body composition assessment issues in athletes · 2019. 5. 29. · body composition 1 assessment...

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Body composition assessment issues in

athletes

Duncan Macfarlane

IHP, HKU

Parts of this lecture were based on lecture notes provided by the Lindsay Carter Anthropometric Archive, AUT, NZ

LEARNING OUTCOMES:

1. Why examine body composition

2. Understand the different levels of assessment of body composition

3. Limitations/benefits of using skinfolds

4. Limitations/benefits of using bioelectric impedance (BIA)

5. Limitations/benefits of using of DXA

WHY ASSESS BODY COMPOSITION:

1. Body composition is a component of fitness

2. It can be a screening tool to assess health risk factors

3. To monitor progress and effectiveness of a training program

4. It can be a screening tool to determine what sport someone is most suited to (???)

My “SomatoMac” iPad project 4

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Basic Two compartment model

TBM = FM + LBM

�  FM = dissectible adipose tissue + fat, including subcutaneous adipose tissue & deep adipose tissue in stores like large intestine & that surrounding joints.

�  LBM = all other tissue + small amount of fat contained within.

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Does fat free mass = lean body mass?

�  NO, FFM implies all other tissue minus all fat.

�  But practically this is not possible to achieve since fat is present inside tissues such as bone & between fibres & fasciculi of muscle.

Models of body composition

• Two-compartment model is used for sport science

• fat mass and fat-free mass

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Methods to assess body fat

Direct methods: �  Dissection

Indirect methods: �  Hydrostatic weighing �  Dual energy x-ray absorptiometry (DEXA, DXA)

Doubly indirect methods: �  Body mass index (BMI) = yuck! �  Bioelectrical impedance analysis (BIA) �  Skinfold/circumference method

9 BMI �  Mass (kg) / square of Height (m)

�  Does NOT distinguish FAT v MUSCLE

10 �  Body Mass=75 kg �  Height= 1.66m �  BMI= 27.2 �  Overweight??

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Levels of assessment

LEVEL 1: DIRECT ASSESSMENT Jan Clarys et al. Direct anatomical evidence of individual differences. The adipose tissue free tissue mass from 7 female and 6 male unembalmed cadavers dissected 48 hours after demise. Muscle 41.9 - 59.4% Bone 16.5 - 25.7% Residual 24.0 - 32.4%

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Level III Skinfold regression equations

�  Follow protocol carefully.

�  Between 150 & 200 equations available.

�  Choose an equation carefully based on correct population.

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Skinfold assumptions (lead to prediction errors)

�  Constant compressibility of skin/tissues. §  Two identical skinfolds can have markedly difference subcutaneous fat

due in part to different compressibility (eg. 34% @ front thigh, but 65% @ supraspinale – Brussels cadaver study)

�  Skin thickness is constant (within/between people). �  There is fixed adipose tissue patterning across limbs

(and people).

�  Constant fat fraction in adipose tissue. �  Fixed proportion of internal to external fat deposits.

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101520253035404550

SScap Abd SSpin Tri Bic Thi Calf

Subject 1 Subject 2

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Anthropometry �  Uses multiple measures. �  Skinfolds, girths, lengths of bones,

breadths of bones, height and weight together.

�  Restricted (15) and Full profile (42) measures.

�  Can take time (10-30min).

It does matter where you take the skinfolds!

Patria Hume and Mike Marfell-Jones

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Hume et al study 2008

Methods

•  Cross-sectional quantitative •  12 subjects

•  27.1 ± 6.5 years •  177.3 ± 7.4 cm •  77.8 ± 12.7 kg

•  8 ISAK-specified skinfold sites •  8 points about ISAK = 9 points in 1-cm grid pattern •  3 x each grid point •  2 x ISAK Criterion Level 4 measurers (TEM<1%) •  Harpenden skinfold callipers @ 0.1 mm accuracy

