IL1B Polymorphism (rs1143634) and IL-1β Plasma Concentration as Predictors of Nutritional Disorders and Prognostic Factors in Multiple Myeloma Patients

Simple Summary

According to the literature, 35–71% of multiple myeloma patients have nutritional disorders. In the development of cachexia and malnutrition, the inflammatory process, accompanied by an increase in the level of proinflammatory cytokines, plays a key role. Interleukin-1β is a cytokine that plays an important role in the mechanisms responsible for muscle and adipose tissue breakdown during malnutrition and cachexia. This study aimed to investigate the association of IL1B gene polymorphism and interleukin-1β plasma concentration with the occurrence of nutritional disorders and survival in patients with multiple myeloma. The presence of the CC genotype of the IL1B gene was associated with a higher plasma concentration of interleukin-1β, a higher risk of cachexia, and poor prognosis. Determination of IL1B polymorphism may be a useful predictive marker of the risk of cachexia and prognostic factor in multiple myeloma patients.

Abstract

Background: Multiple myeloma (MM) is a hematological neoplasm of the early precursor of B-cells. The most characteristic symptoms observed during MM include hypocalcemia, anemia, bacterial infections, and renal damage. Nutritional disorders, especially malnutrition, are noted in about 35–71% of MM patients. Interleukin 1 beta (IL-1β) is a proinflammatory cytokine responsible for muscle atrophy and lipolysis during malnutrition and cachexia. This study aimed to evaluate the usefulness of the IL1B single-nucleotide polymorphism (SNP) (rs1143634) and plasma concentration of IL-1β in the assessment of the risk of nutritional disorders and prognosis in patients with MM. Methods: In our study, 93 patients with the de novo MM were enrolled. The real-time PCR with specific TaqMan probes method was used in genotyping. The IL-1β ELISA kit was used to determine IL-1β concentration in plasma samples. Results: Patients with the CC genotype, compared to the carriers of the other variants of the IL1B, demonstrated significantly higher concentrations of IL-1β in plasma (7.56 vs. 4.97 pg/mL), a significantly higher risk of cachexia (OR = 5.11), and a significantly higher risk of death (HR = 2.03). Moreover, high IL-1β plasma level was related to a significantly higher risk of cachexia (OR = 7.76); however, it was not significantly associated with progression-free survival (PFS) or overall survival (OS). Conclusions: Determination of the IL1B SNP (rs1143634) and plasma concentration of IL-1β may be useful in the assessment of the risk of cachexia and prognosis in patients with MM.

1. Introduction

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of abnormal plasma cells in the bone marrow (BM). In highly developed countries, MM accounts for 1.3% of all cancers, and its incidence is 4.5–6/100,000 per year. MM is the second most common hematological cancer [1,2]. Moreover, MM accounts for 10% of all hematological malignancies [3]. MM most often affects patients over 65 years of age and is 1.5 times more likely to affect men [4,5]. In the course of MM, acute renal failure (19%), anemia (73%), hypercalcemia (15%), and osteolytic lesions are observed in bones (79%) (referred to as CRAB symptoms) [6]. The most common cytogenetic changes occurring in MM are del(1p), del(17p), del(13), gain(1q), t(4;14), t(14;20), and t(14;16) [3]. Frequently observed mutations include those located in KRAS, NRAS, BRAF, EGR1, and FGFR3. Moreover, in approximately 20% of MM cases, there are mutations in the nuclear factor kappa B (NFκB) pathway within the TRAF3, NFKBIA, CYLD, and BIRC2/3 genes [7]. The basic method of treatment in MM is chemotherapy (CTH) based on the use of immunomodulatory drugs (IMIDs) such as thalidomide, lenalidomide, and bortezomib supplemented with autologous hematopoietic stem cell transplantation (aHSCT).

It is estimated that malnutrition occurs in up to 71% of patients with MM before treatment [8]. Moreover, malnutrition during treatment is observed in 35–45.4% of patients [9,10]. The risk factors of malnutrition/cachexia in cancer patients include more advanced disease stage, some tumor locations, more aggressive type of treatment, comorbidities, and low initial body weight [11]. Additionally, after aHSCT, patients are at risk of malnutrition due to CTH-related toxicity, higher risk of infection, and longer hospital stays [12]. Based on the guidelines proposed by the European Society of Clinical Nutrition and Metabolism (ESPEN), malnutrition was divided into three categories: disease-related malnutrition (DRM) with inflammation, DRM without inflammation, and malnutrition without comorbid disease [13]. Diagnostic criteria developed by the Global Leadership Initiative on Malnutrition (GLIM) include unintentional weight loss, low body mass index (BMI), reduced muscle mass, reduced food intake or absorption, and comorbid disease [14]. Cachexia is a multifactorial process that usually occurs in advanced cancer. Anorexia, anemia, muscle wasting, weight loss, and changes in protein, carbohydrate, and lipid metabolism are typical symptoms of cachexia [15,16]. Diagnostic criteria for cachexia include, in the case of cancer, a weight loss of at least 5% over 3–6 months or a BMI < 20 kg/m² as the primary criterion [17], and, additionally, decreased muscle strength, fatigue, anemia, low free fat mass index (FFMI), decreased albumin levels, and increased proinflammatory cytokines, e.g., C-reactive protein. (e.g., CRP). Serum CRP level measurement is a useful marker in the diagnosis of cachexia. However, it should be noted that cachexia may occur without a systemic inflammatory process [18]. The occurrence of malnutrition/cachexia is associated with deterioration of quality of life, increased risk of infection, reduced response to treatment, and shortened overall survival (OS) of patients [19,20].

Thus far, the involvement of interleukin 1 beta (IL-1β) in the pathomechanism of MM has been demonstrated. IL-1β is a cytokine secreted by myeloma cells and it is responsible for bone resorption in MM. IL-1β stimulates stromal cells in the BM to secrete interleukin 6 (IL-6) [21,22]. Moreover, it has been shown that proinflammatory cytokines, including IL-1β, IL-6, interleukin 8 (IL-8), and tumor necrosis factor-alpha (TNF-α), are involved in the breakdown of fat and muscle tissue, which indicates their potential diagnostic usefulness in predicting, diagnosing, and monitoring cancer cachexia [23,24]. However, to date, there are no studies describing the relationship between IL-1β and the nutritional status of MM patients. On the other hand, it is involved in the induction of inflammation (in the hypothalamic–pituitary–adrenal axis) and the mechanisms responsible for the breakdown of muscle fibers and lipolysis [25]. Single-nucleotide polymorphism (SNP) rs11436349 (+3954C>T) of the IL1B gene is a silent polymorphism of exon 5 in chromosome 2. Some studies refer to its functional nature resulting in a higher IL-1β secretion [26]. An association between SNP (rs1143634) IL1B and IL-1β plasma concentration of patients undergoing various therapies has been reported [27,28]. In vitro studies have shown that this SNP influences the increase in IL-1β secretion after stimulation with lipopolysaccharide. Additionally, the occurrence of this SNP translates into an increase in the amount of the active form of IL-1β [29].

