DISTANCE-BASED ANALYSIS OF LONGEVITY-RELATED METABOLOMIC PROFILES Article Swipe
Stefano Monti
,
Paola Sebastiani
,
Zeyuan Song
,
Ziwei Huang
,
Stacy L. Andersen
,
Thomas T. Perls
,
John G. Schumacher
,
Don Melady
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/geroni/igad104.2060
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/geroni/igad104.2060
Longevity is a complex multifactorial trait with many interacting determinants. Identifying each determinant is complicated given that the marginal effect of any one of them may be small. Contemporary high-throughput biomedical assays, such as transcriptomics, proteomics, and metabolomics assays, generate large amounts of very appropriate data for identifying the determinants of longevity, but can be problematic from an analysis viewpoint. For example, the analytes interrogated by these assays are often correlated, include missing data, are often noisy, and typically reflect their contributions to broader underlying, yet often unknown, pathways, processes, and networks. Standard methods for analyzing such data, which focus on each individual analyte’s association with a condition of interest (e.g., long life, health status, etc.) and then exploring commonalities among any associated analytes, can be complemented by multivariate analysis methods that consider the association between different groups of metabolites (e.g., defined by a pathway) and the condition of interest. Distance-based multivariate analysis methods can be used in such settings. We showcase the utility of novel distance-based techniques in the analysis of human and cross-species metabolite data and longevity-related phenotypes collected from the Longevity Consortium (LC). These methods consider the overall similarity of metabolomic profiles among individuals or species with and without, e.g., a long life or a particular age-related disease, or deviations from an optimal longevity associated profile, and have much greater power than aggregated univariate methods when certain assumptions are upheld. We find a number of multivariate patterns in the LC data that could motivate further research.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/geroni/igad104.2060
- https://academic.oup.com/innovateage/article-pdf/7/Supplement_1/632/54741015/igad104.2060.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390082146
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4390082146Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/geroni/igad104.2060Digital Object Identifier
- Title
-
DISTANCE-BASED ANALYSIS OF LONGEVITY-RELATED METABOLOMIC PROFILESWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-01Full publication date if available
- Authors
-
Stefano Monti, Paola Sebastiani, Zeyuan Song, Ziwei Huang, Stacy L. Andersen, Thomas T. Perls, John G. Schumacher, Don MeladyList of authors in order
- Landing page
-
https://doi.org/10.1093/geroni/igad104.2060Publisher landing page
- PDF URL
-
https://academic.oup.com/innovateage/article-pdf/7/Supplement_1/632/54741015/igad104.2060.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/innovateage/article-pdf/7/Supplement_1/632/54741015/igad104.2060.pdfDirect OA link when available
- Concepts
-
Longevity, Univariate, Multivariate statistics, Metabolomics, Multivariate analysis, Identification (biology), Trait, Biology, Computational biology, Bioinformatics, Computer science, Genetics, Machine learning, Ecology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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