Combined Effects of Nutritional and Inflammatory Status on Erythropoiesis Resistance: A Longitudinal Patient-Month Analysis in Hemodialysis Patients

Combined Effects of Nutritional and Inflammatory Status on Erythropoiesis Resistance: A Longitudinal Patient-Month Analysis in Hemodialysis Patients | 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 Combined Effects of Nutritional and Inflammatory Status on Erythropoiesis Resistance: A Longitudinal Patient-Month Analysis in Hemodialysis Patients Yukinobu Ikegishi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9326742/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Nutritional status and inflammation are thought to influence erythropoiesis in hemodialysis patients, but their combined effects on erythropoiesis resistance index (ERI) have not been well characterized in longitudinal settings. Methods: We conducted a retrospective longitudinal study of 57 maintenance hemodialysis patients (684 patient-month observations) between January and December 2025. ERI was analyzed using mixed-effects models with serum albumin and C-reactive protein (CRP) as markers of nutritional status and inflammation. Both continuous models and categorical analyses, including a cumulative nutritional–inflammatory risk score, were performed. Results: Lower albumin was significantly associated with higher ERI, whereas CRP was not independently associated. The interaction between albumin and CRP was not statistically significant. In contrast, the cumulative nutritional–inflammatory risk score showed a graded association with ERI, which remained significant after adjustment for iron-related parameters. Sensitivity analyses restricted to ESA-treated patient-months yielded similar results. Conclusions: In maintenance hemodialysis patients, nutritional and inflammatory status are additively associated with ERI. Although no statistically significant interaction was observed, the cumulative burden of these factors may contribute to variability in ESA responsiveness. These findings support a longitudinal, individualized approach to anemia management. Figures Figure 1 Figure 2 Introduction Anemia is a common and clinically important complication in patients undergoing maintenance hemodialysis [1]. While iron availability and erythropoiesis-stimulating agent (ESA) therapy are central to anemia management [2,3], variability in erythropoietic response remains a major clinical challenge. Increasing evidence suggests that non–iron-related factors, including nutritional status and inflammation, play important roles in modulating erythropoiesis [4–6]. In particular, hypoalbuminemia and elevated inflammatory markers have been associated with reduced responsiveness to ESA therapy in hemodialysis patients. We have previously reported cases of copper deficiency in hemodialysis patients, characterized by severe anemia that was refractory to conventional treatment but improved following intravenous copper administration [7,8]. These cases were notable for the coexistence of multiple risk factors, including malnutrition, inflammation, zinc supplementation, and physiological stress, suggesting a cumulative contribution of multiple factors rather than a single causative mechanism. These observations led us to hypothesize that the cumulative burden of nutritional and inflammatory factors may influence erythropoiesis more broadly in routine hemodialysis care. However, this concept has not been well examined using longitudinal clinical data that account for within-patient variability over time. Therefore, in the present study, we investigated the association between serum albumin and C-reactive protein (CRP), as representative markers of nutritional status and inflammation, and the erythropoiesis resistance index (ERI) using patient-month–level longitudinal data. In addition, we evaluated their additive and potential interaction effects to better understand how these factors are associated with variability in ESA response in maintenance hemodialysis patients. Methods Study design and population This was a single-center, retrospective longitudinal observational study conducted in maintenance hemodialysis patients. Adult patients undergoing maintenance hemodialysis at our center were eligible for inclusion if they had at least 12 consecutive months of available monthly data between January and December 2025 , including hemoglobin, ESA dose, iron indices, and inflammatory markers. Patients with incomplete longitudinal data or receiving dialysis modalities other than maintenance hemodialysis were excluded. The study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the institutional ethics committee, and written informed consent was obtained from all participants. Data collection and variables Clinical and laboratory data were collected retrospectively from electronic medical records and dialysis charts. Data were organized in a longitudinal format, with each row representing one patient-month observation. The following variables were recorded monthly: * Hemoglobin concentration (g/dL) * ESA dose (IU/week), including epoetin alfa or darbepoetin alfa; doses were converted to epoetin equivalents (1 μg darbepoetin alfa ≈ 200 IU epoetin) based on previously published methods [15]. * Intravenous (IV) iron dose (mg/month), aggregated as total monthly dose; months without iron administration were recorded as 0 mg * Transferrin saturation (TSAT, %) * Serum ferritin (ng/mL) * C-reactive protein (CRP, mg/dL) * Serum albumin (g/dL) * Physical stress indicators (hospitalization, infection, or other clinically significant stressors; coded as present or absent) IV iron was administered according to routine clinical practice, typically as intermittent low-dose supplementation based on TSAT and ferritin levels. One patient received a hypoxia-inducible factor prolyl hydroxylase (HIF-PH) inhibitor during the study period and was included in the primary analysis. Definition of ERI The erythropoiesis resistance index (ERI) was calculated as the weekly ESA dose divided by the product of hemoglobin concentration and body weight (ESA dose / [Hb × body weight]) and was used as a marker associated with ESA response. Higher ERI values indicate reduced ESA response. ERI was treated as a time-varying outcome at the patient-month level and was log-transformed as log(ERI + 1) in regression models to reduce skewness and stabilize variance. Exposure variables and grouping The primary exposures of interest were serum albumin and C-reactive protein (CRP), used as representative markers of nutritional status and systemic inflammation, respectively. For descriptive analyses, patient-month observations were categorized according to albumin (<3.5 vs ≥3.5 g/dL) and CRP (<0.3 vs ≥0.3 mg/dL) levels to examine differences in ERI across combinations of nutritional and inflammatory status. To further evaluate cumulative effects, a nutritional-inflammatory risk score was constructed based on the presence of low albumin (<3.5 g/dL) and elevated CRP (≥0.3 mg/dL). Each factor was assigned 1 point, yielding a total score ranging from 0 to 2. In addition to modeling the score as a continuous variable, it was also analyzed as a categorical variable (0, 1, and 2). TSAT and ferritin were treated as iron-related covariates and were included in secondary analyses to assess whether the associations of albumin and CRP with ERI were independent of conventional iron indices. Statistical analysis Descriptive statistics were summarized as medians with interquartile ranges (IQRs) for continuous variables and as counts with percentages for categorical variables. Exploratory analyses, including scatter plots and category-based comparisons, were performed for descriptive purposes only. Because patient-month observations are not independent, these analyses do not account for within-patient correlation and should be interpreted cautiously. Linear mixed-effects models with patient-specific random intercepts were used as the primary analytical approach to account for repeated measurements within individuals. In the primary model, we evaluated the associations of albumin, CRP, and their interaction term (albumin × CRP) with log-transformed ERI [log(ERI + 1)]. To evaluate cumulative effects, the nutritional-inflammatory risk score was also examined in mixed-effects models. Secondary analyses additionally adjusted for iron-related parameters, including TSAT and ferritin, to assess whether the observed associations were independent of conventional iron indices. A sensitivity analysis