循环维生素对传染病的因果关系:整合门德尔随机化和体内证据
Aling Tang1, Zhimin Gong1, Yi Shi1
1Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Frontiers in immunology
|December 17, 2025
概括
这项研究表明,较高的维生素E水平可能会增加病毒感染风险,而细菌感染与较低的维生素D水平有关.败血症也显著降低了小鼠的维生素D水平.
科学领域:
- 营养免疫学 营养免疫学
- 传染病流行病学 传染病流行病学
- 遗传流行病学遗传流行病学
背景情况:
- 感染和维生素水平之间确立的关联.
- 原因方向和病原体特异性关系仍然不清楚.
- 需要对维生素感染因果关系进行强有力的调查.
研究的目的:
- 研究细菌/病毒感染和关键维生素水平 (A,B6,B12,C,D,25...OH) D,E) 之间的遗传因果关系.
- 使用孟德尔随机化确定维生素感染关联的因果方向.
- 用体内败血症模型验证发现.
主要方法:
- 使用英国生物银行和FinnGen GWAS数据进行双样本双向门德尔随机化 (MR).
- 逆方差加权 (IVW) 方法作为初级分析,使用灵敏度分析.
- 结和刺穿 (CLP) 鼠标模型,以评估败血症对25-氧维生素D (25(OH) D的影响.
主要成果:
- 维生素E含量升高与病毒感染风险增加有遗传联系 (OR=1.45).
- 细菌感染与较低的25(OH) D水平 (OR=0.96) 有遗传联系.
- 败血症模型显示,血清25(OH) D水平显著降低.
结论:
- 有证据表明,高维生素E和病毒易感性之间存在因果关系.
- 细菌感染与降低的25...OH) D水平有因果关系.
- 败血症显著降低25(OH) D,突出其在感染严重性的作用.
更多相关视频
08:10A Functional Whole Blood Assay to Measure Viability of Mycobacteria, using Reporter-Gene Tagged BCG or M.Tb BCG lux/M.Tb lux
Published on: September 14, 2011
14.2K
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
11.0K
相关概念视频
Causality in Epidemiology
1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K
What is an Experiment?
17.2K
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
17.2K
Bias in Epidemiological Studies
1.2K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
1.2K
Factors Affecting Illness
5.0K
When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness,...
For instance, risk factors are connected to illness,...
5.0K
Vitamins
2.3K
Vitamins, derived from the Latin word for life, are essential organic substances required in small quantities for optimal growth and overall well-being. Unlike other organic nutrients, vitamins don't act as sources of energy or building materials but rather facilitate these nutrients' utilization by the body. Vitamins are predominantly coenzymes, assisting enzymes in specific chemical actions, like the oxidation of glucose for energy involving B vitamins. Most vitamins are not produced...
2.3K
Criteria for Causality: Bradford Hill Criteria - II
1.1K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.1K
