Introduction
Cachexia is defined by an involuntary weight loss that cannot be fully reversed by conventional nutritional support and leads to progressive functional impairment [ 1 ]. Weight loss in cancer cachexia can be classified according to Martin et al score [ 2 ]. Cachexia is characterized by muscle mass loss with or without fat loss [ 1 ] and can be explained by complex phenomena like a decrease in protein synthesis promoting depletion of muscle tissue [ 1 , 3 ] and an increase in inflammation level [ 3 ]. This inflammation is a key marker of cachexia and is characterized by an increase in biochemical markers such as C-reactive protein (CRP), inflammation marker secreted by the liver in response to increase in interleukine-6 (IL-6) level [ 1 , 4 , 5 ]. During hematological malignancies, cachexia concerns more than 30% of patients, with an increase of mortality risk and a decrease of quality of life [ 6 ]. The role of chemotherapy in this cachexia either by improving cancer-related cachexia or by inducing cachexia itself is well recognized [ 1 ]. Despite identification of mechanisms leading to a loss of muscle mass, the precise identification of cachexia risk factors before cancer treatment remains insufficient and their influence on patient outcome is not defined. Thus, the objective of this work was to identify risk factors of cachexia in myeloma and lymphoma patients, from the outset of cancer diagnosis, to develop strategies to prevent cachexia and its consequences.
Materials and Methods
Patients
This study was based on a prospective and observational study ( NCT02134574 ). One hundred forty-five patients diagnosed with lymphoma or myeloma (aged > 18 years) between 2014 and 2017 were enrolled and completed their treatment. This cohort consisted of a prospective collection of clinical and biological data, from patients consulting for a diagnosis of hematological malignancies within the Department of Clinical Hematology of the Montpellier University Hospital. Clinical responses to treatment of patients were monitored according to international recommendations using prognostic indicators [ 7 , 8 ] and the disease relapse date was identified to determine progression-free survival (PFS). This study was approved by the Institutional Ethics Committee in accordance with the principles of the Helsinki Declaration (National Agency for the Safety of Medicines and Health Products (ANSM), reference: ANSM 13029B-11 of 21/03/2013, Committee for the Protection of Persons (CPP) Sud mediterranee I, reference: 13 24 of 02/04/2013).
Cachexia diagnosis
Each patient losing weight during treatment was considered cachectic, including cachexia induced by treatment. Cachexia was determined at the end of the hematological treatment. For that, a severity stage between 0 and 4 was attributed according to Martin criteria [ 2 ] (where stage 0 is the lowest severity stage and stage 4 is the highest severity stage of cachexia).
Statistical analysis
Distributions of data were tested with the Shapiro-Wilk test. Chi-squared or Fisher’s exact tests were used to compare categorical data. For numerical data, medians were compared using Student’s t -test or Mann-Whitney’s test. Spearman’s correlation test was used to assess the association between two numerical variables. The association between covariates and cachexia was assessed using multivariate logistic regression analysis. The receiver operating characteristics (ROC) curve was used to determine the threshold value to predict cachexia associated with the best sensitivity and specificity according to the Youden index [ 9 ]. Results from Cox regression was internally validated using bootstrap procedure [ 10 , 11 ], generating a total of 1,000 replicates. PFS was measured from the date of the initiation of treatment to the date of relapse and/or progression. PFS was estimated using the Kaplan-Meier method and comparisons were made using the log-rank test. Hazard ratios (HRs) and their 95% confidence intervals (CIs) in univariate and multivariate analyses were calculated using Cox regression analyses. All statistical analyses were performed at the conventional two-tailed α level of 0.05 using R software version 3.0.2.10.
Results
Patients
Of the 145 patients, 89 were men and 56 were women. Thirty of patients were diagnosed for Hodgkin’s lymphoma, 84 patients for non-Hodgkin’s lymphoma and 31 for myeloma. All clinical and biological data are shown in Table 1 .
Table 1
Clinical and Biological Data at Diagnosis
*For all lymphomas. BMI: body mass index; CKD-EPI: chronic kidney disease epidemiology collaboration; CRP: C-reactive protein; FLIPI: follicular lymphoma international prognostic index; HL: Hodgkin lymphoma; IPI: international prognostic index for other lymphoma; IQR: interquartile range; ISS: international scoring system for multiple myeloma; NHL: non-Hodgkin lymphoma; MIPI: mantle cell international prognostic index; PCLI: plasma cell labeling index.
