Association of Abdominal Circumference, Body Mass Index, and Inflammation in Kidney Transplant Recipients

ORIGINAL RESEARCH

Association of Abdominal Circumference, Body Mass Index, and Inflammation in Kidney Transplant Recipients Kristof Nagy, MD,* Akos Ujszaszi, MD,† Adam Remport, MD, PhD,* Csaba P. Kovesdy, MD,‡,§ Istvan Mucsi, MD, PhD,k Miklos Z. Molnar, MD, PhD,‡ and Zoltan Mathe, MD, PhD* Objective: Increased abdominal circumference is a marker of obesity, and it is associated with increased mortality in renal transplant recipients. Recent findings suggest that increased visceral fat deposition is a modifier of inflammation. However, little is known about the association of inflammation with abdominal circumference in kidney transplant recipients. Design: Cross-sectional. Subject: We collected sociodemographic and clinical parameters, medical and transplant history, and laboratory data from 985 prevalent kidney transplant recipients. Abdominal circumference, body mass index (BMI), and inflammatory markers were measured at baseline. Associations of inflammatory markers with abdominal circumference and BMI were examined in unadjusted and adjusted regression models. Results: Mean 6 standard deviation age was a 51 6 13 years, 57% were men, and 21% were diabetics. Patients with abdominal circumference above the median had higher BMI and were older (mean 6 standard deviation: 23.9 6 3.6 vs. 30.1 6 3.9 kg/m2, P , .001; and 48 6 14 vs. 54 6 11 years, P , .001). Furthermore, patients with higher abdominal circumference had higher inflammatory parameters: median (interquartile range) C-reactive protein (mg/L): 2.3 (3.9) versus 4.1 (6.2), P , .001; and IL-6 (pg/mL): 1.9 (2.2) versus 2.3 (2.4), P , .001. In multivariable-adjusted linear regression models, higher abdominal circumference showed significant linear associations with inflammatory markers (standardized regression coefficients (b) of abdominal circumference for lnCRP: babdominal circumference 5 0.29, P , .001; and for lnIL-6: babdominal circumference 5 0.09, P 5 .018). Moreover, in multivariable-adjusted linear regression models, higher BMI showed significant linear associations with inflammatory markers (standardized regression coefficients (b) of BMI for lnCRP: bBMI 5 0.24, P , .001; and for white blood cells: bBMI 5 0.07, P 5 .041). Conclusions: Abdominal circumference and BMI are independently associated with inflammatory markers in prevalent kidney transplant recipients. Ó 2016 by the National Kidney Foundation, Inc. All rights reserved.

Introduction

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ECENT FINDINGS SUGGEST that body fat is not just for energy storage, but it is also a modifier of inflammation.1 Nutrition and inflammation are important component of protein energy wasting syndrome in kidney transplant recipients.2 In addition, both obesity and inflammation are important predictors of outcomes, including mortality and graft survival in * Department of Transplantation and Surgery, Semmelweis University, Budapest, Hungary. † Department of Pathophysiology, Semmelweis University, Budapest, Hungary. ‡ Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee. § Department of Medicine, Nephrology Section, Memphis Veterans Affairs Medical Center, Memphis, Tennessee. k Department of Medicine, Division of Nephrology and Multiorgan Transplant Program, University Health Network and University of Toronto, Toronto, Canada. Financial Disclosures: The authors declare that they have no conflicts of interest. Support: This study was supported by grants from the National Research Fund (OTKA) (F-68841; KTIA-OTKA-EU 7KP-HUMAN-MB08-A81231), ETT (206/09), the Hungarian Kidney Foundation, Hungarian

Journal of Renal Nutrition, Vol -, No - (-), 2016: pp 1-9

renal transplant recipients.3,4 However, the association between obesity and inflammation was not assessed in this population. These findings highlight the importance of body composition and body weight assessment in transplant patients. The most common method used to quantify obesity is calculation of body mass index (BMI); however, a lean and very muscular individual may have a BMI score

