The importance of kidney response over hematologic response in predicting kidney outcome in AL Amyloidosis: a retrospective cohort study

The importance of kidney response over hematologic response in predicting kidney outcome in AL Amyloidosis: a retrospective cohort 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 The importance of kidney response over hematologic response in predicting kidney outcome in AL Amyloidosis: a retrospective cohort study Sungmi Kim, Jinyoung Yang, Kyungho Lee, Junseok Jeon, Sang Eun Yoon, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4003929/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 Light chain amyloidosis, characterized by amyloid fibril deposition in multiple organs, often leads to progression to end-stage kidney disease. This study aimed to identify predictors of kidney survival in patients with kidney amyloidosis, focusing on hematologic and kidney response. Methods This retrospective study included 138 patients diagnosed with kidney amyloidosis between 2011 and 2019. Palladini et al.'s criteria were applied for kidney stage and response, and the 2012 International Society of Amyloidosis criteria for hematologic response. Results Overall, 17 (12.3%) progressed to end-stage kidney disease. Multivariate analysis, considering baseline characteristics, revealed that stage Ⅱ was associated with an increased risk of end-stage kidney disease compared to stage Ⅰ (hazard ratio 3.75; 95% confidence interval 1.38–10.15; P = 0.01). Compared to kidney response, the risk of end-stage kidney disease increased by 8.42 (95% confidence interval 1.71–41.35; P = 0.01) and 7.36 (95% confidence interval 1.25–43.33; P = 0.03) times in stable disease and kidney progression at 6 months, independently on baseline characteristics, respectively, whereas hematologic response showed no association with kidney outcome. Kidney survival was longer in patients with both deep hematologic response and kidney response than in those with only hematologic response (P = 0.004). Conclusion The study underscores the importance of kidney response over hematologic response in predicting end-stage kidney disease and emphasizes the need to assess treatment endpoints, considering organ response alongside hematologic response. AL Amyloidosis Hematologic response Kidney response End-Stage Kidney Disease Predictor Figures Figure 1 Figure 2 Figure 3 Background Light chain amyloidosis is a clonal plasma cell disorder characterized by the deposition of fibrils, which are derived from monoclonal immunoglobulin light chains ( 1 , 2 ). Amyloid fibrils are deposited in many organs, including kidney, heart, liver, and nerve, destructing organ structures and functions ( 3 ). Especially, kidney involvement occurs in about 70% of patients with light chain amyloidosis and results in progressive kidney dysfunction. The incidence rate of progression to end-stage kidney disease (ESKD) in patients with kidney amyloidosis ranged from 10 to 33% in previous studies ( 4 – 6 ). It is one of the main determinants of morbidity and quality of life and also limits therapeutic options ( 7 ). Over the past two decades, advancements in diagnostic tools and treatment options have significantly have improved kidney outcome in light chain amyloidosis. In 2014, Palladini et al. developed the criteria of kidney stage, response, and progression based on proteinuria and estimated glomerular filtration rate (eGFR) and validated these criteria as predictive factors of progression to ESKD ( 5 ). Moreover, several studies highlighted the importance of the early difference between involved and uninvolved free light chain (dFLC) reduction, which is one determinant of hematologic response, as a crucial factor in predicting kidney prognosis ( 8 – 10 ). In this retrospective cohort study, we evaluated kidney outcomes of 138 patients with kidney amyloidosis who received recent standardized care for light chain amyloidosis. The aim of our study was to identify predictors of kidney survival, focusing on hematologic response (both categorical criteria and dFLC changes) and kidney response, as defined by Palladini et al. ( 5 , 10 ). Furthermore, we examined the temporal changes in eGFR and proteinuria in relation to the grade of hematologic response, which could provide insights into predicting kidney response after achieving hematologic response. Methods Study population This retrospective study identified 315 patients diagnosed with AL amyloidosis at Samsung Medical Center between March 2011 and December 2019. Among them, 165 participants (52.4%) had kidney involvement, defined as either biopsy-proven kidney amyloidosis or proteinuria over 0.5g per day, predominantly albuminuria in the presence of the involvement of other organs according to the 2005 International Society of Amyloidosis (ISA) criteria ( 3 ). Individuals were excluded based on the following criteria: concurrent malignancy other than multiple myeloma (n = 4), initiation of hemodialysis within 1 month after diagnosis (n = 9), failure to initiate chemotherapy within 3 months after diagnosis or loss to follow-up within 1 month after diagnosis (n = 14). Finally, a total of 138 participants were included. Data collection and definitions We collected baseline data at the time of diagnosis. Hematologic evaluation of the plasma cell clonality included bone marrow biopsy and determination of monoclonal light chain burden through electrophoresis, immunofixation, and serum free light chain concentrations measurement. The involvement of other organs, such as heart, kidney, liver, gastrointestinal tract, and nerve, was assessed based on the 2005 ISA criteria specific to each organ ( 3 ). For kidney staging, we used the current criteria developed by Palladini et al. ( 5 ). Kidney stage Ⅰ was defined as eGFR equal to or higher than 50 mL/min/1.73m 2 with proteinuria equal or less than 5 g/day, and stage Ⅱ was when eGFR was lower than 50 mL/min/1.73m 2 or when there was proteinuria exceeding 5 g/day. Lastly, stage Ⅲ was assigned for individuals with eGFR lower than 50 mL/min/1.73m 2 and proteinuria greater than 5 g/day. eGFR was calculated using the equation of chronic kidney disease-epidemiology collaboration equation, and proteinuria was estimated by 24-hour urine collection. Hematologic response was assessed at 3 and 6 months in accordance with the 2012 ISA criteria ( 2 ). Given the known impact of deep hematologic response, particularly achieving at least a very good partial response (VGPR), on prognosis and organ response, hematologic response was divided into two groups: complete response/ VGPR and partial response/no response/progression ( 1 , 4 ). The dFLC was calculated by the difference between involved and uninvolved free light chain. The percentage changes in dFLC at 3 and 6 months from baseline were assessed using continuous and categorical variables, with the latter being divided into two groups according to 90% reduction. Kidney response and progression were evaluated at 6 months according to the criteria of Palladini et al. ( 5 ). Kidney progression was defined as a decrease in eGFR of 25% or more, and kidney response was defined as a decrease in proteinuria of 30% or more in the absence of kidney progression. Individuals who did not belong to either kidney response or kidney progression were classified as stable disease. Thereafter, eGFR and proteinuria were evaluated every 6 months for 3 years. Outcomes The primary outcome was kidney survival, defined as progression to ESKD and undergoing dialysis for more than three months. We did not consider cases where temporary dialysis was performed for acute kidney injury treatment. The participants were followed up until initiation of dialysis, death, or the last clinical visit date. Statistical analysis Continuous variables were expressed as median with interquartile range, and categorical variables were expressed as numbers with percentages. Comparison between groups was performed using Kruskal-Wallis test or Mann-Whitney U-test for continuous variables and the chi-square test for categorical variables. Kaplan-Meier curves were plotted for the cumulative incidence, and statistical difference was analyzed using log-rank test. Because a substantial number of patients died before progressing to ESKD during treatment, we accounted for death before requiring dialysis as a competing risk for ESKD using a fine and gray substitution hazard model. Cox regression analysis was performed to identify predictive factors of kidney survival. Multivariate analysis included variables with a P value of < 0.10 in univariate analysis, age and sex. The results were expressed by the hazard ratio (HR) with the 95% confidence interval (CI). Generalized estimating equation was used to investigate the interaction of the time and deep hematologic response in eGFR and proteinuria. Statistical analysis was performed using R Statistical Software, version 4.3.1 (Foundation for Statistical Computing, Vienna, Austria) and SPSS for Windows, version 22 (IBM Co., Armonk, NY, USA). Results Baseline characteristics and overall patients’ survival The baseline clinical characteristics and laboratory data are presented in Table 1 . The age was 64 (56, 70) years and 72 participants (52.2%) were male. A total of 99 participants (71.7%) had heart involvement, and 62 (44.9%) had multiple myeloma concurrently. At baseline, dFLC was 162.2 (63.1, 482.8) mg/L, eGFR was 79 (52, 94) mL/min/1.73m 2 and proteinuria was 3.8 (2.0, 5.9) g/day. According to the revised Mayo staging, 31 (22.5%), 27 (19.6%), 30 (31.2%), and 26 (26.1%) belonged to stage Ⅰ, Ⅱ, Ⅲ, and Ⅳ, respectively. In terms of kidney staging, 70 (50.7%) were in stage Ⅰ, 57 (41.3%) were in stage Ⅱ, and 11 (8.0%) were in stage Ⅲ. Table 1 Baseline characteristics of the patients* Total (N = 138) Kidney stage Ⅰ (N = 70) Kidney stage Ⅱ (N = 57) Kidney stage Ⅲ (N = 11) P value Demographic features Age (year) 64 (56, 70) 65 (56, 73) 63 (54, 68) 66 (62, 69) 0.38 Male sex 72 (52.2) 39 (55.7) 27 (47.7) 6 (54.5) 0.64 BMI (kg/m 2 ) 22.8 (20.9, 24.9) 22.8 (20.8, 24.2) 22.6 (21.2, 25.0) 23.5 (21.3, 25.9) 0.58 Comorbidities Diabetes 20 (14.5) 8 (11.4) 9 (15.8) 3 (27.3) 0.36 Hypertension 28 (20.3) 14 (20.0) 11 (19.3) 3 (27.3) 0.83 Organ involvement Heart 99 (71.7) 48 (68.6) 41 (71.9) 10 (90.9) 0.31 GI tract 21 (15.2) 9 (12.9) 10 (17.5) 2 (18.2) 0.74 Nerve 102 (73.9) 54 (77.1) 40 (70.2) 8 (72.7) 0.67 Liver 25 (18.1) 8 (11.4) 14 (24.6) 3 (27.3) 0.12 Others 23 (16.7) 14 (20.0) 8 (14.0) 1 (9.1) 0.52 Concomitant Multiple myeloma 62 (45.3) 30 (42.9) 26 (46.4) 6 (54.5) 0.75 Laboratory