Mobile Application to Deliver Personalized Nutrition for the Prevention of Alzheimer's Disease

根本生命科学(Dba GB HealthWatch)是一家数字健康和营养基因组学公司。我们的 使命是帮助抗击常见的与饮食和生活方式相关的慢性疾病，提供精确的营养和 先进的移动技术。我们公司开发了HealthWatch 360移动应用程序，用于跟踪 饮食摄入量、体力活动和健康相关症状。这款手机应用程序获得了极好的反响 IOS和Android平台都有评论，注册用户超过70,000人。健康状况- 应用程序中的特定目标根据临床情况提供精细化的营养建议 关于预防饮食引起的慢性病的指南。阿尔茨海默病(AD)是最主要的 在美国，痴呆症是第六大致死原因，也是国家、家庭的主要损失 和照顾者。这个第一阶段的提案是为了开发一个移动工具，该工具将提供 基于遗传风险的个性化营养和饮食计划，以降低AD风险。 目标1：制定确定具体膳食和营养成分的系统程序 与AD关联。使用1000个基因组第三阶段数据库和营养分析 与传统饮食相对应的26个人群，我们将分析是否有特定的营养物质 与易患阿尔茨海默病的遗传变异的频率相关。我们假设一个 当遗传变异是 在给定的人群中被选中的人与富含或耗尽 相关营养素(S)。我们将开发统计模型来量化这些关系。 目标2：将营养模式转化为一套预防阿尔茨海默病的量化建议。 根据我们从目标1获得的营养数据，结合其他循证营养指南 对于AD，我们将根据APP用户的营养状况，综合出一套定性和定量的营养“规则” 基因分型、阿尔茨海默病家族史和其他健康状况。这些基因-和/或表型- 具体规则将估计给定营养物质的理想范围，并修订传统的“规则”(即： 营养建议)被2015-2020年美国膳食指南。 目标3：为预防AD提供个性化膳食计划的手机应用程序。这部手机 应用程序旨在引导、主动和自我执行的AD预防，并针对这些 处于高风险的人群。我们建议开发机器学习算法来创建餐饮计划 对给定的AD风险基因使用修改后的营养范围(目标1和目标2)。用户将能够 修改食物偏好参数(例如，“素食”)，同时保持适当的 营养范围。 该项目的一个关键成果将是一个支持全人群饮食干预的平台，通过 在数字时代，将预防性医疗保健与日常生活无缝结合。

Genben Lifesciences (dba GB HealthWatch) is a digital health and nutritional genomics company. Our mission is to help fight common, diet- and lifestyle-related chronic diseases with precision nutrition and advanced mobile technologies. Our company developed the HealthWatch 360 mobile app for tracking dietary intake, physical activity and health-related symptoms. This mobile app has received excellent reviews for both the iOS and Android platforms and has over 70,000 registered users. Health condition- specific goals featured in the app provide refined nutritional recommendations based on clinical guidelines for the prevention of diet-induced, chronic diseases. Alzheimer’s disease (AD) is the leading cause of dementia in the U.S., the 6th leading cause of mortality and a major cost to the nation, families and caregivers. This phase I proposal is for the development of a mobile tool that will deliver personalized nutrition and meal plans based on genetic risk in order to mitigate AD risk. Aim 1: Develop a systematic process to identify specific dietary and nutritional components associated with AD. Using the 1000 Genomes Phase 3 database and nutritional analyses of the traditional diets that correspond with the 26 populations, we will analyze whether specific nutrients correlate with the frequency of genetic variants that predispose risk of AD. We hypothesize that a population’s fitness would be enhanced and AD risk would be lower when the genetic variants that are selected for in a given population are in equilibrium with a diet that is enriched or depleted with the correlated nutrient(s). We will develop statistical models that will quantify these relationships. Aim 2: Translate nutritional patterns to a set of quantitative recommendations for AD prevention. With the nutrient data we obtain from Aim 1, combined with other evidence-based nutrition guidelines for AD, we will synthesize a set of qualitative and quantitative nutritional “rules” based on the app user’s genotypes, family history of AD and other health conditions. These genotype- and/or phenotype - specific rules will estimate ideal ranges for a given nutrient and amend the conventional “rules” (i.e. nutritional recommendations) by the 2015-2020 Dietary Guidelines for America. Aim 3: Mobile app for delivery of personalized meal plan for the prevention of AD. This mobile application is designed for guided, proactive and self-executed prevention of AD, and targeted at those who are at elevated risk. We propose developing machine-learning algorithms to create meal plans that employ the modified nutrient ranges (from Aims 1 and 2) for a given AD risk genotype. Users will be able to modify food preference parameters (for example, “vegetarian”) while maintaining the appropriate nutrient ranges. A key outcome of this project will be a platform that supports population-wide dietary intervention by seamlessly connecting preventive healthcare with daily life in the digital age.