Whole-Body Protein Metabolism in Chronic Heart Failure: Relationship to Anabolic and Catabolic Hormones
Toth, Michael J
ABSTRACT. Background: Patients with chronic heart failure frequently experience profound wasting during the course of the disease, a condition termed cardiac cachexia. Although protein is the primary structural and functional component of most tissues, few studies have examined the effect of heart failure on protein metabolism. Moreover, no study has assessed the relationship of protein turnover to hormonal alterations thought to promote cachexia. Thus, our goal was to determine if whole-body protein metabolism is altered in heart failure patients and to assess the relationship of protein kinetics to circulating levels of anabolic and catabolic hormones. Methods: We measured whole-body protein metabolism using ^sup 13^C-leucine, body composition, and circulating anabolic and catabolic hormone levels in 10 patients with chronic heart failure and 11 elderly controls. Results: No differences in leucine rate of appearance, oxidation, or nonoxidative disposal were noted between heart failure patients and controls. However, in a subgroup of patients characterized by increased resting energy expenditure for their metabolic body size (n = 4; ≥20% above that predicted from fat-free mass), leucine rate of appearance (mean ± SE; 146 ± 6 µmol/min), an index of protein breakdown, tended to be higher compared with patients with normal resting energy expenditure (n = 5; 120 ± 8 µmol/min) and controls (127 ± 4 µmol/min; p = .06). Alterations in anabolic/catabolic hormone balance did not explain increased protein breakdown in this subgroup, and no correlations were found between hormone levels and protein breakdown in the heart failure group as a whole. In contrast, increased circulating interleukin-6 soluble receptor (r = 0.829; p
Patients with chronic heart failure (HF) frequently lose significant amounts of lean and fat tissue during the course of the disease, a syndrome known as cardiac cachexia.1 Cardiac cachexia has been shown to prolong hospitalization,2 increase complications after surgical procedures,3,4 and reduce physical work capacity.5,6 More importantly, it has been shown to be a strong predictor of mortality in HF patients.7 Despite these detrimental effects, we do not have a clear understanding of the etiology of cardiac cachexia.
Attention has focused in recent years on the potential role of circulating catabolic hormones and cytokines as mediators of cardiac cachexia.8-11 Evidence to support this notion has been provided by studies showing a more catabolic hormonal milieu in cachectic HF patients.9-12 Whether these alterations in hormone levels promote tissue wasting or are merely a consequence of the cachectic state, however, is not clear. A more compelling case for the involvement of these hormones in cardiac cachexia could be made by examining their relationship to the biochemical processes that regulate tissue mass. For most tissues in the body, protein is the primary functional and structural component. Thus, changes in tissue mass are dictated, in large part, by alterations in protein metabolism. To date, however, few studies have measured protein metabolism in HF patients, and no study has attempted to relate these measurements to circulating anabolic and catabolic hormones.
The primary goal of the present study was to examine protein metabolism in HF and to assess its relationship to circulating anabolic and catabolic hormones. We sought to test 2 hypotheses: first, that HF is characterized by increased rates of protein turnover. The rationale underlying this hypothesis is that elevated rates of protein turnover are a common feature of other pathologic conditions characterized by cachexia13-17 and the fact that hormonal alterations thought to cause cardiac cachexia increase protein turnover.18,19 Second, we hypothesized that elevated rates of protein metabolism would be associated with alterations in circulating hormones toward a more catabolic milieu. To accomplish our goals, we measured rates of whole-body protein turnover under postabsorptive conditions using ^sup 13^C-labeled leucine and circulating hormone levels in HF patients and elderly controls.
METHODS
Subjects
Ten male volunteers with chronic HF due to left ventricular systolic dysfunction were recruited from the Heart Failure Clinic of the Cardiology Unit of the University of Vermont (ejection fraction: 32% ± 4% by echocardiography). New York Heart Association (NYHA) functional class averaged 2.5 ± 0.2 with 6 patients in class II, 3 in class III, and 1 in class IV. HF was due to coronary artery disease in 7 patients, defined by a history of myocardial infarction or multivessel coronary obstructions at cardiac catheterization, idiopathic dilated cardiomyopathy in 2 patients, and dilated cardiomyopathy secondary to severe hypertension in 1 patient. HF patients were taking the following medications: diuretics (n = 10; 100%), digoxin (n = 7; 70%), angiotensin-converting enzyme inhibitors/receptor blockers (n = 9; 90%) and β-adrenergic blocking agents (n = 8; 80%). Three of the 9 patients had diabetes mellitus, 1 patient had insulin-dependent diabetes mellitus, and 2 patients had non-insulin-dependent diabetes mellitus (NIDDM). Patients were tested at least 6 months after inpatient management of their HF and were clinically stable and free of peripheral edema.
