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Abstract 15589: Mitochondrial Metabolites Predict Cardiovascular Outcomes and Heart Failure in People With Type 2 Diabetes Mellitus and Vascular Disease: A Tecos Substudy

2020· article· en· W3111507359 on OpenAlexaff
Jessica A. Regan, Jennifer Green, L. Truby, Maggie Nguyen, Stephanie Williams, Robert J. Mentz, Robert W. McGarrah, Yinggan Zheng, John B. Buse, Darren K. McGuire, Eberhard Standl, Paul W. Armstrong, Rury R. Holman, Eric D. Peterson, Svati H. Shah

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsCanadian VIGOUR Centre
Fundersnot available
KeywordsMedicineMaceInternal medicineDiabetes mellitusType 2 diabetesCardiologyMetaboliteType 2 Diabetes MellitusHeart failureEjection fractionMyocardial infarctionEndocrinologyGastroenterology

Abstract

fetched live from OpenAlex

Introduction: People with type 2 diabetes mellitus (DM) have a large burden of cardiovascular (CV) morbidity and mortality, but the likelihood of these outcomes varies and existing risk calculators do not fully capture risk for individuals. Hypothesis: Circulating metabolites characterizing mitochondrial dysfunction may be novel predictors for CV outcomes. Methods: We performed targeted mass-spectrometry metabolite profiling (45 acylcarnitines, 15 amino acids) on baseline plasma samples from 568 cases (498 with major adverse cardiac events (MACE) and 131 with incident hospitalization for heart failure (hHF)) and 568 matched controls (without events) from the patients assigned to placebo in Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS). Matching was based on history of HF, coronary artery disease, BMI, hemoglobin A1C, creatinine, low-density lipoprotein cholesterol, fasting status and ejection fraction. Principal components analysis (PCA) was used for dimensionality reduction, and conditional logistic regression to determine association of PCA factors with MACE or hHF and for individual metabolites within significant factors (false discovery rate q<0.05) Results: Of 12 PCA-derived metabolite factors, three were associated significantly with MACE or hHF; these factors were composed of: 1) short-chain dicarboxylacylcarnitines and long chain acylcarnitines (OR 1.50, q=0.03); 2) medium chain acylcarnitines (OR 1.28, q=0.001); 3) C5:1 and one medium-chain dicarboxylacylcarnitine (OR 1.17, q=0.03). Of these three factors, none were significantly associated with the individual components of MACE. Ten individual metabolites, including short- and medium-chain dicarboxylacylcarnitines and medium- and long-chain acylcarnitines, remained significantly associated with MACE (q<0.05). Two metabolites, one short-chain dicarboxylacylcarnitine and one medium-chain acylcarnitine were associated with hHF (q<0.05). Conclusions: Metabolites reporting on dysregulated mitochondrial fatty acid oxidation and endoplasmic reticulum stress are elevated in individuals with DM who progress to MACE and/or hHF. These biomarkers may improve CV risk prediction in DM patients and help highlight emerging risk mechanisms.

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)0.002
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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