Cite Score:
1.07
ELSEVIER SCOPUS

Malnutrition in Hemodialysis Patients and Predicting Factors: A Cross-Sectional Study

AUTHORS

Farahnaz Joukar 1 , 2 , Zahra Moradi 2 , Farideh Hasavari 2 , Zahra Atrkar Roushan 2 , Asieh Sedighi 2 , Mehrnaz Asgharnezhad 1 , *

AUTHORS INFORMATION

1 Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran

2 Social Determinants of Health Research Center, Guilan University of Medical Sciences, Rasht, Iran

How to Cite: Joukar F, Moradi Z, Hasavari F, Atrkar Roushan Z, Sedighi A, et al. Malnutrition in Hemodialysis Patients and Predicting Factors: A Cross-Sectional Study, Nephro-Urol Mon. Online ahead of Print ; In Press(In Press):e86586. doi: 10.5812/numonthly.86586.

ARTICLE INFORMATION

Nephro-Urology Monthly: In Press (In Press); e86586
Published Online: August 11, 2019
Article Type: Research Article
Received: November 20, 2018
Revised: April 7, 2019
Accepted: May 10, 2019
Crossmark

Crossmark

CHEKING

READ FULL TEXT
Abstract

Background: Protein-energy malnutrition, one of the most important risk factors for cardiovascular diseases, is common in dialysis patients. In this way, several characteristics and socio-economic factors could influence nutritional stats. The diagnosis of malnutrition and its related factors can assist the healthcare team in planning for the care of hemodialysis patients.

Objectives: In this study, we are aimed to determine the nutritional status among hemodialysis patients and characteristics and also socio-economic factors.

Methods: In this cross-sectional study, 312 patients were selected randomly. Modified subjective global assessment (SGA) tool was used for data collection. The data was expressed as mean ± SD and frequency. Logistic regression analysis was performed to detect predicting factors of malnutrition using SPSS software.

Results: About 65.1% of hemodialysis patients suffered from mild-to-moderate malnutrition and 15% of patients were severely malnourished. Most patients were married (82.7%), low- income (63.1%), illiterate (63.8%) and employed (52.2 percent). Following the sub-group analysis, we found significant weight changes in malnourished patients (P value = 0.000). In addition, we found that severely malnourished patients were older, married or divorced, unemployed and lived in large families compared with other groups (P value < 0.05). The illiterate people were in greater risk for malnutrition (AOR = 8.14, 95% CI: 1.8 - 36.89).

Conclusions: Socio-economic factors such as income, education, living conditions, marital status, family size and employment affect nutritional status. Therefore, taking socio-economic factors into account can help the treatment team in the care of hemodialysis patients.

Keywords

Malnutrition Renal Dialysis Patients

Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1. Background

Protein-energy malnutrition (PEM) is linked to increased the morbidity and mortality, which is common in patients with end-stage renal disease (ESRD) on maintenance hemodialysis (HD) therapy (1, 2). PEM leads to reduction in quality of life and increases the hospital cost following the prolonged hospitalization due to infection, delayed wound healing, respiratory muscle mass losses and excessive loss of nutrients through the feces (3, 4). There are several factors which can contribute to malnutrition, including inadequate food intake, anorexia, altered taste sensation, emotional distress, poor diet, comorbid disease and increased metabolism rate owing to inflammation (5). Furthermore, atherosclerosis together with inflammation and also malnutrition affect hemodialysis patients (6, 7). Some predictors of malnutrition have been identified, the ones such as age > 65, male sex, time on dialysis and duration of dialysis (8). Low body mass index (BMI), the marker of malnutrition, is another predictor of poor survival in hemodialysis patients (9). Although the malnutrition is common among HD patients, it is ignored in many specially for some specific simple methods for nutritional evaluation that have a favorable effect on patient situation (5). Nutritional support via care team can improve nutritional status especially in severely malnourished patients (10). In this way, the malnutrition can be estimated by employing a quantitative scoring method and also a subjective global evaluation-dialysis, which is practical and reliable in this section and is the only screening tool suggested by the (ASPEN) American Society for Parenteral and Enteral Nutrition (11).

Thus, the evaluation of nutritional state is an important aspect of the HD patient treatment control. Correspondingly, the evaluation process determination of demographic and socioeconomic main parameters may contribute in distinguishing the high risk rate of patients in order to deliver appropriate care.

2. Objectives

In this study, we tried to evaluated the nutritional status of hemodialysis patients in Razi hospital in Rasht, Iran and determine the demographic and socio-economic risk factors of malnutrition.

