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Record W4388595682 · doi:10.1093/eurheartj/ehad655.167

Assessing myocardial microvascular reactivity with a novel MRI imaging approach as an early biomarker of diabetic heart failure

2023· article· en· W4388595682 on OpenAlexafffund
Sadi Loai, Beiping Qiang, Michael A. Laflamme, H L M Cheng

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsMedicineCardiologyHeart failureInternal medicinePerfusionBlood volumeHeart failure with preserved ejection fractionVasodilationBlood flowBiomarkerEjection fractionPathology

Abstract

fetched live from OpenAlex

Abstract Introduction Microvascular dysfunction (mvD), and more specifically coronary microvascular disease (CMD), has been implicated as the primary hallmark of diabetic cardiomyopathy and HFpEF, afflicting millions of people worldwide. The onset and progression of microvascular disease is driven by vascular inflammation and is characterized by vascular smooth muscle cell thickening and impaired vasomodulation, both of which ultimately reduce perfusion and damage tissue. To meet metabolic demands, the body recruits additional assistance from our microvascular reserve, which is diminished during stress, thus resulting in a dampened vasomodulatory response to stimuli. Diagnosing mvD in the myocardium, therefore, requires a non-invasive method to measure vasomodulation or, more precisely, changes in microvascular blood volume. Purpose Current clinical imaging platforms lack the ability to detect vasomodulation in a sensitive and specific manner. Because of this shortcoming, there is also no literature sex comparison of cardiac microvascular reactivity in response to stress in diabetics. This work aims to address this technology and knowledge gap by developing and demonstrating a novel MRI technique for the specific assessment of vasomodulation without confounding influences from changes in blood oxygen saturation, hematocrit, or flow. Methods Elevated CO2 is a safe and reliable vasodilatory stimulus and is effective in differentiating healthy from diseased vasculature. 10% CO2 was mixed with 21% oxygen and directed into an intubated rat. A blood-pool T1 contrast agent (Ablavar) was injected intravenously as a bolus (0.3mmol/kg) followed by a saline flush, to saturate the blood volume fraction, eliminating sensitivity to molecular oxygen and producing changes in T1 dominated by the blood volume fraction. The extended residency time of Ablavar, which stems from protein binding, allows a prolonged period of stable T1 signal enhancement (approx. 40 minutes). Results Exposure to 10% CO2, a known cardiac vasodilator, elicited conflicting results when compared across sexes. Young female rats demonstrated a strong vasodilatory response within 10 minutes of hypercapnic exposure, quantified through the drastic reduction in T1, while their male counterparts exhibited little to no change. When reverting to room air following 10 minutes of CO2, both male and female animals exhibited strong vasoconstriction. Young pre-diabetic female rats exhibited a blunted response when exposed to 10% CO2, losing their ability to vasodilate and constrict. Conclusion This work described a novel MRI diagnostic tool for highly specific assessment of microvascular vasomodulation and demonstrated a greater vasodilatory response to hypercapnic stimuli in healthy female rats compared to male, along with blunted response in diabetic females. This non-invasive technology will be valuable for early diagnosis of cardiac disease in patients predisposed to developing mvD.T1 myocardial microvascular reactivityBlunted pre-diabetic female stress-CMR

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.333
Teacher spread0.291 · 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
Published2023
Admission routes2
Has abstractyes

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