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Metabolomic Profiling of the Effects of Dapagliflozin in Heart Failure With Reduced Ejection Fraction: DEFINE-HF

2022· article· en· W4281295871 on OpenAlexaff
Senthil Selvaraj, Zhuxuan Fu, Philip G. Jones, Lydia Coulter Kwee, Sheryl L. Windsor, Olga Ilkayeva, Christopher B. Newgard, Kenneth B. Margulies, Mansoor Husain, Silvio E. Inzucchi, Darren K. McGuire, Bertram Pitt, Benjamin M. Scirica, David E. Lanfear, Michael E. Nassif, Ali Javaheri, Robert J. Mentz, Mikhail Kosiborod, Svati H. Shah

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsTed Rogers Centre for Heart Research
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsMedicineEjection fractionDapagliflozinHeart failureMetabolomicsCardiologyInternal medicinePharmacologyBioinformaticsEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

Background: Sodium-glucose cotransporter-2 inhibitors are foundational therapy in patients with heart failure with reduced ejection fraction (HFrEF), but underlying mechanisms of benefit are not well defined. We sought to investigate the relationships between sodium-glucose cotransporter-2 inhibitor treatment, changes in metabolic pathways, and outcomes using targeted metabolomics. Methods: DEFINE-HF (Dapagliflozin Effects on Biomarkers, Symptoms and Functional Status in Patients With HF With Reduced Ejection Fraction) was a placebo-controlled trial of dapagliflozin in HFrEF. We performed targeted mass spectrometry profiling of 63 metabolites (45 acylcarnitines [markers of fatty acid oxidation], 15 amino acids, and 3 conventional metabolites) in plasma samples at randomization and 12 weeks. Using mixed models, we identified principal components analysis–defined metabolite clusters that changed differentially with treatment and examined the relationship between change in metabolite clusters and change in Kansas City Cardiomyopathy Questionnaire scores and NT-proBNP (N-terminal probrain natriuretic peptide). Models were adjusted for relevant clinical covariates and nominal P <0.05 with false discovery rate–adjusted P <0.10 was used to determine statistical significance. Results: Among the 234 DEFINE-HF participants with targeted metabolomic data, the mean age was 62.0±11.1 years, 25% were women, 38% were Black, and mean ejection fraction was 27±8%. Dapagliflozin increased ketone-related and short-chain acylcarnitine as well as medium-chain acylcarnitine principal components analysis–defined metabolite clusters compared with placebo (nominal P =0.01, false discovery rate–adjusted P =0.08 for both clusters). However, ketosis (β-hydroxybutyrate levels >500 μmol/L) was achieved infrequently (3 [2.5%] in dapagliflozin arm versus 1 [0.9%] in placebo arm) and supraphysiologic levels were not observed. Increases in long-chain acylcarnitine, long-chain dicarboxylacylcarnitine, and aromatic amino acid metabolite clusters were associated with decreases in Kansas City Cardiomyopathy Questionnaire scores (ie, worse quality of life) and increases in NT-proBNP levels, without interaction by treatment group. Conclusions: In this study of targeted metabolomics in a placebo-controlled trial of sodium-glucose cotransporter-2 inhibitors in HFrEF, we observed effects of dapagliflozin on key metabolic pathways, supporting a role for altered ketone and fatty acid biology with sodium-glucose cotransporter-2 inhibitors in patients with HFrEF. Only physiologic levels of ketosis were observed. In addition, we identified several metabolic biomarkers associated with adverse HFrEF outcomes. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02653482.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.246
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations92
Published2022
Admission routes1
Has abstractyes

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