Head, Neck, Trunk, and Pelvis Tissue Mass Predictions for Young Adults Using Anthropometric Measures and Dual-Energy X-Ray Absorptiometry

Accurate prediction of wobbling mass (WM), fat mass (FM), lean mass (LM), and bone mineral content (BMC) of living people using regression equations developed from anthropometric measures (lengths, circumferences, breadths, skinfolds) has previously been reported, but only for the extremities. Multiple linear stepwise regression was used to generate comparable equations for the head, neck, trunk, and pelvis of young adults (38 males, 38 females). Equations were validated using actual tissue masses from an independent sample of 13 males and 13 females by manually segmenting full-body dual-energy x-ray absorptiometry scans. Prediction equations exhibited adjusted R2 values ranging from .249 to .940, with more explained variance for LM and WM than BMC and FM, especially for the head and neck. Mean relative errors between predicted and actual tissue masses ranged from −11.07% (trunk FM) to 7.61% (neck FM). Actual and predicted tissue masses from all equations were significantly correlated (R2 = .329 to .937), except head BMC (R2 = .046). These results show promise for obtaining in-vivo head, neck, trunk, and pelvis tissue mass estimates in young adults. Further research is needed to improve head and neck FM and BMC predictions and develop tissue mass prediction equations for older populations.

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Head, Neck, Trunk, and Pelvis Tissue Mass Predictions for Older Adults using Anthropometric Measures and Dual-Energy X-Ray Absorptiometry

International Journal of Kinesiology and Sports Science ◽

10.7575/aiac.ijkss.v.8n.3p.14 ◽

2020 ◽

Vol 8 (3) ◽

pp. 14

Author(s):

Charles A.J. Kahelin ◽

Nicole C. George ◽

Danielle L. Gyemi ◽

David M. Andrews

Keyword(s):

Older Adults ◽

Dual Energy ◽

Regression Equations ◽

Prediction Equations ◽

Tissue Mass ◽

Younger Adults ◽

X Ray ◽

Recreational Activities ◽

Biomechanical Models ◽

Head Neck

Background: Regression equations using anthropometric measurements to predict soft (fat mass [FM], lean mass [LM], wobbling mass [WM]) and rigid (bone mineral content [BMC]) tissue masses of the extremities and core body segments have been developed for younger adults (16-35 years), but not older adults (36-65 years). Tissue mass estimates such as these would facilitate biomechanical modeling and analyses of older adults following fall or collision-related impacts that might occur during sport and recreational activities. Purpose: The purpose of this study was to expand on the previously established tissue mass prediction equations of the head, neck, trunk, and pelvis for healthy, younger adults by generating a comparable set of equations for an older adult population. Methods: A generation sample (38 males, 38 females) was used to create head, neck, trunk, and pelvis tissue mass prediction equations via multiple linear stepwise regression. A validation sample (13 males, 12 females) was used to assess equation accuracy; actual tissue masses were acquired from manually segmented full body Dual-Energy X-ray Absorptiometry scans. Results: Adjusted R2 values for the prediction equations ranged from 0.326 to 0.949, where BMC equations showed the lowest explained variances overall. Mean relative errors between actual and predicted masses ranged from –2.6% to 6.1% for trunk LM and FM, respectively. All actual tissue masses except head BMC (R2 = 0.092) were significantly correlated to those predicted from the equations (R2 = 0.403 to 0.963). Conclusion: This research provides a simple and effective method for predicting head, neck, trunk, and pelvis tissue masses in older adults that can be incorporated into biomechanical models for analyzing sport and recreational activities. Future work with this population should aim to improve core segment BMC predictions and develop equations for the extremities.

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Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

10.1101/2020.09.15.286153 ◽

2020 ◽

Cited By ~ 1

Author(s):

Claudia Kasper ◽

Patrick Schlegel ◽

Isabel Ruiz-Ascacibar ◽

Peter Stoll ◽

Giuseppe Bee

Keyword(s):

Body Composition ◽

Crude Protein ◽

Imaging Techniques ◽

Dual Energy ◽

Growing Pigs ◽

Regression Equations ◽

Chemical Analyses ◽

Prediction Equations ◽

Animal Science ◽

X Ray

AbstractStudies in animal science assessing nutrient and energy efficiency or determining nutrient requirements necessitate gathering exact measurements of body composition or body nutrient contents. Wet chemical analysis methods or standardized dissection are commonly applied, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Among imaging techniques, dual-energy X-ray absorptiometry (DXA) is particularly promising. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. Several calibration regressions have been published, but comparative studies are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (body weight range 20-100 kg) as determined by both DXA and chemical analyses, with R2 ranging between 0.89 for ash and 0.99 for water and crude protein. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses, with the prediction error rCV ranging between 4.3% for crude protein and 12.6% for ash. Finally, we compare existing prediction equations for pigs of a similar range of body weights with the equations derived from our DXA measurements and evaluate their fit with our chemical analyses data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.

