Post-infection brain atrophy accelerates cognitive and molecular changes underlying dementia
Bibliographic record
Abstract
BACKGROUND: Infections have been associated with a greater risk of Alzheimer’s disease and related dementias (ADRD), but it is unclear how infections influence structural brain patterns over time, and whether post-infection brain atrophy can accelerate cognitive decline and molecular changes underlying dementia. METHODS: Using the Baltimore Longitudinal Study of Aging (BLSA; n = 793; mean age = 70.1), we examined how infections relate to longitudinal changes in machine learning-derived, 3 T-MRI neuroimaging signatures, and leveraged the UK Biobank (UKB; 1,120; mean age = 62.9 yrs) to externally validate infection-brain atrophy relationships. Using the BLSA, we also asked if infection history and infection-related brain volume loss were associated with cognitive decline, amyloid-beta PET, and ADRD plasma biomarker trajectories (Aβ42/40, pTau-181, NfL, GFAP). RESULTS: We detected accelerated parieto-temporal atrophy in BLSA participants with a history of upper respiratory tract, bacterial, and urinary tract infections (p < 0.05), as well as influenza and skin/subcutaneous infections (FDR p < 0.05). After demonstrating their associations with longitudinal neuroimaging signatures in the UKB and prevalent dementia in the BLSA, we found that infections were related to a greater burden of ADRD plasma biomarkers and accelerated rates of cognitive decline in BLSA participants. Integrating longitudinal brain scans, cognitive assessments, and plasma biomarker measurements, we identified infection-related changes in verbal memory and NfL that were more prominent among BLSA participants who experienced greater post-infection brain atrophy. CONCLUSION: Along with demonstrating that infections mediate clinically relevant brain atrophy patterns, these findings highlight the consequences of post-infection brain volume loss on longitudinal neurocognitive outcomes and extend our understanding of the biological basis by which infections may contribute to neurodegeneration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".