European Journal of Vascular and Endovascular Surgery

2002

DOI: 10.1053/ejvs.2002.1678

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Serum Apolipoprotein AI Levels in Atherosclerotic and Diabetic Patients

Amalia Dionyssiou-Asteriou

¹

,

M. Papastamatiou

²

,

Ioannis-Anastasios Vatalas

³

et al.

Abstract: Decreased ApoAI appears to be a main component of the dyslipidaemic serum profile observed in patients with atherosclerotic occlusive disease of the lower extremities. Increased Lp(a) levels is an independent risk factor. Decreased HDL-cholesterol is also involved in the dyslipidaemic profile.

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Cited by 12 publications

(4 citation statements)

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“…The association of Apo-AI, Apo-AII and Apo-CII levels was reported with atherosclerotic occlusive disease, CAD and type 2 diabetes associated with CAD [45–47]. In the present study, Apo-AI, Apo-AII and Apo-CII levels were increased in CAD and T2DM_CAD group, however, significantly increased was observed only in CAD group.…”

Section: Discussionsupporting

confidence: 52%

Serum protein signature of coronary artery disease in type 2 diabetes mellitus

¹

,

²

,

³

et al. 2019

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BackgroundCoronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers.MethodsUsing sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers.ResultsOur two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis.ConclusionWe have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1755-5) contains supplementary material, which is available to authorized users.

“…The association of Apo-AI, Apo-AII and Apo-CII levels was reported with atherosclerotic occlusive disease, CAD and type 2 diabetes associated with CAD [45–47]. In the present study, Apo-AI, Apo-AII and Apo-CII levels were increased in CAD and T2DM_CAD group, however, significantly increased was observed only in CAD group.…”

Section: Discussionsupporting

confidence: 52%

Serum protein signature of coronary artery disease in type 2 diabetes mellitus

¹

,

²

,

³

et al. 2019

View full text Add to dashboard Cite

BackgroundCoronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers.MethodsUsing sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers.ResultsOur two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis.ConclusionWe have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1755-5) contains supplementary material, which is available to authorized users.

“…ApoA-I (28 kDa) is the major protein constituent of high density lipoprotein. Decreased apoA-I levels were previously reported in the serum of patients with both ovarian cancer [13], [14], [15] as well as atherosclerosis [16]. Serum lipid and lipoprotein association with cancer has been reported in numerous studies [17], [18], [19].…”

