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The Relationship Between Head Injury and Alzheimer's Disease: A Causal Analysis with Bayesian Networks

Andrei Lixandru

TL;DR

This study investigates whether head injury causally influences Alzheimer's disease (AD) risk by integrating a DAG-based Bayesian network framework with logistic and linear regression analyses on a binary-variable dataset of $n=2149$ patients. The approach estimates an adjusted odds ratio for head injury and a risk difference while controlling for memory complaints, revealing a non-significant protective signal for head injury ($OR = 0.88$, 95% CI $[0.63, 1.22]$) and a strong, significant association for memory complaints ($OR = 4.59$, 95% CI $[3.69, 5.72]$; RD for memory complaints ≈ $0.36$). The findings underscore memory complaints as a robust predictor of AD diagnosis and suggest the head-injury effect, if any, is subtle or context-dependent, warranting larger datasets and more advanced causal modeling. The work highlights the utility of DAGs for causal inference in complex medical histories and informs future preventive strategies for AD risk mitigation.

Abstract

This study examines the potential causal relationship between head injury and the risk of developing Alzheimer's disease (AD) using Bayesian networks and regression models. Using a dataset of 2,149 patients, we analyze key medical history variables, including head injury history, memory complaints, cardiovascular disease, and diabetes. Logistic regression results suggest an odds ratio of 0.88 for head injury, indicating a potential but statistically insignificant protective effect against AD. In contrast, memory complaints exhibit a strong association with AD, with an odds ratio of 4.59. Linear regression analysis further confirms the lack of statistical significance for head injury (coefficient: -0.0245, p = 0.469) while reinforcing the predictive importance of memory complaints. These findings highlight the complex interplay of medical history factors in AD risk assessment and underscore the need for further research utilizing larger datasets and advanced causal modeling techniques.

The Relationship Between Head Injury and Alzheimer's Disease: A Causal Analysis with Bayesian Networks

TL;DR

This study investigates whether head injury causally influences Alzheimer's disease (AD) risk by integrating a DAG-based Bayesian network framework with logistic and linear regression analyses on a binary-variable dataset of patients. The approach estimates an adjusted odds ratio for head injury and a risk difference while controlling for memory complaints, revealing a non-significant protective signal for head injury (, 95% CI ) and a strong, significant association for memory complaints (, 95% CI ; RD for memory complaints ≈ ). The findings underscore memory complaints as a robust predictor of AD diagnosis and suggest the head-injury effect, if any, is subtle or context-dependent, warranting larger datasets and more advanced causal modeling. The work highlights the utility of DAGs for causal inference in complex medical histories and informs future preventive strategies for AD risk mitigation.

Abstract

This study examines the potential causal relationship between head injury and the risk of developing Alzheimer's disease (AD) using Bayesian networks and regression models. Using a dataset of 2,149 patients, we analyze key medical history variables, including head injury history, memory complaints, cardiovascular disease, and diabetes. Logistic regression results suggest an odds ratio of 0.88 for head injury, indicating a potential but statistically insignificant protective effect against AD. In contrast, memory complaints exhibit a strong association with AD, with an odds ratio of 4.59. Linear regression analysis further confirms the lack of statistical significance for head injury (coefficient: -0.0245, p = 0.469) while reinforcing the predictive importance of memory complaints. These findings highlight the complex interplay of medical history factors in AD risk assessment and underscore the need for further research utilizing larger datasets and advanced causal modeling techniques.

Paper Structure

This paper contains 12 sections, 7 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Dataset Overview
  3. Bayesian Network and Model Design
  4. Validation of Network Structure
  5. Methods
  6. Logistic Regression for Odds Ratios
  7. Linear Regression for Risk Difference
  8. Results
  9. Logistic Regression for Odds Ratios
  10. Linear Regression for Risk Difference
  11. Conclusion
  12. Appendix

Figures (4)

  • Figure 1: First proposed DAG for our problem.
  • Figure 2: Odds Ratios with 95% Confidence Intervals
  • Figure 3: Linear Regression Coefficients with 95% Confidence Intervals
  • Figure 4: Regression plot of HeadInjury on Diagnosis (with MemoryComplaints control)