Eleven male volunteers were recruited to serve as controls. Nine of these volunteers were healthy and free of disease, had no signs or symptoms of heart disease, and had normal rest and exercise electrocardiograms. The control group also included 2 individuals with known coronary artery disease but normal left ventricular contraction patterns and no exertional ischemia, as demonstrated by a normal electrocardiographic stress test to exhaustion without angina. Both of these volunteers were treated with aspirin: 1 with a Ca^sup 2+^ channel blocker and the other with an HMG-CoA reductase inhibitor. Because inclusion of these 2 controls did not affect group differences (see Results section), they were retained in the control group. Other medications in the control group were antiepileptic (n = 1), antianxiety (n = 1), 5a-reductase inhibitor (n = 1) and nonsteroidal antiinflammatory (n = 1). Controls had no history of diabetes mellitus, normal fasting blood glucose
Protocol
Volunteers were tested during outpatient and inpatient visits to the University of Vermont General Clinical Research Center. Body composition and exercise capacity were measured on an outpatient basis at least 1 week prior to protein metabolism measurements. For 3 days prior to the inpatient admission, all subjects were provided a standardized, weight-maintenance diet (approximately 55% carbohydrate, 25% fat, 20% protein). Protein intake was standardized and was similar between HF patients and controls as a percentage of the total caloric content of the diet (HF: 19.3 ± 0.3 us control [C]: 19.7 ± 0.4%). All food was provided by the research center. The last meal of the standardized diet was consumed by 7 PM the evening of admission, and subjects were fasted until completion of testing the following day. Volunteers were asked to refrain from exercise the day before admission to prevent any residual effect of physical exercise on protein metabolism or circulating hormone levels. Medications were maintained for all volunteers per normal dosing regimens throughout testing.
On the morning of study, catheters were placed in an antecubital vein for infusion and retrograde in a dorsal hand vein of the contralateral arm. The hand was placed in a warming box to obtain arterialized venous blood. Blood and breath samples were drawn just before the start of infusion for the measurement of baseline ^sup 13^C enrichments. At approximately 6:30 AM, a primed (4.8 µmol/kg), continuous (5.6 µmol/kg/min) infusion of [1,2-^sup 13^C^sub 2^]leucine was started (time 0 minutes). The bicarbonate pool was primed (3.1 µmol/kg) with sodium [^sup 13^C]bicarbonate. Blood and breath samples were drawn at 165, 180, 195, and 210 minutes for measurement of whole-body leucine metabolism. Oxygen consumption and carbon dioxide production rates were determined at 60 and 195 minutes for 15 minutes using the ventilated hood technique (DeltaTrac, Yorba Linda, CA).
Whole-Body Leucine Metabolism
Analytical methods. Plasma leucine and ketoisocaproate (KIC) enrichments were measured by gas chromatography mass spectrometry (GCMS), as described previously.21,22 The amino and keto acids were isolated from plasma and derivatized to the N-heptafluorobutyryl, n-propyl (HFBP) amino acid esters and t-butyldimethylsilyl-quinoxalinol ketoacid derivatives, respectively.21,22 HFBP amino acid derivatives were measured by GCMS (model 5973, Agilent, Palo Alto, CA) by negative chemical ionization and selected monitoring of ions at m/z = 349 and 351 for unlabeled and [1,2-^sup 13^C^sub 2^]leucine, respectively. KIC was measured by electron impact ionization GCMS (model 5971A, Hewlett-Packard, Palo Alto, CA) with selected monitoring of ions at m/z = 259 and 260 for unlabeled and [1,2-^sup 13^C^sub 2^]KIC, respectively. For each measurement, the background corrected enrichment was calculated. The ^sup 13^C enrichment of expired CO2 was measured from breath samples by isotope ratio mass spectrometry (VG SIRA II, Middlewich, Cheshire, United Kingdom).
