TY - JOUR
AU - Fouque,, Denis
AB - Abstract Background It has been a long-standing clinical concern that haemodialysis (HD) patients on afternoon shifts (ASs) are more prone to protein-energy wasting (PEW) than those on morning shifts (MSs), as their dialysis scheme and post-dialysis symptoms may interfere with meal intake. We evaluated the effect of time of day of HD on the evolution of body composition changes and PEW surrogates. Methods We conducted a retrospective study among 9.963 incident HD patients treated in NephroCare centres (2011–16); data were routinely collected in the European Clinical Database. The course of multi-frequency bioimpedance determined lean and fat tissue indices (LTI and FTI) between patients in MSs/ASs over 2 years were compared with linear mixed models. Secondary PEW indicators were body mass index, albumin, creatinine index and normalized protein catabolic rate. Models included fixed (age, sex, vascular access and diabetes mellitus) and random effects (country and patient). Results Mean baseline LTI and FTI were comparable between MSs (LTI: 12.5 ± 2.9 kg/m2 and FTI: 13.7 ± 6.0 kg/m2) and ASs (LTI: 12.4 ± 2.9 kg/m2 and FTI: 13.2 ± 6.1 kg/m2). During follow-up, LTI decreased and FTI increased similarly, with a mean absolute change (baseline to 24 months) of −0.3 kg/m2 for LTI and +1.0 kg/m2 for FTI. The course of these malnutrition indicators did not differ between dialysis shifts (P for interaction ≥0.10). We also did not observe differences between groups for secondary PEW indicators. Conclusions This study suggests that a dialysis shift in the morning or in the afternoon does not impact the long-term nutritional status of HD patients. Regardless of time of day of HD, patients progressively lose muscle mass and increase body fat. body composition, dialysis shift, fat tissue index, lean tissue index, protein-energy wasting KEY LEARNING POINTS What is already known about this subject? the timing of medical therapies impacts on clinical outcomes and side effects in the treatment of many diseases; and it been a long-standing clinical concern that haemodialysis (HD) patients on afternoon shifts are more prone to protein-energy wasting (PEW) than those on morning shifts. What this study adds? having a dialysis shift in the morning or in the afternoon does not impact long-term nutritional status of HD patients; and regardless of time of day of HD, patients progressively lose muscle mass and increase body fat. What impact this may have on practice or policy? this study refutes the idea that the timing of day of HD impacts on the risk of PEW. INTRODUCTION In line with the central role of cyclical rhythms and biological clocks in human physiology [1, 2], the timing of medical therapies impacts on clinical outcomes and side effects in the treatment of diseases such as cancer, hypertension or hyperparathyroidism [3–5]. It is reasonable to hypothesize that such an invasive treatment as maintenance haemodialysis (HD) likely interferes with the patient’s circadian rhythm, given its impact on volume and acid–base regulation, electrolyte composition, medication levels and volume status [6]. Patients on renal replacement therapy typically undergo HD during the morning or afternoon, with time of treatment generally based on space availability or patient preference. Some [7, 8] but not all [6, 9] studies suggest that there may be survival differences between patients assigned to these two shifts. The time of day of HD has also been suggested to impact on other intermediate factors, such as circadian variation of body temperature [10], potassium and phosphorus control [11], intradialytic blood pressure [12] or insomnia/quality of sleep [13, 14]. The protein-energy wasting (PEW) syndrome, resulting in gradual losses of body reserves, is present in 30–50% of dialysis patients [15–17] and contributes to worsening of quality of life, morbidity and mortality [18, 19]. It has been a long-standing clinical concern that HD patients on afternoon shifts (ASs) are more prone to PEW than those on morning shifts (MSs), as their dialysis scheme and post-dialysis symptoms may interfere with the meal intake of lunch and dinner [20]. In contrast, patients on MSs are usually back at home for lunch and their post-dialysis symptoms in the evening are less pronounced. We are not aware of any study confirming or refuting this clinical concern and we set about investigating possible effects of the timing of day of HD on body composition changes and biochemical surrogates of PEW over time. MATERIALS AND METHODS Data source This project utilizes pseudonymized patient data from the European Clinical Database (EuCliD®; Figure 1). EuCliD® is a clinical information system that was developed by Fresenius Medical Care (FME) two decades ago and which is implemented across NephroCare clinics. EuCliD® is part of the FME’s quality improvement and management programmes, and gathers routinely collected medical information including demographics, medical history and comorbidities, laboratory data, dialysis treatment information, vascular access information and clinical outcome data [21, 22]. All clinical data were collected according to standardized clinical protocols and procedures of the NephroCare clinics [21, 22]. All patients provided written informed consent on secondary