SciELO - Scientific Electronic Library Online

vol.35 número2Costes asociados a la desnutrición relacionada con la enfermedad y su tratamiento: revisión de la literaturaDisruptores endocrinos en nutrición artificial índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • En proceso de indezaciónCitado por Google
  • No hay articulos similaresSimilares en SciELO
  • En proceso de indezaciónSimilares en Google


Nutrición Hospitalaria

versión On-line ISSN 1699-5198versión impresa ISSN 0212-1611

Nutr. Hosp. vol.35 no.2 Madrid mar./abr. 2018 


Clinical-nutritional, inflammatory and oxidative stress predictors in hemodialysis mortality: a review

Predictores clínico-nutricionales, inflamatorios y de estrés oxidativo en la mortalidad por hemodiálisis: una revisión

Andreza-P.S. Epifânio1  , Karla-P. Balbino1  , Sônia-M.R. Ribeiro1  , Sylvia-C.C. Franceschini1  , Helen Hermana-M.-Hermsdorff1 

1Department of Nutrition and Health. Universidade Federal de Viçosa. Viçosa, Brazil


The evaluation of clinical-nutrition status is essential to increase life quality and improve clinical outcomes of patients in hemodialysis (HD). In the absence of a gold standard, the goal of this integrative review was to present and discuss the latest scientific literature on the ability of clinical-nutritional indicators and inflammatory and oxidative stress markers to predict morbidity and mortality in HD. In this context, the lean and fat mass indexes have become good predictors of mortality in HD individuals, regardless of BMI. Subjective scoring systems have been more sensitive to malnutrition, and altogether anthropometric indicators may result in an early detection of mortality risk in this population. On the other hand, inflammation in HD, as assessed by C-reactive protein, is not only related to cardiometabolic alterations, but it is also one of the key-points in the development of malnutrition, exacerbated by the state of oxidative stress, which has been identified in this group by the increase of the serum levels of gamma-glutamyl transferase and malondialdehyde.

Key words: Chronic kidney disease; Malnutrition; Hemodialysis; Oxidative stress; Inflammation; Mortality


La evaluación del estado clínico-nutricional es esencial para aumentar la calidad de vida y mejorar los resultados clínicos de los pacientes en hemodiálisis (HD). En ausencia de un patrón oro, el objetivo de esta revisión integrativa fue presentar y discutir la literatura científica más reciente sobre la capacidad de indicadores clínico-nutricionales, y marcadores de estrés oxidativo e inflamatorio, en la predicción de morbilidad y mortalidad en HD. En este contexto, los índices de masa grasa y grasa se han convertido en buenos predictores de mortalidad en individuos con HD, independientemente del IMC. Los sistemas de puntuación subjetiva han sido más sensibles a la desnutrición y, en conjunto, los indicadores antropométricos pueden resultar en una detección temprana del riesgo de mortalidad en esta población. Por otro lado, la inflamación en HD, evaluada por la proteína C reactiva, no solo se relaciona con alteraciones cardiometabólicas, sino que también es uno de los puntos clave en el desarrollo de la desnutrición, exacerbada por el estado de estrés oxidativo, que ha sido identificado en este grupo por el aumento de los niveles séricos de gamma-glutamil transferasa y malondialdehído.

Palabras clave: Enfermedad renal crónica; Desnutrición; Hemodiálisis; Estrés oxidativo; Inflamación; Mortalidad


End-stage renal disease (ESRD), as well as hemodialysis (HD), treatment are marked by clinical-nutritional conditions that increase morbidity and mortality and reduce life quality of patients with this chronic disease 1,2,3. In this sense, hypoalbuminemia, which is also affected by inflammation status and age, is not able to accurately reflect the nutritional state 4. In turn, body mass index (BMI) has been associated with better prognosis of individuals in HD, which is known as "reverse epidemiology" 5,6,7. This relationship is influenced by characteristics such as age, inflammation and related-comorbidities 8. In addition, subjective global assessment may help predict mortality, when properly applied 9.

On the other hand, the progressive deterioration of renal function leads to physiological dysfunctions such as changes in cellular energy metabolism, protein catabolism, insulin resistance and synthesis of mediators of inflammation and oxidative stress 10,11,12. Still, the complex inter-relationship among nutritional indicators and inflammatory and oxidative markers remains the object of investigation on the premise to get better prediction of mortality in patients with ESRD in HD 13.

