Association of IL-6,IL-10,and TNF-α Gene Polymorphism with Malnutrition Inflammation Syndrome and Survival Among End Stage Renal Disease Patients

Abstract

During end stage renal disease (ESRD) inflammatory pathways are activated which may lead to malnutrition inflammation syndrome (MIS). In the present study, 257 ESRD patients and 200 controls were included. Cytokine levels and genotyping was done by polymerase chain reaction–restriction fragment length polymorphism and enzyme-linked immunosorbent assay (ELISA). Risk was estimated through binary logistic regression. Cox proportional hazards regression and Kaplan–Meier were used for survival analysis. Tumor necrosis factor TNF-α-308 AA conferred 3.6-fold higher susceptibility (P=0.001) and higher TNF-α levels (P=0.05). TNF-α-238 AA was associated with 3.3-fold higher susceptibility to ESRD (P=0.002). IL-6-174 CC genotype conferred 3-fold risk to disease (P=0.001) along with higher IL-6 levels (P=0.001). IL-10-1082 GG genotype exhibited 2.2-fold higher susceptibility to disease (P=0.013). IL-10-592 AA/-819 TT genotypes were associated with high C reactive protein (P=0.02) and low IL-10 (P=0.03) levels. TNF-α-308 A allele was significantly associated with 2.3-fold higher risk of malnutrition. TNF-α-GAC, AGC and IL-6-CC were risk haplotypes associated with higher disease susceptibility. Combined analysis revealed 1.6-fold higher susceptibility to disease (P=0.02), there was 2-fold higher susceptibility to malnutrition (P=0.02) in high inflammation group. TNF-α-238 AA genotype was associated with 2.5-fold higher death hazard risk (P=0.02). Our study suggests that TNF-α and its genetic variants are major contributors to susceptibility to MIS in ESRD patients.

Introduction

E nd stage renal disease (ESRD) patients tends to develop malnutrition which may be due to inflammation referred as malnutrition inflammation complex syndrome occurring in ∼20%–60% patient (Jager and others 2001). The increased levels of cytokines, metabolic acidosis, insulin resistance, and oxidative stress may induce malnutrition; however, exact mechanisms are not clear. The progression of anorexia and muscle wasting are major markers of malnutrition which are induced by inflammatory factors. It has been shown that some of the patients on maintenance hemodialysis have diminished appetite with increase in Tumor necrosis factor (TNF)-α and C reactive protein (CRP) levels and poor survival (Kalantar-Zadeh and others 2004).

Inflammation also elicits muscle wasting. Studies on humans and rodents show that muscle wasting is intimately linked to presence of inflammation, which is associated with inflammation mediated activation of specific proteases. Initially caspase-3 activates, and cleaves the intricate structure of muscle, thus exposing a distinctive 14 kDa actin fragment in the insoluble fraction of muscle, followed by activation of the ubiquitin-proteasome system, which further cleaves this protein. In normal healthy condition, there is a state of equilibrium between anabolic and catabolic states (Laviano and others 2010). However, in ESRD the activation of the proteolytic pathways is not compensated by a corresponding increase of the anabolic pathways. Inflammatory pathway can also impair protein synthesis where the major mediators are IL-6 and TNF-α. The synthesis of muscle specific transcription factor Myo-D is stalled by TNF-α which can stimulate transcription factor nuclear factor kappa B thus hindering differentiation of Myo-D (Acharyya and others 2007). A recent study demonstrated that relatively low dose of IL-6 infusion decrease the skeletal muscle protein synthesis (van Hall and others 2008).

There are various cytokines identified to have an effect on appetite that includes IL-6, IL-11α. IL-1β and TNF-α (Plata-Salaman and others 1988). The release and function of IL-1, TNF-α, IL-6, and IL-8 can be inhibited by anti-inflammatory cytokines or regulatory cytokines like IL-10 and IL-4 (Vannier and others 1992). In cachexia mouse model (animals with an adenocarcinoma), on IL-10 gene transfer caused increase in serum IL-10 levels, resulting into decrease in IL-6 levels and impeding further weight loss and hypophagia (Fujiki and others 1997). However, some studies on cancer show that higher levels of IL-10 are associated with weight loss (Fock and others 2008) which might be due to failure of anti-inflammatory control over immune competence (Monk and others 2011). These conflicting results on IL-10 make it an important marker to evaluate its effect on the malnutrition and to find out its role in malnutrition inflammation complex syndrome.

There are inter-individual differences which cannot be explained on the basis of clinical features and they points towards the importance of genetic component. Thus, individuals may have genetically determined tendency for increased amount of cytokine production, thus affecting the clinical phenotype. In published reports, there are very few studies dealing with the genetic aspect of inflammation and malnutrition in ESRD. There is only one study reported so far on IL-6 and IL-10 relating to malnutrition inflammation syndrome (Suhardjono 2006).

The present study aimed to study the genetic aspect of inflammatory cytokines IL-6 (IL-6-174G/C-rs1800795, −572C/G-rs1800796), IL-10 (IL-10-1082 A/G-rs1800896, −592 C/A- rs1800872, −819 C/T-rs1800871), and TNF-α (TNF-α-308 G/Ars1800629, −238 G/A-rs361525, −850 C/T-rs1799724) and their association with malnutrition. The functional aspects have been done by measuring the levels of various cytokines in the serum.

Materials and Methods

Study participants

We prospectively registered 257 ESRD patients on regular hemodialysis from December 2008–2011. They were under regular follow-up in the dialysis unit at Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow. The study was approved by the Ethical committee of SGPGIMS, Lucknow, India. Written informed consent was obtained from both the groups. The exclusion criteria were patients with any active viral infection. Two hundred age, gender, and ethnically matched healthy controls were included. Almost 70% of patients were male so care was taken to incorporate more male controls in our study to exclude gender bias. The male:female ratio in patients was 77.4:21.8 (199/56) and 74.5:24.5 (149/51) in controls. The ethnicity was confirmed by taking the detailed 5 generation pedigree analysis and only those subjects were included who belonged to the state of Uttar Pradesh. The ethnicity of both patients and controls is shown in Supplementary Table S1 (Supplementary Data are available online at www.liebertpub.com/jir). They were selected randomly from the hospital staff after evaluating their biochemical parameters.

