Genetic Study Links Higher BMI to Perceived Facial Aging, but Causality Remains Unproven

In a recent genetic analysis, higher body mass index (BMI) was associated with perceived facial aging; however, the findings do not establish that reducing BMI would prevent or reverse facial aging.¹ The study evaluated shared genetic architecture between facial aging and 15 metabolic traits and used bidirectional Mendelian randomization (MR) to assess potential causal relationships.

Genetic Architecture and Study Design

Facial aging is influenced by genetic, metabolic, and environmental factors and reflects changes involving the skin, subcutaneous fat, muscles, ligaments, and craniofacial structures.² The study used a perception-based facial aging phenotype from the UK Biobank in which participants were classified according to whether they were perceived as younger than, about the same age as, or older than their chronological age. The dataset included 423,999 participants of European ancestry.

Using linkage disequilibrium score regression, BMI demonstrated the strongest positive genetic correlation with facial aging (rg = 0.215; FDR-adjusted P = 3.94 × 10−33). Waist-to-hip ratio also showed a positive genetic correlation, but this association was not significant after adjustment for BMI. High-density lipoprotein cholesterol demonstrated a weaker negative correlation, while other lipid, glycemic, and blood pressure traits did not remain significant after false discovery rate correction.

Mendelian Randomization and Potential Causality

Because BMI showed the strongest and most consistent signal, it was evaluated further using bidirectional MR. After removal of 18 outlier variants with MR-PRESSO, higher genetically predicted BMI was associated with greater odds of appearing older using the inverse-variance weighted approach (OR = 1.053; 95% CI, 1.044-1.063; P = 1.80 × 10−28). Weighted median and MR-Egger analyses showed similar directions.

However, the authors emphasized that the MR findings did not establish a strict unidirectional causal relationship. Significant heterogeneity and evidence of outliers remained, while reverse-direction estimates from facial aging to BMI were also nominally significant but similarly affected by heterogeneity. The investigators therefore interpreted the BMI findings as supportive of a possible contribution rather than definitive evidence of causality.

Candidate Genes and Confounding Factors

The analysis subsequently focused on 4 candidate variants associated with BMI and facial aging. Three met the prespecified colocalization threshold, supporting a shared genetic signal at those loci. SNP-to-gene mapping and gene-based analyses prioritized JAZF1, RAD52, and PPARG. The authors noted that these findings represent statistical prioritization and do not establish that the genes mediate the relationship between BMI and facial aging or that they are therapeutic targets.

An exploratory drug-target analysis identified 5 natural compounds for molecular docking involving RAD52 or PPARG. However, the docking results did not demonstrate direct binding, target engagement, bioavailability, safety, or pharmacologic efficacy. The compounds therefore remain hypothesis-generating and require functional validation.

The authors also highlighted that perceived facial age is a composite phenotype that cannot distinguish cutaneous aging from changes in facial volume, skeletal structure, or other contributors to appearance. Environmental factors such as ultraviolet exposure, smoking, sleep, and nutrition may also modify perceived facial age but were not evaluated through the study’s genetic framework.

Conclusion and Clinical Implications

Several limitations further constrain interpretation. The facial-aging phenotype was perception-based, the genetic datasets were predominantly derived from individuals of European ancestry, and some exposure and outcome datasets may have included overlapping UK Biobank samples. In addition, downstream locus, gene, and molecular docking analyses were computational.

Overall, the findings support a possible contribution of higher BMI to perceived facial aging but do not demonstrate that BMI reduction improves facial aging. The authors identify metabolic health as the more clinically actionable implication, while JAZF1, RAD52, PPARG, and the candidate compounds require functional validation before their roles in facial aging can be established.

References

1. Hu Y, Li KH, He MJ, Yu CS, Wang SB. Body Mass Index and Facial Aging: Mendelian Randomization and Exploratory Target Prioritization. Clin Cosmet Investig Dermatol. 2026;19:624227. Published 2026 Aug 25. doi:10.2147/CCID.S624227

2. Sadick NS, Karcher C, Palmisano L. Cosmetic dermatology of the aging face. Clin Dermatol. 2009;27(3):S3–10. doi:10.1016/j.clindermatol.2008.12.003