Prediction of body composition in elderly men over 75 years of age

Clinical symptoms, signs and tests for identification of impending and current water-loss dehydration in older people

BACKGROUND

There is evidence that water-loss dehydration is common in older people and associated with many causes of morbidity and mortality. However, it is unclear what clinical symptoms, signs and tests may be used to identify early dehydration in older people, so that support can be mobilised to improve hydration before health and well-being are compromised.

OBJECTIVES

To determine the diagnostic accuracy of state (one time), minimally invasive clinical symptoms, signs and tests to be used as screening tests for detecting water-loss dehydration in older people by systematically reviewing studies that have measured a reference standard and at least one index test in people aged 65 years and over. Water-loss dehydration was defined primarily as including everyone with either impending or current water-loss dehydration (including all those with serum osmolality ≥ 295 mOsm/kg as being dehydrated).

SEARCH METHODS

Structured search strategies were developed for MEDLINE (OvidSP), EMBASE (OvidSP), CINAHL, LILACS, DARE and HTA databases (The Cochrane Library), and the International Clinical Trials Registry Platform (ICTRP). Reference lists of included studies and identified relevant reviews were checked. Authors of included studies were contacted for details of further studies.

SELECTION CRITERIA

Titles and abstracts were scanned and all potentially relevant studies obtained in full text. Inclusion of full text studies was assessed independently in duplicate, and disagreements resolved by a third author. We wrote to authors of all studies that appeared to have collected data on at least one reference standard and at least one index test, and in at least 10 people aged ≥ 65 years, even where no comparative analysis has been published, requesting original dataset so we could create 2 x 2 tables.

DATA COLLECTION AND ANALYSIS

Diagnostic accuracy of each test was assessed against the best available reference standard for water-loss dehydration (serum or plasma osmolality cut-off ≥ 295 mOsm/kg, serum osmolarity or weight change) within each study. For each index test study data were presented in forest plots of sensitivity and specificity. The primary target condition was water-loss dehydration (including either impending or current water-loss dehydration). Secondary target conditions were intended as current (> 300 mOsm/kg) and impending (295 to 300 mOsm/kg) water-loss dehydration, but restricted to current dehydration in the final review.We conducted bivariate random-effects meta-analyses (Stata/IC, StataCorp) for index tests where there were at least four studies and study datasets could be pooled to construct sensitivity and specificity summary estimates. We assigned the same approach for index tests with continuous outcome data for each of three pre-specified cut-off points investigated.Pre-set minimum sensitivity of a useful test was 60%, minimum specificity 75%. As pre-specifying three cut-offs for each continuous test may have led to missing a cut-off with useful sensitivity and specificity, we conducted post-hoc exploratory analyses to create receiver operating characteristic (ROC) curves where there appeared some possibility of a useful cut-off missed by the original three. These analyses enabled assessment of which tests may be worth assessing in further research. A further exploratory analysis assessed the value of combining the best two index tests where each had some individual predictive ability.

MAIN RESULTS

There were few published studies of the diagnostic accuracy of state (one time), minimally invasive clinical symptoms, signs or tests to be used as screening tests for detecting water-loss dehydration in older people. Therefore, to complete this review we sought, analysed and included raw datasets that included a reference standard and an index test in people aged ≥ 65 years.We included three studies with published diagnostic accuracy data and a further 21 studies provided datasets that we analysed. We assessed 67 tests (at three cut-offs for each continuous outcome) for diagnostic accuracy of water-loss dehydration (primary target condition) and of current dehydration (secondary target condition).Only three tests showed any ability to diagnose water-loss dehydration (including both impending and current water-loss dehydration) as stand-alone tests: expressing fatigue (sensitivity 0.71 (95% CI 0.29 to 0.96), specificity 0.75 (95% CI 0.63 to 0.85), in one study with 71 participants, but two additional studies had lower sensitivity); missing drinks between meals (sensitivity 1.00 (95% CI 0.59 to 1.00), specificity 0.77 (95% CI 0.64 to 0.86), in one study with 71 participants) and BIA resistance at 50 kHz (sensitivities 1.00 (95% CI 0.48 to 1.00) and 0.71 (95% CI 0.44 to 0.90) and specificities of 1.00 (95% CI 0.69 to 1.00) and 0.80 (95% CI 0.28 to 0.99) in 15 and 22 people respectively for two studies, but with sensitivities of 0.54 (95% CI 0.25 to 0.81) and 0.69 (95% CI 0.56 to 0.79) and specificities of 0.50 (95% CI 0.16 to 0.84) and 0.19 (95% CI 0.17 to 0.21) in 21 and 1947 people respectively in two other studies). In post-hoc ROC plots drinks intake, urine osmolality and axillial moisture also showed limited diagnostic accuracy. No test was consistently useful in more than one study.Combining two tests so that an individual both missed some drinks between meals and expressed fatigue was sensitive at 0.71 (95% CI 0.29 to 0.96) and specific at 0.92 (95% CI 0.83 to 0.97).There was sufficient evidence to suggest that several stand-alone tests often used to assess dehydration in older people (including fluid intake, urine specific gravity, urine colour, urine volume, heart rate, dry mouth, feeling thirsty and BIA assessment of intracellular water or extracellular water) are not useful, and should not be relied on individually as ways of assessing presence or absence of dehydration in older people.No tests were found consistently useful in diagnosing current water-loss dehydration.

