Comorbidity network analysis and genetics of colorectal cancer

Ljubić, Branimir; Pavlovski, Martin; Alshehri, Jumanah; Roychoudhury, Shoumik; Bajic, Vladimir B.; Neste, Christophe Van; Obradović, Zoran

doi:10.1016/j.imu.2020.100492

Cited by 11 publications

(5 citation statements)

References 34 publications

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“…In this population-based study, the most prevalent comorbidities were hypertension, hyperplasia of the prostate, COPD, DM, and IHD. Similar results were found in previous studies in New Zealand and the United States, although the prevalence of comorbidities differed [ 8 , 17 , 46 ]. Variations of prevalence might be related to differences in the study population (eg, some studies focused only on elderly patients among whom comorbidities are more common) and the definition used for chronic conditions (eg, a 1-year look-back period for hospitalization data resulted in a lower yield of comorbid conditions).…”

Section: Discussionsupporting

confidence: 90%

Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study

Qiu,

Wang,

Zhou

et al. 2023

JMIR Public Health Surveill

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Background Patients with colorectal cancer (CRC) often present with multiple comorbidities, and many of these can affect treatment and survival. However, previous comorbidity studies primarily focused on diseases in commonly used comorbidity indices. The comorbid status of CRC patients with respect to the entire spectrum of chronic diseases has not yet been investigated. Objective This study aimed to systematically analyze all chronic diagnoses and diseases co-occurring, using a network-based approach and large-scale administrative health data, and provide a complete picture of the comorbidity pattern in patients newly diagnosed with CRC from southwest China. Methods In this retrospective observational study, the hospital discharge records of 678 hospitals from 2015 to 2020 in Sichuan Province, China were used to identify new CRC cases in 2020 and their history of diseases. We examined all chronic diagnoses using ICD-10 (International Classification of Diseases, 10th Revision) codes at 3 digits and focused on chronic diseases with >1% prevalence in at least one subgroup (1-sided test, P<.025), which resulted in a total of 66 chronic diseases. Phenotypic comorbidity networks were constructed across all CRC patients and different subgroups by sex, age (18-59, 60-69, 70-79, and ≥80 years), area (urban and rural), and cancer site (colon and rectum), with comorbidity as a node and linkages representing significant correlations between multiple comorbidities. Results A total of 29,610 new CRC cases occurred in Sichuan, China in 2020. The mean patient age at diagnosis was 65.6 (SD 12.9) years, and 75.5% (22,369/29,610) had at least one comorbidity. The most prevalent comorbidities were hypertension (8581/29,610, 29.0%; 95% CI 28.5%-29.5%), hyperplasia of the prostate (3816/17,426, 21.9%; 95% CI 21.3%-22.5%), and chronic obstructive pulmonary disease (COPD; 4199/29,610, 14.2%; 95% CI 13.8%-14.6%). The prevalence of single comorbidities was different in each subgroup in most cases. Comorbidities were closely associated, with disorders of lipoprotein metabolism and hyperplasia of the prostate mediating correlations between other comorbidities. Males and females shared 58.3% (141/242) of disease pairs, whereas male-female disparities occurred primarily in diseases coexisting with COPD, cerebrovascular diseases, atherosclerosis, heart failure, or renal failure among males and with osteoporosis or gonarthrosis among females. Urban patients generally had more comorbidities with higher prevalence and more complex disease coexistence relationships, whereas rural patients were more likely to have co-existing severe diseases, such as heart failure comorbid with the sequelae of cerebrovascular disease or COPD. Conclusions Male-female and urban-rural disparities in the prevalence of single comorbidities and their complex coexistence relationships in new CRC cases were not due to simple coincidence. The results reflect clinical practice in CRC patients and emphasize the importance of measuring comorbidity patterns in terms of individual and coexisting diseases in order to better understand comorbidity patterns.

