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20 citation(s) found for "Rajai, N".

Abstract 14362: Retention of CD34+ Progenitor Cells in the Coronary Circulation Correlates With Insulin Levels Depending on Insulin Resistance
Ahmad, Ali , Nardi, Valentina , Rajai, Nazanin , Lerman, Lilach O. , Ӧzcan, Ilke , Toya, Takumi , Lerman, Amir

Circulation | April 23, 2024 | Volume: 144 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.144.suppl_1.14362 Get It

Introduction: Diabetes mellitus and insulin resistance are associated with accelerated vascular injury and progression of coronary atherosclerosis. CD34 + progenitor cells are mobilized and recruited to repair the endothelial and vascular damage. Hypothesis: We hypothesized that the interaction of insulin levels and retention of CD34+ progenitors in the coronary arteries will be different between patients with and without insulin resistance. Methods: In patients with no obstructive coronary artery disease (NOCAD: <40% angiographic stenosis), blood samples were collected concurrently from the proximal aorta and the coronary sinus, and CD34+ cells counted using flow cytometry. CD34+ cells gradient was calculated by subtracting aortic from coronary sinus cell counts, with a higher gradient reflecting lower retention. Patients on antidiabetic medications were excluded. Insulin resistance (IR) was estimated as HOMA-IR index ([insulin]*[glucose]/405)) ≥2. Results: Of 128 patients (mean age 51±11 years, 77% females), 60 (47%) had IR (mean age 51±10 years, 73% females) and 68 (53%) did not (mean age 50±13 years, 79% females). The groups did not differ in age, sex or LDL cholesterol. The IR group had higher BMI and triglyceride levels and lower HDL cholesterol. CD34 + cell gradient correlated directly significantly with insulin levels in patients without IR (r=0.25, p=0.04) (Fig1A) , but inversely in those with IR (r=-0.39, p=0.01) (Fig1B) . Both associations remained significant after adjustment for age, sex, and BMI (IR: r=-0.39, p=0.02; no IR: r=29, p=0.04). Conclusions: In NOCAD patients with IR, higher levels of serum insulin were associated with increased retention of CD34 + cells in the coronary arteries. The current study supports a potential role for CD34 + cells for vascular repair in patients with IR.
Abstract 14029: Association Between Depressive Symptoms Severity and Physiological Age as Determined by Artificial Intelligence-ECG
Medina‐Inojosa, Betsy J. , chen, zhuo, Clark, Matthew M. , Rajai, Nazanin , Friedman, Paul A. , Medina‐Inojosa, José R. , Lerman, Amir , Attia, Zachi I. , López-Jiménez, Francisco

Circulation | December 19, 2023 | Volume: 148 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.148.suppl_1.14029 Get It

Background: Depression is prevalent in almost half of patients with cardiovascular disease (CVD) and is associated with CVD and mortality. We used an artificial intelligence algorithm using ECG that predicts physiological age (ECG-Age) that has been linked to increased total and CVD mortality and CVD risk factors. We hypothesized that depressive symptoms would be associated with physiologic aging, as measured by AI-ECG Methods: A previously validated algorithm was applied to determine ECG-Age to 105,732 consecutive ECGs of individuals age ≥18 who sought care at Mayo Clinic between June 2019 and January 2022 and filled the Patient Health Questionnaire-9 (PHQ-9) within a month of ECG. PHQ-9 ascertains severity and frequency of depressive symptoms with values from 0 - 27 and were categorized as (None≤4, Mild 5-9, Moderate 10-14, Moderately severe 15-19 and Severe ≥20). Delta-Age was defined as ECG-Age minus chronological age and accelerated aging was defined as a Delta-Age >1SD above the mean. Logistic regression analyses associated PHQ-9 scores with positive Delta-Age while adjusting for CVD risk factors Results: Mean chronological age was 57±17 yr and mean ECG-Age 58±14 yr (R2 0.85, P < 0.0001), Delta-Age 0.71±9.40 yr, 58% women, average PHQ-9 5±6, 61% had none, 20% mild, 10 % moderate, 5%moderately severe and 4% had severe depressive symptoms. Delta-Age was 2.10 years higher in those with depressive symptoms, reflecting accelerated aging. Compared to those with no depressive symptoms, those with severe symptoms were 1.5 times more likely to have Delta-Age >1SD (OR 1.52 CI 95%1.73-1.33), independently of CV risk factors and the Charlson comorbidity index. Figure. There is significant attenuation of the association once accounting for age, sex and CVD risk factors Conclusions: Depressive symptoms are associated with physiologic aging, highlighting the role of depression in physiologic aging and the potential of preventive strategies in patients with depression and CVD
Abstract 16913: The Association Between Adverse Childhood Experiences and Physiologic Age as Determined by Artificial Intelligence-ECG
Medina‐Inojosa, Betsy J. , chen, zhuo, Clark, Matthew M. , Mangold, Kathryn, Rajai, Nazanin , Friedman, Paul A. , Medina‐Inojosa, José R. , Lerman, Amir , Attia, Zachi I. , López-Jiménez, Francisco

