Association Between Triglyceride Level and Glycemic Control Among Insulin-Treated Patients With Type 2 Diabetes
Bibliographic record
Abstract
CONTEXT: Elevated blood triglyceride levels are known to increase the risks of diabetes and prediabetes. However, it is still unclear whether elevated triglyceride levels are associated with inadequate glycemic control in patients with type 2 diabetes mellitus. OBJECTIVE: To investigate the association between elevated triglyceride levels and inadequate glycemic control among insulin-treated patients with type 2 diabetes mellitus. DESIGN, SETTING, AND PATIENTS: We recruited 20,108 patients with type 2 diabetes mellitus who were treated with a sufficient dose of insulin. These patients were from the 2013 China National HbA1c Surveillance System study conducted in Mainland China. Multivariate logistic regressions were used to assess the association of triglyceride level with inadequate glycemic control. RESULTS: Overall, 56.0% of the subjects had elevated triglyceride levels (≥1.70 mmol/L); prevalence of HbA1c ≥7.0% (53 mmol/mol) and ≥6.5% (48 mmol/mol) was 67.2% and 83.4%, respectively. The adjusted ORs (95% CIs) of HbA1c ≥7.0% were 1.06 (0.98, 1.15), 1.35 (1.23, 1.48), and 3.12 (2.76, 3.53) for those with triglyceride levels in ranges of 1.70 to 2.29, 2.30 to 3.39, and ≥3.40 mmol/L, respectively, compared with those with triglyceride levels of <1.70 mmol/L. There was a similar association between triglyceride levels and HbA1c ≥6.5%. This association was confirmed by subgroup analyses. There was also a strong nonlinear dose-response relationship between triglyceride level and inadequate glycemic control. CONCLUSIONS: Elevated triglyceride levels were strongly associated with inadequate glycemic control; thus, suppressing triglyceride levels may attain more optimal glycemic control in patients with type 2 diabetes mellitus.
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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 | 0.003 |
| 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 | 0.001 |
| 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".