Cognitive Age Delta as a Marker of Healthy and Pathological Cognitive Aging: The Role of Lifestyle, Cognitive...

Background: Chronological age is an imprecise proxy for cognitive aging. The Cognitive Age Delta (CAD)-the difference between predicted cognitive age and chronological age-offers a scalable, individualized marker of functional brain aging. We examined determinants of CAD in cognitively unimpaired (CU) adults stratified by Alzheimer's disease (AD) and vascular biomarkers. Methods: We analyzed 177 CU participants from the Gipuzkoa Alzheimer Project (Basque Country, Northern Spain) classified as amyloid-negative/vascular-negative (CUA-V-, n = 140), amyloid-positive (CUA+, n = 23), or vascular-positive (CUV+, n = 14) using CSF and MRI criteria; vascular burden was defined as Fazekas ≥ 2 on T2-FLAIR or ≥4 microbleeds on SWI, excluding non-traumatic superficial siderosis and established ischemic lesions. MRI was used solely for vascular classification. Associations with demographic, genetic, lifestyle, and reserve measures were tested with General Linear Models. Results: CAD did not differ across biomarker groups (Kruskal-Wallis H(2) = 0.17, p = 0.91). Median (IQR) CAD values were 0.28 (-4.13, 4.69) for CUA-V-, -0.14 (-3.15, 2.87) for CUA+, and 0.77 (-2.22, 3.76) for CUV+, indicating comparable distributions. Higher vocabulary scores (proxy of cognitive reserve) related to a younger cognitive age in CUA-V- (β = -1.39, p < 0.001) and CUA+ (β = -2.08, p = 0.054). In CUA+, greater sedentary time-particularly computer-based sitting-was also associated with lower CAD (daily sitting β = -2.13, p = 0.009; workday computer sitting β = -2.32, p = 0.015). CAD showed no associations with CSF Aβ42, p-tau or t-tau, APOE ε4 load, or vascular risk factors (all p > 0.05). Conclusions: CAD captures interindividual resilience-related variability beyond classical AD biomarkers. Vocabulary, a marker of lifelong enrichment, emerged as a robust determinant of a younger cognitive age, while amyloid and vascular pathology exerted limited influence at preclinical stages. These findings support CAD as a sensitive, scalable endpoint for identifying protective factors and guiding personalized prevention in early Aging.