20240047065. BIOMARKERS FOR DIAGNOSIS AND TREATMENT OF ENDOCRINE HYPERTENSION, AND METHODS OF IDENTIFICATION THEREOF simplified abstract (INSERM (INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE))

BIOMARKERS FOR DIAGNOSIS AND TREATMENT OF ENDOCRINE HYPERTENSION, AND METHODS OF IDENTIFICATION THEREOF

Organization Name

INSERM (INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE)

Inventor(s)

Maria-Christina Zennaro of Paris (FR)

Emily Jefferson of Dundee (GB)

Parminder Singh Reel of Dundee (GB)

Smarti Reel of Dundee (GB)

Graeme Eisenhofer of Dresden (DE)

Martin Reincke of München (DE)

Félix Beuschlein of Zürich (CH)

Angela Elizabeth Taylor of Birmingham (GB)

Wiebke Arlt of Birmingham (GB)

Katharina Lang of Birmingham (GB)

Alessandro Prete of Birmingham (GB)

Scott Mackenzie of Glasgow (GB)

Eleanor Davies of Glasgow (GB)

BIOMARKERS FOR DIAGNOSIS AND TREATMENT OF ENDOCRINE HYPERTENSION, AND METHODS OF IDENTIFICATION THEREOF - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240047065 titled 'BIOMARKERS FOR DIAGNOSIS AND TREATMENT OF ENDOCRINE HYPERTENSION, AND METHODS OF IDENTIFICATION THEREOF

Simplified Explanation

The disclosed invention is a method for identifying biomarkers to stratify hypertensive patients among different hypertension diseases, specifically endocrine forms of hypertension and primary hypertension. The method uses a machine-learning based approach with a trained classifier on a predefined input dataset to rank combinations of omics biomarkers (such as miRNA, steroids, metanephrines, and small metabolites) based on evaluation parameters. The selected combination of biomarkers is used to stratify the hypertensive patient among the different hypertension diseases.

• The method uses machine learning and a trained classifier to identify biomarkers for stratifying hypertensive patients.
• It ranks combinations of omics biomarkers based on evaluation parameters.
• The selected combination of biomarkers is used to stratify the patient among different hypertension diseases.
• The method can be used to stratify hypertensive patients with endocrine forms of hypertension.
• It operates the trained classifier on a combination of biomarkers determined from the patient to stratify them among different types of hypertensive patients.
• The method can be used for stratifying hypertensive patients with primary hypertension.

Potential applications of this technology:

• Personalized medicine: The method can help in identifying the specific hypertension disease a patient has, allowing for personalized treatment plans.
• Improved patient management: Stratifying hypertensive patients can aid in better management and monitoring of their condition, leading to more effective treatment strategies.
• Drug development: The identified biomarkers can be used in the development of targeted therapies for specific hypertension diseases.

Problems solved by this technology:

• Differentiating between endocrine forms of hypertension and primary hypertension can be challenging, as they may have similar symptoms. This method provides a solution by using biomarkers to accurately stratify patients.
• Traditional diagnostic methods for hypertension may not provide enough information to distinguish between different hypertension diseases. This method offers a more comprehensive approach by considering multiple omics biomarkers.

Benefits of this technology:

• Accurate stratification: The method provides a reliable and accurate way to stratify hypertensive patients among different hypertension diseases, improving diagnosis and treatment decisions.
• Personalized treatment: By identifying the specific hypertension disease, personalized treatment plans can be developed, leading to better patient outcomes.
• Efficient and cost-effective: The machine-learning based approach allows for efficient analysis of biomarkers, potentially reducing the need for extensive and expensive diagnostic tests.

Original Abstract Submitted

the disclosed invention relates to a method for identifying biomarkers for the stratification of hypertensive patients among different hypertension diseases: endocrine forms of hypertension and primary hypertension. the method is a machine-learning based method using one trained classifier on a predefined input dataset to rank several combinations of omics biomarkers (mirna, steroids, metanephrines, small metabolites) on the basis of the computation of at least one evaluation parameter. a combination of biomarkers is selected to stratify the hypertensive patient among said plurality of hypertension diseases. also, the disclosed invention relates to a method for stratifying hypertensive patients, such as hypertensive patients with endocrine forms of hypertension (eht). the method comprises operating a trained classifier on a combination of biomarkers determined from the hypertensive patient to stratify the hypertensive patient among several types of hypertensive patients such as endocrine forms of hypertension and primary hypertension.