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Predicting Mild Cognitive Impairment to Dementia Progression: Optimizing Aβ PET and MMSE Cutoffs
Conference presentation   Open access   Peer reviewed

Predicting Mild Cognitive Impairment to Dementia Progression: Optimizing Aβ PET and MMSE Cutoffs

Rosita Shishegar, Pierrick Bourgeat, Vincent Dore, James D Doecke, Rodrigo Canovas, Simon M Laws, Tenielle Porter, Azadeh Feizpour, Michael W Weiner, Colin L Masters, …
Alzheimer's & dementia, Vol.21(Suppl. 5 (Drug Supplement)), e104130
Wiley Periodicals LLC on behalf of Alzheimer's Association.
Alzheimer's Association International Conference® (Toronto, Canada/Online, 27/07/2025–31/07/2025)
2025
DOI: https://doi.org/10.1002/alz70859_104130
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CC BY V4.0,  Open Access

Abstract

Alzheimer's disease Clinical decision making Clinical medicine Clinical research Clinical trials Cognition Cognition & reasoning Cognitive ability Cognitive impairment Dementia Early intervention Groups High risk Intervention Medical decision making Mini-Mental State Examination Politics Probability Risk Stratification Therapy Thresholds
Background This study aims to determine the optimal threshold for Aβ PET to identify individuals with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer's disease (AD). Additionally, it assesses whether combining β‐amyloid with Mini‐Mental State Examination (MMSE) performance can enhance risk stratification in MCI which can guide clinical decision‐making regarding early therapeutic interventions. Methods We included 686 MCI participants with CDR 0.5 from two cohorts, followed for up to 7 years. Harmonized data from AIBL (N=166) and ADNI (N=520) were analysed using Cox proportional‐hazards models, adjusted for sex, and APOE4 status, with the event of interest being progression to mild dementia due to AD (detected by CDR = 1 and CL>20). Optimal thresholds for MMSE (27) and Aβ (44 CL) were selected to maximize hazard ratios (HR) at 3 years, categorizing participants into low‐risk and high‐risk groups based on cognitive performance and Aβ load. Note that the MMSE score was selected as it is frequently used in clinical practice and in trials. Results Both thresholds showed comparable hazard ratios (Figure 1). However, the MCI high‐cognition group had a significantly higher risk of progressing to AD (measured with risk probability (RP)=1‐survival probability; RP=0.08±0.05) than MCI low‐Aβ (RP=0.01±0.01). Combining both cutoffs improved risk stratification: 51 out of 135 MCI low‐cognition, high‐Aβ progressed to AD within 3 years (50% survival probability, HR=2.00), while only 1 of 308 of the MCI high‐cognition low‐Aβ progressed to AD (RP=0.00±0.01). Furthermore, we tested other cognitive assessments, such as CVLT, which provided similar or even statistically worse results in comparison to MMSE. The low‐cognition high‐Aβ group showed the fastest decline, with an annual rate of decline of 0.34 PACC scores, and an effect size of 0.75, compared to the high‐cognition, low‐Aβ group. Conclusion While cognitive performance alone is not sufficient for predicting MCI‐to‐AD progression, combining Aβ with MMSE cutoffs can enhance risk stratification, providing greater prognostic information and aiding in the design of clinical trials and therapeutic interventions for prodromal AD. This study also highlights the importance of using CL>44 to identify individuals at high risk of progressing to AD.

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