Abstract 15785: Effects of Empagliflozin on Fatty Acid Binding Protein 4 and Body Composition in Patients Without Diabetes but With Risk Factors for Adverse Cardiac Remodeling From the Empa-Heart 2 Cardiolink-7 Randomized Clinical Trial
Bibliographic record
Abstract
Background: Prevention of the progression from stage B to C heart failure is an unmet need. Increased fat composition in skeletal muscle is one of the mechanisms associated with heart failure with preserved ejection fraction. Fatty acid binding protein 4 (FABP4) is known to facilitate the transport of fatty acid to tissue mitochondria for lipid oxidation. Knockdown of FABP4 gene is associated with increased body weight and fat mass in animal models. We sought to determine if Sodium-glucose co-transporter 2 (SGLT2) inhibition with empagliflozin leads to change in blood FABP4 levels and improvement in body composition in patients without diabetes and significant heart failure but with risk factors for adverse cardiac remodeling. Hypothesis: Empagliflozin increases blood FABP4 concentration and lowers body fat composition without changes in skeletal muscle mass. Methods: A total of 42 individuals between 40 and 80 years of age without diabetes but with risk factors for adverse cardiac remodeling were randomized to empagliflozin (10 mg/day; n=21) and placebo (n=21) groups for 6 months. Body composition parameters measured by bio-impedance analysis (InBody-720) included body weight (BW), skeletal muscle mass (SMM) and body fat mass (BFM) at baseline and 6 months follow up. Blood FABP4 concentrations were measured at the same time points. Results: There were no significant differences between two groups in baseline characteristics, BW, SMM, BFM, or FABP4. From baseline to 6 months, empagliflozin compared to placebo significantly increased blood FABP4 concentrations (6067.3 ± 7591.4 pg/ml vs 2714.9 ± 11232.6 pg/ml, p = 0.002), but decreased BW (-1.19 ± 1.01 kg vs 0.34 ± 2.59 kg, p = 0.018) and BFM (-0.68 ± 1.27 kg vs 0.53 ± 2.01 kg, p = 0.024). However, empagliflozin did not significantly affect changes in SMM from baseline to 6 months (-4.01 ± 0.70 kg vs -0.17 ± 0.96 kg, p = 0.387). Conclusions: SGLT2 inhibition with empagliflozin, in patients with neither diabetes nor significant heart failure but with risk factors for adverse cardiac remodeling, increased blood FABP4 concentrations and ameliorated BW and body fat composition with no effect on skeletal muscle mass following 6 months of treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".