2021

DOI: 10.3390/nu13061774

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Triglyceride-Rich Lipoproteins and Their Remnants as Silent Promoters of Atherosclerotic Cardiovascular Disease and Other Metabolic Disorders: A Review

Radu Andy Sascău

¹

,

Alexandra Clément²,

³

et al.

Abstract: While targeting elevated serum levels of low-density lipoprotein cholesterol has been the mainstay of atherosclerosis prevention and treatment for decades, the evidence regarding the atherogenic role of hypertriglyceridemia is still controversial. Various epidemiological population-based studies on statin-treated subjects nominated triglycerides, triglyceride-rich lipoproteins (namely, chylomicrons and very-low-density lipoprotein particles), and their remnants as major determinants of the substantial residual… Show more

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Cited by 30 publications

(24 citation statements)

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“…Several studies have shown that a high level of triglycerides contributes to atherosclerosis (35)(36)(37), while a low level is a risk factor for cerebral hemorrhage (38). It was also reported that a low level of triglycerides was a modest risk factor for HT after IVT (11).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Model for Prediction of Hemorrhage Transformation in Acute Ischemic Stroke After Alteplase

¹

,

Li²,

Wu³

et al. 2022

Front. Neurol.

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Hemorrhage transformation (HT) is the most dreaded complication of intravenous thrombolysis (IVT) in acute ischemic stroke (AIS). The prediction of HT after IVT is important in the treatment decision-making for AIS. We designed and compared different machine learning methods, capable of predicting HT in AIS after IVT. A total of 345 AIS patients who received intravenous alteplase between January 2016 and June 2021 were enrolled in this retrospective study. The demographic characteristics, clinical condition, biochemical data, and neuroimaging variables were included for analysis. HT was confirmed by head computed tomography (CT) or magnetic resonance imaging (MRI) within 48 h after IVT. Based on the neuroimaging results, all of the patients were divided into the non-HT group and the HT group. Then, the variables were applied in logistic regression (LR) and random forest (RF) algorithms to establish HT prediction models. To evaluate the accuracy of the machine learning models, the models were compared to several of the common scales used in clinics, including the multicenter stroke survey (MSS) score, safe implementation of treatments in stroke (SITS) score, and SEDAN score. The performance of these prediction models was evaluated using the receiver operating characteristic (ROC) curve (AUC). Forty-five patients had HT (13.0%) within 48 h after IVT. The ROC curve results showed that the AUCs of HT that were predicted by the RF model, LR model, MSS, SITS, and SEDAN scales after IVT were 0.795 (95% CI, 0.647–0.944), 0.703 (95% CI, 0.515–0.892), 0.657 (95% CI, 0.574–0.741), 0.660 (95% CI, 0.580–0.740) and 0.655 (95% CI, 0.571–0.739), respectively. The RF model performed better than the other models and scales. The top four most influential factors in the RF importance matrix plot were triglyceride, Lpa, the baseline NIHSS, and hemoglobin. The SHapley Additive exPlanation values made the RF prediction model clinically interpretable. In this study, an RF machine learning method was successfully established to predict HT in AIS patients after intravenous alteplase, which the sensitivity was 66.7%, and the specificity was 80.7%.

“…Several studies have shown that a high level of triglycerides contributes to atherosclerosis (35)(36)(37), while a low level is a risk factor for cerebral hemorrhage (38). It was also reported that a low level of triglycerides was a modest risk factor for HT after IVT (11).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Model for Prediction of Hemorrhage Transformation in Acute Ischemic Stroke After Alteplase

¹

,

Li²,

Wu³

et al. 2022

Front. Neurol.

View full text Add to dashboard Cite

Hemorrhage transformation (HT) is the most dreaded complication of intravenous thrombolysis (IVT) in acute ischemic stroke (AIS). The prediction of HT after IVT is important in the treatment decision-making for AIS. We designed and compared different machine learning methods, capable of predicting HT in AIS after IVT. A total of 345 AIS patients who received intravenous alteplase between January 2016 and June 2021 were enrolled in this retrospective study. The demographic characteristics, clinical condition, biochemical data, and neuroimaging variables were included for analysis. HT was confirmed by head computed tomography (CT) or magnetic resonance imaging (MRI) within 48 h after IVT. Based on the neuroimaging results, all of the patients were divided into the non-HT group and the HT group. Then, the variables were applied in logistic regression (LR) and random forest (RF) algorithms to establish HT prediction models. To evaluate the accuracy of the machine learning models, the models were compared to several of the common scales used in clinics, including the multicenter stroke survey (MSS) score, safe implementation of treatments in stroke (SITS) score, and SEDAN score. The performance of these prediction models was evaluated using the receiver operating characteristic (ROC) curve (AUC). Forty-five patients had HT (13.0%) within 48 h after IVT. The ROC curve results showed that the AUCs of HT that were predicted by the RF model, LR model, MSS, SITS, and SEDAN scales after IVT were 0.795 (95% CI, 0.647–0.944), 0.703 (95% CI, 0.515–0.892), 0.657 (95% CI, 0.574–0.741), 0.660 (95% CI, 0.580–0.740) and 0.655 (95% CI, 0.571–0.739), respectively. The RF model performed better than the other models and scales. The top four most influential factors in the RF importance matrix plot were triglyceride, Lpa, the baseline NIHSS, and hemoglobin. The SHapley Additive exPlanation values made the RF prediction model clinically interpretable. In this study, an RF machine learning method was successfully established to predict HT in AIS patients after intravenous alteplase, which the sensitivity was 66.7%, and the specificity was 80.7%.

