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American Journal of Clinical Nutrition, doi:10.3945/ajcn.2008.25906
Vol. 88, No. 6, 1485-1494, December 2008
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© 2008 American Society for Clinical Nutrition

Nutritional status, dietary intake, and body composition

Association of serum prealbumin and its changes over time with clinical outcomes and survival in patients receiving hemodialysis1,2,3,4

Mehdi Rambod1, Csaba P Kovesdy1, Rachelle Bross1, Joel D Kopple1 and Kamyar Kalantar-Zadeh1

1 From the Harold Simmons Center for Kidney Disease Research and Epidemiology (MR and K-Z), the Division of Nephrology and Hypertension (MR, JDK, and KK-Z), and the Bionutrition Services (RB), General Clinical Research Center, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA; the Salem Veterans Administration Medical Center, Salem, VA (CPK); and the School of Public Health, University of California, Los Angeles, CA (JDK and KK-Z)

2 Parts of this study were presented in the form of abstracts, poster, and oral presentations during the annual meetings of the National Kidney Foundation, April 4–8, 2008, Dallas, TX, and during Clinical Nutrition Week, February 10–13, 2008, Chicago, IL.

3 Supported by the National Institutes of Health, National Institute of Diabetes, Digestive, and Kidney Disease (grants K23DK61162 and R21DK078012), investigator-initiated research grant from Watson and DaVita, and a research grant from the philanthropist Mr. Harold Simmons (all for KK-Z), and the General Clinical Research Center from the National Centers for Research Resources, National Institutes of Health (grant M01RR00425).

4 Address reprint requests to K Kalantar-Zadeh, Harold Simmons Center for Kidney Disease Research and Epidemiology, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1124 West Carson Street, C1-Annex, Torrance, CA 90502. E-mail: kamkal{at}ucla.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: In patients receiving maintenance hemodialysis (MHD), a low serum prealbumin is an indicator of protein-energy wasting.

Objective:We hypothesized that baseline serum prealbumin correlates independently with health-related quality of life (QoL) and death and that its change over time is a robust mortality predictor.

Design: Associations and survival predictability of serum prealbumin at baseline and its changes over 6 mo were examined in a 5-y (2001–2006) cohort of 798 patients receiving MHD.

Results: Patients with serum prealbumin ≥ 40 mg/dL had greater mid-arm muscle circumference but lower percentage of total body fat. Both serum interleukin-6 and dietary protein intake correlated independently with serum prealbumin. Measures of QoL indicated better physical health, physical function, and functionality with higher prealbumin concentrations. Although baseline prealbumin was not superior to albumin in predicting survival, in both all and normoalbuminemic (albumin ≥ 3.5 g/dL; n = 655) patients, prealbumin < 20 mg/dL was associated with higher death risk in adjusted models, but further adjustments for inflammatory cytokines mitigated the associations. In 412 patients with baseline prealbumin between 20 and 40 mg/dL whose serum prealbumin was remeasured after 6 mo, a ≥10-mg/dL fall resulted in a death hazard ratio of 1.37 (95% CI: 1.02, 1.85; P = 0.03) after adjustment for baseline measures, including inflammatory markers.

Conclusions: Even though baseline serum prealbumin may not be superior to albumin in predicting mortality in MHD patients, prealbumin concentrations <20 mg/dL are associated with death risk even in normoalbuminemic patients, and a fall in serum prealbumin over 6 mo is independently associated with increased death risk.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Protein-energy wasting (PEW) is common in patients with advanced chronic kidney disease (CKD), including those undergoing maintenance dialysis treatment (1–3). Because poor nutritional status is associated with increased death risk in patients receiving dialysis (4), measuring reliable markers of PEW may lead to timely interventions for patients at risk. Hypoalbuminemia is currently the most commonly used surrogate of PEW in dialysis patients and has a strong association with increased mortality (5, 6), even though a low serum albumin appears to be a strong marker of inflammation rather than nutritional status (2). Several studies have advocated the use of serum prealbumin, also known as transthyretin, as a better surrogate of nutritional status in this patient population (2, 7–9). The National Kidney Foundation Kidney Disease Quality Initiative guideline has recommended prealbumin as a useful measure of nutritional status (1). However, similar to albumin, inflammation can lead to a reduction in serum prealbumin (9, 10). To our knowledge, thus far published studies of the relation between serum prealbumin and mortality in patients with CKD have only studied the baseline measures, ignoring its longitudinal changes over time. It is not known whether serum prealbumin or its changes over time are associated with mortality in normoalbuminemic patients or after controlling for other nutritional and inflammatory markers. The association between prealbumin and other relevant outcomes such as health-related quality of life is not well studied. We hypothesized that serum prealbumin is a reliable and robust marker of nutrition, quality of life, and survival in patients receiving maintenance hemodialysis (MHD), including in normoalbuminemic patients, and that its changes over time can predict mortality independent of baseline measures. To test the foregoing hypothesis, we examined a cohort of 798 MHD patients who were followed for ≤5 y with repeated nutritional and inflammatory measures.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Patient population
We studied MHD patients who were participating in the Nutritional and Inflammatory Evaluation in Dialysis (NIED) Study (11). The original cohort was derived from a pool of {approx}1300 MHD outpatients in 8 DaVita Inc chronic dialysis facilities in the South Bay Los Angeles area (for more details, see NIED Study website at www.NIEDStudy.org). Inclusion criteria were outpatients who had been undergoing MHD for ≥8 wk, were ≥18 y, and who signed a consent form approved by the local Institutional Review Board. Patients with an anticipated life expectancy of <6 mo (eg, because of a metastatic malignancy or advanced HIV/AIDS disease) were excluded. From 1 October 2001 through 31 December 2006, 893 MHD patients from 8 DaVita dialysis facilities in the Los Angeles South Bay area signed the written consent form, approved by the Institutional Review Committee of Harbor-UCLA campus, and underwent periodic evaluations of the NIED Study. For these analyses, data, including baseline serum prealbumin, were available in 798 MHD patients. In a subgroup of 156 randomly selected patients, the protein and energy intakes were estimated with the use of a 3-d diet diary and the total t score of bone mineral density was measured with the use of dual-energy X-ray absorptiometry.

