Impaired<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi mathvariant="bold">β</mml:mi></mml:mrow></mml:math>Cell Function in Chinese Newly Diagnosed Type 2 Diabetes Mellitus with Hyperlipidemia
Bibliographic record
Abstract
The objective is to explore the effects of hyperlipidemia on β cell function in newly diagnosed type 2 diabetes mellitus (T2DM). 208 patients were enrolled in the study and were divided into newly diagnosed T2DM with hyperlipidemia (132 patients) and without hyperlipidemia (76 patients). Demographic data, glucose levels, insulin levels, lipid profiles, homeostasis model assessment for β cell function index (HOMA-β ), homeostasis model assessment for insulin resistance index (HOMA-IR), and quantitative insulin-sensitivity check index (QUICKI) were compared between the two groups. We found that comparing with those of normal lipid levels, the subjects of newly diagnosed T2DM with hyperlipidemia were younger, and had declined HOMA-β . However, the levels of HOMA-β were comparable regardless of different lipid profiles (combined hyperlipidemia, hypertriglyceridemia, and hypercholesterolemia). Multiple stepwise linear regression analysis showed that high fasting plasma glucose (FPG), decreased fasting insulin level (FINS), and high triglyceride (TG) were independent risk factors of β cell dysfunction in newly diagnosed T2DM. Therefore, the management of dyslipidemia, together with glucose control, may be beneficial for T2DM with hyperlipidemia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".