1
|
Lim DJ, Varadarajan V, Quinaglia T, Pezel T, Wu C, Noda C, Heckbert SR, Bluemke D, Ambale-Venkatesh B, Lima JAC. Change in left atrial function and volume predicts incident heart failure with preserved and reduced ejection fraction: Multi-Ethnic Study of Atherosclerosis. Eur Heart J Cardiovasc Imaging 2024:jeae138. [PMID: 38885142 DOI: 10.1093/ehjci/jeae138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/12/2024] [Accepted: 05/22/2024] [Indexed: 06/20/2024] Open
Abstract
AIMS The role of change in left atrial (LA) parameters prior to the onset of heart failure (HF) remains unclear. We used cardiac magnetic resonance (CMR) imaging to investigate the relationship between longitudinal change in LA function and incident HF in a multi-ethnic population with subclinical cardiovascular disease (CVD). METHODS AND RESULTS In this prospective multi-ethnic cohort study, 2470 participants (60 ± 9 years, 47% males), free at baseline of clinical CVD, had LA volume and function assessed via multimodality tissue tracking on CMR imaging at baseline (2000-02) and a second study 9.4 ± 0.6 years later. Free of HF, 73 participants developed incident HF [HF with preserved ejection fraction (HFpEF), n = 39; reduced ejection fraction (HFrEF), n = 34] 7.1 ± 2.1 years after the second study. An annual decrease of 1 SD unit in peak LA strain (ΔLASmax) was most strongly associated with the risk of HFpEF [subdistribution hazard ratios (HR) = 2.56, 95% confidence interval (CI) (1.34-4.90), P = 0.004] and improved model reclassification and discrimination in predicting HFpEF [C-statistic = 0.84, 95% CI (0.79-0.90); net reclassification index (NRI) = 0.34, P = 0.01; and integrated discrimination index (IDI) = 0.02, P = 0.02], whilst an annual decrease of 1 mL/m2 of pre-atrial indexed LA volumes (ΔLAVipreA) was most strongly associated with the risk of HFrEF [subdistribution HR = 1.88, 95% CI (1.44-2.45), P < 0.001] and improved model reclassification and discrimination in predicting HFrEF [C-statistic = 0.81, 95% CI (0.72-0.90); NRI = 0.31, P = 0.03; and IDI = 0.01, P = 0.50] after adjusting for event-specific risk factors and baseline LA measures. CONCLUSION ΔLASmax and ΔLAVipreA were associated with and incrementally predictive of HFpEF and HFrEF, after adjusting for risk factors and baseline LA measures in this population of subclinical CVD.
Collapse
Affiliation(s)
- Daniel J Lim
- School of Medicine, Johns Hopkins University, Baltimore MD, USA
| | | | | | - Theo Pezel
- School of Medicine, Johns Hopkins University, Baltimore MD, USA
| | - Colin Wu
- Office of Biostatistics Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Chikara Noda
- School of Medicine, Johns Hopkins University, Baltimore MD, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - David Bluemke
- Department of Radiology, University of Wisconsin-Madison, WI, USA
| | | | - Joao A C Lima
- School of Medicine, Johns Hopkins University, Baltimore MD, USA
| |
Collapse
|
2
|
Raisi-Estabragh Z, Szabo L, Schuermans A, Salih AM, Chin CWL, Vágó H, Altmann A, Ng FS, Garg P, Pavanello S, Marwick TH, Petersen SE. Noninvasive Techniques for Tracking Biological Aging of the Cardiovascular System: JACC Family Series. JACC Cardiovasc Imaging 2024:S1936-878X(24)00082-2. [PMID: 38597854 DOI: 10.1016/j.jcmg.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 04/11/2024]
Abstract
Population aging is one of the most important demographic transformations of our time. Increasing the "health span"-the proportion of life spent in good health-is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.