Circulation | November 6, 2023 | Volume: 148 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.148.suppl_1.16913 Get It

Background: The gap between electrocardiogram (ECG) artificial intelligence-derived age (ECG-Age) and chronological age (Age-Gap) predicts cardiovascular (CV) comorbidities and mortality reflecting physiologic age. Adverse childhood experiences (ACEs) are traumatic and harmful events that impact adulthood CV outcomes. We hypothesized that ACEs would be associated with physiologic age as determined by the Age-Gap. Methods: We applied a previously validated ECG-Age algorithm to the ECGs of 1,988 consecutive individuals aged ≥18, that within 2 years completed an ACEs questionnaire as part of psychological evaluations for weight management. The ACE questionnaire assesses 3 psychosocial domains (i) childhood abuse (psychological, physical or sexual), (ii) household dysfunction (substance abuse, mental illness, female figure treated violently, criminal behavior and parental separation/divorce) and (iii) neglect (emotional or physical) before age 18; results ranged 0 to 10 and were categorized as burden (0, 1-3, and ≥4) and presence (abuse, household dysfunction, neglect). Age-Gap was operationalized as (≤mean or > mean) positive values reflect physiological aging. Logistic regression analyses tested the association between ACE and Age-Gap. Results: Mean ± SD chronological age was 49±13 yr, ECG-Age 54±12 yr, Age-Gap 4.26±8.95 yr,75% women, average ACE 2±2, 32% experienced 0, 44% 1-3, and 24% ≥4 ACEs. Age-Gap was 1.4 years higher in those who experienced at least 1 ACE. Compared to those who didn’t experience ACE those who faced 1-3 ACE were more likely to have an Age-Gap >mean (OR 1.28 95%CI 1.57-1.04) independent of sex and the age-adjusted Charlson comorbidity index Fig A. Also, they were more prone to experiencing neglect and household dysfunction Fig B. Conclusions: Age-gap was associated with ACEs burden and domains, underscoring the need to identify ACEs during clinical evaluations to prevent ACEs at the individual, family, and community levels.
Prognostic value of peripheral endothelial function on major adverse cardiovascular events above traditional risk factors
Rajai, Nazanin , Sara, Jaskanwal Deep Singh , Rajotia, Arush , López-Jiménez, Francisco , Lerman, Lilach O. , Lerman, Amir , Sara, Jaskanwal D, Lopez-Jimenez, Francisco , Lerman, Lilach O , Lerman, Amir , Toya, Takumi

European Journal of Preventive Cardiology | July 11, 2023 | Volume: 30 | Issue: 16 | Pages: 1781-1788

10.1093/eurjpc/zwad225 | PMID 37431927 Get It

Abstract Aims This study aims to identify whether adding peripheral microvascular dysfunction (PMED), a marker of atherosclerosis to established risk scores has an incremental prognostic value for major adverse cardiovascular events (MACE). Methods and results This is a retrospective study of patients who underwent measuring peripheral arterial tonometry from 2006 to 2020. The optimal cut-off value of the reactive hyperaemia index (RHI) that had maximal prognostic value associated with MACE was calculated. Peripheral microvascular endothelial dysfunction was defined as the RHI lower than the cut-off. Traditional cardiovascular risk factors such as age, sex, congestive heart failure, hypertension, diabetes, stroke, and vascular disease were determined to calculate the CHA2DS2-Vasc score. The outcome was MACE defined as myocardial infarction, heart failure hospitalization, cerebrovascular events, and all-cause mortality. A total of 1460 patients were enrolled (average age 51.4 ± 13.6, 64.1% female). The optimal cut-off value of the RHI was 1.83 in the overall population and in females and males was 1.61 and 1.8, respectively. The risk of MACE during 7 [interquartile range (IQR): 5,11] years of follow-up was 11.2%. Kaplan–Meier analysis showed that lower RHI is associated with worse MACE-free survival (P &lt; 0.001). Multivariate Cox proportional hazard analysis, controlling for classic cardiovascular risk factors or risk scores such as CHA2DS2-Vasc and Framingham risk score revealed that PMED is an independent predictor of MACE. Conclusion Peripheral microvascular dysfunction predicts cardiovascular events. Non-invasive assessment of peripheral endothelial function may be useful in early detection and improving the stratification of high-risk patients for cardiovascular events. Lay summary Key findings
Abstract 13378: Association Between Social Connection and Biological Age as Determined by Artificial Intelligence-Enabled Electrocardiography
Rajai, Nazanin , Suárez, Abraham Báez, Nyman, M. , Lerman, Lilach O. , Medina-Inojosa, Betsy, Lopez‐Jimenez, Francisco, Medina‐Inojosa, José R. , Sheffeh, Mohammad Ali , Attia, Zachi I. , Medina‐Inojosa, Betsy J. , López-Jiménez, Francisco , Lerman, Amir

Circulation | June 15, 2023 | Volume: 146 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.146.suppl_1.13378 Get It