“…Postprandial lipemia has been shown to be associated with increased oxidative stress and decreased FMDs in healthy, male participants ( 22 ). Circulating triglyceride-rich lipoproteins and their remnants are also associated with endothelial dysfunction and CVD risks ( 172 , 173 ). This work showed that the percentage fat content of the meals was inversely associated with the postprandial change in FMD at 3 hours, indicating a reduced vessel response as the fat percentage increased.…”

Section: Discussionmentioning

confidence: 99%

A single, high-fat meal adversely affects postprandial endothelial function: a systematic review and meta-analysis

¹

,

²

,

³

et al. 2022

The American Journal of Clinical Nutrition

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Background Endothelial dysfunction is a predictive risk factor for the development of atherosclerosis and is assessed by flow-mediated dilation (FMD). Although it is known that nitric oxide-dependent endothelial dysfunction occurs after consuming a high-fat meal, the magnitude of the effect and the factors that affect the response are unquantified. Objectives To conduct a systematic review and meta-analysis exploring the quantitative effect of a single high-fat meal on endothelial function and determine the factors that modify the FMD response. Design Six databases were systematically searched for original research published up to January 2022. Eligible studies measured fasting and postprandial FMD following consumption of a high-fat meal. Meta-regression was used to analyze the effect of moderator variables. The protocol was pre-registered in the Prospero database (ID# CRD42020187244). Results There were 131 studies included of which 90 were suitable for quantitative meta-analysis. A high-fat meal challenge transiently caused endothelial dysfunction, decreasing postprandial FMD at 2-hours: -1.02 percentage points (pp) (95% CI: [-1.34, -0.70], P < 0.01, I2 = 93.3%), 3-hours: -1.04 pp ([-1.48, -0.59], P < 0.001, I2 = 84.5%), and 4-hours: -1.19 pp ([-1.53, -0.84], P < 0.01, I2 = 94.6%). Younger, healthy-weight participants exhibited a greater postprandial reduction in FMD% than older, heavier, at-risk groups after a high-fat meal (P < 0.05). Percent fat of the meals was inversely associated with the magnitude of postprandial change in FMD at 3-hours (P < 0.01). Conclusion A single high-fat meal adversely impacts endothelial function, with the magnitude of the impact on postprandial FMD moderated by fasting FMD, participant age, body mass index, and fat content of the meal. Recommendations are made to standardize the design of future postprandial FMD studies and optimize interpretation of results, as high-fat meals are commonly used in clinical studies as a challenge to assess endothelial function and therapeutics.

“… 17–20 According to Sascău et al, triglyceride-rich lipoproteins and their remnants are silenced promoters of atherosclerotic cardiovascular disease and other metabolic diseases. 38 Hadi et al reported that RC is associated with prediabetes, and they suggested that RC might have a certain influence on glucose metabolism. 39 However, the nature of the relationship of RC with new-onset diabetes is unclear, and in this context, the present study investigated the association between them.…”

Section: Discussionmentioning

confidence: 99%

Remnant Cholesterol is an Independent Predictor of New-Onset Diabetes: A Single-Center Cohort Study

Xie¹,

et al. 2021

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Purpose Remnant cholesterol (RC) is the cholesterol of triglyceride-rich lipoproteins, which has a high degree of atherogenic effect. To date, epidemiological evidence supports that higher RC levels lead to a greater risk of adverse cardiovascular events in patients with diabetes, but the nature of the association between RC levels and diabetes risk remains unclear. This study was designed to assess the association of RC with the risk of new-onset diabetes and to investigate whether there is a causal relationship between the two. Patients and Methods The subjects included 15,464 individuals of the general population who participated in a health examination. Subjects were quartered according to the RC quartile, and the Cox proportional hazard model was used to assess the independent association between RC and new-onset diabetes. Results During an average observation period of 6.13 years, 2.41% of the subjects were diagnosed with new-onset diabetes. Kaplan–Meier analysis showed that the 13-year cumulative diabetes rates corresponding to the RC quartile were 8.62%, 2.49%, 12.78%, and 17.91%. Multivariate Cox regression analysis indicated that higher RC levels were independently associated with an increased risk of new-onset diabetes (HR: 2.44, 95% CI: 1.50–3.89). Additionally, according to the results of receiver operating characteristic curve analysis, RC had the largest area under the curve (0.7314) compared to traditional lipid parameters in predicting new-onset diabetes. Conclusion These results indicated that RC is an important independent predictor of new-onset diabetes in the general population.

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