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The impact of cellular networks on disease comorbidity.

Park J, Lee DS, Christakis NA, Barabási AL - Mol. Syst. Biol. (2009)

Bottom Line: The impact of disease-causing defects is often not limited to the products of a mutated gene but, thanks to interactions between the molecular components, may also affect other cellular functions, resulting in potential comorbidity effects.By combining information on cellular interactions, disease-gene associations, and population-level disease patterns extracted from Medicare data, we find statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns in the human population.Our results indicate that such a combination of population-level data and cellular network information could help build novel hypotheses about disease mechanisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Biology, and Computer Science, Center for Complex Network Research, Northeastern University, Boston, MA 02115, USA. perturbation@gmail.com

ABSTRACT
The impact of disease-causing defects is often not limited to the products of a mutated gene but, thanks to interactions between the molecular components, may also affect other cellular functions, resulting in potential comorbidity effects. By combining information on cellular interactions, disease-gene associations, and population-level disease patterns extracted from Medicare data, we find statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns in the human population. Our results indicate that such a combination of population-level data and cellular network information could help build novel hypotheses about disease mechanisms.

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(A) The Pearson correlation between comorbidity and the three quantities (ng, np, ρ̄) that capture cellular-level links between diseases. See also Table I. (B) Average comorbidity for disease pairs satisfying the cellular constraints discussed in the text. See also Table II. (C–E) Average comorbidity for disease pairs with increasing values of ng, np, and ρ̄.
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f2: (A) The Pearson correlation between comorbidity and the three quantities (ng, np, ρ̄) that capture cellular-level links between diseases. See also Table I. (B) Average comorbidity for disease pairs satisfying the cellular constraints discussed in the text. See also Table II. (C–E) Average comorbidity for disease pairs with increasing values of ng, np, and ρ̄.

Mentions: The main question can be formulated as follows: does the existence of these cellular-level links (i.e., nijg>0, nijp>0, ρ̄ij>0) between the two diseases increase the likelihood that individuals simultaneously develop both conditions? We start our investigation by measuring the Pearson correlation between the cellular variables (nijg, nijp, ρ̄ij) and comorbidities (RR and φ) for 83 924 disease pairs. Of these, 2239 pairs are linked through either shared genes (nijg⩾1) or PPIs (nijp⩾1; 658 with shared genes, and 1873 with PPIs). In Figure 2A and Table I we present the Pearson correlation coefficients (PCCs) between the comorbidity measures and the genetic variables. Although ng, in general, has the highest correlation with comorbidity, we do observe positive PCC with all three variables.


The impact of cellular networks on disease comorbidity.

Park J, Lee DS, Christakis NA, Barabási AL - Mol. Syst. Biol. (2009)

(A) The Pearson correlation between comorbidity and the three quantities (ng, np, ρ̄) that capture cellular-level links between diseases. See also Table I. (B) Average comorbidity for disease pairs satisfying the cellular constraints discussed in the text. See also Table II. (C–E) Average comorbidity for disease pairs with increasing values of ng, np, and ρ̄.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2683720&req=5

f2: (A) The Pearson correlation between comorbidity and the three quantities (ng, np, ρ̄) that capture cellular-level links between diseases. See also Table I. (B) Average comorbidity for disease pairs satisfying the cellular constraints discussed in the text. See also Table II. (C–E) Average comorbidity for disease pairs with increasing values of ng, np, and ρ̄.
Mentions: The main question can be formulated as follows: does the existence of these cellular-level links (i.e., nijg>0, nijp>0, ρ̄ij>0) between the two diseases increase the likelihood that individuals simultaneously develop both conditions? We start our investigation by measuring the Pearson correlation between the cellular variables (nijg, nijp, ρ̄ij) and comorbidities (RR and φ) for 83 924 disease pairs. Of these, 2239 pairs are linked through either shared genes (nijg⩾1) or PPIs (nijp⩾1; 658 with shared genes, and 1873 with PPIs). In Figure 2A and Table I we present the Pearson correlation coefficients (PCCs) between the comorbidity measures and the genetic variables. Although ng, in general, has the highest correlation with comorbidity, we do observe positive PCC with all three variables.

Bottom Line: The impact of disease-causing defects is often not limited to the products of a mutated gene but, thanks to interactions between the molecular components, may also affect other cellular functions, resulting in potential comorbidity effects.By combining information on cellular interactions, disease-gene associations, and population-level disease patterns extracted from Medicare data, we find statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns in the human population.Our results indicate that such a combination of population-level data and cellular network information could help build novel hypotheses about disease mechanisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Biology, and Computer Science, Center for Complex Network Research, Northeastern University, Boston, MA 02115, USA. perturbation@gmail.com

ABSTRACT
The impact of disease-causing defects is often not limited to the products of a mutated gene but, thanks to interactions between the molecular components, may also affect other cellular functions, resulting in potential comorbidity effects. By combining information on cellular interactions, disease-gene associations, and population-level disease patterns extracted from Medicare data, we find statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns in the human population. Our results indicate that such a combination of population-level data and cellular network information could help build novel hypotheses about disease mechanisms.

Show MeSH