use of their clinical data for research purposes. The analysis was performed in adherence with the Declaration of Helsinki. FIGURE 1 Open in new tabDownload slide Flow chart with patient numbers. FIGURE 1 Open in new tabDownload slide Flow chart with patient numbers. Study population For this retrospective study, we included incident HD patients treated in 661 NephroCare centres in Europe, Middle-East and Africa (EMEA) and Latin America. Patient selection is presented in Figure 1. Because body composition monitor (BCM) measurements were introduced on a regular basis in 2011, we selected patients starting HD between 1 January 2011 and 30 June 2014. All incident HD patients having at least one BCM measurement available were included in the present study. We then excluded patients who dropped out within the first 6 months [due to death, kidney transplantation, centre change, treatment change (including change to peritoneal dialysis, home HD, treatment stop and spontaneous recovery), any unspecified reasons or lost to follow-up], patients <18 years, patients with metastatic solid tumours [International Classification of Diseases, version 10 (ICD-10): C77–C80], patients treated less or more than three times weekly, patients treated during other shifts than during morning or afternoon (e.g. evening shifts) and patients with missing baseline information on lean tissue index (LTI) and fat tissue index (FTI) as well as on vascular access. After applying these selection criteria, our study population amounted 9963 patients for the primary outcome analyses, coming from the following countries: Argentina (n = 909), Bosnia (n = 250), Brazil (n = 311), Chile (n = 225), Colombia (n = 570), Croatia (n = 24), Czech Republic (n = 311), Estonia (n = 4), France (n = 243), Hungary (n = 257), Italy (n = 470), Poland (n = 1356), Portugal (n = 871), Romania (n = 1254), Russia (n = 637), Serbia (n = 51), Slovakia (n = 150), Slovenia (n = 99), South Africa (n = 34), Spain (n = 861), Turkey (n = 1026) and the UK (n = 50). The index date of the study (baseline) was set at Month 6 after HD initiation (dialysis vintage: 6 months), and information recorded in the preceding 3 months was used to derive study covariates (Figure 2). FIGURE 2 Open in new tabDownload slide Study design. Baseline was defined as the end of the baseline period 6 months after RRT initiation. Exposure (MS and AS) was defined within the last three months of the baseline period. All patients were followed from baseline for 2 years or until drop out. RRT, renal replacement therapy. FIGURE 2 Open in new tabDownload slide Study design. Baseline was defined as the end of the baseline period 6 months after RRT initiation. Exposure (MS and AS) was defined within the last three months of the baseline period. All patients were followed from baseline for 2 years or until drop out. RRT, renal replacement therapy. Study covariates All study covariates are presented as of index date. Study covariates were extracted from EuCliD® and we used records at index date or closest to index date up to 3 months prior. Study covariates included demographic information, co-morbidities, vascular access information, treatment modality and parameters, blood pressure, laboratory and body composition data. ICD-10 codes are used to classify disease information. Diabetes mellitus was defined as E10, E11, E12, E13, E14; cardiovascular disease was defined as G45, G46, H34.0, I09.9, I11.0, I13.0, I13.2, I21, I22, I25.2, I25.5, I42.0, I42.5–I42.9, I43, I50, I60–I69, I70, I71, I73.1, I73.8, I73.9, I77.1, I79.0, I79.2, K55.1, K55.8, K55.9, P29.0, Z95.8, Z95.9; and liver disease as B18, I85.0, I85.9, I86.4, I98.2, K70.0–K70.4, K70.9, K71.1, K71.3–K71.5, K71.7, K72.1, K72.9, K73, K74, K76.0, K76.2–K76.7, K76.8, K76.9, Z94.4. Vascular access types include arteriovenous fistula, graft and central vascular catheter (including temporary, tunnelled and untunnelled central venous catheter). Presented blood pressure was measured before dialysis. Albumin was measured with bromocresol green; in case of bromocresol purple measurements, the values were adjusted by adding 5.5 g/L [23]. Creatinine index was calculated according to Canaud et al. [24]. Body mass index (BMI) was calculated based on the post-dialysis body weight. LTI and FTI were determined with BCM, which is routinely applied in NephroCare clinics. BCM applies the bioimpedance spectroscopy technique and expresses the body composition as a three-compartment model, thus providing information about lean tissue, fat tissue and fluid overload [25, 26]. LTI and FTI represent the respective tissue masses normalized to height squared. BCM measurements are in general performed pre-dialysis throughout the week and on regular intervals in NephroCare clinics (1.6 ± 1.2 months in our study population). These measurements are standardized across all NephroCare centres and are performed by trained personal. Exposure definition The study exposures are being on MS or AS. MS was defined as dialysis start between 6:00 am and 9:00 am, whereas AS was defined as dialysis start between 11:00 am and 2:00 pm. Patients were considered in one or another shift if at least 90% of all treatments recorded in the 3 months prior to baseline were performed in the respective shift. Changes in treatment shifts during follow-up were