| Abstract|| |
Malnutrition is a relatively common problem in patients on hemodialysis (HD) and is associated with increased morbidity and mortality in affected patients. With the aid of subjective global assessment (SGA), a semi-quantitative scale for estimating nutritional status, the malnutrition score (MS), has been developed. The MS incorporates advantages of the SGA while extending the reliability and precision. This study was performed to assess the nutritional status in patients on HD at the Mostafa Khomeini Hospital, Tehran, Iran. Based on the MS, which consists of seven components - - weight change, dietary intake, gastrointestinal (GI) symptoms, functional capacity, comorbidity, subcutaneous fat, and muscle wasting - - we conducted a cross-sectional descriptive-analytic study on 54 HD patients (35 males, 19 females) with age range of 18 to 82 years (mean 44.2 ± 19.8 years). Each component of the MS has a score from one (normal) to five (very severe). Anthropometric measurements including triceps skin-fold thickness (TSF), mid-arm circumference (MAC) and mid-arm muscle circumference (MAMC) were taken on all patients. Also, the body mass index and TSF/MAC ratio were calculated. Relevant laboratory parameters were checked. The duration of HD of the study patients ranged between 5 and 36 months (mean 19.5 ± 1.5 months). Data analysis was carried out using the SPSS, Pearson correlation, 't' test and regression. Based on the MS, 40.7% of patients had malnutrition (mean score 13.8 ± 2.8). There were statistically significant correlations between TSF (p < 0.01), MAC (p = 0.02), MAMC (p = 0.01), TSF/MAC ratio (p < 0.001), BMI (p = 0.028), serum albumin concentration (p = 0.021) and MS. No statistically significant correlation was found between the MS and urea reduction ratio, protein catabolic rate, age, gender, or duration of dialysis. After 1 year, 20.4% of patients died because of dialysis-related complications. The mortality rate did not show significant correlation with age, presence of diabetes mellitus, biochemical parameters, and anthropometric measures. A significant correlation was found between the protein catabolic rate (nPCR) and the mortality rate (regression analysis, p = 0.016); lower values of nPCR were associated with increased mortality. Our study suggests that the MS is a reliable, precise, and rapid method for estimating the nutritional status in patients on HD. The nPCR can be used as a predictor of increased mortality. Further studies with larger sample size and longer duration are required to confirm this observation.
Keywords: Anthropometric measurements, Hemodialysis, Malnutrition score, Subjective global assessment (SGA)
|How to cite this article:|
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;18:397-404
|How to cite this URL:|
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 [serial online] 2007 [cited 2020 Sep 19];18:397-404. Available from: http://www.sjkdt.org/text.asp?2007/18/3/397/33759
| Introduction|| |
Malnutrition is a relatively common problem in chronic dialysis patients, affecting approximately one-third of both hemodialysis (HD) and peritoneal dialysis patients. Malnutrition may occur secondary to poor nutritional intake, increased losses, or to an increase in protein catabolism. The sequelae of malnutrition are numerous and include increased morbidity and mortality, increased hospitalization rate and susceptibility to infection, impaired wound healing, malaise, fatigue, and poor rehabilitation. ,,,,
General estimates of nutritional status may be obtained by comparing ideal with actual body weight and by evaluating the condition of mucous membranes, hair, and skin. Anthropometry can provide a reasonably accurate method for assessing body fat and protein stores. Measurement of skin fold thickness (SFT) at the biceps or triceps provides an estimate of body fat, whereas mid-arm circumference (MAC) and midarm muscle circumference (MAMC) can be used to estimate muscle mass. ,,,,
However, the sensitivity of these methods in detecting early malnutrition, their practicability, and applicability to patients on HD have not been convincing. Bioimpedance analysis is based on the measurement of resistance and reactance when a constant alternating electrical current is applied to a patient. This method and other new methods such as dual-energy X-ray absorptiometry (DEXA) may give reliable results, but they are expensive; and as such, their use is not routine. ,
The subjective global assessment (SGA) was designed to circumvent many of these problems; but its semi-quantitative scale, consisting of three discrete severity levels, restricts its reliability and precision.  Using the components of the conventional SGA, a fully quantitative scoring system for dialysis patients was developed by Kalantar-Zadeh et al. We used this malnutrition score (MS) for nutritional assessment of our patients and compared it with anthropo-metric measurements and biochemical parameters.
| Patients and Methods|| |
This cross-sectional, descriptive-analytic study was conducted in the year 2005 at the Mostafa Khomeini Hospital of Shahed University, Tehran, Iran. The study population composed of 54 patients (35 males and 19 females) who fulfilled the following inclusion criteria:
a) On maintenance conventional HD as a constant modality of renal replacement therapy (thrice weekly)
b) Consent given for participation in the study
c) Absence of active underlying disease (e.g., collagen vascular disease)
d) Absence of active infection
e) No requirement for hospitalization during the month preceding the study
All participants were informed of the purpose of the study, and each patient signed a consent form. Patients ranged in age from 18 to 82 years, with the mean age being 44.2 ± 2.6 years. The hemodialysis duration in the study patients ranged between 5 and 36 months (mean 19.5 ± 1.5 months) and diabetes frequency among patients was 31%.
