Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank
Bibliographic record
Abstract
Abstract A major challenge in multimorbid aging is understanding how diseases co-occur and identifying high-risk groups for accelerated disease development, but to date associations in the relative onset acceleration of disease diagnoses have not been used to characterise disease patterns. This study presents the development and evaluation of a neural network Cox model for predicting onset acceleration risk for age-associated conditions, using demographic, anthropomorphic, imaging and blood biomarker traits from 60,396 individuals and 218,530 outcome events from the UK Biobank. Risk prediction was evaluated with Harrell’s concordance index (C-index). The model performed well on internal (C-index 0.6830 ± 0.0902 , n = 8, 931 ) and external (C-index 0.6461 ± 0.1264 , n = 855 ) test sets, attaining C-index ≥ 0.6 on 38 out of 47 ( 80.9% ) conditions. Inclusion of body composition and blood biomarker input traits were independently important for predictive performance. Kaplan-Meier curves for predicted risk quartiles (log-rank p ≤ 1.16 E − 16 ) indicated robust stratification of individuals into high and low risk groups. Analysis of risk quartiles revealed cardiometabolic, vascular-neuropsychiatric, and digestive-neuropsychiatric disease clusters with strong statistically significant inter-correlated onset acceleration ( r ≥ 0.6 , p ≤ 3.46 E − 5 ), while 13 and 19 conditions were strongly associated with onset acceleration of all cause mortality and all cause morbidity respectively. In prognostic survival analysis, the proportional hazards assumption was met (Schoenfeld residual p > 0.05 ) in 435 out of 435 or 100% (1238 out of 1334 or 92.8%) of cases across outcomes, aHR = 6.11 ± 9.00 ( aHR = 3.67 ± 5.78 ) with (without) Bonferroni correction. The neural architecture of OnsetNet was interpreted with saliency analysis and several significant body composition and blood biomarkers were identified. The results demonstrate that neural network survival models are able to estimate prognostically informative onset acceleration risk, which could be used to improve understanding of synchronicity in the onset of age-associated diseases and reprioritize patients based on disease-specific risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".