determination of muscle mass, fat mass and body water. is bioimpedance … · 2017. 11. 1. ·...

THE SAHLGRENSKA ACADEMY

Determination of muscle mass, fat mass and body water. Is bioimpedance with Impedimed SFB7 consistent with reference methods? - A cross sectional validation study Degree Project in Medicine Jakob Nilsson Programme in Medicine

Gothenburg, Sweden 2017

Supervisor: Lars Ellegård, Associate Professor, Senior Consultant

Department of Internal Medicine and Clinical Nutrition

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Table of Contents

Abbreviations ..................................................................................................................... 3

Abstract ................................................................................................................................. 4

Background ......................................................................................................................... 6

Aim and research questions ....................................................................................... 11 Aim ................................................................................................................................................ 11 Research questions ................................................................................................................. 11

Material and Methods ................................................................................................... 12 Population and data collection ............................................................................................ 12 Measurements ........................................................................................................................... 12 Statistical methods and variable analyses ...................................................................... 16 Data analysis .............................................................................................................................. 18

Ethics .................................................................................................................................. 19

Results ................................................................................................................................ 20 TBSMM estimates, a comparison between DXA and SFB7 ......................................... 20 FM estimates, a comparison between DXA, ADP and SFB7 ........................................ 21 TBW estimates, a comparison between 3H-‐dilution and SFB7 ................................. 22 ECW estimates, a comparison between Br-‐dilution and SFB7 .................................. 22 ICW estimates, a comparison between Dilution Techniques and SFB7 and

between TBK and SFB7 .......................................................................................................... 23

Discussion ......................................................................................................................... 24 Findings ....................................................................................................................................... 24 Adjacent studies ....................................................................................................................... 25 Methodological considerations ........................................................................................... 26 Strengths and Weaknesses ............................................................................................................... 27 Clinical significance and improvements ...................................................................................... 27 Transferability to the ill ...................................................................................................................... 28

Conclusions and Implications .................................................................................... 29

Populärvetenskaplig sammanfattning – (på svenska) ...................................... 31 Acknowledgements ........................................................................................................ 33

References ........................................................................................................................ 33 Figures and Tables ......................................................................................................... 43

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Abbreviations

Abbreviation Meaning Abbreviation Meaning

3H

ADP

ALST

BCM

BMI

Br

CF

CPS

CT

DXA

ECW

ESPEN

FFM

FM

HPLC

ICW

IMAT

Tritium

Air Displacement Plethysmography

Appendicular Lean Soft Tissue

Body Cell Mass

Body Mass Index

Bromide

Calibration Factor

Counts Per Second

Computerized Tomography

Dual-energy X-ray Absorptiometry

Extracellular Water

The European Society of Clinical

Nutrition and Metabolism

Fat Free Mass

Fat Mass

High Performance Liquid

Chromatography

Intracellular Water

Intermuscular Adipose Tissue

IQR

LSTM

MNA

MRI

MUST

NaBr

NRS-2002

rP

SFB7

SAT

TBK

TBSMM

TBW

VAT

Interquartile Ranges

Lean Soft Tissue Mass

Mini Nutritional Assessment

Magnetic Resonance Imaging

Malnutrition Universal

Screening Tool

Sodium Bromide

Nutritional Risk Screening

2002

Pearson correlation

Impedimed SFB7

bioimpedance device

Subcutaneous Adipose Tissue

Total Body Potassium

Total Body Skeletal Muscle

Mass

Total Body Water

Visceral Adipose Tissue

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Abstract

Determination of muscle mass, fat mass and body water. Is bioimpedance with

Impedimed SFB7 consistent with reference methods?

Jakob Nilsson

Degree project, Programme in Medicine, 2016, Department of Internal Medicine and

Clinical Nutrition, Sahlgrenska Academy at University of Gothenburg, Sweden.

Background: Malnutrition and fluid imbalance are common among hospitalized

patients. Bioimpedance is a manageable and inexpensive method to estimate Total

Body Skeletal Muscle Mass (TBSMM), Fat Mass (FM), Total Body Water (TBW),

Extracellular Water (ECW) and Intracellular Water (ICW). However, further

validations against reference methods are required before introducing it to everyday

use in clinical practise.

Aim: To validate SFB7 bioimpedance device’s ability to measure TBSMM, FM,

TBW, ECW and ICW against reference methods in healthy subjects.

Methods: 50 healthy adults (men, n=25; women, n=25) were measured with SFB7

and reference methods: Dual-energy X-ray Absorptiometry (DXA), Air Displacement

Plethysmography (ADP), Dilution Techniques and Total Body Potassium (TBK).

Paired-Samples T-Test, linear regression and Bland Altman plots were used to

compare SFB7 estimates for TBSMM, FM, TBW, ECW and ICW with reference

methods.

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Results: The correlations between estimates by SFB7 and reference methods in men

were high for TBSMM (rP=0.914) and ECW (rP=0.931), however significant

differences in mean and systematic bias were found in both variables. The

correlations between SFB7 and reference methods for FM, TBW and ICW were not

impressive.

The correlations between estimates by SFB7 and reference methods in women were

high for FM (rPDXA=0.898, rPADP=0.890) and ECW (rP=0.907). SFB7 underestimated

FM and overestimated ECW significantly. The correlations between estimates by

SFB7 and reference methods for TBSMM, TBW and ICW were not impressive.

Conclusions: Bioimpedance by Impedimed SFB7 by proprietary software and

equations does not correctly predict TBSMM, FM, TBW, ECW and ICW in healthy

men and women.

Key words: Bioimpedance; body water; total body skeletal muscle mass; fat mass;

validation

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Background

Malnutrition is a condition where a deficiency or imbalance of energy, protein and

other nutrients has caused measurable and adverse changes in body composition,

function or an individual's course of disease (1).

Malnutrition is common among hospitalized patients globally, as well in Europe (2),

Asia (3, 4), Australia (5), Africa (6, 7) and America (8, 9). It results in high costs (10),

longer hospitalization and correlate with infections and worse prognoses (11, 12).

Considerable loss of body cell mass as a result of inadequate nutritional therapy has

been seen in patients in surgical gastrointestinal and orthopaedic wards (13). Parts of

the problem tend to be lack of physician awareness (12) and education (13). A Danish

study shows that patients at nutritional risk rarely are evaluated regarding nutrition

and therefore lack proper planning and monitoring (14).

There are several screening tools to detect malnutrition recommended by The

European Society of Clinical Nutrition and Metabolism (ESPEN) (15). Malnutrition

Universal Screening Tool (MUST) investigate the presence of malnutrition based on

Body Mass Index (BMI), unplanned weight loss and presence of acute disease (16).

Nutritional Risk Screening 2002 (NRS-2002) takes BMI, percent recent weight loss,

change in food intake, severity of disease and age into account (17). The Mini

Nutritional Assessment (MNA) takes height/weight, weight loss, questions related to

lifestyle, medications, mobility and food intake as well as the patients subjective

evaluation of his or hers health and nutrition into consideration (18).

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Meanwhile almost 40 % of the adult population is considered either overweight or

obese (19).

BMI is a simple method to classify underweight, overweight and obesity in adults.

Body weight in kilograms is divided by the square height in metres (kg/m2).

