There are abundant data from short-term metabolic studies in patients with chronic kidney disease (CKD) indicating that metabolic acidosis, a common condition in renal insufficiency, may engender or worsen protein-energy malnutrition, inflammation, and bone disease (1–6). Hence, two separate sets of guidelines within the Kidney Disease Outcome Quality Initiatives (K/DOQI)—Nutrition (7) and Bone Disease guidelines (8)—as well as the European guidelines (9) recommend a serum bicarbonate level >22 mEq/L. In patients with CKD, protein-energy malnutrition and inflammation are closely associated and together are referred to as malnutrition-inflammation complex syndrome (MICS) (10). Because MICS has been implicated as a major cause of poor clinical outcome in patients with CKD, the hypothesis has been advanced that metabolic acidosis, by engendering both malnutrition and negative nitrogen balance (11–13) and inflammation (14,15), may play a major role in increased mortality in this population. However, in contradistinction to most metabolic studies of small populations, which indicate a deleterious effect of acidosis on clinical outcome, the majority of epidemiologic studies in maintenance dialysis patients have indicated an inverse association between small decreases in serum bicarbonate (HCO3−) and improved markers of MICS and also survival (5,16,17). In fact the Dialysis Outcome Practice Pattern Study (DOPPS) (18) recently showed that moderate predialysis acidosis was associated with better nutritional status and lower relative risk for mortality and hospitalization in approximately 7000 maintenance dialysis patients. Hence, the association between metabolic acidosis and survival in dialysis patients has led to confusion pertaining to the effects of metabolic acidosis in this patient population (19). Moreover, interventional studies have yielded inconsistent results in different subgroups of patients with CKD; although in chronic peritoneal dialysis patients mitigating acidemia seems more consistently to improve nutritional status and reduce hospitalizations, the results in maintenance hemodialysis (MHD) patients are mixed (5,16).
We hypothesized that there is an overwhelming effect of the MICS that substantially alters the association between acidosis and survival in these patients. We sought to re-examine the underlying association between metabolic acidosis and alkalosis versus survival that maybe influenced by clinical conditions or other dominating characteristics. We therefore examined these associations using a series of multivariate models in a large sample of MHD patients across the United States.
Materials and Methods
The database used in this study has been described previously (20–22). In summary, the data warehouse of DaVita, Inc., the second largest dialysis care provider in the United States with >500 dialysis facilities and 40,000 maintenance dialysis patients across the country at any given time, includes comprehensive information on virtually all of its patients. A 24-mo cohort (July 1, 2001, through June 30, 2003) of these patients was studied. This period was selected because comprehensive clinical, demographic, and laboratory values during each thrice-weekly hemodialysis session began to be registered electronically in great detail during early to middle 2001. All repeated measures of every relevant variable for each patient within the entry quarter (during the first 13 wk upon the start of observation) were averaged to obtain one quarterly mean value for that variable. The study was approved by Institutional Review Committees of Harbor-UCLA and DaVita.
Cohort Time, Dialysis Vintage, and Death
Cohort time included the number of days a patient participated in the cohort and could vary from 1 to 731 d. Dialysis vintage was defined as the duration of time elapsed between the first day of dialysis treatment and the first day that the patient entered the cohort. The entry quarter was defined as the first quarter in which a patient’s dialysis vintage was >3 mo for at least half the duration of the quarter. By implementing this criterion, any patient who had not maintained in the cohort beyond the first 3 mo of MHD was excluded.
Blood samples were predialysis except for the postdialysis serum urea nitrogen obtained to calculate urea kinetics. Blood samples were drawn using uniform techniques in all dialysis facilities across the nation and were transported to the Central DaVita Laboratory in Deland, FL, within 24 h. All laboratory values were measured via automated and standardized methods in the DaVita Laboratory. Most laboratory values, including complete blood cell counts and serum levels of bicarbonate (HCO3−), urea nitrogen, albumin, creatinine, phosphorus, potassium, and total iron binding capacity (TIBC), were measured at least monthly. Serum ferritin was measured quarterly. Hemoglobin was measured weekly to biweekly in most patients. Single pool Kt/V, a reflection of dialysis dose, and normalized protein nitrogen appearance (nPNA), also known as normalized protein catabolic rate, an estimation of daily protein intake, were calculated monthly in DaVita central laboratory according to Daugirdas et al. (23) using urea kinetic modeling software.
