Adipose-derived miRNAs as potential biomarkers for predicting adulthood obesity and its complications: A systematic review and bioinformatic analysis.
Xiyan LiuHuayi SunLixia ZhengJian ZhangHan SuBingjie LiQianhui WuYunchan LiuYingxi XuXiaoyu SongYang YuPublished in: Obesity reviews : an official journal of the International Association for the Study of Obesity (2024)
Adipose tissue is the first and primary target organ of obesity and the main source of circulating miRNAs in patients with obesity. This systematic review aimed to analyze and summarize the generation and mechanisms of adipose-derived miRNAs and their role as early predictors of various obesity-related complications. Literature searches in the PubMed and Web of Science databases using terms related to miRNAs, obesity, and adipose tissue. Pre-miRNAs from the Human MicroRNA Disease Database, known to regulate obesity-related metabolic disorders, were combined for intersection processing. Validated miRNA targets were sorted through literature review, and enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes via the KOBAS online tool, disease analysis, and miRNA transcription factor prediction using the TransmiR v. 2.0 database were also performed. Thirty miRNAs were identified using both obesity and adipose secretion as criteria. Seventy-nine functionally validated targets associated with 30 comorbidities of these miRNAs were identified, implicating pathways such as autophagy, p53 pathways, and inflammation. The miRNA precursors were analyzed to predict their transcription factors and explore their biosynthesis mechanisms. Our findings offer potential insights into the epigenetic changes related to adipose-driven obesity-related comorbidities.
Keyphrases
- insulin resistance
- adipose tissue
- metabolic syndrome
- weight loss
- high fat diet induced
- type diabetes
- systematic review
- transcription factor
- weight gain
- high fat diet
- skeletal muscle
- gene expression
- public health
- emergency department
- randomized controlled trial
- dna methylation
- depressive symptoms
- cell death
- risk assessment
- endoplasmic reticulum stress
- machine learning
- healthcare
- deep learning
- genome wide
- big data
- meta analyses
- electronic health record