Association of extracellular water/total body water ratio with protein-energy wasting and mortality in patients on hemodialysis

Phase angle is a useful predicting indicator for protein-energy wasting and cardiovascular risk among maintenance hemodialysis patients

Protein-energy wasting (PEW) is a major contributor to the high mortality among maintenance hemodialysis (MHD) patients. Cardiovascular disease (CVD) is the leading cause of death in dialysis patients, and PEW can significantly increase cardiovascular mortality in MHD patients. Previous studies have confirmed that PA may be a good objective indicator for determining the nutritional status and prognosis of MHD patients. Our study aimed to determine the predictive value of phase angle (PA) as detected by bioelectrical impedance analysis (BIA) on PEW and cardiovascular (CV) risk among MHD patients. Our retrospective observational study involved 161 adult patients with HD. The Cardiovascular risk score is a risk model based on the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS). We established LASSO logistic regression analysis model to identify key parameters related to body composition that can predict PEW in MHD patients. The area under the curve (AUC) values for PA, appendicular skeletal muscle mass index (ASMI), body cell mass (BCM), and mid-arm circumference (MAC) in predicting PEW in male MHD patients were relatively large, with 0.708, 0.674, 0.663, and 0.735, respectively. The predicted PEW values of these parameters were slightly lower in female patients than in men. We incorporated PA, ASMI, BCM, and MAC into a model that predicted the incidence of PEW in maintenance hemodialysis patients using LASSO technology. We discovered that the model predicted a greater AUC of PEW occurrence than any single factor, 0.877 for men and 0.76 for women. The results of the univariate logistic regression analysis showed that the low PA tertile array group had a greater incidence of PEW than the high PA group (P < 0.001). Additionally, we also found that lower PA was associated with higher CV risk scores. The PA detected by bioelectrical impedance analysis could predict the risk of PEW and cardiovascular events among patients with MHD. When used in conjunction, PA, ASMI, BCM, and MAC have a high diagnostic efficacy for PEW in patients on maintenance hemodialysis.

Investigation of accordance between nutritional assessment tools, and bio-electrical impedance-derived phase angle, with the global leadership initiative on malnutrition criteria in hemodialysis patients

BACKGROUND

Malnutrition (MN) is a major health concern for patients with chronic kidney disease (CKD) who receive maintenance hemodialysis (MHD). These patients are particularly vulnerable to MN due to their compromised health status, which in turn increases the risk of morbidity and mortality. However, there is limited evidence on the use of reliable and effective tools for assessing MN in this population. This lack of sufficient data highlights the crucial need to assess MN within these patients, considering the significant mortality risk it poses. The first aim of this study was to compare the concurrent validity of three nutritional methods: the 7-point Subjective Global Assessment (7p-SGA), the Nutritional Risk Index (NRI), and the Nutritional Risk Screening from 2002 (NRS-2002) with the Global Leadership Initiative on Malnutrition (GLIM) criteria in MHD patients. The second aim was to investigate the advantage of the bio-electrical impedance-derived phase angle (PhA) in predicting MN.

METHODS

one hundred sixty-eight outpatients (31% women) with a mean age of 56.9 ± 14.7 years and a median dialysis vintage of 48 months were included in this retrospective study. Nutritional scores, anthropometric measurements, biological markers, and body composition parameters were collected.

RESULTS

According to GLIM standards, MN was identified in 80% of these patients. Using logistic regression (LR) analysis, all nutritional scores were significantly associated with GLIM criteria, with optimal sensitivity (94.4%) and specificity (85.7%) for 7p-SGA and NRI, respectively. For discriminating the nutritional risk, the GLIM criteria demonstrated a good agreement with 7p-SGA (Kappa concordance coefficient (κ) = 0.677, p-value<0.001) with a good level of accuracy (Area Under the Curve (AUC) = 0.841; 95% Confidence Interval (CI) = 0.705-0.977; p-value <0.001) when compared to the NRI and NRS-2002 (κ = 0.522, p-value<0.001 and κ = 0.411, p-value = 0.006, respectively). An excellent accuracy was found between PhA and the GLIM-defined MN, with an optimal cut-off value of 5.5° for males and 4.5° for females. The LR showed that arm circumference is the parameter that most influences the decrease of PhA (odds ratio 2.710, 95% CI = 1.597-4.597, p-value <0.001).

