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Lower serum albumin concentration and change in muscle mass: the Health, Aging and Body Composition Study^1,2,3

¹ From the Institute of Health Sciences, Faculty of Earth and Life Sciences, Vrije Universiteit, Amsterdam, Netherlands (MV); the Institute for Research in Extramural Medicine, VU University Medical Center, Amsterdam, Netherlands (MV); the Sticht Center on Aging, Wake Forest University School of Medicine, Winston-Salem, NC (SBK); the Department of Medicine, University of Pittsburgh, Pittsburgh, PA (ABN and BHG); the University of Tennessee Health Science Center, Memphis, TN (FAT); the Prevention Sciences Group, University of California, San Francisco, San Francisco, CA (MCN); the National Institute on Aging, Laboratory of Epidemiology, Demography and Biometry, Bethesda, MD (TBH)

² Supported by National Institute on Aging contracts NO1-AG-6-2101, NO1-AG-6-2103, and NO1-AG-6-2106. The research of MV was made possible by a fellowship of the Royal Netherlands Academy of Arts and Sciences.

³ Reprints not available. Address correspondence to M Visser, Institute of Health Sciences, Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, Netherlands. E-mail: marjolein.visser{at}falw.vu.nl.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Low albumin concentrations in older persons increase the risk of poor health outcomes, including functional decline.

Objective: The aim of the study was to investigate the association between serum albumin concentration and skeletal muscle loss (sarcopenia) in old age.

Design: Serum albumin concentration was measured in 1882 black and white men and women aged 70–79 y participating in the Health, Aging and Body Composition Study. Five-year changes in appendicular skeletal muscle mass (ASMM), total-body fat-free mass (FFM), and trunk lean mass (TLM) were measured by using dual-energy X-ray absorptiometry. Confounders included health and lifestyle factors, which are markers of inflammation and protein intake.

Results: A low albumin concentration (<38 g/L) was observed in 21.2% of the study participants. After adjustment for confounders, the mean (±SE) change in ASMM was –82 ± 26 g per 3-g/L lower albumin concentration (P = 0.002). This association remained after persons with a low albumin concentration (<38 g/L) were excluded. The decline in ASMM in subjects with low albumin concentrations was almost 30% higher (–930 ± 56 g) than that in those with albumin concentrations 42 g/L (–718 ± 38 g; P < 0.01). The association between albumin and change in ASMM remained after additional adjustment for weight change. A weak association was observed for FFM, whereas no association was observed for TLM, which suggests a specific role of albumin in skeletal muscle change.

Conclusions: Lower albumin concentrations, even above the clinical cutoff of 38 g/L, are associated with future loss of ASMM in older persons. Low albumin concentration may be a risk factor for sarcopenia.

Key Words: Sarcopenia • dual-energy X-ray absorptiometry • body composition • aging • protein • inflammation

	INTRODUCTION

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Low albumin concentrations are commonly observed in older persons and are associated with worse health outcomes and mortality (1-4). Low albumin concentration has been used as a marker of (protein) malnutrition (5). However, the finding that low albumin predicts cardiovascular disease risk (6, 7) and the realization that low albumin is not completely normalized in feeding studies (8) has led to research on other aspects of albumin metabolism. Albumin is a negative acute phase protein that decreases with ongoing inflammation (9), and many of the reported associations with albumin may reflect this. Albumin concentration and clinical assessments of nutritional status may reflect different clinical processes (10).

Several studies in patient groups or population samples have shown a relation between low albumin concentration and poor functional status in older persons (11, 12). Even among nondisabled older persons, lower albumin concentrations have been shown to be independently associated with poorer performance as assessed by objective physical performance tests (13). Low albumin concentration is also predictive of a greater decline in functional status (14).

Albumin concentrations have also been related to muscle characteristics, which could potentially explain the association with poor functional status. Cross-sectional studies in older persons have shown a positive association of albumin with appendicular skeletal muscle mass (ASMM) assessed by dual-energy X-ray absorptiometry (DXA) (15, 16) and with calf muscle area assessed by computed tomography (17). However, these studies did not adjust for elevated inflammation status (15, 16) or low protein intake (17), factors that have been associated with low muscle mass (18-20).

In this prospective study the relation between serum albumin concentration and 5-y change in skeletal muscle mass was investigated in well-functioning older men and women participating in the Health, Aging and Body Composition Study. In addition, the potential role of protein intake, inflammation status, and weight change was investigated as modulators of this relation.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study population
The study population consists of 3075 well-functioning black and white men and women aged 70–79 y participating in the Health, Aging, and Body Composition (Health ABC) Study. White participants were recruited from a random sample of Medicare beneficiaries residing in zip codes from the metropolitan areas surrounding Pittsburgh, PA, and Memphis, TN. Black participants were recruited from all age-eligible residents in these geographic areas. After receiving information describing the study, potential participants were screened for eligibility. Eligibility criteria included the following: age 70–79 y in the recruitment period from March 1997 to July 1998; self-report of no difficulty walking one-quarter of a mile or climbing 10 steps without resting; no difficulty performing basic activities of daily living; no reported use of a cane, walker, crutches or other special equipment to get around; no history of active treatment for cancer in the previous 3 y; and no plan to move out of the area in the next 3 y.

