|Year : 2015 | Volume
| Issue : 2 | Page : 302-308
|Geriatric nutritional risk index: A mortality predictor in hemodialysis patients
Mahnaz Edalat-Nejad1, Fatemeh Zameni2, Mahdi Qlich-Khani2, Fatemeh Salehi3
1 Department of Nephrology, Arak University of Medical Sciences, Arak, Iran
2 Department of Internal Medicine, Arak University of Medical Sciences, Arak, Iran
3 Clinical Nutrition Department, Arak University of Medical Sciences, Arak, Iran
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|Date of Web Publication||3-Mar-2015|
| Abstract|| |
Recently, the Geriatric Nutritional Risk Index (GNRI) has been introduced as a valuable tool to assess the nutritional status of hemodialysis (HD) patients. To determine the predictive value of the GNRI score for death in HD, we studied 145 chronic HD patients (%53 men, mean age 60 ± 16 years). The GNRI score was estimated by an equation involving serum albumin and individual's weight and height. According to the highest positive likelihood and risk ratios, the cut-off value of the GNRI for mortality was set at 100. The survival of patients on HD was examined with the Cox proportional hazards model. Mortality was monitored prospectively over an 18-month period, during which 35 patients died. The GNRI (mean 102.6 ± 5.5) was significantly positively correlated with lean body mass, hematocrit, serum lipids and presence of metabolic syndrome. Multivariate Cox proportional hazards analysis demonstrated that the GNRI <100, serum ferritin ≥ 500 μ g/L and age 65 years or older were significant predictors for mortality (hazard ratio 3.691, 95% CI 1.751-7.779, P = 0.001; hazard ratio 3.105, 95% CI 1.536-6.277, P = 0.002; and hazard ratio 2.806, 95% CI 1.297-6.073, P = 0.009, respectively), after adjustment to gender and vintage time. It can be concluded that, in addition to old age, malnutrition (low GNRI) and inflammation (high ferritin) are identified as significant independent risk factors that predict all-cause mortality in HD patients.
|How to cite this article:|
Edalat-Nejad M, Zameni F, Qlich-Khani M, Salehi F. Geriatric nutritional risk index: A mortality predictor in hemodialysis patients. Saudi J Kidney Dis Transpl 2015;26:302-8
|How to cite this URL:|
Edalat-Nejad M, Zameni F, Qlich-Khani M, Salehi F. Geriatric nutritional risk index: A mortality predictor in hemodialysis patients. Saudi J Kidney Dis Transpl [serial online] 2015 [cited 2020 May 27];26:302-8. Available from: http://www.sjkdt.org/text.asp?2015/26/2/302/152445
| Introduction|| |
Assessment of nutritional status in individuals with chronic kidney disease, including long-term hemodialysis (HD) patients, is crucial because malnutrition and wasting syndromes are strong risk factors for morbidity and mortality. ,, There are several methods for the assessment of nutrition, including subjective global assessment (SGA)  and malnutrition- inflammation score.  However, recently, the Geriatric Nutritional Risk Index (GNRI) has been reported as a simple and accurate tool to assess the nutritional status in HD patients. 
The aim of our study was to determine the correlation between GNRI, lean body mass (LBM) and metabolic syndrome (MetS), besides the assessment of the predictability of these factors of mortality in chronic HD patients.
| Materials and Methods|| |
This prospective cohort study was approved by the Ethics Committee of our university. Patients were eligible for entry if they had been designed for chronic HD therapy and were 18 years or older with no clinically active cardiovascular, malignant or infectious diseases on entry. The patients were followed-up for a period of 18 months and excluded from final analysis if they were on dialysis for less than six months (dead or alive). This cut-off level was chosen for reducing patient selection bias by excluding unstable cases or patients with unpredictable underling co-morbidities. Data from 145 patients (mean age 60 ± 16 years; 54% men) were analyzed at the end of the study period. All the patients received daytime dialysis and the total hours per week varied from 8-13.5 h.
The cause of chronic renal failure in 48 (33%) patients was diabetic nephropathy and in 20% of the patients was hypertension. The vintage time was 12-33 months, with an average follow-up period of 15.7 ± 4.5 months. Eighty-eight of the 145 patients were present through the whole study duration.
