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Keywords:

  • body composition;
  • body fat;
  • lean tissue;
  • palliative care;
  • cancer cachexia

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

BACKGROUND

Several investigations that yielded different results in terms of net changes in body composition of weight-losing cancer patients have been reported that employed a variety of methods based on fundamentally different technology. Most of those reports were cross-sectional, whereas to the authors' knowledge there is sparse information available on longitudinal follow-up measurements in relation to other independent methods for the assessment of metabolism and performance.

METHODS

For the current report, the authors evaluated time course changes in body composition (dual-energy X-ray absorptiometry) with measurements of whole body and regional distribution of fat and lean tissue in relation to food and dietary intake, host metabolism (indirect calorimetry), maximum exercise capacity (walking test), and circulating hormones in cancer patients who were receiving palliative care during 4–62 months of follow-up. The entire cohort comprised 311 patients, ages 68 years ± 3 years who were diagnosed with solid gastrointestinal tumors (84 colorectal tumors, 74 pancreatic tumors, 73 upper gastrointestinal tumors, 51 liver-biliary tumors, 3 breast tumors, 5 melanomas, and 21 other tumor types).

RESULTS

Decreased body weight was explained by loss of body fat, preferentially from the trunk, followed by leg tissue and arm tissue, respectively. Lean tissue (fat-free mass) was lost from arm tissue, whereas trunk and leg tissue compartments increased, all concomitant with declines in serum albumin, increased systemic inflammation (C-reactive protein, erythrocyte sedimentation rate), increased serum insulin, and elevated daily caloric intake; whereas serum insulin-like growth factor 1 (IGF-1), resting energy expenditure, and maximum exercise capacity remained unchanged in the same patients. Serum albumin levels (P < 0.001), whole body fat (P < 0.02), and caloric intake (P < 0.001) predicted survival, whereas lean tissue mass did not. Daily intake of fat and carbohydrate was more important for predicting survival than protein intake. Survival also was predicted by serum IGF-1, insulin, leptin, and ghrelin levels (P < 0.02 – P < 0.001). Serum insulin, leptin, and ghrelin (total) levels predicted body fat (P < 0.001), whereas IGF-1 and thyroid hormone levels (T3, free T3) predicted lean tissue mass (P < 0.01). Systemic inflammation primarily explained variation in lean tissue and secondarily explained loss in body fat. Depletion of lean arm tissue was related most to short survival compared with the depletion of lean leg and trunk tissue.

CONCLUSIONS

The current results demonstrated that body fat was lost more rapidly than lean tissue in progressive cancer cachexia, a phenomenon that was related highly to alterations in the levels of circulating classic hormones and food intake, including both caloric amount and diet composition. The results showed importance in the planning of efficient palliative treatment for cancer patients. Cancer 2005. © 2005 American Cancer Society.

The precise mechanism behind weight loss in patients with progressive cancer remains unclear, although several reports have confirmed that elevated resting energy expenditure (REE) and decreased energy intake are significant factors in large numbers of cancer patients who lose weight.1–6 Recently, we evaluated the role of anorexia for negative energy balance in unselected cancer patients.7 Cancer patients who lost weight appeared to have lost a tight coupling between food intake and resting expenditure that is found normally in healthy individuals. Thus, inappropriately low intake, accounting for resting and total energy expenditure, explained weight loss during disease progression with subsequent alteration in body composition.8 In such patients, it has been assumed that the loss of lean tissue is an early and most important change,9 whereas recent analyses in our laboratory have indicated the loss of body fat as the most prominent among alterations that may lead to functional and cognitive alterations in health-related quality of life, although fat stores generally are regarded only as reserves of energy. Fat depots, thus, may be equally as important as skeletal muscles and other lean tissues, considering their strategic function as a possible sensor of energy status and well being in the organism.10 Therefore, the objective of the current analysis was to assess time course changes in regional body fat and lean tissue compartments in relation to other alterations in cancer cachexia, such as systemic inflammation,11 food intake,7 exercise capacity,12 whole body substrate oxidation,2 and circulating levels of hormones of known importance in cachexia among unselected, weight losing cancer patients who are receiving palliative care.13

