- Methodology
- Open Access

# comoR: a software for disease comorbidity risk assessment

- Mohammad Ali Moni
^{1, 2}Email author and - Pietro Liò
^{1}

**4**:8

https://doi.org/10.1186/2043-9113-4-8

© Moni and Liò; licensee BioMed Central Ltd. 2014

**Received: **14 February 2014

**Accepted: **17 April 2014

**Published: **23 May 2014

## Abstract

### Background

The diagnosis of comorbidities, which refers to the coexistence of different acute and chronic diseases, is difficult due to the modern extreme specialisation of physicians. We envisage that a software dedicated to comorbidity diagnosis could result in an effective aid to the health practice.

### Results

We have developed an R software comoR to compute novel estimators of the disease comorbidity associations. Starting from an initial diagnosis, genetic and clinical data of a patient the software identifies the risk of disease comorbidity. Then it provides a pipeline with different causal inference packages (e.g. pcalg, qtlnet etc) to predict the causal relationship of diseases. It also provides a pipeline with network regression and survival analysis tools (e.g. Net-Cox, rbsurv etc) to predict more accurate survival probability of patients. The input of this software is the initial diagnosis for a patient and the output provides evidences of disease comorbidity mapping.

### Conclusions

The functions of the comoR offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high–throughput and clinical data analysis pipelines.

## Keywords

## Introduction

The term “comorbidity” refers to the coexistence or presence of multiple diseases or disorders in relation to a primary disease or disorder in a patient [1]. Multimorbidity can be also defined as coexistence of two or more diseases, but no index disease is considered [2]. A comorbidity relationship between two diseases exists whenever they appear simultaneously in a patient more than chance alone. It represents the co–occurrence of diseases or presence of different medical conditions one after another in the same patient [3, 4]. Some diseases or infections can coexist in one person by coincidence, and there is no pathological association among them. However, in most of the cases, multiple diseases (acute or chronic events) occur together in a patient because of the associations among diseases. These associations can be due to direct or indirect causal relationships and the shared risk factors among diseases [5, 6]. For an instance, people with HIV-1 appear to have a markedly higher rate of end-stage renal disease (ESRD) than the healthy people [7]. It is because some of the risk factors associated with HIV-1 acquisition are the same as those that lead to kidney disease. Patients with chronic kidney disease increase risk of cardiovascular mortality [8]. Thus HIV-1 infections is associated with cardiovascular mortality.

One of the most challenging problems in biomedical research is to understand the complex correlation mechanisms of human diseases. Recent research has increasingly demonstrated that many seemingly dissimilar diseases have common molecular mechanisms. Exploring relations between genes and diseases at the molecular level could greatly facilitate our understanding of pathogenesis, and eventually lead to better diagnosis and treatment. Diseases are more likely to be comorbid if they share associated genes [3]. However, some diseases have direct positive association among them while other diseases may have indirect positive association among them through the biological pathways. The analysis of pathway-disease associations, in addition to gene-disease associations, could be used to clarify the molecular mechanism of a disease. Ashley, Butte, Wheeler, Chen, Klein, Dewey, Dudley, Ormond, Pavlovic, Morgan, Pushkarev, Neff, Hudgins, Gong, Hodges, Berlin, Thorn, Sangkuhl, Hebert, Woon, Sagreiya, Whaley, Knowles, Chou, Thakuria, Rosenbaum, Zaranek, Church, Greely and Quake et al. analysed personal genome, gene-environment interactions and conditionally dependent risks for the clinical assessment [9]. Population-based disease association is also useful in conjunction with molecular and genetic data to discover the molecular origins of disease and disease comorbidity [4]. Patient medical records contain important clarification regarding the co-occurrences of diseases affecting the same patient. To estimate the correlation starting from disease co-occurrence, we need to quantify the strength of the comorbidity risk. Disease Ontology (DO) is also helpful to promote the investigation of diseases and disease risk factors [10].

Comorbidity is an important factor for better risk stratification of patients and treatment planning. The more precise predictions can be made by taking comorbidity into account, the more accurate patient management could be possible. Comorbidity has a significant predictive value on overall survival [11]. Older persons’ survival is highly dependent on it. Comorbidities influence patients treatments and confound survival analysis [12]. For an instance, comorbidity has a major effect on survival in gynaecological cancer, particularly for cancer of the cervix [13]. Many researchers have developed survival analysis software for predicting outcomes of the disease [14–23]. However, all of them are based on the single disease. But survival of patient depends on the disease comorbidity, environment, patient age and treatment plan. Kan et al. performed survival analysis of elderly dialysis patients considering comorbidity risk [24]. They observed that the life expectancy decreases with increasing the number of comorbid diseases. So it is important to consider the comorbidity for more accurate survival prediction.

