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Article ; Online: Unraveling the genomic basis of congenital heart disease.

Darbar, Dawood

2021 Volume 131, Issue 2

Abstract: The genetic, epigenetic, and environmental etiologic basis of congenital heart disease (CHD) for most heart anomalies remains unexplained. In this issue of the JCI, Lahm et al. performed the largest genome-wide association study (GWAS) to date of ... ...

Abstract	The genetic, epigenetic, and environmental etiologic basis of congenital heart disease (CHD) for most heart anomalies remains unexplained. In this issue of the JCI, Lahm et al. performed the largest genome-wide association study (GWAS) to date of European individuals with CHD and clinical subtypes. The comprehensive meta-analysis included over 4000 patients and 8000 controls and uncovered common genetic variants that associated with cardiac anomalies. Lahm and colleagues performed single-cell analysis of induced pluripotent stem cells and heart cells, revealing a role for MACROD2, GOSR2, WNT3, and MSX1 in the developing heart. This study advances our understanding of the genetic basis of common forms of CHD.
MeSH term(s)	Genome-Wide Association Study ; Genomics ; Heart ; Heart Defects, Congenital/genetics ; Humans ; Qb-SNARE Proteins
Chemical Substances	GOSR2 protein, human ; Qb-SNARE Proteins
Language	English
Publishing date	2021-01-19
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Comment
ZDB-ID	3067-3
ISSN	1558-8238 ; 0021-9738
ISSN (online)	1558-8238
ISSN	0021-9738
DOI	10.1172/JCI145377
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Article ; Online: Slow and Steady Does Not Always Win the Race: Re-Evaluating Rate Control in Atrial Fibrillation and Heart Failure With Preserved Ejection Fraction.

Hill, Michael C / Darbar, Dawood

The American journal of cardiology

2023 Volume 198, Page(s) 135–136

MeSH term(s)	Humans ; Atrial Fibrillation ; Stroke Volume ; Heart Failure ; Prognosis
Language	English
Publishing date	2023-05-14
Publishing country	United States
Document type	Editorial ; Comment
ZDB-ID	80014-4
ISSN	1879-1913 ; 0002-9149
ISSN (online)	1879-1913
ISSN	0002-9149
DOI	10.1016/j.amjcard.2023.04.029
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Article ; Online: Sample size determination for adaptive crossover trial in detecting gene-drug interactions.

Chen, Hua Yun / Darbar, Dawood

Statistical methods in medical research

2023 Volume 32, Issue 9, Page(s) 1680–1693

Abstract: Parallel design and crossover design are two of the most frequently used designs for studying drug-gene interactions. Due to the concerns of statistical power and ethics, it is often more prudent to use the crossover design while allowing the patients to ...

Abstract	Parallel design and crossover design are two of the most frequently used designs for studying drug-gene interactions. Due to the concerns of statistical power and ethics, it is often more prudent to use the crossover design while allowing the patients to have choices of not switching the treatment if the first stage treatment is effective. This complicates the calculation of the required sample size to achieve pre-specified statistical power. We propose a method to determine the required sample size with a closed-form formula. The proposed approach is applied to determine the sample size of an adaptive crossover trial in studying gene-drug interaction in treating atrial fibrillation, the most common cardiac arrhythmia in clinical practice. Our simulation study confirms the power achieved by the sample size determined using the proposed approach. Issues related to the adaptive crossover trial are also discussed and practical guidelines are provided.
MeSH term(s)	Humans ; Sample Size ; Cross-Over Studies ; Computer Simulation ; Drug Interactions ; Data Interpretation, Statistical ; Research Design ; Models, Statistical
Language	English
Publishing date	2023-07-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802231181704
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Zs.A 3564: Show issues

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Article ; Online: Deciphering the Electrophysiological Mechanisms for Ibrutinib-Induced Ventricular Arrhythmias.

Kudinov, Alex / Darbar, Dawood

JACC. CardioOncology

2020 Volume 2, Issue 4, Page(s) 630–631

Language	English
Publishing date	2020-11-17
Publishing country	United States
Document type	Editorial
ISSN	2666-0873
ISSN (online)	2666-0873
DOI	10.1016/j.jaccao.2020.10.001
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Article: Genetic Susceptibility to Arrhythmia Phenotypes in a Middle Eastern Cohort of 14,259 Whole-Genome Sequenced Individuals.