Underweight is defined as BMI < 18.5, normal range is defined as BMI 18.5 – 24.99,

overweight is defined as BMI ≥ 25 and obese is defined as BMI ≥ 30 (20). However,

BMI does not say to what extent the weight consists of fat, muscles or fluids,

increasing the need for more precise methodology for determination of body

composition (21).

Dehydration is also common among hospitalized patients and correlates with poor

outcomes, such as death (22, 23). Fluid overload is seen among patients with dialysis

(24), decompensated heart failure (25) and septic chock (26). Fluid overload in

dialysis patients is associated with higher risk of cardiovascular death (27). In acute

conditions, it may lead to pulmonary oedema (28).

Hydration can be approximated by repeated body weight measurements (29), blood

tests (haemoglobin, haematocrit, electrolytes and urea-to-creatinine ratio), urine

samples (osmolality, electrolytes, fluid rate) and clinical observations (blood

circulation, skin turgor and pitting oedema) (30).

Body composition can be described in different ways. Body weight can be defined as

fat mass (FM) + fat free mass (FFM), where FFM on a molecular level includes

water, proteins, glycogen, minerals and essential lipids. On a cellular level total body

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water (TBW) is divided into intracellular water (ICW) and extracellular water (ECW).

On a tissue-system level BW can be divided into adipose tissue, skeletal muscle,

bone, viscera and blood. Body composition can also be described on an atomic level

and a whole body level, the latter concerning body size, shape, exterior and physical

characteristics (31).

Anthropometry comprises several classical ways to measure a subject, including

simply measuring height and body weight. Different body parts breadths,

circumferences and areas are also of interest as well as skinfold thickness and body

volume (32).

In addition, there are more resource-demanding methods to measure body

composition. Imaging techniques such as magnetic resonance imaging (MRI) and

computerized tomography (CT) are considered the most accurate. Neutron activation

is a highly valued method where tissues are depicted by a measurable decay product

arisen when exposing chemical elements to a neutron flux. However, these methods

require very expensive equipment and are limited to research applications. In

addition, CT and Neutron activation exposes the subject to radiation (33). Air

Displacement Plethysmography (ADP), Dual-energy X-ray absorptiometry (DXA),

Dilution Techniques, Whole body counting for Total Body Potassium (TBK) and

bioimpedance are other methods (32).

ADP, a form of densitometry, estimates body volume by calculations of the pressure

changes that occur when a subject is put in a closed chamber. By also weighing the

subject the density will be given. FM can be derived from the density (34).

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For DXA measurements, X-rays with two discrete photon energy levels pass through

the body. Different tissues have different attenuation for each energy level, hence the

remaining photons measured by detectors after passage will reflect an image of body

composition. DXA divides the body into three components: FM, bone mineral (BMC)

and lean soft tissue mass (LSTM) (35). Equations based on LSTM on arms and legs,

called appendicular lean soft tissue (ALST), predominantly consisting of muscles, are

used to predict Total Body Skeletal Muscle Mass (TBSMM) (36).

Dilution Techniques can measure TBW and ECW. To measure TBW the subject is

administered a specific amount of labelled water (e.g. tritium (3H)). To measure ECW

the subject is administered a specific amount of bromide (Br) ions. The tracers (3H

and Br) will distribute in each intended compartment. The concentration of 3H and Br

in blood after an equilibration time will provide the volume of TBW and ECW by

Fick’s dilution principle (c1V1=c2V2) (32). ICW is obtained by subtracting ECW from

TBW (31).

TBK is measured using a whole-body counter. The whole-body counter uses the fact

that radioactive 40K, natural existent in the body, decays producing gamma rays. A

detector system intercepts the gamma rays and a value for TBK can further be derived

from the activity of 40K in the body (32). 98% of all potassium is located within the

cells (37). Body cell mass (BCM), consisting of muscle mass, visceral organs, blood

and brain can be derived from TBK (32). BCM can be recalculated to ICW (38).

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Bioimpedance measures TBW and ECW by allowing an alternating current to pass

through the body water at different frequencies. The current will encounter

impedance: a combination of resistance and reactance (capacitance arisen from cell

membranes). At zero frequencies the current does not penetrate the cell membranes,

thus giving a value for ECW. At high frequencies the current on the contrary does

penetrate the cell membranes, providing a value for both ECW and ICW together

becoming TBW (39). Equations to estimate total body skeletal muscle mass

(TBSMM) (40), FM and FFM (41) have been developed.

Dilution Techniques, whole body counting for TBK, ADP and DXA are considered

reference methods in this context (39). However, they have several drawbacks. The

methods would not allow bedside measurements. Whole-body counters are expensive

and difficult to access. Dilution Techniques are invasive and require advanced

equipment for analysis. DXA and dilution with labelled water expose the subject to

radiation (32). For these reasons, interest has been directed towards measurements,

which allows inexpensive, non-invasive, manageable bedside measurement where test

results can be obtained quickly. However bioimpedance, being an indirect method

requires predictive equations, which has its weaknesses (39, 42).

Measurement with bioimpedance has been shown to be consistent with reference

methods regarding body water in healthy individuals (43, 44), including changes in

ECW during both dehydration and rehydration (45). Bioimpedance is able to estimate

FM (46) as well as Subcutaneous Adipose Tissue (SAT) and skeletal muscle mass in

haemodialysis patient, indicating that it could be used to assess nutritional status (47).

However, bioimpedance lack the ability to estimate TBW and ECW in the overweight

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and obese (48) as well as in patients undergoing severe weight loss due to gastric

reduction surgery (49). Estimating errors have been seen among patients with cancer

(50), haemodialysis (51), other internal medicine disorders (52) and gastrointestinal

conditions (53). Whilst bioimpedance may provide values consistent with reference

methods at a group level, individual differences may occur, as seen in end-stage renal

disease patients (54).

In this study, we aimed to investigate if previous corresponding values between

bioimpedance and reference methods (Dilution Techniques, TBK) for estimating

body water in healthy individuals were reproducible when using an Impedimed SFB7

bioimpedance device (Impedimed SFB7, Australia), further referred to as SFB7. We

also aimed to compare the ability of SFB7 to measure TBSMM compared to DXA,

using external predictive equations. Furthermore we aimed to compare the FM

estimate provided by SFB7 with DXA and ADP. The study was meant to serve as

pilot study for possible further research in the ill.

Aim and research questions

Aim

To validate SFB7’s ability to determine TBSMM, FM, TBW, ECW and ICW in

healthy subjects in comparison with reference methods (DXA, ADP, 3H/Br-dilution,

TBK).

Research questions

• Will SFB7 provide values for TBSMM consistent with DXA?

• Will SFB7 provide values for FM consistent with DXA and ADP?

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• Will SFB7 provide values for TBW consistent with 3H-dilution?

• Will SFB7 provide values for ECW consistent with Br-dilution?

• Will SFB7 provide values for ICW consistent with Dilution Techniques and

whole body counting for TBK?

Material and Methods

Population and data collection

During the fall of 2016 volunteers were asked orally or by posting to participate in

this study. Posters were pinned to billboards at the University of Gothenburg and

Chalmers university of Technology in Sweden. Between October 6th and November

14th 2016, 50 healthy subjects over the age of 20, divided in 25 men and 25 women

had their body composition measured by SFB7, Dilution Techniques, DXA, ADP and

whole body counting for TBK. Healthy was defined as the absence of symptoms and

regular medication. People with claustrophobia were excluded because of the narrow

spaces entailed by the whole body counter and the ADP. Pregnant females were

excluded because of the exposure to radiation.

