Human disease classification in the postgenomic era: a complex systems approach to human pathobiology

Abstract

Contemporary classification of human disease derives from observational correlation between pathological analysis and clinical syndromes. Characterizing disease in this way established a nosology that has served clinicians well to the current time, and depends on observational skills and simple laboratory tools to define the syndromic phenotype. Yet, this time-honored diagnostic strategy has significant shortcomings that reflect both a lack of sensitivity in identifying preclinical disease, and a lack of specificity in defining disease unequivocally. In this paper, we focus on the latter limitation, viewing it as a reflection both of the different clinical presentations of many diseases (variable phenotypic expression), and of the excessive reliance on Cartesian reductionism in establishing diagnoses. The purpose of this perspective is to provide a logical basis for a new approach to classifying human disease that uses conventional reductionism and incorporates the non-reductionist approach of systems biomedicine.

Figure 1

Diagram indicating associations among genetic and environmental factors reduce and their interactions with intermediate phenotypes to yield distinct pathophenotypes. The intermediate phenotypes determine, in part, variation in disease expression and clinical presentation among individuals with equivalent underlying genetic or environmental exposures that predispose to a disease state.

Figure 2

(A) Theoretical human disease network illustrating the relationships among genetic and environmental determinants of the pathophenotypes. Key: G, primary disease genome or proteome; D, secondary disease genome or proteome; I, intermediate phenotype; E, environmental determinants; PS, pathophysiological states leading to P, pathophenotype. (B) Example of this theoretical construct applied to sickle cell disease. Key: red, primary molecular abnormality; gray, disease-modifying genes; yellow, intermediate phenotypes; green, environmental determinants; blue, pathophenotypes.

Figure 3

Examples of modular network representations of disease. Key: G, primary disease genome or proteome; D, secondary disease genome or proteome; E, environmental determinants; I, intermediate phenotype; P, pathophenotype.

Figure 4a

(A) Human disease network. Each node corresponds to a specific disorder colored by class (22 classes, shown in the key to (B)). The size of each node is proportional to the number of genes contributing to the disorder. Edges between disorders in the same disorder class are colored with the same (lighter) color, and edges connecting different disorder classes are colored gray, with the thickness of the edge proportional to the number of genes shared by the disorders connected by it.

Figure 4b

(B) Disease gene network. Each node is a single gene, and any two genes are connected if implicated in the same disorder. In this network map, the size of each node is proportional to the number of specific disorders in which the gene is implicated. (Reproduced with permission from the National Academies Press; Goh et al, in press.)