We hypothesized that in patients with chronic obstructive pulmonary disease, loss of fat-free mass (FFM) and loss of bone mineral density (BMD) were related to (1) each other and may be clinically inapparent, (2) urinary markers of cellular and bone collagen protein breakdown, and (3) severity of lung disease. Eight-one patients and 38 healthy subjects underwent dual-energy X-ray absorptiometry to determine body composition and BMD. Urinary protein breakdown markers, inflammatory mediators, and their soluble receptors were determined. Thirty-three patients had a low fat-free mass index (kg/m2), 17 of whom had a normal body mass index. Thirty-two percent of patients (13% of healthy subjects) had osteoporosis at the hip or lumbar spine. The marker of cellular protein breakdown was elevated in patients and related to lung disease severity and body composition. The marker of bone collagen breakdown was greater in patients with osteoporosis. Inflammatory mediators were elevated in patients. Loss of FFM and loss of BMD were related, occurred commonly, and could be subclinical in patients with chronic obstructive pulmonary disease. Loss of both was greatest with severe lung disease. Increased excretion of cellular and bone collagen protein breakdown products in those with low FFM and BMD indicates a protein catabolic state in these patients.
In patients with chronic obstructive pulmonary disease (COPD), weight loss and a reduced body mass index (BMI) are poor prognostic factors for survival (1–3). Loss of fat-free mass (FFM) may be more important, because even with a normal BMI it is associated with increased morbidity, a poor quality of life, and reduced exercise performance (4, 5). Loss of bone mineral density (BMD) may also occur, with lumbar spine fractures reported in nearly 50% of steroid-naive male patients (6). There is little evidence linking FFM and BMD loss in COPD, whereas in otherwise healthy subjects, osteoporosis was associated with low body weight, fat mass (FM), or FFM, depending on age, sex, and menopausal status (7–11).
Body composition changes in COPD are related to normal aging and to an overall negative energy balance. Nutrient intake may meet recommendations for healthy subjects, but excess energy requirements of the disease state may not be met (12). Causes of extra energy costs include increased oxygen and energy costs of breathing, catabolic intermediary metabolism, and systemic inflammation (13–17). Alterations in body composition cause complications including impaired respiratory muscle function and loss of skeletal muscle mass with reduced force generation and endurance, which may contribute to the increased risk of fractures and disability, all of which become clinically relevant later in the natural history of COPD (4, 18–20).
Body composition can be determined by various methods, with dual-energy X-ray absorptiometry being an established reliable, noninvasive, and quantitative option (21, 22). Although giving body composition status, dual-energy X-ray absorptiometry does not indicate mechanisms associated with any changes detected. Isotopically determined protein turnover has been used in COPD, but is time consuming, expensive, and difficult to apply to large numbers of subjects (23). Simpler methods include the measurement of urinary markers of protein breakdown. Excretion of pseudouridine (5-ribosyluracil, PSU), a modified nucleoside indicator of RNA and hence of cellular protein breakdown, is increased in disease states associated with chronic systemic inflammation (24, 25). It is neither affected by diet, reutilized, nor degraded further (26). Urinary excretion of N-telopeptides of collagen I (NTx), short polypeptide cross-links, is a marker of bone collagen breakdown and has been used to monitor treatment responses in osteoporosis (27–29).
We hypothesized that loss of FFM and loss of BMD are associated and may be clinically inapparent in COPD, and that both are related to the severity of lung disease. Second, we hypothesized that increased breakdown of cellular and bone protein would relate to FFM and BMD loss. To test these ideas we studied patients with a wide spectrum of severity of COPD and determined their FFM, BMD, and excretion of PSU and NTx, and compared them with healthy subjects. Some of the results of this study have been previously reported in the form of abstracts (30–32).
We recruited 81 patients previously diagnosed with COPD from among primary care, respiratory outpatients before pulmonary rehabilitation, when clinically stable: these patients had received no antibiotics or oral corticosteroids, or experienced any increase in respiratory symptoms, in the month before recruitment. Diagnosis was confirmed by spirometry according to GOLD (Global Initiative for Chronic Obstructive Pulmonary Disease) guidelines (33, 34). We also studied 38 healthy subjects (HS). Exclusion criteria for all subjects included neoplastic disease or any disease with an inflammatory or metabolic component, cardiac failure, chronic oral corticosteroid treatment, or weight-lowering drugs. All subjects gave written, informed consent and the study had local research ethics committee approval.
Height and weight (Seca; Vogel & Halke, Hamburg, Germany) were determined barefoot and in lightweight indoor clothing. The BMI was calculated and a low BMI was defined as 19.9 kg/m2 or less (3, 16). FEV1, FVC, and their ratio (FEV1/FVC) were determined. Patients were subdivided into those with FEV1 less than 50% predicted and those with FEV1 greater than 50% predicted (33).
