HDL and Oxidation

Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011-2016

BACKGROUND

Metabolic syndrome (MetS), a worldwide public health problem, affects human health and quality of life in a dramatic manner. A growing evidence base suggests that MetS is strongly associated with levels of systemic immune inflammation. The present study aimed to investigate the possible relationship between the systemic immune-inflammation index (SII), a novel inflammatory marker, and MetS to provide data support for effective MetS prevention by reducing the systemic inflammatory response.

METHODS

We included adult participants with complete SII and MetS information from the 2011-2016 National Health and Nutrition Examination Survey (NHANES). MetS was defined as using the criteria developed by the Adult Treatment Program III of the National Cholesterol Education Program. The formula for SII was as follows: SII = platelet counts × neutrophil counts/ lymphocyte counts. Weighted linear regression was used to assess differences in variables across SII quartile groups after the SII score was divided into 4 quartiles. The independent interaction between SII and MetS was investigated using weighted multivariate logistic regression analysis and subgroup analysis, and the relationship between SII levels and 5 particular MetS items was further explored in depth.

RESULTS

A total of 12,402 participants, 3,489 of whom were diagnosed with MetS, were included in this study. After correcting for covariates, the results of a logistic regression of multistage weighted complex sampling data revealed that participants with higher SII scores had a higher chance of developing MetS (odds ratio (OR) = 1.33, 95% confidence interval (CI): 1.14-1.55) and that SII levels could be used as an independent risk factor to predict that likelihood of MetS onset. In the Q1-Q4 SII quartile group, the risk of developing MetS was 1.33 times higher in the Q4 group, which had the highest level of systemic immune inflammation than in the Q1 group. After adjusting for all confounding factors, SII scores were found to have a negative correlation with high-density lipoprotein cholesterol (OR = 1.29; 95% CI, 0.99-1.67, P = 0.056) and a significant positive correlation with waist circumference (OR = 2.17; 95% CI, 1.65-2.87, P < 0.001) and blood pressure (BP) (OR = 1.65; 95% CI, 1.20-2.27, P = 0.003). Gender, age, and smoking status were shown to alter the positive association between SII and MetS in subgroup analyses and interaction tests (p for interaction < 0.05). Additionally, we demonstrated a nonlinear correlation between SII and MetS. The findings of the restricted cubic spline indicated that there was an inverted U-shaped association between SII and MetS.

CONCLUSIONS

Our findings imply that increased SII levels are related to MetS, and SII may be a simple and cost-effective method to identify individuals with MetS. Therefore, protective measures such as early investigation and anti-inflammatory interventions are necessary to reduce the overall incidence of MetS.

A machine learning approach to personalized predictors of dyslipidemia: a cohort study

Introduction

Mexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment.

Methods

In this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facilitate feature selection, we applied the Variable Importance Measures (VIM) of Random Forest (RF), XGBoost, and Gradient Boosting Machine (GBM). Additionally, to address class imbalance, we employed Synthetic Minority Over-sampling Technique (SMOTE) for dataset resampling. The dataset encompassed anthropometric measurements, biochemical tests, dietary intake, family health history, and other health parameters, including smoking habits, alcohol consumption, quality of sleep, and physical activity.

Results

Our results revealed that the VIM algorithm of RF yielded the most optimal subset of attributes, closely followed by GBM, achieving a balanced accuracy of up to 80%. The selection of the best subset of attributes was based on the comparative performance of classifiers, evaluated through balanced accuracy, sensitivity, and specificity metrics.

Discussion

The top five features contributing to an increased risk of various types of dyslipidemia were identified through the machine learning technique. These features include body mass index, elevated uric acid levels, age, sleep disorders, and anxiety. The findings of this study shed light on significant factors that play a role in dyslipidemia development, aiding in the early identification, prevention, and treatment of this condition.

Advanced glycation end products: Key mediator and therapeutic target of cardiovascular complications in diabetes

The incidence of type 2 diabetes mellitus is growing in epidemic proportions and has become one of the most critical public health concerns. Cardiovascular complications associated with diabetes are the leading cause of morbidity and mortality. The cardiovascular diseases that accompany diabetes include angina, myocardial infarction, stroke, peripheral artery disease, and congestive heart failure. Among the various risk factors generated secondary to hyperglycemic situations, advanced glycation end products (AGEs) are one of the important targets for future diagnosis and prevention of diabetes. In the last decade, AGEs have drawn a lot of attention due to their involvement in diabetic patho-physiology. AGEs can be derived exogenously and endogenously through various pathways. These are a non-homogeneous, chemically diverse group of compounds formed non-enzymatically by condensation between carbonyl groups of reducing sugars and free amino groups of protein, lipids, and nucleic acid. AGEs mediate their pathological effects at the cellular and extracellular levels by multiple pathways. At the cellular level, they activate signaling cascades via the receptor for AGEs and initiate a complex series of intracellular signaling resulting in reactive oxygen species generation, inflammation, cellular proliferation, and fibrosis that may possibly exacerbate the damaging effects on cardiac functions in diabetics. AGEs also cause covalent modifications and cross-linking of serum and extracellular matrix proteins; altering their structure, stability, and functions. Early diagnosis of diabetes may prevent its progression to complications and decrease its associated comorbidities. In the present review, we recapitulate the role of AGEs as a crucial mediator of hyperglycemia-mediated detrimental effects in diabetes-associated complications. Furthermore, this review presents an overview of future perspectives for new therapeutic interventions to ameliorate cardiovascular complications in diabetes.

