Intracoronary Imaging, Cholesterol Efflux, and Transcriptomes After Intensive Statin Treatment

Kini, Annapoorna; Vengrenyuk, Yuliya; Shameer, Khader; Maehara, Akiko; Purushothaman, Meerarani; Yoshimura, Tsuyoshi; Matsumura, Mitsuaki; Aquino, Melissa; Haider, Nezam; Johnson, Kipp W.; Readhead, Ben; Kidd, Brian; Feig, Jonathan E.; Ramanathan, Krishnan; Sweeny, Joseph; Milind, M. Vaidya; Moreno, Pedro R.; Mehran, Roxana; Kovacic, Jason C.; Baber, Usman; Dudley, Joel T.; Narula, Jagat; Sharma, Samin K.

doi:10.1016/j.jacc.2016.10.029

Cited by 61 publications

(54 citation statements)

References 50 publications

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“…Since these structural changes are related to underlying disease aetiology and pathophysiological mechanisms, imaging is obviously widely used in cardiovascular medicine 5. Imaging data sets are complex and stored in a variety of formats (JPEG, MPEG, DICOM, and so on) and are of varying dimension and scale (two dimensional, three dimensional, four dimensional, and so on), thereby representing the leading edge of cardiovascular big data.…”

Section: Introductionmentioning

confidence: 99%

“…Molecular profiling and downstream analyses using statistical and machine learning approaches can also aid in identifying new target molecules for drug discovery and drug repositioning 5 14. Emerging studies suggest that combining pan-omics technologies with imaging will be routine in the future to compile a stratified profile of patient population 5. For example, recently, Li et al demonstrated that layering genomic data on EHR-based phenomics can be used to find and characterise patient subtypes (in this case, for type 2 diabetes mellitus) 15…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning in cardiovascular medicine: are we there yet?

Shameer

Johnson

Glicksberg

et al. 2018

Heart

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383

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Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning in cardiovascular medicine: are we there yet?

Shameer

Johnson

Glicksberg

et al. 2018

Heart

Self Cite

383

229

View full text Add to dashboard Cite

show abstract

“…However, pharmacological intervention aiming to raise serum HDL-C levels failed to reduce the incidence of cardiovascular disease events as assessed in several randomized controlled trials. Study by Hafiane et al supposed that HDL-C mass did not reflect HDL functionality and macrophage-specific cholesterol efflux to apo A-I particles binding to the ABCA1 is considered the most relevant to atherosclerosis [26]. Cholesterol efflux from macrophages is considered a crucial step of reverse cholesterol transport that is responsible for maintaining normal cholesterol balance.…”

Section: Discussionmentioning

confidence: 99%

“…Emerging evidence demonstrated that CEC had an inverse relationship with incidence of cardiovascular events in population-based studies and CEC improve cardiovascular disease risk prediction beyond conventional risk factors [26]. It was also concluded that measures of HDL function might be a better marker of cardiovascular risk and, possibly, of recurrent events than are HDL cholesterol levels [27].…”

Section: Discussionmentioning

confidence: 99%

Roles of cholesterol efflux capacity in the occurrence and prognosis coronary artery disease

Ren

et al. 2019

Preprint

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Background The association between cholesterol efflux capacity (CEC) with the occurrence and prognosis of coronary artery disease (CAD) remains unrevealed. In our study, a systematic review was performed to quantitively analyze the association between CEC and the risk of CAD and follow-up endpoint events of the patients with CAD.Methods A systematic search of electronic databases (PubMed, EMBASE, OVID, Web of Science and Cochrane Library) for studies published until September 2019 was performed. Cohort, case-control studies, and randomized controlled trials that examined the effect of CEC on risk and prognosis of CAD were included.Results Eighteen studies involving a total of 12615 subjects that met the inclusion criteria were included. Among them, 14 studies reported the CEC levels in control and CAD group and 8 of them analyzed the association of CEC with risk of CAD. Four studies reported the prognosis of CAD or acute coronary syndrome (ACS). From the pooled analyses, significantly decreased CEC level was shown in patients with stable CAD in comparison with the control. It was also true in subgroup analysis of the patients with ACS. The decreased CEC was significantly associated with increased risk of CAD (OR=0.65, 95% CI: 0.55-0.75, P<0.001). Decreased CEC level predicted higher all-cause (OR= 0.39, 95% CI: 0.20-0.77, P=0.007) and cardiovascular related mortality (OR= 0.34, 95% CI: 0.13-0.90, P=0.03) risk in patients with CAD. However, CEC levels failed to predict the occurrence of stroke and myocardial infraction in patients with CAD.Conclusions Decreased cholesterol efflux capacity is an independent risk factor for the occurrence of CAD patients, and its level predicts all-cause and cardiovascular related mortality risk in patients with CAD. Prospective studies should further investigate whether CEC control might improve outcomes in CAD patients.

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“…The effect of statin therapy on HDL metabolism is less clear. Previous reports have shown that statins may both decrease [51,52] or increase [53] CEC -the only metric of HDL functionality that has been shown to be predictive of prospective CVD events. A recent study showed that on-statin CEC was inversely associated with incident cardiovascular events and that, in fact, HDL particle number was the strongest HDLrelated biomarker of residual risk in statin-treated patients [54].…”

Section: Discussionmentioning

confidence: 99%

Combined bioactive approach over atherosclerosis risk biomarkers

Scolaro¹

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Intracoronary Imaging, Cholesterol Efflux, and Transcriptomes After Intensive Statin Treatment

Cited by 61 publications

References 50 publications

Machine learning in cardiovascular medicine: are we there yet?

Machine learning in cardiovascular medicine: are we there yet?

Roles of cholesterol efflux capacity in the occurrence and prognosis coronary artery disease

Combined bioactive approach over atherosclerosis risk biomarkers

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