Discovery of new biomarkers for atrial fibrillation using a custom-made proteomics chip

Lind, Lars; Sundström, Johan; Stenemo, Markus; Hagström, Emil; Ärnlöv, Johan

doi:10.1136/heartjnl-2016-309764

Cited by 56 publications

(47 citation statements)

References 30 publications

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“…They also demonstrate that there is a possibility of using individual biomarkers, but their combined use is particularly important for the identification of AF patients. These findings are in line with those of other studies focusing on discovering biomarkers for both AF itself and cardioembolic stroke due to AF [6,14].…”

Section: Resultssupporting

confidence: 92%

“…To our knowledge, this is the first study using a multiple-biomarker approach in patients with stroke of different etiologies. Lind et al [14] found multiple biomarkers, including GDF-15 and IL-6, to be associated with AF in a large cohort study but not in stroke patients. However, the biomarkers that were positively correlated with AF in our pilot study represent a heterogenic group of proteins with wide variety of functions.…”

Section: Resultsmentioning

confidence: 98%

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Biomarkers Associated with Atrial Fibrillation in Patients with Ischemic Stroke: A Pilot Study from the NOR-FIB Study

Lambert¹,

Kong²,

Ratajczak-Tretel³

et al. 2020

Cerebrovasc Dis Extra

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Background and Purpose: Cardioembolic stroke due to paroxysmal atrial fibrillation (AF) may account for 1 out of 4 cryptogenic strokes (CS) and transient ischemic attacks (TIAs). The purpose of this pilot study was to search for biomarkers potentially predicting incident AF in patients with ischemic stroke or TIA. Methods: Plasma samples were collected from patients aged 18 years and older with ischemic stroke or TIA due to AF (n = 9) and large artery atherosclerosis (LAA) with ipsilateral carotid stenosis (n = 8) and age-and sex-matched controls (n = 10). Analyses were performed with the Olink technology simultaneously measuring 184 biomarkers of cardiovascular disease. For bioinformatics, acquired data were analyzed using gene set enrichment analysis (GSEA). Selected proteins were validated using ELISA. Individual receiver operating characteristic (ROC) curves and odds ratios from logistic regression were calculated. A randomForest (RF) model with out-of-bag estimate was applied for predictive modeling. Results: GSEA indicated enrichment of proteins related to inflammatory response in the AF group. Interleukin (IL)-6, growth differentiation factor (GDF)-15, and pentraxin-related protein PTX3 were the top biomarkers on the ranked list for the AF group compared to the LAA group and the control group. ELISA validated increased expression of all tested proteins (GDF-15, PTX3, and urokinase plasminogen activator surface receptor [U-PAR]), except for IL-6. 19 proteins had the area under the ROC curve (AUC) over 0.85 including all of the proteins with significant evolution in the logistic regression. AUCs were very discriminant in distinguishing patients with and without AF (LAA and control group together). GDF-15 alone reached AUC of 0.95. Based on RF model, all selected participants in the tested group were classified correctly, and the most important protein in the model was GDF-15. Conclusions: Our results demonstrate an association between inflammation and AF and that multiple proteins alone and in combination may potentially be used as indicators of AF in CS and TIA patients. However, further studies including larger samples sizes are needed to support these findings. In the ongoing NOR-FIB study, we plan further biomarker assessments in patients with CS and TIA undergoing long-term cardiac rhythm monitoring with insertable cardiac monitors.

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Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 98%

Biomarkers Associated with Atrial Fibrillation in Patients with Ischemic Stroke: A Pilot Study from the NOR-FIB Study

Lambert¹,

Kong²,

Ratajczak-Tretel³

et al. 2020

Cerebrovasc Dis Extra

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“…Of these FGF-23, PlGF, NEMO, sST2, TRAIL-R2, U-PAR have previously been associated with HF incidence and HFpEF,28 29 and all, except NEMO and sST2, have also been associated with CKD or kidney dysfunction 30. FGF-23 and U-PAR have also been associated with incident AF 31. Further evidence indicates the importance of some of the associated proteins in HF.…”