Outcome

Nutritional assessment was done using Subjective Global Assessment (SGA) scores (Blumenkrantz and others 1980). SGA scoring was done on the basis of -Weight change in last 6 months, Dietary intake (solid, semi-solid, liquid, starvation), Gastrointestinal symptoms (nausea, anorexia, vomit, diarrhea) Functional impairment, physical examination-loss of subcutaneous fat, muscle wasting, edema etc.

Biochemical profile

Blood samples for measuring serum biochemical and lipid profiles were obtained in the morning after fasting of 8 h. This included renal function test, S.Albumin, S.Protein, urinary protein level, liver function test, lipid profile, iron profile, CRP, and parathyroid hormone.

Enzyme-linked immunosorbent assay

Cytokine levels in serum were quantified using commercially available kit (Thermo Scientific) for human IL-6, IL-10, and TNF-α. All serum samples were isolated and frozen in aliquots at −80°C until use. The assay was performed in duplicates independently for each sample according to the manufacturer's instructions. The result was expressed as picograms of cytokine per milliliter (pg/mL), based on the standard provided with the kits. For ELISA 25 Normal, 20 Mild, 25 Moderate, and 20 severe patients were included. These levels were compared with 30 healthy controls.

Genotyping

Three milliliters of venous blood sample was collected in EDTA vials and the extraction of genomic DNA was done by using commercial kit (Qiagen). The polymorphisms assessed in the present study were IL-6-174G/C (db SNP ID rs1800795), IL-6-572C/G (db SNP ID rs1800796), IL-10-1082 A/G (db SNP ID rs1800896), IL-10-592 C/A (db SNP ID rs1800872), IL-10-819 C/T (db SNP ID rs1800871) and TNF-α-308 G/A (db SNP ID rs1800629), TNF-α 238 G/A (db SNP ID rs361525), TNF-α-850 C/T-(db SNP ID rs1799724). The details of genotyping protocol are shown in Supplementary Table S2. To ensure reproducibility, 20% of samples were randomly selected and re-genotyped using sequencing. Genotyping of all subjects was blinded to clinical status.

Statistical analysis

Sample size was calculated by Quanto (Ver. 1.1.) for 80% power of study (Gauderman 2002). Statistical analysis was performed by using SPSS statistical software (version 15) and Graph pad Prism (version 5). The biochemical data was compared by using Student's t-test and ANOVA followed by Turkey's multiple comparison test between the groups. The values are expressed as mean±SD. Alleles and genotypic frequencies were calculated by using gene counting method. Comparison of the categorical data, that is, different IL-6, IL-10, and TNF-α genotypes among controls and patients was done by logistic regression analysis to calculate odds ratios (OR) and their 95% confidence intervals (CI). Bonferroni correction was applied in case of multiple comparisons and subgroup analysis. Allele frequencies were tested for Hardy–Weinberg equilibrium. Haplotypes were constructed using SNPStats software. Kaplan–Meier survival analysis and log rank test were used to assess clinical outcome, that is, overall survival in relation to genotype status. The hazard risk and 95% CI was calculated using univariate Cox regression analysis. A two-sided P≤0.05 was considered significant.

Results

Study participants and demographics

The mean age of the patients was 39.3±12.6. The BMI was lower in cases than normal healthy controls (21.2±3.9). Almost all the biochemical parameters were significantly different between both the groups (Supplementary Table S3). The period of dialysis was 2 to 36 months. Almost 70% patients were malnourished at various degrees. We categorized the patients on the basis of SGA into 4 groups normal (n=78), mild (n=38), moderate (n=100) and severe (n=41). BMI was significantly less however, period of dialysis, systolic blood pressure; Erythrocyte Sedimentation Rate was significantly higher in severe SGA group (Supplementary Table S4).

Genotype distribution in patients and controls

TNF-α-308 and −238 AA genotype were showed susceptible genotypes for ESRD (OR=3.6, 95% CI=1.7–7.5, P=0.001 and OR=3.3, 95% CI=1.4–7.3, P=0.002, respectively, Table 1). TNF-α GAC and AGC haplotype was associated with susceptibility to the disease (OR=2.02, 95% CI=1.29–3.16, P=0.002 and OR=2.0, 95% CI=1.22–3.8, P=0.006, Supplementary Table S5).

Table 1.