restricted to patient-month observations with ESA use was performed. Missing data were handled using complete-case analysis. A two-sided p value <0.05 was considered statistically significant. All analyses were performed using R version 4.5.2. Results Cohort and monthly observations A total of 57 maintenance hemodialysis patients contributed 684 patient-month observations. Median hemoglobin was 11.1 g/dL (IQR 10.2–11.9), and median ERI was 14.0 (IQR 8.45–19.34). Median serum albumin was 3.4 g/dL (IQR 3.1–3.7), and median CRP was 0.08 mg/dL (IQR 0.025–0.31). Iron indices were within ranges consistent with routine clinical practice. ERI according to albumin and CRP categories Patient-month observations were categorized according to serum albumin and CRP levels (Figure 1). ERI varied across these groups. Patient-months characterized by both low albumin (<3.5 g/dL) and elevated CRP (≥0.3 mg/dL) tended to show higher ERI values , whereas those with albumin ≥3.5 g/dL and CRP <0.3 mg/dL tended to show lower ERI values . Intermediate ERI levels were observed in the remaining groups. These descriptive findings indicate heterogeneity in ERI across combinations of nutritional and inflammatory status. Association between albumin and ERI stratified by CRP The relationship between serum albumin and ERI was examined according to CRP categories (Figure 1). In patient-months with low CRP (<0.3 mg/dL), albumin showed a modest inverse relationship with ERI. In contrast, in patient-months with elevated CRP (≥0.3 mg/dL), ERI values appeared to be higher across albumin levels , and the inverse relationship between albumin and ERI appeared to be attenuated. These patterns are descriptive and suggest that inflammatory status may be associated with differences in the albumin–ERI relationship , although statistical interaction was not formally demonstrated in the mixed-effects models. In mixed-effects analyses using the high-albumin/low-CRP group as the reference, the low-albumin/high-CRP group showed significantly higher ERI (β = 0.075, p = 0.033), whereas the other groups were not significantly different (Table 4). Cumulative risk analysis To further evaluate the combined influence of nutritional and inflammatory factors, a cumulative risk score was constructed based on the presence of low albumin (<3.5 g/dL) and elevated CRP (≥0.3 mg/dL). Each factor was assigned 1 point, resulting in a total score ranging from 0 to 2. ERI increased progressively with higher cumulative risk scores (Figure 2). Median ERI values were 11.99 (IQR 6.68–15.71) for score 0, 15.02 (IQR 8.94–19.44) for score 1, and 19.65 (IQR 13.75–26.37) for score 2. In a linear mixed-effects model accounting for within-patient repeated measures, cumulative risk score was significantly associated with log(ERI + 1) (β=0.039 per 1-point increase, p=0.023), independent of physical stress. These findings indicate a graded association between cumulative nutritional and inflammatory burden and ERI. Mixed-effects models In mixed-effects models accounting for within-patient repeated measures, lower albumin was significantly associated with higher ERI, whereas CRP was not independently associated with ERI. The interaction term between albumin and CRP showed a positive coefficient but did not reach statistical significance. In analyses using the cumulative nutritional-inflammatory risk score, ERI increased progressively with higher risk scores. The cumulative risk score was significantly associated with log(ERI + 1) (β = 0.039 per 1-point increase, p = 0.023) (Table 2). Secondary analysis In secondary analysis, the association between cumulative nutritional-inflammatory risk and ERI remained significant after additional adjustment for iron-related parameters, including TSAT and ferritin (Table 3). In the extended model, cumulative risk score remained independently associated with log(ERI + 1) (β = 0.036, 95% CI 0.003 to 0.069, p = 0.031). Lower TSAT was independently associated with higher ERI, whereas ferritin was not significantly associated with ERI. In a mixed-effects model including albumin, CRP, and their interaction term, lower albumin was significantly associated with higher ERI, whereas CRP was not independently associated with ERI. The interaction term between albumin and CRP showed a positive coefficient but did not reach statistical significance (β = 0.018, 95% CI -0.004 to 0.040, p = 0.10) (Table 4). Additional analyses including albumin–CRP categories and univariate models are presented in Table 4. In category-based analyses, the low-albumin/high-CRP group showed significantly higher ERI compared with the reference group. Exploratory analyses Exploratory analyses were performed for descriptive purposes and should be interpreted cautiously because patient-month observations are not independent. Scatter plots demonstrated a modest inverse relationship between albumin and ERI. Stratified analyses further showed that ERI tended to be higher in patient-months with elevated CRP compared with those with lower CRP. These descriptive findings were consistent with the results of the mixed-effects models. Discussion Main findings In this longitudinal observational study of maintenance hemodialysis patients, we found that ERI varied according to combinations of nutritional and inflammatory status. Patient-months characterized by both low albumin and elevated CRP tended to show higher ERI , whereas those with higher albumin and low CRP tended to show lower ERI. In mixed-effects models accounting for within-patient correlation, lower albumin was significantly associated with higher ERI, while CRP was not independently associated with ERI. The interaction term between albumin and CRP did not reach statistical significance, although the direction of the association suggested a potential modification of the albumin–ERI relationship by inflammatory status. Interpretation of findings These findings suggest that nutritional status and inflammation are i ndependently and additively associated with ERI in maintenance hemodialysis patients. Serum albumin is commonly used as a marker reflecting nutritional status and overall clinical condition, whereas CRP reflects systemic inflammation [1,5,6]. Importantly, serum albumin reflects not only nutritional status but also inflammation and fluid status, and therefore should be interpreted as a composite marker of overall clinical condition [1]. Categorical analyses demonstrated a graded association between cumulative nutritional–inflammatory burden and ERI, supporting an additive relationship between these factors. In contrast, continuous interaction analyses did not show statistical significance. Taken together, these findings indicate that while the combined burden of nutritional and inflammatory factors is associated with ERI, a synergistic interaction between albumin and CRP was not statistically supported . The discrepancy between categorical and continuous analyses may reflect limited statistical power to detect interaction effects, particularly given the relatively small number of patients. In addition, categorization may better capture clinically meaningful thresholds, whereas continuous models assume linear relationships that may not fully reflect complex biological processes. These findings are consistent with the concept of malnutrition–inflammation complex syndrome (MICS), in which nutritional deficits and inflammation coexist and are associated with adverse outcomes in dialysis populations [1]. However, ERI represents a clinically derived index influenced by ESA dosing, hemoglobin levels, and body weight, and does not directly measure erythropoietic resistance. Therefore, the observed associations should be interpreted as reflecting a combination of biological and clinical factors, including inflammation, comorbidity burden, and physician-driven treatment decisions [4,9]. Comparison with previous studies Previous studies have reported associations between hypoalbuminemia, inflammation, and higher ESA dose requirements in hemodialysis patients [4,5,10]. However, most prior analyses have been cross-sectional and do not account for within-patient variability over time. By using patient-month–level longitudinal data and mixed-effects models, the present study extends prior work by demonstrating that variability in ERI is associated with the combined (additive) burden of nutritional and inflammatory factors over time. Relationship with our previous analysis Our previous reports described copper deficiency in hemodialysis patients, in which anemia refractory