Health-related quality of life in end-stage renal disease patients: the effects of starting dialysis in the first year after the transition period.

BACKGROUND/AIMS: Prevalent dialysis patients have low scores of health-related quality of life (HRQOL) which are associated with increased risk of hospitalization and mortality. Also in CKD-5 non-dialysis patients, HRQOL scores seem to be lower as compared with the general population. This study firstly aimed to compare HRQOL between CKD-5 non-dialysis and prevalent dialysis patients in a cross-sectional analysis and to assess longitudinal changes over 1 year after the dialysis initiation. Secondly, the correlation between HRQOL and physical activity (PA) was explored. METHODS: Cross-sectional 44 CKD-5 non-dialysis, 29 prevalent dialysis, and 20 healthy controls were included. HRQOL was measured by Short Form-36 questionnaires to measure physical and mental domains of health expressed by the physical component summary (PCS) and mental component summary (MCS) scores. PA was measured by a SenseWear™ pro3. Longitudinally, HRQOL was assessed in 38 CKD-5 non-dialysis patients (who were also part of the cross-sectional analysis), before dialysis initiation until 1 year after dialysis initiation. RESULTS: PCS scores were significantly lower both in CKD-5 non-dialysis patients and in prevalent dialysis patients as compared with healthy controls (p < 0.001). MCS scores were significantly lower in both CKD-5 non-dialysis patients (p = 0.003), and in dialysis patients (p = 0.022), as compared with healthy controls. HRQOL scores did not change significantly from the CKD-5 non-dialysis phase into the first year after dialysis initiation. PA was significantly related to PCS in both CKD-5 non-dialysis patients (r = 0.580; p < 0.001), and dialysis patients (r = 0.476; p = 0.009). CONCLUSIONS: HRQOL is already low in the CKD-5 non-dialysis phase. In the first year after dialysis initiation, HRQOL did not change significantly. Given the correlation between PCS score and PA, physical activity programs may be potential tools to improve HRQOL in both CKD-5 non-dialysis as well as in prevalent dialysis patients.

Cardiovascular and Patient Phenotype of Extended Haemodialysis: A Critical Analysis of Studying a Unique Patient Population.

BACKGROUND: Extended haemodialysis (EHD) has been associated with better outcomes compared to conventional (CHD) regimes. The cardiovascular (CV) profile of these patients has not been assessed in detail. METHODS: We report baseline demographic and CV phenotype of 36 CHD and 36 EHD participants to a longitudinal multicentre study. We measured pulse wave velocity (PWV), 24-h ambulatory blood pressure, sublingual dark-field capillaroscopy and vascular biomarkers. RESULTS: EHD patients were younger (p < 0.01), with less CV comorbidity (p = 0.04) and higher dialysis vintage (p < 0.01). Higher PWV in CHD (p = 0.02) was not independent of demographic differences in the 2 groups. Biomarker profiles were similar in EHD and CHD but abnormal compared to healthy controls. CONCLUSION: Although CV profiles in these 2 cohorts were similar, EHD patients were distinct from the CHD population in terms of age and dialysis vintage and appear to comprise a unique group. Direct comparison of outcomes in these groups is challenging due to clinical bias.

Extracellular overhydration linked with endothelial dysfunction in the context of inflammation in haemodialysis dependent chronic kidney disease.

BACKGROUND: Haemodialysis (HD) patients are predisposed to dysregulated fluid balance leading to extracellular water (ECW) expansion. Fluid overload has been closely linked with outcome in these patients. This has mainly been attributed to cardiac volume overload, but the relation between abnormalities in fluid status with micro- and macrovascular dysfunction has not been studied in detail. We studied the interaction of macro- and microvascular factors in states of normal and over- hydration in HD-dependent CKD. METHODS: Fluid compartments [total body water (TBW) and ECW] and overhydration index (OH) were measured with Multifrequency bio-impedance (BCM). Overhydration was defined as OH/ECW>7%. Overhydration was also assessed using the ECW/TBW ratio. Macrocirculation was assessed by pulse-wave velocity (PWV) and mean arterial pressure (MAP) measurements while microcirculation through sublingual capillaroscopy assessment of the Perfused Boundary Region of the endothelial glycocalyx (PBR 5-25mcg). A panel of pro-inflammatory and vascular serum biomarkers and growth factors was analysed. RESULTS: Of 72 HD participants, 30 were in normohydration (N) range and 42 overhydrated according to the OH/ECW ratio. Average ECW/TBW was 0.48±0.03. Overhydrated patients had higher MAP (122.9±22.5 v 111.7±22.2mmHg, p = 0.04) and comorbidities (median Davies score 1.5 v 1.0, p = 0.03). PWV (p = 0.25) and PBR 5-25mcg (p = 0.97) did not differ between the 2 groups. However, Vascular Adhesion Molecule (VCAM)-1, Interleukin-6 and Thrombomodulin, and reduced Leptin were observed in the overhydrated group. Elevation in VCAM-1 levels (OR 1.03; 95% CI 1.01-1.06; p = 0.02) showed a strong independent association with OH/ECW>7% in an adjusted logistic regression analysis and exhibited a strong linear relationship with ECW/TBW (Bata = 0.210, p = 0.03) in an also adjusted model. CONCLUSION: Extracellular fluid overload is significantly linked to microinflammation and markers of endothelial dysfunction. The study provides novel insight in the cardiovascular risk profile associated with overhydration in uraemia.