Qafoud, Fatima / Elshrif, Mohamed / Kunji, Khalid / Althani, Asma / Salam, Amar / Al Suwaidi, Jassim / Asaad, Nidal / Darbar, Dawood / Saad, Mohamad

Journal of clinical medicine

2024 Volume 13, Issue 4

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2024-02-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm13041102
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Article ; Online: The Role of Pharmacogenetics in Atrial Fibrillation Therapeutics: Is Personalized Therapy in Sight?

Darbar, Dawood

Journal of cardiovascular pharmacology

2016 Volume 67, Issue 1, Page(s) 9–18

Abstract: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide requiring therapy. Despite recent advances in catheter-based and surgical therapy, antiarrhythmic drugs (AADs) remain the mainstay of treatment for symptomatic AF. However, ...

Abstract	Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide requiring therapy. Despite recent advances in catheter-based and surgical therapy, antiarrhythmic drugs (AADs) remain the mainstay of treatment for symptomatic AF. However, response in individual patients is highly variable with over half the patients treated with rhythm control therapy experiencing recurrence of AF within a year. Contemporary AADs used to suppress AF are incompletely and unpredictably effective and associated with significant risks of proarrhythmia and noncardiac toxicities. Furthermore, this "one-size" fits all strategy for selecting antiarrhythmics is based largely on minimizing risk of adverse effects rather than on the likelihood of suppressing AF. The limited success of rhythm control therapy is in part due to heterogeneity of the underlying substrate, interindividual differences in disease mechanisms, and our inability to predict response to AADs in individual patients. Genetic studies of AF over the past decade have revealed that susceptibility to and response to therapy for AF is modulated by the underlying genetic substrate. However, the bedside application of these new discoveries to the management of AF patients has thus far been disappointing. This may in part be related to our limited understanding about genetic predictors of drug response in general, the challenges associated with determining efficacy of response to AADs, and lack of randomized genotype-directed clinical trials. Nonetheless, recent studies have shown that common AF susceptibility risk alleles at the chromosome 4q25 locus modulated response to AADs, electrical cardioversion, and ablation therapy. This monograph discusses how genetic approaches to AF have not only provided important insights into underlying mechanisms but also identified AF subtypes that can be better targeted with more mechanism-based "personalized" therapy.
MeSH term(s)	Anti-Arrhythmia Agents/administration & dosage ; Atrial Fibrillation/diagnosis ; Atrial Fibrillation/genetics ; Atrial Fibrillation/therapy ; Genetic Linkage/genetics ; Humans ; Pharmacogenetics/methods ; Pharmacogenetics/trends ; Precision Medicine/methods ; Precision Medicine/trends ; Translational Medical Research/methods ; Translational Medical Research/trends
Chemical Substances	Anti-Arrhythmia Agents
Language	English
Publishing date	2016-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	391970-5
ISSN	1533-4023 ; 0160-2446
ISSN (online)	1533-4023
ISSN	0160-2446
DOI	10.1097/FJC.0000000000000280
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Article ; Online: Atrial Fibrillation in Inherited Channelopathies.

Al-Azaam, Baha'a / Darbar, Dawood

Cardiac electrophysiology clinics

2021 Volume 13, Issue 1, Page(s) 155–163

Abstract: Atrial fibrillation (AF), the common sustained arrhythmia in clinical practice, has major public health implications due to its associated morbidity and increased mortality. The AF epidemic is due to the burgeoning elderly population and the ... ...

Abstract	Atrial fibrillation (AF), the common sustained arrhythmia in clinical practice, has major public health implications due to its associated morbidity and increased mortality. The AF epidemic is due to the burgeoning elderly population and the identification of novel risk factors, for example, genetics. Since the diagnosis of AF has a major impact on the clinical assessment and management of patients with inherited arrhythmia syndromes, improved understanding of the cause and pathogenesis of AF has provided important insights into the underlying pathophysiological mechanisms of this common arrhythmia and identified potential mechanism-based therapies.
MeSH term(s)	Arrhythmias, Cardiac ; Atrial Fibrillation ; Channelopathies ; Humans
Language	English
Publishing date	2021-01-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ISSN	1877-9190
ISSN (online)	1877-9190
DOI	10.1016/j.ccep.2020.10.004
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort.

Qafoud, Fatima / Kunji, Khalid / Elshrif, Mohamed / Althani, Asma / Salam, Amar / Al Suwaidi, Jassim / Darbar, Dawood / Asaad, Nidal / Saad, Mohamad

Journal of clinical medicine

2024 Volume 13, Issue 1

Abstract: Background: Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers ...