to ESA improved after copper supplementation [7,8]. These cases were characterized by the coexistence of multiple risk factors, including malnutrition, inflammation, and physiological stress, suggesting a cumulative contribution of multiple factors. The present findings are conceptually aligned with these observations, in that the coexistence of multiple subclinical stressors may be associated with variability in erythropoietic response even in routine clinical settings. However, the current study does not directly evaluate copper metabolism, and this comparison should be interpreted as hypothesis-generating. Clinical implications These findings have potential implications for anemia management in hemodialysis patients. The additive association of low albumin and elevated CRP with higher ERI suggests that assessment of anemia should not rely solely on iron-related parameters [2,3]. Importantly, these associations remained significant after adjustment for iron-related indices, indicating that nutritional and inflammatory status provide complementary information beyond conventional iron markers [11,12]. In clinical practice, hypoalbuminemia in the presence of inflammation may indicate a higher likelihood of elevated ERI. A longitudinal, individualized approach that integrates nutritional markers and inflammatory status may therefore provide a more comprehensive assessment in routine care [13,14]. Strengths and limitations This study has several strengths, including the use of longitudinal patient-month–level data, repeated assessment of nutritional and inflammatory markers, and the application of mixed-effects models to account for within-patient correlation. Several limitations should be acknowledged. First, this was a single-center observational study, which may limit generalizability. Second, residual confounding and potential reverse causation cannot be excluded. Because ERI incorporates ESA dose, which is influenced by clinical decision-making, the observed associations may partly reflect physician-driven treatment adjustments rather than purely biological resistance. Third, serum albumin is influenced by factors beyond nutrition, including inflammation and fluid status [1]. Fourth, CRP was modeled as a continuous variable in interaction analyses to preserve statistical power; however, different modeling approaches may yield different results. Finally, although HIF-PH inhibitors were used in only one patient in this cohort and are unlikely to have influenced the overall findings , the applicability of these results to patients receiving HIF-PH inhibitors requires further investigation. These considerations suggest that the findings should be interpreted as associational and hypothesis-generating , rather than causal. Conclusions In maintenance hemodialysis patients, ERI is associated with nutritional and inflammatory status. Lower albumin is associated with higher ERI, and cumulative nutritional–inflammatory burden shows a graded association with ERI. While a statistically significant interaction between albumin and CRP was not observed, the results support an additive relationship between these factors. These findings highlight the importance of considering both nutritional status and inflammation, in addition to conventional parameters, in the longitudinal assessment of anemia in hemodialysis patients. Declarations Data Sharing Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Due to ethical restrictions and institutional regulations, individual-level patient data cannot be publicly shared. De-identified aggregate data, analytic code for risk score calculation, and detailed methodological information are available to qualified researchers for the purpose of reproducing the results reported in this article. Reporting Checklist Statement (STROBE) This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The completed STROBE checklist is provided as Supplementary Material. Funding The author received no financial support for the research, authorship, or publication of this article. Conflicts of Interest The author declares that they have no conflicts of interest related to this work. Consent to Publish Consent to Publish declaration: not applicable. Ethics Approval This retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Fuefuki Central Hospital (Approval No. Fuefuki Rin 25-10; January 10, 2026). Written informed consent was obtained from all participants prior to inclusion in the study. Patient data were anonymized before analysis. References Kalantar-Zadeh K, Hoffken B, Wunsch H, et al.Epidemiology of anemia in dialysis patients. Am J Kidney Dis. 2009;54:106–116. Locatelli F, Bárány P, Covic A, et al. Erythropoiesis-stimulating agents and iron therapy in chronic kidney disease. Nephrol Dial Transplant. 2013;28:803–814. Kidney Disease: Improving Global Outcomes (KDIGO) Anemia Work Group. KDIGO 2026 Clinical Practice Guideline for the Management of Anemia in Chronic Kidney Disease (CKD). Kidney Int. 2026;109(Suppl 1S):S1–S99. Macdougall IC, Bircher AJ, Eckardt KU, et al.ESA resistance in chronic kidney disease: mechanisms and management. Nat Rev Nephrol. 2012;8:479–492. Kaysen GA, Müller HG, Ding J, et al. Erythropoietin resistance in end-stage renal disease: role of inflammation and iron. Kidney Int. 2001;59:2241–2249 Weiss G, Goodnough LT. Anemia of chronic disease. N Engl J Med. 2005;352:1011–1023. Ikegishi Y, Abe R, Maehata A, Takiyama Y. Diagnostic pitfalls of ESA-resistant anemia due to functional copper deficiency in a dialysis patient: a myelodysplastic syndrome mimic. CEN Case Reports. 2026;15:37. https://doi.org/10.1007/s13730-025-01042-w Ikegishi Y, Abe R, Maehata A, Takiyama Y. Acute-onset copper deficiency following surgery in a dialysis patient: diagnostic challenges and risk factor interaction. Internal Medicine. 2025; Advance Publication. https://doi.org/10.2169/internalmedicine.5960-25 Panichi V, Rosati A, Bigazzi R, et al. Erythropoietin resistance index and mortality in dialysis patients. Nephrol Dial Transplant. 2008;23:2339–2346. Bradbury BD, Danese MD, Gleeson M, et al. Erythropoietin hyporesponsiveness and mortality in dialysis patients. Am J Kidney Dis. 2009;54:727–735. Coyne DW. Iron indices: what do they really mean? Kidney Int Suppl. 2006;69:S4–S8. Wish JB. Assessing iron status: beyond serum ferritin and transferrin saturation. Clin J Am Soc Nephrol. 2006;1(Suppl 1):S4–S8. Yamamoto H, Nishi S, Tomo T, et al. Practice patterns of anemia management in Japanese hemodialysis patients. Clin Exp Nephrol. 2018;22:109–117. Akizawa T, Okumura H, Alexandre AF, et al. Current status of anemia management in Japanese dialysis patients. Ther Apher Dial. 2015;19(Suppl 1):8–16. Locatelli, F., & Del Vecchio, L. (2023). Resistance to erythropoiesis stimulating agent (ESA) treatment. Handbook of Dialysis Therapy. Tables Table 1. Baseline characteristics of the study population (n = 57) Variable Value Age, years 75 (64–82) Male sex, n (%) 32 (56%) Dialysis vintage, months 74 (37–123) Hemoglobin, g/dL 10.9 (10.0–11.5) ESA dose, IU/week 9000 (4500–12000) Erythropoiesis resistance index (ERI) (ESA dose (IU/kg/week) / Hb) 14.4 (8.3–19.0) IV iron dose, mg/month 0 (0–40) TSAT, % 18 (11–23) Ferritin, ng/mL 58 (31–105) Albumin, g/dL 3.4 (3.1–3.7) CRP, mg/dL 0.10 (0.03–0.44) Dry weight, kg 50.9 (44.5–61.5) Physical stress, n (%) 1 (1.8%) Table 1. Values are presented as median (interquartile range) unless otherwise indicated. ESA: erythropoiesis-stimulating agent ERI: erythropoiesis resistance index TSAT: transferrin saturation CRP: C-reactive protein Table 2. Linear mixed-effects model for ERI Linear mixed-effects models with patient-specific random intercepts were used to evaluate associations between cumulative nutritional–inflammatory risk score and ERI. Variable β coefficient 95% CI p-value Cumulative risk score (per 1-point increase) 0.039 0.005 to 0.073 0.023 Physical stress (present vs absent) 0.183 0.085 to 0.281 <0.001 Outcome: log(ERI + 1) Random effect: patient-specific intercept Table 2. Linear mixed-effects models with patient-level random intercepts were used to account for repeated monthly observations. The outcome variable was log(ERI+1). Model 1 adjusted for clinical stress indicators, albumin, and log-transformed CRP. Model 2 additionally adjusted for iron indices (TSAT and ferritin). IV iron dose represents total intravenous iron administered in the prior month. Table 3. Linear mixed-effects models for ERI (extended model) Linear mixed-effects models with patient-specific random intercepts