Dataset Information

Reference values for skeletal muscle mass and fat mass measured by bioelectrical impedance in 390 565 UK adults.

ABSTRACT:

Background

Loss of skeletal muscle mass (SMM) increases the risk of frailty and, together with excess fat mass (FM), is a risk factor for cardio-metabolic disease. However, use of body composition measurements in nutritional surveillance and routine clinical practice is limited by the lack of reference data. Our aim was to produce age-specific and sex-specific reference values for SMM and FM in the White ethnic adult population in the UK. Secondary objectives were to examine the tracking over time using a subsample of the population with repeated measures of body composition and to assess the validity of these reference values in different ethnic subgroups.

Methods

We used data from segmental bioelectrical impedance analysis (BIA) in 390 565 participants, aged 40-69 years, in the UK Biobank, and data from dual-energy X-ray absorptiometry from n = 905 participants to validate the BIA measurements. SMM was calculated as the sum of the predicted muscle mass from the limbs. The LMS method was used to produce percentile curves for the SMM index (SMMI = SMM/height² ) and the FM index (FMI = FM/height² ). We investigated the validity of the White ethnic reference values by plotting z-scores (99.7% confidence interval) from Black and Asian groups to check if their confidence interval included zero. Longitudinal trajectories were predicted based on the baseline z-scores and the correlation between repeated measurements at follow-up.

Results

The percentile curves show that SMMI declines in men from the age of 40, whereas in women, SMMI is more stable and decreases only slightly among women in the higher percentiles. FMI increases with age in both men and women. Women have higher FMI and lower SMMI than men in all age groups. The validity of the White-based reference values for non-White ethnic groups is poor. Longitudinal trajectories in body composition in the subsample of participants with a follow-up assessment show regression towards the mean in both men and women, with some evidence of declining SMMI only among men. The predicted 90% limits for the expected 5 year trajectories of SMMI and FMI can be used to identify people with unusual trajectories and in clinical practice to identify and track individuals at risk of excessive loss of SMM.

Conclusions

These body composition reference values developed from BIA in a middle/older-aged healthy White ethnic population in the UK could be used as a simple assessment tool for nutritional surveillance and to identify individuals with low SMMI or high FMI who may be at increased risk of disease and/or frailty.

SUBMITTER: Lee MM

PROVIDER: S-EPMC7113534 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Reference values for skeletal muscle mass and fat mass measured by bioelectrical impedance in 390 565 UK adults.

Lee Mei-Man MM Jebb Susan A SA Oke Jason J Piernas Carmen C

Journal of cachexia, sarcopenia and muscle 20200113 2

<h4>Background</h4>Loss of skeletal muscle mass (SMM) increases the risk of frailty and, together with excess fat mass (FM), is a risk factor for cardio-metabolic disease. However, use of body composition measurements in nutritional surveillance and routine clinical practice is limited by the lack of reference data. Our aim was to produce age-specific and sex-specific reference values for SMM and FM in the White ethnic adult population in the UK. Secondary objectives were to examine the tracking ...[more]

PMID: 31943835

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Project description:BackgroundThe fat-to-muscle mass ratio (FMR) might be an indicator to assess type 2 diabetes risk independent of general obesity. However, no longitudinal studies have explored the extent to which total and regional FMRs may confer risks. We aimed to measure the sex-specific associations between FMRs of the arm, leg, trunk and whole body and incident type 2 diabetes.MethodsA total of 464 817 participants (207 286 men and 257 531 women, mean age 56.5 ± 8.2 and 56.2 ± 8.0 years old, respectively) free of diabetes at baseline were included in this prospective cohort study with UK Biobank data. Fat mass and muscle mass were estimated using a bioelectrical impedance assessment device (Tanita BC 418MA). FMR was calculated as fat mass divided by muscle mass in corresponding body parts (total body, arm, leg and trunk). Cox proportional hazard models were used to estimate the aforementioned associations among men and women. Interaction analyses were performed between FMRs and body mass index (BMI) categories (BMI < 25 kg/m2 and BMI ≥ 25 kg/m2 ).ResultsOver the median 11.0 years (5 057 534 person-years) of follow-up, we documented 11 618 cases of type 2 diabetes. There was a significantly positive association between total and regional FMR and incident type 2 diabetes, even after adjusting for BMI and other covariates. Compared with other body parts, FMRs of the whole body and leg showed the strongest relationship among men and women, respectively (hazard ratio per 1 SD, 95% confidence interval: 1.67, 1.55-1.80; 1.45, 1.39-1.53). A significant interaction (P for interaction < 0.001) between BMI category and FMRs of different body parts was observed. In the stratified analysis by BMI category and tertiles of FMRs, overweight/obese individuals with a high FMR tertile tended to have the highest hazard ratio, ranging from 5.91 to 7.94 in whole body and regional areas.ConclusionsIn this large prospective study, higher total and regional FMRs were associated with a higher risk of developing type 2 diabetes, independent of BMI. This association was markedly strengthened in participants with BMI ≥ 25 kg/m2 .

