Urinary Proteomics to Guide Early Intervention to Prevent Complications in Type 2 Diabetes - a Feasibility Study

Phase 4

Not yet recruiting

• Conditions: Type 2 DM; Type 2 DM /Diabetic Nephropathy; Albuminuria
• Interventions: Drug: Semaglutide, 1.34 mg/mL; Drug: Finerenone Oral Tablet; Drug: Dapagliflozin (DAPA)

• Registration Number: NCT06954090
• Lead Sponsor: Steno Diabetes Center Copenhagen

Brief Summary

Title:

Body fluid proteome SIGnatures for persoNALised intervention to prevent cardiovascular and renal complications in diabetes.

Aim:

To explore the feasibility of using urinary proteomic risk scores in clinical practice to identify patients at risk of developing end organ damage and identify which patients should receive additional renocardiovascular protective treatment.

Detailed Description

Background:

Diabetes and its associated complications impose a significant burden on both patients and societies. Despite advancements in lowering blood glucose, the elevated risk of developing cardiovascular disease (CVD) and chronic kidney disease (CKD) remains a pressing concern, underscoring the need for optimized prevention strategies and improved therapeutic options. Recent developments in glucose-lowering drugs, such as sodium-glucosecotransporter- 2-inhibitors (SGLT2-i) and glucagon-like-peptide-1 receptor agonists (GLP1-RA), as well as the use of the non-steroidal mineralocorticoid receptor antagonist (nsMRA) finerenone, have shown promising cardiovascular and renal protection. Currently, there is no reliable method for predicting personalized treatment responses in diabetic complications. Consequently, benefits of treatment are under dispute, due to a large number of patients not responding. The use of SGLT2-i, nsMRA and GLP1-RA in CKD has happened largely in parallel, all agents have demonstrated benefit, but it is not yet clear how to prioritize between the drugs or if all should be combined. This study builds upon previous scientific work that have investigated the urine proteome and identified several biomarkers able to predict early diabetes associated complications.

CKD273 urine proteomic risk score is a well-established tool used to predict the risk of chronic kidney disease (CKD) progression. CAD160 is urine proteomic risk score to predict the risk of coronary artery disease (CAD). HF2 urine proteomic classifier is used to predict the risk of heart failure (HF).

Urine sample analysis is based on capillary electrophoresis coupled with mass spectrometry (CE-MS) to determine these risk scores.

Urine proteomic scores are continous numerical values. Higher score means that the urinary peptide pattern is more similar to that of patients with progressive disease. A lower score indicates a peptide profile more typical of healthy individuals.

In addition a Support Vector Machine (SVM), a supervised machine learning algorithm will perform in silico treatment simulations and calculate the change in classification scores for 3 different potential interventions: GLP1-RA semaglutide, SGT2-i dapagliflozin and GLP1-RA finerenone. Based on these changes (with the largest beneficial change indicating the most effective treatment), the most suitable intervention can be selected and the participent will be allocated.

Design:

Single-centre, open-label, parallel group (intervention group) with 6 months intervention.

Population:

Type 2 diabetes without history of heart failure NYHA Class IV or advanced diabetic kidney disease with an estimated Glomerular Filtration Rate (eGFR) \< 30 ml/min/1.73m2 or urinary albumin creatinine ratio (UACR) \> 200 mg/g.

Objectives:

To assess the feasibility of using proteomic classifiers in clinical practice for response prediction in a prospective study. We will use urinary proteomic classifiers: CKD273, CAD160 and HF2 to identify patients suited for additional medical treatment with sodium-glucose-cotransporter-2 (SGLT2)- inhibitors, glucagon-like-peptide-1 GLP-1 receptor agonists or non-steroidal mineralocorticoid receptor antagonist.

Interventions:

The SGLT2 inhibitor dapagliflozin 10 mg daily, the nsMRA finerenone 10-20 mg daily, and the GLP-1 receptor agonist semaglutide 0.25-1.0 mg once weekly. The medication will be given stepwise according to a prespecified algorithm and guided by the response on UACR.

Endpoints:

Primary endpoint is feasibility of using urinary proteomic classifiers in clinical practice, while secondary endpoints are changes in UACR and urinary proteomic signatures after 6 months of treatment.

Time schedule:

The study is expected to start inclusion June 1st 2025. The recruitment period is 6 months, the intervention period is 6 months and hence the study is expected to be terminated May 31st 2026.

Study Timeline

• Status Verified: 2025-04
• Study First Submitted: 2025-04-14
• Study First Submitted QC: 2025-04-23
• Last Update Submitted: 2025-04-23
• Study First Posted: 2025-05-01
• Last Update Posted: 2025-05-01
• Study Start: 2025-06-01
• Primary Completion: 2026-05-31
• Study Completion: 2026-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 50

Inclusion Criteria

1. Men and women over 18 years of age.
2. Type 2 diabetes with no clinical signs of HF NYHA Class IV
3. Able to understand the written participant information and give informed consent.

