Predicting metabolic syndrome by obesity- and lipid-related indices in mid-aged and elderly Chinese: a population-based cross-sectional study

Association of Three Novel Inflammatory Markers: Lymphocyte to HDL-C Ratio, High-Sensitivity C-Reactive Protein to HDL-C Ratio and High-Sensitivity C-Reactive Protein to Lymphocyte Ratio With Metabolic Syndrome

OBJECTIVE

We aimed to compare the association of three novel inflammatory indicators with metabolic syndrome (MetS) among Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort participants.

METHODS

According to the International Diabetes Federation (IDF) criteria, the cohort participants were divided into the MetS(+) and MetS(-) groups. The lymphocyte to high-density lipoprotein cholesterol (HDL-C) ratio (LHR), high-sensitivity C-reactive protein (hs-CRP) to HDL-C ratio (HCHR) and hs-CRP to lymphocyte ratio (HCLR) were calculated and were compared between the groups. Binary logistic regression (LR) analysis was performed to find the association of the indices with the presence of MetS among men and women. Receiver-operating characteristic (ROC) curve analysis was used to establish cut-off values in predicting MetS for men and women. p-Values <0.05 were considered as statistically significant.

RESULTS

Among a total of 8890 participants (5500 MetS(-) and 3390 MetS(+)), LHR, HCHR and HCLR were significantly higher in the MetS(+) group than in MetS(-) group (p < 0.001). In LR analysis, after adjusting for multiple cofounders, LHR remained an independent factor for the presence of MetS among men (OR: 1.254; 95% CI: 1.202-1.308; p < 0.001) and women (OR: 1.393; 95% CI: 1.340-1.448; p < 0.001). HCHR also remained an independent factor for the presence of MetS only in women (OR: 1.058; 95% CI: 1.043-1.073; p < 0.001). ROC curve analysis showed that LHR had the higher AUC for predicting MetS in both men (AUC: 0.627; 95% CI: 0.611-0.643; p < 0.001) and women (AUC: 0.683; 95% CI: 0.670, 0.696; p < 0.001).

CONCLUSION

This suggests that among both genders, the LHR as an inexpensive and easy-to-access marker has a better diagnostic performance and could be a promising alternative to the traditional expensive inflammatory markers such as hs-CRP for the evaluation of inflammation in patients with MetS.

Association between triglyceride-glucose related indices with the all-cause and cause-specific mortality among the population with metabolic syndrome

BACKGROUND

Triglyceride-glucose (TyG) index has been determined to play a role in the onset of metabolic syndrome (MetS). Whether the TyG index and TyG with the combination of obesity indicators are associated with the clinical outcomes of the MetS population remains unknown.

METHOD

Participants were extracted from multiple cycles of the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 years. Three indicators were constructed including TyG index, TyG combining with waist circumference (TyG-WC), and TyG combining with waist-to-height ratio (TyG-WHtR). The MetS was defined according to the National Cholesterol Education Program (NCPE) Adult Treatment Panel III. Kaplan-Meier (KM) curves, restricted cubic splines (RCS), and the Cox proportional hazard model were used to evaluate the associations between TyG-related indices and mortality of the MetS population. The sensitive analyses were performed to check the robustness of the main findings.

RESULTS

There were 10,734 participants with MetS included in this study, with 5,570 females and 5,164 males. The median age of the study population was 59 years old. The multivariate Cox regression analyses showed high levels of TyG-related indices were significantly associated with the all-cause mortality of MetS population [TyG index: adjustedhazard ratio (aHR): 1.36, 95%confidence interval (CI): 1.18-1.56, p < 0.001; TyG-WHtR index: aHR = 1.29, 95%CI: 1.13-1.47, p < 0.001]. Meanwhile, the TyG-WC and TyG-WHtR index were associated with cardiovascular mortality of the MetS population (TyG-WC: aHR = 1.45, 95%CI: 1.13-1.85, p = 0.004; TyG-WHtR: aHR = 1.50 95%CI: 1.17-1.92, p = 0.002). Three TyG-related indices showed consistent significant correlations with diabetes mortality (TyG: aHR = 4.06, 95%CI: 2.81-5.87, p < 0.001; TyG-WC: aHR = 2.55, 95%CI: 1.82-3.58, p < 0.001; TyG-WHtR: aHR = 2.53 95%CI: 1.81-3.54, p < 0.001). The RCS curves showed a non-linear trend between TyG and TyG-WC indices with all-cause mortality (p for nonlinearity = 0.004 and 0.001, respectively). The sensitive analyses supported the positive correlations between TyG-related indices with mortality of the MetS population.