3. Methods

This cross-sectional study was carried out on 312 patients, aged 18 or older, and admitted for hemodialysis in Razi hospital in Rasht, Iran. We used convenience sampling method to recruit participants. The following inclusion criteria were considered: (1) history of hemodialysis for at least 6 months, at least up to 3 times/week, (2) no hospitalization, (3) no history of parenteral or enteral nutrition.

The main variable was nutritional status, assessed with modified subjective global assessment (SGA) scores (12). Relatively, the altered subjective global assessment (SGA) marks, identified by medical history on seven main topics and also the clinical qualification for four other categories was the remarkably quantitative scoring system including seven elements with a total score ranging from 7 to 35 (7 - 13: normal), (14 - 23: mild to moderately malnourished) and (24 - 35: severely malnourished). The history section of this questionnaire includes five parts: weight/weight modification; dietary intake; main gastrointestinal symptoms; functional capacity; and disease situation morbidities as a related issue to nutritional status. Relative to weight/weight alteration, the patient’s weight loss was recorded along with the current weight from the preceding six months. Additionally, information regarding weight assessment for the SGA was obtained from the patient’s medical history. Other main information was required for the SGA which was gained from the patient’s clinical record. The second part was the physical examination. The physical examination comprises an assessment of the patient focusing on muscle wasting as well as the edema. We did not consider edema or ascites in this section. Other main areas including the eye, and around the triceps and biceps muscles were investigated in order to identify the subcutaneous fat loss. Muscle wasting was evaluated by considering the temporalis muscle, prominence of the clavicles, the contour of the shoulders and also the visibility of the scapula, interosseous muscle between the thumb and forefinger, and the gastrocnemius muscle. Fat loss or muscle wasting are expressed as severe (score: 5), average (score: 3) and unchanged (score: 1). The comorbidities related to malnutrition were evaluated by Charlson comorbidity index (13, 14). The body mass index (BMI) was obtained by using a DS200, scale with an accuracy of 0.5 kg. The researcher was educated on how to measure the HDP with the 7-point SGA as well as using the body fat calipers. Results are presented as mean ± SD and frequency (percentile). P value < 0.05 was determined to be significant. Notably, the logistic regression model was used for assessing probable predicting factors. Variables with a P value less than 0.2 were chosen as a possible factor. Enter method of logistic regression was used for analysis. In this study SPSS version 22 (SPSS, Inc., Chicago, USA) was used for analysis of data.

3.1. Ethical Considerations

This study was reviewed and approved by the Ethical Committee of Guilan University of Medical Sciences (number EP. 3.132.3091). Written informed consents were obtained from all the patients.

4. Results

A total of 312 patients (130 female and 182 male), aged 27 - 86 years (mean age 50.64 ± 13), completed this study. Most of them were married (82.7%); low-income (63.1%); literate (63.8%) and employed (52.2%). Demographic characteristics of the samples are summarized in Table 1.

