Introduction: Multiple myeloma (MM) is a malignant proliferative disease of plasma cells. Abnormally cloned plasma cells secrete large amounts of monoclonal immunoglobulins in the bone marrow of MM patients. Serum urea nitrogen (sUN) is a byproduct of protein metabolism, and its effect on MM patients’ prognoses remains unknown. Therefore, we analyzed MM patients’ clinical data to explore the role of sUN and sUN/serum albumin (sUAR) in the baseline tumor load and MM prognosis of MM patients. Methods: We downloaded the clinical data of 762 MM patients from the MMRF database. After excluding those without baseline sUN, 452 patients were finally included in the study. Smoothed curve fitting, threshold analysis, Tamhane’s T2 test, multivariate-adjusted Cox regression analysis, Kaplan-Meier (K-M) curves, and receiver operating characteristic (ROC) analysis were applied in the study. Results: There were 452 newly diagnosed MM patients included in this study. In most patient groups, sUN and sUAR were positively linked with β2-microglobulin (β2-MG) and lactic dehydrogenase (LDH) according to smoothing curve fitting and threshold analysis. The higher the ISS stage, the greater the values of sUN and sUAR. Furthermore, smoothed curve fitting and threshold analysis showed that sUN was correlated with overall survival (OS), although sUAR had a stronger correlation with OS and could be applied to a broader group. The results of a multivariate-adjusted Cox regression analysis demonstrated that sUN and sUAR were independent prognostic factors for OS. The K-M curve confirmed the correlation between higher sUN and sUAR levels and worse OS. β2-MG and LDH are generally recognized prognostic factors of OS. ROC analysis revealed that sUN might boost β2-MG and LDH’s predictive value and sUAR had a higher predictive value. Conclusion: This retrospective study based on the MMRF database showed that high sUN and sUAR levels were positively associated with β2-MG, LDH, and ISS staging, and sUAR exhibited a stronger correlation with OS than sUN alone.

Multiple myeloma (MM) is a malignant proliferative disease of plasma cells and is characterized by the presence of aberrant clones of plasma cells in the bone marrow [1]. “CRAB” symptoms, which include increased serum calcium (C), renal impairment (R), anemia (A), and bone pain (B), are common MM manifestations. MM is the second most common hematologic malignancy, accounting for approximately 10% of hematologic malignancies, and the incidence of MM in China is gradually rising, and the population is getting younger [2]. Despite the recent development of treatments for MM, it is still an incurable disease.

Monoclonal immunoglobulin levels are unusually high in MM patients due to the significant secretion of aberrant plasma cells in the bone marrow. Animal studies have revealed that the kidney is the main site for the metabolism of Bence Jones proteins and L-chains [3], and MM patients with renal impairment will have a corresponding increase in the blood levels of the relevant immunoglobulins. In MM patients, we often employ Durie and Salmon (DS staging) and the international staging system (ISS)/revised ISS (RISS) to determine the tumor load [4, 5]. In DS staging, hemoglobin, serum calcium, serum M-protein levels, renal impairment, and imaging manifestations are all regarded as indicators [4]. Moreover, it has been demonstrated that β2-microglobulin (β2-MG) and lactic dehydrogenase (LDH) are independent risk factors for MM [5, 6]. Higher levels of β2-MG and LDH in MM patients also suggest a larger tumor load, key components of ISS/RISS staging.

The level of monoclonal immunoglobulins is strongly linked with high tumor load in MM patients [7]. As a byproduct of protein metabolism, serum urea nitrogen (sUN) is an end product of the ornithine cycle in the liver, an essential component of the body's metabolism. In recent years, metabolic reprogramming has been thought to be one of the major hallmarks of tumors [8], and tumor cells have an increased need for nitrogen for proliferation. Subsequently, the pathway of nitrogen metabolism in tumor cells will be changed, as evidenced by the increase in nitrogen utilization and the decrease in nitrogen metabolite content in plasma and urine [9, 10]. The situation is different in MM patients, as the markedly enhanced metabolism of glutamate is present only in the bone marrow, while disturbances in the metabolism of arginine, proline, and the urea cycle are present in both bone marrow and plasma [11‒13], as evidenced by elevated serum levels of the corresponding amino acids and sUN. We therefore hypothesized that sUN levels were correlated with the prognosis of MM patients. The prognosis of MM patients may be influenced by sUN levels, according to research to date [14, 15]. However, no studies have reported whether sUN is an independent prognostic factor in MM or a predictor of MM. Thus, we proposed to obtain a larger sample size using the database to elucidate the relationship between sUN levels and the prognosis of MM patients.

MM patients have low albumin levels due to the activity of inflammatory cytokines such as interleukin-6 secreted in the myeloma microenvironment and competitive depletion of monoclonal globulin’s raw materials [16]. sUN/serum albumin (sUAR) seems to be more representative of the amount of monoclonal globulin metabolites and, consequently, of tumor load. However, whether sUAR is associated with prognosis in MM patients has not been reported. Therefore, we searched and obtained data on MM patients from MMRFs database and performed a retrospective clinical study to analyze the role of sUN and sUAR in the baseline tumor load and MM prognosis of MM patients.