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

Patients

Clinical and metabolic data on cancer patients are collected consecutively in a data base at the Department of Surgery at Sahlgrenska University Hospital (Göteborg, Sweden). These patients are referred for palliative care from the western region of Sweden, which has 2–3 million inhabitants. A computer data base search was performed to identify patients who had at least 1 measurement of body composition in relation to biochemical, metabolic, physiologic, and nutritional tests between 1995 and 2003 for cross-sectional evaluations (n = 311 patients). Among these 311 patients, we identified 132 cancer patients who had at least 2 and an average of 3 measurements of body composition during follow-up (range, 4–62 months). These patients were used for analysis of longitudinal relations between metabolism, physiologic functioning, nutrition, and body composition in cancer patients on evidence-based palliation (systemic antiinflammatory treatment,11 recombinant erythropoietin treatment,12, 14 and specialized nutritional support care13). All patients had generalized malignancy (T4N1M1) with a solid tumor type for which efficient tumor treatment was not available. Gastrointestinal tumors were predominant (Table 1). All patients had insidious or manifest malnutrition (Table 2), and they had been invited to participate in follow-up measurements with biochemical tests every second month and to measurements of body composition, whole body metabolism, and exercise tests every fourth month within randomized, controlled studies.12, 13 All patients were followed until the preterminal phase, with follow-up that ranged between 1 month and 62 months (n = 311 patients). They had received appropriate pain treatment and systemic antiinflammatory medication (indomethacin at a dose of 50 mg × 2) from the inclusion phase until the preterminal phase, as described previously.11 Some patients (n = 50 patients) received recombinant erythropoietin in addition to indomethacin when blood hemoglobin levels became subnormal (< 125 g/L), as described previously,12 and additional patients (n = 141 patients) had received special nutritional support care when their caloric intake became reduced below 90% of resting needs in addition to indomethacin and erythropoietin treatment.13 None of our patients received radiochemotherapy during follow-up or had received any other specific tumor treatment within 6 months of the start of our evaluations. The cohort under longitudinal observations represents a mix of patients with (≈ 50%) and without nutritional support,13 whereas antiinflammatory treatment occurred in all patients, and erythropoietin was provided to all individuals who needed correction of subnormal blood hemoglobin concentrations.12 Thus, our results do not reflect entirely spontaneous alterations in body composition during disease progression; rather, they represent an integrative view of biochemical data, physiologic state, and nutritional state over time according to evidence-based treatment offered to unselected and heterogeneous groups of cancer patients. Informed consent was obtained from all patients, and the Committee for Ethics at the Faculty of Medicine, Göteborg University approved the study protocol.

Table 1. The Number of Unselected Cancer Patients who Experienced Repeated Dual-Energy X-Ray Absorptiometry Measurements during Follow-Up (Range, 4–62 months) According to Major Clinical and Histologic Classificationsa
DiagnosisCohort of all patientsCohort of patients who had repeated DEXA measurements
No. of patients%No. of patients%
  • DEXA: dual-energy X-ray absorptiometry; GI: gastrointestinal.

  • a

    All patients had generalized disease.

  • b

    Sarcomas of unknown origin.

Colorectal carcinoma84274433
Pancreatic carcinoma74242217
Upper GI carcinomas73233224
Liver, biliary carcinoma51162519
Breast carcinoma3< 11< 1
Head and neck carcinomas1< 11< 1
Melanoma5222
Othersb20654
Total311100132100
Table 2. Anthropometry, Physiologic, and Metabolic Data and Biochemical Test Results at the Time Study Patients were Includeda
VariableCohort of all patients (n = 311)Cohort of patients who had repeated DEXA measurements
  • DEXA: dual-energy X-ray absorptiometry; BMI: body mass index; ASAT: aspartate aminotransferase; ALAT: alanine aminotransferase; S-FE: serum iron; TIBC: transferrin iron-binding capacity; IGF-1: insulin-like growth factor 1.

  • a

    Values shown are the mean ± standard error.

