Serum butyrylcholinesterase and concurrent dry-weight change in maintenance hemodialysis: a retrospective longitudinal study

Serum butyrylcholinesterase and concurrent dry-weight change in maintenance hemodialysis: a retrospective longitudinal study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum butyrylcholinesterase and concurrent dry-weight change in maintenance hemodialysis: a retrospective longitudinal study Hirosuke Nakata, Ryo Kamimatsuse, Koichi Nishiwaki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9176845/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Assessing nutrition-related change in maintenance hemodialysis is challenging because commonly used biomarkers such as serum albumin are influenced by inflammation and volume status. Prescribed dry weight (DW) is a clinically important treatment target, but longitudinal DW change may reflect both volume-related and nutrition-related body-mass change. Serum butyrylcholinesterase (BChE), a liver-synthesized protein, may better capture nutrition-related change in this setting. We examined whether longitudinal change in BChE was associated with concurrent change in prescribed DW and compared this association with that of albumin while accounting for volume-related change using human atrial natriuretic peptide (hANP). Methods We conducted a single-center retrospective longitudinal observational study of adults receiving maintenance hemodialysis between September 2024 and December 2025. Monthly clinical data were used to calculate concurrent 3-month changes, expressed as log-ratios (Δlog), in BChE, albumin, hANP, and prescribed DW. The primary analysis evaluated the association between ΔlogBChE and concurrent ΔlogDW across repeated 3-month windows using linear regression with patient-level cluster-robust standard errors. Multivariable models included ΔlogBChE, ΔlogAlb, and ΔloghANP. Results Ninety-six patients were included. In multivariable longitudinal models, ΔlogBChE was independently associated with concurrent ΔlogDW (standardized β = 0.115; P = 0.005), whereas ΔlogAlb was not (P = 0.39). ΔloghANP was also independently associated with ΔlogDW (standardized β = 0.144; P = 0.001). Conclusions In maintenance hemodialysis, longitudinal change in serum BChE was more closely associated with concurrent prescribed DW change than change in albumin, even after accounting for volume-related change. BChE may be a useful adjunct for interpreting DW trajectories when nutritional and volume-related factors coexist. butyrylcholinesterase albumin dry weight hemodialysis nutrition volume status Figures Figure 1 Figure 2 Background Protein–energy wasting (PEW), as defined by the International Society of Renal Nutrition and Metabolism, remains a major clinical problem in patients undergoing maintenance hemodialysis and is strongly associated with adverse outcomes [ 1 – 3 ]. Accurate assessment of nutritional status is therefore important in routine dialysis care. However, in clinical practice, interpreting longitudinal changes in prescribed dry weight is often challenging because nutritional and volume-related factors frequently coexist. Serum albumin is widely used as a surrogate marker of nutrition, but its interpretation is limited because albumin concentrations are strongly influenced by inflammation, fluid dilution, and hepatic acute-phase responses [ 4 ]. As a result, albumin may fail to reflect clinically meaningful changes in body mass over time [ 5 ]. Dry weight (DW) represents the post-dialysis body weight at presumed euvolemia and is a central treatment target in maintenance hemodialysis [ 6 , 7 ]. In routine care, DW is adjusted to avoid both chronic volume overload and excessive ultrafiltration-related symptoms. Importantly, however, DW is also influenced by changes in muscle and fat mass [ 8 ]. Thus, longitudinal DW trajectories may reflect both extracellular volume status and the nutrition-related/body-mass component. From a clinical perspective, unrecognized loss of underlying body mass may result in an inappropriately high prescribed DW relative to true body mass, predisposing patients to persistent volume overload and obscuring early PEW. Conversely, aggressive DW reduction without recognizing nutritional depletion may contribute to intradialytic symptoms. Therefore, markers that help distinguish nutrition-related body-mass change from volume-related change may improve interpretation of longitudinal DW trajectories in maintenance hemodialysis. Serum butyrylcholinesterase (BChE) is synthesized in the liver and reflects protein synthesis capacity and nutritional reserves [ 9 ]. Previous studies have reported associations between BChE and nutritional status in chronic illness, including dialysis populations [ 10 ]. Compared with albumin, which is strongly influenced by inflammation and volume status in hemodialysis patients, BChE provides complementary information related to protein–energy status. While BChE is also affected by inflammation [ 11 ], it may help interpret DW trajectories by reflecting nutritional dynamics beyond albumin alone. However, longitudinal data examining whether BChE helps interpret clinically relevant changes in prescribed DW, particularly in comparison with albumin, remain limited. Natriuretic peptides are released in response to cardiac stretch and are commonly used as supportive biomarkers of volume status. Human atrial natriuretic peptide (hANP) is routinely measured in Japan in hemodialysis practice as a pragmatic volume-related marker [ 12 ]. Higher hANP levels have also been associated with volume overload assessed by body composition analysis [ 13 ]. However, natriuretic peptides also reflect cardiac structure and function and should not be interpreted as stand-alone measures [ 14 , 15 ]. Incorporating a natriuretic peptide marker into longitudinal analyses may help separate volume-related effects from nutrition-related components of DW change. We aimed to compare cross-sectional associations of prescribed DW with BChE and albumin, and to evaluate whether concurrent 3-month changes in BChE were associated with concurrent 3-month changes in prescribed DW independent of albumin and volume-related factors assessed by hANP. We also examined whether BChE provides complementary information for interpreting longitudinal DW change in maintenance hemodialysis. Methods Study design and participants This was a single-center retrospective observational study conducted at a participating institution in Japan. Adult patients receiving maintenance hemodialysis three times weekly between September 2024 and December 2025 were screened. Exclusion criteria were: (1) age < 18 years; (2) DW not stably defined during the observation period or frequent failure to reach the prescribed post-dialysis DW; (3) severe hepatic dysfunction (Child-Pugh class C) or acute liver injury likely to strongly affect BChE; (4) transfer to another facility during follow-up; and (5) missing BChE or DW data. Dry weight definition DW was defined as the prescribed post-dialysis target weight determined by the treating nephrologist based on routine clinical assessment (blood pressure, edema, interdialytic weight gain, and other standard indicators). DW could be adjusted during routine care. Clinical and laboratory variables Monthly laboratory measurements (BChE, albumin, C-reactive protein [CRP], and hANP), prescribed DW, and dialysis adequacy (Kt/V) were obtained as part of routine care. For longitudinal analyses, DW, BChE, albumin, hANP, and CRP were natural log-transformed; 3-month changes were calculated within individuals as ΔlogX = log(X_t) − log(X_t − 3), equivalent to log(X_t/X_t − 3). Concurrent ΔlogDW and changes in biomarkers were defined over the same 3-month window. Blood samples for BChE, albumin, and CRP were collected at the beginning of each month before a midweek dialysis session (predialysis). hANP was measured from a sample obtained immediately before blood return at the end of the same dialysis session. Dialysis modality was recorded as hemodialysis (HD) or hemodiafiltration (HDF). The cardiothoracic ratio (CTR) was obtained from routine chest radiographs (expressed as %). Chest radiographs for CTR assessment were obtained routinely once monthly, typically mid-week during the week of each month. Outcomes The primary endpoint was the association between concurrent 3-month changes in BChE (ΔlogBChE) and concurrent 3-month changes in prescribed DW (ΔlogDW). Secondary endpoints included the association between ΔlogAlb and ΔlogDW, and evaluation of ΔloghANP as a volume-related marker in multivariable models. In prespecified sensitivity analyses, we additionally adjusted for dialysis modality (HD vs HDF) and CTR as pragmatic surrogates for treatment modality and volume/cardiac size. These analyses were intended to examine whether BChE retained an association with DW change under clinically relevant conditions in which volume-related and treatment-related factors coexist. Statistical analysis Continuous variables are summarized as mean (standard deviation) or median (interquartile