Global Salivary Proteomic Profiling of Individuals with the Co-Occurrence of Type 2 Diabetes Mellitus, Dyslipidemia, and Periodontitis
Bibliographic record
Abstract
Human whole saliva contains informative proteins related to disease processes and is important for oral cavity homeostasis. The aim of this study was to investigate the global salivary proteomic profile, including functional enrichment analysis of individuals affected by combinations of poorly/well-controlled type 2 diabetes mellitus (T2DM), dyslipidemia (DL), and periodontitis (P) for the identification of potential disease biomarkers. Biochemical and periodontal evaluations were performed on 150 subjects divided into five groups according to disease combinations. Unstimulated saliva was collected, and proteomic analysis was performed using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS). Results were obtained by searching for the Homo sapiens database of the UniProt catalog using Proteome Discoverer 1.3. PANTHER GO-Slim (PANTHER version 19.0), and Cytoscape 3.10.1 (extensions Metascape) were used to access biological pathways. A total of 1762 proteins were identified in saliva samples. The proteomic profile of T2DM-DL-P groups (G1-poorly controlled T2DM; G2-well-controlled T2DM) were the most diverse and functionally enriched. In G1 and G3, the most abundant protein was TTN, with ENO-1 being highly enriched and associated with the aerobic glycolysis pathway and PFN1 associated with a pro-inflammatory environment. PFN1 was highly enriched in (G1) AKIRIN-2 (immune response), and in (G2) KMT2A (epigenetic regulation) and CALML3 (DNA metabolism and repair). For (G4), the highest abundant protein was RIMS-1, with VIRMA (cytoskeletal organization) being the most enriched. The global salivary proteomics analyses demonstrated significantly altered protein profiles in each group of different pathological combinations, providing new insights into their biology and identifying potential diagnostic and therapeutic candidates for these diseases of growing global concern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".