The dynamic balance of the skin microbiome across the lifespan
Bibliographic record
Abstract
For decades research has centered on identifying the ideal balanced skin microbiome that prevents disease and on developing therapeutics to foster this balance. However, this single idealized balance may not exist. The skin microbiome changes across the lifespan. This is reflected in the dynamic shifts of the skin microbiome's diverse, inter-connected community of microorganisms with age. While there are core skin microbial taxa, the precise community composition for any individual person is determined by local skin physiology, genetics, microbe-host interactions, and microbe-microbe interactions. As a key interface with the environment, the skin surface and its appendages are also constantly exchanging microbes with close personal contacts and the environment. Hormone fluctuations and immune system maturation also drive age-dependent changes in skin physiology that support different microbial community structures over time. Here, we review recent insights into the factors that shape the skin microbiome throughout life. Collectively, the works summarized within this review highlight how, depending on where we are in lifespan, our skin supports robust microbial communities, while still maintaining microbial features unique to us. This review will also highlight how disruptions to this dynamic microbial balance can influence risk for dermatological diseases as well as impact lifelong health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".