Total cholesterol and high density lipoprotein cholesterol ratio is associated with metabolic syndrome in a very elderly Chinese population

Triceps skinfold thickness trajectories and the risk of all-cause mortality: A prospective cohort study

BACKGROUND

The measurement of triceps skinfold (TSF) thickness serves as a noninvasive metric for evaluating subcutaneous fat distribution. Despite its clinical utility, the TSF thickness trajectories and their correlation with overall mortality have not been thoroughly investigated.

AIM

To explore TSF thickness trajectories of Chinese adults and to examine their associations with all-cause mortality.

METHODS

This study encompassed a cohort of 14747 adults sourced from the China Health and Nutrition Survey. Latent class trajectory modeling was employed to identify distinct trajectories of TSF thickness. Subjects were classified into subgroups reflective of their respective TSF thickness trajectory. We utilized multivariate Cox regression analyses and mediation examinations to explore the link between TSF thickness trajectory and overall mortality, including contributory factors.

RESULTS

Upon adjustment for multiple confounding factors, we discerned that males in the 'Class 2: Thin-stable' and 'Class 3: Thin-moderate' TSF thickness trajectories exhibited a markedly reduced risk of mortality from all causes in comparison to the 'Class 1: Extremely thin' subgroup. In the mediation analyses, the Geriatric Nutritional Risk Index was found to be a partial intermediary in the relationship between TSF thickness trajectories and mortality. For females, a lower TSF thickness pattern was significantly predictive of elevated all-cause mortality risk exclusively within the non-elderly cohort.

CONCLUSION

In males and non-elderly females, lower TSF thickness trajectories are significantly predictive of heightened mortality risk, independent of single-point TSF thickness, body mass index, and waist circumference.

Exploring the Potential Performance of Fibroscan for Predicting and Evaluating Metabolic Syndrome using a Feature Selected Strategy of Machine Learning

Metabolic syndrome (MetS) includes several conditions that can increase an individual's predisposition to high-risk cardiovascular events, morbidity, and mortality. Non-alcoholic fatty liver disease (NAFLD) is a predominant cause of cirrhosis, which is a global indicator of liver transplantation and is considered the hepatic manifestation of MetS. FibroScan^® provides an accurate and non-invasive method for assessing liver steatosis and fibrosis in patients with NAFLD, via a controlled attenuation parameter (CAP) and liver stiffness measurement (LSM or E) scores and has been widely used in current clinical practice. Several machine learning (ML) models with a recursive feature elimination (RFE) algorithm were applied to evaluate the importance of the CAP score. Analysis by ANOVA revealed that five symptoms at different CAP and E score levels were significant. All eight ML models had accuracy scores > 0.9, while treebags and random forest had the best kappa values (0.6439 and 0.6533, respectively). The CAP score was the most important variable in the seven ML models. Machine learning models with RFE demonstrated that using the CAP score to identify patients with MetS may be feasible. Thus, a combination of CAP scores and other significant biomarkers could be used for early detection in predicting MetS.

Geospatial analysis of type 2 diabetes mellitus and hypertension in South Sulawesi, Indonesia

The spatial variation of type 2 diabetes mellitus (T2DM) and hypertension and their potential linkage were explored in South Sulawesi Province, Indonesia. The Global Moran's I and regression analysis were utilized to identify the characteristics involved. The methods were performed based on T2DM and hypertension data from 2017 and 2018 acquired from Social Health Insurance Administration in Indonesia. The spatial variation of T2DM and hypertension showed that the prevalence rate of T2DM and hypertension tends to occur randomly (p = 0.678, p = 0.711, respectively). By utilizing Generalized Poisson Regression Analysis, our study showed a significant relationship between T2DM and hypertension (p ≤ 0.001). This research could help policy makers to plan and support projects with the aim of overcoming the risk of T2DM and hypertension.