Table 1. Demographic Characteristics and Socio-Economic Status
Socio-DemographicsN = 312Well Nourished (N = 62) aMildly to Moderately Malnourished (N = 203)aSeverely Malnourished (N = 47) aP Value
Gender0.24
Female13020 (15.4)89 (68.5)21 (16.2)
Male18242 (23.1)114 (62.6)26 (14.3)
Age, y0.00
< 5017244 (25.6)117 (68)11 (6.4)
≥ 5014018 (12.9)86 (61.4)36 (25.7)
Marital status0.00
Single136 (46.2)7 (53.8)0 (0)
Married25853 (20.5)172 (66.7)33 (12.8)
Divorced413 (7.3)24 (58.5)14 (34.1)
Education0.00
Illiterate11310 (8.8)74 (65.5)29 (25.7)
Below diploma10818 (16.7)78 (72.2)12 (11.1)
Diploma and upper9134 (37.4)51 (56)6 (6.6)
Economic situation0.35
Low income19737 (18.8)132 (67)28 (14.2)
Average income and high income11525 (21.7)71 (61.7)19 (16.5)
Employment status0.001
Employee75 (71.4)2 (28.6)0 ( 0 )
Retired11426 (22.8)74 (64.9)14 (12.3)
Worker8717 (19.5)56 (64.4)14 (16.1)
Unemployed10414 (13.5)71 (68.3)19 (18.3)
Number of family members0.00
< 312731 (24.4)83 (65.4)13 (10.2)
3 - 516029 (18.1)109 (68.1)22 (13.8)
> 5252 (8)11 (44)12 (48)
Habitation0.07
Urban20748 (23.2)132 (63.8)27 (13)
Rural10514 (13.3)71 (67.6)20 (19)
Housing conditions0.02
Leased8925 (28.1)56 (62.9)8 (9)
Private22337 (16.6)147 (65.9)39 (17.5)
Living conditions0.000
Alone185 (27.8)11 (61.1)2 (11.1)
With spouse7318 (24.7)48 (65.8)7 (9.6)
With his wife and children22139 (17.6)144 (65.2)38 (17.2)
Spouse Education0.03
Illiterate12019 (15.8)80 (66.7)21 (17.5)
Below diploma11622 (19)82 (70.7)12 (10.3)
Diploma and upper4816 (33.3)30 (62.5)2 (4.2)
Spouse’s employment status0.93
Employed31 (33.3)2 (66.7)0 ( 0 )
Retired9316 (17.2)66 (71)11 (11.8)
Worker365 (13.9)26 (72.2)5 (13.9)
Unemployed15235 (23)98 (64.5)19 (12.5)
Membership in the community0.18
Yes24947 (18.9)168 (67.5)34 (13.7)
No6315 (23.8)35 (55.6)13 (20.6)
Weight loss, %0.000
No4233 (78.6)8 (19)1 (2.4)
< 510925 (22.9)84 (77.1)0 (0)
5 - 101174 (3.4)90 (76.9)23 (19.7)
10 - 15340 (0)19 (55.9)15 (44.1)
> 15100 (0)2 (20)8 (80)
History of transplantation0.45
Yes329 (28.1)19 (59.4)4 (12.5)
No28053 (18.9)184 (65.7)43 (15.4)
Dialysis/week0.07
2378 (21.6)19 (51.4)10 (27)
327554 (19.6)184 (66.9)37 (13.5)
Duration of disease, y0.34
< 1 year247 (29.2)16 (66.7)1 (4.2)
> 1 year28855 (19.1)187 (64.9)46 (16)
Duration of dialysis, y0.44
< 518836 (19.1)123 (65.4)29 (15.4)
5 - 109622 (22.9)63 (65.6)11 (11.5)
> 10284 (14.3)17 (60.7)7 (25)

a Values are presented as No. (%).

The rates of mild to moderate and severe malnutrition were 65.1% and 15% respectively. The rest (19.9%) had normal nutritional status. There is a significant correlation between weight change and nutritional status (P value = 0.000). As shown in Figure 1, the severe weight loss was associated with severe malnutrition.

The relative frequency of weight changes in each nutritional group
Figure 1. The relative frequency of weight changes in each nutritional group

After control of confounding factors, illiterate patients had a significantly higher risk of malnutrition. Low educational level can also increase the risk of malnutrition (Table 2).

Table 2. Education as a Predicting Factor of Malnutrition Among Hemodialysis Patientsa
Variables, EducationNutritional StatusCOR (95%CI)P Value
NormalMalnutritionTotal, No.
Diploma and above34 (37.4)57 (62.6)911
Under diploma18 (16.7)90 (83.3)1083.07 (1.10 - 8.52)0.031
Illiterate10 (8.8)103 (91.2)1138.14 (1.8 - 36.89)0.006

aValues are expressed as No. (%) unless otherwise indicated.

5. Discussion

Hemodialysis patients are always at risk of protein-energy malnutrition (15). In this study, the majority of patients had some degree of malnutrition, even though in some studies, the incidence of malnutrition is underreported (16).

We found that malnutrition is correlated with some demographic characteristics and socio-economic status. Women, the elderly, the unemployed, the widowed, the divorced, the illiterate and those living in large families are more susceptible to malnutrition similar to the previous studies (17, 18). This is perhaps partly because of the more psychological and economical tensions and less supportive initiatives in these patients (19, 20). Contrary to our study, it has been shown that the male sex and the younger age were the predictive factors of malnutrition (21). Duration and frequency of dialysis did not have impact on the nutritional status significantly; however, the results of other studies are inconsistent (22). Nevertheless, high-quality regular dialysis can improve the nutritional status of patients (23). We found that illiteracy can increase the risk of malnutrition, particularly severe malnutrition. Reversed relationship has been found between the level of education and malnutrition, the higher the education, the greater the risk of malnutrition (24), but it seems that higher education can improve the social and economic status and access to health services to reduce the risk of malnutrition. Higher education creates a greater ability to deal with physical and mental problems that can lead to the improvement of nutritional status. Despite the fact that none of the patients in this study was the member of the association of kidney patient support, it seems that community support by providing training and financial assistance for patients can help to improve nutritional status (25). Educated spouses with good economic situation can be a protective factor in the prevention of malnutrition as well (26).

