Explaining Discrepancies Between Total and Segmental DXA and BIA Body Composition Estimates Using Bayesian Regression

Tinsley, Grant M.; Moore, Michael L.; Rafi, Zad; Griffiths, Nelson; Harty, Patrick S.; Stratton, Matthew T.; Benavides, Marqui L.; Dellinger, Jacob R.; Adamson, Brian T.

doi:10.1016/j.jocd.2020.05.003

Cited by 9 publications

(7 citation statements)

References 59 publications

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“…DXA has previously shown good agreement with MRI and computer tomography, including for VAT measurement [ 28 ], although DXA lean mass is quantified indirectly and could be influenced by body hydration and overestimated in obesity [ 29 , 30 ]. BIA, however, additionally overestimates fat-free mass in obesity compared to DXA and, most importantly, is disproportionately sensitive to limb and trunk water content [ 31 – 33 ]. Accordingly, our study has demonstrated that BIA does not agree with DXA for regional body composition, which is more relevant to the detrimental consequences of obesity.…”

Section: Discussionmentioning

confidence: 99%

Association of body-shape phenotypes with imaging measures of body composition in the UK Biobank cohort: relevance to colon cancer risk

et al. 2021

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Background Body mass index (BMI), waist and hip circumference are strongly correlated and do not reflect body composition. A Body Shape Index (ABSI) and Hip Index (HI) define waist and hip size among individuals with the same weight and height and would thus reflect body density. We examined differences in body composition between body-shape phenotypes defined with ABSI and HI and used this information to propose explanations for associations between body-shape phenotypes and colon cancer risk. Methods We used data from the UK Biobank Resource for 15,520 men, 16,548 women with dual-emission X-ray absorptiometry (DXA) measurements; 3997 men, 4402 women with magnetic resonance imaging (MRI) measurements; 200,289 men, 230,326 women followed-up for colon cancer. We defined body-shape phenotypes as: large-ABSI-small-HI (“apple”), small-ABSI-large-HI (“pear”), small-ABSI-small-HI (“slim”), large-ABSI-large-HI (“wide”). We evaluated differences in body composition in linear models and associations with colon cancer risk in Cox proportional hazards models adjusted for confounders and explored heterogeneity by BMI. Results Among individuals with the same height and weight, visceral adipose tissue (VAT) was lowest for “pear” and highest for “apple”, while abdominal subcutaneous adipose tissue (ASAT) was lowest for “slim” and highest for “wide” phenotype. In the gynoid region, differences between “apple” and “pear” phenotypes were accounted for mainly by fat mass in women but by lean mass in men. In men, lean mass was inversely associated with waist size, while the pattern of gynoid fat resembled ASAT in women. Lean and fat mass were higher for higher BMI, but not hand grip strength. Compared to normal weight “pear”, the risk of colon cancer in men (1029 cases) was higher for “apple” phenotype for normal weight (hazard ratio HR = 1.77; 95% confidence interval: 1.16–2.69) and comparably for overweight and obese, higher for “wide” phenotype for overweight (HR = 1.60; 1.14–2.24) and comparably for obese, but higher for “slim” phenotype only for obese (HR = 1.98; 1.35–2.88). Associations with colon cancer risk in women (889 cases) were weaker. Conclusions ABSI-by-HI body-shape phenotypes provide information for body composition. Colon cancer risk in men appears related to ASAT quantity for “slim” and “wide” but to factors determining VAT accumulation for “apple” phenotype.

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Section: Discussionmentioning

confidence: 99%

Association of body-shape phenotypes with imaging measures of body composition in the UK Biobank cohort: relevance to colon cancer risk

et al. 2021

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“…This comparison bridges two levels of body composition assessment, as DXA FM is considered a molecular-level entity (i.e. an estimation of the molecular fat content of the body) while subcutaneous adipose tissue thickness more closely resembles an organ/tissue-level assessment (Tinsley, Moore, et al, 2020;Tinsley, Rodriguez, et al, 2020;Wang et al, 1992). Previously, researchers have entered the sum of skinfolds into body density predictions, most frequently those developed by Jackson and Pollock (1978), with the body density value subsequently entered into a separate equation, such as the Siri equation (Siri, 1961) to yield a 2C FM estimate.…”

Section: Discussionmentioning

confidence: 99%

“…A trained researcher manually adjusted region of interest lines within the enCORE software to designate body segments (i.e. arms, trunk and legs; Tinsley, Moore, et al, 2020; Tinsley, Rodriguez, et al, 2020). In our laboratory, these procedures have produced intraclass correlation coefficient (ICC) values from 0.974 to 0.996 and root‐mean‐square coefficient of variation (RMS‐CV) from 2.6 to 5.4% for total and segmental FM estimates.…”

Section: Methodsmentioning

confidence: 99%

“…The molecular level is employed when the sum of skinfolds estimated by US is utilized in a body density equation [e.g. Jackson and Pollock's equation (Jackson & Pollock, 1978)], with the subsequent body density value inserted into a two‐ or three‐component model equation to ultimately yield estimates of FM and FFM (Tinsley, Moore, et al, 2020; Tinsley, Rodriguez, et al, 2020). In contrast, US‐derived raw skinfold thicknesses can be directly interpreted as an estimate of subcutaneous adipose tissue thickness, which is more closely related to regional tissue‐level assessments.…”

