Environmental Factors in the Home Environment Associated with Lower Cognitive Function
Bibliographic record
Abstract
BACKGROUND: Environmental factors and health have often been studied by geographical region; few studies have focused on indoor environmental quality. The Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) project collects multimodal data from a diverse group of participants representing a range of Type 2 Diabetes Mellitus (T2DM) disease states (normal to insulin-dependent). We used AI-READI data to analyze associations between indoor environmental measures and early indicators of cognitive impairment. METHOD: Participants aged 40 and over with and without Type 2 diabetes are being recruited to the 4-year AI-READI project; data from the first 1067 individuals were released in year 2. Participants with Montreal Cognitive Assessment (MoCA) scores and environmental data were included. MoCA scores were binarized: 0 for scores > 25 (normal) and 1 for scores <=25 (impaired). Self-reported T2DM status was binarized: 0 for no diabetes or lifestyle-controlled diabetes, and 1 for insulin- or oral medication-controlled diabetes. Environmental data were collected for 10 days in participants' homes using a device measuring volatile organic compounds (VOC), nitrogen oxides (NOX), relative light intensity, and particulate matter (PM). RESULT: Out of 1037 participants analyzed, 488 (47%) had impaired cognition. Higher PM concentrations (1, 2.5, 4, and 10 um or smaller) were associated with lower MoCA scores. For example, participants with cognitive impairment had a mean PM2.5 concentration of 21.53 [18.07, 24.99, 95% CI] compared to 11.53 [10.24, 12.82 95% CI] for normal participants. (Figure 1) Lower light levels were associated with lower MoCA scores: median light intensity was 0.03 [0.027, 0.040 95% CI] for normal participants and 0.02 [0.015, 0.026 95% CI] for those with impaired cognition. (Figure 2) We found significant associations between MoCA (binarized) and T2DM (Chi-square, p-value= 4.65e-5). (Figure 3) There were no significant associations between cognitive function and VOC or NOX measures. CONCLUSION: Preliminary results from the AI-READI cohort show an association between cognitive function (MoCA) and measures of ambient light, PM2.5, and T2DM status. Analysis of other factors (VOC, NOX) did not show significant associations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".