Do I lose cognitive function as fast as my twin partner? Analyses based on classes of MMSE trajectories of twins aged 80 and older
Bibliographic record
Abstract
BACKGROUND: Aging is associated with an increasing risk of decline in cognitive abilities. The decline is, however, not a homogeneous process. There are substantial differences across individuals although previous investigations have identified individuals with distinct cognitive trajectories. Evidence is accumulating that lifestyle contributes significantly to the classification of individuals into various clusters. How and whether genetically related individuals, like twins, change in a more similar manner is yet not fully understood. METHODS: In this study, we fitted growth mixture models to Mini Mental State Exam (MMSE) scores from participants of the Swedish OCTO twin study of oldest-old monozygotic and same-sex dizygotic twins with the purpose of investigating whether twin pairs can be assigned to the same class of cognitive change. RESULTS: We identified four distinct groups (latent classes) whose MMSE trajectories followed different patterns of change over time: two classes of high performing individuals who remained stable and declined slowly, respectively, a group of mildly impaired individuals with a fast decline and a small group of impaired individuals who declined more rapidly. Notably, our analyses show no association between zygosity and class assignment. CONCLUSIONS: Our study provides evidence for a more substantial impact of environmental, rather than genetic, influences on cognitive change trajectories in later life.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".