Collapse
Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom; Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Art Schuermans
- Faculty of Medicine, KU Leuven, Leuven, Belgium; Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ahmed M Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Department of Population Health Sciences, University of Leicester, Leicester UK; Department of Computer Science, Faculty of Science, University of Zakho, Zakho, Kurdistan Region, Iraq
| | - Calvin W L Chin
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore; Cardiovascular Academic Clinical Programme, Duke National University of Singapore Medical School, Singapore, Singapore
| | - Hajnalka Vágó
- Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Pankaj Garg
- University of East Anglia, Norwich Medical School, Norwich, United Kingdom; Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
| | - Sofia Pavanello
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy; Padua Hospital, Occupational Medicine Unit, Padua, Italy; University Center for Space Studies and Activities "Giuseppe Colombo" - CISAS, University of Padua, Padua, Italy
| | | | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom; Health Data Research UK, London, United Kingdom
| |
Collapse
|
3
|
Yang X, Li X, Yu N, Yan R, Sun Y, Tang C, Ding W, Ling M, Song Y, Gao H, Gao W, Feng J, Wang S, Zhang Z, Xing Y. Proteomics and β-hydroxybutyrylation Modification Characterization in the Hearts of Naturally Senescent Mice. Mol Cell Proteomics 2023; 22:100659. [PMID: 37805038 PMCID: PMC10685312 DOI: 10.1016/j.mcpro.2023.100659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 10/01/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023] Open
Abstract
Aging is widely accepted as an independent risk factor for cardiovascular disease (CVD), which contributes to increasing morbidity and mortality in the elderly population. Lysine β-hydroxybutyrylation (Kbhb) is a novel post-translational modification (PTM), wherein β-hydroxybutyrate is covalently attached to lysine ε-amino groups. Recent studies have revealed that histone Kbhb contributes to tumor progression, diabetic cardiomyopathy progression, and postnatal heart development. However, no studies have yet reported a global analysis of Kbhb proteins in aging hearts or elucidated the mechanisms underlying this modification in the process. Herein, we conducted quantitative proteomics and Kbhb PTM omics to comprehensively elucidate the alterations of global proteome and Kbhb modification in the hearts of aged mice. The results revealed a decline in grip strength and cardiac diastolic function in 22-month-old aged mice compared to 3-month-old young mice. High-throughput liquid chromatogram-mass spectrometry analysis identified 1710 β-hydroxybutyrylated lysine sites in 641 proteins in the cardiac tissue of young and aged mice. Additionally, 183 Kbhb sites identified in 134 proteins exhibited significant differential modification in aged hearts (fold change (FC) > 1.5 or <1/1.5, p < 0.05). Notably, the Kbhb-modified proteins were primarily detected in energy metabolism pathways, such as fatty acid elongation, glyoxylate and dicarboxylate metabolism, tricarboxylic acid cycle, and oxidative phosphorylation. Furthermore, these Kbhb-modified proteins were predominantly localized in the mitochondria. The present study, for the first time, provides a global proteomic profile and Kbhb modification landscape of cardiomyocytes in aged hearts. These findings put forth novel possibilities for treating cardiac aging and aging-related CVDs by reversing abnormal Kbhb modifications.
Collapse
Affiliation(s)
- Xuechun Yang
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xuehui Li
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Na Yu
- Shandong Precision Medicine Engineering Laboratory of Bacterial Anti-tumor Drugs of Shandong Xinchuang Biotechnology Co., LTD, Jinan, Shandong, China; College of Clinical Medicine, Shandong University, Jinan, Shandong, China
| | - Rong Yan
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yan Sun
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Congmin Tang
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Wenjing Ding
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Mingying Ling
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yiping Song
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Haiqing Gao
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Wenjuan Gao
- Laboratory of Basic Medical Sciences, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Junchao Feng
- Shandong Precision Medicine Engineering Laboratory of Bacterial Anti-tumor Drugs of Shandong Xinchuang Biotechnology Co., LTD, Jinan, Shandong, China
| | - Shaopeng Wang
- Shandong Precision Medicine Engineering Laboratory of Bacterial Anti-tumor Drugs of Shandong Xinchuang Biotechnology Co., LTD, Jinan, Shandong, China
| | - Zhen Zhang
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China.
