在社区轻度认知障碍中,探索BIN1基因多态和海马子子子场体积之间的关联
Jiali Luo1, Junjiao Ping1, Haibo Zhang2
1Department of Psychiatry, The Third People's Hospital of Zhongshan City, Zhongshan, China.
Frontiers in neurology
|February 27, 2025
概括
BIN1 rs10200967 CC基因型可以通过保持海马体积来预防轻度认知障碍 (MCI). 这一发现可能有助于早期阿尔茨海默病 (AD) 检测和预防策略.
科学领域:
- 神经科学是一个神经科学.
- 遗传学 遗传学 是一个
- 放射学 放射学是一门学科.
背景情况:
- 轻度认知障碍 (MCI) 是阿尔茨海默病 (AD) 的早期阶段,需要早期诊断标志物.
- BIN1基因是一个关键的AD易感基因,具有与MCI相关的特定多态性,尽管机制尚不清楚.
研究的目的:
- 研究BIN1基因多态化对MCI发展的影响.
- 在使用MRI的MCI患者中,将BIN1基因型与海马体完整性相关联.
主要方法:
- 招募了52名MCI患者和55名认知正常 (CN) 个体.
- 通过血液样本分析了BIN1 rs10200967,rs1060743和rs4663093的多态性.
- 使用MRI扫描评估海马子区域的体积.
主要成果:
- 与CN个体相比,MCI患者在多个海马子区域表现出显著的缩 (p < 0.05).
- 在MCI和CN组之间没有发现BIN1基因型分布的显著差异 (p > 0.05).
- 在MCI患者中,BIN1 rs10200967基因型与左海马-桃体过渡区域 (lHATA) 缩显著相关 (p <0.05).
结论:
- 在MCI患者中,BIN1 rs10200967 CC基因型与较大的lHATA体积有关.
- 这种基因型可能会对MCI产生保护作用,这表明早期AD检测和预防的潜力.
相关概念视频
01:09Correlation
11.6K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.6K
00:53Correlation and Regression
1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
02:20Correlations
32.4K
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...
32.4K
01:27Correlation and Causation
37.3K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
37.3K
01:25Multiple Regression
2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
01:53Cause and Effect
10.8K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.8K