Sleep-wake variation in body temperature regulates tau secretion and correlates with CSF and plasma tau
Bibliographic record
Abstract
Sleep disturbance is bidirectionally associated with an increased risk of Alzheimer's disease and other tauopathies. While the sleep-wake cycle regulates interstitial and cerebrospinal fluid (CSF) tau levels, the underlying mechanisms remain unknown. Understanding these mechanisms is crucial, given the evidence that tau pathology spreads through neuron-to-neuron transfer, involving the secretion and internalization of pathological tau forms. Here, we combined in vitro, in vivo, and clinical methods to reveal a pathway by which changes in body temperature (BT) over the sleep-wake cycle modulate extracellular tau levels. In mice, a higher BT during wakefulness and sleep deprivation increased CSF and plasma tau levels, while also upregulating unconventional protein secretion pathway I (UPS-I) events including (a) intracellular tau dephosphorylation, (b) caspase 3-mediated cleavage of tau (TauC3), and (c) membrane translocation of tau through binding to phosphatidylinositol 4,5-bisphosphate (PIP2) and syndecan 3. In humans, the increase in CSF and plasma tau levels observed after wakefulness correlated with BT increases during wakefulness. By demonstrating that sleep-wake variation in BT regulates extracellular tau levels, our findings highlight the importance of thermoregulation in linking sleep disturbances to tau-mediated neurodegeneration and the preventative potential of thermal interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".