| | - Yanqiu Xing
- Department of Geriatric Medicine, Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital of Shandong University, Jinan, Shandong, China.
| |
Collapse
|
4
|
Wang Y, Gao T, Wang B. Application of mesenchymal stem cells for anti-senescence and clinical challenges. Stem Cell Res Ther 2023; 14:260. [PMID: 37726805 PMCID: PMC10510299 DOI: 10.1186/s13287-023-03497-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/13/2023] [Indexed: 09/21/2023] Open
Abstract
Senescence is a hot topic nowadays, which shows the accumulation of senescent cells and inflammatory factors, leading to the occurrence of various senescence-related diseases. Although some methods have been identified to partly delay senescence, such as strengthening exercise, restricting diet, and some drugs, these only slow down the process of senescence and cannot fundamentally delay or even reverse senescence. Stem cell-based therapy is expected to be a potential effective way to alleviate or cure senescence-related disorders in the coming future. Mesenchymal stromal cells (MSCs) are the most widely used cell type in treating various diseases due to their potentials of self-replication and multidirectional differentiation, paracrine action, and immunoregulatory effects. Some biological characteristics of MSCs can be well targeted at the pathological features of aging. Therefore, MSC-based therapy is also a promising strategy to combat senescence-related diseases. Here we review the recent progresses of MSC-based therapies in the research of age-related diseases and the challenges in clinical application, proving further insight and reference for broad application prospects of MSCs in effectively combating senesce in the future.
Collapse
Affiliation(s)
- Yaping Wang
- Clinical Stem Cell Center, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, People's Republic of China
- Clinical Stem Cell Center, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People's Republic of China
| | - Tianyun Gao
- Clinical Stem Cell Center, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, People's Republic of China
| | - Bin Wang
- Clinical Stem Cell Center, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, People's Republic of China.
| |
Collapse
|
5
|
Varadarajan V, Gidding S, Wu C, Carr J, Lima JA. Imaging Early Life Cardiovascular Phenotype. Circ Res 2023; 132:1607-1627. [PMID: 37289903 PMCID: PMC10501740 DOI: 10.1161/circresaha.123.322054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/30/2023] [Indexed: 06/10/2023]
Abstract
The growing epidemics of obesity, hypertension, and diabetes, in addition to worsening environmental factors such as air pollution, water scarcity, and climate change, have fueled the continuously increasing prevalence of cardiovascular diseases (CVDs). This has caused a markedly increasing burden of CVDs that includes mortality and morbidity worldwide. Identification of subclinical CVD before overt symptoms can lead to earlier deployment of preventative pharmacological and nonpharmacologic strategies. In this regard, noninvasive imaging techniques play a significant role in identifying early CVD phenotypes. An armamentarium of imaging techniques including vascular ultrasound, echocardiography, magnetic resonance imaging, computed tomography, noninvasive computed tomography angiography, positron emission tomography, and nuclear imaging, with intrinsic strengths and limitations can be utilized to delineate incipient CVD for both clinical and research purposes. In this article, we review the various imaging modalities used for the evaluation, characterization, and quantification of early subclinical cardiovascular diseases.
