Analysing body composition as compositional data: An exploration of the relationship between body composition, body mass and bone strength

Human body composition is made up of mutually exclusive and exhaustive parts (e.g. %truncal fat, %non-truncal fat and %fat-free mass) which are constrained to sum to the same total (100%). In statistical analyses, individual parts of body composition (e.g. %truncal fat or %fat-free mass) have traditionally been used as proxies for body composition, and have been linked with a range of health outcomes. But analysis of individual parts omits information about the other parts, which are intrinsically co-dependent because of the constant sum constraint of 100%. Further, body mass may be associated with health outcomes. We describe a statistical approach for body composition based on compositional data analysis. The body composition data are expressed as logratios to allow relative information about all the compositional parts to be explored simultaneously in relation to health outcomes. We describe a recent extension to the logratio approach to compositional data analysis which allows absolute information about the total of the compositional parts (body mass) to be considered alongside relative information about body composition. The statistical approach is illustrated by an example that explores the relationships between adults’ body composition, body mass and bone strength.

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Individual categorisation of glucose profiles using compositional data analysis

Statistical Methods in Medical Research ◽

10.1177/0962280218808819 ◽

2018 ◽

Vol 28 (12) ◽

pp. 3550-3567 ◽

Cited By ~ 2

Author(s):

Lyvia Biagi ◽

Arthur Bertachi ◽

Marga Giménez ◽

Ignacio Conget ◽

Jorge Bondia ◽

...

Keyword(s):

Data Analysis ◽

Compositional Data ◽

Glucose Monitoring ◽

Glucose Variability ◽

Compositional Data Analysis ◽

Relative Information ◽

Glucose Profiles ◽

Patient Groups ◽

The Times

The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24 h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control.

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Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis

International Journal of Obesity ◽

10.1038/s41366-020-0638-z ◽

2020 ◽

Vol 45 (1) ◽

pp. 266-275 ◽

Cited By ~ 1

Author(s):

Youngwon Kim ◽

Ryan D. Burns ◽

Duck-chul Lee ◽

Gregory J. Welk

Keyword(s):

Physical Activity ◽

Body Mass Index ◽

Data Analysis ◽

Body Mass ◽

Compositional Data ◽

Activity Measurement ◽

Compositional Data Analysis ◽

Lifestyle Behaviors ◽

Linear Regression Models ◽

Index Comparison

Abstract Background/objectives Evidence on the associations between lifestyle movement behaviors and obesity has been established without taking into account the time-constrained nature of categorized, time-based lifestyle behaviors. We examined the associations of sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous PA (MVPA) with body mass index (BMI) using Compositional Data Analysis (CoDA), and compared the associations between a report-based method (24-h Physical Activity Recall; 24PAR) and a monitor-based method (SenseWear Armband; SWA). Subjects/methods Replicate data from a representative sample of 1247 adults from the Physical Activity Measurement Survey (PAMS) were used in the study. Participants completed activity monitoring on two randomly selected days, each of which required wearing a SWA for a full day, and then completing a telephone-administered 24PAR the following day. Relationships among behavioral compositional parts and BMI were analyzed using CoDA via multiple linear regression models with both 24PAR and SWA data. Results Using 24PAR, time spent in sleep (γ = −3.58, p = 0.011), SED (γ = 3.70, p = 0.002), and MVPA (γ = −0.53, p = 0.018) was associated with BMI. Using SWA, time spent in sleep (γ = −5.10, p < 0.001), SED (γ = 8.93, p < 0.001), LPA (γ = −3.12, p < 0.001), and MVPA (γ = −1.43, p < 0.001) was associated with BMI. The SWA models explained more variance in BMI (R2 = 0.28) compared with the 24PAR models (R2 = 0.07). The compositional isotemporal substitution models revealed reductions in BMI when replacing SED by MVPA, LPA (not with 24PAR) or sleep for both 24PAR and SWA, but the effect estimates were larger with SWA. Conclusions Favorable levels of relative time spent in lifestyle movement behaviors were, in general, associated with decreased BMI. The observed associations were stronger using the monitor-based SWA method compared with the report-based 24PAR method.

