3D仮想現実ナビゲーションでアルツハイマー病の超早期変化を発見～血液アルツハイマー病指標と組み合わせたスクリーニングの有用性を実証～

2025-09-11

2025-09-11 学習院大学

藤田医科大学らの共同研究グループは、3D仮想現実(VR)ナビゲーション課題を用いてアルツハイマー病(AD)の超早期変化を検出できる可能性を示した。健常成人111名を対象に「経路統合能」を測定し、血液バイオマーカー(p-tau181、GFAP、NfLなど)と比較したところ、エラー距離は年齢・p-tau181・GFAP・NfLと有意に関連した。多変量解析ではp-tau181とGFAPが独立因子、機械学習解析ではp-tau181が最重要予測因子であった。また、経路統合能のみでp-tau181高値(≥2.2 pg/mL)をAUC 0.86・感度91.7%・特異度77.8%で識別可能であった。MRIでは嗅内野皮質厚がエラー距離と相関したが年齢調整後には消失し、構造変化に先立つ機能的変化を検出した可能性が示唆された。本成果は、非侵襲・短時間・低負担のVR検査と血液検査を組み合わせることで、ADの超早期スクリーニング戦略に貢献しうるものである。

<関連情報>

アルツハイマー病早期発見のための仮想現実ナビゲーション
Virtual reality navigation for the early detection of Alzheimer’s disease

Sayuri Shima^,Reiko Ohdake^,Yasuaki Mizutani^,Harutsugu Tatebe^,Riki Koike^,Atsushi Kasai^,Epifanio Bagarinao^,Kazuya Kawabata^,Akihiro Ueda^,Mizuki Ito^,Junichi Hata^,Shinsuke Ishigaki^,Junichiro Yoshimoto^,Hiroshi Toyama^,Takahiko Tokuda^,Akihiko Takashima^,Hirohisa Watanabe
Frontiers in Aging Neuroscience Published:20 August 2025
DOI:https://doi.org/10.3389/fnagi.2025.1571429

Objective: The development of non-invasive clinical diagnostics is paramount for the early detection of Alzheimer’s disease (AD). Neurofibrillary tangles in AD originate from the entorhinal cortex, a cortical memory area that mediates navigation via path integration (PI). Here, we studied correlations between PI errors and levels of a range of AD biomarkers using a 3D virtual reality navigation system to explore PI as a non-invasive surrogate marker for early detection.

Methods: We examined 111 healthy adults for PI using a head-mounted 3D VR system, AD-related plasma biomarkers (GFAP, NfL, Aβ40, Aβ42, and p-tau181), Apolipoprotein E (ApoE) genotype, and demographic and cognitive assessments. Covariance of PI and AD biomarkers was assessed statistically, including tests for multivariate linear regression, logistic regression, and predictor importance ranking using machine learning, to identify predictive relationships for PI errors.

Results: We found significant positive correlations between PI errors with age and plasma GFAP, p-tau181, and NfL levels. Multivariate analysis identified significant correlations of plasma GFAP (t-value = 2.16, p = 0.0332) and p-tau181 (t-value = 2.53, p = 0.0128) with PI errors. Predictor importance ranking using machine learning and receiver operating characteristic curves identified plasma p-tau181 as the most significant predictor of PI. ApoE genotype and plasma p-tau181 showed positive and negative PI associations (ApoE: coefficient = 0.650, p = 0.037; p-tau181: coefficient = -0.899, p = 0.041). EC thickness exhibited negative correlations with age, mean PI errors, and GFAP, NfL, and p-tau181; however, none of these associations remained significant after adjusting for age in linear regression analyses.

Conclusion: These findings suggest that PI quantified by 3D VR navigation systems may be useful as a surrogate diagnostic tool for the detection of early AD pathophysiology. The hierarchical application of 3D VR PI and plasma p-tau181, in particular, may be an effective combinatorial biomarker for early AD neurodegeneration. These findings advance the application of non-invasive diagnostic tools for early testing and monitoring of AD, paving the way for timely therapeutic interventions and improved epidemiological patient outcomes.