Calculations. Plasma KIC and breath CO2 enrichments from 165, 180, 195, and 210 minutes were used to calculate whole-body leucine kinetics, as described previously.23 The rate of appearance (R^sub a^) of leucine into plasma (µmol/min), an index of protein breakdown, was calculated as: R^sub a^ = I (E^sub i^/E^sub p^ – 1), where I is the infusion rate (µmol/min) of the tracer, and E^sub i^ and E^sub p^ are the enrichment (mpe) of tracer in the infusate and plasma, respectively. Plasma KIC enrichment, a proxy of intracellular leucine enrichment, was used for E^sub p^. Leucine oxidation (C), an index of protein oxidation, was calculated as: C = F^sub 13C^/2 (1/E^sub p^ – 1/E^sub i^) × 100, where F^sub 13C^ is the rate of ^sup 13^CO^sub 2^ excretion into expired air (µmol ^sup 13^C/h) and the constant 2 accounts for the 2 ^sup 13^C labels in the leucine molecule. F^sub 13C^ was calculated as: F^sub 13C^ = F^sub CO2^ × E^sub CO2^/[(60 × 44.6)/(100 × 0.81/0.79)], where F^sub CO2^ is the CO2 production rate (cm^sup 3^/min) and E^sub CO2^ is the enrichment of expired CO2 (mpe × 1000). The constants 60 (min/h) and 44.6 (µmol/cm^sup 3^) convert F^sub CO2^ to µmol/h, the factor 100 changes atom percent excess to a fraction, 0.81 accounts for the retention of ^sup 13^CO^sub 2^ in the bicarbonate pool, and 0.79 for the loss of the 2-^sup 13^C label of leucine in tricarboxylic acid cycle exchange reactions.23 Nonoxidative leucine disposal, an index of protein synthesis, was calculated from the difference between leucine rate of appearance and oxidation. Indirect calorimetry was not performed on one HF patient because of abnormal breathing patterns related to claustrophobia caused by the ventilated hood. Thus, oxidative and nonoxidative leucine disposal data were not calculated on this patient.
Body Composition
Body mass was measured on a digital scale (Scale-Tronix, Inc, Wheaton, IL). Fat mass and fat-free mass were measured by dual energy x-ray absorptiometry, using a Lunar DPX-L densitometer (Lunar Co, Madison, WI).
Hormones and Cytokines
Norepinephrine and epinephrine levels were measured using a radioenzymatic assay, as described previously,24 with an interassay coefficient of variability (CV) of 10% for norepinephrine and 16% for epinephrine. Norepinephrine data for one HF patient were omitted as an outlier (10.2 pg/mL). Insulin levels were determined by radioimmunoassay (Linco Research Inc, St. Charles, MO), with an interassay CV of 7.4%. Insulin-like growth factor-1 (IGF-1) was measured by radioimmunoassay (ALPCO, Windham, NH) with an interassay CV of 5%. C-reactive protein (CRP) was measured by enzyme-linked immunosorbent assay (ELISA), with an interassay CV ranging from 2% to 4%. TNF-α and IL-6 plasma concentrations and soluble receptors levels (TNF-α, sRII and IL-6 sR, respectively) were measured by ultra-sensitive ELISA assays (R&D Systems, Minneapolis, MN) with interassay CVs of 16% and 6% for TNF-α and IL-6 concentration and 9% and 10% for their respective receptors. Cortisol was determined by radioimmunoassay (Diagnostic Products Co, Los Angeles, CA), with an interassay CV of 8.7%.
Resting Energy Expenditure (REE)
REE and substrate use were measured using the ventilated hood technique (DeltaTrac). The average of measurements taken at 60 and 195 minutes during the infusion was calculated. Leucine oxidation data were used to estimate protein oxidation, and rates of carbohydrate and fat oxidation (mg/min) were calculated according to standard equations, as described previously.25 In addition to direct measurements, we predicted REE from fat-free mass (kg) in all volunteers according to the equation: REE (kcal/d) = (14 × fat-free mass) + 804.26 HF patients were then divided into groups with normal or elevated (high) REE for their metabolic body size. High REE was denned as ≥20% above predicted values. The basis for using 20% as a cutoff was derived from previous studies that have shown elevations in REE in HF patients on the order of 15%.27 Thus, a cutoff of ≥20% should be sufficient to identify those patients with resting hypermetabolism. Indirect calorimetry was not performed on one HF patient, because of abnormal breathing patterns related to claustrophobia caused by the ventilated hood.