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Impact of Covariates in Compositional Models and Simplicial Derivatives

Austrian Journal of Statistics ◽

10.17713/ajs.v50i2.1069 ◽

2021 ◽

Vol 50 (2) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Joanna Morais ◽

Christine Thomas-Agnan

Keyword(s):

Data Analysis ◽

Regression Models ◽

Compositional Data ◽

Explanatory Variable ◽

Regression Equation ◽

Compositional Data Analysis ◽

Linear Regression Models ◽

Explanatory Variables ◽

Relative Information ◽

Single Side

In the framework of Compositional Data Analysis, vectors carrying relative information, also called compositional vectors, can appear in regression models either as dependent or as explanatory variables. In some situations, they can be on both sides of the regression equation. Measuring the marginal impacts of covariates in these types of models is not straightforward since a change in one component of a closed composition automatically affects the rest of the composition. Previous work by the authors has shown how to measure, compute and interpret these marginal impacts in the case of linear regression models with compositions on both sides of the equation. The resulting natural interpretation is in terms of an elasticity, a quantity commonly used in econometrics and marketing applications. They also demonstrate the link between these elasticities and simplicial derivatives. The aim of this contribution is to extend these results to other situations, namely when the compositional vector is on a single side of the regression equation. In these cases, the marginal impact is related to a semi-elasticity and also linked to some simplicial derivative. Moreover we consider the possibility that a total variable is used as an explanatory variable, with several possible interpretations of this total and we derive the elasticity formulas in that case.

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The Relationship Between Health Outcomes and Health Expenditure in Europe by Using Compositional Data Analysis

Problemy Ekorozwoju ◽

10.35784/pe.2020.2.11 ◽

2020 ◽

Vol 15 (2) ◽

pp. 99-110

Author(s):

Magdaléna Drastichová ◽

◽

Peter Filzmoser ◽

Keyword(s):

Data Analysis ◽

Health Outcomes ◽

Compositional Data ◽

Compositional Data Analysis ◽

The European Union ◽

Term Care ◽

Outcome Indicators ◽

Healthy Life Years ◽

Life Expectancy At Birth ◽

Life Years

The type of health system in each country and the resources devoted to it determine its outcomes. Relationships between ratios of expenditure to Gross Domestic Product (GDP) classified by provider and indicators reflecting health outcomes in 2015 are examined for 30 countries by means of a compositional data analysis and a regression analysis. The countries in the sample are the European Union (EU-28) countries plus Iceland and Norway. The outcome indicators used are life expectancy at birth (LE); healthy life years in absolute value at birth for females (HLYf) and for males (HLYm); and death rate due to chronic diseases (DR) (response variables). The results indicate that the higher the ratio of expenditure on retailers and other providers of medical goods in relation to other types of expenditure in the composition, the higher the DR indicator and the lower the LE indicator. The ratio of expenditure on residential long-term care facilities in the composition seems to have had a positive effect on both HLY indicators. The effect of expenditure ratios on providers of healthcare system administration and financing is not straightforward.

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Compositional data analysis in epidemiology

Statistical Methods in Medical Research ◽

10.1177/0962280216671536 ◽

2016 ◽

Vol 27 (6) ◽

pp. 1878-1891 ◽

Cited By ~ 10

Author(s):

Mehmet C Mert ◽

Peter Filzmoser ◽

Gottfried Endel ◽

Ingrid Wilbacher

Keyword(s):

Data Analysis ◽

Compositional Data ◽

Statistical Analyses ◽

Analysis Approach ◽

Compositional Data Analysis ◽

Data Sets ◽

Multivariate Statistical Analyses ◽

Multivariate Statistical ◽

Data Set ◽

Relative Information

Compositional data analysis refers to analyzing relative information, based on ratios between the variables in a data set. Data from epidemiology are usually treated as absolute information in an analysis. We outline the differences in both approaches for univariate and multivariate statistical analyses, using illustrative data sets from Austrian districts. Not only the results of the analyses can differ, but in particular the interpretation differs. It is demonstrated that the compositional data analysis approach leads to new and interesting insights.

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Body composition by computed tomography vs. dual energy x-ray absorptiometry: Long-term prediction of all-cause mortality in the Health ABC cohort

The Journals of Gerontology Series A ◽

10.1093/gerona/glab105 ◽

2021 ◽

Author(s):

Samaneh Farsijani ◽

Lingshu Xue ◽

Robert M Boudreau ◽

Adam J Santanasto ◽

Stephen B Kritchevsky ◽

...

