Brain natriuretic peptide and all-cause mortality in patients with kidney failure and haemodialysis treatment

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In patients with kidney failure, inadequate fluid management during haemodialysis may cause fluid overload and overhydration (OH), risk factors for mortality. The aim of this study was to analyse the relationships among BNP, OH and all-cause mortality in patients with kidney failure and haemodialysis. Methods: In this prospective observational study, five-year survival was analysed in 64 patients with kidney failure and haemodialysis. Univariate correlations were performed to analyse the relationships between BNP, OH, and all-cause mortality. Cox regression analysis was performed to adjust the relationship between BNP and all-cause mortality for selected baseline clinical and biochemical characteristics. Results : By the end of the study, 33 patients (52%) had died. Age (r=0.38), BNP level (r=0.48), handgrip strength (r=-0.34), lean tissue index (r=-0.41) and CRP level (r=-0.34, p=0.007) were significantly associated with all-cause mortality. BNP was found to be a significant predictor of all-cause mortality (HR 3.1). However, after adjusting for age, sex, handgrip strength, OH and CRP, BNP was no longer a statistically significant predictor of all-cause mortality. Instead, age, handgrip strength and CRP were significant predictors of all-cause mortality (HR 1.04; HR 0.94 and HR 2.41, respectively). Conclusions : In this study, BNP was correlated with all-cause mortality in patients with kidney failure and haemodialysis, but OH was not. When adjusting for other clinical and biochemical factors, age, inflammation, and handgrip strength were found to be independent and more important predictors of all-cause mortality than BNP. brain natriuretic peptide fluid overload haemodialysis survival analysis Figures Figure 1 Figure 2 Introduction Kidney failure is associated with a significantly increased risk of cardiovascular (CV) mortality, with patients experiencing a 10 to 20 times greater risk than the general population does [1, 2]. One of the key contributors to this elevated risk is overhydration (OH), a common condition among patients with kidney failure undergoing haemodialysis treatment. Approximately 25% of these patients present with 2.5 litres or more OH before dialysis [3–5], and OH has been linked to reduced survival [5–12]. Effective volume management, therefore, becomes a critical aspect of care for patients with kidney failure and haemodialysis. Traditionally, fluid status is assessed clinically [13]. However, given the high prevalence of OH and its significant impact on patient outcomes, there is a need for more accurate and reliable methods for identifying and managing OH. Brain natriuretic peptide (BNP) is a biomarker produced by the heart in response to fluid overload, and elevated levels of BNP and NT-proBNP are associated with increased morbidity and mortality in this population [14, 15]. Previous studies have demonstrated an intraindividual correlation between BNP levels and OH in patients with kidney failure and haemodialysis treatment [16, 17]. Considering the critical importance of volume management in patients with kidney failure on haemodialysis, understanding the role of biomarkers such as BNP in predicting fluid status and its relation to patient outcomes is essential. The aim of this study was therefore to prospectively assess the relationships among BNP, OH, and all-cause mortality in patients with kidney failure undergoing haemodialysis treatment. Materials and methods Study population Baseline data were retrieved from a previously published cross-sectional study [16]. The inclusion criteria were treatment with intermittent haemodialysis for ≥ 3 months, age ≥ 18 years and ability to provide informed consent. The exclusion criterion was having a unipolar pacemaker, as this was considered incompatible with bioimpedance measurements at the time. All study procedures were performed in accordance with the principles of the Declaration of Helsinki, and all study participants provided written informed consent. The study protocol was approved by the Regional Ethical Review Board in Uppsala, Sweden (dnr 2017/006). Eighty-one patients were screened for eligibility, but five did not meet all the inclusion criteria, seven declined study participation, and five enrolled subjects did not enter the study because of renal transplantation (n = 1), recovered renal function (n = 1), conversion to peritoneal dialysis (n = 1), or death (n = 2). Finally, data from 64 individuals were included. Five years after baseline, information on mortality, date and cause of death was extracted from the electronic healthcare records. To further explore the effects of BNP and OH, the study participants were divided into four groups according to their BNP levels and fluid status at baseline. High BNP was defined as BNP ≥ 500 pg/ml [18], and fluid overload was defined as bioimpedance measured as OH ≥ 2.5 L [7, 19]: Group A (low BNP, low OH). Group B (low BNP, high OH). Group C (high BNP, low OH). Group D (high BNP, high OH). Statistical methods The baseline characteristics of the participants were summarized via descriptive statistics. Normally distributed variables are presented as the means with standard deviations (SDs), and nonnormally distributed variables are presented as medians and interquartile ranges (IQRs). Categorical variables are expressed as frequencies (n) and percentages (%). Given the positively skewed distributions of the BNP and CRP values, log-transformation (Log 10 ) of these variables was performed. To analyse linear relationships, the Spearman correlation coefficient was used. Time-to-event (survival) was calculated in months from the start to the end of the study for all study participants, and to compare survival between groups, the Kaplan‒Meier log-rank test was used. Additionally, a Cox regression analysis was performed to adjust the relationship between BNP and all-cause mortality for selected baseline clinical and biochemical characteristics. The level of significance was set to p < 0.05, and the statistical analysis was performed via IBM SPSS Statistics version 28.0. Results Clinical and biochemical characteristics of the study participants The study participants (n = 64) were 70 ± 13 years old and had been treated with haemodialysis for 37 (16–75) months on average. Seventy-seven percent were men. The median BNP was 365 (178–833) pg/ml, and the mean OH was 2.2 ± 1.4 (1.2–3.2) L. The clinical and biochemical characteristics of all the study participants are shown in Table 1 . Table 1 Clinical and biochemical characteristics of the study participants overall and in the four subgroups on the basis of the BNP level and hydration status (OH) at baseline (n = 64) All Participants (n = 64) Group A L-BNP Low OH (n = 29) Group B Low BNP High OH (n = 11) Group C High BNP Low OH (n = 12) Group D High BNP High OH (n = 12) Men/Women (n, %) 49 (77)/15(23) 20 (69)/9 (31) 11 (100)/0 (0) 8 (67)/4 (33) 10 (83)/2 (17) Age (years) 70 ± 13 66 ± 12 62 ± 18 80 ± 7 73 ± 11 Body weight pre-HD (kg) 82.4 ± 18 84 ± 22 82 ± 20 76 ± 18 76 ± 16 Dialysis vintage (months) 37 (16–75) 33 (18–67) 44 (20–92) 55 (23–82) 28 (9–74) Hours/treatment 4.5 (4–4.5) 4.5 (4–4.5) 4.8 (4–5) 4.2 (4–4.5) 4.2 (4–4.5) Treatments/week 3 (3–3) 3 (3–3) 3 (3–3) 3 (2–3) 3 (3–3) SBP (mmHg) 144 ± 26 145 ± 28 141 ± 29 139 ± 28 149 ± 20 DBP (mmHg) 67 ± 16 69 ± 15 65 ± 20 59 ± 17 70 ± 13 Comorbidities Diabetes type 1/2 (n, %) 5 (8)/25 (39) 2 (7)/8 (28) 2 (18)/4 (36) 1 (8)/6 (50) 0 (0)/7 (58) IHD (n, %) 19 (30) 7 (24) 4 (36) 5 (42) 3 (25) Other heart disease (n, %) 21 (33) 11 (38) 1 (9) 3 (25) 6 (50) Laboratory test results Hemoglobin (g/L) 109 ± 13.4 114 ± 11.8 112 ± 14.0 106 ± 11.7 97.5 ± 11.0 Albumin (g/L) 30.3 ± 4.3 31.7 ± 3.4 32 ± 4.0 29 ± 3.4 26 ± 4.5 Phosphate (mmol/L) 1.5 ± 0.5 1.5 ± 0.6 1.3 ± 0.4 1.6 ± 0.6 1.5 ± 0.4 BNP (pg/ml) 365 (178–833) 204 (118–275) 268 (73–406) 1035 (824–1255) 1440 (652–3877) CRP (mg/L) 7.0 (2.7–18) 4.7 (2–17) 4.5 (1.4–18) 11 (3–20) 15.5 (3.8–28) Volume status OH (L) 2 (1.2–3.2) 1.5 (0.9–2.0) 3.4 (3-0-3.7) 1.9 (1.0-2.1) 4.5 (2.7–4.7) NH weight (kg) 80.1 ± 18.4 86 ± 17.9 78.9 ± 19.9 74.6 ± 18.0 72.6 ± 15.5 Target weight (kg) 80.1 ± 18.6 85 ± 17.7 80 ± 22.0 75 ± 20.0 74 ± 15.7 UFV (L) 2.1 (0.9–2.7) 1.4 (0.7–2.8) 2.6 (0.8–2.9) 2.1 (0.9–2.4) 2.5 (0.8–2.7) Nutritional status Handgrip (kg) 24 (20–36) 24 (20–36) 33 (24–41) 19 (16–26) 22.5 (21–33) BMI (kg/m 2 ) 27.3 ± 5.4 28.7 ± 5.2 25.5 ± 6.0 25.7 ± 5.6 25 ± 3.7 LTI (kg/m 2 ) 11.6 ± 2.5 12.3 ± 2.6 12.7 ± 1.6 9.5 ± 1.5 11 ± 2.5 Mortality All-cause mortality (n, %) 33 (52) 12 (41) 4 (36) 10 (83) 7 (58) CV mortality (n, %) 12 (36) 6 (50) 1 (25) 3 (30) 2 (29) The data are expressed