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Record W3036242931 · doi:10.3389/fcvm.2020.00103

Serum GDF15, a Promising Biomarker in Obese Patients Undergoing Heart Surgery

2020· article· en· W3036242931 on OpenAlexafffund
Shreya Sarkar, Stephanie Legere, Ian D. Haidl, Jean S. Marshall, J.B. MacLeod, Christie Aguiar, Sohrab Lutchmedial, Ansar Hassan, Keith R. Brunt, Petra C. Kienesberger, Thomas Pulinilkunnil, Jean‐François Légaré

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsDalhousie UniversitySaint John Regional Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineBiomarkerGDF15Internal medicineHeart failureContext (archaeology)CardiologySystemic inflammationObesityInflammationGastroenterologySurgery

Abstract

fetched live from OpenAlex

Background: Obesity is a risk factor that negatively impacts patient outcomes undergoing heart surgery by mechanisms that are not well defined nor predicated on BMI alone. This knowledge gap has fuelled a search for biomarkers associated with cardiovascular disease that could provide clinical insight to surgeons. One such biomarker is growth differentiation factor15(GDF15), associated with inflammation, metabolism and heart failure outcomes but not yet examined in the context of obesity and cardiac surgery outcomes. Methods: Patients undergoing open-heart surgery were consented and enrolled for blood and tissue (atria) sampling at the time of surgery. Biomarkers analysis was carried out using ELISA and western blot/qPCR respectively. Biomarkers screening was classified by inflammation(NLR, GDF15, Galectin3, ST2, TNFR2), heart failure(HF) /remodeling(NT-proBNP) and metabolism(glycemia, lipid profile). Patients were categorized based on BMI: obese group (BMI ≥30.0) and non-obese group(BMI 20.0- 29.9). Subsequent stratification of GDF15 high patients was conservatively set as being in the 75th percentile. Results: A total of 80 patients undergoing any open-heart surgical interventions were included in the study. Obese (mean BMI= 35.8, n=38) and non-obese (mean BMI= 25.7, n=42) groups had no significant differences in age, sex or co-morbidities. Compared to other biomarkers, plasma GDF15 (mean 1736 vs. 1207ng/l, p2years. However, patients with high GDF15 (>1580ng/l) had reduced survival (65%) compared to the remaining patients with lower GDF15 levels (95%) by Kaplan Meier Analysis (median >2 years; p=0.007). Conclusions: Circulating GDF15 is a salient biomarker likely sourced from heart tissue that appears to predict higher risk obese patients for adverse outcomes. More importantly, elevated GDF15 accounted for more sensitive outcome association that BMI at 2 years post-cardiac surgery, suggesting it heralds links to pathogenicity and should be actively studied prospectively and dynamically in a post-operative follow-up.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.231
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2020
Admission routes2
Has abstractyes

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