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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 57:M302-M307 (2002)
© 2002 The Gerontological Society of America

Influence of Fat-Free Mass and Functional Status on Resting Energy Expenditure in Underweight Elders

Giuseppe Sergia, Alessandra Coina, Mauro Bussolottoa, Paolo Benincàa, Giovanna Tomasia, Claudia Pisenta, Silvia Peruzzaa, Emine M. Inelmena and Giuliano Enzia

a Department of Medical and Surgical Sciences, Division of Geriatrics, University of Padova, Italy

Giuseppe Sergi, Ospedale Geriatrico—Clinica Geriatrica, via Vendramini 7, 35100, Padova, Italy E-mail: giuseppe.sergi{at}tin.it.


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. In underweight elders, resting energy expenditure (REE) and its relationship with fat-free mass (FFM) could be modified by sarcopenia, physical activity, and functional limitation. The aims of this study were to investigate REE and its relationship with quantity and metabolic activity of FFM and to evaluate the influence of functional status on REE in underweight elderly subjects.

Methods. Forty-eight underweight elders (BMI < 20) and 54 normal weight elderly subjects (BMI 20–30) as a control group were selected. Body composition was determined by dual energy x-ray absorptiometry (DEXA). REE was measured by indirect calorimetry. Ability in activities of daily living (ADLs) was assessed by the Katz index.

Results. Underweight elders had significantly lower FFM, FFM index (FFM/height2), and REE than healthy subjects. REE adjusted for FFM with analysis of covariance remained significantly lower in the underweight group (1287 ± 85 vs 1715 ± 139 kcal/day in men, and 1124 ± 63 vs 1366 ± 91 kcal/day in women). Katz index in the underweight group was inversely correlated with REE (r = -0.68; p < .001) even after removal of FFM, FM, and gender, by multiple regression analysis. In this model, FFM and Katz index together explained approximately 54% of REE variability.

Conclusions. Underweight elderly subjects show a hypometabolism due to a reduction of both FFM quantity and its metabolic activity. Functional status in ADLs comes out as an important predictor of REE independently from FFM. The limited physical activity might be the underlying determinant of this hypometabolism, but further investigations are necessary to confirm this issue.

IN the elderly population, the underweight state is often associated with malnutrition, sarcopenia, weakness, and disability (1)(2). Protein-energy malnutrition is highly prevalent among elders (3), and, in addition to leading to an underweight condition, it is an important cause of the age-related decline in muscle mass (4)(5). An accurate estimation of daily energy requirements is particularly important in underweight patients because correct nutritional support will both prevent and treat the malnourished state.

Resting energy expenditure (REE) is the best predictor of overall requirements and usually represents approximately 60% to 75% of the total daily energy requirements of healthy adult subjects (6).

In elderly people, particularly those who are underweight, REE represents the greatest part of the total energy expenditure because of age-related reduction of physical activity (7)(8), while the decline of thermic effect of feeding in elders is not surely demonstrated (9)(10). Fat-free mass (FFM) is the principal determinant of REE, and its effect depends both on its quantity and on its metabolic activity, which may be modulated by race, age, gender, physical activity, and functional and health status. In a previous report, we observed lower REE and FFM values in underweight than in normal weight elderly subjects (11). It is not clear if the observed low REE is due to a FFM depletion or also to an impairment of its metabolic activity. In fact, sarcopenia leads to a reduction of physical activity (12) and likely to a compromise of functional status, which could influence metabolic activity of fat-free mass.

To investigate a possible hypo- or hypermetabolism condition of FFM, it is necessary to normalize REE for quantitative differences in FFM. In previous reports, REE, when normalized for fat-free mass as REE/FFM ratio (ratio method), appeared higher in underweight than in normal weight subjects, suggesting a condition of hypermetabolism (13). Nevertheless, the use of the ratio method to normalize body size-dependent variables has been demonstrated to be not completely correct (14)(15)(16)(17)(18), and hence, a more appropriate statistical approach should be applied (19)(20).