as the means ± SDs, medians (IQRs) or frequencies (percentages), as appropriate. BMI: body mass index; BNP: brain natriuretic peptide; CV: cardiovascular; CRP: C-reactive protein; DBP: diastolic blood pressure; IHD: ischaemic heart disease; LTI: lean tissue index; NH: normal hydration; OH: overhydration; SBP: systolic blood pressure; UFV: ultrafiltration volume; UFR: ultrafiltration rate. When the study participants were divided into four groups on the basis of their BNP level (≥ 500 or < 500 pg/ml) and hydration status measured by bioimpedance (≥ 2.5 L or < 2.5 L), the largest group of patients (45%) was found to have low BNP and low OH (group A). In this group, the median BNP value was 204 pg/ml; in group B (low BNP, high OH), the median BNP was 268 pg/ml; in group C (high BNP, low OH), the median BNP value was 1035 pg/ml; and in group D (high BNP, high OH), the median BNP value was 1440 pg/ml. OH was highest in group D (4.5 L). Patients in groups B and D, who had OH values above the cut-off of 2.5 L, also reported more symptoms related to FO (data not shown). Patients in groups C and D, who had BNP values above the cut-off of 500 pg/ml, were found to be older and have lower handgrip strength and a lower lean tissue index (LTI) but not necessarily lower body mass index (BMI), whereas patients in groups A and B, who were younger, had better nutritional status and higher LTI and handgrip strength values. Most patients were overhydrated before haemodialysis. When the relationship between OH and ultrafiltrated fluid volume during haemodialysis was investigated, participants in groups B and D were found to be at risk for chronic OH with excess fluid left in the body after dialysis. In group B, the prescribed target weight was on average 1.1 kg above normal hydration, as defined by bioimpedance analysis. In group D, this discrepancy was + 1.4 kg. Univariate correlations As shown in Table 2 , BNP correlated significantly and positively with age (r = 0.56, p < 0.001), CRP (r = 0.28, p = 0.027), OH (r = 0.29, p = 0.02) and all-cause mortality (r = 0.48, p < 0.001). BNP was negatively associated with handgrip strength (r = -0.28, p = 0.032), LTI (r = -0.36, p = 0.003) and the albumin level (r = -0.49, p < 0.001). In addition, the lean tissue index (LTI) was significantly correlated with handgrip strength (r = 0.56, p < 0.001). Both LTI (r = 0.36, p = 0.003) and handgrip strength (r = 0.54, p < 0.001) were correlated with sex, with men having higher values than women and negatively correlated with all-cause mortality (r = -0.41, p < 0.001 and r = -0.34, p = 0.008, respectively). Furthermore, a significant correlation between OH and sex was found (r = 0.32, p = 0.009), indicating that OH is more common in men. Age (r = 0.38, p = 0.002), BNP (r = 0.48, p < 0.001), handgrip strength (r = -0.34, p = 0.008), LTI (r = -0.41, p < 0.001) and CRP (r = -0.34, p = 0.007) were associated with all-cause mortality, but OH was not (r = -0.044, p = 0.73). Table 2 Univariate correlations between selected baseline clinical and biochemical characteristics and all-cause mortality Sex (men) HGS (kg) BMI (kg/m²) LTI (kg/m²) CRP (mg/L) BNP (pg/ml) OH (L) Alb (mg/L) All-cause mortality Age (years) -0,051 -,273* -0,077 -,343** 0,028 ,562** 0,006 -,260* ,379** Sex (men) ,540** -0,089 ,365** 0,131 -0,051 ,324** -0,071 -0,020 HGS (kg) 0,095 ,560** -0,093 -,277* 0,145 ,315* -,341** BMI (kg/m²) 0,145 -0,136 -0,141 -0,124 0,034 -0,003 LTI (kg/m²) -0,120 -,363** 0,134 ,272* -,409** CRP (mg/L) ,276* 0,112 -,452** ,335** BNP (pg/ml) ,290* -,491** ,476** OH (L) -0,232 -0,044 Alb (mg/L) -0,233 BNP and CRP are log10 transformed, BMI; body mass index, BNP: brain natriuretic peptide; CRP: C-reactive protein; HGS: handgrip strength; LTI: lean tissue index; OH: overhydration. *p ≤ 0. 05, **p ≤ 0. 01 Survival analyses (Kaplan‒Meier and Cox regression) Kaplan‒Meier analysis was used to analyse the time to event (all-cause mortality) in groups A–D, as previously defined. In total, 33 patients (52%) had died by the end of the study; 12 patients (41%) in group A, 4 (36%) in group B, 10 (83%) in group C and 7 (58%) in group D. Overall, 36% of the deaths were due to cardiovascular disease. The results of the Kaplan‒Meier analysis for all-cause mortality in the four predefined groups A‒D are displayed in Fig. 1 (log rank among all groups p = 0.062). As shown in Fig. 2 , patients with BNP > 500 pg/ml, regardless of OH status (i.e., groups C and D), had a significantly higher mortality rate than patients with BNP ≤ 500 pg/ml (i.e., groups A and B). The log-rank test between these two groups was p = 0.016. A univariate Cox regression model revealed that BNP was a significant predictor of all-cause mortality (HR 3.13, 95% CI: 1.62–6.05) (Table 3 ). Table 3 Model 1. Univariate Cox regression analysis for all-cause mortality including BNP as a risk factor Variable HR 95% CI p value BNP (pg/ml) 3.13 1.62–6.05 < 0.001 BNP: brain natriuretic peptide. BNP is log 10 transformed. When we corrected for age, as shown in Model 2 (Table 4 ), BNP was still a significant predictor of all-cause mortality (HR 2.12, 95% CI: 1.00–4.54), but the hazard ratio decreased from 3.13 to 2.12. Table 4 Model 2. Multivariate Cox regression analysis for all-cause mortality, including BNP and age as risk factors Variables HR 95% CI p value BNP (pg/ml) 2.12 1.00–4.54 0.057 Age (years) 1.04 1.01–1.08 0.02 BNP: brain natriuretic peptide. BNP is log 10 transformed. In Model 3 (Table 5 ), when all variables found to be significantly correlated with all-cause mortality in the univariate correlation analysis, i.e., age, sex, handgrip strength and CRP level (Table 2 ), BNP was no longer a statistically significant predictor of mortality (p = 0.25). However, the point estimate of 1.70 indicates that elevated BNP is still important for all-cause mortality. In this model, age, handgrip strength and CRP were significant predictors of all-cause mortality (HR 1.04, 95% CI: 1.00–1.08; HR 0.94, 95% CI: 0.90–0.99; and HR 2.41, 95% CI: 1.23–4.70, respectively). Table 5 Model 3. Multivariate Cox regression analysis for all-cause mortality adjusted for different risk factors. Variables HR 95% CI P value BNP (pg/ml) 1.70 0.69–4.19 0.25 Age (years) 1.04 1.00–1.08 0.03 Sex (men) 2.20 0.86–5.60 0.10 HGS (kg) 0.94 0.90–0.99 0.013 OH (L) 0.91 0.66–1.26 0.56 CRP (mg/L) 2.41 1.23–4.70 0.01 BNP and CRP are log 10 transformed. BNP: brain natriuretic peptide; CRP: C-reactive protein; HGS: handgrip strength; OH: overhydration. Discussion The primary aim of this study was to assess the relationships among BNP, OH, and all-cause mortality in patients with kidney failure and haemodialysis. For analysis, the study participants were divided into four groups depending on their BNP value and fluid status. The overall five-year all-cause mortality was high, at 51.6%, but this is in line with national registry data from the Swedish renal registry (SNR | Välkommen (medscinet.net)). Patients with elevated BNP levels (> 500 pg/ml) demonstrated markedly greater mortality than those with low BNP levels (< 500 pg/ml), 71% vs. 40%, respectively, highlighting the significant role of BNP as a prognostic biomarker. Baseline BNP, CRP, and low handgrip strength were found to be significantly correlated with all-cause mortality. Importantly, univariate survival analyses revealed that baseline BNP was associated with all-cause mortality five years post assessment, whereas OH was not. The finding that elevated BNP levels serve as a prognostic biomarker of all-cause mortality is in line with previous research. For example, a recent observational study reported that BNP values exceeding 500 pg/ml are strongly associated with increased mortality risk in a similar patient cohort [18]. In our study, as in many other studies, CRP was also found to be a strong and independent predictor of all-cause mortality [20, 21]. Furthermore, a strong association between BNP and CRP was identified, and although no correlation was established between OH and CRP, the highest levels of CRP were observed in the subgroup of patients with the greatest degree of OH (group D). This observation is in line with the hypothesis that OH contributes to the inflammatory state observed in patients with kidney failure. In addition, haemodialysis per se introduces additional systemic inflammation beyond the baseline chronic inflammation seen in patients with kidney failure. Patients receiving haemodialysis are subjected to various factors contributing to inflammation, including the bioincompatibility of dialysis membranes, catheter contamination, and the process of removing waste and excess fluids from the blood, which can activate the immune system and provoke inflammatory responses [22]. In populations with chronic kidney disease (CKD) both with