The entire HCO3− range was divided into 12 categories (<17, ≥27, and 10 increments of 1 mEq/L in between). Ten laboratory variables were also selected to ascertain the patient’s nutritional and/or inflammatory status, together referred to as MICS (10), especially because each of these variables is associated with morbidity and mortality in MHD patients: (1) Serum albumin, which has strong associations with inflammation and comorbid conditions in MHD patients (24–26); (2) nPNA as a marker of daily protein intake; (3) serum TIBC, which is associated with subjective global assessment of nutrition (27); (4) serum ferritin, a possible indicator of inflammation (28); (5) serum creatinine, a marker of muscle mass and meat intake (29); (6) serum calcium, which correlates with coronary calcification (30); (7) serum phosphorus, which correlates with protein intake (30); (8) peripheral white blood cell (WBC) count, which may correlate with serum C-reactive protein (31); (9) percentage of lymphocytes in the WBC, a potential nutritional marker that was shown recently to have independent associations with mortality in MHD patients (32); and (10) blood hemoglobin (33).
Because the dialysis population is a dynamic cohort with a high turnover rate, a nonconcurrent cohort was formed to include all existing MHD patients of the first quarter of observation (q1) and all MHD patients of the subsequent quarters (q2 through q8) of the 24-mo study. A baseline value was created for each measure by left-truncating the first available 3-mo averaged value of the entry quarter for each patient.
In addition to standard descriptive statistics, the Cox proportional hazard regression for truncated and censored data were used to determine whether the 24-mo survival was associated with baseline categories of serum HCO3−. The reference category (with which the death risk in other categories is compared) was the serum HCO3− range between 22 and 22.9 mEq/L. This range of serum HCO3− was chosen as the reference because it was adjacent to and had sample size similar to the modal category, had the highest numbers of death cases, and allowed for the most precise comparison with other HCO3− categories.
For each analysis, three models were examined on the basis of the level of multivariate adjustment: (1) Unadjusted models, which included HCO3− categories, entry quarter, and mortality data; (2) case-mix and dialysis dose models, which were adjusted for age, gender, race and ethnicity, diabetes, vintage categories, primary insurance (Medicare, Medicaid, private, and others), marriage status (married, single, divorced, widowed, and others), standardized mortality ratio of the dialysis clinic during entry quarter as reported by the United States Renal Data System (34), residual renal function during the entry quarter, the Kt/V (single pool), and dialysate bath bicarbonate concentration; and (3) case-mix plus MICS adjusted models, which included all of the above-mentioned covariates as well as 12 indicators of nutritional status and inflammation, including prescribed erythropoietin (EPO) dose (or EPO resistance index, i.e., EPO dose divided by averaged hemoglobin ), protein intake (nPNA), serum phosphorus, calcium, albumin, TIBC, ferritin and creatinine, WBC count, lymphocyte percentage, hemoglobin level, and body mass index (BMI; postdialysis dry weight [kg] divided by height squared [m2] [36,37]). Missing covariate data (<5%) were imputed by the mean or median of the existing values, whichever was appropriate. The only exception was the bath bicarbonate, which had approximately 25% missing data; hence, a dummy variable that also included an additional category for the missing bath bicarbonate value was created. All descriptive and multivariate statistics were carried out by the Stata, version 7.0 (College Station, TX).
The original 2-yr national database of all DaVita MHD patients included 69,819 patients. After implementing the above-mentioned selection criteria, including deleting patients who did not receive MHD treatment for <3 mo (after exclusion of incident patients) or who had missing data, the resulting cohort included 56,385 MHD patients, 36,289 (64%) of whom originated from the first quarter (q1) and 20,096 (36%) of whom originated during the subsequent quarters (q2 through q8). Table 1 shows baseline demographic, clinical, and laboratory characteristics of the entire 56,386 patient cohort. Serum HCO3− mean (±SD) was 21.8 ± 2.8 mEq/L (median 22.0 mEq/L; interquartile range 20.0 to 23.7 mEq/L). Among the reported fresh dialysate bicarbonate concentrations that were used in DaVita MHD patients during the first 3 mo of the cohort, they were mostly 35 mEq/L (76%), 40 mEq/L (20%), and 30 mEq/L (3%).