Overall, the objective of this integrative review was to present and discuss the latest scientific literature on the ability of clinical-nutrition indicators, and inflammatory and oxidative stress markers to predict morbidity and mortality in HD individuals.


A review search was carried out from the databases LILACS, Medline, PubMed, SciELO and BIREME, using the keywords "hemodialysis and mortality", "chronic renal failure", "ESRD", "biochemical markers", "inflammatory markers", "oxidative stress markers", "anthropometric evaluation", "nutritional status", "subjective evaluation", combined with " mortality". Publications carried out from 2010 to 2016 with HD individuals were included.

From the selected articles, a reverse search was carried out for studies whose titles would be eligible. Subsequently, the abstracts were read to ensure compliance with the inclusion criteria and then each article was entirely read to confirm its eligibility. Cohort studies with adults and elderly individuals in HD treatment were included. Articles that were not published in full or those presented as tutorials, editorials, news, letters or comments, reviews and experimental testing were excluded. In addition, studies of acute renal disease, chronic kidney disease under conservative treatment or treatment with peritoneal dialysis, transplantation and nephrotic syndrome were excluded.

During the initial selection process, 155 articles were found, from which 108 were excluded, as shown by Figure 1. Selected papers are related to anthropometric indicators (four studies), subjective global assessment (five studies), oxidative stress markers (two studies), and inflammatory markers (four studies) as predictors of mortality in HD individuals.

Figure 1 Flowchart showing selected studies for this review (2010-2016). 


The 15 selected studies used as anthropometric indicators, BMI, waist circumference (WC), skinfold thickness, arm circumference (AC), Mid-arm circumference (MAC), mid-arm muscle circumference (MAMC) lean tissue index (LTI) and fat tissue index (FTI) and total body fat (TBF), assessed by bioelectrical impedance analysis (BIA). Subjective methods to nutritional assessment were: Subjective Global Assessment (SGA), Modified Subjective Global Assessment (mGSA), Objective Score of Nutrition on Dialysis (OSND), International Society of Renal Nutrition and Metabolism (ISRNM), Malnutrition-Inflammation Score (MIS) and Geriatric Nutritional Risk Index (GNRI).

Hemoglobin, albumin, calcium, phosphorus, parathyroid hormone (PTH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase, total cholesterol (TC), triglycerides (TG), uric acid, urea, creatinine, Kt/V urea (Kt/V), ferritin and transferrin were used as metabolic markers.

The oxidative stress markers used in the studies were plasma concentrations of gamma-glutamyltransferase (GGT), nitric oxide (NO) and malondialdehyde (MDA); while the inflammatory markers were plasma concentrations of C-reactive protein (CRP), interleukin-6 (IL-6), tumor-necrosis factor alpha (TNF-) and theirs receptors (TNFR1 and TNFR 2).

The articles were separated in accordance with categories of predictors of mortality in HD as follows: anthropometric indicators, subjective scores, oxidative stress and inflammation markers (Table I, Table II, Table III, Table IV).

Table I Comparative studies of anthropometric predictors in the mortality of hemodialysis patients (cohort studies, 2010-2016) 

AC: arm circumference; BMI: body mass index; BSF: bicipital skinfold; CRP: C-reactive protein; FTI: fat tissue indices; HD: hemodialysis; LTI: lean tissue indices; MAC: mid-arm circumference; MAMC: mid-arm muscle circumference; TSF: triceps skinfold; SSF: subscapular skinfold.

Table II Comparative studies of subjective predictors in the mortality of hemodialysis patients (cohort studies, 2010-2016) 

AC: arm circumference; BMI: body mass index; HD: hemodialysis; ISRNM: International Society of Nutrition and Metabolism Renal; MAC: mid-arm circumference; MIS: malnutrition-inflammation score; OSND: Objective Score of Nutrition on Dialysis; SGA: subjective global assessment; TSF: triceps skinfold.

Table III Comparative studies of oxidative stress predictors in the mortality of hemodialysis patients (cohort studies, 2010-2016) 

ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index; CRP: C-reactive protein; Cca: corrected calcium/albumin; GGT: gamma-glutamyltransferase; HD: hemodialysis; MDA: malondialdehyde; PC: protein carbonyls; iPTH: intact parathyroid hormone.

