The influence of aging and the microbiome in multiple sclerosis and other neurologic diseases
Bibliographic record
Abstract
The human gut microbiome is well-recognized as a key player in maintaining health. However, it is a dynamic entity that changes across the lifespan. How the microbial changes that occur in later decades of life shape host health or impact age-associated inflammatory neurological diseases such as multiple sclerosis (MS) is still unclear. Current understanding of the aging gut microbiome is largely limited to cross-sectional observational studies. Moreover, studies in humans are limited by confounding host-intrinsic and extrinsic factors that are not easily disentangled from aging. This review provides a comprehensive summary of existing literature on the aging gut microbiome and its known relationships with neurological diseases, with a specific focus on MS. We will also discuss preclinical animal models and human studies that shed light on the complex microbiota-host interactions that have the potential to influence disease pathology and progression in aging individuals. Lastly, we propose potential avenues of investigation to deconvolute features of an aging microbiota that contribute to disease, or alternatively promote health in advanced age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.003 | 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".