Statistical Analysis
Differences between groups were determined by unpaired, Student’s t-tests or Mann-Whitney U tests, depending on the distributional characteristics of the variable. For subgroup analyses, parametric or nonparametric (Kruskal-Wallis H statistic) analysis of variance models were used. Individual leucine metabolism and REE data were statistically adjusted for fat-free mass using a regression-based approach, as described previously.28 Relationships between variables were assessed using Pearson correlation coefficients. Variables that were not normally distributed (eg, cytokines) were log^sub 10^ transformed before correlation analysis. Distributional assumptions of log^sub 10^-transformed variables were verified using the Shapiro-Wilk test. All analyses were conducted with SPSS software 9.0 (SPSS Inc, Chicago, IL). All values are mean ± SE.
RESULTS
Physical characteristics are shown in Table I. No differences in age, body size, or body composition were noted between groups. Similar results were observed when 2 controls with coronary artery disease were excluded from the analyses. Peak aerobic capacity (VO^sub 2^) was lower (p
Leucine metabolism data, statistically adjusted for differences in fat-free mass, are shown in Figure 1. No differences in leucine rate of appearance (HF: 132 ± 8 vs control (C): 127 ± 4 µmol/min), nonoxidative leucine disposal (HF: 111 ± 8 vs C: 106 ± 4 µmol/min), or leucine oxidation (HF: 20 ± 2 vs C: 21 ± 2 µmol/min) were found between groups. Similar results were observed when 2 controls with coronary artery disease were excluded from the analyses. No differences were found between groups in plasma leucine concentration (HF: 120 ± 7 vs C: 131 ± 6 µmol/L).
Although REE was slightly elevated in HF patients compared with controls (HF: 1887 ± 80 vs C: 1664 ± 82), this difference did not reach statistical significance (p = .12). The magnitude elevation in REE, however, is similar to what we have reported previously in a group of HF patients with similar body size and composition.29 No differences in respiratory quotient (HF: 0.791 ± 0.021 vs C: 0.783 ± 0.011), carbohydrate (HF: 87 ± 21 vs C: 75 ± 10 mg/min), or fat oxidation (HF: 97 ± 11 us C: 91 ± 7 mg/min) were noted.
We divided HF patients into groups with normal and high REE for their metabolic body size (ie, fat-free mass) in an attempt to identify patients with elevated rates of protein turnover. Criteria for these group designations are provided in the REE portion of the Methods section. We identified 4 patients with high and 5 with normal REE (Table II). REE was greater (p
Leucine metabolic data in patients with high REE and normal REE and controls are shown in Figure 2. All measures were statistically adjusted for fat-free mass. Leucine rate of appearance showed a strong trend (p = .06) toward being greater in high REE (146 ± 6) compared with normal REE (120 ± 8) and controls (127 ± 4 µmol/min). No differences in leucine oxidation (high REE: 23 ± 2 vs normal REE: 19 ± 2 vs C: 21 ± 2 µmol/min; p = .49) or nonoxidative disposal (high REE: 123 ± 5 vs normal REE: 101 ± 12 vs C: 106 ± 4 µmol/min; p = .16) were found.
