Relationship between Pretreatment Serum Albumin Levels with the Risk of Malignant Pleural Mesothelioma
DOI:
https://doi.org/10.6000/1929-6029.2020.09.08Keywords:
Malignant Pleural Mesothelioma, Serum albumin, Gamma distribution, Generalized additive model, Probabilistic ModelingAbstract
Background: Malignant Pleural Mesothelioma (MPM) is a very rare and aggressive form of cancer. Recently it was found that pretreatment Serum Albumin (SA), the main circulating protein in blood is a significant prognostic factor for MPM patients. The objective of this present article is to show the relationship between pretreatment Serum albumin (SA) levels with the risk of MPM.
Methods: Generalized additive model (GAM), an advanced regression analysis method has been introduced here to find this mathematical relationship between the response variable (SA) and the cofactors.
Results: The main determinates of SA are identified - asbestos exposure, hemoglobin, disease diagnosis status (patients having MPM) are the factors having significant association with SA, whereas duration of asbestos exposure, duration of disease symptoms, total protein (TP), Pleural lactic dehydrogenise (PLD), pleural protein (PP), pleural glucose (PG) and C-reactive protein (CRP) are the significant continuous variables for SA. The non-parametric estimation part of this model shows Lactate dehydrogenase (LDH) and Glucose level are the significant smoothing terms. Additionally it is also found that, second and third order interactions between cofactors are highly significant for SA.
Conclusions: The findings of this present work can conclude that - serum albumin may play the role of a very significant prognostic factor for MPM disease and it has been established here through mathematical modeling. Few of the findings are already been exist in MPM research literature whereas some of the findings are completely new in the literature.
References
Wagner JC, Sleggs CA, Marchand P. Diffuse pleural mesothelioma and asbestos exposure in the North Western Cape Province. Br J Ind Med 1960; 17: 260-71. https://doi.org/10.1136/oem.17.4.260 DOI: https://doi.org/10.1136/oem.17.4.260
Favoni RE, Florio T. Combined chemotherapy with cytotoxic and targeted compounds for the management of human malignant pleural mesothelioma. Trends Pharmacol Sci 2011; 32: 463-79. https://doi.org/10.1016/j.tips.2011.03.011 DOI: https://doi.org/10.1016/j.tips.2011.03.011
Dogan AU, Baris YI, DoganM, Emri S, Steele I, Elmishad AG, et al. Genetic predisposition to fiber carcinogenesis causes a mesothelioma epidemic in Turkey. Cancer Res.2006; 66: 5063-8. https://doi.org/10.1158/0008-5472.CAN-05-4642 DOI: https://doi.org/10.1158/0008-5472.CAN-05-4642
Zervos MD, Bizekis C, Pass HI. Malignant mesothelioma 2008. Current Opinion in Pulmonary Medicine 2008; 14: 303-309. https://doi.org/10.1097/MCP.0b013e328302851d DOI: https://doi.org/10.1097/MCP.0b013e328302851d
Yazicioglu S, Ilcayto R, Balci K, Sayli BS, Yorulmaz B. Pleural calcification, pleural mesotheliomas and bronchial cancers caused by tremolite dust. Thorax1980; 35: 564-569. https://doi.org/10.1136/thx.35.8.564 DOI: https://doi.org/10.1136/thx.35.8.564
McConnochie K, Simonato L, Mavrides P, Christofides P, Pooley FD. Mesothelioma in Cyprus: the role of tremolite. Thorax 1987; 42: 342-347. https://doi.org/10.1136/thx.42.5.342 DOI: https://doi.org/10.1136/thx.42.5.342
Constantopoulos SH, Theodoracopoulos P, Dascalopoulos G, Saratzis N, Sideris K. Metsovo lung outside Metsovo. Chest 1991; 99: 1158-1161. https://doi.org/10.1378/chest.99.5.1158 DOI: https://doi.org/10.1378/chest.99.5.1158
Nishimura SL, Broaddus VC. Asbestos-induced pleural disease. CliniesIn Chest Medicine 1998; 19: 311-329. https://doi.org/10.1016/S0272-5231(05)70079-4 DOI: https://doi.org/10.1016/S0272-5231(05)70079-4
Metintas M, Ozdemir N, Hillerdal G, Ucgun I, Metintas S, et al. Environmental asbestos exposure and malignant pleural mesothelioma. Respiratory Medicine 1999; 93: 349-355. https://doi.org/10.1016/S0954-6111(99)90318-9 DOI: https://doi.org/10.1016/S0954-6111(99)90318-9
