subcutaneous fat in advanced chronic kidney disease patients. A. Witasp1, J. J. ...... 25 Festa A, D'Agostino R Jr, Howard G, Mykkanen L, Tracy RP, Haff- ner SM.
Original Article
| doi: 10.1111/j.1365-2796.2010.02293.x
Increased expression of pro-inflammatory genes in abdominal subcutaneous fat in advanced chronic kidney disease patients A. Witasp1, J. J. Carrero1,2, O. Heimbu¨rger3, B. Lindholm4, F. Hammarqvist5, P. Stenvinkel3 & L. Nordfors1 From the 1Department of Molecular Medicine and Surgery; 2Centre for Gender Medicine; Divisions of 3Renal Medicine; 4Baxter Novum; and 5Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC); Karolinska Institutet, Stockholm, Sweden
¨ rger O, LindAbstract. Witasp A, Carrero JJ, Heimbu holm B, Hammarqvist F, Stenvinkel P, Nordfors L (Karolinska Institutet, Stockholm, Sweden). Increased expression of pro-inflammatory genes in abdominal subcutaneous fat in advanced chronic kidney disease patients. J Intern Med 2011; 269: 410–419. Objectives. Low-grade systemic inflammation, oxidative stress and peripheral insulin resistance are intimately associated and contribute to the increased risk of cardiovascular complications in advanced chronic kidney disease (CKD). Because altered adipose tissue activities have previously been linked to pathophysiological processes in various inflammatory and metabolic diseases we hypothesized that the uraemic milieu in patients with CKD may interact with the adipose tissue, provoking an unfavourable shift in its transcriptional output. Design. Twenty-one adipokine mRNAs were quantified in abdominal subcutaneous adipose tissue (SAT) biopsies and serum ⁄ plasma concentrations of inflammatory markers and related protein products were measured. Setting. The study was conducted at the Karolinska University Hospital, Huddinge, and Karolinska Institutet, Stockholm, Sweden. Subjects. Thirty-seven patients with CKD [15 women, median 58 (interquartile range 49–65) years] and nine nonuraemic individuals [four women, age 62 (45–64) years] were recruited prior to initiation of peritoneal dialysis catheter insertion or elective hernia repair ⁄ laparoscopic cholecystectomy, respectively.
Introduction Patients with advanced chronic kidney disease (CKD) display a spectrum of clinical features includ-
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Results. Even after correction for body mass index, SAT from patients showed a significant upregulation of inflammatory pathway genes interleukin 6 (3.0-fold, P = 0.0002) and suppressor of cytokine signalling 3 (2.5-fold, P = 0.01), as well as downregulation of leptin (2.0-fold, P = 0.03) and the oxidative stress genes uncoupling protein 2 (1.5-fold, P = 0.03) and cytochrome b-245, alpha polypeptide (1.5-fold, P = 0.005), in relation to controls. Conclusions. These gene expression differences suggest that inflammatory and oxidative stress activities may be important features of the intrinsic properties of uraemic adipose tissue, which may have significant effects on the uraemic phenotype. Abbreviations: ADIPOQ, adiponectin; ADIPOR2, adiponectin receptor 2; BMI, body mass index; CKD, chronic kidney disease; CRP, C-reactive protein; Ct, threshold cycle; CVD, cardiovascular disease; CYBA, cytochrome b-245, alpha polypeptide; GFR, glomerular filtration rate; IKBKB, inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta; IL6, interleukin 6; IL6R, interleukin 6 receptor; LEP, leptin; LEPR, leptin receptor; NAMPT, nicotinamide phosphoribosyltransferase; PD, peritoneal dialysis; PIK3R1, phosphoinositide-3-kinase, regulatory subunit 1 (p85 alpha); ROS, reactive oxygen species; SAT, subcutaneous adipose tissue; SLC2A4, solute carrier family 2, facilitated glucose transporter member 4; SOCS3, suppressor of cytokine signalling 3; UCP2, uncoupling protein 2; VCAM1, vascular cell adhesion molecule 1. Keywords: adipose tissue, end-stage renal disease.
ing chronic low-grade inflammation, insulin resistance, dyslipidaemia and accelerated atherosclerosis [1–5]. As this range of features resembles that of the metabolic syndrome in the general population, it
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Gene expression in uraemic fat
has been termed the ‘uraemic–metabolic syndrome’ [6]. This uraemic–metabolic syndrome is highly prevalent in patients with CKD [7–9] and contributes to an elevated cardiovascular risk [10]. Obesity, and specifically abdominal obesity, is thought to significantly contribute to the prevalence and severity of the metabolic syndrome in the general population [11] and to predict mortality and cardiovascular complications in patients with CKD [12]. However, there are conflicting data regarding the role of obesity in patients with advanced CKD in whom obesity has also been associated with a survival advantage [13, 14]. It is clear that obesity is not only characterized by impaired insulin sensitivity but also by enhanced inflammatory signalling in adipose tissue, an endocrine organ that is now recognized as a major site for integration of inflammatory and metabolic pathways [15]. The two main fat depots in the body are visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) located intra-abdominally and peripherally, respectively. Although it has been suggested that these two compartments are biologically distinct [16, 17], the results of a recent gene expression study in obese individuals suggest that both SAT and VAT display pro-inflammatory and insulin resistance-associated profiles, which possibly contributes to metabolic complications [18]. As abdominal obesity in CKD is associated with systemic inflammation [1, 19, 20], it has been suggested that adipose tissue is an important source of inflammation in this patient group. Consistent with this hypothesis, it has been reported that C-reactive protein (CRP), fat mass and abdominal subcutaneous fat are primary determinants of insulin resistance in haemodialysis patients [21, 22]. However, as only clinical correlations have been demonstrated, the precise contribution of uraemic fat to systemic inflammation and insulin resistance has not been fully elucidated. In this study, we hypothesized that uraemia causes alterations in the adipose tissue-specific transcription that may link fat mass to CKD complications, similar to observations in the metabolic syndrome. Thus, we performed mRNA quantification analyses in abdominal SAT from 37 consecutive stage 5 CKD (CKD-5) patients scheduled for peritoneal dialysis (PD) catheter insertion and nine nonuraemic individuals. We targeted molecules that are typically expressed in adipose tissue, as well as genes encoding inflammation, oxidative stress-related and insulin signalling pathway molecules.