Data Collection

Clinical data of 762 MM patients were obtained from the MMRF database (https://portal.gdc.cancer.gov/), and 452 patients were finally included in the study after excluding those without baseline sUN. The following indicators were assessed for each patient in the study: (1) demographic characteristics, including age and sex; (2) MMa ISS stage; (3) laboratory data, including white blood cells (WBC), M protein, lactate dehydrogenase (LDH), β2 microglobulin (β2-MG), albumin, globulin, sUN, serum calcium, and fluorescence in situ hybridization (FISH) analysis (the essential abnormalities to test for were del 13, t(4;14), t(6;14), t(8;14), t(11;14), t(12;14), t(14;16), t(14;20), del 17p, del 1p, and gain 1q), and we divided the patients into two groups according to high risk cytogenetic abnormalities of the mSMART 3.0 criteria (FISHsMART3.0, which means the patient’s FISH results meet any one or more of the mSMART 3.0 criteria for high risk cytogenetic abnormalities (t(4;14), t(14;16), t(14;20), del 17p, and gain 1q)) [17]; and (4) overall survival (OS) within 1,500 days.

Investigation and Statistical Analysis

Continuous data are expressed as the mean ± standard deviation if normally distributed or as median (interquartile range) if skewed. Categorical variables are expressed as numbers or percentages.

Smoothing curve fitting and threshold analysis were used to analyze the relationship between sUN, sUAR and β2-MG, LDH and OS. Tamhane’s T2 test was used to assess differences in sUN and sUAR across ISS stages. Multivariate-adjusted Cox regression analysis revealed independent prognostic factors for OS. Kaplan-Meier (K-M) curves more visually demonstrated the relationship between sUN/sUAR levels and OS. Receiver operating characteristic (ROC) analysis was performed to assess the predictive value of β2-MG combined with LDH for OS after the addition of sUN or sUAR.

All probabilities are two-tailed and calculated using Empower Stats (www.empowerstats.com; X and Y Solution, Inc., Boston, MA, USA), R software (http://www.R-project.org), and SPSS Statistics 22.0. A p value <0.05 was considered to indicate a significant difference.

Clinical data from 762 patients with a primary diagnosis of MM were downloaded from the MMRF database (https://portal.gdc.cancer.gov/). After the screening process shown in Figures 1 and 4, fifty-two patients were finally included in the study after removing patients without baseline sUN.

Fig. 1.

Flowchart of participants. Clinical data of 762 patients were downloaded from the MMRF database; sUN data of 452 patients were not missing and participated in this study. Of these, 149 patients were aged <60, 303 patients were aged ≥60, 185 patients were female, and 267 patients were male.

Fig. 1.

Flowchart of participants. Clinical data of 762 patients were downloaded from the MMRF database; sUN data of 452 patients were not missing and participated in this study. Of these, 149 patients were aged <60, 303 patients were aged ≥60, 185 patients were female, and 267 patients were male.

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Male patients made up 59.07% of the study’s participants, and their median age was 64 (27–87). In addition, 169 patients (38.67%) were ISS stage II, with the ISS stage distribution being virtually consistent. Among the patient’s indicators, all tests (WBC, M protein, LDH, β2-MG, albumin, serum calcium, sUN, sUAR, FISHsMART3.0) were skewed except for globulin, with median M protein of 3.17 g/dL (0.05–12.27), median β2-MG of 3.51 μg/mL (0.21–31.94), and mean globulin of 58.54 g/L (SD 21.32), all of which were above the normal range, while the median values of WBC (5.80*109/L), albumin (36.00 g/L), serum calcium (2.33 mmol/L), and sUN (6.43 mmol/L) were within the normal range (Table 1). The results of FISH analysis in the MMRF database included del 13, t(4;14), t(6;14), t(8;14), t(11;14), t(12;14), t(14;16), t(14;20), del 17p, del 1p, and gain 1q. The variants that appeared more frequently in patients were del 13(30.87%), t(4;14)(11.86%), t(11;14)(17.00%), del 17p(17.90%), and gain 1q(22.82%); while t(6;14)(1.34%), t(8;14)(0.89%), t(12;14)(0.45%), t(14;16)(4.92%), t(14;16)(4.92%), t(14;20)(1.57%), and del 1p(7.16%) appear less frequently (Table 1).

Table 1.