We have developed an R software comoR to compute statistically significant associations among diseases and to predict disease comorbidity risk by using diverse set of data. The input of this software is the initial diagnosis for a patient. To perform the computation of the comorbidity risk, this software uses clinical, gene expression, pathways and ontology data. It provides different comorbidity assessment; integration of genetic information with the comoR output data could be used to infer causal relationships among diseases and to predict more accurate survival probability of patients. The goal of this software is to assist a medical practitioner in decision making in potential treatment.

## Implementation

### Comorbidity based on clinical information

Patient medical records contain important clarification regarding the co-occurrences of diseases affecting the same patient. Two diseases are connected if they are co-expressed in a significant number of patients in a population [4]. To estimate the correlation starting from disease co-occurrence, we need to quantify the strength of the comorbidity risk. We used two comorbidity measures to quantify the strength of comorbidity associations between two diseases: (i) the Relative Risk (fraction between the number of patients diagnosed with both diseases and random expectation based on disease prevalence) as the quantified measures of comorbidity tendency of two disease pairs; and (ii) *ϕ*-correlation (Pearsons correlation for binary variables) to measure the robustness of the comorbidity association. We used the relative risk *R* *R*_{
i
j
} and *ϕ*-correlation *ϕ*_{
i
j
} of observing a pair of diseases *i* and *j* affecting the same patient. The *R* *R*_{
i
j
} allows us to quantify the co-occurrence of disease pairs compared with the random expectation. When two diseases co-occur more frequently than expected by chance, we will get *R* *R*_{
i
j
}>1 and *ϕ*_{
i
j
}>0. The two comorbidity measures are not completely independent of each other. We included edges between disease pairs for which the co-occurrence is significantly greater than the random expectation based on population prevalence of the diseases. Clinical information is from the http://www.icd9data.com in the ICD-9-CM format and collected from [4]. The function comorbidityPatients of the comoR package is able to take input an OMIM id/3 or 5 digit ICD-9-CM code of a disease or a list of gene symbols/Entrez ids and provides comorbidity pattern of diseases based on the relative risk and *ϕ*-correlation between two diseases. comorbidityPatients requires two parameters id list and id type (see details in the Additional file 1). An example and its output (Figure 2) is as follows:

### Gene–disease association

### Pathway–disease association

### Ontology and causal inference to evaluate comorbidity

## Methods

*R*

*R*

_{ i j }) as the quantified measures of comorbidity tendency of two disease pairs and

*ϕ*-correlation (

*ϕ*

_{ i j }) to measure the robustness of the comorbidity association, which are calculated by using following two equations:

where *N* is the total number of patients in the population, *P*_{
i
} and *P*_{
j
} are incidences/prevalences of diseases *i* and *j* respectively. *C*_{
i
j
} is the number of patients that have been diagnosed with both diseases *i* and *j*, and *P*_{
i
}*P*_{
j
} is the random expectation based on disease prevalence. The significance of the relative risk *R* *R*_{
i
j
} is calculated by using the Katz et al. method to estimate confidence intervals [34]. The 99% confidence interval for the *R* *R*_{
i
j
} between two diseases *i* and *j* is calculated by: Lower bounds of the confidence interval (*L* *B*)=*R* *R*_{
i
j
}∗*e* *x* *p*(−2.56∗*σ*_{
i
j
}) and Upper bounds of the confidence interval (*U* *B*)=*R* *R*_{
i
j
}∗*e* *x* *p*(2.56∗*σ*_{
i
j
}), where *σ*_{
i
j
} is given by: ${\sigma}_{\mathit{\text{ij}}}=\frac{1}{{C}_{\mathit{\text{ij}}}}+\frac{1}{{P}_{i}{P}_{j}}-\frac{1}{N}-\frac{1}{{N}^{2}}$. Disease pairs within the 99% confidence interval are only considered if the *LB* value is larger than 1 when *R* *R*_{
i
j
} is larger than 1, or if the *UB* value is smaller than 1 when *R* *R*_{
i
j
} is smaller than 1. For *ϕ*_{
i
j
}>0 comorbidity is larger than expected by chance and for *ϕ*_{
i
j
}<0 comorbidity is smaller than expected by chance. We can determine the significance of *ϕ*≠0 by performing a *t*-test. This consists of calculating *t* according to the formula: $t=\frac{\varphi \sqrt{n-2}}{\sqrt{1-{\varphi}^{2}}}$, where *n* is the number of observations used to calculate *ϕ*.

