Statistical methods for vitamin D targets for functional outcomes in older adults

 描述（由申请人提供）：老年人维持最佳身体机能所需的25-羟基维生素D [25（OH）D]浓度尚不清楚。医学研究所和内分泌学会分别就20 ng/mL或30 ng/mL作为骨骼健康的目标进行了公开辩论，但两组都认为需要更有力的证据来确定骨骼外结果的目标浓度，例如，福尔斯和身体功能。这个问题很重要：>25%的老年人身体机能差，50%的老年人25（OH）D浓度在20和30 ng/mL之间。确定老年人的25（OH）D目标将通过为维生素D补充试验的设计和分析提供信息而产生直接影响。也就是说，目标作为入选/排除标准或基线亚组以及次要结局。我们的解决方案是根据经验识别和验证性别特异性（可能由其他因素分层;例如，使用现有NIH资助的数据集，研究老年人（≥ 65岁）与较低的福尔斯风险和较好的身体功能相关的25（OH）D目标。一个严峻挑战 25（OH）D不能随机化，因此观察数据是解决方案的关键部分。传统的统计方法对这个问题是装备不良的;因此，我们将开发 通过调整和改进结构建模和机器学习方法来开发新的统计方法，并将其应用于我们的目标。我们将使用4项前瞻性队列研究（包括> 12，500名老年人）实现目标：健康，衰老和身体成分，Invecchiare in Chianti，男性骨质疏松性骨折和骨质疏松性骨折研究。我们将：1）识别和验证25（OH）D目标，用于减少福尔斯跌倒、活动性、日常生活活动和日常生活工具活动（功能结果）;以及步态速度、肌肉力量，以及 虚弱（近端条件）（目的1）; 2）表征25（OH）D和血清钙和磷浓度对功能结局/近端条件的联合影响（目的2）;和3）通过内分泌和代谢因子（PTH、血清碱性磷酸酶、aklotho、成纤维细胞生长因子23）量化25（OH）D对功能结局/近端条件的介导（目的3）。我们将用SAS和R统计语言编写代码，并将其传播给科学界。 社区用于其他研究。

 DESCRIPTION (provided by applicant): The concentration of 25-hydroxyvitamin D [25(OH)D] needed for older adults to maintain optimal physical function is unknown. Institute of Medicine and Endocrine Society have publicly debated on 20 ng/mL or 30 ng/mL, respectively, as targets for skeletal health, but both groups agree that stronger evidence is needed to identify target concentrations for extra-skeletal outcomes, e.g., falls and physical function. The problem is significant: >25% of older adults have poor physical function, 50% have 25(OH)D concentrations between 20 and 30 ng/mL. Identifying 25(OH)D targets for older adults will have immediate impact by informing design and analysis of vitamin D supplementation trials. Namely, targets serve as inclusion/exclusion criteria or baseline subgroups and as secondary outcomes. Our solution is to empirically identify and validate sex-specific (possibly stratified by other factors; e.g., race, genetic variants) 25(OH)D targets in older adults (= 65 years) related to lowe risk of falls and better physical function using existing NIH-funded datasets. A critical challenge is that 25(OH)D cannot be randomized; therefore observational data are a crucial part of the solution. Conventional statistical methods are ill-equipped for this problem; thus, we will develop novel statistical methods by adapting and refining structural modeling and machine-learning methods, and apply them to our aims. We will carry out the aims using 4 prospective cohort studies comprising >12,500 older adults: Health, Aging, and Body Composition, Invecchiare in Chianti, Osteoporotic Fractures in Men, and Study of Osteoporotic Fractures. We will: 1) identify and validate 25(OH)D targets for falls reduction, mobility, activities of daily living, and instrumental activities of daily living (functional outcomes); and gait speed, muscle strength, and frailty (proximal conditions) (aim 1); 2) characterize the joint impact of 25(OH)D and serum calcium and phosphorus concentrations on functional outcomes/proximal conditions (aim 2); and 3) quantify mediation of 25(OH)D on functional outcomes/proximal conditions by endocrine and metabolic factors (PTH, serum alkaline phosphatase, aklotho, fibroblast growth factor 23) (aim 3). We will write code in SAS and R statistical languages and disseminate it to the scientific community for use in additional research.