RESUMO
Abstract The peritoneal effects of low-glucose degradation product (GDP)-containing peritoneal dialysis (PD) solutions have been extensively described. To systematically evaluate the efficacy and safety of low GDP solution for PD patients, specifically the effect on residual renal function (RRF) and dialysis adequacy, we conducted a meta-analysis of the published randomized controlled trials (RCTs). Different databases were searched for RCTs that compared low GDP-PD solutions with conventional PD solutions in the treatment of PD patients with continuous ambulatory peritoneal dialysis (CAPD) and automated peritoneal dialysis (APD). The outcomes of RCTs should include RRF and may include small solute clearance, peritoneal transport status, nutritional status, and all-cause mortality. Seven studies (632 patients) were included. Compared with the conventional solution, low-GDP solution preserved RRF in PD patients over time (MD 0.66 mL/min, 95% CI 0.34 to 0.99; p<0.0001), particularly in one year of treatment (p<0.01), and improved weekly Kt/V (MD 0.11, 95% CI 0.05 to 0.17; p=0.0007) without an increased 4-hour D/Pcr (MD 0.00, 95% CI -0.02 to 0.02; p=1.00). Notably, the MD of RRF and urine volume between the two groups tended to decrease as time on PD progressed up to 24 months. Patients using low GDP PD solutions did not have an increased risk of all-cause mortality (MD 0.97, 95% CI 0.50 to 1.88; p=0.93). Our meta-analysis confirms that the low GDP PD solution preserves RRF, improves the dialysis adequacy without increasing the peritoneal solute transport rate and all-cause mortality. Further trials are needed to determine whether this beneficial effect can affect long-term clinical outcomes.
Resumen Los efectos peritoneales de las soluciones de diálisis peritoneal (DP) que contienen productos de degradación bajos en glucosa (PIB) se han descrito ampliamente. Para evaluar sistemáticamente la eficacia y la seguridad de la solución de PIB bajo para pacientes en DP, específicamente el efecto sobre la función renal residual (RRF) y la adecuación de la diálisis, realizamos un metanálisis de los ensayos controlados aleatorios (ECA) publicados. Se realizaron búsquedas en diferentes bases de datos de ECA que compararan la solución de DP de bajo PIB con la solución de DP convencional en el tratamiento de pacientes con EP con CAPD y APD. Los resultados de los ECA deben incluir la RRF y pueden incluir la depuración de solutos pequeños, el estado nutricional, el estado del transporte peritoneal y la mortalidad por todas las causas. Se incluyeron siete estudios (632 pacientes). En comparación con la solución convencional, la solución de bajo PIB preservó la FRR en pacientes con EP a lo largo del tiempo (DM 0,66 mL/min, IC del 95%: 0,34 a 0,99; p<0,0001), particularmente en un año de tratamiento (p<0,01), y mejoró el Kt/V semanal (DM 0,11, IC del 95%: 0,05 a 0,17; p = 0,0007), sin un aumento de D/Pcr a las 4 horas (DM 0,00, IC del 95%: -0,02 a 0,02; p = 1,00). Los pacientes que usaron una solución para DP con bajo contenido de GDP no tuvieron un mayor riesgo de mortalidad por todas las causas (DM 0,97; IC del 95%: 0,50 a 1,88; p = 0,93). Nuestro metanálisis confirma que la solución de DP de bajo PIB preserva la FRR, mejora la adecuación de la diálisis sin aumentar la tasa de transporte peritoneal de solutos y la mortalidad por todas las causas. Se necesitan más ensayos para determinar si este efecto beneficioso puede afectar los resultados clínicos a largo plazo.
RESUMO
Astragali radix(AR,the dried root of Astragalus)is a popular herbal remedy in both China and the United States.The commercially available AR is commonly classified into premium graded(PG)and ungraded(UG)ones only according to the appearance.To uncover novel sensitive and specific markers for AR grading,we took the integrated mass spectrometry-based untargeted and targeted metabolomics ap-proaches to characterize chemical features of PG and UG samples in a discovery set(n=16 batches).A series of five differential compounds were screened out by univariate statistical analysis,including arginine,calycosin,ononin,formononetin,and astragaloside Ⅳ,most of which were observed to be accumulated in PG samples except for astragaloside Ⅳ.Then,we performed machine learning on the quantification data of five compounds and constructed a logistic regression prediction model.Finally,the external validation in an independent validation set of AR(n=20 batches)verified that the five com-pounds,as well as the model,had strong capability to distinguish the two grades of AR,with the pre-diction accuracy＞90％.Our findings present a panel of meaningful candidate markers that would significantly catalyze the innovation in AR grading.