Distribution of Genotypes Between Patients and Controls

Genotype	Patient	Control	OR (95% CI)	P Value
TNF-α-308
GG	160 (62.3)	162 (81.0)	Reference
GA	61 (23.7)	28 (14.0)	2.3 (1.4–3.8)	0.001^a
AA	36 (14.0)	10 (5.0)	3.6 (1.7–7.5)	0.001^a
G	381 (74.1)	352 (88.0)	Reference
A	133 (25.9)	48 (12.0)	2.5 (1.5–3.6)	0.001^a
TNF-α-238
GG	145 (56.4)	125 (62.5)	Reference
GA	81 (31.5)	67 (33.5)	0.9 (0.6–1.3)	0.7
AA	31 (12.1)	8 (4.0)	3.3 (1.4–7.3)	0.002^a
G	371 (72.2)	317 (79.2)	Reference
A	143 (27.8)	83 (20.8)	1.5 (1.0–2.0)	0.01
TNF-α-850
CC	176 (68.5)	151 (75.5)	Reference
CT	51 (19.8)	35 (17.5)	1.2 (0.7–2.0)	0.8
TT	30 (11.7)	14 (7.0)	1.8 (0.9–3.5)	0.07
C	403 (78.4)	337 (84.3)	Reference
T	111 (21.6)	63 (15.8)	1.5 (0.9–1.9)	0.02
IL-6-174
GG	130 (50.6)	148 (71.0)	Reference
GC	91 (35.4)	38 (22.0)	2.9 (1.8–4.6)	0.001^a
CC	36 (14.0)	14 (7.0)	3.2 (1.6–6.4)	0.001^a
G	351 (68.3)	334 (83.5)	Reference
C	163 (31.7)	66 (16.5)	2.3 (1.7–3.2)	0.001^a
IL-6-572
CC	103 (40.1)	97 (48.5)	Reference
GC	108 (42.0)	80 (40.0)	1.3 (0.9–2)	0.1
GG	46 (17.9)	23 (11.5)	2.1 (1.2–3.8)	0.007^a
C	314 (61.1)	274 (68.5)	Reference
G	200 (38.9)	126 (31.5)	1.4 (1.0–1.8)	0.02
IL-10-592
CC	97 (37.7)	60 (30.0)	Reference
CA	108 (42.0)	97 (48.5)	0.6 (0.4–1.0)	0.06
AA	52 (20.3)	43 (21.5)	0.7 (0.4–1.3)	0.4
C	302 (58.7)	217 (54.3)	Reference
A	212 (41.3)	183 (45.7)	1.2 (0.9–1.5)	0.2
IL-10-819
CC	97 (37.7)	60 (30.0)	Reference
CT	108 (42.0)	97 (48.5)	0.6 (0.4–1.0)	0.06
TT	52 (20.3)	43 (21.5)	0.7 (0.4–1.3)	0.3
C	302 (58.7)	217 (54.3)	Reference
T	212 (41.3)	183 (45.7)	1.2 (0.9–1.5)	0.2
IL-10-1082
AA	138 (53.7)	126 (63)	Reference
GA	89 (34.6)	59 (29.5)	1.2 (0.8–1.8)	0.3
GG	30 (11.7)	15 (7.5)	2.2 (1.1–4.4)	0.01^a
A	365 (71.1)	311 (77.7)	Reference
G	149 (28.9)	89 (22.3)	1.4 (1.0–1.9)	0.02

Values in parentheses are percent frequencies.

P≤0.05 significant.

TNF, tumor necrosis factor; OR, odds ratios; CI, confidence intervals.

IL-6-174 CC genotype was associated with higher susceptibility to disease (OR=3.2, 95% CI=1.6–6.4, P=0.001). The C allele was significantly associated with higher susceptibility (OR=2.3, 95% CI=1.7–3.2, P=0.0001). IL-6-572 GG genotype was present in 17.9% patients and in 11.5% controls which showed significant association with higher susceptibility to disease (OR=2.1, 95% CI=1.2–3.8, P=0.007, Table 1). IL-6 CC Haplotype was present 10.1% in controls and 18.2% in patients (OR=2.75, 95% CI=1.70–4.46 P≤0.001) which conferred higher susceptibility to disease (Supplementary Table S5).

IL-10-1082 GG genotype was associated with higher susceptibility to disease on comparing the patient and controls (OR=2.2, 95% CI=1.1–4.4, P=0.013, Table 1).

Genotype distribution in SGA categories

The allele and genotype distribution of TNF-α was compared between SGA categories and it was found that in severe group TNF-α-308 A allele showed significant risk (OR=2.3, 95% CI=1.2–4.2, P=0.007). TNF-α-850 CT was protective against malnutrition (OR=0.2, 95% CI=0.06–0.8, IL-10 P=0.01) (Table 2).

Table 2.

Comparison of Genotype Among Patients on the Basis of Subjective Global Assessment Scores