Thus far, studies have focused on the relationship of this SNP with the development of selected diseases, including cancers such as breast cancer and MM [29,30]. However, one publication presents the impact of the polymorphism studied on the cachexia risk in patients with gastric cancer (GC) [31]. Our previous study showed the impact of the TT genotype of the KIAA1524 gene (686C>T) on shorter OS in MM patients [32].

Considering the above, this study aimed to assess the relationship between SNP of the IL1B gene (rs1143634) or IL-1β plasma concentration and the occurrence of nutritional disorders in patients with MM.

2. Materials and Methods

2.1. Study Group

The study group consisted of 93 consecutively admitted patients meeting the inclusion and exclusion criteria. MM patients were treated at the Department of Hematooncology and Bone Marrow Transplantation of the Medical University of Lublin from 2015 to 2020. Inclusion criteria were MM diagnosed according to SLiM CRAB criteria and International Myeloma Working Group (IMWG) recommendations, treatment naïve. The exclusion criteria were second active malignancy, autoimmunological diseases, and active infections. ISS staging criteria were used in disease staging determination. According to WHO recommendations, anemia was classified by severity into absent or mild (I°) (Hgb 10 g/dL to lower level of normal), moderate (II°) (Hgb 8–10 g/dL), severe (III°) (Hgb 6.5–7.9 g/dL), and life-threatening (IV°) (Hgb < 6.5 g/dL) [33]. Renal function was divided into 2 categories: A if creatinine was <2 mg/dL, and B if creatinine was ≥2 mg/dL [34]. Assessment of patients` nutritional status was performed according to criteria proposed by ESPEN (cachexia) and GLIM (malnutrition) before the start of treatment [13,35]. This study was designed based on the STROBE guidelines. Local ethical approval was obtained from the Bioethical Committee at the Medical University of Lublin (consent number: KE-0254/26/2015). This study was conducted in strict adherence to the principles of the Declaration of Helsinki.

2.2. Blood Collection

A total of 5 mL of peripheral blood was collected into EDTA-containing tubes from each patient participating in this study prior to the treatment in duplicate. The second sample was centrifuged for 15 min at 1000× g to obtain plasma. Samples were stored at −80 °C until laboratory analysis.

2.3. Genotyping

DNA was purified from 200 µL of whole blood using the column method according to the manufacturer’s protocol (DNA Blood Mini Kit, Qiagen, Hilden, Germany). Evaluation of the quality and quantity of the obtained samples was performed using the NanoDrop Lite Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The genotyping analysis was based on the real-time PCR method and performed on a StepOnePlus device (Applied Biosystems, Foster City, CA, USA). According to the manufacturer’s recommendations, the Genotyping Master Mix and TaqMan probes specific for the IL1B SNP (rs1143634) (Thermo Fisher Scientific, Waltham, MA, USA) were used. The thermal cycling protocol was based on the manufacturer’s protocol. All sample tests were performed in triplicate. After the amplification, the obtained genotypes were analyzed with StepOne Software v2.3 (Applied Biosystems, Waltham, MA, USA). Additionally, 10% of the samples were randomly selected and reanalyzed in a sequencing device (3500 Genetic Analyzer, Life Technologies, Carlsbad, CA, USA). A total of 100% consistency of the results was obtained.

2.4. Assessment of IL-1β Plasma Concentration

The ELISA technique was used to assess concentration of IL-1β in plasma samples (Human IL-1β ELISA Kit, Cat. No.BMS224-2, Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s recommendations. The detection range of IL-1β was 0–3.9 pg/mL and the sensitivity was 0.3 pg/mL. Measurement of the optical density (OD) at 450 nm and calculation of the standard curve 5-parameter curve equation and IL-1β concentration were carried out using a Multiskan FC Multiplate Photometer (Thermo Scientific, Waltham, MA, USA). Plasma samples that exceeded the highest concentration of IL-1β were reanalyzed at a higher sample dilution. A Wellwash Versa (Thermo Scientific, Waltham, MA, USA) automatic washer was used.

2.5. Bioelectrical Impendence Analysis

Body composition parameters, including fat-free mass (FFM), were obtained with bioelectrical impedance analysis (BIA). Measurements were performed at supine position in the morning (fasting conditions). Hydration status was controlled within BIA results (no significant deviations were noted). An ImpediMed SFB7 BioImp v1.55 device (Pinkenba, QLD, Australia) was used in BIA measurements.

2.6. Statistical Analysis

MedCalc 15.8 software (MedCalc Software, Ostend, Belgium) was used to analyze the acquired data statistically. In sample size calculation, to reject the null hypothesis, the alpha error was set to 5%, and to achieve an acceptable level of statistical power equal to 80%, the beta error was set at 20%. Calculation was conducted based on a comparison of the percentages of patients with different variants of SNP of the IL1B gene and cachexia as a primary endpoint. Considering the percentage of patients with cachexia in groups with CC genotype (47.1%) and with CT or TT genotype (9.5%), and the ratio of sample sizes in compared groups (1.21), the minimal study group was estimated as 55 patients. A chi-square test was used to assess the IL1B genotypes’ distribution and IL-1β plasma level depending on the selected demographic, clinical, and molecular variables. Results with a p-value < 0.05 were considered statistically significant. Univariable analysis of the risk of malnutrition and cachexia depending on demographic, clinical, and genetic factors was performed using the odds ratio (OR) test accompanied by OR and 95% confidence interval (95% CI) calculation. Multivariable analysis of the risk of malnutrition and cancer cachexia depending on demographic, clinical, and genetic factors was performed using logistic regression for the calculation of OR and 95% CI (results were adjusted to variables indicated by the backward elimination method in which statistically significant variables from the univariable analysis were included; for malnutrition: ISS stage; albumin; for cachexia: 17p/TP53 deletion; albumin level, IL-1 beta plasma concentration/IL1B genotype (CC vs. CT or TT)). Univariable analysis of OS and progression-free survival (PFS) was performed using the two-sided log-rank test (with the calculation of the hazard ratio HR and 95% CI) and visualized with Kaplan–Meier curves. Multivariable OS and PFS analysis was performed using Cox proportional hazard regression models (results were adjusted to variables indicated by the backward elimination method in which statistically significant variables from the univariable analysis were included; for OS: diagnosis; IL1B genotype (CC vs. CT or TT); for PFS: aHSCT; albumin level). Forest plot graphs were used to illustrate the results of the multivariable analysis. The analysis of receiver operating characteristic (ROC) curves was used to determine the cut-off points and to assess the diagnostic usefulness of IL-1β plasma concertation in differentiating the nutritional status of MM patients.

3. Results

3.1. Characteristics of the Study Group

The study group consisted of 93 newly diagnosed and treatment-naïve MM patients (52.7% were females). The median age of patients was 66 years (range: 37–87 years). MM with a monoclonal component constituted almost 90% of the study group. ISS grade 2 or 3 was reported in 31.2% and 37.6% of cases, respectively. The median BMI was 26.59 (range: 14.33–56.82). Nearly half of the patients (49.5%) experienced significant weight loss before MM diagnosis. In the first line of chemotherapy, patients received schemes based on thalidomide—CTD (cyclophosphamide + thalidomide + dexamethasone), bortezomib—V(C)D (bortezomib + cyclophosphamide + dexamethasone), or bortezomib and thalidomide—VTD (bortezomib + thalidomide + dexamethasone) in 29%, 28%, and 43% of cases, respectively. The study group characteristics are presented in

Table 1. General characteristics of the study group.

