Background And Aims: Lipoprotein(a) is a recognized independent cardiovascular risk factor and apolipoprotein B (apoB) level better reflects the risk than LDL-cholesterol. Despite this cardiovascular prediction mostly relies on traditional risk factors. We evaluated the association between Lp(a) and lipid biomarkers of cardiovascular risk in relation to age and sex in apparently healthy individuals.
Methods And Results: 422 presumably healthy subjects aged 19-84 were included. Lipid profile, Lp(a), apoB and small dense low-density lipoprotein cholesterol (sdLDL-C) were assayed. Subjects were divided at desirable cut-points of apoB and LDL-C. A group with elevated apoB (≥100 mg/dL) at low LDL-C (≤115 mg/dL) was appointed as high-risk and a group with low apoB but elevated LDL-C as low-risk. Significantly elevated triglycerides, TG/HDL-C and sdLDL-C were found in high risk group, but Lp(a) levels were comparable. TG/HDL-C was the best predictor of high risk with a very good diagnostic accuracy (AUC = 0.85), whereas Lp(a) had no discriminatory power. Women aged ≤40 with low LDL-C ≤ 100 mg/dL and elevated Lp(a) ≥ 40 mg/dL had higher levels of apoB and sdLDL-C (p = 0.002; p = 0.07) than those with Lp(a) < 40 mg/dL, which was not observed in men. In young females increase of LDL-C and apoB significantly raised the risk of elevated Lp(a).
Conclusions: Women younger than 40 with low LDL-C may be at increased cardiovascular risk associated with elevated Lp(a) and apolipoprotein B levels. Inclusion of Lp(a) and apoB in the routine lipid testing providing information on an individual level may improve the prediction of cardiovascular risk in primary prevention.
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http://dx.doi.org/10.1016/j.numecd.2023.04.010 | DOI Listing |
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
State Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute, Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, 110016, China.
The triglyceride to high density lipoprotein cholesterol (TG/HDL-C) ratio has been consistently linked with the risk of coronary heart disease (CHD). Nevertheless, there is a paucity of studies focusing on acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) or experiencing bleeding events. The study encompassed 17,643 ACS participants who underwent PCI.
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December 2024
Department of Medical and Surgical Sciences, Institute of Cardiology, University of Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, Bologna, 40138, Italy.
Cardiac implantable electronic devices infections (CIEDI) are associated with poor survival despite the improvement in transvenous lead extraction (TLE). Aetiology and systemic involvement are driving factors of clinical outcomes. The aim of this study was to explore their contribute on overall mortality.
View Article and Find Full Text PDFBAY 2413555 is a novel selective and reversible positive allosteric modulator of the type 2 muscarinic acetylcholine (M2) receptor, aimed at enhancing parasympathetic signaling and restoring cardiac autonomic balance for the treatment of heart failure (HF). This study tested the safety, tolerability and pharmacokinetics of this novel therapeutic option. REMOTE-HF was a multicenter, double-blind, randomized, placebo-controlled, phase Ib dose-titration study with two active arms.
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December 2024
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
Sand and dust storms (SDS) can cause adverse health effects, with the oxidative potential (OP) and environmentally persistent free radicals (EPFRs) inducing oxidative stress. We mapped the OP and EPFRs concentrations at 1735 sites in China during SDS periods using experimental data for 2021-2023 and a random forest model. We examined 855,869 hospitalizations during SDS events for 2015-2022 in Beijing, China.
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