Manchester Cancer Research Centre and NIHR Manchester Biomedical Research Centre, Manchester, UK.
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
BMC Cancer. 2021 May 5;21(1):502. doi: 10.1186/s12885-021-08226-4.
Excess body fatness, commonly approximated by a one-off determination of body mass index (BMI), is associated with increased risk of at least 13 cancers. Modelling of longitudinal BMI data may be more informative for incident cancer associations, e.g. using latent class trajectory modelling (LCTM) may offer advantages in capturing changes in patterns with time. Here, we evaluated the variation in cancer risk with LCTMs using specific age recall versus decade recall BMI.
We obtained BMI profiles for participants from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. We developed gender-specific LCTMs using recall data from specific ages 20 and 50 years (72,513 M; 74,837 W); decade data from 30s to 70s (42,113 M; 47,352 W) and a combination of both (74,106 M, 76,245 W). Using an established methodological framework, we tested 1:7 classes for linear, quadratic, cubic and natural spline shapes, and modelled associations for obesity-related cancer (ORC) incidence using LCTM class membership.
Different models were selected depending on the data type used. In specific age recall trajectories, only the two heaviest classes were associated with increased risk of ORC. For the decade recall data, the shapes appeared skewed by outliers in the heavier classes but an increase in ORC risk was observed. In the combined models, at older ages the BMI values were more extreme.
Specific age recall models supported the existing literature changes in BMI over time are associated with increased ORC risk. Modelling of decade recall data might yield spurious associations.
体脂肪过多通常通过一次性测定体重指数（BMI）来估计，与至少 13 种癌症的风险增加有关。对纵向 BMI 数据进行建模对于确定癌症发病的相关性可能更有意义，例如使用潜在类别轨迹模型（LCTM）可能更有利于捕捉随时间变化的模式。在这里，我们使用特定年龄回忆与十年回忆 BMI 来评估 LCTM 与癌症风险的变化。
我们从前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验中获取了参与者的 BMI 数据。我们使用特定年龄 20 岁和 50 岁（72513M；74837W）的回忆数据、30 岁至 70 岁的十年回忆数据（42113M；47352W）以及两者的组合（74106M，76245W）开发了性别特异性 LCTM。使用既定的方法框架，我们测试了 1：7 个线性、二次、三次和自然样条形状的类别，并使用 LCTM 类别成员身份对肥胖相关癌症（ORC）的发病率进行建模。
不同的模型取决于使用的数据类型。在特定年龄回忆轨迹中，只有两个最重的类别与 ORC 风险增加相关。对于十年回忆数据，形状由于较重类别的异常值而出现偏斜，但观察到 ORC 风险增加。在综合模型中，随着年龄的增长，BMI 值更加极端。
特定年龄回忆模型支持现有文献的观点，即 BMI 的变化与 ORC 风险增加有关。对十年回忆数据的建模可能会产生虚假关联。
