The evolving roles of imaging and fluid biomarkers for Alzheimer disease over the past quarter century
Bibliographic record
Abstract
BACKGROUND: At the beginning of the century, Alzheimer disease (AD) biomarkers were only used in research studies and included hippocampal volume and cerebrospinal fluid (CSF) concentrations of Aβ42, total tau, and tau phosphorylated at position 181 (p-tau181). Twenty-five years later, imaging and fluid biomarkers have become critical tools in AD clinical trials and are increasingly being used in clinical care. METHODS: The development of imaging and fluid AD biomarkers will be reviewed. The evolving roles of different modalities of biomarkers in research, clinical trials, and clinical care will be described. RESULTS: In 2004, the first radiotracer that bound amyloid plaques was reported, which enabled visualization of the amount and regional distribution of amyloid plaques in the brains of living individuals via positron emission tomography (PET). Radiotracers binding to insoluble tau aggregates were described in 2013 and 2014. The use of amyloid and tau PET as a reference standard, as well as improvements in fluid biomarker assay technology, led to the first of many accurate AD blood tests in 2017 and 2018. These imaging and fluid biomarkers of AD have undergone waves of development, validation, and regulatory approval. AD research studies now use biomarkers extensively to study the biology of disease. Clinical trials use biomarkers to confirm that participants have AD pathology and to monitor the effects of treatment. AD biomarkers are increasingly being used in the clinical diagnosis of AD. The high acceptability and accessibility of AD blood tests may enable AD biomarker testing to become the standard of care in patients with cognitive impairment. In the future, if trials of preventative treatments are positive, AD biomarker testing of cognitively unimpaired older individuals may become routine. CONCLUSIONS: Advances in imaging and fluid biomarkers over the past quarter century have enabled greater understanding of AD biology and led to the successful development, approval, and clinical use of disease-modifying AD treatments that target amyloid pathology. The clinical availability of AD-specific treatments and high-accuracy AD blood tests is currently transforming the clinical diagnosis and care of patients with AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".