- Email: [email protected]
Malnutrition and inflammation as predictors of mortality in peritoneal dialysis patients
http://www.kidney-international.org & 2006 International Society of Nephrology
Malnutrition and inflammation as predictors of mortality in peritoneal dialysis patients MM Avram1, PA Fein1, MA Rafiq1, T Schloth1, J Chattopadhyay1 and N Mittman1 1
Avram Division of Nephrology, Long Island College Hospital, Brooklyn, New York, USA
Nutritional status is associated with clinical outcomes in dialysis patients. Inflammation may cause malnutrition and increases the risk of poor outcomes. We have investigated the relationship between nutritional markers, an inflammatory marker, and survival in 177 peritoneal dialysis (PD) patients, enrolled from 1991 to 2005. In 53 patients, bioimpedance analysis (BIA) measurements were conducted from November 2000. In a subgroup of 42 patients, we measured high-sensitivity C-reactive protein (CRP) and various nutritional markers including prealbumin serially from May 2003. All the patients were followed to April 2006. Mean enrollment albumin and prealbumin levels were 3.6170.51 g/dl and 35.8711.3 mg/dl, respectively. Mean and median enrollment CRPs were 13.53720.81 (s.d.) and 7.15 mg/l, respectively. Higher enrollment levels of nutritional markers such as albumin (Po0.0001), prealbumin (Po0.0001), and creatinine (P ¼ 0.08) were associated with better survival. Single enrollment level of prealbumin was a significant predictor of mortality (relative risk (RR) ¼ 0.977, P ¼ 0.032) in PD patients followed up to 15 years. The BIA parameter phase angle, a nutritional marker, was independently and inversely associated with mortality risk (RR ¼ 0.536, P ¼ 0.01). Enrollment CRP was a significant independent risk factor for mortality (RR ¼ 1.025, P ¼ 0.035). CRP was inversely correlated with nutritional markers in PD patients. Malnutrition and inflammation are highly prevalent and often coexist in PD patients and each may additionally contribute to the high mortality in these patients. Dietary and therapeutic interventions to improve nutritional and chronic inflammatory status in these patients need further exploration. Kidney International (2006) 70, S4–S7. doi:10.1038/sj.ki.5001968 KEYWORDS: peritoneal dialysis; C-reactive protein; prealbumin; phase angle; malnutrition
Correspondence: MM Avram, Avram Division of Nephrology, The Long Island College Hospital, 339 Hicks Street, Brooklyn, New York 11201, USA. E-mail: [email protected] S4
According to the United States Renal Data System 2005 Report, in 2003 the prevalent dialysis population reached nearly 325,000, 3.8% higher than in 2002 with a predicted increase to 660,000 in 2010.1 Since peaking in 1998, the overall mortality rate in the prevalent dialysis population has fallen by 11%, but the current mortality rate still remains high.1 Identification of various risk factors and aggressive risk factor modification are important strategies to improve outcomes in dialysis patients. Protein energy malnutrition is highly prevalent in hemodialysis2 and peritoneal dialysis (PD)3 patients and is one of the important factors contributing to high mortality in these patients.4 The nutritional status of dialysis patients can be assessed by biochemical assays, anthropometry, and body composition methods such as bioimpedance analysis (BIA). We and others have reported that nutritional markers such as albumin, prealbumin, cholesterol, and creatinine are important predictors of survival in dialysis patients.5–9 Inflammation is also highly prevalent in dialysis patients.10 C-reactive protein (CRP), an acute phase protein, is a wellknown indicator of inflammation.11 Elevated levels of CRP are frequently observed in dialysis patients.12 Elevated levels of CRP and other inflammatory markers have been linked to the increased cardiovascular risk, morbidity, and mortality in dialysis patients.13 CRP has been reported to be an important predictor of mortality in hemodialysis patients.14,15 There are contradictory reports in the literature regarding whether CRP can independently predict all-cause mortality in PD patients. A strong association between protein energy malnutrition and inflammation has been shown in dialysis patients.16 Levels of serum nutritional markers are affected by non-nutritional factor such as inflammation. Few studies have examined the relationship between CRP, nutritional markers and survival in PD patients. We previously reported the importance of prealbumin and CRP in predicting mortality in PD patients.9,12 In this study, we have extended the follow-up of our patients and investigated the importance of nutritional status and inflammation as predictors of mortality and the relationship between nutritional markers and the inflammatory marker, CRP, in PD patients. RESULTS Demographics and patient characteristics
The mean age of PD patients was 54716 (s.d.) years (range: 20–94 years). Average months on dialysis at enrollment were Kidney International (2006) 70, S4–S7
MM Avram et al.: Malnutrition, inflammation, and mortality in PD
Laboratory data
At enrollment, the mean serum prealbumin, albumin, and creatinine were 35.8711 ng/dl, 3.6170.51 g/dl, and 11.897 4.65 mg/dl, respectively. Mean and median enrollment CRP were 13.53720.81 (s.d.) mg/l (range: 0.2–95.8 mg/l) and 7.15 mg/l, respectively. Enrollment CRP level was X10 mg/l in 33% of the patients and X15 mg/l in 17% of the patients. BIA analysis
Mean enrollment body weight and body mass index were 158735.5 (s.d.) lbs (range: 94–256 lbs) and 25.474.94 (s.d.) kg/m2 (range: 15.2–42 kg/m2), respectively. Mean enrollment resistance, reactance and phase angle were 5277106 (s.d.) O, 57719.6 (s.d.) O and 6.1571.601 (s.d.), respectively.