Markers	Geno type	Normal	Mild	Moderate	Severe	Normal vs Mild OR (95% CI), P value	Normal vs Moderate OR (95% CI), P value	Normal vs Severe OR (95% CI), P value
TNF-α-308	GG	55 (70.5)	22 (57.8)	62 (62.0)	21 (51.2)	Reference	Reference	Reference
	GA	16 (20.5)	10 (26.5)	24 (24.0)	11 (26.8)	1.5 (0.6–3.9), 0.3	1.3 (0.6–2.7), 0.4	1.8 (0.7–4.5), 0.2
	AA	7 (9.0)	6 (15.7)	14 (14.0)	9 (22.0)	2.1 (0.64–7.0), 0.2	1.7 (0.6–4.7), 0.2	3.3 (1.1–10), 0.03
	G	126 (80.8)	54 (71.1)	148 (74.0)	53 (64.6)	Reference	Reference	Reference
	A	30 (19.2)	22 (28.9)	52 (26.0)	29 (35.4)	1.7 (0.9–3.2), 0.1	1.4 (0.8–2.3), 0.2	2.3 (1.2–4.2), 0.007^a
	GG	43 (55.1)	23 (60.5)	59 (59.0)	20 (51.2)	Reference	Reference	Reference
TNF-α-238	GA	31 (39.7)	10 (26.3)	26 (26.0)	14 (34.2)	1.8 (0.7–4.3), 0.2	2.0 (1.0–3.9), 0.03	1.2 (0.5–2.8), 0.6
	AA	4 (5.2)	5 (13.1)	15 (15.0)	7 (14.6)	2.8 (0.7–11.1), 0.1	3.2 (1.0–10.2), 0.04	3.8 (0.1–13), 0.04
	G	117 (75.0)	56 (73.6)	144 (72.0)	54 (65.8)	Reference	Reference	Reference
	A	39 (25.0)	20 (26.4)	56 (28.0)	28 (34.1)	1.0 (0.5–2.0), 0.8	1.2 (0.7–1.9), 0.4	1.5 (0.8–2.8), 0.1
	CC	50 (64.1)	12 (57.9)	71 (71.0)	33 (80.5)	Reference	Reference	Reference
TNF-α-850	CT	20 (25.6)	23 (26.3)	18 (18.0)	3 (7.3)	1.1 (0.4–2.8), 0.7	0.63 (0.3–1.3), 0.2	0.2 (0.06–0.8),0.01^a
	TT	8 (10.3)	3 (15.8)	11 (11.0)	5 (12.2)	1.7 (0.5–5.4), 0.3	0.96 (0.36–2.5), 0.9	0.9 (0.2–3.1), 0.9
	C	120 (77.0)	47 (71.0)	160 (80.0)	69 (84.1)	Reference	Reference	Reference
	T	36 (23.0)	29 (29.0)	40 (20.0)	13 (15.9)	1.3 (0.7–2.5), 0.3	0.8 (0.5–1.38), 0.5	0.6 (0.3–1.2), 0.2
IL-6-174	GG	46 (58.9)	22 (52.6)	46 (46.0)	18 (43.9)	Reference	Reference	Reference
	GC	23 (29.5)	10 (34.2)	41 (41.0)	14 (34.1)	1.3 (0.5–3.0), 0.5	1.7 (0.9–3.4), 0.08	1.5 (0.8–3.6), 0.3
	CC	9 (11.6)	6 (13.2)	13 (13.0)	9 (22)	1.2 (0.38–4.2),0.6	1.4 (0.5–3.7), 0.4	2.5 (0.8–7.4), 0.08
	G	115 (73.7)	54 (69.7)	133 (66.5)	50 (61)	Reference	Reference	Reference
	C	41 (26.3)	22 (30.3)	67 (33.5)	32 (39)	1.2 (0.6–2.2),0.5	1.4 (0.8–2.2), 0.1	1.8 (1.0–3.1), 0.05
IL-6-572	CC	33 (42.3)	20 (44.7)	43 (43.0)	14 (34.1)	Reference	Reference	Reference
	GC	37 (47.4)	13 (34.2)	41 (41.0)	17 (41.5)	0.7 (0.3–1.8), 0.5	0.8 (0.4–1.6), 0.6	1.5 (0.6–3.7), 0.3
	GG	8 (10.3)	5 (21.1)	16 (16.0)	10 (24.4)	2.3 (0.79–7.0), 0.1	1.5 (0.6–3.9), 0.3	5.7 (1.8–17), 0.02
	C	103 (66)	53 (61.8)	127 (63.5)	45 (54.9)	Reference	Reference	Reference
	G	53 (34)	23 (38.2)	73 (36.5)	37 (45.1)	1.1 (0.67–2.1), 0.5	1.1 (0.1–1.7), 0.6	1.5 (0.9–2.7), 0.1
IL-10-592	CC	29 (37.2)	17 (39.5)	36 (36.0)	17 (41.5)	Reference	Reference	Reference
	CA	34 (43.6)	13 (42.1)	45 (45.0)	13 (31.7)	0.9 (0.38–2.1), 0.8	1.0 (0.5–2.0),0.8	0.6 (0.2–1.5), 0.3
	AA	15 (19.2)	8 (18.4)	19 (19.0)	11 (26.8)	0.9 (0.3–2.6), 0.8	1.0 (0.4–2.3), 0.9	1.2 (0.4–3.3), 0.6
	C	92 (59.0)	46 (60.5)	117 (58.5)	47 (57.3)	Reference	Reference	Reference
	A	64 (41.0)	30 (39.5)	83 (41.5)	35 (42.7)	0.9 (0.5–1.6), 0.8	1.0 (0.6–1.5), 1.0	1 (0.6–1.8), 0.8
IL-10-819	CC	29 (37.2)	17 (39.5)	36 (36.0)	17 (41.5)	Reference	Reference	Reference
	CT	34 (43.6)	13 (42.1)	45 (45.0)	13 (31.7)	0.9 (0.38–2.1), 0.8	1.0 (0.5–2.0), 0.84	0.6 (0.2–1.5), 0.3
	TT	15 (19.2)	8 (18.4)	19 (19.0)	11 (26.8)	0.9 (0.3–2.6), 0.8	1.0 (0.4–2.3), 0.96	1.2 (0.4–3.3), 0.6
	C	92 (59.0)	46 (60.5)	117 (58.5)	47 (57.3)	Reference	Reference	Reference
	T	64 (41.0)	30 (39.5)	83 (41.5)	35 (42.7)	0.9 (0.5–1.6), 0.8	1.0 (0.6–1.5), 1.0	1 (0.6–1.8), 0.8
	AA	45 (57.7)	15 (47.4)	53 (53.0)	23 (53.6)	Reference	Reference	Reference
IL-10-1082	GA	18 (28.2)	16 (42.1)	36 (36.0)	13 (31.7)	2.3 (1.0–5.6), 0.05	1.69 (0.8–3.3), 0.13	1.1 (0.5–2.7), 0.7
	GG	15 (14.1)	7 (10.5)	11 (11.0)	5 (14.7)	1.5 (0.5–4.7), 0.8	0.6 (0.26–1.5), 0.18	0.8 (0.27–2.8),0.8
	A	108 (71.8)	46 (68.4)	142 (71.0)	59 (69.5)	Reference	Reference	Reference
	G	48 (28.2)	30 (31.6)	58 (29.0)	23 (30.5)	1.1 (0.6–2.1), 0.6	0.9 (0.6–1.5), 1.0	1.1 (0.6–2.0),0.76

Values in parentheses are percent frequencies.

Normal SGA category was taken as reference for comparison with other SGA category.

P≤0.05 significant.

SGA, subjective global assessment.

Genotype distribution in CRP categories

The CRP levels were increased in 39.3% of patients. The patients were further divided into 2 groups on the basis of CRP to assess inflammation as it is one of the predictor of acute phase response. These groups were having inflammation (inflamed) or not having inflammation (noninflamed). Non-inflamed- CRP group revealed more than 1 mg/dL (n=156), while, in case of Inflamed group- CRP was less than 1 mg/dL (n=101) (Pearson and others 2003). The biochemical profile of patients with and without inflammation was almost similar.

The IL-6-174 C allele was present in 39.6% inflamed patients as compared to 26.6% of non-inflamed patients (OR=1.8, 95% CI=1.2–2.6, P=0.002) conferring higher susceptibility in inflamed group. IL-10-592 AA and IL-10-819 TT (OR=2.2, 95% CI=1.1–4.4, P=0.02) genotype was associated with higher susceptibility to inflammation on comparing the inflamed and non-inflamed groups.