findings Hemoglobin (g/dL) 12.3 (10.5, 13.8) 12.3 (11.1, 13.5) 12.0 (10.3, 14.3) 10.8 (9.3, 14.3) 0.79 Albumin (g/dL) 2.7 (2.1, 3.4) 3.1 (2.5, 3.5) 2.4 (2.0, 2.9) a 2.3 (1.9, 2.6) b < 0.01 dFLC, serum (mg/L) 162.2 (63.1, 482.8) 134.1 (65.3, 504.8) 185.9 (63.3, 365.5) 480.6 (63.1, 624.1) 0.50 Affected LC 0.95 Lambda chain 115 (83.3) 59 (84.3) 47 (82.5) 9 (81.8) Kappa chain 23 (16.7) 11 (15.7) 10 (17.5) 2 (18.2) Parameters of kidney disease Proteinuria (g/day) 3.8 (2.0, 5.9) 2.5 (1.4, 3.6) 5.7 (3.9, 3.6) a 9.4 (7.6, 12.0) b, c < 0.01 Serum Creatinine (mg/dL) 0.95 (0.77, 1.26) 0.88 (0.72, 1.05) 0.99 (0.78, 1.57) a 1.73 (1.59, 2.34) b,c < 0.01 eGFR (mL/min/1.73m 2 ) 79 (52, 94) 84 (71, 97) 76 (37, 94) a 31 (27, 41) b,c < 0.01 Revised mayo stage 0.05 Stage Ⅰ 31 (22.6) 19 (27.5) 12 (21.1) 0 (0) Stage Ⅱ 27 (19.7) 15 (21.7) 12 (21.1) 0 (0) Stage Ⅲ 43 (31.4) 17 (24.6) 18 (31.6) 8 (72.7) Stage Ⅳ 36 (26.3) 18 (26.1) 15 (26.3) 3 (27.3) First-line therapy ASCT 41 (29.7) 21 (30.0) 19 (33.3) 1 (9.1) 0.27 Bortezomib-based 54 (39.1) 22 (31.4) 24 (42.1) 8 (72.7) 0.03 Other chemotherapy¶ 43 (31.2) 27 (38.6) 14 (24.6) 2 (18.2) 0.15 * Data are median (IQR) or numbers (percentages). Differences in baseline characteristics among kidney stage groups were assessed with the use of Kruskal-Wallis test for continuous variables and chi-square test for categorical variables. ¶ Other chemotherapy included immunomodulatory agent, daratumumab, and conventional therapy (steroid only, cyclophosphamide, melphalan, etc.). a Kidney stage Ⅰ vs Ⅱ : P < 0.017 by Kruskal-Wallis test. b Kidney stage Ⅰ vs Ⅲ : P < 0.017 by Kruskal-Wallis test. c Kidney stage Ⅱ vs Ⅲ : P < 0.017 by Kruskal-Wallis test. Abbreviation: ASCT, autologous stem cell transplantation; BMI, body mass index; dFLC, difference between involved and uninvolved free light chain; eGFR, estimated glomerular filtration rate; GI, gastrointestinal; IQR, interquartile range; LC, light chain The median follow-up duration was 46.4 months (10.0, 79.4). A total of 53 patients (38.4%) died, of which 47 (34.1%) died before requiring dialysis. The overall survival was different among the revised Mayo stages (P < 0.001). The median overall survival was 36 months in stage Ⅲ and 20 months in stage Ⅳ, while not reached in stage Ⅰ and Ⅱ. Post hoc analysis revealed that the revised Mayo stage Ⅰ had significantly longer overall survival than stage Ⅱ (P = 0.003), Ⅲ (P < 0.001), and Ⅳ (P < 0.001) (Supplementary Fig. 1). Initial kidney staging and kidney survival Out of the 138 patients, 17 (12.3%) progressed to ESKD. By kidney stage, 5 (7.1%), 10 (17.5%), and 2 (18.2%) patients initiated dialysis during the follow-up in stage Ⅰ, Ⅱ, and Ⅲ, respectively. The overall kidney survival varied across three kidney stages (P = 0.02, Fig. 1 ). The cumulative incidence of ESKD at 5 years was 2.1% in kidney stage Ⅰ, 15.5% in stage Ⅱ, and 18.2% in stage Ⅲ, respectively. Alongside kidney staging, diabetes status and hemoglobin levels showed an association with kidney survival in univariate analysis (Table 2 ). In multivariable analysis involving baseline characteristics, only kidney stage Ⅱ exhibited an increased risk of progression to ESKD compared to kidney stage Ⅰ (HR 3.75, 95% CI 1.38–10.15; P = 0.01). Table 2 Baseline parameters and the risk of progression to end-stage kidney disease Progression to ESKD Univariate Multivariate * No. of patients (N = 138) No. (%) HR (95% CI)¶ P value HR (95% CI)¶ P value Diabetes No 118 12 (10.2) Ref. Ref. Yes 20 5 (25.0) 2.42 (0.92, 6.40) 0.08 2.57 (0.79, 8.29) 0.12 Hypertension No 110 13 (11.8) Ref. Yes 28 4 (14.3) 1.14 (0.42, 3.12) 0.80 Heart involvement No 39 2 (5.1) Ref. Yes 99 15 (15.2) 2.95 (0.69, 12.60) 0.15 Hemoglobin (g/dL) 0.79 (0.60 ,1.02) 0.07 0.80 (0.62, 1.03) 0.08 Albumin (g/dL) 0.78 (0.48, 1.26) 0.31 Kidney stage Stage Ⅰ 70 5 (7.1) Ref. Ref. Stage Ⅱ 57 10 (17.5) 3.51 (1.33, 9.25) 0.01 3.75 (1.38, 10.15) 0.01 Stage Ⅲ 11 2 (18.2) 3.68 (0.74, 18.36) 0.11 2.71 (0.48, 15.25) 0.26 Revised mayo stage Stage Ⅰ 31 4 (12.9) Ref. Stage Ⅱ 27 2 (7.4) 0.49 (0.10, 2.33) 0.37 Stage Ⅲ 43 8 (18.6) 1.46 (0.46, 4.63) 0.53 Stage Ⅳ 36 3 (8.3) 0.62 (0.15, 2.54) 0.51 * Model included age, sex, diabetes, hemoglobin levels, and initial kidney stage. ¶ Hazard ratios and P values were estimated with a Substitution hazards model. Abbreviation: CI, confidence interval; ESKD, end-stage kidney disease; HR, hazard ratio; Ref., reference Treatment responses within 6 months and kidney survival At 6 months, 47 (43.1%) out of 109 participants achieved kidney response. In terms of hematologic response, 59 (48.4%) of 122 patients achieved deep hematologic response at 3 months, while 68 (61.3%) of 111 patients achieved deep hematologic response at 6 months. The percentage changes in dFLC from baseline were − 78.9% (-97.0, -45.9) at 3 months and − 85.8% (-96.9, -65.2) at 6 months (Supplementary Table 1). Among treatment response parameters, only kidney response independently predicted kidney survival in multivariate analysis (Table 3 ). Specifically, both stable disease (HR 8.42, 95% CI 1.71–41.35; P = 0.01) and kidney progression (HR 7.36, 95% CI 1.25–43.33; P = 0.03) were associated with an increased risk of ESKD compared to kidney response after adjusting for age, sex, diabetes status, hemoglobin levels, and initial kidney stage. dFLC percentage changes at 6 months were associated with kidney survival in univariate analysis, but this association were marginally significant after adjusting for initial kidney stages. dFLC percentage changes at 3 months were not associated with kidney survival. Hematologic response at both 3 and 6 months did not predict progression to ESKD. Table 3 Hematologic and kidney response and the risk of progression to end-stage kidney disease Progression to ESKD Univariate Multivariate * No. of Patients (N = 138) No. (%) HR (95% CI)¶ P value HR (95% CI)¶ P value Hematologic response at 3 months PR/NR/Progression 63 10 (15.9) Ref. CR/VGPR 59 7 (11.9) 0.68 (0.27, 1.73) 0.42 Hematologic response at 6 months PR/NR/Progression 43 8 (18.6) Ref. CR/VGPR 68 8 (11.8) 0.54 (0.21, 1.39) 0.20 dFLC change at 3 months† 0.96 (0.86, 1.08) 0.50 dFLC change at 3 months Decrease 90% 46 5 (10.9) 0.68 (0.24, 1.92) 0.47 dFLC change at 6 months† 0.93 (0.86, 0.99) 0.03 0.91 (0.81, 1.03) 0.14 dFLC change at 6 months Decrease 90% 50 4 (8.0) 0.37 (0.12, 1.13) 0.08 0.32 (0.09, 1.08) 0.07 Kidney response at 6 months Response 47 2 (4.3) Ref. Ref. Stable disease 30 7 (23.3) 5.17 (1.10, 24.30) 0.04 8.42 (1.71, 41.35) 0.01 Progression 32 7 (21.9) 6.66 (1.38, 37.20) 0.02 7.36 (1.25, 43.33) 0.03 * Model included in the models were age, sex, diabetes, hemoglobin levels, initial kidney stage, and either dFLC change at 6 months (as continuous variables or categorized by more than 90%) or kidney response at 6 months. ¶ Hazard ratios and P values were estimated with a Substitution hazards model. † Every 10 unit increase (The median percentage changes in dFLC were − 78.9% (-97.0, -45.9) at 3 months and − 85.8% (-96.9, -65.2) at 6 months) Abbreviation: CI, confidence interval; CR, Complete response; dFLC, difference between involved and uninvolved free light chain; ESKD, End-stage kidney disease; HR, hazard ratio; NR, No response; PR, Partial response; Ref., reference; VGPR, Very good partial response We categorized participants into four groups based on their hematologic response and kidney response at 6 months and then compared the probabilities of ESKD, as illustrated in Fig. 2 . None of the participants who achieved both deep hematologic response and kidney response progressed to ESKD. Kidney survival was longer in patients with both deep hematologic response and kidney response than those with only hematologic response (P = 0.004). First-line therapy and kidney outcomes We divided patients into subgroups according to first-line therapy to evaluate the impact of treatments on kidney outcomes: autologous stem cell transplantation (ASCT), bortezomib-based, and other chemotherapy. A total of 41 (29.7%) participants underwent upfront ASCT with or without preceding induction chemotherapy. Except for those who underwent upfront ASCT, 54 (39.1%) were treated with bortezomib and 43 (31.2%) received other chemotherapies, including immunomodulatory drugs (thalidomide or lenalidomide), daratumumab, and conventional chemotherapy (cyclophosphamide, melphalan, steroid only, etc.), as the first-line therapy. Supplementary Table 2 shows baseline characteristics of patients according to first-line therapy. Patients who underwent ASCT were younger than those who received chemotherapy, while those who were treated with bortezomib-based chemotherapy had a higher prevalence of heart involvement and a lower eGFR than other groups. Four (9.8%), nine (16.7%), and four (9.3%) patients progressed to ESKD in treatment groups of ASCT, bortezomib-based, and other chemotherapy, respectively. In univariable analysis, types of first-line therapy, ASCT (HR 1.05, 95% CI 0.29–3.83; P = 0.94) and bortezomib-based chemotherapy (HR 2.20, 95% CI 0.72–6.69; P = 0.16) was not associated with ESKD when other chemotherapy was used as the reference category (Supplementary Table 3). Also, we conducted subgroup analysis to evaluate the association between hematologic and kidney responses and ESKD risk in each ASCT and chemotherapy treatment group. Initial kidney stage Ⅱ and kidney response remained predictive of progression to ESKD, particularly in those who received chemotherapy, but not in those who underwent ASCT (P for interaction < 0.01, Supplementary Table 4). Serial changes of eGFR and percentage changes in proteinuria according to hematologic response We investigated the temporal changes in the eGFR and proteinuria over a period of 3 years based on hematologic response (Fig. 3 ). In the deep hematologic response group, eGFR exhibited a marginal decrease from baseline to 6 months (baseline vs. 6 months: 83.3 mL/min/1.73m² vs. 72.3 mL/min/1.73m²; P = 0.07), followed by a subsequent stabilization. The percentage changes in proteinuria from baseline were − 43.8% and − 60.4% during the initial 6 months and the subsequent 6 months, respectively, showing a marginal difference (P = 0.09). Afterward, there was a slight non-significant decrease in proteinuria levels. Conversely, the no deep hematologic response group showed no significant variations in the values of both eGFR and proteinuria assessed every 6 months. Overall, both eGFR (P = 0.38) and percentage changes in proteinuria (P = 0.56) did not exhibit significant differences between hematologic response groups over time. Discussion In our longitudinal