1.0
0.8 Cumulative survival
21.90727 (range: 0–158 months). There were 59% women. The ethnic composition of the population was 60% AfricanAmerican, 21% white, and 19% Hispanic. Thirty-seven percent of the patients were diabetic. The causes of endstage renal disease were diabetes (33.9%), hypertension (36.2%), glomerulonephritis (9.6%), polycystic kidney disease (2.3%), obstruction (1.7%), human immunodeficiency virus (HIV)-associated (3.4%), and other/unknown (11.3%).
> 30 mg/dl, n =122 0.6 P =0.032 0.4
0.2 30 mg/dl, n =55
0
2.50
5.00
7.50
10.00 12.50
Follow-up (years)
Figure 1 | Adjusted survival of PD patients grouped by enrollment prealbumin with regard to all-cause mortality. Survival was adjusted for age, race, gender, and HIV seropositivity.
1.0
6°, n =28
0.9
Survival by nutritional parameters 0.8 Cumulative survival
During the 15 years study period, 89 (50%) patients died. Patients who survived during this period had higher enrollment prealbumin (39.1711.4 vs 32.9710.7 mg/dl, Po0.0001), albumin (3.7770.49 vs 3.4770.49 g/dl, Po0.0001), and creatinine (12.5474.54 vs 11.374.69 mg/dl, P ¼ 0.08) compared to those who did not survive. Patients were stratified by enrollment prealbumin 430 and p30 mg/dl. Fifty-five patients (31.3%) had prealbumin p30 mg/dl. Upon 15 years of observation, the cumulative observed survival of PD patients with enrollment prealbumin 430 mg/dl were significantly better than those of patients with prealbumin p30 mg/dl (Po0.0001). Survival adjusted for age, race, gender, and HIV seropositivity also showed similar results (P ¼ 0.032) (Figure 1). In the multivariate Cox proportional hazards model, adjusting for age, race, gender, and HIV positivity, enrollment prealbumin was an independent predictor (relative risk (RR) ¼ 0.977, P ¼ 0.032) of mortality. If diabetes was introduced into the model, prealbumin ceased to be independent predictor of mortality. Using multivariate logistic regression analysis, enrollment prealbumin was significantly associated with death (odds ratio ¼ 0.964, P ¼ 0.037).
0.7
P =0.036
0.6 <6°, n =25 0.5 0.4 0.3 0.0
1.0
2.0 3.0 4.0 5.0 Follow-up (years)
6.0
Figure 2 | Adjusted survival of PD patients grouped by enrollment phase angle with regard to all-cause mortality. Survival was adjusted for age, race, gender, and diabetic status.
for age, race, gender, and diabetic status (Figure 2). In the multivariate Cox proportional hazards model, adjusting for factors that influence survival such as age, race, gender, and diabetes, phase angle was independent predictor of mortality (RR ¼ 0.536, P ¼ 0.01).
Survival by BIA parameters
Survival by CRP
Survivors had significantly higher enrollment reactance (60.8719.6 vs 47.6716.4 O, P ¼ 0.021) and phase angle (6.5371.68 vs 5.3571.281, P ¼ 0.016) compared to nonsurvivors. Patients with phase angle X61 had significantly better 5 years cumulative survival than that of patients with phase angle o 61 (P ¼ 0.004). Similar results were obtained when survival for the two groups of patients were adjusted
Over the 3-year-study period, levels of enrollment CRP were significantly lower for survivors compared to non-survivors (8.0676.67 vs 26.2734 mg/l, P ¼ 0.022). Mean follow-up CRP was also lower for survivors compared to non-survivors, but the difference did not reach statistical significance (9.2278.48 vs 17.66722.1 mg/l, P ¼ 0.12). Using Kaplan–Meier method, observed 3-year cumulative survival of
Kidney International (2006) 70, S4–S7
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MM Avram et al.: Malnutrition, inflammation, and mortality in PD
Table 1 | Multivariate Cox regression analysis: independent predictors of mortality in peritoneal dialysis patients followed up to 3 years Variable
RR
P-value
Age (years) Gender (male vs female)
1.060 2.22
0.027 0.25
Race African-American vs others Diabetes (yes vs no) HIV (yes vs no) Enrollment CRP (mg/l)
1.62 2.08 2.48 1.025
0.52 0.31 0.31 0.035
CRP, c-reactive protein; HIV, human immunodeficiency virus; RR, relative risk.
