The Impact of Movement Behaviors on Bone Health in Elderly with Adequate Nutritional Status: Compositional Data Analysis Depending on the Frailty Status

The aim of this study was to determine the relationship between bone mass (BM) and physical activity (PA) and sedentary behavior (SB) according to frailty status and sex using compositional data analysis. We analyzed 871 older people with an adequate nutritional status. Fried criteria were used to classify by frailty status. Time spent in SB, light intensity PA (LPA) and moderate-to-vigorous intensity PA (MVPA) was assessed from accelerometry for 7 days. BM was determined by dual-energy X-ray absorptiometry (DXA). The combined effect of PA and SB was significantly associated with BM in robust men and women (p ≤ 0.05). In relation to the other behaviors, SB was negatively associated with BM in robust men while BM was positively associated with SB and negatively with LPA and MVPA in robust women. Moreover, LPA also was positively associated with arm BM (p ≤ 0.01). Finally, in pre-frail women, BM was positively associated with MVPA. In our sample, to decrease SB could be a good strategy to improve BM in robust men. In contrast, in pre-frail women, MVPA may be an important factor to consider regarding bone health.

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Associations between sedentary time, physical activity and bone health among older people using compositional data analysis

PLoS ONE ◽

10.1371/journal.pone.0206013 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0206013 ◽

Cited By ~ 18

Author(s):

Irene Rodríguez-Gómez ◽

Asier Mañas ◽

José Losa-Reyna ◽

Leocadio Rodríguez-Mañas ◽

Sebastien F. M. Chastin ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Older People ◽

Bone Health ◽

Sedentary Time ◽

Compositional Data ◽

Compositional Data Analysis

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The Evaluation of the Impact of a Saharan Event on Particulate Matter Using Compositional Data Analysis

Pollutants ◽

10.3390/pollutants2010001 ◽

2022 ◽

Vol 2 (1) ◽

pp. 1-11

Author(s):

Antonio Speranza ◽

Rosa Caggiano ◽

Vito Summa

Keyword(s):

Data Analysis ◽

Compositional Data ◽

Compositional Data Analysis ◽

Mineral Element ◽

Natural Sources ◽

Element Concentrations ◽

Simultaneous Measurements ◽

Pm10 And Pm2.5 ◽

The Impact ◽

Suburban Site

The proposed approach based on compositional data analysis was applied on simultaneous measurements of the mineral element concentrations of PM10 and PM2.5 from a typical suburban site with and without a Saharan event. The suburban site is located in the city of Rome. The selected mineral elements were Al, Si, Ca, Fe, Ti, Mg, and Sr. The data relating to these elements are reported in a previous study. The considered elements are mainly related to mineral matter. The proposed approach allows statistically validating that the mineral element concentrations of PM during days with a Saharan event differ from those without a Saharan event in terms of mineral element composition and size distribution. In particular, the results showed that the compositional data analysis applied to simultaneous measurements of mineral element concentrations of PM10 and PM2.5 is a helpful technique that can be used to study environmental sites affected by natural sources such as Saharan events. Moreover, the presented technique can be handy in all those conditions where it is important to discriminate whether the occurrence of an exceedance or a violation of the daily limit value established for PM could also be due to natural sources.

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Interpretation of Explanatory Variables Impacts in Compositional Regression Models

Austrian Journal of Statistics ◽

10.17713/ajs.v47i5.718 ◽

2018 ◽

Vol 47 (5) ◽

pp. 1-25 ◽

Cited By ~ 6

Author(s):

Joanna Morais ◽

Christine Thomas-Agnan ◽

Michel Simioni

Keyword(s):

Data Analysis ◽

Regression Models ◽

Compositional Data ◽

Selection Procedure ◽

Original Model ◽

Compositional Data Analysis ◽

Explanatory Variables ◽

Market Shares ◽

Share Model ◽

The Impact

We are interested in modeling the impact of media investments on automobile manufacturer's market shares. Regression models have been developed for the case where the dependent variable is a vector of shares. Some of them, from the marketing literature, are easy to interpret but quite simple (Model A). Alternative models, from the compositional data analysis literature, allow a large complexity but their interpretation is not straightforward (Model B). This paper combines both approaches in order to obtain a performing market share model and develop relevant interpretations for practical use.We prove that Model A is a particular case of Model B, and that an intermediate specification is possible (Model AB). A model selection procedure is proposed. Several impact measures are presented and we show that elasticities are particularly useful: they can be computed from the transformed or from the original model, and they are linked to the simplicial derivatives.

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Association of Body Compositions and Bone Mineral Density in Chinese Children and Adolescents: Compositional Data Analysis

BioMed Research International ◽

10.1155/2021/1904343 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Liu Zhang ◽

Hongjuan Li ◽

Yimin Zhang ◽

Zhenxing Kong ◽

Ting Zhang ◽

...