Factor		Study Group (n = 93)
Gender	Male	44 (47.3%)
	Female	49 (52.7%)
Age (years)	Mean ± standard deviation, median (range)	64.3 ± 9.83 66 (37–87)
	≥65	49 (52.7%)
	<65	44 (47.3%)
Myeloma type	IgG	55 (59.1%)
	IgA	27 (29%)
	Light chains	11 (11.8%)
Light chain type	Kappa	57 (61.3%)
	Lambda	36 (38.7%)
ISS stage	1	29 (31.2%)
	2	29 (31.2%)
	3	35 (37.6%)
Renal function	A	80 (86%)
	B	13 (14%)
Performance status	0	7 (7.5%)
	1	37 (39.8%)
	2	37 (39.8%)
	3	10 (10.8%)
	4	2 (2.2%)
BMI (kg/m2)	Mean ± standard deviation, median (range)	27.06 ± 5.98 26.59 (14.53–56.82)
Body weight loss	Yes	46 (49.5%)
	No	47 (50.5%)
	5%	14 (30.4%)
	10%	32 (69.6%)
Body weight loss (kg)	Mean ± standard deviation, median (range)	5.6 ± 2.4 5 (0–17)
Anemia grade before treatment (WHO)	Absent or I°	51 (54.8%)
	II°	27 (29%)
	III°	14 (15.1%)
	IV°	1 (1.1%)
Treatment protocol	CTD	27 (29%)
	V(C)D	26 (28%)
	VTD	40 (43%)
aHSCT	No	55 (59.1%)
	Yes	38 (40.9%)
del 17p/TP53 No data: n = 32	Absent	45 (73.8%)
	Present	16 (26.2%)
t(4;14) IGH/FGFR3 No data: n = 22	Absent	61 (85.9%)
	Present	10 (14.1%)
t(11;14) IGH/CCND1 No data: n = 22	Absent	63 (88.7%)
	Present	8 (11.3%)
t(14;16) IGH/MAF No data: n = 31	Absent	61 (98.4%)
	Present	1 (1.6%)
Other IGH rearrangement No data: n = 22	Absent	60 (84.5%)
	Present	11 (15.5%)

Abbreviations: aHSCT—autologous hematopoietic stem cell transplantation; BMI—body mass index; CTD—cyclophosphamide, thalidomide, dexamethasone; IGH—immunoglobulin heavy chain; ISS—Multiple Myeloma International Staging System; WHO—World Health Organization; V(C)D—bortezomib, (cyclophosphamide), dexamethasone; VTD—bortezomib, thalidomide, dexamethasone.

.

3.2. Factors Affecting the Risk of Malnutrition

In our study, malnutrition was observed in 68.82% of patients.

3.2.1. Univariable Analysis

According to the univariable analysis, significantly higher risk of malnutrition was observed in patients with stage 3 disease according to the ISS classification (OR = 4.23; p = 0.0089), poor performance status (PS:2–4) (OR = 188.42; p = 0.0003), lower levels of albumin (OR = 5.14; p = 0.0040), and elevated CRP (OR = 2.98; p = 0.0368).

3.2.2. Multivariable Analysis

The multivariable analysis showed a significantly higher risk of malnutrition in patients with stage 3 disease according to the ISS classification (OR = 3.39) and lower albumin levels (OR = 4.56; p = 0.0125) (

Table 2. Malnutrition and cancer cachexia occurrence depending on selected demographic, clinical, and molecular variables.