and without haemodialysis, BNP and CRP are regarded as risk markers for cardiovascular disease and CKD progression [23], and patients who present with inflammation and OH are at increased risk of all-cause mortality compared with those without measurable signs of inflammation and OH [24]. This finding reinforces the importance of monitoring and managing both inflammation and fluid status in patients with kidney failure undergoing haemodialysis to mitigate cardiovascular risk and improve patient outcomes. Previous studies have established a clear association between improved nutrition, physical activity, increased quality of life, and reduced mortality risk in patients with CKD [22]. In the present study, we identified handgrip strength as a robust predictor of all-cause mortality, demonstrating a stronger prognostic value than, e.g., the lean tissue index (LTI), as assessed by bioimpedance analysis. Handgrip strength, a simple yet reliable measure of voluntary muscle function, has been widely recognized as a cost-effective and potent indicator of physical function [25]. While primarily serving as a proxy for muscle mass and physical activity, handgrip strength is also strongly associated with nutritional status, comorbidities, quality of life, and all-cause mortality [26, 27]. Our findings are in keeping with prior observations in the general population, showing that handgrip strength is correlated with both age and sex [28]. Notably, the handgrip strength of our patients was slightly lower than that of the general population, with men exhibiting a mean handgrip strength of 30 kg and women 18 kg, compared with 31 kg and 20 kg, respectively, in an age-matched healthy population [28]. These results underscore the importance of frailty as a determinant of mortality in patients with CKD, emphasizing the need for early identification and intervention. The subgroup with the lowest handgrip strength (group C) had the highest mortality rate, reinforcing the association between reduced muscle strength and poor survival outcomes. Previous studies have demonstrated that higher body mass index (BMI), LTI, and handgrip strength are protective factors in patients undergoing haemodialysis [25, 29]. Interestingly, patients in group B, who exhibited high OH but low levels of BNP and who had the best nutritional status, indicated by higher handgrip strength, LTI, and albumin, had the lowest mortality rates in our study. This finding suggests that nutritional factors may play a more critical role in survival outcomes than OH alone does, suggesting that interventions aiming to improve nutritional status may be even more important than information about fluid restrictions. In the general population, obesity is a well-recognized risk factor for cardiovascular disease and CKD, contributing to increased morbidity and mortality. However, this relationship does not hold universally across all populations. In patients with kidney failure and haemodialysis, observational studies have reported a phenomenon known as the "obesity paradox," where a higher body mass index (BMI) is paradoxically associated with better survival outcomes [29, 30]. However, BMI may not be a precise marker of nutritional status or body composition in this population, as BMI does not distinguish between fat and muscle mass, which is crucial for understanding the health status of dialysis patients [29]. Indeed, weight loss accompanied by muscle gain is associated with improved survival, whereas weight gain combined with muscle loss is detrimental [30]. In our study, the group with the highest mortality (group C) did not have the lowest BMI, but it had the lowest lean tissue index (LTI), indicating that muscle mass depletion, rather than overall weight, may be a more relevant predictor of mortality. This finding highlights the limitations of relying solely on BMI as a measure of health in patients with kidney failure and haemodialysis and underscores the need for more advanced techniques to assess body composition, such as bioimpedance analysis or dual-energy X-ray absorptiometry (DXA), to provide a more nuanced understanding of how muscle and fat mass influence survival. This study has several strengths, including its prospective design and long follow-up period, which increase the reliability of the findings. However, there are also notable limitations. The relatively small sample size limits the generalizability of the results to broader populations and reduces the statistical power to detect differences between subgroups. Additionally, the absence of repeated measurements for key variables, such as body composition and nutritional markers, means that temporal changes could not be assessed. Furthermore, data were collected during the COVID-19 pandemic, which may have influenced mortality outcomes; however, COVID-19 was the cause of death in only one of the 33 deaths, suggesting a minimal direct impact on the study's mortality findings. Conclusion In conclusion, in this cohort of 64 patients with kidney failure and haemodialysis, BNP, but not OH, was found to be correlated with all-cause mortality. Other clinical and biochemical factors, such as age, inflammation, and handgrip strength, were found to be more important factors associated with all-cause mortality. It is important to continue investigating the role of these factors and potential interventions in improving the prognosis and care of patients with kidney failure and receiving haemodialysis treatment. We hence recommend that these findings be confirmed in larger studies. Abbreviations BMI - body mass index BNP - Brain natriuretic peptide CKD - chronic kidney disease CV - cardiovascular DXA - dual-energy X-ray absorptiometry FO – fluid overload LTI - lean tissue index OH - overhydration Declarations Ethics approval and consent to participate All study procedures were performed in accordance with the principles of the Declaration of Helsinki, and all study participants provided written informed consent. The study protocol was approved by the Regional Ethical Review Board in Uppsala, Sweden (dnr 2017/006). Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding The Uppsala University Hospital ALF grants and establishment funding (MKS), the CUWX foundation and the Swedish Kidney Foundation, the Swedish National Strategic Research Initiative EXODIAB (Excellence of Diabetes Research in Sweden) and the Family Ernfors Foundation. Authors' contributions All authors fulfil the ICMJE requirements for authorship. MS: Participated in the data analysis, helped draft the manuscript, and read and approved the final manuscript. RN: Participated in the data analysis, helped draft the manuscript, and read and approved the final manuscript. JM: Contributed to the study conception, study design, data collection and data analysis; made critical revisions to the draft manuscript for important intellectual content; and read and approved the final manuscript. ML: Contributed to the study conception and study design, participated in coordination, and read and approved the final manuscript. HF: Contributed to the study conception and study design, participated in coordination, and read and approved the final manuscript. JS: Contributed to the study conception and study design; participated in coordination, data collection and data analysis; helped draft the manuscript; and read and approved the final manuscript. No funding sources were involved in the study design; the collection, analysis, and interpretation of data; the writing process; or the decision to submit the article for publication. Acknowledgements We thank research nurse Kerstin Marttala at the Department of Nephrology for assisting with the extraction of data from electronic healthcare records at follow-up. We also thank statistician Johan Westerbergh at the Uppsala Research Centre for valuable advice. References Go, A.S., et al., Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med, 2004. 351 (13): p. 1296-305. 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Takai, Range of plasma brain natriuretic peptide (BNP) levels in haemodialysis patients at a high risk of 1-year mortality and their relationship with the nutritional status: a retrospective cohort study in one institute. Renal Replacement Therapy, 2020. 6 (1): p. 32. Cheng, L., et al., The predictive value of bioimpedance-derived fluid parameters for cardiovascular events in patients undergoing haemodialysis. Ren Fail, 2022. 44 (1): p. 1192-1200. Dai, L., E. Golembiewska, B. Lindholm, and P. Stenvinkel, End-Stage Renal Disease, Inflammation and Cardiovascular Outcomes. Contrib Nephrol, 2017. 191 : p. 32-43. Snaedal, S., et al., Comorbidity and acute clinical events as determinants of C-reactive protein variation in haemodialysis patients: implications for patient survival. Am J Kidney Dis, 2009. 