Abstract
OBJECTIVE
This study aims to evaluate the nutritional status of patients with stage 3 and 4 diabetic nephropathy (DN; DN-3 and DN-4) and to explain the effect of DN stages on the prognosis of protein-energy wasting (PEW).
METHODS
Data from demographic characteristics, anthropometric measurements, biochemical findings, food consumption records, and the Subjective Global Assessment (SGA) screening tool of 49 patients (25 DN-3; 24 DN-4) who were followed at the nephrology department were collected. The criteria of the International Society of Renal Nutrition and Metabolism (ISRNM) were used to determine PEW.
RESULTS
56% of DN-3 and 66.7% of DN-4 have been diagnosed with diabetes for over 15 years. The groups differed in total body weight, body-muscle weight, creatinine, microalbuminuria, and eGFR values (p<0.05). Protein (g/kg) intake was only different between the groups (p<0.05). 18.4% of patients had SGA-B score, and 26.5% had PEW.
CONCLUSION
Our study provides a general impression about the presence of PEW in DN patients not receiving dialysis in Turkiye. In patients with DN-3 and DN-4, daily energy and macronutrient intakes are adequate by recommendation. According to ISRNM criteria, the prevalence of PEW increased with advancing disease stage. PEW was observed to be more effective than SGA in assessing malnutrition.
Keywords: Diabetic kidney disease, malnutrition, nephropathy, nutrition, protein energy wasting
Highlight key points
It was demonstrated that 26.5% of the non-dialysis patients with DN had PEW. The existence of PEW was found to be higher in patients with stage 4 DN than stage 3.
Patients diagnosed with PEW were found to have significantly lower body weight, body muscle weight, and waist circumference measurements than non-PEW patients. In addition, patients diagnosed with PEW have higher microalbuminuria and lower eGFR.
It is important that PEW criteria are routinely used to prevent the progression of DN to end-stage renal disease or to detect DN in its early stages.
Diabetes mellitus (DM) is a chronic, progressive disease characterized by hyperglycemia leading to various problems such as neuropathy, nephropathy, skin changes and ulceration, and cardiovascular complications [1, 2]. Diabetic nephropathy (DN) occurs with progressive deterioration of renal function due to hypertension and decreased glomerular filtration rate (GFR). DN is a persistent microvascular complication of DM, defined as high levels of albumin excretion in the urine and impaired renal activity [3, 4].
Chronic kidney disease (CKD) patients have depleted protein and energy stores and accompanying inflammation [5]. The International Society of Renal Nutrition and Metabolism (ISRNM) defines protein-energy wasting (PEW) as the combination of muscle loss, fat loss, malnutrition, and inflammation associated with kidney disease that can occur without insufficient dietary intake [5]. Patients with stage 3 DN (DN-3) and stage 4 DN (DN-4) have been reported to have an increased risk of PEW and a higher PEW rate than non-diabetics [6].
Stage 3 is defined as microalbuminuria and usually occurs 6–15 years after the onset of diabetes. The risk of DN and cardiovascular disease is increased at this stage. Stage 4 is referred to as macroalbuminuria and is characterized by a decrease in GFR of 10–12 ml/min per year. When appropriate treatment is not commenced, it results in end-stage renal disease over the years [7].
Studies on the prevalence of PEW have shown that when the Subjective Global Assessment (SGA) screening tool is used, the prevalence of PEW in stage 3 and 4 CKD patients is 12–18%; this prevalence appears to reach 70% in dialysis patients [8–10]. In this study, we aimed to evaluate the nutritional status of patients with DN-3 and DN-4 and to examine PEW. It is also aimed to explain the effect of nutritional status and DN stages on the prognosis of PEW.
MATERIALS AND METHODS
Ethics Committee Approval
Ethical approval for this study was obtained from the Marmara University Faculty of Medicine Clinical Research Ethics Committee (date: 04.01.2019, decision no: 09.2018.800). All patients signed the “informed consent form” and “consent form”. All procedures were carried out in accordance with the Declaration of Helsinki.