Metintas S, Metintas M, Ucgun I, Oner U. Follow-up of a Turkish cohort living in a rural area. Chest 2002; 22: 2224-2229. https://doi.org/10.1378/chest.122.6.2224 DOI: https://doi.org/10.1378/chest.122.6.2224
Metintas M, Metintas S, Ak G, Erginel S, Alatas F, Kurt E, et al. Epidemiology of pleural. mesothelioma in a population with non-occupational asbestos exposure. Respirology 2008; 13: 117-121. https://doi.org/10.1111/j.1440-1843.2007.01187.x DOI: https://doi.org/10.1111/j.1440-1843.2007.01187.x
Peto J, Decarli A, Vecchia La. C, Levi F, Negri E. The European mesothelioma epidemic. British Journal of Cancer 1999; 79: 666-672. https://doi.org/10.1038/sj.bjc.6690105 DOI: https://doi.org/10.1038/sj.bjc.6690105
Burgers JA, Damhuis RA. Prognostic factors in malignant mesothelioma. Lung Cancer 2004; 45: S49-54. https://doi.org/10.1016/j.lungcan.2004.04.012 DOI: https://doi.org/10.1016/j.lungcan.2004.04.012
McDonald JC, McDonald AD. The epidemiology of mesothelioma in historical context. European Respiratory Journal 1996; 9: 1932-1942. https://doi.org/10.1183/09031936.96.09091932 DOI: https://doi.org/10.1183/09031936.96.09091932
Spirtas R, Beebe GW, Connelly RR, Wright WE, Peters JM, et al. Recent trends in mesothelioma incidence in the United States. American Journal of Industrial Medicine 1986; 9: 397-407. https://doi.org/10.1002/ajim.4700090502 DOI: https://doi.org/10.1002/ajim.4700090502
Peto J, Hodgson JT, Matthews K, Jones JR. Continuing increase in mesothelioma mortality in Britain. Lancet 1995; 345: 535-539. https://doi.org/10.1016/S0140-6736(95)90462-X DOI: https://doi.org/10.1016/S0140-6736(95)90462-X
Leigh J, Corvalan CF, Grimwood A, Berry G, Ferguson DA, et al. The incidence of malignant mesothelioma in Australia 1982-1988. American Journal of Industrial Medicine 1991; 20: 643-655. https://doi.org/10.1002/ajim.4700200507 DOI: https://doi.org/10.1002/ajim.4700200507
National Mesothelioma committee. http://www.mesothelioma-tr.org (accessed November 10, 2014).
Mesothelioma News (accepted: 29.06.11) http://www.meso-theliomanews.com/medical/mesothelioma-diagnosis/pleural mesothelioma
Tanrikulu AC, Senyigit A, Dagli CE, Babayigit C, Abakay A. Environmental malignant pleural mesothelioma in Southeast Turkey. Saudi Medical Journal 2006; 27(10): 1605-1607.
Senyiğit A, Bayram H, Babayiğit C, Topcu F, Nazaroğlu H, Bilici A, et al. Malignant pleural mesothelioma caused by environmental exposure to asbestos in the Southeast of Turkey: CT findings in 117 patients. Respiration 2000; 67(6): 615-622. https://doi.org/10.1159/000056290 DOI: https://doi.org/10.1159/000056290
Senyiğit A, Babayiğit C, Gökirmak M, Topçu F, Asan E, Coşkunsel M, et al. Incidence of malignant pleural mesothelioma due to environmental asbestos fiber exposure in the southeast of Turkey. Respiration 2000; 67(6): 610-614. https://doi.org/10.1159/000056289 DOI: https://doi.org/10.1159/000056289
OrhanEr, Tanrikulu AC, Abakay A, Temurtas F. An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease. Computers & Electrical Engineering 2011; 38: 75-81. https://doi.org/10.1016/j.compeleceng.2011.09.001 DOI: https://doi.org/10.1016/j.compeleceng.2011.09.001
OrhanEr, Tanrikulu AC, Abakay A. Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma. Dicle Medical Journal 2015; 42(1): 5-11. https://doi.org/10.5798/diclemedj.0921.2015.01.0520 DOI: https://doi.org/10.5798/diclemedj.0921.2015.01.0520
Yao ZH, Wan YY, Liu QH, Lin DJ, et al. Serum albumin as a significant prognostic factor in patients with malignant pleural mesothelioma. Tumor Biology 2014; 35(7): 6839-6845. https://doi.org/10.1007/s13277-014-1938-5 DOI: https://doi.org/10.1007/s13277-014-1938-5
Human serum albumin (From Wikipedia, the free encyclopedia) https://en.wikipedia.org/wiki/Human_serum_albumin
R.H. Myers., D.C. Montgomery., G.G.Vining, Generalized Linear Models with Applications in Engineering and the Sciences. New York: John Wiley & Sons; 2002.