People with BMI > 34kg/m2 or < 16kg/m2 were excluded because of the

bioimpedance’s minor ability to measure these (42).

Measurements

Each subject performed all five measurements at the same occasion, over a period of

3.5 hours. The subjects attended the laboratory in the morning, after an overnight fast.

Body weight was measured with the subjects in their underwear to the nearest 0.1kg

using an electronic balance. Height was measured with a stadiometer to the nearest

0.1cm.

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Dilution Techniques. A baseline urine sample, 5-10 ml, was collected, whereupon the

subject drank a solution containing 1ml 3.7MBq/ml 3H, radiation dose 0.07mSv and

45ml 5% sodium bromide (NaBr). A postdose blood sample, 9ml, was collected 3

hours after the oral dose. The baseline urine sample was used to subtract possible

natural occurring 3H and Br from the postdose blood sample.

The serum was centrifuged. Approximately 2 ml serum was sublimated to separate

H2O and 3H from other molecules (proteins, ions etc.). 0.5 ml sublimate + 0.5 ml

standard solution (plasma, 3H 3.7MBq/ml, 1:40 000 diluted) were analysed in a Tri-

Carb liquid scintillation counter. TBW was calculated as:

TBW3H (L) = Dose × 40 000 × (Std. count – blank count)1000 × (Serum count-Urine count)

(1)

where Dose = Amount (in ml) of test solution administered, 40 000 = Standard

solution dilution factor, Std. count = Activity in standard solution, Blank count =

Background activity, 1000 = Conversion from ml to litres, Serum count = Activity in

serum water, Urine count = Activity in zero urine water.

Methanol was used for protein deposit in the bromide analysis whereupon the

supernatant was extracted. The bromide concentration (mmol/L) was determined

using a high performance liquid chromatography (HPLC). ECW, estimated as

corrected bromide space (CBS) was calculated as (55):

CBSBr (L) = Br dose[Br serum]

× 0.90 × 0.95 × 0.94 (2)

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where Br dose = Amount of bromide (in mmol) in test solution administered, [Br

serum] = Concentration (in mmol/L) bromide in serum, 0.90 = correction factor for

bromide distribution in intracellular spaces, 0.95 = correction for the Gibbs-Donnan

effect, 0.94 = correction factor for the concentration of water in serum.

ICW was defined as (31):

ICW(Dilution) (L) = TBW3H – ECWBr (3)

DXA. The DXA measurement was performed using a GE Lunar iDXA (enCORE,

USA) whole body scanner, radiation dose 0.006mSv, measuring FM, lean soft tissue

mass (LSTM) and bone mineral content (BMC).

FFM was defined as (35):

FFMDXA (kg) = LSTMDXA + BMCDXA (4)

TBSMM was calculated using a predictive model based on ALST (36):

TBSMMDXA (kg) = 1.19 × ALSTDXA − 1.65 (5)

SFB7. The measurement was performed right after the DXA measurement giving the

subject a natural rest in supine position for 5-10 minutes. Four electrodes were

positioned on the subject’s right side, two on the hand/wrist and two on the

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foot/ankle. Values for TBW, ECW, ICW, FM and FFM were received using the

proprietary software and equations of SFB7.

TBSMM was calculated using a predictive model, developed in 75 year olds and

derived from equation 5 above, according to equation 2 in Tengvall (40):

TBSMMSFB7 (kg))= −23.953 + (0.333 × Ht) + (−0.004 × Ri) + (−0.010 × Re) + (−1.72

7 × gender) + (0.042 × BW) (6)

where Ri = intracellular fluid resistance, Re = extracellular fluid resistance, Ht =

Height in cm, BW = Body Weight in kg, gender (women = 1; men = 0)

Whole body counting for TBK. The laboratory is a room partly underground, shielded

by iron ore concrete walls, floor and ceiling to reduce background radiation. A

chamber made of plate armour and lead plates further surrounds the detector system

and a bunk intended for the subject. The subjects were dressed in their underwear and

a special coat, intended to avoid radiation (e.g. radon) from their own clothes. The

subjects lay in the chamber for 300 s. The counts per second (CPS) from 40K, a

radioactive isotope of natural body potassium were measured. TBK was calculated as:

TBK (mmol) = CPS (40K) × CF1.195

(7)

where CF = Calibration Factor = 138 × BW/Ht + 66.5, where BW = Body Weight in

kg, Ht = Height in cm. The Calibration Factor was derived from measurements on

plastic phantoms of various sizes, containing known amounts of 40K.

BCM was calculated as (56):

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BCMTBK (kg) = 0.00833 × TBK (8)

ICW was calculated as (38, 57):

ICWTBK (L) = 0.70 × BCM/0.99371 (9)

where 0.70 is the assumed proportion of water in BCM and 0.99371 is the density of

water at 36°C.

ADP. The subjects entered the ADP-device (BodPod, Cosmed, Italy), an ovoid

chamber, measuring body volume, for 2 × 40 s. To reduce body surface area they

wore only their underwear. A swim cap was used to reduce hair volume. An

electronic balance determined their weight. By the density (mass/unit volume), the

Siri Equation was used to calculate percent fat (58):

Percent FatADP = 4.95/density – 4.50 (10)

FM was derived from percent fat:

FMADP (kg) = Percent fat/100 × BW (11)

where BW = Body Weight in kg

Statistical methods and variable analyses

Statistical analyses were performed using IBM® SPSS 24.0 for MAC (SPSS Inc.,

Chicago, IL, USA). Data were analysed in three parts: as a whole group (n=50) and

divided by gender (male, n=25; female, n=25).

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The number of subjects was equal to study populations used in previous similar

research (43) , however limited by a late approval from the regional ethical review

board in Gothenburg.

Estimates from reference methods and SFB7 were compared using linear regression

from which Pearson correlation (rP) was received. Paired-Samples T-Test’s were used

to compare means between a reference method and SFB7 within each group.

Independent-Samples T-Test’s were used to compare estimates between the female

group and the male group. Bland Altman plots examined the difference in a variable

measured with a reference method and SFB7 as a function of their mean. By using

linear regression in the Bland Altman plots, systematic bias could be found. Statistical

significance were equated with p-values < 0.05.

Comparisons were made between:

• TBSMM estimates by SFB7 and DXA

• FM estimates by SFB7 and DXA

• FM estimates by SFB7 and ADP

• FM estimates by ADP and DXA

• FFM estimates by SFB7 and DXA

• TBW estimates by SFB7 and 3H-dilution

• ECW estimates by SFB7 and Br-dilution

• ICW estimates by SFB7 and Dilution Techniques

• ICW estimates by SFB7 and TBK

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Data analysis

A systematic inspection of manually entered data (Height, Body Weight, gender etc.)

for each method was made ensuring correct measurements. Deviating values were

identified by the minimum and maximum values in descriptive statistics (table 1) and

by outliers and extremes in boxplots.

Outliers were defined as values deviating > 1.5 interquartile ranges (IQR) from the

end of the boxes. Extremes were defined as values deviating > 3 IQR from the end of

the boxes. Variables used to identify outliers and extremes are presented in textbox 1.

Textbox 1: Variables used to identify outliers and extremes.