The medical chart of each patient receiving MHD was thoroughly reviewed by a collaborating physician, and data pertaining to underlying kidney disease, cardiovascular history, and other comorbid conditions were extracted. A modified version of the Charlson comorbidity index, ie, without the age and kidney disease components, was used to assess the severity of comorbidities (12, 13). The 798 MHD patients were followed for ≤63 mo, ie, until 31 December 2006.

SF-36 health-related quality of life score
The SF-36, a short-form health-related quality-of-life scoring system with 36 items, which includes 8 independent scales, is a well-documented, self-administered questionnaire and has been widely used and validated in MHD patients (14, 15). The 8 scales of SF-36 are summarized into 2 dimensions: physical health and mental health (15).

Anthropometric measures
Body weight assessment and anthropometric measurements were performed while patients were undergoing a hemodialysis treatment or within 5–20 min after termination of the treatment. Biceps skinfold and triceps skinfold thicknesses were measured with a conventional skinfold caliper with the use of standard techniques as previously described (16, 17).

Near infrared interactance
To measure the percentage of body fat and estimate fat-free body mass, near infrared (NIR) interactance was measured at the same time as the anthropometric measurements (18, 19). A commercial NIR sensor with a CV of 0.5% for total body fat measurement (portable 6100 sensor; Futrex, Gaithersburg, MD; www.futrex.com) was used. NIR measurements were performed by placing the NIR sensor for several seconds on the upper arm in an area without a vascular access for dialysis treatment. NIR measurements of body fat appear to correlate significantly with other nutritional measures in MHD patients (19).

Laboratory tests
Predialysis blood samples and postdialysis serum urea nitrogen were obtained on a mid-week day and coincided chronologically with the drawing of quarterly blood tests in the DaVita facilities. The single-pool Kt/V was used to represent the weekly dialysis dose. All routine laboratory measurements were performed by DaVita Laboratories (Deland, FL) with the use of automated methods.

Serum high sensitivity C-reactive protein (CRP) was measured by a turbidometric immunoassay in which a serum sample is mixed with latex beads coated with antihuman CRP antibodies, forming an insoluble aggregate (WPCI, Osaka, Japan; in mg/L; normal range: <3.0 mg/L) (20, 21). Interleukin-6 (IL-6) and tumor necrosis factor-{alpha} (TNF-{alpha}) were measured with immunoassay kits based on a solid-phase sandwich enzyme-linked immunoabsorbent assay with the use of recombinant human IL-6 and TNF-{alpha} (R&D Systems, Minneapolis, MN; in pg/mL; normal range: IL-6, <9.9 pg/mL; TNF-{alpha}, <4.7 pg/mL) (22, 23). CRP and the cytokines were measured in the General Clinical Research Center Laboratories of Harbor-UCLA Medical Center. Plasma total homocysteine concentrations were determined by HPLC in the Harbor-UCLA Clinical Laboratories. Serum prealbumin was measured with the use of immunoprecipitin analysis in the Harbor-UCLA General Clinical Research Center Laboratories (8).

Statistical methods
Pearson's correlation coefficient (r) was used for analyses of linear associations. Multivariate regression analyses were performed to obtain adjusted P values controlled for case-mix and other covariates. To calculate the relative risks of death, hazard ratios (HRs) were obtained with the use of Cox proportional hazard models after controlling for the covariates. Kaplan-Meier analyses were used to assess the differences in surviving proportions between prealbumin categories. Case-mix and comorbidity covariates included sex, age, race, and ethnicity (Hispanics, blacks, Asians, and others), diabetes mellitus, the modified Charlson comorbidity scale, and dialysis vintage. Laboratory measures of the malnutrition-inflammation complex syndrome (MICS) in fully adjusted Cox models included serum concentrations of CRP, IL-6, and albumin. Nonlinear associations as continuous mortality predictors were studied with the use of restricted cubic splines to examine inappropriate linearity assumptions (24). To mitigate the risk of regression to the mean, baseline serum prealbumin was included as covariate in all Cox and cubic spline models used to assess the death HRs of prealbumin change.