Das RN, Mukherjee S. Joint Mean-Variance Overall Survival Time Fitted Models from Stage III Non-Small Cell Lung Cancer.Epidemiology (Sunnyvale) 2017; 7: 296.
Das RN, Mukherjee S, Panda RN. Association between Body Mass Index and Cardiac Parameters of Worcester Heart Attack Study.BAOJ Cell Mol Cardio 2016; 2: 006.
Mukherjee S, Kapoor S, Banerjee P. Diagnosis and Identification of Risk Factors for Heart Disease Patients Using Generalized Additive Model and Data Mining Techniques. J Cardiovasc Disease Res 2017; 8(4): 137-44. https://doi.org/10.5530/jcdr.2017.4.31 DOI: https://doi.org/10.5530/jcdr.2017.4.31
D. Ruppert., M.P.Wand., R.J.Carroll, Semi parametric Regression, first ed. Cam¬bridge University Press New York; 2003. https://doi.org/10.1017/CBO9780511755453 DOI: https://doi.org/10.1017/CBO9780511755453
T.Hastie., R.Tibshirani, Generalized additive models. John Wiley & Sons, Inc.; 1990.
Hastie T, Tibshirani R. Generalized additive models for medical research. Statisti¬cal Methods in Medical Research 1995; 4: 187-196. https://doi.org/10.1177/096228029500400302 DOI: https://doi.org/10.1177/096228029500400302
SN.Wood, Generalized Additive Models: An Introduction with R. London: Chap¬man and Hall; 2006. https://doi.org/10.1201/9781420010404 DOI: https://doi.org/10.1201/9781420010404
Currie ID, Durban M, Eilers PH. Generalized linear array models with applica¬tions to multidimensional smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2006; 68(2): 259-80. https://doi.org/10.1111/j.1467-9868.2006.00543.x DOI: https://doi.org/10.1111/j.1467-9868.2006.00543.x
P.J.Green., B.W. Silverma, Nonparametric regression and generalized linear models: a roughness penalty approach. CRC Press; 1993. https://doi.org/10.1201/b15710 DOI: https://doi.org/10.1201/b15710
Ruppert D. Selecting the number of knots for penalized splines. Journal of com¬putational and graphical statistics 2002; 11(4): 735-57. https://doi.org/10.1198/106186002853 DOI: https://doi.org/10.1198/106186002853
Eilers PH, Marx BD. Flexible smoothing with B-splines and penalties. Statistical science 1996; 1: 89-102. https://doi.org/10.1214/ss/1038425655 DOI: https://doi.org/10.1214/ss/1038425655
S.Chatterjee., A.S.Hadi, Regression Analysis by Example, fifth ed. John Wiley & Sons, New Jersey; 2006. https://doi.org/10.1002/0470055464 DOI: https://doi.org/10.1002/0470055464
Nelder JA, Lee Y. Generalized linear models for the analysis of Taguchi-type experiments. Applied Stochastic Models and Data Analysis 1991; 7: 107-120. https://doi.org/10.1002/asm.3150070110 DOI: https://doi.org/10.1002/asm.3150070110
Y.Lee., J.A.Nelder., Y.Pawitan, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood. Chapman & Hall, London (2006). https://doi.org/10.1201/9781420011340 DOI: https://doi.org/10.1201/9781420011340
Espinosa E, Feliu J, Zamora P, Gonzalez Baron M, Sanchez JJ, Ordonez A, et al. Serum albumin and other prognostic factors related to response and survival in patients with advanced non-small cell lung cancer. Lung Cancer 1995; 12: 67-76. https://doi.org/10.1016/0169-5002(95)00407-R DOI: https://doi.org/10.1016/0169-5002(95)00407-R
Lu HJ, Chen KW, Tzeng CH, Liu JH, Chiou TJ, Yen CC, et al. Evaluation of prognosis for carcinoma of unknown origin in elderly patients. Oncology 2012; 83: 24-30. https://doi.org/10.1159/000337983 DOI: https://doi.org/10.1159/000337983