Table 2 shows both unadjusted and case-mix and MICS (fully) adjusted correlation coefficients between serum HCO3− and several pertinent clinical and laboratory variables. Serum phosphorus has one of the strongest correlations and an inverse relation with serum HCO3−, indicating that hyperphosphatemic MHD patients tend to have a lower serum HCO3− level. Similar inverse but weaker associations were also observed for serum urea nitrogen and potassium as well as the urea kinetics estimated protein intake.
Table 3 shows the 12 selected serum HCO3− categories. Serum phosphorus and albumin concentrations and protein intake as well as all-cause and cardiovascular mortality rates are listed for each HCO3− group. As depicted in Figure 1A, serum phosphorus was progressively lower in the groups with progressively higher serum HCO3−, corresponding to the same inverse associations mentioned above (see Table 2). Figure 1B displays serum albumin concentrations in incremental HCO3− groups, indicating the inverted J-shaped association between the two laboratory markers with the lowest serum albumin levels observed in the hyperbicarbonatemic MHD patients. However, a strictly downgoing association was observed for the 3-mo averaged urea kinetic indicator of protein intake (nPNA or normalized protein catabolic rate); patients with progressively lower serum HCO3− levels had the highest protein intake, and vice versa (Table 3, Figure 1C). Finally, the high serum HCO3− categories had the greatest 24-mo mortality rates, as demonstrated in Table 3 and Figure 1D, whereas mortality tended to be reduced in lower HCO3− groups.
To examine the adjusted associations between serum HCO3− and prospective mortality in 56,386 MHD patients, we examined Cox proportional hazard models. Figure 2 demonstrates the hazard ratios of all-cause (left) and cardiovascular (right) mortality. The HCO3− category of 22 to <23 mEq/L was the reference group in all models. The lowest unadjusted mortality was associated with a predialysis HCO3− in the 17 to 23 mEq/L range, whereas values ≥23 mEq/L were associated with progressively higher death rates, a J-shaped association. After adjustment for case mix and dialysis dose variables, a U-shaped association was noticed with increased mortality at both ends of the HCO3− spectrum (<17 and >27 mEq/L). After additional multivariate adjustment for 12 potential markers of the MICS, the association transformed into a reversed J-shaped relationship, so a HCO3− concentration of ≥22 mEq/L was associated with less risk for death, whereas HCO3− levels <22 mEq/L were associated with the highest death risk. Similar trends in associations between serum HCO3− and mortality were also observed when the cohort was divided into two subcohorts of incident (vintage <6 mo) and prevalent (vintage ≥6 mo) MHD patients (Figure 3); however, the associations and transitions across death risk of adjacent HCO3− groups and among three levels of adjustment were less smooth. This maybe due to mitigated statistical power and increased background noise caused by a 50% reduction in the number of deaths and sample sizes studied in each group. Figure 4 gives a schematic illustration of the change in the magnitude and the direction of the associations at the three above-mentioned multivariate adjustment levels.
Sensitivity analyses, which included constructing the same multivariate models using the subcohort of 36,289 (64%) patients from q1 or only patients with the two longest vintage (>2 yr) resulted in similar hazard ratios and trends. Further subgroup analyses indicated similar patterns for both patients with and without diabetes and for MHD patients in different age and race groups, dialysis dose ranges, and serum albumin categories (data not shown). Stepwise addition of some of the case-mix and MICS covariates to the unadjusted model showed that the age and nPNA contributed substantially to the observed differences among the three models. Inclusion of serum potassium in the fully adjusted model did not substantially change the magnitude or direction of the associations. Relevant interactions, such as those between serum HCO3− and albumin, phosphorus, or nPNA, did not account for the differences observed among the three levels of adjustment. Addition of EPO resistance index in lieu of EPO dose did not change the associations (data not show).
