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Characterisation, identification, clustering, and classification of disease.

A J Webster1, K Gaitskell2,3, I Turnbull2

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Easily measured risk factors like height and body mass index (BMI) can effectively characterize diseases in large datasets. This approach identifies disease clusters with shared risk factors and potential common causes, offering new insights into multimorbidity.

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Area of Science:

  • Biomedical Informatics
  • Epidemiology
  • Genetics

Background:

  • Quantifying multimorbidity distribution and determinants is crucial for novel data-driven disease classifications.
  • Existing studies utilize molecular information, disease incidence age, and disease trajectories for disease clustering.
  • The potential of easily measured risk factors for disease characterization remains underexplored.

Purpose of the Study:

  • To investigate if easily measured risk factors (height, BMI) can characterize diseases in UK Biobank data.
  • To apply rigorous statistical methods for clinically relevant disease comparisons and clusters.
  • To explore the utility of risk factors in understanding disease relationships and multimorbidity patterns.

Main Methods:

  • Analysis of over 400 common diseases using clinical and epidemiological criteria.
  • Application of conventional proportional hazards models to assess associations with 12 established risk factors.
  • Utilizing UK Biobank data for risk factor analysis and disease clustering.

Main Results:

  • Sex-dependent associations between disease risk and BMI were observed for several diseases.
  • A significant proportion of diseases in both sexes were identifiable by their specific risk factors.
  • Diseases with similar risk factor profiles tended to cluster together, including 10 diseases classified as 'Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified'.

Conclusions:

  • Easily measured risk factors can effectively characterize and cluster diseases, providing a new perspective on disease interactions.
  • Identified disease clusters often share known or suggest potential unconfirmed pathogenic mechanisms.
  • This approach offers novel insights into the interplay between biological pathways, risk factors, and multimorbidity patterns.