TBSMM measured by SFB7 and DXA

FM measured by SFB7, DXA and ADP

FFM measured by SFB7 and DXA

TBW measured by SFB7 and 3H-dilution

ECW measured by SFB7 and Br-dilution

ICW measured SFB7 Dilution Techniques and TBK

Difference in TBSMM (DXA-SFB7)

Difference in FM (DXA-SFB7, ADP-SFB7)

Difference in FFM (DXA-SFB7)

Difference in TBW (3H-dilution - SFB7)

Difference in ECW (Br-dilution - SFB7)

Difference in ICW (Dilution Techniques-SFB7, TBK-SFB7)

Outliers and extremes were examined in the whole group, male group and the female

group.

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Ethics

The study contributed to a greater insight for each subject regarding body

composition. There were examples where the subject’s test results were not consistent

with their own appreciation, e.g. regarding body fat. This could possibly result in

negative alterations in self-image, eating habits and exercise. Furthermore, there was

a risk to detect abnormal nutritional status or an illness. One subject had to recur for

further investigations because of a tendency to low bone mineral density.

The radiation dosage from the DXA (0.006mSv) and 3H-dilution (0.07mSv)

measurement was less than the dosage from a dental X-ray, however not to be

considered negligible. A few subjects found the blood and urine specimen collection

unpleasant. The author underwent all the measurements.

Data could be linked to the subjects only in connection with the measurements.

Before data processing all names were replaced with a key. Informed consent was

obtained from all participants.

A medical doctor with specialized expertise in clinical nutrition reviewed all the

measured values. Adequate follow-up was available in case of deviation.

The regional ethical review board in Gothenburg (application number: 773-16) and

the Radiation Protection Committee (application number: 16-43) in the Region of

Västra Götaland approved this study.

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Results

There was no significant difference in age between the genders. The male group was

taller, weighed more and had a larger BMI. For full descriptive statistic, see table 1.

All 50 participants took part in all measurements. Due to technical errors 9 subjects

repeated the TBK measurement and 2 subjects repeated the SFB7 measurement

within a week from the original occasion. These were included. The systematic

inspection of manually entered data and descriptive statistics (table 1) required no

exclusion. 31 outliers and 6 extremes in 12 subjects were found. One female, who was

an outlier in the “difference in ICW (TBK-SFB7)” variable and who repeated the

TBK measurement at a different occasion was excluded in a recast of statistics

regarding TBK. The remaining outliers and extremes were included.

SFB7 measurements for each variable (TBSMM, FM, TBW, ECW and ICW) are

further validated under separate subheadings.

TBSMM estimates, a comparison between DXA and SFB7

SFB7 underestimates mean TBSMM in comparison with DXA in all three groups

(table 2). The underestimation was more pronounced in the male group than in the

female group (p = 0.012).

The correlation between DXATBSMM and SFB7TBSMM was high in the whole group (rP

= 0.943, p < 0.001) and the male group (rP = 0.914, p < 0.001) but only moderate in

the female group (rP = 0.572, p = 0.003) (table 2, figure 1).

A systematic bias was found in both the whole group and the male group according to

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the linear regression in Bland Altman plot. The correlation was positive in both the

whole group (rP = 0.450, p = 0.001) and the male group (rP = 0.779, p < 0.001),

reflecting an increasing underestimation by SFB7 as mean TBSMM increases. No

systematic bias was found in the female group (table 2, figure 2).

FM estimates, a comparison between DXA, ADP and SFB7

SFB7 underestimates mean FM in comparison with DXA in all three groups (table 3).

There was no significant difference in mean FM between the genders (p = 0.444).

The correlation between DXAFM and SFB7FM was high in the female group (rP =

0.898, p <0.001), somewhat lower in the whole group (rP = 0.816, p < 0.001) and

moderate in the male group (rP = 0.636, p = 0.001) (table 3, figure 3a-c).

SFB7’s underestimation of FM in comparison with DXA was balanced by an

overestimation of FFM (table 3).

SFB7 underestimates mean FM in comparison with ADP in the whole group and the

female group. There was no significant difference in mean FM between ADP and

SFB7 in the male group. The correlation between ADPFM and SFB7FM was high in the

female group (rP = 0.890, p < 0.001), moderate in the whole group (rP = 0.745, p <

0.001) and low in the male group (0.408, p < 0.043) (table 3, figure 3d-f).

No systematic bias was found in any of the groups, neither when comparing SFB7 to

DXA or SFB7 to ADP, according to the linear regression in Bland Altman plot.

However, a wide spread along the y-axis and a wide spread in the same vertical line is

prominent in the plots, especially in the whole group and the male group (table 3,

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figure 4). The wide spread in the same vertical line reflects how SFB7 both

underestimates and overestimates FM by several kg among subjects with the

approximate same amount of FM.

The correlation between FM measured by the reference methods (ADP and DXA)

was high in all three groups. However, ADP tends to provide lower estimates for FM

than DXA. No systematic bias was found, according to the linear regression in Bland

Altman plot (table 3).

TBW estimates, a comparison between 3H-dilution and SFB7

SFB7 slightly overestimates mean TBW in comparison with 3H-dilution in the whole

group. In both the male group and the female group SFB7 mean difference for TBW

were consistent with Dilution Techniques. The correlation between 3H-dilutionTBW

and SFB7TBW was high in the whole group (rP = 0.958, p < 0.001), rather high in the

male group (rP = 0.880, p < 0.001) but moderate in the female group (rP = 0.763, p <

0.001) (table 4, figure 5).

There was no systematic bias in any of the groups, according to the linear regression

in Bland Altman plot. However, a wide spread along the y-axis and in the same

vertical line is prominent in the plots (table 4, figure 6).

ECW estimates, a comparison between Br-dilution and SFB7

SFB7 overestimates mean ECW in comparison with Br-dilution in all three groups

(table 5). The overestimation is more pronounced in the male group than in the

female group (p < 0.001).

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The correlation between Br-dilutionECW and SFB7ECW was high in all three groups

(table 5, figure 7).

A systematic bias was found in the whole group (rP = -0.790, p < 0.001) and the male

group (rP = -0.487, p < 0.014), according to the linear regression in Bland Altman

plot. The correlation was negative in the two groups, reflecting an increasing

overestimation of ECW by SFB7 as mean ECW increases. No systematic bias was

found in the female group (table 5, figure 8).

ICW estimates, a comparison between Dilution Techniques and SFB7 and

between TBK and SFB7

Dilution-SFB7: SFB7 underestimates mean ICW in comparison with Dilution

Techniques in the whole group and the male group. There was no significant

difference in mean ICW between SFB7 and Dilution Techniques in the female group.

The correlation was rather high in the whole group (rP = 0.884, p < 0.001) but

moderate in the male group (rP = 0.667, p < 0.001) and the female group (rP = 0.548,

p = 0.005) (table 6, figure 9a-c).

A systematic bias was found in the whole group according to the linear regression in

Bland Altman plot. The correlation was positive in the whole group (rP = 0.360, p =

0.010), reflecting an increasing underestimation by SFB7 as mean ICW increases. No

systematic bias was found in the male group and the female group. A wide spread

along the y-axis and in the same vertical line is prominent in the plots in all three

groups (table 6, fig. 10a-c).

24

TBK-SFB7: SFB7 overestimates mean ICW in comparison with TBK in the whole

group and the female group while no significant difference in mean ICW was found

in the male group. The correlation was high in the whole group (rP = 0.895, p <

0.001) but moderate in the male group (rP = 0.745, p < 0.001), and the female group

(rP = 0.507, p = 0.010) (table 6, fig. 9d-f).

