Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank

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 characterize 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.6830</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.0902</mml:mn></mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>8</mml:mn> <mml:mo>,</mml:mo> <mml:mn>931</mml:mn></mml:mrow> </mml:math> ) and external (C-index <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.6461</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.1264</mml:mn></mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>855</mml:mn></mml:mrow> </mml:math> ) test sets, attaining C-index <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>≥</mml:mo> <mml:mn>0.6</mml:mn></mml:mrow> </mml:math> on 38 out of 47 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>80.9</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> ) conditions. Inclusion of body composition and blood biomarker input traits was independently important for predictive performance. Kaplan-Meier curves for predicted risk quartiles (log-rank <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo>≤</mml:mo> <mml:mn>1.16</mml:mn> <mml:mi>E</mml:mi> <mml:mo>-</mml:mo> <mml:mn>16</mml:mn></mml:mrow> </mml:math> ) 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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>r</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>0.6</mml:mn></mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo>≤</mml:mo> <mml:mn>3.46</mml:mn> <mml:mi>E</mml:mi> <mml:mo>-</mml:mo> <mml:mn>5</mml:mn></mml:mrow> </mml:math> ), 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo>></mml:mo> <mml:mn>0.05</mml:mn></mml:mrow> </mml:math> ) in 435 out of 435 or 100% (1238 out of 1334 or 92.8%) of cases across outcomes, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>a</mml:mi> <mml:mi>H</mml:mi> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>6.11</mml:mn> <mml:mo>±</mml:mo> <mml:mn>9.00</mml:mn></mml:mrow> </mml:math> ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>a</mml:mi> <mml:mi>H</mml:mi> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3.67</mml:mn> <mml:mo>±</mml:mo> <mml:mn>5.78</mml:mn></mml:mrow> </mml:math> ) 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.