Thongprayoon, Charat

ABSTRACT Background Sodium–glucose co-transporter 2 inhibitors (SGLT2is) are emerging as an essential part of the standard of care for proteinuria in patients with chronic kidney disease. To date, no study has specifically evaluated the effects of body mass index (BMI) on the antiproteinuric efficacy of SGLT2is. Here we report the impact of BMI on the antiproteinuric efficacy of SGLT2is in non-diabetic patients with glomerular diseases. Methods This is a retrospective, multicentre, international observational cohort study that included non-diabetic patients with biopsy-proven glomerular disease and proteinuria >0.5 g/24 h who received SGLT2is between 2016 and 2023. Laboratory values, including proteinuria and estimated glomerular filtration rate (eGFR), were obtained at baseline and after 3–6 months. Changes in laboratory values over time were analysed using the paired signed-rank test. Results A total of 300 patients met the inclusion criteria. The median age was 51.82 years [interquartile range (IQR) 41–62.65], 64.7% were male and 92.7% were white. The most common glomerular disease was immunoglobulin A nephropathy (40.3%). The median eGFR was 52.26 ml/min/1.73 m2 (IQR 36.41–74.01), the median proteinuria was 1.60 g/24 h (IQR 1.15–2.91) and the median serum albumin was 4.08 g/dl (IQR 3.80–4.30). Proteinuria reduction after SGLT2i initiation was significant only in overweight and obese patients (P < .001 versus 0.18). Patients with normal BMI did not experience the expected early decrease in eGFR (P = .16). Conclusions SGLT2is are ineffective in proteinuria reduction in patients with a BMI <25 kg/m2, which contrasts with the significant proteinuria reduction in overweight and obese patients with glomerulopathies.

ABSTRACT Background Membranous nephropathy (MN) associated with phospholipase A2 receptor (PLA2R) antibodies is the most common cause of nephrotic syndrome in non-diabetic adult patients. This study investigated the relationship between clinical phenotypes and renal outcomes in this population, emphasizing the potential for phenotype-based treatment stratification. Methods We conducted a retrospective, multi-center cohort study of PLA2R-positive MN. Unsupervised cluster analysis grouped patients based on clinicopathological characteristics. Primary outcomes included complete or partial remission within 2 years of biopsy and end-stage kidney disease (ESKD) or death during follow-up. Results Among 178 patients, three distinct clusters emerged (n = 89, 70, and 19). Within 2 years of biopsy, 102 patients (57%) achieved complete or partial remission. Cluster 1, characterized by the mildest disease markers, including the lowest body mass index, serum anti-PLA2R titer, serum creatinine, proteinuria, and triglycerides, had the highest remission rate of 72%, compared with 50% in cluster 2 and 54% in cluster 3. Cluster 2 had significantly lower remission compared to cluster 1 (HR 0.64, 95% CI 0.42–0.96, P = .03); results were similar in cluster 3, albeit not statistically significant (HR 0.50, 95% CI 0.23–1.09, P = .08). Elevated anti-PLA2R levels (>100 U/ml) and proteinuria (>8 g/24-hour) predicted reduced remission (HR 0.56, 95% CI 0.36–0.88, P = .01; HR 0.43, 95% CI 0.26–0.73, P = .002, respectively), while high high-density lipoprotein levels were protective (HR 1.01, 95% CI 1.00–1.02, P = .048). Overall, 21 patients (12%) developed ESKD or died. ESKD-free survival differed significantly across clusters (log-rank test P = .04). Conclusions Unsupervised clustering identified distinct clinical phenotypes in PLA2R-positive MN, each associated with different renal prognoses. Phenotype-based risk stratification could enhance treatment precision, improve patient outcomes, and potentially reduce treatment-related adverse effects.

Background/Objectives: It is often challenging for patients to understand treatment options, their mechanisms of action, and the potential side effects of each treatment option for glomerular disorders. This study explored the ability of ChatGPT to simplify these treatment options to enhance patient understanding. Methods: GPT-4 was queried on sixty-seven glomerular disorders using two distinct queries for a general explanation and an explanation adjusted for an 8th grade level or lower. Accuracy was rated on a scale of 1 (incorrect) to 5 (correct and comprehensive). Readability was measured using the average of the Flesch–Kincaid Grade (FKG) and SMOG indices, along with the Flesch Reading Ease (FRE) score. The understandability score (%) was determined using the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P). Results: GPT-4’s general explanations had an average readability level of 12.85 ± 0.93, corresponding to the upper end of high school. When tailored for patients at or below an 8th-grade level, the readability improved to a middle school level of 8.44 ± 0.72. The FRE and PEMAT-P scores also reflected improved readability and understandability, increasing from 25.73 ± 6.98 to 60.75 ± 4.56 and from 60.7% to 76.8% (p < 0.0001 for both), respectively. The accuracy of GPT-4’s tailored explanations was significantly lower compared to the general explanations (3.99 ± 0.39 versus 4.56 ± 0.66, p < 0.0001). Conclusions: ChatGPT shows significant potential for enhancing the readability and understandability of glomerular disorder therapies for patients, but at a cost of reduced comprehensiveness. Further research is needed to refine the performance, evaluate the real-world impact, and ensure the ethical use of ChatGPT in healthcare settings.