The association between a dietary habits score and the risk of metabolic syndrome: A cohort study

Comparison of atherogenic indices for predicting the risk of metabolic syndrome in Southwest Iran: results from the Hoveyzeh Cohort Study (HCS)

BACKGROUND

Metabolic syndrome (MetS) is a cluster of risk factors related to diabetes and cardiovascular disease (CVD). Given that early identification of MetS might decrease CVD risk, it is imperative to establish a simple and cost-effective method to identify individuals at risk of MetS. The purpose of this study was to explore the relationships between several atherogenic indices (including AIP, TyG index, non-HDL-C, LDL-c/HDL-c, and TC/HDL-c) and MetS, and to assess the ability of these indices to predict MetS.

METHODS

The present cross-sectional study was conducted using baseline data from 9809 participants of the Hoveyzeh Cohort Study (HCS). MetS was defined based on the International Diabetes Federation (IDF). To examine the discriminatory abilities of each atherogenic indices in the identification of MetS, a receiver-operating characteristic curve was conducted. Logistic regression analysis was also performed to evaluate the relationship between atherogenic indices and MetS.

RESULTS

All of the atherogenic indices including the TyG index, AIP, non-HDL-C, TC/HDL-c, and LDL-c/HDL-c were significantly higher in participants with MetS than in those without MetS. According to the ROC curve analysis, the TyG index revealed the highest area under the curve (0.79 and 0.85 in men and women, respectively), followed by the AIP (0.76 and 0.83 in men and women, respectively). The best cutoff values for the TyG index and AIP were 8.96 and 0.16 for men and 8.84 and 0.05 for women, respectively. The TyG index and AIP were also strongly associated with MetS.

CONCLUSION

Among the 5 atherogenic indices evaluated, the TyG index and AIP were strongly related to MetS. The TyG index also demonstrated superior discriminative ability compared to other atherogenic indices in predicting MetS.

Metabolic syndromes increase significantly with the accumulation of bad dietary habits

BACKGROUND

The association between dietary habits and metabolic syndrome (MetS) has not been well documented, due to the complexity and individualization of dietary culture in the Chinese population.

OBJECTIVE

To construct a composite score from various bad dietary habits and to evaluate their comprehensive association with the prevalence of MetS and its components among Chinese men and women across various age groups.

SETTING

Serial cross-sectional studies.

METHODS

Twenty-three dietary habits were assessed through face-to-face interviews with 98,838 males and 83,099 females in health check-up programs from 2015 to 2021, among which eighteen bad dietary habits were observed to be associated independently with total MetS. The total score of bad dietary habits was composed of four categories via variable clustering analysis, including irregular dietary habits, unhealthy dietary flavors, unbalanced dietary structure, and high-fat diet. The 2016 Chinese guideline for the management of dyslipidemia in adults was used to define MetS.

RESULTS

Men had a higher score of bad dietary habits than women (9.63 ± 3.11 vs. 8.37 ± 3.23), which decreased significantly with increasing age in both males and females (Pinteraction<0.01). The prevalence of total MetS increased significantly with the cumulative score of bad dietary habits in both males (highest quintile vs. lowest quintile: OR, 1.90; 95% confidence interval [CI], 1.80-2.00; Plinear<0.01) and females (OR, 2.23; 95% CI, 2.02-2.46; Plinear<0.01) after adjusted for age, education, smoking status, alcohol consumption, and physical activities. These linear trends were also observed for each MetS component (all Plinear<0.01). The role of irregular dietary habits and high-fat diet on MetS prevalence are much higher in males than in females, while unhealthy dietary flavors and unbalanced dietary structure had a greater influence on females.

CONCLUSIONS

The accumulation of bad dietary habits contributes to the MetS developments. Thus, individualized lifestyle interventions are needed to correct bad dietary habits with regard to gender differences.