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Hume et al study 2008

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Hume et al study 2008

Supraspinale

Triceps Biceps

Iliac crest Abdominal

Subscapular

Calf Thigh

Subscapular showed least # of differences

Abdominal showed most # of differences

Red shows significant differences

Size of the circle is the size of the Cohen effect

Iliac crest shows an anterior trend

22  Body  Fat  Equa,on   Mean  Pred  %BF   Mean  DXA  (NH)   B_A  Bias   B_A  LoA   PE  %     CV  %   Effect  Size  

Males                              

Sloan  1967   8.7   19.2   10.5    4.6  -­‐  16.3   -­‐56.6   58.0   1.5  

Wilmore  1969     11.2   19.2   8.0    3.7  -­‐  12.3   -­‐41.7   37.5   1.5  

Katch  1973     9.2   19.2   10.0    6.2  -­‐  13.8   -­‐53.3   52.2   1.6  

Durnin  1974     13.9   19.2   5.2    0.1  -­‐  10.4   -­‐29.0   25.6   1.0  

Withers  1987a     9.4   19.2   9.7    5.7  -­‐  13.7   -­‐52.5   51.4   1.5  

Lean  1996a   10.4   19.2   8.7    3.8  -­‐  13.6   -­‐46.8   44.7   1.4  

Lean  1996b   12.4   19.2   6.8    0.1  -­‐  13.5   -­‐35.0   31.6   1.3  

Peterson  2003   16.4   19.2   2.7    -­‐1.0  -­‐  6.4   -­‐14.6   11.8   0.6  

Garcia  2005     9.4   19.2   9.7   1.8  -­‐  17.6   -­‐54.1   60.8   1.3  

MACFARLANE  2014   23.1   23.1   0.0    -­‐5.1  -­‐  5.2   0.2   5.1   0.3  

Females                              

Sloan  1962     21.7   31.0   9.3   3.5    -­‐    15.1   -­‐30.2   25.5   1.4  

Katch  1968     24.3   31.0   6.7    -­‐4.4  -­‐  17.9   -­‐22.9   21.6   1.0  

Wilmore  1970     24.9   31.0   6.2   0.6    -­‐    11.7   -­‐19.5   15.6   1.2  

Durnin  1974     28.1   31.0   2.9    -­‐2.5    -­‐    8.3   -­‐9.4   8.5   0.7  

Pollock  1975a   21.0   31.0   10.0    2.7    -­‐    17.3   -­‐32.9   28.6   1.4  

Lewis  1978   26.1   31.0   4.9    -­‐13.1    -­‐    23   -­‐19.8   18.2   1.0  

Jackson  1980a   20.4   31.0   10.6    3.4  -­‐  17.9   -­‐35.4   31.6   1.4  

Jackson  1980b   19.8   31.0   11.2    4.6  -­‐  17.9   -­‐37.3   33.5   1.4  

Thorland  1984     22.0   31.0   9.0    -­‐0.5  -­‐  18.6   -­‐30.4   27.7   1.2  

Withers  1987b     21.4   31.0   9.7    4.4  -­‐  15.0   -­‐32.0   27.5   1.4  

Withers  1987c/d     20.5   31.0   10.5    6.1  -­‐  14.9   -­‐34.4   29.8   1.5  

Lean  1996a   24.1   31.0   7.0    0.8  -­‐  13.2   -­‐22.4   18.7   1.2  

Lean  1996b   24.5   31.0   6.6    -­‐1.0  -­‐  14.2   -­‐20.1   17.3   1.3  

Peterson  2003     27.5   31.0   3.5    -­‐2.0  -­‐  9.0   -­‐11.4   9.6   0.7  

MACFARLANE  2014   31.7   31.5   -­‐0.2    -­‐8.6  -­‐  8.2   3.7   7.6   0.6  

Data on HK Young Adults

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Level III Anthropometry

Advantages: �  Convenient and cheap method. �  Equipment is portable. �  Relatively non-invasive? �  Quite good for “follow-up” analysis Disadvantages: �  Numerous assumptions - Can take a lot of time for 42 Full sites –

even for 15 Restricted sites �  Many equations – they do not agree

§  (%Body Fat equations = wrong; keep as a simple Sum of Skin-Folds) �  Requires a GOOD measurer (ISAK L1)

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Level III Bioelectrical Impedance

�  Measures total body water, not body fat. �  Uses relationship between H20 and fat. �  Depends on equation in ‘black box’.

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Level III Bioelectrical impedance

�  Hand to foot lying. �  Hand to hand standing. �  Foot to foot standing. �  Muscle electrical activity

filtered out?

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Level III Bioelectrical impedance

�  Passes electrical current through electrodes placed on hand, wrist, ankle, and foot.

�  Measures resistance to current. �  Muscle holds more water so resistance is less than in

fat. �  Measures total body water which can be used to

measure fat free mass based on assumption percentage of total body water in fat free mass (73%).

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Level III Bioelectrical impedance

Advantages: �  Convenient and rapid method. �  Equipment is portable. �  Non-invasive. �  Correlates well with hydrostatic weighing (0.93). Disadvantages: �  Subject must be normally hydrated. �  Software is not uniform across machines (racial norms?) �  3-5 % error? – even higher? �  - does it monitor changes over time ? (eg. v DXA?)