| Cachexia cohort (n = 145) | |||
| n (%) | Median (IQR) | Range | |
| Age at diagnosis (years) | - | 62.4 (48.0 - 70.9) | 20.0 - 90.6 |
| Male sex | 89 (61) | - | |
| Weigh at diagnosis (kg) | - | 71.0 (60.0 - 82.0) | 40.7 - 116.8 |
| Height (cm) | - | 170.0 (164.0 - 176.0) | 145.0 - 197.0 |
| BMI at diagnosis | - | 24.3 (21.5 - 27.6) | 16.2 - 39.4 |
| Tobacco status | |||
| Never smoked | 66 (45.5) | - | |
| Old smoker | 55 (38) | - | |
| Actual smoker | 24 (16.5) | - | |
| Hemoglobin (g/L) | 143 (99) | 13.0 (11.5 - 14.1) | 7.5 - 17.4 |
| Total proteins (g/L) | 122 (84) | 71.5 (68.0 - 76.0) | 38.0 - 106.0 |
| Albumin (g/L) | 122 (84) | 40.2 (36.1 - 42.9) | 17.1 - 48.6 |
| CRP (mg/L) | 114 (79) | 5.0 (2.1 - 18.4) | 0.3 - 245.7 |
| Creatinine (µmol/L) | 141 (97) | 73.0 (60.0 - 86.0) | 44.0 - 256.0 |
| CKD-EPI (mL/min/1.73 m 2 ) | 140 (97) | ||
| < 60 | 19 (14) | - | |
| > 120 | 16 (11) | - | |
| 60 - 120 | 100 (71) | 62.4 (48.9 - 70.9) | 20.0-90.6 |
| Pathology | |||
| HL | 28 (19) | - | |
| NHL | 86 (59) | - | |
| Myeloma | 31 (22) | - | |
| Lymphoma | |||
| Ann Arbor stage* I-II | 37/97 | - | |
| FLIPI < 2 | 8/13 | - | |
| MIPI < 3 | 3/6 | - | |
| IPI < 3 | 18/29 | - | |
| Myeloma | |||
| ISS index I | 16/25 | - | |
| PCLI (%) | 24 | 0.23 (0.10 - 0.61) | |
| Ratio tumoral/normal plasmocytes | 27 | 22.8 (6.4 - 49.0) |
Cachexia prevalence
The average time interval between the two weight measurements was 8 months (i.e. treatment time). Fifty-five out of 145 patients (38%) present a cachectic state at the end of treatment ( Fig. 1 ): 11 with a weight loss of severity 0 (7.5% of 145 patients), 17 with a severity 1 (12%), 11 with a severity 2 (7.5%), 13 with a severity 3 (9%) and three with a severity 4 (2%).

Risk factors for cachexia and patients’ outcome
Classic disease prognostic scores were not significantly associated with cachexia ( Table 2 ). Among the biological and clinical data, only CRP seems to be associated with cachexia. The ROC curves were used to determine a threshold value of 54.0 mg/L for CRP level at diagnosis that discriminated between patients who were cachectic or not cachectic during treatment. A CRP level greater than 54.0 mg/L at diagnosis appears to be a risk factor for cachexia during treatment (P = 0.023, odds ratio (OR): 5.94, CI: 1.55 - 39.14). These results were confirmed by bootstrap analyse, and a CRP > 54 mg/L still appears to be a risk factor for cachexia (OR: 8.17, CI: 3.44 - 19.41) ( Table 2 ). Nine patients died as a result of treatment and 34 relapsed (median: 26.3 months; min.: 7.3; max.: 45.0; interquartile range (IQR): 18.5 - 33.2). At diagnosis, CRP > 54 mg/L appears to be a risk factor for relapse (P = 0.012, HR: 2.96, CI: 1.22 - 7.14), as well as Ann Arbor stage (P = 0.033, HR: 0.32, IQR: 0.11 - 0.96).
Table 2
Association Between Parameters Measured at Diagnosis and Cachexia During Treatment
*For all lymphomas. CI: confidence interval; CRP: C-reactive protein; DLBCL: diffuse large B-cell lymphoma; FLIPI: follicular lymphoma international prognostic index; IPI: international prognostic index for other lymphoma; ISS: international scoring system for multiple myeloma; HL: Hodgkin lymphoma; MIPI: mantle cell international prognostic index; NHL: non-Hodgkin lymphoma; OR: odds ratio; PCLI: plasma cell labeling index.