Whole body composition and BMD of the lumbar spine and hip were determined with either a QDR 2000+ or Discovery bone densitometer (Hologic, Bedford, MA) with the patient supine. The total effective radiation dose was 5.2 μSv (35). Thirty patients underwent repeated measurements on the same day after repositioning. The coefficient of variation was less than 2.2% for lumbar spine and hip BMD, whole body bone mineral content, FM, and FFM. FM and FFM (excluding bone mineral content) were expressed as ratios to height squared to give indices, FMI and FFMI, (36, 37). A low FFMI or FMI was defined as less than the lower 5th percentile of the healthy subjects for that sex (5). The BMD is presented as absolute figures and as a T score (standard deviation [SD] from a young, sex-specific reference mean BMD) (38).
A 20-ml second void urine sample was obtained. Urinary creatinine and PSU were analyzed by high-performance liquid chromatography (39). Urine NTx concentration was measured by ELISA (Osteomark, Quidel, UK). Both PSU and NTx were expressed as a ratio to urinary creatinine concentration. To allow for differences in FFM, PSU and NTx were expressed as ratios to FFMI: PSU/FFMI (μmol/mmol/kg/m2) and NTx/FFMI (nmol/mmol/kg/m2).
Interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and its soluble receptors (TNF-α srI and srII) were measured by ELISA (R&D Systems Europe, Abingdon, UK) on batched samples with the assayist blind to subject identity (40). Detection limits were as follows: IL-6 (0.039 pg/ml), TNF-α (0.12 pg/ml), TNF-α srI (0.77 pg/ml), and TNF-α srII (0.6 pg/ml). IL-6 sr was measured by an in-house ELISA using paired antibodies (detection limit, 0.034 ng/ml; R&D Systems Europe). The coefficient of variation was less than 10% for intra- and interassay variation for all assays.
Data analysis was performed with the Statistical Package for the Social Sciences (SPSS, Chicago, IL), version 11.0. Results are presented as arithmetic or geometric (nonnormally distributed) means and 95% confidence interval. Analyses included the χ2 test, independent t test, one-way analysis of variance with a post hoc Tukey test, Pearson correlation, and multiple regression. A p < 0.05 was considered significant.
Demographic data for all subjects are presented in Table 1
Patients (n = 81)
Healthy Subjects (n = 38)
|Sex||43 male||19 male|
|Age, yr; median (range)||65.8 (39–82)||60.8 (44–77)*|
|FEV1, L; median (range)||1.02 (0.3–3.57)||2.81 (1.46–4.39)*|
|FEV1, % predicted; median (range)||44 (16–105)||104 (83–148)*|
|History of smoking, n||81||16|
|Smoking status, pack-years; median (range)||39 (10–100)||0 (0–75)|
|ICS, median (range)||400 (0–4,000)||0|
|BMI, kg/m2; arithmetic mean (95% CI)||23.95 (22.91, 25.0)||27.02 (25.69, 28.36)*|
|BMD total lumbar, g/cm2; arithmetic mean (95% CI)||0.9 (0.861, 0.94)||1.043 (0.974, 1.111)*|
|BMD total hip, g/cm2; arithmetic mean (95% CI)||0.797 (0.75, 0.84)||0.964 (0.9, 1.03)*|
|Total FFM, kg; arithmetic mean (95% CI)||43.97 (41.63, 46.3)||50.15 (46.42, 53.87)*|
|Total FFMI, kg/m2; arithmetic mean (95% CI)||16.17 (15.53, 16.82)||17.8 (16.92, 18.64)*|
|Total FMI, kg/m2; arithmetic mean (95% CI)||7.25 (6.60, 7.91)||8.65 (7.85, 9.46)†|
|Height of males, m; arithmetic mean (95% CI)||1.69 (1.67, 1.71)||1.72 (1.69, 1.76)|
|Height of females, m; arithmetic mean (95% CI)|| 1.58 (1.56, 1.61)‡|| 1.61 (1.59, 1.64)‡|
Of the 81 patients, 61 had a normal BMI (25.77 [24.77, 26.77] kg/m2) and 20 patients had a low BMI (18.40 [17.84, 18.96] kg/m2). The lower 5th percentile for FFMI was 16.89 kg/m2 for healthy males and 13.28 kg/m2 for healthy females. On the basis of this, 33 patients had a low FFMI and 48 had a normal FFMI. A normal BMI–normal FFMI occurred in 44 patients (22 male), a normal BMI–low FFMI was found for 17 patients (13 male), a low BMI–low FFMI occurred in 16 patients (7 male), and 4 patients (1 male) had a low BMI–normal FFMI. There was no age difference between groups (Table 2)
Normal BMI–Normal FFMI (n = 44)
Normal BMI–Low FFMI (n = 17)
