Dataset Information

Latent class analysis of obesity-related characteristics and associations with body mass index among young children.

ABSTRACT: Objective:Identifying how obesity-related characteristics cluster in populations is important to understand disease risk. Objectives of this study were to identify classes of children based on obesity-related variables and to evaluate the associations between the identified classes and overweight and obesity. Methods:A cross-sectional study was conducted among children 3-11 years of age (n = 5185) from the TARGet Kids! network (2008-2018). Latent class analysis was used to identify distinct classes of children based on 15 family, metabolic, health behaviours and school-related variables. Associations between the identified latent classes and overweight and obesity were estimated using multinomial logistic regression. Results:Six classes were identified: Class 1: 'Family and health risk behaviours' (20%), Class 2: 'Metabolic risk' (7%), Class 3: 'High risk' (6%), Class 4: 'High triglycerides' (21%), Class 5: 'Health risk behaviours and developmental concern' (22%), and Class 6: 'Healthy' (24%). Children in Classes 1-5 had increased odds of both overweight and obesity compared with 'Healthy' class. Class 3 'High risk' was most strongly associated with child overweight (odds ratio [OR] 1.9, 95% confidence interval [CI] 1.2, 3.2) and obesity (OR 3.3, 95% CI 1.7, 6.7). Conclusions:Distinct classes of children identified based on obesity-related characteristics were all associated with increased obesity; however, the magnitude of risk varied depending on number of at-risk characteristics. Understanding the clustering of obesity characteristics in children may inform precision public health and population prevention interventions.

SUBMITTER: Anderson LN

PROVIDER: S-EPMC7448165 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Latent class analysis of obesity-related characteristics and associations with body mass index among young children.

Anderson Laura N LN Sandhu Ravinder R Keown-Stoneman Charles D G CDG De Rubeis Vanessa V Borkhoff Cornelia M CM Carsley Sarah S Maguire Jonathon L JL Birken Catherine S CS

Obesity science & practice 20200407 4

<h4>Objective</h4>Identifying how obesity-related characteristics cluster in populations is important to understand disease risk. Objectives of this study were to identify classes of children based on obesity-related variables and to evaluate the associations between the identified classes and overweight and obesity.<h4>Methods</h4>A cross-sectional study was conducted among children 3-11 years of age (<i>n</i> = 5185) from the <i>TARGet Kids!</i> network (2008-2018). Latent class analysis was u ...[more]

PMID: 32874674

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Project description:Importance:A total of 25.7 million children in the United States are classified as overweight or obese. Obesity is associated with deficits in executive function, which may contribute to poor dietary decision-making. Less is known about the associations between being overweight or obese and brain development. Objective:To examine whether body mass index (BMI) is associated with thickness of the cerebral cortex and whether cortical thickness mediates the association between BMI and executive function in children. Design, Setting, and Participants:In this cross-sectional study, cortical thickness maps were derived from T1-weighted structural magnetic resonance images of a large, diverse sample of 9 and 10-year-old children from 21 US sites. List sorting, flanker, matrix reasoning, and Wisconsin card sorting tasks were used to assess executive function. Main Outcomes and Measures:A 10-fold nested cross-validation general linear model was used to assess mean cortical thickness from BMI across cortical brain regions. Associations between BMI and executive function were explored with Pearson partial correlations. Mediation analysis examined whether mean prefrontal cortex thickness mediated the association between BMI and executive function. Results:Among 3190 individuals (mean [SD] age, 10.0 [0.61] years; 1627 [51.0%] male), those with higher BMI exhibited lower cortical thickness. Eighteen cortical regions were significantly inversely associated with BMI. The greatest correlations were observed in the prefrontal cortex. The BMI was inversely correlated with dimensional card sorting (r?=?-0.088, P?<?.001), list sorting (r?=?-0.061, P?<?.003), and matrix reasoning (r?=?-0.095, P?<?.001) but not the flanker task. Mean prefrontal cortex thickness mediated the association between BMI and list sorting (mean [SE] indirect effect, 0.014 [0.008]; 95%?CI, 0.001-0.031) but not the matrix reasoning or card sorting task. Conclusions and Relevance:These results suggest that BMI is associated with prefrontal cortex development and diminished executive functions, such as working memory.

Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.

Dataset Information

Latent class analysis of obesity-related characteristics and associations with body mass index among young children.

Publications

Latent class analysis of obesity-related characteristics and associations with body mass index among young children.

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