Clinicopathologic predictors of renal response and survival in newly diagnosed multiple myeloma with renal injury: a retrospective study.

Introduction

Multiple myeloma (MM) is a hematologic malignancy characterized by the presence of abnormal clonal plasma cells in the bone marrow. These cells have the potential of uncontrolled growth, producing excessive quantities of monoclonal immunoglobulins and disrupting the bone marrow microenvironment, and then triggering multi-organ dysfunction [ 1 – 3 ]. Renal impairment (RI) is a common complication of MM, affecting 20%–40% of the newly diagnosed MM (NDMM) patients [ 4 ]. Besides, some patients progress to RI during the treatment process of disease, with approximately 2% to 4% requiring dialysis [ 5 – 7 ]. Evidence has demonstrated renal recovery is associated with improved survival, and the emergence of novel anti-MM therapies really improves myeloma-related RI [ 4 , 8 – 10 ]. However, the NDMM patients with RI still have a lower overall survival rate compared to those without RI [ 8 , 9 , 11 ]. It is important to disclose the association factors of RI in the NDMM, as well as to clarify the influencing factors of renal response targeting MM-related RI, aiming to find new potent target for its treatment.

Studies have demonstrated that MM-related RI is multi-factorial; long-standing RI can be attributed to age-related co-morbidities such as hypertension and diabetes mellitus [ 12 , 13 ]. In addition, Yadav et al. [ 14 ] revealed higher serum immunoglobulin free light chain (FLC) levels at diagnosis was closely associated with the severity of RI regardless of the paraprotein type; this can be reversed by the rapid lowering of the involved FLC with effective anti-myeloma therapy [ 15 ]. Also, earlier studies have suggested that the urinary albumin level was lower in myeloma patients with cast nephropathy than that in patients with renal amyloidosis [ 16 , 17 ]. Urinary albumin content reflects the pathogenesis of RI and predicted renal response in myeloma patients [ 18 ]. Moreover, it is reported that serum erythropoietin (EPO) levels > 25 mIU/mL is an independent predictive factor for major renal response and may predict renal reversibility more accurately in patients with urinary albumin level ≤ 25% [ 19 ].

In the current study, we tried to completely investigate the influencing factors of RI, RI response and survival in NDMM patients based on the clinical indexes at diagnosis, including the patients’ information, laboratory examination data and FISH results.

Patients and methods

Study design and patients

This was a prospective observational study. A total of 647 NDMM patients aged ≥ 18 years treated at Zhabei Central Hospital in Shanghai Jing’an District (Shanghai, China) between June 1, 2013, and December 31, 2023, were included in this study. Among them, 182 patients were diagnosed with RI and 465 patients were without RI. MM was diagnosed according to International Myeloma Working Group (IMWG) consensus statement in 2014 [ 20 ], as well as the MM-related RI, including a serum creatinine (Cr) level higher than 2 mg/dL (177 μmol/L) or a reduced Cr clearance (< 40 mL/min) [ 20 – 22 ]. Also, the renal response was evaluated according to the IMWG consensus statement in 2014 [ 20 ]; detailly, patients with complete response (CR), partially response (PR) and minimal response (MR) were all considered as the responder. The comprehensive clinical information, including age, sex, M protein type, serum free light chain κ/λ ratio, Durie-Salmon (DS) stage, International Staging System (ISS), R2-ISS, serum Cr (μmol/L), hemoglobin (g/L), serum albumin (g/L), estimated glomerular filtration rate (eGFR) (ml*min-1*(1.73 m2)-1), uric acid (μmol/L), urea (mmol/L), urinary protein/creatinine ratio (mg/g Cr), urinary albumin/creatinine ratio (mg/g Cr), lactate dehydrogenase (LDH) (U/L), serum calcium (mmol/L), β2-microglobulin (β2-MG) (mg/L), cystatin C (Cyc) (mg/L), plasma cell percentage, cytogenetic profiles including t(4;14), t(11;14), t(14;16), del(17p), del(13q) and gain(1q21) detected by fluorescence in situ hybridization (FISH), comorbidity (diabetes, hypertension) and treatment protocols were recorded for this study.

Statistical analysis

Statistical analyses were conducted using SPSS (version 24.0), the statistical analysis platform ( https://bioinfograph.rightongene.com:18080/#/login ) developed based on the R software (version 3.6.1) and the R software. Categorical data were reported as frequency counts and percentages. Statistical significance was determined by a two-tailed test with a P value of less than 0.05. Detail descriptions of the statistical methods for logistic and Cox were listed as follows.