Low BMI–Low FFMI (n = 16)
|Age, yr; median (range)||66.5 (50–81)||67 (54–80)||64.5 (39–82)|
|BMD total lumbar, g/cm2; arithmetic mean (95% CI)||0.950 (0.898, 1.002)||0.861 (0.764, 0.958)||0.840 (0.763, 0.916)|
|BMD total hip, g/cm2; arithmetic mean (95% CI)||0.886 (0.824, 0.947)||0.725 (0.638, 0.812)*||0.663 (0.597, 0.7280)†|
|Total FFMI, kg/m2; arithmetic mean (95% CI)||17.838 (17.058, 18.619)||14.878 (13.861, 15.895)†||13.281 (12.492, 14.072)†|
|Total FMI, kg/m2; arithmetic mean (95% CI)||9.036 (8.261, 9.811)||6.164 (5.344, 6.984)†||4.471 (3.6, 5.345)†|
|PSU/FFMI, μmol/mmol/kg/m2; geometric mean (95% CI)||1.726 (1.524, 1.95)||2.265 (1.774, 2.891)||2.366 (1.945, 2.884)*|
geometric mean (95% CI)|| 1.892 (1.479, 2.421)|| 2.203 (1.127, 4.315)|| 2.812 (2.014, 3.945)|
The whole body FFMI was less in patients than in HS (Table 1). Among patients, the whole body FFMI was less in the low BMI–low FFMI patients and in those with a normal BMI–low FFMI compared with the normal BMI–normal FFMI group (p < 0.001). There was no difference in FFMI between the normal BMI–low FFMI and low BMI–low FFMI groups (Table 2). The FFMI was less in patients with FEV1 less than 50% predicted (15.33 [14.49, 16.17] kg/m2) compared with patients with FEV1 greater than 50% predicted (17.28 [16.36, 18.2] kg/m2) (p = 0.005). Multiple regression analysis, with FFMI as the dependent variable, explored the contribution of age (nonsignificant), sex (p < 0.001), FEV1 (p < 0.001), and IL-6 (p = 0.038). Overall, the adjusted r2 was 0.451.
The total FMI was lower in patients than in healthy subjects (Table 1), but was not different between patients whose FEV1 was greater or lesser than 50% predicted (p = 0.102). Although maintaining a BMI greater than 19.9 kg/m2, patients with normal BMI–low FFMI had a significantly lower FMI than those with normal BMI–normal FFMI (Table 2).
The total BMD at both the lumbar and hip regions of patients was less than for HS (both p < 0.001) (Table 1), as it was for individual lumbar sites (L1–L4) and hip sites (femoral neck, trochanter, and intertrochanteric region) (p < 0.02 for all). There were lower BMD and T scores for the total hip site in patients with low BMI–low FFMI or with normal BMI–low FFMI compared with normal BMI–normal FFMI patients. There was no difference between these subgroups for the total lumbar region (Figure 1and Table 2). The BMD was less at the hip in those with FEV1 less than 50% predicted compared with those with milder disease (p = 0.002), with no difference at the lumbar spine (Figure 2) .
Using multiple regression analyses with total BMD at the lumbar and hip sites as the dependent variable, associations with FFMI, FMI, smoking pack-years, age, sex, IL-6, inhaled corticosteroid dose, and actual FEV1 were explored among patients. At the hip site, only total FFMI (p < 0.001) revealed a significant effect on BMD, with overall adjusted r2 = 0.595. Similar relationships were found at the lumbar site BMD with FFMI (p = 0.002), but not with the other variables (r2 = 0.334 for all variables). Among the HS, none of these variables had a significant effect on BMD, with adjusted r2 = 0.301 (hip) and r2 = 0.145 (lumbar region).
For the HS, at the total lumbar site 5 (13.2%) had osteoporotic T scores (less than −2.5) and 10 (26.3%) had osteopenia (T score less than −1 but greater than −2.5) (38). At the total hip site 2 had osteoporosis (5%) and 11 had osteopenia (28.9%). Among the patients, at the total lumbar site 22 (15 female) had osteoporosis (27.2%) and 31 (14 female) had osteopenia (38.3%). At the total hip site, 16 (13 female) had osteoporotic T scores (19.8%) and 42 (18 female) had osteopenic levels (51.9%). Frequency of bone loss (either osteoporosis or osteopenia) at either the hip or lumbar spine was different between HS and patients when related to the severity of lung disease (χ2, p < 0.001) (Figure 3). Bone loss was also different between the HS and patient groups on the basis of body composition (χ2, p < 0.001) (Figure 4) .