To compare the mortality predictability of serum concentrations of albumin and prealbumin, receiver operating characteristic (ROC) curves were constructed in which death was the reference variable and the unadjusted or fully adjusted death hazard score of prealbumin or albumin were the predicting variables. The differences of the areas under ROC curves were examined and compared with the use of the ROCCOMP command in STATA (Stata Corp, College Station, TX). Sensitivity (y-axis) was plotted against one minus specificity (x-axis) for each possible cutoff value of hazard score of the Cox models, including these 2 laboratory values and death as the dependent (reference) variable (25). The area under the curve (AUC) represents the discriminative power of the test. Values are expected to be between 0.5 (indicating no discriminative ability) and 1.0 (indicating highest detection accuracy).

Fiducial limits are given as mean ± SD or median and interquartile range; risk ratios include 95% CIs. A P value < 0.05 or a 95% CI that did not span 1.0 was considered to be statistically significant. Descriptive and multivariate statistics were performed with the statistical software STATA version 10.0 (Stata Corp).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The average (mean ± SD) baseline serum prealbumin in the 798 MHD patients was 28.3 ± 9.6 mg/dL (median: 28.0; minimum: 4.0; maximum: 59.0; interquartile range: 7–55 mg/dL). Four a priori categories of serum prealbumin concentrations with 10 mg/dL increments were selected, based on the practical clinical utility of the cutoffs: <20 mg/dL (n = 147; 18%), 20 to <30 mg/dL (n = 316; 40%), 30 to <40 mg/dL (n = 248; 31%), and ≥40 mg/dL (n = 87; 11%). The relevant demographic and clinical and laboratory measures in all 798 MHD patients as well as across the 4 categories of prealbumin are shown in Table 1Go. The proportion of female or diabetic patients was higher in the groups with lower prealbumin concentrations. The MHD patients with lower prealbumin had more comorbidities according to the modified Charlson score, smaller mid-arm muscle circumference, higher NIR-measured percentage of total body fat, lower normalized protein nitrogen appearance (nPNA) [normalized protein catabolic rate (nPCR)] as the surrogate of dietary protein intake, and incrementally higher mortality rates. Patients with serum prealbumin concentrations ≥ 40 mg/dL also had higher mid-arm muscle circumference and the lowest body fat content than did patients in the group with the lowest serum prealbumin. Among laboratory measures, serum albumin and homocysteine concentrations were lower and serum CRP and IL-6 were higher in the groups with lower serum concentrations of prealbumin.


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  		TABLE 1. Baseline demographic, clinical, and laboratory values in total serum prealbumin and according to the 4 a priori–selected groups of serum prealbumin in 798 patients receiving maintenance hemodialysis1

 
We examined the association of relevant clinical and nutritional measures with serum prealbumin concentrations in all 798 MHD patients as shown in Table 2Go. Serum prealbumin was correlated with the nPNA (nPCR) as the surrogate of dietary protein intake as well as with serum albumin, creatinine, and ferritin and logarithm values of serum CRP and IL-6. The relative contributions of inflammation, represented by serum IL-6 concentration, and dietary protein intake, represented by nPNA, to the variability of serum prealbumin in all MHD patients is shown in Figure 1Go. Although serum prealbumin had a slightly stronger association with IL-6 than with nPNA, at all values of multivariate adjustment, both nPNA and IL-6 maintained their robust and independent correlations with serum prealbumin (P < 0.001) without any statistical interaction between these 2 (data not shown).


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  		TABLE 2. Unadjusted and multivariate adjusted Pearson's correlation coefficient of baseline serum prealbumin and other relevant variables in 798 patients receiving maintenance hemodialysis1

 

Figure 1
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  		FIGURE 1.. Concurrent association of normalized protein nitrogen appearance (nPNA) [normalized protein catabolic rate (nPCR)], an indirect measure of dietary protein intake, and serum interleukin-6, a surrogate of inflammation, with serum prealbumin (transthyretin) concentration in 798 patients receiving maintenance hemodialysis.

 
We examined the association of serum prealbumin increments with self-reported health-related quality of life as shown in Figure 2Go. The averaged standardized scores of SF-36 among 645 MHD patients who answered this quality-of-life questionnaire showed better quality of life with higher serum prealbumin values; this trend was more prominent for the physical health dimension as well as the physical function and functionality scales.


Figure 2
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  		FIGURE 2.. Standardized SF-36 quality-of-life scores in the 4 groups of prealbumin in 645 patients receiving maintenance hemodialysis. The 4 groups of serum prealbumin are < 20 mg/dL (n = 111), 20 to <30 mg/dL (n = 264), 30 to <40 mg/dL (n = 197), and ≥40 mg/dL (n = 73). *P < 0.05.