Studying a large national dialysis database, we found inverse associations between 3-mo averaged predialysis serum HCO3− and several nutritional indicators, including serum phosphorus, potassium, and urea levels as well as nPNA, an indicator of protein intake, in >56,000 MHD patients. These findings may indicate that a high protein intake may lead to a low serum HCO3− level at the start of the subsequent dialysis treatment. Moreover, we initially found improved survival rates in the acidotic range of serum HCO3−, whereas normal to alkalotic HCO3− levels seemed to be associated with higher all-cause and cardiovascular mortality. These associations, however, reversed almost entirely after multivariate adjustment for case mix and markers of malnutrition and inflammation, together also known as MICS. This striking reversal may indicate that the associations described between serum HCO3− and mortality by others (17,18) and also found in our unadjusted models are essentially due to the confounding effect of MICS. Moreover, because the reversal of these associations was not altered with the inclusion of serum phosphorus in the multivariate models, the role of the commonly used acidogenic phosphorus binder sevelamer hydrochloride in causing hypobicarbonatemia and its related adverse effects seems relatively immaterial. Our findings may have significant implications for the clinical treatment of >300,000 maintenance dialysis patients in the United States and many more in other countries.
Metabolic acidosis occurs commonly in patients with CKD (3,4,38,39). Acidemia is believed to be an important cause of morbidity and many adverse consequences in patients with CKD and ESRD (2,5,38,40,41). The acidosis of renal failure may contribute to protein-energy malnutrition, another common condition and a risk factor for poor outcome (41–44). Moreover, a chronic state of inflammation is commonly observed in renal failure and may predispose to both protein-energy malnutrition and increased rate of cardiovascular and atherosclerotic disease in this population (24,26). MICS is a common condition in patients with stages 4 and 5 CKD. Several mechanisms may contribute to the development of malnutrition from metabolic acidosis in patients with CKD: (1) Increased protein catabolism, (2) decreased protein synthesis, (3) endocrine abnormalities including insulin resistance, (4) reduction in serum leptin levels, and (5) inflammation per se (3–5,16,44,45).
Despite that much research indicates a catabolic response to metabolic acidosis, including subnormal levels of essential branched-chain amino acids, increased protein breakdown, and abnormalities in bone metabolism and hormonal responses (46–48), the vast majority of recent epidemiologic studies in maintenance dialysis patients have shown an inverse relationship between metabolic acidosis and nutritional status (14,18,49–55). Uribarri (50) reported a significant inverse relationship between serum HCO3− and nPNA and found that MHD patients with a HCO3− ≤ 21 mEq/L (versus those ≥25 mEq/L) had a higher predialysis serum creatinine and urea level. Dumler et al. (51) reported a higher serum albumin, creatinine, and nPNA in MHD patients with metabolic acidosis. Gao et al. (52) found an inverse association between predialysis serum HCO3− and serum urea, phosphorus, and uric acid in 50 MHD patients. Lin et al. (14) reported a higher BMI, triceps skinfold thickness, dietary protein intake, nPNA, and serum potassium in acidemic MHD patients. Finally, two epidemiologic studies with large sample sizes reported by Leavey et al. (53) (n = 3891) and Chauveau et al. (54) (n = 7123) found significant inverse relationships between predialysis HCO3− and serum albumin as well as serum prealbumin, nPNA, and BMI, respectively. An epidemiologic analysis of a large cohort of MHD patients by Lowrie et al. (17) showed that the association between the baseline serum HCO3− and prospective mortality was J-shaped, in that the risk for death was higher when serum HCO3− was either <17.5 or >25 mEq/L. This inverse association was confirmed recently by DOPPS investigators (18), who concluded that moderate predialysis acidosis seems to be associated with better nutritional status and lower relative risk for mortality or hospitalization. Although Lowrie et al. (56–59) discussed the possibility that confounding factors might alter or blunt the risks associated with acidemic dialysis patients, to our knowledge, our study is the first to seek to pinpoint the substantial impact of elements of MICS that lead to the alterations of the unadjusted associations.
It was shown recently that subjective reported appetite is a strong predictor of survival in MHD patients (60). A higher protein intake, as indicated by an increased nPNA, is associated with decreased mortality and hospitalization (61). This relationship seems to confound the underlying association between low serum HCO3− levels and increased protein catabolism with subsequent wasting and poor outcomes. Hence, it is possible but not shown unequivocally previously that MICS-adjusted associations differ substantially from unadjusted ones. With regard to correlations with outcome, our study, hence, is one of the first to suggest the plausibility of the foregoing hypothesis.