patients with CRPo15 mg/l was significantly (P ¼ 0.01) higher than those with CRPX15 mg/l. Similar analysis with mean follow-up CRP showed the difference in cumulative survival between the two groups of patients, but the results did not reach statistical significance (P ¼ 0.26). Adjusted (age, race, gender, diabetes, and HIV seropositivity) survival of patients with CRPo15 mg/l was higher (P ¼ 0.046) than those with CRPX15 mg/l. The independent predictors of survival as determined by Cox regression analysis are shown in Table 1. Serum CRP (RR ¼ 1.025, P ¼ 0.035) and age (RR ¼ 1.060, P ¼ 0.027) were significant independent predictors of mortality. For every mg/l increase in serum CRP at enrollment, there was a 2.5% increase in mortality risk. Correlations between CRP, nutritional markers, and BIA parameters
Serum nutritional markers such as prealbumin (r ¼ 0.55, Po0.0001 for reactance and r ¼ 0.54, Po0.0001 for phase angle), albumin (r ¼ 0.55, Po0.0001 for reactance and r ¼ 0.54, Po0.0001 for phase angle), and creatinine (r ¼ 0.10, P ¼ 0.05 for reactance and r ¼ 0.27, P ¼ 0.07 for phase angle) were correlated with BIA parameters. Enrollment CRP was inversely and strongly correlated with prealbumin (r ¼ 0.5, P ¼ 0.034) and weakly with albumin (r ¼ 0.29, P ¼ 0.06) and creatinine (r ¼ 0.31, P ¼ 0.05). DISCUSSION
The results of this study confirm that malnutrition is highly prevalent in PD patients and nutritional status remains strong predictor of mortality in these patients. A single random enrollment level of prealbumin was a significant predictor of long-term (15 years) survival in both diabetic and non-diabetic PD patients. Chertow et al.17 recently reported that in hemodialysis patients lower prealbumin concentrations were associated with mortality and hospitalization due to infection, independent of serum albumin and other clinical characteristics. BIA indexes, reactance, and phase angle were significantly correlated with nutritional markers such as albumin, prealbumin, and creatinine, which is in agreement with previously published papers demonstrating that the BIA indexes strongly reflect nutritional status in PD patients.18–20 It has been reported that there are some alterations in tissue electrical S6
properties with malnutrition that can be detected by BIA.21 Phase angle has been reported to be a simple and reliable method for the routine assessment of nutritional status in PD patients.22 Lower values of phase angle were independently and strongly associated with increased mortality, which may reflect a higher mortality rate in malnourished PD patients. Our study confirms that elevated level of CRP is independently and significantly associated with an increased all-cause mortality in PD patients. This suggests that inflammation per se has an important effect on mortality in PD patients. There are conflicting data in the literature concerning elevated CRP levels and death in PD patients. Several authors have reported that CRP is a strong independent predictor of cardiovascular and all-cause mortality in PD patients.23,24 In contrast, other authors could not demonstrate CRP as an independent predictor of death in a multivariate model.25,26 The difference in sample size, followup duration, and the extent of adjustment for confounding covariates may explain the disparity between various studies. Recently, it has been reported that the averaged value of serum CRP is more predictive of prognosis compared to the baseline values in PD patients.27 However, in this study, levels of mean follow-up CRP was not associated with mortality in PD patients, which is in agreement with a previously published report.28 CRP was correlated with nutritional markers such as albumin, prealbumin, and creatinine, implicating an association between malnutrition and inflammation in PD patients, which confirms previously published reports.24,29 Proinflammatory cytokines including CRP can adversely affect nutritional status by inhibiting appetite,30 inducing protein breakdown in muscle31 and increasing energy expenditure.32 It has been reported that malnutrition and inflammation tend to occur concurrently and coexist in dialysis patients and this has been termed ‘malnutrition inflammation complex syndrome’.33 Early diagnosis of this syndrome is important to identify high-risk patients. Interventions such as nutritional support, changes in dialysis, and drug therapy to reduce malnutrition and inflammation may decrease morbidity and mortality of these patients. In summary, higher enrollment values of nutritional markers were associated with better survival in PD patients. Single enrollment levels of prealbumin predict long-term survival (up to 15 years) in PD patients. Phase angle, a nutritional marker, is a strong independent predictor of mortality in PD patients up to 6 years. Higher enrollment levels of CRP independently predict increased mortality in PD patients monitored up to 3 years. CRP is inversely correlated with nutritional markers in PD patients. CONCLUSION
Malnutrition and inflammation are common comorbid conditions and coexist in PD patients. There is a strong association between measures of malnutrition, particularly prealbumin and inflammation in PD patients. Dietary and therapeutic interventions to improve nutritional and chronic Kidney International (2006) 70, S4–S7
MM Avram et al.: Malnutrition, inflammation, and mortality in PD
inflammatory status in dialysis patients need further exploration.
9.
10.
MATERIALS AND METHODS Patients One hundred seventy-seven PD patients treated at the Avram Center for Kidney Diseases were enrolled in the study from 1991 to 2005. On enrollment, demographic, clinical, and biochemical data were recorded. BIA measurements were conducted in 53 patients using an impedance plethysmograph (800 mA and 50 kHz) from 2000. Patients’ electrical impedance values, resistance, and reactance were measured and the body composition parameters including phase angle were determined using Cyprus version 2.0 (BIA-101; RJL Systems Inc., Clinton Township, MI, USA). In 42 patients, highsensitivity CRP was measured serially from May 2003. Prealbumin and CRP were measured by immunoturbidimetric method (Spectra Labs, Rockleigh, NJ, USA). All the patients were followed until April 2006. In addition to the single enrollment CRP value, we also evaluated the averaged value of all the available serial measurements for CRP over the study period, which we call ‘mean follow-up’ values. The Institutional Review Board approved the study protocol and informed consents were obtained from study patients. Statistical analysis Continuous variables were reported as mean7s.d. For selected comparisons between group means, parametric (t-test) or nonparametric (Mann–Whitney test) tests were used. Correlations were reported as either the Pearson correlation coefficient or the Spearman rank-correlation coefficient. Observed survival was computed by Kaplan–Meier method.34 Log-rank test was used to assess the differences between survival curves. Independent predictors of survival were determined by Cox regression analysis. Calculations were performed using SPSS for Windows 12.0.1 (SPSS Inc., Chicago, IL, USA).
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ACKNOWLEDGMENTS
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This work was supported in part by the grants from the Kidney and Urology Foundation of America and the Nephrology Foundation of Brooklyn.