Introduction: Increasing evidence unravels the role of loneliness and social connection in cardiovascular diseases potentially mediating accelerated biological aging. The present study aims to explore the association between social connection and biological aging as determinate by artificial intelligence-enabled electrocardiography (ECG). Methods: This is an observational retrospective cohort of adults aged ≥18 seen at Mayo Clinic from 2019-2022 who completed the social determinants of health questionnaire and had a 12-lead ECG within one year of completing the questionnaire. Five questions were asked to understand each patient’s social connection . The answers were scored to assess their level of social connection from socially isolated (score of 0) to socially connected (score of 4). The biological age was predicted from ECGs using the previously developed convolutional neural network (AI-ECG age). Delta age (Δage) was defined as AI-ECG age minus chronological age, where positive values reflect an older than expected age. Results: We included 280,323 subjects (chronological age 59.7±16.4 years, 51% female). The average AI-ECG age was 59.5±13.5 year. Better social connection status correlated with lower delta age (β=-0.33 (95%CI, -0.38 and -0.28, p<0.001, adjusted to chronological age and sex). Conversely, individuals reporting the least social connection were an average of 2 years older than expected whereas those with the most social connection were 2 years younger than expected by AI-ECG (Figure 1). Conclusions: Social connection is strongly associated with slower biological aging compared to chronological aging, independent of the conventional cardiovascular risk factors. This observation underscores the need to address social connection as a healthcare determinant.
Abstract 13374: Effect of Moderate to Strenuous Exercise on Biological Aging as Determined by Artificial-Enabled Electrocardiography
Rajai, Nazanin , Medina-Inojosa, Betsy, Suárez, Abraham Báez, Nyman, M. , Lerman, Lilach O. , Medina‐Inojosa, Betsy J. , Sheffeh, Mohammad Ali , Attia, Zachi I. , Medina‐Inojosa, José R. , López-Jiménez, Francisco , Lerman, Amir

Circulation | June 15, 2023 | Volume: 146 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.146.suppl_1.13374 Get It

Introduction: The physiological changes associated with aging have deleterious effects on cardiovascular function. In addition, increasing evidence showed the effect of exercise on improving cardiovascular health and reducing the aging process. The present study sought to demonstrate the association between exercise and biological age, estimated by our previously reported method using artificial intelligence-enabled electrocardiography (ECG). Methods: This retrospective observational study enrolled adults aged ≥18 years who have been evaluated at Mayo Clinic between 2019-2022 and completed the exercise questionnaire. The total time spent in moderate to strenuous exercise was calculated by multiplying the number of days engaging in exercise per week by the average minutes per day. The biological age was determined by applying our previously developed convolutional neural network model on the 12-lead ECG records (AI-ECG). The ECG records were within one year of subjects’ visits (in case of multiple visits, the closest one to the questionnaire was chosen). Positive Age-Gap (AI-ECG minus chronological age) reflects a person identified as being older by AI-ECG compared to his/her chronological age. Results: A total of 268,002 subjects (51% female) were recruited (mean age 59.7±16.4 years). Of those, 25.7% were not engaged in any exercise. The median day of engaging in exercise was 3 (Interquartile range, 2,5) per week, and the average time of exercising was 31.3±30 minutes/day. Age-Gap diminished progressively with increased duration of moderate to strenuous exercise (Figure 1). Linear regression analysis revealed exercise is an independent predictor of Age-Gap, (β= -0.043, 95%CI, -0.045 and -0.041, p<0.001). Conclusions: Engaging in moderate to strenuous exercise is associated with slower biological aging relative to chronological age as determined by AI-enabled ECG.
Abstract 13368: Effect of Peripheral Endothelial Function Assessment in Improving the Risk Stratification of Major Cardiovascular Events Beyond CHA2DS2-Vasc
Rajai, Nazanin , Sara, Jaskanwal Deep Singh , Lerman, Lilach O. , Toya, Takumi , López-Jiménez, Francisco , Lerman, Amir

Circulation | June 15, 2023 | Volume: 146 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.146.suppl_1.13368 Get It

Introduction: Peripheral microvascular endothelial dysfunction (PMED) is a marker and precursor of atherosclerosis and is associated with an increased risk of cardiovascular events. The present study aims to identify the best cut-off value of PMED, assessed by the reactive hyperemia index, in predicting major adverse cardiovascular events (MACE). Additionally, we investigate whether integrating PMED to CHA2DS2-Vasc score improves the predicting accuracy of cardiovascular disease. Methods: this is a retrospective study of patients who underwent reactive hyperemia peripheral arterial tonometry (PAT). The optimal cut-off value of reactive hyperemia index associated with MACE was calculated using the area under the receiver-operating curve and the maximum Youden index. Traditional cardiovascular risk factors such as age, sex, congestive heart failure, hypertension, diabetes, stroke, vascular disease were determined to calculate the CHA2DS2-VASc score. MACE was defined as myocardial infarction, heart failure hospitalization, and cerebrovascular events. Results: 1,690 patients were enrolled (average age 51.1±13.9, 64.1% female). The optimal cut-off value of the reactive hyperemia index was 2.0 in the overall population and females and 2.17 in males. The risk of MACE during 10 (Interquartile range: 4,16) years of follow-up was 15.3%. Multivariable logistic regression revealed that PMED as an independent predictor of MACE (PMED: OR, 3.6, 95% CI, 2.6-4.9, p<0.001) (Table1). Furthermore, integrating PMED to CHA2DS2-VASc significantly improved the predictive accuracy (area under the curve, 0.747 vs. 0.708, p<0.001). Conclusion: PMED is a strong predictor of cardiovascular events. Non-invasive assessment of peripheral endothelial function may be useful in early detection and improving the stratification of high-risk patients for cardiovascular events.
Abstract 11957: Physical Training Augmented With Whole Body Electronic Muscle Stimulation is Superior to Conventional Training Alone in Healthy Subjects, a Pilot Randomized Controlled Trial
Sara, Jaskanwal Deep Singh , Rajai, Nazanin , Breuer, Logan, Bjerke, Jacob, Olson, Thomas P. , Nagai, Takashi , Schilaty, Nathan D. , Lerman, Amir

