Improvements in Cardiometabolic Risk Factors by Weight Reduction: A Post Hoc Analysis of Adults With Obesity Randomly Assigned to Tirzepatide
Bibliographic record
Abstract
BACKGROUND: Tirzepatide reduced weight and improved cardiometabolic risk factors for participants in the SURMOUNT-1 trial. The changes in cardiometabolic risk factors by degree of tirzepatide-induced weight reduction across a wide spectrum of weight loss have not been reported. OBJECTIVE: To determine changes in cardiometabolic risk factors by weight reduction. DESIGN: Post hoc analysis of the phase 3, randomized, double-blind, SURMOUNT-1 trial (ClinicalTrials.gov: NCT04184622). SETTING: 119 sites in 9 countries. PARTICIPANTS: = 1605) with obesity, or overweight with weight-related complications (excluding diabetes), randomly assigned to tirzepatide treatment groups. INTERVENTION: Once-weekly tirzepatide, 5, 10, or 15 mg. MEASUREMENTS: Changes from baseline to week 72 in cardiometabolic risk factors by weight reduction. RESULTS: were observed even with modest weight reduction, with the steepest effect occurring between less than 5% and less than 20% weight reduction. Improvements in levels of triglycerides, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, and non-HDL cholesterol were primarily observed only after weight reductions greater than 10%. Results were consistent after adjustment for baseline differences. LIMITATIONS: The analysis was post hoc and should be regarded as hypothesis-generating. Duration and sample size precluded evaluation of cardiovascular outcomes. CONCLUSION: In SURMOUNT-1, tirzepatide-associated improvements in cardiometabolic risk factors positively related to the degree of weight reduction, but the pattern varied depending on outcome measure. PRIMARY FUNDING SOURCE: Eli Lilly and Company.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".