Using age- and sex-specific reference ranges (Z score), 26 of the patients had an SD less than −1 for the total lumbar region, as did 28 for the total hip site, compared with 7 HS at the lumbar region and 2 HS at the hip region. There was no difference in the sex mix for those with a low Z score in either of the subject groups.
The PSU/FFMI was greater in patients than in HS (Table 3)
Patients (n = 81)
Healthy Subjects (n = 38)
|PSU/FFMI, μmol/mmol/kg/m2||1.963 (1.786, 2.158)||1.622 (1.449, 1.82)*|
|NTx/FFMI, nmol/mmol/kg/m2||1.954 (1.644, 2.328)||2.158 (1.766, 2.636)|
|IL-6, pg/ml||3.041 (2.618, 3.532)||1.413 (1.104, 1.811)†|
|TNF-α, pg/ml||2.244 (1.959, 2.576)||1.795 (1.500, 2.148)*|
|IL-6 sr, ng/ml||18.66 (16.98, 20.42)||18.37 (17.22, 19.54)|
|TNF-α srI, pg/ml||1,285.29 (1,196.74, 1,380.38)||1,078.95 (984.01, 1,180.32)†|
|TNF-α srII, pg/ml||2,697.74 (2,511.89, 2,904.02)|| 2,172.7 (2,004.47, 2,360.48)†|
NTx was not different between patients and HS or between patients on the basis of body composition, but was greater in those with severe lung disease (Table 3 and Figure 5). The NTx/FFMI inversely related to actual FEV1 (r = −0.33, p = 0.003) in patients. The NTx/FFMI was greater in patients with osteoporosis at the lumbar spine (3.296 [2.084, 5.211] nmol/mmol/kg/m2) than in those with no osteoporosis (1.845 [1.496, 2.275] nmol/mmol/kg/m2) (p = 0.009) and greater in those with osteoporosis at the hip (4.887 [3.214, 7.43] nmol/mmol/kg/m2) than in those with no osteoporosis (1.766 [1.432, 2.163] nmol/mmol/kg/m2) (p < 0.001). The correlation of the NTx/FFMI to BMD of the lumbar region was r = −0.337 (p = 0.002) and to the hip region the correlation was r = −0.5 (p < 0.001). PSU and NTx were related (r = 0.236, p = 0.034).
Circulating concentrations of IL-6 (p < 0.001), TNF-α (p < 0.05), TNF-α srI (n = 73 patients and 38 HS, p < 0.001) and TNF-α srII (n = 73 patients and 38 HS, p < 0.001) were all greater in patients than in HS (Table 3). IL-6 sr (n = 72 patients and 38 HS) was not different between patients and HS. Only TNF-α was linked to the severity of lung disease, being greater in those with mild disease (2.649 [2.143, 3.273] pg/ml) compared with those with severe disease (1.982 [1.656, 2.371] pg/ml) (p = 0.038); otherwise cytokines and soluble receptors were unrelated to body composition, lung disease severity, and presence of bone disease. In the patients, both TNF-α srI and TNF-α srII were related to IL-6 (r = 0.33, p = 0.004 and r = 0.42, p < 0.001, respectively) and to each other (r = 0.874, p < 0.001), but not to TNF-α.
In patients with COPD encompassing a wide spectrum of disease severity we found a relationship between loss of FFM and BMD. In addition, urinary markers of cellular and bone collagen protein breakdown were related, further supporting the association between loss of FFM and BMD. In the context of COPD the losses from these two protein-rich body compartments may be linked by common mechanisms leading to proteolysis, which adds to the primary impacts of the lung disease leading to loss of skeletal muscle mass, excess bone loss, and progressive disability.
Within our group of patients 21% had “hidden” loss of FFM with a normal BMI, similar to other studies (4, 5, 16). This subgroup of patients resembled those with low BMI–low FFM, with no difference between their FFMI or their T scores at the hip, indicating a high level of subclinical bone disease, with 15 of these 17 having either osteoporosis or osteopenia. In the patients with normal BMI–low FFM there was preferential FFM loss, with some FM loss despite maintaining a normal BMI. These findings emphasize the added value and need for anthropometric assessment rather than simple weight, height, and BMI (4, 5, 16). The FFMI was related to the severity of lung disease based on FEV1 and to circulating IL-6 concentrations. The increased PSU in those with low FFMI probably reflects breakdown of cellular tissues, such as skeletal muscle, leading to cachexia and physical disability. This could be considered a maladaptive response in individuals with already depleted FFM, but such interpretation needs to be made cautiously, as PSU is a measure only of breakdown. Protein turnover studies in COPD suggest both an increase in protein synthesis and breakdown, with the latter predominating (23). Hence, we chose to measure PSU as an indicator of the prominent process likely to be occurring in patients with COPD and loss of FFM.