HDL functionality is dependent on hepatocyte stress defense factors Nrf1 and Nrf2

High density lipoproteins (HDL) promote homeostasis and counteract stressful tissue damage that underlie cardiovascular and other diseases by mediating reverse cholesterol transport, reducing inflammation, and abrogating oxidative damage. However, metabolically stressful conditions associated with atherosclerosis can impair these effects. Hepatocytes play a major role in the genesis and maturation of circulating HDL, and liver stress elicits marked regulatory changes to circulating HDL abundance and composition, which affect its functionality. The mechanisms linking liver stress to HDL function are incompletely understood. In this study, we sought to determine whether stress defending transcription factors nuclear factor erythroid 2 related factor-1 (Nrf1) and -2 (Nrf2) promote hepatocyte production of functional HDL. Using genetically engineered mice briefly fed a mild metabolically stressful diet, we investigated the effect of hepatocyte-specific deletion of Nrf1, Nrf2, or both on circulating HDL cholesterol, protein composition, and function. Combined deletion, but not single gene deletion, reduced HDL cholesterol and apolipoprotein A1 levels as well as the capacity of HDL to accept cholesterol undergoing efflux from cultured macrophages and to counteract tumor necrosis factor α-induced inflammatory effect on cultured endothelial cells. This coincided with substantial alteration to the HDL proteome, which correlated with liver gene expression profiles of corresponding proteins. Thus, our findings show complementary actions by hepatocyte Nrf1 and Nrf2 play a role in shaping HDL abundance and composition to promote production of functionally viable HDL. Consequently, our study illuminates the possibility that enhancing stress defense programming in the liver may improve atheroprotective and perhaps other health promoting actions of HDL.

Proteomic Exploration of Paraoxonase 1 Function in Health and Disease

High-density lipoprotein (HDL) exhibits cardio- and neuro-protective properties, which are thought to be promoted by paraoxonase 1 (PON1), a hydrolytic enzyme associated with an HDL subfraction also enriched with an anticoagulant protein (PROS1) and amyloid beta-transport protein clusterin (CLU, APOJ). Reduced levels of PON1 activity, characterized biochemically by elevated levels of homocysteine (Hcy)-thiolactone, oxidized lipids, and proteins modified by these metabolites in humans and mice, are associated with pathological abnormalities affecting the cardiovascular system (atherothrombosis) and the central nervous system (cognitive impairment, Alzheimer's disease). The molecular bases of these abnormalities have been largely unknown. Proteomic and metabolic studies over the past decade have significantly contributed to our understanding of PON1 function and the mechanisms by which PON1 deficiency can lead to disease. Recent studies discussed in this review highlight the involvement of dysregulated proteostasis in the pro-oxidative, pro-atherothrombotic, and pro-amyloidogenic phenotypes associated with low PON1 activity.

Role of Lipoprotein Levels and Function in Atherosclerosis Associated with Autoimmune Rheumatic Diseases

Immune and inflammatory mediators in autoimmune rheumatic diseases induce modification in the activity of enzymes pivotal for lipid metabolism and promote a proatherogenic serum lipid profile. However, disturbances in low- and high-density lipoprotein composition and increased lipid oxidation also occur. Therefore, lipoprotein dysfunction causes intracellular cholesterol accumulation in macrophages, smooth muscle cells, and platelets. Overall, both plaque progression and acute cardiovascular events are promoted. Single rheumatic diseases may present a particular pattern of lipid disturbances so that standard methods to evaluate cardiovascular risk may not be accurate enough. In general, antirheumatic drugs positively affect lipid metabolism in these patients.

Advanced Glycation End Products and Their Effect on Vascular Complications in Type 2 Diabetes Mellitus

Diabetes is well established as a chronic disease with a high health burden due to mortality or morbidity from the final outcomes of vascular complications. An increased duration of hyperglycemia is associated with abnormal metabolism. Advanced glycation end products (AGEs) are nonenzymatic glycated forms of free amino acids that lead to abnormal crosslinking of extra-cellular and intracellular proteins by disrupting the normal structure. Furthermore, the interaction of AGEs and their receptors induces several pathways by promoting oxidative stress and inflammation. In this review, we discuss the role of AGEs in diabetic vascular complications, especially type 2 DM, based on recent clinical studies.