The Ratios of Triglycerides and C-reactive protein to High density-lipoprotein -cholesterol as valid biochemical markers of the Nascent Metabolic Syndrome

Triglyceride-Rich Lipoprotein Metabolism: Key Regulators of Their Flux

The residual risk for arteriosclerotic cardiovascular disease after optimal statin treatment may amount to 50% and is the consequence of both immunological and lipid disturbances. Regarding the lipid disturbances, the role of triglyceride-rich lipoproteins (TRLs) and their remnants has come to the forefront in the past decade. Triglycerides (TGs) stand as markers of the remnants of the catabolism of TRLs that tend to contain twice as much cholesterol as compared to LDL. The accumulation of circulating TRLs and their partially lipolyzed derivatives, known as "remnants", is caused mainly by ineffective triglyceride catabolism. These cholesterol-enriched remnant particles are hypothesized to contribute to atherogenesis. The aim of the present narrative review is to briefly summarize the main pathways of TRL metabolism, bringing to the forefront the newly discovered role of apolipoproteins, the key physiological function of lipoprotein lipase and its main regulators, the importance of the fluxes of these particles in the post-prandial period, their catabolic rates and the role of apo CIII and angiopoietin-like proteins in the partition of TRLs during the fast-fed cycle. Finally, we provide a succinct summary of the new and old therapeutic armamentarium and the outcomes of key current trials with a final outlook on the different methodological approaches to measuring TRL remnants, still in search of the gold standard.

Waist Circumference Is an Essential Factor in Predicting Insulin Resistance and Early Detection of Metabolic Syndrome in Adults

BACKGROUND

Metabolic syndrome (Met-S) is considered one of the most important health problems of the 21st century. It includes a group of metabolic disorders that increase the risk of cardiovascular diseases such as overweight and obesity, elevated lipid profile and blood pressure and insulin resistance (IR). Based on the information mentioned above in which there seems to be a relationship between IR and Met-S, the objective of this work was twofold: on the one hand, to assess the relationship between the values of different insulin resistance risk scales and Met-S determined with three different scales, and on the other, to determine whether any of the components of Met-S predispose more to the appearance of IR.

METHODS

A descriptive cross-sectional study of 418,343 workers. Waist circumference was measured and evaluated together with six formulas to assess the insulin resistance index. Categorical variables were evaluated by calculating the frequency and distribution of each one. For quantitative variables, mean and standard deviation were determined, and Student's t-test was applied, while for qualitative variables, the chi-square test was performed. The usefulness of the different risk scales for insulin resistance for predicting metabolic syndrome was evaluated using ROC curves, the area under the curve (AUC), as well as their cut-off points for sensitivity, specificity, and the Youden index.

RESULTS

People with metabolic syndrome applying any criteria had higher values in the IR risk scales. The different IR scales made it possible to adequately classify people with metabolic syndrome. Of the three definitions of Met-S, the one that showed the greatest relationship with IR was IDF.

CONCLUSIONS

Most risk scales for insulin resistance enable the presence of metabolic syndrome to be adequately classified, finding the best ones if the International Diabetes Federation (IDF) criteria are applied. Of the elements included in the Met-S, the one that seems to increase the risk of presenting IR the most is waist circumference; hence, the Met-S definition that is most related to IR is that of the IDF, which is the only one of the three in which a high value of waist circumference is necessary to be able to diagnose Met-S. Waist circumference can be considered the central essential component for detecting insulin resistance and, therefore, the early detection of metabolic syndrome.

Correlates of Insulin Resistance in Nascent Metabolic Syndrome

Background

Metabolic Syndrome (MetS), a major global problem, is a cluster of cardio-metabolic risk factors that predisposes to both type 2 diabetes mellitus (T2DM) and premature atherosclerotic cardiovascular disease (ASCVD). Insulin resistance is a major underpinning of MetS.

Objectives

We investigated the relationship between insulin resistance and biomarkers of inflammation, oxidative stress, free fatty acids (FFA) levels and adipokine dysregulation in a cohort of nascent MetS.

Design

This was a cross-sectional study comparing patients with MetS with matched controls.