study involving 138 patients with kidney amyloidosis, the initial kidney stage emerged as an independent predictor of progression to ESKD. Notably, in stage Ⅱ, the risk of ESKD increased by 3.75 times compared to stage Ⅰ, emphasizing the critical importance of initiating appropriate hematological treatment before kidney involvement reaches a more severe stage. Our analysis has brought that kidney response, rather than hematologic response following chemotherapy, exhibited a substantial association with progression to ESKD. Of particular concern is the finding that, even among those achieving deep hematologic response at the 6-month mark, subsequent improvements in eGFR and proteinuria after 12 months were insignificant, and especially, a lack of kidney response had association with an increased risk of ESKD. This observation implies that despite a favorable hematologic response, additional interventions may be required when the kidney response remains suboptimal. This is in line with recent arguments suggesting that patients who have not received sufficient organ responses need new treatment targeting the amyloid deposits ( 11 , 12 ). The achievement of hematologic response is a crucial goal in the treatment of AL amyloidosis, aiming to impede the further accumulation of amyloid fibrils. Previous studies have demonstrated its association with overall survival ( 2 , 13 ), as well as organ responses, including kidney response ( 4 , 6 ) and kidney survival ( 4 , 5 ). However, recent research indicates a shifting emphasis towards the importance of evaluating organ response, encompassing the heart, kidneys, liver, and more, as a key prognostic factor compared to hematologic response ( 14 – 17 ). In line with these insights, our study consistently found that assessing kidney response holds more significance in predicting progression to ESKD than hematologic response alone. Unlike multiple myeloma, where morbidity and mortality primarily stem from plasma cell proliferation, the presence of organ dysfunction adversely affects the patient’s prognosis in AL amyloidosis ( 3 ). Given this intricate pathophysiology, there is a growing need to expand current treatment surrogate endpoints, which are highly dependent on hematologic response, to encompass organ responses as well ( 18 , 19 ). The current standard for evaluating kidney response in AL amyloidosis involves categorizing it into three stages: progression, stable, and response ( 5 ). Our study found that the hazard ratio for progression to ESKD was comparable between stable disease and kidney progression. In post hoc analysis, the risk of ESKD was not different between kidney progression and stable disease (P = 0.80, data not shown). This suggests that stable disease has limited discriminatory value compared to kidney progression, and kidney response serves as a more reliable predictor of kidney survival. There is a need for further exploration and confirmation of criteria related to kidney response, which is widely utilized in AL amyloidosis, particularly those associated with long-term kidney outcomes. Questions also remain regarding the timing of evaluating organ response. Notably, in the group with deep hematologic response, there was no significant reduction in proteinuria after 12 months, indicating that the 12-month mark post-treatment initiation may be an appropriate time point for kidney response evaluation. This observation aligns with a study conducted at the Mayo Clinic, which suggested a median time of around 11 months to reach kidney response ( 20 ). As a determinant of hematologic response, dFLC changes have been identified as an independent predictor of overall patient survival ( 9 , 21 ) and kidney survival in patients with AL amyloidosis ( 5 , 8 , 9 ). Especially, dFLC is preferred over levels of involved free light chain or free light chain ratio for assessment of light chain burden in individuals with kidney failure because it is less likely to be confounded by kidney function ( 22 ). While it did not emerge as a predictor of kidney survival in our study, it showed an association with kidney response. Specifically, the percentage changes in dFLC at 3 months (no kidney response vs. kidney response: -72.6 vs. -89.3; P = 0.10) and 6 months (-83.3 vs. -90.7; P = 0.02) were more substantial in patients who achieved kidney response at 6 months compared to those who did not (Supplementary Table 1). This study has several limitations. Firstly, its retrospective design introduced a notable constraint, with slight variations in the timing of response assessments. Also, kidney responses at 6 months were unavailable for two patients due to missing laboratory results. Secondly, advancements in therapeutic strategies over the study period, such as increased utilization of bortezomib and autologous stem cell transplantation since 2014, may have influenced patients outcomes ( 23 ). This enhancement in patient survival allowed for a comprehensive assessment of kidney outcomes in many cases. However, analysis involving first-line therapy was limited by small numbers of patients in each treatment group and selection bias in that individuals who underwent ASCT were younger and had better organ function than those who received other treatments. Third, while kidney amyloidosis remains the predominant cause of progression to ESKD, other contributing factors may have included treatment-related nephrotoxicity, cardiorenal syndrome, and the progression of chronic kidney disease. Conclusion Our study underscores the importance of kidney response, rather than post-chemotherapy hematologic response, in predicting the progression to ESKD. While a significant reduction in dFLC substantially increased likelihood of kidney response, discrepancies between dFLC changes and improvements in kidney parameters were noted in some cases. Additionally, even among patients exhibiting deep hematologic response at the 6-month mark, subsequent improvements in eGFR and proteinuria after 12 months were found to be insignificant. This highlights the imperative for new treatment strategies to improve the prognosis of patients with insufficient organ responses. Further research is warranted to establish specific criteria and optimal timings for organ responses. Abbreviations CI confidence interval dFLC difference between involved and uninvolved free light chain eGFR estimated glomerular filtration ESKD end-stage kidney disease HR hazard ratio ISA International Society of Amyloidosis VGPR very good partial response Declarations Ethics approval and consent to participate Clinical investigations were conducted in accordance with the principles of the Declaration of Helsinki. This study was approved by the Institutional Review Board of Samsung Medical Center (IRB file no. SMC 2023-07-058). The need for informed patient consent was waived by the Institutional Review Board of Samsung Medical Center due to the retrospective design of the study. Consent for publication Not applicable Availability of data and materials All data associated with the present study are available from the corresponding author on reasonable request. Competing interests The authors of this manuscript declare that they have no competing interests. Funding This study was supported by a grant from the Samsung Biomedical Research Institute (grant no.OTA1901971). Authors’ contributions Jung Eun Lee designed and supervised the study. Sungmi Kim and Jinyoung Yang wrote the original manuscript draft as the first authors. Kyungho Lee and Junseok Jeon were involved in the patient recruitment and planned studies. Sang Eun Yoon and Darae Kim had all access to data and conducted the analysis. Jin-Oh Choi, Seok Jin Kim, and Kihyun Kim were responsible for the interpretation of the results of the analysis and the critical revision of the article. All authors have read and approved the final manuscript. Acknowledgements The authors thank Sang Ah Chi, senior statistician at the Biomedical Statistics Center, Samsung Medical Center, Seoul, South Korea for her dedicated efforts in statistical analysis. References Ryšavá R. AL amyloidosis: advances in diagnostics and treatment. Nephrol Dial Transpl. 2019;34(9):1460–6. Palladini G, Dispenzieri A, Gertz MA, Kumar S, Wechalekar A, Hawkins PN, et al. New criteria for response to treatment in immunoglobulin light chain amyloidosis based on free light chain measurement and cardiac biomarkers: impact on survival outcomes. J Clin Oncol. 2012;30(36):4541–9. Gertz MA, Comenzo R, Falk RH, Fermand JP, Hazenberg BP, Hawkins PN et al. Definition of organ involvement and treatment response in immunoglobulin light chain amyloidosis (AL): a consensus opinion from the 10th International Symposium on Amyloid and Amyloidosis, Tours, France, 18–22 April 2004. Am J Hematol. 2005;79(4):319 – 28. Kastritis E, Gavriatopoulou M, Roussou M, Migkou M, Fotiou D, Ziogas DC, et al. Renal outcomes in patients with AL amyloidosis: Prognostic factors, renal response and the impact of therapy. Am J Hematol. 2017;92(7):632–9. Palladini G, Hegenbart U, Milani P, Kimmich C, Foli A, Ho AD, et al. A staging system for renal outcome and early markers of renal response to chemotherapy in AL amyloidosis. Blood. 2014;124(15):2325–32. Drosou ME, Vaughan LE, Muchtar E, Buadi FK, Dingli D, Dispenzieri A, et al. Comparison of the current renal staging, progression and response criteria to predict renal survival in AL amyloidosis using a Mayo cohort. Am J Hematol. 2021;96(4):446–54. Merlini G, Seldin DC, Gertz MA. Amyloidosis: pathogenesis and new therapeutic options. J Clin Oncol. 2011;29(14):1924–33. Rezk T, Lachmann HJ, Fontana M, Sachchithanantham S, Mahmood S, Petrie A, et al. Prolonged renal survival in light chain amyloidosis: speed and magnitude of light chain reduction is the crucial factor. Kidney Int. 2017;92(6):1476–83. Pinney JH, Lachmann HJ, Bansi L, Wechalekar AD, Gilbertson JA, Rowczenio D, et al. Outcome in renal Al amyloidosis after chemotherapy. J Clin Oncol. 2011;29(6):674–81. Kumar S, Dispenzieri A, Lacy MQ, Hayman SR, Buadi FK, Colby C, et al. Revised prognostic staging system for light chain amyloidosis incorporating cardiac biomarkers and serum free light chain measurements. J Clin Oncol. 2012;30(9):989–95. Gertz MA, Landau H, Comenzo RL, Seldin D, Weiss B, Zonder J, et al. First-in-Human Phase I/II Study of NEOD001 in Patients With Light Chain Amyloidosis and Persistent Organ Dysfunction. J Clin Oncol. 