CalR: A toolkit and repository for experiments of energy homeostasis using indirect calorimetry

CalR：使用间接量热法进行能量稳态实验的工具包和存储库

• 批准号: 10338235
• 负责人: ALEXANDER BANKS
• 金额: $ 43.23万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10338235
• 关键词: Address; Affect; Animal Model; Animals; Awareness; Basal metabolic rate; Body Weight; Body Weight Changes; Body Weight decreased; Burn injury; Carbon Dioxide; Communities; Complex Analysis; Computer software; Consensus; Consumption; Data; Data Analyses; Data Files; Data Set; Deposition; Development; Disease; Eating; Energy Intake; Energy Metabolism; Equation; Equilibrium; Fatty acid glycerol esters; Feedback; Floods; Food Energy; Future; Gases; Goals; Health; Home; Homeostasis; Human body; Indirect Calorimetry; Infrastructure; Internet; Laboratories; Linear Regressions; Literature; Longevity; Manuals; Measurement; Measures; Metabolic; Metabolism; Metadata; Methodology; Methods; Modeling; Modernization; Mouse Strains; Noise; Obesity; Pathogenicity; Pattern; Physiological; Pilot Projects; Policies; Process; Quality Control; Regulation; Reproducibility; Research; Research Personnel; Resources; Sample Size; Scientist; Series; Site; Speed; Standardization; Statistical Data Interpretation; Statistical Methods; Surveys; System; Techniques; Testing; Texas; Thinness; Time; Time Series Analysis; United States National Institutes of Health; Visualization; Visualization software; Weight; Weight Gain; base; cancer cachexia; comparative; computerized data processing; computing resources; data cleaning; data handling; data management; data reduction; data repository; data sharing; density; design; energy balance; experimental analysis; experimental study; flexibility; food surveillance; graphical user interface; improved; instrument; large datasets; metabolic rate; outreach; repository; tool; trend; usability; user-friendly

Project Summary Modern indirect calorimetry systems allow for high-density multi-dimensional time-series measurements of components affecting body weight and energy homeostasis. Indirect calorimetry is used for understanding the factors influencing pathogenic changes to body weight—examples include weight loss with cancer cachexia, or weight gain with obesity. These indirect calorimetry systems generate a flood of raw data that requires time- consuming manual manipulation for formatting, data cleaning, quality control, and visualization. Beyond data handling, analysis of indirect calorimetry experiments requires specialized statistical treatment to account for differential contributions of fat mass and lean mass to metabolic rates. Surprisingly, no tools or resources exist to address these shortcomings. Comparisons between experiments are rarely performed due to the different types of instruments with varying units of measurement and ad hoc statistical treatments of data. To address this critical need, we propose the creation of a free online tool, CalR, that helps scientists quickly and efficiently analyze indirect calorimetry data by providing standardized methods for reproducible research and a site to store and aggregate datasets. The preliminary version of CalR we launched is a user-friendly but sophisticated web tool that uses a graphical user interface to import data files from different instruments, quickly visualize experimental results, and perform basic statistical analyses. After several years of iterative development, in addition to constructive feedback from a user survey, it is clear that additional functionality and statistical methods need to be developed to realize the potential of this project. The broad goal of this research is the development of a framework that delivers modern tools for the analysis of the physiological data affecting body weight. These new tools, built on the existing CalR software, will continue to be freely provided to the scientific community. Specific aims will establish three aspects of this framework: 1) New analysis features intended to improve prediction of body weight change, automatic determination of metabolic flexibility, improved determination of metabolic rates, and better visualizations. 2) New statistical methods incorporating additional covariates into analysis and a module to determine statistical power for metabolic experiments. 3) The development of a repository where indirect calorimetry data can be deposited and made broadly accessible for large-scale analysis. For each aim, we will develop and thoroughly test each component, after which we will provide a functional web tool online to share with the thousands of scientists who use our free software platform. These efforts will extract more nuanced information and will improve the interpretation of each experiment for users in all fields where body weight impacts disease processes.

项目摘要 现代间接量热法允许高密度的多维时间序列测量 影响体重和能量体内稳态的成分。间接量热法用于理解 因素会影响体重的致病性变化 - 例子包括癌症恶病质的体重减轻，或 体重增加随肥胖而增加。这些间接的量热法系统会产生大量的原始数据，这些数据需要时间 - 食用手动操作，以进行格式，数据清洁，质量控制和可视化。超越数据 处理，间接量热法实验的分析需要专门的统计处理以说明 脂肪质量和瘦质量对代谢率的差异贡献。令人惊讶的是，没有工具或资源 解决这些缺点。由于不同 具有不同单位测量和临时统计处理的工具类型。解决 我们提出了这一至关重要的需要 通过提供可再现研究的标准化方法来分析间接量热法数据 存储和聚合数据集。我们推出的Calr的初步版本是用户友好但精致的 使用图形用户界面从不同工具导入数据文件的Web工具，快速可视化 实验结果，并进行基本的统计分析。经过几年的迭代发展， 除了来自用户调查的建设性反馈外，很明显，其他功能和统计 需要开发方法来实现该项目的潜力。这项研究的广泛目标是 开发一个框架，该框架提供现代工具，以分析影响身体的物理数据 重量。这些基于现有CALR软件的新工具将继续免费提供给科学 社区。具体目标将建立此框架的三个方面：1）旨在的新分析功能 改善体重变化的预测，自动确定代谢灵活性，改善 确定代谢率和更好的可视化。 2）编码其他的新统计方法 协变量分析和一个模块，以确定代谢实验的统计能力。 3） 开发一个存储库，可以存放间接量热法数据，并可以广泛访问 大规模分析。对于每个目标，我们将开发并彻底测试每个组件，然后我们将 在线提供功能性的Web工具，与数千位使用我们的免费软件的科学家共享 平台。这些努力将提取更多细微的信息，并将改善每个人的解释 在体重影响疾病过程的所有领域的用户实验。