bioRxivNeurosciencePreprint
Clinical and biomarker changes in sporadic Alzheimer's disease
Junjie ZhuoiD, Yuanchao ZhangiD, Bing LiuiD, Yong LiuiD, Xiaoqing Alice ZhouiD, Perry Francis Bartlett, Tianzi JiangiD, the Alzheimer’s Disease Neuroimaging Initiative
Abstract
IMPORTANCEThe dynamic changes of biomarkers and clinical profiles in sporadic Alzheimers disease (SAD) are poorly understood. OBJECTIVETo evaluate the impact of amyloid-{beta} (A{beta}) biomarkers on SAD by measuring the dynamic changes in biomarkers and clinical profiles in the progression of SAD. DESIGN AND SETTINGThis retrospective and longitudinal study analyzed clinical and biomarker data from 665 participants (mean follow-up 4.90 {+/-} 2.83 years) from a subset of the AD Neuroimaging Initiative (ADNI) participants collected from August 2005 to December 2018. By aligning the timing of the changes in the various biomarkers with the stable normal cognition (CN) baseline and mild cognitive impairment (MCI) or AD onset timepoints, we combined data from the stable CN, CN conversion to MCI (CN2MCI), and MCI conversion to AD (MCI2AD) groups to identify the trajectories associated with the progression of AD. PARTICIPANTSThe participants were 294 CN, 69 CN2MCI, 300 MCI2AD, and 24 who converted from CN to MCI to AD (CN2MCI2AD) (of whom 22 were also included in the CN2MCI). EXPOSURESAmyloid-{beta} measured by florbetapir positron emission tomography (PET) or cerebrospinal fluid assay of amyloid-{beta} (CSF A{beta}42). MAIN OUTCOMES AND MEASURESThe measures included the 13-item cognitive subscale of the AD Assessment Scale (ADAS13, as a clinical measure), hippocampal volume, and the fluorodeoxyglucose (FDG) PET standardized uptake value ratio (SUVR). RESULTSThe CN, CN2MCI, and MCI2AD subgroups median (interquartile range [IQR]) annual changes in ADAS13 were (0.388 [-0.278, 0.818], 1.000 [0.239, 2.330], and 3.388 [1.750, 6.169]). The annual changes in hippocampal volume for each group were (-0.005 %ICV [-0.011, -0.001], -0.006 %ICV [-0.012, -0.002], and -0.014 %ICV [-0.021, -0.009]). The annual changes in FDG PET SUVR for each group were (-0.011 [-0.030, 0.010], -0.027 [-0.056, -0.012], and -0.039 [-0.063, 0.014]). Changes in the amyloid biomarkers were inconsistent with clinical profile changes. The annual changes in CSF A{beta}42 for each group were (-1.500 pg/ml [-6.000, 4.000], -2.200 [-5.667, 4.000], and -2.000 [-7.000, 2.650]) and in A{beta} PET SUVR for each group were (0.004 [-0.002, 0.012], 0.004 [-0.001,0.011], and 0.005 [-0.006, 0.014]). During the stable CN and CN2MCI stages, subjects with elevated and those with normal amyloid showed no significant differences (likelihood ratio test, p < .01) in clinical measures, hippocampal volume, or FDG. CONCLUSIONS AND RELEVANCEHippocampal volume and FDG associated with clinical profiles impairment in the SAD progression. A{beta} alone is not associated with clinical profiles, hippocampal volume, and FDG impairment in the preclinical stage of SAD. Key PointsQuestion: What is the role of amyloid-{beta} in dynamic changes in biomarkers and clinical profiles in the progression of sporadic Alzheimers disease? Findings: The changes of the hippocampal volume and FDG that were consistent with the changes of the clinical profiles showed a non-linear change in the initial stage and an accelerated non-linear change during MCI2AD, changes in amyloid biomarkers were inconsistent with the clinical profile. Cognitively normal people with elevated or normal amyloid showed no significant differences in clinical measures, hippocampal volume, or FDG. Meaning: Amyloid-{beta} alone may not be used as the central index for defining the preclinical stage of SAD.
- organism: human
- assay: imaging
- assay: proteomics
- research focus: differential expression
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Identifying Changepoints in Biomarkers During the Preclinical Phase of AD
Laurent Younes B, Marilyn Albert, … Michael I. Miller·Frontiers in Aging NeurosciencePeer-reviewed·Apr 2, 2019
- Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration
Alexei Taylor, Fengqing Zhang, … the Alzheimer’s Disease Neuroimaging Initiative·NeuroImagePeer-reviewed·Sep 9, 2022
- Cortical thickness changes precede high levels of amyloid by at least seven years
James M. Roe, William Jagust, … Yunpeng Wang·bioRxiv·Aug 14, 2025
- Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease
Elizabeth Levitis, Jacob W. Vogel, … Alzheimer’s Disease Neuroimaging Initiative·Brain CommunicationsPeer-reviewed·Apr 12, 2022
- Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease
Jeffrey R. Petrella, Wenrui Hao, … for the Alzheimer’s Disease Computational Modeling Initiative·Computational and Mathematical Methods in MedicinePeer-reviewed·Feb 3, 2019
- Data-driven models of dominantly-inherited Alzheimer’s disease progression
Neil P. Oxtoby, Alexandra L. Young, … Daniel C. Alexander·BrainPeer-reviewed·Feb 8, 2018