The aims of this study were to investigate resting energy expenditure and its relationship with quantity and metabolic activity of fat-free mass, and to evaluate the influence of functional status on REE in underweight elderly subjects.


   Methods
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 Abstract
 Methods
Results
 Discussion
 References
 
Subjects
This survey was performed in the Geriatric Hospital in Padova. The study design was approved by the Ethical Committee of the University of Padova; an individual written informed consent was also obtained. The participants were 102 elderly subjects divided into two groups according to their nutritional status. Forty-eight underweight subjects (26 women aged 80 ± 7 years and 22 men aged 79 ± 9 years) with body mass index (BMI) < 20 kg/m2 were selected. These subjects were recruited in our hospital among patients admitted for nonrelevant clinical problems. In particular, seven were admitted to the hospital for mild hypertension, six for constipation, nine for social problems, eight for lipothymia or syncope, eight for initial cognitive deterioration, and 10 for osteoarthrosis. We excluded subjects affected by acute illness; severe liver, heart, or kidney failure; endocrinopathy; cancer; and inflammation states as well as patients treated with steroid hormones and other drugs interfering with resting metabolic rate. We included only subjects with stable weight in the previous 6 months. Body weight variation >= 3% in this period was an exclusion criterion. The weight stability was confirmed by weighing the subjects at admission and about 6 days after the test.

Individuals with Mini-Mental State Examination (MMSE) scores < 17 and those who were unable to collaborate for the execution of all instrumental examinations were excluded. As a control group, 54 healthy elderly subjects, 27 women aged 79 ± 9 years and 27 men aged 77 ± 7 years with BMIs ranging from 20 to 30, were recruited as volunteers. In order to exclude diseases or therapies affecting nutritional status and REE, a brief history was charted, and a physical examination was performed.

Measures
On the same day, all subjects submitted to several measurements.

Anthropometry..-- Body weight was measured to the nearest 0.1 kg, and height was determined to the nearest 0.1 cm with subjects wearing light clothing and no shoes. Body mass index was calculated as weight in kilograms divided by height in meters squared.

Biochemical analysis..-- To assess clinical conditions, routine biochemical analyses were performed. Moreover, to exclude states of inflammation, we evaluated erithrocyte sedimentation rate, reactive protein, fibrinogen, and leukocytes.

Body composition..-- Fat-free mass and fat mass (FM) were obtained by dual energy x-ray absorptiometry (DEXA) with fan-beam technology (QDR 4500W, Hologic Inc., Waltham, MA). A standardized procedure for patient positioning and QDR software was used. The DEXA scans were analyzed with the Hologic software for body composition evaluation, version 8.21. DEXA presented a good reproducibility in determination of soft tissue composition (21), and a good agreement was found in elderly subjects (22). Fan-beam DEXA technology for measuring fat and fat-free mass has also been recently validated (23)(24)(25)(26)(27). Fat-free mass and fat mass were expressed as absolute value and as a percentage of total body weight. FFM was also normalized for height as FFM index (FFMI), calculated as FFM divided by height squared (28). Moreover, appendicular skeletal muscle mass (ASMM) was measured as the sum of the fat-free soft tissue masses of arms and legs, as described by Heymsfield and colleagues (29). ASMM index (ASMMI) was calculated as ASMM divided by height squared (12).

Resting energy expenditure (REE)..-- This was measured in standard condition by an open-circuit indirect calorimeter (2900 Metabolic System, SensorMedics Corp., Yorba Linda, CA) and a ventilated hood system. In all subjects, REE was measured in the early morning, after a 12-hour fast, and after 30 minutes more rest in bed for habituation. Before each measurement period, atmospheric pressure and mixing chamber temperature were entered into the system. Calibration of gas flow was performed with a calibrated syringe and then measured. Carbon dioxide and oxygen concentrations were compared with span gas (with known oxygen and carbon dioxide fractions) and pure nitrogen (as zero level gas). The validation of the indirect calorimetry method was determined twice a month by repeated measures of acetone combustion showing good accuracy (error of 2.4%) and precision (CV = 1.5%).