Circulation | June 15, 2023 | Volume: 146 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.146.suppl_1.11957 Get It

Introduction: Physical activity is protective against cardiovascular (CV) disease and mortality, yet more than 25% of adults do not perform any regular exercise. Whole body electronic muscle stimulation (WB-EMS) is a novel FDA-approved device used for physical training that simultaneously stimulates all major muscle groups using percutaneous electrical impulse transmission during exercise. Preliminary studies demonstrated increased muscle mass, reduced fat mass, and improved functional capacity in elderly individuals who trained with WB-EMS. We evaluated the effects of training with WB-EMS on measures of CV health. Methods: Healthy adults were randomized to physical training with versus without WB-EMS for one 20-minute session each week for 16 weeks. Participants wore a specifically designed vest and arm and leg straps connected to the WB-EMS device (Miha Bodytec, Germany). Biphasic electrical stimulation was delivered through the vest and straps (4 sec on, 4 sec off) at a frequency that elicited a score of 5-6 on the Borg perceived exertion scale when each major muscle group was stimulated. Individuals randomized to no WB-EMS wore the same equipment but received no stimulation. Sessions were provided by trainers certified in WB-EMS training and consisted of a fixed protocol of simple exercises such as squats and lunges. Temporal changes in measures of CV health were compared between groups. Results: Forty-one individuals were recruited between 02/2021 and 02/2022 (n=24 WB-EMS, n=17 no WB-EMS; mean age 35.9±11.2 yrs; 61.3% females; median BMI 24.3 (21.8, 28.1) kg/m 2 . After 16 weeks there were significant and favorable differences in the intervention group compared to controls in waist: hip ratio (-1.2 cm vs. 0.9, p=0.05); total cholesterol (-1.1 mg/kg vs. 14.2 mg/kg, p=0.02); anaerobic threshold (68.2% vs. 30.8%, p=0.03); breathing efficiency (VE/VCO2: -10.36 vs -7.38, p=0.01); and peripheral endothelial function, measured with reactive hyperemia peripheral arterial tonometry (-0.009 vs. -0.29, p=0.06). Conclusions: Once weekly training with WB-EMS yielded more favorable changes in CV biomarkers compared to conventional training alone in healthy adults, and thus may offer an effective and time-efficient method of physical training.
Coronary microvascular dysfunction is an independent predictor of developing cancer in patients with non-obstructive coronary artery disease
Rajai, Nazanin , Ahmad, Ali , Sara, Jaskanwal Deep Singh , Herrmann, Joerg , Lerman, Lilach O. , Lerman, Amir , Sara, Jaskanwal D, Lerman, Amir , Toya, Takumi

European Journal of Preventive Cardiology | August 22, 2022 | Volume: 30 | Issue: 3 | Pages: 209-216