Table IV Comparative studies of inflammatory predictors in the mortality of hemodialysis patients (cohort studies, 2010-2016) 

BMI: body mass index; CRP: C-reactive protein; GNRI: geriatric nutritional risk index; HD: hemodialysis; IL-6: interleukin-6; TNF-: tumor necrosis factor-alfa; sTNFR1 and 2: tumor necrosis factor receptors 1 and 2; VFA: visceral fat area; SFA: subcutaneous fat area; SGA: Subjective Global Assessment.


Studies show that protein-energy malnutrition is present in a range of 23% to 76% of individuals in HD 14. Unlike the general population, a higher BMI in these individuals was associated with better survival, fact presented as reverse epidemiology in the literature 15. However, BMI measures did not present differences between lean and fat mass, making it difficult to quantitatively understand which components of body composition are related to survival in HD individuals with ESRD 16. In order to evaluate body compartments, the studies use the Body Composition Monitor (BCM) based on spectroscopic bioimpedance (BIS). Bioimpedance methodologies, the BCM expresses body composition as a three-compartment model, providing overhydration, lean tissue index (LTI), and fat tissue index (FTI), whereby LTI and FTI are the respective tissue masses normalized to height squared. Also, LTI and FTI percentiles (< 10th percentile [low]; 10th-90th percentile [normal]; and > 90th percentile [high]) relative to an age- and sex-matched healthy population are supplied. The three-compartment model of the BCM has been validated against standard reference methods for assessment of fluid status and body composition in dialysis patients. Castelhano et al. 17 have indicated LTI and FTI reference values (10th and 90th percentiles), adjusted for age and sex, in which lean tissue below the 10th percentile was associated with higher mortality in HD (OR: 1.57). Similarly, considering age, sex and diabetes mellitus, higher percentage of lean body mass was associated with better survival 18,19 in HD individuals for the same population.

Moreover, Rosenbergeret et al. 20 evaluated the relationship between scarcity of lean tissue (expressed as LTI below the 10th percentile) and survival in HD. The possible causes to worse survival 21,22 included malnutrition and inflammation signal 21 as lean mass stocks uremic toxins, in which case, the smaller amount of lean tissue could indicate a higher concentration of uremic toxins in the blood 21,23.

The relation of muscle mass and increased mortality caused by infections in HD individuals is well known, since subjects with ESRD develop an acquired immune deficiency that may be exacerbated in those with low muscle mass and hipoalbuminemia 24. In this sense, Marcellid et al. 16 had found that body composition by multifrequency bioimpedance and LTI and FTI between the 10th and 90th percentiles were associated with improved survival, while the low LTI and FTI, and especially the combination of both, were associated with increased mortality, regardless of BMI.

Other anthropometric and body composition indicators do not qualify as common practices. TBF and simple anthropometric measures, such as MAC, AC and triceps skinfold thickness (TSF) are generally used 25,26. Stosovic et al. 27 found that the TBF, TSF, AC and MAC were independent predictors of mortality for individuals in HD. The predictive values ​​of all these anthropometric indicators for mortality were similar, except for BMI. When these indicators were altogether tested, the AC was the indicator with the greatest power to predict mortality and showed an average reduction of 8.0% in the proportional mortality risk.

Furthermore, the reduction of muscle mass in this population 28 is associated with hypoalbuminemia and PEM, which indicate inflammatory conditions 29. Su et al. 24 have reported that the decline in lean body mass over time, estimated by MAC and skinfold measurements were associated with higher risk of all specific causes of mortality in HD individuals. These relationships were particularly strong in those with BMI < 25 kg/m2.

It is noteworthy that BMI values for individuals in HD are usually higher than for general population, although many of them may present LTI values below percentile 10 30, suggesting that body composition related to lean tissue is more important than BMI isolated. In addition, interventions to keep lean and fat mass suitable are favorable for survival in population with ESRD. Thus, AC measure and the body fat distribution (as LTI and FTI) were altogether promissory predictors of morbidity and mortality in HD, reinforcing the importance of lean and fatty tissues in evaluating the survival of HD patients independently of BMI, making them (mainly AC) good indicators in clinical practice.


