Influence of intergenerational social mobility on brain structure and global cognition: findings from the Whitehall II study across 20 years
Bibliographic record
Abstract
BACKGROUND: Whether changes in socioeconomic position (SEP) across generations, i.e. intergenerational social mobility, influence brain degeneration and cognition in later life is unclear. OBJECTIVE: To examine the association of social mobility, brain grey matter structure and global cognition. METHODS: We analysed T1 brain MRI data of 771 old adults (69.8 ± 5.2 years) from the Whitehall II MRI substudy, with MRI data collected between 2012 and 2016. Social mobility was defined by SEP changes from their fathers' generation to mid-life status. Brain structural outcomes include grey matter (GM) volume and cortical thickness (CT) covering whole brain. Global cognition was measured by the Mini Mental State Examination. We firstly conducted analysis of covariance to identify regional difference of GM volume and cortical thickness across stable high/low and upward/downward mobility groups, followed with diagonal reference models studying the relationship between mobility and brain cognitive outcomes, apart from SEP origin and destination. We additionally conducted linear mixed models to check mobility interaction over time, where global cognition was derived from three phases across 2002 to 2017. RESULTS: Social mobility related to 48 out of the 136 GM volume regions and 4 out of the 68 CT regions. Declined volume was particularly seen in response to downward mobility, whereas no independent association of mobility with global cognition was observed. CONCLUSION: Despite no strong evidence supporting direct influence of mobility on global cognition in later life, imaging findings warranted a severe level of neurodegeneration due to downward mobility from their father's generation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".