Keyword(s):

Body Composition ◽

Data Analysis ◽

Fat Mass ◽

Compositional Data ◽

Dual Energy ◽

Compositional Data Analysis ◽

Muscle Area ◽

X Ray ◽

All Cause Mortality ◽

Body Compartments

Abstract Background Body composition assessment by computed tomography (CT) predicts health outcomes in diverse populations. However, its performance in predicting mortality has not been directly compared to dual-energy-X-ray-absorptiometry (DXA). Additionally, the association between different body compartments and mortality, acknowledging the compositional nature of human body, is not well-studied. Compositional Data Analysis, that is applied to multivariate proportion-type dataset, may help to account for the inter-relationships of body compartments by constructing log-ratios of components. Here, we determined the associations of baseline CT-based measures of mid-thigh cross-sectional areas vs. DXA measures of body composition with all-cause mortality in Health ABC cohort, using both traditional (individual body compartments) and Compositional Data Analysis (using ratios of body compartments) approaches. Methods The Health ABC study assessed body composition in 2911 older adults in 1996-97. We investigated the individual and ratios of (by Compositional Analysis) body compartments assessed by DXA (lean, fat, and bone-mass) and CT (muscle, subcutaneous fat area, intermuscular fat (IMF), and bone) on mortality, using Cox proportional hazard models. Results Lower baseline muscle area by CT (HRmen=0.56 [95%CI: 0.48-0.67], HRwomen=0.60 [0.48-0.74]), fat-mass by DXA (HRmen=0.48 [0.24-0.95]) were predictors of mortality in traditional Cox-regression analysis. Consistently, Compositional Data Analysis revealed that lower muscle area vs. IMF, muscle area vs. bone area, and lower fat-mass vs. lean-mass were associated with higher mortality in both sexes. Conclusion Both CT measure of muscle area and DXA fat-mass (either individually or relative to other body compartments) were strong predictors of mortality in both sexes in a community research setting.

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A systematic review of compositional data analysis studies examining associations between sleep, sedentary behaviour, and physical activity with health outcomes in adults

Applied Physiology Nutrition and Metabolism ◽

10.1139/apnm-2020-0160 ◽

2020 ◽

Vol 45 (10 (Suppl. 2)) ◽

pp. S248-S257 ◽

Cited By ~ 1

Author(s):

Ian Janssen ◽

Anna E. Clarke ◽

Valerie Carson ◽

Jean-Philippe Chaput ◽

Lora M. Giangregorio ◽

...

Keyword(s):

Physical Activity ◽

Systematic Review ◽

Data Analysis ◽

Health Outcomes ◽

Sedentary Behaviour ◽

Compositional Data ◽

Compositional Data Analysis ◽

Movement Behaviour ◽

Time Out ◽

All Cause Mortality

This systematic review determined if the composition of time spent in movement behaviours (i.e., sleep, sedentary behaviour (SED), light physical activity, and moderate-to-vigorous physical activity (MVPA)) is associated with health in adults. Five electronic databases were searched in August 2019. Studies were eligible for inclusion if they were peer-reviewed, examined community-dwelling adults, and used compositional data analysis to examine the associations between the composition of time spent in movement behaviours and health outcomes. Eight studies (7 cross-sectional, 1 prospective cohort) of >12 000 unique participants were included. Findings indicated that the 24-h movement behaviour composition was associated with all-cause mortality (1 of 1 analyses), adiposity (4 of 4 analyses), and cardiometabolic biomarkers (8 of 15 analyses). Reallocating time into MVPA from other movement behaviours was associated with favourable changes to most health outcomes and taking time out of SED and reallocating it into other movement behaviours was associated with favourable changes to all-cause mortality. The quality of evidence was very low for all health outcomes. In conclusion, these findings support the notion that the composition of movement across the entire 24-h day matters, and that recommendations for sleep, SED, and physical activity should be combined into a single public health guideline. (PROSPERO registration no.: CRD42019121641.) Novelty The 24-h movement behaviour composition is associated with a variety of health outcomes. Reallocating time into MVPA is favourably associated with health. Reallocating time out of SED is associated with favourable changes to mortality risk.

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