Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

Chu, Su H.; Huang, Mengna; Kelly, Rachel S.; Benedetti, Elisa; Siddiqui, Jalal K.; Zeleznik, Oana A.; Pereira, Alexandre; Herrington, David M.; Wheelock, Craig E.; Krumsiek, Jan; McGeachie, Michael J.; Moore, Steven C.; Kraft, Peter; Mathé, Ewy A.; Lasky‐Su, Jessica

doi:10.3390/metabo9060117

Cited by 51 publications

(35 citation statements)

References 164 publications

(185 reference statements)

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“…Furthermore, there is a demand for standardized and calibrated analytical procedures and new analytical methods to characterize the steroid metabolome [84,93,94]. The integration of multidimensional omics data has achieved considerable success, and there is still room for improvement, especially in the construction of multi-omics prediction models [95,96,97].…”

Section: Discussionmentioning

confidence: 99%

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

et al. 2019

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Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference.

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Section: Discussionmentioning

confidence: 99%

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

et al. 2019

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“…To date, many studies have focused on single nutrients or foods versus dietary patterns which is a disconnect with the reality that diet as a whole is more than the sum of its parts [65]. With respect to omics approaches, choice of the source(s) of biological samples, sample preparation methods, analytic platform (e.g., nuclear magnetic resonance or liquid chromatography-mass spectroscopy for metabolomics), impose various strengths and limitations on the measurement and interpretation of data as summarized previously [66]. The omics approach also needs to be matched to the study aim.…”

Section: Challenges and Future Opportunitiesmentioning

confidence: 99%

“…The integration of different omics data types is an ongoing challenge [66]. Temporality, that is assumptions about the direction of temporal effects, need to be made, with the exception of genetics where it is reasonable to expect genetic variation precedes most other omic measurements.…”

Section: Challenges and Future Opportunitiesmentioning

confidence: 99%

An Integrative Approach to Assessing Diet–Cancer Relationships

Murphy

2020

Metabolites

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The relationship between diet and cancer is often viewed with skepticism by the public and health professionals, despite a considerable body of evidence and general consistency in recommendations over the past decades. A systems biology approach which integrates ‘omics’ data including metabolomics, genetics, metagenomics, transcriptomics and proteomics holds promise for developing a better understanding of how diet affects cancer and for improving the assessment of diet through biomarker discovery thereby renewing confidence in diet–cancer links. This review discusses the application of multi-omics approaches to studies of diet and cancer. Considerations and challenges that need to be addressed to facilitate the investigation of diet–cancer relationships with multi-omic approaches are also discussed.

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“…54 Integrative omic approaches are now used to evaluate multiple forms of omic data together and more recently have become a focus in allergybased research. 42,54,55 Statistical methods to integrate multiomic data are emerging, and multiple review articles have summarized these approaches 1,56 and provide additional insight into important considerations when analyzing multiomic data in epidemiological studies. In the field of allergy and immunology multiomic integration is starting to provide important insights into disease pathophysiology.…”

Section: Integrative Omicsmentioning

confidence: 99%

The nuts and bolts of omics for the clinical allergist

Virkud

Kelly

Wood

et al. 2019

Annals of Allergy, Asthma & Immunology

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The term omics (aka multi-omics) refers to the usage of high-throughput technology to characterize a set of molecules relevant to a field of biology, and has grown to encompass a variety of "-omes," including the genome (DNA), transcriptome (RNA), proteome (proteins), metabolome (metabolites), microbiome (microbiota), and exposome (exposures). Integrative omics refers to the study of how different omics data interact to affect a condition or disease of interest. Personalized medicine is an approach to studying disease origin, pathogenesis, progression, and treatment that uses information specific to an individual (for example, their genome or transcriptome) to identify the optimal treatment option for that individual. Endotype refers to a subtype of a disease that is defined by a distinct pathophysiologic mechanism, in contrast with a phenotype, which describes characteristics of a disease without implying a different underlying mechanism.

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Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

Cited by 51 publications

References 164 publications

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

An Integrative Approach to Assessing Diet–Cancer Relationships

The nuts and bolts of omics for the clinical allergist

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