In this sense, SGA is a simple method for assessing the nutritional status in many patients, including those with ESRD. Vogt and Caramori 31 had presented prevalence of malnutrition of 26.3% by SGA, 25.2% for MIS (MIS > 8) and 28.8% by criteria based on ISRNM, and malnutrition evaluated by SGA and MIS was able to predict mortality in a period of 15.5 ± 5.4 months. The study of Zuijdewijnet et al. 32 highlighted the SGA and MIS, after comparing eight evaluation tests of nutritional risk, as better predictors of mortality. As results, these studies show the impact of changes in scores of SGA and MIS on clinical outcomes and mortality risk. Another study has shown that an 1-point increase in SGA, 12 months after dialysis, was independently associated with an increase of 34% in all-cause mortality risk when tested with albumin, CRP, and BMI 33. The same finding was discussed by Chan et al. 34, after adjusting for all variables including age, sex, HD time, serum albumin, body mass index and smoking. In addition, they observed that the SGA, considering mild malnutrition (B) and moderate (C) independently predicts mortality.

The study of Beberashvili et al. 35 has found significant associations with hospitalization and mortality in individuals in HD through a comprehensive OSND scoring system. Both variables were significantly correlated with inpatient days and frequency, as well as lean and fat body mass, MIS, blood pressure and IL-6 values 35,36. The results found by Beberashvili et al. 37 indicate that MIS had reliability and good concurrent and predictive validity 37.

Even so, these scoring systems should be considered as complementary clinical markers of malnutrition state, while application of SGA and MIS could result in early detection of malnutrition compared to metabolic and inflammatory markers, or classic anthropometric indicators 38. Therefore, SGA and MIS may represent good tools for application in clinical practice, as they may contribute to an early identification of malnutrition.


The presence of oxidative stress, as well as anthropometric and subjective evaluation, has been related to the increase in morbidity and mortality in cardiac surgical patients, critically ill and renal patients requiring HD 39,40.

In HD individuals, increased oxidative stress results from an imbalance between pro-oxidant activity and anti-oxidant systems, more intensely, contributing to increased morbidity and mortality 41,42. Diabetes mellitus, advanced age, inflammation, excess of uremic toxins, bio-incompatibility of dialysis membranes 43 and intravenous iron therapy 44 are the main causes of increased pro-oxidant activity in these individuals 42.

Excess of oxygen reactive species (ROS) can produce cellular damage, interacting with biomolecules (proteins, lipids and nucleic acids) and, thus, have negative effects on tissue function and structure. ROS can react with polyunsaturated fatty acids that produce lipid hydro peroxides. MDA, a linolenic acid product of decomposition of the main final oxidation reactions of polyunsaturated fatty acids, is a useful indicator to evaluate oxidative damage 45,46. MDA can still interact with DNA and proteins, and can lead to mutagenic and cytotoxic effects and, possibly, is involved in the pathogenesis of various diseases, such as atherosclerosis 47). Low MDA values, which suggest a lower intensity of oxidative stress, are associated with better survival 45.

Rusu et al. 48 observed that the increase in MDA is associated with a higher ratio of albumin corrected calcium (cCA). It is known that elevated cCA is a predictor of mortality, since hypercalcemia and increased ROS can act synergistically in aggravating the severe vascular lesions found in HD individuals. MDA has a high predictive value for the mortality of these individuals and is related to the survival of this population, especially when associated with cardiovascular diseases.

GGT 49, a recognized biomarker for liver disease, was another marker of oxidative stress presented as a predictor of mortality in HD individuals. It is an enzyme with important role in the extracellular catabolism of glutathione, a representative intracellular antioxidant 50. GGT-mediated oxidative stress may be involved in the formation of coronary atherosclerotic plaques and endothelial dysfunction 51,52,53.

In this sense, oxidative stress may play an important role in the pathogenesis of atherosclerosis and cardiovascular risk in ESRD, thus contributing to the increase in mortality. More studies are necessary to identify potential biomarkers in these people. Still, an early evaluation through GGT and MDA may contribute to a better monitoring of oxidative stress presence, which is common among individuals with chronic kidney disease.