We confirmed previous reports of a greater degree of systemic inflammation in our patients (16, 41). Increased concentrations of circulating IL-6 and TNF-α in patients may reflect a potential pathophysiological mechanism, as in animal and in vitro studies both are associated with skeletal muscle atrophy and increased protein breakdown (42–46). Increased release of TNF-α by mononuclear cells has been reported in weight-losing patients with COPD (47). However, we found no relationships between TNF-α and its soluble receptors or IL-6 sr and altered body composition in this study, which may reflect the wide range of lung disease severity we studied in this cross-sectional review, in which the weight stability status of our patients was not determined (16, 47). Despite our exclusion criteria, other confounding influences may have occurred as a result of inapparent inflammatory disease.
High levels of bone disease occurred in the patients, with more than 80% meeting the definition of osteoporosis or osteopenia, which had not been previously diagnosed. One-third of our patients had osteoporosis, compared with 13% of the HS, which agrees with the reported high levels of serious bone disease in COPD and is similar to reports of 36% osteoporosis and 56% of patients with bone loss in studies using mixed disease groups and Z scores to define bone loss (6, 48, 49). Linkage between loss of FFM and bone disease may indicate associated mechanisms of loss and our findings of increased protein breakdown in the cellular and bone extracellular matrix compartments support this view. A key feature of our patients was the stronger association of both lung disease and the link between FFM and bone loss at the hip compared with the lumbar spine. The lack of separation of lumbar BMD and T score between the patient groups according to body composition or airway obstruction may be due to the confounding effect of degenerative spine disease and loss of vertebral height leading to an apparently greater BMD in the presence of osteoporosis. As with loss of FFM, systemic inflammation may have an association with loss of bone with similar relationships reported in other chronic diseases with depletion of FFM (50–52). In vitro, both IL-6 and TNF-α stimulate osteoclasts and increase bone resorption (53–57). Performing dual-energy X-ray absorptiometry scans on every patient with COPD is not a practical option, but simple markers including anthropometry (such as skinfold anthropometry and bioelectrical impedance) may assist in the identification of patients who have FFM loss and who subsequently are at greater risk of bone disease in larger community-based populations (58).
Definitions of the severity of lung disease, FFM, and osteoporosis may seem arbitrary and could impose limitations on group analyses. To define the severity of lung disease we opted for subdivision based on less or greater than 50% predicted FEV1, a division in both the GOLD and NICE (National Institute for Clinical Excellence) guidelines (33, 59). There is no consensus on the definition of low FFM and various criteria have been used (4, 5, 16, 60). In this study low FFMI was defined as less than the lower 5th percentile of the HS to obtain an abnormal group with environmental and lifestyle factors similar to those of the local population. There is no recognized definition of osteoporosis in men, but it is generally accepted to use the same definition as for postmenopausal women (61). In this observational study of altered body composition in COPD we did not explore the possible contribution of physical inactivity and catabolic/anabolic hormone balance to FFM and bone loss, so few conclusions as to causative mechanisms can be drawn; however, our findings suggest future studies would be worthwhile to define such interactive processes (17, 62, 63). It was not possible to obtain a cumulative dose of inhaled corticosteroid usage for all patients. Although more than half of the patients were taking inhaled corticosteroids, only 14 were taking doses greater than 1,000 μg/day, which may have had an influence on bone loss, although this remains unclear and is difficult to separate from other influences, not the least being the more severe lung disease for which it is prescribed (59, 64).
We have demonstrated that loss of BMD and FFM are related and are associated with the severity of lung disease and with urinary markers of protein breakdown. Even in milder COPD, there was a high prevalence of osteoporosis that was not suspected on clinical grounds. With the predicted worldwide increase in COPD and the personal, clinical, and fiscal implications of progressive disability and secondary complications, such as hip and spinal fractures, the identification of patients with undetected bone disease and a high risk of osteoporosis is of considerable importance. Although many of the complications of COPD are associated with increasingly severe lung disease, this study extends earlier work by demonstrating high levels of clinically inapparent bone loss occurring in patients with milder degrees of lung disease severity.