No systematic bias was found in any of the groups, according to the linear regression

in Bland Altman plot, however a wide spread along the y-axis and in the same vertical

line is prominent in the plots (table 6, fig. 10d-f).

When excluding the subject who was an outlier and who repeated the TBK

measurement at a different occasion, no significant difference in mean ICW between

TBK and SFB7 occurred (whole group, p = 0.847; female group, p = 0.715) according

to Independent-Samples T-Test. In both groups, the correlation between TBKICW and

SFB7ICW improved slightly and as before the exclusion, no systematic bias was found

(table 6).

Discussion

Findings

We have validated SFB7’s ability to predict TBSMM, FM, TBW, ECW and ICW in a

healthy study population. Unfortunately, this study shows a lack in SFB7’s ability to

predict all these values in men, women and when combining the genders.

25

Adjacent studies

The TBSMM equation for bioimpedance was derived from DXA-estimates. The

equation was developed in 75 year olds from Sweden (n=98; men, n=50; women,

n=48; medication use in 87/98). In comparison with our whole group (n=50) a similar

correlation was found between TBSMM measured by DXA and bioimpedance (rPn=98

= 0.96, rPn=50 = 0.943), however no systematic bias was found in the other study.

Apart from the age difference, the latter could be explained by their use of a different

bioimpedance analyser (Xitron Hydra 4200) and DXA-scanner (Lunar Prodigy) (40).

Bioimpedance underestimating FM in comparison with DXA has been shown in a

previous study with a slightly different population (Norwegian; n=93; men, n=57;

women, n=36; age 51 ± 11.5 years; BMI 30.9 ± 4.5 kg/m2), using the same DXA but

a different bioimpedance analyser (BodyScout). The difference in mean FM between

DXA and bioimpedance was more pronounced in that study (Δmean FMn=93 = 4.1 ±

3.6kg, Δmean FMn=50 = 2.8 ± 3.4kg) but they presented a higher correlation (rPn=93 =

0.95, rPn=50 = 0.816). In contrast to our study, their difference in mean FM was

significantly higher in the male group than in the female group and a systematic bias

was found in the whole group. The study concluded that the proprietary software of

the bioimpedance device should not be used in estimation of body FM on an

individual level (59).

A previous study in healthy subjects (n=73, 36.2 ± 10.0 years) presented a similar

correlation between ICW measured by TBK and bioimpedance (rPn=73 = 0.85; rPn=50 =

0.895). However, their difference in mean ICW was more appealing (0.08 litres). The

differences could be explained by their use of a different bioimpedance analyser

26

(Xitron 4000B), different gender distribution (63 men, 10 women), nationality

(Italian) and probable differences in whole-Body Counting constructions and

calibrations (44).

A previous Dietary Thesis at the University of Gothenburg in Sweden (n=42, equal

gender distribution, healthy subjects, age 47.4 ± 18.6 years) presented a similar

correlation between TBW measured by 3H-dilution and bioimpedance (r2n=42 = 0.94;

r2n=50 = 0.917; where r2 = rP squared) and ECW measured by Br-dilution and

bioimpedance (r2n=42 = 0.93; r2

n=50 = 0.914) but a higher correlation between ICW

measured by Dilution Techniques and bioimpedance (r2n=42 = 0.90; r2

n=50 = 0.781).

They used the same Dilution Techniques as in our study. Bioimpedance

overestimated FFM in comparison with DXA in our study, while no significant

difference in mean FFM was found in their study. The correlation between DXAFFM

and bioimpedanceFFM was similar (r2n=42 = 0.90; r2

n=50 = 0.925). Differences could be

explained by their use of a different bioimpedance analyser (Xitron Hydra 4200

devices (Xitron Technologies, San Diego, USA) and DXA-scanner (Lunar progidy,

GE Lunar Corp, Madison, USA) (43).

Methodological considerations

In reference to a high frequency of malnutrition (2-14) and fluid imbalance (22, 23)

among hospitalized patients, this study is a relevant contribution to the pursuit of

simpler methods to estimate body composition. The research questions were concise

and designed to be answered with a simple yes or no. The accuracy in choice of

reference methods and statistical analyses provides a high credibility to the answers.

The study was performed in a controlled environment. Competent personnel with

many years of experience in the field performed a key part of the measurements.

27

Strengths and Weaknesses

One of the greatest strengths of this study was the usage of several reference methods

and statistical analyses. Since the study population was evenly divided by gender, we

were able to make proper comparisons in SFB7’s ability to determine body

composition estimates between men and women.

Considering weaknesses, the study population was very homogenous. A large part of

the group had a great common interest in fitness, among them elite athletes on a

national level. Higher correlation was often found in the whole group (n = 50) than in

the gender groups (n = 25), indicating the need of a larger study population to validate

SFB7’s ability to determine body composition estimates in men and women

separately. Also, several subjects repeated the TBK and SFB7 measurement on a

different occasion, during which time normal variations in body composition may

have occurred, possibly resulting in inaccurate values.

To further elucidate the value of bioimpedance in clinical settings it would have been

of interest to compare devices from different brands and to use more accurate

reference methods such as MRI-scans.

Clinical significance and improvements Whether SFB7’s inability to predict body composition estimates lies within the

software used to determine raw data (resistance, reactance) or the equations further

used to predict body composition estimates or within both is for us impossible to say.

Since we do not know the equations used to predict these values, speculations for

improvements are difficult. A possibility is to use more individualized values for body

density. Bioimpedance equations rely on an appreciated value for body density

28

(1.05g/ml). The ADP in this study provided values for body density extending from

1.01 – 1.08g/ml, indicating room for improvement.

An attempt to develop bioimpedance equations for TBW and ECW specific for elite

athletes was made recently (60). Even though our study population should not be

considered elite athletes, they sure differed from the general population regarding

physical fit. Perhaps data collected in this study could underpin a cross-validation

study focusing on developing bioimpedance equations specific for moderate fitness

performers and athletes.

The equation used to predict TBSMM (40) needs improvement to fit a wider

population. Among its weaknesses is the simplification were a differentiating between

men and women is made using only a constant (−1.727).

Since SFB7 provides unsatisfying values in our healthy study population we cannot

expect it to provide proper values in patients with additionally medication and

diagnoses affecting fluid balance.

Perhaps, as suggested by a recent study in peritoneal dialysis patients, we should

apply caution, and not let bioimpedance replace our subjective assessment (61).

Transferability to the ill The results from this study are not applicable to the ill, nor were they intended to. If

SFB7 had provided more satisfying body composition estimates in our healthy study

population, further research in the ill would have been of interest. Our homogenous,

29

young and healthy study population, including several lean and muscular subjects, is

quite the opposite of the typical elderly patient.

As known from previous research, muscle mass tend to decrease with age (62).

When comparing TBSMM determined by equation 2 in Tengvall and SFB7 with

TBSMM determined by DXA, a systematic bias was found in our whole group and

male group reflecting a lack of precision in SBF7’s ability to determine TBSMM

dependent on the subjects’ amount of muscle mass.

TBW tends to decrease with age as well. Whether it is due to the loss of ICW and

BCM, or ECW is controversial (63). However it may have an impact on the

bioimpedance estimates.

The elderly has an increased amount of Visceral Adipose Tissue (VAT) in

comparison with the young (64), which may have an impact on the bioimpedance

estimates for FM.