Collapse
Affiliation(s)
- Vinithra Varadarajan
- Division of Cardiology, Department of Medicine Johns Hopkins University, Baltimore, MD
| | | | - Colin Wu
- Department of Medicine, National Heart, Lung and Blood Institute, Bethesda, MD
| | - Jeffrey Carr
- Department Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN
| | - Joao A.C. Lima
- Division of Cardiology, Department of Medicine Johns Hopkins University, Baltimore, MD
| |
Collapse
|
6
|
Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 77] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
Collapse
Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| |
Collapse
|
7
|
Akintoye E, Saijo Y, Braghieri L, Badwan O, Patel H, Dabbagh MM, El Dahdah J, Jellis CL, Desai MY, Rodriguez LL, Grimm RA, Griffin BP, Popović ZB. Impact of Age and Sex on Left Ventricular Remodeling in Patients With Aortic Regurgitation. J Am Coll Cardiol 2023; 81:1474-1487. [PMID: 37045517 PMCID: PMC9982944 DOI: 10.1016/j.jacc.2023.02.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/17/2023] [Accepted: 02/09/2023] [Indexed: 04/14/2023]
Abstract
BACKGROUND Current guidelines for aortic regurgitation (AR) recommend the same linear left ventricular (LV) dimension for intervention regardless of age and sex. OBJECTIVES The purpose of this study was to evaluate the impact of age and sex on the degree of LV remodeling and outcomes. METHODS We included consecutive patients with severe AR who were serially monitored by echocardiogram between 2010 and 2016. The 2 main endpoints were as follows: 1) LV end-systolic volume indexed to body surface area (LVESVi) and LV end-diastolic volume indexed to body surface area; and 2) adverse events (AE). We evaluated the longitudinal rate of LV remodeling and determined the association between LV volume and AE by age and sex. RESULTS A total of 525 adult patients (26% women) with a median echocardiogram follow-up of 2.0 years (IQR: 1.0-3.6 years) were included. At baseline, older patients (age ≥60 years) had smaller LV volumes compared with younger patients (age <60 years), eg, the mean LVESVi was 27.3 mL/m2 vs 32.3 mL/m2, respectively. Similarly, women had smaller LV volumes compared with men (mean LVESVi was 23.3 mL/m2 vs 32.4 mL/m2). On serial evaluation, older patients and women maintained smaller LV volumes compared with younger patients and men, respectively. There were 210 (40%) AE during follow-up. The optimal discriminatory threshold for AE varies by age and sex, eg, the LVESVi threshold was highest for young men (50 mL/m2), intermediate for older men (35 mL/m2), and lowest for women (27 mL/m2). CONCLUSIONS On serial evaluation, older patients and women with chronic AR maintained smaller LV volumes than younger patients and men, respectively, and develop AE at lower LV volumes.
Collapse
Affiliation(s)
- Emmanuel Akintoye
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA. https://twitter.com/eakintoyeMD
| | - Yoshihito Saijo
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Lorenzo Braghieri
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Osamah Badwan
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Hardik Patel
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - M Marwan Dabbagh
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Joseph El Dahdah
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Christine L Jellis
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA. https://twitter.com/ChrisJellisMD
| | - Milind Y Desai
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA. https://twitter.com/DesaiMilindY
| | - L Leonardo Rodriguez
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Richard A Grimm
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Brian P Griffin
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA. https://twitter.com/BrianGriffinMD
| | - Zoran B Popović
- Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
| |
Collapse
|
8
|
Hu Z, Xiao J, Mao X, Xie Y, Kwan AC, Song SS, Fong MW, Wilcox AG, Li D, Christodoulou AG, Fan Z. MR Multitasking-based multi-dimensional assessment of cardiovascular system (MT-MACS) with extended spatial coverage and water-fat separation. Magn Reson Med 2023; 89:1496-1505. [PMID: 36336794 PMCID: PMC9892247 DOI: 10.1002/mrm.29522] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/25/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE To extend the MR MultiTasking-based Multidimensional Assessment of Cardiovascular System (MT-MACS) technique with larger spatial coverage and water-fat separation for comprehensive aortocardiac assessment. METHODS MT-MACS adopts a low-rank tensor image model for 7D imaging, with three spatial dimensions for volumetric imaging, one cardiac motion dimension for cine imaging, one respiratory motion dimension for free-breathing imaging, one T2-prepared inversion recovery time dimension for multi-contrast assessment, and one T2*-decay time dimension for water-fat separation. Nine healthy subjects were recruited for the 3T study. Overall image quality was scored on bright-blood (BB), dark-blood (DB), and gray-blood (GB) contrasts using a 4-point scale (0-poor to 3-excellent) by two independent readers, and