Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

391
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:  
391
Regression Toward the Mean01:52

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
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

292
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
292
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

206
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
206
Causality in Epidemiology01:21

Causality in Epidemiology

518
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...
518
Randomized Experiments01:13

Randomized Experiments

7.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical epidemiology of snakebite envenoming in hospitals 11 provinces of Yangtze River Basin and southern regions of China: A retrospective hospital-based analysis.

PLoS neglected tropical diseases·2026
Same author

Awareness and management of sepsis among emergency medicine practitioners in China: a national cross-sectional study.

BMC emergency medicine·2026
Same author

Corrigendum: Spatial Accessibility Analysis of Snake Antivenom.

International journal of public health·2026
Same author

Spatial epidemiological analysis of chronic obstructive pulmonary disease in Qingdao City, China.

Respiratory research·2026
Same author

Unveiling the burden of snakebite injuries: an EQ-5D-5 L-based evaluation of health-related quality of life.

BMC public health·2025
Same author

The endogenous glutamatergic transmitter system promotes collagen synthesis in cardiac fibroblasts under hypoxia.

Frontiers in cardiovascular medicine·2025

Related Experiment Video

Updated: Jul 29, 2025

Author Spotlight: Advanced Integrated Model for Sepsis-Induced Myopathy and Single-Cell Metabolic Analysis
04:01

Author Spotlight: Advanced Integrated Model for Sepsis-Induced Myopathy and Single-Cell Metabolic Analysis

Published on: June 14, 2024

819

Exploring the Causality Between Body Mass Index and Sepsis: A Two-Sample Mendelian Randomization Study.

Juntao Wang1, Yanlan Hu1, Jun Zeng2

  • 1International School of Public Health and One Health, Hainan Medical University, Haikou, Hainan, China.

International Journal of Public Health
|May 19, 2023
PubMed
Summary

Higher body mass index (BMI) is causally linked to an increased risk of sepsis. Managing BMI may be a strategy to help prevent sepsis development.

Keywords:
Mendelian randomizationbody mass indexinstrumental variableobesitysepsis

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

277
Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass
07:44

Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass

Published on: July 14, 2023

1.2K

Related Experiment Videos

Last Updated: Jul 29, 2025

Author Spotlight: Advanced Integrated Model for Sepsis-Induced Myopathy and Single-Cell Metabolic Analysis
04:01

Author Spotlight: Advanced Integrated Model for Sepsis-Induced Myopathy and Single-Cell Metabolic Analysis

Published on: June 14, 2024

819
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

277
Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass
07:44

Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass

Published on: July 14, 2023

1.2K

Area of Science:

  • Epidemiology
  • Genetics
  • Public Health

Background:

  • Observational studies suggest a link between obesity and sepsis, but causality remains unclear.
  • Body Mass Index (BMI) is a key indicator of obesity.
  • Sepsis is a life-threatening condition requiring clear etiological understanding.

Purpose of the Study:

  • To investigate the causal relationship between Body Mass Index (BMI) and sepsis.
  • To utilize Mendelian randomization (MR) to assess causality, overcoming limitations of observational studies.

Main Methods:

  • A two-sample Mendelian randomization (MR) approach was employed.
  • Genome-wide association studies (GWAS) identified single-nucleotide polymorphisms (SNPs) for BMI as instrumental variables.
  • Inverse variance-weighted, MR-Egger regression, and weighted median methods were used, with sensitivity analyses for validity.

Main Results:

  • Increased BMI showed a causal association with a higher risk of sepsis (OR 1.32, 95% CI 1.21-1.44).
  • A causal link was also observed for streptococcal septicemia (OR 1.46, 95% CI 1.11-1.91).
  • No significant causal relationship was found for puerperal sepsis (OR 1.06, 95% CI 0.87-1.28).

Conclusions:

  • This study provides evidence supporting a causal relationship between higher BMI and sepsis risk.
  • Controlling BMI may represent a viable strategy for sepsis prevention.
  • Findings highlight the importance of weight management in reducing sepsis incidence.