Long-term effects of PM2.5 constituents on metabolic syndrome and mediation effects of serum uric acid

Exposure to particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) was associated with the risk for metabolic syndrome (MetS) in the general population, but the contributions of individual PM2.5 constituents to this association and the potential pathway between PM2.5 constituents and MetS risk are not well elaborated. This study aimed to investigate associations between PM2.5 constituents and MetS in general populations, relative importance of PM2.5 constituents to and mediation effects of serum uric acid (SUA) on those associations. The 48,148 participants from a provincially representative cohort established in southwest China were included. The 3-year average concentrations of PM2.5 and its constituents (nitrate [NO3-], sulfate [SO42-], ammonium [NH4+], organic matter [OM], and black carbon [BC]) were estimated using a series of machine-learning models. Multivariate logistic regression and weighted quantile sum regression were used to estimate effects of independent PM2.5 constituents on MetS and their contributions to the joint effect. Mediation analysis examined the potential mediation effects of SUA on the associations between PM2.5 constituents and MetS. Each interquartile range (IQR) increase in the concentration of PM2.5 constituents was all positively associated with the increased MetS odds, including SO42- (OR = 1.15 [1.11, 1.19]]), NO3- (OR = 1.12 [1.08, 1.16]), NH4+ (OR = 1.13 [1.09, 1.17]), OM (OR = 1.09 [1.06, 1.13]), and BC (OR = 1.09 [1.06, 1.13]). Their joint associations on MetS were mainly attributed to SO42- (weight=46.1%) and NH4+ (44.0%). The associations of PM2.5 constituents with abnormal MetS components were mainly attributed to NH4+ for elevated BP (51.6%) and reduced HDL-C (97.0%), SO42- for elevated FG (68.9%), NO3- for elevated TG (51.0%), and OM for elevated WC (63.0%). Percentages mediated by SUA for the associations of PM2.5, SO42-, NO3-, and BC with MetS were 13.6%, 13.1%, 10.6%, and 11.1%, respectively. Long-term exposure to PM2.5 constituents, mainly NH4+ and SO42-, was positively associated with MetS odds, partially mediated by SUA.

An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study

BACKGROUND

Various predictive models have been developed for predicting the incidence of coronary heart disease (CHD), but none of them has had optimal predictive value. Although these models consider diabetes as an important CHD risk factor, they do not consider insulin resistance or triglyceride (TG). The unsatisfactory performance of these prediction models may be attributed to the ignoring of these factors despite their proven effects on CHD. We decided to modify standard CHD predictive models through machine learning to determine whether the triglyceride-glucose index (TyG-index, a logarithmized combination of fasting blood sugar (FBS) and TG that demonstrates insulin resistance) functions better than diabetes as a CHD predictor.

METHODS

Two-thousand participants of a community-based Iranian population, aged 20-74 years, were investigated with a mean follow-up of 9.9 years (range: 7.6-12.2). The association between the TyG-index and CHD was investigated using multivariate Cox proportional hazard models. By selecting common components of previously validated CHD risk scores, we developed machine learning models for predicting CHD. The TyG-index was substituted for diabetes in CHD prediction models. All components of machine learning models were explained in terms of how they affect CHD prediction. CHD-predicting TyG-index cut-off points were calculated.

RESULTS

The incidence of CHD was 14.5%. Compared to the lowest quartile of the TyG-index, the fourth quartile had a fully adjusted hazard ratio of 2.32 (confidence interval [CI] 1.16-4.68, p-trend 0.04). A TyG-index > 8.42 had the highest negative predictive value for CHD. The TyG-index-based support vector machine (SVM) performed significantly better than diabetes-based SVM for predicting CHD. The TyG-index was not only more important than diabetes in predicting CHD; it was the most important factor after age in machine learning models.

CONCLUSION

We recommend using the TyG-index in clinical practice and predictive models to identify individuals at risk of developing CHD and to aid in its prevention.