CONCLUSION

Based on the results of the present study, 7p-SGA is the most sensitive score in identifying MN diagnosed by GLIM criteria. Nonetheless, NRI exhibits greater specificity. PhA is a valuable marker for MN in MHD patients.

Tirzepatide Reduces Fat Mass and Provides Good Glycaemic Control in Type 2 Diabetes Patients Undergoing Haemodialysis: A Single-Centre Retrospective Study

OBJECTIVE

Tirzepatide is an injectable peptide approved by the US Food and Drug Administration for the treatment of Type 2 diabetes (T2DM). Its weight-loss effect primarily targets fat reduction; however, such effect on patients with chronic kidney disease (CKD) undergoing haemodialysis (HD) has not been reported.

METHODS

Nine patients with CKD undergoing HD received weekly tirzepatide doses (2.5-7.5 mg) once a week. Evaluations encompassed tirzepatide's impact on dry weight (DW) and body composition assessed at baseline and study conclusion using bioelectrical impedance analysis. This longitudinal study included nine patients, with a median age of 53 years and median HD duration of 4 years.

RESULTS

Tirzepatide treatment significantly decreased glycated albumin compared with the value at baseline (22.7 ± 5.4 vs. 18.3 ± 2.5%, p = 0.028, respectively). Significant reductions were observed in DW (-1.0 kg, p = 0.024) and body mass index (-0.6 kg/m2, p = 0.050) following tirzepatide administration. Total fat mass was also reduced, but not significantly (- 2.51% from baseline, p = 0.214). In contrast, skeletal muscle mass was not decreased (-1.02% from baseline, p = 0.722). No serious side effects other than nausea were observed during the study period.

CONCLUSION

Tirzepatide effectively provides good glycaemic control in T2DM patients undergoing HD, decreasing DW by reducing body fat mass without increasing frailty risk.

Development and validation of a model for predicting the risk of cardiovascular events in maintenance hemodialysis patients

The mortality rates for patients undergoing hemodialysis (HD) remain unacceptably high compared to the general population, and more specific information about the causes of death is not known. The study aimed to develop and validate a risk prediction model that uses common clinical factors to predict the probability of cardiovascular events in maintenance hemodialysis (MHD) patients. The study involved 3488 adult patients who received regular scheduled hemodialysis treatment at 20 hemodialysis centers in southwest China between June 2015 and August 2020, with follow-up until August 2021. The optimal parameter set was identified by multivariable Cox regression analyses and Cross-LASSO regression analyses and was used to establish a nomogram for predicting the risk of cardiovascular events in maintenance hemodialysis patients at 3 and 5 years. The performance of the model was evaluated using the consistency index (Harrell's C-index), the area under the receiver operating characteristic (ROC) curve, and calibration plots. The model was validated by tenfold cross-validation and bootstrapping with 1000 resamples. In the derivation cohort, the model yields an AUC of 0.764 [95% confidence interval (CI), 0.737-0.790] and 0.793 [CI, 0.757-0.829] for predicting the risk of cardiovascular events of MHD patients at 3 and 5 years. In the internal validation cohort AUC of 0.803 [95% CI, 0.756-0.849], AUC of 0.766 [95% CI, 0.686-0.846], and the external validation cohort AUC of 0.826 [95% CI, 0.765-0.888], AUC of 0.817 [95% CI, 0.745-0.889] at 3 and 5 years. The model's calibration curve is close to the ideal diagonal. By tenfold cross-validation analyses, the 3- and 5-year risk of cardiovascular events (AUC 0.732 and 0.771, respectively). By the bootstrap resampling method, the derivation cohort and validation cohort (Harrell's C-index 0.695 and 0.667, respectively) showed good uniformity with the model. The constructed model accurately predicted cardiovascular events of MHD patients in the 3rd and 5th years after dialysis. And the further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.