Study Design and Subjects
This descriptive cross-sectional study was conducted in Marmara University Faculty of Medicine, Department of Nephrology, between January 2019 and June 2019. All patients who applied to the nephrology department and were diagnosed with DN were contacted within these dates, and 49 patients diagnosed with DN-3 (n=25) and DN-4 (n=24) patients who met the research criteria and volunteered to participate in the study were included. When the sample size was calculated using the sample article in the G-power analysis with a 95% CI, 5% error, both groups were found to have 13 people each [11].
Patients diagnosed with DN-3 and DN-4 without any chronic disease other than DN (except diabetes and hypertension) were enrolled in the study. Patients aged 25–75 years were selected for the study because DN is reported to be more common in older adults and those with diabetes duration between 15 and 30 years [12, 13]. Individuals who did not meet the inclusion criteria and who had a major illness that would affect their ability to record food consumption using the 3-day recall method were excluded from the study.
Data Collection Tool
In the study, patients were provided with a questionnaire via face-to-face interview. The questionnaire includes individuals’ demographic and anthropometric characteristics, biochemical findings and 3-day food records. The SGA screening tool was used to assess patients’ nutritional status, and the ISRNM criteria were utilized for determining PEW.
Anthropometric Measurements
The patient’s weight, body fat percentage, body mass index (BMI), fat mass, and muscle mass were obtained using the bioelectrical impedance analysis technique with the Inbody 120 brand scale. A seca mechanical height was used to gauge the patients’ height and waist circumference was measured with a tape measure. The individuals’ height (m) and body weight (kg) values were used to calculate their BMI.
Food Consumption Record
Food consumption records of the patients were taken for 3 consecutive days, including 2-weekdays and 1-weekend day. Energy and macronutrient analyses of food consumption records were evaluated in the BeBis 8.1 program. The percentages of energy and macronutrients in the food consumption records were calculated based on the Recommended Daily Allowance (RDA). Energy and macronutrient intakes by RDA are considered “inadequate intake” for <66%, “adequate intake” for 67–133%, and “excessive intake” for >133% [14].
Subjective Global Assessment (SGA) Screening Tool
SGA is a tool that assesses nutritional status by using both objective and subjective components. It consists of three sections, including the patient’s history, physical examination, and SGA score. The history section includes questions related to weight change, change in food intake, gastrointestinal symptoms, functional capacity, illness, and nutritional needs. The physical examination section evaluates subcutaneous fat loss (triceps, chest), loss of muscle mass, edema, and ascites. The scores of these assessments are classified into the following categories: SGA-A (well-nourished), SGA-B (moderately-malnourished) and SGA-C (severely-malnourished) [15].
Biochemical Parameters
Blood glucose, serum albumin, microalbuminuria, total cholesterol, creatinine, GFR, and C-reactive protein (CRP) levels were retrospectively obtained from the biochemical findings of the patients within the last three months.
Protein-Energy Wasting (PEW) Diagnostic Criteria
Following the ISRNM, four basic parameters (serum biochemistry, body mass, muscle mass, and dietary intake) are evaluated as diagnostic criteria for PEW. Clinical diagnostic criteria of PEW in CKD require serum biochemistry (low serum albumin or low serum prealbumin or low serum cholesterol), body mass (BMI <23 kg/m2 or unintentional weight loss over time or total body fat percentage), muscle mass (muscle loss or decreased upper middle arm muscle circumference), and dietary intake (unintentional low protein intake or undesirable low dietary energy intake). At least three of the four categories (and at least one test in each of the selected categories) must be met for a diagnosis of PEW due to kidney disease [16].
Statistical Analysis
The Statistical Package for Social Sciences (SPSS) 22.0 software (IBM Corp.; Armonk, NY, USA) was used for statistical analysis. Descriptive statistics were calculated for demographic data. Independent Sample T-test, Mann-Whitney U, Chi-square, and Spearman tests were used to analyze the data. The p-value <0.05 was considered statistically significant.
RESULTS
Our study involved 49 patients diagnosed with DN; 40.8% of the participants were >65 years of age and 61.2% were men. In our study, the body weight and muscle weight of DN-3 patients were higher than DN-4 patients, and this difference was significant (p=0.007; p<0.001, respectively). At the same time, no muscle wasting was observed in DN-3 patients, whereas muscle wasting was found in DN-4 patients (p=0.019). PEW was found in 26.5% of all patients, and 12 of 13 patients with PEW were in the DN-4 group (p=0.001). SGA-B was found in 18.4% of all patients. It was found that the year of kidney disease was negatively correlated with body weight (kg), BMI (kg/m2), and waist circumference (cm) in DN-3 patients and it was also negatively correlated with body fat weight (kg), body fat percentage (%), waist circumference (cm) in DN-4 patients. (p<0.05; Table 1).