AUTHORS' CONCLUSIONS

There is limited evidence of the diagnostic utility of any individual clinical symptom, sign or test or combination of tests to indicate water-loss dehydration in older people. Individual tests should not be used in this population to indicate dehydration; they miss a high proportion of people with dehydration, and wrongly label those who are adequately hydrated.Promising tests identified by this review need to be further assessed, as do new methods in development. Combining several tests may improve diagnostic accuracy.

The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases

Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases.

Body composition by whole-body bioelectrical impedance and prediction of clinically relevant outcomes: overvalued or underused?

BACKGROUND/OBJECTIVES

Body composition calculated using whole-body bioelectrical impedance analysis (BIA), almost invariably with height (H) and often with weight (W), can help patient management and predict clinical outcomes. This study aimed to examine the merits of this approach compared with simple anthropometry (W+H).

SUBJECTS/METHODS

Use was made of original data and validation studies based on reference body composition methods: water dilution, densitometry, dual-energy X-ray absorptiometry, and more robust methods. Prediction of clinical outcomes, including mortality and length of hospital stay, was examined in six studies of chronic obstructive pulmonary disease and a study with multiple patient groups. Vector analysis, phase angle, multi-frequency BIA and segmental impedance were not considered.

RESULTS

In a broad range of study populations, from neonates to older people, in health and disease, body composition calculated using BIA with simple anthropometry frequently offered no advantage over W+H alone, but in some situations it was superior and in others inferior. In predicting clinically relevant outcomes, the fat-free mass index (FFMI), established using BIA, had comparable and sometimes greater power than body mass index (BMI), but none of the reviewed papers used FFMI calculated from W+H or BMI to predict clinical outcomes.

CONCLUSIONS

A variable and generally weak evidence base was found to suggest that BIA with anthropometry is better at predicting body composition than simple anthropometry alone. No evidence was found from the reviewed studies that FFMI calculated from BIA and anthropometry was better at predicting clinical outcomes than FFMI calculated by simple anthropometry alone.

Precision of GE Lunar iDXA for the measurement of total and regional body composition in nonobese adults

Dual-energy X-ray absorptiometry (DXA) is a well-accepted technique for measuring body composition. Knowledge of measurement precision is critical for monitoring of changes in bone mineral content (BMC), and fat and lean masses. The purpose of this study was to characterize in vivo precision of total body and regional body composition parameters using the GE Lunar iDXA (GE Healthcare Lunar, Madison, WI) system in a sample of nonobese subjects. We also evaluated the difference between expert and automatic region-of-interest (ROI) analysis on body composition precision. To this end, 2 total body scans were performed on each subject with repositioning between scans. Total body precision for BMC, fat and lean mass were 0.5%, 1.0%, and 0.5% coefficient of variation (CV), respectively. Regional body composition precision error was less than 2.5% CV for all regions except arms. Precision error was higher for the arms (CV: BMC 1.5%; fat mass 2.8%; lean mass 1.6%), likely owing to the placement of arms relative to torso leading to differences in ROI. There was a significant correlation between auto ROI and expert ROI (r>0.99). Small, but statistically significant differences were found between auto and manual ROI. Differences were small in total body, leg, trunk, and android and gynoid regions (0.004-2.8%), but larger in arm region (3.0-6.3%). Total body and regional precision for iDXA are small and it is suggested that iDXA may be useful for monitoring changes in body composition during longitudinal trials.