Keyword(s):

Bone Mineral Density ◽

Data Analysis ◽

Children And Adolescents ◽

Bone Mineral ◽

Compositional Data ◽

Compositional Data Analysis ◽

Mineral Density ◽

Body Compositions ◽

The Relationship ◽

Chinese Children And Adolescents

The purpose of this study was to investigate the relationship between body compositions and bone mineral density (BMD) and the effect of composition substitution among Chinese children and adolescents without the influence of multicollinearity. A dual-energy X-ray absorptiometry scan was used to determine the amount of truncal fat (TF), nontruncal fat (NTF), fat-free mass (FFM), and BMD. The compositional data analysis and the compositional proportional substitution analysis were conducted to determine the effect of each part of body compositions on BMD and its substitution effects. Four hundred sixty-six (466) (boys: 51.9%) participants completed this cross-sectional study. For girls, in the overweight group, the relationship between TF and the BMD was positive ( β = 2.943 e − 01 , p = 0.006 ) while the NTF showed the opposite trend ( β = − 2.358 e − 01 , p = 0.009 ). When 4% NTF or FFM was substituted by TF, the BMD increased by about 0.1 and 0.05 units ( p < 0.05 ), respectively. For boys, the association between FFM and BMD was statistically positive ( β = 4.091 e − 02 , p = 0.0001 ). There was a positive correlation between TF and BMD ( β = 7.963 e − 02 , p = 0.036 ). But with the increase of BMI, this correlation shifted in the opposite direction. In conclusion, compared to TF and NTF, FFM had a better protective effect on BMD, especially for boys. The risk of NTF accumulation on BMD was greater than that of TF accumulation. Compared with girls, boys were more sensitive to the amount of TF.

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Compositional analysis of dietary patterns

Statistical Methods in Medical Research ◽

10.1177/0962280218790110 ◽

2018 ◽

Vol 28 (9) ◽

pp. 2834-2847 ◽

Cited By ~ 2

Author(s):

M Solans ◽

G Coenders ◽

R Marcos-Gragera ◽

A Castelló ◽

E Gràcia-Lavedan ◽

...

Keyword(s):

Data Analysis ◽

Dietary Patterns ◽

Pattern Analysis ◽

Compositional Data ◽

Compositional Analysis ◽

Principal Component ◽

Compositional Data Analysis ◽

Trade Offs ◽

Dietary Pattern Analysis ◽

The Relationship

Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others.

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Compositional Data Analysis in Time-Use Epidemiology: What, Why, How

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17072220 ◽

2020 ◽

Vol 17 (7) ◽

pp. 2220 ◽

Cited By ~ 9

Author(s):

Dorothea Dumuid ◽

Željko Pedišić ◽

Javier Palarea-Albaladejo ◽

Josep Antoni Martín-Fernández ◽

Karel Hron ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Sedentary Behavior ◽

Time Use ◽

Compositional Data ◽

Compositional Data Analysis ◽

Health It ◽

Future Directions ◽

The Relationship ◽

Daily Time

In recent years, the focus of activity behavior research has shifted away from univariate paradigms (e.g., physical activity, sedentary behavior and sleep) to a 24-h time-use paradigm that integrates all daily activity behaviors. Behaviors are analyzed relative to each other, rather than as individual entities. Compositional data analysis (CoDA) is increasingly used for the analysis of time-use data because it is intended for data that convey relative information. While CoDA has brought new understanding of how time use is associated with health, it has also raised challenges in how this methodology is applied, and how the findings are interpreted. In this paper we provide a brief overview of CoDA for time-use data, summarize current CoDA research in time-use epidemiology and discuss challenges and future directions. We use 24-h time-use diary data from Wave 6 of the Longitudinal Study of Australian Children (birth cohort, n = 3228, aged 10.9 ± 0.3 years) to demonstrate descriptive analyses of time-use compositions and how to explore the relationship between daily time use (sleep, sedentary behavior and physical activity) and a health outcome (in this example, adiposity). We illustrate how to comprehensively interpret the CoDA findings in a meaningful way.

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Relations between socio-economic factors and nutritional diet in Vietnam from 2004 to 2014: New insights using compositional data analysis

Statistical Methods in Medical Research ◽

10.1177/0962280218770223 ◽

2018 ◽

Vol 28 (8) ◽

pp. 2305-2325 ◽

Cited By ~ 7

Author(s):

Huong Thi Trinh ◽

Joanna Morais ◽

Christine Thomas-Agnan ◽

Michel Simioni

Keyword(s):

Data Analysis ◽

Compositional Data ◽

Calorie Intake ◽

Compositional Data Analysis ◽

Diet Patterns ◽

Total Calorie ◽

Food Expenditure ◽

The Impact ◽

Total Calorie Intake

This paper contributes to the analysis of the impact of socioeconomic factors, like food expenditure level and urbanization, on diet patterns in Vietnam, from 2004 to 2014. Contrary to the existing literature, we focus on the diet balance in terms of macronutrients consumption (protein, fat and carbohydrate) and we take into account the fact that the volumes of macronutrients are not independent. In other words, we are interested in the shares of each macronutrient in the total calorie intake. We use compositional data analysis (CODA), adapted to deal with the relative information contained in shares, to describe the evolution of diet patterns over time, and to model the impact of household characteristics on the macronutrient shares vector. We compute food expenditure elasticities of macronutrient shares, and we compare them to classical elasticities for macronutrient volumes and total calorie intake. The compositional model highlights the important role of many factors in the determination of diet choices and we will focus mainly on the role of food expenditure. Our results are consistent with the rest of the literature, but they have the advantage to highlight the substitution effects between macronutrients in the context of nutrition transition.

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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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