Inflammatory Markers and Haptoglobin Polymorphism in Saudi with Non-insulin-dependent Diabetes Mellitus
Bibliographic record
Abstract
OBJECTIVES: Haptoglobin (Hp) polymorphism associated with clinical evolution of several inflammatory diseases and considered as a predictive factor for development of diabetes complications. We designed the present study to investigate the frequency distribution of Hp phenotypes among Saudi with non-insulin-dependent diabetes mellitus compared to healthy nondiabetic subjects. Moreover, we explored the possibility of relationship between serum levels of inflammatory markers (namely, high-sensitive C-reactive proteins "hs-CRP", interleukin (IL)-6, and Hp) and Hp phenotypes. METHODS: In the present case-control study, we enrolled 60 type 2 diabetic patients as the study group and 60 healthy subjects as the control group. We assayed serum levels of Hp and hs-CRP by immunoturbidimetric method; while IL-6 was measured by ELISA. Native polyacrylamide gel electrophoresis was used for determination of Hp phenotypes. RESULTS: In type 2 diabetics, serum concentrations of IL-6, hs-CRP, and Hp were significantly elevated and correlated to body mass index. Moreover, there was a significant correlation between plasma glucose level and Hp (r = 0.577, p = 0.000), IL-6 (r = 0.448, p = 0.000), and hs-CRP (r = 0.380, p = 0.001). In addition, data demonstrated a positive correlation between HbA1c and Hp (r = 0.521, p = 0.000), IL-6 (r = 0.420, p = 0.001), and hs-CRP (r = 0.353, p = 0.008). Hp 2-1 phenotype predominated among subjects in both study and control groups. No significant association between Hp phenotypes with any of the investigated inflammatory markers was documented. CONCLUSION: Inflammation may represent the link between type 2 diabetes and obesity. Hp 2-1 was the predominant phenotype among Saudi type 2 diabetics as well as healthy subjects. In addition to Hp; other possible genetic polymorphisms like CRP may have its effect on diabetes through different mechanisms.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".