Biomarkers

Background: With the increase in life expectancy and the rising prevalence of Alzheimer's Disease (AD), the integration of biomarkers for early diagnosis is crucial. The Mayo Preclinical Alzheimer's Cognitive Composite (Mayo-PACC), encompassing the Rey-Auditory Verbal Learning Test (RAVLT), Trail Making Test - B (TMT B), and semantic fluency, is designed to detect cognitive changes in preclinical AD. This study investigates gender-based differences in the predictive efficacy of Mayo-PACC for AD biomarkers following the ATN (Amyloid, Tau, Neurodegeneration) criteria.

Method: The study included 112 patients diagnosed with Mild Cognitive Impairment (MCI), comprising 38% women and 61% men, matched by age (mean ± SD: 68.73 ± 10.70 years), education (14.21 ± 3.56 years), and cognitive performance (Mayo-PACC p= 0.76). Neuropsychological assessments were conducted using the Mayo-PACC, and cerebrospinal fluid biomarkers (amyloid β1-42, total tau, and phosphorylated tau at threonine 181) were quantified by ELISA (Fujirebio, Japan). Biomarkers were dichotomized using cutoff values previously determined in our laboratory.

Result: Random Forest models indicated an overall area under the curve (AUC) of 0.67 for the Mayo-PACC. In gender-specific analyses, women showed a lower predictive capacity (AUC: 0.43), with TMT B (35.42%), RAVLT (32.84%), and semantic fluency (31.74%) as key predictors. For men, a higher AUC of 0.90 was observed, with TMT B (35.49%) and semantic fluency (34.91%) being the most influential. When examining individual ATN biomarkers, women showed an AUC of 0.25 for Aβ1-42, 0.54 for N, and 0.40 for T, with RAVLT (40.51%) as a prominent feature. Men exhibited AUCs of 0.63 for Aβ1-42, 0.83 for N, and 0.48 for T, with semantic fluency (33.95%), and RAVLT (33.12%) being significant predictors.

Conclusion: The study highlights notable gender differences in the predictive performance of neuropsychological composites for AD biomarkers. Biomarker prediction was more accurate in men. These findings suggest the importance of gender-specific approaches in predictive modeling for AD diagnosis, underscoring the potential to enhance diagnostic precision in clinical practice.