Inflammation has recently been recognized as an essential component in ESRD in HD, playing a unique role in its pathophysiology and is responsible, in part, by CVD mortality end all other causes 54. Moreover, inflammation is related to the development of PEM and other comorbidities. In fact, the increase in release or activation of pro-inflammatory cytokines such as IL-6 or TNF-, as well as acute phase protein CRP, may suppress appetite, cause muscle proteolysis and hypoalbuminemia 55. Furthermore, PEM and inflammation contribute independently to hypoalbuminemia and thus increase the risk for mortality in ESRD in HD 56.

Inflammation is often present in individuals in HD, and the use of central venous catheter has been linked to chronic inflammatory state 57. Faria et al. 58 have found that its use as vascular access for HD procedure was independently associated with mortality in patients with high concentrations of CRP and low triglycerides.

In this context, the inflammation as assessed by CRP is present between 30% and 60% of American north and Europeans individuals in dialysis 59. In addition, values of CRP higher than 5 mg/L 60 or 10 mg/L 61,62 have been positively associated to cardiovascular mortality. Inflammatory markers such as TNF- and CRP are powerful independent predictors of risk for atherosclerosis, cardiovascular disease and mortality in HD individuals 63. The study of Nakagawa et al. 64 has shown that TNF- and CRP were positively associated with the causes of cardiovascular mortality, after adjusting for age and sex. When stratified by GNRI, TNF- and CRP were positively associated with all-cause mortality, only in malnourished individuals. This is supported by the finding of Carlsson et al. 65, concerning the slightly higher values of TNFR 1 and 2 in subjects with malnutrition. However, inflammation can elevate risk of mortality in patients with ESRD in HD by increasing cardiovascular risk and malnutrition.

Thus, the inflammation in individuals in HD, particularly evaluated by the CRP, is not only related to the cardiovascular alterations, including atherosclerosis, but it is also one of the key points in the development of PEM, stimulated by the oxidative stress. This fact can be reversed through a better follow-up of these individuals through PCR, TNF-, IL-6, identifying the evolution of inflammation and providing better nutritional support, aiming to improve the clinical picture of the individual with ESRD in HD.


In the absence of a gold standard to assess the mortality risk of HD individuals, application of one of the subjective methods together with adiposity and lean mass indicators, and CRP concentration in the clinical-nutritional practice could offer more accurate mortality risk in this population. Although oxidative stress biomarkers in ESRD are important, more studies are necessary to identify a recognized oxidative stress marker as a mortality predictor in HD.


1. Vollmer WM, Wahl PW, Blagg CR. Survival with dialysis and transplantation in patients with end-stage renal disease. N Engl J Med 1983;30:1553-8. [ Links ]

2. Held JP, Brunner F, Odaka M, et al. Five-year survival for end-stage renal disease patients in the United States, Europe, and Japan, 1982 to 1987, Am J Kidney Dis 1990;15:451-7. [ Links ]

3. Jager KJ, Lindholm B, Goldsmith D, et al. Cardiovascular and non-cardiovascular mortality in dialysis patients: where is the link? Kidney Int 2011;1(Suppl:1):S21-S23. [ Links ]

4. Gama-Axelsson T, Heimbürger O, Stenvinkel P, et al. Serum Albumin as Predictor of Nutritional Status in Patients with ESRD. Clin J Am Soc Nephrol? 2012;7:1446-53. [ Links ]

5. Herselman M, Esau N, Kruger JM, et al. Relationship between body mass indexand mortality in adults on maintenance hemodialysis: a systematic review. J Ren Nutr 2010;20:281-92. [ Links ]

6. Kalantar-Zadeh K, Block G, Humphreys MH, et al. Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int 2003;63:793-808. [ Links ]

7. Leavey SF, McCullough K, Hecking E, et al. Body mass index and mortality in 'healthier' as compared with 'sicker' haemodialysis patients: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrol Dial Transplant 2001;16:2386-94. [ Links ]

8. Hoogeveen EK, Halbesma N, Rothman KJ, et al. Obesity and Mortality Risk among Younger Dialysis Patients. Clin J Am Soc Nephrol 2012;7:280-8. [ Links ]

9. Baker JP, Detsky AS, Wesson DE, et al. Nutritional assessment: a comparison of clinical judgment and objective measurements. N Engl J Med 1982;306:967-72. [ Links ]

