Understanding the role of host metabolites in the induction of immune senescence: Future strategies for keeping the ageing population healthy
Bibliographic record
Abstract
Advancing age is accompanied by significant remodelling of the immune system, termed immune senescence, and increased systemic inflammation, termed inflammageing, both of which contribute towards an increased risk of developing chronic diseases in old age. Age-associated alterations in metabolic homeostasis have been linked with changes in a range of physiological functions, but their effects on immune senescence remains poorly understood. In this article, we review the recent literature to formulate hypotheses as to how an age-associated dysfunctional metabolism, driven by an accumulation of key host metabolites (saturated fatty acids, cholesterol, ceramides and lactate) and loss of other metabolites (glutamine, tryptophan and short-chain fatty acids), might play a role in driving immune senescence and inflammageing, ultimately leading to diseases of old age. We also highlight the potential use of metabolic immunotherapeutic strategies targeting these processes in counteracting immune senescence and restoring immune homeostasis in older adults. LINKED ARTICLES: This article is part of a themed issue on Inflammation, Repair and Ageing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.9/issuetoc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".