not considered but we expect this to minimally affect our results, as on average 89% of the treatments during follow-up were performed in the respective dialysis shift as defined at baseline. Outcome assessment The primary outcome of the study is the changes in LTI and FTI over the 2-year follow-up. As secondary outcomes, we considered other PEW surrogate indicators including BMI, albumin, creatinine index and normalized protein catabolic rate (nPCR). Of note, for our secondary outcome analyses, patients with missing secondary outcome information at baseline were excluded. All patients were then followed for 2 years or until drop out due to death, kidney transplantation, centre change, treatment change (including change to peritoneal dialysis, home HD, treatment stop and spontaneous recovery), any unspecified reasons or lost to follow-up. Statistical analyses Summary statistic measures for patient and treatment characteristics as of baseline were calculated by descriptive statistics. Normally distributed variables are presented as mean ± standard deviation (SD); not normally distributed variables are summarized as median with 25th and 75th percentiles. To compare both treatment groups at baseline, we used the Student’s t-test for normally distributed continuous variables, the Wilcoxon rank-sum test for not normally distributed continuous variables and the Pearson’s Chi-squared test for dichotomous variables. To display the changes of the primary (LTI and FTI) and secondary (BMI, albumin, creatinine index and nPCR) outcome variables over time, the absolute change to baseline (delta) was calculated for each patient and 1-month intervals. The last available value per interval was used for calculation. Missing values were not replaced. Number of patients with available measurements is presented every 3 months. Descriptive statistics were calculated for the delta of each parameter and interval and presented graphically as mean ± SD. By using inferential statistical analysis, we first evaluated the changes of LTI and FTI in the total cohort over the 2-year follow-up. We applied for each of the two outcomes a linear mixed model including time as fixed effect as well as country and patient as random effect. We used an autoregressive process of first order as covariance structure to consider the specific structure of the repeated measurements in irregular time intervals. As both outcomes were not normally or log-normally distributed, they were transformed with Box–Cox transformation [27]. Additionally, we compared the course of LTI and FTI between patients treated in the MS and AS groups with inferential statistical analysis. By calculating the ‘treatment × time’ interaction term of the linear mixed model, we tested the two-sided hypothesis that the linear slope of the respective parameter over the 2-year follow-up differs between the MS versus AS against the null hypothesis that the slopes of the two treatment groups are parallel. In addition, the linear mixed models included fixed effects [treatment group (morning versus afternoon), time (1 month) and the factors age (years), sex (male), vascular access (catheter yes) and diabetes mellitus as main effects and each in interaction with time] and the same random effects and correlation structure as described in the first model. Also, in these two models, the outcomes were transformed with Box–Cox transformation. Inferential statistical analysis was also performed for the secondary outcomes. Subgroup analyses Descriptive statistics of the changes of LTI and FTI over time were performed in a subgroup analysis of patients from different regions with presumably similar eating patterns: (i) Mediterranean region: Bosnia, Croatia, Italy, Portugal, Serbia, Slovenia, Spain, Turkey and France; (ii) North and East Europe: Estonia, Poland, Russia, Czech Republic, Hungary, Romania, Slovakia and the UK; and (iii) Latin America: Argentina, Brazil, Chile and Colombia. Due to the low number of patients from South Africa (n = 34), no subgroup analysis was performed for these patients. The statistical software SAS (version 9.4, SAS Institute Inc.) was used for all analyses. RESULTS Of 9963 patients within the study cohort, 4858 patients were treated during MSs and 5105 during ASs (Figure 1). Most patients came from the EMEA region (79.8%) and the remaining (20.2%) from Latin America. In EMEA, the number of patients was balanced between MS (52.0%) and AS (48.1%), whereas in Latin America, more patients were treated during ASs (63.8%). Baseline characteristics overall and by MS/AS are presented in Table 1. When comparing both treatment groups, a higher proportion of women was treated during the afternoon (44.1%) compared with MSs (39.5%). Moreover, patients treated in the afternoon were on average 2.3 years older and had slightly more catheters as vascular access (24.3% versus 22.5%). Additionally, conventional HD was slightly more common in the ASs (68.7% versus 62.0%) and, accordingly, a lower frequency of haemodiafiltration (31.0% versus 37.7%). Primary and secondary outcome parameters did not differ substantially at baseline, except for BMI being slightly more elevated in patients of the AS. Table 1 Baseline characteristics of the study population Parameter . Total (n = 9963) . MS (n = 4858) . AS (n = 5105) . Absolute difference . P-value . Age, years 64 ± 15 62 ± 15 65 ± 14 3 <0.001 Gender: female, % 42 40 44 4 <0.001 Comorbidities  Diabetes mellitus, % 30 29 30 1 0.2  Cardiovascular disease, % 29 29 29 0 0.5  Liver disease, % 6 6 5 1 0.10 Vascular access, % 0.10  Arteriovenous fistula 74 75 73 2  Graft 3 3 3 0  Central vascular catheter 23 23 24 1 Treatment modality, % <0.001  HD 65.4 62.0 68.7 6.7  Online haemodiafiltration 34.3 37.7 31.0 6.7  Other 0.3 0.3 0.3 0 Treatment parameter  OCM Kt/V 1.6 ± 0.4 1.6 ± 0.4 1.5 ± 0.4 0.1 <0.001  Effective treatment time, min 249 ± 18 250 ± 17 248 ± 18 2 <0.001  Convection volume, La 25 ± 8 25 ± 8 25 ± 8 0 0.8 Dialysate sodium, % <0.001  138 mmol/L 69 68 71 3  140 mmol/L 28 30 26 4  Other 3 2 4 2 Dialysate potassium, % <0.001  1.5 mmol/L 8 10 6 4  2.0 mmol/L 56 54 57 3  3.0 mmol/L 33 33 34 1  Other 4 4 4 0 Dialysate calcium, % 0.12  1.25 mmol/L 20 20 20 0  1.50 mmol/L 74 74 74 0  1.75 mmol/L 4 4 4 0  Other 2 2 2 0 Dialysate bicarbonate, % <0.001  31 mmol/L 6 5 7 2  32 mmol/L 76 77 76 1  35 mmol/L 15 16 14 2  Other 3 3 3 0 Body composition  LTI, kg/m² 12.4 ± 2.9 12.5 ± 2.9 12.4 ± 2.9 0.1 0.01  FTI, kg/m² 13.5 ± 6.0 13.7 ± 6.0 13.2 ± 6.1 0.5 <0.001  BMI, kg/m² 26.3 ± 5.4 26.5 ± 5.4 26.0 ± 5.4 0.5 <0.001  Fluid overload, L 2.3 ± 1.9 2.1 ± 1.8 2.5 ± 1.9 0.4 <0.001  Body weight, kg 71 ± 16 72 ± 16 70 ± 16 2 <0.001 Blood pressure, mmHg  Systolic 138 ± 24 139 ± 24 136 ± 23 3 <0.001  Diastolic 71 ± 14 72 ± 14 71 ± 14 1 <0.001 Lab values  Albumin, g/L 38.5 ± 4.3 38.5 ± 4.3 38.4 ± 4.4 0.1 0.6  Creatinine, mg/dL 7.0 ± 2.3 7.2 ± 2.4 6.9 ± 2.3 0.3 <0.001  Creatinine index, mg/kg/day 18.6 ± 2.5 18.8 ± 2.5 18.4 ± 2.5 0.4 <0.001  nPCR, g/kg/day 1.1 ± 0.3 1.1 ± 0.3 1.0 ± 0.3 0.1 0.002  CRP, mg/L 4.6 (1.9–11.3) 4.4 (1.8– 11.0) 4.9 (2.0–12.0) 0.5 0.04  Haemoglobin, g/dL 11.2 ± 1.5 11.2 ± 1.5 11.1 ± 1.5 0.1 0.001  Ferritin, µg/L 373 (204–627) 361 (202–612) 384 (206–644) 23 0.01  Uric acid, mg/dL 5.9 ± 1.5 6.0 ± 1.5 5.9 ± 1.5 0.1 0.3  Sodium, mmol/L 138 ± 4 138 ± 4 138 ± 4 0 <0.001  Phosphate, mmol/L 1.5 ± 0.5 1.6 ± 0.5 1.5 ± 0.4 0.1 <0.001  Calcium, mmol/L 2.2 ± 0.2 2.2 ± 0.2 2.2 ± 0.2 0 0.03  Total cholesterol, mg/dL 172 ± 44 169 ± 43 174 ± 44 5 <0.001  HDL cholesterol, mg/dL 44 ± 15 44 ± 15 44 ± 15 0 0.4  LDL cholesterol, mg/dL 100 ± 37 100 ± 37 101 ± 38 1 0.5  Triglycerides, mg/dL 135 (97–187) 129 (93–176) 143 (104–200) 14 <0.001 Parameter . Total (n = 9963) . MS (n = 4858) . AS (n = 5105) . Absolute difference . P-value . Age, years 64 ± 15 62 ± 15 65 ± 14 3 <0.001 Gender: female, % 42 40 44 4 <0.001 Comorbidities  Diabetes mellitus, % 30 29 30 1 0.2  Cardiovascular disease, % 29 29 29 0 0.5  Liver disease, % 6 6 5 1 0.10 Vascular access, % 0.10  Arteriovenous fistula 74 75 73 2  Graft 3 3 3 0  Central vascular catheter 23 23 24 1 Treatment modality, % <0.001  HD 65.4 62.0 68.7 6.7  Online haemodiafiltration 34.3 37.7 31.0 6.7  Other 0.3 0.3 0.3 0 Treatment parameter  OCM Kt/V 1.6 ± 0.4 1.6 ± 0.4 1.5 ± 0.4 0.1 <0.001  Effective treatment time, min 249 ± 18 250 ± 17 248 ± 18 2 <0.001  Convection volume, La 25 ± 8 25 ± 8 25 ± 8 0 0.8 Dialysate sodium, % <0.001  138 mmol/L 69 68 71 3  140 mmol/L 28 30 26 4  Other 3 2 4 2 Dialysate potassium, % <0.001  1.5 mmol/L 8 10 6 4  2.0 mmol/L 56 54 57 3  3.0 mmol/L 33 33 34 1  Other 4 4 4 0 Dialysate calcium, % 0.12  1.25 mmol/L 20 20 20 0  1.50 mmol/L 74 74 74 0  1.75 mmol/L 4 4 4 0  Other 2 2 2 0 Dialysate bicarbonate, % <0.001  31 mmol/L 6 5 7 2  32 mmol/L 76 77 76 1  35 mmol/L 15 16 14 2  Other 3 3 3 0 Body composition  LTI, kg/m² 12.4 ± 2.9 12.5 ± 2.9 12.4 ± 2.9 0.1 0.01  FTI, kg/m² 13.5 ± 6.0 13.7 ± 6.0 13.2 ± 6.1 0.5 <0.001  BMI, kg/m² 26.3 ± 5.4 26.5 ± 5.4 26.0 ± 5.4 0.5 <0.001  Fluid overload, L 2.3 ± 1.9 2.1 ± 1.8 2.5 ± 1.9 0.4 <0.001  Body weight, kg 71 ± 16 72 ± 16 70 ± 16 2 <0.001 Blood pressure, mmHg  Systolic 138 ± 24 139 ± 24 136 ± 23 3 <0.001  Diastolic 71 ± 14 72 ± 14 71 ± 14 1 <0.001 Lab values  Albumin, g/L 38.5 ± 4.3 38.5 ± 4.3 38.4 ± 4.4 0.1 0.6  Creatinine, mg/dL 7.0 ± 2.3 7.2 ± 2.4 6.9 ± 2.3 0.3 <0.001  Creatinine index, mg/kg/day 18.6 ± 2.5 18.8 ± 2.5 18.4 ± 2.5 0.4 <0.001  nPCR, g/kg/day 1.1 ± 0.3 1.1 ± 0.3 1.0 ± 0.3 0.1 0.002  CRP, mg/L 4.6 (1.9–11.3) 4.4 (1.8– 11.0) 4.9 (2.0–12.0) 0.5 0.04  Haemoglobin, g/dL 11.2 ± 1.5 11.2 ± 1.5 11.1 ± 1.5 0.1 0.001  Ferritin, µg/L 373 (204–627) 361 (202–612) 384 (206–644) 23 0.01  Uric acid, mg/dL 5.9 ± 1.5 6.0 ± 1.5 5.9 ± 1.5 0.1 0.3  Sodium, mmol/L 138 ± 4 138 ± 4 138 ± 4 0 <0.001  Phosphate, mmol/L 1.5 ± 0.5 1.6 ± 0.5 1.5 ± 0.4 0.1 <0.001  Calcium, mmol/L 2.2 ± 0.2 2.2 ± 0.2 2.2 ± 0.2 0 0.03  Total cholesterol, mg/dL 172 ± 44 169 ± 43 174 ± 44 5 <0.001  HDL cholesterol, mg/dL 44 ± 15 44 ± 15 44 ± 15 0 0.4  LDL cholesterol, mg/dL 100 ± 37 100 ± 37 101 ± 38 1 0.5  Triglycerides, mg/dL 135 (97–187) 129 (93–176) 143 (104–200) 14 <0.001 Data are presented as mean ± SD, median (q1–q3) or %, as appropriate. Clinical data within the last 3 months of the baseline period were used. Statistical analysis was performed with the t-test for normally distributed continuous variables, with the Wilcoxon rank-sum test for not normally distributed continuous variables and with the Pearson’s Chi-squared test for dichotomous variables. a Only for HDF patients. OCM, online clearance monitoring; CRP, C-reactive protein; HDL, high-density lipoprotein; LDL, low-desitiy