Population description of MM patients

Baseline characteristicsN (%)/median (interquartile range)Baseline characteristicsN (%)/median (interquartile range)
Sex FISH analysis 
 Female 185 (40.93)  Del 13 138 (30.87) 
 Male 267 (59.07)  t(4;14) 53 (11.86) 
Age 64 (27–87)  t(6;14) 6 (1.34) 
ISS stage    
 1 147 (33.64)  t(8;14) 4 (0.89) 
 2 169 (38.67)  t(11;14) 76 (17.00) 
 3 121 (27.69)  t(12;14) 2 (0.45) 
Race    
 White 346 (79.36)  t(14;16) 7 (1.57) 
 Black or African American 72 (16.51)  t(14;20) 80 (17.90) 
 Asian 7 (1.61)  Del 17p 32 (7.16) 
 Not reported 11 (2.52)  Gain 1q 102 (22.82) 
  White blood cell, ×109/L 5.80 (3.00) 
  LDH, ukat/L 2.75 (1.11) 
Ca, mmol/L 2.33 (0.25) β2-MG, mg/mL 3.51 (3.28) 
Albumin, g/L 36.00 (7.85) Blood urea nitrogen, mmol/L 6.43 (3.57) 
Globulin, g/L 58.54±21.32 Blood urea nitrogen/albumin, mg/g 5.17 (3.19) 
Baseline characteristicsN (%)/median (interquartile range)Baseline characteristicsN (%)/median (interquartile range)
Sex FISH analysis 
 Female 185 (40.93)  Del 13 138 (30.87) 
 Male 267 (59.07)  t(4;14) 53 (11.86) 
Age 64 (27–87)  t(6;14) 6 (1.34) 
ISS stage    
 1 147 (33.64)  t(8;14) 4 (0.89) 
 2 169 (38.67)  t(11;14) 76 (17.00) 
 3 121 (27.69)  t(12;14) 2 (0.45) 
Race    
 White 346 (79.36)  t(14;16) 7 (1.57) 
 Black or African American 72 (16.51)  t(14;20) 80 (17.90) 
 Asian 7 (1.61)  Del 17p 32 (7.16) 
 Not reported 11 (2.52)  Gain 1q 102 (22.82) 
  White blood cell, ×109/L 5.80 (3.00) 
  LDH, ukat/L 2.75 (1.11) 
Ca, mmol/L 2.33 (0.25) β2-MG, mg/mL 3.51 (3.28) 
Albumin, g/L 36.00 (7.85) Blood urea nitrogen, mmol/L 6.43 (3.57) 
Globulin, g/L 58.54±21.32 Blood urea nitrogen/albumin, mg/g 5.17 (3.19) 

Mean ± SD/median (interquartile range)/N (%).

ISS stage, International Staging System; β2-MG, Beta-2-microglobulin.

sUN and sUAR Were Positively Correlated with LDH, β2-MG, and ISS Staging

Smoothing curves revealed a correlation between sUN and LDH (p < 0.001) (Fig 2a) and a correlation with β2-MG (p < 0.001) (Fig. 2b). Similarly, sUAR was correlated with LDH (p = 0.006) (Fig. 2c) and β2-MG (p < 0.001) (Fig. 2d). sUN and sUAR were positively correlated with β2-MG and LDH, respectively.

Fig. 2.

Smoothing curve fitting of sUN, sUAR, LDH, and β2-MG. a, b A positive and statistically significant linear correlation of sUN with LDH and β2-MG without other adjusted variables. c, d A positive and statistically significant linear correlation between sUAR and LDH and β2-MG without other adjustment variables. The table shows the results of the one-way analysis of sUN and sUAR with LDH and β2-MG, respectively.

Fig. 2.

Smoothing curve fitting of sUN, sUAR, LDH, and β2-MG. a, b A positive and statistically significant linear correlation of sUN with LDH and β2-MG without other adjusted variables. c, d A positive and statistically significant linear correlation between sUAR and LDH and β2-MG without other adjustment variables. The table shows the results of the one-way analysis of sUN and sUAR with LDH and β2-MG, respectively.

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Further analysis of the threshold effect of smoothing curves of sUN with LDH and β2-MG was performed. The results showed that the positive correlation with LDH was more significant when sUN was lower than 14.28 mmol/L (p < 0.0001), while the positive correlation with LDH was not significant when sUN was higher than 14.28 mmol/L (p > 0.05); the positive correlation with β2-MG was more significant when sUN was lower than 13.92 mmol/L (p < 0.0001), while the positive correlation with β2-MG was higher than 13.92 mmol/L (p < 0.0001). The positive correlation with β2-MG was also more significant when sUN was lower than 13.92 mmol/L (p < 0.0001), while the positive correlation with β2-MG was weakened when sUN was higher than 13.92 mmol/L (p = 0.0015) (Table 2). Analyzing the threshold effect of the smooth fitting curves of sUAR with LDH and β2-MG showed that the positive correlation with LDH was more significant when sUAR was lower than 11.25 mg/g (p = 0.0004), while there was no significant correlation with LDH when sUAR was higher than 11.25 mg/g (p = 0.8883); the positive correlation with β2-MG was more significant when sUAR was lower than 9.74 mg/g (p < 0.0001) and weakened when sUAR was higher than 9.74 mg/g (p = 0.0001). When sUAR was below 9.74 mg/g, the positive correlation with β2-MG was more significant (p < 0.0001), while it was less significant when sUAR was greater than 9.74 mg/g (p = 0.0061) (Table 3).

Table 2.

Threshold analysis of sUN, and LDH, β2-MG

Model IModel II
one line effectturning point (K)< K effect 1> K effect 2effect 2–1
LDH 0.05 (0.02, 0.09)** 14.28 0.14 (0.08, 0.19)*** −0.04 (−0.11, 0.02) −0.18 (−0.28, −0.07)*** 
B2MG 0.37 (0.30, 0.44)*** 13.92 0.54 (0.41, 0.66)*** 0.17 (0.03, 0.31)* −0.36 (−0.59, −0.14)** 
Model IModel II
one line effectturning point (K)< K effect 1> K effect 2effect 2–1
LDH 0.05 (0.02, 0.09)** 14.28 0.14 (0.08, 0.19)*** −0.04 (−0.11, 0.02) −0.18 (−0.28, −0.07)*** 
B2MG 0.37 (0.30, 0.44)*** 13.92 0.54 (0.41, 0.66)*** 0.17 (0.03, 0.31)* −0.36 (−0.59, −0.14)** 

Demonstrated a good positive correlation between sUN and LDH when sUN <14.28 mmol/L (OR = 0.14, 95% CI [0.08, 0.19]; p < 0.0001) and sUN had a good positive correlation with β2-MG when sUN <13.92 mmol/L (OR = 0.54. 95% CI [0.41, 0.66]; p < 0.0001).

