Hypertriglyceridemia and Kidney Prognosis in ADPKD: Insights from Attribute-Based Cross-Classification by Sex and Age

Background: The 2024 KDIGO conference emphasized shifting from a one-size-fits-all model to individualized CKD care. Hypertriglyceridemia may contribute to disease progression in autosomal dominant polycystic kidney disease (ADPKD), but its prognostic impact remains unclear, especially across sex and age groups. This study examined its effect 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). Hypertriglyceridemia was defined as serum triglycerides ≥150 mg/dL or use of lipid-lowering agents. 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 via Cox regression. Interaction between hypertriglyceridemia and age ≥50 was evaluated in multivariable models. Mean follow-up was 6.9 years; 266 patients experienced renal events. Results: Hypertriglyceridemia was not linked to worse renal prognosis in the overall cohort (HR=1.06, P=0.719). No significant interaction was seen between hypertriglyceridemia and age ≥50 in either sex (interaction P=0.452 men, 0.678 women). Cross-classification showed a significant and clinically relevant association between hypertriglyceridemia and poor outcomes in women <50 years (HR=3.15, P=0.018). No significant associations appeared in other subgroups. These findings suggest hypertriglyceridemia may detrimentally affect renal prognosis in younger women with ADPKD, underscoring the value of attribute-based risk assessment. Conclusion: Hypertriglyceridemia may be a modifiable risk factor for renal progression in ADPKD, especially in younger women. Attribute-based cross-classification offers useful insight for personalized risk. Funding: NIDDK SupportAttribute-Based Cross-Classification Analysis by Sex and Age