ArticleFrontiers in endocrinology2025
Multi-fluid, multi-omics signatures of insulin resistance and incident type 2 diabetes among Puerto Rican adults.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Introduction: Previous studies have examined the prediction of insulin resistance and type 2 diabetes (T2D) using plasma or saliva omics, but none have combined metabolomics and proteomics from multiple biofluids, such as plasma and saliva. Among Puerto Rican adults, a high-risk population with health disparities, we sought to determine whether adding saliva improves T2D prediction over plasma alone. Methods: In this pilot matched case-control study within the San Juan Overweight and Obese Adults Longitudinal Study (SOALS), we analyzed baseline samples from 40 healthy participants, 20 of whom developed T2D at follow-up (year 3) and 20 age- and sex-matched controls. We profiled 7,595 proteins in plasma and saliva (SomaScan) and 1,051 plasma and 635 saliva metabolites [ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS); Metabolon, Inc.] for analysis. We evaluated nine omics signatures combining biofluid (plasma, saliva, or both) and omics (metabolomics, proteomics, or both). Nested elastic net regression with leave-one-out cross-validation identified insulin resistance signatures, and receiver operating characteristic (ROC) curves [area under the curve (AUC)] assessed their predictive performance for T2D. We used multivariable conditional logistic regression to evaluate associations between omics scores and incident T2D. Results: The strongest T2D prediction was observed for plasma proteomics and multi-omics, multi-fluid proteomics, and multi-omics signatures (AUCs: 0.80-0.83). Saliva proteomics, metabolomics, and multi-omics, along with plasma metabolomics and multi-fluid metabolomics, exhibited limited prediction (AUCs: 0.51-0.67). Plasma proteomics, multi-omics, and multi-fluid multi-omics were positively associated with T2D [hazard ratios (HRs): 3.00-3.68]. Conclusion: Plasma proteomic signatures provided the strongest T2D prediction. Adding saliva data did not improve predictive performance of plasma data.
Indexed as
Identifiers
What OpenQuestion holds
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.