The authors thank the staff at Ely Bridge Surgery, North Cardiff Medical Practice, and the Pulmonary Rehabilitation Department and Consultant Respiratory Physicians at Llandough Hospital, for the inclusion of patients.
|1.||Wilson DO, Rogers RM, Wright EC, Anthonisen NR. Body weight in chronic obstructive pulmonary disease. Am Rev Respir Dis 1989;139:1435–1438.|
|2.||Gray-Donald K, Gibbons L, Shapiro SH, Macklem PT, Martin JG. Nutritional status and mortality in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1996;153:961–966.|
|3.||Landbo C, Prescott E, Lange P, Vestbo J, Almdal TP. Prognostic value of nutritional status in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1999;160:1856–1861.|
|4.||Schols AMWJ, Soeters PB, Dingemans AMC, Mostert R, Frantzen PJ, Wouters EFM. Prevalence and characteristics of nutritional depletion in patients with stable COPD eligible for pulmonary rehabilitation. Am Rev Respir Dis 1993;147:1151–1156.|
|5.||Shoup R, Dalsky G, Warner S, Davies M, Connors M, Khan M, Khan F, ZuWallack R. Body composition and health-related quality of life in patients ith obstructive airways disease. Eur Respir J 1997;10:1576–1580.|
|6.||McEvoy CE, Ensrud KE, Bender E, Genant HK, Yu W, Griffith JM, Niewoehner DE. Association between corticosteroid use and vertebral fractures in older men with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998;157:704–709.|
|7.||Edelstein SL, Barrett-Connor E. Relation between body size and bone mineral density in elderley men and women. Am J Epidemiol 1993;138:160–169.|
|8.||Baumgartner RN, Stauber PM, Koehler KM, Romero L, Garry PJ. Associations of fat and muscle masses with bone mineral in elderly men and women. Am J Clin Nutr 1996;63:365–372.|
|9.||Coin A, Sergi G, Benincà P, Lupoli L, Cinti G, Ferrara L, Benedetti G, Tomasi G, Pisent C, Enzi G. Bone mineral density and body composition in underweight and normal elderly subjects. Osteoporos Int 2000;11:1043–1050.|
|10.||Incalzi RA, Caradonna P, Ranieri P, Basso S, Fuso L, Pagano F, Ciappi C, Pistelli R. Correlates of osteoporosis in chronic obstructive pulmonary disease. Respir Med 2000;94:1079–1084.|
|11.||Bakker I, Twisk JWR, van Mechelen W, Kemper HCG. Fat-free body mass is the most important body composition determinant of 10-year longitudinal development of lumbar bone in adult men and women. J Clin Endocrino Metab 2003;88:2607–2613.|
|12.||Hunter AMB, Carey MA, Larsh HW. The nutritional status of patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1981;96:556–565.|
|13.||Donahue M, Rogers RM, Wilson DO, Pennock BE. Oxygen consumption of the respiratory muscles in normal and in malnourished patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1989;94:1260–1263.|
|14.||Schols AM, Mostert R, Soeters PB, Wouters EF. Body composition and exercise performance in patients with chronic obstructive pulmonary disease. Thorax 1991;46:695–699.|
|15.||Baarends EM, Schols AM, Pannemans DL, Westerterp KR, Wouters EF. Total free living energy expenditure in patients with severe chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1997;155:549–554.|
|16.||Eid AA, Ionescu AA, Nixon LS, Lewis-Jenkins V, Matthews SB, Griffiths TL, Shale DJ. Inflammatory response and body composition in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2001;164:1414–1418.|
|17.||Debigaré R, Marquis K, Côté CH, Tremblay RR, Michaud A, LeBlanc P, Maltais F. Catabolic/anabolic balance and muscle wasting in patients with COPD. Chest 2003;124:83–89.|
|18.||Leech JA, Dullberg C, Kellie S, Pattee L, Gay J. Relationship of lung function to severity of osteoporosis in women. Am Rev Respir Dis 1990;141:68–71.|
|19.||Nishimura Y, Tsutsumi M, Nakata H, Tsunenari T, Maeda H, Yokoyama M. Relationship between respiratory muscle strength and lean body mass in men with COPD. Chest 1995;107:1232–1236.|
|20.||Baarends AM, Schols AMWJ, Mostert R, Wouters EFM. Peak exercise response in relation to tissue depletion in patients with chronic obstructive pulmonary disease. Eur Respir J 1997;10:2807–2813.|
|21.||Visser M, Fuerst T, Lang T, Salamone L, Harris TB, Dual-energy X-ray Absorptiometry and Body Composition Working Group. Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass: health, aging and body composition study. J Appl Physiol 1999;87:1513–1520.|