| Original data | Bootstrap analysis (1,000 replicates) | ||
| OR (95% CI) | P | Mean OR (95% CI) | |
| Total proteins > 72.5 g/L, n = 54/122 | 1.62 (0.77 - 3.47) | 0.208 | 1.48 (1.38 - 1.58) |
| Albumin > 41.3 g/L, n = 43/122 | 1.21 (0.56 - 2.65) | 0.635 | 1.39 (1.29 - 1.49) |
| CRP > 54.0 mg/L, n = 16/114 | 5.94 (1.55 - 39.14) | 0.023 | 8.17 (3.44 - 19.41) |
| Hemoglobin > 12.0 g/L, n = 90/143 | 1.39 (0.69 - 2.79) | 0.353 | 1.14 (1.03 - 1.27) |
| Creatinine > 93.5 µmol/L, n = 26/141 | 0.30 (0.12 - 0.73) | 0.009 | 0.78 (0.64 - 0.96) |
| Pathology | |||
| HL, n = 30 | Ref | NA | |
| NHL, n = 84 | 0.45 (0.17 - 1.16) | 0.098 | NA |
| Myeloma, n = 31 | 0.37 (0.12 - 1.11) | 0.077 | NA |
| Treatment per pathology | |||
| HL, n = 30 | Ref | NA | |
| DLBCL, n = 35 | 0.36 (0.12 - 1.06) | 0.063 | NA |
| Other lymphoma, n = 49 | 0.52 (0.19 - 1.46) | 0.217 | NA |
| Myeloma, n = 31 | 0.34 (0.12 - 1.11) | 0.077 | NA |
| Lymphoma | |||
| Ann Arbor stage* I-II, n = 37/97 | 1.15 (0.49 - 2.73) | 0.752 | 1.37 (1.26 - 1.49) |
| FLIPI < 2, n = 8/13 | 0.36 (0.01 - 10.68) | 0.509 | NA |
| MIPI < 3, n = 3/6 | 6.29 × 10 8 (0 - NA) | 0.997 | NA |
| IPI < 3, n = 18/29 | 0.38 (0.08 - 1.65) | 0.202 | 0.74 (0.52 - 1.06) |
| Myeloma | |||
| ISS index I, n = 16/25 | 2.89 (0.24 - 67.90) | 0.414 | NA |
| PCLI < 0.63 (%), n = 18/24 | 0.20 (0.01 - 1.58) | 0.142 | NA |
| Ratio tumoral/normal plasmocytes < 8.15 , n = 9/27 | 0.51 (0.09 - 2.57) | 0.350 | 0.76 (0.47 - 1.24) |
Discussion
CRP level at diagnosis: a risk factor for cachexia at the end of treatment of lymphoma or myeloma
High CRP level at cancer diagnosis appears to be a cachexia risk factor for 38% of patients losing weight during hematological treatment. These results are consistent with our bootstrap analysis, but had never been highlighted in hematology. Indeed, the increase in inflammation level, symbolized by an increase in CRP level, is one of the major mechanisms of cachexia that can lead to loss of muscle mass and increase mortality risk [ 1 , 12 ].
CRP level at diagnosis: a risk factor for relapse of lymphoma or myeloma
In addition, high CRP level at diagnosis appears to be a risk factor for relapse and therefore a risk factor for early mortality. This relationship could reflect a cachexia not previously diagnosed or sarcopenia (i.e. muscle deconditioning linked to advanced age [ 13 ]). Indeed, inflammation could reflect sarcopenia development [ 14 ] instead of cancer-related cachexia, seeing that the median age of the patients in the present study is 62 years. Finally, a high level of inflammation could also reflect advanced stages in cancer at diagnosis [ 15 ]. These data can enable clinicians to identify patients at risk for cachexia and implement preventive strategies to reduce its prevalence.
Limitations
This study presents different limitations or bias. Cachexia was only assessed at the end of treatment. We have no follow-up of the evolution of patients’ weight loss during treatment. Furthermore, we used only weight loss to diagnose cachexia, which can be insufficient [ 16 ]. Computed tomography scan or magnetic resonance imaging could be used due to their higher precision about body composition.
Perspectives
In our study, 38% of patients are cachectic during treatment for a lymphoma or a myeloma. Beyond the probable changes in quality of life, we suggest also that these patients can present an increased risk of mortality [ 2 , 17 ]. With the identification of high CRP level as a diagnostic cachexia risk factor and a relapse risk factor, the implementation of preventive strategies is possible, favorable and recommended [ 12 ]. Indeed, cachexia is a phenomenon that is not completely reversible, whose main mechanisms are an increase in the level of inflammation and a negative imbalance in protein synthesis [ 1 ]. The practice of physical activity, and particularly adapted physical activity, would be one of the ways to prevent its appearance and is part of the management recommendations [ 12 , 18 , 19 ].
Conclusion
This study has highlighted that high CRP level at diagnosis seems to be a risk factor for cachexia during treatment and a risk factor for relapse.
None.
Financial Disclosure
This work is supported by a public grant overseen by the European funds of regional development (reference no.:2015 009260/LR 0001082).
Conflict of Interest
All authors declare that they have no conflict of interest.
Informed Consent
Each patient provided a written informed consent.
Author Contributions
Joris Mallard, Anne Laure Gagez and Guillaume Cartron have written the article. Guillaume Cartron worked on the study design and Pierre Louis Bernard, Aline Herbinet, Jonathan Maury and Cedric Baudinet read and approuve the final manuscript, and help for the results analysis. All authors accepted the final version of the manuscript.