Variable	Malnutrition				Cancer Cachexia
			Univariable	Multivariable			Univariable	Multivariable
	No (n = 29)	Yes (n = 64)	OR [95% CI] p	OR [95% CI] p	No (n = 65)	Yes (n = 28)	OR [95% CI] p	OR [95% CI] p
Gender
Men	11 (25%)	33 (75%)	1.74 [0.71–4.27]	1.70 [0.63–4.58]	28 (63.6%)	16 (36.4%)	1.76 [0.72–4.31]	1.31 [0.31–5.21]
Women	18 (36.7%)	31 (63.3%)	0.2248	0.2891	37 (75.5%)	12 (24.5%)	0.2149	0.7170
Age
≥65	14 (28.6%)	35 (71.4%)	1.29 [0.54–3.11]	0.61 [0.21–1.76]	31 (63.3%)	18 (36.7%)	1.97 [0.79–4.92]	0.76 [0.17–3.28]
<65	15 (34.1%)	29 (65.9%)	0.5666	0.3586	34 (77.3%)	10 (22.7%)	0.1445	0.7101
Diagnosis
MM with a monoclonal component	27 (32.9%)	55 (67.1%)	0.45 [0.09–2.24]	0.47 [0.09–2.57]	57 (69.55)	25 (30.5%)	1.17 [0.29–4.78]	2.43 [0.21–28.07]
Light chain disease	2 (18.2%)	9 (81.8%)	0.3316	0.3856	8 (72.7%)	3 (27.3%)	0.8273	0.4757
Monoclonal protein class
IgA	8 (29.6%)	19 (70.4%)	0.80 [0.29–2.16]	0.63 [0.2–1.94]	17 (63%)	10 (37%)	0.64 [0.24–1.70]	0.35 [0.07–1.82]
IgG	19 (34.5%)	36 (65.5%)	0.6565	0.4192	40 (72.7%)	15 (27.3%)	0.3684	0.2138
N/a: n = 11
Light chain type
Lambda	10 (27.8%)	26 (72.2%)	1.30 [0.52–3.24]	1.21 [0.45–3.28]	23 (63.9%)	13 (36.1%)	1.58 [0.64–3.89]	1.29 [0.32–5.28]
Kappa	19 (33.3%)	38 (66.7%)	0.5736	0.7047	42 (73.7%)	15 (26.3%)	0.3175	0.7206
ISS stage
3	5 (14.3%)	30 (85.7%)	4.23 [1.43–12.49]	3.39 [1.11–10.41]	20 (57.1%)	15 (42.9%)	2.60 [1.04–6.45]	0.99 [0.22–4.45]
1, 2	24 (41.4%)	34 (58.6%)	0.0089 *	0.0327 *	45 (77.6%)	13 (22.4%)	0.0400 *	0.9880
Renal function
B	2 (15.4%)	11 (54.6%)	2.80 [0.58–13.55]	0.90 [0.14–5.57]	7 (53.8%)	6 (46.2%)	2.56 [0.68–7.47]	0.49 [0.06–3.66]
A	27 (33.7%)	53 (66.2%)	0.2002	0.9067	58 (72.5%)	22 (27.5%)	0.1815	0.4857
Stage of chronic kidney disease
G3a,G3b, G4, G5D	8 (24.2%)	25 (75.8%)	1.68 [0.64–4.38]	0.51 [0.15–1.82]	20 (60.6%)	13 (39.4%)	1.95 [0.78–4.85]	0.85 [0.18–4.12]
G1,G2	21 (35%)	39 (65%)	0.2864	0.3017	45 (75%)	15 (25%)	0.1506	0.8456
Performance status
2–4	0 (0%)	49 (100%)	188.42 [10.87–3266.72]	-[-]	30 (61.2%)	19 (38.8%)	2.46 [0.97–6.24]	0.52 [0.11–2.48]
0, 1	29 (65.9%)	15 (34.1%)	0.0003 *	0.9939	35 (79.5%)	9 (20.5%)	0.0578	0.4152
Treatment protocol (1)
CTD	5 (18.5%)	22 (81.5%)	2.51 [0.84–7.50]	0.57 [0.17–1.89]	18 (66.7%)	9 (33.3%)	1.24 [0.47–3.23]	1.07 [0.21–5.46]
V(C)D, VTD	24 (36.4%)	42 (63.6%)	0.0982	0.3625	47 (71.2%)	19 (28.8%)	0.6647	0.9381
Treatment protocol (2)
VTD	10 (35.7%)	18 (64.3%)	0.74 [0.29–1.90]	1.29 [0.46–3.64]	20 (71.4%)	8 (28.6%)	0.90 [0.34–2.38]	1.30 [0.28–5.98]
CTD, V(C)D	19 (29.2%)	46 (70.8%)	0.5365	0.6288	45 (69.2%)	20 (30.8%)	0.8322	0.7354
Anemia before treatment (WHO)
Yes	21 (28%)	54 (72%)	2.06 [0.71–5.92]	1.01 [0.28–3.61]	49 (65.3%)	26 (34.7%)	4.24 [0.90–19.90]	0.63 [0.04–8.71]
No	8 (44.4%)	10 (55.6%)	0.1813	0.9923	16 (88.9%)	2 (11.1%)	0.0666	0.7317
Platelets
Low	3 (25%)	9 (75%)	1.42 [0.35–5.68]	0.64 [0.13–3.18]	7 (58.3%)	5 (41.7%)	1.80 [0.52–6.26]	0.93 [0.09–9.46]
Normal	26 (32.1%)	55 (67.9%)	0.6216	0.5885	58 (71.6%)	23 (28.4%)	0.3543	0.9547
Albumins
Low	4 (11.8%)	30 (88.2%)	5.14 [1.72–17.66]	4.56 [1.39–15.03]	15 (44.1%)	19 (55.9%)	7.04 [2.64–18.76]	5.54 [1.27–24.1]
Normal	25 (42.4%)	34 (57.6%)	0.0040 *	0.0125 *	50 (84.7%)	9 (15.3%)	0.0001 *	0.0225 *
CRP
High	6 (17.6%)	28 (82.4%)	2.98 [1.07–8.31]	2.17 [0.72–6.59]	6 (17.6%)	28 (82.4%)	N/a	N/a
Normal	23 (39%)	36 (61%)	0.0368 *	0.1705	59 (100%)	0 (0%)
LDH
High	0 (0%)	10 (100%)	11.37 [0.64–200.93]	-[-]	4 (40%)	6 (60%)	4.16 [1.07–16.14]	3.28 [0.25–43.74]
Normal	29 (34.9%)	54 (65.1%)	0.0972	0.9937	61 (73.5%)	22 (26.5%)	0.0394 *	0.3678
Calcium
High	4 (19%)	17 (81%)	2.26 [0.68–7.44]	1.5 [0.41–5.51]	11 (52.4%)	10 (47.6%)	2.72 [0.99–7.48]	2.75 [0.41–18.33]
Normal	25 (34.7%)	47 (65.3%)	0.1800	0.5400	54 (75%)	18 (25%)	0.0513	0.2944
B2M
High	23 (29.5%)	55 (70.5%)	1.59 [0.51–4.99]	0.95 [0.27–3.32]	52 (66.7%)	26 (33.3%)	3.25 [0.68–15.49]	-[-]
Normal	6 (40%)	9 (60%)	0.4234	0.9332	13 (86.7%)	2 (13.3%)	0.1390	0.9946
Creatinine
High	7 (21.9%)	25 (78.1%)	2.01 [0.75–5.41]	0.6 [0.16–2.17]	18 (56.2%)	14 (43.7%)	2.61 [1.04–6.54]	0.76 [0.14–4.16]
Normal	22 (36.1%)	39 (63.9%)	0.1645	0.4355	47 (77%)	14 (23%)	0.0406 *	0.7545
eGFR
Low	15 (24.6%)	46 (75.4%)	2.38 [0.96–5.92]	1.51 [0.55–4.13]	41 (67.2%)	20 (32.8%)	1.46 [0.56–3.83]	1.65 [0.33–8.28]
Normal	14 (43.7%)	18 (56.2%)	0.0611	0.4221	24 (75%)	8 (25%)	0.4380	0.5425
del 17p/TP53
Present	2 (12.5%)	14 (87.5%)	4.25 [0.86–21.04]	1.47 [0.63–3.41]	8 (50%)	8 (50%)	4.62 [1.33–16.02]	5.2 [1.17–23.21]
Absent	17 (37.8%)	28 (62.2%)	0.0762	0.3678	37 (82.2%)	89 (17.8%)	0.0157 *	0.0307 *
No data: n = 32
t(4;14) IGH/FGFR3
Present	4 (40%)	6 (60%)	0.63 [0.16–2.50]	0.51 [0.11–2.27]	7 (70%)	3 (30%)	1.11 [0.26–4.79]	1.92 [0.24–15.49]
Absent	18 (29.5%)	43 (70.5%)	0.5085	0.3736	44 (72.1%)	17 (27.9%)	0.8896	0.5393
No data: n = 22
t(11;14) IGH/CCND1
Present	4 (50%)	4 (50%)	0.40 [0.09–1.77]	0.46 [0.09–2.27]	6 (75%)	2 (25%)	0.83 [0.15–4.52]	0.45 [0.04–5.54]
Absent	18 (28.6%)	45 (71.4%)	0.2280	0.3404	45 (71.4%)	18 (28.6%)	0.8326	0.5366
No data: n = 22
t(14;16) IGH/MAF
Present	1 (100%)	0 (0%)	0.14 [0.005–3.70]	-[-]	1 (100%)	0 (0%)	0.82 [0.03–20.99]	-[-]
Absent	21 (30%)	49 (70%)	0.2424	0.9920	50 (71.4%)	20 (28.6%)	0.9052	0.9950
No data: n = 22
Other IGH rearrangement
Present	2 (18.2%)	9 (81.8%)	2.25 [0.44–11.41]	2.05 [0.38–11.03]	7 (63.6%)	4 (36.4%)	1.57 [0.40–6.09]	1.1 [0.2–6.01]
Absent	20 (33.3%)	40 (66.7%)	0.3276	0.4008	44 (73.3%)	16 (26.7%)	0.5133	0.9094
No data: n = 22
IL1B genotype (rs1143634)
CC	15 (29.4%)	36 (70.6%)	1.20 [0.50–2.89]	0.93 [0.35–2.48]	27 (52.9%)	24 (47.1%)	8.44 [2.63–27.15]	5.11 [1.25–20.92]
TT or TC	14 (33.3%)	28 (66.7%)	0.6847	0.8935	38 (90.5%)	4 (9.5%)	0.0003 *	0.0233 *
IL1B genotype (rs1143634)
TT	2 (15.4%)	11 (84.6%)	2.80 [0.58–13.55]	3.87 [0.73–20.43]	11 (84.6%)	2 (15.4%)	0.38 [0.08–1.83]	2.81 [0.31–25.77]
TC or CC	27 (33.7%)	53 (66.2%)	0.2002	0.1110	54 (67.5%)	26 (32.5%)	0.2263	0.3607
IL-1β plasma level [pg/mL]
Low	17 (36.2%)	30 (63.8%)	0.62 [0.256–1.51]	1.37 [0.52–3.59]	42 (89.4%)	5 (10.6%)	8.40 [2.82–25.05]	7.76 [1.66–36.31]
High	12 (26.1%)	34 (73.9%)	0.2956	0.5229	23 (50%)	23 (50%)	0.0001 *	0.0092 *

