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相关概念视频

Obesity01:24

Obesity

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Coefficient of Correlation01:12

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Regression Toward the Mean01:52

Regression Toward the Mean

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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...
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Odds Ratio01:09

Odds Ratio

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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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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:
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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相关实验视频

Updated: May 15, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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乙酸细胞数量与超重状态之间的关联:非线性,双向研究.

Xinghai Yue1, Chenchen Wang1, Xixu Zhang2

  • 1The Second Clinical College, Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China.

Lipids in health and disease
|May 10, 2025
PubMed
概括

乙氨基细胞计数显示了与超重和肥胖的反向U形联系,这表明与体重相关的免疫机制在免疫机制中发挥了作用. 这项研究分析了NHANES关于超重状况和氨酸细胞的数据.

关键词:
体重指数 (Body Mass Index) 是一个指数.埃索诺菲尔是什么意思尼汉斯 (NHANES) 是一个名人.肥胖问题 肥胖问题体重过重的情况

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科学领域:

  • 免疫学 免疫学 免疫学
  • 代谢健康 代谢健康
  • 公共卫生 营养 公共卫生 营养

背景情况:

  • 乙氨基酸水平与超重/肥胖之间的相关性受到争论,动物和人类研究的结果相互矛盾.
  • 现有的研究在临床环境中呈现出正相关性,与动物模型所建议的负相关性形成鲜明对比.
  • 需要进一步调查eosinophil数量和超重状态之间的潜在非线性关系.

研究的目的:

  • 通过使用国家健康和营养检查调查 (NHANES) 数据,研究乙酸细胞数量和超重状态之间的关联.
  • 探索埃索诺菲尔数量和相关的代谢指标,如体重指数 (BMI),甘油三和高密度脂蛋白 (HDL) 之间的关系.
  • 为了确定是否存在一种非线性关联,在乙酸细胞水平和超重/肥胖之间.

主要方法:

  • 利用了来自NHANES 1999-2018的数据,包括对氨酸细胞计数的实验室测量.
  • 定义的超重状态是身体质量指数 (BMI) ≥25 kg/m2.
  • 使用加权后勤/线性回归,子组分析和受限立方线 (RCS) 来分析关联.

主要成果:

  • 包括77,217名参与者;39,111人被归类为超重或肥胖 (BMI ≥25).
  • 在乙酸细胞数量和超重状态之间发现了显著的正相关性 (OR: 2.38,95% CI: 1.81-3.12).
  • 观察到与肥胖症,BMI和甘油三酸的好相关性,与HDL的负相关性. 非线性分析显示了一个反转的U形关系.

结论:

  • 在乙氨基酸细胞数量和超重状态,肥胖和BMI之间存在一个反转的U形关系.
  • 乙氨基基酸计数与包括HDL和甘油三酸在内的代谢标志物有显著的关联.
  • 这些发现突出了乙氨基酸在超重/肥胖中的潜在作用,并提供了有关免疫机制的见解.