were used to evaluate associations between cumulative risk score and ERI. Variable β coefficient 95% CI p-value Cumulative risk score (per 1-point increase) 0.036 0.003 to 0.069 0.031 Physical stress (present vs absent) 0.178 0.081 to 0.275 <0.001 TSAT (per 1% increase) -0.009 -0.014 to -0.004 <0.001 Ferritin (per 10 ng/mL increase) 0.001 -0.001 to 0.003 0.42 Outcome: log(ERI + 1) Random effect: patient-specific intercept Table 3. Linear mixed-effects model for ERI with additional adjustment for iron-related parameters Linear mixed-effects models with patient-specific random intercepts were used to evaluate the association between cumulative nutritional–inflammatory risk score and erythropoiesis resistance index (ERI). The outcome variable was log-transformed ERI [log(ERI + 1)]. The model included cumulative risk score and physical stress indicators as fixed effects, with additional adjustment for iron-related parameters, including transferrin saturation (TSAT) and ferritin. β coefficients represent the change in log(ERI + 1) per unit increase in each variable. Table 4. Mixed-effects models for ERI: albumin, CRP, interaction, and category-based analyses (A) Continuous model Variable β coefficient 95% CI p-value Albumin (per 1 g/dL increase) -0.106 (-0.185 to -0.027) 0.008 CRP (per 1 mg/dL increase) -0.035 (-0.083 to 0.013) 0.15 Albumin × CRP 0.018 (-0.004 to 0.040) 0.10 (B) Albumin–CRP categories (reference: high albumin / low CRP) Category β coefficient 95% CI p-value High albumin / High CRP 0.055 (-0.022 to 0.132) 0.166 Low albumin / Low CRP 0.044 (-0.010 to 0.098) 0.108 Low albumin / High CRP 0.075 (0.006 to 0.144) 0.033 (C) Univariate analyses Variable β coefficient 95% CI p-value Albumin -0.110 (-0.179 to -0.041) 0.002 CRP 0.010 (0.001 to 0.019) 0.038 Outcome: log(ERI + 1) Model: linear mixed-effects model with patient-specific random intercept Table 4. Mixed-effects models for ERI: albumin, CRP, interaction, and category-based analyses. Linear mixed-effects models with patient-specific random intercepts were used to evaluate the associations between serum albumin, C-reactive protein (CRP), and erythropoiesis resistance index (ERI). The outcome variable was log-transformed ERI [log(ERI + 1)]. Panel (A) shows the primary model including albumin, CRP, and their interaction term (albumin × CRP) as fixed effects. Panel (B) presents analyses based on categories defined by albumin (<3.5 vs ≥3.5 g/dL) and CRP (<0.3 vs ≥0.3 mg/dL), using the high-albumin/low-CRP group as the reference. Panel (C) shows univariate associations between each variable and ERI. β coefficients represent the change in log(ERI + 1) per unit increase (continuous variables) or relative to the reference group (categorical variables). ERI, erythropoiesis resistance index; CRP, C-reactive protein. Additional Declarations No competing interests reported. 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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-9326742","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623010772,"identity":"c2e97fe1-2b20-4958-9cee-7868dd8eb0e7","order_by":0,"name":"Yukinobu Ikegishi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACZjYgYcCQwMbewMAMEzQgTgvPAWK1MLCByQQGiQSEFrzA4Dhb4sMvBXV5fJJvDD8XVNgw8Lc3MBQX4NNymO2wsYzB4WI26Rxj6Rln0hgkzhxgMJ6BVwt7m7SEwYHENukcA2netsMMBkAXGvPg19L+W8KgLrFN8ozxbyK1sB1j/GDAnNgmwWNGnC2Sh9mSpYEaE9t40sqsec6k8UicOdiA1y98548Zfvzxpy5xfvvhzbd5Kmzk+NubjxnjCzGFA8DYhDiDAxyBQDZjmzEeHQzyDUAlP8BM9gcwQebH+LSMglEwCkbBiAMAXbJGJ/pgVTwAAAAASUVORK5CYII=","orcid":"","institution":"Fuefuki Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yukinobu","middleName":"","lastName":"Ikegishi","suffix":""}],"badges":[],"createdAt":"2026-04-05 14:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9326742/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9326742/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106972457,"identity":"81a0e0d7-5005-4958-86d7-857a7b755ad6","added_by":"auto","created_at":"2026-04-15 10:23:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDescriptive relationships between albumin, CRP, and ERI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot showing the relationship between serum albumin and ERI, stratified by CRP levels.\u003c/p\u003e\n\u003cp\u003e(B) Distribution of ERI across categories defined by albumin (\u0026lt;3.5 vs ≥3.5 g/dL) and CRP (\u0026lt;0.3 vs ≥0.3 mg/dL).\u003c/p\u003e\n\u003cp\u003eThese analyses are descriptive and do not account for within-patient correlation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9326742/v1/49d3a26fd1016fb0848e9975.png"},{"id":106972769,"identity":"27adfd70-ab3a-4ae1-ae2b-dc56aba8bc5b","added_by":"auto","created_at":"2026-04-15 10:24:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eERI according to cumulative nutritional–inflammatory risk score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of erythropoiesis resistance index (ERI) according to cumulative risk score at the patient-month level.\u003c/p\u003e\n\u003cp\u003eThe cumulative risk score was defined as the sum of the following factors: low albumin (\u0026lt;3.5 g/dL) and elevated CRP (≥0.3 mg/dL), yielding a score of 0–2.\u003c/p\u003e\n\u003cp\u003eERI increased progressively with higher risk scores, with median ERI values of 11.99 (IQR 6.68–15.71) for score 0, 15.02 (IQR 8.94–19.44) for score 1, and 19.65 (IQR 13.75–26.37) for score 2.\u003c/p\u003e\n\u003cp\u003eThese comparisons are descriptive and do not account for within-patient correlation.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9326742/v1/e2c808adc33fbb997a327da3.png"},{"id":106973612,"identity":"e6e06c8b-0a8e-4aa5-ac16-f24fcc19195a","added_by":"auto","created_at":"2026-04-15 10:28:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1158582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9326742/v1/bd06d67e-3844-47f6-96cc-7a9db4ce748f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combined Effects of Nutritional and Inflammatory Status on Erythropoiesis Resistance: A Longitudinal Patient-Month Analysis in Hemodialysis Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnemia is a common and clinically important complication in patients undergoing maintenance hemodialysis [1]. While iron availability and erythropoiesis-stimulating agent (ESA) therapy are central to anemia management [2,3], variability in erythropoietic response remains a major clinical challenge. Increasing evidence suggests that non–iron-related factors, including nutritional status and inflammation, play important roles in modulating erythropoiesis [4–6]. In particular, hypoalbuminemia and elevated inflammatory markers have been associated with reduced responsiveness to ESA therapy in hemodialysis patients.\u003c/p\u003e\n\u003cp\u003eWe have previously reported cases of copper deficiency in hemodialysis patients, characterized by severe anemia that was refractory to conventional treatment but improved following intravenous copper administration [7,8]. These cases were notable for the coexistence of multiple risk factors, including malnutrition, inflammation, zinc supplementation, and physiological stress, suggesting a cumulative contribution of multiple factors rather than a single causative mechanism.\u003c/p\u003e\n\u003cp\u003eThese observations led us to hypothesize that the cumulative burden of nutritional and inflammatory factors may influence erythropoiesis more broadly in routine hemodialysis care. However, this concept has not been well examined using longitudinal clinical data that account for within-patient variability over time.\u003c/p\u003e\n\u003cp\u003eTherefore, in the present study, we investigated the association between serum albumin and C-reactive protein (CRP), as representative markers of nutritional status and inflammation, and the erythropoiesis resistance index (ERI) using patient-month–level longitudinal data. In addition, we evaluated their additive and potential interaction effects to better understand how these factors are associated with variability in ESA response in maintenance hemodialysis patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a single-center, retrospective longitudinal observational study conducted in maintenance hemodialysis patients. Adult patients undergoing maintenance hemodialysis at our center were eligible for inclusion if they had at least 12 consecutive months of available monthly data \u003cu\u003ebetween January and December 2025\u003c/u\u003e, including hemoglobin, ESA dose, iron indices, and inflammatory markers. Patients with incomplete longitudinal data or receiving dialysis modalities other than maintenance hemodialysis were excluded.