range) as appropriate. Associations between changes in biomarkers and changes in DW were evaluated using linear regression. Because repeated observations were available for each patient, models used cluster-robust standard errors at the patient level. Multivariable models included markers reflecting volume status (ΔloghANP) and other prespecified covariates as appropriate. In prespecified sensitivity analyses, we additionally adjusted for dialysis modality (HD vs HDF) and CTR as pragmatic surrogates for modality and volume/cardiac size; CTR was treated as a time-varying covariate measured at the start of each 3-month window. Delivered Kt/V was likewise entered as a time-varying covariate measured at the start of each 3-month window (t − 3). Statistical approach for overlapping rolling windows Because 3-month rolling windows overlap within individuals, observations are not independent. Therefore, our primary analyses used ordinary least squares with patient-clustered robust standard errors. As sensitivity analyses addressing within-patient dependence under alternative modeling frameworks, we additionally fitted (i) a linear mixed-effects model with a random intercept for patient and (ii) a generalized estimating equations model with an exchangeable working correlation. Two-sided P values < 0.05 were considered statistically significant. Statistical analyses were performed using EZR, based on R version 4.5.2. Results Study population and data completeness Among 113 screened patients, 17 were excluded because DW could not be stably defined, leaving 96 patients in the analytic cohort. Monthly measurements of BChE, albumin, hANP, CRP, and Kt/V were available for the study variables, and the analytic dataset used for longitudinal 3-month windows had no missing values for variables used in the primary longitudinal models. Baseline patient characteristics are summarized in Table 1 . Table 1 Baseline characteristics of the study cohort Values are presented as mean ± SD, median [IQR], or n (%), as appropriate. Characteristic All patients (N = 96) Age, y 72.40 ± 12.10 Sex, male 62 (64.6%) Dialysis vintage, months 67.00 [36.75, 103.30] Diabetes mellitus 43 (44.8%) Body mass index, kg/m² 23.60 ± 3.90 Dry weight, kg 63.20 ± 12.30 Kt/V 1.44 ± 0.26 Hemoglobin, g/dL 11.10 ± 1.40 Ferritin, ng/mL 132.00 [73.75, 281.00] Transferrin saturation, % 27.30 ± 12.40 Butyrylcholinesterase, U/L 217.60 ± 62.50 Albumin, g/dL 3.54 ± 0.40 C-reactive protein, mg/dL 0.17 [0.07, 0.38] hANP, pg/mL 52.00 [26.20, 87.45] GNRI 92.80 ± 6.80 At baseline (month 0), BChE showed moderate correlations with serum albumin (Spearman ρ = 0.298, P = 0.0032) and GNRI (Spearman ρ = 0.393, P < 0.0001), supporting its relevance as a nutrition-related marker (Additional file 1: Table S1 ). Table 2 provides a cross-sectional snapshot of factors associated with baseline DW, highlighting that DW reflects both body size/nutritional reserves and volume-related conditions; these baseline findings helped contextualize subsequent longitudinal models. Table 2 Cross-sectional associations of baseline prescribed dry weight with clinical and laboratory parameters Outcome: prescribed dry weight (baseline, month 0) Predictor β (95% CI) P value BChE 0.215 (0.083, 0.348) 0.0015 Albumin 0.488 (0.214, 0.763) < 0.001 C-reactive protein 0.020 (-0.005, 0.045) 0.12 hANP -0.054 (-0.105, -0.003) 0.038 Values are unstandardized regression coefficients estimated using linear regression with robust standard errors. N = 96. Primary longitudinal analysis: concurrent 3-month changes In the primary analysis using concurrent 3-month windows, ΔlogBChE was positively associated with ΔlogDW after accounting for volume-related changes, whereas ΔlogAlb showed weaker or non-significant associations. These findings suggest that BChE and albumin may differ in their longitudinal associations with DW change. In models that included ΔlogBChE, ΔlogAlb, and ΔloghANP, ΔlogBChE remained independently associated with ΔlogDW (standardized β = 0.115; P = 0.005), while ΔlogAlb was not (P = 0.39). ΔloghANP was also associated with ΔlogDW (standardized β = 0.144; P = 0.001). These associations are summarized in Fig. 1. Detailed model outputs are provided in Table 3 . Fully adjusted results are shown in Additional file 1: Table S2 . A sensitivity model replacing ΔlogAlb with ΔGNRI is shown in Additional file 1: Table S3, and sensitivity analyses additionally including CTR and dialysis modality are shown in Additional file 1: Table S4. Alternative approaches to within-patient dependence (LMM/GEE) are summarized in Additional file 1: Table S5. In the fully adjusted model, delivered Kt/V was independently associated with ΔlogDW (unstandardized β = 0.009; standardized β = 0.204; P < 0.001), and diabetes mellitus was also independently associated with ΔlogDW (unstandardized β = -0.003; standardized β = -0.181; P < 0.001). Figure 1. Forest plot of standardized associations with 3-month change in prescribed dry weight. Forest plot showing standardized regression coefficients (β) with 95% confidence intervals from longitudinal models predicting 3-month change in prescribed dry weight on the natural-log scale (ΔlogDW), calculated over rolling 3-month windows (ΔlogDW = ln[DW(t)] − ln[DW(t − 3)] = ln{DW(t)/DW(t − 3)}). Model A includes ΔlogAlb and ΔloghANP; Model B includes ΔlogBChE and ΔloghANP; Model C includes ΔlogBChE, ΔlogAlb, and ΔloghANP; the fully adjusted model additionally includes age, sex, diabetes mellitus, dialysis vintage, and Kt/V. Standardized β coefficients allow comparison of effect sizes across predictors. Patient-clustered robust standard errors were used to account for within-patient dependence due to overlapping windows. Table 3 Primary model: association of concurrent 3-month changes with ΔlogDW (rolling 3-month windows). ΔlogX denotes the difference in natural log-transformed values between the start and end of each 3-month window (i.e., log[X_end/X_start]). Variable β (95% CI) Standardized β P value ΔlogBChE (3 months) 0.050 (0.015, 0.085) 0.115 0.005 ΔlogAlb (3 months) -0.024 (-0.079, 0.031) -0.036 0.39 ΔloghANP (3 months) 0.011 (0.004, 0.018) 0.144 0.001 In sensitivity analyses additionally adjusting for dialysis modality and CTR, the association between concurrent ΔlogBChE and ΔlogDW remained statistically significant (β = 0.041, 95% CI 0.005–0.077, P = 0.026). CTR showed a borderline association with ΔlogDW (β = -0.000453, P = 0.068), and dialysis modality showed a borderline association (β = 0.0057, P = 0.054). Detailed model outputs are provided in Additional file 1: Table S4. Within-patient trajectories Representative individual trajectories of BChE, albumin, and DW are shown in Additional file 2: Figure S1 . BChE demonstrated relatively smooth within-patient changes, whereas albumin displayed greater short-term variability. DW changed in a stepwise manner consistent with clinical adjustment. A conceptual framework separating volume and nutrition-related/body-mass axes, intended to aid clinical interpretation of DW trajectories, is presented in Fig. 2. Figure 2. Proposed conceptual framework of determinants of dry weight in hemodialysis patients. The model separates a volume axis (hANP) and a nutrition-related/body-mass axis (BChE). Albumin is depicted as a composite marker influenced by nutrition, inflammation, and fluid dilution. Sensitivity analyses To assess the robustness of inference given overlapping 3-month rolling windows, we re-estimated the primary model using a linear mixed-effects model (random intercept for patient) and a generalized estimating equations model with an exchangeable working correlation (Additional file 1: Table S5). The direction of association for ΔlogBChE and ΔloghANP was consistent across approaches, and the precision of inference for ΔlogBChE varied with the correlation assumptions. Discussion In this longitudinal study of patients undergoing maintenance hemodialysis, we found that serum BChE was more consistently associated with concurrent prescribed DW change than albumin, even after accounting for volume-related factors and relevant clinical covariates. These findings suggest that BChE may provide complementary information for interpreting longitudinal DW change when nutritional and volume-related factors coexist. Our findings support a dual-axis model in which DW reflects both extracellular volume status and the nutrition-related/body-mass component. DW is conventionally regarded as a surrogate for euvolemia; however, in clinical practice, it is also influenced by changes in muscle and fat mass [ 6 ]. Our data illustrate this dual nature. Changes in hANP, a marker of atrial stretch and volume load [ 12 – 15 ], were independently associated with changes in DW, supporting its role as a volume-related axis. In parallel, changes in serum BChE were independently associated with changes in DW, suggesting that BChE may reflect the nutrition-related/body-mass component of DW change beyond volume-related shifts. Although CTR is commonly used as a pragmatic surrogate of volume/cardiac size in hemodialysis practice [ 16 ], it was not independently associated with concurrent ΔlogDW in our sensitivity model. This may reflect that CTR captures both chronic cardiac remodeling and volume status and may be less sensitive to short-term volume dynamics than hANP. Importantly, adjustment for CTR and dialysis modality did not materially alter the ΔlogBChE–ΔlogDW association, supporting the robustness of BChE as a clinically informative marker of the body-mass component of DW change. Notably, an inverse correlation between ΔlogBChE and ΔloghANP is clinically plausible because improvements in volume status (lower hANP) may occur alongside recovery of nutritional reserves and/or reduced dilution (higher BChE). In our rolling-window dataset, concurrent ΔlogBChE and ΔloghANP were moderately inversely correlated (Spearman ρ = -0.315; P < 0.0001; Additional file 1: Table S6). Accordingly, ΔloghANP and ΔlogBChE can move in opposite directions while remaining jointly informative, as they preferentially reflect volume-related versus nutrition-related components of DW change. Although albumin has long been used as a nutritional marker in dialysis patients, it is well established that albumin is strongly influenced by inflammation and fluid dilution [ 4 , 17 ]. In our study, albumin was associated with DW in cross-sectional analyses but failed to be associated with longitudinal DW changes. This discrepancy highlights albumin’s role as a composite or downstream marker rather than a reliable marker for interpreting short- to intermediate-term changes in nutritional or body-composition status in routine dialysis care. Several mechanisms may explain why BChE tracked nutrition-related DW changes more consistently than albumin. First, the circulating half-life of serum albumin is approximately 19–21 days [ 18 ], whereas BChE has a shorter half-life (approximately 12 days) [ 9 ]. This difference may allow BChE to respond more promptly to short-term changes in protein-energy status. Second, albumin (approximately 66.5 kDa) is substantially smaller than the predominant tetrameric form of BChE (approximately 340 kDa) [ 19 , 20 ]. Although we did not measure dialytic losses directly, which remains speculative, the larger molecular size of BChE makes substantial dialytic removal less likely, potentially reducing susceptibility to dialysis-related variability compared with albumin. Finally, albumin is strongly influenced by inflammation and fluid dilution, whereas BChE may provide a complementary signal that is more useful for interpreting whether a change in prescribed DW is accompanied by a nutrition-related/body-mass change in routine practice. Several composite indices, such as GNRI, are used in dialysis practice to assess nutritional risk [ 21 ], but many incorporate serum albumin and therefore inherit its susceptibility to inflammation and dilution. In our cohort, baseline GNRI values suggested a substantial burden of nutritional risk, yet longitudinal interpretation remains challenging when albumin fluctuates for non-nutritional reasons. Beyond albumin, circulating markers such as prealbumin (transthyretin), transferrin, and total cholesterol have been used to reflect protein–energy status [ 1 , 22 ]; however, each can be influenced by inflammation, hepatic synthesis, and dilution. Our findings support BChE as a complementary biochemical marker that may be particularly useful for longitudinal monitoring when the clinical question is how to interpret a change in prescribed DW in the context of possible coexisting nutritional and volume-related changes. Higher baseline Kt/V was positively associated with subsequent 3-month ΔlogDW (Additional file 1: Table S2 ). This could reflect better dialysis delivery and overall clinical status, which could facilitate appetite and nutritional recovery. However, Kt/V is also influenced by body size (V), and therefore its association with changes in prescribed dry weight should be interpreted cautiously without implying causality. Consistent with prior literature, inadequate delivered Kt/V has been associated with worse outcomes [ 23 ] and current guidelines recommend maintaining a minimum delivered spKt/V in thrice-weekly hemodialysis [ 24 ]; nonetheless, the benefit of increasing Kt/V beyond conventional targets is not uniform across studies [ 25 ]. Importantly, the association between ΔlogBChE and ΔlogDW was robust to adjustment for Kt/V and other clinical covariates, supporting the interpretation that BChE reflects a nutrition-related/body-mass component of DW trajectories beyond volume-related changes. Serum BChE is synthesized in the liver and reflects protein synthesis capacity and nutritional reserves [ 9 ]. Compared with albumin, BChE may provide a complementary signal that is less dominated by acute-phase and dilutional effects than albumin in routine practice. Our finding that increases in BChE were associated with increases in DW is consistent with the hypothesis that BChE may track changes in underlying body mass, supporting its potential utility as a practical marker of the nutrition-related/body-mass component in hemodialysis patients. Combining ΔloghANP and ΔlogBChE may help distinguish volume-driven DW adjustment from nutrition-related body-mass change. Future multicenter prospective studies with direct body-composition measures are needed to validate whether ΔlogBChE distinguishes tissue loss from volume-driven DW adjustment and to confirm its utility in broader hemodialysis populations. Finally, BChE may have broader clinical relevance beyond nutritional assessment. Lower serum BChE has been associated with worse survival in maintenance hemodialysis cohorts. For example, low butyrylcholinesterase activity/level predicted higher all-cause mortality and provided prognostic information alongside conventional markers such as albumin and CRP [ 26 , 27 ]. Although mortality was not assessed in our study, these prior findings support the potential clinical relevance of BChE-informed interpretation of DW trajectories and motivate future prospective validation. Several limitations warrant consideration. First, DW is a clinically prescribed target that may be modified in response to symptoms, interdialytic weight gain, blood pressure trends, and clinician judgment; therefore, changes in DW do not uniquely represent changes in body composition. Because prescribed DW is determined by clinician judgment potentially informed by volume-related clinical information (and biomarkers such as hANP in some settings), incorporation bias cannot be excluded. Second, although we incorporated a volume-related biomarker (hANP), we did not have concurrent objective measures of extracellular water (e.g., bioimpedance spectroscopy) or echocardiographic parameters; although CTR was available from routine radiographs and included in sensitivity analyses, it was not measured with a standardized protocol, and residual confounding by volume status cannot be excluded. Third, unmeasured time-varying factors—including intercurrent infection or hospitalization, dietary intake and counseling, medication changes, and dialysis-related parameters such as membrane protein loss or convective dose—may have influenced both BChE and DW trajectories. Fourth, laboratory measurements were obtained monthly and hANP was sampled post-dialysis, whereas other laboratory measurements were obtained predialysis; this difference in sampling timing may introduce variability. Finally, this was a single-center observational study; thus, external validity may be limited, and causal inference is not possible. Accordingly, our findings should be interpreted as hypothesis-generating and require validation in larger prospective studies with direct body-composition assessment. Conclusions In maintenance hemodialysis, longitudinal change in serum BChE was more closely associated with concurrent prescribed DW change than change in albumin, even after accounting for volume-related change. These findings suggest that BChE may provide complementary information for interpreting longitudinal DW change, particularly when nutritional and volume-related factors may coexist. Abbreviations DW dry weight BChE butyrylcholinesterase hANP human atrial natriuretic peptide PEW protein-energy wasting CRP C-reactive protein Kt/V Dialysis adequacy index where K is urea clearance, t is dialysis time, and V is urea distribution volume HD hemodialysis HDF hemodiafiltration CTR cardiothoracic ratio GNRI Geriatric Nutritional Risk Index Declarations Ethics approval and consent to participate: This study was approved by the Ethics Committee of Uji Tokushukai Medical Center, Japan (approval No. 2026-03). Given the retrospective observational design and the use of routinely collected clinical data, the requirement for written informed consent was waived; patients were provided the opportunity to opt out in accordance with institutional policy and the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. Funding: No funding was received for this study. Author Contribution Research idea and study design: HN; data acquisition: HN, RK, KN; data analysis/interpretation: HN; statistical analysis: HN; supervision or mentorship: RK, KN. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. Acknowledgments: None. Data Availability The datasets generated and/or analyzed during the current study are not publicly available because they contain potentially identifiable clinical information but are available from the corresponding author on reasonable request, subject to institutional approval and applicable data protection requirements. References Fouque D, Kalantar-Zadeh K, Kopple J, Cano N, Chauveau P, Cuppari L, et al. 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Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additionalfile2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 28 Mar, 2026 Editor invited by journal 23 Mar, 2026 Editor assigned by journal 21 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 20 Mar, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9176845","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615381256,"identity":"0f79fea7-3f5c-4102-9a1e-44e98f048235","order_by":0,"name":"Hirosuke Nakata","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACA2YwlQDEjI0PEipswIwDRGnhYWA+bPDhTBpISwN+LQxwLWxpkjPbDoO5eLWYs/M+fFxQkyZvz37G2JjnzHm7te2HgbbU2ETj0mLZzG5sPONYjmEPT47hY56K28nbziQCtRxLy23A5bDDbGzSPGwVjD0MOSBbbiebHQBqYWw4TEDLvwr7Hv43ZtK8beeSzc4/JEILb1tOYo9EGsj7B+zMbhC2hdmYty8tuefGY1AgJyeY3QDakoDPL+ePMT7m+ZZs296fCIpKO3uz8+kPH3yoscGpBQMkglUmEKscBOxJUTwKRsEoGAUjAwAAtRJixQY8Hg0AAAAASUVORK5CYII=","orcid":"","institution":"Uji Tokushukai Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Hirosuke","middleName":"","lastName":"Nakata","suffix":""},{"id":615381257,"identity":"aa3084f7-cbfb-46ff-8c3c-f626a701080a","order_by":1,"name":"Ryo Kamimatsuse","email":"","orcid":"","institution":"Uji Tokushukai Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Kamimatsuse","suffix":""},{"id":615381258,"identity":"e4f43f58-4975-4596-b269-c409f4fbcbea","order_by":2,"name":"Koichi Nishiwaki","email":"","orcid":"","institution":"Uji Tokushukai Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Nishiwaki","suffix":""}],"badges":[],"createdAt":"2026-03-20 08:39:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9176845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9176845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106188778,"identity":"1846f157-184b-4a48-8bf8-6366a05c3c88","added_by":"auto","created_at":"2026-04-05 17:06:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178014,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of standardized associations with 3-month change in prescribed dry weight.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9176845/v1/fbf40148c05b60e599e76b40.png"},{"id":106188780,"identity":"70723365-93e8-43bf-91c7-8d6dbda34614","added_by":"auto","created_at":"2026-04-05 17:06:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29004,"visible":true,"origin":"","legend":"\u003cp\u003eProposed conceptual framework of determinants of dry weight in hemodialysis patients.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9176845/v1/f9f03007a3abd0b9abc0504c.png"},{"id":106405706,"identity":"520807be-7fe2-4257-ac34-4419661d6861","added_by":"auto","created_at":"2026-04-08 09:28:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":861543,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9176845/v1/cf98be4e-1882-413c-9ec9-06a7d4a44770.pdf"},{"id":106403019,"identity":"33580bc9-3a22-49c0-ac74-b40116f6695f","added_by":"auto","created_at":"2026-04-08 09:13:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21486,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9176845/v1/02d31daaf95d32596508672a.docx"},{"id":106402980,"identity":"4e0ae30f-d2e1-4cee-851f-4b53767f563c","added_by":"auto","created_at":"2026-04-08 09:13:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7167464,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9176845/v1/603c48b493728e198555d8de.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum butyrylcholinesterase and concurrent dry-weight change in maintenance hemodialysis: a retrospective longitudinal study","fulltext":[{"header":"Background","content":"\u003cp\u003eProtein\u0026ndash;energy wasting (PEW), as defined by the International Society of Renal Nutrition and Metabolism, remains a major clinical problem in patients undergoing maintenance hemodialysis and is strongly associated with adverse outcomes [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Accurate assessment of nutritional status is therefore important in routine dialysis care. However, in clinical practice, interpreting longitudinal changes in prescribed dry weight is often challenging because nutritional and volume-related factors frequently coexist. Serum albumin is widely used as a surrogate marker of nutrition, but its interpretation is limited because albumin concentrations are strongly influenced by inflammation, fluid dilution, and hepatic acute-phase responses [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a result, albumin may fail to reflect clinically meaningful changes in body mass over time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDry weight (DW) represents the post-dialysis body weight at presumed euvolemia and is a central treatment target in maintenance hemodialysis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In routine care, DW is adjusted to avoid both chronic volume overload and excessive ultrafiltration-related symptoms. Importantly, however, DW is also influenced by changes in muscle and fat mass [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, longitudinal DW trajectories may reflect both extracellular volume status and the nutrition-related/body-mass component. From a clinical perspective, unrecognized loss of underlying body mass may result in an inappropriately high prescribed DW relative to true body mass, predisposing patients to persistent volume overload and obscuring early PEW. Conversely, aggressive DW reduction without recognizing nutritional depletion may contribute to intradialytic symptoms. Therefore, markers that help distinguish nutrition-related body-mass change from volume-related change may improve interpretation of longitudinal DW trajectories in maintenance hemodialysis.\u003c/p\u003e \u003cp\u003eSerum butyrylcholinesterase (BChE) is synthesized in the liver and reflects protein synthesis capacity and nutritional reserves [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous studies have reported associations between BChE and nutritional status in chronic illness, including dialysis populations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Compared with albumin, which is strongly influenced by inflammation and volume status in hemodialysis patients, BChE provides complementary information related to protein\u0026ndash;energy status. While BChE is also affected by inflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], it may help interpret DW trajectories by reflecting nutritional dynamics beyond albumin alone. However, longitudinal data examining whether BChE helps interpret clinically relevant changes in prescribed DW, particularly in comparison with albumin, remain limited.\u003c/p\u003e \u003cp\u003eNatriuretic peptides are released in response to cardiac stretch and are commonly used as supportive biomarkers of volume status. Human atrial natriuretic peptide (hANP) is routinely measured in Japan in hemodialysis practice as a pragmatic volume-related marker [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Higher hANP levels have also been associated with volume overload assessed by body composition analysis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, natriuretic peptides also reflect cardiac structure and function and should not be interpreted as stand-alone measures [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Incorporating a natriuretic peptide marker into longitudinal analyses may help separate volume-related effects from nutrition-related components of DW change.\u003c/p\u003e \u003cp\u003eWe aimed to compare cross-sectional associations of prescribed DW with BChE and albumin, and to evaluate whether concurrent 3-month changes in BChE were associated with concurrent 3-month changes in prescribed DW independent of albumin and volume-related factors assessed by hANP. We also examined whether BChE provides complementary information for interpreting longitudinal DW change in maintenance hemodialysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis was a single-center retrospective observational study conducted at a participating institution in Japan. Adult patients receiving maintenance hemodialysis three times weekly between September 2024 and December 2025 were screened. Exclusion criteria were: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (2) DW not stably defined during the observation period or frequent failure to reach the prescribed post-dialysis DW; (3) severe hepatic dysfunction (Child-Pugh class C) or acute liver injury likely to strongly affect BChE; (4) transfer to another facility during follow-up; and (5) missing BChE or DW data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDry weight definition\u003c/h3\u003e\n\u003cp\u003eDW was defined as the prescribed post-dialysis target weight determined by the treating nephrologist based on routine clinical assessment (blood pressure, edema, interdialytic weight gain, and other standard indicators). DW could be adjusted during routine care.