*—Statistically significant result. Abbreviations: B2M—beta-2-microglobulin; CI—confidence interval; CRP—C-reactive protein; CTD—cyclophosphamide, thalidomide, dexamethasone; eGFR—estimated glomerular filtration rate; IgA—immunoglobulin A; IgG—immunoglobulin G; IGH—immunoglobulin heavy chain; IL-1β—interleukin 1 beta; ISS—Multiple Myeloma International Staging System; LDH—lactate dehydrogenase; N/a—not applicable; OR—odds ratio; WHO—World Health Organization; V(C)D—bortezomib, (cyclophosphamide), dexamethasone; VTD—bortezomib, thalidomide, dexamethasone.

; Figure 1A and Figure 2).

3.3. Factors Affecting the Risk of Cachexia

Cachexia was noted in 30.1% of MM patients.

3.3.1. Univariable Analysis

According to the univariable analysis, significantly higher risk of cachexia was observed in patients with stage 3 disease according to the ISS classification (OR = 2.6; p = 0.0400), lower albumin levels (OR = 7.04; p = 0.0001), increased LDH (OR = 4.16; p = 0.0394), elevated creatinine (OR = 2.61; p = 0.0406), and with the presence of 17p/TP53 deletion (OR = 4.62; p = 0.0157). Moreover, patients with the CC genotype of the IL1B gene had significantly more than 8-fold higher risk of cachexia (OR = 8.44; p = 0.0003). Similarly, a significantly more than 8-fold higher risk of cachexia was noted in patients with higher IL-1β plasma levels (OR = 8.40; p = 0.0001).

3.3.2. Multivariable Analysis

The multivariable analysis showed a significantly higher risk of cachexia in patients with lower albumin levels (OR = 5.54; p = 0.0225), with the presence of 17p/TP53 deletion (OR = 5.20; p = 0.0307), with the CC genotype of IL1B gene (OR = 5.11; p = 0.0233), and with higher levels of IL-1β (OR = 7.76; p = 0.0092) (Table 2; Figure 1B).

3.4. Progression-Free Survival

3.4.1. Univariable Analysis

According to the univariable analysis, the factors significantly correlated with higher risk of PFS reduction were male sex (HR = 1.70; p = 0.0402), higher stage of chronic kidney disease (>G2: HR = 2.42; p = 0.0004), anemia before treatment (HR = 2.21; p = 0.0385), low albumin level (HR = 2.69; p = 0.0001), and high level of creatinine (HR = 2.21; p = 0.0017). A significantly lower risk of PFS reduction in patients treated with VTD, as compared to those treated with CTD or V(C)D schemes (HR = 0.51; p = 0.0387) and aHSCT (HR = 0.37; p = 0.0005), was found. Additionally, a significantly higher risk of PFS reduction was observed in patients with the CC variant of the IL1B gene (rs1143634) as compared to patients with other variants (median PFS: 24 vs. 25 months; HR = 1.69; p = 0.0424) (Figure 3A).

3.4.2. Multivariable Analysis

A multivariable analysis revealed a significantly lower risk of PFS reduction in patients in whom aHSCT was applied (HR = 0.43; p = 0.0065). On the other hand, patients with lower levels of albumin had significantly higher risk of PFS reduction (HR = 2.40; p = 0.0017).

Detailed data on the relationship between selected demographic, clinical, and molecular variables and PFS are presented in

Table 3. Survival of MM patients depending on selected demographic, clinical, and molecular variables.

