Genetic Variants in PHACTR1 & LPL Mediate Restenosis Risk in Coronary Artery Patients
Bibliographic record
Abstract
Cynthia Al Hageh,1,* Stephanie Chacar,2,* Thenmozhi Venkatachalam,2 Dominique Gauguier,3,4 Antoine Abchee,5 Elie Chammas,6 Hamdan Hamdan,2 Siobhan O’Sullivan,1 Pierre Zalloua,1,7,8 Moni Nader2,7 1Department of Molecular Biology and Genetics, College of Medicine and Health Sciences, Khalifa University for Science and Technology, Abu Dhabi, United Arab Emirates; 2Department of Physiology and Immunology College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, UAE; 3McGill University and Genome Quebec Innovation Centre, Montreal, QC, H3A 0G1, Canada; 4Université Paris Cité, INSERM, Paris, France; 5Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates; 6School of Medicine, Lebanese University, Beirut, Lebanon; 7Biotechnology Center, Khalifa University for Science and Technology, Abu Dhabi, United Arab Emirates; 8Harvard T.H. Chan School of Public Health, Boston, MA, USA*These authors contributed equally to this workCorrespondence: Pierre Zalloua; Moni Nader, College of Medicine and Health Sciences, Khalifa University for Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates, Email pierre.zalloua@ku.ac.ae; moni.nader@ku.ac.aeBackground and Objective: Coronary artery disease (CAD) is a major cause of death worldwide. Revascularization via stent placement or coronary artery bypass grafting (CABG) are standard treatments for CAD. Despite a high success rate, these approaches are associated with long-term failure due to restenosis. Risk factors associated with restenosis were investigated using a case-control association study design.Methods: Five thousand two hundred and forty-two patients were enrolled in this study and were assigned as follows: Stenosis Group: 3570 patients with CAD > 50% without a prior stent or CABG (1394 genotyped), and Restenosis Group: 1672 patients with CAD > 50% and prior stent deployment or CABG (705 genotyped). Binomial regression models were applied to investigate the association of restenosis with diabetes, hypertension, and dyslipidemia. The genetic association with restenosis was conducted using PLINK 1.9.Results: Dyslipidemia is a major risk factor (Odds Ratio (OR) = 2.14, P-value < 0.0001) for restenosis particularly among men (OR = 2.32, P < 0.0001), while type 2 diabetes (T2D) was associated with an increased risk of restenosis in women (OR = 1.36, P = 0.01). The rs9349379 (PHACTR1) and rs264 (LPL) were associated with an increased risk of restenosis in our patients. PHACTR1 variant was associated with increased risk of restenosis mainly in women and in diabetic patients, while the LPL variant was associated with increased risk of restenosis in men.Conclusion: The rs9349379 in PHACTR1 gene is significantly associated with restenosis, this association is more pronounced in women and in diabetic patients. The rs264 in LPL gene was associated with increased risk of restenosis in male patients.Keywords: PHACTR1, LPL, diabetes, restenosis
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".