To find the independent associated factors of RI in NDMM patients, univariate Chi-square or Fisher’s exact test analysis was performed to explore the influencing factor of RI in the 647 NDMM patients among the 28 variables, including sex, age (≥ 65), serum secreted monoclonal immunoglobulin type of IgG, light chain type of κ, DS stage (III), ISS stage (III), R2-ISS stage (1.5–5), serum free light chain (≤ 0.01 or ≥ 100), t(11;14), t(4;14), t(14;16), del(17p), del(13q), gain(1q21), hemoglobin (≥ 100), LDH (≥ 250), albumin (≥ 35), β2-MG (≥ 5.5), serum calcium (> 2.65), plasma cells of BM (≥ 50%), extramedullary plasmacytoma, amyloidosis, comorbidity (diabetes, hypertension), urea (≥ 8.3), uric acid (≥ 340), Cyc (≥ 1.03), urinary protein/Cr (≥ 200) and urinary albumin/Cr (≥ 30). Then, the factors with P < 0.05 and no close association in clinic were submitted to stepAIC Multiple stepwise regression and the multivariate logistic analysis. Specifically, the related variables, such as DS stage and serum calcium levels; ISS stage with β2-MG and albumin; R2-ISS stage and del(17p), LDH, t(4;14), gain(1q21); chronic kidney disease (CKD) stage and eGFR level before treatment, could not be included in the model at the same time. During this analysis, internal validation was carried out using the bootstrap resampling ( n = 1000) in which 647 bootstrap samples were used to quantify the optimism-corrected area under the curve (AUC) [ 23 ]. In addition, the k-means cluster method was used to assess the association between the 28 variables and RI incidence in NDMM patients.

To screen and validate the predictive markers of renal response, 193 NDMM patients with RI were randomly divided into training and validation cohorts with a ratio of 3:1. The support vector machine-recursive feature elimination (SVM-RFE) machine learning algorithms can be used to find the best variables as a surveillant machine learning method to support vectors by cutting the features in half each round if there are many features [ 24 ]. Thirty-two variables including sex, age (≥ 65), serum secreted monoclonal immunoglobulin type of IgG, light chain type of κ, DS stage (III), ISS stage (III), R2-ISS stage (1.5–5), serum free light chain (≤ 0.01 or ≥ 100), t(11;14), t(4;14), t(14;16), del(17p), del(13q), gain(1q21), hemoglobin (≥ 100),LDH (≥ 250), albumin (≥ 35),β2-MG (≥ 5.5), serum calcium (> 2.65), plasma cells of BM (≥ 50%), extramedullary plasmacytoma, amyloidosis, comorbidity (diabetes, hypertension), treatment regimen (PI-based regimen), Cr level before treatment (< 355), eGFR level before treatment (< 12.11), CKD stage (G4-G5), urea (≥ 8.3), uric acid (≥ 340), Cyc (≥ 1.03), urinary protein/Cr (≥ 200) and urinary albumin/Cr (≥ 30). Among these variables, the optimal cut-off levels of the levels of Cr and eGFR before treatment were identified using AddFor algorithms [ 25 ]. Similarly, the related variables, such as ISS stage and β2-MG, albumin; R2-ISS stage and del(17p), LDH, t(4;14), gain(1q21); CKD stage and eGFR level before treatment, could not be included in the model at the same time. The SVM classifier from the R package ( http://github.com/johncolby/SVM-RFE ) was adopted to classify the selected biomarkers in predicting the RI response. k = 10 was the setting for the k-fold cross validation. In our study, we employed the receiver operating characteristic (ROC) analysis to assess the diagnostic accuracy of this model in distinguishing the RI responders from non-responders in 193 NDMM patients with RI, and the AUC was calculated to measure the predictive capability of the algorithms. ROC analysis is a widely accepted statistical technique in medical research for evaluating the performance of diagnostic tests, predictive models, or classification methods.

For survival analysis, the optimal cut-off level of continuous variables (the level of Cr before treatment) was identified using AddFor algorithms [ 25 ]. Then, the Kaplan–Meier curves with log-rank test was used to assess the effect of clinical clinicopathologic features on the overall survival of NDMM patients with RI. The Schoenfeld residuals analysis was used to assess whether the Proportional Hazards Assumption (PH assumption) holds of these variables. Time-dependent mean C-index was performed to assessed the prognostic predictive capacity of each indicator ( P < 0.05), with resample (bootstrap, n = 1000) for internal validation [ 26 ]. Next, these factors were included in the multivariate Cox analysis and the results were demonstrated using the forest plot and nomogram.

