Biomarkers of the ageing immune system and their association with frailty – A systematic review
Bibliographic record
Abstract
INTRODUCTION: Ageing is associated with several physiological changes, including changes in the immune system. Age-related changes in the innate and adaptive immune system are thought to contribute to frailty. Understanding the immunological determinants of frailty could help to develop and deliver more effective care to older people. This systematic review aims to study the association between biomarkers of the ageing immune system and frailty. METHODS: The search strategy was performed in PubMed and Embase, using the keywords "immunosenescence", "inflammation", "inflammaging" and "frailty". We included studies that investigated the association of biomarkers of the ageing immune system and frailty cross-sectionally in older adults, without an active disease that affects immune parameters. Three independent researchers selected the studies and performed data extraction. Study quality was assessed using the Newcastle-Ottawa scale adapted for cross-sectional studies. RESULTS: A total of 44 studies, with a median number of 184 participants, was included. Study quality was good in 16 (36 %), moderate in 25 (57 %) and poor in 3 (7 %) of studies. The most frequently studied inflammaging biomarkers were IL-6, CRP and TNF-α. Associations with frailty were observed for increased levels of (i) IL-6 in 12 of 24 studies, (ii) CRP in 7 of 19 studies, and (ii) TNF-α in 4 of 13 studies. In none of the other studies were associations observed of frailty with these biomarkers. Different types of T-lymphocyte subpopulations were studied but each subset was studied only once, and the study sample sizes were low. CONCLUSION: Our review of 44 studies on the relation between immune biomarkers and frailty identified IL-6 and CRP as the biomarkers that were most consistently associated with frailty. T-lymphocyte subpopulations were investigated but too infrequently to draw strong conclusions yet, although initial results are promising. Additional studies are required in order to further validate these immune biomarkers in larger cohorts. Furthermore, prospective studies in more uniform settings and larger cohorts are needed to further investigate the association with immune candidate biomarkers for which potential associations with ageing and frailty were previously observed, before these can be used in clinical practice to help assess frailty and improve the care treatments of older patients.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".