Some calcium-free phosphate binders may be implicated as a cause of metabolic acidosis and its adverse effects in MHD patients (5). Until late 1990s, most MHD patients were treated exclusively with aluminum- and calcium-based phosphate binders, which are alkaline. In the past several years, including during the studied cohort (2001 to 2003), sevelamer hydrochloride has increasingly become the primary phosphorus binders in patients with CKD because of the concerns related both to aluminum toxicity and to coronary artery calcification as a result of higher calcium intake (62,63). Each sevelamer capsule also includes some amount of nitrogen and chloride, which may lead to some decrease in HCO3− levels and some increase in the calculated nPNA, respectively. This might result in the reverse association between serum HCO3− and both serum phosphorus and nPNA, assuming that hyperphosphatemic patients received higher than average doses of sevelamer. However, given the small amount of change in nPNA as a result of intake of nitrogen contained in sevelamer, it is highly unlikely that the difference between the two ends of the HCO3− spectrum found in our study is the result of sevelamer intake. Hence, we believe that the association between high serum phosphorus and low serum HCO3− found in our study as well as by DOPPS investigators (18) is merely a reflection of increased protein intake, which will also lead to hyperphosphatemia.
A limitation of our study is lack of information on outpatient medications such as phosphorus binders and lack of explicit laboratory markers of inflammation such as C-reactive protein. However, we did use data on serum albumin, ferritin, and TIBC and WBC count and lymphocyte percentage, which also tend to reflect the presence or absence of inflammation (27,28,31). Another limitation of our analysis is that there is no proven steady state that is required for accurate calculation for nPNA equation. However, the 3-mo averaging of the nPNA values along with large sample size should mitigate this inherent limitation of this particular MICS surrogate. Our study is based on only a 24-mo follow-up, rather than longer periods of observation, so the results may not apply to long-term survival. Nonetheless, two thirds of MHD patients die within the first 5 yr of initiation of dialysis (34). The narrow time window of our study ensures that confounding by changes in practice or technology is minimal. Our study by far has the largest sample size to date, and our data originate from one dialysis care provider that has uniform patient treatment practices when compared with DOPPS data (18); all laboratory measurements are performed in one single facility, and most data are 3-mo means of several measures. Hence, measurement variability is minimized. However, we agree that there are serious problems with the accuracy of the measurement of serum bicarbonate in large-scale studies (64). Moreover, epidemiologic analyses can identify only associations; causal relationships need to be demonstrated by clinical trials. Hence, the results of epidemiologic data analyses should be considered with caution.
Our study provides further evidence that the MICS may be the substantial contributor to the counterintuitive associations between serum HCO3− and mortality, which is similar to what has been described as risk-factor paradox or reverse epidemiology in this patient population (19). On the basis of our findings, the data presented by Bommer et al. (18) indicating that moderate predialysis acidosis is associated with lower relative risk for mortality or hospitalization may be viewed at a different angle. Although the MICS-adjusted associations between serum HCO3− and mortality are in the opposite direction of the original association, it is important to appreciate that physicians usually treat a patient on the basis of his or her unadjusted data. A low serum HCO3− level indeed is a marker of prolonged survival, probably because it reflects higher protein intake in MHD patients. If this concept indeed is true, then the results of this study provide further support for the need for increased attention to malnutrition, inflammation, low appetite, and inadequate food intake in MHD patients.
This work is supported by a Young Investigator Award from the National Kidney Foundation and by National Institute of Diabetes and Digestive and Kidney Diseases grant DK61162 to K.K.-Z.
Published online ahead of print. Publication date available at http://www.cjasn.org.