Differences in circulating anabolic and catabolic hormones and cytokines among HF patients with high REE, normal REE, and controls are shown in Table III. No differences were found in norepinephrine, epinephrine, insulin, CRP, tumor necrosis factor (TNF)-α, or cortisol levels among groups. IGF-1 was greater in normal-REE patients compared with high-REE patients and controls (p = .02). Both TNF-α RII and IL-6sR were greater in normal- and high-REE patients compared with controls (both p
Correlations between leucine metabolism variables and hormones in HF patients are shown in Table IV. IGF-1 levels were negatively related to leucine oxidation (p
DISCUSSION
Increased rates of protein turnover were not evident when HF patients were considered as a single homogenous group, suggesting that HF, per se, does not alter protein metabolism. Despite the absence of group differences, there was considerable variability in protein metabolism in HF patients (eg, variance of protein breakdown and synthesis was twice that of controls). To explore the nature of this variability and to identify patients that might exhibit elevated rates of protein turnover, we divided patients into groups with normal and elevated REE for their metabolic body size. The rationale behind this approach is that increased REE often accompanies increased protein turnover in disease states characterized by cachexia.30,31 This may be due to the fact that catabolic hormones thought to induce cardiac cachexia increase both thermogenesis and protein turnover.18,19 After patients were divided into groups with normal and high REE, a strong trend (p = .06) toward increased (15%-22%) protein breakdown was found in those patients with resting hypermetabolism. Although these group differences did not reach statistical significance, the magnitude increase in protein breakdown was similar to findings in other cachectic conditions.17,32 This type of heterogeneity in protein metabolism among HF patients is consistent with variability in protein turnover observed in other disease states characterized by cachexia.33 More importantly, the fact that increased REE and protein breakdown segregate in this subgroup of patients suggests that they could be at greater risk for developing cachexia.
Our findings differ from other studies that have found reduced whole-body protein turnover in HF.34,35 Divergent results among studies are likely due to the different conditions under which protein metabolism was measured. Morrison et al34 found reduced rates of protein turnover in HF patients studied after inpatient clinical management. Because hospitalization is accompanied by bedrest and malnutrition,36 2 factors known to decrease protein turnover,37,38 their results may relate to factors associated with hospitalization, rather than HF. Cortes et al35 found a lower rate of whole-body protein synthesis in HF patients compared with controls during 10 hours of oral feeding. These findings are not directly comparable to our study or the work of Morrison et al,34 however, because these studies examined postabsorptive protein metabolism. Thus, variability in the conditions under which measurements have been conducted prevents conclusions from being drawn regarding the effect of HF on protein metabolism.
We further explored the role of circulating hormones and cytokines on the regulation of whole-body protein metabolism. No differences in hormone levels were found between high-REE and normal-REE groups to explain elevated protein breakdown in the high REE group. Furthermore, in the HF group as a whole, correlations between hormone levels and leucine rate of appearance were not found. Thus, our results argue against the notion that changes in circulating anabolic or catabolic hormones contribute to altered whole-body protein turnover or hypermetabolism.
In contrast to leucine appearance data, leucine oxidation was negatively related to circulating IGF-1 levels. This relationship is in keeping with the effect of IGF-1 to reduce leucine oxidation39,40 and suggests that reductions in plasma IGF-1 within the HF population could promote the irreversible loss of protein substrates (ie, increased amino acid oxidation). Such a role for IGF-1 in the regulation of protein metabolism may be relevant to the development of cardiac cachexia in HF patients, considering that some patients exhibit reduced circulating IGF-19 and a diminished IGF-1 response to GH administration.41 Indeed, these patients are characterized by reduced protein reserves and other features of cardiac cachexia.9
We also found a positive relationship between circulating IL-GsR levels and leucine oxidation in HF patients. The role of the IL-6 system in the development of cachexia has been demonstrated repeatedly.42,43 The reason why IL-6sR, rather than IL-6, correlated with leucine oxidation, however, is not readily apparent. Recent studies suggest that IL-6sR may facilitate some of the effects of IL-6 at the cellular level.44 Moreover, because a portion of circulating IL-6sR is generated from proteolytic cleavage of the membrane-bound receptor,44 it may more closely reflect the action of IL-6 at the level of target tissues. Assuming that circulating IL-6sR levels are, at minimum, an indicator of immune activation in HF patients, the nature of their association to leucine oxidation agrees with studies showing that cytokines increase protein oxidation.45,46 Increasing IL-6 and its soluble receptor with increasing disease severity in HF47 could stimulate protein oxidation.
