Faculty Opinions recommendation of Restoring metabolism of myeloid cells reverses cognitive decline in ageing.
Bibliographic record
Abstract
Ageing is characterized by the development of persistent pro-inflammatory responses that contribute to atherosclerosis, metabolic syndrome, cancer and frailty1-3. The ageing brain is also vulnerable to inflammation, as demonstrated by the high prevalence of age-associated cognitive decline and Alzheimer's disease4-6. Systemically, circulating pro-inflammatory factors can promote cognitive decline7,8, and in the brain, microglia lose the ability to clear misfolded proteins that are associated with neurodegeneration9,10. However, the underlying mechanisms that initiate and sustain maladaptive inflammation with ageing are not well defined. Here we show that in ageing mice myeloid cell bioenergetics are suppressed in response to increased signalling by the lipid messenger prostaglandin E2 (PGE2), a major modulator of inflammation11. In ageing macrophages and microglia, PGE2 signalling through its EP2 receptor promotes the sequestration of glucose into glycogen, reducing glucose flux and mitochondrial respiration. This energy-deficient state, which drives maladaptive pro-inflammatory responses, is further augmented by a dependence of aged myeloid cells on glucose as a principal fuel source. In aged mice, inhibition of myeloid EP2 signalling rejuvenates cellular bioenergetics, systemic and brain inflammatory states, hippocampal synaptic plasticity and spatial memory. Moreover, blockade of peripheral myeloid EP2 signalling is sufficient to restore cognition in aged mice. Our study suggests that cognitive ageing is not a static or irrevocable condition but can be reversed by reprogramming myeloid glucose metabolism to restore youthful immune functions. PMID: 33473210 Funding information This work was supported by: American Heart Association-American Stroke Association, United States NIA NIH HHS, United States Grant ID: P50 AG047366 NIA NIH HHS, United States Grant ID: R01 AG048232 NIH HHS, United States Grant ID: 1S10RR02678001 NCI NIH HHS, United States Grant ID: F30 CA228215 NIA NIH HHS, United States Grant ID: RF1 AG058047 NIA NIH HHS, United States Grant ID: P30 AG066515 NINDS NIH HHS, United States Grant ID: R21 NS087639 NIDDK NIH HHS, United States Grant ID: DP1 DK113643 Wellcome Trust, United Kingdom NCI NIH HHS, United States Grant ID: R35 CA220434 More Less keyboard_arrow_down
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.546 | 0.336 |
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".