their interreader agreement was evaluated. Myocardial wall thickness and left ventricular ejection fraction (LVEF) were quantified on DB and BB contrasts, respectively. The agreement in these metrics between MT-MACS and conventional breath-held, electrocardiography-triggered 2D sequences were evaluated. RESULTS MT-MACS provides both water-only and fat-only images with excellent image quality (average score = 3.725/3.780/3.835/3.890 for BB/DB/GB/fat-only images) and moderate to high interreader agreement (weighted Cohen's kappa value = 0.727/0.668/1.000/1.000 for BB/DB/GB/fat-only images). There were good to excellent agreements in myocardial wall thickness measurements (intraclass correlation coefficients [ICC] = 0.781/0.929/0.680/0.878 for left atria/left ventricle/right atria/right ventricle) and LVEF quantification (ICC = 0.716) between MT-MACS and 2D references. All measurements were within the literature range of healthy subjects. CONCLUSION The refined MT-MACS technique provides multi-contrast, phase-resolved, and water-fat imaging of the aortocardiac systems and allows evaluation of anatomy and function. Clinical validation is warranted.
Collapse
Affiliation(s)
- Zhehao Hu
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of BioengineeringUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Jiayu Xiao
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Xianglun Mao
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Yibin Xie
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Alan C. Kwan
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Smidt Heart InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Shlee S. Song
- Department of NeurologyCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Michael W. Fong
- Division of Cardiovascular MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Cardiovascular Thoracic InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Alison G. Wilcox
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Debiao Li
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of BioengineeringUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of BioengineeringUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Zhaoyang Fan
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of Radiation OncologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| |
Collapse
|
9
|
Álvarez-Garcia J, Popova E, Vives-Borrás M, de Nadal M, Ordonez-Llanos J, Rivas-Lasarte M, Moustafa AH, Solé-González E, Paniagua-Iglesias P, Garcia-Moll X, Viladés-Medel D, Leta-Petracca R, Oristrell G, Zamora J, Ferreira-González I, Alonso-Coello P, Carreras-Costa F. Myocardial injury after major non-cardiac surgery evaluated with advanced cardiac imaging: a pilot study. BMC Cardiovasc Disord 2023; 23:78. [PMID: 36765313 PMCID: PMC9911951 DOI: 10.1186/s12872-023-03065-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 01/13/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Myocardial injury after non-cardiac surgery (MINS) is a frequent complication caused by cardiac and non-cardiac pathophysiological mechanisms, but often it is subclinical. MINS is associated with increased morbidity and mortality, justifying the need to its diagnose and the investigation of their causes for its potential prevention. METHODS Prospective, observational, pilot study, aiming to detect MINS, its relationship with silent coronary artery disease and its effect on future adverse outcomes in patients undergoing major non-cardiac surgery and without postoperative signs or symptoms of myocardial ischemia. MINS was defined by a high-sensitive cardiac troponin T (hs-cTnT) concentration > 14 ng/L at 48-72 h after surgery and exceeding by 50% the preoperative value; controls were the operated patients without MINS. Within 1-month after discharge, cardiac computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) studies were performed in MINS and control subjects. Significant coronary artery disease (CAD) was defined by a CAD-RADS category ≥ 3. The primary outcomes were prevalence of CAD among MINS and controls and incidence of major cardiovascular events (MACE) at 1-year after surgery. Secondary outcomes were the incidence of individual MACE components and mortality. RESULTS We included 52 MINS and 12 controls. The small number of included patients could be attributed to the study design complexity and the dates of later follow-ups (amid COVID-19 waves). Significant CAD by CCTA was equally found in 20 MINS and controls (30% vs 33%, respectively). Ischemic patterns (n = 5) and ischemic segments (n = 2) depicted by cardiac MRI were only observed in patients with MINS. One-year MACE were also only observed in MINS patients (15.4%). CONCLUSION This study with advanced imaging methods found a similar CAD frequency in MINS and control patients, but that cardiac ischemic findings by MRI and worse prognosis were only observed in MINS patients. Our results, obtained in a pilot study, suggest the need of further, extended studies that screened systematically MINS and evaluated its relationship with cardiac ischemia and poor outcomes. Trial registration Clinicaltrials.gov identifier: NCT03438448 (19/02/2018).
