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Record W4401162220 · doi:10.1016/j.celrep.2024.114573

Growth differentiation factor 15 alleviates diastolic dysfunction in mice with experimental diabetic cardiomyopathy

2024· article· en· W4401162220 on OpenAlexaff
Jordan S. F. Chan, Seyed Amirhossein Tabatabaei Dakhili, Maria Areli Lorenzana‐Carrillo, Keshav Gopal, Serena M. Pulente, Amanda A. Greenwell, Kunyan Yang, Christina T. Saed, Magnus J. Stenlund, Sally R. Ferrari, Indiresh A Mangra-Bala, Tanin Shafaati, Rakesh Bhat, Farah Eaton, Michael Overduin, Sebastian B. Jørgensen, Gregory R. Steinberg, Erin E. Mulvihill, Gopinath Sutendra, John R. Ussher

Bibliographic record

VenueCell Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsMcMaster UniversityUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsDiabetic cardiomyopathyCardiomyopathyInternal medicineDiastoleMedicineCardiologyEndocrinologyDiabetes mellitusHeart failureBlood pressure

Abstract

fetched live from OpenAlex

Growth differentiation factor 15 (GDF15) is a peptide with utility in obesity, as it decreases appetite and promotes weight loss. Because obesity increases the risk for type 2 diabetes (T2D) and cardiovascular disease, it is imperative to understand the cardiovascular actions of GDF15, especially since elevated GDF15 levels are an established biomarker for heart failure. As weight loss should be encouraged in the early stages of obesity-related prediabetes/T2D, where diabetic cardiomyopathy is often present, we assessed whether treatment with GDF15 influences its pathology. We observed that GDF15 treatment alleviates diastolic dysfunction in mice with T2D independent of weight loss. This cardioprotection was associated with a reduction in cardiac inflammation, which was likely mediated via indirect actions, as direct treatment of adult mouse cardiomyocytes and differentiated THP-1 human macrophages with GDF15 failed to alleviate lipopolysaccharide-induced inflammation. Therapeutic manipulation of GDF15 action may thus have utility for both obesity and diabetic cardiomyopathy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.470

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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 designBench or experimental
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

Citations15
Published2024
Admission routes1
Has abstractyes

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