Assessment of cardiometabolic risk and prevalence of meeting treatment guidelines among patients with type 2 diabetes stratified according to their use of insulin and/or other diabetic medications: results from <scp>INSPIRE ME IAA</scp>
Bibliographic record
Abstract
AIM: Visceral adipose tissue (VAT) and liver fat (LF) are strongly associated with type 2 diabetes. It is not known, however, how diabetes treatment and/or risk factor management modulates the association between VAT, LF and diabetes. The aim was to determine the level of VAT and LF in patients with type 2 diabetes according to their treatment status and achievement of the American Diabetes Association's (ADA) diabetes management goals. METHODS: We performed a cross-sectional analysis of the baseline data of the International Study of the Prediction of Intra-Abdominal Adiposity and its Relationship with Cardiometabolic risk/Intra-Abdominal Adiposity (INSPIRE ME IAA), a 3-year prospective cardiometabolic imaging study conducted in 29 countries. Patients (n = 3991) were divided into four groups: (i) those without type 2 diabetes (noT2D n = 1003 men, n = 1027 women); (ii) those with type 2 diabetes but not treated with diabetes medications (T2Dnomeds n = 248 men, n = 198 women); (iii) those with type 2 diabetes and treated with diabetes medications but not yet using insulin (T2Dmeds-ins n = 591 men, n = 484 women) and (iv) those with type 2 diabetes and treated with insulin (T2Dmeds+ins n = 233 men, n = 207 women). Abdominal and liver adiposity were measured by computed tomography. RESULTS: Fewer patients with high VAT or LF achieved the ADA's goals for high-density lipoprotein cholesterol (HDL-C) or triglycerides compared to patients with low VAT or LF. Visceral adiposity (p = 0.02 men, p = 0.003 women) and LF (p = 0.0002 men, p = 0.0004 women) increased among patients who met fewer of the ADA treatment criteria, regardless of type 2 diabetes treatment. CONCLUSION: Residual cardiometabolic risk exists among patients with type 2 diabetes characterized by elevated VAT and LF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".