This study explores the potential of cerebrospinal fluid (CSF) protein ratios to reflect Alzheimer's disease (AD) pathology and neurodegeneration. Using a multiplex antibody-based suspension bead array, researchers measured 49 proteins in CSF from two cohorts: the Swedish GEDOC memory clinic (n=148 A-T- and 65 A+T+) and the Amsterdam Dementia Cohort (n=26 A-T- and 26 A+T+). Proteins were clustered based on their correlation with CSF amyloid beta (Aβ42), tau (t-tau, p-tau), and neurofilament light chain (NfL) levels. Support vector machine (SVM) modeling identified protein pairs that showed improved performance in distinguishing A+T+ from A-T- individuals compared to single proteins or pairs from the same cluster. The results showed that proteins from two clusters—tau-associated (e.g., NRGN, GAP43, SNCB) and amyloid-associated (e.g., PTPRN2, NCAN, CHL1)—combined in pairs significantly enhanced their ability to discriminate between AD pathology-affected and unaffected individuals. These pairs also showed stronger correlations with cognitive decline, as measured by cognitive scores. The findings were validated in an independent cohort. Protein pairs, particularly GAP43/PTPRN2 and SNCB/PTPRN2, demonstrated high accuracy in distinguishing AD-diagnosed individuals with negative AT status from those with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). These pairs showed strong negative correlations with cognitive scores, indicating their potential as biomarkers for monitoring AD pathology and neurodegeneration. The study highlights the potential of protein ratios to serve as valuable tools for monitoring AD pathology and neurodegeneration, particularly in clinical trials for novel therapies. The results suggest that combining brain-derived proteins in pairs enhances their capacity to reflect disease pathology and cognitive decline, potentially due to adjustment of inter-individual variability. This approach offers a promising alternative to traditional biomarkers for monitoring disease progression and treatment efficacy in AD.This study explores the potential of cerebrospinal fluid (CSF) protein ratios to reflect Alzheimer's disease (AD) pathology and neurodegeneration. Using a multiplex antibody-based suspension bead array, researchers measured 49 proteins in CSF from two cohorts: the Swedish GEDOC memory clinic (n=148 A-T- and 65 A+T+) and the Amsterdam Dementia Cohort (n=26 A-T- and 26 A+T+). Proteins were clustered based on their correlation with CSF amyloid beta (Aβ42), tau (t-tau, p-tau), and neurofilament light chain (NfL) levels. Support vector machine (SVM) modeling identified protein pairs that showed improved performance in distinguishing A+T+ from A-T- individuals compared to single proteins or pairs from the same cluster. The results showed that proteins from two clusters—tau-associated (e.g., NRGN, GAP43, SNCB) and amyloid-associated (e.g., PTPRN2, NCAN, CHL1)—combined in pairs significantly enhanced their ability to discriminate between AD pathology-affected and unaffected individuals. These pairs also showed stronger correlations with cognitive decline, as measured by cognitive scores. The findings were validated in an independent cohort. Protein pairs, particularly GAP43/PTPRN2 and SNCB/PTPRN2, demonstrated high accuracy in distinguishing AD-diagnosed individuals with negative AT status from those with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). These pairs showed strong negative correlations with cognitive scores, indicating their potential as biomarkers for monitoring AD pathology and neurodegeneration. The study highlights the potential of protein ratios to serve as valuable tools for monitoring AD pathology and neurodegeneration, particularly in clinical trials for novel therapies. The results suggest that combining brain-derived proteins in pairs enhances their capacity to reflect disease pathology and cognitive decline, potentially due to adjustment of inter-individual variability. This approach offers a promising alternative to traditional biomarkers for monitoring disease progression and treatment efficacy in AD.