Results

Clinical characteristics

A total of 647 NDMM patients were included in this study, including 402 males and 245 females, with 342 (52.86%) patients aged ≥ 65 years. IgG type was the most common secreted monoclonal immunoglobulin, which accounts for 46.83%, and the distribution on light chain restriction showed a similar incidence of κ and λ type (52.55% and 46.37%, respectively). Most of the patients (93.51%, 605/647) were diagnosed with a stage III on the DS stage, and 56.92% (362/636) patients were divided into the intermediate-high risk group based on the R2-ISS. Regarding the FISH results, 89 (14.15%) patients were positive for t(11; 14), 94 (14.94%) were positive for t(4;14), 10 (1.62%) patients were positive for t(14;16), 87 (13.51%) patients showed del (17p), 246 (38.20%) showed del (13q), and about half of the patients (50.93%, 328/644) demonstrated 1q21 gains. Most of the patients (79.91%, 517/647) received ubiquitin proteasome inhibitors (PI)-based regimen.166 (25.66%) showed more than 50% plasma cells in the bone marrow (BM), an identified risk factor of MM [ 27 ]. Among the 647 patients, 193 patients had a ≥ 177 μmol/L Cr level and were defined as the MM-related RI. The detailly clinicopathologic data are shown in Table 1 . Table 1 Characteristics of the study populations Characteristics Total cohort ( n = 647, %) Sex Female 245 (37.87) Male 402 (62.13) Age at diagnosis, years ≥ 65 342 (52.86) < 65 305 (47.14) Secreted monoclonal immunoglobulin Detected in urine only 145 (22.41) IgG 303 (46.83) IgA 139 (21.48) IgD 52 (8.03) IgM 1 (0.15) Non-secretory 7 (1.08) Light chain restriction Non-secretory 7 (1.08) κ 340 (52.55) λ 300 (46.37) DS stage I 9 (1.39) II 33 (5.10) III 605 (93.51) ISS stage I 127 (19.63) II 203 (31.38) III 317 (48.99) R2-ISS Low-risk 56 (8.90) Low-intermediate risk 110 (17.49) Intermediate-high risk 355 (56.44) High-risk 108 (17.17) Missing 18 Serum-free light chain 0.01–100 376 (58.84) ≤ 0.01, ≥ 100 263 (41.16) Missing 8 t(11; 14) in FISH No 540 (85.85) Yes 89 (14.15) Missing 18 t(4; 14) in FISH No 535 (85.06) Yes 94 (14.94) Missing 18 t(14; 16) in FISH No 619 (98.41) Yes 10 (1.62) Missing 18 Del(17p) in FISH No 557 (86.49) Yes 87 (13.51) Missing 3 Del(13q) in FISH No 398 (61.80) Yes 246 (38.20) Missing 3 Gain(lq21) in FISH No 316 (49.07) Yes 328 (50.93) Missing 3 Hemoglobin level, g/L ≤ 100 401 (61.98) > 100 g/l 246 (38.02) Serum LDH, U/L < 250 496 (76.66) ≥ 250 151 (23.34) Serum albumin, g/L < 35 332 (51.31) ≥ 35 315 (48.69) β2-MG, mg/L < 3.5 206 (31.84) 3.5–5.5 124 (19.17) ≥ 5.5 317 (48.99) Serum calcium level, mmol/L ≤ 2.65 536 (82.84) > 2.65 111 (17.16) Extramedullary plasmacytoma No 525 (81.14) Yes 122 (18.86) Amyloidosis No 624 (96.45) Yes 23 (3.55) Plasma cells of BM, % < 50 477 (74.30) ≥ 50 165 (25.70) Missing 5 Comorbidity (diabetes, hypertension) No 369 (57.03) Yes 278 (42.97) Treatment regimens PI-based regimen 517 (79.91) Thalidomide based regimen 92 (14.22) Other treatment options 38 (5.87) RI No 454 (70.17) Yes 193 (29.83) eGFR, ml*min-1*(1.73 m2)-1 < 60 262 (40.49) ≥ 60 385 (59.51)) Urea, mmol/L < 8.3 377 (58.27) ≥ 8.3 270 (41.73) Uric acid, μmol/L < 340 342 (52.86) ≥ 340 305 (47.14) Cyc, mg/L < 1.03 113 (17.47) ≥ 1.03 534 (82.53) Urinary protein/creatinine, mg/g Cr < 200 226 (34.93) ≥ 200 421 (65.07) Urinary albumin/creatinine, mg/g Cr < 30 284 (43.89) ≥ 30 363 (56.11) Cr, creatinine; Cyc, cystatin C; DS, Durie-Salmon; eGFR, estimated glomerular filtration rate; ISS, International Staging System; LDH, lactate dehydrogenase; β2-MG, β2-microglobulin; PI, ubiquitin proteasome inhibitors