Effective Prevention and Management Tools for Metabolic Syndrome Based on Digital Health-Based Lifestyle Interventions Using Healthcare Devices

Digital health-based lifestyle interventions (e.g., mobile applications, short messaging service, wearable devices, social media, and interactive websites) are widely used to manage metabolic syndrome (MetS). This study aimed to confirm the utility of self-care for prevention or management of MetS. We recruited 106 participants with one or more MetS risk factors from December 2019 to September 2020. Participants were provided five healthcare devices and applications. Characteristics were compared at baseline and follow-up to examine changes in risk factors, engagement, persistence, and physical activity (analyzed through device use frequency and lifestyle interventions performed). Participants with 1-2 MetS risk factors showed statistically significant reductions in waist circumference (WC) and blood pressure (BP). Participants with ≥3 MetS risk factors showed statistically significant reductions in risk factors including weight, body mass index, WC, BP, and fasting blood sugar (FBS). The prevention and improvement groups used more healthcare devices than the other groups. Smartwatch was the most frequently used device (5 times/week), and physical activity logged more than 7000 steps/week. WC, BP, and FBS of the improvement group were reduced by more than 40%. Based on engagement, persistence, and physical activity, digital health-based lifestyle interventions could be helpful for MetS prevention and management.

Dietary habits and the 10-year risk of overweight and obesity in urban adult population: A cohort study predicated on Yazd Healthy Heart Project

BACKGROUND AND OBJECTIVE

Overweight and obesity are thought to be associated with increased risk of chronic disease in the Middle East. The present study aimed to determine the association between dietary habits and the incidence of overweight and obesity in urban adult population in the central part of Iran after a 10-year follow-up.

METHODS

This cohort study was initiated with 2000 participation aged 20-74 years from Yazd city in Iran based on Yazd Healthy Heart Project (YHHP). The participants without overweight and obesity at the baseline of the study were followed up to 10 years. Demographic data, anthropometric measurements, behavioral and metabolic risk factors of cardiovascular diseases and dietary habits were assessed at baseline and phase II.

RESULTS

After a 10-year follow up, 516 non-overweight and 1068 non-obese participants were included for the final analysis. Once adjustments were made for all potential confounders including age, sex, smoking, economic status, physical activity and education, it was identified that lack of weight control increased the risk of obesity (hazard ratio; 95% CI) in total population (1.9; 1.06, 3.4), as well as the risk of overweight (2.39; 1.07, 5.27) and obesity (2.65; 1.13, 6.25) in men. Moreover, consumption of mayonnaise increased the 10-year risk of overweight in women (6.09; 1.2, 30.99).

CONCLUSIONS

As revealed by the present study, unhealthy dietary habits can increase the incidence of overweight and obesity in central part of Iran. Therefore, changing the lifestyle appears to be urgent in reducing the risk of overweight and obesity.

Serum FFAs profile analysis of Normal weight and obesity individuals of Han and Uygur nationalities in China

Background

Han and Uygur are the two main nationalities living in Xinjiang, China. There are significant differences in the incidence of metabolic diseases for two nationalities, but the specific reasons are not clear. Obesity is an important risk factor for the development of metabolic syndrome, which may be closely related to the increase of serum free fatty acids (FFAs) content. This study aims to use metabolomics to compare the changes of serum FFAs profiles between normal weight (NW) and obese (OB) individuals of two nationalities, screening out the differential FFAs, predicting and evaluating their relationship with diseases.

Methods

Thirty-four kinds of FFAs in serum were detected by ultra-high-pressure liquid chromatography–mass spectrometry (UHPLC-MS) and distinctions in FFAs profiles were evaluated using a metabolomics method while Receiver operating characteristics (ROC) and logistic regression models were used to explore FFAs significant for diagnosing obesity and obesity-associated comorbidities.

Results

In the Han nationality, ten kinds of FFAs (C7:0, C8:0, C9:0, C10:0, C11:0, C14:0, C18:2, C20:3, C20:4 and C22:6) showed significant differences between NW and OB individuals. These differential FFAs may be related to hypertension and gestational diabetes mellitus. In the Uygur nationality, C20:3 and C20:5 showed significant differences between NW and OB individuals. C9:0 and C19:0, which were screened out among the female subjects, showed a good ability to predict obesity status in Uygur females (AUC = 0.950).

Conclusion

In both the Han and Uygur nationalities, the FFAs profiles of NW individuals differed from those of OB individuals. The significantly differential FFAs are closely related to obesity and may be important risk factors for obesity and related metabolic diseases.