Cytokine levels

When IL-6, TNF-α and IL-10 were compared between patients and control group all the studied cytokines were significantly higher among patient group. On comparing the levels between SGA categories, the severe group of SGA patients showed higher levels of proinflammatory cytokines IL-6 (P=0.03) and TNF-α (P=0.04), whereas IL-10 levels were similar in all groups. On comparing the levels between inflamed and noninflamed groups, the inflamed group showed higher levels of IL-6 (P=0.008) and TNF-α (P=0.03) and lower levels of IL-10 (P=0.04). Phenotype assignment of serum levels to genotypes-IL-10-1082A, IL-10-819T, IL-10-592A alleles was low producers of IL-10. TNF-α-308A, TNF-α-238A, TNF-α-850T allele were high producer of TNF-α. IL-6-174 C, IL-6-572 G was high producers of IL-6 (Fig. 1A–C).

FIG. 1.

(A) Serum IL-6 level in different groups and genotypes. (B) Serum TNF-α level in different groups and genotypes. (C) Serum IL-10 level in different groups and genotypes. Cnt, control; Pt, patient; SGA groups: Norm, normal; Mod, moderate; Sevr, severe; CRP group: Inf, inflamed; N. Inf, non-inflamed; SGA, subjective global assessment; TNF, tumor necrosis factor; CRP, C reactive protein.

Combined effect

To analyze the combined effect of these markers, we labeled them as high producer and low producer alleles depending upon the serum levels of these cytokines. The presence of 0–4 high IL-6, TNF-α, and low IL-10 producer alleles were grouped into low inflammation group. Five to 8 high IL-6, TNF-α, and low IL-10 producer alleles were grouped into high inflammation group. The high inflammation group showed higher susceptibility associated with the disease on comparing patients and controls (OR=1.6, 95% CI=1.1–2.3, P=0.02). On comparing high inflammation and low inflammation groups in SGA categories, the differences were not significant. However, on combining the mild, moderate and severe groups into single group of malnourished individuals and further comparing with the normal SGA group the difference was significant (OR=1.9, 95% CI=1.1–3.4, P=0.02) (Table 3).

Table 3.

Combined Analysis of Various Groups

Group	Patient	Control	OR (95% CI)	P value
Low inflammation	146 (56.8)	135 (67.5)	Reference
High inflammation	111 (43.2)	65 (32.5)	1.6 (1.1–2.3)	0.02^a

Group	Normal	Malnourished	OR (95% CI)	P value
Low inflammation	53 (67.9)	93 (51.9)	Reference
High inflammation	25 (32.1)	86 (48.1)	1.9 (1.1–3.4)	0.02^a

Group	Inflamed	Non-inflamed	OR (95% CI)	P value
Low inflammation	49 (48.5)	97 (62.1)	Reference
High inflammation	52 (52.5)	59 (37.8)	1.7 (1.0–2.8)	0.03

Values in parentheses are percent frequencies.

Low inflammation: 0–4 alleles of high IL-6 (−174C, −572G), TNF-α (−308A, −238A, −850-T) and low IL-10 (−1082A, −819T, −592A) producer.

High inflammation: 5–8 alleles of high producing IL-6 (−174C, −572G), TNF-α (−308A, −238A, −850-T) and low IL-10 (−1082A, −819T, −592A) producer

P≤0.05 significant.

Survival analysis

The patients included in our study were followed up to 3 years. The mean survival of the patient's was 19.6±10.5 months. Kaplan–Meier and Cox regression analysis revealed that TNF-α-238 AA genotype was significantly associated with higher death hazard ratio (HR=2.5, 95% CI=1.2–5.5, P=0.02, Fig. 2A). More so high inflammation group was significantly associated with higher death hazard (HR=2.2, 95% CI=1.2–3.6, P=0.007) (Fig. 2B). When survival analysis was done, we observed that severe group (HR=4.1, 95% CI=1.8–9.4, P=0.001) was significantly associated with higher death hazard (Supplementary Table S6).

FIG. 2.

(A) Kaplan–Meier curve of TNF-α-238 G/A genotypes on the survival outcome of ESRD patients. (B) Kaplan–Meier curve of combined risk genotypes on the survival outcome of ESRD patients. ESRD, end stage renal disease.

Discussion

In this study, we have investigated the association and interaction of single nucleotide polymorphisms (SNP) in TNF-α, IL-6, and IL-10 among 257 patients and 200 controls. Our results revealed high producer genotypes of IL-6 and TNF-α were associated with malnutrition and inflammation. The low producer genotypes of IL-10 were associated with inflammation. Our results indicated TNF-α 238 AA was high risk genotype as it was not only associated with 3.3-fold higher susceptibility to disease and 2.5-fold higher death hazard. The combined analysis revealed that the high inflammation group was associated with 1.8-fold higher risk of disease and 2-fold higher risk of death in patients.

Increased serum cytokine levels and their soluble receptors in patients with differing grades of renal failure have been related with the worsening of renal function (Descamps-Latscha and others 1995). Thus, ESRD is an inflamed state and mediators of inflammation are activated. The outcome of inflammation is number of metabolic changes that are frequently illustrated by negative energy balance, increased thermogenesis and anorexia which are intervened by cytokines (Dantzer and others 2007; Argiles and others 2009). Chronic inflamed state is associated with malnutrition and high mortality risk. However, only some patients with chronic disease may develop cachexia pointing towards intra individual genetic variation.

The markers which cause inflammation induced malnutrition have been investigated. One such marker is TNF-α which is multifunctional cytokine and possess anorectic properties (Stenvinkel and others 2004). Elevated levels of this cytokine cause muscle protein catabolism via activation of NFκB (Guttridge and others 2000) and ubiquitin proteasome pathway (Mitch and others 1999). In our study, elevated levels of TNF-α were observed in severe group of malnutrition which links TNF-α with malnutrition this is in concordance with a study on patients on dialysis where surrogate markers of malnutrition and anorexia have been associated with elevated levels of TNF-α (Kalantar-Zadeh and others 2004). TNF-α 308 G/A transition is associated with a state of high TNF-α production and susceptibility to several diseases (Vatay and others 2003; Balakrishnan and others 2004) and might be an important functional polymorphism that influences both the prevalence of inflammation and its associated phenotypes in ESRD (Vatay and others 2003; Balakrishnan and others 2004). In the present study, TNF-α-308AA genotype is significantly associated with higher production of TNF-α level which is in concordance with previous reports (Balakrishnan and others 2004; Spriewald and others 2005). Various studies have linked the TNF-α-308 GA and AA genotype to higher comorbidity and lower S-albumin (Balakrishnan and others 2004) and A allele with higher risk to death (Jaber and others 2004). In our study, TNF-α-308 AA genotype is associated with 3-fold higher susceptibility to the disease along with similar 2-fold higher and risk to malnutrition at the allelic level. TNF-α-238 AA genotype has been previously linked to higher susceptibility to ESRD (Spriewald and others 2005; Ranganath and others 2009). TNF-α-238 A allele is also associated with higher levels of TNF-α in ESRD (Spriewald and others 2005). In the present study, the TNF-α-238 AA genotype is associated with 3.3-fold higher risk of disease on comparing patients and controls and 2.5-fold higher mortality risk. Recently, TNF-α-850C/T polymorphism is found to be associated with hypertensive chronic kidney disease (Yoshida and others 2009). Further TNF-α-850 CT genotype was protective against malnutrition.

