FreshRSS

🔒
❌ Acerca de FreshRSS
Hay nuevos artículos disponibles. Pincha para refrescar la página.
AnteayerTus fuentes RSS

Classifying self‐management clusters of patients with mild cognitive impairment associated with diabetes: A cross‐sectional study

Abstract

Aims and Objectives

This study aims to propose a self-management clusters classification method to determine the self-management ability of elderly patients with mild cognitive impairment (MCI) associated with diabetes mellitus (DM).

Background

MCI associated with DM is a common chronic disease in old adults. Self-management affects the disease progression of patients to a large extent. However, the comorbidity and patients' self-management ability are heterogeneous.

Design

A cross-sectional study based on cluster analysis is designed in this paper.

Method

The study included 235 participants. The diabetes self-management scale is used to evaluate the self-management ability of patients. SPSS 21.0 was used to analyse the data, including descriptive statistics, agglomerative hierarchical clustering with Ward's method before k-means clustering, k-means clustering analysis, analysis of variance and chi-square test.

Results

Three clusters of self-management styles were classified as follows: Disease neglect type, life oriented type and medical dependence type. Among all participants, the percentages of the three clusters above are 9.78%, 32.77% and 57.45%, respectively. The difference between the six dimensions of each cluster is statistically significant.

Conclusion(s)

This study classified three groups of self-management styles, and each group has its own self-management characteristics. The characteristics of the three clusters may help to provide personalized self-management strategies and delay the disease progression of MCI associated with DM patients.

Relevance to clinical practice

Typological methods can be used to discover the characteristics of patient clusters and provide personalized care to improve the efficiency of patient self-management to delay the progress of the disease.

Patient or public contribution

In our study, we invited patients and members of the public to participate in the research survey and conducted data collection.

Metabolomic signature between diabetic and non-diabetic obese patients: A protocol for systematic review

by Yuxing Tai, Xiaoqian Yang, Xiaochao Gang, Zhengri Cong, Sixian Wang, Peizhe Li, Mingjun Liu

Background

Type 2 diabetes mellitus (T2DM) is a chronic and progressive condition defined by hyperglycemia caused by abnormalities in insulin production, insulin receptor sensitivity, or both. Several studies have revealed that higher body mass index (BMI) is associated with increasing risk of developing diabetes. In this study, we perform a protocol for systematic review to explore metabolite biomarkers that could be used to identify T2DM in obese subjects.

Methods

The protocol of this review was registered in PROSPERO (CRD42023405518). Three databases, EMBASE, PubMed, and Web of Science were selected to collect potential literature from their inceptions to July December 2023. Data for collection will include title, authors, study subjects, publication date, sample size, detection and analytical platforms, participant characteristics, biological samples, confounding factors, methods of statistical analysis, the frequency and directions of changes in potential metabolic biomarkers, and major findings. Pathway analysis of differential metabolites will be performed with MetaboAnalyst 5.0 based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Human Metabolome Database.

Results

The results of this systematic review will be published in a peer-reviewed journal.

Conclusion

This systematic review will summarize the potential biomarkers and metabolic pathways to provide a new reference for the prevention and treatment of T2DM in obese subjects.

❌