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REFERENCES
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1. US Renal Data System. USRDS 2005 Annual Data Report: Atlas of End-Stage Renal Disease in the United States. National Institute of Health, National Institute of Diabetes and Digestive and Kidney Diseases: Bethesda, MD, 2005. 2. Wolfson M, Strong CJ, Minturn D et al. Nutritional status and lymphocyte function in maintenance hemodialysis patients. Am J Clin Nutr 1984; 39: 547–555. 3. Marckmann P. Nutritional status of patients on hemodialysis and peritoneal dialysis. Clin Nephrol 1988; 29: 75–78. 4. Acchiardo SR, Moore LW, Latour PA. Malnutrition as the main factor in morbidity and mortality of dialysis patients. Kidney Int Suppl 1983; 24: S199–S203. 5. Avram MM, Goldwasser P, Erroa M et al. Predictors of survival in continuous ambulatory peritoneal dialysis patients: the importance of prealbumin and other nutritional and metabolic markers. Am J Kidney Dis 1994; 23: 91–98. 6. Avram MM, Mittman N, Bonomini L et al. Markers for survival in dialysis: a seven year prospective study. Am J Kidney Dis 1995; 26: 209–219. 7. Avram MM, Sreedhara R, Fein PA et al. Survival of hemodialysis and peritoneal dialysis over 12 years with emphasis on nutritional parameters. Am J Kidney Dis 2001; 37: S77–S80. 8. Lowrie EG, Huang WH, Lew NL. Death risk predictors among peritoneal dialysis and hemodialysis patients: a preliminary comparison. Am J Kidney Dis 1995; 26: 220–228.
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27.
28.
29. 30.
31.
32.
33.
34.
Mittman N, Avram MM, Oo KK et al. Serum prealbumin predicts survival in hemodialysis and peritoneal dialysis: 10 years of prospective observation. Am J Kidney Dis 2001; 38: 1358–1364. Stenvinkel P, Alvestrand A. Inflammation in end-stage renal disease: sources, consequences, and therapy. Semin Dial 2002; 15: 329–337. Lacson EJ, Levin NW. C-reactive protein and end-stage renal disease. Semin Dial 2004; 17: 438–448. Avram MM, Fein PA, Schloth T et al. Association between C-reactive protein and clinical outcomes in peritoneal dialysis patients. Adv Periton Dial 2005; 21: 154–158. Zimmerman J, Herrlinger S, Pruy A et al. Inflammation enhances cardiovascular risk and mortality in hemodialysis patients. Kidney Int 1999; 55: 648–658. Yeun JY, Levine RA, Mantadilok V et al. C-reactive protein predicts all-cause and cardiovascular mortality in hemodialysis patients. Am J Kidney Dis 2000; 35: 469–476. Wanner C, Metzger T. C-reactive protein a marker for all-cause and cardiovascular mortality in hemodialysis patients. Nephrol Dial Transplant 2002; 17: 29–32. Kalantar-Zadeh K, Kopple JD. Relative contribution of nutrition and inflammation to clinical outcome in dialysis patients. Am J Kidney Dis 2001; 38: 1343–1350. Chertow GM, Goldstein-Fuchs DJ, Lazarus JM et al. Prealbumin, mortality, and cause-specific hospitalization in hemodialysis patients. Kidney Int 2005; 68: 2794–2800. Edefonti A, Picca M, Damiani B et al. Prevalence of malnutrition assessed by bioimpedance analysis and anthropometry in children on peritoneal dialysis. Periton Dial Int 2001; 21: 172–179. Fein PA, Gundumalla G, Jorden A et al. Usefulness of bioelectrical impedance analysis in monitoring nutrition status and survival in peritoneal dialysis patients. Adv Periton Dial 2002; 18: 195–199. Mushnick R, Fein PA, Mittman N et al. Relationship of bioelectrical impedance parameters to nutrition and survival in peritoneal dialysis patients. Kidney Int Suppl 2003; 64: 53–56. Heymsfield SB, Barbosa-Silva MC, Barros AJ et al. Can bioelectrical impedance analysis identify malnutrition in preoperative nutrition assessment? Nutrition 2003; 19: 422–426. Passadakis P, Sud K, Dutta A et al. Bioelectrical impedance analysis in the evaluation of the nutritional status of continuous ambulatory peritoneal dialysis patients. Adv Periton Dial 1999; 15: 147–152. Wang AY-M, Woo J, Lam CW-K et al. Is a single time point C-reactive protein predictive of outcome in peritoneal dialysis patients? J Am Soc Nephrol 2003; 14: 1871–1879. Noh H, Lee SW, Kang SW. Serum C-reactive protein: a predictor of mortality in continuous ambulatory peritoneal dialysis patients. Periton Dial Int 1998; 18: 387–394. Herzig KA, Purdie DM, Chang W et al. Is C-reactive protein a useful predictor of outcome in peritoneal dialysis patients? J Am Soc Nephrol 2001; 12: 814–821. Owen WF, Lowrie EG. C-reactive protein as an outcome predictor for maintenance hemodialysis patients. Kidney Int 1998; 54: 627–636. Ates K, Ates A, Ekmekci Y et al. The time course of serum C-reactive protein is more predictive of mortality than its baseline level in peritoneal dialysis patients. Periton Dial Int 2005; 25: 256–268. Pupim LB, Caglar K, Hakim RM et al. Uremic malnutrition is a predictor of death independent of inflammatory status. Kidney Int 2004; 66: 2054–2060. Fein PA, Mittman N, Gadh R et al. Malnutrition and inflammation in peritoneal dialysis patients. Kidney Int Suppl 2003; 87: 87–91. Kalantar-Zadeh K, Block G, McAllister CJ et al. Appetite and inflammation, nutrition, anemia, and clinical outcome in hemodialysis patients. Am J Clin Nutr 2004; 80: 299–307. Mitch WE. Insights into the abnormalities of chronic renal disease attributed to malnutrition. J Am Soc Nephrol 2002; 13(Suppl): S22–S27. Utaka S, Avesani CM, Draibe SA et al. Inflammation is associated with increased energy expenditure in patients with chronic kidney disease. Am J Clin Nutr 2005; 82: 801–805. Kalantar-Zadeh K, Ikizler TA, Block G et al. Malnutrition–inflammation complex syndrome in dialysis patients: causes and consequences. Am J Kidney Dis 2003; 42: 864–881. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958; 53: 457–481.
