# An integrated CSF-serum biomarker model for predicting clinical progression in Alzheimer’s disease

## TL;DR

This study develops a model combining cerebrospinal fluid and blood biomarkers to predict Alzheimer's disease progression with high accuracy.

## Contribution

A novel integrated CSF-serum biomarker model for Alzheimer's disease prediction is developed and validated.

## Key findings

- The model achieved an AUC of 0.92 in training, 0.86 in testing, and 0.83 in external validation.
- Higher A/G ratio was linked to reduced AD progression risk, while higher PLR increased risk.
- Calibration was strong with a mean absolute error of 0.039.

## Abstract

The early and accurate identification of Alzheimer’s disease (AD) remains a significant clinical challenge. Integrating novel peripheral blood-based biomarkers with established cerebrospinal fluid (CSF) measures may offer a promising strategy to enhance diagnostic accuracy and risk stratification.

This study enrolled 91 participants who underwent CSF and serum testing. The cohort was randomly divided into a training set (n = 63) and an internal testing set (n = 28). External validation was performed using matched data (n = 30) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (total n = 639). Data collected included demographics, Mini-Mental State Examination (MMSE) total scores, the Functional Activities Questionnaire (FAQ) total scores, CSF phosphorylated tau (pTau181) and amyloid-β (Aβ42) levels, and serum indices such as the albumin-to-globulin (A/G) ratio and platelet-to-lymphocyte ratio (PLR). Predictor selection was performed via univariate and multivariate logistic regression, and a nomogram was developed from the final model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves with mean absolute error (MAE), and decision curve analysis (DCA).

The final predictive model incorporated CSF pTau181, A/G ratio, and PLR (using a cut-off ≥113.22). It demonstrated robust discrimination, achieving an AUC of 0.92 in the training set, 0.86 in the testing set, and 0.83 upon external validation. Calibration was excellent (MAE = 0.039). In the testing set, sensitivity was 0.83 and specificity was 0.86. A higher A/G ratio was associated with a reduced risk of AD progression, whereas a higher PLR was associated with an increased risk.

The combined CSF-peripheral blood biomarker model demonstrates robust discrimination and calibration for predicting AD progression. By linking central tau pathology with peripheral nutritional and inflammatory status, it may aid clinical risk stratification and guide management strategies focused on nutrition and inflammation. Further large-scale, prospective validation is warranted.