Table 1.
Demographic and anthropometric characteristics of the patients and the relationship between anthropometric measurements and year of kidney disease in patients
| Variables | Stage of disease | χ2 | p | ||||
|---|---|---|---|---|---|---|---|
| Stage 3 DN (n=25) | Stage 4 DN (n=24) | Total (n=49) | |||||
| % | % | % | |||||
| Gender | 15.414 | <0.001* | |||||
| Male | 88 | 33.3 | 61.2 | ||||
| Female | 12 | 66.7 | 38.8 | ||||
| Age | 0.214 | 0.773 | |||||
| <65 years | 56 | 62.5 | 59.2 | ||||
| ≥65 years | 44 | 37.5 | 40.8 | ||||
| Type of diabetes | 0.271 | 0.667 | |||||
| Type 1 DM | 8 | 12.5 | 10.2 | ||||
| Type 2 DM | 92 | 87.5 | 89.8 | ||||
| Age at kidney disease diagnosis | 0.698 | 0.538 | |||||
| <9 years | 64 | 75 | 69.4 | ||||
| ≥9 years | 36 | 25 | 30.6 | ||||
| Age at diabetes diagnosis | 0.587 | 0.561 | |||||
| ≤15 years | 44 | 33.3 | 38.8 | ||||
| >15 years | 56 | 66.7 | 61.2 | ||||
| PEW | 13.293 | 0.001* | |||||
| PEW exists | 4 | 50 | 26.5 | ||||
| PEW not exists | 96 | 50 | 73.5 | ||||
| SGA score | 3.659 | 0.074 | |||||
| SGA-A | 92 | 70.8 | 81.6 | ||||
| SGA-B | 8 | 29.2 | 18.4 | ||||
| Anthropometric measurements | Median (Q1–Q3) | U | > | Z | p | ||
| Stage 3 DN (n=25) | Stage 4 DN (n=24) | ||||||
| Body weight (kg) | 84.1 (76.8–95.7) | 73.0 (67.1–86.3) | 164.5 | -2.710 | 0.007* | ||
| BMI (kg/m2) | 29.2 (26.4–34.1) | 27.6 (24.5–35.7) | 264.5 | -0.710 | 0.478 | ||
| Body fat weight (kg) | 24.0 (20.1–33.1) | 26.3 (14.3–39.8) | 280.5 | -0.390 | 0.697 | ||
| Body fat percentage (%) | 29.2 (25.5–37.4) | 38.9 (23.5–46.2) | 267.5 | -0.650 | 0.516 | ||
| Body muscle weight (kg) | 34.5 (29.3–37.7) | 26.0 (23.0–29.8) | 102.5 | -3.951 | <0.001* | ||
| Waist circumference (cm) | 103.0 (97.0–112.5) | 100.0 (88.0–111.0) | 223.0 | -1.541 | 0.123 | ||
| Muscle loss (%) | 0.0 (0.0–0.0) | 0.0 (0.0–4.1) | 209.0 | -2.343 | 0.019* | ||
| Anthropometric measurements | Year of kidney disease | ||||||
| Stage 3 DN (n=25) | Stage 4 DN (n=24) | ||||||
| r | p | r | p | ||||
| Body weight (kg) | -0.444 | 0.026* | -0.386 | 0.062 | |||
| BMI (kg/m2) | -0.424 | 0.035* | -0.371 | 0.074 | |||
| Body muscle weight (kg) | -0.192 | 0.358 | 0.140 | 0.515 | |||
| Body fat weight (kg) | -0.288 | 0.163 | -0.453 | 0.026** | |||
| Body fat percentage (%) | -0.119 | 0.570 | -0.437 | 0.033** | |||
| Waist circumference (cm) | -0.526 | 0.007* | -0.456 | 0.025** | |||
| Muscle loss (%) | 0.221 | 0.288 | -0.058 | 0.786 | |||
DN: Diabetic nephropathy; DM: Diabetes mellitus; SGA: Subjective global assessment; BMI: Body mass index; *: Fisher’s exact chi-square p<0.05; Mann-Whitney U test; Asym p. Sig. (2-tailed); **: Spearman correlation; Sig. (2-tailed) p<0.05.
