The Effect of Social Determinants of Health on Cognitive Resilience to Alzheimer's Disease, Determined by Plasma <i>p</i> ‐tau217 in the Prospective INDE Cohort in Thailand: A Story from a Middle‐Income Country
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDOH) contribute to cognitive resilience, ranging from healthy populations to aging individuals with cognitive impairment, including those with Alzheimer's disease (AD) and related dementias. Plasma phosphorylated tau 217 (p-tau217) has been proven to be a specific biomarker reflecting AD pathology and severity (Jack et al., 2024). We aim to assess factors contributing to cognitive resilience among the Thai population. METHOD: We prospectively enrolled participants into the INDE cohort in King Chulalongkorn Memorial Hospital, Bangkok, Thailand (NCT06375213), collecting exhaustive clinical information, neuropsychological tests, and plasma p-tau217. A regression model was fitted with Montreal Cognitive Assessment (MoCA) scores and plasma p-tau217 levels to calculate residuals, representing cognitive resilience. Positive residuals indicate high resilience, whereas negative residuals indicate low resilience. A second regression model examined factors associated with cognitive resilience, focusing on each SDOH, with adjustments for age and sex. RESULT: Among 297 participants (73.4% female and median age 66 years [IQR: 61, 71]), 166 (55.9%) had high resilience (Table 1). A linear regression model showed an inverse relationship between MoCA and log-transformed plasma p-tau217 levels (β = -3.96, p < 0.01), and a box-and-whisker plot illustrated MoCA distribution across resilience groups (Figure 1). Multivariable linear regression analysis demonstrated that property ownership exceeding 10-million-baht, higher educational attainment, and meeting American Heart Association (AHA) physical activity recommendations were significantly associated with greater cognitive resilience. Higher education level correlated with increased resilience in a progressive manner. In contrast, greater distance from the city, current smoking, longer sleep duration, and increased daily sitting time were significantly linked to lower resilience (Table 2). CONCLUSION: SDOH notably impacted cognitive resilience, determined by the residuals of p-tau217 and cognitive score. Public policy and clinical intervention regarding factors associated with cognitive resilience are warranted, even in low- and middle-income countries (LMICs).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".