效果大小和推断统计技术与机器学习相结合,用于评估益生素度和代谢恒常度之间的关联
Alan Carvalho Dias1, Rafael Henriques Jácomo2, Lidia Freire Abdalla Nery2
1Sabin Medicina Diagnóstica, Brasilia, Federal District, Brazil; Post-Graduation in Health Sciences, University of Brasilia, Brasilia, Federal District, Brazil.
Clinica chimica acta; international journal of clinical chemistry
|December 4, 2023
概括
低血清素水平与代谢障碍有关. 这项研究确定了与葡萄糖和脂质代谢变化相关的特定益生菌度范围或"灰色区域",为代谢健康提供了洞察力.
科学领域:
- 内分泌学 在内分泌学.
- 代谢健康 代谢健康
- 临床生物化学 临床生物化学
背景情况:
- 益生素水平的指导方针各不相同,其中一个"恒温功能性增加过渡性益生素血症" (HomeoFIT-PRL) 范围表明潜在的代谢益处.
- 了解益生菌度与代谢参数之间的关系对于识别健康风险至关重要.
研究的目的:
- 为了研究平均益生素度与糖分 (葡萄糖) 和脂质 (脂肪) 代谢中障碍之间的关联.
- 为了确定与代谢变化相关的特定益生菌度"灰色区域".
主要方法:
- 一项针对65,795名成年人的大型横截面研究分析了与代谢测试 (HOMA-IR,葡萄糖,胰岛素,脂质) 一起的益生菌水平.
- 数据被按益乳素结果划分,并使用"分层多标准分析群体之间的差异 - 统计和效果大小方法" (HiMADiG-SESA).
- 机器学习确定了转折点和置信区间,以确定代谢变化的灰色区域.
主要成果:
- 代谢测试的平均值对于低于7ng/mL的益生菌水平 (胰岛素除外) 有显著差异.
- 在HomeoFIT-PRL范围 (25-100 ng/mL),代谢平均值通常较低,除了HDL-c.
- 鉴定了与代谢变化相关的平均益生菌素的灰色区域:9.58-12.87 ng/mL用于甘油代谢和13.81-18.73 ng/mL用于脂代谢.
结论:
- 在已识别的灰色区域以下的平均益生素度和代谢测试结果之间存在强烈的相关性.
- 这些发现表明,益生菌在代谢障碍的发展中可能发挥作用.
- 平均益乳素水平可以为个体的新陈代谢平衡提供有价值的见解.
相关概念视频
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis
43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
Statistical Hypothesis Testing
1.9K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
1.9K
Statistical Methods for Analyzing Epidemiological Data
372
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
372
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
133
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
133
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
130
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
130