Society of Hypertension, Hungarian Society of Nephrology, and the Foundation for Prevention in Medicine. M.Z.M. received grants from the National Developmental Agency (KTIA-OTKA-EU 7KP-HUMAN-MB08-A-81231) from the Research and Technological Innovation Fund and was also supported by Hungarian Kidney Foundation. Address correspondence to Miklos Z. Molnar, MD, PhD, FEBTM, FERA, FASN, Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, 956 Court Ave, Suite B216B, Memphis, TN 38163. E-mail: [email protected] or Zoltan Mathe, MD, PhD,

Department of Transplantation and Surgery, Semmelweis University, Baross utca 23., Budapest, Hungary. E-mail: [email protected] Ó

2016 by the National Kidney Foundation, Inc. All rights reserved. 1051-2276/$36.00 http://dx.doi.org/10.1053/j.jrn.2016.02.007

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suggesting obesity without having significant body fat. Abdominal circumference may be a more accurate parameter to represent the amount of visceral and abdominal body fat.5 Abdominal obesity is a predictor of all-cause and cardiovascular mortality in patients with various stages of chronic kidney disease, including kidney transplant recipients, while there is reverse correlation between BMI and survival rates in dialysed populations.6,7 A recent study highlighted that inflammation is an effect modifier of outcomes in hemodialysis patients, indicating that obesity provides survival advantage only in patients with inflammation.8 In renal transplant recipients, inflammation is an independent predictor of poor clinical outcomes.9,10 Proinflammatory cytokines predict cardiovascular events and all-cause mortality. Graft loss is also associated with markers of inflammation.9,10 Furthermore, in the general, population obesity can be associated with low-grade chronic inflammation.11 Findings from the general population cannot be directly extrapolated to transplant patients, due to the immunologically active graft and to immunosuppressive therapy. Therefore, the association between inflammation and obesity requires additional investigation in kidney transplant recipients. To further inform this field, our primary objective was to analyze the association between abdominal circumference, BMI, and inflammatory markers in renal transplant recipients. We hypothesized that increased abdominal circumference and BMI are associated with elevated inflammatory markers.

Methods Patient Population and Data Collection We invited all prevalent kidney transplant recipients 18 years of age or older (n 5 1,214) who were followed at a single transplant outpatient clinic on December 31, 2006, to participate in this observational study. Exclusion criteria were acute rejection within the last 4 weeks, current hospitalization, transplantation in the previous 3 months, acute infection, or bleeding. Baseline assessments were conducted between February 2007 and August 2007.2,4,7,12,13 Demographic data and details of medical history were collected at baseline, when information on age, gender, menopause status, etiology of chronic kidney disease, transplantation-related data including immunosuppressant medication, and comorbidities (the modified Charlson Comorbidity Index) were obtained.14 Estimated glomerular filtration rate was calculated using the chronic kidney disease epidemiology collaboration equation.15 The study was approved by the Ethics Committee of our University (49/2006). Before enrollment, patients received detailed written and verbal information regarding the aims and protocol of the study and gave written consent to participate.

Laboratory Data All laboratory data were collected and measured at the baseline clinic visit and included adipocytokines, TNF-a, IL-6, blood hemoglobin, serum C-reactive protein (CRP), serum creatinine, blood urea nitrogen, and serum albumin levels. Serum samples were also collected at the time of the baseline assessment and stored at 270 C for future use. Serum cytokines’ concentrations were measured using immunoassay kits based on solid-phase sandwich enzyme-linked immunosorbent assay (R&D Systems, Minneapolis, MN, Coefficient of Variation,10%). Transplantation-Related Data and Donor Characteristics Transplant-related data extracted from the medical records included current medications (including current immunosuppressive treatment), transplant vintage, that is, time elapsed since the date of transplantation, length of time on dialysis, type of allograft, history of treated acute rejection(s) after transplantation, human leukocyte antigen mismatch, panel reactive antibodies titer, cold ischemia time, donor age, and gender. Immunosuppressive Therapy Standard maintenance immunosuppressive therapy consisted of prednisolone, with either cyclosporine A microemulsion formulation (Neoral) (CsA) or tacrolimus, combined with mycophenolate mofetil or azathioprine or sirolimus. Statistical Analysis Statistical analyses were carried out using STATA 13 (StataCorp, College Station, TX). Data were summarized using proportions, means (6standard deviation), or medians (interquartile range) as appropriate. Categorical variables were compared with chi-square test, and continuous variables were compared using Student’s t test or the Mann–Whitney U test, Kruskal–Wallis H test, or analysis of variance as appropriate. In all statistics, two-sided tests were used and the results were considered statistically significant if P was ,.05. The association between abdominal circumference and inflammatory markers was assessed using cubic spline analyses and multivariable linear regression models. Analogous analyses were also conducted for BMI and inflammation. The variables entered in the multivariable-adjusted models were selected based on theoretical considerations; we included predictors in the models which were known to be associated both with inflammation and with obesity based on scientific evidence and which were available in our database. Three regression models were examined with incremental levels of multivariable adjustment: (1) unadjusted model; (2) case-mix model was age, gender, estimated glomerular filtration rate, end-stage renal disease time, Charlson Comorbidity Index, steroid use, blood hemoglobin, serum transferrin, soluble transferrin receptor,