CONCLUSION

Our study highlights the clinical value of TyG-related indices in predicting the survival of the MetS population. TyG-related indices would be the surrogate biomarkers for the follow-up of the MetS population.

Comparison of seven anthropometric indexes to predict hypertension plus hyperuricemia among U.S. adults

Purpose

This study aims to compare the association of hypertension plus hyperuricemia (HTN-HUA) with seven anthropometric indexes. These include the atherogenic index of plasma (AIP), lipid accumulation product (LAP), visceral adiposity index (VAI), triglyceride-glucose index (TyG), body roundness index (BRI), a body shape index (ABSI), and the cardiometabolic index (CMI).

Methods

Data was procured from the National Health and Nutrition Examination Survey (NHANES), which recruited a representative population aged 18 years and above to calculate these seven indexes. Logistic regression analysis was employed to delineate their correlation and to compute the odds ratios (OR). Concurrently, receiver operating characteristic (ROC) curves were utilized to evaluate the predictive power of the seven indexes.

Results

A total of 23,478 subjects were included in the study. Among these, 6,537 (27.84%) were patients with HUA alone, 2,015 (8.58%) had HTN alone, and 2,836 (12.08%) had HTN-HUA. The multivariate logistic regression analysis showed that the AIP, LAP, VAI, TyG, BRI, ABSI, and CMI were all significantly associated with concurrent HTN-HUA. The OR for the highest quartile of the seven indexes for HTN-HUA were as follows: AIP was 4.45 (95% CI 3.82-5.18), LAP was 9.52 (95% CI 7.82-11.59), VAI was 4.53 (95% CI 38.9-5.28), TyG was 4.91 (95% CI 4.15-5.80), BRI was 9.08 (95% CI 7.45-11.07), ABSI was 1.71 (95% CI 1.45 -2.02), and CMI was 6.57 (95% CI 5.56-7.76). Notably, LAP and BRI demonstrated significant discriminatory abilities for HTN-HUA, with area under the curve (AUC) values of 0.72 (95% CI 0.71 - 0.73) and 0.73 (95% CI 0.72 - 0.74) respectively.

Conclusion

The AIP, LAP, VAI, TyG, BRI, ABSI, and CMI all show significant correlation with HTN-HUA. Notably, both LAP and BRI demonstrate the capability to differentiate cases of HTN-HUA. Among these, BRI is underscored for its effective, non-invasive nature in predicting HTN-HUA, making it a superior choice for early detection and management strategies.

Investigating the Influence of Heavy Metals and Environmental Factors on Metabolic Syndrome Risk Based on Nutrient Intake: Machine Learning Analysis of Data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES)

This study delves into the complex interrelations among nutrient intake, environmental exposures (particularly to heavy metals), and metabolic syndrome. Utilizing data from the Korea National Health and Nutrition Examination Survey (KNHANES), machine learning techniques were applied to analyze associations in a cohort of 5719 participants, categorized into four distinct nutrient intake phenotypes. Our findings reveal that different nutrient intake patterns are associated with varying levels of heavy metal exposure and metabolic health outcomes. Key findings include significant variations in metal levels (Pb, Hg, Cd, Ni) across the clusters, with certain clusters showing heightened levels of specific metals. These variations were associated with distinct metabolic health profiles, including differences in obesity, diabetes prevalence, hypertension, and cholesterol levels. Notably, Cluster 3, characterized by high-energy and nutrient-rich diets, showed the highest levels of Pb and Hg exposure and had the most concerning metabolic health indicators. Moreover, the study highlights the significant impact of lifestyle habits, such as smoking and eating out, on nutrient intake phenotypes and associated health risks. Physical activity emerged as a critical factor, with its absence linked to imbalanced nutrient intake in certain clusters. In conclusion, our research underscores the intricate connections among diet, environmental factors, and metabolic health. The findings emphasize the need for tailored health interventions and policies that consider these complex interplays, potentially informing future strategies to combat metabolic syndrome and related health issues.

