Associations of ABCG1-mediated cholesterol efflux capacity with coronary artery lipid content assessed by near-infrared spectroscopy

Takata, Kohei; Honda, Satoshi; Sidharta, S.; Duong, My Ngan; Shishikura, Daisuke; Kim, Susan W.; Andrews, Jordan; Bartolo, Belinda A. Di; Psaltis, Peter J.; Bursill, Christina A.; Worthley, M.; Nicholls, Stephen J.

doi:10.21037/cdt.2018.11.04

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Thus, some of these ABCG1 SNPs have a protective role in the development of CAD and MI, due to the contribution of ABCG1 expression to the RCT capacity. This is also supported by the finding that the cholesterol efflux from cells to HDL, mediated by ABCG1, shows an inverse correlation with lipid accumulation in the coronary artery wall of patients with acute coronary insufficiency [258].…”

Section: Changes At the Genome Levelsupporting

confidence: 52%

Genomic Variants and Multilevel Regulation of ABCA1, ABCG1, and SCARB1 Expression in Atherogenesis

Rozhkova

Дмитриева

Nosova

et al. 2021

JCDD

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Atheroprotective properties of human plasma high-density lipoproteins (HDLs) are determined by their involvement in reverse cholesterol transport (RCT) from the macrophage to the liver. ABCA1, ABCG1, and SR-BI cholesterol transporters are involved in cholesterol efflux from macrophages to lipid-free ApoA-I and HDL as a first RCT step. Molecular determinants of RCT efficiency that may possess diagnostic and therapeutic meaning remain largely unknown. This review summarizes the progress in studying the genomic variants of ABCA1, ABCG1, and SCARB1, and the regulation of their function at transcriptional and post-transcriptional levels in atherosclerosis. Defects in the structure and function of ABCA1, ABCG1, and SR-BI are caused by changes in the gene sequence, such as single nucleotide polymorphism or various mutations. In the transcription initiation of transporter genes, in addition to transcription factors, long noncoding RNA (lncRNA), transcription activators, and repressors are also involved. Furthermore, transcription is substantially influenced by the methylation of gene promoter regions. Post-transcriptional regulation involves microRNAs and lncRNAs, including circular RNAs. The potential biomarkers and targets for atheroprotection, based on molecular mechanisms of expression regulation for three transporter genes, are also discussed in this review.

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Section: Changes At the Genome Levelsupporting

confidence: 52%

Genomic Variants and Multilevel Regulation of ABCA1, ABCG1, and SCARB1 Expression in Atherogenesis

Rozhkova

Дмитриева

Nosova

et al. 2021

JCDD

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“…These studies rarely defined the underlying mechanisms of CXCL12 in vitro. Dyslipidemia is the lead-cholesterol onto lipid-free apoA-I, while ABCG1 mainly facilitates cholesterol efflux onto HDL (35)(36)(37)(38). Therefore, the CXCL12-induced decrease in cholesterol efflux to apoA-I but not to HDL implies that CXCL12 reduces cholesterol efflux by downregulating ABCA1 expression.…”

Section: Discussionmentioning

confidence: 99%

CXCL12 promotes atherosclerosis by downregulating ABCA1 expression via the CXCR4/GSK3β/β-cateninT120/TCF21 pathway

Gao

et al. 2019

Journal of Lipid Research

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“…Presently, artificial intelligence (AI) and particularly deep-learning show strong promise for automatic segmentation of CMR images [ 5 – 8 ]. While the current AI-based methods have been successfully used for delineating the adult heart disease, they are not yet reliable for segmenting the CMR images of CHD patients, and particularly in children [ 8 , 9 ]. The foremost basis for this shortcoming is the anatomical heterogeneity and lack of large CMR databases that include data from a diverse group of CHD subjects acquired by diverse scanners and pulse sequences.…”

Section: Introductionmentioning

confidence: 99%

“…Another limitation is overfitting, especially over training, to image patterns in a specific dataset that includes images from the same scanner model/vendor, as also reported by Bai et al [ 7 ]. Dealing with limited data is a major challenge in designing effective neural networks for pediatric CMR, particularly for CHD subjects, and necessitates innovative approaches [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases

Karimi-Bidhendi

Arafati

Cheng

et al. 2020

Journal of Cardiovascular Magnetic Resonance

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Background For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging offers non-invasive and non-ionizing assessment of CHD patients. However, although CMR data facilitates reliable analysis of cardiac function and anatomy, clinical workflow mostly relies on manual analysis of CMR images, which is time consuming. Thus, an automated and accurate segmentation platform exclusively dedicated to pediatric CMR images can significantly improve the clinical workflow, as the present work aims to establish. Methods Training artificial intelligence (AI) algorithms for CMR analysis requires large annotated datasets, which are not readily available for pediatric subjects and particularly in CHD patients. To mitigate this issue, we devised a novel method that uses a generative adversarial network (GAN) to synthetically augment the training dataset via generating synthetic CMR images and their corresponding chamber segmentations. In addition, we trained and validated a deep fully convolutional network (FCN) on a dataset, consisting of $$64$$ 64 pediatric subjects with complex CHD, which we made publicly available. Dice metric, Jaccard index and Hausdorff distance as well as clinically-relevant volumetric indices are reported to assess and compare our platform with other algorithms including U-Net and cvi42, which is used in clinics. Results For congenital CMR dataset, our FCN model yields an average Dice metric of $$91.0\mathrm{\%}$$ 91.0 % and $$86.8\mathrm{\%}$$ 86.8 % for LV at end-diastole and end-systole, respectively, and $$84.7\mathrm{\%}$$ 84.7 % and $$80.6\mathrm{\%}$$ 80.6 % for RV at end-diastole and end-systole, respectively. Using the same dataset, the cvi42, resulted in $$73.2\mathrm{\%}$$ 73.2 % , $$71.0\mathrm{\%}$$ 71.0 % , $$54.3\mathrm{\%}$$ 54.3 % and $$53.7\mathrm{\%}$$ 53.7 % for LV and RV at end-diastole and end-systole, and the U-Net architecture resulted in $$87.4\mathrm{\%}$$ 87.4 % , $$83.9\mathrm{\%}$$ 83.9 % , $$81.8\mathrm{\%}$$ 81.8 % and $$74.8\mathrm{\%}$$ 74.8 % for LV and RV at end-diastole and end-systole, respectively. Conclusions The chambers’ segmentation results from our fully-automated method showed strong agreement with manual segmentation and no significant statistical difference was found by two independent statistical analyses. Whereas cvi42 and U-Net segmentation results failed to pass the t-test. Relying on these outcomes, it can be inferred that by taking advantage of GANs, our method is clinically relevant and can be used for pediatric and congenital CMR segmentation and analysis.

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Associations of ABCG1-mediated cholesterol efflux capacity with coronary artery lipid content assessed by near-infrared spectroscopy

Cited by 9 publications

References 28 publications

Genomic Variants and Multilevel Regulation of ABCA1, ABCG1, and SCARB1 Expression in Atherogenesis

Genomic Variants and Multilevel Regulation of ABCA1, ABCG1, and SCARB1 Expression in Atherogenesis

CXCL12 promotes atherosclerosis by downregulating ABCA1 expression via the CXCR4/GSK3β/β-cateninT120/TCF21 pathway

Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases

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