Cross-species transcriptomics identifies obesity associated genes between human and mouse studies.
Animesh AcharjeeSusanne N WijesingheDominic RussGeorgios GkoutosSimon W JonesPublished in: Journal of translational medicine (2024)
The present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.
Keyphrases
- weight loss
- metabolic syndrome
- insulin resistance
- type diabetes
- genome wide
- high fat diet induced
- weight gain
- machine learning
- bariatric surgery
- adipose tissue
- genome wide identification
- endothelial cells
- gene expression
- bioinformatics analysis
- case report
- randomized controlled trial
- dna methylation
- body mass index
- single cell
- transcription factor
- risk assessment
- climate change
- knee osteoarthritis
- deep learning
- cancer therapy
- rna seq