Table 3.

Threshold analysis of sUAR, and LDH, β2-MG

Model IModel II
one line effectturning point (K)< K effect 1> K effect 2effect 2–1
LDH 0.07 (0.03, 0.11)** 11.25 0.13 (0.06, 0.20)*** −0.01 (−0.08, 0.07) −0.13 (−0.26, −0.01)* 
B2MG 0.46 (0.37, 0.54)*** 9.74 0.74 (0.58, 0.90)*** 0.20 (0.06, 0.35)** −0.54 (−0.80, −0.28)*** 
Model IModel II
one line effectturning point (K)< K effect 1> K effect 2effect 2–1
LDH 0.07 (0.03, 0.11)** 11.25 0.13 (0.06, 0.20)*** −0.01 (−0.08, 0.07) −0.13 (−0.26, −0.01)* 
B2MG 0.46 (0.37, 0.54)*** 9.74 0.74 (0.58, 0.90)*** 0.20 (0.06, 0.35)** −0.54 (−0.80, −0.28)*** 

Demonstrated a good positive correlation between sUAR and LDH when sUAR <11.25 mmol/L (OR = 0.13, 95% CI [0.06, 0.20]; p = 0.0004) and sUAR had a good positive correlation with β2-MG when sUAR <9.74 mmol/L (OR = 0.74, 95% CI [0.58, 0.90]; p < 0.0001).

It was analyzed whether patients with distinct ISS stages (I, II, and III) had varied sUN and sUAR values. The results indicated that sUN levels differed between groups of ISS stages (p < 0.001), and further multiple comparisons of the groups revealed that sUN levels in patients with ISS stage II were not significantly different from those in patients with ISS stage I (p > 0.05), whereas sUN levels in patients with ISS stage III were significantly higher than those in patients with ISS stage II (p < 0.001) and significantly higher than those of stage I patients (p < 0.001). Patients’ sUAR levels increased in ISS stage III, and the difference was statistically significant (Fig. 3b).

Fig. 3.

sUN and sUAR in different ISS stages. The variance of each group was not equal, so Tamhane’s T2 test was used for analysis. The bar graph shows that (a) ISS stage III patients had significantly higher sUN than ISS stage II (p < 0.001) and stage I (p < 0.001) patients. b ISS stage II patients had significantly higher sUAR than ISS stage I patients (p < 0.001), and stage III patients had significantly higher sUAR than stage II patients (p < 0.001).

Fig. 3.

sUN and sUAR in different ISS stages. The variance of each group was not equal, so Tamhane’s T2 test was used for analysis. The bar graph shows that (a) ISS stage III patients had significantly higher sUN than ISS stage II (p < 0.001) and stage I (p < 0.001) patients. b ISS stage II patients had significantly higher sUAR than ISS stage I patients (p < 0.001), and stage III patients had significantly higher sUAR than stage II patients (p < 0.001).

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sUAR Was More Correlated with OS Than sUN and Applicable to a Larger Group

Smoothing curve fitting revealed that sUN and sUAR were negatively correlated with OS in MM patients, with sUAR showing a greater correlation (p < 0.05) (Fig. 4). The patients were split into two groups based on the median values of sUN and sUAR, which were 6.43 mmol/L and 5.23 mg/g, respectively. Among patients with sUN ≤ 6.43 mmol/L, 85.71% did not encounter a mortality event; among patients with sUN >6.43 mmol/L, 76.02% did not encounter a mortality event (Fig. 4a). A total of 85.78% of patients with sUAR ≤ 5.23 mg/g and 76.44% of patients with sUAR >5.23 mg/g did not experience a mortality event (Fig. 4b).

Fig. 4.

Smoothing curves of sUN, sUAR, and OS. a, b A positive correlation between sUN, sUAR, and OS in MM patients, and the relationship between sUAR and OS is more significant (p < 0.05). The table below the figure shows the distribution of patients’ OS and vital status on both sides of the median when the median of sUN and sUAR is used as the boundary.

Fig. 4.

Smoothing curves of sUN, sUAR, and OS. a, b A positive correlation between sUN, sUAR, and OS in MM patients, and the relationship between sUAR and OS is more significant (p < 0.05). The table below the figure shows the distribution of patients’ OS and vital status on both sides of the median when the median of sUN and sUAR is used as the boundary.

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Additional threshold analysis of the smoothing curve fitting of sUN/sUAR and OS revealed that when sUN was above 4.54 mmol/L, the positive correlation between sUN and OS was more significant (p = 0.0038) (Table 4); similarly, when sUAR was below 9.63 mg/g compared to when it was above 9.63 mg/g, the positive correlation between sUAR and OS was more significant (p = 0.0026) (Table 4). Among the patients, 390 (86.3%) had sUN >4.54 mmol/L, and 402 (88.9%) had sUAR ≤ 9.63 mg/g, indicating that the correlation between sUAR and patient mortality events was more applicable.

