UNIQUE MICROBIOME SIGNATURES RELATED TO CANCER SURVIVORSHIP AND COGNITIVE DECLINE IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Significant scientific advancements have resulted in new and effective therapies for patients with cancer. Although this leads to an increased cancer survivor population, there are several long-term side effects of cancer treatments, including a high risk of developing dementia. Emerging evidence shows that the gut microbiota significantly contributes to brain health and dementia pathology; however, its role in cancer treatment-related side effects, specifically in older adults, is not well known. Here, we used 110 participants from our MiaGB (Microbiome in aging Gut and Brain) consortium aged 60 years and older and analyzed their gut microbiome (using whole genome sequencing) with cognitive function (using Montreal Cognitive Assessment (MoCA) and MiniCog). We observed an 8% increase in cognitive impairment (CI) in cancer survivors compared to non-cancer participants (46% vs 38%, respectively). When only comparing cognitively healthy versus CI, the abundance of Escherichia coli was significantly higher in the CI group, while Streptococcus thermophilus was higher in the cognitively healthy group. Additionally, the abundances of Dorea sp. CAG 317, Ruminococcus gnavus, Roseburia faecis, and Eggerthella lenta were significantly increased in the gut of cancer survivors compared to non-cancer participants. Specifically, the abundance of Dorea sp. CAG 317 was increased in cancer survivors with CI. These findings suggest that unique microbiome signatures induced by cancer treatments may impair cognitive function. Furthermore, understanding the associations between the microbiota and cognitive function in cancer survivors will aid the development of therapeutics to combat cancer treatment-related side effects, which are a growing public health concern.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".