ABDOMINAL CIRCUMFERENCE, BMI AND INFLAMMATION

ferritin, calcium, phosphate, and parathyroid hormone; (3) Final model was adjusted for variables in case-mix model, as well as serum albumin, prealbumin, cholesterol, highdensity lipoprotein cholesterol, and triglyceride level. Only 2% of the data were missing in our final models (Table S1). Missing data were imputed with median values.

Results Demographics and Baseline Characteristics Of the 1,214 potential patients, 17% refused to participate in the study and 1% were excluded based on various inclusion/exclusion criteria. In our cohort, eight people had missing body composition data, and the final study sample therefore included 985 patients (Fig. S1). The proportion of men among participants was lower than among those who refused to participate (57% vs. 67% males; P 5 .008), but there was no difference in age between the two groups (51 6 13 vs. 52 6 13 years; P 5 .66). Baseline characteristics of the 985 patients divided into two groups by the World Health Organization suggested categories of abdominal circumference are shown in Table 1. Generally, patients with higher abdominal circumference were older, more likely to be male and diabetic, and had higher inflammatory markers. Table S2 shows clinically relevant correlations of different parameters with abdominal circumference. Baseline characteristics of the 985 patients divided into four groups by the World Health Organization suggested categories of BMI are shown in Table S3. Generally, patients with higher BMI were older, more likely to be diabetic, had higher prevalence of coronary heart disease, and had higher inflammatory markers. Table S4 shows clinically relevant correlations of different parameters with BMI. In addition, correlations of inflammatory parameters with kidney function are demonstrated in Table S5. Abdominal Circumference, BMI, and Inflammatory Markers To characterize the association between inflammatory and body composition markers, first, we conducted unadjusted cubic spline analyses. Our results showed a linearly increasing association between abdominal circumference and natural log-transformed (ln) CRP, IL-6, and TNF-a levels and a threshold-type association with white blood cell (WBC) (Fig. 1). To further analyze the association between the inflammatory markers and abdominal circumference, we performed multivariable-adjusted linear regression models (Table 2). Adjustment for case-mix and additional nutritional factors abrogated the associations with WBC and lnTNF-a, but lnCRP and lnIL-6 levels remained significantly associated with abdominal circumference. To further analyze the association between inflammatory markers and BMI, we performed unadjusted cubic spline analyses and multivariable-adjusted linear regression

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models (Table 3, Figure 2). Adjustment for case-mix and additional nutritional factors abrogated the associations with lnIL-6 and lnTNF-a, but lnCRP level and WBC remained significantly associated with BMI (Table 3).

