Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey.
Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey.
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DOI：
10.1017/s000711452200263x
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发表时间：
2023-06-14
影响因子：
3.6
通讯作者：
Crozier, Sarah R.
中科院分区：
文献类型：
--
作者：
Dalrymple, Kathryn V.;Vogel, Christina;Godfrey, Keith M.;Baird, Janis;Hanson, Mark A.;Cooper, Cyrus;Inskip, Hazel M.;Crozier, Sarah R.
关键词：
人们越来越感兴趣的是建立纵向饮食数据的模型，并将个体分类为亚组(潜伏类)，这些亚组遵循类似的轨迹。这些轨迹可以确定适合饮食干预的人群和时间点。本文对两种潜在类方法：基于群体的轨迹建模(GBTM)和生长混合建模(GMM)进行了比较和综述。分析了南安普顿纵向妇女调查中2963对母子二人组的数据。连续膳食质量指数(DQI)是通过主成分分析从母亲孕前、孕11周和孕34周、子代6个月和12个月以及3、6-7和8-9岁收集的。采用从1到6个类别的正演模拟方法来确定DQI潜在类别的最佳数量。使用Akaike和贝叶斯信息标准、类分配概率、正确分类的几率比、组成员资格和熵对模型进行评估。这两种方法都认为有5个班是最优的，两种方法的班级分配之间存在很强的相关性(Spearman‘s=0.98)。饮食轨迹被划分为稳定的水平线，并被定义为差(GMM=4%和GBTM=5%)、差-中等(23%，23%)、中等(39%，39%)、中等-好(27%，28%)和最好(7%，6%)。GBTM和GMM都适用于确定饮食轨迹。建议使用GBTM，因为它的计算量较小，但结果可以使用GMM进行确认。孕前饮食质量轨迹的稳定性强调了从怀孕前开始促进饮食改善的重要性。
There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother–child dyads from the longitudinal Southampton Women’s Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6–7 and 8–9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman’s = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.
影响因子：
4.7
作者：
Crozier, S. R.;Robinson, S. M.;Borland, S. E.;Inskip, H. M.
通讯作者：
Inskip, H. M.