Urinary nitrogen excretion was determined during the entire REE measurement to evaluate protein oxidation. The energy expenditure was calculated from VO2 and VCO2; the energy equivalent of VO2 was corrected for the nonprotein respiratory quotient (RQ), and REE measurements were later extrapolated within 24 hours.

Cognitive and functional evaluation..-- Mini-Mental State Examination and the Katz index of activities of daily living were used. The Katz index tests the level of functional independence in six categories (30): bathing, dressing, toileting, transferring, continence, and feeding. Each item was assessed on a three-level scale (0 = independent, 1 = human help, 2 = totally dependent) with a total score ranging from 0 to 12, indicating an increasing degree of dependence (31).

Statistical analysis..-- Data were analyzed with Systat statistical software, version 8.01 for Windows (SPSS Inc., Chicago, IL). The results were expressed as mean ± standard deviation. Differences of the variables between normal and underweight people were evaluated in both genders by Student's unpaired two-sided t test.

The normalization of REE for fat-free mass variations in underweight and healthy subjects was performed with two different procedures. First, we used the ratio method, dividing REE by FFM-kg. Therefore, as already reported, the use of the REE/FFM ratio is appropriate only when the mathematical equation relating the two parameters is equal to one and REE is in constant proportion to FFM with an intercept equal to zero (15)(16)(17)(18). Because the relation between FFM and REE is usually expressed by equations with intercepts different from zero as suggested by others (14)(19), we adjusted REE for FFM differences in the two groups by analysis of covariance (ANCOVA).

The relationship between REE and body composition (FFM and FM) was evaluated in underweight and normal weight subjects by linear regression models. In addition, a linear regression model between REE and the Katz index was performed in the underweight group. Then, a multiple regression analysis was used to determine whether the Katz index was associated to REE after adjustment for FFM and FM.

Finally, we applied the same procedures for the FFM-normalization of REE in a subgroup of underweight able subjects, selected for Katz index <= 2.


   Results
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 Abstract
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 Results
Discussion
 References
 
Anthropometric characteristics and body composition values of the subjects are shown in Table 1 . The mean age was similar in the two groups for both genders. BMI was obviously lower in underweight patients than in healthy subjects, in men (17.7 ± 1.6 vs 25.8 ± 2.5 kg/m2) as well as in women (16.9 ± 1.7 vs 26.2 ± 2.6 kg/m2). Underweight subjects had significantly lower mean weight, FFM, ASMM, and FM in kilograms. Moreover, this group had FFM in percent of body weight higher, and FFMI and ASMMIs significantly lower, than healthy subjects.


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  		Table 1. Anthropometric and Body Composition Parameters in Normal Weight and Underweight Subjects by Gender

 
Normal weight subjects were completely independent in ADLs. Underweight subjects presented a large variability in Katz index (mean 3.5 ± 4.1). In particular, 27 could be considered independent (Katz <= 2), eight were severely disabled (Katz >= 10), and among them, two were bedridden for severe arthrosis.

The measured and normalized data of resting metabolic rate in normal weight and underweight subjects, by gender, are shown in Table 2 . REE was significantly lower in the underweight subjects than in the healthy group in both genders: 1349 ± 204 vs 1693 ± 284 kcal/day in men, and 1086 ± 264 vs 1389 ± 196 kcal/day in women.


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  		Table 2. Resting Energy Expenditure (REE) Values in Normal Weight, Underweight, and Underweight Able (Katz Index <= 2) Subjects, by Gender

 
When REE was expressed in kilograms of FFM (ratio method), it was significantly higher in underweight men and similar in the two groups of women. On the contrary, after adjustment for FFM using the covariance method, REE was significantly lower in the underweight subjects in both genders (p < .001).