Comparison of metabolic syndrome prevalence and characteristics using five different definitions in China: a population-based retrospective study

Background

Metabolic syndrome (MetS) is on the rise in developing countries and is characterized by a series of indications of metabolic disturbance. However, the prevalence of MetS varies under different definitions. The study aimed to compare five definitions of MetS in the China adult population, to explore their prevalence, characteristics and agreement.

Methods

The data for the retrospective study came from the China Health and Retirement Longitudinal Study (CHARLS), consisting of 9,588 participants (≥45). MetS definitions from International Diabetes Federation (IDF) (2006), National Cholesterol Education Program Adult Treatment Panel III (ATPIII) (2005), National Cholesterol Education Program Adult Treatment Panel III (ATPIII) (2001), Chinese Diabetes society (CDS) (2004) and the World Health Organization (WHO) (1999). We used binary and multivariable logistic analysis to explore factors connected with MetS.

Results

The five definitions of MetS led to different prevalence of MetS:34.52% by IDF (2006), 38.63% by ATP (2005), 25.94% by ATP (2001), 26.31% by CDS (2004), 21.57% by WHO (1999). According to the definition of IDF (2006) (22.32% vs. 45.06%), ATPIII (2005) definition (27.99% vs. 47.82%), ATPIII (2001) definition (15.37% vs. 35.07%), CDS (2004) definition (19.96% vs. 31.80%), and WHO (1999) definition (17.44% vs. 25.14%), the prevalence of MetS in men was low but in women was high. The agreement between the five definitions for men was good except for the IDF (2006) definition and ATPIII (2001) definition (kappa = 0.51), with kappa values from 0.64 to 0.85. For women, the agreement between the five definitions was good ranging from 0.67 to 0.95, however, except for the definition of CDS (2004) and the definition of IDF (2006) (kappa = 0.44), the definition of WHO (1999) and the definition of IDF (2006) (kappa = 0.55), and the definition of WHO (1999) and the definition of ATPIII (2005) (kappa = 0.54). Binary logistic analysis indicated that although the impact and relevance varied by sex and definition, age, education, marital status, current residence, current smoking, alcohol using, taking activities and number of chronic diseases were factors connected to MetS.

Conclusion

the prevalence and characteristics of the five definitions of MetS are different in the Chinese population. Therefore, it is vital to use the same definition for a country to diagnose MetS. On the other side, a lower prevalence in men than in women and the consistency of five MetS definitions are good in men but relatively poor in women.

The combination of body mass index and fasting plasma glucose is associated with type 2 diabetes mellitus in Japan: a secondary retrospective analysis

Background

Body mass index (BMI) and fasting plasma glucose (FPG) are known risk factors for type 2 diabetes mellitus (T2DM), but data on the prospective association of the combination of BMI and FPG with T2DM are limited. This study sought to characterize the association of the combination of BMI and FPG (ByG) with T2DM.

Methods

The current study used the NAGALA database. We categorized participants by tertiles of ByG. The association of ByG with T2DM was expressed with hazard ratios (HRs) with 95% confidence intervals (CIs) after adjustment for potential risk factors.

Results

During a median follow-up of 6.19 years in the normoglycemia cohort and 5.58 years in the prediabetes cohort, the incidence of T2DM was 0.75% and 7.79%, respectively. Following multivariable adjustments, there were stepwise increases in T2DM with increasing tertiles of ByG. After a similar multivariable adjustment, the risk of T2DM was 2.57 (95% CI 2.26 - 2.92), 1.97 (95% CI 1.53 - 2.54) and 1.50 (95% CI 1.30 - 1.74) for a per-SD change in ByG in all populations, the normoglycemia cohort and the prediabetes cohort, respectively.

Conclusion

ByG was associated with an increased risk of T2DM in Japan. The result reinforced the importance of the combination of BMI and FPG in assessing T2DM risk.