Discussion To the best of our knowledge, this is the first observational cohort study demonstrating the association of abdominal circumference, BMI, and inflammation in prevalent kidney transplant recipients. We used abdominal circumference as the primary marker of obesity because it describes visceral and abdominal body fat more precisely than BMI.7 Despite BMI being the most common method for assessing obesity, it cannot distinguish between obesity and higher muscle mass. Therefore, a combination of BMI and abdominal circumference may be a better approach to assess obesity in any population. We hypothesized that increased abdominal circumference and increased BMI are associated with elevated inflammatory markers, hence linking obesity to inflammation in this patient population. Our hypothesis was confirmed because both increased abdominal circumference and high BMI were associated with increased inflammatory parameters in both unadjusted and multivariableadjusted models. Our investigation revealed an association between obesity and inflammation in renal transplant recipients. In multivariable-adjusted models, we found the strongest association of both abdominal circumference and BMI with CRP. This association has also been observed in the general population.16 One possible mechanism that may link CRP and obesity is the effect of proinflammatory interleukins. Several interleukins are synthetized by adipocytes, and their levels are elevated in obesity and induce CRP production in the liver.17 Supporting this hypothesis, we demonstrated correlations between CRP and interleukins (Table S5); however, linking CRP and obesity require further investigation. In addition to the correlation with CRP, proinflammatory interleukins such as IL-6 and TNF-a are also associated with abdominal circumference, but not with BMI in our multivariable-adjusted models. IL-6 and TNF-a are mainly synthetized in visceral fat tissue.9,18 Moreover, approximately 30 percent of circulating IL-6 is produced by adipocytes.19 Jannsen et al.20 demonstrated that abdominal circumference is a more specific parameter and predictor of visceral body fat and obesity-related health risk. It is also noteworthy that the kidneys presumably eliminate these cytokines, and their levels correlated negatively with graft function in our cohort (Table S5). However, the association of cytokines with abdominal circumference remained significant even after adjusting for renal function in our models. These findings further support the assumption that adipocytes might by responsible for elevated cytokine levels.

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Table 1. Baseline Characteristics of the Patients Divided by WHO Suggested Cutoff Values of Abdominal Circumference

Baseline Characteristics Abdominal circumference (cm) BMI (kg/m2) Demographics/comorbidities Age (y) Gender, female (%) Presence of hypertension (%) Presence of diabetes (%) Presence of coronary heart disease (%) Charlson Comorbidity Index Smoking (%) Time since last transplant (mo) Previous time on dialysis (mo) Total ESRD time (mo) Laboratory parameters eGFR (CKD-EPI) (mL/min/1.73 m2) Blood Hgb (g/L) Serum phosphate (mmol/L) Serum calcium (mmol/L) Serum parathyroid hormone (pg/mL) Serum ferritin (ng/mL) Serum transferrin (g/L) Serum sol. transferrin receptor (mg/L) Inflammatory markers CRP (mg/L) Interleukin-6 (pg/mL) Tumor necrosis factor a (pg/mL) White blood cell count (10^9/L) Malnutrition-inflammation score Serum albumin (g/L) Nutritional markers (serum) Total cholesterol (mmol/L) LDL cholesterol (mmol/L) HDL cholesterol (mmol/L) Triglyceride (mmol/L) Transplantation-related data Primary cause of ESRD (%) Chronic GN Chronic TIN ADPKD Diabetic nephropathy Hypertensive nephropathy Others or unknown Cold ischemic time (minute) History of delayed graft function (%) History of acute rejection (%) HLA mismatches (%) 0 1 2 3 4 5 6 Immunosuppression Steroid use (%) Cyclosporine use (%) Tacrolimus use (%) Azathioprine use (%) MMF use (%)

All Patients (n 5 985)

Abdominal Circumference #88 cm in Women and #102 in Men (n 5 441)

Abdominal Circumference .88 cm in Women and .102 cm in Men (n 5 544)

P Value

98.8 6 14.4 27.0 6 4.9

87.4 6 9.6 23.5 6 3.4

108.0 6 10.6 29.9.1 6 4.0

,.001 ,.001

51 6 13 43 94 21 9 2 (2) 18 72 (75) 20 (29) 108 (86)

47 6 14 38 91 15 7 2 (1) 23 72 (74) 20 (33) 111 (95)

54 6 11 47 96 26 10 2 (2) 15 72 (75) 20 (28) 104 (82)

,.001 .004 ,.001 ,.001 .116 ,.001 ,.001 .495 .699 .102

52.6 6 21.8 135 6 17 1.08 6 0.29 2.36 6 0.15 67.2 (56.0) 161 (301) 2.36 6 0.46 3.77 6 1.77

53.2 6 22.7 134 6 18 1.10 6 0.35 2.35 6 0.17 64.6 (54.0) 162 (338) 2.29 6 0.44 3.52 6 1.70

52.1 6 21.0 136 6 16 1.05 6 0.22 2.36 6 0.14 70.0 (62.0) 160.5 (287) 2.41 6 0.47 3.97 6 1.61

.469 .034 .008 .392 .018 .288 ,.001 ,.001

3.1 (5.2) 2.1 (2.3) 2.1 (1.3) 7.9 6 2.3 3 (3) 40.2 6 4.1

2.3 (3.4) 1.9 (2.0) 2.1 (1.4) 7.7 6 2.3 3 (3) 40.4 6 4.3

4.2 (6.6) 2.3 (2.7) 2.0 (1.3) 8.1 6 2.3 3 (2) 40.1 6 4.0

,.001 ,.001 .790 .011 .036 .198

5.5 6 1.3 3.2 6 0.9 1.3 6 0.4 1.7 (1.3)

5.4 6 1.3 3.1 6 0.9 1.4 6 0.5 1.5 (1.1)

23 13 18 5 7 35 1248 6 350 26 15

26 13 14 5 6 37 1218 6 390 22 17

20 13 21 4 7 34 1271 6 313 29 14

1 5 22 46 21 4 1

1 3 22 46 22 6 0

1 6 22 46 21 3 2

81 49 43 4 78

78 46 44 4 76

84 51 41 4 80

5.6 6 1.3 3.2 6 0.93 1.3 6 0.4 1.9 (1.5)

.003 .197 ,.001 ,.001 .030

,.001 .265 .014 .006

.013 .142 .331 .884 .230 (Continued )

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ABDOMINAL CIRCUMFERENCE, BMI AND INFLAMMATION Table 1. Baseline Characteristics of the Patients Divided by WHO Suggested Cutoff Values of Abdominal Circumference (Continued )

Baseline Characteristics

All Patients (n 5 985)

Abdominal Circumference #88 cm in Women and #102 in Men (n 5 441)

Abdominal Circumference .88 cm in Women and .102 cm in Men (n 5 544)

Sirolimus use (%) Everolimus use (%)

8 2

9 2

7 2

P Value .465 .618

ADPKD, autosomal dominant polycystic kidney disease; BMI, body mass index; CKD-EPI, chronic kidney disease epidemiology collaboration; CRP, C-reactive protein; eGFR, estimated GFR; ESRD, end-stage renal disease; GN, glomerulonephritis; HDL, high-density lipoprotein; Hgb, hemoglobin; HLA, human leukocyte antigen; LDL, low-density lipoprotein; MMF, mycophenolate mofetil; SD, standard deviation; TIN, tubulointerstitialis nephritis; WHO, World Health Organization. Values are in median with interquartile range or in mean 6 SD.

After full multivariable adjustment, WBC was not associated with abdominal circumference and was only weakly associated with BMI. Previous study showed that WBC count is independently associated with BMI in diabetic patients.21 The main reason why this association cannot be detected in our sample is the effects of immune suppression on WBC count in our patient population. However, further studies are needed whether obesity markers and changing of these markers have any effect on WBC in kidney transplant recipients. There are several plausible physiological explanations for why obesity could induce or be associated with inflamma-

tion in kidney transplant recipients. First, in obese individuals, fat tissue is infiltrated with immune cells that migrate and embed mainly in visceral fat tissue.22 The migrated M1 proinflammatory macrophages and natural-killer T cells are responsible for insulin insensitivity and cytokine production, further supporting the connection of obesity with inflammation.22,23 It should be highlighted that in transplant recipients, obesity-associated inflammation results in an imbalance between T-cell subgroups, increasing the ratio of CD81 T cells to CD41 and inhibiting the immunosuppressive T regulatory cells, which may increase the rejection risk.24 The main proinflammatory cytokine

Figure 1. Unadjusted association between abdominal circumference (explanatory variable) and lnCRP, lnIL-6, lnTNF-a, and WBC (dependent variables) in 985 kidney transplant recipients using linear regression analysis with additional distributional histograms of the inflammatory markers.

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Table 2. Association of Abdominal Circumference (Explanatory Variable) and Different Inflammatory Markers (Dependent Variables) Using Multilevel and Multivariate Linear Regression Analyses in 985 Kidney Transplant Recipients

Dependent Variables lnCRP Unadjusted Case-mix Fully adjusted WBC Unadjusted Case-mix Fully adjusted lnIL-6 Unadjusted Case-mix Fully adjusted lnTNF-a Unadjusted Case-mix Fully adjusted

Regression Coefficient (RC) of Abdominal Circumference

Standardized RC of Abdominal Circumference

0.022 0.023 0.023

0.272 0.292 0.289

0.018 0.012 0.009

Lower CI 95% of RC

Upper CI 95% of RC

P Value

0.017 0.018 0.018

0.027 0.029 0.029

,.001 ,.001 ,.001

0.110 0.072 0.054

0.008 0.000 20.004

0.028 0.023 0.021

.001 .052 .163

0.008 0.005 0.005

0.137 0.087 0.085

0.004 0.001 0.001

0.011 0.009 0.009

,.001 .013 .018

0.003 0.003 0.002

0.078 0.080 0.066

0.001 0.000 0.000

0.005 0.005 0.005

.015 .029 .085

CI, confidence interval; eGFR, estimated glomerular filtration rate; ESRD, end-stage renal disease; HDL, high-density lipoprotein; WBC, white blood cell; lnCRP, natural logarithm of C-reactive protein; lnIL-6, natural logarithm of Interleukin-6; lnTNF-Alpha, natural logarithm of tumor necrosis factor-alpha. Case-mix model: adjusted for age, gender, eGFR, ESRD time, Charlson comorbidity score, steroid use, hemoglobin, transferrin, soluble transferrin receptor, ferritin, calcium, phosphate, and parathyroid hormone. Fully adjusted model: adjusted for variable from case-mix model and albumin, prealbumin, cholesterol, HDL cholesterol, and triglyceride level.

that could be responsible for fat-associated inflammation is TNF-a, which also could be responsible for insulin resistance leading to diabetes, obesity, and further kidney injury.18 Furthermore, there are many other cytokines synthetized in visceral fat that presumably play an important role in the common pathophysiology of inflammation and obesity.18 Finally, recent in vitro studies demonstrated that in adipocytes, intracellular pathways and toll-like receptors could be activated inducing inflammation through different kinases.25,26 The prevalence of obesity is increasing rapidly in the western world, affecting more than one-third of developed countries’ population.27 It is responsible for worsening kidney function directly or indirectly through increased risk for diabetes and hypertension.28,29 In renal transplant recipients, the effects of pretransplant obesity on long-term posttransplant outcomes are controversial and might be modified by inflammation.3,30 We previously showed an association between increased pretransplant BMI and delayed graft function, raising the possibility that obesity might induce a proinflammatory environment that is an important modifier of early graft function and graft survival.31 Gore et al.32 presented similar results in a large multicenter cohort. Recently, Nicoletto et al.30 emphasized that BMI is not a predictor of mortality; however, Deetman et al.33 uncovered the limitations of BMI as a predictor by adjusting for urinary creatinine and suggesting that higher muscle mass may offer a potential explanation for Nicoletto’s finding. There are few studies using abdominal circumference as a marker