\u003c/p\u003e\n\u003ch3\u003eClinical and laboratory variables\u003c/h3\u003e\n\u003cp\u003eMonthly laboratory measurements (BChE, albumin, C-reactive protein [CRP], and hANP), prescribed DW, and dialysis adequacy (Kt/V) were obtained as part of routine care. For longitudinal analyses, DW, BChE, albumin, hANP, and CRP were natural log-transformed; 3-month changes were calculated within individuals as ΔlogX\u0026thinsp;=\u0026thinsp;log(X_t)\u0026thinsp;\u0026minus;\u0026thinsp;log(X_t\u0026thinsp;\u0026minus;\u0026thinsp;3), equivalent to log(X_t/X_t\u0026thinsp;\u0026minus;\u0026thinsp;3). Concurrent ΔlogDW and changes in biomarkers were defined over the same 3-month window. Blood samples for BChE, albumin, and CRP were collected at the beginning of each month before a midweek dialysis session (predialysis). hANP was measured from a sample obtained immediately before blood return at the end of the same dialysis session.\u003c/p\u003e \u003cp\u003eDialysis modality was recorded as hemodialysis (HD) or hemodiafiltration (HDF). The cardiothoracic ratio (CTR) was obtained from routine chest radiographs (expressed as %). Chest radiographs for CTR assessment were obtained routinely once monthly, typically mid-week during the week of each month.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was the association between concurrent 3-month changes in BChE (ΔlogBChE) and concurrent 3-month changes in prescribed DW (ΔlogDW). Secondary endpoints included the association between ΔlogAlb and ΔlogDW, and evaluation of ΔloghANP as a volume-related marker in multivariable models. In prespecified sensitivity analyses, we additionally adjusted for dialysis modality (HD vs HDF) and CTR as pragmatic surrogates for treatment modality and volume/cardiac size. These analyses were intended to examine whether BChE retained an association with DW change under clinically relevant conditions in which volume-related and treatment-related factors coexist.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are summarized as mean (standard deviation) or median (interquartile range) as appropriate. Associations between changes in biomarkers and changes in DW were evaluated using linear regression. Because repeated observations were available for each patient, models used cluster-robust standard errors at the patient level. Multivariable models included markers reflecting volume status (ΔloghANP) and other prespecified covariates as appropriate. In prespecified sensitivity analyses, we additionally adjusted for dialysis modality (HD vs HDF) and CTR as pragmatic surrogates for modality and volume/cardiac size; CTR was treated as a time-varying covariate measured at the start of each 3-month window. Delivered Kt/V was likewise entered as a time-varying covariate measured at the start of each 3-month window (t\u0026thinsp;\u0026minus;\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical approach for overlapping rolling windows\u003c/h2\u003e \u003cp\u003eBecause 3-month rolling windows overlap within individuals, observations are not independent. Therefore, our primary analyses used ordinary least squares with patient-clustered robust standard errors. As sensitivity analyses addressing within-patient dependence under alternative modeling frameworks, we additionally fitted (i) a linear mixed-effects model with a random intercept for patient and (ii) a generalized estimating equations model with an exchangeable working correlation.\u003c/p\u003e \u003cp\u003eTwo-sided P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Statistical analyses were performed using EZR, based on R version 4.5.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and data completeness\u003c/h2\u003e \u003cp\u003eAmong 113 screened patients, 17 were excluded because DW could not be stably defined, leaving 96 patients in the analytic cohort. Monthly measurements of BChE, albumin, hANP, CRP, and Kt/V were available for the study variables, and the analytic dataset used for longitudinal 3-month windows had no missing values for variables used in the primary longitudinal models. Baseline patient characteristics are summarized 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\u003eBaseline characteristics of the study cohort \u003cem\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median [IQR], or n (%), as appropriate.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients (N\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.40\u0026thinsp;\u0026plusmn;\u0026thinsp;12.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (64.6%)\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\u003e67.00 [36.75, 103.30]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry weight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.20\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKt/V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerritin, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.00 [73.75, 281.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransferrin saturation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.30\u0026thinsp;\u0026plusmn;\u0026thinsp;12.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButyrylcholinesterase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217.60\u0026thinsp;\u0026plusmn;\u0026thinsp;62.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17 [0.07, 0.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehANP, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.00 [26.20, 87.45]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.80\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\u003eAt baseline (month 0), BChE showed moderate correlations with serum albumin (Spearman ρ\u0026thinsp;=\u0026thinsp;0.298, P\u0026thinsp;=\u0026thinsp;0.0032) and GNRI (Spearman ρ\u0026thinsp;=\u0026thinsp;0.393, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), supporting its relevance as a nutrition-related marker (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a cross-sectional snapshot of factors associated with baseline DW, highlighting that DW reflects both body size/nutritional reserves and volume-related conditions; these baseline findings helped contextualize subsequent longitudinal models.\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\u003eCross-sectional associations of baseline prescribed dry weight with clinical and laboratory parameters \u003cem\u003eOutcome: prescribed dry weight (baseline, month 0)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\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\u003eBChE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.215 (0.083, 0.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.488 (0.214, 0.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020 (-0.005, 0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehANP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.054 (-0.105, -0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\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\u003e \u003cem\u003eValues are unstandardized regression coefficients estimated using linear regression with robust standard errors. N\u0026thinsp;=\u0026thinsp;96.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrimary longitudinal analysis: concurrent 3-month changes\u003c/h2\u003e \u003cp\u003eIn the primary analysis using concurrent 3-month windows, ΔlogBChE was positively associated with ΔlogDW after accounting for volume-related changes, whereas ΔlogAlb showed weaker or non-significant associations. These findings suggest that BChE and albumin may differ in their longitudinal associations with DW change. In models that included ΔlogBChE, ΔlogAlb, and ΔloghANP, ΔlogBChE remained independently associated with ΔlogDW (standardized β\u0026thinsp;=\u0026thinsp;0.115; P\u0026thinsp;=\u0026thinsp;0.005), while ΔlogAlb was not (P\u0026thinsp;=\u0026thinsp;0.39). ΔloghANP was also associated with ΔlogDW (standardized β\u0026thinsp;=\u0026thinsp;0.144; P\u0026thinsp;=\u0026thinsp;0.001). These associations are summarized in Fig.\u0026nbsp;1. Detailed model outputs are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Fully adjusted results are shown in Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. A sensitivity model replacing ΔlogAlb with ΔGNRI is shown in Additional file 1: Table S3, and sensitivity analyses additionally including CTR and dialysis modality are shown in Additional file 1: Table S4. Alternative approaches to within-patient dependence (LMM/GEE) are summarized in Additional file 1: Table S5. In the fully adjusted model, delivered Kt/V was independently associated with ΔlogDW (unstandardized β\u0026thinsp;=\u0026thinsp;0.009; standardized β\u0026thinsp;=\u0026thinsp;0.204; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and diabetes mellitus was also independently associated with ΔlogDW (unstandardized β = -0.003; standardized β = -0.181; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1.