Abstract 25: Mild Cognitive Impairment the New Risk Factor for Stroke?
Bibliographic record
Abstract
Background and Purpose: Hypertension, prediabetes and type 2 diabetes are major risk factors for stroke, particularly among elderly African Americans (AAs). However, whether there are racial differences in the characteristics of patients with mild cognitive impairment (MCI) are unknown. The purpose of this study is to explore racial differences in MCI, blood pressure and glucose levels among older AAs and White Americans (WAs). Methods: We recruited 79 free living older adults (>65 years) (40 AAs and 39 WAs). Cognitive impairment was measured using the Montreal Cognitive Assessment (MoCA). We defined MCI as MoCA score between 18-26. In addition, systolic and diastolic blood pressure and hemoglobin A1C (A1C) were obtained in each participant. Results: The mean age of our group was 71.4±5.0 years and body mass index 29.1±5.9 kg/m 2 . The AAs were younger than WAs (70.3±5.1 vs. 72.4±4.7 years, p=0.06), there were no difference in body mass index (29.1±5.9 vs 27.7±5.4kg/m 2 , p=0.26). We found racial differences in MCI between our AA and WA participants. The AAs in our group had significantly lower MoCA scores compared to WAs (21±4.3 vs 25.5±3.2, p=0.0004). In addition, the systolic blood pressure (137.4±17.1 vs.128.25±14.9 mmHg, p=0.01) and diastolic blood pressure (77.3±10.8 vs.72.9±9 mmHg, p=0.05) were statistically higher in our AAs compared to WAs. Finally, the A1C was statistically higher in our AA vs. WA participants (5.8±0.4 vs. 5.5±0.29%, p=0.001). Conclusions: Our pilot data clearly demonstrates racial differences in MCI. Our study confirms that AAs with MCI are younger, have higher blood pressure and A1C levels when compared to WAs. Therefore, future studies are warranted to determine whether treatment of blood pressure and dysglycemia can reverse MCI in older AAs.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".