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: 10544759
• 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/10544759
• 关键词: Address; Affect; 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 Aggregation; Data Analyses; Data Files; Data Set; Deposition; Development; Dimensions; 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; 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; Recommendation; 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; 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; model organism; outreach; repository; tool; trend; usability; user-friendly; web-based tool

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 现代间接量热系统允许高密度多维时间序列测量 影响体重和能量平衡的成分。间接量热法用于了解 影响体重致病性变化的因素-例子包括癌症恶病质引起的体重减轻，或 体重增加伴随肥胖。这些间接量热系统产生大量的原始数据，需要时间- 使用手动操作进行格式化、数据清理、质量控制和可视化。除了数据 间接量热法实验的处理和分析需要专门的统计处理， 脂肪量和瘦肉量对代谢率的不同贡献。令人惊讶的是， 来解决这些缺点。实验之间的比较很少进行，由于不同的 不同计量单位的工具类型和对数据的特别统计处理。解决 为了满足这一关键需求，我们建议创建一个免费的在线工具CalR，以帮助科学家快速有效地工作。 分析间接量热数据，提供标准化的方法进行可重复的研究和网站， 存储和聚合数据集。我们推出的CalR的初步版本是一个用户友好但复杂的 使用图形用户界面从不同仪器导入数据文件的Web工具， 实验结果，并进行基本的统计分析。经过几年的迭代开发， 除了来自用户调查的建设性反馈外，很明显， 需要制定方法来实现这一项目的潜力。这项研究的主要目标是 开发一个框架，提供现代工具，用于分析影响身体的生理数据 重量.这些建立在现有CalR软件基础上的新工具将继续免费提供给科学界， 社区具体目标将确立这一框架的三个方面：1）新的分析特征， 改进体重变化预测，自动测定代谢灵活性，改进 代谢率的测定和更好的可视化。2)新的统计方法， 协变量分析和一个模块来确定代谢实验的统计功效。3)的 开发一个储存库，可储存间接量热数据，并可供广泛使用， 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 提供一个功能强大的网络工具，与成千上万使用我们免费软件的科学家分享 平台这些努力将提取更多细微差别的信息，并将改善对每一个问题的解释。 为体重影响疾病过程的所有领域的用户进行实验。