Kaizu and others (2003) reported that the IL-6 levels were increased in muscles of inflamed chronic kidney disease (CKD) as compared to controls which signifies the catabolic nature of IL-6. Increased levels of IL-6 were also associated with higher energy expenditure in ESRD patients on HD (Kamimura and others 2007) and decrease in body fat content of rodents (Stenlof and others 2003). In our study, significantly higher levels of IL-6 were detected in severe SGA group as compared to normal SGA group which is in agreement with the above findings. The SNPs within the IL-6 gene are believed to be of major importance as there is a strong association between circulating IL-6 levels and outcome in ESRD patients (Panichi and others 2004). The results in the literature on association between the IL-6-174 promoter G/C SNP and both plasma IL-6 and CRP levels are contradictory (Gaudino and others 2003). In our study IL-6-174 C allele is associated with 1.8-fold higher risk of higher inflammation which was evident by higher serum level of IL-6 detected in this genotype. There are various studies which support this trend (Muller-Steinhardt and others 2007; Wypasek and others 2010) but there are others which attribute the higher IL-6 levels to G allele (Balakrishnan and others 2004; Spriewald and others 2005). IL-6-174 CC genotype is shown to be associated with high blood pressure and LVH in hemodialysis patients (Losito and others 2003). It is also associated with higher comorbidity (ICED) and functional scores (Karnofsky Index) (Balakrishnan and others 2004) and incidence of cardiovascular events and mortality (Aker and others 2009). A recent study showed association of IL-6-572 G/C genetic variant with glomerular filtration rate and CKD prevalence (Okada and others 2012). IL-6-572 GG is also associated with stroke (Timasheva and others 2008) and atherosclerosis (Yamaguchi and others 2006). In this study IL-6-174 CC genotype was associated with 3-fold higher risk of ESRD. IL-6-572 GG genotype was associated with 2-fold higher risk of ESRD on comparing the patients with controls. This genotype was also significantly associated with higher IL-6 levels.

IL-10 is anti-inflammatory immune-regulating cytokine, linked to stress reaction and inflammation. It is induced by catecholamines or TNF-α. Increased plasma level of IL-10 in ESRD is detected due to reduced filtration from kidneys may be due to glomerular filtration and tubular metabolism which may affect plasma half-life (Morita and others 1997). Our results also show similar trend. IL-10 levels are elevated in uremia, and may counter act against proinflammatory cytokines; therefore, maintain the immune balance. However, it is reported in animal model that high levels of IL-10 are associated with weight loss (Fock and others 2008). In our study, no significant difference in levels of IL-10 was observed in any of the SGA group. Various SNPs in the promoter region of IL-10 have been reported to be associated with high levels of IL-10 in the supernatant of stimulated cultured cells. It is interesting to note that these polymorphisms are also associated with elevated IL-10 among uremic patients (Eskdale and others 1995). Further carriers of the homozygous G/G at position −1082 of the IL-10 promoter produce 30% more cytokine on definite stimulus than A/A carriers. IL-10-1082 GG and GA genotype has been associated with higher Karnofsky index (Balakrishnan and others 2004). In the present study GG genotype was associated with 2.7-fold higher susceptibility to disease. IL-10-1082 G allele is also associated with developing cachexia in cancer (Deans and others 2009; Sun and others 2010). The IL-10-592/-819 AA/TT was significantly associated with lower IL-10 levels and 2.0-fold higher risk of inflammation in the inflamed group of patients which is in agreement with a recent study on ischemic heart disease which also demonstrated similar results (Xie and others 2010).

We may hypothesize on the basis of above results that TNF-α might be major marker as TNF-α-308 A allele is associated with both higher susceptibility to disease and malnutrition and on the other hand TNF-α-238 AA genotype has shown association with higher susceptibility and death hazard. IL-10-592 AA/IL-10-819 TT showed higher risk of inflammation in patients. The levels of IL-6 and TNF-α are elevated in the severe group of malnutrition and in inflamed (elevated CRP group) patients which proves the presence of inflammation induced malnutrition. IL-10 levels are increased in non-inflamed patients but no significant difference was observed in malnourished categories showing that it may be protective in nature against inflammation. Further studies on anti-inflammatory markers are required for establishing their role in malnutrition. The combined effect model of genotypes associated with high IL-6, TNF-α and low IL-10 revealed association with disease susceptibility, poor nutritional and survival status. The main drawback of the study is that most of P values were of marginal significance. This may be due to the small sample size. To get best results under the present circumstances we have shown only those P values which were of real significance. As the study design used is very stringent hence the sample size was reduced. To strengthen our findings, there is a need for further studies on larger cohorts for validating the above findings and identifying other factors associated with malnutrition inflammation complex syndrome which will help in better understanding of the mechanisms and also the management of the patients.

Footnotes

Acknowledgments

Richa Sharma is receiving her Doctoral Fellowship from the Department of Biotechnology, Government of India, New Delhi, India.

This study was supported by intramural funding from Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow (U.P.) India.

Author Disclosure Statement

No competing financial interests exist.