Table 4.

Threshold analysis of sUN, sUAR, and OS

Model IModel II
one line effectturning point(K)< K effect 1> K effect 2effect 2–1
sUN 1.05 (1.02, 1.08)** 4.54 1.05 (0.54, 2.02) 1.05 (1.02, 1.08)** 1.00 (0.52, 1.95) 
sUAR 1.07 (1.03, 1.10)*** 9.63 1.18 (1.06, 1.31)** 1.02 (0.94, 1.10) 0.86 (0.74, 1.01) 
Model IModel II
one line effectturning point(K)< K effect 1> K effect 2effect 2–1
sUN 1.05 (1.02, 1.08)** 4.54 1.05 (0.54, 2.02) 1.05 (1.02, 1.08)** 1.00 (0.52, 1.95) 
sUAR 1.07 (1.03, 1.10)*** 9.63 1.18 (1.06, 1.31)** 1.02 (0.94, 1.10) 0.86 (0.74, 1.01) 

The results showed a more significant positive correlation between sUN and OS when sUN >4.54 mmol/L (OR = 1.05, 95% CI [1.02, 1.08]; p = 0.0038) and a more significant positive correlation between sUAR and patient OS when sUAR <9.63 mg/g (OR = 1.18, 95% CI [1.06, 1.31]; p = 0.0026).

sUAR Is an Independent Factor for OS and sUN in Partial MM Patients

Plotting the K-M curves of sUN, sUAR, and OS separately demonstrated more intuitively that higher sUN levels (>6.43 mmol/L) were significantly associated with poorer OS than lower sUN levels (≤6.43 mmol/L) (p = 0.0039), and similarly, higher sUAR levels (>5.23 mg/g) were significantly associated with poorer patient OS than lower sUAR levels (≤5.23 mg/g) (p = 0.0075) (Fig. 5). Cox regression analysis of sUN, sUAR, and OS showed that after adjusting for age and sex factors, there was still a significant correlation between sUN and OS (p = 0.0241); after adjusting for potentially relevant factors (age, sex, LDH, β2-MG, M protein, serum calcium, WBC, globulin, FISHsMART3.0), there was no significant correlation between sUN and patients’ OS (p = 0.0710) (Table 5). The correlation between sUAR and patient OS remained statistically significant (p = 0.0103) after adjusting for age and sex factors, and the correlation between sUAR and patient OS remained statistically significant (p = 0.0310) after adjusting for potentially relevant factors (Table 6).

Fig. 5.

K-M curves of sUN, sUAR, and OS. The results showed a statistically significant difference in OS between the two sides within 1,500 days, bounded by the median sUN (p = 0.0039). Similarly, there was a significant difference in OS between the two sides within 1,500 days, bounded by the median sUAR (p = 0.0075).

Fig. 5.

K-M curves of sUN, sUAR, and OS. The results showed a statistically significant difference in OS between the two sides within 1,500 days, bounded by the median sUN (p = 0.0039). Similarly, there was a significant difference in OS between the two sides within 1,500 days, bounded by the median sUAR (p = 0.0075).

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Table 5.

Cox regression analysis of sUN and OS adjusted by age and sex

Table 5.

Cox regression analysis of sUN and OS adjusted by age and sex

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Table 6.

Cox regression analysis of sUAR and OS adjusted by age and sex

Table 6.

Cox regression analysis of sUAR and OS adjusted by age and sex

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Sex and age were used as stratification variables in the multivariate-adjusted Cox regression analysis. In male MM patients, there was still a significant correlation between sUN and patients’ OS after adjusting for potentially relevant factors (p = 0.0317) (Table 5), and similarly, there was still a significant correlation between sUAR and patients’ OS (p = 0.0364) (Table 6); however, in female MM patients, there was no significant correlation (p > 0.05) between sUN, sUAR and patients’ OS with or without adjustment for other variables (Tables 5, 6). When age was used as a stratification variable, the results showed that among MM patients ≥60 years old, there was still a significant correlation between sUN and patients’ OS after adjusting for potentially relevant factors (p = 0.0432) (Table 5), and similarly, there was still a significant correlation between sUAR and patients’ OS (p = 0.0252) (Table 6); however, among MM patients <60 years old, there was no significant correlation between sUN and patients’ OS with or without adjustment for other variables (p > 0.05) (Table 5), and no significant correlation between sUAR and patients’ OS after adjustment for other variables compared to before adjustment (p > 0.05) (Table 6).

Thus, multivariate-adjusted Cox regression analysis showed that sUAR was an independent predictive factor for OS. Both sUN and sUAR were independent predictive factors for OS in the group of MM patients older than 60 years old and in male patients; however, the correlation between sUAR and OS was more significant (Tables 5, 6).

sUN and sUAR Increase the Predictive Value of β2-MG and LDH for OS

ROC analysis of sUN and sUAR showed that sUN could improve the predictive value of β2-MG and LDH compared to their predictive value of OS in patients (AUC = 0.638 vs. AUC = 0.617, p = 0.02), and sUAR could similarly improve the predictive value with a greater increase (AUC = 0.651 vs. 0.619, p = 0.007) (Fig. 6).