What do our results reveal about the effect of HF on protein metabolism and their implications for nutrition management? An answer to this question requires that we consider the population from which the data were gathered. Our HF patients had relatively mild to moderate disease and were not characterized by cachexia. Although some might consider it a limitation that we did not study cachectic patients, we view this as a strength of our study because we are certain that alterations in protein metabolism are not a consequence of the cachectic state. In this context, we have identified a subgroup of patients relatively early in the course of the disease that are characterized by resting hypermetabolism and increased protein turnover.33 Studies are needed to determine whether this phenotype predicts which HF patients develop cachexia over time. Importantly, alterations in anabolic and catabolic hormone levels widely believed to be a cause of cardiac cachexia11 were not present in those patients with resting hypermetabolism and increased protein turnover. This finding would challenge the notion that endocrine abnormalities associated with the HF syndrome predispose to metabolic abnormalities associated with cachexia, namely, hypermetabolism and excessive protein breakdown. We should point out, however, that endocrine alterations would favor the irreversible disposal of protein substrates (ie, amino acid oxidation) in HF patients (Figure 3). Thus, there may indeed be some role for hormonal abnormalities in the development of cardiac cachexia. The alterations in energy and protein metabolism observed in the present study may reflect a constellation of metabolic adaptations to the HF syndrome that predispose to cachexia as the disease progresses, although this hypothesis requires careful longitudinal study for confirmation. Identification of such patients may be advantageous because early nutrition intervention may mitigate the loss of body mass and protein reserves and, in turn, reduce mortality associated with cachexia.7
In summary, our results suggest that altered wholebody protein metabolism is not a generalized feature of the HF syndrome. In a subgroup of patients characterized by resting hypermetabolism, however, there is a trend toward elevated rates of protein turnover. Although we were unable to identify any circulating hormones that were related to increased protein turnover in HF patients, we found correlations between leucine oxidation and both circulating IGF-I and IL-GsR. Future studies that examine protein metabolism and hormone levels in HF patients over time or under catabolic conditions are needed to clarify whether these metabolic alterations predispose to the development of cardiac cachexia.
ACKNOWLEDGMENTS
The authors thank all the participants who volunteered their time for the study. The work was supported by grants from the National Institutes of Health (AM-02125; AG-17494; RR-00109) and American Federation for Aging Research.
REFERENCES
1. Pitman JG, Cohen P. The pathogenesis of cardiac cachexia. N Engl J Med. 1964;271:403-409.
2. Rich MW, Keller AJ, Schechtman KB, Marshall WG, Kouchoukos NT. Increased complications and prolonged hospital stay in elderly cardiac surgical patients with low serum albumin. Am J Cardlol. 1989;63:714-718.
3. Blackburn GL, Gibbons GW, Bothe A, Benotti PN, Harken DE, McEnany TM. Nutritional support in cardiac cachexia. J Thoracic Cardiovasc Surg. 1977;73:489-494.
4. Abel RM, Fischer JE, Buckley MJ, Barnett O, Austen WJ. Malnutrition in cardiac surgical patients: results of a prospective, randomized evaluation of early postoperative parenteral nutrition. Arch Surg. 1976;111:45-50.
5. Toth MJ, Gottlieb SS, Fisher ML, Poehlman ET. Skeletal muscle atrophy and peak oxygen consumption in heart failure. Am J Cardiol. 1997;79:1267-1269.
6. Harrington D, Anker S, Chua TP, et al. Skeletal muscle function and its relation to exercise tolerance in chronic heart failure. J Am Coll Cardiol. 1997;30:1758-1764.
7. Anker SD, Ponikowski P, Varney S, et al. Wasting as independent risk factor for mortality in chronic heart failure. Lancet. 1997;349:1050-1053.
8. Gosker HR, Wouters EFM, van der Vusse GJ, Schols AM. Skeletal muscle dysfunction in chronic obstructive pulmonary disease and chronic heart failure: underlying mechanims and therapy perspectives. Am J Clin Nutr. 2000;71:1033-1047.
9. Niebauer J, Pflaum CD, Clark AL, et al. Deficient insulin-like growth factor I in chronic heart failure predicts altered body composition, anabolic deficiency, cytokine and neurohumoral activation. J Am Coll Cardiol. 1998;32:393-397.
10. Anker SD, Clark AL, Kemp M, et al. Tumor necrosis factor and steroid metabolism in chronic heart failure: possible relation to muscle wasting. JAm Coll Cardiol. 1997;30:997-1001.
11. Anker SD, Chua TP, Ponikowski P, et al. Hormonal changes and catabolic/anabolic imbalance in chronic heart failure and their importance for cardiac cachexia. Circulation. 1997;96:526-534.
12. Anker SD, Ponikowski PP, Clark AL, et al. Cytokines and neurohormones relating to body composition alterations in the wasting syndrome of chronic heart failure. Eur Heart J. 1999;20:683-693.
