Abstract
We investigated the relationship between geriatric nutritional risk index (GNRI) and subpopulation lymphocyte counts (SLCs) in hemodialysis (HD) and peritoneal dialysis (PD) patients and evaluated whether they can be helpful in the diagnosis of malnutrition in these patients. We examined the GNRI and SLCs of 50 HD patients (mean: 55.8 ± 12.7 years; 28 men and 22 women) and 16 Continuous Ambulatory Peritoneal Dialysis (CAPD) patients (mean: 49.8 ± 14.5 years; 10 men and six women). The GNRI is calculated based on the serum albumin level, dry weight, and ideal body weight and uses the following equation: GNRI = [14.89 × albumin (g/dL)] + [41.7 × (weight/ideal body weight)]. SLCs were evaluated using flow cytometry. T-tests and χ2 tests were performed to compare the two groups. Logistic regression analysis was performed for predicting malnutrition in dialysis patients. The average GNRI value was 100.1 ± 8.4 in HD patients and 99.2 ± 8.1 in PD patients, and no significant differences in GNRI or SLC were observed between the two groups. SLCs were higher in patients with higher GNRI (GNRI ≥ 100) although there was no statistical difference. Logistic regression for predicting malnutrition according to GNRI revealed that age, female sex, and CD19 counts predicted malnutrition in HD and PD patients. These results suggest that GNRI and SLCs (especially CD19 count) may be significant nutritional markers in these patients.
Introduction
Malnutrition is highly prevalent in maintenance dialysis patients and is associated with an increased risk of mortality.Citation1,Citation2 Thus, regular nutritional assessment is recommended for all dialysis patients. Serial assessment of nutritional status for detection and management of protein energy wasting (PEW) is recommended using old and new scoring tools, including the subjective global assessment, malnutrition-inflammation score, geriatric nutritional risk index (GNRI), and PEW definition criteria.Citation1,Citation2
To the best of our knowledge, no study has examined the relationship between GNRI and subpopulation lymphocyte counts (SLCs) in hemodialysis (HD) and peritoneal dialysis (PD) patients.
The GNRI is a simple and accurate method to assess nutritional condition using only three parameters: body weight, height, and serum albumin levels.Citation1,Citation3 Recently, the GNRI was found to be a reliable screening tool for malnutrition in HD and PD patients.Citation3,Citation4
Quantities of ingested nutrients influence circulating lymphocyte counts in PD patients.Citation5 Thus, total lymphocyte count (TLC) and SLCs could be helpful in monitoring the nutritional status of PD patients.
There is some controversy regarding the use of TLC as a marker for protein-calorie malnutritionCitation6,Citation7; however, evaluation of TLC has been shown to be helpful in monitoring nutritional status and assessing prognosis in PD patients.Citation5,Citation8–10
Among SLCs, CD19 counts were reported to decrease over the course of PDCitation11, and higher CD19 counts were associated with better clinical and laboratory scores, indicating an adequate PD treatment.Citation5
In the present study, we investigated the relationship between GNRI and SLCs in HD and PD patients and evaluated whether they can be helpful in the diagnosis of malnutrition in these patients.
Methods
Patients
We enrolled 66 patients (50 HD and 16 CAPD patients) receiving stable maintenance dialysis at the Kosin University Gospel Hospital. The study inclusion criteria were ongoing HD or PD therapy for more than 6 months and stable condition. Exclusion criteria included cardiovascular disease, respiratory disease, gastrointestinal disease, neurologic disease, malignancy, severe infection, or use of medications (corticosteroids, cyclosporine, or other major immunosuppressive drugs) affecting TLC. HD patients underwent maintenance HD three times a week using conventional bicarbonate-buffered dialysate. PD patients were dialyzed using glucose PD solutions and the twin-bag connection system.
The study protocol was approved by the ethics committee of our hospital.
Data collection
Clinical and laboratory data from the enrolled patients were gathered from medical records. We collected clinical data, such as sex, age, body mass index (BMI), and the presence of diabetes mellitus (DM), and laboratory results. Blood laboratory tests including SLCs were performed once a month and averaged over 2 months. SLCs were evaluated using flow cytometry. The analyses included CD3 cells (T lymphocytes), CD4 cells (helper lymphocytes), CD8 cells (cytotoxic suppressor lymphocytes), and CD19 cells (B lymphocytes).
Body weight was measured after each dialysis session and averaged over 1 month.
BMI (kg/m2) was calculated using dry weight. Serum albumin levels were measured by bromocresol green. We calculated GNRI using serum albumin values, dry weight, and ideal body weight. GNRI was calculated by modifying the nutritional risk index for elderly subjects, as reported by Yamada et al.Citation3:
Note that body weight/ideal body weight was greater than 1 when a subject’s body weight exceeded his ideal body weight. Ideal body weight was calculated using height and a BMI of 22, which is reportedly associated with the lowest morbidity rate in the Asian population. GNRI was calculated twice at the first and second month of observation.
Subjects were divided into two groups according to dialysis method (HD or PD), GNRI (≥ 100 or < 100), and CD19 counts (≥ 100/mm3 or < 100/mm3).
To determine a possible GNRI cutoff value, patients were divided into two groups on the basis of serum albumin level. The malnutrition group was defined as patients with serum albumin under 3.0 g/dL because of our patient’s malnutrition.
