Department of Pediatrics, Faculty of Medicine, Nutrition and Metabolism, University of São Paulo.
The Microsoft Research, Centre for Computational and Systems Biology (COSBI), University of Trento, Rovereto, Italy.
Mol Nutr Food Res. 2018 Mar;62(6):e1700613. doi: 10.1002/mnfr.201700613. Epub 2018 Feb 22.
Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals.
A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures.
The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals.
微量营养素在食物中的含量很少,协同作用,并且需要不同的时间才能看到健康状况和疾病风险的变化。这些基本原则被纳入一项干预研究,旨在为人群和个体的食物生物活性设定目标推荐值制定新的实验策略。
对 9-13 岁的儿童进行为期 6 周的多种维生素/矿物质干预。参与者(136 人):(i)为自身对照(n-of-1);(ii)监测依从性;(iii)在基线、干预后和 6 周洗脱期后测量 36 种循环维生素形式、30 种临床、人体测量和饮食摄入参数;(iv)将其遗传背景作为维生素基线或反应的修饰剂。次年(135 名参与者)重复相同的干预。大多数维生素反应良好,许多临床参数朝着改善代谢健康的方向变化。任何代谢物的基线水平都可以预测其对干预的反应。确定弹性网络惩罚回归模型,并根据多种维生素/临床基线测量来显著预测对干预的反应。
该研究设计、计算方法和结果是朝着为个人优化维生素水平和健康参数制定建议迈出的一步。