Batis Carolina, Mendez Michelle A, Sotres-Alvarez Daniela, Gordon-Larsen Penny, Popkin Barry
Department of Nutrition, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
J Epidemiol Community Health. 2014 Aug;68(8):773-9. doi: 10.1136/jech-2013-203560. Epub 2014 Apr 12.
Most research on dietary patterns and health outcomes does not include longitudinal exposure data. We used an innovative technique to capture dietary pattern trajectories and their association with haemoglobin A1c (HbA1c), homeostasis model of insulin resistance (HOMA-IR) and prevalence of newly diagnosed diabetes.
We included 4096 adults with 3-6 waves of diet data (1991-2006) and biomarkers measured in 2009 from the China Health and Nutrition Survey. Diet was assessed with three 24-h recalls and a household food inventory. We used a dietary pattern previously identified with reduced rank regression that positively predicted diabetes in 2006 (high in wheat products and soy milk and low in rice, legumes, poultry, eggs and fish). We estimated a score for this dietary pattern for each subject at each wave. Using latent class trajectory analysis, we grouped subjects with similar dietary pattern score trajectories over time into five classes.
Three trajectory classes were stable over time, and in two classes the diet became unhealthier over time (upward trend in dietary pattern score). Among two classes with similar scores in 2006, the one with the lower (healthier) initial score had an HbA1c 1.64% lower (-1.64 (95% CI -3.17 to -0.11)) and non-significantly a HOMA-IR 6.47% lower (-6.47 (-17.37 to 4.42)) and lower odds of diabetes (0.86 (0.44 to 1.67)).
Our findings suggest that dietary pattern trajectories with healthier scores longitudinally had a lower HbA1c compared with those with unhealthier scores, even when the trajectories had similar scores in the end point.
大多数关于饮食模式与健康结果的研究未纳入纵向暴露数据。我们采用一种创新技术来获取饮食模式轨迹及其与糖化血红蛋白（HbA1c）、胰岛素抵抗稳态模型（HOMA-IR）以及新诊断糖尿病患病率之间的关联。
我们纳入了4096名成年人，他们有3至6轮饮食数据（1991 - 2006年）以及2009年测量的生物标志物，数据来自中国健康与营养调查。通过三次24小时饮食回顾和家庭食物清单评估饮食情况。我们使用一种先前通过降秩回归确定的饮食模式，该模式在2006年能正向预测糖尿病（小麦制品和豆浆含量高，而大米、豆类、家禽、鸡蛋和鱼类含量低）。我们在每一轮为每个受试者估算该饮食模式的得分。使用潜在类别轨迹分析，我们将随时间具有相似饮食模式得分轨迹的受试者分为五类。
三个轨迹类别随时间保持稳定，在两个类别中饮食随时间变得更不健康（饮食模式得分呈上升趋势）。在2006年得分相似的两个类别中，初始得分较低（更健康）的那个类别糖化血红蛋白低1.64%（-1.64（95%可信区间 -3.17至 -0.11）），胰岛素抵抗稳态模型非显著低6.47%（-6.47（-17.37至4.42）），糖尿病发病几率也较低（0.86（0.44至1.67））。
我们的研究结果表明，纵向来看，得分更健康的饮食模式轨迹相比得分不健康的轨迹，糖化血红蛋白水平更低，即便这些轨迹在终点时得分相似。