Age (yrs)68 ± 366 ± 3
Normal weight (kg)75 ± 1476 ± 14
BMI  
 Before disease (kg/m2)25.7 ± 0.325.6 ± 0.2
 At inclusion (kg/m2)23.6 ± 0.223.5 ± 0.2
 Weight loss at admission (%)10 ± 98 ± 9
Weight loss at admission (kg)8 ± 56 ± 5
Metabolic and nutritional measures  
 Body temp (°C)36.8 ± 0.536.7 ± 0.4
 Predicted resting energy expended (Kcal)1370 ± 2281411 ± 230
 Daily caloric intake (Kcal)1714 ± 6211812 ± 549
 Triceps skinfold (mm)11.6 ± 5.611.8 ± 5.0
 Arm muscle circumference (cm)23.4 ± 3.123.9 ± 3.6
 Total body fat (kg)17,379 ± 905318,430 ± 8543
 Lean body mass (kg)46,544 ± 940546,952 ± 9661
Physiologic measures  
 Heart rate (beats/min)76 ± 1371 ± 11
 Systolic blood pressure (mm/Hg)137 ± 22140 ± 22
 Diabolic blood pressure (mm/Hg)78 ± 1078 ± 10
 Maximum exercise capacity (watt)76 ± 4090 ± 40
Blood/serum/plasma concentration  
 Serum creatinine (μmol/L)92 ± 2591 ± 25
 Bilirubin (μmol/L)16 ± 510 ± 8
 Alkaline phosphatase (μkat/L)9.8 ± 116.6 ± 8.5
 ASAT (μkat/L)0.8 ± 1.00.5 ± 0.4
 ALAT (μkat/L)0.7 ± 0.70.6 ± 0.5
 Hemoglobin (g/L)117 ± 16119 ± 15
 Erythrocyte sedimentation rate (mm/hr)43 ± 3036 ± 27
 C-reactive protein (μmol/L)45 ± 5428 ± 42
 Albumin (g/L)33 ± 635 ± 5
 S-FE (μmol/L)9 ± 510 ± 5
 TIBC (μmol/L)51 ± 1156 ± 11
 Insulin (mU/L)13 ± 1113 ± 11
 IGF-1 (μg/L)114 ± 68137 ± 73
 Free T3 (pmol/L)3.8 ± 2.43.8 ± 1.3
 Total T3 (pmol/L)1.5 ± 0.41.6 ± 0.4
 Leptin (ng/mL)8 ± 88 ± 9
 Ghrelin (total pg/mL)782 ± 440794 ± 460
 Erythropoietin (IU/L)20 ± 1417 ± 10

Dietary Intake

Patients were instructed to complete a 4-day dietary record at home. Two weekend days were included to observe variations in food intake that may have occurred between weekdays and weekends. The amounts of all food and beverages were recorded in household measures. Preparation procedures were noted. Patients were asked to clarify incomplete recording and to estimate serving sizes with the aid of photographs.15 The household measures were converted into grams using the Swedish Food Weight Tables. Intake of energy and nutrients was calculated using the software Kostsvar (AIVO AB, Stockholm, Sweden). The nutrient data base used was the Swedish National Food Composition Tables,16 which accounts for nutrient loss during food preparation. Dietary records were validated repeatedly by measurements of total urinary nitrogen.17

Energy Expenditure

REE was determined in the morning after an overnight fast, from 8:00 AM to 9:00 AM or between 10:00 AM and 11.30 AM at our institution, by indirect calorimetry (Deltatrat; Datex, Helsinki, Finland), which was performed in the supine position according to standard criteria, as described elsewhere.1, 2, 18

Nutritional State

Body weight was recorded in light, indoor clothing on a digital electronic scale. Patients were asked to state their habitual weight before the onset of disease. Weight loss was calculated as the difference between current body weight and habitual body weight and was expressed as a percentage. Body height was measured in centimeters using a wall-mounted stadiometer. Body composition was measured by the dual-energy X-ray absorptiometry (DEXA) technique, as described elsewhere.7, 12

Blood Tests

Blood tests, such as serum albumin, blood hemoglobin, erythrocyte sedimentation rate, C-reactive protein, liver function tests (aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, serum bilirubin) and serum creatinine and thyroid hormones (total T3; free T3) all were routine hospital tests. Radioimmunoassays were used for measurements of serum insulin (Linco Research Inc., St. Charles, MO), insulin-like growth factor 1 (IGF-1) (Mediagnost, Reutlinger, Germany), leptin (Linco Research Inc.), and ghrelin (total) (Phoenix Peptides, Karlsruhe, Germany).

