Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Rubeis, Vanessa De; Andreacchi, Alessandra T.; Sharpe, Isobel; Griffith, Lauren; Keown‐Stoneman, Charles; Anderson, Laura N.

doi:10.1002/osp4.456

Cited by 21 publications

(35 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The life-course trajectories described in this study are consistent with those previously reported in the literature. Several studies have used repeated measures of BMI, waist circumference or body shape to approximate unobserved sub-groups of developmental trajectories of weight through different periods of life and estimated their association to different outcomes, including type 2 diabetes 31 .…”

Section: Discussionmentioning

confidence: 99%

Life-course trajectories of weight and their impact on the incidence of type 2 diabetes

Yacamán-Méndez

Lagerros

Zhou

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Although exposure to overweight and obesity at different ages is associated to a higher risk of type 2 diabetes, the effect of different patterns of exposure through life remains unclear. We aimed to characterize life-course trajectories of weight categories and estimate their impact on the incidence of type 2 diabetes. We categorized the weight of 7203 participants as lean, normal or overweight at five time-points from ages 7–55 using retrospective data. Participants were followed for an average of 19 years for the development of type 2 diabetes. We used latent class analysis to describe distinctive trajectories and estimated the risk ratio, absolute risk difference and population attributable fraction (PAF) associated to different trajectories using Poisson regression. We found five distinctive life-course trajectories. Using the stable-normal weight trajectory as reference, the stable overweight, lean increasing weight, overweight from early adulthood and overweight from late adulthood trajectories were associated to higher risk of type 2 diabetes. The estimated risk ratios and absolute risk differences were statistically significant for all trajectories, except for the risk ratio of the lean increasing trajectory group among men. Of the 981 incident cases of type 2 diabetes, 47.4% among women and 42.9% among men were attributable to exposure to any life-course trajectory different from stable normal weight. Most of the risk was attributable to trajectories including overweight or obesity at any point of life (36.8% of the cases among women and 36.7% among men). The overweight from early adulthood trajectory had the highest impact (PAF: 23.2% for woman and 28.5% for men). We described five distinctive life-course trajectories of weight that were associated to increased risk of type 2 diabetes over 19 years of follow-up. The variability of the effect of exposure to overweight and obesity on the risk of developing type 2 diabetes was largely explained by exposure to the different life-course trajectories of weight.

show abstract

Section: Discussionmentioning

confidence: 99%

Life-course trajectories of weight and their impact on the incidence of type 2 diabetes

Yacamán-Méndez

Lagerros

Zhou

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…One of these studies grouped BMI trajectories by sex (male and female) and age (>65, 50–65, and <50 years), resulting in six different groups of growth trajectories 28 . All studies considered sex, age, or lifestyle habits as covariates 13,15,25 . Only one study found that, although all BMI trajectories in adolescents were not significantly associated with diabetes onset, the tri‐ponderal mass index (TMI) trajectories were associated with diabetes onset 26 .…”

Section: Resultsmentioning

confidence: 99%

“…Can different changes in the trends of BMI levels be used to estimate the risks of subsequent diabetes? Group‐based trajectory modeling approaches have become increasingly popular in recent years and have been used to determine the long‐term trajectory of BMI and its association with subsequent diabetes 11–13 …”

Section: Introductionmentioning

confidence: 99%

Association between distinct body mass index trajectories according to the group‐based trajectory modeling and the risk of incident diabetes: A systematic review