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the institutional ethics committee, and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and laboratory data were collected retrospectively from electronic medical records and dialysis charts. Data were organized in a longitudinal format, with each row representing one patient-month observation.\u003c/p\u003e\n\u003cp\u003eThe following variables were recorded monthly:\u003c/p\u003e\n\u003cp\u003e* Hemoglobin concentration (g/dL)\u003c/p\u003e\n\u003cp\u003e* ESA dose (IU/week), including epoetin alfa or darbepoetin alfa; doses were converted to epoetin equivalents (1 μg darbepoetin alfa ≈ 200 IU epoetin)\u003cu\u003e\u0026nbsp;based on previously published methods [15].\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e* Intravenous (IV) iron dose (mg/month), aggregated as total monthly dose; months without iron administration were recorded as 0 mg\u003c/p\u003e\n\u003cp\u003e* Transferrin saturation (TSAT, %)\u003c/p\u003e\n\u003cp\u003e* Serum ferritin (ng/mL)\u003c/p\u003e\n\u003cp\u003e* C-reactive protein (CRP, mg/dL)\u003c/p\u003e\n\u003cp\u003e* Serum albumin (g/dL)\u003c/p\u003e\n\u003cp\u003e* Physical stress indicators (hospitalization, infection, or other clinically significant stressors; coded as present or absent)\u003c/p\u003e\n\u003cp\u003eIV iron was administered according to routine clinical practice, typically as intermittent low-dose supplementation based on TSAT and ferritin levels.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eOne patient received a hypoxia-inducible factor prolyl hydroxylase (HIF-PH) inhibitor during the study period and was included in the primary analysis.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of ERI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe erythropoiesis resistance index (ERI) was calculated as the weekly ESA dose divided by the product of hemoglobin concentration and body weight (ESA dose / [Hb × body weight]) and was used as a marker associated with ESA response. Higher ERI values indicate reduced ESA response.\u003c/p\u003e\n\u003cp\u003eERI was treated as a time-varying outcome at the patient-month level and was log-transformed as log(ERI + 1) in regression models to reduce skewness and stabilize variance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure variables and grouping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary exposures of interest were serum albumin and C-reactive protein (CRP), used as representative markers of nutritional status and systemic inflammation, respectively.\u003c/p\u003e\n\u003cp\u003eFor descriptive analyses, patient-month observations were categorized according to albumin (\u0026lt;3.5 vs ≥3.5 g/dL) and CRP (\u0026lt;0.3 vs ≥0.3 mg/dL) levels to examine differences in ERI across combinations of nutritional and inflammatory status.\u003c/p\u003e\n\u003cp\u003eTo further evaluate cumulative effects, a nutritional-inflammatory risk score was constructed based on the presence of low albumin (\u0026lt;3.5 g/dL) and elevated CRP (≥0.3 mg/dL). Each factor was assigned 1 point, yielding a total score ranging from 0 to 2.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eIn addition to modeling the score as a continuous variable, it was also analyzed as a categorical variable (0, 1, and 2).\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTSAT and ferritin were treated as iron-related covariates and were included in secondary analyses to assess whether the associations of albumin and CRP with ERI were independent of conventional iron indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were summarized as medians with interquartile ranges (IQRs) for continuous variables and as counts with percentages for categorical variables.\u003c/p\u003e\n\u003cp\u003eExploratory analyses, including scatter plots and category-based comparisons, were performed for descriptive purposes only. Because patient-month observations are not independent, these analyses do not account for within-patient correlation and should be interpreted cautiously.\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models with patient-specific random intercepts were used as the primary analytical approach to account for repeated measurements within individuals.\u003c/p\u003e\n\u003cp\u003eIn the primary model, we evaluated the associations of albumin, CRP, and their interaction term (albumin × CRP) with log-transformed ERI [log(ERI + 1)].\u003c/p\u003e\n\u003cp\u003eTo evaluate cumulative effects, the nutritional-inflammatory risk score was also examined in mixed-effects models.\u003c/p\u003e\n\u003cp\u003eSecondary analyses additionally adjusted for iron-related parameters, including TSAT and ferritin, to assess whether the observed associations were independent of conventional iron indices.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eA sensitivity analysis restricted to patient-month observations with ESA use was performed.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMissing data were handled using complete-case analysis.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eA two-sided p value \u0026lt;0.05 was considered statistically significant. All analyses were performed using R version 4.5.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCohort and monthly observations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 57 maintenance hemodialysis patients contributed 684 patient-month observations. Median hemoglobin was 11.1 g/dL (IQR 10.2–11.9), and median ERI was 14.0 (IQR 8.45–19.34). Median serum albumin was 3.4 g/dL (IQR 3.1–3.7), and median CRP was 0.08 mg/dL (IQR 0.025–0.31). Iron indices were within ranges consistent with routine clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eERI according to albumin and CRP categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient-month observations were categorized according to serum albumin and CRP levels (Figure 1).\u003c/p\u003e\n\u003cp\u003eERI varied across these groups. Patient-months characterized by both low albumin (\u0026lt;3.5 g/dL) and elevated CRP (≥0.3 mg/dL) \u003cu\u003etended to show higher ERI values\u003c/u\u003e, whereas those with albumin ≥3.5 g/dL and CRP \u0026lt;0.3 mg/dL\u003cu\u003e\u0026nbsp;tended to show lower ERI values\u003c/u\u003e. Intermediate ERI levels were observed in the remaining groups.\u003c/p\u003e\n\u003cp\u003eThese descriptive findings indicate heterogeneity in ERI across combinations of nutritional and inflammatory status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between albumin and ERI stratified by CRP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between serum albumin and ERI was examined according to CRP categories (Figure 1).\u003c/p\u003e\n\u003cp\u003eIn patient-months with low CRP (\u0026lt;0.3 mg/dL), albumin showed a modest inverse relationship with ERI. In contrast, in patient-months with elevated CRP (≥0.3 mg/dL), ERI values \u003cu\u003eappeared to be higher across albumin levels\u003c/u\u003e, and the inverse relationship between albumin and ERI \u003cu\u003eappeared to be attenuated.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThese patterns are descriptive and suggest that inflammatory status \u003cu\u003emay be associated with differences in the albumin–ERI relationship\u003c/u\u003e, although statistical interaction was not formally demonstrated in the mixed-effects models.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eIn mixed-effects analyses using the high-albumin/low-CRP group as the reference, the low-albumin/high-CRP group showed significantly higher ERI (β = 0.075, p = 0.033), whereas the other groups were not significantly different (Table 4).\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative risk analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluate the combined influence of nutritional and inflammatory factors, a cumulative risk score was constructed based on the presence of low albumin (\u0026lt;3.5 g/dL) and elevated CRP (≥0.3 mg/dL). Each factor was assigned 1 point, resulting in a total score ranging from 0 to 2.\u003c/p\u003e\n\u003cp\u003eERI increased progressively with higher cumulative risk scores (Figure 2). Median ERI values were 11.99 (IQR 6.68–15.71) for score 0, 15.02 (IQR 8.94–19.44) for score 1, and 19.65 (IQR 13.75–26.37) for score 2.