Exercise Capacity: Exercise Test

The exercise started with the patient standing on the treadmill with all equipment connected for 1 minute and thereafter walking 1.5 km per hour for 2 minutes. The test continued with walking at 1.5 km per hour at a 12% elevation (α = 6.9 °) for 1 minute; thereafter, the speed was increased 0.1 km per hour every 10th second until the patient finished the test. The speed at which the patient finished the test was defined as maximal exercise power. The maximal mechanical power (W) at exercise was calculated automatically by the software provided by the manufacturer (Cardionics Inc., Webster, TX) and accounted for body weight, walking speed, and the elevation angle of the treadmill. Oxygen uptake and carbon dioxide production were measured with Medical Graphics System CPX equipment (Medical Graphics Corp., St. Paul, MN), which was calibrated with 21% O2 in N2 and 12% CO2 in N2 before each test, as described elsewhere.12

Statistics and Calculations

The results are presented as mean ± standard error of the mean. Survival curves were constructed using the Kaplan–Meier technique, and statistical analyses of time course changes were tested with log-rank tests or by using an analysis of variance (ANOVA) for repeated measures when appropriate. Several group comparisons were tested by factorial ANOVA. Cumulative net changes during the entire follow-up period were calculated as the percentage change from the first to the last available measurement in each patient (see Results, below). Multiple regression analysis was performed according to standard statistics (Statview® version 5.0; Abacus Concepts Inc., Berkeley, CA). P values < 0.05 in 2-tailed tests were regarded statistically significant. Possible deviations from zero were tested by a one-group t test.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

Cancer patients in the current evaluation had reduced survival. Patients who were subjected to repeated measurements of body composition, for natural reasons, had significantly longer survival (P < 0.001) compared with the entire cohort of 311 patients (Fig. 1). Time course changes of whole body fat (g) and lean tissue (g) during the entire follow-up period in all patients (range, 4–62 months) are shown in Figures 2 and 3. This absolute information may imply falsely that lean tissue was most vulnerable during disease progression, because absolute information depends to some extent on differences in survival. However, a different pattern appeared when body fat and lean tissue were expressed in percent changes from baseline values at the time of inclusion for each patient during follow-up. Then, it was evident that body fat was lost (P < 0.01) and that lean tissue was maintained or even increased slightly (P < 0.05) (Fig. 4). Body compartmental analyses revealed that fat was lost preferentially from the trunk, followed by leg and arm tissue compartments (P < 0.01). By contrast, lean tissue was lost preferentially from arm tissues, whereas leg and trunk tissues gained relative weight (P < 0.01) (Fig. 4). Similarly, it was demonstrated that the percent decreases in body weight (P < 0.01) and serum albumin (P < 0.001) appeared concomitant with a relative increase in systemic inflammation (erythrocyte sedimentation rate, C-reactive protein; P < 0.001), serum insulin (P < 0.001), and daily caloric intake (P < 0.001); whereas serum IGF-1, REE, and maximum exercise capacity essentially remained unchanged during 4–62 months of follow-up in the same patients (P < 0.10) (Fig. 5). There was a large, cross-sectional variation between whole body fat and lean tissue mass in the current groups of weight-losing cancer patients who experienced repeated DEXA measurements (Fig. 6).

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Figure 1. Survival curves are shown for all patients (n = 311 patients) and for patients who were subjected to repeated dual-energy X-ray absorptiometry (DEXA) measurements (n = 132 patients) during follow-up (for diagnoses, see Table 1).

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Figure 2. Time course changes of whole body fat are illustrated for the 132 cancer patients who were subjected to repeated dual-energy X-ray absorptiometry measurements (see Table 1). All patients were followed until the preterminal phase (range, 4–62 months). Bars indicate mean values, and standard errors of the mean are indicted by error bars.

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Figure 3. Time course changes of lean tissue are illustrated for the 132 cancer patients who were subjected to repeated dual-energy X-ray absorptiometry measurements (see Table 1). All patients were followed until the preterminal phase. Measurements of lean tissue were performed simultaneously with whole body fat determination in all patients (see Fig. 2). Bars indicate mean values, and standard errors of the mean are indicted by error bars.