10. Slee AD. Exploring metabolic dysfunction in chronic kidney disease. Nutrition & Metabolism 2012;9:36. DOI: 10.1186/1743-7075-9-36. [ Links ]

11. Cachofeiro V, Goicochea M, De Vinuesa SG, et al. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease. Kidney Int 2008;74(Suppl:111):S4-S9. [ Links ]

12. Oberg BP, McMenamin E, Lucas FL, et al. Increased prevalence of oxidant stress and inflammation in patients with moderate to severe chronic kidney disease. Kidney Int 2004;65:1009-16. [ Links ]

13. Xu H, Watanabe M, Qureshi AR. Oxidative DNA damage and mortality in hemodialysis and peritoneal dialysis patients. Perit Dial Int 2015;35:206-15. [ Links ]

14. Oliveira CMC, Kubrusly M, Mota RS, et al. Desnutrição na insuficiência renal crônica: qual o melhor método diagnóstico na prática clínica? Jornal Brasileiro de Nefrologia 2010;32:57-70. [ Links ]

15. Bossola M, Giungi S, Tazza L, et al. Is there any survival advantage of obesity in Southern European haemodialysis patients? Nephrol Dial Transplant 2010;25:318-9. [ Links ]

16. Marcelli D, Usvyat LA, Kotanko P, et al. Body Composition and Survival in Dialysis Patients: Results from an International Cohort Study. Clin J Am Soc Nephrol ?2015;10:1192-200. [ Links ]

17. Castellano S, Palomares I, Moissl U, et al. Risk identification in haemodialysis patients by appropriate body composition assessment. Nefrologia 2016;36:268-74. [ Links ]

18. Beddhu S, Pappas LM, Ramkumar N, et al. Effects of body size and body composition on survival in hemodialysis patients. J Am Soc Nephrol 2003;14:2366-72. [ Links ]

19. Kalantar-Zadeh K, Streja E, Kovesdy CP, et al. The Obesity Paradox and Mortality Associated With Surrogates of Body Size and Muscle Mass in Patients Receiving Hemodialysis. Mayo Clinic Proceedings 2010;85:991-1001. [ Links ]

20. Rosenberger J, Kissova V, Majernikova M, et al. Body composition monitor assessing malnutrition in the hemodialysis population independently predicts mortality. J Ren Nutr 2014;24:172-6. [ Links ]

21. Huang CX, Tighiouart H, Beddhu S, et al. Both low muscle mass and low fat areassociated with higher all-cause mortality in hemodialysis patients. Kidney Int 2010;77(7):624-9. [ Links ]

22. Jialin W, Yi Z, Weijie Y. Relationship between body mass indexand mortality in hemodialysis patients: A meta-analysis. Nephron Clin Pract 2012;121(3-4):c102-11. [ Links ]

23. Park J, Ahmadi SF, Streja E, et al. Obesity paradox in end-stage kidney disease patients. Prog Cardiovasc Dis 2014; 56:415-25. [ Links ]

24. Su CT, Yabes J, Pike F, et al. Changes in anthrophometry and mortality in maintenance hemodialysis patients in the HEMO Study. Am J Kidney Dis 2013;62:1141-50. [ Links ]

25. Kramer H, Shoham D, McClure LA, et al. Association of Waist Circumference and Body Mass Index With All-Cause Mortality in CKD: The REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study. Am J kidney Dis 2011;58:177-85. [ Links ]

26. Weber J, Kelley J. Health Assessment in Nursing. 4th ed. Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2010. [ Links ]

27. Stosovic M, Stanojevic M, Simic-Ogrizovic S, et al. The predictive value of anthropometric parameters on mortality in hemodialysis patients Nephrol Dial Transplant 2011;26:1367-74. [ Links ]

28. Allon M, Depner TA, Radeva M, et al. Impact of dialysis dose and membrane on infection-related hospitalization and death: results of the HEMO Study. J Am Soc Nephrol 2013;14:1863-70. [ Links ]

29. Kaysen GA, Dubin JA, Muller HG, et al. The acute-phase response varies with time and predicts sérum albumin levels in hemodialysis patients. The HEMO Study Group. Kidney Int 2010;58:346-52. [ Links ]

