The association of weight status with glycemic control, diabetes related complications and anti-hyperglycemic medication use in patients with type 2 diabetes mellitus: The results of the Australian National Diabetes Audit (ANDA) 2015–2022
Bibliographic record
Abstract
AIMS: Type 2 diabetes has reached pandemic proportions; and obesity is considered one of its main drivers. We investigated the association of weight status with glycemic control, diabetes related complications and anti-hyperglycemic medication use among adults living with type 2 diabetes mellitus (T2DM). METHODS: We analysed data from the 2015 to 2022 cross-sectional Australian National Diabetes Audits (ANDA) to explore the association of weight status with glycemic control and diabetes complications. RESULTS: ) were included. 71 % of patients had above-target glycemia (HbA1c > 7 %). Odds of being moderately above target (HbA1c 7.1-9 %) or greatly above target (HbA1c > 9 %) were higher in patients with obesity (OR 1.20, 95 % CI 1.05, 1.37 and OR 1.43 95 % CI 1.22, 1.68 respectively). Patients with obesity were more likely to have cardiovascular disease, diabetic foot ulcers and peripheral vascular disease, despite use of a greater number of anti-hyperglycemic drugs. CONCLUSIONS: Patients with T2DM and obesity have poorer glycemic control, higher utilisation of anti-glycemic medications and greater odds of diabetes related complications. Approaches to optimise glycemic control that also deliver weight reduction should be an integral component of diabetes management to help improve health outcomes for people with T2DM.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".