Modeling the enigma of complex disease etiology

Abstract

Background: Complex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors.

Methods: Here we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model.

Results: We provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves.

Conclusions: The Human Disease Ontology's Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.

Keywords: Asthma; Diabetes; Disease etiology; Environmental drivers; Fetal alcohol syndrome; Genetics; Pathophysiology.

Fig. 1

Spectrum of disease genetic etiology and environmental drivers. Examples of diseases that result from entirely environmental, a mixture of environmental and genetic (multifactorial), or entirely genetic etiology

Fig. 2

Modeling the complexity of disease etiology. Encompassing genetics, epigenetics, social determinants of health, environmental drivers and other host factors

Fig. 3

Driver Assessment to DO classification workflow: the established workflow enables testing of the Complex Disease model through the identification of drivers for specific complex diseases. Assessment results in (1) identifying the genetics, epigenetics, social determinants of health, environmental drivers and other host factor drivers for a disease (2) updating the disease driver terms in the DO’s DISDRIV ontology, (3) revision of the DO classification (addition or removal of disease terms, defining, and (4) defining disease to driver relationships by including an ontology axiom statements to define the disease to driver association (e.g. FAS ‘has_disease_driver’ alcohol)

Fig. 4

Asthma classification. a Asthma classification before refactoring; b Refactored asthma classification. Including endotypes, expansion of subtypes

Fig. 5

Diabetes mellitus reclassification. Showing the reclassification of diabetes mellitus following the recent review