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Interpretation of Multivariate Association Patterns between Multicollinear Physical Activity Accelerometry Data and Cardiometabolic Health in Children-A Tutorial.

ABSTRACT: Associations between multicollinear accelerometry-derived physical activity (PA) data and cardiometabolic health in children needs to be analyzed using an approach that can handle collinearity among the explanatory variables. The aim of this paper is to provide readers a tutorial overview of interpretation of multivariate pattern analysis models using PA accelerometry data that reveals the associations to cardiometabolic health. A total of 841 children (age 10.2 ± 0.3 years) provided valid data on accelerometry (ActiGraph GT3X+) and six indices of cardiometabolic health that were used to create a composite score. We used a high-resolution PA description including 23 intensity variables covering the intensity spectrum (from 0-99 to ≥10000 counts per minute), and multivariate pattern analysis to analyze data. We report different statistical measures of the multivariate associations between PA and cardiometabolic health and use decentile groups of PA as a basis for discussing the meaning and impact of multicollinearity. We show that for high-resolution accelerometry data; considering all explanatory variables is crucial to obtain a correct interpretation of associations to cardiometabolic health; which is otherwise strongly confounded by multicollinearity in the dataset. Thus; multivariate pattern analysis challenges the traditional interpretation of findings from linear regression models assuming independent explanatory variables.

SUBMITTER: Aadland E

PROVIDER: S-EPMC6680435 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Interpretation of Multivariate Association Patterns between Multicollinear Physical Activity Accelerometry Data and Cardiometabolic Health in Children-A Tutorial.

Aadland Eivind E Andersen Lars Bo LB Resaland Geir Kåre GK Kvalheim Olav Martin OM

Metabolites 20190702 7

Associations between multicollinear accelerometry-derived physical activity (PA) data and cardiometabolic health in children needs to be analyzed using an approach that can handle collinearity among the explanatory variables. The aim of this paper is to provide readers a tutorial overview of interpretation of multivariate pattern analysis models using PA accelerometry data that reveals the associations to cardiometabolic health. A total of 841 children (age 10.2 ± 0.3 years) provided valid data ...[more]

PMID: 31269708

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Project description:BackgroundThe analysis of associations between accelerometer-derived physical activity (PA) intensities and cardiometabolic health is a major challenge due to multicollinearity between the explanatory variables. This challenge has facilitated the application of different analytic approaches within the field. The aim of the present study was to compare association patterns of PA intensities with cardiometabolic health in children obtained from multiple linear regression, compositional data analysis, and multivariate pattern analysis.MethodsA sample of 841 children (age 10.2 ± 0.3 years; BMI 18.0 ± 3.0; 50% boys) provided valid accelerometry and cardiometabolic health data. Accelerometry (ActiGraph GT3X+) data were characterized into traditional (four PA intensity variables) and more detailed categories (23 PA intensity variables covering the intensity spectrum; 0-99 to ≥10,000 counts per minute). Several indices of cardiometabolic health were used to create a composite cardiometabolic health score. Multiple linear regression and multivariate pattern analyses were used to analyze both raw and compositional data.ResultsBesides a consistent negative (favorable) association between vigorous PA and the cardiometabolic health measure using the traditional description of PA data, associations between PA intensities and cardiometabolic health differed substantially depending on the analytic approaches used. Multiple linear regression lead to instable and spurious associations, while compositional data analysis showed distorted association patterns. Multivariate pattern analysis appeared to handle the raw PA data correctly, leading to more plausible interpretations of the associations between PA intensities and cardiometabolic health.ConclusionsFuture studies should consider multivariate pattern analysis without any transformation of PA data when examining relationships between PA intensity patterns and health outcomes.Trial registrationThe study was registered in Clinicaltrials.gov 7th of April 2014 with identification number NCT02132494 .

Project description:BackgroundStrongly multicollinear covariates, such as those typically represented in metabolomics applications, represent a challenge for multivariate regression analysis. These challenges are commonly circumvented by reducing the number of covariates to a subset of linearly independent variables, but this strategy may lead to loss of resolution and thus produce models with poorer interpretative potential. The aim of this work was to implement and illustrate a method, multivariate pattern analysis (MVPA), which can handle multivariate covariates without compromising resolution or model quality.ResultsMVPA has been implemented in an open-source R package of the same name, mvpa. To facilitate the usage and interpretation of complex association patterns, mvpa has also been integrated into an R shiny app, mvpaShiny, which can be accessed on www.mvpashiny.org . MVPA utilizes a general projection algorithm that embraces a diversity of possible models. The method handles multicollinear and even linear dependent covariates. MVPA separates the variance in the data into orthogonal parts within the frame of a single joint model: one part describing the relations between covariates, outcome, and explanatory variables and another part describing the "net" predictive association pattern between outcome and explanatory variables. These patterns are visualized and interpreted in variance plots and plots for pattern analysis and ranking according to variable importance. Adjustment for a linear dependent covariate is performed in three steps. First, partial least squares regression with repeated Monte Carlo resampling is used to determine the number of predictive PLS components for a model relating the covariate to the outcome. Second, postprocessing of this PLS model by target projection provided a single component expressing the predictive association pattern between the outcome and the covariate. Third, the outcome and the explanatory variables were adjusted for the covariate by using the target score in the projection algorithm to obtain "net" data. We illustrate the main features of MVPA by investigating the partial mediation of a linearly dependent metabolomics descriptor on the association pattern between a measure of insulin resistance and lifestyle-related factors.ConclusionsOur method and implementation in R extend the range of possible analyses and visualizations that can be performed for complex multivariate data structures. The R packages are available on github.com/liningtonlab/mvpa and github.com/liningtonlab/mvpaShiny.