53 (6): p. 1024-33. Maraj, M., et al., Malnutrition, Inflammation, Atherosclerosis Syndrome (MIA) and Diet Recommendations among End-Stage Renal Disease Patients Treated with Maintenance Haemodialysis. Nutrients, 2018. 10 (1). D'Marco, L., A. Bellasi, and P. Raggi, Cardiovascular biomarkers in chronic kidney disease: state of current research and clinical applicability. Dis Markers, 2015. 2015 : p. 586569. Dekker, M.J., et al., Impact of fluid status and inflammation and their interaction on survival: a study in an international haemodialysis patient cohort. Kidney Int, 2017. 91 (5): p. 1214-1223. Cheng, Y., et al., Chronic kidney disease: prevalence and association with handgrip strength in a cross-sectional study. BMC Nephrol, 2021. 22 (1): p. 246. Lee, S.Y., Handgrip Strength: An Irreplaceable Indicator of Muscle Function. Ann Rehabil Med, 2021. 45 (3): p. 167-169. Suliman, M.E., et al., Handgrip strength and mortality in a cohort of kidney failure patients: Comparative analysis of different normalization methods. Nutrition, 2024. 125 : p. 112470. de Araujo Amaral, C., et al., Factors associated with low handgrip strength in older people: data of the Study of Chronic Diseases (Edoc-I). BMC Public Health, 2020. 20 (1): p. 395. Kittiskulnam, P. and K.L. Johansen, The obesity paradox: A further consideration in dialysis patients. Semin Dial, 2019. 32 (6): p. 485-489. Kalantar-Zadeh, K., et al., The obesity paradox and mortality associated with surrogates of body size and muscle mass in patients receiving haemodialysis. Mayo Clin Proc, 2010. 85 (11): p. 991-1001 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Jun, 2025 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 12 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviews received at journal 10 Nov, 2024 Reviewers agreed at journal 06 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviews received at journal 01 Nov, 2024 Reviewers agreed at journal 01 Nov, 2024 Reviewers agreed at journal 01 Nov, 2024 Reviewers invited by journal 01 Nov, 2024 Editor invited by journal 31 Oct, 2024 Editor assigned by journal 30 Oct, 2024 Submission checks completed at journal 30 Oct, 2024 First submitted to journal 23 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5318878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376329569,"identity":"29765ee0-12b9-458c-938e-41ad2d3dea21","order_by":0,"name":"Maria K Svensson","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"K","lastName":"Svensson","suffix":""},{"id":376329570,"identity":"dc6a777c-80b3-463b-b0ea-fd28e206955b","order_by":1,"name":"Rita Nassar","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Rita","middleName":"","lastName":"Nassar","suffix":""},{"id":376329571,"identity":"3232782f-6612-4cad-b501-9ab366bd2db1","order_by":2,"name":"Jan Melin","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Melin","suffix":""},{"id":376329572,"identity":"adbb4970-408a-412b-81cc-1a38798b204c","order_by":3,"name":"Magnus Lindberg","email":"","orcid":"","institution":"University of Gävle","correspondingAuthor":false,"prefix":"","firstName":"Magnus","middleName":"","lastName":"Lindberg","suffix":""},{"id":376329575,"identity":"0d071e51-6659-4bd7-beb6-d18319367215","order_by":4,"name":"Hans Furuland","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Furuland","suffix":""},{"id":376329579,"identity":"ed410241-19d7-4892-a966-2898fbfc8aaa","order_by":5,"name":"Jenny Stenberg","email":"data:image/png;base64,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","orcid":"","institution":"Uppsala University","correspondingAuthor":true,"prefix":"","firstName":"Jenny","middleName":"","lastName":"Stenberg","suffix":""}],"badges":[],"createdAt":"2024-10-23 12:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5318878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5318878/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-025-04251-8","type":"published","date":"2025-06-23T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70295861,"identity":"45d1af85-ff81-4d6e-abaa-3ae105248f59","added_by":"auto","created_at":"2024-12-02 00:27:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30740,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier analysis of all-cause mortality in the four predefined groups A‒D. The figure depicts cumulative survival on y-axel and study observation time, which was up to 60 months, on x-axel.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5318878/v1/6403d348614ed297578a51a0.png"},{"id":70295863,"identity":"8cb5b28b-d5da-45a6-a85b-caf095ad79a3","added_by":"auto","created_at":"2024-12-02 00:27:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23380,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier analysis of all-cause mortality in patients with a BNP value ≥500 or \u0026lt;500 (pg/ml) (log-rank p=0.016). The figure curve depicts the cumulative survival on the y-axel and the study observation time, which was up to 60 months, on the x-axel.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5318878/v1/777720188e9d6a5420be519c.png"},{"id":85686246,"identity":"9f5324d2-78b6-447c-bc37-64ddbe0f8da2","added_by":"auto","created_at":"2025-06-30 16:05:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1032925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5318878/v1/aa4246e5-89a3-46df-9e48-040574ff077b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain natriuretic peptide and all-cause mortality in patients with kidney failure and haemodialysis treatment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney failure is associated with a significantly increased risk of cardiovascular (CV) mortality, with patients experiencing a 10 to 20 times greater risk than the general population does [1, 2]. One of the key contributors to this elevated risk is overhydration (OH), a common condition among patients with kidney failure undergoing haemodialysis treatment. Approximately 25% of these patients present with 2.5 litres or more OH before dialysis [3\u0026ndash;5], and OH has been linked to reduced survival [5\u0026ndash;12]. Effective volume management, therefore, becomes a critical aspect of care for patients with kidney failure and haemodialysis.\u003c/p\u003e \u003cp\u003eTraditionally, fluid status is assessed clinically [13]. However, given the high prevalence of OH and its significant impact on patient outcomes, there is a need for more accurate and reliable methods for identifying and managing OH. Brain natriuretic peptide (BNP) is a biomarker produced by the heart in response to fluid overload, and elevated levels of BNP and NT-proBNP are associated with increased morbidity and mortality in this population [14, 15]. Previous studies have demonstrated an intraindividual correlation between BNP levels and OH in patients with kidney failure and haemodialysis treatment [16, 17].\u003c/p\u003e \u003cp\u003eConsidering the critical importance of volume management in patients with kidney failure on haemodialysis, understanding the role of biomarkers such as BNP in predicting fluid status and its relation to patient outcomes is essential. The aim of this study was therefore to prospectively assess the relationships among BNP, OH, and all-cause mortality in patients with kidney failure undergoing haemodialysis treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eBaseline data were retrieved from a previously published cross-sectional study [16]. The inclusion criteria were treatment with intermittent haemodialysis for \u0026ge;\u0026thinsp;3 months, age\u0026thinsp;\u0026ge;\u0026thinsp;18 years and ability to provide informed consent. The exclusion criterion was having a unipolar pacemaker, as this was considered incompatible with bioimpedance measurements at the time. All study procedures were performed in accordance with the principles of the Declaration of Helsinki, and all study participants provided written informed consent. The study protocol was approved by the Regional Ethical Review Board in Uppsala, Sweden (dnr 2017/006).\u003c/p\u003e \u003cp\u003eEighty-one patients were screened for eligibility, but five did not meet all the inclusion criteria, seven declined study participation, and five enrolled subjects did not enter the study because of renal transplantation (n\u0026thinsp;=\u0026thinsp;1), recovered renal function (n\u0026thinsp;=\u0026thinsp;1), conversion to peritoneal dialysis (n\u0026thinsp;=\u0026thinsp;1), or death (n\u0026thinsp;=\u0026thinsp;2). Finally, data from 64 individuals were included.