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

Sponsor
Steno Diabetes Center Copenhagen
Study ID
NCT06954090
Phase
PHASE4
Status
Enrolling By Invitation

Conditions

  • Albuminuria
  • Type 2 DM
  • Type 2 DM /Diabetic Nephropathy

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • Semaglutide, 1.34 mg/mL — DRUG
    Semaglutide will be introduced at a dose of 0.25 mg/week subcutaneous injection, escalated to 0.5 and 1.0 mg/week after 4 and 8 weeks if tolerated.
  • Finerenone Oral Tablet — DRUG
    Finerenone will be introduced at a dose of 10 mg/day in patients with a serum potassium level \< 4.8 mmol/l and eGFR \< 60 ml/min/1.73 m2 and escalated to 20 mg/day after 4 weeks if the serum potassium level is still \< 4.8 mmol/l. Starting dose is 20 mg/day if eGFR ≥ 60 ml/min/1.73 m2. The dosage will be reduced or discontinued in patients who develop hyperkalemia (serum potassium \> 5.5 mmol/l).
  • Dapagliflozin (DAPA) — DRUG
    Dapagliflozin will be introduced at a dose of 10 mg/day. The dose can be reduced at any time during the trial if required by the subject's tolerance to the product.

Study Details

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.

Key Dates

Start date
Nov 20, 2025
Status verified
Nov 2025
Primary completion
Nov 30, 2026
Completion
May 31, 2027

Study Design

Enrollment
50 participants (estimated)
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER

Arms

  • Active Comparator: Semaglutide
    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.
  • Active Comparator: Finerenone
    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.
  • Active Comparator: Dapagliflozin
    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 Measure

Proteomic feasibility [ Time Frame: 2 weeks from sampling ]

Related Studies