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Section: Discussionsupporting

confidence: 90%

Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study

Qiu,

Wang,

Zhou

et al. 2023

JMIR Public Health Surveill

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“…This study is a preliminary excursion into the various factors that influence the development of comorbidities post mild TBI. In the future, we intend to build upon this work to study pairwise disease co-occurrences at scale using graph networks and network analysis ( Fotouhi et al, 2018 ; Ljubic et al, 2020 ; Lee and Park, 2021 ). In doing so, a larger avenue of analysis is opened to visualize and understand disease comorbidities as differences in network structures across scales ( Fotouhi et al, 2018 ; Ljubic et al, 2020 ; Lee and Park, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…In the future, we intend to build upon this work to study pairwise disease co-occurrences at scale using graph networks and network analysis ( Fotouhi et al, 2018 ; Ljubic et al, 2020 ; Lee and Park, 2021 ). In doing so, a larger avenue of analysis is opened to visualize and understand disease comorbidities as differences in network structures across scales ( Fotouhi et al, 2018 ; Ljubic et al, 2020 ; Lee and Park, 2021 ). Comorbidities are understood as unique spatial organisations of statistically significant interconnected disease nodes with varying depths of connectedness ( Fotouhi et al, 2018 ; Kim et al, 2018 ; Lee and Park, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Prevalence of comorbidities post mild traumatic brain injuries: a traumatic brain injury model systems study

Sudhakar,

Sridhar,

Char

et al. 2023

Front. Hum. Neurosci.

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Traumatic brain injury (TBI) is associated with an increased risk of long-lasting health-related complications. Survivors of brain trauma often experience comorbidities which could further dampen functional recovery and severely interfere with their day-to-day functioning after injury. Of the three TBI severity types, mild TBI constitutes a significant proportion of total TBI cases, yet a comprehensive study on medical and psychiatric complications experienced by mild TBI subjects at a particular time point is missing in the field. In this study, we aim to quantify the prevalence of psychiatric and medical comorbidities post mild TBI and understand how these comorbidities are influenced by demographic factors (age, and sex) through secondary analysis of patient data from the TBI Model Systems (TBIMS) national database. Utilizing self-reported information from National Health and Nutrition Examination Survey (NHANES), we have performed this analysis on subjects who received inpatient rehabilitation at 5 years post mild TBI. Our analysis revealed that psychiatric comorbidities (anxiety, depression, and post-traumatic stress disorder (PTSD)), chronic pain, and cardiovascular comorbidities were common among survivors with mild TBI. Furthermore, depression exhibits an increased prevalence in the younger compared to an older cohort of subjects whereas the prevalence of rheumatologic, ophthalmological, and cardiovascular comorbidities was higher in the older cohort. Lastly, female survivors of mild TBI demonstrated increased odds of developing PTSD compared to male subjects. The findings of this study would motivate additional analysis and research in the field and could have broader implications for the management of comorbidities after mild TBI.

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“…Following the data filtering of mTBI subjects from the database (with age >=16 years), we computed phi correlation coefficient (φ) to quantify the strength of association between each unique pair of the 26 comorbidities (Fotouhi et al 2018; Ljubic et al 2020; Lee and Park 2021). φ is a non-parametric statistic suitable for analysing relationships between binary variables (Barabási et al 2011; Fotouhi et al 2018; Ljubic et al 2020; Lee and Park 2021). To compute φ, we first constructed a 2*2 matrix for each pair of diseases (D1, D2) as per Table 1.…”

Section: Methodsmentioning

confidence: 99%

Exploring Comorbidity Networks in Mild Traumatic Brain Injury Subjects through Graph Theory: A Traumatic Brain Injury Model Systems Study

Mehta,

Sudhakar

2024

Preprint

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Traumatic brain injuries (TBIs) are characterized by myriad comorbidities that affect the functioning of the affected individuals. The comorbidities that TBI subjects experience span a wide range, ranging from psychiatric diseases to those that affect the various systems of the body. This is compounded by the fact that the problems that TBI subjects face could span over an extended period post-primary injury. Further, no drug exists to prevent the spread of secondary injuries after a primary impact. In this study, we employed graph theory to understand the patterns of comorbidities after mild TBIs. Upon application of network analysis and a novel clustering algorithm, we discovered interesting associations between comorbidities in young and old subjects with the condition. Specifically, bipolar disorder was seen as related to cardiovascular comorbidities, a pattern that was observed only in the young subjects. Similar associations between obsessive-compulsive disorder and rheumatoid arthritis were observed in young subjects. Psychiatric comorbidities exhibited differential associations with non-psychiatric comorbidities depending on the age of the cohort. The study results might have implications for effective surveillance and the management of comorbidities post mild TBIs.

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Comorbidity network analysis and genetics of colorectal cancer

Cited by 11 publications

References 34 publications

Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study

Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study

Prevalence of comorbidities post mild traumatic brain injuries: a traumatic brain injury model systems study

Exploring Comorbidity Networks in Mild Traumatic Brain Injury Subjects through Graph Theory: A Traumatic Brain Injury Model Systems Study

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