of obesity. We demonstrated that higher abdominal circumference is associated with increased mortality, whereas elevated BMI portends a survival advantage in kidney transplant patients.7 Markers of inflammation are also important predictors of outcomes. According to Dahle et al.10 and Abedine et al.,9 CRP and IL-6 are independent predictors of graft loss in kidney transplant recipients. These findings emphasize that both abdominal obesity and inflammation may have negative effects on survival and graft function. As we presented above, there is an association between inflammation and obesity in transplanted patients, and it requires further investigation to determine whether inflammation predicts outcomes independently from obesity and also whether inflammation may also be an effect modifier in the association between BMI and outcomes in kidney transplant recipients. Strength of our study includes the examination of a large cohort of kidney transplant recipients and minimal missing data given the protocol-driven study design and data collection. In addition, our analyses accounted for important confounders of the abdominal circumference and the BMI inflammation associations such as residual graft function and transplantation-related data. To the best of our knowledge, our study is the first to investigate the association of abdominal obesity and BMI with inflammation in kidney transplant recipients. The interpretation of our study should be tempered by potential limitations. One of the major limitations of this study was that inflammatory markers and abdominal circumference and BMI were measured only at baseline;

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ABDOMINAL CIRCUMFERENCE, BMI AND INFLAMMATION

Figure 2. Unadjusted association between BMI (explanatory variable) and lnCRP, lnIL-6, lnTNF-a, and WBC (dependent variables) in 985 kidney transplant recipients using linear regression analysis with additional distributional histograms of the inflammatory markers.

Table 3. Association of BMI (Explanatory Variable) and Different Inflammatory Markers (Dependent Variables) Using Multilevel and Multivariate Linear Regression Analyses in 985 Kidney Transplant Recipients Dependent Variables lnCRP Unadjusted Case-mix Fully adjusted WBC Unadjusted Case-mix Fully adjusted lnIL-6 Unadjusted Case-mix Fully adjusted lnTNF-a Unadjusted Case-mix Fully adjusted

Regression Coefficient (RC) of BMI

Standardized RC of BMI

0.062 0.059 0.057

0.264 0.248 0.240

0.052 0.038 0.034

Lower CI 95% of RC

Upper CI 95% of RC

P Value

0.048 0.044 0.042

0.077 0.073 0.071

,.001 ,.001 ,.001

0.109 0.080 0.071

0.022 0.007 0.001

0.081 0.069 0.066

.001 .017 .041

0.017 0.008 0.008

0.100 0.049 0.047

0.006 20.002 20.003

0.027 0.019 0.018

.002 .126 .146

0.004 0.005 0.004

0.044 0.051 0.040

0.000 20.001 20.003

0.011 0.012 0.011

.169 .126 .235

BMI, body mass index; CI, confidence interval; eGFR, estimated glomerular filtration rate; ESRD, end-stage renal disease; HDL, high-density lipoprotein; lnCRP, natural logarithm of C-reactive protein; lnIL-6, natural logarithm of Interleukin-6; lnTNF-Alpha, natural logarithm of tumor necrosis factor-alpha; WBC, white blood cell. Case-mix model: adjusted for age, gender, eGFR, ESRD time, Charlson comorbidity score, steroid use, hemoglobin, transferrin, soluble transferrin receptor, ferritin, calcium, phosphate, and parathyroid hormone. Fully adjusted model: adjusted for variable from case-mix model and albumin, prealbumin, cholesterol, HDL cholesterol, and triglyceride level.

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therefore, we did not have the ability to follow change in levels over time. We examined patients from a single transplant center. This center cares for two-thirds of the transplant population from our country. As our country does not have a national registry, we cannot compare our cohort with the entire national transplant population. However, the baseline characteristics of our prevalent cohort are very similar to other published cohorts from Europe. Additionally, models could only be adjusted for identified confounders for which we had available data. Therefore, we cannot rule out residual confounding.

Conclusions In our large and contemporary cohort of almost a thousand kidney transplant recipients, we found that markers of obesity, such as BMI and abdominal circumference, showed moderate-to-strong independent associations with inflammatory markers. Further studies are needed to examine whether changes in BMI and/or body composition have any effect on inflammatory markers or on clinical outcomes in kidney transplant recipients.

Practical Application The clinical relevance of these findings is the better understanding of the association of obesity and inflammation in renal transplant recipients. We demonstrate an existing independent association between inflammation and obesity in our cohort. Our results raise the question whether any intervention affecting obesity might have an effect on inflammation. This question needs to be answered in future interventional trials. Supplementary Data

Supplementary data related to this article can be found at http://dx.doi.org/10.1053/j.jrn.2016.02.007.

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