Variable	Progression Free Survival			Overall Survival
	Univariable		Multivariable	Univariable		Multivariable
	mPFS (Months) 18	HR (95% CI) p	HR (95% CI) p	mOS (Months) 25	HR (95% CI) p	HR (95% CI) p
Gender
Men	24	1.7 (1.01–2.87)	1.38 (0.8–2.39)	34	1.50 (0.81–2.77)	1.5 (0.8–2.81)
Women	25	0.0402 *	0.2485	47	0.1911	0.2125
Age
≥65	17	1.49 (0.89–2.50)	0.63 (0.33–1.21)	38	1.30 (0.71–2.41)	1.17 (0.62–2.2)
<65	30	0.1287	0.1687	45	0.3971	0.6286
Diagnosis
MM with a monoclonal component	25	0.64 (0.2–1.48)	0.43 (0.16–1.19)	47	0.33 (0.12–0.93)	0.30 (0.13–0.72)
Light chain disease	15	0.2082	0.1056	16	0.0011 *	0.0076 *
Monoclonal protein class
IgA	24	1.10 (0.59–2.02)	0.79 (0.40–1.58)	47	0.62 (0.29–1.33)	0.49 (0.23–1.07)
IgG	25	0.7522	0.5162	45	0.1780	0.0766
N/a: n = 11
Light chain type
Lambda	17	1.10 (0.64–1.90)	1.12 (0.65–1.92)	33	1.57 (0.82–3)	1.38 (0.73–2.62)
Kappa	26	0.7174	0.6845	48	0.1446	0.3214
ISS stage
3	17	1.39 (0.8–2.40)	1.22 (0.58–2.56)	33	1.3 (0.68–2.46)	0.96 (0.8–1.86)
1, 2	26	0.2114	0.6018	45	0.4061	0.9156
Renal function
B	15	1.75 (0.79–3.89)	1.1 (0.56–2.17)	24	1.82 (0.74–4.48)	0.9 (0.39–2.09)
A	25	0.0854	0.7773	45	0.1018	0.8109
Stage of chronic kidney disease
G3a, G3b,G4,G5D	13	2.42 (1.36–4.31)	1.53 (0.85–2.74)	25	2.08 (1.09–3.98)	1.33 (0.68–2.62)
G1, G2	34	0.0004 *	0.1568	47	0.0153 *	0.4091
Performance status
2–4	25	1.25 (0.75–2.11)	0.73 (0.41–1.27)	33	1.7 (0.92–3.15)	1.15 (0.6–2.21)
0, 1	24	0.3872	0.2653	47	0.0849	0.6632
Treatment protocol (1)
CTD	24	1.68 (0.67–2.04)	1.01 (0.57–1.79)	33	0.96 (0.51–1.8)	0.83 (0.42–1.63)
V(C)D, VTD	25	0.5624	0.9609	43	0.8874	0.5900
Treatment protocol (2)
VTD	36	0.51 (0.29–0.90)	0.74 (0.37–1.47)	38	1.03 (0.51–2.07)	1.16 (0.57–2.38)
CTD, V(C)D	17	0.0387 *	0.3923	47	0.9232	0.6752
aHSCT
Yes	42	0.37 (0.22–0.62)	0.43 (0.24–0.79)	45	0.60 (0.32–1.1)	0.66 (0.34–1.26)
No	15	0.0005 *	0.0065 *	38	0.1094	0.2111
Body weight loss before treatment
Yes	18	1.46 (0.87–2.45)	1.09 (0.62–1.91)	33	1.36 (0.73–2.52)	1.3 (0.67–2.53)
No	34	0.1471	0.7602	46	0.3181	0.4368
Anemia before treatment (WHO)
Yes	18	2.21 (1.2–4.07)	1.11 (0.45–2.75)	38	1.84 (0.92–3.65)	1.64 (0.55–4.88)
No	-	0.0385 *	0.8137	48	0.1263	0.3719
Platelets
Low	12	1.82 (0.78–4.24)	1.2 (0.59–2.43)	24	1.60 (0.65–3.94)	1.28 (0.58–2.83)
Normal	25	0.0753	0.6086	43	0.2253	0.5421
Albumins
Low	12	2.69 (1.49–4.84)	2.4 (1.39–4.14)	28	2.68 (1.33–5.37)	3.14 (1.64–6.02)
Normal	37	0.0001 *	0.0017 *	48	0.0007 *	0.0006*
CRP
High	24	1.48 (0.85–2.58)	1.05 (0.59–1.86)	30	1.79 (0.92–3.49)	1.51 (0.81–2.84)
Normal	26	0.1268	0.8611	46	0.0560	0.1973
LDH
High	12	1.71 (0.68–4.29)	1.19 (0.56–2.54)	22	1.55 (0.51–4.74)	0.85 (0.32–2.56)
Normal	25	0.1468	0.6489	45	0.3468	0.7491
Calcium
High	25	1.24 (0.66–2.31)	1.21 (0.67–2.19)	25	1.62 (0.46–3.45)	1.37 (0.68–2.77)
Normal	25	0.4689	0.5284	46	0.1541	0.3748
B2M
High	24	1.85 (0.97–3.52)	1.32 (0.59–2.96)	38	1.76 (0.85–3.64)	1.34 (0.55–3.27)
Normal	-	0.1093	0.4951	52	0.1826	0.5242
Creatinine
High	14	2.21 (1.25–3.92)	1.5 (0.87–2.59)	28	2.04 (1.06–3.95)	1.2 (0.6–2.4)
Normal	34	0.0017 *	0.1499	47	0.0182 *	0.6110
eGFR
Low	24	1.76 (1.02–3.05)	1.24 (0.66–2.33)	41	1.29 (0.67–2.49)	0.96 (0.47–1.96)
Normal	-	0.0634	0.5088	45	0.4557	0.9055
del 17p/TP53
Present	15	1.69 (0.78–3.68)	1.23 (0.82–1.84)	30	1.97 (0.68–5.71)	1.03 (0.6–1.78)
Absent	25	0.1191	0.3247	52	0.1106	0.9128
No data: n = 32
t(4;14) IGH/FGFR3
Present	42	0.87 (0.36–2.12)	1.32 (0.47–3.65)	-	1.26 (0.40–4.02)	1.52 (0.51–4.55)
Absent	24	0.7707	0.5994	46	0.6608	0.4514
No data: n = 22
t(11;14) IGH/CCND1
Present	9	1.61 (0.57–4.56)	1.56 (0.66–3.7)	46	1.08 (0.36–3.22)	1.16 (0.39–3.43)
Absent	25	0.2657	0.3166	45	0.8852	0.7950
No data: n = 22
t(14;16) IGH/MAF
Present	9	4.68 (0.07–315.0.4)	2.2 (0.29–17.08)	28	3.3 (0.1–113.16)	2.35 (0.3–18.53)
Absent	25	0.0872	0.4534	46	0.2082	0.4189
No data: n = 22
Other IGH rearrangement
Present	17	1.13 (0.48–2.64)	1.08 (0.48–2.46)	-	1.15 (0.38–3.51)	1.42 (0.48–4.19)
Absent	25	0.7620	0.8487	45	0.7936	0.5287
No data: n = 22
IL1B genotype (rs1143634)
CC	24	1.69 (1.01–2.84)	1.28 (0.72–2.27)	30	2.04 (1.1–3.78)	2.03 [1.06–3.88]
TT or TC	25	0.0424 *	0.4064	48	0.0184 *	0.0337 *
IL1B genotype (rs1143634)
TT	-	0.60 (0.28–1.26)	0.76 (0.3–1.92)	-	0.14 (0.06–0.32)	0.16 (0.02–1.15)
TC or CC	24	0.2549	0.5695	38	0.0198 *	0.0700
IL-1β plasma level [pg/mL]
Low	25	0.89 (0.53–1.5)	0.97 (0.57–1.66)	46	0.70 (0.38–1.31)	1.20 (0.64–2.23)
High	24	0.6655	0.9164	34	0.2507	0.5724

*—Statistically significant result. Abbreviations: aHSCT—autologous hematopoietic stem cell transplantation; B2M—beta-2-microglobulin; CI—confidence interval; CRP—C-reactive protein; CTD—cyclophosphamide, thalidomide, dexamethasone; eGFR—estimated glomerular filtration rate; HR—hazard ratio; IgA—immunoglobulin A; IgG—immunoglobulin G; IGH—immunoglobulin heavy chain; IL-1β—interleukin 1 beta; ISS—Multiple Myeloma International Staging System; LDH—lactate dehydrogenase; mOS—median overall survival; mPFS—median Progression-free survival; N/a—not applicable; WHO—World Health Organization; V(C)D—bortezomib, (cyclophosphamide), dexamethasone; VTD—bortezomib, thalidomide, dexamethasone.

.

3.5. Overall Survival

3.5.1. Univariable Analysis

The univariable analysis identified the following factors as significantly related to a higher risk of death: higher stage of chronic kidney disease (>G2: HR = 2.08; p = 0.0153), low albumin levels (HR = 2.68; p = 0.0007), and high creatinine levels (HR = 2.04; p = 0.0182). On the other hand, a significantly lower risk of death was observed in the subjects with MM with monoclonal component (HR = 0.33; p = 0.0011). Moreover, we observed a higher risk of death in patients with the CC variant of the IL1B gene (rs1143634) as compared to those with other genotypes (median OS: 30 months vs. 48 months; HR = 2.04; p = 0.0184) (Figure 3B). On the other hand, a significantly lower risk of death was observed in the subjects with the TT variant of the IL1B gene (rs1143634) as compared to carriers of the C allele (HR = 0.14; p = 0.0198) (Figure 3C).

