Clinical implications of intentional weight loss in people living with type 2 diabetes: A real‐world database study
Bibliographic record
Abstract
Abstract Aims This study aimed to evaluate the impact of intentional weight loss on the risk of microvascular and macrovascular complications in people with obesity and type 2 diabetes (T2D), while accounting for changes in glycaemic control. Materials and Methods Data from adults living with obesity (body mass index [BMI] ≥30 kg/m 2 ) and T2D were extracted from Clinical Practice Research Datalink (CPRD) Aurum database from 2006 to 2022. Incidence of microvascular (retinopathy, neuropathy, and chronic kidney disease [CKD]) and macrovascular (myocardial infarction [MI], peripheral artery disease [PAD], and stroke) complications following a 4‐year intentional weight loss period were quantified, and risk of outcomes estimated using Cox proportional hazard regression. Results Data from 100 507 people (mean age 53.3 years; 58% male) was included. Mean [SD] relative reduction in BMI was −2.2% [9.1%] and mean [SD] absolute change in HbA 1c was 0.0% [1.9%]. Risk of composite microvascular and macrovascular complications was significantly reduced per 1% BMI reduction (hazard ratio [HR] 0.990 [95% CI 0.988, 0.992] and 0.996 [95% CI 0.994, 0.998], respectively) and per 1%‐point decrease in HbA 1c (HR 0.875 [95% CI 0.866, 0.884] and 0.872 [95% CI 0.862, 0.883]). Results were consistent across all subgroups. Reductions in BMI and HbA 1c were associated with reduced risk of retinopathy, neuropathy, CKD, and PAD. Reductions in HbA 1c ‐only were associated with reduced risk of MI and stroke. Conclusions A reduction in BMI and HbA 1c by intentional weight loss, independently and concomitantly, was associated with a reduced risk of microvascular and macrovascular complications in people with obesity and T2D.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".