According to the biochemical parameters of the patients, CRP, glucose, and albumin values were higher than the reference values, but there was no difference by the disease stage (Table 2). Creatinine and microalbuminuria levels were lower (p<0.001) and eGFR values were higher (p<0.001) in patients with DN-3 compared to DN-4.
Table 2.
Biochemical parameters of the patients
| Biochemical parameters | Stage of disease, Median (Q1–Q3) | U | Z | p | |
|---|---|---|---|---|---|
| Stage 3 DN (n=25) | Stage 4 DN (n=24) | ||||
| CRP (mg/L) | 5.51 (3.13–16.2) | 5.9 (3.1–15.0) | 284.5 | -0.310 | 0.756 |
| Glucose (mg/dl) | 148.0 (126.5–182.0) | 134.0 (115.5–189.2) | 243.5 | -1.130 | 0.258 |
| Albumin (g/dl) | 3.9 (3.7–4.2) | 3.8 (3.6–3.9) | 218.0 | -1.650 | 0.099 |
| Creatinine (mg/dl) | 2.5 (2.0–2.7) | 3.2 (2.6–3.5) | 115.5 | 93.692 | <0.001* |
| Cholesterol (mg/dl) | 183.0 (154.5–209.5) | 185.0 (163.5–223.2) | 258.5 | -0.830 | 0.406 |
| Microalbuminuria (mg/dl) | 58.0 (35.5–207.5) | 794.5 (426.8–2295.5) | 0.0 | -6.001 | <0.001* |
| eGFR (ml/min/1.73 m2) | 37.8 (30.2–49.9) | 23.2 (15–45.4) | 18.0 | -5.641 | <0.001* |
DN: Diabetic nephropathy; CRP: C-Reactive protein; eGFR: Estimated glomerular filtration rate; *: Mann-Whitney U test; Asym p. Sig (2-tailed) p<0.05. Reference values: CRP, 0–5.5 mg/L; Glucose, 70–100 mg/dl; Albumin, 3.5–5.5 g/dl; Creatinine, 0.5–1.2 mg/dl; Cholesterol, <200 mg/dl; Microalbuminuria, <30 mg/dl normal 30–300 mg/dl microalbuminuria >300 mg/dl macroalbuminuria; eGFR (ml/min/1.73 m2), ≥90 Stage 1, 50–89 Stage 2, 30–49 Stage 3, 15–29 Stage 4, <15 Stage 5.
Table 3 shows energy, carbohydrate, and fat intakes did not differ between the groups, while protein intakes per weight were higher in DN-4 patients (p<0.05).
Table 3.
Patients’ daily energy and nutrient intakes according to the RDA
| Energy and nutrient intake | Stage of disease | U | Z | p | |||
|---|---|---|---|---|---|---|---|
| Stage 3 DN (n=25) | Stage 4 DN (n=24) | ||||||
| Median (Q1–Q3) | RDA (%) | Median (Q1–Q3) | RDA (%) | ||||
| Energy (kcal/day) | 1414.5 (836–2238.3) | 74.4 | 1457.1 (930–2000.2) | 76.7 | 263.0 | -0.740 | 0.459 |
| Carbohydrate (g/day) | 147.7 (74.8–227.8) | 113.6 | 159.5 (85.2–228.8) | 122.7 | 238.0 | -1.240 | 0.215 |
| Carbohydrate (%TE) | 41.3 (32.4–57.8) | 77 | 43.5 (32.6–53.2) | 79 | 224.5 | -1.510 | 0.131 |
| Protein (g/day) | 56 (34.3–84.5) | 88.1 | 53.2 (34.5–88.2) | 83.7 | 286.5 | -0.270 | 0.787 |
| Protein (g/kg) | 0.63 (0.26–0.88) | 75.9 | 0.79 (0.28–2.15) | 95.2 | 198.5 | -2.031 | 0.042* |
| Fat (g/day) | 65.2 (41.1–111.1) | 100.3 | 64.5 (42.7–94.8) | 99.2 | 284.0 | -0.320 | 0.749 |
DN: Diabetic nephropathy; RDA: Recommended daily allowance; %TE: Total energy percentage; *: Mann-Whitney U test; Asym p. Sig (2-tailed); PEW: Protein-energy waste; DN: Diabetic nephropathy; BMI: Body mass index; SGA: Subjective global assessment; **: Fisher’s Exact chi-square. p<0.05.