Kan

Liu

et al. 2022

Obesity Reviews

View full text Add to dashboard Cite

We aimed to determine the association between distinct body mass index (BMI) trajectories, using group-based trajectory modeling, and the subsequent risk of incident diabetes. Five databases were systematically searched. Fourteen population-based cohort studies that summarized the association between different BMI trajectories and subsequent diabetes, with the four most common BMI trajectories including the "stable," "increasing," "decreasing," and "turning" groups, were included. The rapid increase and stable high-level BMI groups showed the strongest association with the subsequent risk of diabetes compared with the stable normal BMI group. Increased baseline BMI levels resulted in a steeper slope and greater risk of subsequent diabetes. In the decreasing BMI group, one study reported that those aged >50 years showed the highest incidence of subsequent diabetes, whereas the other two studies reported no association between these two variables. In the turning group, an increase followed by a decrease in BMI levels from adolescence to late adulthood could reduce the risk of developing diabetes, although the residual risk remained. By contrast, the incidence of subsequent diabetes remained high in the middle-aged BMI-turning group. This study can provide further insights for identifying populations at high risk of diabetes and for developing targeted prevention strategies.

show abstract

“…Group-based discrete trajectory mixture models were employed to develop trajectories of built environments, BMI, and waist circumference across the period of both cohorts combined. This has been used by previous studies in modelling the trajectories of BMI [ 31 ] and mental health [ 47 ]. Group-based discrete trajectory mixture models partition individuals into some groups, denoting differences in trajectory courses.…”

Section: Methodsmentioning

confidence: 99%

“…For example, while there is a legion of linking green space with health, longitudinal evidence linking green space qualities and health remains volumetrically smaller [ 27 ] and, but for some recent studies [ 28 , 29 ], rarely examines how subjective and or objective changes in those qualities may lead to changes in health status [ 30 ]. This is a widespread issue in the built environment and health literature as reported in a scoping review [ 31 ], which summarised a number of studies that measured changes (i.e., developmental trajectories) in weight status (e.g., BMI), including child and adolescent participants, but none of these studies evaluated changes in built environments as exposure of interest. This present study aimed to enrich the current literature by examining the associations between changes in a range of built environments and developmental trajectories of weight status among Australian children from age 2 to 15 years.…”

Section: Introductionmentioning

confidence: 99%

Association between built environments and weight status: evidence from longitudinal data of 9589 Australian children

2022

View full text Add to dashboard Cite

Background No studies appear to examine potential associations between changes in built environments across childhood and the developmental trajectories of child weight status. Objective Examine the developmental trajectories of child weight status with respect to changes in childhood exposure to the built environments. Methods This study used data of 9589 children with biennial follow-up (2004–2016), retrieved from the Longitudinal Study of Australian Children. Changes in objectively-measured child weight status (i.e., body mass index-BMI, waist circumference) were investigated in relation to changes in seven built environments (i.e., neighbourhood safety; green space quality; footpaths and street conditions; public transport; shopping facilities; basic services; and local traffic) subjectively reported by caregivers. Group-based discrete trajectory mixture models were used to classify children according to their developmental trajectories of built environments and weight status. Multilevel multinomial logistic regression was employed to examine associations between built environments and child weight status adjusted for confounding. Results Two, four, and six trajectory groups were developed for built environment variables. Three groups namely “moderate”, “high”, and “extreme increase” were generated for each BMI and waist circumference. Findings from multilevel analyses indicated that growing up in neighbourhoods that are considered highly safe, with better quality of green space nearby, and in areas with low local traffic over time are protective against unhealthy weight increase in childhood. Meanwhile, living with better access to shopping facilities and basic services was associated with an unhealthy increase in BMI and/or waist circumference. No clear associations appeared between the quality of footpath and street conditions, access to public transport, and child weight status. Conclusions Built environments might act either as a risk or protective factor of an unhealthy increase in child weight status. Enabling health-promoting neighbourhoods (i.e., highly safe, quality green space nearby, low local traffic) is important to support a healthy weight trajectory across childhood.

show abstract

Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Cited by 21 publications

References 74 publications

Life-course trajectories of weight and their impact on the incidence of type 2 diabetes

Life-course trajectories of weight and their impact on the incidence of type 2 diabetes

Association between distinct body mass index trajectories according to the group‐based trajectory modeling and the risk of incident diabetes: A systematic review

Association between built environments and weight status: evidence from longitudinal data of 9589 Australian children

Contact Info

Product

Resources

About