Pathogen Burden, Blood Biomarkers, and Functional Aging in Community-Dwelling Older Adults
Bibliographic record
Abstract
BACKGROUND: Lifelong accumulation of latent or persistent or repeated infections may be a contributing factor to the deterioration of physical and cognitive function associated with functional aging, but the evidence is limited and the biological underpinnings are unclear. METHODS: We profiled the seropositivity for common viral, bacterial, and plasmodial pathogens of local importance in community-living older adults in 2 studies involving 745 older adults (mean age 67.0, SD: 7.7 years), and 142 older adults (mean age 72.7, SD: 8.3 years). Pathogen load was related to different sets of age-related physical and cognitive measures of functional aging and the Frailty Index (FI), and plasma levels of biomarkers of inflammation, innate and adaptive immunity, and other physiological functions. RESULTS: High pathogen load was associated with impaired gait speed (GS; p < .015), functional mobility (performance-oriented mobility assessment [POMA]; p < .029), cognitive function (Mini-Mental State Examination [MMSE]; p < .05), and increased FI; p < .05). High pathogen load was significantly associated with C3a complement activity (p < .001), matrix metalloproteinase-7, macrophage inflammatory protein-1α (p < .05), and monocyte chemoattractant protein 2 (p = .028). Blood biomarkers did not fully explain the observed association between pathogen load and functional aging measures. CONCLUSIONS: This study provides novel evidence linking lifelong cumulated numbers of latent, persistent, or repeated infection to functional aging, plausibly via inflammatory and immune and other biological factors.
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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 | 0.000 | 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".