Summary
Type 2 Diabetes Mellitus (T2DM) is a heterogeneous disease, associated with mortality and severe morbidity, and affecting hundreds of millions of people around the globe. Despite some misconception accepted in the public, current models to predict the onset of T2DM have insufficient power, and are generally limited to individuals that already progressed into pre-diabetes states, at which the assignment of disease-preventing treatments is largely ineffective. Clinical-level prediction power (AUC>0.9) among non-diabetic individuals would enable effective pre-emptive strategies, but the way to produce such predictions remined unknown. Based on a new understanding of enhancer-silencer regulatory networks, I have developed a new class of supreme epigenetic biomarkers. Disease-prediction models that built on the basis of these novel understanding and biomarkers, significantly overpower the best currently-available prediction models. Importantly, these pioneering models can identify the people at risk well before the appearance of any clinical symptoms. Strong preliminary evidence signified the potential to develop the first clinical test to identify the risk to develop T2DM and associated diseases among non-pre-diabetic individuals at the required prediction power, and in time to deliver effective disease-preventing treatments. Validation of these results and demonstration of the effectiveness of the new clinical-level test will allows the directions of the effective (though expensive) preventing treatments to the people at risk, thus dramatically improve the way we are dealing with this harmful disease.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101158206 |
| Start date: | 01-04-2024 |
| End date: | 30-09-2025 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
Type 2 Diabetes Mellitus (T2DM) is a heterogeneous disease, associated with mortality and severe morbidity, and affecting hundreds of millions of people around the globe. Despite some misconception accepted in the public, current models to predict the onset of T2DM have insufficient power, and are generally limited to individuals that already progressed into pre-diabetes states, at which the assignment of disease-preventing treatments is largely ineffective. Clinical-level prediction power (AUC>0.9) among non-diabetic individuals would enable effective pre-emptive strategies, but the way to produce such predictions remined unknown. Based on a new understanding of enhancer-silencer regulatory networks, I have developed a new class of supreme epigenetic biomarkers. Disease-prediction models that built on the basis of these novel understanding and biomarkers, significantly overpower the best currently-available prediction models. Importantly, these pioneering models can identify the people at risk well before the appearance of any clinical symptoms. Strong preliminary evidence signified the potential to develop the first clinical test to identify the risk to develop T2DM and associated diseases among non-pre-diabetic individuals at the required prediction power, and in time to deliver effective disease-preventing treatments. Validation of these results and demonstration of the effectiveness of the new clinical-level test will allows the directions of the effective (though expensive) preventing treatments to the people at risk, thus dramatically improve the way we are dealing with this harmful disease.Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
Images
No images available.
Geographical location(s)