Materials and methods The experiments were undertaken with the understanding and informed consent of all participants. The study protocol was approved by the Ethics Committee at the Karolinska Institutet. Patients Adipose tissue gene expression analyses were performed in a subset of patients with CKD-5 enrolled from a larger cohort that has previously been described in detail [4]. Patients with acute infection, active vasculitis or liver disease at the time of evaluation were excluded, as were patients younger than 18 or older than 70 years. Thirty-seven consecutive patients with CKD-5 [age 58 (49–65) years, 22 (59%) men and glomerular filtration rate (GFR) 6.8 (6.0–8.6) mL min)1] were recruited shortly before initiating PD at the Karolinska University Hospital at Huddinge, Stockholm, during the period 1994– 2008, and tissue specimens were collected during PD catheter insertion. The causes of renal failure amongst the 37 patients were diabetic nephropathy (n = 11; 30%), polycystic kidney disease (n = 6; 16%), chronic glomerulonephritis (n = 4; 11%), nephrosclerosis (n = 3; 8%) and other or unknown diseases (n = 13; 35%). The presence of clinically manifest cardiovascular disease (CVD) was described in 15 patients (41%) and diabetes mellitus type-1 (n = 6) or type-2 (n = 10) was reported in 16 (42%) patients (baseline data for diabetic and nondiabetic patients are presented in Table 1). Eighteen patients (49%) were receiving statin medication and 21 patients (57%) were treated with beta blockers. Nonuraemic control subjects The control subjects consisted of patients scheduled for surgical treatment of noninflammatory conditions (cholelithiasis and hernia) at the Karolinska University Hospital. Nine consecutive patients (cholelithiasis n = 3 and hernia n = 6; 5 [56%] men, age 62 [45– 64] years) were recruited prior to the surgical procedure. The exclusion criteria were signs of preoperative systemic inflammation, clinically evident CVD or diabetes mellitus. Blood and tissue sampling Approximately 1 g SAT was sampled from the lower abdominal region at the beginning of surgery, immediately snap-frozen in isopentane and stored at
ª 2010 The Association for the Publication of the Journal of Internal Medicine Journal of Internal Medicine 269; 410–419
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Table 1 Clinical and laboratory data of patients with CKD-5 and controls CKD-5 patients All patients
Nondiabetic patients
Diabetic patients
Controls
n
37
21
16
9
Age (years)
58 (49–65)
54 (37–64)
62 (55–68)
62 (45–64)
Sex (% males)
59
52
69
56
Diabetes (%)
43
–
–
–
CVD (%)
41
27
731,**
–
7 (6–9)
6 (5–8)
8 (7–9)1,*
95 (75–100)2,***
23.7 (21.8–25.9)
23.7 (21.1–25.8)
24.1 (22.4–26.4)
28.1 (26.9–29.4)2,***
GFR (mL min)1) )2
BMI (kg m ) Serum albumin (g L)1)
34.0 (32.3–36.0)
34.5 (33.0–36.0)
34.0 (30.3–37.0)
37.0 (33.0–38.5)
Total cholesterol (mmol L)1)
4.3 (3.6–5.2)
4.3 (3.2–4.8)
4.4 (3.8–5.2)
4.7 (4.5–5.7)
Triglycerides (mmol L)1)
1.6 (1.0–2.3)
1.8 (1.0–2.7)
1.6 (1.0–2.5)
1.5 (1.2–1.7)
Plasma glucose (mmol L)1)
5.0 (4.7–6.6)
4.9 (4.7–5.2)
5.8 (4.5–10.3)
5.4 (5.0–6.1)
1,
HbA1c%
4.6 (4.3–5.1)
4.5 (4.2–4.8)
5.2 (4.6–7.4) **
4.5 (4.3–4.8)
hsCRP (mg L)1)
4.2 (1.5–10.0)
2.7 (1.6–10.0)
6.5 (1.2–11.2)
3.5 (2.3–8.5)
IL6 (ng mL)1)
6.9 (4.3–10.7)
6.7 (4.0–10.0)
7.0 (4.7–11.3)
2.7 (2.3–4.3)2,***
1050 (877–1230)
1017 (845–1170)
1087 (955–1243)
593 (526–640)2,***
)1
VCAM1 (ng mL )
Values are presented as median (25th–75th quartile). Group differences were assessed with nonparametric Wilcoxon rank-sum test. 1 Differences between diabetic and nondiabetic patients. 2 Differences between all patients and controls. *, **, ***P-values