Of the 3075 participants, we excluded those with missing baseline data on albumin concentration (n = 28), those who reported oral steroids use at baseline (n = 69), and those who had missing baseline data on body composition (n = 19). We also excluded those with missing protein intake data obtained at the first annual follow-up examination (n = 441). Of the 2518 participants with complete data, 1882 had 5-y follow-up data on body composition [n = 1866 for change in ASMM, n = 1784 for change in total body fat-free mass (FFM), and n = 1849 for change in trunk lean mass (TLM)]. The reasons for not having 5-y follow-up data on body composition were death (n = 250), no clinic examination (home examination n = 262; proxy interview n = 80), and no contact (n = 42).

The Health ABC Study was reviewed and approved by the Institutional Review Boards at the University of Tennessee and the University of Pittsburgh. All participants provided informed consent before participating in the study.

Albumin and inflammatory markers
Blood samples were collected at the clinic in the morning after the subjects had fasted overnight for 8 h. After processing, the specimens were portioned into cryovials, frozen at –70 °C, and shipped to the Health ABC Core Laboratory at the University of Vermont. Serum albumin concentration was measured by using the bromocresol green method (Vitros; Ortho-Clinical Diagnostics Inc, Rochester NY). The plasma concentrations of interleukin 6 (IL-6) and C-reactive protein (CRP) were used as indicators of inflammation status. Plasma IL-6 concentration was measured in duplicate by means of a commercial enzyme-linked immunosorbent assay (ELISA; High Sensitivity HS600 Quantikine kit; R&D Systems Inc, Minneapolis, MN). Serum concentrations of CRP were also measured in duplicate by ELISA based on purified protein and polyclonal anti-CRP antibodies (Calbiochem-Novabiochem Corp, San Diego, CA). The CRP assay was standardized according to the World Health Organization’s First International Reference Standard and had a sensitivity of 0.08 µg/mL. Assays of blind duplicates collected for 150 participants yielded an average interassay CV of 2.0% for albumin, 10.3% for IL-6, and 8.0% for CRP.

Body composition
Body composition at baseline and the 5-y follow-up was assessed by using fan-beam dual-energy X-ray absorptiometry (model QDR4500, software version 8.21; Hologic, Waltham, MA). Information regarding the extensive quality-assurance protocol of this measurement in the Health ABC Study was described elsewhere (21). The sum of nonfat, nonbone tissue of both arms and legs was used to represent ASMM (22). The nonfat, nonbone tissue of the trunk was used as a measure of TLM, which represented the nonmuscle component of lean body mass. We also used total body FFM as an overall measure of total body composition.

Protein intake
A modified Block 98 food-frequency questionnaire (FFQ) was administered by a trained dietary interviewer to estimate the individual participant’s usual nutrient intakes. Dietary information was not obtained at the baseline examination but at the first annual follow-up examination. The FFQ, developed and modified by Block Dietary Data Systems (Berkeley, CA) for the Health ABC Study, was based on age-appropriate intake data from the third National Health and Nutrition Examination Survey. The food lists were based on the survey 24-h dietary recall data for those aged >65 y, either non-Hispanic white or black, and residing in either the northeast or the south. A total of 108 food items was included. All interviews were periodically monitored throughout the study to ensure the quality and consistency of the data collection procedures. Wood blocks, real food models, and flash cards were used to help participants estimate portion size. Protein and energy intakes were estimated by Block Dietary Data Systems. Dietary information for 57 persons was excluded because of serious errors. The residual protein intake (g/d), after total energy intake was accounted for, was calculated as a measure of protein intake (23). Those who reported extreme sex-specific residual protein intakes (lowest and highest 1%) were excluded from the analyses (n = 50). Persons in the lowest sex-specific quintile of residual protein intake were considered to have a low protein intake.