\u003c/p\u003e\n\u003cp\u003eIn a linear mixed-effects model accounting for within-patient repeated measures, cumulative risk score was significantly associated with log(ERI + 1) (β=0.039 per 1-point increase, p=0.023), independent of physical stress.\u003c/p\u003e\n\u003cp\u003eThese findings indicate a graded association between cumulative nutritional and inflammatory burden and ERI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMixed-effects models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn mixed-effects models accounting for within-patient repeated measures, lower albumin was significantly associated with higher ERI, whereas CRP was not independently associated with ERI. The interaction term between albumin and CRP showed a positive coefficient but did not reach statistical significance.\u003c/p\u003e\n\u003cp\u003eIn analyses using the cumulative nutritional-inflammatory risk score, ERI increased progressively with higher risk scores. The cumulative risk score was significantly associated with log(ERI + 1) (β = 0.039 per 1-point increase, p = 0.023) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn secondary analysis, the association between cumulative nutritional-inflammatory risk and ERI remained significant after additional adjustment for iron-related parameters, including TSAT and ferritin (Table 3).\u003c/p\u003e\n\u003cp\u003eIn the extended model, cumulative risk score remained independently associated with log(ERI + 1) (β = 0.036, 95% CI 0.003 to 0.069, p = 0.031). Lower TSAT was independently associated with higher ERI, whereas ferritin was not significantly associated with ERI.\u003c/p\u003e\n\u003cp\u003eIn a mixed-effects model including albumin, CRP, and their interaction term, lower albumin was significantly associated with higher ERI, whereas CRP was not independently associated with ERI. The interaction term between albumin and CRP showed a positive coefficient but did not reach statistical significance (β = 0.018, 95% CI -0.004 to 0.040, p = 0.10) (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAdditional analyses including albumin–CRP categories and univariate models are presented in Table 4.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eIn category-based analyses, the low-albumin/high-CRP group showed significantly higher ERI compared with the reference group.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExploratory analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploratory analyses were performed for descriptive purposes and should be interpreted cautiously because patient-month observations are not independent.\u003c/p\u003e\n\u003cp\u003eScatter plots demonstrated a modest inverse relationship between albumin and ERI. Stratified analyses further showed that ERI tended to be higher in patient-months with elevated CRP compared with those with lower CRP.\u003c/p\u003e\n\u003cp\u003eThese descriptive findings were consistent with the results of the mixed-effects models.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMain findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this longitudinal observational study of maintenance hemodialysis patients, we found that ERI varied according to combinations of nutritional and inflammatory status. Patient-months characterized by both low albumin and elevated CRP\u003cu\u003e\u0026nbsp;tended to show higher ERI\u003c/u\u003e, whereas those with higher albumin and low CRP \u003cu\u003etended to show lower ERI.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn mixed-effects models accounting for within-patient correlation, lower albumin was significantly associated with higher ERI, while CRP was not independently associated with ERI. The interaction term between albumin and CRP did not reach statistical significance, although the direction of the association suggested a potential modification of the albumin–ERI relationship by inflammatory status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings suggest that nutritional status and inflammation are i\u003cu\u003endependently and additively associated with ERI\u003c/u\u003e in maintenance hemodialysis patients. Serum albumin is commonly used as a marker reflecting nutritional status and overall clinical condition, whereas CRP reflects systemic inflammation [1,5,6]. Importantly, serum albumin reflects not only nutritional status but also inflammation and fluid status, and therefore should be interpreted as a composite marker of overall clinical condition [1].\u003c/p\u003e\n\u003cp\u003eCategorical analyses demonstrated a graded association between cumulative nutritional–inflammatory burden and ERI, supporting \u003cu\u003ean additive relationship\u003c/u\u003e between these factors. In contrast, continuous interaction analyses did not show statistical significance. Taken together, these findings indicate that while the combined burden of nutritional and inflammatory factors is associated with ERI,\u003cu\u003e\u0026nbsp;a synergistic interaction between albumin and CRP was not statistically supported\u003c/u\u003e.\u003c/p\u003e\n\u003cp\u003eThe discrepancy between categorical and continuous analyses may reflect limited statistical power to detect interaction effects, particularly given the relatively small number of patients. In addition, categorization may better capture clinically meaningful thresholds, whereas continuous models assume linear relationships that may not fully reflect complex biological processes.\u003c/p\u003e\n\u003cp\u003eThese findings are consistent with the concept of malnutrition–inflammation complex syndrome (MICS), in which nutritional deficits and inflammation coexist and are associated with adverse outcomes in dialysis populations [1]. However, ERI represents a clinically derived index influenced by ESA dosing, hemoglobin levels, and body weight, and does not directly measure erythropoietic resistance. Therefore, the observed associations should be interpreted as reflecting a combination of biological and clinical factors, including inflammation, comorbidity burden, and physician-driven treatment decisions [4,9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with previous studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies have reported associations between hypoalbuminemia, inflammation, and higher ESA dose requirements in hemodialysis patients [4,5,10]. However, most prior analyses have been cross-sectional and do not account for within-patient variability over time. By using patient-month–level longitudinal data and mixed-effects models, the present study extends prior work by demonstrating that variability in ERI is associated with the \u003cu\u003ecombined (additive) burden\u0026nbsp;\u003c/u\u003eof nutritional and inflammatory factors over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship with our previous analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur previous reports described copper deficiency in hemodialysis patients, in which anemia refractory to ESA improved after copper supplementation [7,8]. These cases were characterized by the coexistence of multiple risk factors, including malnutrition, inflammation, and physiological stress, suggesting a cumulative contribution of multiple factors.\u003c/p\u003e\n\u003cp\u003eThe present findings are conceptually aligned with these observations, in that the coexistence of multiple subclinical stressors may be associated with variability in erythropoietic response even in routine clinical settings. However, the current study does not directly evaluate copper metabolism, and this comparison should be interpreted as hypothesis-generating.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings have potential implications for anemia management in hemodialysis patients. The additive association of low albumin and elevated CRP with higher ERI suggests that assessment of anemia should not rely solely on iron-related parameters [2,3]. Importantly, these associations remained significant after adjustment for iron-related indices, indicating that nutritional and inflammatory status provide complementary information beyond conventional iron markers [11,12].\u003c/p\u003e\n\u003cp\u003eIn clinical practice, hypoalbuminemia in the presence of inflammation may indicate a higher likelihood of elevated ERI. A longitudinal, individualized approach that integrates nutritional markers and inflammatory status may therefore provide a more comprehensive assessment in routine care [13,14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several strengths, including the use of longitudinal patient-month–level data, repeated assessment of nutritional and inflammatory markers, and the application of mixed-effects models to account for within-patient correlation.