ArticleScientific reports2022
Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- The kynurenine pathway in depression and schizophrenia: convergent signals, divergent states, and clinical signatures.EXCLI journal · 2026Review
- Incretin-based approaches for type 2 diabetes therapy: effects on circulating cytokines and adipocyte's secretome.BMC endocrine disorders · 2025Article
- Application of Clinical Blood Metabogram to Type 2 Diabetes Mellitus.Metabolites · 2024Article
- Clinical Blood Metabogram: Application to Overweight and Obese Patients.Metabolites · 2023Article
- Current State and Future Perspectives on Personalized Metabolomics.Metabolites · 2023Review
- Time-resolved molecular measurements reveal changes in astronauts during spaceflight.Frontiers in physiology · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals' multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.