lipoprotein; HDF, hemodiafiltration; ESKD, end-stage kidney disease. Open in new tab Table 1 Baseline characteristics of the study population Parameter . Total (n = 9963) . MS (n = 4858) . AS (n = 5105) . Absolute difference . P-value . Age, years 64 ± 15 62 ± 15 65 ± 14 3 <0.001 Gender: female, % 42 40 44 4 <0.001 Comorbidities  Diabetes mellitus, % 30 29 30 1 0.2  Cardiovascular disease, % 29 29 29 0 0.5  Liver disease, % 6 6 5 1 0.10 Vascular access, % 0.10  Arteriovenous fistula 74 75 73 2  Graft 3 3 3 0  Central vascular catheter 23 23 24 1 Treatment modality, % <0.001  HD 65.4 62.0 68.7 6.7  Online haemodiafiltration 34.3 37.7 31.0 6.7  Other 0.3 0.3 0.3 0 Treatment parameter  OCM Kt/V 1.6 ± 0.4 1.6 ± 0.4 1.5 ± 0.4 0.1 <0.001  Effective treatment time, min 249 ± 18 250 ± 17 248 ± 18 2 <0.001  Convection volume, La 25 ± 8 25 ± 8 25 ± 8 0 0.8 Dialysate sodium, % <0.001  138 mmol/L 69 68 71 3  140 mmol/L 28 30 26 4  Other 3 2 4 2 Dialysate potassium, % <0.001  1.5 mmol/L 8 10 6 4  2.0 mmol/L 56 54 57 3  3.0 mmol/L 33 33 34 1  Other 4 4 4 0 Dialysate calcium, % 0.12  1.25 mmol/L 20 20 20 0  1.50 mmol/L 74 74 74 0  1.75 mmol/L 4 4 4 0  Other 2 2 2 0 Dialysate bicarbonate, % <0.001  31 mmol/L 6 5 7 2  32 mmol/L 76 77 76 1  35 mmol/L 15 16 14 2  Other 3 3 3 0 Body composition  LTI, kg/m² 12.4 ± 2.9 12.5 ± 2.9 12.4 ± 2.9 0.1 0.01  FTI, kg/m² 13.5 ± 6.0 13.7 ± 6.0 13.2 ± 6.1 0.5 <0.001  BMI, kg/m² 26.3 ± 5.4 26.5 ± 5.4 26.0 ± 5.4 0.5 <0.001  Fluid overload, L 2.3 ± 1.9 2.1 ± 1.8 2.5 ± 1.9 0.4 <0.001  Body weight, kg 71 ± 16 72 ± 16 70 ± 16 2 <0.001 Blood pressure, mmHg  Systolic 138 ± 24 139 ± 24 136 ± 23 3 <0.001  Diastolic 71 ± 14 72 ± 14 71 ± 14 1 <0.001 Lab values  Albumin, g/L 38.5 ± 4.3 38.5 ± 4.3 38.4 ± 4.4 0.1 0.6  Creatinine, mg/dL 7.0 ± 2.3 7.2 ± 2.4 6.9 ± 2.3 0.3 <0.001  Creatinine index, mg/kg/day 18.6 ± 2.5 18.8 ± 2.5 18.4 ± 2.5 0.4 <0.001  nPCR, g/kg/day 1.1 ± 0.3 1.1 ± 0.3 1.0 ± 0.3 0.1 0.002  CRP, mg/L 4.6 (1.9–11.3) 4.4 (1.8– 11.0) 4.9 (2.0–12.0) 0.5 0.04  Haemoglobin, g/dL 11.2 ± 1.5 11.2 ± 1.5 11.1 ± 1.5 0.1 0.001  Ferritin, µg/L 373 (204–627) 361 (202–612) 384 (206–644) 23 0.01  Uric acid, mg/dL 5.9 ± 1.5 6.0 ± 1.5 5.9 ± 1.5 0.1 0.3  Sodium, mmol/L 138 ± 4 138 ± 4 138 ± 4 0 <0.001  Phosphate, mmol/L 1.5 ± 0.5 1.6 ± 0.5 1.5 ± 0.4 0.1 <0.001  Calcium, mmol/L 2.2 ± 0.2 2.2 ± 0.2 2.2 ± 0.2 0 0.03  Total cholesterol, mg/dL 172 ± 44 169 ± 43 174 ± 44 5 <0.001  HDL cholesterol, mg/dL 44 ± 15 44 ± 15 44 ± 15 0 0.4  LDL cholesterol, mg/dL 100 ± 37 100 ± 37 101 ± 38 1 0.5  Triglycerides, mg/dL 135 (97–187) 129 (93–176) 143 (104–200) 14 <0.001 Parameter . Total (n = 9963) . MS (n = 4858) . AS (n = 5105) . Absolute difference . P-value . Age, years 64 ± 15 62 ± 15 65 ± 14 3 <0.001 Gender: female, % 42 40 44 4 <0.001 Comorbidities  Diabetes mellitus, % 30 29 30 1 0.2  Cardiovascular disease, % 29 29 29 0 0.5  Liver disease, % 6 6 5 1 0.10 Vascular access, % 0.10  Arteriovenous fistula 74 75 73 2  Graft 3 3 3 0  Central vascular catheter 23 23 24 1 Treatment modality, % <0.001  HD 65.4 62.0 68.7 6.7  Online haemodiafiltration 34.3 37.7 31.0 6.7  Other 0.3 0.3 0.3 0 Treatment parameter  OCM Kt/V 1.6 ± 0.4 1.6 ± 0.4 1.5 ± 0.4 0.1 <0.001  Effective treatment time, min 249 ± 18 250 ± 17 248 ± 18 2 <0.001  Convection volume, La 25 ± 8 25 ± 8 25 ± 8 0 0.8 Dialysate sodium, % <0.001  138 mmol/L 69 68 71 3  140 mmol/L 28 30 26 4  Other 3 2 4 2 Dialysate potassium, % <0.001  1.5 mmol/L 8 10 6 4  2.0 mmol/L 56 54 57 3  3.0 mmol/L 33 33 34 1  Other 4 4 4 0 Dialysate calcium, % 0.12  1.25 mmol/L 20 20 20 0  1.50 mmol/L 74 74 74 0  1.75 mmol/L 4 4 4 0  Other 2 2 2 0 Dialysate bicarbonate, % <0.001  31 mmol/L 6 5 7 2  32 mmol/L 76 77 76 1  35 mmol/L 15 16 14 2  Other 3 3 3 0 Body composition  LTI, kg/m² 12.4 ± 2.9 12.5 ± 2.9 12.4 ± 2.9 0.1 0.01  FTI, kg/m² 13.5 ± 6.0 13.7 ± 6.0 13.2 ± 6.1 0.5 <0.001  BMI, kg/m² 26.3 ± 5.4 26.5 ± 5.4 26.0 ± 5.4 0.5 <0.001  Fluid overload, L 2.3 ± 1.9 2.1 ± 1.8 2.5 ± 1.9 0.4 <0.001  Body weight, kg 71 ± 16 72 ± 16 70 ± 16 2 <0.001 Blood pressure, mmHg  Systolic 138 ± 24 139 ± 24 136 ± 23 3 <0.001  Diastolic 71 ± 14 72 ± 14 71 ± 14 1 <0.001 Lab values  Albumin, g/L 38.5 ± 4.3 38.5 ± 4.3 38.4 ± 4.4 0.1 0.6  Creatinine, mg/dL 7.0 ± 2.3 7.2 ± 2.4 6.9 ± 2.3 0.3 <0.001  Creatinine index, mg/kg/day 18.6 ± 2.5 18.8 ± 2.5 18.4 ± 2.5 0.4 <0.001  nPCR, g/kg/day 1.1 ± 0.3 1.1 ± 0.3 1.0 ± 0.3 0.1 0.002  CRP, mg/L 4.6 (1.9–11.3) 4.4 (1.8– 11.0) 4.9 (2.0–12.0) 0.5 0.04  Haemoglobin, g/dL 11.2 ± 1.5 11.2 ± 1.5 11.1 ± 1.5 0.1 0.001  Ferritin, µg/L 373 (204–627) 361 (202–612) 384 (206–644) 23 0.01  Uric acid, mg/dL 5.9 ± 1.5 6.0 ± 1.5 5.9 ± 1.5 0.1 0.3  Sodium, mmol/L 138 ± 4 138 ± 4 138 ± 4 0 <0.001  Phosphate, mmol/L 1.5 ± 0.5 1.6 ± 0.5 1.5 ± 0.4 0.1 <0.001  Calcium, mmol/L 2.2 ± 0.2 2.2 ± 0.2 2.2 ± 0.2 0 0.03  Total cholesterol, mg/dL 172 ± 44 169 ± 43 174 ± 44 5 <0.001  HDL cholesterol, mg/dL 44 ± 15 44 ± 15 44 ± 15 0 0.4  LDL cholesterol, mg/dL 100 ± 37 100 ± 37 101 ± 38 1 0.5  Triglycerides, mg/dL 135 (97–187) 129 (93–176) 143 (104–200) 14 <0.001 Data are presented as mean ± SD, median (q1–q3) or %, as appropriate. Clinical data within the last 3 months of the baseline period were used. Statistical analysis was performed with the t-test for normally distributed continuous variables, with the Wilcoxon rank-sum test for not normally distributed