The linear regression between FFM, FM, and REE is shown in Table 3 . The relationship between FFM and REE (Fig. 1) was stronger in the normal weight group (r = .69; p < .001) than in underweight elders (r = .50; p < .001). On the other hand, the correlation between FM and REE was not significant in both groups. The Katz index of ADLs in the underweight group (Fig. 2) was inversely correlated with REE (r = -.68), and this association was confirmed after removal of FFM, FM, and gender by a multiple regression analysis model where Katz, FFM, FM, and gender were entered as independent variables (Table 4 ). In this model, the FFM and Katz index explained about 54% of REE variability (r = .74; p < .001). FM and gender did not significantly influence this relation.


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  		Table 3. Linear Regression Analysis Between REE and Body Composition in Underweight and Normal Weight Groups

 


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  		Figure 1. Relationship between resting energy expenditure (REE) and fat-free mass (FFM) in the normal group (full triangles and solid line; r = .69; p < .001) and the underweight group (empty squares and dotted line; r = .50; p < .001).

 


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  		Figure 2. Relationship between resting energy expenditure (REE) and the Katz index of the activities of daily living in underweight subjects (r = -.68; p < .001).

 

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  		Table 4. Multiple Regression Model in REE Prediction, Using FFM, FM, Katz Index, and Gender as Independent Variables

 
Table 2 shows measured and normalized REE results when considering only able subjects, with Katz index <= 2. Measured REE remained lower, even if not significantly, in underweight than in normal weight elders in both genders. REE/FFM ratio was significantly higher in underweight able subjects in both genders (p < .05). On the contrary, REE adjusted for FFM using the ANCOVA was significantly lower in underweight than in normal weight elders (p < .001), in both genders.


   Discussion
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 Abstract
 Methods
 Results
 Discussion
References
 
To investigate the influence of body composition and functional status on REE in underweight elders, two groups of elderly subjects selected for different nutritional status were studied. The first one included hospitalized underweight subjects selected for BMI < 20, and the second one included normal weight subjects with good nutritional and health status. Body mass index is a routine nutritional parameter, and BMI < 20 is widely used in literature (32)(33)(34)(35) to indicate an underweight condition in the elderly population. Moreover, this cutoff represents the 5th percentile of the distribution of BMI in a sample of Italian elderly subjects (36).

As discussed in our previous report (11), to evaluate FFM in relation to body size and nutritional status, we used the height normalized FFM (FFMI), because FFM expressed as absolute weight or as percentage of body weight is unsatisfactory (28). Almost all underweight elders presented FFMI values below the 5th percentile of a general old population that is 15.3 for men and 17.7 for women (unpublished data). Moreover, they presented very low ASMM and ASMMI mean values. ASMMI has been recently used as a parameter to define sarcopenia (12), using as cutoff two standard deviations below the gender-specific means of the Rosetta Study young adults (7.26 for men and 5.45 for women). Because of the absence of an Italian reference for this parameter, we used these cutoffs to identify the underweight sarcopenic patients. We found that all underweight men and 77% of underweight women may be considered sarcopenic.

The main goal of this survey was to investigate REE and its relationship with fat-free mass.

Resting metabolic rate explains the greatest part of the daily energy output in a person of average physical activity level and depends on body size and body composition (6). Aging is associated with a decrease in metabolic rate due to the age-related decline in FFM, but the loss of FFM in elders does not fully account for the lower REE (6). In fact, REE, adjusted for FFM, is lower in normal-weight elders than in young subjects (19).