10.1093/eurjpc/zwac184 | PMID 35989450 Get It

Abstract Aims Cardiovascular disease and cancer share common pathogenesis and risk factors. Coronary microvascular dysfunction (CMD), reflecting impaired coronary microvascular dilation in response to stress, is related to a higher risk of major cardiovascular events; however, its association with cancer has not been explored. Methods and results A retrospective study on 1042 patients with non-obstructive coronary artery diseases (NOCADs) was performed. Data regarding demographic, clinical history, diagnostic coronary reactivity test, and cancer occurrence were collected. Coronary microvascular dysfunction was defined as coronary flow reserve (the ratio of hyperaemic blood flow to resting blood flow) ≤2.5. Thirty-four per cent had CMD (67.4% female and the average age was 52.4 ± 12.2 years). Of 917 patients with no history of cancer, 15.5% developed cancer during follow-up [median of 9 (4, 16) years]. Kaplan–Meier analysis showed that CMD patients had lower cancer-free survival compared with those without CMD (log-rank P = 0.005). Cox proportional hazard analyses showed that after adjusting for age, sex, hypertension, diabetes, smoking, and glomerular filtration rate, CMD is independently associated with cancer [hazard ratio, 1.4; 95% confidence interval (CI), 1.09–2.04; P = 0.04]. The rate of major adverse cardiovascular events (MACE) was significantly higher in CMD patients compared with that in non-CMD patients who had a previous history of cancer [odds ratio (OR), 2.5; 95% CI, 1–6.2; P = 0.04] and those with no history of cancer (OR, 1.4; 95% CI, 1.01–1.9; P = 0.044). Conclusion Coronary microvascular dysfunction is associated with cancer incidence in patients presenting with NOCADs. This study emphasizes follow-up in patients with CMD to evaluate the risk of MACE as well as potential malignant diseases.
Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019
Kocarnik, Jonathan , Compton, Kelly, Dean, Frances , Fu, Weijia , Gaw, Brian, Harvey, James , Henrikson, Hannah , Lu, Dan , Pennini, Alyssa, Xu, Rixing, Ababneh, Emad , Abbasi‐Kangevari, Mohsen , Abbastabar, Hedayat , Abd‐Elsalam, Sherief , Abedi, Aidin , Abidi, Hassan , Abolhassani, Hassan , Adedeji, Isaac Akinkunmi , Advani, Shailesh M, Aghaali, Mohammad , Ahmad, Tauseef , Rashid, Tarik A. , Salih, Yusra Ahmed , Aklilu, Addis , Akram, Tayyaba , Akunna, Chisom Joyqueenet , Hamad, Hanadi Al , Alahdab, Fares , Alimohamadi, Yousef, Alipour, Vahid, Alkhayyat, Motasem , Almasri, Nihad A. , Al‐Maweri, Sadeq Ali , Almustanyir, Sami , Alonso, Nivaldo , Alvis‐Guzmán, Nelson , Amu, Hubert , Ansari, Fereshteh, Antwi, Maxwell Hubert , Anvari, Davood , Anyasodor, Anayochukwu Edward , Aqeel, Muhammad , Arabloo, Jalal , Aremu, Olatunde , Ariffin, Hany , Aripov, Timur , Arshad, Muhammad , Artaman, Al, Arulappan, Judie , Jafarabadi, Mohammad Asghari , Ashraf, Tahira , Atorkey, Prince , Ausloos, Marcel , Awedew, Atalel Fentahun , Quintanilla, Beatriz Paulina Ayala , Ayenew, Temesgen , Azab, Mohammed A. , Azadnajafabad, Sina , Jafari, Amirhossein Azari , Azarian, Ghasem , Badiye, Ashish , Bahadory, Saeed , Baig, Atif Amin , Baker, Jennifer L. , Balakrishnan, Senthilkumar , Bärnighausen, Till , Barone‐Adesi, Francesco , Barrow, Amadou , Belgaumi, Uzma , Bezabhe, Woldesellassie, Bezabih, Yihienew Mequanint , Bhagat, Devidas S. , Bhagavathula, Akshaya Srikanth , Bhardwaj, Nikha , Bhaskar, Sonu , Bhojaraja, Vijayalakshmi S, Bibi, Sadia , Bijani, Ali , Bisignano, Catherine, Bolarinwa, Obasanjo Afolabi , Bolla, Srinivasa Rao , Braithwaite, Dejana , Brar, Amanpreet , Brenner, Hermann , Bustamante-Teixeira, Maria Teresa , Butt, Nadeem Shafique , Butt, Zahid A , Santos, Florentino Luciano