Association of predicted lean body mass and fat mass with cardiovascular events in patients with type 2 diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: Previous studies have found that predicted fat mass and lean body mass may act differently on adverse events. However, the cardiovascular prognostic value of lean body mass and fat mass in patients with type 2 diabetes mellitus (T2DM) has not yet been investigated. We sought to investigate the relation between predicted lean body mass or fat mass and the risk of cardiovascular disease in patients with T2DM. METHODS: We conducted a post hoc analysis of data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study to investigate the relation between the predicted lean body mass or fat mass and major adverse cardiovascular events in patients with T2DM. We used sex-specific quartiles of predicted lean body mass index (BMI; kg/m2) and fat mass index (kg/m2). We defined a major adverse cardiovascular event as a composite of nonfatal myocardial infarction, nonfatal stroke or death from cardiovascular causes. RESULTS: After a mean follow-up period of 8.8 years, we found that a major cardiovascular event occurred in 1801 of 10 251 patients (17.8%). Predicted lean BMI was not associated with major cardiovascular events (p = 0.34). Compared with patients in the first quartile (incidence rate 16.4%; 17.2%, 17.5% and 19.8% for the second, third and four quartiles, respectively) of predicted fat mass index, those in the fourth quartile had a hazard ratio of 1.53 (95% confidence interval 1.23–1.91). INTERPRETATION: In patients with T2DM, we found that predicted fat mass had a strong positive association with a higher risk of a major adverse cardiovascular event. Increasing lean body mass did not have a protective role. Trial registration:ClinicalTrials.gov., no. NCT00000620
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".