Eight decades of follow-up link life course exposures to proteomic organ ageing and longevity
Bibliographic record
Abstract
ABSTRACT The pace of organ ageing varies substantially between individuals, yet drivers of variability remain poorly understood. This gap is critical, given only 20-30% of longevity is genetically inherited 1,2 and age-related diseases are leading causes of morbidity and mortality 3 . Proteomic clocks allow organ ageing to be estimated from blood sampling 4 , facilitating study of how life course exposures shape biological ageing heterogeneity. Here, we leverage the unique design of the MRC National Survey of Health and Development (NSHD), the world’s oldest continuously followed birth cohort, to track 1,803 individuals across eight decades since birth in 1946. At mean age 63.2 years, we estimated proteomic ageing in seven organs. Despite near identical chronological ages, participants’ proteomes revealed biological ageing disparities spanning decades. Extreme ageing in multiple organs was a strong prognostic indicator for all-cause mortality over the following 15 years (HR=6.62 for ≥ 4 extremely aged organs). Adversity and being overweight in adolescence associated with accelerated ageing decades later in life. Completing secondary school education and maintaining physical activity linked to relative biological youth. Mediation analyses indicated liver, kidney and immune ageing linked life course exposures to mortality. Across 10,776 plasma protein targets, we identified 143 predictors of longevity, including MED9, strongly linked to diverse socio-behavioural exposures. These findings provide unique insights into which factors are likely to shape how we age, when in life they may be influential, and how biological effects emerge, informing healthy ageing promotion.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".