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. First, this was a single-center observational study, which may limit generalizability. Second, residual confounding and potential reverse causation cannot be excluded. \u003cu\u003eBecause ERI incorporates ESA dose, which is influenced by clinical decision-making, the observed associations may partly reflect physician-driven treatment adjustments rather than purely biological resistance.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThird, serum albumin is influenced by factors beyond nutrition, including inflammation and fluid status [1]. Fourth, CRP was modeled as a continuous variable in interaction analyses to preserve statistical power; however, different modeling approaches may yield different results. Finally, although \u003cu\u003eHIF-PH inhibitors were used in only one patient in this cohort and are unlikely to have influenced the overall findings\u003c/u\u003e, the applicability of these results to patients receiving HIF-PH inhibitors requires further investigation.\u003c/p\u003e\n\u003cp\u003eThese considerations suggest that the findings should be interpreted as \u003cu\u003eassociational and hypothesis-generating\u003c/u\u003e, rather than causal.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn maintenance hemodialysis patients, \u003cu\u003eERI is associated with nutritional and inflammatory status.\u003c/u\u003e Lower albumin is associated with higher ERI, and cumulative nutritional–inflammatory burden shows a graded association with ERI.\u003c/p\u003e\n\u003cp\u003eWhile a statistically significant interaction between albumin and CRP was not observed, the results support \u003cu\u003ean additive relationship\u003c/u\u003e between these factors. These findings highlight the importance of considering both nutritional status and inflammation, in addition to conventional parameters, in the longitudinal assessment of anemia in hemodialysis patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. Due to ethical restrictions and institutional regulations, individual-level patient data cannot be publicly shared. De-identified aggregate data, analytic code for risk score calculation, and detailed methodological information are available to qualified researchers for the purpose of reproducing the results reported in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReporting Checklist Statement (STROBE)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The completed STROBE checklist is provided as Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author received no financial support for the research, authorship, or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that they have no conflicts of interest related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Fuefuki Central Hospital (Approval No. Fuefuki Rin 25-10; January 10, 2026). Written informed consent was obtained from all participants prior to inclusion in the study. Patient data were anonymized before analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKalantar-Zadeh K, Hoffken B, Wunsch H, et al.Epidemiology of anemia in dialysis patients. \u003cem\u003eAm J Kidney Dis.\u003c/em\u003e 2009;54:106\u0026ndash;116.\u003c/li\u003e\n \u003cli\u003eLocatelli F, B\u0026aacute;r\u0026aacute;ny P, Covic A, et al. Erythropoiesis-stimulating agents and iron therapy in chronic kidney disease. Nephrol Dial Transplant. 2013;28:803\u0026ndash;814.\u003c/li\u003e\n \u003cli\u003eKidney Disease: Improving Global Outcomes (KDIGO) Anemia Work Group. KDIGO 2026 Clinical Practice Guideline for the Management of Anemia in Chronic Kidney Disease (CKD). Kidney Int. 2026;109(Suppl 1S):S1\u0026ndash;S99.\u003c/li\u003e\n \u003cli\u003eMacdougall IC, Bircher AJ, Eckardt KU, et al.ESA resistance in chronic kidney disease: mechanisms and management. \u003cem\u003eNat Rev Nephrol.\u003c/em\u003e 2012;8:479\u0026ndash;492.\u003c/li\u003e\n \u003cli\u003eKaysen GA, M\u0026uuml;ller HG, Ding J, et al. Erythropoietin resistance in end-stage renal disease: role of inflammation and iron. Kidney Int. 2001;59:2241\u0026ndash;2249\u003c/li\u003e\n \u003cli\u003eWeiss G, Goodnough LT. Anemia of chronic disease. N Engl J Med. 2005;352:1011\u0026ndash;1023.\u003c/li\u003e\n \u003cli\u003eIkegishi Y, Abe R, Maehata A, Takiyama Y. Diagnostic pitfalls of ESA-resistant anemia due to functional copper deficiency in a dialysis patient: a myelodysplastic syndrome mimic. CEN Case Reports. 2026;15:37. https://doi.org/10.1007/s13730-025-01042-w\u003c/li\u003e\n \u003cli\u003eIkegishi Y, Abe R, Maehata A, Takiyama Y. Acute-onset copper deficiency following surgery in a dialysis patient: diagnostic challenges and risk factor interaction. Internal Medicine. 2025; Advance Publication. https://doi.org/10.2169/internalmedicine.5960-25\u003c/li\u003e\n \u003cli\u003ePanichi V, Rosati A, Bigazzi R, et al. Erythropoietin resistance index and mortality in dialysis patients. Nephrol Dial Transplant. 2008;23:2339\u0026ndash;2346.\u003c/li\u003e\n \u003cli\u003eBradbury BD, Danese MD, Gleeson M, et al. Erythropoietin hyporesponsiveness and mortality in dialysis patients. Am J Kidney Dis. 2009;54:727\u0026ndash;735.\u003c/li\u003e\n \u003cli\u003eCoyne DW. Iron indices: what do they really mean? Kidney Int Suppl. 2006;69:S4\u0026ndash;S8.\u003c/li\u003e\n \u003cli\u003eWish JB. Assessing iron status: beyond serum ferritin and transferrin saturation. Clin J Am Soc Nephrol. 2006;1(Suppl 1):S4\u0026ndash;S8.\u003c/li\u003e\n \u003cli\u003eYamamoto H, Nishi S, Tomo T, et al. Practice patterns of anemia management in Japanese hemodialysis patients. Clin Exp Nephrol. 2018;22:109\u0026ndash;117.\u003c/li\u003e\n \u003cli\u003eAkizawa T, Okumura H, Alexandre AF, et al. Current status of anemia management in Japanese dialysis patients. Ther Apher Dial. 2015;19(Suppl 1):8\u0026ndash;16.\u003c/li\u003e\n \u003cli\u003eLocatelli, F., \u0026amp; Del Vecchio, L. (2023). Resistance to erythropoiesis stimulating agent (ESA) treatment. Handbook of Dialysis Therapy.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of the study population (n = 57)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e75 (64\u0026ndash;82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eMale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e32 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eDialysis vintage, months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e74 (37\u0026ndash;123)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e10.9 (10.0\u0026ndash;11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eESA dose, IU/week\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e9000 (4500\u0026ndash;12000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eErythropoiesis resistance index (ERI)\u003c/p\u003e\n \u003cp\u003e(ESA dose (IU/kg/week) / Hb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e14.4 (8.3\u0026ndash;19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eIV iron dose, mg/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e0 (0\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eTSAT, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e18 (11\u0026ndash;23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eFerritin, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e58 (31\u0026ndash;105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e3.4 (3.1\u0026ndash;3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eCRP, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e0.10 (0.03\u0026ndash;0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eDry weight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e50.9 (44.5\u0026ndash;61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003ePhysical stress, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValues are presented as median (interquartile range) unless otherwise indicated.\u003c/p\u003e\n\u003cp\u003eESA: erythropoiesis-stimulating agent\u003c/p\u003e\n\u003cp\u003eERI: erythropoiesis resistance index\u003c/p\u003e\n\u003cp\u003eTSAT: transferrin saturation\u003c/p\u003e\n\u003cp\u003eCRP: C-reactive protein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Linear mixed-effects model for ERI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models with patient-specific random intercepts were used to evaluate associations between cumulative nutritional\u0026ndash;inflammatory risk score and ERI.