Jorge Alberto Rodriguez Sabillon

Background: Semaglutide, a GLP-1 receptor agonist, has shown promising nephroprotective effects in clinical trials, though real-world data on its long-term impact on renal function in high-risk diabetic nephropathy patients remain scarce. Methods: We conducted a multicenter, retrospective observational study involving 156 patients with type 2 diabetes and chronic kidney disease (CKD) treated with subcutaneous semaglutide between 2019 and 2023. Inclusion required an eGFR > 15 mL/min/1.73 m² or albuminuria > 30 mg/g and at least two years of follow-up. The primary outcome was the change in eGFR slope after semaglutide initiation. Subgroup analyses were performed based on baseline eGFR, albuminuria, and SGLT2i co-treatment. Results: In the whole study population, the median eGFR slope significantly improved from −3.29 (IQR 7.54) to −0.79 (IQR 6.01) mL/min/1.73 m²/year post-treatment (p < 0.001). Multiple linear regression showed a hazard ratio for the effect of semaglutide on the eGFR slope of 4.06 (2.43–5.68), p < 0.001. In patients with baseline eGFR < 60 mL/min/1.73 m², the slope improved from −3.77 to −1.01 (p < 0.0001), while patients on concurrent SGLT2i therapy saw slope changes from −2.96 to −0.37 (p < 0.0001). Patients with albuminuria 30–1000 mg/g also improved from −2.96 to −0.04 (p < 0.0001); however, those > 1000 mg/g did not show a significant change (p = 0.184). Semaglutide also reduced BMI (p = 0.04), HbA1c (p = 0.002), triglycerides (p = 0.001), CRP (p = 0.003), and GGT values (p = 0.004). Conclusions: In real-world practice, semaglutide significantly attenuated renal function decline in high-risk diabetic patients, particularly those with advanced CKD or concurrent SGLT2i therapy. These findings support its nephroprotective role beyond glycemic control.

Background: Semaglutide, a GLP-1 receptor agonist, has shown promising nephroprotective effects in clinical trials, though real-world data on its long-term impact on renal function in high-risk diabetic nephropathy patients remain scarce. Methods: We conducted a multicenter, retrospective observational study involving 156 patients with type 2 diabetes and chronic kidney disease (CKD) treated with subcutaneous semaglutide between 2019 and 2023. Inclusion required an eGFR >15 mL/min/1.73 m² or albuminuria >30 mg/g and at least two years of follow-up. The primary outcome was the change in eGFR slope after semaglutide initiation. Subgroup analyses were performed based on baseline eGFR, albuminuria, and SGLT2i co-treatment. Results: In the whole study population, the median eGFR slope significantly improved from -3.29 (IQR 7.54) to -0.79 (IQR 6.01) mL/min/1.73 m²/year post-treatment (p< 0.001). Multiple linear regression showed a hazard ratio for the effect of semaglutide on the eGFR slope of 4.06 (2.43-5.68), p< 0.001. In patients with baseline eGFR 1,000 mg/g did not show a significant change (p=0.184). Semaglutide also reduced BMI (p=0.04), HbA1c (p=0.002), triglycerides (p=0.001), CRP (p=0.003), and GGT values (p=0.004). Conclusions: In real-world practice, semaglutide significantly attenuated renal function decline in high-risk diabetic patients, particularly those with advanced CKD or concurrent SGLT2i therapy. These findings support its nephroprotective role beyond glycemic control.

Aims: Serum creatinine and albuminuria are the core of most CKD prediction and progression risk models. Several biomarkers have been introduced to improve these results such as beta‐2‐microglobulin (B2M) and cystatin C (CysC). Nevertheless, few clinical comparisons of these biomarkers are available. We have compared serum B2M levels with albuminuria, CysC levels, and the CKD‐EPI GFR equations.Designs and Methods: A sample of 434 patients were studied: 234 males and 200 females, the mean age was 58.3 ± 15.0 years, and 33.4% have diabetes mellitus. In all patients, plasma B2M, CysC, creatinine, and urinary albumin excretion were analyzed. EGFR was calculated using CKD‐EPI equations for creatinine, CysC, and creatinine‐CysC. The risk of death and CKD progression was evaluated using ROC curves and Cox proportional hazards survivorship models.Results: For mortality, the highest area under the curve (AUC) was for CysC (0.775, 0.676–0.875). The lowest sensitivity was shown by eGFR (creatinine) (0.298, 0.195–0.401, p < 0.001), eGFR (CysC) (0.216, 0.118–0.314, p < 0.001), and eGFR (creatinine + CysS) (0.218, 0.124–0.312, p < 0.001). For progression to advanced CKD, the highest AUC was for CysC (0.908, 0.862–0.954). The lowest sensitivity was shown by eGFR (creatinine) (0.184, 0.106–0.261, p < 0.001), eGFR (CysC) (0.095, 0.048–0.14, p < 0.001), and eGFR (creatinine+ CysC) (0.087, 0.040–0.134, p < 0.001). CysC, after age, was the second‐best marker of life risk. Contrariwise, for CKD progression, CysC, and albuminuria were the best markers.Conclusions: The best biomarker of mortality and risk of progression to CKD was CysC. Albuminuria and B2M were the next best options to be used. The lowest sensitivity was shown by estimated eGFR.

Objetives: Adequate donor-recipient matching is necessary to achieve the best renal function after transplantation. The nephron load received from the donor, which can be reflected in variables such as age, sex and body mass index, may influence the evolution of the graft. Our objetive was develop a combined score according to the donor-recipient pairing in age, sex and body mass index, and assess its influence on the evolution of the renal graft. Materials and methods: Transplant patients between 2013 and 2020 who had at least 1 year of post-transplant follow-up. A ranking of 0 to 2 points was awarded according to the match between donor and recipient for sex, age and body mass index individually, with higher rank to greater mismatch. Subsequently, a Score with value of 0 to 6 points was created with the combined ranking of the indivudual variables, analyzing its influence on the evolution of the graft by multivariate linear regression. Results: 280 patients followed for 4.3 years of median. Worse renal function was observed at the end of follow-up as the range of each variable increased separately, and the Score created, with a worsening of renal function as the combined Score increased in the multivariate analysis: Coefficient B= -2.4 ml/min, p=0.018. Conclusions: An adequate match between donor and recipient in age, sex and body mass index using this combined Score can help achieve better kidney function after transplantation.