Predicting brain amyloid load with digital and blood-based biomarkers
Abstract Background With the recent approval of anti-β-amyloid (Aβ) treatment for Alzheimer’s disease (AD), a demand has emerged for scalable, convenient and accurate estimations of brain Aβ burden for the detection of AD that would enable timely, accurate and reliable diagnosis in one’s primary car...
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BMC
2025-07-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-025-01801-y |
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| author | Weineng Chen Yu Liao Xinchong Shi Fengjuan Su Haifan Kong Yingying Fang Yifan Zheng Jiayi Zhou Ganqiang Liu Xianbo Zhou Xiaoli Yao Curtis B. Ashford Feng Li Long Yang Michael F. Bergeron J. Wesson Ashford Xiangsong Zhang Zhong Pei |
| author_facet | Weineng Chen Yu Liao Xinchong Shi Fengjuan Su Haifan Kong Yingying Fang Yifan Zheng Jiayi Zhou Ganqiang Liu Xianbo Zhou Xiaoli Yao Curtis B. Ashford Feng Li Long Yang Michael F. Bergeron J. Wesson Ashford Xiangsong Zhang Zhong Pei |
| author_sort | Weineng Chen |
| collection | DOAJ |
| description | Abstract Background With the recent approval of anti-β-amyloid (Aβ) treatment for Alzheimer’s disease (AD), a demand has emerged for scalable, convenient and accurate estimations of brain Aβ burden for the detection of AD that would enable timely, accurate and reliable diagnosis in one’s primary care physician’s (PCPs) office as called for recently by World Health Organization (WHO). Methods MemTrax, a 2-minute online memory test, was selected as the digital biomarker of cognitive impairment, and blood-based biomarkers (BBMs) including Aβ42, Aβ40, P-tau181, GFAP and NfL were used to estimate AD-related metrics in different groups of elderly individuals (n = 349) for comparison with Aβ PET scans of brain Aβ burden. The correlations between MemTrax, MoCA, BBMs and brain Aβ burden, expressed in centiloid (CL) values, were analyzed for predicting CL value alone or in combinations using machine-learning (ML). Results Both MemTrax and the MoCA were able to differentiate Aβ status similarly. Integration of MemTrax and BBMs using ML, however, significantly improved the AUCs (over the same with MoCA) for differentiating Aβ status. MemTrax and p-Tau181/Aβ42 composite showed the strongest relationship with CL value among other BBMs. Most importantly, regression analyses of MemTrax and p-Tau181/Aβ42 aptly predicted CL values. Conclusion The combination of MemTrax and BBMs provides an accurate, convenient, non-invasive, cost-effective and scalable way to estimate Aβ load, which provides an opportunity for mass screening and timely and accurate diagnosis of AD. Our findings could also facilitate more effective AD clinical management in the PCPs office worldwide for more equitable access to current standard of care. |
| format | Article |
| id | doaj-art-c4722fe4c3c44da7b11fa45824e3dcc8 |
| institution | Kabale University |
| issn | 1758-9193 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-c4722fe4c3c44da7b11fa45824e3dcc82025-08-20T04:01:24ZengBMCAlzheimer’s Research & Therapy1758-91932025-07-0117111310.1186/s13195-025-01801-yPredicting brain amyloid load with digital and blood-based biomarkersWeineng Chen0Yu Liao1Xinchong Shi2Fengjuan Su3Haifan Kong4Yingying Fang5Yifan Zheng6Jiayi Zhou7Ganqiang Liu8Xianbo Zhou9Xiaoli Yao10Curtis B. Ashford11Feng Li12Long Yang13Michael F. Bergeron14J. Wesson Ashford15Xiangsong Zhang16Zhong Pei17Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityShenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen UniversityDepartment of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityDepartment of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityDepartment of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityDepartment of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityDepartment of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityShenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen UniversityCenter for Alzheimer’s Research, Washington Institute of Clinical Research, Vienna, VA, USA; AstraNeura, Co., Ltd.Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityMemTrax, LLCKunming Escher Technology Co. LtdCenter for Clinical Pharmacy, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Health Sciences, University of HartfordDepartment of Psychiatry & Behavioral Sciences, War Related Illness & Injury Study Center, VA Palo Alto Health Care System, Stanford UniversityDepartment of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen UniversityAbstract Background With the recent approval of anti-β-amyloid (Aβ) treatment for Alzheimer’s disease (AD), a demand has emerged for scalable, convenient and accurate estimations of brain Aβ burden for the detection of AD that would enable timely, accurate and reliable diagnosis in one’s primary care physician’s (PCPs) office as called for recently by World Health Organization (WHO). Methods MemTrax, a 2-minute online memory test, was selected as the digital biomarker of cognitive impairment, and blood-based biomarkers (BBMs) including Aβ42, Aβ40, P-tau181, GFAP and NfL were used to estimate AD-related metrics in different groups of elderly individuals (n = 349) for comparison with Aβ PET scans of brain Aβ burden. The correlations between MemTrax, MoCA, BBMs and brain Aβ burden, expressed in centiloid (CL) values, were analyzed for predicting CL value alone or in combinations using machine-learning (ML). Results Both MemTrax and the MoCA were able to differentiate Aβ status similarly. Integration of MemTrax and BBMs using ML, however, significantly improved the AUCs (over the same with MoCA) for differentiating Aβ status. MemTrax and p-Tau181/Aβ42 composite showed the strongest relationship with CL value among other BBMs. Most importantly, regression analyses of MemTrax and p-Tau181/Aβ42 aptly predicted CL values. Conclusion The combination of MemTrax and BBMs provides an accurate, convenient, non-invasive, cost-effective and scalable way to estimate Aβ load, which provides an opportunity for mass screening and timely and accurate diagnosis of AD. Our findings could also facilitate more effective AD clinical management in the PCPs office worldwide for more equitable access to current standard of care.https://doi.org/10.1186/s13195-025-01801-yAlzheimer’s diseaseMemTraxMoCABlood biomarkersAβCentiloid |
| spellingShingle | Weineng Chen Yu Liao Xinchong Shi Fengjuan Su Haifan Kong Yingying Fang Yifan Zheng Jiayi Zhou Ganqiang Liu Xianbo Zhou Xiaoli Yao Curtis B. Ashford Feng Li Long Yang Michael F. Bergeron J. Wesson Ashford Xiangsong Zhang Zhong Pei Predicting brain amyloid load with digital and blood-based biomarkers Alzheimer’s Research & Therapy Alzheimer’s disease MemTrax MoCA Blood biomarkers Aβ Centiloid |
| title | Predicting brain amyloid load with digital and blood-based biomarkers |
| title_full | Predicting brain amyloid load with digital and blood-based biomarkers |
| title_fullStr | Predicting brain amyloid load with digital and blood-based biomarkers |
| title_full_unstemmed | Predicting brain amyloid load with digital and blood-based biomarkers |
| title_short | Predicting brain amyloid load with digital and blood-based biomarkers |
| title_sort | predicting brain amyloid load with digital and blood based biomarkers |
| topic | Alzheimer’s disease MemTrax MoCA Blood biomarkers Aβ Centiloid |
| url | https://doi.org/10.1186/s13195-025-01801-y |
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