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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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Two examples of disease (disorder) pairs with significant comorbidity that are connected at the cellular level through either shared genes (A) or protein–protein interactions (A and B). (A) Alzheimer's disease and myocardial infarction (P≈10−5). (B) Autonomic nervous system disorder and carpal tunnel syndrome (P≈10−148).
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f3: Two examples of disease (disorder) pairs with significant comorbidity that are connected at the cellular level through either shared genes (A) or protein–protein interactions (A and B). (A) Alzheimer's disease and myocardial infarction (P≈10−5). (B) Autonomic nervous system disorder and carpal tunnel syndrome (P≈10−148).

Mentions: The observed correlations suggest that a combination of disease data and cellular network information may assist us in identifying new comorbidity patterns alongside their potential genetic origin. Indeed, upon inspection of the 2239 disease pairs that are genetically linked (i.e., nijg⩾1 or nijp⩾1), we find several disease pairs whose comorbidity patterns are already well known to the medical community, such as diabetes and obesity (Evans et al, 2002), or breast cancer and osteosarcoma (Knowling and Basco, 1986). At the same time, due to the aforementioned mismatch between disease names used by clinicians (within the ICD-9 coding scheme) and by geneticists (within the OMIM tabulation), several highly comorbid disease pairs are readily anticipated (such as diabetes and hypoglycemia, as hypoglycemia is a common side effect of the treatment of diabetes) or cases in which one disease is a broader version of the other (such as mononeuritis and hereditary peripheral neuropathy). Such mapping limitations notwithstanding, we find several interesting disease pairs that are linked at the cellular level and also show significant comorbidity. For example, consider Alzheimer's disease (ICD-9-CM 331) and myocardial infarction (ICD-9-CM 410.9), for which earlier comorbidity studies were either inconclusive or contradictory (Bursi et al, 2006). As Figure 3A shows, we not only find statistically significant comorbidity (P≈10−5) between the two, but the figure suggests that the shared ACE and APOE genes may contribute to the observed effect. Similarly, we observe significant comorbidity (P≈10−148) between autonomic nervous system disorder (ICD-9-CM 337.9) and carpal tunnel syndrome (ICD-9-CM 354, Figure 3B). A known mechanism is L-chain amyloidosis, which may affect the autonomic nervous system and causes carpal tunnel syndrome when the amyloid infiltrates the flexor retinaculum of the patient's wrist (Haan and Peters, 1994). Figure 3B, however, suggests that a PPI between the associated genes of each disorder may also play a role in the observed effect. Although there may be additional possible physiological or social explanations for some of the observed comorbidities (see SI), the method described above has the potential to offer new, testable hypotheses about the biological basis of disease interrelationships. These examples were selected only to demonstrate the potential of the combined investigation of the network and population-level data in identifying potentially interesting disease pairs worthy of further study. A more detailed description of these disease pairs, along with the complete list of the 2239 genetically linked disease pairs and their genetic associations are provided in the SI.


The impact of cellular networks on disease comorbidity.

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

Two examples of disease (disorder) pairs with significant comorbidity that are connected at the cellular level through either shared genes (A) or protein–protein interactions (A and B). (A) Alzheimer's disease and myocardial infarction (P≈10−5). (B) Autonomic nervous system disorder and carpal tunnel syndrome (P≈10−148).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2683720&req=5

f3: Two examples of disease (disorder) pairs with significant comorbidity that are connected at the cellular level through either shared genes (A) or protein–protein interactions (A and B). (A) Alzheimer's disease and myocardial infarction (P≈10−5). (B) Autonomic nervous system disorder and carpal tunnel syndrome (P≈10−148).
Mentions: The observed correlations suggest that a combination of disease data and cellular network information may assist us in identifying new comorbidity patterns alongside their potential genetic origin. Indeed, upon inspection of the 2239 disease pairs that are genetically linked (i.e., nijg⩾1 or nijp⩾1), we find several disease pairs whose comorbidity patterns are already well known to the medical community, such as diabetes and obesity (Evans et al, 2002), or breast cancer and osteosarcoma (Knowling and Basco, 1986). At the same time, due to the aforementioned mismatch between disease names used by clinicians (within the ICD-9 coding scheme) and by geneticists (within the OMIM tabulation), several highly comorbid disease pairs are readily anticipated (such as diabetes and hypoglycemia, as hypoglycemia is a common side effect of the treatment of diabetes) or cases in which one disease is a broader version of the other (such as mononeuritis and hereditary peripheral neuropathy). Such mapping limitations notwithstanding, we find several interesting disease pairs that are linked at the cellular level and also show significant comorbidity. For example, consider Alzheimer's disease (ICD-9-CM 331) and myocardial infarction (ICD-9-CM 410.9), for which earlier comorbidity studies were either inconclusive or contradictory (Bursi et al, 2006). As Figure 3A shows, we not only find statistically significant comorbidity (P≈10−5) between the two, but the figure suggests that the shared ACE and APOE genes may contribute to the observed effect. Similarly, we observe significant comorbidity (P≈10−148) between autonomic nervous system disorder (ICD-9-CM 337.9) and carpal tunnel syndrome (ICD-9-CM 354, Figure 3B). A known mechanism is L-chain amyloidosis, which may affect the autonomic nervous system and causes carpal tunnel syndrome when the amyloid infiltrates the flexor retinaculum of the patient's wrist (Haan and Peters, 1994). Figure 3B, however, suggests that a PPI between the associated genes of each disorder may also play a role in the observed effect. Although there may be additional possible physiological or social explanations for some of the observed comorbidities (see SI), the method described above has the potential to offer new, testable hypotheses about the biological basis of disease interrelationships. These examples were selected only to demonstrate the potential of the combined investigation of the network and population-level data in identifying potentially interesting disease pairs worthy of further study. A more detailed description of these disease pairs, along with the complete list of the 2239 genetically linked disease pairs and their genetic associations are provided in the SI.

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
Related in: MedlinePlus