Serum Endothelial Cell–Specific Molecule 1 (Endocan) Levels in Patients With Acute Myocardial Infarction and Its Clinical Significance

Qiu, Chongrong; Fu, Qiang; Sui, Jian; Zhang, Qian; Wei, Peng; Wu, Yan Yan; Zhu, Kongbo; Lü, Yi; Zong, Bing

doi:10.1177/0003319716651349

Cited by 37 publications

(29 citation statements)

References 36 publications

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“…Levels of ESM-1, with the strongest association to phenogroups of any of the proteins, has previously been negatively associated with BMI and insulin resistance 33 34. Further evidence supports a role for this protein as a marker of endothelial dysfunction (upregulated in acute MI, CKD and hypertension35), which is also consistent with a purported model of global inflammation driving microvascular endothelial dysfunction in HFpEF 10 11. Furthermore, lower levels of both FGF-23 and TRAIL-R2 were recently found to associate with resilience against new cardiovascular events in a cohort of patients with stable CAD 36.…”

Section: Discussionmentioning

confidence: 78%

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

Hedman¹,

Hage²,

Sharma³

et al. 2020

Heart

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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Section: Discussionmentioning

confidence: 78%

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

Hedman¹,

Hage²,

Sharma³

et al. 2020

Heart

104

View full text Add to dashboard Cite

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“…ESM‐1 is a soluble sulfate proteoglycan involved in neoangiogenesis. This protein has been previously linked to smoking in a negative fashion and to prevalent myocardial infarction in a positive fashion in cross‐sectional studies. Expression of ESM‐1 has been detected in adipocytes, but contrary to our findings, it was reported that plasma levels of ESM‐1 were reduced in women with obesity and increased following weight reduction in a small cross‐sectional study .…”

Section: Discussionmentioning

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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“…Additionally, endocan, by causing vascular endothelial cell proliferation, can play a major role in the development of endothelial dysfunction, which is regarded as an initial lesion in atherosclerosis development. Lastly, it is thought that endocan can increase coagulation by playing a role in the thrombosis process …”

Section: Discussionmentioning

confidence: 99%

Endocan as a marker of disease severity in pulmonary thromboembolism

Kuluöztürk

İn

İlhan

2019

Clinical Respiratory J

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Introduction The aim of this study is to determine the serum endocan levels in patients with pulmonary thromboembolism (PTE) and investigate whether a relationship exists between serum endocan levels and the disease severity. Materials and Methods The study included 85 patients with acute PTE and 40 healthy control subjects. The patients with PTE were divided into three groups at admission as “high‐risk”, “intermediate‐risk” and “low‐risk”, considering the guidelines of the European Society of Cardiology. Serum endocan levels in all participants' blood samples were measured. Results The mean serum endocan levels were significantly higher in the PTE group, compared to the control subjects (P < 0.001). Serum endocan levels were significantly higher in the “high‐risk” group when compared with patients in the “low‐risk” and “intermediate‐risk” groups (P < 0.001 and P < 0.01 respectively). Similarly, serum endocan levels were higher in the “intermediate‐risk” group compared to those in the “low‐risk” group (P < 0.001). There was a negative correlation between serum endocan levels and partial oxygen pressure (r = −0.262, P = 0.016), whereas a positive correlation was found between the serum endocan levels and systolic pulmonary arterial pressure (r = 0.296, P = 0.006). Additionally, endocan had an area under the curve in the receiver operating characteristic curve of 0.837 (0.768‐0.907; 95% CI; P < 0.001) and cut‐off value was 194.5 pg/mL (sensitivity 80%, specificity 72.5%). Conclusion Serum endocan levels were higher and related to the severity of the disease in PTE patients. Additionally, endocan could be an indicator to be used in the diagnosis of PTE and in the prediction of the disease severity.

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Serum Endothelial Cell–Specific Molecule 1 (Endocan) Levels in Patients With Acute Myocardial Infarction and Its Clinical Significance

Cited by 37 publications

References 36 publications

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

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

Endocan as a marker of disease severity in pulmonary thromboembolism

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