Fig. 6.

ROC curves of sUN and sUAR. On the basis of LDH and β2-MG prediction, sUN and sUAR both increased the predictive effect of the model, and sUAR increased more significantly (AUC = 0.651 vs. AUC = 0.619, p = 0.007).

Fig. 6.

ROC curves of sUN and sUAR. On the basis of LDH and β2-MG prediction, sUN and sUAR both increased the predictive effect of the model, and sUAR increased more significantly (AUC = 0.651 vs. AUC = 0.619, p = 0.007).

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In this study, we found that high sUN and sUAR levels were positively correlated with β2-MG and LDH. Further study discovered that sUN and sUAR were correlated with OS and more significantly in male patients and patients over the age of 60. The prognostic model for MM patients was constructed by combining β2-MG and LDH with sUN or sUAR, and it was found that sUN and sUAR increased β2-MG and LDH’s predictive value for OS.

Since the International Myeloma Working Group revised the definition and diagnostic criteria for MM in 2014, there has been a proliferation of studies on MM biomarkers to predict prognosis, including clonal myeloplasmacytic, extramedullary plasmacytoma, the “CRAB” symptoms [18], chromosomal alterations such as t(4;14), t(14;16), del(17p), gain(1q) [19], heavy/light chains in different subtypes [20], and imaging alterations such as PET or PET/CT [21, 22]. However, because MM patients may have renal failure, these indicators are still lacking, and additional biomarkers are required to more accurately predict the outcome and prognosis to help stratify MM patients. Due to the massive accumulation of monoclonal immunoglobulins and the presence of renal damage, the concentration of nitrogen-containing metabolites in MM patients rises [23]. Other study has shown alterations in gut microbes associated with urea nitrogen metabolism in MM patients [24]. Prior studies have shown that patients with MM have a worse prognosis when their urea nitrogen levels are high [25]; however, larger sample size and more quantitative researches are still needed.

Our study revealed a positive correlation between sUN and β2-MG and LDH as the ISS stage of MM patients, while the available studies showed that changes in the urea cycle of MM cells were detected during the progression of monoclonal gammopathy of undetermined significance (MGUS) to MM, but there were no significant changes in LDH [26]. Combined with the above findings, we reasonably hypothesize that the urea cycle in MM cells is not only limited to the progression of MGUS to MM but also shows significant changes during the progression of MM. Our study showed that high sUN levels were associated with poorer OS in patients. The revised ISS staging showed that albumin, β2-MG, LDH, del(17p), t(4;14), and 1q+ are all independent correlates of OS in patients [27]. In our study, we found that sUN was also an independent risk factor for OS in MM patients older than 60 years old and in male patients.

Studies have demonstrated that sUAR is a critical factor in determining the prognosis of patients with aspiration pneumonia, chronic obstructive pulmonary disease, geriatric patients with gastrointestinal bleeding, and patients with Escherichia coli bacteremia, and all suggest that sUAR is a valid predictor of patient prognosis [28‒31]. In the available studies, albumin often represents malnutrition and systemic inflammatory responses in patients [28‒31]. In MM patients, albumin serves as both an indicator of the degree of inflammation and a key criterion in the ISS stage. Thus, sUAR, rather than sUN alone, is more representative of the level of monoclonal globulin metabolites, and taking albumin into account will improve the prediction. We also plotted the K-M curves of albumin and patient OS, and sUAR had a more significant effect on patient OS than albumin alone (online suppl. Fig. 1; for all online suppl. material, see https://doi.org/10.1159/000538479).

As a retrospective study, there are still some limitations in our study. Due to the clinical data obtained from the database of patients, some significant clinical information is missing. Although the metabolic pathway of monoclonal immunoglobulins in MM patients is similar to the conventional protein, the difference on account of different isoforms of immunoglobulins, and the metabolic rate varies between isoforms [32]. However, this study lacks data on further typing of MM patients. The main ethnicity of the included patients was Caucasian, which has certain population limitations. Nevertheless, since there is no denying that MM patients with renal failure have elevated levels of UN, we conducted a univariate analysis (online suppl. Table 1) of creatinine level with UN and UAR and discovered that there was a positive correlation between the two. However, because the MMRF database did not contain information on patients’ GFR, we were unable to conduct a more in-depth analysis of renal function with UN and UAR and therefore we were unable to fully rule out the impact of renal impairment in MM patients on the study results. This study is retrospective rather than prospective, so our findings need to be proven in more various ethnic populations or prospective studies. Although basic studies on nitrogen metabolism in MM can explain some of the findings of our study [11, 26], sUAR, a more predictive indicator, has not been studied to prove whether there is a correlation between sUAR and albumin in MM patients due to the lack of basic studies. The relationship between sUAR and MM cell survival and progression remains to be further explored.

