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Record W2278774990 · doi:10.1161/atvb.34.suppl_1.42

Abstract 42: Haptoglobin Genotype as a Marker of Vascular Intraplaque Hemorrhage

2014· article· en· W2278774990 on OpenAlexaff
Tina Binesh Marvasti, Navneet Singh, Pascal N. Tyrrell, Betty Wong, David E.C. Cole, Alan R. Moody

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2014
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsHaptoglobinMedicineInternal medicineMagnetic resonance imagingGenotypeStroke (engine)GenotypingCardiologyStenosisMyocardial infarctionLogistic regressionPathologyGastroenterologyRadiologyGene

Abstract

fetched live from OpenAlex

Background: Intraplaque hemorrhage (IPH), a component of late stage atherosclerosis, is a critical factor in plaque destabilization leading to stroke and myocardial infarction. Magnetic resonance imaging (MRI) is able to identify IPH and allows for identification of high risk patients. Currently however, no routinely available tests are conducted to identify individuals at risk of IPH. IPH is a rich source of free hemoglobin (Hb), a potent oxidant. The plasma protein haptoglobin (Hp) binds Hb forming a Hb-Hp complex, that can be engulfed by tissue macrophages.The reduction in oxidative stress decreases vascular inflammation. In humans, the Hp gene has three different genotypes: Hp1-1, Hp2-2 and Hp1-2. The Hb-Hp2-2 complex has a lower binding affinity to macrophages, resulting in higher oxidative burden. Numerous studies have demonstrated a higher risk of cardiovascular events in Hp2-2 individuals. We therefore hypothesized that patients of a Hp2-2 genotype have a greater risk of MR-IPH. Methods: From 2010 to 2014, patients with non-surgical carotid artery disease (30-95% stenosis) underwent 3T carotid MRI that included a MR-IPH sequence. A radiologist with 25 years of expertise in MR-IPH imaging determined the presence of MR-IPH using widely accepted methods. Patients’ charts were blindly reviewed for demographics and medical history. Patients were genotyped for Hp1/2 using an established PCR genotyping method. Statistical association analysis was performed using logistic regression. Results and Conclusions: Out of the 80 recruited patients (mean age, 72.8 years; range 52-100), those with Hp2-2 genotype vs. Hp1-1/Hp1-2 had a prevalence of MR-IPH of 65% compared to 41%(p=0.09). Non-diabetics (HbA1c <6.5%) with Hp2-2 genotype had an increased risk of IPH compared to non-diabetics with Hp1-1/Hp 1-2 genotype (OR=3, 95% CI 1.04-8.73, p=0.03). Males were also at an increased risk of IPH (OR=4.05, 95% CI 1.53-10.70, p=0.004) and the majority (71%) of males with IPH were of the Hp2-2 genotype. In conclusion, non-diabetic Hp2-2 male patients with history of vascular disease are the population with the highest risk of developing IPH and they can be identified using simple haptoglobin genotype testing.

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 score0.004
Threshold uncertainty score0.012

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.020
GPT teacher head0.264
Teacher spread0.244 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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