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Malnutrition and inflammation as predictors of mortality in peritoneal dialysis patients MM Avram1, PA Fein1, MA Rafiq1, T Schloth1, J Chattopadhyay1 and N Mittman1 1
Avram Division of Nephrology, Long Island College Hospital, Brooklyn, New York, USA
Nutritional status is associated with clinical outcomes in dialysis patients. Inflammation may cause malnutrition and increases the risk of poor outcomes. We have investigated the relationship between nutritional markers, an inflammatory marker, and survival in 177 peritoneal dialysis (PD) patients, enrolled from 1991 to 2005. In 53 patients, bioimpedance analysis (BIA) measurements were conducted from November 2000. In a subgroup of 42 patients, we measured high-sensitivity C-reactive protein (CRP) and various nutritional markers including prealbumin serially from May 2003. All the patients were followed to April 2006. Mean enrollment albumin and prealbumin levels were 3.6170.51 g/dl and 35.8711.3 mg/dl, respectively. Mean and median enrollment CRPs were 13.53720.81 (s.d.) and 7.15 mg/l, respectively. Higher enrollment levels of nutritional markers such as albumin (Po0.0001), prealbumin (Po0.0001), and creatinine (P ¼ 0.08) were associated with better survival. Single enrollment level of prealbumin was a significant predictor of mortality (relative risk (RR) ¼ 0.977, P ¼ 0.032) in PD patients followed up to 15 years. The BIA parameter phase angle, a nutritional marker, was independently and inversely associated with mortality risk (RR ¼ 0.536, P ¼ 0.01). Enrollment CRP was a significant independent risk factor for mortality (RR ¼ 1.025, P ¼ 0.035). CRP was inversely correlated with nutritional markers in PD patients. Malnutrition and inflammation are highly prevalent and often coexist in PD patients and each may additionally contribute to the high mortality in these patients. Dietary and therapeutic interventions to improve nutritional and chronic inflammatory status in these patients need further exploration. Kidney International (2006) 70, S4–S7. doi:10.1038/sj.ki.5001968 KEYWORDS: peritoneal dialysis; C-reactive protein; prealbumin; phase angle; malnutrition
Correspondence: MM Avram, Avram Division of Nephrology, The Long Island College Hospital, 339 Hicks Street, Brooklyn, New York 11201, USA. E-mail: [email protected] S4
According to the United States Renal Data System 2005 Report, in 2003 the prevalent dialysis population reached nearly 325,000, 3.8% higher than in 2002 with a predicted increase to 660,000 in 2010.1 Since peaking in 1998, the overall mortality rate in the prevalent dialysis population has fallen by 11%, but the current mortality rate still remains high.1 Identification of various risk factors and aggressive risk factor modification are important strategies to improve outcomes in dialysis patients. Protein energy malnutrition is highly prevalent in hemodialysis2 and peritoneal dialysis (PD)3 patients and is one of the important factors contributing to high mortality in these patients.4 The nutritional status of dialysis patients can be assessed by biochemical assays, anthropometry, and body composition methods such as bioimpedance analysis (BIA). We and others have reported that nutritional markers such as albumin, prealbumin, cholesterol, and creatinine are important predictors of survival in dialysis patients.5–9 Inflammation is also highly prevalent in dialysis patients.10 C-reactive protein (CRP), an acute phase protein, is a wellknown indicator of inflammation.11 Elevated levels of CRP are frequently observed in dialysis patients.12 Elevated levels of CRP and other inflammatory markers have been linked to the increased cardiovascular risk, morbidity, and mortality in dialysis patients.13 CRP has been reported to be an important predictor of mortality in hemodialysis patients.14,15 There are contradictory reports in the literature regarding whether CRP can independently predict all-cause mortality in PD patients. A strong association between protein energy malnutrition and inflammation has been shown in dialysis patients.16 Levels of serum nutritional markers are affected by non-nutritional factor such as inflammation. Few studies have examined the relationship between CRP, nutritional markers and survival in PD patients. We previously reported the importance of prealbumin and CRP in predicting mortality in PD patients.9,12 In this study, we have extended the follow-up of our patients and investigated the importance of nutritional status and inflammation as predictors of mortality and the relationship between nutritional markers and the inflammatory marker, CRP, in PD patients. RESULTS Demographics and patient characteristics
The mean age of PD patients was 54716 (s.d.) years (range: 20–94 years). Average months on dialysis at enrollment were Kidney International (2006) 70, S4–S7
MM Avram et al.: Malnutrition, inflammation, and mortality in PD
Laboratory data
At enrollment, the mean serum prealbumin, albumin, and creatinine were 35.8711 ng/dl, 3.6170.51 g/dl, and 11.897 4.65 mg/dl, respectively. Mean and median enrollment CRP were 13.53720.81 (s.d.) mg/l (range: 0.2–95.8 mg/l) and 7.15 mg/l, respectively. Enrollment CRP level was X10 mg/l in 33% of the patients and X15 mg/l in 17% of the patients. BIA analysis
Mean enrollment body weight and body mass index were 158735.5 (s.d.) lbs (range: 94–256 lbs) and 25.474.94 (s.d.) kg/m2 (range: 15.2–42 kg/m2), respectively. Mean enrollment resistance, reactance and phase angle were 5277106 (s.d.) O, 57719.6 (s.d.) O and 6.1571.601 (s.d.), respectively.
