Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents.

Objective: Hypertriglyceridemic waist (HW), hypertriglyceridemic waist-to-height ratio (HWHtR), and waist-to-hip ratio (WHR) have been shown to be indicators of cardiometabolic risk factors. However, it is not clear which indicator is more suitable for children and adolescents. We aimed to investigate the relationship between HW, HWHtR, WHR, and cardiovascular risk factors clustering to determine the best screening tools for cardiometabolic risk in children and adolescents. Methods: This was a national cross-sectional study. Anthropometric and biochemical variables were assessed in approximately 70,000 participants aged 6-18 years from seven provinces in China. Demographics, physical activity, dietary intake, and family history of chronic diseases were obtained through questionnaires. ANOVA, χ 2 and logistic regression analysis was conducted. Results: A significant sex difference was observed for HWHtR and WHR, but not for HW phenotype. The risk of cardiometabolic health risk factor clustering with HW phenotype or the HWHtR phenotype was significantly higher than that with the non-HW or non-HWHtR phenotypes among children and adolescents (HW: OR = 12.22, 95% CI: 9.54-15.67; HWHtR: OR = 9.70, 95% CI: 6.93-13.58). Compared with the HW and HWHtR phenotypes, the association between risk of cardiometabolic health risk factors (CHRF) clustering and high WHR was much weaker and not significant (WHR: OR = 1.14, 95% CI: 0.97-1.34). Conclusion: Compared with HWHtR and WHR, the HW phenotype is a more convenient indicator withhigher applicability to screen children and adolescents for cardiovascular risk factors.

Association Between Eating Speed and Metabolic Syndrome: A Systematic Review and Meta-Analysis.

Objective: This review aimed to systematically summarize and meta-analyze the association between eating speed and metabolic syndrome (MetS). Methods: Following the Preferred Reporting Items for Systematic Reviews, and Meta Analyses (PRISMA) guidelines, four electronic databases (PubMed, Web of Science, MEDLINE, and EMBASE) were searched until March 2021 to identify eligible articles based on a series of inclusion and exclusion criteria. Heterogeneity was examined using I 2 statistics. Using random-effects models, the pooled odds ratios (ORs), and 95% CIs were calculated to evaluate the association between eating speed with MetS and its components, including central obesity, blood pressure (BP), high-density lipoprotein cholesterol (HDL), triglyceride (TG), and fasting plasma glucose (FPG). Results: Of the 8,500 original hits generated by the systematic search, 29 eligible studies with moderate-to-high quality were included, involving 465,155 subjects. The meta-analysis revealed that eating faster was significantly associated with higher risks of MetS (OR = 1.54, 95% CI: 1.27-1.86), central obesity (OR = 1.54, 95% CI: 1.37-1.73), elevated BP (OR = 1.26, 95% CI: 1.13-1.40), low HDL (OR = 1.23, 95% CI: 1.15-1.31), elevated TG (OR = 1.29, 95% CI: 1.18-1.42), and elevated FPG (OR = 1.16, 95% CI: 1.06-1.27) compared to eating slowly. Conclusions: The results of the review indicated that eating speed was significantly associated with MetS and its components. Interventions related to decreasing eating speed may be beneficial for the management of MetS. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021242213, identifier: CRD42021242213.

Prevalence of Hyperhomocysteinemia in China: An Updated Meta-Analysis.

We conducted a meta-analysis to systematically assess the prevalence of hyperhomocysteinemia (HHcy) in China, its change over time, and its determinants. Literature searches were conducted using English databases (PubMed, Embase, and Web of Science) and Chinese databases (CNKI, CBM, VIP, and Wanfang). The time ranges were from Jan 2014 to Mar 2021 in China. We adopted the random effects model to estimate the pooled positive rates of HHcy and corresponding 95% confidence intervals (95% CI). To find the sources of heterogeneity, we performed subgroup analysis and meta-regression. A total of 29 related articles were identified involving 338,660 participants with 128,147 HHcy cases. The estimated prevalence of HHcy in China was 37.2% (95% CI: 32.6-41.8%, I2 = 99.8%, p for heterogeneity < 0.001). The trend of HHcy prevalence was gradually upward over time, with increases during 2015-2016 (comparison to 2013-2014, p < 0.001), but steady between 2015-2016 and 2017-2018. Subgroup analysis showed that the prevalence was higher in the elderly over 55 years old, males, and residents in the north, inland, and rural China (for each comparison, p < 0.001). Meta-regression analysis revealed that age and area of study contributed to 42.3% of the heterogeneity between studies. The current meta-analysis provides strong evidence that the prevalence of HHcy is increasing in China, and varies substantially across different ages, genders, and geographic distribution. Accordingly, high-risk population groups should be focused on, and public health policies and strategies should be carried out to prevent and control HHcy in China.