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Aim: To conduct a post hoc subgroup analysis of patients with type 2 diabetes (T2D) from the RECAP study, who were treated with sodium-glucose cotransporter-2 (SGLT2) inhibitor and glucagon-like peptide 1 receptor agonist (GLP-1RA) combination therapy, focusing only on those patients who had chronic kidney disease (CKD), to examine whether the composite renal outcome differed between those who received SGLT2 inhibitor treatment first and those who received a GLP-1RA first., Methods: We included 438 patients with CKD (GLP-1RA-first group, n = 223; SGLT2 inhibitor-first group, n = 215) from the 643 T2D patients in the RECAP study. The incidence of the composite renal outcome, defined as progression to macroalbuminuria and/or a ≥50% decrease in estimated glomerular filtration rate (eGFR), was analysed using a propensity score (PS)-matched model. Furthermore, we calculated the win ratio for these composite renal outcomes, which were weighted in the following order: (1) both a ≥50% decrease in eGFR and progression to macroalbuminuria; (2) a decrease in eGFR of ≥50% only; and (3) progression to macroalbuminuria only., Results: Using the PS-matched model, 132 patients from each group were paired. The incidence of renal composite outcomes did not differ between the two groups (GLP-1RA-first group, 10%; SGLT2 inhibitor-first group, 17%; odds ratio 1.80; 95% confidence interval [CI] 0.85 to 4.26; p = 0.12). The win ratio of the GLP-1RA-first group versus the SGLT2 inhibitor-first group was 1.83 (95% CI 1.71 to 1.95; p < 0.001)., Conclusion: Although the renal composite outcome did not differ between the two groups, the win ratio of the GLP-1RA-first group versus the SGLT2 inhibitor-first group was significant. These results suggest that, in GLP-1RA and SGLT2 inhibitor combination therapy, the addition of an SGLT2 inhibitor to baseline GLP-1RA treatment may lead to more favourable renal outcomes., (© 2024 John Wiley & Sons Ltd.)

Diabetic kidney disease (DKD) is a key determinant of morbidity and mortality in patients with type 1 diabetes (T1D). Identifying factors associated with early glomerular filtration rate (GFR) decline in T1D is important in prevention or early intervention for DKD. This study investigated whether phosphate metabolism, including fibroblast growth factor 23 (FGF23) is associated with the kidney function of patients with T1D. We randomly recruited 118 patients with T1D with a normal or mildly impaired kidney function [chronic kidney disease (CKD) stages of G1/G2, A1/A2], and measured their serum FGF23 levels. Serum FGF23 was significantly negatively associated with the estimated GFR (eGFR) (r = -0.292, P = 0.0016), but not urinary albumin creatinine ratio (UACR), and positively associated with serum phosphate (Pi; r = 0.273, P = 0.0027). Serum FGF23 increased with decreasing eGFR quartiles (P for linear trend = 0.0371), while FGF23 was modestly higher in the higher quartiles of UACR (not statistically significant). The multiple linear regression analysis also showed a significant inverse association between FGF23 and eGFR (Model 1: β = -0.149, P = 0.0429; Model 2: β = -0.141, P = 0.0370). The association remained significant after adjustment for Pi. We identified that FGF23 was inversely associated with the eGFR in T1D patients with a normal or mildly impaired kidney function., Competing Interests: The authors have declared that no competing interests exist.

This study aimed to develop a simplified model for predicting end-stage kidney disease (ESKD) in patients with diabetes. The cohort included 2549 individuals who were followed up at Kyushu University Hospital (Japan) between January 1, 2008 and December 31, 2018. The outcome was a composite of ESKD, defined as an eGFR < 15 mL min -1 [1.73 m] -2 , dialysis, or renal transplantation. The mean follow-up was 5.6 [Formula: see text] 3.7 years, and ESKD occurred in 176 (6.2%) individuals. Both a machine learning random forest model and a Cox proportional hazard model selected eGFR, proteinuria, hemoglobin A1c, serum albumin levels, and serum bilirubin levels in a descending order as the most important predictors among 20 baseline variables. A model using eGFR, proteinuria and hemoglobin A1c showed a relatively good performance in discrimination (C-statistic: 0.842) and calibration (Nam and D'Agostino [Formula: see text] 2 statistic: 22.4). Adding serum albumin and bilirubin levels to the model further improved it, and a model using 5 variables showed the best performance in the predictive ability (C-statistic: 0.895, [Formula: see text] 2 statistic: 7.7). The accuracy of this model was validated in an external cohort (n = 5153). This novel simplified prediction model may be clinically useful for predicting ESKD in patients with diabetes., (© 2022. The Author(s).)

Fibroblast growth factor (FGF) 23 is a bone-derived phosphotropic hormone that regulates phosphate and vitamin D metabolism. FGF23 mainly affects kidney function via the FGF receptor (FGFR)/α-Klotho complex. The physiological roles of FGF23 and α-Klotho in the regulation of mineral homeostasis have been well established. In addition, recent studies have reported that FGF23 has various effects on many other tissues, sometimes in an α-Klotho-independent manner, especially under pathological conditions. However, how FGF23 works in these tissues without α-Klotho is not entirely clear. Here we review the recent reports concerning the actions of FGF23 on various tissues and discuss the remaining questions about FGF23., (Copyright © 2018 Elsevier Ltd. All rights reserved.)