The MS consists of seven features: weight change, dietary intake, gastrointestinal (GI) symptoms, functional capacity, co-morbidity, subcutaneous fat, and signs of muscle wasting. Each component has a score from one (normal) to five (very severe). Thus, the MS is a number between 7(normal) and 35 (severely malnourished); a lower score denotes tendency towards a normal nutritional status and higher score is an indicator of malnutrition  [Table - 1]. Based on the MS, the total nutritional scoring for each patient was assessed by two physicians separately, within an interval of 10 to 20 minutes (mean 15 ± 2 minutes).
Body dry weight and skin-fold thickness at the triceps (TSF) were measured between 10 and 20 minutes after termination of the dialysis session. The MAC and TSF were measured using a metal tape measure and caliper respectively. All measurements were performed three times on the non-access arm, and the average result was registered in a questionnaire. MAMC was calculated by the following formula: MAMC = MAC - (3.1415*TSF).
Then, the ratio of TSF:MAC was obtained and registered. The BMI was calculated as the ratio between enddialysis body weight in kg and the square of height in meters (kg/m 2) .
Blood samples were taken from all patients just before the beginning of a dialysis session for measuring the following parameters: serum albumin, creatinine, BUN, cholesterol, triglyceride, and hematocrit. Also, a postdialysis blood sample was taken for measuring the BUN.
The urea reduction ratio (URR) was calculated by the formula
protein catabolic rate (nPCR) was calculated using the equation of Gotch and Sargent based on the intra-dialytic urea appearance rate :
ID rise BUN = C 0 -C T
ID interval : intra-dialytic interval (hours);
C 0 : pre-dialysis BUN of the next session (mg/dl); C T : postdialysis BUN (mg/dl).
The URR correlates with kt/v and can be used as an indicator of dialysis efficacy. , All the above data were registered in a questionnaire. During the period of one year, the patients' mortality rate was also assessed.
All data analyses were carried out using the SPSS v.11.5 software package. Associations between variables were assessed using Pearson's correlation, x2 test. Mean and SD for continuous variables were calculated for males and females. Differences between means were analyzed using the't' test. Descriptive statistics and regression analysis were carried out with the statistical software. A 'p' value of <0.05 was considered statistically significant.
| Results|| |
[Table - 2] shows the demographic data of the study patients. We found that 40.7% of patients had malnutrition (mean score: 13.8 ± 2.8). Of them, 5.6% had severe malnutrition (score of 23-35) and 35.1% had mild to moderate malnutrition (score of 11-22).
Quantitative nutritional scores, as well as the TSF, MAC, MAMC, BMI, and the biochemical parameters, were not significantly different between men and women (p = 0.64). However, there were significant gender-specific differences in weight, height, and URR [Table - 2]. Women had higher URR but lower weight and height in comparison with men.
Based on Pearson correlation, the differences in MS between various age groups were not significant (p = 0.8). Also, the Pearson correlation showed significant correlation between the MS and the anthropometric parameters including TSF (p < 0.01), MAC (p = 0.02), MAMC (p = 0.01), TSF/MAC ratio (p < 0.001), and BMI (p = 0.028), indicating a smaller MAC and MAMC for patients having a higher nutritional score or a stronger tendency towards malnutrition. The MS did not correlate significantly with duration of dialysis (p=0.297) and other laboratory parameters, except serum albumin concentration (p = 0.021).