\u003c/p\u003e \u003cp\u003eFive years after baseline, information on mortality, date and cause of death was extracted from the electronic healthcare records. To further explore the effects of BNP and OH, the study participants were divided into four groups according to their BNP levels and fluid status at baseline. High BNP was defined as BNP\u0026thinsp;\u0026ge;\u0026thinsp;500 pg/ml [18], and fluid overload was defined as bioimpedance measured as OH\u0026thinsp;\u0026ge;\u0026thinsp;2.5 L [7, 19]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGroup A (low BNP, low OH).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGroup B (low BNP, high OH).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGroup C (high BNP, low OH).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGroup D (high BNP, high OH).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eThe baseline characteristics of the participants were summarized via descriptive statistics. Normally distributed variables are presented as the means with standard deviations (SDs), and nonnormally distributed variables are presented as medians and interquartile ranges (IQRs). Categorical variables are expressed as frequencies (n) and percentages (%). Given the positively skewed distributions of the BNP and CRP values, log-transformation (Log\u003csup\u003e10\u003c/sup\u003e) of these variables was performed.\u003c/p\u003e \u003cp\u003eTo analyse linear relationships, the Spearman correlation coefficient was used. Time-to-event (survival) was calculated in months from the start to the end of the study for all study participants, and to compare survival between groups, the Kaplan‒Meier log-rank test was used. Additionally, a Cox regression analysis was performed to adjust the relationship between BNP and all-cause mortality for selected baseline clinical and biochemical characteristics. The level of significance was set to p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the statistical analysis was performed via IBM SPSS Statistics version 28.0.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eClinical and biochemical characteristics of the study participants\u003c/h2\u003e \u003cp\u003eThe study participants (n\u0026thinsp;=\u0026thinsp;64) were 70\u0026thinsp;\u0026plusmn;\u0026thinsp;13 years old and had been treated \u003cem\u003ewith\u003c/em\u003e haemodialysis for 37 (16\u0026ndash;75) months on average. Seventy-seven percent were men. The median BNP was 365 (178\u0026ndash;833) pg/ml, and the mean OH was 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (1.2\u0026ndash;3.2) L. The clinical and biochemical characteristics of all the study participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and biochemical characteristics of the study participants overall and in the four subgroups on the basis of the BNP level and hydration status (OH) at baseline (n\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003cp\u003eL-BNP\u003c/p\u003e \u003cp\u003eLow OH\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003cp\u003eLow BNP\u003c/p\u003e \u003cp\u003eHigh OH\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003cp\u003eHigh BNP\u003c/p\u003e \u003cp\u003eLow OH\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup D\u003c/p\u003e \u003cp\u003eHigh BNP\u003c/p\u003e \u003cp\u003eHigh OH (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen/Women (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (77)/15(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (69)/9 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (100)/0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (67)/4 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (83)/2 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight pre-HD (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis vintage (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (16\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (18\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (20\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (23\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (9\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHours/treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 (4\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5 (4\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8 (4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2 (4\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2 (4\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (3\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e149\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes type 1/2 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8)/25 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7)/8 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (18)/4 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (8)/6 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)/7 (58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther heart disease (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory test results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365 (178\u0026ndash;833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (118\u0026ndash;275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268 (73\u0026ndash;406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1035 (824\u0026ndash;1255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1440\u003c/p\u003e \u003cp\u003e(652\u0026ndash;3877)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (2.7\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7 (2\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (1.4\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (3\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.5 (3.8\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVolume status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOH (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.2\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (0.9\u0026ndash;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4 (3-0-3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9 (1.0-2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5 (2.7\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.9\u0026thinsp;\u0026plusmn;\u0026thinsp;19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUFV (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1 (0.9\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (0.7\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6 (0.8\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1 (0.9\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5 (0.8\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutritional status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandgrip (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (20\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (20\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (24\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (16\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5 (21\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV mortality (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs, medians (IQRs) or frequencies (percentages), as appropriate. BMI: body mass index; BNP: brain natriuretic peptide; CV: cardiovascular; CRP: C-reactive protein; DBP: diastolic blood pressure; IHD: ischaemic heart disease; LTI: lean tissue index; NH: normal hydration; OH: overhydration; SBP: systolic blood pressure; UFV: ultrafiltration volume; UFR: ultrafiltration rate.\u003c/p\u003e \u003cp\u003eWhen the study participants were divided into four groups on the basis of their BNP level (\u0026ge;\u0026thinsp;500 or \u0026lt;\u0026thinsp;500 pg/ml) and hydration status measured by bioimpedance (\u0026ge;\u0026thinsp;2.5 L or \u0026lt;\u0026thinsp;2.5 L), the largest group of patients (45%) was found to have low BNP and low OH (group A). In this group, the median BNP value was 204 pg/ml; in group B (low BNP, high OH), the median BNP was 268 pg/ml; in group C (high BNP, low OH), the median BNP value was 1035 pg/ml; and in group D (high BNP, high OH), the median BNP value was 1440 pg/ml. OH was highest in group D (4.5 L). Patients in groups B and D, who had OH values above the cut-off of 2.5 L, also reported more symptoms related to FO (data not shown). Patients in groups C and D, who had BNP values above the cut-off of 500 pg/ml, were found to be older and have lower handgrip strength and a lower lean tissue index (LTI) but not necessarily lower body mass index (BMI), whereas patients in groups A and B, who were younger, had better nutritional status and higher LTI and handgrip strength values.\u003c/p\u003e \u003cp\u003eMost patients were overhydrated before haemodialysis. When the relationship between OH and ultrafiltrated fluid volume during haemodialysis was investigated, participants in groups B and D were found to be at risk for chronic OH with excess fluid left in the body after dialysis. In group B, the prescribed target weight was on average 1.1 kg above normal hydration, as defined by bioimpedance analysis. In group D, this discrepancy was +\u0026thinsp;1.4 kg.