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

Abstract

Summary 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.

Clustering of Lifestyle and Health Behaviours in Australian Children and Their Relationship with Obesity, Self-Rated Health and Quality of Life

“…The analytical method LCA designates each participant a 'best' class assignment based on their maximum likelihood of belonging to each of the identi ed distinct classes. Participants within the same class are regarded as homogeneous based on the indicator variables [3,30]. Analyses were conducted using the LCA procedure in Stata version 16 software.…”

“…For example, weight and height data to calculate BMI were collected through trained professionals. Moreover, using LCA is an advanced statistical approach that ensures diverse bene ts to estimate more precisely [3].…”

“…Thirdly, the record of the children's health behaviour patterns is based on children's memory. It might be possible to forget past incidents, leading to bias, or over and underreporting [3,22].…”

“…It is well established that a healthy lifestyle can reduce morbidity rates for all sections of the population, including children [1][2][3]. However, while many studies have linked sub-optimal children's health and lifestyle environments with adult disease, few have focused on the association of identi ed clusters and obesity, self-rated health and quality of life in children [4][5][6].…”

Influence of Lifestyle Patterns On Myopia In Students Aged 9 To 18 In Tianjin, China: A Latent Class Analysis

“…Lifestyle or behavioral factors are often used as indicators for classi cation. LCA has been used in several studies to explorer lifestyle factors associated with oral health 11 , obesity 12 , mental health 13 , and other health related outcomes but few with myopia.…”