摘要
End-stage renal disease(ESRD)significantly impacts patients’quality of life and poses substantial socioeconomic burdens.Dietary interventions are crucial for managing ESRD,yet high-quality evidence and analysis specifically linking diet to mortality outcomes is scarce.Methods:We conducted a comprehensive study involving 656 peritoneal dialysis(PD)patients over 12 years,with an average follow-up every 3 months.Dietary intake was meticulously recorded using a 3-day dietary record method,integrated with detailed health records and outcomes.We employed a 2-stage model to evaluate nonlinear relationships between dietary nutrients and mortality risk,accounting for various confounding factors.Findings:Our analysis revealed that 14 out of 26 nutritional elements lack guidelines for ESRD and PD patients,with 13 showing significant associations with mortality.For example,while guidelines suggest a dietary protein intake of 1.0 to 1.2 g/kg/d,our findings indicate an optimal range of 0.88 to 1.13 g/kg/d.Similarly,the recommended dietary energy intake of 25 to 35 kcal/kg/d was refined to 26 to 42 kcal/kg/d.We identified that 69% of dietary intake-outcome relationships are nonlinear,especially in patients with poor health status.Interpretations:Our study provides detailed dietary intake thresholds that correlate with improved prognosis in ESRD patients,enhancing current guidelines.The findings highlight the importance of personalized nutritional management and underscore the nonlinear nature of nutrient–disease relationships,particularly in severely ill patients.This approach can refine dietary recommendations and improve patient care in ESRD.
基金
supported by the National Natural Science Foundation of China(62402017 and 82470774)
Xuzhou Scientific Technological Projects(KC23143)
the Clinical Cohort Construction Program of Peking University Third Hospital(No.BYSYDL2023004)
Peking University Medicine plus X Pilot Program-Key Technologies R&D Project(2024YXXLHGG007)
the receipt of studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science(Grant Ref:218529/Z/19/Z).