13. Melville S, McNurlan MA, Calder AG, Garlick PJ. Increased protein turnover despite normal energy metabolism and responses to feeding in patients with lung cancer. Cancer Res. 1990;50:1125-1131.
14. Birkhahn RH, Long CL, Fitkin D, Geiger JW, Blakemore WS. Effects of major skeletal trauma on whole body protein turnover in man measured by L-[^sup 14^C]-leucine. Surgery. 1980;88:294-300.
15. Kien CL, Young VR, Rohrbaugh DK, Burke JF. Increased rates of whole body protein synthesis and breakdown in children recovering from burns. Ann Surg. 1978;187:383-391.
16. Yarasheski KE, Zachwieja JJ, Gischler J, Crowley J, Horgan MM, Powderly WG. Increased plasma GIn and Leu Ra and inappropriately low muscle protein synthesis rates in AIDS wasting. Am J Physiol. 1998;275:E577-E583.
17. Rail LC, Rosen CJ, Dolnikowski G, et al. Protein metabolism in rheumatoid arthritis and aging: effect of muscle strength training and tumor necrosis factor alpha. Arthritis Rheum. 1996;39: 1115-1124.
18. Matthews DE, Battezzati A. Regulation of protein metabolism during stress. Curr Opin Gen Surg. 1993;72-77.
19. Brillon DJ, Zheng B, Campbell RG, Matthews DE. Effect of cortisol on energy expenditure and amino acid metabolism in humans. Am J Physiol. 1995;268:E501-E513.
20. Toth MJ, Matthews DE, Ades PA, et al. Skeletal muscle myofibrillar protein metabolism in heart failure: relationship to immune activation and functional capacity. Am J Physiol. 2005; 288:E685-E692.
21. Matthews DE, Harkin R, Battezzati A, Brillon DJ. Splanchnic bed utilization of enterai alpha-ketoisocaproate in humans. Metab Clin Exp. 1999;48:1555-1563.
22. Matthews DE, Pesola G, Campbell RG. Effect of epinephrine on amino acid and energy metabolism in humans. Am J Physiol. 1990;258:E948-E956.
23. Toth MJ, MacCoss MJ, Poehlman ET, Matthews DE. Recovery of ^sup 13^CO^sub 2^ from infused [1-^sup 13^C]leucine and [1,2-^sup 13^C^sub 2^]leucine in healthy humans. Am J Physiol. 2001;281:E233-E241.
24. Kennedy RH, Ziegler MG. A more sensitive and specific radioenzymatic assay for catecholamines. Life Sci. 1990;47:2143-2153.
25. Toth MJ, Sites CK, Cefalu WT, Matthews DE, Poehlman ET. Determinants of insulin-stimulated glucose disposal in middleaged, premenopausal women. Am J Physiol. 2001;281:E113-E121.
26. Arciero PJ, Goran MI, Gardner AW, Ades PA, Tyzbir RS, Poehlman ET. A practical equation to predict resting metabolic rate in older men. Metabolism. 1993;42:950-957.
27. Riley M, Elborn JS, McKane WR, Bell N, Stanford CF, Nicholls DP. Resting energy expenditure in chronic cardiac failure. Clin Sci. 1991;80:633-639.
28. Toth MJ, Goran MI, Ades PA, Howard DB, Poehlman ET. Examination of data normalization procedures for expressing peak VO^sub 2^ data. J Appl Physiol. 1993;75:2288-2292.
29. Toth MJ, Gottlieb SS, Goran MI, Fisher ML, Poehlman ET. Daily energy expenditure in free-living heart failure patients. Am J Physiol. 1997;272:E469-E475.
30. Roubenoff R, Roubenoff RA, Cannon JG, et al. Rheumatoid cachexia: cytokine-driven hypermetabolism accompanying reduced body cell mass in chronic inflammation. J Clin Invest. 1993;93:2379-2386.
31. Grunfeld C, Pang M, Shimizu L, Shigenaga JK, Jensen P, Feingold KR. Resting energy expenditure, caloric intake, and shortterm weight change in human immunodeficiency virus infection and the acquired immunodeficiency syndrome. Am J Clin Nutr. 1992;55:455-460.
32. Macallan DC, McNurlan MA, Milne E, Calder AG, Garlick PJ, Griffin GE. Whole-body protein turnover from leucine kinetics and the response to nutrition in human immunodeficiency virus infection. Am J Clin Nutr. 1995;61:818-826.
33. Biolo G, Antonione R, Barazzoni R, Zanettic M, Guarnieri G. Mechanisms of altered protein turnover in chronic diseases: a review of human kinetic studies. Curr Opin Clin Nutr Metab Care. 2003;6:55-63.
34. Morrison WL, Gibson JNA, Rennie MJ. Skeletal muscle and whole body protein turnover in cardiac cachexia: influence of branched-chain amino acid administration. Eur J Clin Invest. 1988;18:648-654.
35. Cortes CW, Thompson PD, Moyna NM, et al. Protein kinetics in stable heart failure patients. J Appl Physiol. 2003;94:295-300.
36. Weinsier RL, Hunker EM, Krumdieck CL, Butterworth CE. Hospital malnutrition: a prospective evaluation of general medical patients during the course of hospitalization. Am J Clin Nutr. 1979;32:418-426.
37. Bettany GEA, Ang BC, Georgiannos SN, Halliday D, Powell-Tuck J. Bed rest decreases whole-body protein turnover in post-absorptive man. Clin Sci. 1996;90:73-75.
38. Stein TP, Rumpler WV, Leskiw MJ, Schluter MD, Staples R, Bodwell CE. Effect of reduced dietary intake on energy expenditure, protein turnover and glucose cycling in man. Metab Clin Exp. 1991;40:478-483.
39. Turkalj I, Keller U, Ninnis R, Vosmeer S, Stauffacher W. Effect of increasing doses of recombinant human insulin-like growth factor-1 on glucose, lipid and leucine metabolism in man. J Clin Endocr Metab. 1992;75:1186-1191.
40. Hussain MA, Schmitz O, Mengel A, et al. Insulin-like growth factor I stimulates lipid oxidation, reduces protein oxidation and enhance insulin sensitivity. J Clin Invest. 1993;92:2249-2256.
41. Anker SD, Volterrani M, Pflaum CD, et al. Acquired growth hormone resistance in patients with chronic heart failure: implications for therapy with growth hormone. J Am Coll Cardiol. 2001;38:443-452.
42. Strassmann G, Fong M, Kenney JS, Jacob CO. Evidence for the involvement of interleukin 6 in experimental cancer cachexia. J Clin Invest. 1992;89:1681-1684.
43. Tsujinaka T, Fujita J, Ebisui C, et al. Interleukin 6 receptor antibody inhibits muscle atrophy and modulates proteolytic systems in interleukin 6 transgenic mice. J Clin Invest. 1996;97: 244-249.
44. Jones SA, Rose-John S. The role of soluble receptors in cytokine biology: the agonist properties of the sIL-6R/IL-6 complex. Biochim Biophys Acta. 2002;1592:251-263.
45. Sakurai Y, Zhang XJ, Wolfe RR. TNF directly stimulates glucose uptake and leucine oxidation and inhibits FFA flux in conscious dogs. Am J Physiol. 1996;270:E864-E872.
46. Garcia-Martinez C, Llovera M, Lopez-Soriano FJ, del Santo B, Argiles JM. Lipopolysaccharide (LPS) increases the in vivo oxidation of branched-chain amino acids in the rat: a cytokine effect. Mol Cell Biochem. 1995;148:9-15.
47. Tsutamoto T, Hisanaga T, Wada A, et al. Interleukin-6 spillover in the peripheral circulation increases with the severity of heart failure, and the high plasma level of interleukin-6 is an important prognostic predictor in patients with congestive heart failure. J Am Coll Cardiol. 1998;31:391-398.
Michael J. Toth, PhD; and Dwight E. Matthews, PhD
From the Department of Medicine, University of Vermont, Burlington, Vermont
Received for publication August 5, 2005.
Accepted for publication December 29, 2005.
Correspondence: Michael J. Toth, PhD, Health Science Research Facility 126 B, 149 Beaumont Ave., University of Vermont, Burlington, VT 05405. Electronic mail may be sent to michael.toth@uvm.edu.
Copyright American Society for Parenteral and Enteral Nutrition May/Jun 2006
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