\u003c/b\u003e Forest plot of standardized associations with 3-month change in prescribed dry weight.\u003c/p\u003e \u003cp\u003e Forest plot showing standardized regression coefficients (β) with 95% confidence intervals from longitudinal models predicting 3-month change in prescribed dry weight on the natural-log scale (ΔlogDW), calculated over rolling 3-month windows (ΔlogDW\u0026thinsp;=\u0026thinsp;ln[DW(t)]\u0026thinsp;\u0026minus;\u0026thinsp;ln[DW(t\u0026thinsp;\u0026minus;\u0026thinsp;3)]\u0026thinsp;=\u0026thinsp;ln{DW(t)/DW(t\u0026thinsp;\u0026minus;\u0026thinsp;3)}). Model A includes ΔlogAlb and ΔloghANP; Model B includes ΔlogBChE and ΔloghANP; Model C includes ΔlogBChE, ΔlogAlb, and ΔloghANP; the fully adjusted model additionally includes age, sex, diabetes mellitus, dialysis vintage, and Kt/V. Standardized β coefficients allow comparison of effect sizes across predictors. Patient-clustered robust standard errors were used to account for within-patient dependence due to overlapping windows.\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\u003ePrimary model: association of concurrent 3-month changes with ΔlogDW (rolling 3-month windows). ΔlogX denotes the difference in natural log-transformed values between the start and end of each 3-month window (i.e., log[X_end/X_start]).\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized β\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\u003eΔlogBChE (3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050 (0.015, 0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔlogAlb (3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.024 (-0.079, 0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔloghANP (3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011 (0.004, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\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 sensitivity analyses additionally adjusting for dialysis modality and CTR, the association between concurrent ΔlogBChE and ΔlogDW remained statistically significant (β\u0026thinsp;=\u0026thinsp;0.041, 95% CI 0.005\u0026ndash;0.077, P\u0026thinsp;=\u0026thinsp;0.026). CTR showed a borderline association with ΔlogDW (β = -0.000453, P\u0026thinsp;=\u0026thinsp;0.068), and dialysis modality showed a borderline association (β\u0026thinsp;=\u0026thinsp;0.0057, P\u0026thinsp;=\u0026thinsp;0.054). Detailed model outputs are provided in Additional file 1: Table S4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWithin-patient trajectories\u003c/h2\u003e \u003cp\u003eRepresentative individual trajectories of BChE, albumin, and DW are shown in Additional file 2: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. BChE demonstrated relatively smooth within-patient changes, whereas albumin displayed greater short-term variability. DW changed in a stepwise manner consistent with clinical adjustment. A conceptual framework separating volume and nutrition-related/body-mass axes, intended to aid clinical interpretation of DW trajectories, is presented in Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2.\u003c/b\u003e Proposed conceptual framework of determinants of dry weight in hemodialysis patients.\u003c/p\u003e \u003cp\u003e The model separates a volume axis (hANP) and a nutrition-related/body-mass axis (BChE). Albumin is depicted as a composite marker influenced by nutrition, inflammation, and fluid dilution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eTo assess the robustness of inference given overlapping 3-month rolling windows, we re-estimated the primary model using a linear mixed-effects model (random intercept for patient) and a generalized estimating equations model with an exchangeable working correlation (Additional file 1: Table S5). The direction of association for ΔlogBChE and ΔloghANP was consistent across approaches, and the precision of inference for ΔlogBChE varied with the correlation assumptions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal study of patients undergoing maintenance hemodialysis, we found that serum BChE was more consistently associated with concurrent prescribed DW change than albumin, even after accounting for volume-related factors and relevant clinical covariates. These findings suggest that BChE may provide complementary information for interpreting longitudinal DW change when nutritional and volume-related factors coexist. Our findings support a dual-axis model in which DW reflects both extracellular volume status and the nutrition-related/body-mass component.\u003c/p\u003e \u003cp\u003eDW is conventionally regarded as a surrogate for euvolemia; however, in clinical practice, it is also influenced by changes in muscle and fat mass [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Our data illustrate this dual nature. Changes in hANP, a marker of atrial stretch and volume load [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], were independently associated with changes in DW, supporting its role as a volume-related axis. In parallel, changes in serum BChE were independently associated with changes in DW, suggesting that BChE may reflect the nutrition-related/body-mass component of DW change beyond volume-related shifts. Although CTR is commonly used as a pragmatic surrogate of volume/cardiac size in hemodialysis practice [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], it was not independently associated with concurrent ΔlogDW in our sensitivity model. This may reflect that CTR captures both chronic cardiac remodeling and volume status and may be less sensitive to short-term volume dynamics than hANP. Importantly, adjustment for CTR and dialysis modality did not materially alter the ΔlogBChE\u0026ndash;ΔlogDW association, supporting the robustness of BChE as a clinically informative marker of the body-mass component of DW change.\u003c/p\u003e \u003cp\u003eNotably, an inverse correlation between ΔlogBChE and ΔloghANP is clinically plausible because improvements in volume status (lower hANP) may occur alongside recovery of nutritional reserves and/or reduced dilution (higher BChE). In our rolling-window dataset, concurrent ΔlogBChE and ΔloghANP were moderately inversely correlated (Spearman ρ = -0.315; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Additional file 1: Table S6). Accordingly, ΔloghANP and ΔlogBChE can move in opposite directions while remaining jointly informative, as they preferentially reflect volume-related versus nutrition-related components of DW change.\u003c/p\u003e \u003cp\u003eAlthough albumin has long been used as a nutritional marker in dialysis patients, it is well established that albumin is strongly influenced by inflammation and fluid dilution [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In our study, albumin was associated with DW in cross-sectional analyses but failed to be associated with longitudinal DW changes. This discrepancy highlights albumin\u0026rsquo;s role as a composite or downstream marker rather than a reliable marker for interpreting short- to intermediate-term changes in nutritional or body-composition status in routine dialysis care.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may explain why BChE tracked nutrition-related DW changes more consistently than albumin. First, the circulating half-life of serum albumin is approximately 19\u0026ndash;21 days [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], whereas BChE has a shorter half-life (approximately 12 days) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This difference may allow BChE to respond more promptly to short-term changes in protein-energy status. Second, albumin (approximately 66.5 kDa) is substantially smaller than the predominant tetrameric form of BChE (approximately 340 kDa) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although we did not measure dialytic losses directly, which remains speculative, the larger molecular size of BChE makes substantial dialytic removal less likely, potentially reducing susceptibility to dialysis-related variability compared with albumin. Finally, albumin is strongly influenced by inflammation and fluid dilution, whereas BChE may provide a complementary signal that is more useful for interpreting whether a change in prescribed DW is accompanied by a nutrition-related/body-mass change in routine practice.\u003c/p\u003e \u003cp\u003eSeveral composite indices, such as GNRI, are used in dialysis practice to assess nutritional risk [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], but many incorporate serum albumin and therefore inherit its susceptibility to inflammation and dilution. In our cohort, baseline GNRI values suggested a substantial burden of nutritional risk, yet longitudinal interpretation remains challenging when albumin fluctuates for non-nutritional reasons. Beyond albumin, circulating markers such as prealbumin (transthyretin), transferrin, and total cholesterol have been used to reflect protein\u0026ndash;energy status [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; however, each can be influenced by inflammation, hepatic synthesis, and dilution. Our findings support BChE as a complementary biochemical marker that may be particularly useful for longitudinal monitoring when the clinical question is how to interpret a change in prescribed DW in the context of possible coexisting nutritional and volume-related changes.\u003c/p\u003e \u003cp\u003eHigher baseline Kt/V was positively associated with subsequent 3-month ΔlogDW (Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This could reflect better dialysis delivery and overall clinical status, which could facilitate appetite and nutritional recovery. However, Kt/V is also influenced by body size (V), and therefore its association with changes in prescribed dry weight should be interpreted cautiously without implying causality.\u003c/p\u003e \u003cp\u003eConsistent with prior literature, inadequate delivered Kt/V has been associated with worse outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and current guidelines recommend maintaining a minimum delivered spKt/V in thrice-weekly hemodialysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; nonetheless, the benefit of increasing Kt/V beyond conventional targets is not uniform across studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Importantly, the association between ΔlogBChE and ΔlogDW was robust to adjustment for Kt/V and other clinical covariates, supporting the interpretation that BChE reflects a nutrition-related/body-mass component of DW trajectories beyond volume-related changes.\u003c/p\u003e \u003cp\u003eSerum BChE is synthesized in the liver and reflects protein synthesis capacity and nutritional reserves [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Compared with albumin, BChE may provide a complementary signal that is less dominated by acute-phase and dilutional effects than albumin in routine practice. Our finding that increases in BChE were associated with increases in DW is consistent with the hypothesis that BChE may track changes in underlying body mass, supporting its potential utility as a practical marker of the nutrition-related/body-mass component in hemodialysis patients. Combining ΔloghANP and ΔlogBChE may help distinguish volume-driven DW adjustment from nutrition-related body-mass change. Future multicenter prospective studies with direct body-composition measures are needed to validate whether ΔlogBChE distinguishes tissue loss from volume-driven DW adjustment and to confirm its utility in broader hemodialysis populations.\u003c/p\u003e \u003cp\u003eFinally, BChE may have broader clinical relevance beyond nutritional assessment. Lower serum BChE has been associated with worse survival in maintenance hemodialysis cohorts. For example, low butyrylcholinesterase activity/level predicted higher all-cause mortality and provided prognostic information alongside conventional markers such as albumin and CRP [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although mortality was not assessed in our study, these prior findings support the potential clinical relevance of BChE-informed interpretation of DW trajectories and motivate future prospective validation.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. First, DW is a clinically prescribed target that may be modified in response to symptoms, interdialytic weight gain, blood pressure trends, and clinician judgment; therefore, changes in DW do not uniquely represent changes in body composition. Because prescribed DW is determined by clinician judgment potentially informed by volume-related clinical information (and biomarkers such as hANP in some settings), incorporation bias cannot be excluded. Second, although we incorporated a volume-related biomarker (hANP), we did not have concurrent objective measures of extracellular water (e.g., bioimpedance spectroscopy) or echocardiographic parameters; although CTR was available from routine radiographs and included in sensitivity analyses, it was not measured with a standardized protocol, and residual confounding by volume status cannot be excluded. Third, unmeasured time-varying factors\u0026mdash;including intercurrent infection or hospitalization, dietary intake and counseling, medication changes, and dialysis-related parameters such as membrane protein loss or convective dose\u0026mdash;may have influenced both BChE and DW trajectories. Fourth, laboratory measurements were obtained monthly and hANP was sampled post-dialysis, whereas other laboratory measurements were obtained predialysis; this difference in sampling timing may introduce variability.\u003c/p\u003e \u003cp\u003eFinally, this was a single-center observational study; thus, external validity may be limited, and causal inference is not possible. Accordingly, our findings should be interpreted as hypothesis-generating and require validation in larger prospective studies with direct body-composition assessment.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn maintenance hemodialysis, longitudinal change in serum BChE was more closely associated with concurrent prescribed DW change than change in albumin, even after accounting for volume-related change. These findings suggest that BChE may provide complementary information for interpreting longitudinal DW change, particularly when nutritional and volume-related factors may coexist.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edry weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBChE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebutyrylcholinesterase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehANP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman atrial natriuretic peptide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePEW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein-energy wasting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKt/V\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDialysis adequacy index where K is urea clearance, t is dialysis time, and V is urea distribution volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemodialysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemodiafiltration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiothoracic ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGNRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeriatric Nutritional Risk Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e This study was approved by the Ethics Committee of Uji Tokushukai Medical Center, Japan (approval No. 2026-03). Given the retrospective observational design and the use of routinely collected clinical data, the requirement for written informed consent was waived; patients were provided the opportunity to opt out in accordance with institutional policy and the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eResearch idea and study design: HN; data acquisition: HN, RK, KN; data analysis/interpretation: HN; statistical analysis: HN; supervision or mentorship: RK, KN. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available because they contain potentially identifiable clinical information but are available from the corresponding author on reasonable request, subject to institutional approval and applicable data protection requirements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFouque D, Kalantar-Zadeh K, Kopple J, Cano N, Chauveau P, Cuppari L, et al. A proposed nomenclature and diagnostic criteria for protein\u0026ndash;energy wasting in acute and chronic kidney disease. Kidney Int. 2008;73:391\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy CP, Kalantar-Zadeh K, WHY IS PROTEIN-ENERGY WASTING, ASSOCIATED WITH MORTALITY IN CHRONIC KIDNEY DISEASE?. Semin Nephrol. 2009;29:3\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreau-Gaudry X, Jean G, Genet L, Lataillade D, Legrand E, Kuentz F, et al. A Simple Protein\u0026ndash;Energy Wasting Score Predicts Survival in Maintenance Hemodialysis Patients. J Ren Nutr. 2014;24:395\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaysen GA, Dubin JA, M\u0026uuml;ller H-G, Rosales L, Levin NW, Mitch WE, et al. Inflammation and reduced albumin synthesis associated with stable decline in serum albumin in hemodialysis patients. Kidney Int. 2004;65:1408\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans DC, Corkins MR, Malone A, Miller S, Mogensen KM, Guenter P, et al. The Use of Visceral Proteins as Nutrition Markers: An ASPEN Position Paper. Nutr Clin Pract. 2021;36:22\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaeger JQ, Mehta RL. Assessment of Dry Weight in Hemodialysis: An Overview. J Am Soc Nephrol. 1999;10:392\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInoue H, Oya M, Aizawa M, Wagatsuma K, Kamimae M, Kashiwagi Y, et al. Predicting dry weight change in Hemodialysis patients using machine learning. BMC Nephrol. 