Introduction Marfan syndrome (MFS) is an autosomal dominant condition characterised by a wide array of pleiotropic manifestations that affect the cardiovascular, skeletal, ocular and pulmonary systems. This phenotypic diversity arises from the pathogenic variability of the over 3000 identified FBN1 variants. Despite extensive research, correlations between specific FBN1 genotypes and aortic phenotypes remain inconclusive. Methods A comprehensive systematic review and meta-analysis was conducted on data collected from PubMed, Scopus and ScienceDirect up to 1 March 2025. All quantitative studies that reported aortic outcome data and met inclusion criteria were analysed. The primary endpoints assessed were aortic aneurysm, dissection and surgery. Results Our search strategy identified 17 studies, of which 11 were suitable for meta-analysis. We analysed data from over 6000 adults and conducted genotype-phenotype correlation analyses for six variant classes. Our findings indicate that haploinsufficiency (HI) variants are associated with a 2.5-fold increased risk of developing an aortic presentation compared with dominant negative (DN) variants (pooled RR 2.62; 95% CI 1.90 to 3.61; p<0.001, τ 2 =0.09, I²=50.4%). Our analysis of the missense cohort revealed a significant positive correlation between substitutions of or by cysteine and adverse aortic events (pooled RR 2.21; 95% CI 1.18 to 4.15; p<0.001, τ 2 =0.12, I²=76.1%). Subgroup analyses by structural variant classification ranked HI variants as the highest risk, followed by missense and splicing mutations (pooled proportion=0.18 and 0.15). Conclusions We found significant genotype-aortic phenotype correlations among FBN1 variant classes. Specifically, HI and cysteine-involving variants present the greatest risk and exhibit larger baseline aortic root diameters. Splicing variants, while traditionally grouped under the HI class, demonstrated an aortic risk more comparable to that of missense mutations. In the era of precision medicine, these findings empower clinicians to move beyond one-size-fits-all criteria and tailor monitoring intervals and elective repair decisions according to patient genetic profiles.
Korukonda et al. (Thu,) studied this question.