Abstract: SA-PO0558
Overweight/Obesity and Kidney Prognosis in ADPKD: Insights from Attribute-Based Cross-Classification by Sex and Age
Session Information
- Cystic Kidney Diseases: Clinical Research
November 08, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1201 Genetic Diseases of the Kidneys: Monogenic Kidney Diseases
Author
- Ushio, Yusuke, Tokyo Joshi Ika Daigaku, Shinjuku, Tokyo, Japan
Background
Overweight and obesity may contribute to disease progression in autosomal dominant polycystic kidney disease (ADPKD), but its prognostic impact remains unclear, particularly across sex and age groups. This study investigated its influence on renal outcomes using an Attribute-Based Medicine (ABM) approach with sex–age cross-classification.
Methods
We analyzed 553 ADPKD patients not receiving renal replacement therapy (median age: 43 years; eGFR: 55.9 mL/min/1.73 m2; total kidney volume: 1335.4 mL). Patients were cross-classified by sex (men/women) and age (<50/≥50 years). The renal outcome—≥30% eGFR decline or initiation of renal replacement therapy—was assessed using Cox regression. The mean follow-up was 6.9 years; 266 patients experienced renal events.
Results
Overweight/obesity was significantly associated with poorer renal outcomes (HR=1.43, P=0.042). No significant interaction was found between overweight/obesity and age ≥50 in either sex (interaction P=0.928 in men, P=0.168 in women), although women showed a trend toward greater age-related impact. Cross-classification revealed that overweight/obesity was strongly associated with poorer renal outcomes in women <50 years (HR=3.20, P=0.004), and to a lesser extent in men <50 years (HR=1.82, P=0.037). No significant associations were observed in either sex ≥50 years. These findings suggest a greater impact in younger patients, particularly women.
Conclusion
Overweight and obesity are important modifiable risk factors for renal progression in ADPKD, particularly in younger patients. Attribute-Based Medicine (ABM) using cross-classification by sex and age provides novel insights into individualized risk stratification.