Does the low-density lipoprotein cholesterol play a key role in predicting metabolic syndrome in the Iranian adult population?

Investigation of the Relationship Between Serum Low-Density Lipoprotein Cholesterol Levels with Genetic Polymorphisms, Gut Microbiota, and Nutrition

Background: To prevent cardiovascular disease (CVD), it is important to determine the factors that are associated with its development. High serum low-density lipoprotein (LDL) cholesterol (LDL-C) levels are a modifiable prevention and treatment target known to contribute to the development of CVD, but the factors affecting blood cholesterol levels, including LDL-C, remain controversial. Objective: In this study, the factors (genetic, nutritional, and gut microbiota) thought to be effective on serum LDL-C levels were discussed from a holistic perspective, and the effects of the relationship between these factors on LDL-C levels were examined. Methods: The study was carried out with 609 adults (48% male) who applied to a private health institution between 2016 and 2022. Results: It was observed that serum LDL-C levels were positively correlated with body mass index (BMI) (P = 0.000) and different ApoE alleles had significant effects on LDL-C levels. It was observed that the highest LDL-C levels were in the ɛ4+ group, followed by ɛ3+ and ɛ2+ groups, respectively (P = 0.000). Results showed that dietary cholesterol and fiber consumption did not significantly affect serum LDL-C levels (P = 0.705 and P = 0.722, respectively). It was also observed that enterotypes and the butyrate synthesis potential of intestinal microbiota did not cause significant changes in serum LDL-C levels (P = 0.369 and P = 975, respectively). Conclusion: Serum LDL-C levels are affected by modifiable factors such as BMI and nonmodifiable factors such as APOE genotype. By identifying these factors and conducting further studies on them, new ways to improve serum LDL-C levels, which is an important factor in the development of CVD, can be identified. In addition, no significant effect of gene-nutrient or microbiota-nutrient interactions on serum LDL-C levels was detected. Further research is needed, especially on the relationship between intestinal microbiota and serum LDL levels.

Lipid accumulation product is a better predictor of metabolic syndrome in Chinese adolescents: a cross-sectional study

Aim

Confirm and compare the degree of associations of non-traditional lipid profiles and metabolic syndrome (MetS) in Chinese adolescents, determine the lipid parameter with better predictive potential, and investigate their discriminatory power on MetS.

Methods

Medical measurements, including anthropometric measurements and biochemical blood tests, were undergone among a total sample of 1112 adolescents (564 boys and 548 girls) aged from 13 to 18 years. Univariate and multivariate logistic regression analyses were applied for assessing the relationships between the levels of traditional/non-traditional lipid profiles and MetS. We performed Receiver Operating Characteristic (ROC) analyses to mensurate the effectiveness of lipid accumulation product (LAP) on the diagnosis of MetS. Meanwhile, areas under the ROC curve and the cut-off values were calculated for MetS and its components.

Results

Univariate analysis showed that all our lipid profiles were closely associated with MetS (P< 0.05). LAP index showed the closest association with MetS than the other lipid profiles. Additionally, ROC analyses indicated that the LAP index showed sufficient capabilities to identify adolescents with MetS and its components.

Conclusion

The LAP index is a simple and efficient tool to identify individuals with MetS in Chinese adolescents.

Association between serum low-density lipoprotein cholesterol and metabolic syndrome in a working population

Background

The studies, investigating the association of low-density lipoprotein cholesterol (LDL-C) with metabolic syndrome (MetS) are limited with controversial conclusions. Therefore, this study aimed at revealing the specific relationship between the serum LDL-C levels and MetS prevalence in a large working population.

Methods

Secondary data analysis of a cross-sectional study, conducted between 2012 and 2016 in Spain on participants aged within the range of 20–70 years, involved 60,799 workers. Logistic regression analysis was applied to evaluate the association between the levels of serum LDL-C and MetS prevalence.

Results

Among the 60,799 workers, the prevalence of MetS was 9.0%. The odds ratios (95% confidence intervals) of MetS prevalence were 1.27 (1.16–1.39) and 1.53 (1.41–1.65) for the individuals with the LDL-C levels in lower (< 103.8 mg/dL) and upper (> 135.8 mg/dL) tertiles as compared to those with the LDL-C levels in middle tertile (103.8–135.8 mg/dL) in the studied population. Similarly, a U-shaped relationship was also observed in male cohort. The serum LDL-C levels associated with the lowest risk of current MetS were 113.6 mg/dL and 117.6 mg/dL in the overall studied population and male cohort, respectively. The female workers with the levels of LDL-C higher than 135.0 mg/dL had an increased prevalence of MetS (P < 0.05).