1. Franch HA, Raissi S, Wang X, Zheng B, Bailey JL, Price SR: Acidosis impairs insulin receptor substrate-1-associated phosphoinositide 3-kinase signaling in muscle cells: Consequences on proteolysis. Am J Physiol Renal Physiol 287: F700–F706, 2004
2. Mitch WE: Influence of metabolic acidosis on nutrition. Am J Kidney Dis 29: xlvi–xlviii, 1997
3. Franch HA, Mitch WE: Catabolism in uremia: The impact of metabolic acidosis. J Am Soc Nephrol 9: S78–S81, 1998
4. Bailey JL, Mitch WE: Twice-told tales of metabolic acidosis, glucocorticoids, and protein wasting: What do results from rats tell us about patients with kidney disease? Semin Dial 13: 227–231, 2000
5. Kalantar-Zadeh K, Mehrotra R, Fouque D, Kopple JD: Metabolic acidosis and malnutrition-inflammation complex syndrome in chronic renal failure. Semin Dial 17: 445–465, 2004
6. Uribarri J, Levin NW, Delmez J, Depner TA, Ornt D, Owen W, Yan G: Association of acidosis and nutritional parameters in hemodialysis patients. Am J Kidney Dis 34: 493–499, 1999
7. Clinical practice guidelines for nutrition in chronic renal failure. K/DOQI, National Kidney Foundation. Am J Kidney Dis 35[Suppl 2]: S1–S140, 2000
8. National Kidney Foundation: K/DOQI clinical practice guidelines for bone metabolism and disease in chronic kidney disease. Am J Kidney Dis 42[Suppl 3]: S1–S202, 2003
9. Locatelli F, Fouque D, Heimburger O, Drueke TB, Cannata-Andia JB, Horl WH, Ritz E: Nutritional status in dialysis patients: A European consensus. Nephrol Dial Transplant 17: 563–572, 2002
10. 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 42: 864–881, 2003
11. Ballmer PE, McNurlan MA, Hulter HN, Anderson SE, Garlick PJ, Krapf R: Chronic metabolic acidosis decreases albumin synthesis and induces negative nitrogen balance in humans. J Clin Invest 95: 39–45, 1995
12. Reaich D, Channon SM, Scrimgeour CM, Goodship TH: Ammonium chloride-induced acidosis increases protein breakdown and amino acid oxidation in humans. Am J Physiol 263: E735–E739, 1992
13. Papadoyannakis NJ, Stefanidis CJ, McGeown M: The effect of the correction of metabolic acidosis on nitrogen and potassium balance of patients with chronic renal failure. Am J Clin Nutr 40: 623–627, 1984
14. Lin SH, Lin YF, Chin HM, Wu CC: Must metabolic acidosis be associated with malnutrition in haemodialysed patients? Nephrol Dial Transplant 17: 2006–2010, 2002
15. Pickering WP, Price SR, Bircher G, Marinovic AC, Mitch WE, Walls J: Nutrition in CAPD: Serum bicarbonate and the ubiquitin-proteasome system in muscle. Kidney Int 61: 1286–1292, 2002
16. Mehrotra R, Kopple JD, Wolfson M: Metabolic acidosis in maintenance dialysis patients: Clinical considerations. Kidney Int Suppl 64: S13–S26, 2003
17. Lowrie EG, Lew NL: Death risk in hemodialysis patients: The predictive value of commonly measured variables and an evaluation of death rate differences between facilities. Am J Kidney Dis 15: 458–482, 1990
18. Bommer J, Locatelli F, Satayathum S, Keen ML, Goodkin DA, Saito A, Akiba T, Port FK, Young EW: Association of predialysis serum bicarbonate levels with risk of mortality and hospitalization in the Dialysis Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis 44: 661–671, 2004
19. Kalantar-Zadeh K, Block G, Humphreys MH, Kopple JD: Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int 63: 793–808, 2003
20. Kalantar-Zadeh K, Regidor DL, McAllister CJ, Michael B, Warnock DG: Time-dependent associations between iron and mortality in hemodialysis patients. J Am Soc Nephrol 16: 3070–3080, 2005
21. Kalantar-Zadeh K, Kopple JD, Kilpatrick RD, McAllister CJ, Shinaberger CS, Gjertson DW, Greenland S: Association of morbid obesity and weight change over time with cardiovascular survival in hemodialysis population. Am J Kidney Dis 46: 489–500, 2005
