The occurrence of two or more diseases in the same patient is known as comorbidity. It poses significant difficulties in terms of disease diagnosis and therapy. Comorbidities are frequently associated with more negative health outcomes compared to single disorders, including lower quality of life, greater mortality rate, and higher economic burden.
Understanding the processes of comorbidities could aid in early detection, treatment, and management, eventually lowering the worldwide disease burden associated with it.
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Patterns of comorbidity among patients
It has been proposed that shared genetic components such as disease-associated genes or metabolic pathways can explain comorbidities in some circumstances. As a result, common disease-associated genes could be used to investigate disease comorbidity. In this regard, current research has shed light on patient comorbidity patterns. They discovered that many illness pairs that share genes do not show considerable comorbidity, and vice versa: comorbid disorders may not share a genetic component.
As gene products rarely act in isolation, it is necessary to analyze the interconnections between disease-associated gene products to properly comprehend disease comorbidities. For example, diseases may be related by the interaction of two proteins, each of which is linked to a separate disease. Protein-protein interactions can thus influence comorbidity linkages even if there are no genes in common.
The biological and genetic basis of comorbid diseases
Large-scale genome-wide association studies (GWASs) have shown overlapped genetic risks for a few commonly comorbid diseases at the genomic locus level, i.e. single-nucleotide polymorphisms (SNPs) or genes implying that comorbid relationships may have a biological foundation. For example, GWASs have discovered 38 SNPs linked to asthma and allergy disorders, and 244 genomic loci linked to ankylosing spondylitis, Crohn's disease, psoriasis, primary sclerosing cholangitis, and ulcerative colitis.
Sánchez-Valle et al. discovered that illness connections deduced from similarities between patients' gene expression profiles show considerable overlaps with epidemiologically reported comorbid linkages, indicating that comorbidity is genetically based. Some studies have also found that diseases with a high likelihood of concurrency share more genes. These discoveries have added to our understanding of the biological genesis of comorbidity.
Due to the molecular connections among genes, malfunctions produced by disease risk loci can spread through cellular networks. To this purpose, several studies use network-level evidence, such as protein-protein interactions (PPIs) and molecular pathways to capture the genetic similarities across comorbidities.
By merging information on cellular contacts, disease–gene relationships, and Medicare data, Park et al. discovered a substantial positive link between the number of shared PPIs and the level of disease concurrency. There has also been a dramatic increase in the number of common pathways between malignancies and comorbid Mendelian disorders.
These findings suggest that aberrant entanglement in molecular networks may play a role in a patient's coexistence of comorbid disorders. Similarly, several comorbidities have been reported to have comparable general genetic structures as evaluated by genetic correlations, such as the common genetic correlations among comorbid psychiatric disorders.
Phenotype-genotype comorbidity in rare diseases
Rare diseases affect a small number of people individually, yet when taken together, they affect millions. It is necessary to improve the diagnosis and knowledge of the underlying mechanisms. The small number of people afflicted makes genetic and molecular investigation of uncommon diseases difficult. By collecting information from people with comparable traits and looking for overlap in terms of shared genes and underlying functional systems, phenotypic comorbidity analysis can assist to correct this.
However, a few research projects have merged comorbidity with genetic data analysis. A paper published in PLOS Genetics describes a computational strategy that links patient phenotypes based on phenotypic co-occurrence and leverages genomic information from patient mutations to assign genes to the phenotypes, which are then applied to find enriched functional systems.
To create functionally coherent phenotypic clusters these traits are clustered using network analysis. They used the method on the DECIPHER database, which has clinical and genomic data for thousands of patients with a variety of uncommon illnesses and copy number variations. They were able to create clusters of abnormalities that co-occur in many patients who had mutations in genes associated with comparable functional systems by merging genomic and phenotypic data from thousands of patients.
This approach has a wide range of possible applications for other diseases; by making the workflow public, it may be applied to any other data set with the necessary information.
Comorbidity in mental illness
Comorbidity in mental disease is widespread, making mental health more complicated than just having one diagnosis. Mental health illnesses do not exist in a vacuum, according to research. It is influenced by underlying shared biology.
Research studies of families and twin siblings have shown that some common psychiatric disorders are accounted for partly due to shared genetic risk factors. Some genes linked to bipolar illness and schizophrenia, for example, are found in both conditions. Attention Deficit Hyperactivity Disorder, or ADHD; major depressive disorder; bipolar disorder; schizophrenia; and anti-social disorders are all examples of afflictions that frequently co-occur.
Because of these shared genetic risk factors, having a family history of one mental health disorder may put you at risk for other mental health conditions as well. For example, studies suggest that if your family has a history of ADHD, you're more likely to develop depression, anti-social disorders, or bipolar illness than people who don't have a family history of ADHD.
Comorbidity must be taken into account during both diagnosis and therapy, according to scientists. Better mental health outcomes are the eventual objective of understanding and incorporating this genetic information.
- Dong et, al. (2021). A global overview of genetically interpretable comorbidities among common diseases in UK Biobank. MedRxiv 2021.01.15.21249242; doi: https://doi.org/10.1101/2021.01.15.21249242.
- Rubio-Perez, C et, al. (2017). Genetic and functional characterization of disease associations explains comorbidity. Scientific reports, 7(1), 6207. https://doi.org/10.1038/s41598-017-04939-4
- Pharmacogenetics. (2019). Mental Illness Comorbidity: The Genetic Connection. [online] Genomind. Available at: https://www.genomind.com/blog/mental-illness-comorbidity-the-genetic-connection.
- Díaz-Santiago E, Jabato FM, Rojano E, Seoane P, Pazos F, et al. (2020) Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases. PLOS Genetics 16(10): e1009054. https://doi.org/10.1371/journal.pgen.1009054