Investigating the relationship between IGF-I, -II and IGFBP-3 concentrations and later-life cognition and brain volume
Bibliographic record
Abstract
Abstract Background The insulin/insulin-like signalling (IIS) pathways, including Insulin-like Growth Factors (IGFs), varies with age. However, their association with late-life cognition and neuroimaging parameters is not well characterised. Methods Using data from the British 1946 birth cohort we investigated associations of IGF-I, -II and IGFBP-3 (measured at 53 and 60-64 years) with cognitive performance (word learning test (WLT) and visual letter search (VLS) – at 60-64y and 69y) and cognitive state (Addenbrooke’s Cognitive Exam-III (ACE-III) - at 69-71y), and in a proportion, quantified neuroimaging measures (whole brain volume (WBV); white matter hyperintensity volume (WMHV); hippocampal volume (HV)). Regression models included adjustments for demographic, lifestyle and health factors. Results Higher IGF-I and IGF-II at 53y was associated with higher ACE-III scores (ß 0.07 95%CI [0.02,0.12]; score ACE-III 89.48 [88.86,90.1], respectively). IGF-II at age 53y was additionally associated with higher WLT scores (score WLT 20 [19.35,20.65]). IGFBP-3 at 60-64y was associated with favourable VLS score at 60-64y and 69y (ß 0.07 [0.01,0.12]; ß 0.07 [0.02,0.12], respectively), higher memory and cognitive state at 69y (ß 0.07 [0.01,0.12]; ß 0.07 [0.01,0.13], respectively) and reduced WMHV (ß -0.1, [-0.21,-0.00]). IGF-I/IGFBP-3 at 60-64y was associated with lower VLS scores at 69y (ß -0.08, [-0.15,-0.02]). Conclusions Increased measure in IIS parameters (IGF-I, -II and IGFBP-3) relate to better cognitive state in later life. There were apparent associations with specific cognitive domains (IGF-II relating to memory; IGFBP-3 to memory, processing speed and WMHV; and IGF-I/IGFBP-3 molar ratio with slower processing speed). IGFs and IGFBP-3 are associated with favourable cognitive function outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".