Abstract Details

Title Brain Age Gap as a Predictive Biomarker for Neurodegenerative Disease Progression: A Systematic Review and Meta-analysis

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P7 - Poster Session 7 (8:00 AM-9:00 AM)

Poster/Presentation
Number 12-011

Objective To perform a meta-analysis assessing the diagnostic performance of MRI-derived brain age gap (BAG) in differentiating patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and Parkinson’s disease (PD) from healthy controls.

Background One promising biomarker for neurodegenerative diseases is the brain age gap (BAG) , which is the difference between chronological age and brain age as determined by MRI. Although elevated BAG has been linked to Parkinson's disease (PD) , Alzheimer's disease (AD) , and mild cognitive impairment (MCI) , no meta-analysis has measured it's diagnostic value across disorders .

Design/Methods we conducted a comprehensive search of PubMed , PMC and relevent imaging journals up to October 2025 for studies that reported group-wise brain age gap (BAG) means and standard deviations, using structural T1-weighted MRI and machine learning to estimate brain age. A total of 3560 participants from six eligible studies ( AD=1240 ; MCI = 920; PD = 1080 ; controls = 320 ) were included. Pooled standardised mean differences ( SMD ) in BAG between patients and controls were calculated using random-effects meta-analyses.

Results The patients brain appeared to age faster than those of the control group, with a significantly higher brain age gap (BAG) (pooled SMD=1.12; 95% CI, 0.89–1.36; p<0.001). AD had the highest SMD (1.35; 95% CI, 1.08–1.61), with lower values seen in MCI (0.89; 95% CI, 0.65–1.13) and PD (0.98; 95% CI, 0.75–1.21). The model architecture (deep learning vs. atlas-based; p=0.03) contributed to the moderate heterogeneity ( I²=58%). Robustness was confirmed by sensitivity analyses.

Conclusions MRI-derived BAG effectively distinguishes between healthy ageing and neurodegenerative patients, with biggest effects in AD. Our results validate BAG as a transdiagnostic biomarker for tracking the course of a disease. Clinical translation and the creation of guidelines will be facilitated by standardised reporting and increased research in other disorders.

Authors/Disclosures
SAI VENKATA MANOJ KOTHARU, MBBS PRESENTER	Dr. KOTHARU has nothing to disclose.
Simranjeet S. Nagoke, MBBS	Dr. Nagoke has nothing to disclose.
Aditi Agarwal	Aditi Agarwal has nothing to disclose.
Binay Panjiyar, MBBS	Dr. Panjiyar has nothing to disclose.
Govind S. Mann, MBBS	Dr. Mann has nothing to disclose.
Dheekshitha S. Korlakunta, MBBS	Miss Korlakunta has nothing to disclose.
Bhavana Korlakunta, MBBS	Dr. Korlakunta has nothing to disclose.

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