An underweight state in the elderly population determines an ulterior decrease of REE and FFM and is often associated with disability (37). Also, our underweight patients presented lower REE and FFM values than the control group, and it is crucial to assess whether this low resting metabolic rate is attributable only to a decrease of FFM or of its metabolic activity. In order to clarify this question, it is necessary to normalize REE for differences in fat-free mass. In a previous study, normalized REE (REE/FFM = ratio method) was significantly higher in underweight elderly subjects, suggesting malnutrition related to hypermetabolism (13). This hypermetabolism in malnourished subjects was explained with the preponderance of visceral components of FFM, characterized by higher oxygen consumption because of decrease of muscle mass.

In our study, the REE/FFM ratio was similar in both normal and underweight groups (Table 2 ). Moreover, the ratio method provided misleading conclusions regarding differences in REE (14)(15)(16)(17)(18)(19)(20). On the contrary, the normalization of REE for FFM, using the analysis of covariance method, revealed a hypometabolism in underweight men and women. Consequently, the lower REE in the underweight elderly subjects is not fully explained by the lower FFM, and a decrease in metabolic activity of FFM is supposed.

The factors that differentiate underweight from normal weight subjects could influence this observed hypometabolism. They were, besides sarcopenia, the impairment in ADLs and the presence of chronic diseases in disabled subjects.

Underweight subjects with a greater impairment in ADLs were prevalently affected by osteoarthrosis and by a mild cognitive decline, because chronic diseases possibly influencing energy metabolism were excluded. Moreover, the subjects did not present with inflammatory conditions because mean values of inflammation indices were similar in the two groups, and subjects with altered values on these tests were excluded. For this reason, we think that morbidity itself is not responsible for the energy metabolism alteration occurring in underweight subjects.

ADL score seems to play an important role in determining REE in underweight subjects. Our results demonstrate that the more disability worsens, the more REE decreases, independently from FFM and FM. Moreover, the Katz index in these subjects was the best predictor of REE.

The relationship between disability and energy expenditure in elders has not been investigated in the literature; however, we think that the association found in our study suggests a possible explanation of hypometabolism in underweight elders. Therefore, it is also interesting to investigate if the severe loss of FFM is associated with hypometabolism independently from disability. Underweight able subjects presented REE values adjusted for FFM lower than normal weight elders, so other factors, in addition to disability, should be researched.

Sarcopenia leads to a decrement of physical activity (12)(38). Then, also, our ADL-able underweight elders could have a reduction of physical activity, which reaches the lowest degree in underweight disabled subjects. We suppose that the impairment of physical activity could explain the hypometabolism in underweight subjects. In fact, in older individuals, moderate physical exercise increases REE without variations in body composition (39)(40); this effect seems to be related to higher sympathetic nervous system activity. On the contrary, a decrease of physical activity could lead to a readjustment of FFM metabolic activity with a final reduction of REE. In fact, in paraplegic men, measured resting metabolic rate was found to be lower than predicted, especially when FFM was relatively low (41). In any case, the influence of low physical activity on REE in elders has not been sufficiently investigated.

A limit of our study is that we cannot better demonstrate the role of physical activity on REE because we did not measure the physical performance. In fact, we used the ADL scale as the sole index of physical impairment and disability. In this scale, incontinence represents a nonhierarchical activity of daily living and may be a possible confounding effect. Therefore, the correlations between REE and the Katz index were not modified when ADL total score was calculated excluding the incontinence item.

In conclusion, in underweight elderly subjects, hypometabolism occurs for a reduction of both FFM quantity and metabolic activity. Functional status in ADL is an important predictor of REE independently from FFM. The limited physical activity might be the underlying determinant of hypometabolism in underweight elders, but further investigations are necessary to confirm this.