Intermuscular Adipose Tissue (IMAT) increases in the elderly (65) which may affect

the bioimpedance ability to estimate both TBSMM and FM.

Conclusions and Implications

SFB7’s proprietary software and equations does not provide values for FM, TBW,

ECW and ICW consistent with reference methods in healthy subjects. Equation 2 in

Tengvall applied on SFB7 does not correctly estimate TBSMM.

30

We are brought further away from the vision of a manageable method to detect

deviant nutrition and fluid imbalance among hospitalized patients. Improvements of

SFB7’s equations and equation 2 in Tengvall in general and/or development of more

individualized equations are needed. The SFB7’s software should be reviewed.

This underlines the need to scrutinize proprietary equation if possible, as these have a

profound impact on the performance of the bioimpedance’s output. Thus,

bioimpedance methods are not just population specific, but also device- and equation

specific.

31

Populärvetenskaplig sammanfattning – (på svenska)

Bestämning av muskelmassa, fettmassa och kroppsvatten. Är bioimpedans med

Impedimed SFB7 överensstämmande med referensmetoder?

Det är vanligt att patienter på sjukhus lider av undernäring och rubbad vätskebalans.

Detta leder till längre vårdtider, sjukare patienter, högre vårdkostnader och i värsta

fall döden.

Bioimpedans mäter det elektriska motståndet i kroppen och härleder utifrån

ekvationer värden för kroppens olika vattenrum (totalt vatten, vattnet utanför cellerna

och vattnet inuti cellerna). Beroende på fabrikat kan även värden för fettmassa och

muskelmassa erhållas. Det är en lätthanterlig och billig metod, där man likt vid en

EKG-undersökning fäster elektroder till kroppen. Svar fås på bara några sekunder.

Metoden skulle kunna vara till hjälp inom sjukvården för att identifiera patienter i

behov av extra näring, vätsketerapi och vätskedrivande.

Innan bioimpedans kan börja användas som rutin inom sjukvården krävs dock fler

studier som utvärderar metoden gentemot referensmetoder. I vår studie lät vi 50 friska

forskningspersoner, hälften män och hälften kvinnor, mäta sig med en bioimpedans-

apparat av modellen Impedimed SFB7 (härefter hänvisad till som SFB7) och 4

referensmetoder under en och samma förmiddag. SFB7 ger värden för de olika

vattenrummen samt fettmassa. För att få bioimpedans-värden även för muskelmassa

använde vi en extern ekvation. Referensmetoderna bestod av följande:

32

• Dual-energy X-ray Absorptiometry (DXA), en röntgenundersökning som

bland annat mäter fettmassa och muskelmassa.

• Air Displacement Pletysmography (ADP), en kammare som med hjälp av

tryckförändringar beräknar kroppens densitet och därifrån härleder värden för

fettmassa.

• Helkroppsräknare för totalkalium, en äldre teknik där kroppens radioaktivitet

från kalium mäts med strålningsdetektorer i en kammare. Utifrån kroppens

kaliuminnehåll kan vattnet inuti cellerna beräknas.

• En utspädningsteknik där forskningspersonerna fick dricka tritium (som

fördelar sig i totalt vatten) och bromid (som fördelar sig i vattnet utanför

cellerna). Ämnenas koncentrationer analyserades därefter i ett blodprov varpå

beräkningar för de olika vattenrummens volymer kunde göras.

SFB7’s värden för fettmassa och de olika vattenrummen jämfördes med

referensmetodernas motsvarande värden. Detsamma gällde för muskelmassan som

SFB7 beräknade med hjälp av den externa ekvationen. Hur väl mätningarna stämde

överens med varandra undersöktes med medelvärdesjämförelser och korrelation

(sambandet mellan SFB7’s värden för en kroppskomponent och en referensmetods

värden för samma komponent). Vidare undersöktes om SFB7 gjorde några

systematiska fel beroende på hur mycket av en kroppskomponent

forskningspersonerna hade.

Resultaten var dock nedslående. SFB7 gav värden för både fettmassa och de olika

vattenrummen, som med en eller flera statistiska metoder skiljde sig från

33

referensmetoderna på ett betydande sätt. Detsamma gällde de värden för muskelmassa

som SFB7 tog fram med hjälp av den externa ekvationen.

Förbättringar av SFB7’s mjukvara och/eller dess ekvationer, samt den externa

ekvationen som användes för att beräkna muskelmassa krävs innan mätningar på både

friska individer och patienter inom sjukvården är lämpligt.

Acknowledgements

The author acknowledges the contributions of the participants, V. Malmros for the

H3- and Br-dilution assay, V. Malmros and M. Hoppe for the bioimpedance and DXA

measurements, M. Isaksson, N. Dilan and M. Hjellström for the TBK measurements,

E. Järvinen for providing BodPod manual and instructions and L. Ellegård and I.

Bosaeus for supervision.

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43

Figures and Tables

pM

eanSD

Min

Max

Mean

SDM

inM

axM

eanSD

Min

Max

0.120A

ge (years)26.4

6.220.0

61.127.8

7.820.2

61.125.1

3.520.0

34.4< 0.001

Height (cm

)174.0

9.1153.5

197.0180.7

6.3169.5

197.0167.2

6.0153.5

177.0< 0.001

Weight (kg)

70.112.1

47.699.3

79.09.0

64.599.3

61.17.0

47.681.6

< 0.001B

MI (kg/m

2)23.0

2.316.3

27.324.2

1.821.1

27.321.8

2.216.3

27.0< 0.001

SFB7

TB

SMM (kg)

24.46.1

10.735.3

29.72.7

25.735.3

19.02.9

10.722.5

< 0.001SFB

7FM (kg)

12.85.8

1.938.7

11.24.7

1.917.8

14.36.5

3.938.7

0.060SFB

7FFM (kg)

57.312.6

37.386.3

67.88.6

54.486.3

46.74.5

37.353.9

< 0.001SFB

7T

BW (l)

41.99.2

27.363.2

49.66.3

39.863.2

34.23.3

27.339.5

< 0.001SFB

7E

CW (l)

17.44.0

11.128.5

20.82.7

16.728.6

14.01.3

11.116.4

< 0.001SFB

7IC

W (l)24.5

5.415.3

36.828.8

3.923.2

36.820.2

2.515.3

24.4< 0.001

DX

AT

BSM

M (kg)27.3

7.216.0

43.033.5

4.426.6

43.021.1

2.316.0

25.4< 0.001

DX

AFM (kg)

15.65.4

7.136.9

14.44.5

7.125.5

16.76.0

8.837.0

0.129D

XA

FFM (kg)55.2

12.335.3

85.265.6

8.455.0

85.244.9

4.035.3

52.1< 0.001

AD

PFM (kg)

13.95.5

5.234.1

12.34.5

5.924.8

15.66.1

5.234.1

0.036A

DP

BD (kg/l)

1.050.02

1.011.08

1.060.01

1.031.08

1.040.02

1.011.07

< 0.0013H

TB

W (l)41.1

9.025.7

63.748.6

6.241.1

63.733.6

3.325.7

39.8< 0.001

Br

EC

W (l)15.4

2.810.0

23.217.5

2.214.4

23.213.4

1.410.0

16.3< 0.001

3H-B

rIC

W (l)25.6

6.515.7

41.131.1

4.325.9

41.120.2

2.415.7

25.6< 0.001

TB

KIC

W (l)23.0

5.813.7

37.027.8

4.122.4

37.018.1

2.013.7

21.9< 0.001

p = by Independent-Samples T-Test, com

paring means betw

een the male group and the fem

ale group

Table 1: Descriptive statistic in 50 healthy subjects, presented as m

ean, standard deviation, min and m

ax.W

hole group (n=50)M

ale group (n=25)Fem

ale group (n=25)