Abstract	Background: Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers and can predict early stages of diseases. In this study, we evaluated the association between ECG traits, clinical biomarkers, and diseases and developed risk scores to predict the risk of developing coronary artery disease (CAD) in the Qatar Biobank. Methods: This study used 12-lead ECG data from 13,827 participants. The ECG traits used for association analysis were RR, PR, QRS, QTc, PW, and JT. Association analysis using regression models was conducted between ECG variables and serum electrolytes, sugars, lipids, blood pressure (BP), blood and inflammatory biomarkers, and diseases (e.g., type 2 diabetes, CAD, and stroke). ECG-based and clinical risk scores were developed, and their performance was assessed to predict CAD. Classical regression and machine-learning models were used for risk score development. Results: Significant associations were observed with ECG traits. RR showed the largest number of associations: e.g., positive associations with bicarbonate, chloride, HDL-C, and monocytes, and negative associations with glucose, insulin, neutrophil, calcium, and risk of T2D. QRS was positively associated with phosphorus, bicarbonate, and risk of CAD. Elevated QTc was observed in CAD patients, whereas decreased QTc was correlated with decreased levels of calcium and potassium. Risk scores developed using regression models were outperformed by machine-learning models. The area under the receiver operating curve reached 0.84 using a machine-learning model that contains ECG traits, sugars, lipids, serum electrolytes, and cardiovascular disease risk factors. The odds ratio for the top decile of CAD risk score compared to the remaining deciles was 13.99. Conclusions: ECG abnormalities were associated with serum electrolytes, sugars, lipids, and blood and inflammatory biomarkers. These abnormalities were also observed in T2D and CAD patients. Risk scores showed great predictive performance in predicting CAD.
Language	English
Publishing date	2024-01-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm13010276
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The Pharmacogenomics of a Mutation 'Hotspot' for the Short QT Syndrome.

Darbar, Dawood / McCauley, Mark

JACC. Clinical electrophysiology

2017 Volume 3, Issue 7, Page(s) 744–746

MeSH term(s)	Arrhythmias, Cardiac ; Humans ; Mutation ; Pharmacogenetics
Language	English
Publishing date	2017-09-29
Publishing country	United States
Document type	Editorial ; Research Support, N.I.H., Extramural ; Comment
ZDB-ID	2846739-5
ISSN	2405-500X ; 2405-5018 ; 2405-500X
ISSN (online)	2405-500X ; 2405-5018
ISSN	2405-500X
DOI	10.1016/j.jacep.2016.12.024
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Article ; Online: Correlations between Resting Electrocardiogram Findings and Disease Profiles

Fatima Qafoud / Khalid Kunji / Mohamed Elshrif / Asma Althani / Amar Salam / Jassim Al Suwaidi / Dawood Darbar / Nidal Asaad / Mohamad Saad

Journal of Clinical Medicine, Vol 13, Iss 1, p

Insights from the Qatar Biobank Cohort

2024 Volume 276

Abstract	Background: Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers and can predict early stages of diseases. In this study, we evaluated the association between ECG traits, clinical biomarkers, and diseases and developed risk scores to predict the risk of developing coronary artery disease (CAD) in the Qatar Biobank. Methods: This study used 12-lead ECG data from 13,827 participants. The ECG traits used for association analysis were RR, PR, QRS, QTc, PW, and JT. Association analysis using regression models was conducted between ECG variables and serum electrolytes, sugars, lipids, blood pressure (BP), blood and inflammatory biomarkers, and diseases (e.g., type 2 diabetes, CAD, and stroke). ECG-based and clinical risk scores were developed, and their performance was assessed to predict CAD. Classical regression and machine-learning models were used for risk score development. Results: Significant associations were observed with ECG traits. RR showed the largest number of associations: e.g., positive associations with bicarbonate, chloride, HDL-C, and monocytes, and negative associations with glucose, insulin, neutrophil, calcium, and risk of T2D. QRS was positively associated with phosphorus, bicarbonate, and risk of CAD. Elevated QTc was observed in CAD patients, whereas decreased QTc was correlated with decreased levels of calcium and potassium. Risk scores developed using regression models were outperformed by machine-learning models. The area under the receiver operating curve reached 0.84 using a machine-learning model that contains ECG traits, sugars, lipids, serum electrolytes, and cardiovascular disease risk factors. The odds ratio for the top decile of CAD risk score compared to the remaining deciles was 13.99. Conclusions: ECG abnormalities were associated with serum electrolytes, sugars, lipids, and blood and ...
Keywords	ECG ; arrythmia ; risk scores ; Qatar Biobank ; type 2 diabetes ; cardiovascular diseases ; Medicine ; R
Subject code	610
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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