No correlation between either the nPCR or the URR and the MS was found. Multiple regression analysis showed similar results for relationship between the MS and MAMC, MAC, and TSF. [Table - 3] shows the Pearson correlation coefficients between the MS and other parameters. At the end of one year's follow-up, 20.4% of patients died because of ESRD complications. The mortality rate did not show any statistically significant correlation with age (p = 0.74), gender (p = 0.26), diabetes mellitus (p = 0.74), laboratory, or anthropometric parameters. Multiple regression analysis showed significant relationship between the mortality and nPCR (p = 0.016). Thus, lower values of nPCR were associated with increased mortality. [Table - 4] shows this relationship.
| Discussion|| |
Malnutrition is a common problem in dialysis patients, affecting approximately one-third of both hemodialysis and peritoneal dialysis patients. Malnutrition increases patients' mortality and morbidity. Despite this, the nutritional status of dialysis patients is frequently ignored. ,,,,,
There are several methods of estimating nutritional status, ranging from the anthropometric measurements  to costly and time consuming methods such as DEXA and bioelectrical impedance, which are used only in a few research centers.  However, the reliability of these methods in detecting malnutrition and their practicability has not been shown. 
Kelly et al. developed an HD prognostic nutrition index (HD-PNI), which incorporated the number of days hospitalized for each patient; but it is too cumbersome for routine use. 
Based on conventional SGA, KalantarZadeh et al. developed the MS, which can be performed in minutes and which reliably assesses the nutritional status of dialysis patients.  In our study, we used the MS for assessment of nutritional status of HD patients. Kalantar-Zadeh showed that the Pearson correlation coefficients between the MS and biceps skin-fold (r = -0.32), MAC (r = -0.55), MAMC (r = -0.66), BMI (r = 0.35), total iron-binding capacity (TIBC, r = -0.77), and the serum albumin (r = -0.36) were all significant. The MS also showed a significant correlation with age (r = +0.34) and dialysis duration (r = +0.28).We could not find the latter correlations in our study. This may be attributed to lower age and shorter dialysis duration of our patients in comparison with Kalantar-Zadeh study (mean value of age 57.2 ± 12.9 versus 44.2 ± 19.8 years and dialysis duration 3 ± 2.18 years versus 19.57 ± 1.5 months).
There was no correlation between the MS and gender, URR, or nPCR in the KalantarZadeh study, as well as our study. This may be because urea modeling depends on many assumptions, such as constant protein intake.  On the other hand, we used only URR at the time the MS was assessed. Therefore, if we had averaged URRs over a longer period, we might have found a correlation with the MS. Kalantar-Zadeh et al. also did not find such correlation between above variables.  Females had greater URR than males; this may be attributed to small body surface area in them; however, the BMI was approximately equal in both genders, and we could not find any other explanation for this difference. URR being different but BMI being approximately equal. Similar studies have shown that an inadequate dose of dialysis was more likely among males, blacks, and those with larger body surface areas. , Also, a significant survival benefit for women receiving a high dialysis dose was observed upon subgroup analysis. , In our study, we found that the mortality rate at one year (20.4%) significantly correlated with nPCR. Thus, if we find a similar correlation between the MS and nPCR in studies over longer periods, we can use the nPCR as a predictor of increased mortality. Also, the mortality rate did not show significant correlation with age, diabetes mellitus, and serum albumin concentration. It may be attributed to relatively low age of the patients, small sample size of diabetic patients (17 subjects), and short duration of maintenance dialysis and patient follow-up (1 year) in comparison to other studies. ,, It has been shown that survival declines with increasing age, with patients under the age of 45 years doing best.
In this study, based on the MS, 59.3% of patients were well nourished (MS = 7-10), 35.1% had mild to moderate malnutrition (MS = 11-22), and 5.6% had severe malnutrition (MS = 23-35). Jerin et al. reported that 42.8% of their study population had malnutrition, which is similar to our study.  Tirmentajn showed that among 43 HD patients, 46.6% had malnutrition. It seems that the nutritional status in our patients is similar to other centers. 
We showed that the MS is compatible with the anthropometric measurement results and can be used as a reliable, rapid, and precise method for nutritional assessment in office, hospital and HD centers. It is preferred in comparison with other time-consuming methods for nutritional assessment.
| Conclusions|| |
The nutritional status in patients on HD needs more attention. Regular periodic assessment of nutritional status based on the MS allows implementation of preventative interventions, such as nutritional counseling or psychosocial interventions, as well as use of dietary supplements, in order to decrease patients' mortality and morbidity.