Association of hypertension with the triglyceride-glucose index and its associated indices in the Chinese population: A 6-year prospective cohort study

The authors aim to assess the correlation between hypertension and the triglyceride-glucose (TyG) index and its associated indices, and to compare their abilities to identify hypertension. Four thousand eight hundred and sixty-six non-hypertensive participants were enrolled from the China National Health Survey in 2009. The data on new-onset hypertension were gathered in both 2011 and 2015. The TyG index and its associated indices were derived from the fasting triglyceride, blood glucose levels, and anthropometric parameters. Multivariate logistic regression analyses and receiver operating characteristic curve analysis were used. The adjusted odds ratio (OR) and 95% confidence interval (CI) for the new-onset hypertension for the TyG-waist-to-height ratio (TyG-WHtR), TyG-waist circumference (TyG-WC), TyG-waist-to-hip ratio (TyG-WHR), TyG-body mass index (TyG-BMI), and TyG index were 1.379 (1.230-1.546), 1.002 (1.001-1.003), 1.156 (1.069-1.251), 1.007 (1.005-1.009), and 1.187 (1.051-1.341), respectively. In addition, comparing the lowest quartile (Q1) group with the highest quartile (Q4), the adjusted OR and 95% CI for the new-onset hypertension were found to be 1.86 (1.48-2.35), 1.93 (1.53-2.43), 1.71 (1.36-2.16), 2.00 (1.60-2.50), and 1.49 (1.19-1.88) for TyG-WHtR, TyG-WC, TyG-WHR, TyG-BMI, and TyG index, respectively, among all participants. The TyG-WHtR had the largest area under the curve (AUC) for hypertension (AUC, 0.628; 95% CI, 0.614-0.641) in all participants. Stratified analysis also indicated that the TyG-WHtR exhibited the greatest AUC in both males (AUC, 0.608; 95% CI, 0.587-0.629) and females (AUC, 0.648; 95% CI, 0.629-0.666). In conclusions, the TyG index and its associated indices were positively associated with hypertension. Among these indices, TyG-WHtR was the most valuable indicator for predicting hypertension.

Changes in the triglyceride glucose-body mass index estimate the risk of stroke in middle-aged and older Chinese adults: a nationwide prospective cohort study

BACKGROUND

Stroke was reported to be highly correlated with the triglyceride glucose-body mass index (TyG-BMI). Nevertheless, literature exploring the association between changes in the TyG-BMI and stroke incidence is scant, with most studies focusing on individual values of the TyG-BMI. We aimed to investigate whether changes in the TyG-BMI were associated with stroke incidence.

METHODS

Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), which is an ongoing nationally representative prospective cohort study. The exposures were changes in the TyG-BMI and cumulative TyG-BMI from 2012 to 2015. Changes in the TyG-BMI were classified using K-means clustering analysis, and the cumulative TyG-BMI was calculated as follows: (TyG-BMI2012 + TyG-BMI2015)/2 × time (2015-2012). Logistic regressions were used to determine the association between different TyG-BMI change classes and stroke incidence. Meanwhile, restricted cubic spline regression was applied to examine the potential nonlinear association of the cumulative TyG-BMI and stroke incidence. Weighted quantile sum regression was used to provide a comprehensive explanation of the TyG-BMI by calculating the weights of FBG, triglyceride-glucose (TG), and BMI.

RESULTS

Of the 4583 participants (mean [SD] age at baseline, 58.68 [9.51] years), 2026 (44.9%) were men. During the 3 years of follow-up, 277 (6.0%) incident stroke cases were identified. After adjusting for potential confounders, compared to the participants with a consistently low TyG-BMI, the OR for a moderate TyG-BMI with a slow rising trend was 1.01 (95% CI 0.65-1.57), the OR for a high TyG-BMI with a slow rising trend was 1.62 (95% CI 1.11-2.32), and the OR for the highest TyG-BMI with a slow declining trend was 1.71 (95% CI 1.01-2.89). The association between the cumulative TyG-BMI and stroke risk was nonlinear (Passociation = 0.017; Pnonlinearity = 0.012). TG emerged as the primary contributor when the weights were assigned to the constituent elements of the TyG-BMI (weight2012 = 0.466; weight2015 = 0.530).

CONCLUSIONS

Substantial changes in the TyG-BMI are independently associated with the risk of stroke in middle-aged and older adults. Monitoring long-term changes in the TyG-BMI may assist with the early identification of individuals at high risk of stroke.