In this study, the weightiest change was seen in malnourishment. The inflammation may often induce weight loss or a condition of malnutrition (27). As previous studies have shown, there is a remarkably significant negative correlation between altered Subjective Global Assessment-Dialysis Malnutrition Score and also the anthropometric evaluation such as triceps skin fold thickness, mid arm circumference, and mid arm muscle circumferences (28). It is well worth noting that decreased fat mass is correlated with cardio-vascular diseases CVD and other markers of malnutrition (22).

5.1. Conclusions

In summary, more attention must be paid to high risk patients with regard to demographic and socio-economic status in treatment programs.

Acknowledgements

Footnotes

References

  • 1. Siddiqui UA, Halim A, Hussain T. Nutritional profile and inflammatory status of stable chronic hemodialysis patients at Nephrology Department, Military Hospital Rawalpindi. J Ayub Med Coll Abbottabad. 2007;19(4):29-31. [PubMed: 18693592].
  • 2. Abdallah SAK, Yousif YB. Nutritional intervention of adequate calorie and protein intake improve malnutrition among hemodialysis patients. 5th European Nutrition and Dietetics Conference. Rome, Italy. 2016. p. 1110-9.
  • 3. Tsai AC, Chang MZ. Long-form but not short-form Mini-Nutritional Assessment is appropriate for grading nutritional risk of patients on hemodialysis--a cross-sectional study. Int J Nurs Stud. 2011;48(11):1429-35. doi: 10.1016/j.ijnurstu.2011.05.004. [PubMed: 21640347].
  • 4. Mollaoglu M. Quality of life in patients undergoing hemodialysis. Hemodialysis. InTech; 2013. doi: 10.5772/52277.
  • 5. Janardhan V, Soundararajan P, Rani NV, Kannan G, Thennarasu P, Chacko RA, et al. Prediction of malnutrition using modified subjective global assessment-dialysis malnutrition score in patients on hemodialysis. Indian J Pharm Sci. 2011;73(1):38-45. doi: 10.4103/0250-474X.89755. [PubMed: 22131620]. [PubMed Central: PMC3224408].
  • 6. Kirushnan BB, Rao BS, Annigeri R, Balasubramanian S, Seshadri R, Prakash KC, et al. Impact of malnutrition, inflammation, and atherosclerosis on the outcome in hemodialysis patients. Indian J Nephrol. 2017;27(4):277-83. doi: 10.4103/0971-4065.202830. [PubMed: 28761229]. [PubMed Central: PMC5514823].
  • 7. Carrera-Jimenez D, Miranda-Alatriste P, Atilano-Carsi X, Correa-Rotter R, Espinosa-Cuevas A. Relationship between nutritional status and gastrointestinal symptoms in geriatric patients with end-stage renal disease on dialysis. Nutrients. 2018;10(4). doi: 10.3390/nu10040425. [PubMed: 29596313]. [PubMed Central: PMC5946210].
  • 8. Stenvinkel P. Inflammatory and atherosclerotic interactions in the depleted uremic patient. Blood Purif. 2001;19(1):53-61. doi: 10.1159/000014479. [PubMed: 11114578].
  • 9. Valtuille R, Casos ME, Fernandez EA, Guinsburg A, Marelli C. Nutritional markers and body composition in hemodialysis patients. Int Sch Res Notices. 2015;2015:695263. doi: 10.1155/2015/695263. [PubMed: 27347538]. [PubMed Central: PMC4897264].
  • 10. Tappenden KA, Quatrara B, Parkhurst ML, Malone AM, Fanjiang G, Ziegler TR. Critical role of nutrition in improving quality of care: An interdisciplinary call to action to address adult hospital malnutrition. J Acad Nutr Diet. 2013;113(9):1219-37. doi: 10.1016/j.jand.2013.05.015. [PubMed: 23871528].
  • 11. Steiber AL, Kalantar-Zadeh K, Secker D, McCarthy M, Sehgal A, McCann L. Subjective Global Assessment in chronic kidney disease: a review. J Ren Nutr. 2004;14(4):191-200. doi: 10.1053/j.jrn.2004.08.004. [PubMed: 15483778].
  • 12. Kwon YE, Kee YK, Yoon CY, Han IM, Han SG, Park KS, 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(7). e2714. doi: 10.1097/MD.0000000000002714. [PubMed: 26886609]. [PubMed Central: PMC4998609].
