Neuroinflammation is increasingly recognized as a central feature of neurodegenerative disease pathogenesis, and the neutrophil-to-lymphocyte ratio (NLR), a readily accessible marker of systemic inflammation, may reflect underlying neuroinflammatory processes; however, whether NLR plays a causal role in neurodegeneration remains unclear. To evaluate this, two-sample Mendelian randomization (MR) analyses were applied to assess potential causal relationships between NLR and four neurodegenerative conditions — Parkinson's disease (PD; n = 56,306), Lewy body dementia (LBD; n = 6,618), Alzheimer's disease (AD; n = 7,409), and frontotemporal dementia (FTD; n = 4,218) — using published genome-wide association study (GWAS) summary statistics. Genetic instruments for NLR were derived from UK Biobank GWAS summary statistics for neutrophil and lymphocyte counts (n = 361,194, European ancestry), with a propagation-of-error approach used to construct a composite inflammatory phenotype defined as the difference between the two cell counts. Instruments were selected by clumping common SNPs (MAF > 0.01) at genome-wide significance (p < 5×10⁻⁸) to a pairwise linkage disequilibrium threshold of r² < 0.01. MR analyses revealed no statistically significant causal relationship between NLR and any of the four neurodegenerative diseases examined (all p > 0.05); although positive causal effect estimates were observed for PD and LBD, effects were minimal with wide confidence intervals crossing the null for all outcomes. These findings suggest that while NLR may capture early inflammatory changes associated with neurodegeneration, it is unlikely to be causally involved in disease pathogenesis, highlighting the need for future studies investigating specific upstream inflammatory pathways and expanding analyses to more diverse populations.

Lavanya Garg is a rising Junior at Brown University concentrating in Computational Biology. Her interests lie at the intersection of data science and medicine, particularly in using computational approaches to understand genetic variation and disease. She has conducted research using genomic datasets and simulations to study evolutionary patterns and health outcomes. Lavanya has also gained experience in healthcare and laboratory settings through internships and research programs. Beyond academics, she is actively involved in leadership and advocacy initiatives supporting women in STEM and expanding access to scientific education.