Project description:BackgroundSedentary behavior is a modifiable risk factor for cardiometabolic health; however, the assessment of total sedentary time may not capture youth's highly active and interrupted activity patterns. This study examined the associations between sedentary activity patterns and cardiometabolic risk factors among Mexican youth, who have a disproportionate burden of metabolic diseases, using a repeated measure design out of a longitudinal data.Methods570 subjects in the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) birth cohort, who were followed up to three-time points during adolescence, were included. Bout duration, and frequency and percentages of waking time spent in specific intensities of activity, were quantified using ActiGraph wGT3X-BT wrist accelerometers. Self-reported questionnaires were used to query the usual duration of different sedentary behaviors. Outcomes were fasting lipid profile, markers for glucose homeostasis, anthropometry, and blood pressure. Associations were modeled using linear mixed-effects models, and isotemporal substitution approach was additionally used to assess the effect of replacing objectively assessed sedentary activity with other activity intensities, adjusting for potential confounders.ResultsEach hour of self-reported screen-based time was positively associated with diastolic blood pressure (mm Hg) [β = 0.30, 95% confidence interval (95% CI) = 0.10, 0.51], and an hour of other sedentary time was associated with log serum glucose (mg/dL) [β = 0.01, 95% CI = 0.004, 0.017]. Substitution models showed that replacing 5% of sedentary time with moderate to vigorous physical activity (MVPA) was associated with lower waist circumference (cm) [β = - 1.35, 95% CI = - 1.91, - 0.79] and log serum triglycerides (mg/dL) [β = - 0.11, 95% CI = - 0.18, - 0.03]. Substituting one uninterrupted sedentary bout with light activity was associated with lower insulin (μIU/mL) [β = - 0.06, 95% CI = - 0.10, - 0.02].ConclusionsSedentary time was associated with cardiometabolic risk factors in Mexican youth in a context-specific manner. Replacing sedentary time with higher intensities was associated with improvements in some cardiometabolic markers.

Project description:Background/objectivesPrenatal growth, which is widely marked by birthweight, may have a pivotal role in affecting the lifelong risk of cardiometabolic disorders; however, comprehensive evaluation of its relations with childhood cardiometabolic risk patterns and the ethnic and gender disparities in national representative populations is still lacking. The aim of this study was to evaluate the associations between birthweight and comprehensive patterns of cardiometabolic risk in a nationally representative sample of children and adolescents.Subjects/methodsProspective analyses were performed using data from 28 153 children 0 to 15 years in the National Health and Nutrition Examination Survey from 1999 through 2014. We defined childhood cardiometabolic disorders using standard definitions for obesity, high blood pressure, hyperglycemia and dyslipidemia.ResultsFive birthweight categories <2.5, 2.5-3.0, 3.0-3.5, 3.5-4.2 and ⩾4.2 kg accounted for 8.2%, 17.9%, 35.7%, 27.9% and 10.4% of the population, respectively. In all children, with increasing birthweight, we observed significantly increasing trends of the risk of general and central obesity (P for trend <0.01) and significantly decreasing trends of the risk of high systolic blood pressure (SBP), high HbA1c and low high-density lipoprotein cholesterol (HDL-C) (P for trend <0.05). The associations were independent of current body mass index (BMI). In addition, we found that the relations of birthweight with high waist circumference in Black children showed U-shape, as well as high SBP in Mexican and Hispanic children. Moreover, we found that the associations of low birthweight with high SBP and low HDL-C appeared to more prominent significant in boys, whereas the inverse association with high HbA1c was more evident in girls.ConclusionsOur data indicate that birthweight is significantly related to childhood cardiometabolic risk, independent of current BMI, and the associations exhibit race and gender-specific patterns.

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Interpretation of Multivariate Association Patterns between Multicollinear Physical Activity Accelerometry Data and Cardiometabolic Health in Children-A Tutorial.

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Interpretation of Multivariate Association Patterns between Multicollinear Physical Activity Accelerometry Data and Cardiometabolic Health in Children-A Tutorial.

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