  • 13. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676-82. doi: 10.1093/aje/kwq433. [PubMed: 21330339].
  • 14. Lee JH, Hutzler LH, Shulman BS, Karia RJ, Egol KA. Does risk for malnutrition in patients presenting with fractures predict lower quality measures? J Orthop Trauma. 2015;29(8):373-8. doi: 10.1097/BOT.0000000000000298. [PubMed: 26197021].
  • 15. Pasticci F, Fantuzzi AL, Pegoraro M, McCann M, Bedogni G. Nutritional management of stage 5 chronic kidney disease. J Ren Care. 2012;38(1):50-8. doi: 10.1111/j.1755-6686.2012.00266.x. [PubMed: 22369595].
  • 16. Afshar R, Sanavi S, Izadi-Khah A. Assessment of nutritional status in patients undergoing maintenance hemodialysis: A single-center study from Iran. Saudi J Kidney Dis Transpl. 2007;18(3):397-404. [PubMed: 17679753].
  • 17. Alharbi KA. Assessment of nutritional status of patients on hemodilaysis: a single center study from Jeddah, Saudi Arabia [dissertation]. Florida International University; 2010.
  • 18. Akbari Sari A, Rezaei S, Homaie Rad E, Dehghanian N, Chavehpour Y. Regional disparity in physical resources in the health sector in iran: A comparison of two time periods. Iran J Public Health. 2015;44(6):848-54. [PubMed: 26258098]. [PubMed Central: PMC4524310].
  • 19. Homaie Rad E, Mostafavi H, Delavari S, Mostafavi S. Health-related quality of life in patients on hemodialysis and peritoneal dialysis: A meta-analysis of iranian studies. Iran J Kidney Dis. 2015;9(5):386-93. [PubMed: 26338163].
  • 20. Samadi A, Homaie Rad E. Determinants of healthcare expenditure in Economic Cooperation Organization (ECO) countries: Evidence from panel cointegration tests. Int J Health Policy Manag. 2013;1(1):63-8. doi: 10.15171/ijhpm.2013.10. [PubMed: 24596838]. [PubMed Central: PMC3937933].
  • 21. Feroze U, Noori N, Kovesdy CP, Molnar MZ, Martin DJ, Reina-Patton A, et al. Quality-of-life and mortality in hemodialysis patients: Roles of race and nutritional status. Clin J Am Soc Nephrol. 2011;6(5):1100-11. doi: 10.2215/CJN.07690910. [PubMed: 21527646]. [PubMed Central: PMC3087777].
  • 22. Kadiri Mel M, Nechba RB, Oualim Z. Factors predicting malnutrition in hemodialysis patients. Saudi J Kidney Dis Transpl. 2011;22(4):695-704. [PubMed: 21743213].
  • 23. Segall L, Mardare NG, Ungureanu S, Busuioc M, Nistor I, Enache R, et al. Nutritional status evaluation and survival in haemodialysis patients in one centre from Romania. Nephrol Dial Transplant. 2009;24(8):2536-40. doi: 10.1093/ndt/gfp110. [PubMed: 19297358].
  • 24. Tayyem RF, Mrayyan MT. Assessing the prevalence of malnutrition in chronic kidney disease patients in jordan. J Ren Nutr. 2008;18(2):202-9. doi: 10.1053/j.jrn.2007.10.001. [PubMed: 18267213].
  • 25. Lillis C, LeMone P, LeBon M, Lynn P. Study guide for fundamentals of nursing: The art and science of nursing care. Lippincott Williams & Wilkins; 2010.
  • 26. Stojanovic M, Stojanovic D, Stefanovic V. The impact of malnutrition on mortality in patients on maintenance hemodialysis in Serbia. Artif Organs. 2008;32(5):398-405. doi: 10.1111/j.1525-1594.2008.00558.x. [PubMed: 18471169].
  • 27. Memoli B, Guida B, Saravo MT, Nastasi A, Trio R, Liberti R, et al. [Predictive and diagnostic factors of malnutrition in hemodialysis patients]. G Ital Nefrol. 2002;19(4):456-66. Italian. [PubMed: 12369050].
  • 28. Tapiawala S, Vora H, Patel Z, Badve S, Shah B. Subjective global assessment of nutritional status of patients with chronic renal insufficiency and end stage renal disease on dialysis. J Assoc Physicians India. 2006;54:923-6. [PubMed: 17334008].
  • COMMENTS

    LEAVE A COMMENT HERE: