- Email: [email protected]
Nutritional Status and Intrarenal Resistive Indices After Kidney Transplantation
Nutritional Status and Intrarenal Resistive Indices After Kidney Transplantation A. Kolonko, J. Chudek, A. Kujawa-Szewieczek, and A. Wie¸cek ABSTRACT Background. Obesity predicts vascular stiffness, which is prevalent among kidney transplant patients. However, the influence of obesity has not been established on parameters of renal vascular resistance variation. The aim of this study was to analyze the influence of nutritional status on intrarenal resistive parameters as measured in the early period after successful kidney transplantation by Doppler ultrasound. Methods. Both pulsatility index (PI) and resistance index (RI) in the kidney graft were measured by Doppler sonography twice: at 2 to 4 days and before hospital discharge (mean 22 days; 95% confidence interval 21–23) after transplantation. Nutritional status was scored according to World Health Organization criteria. Results. Among 513 patients, 29 were underweight; 280, normal; 166, overweight; and 38, obese. Both PI and RI values were significantly increased consistent with recipient nutritional status (analysis of variance: P ⬍ .001). Post hoc analysis showed significant differences in PI and RI measurements for obese versus underweight or normal weight groups. Multivariate analysis revealed an influence of body mass index on PI and RI measurements before hospital discharge to be independent of other variables, including recipient age, prior delayed graft function and cold ischemia time. Conclusions. Excessive nutritional status was associated with increased renal vascular resistance among kidney transplant patients. Nutritional status should be considered for the proper interpretation of intrarenal Doppler measurements. OPPLER MEASUREMENT of renal vascular resistance is an useful tool to manage kidney transplant recipients, especially in the early postoperative period. It can establish the cause of kidney graft dysfunction1–3 and identify patients with an increased risk of long-term graft loss or death.4 – 6 However, in the early posttransplant period, several clinical factors may influence intrarenal resistive indices, that is, pulsatility index (PI) and resistance index (RI), namely, delayed graft function (DGF), acute rejection episodes, acute calcineurin inhibitor toxicity, and vascular complications.1–3,7 In stable kidney transplant recipients, the values of resistive indices are strongly related to markers of pulse wave velocity (PWV), pulse pressure, anklebrachial index (ABI), and intima-media thickness (IMT) as well as to predictors of vascular stiffness including recipient age and Framingham risk score.8 –10 Obesity is one of the predictors of vascular stiffness. It is highly prevalent among kidney transplant patients.11,12
D
Since its influence on variations in renal vascular resistance has not been established. We sought to analyze the influence of nutritional status on intrarenal resistive parameters as measured by Doppler ultrasound in the early period after kidney transplantation. METHODS We analyzed 513 consecutive first cadaveric kidney graft recipients transplanted from 1998 to 2010 excluding patients with primary graft nonfunction or early acute rejection episodes. Data were retrieved from our center-operated transplant patient registry. From the Departments of Nephrology, Endocrinology and Metabolic Diseases (A.K., J.C., A.K.-S., A.W.) and Pathophysiology (J.C.), Medical University of Silesia, Katowice, Poland. Address reprint requests to Dr Aureliusz Kolonko, Department of Nephrology, Endocrinology and Metabolic Diseases; 40-027 Katowice, Francuska 20/24, Poland. E-mail: [email protected]
© 2013 by Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010-1710
0041-1345/–see front matter http://dx.doi.org/10.1016/j.transproceed.2012.11.018
Transplantation Proceedings, 45, 1625–1629 (2013)
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The causes of chronic renal disease were chronic glomerulonephritis (51.5%), interstitial nephritis (11.3%), polycystic kidney disease (8.4%), diabetic nephropathy (5.3%), hypertensive nephropathy (7.4%), other diseases or unknown conditions (16.1%). Most patients were prescribed cyclosporine or tacrolimus, mycophenolate mofetil or azathioprine or sirolimus, and steroids (Table 1). Induction therapy included basiliximab (n ⫽ 38; Simulect, Novartis, Basel, Switzerland) or antithymocyte globulin (n ⫽ 4; Fresenius, Bad Homburg, Germany). PI and RI in the kidney graft were measured by Doppler sonography twice: at 2– 4 day after transplantation and before hospital discharge (mean ⫽ 22; 95% confidence interval 21–23). Doppler examinations employed an Acuson machine (Aspen, Mountain View, Calif, USA), and after 2005, with a Siemens machine (Sonoline Antares, Mountain View, Calif, USA). Both apparatus were equipped with 2.5 to 4.0-MHz micro-convex-array transducers to visualize 3–5 segmental arteries to record and analyze the Doppler spectrum. The peak systolic velocity (Vmax) and end-diastolic velocity (Vmin) were measured to calculate segmental arterial resistive indices as PI ⫽ 2 ⫻ (Vmax ⫺ Vmin)/Vmax ⫹ Vmin and RI ⫽ 1 ⫺ (Vmin/Vmax). Patient nutritional status was scored according to World Health Organization criteria, based on anthropometric measurements performed before hospital discharge, corresponding to the second Doppler assessment. DGF was defined as a need for dialysis therapy during first week after transplantation. Statistical analyses were performed using STATISTICA 10.0 PL for Windows software package (StatSoft Polska, Kraków, Poland)
and MedCalc 12.3.0.0. (Mariakerke, Belgium). Results are presented as mean values and 95% confidence intervals. To compare groups, we used the chi-square test for qualitative variables, and analysis of variance (ANOVA), followed by Tukey test for quantitative ones. Correlation coefficients were calculated according to Pearson. We performed backward stepwise multivariate regression analyses for dependent variables (PI or RI in both time points) including potential explanatory variables (recipient age, delayed graft function, body mass index [BMI], history of previous cardiovascular episodes or diabetes, cold ischemia time [CIT], human leukocyte antigen [HLA] mismatches, and corresponding calcineurin inhibitor levels). In all statistical tests, P values below .05 were considered to be significant.
RESULTS
Among 513 patients, 29 were underweight (UW); 280, normal weight (NW); 166, overweight (OW); and 38, obese (OB). Significant differences were observed among analyzed groups in age, previous cardiovascular episodes, DGF frequency and trough levels of both calcineurin inhibitors at the time of the first posttransplant measurement (Table 1). In the first measurement, PI and RI were: UW 1.59 (1.34 –1.85) and 0.75 (0.71– 0.79); NW 1.60 (1.53–1.68) and 0.76 (0.74 – 0.77); OW 1.71 (1.62–1.80) and 0.79 (0.77– 0.80); OB 2.03 (1.81– 2.25) and 0.84 (0.81– 0.86), respectively (Fig 1A and B). Similarly, in the second measurement
Table 1. Characteristics of Patients in Groups Defined According to World Health Organization Nutritional Status Criteria
Age (y) Gender (M/F) BMI (kg/m2) Duration of dialysis (mo) Hypertension (%) Previous cardiovascular episodes (%) Diabetes (%) Mismatch HLA I Mismatch HLA II CIT (ho) Highly sensitized patients (PRA ⬎ 30%), % Calcineurin inhibitors (CyA/Tc) Antimetabolic drugs (AZA/MMF) CyA C0 first (ng/mL) CyA C2 first (ng/mL) Tc C0 (ng/mL) CyA C0 last (ng/mL) CyA C2 last (ng/mL) Tc C0 last (ng/mL) Donor age (y) Donor serum creatinine (mol/L) DGF (%)
Underweight (BMI ⬍ 18.5), n ⫽ 29
Normal Weight (BMI 18.5–24.9), n ⫽ 280
Overweight (BMI 25–29.9), n ⫽ 166
Obese (BMI ⬎30), n ⫽ 38
ANOVA
32 (28–36) 15/14 17.1 (16.8–17.5) 31 (23–39) 23 (79.3%) 0 0 2.38 (1.98–2.78) 0.97 (0.75–1.18) 18 (16–21) 1 (3.5%) 15/14 10/17 146 (70–222) 821 (543–1099) 9.2 (5.2–13.2) 195 (136–254) 1016 (715–1316) 9.7 (7.2–12.2) 38 (33–44) 92 (77–106) 4 (13.8%)
41 (40–43) 169/111 22.1 (21.9–22.3) 31 (28–34) 242 (86.4%) 10 (3.6%) 17 (6.1%) 2.52 (2.40–2.64) 0.9 (0.82–0.97) 19 (18–20) 9 (3.2%) 195/85 100/160 165 (145–181) 811 (754–869) 11.6 (10.0–13.2) 258 (239–278) 1168 (1108–1228) 11.9 (10.8–13.1) 41 (40–43) 112 (104–119) 79 (28.2%)
48 (46–50) 124/42 27.3 (27.1–27.5) 27 (23–30) 149 (89.8%) 12 (7.2%) 18 (10.8%) 2.55 (2.41–2.69) 0.99 (0.89–1.09) 19 (18–20) 0 123/43 55/101 200 (178–221) 887 (822–952) 14.7 (12.5–16.9) 277 (254–301) 1208 (1132–1285) 13.5 (11.7–15.3) 42 (40–44) 119 (108–130) 66 (39.8%)
51 (48–54) 21/17 32.3 (31.7–33.0) 29 (22–35) 29 (76.3%) 5 (13.2%) 2 (5.3%) 2.45 (2.10–2.80) 0.69 (0.50–0.89) 18 (15–20) 0 31/7 22/16 200 (157–244) 998 (854–1141) 13.5 (6.8–20.1) 285 (208–362) 1190 (1017–1362) 10.3 (7.3–13.1) 41 (37–46) 110 (91–129) 22 (57.9%)
⬍.001 NS ⬍.001 NS NS .003 NS NS NS NS NS .01 NS .03 NS .04 NS NS NS NS NS ⬍.001
Data shown as means ⫾ 95% confidence interval or frequencies. BMI, body mass index; HLA, human leukocyte antygen; CIT, cold ischemia time; PRA, panel-reactive antibodies; CyA, cyclosporine; Tc, tacrolimus; AZA, azathioprine; MMF, mycophenolic acid; C0 first, cyclosporine trough level; C2 first, cyclosporine blood concentration 2 h after oral intake (both concentrations measured at the time of first Doppler measurement) Tc C0, tacrolimus trough level, measured at the time of first Doppler measurement; C0 last, cyclosporine trough level; C2 last, cyclosporine blood concentration 2 hours after oral intake (both concentrations measured at the time of last Doppler measurement); Tc C0 last, tacrolimus trough level (measured at the time of last Doppler measurement); DGF, delayed graft function; NS, not significant; ANOVA, analysis of variance.
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Fig 1. The values of intrarenal resistance indices measured within the segmental arteries of the kidney graft in four groups, according to recipients nutritional status. (A, B) Results of first measurement (at 2– 4 days posttransplant). (C, D) Results of last measurement (before discharge of the patient from the hospital). *P ⬍ .05 vs normal weight and underweight; **P ⬍ .01 vs normal weight and underweight. CI, confidence interval; SD, standard deviation.
they were: UW 1.37 (1.29 –1.44) and 0.72 (0.70 – 0.74); NW 1.43 (1.38 –1.47) and 0.73 (0.72– 0.74); OW 1.53 (1.47– 1.59) and 0.75 (0.74 – 0.77); OB 1.74 (1.54 –1.94) and 0.79 (0.76 – 0.83), respectively (Fig 1C and D). In both measurements PI and RI values were significantly higher with increased recipient nutritional status (ANOVA: P ⬍ .001). Upon post hoc analysis, we demonstrated significant differences for PI and RI in both measurements for obese versus UW or NW patients. Significant, positive correlations were observed between BMI and both resistive parameters (PI and RI) at the two measurement time points: for first, PI: 0.162 and RI: 0.188; for the second, PI: 0.173 and RI: 0.180 (all with P ⬍ .001). Multivariate regression analysis revealed the influence of BMI on PI2 and RI2 values to be independent of recipient age, prior DGF, or CIT (Table 2). Corresponding BMI influence on intrarenal resistance parameters measured during the first days after transplantation was only confirmed for PI (Table 2).
DISCUSSION
Our study showed excessive nutritional status to be associated with significantly higher renal vascular resistance among kidney transplant recipients. During the first days after transplantation, the differences between NW and OB subjects were 0.43 for PI and 0.08 for RI. These differences slightly decreased during the subsequent 3 weeks, but were still considerable at the time of hospital discharge: 0.29 for PI and 0.06 for RI. The differences observed in PI and RI measured in kidney graft arteries were probably related to the association between obesity and arterial stiffness, as shown in the general population,11 in children,13 as well as in patients with chronic kidney disease.14 Among patients with metabolic syndrome, Buscemi et al demonstrated increasing trends in IMT, PI, and RI across groups, of increasing BMI.15 In a recent study, Kawai et al indicated that RI might be useful to evaluate early renal damage more
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Table 2. The Results of Multivariate Regression Analyses for Intrarenal Resistive Indices Measured Shortly Posttransplant (PI1 and RI1) or Before Discharge from the Hospital (PI2 and RI2) Multivariate Analysis for PI1 Independent Variable
Beta
DGF Recipient age (per year) BMI (per 1 kg/m2)
0.375 — 0.105
P
⬍0.001 0.03
Multivariate Analysis for PI2 Beta
Recipient age (per year) CIT (per hour) DGF BMI (per 1 kg/m2)
0.178 0.134 0.069 0.101
P
⬍.001 .003 .12 .02
Multivariate Analysis for RI1 Beta
0.424 0.138 —
P
⬍.001 .002
Multivariate Analysis for RI2 Beta
0.175 0.127 0.072 0.089
P
⬍.001 .005 .11 .05
Data shown as means ⫾ 95% confidence interval. PI1 and RI1: pulsatility and resistance indices measured 2– 4 days posttransplant; DGF, delayed graft function; BMI, body mass index; PI2 and RI2, pulsatility and resistance indices measured before discharge from the hospital; CIT, cold ischemia time.
effectively than estimated glomerular filtration rate (eGFR).16 In that study BMI significantly correlated with RI both upon univariate and multiple regression analyses.16 The effect of obesity on kidney transplant outcomes is still controversial. The prevalence of OW or OB kidney transplant candidates has constantly increased over the last 2 decades17 as the consequence of lifestyle changes. Donorrecipient body weight ratio in pediatric population directly correlate with eGFR values in the early and late posttransplant periods.18 In adults, obesity is recognized to be a significant independent risk factor for complications of wound healing,19 DGF,19,20 acute rejection episodes,20 graft failure,19,21 and patient death.21 An association has been clearly demonstrated between nutritional markers and predictors of vascular stiffness among stable kidney transplant recipients. Heine et al, observed increased resistance indices to be independently associated with higher age, IMT, but lower BMI.9 Of note, this study included a relatively small patient group (n ⫽ 105), with a relatively wide range of posttransplant observation periods (6 –237 months). Moreover, renal resistive indices were increased in patients with pathologic compared with physiologic ABI.9 Furthermore, Schwenger et al demonstrated recipient age, PWV, pulse pressure, and IMT to be independent factors influencing RI values among stable transplant patients.8 In our study, the BMI groups differed in the prevalence of previous cardiovascular episodes, proportionate to age differences. It has been established that age is an independent factor augmenting intrarenal RI both in healthy adults and kidney transplant recipients.8,9,22 Thus, as we observed significant differences in patient characteristics, of age, previous cardiovascular episodes, first posttransplant calcineurin inhibitors trough levels, and DGF incidence, we performed a multivariate analysis including all of these
variables. The regression analysis for the early postoperative period confirmed the independent impact of BMI only on PI (measured at 2– 4 posttransplant days), showing a strong influence on DGF (beta ⫽ 0.375 for PI and beta ⫽ 0.424 for RI). Thus, significantly higher PI and RI values on early posttransplant measurements were attributed mainly to differences in DGF prevalence. A similar DGF distribution according to recipient BMI has also been shown by other authors.23 In contrast, at discharge, the regression analysis showed a considerably weaker influence of postoperative DGF history (beta ⫽ 0.072), while BMI became the important parameters for RI variability (beta ⫽ 0.089 per 1 kg/m2 increase). Based on the results of our multivariate analyses, we concluded that the values of kidney graft resistance parameters measured at approximately 3 weeks after transplantation were independently related to recipient nutritional status. It is worth while to note that we excluded from our analysis all patients with primary graft nonfunction and acute rejection episodes in the early postoperative period, seeking to avoid misinterpretations of the usually high PI and RI values that an observed in these clinical conditions. The substantial differences in intrarenal resistive indices between patients with varying nutritional status must be kept in mind, as higher RI values are unfavorable prognostic markers for both patient and kidney graft survival.4 Thus, our present work revealed recipient BMI to be another potential cofactor influencing Doppler spectrum in the kidney graft. Our study had some limitations. The time point of the secondary Doppler examination may be not optimal to analyze BMI influence on PI and RI, due to the slow recovery after DGF in these patients, which may still affect intrarenal resistance parameters before discharge. Additionally, the group of obese patients was relatively small and did not include morbidly obese incidentally transplanted in Poland. In conclusion, excessive nutritional status in transplanted patients was associated with significantly increased renal vascular resistance. Thus, nutritional status should be considered in the interpretation of intrarenal Doppler measurements in these patients.
REFERENCES 1. Friedewald SM, Molmenti EP, Friedewald JJ, et al. Vascular and nonvascular complications of renal transplants: sonographic evaluation and correlation with other imaging modalities, surgery, and pathology. J Clin Ultrasound. 2005;33:127–139. 2. Chudek J, Kolonko A, Król R, et al. The intrarenal vascular resistance parameters measured by duplex Doppler ultrasound shortly after kidney transplantation in patients with immediate, slow, and delayed graft function. Transplant Proc. 2006;38:42– 45. 3. Jimenez C, Lopez MO, Gonzalez E, et al. Ultrasonography in kidney transplantation: values and new developments. Transplant Rev. 2009;23:209 –213. 4. Radermacher J, Mengel M, Ellis S, et al. The renal arterial resistance index and renal allograft survival. N Engl J Med. 2003; 349:115–124.
NUTRITIONAL STATUS AND INTRARENAL RESISTIVE INDICES 5. Kolonko A, Chudek J, Zejda JE, et al. Impact of early kidney resistance index on kidney graft and patient survival during 5-year follow-up. Nephrol Dial Transplant. 2012;27:1225–1231. 6. McArthur C, Geddes CC, Baxter GM. Early measurement of pulsatility and resistive indexes: correlation with long-term renal transplant function. Radiology. 2011;259:278 –285. 7. Boger CA, Rummele P, Mihatsch MJ, et al. Reverse diastolic intrarenal flow due to calcineurin inhibitor (CNI) toxicity. Am J Transplant. 2006;6:1963–1967. 8. Schwenger V, Keller T, Hofmann N, et al. Color Doppler indices of renal allografts depend on vascular stiffness of the transplant recipients. Am J Transplant. 2006;6:2721–2724. 9. Heine GH, Gerhart MK, Ulrich C, et al. Renal Doppler resistance indices are associated with systemic atherosclerosis in kidney transplant recipients. Kidney Int. 2005;68:878 – 885. 10. Gerhart MK, Seiler S, Grun OS, et al. Indices of systemic atherosclerosis are superior to ultrasound resistance indices for prediction of allograft survival. Nephrol Dial Transplant. 2010;25: 1294 –1300. 11. Wildman RP, Mackey RH, Bostom A, et al. Measures of obesity are associated with vascular stiffness in young and older adults. Hypertension. 2003;42:468 – 473. 12. Lentine KL, Axelrod D, Abbott KC. Interpreting body composition in kidney transplantation: weighting candidate selection, prognostication, and interventional strategies optimize health. Clin J Am Soc Nephrol. 2011;6:1238 –1240. 13. Tounian P, Aggoun Y, Dubern B, et al. Presence of increased stiffness of the common carotid artery and endothelial dysfunction in severely obese children: a prospective study. Lancet. 2001;358:1400 –1404. 14. Locsey L, Szlanka B, Borbas B, et al. Arterial stiffness in chronic renal failure and after renal transplantation. Transplant Proc. 2010;42:2299 –2303.
1629 15. Buscemi S, Verga S, Batsis JA, et al. Intra-renal hemodynamisc and carotid intima-media thickness in the metabolic syndrome. Diabetes Res Clin Pract. 2009;86:177–185. 16. Kawai T, Kamide K, Onishi M, et al. Usefulness of the resistive index in renal Doppler ultrasonography as an indicator of vascular damage in patients with risks of atherosclerosis. Nephrol Dial Transplant. 2011;26:3256 –3262. 17. Lin J, McGovern ME, Brunelli SM, et al. Longitudinal trends and influence of BMI mismatch in living kidney donors and their recipients. Int Urol Nephrol. 2011;43:891– 897. 18. Spatenka J, Seeman T, Foltynowa E, et al. Effect of donor/ recipient body weight ratio, donor weight, recipient weight and donor age on kidney graft function in children. Nephrol Dial Transplant. 2012;27:820 – 824. 19. Zrim S, Furlong T, Grace BS, et al. Body mass index and postoperative complications in kidney transplant recipients. Nephrology. 2012;17:582–587. 20. Chang SH, Coates PTH, McDonald SP. Effects of body mass index at transplant on outcomes of kidney transplantation. Transplantation. 2007;84:981–987. 21. Meier-Kriesche H-U, Arndorfer JA, Kaplan B. The impact of body mass index on renal transplant outcomes: a significant independent risk factor for graft failure and patient death. Transplantation. 2002;73:70 –74. 22. Kaiser C, Gotzberger M, Landauer N, et al. Age dependency of intrarenal resistance index (RI) in healthy adults and patients with fatty liver disease. Eur J Med Res. 2007;12:191–195. 23. Weissenbacher A, Jara M, Ulmer H, et al. Recipient and donor body mass index as important risk factors for delayed kidney graft function. Transplantation. 2012;93:524 –529.
D
Since its influence on variations in renal vascular resistance has not been established. We sought to analyze the influence of nutritional status on intrarenal resistive parameters as measured by Doppler ultrasound in the early period after kidney transplantation. METHODS We analyzed 513 consecutive first cadaveric kidney graft recipients transplanted from 1998 to 2010 excluding patients with primary graft nonfunction or early acute rejection episodes. Data were retrieved from our center-operated transplant patient registry. From the Departments of Nephrology, Endocrinology and Metabolic Diseases (A.K., J.C., A.K.-S., A.W.) and Pathophysiology (J.C.), Medical University of Silesia, Katowice, Poland. Address reprint requests to Dr Aureliusz Kolonko, Department of Nephrology, Endocrinology and Metabolic Diseases; 40-027 Katowice, Francuska 20/24, Poland. E-mail: [email protected]
© 2013 by Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010-1710
0041-1345/–see front matter http://dx.doi.org/10.1016/j.transproceed.2012.11.018
Transplantation Proceedings, 45, 1625–1629 (2013)
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The causes of chronic renal disease were chronic glomerulonephritis (51.5%), interstitial nephritis (11.3%), polycystic kidney disease (8.4%), diabetic nephropathy (5.3%), hypertensive nephropathy (7.4%), other diseases or unknown conditions (16.1%). Most patients were prescribed cyclosporine or tacrolimus, mycophenolate mofetil or azathioprine or sirolimus, and steroids (Table 1). Induction therapy included basiliximab (n ⫽ 38; Simulect, Novartis, Basel, Switzerland) or antithymocyte globulin (n ⫽ 4; Fresenius, Bad Homburg, Germany). PI and RI in the kidney graft were measured by Doppler sonography twice: at 2– 4 day after transplantation and before hospital discharge (mean ⫽ 22; 95% confidence interval 21–23). Doppler examinations employed an Acuson machine (Aspen, Mountain View, Calif, USA), and after 2005, with a Siemens machine (Sonoline Antares, Mountain View, Calif, USA). Both apparatus were equipped with 2.5 to 4.0-MHz micro-convex-array transducers to visualize 3–5 segmental arteries to record and analyze the Doppler spectrum. The peak systolic velocity (Vmax) and end-diastolic velocity (Vmin) were measured to calculate segmental arterial resistive indices as PI ⫽ 2 ⫻ (Vmax ⫺ Vmin)/Vmax ⫹ Vmin and RI ⫽ 1 ⫺ (Vmin/Vmax). Patient nutritional status was scored according to World Health Organization criteria, based on anthropometric measurements performed before hospital discharge, corresponding to the second Doppler assessment. DGF was defined as a need for dialysis therapy during first week after transplantation. Statistical analyses were performed using STATISTICA 10.0 PL for Windows software package (StatSoft Polska, Kraków, Poland)
and MedCalc 12.3.0.0. (Mariakerke, Belgium). Results are presented as mean values and 95% confidence intervals. To compare groups, we used the chi-square test for qualitative variables, and analysis of variance (ANOVA), followed by Tukey test for quantitative ones. Correlation coefficients were calculated according to Pearson. We performed backward stepwise multivariate regression analyses for dependent variables (PI or RI in both time points) including potential explanatory variables (recipient age, delayed graft function, body mass index [BMI], history of previous cardiovascular episodes or diabetes, cold ischemia time [CIT], human leukocyte antigen [HLA] mismatches, and corresponding calcineurin inhibitor levels). In all statistical tests, P values below .05 were considered to be significant.
RESULTS
Among 513 patients, 29 were underweight (UW); 280, normal weight (NW); 166, overweight (OW); and 38, obese (OB). Significant differences were observed among analyzed groups in age, previous cardiovascular episodes, DGF frequency and trough levels of both calcineurin inhibitors at the time of the first posttransplant measurement (Table 1). In the first measurement, PI and RI were: UW 1.59 (1.34 –1.85) and 0.75 (0.71– 0.79); NW 1.60 (1.53–1.68) and 0.76 (0.74 – 0.77); OW 1.71 (1.62–1.80) and 0.79 (0.77– 0.80); OB 2.03 (1.81– 2.25) and 0.84 (0.81– 0.86), respectively (Fig 1A and B). Similarly, in the second measurement
Table 1. Characteristics of Patients in Groups Defined According to World Health Organization Nutritional Status Criteria
Age (y) Gender (M/F) BMI (kg/m2) Duration of dialysis (mo) Hypertension (%) Previous cardiovascular episodes (%) Diabetes (%) Mismatch HLA I Mismatch HLA II CIT (ho) Highly sensitized patients (PRA ⬎ 30%), % Calcineurin inhibitors (CyA/Tc) Antimetabolic drugs (AZA/MMF) CyA C0 first (ng/mL) CyA C2 first (ng/mL) Tc C0 (ng/mL) CyA C0 last (ng/mL) CyA C2 last (ng/mL) Tc C0 last (ng/mL) Donor age (y) Donor serum creatinine (mol/L) DGF (%)
Underweight (BMI ⬍ 18.5), n ⫽ 29
Normal Weight (BMI 18.5–24.9), n ⫽ 280
Overweight (BMI 25–29.9), n ⫽ 166
Obese (BMI ⬎30), n ⫽ 38
ANOVA
32 (28–36) 15/14 17.1 (16.8–17.5) 31 (23–39) 23 (79.3%) 0 0 2.38 (1.98–2.78) 0.97 (0.75–1.18) 18 (16–21) 1 (3.5%) 15/14 10/17 146 (70–222) 821 (543–1099) 9.2 (5.2–13.2) 195 (136–254) 1016 (715–1316) 9.7 (7.2–12.2) 38 (33–44) 92 (77–106) 4 (13.8%)
41 (40–43) 169/111 22.1 (21.9–22.3) 31 (28–34) 242 (86.4%) 10 (3.6%) 17 (6.1%) 2.52 (2.40–2.64) 0.9 (0.82–0.97) 19 (18–20) 9 (3.2%) 195/85 100/160 165 (145–181) 811 (754–869) 11.6 (10.0–13.2) 258 (239–278) 1168 (1108–1228) 11.9 (10.8–13.1) 41 (40–43) 112 (104–119) 79 (28.2%)
48 (46–50) 124/42 27.3 (27.1–27.5) 27 (23–30) 149 (89.8%) 12 (7.2%) 18 (10.8%) 2.55 (2.41–2.69) 0.99 (0.89–1.09) 19 (18–20) 0 123/43 55/101 200 (178–221) 887 (822–952) 14.7 (12.5–16.9) 277 (254–301) 1208 (1132–1285) 13.5 (11.7–15.3) 42 (40–44) 119 (108–130) 66 (39.8%)
51 (48–54) 21/17 32.3 (31.7–33.0) 29 (22–35) 29 (76.3%) 5 (13.2%) 2 (5.3%) 2.45 (2.10–2.80) 0.69 (0.50–0.89) 18 (15–20) 0 31/7 22/16 200 (157–244) 998 (854–1141) 13.5 (6.8–20.1) 285 (208–362) 1190 (1017–1362) 10.3 (7.3–13.1) 41 (37–46) 110 (91–129) 22 (57.9%)
⬍.001 NS ⬍.001 NS NS .003 NS NS NS NS NS .01 NS .03 NS .04 NS NS NS NS NS ⬍.001
Data shown as means ⫾ 95% confidence interval or frequencies. BMI, body mass index; HLA, human leukocyte antygen; CIT, cold ischemia time; PRA, panel-reactive antibodies; CyA, cyclosporine; Tc, tacrolimus; AZA, azathioprine; MMF, mycophenolic acid; C0 first, cyclosporine trough level; C2 first, cyclosporine blood concentration 2 h after oral intake (both concentrations measured at the time of first Doppler measurement) Tc C0, tacrolimus trough level, measured at the time of first Doppler measurement; C0 last, cyclosporine trough level; C2 last, cyclosporine blood concentration 2 hours after oral intake (both concentrations measured at the time of last Doppler measurement); Tc C0 last, tacrolimus trough level (measured at the time of last Doppler measurement); DGF, delayed graft function; NS, not significant; ANOVA, analysis of variance.
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Fig 1. The values of intrarenal resistance indices measured within the segmental arteries of the kidney graft in four groups, according to recipients nutritional status. (A, B) Results of first measurement (at 2– 4 days posttransplant). (C, D) Results of last measurement (before discharge of the patient from the hospital). *P ⬍ .05 vs normal weight and underweight; **P ⬍ .01 vs normal weight and underweight. CI, confidence interval; SD, standard deviation.
they were: UW 1.37 (1.29 –1.44) and 0.72 (0.70 – 0.74); NW 1.43 (1.38 –1.47) and 0.73 (0.72– 0.74); OW 1.53 (1.47– 1.59) and 0.75 (0.74 – 0.77); OB 1.74 (1.54 –1.94) and 0.79 (0.76 – 0.83), respectively (Fig 1C and D). In both measurements PI and RI values were significantly higher with increased recipient nutritional status (ANOVA: P ⬍ .001). Upon post hoc analysis, we demonstrated significant differences for PI and RI in both measurements for obese versus UW or NW patients. Significant, positive correlations were observed between BMI and both resistive parameters (PI and RI) at the two measurement time points: for first, PI: 0.162 and RI: 0.188; for the second, PI: 0.173 and RI: 0.180 (all with P ⬍ .001). Multivariate regression analysis revealed the influence of BMI on PI2 and RI2 values to be independent of recipient age, prior DGF, or CIT (Table 2). Corresponding BMI influence on intrarenal resistance parameters measured during the first days after transplantation was only confirmed for PI (Table 2).