*D*and a set of human genes

*G*, gene-disease associations attempt to find whether gene

*g*∈

*G*is associated with disease

*d*∈

*D*. If

*G*

_{ i }and

*G*

_{ j }, the sets of significant up and down dysregulated genes associated with diseases

*i*and

*j*respectively, then the number of shared dysregulated genes $\left({n}_{\mathit{\text{ij}}}^{g}\right)$ associated with both diseases

*i*and

*j*is as follows:

*p*−

*v*

*a*

*l*

*u*

*e*is adjusted by false discovery rate (FDR). The hypergeometric p-value is calculated using the following formula:

where *N* is the total number of reference genes, *M* is the number of genes that are associated to the disease of interest, *n* is the size of the list of genes of interest and *k* is the number of genes within that list which are associated to the disease.

*DA*and

*DB*is calculated based on disease semantic value. Formally, a DO term or a disease

*A*can be represented as a graph

*D*

*A*

*G*

_{ A }=(

*A*,

*T*

_{ A },

*E*

_{ A }), where

*T*

_{ A }is the set of all diseases or DO terms in

*D*

*A*

*G*

_{ A }, including term

*A*itself and all of its ancestor terms in the DO graph, and

*E*

_{ A }is the set of corresponding edges that connect the DO terms in

*D*

*A*

*G*

_{ A }. To encode the semantic of a DO term in a measurable format to enable a quantitative comparison, Wang firstly defined the semantic value of term

*A*as the aggregate contribution of all terms in

*D*

*A*

*G*

_{ A }to the semantics of term

*A*, terms closer to term

*A*in

*D*

*A*

*G*

_{ A }contribute more to its semantics [30]. Thus, we defined the contribution of a disease or DO term

*t*in

*D*

*A*

*G*

_{ A }to the semantics of DO term

*A*as the

*D*value of disease or term

*t*related to disease or term

*A*,

*D*

_{ A }(

*t*), which can be calculated as:

*w*

_{ e }is the semantic contribution factor for edge

*e*(

*e*∈

*E*

_{ A }) linking term or disease

*t*with its child term or disease ${t}^{{}^{\prime}}$. It is assigned between 0 and 1 according to the types of associations. Term

*A*contributes to its own is defined as one. Then the semantic value of DO term or disease

*A*,

*D*

*V*(

*A*) is calculated as:

*A*and

*B*, the semantic similarity between these two terms or disease is defined as:

where *D*_{
A
}(*t*) is the semantic value of disease *t* related to DO term or disease *A* and *D*_{
B
}(*t*) is the semantic value of DO term or disease *t* associated to DO term or disease *B*.

## Comparison with similar software

An R package “comorbidities” that has functions to categorize comorbidites into the Deyo-Charlson index, the original Elixhauser index of 30 comorbidities, and the AHRQ comorbidity index of 29 diagnoses [35, 36]. This package provides total comorbidity count or the total Charlson score. But comoR provides relative risk, *ϕ*-correlation, associated genes, pathway and p-value between the comorbidity diseases. It could provide comorbidity associations among all diseases. So comoR is more useful than “comorbidities”.

*β*) values of 5 genes in five diseases conditions(breast cancer, colon cancer, ovarian cancer, liver cancer and osteosarcoma) by using Net-Cox. For this comparative study we have considered five NCBI GEO data sets, accession numbers are GSE3494, GSE17536, GSE26712, GSE10141 and GSE21257 [39–43]. The comparative coefficient (

*β*) values of five significant genes (BRCA1, BRCA2, PTEN, TGFB2 and TP53) in 5 diseases conditions are reported in the Table 1. It is observed that diseases may coexist in the same patient. Our software is able to predict occurrence of other diseases in relation to primary disease. So the comorbidity output of our software could be helpful for more accurate survival analysis. So, comoR could be integrate as a pipeline with the survival analysis softwares.