Statistical analysis
Data are expressed as a mean ± standard deviation. T-tests and χ2 tests were performed for comparisons between the two groups. The odds ratios (OR) were calculated using a logistic regression model. p-values < 0.05 were considered statistically significant. All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS), version 18.0 (SPSS Inc., Chicago, IL).
To determine the GNRI cutoff value, positive likelihood ratio was calculated as follows:
Results
Subject characteristics
The clinical characteristics of the patients according to dialysis method are shown in . GNRI data represented a normal distribution, and the average GNRI value was 100.1 ± 8.4 in HD patients and 99.2 ± 8.1 in PD patients. No significant differences in GNRI and SLC were observed between the two groups. The levels of potassium and albumin were higher in HD patients. Patient clinical characteristics according to GNRI are shown in . No significant differences in age, sex, or presence of DM were observed between the two groups. SLCs were higher in patients with higher GNRI although there was no statistical difference. The levels of BUN and albumin were higher in patients with higher GNRI.
Table 1. Clinical characteristics of 66 dialysis patients according to the methods of dialysis at the start of the study.
Table 2. Clinical characteristics of 66 dialysis patients according to GNRI at the start of the study.
Factors for predicting malnutrition
Logistic regression analysis was performed for investigating the causal relationship between independent variables and GNRI. The results of the logistic regression for predicting malnutrition according to GNRI are shown in . The results showed that age > 60 years (OR: 10.783, 95% CI: 1.936–60.059, p = 0.007), female sex (OR: 6.643, 95% CI: 1.269–34.788, p = 0.025), and CD19 count < 100 (OR: 9.202, 95% CI: 1.481–57.191, p = 0.017) predicted malnutrition in HD and PD patients.
Table 3. Logistic regression for predicting malnutrition according to GNRI at the start of the study.
Patient clinical characteristics according to CD19 count are shown in . Duration of dialysis was shorter, and SLC and albumin levels were higher, in the group with higher CD19 counts.
Table 4. Clinical characteristics of 66 dialysis patients according to CD19 count at the start of the study.
Discussion
In this study, we examined the relationship between GNRI and SLC in HD and PD patients using logistic regression.
Several studies have suggested that GNRI is the simplest and most accurate index to assess malnutrition in HD patients.Citation3,Citation12–14 Similarly, a recent study of 486 PD patients over 14 years showed that GNRI is also able to predict nutritional status and prognosis in PD patients, although the role of GNRI in PD patients had been unclear previously. According to this study, GNRI is significantly associated with creatinine, albumin, BMI, arm circumference, fat mass index, and comorbidity.Citation4
Yamada et al. suggested that the most accurate GNRI cutoff value to identify malnutrition in HD patients was less than 91.2 based on the malnutrition-inflammation score.Citation3 In a study of 753 HD patients, Panichi et al. demonstrated that GNRI less than 92, the lowest quartile, was strongly associated with malnutrition and poor outcomes.Citation13
In addition, a GNRI cutoff value of 96.4 is associated with significant reduction of lean mass index according to Kang et al.Citation4
In this study, to determine a possible GNRI cutoff value for malnutrition, the sensitivity, specificity, and positive likelihood ratio were examined. A GNRI value of 100 was shown to indicate the highest value of positive likelihood ratio (data not shown).
It appears that dietary intake influences circulating lymphocyte counts in PD patients. In a study of 55 uremic patients, Grzegorzewska and Leander proposed that effectiveness of nutrition can be monitored by evaluating TLC and SLC.Citation5
TLC has been used as an index to represent nutritional status, but its role in assessing malnutrition and prognosis in HD and PD patients is debatable.Citation8,Citation15 Ateş et al. reported that volume status is more closely related to TLC than nutritional status in PD patients.Citation8
Among the SLCs in the present study, CD19 count, a marker of B lymphocytes, is associated with GNRI. These cells play an important role in initiating the immune response through antigen-independent and immunoglobulin-induced activation of B cells.Citation16
Palop et al. measured changes in cell immunity during a 3-year follow-up of PD patients. TLC, CD8, and CD19 cell counts were significantly decreased over time.Citation11
Grzegorzewska and Leander reported that CD19 cell count is positively correlated with intake of potassium, copper, vitamin A, and beta-carotene, and its increase improves insomnia, weakness, and clinical symptoms (i.e., dysgeusia, anorexia, nausea, vomiting) and other indicators (i.e., serum albumin, hematocrit, Lean Body Mass (LBM)) that reflect nutritional status.Citation5
In uremic patients, the relationship between malnutrition and immune function has been known for some time. Glassock postulated that cellular immune dysfunction observed in these patients may be the consequence of nutritional deficiency rather than uremic toxins or endogenous factors.Citation17 More research is needed to assess the nutritional function of immune cells in these patients.
Our study has several limitations. It is a prospective study, but the observational period was only 2 months, and the sample size was too small. A well-constructed study with a larger sample size and a longer follow-up period is needed.
In addition, it may be controversial that malnutrition is defined as serum albumin under 3.0 g/dL in the process of obtaining the GNRI cutoff value; however, the absence of a precise definition of malnutrition in dialysis patients led to our use of serum albumin level.
Finally, further studies on the relationship between SLC and mortality are needed.
Conclusions
These results suggest that GNRI and SLCs (especially CD19 count) may be significant nutritional markers in HD and PD patients.
Disclosure statement
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
Funding
This study was supported by a grant from the Kosin University College of Medicine (2014).
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