1.0
0.8 Cumulative survival
21.90727 (range: 0–158 months). There were 59% women. The ethnic composition of the population was 60% AfricanAmerican, 21% white, and 19% Hispanic. Thirty-seven percent of the patients were diabetic. The causes of endstage renal disease were diabetes (33.9%), hypertension (36.2%), glomerulonephritis (9.6%), polycystic kidney disease (2.3%), obstruction (1.7%), human immunodeficiency virus (HIV)-associated (3.4%), and other/unknown (11.3%).
> 30 mg/dl, n =122 0.6 P =0.032 0.4
0.2 30 mg/dl, n =55
0
2.50
5.00
7.50
10.00 12.50
Follow-up (years)
Figure 1 | Adjusted survival of PD patients grouped by enrollment prealbumin with regard to all-cause mortality. Survival was adjusted for age, race, gender, and HIV seropositivity.
1.0
6°, n =28
0.9
Survival by nutritional parameters 0.8 Cumulative survival
During the 15 years study period, 89 (50%) patients died. Patients who survived during this period had higher enrollment prealbumin (39.1711.4 vs 32.9710.7 mg/dl, Po0.0001), albumin (3.7770.49 vs 3.4770.49 g/dl, Po0.0001), and creatinine (12.5474.54 vs 11.374.69 mg/dl, P ¼ 0.08) compared to those who did not survive. Patients were stratified by enrollment prealbumin 430 and p30 mg/dl. Fifty-five patients (31.3%) had prealbumin p30 mg/dl. Upon 15 years of observation, the cumulative observed survival of PD patients with enrollment prealbumin 430 mg/dl were significantly better than those of patients with prealbumin p30 mg/dl (Po0.0001). Survival adjusted for age, race, gender, and HIV seropositivity also showed similar results (P ¼ 0.032) (Figure 1). In the multivariate Cox proportional hazards model, adjusting for age, race, gender, and HIV positivity, enrollment prealbumin was an independent predictor (relative risk (RR) ¼ 0.977, P ¼ 0.032) of mortality. If diabetes was introduced into the model, prealbumin ceased to be independent predictor of mortality. Using multivariate logistic regression analysis, enrollment prealbumin was significantly associated with death (odds ratio ¼ 0.964, P ¼ 0.037).
0.7
P =0.036
0.6 <6°, n =25 0.5 0.4 0.3 0.0
1.0
2.0 3.0 4.0 5.0 Follow-up (years)
6.0
Figure 2 | Adjusted survival of PD patients grouped by enrollment phase angle with regard to all-cause mortality. Survival was adjusted for age, race, gender, and diabetic status.
for age, race, gender, and diabetic status (Figure 2). In the multivariate Cox proportional hazards model, adjusting for factors that influence survival such as age, race, gender, and diabetes, phase angle was independent predictor of mortality (RR ¼ 0.536, P ¼ 0.01).
Survival by BIA parameters
Survival by CRP
Survivors had significantly higher enrollment reactance (60.8719.6 vs 47.6716.4 O, P ¼ 0.021) and phase angle (6.5371.68 vs 5.3571.281, P ¼ 0.016) compared to nonsurvivors. Patients with phase angle X61 had significantly better 5 years cumulative survival than that of patients with phase angle o 61 (P ¼ 0.004). Similar results were obtained when survival for the two groups of patients were adjusted
Over the 3-year-study period, levels of enrollment CRP were significantly lower for survivors compared to non-survivors (8.0676.67 vs 26.2734 mg/l, P ¼ 0.022). Mean follow-up CRP was also lower for survivors compared to non-survivors, but the difference did not reach statistical significance (9.2278.48 vs 17.66722.1 mg/l, P ¼ 0.12). Using Kaplan–Meier method, observed 3-year cumulative survival of
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MM Avram et al.: Malnutrition, inflammation, and mortality in PD
Table 1 | Multivariate Cox regression analysis: independent predictors of mortality in peritoneal dialysis patients followed up to 3 years Variable
RR
P-value
Age (years) Gender (male vs female)
1.060 2.22
0.027 0.25
Race African-American vs others Diabetes (yes vs no) HIV (yes vs no) Enrollment CRP (mg/l)
1.62 2.08 2.48 1.025
0.52 0.31 0.31 0.035
CRP, c-reactive protein; HIV, human immunodeficiency virus; RR, relative risk.