 
Over the 5 y of the cohort, 228 (29%) patients died, 91 (11%) underwent transplantation, and 148 (19%) left the cohort without further follow-up information. The cubic splines graphs (Figure 3Go) show the associations between baseline serum prealbumin and mortality in the 5-y cohort of all 798 MHD patients. Patients with lower prealbumin concentrations, especially <20 mg/dL, showed higher death risks, including after adjustment for MICS markers, serum albumin, or both. However, these relations mitigated after additional multivariate adjustment for makers of inflammation, including serum CRP, IL-6, and TNF-{alpha}. The Kaplan-Meier survival curves for the 4 a priori–selected groups of serum prealbumin concentrations are shown in Figure 4Go.


Figure 3
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  		FIGURE 3.. Mortality predictability of serum prealbumin in 798 patients receiving maintenance hemodialysis (from October 2001 to January 2007). (A) Adjusted for case-mix variables, (B) adjusted for case-mix variables and serum albumin, (C) adjusted for case-mix and malnutrition-inflammation complex syndrome (MICS) variables (including serum albumin), and (D) adjusted for case-mix, MICS, and inflammation variables. Case-mix variables include age, sex, race-ethnicity, diabetes mellitus, dialysis vintage, primary insurance, marital status, modified Charlson comorbidity score, dialysis dose (in Kt/V), and kidney residual urine. MICS variables include albumin, erythropoietin dose, creatinine, hemoglobin, phosphorus, total iron-binding capacity, normalized protein catabolic rate, bicarbonate, calcium, ferritin, white blood cell count, lymphocyte percentage, BMI, and vitamin D dose. Inflammatory variables include C-reactive protein, interleukin-6, and tumor necrosis factor-{alpha}.

 

Figure 4
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  		FIGURE 4.. Kaplan-Meier proportion of surviving patients receiving maintenance hemodialysis after 5 y of observation according to the 4 a priori–selected groups of serum prealbumin in 798 patients receiving maintenance hemodialysis. (Top) Unadjusted; (bottom) adjusted for age and sex. Serum prealbumin cutoffs are < 20 mg/dL (n = 147), 20 to <30 mg/dL (n = 316), 30 to <40 mg/dL (n = 248), and ≥40 mg/dL (n = 87).

 
To further examine whether the mortality predictability of low serum prealbumin holds independent of hypoalbuminemia, we compared death HRs of serum prealbumin increments in all 798 MHD patients with the subset of 655 patients who had a normal serum albumin, ie, ≥3.5 g/dL. As shown in Table 3Go, a serum prealbumin <20 mg/dL (18% of all and 12% of normoalbuminemic patients) was associated with a 63–73% increased death risk ratio in case-mix and MICS-adjusted models. Sensitivity analyses with the use of more parsimonious models with limited number of covariates showed similar trends (data not shown). The interaction between serum albumin and prealbumin was not statistically significant in any of the multivariate models (P > 0.20).


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  		TABLE 3. Hazard ratios (HRs) and 95% CIs of 5-y (from October 2001 to January 2007) mortality according to the 4 a priori–selected groups of serum prealbumin in all 798 patients receiving maintenance hemodialysis and in 655 normoalbuminemic (albumin ≥ 3.5 g/dL) patients1

 
To compare mortality predictability of serum albumin and prealbumin with each other, we performed ROC curve analyses. As shown in Figure 5Go, the mortality-predictability of serum albumin appeared superior to prealbumin in unadjusted models (AUCs: 0.66 compared with 0.59; P = 0.004). However, Cox models, including case-mix variables in addition to albumin or prealbumin, showed similar AUCs (0.79 compared with 0.79; P = 0.9), indicating that the unadjusted prognostic superiority of serum albumin was contributed significantly by patients' demographics, whereas this was less so for serum prealbumin.


Figure 5
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  		FIGURE 5.. Receiver operating characteristic (ROC) curves of probabilities obtained from hazard regression models of serum albumin and prealbumin as independent variables and all-cause mortality as dependent (reference) variable in unadjusted (top) and case-mix adjusted (bottom) formats. Larger area under the curve (AUC) indicates higher prognostic value.

 
To examine whether longitudinal changes in serum prealbumin over time affected survival in MHD patients whose serum prealbumin was remeasured after 6 mo (n = 566), the magnitude and direction of change in serum prealbumin concentrations were calculated. The cubic splines graphs for the associations between changes in serum prealbumin and mortality in the 5-y cohort of 566 MHD patients are shown in Figure 6Go. A consistent trend was noticed in that a fall in serum prealbumin was associated with worsening death risk, whereas a rise in serum prealbumin showed a tendency toward greater survival, at least in the least-adjusted models. The HRs of the a priori–selected groups of prealbumin change are shown in Table 4Go. Patients whose serum prealbumin fell >10 mg/dL over the first 6 mo of observation had a 78% increased death risk ratio compared with patients with stable prealbumin values. Additional analyses to model the continuous values of change in prealbumin over 6 mo showed that in patients with a baseline serum prealbumin between 20 and 40 mg/dL (73% of the patients), a drop by 10 mg/dL was associated with 37% increase in death risk (HR: 1.37; 95% CI: 1.02, 1.85; P = 0.03) independent of baseline markers of MICS, serum albumin, and inflammatory markers (see Table 4Go).


