Mendelian Randomization: A Robust Approach for Causal Inference in Observational Data (Motivated by the Trending Study on Cheese Intake and Osteoarthritis by Song Wen et al.)

Mendelian Randomization (MR) is an advanced causal inference method that leverages genetic variants as instrumental variables (IVs) to mitigate confounding and reverse causation in observational studies. By mimicking the principles of randomized controlled trials (RCTs), MR offers a robust framework for estimating causal effects without the logistical and ethical constraints of prospective studies. This paper provides an overview of MR, using the Song Wen et al. (2024) study on cheese intake and osteoarthritis (OA) as a case study to illustrate its application in nutritional epidemiology.MR relies on three core assumptions: (1) Relevance—genetic variants must be strongly associated with the exposure; (2) Independence—genetic variants should not correlate with confounders, and (3) Exclusion Restriction—genetic variants should influence the outcome only through the exposure. The Song Wen et al. (2024) study applied a two-sample MR approach using genome-wide association study (GWAS) data to assess whether genetically predicted cheese intake influences OA risk. Their findings suggest a potential protective effect, particularly for knee OA (OR = 0.52, 95% CI = 0.42–0.66, p = 4.11 × 10⁻⁸).Key strengths of MR include its ability to estimate lifelong exposure effects and reduce confounding, while limitations include weak instrument bias, pleiotropy, and population stratification. Future directions include integrating multivariable MR, polygenic risk scores, and large-scale biobanks to enhance statistical power and applicability across diverse populations. By refining MR methodologies, researchers can strengthen causal inference in public health and clinical decision-making, advancing evidence-based interventions for chronic diseases.