Conclusions

The low and high levels of serum LDL-C were associated with an increased prevalence of MetS in the working population and in male workers. Only the high (> 135.0 mg/dL) levels of LDL-C increased MetS prevalence in female workers.

Triglyceride to high-density lipoprotein cholesterol and low-density lipoprotein cholestrol to high-density lipoprotein cholesterol ratios are predictors of cardiovascular risk in Iranian adults: Evidence from a population-based cross-sectional study

BACKGROUND

The superiority of TG/HDL-C and LDL-C/HDL-C ratios in predicting CVD risk is a matter of debates. Thus, the objective of this study was to compare TG/HDL-C and LDL-C to HDL-C ratios in predicting the risk of CVD events.

METHODS

In a population-based cross-sectional study, 567 representative participants aged 40 years or older were entered in the study in Babol, North of Iran. The demographic data, anthropometric measures, and the cardio metabolic risk factors were measured. The individual risk of CVD events was assessed by ACC/AHA risk model. ROC analysis was applied to estimate the diagnostic accuracy and the optimal cut-off points of TG/HDL-C and LDL-C/HDL-C ratios.

RESULTS

The AUC of TG/HDL-C and LDL-C/HDL-C ratios were rather similar and both parameters significantly predicted CVD risk in men comparably, and TG/HDL-C at optimal cutoff point of 3.6 produced 75% sensitivity and 39% specificity. However,in women TG/HDL-C with AUC of 0.65( p=0.091) at optimal cutoff value of 3.4 produced a sensitivity of 82% and specificity of 51%. The LDL-C/HDL-C ratio had no discriminative ability in predicting CVD risk in women. The adjusted OR of TG/HDL-C at 2nd quartile was significant (OR=3.22, 95% CI:1.25-8.29) and a greater association was found with 3rd and 4rth quartiles.

CONCLUSION

Both TG/HDL-C and LDL-C/HDL-C ratios comparably predict CVD risk in men, whereas in women only TG/ HDL-C is a significant predictor but not LDL-C/HDL-C.

Identification of Traditional Chinese Medicine Constitutions and Physiological Indexes Risk Factors in Metabolic Syndrome: A Data Mining Approach

Objective

In order to find the predictive indexes for metabolic syndrome (MS), a data mining method was used to identify significant physiological indexes and traditional Chinese medicine (TCM) constitutions.

Methods

The annual health check-up data including physical examination data; biochemical tests and Constitution in Chinese Medicine Questionnaire (CCMQ) measurement data from 2014 to 2016 were screened according to the inclusion and exclusion criteria. A predictive matrix was established by the longitudinal data of three consecutive years. TreeNet machine learning algorithm was applied to build prediction model to uncover the dependence relationship between physiological indexes, TCM constitutions, and MS.

Results

By model testing, the overall accuracy rate for prediction model by TreeNet was 73.23%. Top 12.31% individuals in test group (n=325) that have higher probability of having MS covered 23.68% MS patients, showing 0.92 times more risk of having MS than the general population. Importance of ranked top 15 was listed in descending order . The top 5 variables of great importance in MS prediction were TBIL difference between 2014 and 2015 (D_TBIL), TBIL in 2014 (TBIL 2014), LDL-C difference between 2014 and 2015 (D_LDL-C), CCMQ scores for balanced constitution in 2015 (balanced constitution 2015), and TCH in 2015 (TCH 2015). When D_TBIL was between 0 and 2, TBIL 2014 was between 10 and 15, D_LDL-C was above 19, balanced constitution 2015 was below 60, or TCH 2015 was above 5.7, the incidence of MS was higher. Furthermore, there were interactions between balanced constitution 2015 score and TBIL 2014 or D_LDL-C in MS prediction.

Conclusion

Balanced constitution, TBIL, LDL-C, and TCH level can act as predictors for MS. The combination of TCM constitution and physiological indexes can give early warning to MS.