30. Honda H, Qureshi AR, Axelsson J, et al. Obese sarcopenia in patients with end-stage renal disease is associated with inflammation andincreased mortality. Am J Clin Nutr 2007;86:633-8. [ Links ]

31. Vogt BP, Caramori JCT. Are nutritional composed scoring systems and protein-energy wasting score associated with mortality in maintenance hemodialysis patients? J Ren Nutr 2016;26:183-9. [ Links ]

32. De Roij van Zuijdewijn CL, ter Wee PM, et al. A comparison of 8 nutrition-related tests to predict mortality in hemodialysis patients J Ren Nutr 2015;25:412-9. [ Links ]

33. Kwon YE, Kee YK, Yoon CY, et al. Change of nutritional status assessed using subjective global assessment is associated with all-cause mortality in incident dialysis patients. Medicine (Baltimore) 2016;95:e2714. [ Links ]

34. Chan M, Kelly J, Batterham M, et al. Malnutrition (subjective global assessment) scores and serum albumin levels, but not body mass index values, at initiation of dialysis are independent predictors of mortality: a 10-year clinical cohort study. J Ren Nutr 2012;22:547-57. [ Links ]

35. Beberashvili I, Azar A, Sinuani I, et al. Objective Score of Nutrition on Dialysis (OSND) as an alternative for the malnutrition-inflammation score in assessment of nutritional risk of haemodialysis patients. Nephrol Dial Transplant 2010;25:2662-71. [ Links ]

36. Beberashvili I, Sinuani I, Azar A, et al. IL-6 Levels, Nutritional Status, and Mortality in Prevalent Hemodialysis Patients. Clin J Am Soc Nephrol 2011;6:2253-63. [ Links ]

37. Beberashvili I, Azar A, Sinuani I, et al. Comparison Analysis of Nutritional Scores for Serial Monitoring of Nutritional Status in Hemodialysis Patients. Clin J Am Soc Nephrol 2013;8:443-51. [ Links ]

38. Toledo FR, Antunes AA, Vannini FC, et al. Validity of malnutrition scores for predicting mortality in chronic hemodialysis patients. Int Urol Nephrol 2013;45:1747-52. [ Links ]

39. Goodyear-Bruch C, Pierce JD. Oxidative stress in critically ill patients. Am J Crit Care 2002;11:543-51. [ Links ]

40. Small DM, Coombes JS, Bennett N, et al. Oxidative stress, anti-oxidant therapies and chronic kidney disease. Nephrology 2012;17:311-21. [ Links ]

41. Inal M, Kanbak G, Sen S, et al. Antioxidant status and lipid peroxidation in hemodialysis patients undergoing erythropoietin and erythropoietin-vitamin E combined therapy. Free Radic Res 1999;31:211-6. [ Links ]

42. Locatelli F, Canaud B, Eckardt KU, et al. Oxidative stress in end-stage renal disease: an emerging threat to patient outcome. Nephrol Dial Transplant 2003;18:1272-80. [ Links ]

43. Elkabbaj D, Bahadi A, Cherrah Y, et al. Impact of improving quality of dialysis fluid on oxidative stress and lipid profile in hemodialysis patients. ISRN Nephrol 2013;2013:717-849. [ Links ]

44. Fishbane S, Mathew A, Vaziri ND. Iron toxicity: relevance for dialysis patients. Nephrol Dial Transplant 2014;29:255-9. [ Links ]

45. Del Rio D, Stewart AJ, Pellegrini N. A review of recent studies on malondialdehyde as toxic molecule and biological marker of oxidative stress. Nutr Metab Cardiovasc Dis 2005;15:316-28. [ Links ]

46. Sung CC, Hsu YC, Chen CC, et al. Oxidative stress and nucleic acid oxidation in patients with chronic kidney disease. Oxid Med Cell Longev 2013;2013:301982. [ Links ]

47. Roehrs M , Valentini J, Bulcão R, et al. The plasma retinol levels as pro-oxidant/oxidant agents in haemodialysis patients. Nephrol Dial Transplant 2009;24:2212-8. [ Links ]

48. Rusu CC, Racasan S, Kacso IM, et al. Malondialdehyde can predict survival in hemodialysis patients. Clujul Medical 2016;89:250-6. [ Links ]