2016;34(10):1097–103. Milani P, Merlini G, Palladini G. Novel Therapies in Light Chain Amyloidosis. Kidney Int Rep. 2018;3(3):530–41. Jimenez-Zepeda VH, Lee H, McCulloch S, Tay J, Duggan P, Neri P, et al. Treatment response measurements and survival outcomes in a cohort of newly diagnosed AL amyloidosis. Amyloid. 2021;28(3):182–8. Palladini G, Barassi A, Klersy C, Pacciolla R, Milani P, Sarais G, et al. The combination of high-sensitivity cardiac troponin T (hs-cTnT) at presentation and changes in N-terminal natriuretic peptide type B (NT-proBNP) after chemotherapy best predicts survival in AL amyloidosis. Blood. 2010;116(18):3426–30. Leung N, Dispenzieri A, Fervenza FC, Lacy MQ, Villicana R, Cavalcante JL, et al. Renal response after high-dose melphalan and stem cell transplantation is a favorable marker in patients with primary systemic amyloidosis. Am J Kidney Dis. 2005;46(2):270–7. Wechalekar A, Merlini G, Gillmore JD, Russo P, Lachmann HJ, Obici L, et al. Role of NT-ProBNP to Assess the Adequacy of Treatment Response in AL Amyloidosis. Blood. 2008;112(11):1689. Sidana S, Milani P, Binder M, Basset M, Tandon N, Foli A, et al. A validated composite organ and hematologic response model for early assessment of treatment outcomes in light chain amyloidosis. Blood Cancer J. 2020;10(4):41. Comenzo RL, Reece D, Palladini G, Seldin D, Sanchorawala V, Landau H, et al. Consensus guidelines for the conduct and reporting of clinical trials in systemic light-chain amyloidosis. Leukemia. 2012;26(11):2317–25. Manwani R, Cohen O, Sharpley F, Mahmood S, Sachchithanantham S, Foard D, et al. A prospective observational study of 915 patients with systemic AL amyloidosis treated with upfront bortezomib. Blood. 2019;134(25):2271–80. Leung N, Glavey SV, Kumar S, Dispenzieri A, Buadi FK, Dingli D, et al. A detailed evaluation of the current renal response criteria in AL amyloidosis: is it time for a revision? Haematologica. 2013;98(6):988–92. Kumar SK, Dispenzieri A, Lacy MQ, Hayman SR, Buadi FK, Zeldenrust SR, et al. Changes in serum-free light chain rather than intact monoclonal immunoglobulin levels predicts outcome following therapy in primary amyloidosis. Am J Hematol. 2011;86(3):251–5. Katzmann JA, Clark RJ, Abraham RS, Bryant S, Lymp JF, Bradwell AR, et al. Serum Reference Intervals and Diagnostic Ranges for Free κ and Free λ Immunoglobulin Light Chains: Relative Sensitivity for Detection of Monoclonal Light Chains. Clin Chem. 2002;48(9):1437–44. Yoon SE, Kim D, Choi JO, Min JH, Kim BJ, Kim JS, et al. A comprehensive overview of AL amyloidosis disease characteristics accumulated over two decades at a single referral center in Korea. Int J Hematol. 2023;117(5):706–17. 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-4003929","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276552193,"identity":"c12a91a8-4cf5-4715-8be1-715fef64fbf9","order_by":0,"name":"Sungmi Kim","email":"","orcid":"","institution":"Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Sungmi","middleName":"","lastName":"Kim","suffix":""},{"id":276552194,"identity":"024fc678-ad73-4f1b-a704-cd62b8d3daba","order_by":1,"name":"Jinyoung Yang","email":"","orcid":"","institution":"Division of Infectious disease, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Jinyoung","middleName":"","lastName":"Yang","suffix":""},{"id":276552195,"identity":"20e8ecb9-2aa9-45bd-a0fe-ac729d265937","order_by":2,"name":"Kyungho Lee","email":"","orcid":"","institution":"Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Kyungho","middleName":"","lastName":"Lee","suffix":""},{"id":276552196,"identity":"fe12d7b4-326e-4c01-8e55-f579e0cfd1a8","order_by":3,"name":"Junseok Jeon","email":"","orcid":"","institution":"Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Junseok","middleName":"","lastName":"Jeon","suffix":""},{"id":276552197,"identity":"47ccb482-10ad-40c6-929c-a281aac50559","order_by":4,"name":"Sang Eun Yoon","email":"","orcid":"","institution":"Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Sang","middleName":"Eun","lastName":"Yoon","suffix":""},{"id":276552198,"identity":"54997dfe-cd1d-44de-b6a1-82a5d42c3ad9","order_by":5,"name":"Darae Kim","email":"","orcid":"","institution":"Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Darae","middleName":"","lastName":"Kim","suffix":""},{"id":276552199,"identity":"ad331b0d-9b90-4aca-9e44-5a3cac82bf60","order_by":6,"name":"Jin-Oh Choi","email":"","orcid":"","institution":"Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Jin-Oh","middleName":"","lastName":"Choi","suffix":""},{"id":276552200,"identity":"e8a6558c-757a-4eac-9fdf-37bcd44a2c89","order_by":7,"name":"Seok Jin Kim","email":"","orcid":"","institution":"Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Seok","middleName":"Jin","lastName":"Kim","suffix":""},{"id":276552201,"identity":"29f72a66-bb05-4c2e-8d1c-2321278ac34e","order_by":8,"name":"Kihyun Kim","email":"","orcid":"","institution":"Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":false,"prefix":"","firstName":"Kihyun","middleName":"","lastName":"Kim","suffix":""},{"id":276552202,"identity":"0fae9870-9016-41cf-ae6a-4069e1c4eda5","order_by":9,"name":"Jung Eun Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACxgYIbcfYwAxkVpCgJZm5AcQ8Q4pt7SAtjG1EKGWekXv4xce2O8y8DYyNDz7Oq5PX7T9j9vALg52cbgMO02fkpVnObHvGJ9nA2Gw4c9thw203csyNZRiSjc0O4NKSY2bM23aY2bCBsU2ad9sBxm03eMykJRgOJG7Dp+Vv22HG/QcY23//nVNnv+38GYJajB8zArU0Am1hBoY0UGWOmeQHfFp63pgx9pw7nMzYzNgs2XPscPK2G2ll0gwGuP1i2J5j/OFH2WE7xvbmgx9+1NTZbjt/eJvkjwo7OZxaGhjYJMAsZiRRZh4D7MpBQB4o/wHTuT9w6xgFo2AUjIKRBwAYZWLa4r1oZQAAAABJRU5ErkJggg==","orcid":"","institution":"Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul","correspondingAuthor":true,"prefix":"","firstName":"Jung","middleName":"Eun","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-03-01 17:00:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4003929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4003929/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52105995,"identity":"8e78c67d-7bd9-4e24-9b0f-5a68cc100a62","added_by":"auto","created_at":"2024-03-06 19:34:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82511,"visible":true,"origin":"","legend":"\u003cp\u003eProgression to end-stage kidney disease according to kidney stage\u003c/p\u003e\n\u003cp\u003eThe cumulative incidence was estimated with accounting for death as a competing risk, and survival curves were compared by log-rank test. The overall incidence of ESKD varied by initial kidney stages (P = 0.02), and especially, there were significant differences between stage Ⅰ and stage Ⅱ (P = 0.007), and between stage Ⅰ and stage Ⅲ (P = 0.007).\u003c/p\u003e\n\u003cp\u003eESKD, end-stage kidney disease\u003c/p\u003e","description":"","filename":"Figure1..png","url":"https://assets-eu.researchsquare.com/files/rs-4003929/v1/b28da7550b4e2ae7a5ddb42f.png"},{"id":52105993,"identity":"ef840a6e-8a2a-4ef1-b321-ebdb495d3d69","added_by":"auto","created_at":"2024-03-06 19:34:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104237,"visible":true,"origin":"","legend":"\u003cp\u003eProgression to end-stage kidney disease according to kidney and hematologic response at 6 months\u003c/p\u003e\n\u003cp\u003eThe participants were categorized into four groups based on hematologic response and kidney response at 6 months. Kidney survival was longer in patients with both deep hematologic response and kidney response than those with only hematologic response (P = 0.004). Deep hematologic response was defined as the achievement of at least VGPR.\u003c/p\u003e\n\u003cp\u003eESKD, end-stage kidney disease; HR, hematologic response; VGPR, very good partial response\u003c/p\u003e","description":"","filename":"Figure2..png","url":"https://assets-eu.researchsquare.com/files/rs-4003929/v1/b69ceda103ef123d52805bd6.png"},{"id":52105994,"identity":"794d8a4f-1fcc-447d-9984-f01539c0e978","added_by":"auto","created_at":"2024-03-06 19:34:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98748,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in estimated glomerular filtration rate and proteinuria according to hematologic response\u003c/p\u003e\n\u003cp\u003eIn the deep hematologic response group, eGFR exhibited a marginal decrease from baseline to 6 months (P = 0.07), followed by a subsequent stabilization (A). The percentage changes in proteinuria were -43.8% and -60.4% during the initial 6 months and the subsequent 6 months, respectively, showing a marginal difference (P = 0.09, B). Afterward, there was a slight non-significant decrease in proteinuria levels.\u003cstrong\u003e \u003c/strong\u003eOverall, eGFR (P = 0.38) and percentage changes in proteinuria from baseline (P = 0.56) did not differ between hematologic response groups as time went by. Generalized estimated equation was used to estimate the interaction of the grade of hematologic response and time in eGFR and proteinuria. Deep hematologic response was defined as the achievement of at least VGPR.\u003c/p\u003e\n\u003cp\u003eeGFR, estimated glomerular filtration rate; HR, hematologic response; VGPR, very good partial response\u003c/p\u003e\n\u003cp\u003e* P value\u003cstrong\u003e \u003c/strong\u003efor decrease in eGFR and percentage changes in proteinuria in the deep hematologic response group\u003c/p\u003e","description":"","filename":"Figure3..png","url":"https://assets-eu.researchsquare.com/files/rs-4003929/v1/ae7a8adf6422c2157e4a281c.png"},{"id":53468026,"identity":"af28ed1b-41bb-4889-994f-13d35c9c106a","added_by":"auto","created_at":"2024-03-26 10:50:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":679192,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4003929/v1/5247892c-c516-42dd-884e-107082a7f231.pdf"},{"id":52105996,"identity":"fae4b62c-5866-49b6-8f18-e6bb8a249221","added_by":"auto","created_at":"2024-03-06 19:34:57","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":90088,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4003929/v1/baed5ca9dd705b77e8fcb595.