We used clinical data of MM patients from the MMRF database, and the resulting analysis showed that sUAR and sUN were positively correlated with β2-MG and LDH levels in MM patients. In MM patients with different ISS stages, the higher the ISS stage, the higher the levels of sUN and sUAR. Further studies found that sUN and sUAR correlated with patients’ OS, and MM patients with high levels of sUN and sUAR had poorer OS, which was more pronounced in male patients and in patients over the age of 60. sUN and sUAR can be used as clinical biomarkers to predict MM prognosis, and sUAR has a better predictive effect than sUN alone. Since this study is based on hypotheses generated from retrospective observational studies, additional patients or prospective studies are needed to validate our findings.

The authors are grateful to the other members in Bone Marrow Transplantation Center, The First Affiliated Hospital, School of Medicine, Zhejiang University for helpful discussions.

Patient consent was not required as this study was based on publicly available data. MMRF database belongs to public database. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on the open source data, and approved by Ethical Inspection of the First Affiliated Hospital, College of Medicine, Zhejiang University.

The authors declare that they have no competing interests.

This work was supported by National Natural Science Foundation of China (82100212).

Mengmeng Dong designed research, guided the project processing, and analyzed data and the paper writing. Zhen Cai designed and supervised the research. Jiaqi Shao performed the clinic data collection, analyzed data and wrote the paper. Enfan Zhang and Haoguang Chen partially analyzed data.

The data that support the findings of this study are not publicly available due to protection of patient privacy but are available from the corresponding author upon reasonable request, please contact dmmzju@zju.edu.cn.