Project description:BackgroundAccurate volume measurement is important in the management of patients with congestive heart failure or renal insufficiency. A bioimpedance analyser can estimate total body water in litres and has been widely used in clinical practice due to its non-invasiveness and ease of results interpretation. To change impedance data to volumetric data, bioimpedance analysers use equations derived from data from healthy subjects, which may not apply to patients with other conditions. Bioelectrical impedance vector analysis (BIVA) was developed to overcome the dependence on those equations by constructing vector plots using raw impedance data. BIVA requires normal reference plots for the proper interpretation of individual vectors. The aim of this study was to construct normal reference vector plots of bioelectrical impedance for Koreans.MethodsBioelectrical impedance measurements were collected from apparently healthy subjects screened according to a comprehensive physical examination and medical history performed by trained physicians. Reference vector contours were plotted on the RXc graph using the probability density function of the bivariate normal distribution. We further compared them with those of other ethnic groups.ResultsA total of 242 healthy subjects aged 22 to 83 were recruited (137 men and 105 women) between December 2015 and November 2016. The centers of the tolerance ellipses were 306.3 Ω/m and 34.9 Ω/m for men and 425.6 Ω/m and 39.7 Ω/m for women. The ellipses were wider for women than for men. The confidence ellipses for Koreans were located between those for Americans and Spaniards without overlap for both genders.ConclusionThis study presented gender-specific normal reference BIVA plots and corresponding tolerance and confidence ellipses on the RXc graph, which is important for the interpretation of BIA-reported volume status in patients with congestive heart failure or renal insufficiency. There were noticeable differences in reference ellipses with regard to gender and ethnic groups.

Project description:IntroductionMethods of body composition estimation such as dual-energy X-ray absorptiometry (DXA), anthropometry, and bioimpedance (BIA) are used for the estimation of skeletal muscle mass (SMM) and lean body mass (LBM). No previous studies have examined whether these methods generate comparable results, or whether they are valid by using DXA as the reference. The aims of the present investigation were: (a) to assess the differences between DXA, anthropometry, and BIA in the estimation of SMM and LBM, taking into consideration the impact of sex and hydration status; and (b) to examine the agreement of anthropometry and BIA as compared to DXA for the estimation of SMM and LBM.MethodsA descriptive cross-sectional design was followed with 262 healthy young adults (159 males and 103 females). LBM and SMM were assessed by anthropometry with the formulas from Lee et al. and Kulkarni et al. for LBM; and Kerr (opt a), Kerr (opt b), Lee et al., Poortmans, Matiegka, Martin et al., Drinkwater and Ross, and Heymsfield et al. for SMM; by BIA with the formula reported by the TANITA MC-780-MA software for LBM and SMM; and DXA with the formula reported by the Hologic Horizon software for LBM, and the conversion by Kim et al. for SMM.ResultsSignificant differences were found for both SMM and LBM in kg, and percentages between most methods and formulas for the overall sample (p < 0.001-0.003) and divided by sex (p < 0.001-0.035). Hydration status did not have a significant effect on the differences between methods and formulas (p = 0.058-0.870). Lin's coefficient revealed limited agreement among the majority of formulas and methods (CCC = 0.007-0.880). The Bland-Altman analysis showed significant differences in most methods and formulas, both in the overall sample and divided by sex, when using SMM and LBM with DXA as the reference (p < 0.001-0.030).ConclusionThere is a lack of agreement between methods and formulas for assessing SMM and LBM. Sex was found to be a significant factor in this analysis. Furthermore, significant differences were observed between most formulas and methods as compared to DXA, except for the equations to estimate SMM with anthropometry by Poortmans.