**Comparative values of genes co-expression and functional linkage network based penalised Cox regression coefficient (**
β
**) of five significant genes (BRCA1, BRCA2, PTEN, TGFB2 and TP53) in five diseases conditions (breast cancer, colon cancer, ovarian cancer, liver cancer and osteosarcoma)**

Disease name | Network type | BRCA1 | BRCA2 | PTEN | TGFB2 | TP53 |
---|---|---|---|---|---|---|

Co-expression | 8.1253 | 58.4088 | 9.9136 | 31.5791 | 17.6486 | |

Breast cancer | Functional linkage | 1.3637 | 42.1227 | 53.2586 | 19.9091 | 23.4185 |

Co-expression | 22.4097 | 18.3406 | 17.8181 | 28.2778 | 24.0951 | |

Colon cancer | Functional linkage | 40.4169 | 23.6457 | 37.3934 | 17.9620 | 20.2739 |

Co-expression | 42.5902 | 155.2418 | -0.0751 | -0.4850 | 27.1997 | |

Ovarian cancer | Functional linkage | 24.1814 | 14.8738 | 33.2762 | 27.0234 | -22.8965 |

Co-expression | 5.7010 | 10.2188 | 41.2701 | 29.6339 | 3.2189 | |

Liver cancer | Functional linkage | 13.3196 | 11.4365 | 7.3683 | 3.1508 | 1.9305 |

Co-expression | 11.8679 | 10.5565 | -1.3561 | -8.1221 | 4.4491 | |

Osteosarcoma | Functional linkage | 51.3299 | 17.1618 | 15.1504 | 4.2642 | 5.3983 |

## Discussion

Exploring associations among diseases at the molecular and clinical levels could greatly facilitate our understanding of pathogenesis, and eventually lead to better diagnosis and treatment. If two diseases have associated comorbidity, the occurrence of one of them in a patient may increase the likelihood of developing the other diseases. Development of methods integrating genetic and clinical data will assist clinical decision making and represent a large step towards individualised medicine. Hidalgo et al. analysed comorbidity associations using the medical records [4]. To our knowledge, there is no available R software package for the prediction of disease comorbidities. An R package “comorbodoties” is able to categorises ICD-9-CM codes based on published 30 comorbidity indices using Deyo adaptation of Charlson index and the Elixhauser index [35, 36]. We have developed comoR, an R package that implements different statistical approach for the prediction of disease comorbidity using divers set of data.

Advances in high-throughput molecular assay technologies in the fields of genomics, proteomics and other omics is increasing the diagnostic and therapeutic strategies, and systems-driven strategies for personalised treatment. In particular, the availability of these data sets for many different diseases presents a ripe opportunity to use data-driven approaches to advance our current knowledge of disease relationships in a systematic way. Patient’s genetic/genomic data is becoming important for clinical decision making, including disease risk assessment, disease diagnosis and subtyping, drug therapy and dose selection [44]. In the future, clinicians will have to consider genetic/genomic implications to patient care throughout their clinical workflow, including electronic prescribing of medications. The identified disease patterns can then be further investigated with regards to their diagnostic utility or help in the prediction of novel therapeutic targets. Therefore, comoR could be helpful for the personalised medicine system. This software will provide us to detect many diseases at the earliest detectable phase, weeks, months, and maybe years before symptoms appear. Thus it could be applicable in the personalised medicine and in clinical bioinformatics.

## Conclusion

Doctors need to be kept updated on novel information on likely comorbidities of diseases. The comoR software provides a robust approach to study disease comorbidities, which can be easily integrated into pipelines for high-throughput and clinical data analysis and to predict causal inference of a disease. This software will help to gain a better understanding of the complex pathogenesis of disease risk phenotypes and the heterogeneity of disease comorbidity. Thus it could be applicable in the personalised medicine and in clinical bioinformatics.

## Availability and requirements

The software package comoR has been written in the platform independent R programming language. It requires R version 3.0.1 or newer to run. The software is freely available at http://www.cl.cam.ac.uk/~mam211/comoR/ and will appear in Comprehensive R Archive Network (CRAN) at (http://cran.r-project.org/).

## Declarations

### Acknowledgements

This work is supported by the EU Mission T2D project.

## Authors’ Affiliations

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