Section: Introductionmentioning

confidence: 99%

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Cross‐sectional and longitudinal associations between subcutaneous adipose tissue thickness and dual‐energy X‐ray absorptiometry fat mass

Green

Smith

Stratton

et al. 2021

Clin Physio Funct Imaging

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The present study examined cross-sectional and longitudinal relationships between total and segmental subcutaneous tissue thicknesses from ultrasonography (US) and total and segmental fat mass (FM) estimates from dual-energy X-ray absorptiometry (DXA). Traditional US FM estimates were also examined. Twenty resistancetrained males (mean ± SD; age: 22.0 ± 2.6 years; body mass: 74.8 ± 11.5 kg; DXA fat: 17.5 ± 4.5%) completed a 6-week supervised resistance training programme while consuming a hypercaloric diet. Pre-and post-intervention body composition was assessed by DXA and B-mode US. Data were analysed using Pearson's correlation (r), Lin's correlation coefficient (CCC), paired t-tests, Wilcoxon signed-rank tests and Bland-Altman analysis, as appropriate. Cross-sectionally, correlations were observed between total DXA FM and total subcutaneous tissue thickness (r = 0.88). Longitudinally, a correlation was observed between total DXA FM changes and total subcutaneous tissue changes (r = 0.49, CCC = 0.38). Correlations of similar magnitudes were observed for the upper body and trunk estimates, but DXA FM changes were unrelated to subcutaneous tissue changes for the lower body and arms. Cross-sectionally, US 2-compartment FM and DXA FM were correlated (r = 0.91, CCC = 0.83). Longitudinally, a weaker correlation was observed (r = 0.47, CCC = 0.33). In summary, longitudinal associations between US and DXA are weaker than cross-sectional relationships; additionally, correlations between US subcutaneous tissue and whole-body DXA FM appear to be driven by the trunk region rather than appendages. Reporting raw skinfold thicknesses rather than FM estimates alone may improve the utility of techniques based on subcutaneous tissue thickness, such as US and skinfolds.

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“…Eine endgültige Bewertung des Nutzens bzw. der Zuverlässigkeit sonographischer Methoden oder der Bioimpedanzanalyse zur Erfassung der Körpermuskelmasse ist derzeit noch ausstehend [ 56 , 57 ]. Trotzdem soll im Folgenden auch ein kurzer Überblick über die Charakteristika beider diagnostischer Verfahren gegeben werden.…”

Section: Methoden Zur Bestimmung Des Ernährungszustandsunclassified

Erfassung und apparatives Monitoring des Ernährungsstatus von Patient*innen auf der Intensiv- und Intermediate Care Station

Weimann

Hartl

Adolph

et al. 2022

Med Klin Intensivmed Notfmed

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ZusammenfassungDie Erhebung des Ernährungsstatus zum Zeitpunkt der Aufnahme im Intensiv- oder Intermediate Care Bereich hat sowohl prognostische als auch therapeutische Relevanz im Hinblick auf die Planung einer individualisierten medizinischen Ernährungstherapie (engl. „medical nutrition therapy“, MNT). Diese Planung wird im Rahmen der Erstversorgung eines vital bedrohlichen Krankheitsbilds nachvollziehbar nicht priorisiert, jedoch im weiteren Verlauf häufig auch oft nicht mehr angemessen durchgeführt. Vor allem bei längerer Verweildauer besteht das Risiko einer Mangelernährung mit Aufbau eines kumulativen, prognoserelevanten Makro- und/oder Mikronährstoffdefizits. Bisher gibt es für Patient*innen auf Intensiv- und Intermediate Care Einheiten keine strukturierten Empfehlungen zur Erhebung des Ernährungsstatus. Das vorliegende Positionspapier der Sektion Metabolismus und Ernährung der Deutschen Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin (DIVI) beinhaltet konsensbasierte Empfehlungen zur Erfassung und zum apparativen Monitoring des Ernährungsstatus von Patient*innen auf Intensiv- und Intermediate Care Stationen. Diese Empfehlungen ergänzen die aktuelle S2k-Leitlinie „Klinische Ernährung in der Intensivmedizin“ der Deutschen Gesellschaft für Ernährungsmedizin (DGEM) und der DIVI.

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Explaining Discrepancies Between Total and Segmental DXA and BIA Body Composition Estimates Using Bayesian Regression

Cited by 9 publications

References 59 publications

Association of body-shape phenotypes with imaging measures of body composition in the UK Biobank cohort: relevance to colon cancer risk

Association of body-shape phenotypes with imaging measures of body composition in the UK Biobank cohort: relevance to colon cancer risk

Cross‐sectional and longitudinal associations between subcutaneous adipose tissue thickness and dual‐energy X‐ray absorptiometry fat mass

Erfassung und apparatives Monitoring des Ernährungsstatus von Patient*innen auf der Intensiv- und Intermediate Care Station

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