Identification of the influencing factors of RI in NDMM

Then, we explored the associated factors of MM-related RI. In the current cohort of 647 MM patients, 193 patients (29.83%) were with RI (RI group) and454 (70.17%) patients were diagnosed without RI (non-RI group). The univariate analysis revealed several factors showed significant difference between the RI and non-RI, including the proportion of male patients (70.98% vs. 58.37%, P = 0.003), secreted monoclonal immunoglobulin of IgG type (52.64% vs. 33.16%, P < 0.001), light chain of κ type (46.11% vs. 55.29%, P = 0.033), ISS stage (90.16% vs. 31.50% for stage III, P < 0.001), R2-ISS stage (98.92% vs. 63.06% for 1.5–5 score, P < 0.001), serum free κ/λ light chain (58.12% vs. 33.93% for dysregulation, P < 0.001), t(11;14) (19.46% vs. 11.94% for positive, P = 0.002), Del(17p) (20.31% vs. 10.62% for positive, P = 0.032), decreased hemoglobin (86.01% vs. 51.99%, P < 0.001), elevated LDH (35.23% vs. 18.28%, P < 0.001), β2-MG (90.16% vs. 31.50% for ≥ 5.5 mg/L, P < 0.001), elevated serum calcium (31.61% vs. 11.01%, P < 0.001), plasma cell percentage (34.74% vs. 21.90% for ≥ 50% percentage, P = 0.013), elevated urea (92.23% vs. 20.26%, P < 0.001), uric acid (74.61% vs. 35.46%, P < 0.001), elevated Cyc (100% vs. 75.11%, P < 0.001), elevated urinary protein/creatinine ratio (88.08% vs. 55.29%, P < 0.001), and elevated urinary albumin/creatinine ratio (77.20% vs. 47.14%, P < 0.001) (Table 2 ). Then, the variates with significant differences between the RI and non-RI groups were submitted to the stepAIC Multiple stepwise regression to choose variables. Finally, we find 6 variables were strongly associated with the incidence of RI, including the secreted monoclonal immunoglobulin of IgG type, serum freeκ/λ light chain, elevated serum calcium (> 2.65), elevated urea (≥ 8.3), elevated uric acid (≥ 340), and ISS stage of III (Fig. 1 A). The Calibration curve was constructed and verified using resampling ( n = 1000) with bootstrap techniques, achieving an AUC of 93.7% (Fig. 1 B). Also, we assessed the association between the original 28 variables and the incidence of RI using k-means cluster. Three clusters were constructed, but they cannot distribute the RI and non-RI patients well (Fig. 1 C). Table 2 Comparative analysis of the clinical characteristics of non-RI and RI group Characteristics Non-RI group ( n = 454, %) RI group ( n = 193, %) χ 2 P Sex 9.16 0.002 female 189 (41.63) 56 (29.02) male 265 (58.37) 137 (70.98) Age at diagnosis, year 0.479 0.489 ≥ 65 244 (53.74) 98 (50.78) < 65 210 (46.26) 95 (49.22) Secreted monoclonal immunoglobulin 37.24 < 0.001 Detected in urine only 79 (17.40) 66 (34.20) IgG 239 (52.64) 64 (33.16) IgA 29 (6.39) 39 (20.21) IgD 100 (22.03) 23 (11.92) IgM 0 (0.00) 1 (0.52) not secreted 7 (1.54) 0 (0.00) Secreted monoclonal immunoglobulin IgG 239 (52.64) 64 (33.16) 20.65 < 0.001 other 215 (47.36) 129 (66.84) Light chain 8.224 0.013 not secreted 7 (1.54) 0 (0.00) κ 251 (55.29) 89 (46.11) λ 196 (43.17) 104 (53.89) Light chain 4.57 0.033 κ 251 (55.29) 89 (46.11) other 203 (44.71) 104 (53.89 DS 5.19 0.023 I-II 36 (7.93) 6 (3.11) III 418 (92.07) 187 (96.89) ISS 186.46 < 0.001 I-II 311 (68.50) 19 (9.84) III 143 (31.50) 174 (90.16) R2-ISS 86.42 < 0.001 0–1 164 (36.94) 2 (1.08) 1.5–5 280 (63.06) 183 (98.92) Serum-free light chain 32.35 < 0.001 0.01–100 296 (66.07) 80 (41.88) ≤ 0.01, ≥ 100 152 (33.93) 111 (58.12) t(11; 14) in FISH 6.08 0.014 Negative 391 (88.06) 149 (80.54) Positive 53 (11.94) 36 (19.46) t(4; 14) in FISH 0.01 0.931 Negative 378 (85.14) 157 (84.86) Positive 66 (14.86) 28 (15.14) t(14; 16) in FISH 0.459 0.549 Negative 438 (98.65) 181 (97.84) Positive 6 (1.35) 4 (2.16) Del(17p) in FISH 10.84 0.001 Negative 404 (89.38) 153 (79.69) Positive 48 (10.62) 39 (20.31) Del(13q) in FISH 1.84 0.175 Negative 287 (63.50) 111 (57.81) Positive 165 (36.50) 81 (42.19) Gain(lq21) in FISH 0.04 0.835 Negative 223 (49.34) 93 (48.44) Positive 229 (50.66) 99 (51.56) Hemoglobin level, g/L 67.41 < 0.001 ≤ 100 235 (51.99) 166 (86.01) > 100 219 (48.01) 27 (13.99) Serum LDH, U/L 21.75 < 0.001 < 250 371 (81.72) 125 (64.77) ≥ 250 83 (18.28) 68 (35.23) Serum albumin, U/L 1.88 0.171 < 35 225 (49.56) 107 (55.44) ≥ 35 229 (50.44) 86 (44.56) β2-MG, mg/L 189.37 < 0.001 < 3.5 201 (44.27) 5 (2.59) 3.5 ≤ β2-MG < 5.5 110 (24.23) 14 (7.25) ≥ 5.5 143 (31.50) 174 (90.16) Serum calcium level, mmol/L 40.41 < 0.001 ≤ 2.65 404 (88.99) 132 (68.39) > 2.65 50 (11.01) 61 (31.61) Plasma cells of BM, % 11.54 0.001 < 50 353 (78.10) 124 (65.26) ≥ 50 99 (21.90) 66 (34.74) Extramedullary plasmacytoma 3.40 0.065 No 360 (79.30) 165 (85.49) Yes 94 (20.70) 28 (14.51) Amyloidosis 0.99 0.321 No 440 (96.92) 184 (95.34) Yes 14 (3.08) 9 (4.66) Comorbidity (diabetes, hypertension) 1.96 0.161 No 267 (58.81) 102 (52.85) Yes 187 (41.19) 91 (47.15) Urea, mmol/L 288.43 < 0.001 < 8.3 362 (79.74) 15 (7.77) ≥ 8.3 92 (20.26) 178 (92.23) Uric acid, μmol/L 83.30 < 0.001 < 340 293 (64.54) 49 (25.39) ≥ 340 161 (35.46) 144 (74.61) Cyc, mg/L 58.20 < 0.001 < 1.03 113 (24.89) 0 (0.00) ≥ 1.03 341 (75.11) 193 (100.00) Urinary protein/creatinine, mg/g Cr 64.09 < 0.001 < 200 203 (44.71) 23 (11.92) ≥ 200 251 (55.29) 170 (88.08) Urinary albumin/creatinine, mg/g Cr 49.71 < 0.001 < 30 240 (52.86) 44 (22.80) ≥ 30 214 (47.14) 149 (77.20) Cr, creatinine; Cyc, cystatin C; DS, Durie-Salmon; ISS, International Staging System; LDH, lactate dehydrogenase; β2-MG, β2-microglobulin Fig. 1 The association factors of RI in NDMM patients. A Forest plot demonstrating the significant association factors of RI in NDMM patients following the bootstrap resampling ( n = 1000) for internal validation. B The calibration curve was constructed and verified using resampling ( n = 1000) with bootstrap techniques, achieving an AUC of 93.7% (AUC, area under the curve). C K-means cluster was performed to assess the association between the original 28 variables and the incidence of RI