Resting metabolic rate in old-old women with and without frailty: variability and estimation of energy requirements

OBJECTIVES

To measure resting metabolic rate (RMR) in old-old adults living in the community and examine the association between measured RMR and frailty status and compare it with expected RMR generated by a predictive equation.

DESIGN

Physiological substudy conducted as a home visit within an observational cohort study.

SETTING

Baltimore City and County, Maryland.

PARTICIPANTS

Seventy-seven women aged 83 to 93 enrolled in the Women's Health and Aging Study II.

MEASUREMENTS

Resting metabolic rate with indirect calorimetry, frailty status, fat-free mass, ambient and body temperature, expected RMR according to the Mifflin-St. Jeor equation.

RESULTS

Average RMR was 1,119 ± 205 kcal/d (range 595-1,560 kcal/d). Agreement between observed and expected RMR was biased and poor (between-subject coefficient of variation 38.0%, 95% confidence interval = 35.1-40.8). Variability of RMR was greater in frail individuals (heteroscedasticity F-test P = .02). Low and high RMR were associated with being frail (odds ratio 5.4, P = .04) and slower self-selected walking speed (P < .001) after adjustment for covariates.

CONCLUSION

Equations to predict RMR that are not validated in old-old adults appear to correlate poorly with measured RMR. RMR is highly variable in old-old women, with deviations from the mean predicting clinical frailty. These exploratory findings suggest a pathway to clinical frailty through high or low RMR.

Biological markers of protein-energy malnutrition

The biological markers of undernutrition fall into three categories: (a) those that measure structure; (b) those that measure function; and (c) indices of the above two. Ideally a marker of nutritional status should have the following characteristics: specific to nutritional status; sensitive to changes in nutritional status; reproducible; simple to measure; inexpensive and widely available. Unfortunately there are no such markers, and therefore individuals involved in the assessment of nutritional status should be aware of the advantages and disadvantages of the markers they use. For example, body composition can be assessed using sophisticated techniques that make fewer assumptions than simple bedside techniques (1). However, these sophisticated techniques (eg neutron activation, and combinations of techniques such as hydro-densitometry, water dilution techniques and dual-energy X-ray absorptiometry) are not widely available and some of them are labour intensive. On the other hand simple bedside techniques, such as those based on skinfold thicknesses can be applied widely because they are easy and quick to perform, but they are probably not as accurate as the classic body composition techniques (hydro-densitometry or water dilution techniques) or other sophisticated methods based on the assessment of multiple body compartments (1). Therefore the choice of method depends not only on the availability of investigative tools, but also on the practicalities of using them in individuals, a small group of individuals, or large groups of individuals, (eg national surveys during famine and non-famine conditions). In this brief review only some aspects concerned with simple bedside or laboratory methods will be discussed.

Laboratory and field measurements of body composition

Objective

This background paper was prepared in response to a request to review the concepts related to measurement of body composition, to discuss laboratory and field methods of assessing body composition and to discuss the practical applications of the methods – how they might be used singly or in combination to provide data for a selected population.

Design

The common laboratory and field methods are described and discussed, with particular attention to the assumptions involved and the applicability of the methods to the different population groups. Most measurements of body composition are made in the field, at the bedside or clinic as opposed to in the laboratory. The laboratory methods have a role to play in their own right, in research into new concepts, models and methods. However, they are particularly important in establishing the accuracy of the field methods.

Setting

Field, bedside and laboratory studies.

Subjects

Children, adults, the elderly, ethnic groups.

Results

Laboratory estimates of body compositions are best performed by multi-component methods or by 2-component methods adjusted for to the populations under investigation. There is a scarcity of data for most of the populations in the world.