3.5.2. Multivariable Analysis

The multivariable analysis confirmed the independent prognostic value of the following factors: MM with a monoclonal component (HR = 0.30; p = 0.076), low albumin levels (HR = 3.14; p = 0.0006), and CC genotype of IL1B gene (rs1143634) (HR = 2.03; p = 0.0337). A graphical representation of the results of the multivariable analysis of PFS and OS is shown in Figure 2A,B.

Detailed data on the relationship between selected demographic, clinical, and molecular variables and OS are presented in Table 3.

3.6. The Association between Gender and Demographic, Clinical, and Molecular Factors

We observed no statistically significant differences in IL-1β concentration and IL1B SNP depending on gender and other demographic, clinical, and molecular variables (Table S1).

3.7. The Association between IL1B Genotypes’ Distribution and Demographic, Clinical, and Molecular Factors

Elevated levels of CRP were noted to be significantly more common in patients with the CC genotype than in the carriers of the T allele (54.9% vs. 29.2%; p = 0.0003). We observed significantly higher IL-1β concentrations in the carriers of CC compared to the other variants of the IL1B gene (68.6% vs. 50.7%; p = 0.0002; Figure 4). The detailed data are presented in the Table S2.

Patients with the CC genotype had significantly higher IL-1β concentrations in plasma (7.56 vs. 4.97 pg/mL; p < 0.0001). Moreover, patients with CC genotype had significantly higher CRP concentration as compared to those with other variants of the IL1B gene (8.3 vs. 2 mg/L; p = 0.0014). Significantly lower albumin level was noted in CC genotype carriers (3.60 vs. 3.85 g/dL; p = 0.0165) (Table S3).

Patients with elevated CRP had significantly higher IL-1β plasma concentration than patients with normal CRP levels (8.35 vs. 5.38 pg/mL; p = 0.0004) (Table S4). Moreover, we found a significant, weak, positive correlation between CRP and IL-1β plasma concentration (r = 0.295; p = 0.0045; Table S5; Figure 4B).

3.8. Diagnostic Usefulness of the Assessment of the IL1B SNP (rs1143634) and IL-1β Concentration in Predicting Nutritional Disorders

The assessment of IL1B SNP and IL-1β plasma concentration demonstrated a significant diagnostic usefulness in the prediction of cachexia. The CC genotype of the IL1B gene was characterized with 85.7% sensitivity and 58.5% specificity in the prediction of cachexia (AUC = 0.72; p < 0.0001). At the cut-off of >6.7 pg/mL, 78.6% sensitivity and 72.3% specificity in the prediction of cachexia was noted (AUC = 0.788; p ≤ 0.0001; Figure 5). Joint analysis of IL-1β and CRP levels was characterized by high diagnostic usefulness in predicting the risk of cancer cachexia (AUC = 0.956; p < 0.0001).

The assessment of IL1B SNP and IL-1β plasma concentration demonstrated a nonsignificant diagnostic usefulness in the prediction of malnutrition. The CC genotype of IL1B gene was characterized with 56.2% sensitivity and 48.2% specificity in the prediction of malnutrition (AUC = 0.52; p = 0.6894). At the cut-off of >5.1 pg/mL, 75% sensitivity and 48.3% specificity in the prediction of malnutrition was observed (AUC = 0.59; p = 0.1634).

4. Discussion

An important phenomenon accompanying cachexia is the inflammatory process, during which the secretion of proinflammatory cytokines is observed, including IL-1β, Il-6, IL8, TNF-α, and interferon-gamma (INF-γ) [33]. Moreover, in the BM of MM patients, cancer cells stimulate the secretion of IL-1β and IL-6 [21]. To the best of our knowledge, this study is the first to describe this type of relationship in MM. Due to the lack of this type of papers in MM, we decided to compare our results with those obtained in GC, gastroesophageal junction (GEJ) cancer, or chronic obstructive pulmonary disease (COPD). As in the case of nutritional disorders, there are no studies in the available literature describing the association of the SNP we examined with OS in patients with MM.

In our study including de novo diagnosed and previously untreated MM patients, cachexia was noted in 30.1% and malnutrition was observed in 68.8% of cases. Mallard et al. conducted a study on 31 patients with MM, 30 with Hodgkin’s lymphoma, and 84 with non-Hodgkin’s lymphoma. They described that cachexia was noted in 38% of patients treated due to hematological malignancies [36]. In turn, in the study conducted by Olleros et al. in a group of 58 patients with MM, 45.4% were moderately or severely malnourished [10]. Kim et al. conducted a study on 216 patients with MM. Using the Subjective Global Assessment (SGA) scale, they showed that 71% of patients had malnutrition, of which 23% had severe malnutrition requiring nutritional intervention [8]. Meanwhile, Garzón Herazo et al. conducted a study on 124 patients with MM treated with aHSCT. Based on the nutritional risk index (NRI < 97.5) calculated before aHSCT, they found that 35% of patients were moderately or severely malnourished Thus, our data are consistent with the available literature.

The presence of rapidly proliferating cancer cells contributes to the development of a systemic inflammatory process that underlies the processes related to the remodeling of muscle and fat tissue [37]. IL-1β promotes muscle fiber breakdown by activating the nuclear factor kappa B (NF-κB) pathway and increasing IL-6 levels. TNF-α, also called cachectin, IL-6, IL-1β, and IFN-γ, activates the NF-κB pathway and promotes the transcription of E3 ubiquitin-proteasome ligase in muscle atrophy [23]. Moreover, in the course of cachexia, browning of adipose tissue is observed, which is mainly mediated by proinflammatory cytokines, e.g., TNF-α, IL-6, and IL-1β [38,39]. Furthermore, IL-1β plays an important role in activating lipolysis by promoting adipose tissue triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL). High levels of IL-1β promote the production of lipid mobilizing factor (LMF), which stimulates increased lipolysis [23]. Gherardi et al. showed that an increased concentration of IL-1β is observed in the serum of MM patients [40,41]. In our study, an increase in IL-1β levels in the plasma of MM patients was associated with an increase in the risk of developing cachexia. Bębnowska et al. showed that IL-1β stimulates the production of IL-6 and IL-10 in patients with MM [42]. Kline et al., in studies using cultures of stromal cells derived from the bone marrow of MM patients, showed that in addition to IL-6, IL-1β also induces an increase in the secretion of IL-8 (to the greatest extent), monocyte chemoattractant protein (MCP-1), and granulocyte-macrophage colony-stimulating factor (GM-CSF) [43].