Patients and Methods

Participants included 47 patients with MetS and 41 controls. Persons with diabetes, ASCVD, smoking and macro-inflammation were excluded. Fasting blood was obtained for both plasma and monocyte isolation. Homeostasis model assessment insulin resistance index (HOMA-IR) was calculated from fasting glucose and insulin levels.

Results

The patients were insulin resistant as determined by a valid measure, HOMA-IR. HOMA-IR increased with increasing severity of MetS and correlated with cardio-metabolic features, hsCRP, FFA levels, and adipose tissue insulin resistance. Insulin resistance also correlated with biomarkers of oxidative stress and both circulating and cellular biomarkers of inflammation. Receiver operating Characteristic (ROC) curve analysis revealed that HOMA-IR was an excellent predictor of MetS with an area under the curve of 0.80.

Conclusion

In our patients with nascent MetS we show that they have significant insulin resistance. Based on our findings, elevated FFA levels, oxidative stress and inflammation could contribute to the insulin resistance.

The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis

Background

Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, and metabolic syndrome (MetS) is the most common metabolic disorder that contains hypertension, dyslipidemia, and obesity. Despite clinical evidence suggested potential associations between SLE and MetS, the underlying pathogenesis is yet unclear.

Methods

The microarray data sets of SLE and MetS were obtained from the Gene Expression Omnibus (GEO) database. To identify the shared genes between SLE and MetS, the Differentially Expressed Genes (DEGs) analysis and the weighted gene co-expression network analysis (WGCNA) were conducted. Then, the GO and KEGG analyses were performed, and the protein-protein interaction (PPI) network was constructed. Next, Random Forest and LASSO algorithms were used to screen shared hub genes, and a diagnostic model was built using the machine learning technique XG-Boost. Subsequently, CIBERSORT and GSVA were used to estimate the correlation between shared hub genes and immune infiltration as well as metabolic pathways. Finally, the significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data.

Results

Using limma and WGCNA, we identified 153 shared feature genes, which were enriched in immune- and metabolic-related pathways. Further, 20 shared hub genes were screened and successfully used to build a prognostic model. Those shared hub genes were associated with immunological and metabolic processes in peripheral blood. The scRNA-seq results verified that TNFSF13B and OAS1, possessing the highest diagnostic efficacy, were mainly expressed by monocytes. Additionally, they showed positive correlations with the pathways for the metabolism of xenobiotics and cholesterol, both of which were proven to be active in this comorbidity, and shown to be concentrated in monocytes.

Conclusion

This study identified shared hub genes and constructed an effective diagnostic model in SLE and MetS. TNFSF13B and OAS1 had a positive correlation with cholesterol and xenobiotic metabolism. Both of these two biomarkers and metabolic pathways were potentially linked to monocytes, which provides novel insights into the pathogenesis and combined therapy of SLE comorbidity with MetS.

A comparison of the ratios of C-reactive protein and triglycerides to high-density lipoprotein-cholesterol as biomarkers of metabolic syndrome in African Americans and non-Hispanic Whites

AIMS

The ratio of triglycerides (TG) to high density lipoprotein-cholesterol (HDL-C) is a validated biomarker of insulin resistance and metabolic syndrome (MetS). In African-Americans (AA) there is concern about this ratio because their mean TG level is lower than the general population. As an alternative approach, we examined the CRP:HDL-C ratio in both AA and non-Hispanic whites (NHW) in the NHANES study for its association with MetS.

METHODS

A total of n = 3541 individuals were studied from the NHANES data. Fasting blood samples were obtained for lipids, insulin, and CRP. TG and CRP ratios to HDL-C were calculated.

RESULTS

The TG:HDL-C ratio was significantly increased in NHW compared to AA, but the CRP:HDL-C ratio did not differ between NHW and AA groups. Both ratios were significantly increased in MetS patients versus controls (both races) and increased with greater severity of MetS. Receiver Operating Characteristic (ROC) curve analysis showed that the TG:HDL-C area under the curve was superior to CRP:HDL-C in predicting MetS in both AA and NHW patients.

CONCLUSION

In this large NHANES study, the TG:HDL-C ratio is a superior predictor of MetS in both AA and NHW persons despite the lower TG levels in AA persons.