Conclusions

Energy requirements based on body weight are an approximation since they do not take into account differences in body composition, which will better determine the true requirements. The measurement of body composition occurs in many branches of biology and medicine. It is used in the assessment of nutritional and growth status and in disease states and their treatment. Energy stores, skeletal muscle and protein content can be determined and changes monitored. In human energetics, body composition is widely used for the standardisation of other variables, such as basal metabolic rate (BMR), in the assessments of ethnic and environmental differences and of variability and adaptation to different levels of nutrition. Choosing a method is very problematic. Users want simple, inexpensive, rapid, safe accurate methods to measure body composition but speed and simplicity come at the expense of accuracy. Recommendations are made for age, sex, and in some cases, fatness and ethnic specific methods.

Body composition by hydrometry (deuterium oxide dilution) and bioelectrical impedance in subjects aged >60 y from rural regions of Cuba, Chile and Mexico

BACKGROUND

In Latin American and Caribbean countries such as Chile, Mexico and Cuba, the population over 60 y has increased steadily. In this age group, there is scarce information about body composition, particularly for those living in rural areas.

OBJECTIVE

The purpose of this study was to determine body composition in free-living and healthy elderly subjects >60 y from rural areas of Chile, Cuba and Mexico using deuterium oxide dilution and bioelectrical impedance (BIA) and to develop and cross-validate a predictive equation for this group of subjects by BIA for future use as a field technique.

SUBJECTS

The study included 133 healthy subjects (73 males and 60 females) >60 y from rural regions of Cuba, Chile and Mexico.

MEASUREMENTS

Total body water, body weight, height and other anthropometric and BIA variables (resistance and reactance) were measured.

METHODS

Total body water was determined by deuterium oxide dilution, and fat-free mass (FFM)/fat mass were derived from this measurement. The total sample was used in a split-sample internal cross-validation. BIA and other anthropometric variables were integrated to multiple regression model to design the best predictive equation, which was validated in the other sample. ANOVA, multiple regression and Bland and Altman's procedure were used to analyze the data.

RESULTS

Body weight, percentage of fat and fat-free mass were lower in the Cuban men and women compared with Chilean and Mexican men and women. The best predictive equation of the FFM was: FFM kg=(-7.71+(H(2)/R x 0.49)+(country or ethnicity x 1.12)+(body weight x 0.27)+(sex x 3.49)+(Xc x 0.13)), where H(2) is height(2) (cm); R is resistance (Omega); country: Chile=1, Mexico=2 and Cuba=3; sex: women=0 and men=1; body weight (kg) and Xc is reactance (Omega). R(2) was 0.944 and the root mean square error (RMSE) was 2.08 kg. The mean+/-s.d. of FFM prediction was 44.2+/-9.2 vs 44.6+/-10.1. The results of cross-validation showed no significant difference with the line of identity, showing that the predicted equation was accurate. The intercept (=-0.32) was not significantly different from zero (P=0.89) and the slope (=1.02) not significantly different from 1.0 (P>0.9). The R(2) was 0.86, RMSE=3.86 kg of FFM and the pure error was 3.83.

CONCLUSION

The new BIA equation is accurate, precise and showed good agreement. The use of this equation could improve the estimates of body composition for the elderly population for these regions, as well as enhancing the opportunity to conduct studies in the elderly population from Latin America.

New bioimpedance model accurately predicts lower limb muscle volume: validation by magnetic resonance imaging

Conventional bioimpedance analysis (BIA) methods now simplify the representation of lower limb geometry and electrical properties for body composition estimation. In the present study, a three-dimensional model of the lower limb was assembled by segmentation of magnetic resonance cross-sectional images (MRI) for adipose tissue, skeletal muscle, and bone. An electrical network was then associated with this model. BIA and MRI measurements were made in six lean subjects (3 men and 3 women, age 32.2 +/- 6.9 yr). Assuming 0.85 S/m for the longitudinal conductivity of the muscle, the model predicted in the examined subjects an impedance profile that conformed well to the BIA impedance profile; predicted and measured resistances were similar (261.3 +/- 7.7 vs. 249 +/- 9 Omega; P = not significant). The resistance profile provided, through a simpler model, muscle area estimates along the lower limb and total leg muscle volume (mean 4,534 cm(3) for men and 4,071 cm(3) for women) with a mean of the absolute value of relative error with respect to MRI of 6.2 +/- 3.9. The new approach suggests that BIA can reasonably estimate the distribution and volume of muscles in the lower extremities of lean subjects.