Other covariates
Other covariates included demographics (sex, race, and study site), lifestyle variables (physical activity, smoking status, and alcohol consumption), and health variables (chronic disease and antiinflammatory drug use) assessed at baseline and follow-up. The time spent on gardening, heavy chores, light house work, grocery shopping, laundry, climbing stairs, walking for exercise, walking for other purposes, aerobics, weight or circuit training, high-intensity exercise activities, and moderate-intensity exercise activities in the past 7 d was obtained as was information on the intensity level at which each activity was performed. For each participant, the scores of all performed activities were summed to create an overall physical activity score in kilocalories per kilogram per week (24). A dichotomous variable was created for weight training (yes or no). Smoking status was categorized as current, former, and never smokers. The number of alcoholic beverages ingested in a typical week during the past 12 mo was categorized as none, 7/wk, and >7/wk. Current presence of disease was determined by using self-reported physician-diagnosed disease information, clinic data, and medication use and included cerebrovascular disease; coronary heart disease; peripheral arterial disease; congestive heart failure; current symptomatic hip, knee, or hand osteoarthritis; pulmonary disease, diabetes mellitus; and depression. Serum creatinine concentration (mg/dL; Vitros, Ortho-Clinical Diagnostics Inc, Rochester NY) was used as a measure of renal function. Daily use of antiinflammatory drugs was determined from drug data coded by using the Iowa Drug Information System ingredient codes.

Statistical analyses
Analyses were performed by using SAS software version 8 (SAS Institute Inc, Cary, NC). Albumin concentration was used as a continuous variable, with regression coefficients expressed per population SD of albumin (ie, 3 g/L). In addition, albumin concentration was used as a categorical variable to examine a potential nonlinear relation: low (<38 g/L, which is considered the clinical cutoff for low albumin concentrations; 25), intermediate (38-41.9 g/L), and high (42 g/L, which is the reference category). To test for trend, the 3 categories were entered in the model as an ordinal variable. Differences in sample characteristics between albumin categories were tested using chi-square statistics for categorical variables and linear regression analysis for continuous variables. Multiple linear regression analysis was used to test the association of albumin concentration with change in ASMM, FFM, or TLM. Both absolute change (follow-up value minus baseline value, in kg) and relative change (absolute change divided by baseline value x 100; in %) in ASMM, FFM, and TLM was used as the study outcome. In the first model, adjustment was made for sex, race, study site, total body fat, and follow-up time. When absolute change in ASMM (or FFM or TLM) was used as the study outcome, additional adjustment was made for baseline ASMM (or FFM or TLM). In a second model, additional adjustment was made for physical activity, smoking status, alcohol consumption, chronic disease, serum creatinine concentration, and antiinflammatory drug use. In a third model, the 2 inflammatory markers were additionally included, and in a final model protein intake was included. To examine whether the relation could be explained by weight change, we also adjusted for 5-y weight change. Potential sex or racial differences in the relation of albumin with change in ASMM, FFM, or TLM were assessed in stratified analyses, and interactions were tested by using product terms in additional analyses.

	RESULTS

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Compared with the 1882 participants included in the longitudinal analyses, those excluded (n = 1193) were older ( ± SD: 74.0 ± 2.9 y compared with 73.4 ± 2.8 y), more likely to be black (52.1% compared with 35.1%), had a lower albumin concentration (39.5 ± 3.2 compared with 40.0 ± 3.1 g/L), had higher IL-6 (2.86 ± 2.30 compared with 2.23 ± 1.87 pg/mL) and CRP (3.67 ± 5.89 compared with 2.58 ± 3.71 mg/L) concentrations, and had a higher percentage of persons with a low protein intake (52.7% compared with 20.0%); P < 0.0001 for all differences. Sex (49.8% compared with 52.6% female), ASMM (20.7 ± 5.1 compared with 20.4 ± 5.0 kg), FFM (50.0 ± 10.5 compared with 49.6 ± 10.6 kg), and TLM (24.0 ± 4.9 compared with 23.8 ± 4.9 kg) were not significantly different between those excluded and included from the longitudinal analyses.

A low serum albumin concentration (<38 g/L) was observed in 399 (21.2%) participants. Participants with low serum albumin were more likely to be female or black, were less likely to live in Pittsburgh and the surrounding areas, were less likely to have diabetes mellitus, had a lower number of prevalent chronic diseases, had more total body fat, had a relatively longer follow-up time between the baseline and the 5-y follow-up examination, and had a lower serum creatinine concentration (Table 1). Albumin was not associated with baseline ASMM, FFM, or TLM. As expected, a strong association between serum albumin concentration and inflammation status was observed. Low serum albumin was related to higher CRP and higher IL-6 concentrations. Serum albumin concentration was not associated with a low protein intake.