Are BIA machines reliable? - Yes – but MUST be under identical conditions

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Macfarlane study: DXA v BIA to track changes over 3 months 29

!Table!2.!!Baseline,!change1score!after!intervention!and!statistics!for!BIA!and!DXA!measurements,!as!well!as!comparisons!of!change1scores!(delta)!between!devices:!means!±!SD.!!

! Baseline,!g!

Change!after!intervention,!

g,!(%)!

Change:!!p1value!!(t1test)!

Change:!Effect!size!

Correlation,!r!

(p1value)!Weight!(g)! 65287!±!

13603!1313!±!1500!(10.5%)!

0.008! 0.20!(small)!

!

Fat!DXA!(g)! 21981!±!6589!

1802!±!1096!(13.6%)!

<0.001! 0.16!(small)!

!

Fat!BIA!(g)! 19554!±!6898!

1485!±!1539!(12.5%)!

<0.001! 0.17!(small)!

!

delta1Fat!(DXA!v!BIA)!

! ! 0.003! 0.72!(large)!

0.511!(<0.001)!

Muscle!DXA!(g)!

42561!±!9485!

477!±!966!(+1.1%)!

<0.001! 0.09!(trivial)!

!

Muscle!BIA!(g)!

43255!±!9967!

84!±!1201!(+0.2%)!

0.425! 0.08!(trivial)!

!

delta1Muscle!(DXA!v!BIA)!

! ! <0.001! 0.79!(large)!

0.362!(<0.001)!

Bone!DXA!(g)! 2143!±!392!

7!±!41!(+0.3%)!

0.052! 0.08!(trivial)!

!

Bone!BIA!(g)! 2543!±!496!

14!±!91!(+0.6%)!

0.074! 0.11!(trivial)!

!

delta1Bone!(DXA!v!BIA)!

! ! 0.392! 0.93!(large)!

0.172!(0.047)!

!

%BF by DXA = 33.7%

%BF by BIA = 30.0%

= 3.7% higher by DXA

%muscle by DXA = 65.2%

%muscle by BIA = 66.3%

= 1.1% lower by DXA

Baseline:

Macfarlane study: DXA v BIA to track changes over 3 months 30

!Table!2.!!Baseline,!change1score!after!intervention!and!statistics!for!BIA!and!DXA!measurements,!as!well!as!comparisons!of!change1scores!(delta)!between!devices:!means!±!SD.!!

! Baseline,!g!

Change!after!intervention,!

g,!(%)!

Change:!!p1value!!(t1test)!

Change:!Effect!size!

Correlation,!r!

(p1value)!Weight!(g)! 65287!±!

13603!1313!±!1500!(10.5%)!

0.008! 0.20!(small)!

!

Fat!DXA!(g)! 21981!±!6589!

1802!±!1096!(13.6%)!

<0.001! 0.16!(small)!

!

Fat!BIA!(g)! 19554!±!6898!

1485!±!1539!(12.5%)!

<0.001! 0.17!(small)!

!

delta1Fat!(DXA!v!BIA)!

! ! 0.003! 0.72!(large)!

0.511!(<0.001)!

Muscle!DXA!(g)!

42561!±!9485!

477!±!966!(+1.1%)!

<0.001! 0.09!(trivial)!

!

Muscle!BIA!(g)!

43255!±!9967!

84!±!1201!(+0.2%)!

0.425! 0.08!(trivial)!

!

delta1Muscle!(DXA!v!BIA)!

! ! <0.001! 0.79!(large)!

0.362!(<0.001)!

Bone!DXA!(g)! 2143!±!392!

7!±!41!(+0.3%)!

0.052! 0.08!(trivial)!

!

Bone!BIA!(g)! 2543!±!496!

14!±!91!(+0.6%)!

0.074! 0.11!(trivial)!

!

delta1Bone!(DXA!v!BIA)!

! ! 0.392! 0.93!(large)!

0.172!(0.047)!

!

BIA only measured 61% of the Fat losses seen by DXA

BIA only measured 18% of the Muscle gains seen by DXA

NOTE: Mass dropped only 313g, but Fat loss was 802g, with Muscle gain 477g

= do not use weight scales alone!

WHY is BIA different to DXA:

•  All mainly due to “different assumptions” and “different calibrations” – lack of Direct Level 1 criterion comparisons

•  Eg. Max car speed = distance/time ; eg. 1km in 18sec = 200kph

Pat Fox + = 250kph ?

