728-P: Biomarkers of Low-Grade Inflammation in Novel Subtypes of Patients at Risk for Diabetes
Bibliographic record
Abstract
In the Tübingen Family Study recently 6 subtypes of prediabetic metabolism were identified. Cluster 3, 5 and 6 have a moderate to high risk for T2D or its associated complications. Cluster 3 is characterized by low insulin secretion, cluster 5 by insulin resistance and high liver fat content. Individuals in cluster 6 have an elevated risk of nephropathy despite a delayed diabetes development. Low-grade inflammation has been implicated in the development of T2D and cardiorenal complications. We hypothesized that the subtypes differ in levels of inflammatory biomarkers. Thus, we measured 31 inflammatory biomarkers associated with metabolic disease. We used Bio-Plex® multiplex immunoassays in 145 individuals who were recruited in Tübingen from the Prediabetes Lifestyle Intervention Study (PLIS) - a multicenter study conducted in individuals with ADA-defined prediabetes. Cluster comparison was done using ANOVA models, raw unadjusted values are reported. We identified eight biomarkers which differed among diabetes-risk subtypes, namely MIP1a, MIP1b, IL18, Osteopontin (OPN), IFNy, TNFb, and soluble TNFa-Receptor (sTNF-R)- 1 and -2. High risk cluster 3, 5 and 6 had significantly elevated levels of several inflammatory biomarkers compared to low risk cluster 2 with the lowest levels of these biomarkers. Within the high-risk groups, cluster 5 had increased biomarker levels compared to cluster 3 (e.g., IL-18: cluster 5: 86.2 pg/ml ± 3.9 vs cluster 3: 66.8 pg/ml ± 4.8, p=0.018; OPN: 16.8 ng/ml ± 1.0 vs. 12.8 ng/ml ± 0.8, p = 0.022; MIP1a 2.2 pg/ml ± 0.1 vs. 1.6 pg/ml ± 0.1, p < 0.001). Cluster 6 showed lower levels of IFNy than cluster 3 (2.2pg/ml ± 0.3 vs 3.5pg/ml ± 0.2, P=0.019). Our data show that the novel T2D risk subtypes differ in inflammatory biomarkers. This suggests that a low-grade inflammation may be involved in the progression from prediabetes to T2D and complications. A more precise mechanistic understanding is needed in order to establish precise preventive strategies for different risk phenotypes. Disclosure S.Katzenstein: None. J.Szendroedi: None. M.Roden: Advisory Panel; Eli Lilly and Company, Consultant; TARGET PharmaSolutions, Inc., Research Support; Boehringer-Ingelheim, Novo Nordisk, Novartis, Sanofi. L.Fritsche: None. N.Stefan: Advisory Panel; Pfizer Inc., Research Support; Sanofi, Speaker's Bureau; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Lilly Diabetes, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. R.Wagner: Advisory Panel; Daiichi Sankyo, Speaker's Bureau; Novo Nordisk, Sanofi. A.Fritsche: Advisory Panel; Novo Nordisk, Lilly, Sanofi, Boehringer-Ingelheim, Speaker's Bureau; AstraZeneca, SYNLAB Holding Deutschland GmbH. A.L.Birkenfeld: None. R.Jumpertz von schwartzenberg: Other Relationship; Sanofi, Amgen Inc., Lilly, Novo Nordisk. A.Sandforth: None. V.Minelli faiao: None. J.Seissler: None. S.R.Bornstein: None. N.Perakakis: Advisory Panel; Bayer Inc., Other Relationship; Novo Nordisk, Novo Nordisk. A.Schürmann: None. S.Kabisch: Other Relationship; Berlin-Chemie AG, Sanofi, Lilly, Boehringer Ingelheim Inc., JuZo Akademie, Research Support; J. Rettenmaier / Söhne, Wilhelm-Doerenkamp Foundation, German Center of Diabetes Research. M.Blüher: Advisory Panel; Boehringer Ingelheim Inc., Lilly, Novo Nordisk, Consultant; Novo Nordisk Foundation, Speaker's Bureau; Amgen Inc., AstraZeneca, Bayer Inc., Daiichi Sankyo, Novartis, Sanofi-Aventis Deutschland GmbH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".