Received July 2, 2001

Accepted October 2, 2001


   References
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 Abstract
 Methods
 Results
 Discussion
 References
 

  1. Boult C, Krinke UB, Urdangarin CF, Skarin V, 1999. The validity of nutritional status as a marker for future disability and depressive symptoms among high risk older adults. J Am Geriatr Soc. 47:995-999. [Medline]
  2. Sonn U, Rothenberg E, Steen B, 1998. Dietary intake and functional ability between 70 and 76 years of age. Aging (Milano). 10:324-331. [Medline]
  3. Constants T, Alix E, Dardaine V, 2000. Protein energy malnutrition. Diagnostic methods and epidemiology. Presse Med. 29:2171-2176.
  4. Paw MJMCA, Dekker JM, Feskens EJM, et al. 1999. How to select a frail elderly population? A comparison of three working definitions. J Clin Epidemiol. 52:1015-1021. [Medline]
  5. Van den Broeck J, 1995. Malnutrition and mortality. J R Soc Med. 88:487-490. [Medline]
  6. Poehlman ET, 1993. Regulation of energy expenditure in aging humans. J Am Geriatr Soc. 41:552-559. [Medline]
  7. Westerterp KR, 2000. Daily physical activity and ageing. Curr Opin Clin Nutr Metab Care. 3:485-488. [Medline]
  8. Bijnen FC, Feskens EJ, Caspersen CJ, Mosterd WL, Kromhout D, 1998. Age, period, and cohort effects on physical activity among elderly men during 10 years of follow-up: the Zutphen Elderly Study. J Gerontol Med Sci. 53A:M235-M241. [Abstract]
  9. Pannemans DL, Bouten CV, Westerterp KR, 1995. 24 h energy expenditure during a standardized activity protocol in young and elderly men. Eur J Clin Nutr. 49:49-56. [Medline]
  10. Visser M, Deurenberg P, van Staveren WA, et al. 1995. Resting metabolic rate and diet-induced thermogenesis in young and elderly subjects: relationship with body composition, fat distribution and physical activity level. Am J Clin Nutr. 61:772-778. [Abstract/Free Full Text]
  11. Coin A, Sergi G, Benincà P, et al. 2000. Bone mineral density and body composition in underweight and normal elderly subjects. Osteoporos Int. 11:1043-1050. [Medline]
  12. Baumgartner RN, Koehler KM, Gallagher D, et al. 1998. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 147:755-763. [Abstract/Free Full Text]
  13. Campillo B, Bories PN, Devanlay M, et al. 1992. Aging, energy expenditure and nutritional status: evidence for denutrition-related hypermetabolism. Ann Nutr Metab. 36:265-272. [Medline]
  14. Ravussin E, Bogardus C, 1989. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr. 49:968-975.
  15. Toth MJ, Goran MI, Ades PA, et al. 1993. Examination of data normalization procedures for expressing peak VO2 data. J Appl Physiol. 75:2288-2292. [Abstract/Free Full Text]
  16. Tanner JM, 1949. Fallacy of the pre-weight and pre-surface area standards, and their relation to spurious correlation. J Appl Physiol. 2:1-15. [Free Full Text]
  17. Katch V, 1972. Correlational v ratio adjustments of body weight in exercise-oxygen studies. Ergonomics. 15:671-680. [Medline]
  18. Katch VL, 1973. Use of the oxygen-body weight ratio in correlational analyses: spurious correlations and statistical considerations. Med Sci Sports Exerc. 5:253-257.
  19. Poehlman E, Toth MJ, 1995. Mathematical ratios lead to spurious conclusions regarding age- and sex-related differences in resting metabolic rate. Am J Clin Nutr. 61:482-485. [Abstract/Free Full Text]
  20. Vaughan L, Zurlo F, Ravussin E, 1991. Aging and energy expenditure. Am J Clin Nutr. 53:821-825. [Abstract/Free Full Text]
  21. Lukaski HC, 1993. Soft tissue composition and bone mineral status. Evaluation by dual energy x-ray absorptiometry. J Nutr. 123: (suppl 2) 438-443.
