出版社：Society for Longitudinal and Life Course Studies
摘要：The aims of this study are 1) to analyse developmental trajectories of body fatness from adolescence into adulthood, thereby determining the number and characteristics of distinct body fatness trajectories, and 2) to relate these distinct subgroups to indicators of cardiovascular disease risk, revealing subgroups specifically at risk. This paper will illustrate in more detail the application of Latent Class Growth (Mixture) Modelling (LCGMM) on longitudinal, observational data. Data were obtained from the Amsterdam Growth and Health Longitudinal Study, an ongoing observational study of apparently healthy participants (n=336). Participants were followed up from 13-42 years of age. Body Mass Index was used as a marker for body fatness and cardiovascular diseases (CVD)-risk factors included Mean Arterial Pressure and HDL-Cholesterol. LCGMM was used for the identification of developmental trajectories of body fatness, and linear regression analyses were used for the associations between the trajectories and CVD-risk. Analyses revealed three distinct trajectories; a "normative" trajectory (88.4%), a progressively overweight trajectory (4.5%) and a progressively overweight but stabilising trajectory (7.1%). Significant differences in CVD-risk between these trajectories appeared. These results show that body fatness development throughout life is heterogeneous, showing differences in CVD-risk. This paper also demonstrates that LCGMM is a promising technique to distinguish between subjects with different developmental trajectories. PURPOSE: The purpose of this study was to analyse life course developmental patterns of body fatness. Hereby, we can determine the number and characteristics of distinct body fatness trajectories. Also, we related these trajectories to cardiovascular disease (CVD) risk indicators, revealing subgroups specifically at-risk. METHODS: We obtained data from the Amsterdam Growth Study (n=325), an ongoing observational study of apparently healthy participants. Body Mass Index (BMI) and skinfolds (SSF) were used as indicators for body fatness, and CVD-risk was quantified by blood pressure, HDL-cholesterol and Triglycerides. Contemporary techniques (latent class growth mixture modelling, LCGMM) were used for the identification of developmental trajectories of body fatness and linear regression analyses were used for the associations between the trajectories and CVD-risk. RESULTS: Analyses revealed three trajectories for each fatness indicator. For BMI, a normative (85.9%), a chronic overweight (5.5%), and an overweight, but stabilising group (8.6%) was found, while a normative (75.1%), an increasing (16.3%) and a fluctuating group (8.6%) was found for SSF. Significant differences in CVD-risk between these trajectories appeared. CONCLUSIONS: These results show that body fatness development throughout life is heterogeneous, showing differences in CVD-risk. This paper also demonstrates that LCGMM is a promising technique to distinguish between subjects with different developmental trajectories.
