Frank's Sign as a Dose-Dependent Marker of White Matter Burden in CADASIL: A Brain MRI Study

Background/Objectives: Frank's sign, a diagonal earlobe crease, may reflect systemic microvascular dysfunction. We investigated whether Frank's sign serves as a clinical marker of white matter hyperintensity (WMH) burden in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), a monogenic model of pure cerebral small vessel disease. Methods: We analyzed 81 genetically confirmed CADASIL patients (61.8 ± 12.6 years, 40.7% female) and 54 age/sex-matched controls (70.3 ± 6.6 years, 48.1% female). Frank's sign was detected using deep learning from brain MRI-reconstructed 3D facial surfaces. WMH volumes were automatically quantified and adjusted for confounders using Random Forest regression residuals. We compared Frank's sign prevalence between groups, assessed within-CADASIL associations, and evaluated dose-response relationships across WMH tertiles. Results: Frank's sign prevalence was significantly higher in CADASIL versus controls (66.7% vs. 42.6%, p = 0.020), with strengthened association after multivariate adjustment (OR = 4.214, 95% CI: 1.128-15.733, p = 0.032). Within CADASIL, Frank's sign-positive patients showed 72% greater WMH burden (51.5 ± 27.1 vs. 30.0 ± 26.1 mL, p < 0.001) and lower Consortium to Establish a Registry for Alzheimer's Disease (CERAD) total scores (57.7 ± 19.6 vs. 71.2 ± 22.8, p = 0.006), but similar lacunes, microbleeds, and hippocampal volumes. A robust dose-response relationship emerged across WMH tertiles, with Frank's sign prevalence increasing from 37.0% (lowest) to 74.1% (highest tertile; adjusted OR = 3.571, 95% CI: 1.134-11.253, p = 0.030). Conclusions: Frank's sign represents an accessible biomarker of WMH burden in CADASIL, demonstrating disease-specificity and dose-response characteristics independent of vascular risk factors. The automated MRI-based detection method of Frank's sign enables retrospective analysis of existing neuroimaging databases, transforming a bedside observation into a quantifiable neuroimaging biomarker for genetic small vessel disease stratification.