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Figure 4. Relative changes to inclusion values of fat content (F) and lean tissue (lean body mass [LBM]) in arm, leg, and abdominal (abd) tissue compartments are illustrated for the 132 cancer patients who were followed until the preterminal phase (see Table 1 and Figs. 2 and 3). Fat and lean tissue totals (tot) were determined from whole body values in dual-energy X-ray absorptiometry measurements and are presented as the mean of the net relative alterations in each patient during the entire follow-up period. Regional fat and lean tissue alterations all differed significantly according to a factorial analysis of variance (P < 0.01).

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Figure 5. Relative changes to inclusion values of body weight, erythrocyte sedimentation rate (ESR), serum albumin, serum insulin-like growth factor 1 (IGF-1), serum insulin, resting energy expenditure (REE), maximum exercise capacity (watt), daily food intake (Kcal), whole body fat (Fat-tot), and lean tissue (LBM-tot) are illustrated for the 132 cancer patients who were followed until the preterminal phase (see Table 1). All measures are presented as the mean of the net relative alterations in all patients during the entire follow-up period. C-reactive protein showed a similar change as ESR (data not shown). Asterisks indicate P values < 0.05 compared with no change (1 group t test).

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Figure 6. The relation between whole body fat and lean tissue is illustrated for all observations in the 132 patients who were subjected to repeated dual-energy X-ray absorptiometry measurements during follow-up until the preterminal phase (see Table 1).

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Multiple regression analyses on cross-sectional observations were performed with survival, energy balance, food intake, body fat, and lean tissue as dependent variable on all 311 patients. These computations revealed that serum albumin (P < 0.0001), whole body fat (P < 0.02), and caloric intake (P < 0.0001) were significant predictors of survival (P < 0.001), whereas lean tissue lacked predictive significance (Table 3). Daily fat intake and carbohydrate intake were significantly more important for predicting survival compared with protein intake, and daily caloric intake predicted body fat better than whole body lean tissues (not shown). Serum IGF-1, as expected, predicted survival in combination with other serum hormones, such as insulin, leptin, ghrelin, and T3, all of which lacked significance as mathematical predictors of survival (not shown). Energy balance, which determines the development of body composition, was predicted significantly by daily intake of protein, carbohydrate, and particularly fat, whereas whole body oxidation of carbohydrate and fat lacked the ability to predict energy balance (Table 4). Consequently, it was interesting to note that serum insulin, leptin (P < 0.0001), and ghrelin (P < 0.008) predicted body fat, whereas plasma IGF-1 and serum total T3 were only borderline predictors (P < 0.10) (Table 5). By contrast, IGF-1, insulin, and ghrelin predicted body lean tissue content, whereas leptin had only borderline significance, and T3 had no such effect (not shown). Systemic inflammation (C-reactive protein, erythrocyte sedimentation rate) was more predictive of lean tissues than of body fat (not shown). Depletion of arm tissue components (fat, lean tissue) was most related to short survival compared with depletion of leg and trunk tissue.

Table 3. Multiple Regression Analysis with Survival (Days) as the Dependent Variable and with Measures of Systemic Inflammation (Hemoglobin, ESR, and Serum Albumin), Physical Functioning (Maximum Exercise Capacity [Watt]), Body Components (Fat, Lean Tissues [kg]), and Daily Caloric Intake (Kcal) as Independent Variables in 311 Cancer Patientsa
VariableCoefficientStandard coefficientP value
  • ESR: erythrocyte sedimentation rate; S-alb; serum albumin; Hb: hemoglobin; Max: maximum.

  • a

    Correlation coefficient (r) = 0.42; adjusted r2 = 0.16; P < 0.0001; counts > 500.

  • b

    Variable was significant.