References

Acharyya

, Guttridge

. 2007. Cancer cachexia signaling pathways continue to emerge yet much still points to the proteasome. Clin Cancer Res, 13,5:1356–1361.

Aker

, Bantis

, Reis

, Kuhr

, Schwandt

, Grabensee

, Heering

, Ivens

. 2009. Influence of interleukin-6 g-174c gene polymorphism on coronary artery disease, cardiovascular complications and mortality in dialysis patients. Nephrol Dial Transplant, 24,9:2847–2851.

Argiles

, Busquets

, Toledo

, Lopez-Soriano

. 2009. The role of cytokines in cancer cachexia. Curr Opin Support Palliat Care, 3,4:263–268.

Balakrishnan

, Guo

, Rao

, Jaber

, Tighiouart

, Freeman

, Huang

, King

, Pereira

. 2004. Cytokine gene polymorphisms in hemodialysis patients: association with comorbidity, functionality, and serum albumin. Kidney Int, 65,4:1449–1460.

Blumenkrantz

, Kopple

, Gutman

, Chan

, Barbour

, Roberts

, Shen

, Gandhi

, Tucker

, Curtis

, Coburn

. 1980. Methods for assessing nutritional status of patients with renal failure. Am J Clin Nutr, 33,7:1567–1585.

Dantzer

, Kelley

. 2007. Twenty years of research on cytokine-induced sickness behavior. Brain Behav Immun, 21,2:153–160.

Deans

, Tan

, Ross

, Rose-Zerilli

, Wigmore

, Howell

, Grimble

, Fearon

. 2009. Cancer cachexia is associated with the IL10-1082 gene promoter polymorphism in patients with gastroesophageal malignancy. Am J Clin Nutr, 89,4:1164–1172.

Descamps-Latscha

, Herbelin

, Nguyen

, Roux-Lombard

, Zingraff

, Moynot

, Verger

, Dahmane

, de Groote

, Jungers

et al. 1995. Balance between IL-1 beta, TNF-alpha, and their specific inhibitors in chronic renal failure and maintenance dialysis. Relationships with activation markers of t cells, b cells, and monocytes. J Immunol, 154,2:882–892.

Eskdale

, Gallagher

. 1995. A polymorphic dinucleotide repeat in the human IL-10 promoter. Immunogenetics, 42,5:444–445.

10.

Fock

, Vinolo

, Crisma

, Nakajima

, Rogero

, Borelli

. 2008. Protein-energy malnutrition modifies the production of interleukin-10 in response to lipopolysaccharide (LPS) in a murine model. J Nutr Sci Vitaminol (Tokyo), 54,5:371–377.

11.

Fujiki

, Mukaida

, Hirose

, Ishida

, Harada

, Ohno

, Bluethmann

, Kawakami

, Akiyama

, Sone

, Matsushima

. 1997. Prevention of adenocarcinoma colon 26-induced cachexia by interleukin 10 gene transfer. Cancer Res, 57,1:94–99.

12.

Gauderman

. 2002. Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol, 155,5:478–484.

13.

Gaudino

, Andreotti

, Zamparelli

, Di Castelnuovo

, Nasso

, Burzotta

, Iacoviello

, Donati

, Schiavello

, Maseri

, Possati

. 2003. The −174g/c interleukin-6 polymorphism influences postoperative interleukin-6 levels and postoperative atrial fibrillation. Is atrial fibrillation an inflammatory complication? Circulation, 108,Suppl 1:II195–199.

14.

Guttridge

, Mayo

, Madrid

, Wang

, Baldwin

Jr . 2000. NF-kappab-induced loss of myod messenger rna: Possible role in muscle decay and cachexia. Science, 289,5488:2363–2366.

15.

Jaber

, Rao

, Guo

, Balakrishnan

, Perianayagam

, Freeman

, Pereira

. 2004. Cytokine gene promoter polymorphisms and mortality in acute renal failure. Cytokine, 25,5:212–219.

16.

Jager

, Merkus

, Huisman

, Boeschoten

, Dekker

, Korevaar

, Tijssen

, Krediet

. 2001. Nutritional status over time in hemodialysis and peritoneal dialysis. J Am Soc Nephrol, 12,6:1272–1279.

17.

Kaizu

, Ohkawa

, Odamaki

, Ikegaya

, Hibi

, Miyaji

, Kumagai

. 2003. Association between inflammatory mediators and muscle mass in long-term hemodialysis patients. Am J Kidney Dis, 42,2:295–302.

18.

Kalantar-Zadeh

, Block

, McAllister

, Humphreys

, Kopple

. 2004. Appetite and inflammation, nutrition, anemia, and clinical outcome in hemodialysis patients. Am J Clin Nutr, 80,2:299–307.

19.

Kalantar-Zadeh

, Kopple

, Humphreys

, Block

. 2004. Comparing outcome predictability of markers of malnutrition-inflammation complex syndrome in haemodialysis patients. Nephrol Dial Transplant, 19,6:1507–1519.

20.

Kamimura

, Draibe

, Dalboni

, Cendoroglo

, Avesani

, Manfredi

, Canziani

, Cuppari

. 2007. Serum and cellular interleukin-6 in haemodialysis patients: relationship with energy expenditure. Nephrol Dial Transplant, 22,3:839–844.

21.

Laviano

, Krznaric

, Sanchez-Lara

, Preziosa

, Cascino

, Rossi Fanelli

. 2010. Chronic renal failure, cachexia, and ghrelin. Int J Pept 10.1155/2010/648045

22.

Losito

, Kalidas

, Santoni

, Jeffery

. 2003. Association of interleukin-6-174g/c promoter polymorphism with hypertension and left ventricular hypertrophy in dialysis patients. Kidney Int, 64,2:616–622.

23.

Mitch

, Du

, Bailey

, Price

. 1999. Mechanisms causing muscle proteolysis in uremia: the influence of insulin and cytokines. Miner Electrolyte Metab, 25,4–6:216–219.

24.

Monk

, Steevels

, Hillyer

, Woodward

. 2011. Constitutive, but not challenge-induced, interleukin-10 production is robust in acute pre-pubescent protein and energy deficits: new support for the tolerance hypothesis of malnutrition-associated immune depression based on cytokine production in vivo. Int J Environ Res Public Health, 8,1:117–135.