Figure 6
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  		FIGURE 6.. Mortality predictability of changes in serum prealbumin during a 6-mo period in 566 patients receiving maintenance hemodialysis (from October 2001 to January 2007). (Top) Adjusted for baseline prealbumin; (bottom) fully adjusted for baseline prealbumin plus case-mix, malnutrition-inflammation complex syndrome (MICS), and inflammatory variables. Case-mix variables include age, sex, race-ethnicity, diabetes mellitus, dialysis vintage, primary insurance, marital status, Charlson comorbidity score, dialysis dose (in Kt/V), and kidney residual urine. MICS variables include albumin, erythropoietin dose, creatinine, hemoglobin, phosphorus, total iron-binding capacity, normalized protein catabolic rate, bicarbonate, calcium, ferritin, white blood cell count, lymphocyte percentage, BMI, and vitamin D dose. Inflammatory variables include C-reactive protein, interleukin-6, and tumor necrosis factor-{alpha}.

 

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  		TABLE 4. Hazard ratios (HR) and 95% CIs of 5-y mortality according to the 3 a priori–selected groups of change in serum prealbumin and according to the continuous measure of change in serum prealbumin in 566 patients receiving maintenance hemodialysis (from October 2001 to January 2007)1

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In 798 patients receiving MHD who participated in a prospective 5-y study in Southern California, we found that patients with the highest serum concentrations of prealbumin (≥40 mg/dL) had higher serum albumin and creatinine concentrations, greater mid-arm muscle circumference, and lower percentage of total body fat, and they tended to have higher dietary protein and calorie intakes. Serum prealbumin was somewhat equally correlated with both markers of nutritional status and inflammation without any statically significant interaction between dietary protein intake and inflammatory cytokine. Standardized measures of quality of life indicated better physical health, physical function, and functionality in MHD patients with higher serum concentrations of prealbumin. A consistent trend of poor survival was noticed with low serum concentrations of prealbumin even among normoalbuminemic patients, although these associations were weaker after controlling for inflammatory markers, including serum CRP, TNF-{alpha}, and IL-6. Comparing case-mix adjusted death hazard models, both serum albumin and prealbumin concentrations showed similar prognostic values of predicting mortality. A fall in serum prealbumin beyond 10 mg/dL over 6 mo was associated with worsening death risk in the subsequent years. These findings underscore the clinically relevant associations of serum prealbumin with quality of life and survival and indicate that it is a useful marker to risk-stratify even normoalbuminemic patients and that longitudinal changes over time in serum prealbumin can modify the risk of death in MHD patients independent of baseline measures of nutritional status or inflammation.

Patients with CKD have an exceptionally high mortality rate and a high burden of cardiovascular disease (26). About 1 of every 5 of the 400 000 patients receiving MHD in the United States dies every year (27). Even though half of all these deaths are attributed to cardiovascular disease (27), measures of PEW and not traditional cardiovascular risk factors are the strongest predictors of mortality in MHD patients (2). The confounding effects of PEW, also known as MICS, on associations between traditional cardiovascular risk factors and clinical outcome are so strong that these associations appear paradoxically inverted (28, 29). Hence, a low, rather than a high, body mass index or serum cholesterol concentration is paradoxically associated with increased mortality in MHD patients (28–32). This phenomenon, known as reverse epidemiology or altered risk factor patterns (33), is also observed in chronic heart failure and other chronic disease states with wasting syndrome (34, 35) and underscores the striking role that nutritional and inflammatory status plays in short-term survival of populations with chronic disease states (34). Therefore, reliable markers of MICS with stronger and more robust associations with morbidity and mortality in MHD patients are needed, so that patients at risk can better be identified for focused nutritional interventions.

Prealbumin is a 54-kDa protein synthesized primarily by the liver (7). Its main function is to transport thyroxine and indirectly vitamin A, because it serves as a carrier protein for retinol binding protein (9). A rise or fall in protein and energy intakes leads to parallel changes in circulating prealbumin concentrations (36). Similar to serum albumin, prealbumin, too, is considered a negative acute-phase reactant, because its serum concentrations may decrease in inflammation (37). In contrast to serum albumin, however, the half-life of prealbumin is relatively short, ie, 2–3 d (36, 37). Hence, it may be a more sensitive indicator of nutritional status than either serum albumin or transferrin according to the reports of Kalantar-Zadeh et al (31), Sreedhara et al (38), Mittman et al (39), Goldwasser et al (40), and Chertow et al (7, 8). In our current study, even though there was no superiority of serum prealbumin to albumin in predicting mortality, we found that among normoalbuminemic patients a low serum prealbumin was still associated with increased death risk. More interestingly, a drop in serum prealbumin by ≥10 mg/dL over 6 mo was a robust predictor of increased mortality independent of other baseline nutritional or inflammatory markers. To the best of our knowledge, our study is the first one to examine the effect of changes in circulating prealbumin over time on mortality and the only study with concomitant measures of nutritional and inflammatory markers and body composition, especially after controlling for several explicit markers of inflammation and cytokines, ie, CRP, IL-6, and TNF-{alpha}.