Intercept−733.4< 0.006
S-alb21.0210.266< 0.0001b
Hb1.1610.0440.47
Daily food intake0.1640.244< 0.0001b
Body fat0.0060.124< 0.02b
Lean tissue−0.001−0.0200.75
ESR−0.735−0.0450.48
Max. exercise capacity−0.766−0.0740.29
Table 4. Multiple Regression Analysis with Energy Balance (Kcal) as the Dependent Variable and Measures of Resting Energy Expenditure (Kcal), Whole Body Substrate Oxidation (g/day), and Daily Substrate Intake (g/L) as Independent Variables in 311 Cancer Patientsa
VariableCoefficientStandard coefficientP value
  • CHO oxide: carbohydrate oxidation.

  • a

    Correlation coefficient (r) = 0.99; adjusted r2 = 0.98; P < 0.0001; the variables shown were not independent, which may explain the high correlation coefficient; counts > 500.

  • b

    Variable was significant.

Intercept−39.1570.23
Fat intake8.9930.479< 0.0001b
Protein intake4.2390.159< 0.0001b
Carbohydrate intake3.9100.472< 0.0001b
CHO oxide−0.256−0.0360.67
Fat oxide−0.595−0.0390.67
Resting energy expenditure−0.898−0.402< 0.0001b
Table 5. Multiple Regression Analysis with Whole Body Fat (kg) as the Dependent Variable and Circulating Hormone Concentration (μmol/L) as Independent Variables in 114 of 311 Cancer Patientsa
VariableCoefficientStandard coefficientP valuea
  • IGF-1: insulin-like growth factor 1.

  • a

    Correlation coefficient (r) = 0.76; adjusted r2 = 0.55; P < 0.0001; n = 114 patients.

  • b

    Difference was significant.

  • c

    Because ghrelin was introduced for follow-up measurements most recently, counts were > 200.

Intercept6733.017< 0.02b
Total T32981.2630.120< 0.07
Leptin792.7740.637< 0.0001b
Insulin74.5820.130< 0.05b
IGF-115.6620.117< 0.08
Ghrelinc−2.645−0.176< 0.008b

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

For decades, measurements of body composition have been regarded a gold standard of nutritional assessment in individuals with undernutrition.19 Numerous investigations on alterations of body composition in undernourished individuals and patients have been published based on a variety of methods, including plain antropometric measurements,20–22 whole body potassium determinations,1, 23–25 neutron activation analysis,26, 27 computerized tomography,28 bioelectric impedance measurements,29, 30 and DEXA measurements.20, 31–34 All of these techniques have various advantages and limitations. During recent years, the DEXA technique has become a gold standard due to its reproducibility and simplicity, particularly in repeated follow-up measurements. The DEXA technique measures body mineral content, lean tissues, and body fat as independent variables based on the attenuation of X-ray radiation through body tissues. Thus, bone mineral content and body fat are well defined components of body composition, whereas the lean tissue compartments consist of cell masses and body water distributed in intracellular and extracellular volumes.35 Ideally, each measurements of body composition with the DEXA technique should include an independent variable of intracellular and extracellular water to allow correct determination of lean body mass.35 Although reasonable methods for such determinations are available, they usually are not suitable for applications in follow-up measurements of severely ill patients with progressive disease, and previous investigations have reported normal distribution volumes of extracellular and intracellular water in most weight-losing cancer patients.35 Therefore, our current determinations of body components may have been hampered by uncertainty in determining small alterations in body fluids, although most of our patients had no signs of overt water retention. However, regional changes in lean tissue mass may reflect small but clinically undetectable changes in water distribution. Our previous studies of body composition in severely ill patients with progressive disease were based on measurements of body potassium or nitrogen content, including determinations of whole body water based on isotope dilution techniques, all with recognized practical and conceptual limitations.1, 8 Therefore, we regard our current data on lean tissue or fat-free mass as appropriate for follow-up measurement on unselected patients.

Published results on altered body composition in cachectic cancer patients are at variance: Some studies confirm a decline in lean body mass,20, 21 others emphasize the loss of body fat as most pronounced,36 whereas some reports suggest a possibility of reduced quality of life with decreased physical functioning but with unchanged body composition21, 24, 32 due to a proportional loss of both fat and lean tissue, and particularly loss of skeletal muscles.21, 27, 32 These discrepancies may depend on the fact that various techniques with different limitations were used or that various groups of patients have different time course changes.33 However, most studies indicate that body fat declines when patients suffer from progressive disease, whereas alterations in fat-free mass or lean body mass may be either inconclusive or decreased.21, 37 A well established opinion in medical handbooks and consensus discussions is that lean tissues, particularly skeletal muscles, are lost rapidly, a fact that may represent an important issue from the patient's perspective. Conceptually, it has been claimed that loss of body fat may reflect anorexia and depressed energy intake, whereas loss of skeletal muscles would imply increased catabolism.38 However, our own preclinical and clinical evaluations in tumor-bearing hosts usually have not indicated increased total muscle breakdown rates.39 Rather, the loss of skeletal muscles seems to be dependent on increased net breakdown40, 41 that is dependent primarily on depressed protein synthesis,42, 43 sometimes with regional differences among muscle compartments.44 Therefore, it is interesting to note that lean tissue mass in arm and leg compartments differed in directions over time. A relative increase in lean tissue compartments across the trunk (abdominal area) also has been observed by others in cancer patients.45 These observations agree with our results in preclinical studies indicating retention of visceral proteins in tumor-bearing hosts.46

A lesser surprise was our finding that body fat declined in all body compartments along with disease progression. However, this observation should be considered along with the fact that overall caloric intake was maintained or even increased in the same patients (Fig. 5). Thus, calories and nutrients seem to be used preferentially to support lean tissues compared with energy storing in fat. It is not likely that such retention of nutrients in lean tissue only represented uptake into acute-phase reactants or tumor compartments, because the maximum exercise capacity was preserved and increased numerically, a fact that also argues against the view that the maintenance of lean trunk and leg tissues was water retention only. Therefore, proportions of maintained or increased food intake seemed to be diverted into skeletal muscle cells, particularly in leg tissues and visceral proteins, as demonstrated in tumor-bearing mice.47

In our previous analyses, we demonstrated that weight loss is predicted by systemic inflammation and resting energy metabolism in the development of cancer cachexia.11 Previous observations suggested that alterations in whole body fat and patients' adrenergic activity predicted alterations in resting metabolism, whereas lean tissue mass did not.11 Such observations have support in the current analysis, in which body fat and daily food intake predicted survival, whereas alterations in lean tissue lacked this prediction entirely (Table 3).

A number of previous observations emphasized that cancer-induced weight loss was related to increased REE, which can be attenuated by cyclooxygenase inhibition by providing either indomethacin,11, 36 ibuprophen, or fish oils.48, 49 It is likely that such effects are related to decreased inflammation after treatment. It also is well established that host metabolic alterations and improvements in body composition are interrelated across pathways of lipolysis, lipogenesis, substrate oxidation, anorexia, and inflammation, in which the mediators are cytokines, fatty acids, and particularly eicosanoides.6, 50, 51 The current study extends such observations, indicating that the classic hormone system probably is as important as cytokine mediators, because serum insulin, leptin, and ghrelin levels were related to loss of body fat, whereas serum IGF-1 and thyroid hormone status were borderline factors (Table 5). Such interrelations among circulating hormones most likely are related to energy intake and, thus, to long-term energy balance, perhaps more than REE (Table 4).52, 53 Thus, long-term energy balance in weight-losing cancer patients was most dependent on daily fat intake followed by protein and carbohydrate intake, again, more than alterations in REE, which, to date, have been the focus for explaining weight loss in patients with cancer. Our current findings suggest that support of food intake should be a rewarding approach to the palliation of cancer patients. This conclusion is supported by recently conducted, prospective evaluations on the role of specialized nutrition care for weight-losing cancer patients in a randomized study.13 Thus, the current results imply that diets should be fat-enriched in supportive nutrition to achieve optimal effects on physical functioning and survival,9, 10 although other dietary compounds also may contribute.54

The current evaluation demonstrated that net alterations in body fat are more pronounced in progressive cancer cachexia compared with long-term alterations in lean tissue mass. The observed metabolic alterations were related highly to circulating levels of classic hormones with known importance for energy homeostasis, storage of nutrients, and intermediary host metabolism when they were assessed in cross-sectional analyses on all patients. The results of the current study demonstrate that cancer cachexia is related highly to factors that determine food intake (appetite), which includes the amount of calories as well as diet composition.54–56 This information is important in the planning of palliative care for patients with cancer.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES