Study reveals obesity's link to increased risk of multiple sclerosis and ischemic stroke

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In a recent study published in Scientific Reports, researchers from China used Mendelian randomization (MR) to assess the genetic relationship between body mass index (BMI) and multiple neurological diseases.

They found that BMI shows a genetic causal relationship with multiple sclerosis (MS) and ischemic stroke (IS), but not with Parkinson’s disease (PD), Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and epilepsy (EP).

Study: Genetic causal role of body mass index in multiple neurological diseases. Image Credit: New Africa/Shutterstock.comStudy: Genetic causal role of body mass index in multiple neurological diseases. Image Credit: New Africa/


BMI is widely used for obesity assessment owing to its simplicity and sensitivity. Economic changes and lifestyle shifts have increased obesity risk globally. Elevated BMI is linked to various diseases and higher mortality rates, including type 2 diabetes, hypertension, coronary heart disease, musculoskeletal disorders, and neoplastic growth.

Neurological diseases cover a broad spectrum of nervous system conditions, including neurodegenerative, cerebrovascular, infectious, oncological, and hereditary disorders.

While PD is characterized by dopamine concentration changes and Lewy body presence, AD is linked to β-amyloid deposition and tau protein phosphorylation. ALS affects motor neurons, while MS is a demyelinating disease mediated by the immune system.

IS is associated with various risk factors like hypertension and diabetes, and EP arises from synchronized neuronal discharges due to genetic or structural abnormalities.

MR is a method to assess causal relationships between exposures and outcomes using genetic instrumental variables, including single nucleotide polymorphisms (SNPs). The method is robust against the effects of confounders and reverse causation.

Therefore, researchers in the present study investigated the genetic links between BMI and neurological diseases using MR analysis, aiming to inform disease management strategies.

About the study

The present study used SNPs from a genome-wide association study (GWAS) dataset as instrumental variables to explore genetic causality between exposure and outcome factors.

The study followed stringent criteria for MR studies, ensuring robust correlations between instrumental variables and exposure factors while controlling for potential confounders.

Data on BMI indicators were obtained from the Integrative Epidemiology Unit (IEU) database, comprising nearly one million participants of European ancestry, with measurements for over seven million SNPs.

Data for various neurological diseases were sourced from the IEU database, including PD, AD, MS, ALS, IS, EP cases, and respective control groups.

The participants were predominantly of European origin, except for ALS and EP, which comprised individuals of multiple races and regions.

Quality control procedures were implemented for all disease data. SNPs significantly associated with BMI were subjected to cluster analysis to exclude redundant effects. SNPs causally linked to PD, AD, MS, ALS, IS, EP, and those related to disease confounders were excluded.

Two-sample MR analysis was employed, with inverse variance weighting (IVW) as the primary analytical approach, supported by weighted median, MR Egger, simple mode, and weighted mode. Further, the sensitivity analysis employed the MR-Egger method, Cochran Q test, and leave-one-out method to assess horizontal pleiotropy, heterogeneity, and robustness of the causal relationship between BMI indicators and neurological diseases.

Results and discussion

As per the study, significant genome-wide associations were found between BMI indicators and SNPs for PD (42), AD (42), MS (39), ALS (42), IS (42), and EP (31). The IVW analysis showed no genetic causality between BMI and PD, AD, ALS, and epilepsy (P > 0.05).

However, a positive genetic causality was found between BMI and MS (P = 0.035) and IS (P = 0.000). The findings suggest that a higher BMI is associated with increased risk for MS and IS.

Further, the weighted median analysis showed causal relationships between BMI and MS, IS, while the simple mode suggested a relationship with IS alone. Interestingly, MR Egger and weighted mode analyses showed no causal relationship between BMI and the studied diseases.

Results of the sensitivity analysis corroborated with the main findings. No significant heterogeneity or pleiotropy was found, and the findings were confirmed to be stable and reliable.

The findings are strengthened with the use of robust instrumental SNPs derived from the most comprehensive GWAS database so far.

However, the study is limited by its focus on patients of European ancestry, potential incomplete control of all neurological disorder risk factors, and reliance solely on BMI, without considering other body composition metrics.

Future studies involving waist circumference, waist-to-hip ratio, body fat percentage, and bioelectrical impedance could potentially reduce the bias in the results.


In conclusion, the study demonstrates MR analysis's utility in exploring genetic causal links between BMI and neurological diseases.

While no causal relationship was found with PD, AD, ALS, or EP, a genetic causal association of BMI was identified with MS and IS, suggesting that an increased BMI may increase the risk of MS and IS.

These findings highlight obesity's potential role as a risk factor in neurological disorders, paving the way for prevention and treatment strategies for improved health outcomes.

Journal reference:
Dr. Sushama R. Chaphalkar

Written by

Dr. Sushama R. Chaphalkar

Dr. Sushama R. Chaphalkar is a senior researcher and academician based in Pune, India. She holds a PhD in Microbiology and comes with vast experience in research and education in Biotechnology. In her illustrious career spanning three decades and a half, she held prominent leadership positions in academia and industry. As the Founder-Director of a renowned Biotechnology institute, she worked extensively on high-end research projects of industrial significance, fostering a stronger bond between industry and academia.  


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