ArticleDiabetologia2023
Skeletal muscle and intermuscular adipose tissue gene expression profiling identifies new biomarkers with prognostic significance for insulin resistance progression and intervention response.
Article in Diabetologia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 8 citations in OpenAlex.
- Spatial transcriptomics maps distinct signatures of human intermuscular adipose expansion in mice.medRxiv : the preprint server for health sciences · 2026Article
- Comparative effects of exercise modalities on ectopic fat reduction in overweight and obese children and adolescents: a narrative review.European journal of pediatrics · 2026Review
- Integrative analysis of mitochondrial and immune pathways in diabetic kidney disease: identification of AASS and CASP3 as key predictors and therapeutic targets.Renal failure · 2025Article
- Insights into intramuscular adipose-muscle signaling in the diabetic lower extremity.Journal of clinical & translational endocrinology · 2025Article
- Identification of the shared gene signatures and biological mechanism in type 2 diabetes mellitus and hepatocellular carcinoma.Clinical and experimental hepatology · 2025Article
- Network-based systematic dissection of exercise-induced inhibition of myosteatosis in older individuals.The Journal of physiology · 2025Article
- Enhancing diabetic muscle repair through W-GA nanodots: a nanomedicinal approach to ameliorate myopathy in type 2 diabetes.Burns & trauma · 2025Article
- Chronically Increased Levels of Circulating Insulin Secondary to Insulin Resistance: A Silent Killer.Biomedicines · 2024Review
- Intermuscular adipose tissue accumulation is associated with higher tissue sodium in healthy individuals.Physiological reports · 2024Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
aims/hypothesisAlthough insulin resistance often leads to type 2 diabetes mellitus, its early stages are often unrecognised, thus reducing the probability of successful prevention and intervention. Moreover, treatment efficacy is affected by the genetics of the individual. We used gene expression profiles from a cross-sectional study to identify potential candidate genes for the prediction of diabetes risk and intervention response.
methodsUsing a multivariate regression model, we linked gene expression profiles of human skeletal muscle and intermuscular adipose tissue (IMAT) to fasting glucose levels and glucose infusion rate. Based on the expression patterns of the top predictive genes, we characterised and compared individual gene expression with clinical classifications using k-nearest neighbour clustering. The predictive potential of the candidate genes identified was validated using muscle gene expression data from a longitudinal intervention study.
resultsWe found that genes with a strong association with clinical measures clustered into three distinct expression patterns. Their predictive values for insulin resistance varied substantially between skeletal muscle and IMAT. Moreover, we discovered that individual gene expression-based classifications may differ from classifications based predominantly on clinical variables, indicating that participant stratification may be imprecise if only clinical variables are used for classification. Of the 15 top candidate genes, ST3GAL2, AASS, ARF1 and the transcription factor SIN3A are novel candidates for predicting a refined diabetes risk and intervention response. CONCLUSION/
interpretationOur results confirm that disease progression and successful intervention depend on individual gene expression states. We anticipate that our findings may lead to a better understanding and prediction of individual diabetes risk and may help to develop individualised intervention strategies.
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