Another interesting and novel finding in our study was the inverse association between serum prealbumin and the percentage of total body fat, in that in patients with higher prealbumin there was a lower, rather than higher, proportion of body fat (Table 1Go), whereas the mid-arm muscle circumference, lean body mass, protein intake (nPNA), and serum concentrations of creatinine and albumin were all higher. The latter is in contradistinction to serum albumin, for which we recently showed a positive association with body fat, which per se was paradoxically associated with greater survival. Thus, unlike serum albumin (19), serum prealbumin appears to act in the opposite direction of serum albumin, at least for its association with body fat. Furthermore, our study showed an association of prealbumin with components of health-related quality of life, which per se is a predictor of survival in MHD patients (14, 41). We found that this association was stronger for several physical health components of the SF-36 compared with mental health components. Additional studies are necessary to examine the clinical implications of these findings.

Our study also examined the implication of longitudinal changes in serum prealbumin over time. Patients whose prealbumin decreased after 6 mo had a higher death risk subsequently, compared with those with stable serum prealbumin, especially among patients with a baseline serum prealbumin between 20 and 40 mg/mL (Table 4Go). Although the opposite association, ie, between a rise in prealbumin and improved survival, was not evident except for a trend in the spline graphs (Figure 6Go), these general findings may imply that monitoring changes in serum prealbumin can help identify patients at risk, who may benefit from nutritional interventions, especially if the prealbumin fall is >10 mg/dL over 6 mo. Relevant to our findings, Vehe et al (42) showed that nutritional support for 4 wk led to a significant rise in serum prealbumin from 15.3 ± 7.8 mg/dL to 24.6 ± 19.0 mg/dL in 14 CKD patients (P < 0.01). Although Mortelmans et al (43) did not show a significant increase in serum prealbumin in 16 MHD patients given intradialytic parenteral nutrition over a 9-mo period, a recent randomized controlled trial in 186 malnourished MHD patients who also received oral nutritional supplements with or without 1 y of intradialytic parenteral nutrition showed that an increase in prealbumin of >30 mg/L within 3 mo independently predicted a 54% decrease in 2-y mortality and improved general well-being (2).

A potential limitation of the present study is a selection bias during enrollment. However, because the mortality in our cohort was less than the base population, it might be argued that a selection bias with such a direction generally would lead to a bias toward the null hypothesis, so, without this bias, our positive results might have been even stronger. The strengths of our study include the sample size, which was moderately large, the comprehensive clinical and laboratory evaluations with repeated measures of serum prealbumin, concomitant assessment of quality of life and body composition measures, and detailed evaluation of comorbid states by study physicians at baseline. Unlike previous cohorts that were studied, ours was extensively characterized for markers of inflammation and nutritional status, including direct measurements of total body fat. The availability of these measures allowed us to show that prealbumin was able to predict mortality risk independent of influences from other known inflammatory markers or comorbid states in this group of MHD patients. Another strength of this cohort is that the subjects were selected randomly without having any prior knowledge of their inflammatory status. Finally, the same blood specimens that were used to measure markers of MICS and cytokines were also used for the prealbumin measurements.

In conclusion, we found that serum prealbumin correlated with several surrogates of body composition, inflammation, and health-related quality of life in MHD patients and that a low baseline serum prealbumin even in normoalbuminemic patients was independently associated with a trend toward increased death risk. Most importantly, a decline in serum prealbumin over 6 mo was an independent death predictor. Understanding the role of prealbumin as an indicator of outcome in the MHD population may lead to more useful strategies to identify patients at risk of PEW and to the development of focused nutritional interventions to improve nutritional status and, hence, survival in almost a half million dialysis patients and the many millions of patients with CKD in the United States and as well as throughout the world. Randomized controlled trials are needed to examine the clinical implications of our findings.


   ACKNOWLEDGMENTS
 
We thank Ms. Stephanie Griffith, at Harbor-UCLA GCRC Core Laboratories, for the management of blood samples and measuring inflammatory markers.

The author's responsibilities were as follows—MR: Conducted and analyzed study data and wrote, reviewed, and approved the manuscript; JDK, RB, and CPK: analysis and interpretation of the data, and reviewed, amended, and approved the manuscript; KK-Z (principal investigator): designed, conducted, and analyzed the NIED study, and wrote the manuscript.