Abbreviations; SFB

7, Impedim

ed SFB7 B

ioimpedance device; D

XA

, Dual-Energy X

-ray Absorptiom

etry; AD

P, Air D

isplacement

Pletysmography; 3H

, Tritium; B

r, Brom

ide; TBK

, Total Body Potassium

; BM

I, Body M

ass Index; TBSM

M, Total B

ody Skeletal Muscle M

ass; FM

, Fat Mass; FFM

, Fat Free Mass; TB

W, Total B

ody Water; EC

W, Extracellular W

ater; ICW

, Intracellular Water; B

D, B

ody Density; SD

, Standard D

eviation; min, m

inimum

; max, m

aximum

44

Table 2: Difference in TBSMM estimates between DXA and SFB7, analysed with Paired-Samples T-Test, linear regression and Bland Altman plot with linear regression. Difference in mean Linear regression

in BA n kg p1 rP p2 LoA rP p3 TBSMM DXA-SFB7 (whole group)

50 2.9 ± 2.5 <0.001 0.943 <0.001 -1.9 – 7.8 0.450 0.001

TBSMM DXA-SFB7 (male)

25 3.8 ± 2.2 <0.001 0.914 <0.001 -0.5 – 8.1 0.779 <0.001

TBSMM DXA-SFB7 (female)

25 2.1 ± 2.5 <0.001 0.572 0.003 -2.8 – 6.9 -0.268 0.194

Abbreviations; TBSMM, Total Body Skeletal Muscle Mass; DXA, Dual-energy X-ray Absorptiometry; SFB7, Impedimed SFB7 bioimpedance device; BA, Bland Altman plot Data presented as difference in mean in kg ± 1 Standard Deviation (SD), ± 1.96 SD Limits of Agreement (LoA) and Pearson correlation (rP) p1 = by Paired-Samples T-Test. p2 = by linear regression. p3 = by linear regression in Bland Altman plot

Figure 1: Correlation between TBSMM in kg measured by DXA (y-axis) and SFB7 (x-axis). a = whole group, b = male group, c = female group. Black line, regression line. Abbreviations; TBSMM, Total Body Skeletal Muscle Mass; SFB7, Impedimed SFB7 bioimpedance device; DXA, Dual-energy X-ray Absorptiometry

Figure 2: Bland Altman Plots with linear regression displaying the relationship between difference in TBSMM (TBSMMΔ) and mean TBSMM (TBSMMx ̄), where TBSMMΔ = DXATBSMM-SFB7TBSMM, TBSMMx ̄ = (DXATBSMM +SFB7TBSMM)/2. a = whole group, b = male group, c = female group. Continuous horizontal line, difference in mean; crosshatched lines, difference in mean ± 1.96 SD, Limits of Agreement (LoA); grey line, regression line. Abbreviations; TBSMM, Total Body Skeletal Muscle Mass; SFB7, Impedimed SFB7 bioimpedance device; DXA, Dual-energy X-ray Absorptiometry

45

Figure 3: Correlation between FM measured by DXA (y-axis) and SFB7 (x-axis) (a-c) and by ADP (y-axis) and SFB7 (x-axis) (d-f). a,d = whole group, b,e = male group, c,f = female group. Black line, regression line. Abbreviations; FM; Fat Mass, SFB7, Impedimed SFB7 bioimpedance device; DXA, Dual-energy X-ray Absorptiometry; ADP, Air Displacement Plethysmography

Figure 4: Bland Altman Plots with linear regression displaying the relationship between the difference in FM (FMΔ) and mean FM (FMx ̄), where FMΔ (a-c) = DXAFM-SFB7FM, FMΔ (d-f) ADPFM-SFB7FM, FMx ̄ (a-c) = (DXAFM+SFB7FM)/2, FMx ̄ (d-f) = (ADPFM+SFB7FM)/2. a,d = whole group, b,e = male group, c,f = female group. Continuous horizontal line, difference in mean; crosshatched lines, difference in mean ± 1.96 SD, Limits of Agreement (LoA); grey line, regression line. Abbreviations; FM; Fat Mass, SFB7, Impedimed SFB7 bioimpedance device; DXA, Dual-Energy X-ray Absorptiometry; ADP, Air Displacement Plethysmography

46

Table 3: Difference in FM and FMM estimates between SFB7 and reference methods, analysed with Paired-Samples T-Test, linear regression and Bland Altman plot with linear regression. Difference in mean Linear regression in BA n kg p1 rP p2 LoA rP p3 FM DXA-SFB7 (whole group)

50 2.8 ± 3.4 <0.001 0.816 <0.001 -3.9 – 9.5 -0.126 0.385

FM DXA-SFB7 (male group)

25 3.2 ± 4.0 0.001 0.636 0.001 -4.6 – 10.9 -0.047 0.824

FM DXA-SFB7 (female group)

25 2.4 ± 2.9 <0.001 0.898 <0.001 -3.2 – 8.0 -0.165 0.431

FM ADP-SFB7 (whole group)

50 1.2 ± 4.1 0.049 0.745 <0.001 -6.8 – 9.1 -0.082 0.573

FM ADP-SFB7 (male group)

25 1.1 ± 5.0 0.289 0.408 0.043 -8.7 – 10.9 -0.059 0.779

FM ADP-SFB7 (female group)

25 1.2 ± 3.0 0.047 0.890 <0.001 -4.6 – 7.1 -0.150 0.475

FM ADP-DXA (whole group)

50 -1.6 ± 1.7 <0.001 0.953 <0.001 -4.9 – 1.7 0.061 0.675

FM ADP-DXA (male group)

25 -2.1 ± 1.9 <0.001 0.907 <0.001 -5.9 – 1.7 -0.042 0.841

FM ADP-DXA (female group)

25 -1.2 ± 1.3 <0.001 0.979 <0.001 -3.6 – 1.3 0.021 0.921

FFM DXA-SFB7 (whole group)

50 -2.0 ± 3.5 <0.001 0.962 <0.001 -8.8 – 4.8 -0.090 0.535

FFM DXA-SFB7 (male group)

25 -2.2 ± 4.1 0.011 0.887 <0.001 -10.2 – 5.7 -0.035 0.868

FFM DXA-SFB7 (female group)

25 -1.8 ± 2.8 0.004 0.786 <0.001 -7.4 – 3.8 -0.194 0.354

Abbreviations; FM; Fat Mass, FFM; Fat Free Mass; SFB7, Impedimed SFB7 bioimpedance device; DXA, Dual-energy X-ray Absorptiometry; ADP, Air Displacement Plethysmography; BA, Bland Altman plot Data presented as difference in mean in kg ± 1 Standard Deviation (SD), ± 1.96 SD Limits of Agreement (LoA) and Pearson correlation (rP) p1 = by Paired-Samples T-Test. p2 = by linear regression. p3 = by linear regression in Bland Altman plot