NO – the car’s speedometer was not calibrated correctly

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Level II methods: Indirect measurement and use

of assumptions based on qualitative measures

�  Underwater weighing - accurate, cheaper, uncomfortable, current ‘gold standard’?

�  Medical imaging, CT scan, MRI – accurate, $! �  DXA - accurate but $!, future ‘gold standard’?

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Dual energy X-ray absorptiometry (DEXA)

�  Primarily used to determine bone mineral density (BMD).

�  Can measure regional or total body composition (often using a Fan Beam X-ray – very low dose)

�  With further research, could be new gold standard? – some say it already is the gold standard?

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Advantages: �  +/-2% error fat, +/-1% error

bone? – better now? �  Directly gives % fat without

relying on assumed density. �  Low radiation dose. �  Minimal effort by subject. �  Minimal amount of time

needed to perform. �  Gives total or regional values. �  Technician does not have to be

trained extensively.

Disadvantages: �  Costs HK$600,000 per

scanner. �  Computer software is not

uniform for every scanner. �  Software values do not

account for extremes of % body fat.

�  Software is based on adult values, not all races have norms

�  Very obese hard to fit on table (mirror half of them?)

Dual energy X-ray absorptiometry (DXA)

Example of Output 35

Details of Output (BMC/BMD) 36

Details of Output (fracture risk) 37

Details of Output (body comp) 38

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How reliable is DXA?

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But differences can be seen between two scans

Female Athlete – scanned twice within 15 minutes: �  1st scan: % Body Fat = 22.5% �  2nd scan: % Body Fat = 21.5% �  In comparison – her “InBody” BIA value = 12% �  = Huge difference (and very few elite females are at 12%!!) Myself – scanned twice within 15 minutes: �  1st scan: % Body Fat = 22.8% �  2nd scan: % Body Fat = 23.9%

§  - these 1% total difference in DXA %body-fat are HIGH (not sure if this is due to our aging DXA)

�  We have just purchased a new Hologic DXA and happy to check values compared to the HKSI DXA

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But differences can be due to software changes too!

In 2009 Hologic changed their DXA software due to the USA NHANES analysis study (Kelly et al, 2009, PLoS ONE) to increase %body fat by about 4-5%!!

- So comparisons with previous pre-2009 data = almost impossible - aarrgghh!

Female Elite Athlete – same scan – but different software (+/- NHANES adjustment): �  Scan using 2008 (pre-NHANES) analysis: % Body Fat = 16.7% (51.5kg Lean tissue) �  Scan using 2009 (NHANES) analysis: % Body Fat = 21.2% (48.6kg Lean tissue) �  = +4.5%BF = Huge difference (due entirely to different calibration/analysis)

�  (another female: pre-NHANES=38.3% v NHANES=41.6 = +3.3%BF difference)

Male non-elite – same scan – but different software (+/- NHANES adjustment): �  Scan using 2008 (pre-NHANES) analysis: % Body Fat = 16.1% (64.2kg Lean tissue) �  Scan using 2009 (NHANES) analysis: % Body Fat = 20.7% (60.5kg Lean tissue) �  = +4.6%BF = Huge difference (due entirely to different calibration/analysis)

�  (another male: pre-NHANES=11.8% v NHANES=16.6% = +4.8%BF difference)

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How to minimize variations between scans Two good studies by Nana et al (MSSE, 2012 & 2013)

�  2012 �  – showing �  Fat mass here only

�  concluded none of the changes in body fat were substantial �  but consuming the meal did increase lean mass to a small degree

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How to minimize variations between scans Nana et al (MSSE, 2013)

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How to minimize variations between scans Nana et al (MSSE, 2013 - endurance)

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How to minimize variations between scans Nana et al (2013) concluded: �  Fat mass – showed small/minimal changes after exercise in both

strength & endurance athletes

�  Lean mass – also showed small/minimal changes after exercise in both strength & endurance athletes

�  Despite these small/minimal changes – they still suggest athletes should fast overnight and perform a DXA when completely rested in the morning with NO exercise prior §  Also – hydration status is important (euhydration) ideally using specific

gravity of urine checked, and void bladder before scan §  - These rules were derived for “statistical reasons” rather than for

“functional reasons” – to me the differences are small §  - ?? Ideally fast overnight, but if not practical – no exercise prior and no

meal within 2hr?, and ensure a constant level of hydration = more critical