1.
BrigleRogers
KB
,
Rogers
B
.
Pathobiology and diagnosis of multiple myeloma
.
Semin Oncol Nurs
.
2017
;
33
(
3
):
225
36
.
2.
Chinese Hematology Association Chinese Society of Hematology Chinese Myeloma Committee-Chinese Hematology Association
.
[The guidelines for the diagnosis and management of multiple myeloma in China (2020 revision)]
.
Zhonghua Nei Ke Za Zhi
.
2020
;
59
(
5
):
341
6
.
3.
Wochner
RD
,
Strober
W
,
Waldmann
TA
.
The role of the kidney in the catabolism of Bence Jones proteins and immunoglobulin fragments
.
J Exp Med
.
1967
;
126
(
2
):
207
21
.
4.
D’Anastasi
MM
,
Notohamiprodjo
M
,
Schmidt
GP
,
Dürr
HR
,
Reiser
MF
,
Baur-Melnyk
A
.
Tumor load in patients with multiple myeloma: β2-microglobulin levels versus whole-body MRI
.
AJR Am J Roentgenol
.
2014
;
203
(
4
):
854
62
.
5.
Bataille
R
,
Magub
M
,
Grenier
J
,
Donnadio
D
,
Sany
J
.
Serum beta-2-microglobulin in multiple myeloma: relation to presenting features and clinical status
.
Eur J Cancer Clin Oncol
.
1982
;
18
(
1
):
59
66
.
6.
Dimopoulos
MA
,
Barlogie
B
,
Smith
TL
,
Alexanian
R
.
High serum lactate dehydrogenase level as a marker for drug resistance and short survival in multiple myeloma
.
Ann Intern Med
.
1991
;
115
(
12
):
931
5
.
7.
Durie
BG
,
Salmon
SE
.
A clinical staging system for multiple myeloma. Correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival
.
Cancer
.
1975
;
36
(
3
):
842
54
.
8.
Pavlova
NN
,
Thompson
CB
.
The emerging hallmarks of cancer metabolism
.
Cell Metab
.
2016
;
23
(
1
):
27
47
.
9.
Keshet
R
,
Szlosarek
P
,
Carracedo
A
,
Erez
A
.
Rewiring urea cycle metabolism in cancer to support anabolism
.
Nat Rev Cancer
.
2018
;
18
(
10
):
634
45
.
10.
Lee
JS
,
Adler
L
,
Karathia
H
,
Carmel
N
,
Rabinovich
S
,
Auslander
N
, et al
.
Urea cycle dysregulation generates clinically relevant genomic and biochemical signatures
.
Cell
.
2018
;
174
(
6
):
1559
70.e22
.
11.
Fei
F
,
Ma
T
,
Zhou
X
,
Zheng
M
,
Cao
B
,
Li
J
.
Metabolic markers for diagnosis and risk-prediction of multiple myeloma
.
Life Sci
.
2021
;
265
:
118852
.
12.
Evans
LA
,
Anderson
EA
,
Jessen
E
,
Nandakumar
B
,
Atilgan
E
,
Jevremovic
D
, et al
.
Overexpression of the energy metabolism transcriptome within clonal plasma cells is associated with the pathogenesis and outcomes of patients with multiple myeloma
.
Am J Hematol
.
2022
;
97
(
7
):
895
902
.
13.
Gavriatopoulou
M
,
Paschou
SA
,
Ntanasis-Stathopoulos
I
,
Dimopoulos
MA
.
Metabolic disorders in multiple myeloma
.
Int J Mol Sci
.
2021
;
22
(
21
):
11430
.
14.
Kyle
RA
.
Prognostic factors in multiple myeloma
.
Stem Cell
.
1995
;
13
(
Suppl 2
):
56
63
.
15.
Matzner
Y
,
Benbassat
J
,
Polliack
A
.
Prognostic factors in multiple myeloma: a retrospective study using conventional statistical methods and a computer program
.
Acta Haematol
.
1978
;
60
(
5
):
257
68
.
16.
Palumbo
A
,
Avet-Loiseau
H
,
Oliva
S
,
Lokhorst
HM
,
Goldschmidt
H
,
Rosinol
L
, et al
.
Revised international staging system for multiple myeloma: a report from international myeloma working group
.
J Clin Oncol
.
2015
;
33
(
26
):
2863
9
.
17.
Mikhael
JR
,
Dingli
D
,
Roy
V
,
Reeder
CB
,
Buadi
FK
,
Hayman
SR
, et al
.
Management of newly diagnosed symptomatic multiple myeloma: updated Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines 2013
.
Mayo Clin Proc
.
2013
;
88
(
4
):
360
76
.
18.
Rajkumar
SV
,
Dimopoulos
MA
,
Palumbo
A
,
Blade
J
,
Merlini
G
,
Mateos
MV
, et al
.
International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma
.
Lancet Oncol
.
2014
;
15
(
12
):
e538
48
.
19.
Pawlyn
C
,
Davies
FE
.
Toward personalized treatment in multiple myeloma based on molecular characteristics
.
Blood
.
2019
;
133
(
7
):
660
75
.
20.
Chae
H
,
Han
E
,
Yoo
J
,
Lee
J
,
Lee
JJ
,
Cha
K
, et al
.
Heavy/light chain assay as a biomarker for diagnosis and follow-up of multiple myeloma
.
Clin Chim Acta
.
2018
;
479
:
7
13
.
21.
McDonald
JE
,
Kessler
MM
,
Gardner
MW
,
Buros
AF
,
Ntambi
JA
,
Waheed
S
, et al
.
Assessment of total lesion glycolysis by (18) F FDG PET/CT significantly improves prognostic value of GEP and ISS in myeloma
.
Clin Cancer Res
.
2017
;
23
(
8
):
1981
7
.
22.
Morvan
L
,
Carlier
T
,
Jamet
B
,
Bailly
C
,
Bodet-Milin
C
,
Moreau
P
, et al
.
Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis
.
Int J Comput Assist Radiol Surg
.
2020
;
15
(
1
):
129
39
.
23.
Pham
A
,
Reagan
JL
,
Castillo
JJ
.
Multiple myeloma-induced hyperammonemic encephalopathy: an entity associated with high in-patient mortality
.
Leuk Res
.
2013
;
37
(
10
):
1229
32
.
24.
Jian
X
,
Zhu
Y
,
Ouyang
J
,
Wang
Y
,
Lei
Q
,
Xia
J
, et al
.
Alterations of gut microbiome accelerate multiple myeloma progression by increasing the relative abundances of nitrogen-recycling bacteria
.
Microbiome
.
2020
;
8
(
1
):
74
.
25.
Dawson
AA
,
Ogston
D
.
Factors influencing the prognosis in myelomatosis
.
Postgrad Med J
.
1971
;
47
(
552
):
635
8
.
26.
Ludwig
C
,
Williams
DS
,
Bartlett
DB
,
Essex
SJ
,
McNee
G
,
Allwood
JW
, et al
.
Alterations in bone marrow metabolism are an early and consistent feature during the development of MGUS and multiple myeloma
.
Blood Cancer J
.
2015
;
5
(
10
):
e359
.
27.
D'Agostino
M
,
Cairns
DA
,
Lahuerta
JJ
,
Wester
R
,
Bertsch
U
,
Waage
A
, et al
.
Second revision of the International staging system (R2-ISS) for overall survival in multiple myeloma: a European myeloma network (EMN) report within the harmony project
.
J Clin Oncol
.
2022
;
40
(
29
):
3406
18
.
28.
Tokgoz Akyil
F
,
Yalcinsoy
M
,
Hazar
A
,
Cilli
A
,
Celenk
B
,
Kilic
O
, et al
.
Prognosis of hospitalized patients with community-acquired pneumonia
.
Pulmonology
.
2018
;
24
(
3
):
164
9
.
29.
Zeng
Z
,
Ke
X
,
Gong
S
,
Huang
X
,
Liu
Q
,
Huang
X
, et al
.
Blood urea nitrogen to serum albumin ratio: a good predictor of in-hospital and 90-day all-cause mortality in patients with acute exacerbations of chronic obstructive pulmonary disease
.
BMC Pulm Med
.
2022
;
22
(
1
):
476
.
30.
Bae
SJ
,
Kim
K
,
Yun
SJ
,
Lee
SH
.
Predictive performance of blood urea nitrogen to serum albumin ratio in elderly patients with gastrointestinal bleeding
.
Am J Emerg Med
.
2021
;
41
:
152
7
.
31.
Zou
XL
,
Feng
DY
,
Wu
WB
,
Yang
HL
,
Zhang
TT
.
Blood urea nitrogen to serum albumin ratio independently predicts 30-day mortality and severity in patients with Escherichia coli bacteraemia
.
Med Clin
.
2021
;
157
(
5
):
219
25
.
32.
Spiegelberg
HL
,
Fishkin
BG
.
The catabolism of human G immunoglobulins of different heavy chain subclasses. 3. The catabolism of heavy chain disease proteins and of Fc fragments of myeloma proteins
.
Clin Exp Immunol
.
1972
;
10
(
4
):
599
607
.