Section: Discussionmentioning

confidence: 94%

Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning

Hedman¹,

Hage²,

Sharma³

et al. 2020

Heart

107

104

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ObjectiveHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.MethodsWe applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71–83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses.ResultsWe identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (p<0.05). Fifteen out of 86 plasma proteins differed between phenogroups (false discovery rate, FDR<0.05), including biomarkers of HF, AF and kidney function. The composite end point was significantly different between phenogroups (log-rank p<0.001), at short-term (100 days), mid-term (18 months) and longer-term follow-up (1000 days). Phenogroup 2 was older, with poorer diastolic and right ventricular function and higher burden of risk factors as AF (85%), hypertension (83%) and chronic obstructive pulmonary disease (30%). In this group a third experienced the primary outcome to 100 days, and two-thirds to 18 months (HR (95% CI) versus phenogroups 1, 3, 4, 5, 6: 1.5 (0.8–2.9); 5.7 (2.6–12.8); 2.9 (1.5–5.6); 2.7 (1.6–4.6); 2.1 (1.2–3.9)).ConclusionsUsing machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.

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“…Of the 92 proteins, 84 passed our strict quality control criteria (call rate > 75% at all three time points) and were included in downstream analyses. Details regarding the protein arrays, quality control, and processing of data in the PIVUS study have been published previously .…”

Section: Methodsmentioning

confidence: 99%

“…Using the proximal extension assay (PEA) technique for measurement of multiple proteins on a targeted proteomics assay, we have discovered that a range of proteins is related to several cardiovascular and metabolic traits in the community‐based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study . We also recently showed that the majority of 84 cardiovascular candidate proteins increased over time when measured at ages 70, 75, and 80 years in the PIVUS cohort .…”

Section: Introductionmentioning

confidence: 99%

Changes in Proteomic Profiles are Related to Changes in BMI and Fat Distribution During 10 Years of Aging

Lind

Figarska

Sundström

et al. 2019

Obesity

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Objective This study investigated how changes in 84 proteins over a 10‐year period of aging were related to changes in measures of body fat and distribution over the same period. Methods Cardiovascular candidate proteins were measured using the proximal extension assay technique, along with BMI and waist‐hip ratio (WHR), at ages 70, 75, and 80 in 1,016 participants of the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) cohort. Associations of changes in plasma protein levels, BMI, and WHR over time were analyzed using linear mixed models. Results Changes in 19 and 16 proteins were significantly associated with changes in BMI and WHR, respectively (P < 0.00059), over the investigated 10‐year period. Leptin and fatty acid‐binding protein 4 were among the proteins most strongly associated with changes in both BMI and WHR. Four of the proteins significantly tracked with change in BMI (P < 0.00059) but not WHR (P > 0.05): endothelial cell‐specific molecule 1, pentraxin‐related protein PTX3, ST2 protein (also known as interleukin‐1 receptor‐like 1), and spondin‐1. Five proteins tracked with change in WHR (P < 0.00059) but not BMI (P > 0.05): caspase‐8, cathepsin L1, oxidized low‐density lipoprotein receptor 1, interleukin‐6 receptor subunit alpha, and C‐C motif chemokine 20. Conclusions This is the first large longitudinal study of how changes in plasma protein signatures are associated with changes in measures of body fat and distribution over 10 years of aging.

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Discovery of new biomarkers for atrial fibrillation using a custom-made proteomics chip

Abstract: Using a novel proteomics approach, we confirmed the previously reported association between NT-pro-BNP, FGF-23, GDF-15 and incident AF, and also discovered four proteins (FABP4, IL-6, TIM-1 and AM) that could be of importance in the development of AF.

Cited by 56 publications

References 30 publications

Biomarkers Associated with Atrial Fibrillation in Patients with Ischemic Stroke: A Pilot Study from the NOR-FIB Study

Biomarkers Associated with Atrial Fibrillation in Patients with Ischemic Stroke: A Pilot Study from the NOR-FIB Study

Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning

Changes in Proteomic Profiles are Related to Changes in BMI and Fat Distribution During 10 Years of Aging

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