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate correlations\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, BNP correlated significantly and positively with age (r\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CRP (r\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.027), OH (r\u0026thinsp;=\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.02) and all-cause mortality (r\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). BNP was negatively associated with handgrip strength (r = -0.28, p\u0026thinsp;=\u0026thinsp;0.032), LTI (r = -0.36, p\u0026thinsp;=\u0026thinsp;0.003) and the albumin level (r = -0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, the lean tissue index (LTI) was significantly correlated with handgrip strength (r\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both LTI (r\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;=\u0026thinsp;0.003) and handgrip strength (r\u0026thinsp;=\u0026thinsp;0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were correlated with sex, with men having higher values than women and negatively correlated with all-cause mortality (r = -0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and r = -0.34, p\u0026thinsp;=\u0026thinsp;0.008, respectively). Furthermore, a significant correlation between OH and sex was found (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.009), indicating that OH is more common in men. Age (r\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.002), BNP (r\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), handgrip strength (r = -0.34, p\u0026thinsp;=\u0026thinsp;0.008), LTI (r = -0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CRP (r = -0.34, p\u0026thinsp;=\u0026thinsp;0.007) were associated with all-cause mortality, but OH was not (r = -0.044, p\u0026thinsp;=\u0026thinsp;0.73).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate correlations between selected baseline clinical and biochemical characteristics and all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex (men)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHGS (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLTI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNP (pg/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOH (L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAlb (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,273*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,343**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,562**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,260*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,379**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (men)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,540**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,365**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,324**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0,020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,560**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-,277*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,315*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,341**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0,124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0,003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-,363**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,272*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,409**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,276*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,452**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,335**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,290*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,491**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,476**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOH (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0,232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0,044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0,233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eBNP and CRP are log10 transformed, BMI; body mass index, BNP: brain natriuretic peptide; CRP: C-reactive protein; HGS: handgrip strength; LTI: lean tissue index; OH: overhydration. *p\u0026thinsp;\u0026le;\u0026thinsp;0. 05, **p\u0026thinsp;\u0026le;\u0026thinsp;0. 01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analyses (Kaplan‒Meier and Cox regression)\u003c/h2\u003e \u003cp\u003eKaplan‒Meier analysis was used to analyse the time to event (all-cause mortality) in groups A\u0026ndash;D, as previously defined. In total, 33 patients (52%) had died by the end of the study; 12 patients (41%) in group A, 4 (36%) in group B, 10 (83%) in group C and 7 (58%) in group D. Overall, 36% of the deaths were due to cardiovascular disease. The results of the Kaplan‒Meier analysis for all-cause mortality in the four predefined groups A‒D are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (log rank among all groups p\u0026thinsp;=\u0026thinsp;0.062). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, patients with BNP\u0026thinsp;\u0026gt;\u0026thinsp;500 pg/ml, regardless of OH status (i.e., groups C and D), had a significantly higher mortality rate than patients with BNP\u0026thinsp;\u0026le;\u0026thinsp;500 pg/ml (i.e., groups A and B). The log-rank test between these two groups was p\u0026thinsp;=\u0026thinsp;0.016.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA univariate Cox regression model revealed that BNP was a significant predictor of all-cause mortality (HR 3.13, 95% CI: 1.62\u0026ndash;6.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel 1. Univariate Cox regression analysis for all-cause mortality including BNP as a risk factor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62\u0026ndash;6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBNP: brain natriuretic peptide. BNP is log\u003csup\u003e10\u003c/sup\u003e transformed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen we corrected for age, as shown in Model 2 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), BNP was still a significant predictor of all-cause mortality (HR 2.12, 95% CI: 1.00\u0026ndash;4.54), but the hazard ratio decreased from 3.13 to 2.12.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel 2. Multivariate Cox regression analysis for all-cause mortality, including BNP and age as risk factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBNP: brain natriuretic peptide. BNP is log\u003csup\u003e10\u003c/sup\u003e transformed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Model 3 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), when all variables found to be significantly correlated with all-cause mortality in the univariate correlation analysis, i.e., age, sex, handgrip strength and CRP level (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), BNP was no longer a statistically significant predictor of mortality (p\u0026thinsp;=\u0026thinsp;0.25). However, the point estimate of 1.70 indicates that elevated BNP is still important for all-cause mortality. In this model, age, handgrip strength and CRP were significant predictors of all-cause mortality (HR 1.04, 95% CI: 1.00\u0026ndash;1.08; HR 0.94, 95% CI: 0.90\u0026ndash;0.99; and HR 2.41, 95% CI: 1.23\u0026ndash;4.70, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel 3. Multivariate Cox regression analysis for all-cause mortality adjusted for different risk factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (men)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026ndash;5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOH (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026ndash;4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBNP and CRP are log\u003csup\u003e10\u003c/sup\u003e transformed. BNP: brain natriuretic peptide; CRP: C-reactive protein; HGS: handgrip strength; OH: overhydration.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary aim of this study was to assess the relationships among BNP, OH, and all-cause mortality in patients with kidney failure and haemodialysis. For analysis, the study participants were divided into four groups depending on their BNP value and fluid status. The overall five-year all-cause mortality was high, at 51.6%, but this is in line with national registry data from the Swedish renal registry (SNR | V\u0026auml;lkommen (medscinet.net)). Patients with elevated BNP levels (\u0026gt;\u0026thinsp;500 pg/ml) demonstrated markedly greater mortality than those with low BNP levels (\u0026lt;\u0026thinsp;500 pg/ml), 71% vs. 40%, respectively, highlighting the significant role of BNP as a prognostic biomarker. Baseline BNP, CRP, and low handgrip strength were found to be significantly correlated with all-cause mortality. Importantly, univariate survival analyses revealed that baseline BNP was associated with all-cause mortality five years post assessment, whereas OH was not.