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The importance of kidney response over hematologic response in predicting kidney outcome in AL Amyloidosis: a retrospective cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eLight chain amyloidosis is a clonal plasma cell disorder characterized by the deposition of fibrils, which are derived from monoclonal immunoglobulin light chains (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Amyloid fibrils are deposited in many organs, including kidney, heart, liver, and nerve, destructing organ structures and functions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Especially, kidney involvement occurs in about 70% of patients with light chain amyloidosis and results in progressive kidney dysfunction. The incidence rate of progression to end-stage kidney disease (ESKD) in patients with kidney amyloidosis ranged from 10 to 33% in previous studies (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). It is one of the main determinants of morbidity and quality of life and also limits therapeutic options (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past two decades, advancements in diagnostic tools and treatment options have significantly have improved kidney outcome in light chain amyloidosis. In 2014, Palladini et al. developed the criteria of kidney stage, response, and progression based on proteinuria and estimated glomerular filtration rate (eGFR) and validated these criteria as predictive factors of progression to ESKD (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, several studies highlighted the importance of the early difference between involved and uninvolved free light chain (dFLC) reduction, which is one determinant of hematologic response, as a crucial factor in predicting kidney prognosis (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this retrospective cohort study, we evaluated kidney outcomes of 138 patients with kidney amyloidosis who received recent standardized care for light chain amyloidosis. The aim of our study was to identify predictors of kidney survival, focusing on hematologic response (both categorical criteria and dFLC changes) and kidney response, as defined by Palladini et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, we examined the temporal changes in eGFR and proteinuria in relation to the grade of hematologic response, which could provide insights into predicting kidney response after achieving hematologic response.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003e \u003cb\u003eStudy population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis retrospective study identified 315 patients diagnosed with AL amyloidosis at Samsung Medical Center between March 2011 and December 2019. Among them, 165 participants (52.4%) had kidney involvement, defined as either biopsy-proven kidney amyloidosis or proteinuria over 0.5g per day, predominantly albuminuria in the presence of the involvement of other organs according to the 2005 International Society of Amyloidosis (ISA) criteria (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Individuals were excluded based on the following criteria: concurrent malignancy other than multiple myeloma (n\u0026thinsp;=\u0026thinsp;4), initiation of hemodialysis within 1 month after diagnosis (n\u0026thinsp;=\u0026thinsp;9), failure to initiate chemotherapy within 3 months after diagnosis or loss to follow-up within 1 month after diagnosis (n\u0026thinsp;=\u0026thinsp;14). Finally, a total of 138 participants were included.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData collection and definitions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe collected baseline data at the time of diagnosis. Hematologic evaluation of the plasma cell clonality included bone marrow biopsy and determination of monoclonal light chain burden through electrophoresis, immunofixation, and serum free light chain concentrations measurement. The involvement of other organs, such as heart, kidney, liver, gastrointestinal tract, and nerve, was assessed based on the 2005 ISA criteria specific to each organ (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). For kidney staging, we used the current criteria developed by Palladini et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Kidney stage Ⅰ was defined as eGFR equal to or higher than 50 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e with proteinuria equal or less than 5 g/day, and stage Ⅱ was when eGFR was lower than 50 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e or when there was proteinuria exceeding 5 g/day. Lastly, stage Ⅲ was assigned for individuals with eGFR lower than 50 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e and proteinuria greater than 5 g/day. eGFR was calculated using the equation of chronic kidney disease-epidemiology collaboration equation, and proteinuria was estimated by 24-hour urine collection.\u003c/p\u003e \u003cp\u003eHematologic response was assessed at 3 and 6 months in accordance with the 2012 ISA criteria (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Given the known impact of deep hematologic response, particularly achieving at least a very good partial response (VGPR), on prognosis and organ response, hematologic response was divided into two groups: complete response/ VGPR and partial response/no response/progression (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The dFLC was calculated by the difference between involved and uninvolved free light chain. The percentage changes in dFLC at 3 and 6 months from baseline were assessed using continuous and categorical variables, with the latter being divided into two groups according to 90% reduction. Kidney response and progression were evaluated at 6 months according to the criteria of Palladini et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Kidney progression was defined as a decrease in eGFR of 25% or more, and kidney response was defined as a decrease in proteinuria of 30% or more in the absence of kidney progression. Individuals who did not belong to either kidney response or kidney progression were classified as stable disease. Thereafter, eGFR and proteinuria were evaluated every 6 months for 3 years.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOutcomes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe primary outcome was kidney survival, defined as progression to ESKD and undergoing dialysis for more than three months. We did not consider cases where temporary dialysis was performed for acute kidney injury treatment. The participants were followed up until initiation of dialysis, death, or the last clinical visit date.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as median with interquartile range, and categorical variables were expressed as numbers with percentages. Comparison between groups was performed using Kruskal-Wallis test or Mann-Whitney U-test for continuous variables and the chi-square test for categorical variables. Kaplan-Meier curves were plotted for the cumulative incidence, and statistical difference was analyzed using log-rank test. Because a substantial number of patients died before progressing to ESKD during treatment, we accounted for death before requiring dialysis as a competing risk for ESKD using a fine and gray substitution hazard model. Cox regression analysis was performed to identify predictive factors of kidney survival. Multivariate analysis included variables with a P value of \u0026lt;\u0026thinsp;0.10 in univariate analysis, age and sex. The results were expressed by the hazard ratio (HR) with the 95% confidence interval (CI). Generalized estimating equation was used to investigate the interaction of the time and deep hematologic response in eGFR and proteinuria. Statistical analysis was performed using R Statistical Software, version 4.3.1 (Foundation for Statistical Computing, Vienna, Austria) and SPSS for Windows, version 22 (IBM Co., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eBaseline characteristics and overall patients\u0026rsquo; survival\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe baseline clinical characteristics and laboratory data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The age was 64 (56, 70) years and 72 participants (52.2%) were male. A total of 99 participants (71.7%) had heart involvement, and 62 (44.9%) had multiple myeloma concurrently. At baseline, dFLC was 162.2 (63.1, 482.8) mg/L, eGFR was 79 (52, 94) mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e and proteinuria was 3.8 (2.0, 5.9) g/day. According to the revised Mayo staging, 31 (22.5%), 27 (19.6%), 30 (31.2%), and 26 (26.1%) belonged to stage Ⅰ, Ⅱ, Ⅲ, and Ⅳ, respectively. In terms of kidney staging, 70 (50.7%) were in stage Ⅰ, 57 (41.3%) were in stage Ⅱ, and 11 (8.0%) were in stage Ⅲ.\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 patients*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKidney stage Ⅰ\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKidney stage Ⅱ\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKidney stage Ⅲ\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (56, 70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (56, 73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (54, 68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (62, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8 (20.9, 24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8 (20.8, 24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.6 (21.2, 25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.5 (21.3, 25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrgan involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNerve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcomitant Multiple myeloma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.3 (10.5, 13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3 (11.1, 13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (10.3, 14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8 (9.3, 14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\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\u003e2.7 (2.1, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1 (2.5, 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (2.0, 2.9)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3 (1.9, 2.6)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC, serum (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.2 (63.1, 482.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.1 (65.3, 504.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185.9 (63.3, 365.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e480.6 (63.1, 624.