Multi-fluid, multi-omics signatures of insulin resistance and incident type 2 diabetes among Puerto Rican adults.

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.