continuous variables and with the Pearson’s Chi-squared test for dichotomous variables. a Only for HDF patients. OCM, online clearance monitoring; CRP, C-reactive protein; HDL, high-density lipoprotein; LDL, low-desitiy lipoprotein; HDF, hemodiafiltration; ESKD, end-stage kidney disease. Open in new tab During 2 years of follow-up, 3047 patients (1448 in the MS and 1599 in the AS) were censored. Of these, 1747 patients died, 537 were transplanted, 448 were transferred to another centre and 315 patients were censored due to other reasons (e.g. change to PD, home dialysis and lost to follow-up). Throughout follow-up, on average 89% of all subsequent dialysis sessions were performed in the same treatment shift defined at baseline. In average, 14.7 ± 8.4 BCM measurements were performed per patient during follow-up. Mean delta changes of LTI and FTI during follow-up are displayed in Figure 3. Overall, LTI slightly decreased (−0.3 kg/m2) and FTI gradually increased (+1.0 kg/m2). Results from our linear mixed models showed both these body composition changes to be statistically significant (LTI: P < 0.001; FTI: P < 0.001). FIGURE 3 Open in new tabDownload slide Changes in the primary outcome variables LTI and FTI over time. The absolute change to baseline (delta) was calculated for each patient and 1 month intervals and presented as mean ± SD. FIGURE 3 Open in new tabDownload slide Changes in the primary outcome variables LTI and FTI over time. The absolute change to baseline (delta) was calculated for each patient and 1 month intervals and presented as mean ± SD. We did not observe any differences between the changes in LTI or FTI between patients assigned to MS or AS in descriptive analyses (Figure 3) and in our multivariable-adjusted linear mixed models (LTI: P = 0.2; FTI: P = 0.10; Table 2). When transforming results presented in Table 2 into the original scale for a reference patient (mean age and most frequent category for all covariates), the estimated average change per month was −0.006 kg/m2 (MS) and −0.010 kg/m2 (AS) for LTI, and 0.027 kg/m2 (MS) and 0.037 kg/m2 (AS) for FTI. When stratifying patients according to the geographic location, participants from Mediterranean regions, North and East Europe as well as Latin America showed a comparable course of LTI and FTI as the total population, regardless of the dialysis shift (Supplementary data, Figure S1). Table 2 Inference statistics for LTI and FTI comparing MS and AS with adjustment for age, sex, vascular access and diabetes mellitus Parameter . Estimate . SE . P-value . Box–Cox transformed LTI (kg/m²)  Intercept 4.5674 0.0294 <0.001  Treatment (AS) 0.0155 0.0078 0.048  Time (1 month) 0.00004 0.0014 1.0  Treatment (AS) × time (1 month) −0.0007 0.0006 0.2  Age, years −0.0121 0.0003 <0.001  Gender (male) 0.4018 0.0079 <0.001  Vascular access (catheter) −0.1121 0.0094 <0.001  Diabetes mellitus (yes) −0.0903 0.0085 <0.001 Box–Cox transformed FTI (kg/m²)  Intercept 4.7809 0.1157 <0.001  Treatment (AS) −0.2351 0.0344 <0.001  Time (1 month) 0.0323 0.0054 <0.001  Treatment (AS) × time (1 month) 0.0038 0.0023 0.10  Age, years 0.0217 0.0012 <0.001  Gender (male) −1.0093 0.0346 <0.001  Vascular access (catheter) −0.0277 0.0411 0.5  Diabetes mellitus (yes) −0.7258 0.0377 <0.001 Parameter . Estimate . SE . P-value . Box–Cox transformed LTI (kg/m²)  Intercept 4.5674 0.0294 <0.001  Treatment (AS) 0.0155 0.0078 0.048  Time (1 month) 0.00004 0.0014 1.0  Treatment (AS) × time (1 month) −0.0007 0.0006 0.2  Age, years −0.0121 0.0003 <0.001  Gender (male) 0.4018 0.0079 <0.001  Vascular access (catheter) −0.1121 0.0094 <0.001  Diabetes mellitus (yes) −0.0903 0.0085 <0.001 Box–Cox transformed FTI (kg/m²)  Intercept 4.7809 0.1157 <0.001  Treatment (AS) −0.2351 0.0344 <0.001  Time (1 month) 0.0323 0.0054 <0.001  Treatment (AS) × time (1 month) 0.0038 0.0023 0.10  Age, years 0.0217 0.0012 <0.001  Gender (male) −1.0093 0.0346 <0.001  Vascular access (catheter) −0.0277 0.0411 0.5  Diabetes mellitus (yes) −0.7258 0.0377 <0.001 LTI and FTI were Box–Cox transformed with the formula Y^=(Yλ−1)/λ with λ = 0.35 for LTI and λ = 0.55 for FTI. Open in new tab Table 2 Inference statistics for LTI and FTI comparing MS and AS with adjustment for age, sex, vascular access and diabetes mellitus Parameter . Estimate . SE . P-value . Box–Cox transformed LTI (kg/m²)  Intercept 4.5674 0.0294 <0.001  Treatment (AS) 0.0155 0.0078 0.048  Time (1 month) 0.00004 0.0014 1.0  Treatment (AS) × time (1 month) −0.0007 0.0006 0.2  Age, years −0.0121 0.0003 <0.001  Gender (male) 0.4018 0.0079 <0.001  Vascular access (catheter) −0.1121 0.0094 <0.001  Diabetes mellitus (yes) −0.0903 0.0085 <0.001 Box–Cox transformed FTI (kg/m²)  Intercept 4.7809 0.1157 <0.001  Treatment (AS) −0.2351 0.0344 <0.001  Time (1 month) 0.0323 0.0054 <0.001  Treatment (AS) × time (1 month) 0.0038 0.0023 0.10  Age, years 0.0217 0.0012 <0.001  Gender (male) −1.0093 0.0346 <0.001  Vascular access (catheter) −0.0277 0.0411 0.5  Diabetes mellitus (yes) −0.7258 0.0377 <0.001 Parameter . Estimate . SE . P-value . Box–Cox transformed LTI (kg/m²)  Intercept 4.5674 0.0294 <0.001  Treatment (AS) 0.0155 0.0078 0.048  Time (1 month) 0.00004 0.0014 1.0  Treatment (AS) × time (1 month) −0.0007 0.0006 0.2  Age, years −0.0121 0.0003 <0.001  Gender (male) 0.4018 0.0079 <0.001  Vascular access (catheter) −0.1121 0.0094 <0.001  Diabetes mellitus (yes) −0.0903 0.0085 <0.001 Box–Cox transformed FTI (kg/m²)  Intercept 4.7809 0.1157 <0.001  Treatment (AS) −0.2351 0.0344 <0.001  Time (1 month) 0.0323 0.0054 <0.001  Treatment (AS) × time (1 month) 0.0038 0.0023 0.10  Age, years 0.0217 0.0012 <0.001  Gender (male) −1.0093 0.0346 <0.001  Vascular access (catheter) −0.0277 0.0411 0.5  Diabetes mellitus (yes) −0.7258 0.0377 <0.001 LTI and FTI were Box–Cox transformed with the formula Y^=(Yλ−1)/λ with λ = 0.35 for LTI and λ = 0.55 for FTI. Open in new tab Similarly, we did not find any substantial differences in our secondary outcome variables (BMI: P = 0.7; albumin: P = 0.12; creatinine index: P = 0.9; and nPCR: P = 0.5) between patients treated in the MS or AS (Figure 4). FIGURE 4 Open in new tabDownload slide Changes in the secondary outcome variables albumin, creatinine index, BMI and nPCR over time. The absolute change to baseline (delta) was calculated for each patient and 1-month interval and presented as mean ± SD. FIGURE 4 Open in new tabDownload slide Changes in the secondary outcome variables albumin, creatinine index, BMI and nPCR over time. The absolute change to baseline (delta) was calculated for each patient and 1-month interval and presented as mean ± SD. DISCUSSION This retrospective cohort study suggests that patients initiating maintenance HD tend to lose lean tissue mass and gain fat mass. However, patients treated during MSs display similar changes in body composition (LTI and FTI) and PEW indicators (BMI, albumin, creatinine index and nPCR) as patients treated during ASs. Thus, our results do not support the hypothesis that the time of day of dialysis makes patients prone to PEW. Research suggests that a more equal distribution of protein and energy throughout the day may be optimal for increasing muscle protein synthesis [28, 29]. There is scarce information on whether eating patterns in HD patients undergoing different dialysis shifts may vary, but existing studies collectively suggest that energy and calorie intake is lower during lunch for patients dialysing in the afternoon. In an Italian cross-sectional analysis of 94 HD patients responding 3-day food recalls, Cupisti et al. [30] reported that patients dialysing in the afternoon consumed significantly less kcal and energy during lunch than patients dialysing in the morning, but no differences were observed in reported energy and calorie intake during breakfast or dinner across dialysis shifts. Another small US study evaluated 24-h dietary recalls also of 94 HD patients [20] and observed that patients dialysing in the morning ingested the least amount of protein and total kcal at breakfast, while patients dialysing in the AS ingested the least amount of protein and total kcal at lunch. Divergences in breakfast findings may be attributed to the eating patterns of those countries: whereas in Italy typical eating patterns include higher protein/energy intake during lunch, in the USA, there is a tendency to include higher protein and energy intakes at dinner. At study inclusion, patients dialysed in the morning tended to be slightly younger and less comorbid, with marginally better indicators of nutritional status and body composition. This agrees with small cross-sectional reports observing that patients being dialysed in the MS have a higher BMI [6–8], higher creatinine levels [6, 9] and higher nPCR [9], and were less often undernourished [7] than patients dialysed in the afternoon. However, this apparent better nutritional status in MS patients was never explored, to our knowledge, in multivariable analysis, and our analysis shows differences to be mostly abrogated by adjustment. We thus speculate that previously reported differences in nutritional status may be attributed to differences in demographic, socio-economic status and comorbidities across both shifts. To the best of our knowledge, ours is the first and sole study that explores whether these differences in nutrient intake because of time of the day of HD may clinically affect PEW risk over time. We cannot corroborate whether energy and calorie intake differed by time of the day of HD, but we can convincingly show that clinically these differences, if they de facto exist, do not seem to translate into more or less rapid changes in body composition or PEW indicators. A caveat in the interpretation of our findings is that we lack information on whether patients received meals or were allowed to eat snacks during or after the dialysis. It has been demonstrated that provision of protein and energy-rich meals during the dialysis session can counteract the catabolism induced by the dialysis procedure [31]. However, such hospital practice would apply equally to patients in the MS or in the AS and should not dramatically change our conclusions. Differences in nutrient intake by time of the day of HD have been argued as a possible mechanism to explain observed survival differences between HD shifts in studies performed ∼20 years ago [7, 8], whereby patients in the AS seemed to exhibit a higher mortality risk. These differences in survival have, however, been refuted in subsequent more contemporary studies after adjustment for socio-economic confounding factors [6, 9]. Our study also shows that collectively, patients initiating