Lipoprotein-associated phospholipase A2 predicted cardiovascular disease in obstructive sleep apnea syndrome

Ruan

Evidence-Based Complementary and Alternative Medicine

2021

Acute heart failure (AHF) occurs mostly in the elderly, which is a syndrome that occurs in the later stages of the development of cardiovascular disease. Due to the sharp decline in the patient’s heart function, the patient has a high mortality rate and a poor prognosis, which seriously threaten the life safety of the elderly. Therefore, early diagnosis of AHF and timely treatment are extremely important. Lipoprotein-associated phospholipase A2 (LP-PLA2) is a newly discovered cardiovascular-specific inflammatory marker, which is closely related to the formation and progression of atherosclerotic plaque and the occurrence and development of coronary heart disease. Soluble growth stimulation expression gene 2 (sST2) protein is a protein produced under the induction of mechanical stress in cardiomyocytes. It can act as a decoy receptor to mediate the interleukin-33 (IL-33)/ST2 signaling pathway to bind to IL-33, thereby reducing the myocardial protective effect and leading to myocardial remodeling. The purpose of this study was to explore the serum LP-PLA2 and sST2 levels in AHF patients and to analyze their correlation with the disease and their prognostic value. The results showed that the levels of serum LP-PLA2 and sST2 were increased in AHF patients, and the levels of serum LP-PLA2 and sST2 in patients with adverse prognostic events were higher than those in patients without adverse prognostic events. The levels of serum LP-PLA2 and sST2 are closely related to the degree of patients’ illness, among which the combined prediction of AHF patients with LP-PLA2 and sST2 has the highest value, which is worthy of promotion.

Section: Introductionmentioning

confidence: 99%

Correlation between Serum LP-PLA2 and sST2 Levels and the Condition of Patients with Acute Heart Failure and Their Prognostic Value

Ruan

Evidence-Based Complementary and Alternative Medicine

2021

“…It means that Lp-PLA2 could catalyze the hydrolysis of sn-2 residue, resulting in producing lysophosphatidylcholine (lyso-PC) and nonesterified fatty acids that are associated with oxidative stress and inflammation response [ 8 ]. Due to the proinflammatory effects and various studies, which had illustrated that the Lp-PLA2 was correlated with a wide range of cardiovascular diseases [ 9 – 11 ], Lp-PLA2 is gradually identified as a reliable biomarker for the risk of clinical cardiovascular events.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Plasma Lipoprotein-Associated Phospholipase A2 Concentrations and Apolipoprotein in Stable Coronary Artery Disease Patients

Yang¹,

Tang²,

Cao

et al. 2020

Disease Markers

Background. Increasing evidence states that the plasma lipoprotein-associated phospholipase A2 (Lp-PLA2) levels and apolipoprotein particles are regarded as the risk maker for cardiovascular heart disease. Nevertheless, the issue about whether Lp-PLA2 is associated with apolipoprotein particles in individuals who have been diagnosed as stable coronary artery disease (CAD) remains largely unexplored. Method. All 569 participants engaged in this research, who never took lipid-lowering drugs, had been divided into groups by the coronary angiography (CAG), namely, stable CAD: n=291; non-CAD: n=278. The results concerning Lp-PLA2 levels were calculated by Elisa Kit, while apolipoprotein particles were measured by the department of laboratory. Results. The plasma concentration of Lp-PLA2 was remarkably higher in stable CAD group than the non-CAD group (136.0±60.5 ng/mL vs. 113.2±65.6 ng/mL, P<0.001). Pearson correlation analyses explained the plasma Lp-PLA2 concentration was correlated with apoB (r=0.390, P<0.001) and apoB/apoA1 (r=0.450, P<0.001), not associated with apoA1 (r=−0.099, P=0.101). Conversely, the association remains unobserved among non-CAD patients except apoA1. Moreover, multiple linear regression revealed the relations between Lp-PLA2 concentrations and apoB (β=0.390, P<0.001), as well as apoB/apoA1 (β=0.450, P<0.001), but not apoA1 (β=−0.099, P=0.121). After adjustment for several risk factors regarding CAD, like hypertension, gender, smoking, age, and diabetes mellitus, there had still been positive associations between the Lp-PLA2 concentration and apoB (β=0.364, P<0.001), as well as apoB/apoA1 (β=0.390, P<0.001). Conclusion. The plasma levels of Lp-PLA2 provide positively a key link with apoB, apoB/apoA-1 among stable CAD, denoting the communication between Lp-PLA2 and apolipoprotein particles in the state of CAD.

IEEE Trans. Neural Syst. Rehabil. Eng.

“…O BSTRUCTIVE sleep apnea syndrome is a common sleep-related respiratory disease in clinic. It is typically characterized by paroxysmal stenosis or even closure of the upper respiratory tract during sleep, and results in decreased cerebral oxygen saturation and intermittent hypoxia [1] and would further lead to a variety of concurrent diseases [2], [3]. At present, the gold standard in diagnosing OSA is polysomnography (PSG), which records electroencephalography (EEG), electrocardiography (ECG), electrooculography (EMG), respiratory state, etc.…”

Section: Introductionmentioning

confidence: 99%

A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment

Huang

Tang

Luo

et al. 2023

Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitive measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics.