Small-dense LDL/large-buoyant LDL ratio associates with the metabolic syndrome

Debunking the Myth: Eggs and Heart Disease

Eggs, which are often considered a complete food, have recently been scrutinized by the media as a potential cause of cardiovascular disease. However, the media hasn't shown the same enthusiasm for processed foods high in fructose, the consumption of refined cooking oil, seed oils, and carbohydrate-rich meals, the connection between these factors and metabolic diseases, or the potential long-term impacts on population comorbidities, as they have for criticizing egg yolks as a cause for cardiovascular disease in recent times. This review investigates the relationship between eggs and lipid levels, glucose levels, atherosclerosis, and antioxidant properties, as well as comparing them to cholesterol-free egg controls. We conducted the review in response to a recent trend of discarding nutritious and energy-rich egg yolks due to the belief propagated by the media that removing egg yolks from a normal diet is cardioprotective after the media started to blame egg yolks as the cause of the recent surge in heart attacks. However, the media fails to highlight the fact that eggs have been an integral part of the human diet since the domestication of hens. On the other hand, recent additions to the human diet a few decades ago, such as fructose-rich breakfast cereals, coffee beverages with sugar levels comparable to candy bars, protein supplements for diabetics that are notorious for raising blood glucose levels, and the heightened consumption of seed oil, which causes inflammation, have been responsible for the surge in cardiovascular events in recent times. Social media platforms often showcase visually appealing junk food products and sugary beverages as a sign of wealth, promoting unhealthy processed food and ultimately causing a decline in an individual's lifespan and overall health.

Specific Increase in Small Dense Low-Density Lipoprotein-Cholesterol Levels beyond Triglycerides in Patients with Diabetes: Implications for Cardiovascular Risk of MAFLD

AIMS

Small dense (sd) low-density lipoprotein (LDL)-cholesterol (C) is the most powerful predictor of cardiovascular (CV) disease among lipid biomarkers and is generated by hypertriglyceridemia and insulin resistance. Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly proposed liver disease with a high CV risk. We investigated the specific association of sdLDL-C with MAFLD beyond triglycerides (TG) and obesityMethods: Participants were 839 non-alcoholic drinkers with type 2 diabetes enrolled in a regional diabetes cohort. Fatty liver (FL) and visceral fat area (VFA) was detected by computed tomography scan. sdLDL-C and LDL-TG were measured by our established homogeneous assay. TG rich lipoprotein (TRL) was calculated by subtracting LDL-C plus HDL-C from total-C. Grade of sdLDL-C (≤ 24, 25-34, 35-44, and ≥ 45 mg/dL) was classified according to the Hisayama study.

RESULTS

Compared to non-FL counterparts, FL subjects were younger, predominantly male and smokers; and had higher body mass index (BMI), VFA, hemoglobin A1c, C-peptide, TG, and sdLDL-C, while had similar levels of LDL-C, LDL-TG, and TRL-C. Multivariate logistic analysis revealed that sdLDL-C was the most powerful lipid parameter for identifying FL, independent of TG, HDL-C, BMI, and VFA. The independent association between TG and FL was lost when sdLDL-C was added to the analysis. These results remained the same when lipid-lowering drug users were excluded. After adjustment for confounders, the odds ratio for FL was 2.4-2.7 at sdLDL ≥ 35 mg/dL based on sdLDL ≤ 24 mg/dL.

CONCLUSIONS

sdLDL-C levels are specifically elevated in patients with diabetes and MAFLD, independent of TG and VFA, suggesting liver-centered metabolic abnormalities.

Transitioning from having no metabolic abnormality nor obesity to metabolic impairment in a cohort of apparently healthy adults

INTRODUCTION

The global prevalence of metabolic syndrome and its association with increased morbidity and mortality has been rigorously studied. However, the true prevalence of "metabolic health", i.e. individuals without any metabolic abnormalities is not clear. Here, we sought to determine the prevalence of "metabolically healthy" individuals and characterize the "transition phase" from metabolic health to development of dysfunction over a follow-up period of 5 years.

METHODS

We included 20,507 individuals from the Tel Aviv Sourasky Medical Center Inflammation Survey (TAMCIS) which comprises apparently healthy individuals attending their annual health survey. A second follow-up visit was documented after 4.8 (± 0.6) years. We defined a group of metabolically healthy participants without metabolic abnormalities nor obesity and compared their characteristics and change in biomarkers over time to participants who developed metabolic impairment on their follow-up visit. The intersections of all metabolic syndrome components and elevated high sensitivity C-reactive protein (hs-CRP) were also analyzed.

RESULTS

A quarter of the cohort (5379 individuals, (26.2%) did not fulfill any metabolic syndrome criteria during their baseline visit. A total of 985 individuals (12.7% of returning participants) developed metabolic criteria over time with hypertension being the most prevalent component to develop among these participants. Individuals that became metabolically impaired over time demonstrated increased overlap between metabolic syndrome criteria and elevated hs-CRP levels. The group that became metabolically impaired over time also presented higher delta values of WBC, RBC, liver biomarkers, and uric acid compared with participants who were consistently metabolically impaired. LDL-C (low-density lipoprotein cholesterol) delta levels were similar.

CONCLUSIONS

Roughly one-quarter of apparently healthy adults are defined as "metabolically healthy" according to current definitions. The transition from health to metabolic dysfunction is accompanied with active inflammation and several non-metabolic syndrome biomarkers. Aggressive screening for these biomarkers, blood pressure and hs-CRP might help identify apparently healthy individuals at increased risk of developing metabolic syndrome over time.

Small LDL subfractions are associated with coronary atherosclerosis despite no differences in conventional lipids

Lipoprotein subfractions currently represent a new source of cardiovascular disease (CVD) risk markers that may provide more information than conventional lipid measures. We aimed to investigate whether lipoprotein subfractions are associated with coronary atherosclerosis in patients without prior known CVD. Fasting serum samples from 60 patients with suspected coronary artery disease (CAD) were collected before coronary angiography and analyzed by nuclear magnetic resonance (NMR) spectroscopy. The severity of coronary atherosclerosis was quantified by the Gensini score (≤20.5 = nonsignificant coronary atherosclerosis, 20.6-30.0 = intermediate coronary atherosclerosis, ≥30.1 = significant CAD). Differences in lipoprotein subfractions between the three Gensini groups were assessed by two-way ANOVA, adjusted for statin use. Despite no differences in conventional lipid measures between the three Gensini groups, patients with significant CAD had higher apolipoprotein-B/apolipoprotein-A1 ratio, 30% more small and dense low-density lipoprotein 5 (LDL-5) particles, and increased levels of cholesterol, triglycerides, and phospholipids within LDL-5 compared with patients with nonsignificant coronary atherosclerosis and intermediate coronary atherosclerosis (P ≤ 0.001). In addition, the low-density lipoprotein (LDL) cholesterol/high-density lipoprotein cholesterol ratio, and triglyceride levels of LDL 4 were significantly increased in patients with significant CAD compared with patients with nonsignificant coronary atherosclerosis. In conclusion, small and dense lipoprotein subfractions were associated with coronary atherosclerosis in patients without prior CVD. Additional studies are needed to explore whether lipoprotein subfractions may represent biomarkers offering a clinically meaningful improvement in the risk prediction of CAD.

Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population

Purpose. Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. Methods. In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfractions (LDLC-1 to LDLC-7) were classified and quantified using the Lipoprint system. Six predictive models (k-nearest neighbor classifier (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)) were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), recall (sensitivity), accuracy, precision, and F1 score. The selected features were analyzed and ranked. Results. While predicting the CAD development risk of the CAD patients without lipid-lowering therapy in the test set, all models obtained AUC values above 0.94, and the accuracy, precision, recall, and F1 score were above 0.84, 0.85, 0.92, and 0.88, respectively. While predicting the CAD development risk of all CAD patients in the test set, all models obtained AUC values above 0.91, and the accuracy, precision, recall, and F1 score were above 0.87, 0.94, 0.87, and 0.92, respectively. Importantly, small dense LDL-C (sdLDL-C) and LDLC-4 play pivotal roles in predicting CAD risk. Conclusions. In the present study, machine learning tools combining both clinical data and blood lipid profile showed excellent overall predictive power. It suggests that machine learning tools are suitable for predicting the risk of CAD development in the near future.

Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development

Background: Coronary artery disease (CAD) is the leading cause of death worldwide, which has a long asymptomatic period of atherosclerosis. Thus, it is crucial to develop efficient strategies or biomarkers to assess the risk of CAD in asymptomatic individuals.

Methods: A total of 356 consecutive CAD patients and 164 non-CAD controls diagnosed using coronary angiography were recruited. Blood lipids, other baseline characteristics, and clinical information were investigated in this study. In addition, low-density lipoprotein cholesterol (LDL-C) subfractions were classified and quantified using the Lipoprint system. Based on these data, we performed comprehensive analyses to investigate the risk factors for CAD development and to predict CAD risk.

Results: Triglyceride, LDLC-3, LDLC-4, LDLC-5, LDLC-6, and total small and dense LDL-C were significantly higher in the CAD patients than those in the controls, whereas LDLC-1 and high-density lipoprotein cholesterol (HDL-C) had significantly lower levels in the CAD patients. Logistic regression analysis identified male [odds ratio (OR) = 2.875, P < 0.001], older age (OR = 1.018, P = 0.025), BMI (OR = 1.157, P < 0.001), smoking (OR = 4.554, P < 0.001), drinking (OR = 2.128, P < 0.016), hypertension (OR = 4.453, P < 0.001), and diabetes mellitus (OR = 8.776, P < 0.001) as clinical risk factors for CAD development. Among blood lipids, LDLC-3 (OR = 1.565, P < 0.001), LDLC-4 (OR = 3.566, P < 0.001), and LDLC-5 (OR = 6.866, P < 0.001) were identified as risk factors. To predict CAD risk, six machine learning models were constructed. The XGboost model showed the highest AUC score (0.945121), which could distinguish CAD patients from the controls with a high accuracy. LDLC-4 played the most important role in model construction.

Conclusions: The established models showed good performance for CAD risk prediction, which can help screen high-risk CAD patients in asymptomatic population, so that further examination and prevention treatment might be taken before any sudden or serious event.

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

BACKGROUND

The discovery of robust and trans-ethnically replicated DNA methylation markers of metabolic phenotypes, has hinted at a potential role of epigenetic mechanisms in lipid metabolism. However, DNA methylation and the lipid compositions and lipid concentrations of lipoprotein sizes have been scarcely studied. Here, we present an epigenome-wide association study (EWAS) (N = 5414 total) of mostly lipid-related metabolic measures, including a fine profiling of lipoproteins. As lipoproteins are the main players in the different stages of lipid metabolism, examination of epigenetic markers of detailed lipoprotein features might improve the diagnosis, prognosis, and treatment of metabolic disturbances.

RESULTS

We conducted an EWAS of leukocyte DNA methylation and 226 metabolic measurements determined by nuclear magnetic resonance spectroscopy in the population-based KORA F4 study (N = 1662) and replicated the results in the LOLIPOP, NFBC1966, and YFS cohorts (N = 3752). Follow-up analyses in the discovery cohort included investigations into gene transcripts, metabolic-measure ratios for pathway analysis, and disease endpoints. We identified 161 associations (p value < 4.7 × 10-10), covering 16 CpG sites at 11 loci and 57 metabolic measures. Identified metabolic measures were primarily medium and small lipoproteins, and fatty acids. For apolipoprotein B-containing lipoproteins, the associations mainly involved triglyceride composition and concentrations of cholesterol esters, triglycerides, free cholesterol, and phospholipids. All associations for HDL lipoproteins involved triglyceride measures only. Associated metabolic measure ratios, proxies of enzymatic activity, highlight amino acid, glucose, and lipid pathways as being potentially epigenetically implicated. Five CpG sites in four genes were associated with differential expression of transcripts in blood or adipose tissue. CpG sites in ABCG1 and PHGDH showed associations with metabolic measures, gene transcription, and metabolic measure ratios and were additionally linked to obesity or previous myocardial infarction, extending previously reported observations.

CONCLUSION

Our study provides evidence of a link between DNA methylation and the lipid compositions and lipid concentrations of different lipoprotein size subclasses, thus offering in-depth insights into well-known associations of DNA methylation with total serum lipids. The results support detailed profiling of lipid metabolism to improve the molecular understanding of dyslipidemia and related disease mechanisms.

LDL and HDL lipoprotein subfractions in multiple sclerosis patients with decreased insulin sensitivity

Objectives. Increased metabolic and cardiovascular morbidity has been reported in multiple sclerosis (MS) patients. Previously, we have found decreased insulin sensitivity and hyperinsulinemia in a group of newly diagnosed MS patients. We hypothesize that these features may be associated with an altered lipid profile and low, intermediate, or high density lipoprotein (LDL, IDL, HDL) subclasses accelerating atherosclerosis and thus contributing to the cardiovascular risk increase in these patients.

Subjects and methods. In a group of 19 newly diagnosed untreated MS patients with previously found hyperinsulinemia and insulin resistance and a matched group of 19 healthy controls, the lipoprotein subclasses profile was determined. Polyacrylamide gel electrophoresis was used to separate and measure the LDL (large LDL and small dense LDL), HDL (large, intermediate and small), and IDL (A, B and C) subclasses with the Lipoprint© System (Quantimetrix Corporation, Redondo Beach, CA, USA).

Results. No difference was found either in the conventional lipid or lipoprotein subclasses profile between the MS patients and healthy controls. We found an inverse association between the level of IDL-B with fasting insulin (r=–0.504, p=0.032), the insulin resistance estimated by homeo-static model assessment – insulin resistance (HOMA-IR) (r=–0.498, p=0.035), insulin response expressed as area under the curve (AUC; r=–0.519, p=0.027), and area above the baseline (AAB; r=–0.476, p=0.045) and positive association with insulin sensitivity estimated by insulin sensitivity index (ISI) Matsuda (r=0.470, 0.048) in MS patients, but not in healthy controls suggesting the first signs in lipoprotein subclasses profile change.

Conclusions. Our data indicate that changes in lipoprotein profile and subclasses are preceded by insulin resistance and hyperinsulinemia in patients with newly diagnosed MS.

Small, dense LDL: an update

PURPOSE OF REVIEW

In this review, we summarize the latest findings on small, dense LDL (sdLDL) atherogenic particles, including their associations with other biomarkers.

RECENT FINDINGS

Increased sdLDL levels have been reported not only in different metabolic disorders such as diabetes, obesity and metabolic syndrome, but also in patients with rheumatoid and psoriatic arthritis as well as hypothyroidism. A wide range of lipid-lowering, as well as other drug classes, including novel antidiabetic agents and nutraceuticals, exert favourable effects on these atherogenic particles. The 'gold standard' methodology for the assessment of sdLDL has not been established yet. However, the association between sdLDL and several biomarkers could facilitate their assessment.

SUMMARY

Estimation of sdLDL in daily clinical practice may help with the identification of patients at high cardiovascular risk and further contribute in directing specific interventions to prevent and/or decrease such risk.

The small dense LDL particle/large buoyant LDL particle ratio is associated with glucose metabolic status in pregnancy

BACKGROUND

The lipoprotein subfraction particle profile can be used to improve clinical assessments of cardiovascular disease risk and contribute to early detection of atherogenic dyslipidemia. Lipid alterations in gestational diabetes have been extensively studied, but the results have been inconsistent. Here, we investigated serum lipoprotein subfraction particle levels and their association with glucose metabolic status in pregnancy.

METHODS

Twenty-eight pregnant women with gestational diabetes and 56 pregnant women with normal glucose tolerance matched for body mass index were enrolled in this study. We assessed fasting serum lipid concentrations and lipoprotein subfraction particle levels in participants between 24 and 28 weeks of gestation.

RESULTS

The level of low-density lipoprotein (LDL) cholesterol was significantly lower in women with gestational diabetes than in those with normal glucose tolerance, but the triglyceride and high-density lipoprotein (HDL) cholesterol levels of the two groups were similar. Lipoprotein particle analysis showed that very-low-density lipoprotein (VLDL) particle number and the small dense LDL particle/large buoyant LDL particle (sdLDL-P/lbLDL-P) ratio were significantly higher in women with gestational diabetes than in those with normal glucose tolerance (P = 0.013 and P = 0.015, respectively). In multivariate analysis, fasting glucose was independently and positively associated with sdLDL-P/lbLDL-P ratio even after adjustment for maternal age, gestational weight gain, BMI and LDL cholesterol (standardized Beta = 0.214, P = 0.029).

CONCLUSIONS

The sdLDL-P/lbLDL-P ratio is higher in GDM compared with non-diabetic pregnant women, and positively and independently associated with fasting glucose in pregnant women.

Comprehensive assessment of lipoprotein subfraction profiles according to glucose metabolism status, and association with insulin resistance in subjects with early-stage impaired glucose metabolism

BACKGROUND

Early detection of atherogenic dyslipidemia is crucial. We investigated lipoprotein subfraction parameters according to glucose metabolism status.

METHODS

We recruited 1255 lipid-lowering drug-naïve subjects with normal fasting glucose (NFG; n=200, 15.9%), impaired fasting glucose (IFG; n=443, 35.3%), or type 2 diabetes (T2D; n=612, 48.8%). Lipoprotein subfractions (1-7) were determined by polyacrylamide gel electrophoresis, separating low-density lipoprotein (LDL) into large buoyant LDL (lbLDL, LDL1-2) and small dense LDL (sdLDL, LDL3-7). Lipoprotein subfraction parameters including the sdLDL% (LDL3-7/LDL1-7), the sdLDL/lbLDL ratio (LDL3-7/LDL1-2), and weighted LDL subfraction (LDLSF) scores, were compared between groups. Their associations with insulin resistance, estimated using the homeostasis model assessment of insulin resistance, were examined.

RESULTS

The concentrations of sdLDL particles were significantly higher in subjects with T2D and IFG than in those with NFG (15.78±13.47mg/dl and 14.60±14.33mg/dl, respectively, vs. 12.22±12.31mg/dl). Compared with those with NFG, subjects with IFG or T2D had significantly a higher sdLDL% (15.98±15.26% vs. 19.50±16.21% or 21.46±16.81%, respectively), a higher sdLDL/lbLDL ratio (0.24±0.30 vs. 0.31±0.37 or 0.35±0.39), and a higher LDLSF score (2.08±0.91 vs. 2.30±1.14 or 2.36±1.17). These lipoprotein subfraction parameters had stronger associations with insulin resistance compared to conventional lipid profiles in the IFG and T2D groups.

CONCLUSIONS

Atherogenic dyslipidemia is initiated in an early stage of impaired glucose metabolism, when early intervention might be required.