2023;24:196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin J, Kim H, Jeon HE, Kwon S, Hwang JH, Son H-E, et al. Optimizing hemodialysis management using bioelectrical impedance analysis: emphasizing the significance of the phase angle. Ren Fail. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/0886022X.2025.2567037\u003c/span\u003e\u003cspan address=\"10.1080/0886022X.2025.2567037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantarpia L, Grandone I, Contaldo F, Pasanisi F. Butyrylcholinesterase as a prognostic marker: a review of the literature. J Cachexia Sarcopenia Muscle. 2013;4:31\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkamoto T, Tsutaya C, Hatakeyama S, Konishi S, Okita K, Tanaka Y, et al. Low serum butyrylcholinesterase is independently related to low fetuin-A in patients on hemodialysis: a cross-sectional study. Int Urol Nephrol. 2018;50:1713\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamp\u0026oacute;n N, Hermida-Cadahia EF, Riveiro A, Tutor JC. Association between butyrylcholinesterase activity and low-grade systemic inflammation. Ann Hepatol. 2012;11:356\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuwahara M, Matsushita K, Yoshinaga H, Aki M, Fujisaki N, Kagawa S. Clinical significance of human atrial natriuretic peptide (HANP) in patients on maintenance hemodialysis\u0026mdash;HANP as a parameter to determine the dry weight (D.W). Hinyokika Kiyo. 1992;38:5\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkamoto M, Fukui M, Kurusu A, Shou I, Maeda K, Hamada C, et al. Usefulness of a Body Composition Analyzer, InBody 2.0, in Chronic Hemodialysis Patients. Kaohsiung J Med Sci. 2006;22:207\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakatani T, Naganuma T, Masuda C, Sugimura T, Uchida J, Takemoto Y, et al. The prognostic role of atrial natriuretic peptides in hemodialysis patients. Blood Purif. 2003;21:395\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoffy S, Rosner MH. Natriuretic Peptides in ESRD. Am J Kidney Dis. 2005;46:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYotsueda R, Taniguchi M, Tanaka S, Eriguchi M, Fujisaki K, Torisu K, et al. Cardiothoracic Ratio and All-Cause Mortality and Cardiovascular Disease Events in Hemodialysis Patients: The Q-Cohort Study. Am J Kidney Dis. 2017;70:84\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumler F. Hypoalbuminemia is a marker of overhydration in chronic maintenance patients on dialysis. ASAIO J. 2003;49:282\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrinsen BHCMT, de Sain-van MGM. Albumin turnover: experimental approach and its application in health and renal diseases. Clin Chim Acta. 2004;347:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniProt Consortium. UniProtKB\u0026mdash;Albumin (ALB), Homo sapiens (Human). Accession P02768. UniProt. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/uniprotkb/P02768/entry\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/uniprotkb/P02768/entry\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 14 Feb, 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJasiecki J, Szczoczarz A, Cysewski D, Lewandowski K, Skowron P, Waleron K, et al. Butyrylcholinesterase\u0026ndash;Protein Interactions in Human Serum. Int J Mol Sci. 2021;22:10662.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiella MC. Nutritional Evaluation of Patients Receiving Dialysis for the Management of Protein-Energy Wasting: What is Old and What is New? J Ren Nutr. 2013;23:195\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkizler TA, Burrowes JD, Byham-Gray LD, Campbell KL, Carrero J-J, Chan W, et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am J Kidney Dis. 2020;76:S1\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePort FK, Wolfe RA, Hulbert-Shearon TE, McCullough KP, Ashby VB, Held PJ. High dialysis dose is associated with lower mortality among hemodialysis patients. Am J Kidney Dis. 2004;43:1014\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Kidney Foundation. KDOQI Clinical Practice Guideline for Hemodialysis Adequacy: 2015 Update. Am J Kidney Dis. 2015;66:884\u0026ndash;930.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEknoyan G, Beck GJ, Cheung AK, Daugirdas JT, Greene T, Kusek JW, et al. Effect of dialysis dose and membrane flux in maintenance hemodialysis. N Engl J Med. 2002;347:2010\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStojanov MD, Jovičić DM, Djurić SP, Konjević MM, Todorović ZM, Prostran MŠ. Butyrylcholinesterase activity and mortality risk in hemodialysis patients: Comparison to hsCRP and albumin. Clin Biochem. 2009;42:22\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukushi K, Okamoto T, Ozaki Y, Ozaki K, Sasaki D, Miura Y, et al. Butyrylcholinesterase level as an independent prognostic factor for overall survival in patients on maintenance hemodialysis: a single-center retrospective study. Clin Exp Nephrol. 2022;26:190\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"butyrylcholinesterase, albumin, dry weight, hemodialysis, nutrition, volume status","lastPublishedDoi":"10.21203/rs.3.rs-9176845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9176845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAssessing nutrition-related change in maintenance hemodialysis is challenging because commonly used biomarkers such as serum albumin are influenced by inflammation and volume status. Prescribed dry weight (DW) is a clinically important treatment target, but longitudinal DW change may reflect both volume-related and nutrition-related body-mass change. Serum butyrylcholinesterase (BChE), a liver-synthesized protein, may better capture nutrition-related change in this setting. We examined whether longitudinal change in BChE was associated with concurrent change in prescribed DW and compared this association with that of albumin while accounting for volume-related change using human atrial natriuretic peptide (hANP).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a single-center retrospective longitudinal observational study of adults receiving maintenance hemodialysis between September 2024 and December 2025. Monthly clinical data were used to calculate concurrent 3-month changes, expressed as log-ratios (Δlog), in BChE, albumin, hANP, and prescribed DW. The primary analysis evaluated the association between ΔlogBChE and concurrent ΔlogDW across repeated 3-month windows using linear regression with patient-level cluster-robust standard errors. Multivariable models included ΔlogBChE, ΔlogAlb, and ΔloghANP.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNinety-six patients were included. In multivariable longitudinal models, ΔlogBChE was independently associated with concurrent ΔlogDW (standardized β\u0026thinsp;=\u0026thinsp;0.115; P\u0026thinsp;=\u0026thinsp;0.005), whereas ΔlogAlb was not (P\u0026thinsp;=\u0026thinsp;0.39). ΔloghANP was also independently associated with ΔlogDW (standardized β\u0026thinsp;=\u0026thinsp;0.144; P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn maintenance hemodialysis, longitudinal change in serum BChE was more closely associated with concurrent prescribed DW change than change in albumin, even after accounting for volume-related change. BChE may be a useful adjunct for interpreting DW trajectories when nutritional and volume-related factors coexist.\u003c/p\u003e","manuscriptTitle":"Serum butyrylcholinesterase and concurrent dry-weight change in maintenance hemodialysis: a retrospective longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:06:18","doi":"10.21203/rs.3.rs-9176845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-19T11:35:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201552858543778948150051736460009106331","date":"2026-05-12T07:41:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288087405554399762128115670118107005522","date":"2026-05-11T07:28:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T10:03:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272303554445085390418877624388971501989","date":"2026-04-08T17:37:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-28T16:26:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T05:31:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-21T07:46:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T07:45:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-03-20T08:34:25+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":"3ce860ef-3af0-492e-bc01-8f8bbc28f0ab","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-19T11:35:43+00:00","index":96,"fulltext":""},{"type":"reviewerAgreed","content":"201552858543778948150051736460009106331","date":"2026-05-12T07:41:40+00:00","index":93,"fulltext":""},{"type":"reviewerAgreed","content":"288087405554399762128115670118107005522","date":"2026-05-11T07:28:18+00:00","index":90,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-05T17:06:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:06:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9176845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9176845","identity":"rs-9176845","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}