Collapse
Affiliation(s)
- Jesús Álvarez-Garcia
- grid.411347.40000 0000 9248 5770Department of Cardiology, Hospital Universitario Ramon y Cajal, M-607, 9,100, 28034 Madrid, Spain ,grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain ,grid.512890.7Centro de Investigación Biomédica en Red Enfermedades Cardiovaculares (CIBERCV), Madrid, Spain
| | - Ekaterine Popova
- IIB SANT PAU, Institut d'Investigació Biomèdica Sant Pau, Sant Quintí 77, 08041, Barcelona, Spain. .,Centro Cochrane Iberoamericano, Sant Antoni Maria Claret, 167, 08025, Barcelona, Spain.
| | - Miquel Vives-Borrás
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain ,grid.507085.fFundació Institut d’Investigació Sanitària Illes Balears (IdISBa), Department of Cardiology, Carretera de Valldemossa, 79, 07120 Palma, Balearic Islands Spain ,grid.411164.70000 0004 1796 5984Department of Cardiology, Hospital Universitari Son Espases, Carretera de Valldemossa, 79, Palma, Illes Balears Spain
| | - Miriam de Nadal
- Department of Anaesthesiology and Intensive Care, Hospital Universitari Vall d'Hebron, Passeig de la Vall d'Hebron, 119, 08035, Barcelona, Spain.
| | - Jordi Ordonez-Llanos
- grid.413396.a0000 0004 1768 8905Department of Biochemistry, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain ,Foundation for Clinical Biochemistry & Molecular Pathology, Barcelona, Spain
| | - Mercedes Rivas-Lasarte
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain ,grid.73221.350000 0004 1767 8416Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda, C. Joaquín Rodrigo, 1, 28222 Majadahonda, Madrid, Spain
| | - Abdel-Hakim Moustafa
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain
| | - Eduard Solé-González
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain ,grid.410458.c0000 0000 9635 9413Department of Cardiology, Hospital Clinic i Provincial, C. de Villarroel, 170, 08036 Barcelona, Spain
| | - Pilar Paniagua-Iglesias
- grid.413396.a0000 0004 1768 8905Department of Anaesthesia and Pain Management, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain
| | - Xavier Garcia-Moll
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain
| | - David Viladés-Medel
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain
| | - Rubén Leta-Petracca
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain
| | - Gerard Oristrell
- grid.411083.f0000 0001 0675 8654Department of Cardiology, Hospital Universitari Vall d’Hebron, Passeig de la Vall d’Hebron, 119, 08035 Barcelona, Spain
| | - Javier Zamora
- grid.411347.40000 0000 9248 5770Clinical Biostatistics Unit, IRYCIS, Hospital Universitario Ramon y Cajal, M-607, 9,100, 28034 Madrid, Spain ,grid.466571.70000 0004 1756 6246CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ignacio Ferreira-González
- grid.411083.f0000 0001 0675 8654Department of Cardiology, Hospital Universitari Vall d’Hebron, Passeig de la Vall d’Hebron, 119, 08035 Barcelona, Spain ,grid.466571.70000 0004 1756 6246CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Pablo Alonso-Coello
- IIB SANT PAU, Institut d’Investigació Biomèdica Sant Pau, Sant Quintí 77, 08041 Barcelona, Spain ,grid.466571.70000 0004 1756 6246CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Francesc Carreras-Costa
- grid.413396.a0000 0004 1768 8905Department of Cardiology, Hospital de la Santa Creu i Sant Pau, Sant Quintí 89, 08026 Barcelona, Spain ,grid.512890.7Centro de Investigación Biomédica en Red Enfermedades Cardiovaculares (CIBERCV), Madrid, Spain
| |
Collapse
|
10
|
Ribeiro ASF, Zerolo BE, López-Espuela F, Sánchez R, Fernandes VS. Cardiac System during the Aging Process. Aging Dis 2023:AD.2023.0115. [PMID: 37163425 PMCID: PMC10389818 DOI: 10.14336/ad.2023.0115] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/15/2023] [Indexed: 05/12/2023] Open