|22.||Steiner MC, Barton RL, Singh SJ, Morgan MD. Bedside methods versus dual energy X-ray absorptiometry for body composition measurement in COPD. Eur Respir J 2002;19:626–631.|
|23.||Engelen MPKJ, Deutz NEP, Wouters EFM, Schols AMWJ. Enhanced levels of whole-body protein turnover in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2000;162:1488–1492.|
|24.||Ionescu AA, Nixon LS, Luzio S, Lewis-Jenkins V, Evans WD, Stone MD, Owens DR, Routledge PA, Shale DJ. Pulmonary function, body composition, and protein catabolism in adults with cystic fibrosis. Am J Respir Crit Care Med 2002;165:495–500.|
|25.||Intrieri M, Calcagno G, Oriani G, Pane F, Zarrilli F, Cataldo PT, Foggia M, Piazza M, Salvatore F, Sacchetti L. Pseudouridine and 1-ribosylpyridin-4-one-3-carboxamide (PCNR) serum concentrations in human immunodeficiency virus type 1-infected patients are independent predictors for AIDS progression. J Infect Dis 1996;174:199–203.|
|26.||Borek EC, Kerr SJ. Atypical transfer RNAs and their origin in neoplastic cells. Adv Cancer Res 1972;15:163–190.|
|27.||Hanson DA, Weis MAE, Bollen AM, Maslan SL, Singer FR, Eyre DR. A specific immunoassay for monitoring human bone resorption: quantification of type I collagen cross-linked N-telopeptides in urine. J Bone Miner Res 1992;7:1251–1258.|
|28.||Garnero P, Shih WJ, Gineyts E, Karpf DB, Delmas PD. Comparison of new biochemical markers of bone turnover in late postmenopausal osteoporotic women in response to alendronate treatment. J Clin Endocrinol Metab 1994;79:1693–1700.|
|29.||Aris RM, Lester GE, Caminiti M, Blackwood AD, Hensler M, Lark RK, Hecker TM, Renner JB, Guillen U, Brown SA, et al. Efficacy of alendronate in cystic fibrosis adults with low bone density. Am J Respir Crit Care Med 2004;169:77–82.|
|30.||Bolton CE, Ionescu AA, Shiels K, Nixon LS, Pettit RJ, Evans WD, Edwards PH, Griffiths TL, Shale DJ. Evidence for cellular and bone connective tissue protein breakdown in patients with COPD [abstract]. Thorax 2003;58(Suppl iii): iii10. S29.|
|31.||Bolton CE, Ionescu AA, Shiels KM, Nixon LS, Pettit RJ, Evans WD, Edwards PH, Linnane SJ, Griffiths TL, Shale DJ. Fat free mass status in patients with COPD [abstract]. Am J Respir Crit Care Med 2004;169:A615.|
|32.||Bolton CE, Ionescu AA, Shiels KM, Nixon LS, Pettit RJ, Evans WD, Edwards PH, Linnane SJ, Griffiths TL, Shale DJ. Bone loss in patients with chronic obstructive pulmonary disease (COPD) [abstract]. Am J Respir Crit Care Med 2004;169:A616.|
|33.||Global Initiative for Chronic Obstructive Pulmonary Disease. Global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease. NHLBI/WHO workshop report, 2001. Available at URL(accessed October 2004).|
|34.||Fabbri LM, Hurd SS, GOLD Scientific Committee. Global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease: 2003 update. Eur Respir J 2003;22:1–2.|
|35.||Blake GM, Wahner HW, Fogelman I. The Evaluation of osteoporosis: dual energy X-ray absorptiometry and ultrasound in clinical practice. London: Martin Dunitz; 1998.|
|36.||VanItallie TB, Yang MU, Heymsfield SB, Funk RC, Boileau RA. Height-normalised indices of the body's fat-free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr 1990;52:953–959.|
|37.||Bolton CE, Ionescu AA, Evans WD, Pettit RJ, Shale DJ. Altered tissue distribution in adults with cystic fibrosis. Thorax 2003;58:885–889.|
|38.||World Health Organization Study Group. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO Study Group. Geneva, Switzerland: World Health Organization; 1994. Technical Report Series 843.|
|39.||Li Y, Wang S, Zhong Z. Simultaneous determination of pseudouridine and creatinine in urine of normal children and patients with leukaemia by high performance liquid chromatography. Biomed Chromatogr 1992;6:191–193.|
|40.||Ionescu AA, Chatham K, Davies CA, Nixon LS, Enright S, Shale DJ. Inspiratory muscle function and body composition in cystic fibrosis. Am J Respir Crit Care Med 1998;158:1271–1276.|
|41.||Pouw EM, Schols AM, Deutz NE, Wouters EF. Plasma and amino acid levels in relation to resting energy expenditure and inflammation in stable chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998;158:797–801.|