Although age, years of renal disease and diabetes were higher in patients with SGA-B, no significant difference was found (p>0.05). There was a statistically significant difference in body and muscle weight, muscle loss, and eGFR values by SGA scores (p<0.05). The eGFR values, body and muscle weight were more elevated in patients with SGA-A than those with SBA-B, and muscle loss was lower (Table 4).
Table 4.
The relationship between patients’ SGA scores and variables
| Variables | SGA score, Median (Q1–Q3) | U | Z | p | |
|---|---|---|---|---|---|
| SGA-A (n=40) | SGA-B (n=9) | ||||
| Age | 63 (57.0–68.7) | 65 (45.0–71.0) | 179.5 | -0.013 | 0.990 |
| Year of kidney disease | 5 (3.2–10.0) | 6 (4.5–9.0) | 166.0 | -0.364 | 0.716 |
| Duration of diabetes | 17 (12.2–25.0) | 18 (13.5–20.0) | 172.5 | -0.195 | 0.846 |
| Anthropometric measurements | |||||
| Body weight (kg) | 83.7 (74.8–93.7) | 66.6 (49.9–80.6) | 81.5 | -2.543 | 0.011* |
| BMI (kg/m2) | 29.4 (26.0–34.7) | 27.5 (20.7–33.5) | 125.5 | -1.407 | 0.159 |
| Body fat weight (kg) | 25.8 (19.0–35.9) | 19.0 (10.9–33.7) | 135.0 | -1.162 | 0.245 |
| Body fat percentage (%) | 29.9 (24.6–43.8) | 28.9 (23.5–43.6) | 169.5 | -0.271 | 0.786 |
| Body muscle weight (kg) | 31.3 (26.3–35.9) | 24.1 (21.3–26.1) | 56.0 | -3.202 | 0.001* |
| Waist circumference (cm) | 103.0 (96.2–111.0) | 98.0 (81.5–109.5) | 117.5 | -1.615 | 0.106 |
| Muscle loss (%) | 0.0 (0.0–0.0) | 4.7 (3.8–5.7) | 6.5 | -5.766 | 0.001* |
| Energy and nutrient intake | |||||
| Energy (kcal/day) | 1502.3 (1229.9–1594.2) | 1274.7 (1097.6–1794.9) | 162.0 | -0.465 | 0.642 |
| Carbohydrate (g/day) | 157.9 (136.4–180.3) | 121.8 (98.9–208.1) | 167.0 | -0.336 | 0.737 |
| Fat (g/day) | 62.6 (52.3–75.2) | 64.3 (52.3–70.0) | 172.5 | -0.194 | 0.846 |
| Biochemical parameters | |||||
| Albumin (g/dl) | 3.9 (3.7–4.1) | 3.7 (3.1–4.0) | 120.0 | -1.558 | 0.119 |
| Glucose (mg/dl) | 139.0 (118.0–177.0) | 138.0 (118.5–216.5) | 154.5 | -0.658 | 0.510 |
| Creatinine (mg/dl) | 2.65 (2.3–3.1) | 3.0 (2.0–3.6) | 170.5 | -0.245 | 0.806 |
| Cholesterol (mg/dl) | 183.5 (156.5–210.2) | 198.0 (164.0–300.5) | 138.5 | -1.072 | 0.284 |
| CRP (mg/L) | 5.7 (3.1–15.5) | 5.9 (3.1–12.7) | 161.5 | -0.478 | 0.632 |
| Microalbuminuria (mg/dl) | 250.5 (50.5–559.5) | 689.0 (198.5–1574.0) | 124.0 | -1.446 | 0.148 |
| eGFR (ml/min/1.73 m2) | 32.2 (15.2–49.9) | 23.6 (15.1–33.5) | 100.0 | -2.066 | 0.039* |
SGA: Subjective global assessment; BMI: Body mass index; CRP: C-Reactive protein; eGFR: Estimated glomerular filtration rate; *: Mann-Whitney U test; Asymp. Sig. (2-tailed) p<0.05.
Table 5 states that body weight, body muscle weight, and waist circumference decreased in patients with PEW (p=0.009, 0.002, 0.044, respectively). In addition, these patients had higher microalbuminuria levels and lower eGFR values (p=0.002, 0.005, respectively).
Table 5.
Relationship of PEW status with biochemical parameters and anthropometric measurements
| Variables | PEW status, Median (Q1–Q3) | U | Z | p | |
|---|---|---|---|---|---|
| Exists (n=13) | Not exists (n=36) | ||||
| Age | 64 (53–67) | 62.5 (56.2–69) | 228.0 | -0.139 | 0.892 |
| Year of kidney disease | 6 (3.5–10) | 5.5 (3.2–9.7) | 212.0 | -0.502 | 0.616 |
| Duration of diabetes | 20 (16.5–21) | 16 (12–25) | 197.0 | -0.842 | 0.400 |
| Biochemical Parameters | |||||
| Glucose (mg/dl) | 136.0 (117.0–201.5) | 140.0 (119.0–178.7) | 228.0 | -0.136 | 0.892 |
| Microalbuminuria (mg/dl) | 689.0 (445.0–1811.0) | 207.5 (41.2–398.5) | 100.0 | -3.035 | 0.002* |
| eGFR (ml/min/1.73 m2) | 20.7 (16.8–29.7) | 30.4 (27.9–43.7) | 111.0 | -2.786 | 0.005* |
| Creatinine (mg/dl) | 2.5 (1.9–3.2) | 2.2 (1.6–2.4) | 149.0 | -1.925 | 0.054 |
| CRP (mg/dl) | 6.3 (3.19–15.1) | 5.7 (3.1–15.2) | 230.5 | -0.079 | 0.937 |
| Anthropometric measurements | |||||
| Body weight (kg) | 70.6 (61.1–81.8) | 84 (74.8–94.6) | 118.0 | -2.627 | 0.009* |
| Body muscle weight (kg) | 25.3 (22.7–27.6) | 31.9 (26.5–36.1) | 98.0 | -3.080 | 0.002* |
| Body fat weight (kg) | 19.0 (13.7–36.8) | 26.1 (19.8–35.9) | 171.1 | -1.427 | 0.154 |
| Waist circumference (cm) | 99 (86–106.5) | 103 (97–113.2) | 145.0 | -2.017 | 0.044* |
PEW: Protein-energy wasting; eGFR: Estimated glomerular filtration rate; CRP: C-Reactive protein; *: Mann Whitney U test; Asymp. Sig. (2-tailed) p<0.05.
DISCUSSION
Diabetic nephropathy is a major cause of morbidity and mortality in patients with DM and increases the risk of renal disease progression. Therefore, DN should be diagnosed early, and its progression should be prevented and/or slowed down with appropriate interventions [17].
In our study, over half of patients with DN have been diagnosed with DM for >15 years. It is known that patients with type 1 or type 2 DM have a high risk of developing nephropathy within 15 years after the onset of the disease. [18]. Patients with diabetes duration between 15 and 30 years had a higher prevalence of DN diagnosis [13]. The fact that the year of diabetes diagnosis was more than 15 years in the majority of patients, and this was higher in DN-4, may increase the existence of PEW and consequently progress to end-stage renal disease.
In patients with CKD, it is first recommended to assess body composition for nutritional evaluation [19]. In our study, the total body weight and muscle weight of patients with DN-3 were higher, and muscle loss was increased in patients with DN-4. It was also found that body weight, waist circumference, and BMI in DN-3 and body fat weight, body fat percentage and waist circumference in DN-4 decreased with increasing years of renal disease. Similarly, diabetic CKD patients had reduced body muscle mass, elevated fat mass, and a lower BMI compared to non-diabetic CKD patients [20]. Changes in body weight, muscle mass, and fat mass are expected as a result of increased exposure to inflammation, increased anorexia and susceptibility to malnutrition as the renal disease year increases.