The available literature focuses on the association of SNPs of genes encoding proinflammatory cytokines, i.e., IL-6: rs1800795 (-174G>C), TNF-α: rs1800629 (-308G>A), or IL1B rs16944 (-511C>T), with the development of MM [44,45,46]. Similarly, in the case of the rs1143634, SNP we studied, available studies describe its relationship with the risk of developing selected cancers, including MM [28,29]. It should be emphasized that in the literature, there are no studies describing the association of SNPs of genes encoding proinflammatory cytokines, including those located within IL1B, in particular rs1143634, with the risk of nutritional disorders in MM [47,48]. Zhang et al. performed an IL1B SNP analysis (rs1143634) on a group of 214 patients with locally advanced gastric cancer. The T allele of this gene was significantly more common in patients with cachexia compared to patients without cachexia (12.1% vs. 5.7%). Therefore, the presence of the T allele was associated with a significant, over 2.5-fold higher, risk of cachexia (OR = 2.51; 95% CI: 1.18–5.35) [31]. In another study by Jatoi et al., 44 patients with confirmed gastric cancer and gastroesophageal junction cancer with metastases were enrolled, and the IL1B SNP (rs1143634) was analyzed. Compared to patients with the CC genotype, carriers of the T allele showed a significant increase in body weight during the study and a significantly lower risk of death (HR = 0.3) [49]. In the study conducted by Broekhuizen et al., SNP rs16944 IL1B (-511T>C) was analyzed in a group of 99 patients with COPD. The occurrence of the CC genotype was associated with a higher incidence of cachexia compared to other genotype variants (46.3% vs. 22.7%) [50]. Therefore, our results, in which MM patients with the CC genotype had a higher risk of cachexia and shorter OS compared to carriers of the T allele, are consistent with those obtained by Jatoi et al. and Broekhuizen et al., while contrary to those presented in the work of Zhang et al. (only regarding risk of cachexia). However, it should be noted that the study conducted by Zhang et al. involved Asian patients, in whom the allele distribution was substantially different from that observed in our study (the TT genotype was not noted in any patient, while in our study it was recorded in 13 patients). Additionally, the study mentioned above involved GC patients, whereas ours included MM patients; therefore, the studied SNP could have different clinical consequences (including risk of cachexia). The importance of the above differences (race, cancer type) has already been indicated in the literature [51,52].

As in the case of nutritional disorders, there are no studies in the available literature describing the association of the SNP we examined with OS in patients with MM. Therefore, we report the association of other SNPs located in the gene we studied with the survival of MM patients. In a study conducted by Vangsted et al. in 348 MM patients, SNP rs1143627 of the IL1B gene (-31C>T) was analyzed. In this group, 185 patients were treated with high doses of melphalan and aHSCT, while 163 patients were treated with other regimens. The presence of the C allele was associated with a significantly longer OS (mOS: 80.1 vs. 48.5 months; HR = 0.6; 95% CI: 0.5–0.9) and a nonsignificantly longer time to treatment failure (TTF) compared to the presence of the TT genotype (29.9 vs. 27.2 months; HR = 0.9; 95% CI: 0.6–1.2) [53]. On the other hand, in our study, the CC genotype was associated with shorter OS compared with other variants of IL1B (rs1143634) genotypes. Although we examined a different SNP, localization in the same gene suggests its involvement in cancer progression and, therefore, the potential usefulness of determining this gene’s status in prognostication.

In another study, Vangsted et al. analyzed the association of several SNPs of the IL1B gene, including rs16944 (-511C>T), rs4848306 (-3737 C>T), and rs1143623 (-1464 G>C) with TTF and OS in patients with MM. The study included 348 patients with MM treated with high doses of melphalan and stem cells, of which 146 patients received interferon-alpha (INF-α) maintenance treatment. The disease recurred in 243 patients, and among this group, 177 people were treated with thalidomide and 74 received bortezomide. In the case of the SNP rs4848306 of the IL1B gene, in T allele carriers compared to CC homozygotes, a significantly higher risk of death (62.1 vs. 85.8 months; HR = 1.8; 95% CI: 1.2–2.6) and a significantly higher risk of TTF shortening (26 vs. 32.8 months; HR = 1.4; 95% CI: 1–1.9) was observed. In turn, in the case of the SNP rs16944 of the IL1B gene, in carriers of the TT homozygotes compared to CC or CT genotypes, a significantly lower risk of death was observed (93.9 vs. 54.9 or 74.4 months; HR = 0.5; 95% CI: 0.3–1) and a nonsignificant difference in the risk of shortening TTF (34.4 vs. 27.8 or 27.6 months; HR = 1; 95% CI: 0.6–1.5). However, in the case of rs1143623 of the IL1B gene, a significantly lower risk of death was observed in carriers of the C allele compared to GG homozygotes (81.6 vs. 56.7 months; HR = 0.6; 95% CI: 0.5–0.9) and a nonsignificant difference in the risk of TTF shortening (28.9 vs. 28.4 months; HR = 1; 95% CI: 0.8–1.3) [46].

Also, in this case, we examined different SNP; however, considering the explanation mentioned in the previous paragraph, utilization of determining this gene’s status as a prognostic factor could be suggested. In our previous study including 128 patients with MM diagnosed de novo, we noted a higher risk of death in the carriers of the TT genotype (rs2278911; 686C>T) of the KIAA1524 gene (mOS: 8 months vs. 45 months; HR = 0.53). Moreover, patients with the CC genotype had a significantly lower risk of OS reduction compared to other KIAA1524 genotypes (HR = 0.41). In silico analysis showed that the KIAA1524 gene correlates significantly with IL-6, one of the key proinflammatory cytokines, which is further evidence of the link between severe inflammation and unfavorable prognosis in MM [33].

It should be considered that performing SNP analysis may have an advantage over simply measuring CRP or IL-1β levels because its determination is independent of the patient’s current status (presence of inflammation unrelated to cancer and/or the development of other diseases, nutrition, comorbidities, previous treatment affecting the immune system, etc.). For this reason, as a predisposition factor to the development of cachexia or a worse prognosis, SNPs are not susceptible to confounding factors. At the same time, in the era of technological progress, the determination of SNPs is relatively simple and affordable. On the other hand, the fact that the indicated SNPs do not reflect the current state of the immune system, but, rather, some predispositions, may be perceived as a disadvantage.

A limitation of the study includes the lack of determination of other proinflammatory cytokines that are involved in the development of nutritional disorders. To date, numerous SNPs were associated with the development of cachexia or poor survival in various cancers; thus, combined with our results, this may suggest that the studied SNP may also be a useful predictive (regarding nutritional disorders) and prognostic factor in patients with MM.

In the context of OS, conclusions regarding the advantage of IL1B SNP over IL-1β concentration (categorized for this analysis) determination can be drawn based on the comparison of HR values. Higher HR values (and, above all, statistically significant) were obtained for SNP.

Potential practical implications of the conducted research include the utilization of IL-1β concentration or IL1B SNP (rs1143634) determination in the assessment of the predispositions to nutritional deficiencies, facilitation of the diagnosis of such type of disorders, the development of anti-IL1B therapy to treat or prevent malnutrition and cachexia, assessment of patients regarding the level of IL-1β or IL1B SNP (rs1143634) to assess predispositions, diagnosis of this type of disorder, implementation of appropriate supportive treatment (e.g., nutritional) in diagnosed or at-risk patients, the development of anti-IL1B therapy to treat or prevent nutritional deficiencies, and, finally, for prognostic purposes in MM patients. Moreover, determining the level of IL-1β with other parameters increases the diagnostic utility in assessing the risk of developing cachexia.

5. Conclusions

Determination of the IL1B SNP (rs1143634) and plasma concentration of IL-1β may be useful in the assessment of the risk of cachexia and prognosis in patients with MM.