\u003c/p\u003e \u003cp\u003eThe finding that elevated BNP levels serve as a prognostic biomarker of all-cause mortality is in line with previous research. For example, a recent observational study reported that BNP values exceeding 500 pg/ml are strongly associated with increased mortality risk in a similar patient cohort [18]. In our study, as in many other studies, CRP was also found to be a strong and independent predictor of all-cause mortality [20, 21]. Furthermore, a strong association between BNP and CRP was identified, and although no correlation was established between OH and CRP, the highest levels of CRP were observed in the subgroup of patients with the greatest degree of OH (group D). This observation is in line with the hypothesis that OH contributes to the inflammatory state observed in patients with kidney failure. In addition, haemodialysis per se introduces additional systemic inflammation beyond the baseline chronic inflammation seen in patients with kidney failure. Patients receiving haemodialysis are subjected to various factors contributing to inflammation, including the bioincompatibility of dialysis membranes, catheter contamination, and the process of removing waste and excess fluids from the blood, which can activate the immune system and provoke inflammatory responses [22]. In populations with chronic kidney disease (CKD) both with and without haemodialysis, BNP and CRP are regarded as risk markers for cardiovascular disease and CKD progression [23], and patients who present with inflammation and OH are at increased risk of all-cause mortality compared with those without measurable signs of inflammation and OH [24]. This finding reinforces the importance of monitoring and managing both inflammation and fluid status in patients with kidney failure undergoing haemodialysis to mitigate cardiovascular risk and improve patient outcomes.\u003c/p\u003e \u003cp\u003ePrevious studies have established a clear association between improved nutrition, physical activity, increased quality of life, and reduced mortality risk in patients with CKD [22]. In the present study, we identified handgrip strength as a robust predictor of all-cause mortality, demonstrating a stronger prognostic value than, e.g., the lean tissue index (LTI), as assessed by bioimpedance analysis. Handgrip strength, a simple yet reliable measure of voluntary muscle function, has been widely recognized as a cost-effective and potent indicator of physical function [25]. While primarily serving as a proxy for muscle mass and physical activity, handgrip strength is also strongly associated with nutritional status, comorbidities, quality of life, and all-cause mortality [26, 27].\u003c/p\u003e \u003cp\u003eOur findings are in keeping with prior observations in the general population, showing that handgrip strength is correlated with both age and sex [28]. Notably, the handgrip strength of our patients was slightly lower than that of the general population, with men exhibiting a mean handgrip strength of 30 kg and women 18 kg, compared with 31 kg and 20 kg, respectively, in an age-matched healthy population [28]. These results underscore the importance of frailty as a determinant of mortality in patients with CKD, emphasizing the need for early identification and intervention. The subgroup with the lowest handgrip strength (group C) had the highest mortality rate, reinforcing the association between reduced muscle strength and poor survival outcomes. Previous studies have demonstrated that higher body mass index (BMI), LTI, and handgrip strength are protective factors in patients undergoing haemodialysis [25, 29]. Interestingly, patients in group B, who exhibited high OH but low levels of BNP and who had the best nutritional status, indicated by higher handgrip strength, LTI, and albumin, had the lowest mortality rates in our study. This finding suggests that nutritional factors may play a more critical role in survival outcomes than OH alone does, suggesting that interventions aiming to improve nutritional status may be even more important than information about fluid restrictions.\u003c/p\u003e \u003cp\u003eIn the general population, obesity is a well-recognized risk factor for cardiovascular disease and CKD, contributing to increased morbidity and mortality. However, this relationship does not hold universally across all populations. In patients with kidney failure and haemodialysis, observational studies have reported a phenomenon known as the \"obesity paradox,\" where a higher body mass index (BMI) is paradoxically associated with better survival outcomes [29, 30]. However, BMI may not be a precise marker of nutritional status or body composition in this population, as BMI does not distinguish between fat and muscle mass, which is crucial for understanding the health status of dialysis patients [29]. Indeed, weight loss accompanied by muscle gain is associated with improved survival, whereas weight gain combined with muscle loss is detrimental [30]. In our study, the group with the highest mortality (group C) did not have the lowest BMI, but it had the lowest lean tissue index (LTI), indicating that muscle mass depletion, rather than overall weight, may be a more relevant predictor of mortality. This finding highlights the limitations of relying solely on BMI as a measure of health in patients with kidney failure and haemodialysis and underscores the need for more advanced techniques to assess body composition, such as bioimpedance analysis or dual-energy X-ray absorptiometry (DXA), to provide a more nuanced understanding of how muscle and fat mass influence survival.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including its prospective design and long follow-up period, which increase the reliability of the findings. However, there are also notable limitations. The relatively small sample size limits the generalizability of the results to broader populations and reduces the statistical power to detect differences between subgroups. Additionally, the absence of repeated measurements for key variables, such as body composition and nutritional markers, means that temporal changes could not be assessed. Furthermore, data were collected during the COVID-19 pandemic, which may have influenced mortality outcomes; however, COVID-19 was the cause of death in only one of the 33 deaths, suggesting a minimal direct impact on the study's mortality findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, in this cohort of 64 patients with kidney failure and haemodialysis, BNP, but not OH, was found to be correlated with all-cause mortality. Other clinical and biochemical factors, such as age, inflammation, and handgrip strength, were found to be more important factors associated with all-cause mortality. It is important to continue investigating the role of these factors and potential interventions in improving the prognosis and care of patients with kidney failure and receiving haemodialysis treatment. We hence recommend that these findings be confirmed in larger studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI - body mass index\u003c/p\u003e\n\u003cp\u003eBNP - Brain natriuretic peptide\u003c/p\u003e\n\u003cp\u003eCKD - chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCV - cardiovascular\u003c/p\u003e\n\u003cp\u003eDXA - dual-energy X-ray absorptiometry\u003c/p\u003e\n\u003cp\u003eFO \u0026ndash; fluid overload\u003c/p\u003e\n\u003cp\u003eLTI - lean tissue index\u003c/p\u003e\n\u003cp\u003eOH - overhydration\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll study procedures were performed in accordance with the principles of the Declaration of Helsinki, and all study participants provided written informed consent. The study protocol was approved by the Regional Ethical Review Board in Uppsala, Sweden (dnr 2017/006).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing\u0026nbsp;interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe Uppsala University Hospital ALF grants and establishment funding (MKS), the CUWX foundation and the Swedish Kidney Foundation, the Swedish National Strategic Research Initiative EXODIAB (Excellence of Diabetes Research in Sweden) and the Family Ernfors Foundation.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAll authors fulfil the ICMJE requirements for authorship.