Assessing Cognitive and Gut Health in North Carolina’s Rural Indigenous Population via Mobile Clinic
Bibliographic record
Abstract
Abstract Objective Alzheimer’s disease disproportionately affects communities of color, including rural North Carolina. These communities experience environmental hazards, limited nutritious food access, and healthcare barriers. To ensure cultural relevance, we collaborated with community members and regional research partners to adapt the Alzheimer’s Gut Microbiome Project protocol (AGMP). Using a mobile clinic, we investigated the relationship between cognitive function, the gut microbiome (GMB), and the metabolome in and around Robeson County, the tribal headquarters of the Lumbee Tribe. Methods After tribal IRB approval and informed consent, participants underwent fasting glucose and cholesterol testing via finger prick and biomarker collection through venipuncture. The AGMP protocol for metabolomics and metagenomics was followed. Participants completed cognitive assessments and provided demographic, lifestyle, medical, environmental, and dietary information through structured questionnaires. A home stool collection kit with detailed instructions was provided. Results Of the 65 individuals expressing interest, 44 enrolled, and 39 provided both plasma and stool samples. Only three participants did not return stool samples. All participants identified as Indigenous with a mean age of 72 (SD 8); most were women (77%) with a mean of 13 years of education (SD 3). The mean Healthy Eating Index score was 64 (SD 12), and the mean 3-point adjusted Montreal Cognitive Assessment Score was 21 (SD 4). GMB analyses are ongoing. Discussion Findings of the association between cognitive function and the GMB will be presented. The high rate of biospecimen collection highlights the effectiveness of our community-centered mobile clinic research approach in a rural area.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".