JDK and KK-Z have received honoraria or research grants from NovoNordisk, the manufacturer of growth hormone (Norditropin), which is currently being tested in a Phase III trial in malnourished dialysis patients. KK-Z has received honoraria or research grants from Abbott Nutrition, the manufacturer of Nepro and Oxepa, and from Nutripletion and Pentec, providers of intradialytic parenteral nutrition. None of the other authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. KDOQI; National Kidney Foundation. KDOQI clinical practice guidelines and clinical practice recommendations for anemia in chronic kidney disease. Am J Kidney Dis 2006;47(suppl):S11–145.[CrossRef][Medline]
  2. Fouque D, Kalantar-Zadeh K, Kopple J, et al. A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease. Kidney Int 2008;73:391–8.[CrossRef][Medline]
  3. Kalantar-Zadeh K. Recent advances in understanding the malnutrition-inflammation-cachexia syndrome in chronic kidney disease patients: What is next? Semin Dial 2005;18:365–9.[CrossRef][Medline]
  4. Pupim LB, Caglar K, Hakim RM, Shyr Y, Ikizler TA. Uremic malnutrition is a predictor of death independent of inflammatory status. Kidney Int 2004;66:2054–60.[CrossRef][Medline]
  5. Lacson E Jr, Ikizler TA, Lazarus JM, Teng M, Hakim RM. Potential impact of nutritional intervention on end-stage renal disease hospitalization, death, and treatment costs. J Ren Nutr 2007;17:363–71.[CrossRef][Medline]
  6. Kalantar-Zadeh K, Kilpatrick RD, Kuwae N, et al. Revisiting mortality predictability of serum albumin in the dialysis population: time dependency, longitudinal changes and population-attributable fraction. Nephrol Dial Transplant 2005;20:1880–8.[Abstract/Free Full Text]
  7. Chertow GM, Ackert K, Lew NL, Lazarus JM, Lowrie EG. Prealbumin is as important as albumin in the nutritional assessment of hemodialysis patients. Kidney Int 2000;58:2512–7.[CrossRef][Medline]
  8. Chertow GM, Goldstein-Fuchs DJ, Lazarus JM, Kaysen GA. Prealbumin, mortality, and cause-specific hospitalization in hemodialysis patients. Kidney Int 2005;68:2794–800.[CrossRef][Medline]
  9. Kopple JD, Mehrotra R, Suppasyndh O, Kalantar-Zadeh K. Observations with regard to the National Kidney Foundation K/DOQI clinical practice guidelines concerning serum transthyretin in chronic renal failure. Clin Chem Lab Med 2002;40:1308–12.[CrossRef][Medline]
  10. Kaysen GA. Effects of inflammation on plasma composition and endothelial structure and function. J Ren Nutr 2005;15:94–8.[CrossRef][Medline]
  11. Colman S, Bross R, Benner D, et al. The Nutritional and Inflammatory Evaluation in Dialysis patients (NIED) study: overview of the NIED study and the role of dietitians. J Ren Nutr 2005;15:231–43.[CrossRef][Medline]
  12. Mehrotra R, Kermah D, Fried L, et al. Chronic peritoneal dialysis in the United States: declining utilization despite improving outcomes. J Am Soc Nephrol 2007;18:2781–8.[Abstract/Free Full Text]
  13. Beddhu S, Bruns FJ, Saul M, Seddon P, Zeidel ML. A simple comorbidity scale predicts clinical outcomes and costs in dialysis patients. Am J Med 2000;108:609–13.[CrossRef][Medline]
  14. Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. Association among SF36 quality of life measures and nutrition, hospitalization, and mortality in hemodialysis. J Am Soc Nephrol 2001;12:2797–806.[Abstract/Free Full Text]
  15. Diaz-Buxo JA, Lowrie EG, Lew NL, Zhang H, Lazarus JM. Quality-of-life evaluation using Short Form 36: comparison in hemodialysis and peritoneal dialysis patients. Am J Kidney Dis 2000;35:293–300.[CrossRef][Medline]
  16. Kalantar-Zadeh K, Kleiner M, Dunne E, et al. Total iron-binding capacity-estimated transferrin correlates with the nutritional subjective global assessment in hemodialysis patients. Am J Kidney Dis 1998;31:263–72.[Medline]
  17. Williams AJ, McArley A. Body composition, treatment time, and outcome in hemodialysis patients. J Ren Nutr 1999;9:157–62.[Medline]
  18. Kalantar-Zadeh K, Block G, Kelly MP, Schroepfer C, Rodriguez RA, Humphreys MH. Near infra-red interactance for longitudinal assessment of nutrition in dialysis patients. J Ren Nutr 2001;11:23–31.[Medline]
  19. Kalantar-Zadeh K, Kuwae N, Wu DY, et al. Associations of body fat and its changes over time with quality of life and prospective mortality in hemodialysis patients. Am J Clin Nutr 2006;83:202–10.[Abstract/Free Full Text]