Project description:IntroductionPhase angle (PhA) is a ratio of reactance and resistance {arctangent (reactance (Xc)/resistance (R)) × (180°/π)}, which can be obtained by bioelectrical impedance analysis (BIA). PhA indicates cellular health and integrity, and it is also considered as a prognostic tool in medical disorders and an indicator of nutritional status (especially of muscle quality) in patients with obesity. However, PhA has limited usefulness in clinical practice because of a lackness of reference values for Chinese overweight and obese populations. The main aim of this study was to show PhA reference data in different age and BMI groups by sex. In addition, we also study the association of age, sex, and BMI on PhA.MethodsA total of 1729 overweight and obese participants were included in this study. PhA and body composition were measured using segmental multifrequency BIA. Differences in mean values for variables were tested by one-way analysis of variance. Multiple regression analysis was used to assess the associations of PhA with age, sex and BMI.ResultsMultiple regression analysis showed that age, sex and BMI were significant (P < 0.05) independent influence factors of PhA in Chinese overweight and obese adults when age and BMI were continues variables. The mean PhA value for all participants was 5.5°. Mean BMI, age, weight, height and 50kHz-PhA were significantly higher (P < 0.001) in male participants than female ones. In age groups and BMI groups, mean 50kHz-PhA was significantly higher (P < 0.005) in male participants than female ones. When age groups and BMI groups were categorical variables, multiple regression analysis showed that different age groups (46-55 years and ≥ 56 years) had a significantly lower (P < 0.005) PhA as compared with the baseline group (18-25 years) and different BMI groups (≥ 28 kg/m2) had a significantly higher (P < 0.05) PhA as compared with the baseline group (24-27.9 kg/m2).ConclusionPhA differed according to age, sex and BMI. Reference data in this study can be taken into consideration when deriving the reference values for overweight and obese Chinese populations.

Project description:BACKGROUND: The measurements of body mass index (BMI) and percentage of body fat are used in many clinical situations. However, special tools are required to measure body fat. Many formulas are proposed for estimation but these use constant coefficients of age. Age spectrum might affect the predicted value of the body composition due to body component alterations, and the coefficient of age for body fat prediction might produce inconsistent results. The objective of this study was to identify variations of BMI and body fat across the age spectrum as well as compare results between BMI predicted body fat and bioelectrical impedance results on age. METHODS: Healthy volunteers were recruited for this study. Body fat was measured by bioelectrical impedance. The age spectrum was divided into three groups (younger: 18-39.9; middle: 40-59.9; and older: ≥60 years). Comparison of body composition covariates including fat mass (FM), fat free mass (FFM), percentage FM (PFM), percentage FFM (PFFM), FM index (FMI) and FFM index (FFMI) in each weight status and age spectrum were analyzed. Multivariable linear regression coefficients were calculated. Coefficient alterations among age groups were tested to confirm the effect of the age spectrum on body composition covariates. Measured PFM and calculated PFM from previous formulas were compared in each quarter of the age spectrum. RESULTS: A total of 2324 volunteers were included in this study. The overall body composition and weight status, average body weight, height, BMI, FM, FFM, and its derivatives were significantly different among age groups. The coefficient of age altered the PFM differently between younger, middle, and older groups (0.07; P = 0.02 vs 0.13; P < 0.01 vs 0.26; P < 0.01; respectively). All coefficients of age alterations in all FM- and FFM-derived variables between each age spectrum were tested, demonstrating a significant difference between the younger (<60 years) and older (≥60 years) age groups, except the PFFM to BMI ratio (difference of PFM and FMI [95% confidence interval]: 17.8 [12.8-22.8], P < 0.01; and 4.58 [3.4-5.8], P < 0.01; respectively). The comparison between measured PFM and calculated PFM demonstrated a significant difference with increments of age. CONCLUSION: The relationship between body FM and BMI varies on the age spectrum. A calculated formula in older people might be distorted with the utilization of constant coefficients.

Dataset Information

Reference values for skeletal muscle mass and fat mass measured by bioelectrical impedance in 390 565 UK adults.

Background

Methods

Results

Conclusions

Publications

Reference values for skeletal muscle mass and fat mass measured by bioelectrical impedance in 390 565 UK adults.

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