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffected LC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters of kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (2.0, 5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (1.4, 3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7 (3.9, 3.6)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4 (7.6, 12.0)\u003csup\u003eb, c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.77, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.72, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.78, 1.57) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73 (1.59, 2.34)\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (52, 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (71, 97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (37, 94) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (27, 41)\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised mayo stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅰ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-line therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBortezomib-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther chemotherapy\u0026para;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Data are median (IQR) or numbers (percentages). Differences in baseline characteristics among kidney stage groups were assessed with the use of Kruskal-Wallis test for continuous variables and chi-square test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u0026para; Other chemotherapy included immunomodulatory agent, daratumumab, and conventional therapy (steroid only, cyclophosphamide, melphalan, etc.).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Kidney stage Ⅰ vs Ⅱ : \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.017 by Kruskal-Wallis test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Kidney stage Ⅰ vs Ⅲ : \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.017 by Kruskal-Wallis test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003e Kidney stage Ⅱ vs Ⅲ : \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.017 by Kruskal-Wallis test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviation: ASCT, autologous stem cell transplantation; BMI, body mass index; dFLC, difference between involved and uninvolved free light chain; eGFR, estimated glomerular filtration rate; GI, gastrointestinal; IQR, interquartile range; LC, light chain\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe median follow-up duration was 46.4 months (10.0, 79.4). A total of 53 patients (38.4%) died, of which 47 (34.1%) died before requiring dialysis. The overall survival was different among the revised Mayo stages (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The median overall survival was 36 months in stage Ⅲ and 20 months in stage Ⅳ, while not reached in stage Ⅰ and Ⅱ. Post hoc analysis revealed that the revised Mayo stage Ⅰ had significantly longer overall survival than stage Ⅱ (P\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.003), Ⅲ (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Ⅳ (P\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInitial kidney staging and kidney survival\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOut of the 138 patients, 17 (12.3%) progressed to ESKD. By kidney stage, 5 (7.1%), 10 (17.5%), and 2 (18.2%) patients initiated dialysis during the follow-up in stage Ⅰ, Ⅱ, and Ⅲ, respectively. The overall kidney survival varied across three kidney stages (P\u0026thinsp;=\u0026thinsp;0.02, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The cumulative incidence of ESKD at 5 years was 2.1% in kidney stage Ⅰ, 15.5% in stage Ⅱ, and 18.2% in stage Ⅲ, respectively. Alongside kidney staging, diabetes status and hemoglobin levels showed an association with kidney survival in univariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In multivariable analysis involving baseline characteristics, only kidney stage Ⅱ exhibited an increased risk of progression to ESKD compared to kidney stage Ⅰ (HR 3.75, 95% CI 1.38\u0026ndash;10.15; P\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \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\u003eBaseline parameters and the risk of progression to end-stage kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eProgression to ESKD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of patients (N\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u0026para;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u0026para;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.42 (0.92, 6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.57 (0.79, 8.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.42, 3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.95 (0.69, 12.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.60 ,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80 (0.62, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.48, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅰ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51 (1.33, 9.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.75 (1.38, 10.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.68 (0.74, 18.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.71 (0.48, 15.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised mayo stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅰ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49 (0.10, 2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46 (0.46, 4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.15, 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Model included age, sex, diabetes, hemoglobin levels, and initial kidney stage.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026para; Hazard ratios and P values were estimated with a Substitution hazards model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviation: CI, confidence interval; ESKD, end-stage kidney disease; HR, hazard ratio; Ref., reference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTreatment responses within 6 months and kidney survival\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt 6 months, 47 (43.1%) out of 109 participants achieved kidney response. In terms of hematologic response, 59 (48.4%) of 122 patients achieved deep hematologic response at 3 months, while 68 (61.3%) of 111 patients achieved deep hematologic response at 6 months. The percentage changes in dFLC from baseline were \u0026minus;\u0026thinsp;78.9% (-97.0, -45.9) at 3 months and \u0026minus;\u0026thinsp;85.8% (-96.9, -65.2) at 6 months (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAmong treatment response parameters, only kidney response independently predicted kidney survival in multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, both stable disease (HR 8.42, 95% CI 1.71\u0026ndash;41.35; P\u0026thinsp;=\u0026thinsp;0.01) and kidney progression (HR 7.36, 95% CI 1.25\u0026ndash;43.33; P\u0026thinsp;=\u0026thinsp;0.03) were associated with an increased risk of ESKD compared to kidney response after adjusting for age, sex, diabetes status, hemoglobin levels, and initial kidney stage. dFLC percentage changes at 6 months were associated with kidney survival in univariate analysis, but this association were marginally significant after adjusting for initial kidney stages. dFLC percentage changes at 3 months were not associated with kidney survival. Hematologic response at both 3 and 6 months did not predict progression to ESKD.\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\u003eHematologic and kidney response and the risk of progression to end-stage kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eProgression to ESKD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of Patients (N\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u0026para;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u0026para;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic response\u003c/p\u003e \u003cp\u003eat 3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR/NR/Progression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR/VGPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.27, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic response\u003c/p\u003e \u003cp\u003eat 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR/NR/Progression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR/VGPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.21, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC change at 3 months\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.86, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC change at 3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecrease\u0026thinsp;\u0026lt;\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecrease\u0026thinsp;\u0026gt;\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.24, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC change at 6 months\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.86, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.81, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC change at 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecrease\u0026thinsp;\u0026lt;\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecrease\u0026thinsp;\u0026gt;\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37 (0.12, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32 (0.09, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney response at 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003cp\u003e(1.10, 24.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003cp\u003e(1.71, 41.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003cp\u003e(1.38, 37.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.36\u003c/p\u003e \u003cp\u003e(1.25, 43.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Model included in the models were age, sex, diabetes, hemoglobin levels, initial kidney stage, and either dFLC change at 6 months (as continuous variables or categorized by more than 90%) or kidney response at 6 months.