Figure 5: Correlation between TBW in litres measured by Tritium dilution (y-axis) and SFB7 (x-axis). a = whole group, b = male group, c = female group. Black line, regression line. Abbreviations; TBW, Total Body Water; SFB7, Impedimed SFB7 bioimpedance device; 3H, Tritium dilution

47

Table 4: Difference in TBW estimates between Tritium dilution and SFB7, analysed with Paired-Samples T-Test, linear regression and Bland Altman plot with linear regression. Difference in mean Linear regression in BA

n litres p1 rP p2 LoA rP p3 TBW 3H-SFB7 (whole group)

50 -0.8 ± 2.7 0.032 0.958 <0.001 -6.1 – 4.4 -0.072 0.619

TBW 3H-SFB7 (male group)

25 -1.0 ± 3.0 0.110 0.880 <0.001 -7.0 – 5.0 -0.035 0.867

TBW 3H-SFB7 (female group)

25 -0.7 ± 2.3 0.165 0.763 <0.001 -5.1 – 3.8 -0.009 0.966

Abbreviations; TBW, total body water; SFB7, Impedimed SFB7 bioimpedance device; 3H, Tritium dilution; BA, Bland Altman plot Data presented as difference in mean in litres ± 1 Standard Deviation (SD), ± 1.96 SD Limits of Agreement (LoA) and Pearson correlation (rP) p1 = by Paired-Samples T-Test. p2 = by linear regression. p3 = by linear regression in Bland Altman plot

Table 5: Difference in ECW estimates between Bromide dilution and SFB7, analysed with Paired-Samples T-Test, linear regression and Bland Altman plot with linear regression. Difference in mean Linear regression in BA n litres p1 rP p2 LoA rP p3 ECW Br-SFB7 (whole group)

50 -1.9 ± 1.6 <0.001 0.956 <0.001 -5.1 – 1.2 -0.790 <0.001

ECW Br-SFB7 (male group)

25 -3.3 ± 1.0 <0.001 0.931 <0.001 -5.3 – -1.2 -0.487 0.014

ECW Br-SFB7 (female group)

25 -0.6 ± 0.6 <0.001 0.907 <0.001 -1.8 – 0.6 0.165 0.431

Abbreviations; ECW, Extracellular Water: SFB7, Impedimed SFB7 bioimpedance device; Br, Bromide dilution; BA, Bland Altman plot Data presented as difference in mean in litres ± 1 Standard Deviation (SD), ± 1.96 SD Limits of Agreement (LoA) and Pearson correlation (rP) p1 = by Paired-Samples T-Test. p2 = by linear regression. p3 = by linear regression in Bland Altman plot

Figure 6: Bland Altman Plots with linear regression displaying the relationship between difference in TBW (TBWΔ) and mean TBW (TBWx ̄), where TBWΔ = 3HTBW-SFB7TBW, TBWx ̄ = (3HTBW+SFB7TBW)/2. a = whole group, b = male group, c = female group. Continuous horizontal line, difference in mean; crosshatched lines, difference in mean ± 1.96 SD, Limits of Agreement (LoA); grey line, regression line. Abbreviations; TBW, Total Body Water; SFB7, Impedimed SFB7 bioimpedance device; 3H, Tritium dilution

48

Table 6: Difference in ICW estimates between Dilution Techniques and SFB7 and between TBK and SFB7, analysed with Paired-Samples T-Test, linear regression and Bland Altman plot with linear regression. Difference in mean Linear regression in BA n litres p1 rP p2 LoA rP p3 ICW Dil-SFB7 (whole group)

50 1.1 ± 3.1 0.013 0.884 <0.001 - 4.9 – 7.1 0.360 0.010

ICW Dil-SFB7 (male group)

25 2.3 ± 3.3 0.002 0.667 <0.001 -4.2 – 8.8 0.131 0.533

ICW Dil-SFB7 (female group)

25 0.0 ± 2.3 0.934 0.548 0.005 -4.6 – 4.5 -0.075 0.723

ICW TBK-SFB7 50 -1.6 ± 2.6 <0.001 0.895 <0.001 -6.7 – 3.5 0.151 0.295 (whole group) 49* -1.7 ± 2.5 <0.001 0.901 <0.001 -6.6 – 3.3 0.211 0.146 ICW TBK-SFB7 (male group)

25 -1.1 ± 2.9 0.074 0.745 <0.001 -6.7 – 4.5 0.073 0.729

ICW TBK-SFB7 25 -2.1 ± 2.3 <0.001 0.507 0.010 -6.5 – 2.4 -0.268 0.196 (female group) 24* -2.3 ± 2.0 <0.001 0.580 0.003 -6.3 – 1.7 -0.180 0.399 Abbreviations; ICW, Intracellular Water; SFB7, Impedimed SFB7 bioimpedance device; Dil, Dilution Techniques; TBK, Total Body Potassium; BA, Bland Altman plot Data presented as difference in mean in litres ± 1 Standard Deviation (SD), Pearson correlation (rP), ± 1.96 SD Limits of Agreement (LoA) p1 = by Paired-Samples T-Test. p2 = by linear regression. p3 = by linear regression in Bland Altman plot *One subject excluded due to repeated TBK measurement at a different occasion.

Figure 8: Bland Altman Plots with linear regression displaying the relationship between difference in ECW (ECWΔ) and mean ECW (ECWx ̄), where ECWΔ = BrECW-SFB7ECW, ECWx ̄ = (BrECW +SFB7ECW)/2. a = whole group, b = male group, c = female group. Continuous horizontal line, difference in mean; crosshatched lines, difference in mean ± 1.96 SD, Limits of Agreement (LoA); grey line, regression line (hardly visible in c). Abbreviations; ECW, Extracellular Water; SFB7, Impedimed SFB7 bioimpedance device; Br, Bromide dilution

Figure 7: Correlation between ECW in litres measured by SFB7 (x-axis) and Bromide dilution (y-axis). a = whole group, b = male group, c = female group. Black line, regression line. Abbreviations; ECW, Extracellular Water; SFB7, Impedimed SFB7 bioimpedance device; Br, Bromide dilution

49

Figure 9: Correlation between ICW in litres measured by SFB7 (x-axis) and Dilution Techniques (y-axis) (a-c) and by SFB7 (x-axis) and TBK (y-axis) (d-f). a,d = whole group, b,e = male group, c,f = female group. Black line, regression line. Abbreviations; ICW, Intracellular Water; SFB7, Impedimed SFB7 bioimpedance device; Dil, Dilution Techniques; TBK, Total Body Potassium

Figure 10: Bland Altman plots with linear regression displaying the relationship between difference in ICW (ICWΔ) and mean ICW (ICWx ̄), where ICWΔ (a-c) = ICWDil-ICWSFB7, ICWΔ (d-f) ICWTBK-ICWSFB7, ICWx ̄ (a-c) = (ICWDil+ICWSFB7)/2, ICWx ̄ (d-f) = (ICWTBK+ICWSFB7)/2. a,d = whole group, b,e = male group, c,f = female group. Continuous horizontal line, difference in mean; crosshatched lines, difference in mean ± 1.96 SD, Limits of Agreement (LoA); grey line, regression line. Abbreviations: ICW, Intracellular Water; SFB7, Impedimed SFB7 bioimpedance device; Dil, Dilution Techniques; TBK, Total Body Potassium

determination of muscle mass, fat mass and body water. is bioimpedance … · 2017. 11. 1. ·...

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