Currently, a high prevalence of hypophosphatemia exists in Portuguese HD patients. This condition is associated with worst nutritional and body composition parameters. In the context of additional indices of malnutrition (low albumin, low BMI or severe overhydration), hypophosphatemic patients presented higher mortality risk.
Monitoring nutritional parameters is an integral part of hemodialysis (HD) patient treatment program. The purpose of this study was to evaluate the impact of the personalized nutritional counseling (PNC) on calcium-phosphorus metabolism, potassium, albumin, protein intake, interdialytic weight gain (IDWG), body composition parameters and fluid overload in HD patients. This was a multicenter longitudinal intervention study with 6 months of follow-up and 731 patients on maintenance HD from 34 dialysis units in Portugal were enrolled. Biochemical and body composition parameters were measured at baseline, 1, 3 and 6 months after the PNC. Patient's mean age was 64.9 (95% confidence interval [CI]: 63.8-66.0) years and mean HD time was 59.8 (95% CI: 55.3-64.3) months. Regarding data comparison collected before PNC vs. 6 months after, we obtained, respectively, the following results: patients with normalized protein catabolic rate (nPCR) ≥ 1 g/kg/day = 66.5% vs. 73.5% (P = 0.002); potassium > 5.5 mEq/L = 52% vs. 35.8% (P < 0.001); phosphorus between 3.5 and 5.5 mg/dL = 43.2% vs. 52.5% (P < 0.001); calcium/phosphorus (Ca/P) ratio ≤ 50 mg/dL = 73.2 % vs. 81.4% (P < 0.001); albumin ≥ 4.0 g/dL = 54.8% vs. 55% (P = 0.808); presence of relative overhydration = 22.4% vs. 25% (P = 0.283); IDWG > 4.5% = 22.3% vs. 18.2% (P = 0.068). PNC resulted in a significant decrease in the prevalence of hyperkalemia, hypophosphatemia and also showed amelioration in Ca/P ratio, nPCR and an increase in P of hyphosphatemic patients. Our study suggests that dietetic intervention contributes to the improvement of important nutritional parameters in patients receiving hemodialysis treatment.
Objective Evaluate which of two combinations of parameters based on International Society of Renal Nutrition and Metabolism recommendations could better identify patients with protein‐energy wasting (PEW) and to compare the relationship of these two combinations with other clinical and body composition parameters. Methods This was a multicentre longitudinal study with 24 months of follow‐up. The PEW patients were characterized by: Group A (GA) – normalized protein catabolic rate (nPCR) < 1.0 g/kg per day, albumin <3.8 g/dL and body cell mass index (BCMI) < 6.4 kg/m2 (n = 203); Group B (GB) – nPCR <1.0 g/kg per day, albumin <3.8 g/dL and body mass index (BMI) <23 kg/m2 (n = 109). All the patients who did not meet these requirements were considered “well‐nourished” (GA: n = 1818; GB: n = 3292). Results When compared to the well‐nourished patients, PEW patients in the GA presented higher age, Kt/V, C‐reactive protein, relative overhydration, fat tissue index (FTI); lower creatinine, albumin, nPCR, PTH, haemoglobin, phosphorus, calcium X phosphorus product, potassium, dry weight, BMI, BCMI, lean tissue index, %IDWG . In the GB, well‐nourished patients FTI was significantly higher. In Cox analysis, the combination with BCMI was a strong independent predictor of mortality in these patients (hazard ratio: 1.48; confidence interval: 1.00–2.19; P = 0.048), even after adjustment. Although GB combination seemed to be also a predictor of death (hazard ratio: 2.67; confidence interval: 1.92–3.71; P < 0.001), when adjusted, the association remained no longer significant. Conclusion A new combination of parameters including protein intake, albumin and BCMI demonstrated significant associations with other nutrition and inflammation parameters as well as with mortality.
Depending on the gender, different parameters such as protein intake, potassium, phosphorus, body mass index and albumin are associated with mortality in patients undergoing HD. Albumin <3.5 g/dl is an independent mortality predictor in both genders, whereas a body mass index <23 kg/m is an independent predictor of death, but only in men.
Introduction: Body cell mass (BCM) is a useful nutritional marker and is not affected by changes in the hydration status that commonly occur in hemodialysis (HD) patients. This study aimed to examine the association between body cell mass index (BCMI) and nutritional parameters, as well as its relationship with long-term survival in these patients.Methods: This longitudinal prospective multicenter study followed a cohort of patients in HD for 24 months. The clinical parameters of 2527 patients (mean age 70.3 AE 14.6 years, 55.8% male and mean HD vintage 58 (IQR:33-95) months) were measured and their body composition parameters were assessed by a body composition monitor before the HD session. BCM was converted to BCMI (BCM/height 2 ) and, taking into account the mean value (6.4 AE 2.1 kg/m 2 ), we split our study population into 2 groups: G1 (BCMI <6.4 kg/m 2 ; n = 1366) and G2 (BCMI ≥ 6.4 kg/m 2 ; n = 1161). All statistical tests were performed using SPSS 20.0 software. A P value lower than 0.05 was considered statistically significant.Findings: Patients with a BCMI <6.4 kg/m 2 displayed higher age (P < 0.001), dialysis adequacy (Kt/V) (P < 0.001), total cholesterol (TC) (P = 0.033), high-density lipoprotein cholesterol (HDL-C) (P < 0.001), relative overhydration (overhydration/extracellular water [OH/ECW]) (P < 0.001), CRP (P < 0.001), fat tissue index (FTI) (P < 0.001) and lower normalized protein equivalent of nitrogen appearance (nPNA) (P < 0.001), albumin (P < 0.001), serum creatinine (P < 0.001), creatinine index (P < 0.001), potassium (P < 0.001), phosphorus (P < 0.001), calcium/phosphorus product (Ca X P) (P < 0.001), lean tissue index (LTI) (P < 0.001) and body mass index (BMI) (P = 0.046). The Kaplan-Meier survival curve showed a significantly better survival in female and male patients with BCMI ≥6.4 kg/m 2 (P = 0.001 and P < 0.001, respectively). In the cox regression analysis, a significantly higher mortality risk was observed in G1 patients (P = 0.001).Discussion: Our study showed that a low BCMI was a mortality predictor and was associated with worse nutritional parameters in patients undergoing HD.
Background: Physical inactivity and muscle wasting potentiate each other and are highly prevalent among hemodialysis (HD) patients. The authors evaluated the association between physical activity (PA), clinical, nutritional, and body composition parameters in HD patients. Methods: Multicenter cross-sectional study with 581 HD patients. Clinical, body composition, dietary intake, and PA data were recorded. For the analysis, patients were divided into active (follow World Health Organization recommendations) and inactive groups. Results: A total of 20% of the patients followed World Health Organization recommendations on PA. Differences between physically active and physically inactive patients were observed in age, biochemical parameters and total body water, intracellular water, lean tissue index (LTI), body cell mass, energy, and protein intake. PA was a predictor of higher LTI, body cell mass, and energy intake independently of age, gender, presence of diabetes, dialysis adequacy, and dialysis vintage. Controlling for the effect of age, walking and vigorous PA were positively correlated with energy and protein intake. Vigorous PA was also positively correlated with LTI. Conclusion: The PA is a predictor of higher LTI, body cell mass, and energy intake. Vigorous PA is associated with an improved body composition and dietary pattern, whereas walking seems to be also associated with a favorable nutritional status.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.