Serum lipids and glucose concentrations were measured with the standard laboratory methods. The average of the measurements of three consecutive months was considered as the baseline value. Blood pressure (BP) was assessed in the sitting position and the patients were considered hypertensive if they had any one of these three criteria: Positive history of hypertension, taking antihypertensive treatments or mean BP greater than ≥130/85 mm Hg at the end of the dialysis session in spite of achieving appropriate dry weight on most occasions, estimated by a review of patients' BP recorded on dialysis charts.
Body mass index (BMI) was calculated as dry weight in kilograms divided by the square of height in meters. We assumed dry weight as the weight recorded at the end of dialysis. Waist circumference was calculated as the average of two measurements taken after inspiration and expiration at the midpoint between the costal margin and the iliac crest.
We used two definitions for metabolic syndrome provided by the International Diabetes Federation (IDF)  and the revised National Cholesterol Education Program (NCEP), respectively. The revised NCEP and IDF definitions of MetS are similar and they can identify many of the same individuals as having MetS.
The GNRI was calculated by modifying the Nutritional Risk Index (NRI) for elderly patients,  as has been reported by Yamada et al,  as follows:
GNRI = [1.489 + albumin (g/L)] + [41.7 × dry weight/ideal BW]
The ideal BW was calculated using height and an idealized BMI of 22.
The cut-off value of GNRI for mortality was set at 100 according to the highest positive likelihood and risk ratios of our cohort (2.30 and 1.47, respectively).
LBM was estimated by an equation  that has been derived from weight, height, serum creatinine (SCr) and urea reduction ratio (URR) at the end of the dialysis treatment, as follows: LBM = 0.34 × SCr (mg/dL) + 5.58 × (1 if female; 0 if male) + 0.30 × weight (in kg) + 0.67 × height (in inches) - 0.23 × URR - 5.75 URR = [(Pre-dialysis Urea - Post-dialysis Urea)/Pre-dialysis Urea] × 100
| Statistical Analysis|| |
All statistical analyses were performed using SPSS software, version 17.0 (SPSS Inc., Chicago, IL, USA).
Results were expressed as the mean values ± SD or as range of values for variables that did not follow a normal distribution. Categorical data were presented as percentages and were compared among groups by χ2 tests. Correlation between GNRI and laboratory parameters were assessed using the Pearson correlation coefficients or the Spearman rank order correlation coefficients as appropriate.
Uni-variate analysis was performed to identify the risk factors that were statistically related to mortality. Variables identified from the uni-variate analysis as potential predictors were included in the multivariate analysis. The P-value for entry into the model was ≤ 0.1. The cut-off value of the variables were based on the distribution within the cohort and the highest values for the positive likelihood and risk ratios.
Survival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The uni-variate and multivariate Cox regression analyses were presented as hazard ratio (HR; CI).
All statistical tests were two-sided, with a value for P <0.05 defining significance.
| Results|| |
No patient was lost to follow-up. Of the 145 study patients, the diagnosis of MetS was confirmed by the IDF definition in 70 (48.2%) subjects and also 112 (77.2%) patients fulfilled the criteria for NCEP-ATP III. The clinical characteristics of the patients are shown in [Table 1].
|Table 1: Clinical characteristics of the cohort at study entry, geriatric nutritional risk index (GNRI) <100 versus >100, n = 145.|
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The mean value of the GNRI score was 102.6 ± 5.5 (range of 87.3-116.4). The GNRI significantly and positively correlated with LBM, the presence of MetS and the serum lipids levels [Table 2]. There were no significant differences of GNRI score due to gender or presence of diabetes (P = 0.254 and P = 0.441, respectively).
|Table 2: Correlation coefficients between GNRI score and MetS and nutritional parameters that are not included in the Geriatric Nutritional Risk Index (GNRI) score in the study patients.|
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During the 18-month follow-up period, 35 patients died. The GNRI score of the surviving patients was significantly greater than that of those who died (103.1 ± 5.1 vs. 100.89 ± 6.4, P = 0.040).