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026para; Hazard ratios and P values were estimated with a Substitution hazards model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026dagger; Every 10 unit increase (The median percentage changes in dFLC were \u0026minus;\u0026thinsp;78.9% (-97.0, -45.9) at 3 months and \u0026minus;\u0026thinsp;85.8% (-96.9, -65.2) at 6 months)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviation: CI, confidence interval; CR, Complete response; dFLC, difference between involved and uninvolved free light chain; ESKD, End-stage kidney disease; HR, hazard ratio; NR, No response; PR, Partial response; Ref., reference; VGPR, Very good partial response\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe categorized participants into four groups based on their hematologic response and kidney response at 6 months and then compared the probabilities of ESKD, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. None of the participants who achieved both deep hematologic response and kidney response progressed to ESKD. Kidney survival was longer in patients with both deep hematologic response and kidney response than those with only hematologic response (P\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst-line therapy and kidney outcomes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e We divided patients into subgroups according to first-line therapy to evaluate the impact of treatments on kidney outcomes: autologous stem cell transplantation (ASCT), bortezomib-based, and other chemotherapy. A total of 41 (29.7%) participants underwent upfront ASCT with or without preceding induction chemotherapy. Except for those who underwent upfront ASCT, 54 (39.1%) were treated with bortezomib and 43 (31.2%) received other chemotherapies, including immunomodulatory drugs (thalidomide or lenalidomide), daratumumab, and conventional chemotherapy (cyclophosphamide, melphalan, steroid only, etc.), as the first-line therapy. Supplementary Table\u0026nbsp;2 shows baseline characteristics of patients according to first-line therapy. Patients who underwent ASCT were younger than those who received chemotherapy, while those who were treated with bortezomib-based chemotherapy had a higher prevalence of heart involvement and a lower eGFR than other groups. Four (9.8%), nine (16.7%), and four (9.3%) patients progressed to ESKD in treatment groups of ASCT, bortezomib-based, and other chemotherapy, respectively. In univariable analysis, types of first-line therapy, ASCT (HR 1.05, 95% CI 0.29\u0026ndash;3.83; P\u0026thinsp;=\u0026thinsp;0.94) and bortezomib-based chemotherapy (HR 2.20, 95% CI 0.72\u0026ndash;6.69; P\u0026thinsp;=\u0026thinsp;0.16) was not associated with ESKD when other chemotherapy was used as the reference category (Supplementary Table\u0026nbsp;3). Also, we conducted subgroup analysis to evaluate the association between hematologic and kidney responses and ESKD risk in each ASCT and chemotherapy treatment group. Initial kidney stage Ⅱ and kidney response remained predictive of progression to ESKD, particularly in those who received chemotherapy, but not in those who underwent ASCT (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSerial changes of eGFR and percentage changes in proteinuria according to hematologic response\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe investigated the temporal changes in the eGFR and proteinuria over a period of 3 years based on hematologic response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the deep hematologic response group, eGFR exhibited a marginal decrease from baseline to 6 months (baseline vs. 6 months: 83.3 mL/min/1.73m\u0026sup2; vs. 72.3 mL/min/1.73m\u0026sup2;; P\u0026thinsp;=\u0026thinsp;0.07), followed by a subsequent stabilization. The percentage changes in proteinuria from baseline were \u0026minus;\u0026thinsp;43.8% and \u0026minus;\u0026thinsp;60.4% during the initial 6 months and the subsequent 6 months, respectively, showing a marginal difference (P\u0026thinsp;=\u0026thinsp;0.09). Afterward, there was a slight non-significant decrease in proteinuria levels. Conversely, the no deep hematologic response group showed no significant variations in the values of both eGFR and proteinuria assessed every 6 months. Overall, both eGFR (P\u0026thinsp;=\u0026thinsp;0.38) and percentage changes in proteinuria (P\u0026thinsp;=\u0026thinsp;0.56) did not exhibit significant differences between hematologic response groups over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our longitudinal study involving 138 patients with kidney amyloidosis, the initial kidney stage emerged as an independent predictor of progression to ESKD. Notably, in stage Ⅱ, the risk of ESKD increased by 3.75 times compared to stage Ⅰ, emphasizing the critical importance of initiating appropriate hematological treatment before kidney involvement reaches a more severe stage. Our analysis has brought that kidney response, rather than hematologic response following chemotherapy, exhibited a substantial association with progression to ESKD. Of particular concern is the finding that, even among those achieving deep hematologic response at the 6-month mark, subsequent improvements in eGFR and proteinuria after 12 months were insignificant, and especially, a lack of kidney response had association with an increased risk of ESKD. This observation implies that despite a favorable hematologic response, additional interventions may be required when the kidney response remains suboptimal. This is in line with recent arguments suggesting that patients who have not received sufficient organ responses need new treatment targeting the amyloid deposits (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe achievement of hematologic response is a crucial goal in the treatment of AL amyloidosis, aiming to impede the further accumulation of amyloid fibrils. Previous studies have demonstrated its association with overall survival (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), as well as organ responses, including kidney response (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and kidney survival (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, recent research indicates a shifting emphasis towards the importance of evaluating organ response, encompassing the heart, kidneys, liver, and more, as a key prognostic factor compared to hematologic response (\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In line with these insights, our study consistently found that assessing kidney response holds more significance in predicting progression to ESKD than hematologic response alone. Unlike multiple myeloma, where morbidity and mortality primarily stem from plasma cell proliferation, the presence of organ dysfunction adversely affects the patient\u0026rsquo;s prognosis in AL amyloidosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Given this intricate pathophysiology, there is a growing need to expand current treatment surrogate endpoints, which are highly dependent on hematologic response, to encompass organ responses as well (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current standard for evaluating kidney response in AL amyloidosis involves categorizing it into three stages: progression, stable, and response (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Our study found that the hazard ratio for progression to ESKD was comparable between stable disease and kidney progression. In post hoc analysis, the risk of ESKD was not different between kidney progression and stable disease (P\u0026thinsp;=\u0026thinsp;0.80, data not shown). This suggests that stable disease has limited discriminatory value compared to kidney progression, and kidney response serves as a more reliable predictor of kidney survival. There is a need for further exploration and confirmation of criteria related to kidney response, which is widely utilized in AL amyloidosis, particularly those associated with long-term kidney outcomes. Questions also remain regarding the timing of evaluating organ response. Notably, in the group with deep hematologic response, there was no significant reduction in proteinuria after 12 months, indicating that the 12-month mark post-treatment initiation may be an appropriate time point for kidney response evaluation. This observation aligns with a study conducted at the Mayo Clinic, which suggested a median time of around 11 months to reach kidney response (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a determinant of hematologic response, dFLC changes have been identified as an independent predictor of overall patient survival (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and kidney survival in patients with AL amyloidosis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Especially, dFLC is preferred over levels of involved free light chain or free light chain ratio for assessment of light chain burden in individuals with kidney failure because it is less likely to be confounded by kidney function (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). While it did not emerge as a predictor of kidney survival in our study, it showed an association with kidney response. Specifically, the percentage changes in dFLC at 3 months (no kidney response vs. kidney response: -72.6 vs. -89.3; P\u0026thinsp;=\u0026thinsp;0.10) and 6 months (-83.3 vs. -90.7; P\u0026thinsp;=\u0026thinsp;0.02) were more substantial in patients who achieved kidney response at 6 months compared to those who did not (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, its retrospective design introduced a notable constraint, with slight variations in the timing of response assessments. Also, kidney responses at 6 months were unavailable for two patients due to missing laboratory results. Secondly, advancements in therapeutic strategies over the study period, such as increased utilization of bortezomib and autologous stem cell transplantation since 2014, may have influenced patients outcomes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This enhancement in patient survival allowed for a comprehensive assessment of kidney outcomes in many cases. However, analysis involving first-line therapy was limited by small numbers of patients in each treatment group and selection bias in that individuals who underwent ASCT were younger and had better organ function than those who received other treatments. Third, while kidney amyloidosis remains the predominant cause of progression to ESKD, other contributing factors may have included treatment-related nephrotoxicity, cardiorenal syndrome, and the progression of chronic kidney disease.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study underscores the importance of kidney response, rather than post-chemotherapy hematologic response, in predicting the progression to ESKD. While a significant reduction in dFLC substantially increased likelihood of kidney response, discrepancies between dFLC changes and improvements in kidney parameters were noted in some cases. Additionally, even among patients exhibiting deep hematologic response at the 6-month mark, subsequent improvements in eGFR and proteinuria after 12 months were found to be insignificant. This highlights the imperative for new treatment strategies to improve the prognosis of patients with insufficient organ responses. Further research is warranted to establish specific criteria and optimal timings for organ responses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003edFLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifference between involved and uninvolved free light chain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eend-stage kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Society of Amyloidosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVGPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003every good partial response\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\u003c/p\u003e\n\u003cp\u003eClinical investigations were conducted in accordance with the principles of the Declaration of Helsinki. This study was approved by the Institutional Review Board of Samsung Medical Center (IRB file no. SMC 2023-07-058). The need for informed patient consent was waived by the Institutional Review Board of Samsung Medical Center due to the retrospective design of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data associated with the present study are available from the corresponding author on reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from the Samsung Biomedical Research Institute (grant no.OTA1901971). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJung Eun Lee\u0026nbsp;designed and supervised the study.\u0026nbsp;Sungmi Kim and Jinyoung Yang wrote the original manuscript draft as the first authors.\u0026nbsp;Kyungho Lee\u0026nbsp;and Junseok Jeon\u0026nbsp;were involved in the patient recruitment and planned studies. Sang Eun Yoon and Darae Kim had all access to data and conducted the analysis.\u0026nbsp;Jin-Oh Choi,\u0026nbsp;Seok Jin Kim, and Kihyun Kim\u0026nbsp;were\u0026nbsp;responsible for the\u0026nbsp;interpretation of the results of the analysis and the critical revision of the article. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Sang Ah Chi, senior statistician at the Biomedical Statistics Center, Samsung Medical Center, Seoul, South Korea for her dedicated efforts in statistical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRyšav\u0026aacute; R. AL amyloidosis: advances in diagnostics and treatment. Nephrol Dial Transpl. 2019;34(9):1460\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalladini G, Dispenzieri A, Gertz MA, Kumar S, Wechalekar A, Hawkins PN, et al. New criteria for response to treatment in immunoglobulin light chain amyloidosis based on free light chain measurement and cardiac biomarkers: impact on survival outcomes. J Clin Oncol. 2012;30(36):4541\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGertz MA, Comenzo R, Falk RH, Fermand JP, Hazenberg BP, Hawkins PN et al. Definition of organ involvement and treatment response in immunoglobulin light chain amyloidosis (AL): a consensus opinion from the 10th International Symposium on Amyloid and Amyloidosis, Tours, France, 18\u0026ndash;22 April 2004. Am J Hematol. 2005;79(4):319\u0026thinsp;\u0026ndash;\u0026thinsp;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKastritis E, Gavriatopoulou M, Roussou M, Migkou M, Fotiou D, Ziogas DC, et al. Renal outcomes in patients with AL amyloidosis: Prognostic factors, renal response and the impact of therapy. Am J Hematol. 2017;92(7):632\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalladini G, Hegenbart U, Milani P, Kimmich C, Foli A, Ho AD, et al. A staging system for renal outcome and early markers of renal response to chemotherapy in AL amyloidosis. Blood. 2014;124(15):2325\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrosou ME, Vaughan LE, Muchtar E, Buadi FK, Dingli D, Dispenzieri A, et al. Comparison of the current renal staging, progression and response criteria to predict renal survival in AL amyloidosis using a Mayo cohort. Am J Hematol. 2021;96(4):446\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerlini G, Seldin DC, Gertz MA. Amyloidosis: pathogenesis and new therapeutic options. J Clin Oncol. 2011;29(14):1924\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezk T, Lachmann HJ, Fontana M, Sachchithanantham S, Mahmood S, Petrie A, et al. Prolonged renal survival in light chain amyloidosis: speed and magnitude of light chain reduction is the crucial factor. Kidney Int. 2017;92(6):1476\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinney JH, Lachmann HJ, Bansi L, Wechalekar AD, Gilbertson JA, Rowczenio D, et al. Outcome in renal Al amyloidosis after chemotherapy. J Clin Oncol. 2011;29(6):674\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Dispenzieri A, Lacy MQ, Hayman SR, Buadi FK, Colby C, et al. Revised prognostic staging system for light chain amyloidosis incorporating cardiac biomarkers and serum free light chain measurements. J Clin Oncol. 2012;30(9):989\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGertz MA, Landau H, Comenzo RL, Seldin D, Weiss B, Zonder J, et al. First-in-Human Phase I/II Study of NEOD001 in Patients With Light Chain Amyloidosis and Persistent Organ Dysfunction. J Clin Oncol. 2016;34(10):1097\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilani P, Merlini G, Palladini G. Novel Therapies in Light Chain Amyloidosis. Kidney Int Rep. 2018;3(3):530\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJimenez-Zepeda VH, Lee H, McCulloch S, Tay J, Duggan P, Neri P, et al. Treatment response measurements and survival outcomes in a cohort of newly diagnosed AL amyloidosis. Amyloid. 2021;28(3):182\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalladini G, Barassi A, Klersy C, Pacciolla R, Milani P, Sarais G, et al. The combination of high-sensitivity cardiac troponin T (hs-cTnT) at presentation and changes in N-terminal natriuretic peptide type B (NT-proBNP) after chemotherapy best predicts survival in AL amyloidosis. Blood. 2010;116(18):3426\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung N, Dispenzieri A, Fervenza FC, Lacy MQ, Villicana R, Cavalcante JL, et al. Renal response after high-dose melphalan and stem cell transplantation is a favorable marker in patients with primary systemic amyloidosis. Am J Kidney Dis. 2005;46(2):270\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWechalekar A, Merlini G, Gillmore JD, Russo P, Lachmann HJ, Obici L, et al. Role of NT-ProBNP to Assess the Adequacy of Treatment Response in AL Amyloidosis. Blood. 2008;112(11):1689.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSidana S, Milani P, Binder M, Basset M, Tandon N, Foli A, et al. A validated composite organ and hematologic response model for early assessment of treatment outcomes in light chain amyloidosis. Blood Cancer J. 2020;10(4):41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComenzo RL, Reece D, Palladini G, Seldin D, Sanchorawala V, Landau H, et al. Consensus guidelines for the conduct and reporting of clinical trials in systemic light-chain amyloidosis. Leukemia. 2012;26(11):2317\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManwani R, Cohen O, Sharpley F, Mahmood S, Sachchithanantham S, Foard D, et al. A prospective observational study of 915 patients with systemic AL amyloidosis treated with upfront bortezomib. Blood. 2019;134(25):2271\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung N, Glavey SV, Kumar S, Dispenzieri A, Buadi FK, Dingli D, et al. A detailed evaluation of the current renal response criteria in AL amyloidosis: is it time for a revision? Haematologica. 2013;98(6):988\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar SK, Dispenzieri A, Lacy MQ, Hayman SR, Buadi FK, Zeldenrust SR, et al. Changes in serum-free light chain rather than intact monoclonal immunoglobulin levels predicts outcome following therapy in primary amyloidosis. Am J Hematol. 2011;86(3):251\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatzmann JA, Clark RJ, Abraham RS, Bryant S, Lymp JF, Bradwell AR, et al. Serum Reference Intervals and Diagnostic Ranges for Free κ and Free λ Immunoglobulin Light Chains: Relative Sensitivity for Detection of Monoclonal Light Chains. Clin Chem. 2002;48(9):1437\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon SE, Kim D, Choi JO, Min JH, Kim BJ, Kim JS, et al. A comprehensive overview of AL amyloidosis disease characteristics accumulated over two decades at a single referral center in Korea. Int J Hematol. 2023;117(5):706\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"AL Amyloidosis, Hematologic response, Kidney response, End-Stage Kidney Disease, Predictor","lastPublishedDoi":"10.21203/rs.3.rs-4003929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4003929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLight chain amyloidosis, characterized by amyloid fibril deposition in multiple organs, often leads to progression to end-stage kidney disease. This study aimed to identify predictors of kidney survival in patients with kidney amyloidosis, focusing on hematologic and kidney response.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 138 patients diagnosed with kidney amyloidosis between 2011 and 2019. Palladini et al.'s criteria were applied for kidney stage and response, and the 2012 International Society of Amyloidosis criteria for hematologic response.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 17 (12.3%) progressed to end-stage kidney disease. Multivariate analysis, considering baseline characteristics, revealed that stage Ⅱ was associated with an increased risk of end-stage kidney disease compared to stage Ⅰ (hazard ratio 3.75; 95% confidence interval 1.38\u0026ndash;10.15; P\u0026thinsp;=\u0026thinsp;0.01). Compared to kidney response, the risk of end-stage kidney disease increased by 8.42 (95% confidence interval 1.71\u0026ndash;41.35; P\u0026thinsp;=\u0026thinsp;0.01) and 7.36 (95% confidence interval 1.25\u0026ndash;43.33; P\u0026thinsp;=\u0026thinsp;0.03) times in stable disease and kidney progression at 6 months, independently on baseline characteristics, respectively, whereas hematologic response showed no association with kidney outcome. Kidney survival was longer in patients with both deep hematologic response and kidney response than in those with only hematologic response (P\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study underscores the importance of kidney response over hematologic response in predicting end-stage kidney disease and emphasizes the need to assess treatment endpoints, considering organ response alongside hematologic response.\u003c/p\u003e","manuscriptTitle":"The importance of kidney response over hematologic response in predicting kidney outcome in AL Amyloidosis: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 19:34:52","doi":"10.21203/rs.3.rs-4003929/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":"bf5d3ada-e0a8-4a14-9355-54c1a6e4e799","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-26T10:50:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 19:34:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4003929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4003929","identity":"rs-4003929","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}