The Role of Allostatic Load in Cognitive Impairment and Dementia in Latin America Population
Bibliographic record
Abstract
BACKGROUND: Latin America (LA) experiences disparities driven by social determinants of health and low socioeconomic status, which increase the risk of dementia. These adverse factors may contribute to dementia through allostasis, the body's adaptive response to stressors. Chronic stressors can lead to allostatic overload, disrupting physiological systems and increasing the risk of diseases like dementia. While chronic stress is linked to cognitive decline, further research is needed to understand cumulative effects and develop an allostatic index (ALI) specific to the LA population. METHOD: We analyzed data from 8,044 participants across four LA cohorts: Chilean National Health Survey (Chile), GERO (Chile), ReDLat, and Costa Rica, including individuals with and without cognitive impairment or dementia. ALI was calculated using metabolic, cardiovascular, immune, neuroendocrine, and anthropometric biomarkers. Specific indices were developed for each cohort, considering cutoff values, percentiles, and risk quartiles. Logistic regression models examined associations between ALI and cognitive status, assessed with the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). RESULT: Higher ALI was associated with increased risk of cognitive impairment and dementia. In Costa Rica, ALI was significantly associated with cognitive impairment (OR = 1.048; p = 0.021). In the ReDLat cohort, higher ALI correlated with worse clinical cognitive status (OR = 1.068; p = 0.031). In the GERO Chile cohort, ALI showed significant impact (OR = 1.17; p = 0.04). In the CNHS, higher ALI notably increased cognitive impairment risk (OR = 7.15; p = 0.015). CONCLUSION: ALI is a relevant marker for identifying cognitive impairment and understanding its relationship with chronic stress. Monitoring biomarkers such as systolic blood pressure and waist-to-height ratio can improve the characterization of dementia in LA populations. These findings highlight the importance of addressing chronic stress and its biological impacts in diverse settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".