Kaplan-Meier analyses revealed that patients with GNRI <100 (n = 42) had a significantly lower survival rate compared with those with GNRI ≥ 100 (log-rank test, P = 0.003) [Figure 1].
|Figure 1: Kaplan–Meier survival curve by serum ferritin (A), GNRI (B), age (C) and gender (D) categories.|
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Hazard ratios and 95% confidence intervals (CI) of mortality using Cox proportional hazard models, based on initial values at the start of the prospective cohort and time to death, are shown in [Table 3]. The model was controlled for age, gender, BMI, diabetes status, MetS, LBM and serum lipids to estimate the relative risks. Prospective mortality showed the strongest association with the low GNRI. The relative risk for death with a GNRI less than 100 was 3.691 (95% CI 1.751-7.779, P = 0.001). Also, the relative risk for death with serum ferritin ≥500 μ g/L and age 65 years or older were 3.105 (95% 1.536-6.277, P = 0.002) and 2.806 (95% CI 1.297-6.073, P = 0.009), respectively, after adjustment for other parameters (gender, HD vintage, presence of diabetes, MetS and serum biochemistries, [Table 3].
|Table 3: Cox proportional hazards analysis of factors that predicted mortality in the study patients.|
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| Discussion|| |
Our results showed that three major risk factors: Old age, inflammation (high ferritin level) and malnutrition (low GNRI) were strong predictors of short-term mortality in HD patients.
The NRI which, combines two nutritional indicators (albumin and weight), was first described by Buzby et al (1988) , to score the severity of post-operative complications. By extension, it has been used as an index of malnutrition in hospitalized adults by Naber et al (1997).  Thereafter, Bouillanne et al (2005)  replaced the usual weight in the NRI formula by the ideal weight according to the Lorentz formula, creating the GNRI as a predictor of nutrition-related risk of mortality. In our study, we examined the predictive value of the GNRI for mortality by excluding the patients on HD less than six months (despite status) from the final analysis.
There were limited published data about the influence of the GNRI on mortality of HD patients. Our study is similar to the study by Kobayashi et al,  except for our shorter follow-up period (60 versus 18 months). Weight and serum albumin values as two components of the GNRI and frequently fluctuate through time, especially during acute inter-current illnesses; accordingly, we prefer to examine the prognostic significance of the GNRI score on short-term survival. Kobayashi et al showed that the GNRI ≤90 was a significant predictor for mortality after adjustment for the parameters of age, gender, HD vintage and presence of diabetes.
Our cut-off point and the average score for the GNRI were higher than the previous reports, ,,,, but we found a strong association of GNRI <100 and mortality in our study population. We hypothesized that the difference with the other studies resulted from our arbitrary selection of a BMI of 22 for the calculations of the GNRI.
In our study, the high ferritin level (≥500 μ g/L) was associated with a high risk of mortality. This finding suggests the importance of the inflammatory millieu due to CKD as a risk factor for mortality besides the nutritional status.
Surprisingly, the patients with GNRI <100 were somewhat younger than the individuals with GNRI ≥100. Nevertheless, old age (≥65 years) was a strong mortality predictive factor in our study; our data showed that advanced age was a more powerful factor in the prediction of the outcome in our patients than the GNRI.
The cumulative survival was markedly lower in our diabetic patients (0.68 vs 0.79); however, they were not at a greater risk for death than non-diabetics (P = 0.092; curve not shown). Also, despite the high prevalence of the MetS and its strong and positive correlation with the GNRI score (especially by the NCEP definition), this factor had no role in predicting the outcome in our patients. However, the different prevalence of MetS with the NCEP and the IDF definitions (77% versus 48%) offers debate about the extension of the use of this syndrome in HD patients.
The findings in our report had at least three limitations: Firstly, the GNRI value was not sensitive to weight changes in overweight individuals. On the other hand, weight loss did not reflect on the GNRI score until the patient's weight was greater than the ideal body weight. This limitation makes the GNRI a less useful tool for overweight patients. Secondly, this cohort study was not individualized for the cardiovascular and the non-cardiovascular causes of death; however, we excluded cases with malignancy, trauma or accident as causes of death. Thirdly, this study was carried out in a single center with nearly small population; therefore, for better conclusions, a multi-center larger study is needed to establish the role of the GNRI as a tool for predicting the outcome of the HD patients.
We conclude that our study demonstrated that the GNRI in association with old age and high serum ferritin were significant predictors for short-term mortality in HD patients.
Conflict of interest: None declared.
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Dr. Mahnaz Edalat-Nejad
Arak University of Medical Sciences, Arak
[Table 1], [Table 2], [Table 3]
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