Our study detected lower eGFR values and higher levels of microalbuminuria and serum creatinine in patients with DN-4 compared to DN-3. CKD patients with DN have been found to lower eGFR levels compared to those without DN [21]. CKD patients with PEW have low albumin levels, total lymphocyte count, fat and muscle mass, high proteinuria, and Na/K ratio [22]. Our outcomes are similar to those in the literature, as the eGFR level decreases and the microalbuminuria level increases as the disease stage progresses.
In non-dialyzed CKD stage 3–5 patients, energy intake is 25–35 kcal/ideal kg/day for ideal weight, while protein intake is recommended as 0.8–0.9 g/kg/day for diabetics. It is generally expected to obtain 50–60% of their total energy intake from carbohydrates and 30% from fat [23]. Our study showed no significant difference in daily energy and protein intake between DN-3 and DN-4. However, patients with DN-4 had a higher protein intake (g/kg) than DN-3. Both groups’ daily energy and macronutrient intakes were also adequate by RDA. A study reported a significant reduction in urinary albumin excretion in individuals with type 2 DM and microalbuminuria after restricting carbohydrate intake to 38% of total energy for 12 months [24]. Other studies have demonstrated that energy and macronutrient intake are significantly reduced in patients diagnosed with PEW [22, 25]. Although the anthropometric parameters of the patients with DN-3 in our study were better, the daily energy intake and protein intake of the patients with DN-4 were relatively higher. This may indicate enhanced catabolism in patients with DN-4. Inadequate energy and macronutrient intake in renal patients with PEW may lead to malnutrition and the inability to compensate for catabolism in DN.
SGA is a valid tool for assessing malnutrition in CKD [15]. In this study, the majority of the patients were well-nourished, and only 18.4% were moderately malnourished on the SGA score. A study stated that 56.3% had moderate malnutrition and 8.1% had severe malnutrition in hemodialysis patients [26]. In our study, moderately-malnourished patients had lower body weight, less muscle weight, higher muscle loss, and fewer eGFR levels. It was observed that the energy and carbohydrate intakes of moderately-malnourished individuals were lower, although no significant difference was found. In addition, the body weights of patients with SGA-B were statistically lower than patients with SGA-A, and this finding is consistent with other studies in the literature [15, 27]. A study evaluating malnutrition with SGA reported that the incidence of malnutrition increased gradually with the progression of kidney disease and the presence of malnutrition was associated with lower body weight, hemoglobin, total protein, albumin, pre-albumin, and reduced food intake. This study reported the need for a more detailed and sensitive scale for the assessment of malnutrition, especially in stage 3–5 CKD patients [28].
PEW is a valuable tool for early diagnosis of alterations in nutrient intake which includes a combination of several SGA parameters [15]. We determined that 26.5% of the patients had PEW by ISRNM diagnostic criteria and PEW was higher in DN-4 than DN-3. In non-diabetic nephropathy patients, PEW was more common in stage 4 nephropathy than stage 3 [15]. In patients with DN receiving dialysis, the PEW rate was 21–23% based on ISRNM criteria [29]. In another study, the prevalence of PEW among pre-dialysis CKD patients was 33.3% [30]. Pérez-Torres et al. [22] found the prevalence of PEW to be 30.1% and SGA-B to be 27.9% in advanced CKD patients, and there was no difference between the two methods. Meanwhile, another study demonstrated that PEW was more effective than SGA. Using an albumin cut-off value of 3.8 mg/dl instead of 3.5 mg/dl in PEW criteria provides superiority over SGA because PEW successfully predicts and is more sensitive to detect malnutrition [8]. Furthermore, Ho et al. [8] demonstrated that obligatory BMI assessment, in addition to the original PEW criteria, provided a better measure of mortality rate. These PEW rates may differ due to patients with CKD at different stages, dialysis and diabetic status.
Low serum albumin, microalbuminuria, high GFR levels, low BMI and unintentional weight loss, and low muscle and/or fat mass are indicators of PEW [25]. In this study, albumin levels were lower in DN-4 and patients with SGA-B, although not statistically significant. Microalbuminuria was observed in DN-3 patients, macroalbuminuria was noted in DN-4 patients and patients with PEW had more albuminuria than those without PEW. In research in Turkiye, 36% of CKD patients receiving dialysis had albumin levels below 3.8 mg/dl [11]. In the majority of hemodialysis patients, serum albumin levels are below 3.7 mg/dl [31]. Studies have confirmed that the albumin levels of patients with PEW were lower than the patients without PEW [8, 11, 22]. Oral energy supplementation for 2 months among hemodialysis patients with PEW has improved hemoglobin, albumin and nutrient intake [32]. In non-dialysis CKD patients who followed up for 2 years, oral nutritional support resulted in improvements in BMI, serum albumin and inflammatory markers [33]. It is critical to screen for PEW in patients who have had diabetes for many years and in patients with early-stage CKD and to provide appropriate nutritional support to patients with PEW.