Table 2

Table 3

Table 4

Table 4

Table 4

Table 4

Figure 1

Table 4

Figure 1

View larger version (22K): [in this window] [in a new window]   FIGURE 1.. Adjusted mean (±SE) relative 5-y change in appendicular skeletal muscle mass (ASMM; n = 1866), total-body fat-free mass (FFM; n = 1784), and trunk lean mass (TLM; n = 1849) according to categories of baseline serum albumin concentration: , low (<38 g/L); , intermediate (38-41.9 g/L); and , high (42 g/L). Multiple linear regression analysis was used to test the association of albumin concentration with change in ASMM, FFM, or TLM, adjusted for sex, race, study site, age, follow-up time, total body fat, smoking status, alcohol consumption, physical activity, antiinflammatory drug use, chronic disease, depression, serum creatinine concentration, C-reactive protein concentration, interleukin 6 concentration, and low protein intake. **Significantly different from 42 g/L category, P < 0.05.

Tables 3 and 4

	DISCUSSION

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The results of this study suggest that a lower serum albumin concentration in older persons was associated with a greater loss of ASMM during a 5-y follow-up. Lower serum albumin concentrations could have been caused by ongoing inflammation, poor health status, and malnutrition—factors that have also been shown to be associated with low muscle mass or accelerated muscle loss. However, after careful adjustment for known health factors, including chronic disease, inflammation status, poor nutritional intake, and known lifestyle factors (including physical activity and smoking—factors that have been shown to be associated with lower albumin concentration and low muscle mass), the relation between lower albumin concentration and skeletal muscle loss remained. The relation with change in ASMM even persisted after additional adjustment for 5-y weight change, which suggests that the observed muscle loss was accelerated beyond what would be expected for weight loss.

A much weaker association was observed for the association between lower albumin concentration and change in total-body FFM, whereas no association was observed for change in TLM. These results suggest that low serum albumin may increase the loss of ASMM with aging and that the major body organs, represented by TLM, are relatively protected from this loss.

It is of interest that the association between serum albumin concentration and change in ASMM was still observed after exclusion of participants with clinically low (<38g/L) serum albumin concentrations. An association between serum albumin and loss of grip strength in old age has also been reported in the clinically normal range of 38 g/L (26). This suggests that suboptimal albumin concentrations within the normal range could still increase the risk of sarcopenia.

The mechanisms explaining the observed relation between lower serum albumin concentration and loss of ASMM are not clear. Serum albumin concentration may be a marker of protein status of the body, with lower values indicating a diminished protein reserve, stimulating catabolic processes leading to muscle breakdown. However, albumin concentration is well maintained even in the presence of a negative nitrogen balance caused by a low-protein diet (20). Several other mechanisms can also be hypothesized. Several studies have indicated the iron-binding antioxidant properties of albumin (27) and albumin is a specific modulator of cellular glutathione, one of the main body antioxidants (28). Oxidative damage may play a crucial role in the decline of skeletal muscle with aging (29). In addition, increased concentrations of free cortisol have been observed in hypoalbuminemic persons (30), which potentially stimulates muscle breakdown, especially in inactive persons (31). Albumin also has been shown to activate the phosphatidyl-inositol 3-kinase pathway (32), thereby mediating muscle hypertrophy (33, 34). Because free testosterone is a feedback regulator of plasma testosterone, albumin may also affect serum testosterone (35). Last, serum albumin concentration could be a marker of underlying disease, such as chronic renal failure, which has been shown to be associated with muscle wasting (36). Future studies are needed to investigate the potential direct and indirect pathways through which serum albumin could influence muscle mass and muscle function.

The strengths of this study were its large study sample, which included black and white men and women; the use of accurate regional body-composition measures by dual-energy X-ray absorptiometry; and the long (5 y) follow-up. Moreover, careful adjustment was made for important confounders of the association under study, including lifestyle variables, 2 inflammatory markers, and dietary intake. A limitation of the study is that no repeated measures of serum albumin concentration over time were available and that information on dietary intake was obtained 1 y after the baseline examination, when serum albumin concentrations were measured. Because of its half-life of 21 d and its response to acute inflammation, serum albumin may vary over time, and repeated measures would have allowed us to more accurately examine the association with ASMM. Our study was observational, and a causal relation between albumin and change in ASMM can only be inferred. Finally, selective loss of follow-up may have influenced our results. Persons not included in the statistical analyses were older, had higher concentrations of the inflammatory markers, had a lower albumin concentration, and were more likely to have a low protein intake. Therefore, our results are likely an underestimation, which stresses the importance of albumin as a risk factor for sarcopenia.

In conclusion, lower albumin concentrations, even those above the clinical cutoff of 38 g/L, are associated with a future loss of ASMM in older men and women. These results suggest that low albumin concentration may be a risk factor for sarcopenia.

	ACKNOWLEDGMENTS

 
MV, SBK, ABN, MCN, and TBH were responsible for the study concept and design. MV was responsible for the data analysis and interpretation and the draft of the manuscript. MV, SBK, ABN, BHG, FAT, MCN, and TBH were responsible for the critical revision of the manuscript for important intellectual content. None of the authors had a conflict of interest.

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