Examining the Impact of Socioeconomic Position Across the Life Course on Cognitive Function and Brain Structure in Healthy Aging
Bibliographic record
Abstract
This study explores the relationship of life-course intergenerational social mobility with cognitive function and brain structure in older adults using Diagonal Reference Models. Data from the Irish Longitudinal Study on Ageing, a population-based cohort of adults aged 50 years and older (N = 4 620 participants; mean age: 66.1; standard deviation: 9.1; 55% female) was used for analysis. Brain magnetic resonance imaging data were available for 464 participants. Social mobility was characterized as the difference between childhood socioeconomic position (SEP; ie, father's occupation) and adulthood SEP (ie, own occupation). The Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), cortical thickness, and total gray matter volume (GMV) served as global cognitive and brain measures. Exploratory analyses included the volumes of the ventromedial prefrontal cortex (vmPFC), anterior cingulate (AC), hippocampus, and amygdala. A social gradient in cognitive function was observed among the intergenerationally stable; brain structure was not as clearly socially patterned. Adulthood SEP was significantly associated with MoCA (weight = 0.76; p < .001), MMSE (weight = 0.91; p < .001), GMV (weight = 0.77; p = .002), and AC volume (weight = 0.76; p < .001), whereas childhood SEP was associated with vmPFC volume (weight = 1.00; p = .003). There was no independent association of social mobility with any of the outcomes. Together our results suggest that both childhood and adulthood SEP are important in shaping later-life brain health, but that adulthood SEP predominates in terms of its influence. This is potentially an important insight as it suggests that brain health may be modifiable if socioeconomic circumstances change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".