22. Kalantar-Zadeh K, Kilpatrick RD, McAllister CJ, Greenland S, Kopple JD: Reverse epidemiology of hypertension and cardiovascular death in the hemodialysis population: The 58th annual fall conference and scientific sessions. Hypertension 45: 811–817, 2005
23. Daugirdas JT: The post:pre dialysis plasma urea nitrogen ratio to estimate K.t/V and NPCR: Validation. Int J Artif Organs 12: 420–427, 1989
24. Kaysen GA, Dubin JA, Muller HG, Mitch WE, Rosales LM, Levin NW: Relationships among inflammation nutrition and physiologic mechanisms establishing albumin levels in hemodialysis patients. Kidney Int 61: 2240–2249, 2002
25. Owen WF Jr, Lew NL, Liu Y, Lowrie EG, Lazarus JM: The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis. N Engl J Med 329: 1001–1006, 1993
26. Kaysen GA, Dubin JA, Muller HG, Rosales L, Levin NW, Mitch WE: Inflammation and reduced albumin synthesis associated with stable decline in serum albumin in hemodialysis patients. Kidney Int 65: 1408–1415, 2004
27. Kalantar-Zadeh K, Kleiner M, Dunne E, Ahern K, Nelson M, Koslowe R, Luft FC: Total iron-binding capacity-estimated transferrin correlates with the nutritional subjective global assessment in hemodialysis patients. Am J Kidney Dis 31: 263–272, 1998
28. Kalantar-Zadeh K, Rodriguez RA, Humphreys MH: Association between serum ferritin and measures of inflammation, nutrition and iron in haemodialysis patients. Nephrol Dial Transplant 19: 141–149, 2004
29. Beddhu S, Samore MH, Roberts MS, Stoddard GJ, Pappas LM, Cheung AK: Creatinine production, nutrition, and glomerular filtration rate estimation. J Am Soc Nephrol 14: 1000–1005, 2003
30. Block GA, Klassen PS, Lazarus JM, Ofsthun N, Lowrie EG, Chertow GM: Mineral metabolism, mortality, and morbidity in maintenance hemodialysis. J Am Soc Nephrol 15: 2208–2218, 2004
31. Reddan DN, Klassen PS, Szczech LA, Coladonato JA, O’Shea S, Owen WF Jr, Lowrie EG: White blood cells as a novel mortality predictor in haemodialysis patients. Nephrol Dial Transplant 18: 1167–1173, 2003
32. Kuwae N, Kopple J, Kalantar-Zadeh K: A low lymphocyte percentage is a predictor of mortality and hospitalization in hemodialysis patients. Clin Nephrol 63: 22–34, 2005
33. Lowrie EG: Acute-phase inflammatory process contributes to malnutrition, anemia, and possibly other abnormalities in dialysis patients. Am J Kidney Dis 32: S105–S112, 1998
34. United States Renal Data System: USRDS 2003 Annual Data Report: Atlas of End Stage Renal Diseases in the United States, Bethesda, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2003
35. Kalantar-Zadeh K, McAllister CJ, Lehn RS, Lee GH, Nissenson AR, Kopple JD: Effect of malnutrition-inflammation complex syndrome on EPO hyporesponsiveness in maintenance hemodialysis patients. Am J Kidney Dis 42: 761–773, 2003
36. Kalantar-Zadeh K, Abbott KC, Salahudeen AK, Kilpatrick RD, Horwich TB: Survival advantages of obesity in dialysis patients. Am J Clin Nutr 81: 543–554, 2005
37. Salahudeen AK: Obesity and survival on dialysis. Am J Kidney Dis 41: 925–932, 2003
38. Bailey JL, Mitch WE: The search for the uremic toxin: The case for metabolic acidosis. Wien Klin Wochenschr 109: 7–12, 1997
39. Enia G, Catalano C, Zoccali C, Maggiore Q, Poon TF, Ward MK, Kerr DN: Hyperchloraemia: A non-specific finding in chronic renal failure. Nephron 41: 189–192, 1985
40. Mitch WE: Metabolic acidosis stimulates protein metabolism in uremia. Miner Electrolyte Metab 22: 62–65, 1996
41. Bailey JL, Wang X, England BK, Price SR, Ding X, Mitch WE: The acidosis of chronic renal failure activates muscle proteolysis in rats by augmenting transcription of genes encoding proteins of the ATP-dependent ubiquitin-proteasome pathway. J Clin Invest 97: 1447–1453, 1996
42. Kalantar-Zadeh K, Fouque D, Kopple JD: Outcome research, nutrition, and reverse epidemiology in maintenance dialysis patients. J Ren Nutr 14: 64–71, 2004
43. Mitch WE: Uremic acidosis and protein metabolism. Curr Opin Nephrol Hypertens 4: 488–492, 1995