References
1 Fearon K Strasser F Anker SD Bosaeus I Bruera E Fainsinger RL Jatoi A et al Definition and classification of cancer cachexia: an international consensus Lancet Oncol 2011 12 5 489 495 10.1016/S1470-2045(10)70218-7 21296615
2 Martin L Senesse P Gioulbasanis I Antoun S Bozzetti F Deans C Strasser F et al Diagnostic criteria for the classification of cancer-associated weight loss J Clin Oncol 2015 33 1 90 99 10.1200/JCO.2014.56.1894 25422490
3 Aoyagi T Terracina KP Raza A Matsubara H Takabe K Cancer cachexia, mechanism and treatment World J Gastrointest Oncol 2015 7 4 17 29 10.4251/wjgo.v7.i4.17 25897346 PMC4398892
4 Evans WJ Morley JE Argiles J Bales C Baracos V Guttridge D Jatoi A et al Cachexia: a new definition Clin Nutr 2008 27 6 793 799 10.1016/j.clnu.2008.06.013 18718696
5 Morley JE Thomas DR Wilson MM Cachexia: pathophysiology and clinical relevance Am J Clin Nutr 2006 83 4 735 743 10.1093/ajcn/83.4.735 16600922
6 Pressoir M Desne S Berchery D Rossignol G Poiree B Meslier M Traversier S et al Prevalence, risk factors and clinical implications of malnutrition in French Comprehensive Cancer Centres Br J Cancer 2010 102 6 966 971 10.1038/sj.bjc.6605578 20160725 PMC2844030
7 Durie BG Harousseau JL Miguel JS Blade J Barlogie B Anderson K Gertz M et al International uniform response criteria for multiple myeloma Leukemia 2006 20 9 1467 1473 10.1038/sj.leu.2404284 16855634
8 Cheson BD Fisher RI Barrington SF Cavalli F Schwartz LH Zucca E Lister TA et al Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification J Clin Oncol 2014 32 27 3059 3068 10.1200/JCO.2013.54.8800 25113753 PMC4979083
9 Youden WJ Index for rating diagnostic tests Cancer 1950 3 1 32 35 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3 15405679
10 Efron B Bootstrap methods: another look at the jackknife Ann Stat 1979 7 1 26 10.1214/aos/1176344552
11 Efron B Tibshirani R Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy Stat Sci 1986 1 54 75 10.1214/ss/1177013815
12 Gould DW Lahart I Carmichael AR Koutedakis Y Metsios GS Cancer cachexia prevention via physical exercise: molecular mechanisms J Cachexia Sarcopenia Muscle 2013 4 2 111 124 10.1007/s13539-012-0096-0 23239116 PMC3684702
13 Muscaritoli M Anker SD Argiles J Aversa Z Bauer JM Biolo G Boirie Y et al Consensus definition of sarcopenia, cachexia and pre-cachexia: joint document elaborated by Special Interest Groups (SIG) "cachexia-anorexia in chronic wasting diseases" and "nutrition in geriatrics" Clin Nutr 2010 29 2 154 159 10.1016/j.clnu.2009.12.004 20060626
14 Bowen TS Schuler G Adams V Skeletal muscle wasting in cachexia and sarcopenia: molecular pathophysiology and impact of exercise training J Cachexia Sarcopenia Muscle 2015 6 3 197 207 10.1002/jcsm.12043 26401465 PMC4575550
15 Marelli G Sica A Vannucci L Allavena P Inflammation as target in cancer therapy Curr Opin Pharmacol 2017 35 57 65 10.1016/j.coph.2017.05.007 28618326
16 Roeland EJ Ma JD Nelson SH Seibert T Heavey S Revta C Gallivan A et al Weight loss versus muscle loss: re-evaluating inclusion criteria for future cancer cachexia interventional trials Support Care Cancer 2017 25 2 365 369 10.1007/s00520-016-3402-0 27628947
17 Farkas J von Haehling S Kalantar-Zadeh K Morley JE Anker SD Lainscak M Cachexia as a major public health problem: frequent, costly, and deadly J Cachexia Sarcopenia Muscle 2013 4 3 173 178 10.1007/s13539-013-0105-y 23539127 PMC3774921
18 Antoun S Raynard B Muscle protein anabolism in advanced cancer patients: response to protein and amino acids support, and to physical activity Ann Oncol 2018 29 suppl_2 ii10 ii17 10.1093/annonc/mdx809 29506227
19 Radbruch L Elsner F Trottenberg P Strasser F Fearon K Clinical practice guidelines on Cancer Cachexia in advanced cancer patients Eur Palliat Care Resarch Collab 2010