Evaluation of a model for total body protein mass based on dual-energy X-ray absorptiometry: comparison with a reference four-component model

The aim of the present study was to evaluate a model of body composition for assessing total body protein (TBP) mass using dual-energy X-ray absorptiometry (DXA), with either measured or assumed total body water (TBW); it was intended to provide a less complex or demanding alternative technique to, for example, the four-component model (4-CM). The following measurements were obtained in healthy adults (n 46) aged 18--62 years, and children (n 30) aged 8--12 years: body weight (BWt), body volume (BV; under-water weighing), TBW ((2)H-dilution space or predicted using an assumed hydration fraction of fat-free mass (HF(ffm))), bone mineral content (BMC; DXA) and fat-free soft tissue (FFST; DXA). TBP was calculated using the 4-CM (TBP = 3.05BWt -- 0.290TBW -- 2.734BMC -- 2.74BV) and the DXA model (TBP = FFST -- 0.2302BMC -- TBW). DXA measurements were obtained using the Lunar DPX (Lunar Radiation Corporation, Madison, WI, USA) or Hologic QDR 1000/W (Hologic, Waltham, MA, USA). Precision of the DXA model for TBP with measured TBW (4.6--6.8 % mean TBP) was slightly worse than the 4-CM (4.0--5.4 %), whereas that modelled with assumed HF(ffm) was more precise (2.4--5.2 %) because it obviated imprecision associated with measuring TBW. Agreement between the 4-CM and DXA model with measured TBW was also worse (e.g. bias, 15 % of the mean; 95 % limits of agreement up to +/-39 % for adults measured on the Lunar DPX) than when a constant for HF(ffm) was assumed (3.7 % and +/-21 % respectively). Most of the variability in agreement between these various models was due to interpretation of biological factors, rather than to measurement imprecision. Therefore, the DXA model, which is less complex and demanding than the 4-CM, is of value for assessing TBP in groups of healthy subjects, but is of less value for individuals in whom there may be substantial differences from reference 4-CM estimates.

Body water spaces and cellular hydration during healthy aging

Age-related changes in the proportions of intracellular or extra-cellular water to total body water and in the ratio of total body water to fat-free mass are debatable. These are important issues both for medical reasons (dehydration is a threat in the diseased elderly) and for methodological reasons (most techniques for assessing of body composition assume constant hydration of the fat-free mass). This study compared hydration in young and elderly (60 years) people. In the first part of the study, we analyzed the literature and computed the ratio of total body water over fat-free mass, Hf. Eligible studies involved independent measurements of fat-free mass and total body water. Hf did not appear to change with age. The second part of this study computed Hf in 103 individuals studied in our laboratory. The mean values were not different in young (73.2 +/- 2.4%) and elderly people (73.4 +/- 2.4%). At all ages, the proportion of intracellular or extracellular water (as measured by bromide dilution) to total body water (as measured by oxygen 18 dilution) was similar. The same finding holds for the proportion of intracellular water to fat-free mass. We conclude that hydration of fat-free mass and cellular hydration are not affected in healthy aging.

Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine

Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.

Nutrition, ageing and ill health

There are physical, mental, social and environmental changes which take place with ageing; for example, decreased physical activity, increase in body fat, decrease in lean body mass and consequently decreased energy intake may be associated with physiological functions that affect metabolism, nutrient intake, physical activity and risk of disease. There are now many studies which have found that undernutrition is prevalent and often unrecognized in patients admitted to hospitals and institutions. There is also evidence which links protein-energy undernutrition or its markers with clinical outcomes in acute and non-acute hospital settings and that nutritional supplements can improve outcomes in some of these settings. However, most clinically-available nutrition screening instruments lack sensitivity and specificity, and abnormal nutritional indicators may simply reflect effects of age, functional disability, or severe underlying disease. Thus, causal relationship cannot be assumed without a sufficiently powerful intervention study which adequately adjusts for the effects of non-nutritional factors, such as the number and severity of co-morbid conditions on clinical outcome.