49. Park WY, Koh ES, Kim S-H, et al. Serum Gamma-Glutamyltransferase Levels Predict Clinical Outcomes in Hemodialysis Patients. Yu M-L, ed. PLoS ONE 2015;10:e0138159. [ Links ]

50. Del Vecchio L, Locatelli F, Carini M. What we know about oxidative stress in patients with chronic kidneydisease on dialysis -clinical effects, potential treatment, and prevention. Semin Dial 2011;24:56-64. [ Links ]

51. Dominici S, Paolicchi A, Corti A, et al. Prooxidant reactions promoted by solubleand cell-bound gamma-glutamyltransferase activity. Methods Enzymol 2005;401:484-501. [ Links ]

52. Jiang S, Jiang D, Tao Y. Role of gamma-glutamyltransferase in cardiovascular diseases. Exp Clin Cardiol 2013;18:53-6. [ Links ]

53. Breitling LP, Claessen H, Drath C, et al. Gamma-glutamyltransferase, general and cause-specific mortality in 19,000 construction workers followed over 20 years. J Hepatol 2011;55:594-601. [ Links ]

54. Osorio A, Ortega E, de Haro T, et al. Lipid profiles and oxidative stress parameters in male and fe­male hemodialysis patients. Mol Cell Biochem 2011;353:59-63. [ Links ]

55. Zargari M, Sedighi O. Influence of Hemodialysis on Lipid Peroxidation, Enzymatic and Non-Enzymatic Antioxidant Capacity in Chronic Renal Failure Patients. Nephro Urol Monthly 2015;7(4):e28526. [ Links ]

56. Barreto DV, Barreto FC, Liabeuf S, et al. Plasma interleukin-6 is independently associated with mortality in both hemodialysis and predialysis patients with chronic kidney disease. Kidney Int 2010;6:550-6. [ Links ]

57. Goldstein SL, Leung JC, Silverstein DM. Pro- and anti-inflammatory cytokines in chronic pediatric dialysis patients: effect of aspirin. Clin J Am Soc Nephrol 2006;1:979-86. [ Links ]

58. Do Sameiro-Faria M, Ribeiro S, Costa E, et al. Risk Factors for Mortality in Hemodialysis Patients: Two-Year Follow-Up Study. Disease Markers 2013;35:791-8. [ Links ]

59. Don BR, Kim K, Li J, et al. The effect of etanercept on suppression of the systemic inflammatory response in chronic hemodialysis patients. Clin Nephrol 2010;6:431-8. [ Links ]

60. Bazeley J, Bieber B, Li Y, et al. C-Reactive Protein and Prediction of 1-Year Mortality in Prevalent Hemodialysis Patients. Clin J Am Soc Nephrol 2011;6:2452-61. [ Links ]

61. Neirynck N, Glorieux G, Schepers E, et al. Pro-inflammatory cytokines and leukocyte oxidative burst in chronic kidney disease: culprits or innocent bystanders? Nephrol Dial Transplant 2015;0:1-10. [ Links ]

62. Yeun JY, Levine RA, Mantadilok V, et al. C-reactive protein predicts all-cause and cardiovascular mortality in hemodialysis patients. Am J Kidney Dis 2000;35:469-76. [ Links ]

63. Wang AY, Lam CW, Wang M, et al. Increased circulating inflammatory proteins predict a worse prognosis with valvular calcification in end-stage renal disease: a prospective cohort study. Am J Nephrol 2008;28: 647-53. [ Links ]

64. Nakagawa N, Matsuki M, Yao N, et al. Impact of Metabolic Disturbances and Malnutrition-Inflammation on 6-Year Mortality in Japanese Patients Undergoing Hemodialysis. Ther Apher Dial 2015;19(1):30-9. [ Links ]

65. Carlsson AC, Carrero J-J, Stenvinkel P, et al. High Levels of Soluble Tumor Necrosis Factor Receptors 1 and 2 and Their Association with Mortality in Patients Undergoing Hemodialysis. Cardiorenal Med 2015;5:89-95. [ Links ]

Received: May 08, 2017; Accepted: September 08, 2017

Correspondence: Helen Hermana M Hermsdorff. Department of Nutrition and Health. Universidade Federal de Viçosa. Av. PH Rolfs, s/n. Viçosa, Minas Gerais, 36570-900, Brazil e-mail:

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License