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 328px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 328px;\"\u003e\n \u003cp\u003eCumulative risk score (per 1-point increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.005 to 0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 328px;\"\u003e\n \u003cp\u003ePhysical stress (present vs absent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.085 to 0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOutcome: log(ERI + 1)\u003c/p\u003e\n\u003cp\u003eRandom effect: patient-specific intercept\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models with patient-level random intercepts were used to account for repeated\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emonthly observations. The outcome variable was log(ERI+1). Model 1 adjusted for clinical stress indicators,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ealbumin, and log-transformed CRP. Model 2 additionally adjusted for iron indices (TSAT and ferritin). IV\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eiron dose represents total intravenous iron administered in the prior month.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Linear mixed-effects models for ERI (extended model)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models with patient-specific random intercepts were used to evaluate associations between cumulative risk score and ERI.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 375px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 375px;\"\u003e\n \u003cp\u003eCumulative risk score (per 1-point increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.003 to 0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 375px;\"\u003e\n \u003cp\u003ePhysical stress (present vs absent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.081 to 0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 375px;\"\u003e\n \u003cp\u003eTSAT (per 1% increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e-0.014 to -0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 375px;\"\u003e\n \u003cp\u003eFerritin (per 10 ng/mL increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e-0.001 to 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOutcome: log(ERI + 1)\u003c/p\u003e\n\u003cp\u003eRandom effect: patient-specific intercept\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Linear mixed-effects model for ERI with additional adjustment for iron-related parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models with patient-specific random intercepts were used to evaluate the association between cumulative nutritional\u0026ndash;inflammatory risk score and erythropoiesis resistance index (ERI).\u003c/p\u003e\n\u003cp\u003eThe outcome variable was log-transformed ERI [log(ERI + 1)]. The model included cumulative risk score and physical stress indicators as fixed effects, with additional adjustment for iron-related parameters, including transferrin saturation (TSAT) and ferritin.\u003c/p\u003e\n\u003cp\u003e\u0026beta; coefficients represent the change in log(ERI + 1) per unit increase in each variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Mixed-effects models for ERI: albumin, CRP, interaction, and category-based analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) Continuous model\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"709\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eAlbumin (per 1 g/dL increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e-0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e(-0.185 to -0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eCRP (per 1 mg/dL increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e(-0.083 to 0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eAlbumin \u0026times; CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e(-0.004 to 0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e(B) Albumin\u0026ndash;CRP categories (reference: high albumin / low CRP)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"718\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 281px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 281px;\"\u003e\n \u003cp\u003eHigh albumin / High CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e(-0.022 to 0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 281px;\"\u003e\n \u003cp\u003eLow albumin / Low CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e(-0.010 to 0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 281px;\"\u003e\n \u003cp\u003eLow albumin / High CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e(0.006 to 0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e(C) Univariate analyses\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"728\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e(-0.179 to -0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e(0.001 to 0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOutcome: log(ERI + 1)\u003c/p\u003e\n\u003cp\u003eModel: linear mixed-effects model with patient-specific random intercept\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Mixed-effects models for ERI: albumin, CRP, interaction, and category-based analyses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models with patient-specific random intercepts were used to evaluate the associations between serum albumin, C-reactive protein (CRP), and erythropoiesis resistance index (ERI).\u003c/p\u003e\n\u003cp\u003eThe outcome variable was log-transformed ERI [log(ERI + 1)].\u003c/p\u003e\n\u003cp\u003ePanel (A) shows the primary model including albumin, CRP, and their interaction term (albumin \u0026times; CRP) as fixed effects.\u003c/p\u003e\n\u003cp\u003ePanel (B) presents analyses based on categories defined by albumin (\u0026lt;3.5 vs \u0026ge;3.5 g/dL) and CRP (\u0026lt;0.3 vs \u0026ge;0.3 mg/dL), using the high-albumin/low-CRP group as the reference.\u003c/p\u003e\n\u003cp\u003ePanel (C) shows univariate associations between each variable and ERI.\u003c/p\u003e\n\u003cp\u003e\u0026beta;\u0026nbsp;coefficients represent the change in log(ERI + 1) per unit increase (continuous variables) or relative to the reference group (categorical variables).\u003c/p\u003e\n\u003cp\u003eERI, erythropoiesis resistance index; CRP, C-reactive protein.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9326742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9326742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background: Nutritional status and inflammation are thought to influence erythropoiesis in hemodialysis patients, but their combined effects on erythropoiesis resistance index (ERI) have not been well characterized in longitudinal settings.\nMethods: We conducted a retrospective longitudinal study of 57 maintenance hemodialysis patients (684 patient-month observations) between January and December 2025. ERI was analyzed using mixed-effects models with serum albumin and C-reactive protein (CRP) as markers of nutritional status and inflammation. Both continuous models and categorical analyses, including a cumulative nutritional–inflammatory risk score, were performed.\nResults: Lower albumin was significantly associated with higher ERI, whereas CRP was not independently associated. The interaction between albumin and CRP was not statistically significant. In contrast, the cumulative nutritional–inflammatory risk score showed a graded association with ERI, which remained significant after adjustment for iron-related parameters. Sensitivity analyses restricted to ESA-treated patient-months yielded similar results.\nConclusions: In maintenance hemodialysis patients, nutritional and inflammatory status are additively associated with ERI. Although no statistically significant interaction was observed, the cumulative burden of these factors may contribute to variability in ESA responsiveness. These findings support a longitudinal, individualized approach to anemia management.","manuscriptTitle":"Combined Effects of Nutritional and Inflammatory Status on Erythropoiesis Resistance: A Longitudinal Patient-Month Analysis in Hemodialysis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 10:13:13","doi":"10.21203/rs.3.rs-9326742/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e90e860-11cb-47b7-9337-9c4056b91a73","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T05:24:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 10:13:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9326742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9326742","identity":"rs-9326742","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}