Impact of diabetes on the progression of Alzheimer’s disease via trajectories of amyloid–tau–neurodegeneration (ATN) biomarkers
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is characterized by the accumulation of abnormal proteins, such as β-amyloid and tau, in the brain, which precedes cognitive impairment. Although diabetes mellitus (DM) is a well-established risk factor for AD, few studies have investigated how the presence of DM affects the sequential pathogenesis of AD, specifically within the amyloid-tau-neurodegeneration (ATN) and cognition framework. OBJECTIVES: This study aims to investigate the trajectories of ATN biomarkers in relation to the presence of DM in the preclinical and prodromal stages of AD. DESIGN: Participants with normal cognition (CN) or mild cognitive impairment (MCI) at baseline were included. Subjects were followed for 12-192 months, with neuroimaging and cognitive assessments conducted at every 12 or 24 months. SETTING: This study utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. PARTICIPANTS: A total of 603 participants aged 55-90 years were included, comprising 284 CN (25 with DM, 259 without DM) and 319 MCI (39 with DM, 280 without DM) individuals. MEASUREMENTS: ATN biomarkers were identified using florbetapir positron emission tomography (PET), flortaucipir PET, and magnetic resonance imaging (MRI), respectively. Cognition was assessed using the Clinical Dementia Rating-Sum of Boxes (CDR-SB) and Mini-Mental State Examination (MMSE). Moderation analysis was conducted to investigate the effect of DM on the association between ATN biomarkers of AD. RESULTS: Elevated amyloid standardized uptake value ratios (SUVRs) were associated with increased tau levels in the hippocampus, and this association was significantly enhanced by the presence of DM in MCI participants (p = 0.021). DM also strengthened the association between increased tau SUVR levels and neurodegeneration (indicated by decreased entorhinal cortical volumes; p = 0.005) in those with MCI. Furthermore, DM enhanced the association of decreased entorhinal (p = 0.012) and middle temporal cortex (p = 0.031) volumes with increased (worsened) CDR-SB scores in MCI participants. However, DM did not predict significant longitudinal changes in ATN pathology or cognitive decline in CN participants. CONCLUSIONS: Our study suggests that DM may increase the risk of AD by accelerating each step of the A-T-N cascade in the prodromal stage of AD, underscoring the importance of DM management in preventing the MCI conversion to AD.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".