|42.||Tsujinaka T, Ebisui C, Fujita J, Kishibuchi M, Morimoto T, Ogawa A, Katsume A, Obsugi Y, Kominami E, Monden M. Muscle undergoes atrophy in association with increase of lysosomal cathepsin activity in interleukin-6 transgenic mouse. Biochem Biophys Res Commun 1995;207:168–174.|
|43.||Li Y-P, Schwartz RJ, Waddell ID, Holloway BR, Reid MB. Skeletal muscle myocytes undergo protein loss and reactive oxygen-mediated NF-κB activiation in response to tumour necrosis factor α. FASEB J 1998;12:871–880.|
|44.||Li Y-P, Reid MB. NF-κB mediates the protein loss induced by TNF-α in differentiated skeletal muscle myotubes. Am J Physiol Regul Integr Comp Physiol 2000;279:R1165–R1170.|
|45.||Langen RCJ, Schols AMWJ, Kelders MCJM, Wouters EFM, Janssen-Heininger YMW. Inflammatory cytokines inhibit myogenic differentiation through activation of nuclear factor-κB. FASEB J 2001;15:1169–1180.|
|46.||Kosmidou I, Vassilakopoulos T, Xagorari A, Zakynthinos S, Papapetropoulos A, Roussos C. Production of interleukin-6 by skeletal myotubes. Am J Respir Cell Mol Biol 2002;26:587–593.|
|47.||De Godoy I, Donahoe M, Calhoun WJ, Mancino J, Rogers RM. Elevated TNF-α production by peripheral blood monocytes of weight-losing COPD patients. Am J Respir Crit Care Med 1996;153:633–637.|
|48.||Engelen MPKJ, Schols AMWJ, Heidendal GAK, Wouters EFM. Dual-energy X-ray absorptiometry in the clinical evaluation of body composition and bone mineral density in patients with chronic obstructive pulmonary disease. Am J Clin Nutr 1998;68:1298–1303.|
|49.||Iqbal F, Michaelson J, Thaler L, Rubin J, Roman J, Nanes MS. Declining bone mass in men with chronic pulmonary disease. Chest 1999;116:1616–1624.|
|50.||Espat NJ, Moldawer LL, Copeland EM. Cytokine-mediated alterations in host metabolism prevent nutritional repletion in cachetic cancer patients. J Surg Oncol 1995;58:77–82.|
|51.||Anker SD, Clark AL, Teixeira MM, Hellewell PG, Coates AJS. Loss of bone mineral in patients with cachexia due to chroic heart failure. Am J Cardiol 1999;83:612–615.|
|52.||Ionescu AA, Evans WD, Pettit RJ, Nixon LS, Stone MD, Shale DJ. Hidden depletion of fat-free mass and bone mineral density in adults with cystic fibrosis. Chest 2003;124:2220–2228.|
|53.||Bertolini DR, Nedwin GE, Bingman TS, Smith DD, Mundy GR. Stimulation of bone resorption and inhibition of bone formation in vitro by human tumour necrosis factor. Nature 1986;319:516–518.|
|54.||Gowen M, Mundy GR. Actions of recombinant interleukin 1, interleukin 2 and interferon γ on bone resorption in vitro. J Immunol 1986;136:2478–2482.|
|55.||Raisz LG. Local and systemic factors in the pathogenesis of osteoporosis. N Engl J Med 1988;318:818–828.|
|56.||Manolagas SC, Jilka RL. Bone marrow, cytokines and bone remodelling. N Engl J Med 1995;332:305–311.|
|57.||Neale SD, Schulze E, Smith R, Athanasou NA. The influence of serum cytokines and growth factors on osteoclast formation in Paget's disease. QJM 2002;95:233–240.|
|58.||Scottish Intercollegiate Guidelines Network. Management of osteoporosis: a national clinical guideline. Available at(accessed October 2004).|
|59.||National Collaborating Centre for Chronic Conditions. Chronic obstructive pulmonary disease: national clinical guideline on management of chronic obstructive pulmonary disease in adults in primary and secondary care. Thorax 2004;59:1–232.|
|60.||Creutzberg EC, Schols AMWJ, Weling-Scheepers CAPM, Buurman WA, Wouters EFM. Characterization of nonresponse to high calorie oral nutritional therapy in depleted patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2000;161:745–752.|
|61.||Melton L, Orwoll E, Wasnich R. Does bone density predict fractures comparably in men and women? Osteoporos Int 2001;12:707–709.|
|62.||Coupland C, Cliffe S, Bassey E, Grainge M, Hosking D, Chilvers C. Habitual physical activity and bone mineral density in postmenopausal women in England. Int J Epidemiol 1999;28:241–246.|
|63.||Nguyen TV, Center JR, Eisman JA. Osteoporosis in elderly men and women: effects of dietary calcium, physical activity and body mass index. J Bone Miner Res 2000;15:322–331.|
|64.||Lee TA, Weiss KB. Fracture risk associated with inhaled corticosteroid use in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2004;169:855–859.|