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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

(A) Age distribution of the patients in the adverse event reports. (b) Gender distribution. (C) Distribution of disease semantic types.Notes: T047, disease or syndrome; T020, acquired abnormality; T046, pathologic function; T184, sign or symptom; T033, finding; T190, anatomical abnormality; T191, neoplastic process; T048, mental or behavioral dysfunction; T049, cell or molecular dysfunction; T019, congenital abnormality; T037, injury or poisoning.
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f1-cin-suppl.1-2014-037: (A) Age distribution of the patients in the adverse event reports. (b) Gender distribution. (C) Distribution of disease semantic types.Notes: T047, disease or syndrome; T020, acquired abnormality; T046, pathologic function; T184, sign or symptom; T033, finding; T190, anatomical abnormality; T191, neoplastic process; T048, mental or behavioral dysfunction; T049, cell or molecular dysfunction; T019, congenital abnormality; T037, injury or poisoning.

Mentions: We extracted the patient–disease pairs from the adverse event reports for comorbidity mining. The adverse event reports contain records of 3,354,043 patients. Among all patients, 2,213,399 (66%) and 3,153,795 (94%) have their age and gender information available. Figure 1(a,b) shows the distributions of age and gender. Different from the Medicare claims, which only contain patients of age 65 years or older, the adverse event reports have patients aged from one day to hundreds of years. With both the disease and demographics data for millions of patients, we were able to study the potential effects of age and gender on the change of disease comorbidity patterns. For comorbidity extraction, we stratified patients into five groups based on their ages (Fig. 1a) and two groups based on their genders (Fig. 1b).


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

Chen Y, Xu R - Cancer Inform (2014)

(A) Age distribution of the patients in the adverse event reports. (b) Gender distribution. (C) Distribution of disease semantic types.Notes: T047, disease or syndrome; T020, acquired abnormality; T046, pathologic function; T184, sign or symptom; T033, finding; T190, anatomical abnormality; T191, neoplastic process; T048, mental or behavioral dysfunction; T049, cell or molecular dysfunction; T019, congenital abnormality; T037, injury or poisoning.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-suppl.1-2014-037: (A) Age distribution of the patients in the adverse event reports. (b) Gender distribution. (C) Distribution of disease semantic types.Notes: T047, disease or syndrome; T020, acquired abnormality; T046, pathologic function; T184, sign or symptom; T033, finding; T190, anatomical abnormality; T191, neoplastic process; T048, mental or behavioral dysfunction; T049, cell or molecular dysfunction; T019, congenital abnormality; T037, injury or poisoning.
Mentions: We extracted the patient–disease pairs from the adverse event reports for comorbidity mining. The adverse event reports contain records of 3,354,043 patients. Among all patients, 2,213,399 (66%) and 3,153,795 (94%) have their age and gender information available. Figure 1(a,b) shows the distributions of age and gender. Different from the Medicare claims, which only contain patients of age 65 years or older, the adverse event reports have patients aged from one day to hundreds of years. With both the disease and demographics data for millions of patients, we were able to study the potential effects of age and gender on the change of disease comorbidity patterns. For comorbidity extraction, we stratified patients into five groups based on their ages (Fig. 1a) and two groups based on their genders (Fig. 1b).

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