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Multiple Sclerosis and Hematologic Malignancies: A Bidirectional Mendelian Randomization Study.

Qiongqiong Su1, Xiaolei Wei1, Yongqiang Wei1

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Genetic liability to multiple sclerosis (MS) may increase the risk of certain hematologic malignancies (HM), specifically leukemia and Hodgkin lymphoma. Further research is needed to confirm these findings in diverse populations.

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Area of Science:

  • Genetics
  • Epidemiology
  • Oncology

Background:

  • Observational studies suggest a link between multiple sclerosis (MS) and hematologic malignancies (HM), but results are inconsistent.
  • Understanding the causal relationship between MS and HM is crucial for patient care and research.

Purpose of the Study:

  • To investigate the potential causal relationship between multiple sclerosis (MS) and various hematologic malignancies (HM) using a bidirectional Mendelian randomization (MR) approach.
  • To assess whether genetic predisposition to MS influences the risk of developing HM.

Main Methods:

  • A bidirectional two-sample Mendelian randomization (MR) analysis was performed.
  • Genome-wide association study (GWAS) summary statistics for MS (n=115,803) and HM (n=218,792) were utilized.
  • Inverse-variance-weighted (IVW) method and sensitivity analyses were employed to assess causality and rule out pleiotropy.

Main Results:

  • Genetic liability to MS was significantly associated with increased odds of unspecified leukemia (OR 1.311, P=0.048) and Hodgkin lymphoma (HL) (OR 1.224, P=0.009).
  • No significant associations were found for other leukemia, lymphoma, or plasma-cell neoplasm subtypes.
  • Sensitivity analyses indicated no substantial heterogeneity or directional pleiotropy for the significant findings.

Conclusions:

  • This study provides genetic evidence suggesting that individuals with a higher genetic predisposition to MS have an elevated risk of developing certain hematologic malignancies, including leukemia and Hodgkin lymphoma.
  • These findings warrant cautious interpretation and require validation in larger, multi-ancestry datasets using diverse causal inference frameworks.