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How to build personalized multi-omics comorbidity profiles.

ABSTRACT: Multiple diseases (acute or chronic events) occur together in a patient, which refers to the disease comorbidities, because of the multi ways associations among diseases. Due to shared genetic, molecular, environmental, and lifestyle-based risk factors, many diseases are comorbid in the same patient. Methods for integrating multiple types of omics data play an important role to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Moreover, integrated omics and clinical information may potentially improve prediction accuracy of disease comorbidities. However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis. With the availability of the wide spread huge omics, phenotype and ontology information, it is becoming more and more practical to help doctors in clinical diagnostics and comorbidity prediction by providing appropriate software tool. We developed an R software POGO to compute novel estimators of the disease comorbidity risks and patient stratification. Starting from an initial diagnosis, omics and clinical data of a patient the software identifies the association risk of disease comorbidities. The input of this software is the initial diagnosis of a patient and the output provides evidence of disease comorbidities. The functions of POGO offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high-throughput and clinical data analysis pipelines. POGO is compliant with the Bioconductor standard and it is freely available at www.cl.cam.ac.uk/~mam211/POGO/.

SUBMITTER: Moni MA

PROVIDER: S-EPMC4478898 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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How to build personalized multi-omics comorbidity profiles.

Moni Mohammad Ali MA Liò Pietro P

Frontiers in cell and developmental biology 20150624

Multiple diseases (acute or chronic events) occur together in a patient, which refers to the disease comorbidities, because of the multi ways associations among diseases. Due to shared genetic, molecular, environmental, and lifestyle-based risk factors, many diseases are comorbid in the same patient. Methods for integrating multiple types of omics data play an important role to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Moreover, ...[more]

PMID: 26157799

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Project description:BackgroundAnorexia Nervosa (AN) is a serious mental illness characterized by emaciation, an intense fear of gaining weight despite being underweight, and distorted body image. Few treatments reverse the core symptoms in AN such as profound aversion to food and food avoidance. Consequently, AN has a chronic and relapsing course and the highest mortality rate of any psychiatric illness. A more complete understanding of the disease pathogenesis is needed in order to develop better treatments and improve AN outcome. The pathogenesis and psychopathophysiology of AN can be better elucidated by combining longitudinal phenotyping with multiple "omics" techniques, including genomics, proteomics, lipidomics, and metabolomics.DesignThis paper summarizes the key findings of a series of interrelated studies including new experimental data and previously published data, and describes our current initiatives and future directions.ResultsExon sequencing data was analyzed in 1205 AN and 1948 controls. Targeted metabolomics, lipidomics, and proteomics data were collected in two independent convenience samples consisting of 75 subjects with eating disorders and 61 sex- and age-matched healthy controls. Study participants were female and the mean age was 22.9 (4.9 [SD]) years. Epoxide hydrolase 2 (EPHX2) genetic variations were significantly associated with AN risk, and epoxide hydrolase (sEH) activity was elevated in AN compared to controls. The polyunsaturated fatty acids (PUFAs) and eicosanoids data revealed that cytochrome P450 pathway was implicated in AN, and AN displayed a dysregulated postprandial metabolism of PUFAs and sEH-dependent eicosanoids.Implication and current initiativesCollectively, our data suggest that dietary factors may contribute to the burden of EPHX2-associated AN susceptibility and affect disease outcome. We are implementing new investigations using a longitudinal study design in order to validate and develop an EPHX2 multi-omics biomarker system. We will test whether sEH-associated postprandial metabolism increases AN risk and affects treatment outcome through an ω-6 rich breakfast challenge. Participants will include 100 ill AN patients, 100 recovered AN patients, and 100 age- and race-matched healthy women. These data will allow us to investigate 1) how genetic and dietary factors independently and synergistically contribute to AN risk and progression, and 2) if clinical severity and treatment response in AN are affected by sEH activity and eicosanoid dysregulation. Results of our study will 1) identify clinically relevant biomarkers, 2) unravel mechanistic functions of sEH, and 3) delineate contributory roles of dietary PUFAs and cytochrome P450 pathway eicosanoids for the purpose of developing novel AN treatments and improving disease prognosis.

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How to build personalized multi-omics comorbidity profiles.

Publications

How to build personalized multi-omics comorbidity profiles.

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