dialysis tend to progressively lose LTI (−0.3 kg/m2 on average over 2 years) and increase in FTI (+1.0 kg/m2). This is in agreement with previous findings [32] and with the established view of ESKD/dialysis as a catabolic/wasting state [15, 16]. These divergences in body composition would appear masked if only BMI (+0.5 kg/m2 on average over 2 years in our study) or total body weight (+1.4 kg) was evaluated, exemplifying how analysis of body composition in routine clinical practice may offer advantages over simply looking at body size [33, 34]. Moreover, it is important to note that optimal management of the nutritional status of HD patients is important for long-term nutritional outcomes, irrespective of the dialysis shift. A timely and proper identification of patients with mild symptoms may help to start early interventions such as monitoring nutritional intake, reducing metabolic and hormonal disturbances, or providing appetite stimulants and exercise programmes. In addition to lacking information about meal intake, further limitations should be taken into consideration when evaluating our results. The retrospective and observational design of our study does not allow any conclusions to be drawn regarding causality. However, randomized controlled trials are unlikely to be carried out to answer this research question. We lacked information regarding residual renal function in the present study. However, as patients are not selected for one of the treatment shifts based on medical reasons but instead based on patients’ preference and space availability, we do not expect differences in residual renal function between the two groups. BCM measurements were not performed at one distinct day of the week, which may induce some variability in the fluid status. However, this may have been mitigated by comparing changes in bioimpedance measurements from the same patient. Finally, although our study population consists of patients from many different countries, all patients were treated by the same dialysis provider and may not extrapolate to other countries or providers. In conclusion, this study suggests that being dialysed in the morning or in the afternoon does not impact long-term nutritional status of HD patients. Regardless of time of day of HD, patients progressively lose muscle mass and increase body fat. SUPPLEMENTARY DATA Supplementary data are available at ndt online. ACKNOWLEDGEMENTS The authors thank all physicians and nurses working at the NephroCare dialysis centres for their efforts in handling the EuCliD® data, which made this project possible. The European Renal Nutrition Working Group is an initiative of and supported by the European Renal Association – European Dialysis Transplant Association. J.J.C. acknowledges support from the Swedish Research Council (2019-01059). FUNDING We did not receive funding for this work. AUTHORS’ CONTRIBUTIONS All authors have made a significant contribution to the conception, design, execution or interpretation of the reported study. conceptualization, writing—original draft was done by J.J.C.; methodology, writing—original draft by A.M.Z.; formal analysis, data curation, writing—review and editing by M.W.; resources, writing—review and editing by S.S.; conceptualization, writing—review and editing by B.C.; conceptualization, writing—review and editing by A.G.; methodology, writing—review and editing by A.C.W.; and conceptualization, writing—review and editing by D.F. All authors approved the final manuscript. CONFLICT OF INTEREST STATEMENT A.M.Z., M.W., S.S., B.C., A.G. and A.C.W. are employees of Fresenius Medical Care and may hold stock in the company. J.J.C. has received speaker fees from Abbott Laboratories and Nutricia. D.F. has received lecture fees from Sanofi, Vifor Pharma, Fresenius Kabi and a research grant from Fresenius Medical Care. Results presented in this article have not been published previously in whole or part, except in abstract format. DATA AVAILABILITY STATEMENT The EuCliD database is a comprehensive electronic therapy information system which has been established within the framework of Fresenius Medical Care’s quality improvement and management programs in NephroCare clinics. Data from this database cannot be shared publicly. REFERENCES 1 Kooman JP , Usvyat L , van der Sande FM et al. ‘ Time and time again’: oscillatory and longitudinal time patterns in dialysis patients . Kidney Blood Press Res 2012 ; 35 : 534 – 548 Google Scholar Crossref Search ADS PubMed WorldCat 2 Firsov D , Bonny O. Circadian rhythms and the kidney . Nat Rev Nephrol 2018 ; 14 : 626 – 635 Google Scholar Crossref Search ADS PubMed WorldCat 3 Hermida RC , Calvo C , Ayala DE et al. Administration-time-dependent effects of doxazosin GITS on ambulatory blood pressure of hypertensive subjects . 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Winter and Denis Fouque contributed equally to this work. © The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Evolution of body composition and wasting indicators by time of day of haemodialysis
JO - Nephrology Dialysis Transplantation
DO - 10.1093/ndt/gfaa253
DA - 2021-01-25
UR - https://www.deepdyve.com/lp/oxford-university-press/evolution-of-body-composition-and-wasting-indicators-by-time-of-day-of-rFof4oicDF
SP - 346
EP - 354
VL - 36
IS - 2
DP - DeepDyve
ER -