliskan, Kadir / Manintveld, Olivier C / Constantinescu, Alina A / Germans, Tjeerd / van Ramshorst, Jan / Umans, Victor A W M / Boersma, Eric / Rizopoulos, Dimitris / Kardys, Isabella

    International journal of cardiology

    2022  Volume 364, Page(s) 77–84

    Abstract: Background: This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and ... ...

    Abstract Background: This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis.
    Methods: Clinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted.
    Results: Clustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4-2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12-0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only.
    Conclusions: Clustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication.
    MeSH term(s) Biomarkers ; Heart Failure ; Heart Transplantation ; Humans ; Prognosis ; Stroke Volume ; Ventricular Dysfunction, Left ; Ventricular Function, Left
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-06-15
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 779519-1
    ISSN 1874-1754 ; 0167-5273
    ISSN (online) 1874-1754
    ISSN 0167-5273
    DOI 10.1016/j.ijcard.2022.06.020
    Database MEDical Literature Analysis and Retrieval System OnLINE

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