Caetano dos , Cao, Yin , Carreras, Giulia , Catalá-López, Ferrán , Cembranel, Francieli , Cerin, Ester , Cernigliaro, Achille , Chakinala, Raja Chandra , Chattu, Soosanna Kumary , Chattu, Vijay Kumar , Chaturvedi, Pankaj, Chimed‐Ochir, Odgerel , Cho, Daniel , Chu, Dinh‐Toi , Conde, João , Cortés, Sandra , Cortesi, Paolo Angelo, Costa, Vera Marisa , Cunha, Amanda Ramos da , Dadras, Omid , Dagnew, Amare Belachew , Dandona, Lalit , Dandona, Rakhi , Hoz, Fernando de la , Dhamnetiya, Deepak , Dhimal, Mandira Lamichhane , Díaz, Daniel , Djalalinia, Shirin , Doaei, Saeid , Dorostkar, Fariba, Driscoll, Tim , Ebrahimi, Hedyeh , Eftekharzadeh, Sahar , Tantawi, Maha El , El‐Abid, Hassan , Elbarazi, Iffat , Elhabashy, Hala Rashad , Elhadi, Muhammed , Eshrati, Babak, Esmaeilzadeh, Firooz , Etemadi, Arash , Ezzikouri, Sayeh , Faisaluddin, Mohammed, Fares, Jawad , Farzadfar, Farshad , Feroze, Abdullah , Desideri, Lorenzo Ferro , Fischer, Florian , Fisher, James L. , Fukumoto, Takeshi , Gad, Mohamed M. , Fonseca, Mariana Gaspar, Obsa, Abera Getachew, Ghafourifard, Mansour , Ghashghaee, Ahmad , Ghith, Nermin , Gholamalizadeh, Maryam , Gilani, Syed Amir , Gizaw, Abraham Tamirat , Glasbey, James , Golechha, Mahaveer , Goleij, Pouya , Gopalani, Sameer Vali , Gorini, Giuseppe , Goudarzi, Houman , Gubari, Mohammed Ibrahim Mohialdeen , Guerra, Maximiliano Ribeiro, Guha, Avirup , Gunasekera, Sanjeeva , Gupta, Bhawna , Gupta, Vivek , Gutiérrez, Reyna Alma, Hafezi‐Nejad, Nima , Haider, Mohammad Rifat , Hameed, Sajid , Hanif, Asif , Haque, Shafiul , Harlianto, Netanja I. , Hasaballah, Ahmed I , Hay, Simon I , Heidari, Golnaz , Herrera-Serna, Brenda Yuliana , Herţeliu, Claudiu , Hezam, Kamal , Hossain, Mohammad Bellal , Hosseini, Mohammad‐Salar , Hosseini, Mostafa, Hosseinzadeh, Mehdi , Hostiuc, Mihaela , Househ, Mowafa , Hsaïri, Mohamed, Huang, Junjie , Hugo, Fernando Neves , Hussain, Rabia , Hussein, Nawfal R , Hwang, Bing‐Fang , Ibitoye, Segun Emmanuel , Ida, Fidelia , Ikuta, Kevin S , Ilić, Irena , Irham, Lalu Muhammad , Islam, Jessica Y. , Islam, Sheikh Mohammed Shariful , Ismail, Nahlah Elkudssiah , Isola, Gaetano , Iwagami, Masao , Jacob, Louis , Jain, Vardhmaan , Jakovljević, Mihajlo, Javaheri, Tahereh , Jayaram, Shubha, Jha, Ravi Prakash , Jonas, Jost B, Joó, Tamás , Joseph, Nitin , Joukar, Farahnaz, Jürisson, Mikk , Kalankesh, Leila R , Kalhor, Rohollah , Kalkonde, Yogeshwar , Kamath, Ashwin , Al-Salihi, Nawzad Kameran, Kandel, Himal , Kapoor, Neeti , Kasa, Ayele Semachew , Keshavarz, Pedram , Keykhaei, Mohammad , Khader, Yousef , Khalilov, Rovshan , Khan, Maseer , Khan, Md Nuruzzaman , Khan, Moien AB , Khang, Young-Ho, Khater, Amir M, Kısa, Adnan , Kısa, Sezer , Kopec, Jacek A , Koteeswaran, Rajasekaran , Koul, Parvaiz A , Laxminarayana, Sindhura Lakshmi Koulmane , Biçer, Burcu Küçük , Kugbey, Nuworza , Kumar, G Anil , Kurmi, Om , Kutluk, Tezer, Lami, Faris Hasan, Lee, Yo Han , Leigh, James , Leong, Elvynna , Li, Ming‐Chieh , Liu, Xuefeng , Loureiro, Joana A. , Lunevičius, Raimundas , Razek, Muhammed Magdy Abd El, Majeed, Azeem , Makki, Alaa, Male, Shilpa, Malik, Ahmad Azam , Martini, Santi , Masoumi, Seyedeh Zahra , Mehrotra, Ravi , Mendoza, Walter, Mengesha, Endalkachew Worku , Mesregah, Mohamed Kamal , Jonasson, Junmei Miao , Miazgowski, Bartosz , Miazgowski, Tomasz , Michałek, Irmina Maria , Miller, Ted R. , Mirzaei, Hamid Reza , Misra, Sanjeev , Mithra, Prasanna , Moghadaszadeh, Masoud, Mohammad, Karzan Abdulmuhsin , Mohammad, Yousef , Mohammadi, Mokhtar , Mohammadi, Seyyede Momeneh, Mohammadian-Hafshejani, Abdollah , Mohammed, Shafiu , Moka, Nagabhishek , Mokdad, Ali H. , Molokhia, Mariam , Monasta, Lorenzo , Moosavi, Mohammad Amin , Moradi, Yousef , Moraga, Paula , Morgado-da-Costa, Joana, Mubarik, Sumaira , Mwanri, Lillian , Nagarajan, Ahamarshan Jayaraman , Nagata, Chie , Naimzada, Mukhammad David , Nangia, Vinay, Naqvi, Atta Abbas , Swamy, Sreenivas Narasimha, Negoi, Ionuţ , Negru, Șerban , Kandel, Sandhya Neupane , Nguyen, Cuong Tat , Niazi, Robina Khan, Nnaji, Chukwudi A , Noor, Nurulamin M , Nzoputam, Chimezie Igwegbe , Oancea, Bogdan , Ogbo, Felix Akpojene , Olagunju, Andrew T , Olakunde, Babayemi O. , Omar, Emad, Bali, Ahmed Omar , Omonisi, Abidemi , Ong, Sokking, Onwujekwe, Obinna , Ortega-Altamirano, Doris V , Otstavnov, Stanislav S , Mahesh, P A , Pakshir, Keyvan , Pană, Adrian , Panagiotakos, Demosthenes B. , Panda‐Jonas, Songhomitra, Park, Eun‐Cheol , Park, Eun‐Kee , Kan, Fatemeh Pashazadeh , Patel, Harsh K. , Patel, Jenil , Pati, Siddhartha , Paudel, Uttam , Pereira, David M. , Pereira, Renato B. , Perianayagam, Arokiasamy , Pillay, Julian David , Pirouzpanah, Saeed , Podder, Indrashis , Postma, Maarten J. , Prashant, Akila , Preoțescu, Liliana , Rabiee, Navid , Radfar, Amir , Radhakrishnan, Raghu , Radhakrishnan, Venkatraman , Rahim, Fakher , Rahimzadeh, Shadi , Rahman, Mosiur , Rahman, Muhammad Aziz , Rajai, Nazanin , Rajesh, Aashish , Ram, Pradhum , Ramezanzadeh, Kiana, Ranabhat, Kamal , Rao, Chythra R , Rao, Sowmya J, Rawassizadeh, Reza , Razeghinia, Mohammad Sadegh , Rezapour, Aziz, Buendía, Jefferson Antonio , Rohloff, Peter , Rwegerera, Godfrey Mutashambara , Manjula, S, Saddik, Basema , Saeed, Umar , Sahoo, Harihar , Salehi, Sana , Salem, Marwa Rashad , Samaei, Mehrnoosh, Samy, Abdallah M , Sanabria, Juan , Sankararaman, Senthilkumar , Santric-Milicevic, Milena M , Sardiwalla, Yaeesh , Sarveazad, Arash , Sathian, Brijesh , Sawhney, Monika, Šekerija, Mario , Seylani, Allen, Shafaat, Omid , Shaikh, Masood Ali , Shannawaz, Mohammed , Sheikhbahaei, Sara , Shetty, Adithi , Shetty, Jeevan K. , Shetty, Pavanchand H, Shirkoohi, Reza , Shivarov, Velizar , Malleshappa, Sudeep K Siddappa , Silva, Diego Augusto Santos , Sintayehu, Yitagesu , Skryabin, Valentin Yurievich , Soeberg, Matthew, Sofi‐Mahmudi, Ahmad , Steiropoulos, Paschalis , Straif, Kurt, Subedi, Ranjeeta , Sultana, Saima , Szerencsés, Viktória , Tabuchi, Takahiro , Tadbiri, Hooman , Takahashi, Ken , Tan, Ker‐Kan , Tat, Vivian Y, Tedla, Bemnet , Tefera, Yonas Getaye , Temsah, Mohamad‐Hani , Tesfay, Fisaha Haile , Tessema, Gizachew Assefa , Thavamani, Aravind , Chandrasekar, Viveksandeep Thoguluva , Touvier, Mathilde , Tovani‐Palone, Marcos Roberto , Traini, Eugenio , Tran, Mai Thi Ngoc , Tripathy, Jaya Prasad , Tusa, Biruk Shalmeno , Ullah, Irfan , Ullah, Saif , Umapathi, Krishna Kishore , Vaezi, Maryam, Tahbaz, Sahel Valadan , Velazquez, Diana Zuleika , Violante, Francesco Saverio , Volovici, Victor , Vu, Giang Thu , Waheed, Yasir , Ward, Paul , Wen, Yi Feng , Westerman, Ronny, Yadav, Lalit , Jabbari, Seyed Hossein Yahyazadeh, Yang, Lin , Yazie, Taklo Simeneh , Yeshaw, Yigizie , Younis, Mustafa Z , Yousefi, Zabihollah , Yunusa, Ismaeel , Zadnik, Vesna , Zare, Fariba , Застрожин, Михаил Сергеевич , Zastrozhina, Anasthasia, Zhang, Jianrong , Zhong, Chenwen, Zhu, Cong , Zimmermann, Ivan Ricardo , Fitzmaurice, Christina , Murray, Christopher J L , Force, Lisa M , Ong, Sok King , Eftekhari, Aziz , Guimarães, André Luiz Sena , Kumar, Vivek , Lami, Faris , Liu, Xuefeng , Baig, Atif Amin , Thoguluva Chandrasekar, Viveksandeep, Kumar, Vivek , Cao, Yin , Brenner, Hermann , Farzadfar, Farshad , Islam, Sheikh Mohammed Shariful , Rahman, Muhammad Aziz , Guha, Avirup , Ebrahimi, Hedyeh , Volovici, Victor , Bhagavathula, Akshaya Srikanth , Jakovljević, Mihajlo , Elhadi, Muhammed , Abdoli, Amir , Adnani, Qorinah Estiningtyas Sakilah , Afzal, Muhammad Sohail, Ahinkorah, Bright Opoku , Ahmad, Sajjad , Ahmadi, Ali , Ahmadi, Sepideh , Akalu, Gizachew Taddesse , Al‐Aly, Ziyad , Ali, Saqib , Aljunid, Syed Mohamed , Almasi‐Hashiani, Amir , Anbesu, Etsay Woldu , Ancuceanu, Robert , Ansari‐Moghaddam, Alireza , Arab-Zozani, Morteza , Asemi, Zatollah , Aujayeb, Avinash , Azzam, Ahmed Y , Banach, Maciej , Barra, Fabio , Behzadifar, Masoud , Bezabhe, Woldesellassie M Mequanint , Bhardwaj, Pankaj , Bhattacharyya, Krittika , Biondi, Antonio , Bjørge, Tone , Bleyer, Archie , Blyuss, Oleg B. , Chaturvedi, Pankaj, Christopher, Devasahayam Jesudas , Chung, Michael T. , Cortesi, Paolo Angelo , Belachew, Amare , Dahlawi, Saad M A , Dai, Xiaochen , Darwesh, Aso Mohammad , Neves, José das , Demis, Asmamaw , Denova‐Gutiérrez, Edgar , Dhimal, Meghnath , Dianatinasab, Mostafa , Do, Huyen Phuc , Doaei, Saeid , Figueiredo, Francisco Winter dos Santos , El-Jaafary, Shaimaa I , Eskandarieh, Sharareh , Faraon, Emerito Jose A , Ferrero, Simone , Filip, Irina , Foroutan, Masoud , Gaál, Péter , Gadanya, Muktar A , Gallus, Silvano , Gomez, Ricardo Santiago , Grosso, Giuseppe , Gupta, Veer Bala , Haj‐Mirzaian, Arvin , Halwani, Rabih , Hamadeh, Randah R , Hamidi, Samer , Haro, Josep María , Hassanipour, Soheil , Hay, Roderick J. , Hayat, Khezar , Heidari, Mohammad , Holla, Ramesh , Hossain, Md Mahbub , Hostiuc, Sorin , Iavicoli, Ivo , Ilesanmi, Olayinka Stephen , Ilić, Milena , Islam, Rakibul M. , Jazayeri, Seyed Behzad , Kabir, Ali , Kahrizi, Danial , Kaliyadan, Feroze , Karch, André , Katikireddi, Srinivasa Vittal , Kauppila, Joonas H. , Kavetskyy, Taras , Kebede, Sewnet Adem , Khan, Gulfaraz , Khang, Young-Ho, Khayamzadeh, Maryam , Kim, Gyu Ri , Kim, Yun Jin , Kissimova‐Skarbek, Katarzyna , Koyanagi, Ai , Kumar, Narinder , Kumar, Nithin , Vecchia, Carlo La , Landires, Iván , Lauriola, Paolo , Lee, Sang Hyuck , Lee, Shaun Wen Huey , Lee, Wei‐Chen , Li, Jiarui , Mansournia, Mohammad Alì , Mathur, Prashant , McKee, Martin , Menezes, Ritesh G. , Meštrović, Tomislav , Mirzaei, Hamed , Moni, Mohammad Ali , Morrison, Shane D. , Mosapour, Abbas , Nagaraju, Shankar Prasad , Ndejjo, Rawlance , Nduaguba, Sabina O. , Nguyen, Huong Lan Thi , Núñez-Samudio, Virginia , Оchir, Chimedsuren , Odukoya, Oluwakemi Ololade , Ong, Sokking, Orru, Hans , Otstavnov, Nikita , Owolabi, Mayowa , Padubidri, Jagadish Rao , Pardhan, Shahina , Pattanshetty, Sanjay M. , Pishgar, Farhad , Pourjafar, Hadi , Rabiee, Mohammad , Rafiee, Ata , Rahmani, Amir Masoud , Rakovac, Ivo , Ranasinghe, Priyanga Diloshini , Renzaho, André M. N. , Rezaei, Negar , Rezaei, Nima , Roberts, Thomas J. , Romoli, Michele , Ronfani, Luca , Roshandel, Gholamreza , Sabour, Siamak , Sahebkar, Amirhossein , Salimzadeh, Hamideh , Şaylan, Mete , Schneider, Ione Jayce Ceola , Shaghaghi, Zahra , Shamsoddin, Erfan , Sharma, Rajesh , Sheikh, Aziz , Shibuya, Kenji , Shivakumar, K. M. , Siabani, Soraya , Singh, Jasvinder A. , Skryabina, Anna Aleksandrovna , Sotoudeh, Houman , Sufiyan, Mu’awiyyah Babale , Sultan, Iyad Yasin , Sur, Daniel Genel , Szócska, Miklós , Tabarés‐Seisdedos, Rafael , Taherkhani, Amir , Talaat, Iman Mamdouh , Tehrani‐Banihashemi, Arash , Thapar, Rekha , Thomas, Nihal , Tohidinik, Hamid Reza , Tran, Bach Xuan , Tran, Khanh Bao , Unnikrishnan, Bhaskaran , Upadhyay, Era , Vacante, Marco , Vaezi, Maryam, Veroux, Massimiliano , Vlassov, Vasily , Vo, Bay, Wamai, Richard G. , Winkler, Andrea Sylvia , Yaya, Sanni , Yonemoto, Naohiro , Yu, Chuanhua , Yüce, Deniz , Zhou, Linghui , Ziapour, Arash , Olagunju, Andrew T , Shivakumar, KM , Baig, Atif Amin , Alahdab, Fares , Asghari-Jafarabadi, Mohammad , Rahim, Fakher

JAMA Oncology | December 30, 2021 | Volume: 8 | Issue: 3 | Pages: 420

10.1001/jamaoncol.2021.6987 | PMID 34967848 Get It