  20. Erbagci AB, Tarakcioglu M, Aksoy M, et al. Diagnostic value of CRP and Lp(a) in coronary heart disease. Acta Cardiol 2002;57:197–204.[CrossRef][Medline]
  21. Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med 2002;347:1557–65.[Abstract/Free Full Text]
  22. Beutler B, Cerami A. The biology of cachectin/TNF-a primary mediator of the host response. Annu Rev Immunol 1989;7:625–55.[Medline]
  23. Pecoits-Filho R, Barany P, Lindholm B, Heimburger O, Stenvinkel P. Interleukin-6 is an independent predictor of mortality in patients starting dialysis treatment. Nephrol Dial Transplant 2002;17:1684–8.[Abstract/Free Full Text]
  24. Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med 1989;8:551–61.[Medline]
  25. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004;159:882–90.[Abstract/Free Full Text]
  26. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 2004;351:1296–305.[Abstract/Free Full Text]
  27. United States Renal Data System: excerpts from the USRDS 2005 Annual Data Report: Atlas of End-Stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. Am J Kid Dis 2006;47(suppl 1):1–286.[Medline]
  28. Kilpatrick RD, McAllister CJ, Kovesdy CP, Derose SF, Kopple JD, Kalantar-Zadeh K. Association between serum lipids and survival in hemodialysis patients and impact of race. J Am Soc Nephrol 2007;18:293–303.[Abstract/Free Full Text]
  29. Kalantar-Zadeh K, Abbott KC, Salahudeen AK, Kilpatrick RD, Horwich TB. Survival advantages of obesity in dialysis patients. Am J Clin Nutr 2005;81:543–54.[Abstract/Free Full Text]
  30. Nakai S, Akiba T, Kazama J, et al. Effects of serum calcium, phosphorous, and intact parathyroid hormone levels on survival in chronic hemodialysis patients in Japan. Ther Apher Dial 2008;12:49–54.[CrossRef][Medline]
  31. Kalantar-Zadeh K, Ikizler TA, Block G, Avram MM, Kopple JD. Malnutrition-inflammation complex syndrome in dialysis patients: causes and consequences. Am J Kidney Dis 2003;42:864–81.[Medline]
  32. McClellan WM, Chertow GM. Beyond Framingham: cardiovascular risk profiling in ESRD. J Am Soc Nephrol 2005;16:1539–41.[Free Full Text]
  33. Kalantar-Zadeh K. What is so bad about reverse epidemiology anyway? Semin Dial 2007;20:593–601.[CrossRef][Medline]
  34. Kalantar-Zadeh K, Abbott KC, Kronenberg F, Anker SD, Horwich TB, Fonarow GC. Epidemiology of dialysis patients and heart failure patients. Semin Nephrol 2006;26:118–33.[CrossRef][Medline]
  35. Kalantar-Zadeh K, Horwich TB, Oreopoulos A, et al. Risk factor paradox in wasting diseases. Curr Opin Clin Nutr Metab Care 2007;10:433–42.[CrossRef][Medline]
  36. Spiekerman AM. Proteins used in nutritional assessment. Clin Lab Med 1993;13:353–69.[Medline]
  37. Myron Johnson A, Merlini G, Sheldon J, Ichihara K. Clinical indications for plasma protein assays: transthyretin (prealbumin) in inflammation and malnutrition. Clin Chem Lab Med 2007;45:419–26.[CrossRef][Medline]
  38. Sreedhara R, Avram MM, Blanco M, Batish R, Avram MM, Mittman N. Prealbumin is the best nutritional predictor of survival in hemodialysis and peritoneal dialysis. Am J Kidney Dis 1996;28:937–42.[Medline]
  39. Mittman N, Avram MM, Oo KK, Chattopadhyay J. Serum prealbumin predicts survival in hemodialysis and peritoneal dialysis: 10 years of prospective observation. Am J Kidney Dis 2001;38:1358–64.[CrossRef][Medline]
  40. Goldwasser P, Michel MA, Collier J, et al. Prealbumin and lipoprotein(a) in hemodialysis: relationships with patient and vascular access survival. Am J Kidney Dis 1993;22:215–25.[Medline]
  41. Kalantar-Zadeh K, Unruh M. Health related quality of life in patients with chronic kidney disease. Int Urol Nephrol 2005;37:367–78.[CrossRef][Medline]
  42. Vehe KL, Brown RO, Moore LW, Acchiardo SR, Luther RW. The efficacy of nutrition support in infected patients with chronic renal failure. Pharmacotherapy 1991;11:303–7.[Medline]
  43. Mortelmans AK, Duym P, Vandenbroucke J, et al. Intradialytic parenteral nutrition in malnourished hemodialysis patients: a prospective long-term study. JPEN J Parenter Enteral Nutr 1999;23:90–5.[Abstract/Free Full Text]
Received for publication January 22, 2008. Accepted for publication August 18, 2008.




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