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<p>Cognitive decline in older adults, particularly during the preclinical stages of Alzheimer’s disease (AD), presents a critical opportunity for early detection and intervention. While T1-weighted MRI is widely used in AD research, its capacity to identify early vulnerability and monitor longitudinal progression remains incompletely characterized. We analyzed longitudinal T1-weighted MRI data from 224 cognitively unimpaired older adults followed for up to 12&#xa0;years. Participants were stratified by clinical outcome into converters to mild cognitive impairment (HC-converters, <i>n</i> = 112) and stable controls (HC-stable, <i>n</i> = 112). Groups were matched at baseline for age (mean ~ 74–75&#xa0;years), education (~ 16.4&#xa0;years), and cognitive scores (MMSE ≈ 29; CDR-SB ≈ 0.04). Four MRI-derived biomarkers were examined: brain-predicted age difference (brain-PAD), mean cortical thickness, AD-cortical signature, and hippocampal volume. Brain-PAD showed the strongest baseline association with future conversion (<i>β</i> = 1.25, <i>t</i> = 3.52, <i>p</i> = 0.0009) and highest classification accuracy (AUC = 0.66; sensitivity = 62%, and specificity = 67%). Longitudinal mixed-effects models focusing on the group × time interaction revealed a significant positive slope in brain-PAD for converters (<i>β</i> = 0.0079, <i>p</i> = 0.003) and a non-significant trend in stable controls (<i>β</i> = 0.0047, <i>p</i> = 0.075), indicating incipient divergence in brain aging trajectories during the preclinical window. Hippocampal volume and AD-cortical signature declined similarly in both groups. The mean cortical thickness had limited discriminative or dynamic utility. These findings support brain-PAD, derived from routine T1-weighted MRI using machine learning, as a sensitive, performance-independent biomarker for early risk stratification and monitoring of cognitive aging trajectories.</p>

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Brain age as an accurate biomarker of preclinical cognitive decline: evidence from a 12-year longitudinal study

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Odelia Elkana,
  • Iman Beheshti

摘要

Cognitive decline in older adults, particularly during the preclinical stages of Alzheimer’s disease (AD), presents a critical opportunity for early detection and intervention. While T1-weighted MRI is widely used in AD research, its capacity to identify early vulnerability and monitor longitudinal progression remains incompletely characterized. We analyzed longitudinal T1-weighted MRI data from 224 cognitively unimpaired older adults followed for up to 12 years. Participants were stratified by clinical outcome into converters to mild cognitive impairment (HC-converters, n = 112) and stable controls (HC-stable, n = 112). Groups were matched at baseline for age (mean ~ 74–75 years), education (~ 16.4 years), and cognitive scores (MMSE ≈ 29; CDR-SB ≈ 0.04). Four MRI-derived biomarkers were examined: brain-predicted age difference (brain-PAD), mean cortical thickness, AD-cortical signature, and hippocampal volume. Brain-PAD showed the strongest baseline association with future conversion (β = 1.25, t = 3.52, p = 0.0009) and highest classification accuracy (AUC = 0.66; sensitivity = 62%, and specificity = 67%). Longitudinal mixed-effects models focusing on the group × time interaction revealed a significant positive slope in brain-PAD for converters (β = 0.0079, p = 0.003) and a non-significant trend in stable controls (β = 0.0047, p = 0.075), indicating incipient divergence in brain aging trajectories during the preclinical window. Hippocampal volume and AD-cortical signature declined similarly in both groups. The mean cortical thickness had limited discriminative or dynamic utility. These findings support brain-PAD, derived from routine T1-weighted MRI using machine learning, as a sensitive, performance-independent biomarker for early risk stratification and monitoring of cognitive aging trajectories.