44. Mitch WE: Insights into the abnormalities of chronic renal disease attributed to malnutrition. J Am Soc Nephrol 13[Suppl 1]: S22–S27, 2002
45. Hara Y, May RC, Kelly RA, Mitch WE: Acidosis, not azotemia, stimulates branched-chain, amino acid catabolism in uremic rats. Kidney Int 32: 808–814, 1987
46. Swendseid ME, Wang M, Vyhmeister I, Chan W, Siassi F, Tam CF, Kopple JD: Amino acid metabolism in the chronically uremic rat. Clin Nephrol 3: 240–246, 1975
47. Graham KA, Hoenich NA, Tarbit M, Ward MK, Goodship TH: Correction of acidosis in hemodialysis patients increases the sensitivity of the parathyroid glands to calcium. J Am Soc Nephrol 8: 627–631, 1997
48. Graham KA, Reaich D, Channon SM, Downie S, Gilmour E, Passlick-Deetjen J, Goodship TH: Correction of acidosis in CAPD decreases whole body protein degradation. Kidney Int 49: 1396–1400, 1996; published erratum appears in Kidney Int 51: 1662, 1997
49. Kang SW, Lee SW, Lee IH, Kim BS, Choi KH, Lee HY, Han DS: Impact of metabolic acidosis on serum albumin and other nutritional parameters in long-term CAPD patients. Adv Perit Dial 13: 249–252, 1997
50. Uribarri J: Moderate metabolic acidosis and its effects on nutritional parameters in hemodialysis patients. Clin Nephrol 48: 238–240, 1997
51. Dumler F, Falla P, Butler R, Wagner C, Francisco K: Impact of dialysis modality and acidosis on nutritional status. ASAIO J 45: 413–417, 1999
52. Gao H, Lew SQ, Bosch JP: Moderate metabolic acidosis and its effects on serum parameters in hemodialysis patients. Nephron 86: 135–138, 2000
53. Leavey SF, Strawderman RL, Young EW, Saran R, Roys E, Agodoa LY, Wolfe RA, Port FK: Cross-sectional and longitudinal predictors of serum albumin in hemodialysis patients. Kidney Int 58: 2119–2128, 2000
54. Chauveau P, Fouque D, Combe C, Laville M, Canaud B, Azar R, Cano N, Aparicio M, Leverve X: Acidosis and nutritional status in hemodialyzed patients. French Study Group for Nutrition in Dialysis. Semin Dial 13: 241–246, 2000
55. Kung SC, Morse SA, Bloom E, Raja RM: Acid-base balance and nutrition in peritoneal dialysis. Adv Perit Dial 17: 235–237, 2001
56. Lowrie EG, Lew NL: Commonly measured laboratory variables in hemodialysis patients: Relationships among them and to death risk. Semin Nephrol 12: 276–283, 1992
57. Lowrie E: The problem: Dialysis and death; how to prevent it. In: Death on Hemodialysis: Preventable or Inevitable?, edited by Friedman EA, Hingham MA, Kluwer Academic Publishers, 1994, pp 121–142
58. Lowrie EG: Chronic dialysis treatment: Clinical outcome and related processes of care. Am J Kidney Dis 24: 255–266, 1994
59. Lowrie EG, Teng M, Lew NL, Lacson EJ, Lazarus JM, Owen WF: Toward a continuous quality improvement paradigm for hemodialysis providers with preliminary suggestions for clinical practice monitoring and measurement. Hemodial Int 7: 28–51, 2003
60. Kalantar-Zadeh K, Block G, McAllister CJ, Humphreys MH, Kopple JD: Appetite and inflammation, nutrition, anemia and clinical outcome in hemodialysis patients. Am J Clin Nutr 80: 299–307, 2004
61. Shinaberger CS, Kilpatrick RD, McAllister CJ, Kopple JD, Kalantar-Zadeh K: Exploring time-dependent associations between urea dynamic calculated protein intake and cardiovascular mortality in hemodialysis patients. 37th Annual Conference of the American Society of Nephrology [Abstract]. J Am Soc Nephrol 15[Suppl]: 385A–386A, 2004
62. Chertow GM, Burke SK, Raggi P: Sevelamer attenuates the progression of coronary and aortic calcification in hemodialysis patients. Kidney Int 62: 245–252, 2002
63. Goodman WG, Goldin J, Kuizon BD, Yoon C, Gales B, Sider D, Wang Y, Chung J, Emerick A, Greaser L, Elashoff RM, Salusky IB: Coronary-artery calcification in young adults with end-stage renal disease who are undergoing dialysis. N Engl J Med 342: 1478–1483, 2000
64. Kirschbaum B: Spurious metabolic acidosis in hemodialysis patients. Am J Kidney Dis 35: 1068–1071, 2000