P2-15 Genetic polymorphisms of innate immunity-related inflammatory pathways and their association with factors related to type 2 diabetes
Bibliographic record
Abstract
Introduction Type 2 diabetes mellitus (T2DM) has been linked to a state of chronic inflammation due to innate immunity. Serum levels of pro-inflammatory cytokines are elevated in the early stages of T2DM and increase with disease progression. Genetic variation can affect the innate immune response to environmental factors, and may determine an individual's risk of disease. Methods We conducted a cross-sectional study in 7384 subjects from the TwinsUK Registry to evaluate the association between 18 single nucleotide polymorphisms (SNPs) in five genes (TLR4, IL1A, IL6, TNFA, and CRP) along the innate immunity-related inflammatory pathway and biomarkers of predisposition to T2DM [fasting insulin and glucose, HDL- and LDL- cholesterols, triglycerides (TGs), amyloid-A, sensitive C reactive protein (sCRP) and vitamin D binding protein (VDBP) and body mass index (BMI)]. Results Of the 18 SNPs examined (18 SNPs with 9 phenotypes), 14 were significantly associated with a metabolic risk factors for T2D (P£0.0027). Fasting insulin was associated with SNPs in IL6, TLR4 and TNFA, whereas serum LDL-C was associated with variants of IL1A and IL6. Serum CRP level was associated with SNPs in IL1A, IL6, TLR4 and CRP. Correlation among the different factors related to risk of T2DM showed a significant (p<0.0001) positive correlation between BMI and glucose (r=0.22), insulin (r=0.23), amyloid-A (r=0.23), sCRP (r=0.37), LDL-C (r=0.09) and TGs (r=0.32). Conclusion Genetic variants in the innate immunity pathway are associated with biomarkers of T2DM and metabolic syndrome, an observation that may provide a rationale for studying their use in early disease risk prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".