Physical Activity in End-Stage Renal Disease Patients: The Effects of Starting Dialysis in the First 6 Months after the Transition Period.

OBJECTIVES: Physical inactivity in end-stage renal disease (ESRD) patients is associated with increased mortality, and might be related to abnormalities in body composition (BC) and physical performance. It is uncertain to what extent starting dialysis influences the effects of ESRD on physical activity (PA). This study aimed to compare PA and physical performance between stage 5 chronic kidney disease (CKD-5) non-dialysis and dialysis patients, and healthy controls, to assess alterations in PA during the transition from CKD-5 non-dialysis to dialysis, and to relate PA to BC. METHODS: For the cross-sectional analyses 44 CKD-5 non-dialysis patients, 29 dialysis patients, and 20 healthy controls were included. PA was measured by the SenseWear™ pro3. Also, the walking speed and handgrip strength (HGS) were measured. BC was measured by the Body Composition Monitor©. Longitudinally, these parameters were assessed in 42 CKD-5 non-dialysis patients (who were also part of the cross-sectional analysis), before the start of dialysis and 6 months thereafter. RESULTS: PA was significantly lower in CKD-5 non-dialysis patients as compared to that in healthy controls but not as compared to that in dialysis patients. HGS was significantly lower in dialysis patients as compared to that in healthy controls. Walking speed was significantly lower in CKD-5 non-dialysis patients as compared to that in healthy controls but not as compared to that in dialysis patients. Six months after starting dialysis, activity related energy expenditure (AEE) and walking speed significantly increased. CONCLUSIONS: PA is already lower in CKD-5 non-dialysis patients as compared to that in healthy controls and does not differ from that of dialysis patients. However, the transition phase from CKD-5 non-dialysis to dialysis is associated only with a modest improvement in AEE.

Body composition in dialysis patients: a functional assessment of bioimpedance using different prediction models.

OBJECTIVES: The assessment of body composition (BC) in dialysis patients is of clinical importance given its role in the diagnosis of malnutrition and sarcopenia. Bioimpedance techniques routinely express BC as a 2-compartment (2-C) model distinguishing fat mass (FM) and fat-free mass (FFM), which may be influenced by the hydration of adipose tissue and fluid overload (OH). Recently, the BC monitor was introduced which applies a 3-compartment (3-C) model, distinguishing OH, adipose tissue mass, and lean tissue mass. The aim of this study was to compare BC between the 2-C and 3-C models and assess their relation with markers of functional performance (handgrip strength [HGS] and 4-m walking test), as well as with biochemical markers of nutrition. METHODS: Forty-seven dialysis patients (30 males and 17 females) (35 hemodialysis, 12 peritoneal dialysis) with a mean age of 64.8 ± 16.5 years were studied. 3-C BC was assessed by BC monitor, whereas the obtained resistivity values were used to calculate FM and FFM according to the Xitron Hydra 4200 formulas, which are based on a 2-C model. RESULTS: FFM (3-C) was 0.99 kg (95% confidence interval [CI], 0.27 to 1.71, P = .008) higher than FFM (2-C). FM (3-C) was 2.43 kg (95% CI, 1.70-3.15, P < .001) lower than FM (2-C). OH was 1.4 ± 1.8 L. OH correlated significantly with ΔFFM (FFM 3-C - FFM 2-C) (r = 0.361; P < .05) and ΔFM (FM 3-C - FM 2-C) (r = 0.387; P = .009). HGS correlated significantly with FFM (2-C) (r = 0.713; P < .001), FFM (3-C) (r = 0.711; P < .001), body cell mass (2-C) (r = 0.733; P < .001), and body cell mass (3-C) (r = 0.767; P < .001). Both physical activity (r = 0.456; P = .004) and HGS (r = 0.488; P = .002), but not BC, were significantly related to walking speed. CONCLUSIONS: Significant differences between 2-C and 3-C models were observed, which are partly explained by the presence of OH. OH, which was related to ΔFFM and ΔFM of the 2-C and 3-C models, is therefore an important parameter for the differences in estimation of BC parameters of the 2-C and 3-C models. Both FFM (3-C) and FFM (2-C) were significantly related to HGS. Bioimpedance, HGS, and the 4-m walking test may all be valuable tools in the multidimensional nutritional assessment of both hemodialysis and peritoneal dialysis patients.