Exclusion Criteria

1. Heart failure NYHA class IV at screening
2. Moderately - or severely increased albuminuria with a UACR ≥ 200 mg/g or CKD with an eGFR < 30 ml/min/1.73m2 at the screening visit.
3. A female who is pregnant, breastfeeding, or intends to become pregnant, or women of childbearing potential (WOCBP) who are not using highly effective contraceptive methods.
4. Receiving therapy with all three of the study medication prior to enrolment.
5. Myocardial infarction, unstable angina, stroke, or transient ischemic attack within 12 weeks prior to enrolment
6. Known or suspected hypersensitivity to the study medications or related products
7. History of pancreatitis at the screening visit
8. Body mass index < 18.5 kg/m2 at the screening visit
9. Type 1 diabetes
10. Serum potassium > 5.0 mmol/L at the screening visit
11. Addison's Disease
12. Concomitant treatment with strong CYP3A4 inhibitors (e.g., itraconazole, ketoconazole, ritonavir, nelfinavir, cobicistat, clarithromycin, telithromycin, nefazodone)
13. Treatment with a potassium-sparing diuretic (amiloride, triamterene)
14. Treatment with other mineralocorticoid receptor antagonist than finerenone (e.g., spironolactone, eplerenone, esaxerenone, canrenone)
15. Elevated Alanine Aminotransferase (ALT) > 3x upper normal limit, autoimmune hepatitis, and/or severe hepatic impairment (including but not limited to a history of hepatic encephalopathy, a history of oesophageal varices or a history of portocaval shunt.)
16. Autosomal dominant or autosomal recessive polycystic kidney disease
17. Lupus nephritis or ANCA-associated vasculitis, or any other primary or secondary kidney disease requiring immunosuppressive therapy within 6 months prior to screening
18. Kidney transplant or dialysis
19. Presence or history of malignant neoplasms (except basal cell skin cancer or squamous cell skin cancer) within five years before screening.
20. Any other history, condition, therapy, or uncontrolled intercurrent illness that could, as judged by the investigator, affect participant safety or compliance with study requirements.
21. Known or suspected abuse of narcotics.
22. Participant in another intervention study,
23. Vulnerable (i.e., under guardianship) or mentally incapacitated subjects (i.e., not able to understand and sign the informed consent)

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Semaglutide	Semaglutide, 1.34 mg/mL	3 urine proteomic risk scores will be measured in the study. The CKD273 urine proteomic risk score, a well-established tool used to predict the risk of chronic kidney disease (CKD) progression, CAD160 urine proteomic risk score to predict the risk of coronary artery disease (CAD) and HF2 urine proteomic classifier to predict the risk of heart failure (HF). In addition a Support Vector Machine (SVM), a supervised machine learning algorithm will perform in silico treatment simulations and calculate the change in classification scores for 3 different potential interventions: GLP1-RA semaglutide, SGT2-i dapagliflozin and GLP1-RA finerenone. Based on these changes (with the largest beneficial change indicating the most effective treatment), the most suitable intervention can be selected and the participent will be allocated.
Finerenone	Finerenone Oral Tablet	3 urine proteomic risk scores will be measured in the study. The CKD273 urine proteomic risk score, a well-established tool used to predict the risk of chronic kidney disease (CKD) progression, CAD160 urine proteomic risk score to predict the risk of coronary artery disease (CAD) and HF2 urine proteomic classifier to predict the risk of heart failure (HF). In addition a Support Vector Machine (SVM), a supervised machine learning algorithm will perform in silico treatment simulations and calculate the change in classification scores for 3 different potential interventions: GLP1-RA semaglutide, SGT2-i dapagliflozin and GLP1-RA finerenone. Based on these changes (with the largest beneficial change indicating the most effective treatment), the most suitable intervention can be selected and the participent will be allocated.
Dapagliflozin	Dapagliflozin (DAPA)	3 urine proteomic risk scores will be measured in the study. The CKD273 urine proteomic risk score, a well-established tool used to predict the risk of chronic kidney disease (CKD) progression, CAD160 urine proteomic risk score to predict the risk of coronary artery disease (CAD) and HF2 urine proteomic classifier to predict the risk of heart failure (HF). In addition a Support Vector Machine (SVM), a supervised machine learning algorithm will perform in silico treatment simulations and calculate the change in classification scores for 3 different potential interventions: GLP1-RA semaglutide, SGT2-i dapagliflozin and GLP1-RA finerenone. Based on these changes (with the largest beneficial change indicating the most effective treatment), the most suitable intervention can be selected and the participent will be allocated.

Primary Outcome Measures

Name	Time	Method
Proteomic feasibility	2 weeks from sampling	Achieve urine proteomic results within 2 weeks of sampling for at least 90% of the participants in clinical practice.
Evaluation of medical treatment	3 weeks from sampling	Ensure that urine proteomic results are interpreted for evaluating medical treatment in at least 90% of participants.

Secondary Outcome Measures

Name	Time	Method
Urine Albumin-to-Creatinine Ratio	Over the 6 month of the follow up from screening visit to the end of study.	Changes in UACR from screening visit to the end of study
Urinary proteomic signatures	Over the 6 month of the follow up from screening visit to the end of study.	Changes in urinary proteomic signatures from screening visit to the end of study: Urine proteomic risk scores are continous numerical values. Higher score means urinary peptide pattern is more similar to that of patients with progressive disease. A lower score indicates a peptide profile more typical of healthy individuals.; CKD273, CAD160 and HF2 urine proteomic risk-scores and their changes will be measured from urine samples during the study.

Trial Locations

Locations (1)

Steno Diabetes Center Copenhagen; 🇩🇰 Herlev, Hajdú-Bihar, Denmark