25.

Morita

, Yamamura

, Kashihara

, Makino

. 1997. Increased production of interleukin-10 and inflammatory cytokines in blood monocytes of hemodialysis patients. Res Commun Mol Pathol Pharmacol, 98,1:19–33.

26.

Muller-Steinhardt

, Ebel

, Hartel

. 2007. The impact of interleukin-6 promoter−597/-572/-174genotype on interleukin-6 production after lipopolysaccharide stimulation. Clin Exp Immunol, 147,2:339–345.

27.

Okada

, Wakai

, Naito

, Morita

, Kawai

, Hamajima

, Hara

, Takashima

, Suzuki

, Takezaki

, Ohnaka

, Arisawa

, Hirohata

, Matsuo

, Mikami

, Kubo

, Tanaka

. 2012. Pro-/anti-inflammatory cytokine gene polymorphisms and chronic kidney disease: a cross-sectional study. BMC Nephrol, 13:2.

28.

Panichi

, Maggiore

, Taccola

, Migliori

, Rizza

, Consani

, Bertini

, Sposini

, Perez-Garcia

, Rindi

, Palla

, Tetta

. 2004. Interleukin-6 is a stronger predictor of total and cardiovascular mortality than c-reactive protein in haemodialysis patients. Nephrol Dial Transplant, 19,5:1154–1160.

29.

Pearson

, Mensah

, Alexander

, Anderson

, Cannon

3rd , Criqui

, Fadl

, Fortmann

, Hong

, Myers

, Rifai

, Smith

Jr. , Taubert

, Tracy

, Vinicor

. 2003. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the centers for disease control and prevention and the american heart association. Circulation, 107,3:499–511.

30.

Plata-Salaman

, Oomura

, Kai

. 1988. Tumor necrosis factor and interleukin-1 beta: suppression of food intake by direct action in the central nervous system. Brain Res, 448,1:106–114.

31.

Ranganath

, Tripathi

, Sharma

, Sankhwar

, Agrawal

. 2009. Role of non-HLA genetic variants in end-stage renal disease. Tissue Antigens, 74,2:147–155.

32.

Spriewald

, Witzke

, Wassmuth

, Wenzel

, Arnold

, Philipp

, Kalden

. 2005. Distinct tumour necrosis factor alpha, interferon gamma, interleukin 10, and cytotoxic t cell antigen 4 gene polymorphisms in disease occurrence and end stage renal disease in wegener's granulomatosis. Ann Rheum Dis, 64,3:457–461.

33.

Stenlof

, Wernstedt

, Fjallman

, Wallenius

, Jansson

. 2003. Interleukin-6 levels in the central nervous system are negatively correlated with fat mass in overweight/obese subjects. J Clin Endocrinol Metab, 88,9:4379–4383.

34.

Stenvinkel

, Heimburger

, Lindholm

. 2004. Wasting, but not malnutrition, predicts cardiovascular mortality in end-stage renal disease. Nephrol Dial Transplant, 19,9:2181–2183.

35.

Suhardjono. 2006. Malnutrition-inflammation syndrome in a hemodialysis population: the influence of polymorphic IL-6-174 and IL-10-1082 genes. Acta Med Indones, 38,3:145–149.

36.

Sun

, Sun

, Zhang

, Song

, Zheng

. 2010. Association of interleukin-10 gene polymorphism with cachexia in chinese patients with gastric cancer. Ann Clin Lab Sci, 40,2:149–155.

37.

Timasheva

, Nasibullin

, Zakirova

, Mustafina

. 2008. Association of interleukin-6, interleukin-12, and interleukin-10 gene polymorphisms with essential hypertension in tatars from Russia. Biochem Genet, 46,1–2:64–74.

38.

van Hall

, Steensberg

, Fischer

, Keller

, Moller

, Moseley

, Pedersen

. 2008. Interleukin-6 markedly decreases skeletal muscle protein turnover and increases nonmuscle amino acid utilization in healthy individuals. J Clin Endocrinol Metab, 93,7:2851–2858.

39.

Vannier

, Miller

, Dinarello

. 1992. Coordinated antiinflammatory effects of interleukin 4: interleukin 4 suppresses interleukin 1 production but up-regulates gene expression and synthesis of interleukin 1 receptor antagonist. Proc Natl Acad Sci U S A, 89,9:4076–4080.

40.

Vatay

, Bene

, Kovacs

, Prohaszka

, Szalai

, Romics

, Fekete

, Karadi

, Fust

. 2003. Relationship between the tumor necrosis factor alpha polymorphism and the serum c-reactive protein levels in inflammatory bowel disease. Immunogenetics, 55,4:247–252.

41.

Wypasek

, Undas

, Sniezek-Maciejewska

, Kapelak

, Plicner

, Stepien

, Sadowski

. 2010. The increased plasma c-reactive protein and interleukin-6 levels in patients undergoing coronary artery bypass grafting surgery are associated with the interleukin-6-174g>c gene polymorphism. Ann Clin Biochem, 47,Pt 4:343–349.

42.

Xie

, Myint

, Zhao

, Li

, Wang

, Liang

, Wu

. 2010. Relationship between −592a/c polymorphism of interleukin-10 (IL-10) gene and risk of early carotid atherosclerosis. Int J Cardiol, 143,1:102–104.

43.

Yamaguchi

, Yamada

, Metoki

, Yoshida

, Satoh

, Ichihara

, Kato

, Kameyama

, Yokoi

, Matsuo

, Segawa

, Watanabe

, Nozawa

. 2006. Genetic risk for atherothrombotic cerebral infarction in individuals stratified by sex or conventional risk factors for atherosclerosis. Int J Mol Med, 18,5:871–883.

44.

Yoshida

, Kato

, Yokoi

, Watanabe

, Metoki

, Satoh

, Aoyagi

, Nishigaki

, Nozawa

, Yamada

. 2009. Association of candidate gene polymorphisms with chronic kidney disease in Japanese individuals with hypertension. Hypertens Res, 32,5:411–418.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB