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Record W4387959736 · doi:10.1183/13993003.01253-2023

Cystic fibrosis and the cardiovascular system: the unexpected heartache

2023· letter· en· W4387959736 on OpenAlexaff
Helge Hebestreit, Christina S. Thornton

Bibliographic record

VenueEuropean Respiratory Journal · 2023
Typeletter
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePopulationCystic fibrosisDiabetes mellitusDiseaseInternal medicineHeart failureCohortBody mass indexIntensive care medicineEndocrinologyEnvironmental health

Abstract

fetched live from OpenAlex

In recent years, advancements in medical care and therapies have significantly improved the life expectancy of individuals with cystic fibrosis (CF). As a result, an increasing number of people with CF are now reaching adulthood and experience the long-term consequences of the disease. One of the emerging challenges faced by this growing population is the increased risk of cardiac disease [1]. The rate of cardiovascular disease (CVD) in CF varies among individuals and studies, and the exact prevalence is not well-established. Indeed, a higher body mass index (BMI), lipid metabolism and smoking, all traditional cardiac risk factors, are often attenuated in CF disease [2]. However, it is generally recognised that people with CF have an increased risk of developing cardiovascular complications compared to the general population, which historically has been associated as secondary to progressive lung disease and respiratory failure. Other factors, including presence of CF-related diabetes, high salt dietary intake, chronic kidney disease and chronic inflammation ( i.e. similar to those of other chronic diseases, including HIV, rheumatoid arthritis and systemic lupus erythematosus) all render cardiac risk in this population [2]. Studies have reported varying prevalence rates of CVD, ranging from around 10% to 30% in the CF population. The most common cardiovascular conditions seen in CF patients include atherosclerosis, hypertension and heart failure; however, other clinical sequalae include effects on the aorta, pulmonary hypertension, and peripheral vascular disease [1]. More recently, a multicentre retrospective cohort of people with CF and SARS-CoV-2 infection (n=422) reported that nearly half had a history of diabetes (47%) or hypertension (48%) [3]. Furthermore, 22.5% had a history of ischaemic heart disease, suggesting that CVD might be under-reported in this population. Finally, all reported cardiac risk factors were significantly higher in people with CF compared to those without CF (all p-values <0.01). Consequently, there is a need for proactive cardiac monitoring and management in people with CF to identify and address cardiovascular risk factors early on. Several mechanisms underpin CVD risk in CF [4]. One is via chronic hypoxaemia in CF caused by ventilation heterogeneity and destruction of lung tissue, leading to pulmonary vasoconstriction, pulmonary hypertension and cor pulmonale. A second is through the presence of cystic fibrosis transmembrane conductance regulator (CFTR) protein in cardiac myocytes, including atrial and ventricular myocytes, as well as blood vessels. Similar to its role in epithelial cells, CFTR in the heart plays a role in chloride ion conduction. Finally, the loss of CFTR function can impact myocyte contractility, intracellular calcium signalling, myocardial fibrosis and heart remodelling. Evaluation of the cardiovascular system is important in cystic fibrosis patients

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.030
GPT teacher head0.269
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations9
Published2023
Admission routes1
Has abstractyes

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