# Individualized prediction of clinical progression to dementia using plasma biomarkers in non-demented elderly

## TL;DR

This study uses blood biomarkers to predict dementia risk in elderly individuals with cognitive decline, improving individualized clinical predictions.

## Contribution

The study introduces individualized dementia progression models using plasma biomarkers like pTau181, pTau217, GFAP, and NfL in non-demented elderly.

## Key findings

- Plasma GFAP improves dementia risk prediction in MCI patients beyond age, sex, and MMSE.
- pTau181 and pTau217 enhance Alzheimer's dementia prediction models in MCI individuals.
- The models are externally validated and robust in clinical settings.

## Abstract

We aimed to develop individualized predictions for risk of developing any-cause dementia and Alzheimer’s disease (AD) dementia, in individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI), using plasma phosphorylated-tau-181 (pTau181), phosphorylated-tau-217 (pTau217; in a subset), amyloid beta1-42/1–40 (Aβ42/40), glial fibrillary acidic protein (GFAP) and/or neurofilament light (NfL).

From the Amsterdam Dementia Cohort we included 314 individuals with SCD (age 61 ± 9 years, n = 184 (59%) male, MMSE 29 ± 1) and 253 individuals with MCI (age 65 ± 7 years, n = 165 (65%) male, MMSE 27 ± 2), who had annual follow-up (median duration 2.4 years). Cox proportional hazards regression models were used to calculate probabilities for progression to dementia and were externally validated in MEMENTO and AIBL cohorts.

During follow-up 20 SCD and 99 MCI patients developed dementia. For MCI patients who progressed to any form of dementia, plasma GFAP contributed on top of age, sex, and MMSE score in the parsimonious individualized prognostic model (C-index = 0.69 [95%CI = 0.63; 0.76]). With AD-dementia as the outcome, GFAP and pTau181 were selected in the parsimonious model on top of the demographic variables (C-index = 0.71 [95%CI = 0.65; 0.76]). In the subset of 197 MCI individuals with pTau217 measurements, pTau217 was selected in the parsimonious model on top of the demographic variables (C-index = 0.75 [95%CI = 0.69; 0.79]). External validation demonstrated that the models are robust in a memory clinic setting.

Our prediction models have utility for clinical practice to calculate progression probabilities for development of dementia in individual patients living with MCI over a 1-, 3- and 5-year time period.

The online version contains supplementary material available at 10.1186/s13195-025-01925-1.