Abstract
The aging process is accompanied by a continuous decline of the cardiac system, disrupting the homeostatic regulation of cells, organs, and systems. Aging increases the prevalence of cardiovascular diseases, thus heart failure and mortality. Understanding the cardiac aging process is of pivotal importance once it allows us to design strategies to prevent age-related cardiac events and increasing the quality of live in the elderly. In this review we provide an overview of the cardiac aging process focus on the following topics: cardiac structural and functional modifications; cellular mechanisms of cardiac dysfunction in the aging; genetics and epigenetics in the development of cardiac diseases; and aging heart and response to the exercise.
Collapse
Affiliation(s)
| | - Blanca Egea Zerolo
- Escuela de Enfermería y Fisioterapia San Juan de Dios. Universidad Pontificia Comillas, Madrid, Spain
| | - Fidel López-Espuela
- Metabolic Bone Diseases Research Group, Nursing and Occupational Therapy College, University of Extremadura, Caceres, Spain
| | - Raúl Sánchez
- Unidad de Cardiopatías Congénitas, Hospital Universitario La Paz, Madrid, Spain
| | - Vítor S Fernandes
- Departamento de Fisiología, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain
| |
Collapse
|
11
|
Shabani M, Ostovaneh MR, Ma X, Ambale Venkatesh B, Wu CO, Chahal H, Bakhshi H, McClelland RL, Liu K, Shea SJ, Burke G, Post WS, Watson KE, Folsom AR, Bluemke DA, Lima JAC. Pre-diagnostic predictors of mortality in patients with heart failure: The multi-ethnic study of atherosclerosis. Front Cardiovasc Med 2022; 9:1024031. [PMID: 36620619 PMCID: PMC9812565 DOI: 10.3389/fcvm.2022.1024031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
Background There are multiple predictive factors for cardiovascular (CV) mortality measured at, or after heart failure (HF) diagnosis. However, the predictive role of long-term exposure to these predictors prior to HF diagnosis is unknown. Objectives We aim to identify predictive factors of CV mortality in participants with HF, using cumulative exposure to risk factors before HF development. Methods Participants of Multi-Ethnic Study of Atherosclerosis (MESA) with incident HF were included. We used stepwise Akaike Information Criterion to select CV mortality predictors among clinical, biochemical, and imaging markers collected prior to HF. Using the AUC of B-spline-corrected curves, we estimated cumulative exposure to predictive factors from baseline to the last exam before HF. The prognostic performance for CV mortality after HF was evaluated using competing risk regression with non-CV mortality as the competing risk. Results Overall, 375 participants had new HF events (42.9% female, mean age: 74). Over an average follow-up of 4.7 years, there was no difference in the hazard of CV death for HF with reduced versus preserved ejection fraction (HR = 1.27, p = 0.23). The selected predictors of CV mortality in models with the least prediction error were age, cardiac arrest, myocardial infarction, and diabetes, QRS duration, HDL, cumulative exposure to total cholesterol and glucose, NT-proBNP, left ventricular mass, and statin use. The AUC of the models were 0.72 when including the latest exposure to predictive factors and 0.79 when including cumulative prior exposure to predictive factors (p = 0.20). Conclusion In HF patients, besides age and diagnosed diabetes or CVD, prior lipid profile, NT-proBNP, LV mass, and QRS duration available at the diagnosis time strongly predict CV mortality. Implementing cumulative exposure to cholesterol and glucose, instead of latest measures, improves predictive accuracy for HF mortality, though not reaching statistical significance.