patients with CRPo15 mg/l was significantly (P ¼ 0.01) higher than those with CRPX15 mg/l. Similar analysis with mean follow-up CRP showed the difference in cumulative survival between the two groups of patients, but the results did not reach statistical significance (P ¼ 0.26). Adjusted (age, race, gender, diabetes, and HIV seropositivity) survival of patients with CRPo15 mg/l was higher (P ¼ 0.046) than those with CRPX15 mg/l. The independent predictors of survival as determined by Cox regression analysis are shown in Table 1. Serum CRP (RR ¼ 1.025, P ¼ 0.035) and age (RR ¼ 1.060, P ¼ 0.027) were significant independent predictors of mortality. For every mg/l increase in serum CRP at enrollment, there was a 2.5% increase in mortality risk. Correlations between CRP, nutritional markers, and BIA parameters
Serum nutritional markers such as prealbumin (r ¼ 0.55, Po0.0001 for reactance and r ¼ 0.54, Po0.0001 for phase angle), albumin (r ¼ 0.55, Po0.0001 for reactance and r ¼ 0.54, Po0.0001 for phase angle), and creatinine (r ¼ 0.10, P ¼ 0.05 for reactance and r ¼ 0.27, P ¼ 0.07 for phase angle) were correlated with BIA parameters. Enrollment CRP was inversely and strongly correlated with prealbumin (r ¼ 0.5, P ¼ 0.034) and weakly with albumin (r ¼ 0.29, P ¼ 0.06) and creatinine (r ¼ 0.31, P ¼ 0.05). DISCUSSION
The results of this study confirm that malnutrition is highly prevalent in PD patients and nutritional status remains strong predictor of mortality in these patients. A single random enrollment level of prealbumin was a significant predictor of long-term (15 years) survival in both diabetic and non-diabetic PD patients. Chertow et al.17 recently reported that in hemodialysis patients lower prealbumin concentrations were associated with mortality and hospitalization due to infection, independent of serum albumin and other clinical characteristics. BIA indexes, reactance, and phase angle were significantly correlated with nutritional markers such as albumin, prealbumin, and creatinine, which is in agreement with previously published papers demonstrating that the BIA indexes strongly reflect nutritional status in PD patients.18–20 It has been reported that there are some alterations in tissue electrical S6
properties with malnutrition that can be detected by BIA.21 Phase angle has been reported to be a simple and reliable method for the routine assessment of nutritional status in PD patients.22 Lower values of phase angle were independently and strongly associated with increased mortality, which may reflect a higher mortality rate in malnourished PD patients. Our study confirms that elevated level of CRP is independently and significantly associated with an increased all-cause mortality in PD patients. This suggests that inflammation per se has an important effect on mortality in PD patients. There are conflicting data in the literature concerning elevated CRP levels and death in PD patients. Several authors have reported that CRP is a strong independent predictor of cardiovascular and all-cause mortality in PD patients.23,24 In contrast, other authors could not demonstrate CRP as an independent predictor of death in a multivariate model.25,26 The difference in sample size, followup duration, and the extent of adjustment for confounding covariates may explain the disparity between various studies. Recently, it has been reported that the averaged value of serum CRP is more predictive of prognosis compared to the baseline values in PD patients.27 However, in this study, levels of mean follow-up CRP was not associated with mortality in PD patients, which is in agreement with a previously published report.28 CRP was correlated with nutritional markers such as albumin, prealbumin, and creatinine, implicating an association between malnutrition and inflammation in PD patients, which confirms previously published reports.24,29 Proinflammatory cytokines including CRP can adversely affect nutritional status by inhibiting appetite,30 inducing protein breakdown in muscle31 and increasing energy expenditure.32 It has been reported that malnutrition and inflammation tend to occur concurrently and coexist in dialysis patients and this has been termed ‘malnutrition inflammation complex syndrome’.33 Early diagnosis of this syndrome is important to identify high-risk patients. Interventions such as nutritional support, changes in dialysis, and drug therapy to reduce malnutrition and inflammation may decrease morbidity and mortality of these patients. In summary, higher enrollment values of nutritional markers were associated with better survival in PD patients. Single enrollment levels of prealbumin predict long-term survival (up to 15 years) in PD patients. Phase angle, a nutritional marker, is a strong independent predictor of mortality in PD patients up to 6 years. Higher enrollment levels of CRP independently predict increased mortality in PD patients monitored up to 3 years. CRP is inversely correlated with nutritional markers in PD patients. CONCLUSION
Malnutrition and inflammation are common comorbid conditions and coexist in PD patients. There is a strong association between measures of malnutrition, particularly prealbumin and inflammation in PD patients. Dietary and therapeutic interventions to improve nutritional and chronic Kidney International (2006) 70, S4–S7
MM Avram et al.: Malnutrition, inflammation, and mortality in PD
inflammatory status in dialysis patients need further exploration.
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MATERIALS AND METHODS Patients One hundred seventy-seven PD patients treated at the Avram Center for Kidney Diseases were enrolled in the study from 1991 to 2005. On enrollment, demographic, clinical, and biochemical data were recorded. BIA measurements were conducted in 53 patients using an impedance plethysmograph (800 mA and 50 kHz) from 2000. Patients’ electrical impedance values, resistance, and reactance were measured and the body composition parameters including phase angle were determined using Cyprus version 2.0 (BIA-101; RJL Systems Inc., Clinton Township, MI, USA). In 42 patients, highsensitivity CRP was measured serially from May 2003. Prealbumin and CRP were measured by immunoturbidimetric method (Spectra Labs, Rockleigh, NJ, USA). All the patients were followed until April 2006. In addition to the single enrollment CRP value, we also evaluated the averaged value of all the available serial measurements for CRP over the study period, which we call ‘mean follow-up’ values. The Institutional Review Board approved the study protocol and informed consents were obtained from study patients. Statistical analysis Continuous variables were reported as mean7s.d. For selected comparisons between group means, parametric (t-test) or nonparametric (Mann–Whitney test) tests were used. Correlations were reported as either the Pearson correlation coefficient or the Spearman rank-correlation coefficient. Observed survival was computed by Kaplan–Meier method.34 Log-rank test was used to assess the differences between survival curves. Independent predictors of survival were determined by Cox regression analysis. Calculations were performed using SPSS for Windows 12.0.1 (SPSS Inc., Chicago, IL, USA).
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ACKNOWLEDGMENTS
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This work was supported in part by the grants from the Kidney and Urology Foundation of America and the Nephrology Foundation of Brooklyn.
