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Mitral valve phenotypes in SMAD3-related thoracic aortic disease: insights from the Montalcino Aortic Consortium

2024· article· en· W4403807020 on OpenAlexaff
Sumit Prakash, Kishan L. Asokan, Wannes Renders, Laura Muiño Mosquera, Julie De Backer, David W. Jantzen, Anji T. Yetman, Gisela Teixidó‐Turà, Richmond Jeremy, Edward G. Jones, Shaine A. Morris, Maral Ouzonian, Alan C. Braverman, Guillaume Jondeau, Dianna M. Milewicz

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCardiologyInternal medicineAortic valveThoracic aortaAorta

Abstract

fetched live from OpenAlex

Abstract Background SMAD3 pathogenic variants (PV) predispose to heritable thoracic aortic aneurysms and dissections (HTAD). Mitral annular disjunction (MAD) associated with mitral valve prolapse (MVP) and mitral regurgitation (MR) was identified as a potential new marker of disease severity in Marfan syndrome. The prevalence and prognostic impact of these mitral phenotypes in other forms of HTAD are less known. Purpose We hypothesize that mitral abnormalities are increased in SMAD3-related HTAD and are associated with increased risk for valve and aortic complications. Methods The Montalcino Aortic Consortium (MAC) registry has enrolled participants with PV in 15 HTAD genes. Subjects >16 years old with complete imaging and clinical data were included in this study. MVP was defined according to current guidelines. MR was classified as mild, moderate, or severe. MAD was defined by a >3-millimeter gap between the posterior mitral valve leaflet hinge point and the inferolateral myocardium as confirmed by direct measurement of echocardiogram images. Frequencies of MVP, MR, and MAD were compared between MAC participants with SMAD3 PV and other HTAD PV. Associations between clinical and echo characteristics and the composite outcome of arrhythmia, aortic surgery, aortic dissection, or congestive heart failure was evaluated with multivariable logistic regression. Results In 671 MAC participants (129 with SMAD3 PV, 36 [IQR 19-51] years, 49% female), the prevalence of MVP was 15% and the prevalence of MR was 22%. Both MVP (31/129, 24%, vs. 67/542, 12%, OR 2.2 [1.4-3.6], P<0.001) and MR (40/129, 31%, vs. 108/542, 20%, OR 1.8 [1.2-2.8], P<0.006) were more common in participants with SMAD3 PV compared to other PV and were further enriched in cases with SMAD3 missense PV compared to loss of function PV (MR or MVP: 26/48, 54% vs. 7/30, 23%, OR 3.9 [1.4-10.8], P<0.007). Images were available from 238 MAC participants (67 with SMAD3 PV) to assess for MAD. MAD (29/67, 43%, vs. 28/171, 16%, OR 3.9 [2.1-7.3], P<0.0001), or the composite of MR, MVP, or MAD (42/67, 63%, vs. 69/171, 35%, OR 3.2 [1.8-5.7], P<0.0001) was more common with SMAD3 PV compared to other PV (Figure 1). Twelve participants with SMAD3 PV (18%) had prominent mitral phenotypes (>mild MR, >10 mm MAD, or MAD with MVP) but no significant aortic dilation (Z<3). When controlling for age and sex, MR or MVP, but not MAD, was independently associated with the composite outcome (OR 16 [2.4-110], Figure 2). Conclusion Mitral valve pathology, including MVP, MR, and MAD, is increased in individuals with SMAD3 PV compared to other HTAD PV. Prominent mitral phenotypes related to SMAD3 missense PV may identify a high-risk subgroup with adverse cardiovascular outcomes. Because congenital mitral disease may be the primary presenting feature of SMAD3 PV, genetic testing for HTAD should be considered for such patients, especially if they also have a family history of thoracic aortic disease.

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)0.004
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.342
Teacher spread0.317 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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