We may also use the nPCR for this purpose. Further studies with larger sample size conducted over longer duration are suggested.
| References|| |
|1.||Steiber AL, Handu DJ, Cataline DR, Deighton TR, Weatherspoon LJ. The impact of nutrition intervention on a reliable morbidity and mortality indicator: the hemodialysis-prognostic nutrition index. J Ren Nutr 2003;13(3):186-90. |
|2.||Chertow GM, Bullard A, Lazarus JM. Nutrition and the dialysis prescription. Am J Nephrol 1996;16:79-89. [PUBMED] |
|3.||Detsky AS, Baker JP, Mendelson RA, Wolman SL, Wesson DE, Jeejeebhoy KN. Evaluating the accuracy of nutritional assesment techniques applied to hospitalized patients: methodology and comparisons. J Parenter Enteral Nutr 1987;8:153-9. |
|4.||Hakim R, Tevin N. Malnutrition in hemodialysis patients. Am J Kidney Dis 1993;21:125-37. |
|5.||Daugirdas JT, Ing T, Blake PG. Hand book of dialysis, 3rd ed, Boston, L.W.W, 2001;23: 420-6. |
|6.||Chertow GM, Lowrie EG, Wilmore DW, et al. Nutritional assessment with bioelectrical impedance analysis in maintenance hemodialysis patients. J Am Soc Nephrol 1995; 6:75-81. [PUBMED] |
|7.||Kalantar-Zadeh K, Kleiner M, Dunn E, Lee GH, Luft FC. A modified quantitative subjective global assessment of nutrition for dialysis patients. Nephrol Dial Transplant 1999;14(7):1732-8. |
|8.||Lowrie EG, Lew NL. The urea reduction ratio (URR). A simple method for eveluation hemodialysis treatment. Contem Dial Nephrol 1992;12:11-3. |
|9.||Gotch FA, Sargent JA. A mechanistic analysis of the national cooperative dialysis study. Kidney Int 1985;28:526-34. [PUBMED] |
|10.||NKF-DOQI Clinical Guidelines for Hemodialysis Adequacy. v. Hemodialysis dose trouble shooting. Am J kidney Dis 2001;37(S1):S42. |
|11.||Blake PG. Adequacy of dialysis. Kidney Int 2003;63:1587-9. [PUBMED] [FULLTEXT]|
|12.||Gold Wasser P, Mittman N, Antignani A, et al. Predictors of mortality in hemodialysis patients. J Am Soc Nephrol 1993;3:1613-6. |
|13.||Nelson EE, Hong CD, Pesce AL, Peterson DW, Singh S, Pollak VE. Anthropometric norms of dialysis population. Am J Kidney Dis 1990;16:32-7. [PUBMED] |
|14.||Heymsfield SB, Tighc A, Wang Z. Nutritional assessment by anthropometric and biochemical methods. Modern nutrition in health and disease, Philadelphia, 1998;812-41. |
|15.||Kelly MP, Kight MA, Migliore V. Positive and negative deviance: keys to elucidating thresholds of nutritional care. J Renal Nutr 1995;5:124-32. |
|16.||Druml W. Malnutrition is bad, but how can one detect malnutrition? Nephrol Dial Transplant 1997;12:2225-7. [PUBMED] [FULLTEXT]|
|17.||17.Depner T, Daugirdas J, Greene T, et al. Dialysis dose and the effect of gender and body size on outcome in the HEMO study. Kidney Int 2004;65:1386-7. |
|18.||Eknoyan G, Beck GJ, Cheung AK, et al. Effect of dialysis dose and membrane flux in maintenance hemodialysis. N Engl J Med 2002;347:2010-3. [PUBMED] [FULLTEXT]|
|19.||Port FK, Wolfe RA, Hulbert-Shearon TE, et al. High dialysis dose is associated with lower mortality among women but not among men. Am J Kidney Dis 2004;43:1014-7. [PUBMED] [FULLTEXT]|
|20.||Introduction and summary proceeding from the morbidity, mortality and prescription of dialysis symposium, Dallas, TX, September 15-17,1989. Am J Kidney Dis 1990;15:375. |
|21.||Mailloux Lu, Bellucci AG. Survival estimates for 683 patients starting dialysis from 1970-1989: Identification of risk factors for survival. Clin Nephrol 1994;42:127-31. |
|22.||Kopple JD, Low NL. Body weight-forheight relationship predict mortality in maintenance hemodialysis patients. Kidney Int 1999;56:1136-8. |
|23.||Jerin L, Ladavac R, Kuzmanovic G, Dodic D, Griparic D. Subjective general assessment of nutritional status in patients with chronic renal failure and regular hemodialysis. Acta Med Croatica. 2003;57(1):23-8. |
|24.||Tirmentajn-Jankovie B, Dimkovic N. Simple methods for nutritional status assessment in patients treated with repeated hemodialysis. Med Progl 2004;57(9-10):43944. |
Assistant Professor of Nephrology, Mostafa Khomeini Hospital, Italia St, Tehran
[Table - 1], [Table - 2], [Table - 3], [Table - 4]