Models used to predict the RI response in NDMM patients

Next, the clinicopathologic data were included to construct a model to predict the RI response of NDMM patients with RI. Five variables including calcium, DS stage, Cr level before treatment, age and gender were included in the final model with a sensitivity, specificity, AUC, and accuracy of 86.75%, 51.15%, 78.30% and 72.99% in the training group (Fig. 2 A, Table 3 ), and 79.31%, 52.94%, 72.40% and 69.57% in the validation group (Fig. 2 B, Table 4 ). Fig. 2 Performance of the model used to predict RI response in NDMM patients following treatment. ROC curves reflecting the performance of the model in both the A training and B validation cohorts. (AUC, area under the curve) Table 3 The specificity, sensitivity and accuracy of the model used to predict the RI response in the training set RI response Predict model Response Non-response Response 72 11 Non-response 26 28 Specificity 51.15% Sensitivity 86.75% Accuracy 72.99% Table 4 The specificity, sensitivity and accuracy of the model used to predict the RI response in the validation set RI response Predict model Response Non-response Response 23 6 Non-response 8 9 Specificity 52.94% Sensitivity 79.31% Accuracy 69.57%

Identification of the influencing factors of OS of NDMM patients with RI

Also, we analyzed the influencing factors of OS of the NDMM patients with RI. The log-rank test showed that age ≥ 65 years (Fig. 3 A), β2-MG level ≥ 5.5 (Fig. 3 B), del(13q) (Fig. 3 C), LDH level ≥ 250 (Fig. 3 D), urinary albumin/Cr (≥ 30) (Fig. 3 E), Cr level before treatment more than 369 (Fig. 3 F), CKD stage of G4 and G5 (Fig. 3 G), RI non-response (Fig. 3 H) and ISS stage of III (F i g. 3 I) were associated with the shorter OS time in NDMM patients with RI. Then, we performed Schoenfeld residuals analysis to assess whether the PH assumption holds of these variables. The results showed all of these variables with P < 0.05 showed no significant association with the overall survival time of NDMM patients (Fig. 4 ). The multivariable Cox analysis demonstrated that both LDH ≥ 250 and CKD stage of G4 and G5 were the independent factors of OS (Fig. 5 A). The nomogram showed the contribution of these 2 factors to the 1-year, 2-year and 3-year OS of NDMM patients with RI (Fig. 5 B). In addition, the time-dependent ROC showed that the LDH and Cr level which were included in the final model showed the relative higher C-index among the variables at 1, 3, and 5 years (Fig. 5 C). Fig. 3 Factors influencing the overall survival of NDMM patients with RI. Kaplan–Meier curves showed the relationships between A age (≥ 65), B β2-MG level (≥ 5.5), C del(13q), D LDH level (≥ 250), E urinary albumin/Cr (≥ 30), F Cr level before treatment (≥ 369), G CKD stage of G4 and G5, H RI response and I ISS stage of III and the overall survival of NDMM patients with RI Fig. 4 Schoenfeld residuals for the factors included in the multivariate Cox model (Global Schoenfeld Test P = 0.3524). The factors including age, β2-MG, del(13q), LDH, urinary albumin/Cr, Cr level, CKD stage and RI response Fig. 5 Performance of the Cox model used to predict the prognosis. A Multivariable Cox analysis of the influencing factors of overall survival. B Nomogram showed the contributions of LDH and the ISS stage of III to the 1-year, 2-year and 3-year survival of NDMM patients with RI. C The time-dependent C-index of indicators for diagnosing overall survival in NDMM patients with RI

Discussion

RI is a prevalent and ominous comorbidity of MM, significantly impacting patients’ quality of life and survival [ 28 ]. Renal function is promising reversed in the early stages of renal dysfunction, potentially extending the survival time of MM patients. Early diagnosis and intervention are essential for NDMM patients with RI, as they can help decrease abnormal monoclonal immunoglobulin levels in the bloodstream and mitigate renal damage [ 29 ]. The incidence of RI is about at 24% among Chinese patients, which (193/647, 29.82%) was slightly higher in the current cohort. This difference may be caused by the different definitions across studies [ 30 ] or various populations. In the current study, we found some clinicopathologic features were strongly associated with the incidence, response and survival of NDMM patients with RI, which may contribute to the prognosis improvement.

Marked changes in the routinely adopted clinical measures of kidney function such as Cr are reliant on a number of non-renal factors (comorbidity, muscle mass, medication, age) and are not an early and immediate indicator of renal injury. Several candidate molecules released from injured tubular cells have emerged as early and sensitive markers [ 31 , 32 ]. Herein, we assessed the association between clinicopathologic data and RI in NDMM patients. The multi-factorial logistic regression analysis identified 6 independent risk factors influencing the incidence of renal damage in NDMM, including the secreted monoclonal immunoglobulin of IgG type, serum free κ/λ light chain, elevated serum calcium, elevated urea, elevated uric acid and ISS stage of III. Researchers have found there is a close relationship between monoclonal immunoglobulin and renal diseases [ 33 , 34 ]. In MM patients, malignant proliferation of plasma cells leads to the mass secretion of abnormal immunoglobulins, which contain excessive amounts of free light chains. These free light chains exceed the reabsorption and catabolic capacity of renal tubules, resulting in their deposition in the kidneys [ 35 ]. Consistently, the present study demonstrated that the secreted monoclonal immunoglobulin and serum free κ/λ light chain were significantly associated with the incidence of RI. High serum calcium can lead to tubular injury, as calcium salts may deposit in renal tissues, resulting in renal interstitial fibrosis and glomerular sclerosis. These pathological changes directly affect renal function. In addition, it triggers the increased renal burden, the body excretes excess calcium through urine to reduce blood calcium levels in a state of hypercalcemia. However, this process increases the workload on the kidneys, which may damage renal function over time [ 36 – 38 ]. Hyperuricemia is not only associated with an increased risk of cardiovascular diseases, but may also cause damage to the kidneys [ 39 ]. It serves as an indicator of CKD and may even be an independent risk factor for the progression of CKD [ 40 ]. In addition to the imaging of renal perfusion and GFR, the corticomedullary gradient of solute sodium and urea have been considered as potential markers of renal function [ 41 , 42 ]. A previous study confirmed the potential of monitoring renal function of urea CEST (chemical exchange saturation transfer) through capturing the spatially varying urea concentrating ability of the kidneys [ 43 ]. In a published cohort with 85 MM-related RI patients, most cases (42, 49.41%) were diagnosed at ISS stage III [ 44 ]. In the present study, 134 (89.93%) and 102 (32.91%) patients with RI and without RI were at an ISS stage of III, respectively, suggesting a tendency of higher ISS stage and higher RI incidence. Similarly, Shi et al. [ 45 ] reported that 89.2% and 31.1% patients at ISS stage of III were diagnosed with RI and without RI, respectively. Royal et al. [ 46 ] suggested that β2-MG was independently correlated with a higher glomerular filtration rate in MM patients. Consistently, the current study demonstrated that β2-MG was a significant influencing factor of RI in NDMM patients as demonstrated by the strong relationship between ISS stage and RI indicate.

In addition, this study constructed a model including calcium, DS stage, Cr level before treatment, age and gender to predict the RI response in NDMM patients. However, the accuracy was not very satisfied, with 72.99% in the training group and 69.57% in the validation group. Similarly, age was shown to be related to the renal response in another study [ 47 ]; detailly, the researchers demonstrated that renal responders were younger than the non-responders (median age: 61 years; 77 years). In another study including 181 NDMM patients with RI, the researchers find that the hypercalcemia, gain(1q21) and hematological response over a partial response were independent predictive factors for renal function response. In the present study, we aimed to establish the model to predict the response of RI; thus, the hematological response was not included, but we assessed whether the treatment regimen (PI-based method) affects the response of RI and the results showed no significant difference. Beyond the included factors, serum erythropoietin levels > 25 mIU/mL and a percentage of urinary albumin excretion ≤ 25% were found to be the significant factors that influenced the major renal responses [ 19 ], suggesting the variables included in the current study may not enough to get the satisfying results, other variables such as the mutations, expression profiles as well as the proteomics should be included to get a comprehensive and convincing model.

Also, we evaluated the influencing factors of prognosis in NDMM patients with RI. The results showed that age ≥ 65 years, β2-MG level ≥ 5.5, del(13q), LDH level ≥ 250, urinary albumin/Cr (≥ 30), Cr level before treatment more than 369, CKD stage of G4 and G5, RI non-response and ISS stage of III were the adverse prognostic factors of OS in NDMM patients with RI. LDH shows significant clinical relevance in patients with MM, particularly for predicting RI and prognosis. In the R-ISS and R2-ISS, an elevated LDH level is a criterion for assessing the prognosis of MM patients [ 48 – 50 ]. Cytogenetic abnormality, including t(11;14), t(4;14),t(14;16), del(17p), del(13q) and gain(1q21) are common cytogenetic events in MM. Herein, we found that t(11;14) and del(17p) were more common in patients with RI than those without RI. In addition, we found that del(13q) was an adverse factor of the overall survival of NDMM patients with RI. Consistently, evidence has shown that del(13q) was independently associated with poorer overall survival of MM patients [ 51 ]. Our further analysis showed that both LDH (≥ 250 U/L) and CKD stage (G4-G5) were the independent influencing factors of the overall survival of NDMM patients with RI.

Our findings may provide some suggestions to the clinic. Detailly, this study clarified that the secreted monoclonal immunoglobulin of IgG type, serum free κ/λ light chain, elevated serum calcium, elevated urea, elevated uric acid and ISS stage of III were closely associated with the incidence of RI, indicating the drugs or diet targeting decreasing the levels of serum calcium, urea and uric acid may be usefully to prevent MM patients from RI. Specifically, Ling et al. [ 52 ] reported that the renal function was improved significantly when the serum calcium levels were successfully controlled. Chen et al. [ 53 ] reported that the low-purine diet (LDP) could decrease serum uric acid levels in gout patients, especially overproduction type through a prospective cohort study. In addition, Patel et al. [ 54 ] found that administration of Roflumilast, a PDE4 inhibitor, remarkably decrease serum creatinine, urea and uric acid in adenine-induced chronic kidney disease rat models. These findings suggest that the LDP may prevent MM patients from RI. In addition, the drugs used to decrease uric acid levels are Febuxostat, Probenecid, Allopurinol, as well as the Roflumilast which may be used to treat MM-related RI. In addition, we established a model to predict the renal response in MM patients. Based on this, we suggest that the prediction should be carried out when the MM patients with RI come in the hospital and obtaining these 5 variables, then different regimens should be given according to the prediction results. Detailly, ubiquitin proteasome inhibitors and thalidomide-based regimen could be given to the predicted responders as our results revealed that the treatment regimen should no significant association with the renal response. Otherwise, the predicted non-responders can choose other medications, such as the new quadruplet and triplet combinations, including proteasome inhibitors, immunomodulatory drugs, and anti-CD38 monoclonal antibodies, as well as the conjugated antibodies, chimeric antigen receptor T-cells, and T-cell engagers, which are well tolerated and effective in patients with moderate renal impairment [ 22 ]. In addition, these patients can participate in clinical trials at the beginning of the treatment.

This study more comprehensively analyzed the relationship between examination data and/or clinical data and the incidence of RI, RI response and survival of NDMM patients with RI in a cohort of 647 NDMM patients; especially, the FISH indexes [t(4;14), t(11;14), t(14;16), del(17p), del(13q) and gain(1q21)] were included. Following the current study, we found some strong factor influencing the RI incidence, response and survival. However, several limitations should be clarified. One is that the samples were only from one center and we intend to carry out a prospective multicenter clinical trial next. As an alternation, resampling methods were used in the current study to increase the accuracy of results. Second, the sample size is small with only 193 RI patients. Third, the data of mutation, transcriptome and proteomics were not included. Due to the low rate of renal biopsies at our center, pathological types were not included in the study.

In conclusion, this retrospective study clarified the relationship between clinicopathologic characteristics and the incidence of renal injury, response and survival in NDMM patients. Detailly, we found that the secreted monoclonal immunoglobulin of IgG type, serum free κ/λ light chain, elevated serum calcium, elevated urea, elevated uric acid and ISS stage of III were closely associated with the incidence of RI, which may help develop new potent strategies for the treatment of RI in NDMM patients. Elevated LDH and CKD stage (G4-G5) were the independent adverse factors of the OS of NDMM patients with RI. In addition; this study provided a model to predict the response of RI using 5 clinical features, including calcium, DS stage, Cr level before treatment, age and gender. Overall, this study clarifies the association factor of the incidence, response and prognosis of NDMM patients with RI more comprehensively, which may help improve the survival of NDMM patients with RI.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Wei Wei, Haotian Shi and Haimin Chen have contributed equally to this work.

Author contributions

Daolin Wei, Fan Zhou and Wei Wei designed and provided supervision of the study; Wei Wei, Haotian Shi, Haimin Chen, Xiaoling Chen collected and analyzed the data; Rong Peng, Wenjun Yu, Lixia Wu, Nian Zhou, Wenhao Zhao, Weiwei Xu, Yan Zhou, and Jingjing Yu collected the data. The first draft of the manuscript was written by Wei Wei, Haotian Shi, and Haimin Chen, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 82070226) and the Health Committee Foundation of Jing'an District, Shanghai (Grant No. 2021MS10).

Data availability

No datasets were generated or analyzed during the current study.

Declarations

Conflicts of interest

The authors declare no competing interests.

Ethical approval

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee of Zhabei Central Hospital in Shanghai Jing’an District (no. ZBLL2024101201001).

Consent to participate

Informed consent was obtained from all subjects involved in the study.

Consent for publication

Not applicable.

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