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Mining cancer-specific disease comorbidities from a large observational health database.

Chen Y, Xu R - Cancer Inform (2014)

Bottom Line: We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group.We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis.Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

View Article: PubMed Central - PubMed

Affiliation: Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

ABSTRACT
Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers. We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

No MeSH data available.


Related in: MedlinePlus

The left panel shows the average comorbidity score of six diseases classes.Notes: The right panel shows their prevalence in each age group, which is the number of patients who have diseases belonging to a disease class. For cardiovascular diseases, the relevance score and prevalence have similar variation trends, so their comorbidity relationship with cancers might be overestimated.
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f3-cin-suppl.1-2014-037: The left panel shows the average comorbidity score of six diseases classes.Notes: The right panel shows their prevalence in each age group, which is the number of patients who have diseases belonging to a disease class. For cardiovascular diseases, the relevance score and prevalence have similar variation trends, so their comorbidity relationship with cancers might be overestimated.

Mentions: Figure 3 shows the variation trends of cancer comorbidity patterns for six disease classes, which have non-zero relevance scores in all age groups (the disease classes with asterisks in Table 1). Cardiovascular diseases have a stronger association with cancers when patients become elder and the association peaks in the age group 60–80. Respiration disorders occur more frequently among younger cancer patients, particularly in the age group <20. The other disease classes have relatively stable comorbidity associations with cancer when patient ages increase.


Mining cancer-specific disease comorbidities from a large observational health database.

Chen Y, Xu R - Cancer Inform (2014)

The left panel shows the average comorbidity score of six diseases classes.Notes: The right panel shows their prevalence in each age group, which is the number of patients who have diseases belonging to a disease class. For cardiovascular diseases, the relevance score and prevalence have similar variation trends, so their comorbidity relationship with cancers might be overestimated.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216041&req=5

f3-cin-suppl.1-2014-037: The left panel shows the average comorbidity score of six diseases classes.Notes: The right panel shows their prevalence in each age group, which is the number of patients who have diseases belonging to a disease class. For cardiovascular diseases, the relevance score and prevalence have similar variation trends, so their comorbidity relationship with cancers might be overestimated.
Mentions: Figure 3 shows the variation trends of cancer comorbidity patterns for six disease classes, which have non-zero relevance scores in all age groups (the disease classes with asterisks in Table 1). Cardiovascular diseases have a stronger association with cancers when patients become elder and the association peaks in the age group 60–80. Respiration disorders occur more frequently among younger cancer patients, particularly in the age group <20. The other disease classes have relatively stable comorbidity associations with cancer when patient ages increase.

Bottom Line: We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group.We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis.Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

View Article: PubMed Central - PubMed

Affiliation: Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

ABSTRACT
Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers. We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

No MeSH data available.


Related in: MedlinePlus