DISCUSSION
Our study showed excessive nutritional status to be associated with significantly higher renal vascular resistance among kidney transplant recipients. During the first days after transplantation, the differences between NW and OB subjects were 0.43 for PI and 0.08 for RI. These differences slightly decreased during the subsequent 3 weeks, but were still considerable at the time of hospital discharge: 0.29 for PI and 0.06 for RI. The differences observed in PI and RI measured in kidney graft arteries were probably related to the association between obesity and arterial stiffness, as shown in the general population,11 in children,13 as well as in patients with chronic kidney disease.14 Among patients with metabolic syndrome, Buscemi et al demonstrated increasing trends in IMT, PI, and RI across groups, of increasing BMI.15 In a recent study, Kawai et al indicated that RI might be useful to evaluate early renal damage more
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KOLONKO, CHUDEK, KUJAWA-SZEWIECZEK ET AL
Table 2. The Results of Multivariate Regression Analyses for Intrarenal Resistive Indices Measured Shortly Posttransplant (PI1 and RI1) or Before Discharge from the Hospital (PI2 and RI2) Multivariate Analysis for PI1 Independent Variable
Beta
DGF Recipient age (per year) BMI (per 1 kg/m2)
0.375 — 0.105
P
⬍0.001 0.03
Multivariate Analysis for PI2 Beta
Recipient age (per year) CIT (per hour) DGF BMI (per 1 kg/m2)
0.178 0.134 0.069 0.101
P
⬍.001 .003 .12 .02
Multivariate Analysis for RI1 Beta
0.424 0.138 —
P
⬍.001 .002
Multivariate Analysis for RI2 Beta
0.175 0.127 0.072 0.089
P
⬍.001 .005 .11 .05
Data shown as means ⫾ 95% confidence interval. PI1 and RI1: pulsatility and resistance indices measured 2– 4 days posttransplant; DGF, delayed graft function; BMI, body mass index; PI2 and RI2, pulsatility and resistance indices measured before discharge from the hospital; CIT, cold ischemia time.
effectively than estimated glomerular filtration rate (eGFR).16 In that study BMI significantly correlated with RI both upon univariate and multiple regression analyses.16 The effect of obesity on kidney transplant outcomes is still controversial. The prevalence of OW or OB kidney transplant candidates has constantly increased over the last 2 decades17 as the consequence of lifestyle changes. Donorrecipient body weight ratio in pediatric population directly correlate with eGFR values in the early and late posttransplant periods.18 In adults, obesity is recognized to be a significant independent risk factor for complications of wound healing,19 DGF,19,20 acute rejection episodes,20 graft failure,19,21 and patient death.21 An association has been clearly demonstrated between nutritional markers and predictors of vascular stiffness among stable kidney transplant recipients. Heine et al, observed increased resistance indices to be independently associated with higher age, IMT, but lower BMI.9 Of note, this study included a relatively small patient group (n ⫽ 105), with a relatively wide range of posttransplant observation periods (6 –237 months). Moreover, renal resistive indices were increased in patients with pathologic compared with physiologic ABI.9 Furthermore, Schwenger et al demonstrated recipient age, PWV, pulse pressure, and IMT to be independent factors influencing RI values among stable transplant patients.8 In our study, the BMI groups differed in the prevalence of previous cardiovascular episodes, proportionate to age differences. It has been established that age is an independent factor augmenting intrarenal RI both in healthy adults and kidney transplant recipients.8,9,22 Thus, as we observed significant differences in patient characteristics, of age, previous cardiovascular episodes, first posttransplant calcineurin inhibitors trough levels, and DGF incidence, we performed a multivariate analysis including all of these
variables. The regression analysis for the early postoperative period confirmed the independent impact of BMI only on PI (measured at 2– 4 posttransplant days), showing a strong influence on DGF (beta ⫽ 0.375 for PI and beta ⫽ 0.424 for RI). Thus, significantly higher PI and RI values on early posttransplant measurements were attributed mainly to differences in DGF prevalence. A similar DGF distribution according to recipient BMI has also been shown by other authors.23 In contrast, at discharge, the regression analysis showed a considerably weaker influence of postoperative DGF history (beta ⫽ 0.072), while BMI became the important parameters for RI variability (beta ⫽ 0.089 per 1 kg/m2 increase). Based on the results of our multivariate analyses, we concluded that the values of kidney graft resistance parameters measured at approximately 3 weeks after transplantation were independently related to recipient nutritional status. It is worth while to note that we excluded from our analysis all patients with primary graft nonfunction and acute rejection episodes in the early postoperative period, seeking to avoid misinterpretations of the usually high PI and RI values that an observed in these clinical conditions. The substantial differences in intrarenal resistive indices between patients with varying nutritional status must be kept in mind, as higher RI values are unfavorable prognostic markers for both patient and kidney graft survival.4 Thus, our present work revealed recipient BMI to be another potential cofactor influencing Doppler spectrum in the kidney graft. Our study had some limitations. The time point of the secondary Doppler examination may be not optimal to analyze BMI influence on PI and RI, due to the slow recovery after DGF in these patients, which may still affect intrarenal resistance parameters before discharge. Additionally, the group of obese patients was relatively small and did not include morbidly obese incidentally transplanted in Poland. In conclusion, excessive nutritional status in transplanted patients was associated with significantly increased renal vascular resistance. Thus, nutritional status should be considered in the interpretation of intrarenal Doppler measurements in these patients.
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