  22. Baumgartner RN, Stauber PM, McHugh D, et al. 1995. Cross-sectional age differences in body composition in persons 60+ years of age. J Gerontol Med Sci. 50A:M307-M316. [Abstract]
  23. Fuerst T, Genant HK, 1996. Evaluation of body composition and total body bone mass with the Hologic QDR-4500. Osteoporos Int. 6: (suppl) 202
  24. Visser M, Fuerst T, Lang T, Salamone L, Harris TB, for the Health Aging, Body Composition Study-Dual-Energy X-Ray Absorptiometry and Body Composition Working Group 1999. Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass. J Appl Physiol. 87:1513-1520. [Abstract/Free Full Text]
  25. Mazess RB, Barden HS, 2000. Evaluation of differences between fan-beam and pencil-beam densitometers. Calcif Tissue Int. 67:291-296. [Medline]
  26. Ellis KJ, Shypailo RJ, 1998. Bone mineral and body composition measurements: cross-calibration of pencil-beam and fan-beam dual-energy X-ray absorptiometers. J Bone Miner Res. 13:1613-1618. [Medline]
  27. Modlesky CM, Evans EM, Millard-Stafford ML, Collins MA, Lewis RD, Cureton KJ, 1999. Impact of bone mineral estimates on percent fat estimates from a four-component model. Med Sci Sports Exerc. 31:1861-1868. [Medline]
  28. Van Itallie TB, Yang MU, Heymsfield SB, et al. 1990. Height-normalized indices of the body's fat-free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr. 52:953-959. [Abstract/Free Full Text]
  29. Heymsfield SB, Smith R, Aulet M, et al. 1990. Appendicular skeletal muscle mass: measurement by dual photon absorptiometry. Am J Clin Nutr. 52:214-218. [Abstract/Free Full Text]
  30. Katz S, Ford AB, Moskowitz RW, et al. 1963. Studies of illness in the aged. The index of ADL: a standardized measure of biological and psychosocial function. JAMA. 185:94-101.
  31. Sullivan DH, Patch GA, Wallas RC, et al. 1990. Impact of nutritional status on morbidity and mortality in a select population of geriatric patients. Am J Clin Nutr. 51:749-758. [Abstract/Free Full Text]
  32. Mattila K, Haavisto M, Rajala S, 1986. Body mass index and mortality in the elderly. BMJ. 292:867-868.
  33. McWhirter JP, Pennington CR, 1994. Incidence and recognition of malnutrition in hospital. Br J Nutr. 308:945-948.
  34. Launer LJ, Harris T, 1996. Weight, height and body mass index distribution in geographically and ethnically diverse samples of older persons. Age Ageing. 25:300-306. [Abstract/Free Full Text]
  35. Corish CA, Flood P, Mulligan S, et al. 2000. Apparent low frequency of undernutrition in Dublin hospital in-patients: should we review the anthropometric thresholds for clinical practice?. Br J Nutr. 84:325-335. [Medline]
  36. Melchionda N, Gatto MRA, Enzi G, et al. 1992. Issue relative to anthropometry of the elderly. Age Nutr. 3:33-44.
  37. Romagnoni F, Zuliani G, Bollini C, et al. 1999. Disability is associated with malnutrition in institutionalized elderly people. The IRA study. Istituto di Riposo per Anziani. Aging. 11:194-199. [Medline]
  38. Evans WJ, 1995. What is sarcopenia?. J Gerontol A Biol Sci Med Sci. 50A: (Special Issue) 5-8.
  39. Poehlman ET, Gardner AW, Goran MI, 1992. Influence of endurance training on energy intake, norepinephrine kinetics, and metabolic rate in older individuals. Metabolism. 41:941-948. [Medline]
  40. Poehlman ET, McAuliffe T, Danforth E, Jr 1990. Effects of age and level of physical activity on plasma norepinephrine kinetics. Am J Physiol. 258:256-262.
  41. Sedlock DA, Laventure SJ, 1990. Body composition and resting energy expenditure in long term spinal cord injury. Paraplegia. 28:448-454. [Medline]



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