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 ≤ 1.16E − 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 ≤ 3.46E − 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.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study was funded by Calico Life Sciences LLC.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The study used ONLY openly available human data that were originally located at: www.ukbiobank.ac.uk
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Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
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Footnotes
Improved readability in the text and figures.
Data Availability
Participant data from the UK Biobank cohort was obtained through UK Biobank Access Application number 44584. The UK Biobank has approval from the North West Multi-center Research Ethics Committee (REC reference: 11/NW/0382). All methods were performed in accordance with the relevant guidelines and regulations, and informed consent was obtained from all participants. Researchers may apply to use the UK Biobank data resource by submitting a health-related research proposal that is in the public interest. More information may be found on the UK Biobank researchers and resource catalogue pages (www.ukbiobank.ac.uk).