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Predicting Football Players' Dual-Energy X-Ray Absorptiometry Body Composition Using Standard Anthropometric Measures

Journal of Athletic Training ◽

10.4085/1062-6050-47.3.12 ◽

2012 ◽

Vol 47 (3) ◽

pp. 257-263 ◽

Cited By ~ 19

Author(s):

Jonathan M. Oliver ◽

Brad S. Lambert ◽

Steven E. Martin ◽

John S. Green ◽

Stephen F. Crouse

Keyword(s):

Body Composition ◽

Health Risk ◽

National Collegiate Athletic Association ◽

Skinfold Thickness ◽

Dual Energy ◽

Football Players ◽

Regression Equations ◽

Anthropometric Measures ◽

X Ray ◽

Collegiate Football

Context: The recent increase in athlete size, particularly in football athletes of all levels, coupled with the increased health risk associated with obesity warrants continued monitoring of body composition from a health perspective in this population. Equations developed to predict percentage of body fat (%Fat) have been shown to be population specific and might not be accurate for football athletes. Objective: To develop multiple regression equations using standard anthropometric measurements to estimate dual-energy x-ray absorptiometry %Fat (DEXA%Fat) in collegiate football players. Design: Controlled laboratory study. Patients and Other Participants: One hundred fifty-seven National Collegiate Athletic Association Division IA football athletes (age = 20 ± 1 years, height = 185.6 ± 6.5 cm, mass = 103.1 ± 20.4 kg, DEXA%Fat = 19.5 ± 9.1%) participated. Main Outcome Measure(s): Participants had the following measures: (1) body composition testing with dual-energy x-ray absorptiometry; (2) skinfold measurements in millimeters, including chest, triceps, subscapular, midaxillary, suprailiac, abdominal (SFAB), and thigh; and (3) standard circumference measurements in centimeters, including ankle, calf, thigh, hip (AHIP), waist, umbilical (AUMB), chest, wrist, forearm, arm, and neck. Regression analysis and fit statistics were used to determine the relationship between DEXA%Fat and each skinfold thickness, sum of all skinfold measures (SFSUM), and individual circumference measures. Results: Statistical analysis resulted in the development of 3 equations to predict DEXA%Fat: model 1, (0.178 • AHIP) + (0.097 • AUMB) + (0.089 • SFSUM) − 19.641; model 2, (0.193 • AHIP) + (0.133 • AUMB) + (0.371 • SFAB) − 23.0523; and model 3, (0.132 • SFSUM) + 3.530. The R2 values were 0.94 for model 1, 0.93 for model 2, and 0.91 for model 3 (for all, P < .001). Conclusions: The equations developed provide an accurate way to assess DEXA%Fat in collegiate football players using standard anthropometric measures so athletic trainers and coaches can monitor these athletes at increased health risk due to increased size.

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The accuracy of dual energy X-ray absorptiometry (DXA), weight, and P2 back fat to predict half-carcass and primal-cut composition in pigs within and across research experiments

Australian Journal of Agricultural Research ◽

10.1071/ar04052 ◽

2004 ◽

Vol 55 (9) ◽

pp. 973 ◽

Cited By ~ 27

Author(s):

D. Suster ◽

B. J. Leury ◽

C. D. Hofmeyr ◽

D. N. D'Souza ◽

F. R. Dunshea

Keyword(s):

Mineral Content ◽

Dual Energy ◽

Regression Equations ◽

Lean Tissue ◽

Large White ◽

Tissue Mass ◽

X Ray ◽

Fat Composition ◽

Non Destructive ◽

Total Tissue

A Hologic QDR4500A dual energy X-ray absorptiometer (DXA) was used to measure body composition in 199 half-carcasses ranging from 15 to 48 kg. Half-carcasses were from animals of mixed sex and of either Large White × Landrace or Large White × Landrace × Duroc descent. Half-carcasses were selected from 5 different experiments to evaluate DXA accuracy within and across experiments. Values determined by DXA including total tissue mass, fat tissue mass, lean tissue mass, and bone mineral content, for the half-carcass and the shoulder, loin, belly, and ham primal cuts were evaluated by comparison with manually dissected composition. Relationships between manually dissected values and measurements of weight and backfat at the P2 site were also evaluated. Manually dissected values were strongly related to DXA-derived values, more so than with weight and P2 or a combination of both, particularly in the measurement of fat composition. In contrast to estimates derived from weight and P2, DXA-derived estimates remained accurate even when between-experiment variation was included. However, because DXA estimates were different from manually dissected values, they would need to be adjusted with the use of appropriate regression equations to correct the in-built algorithms. These results demonstrate the efficacy of DXA as a non-destructive method for determining the composition of the half-carcass and primal cuts, and its greater precision than current routinely used methods.

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Dual energy X-ray absorptiometry is a valid tool for assessing in vivo body composition of broilers

Animal Production Science ◽

10.1071/an17637 ◽

2019 ◽

Vol 59 (5) ◽

pp. 993 ◽

Cited By ~ 1

Author(s):

Camila Angelica Gonçalves ◽

Nilva Kazue Sakomura ◽

Edney Pereira da Silva ◽

Silvana Martinez Baraldi Artoni ◽

Rafael Massami Suzuki ◽

...