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REFERENCES
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1. US Renal Data System. USRDS 2005 Annual Data Report: Atlas of End-Stage Renal Disease in the United States. National Institute of Health, National Institute of Diabetes and Digestive and Kidney Diseases: Bethesda, MD, 2005. 2. Wolfson M, Strong CJ, Minturn D et al. Nutritional status and lymphocyte function in maintenance hemodialysis patients. Am J Clin Nutr 1984; 39: 547–555. 3. Marckmann P. Nutritional status of patients on hemodialysis and peritoneal dialysis. Clin Nephrol 1988; 29: 75–78. 4. Acchiardo SR, Moore LW, Latour PA. Malnutrition as the main factor in morbidity and mortality of dialysis patients. Kidney Int Suppl 1983; 24: S199–S203. 5. Avram MM, Goldwasser P, Erroa M et al. Predictors of survival in continuous ambulatory peritoneal dialysis patients: the importance of prealbumin and other nutritional and metabolic markers. Am J Kidney Dis 1994; 23: 91–98. 6. Avram MM, Mittman N, Bonomini L et al. Markers for survival in dialysis: a seven year prospective study. Am J Kidney Dis 1995; 26: 209–219. 7. Avram MM, Sreedhara R, Fein PA et al. Survival of hemodialysis and peritoneal dialysis over 12 years with emphasis on nutritional parameters. Am J Kidney Dis 2001; 37: S77–S80. 8. Lowrie EG, Huang WH, Lew NL. Death risk predictors among peritoneal dialysis and hemodialysis patients: a preliminary comparison. Am J Kidney Dis 1995; 26: 220–228.
Kidney International (2006) 70, S4–S7
27.
28.
29. 30.
31.
32.
33.
34.
Mittman N, Avram MM, Oo KK et al. Serum prealbumin predicts survival in hemodialysis and peritoneal dialysis: 10 years of prospective observation. Am J Kidney Dis 2001; 38: 1358–1364. Stenvinkel P, Alvestrand A. Inflammation in end-stage renal disease: sources, consequences, and therapy. Semin Dial 2002; 15: 329–337. Lacson EJ, Levin NW. C-reactive protein and end-stage renal disease. Semin Dial 2004; 17: 438–448. Avram MM, Fein PA, Schloth T et al. Association between C-reactive protein and clinical outcomes in peritoneal dialysis patients. Adv Periton Dial 2005; 21: 154–158. Zimmerman J, Herrlinger S, Pruy A et al. Inflammation enhances cardiovascular risk and mortality in hemodialysis patients. Kidney Int 1999; 55: 648–658. Yeun JY, Levine RA, Mantadilok V et al. C-reactive protein predicts all-cause and cardiovascular mortality in hemodialysis patients. Am J Kidney Dis 2000; 35: 469–476. Wanner C, Metzger T. C-reactive protein a marker for all-cause and cardiovascular mortality in hemodialysis patients. Nephrol Dial Transplant 2002; 17: 29–32. Kalantar-Zadeh K, Kopple JD. Relative contribution of nutrition and inflammation to clinical outcome in dialysis patients. Am J Kidney Dis 2001; 38: 1343–1350. Chertow GM, Goldstein-Fuchs DJ, Lazarus JM et al. Prealbumin, mortality, and cause-specific hospitalization in hemodialysis patients. Kidney Int 2005; 68: 2794–2800. Edefonti A, Picca M, Damiani B et al. Prevalence of malnutrition assessed by bioimpedance analysis and anthropometry in children on peritoneal dialysis. Periton Dial Int 2001; 21: 172–179. Fein PA, Gundumalla G, Jorden A et al. Usefulness of bioelectrical impedance analysis in monitoring nutrition status and survival in peritoneal dialysis patients. Adv Periton Dial 2002; 18: 195–199. Mushnick R, Fein PA, Mittman N et al. Relationship of bioelectrical impedance parameters to nutrition and survival in peritoneal dialysis patients. Kidney Int Suppl 2003; 64: 53–56. Heymsfield SB, Barbosa-Silva MC, Barros AJ et al. Can bioelectrical impedance analysis identify malnutrition in preoperative nutrition assessment? Nutrition 2003; 19: 422–426. Passadakis P, Sud K, Dutta A et al. Bioelectrical impedance analysis in the evaluation of the nutritional status of continuous ambulatory peritoneal dialysis patients. Adv Periton Dial 1999; 15: 147–152. Wang AY-M, Woo J, Lam CW-K et al. Is a single time point C-reactive protein predictive of outcome in peritoneal dialysis patients? J Am Soc Nephrol 2003; 14: 1871–1879. Noh H, Lee SW, Kang SW. Serum C-reactive protein: a predictor of mortality in continuous ambulatory peritoneal dialysis patients. Periton Dial Int 1998; 18: 387–394. Herzig KA, Purdie DM, Chang W et al. Is C-reactive protein a useful predictor of outcome in peritoneal dialysis patients? J Am Soc Nephrol 2001; 12: 814–821. Owen WF, Lowrie EG. C-reactive protein as an outcome predictor for maintenance hemodialysis patients. Kidney Int 1998; 54: 627–636. Ates K, Ates A, Ekmekci Y et al. The time course of serum C-reactive protein is more predictive of mortality than its baseline level in peritoneal dialysis patients. Periton Dial Int 2005; 25: 256–268. Pupim LB, Caglar K, Hakim RM et al. Uremic malnutrition is a predictor of death independent of inflammatory status. Kidney Int 2004; 66: 2054–2060. Fein PA, Mittman N, Gadh R et al. Malnutrition and inflammation in peritoneal dialysis patients. Kidney Int Suppl 2003; 87: 87–91. Kalantar-Zadeh K, Block G, McAllister CJ et al. Appetite and inflammation, nutrition, anemia, and clinical outcome in hemodialysis patients. Am J Clin Nutr 2004; 80: 299–307. Mitch WE. Insights into the abnormalities of chronic renal disease attributed to malnutrition. J Am Soc Nephrol 2002; 13(Suppl): S22–S27. Utaka S, Avesani CM, Draibe SA et al. Inflammation is associated with increased energy expenditure in patients with chronic kidney disease. Am J Clin Nutr 2005; 82: 801–805. Kalantar-Zadeh K, Ikizler TA, Block G et al. Malnutrition–inflammation complex syndrome in dialysis patients: causes and consequences. Am J Kidney Dis 2003; 42: 864–881. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958; 53: 457–481.
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