Section: Discussionmentioning

confidence: 95%

Validation of Candidate Serum Ovarian Cancer Biomarkers for Early Detection

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²

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et al. 2007

Biomark�Insights

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ObjectiveWe have previously analyzed protein profiles using Surface Enhanced Laser Desorption and Ionization Time-Of-Flight Mass Spectroscopy (SELDI-TOF-MS) [Kozak et al. 2003, Proc. Natl. Acad. Sci. U.S.A. 100:12343–8] and identified 3 differentially expressed serum proteins for the diagnosis of ovarian cancer (OC) [Kozak et al. 2005, Proteomics, 5:4589–96], namely, apolipoprotein A-I (apoA-I), transthyretin (TTR) and transferin (TF). The objective of the present study is to determine the efficacy of the three OC biomarkers for the detection of early stage (ES) OC, in direct comparison to CA125.MethodsThe levels of CA125, apoA-I, TTR and TF were measured in 392 serum samples [82 women with normal ovaries (N), 24 women with benign ovarian tumors (B), 85 women with ovarian tumors of low malignant potential (LMP), 126 women with early stage ovarian cancer (ESOC), and 75 women with late stage ovarian cancer (LSOC)], obtained through the GOG and Cooperative Human Tissue Network. Following statistical analysis, multivariate regression models were built to evaluate the utility of the three OC markers in early detection.ResultsMultiple logistic regression models (MLRM) utilizing all biomarker values (CA125, TTR, TF and apoA-I) from all histological subtypes (serous, mucinous, and endometrioid adenocarcinoma) distinguished normal samples from LMP with 91% sensitivity (specificity 92%), and normal samples from ESOC with a sensitivity of 89% (specificity 92%). MLRM, utilizing values of all four markers from only the mucinous histological subtype showed that collectively, CA125, TTR, TF and apoA-I, were able to distinguish normal samples from mucinous LMP with 90% sensitivity, and further distinguished normal samples from early stage mucinous ovarian cancer with a sensitivity of 95%. In contrast, in serum samples from patients with mucinous tumors, CA125 alone was able to distinguish normal samples from LMP and early stage ovarian cancer with a sensitivity of only 46% and 47%, respectively. Furthermore, collectively, apoA-I, TTR and TF (excluding CA-125) distinguished i) normal samples from samples representing all histopathologic subtypes of LMP, with a sensitivity of 73%, ii) normal samples from ESOC with a sensitivity of 84% and iii) normal samples from LSOC with a sensitivity of 97%. More strikingly, the sensitivity in distinguishing normal versus mucinous ESOC, utilizing apoA-I, TF and TTR (CA-125 excluded), was 95% (specificity 86%; AUC 95%).ConclusionsThese results suggest that the biomarker panel consisting of apoA-I, TTR and TF may significantly improve early detection of OC.

“…ApoA1 is a key protein component of high-density lipoprotein (HDL) that plays an important role in lipoprotein metabolism, reverse cholesterol transport, and atherosclerosis prevention . ApoA1 levels were also found to be lower in the serum of patients with ovarian cancer and atherosclerosis . As a result, development of efficient detection methods for precise detection of ApoA1 is critical for early diagnosis and treatment of associated diseases.…”

Section: Introductionmentioning

confidence: 99%

“… 28 ApoA1 levels were also found to be lower in the serum of patients with ovarian cancer 29 and atherosclerosis. 30 As a result, development of efficient detection methods for precise detection of ApoA1 is critical for early diagnosis and treatment of associated diseases. As part of the standard clinical screening, ELISA is normally used to assess the level of ApoA1 molecules in biofluids.…”

Section: Introductionmentioning

confidence: 99%

Label-Free Electrochemiluminescence Nano-aptasensor for the Ultrasensitive Detection of ApoA1 in Human Serum

¹

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²

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³

et al. 2022

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A molybdenum sulfide/zirconium oxide/Nafion (MoS2/ZrO2/Naf) based electrochemiluminescence (ECL) aptasensor for the selective and ultrasensitive detection of ApoA1 is proposed, with Ru(bpy)3 2+ as the luminophore. The chitosan (CS) modification on the nanocomposite layer allowed glutaraldehyde (GLUT) cross-linking, resulting in the immobilization of ApoA1 aptamers. Scanning electron microscopy, tunneling electron microscopy, and energy dispersive X-ray spectroscopy were used to characterize the nanocomposite, while electrochemiluminescence (ECL), cyclic voltammetry, and electrochemical impedance spectroscopy were used to analyze the aptasensor assembly. The nanocomposite was used as an electrode modifier, which increased the intensity of the ECL signal. Due to the anionic environment produced on the sensor surface following the specific interaction of the ApoA1 biomarker with the sensor, more Ru(bpy)3 2+ were able to be electrostatically attached to the aptamer-ApoA1 complex, resulting in enhanced ECL signal. The ECL aptasensor demonstrated outstanding sensitivity for ApoA1 under optimal experimental conditions, with a detection limit of 53 fg/mL and a wide linear dynamic range of 0.1–1000 pg/mL. The potential practical applicability of this aptasensor was validated by analyzing ApoA1 in human serum samples, with recovery rates of 94–108% (n = 3). The proposed assay was found to be substantially better compared to the commercially available enzyme-linked immunosorbent assay method, as reflected from over 1500 times improvement in the detection limit for ApoA1.

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