Comparison of Brain Age With Standard MRI Assessment for Dementia Risk Stratification in Subjective and Mild Cognitive Impairment

MRI-derived brain aging trajectories across the Alzheimer's continuum: links to cognition, pathology and lifestyle risk factors

BAG is a reliable non-invasive marker of structural brain health sensitive to AD pathology and to modifiable AD risk and supports its relevance for early risk stratification and prevention-oriented research.

Predicting future brain atrophy based on longitudinal MRI

Background Neuron loss is a hallmark of neurodegenerative diseases and leads to brain atrophy detectable with magnetic resonance imaging (MRI). Accurate prediction of future atrophy is valuable for research in Alzheimer's disease (AD) and related dementias. Objective This study aimed to predict annualized percentage changes in hippocampal, ventricular, and total gray matter (TGM) volumes in individuals ranging from cognitively normal to dementia, and to evaluate whether longitudinal MRI-derived change measures improve prediction performance compared with single-time-point MRI information. Methods Using elastic net regression, we compared baseline models based on single-timepoint MRI information with longitudinal models incorporating prior MRI-derived change measures. Both approaches were evaluated as MRI-only and MRI + risk-factor variants, with risk factors including age, sex, APOE4, and diagnostic status. Results In cross-validated analyses using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, the longitudinal MRI + risk-factor model performed best, yielding Pearson correlations of 0.62 for hippocampal atrophy, 0.51 for ventricular enlargement, and 0.41 for TGM atrophy. Longitudinal models consistently outperformed single time-point models, and adding risk factors improved predictive performance beyond MRI alone. External validation using the Australian Imaging, Biomarkers and Lifestyle cohort confirmed these findings. Predicted atrophy outperformed present-day regional volumes in identifying individuals progressing from normal cognition to MCI/dementia and from MCI to dementia. Conclusions MRI-derived longitudinal features enhance atrophy prediction, and predicted atrophy rates provide sensitive markers of future cognitive decline. These findings support the potential utility of predicted atrophy for cohort enrichment and therapeutic trial design.

Do cerebral white matter lesions influence the rate of progression from mild cognitive impairment to dementia?

An Integrative Multimodal Model for Early Diagnosis of Dementia and Differential Diagnosis of Alzheimer's Disease Using Neuroimaging, Polygenic Risk, and Cognitive Assessments

Background: Early diagnosis and etiological classification of dementia remain challenging, as clinicians typically lack tools to integrate cognitive, neuroimaging, and genetic data quantitatively. We developed and validated multimodal risk models to support early diagnosis of dementia and differential diagnosis of Alzheimer's disease (AD) versus non-AD dementias in real-world clinical settings and translated model outputs into individualized risk reports. Methods: Utilizing real-world clinical cohorts (n = 1,100 for early diagnosis of dementia, using clinical diagnoses up to three years after clinical assessment; n = 788 for AD differential diagnosis) from Norwegian Memory Clinics, we trained and validated the Multimodal Hazard Score for Real-World Data (MHS-RWD) model integrating demographics (age, sex), cognitive assessments (MMSE-NR3 or CERAD 10-word delayed recall), the MRI-derived Imaging Hazard Score, and the Polygenic Hazard Score. Discrimination performance was examined using the area under the receiver operating characteristic curve (AUC). Results: In real-world clinical data, the MHS-RWD consistently outperformed any single predictor used alone. For early diagnosis of dementia, the full model achieved an AUC of 0.89 in females and 0.84 in males. For the differential diagnosis of AD from other dementias, the multimodal model yielded an AUC of 0.91 in females and 0.83 in males. A patient-level risk report was designed to present individualized risk estimates. Conclusions: Multimodal integration of cognitive, neuroimaging, and polygenic data in the MHS-RWD tool yields strong discrimination for both early diagnosis of dementia and AD differential diagnosis. The tool relies on data obtainable in clinical care, and genetic information that is becoming increasingly available in routine practice. Delivered through intuitive patient-level risk reports, it could support etiologically informed dementia decisions in real-world settings, with potential utility in primary care.

A score based on MRI imaging variables can predict time to moderate progression in mild cognitive impairment: Multimodal data integration study

Background Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD). Because there is no treatment for AD, it is vital to establish which patients are at a higher risk of disease progression, and might benefit from early intervention. Objective This study investigates whether a score based on clusters of MRI and lipidomic variables can predict the time to moderate progression in MCI. Methods We analyzed MRI and lipidomics data from 480 individuals with MCI (mean age 72.14 years, range 55–90 years). Moderate progression was defined as a ≥ 2.5-point increase in the Clinical Dementia Rating Scale Sum of Boxes (baseline mean CDR-SB of 1.2, range 0–10 points). Multi-source data integration and clustering methods were used to derive predictive scores. Results The MRI-based score outperformed the lipidomics score (c-index: 0.754 versus 0.666). A 1-unit increase in the MRI score was linked to a 13% higher risk of moderate progression. Individuals in the top 25% of MRI scores had a 3-fold greater risk of decline. Conclusions An MRI-based score offers a simple, effective tool to predict moderate MCI progression and identify high-risk individuals for AD.

Hearing in noise and cognitive status in older adults: Biomarker associations and bidirectional survival analyses

Background Hearing impairment (HI) has been identified as a potentially modifiable risk factor for dementia, yet its relationship with Alzheimer's disease (AD) neuropathology and the temporal directionality remain unclear, particularly in late life. Objective To examine cross-sectional and longitudinal associations between HI, clinical cognitive status, and AD biomarkers in community-dwelling older adults. Methods We included 474 DRIVES Project participants (mean age 73.5 ± 5.2 years). Hearing was assessed using the NIH Toolbox Words-in-Noise (WIN) test. Clinical cognitive status was assessed using Clinical Dementia Rating (CDR). AD biomarkers included cerebrospinal fluid (CSF) Aβ42/Aβ40, t-tau/Aβ42, and p-tau/Aβ42 ratios and amyloid PET imaging. Multivariable linear, logistic, Cox proportional hazards, and Fine–Gray competing-risk models were used. Results HI was more prevalent among participants with mild cognitive impairment (MCI; defined as CDR = 0.5) than cognitively normal individuals (50% versus 24%, p < 0.001), and MCI status was independently associated with worse baseline WIN thresholds (β = 1.55 dB SNR; 95% CI 0.72–2.37). HI was not cross-sectionally associated with CSF amyloid or tau ratios or amyloid PET positivity. Longitudinally, baseline MCI was associated with a higher risk of incident HI (Cox HR = 2.63, 95% CI 1.48–4.67; Fine–Gray sHR = 2.79, 95% CI 1.06–7.40), whereas baseline HI was not associated with subsequent CDR progression. Conclusions In older adults, HI was associated with MCI but not core AD biomarkers or future CDR progression, suggesting that late-life HI may reflect cognitive vulnerability or broader brain aging rather than independently driving AD pathology.