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Lipid-rich Plaques Detected by Near-infrared Spectroscopy Are More Frequently Exposed to High Shear Stress.

ABSTRACT: High wall shear stress (WSS) and near-infrared spectroscopy (NIRS) detected lipid-rich plaque (LRP) are both known to be associated with plaque destabilization and future adverse cardiovascular events. However, knowledge of spatial co-localization of LRP and high WSS is lacking. This study investigated the co-localization of LRP based on NIRS and high WSS. Fifty-three patients presenting acute coronary syndrome underwent NIRS-intravascular-ultrasound (NIRS-IVUS) imaging of a non-culprit coronary artery. WSS was obtained using WSS profiling in 3D-reconstructions of the coronary arteries based on fusion of IVUS-segmented lumen and CT-derived 3D-centerline. Thirty-eight vessels were available for final analysis and divided into 0.5 mm/45° sectors. LRP sectors, as identified by NIRS, were more often colocalized with high WSS than sectors without LRP. Moreover, there was a dose-dependent relationship between lipid content and high WSS exposure. This study is a first step in understanding the evolution of LRPs to vulnerable plaques. Graphical Abstract.

SUBMITTER: Hartman EMJ

PROVIDER: S-EPMC8219563 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Lipid-rich Plaques Detected by Near-infrared Spectroscopy Are More Frequently Exposed to High Shear Stress.

Hartman Eline M J EMJ De Nisco Giuseppe G Kok Annette M AM Hoogendoorn Ayla A Coenen Adriaan A Mastik Frits F Korteland Suze-Anne SA Nieman Koen K Gijsen Frank J H FJH van der Steen Anton F W AFW Daemen Joost J Wentzel Jolanda J JJ

Journal of cardiovascular translational research 20201009 3

High wall shear stress (WSS) and near-infrared spectroscopy (NIRS) detected lipid-rich plaque (LRP) are both known to be associated with plaque destabilization and future adverse cardiovascular events. However, knowledge of spatial co-localization of LRP and high WSS is lacking. This study investigated the co-localization of LRP based on NIRS and high WSS. Fifty-three patients presenting acute coronary syndrome underwent NIRS-intravascular-ultrasound (NIRS-IVUS) imaging of a non-culprit coronary ...[more]

PMID: 33034862

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Project description:BackgroundLipid content in coronary atheromatous plaques, measured by near-infrared spectroscopy (NIRS), can predict the risk of future coronary events. Biomarkers that reflect lipid content in coronary plaques may therefore improve coronary artery disease (CAD) risk assessment.PurposeWe aimed to investigate the association between circulating lipoprotein subfractions and lipid content in coronary atheromatous plaques in statin-treated patients with stable CAD undergoing percutaneous coronary intervention.Methods56 patients with stable CAD underwent three-vessel imaging with NIRS when feasible. The coronary artery segment with the highest lipid content, defined as the maximum lipid core burden index within any 4 mm length across the entire lesion (maxLCBI4mm), was defined as target segment. Lipoprotein subfractions and Lipoprotein a (Lp(a)) were analyzed in fasting serum samples by nuclear magnetic resonance spectroscopy and by standard in-hospital procedures, respectively. Penalized linear regression analyses were used to identify the best predictors of maxLCBI4mm. The uncertainty of the lasso estimates was assessed as the percentage presence of a variable in resampled datasets by bootstrapping.ResultsOnly modest evidence was found for an association between lipoprotein subfractions and maxLCBI4mm. The lipoprotein subfractions with strongest potential as predictors according to the percentage presence in resampled datasets were Lp(a) (78.1 % presence) and free cholesterol in the smallest high-density lipoprotein (HDL) subfractions (74.3 % presence). When including established cardiovascular disease (CVD) risk factors in the regression model, none of the lipoprotein subfractions were considered potential predictors of maxLCBI4mm.ConclusionIn this study, serum levels of Lp(a) and free cholesterol in the smallest HDL subfractions showed the strongest potential as predictors for lipid content in coronary atheromatous plaques. Although the evidence is modest, our study suggests that measurement of lipoprotein subfractions may provide additional information with respect to coronary plaque composition compared to traditional lipid measurements, but not in addition to established risk factors. Further and larger studies are needed to assess the potential of circulating lipoprotein subfractions as meaningful biomarkers both for lipid content in coronary atheromatous plaques and as CVD risk markers.

Project description:Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model's superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development.

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Lipid-rich Plaques Detected by Near-infrared Spectroscopy Are More Frequently Exposed to High Shear Stress.

Publications

Lipid-rich Plaques Detected by Near-infrared Spectroscopy Are More Frequently Exposed to High Shear Stress.

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