Our study found that the body weight, body muscle weight and waist circumference measurements of patients with PEW were significantly lower than those without PEW. Vanden Wyngaert et al. [34] stated that PEW is a predictor of increased fall risk and impaired exercise capacity in hemodialysis patients. Changes such as insufficient nutrient intake due to appetite loss and dietary factors, increased energy expenditure, acidosis, multi-catabolic endocrine disorders, and continuous inflammation lead to excessive muscle and adipose loss [6]. Therefore, it is expected that the deterioration of nutritional status in patients with PEW is accompanied by inflammation [16]. In our study, CRP values were found to be higher in patients with PEW, although not statistically significant. At the same time, CRP values were above the reference values in both groups, indicating inflammation in patients. PEW, which uses total cholesterol, serum albumin and BMI values for diagnostic criteria, reflects malnutrition and inflammation, and PEW values are associated with morbidity and mortality [16]. In a large cohort study by Ho et al. [8], PEW was associated with a higher risk of mortality at 3 months and 1 year. It was also stated that a modest decrease in BMI, serum albumin and total cholesterol levels based on PEW criteria caused by malnutrition results in worse survival.
The limitations of our study include the single-center design of the study and the small population. The evaluation and comparison of malnutrition in DN patients according to the PEW criteria defined by ISRNM and using the SGA screening tool is a strength of this study. Our study determined that the presence of PEW in non-dialysis patients with diabetic nephropathy is at significant levels while PEW studies in the literature have generally focused on renal patients receiving dialysis. Therefore, it is considered that PEW assessment may predict malnutrition in this patient group.
Conclusion
This study presents an overview of the PEW assessment in diabetic nephropathy non-dialysis patients in Turkiye with the aim of enhancing awareness of malnutrition and encouraging early intervention. This study evaluated the nutritional status of patients with DN, and all patients had adequate daily energy and macronutrient intakes by RDA. It was reported that 26.5% of the patients had PEW. Furthermore, in a comparison of SGA and PEW used to evaluate malnutrition, PEW was assessed to be more effective. PEW was observed to affect albuminuria, GFR, body weight, body muscle weight, and waist circumference. PEW, related to mortality and morbidity risk, is an important tool that reflects inflammation and malnutrition as assessed by serum albumin, total cholesterol, CRP, GFR, BMI, and muscle mass. Therefore, it is important to use PEW criteria routinely to prevent the progression of DN to end-stage renal disease or to detect DN in the early stages. There should be longitudinal cohort studies on the effectiveness of PEW to predict malnutrition in this patient group.
Footnotes
Cite this article as: Karakas E, Colak H, Gunes FE, Karakoyun B. Determination of nutritional status and protein-energy wasting in patients with diabetic nephropathy. North Clin Istanb 2024;11(6):560–568.
This study was presented at Joint Congress of FEPS and Turkish Society of Physiological Sciences, and published in abstract form in Acta Physiologica 2023; Vol. 237 (Suppl. 727): 64–65.
Ethics Committee Approval
The Marmara University Clinical Research Ethics Committee granted approval for this study (date: 04.01.2019, number: 09.2018.800).
Authorship Contributions
Concept – EK; Design – EK, FEG; Supervision – BK; Fundings – EK; Materials – EK, HC; Data collection and/or processing – EK, HC, FEG, BK; Analysis and/or interpretation – EK, HC; Literature review – EK, HC; Writing – EK, HC; Critical review – HC, FEG, BK.
Conflict of Interest
No conflict of interest was declared by the authors.
Use of AI for Writing Assistance
This article does not include any work by artificial intelligence – assisted technologies.
Financial Disclosure
The authors declared that this study has received no financial support.
Peer-review
Externally peer-reviewed.
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