Wang CY, Hsieh MJ, Chiu YC, Li SH, Huang HW, Fang FM, et al. Higher serum C-reactive protein concentration and hypoalbuminemia are poor prognostic indicators in patients with esophageal cancer undergoing radiotherapy. RadiotherOncol 2009; 92: 270-5. https://doi.org/10.1016/j.radonc.2009.01.002 DOI: https://doi.org/10.1016/j.radonc.2009.01.002
Lambert JW, Ingham M, Gibbs BB, Given RW, Lance RS, Riggs SB. Using preoperative albumin levels as a surrogate marker for outcomes after radical cystectomy for bladder cancer. Urology 2013; 81: 587-92. https://doi.org/10.1016/j.urology.2012.10.055 DOI: https://doi.org/10.1016/j.urology.2012.10.055
Spirtas R, Heineman EF, Bernstein L, et al Malignant mesothelioma: attributable risk of asbestosexposure. Occupational and Environmental Medicine 1994; 51: 804-811. https://doi.org/10.1136/oem.51.12.804 DOI: https://doi.org/10.1136/oem.51.12.804
Whitwell F, Rawcliffe RM Diffuse malignant pleural mesothelioma and asbestos exposure Thorax 1971; 26: 6-22. https://doi.org/10.1136/thx.26.1.6 DOI: https://doi.org/10.1136/thx.26.1.6
Berardi R, Fiordoliva I, De Lisa M, Ballatore Z, et al Clinical and pathologic predictors of clinical outcome of malignant pleural mesothelioma. Tumori 2016; 102(2): 190-5. https://doi.org/10.5301/tj.5000418 DOI: https://doi.org/10.5301/tj.5000418
Momenin N, Colletti PM, Kaptein EM. Low pleural fluid-to-serum glucose gradient indicatespleuroperitoneal communication in peritoneal dialysis patients: presentation of two cases and a review of the literature. Nephrol Dial Transplant 2012; 27: 1212-1219. https://doi.org/10.1093/ndt/gfr393 DOI: https://doi.org/10.1093/ndt/gfr393
Limthongkul S. The pathogenesis of low pleural fluid glucose in acidotic malignant pleural effusions. J Med Assoc Thai. 1989; 72(9): 492-7.
Khaleeq G, Musani AI. Emerging paradigms in the management of malignant pleural effusions. Respir Med 2008; 102(7): 939-48. https://doi.org/10.1016/j.rmed.2008.01.022 DOI: https://doi.org/10.1016/j.rmed.2008.01.022
Nojiri S1, Gemba K, Aoe K, Kato K, Yamaguchi T, Sato T, Kubota K, Kishimoto T. Survival and prognostic factors in malignant pleural mesothelioma: a retrospective study of 314 patients in the west part of Japan. Jpn J ClinOncol. 2011; 41(1): 32-9. https://doi.org/10.1093/jjco/hyq159 DOI: https://doi.org/10.1093/jjco/hyq159
Ghanim B, Hoda MA, Winter MP, Berger LW. Pretreatment Serum C-Reactive Protein Levels Predict Benefit From Multimodality Treatment Including Radical Surgery in Malignant Pleural Mesothelioma. Annals of surgery 2012; 256(2): 357-62. https://doi.org/10.1097/SLA.0b013e3182602af4 DOI: https://doi.org/10.1097/SLA.0b013e3182602af4
Zhuo Y, Lin L, Wei S, Zhang M. Pretreatment elevated serum lactate dehydrogenase as a significant prognostic factor in malignant mesothelioma- A meta-analysis. Medicine (Baltimore) 2016; 95(52): e5706. https://doi.org/10.1097/MD.0000000000005706 DOI: https://doi.org/10.1097/MD.0000000000005706
Mukherjee S. Malignant Mesothelioma Disease Diagnosis using Data Mining Techniques. Applied Artificial Intelligence 2018; 32(3): 293-308. https://doi.org/10.1080/08839514.2018.1451216 DOI: https://doi.org/10.1080/08839514.2018.1451216
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