Keyword(s):

Body Composition ◽

Crude Protein ◽

Dual Energy ◽

The Body ◽

Invasive Technique ◽

Composition Analysis ◽

Regression Equations ◽

X Ray ◽

Non Invasive

The use of non-invasive techniques to estimate body composition in animals in vivo conforms to the desire to improve the welfare of animals during research and also has the potential to advance scientific research. The purpose of the present study was to determine a predictive equation of the dual energy X-ray absorptiometry (DXA) method for broilers by comparing the measurement of body composition using DXA with that by chemical analysis. In total, 720 day-old Cobb500 broilers were distributed into a split-plot arrangement 3 (crude protein concentrations of diets) × 2 (genders) × 2 (methods of chemical body evaluation), with six replications of 20 birds each. To promote the modification of the body composition of broilers, diets varied in the crude protein concentration, which was 70%, 100% and 130% of the required. Two hundred and sixteen birds in different ages were evaluated by its bodyweight, lean, fat and ash contents. The data were submitted to ANOVA and it was demonstrated that the dietary crude protein levels applied allowed a greater variation of the body composition of the birds. Also, the results indicated that the DXA method did not predict fat mass, lean mass or bone mineral content as well as did chemical composition analysis, resulting in the need to develop regression equations for improving the in vivo prediction of these chemical components. The regression equations developed here enable the feather-free body composition of individual broilers to be directly estimated throughout growth using the DXA non-invasive technique.

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Accuracy of dual energy X-ray absorptiometry, weight, longissimus lumborum muscle depth and GR fat depth to predict half carcass composition in sheep

Australian Journal of Experimental Agriculture ◽

10.1071/ea07039 ◽

2007 ◽

Vol 47 (10) ◽

pp. 1165 ◽

Cited By ~ 14

Author(s):

F. R. Dunshea ◽

D. Suster ◽

P. J. Eason ◽

R. D. Warner ◽

D. L. Hopkins ◽

...

Keyword(s):

Mineral Content ◽

Dual Energy ◽

Carcass Composition ◽

Regression Equations ◽

Tissue Mass ◽

X Ray ◽

Longissimus Lumborum ◽

The Relationship ◽

Non Destructive ◽

Total Tissue

A Hologic QDR4500W dual energy X-ray absorptiometer (DXA) was used to measure body composition in 60 sheep half carcasses ranging from 8 to 28 kg. Half carcasses were from ewes and wethers of mixed genetics. Values determined by DXA, including total tissue mass (TTM), lean tissue mass (LTM), fat tissue mass (FTM) and bone mineral content (BMC), for the half carcass were evaluated by comparison with chemically determined composition. In the case of BMC, the relationship was with chemically determined ash content. Liveweight and chemically determined lean, fat and ash were strongly related to DXA-derived values for TTM, LTM, FTM and BMC, respectively (R2 = 0.999, 0.986, 0.989 and 0.920, respectively). However, because DXA estimates were different from chemically determined values in this sample of carcasses, they needed to be adjusted with the use of appropriate regression equations to correct the in-built algorithms. These data demonstrate the efficacy of DXA as a non-destructive method for determining the composition of the sheep half carcass.

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Predicting in Vivo Soft Tissue Masses of the Lower Extremity Using Segment Anthropometric Measures and DXA

Journal of Applied Biomechanics ◽

10.1123/jab.21.4.371 ◽

2005 ◽

Vol 21 (4) ◽

pp. 371-382 ◽

Cited By ~ 22

Author(s):

Jeffrey D. Holmes ◽

David M. Andrews ◽

Jennifer L. Durkin ◽

James J. Dowling

Keyword(s):

Soft Tissue ◽

Lower Extremity ◽

Prediction Equation ◽

Regression Equations ◽

Anthropometric Measures ◽

Soft Tissue Masses ◽

Highly Correlated ◽

Relative Errors ◽

Dxa Scans

The purpose of this study was to derive and validate regression equations for the prediction of fat mass (FM), lean mass (LM), wobbling mass (WM), and bone mineral content (BMC) of the thigh, leg, and leg + foot segments of living people from easily measured segmental anthropometric measures. The segment masses of 68 university-age participants (26 M, 42 F) were obtained from full-body dual photon x-ray absorptiometry (DXA) scans, and were used as the criterion values against which predicted masses were compared. Comprehensive anthropometric measures (6 lengths, 6 circumferences, 8 breadths, 4 skinfolds) were taken bilaterally for the thigh and leg for each person. Stepwise multiple linear regression was used to derive a prediction equation for each mass type and segment. Prediction equations exhibited high adjustedR2values in general (0.673 to 0.925), with higher correlations evident for the LM and WM equations than for FM and BMC. Predicted (equations) and measured (DXA) segment LM and WM were also found to be highly correlated (R2= 0.85 to 0.96), and FM and BMC to a lesser extent (R2= 0.49 to 0.78). Relative errors between predicted and measured masses ranged between 0.7% and –11.3% for all those in the validation sample (n= 16). These results on university-age men and women are encouraging and suggest that in vivo estimates of the soft tissue masses of the lower extremity can be made fairly accurately from simple segmental anthropometric measures.

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