\u003c/p\u003e\n\u003cp\u003eMS: Participated in the data analysis, helped draft the manuscript, and read and approved the final manuscript. RN: Participated in the data analysis, helped draft the manuscript, and read and approved the final manuscript. JM: Contributed to the study conception, study design, data collection and data analysis;\u0026nbsp;made critical revisions to the draft manuscript for important intellectual content; and read and approved the final manuscript. ML: Contributed to the study conception and study design, participated in coordination, and read and approved the final manuscript. HF: Contributed to the study conception and study design, participated in coordination, and read and approved the final manuscript. JS: Contributed to the study conception and study design; participated in coordination, data collection and data analysis; helped draft the manuscript; and read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eNo funding sources were involved in the study design; the collection, analysis, and interpretation of data; the writing process; or the decision to submit the article for publication.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank research nurse Kerstin Marttala at the Department of Nephrology for assisting with the extraction of data from electronic healthcare records at follow-up. We also thank statistician Johan Westerbergh at the Uppsala Research Centre for valuable advice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGo, A.S., et al., \u003cem\u003eChronic kidney disease and the risks of death, cardiovascular events, and hospitalization.\u003c/em\u003e N Engl J Med, 2004. \u003cstrong\u003e351\u003c/strong\u003e(13): p. 1296-305.\u003c/li\u003e\n\u003cli\u003eCozzolino, M., et al., \u003cem\u003eCardiovascular disease in dialysis patients.\u003c/em\u003e Nephrol Dial Transplant, 2018. \u003cstrong\u003e33\u003c/strong\u003e(suppl_3): p. iii28-iii34.\u003c/li\u003e\n\u003cli\u003eMoissl, U., et al., \u003cem\u003eBioimpedance-guided fluid management in haemodialysis patients.\u003c/em\u003e Clin J Am Soc Nephrol, 2013. \u003cstrong\u003e8\u003c/strong\u003e(9): p. 1575-82.\u003c/li\u003e\n\u003cli\u003eMathilakath, N.C., et al., \u003cem\u003ePrevalence of Overhydration in Patients on Maintenance Haemodialysis As Determined by Body Composition Monitor and Effects of Attaining Target Dry Weight.\u003c/em\u003e Cureus, 2022. \u003cstrong\u003e14\u003c/strong\u003e(9): p. e29509.\u003c/li\u003e\n\u003cli\u003eOnofriescu, M., et al., \u003cem\u003eOverhydration, Cardiac Function and Survival in Haemodialysis Patients.\u003c/em\u003e PLoS One, 2015. \u003cstrong\u003e10\u003c/strong\u003e(8): p. e0135691.\u003c/li\u003e\n\u003cli\u003eDavies, S.J. and A. 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Raggi, \u003cem\u003eCardiovascular biomarkers in chronic kidney disease: state of current research and clinical applicability.\u003c/em\u003e Dis Markers, 2015. \u003cstrong\u003e2015\u003c/strong\u003e: p. 586569.\u003c/li\u003e\n\u003cli\u003eDekker, M.J., et al., \u003cem\u003eImpact of fluid status and inflammation and their interaction on survival: a study in an international haemodialysis patient cohort.\u003c/em\u003e Kidney Int, 2017. \u003cstrong\u003e91\u003c/strong\u003e(5): p. 1214-1223.\u003c/li\u003e\n\u003cli\u003eCheng, Y., et al., \u003cem\u003eChronic kidney disease: prevalence and association with handgrip strength in a cross-sectional study.\u003c/em\u003e BMC Nephrol, 2021. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 246.\u003c/li\u003e\n\u003cli\u003eLee, S.Y., \u003cem\u003eHandgrip Strength: An Irreplaceable Indicator of Muscle Function.\u003c/em\u003e Ann Rehabil Med, 2021. \u003cstrong\u003e45\u003c/strong\u003e(3): p. 167-169.\u003c/li\u003e\n\u003cli\u003eSuliman, M.E., et al., \u003cem\u003eHandgrip strength and mortality in a cohort of kidney failure patients: Comparative analysis of different normalization methods.\u003c/em\u003e Nutrition, 2024. \u003cstrong\u003e125\u003c/strong\u003e: p. 112470.\u003c/li\u003e\n\u003cli\u003ede Araujo Amaral, C., et al., \u003cem\u003eFactors associated with low handgrip strength in older people: data of the Study of Chronic Diseases (Edoc-I).\u003c/em\u003e BMC Public Health, 2020. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 395.\u003c/li\u003e\n\u003cli\u003eKittiskulnam, P. and K.L. Johansen, \u003cem\u003eThe obesity paradox: A further consideration in dialysis patients.\u003c/em\u003e Semin Dial, 2019. \u003cstrong\u003e32\u003c/strong\u003e(6): p. 485-489.\u003c/li\u003e\n\u003cli\u003eKalantar-Zadeh, K., et al., \u003cem\u003eThe obesity paradox and mortality associated with surrogates of body size and muscle mass in patients receiving haemodialysis.\u003c/em\u003e Mayo Clin Proc, 2010. \u003cstrong\u003e85\u003c/strong\u003e(11): p. 991-1001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"brain natriuretic peptide, fluid overload, haemodialysis, survival analysis","lastPublishedDoi":"10.21203/rs.3.rs-5318878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5318878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Brain natriuretic peptide (BNP) is a biomarker secreted from the heart in response to fluid overload. In patients with kidney failure, inadequate fluid management during haemodialysis may cause fluid overload and overhydration (OH), risk factors for mortality. The aim of this study was to analyse the relationships among BNP, OH and all-cause mortality in patients with kidney failure and haemodialysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this prospective observational study, five-year survival was analysed in 64 patients with kidney failure and haemodialysis. Univariate correlations were performed to analyse the relationships between BNP, OH, and all-cause mortality. Cox regression analysis was performed to adjust the relationship between BNP and all-cause mortality for selected baseline clinical and biochemical characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: By the end of the study, 33 patients (52%) had died. Age (r=0.38), BNP level (r=0.48), handgrip strength (r=-0.34), lean tissue index (r=-0.41) and CRP level (r=-0.34, p=0.007) were significantly associated with all-cause mortality. BNP was found to be a significant predictor of all-cause mortality (HR 3.1). However, after adjusting for age, sex, handgrip strength, OH and CRP, BNP was no longer a statistically significant predictor of all-cause mortality. Instead, age, handgrip strength and CRP were significant predictors of all-cause mortality (HR 1.04; HR 0.94 and HR 2.41, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: In this study, BNP was correlated with all-cause mortality in patients with kidney failure and haemodialysis, but OH was not. When adjusting for other clinical and biochemical factors, age, inflammation, and handgrip strength were found to be independent and more important predictors of all-cause mortality than BNP.\u003c/p\u003e","manuscriptTitle":"Brain natriuretic peptide and all-cause mortality in patients with kidney failure and haemodialysis treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 00:27:39","doi":"10.21203/rs.3.rs-5318878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-12T12:33:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-11T14:04:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-10T21:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10930478200661767888263936449096685375","date":"2024-11-06T15:46:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99109171912219305139161010080530296313","date":"2024-11-04T21:59:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-01T06:51:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335315538775172450925183906064354640300","date":"2024-11-01T06:40:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45531106221827490728614750746458947966","date":"2024-11-01T06:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-01T05:56:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-31T05:26:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-30T10:05:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-30T10:04:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2024-10-23T12:12:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c626f0ee-44e9-4496-83c7-ada97a9d6da9","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T16:03:28+00:00","versionOfRecord":{"articleIdentity":"rs-5318878","link":"https://doi.org/10.1186/s12882-025-04251-8","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-06-23 15:57:49","publishedOnDateReadable":"June 23rd, 2025"},"versionCreatedAt":"2024-12-02 00:27:39","video":"","vorDoi":"10.1186/s12882-025-04251-8","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04251-8","workflowStages":[]},"version":"v1","identity":"rs-5318878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5318878","identity":"rs-5318878","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}