Collapse
Affiliation(s)
- Mahsima Shabani
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Mohammad R. Ostovaneh
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States,Penn State Health Milton S. Hershey Medical Center, Hershey, PA, United States
| | - Xiaoyang Ma
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, United States
| | | | - Colin O. Wu
- Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Harjit Chahal
- Medstar Heart and Vascular Institute, Washington, DC, United States
| | - Hooman Bakhshi
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States,Inova Heart and Vascular Institute, Falls Church, VA, United States
| | - Robyn L. McClelland
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Kiang Liu
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Steven J. Shea
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, United States,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Gregory Burke
- Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, United States
| | - Wendy S. Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Karol E. Watson
- Division of Cardiology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Aaron R. Folsom
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States
| | - David A. Bluemke
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - João A. C. Lima
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States,Department of Radiology, Johns Hopkins University, Baltimore, MD, United States,*Correspondence: João A. C. Lima,
| |
Collapse
|
12
|
Puyol-Antón E, Ruijsink B, Mariscal Harana J, Piechnik SK, Neubauer S, Petersen SE, Razavi R, Chowienczyk P, King AP. Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation. Front Cardiovasc Med 2022; 9:859310. [PMID: 35463778 PMCID: PMC9021445 DOI: 10.3389/fcvm.2022.859310] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/02/2022] [Indexed: 12/12/2022] Open
Abstract
Background Artificial intelligence (AI) techniques have been proposed for automation of cine CMR segmentation for functional quantification. However, in other applications AI models have been shown to have potential for sex and/or racial bias. The objective of this paper is to perform the first analysis of sex/racial bias in AI-based cine CMR segmentation using a large-scale database. Methods A state-of-the-art deep learning (DL) model was used for automatic segmentation of both ventricles and the myocardium from cine short-axis CMR. The dataset consisted of end-diastole and end-systole short-axis cine CMR images of 5,903 subjects from the UK Biobank database (61.5 ± 7.1 years, 52% male, 81% white). To assess sex and racial bias, we compared Dice scores and errors in measurements of biventricular volumes and function between patients grouped by race and sex. To investigate whether segmentation bias could be explained by potential confounders, a multivariate linear regression and ANCOVA were performed. Results Results on the overall population showed an excellent agreement between the manual and automatic segmentations. We found statistically significant differences in Dice scores between races (white ∼94% vs. minority ethnic groups 86-89%) as well as in absolute/relative errors in volumetric and functional measures, showing that the AI model was biased against minority racial groups, even after correction for possible confounders. The results of a multivariate linear regression analysis showed that no covariate could explain the Dice score bias between racial groups. However, for the Mixed and Black race groups, sex showed a weak positive association with the Dice score. The results of an ANCOVA analysis showed that race was the main factor that can explain the overall difference in Dice scores between racial groups. Conclusion We have shown that racial bias can exist in DL-based cine CMR segmentation models when training with a database that is sex-balanced but not race-balanced such as the UK Biobank.
Collapse
Affiliation(s)
- Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Bram Ruijsink
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Jorge Mariscal Harana
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Stefan K. Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Steffen E. Petersen
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Phil Chowienczyk
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- British Heart Foundation Centre, King’s College London, London, United Kingdom
| | - Andrew P. King
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| |
Collapse
|
13
|
The Role of Oxidative Stress in the Aging Heart. Antioxidants (Basel) 2022; 11:antiox11020336. [PMID: 35204217 PMCID: PMC8868312 DOI: 10.3390/antiox11020336] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/25/2022] [Accepted: 01/27/2022] [Indexed: 12/17/2022] Open
Abstract
Medical advances and the availability of diagnostic tools have considerably increased life expectancy and, consequently, the elderly segment of the world population. As age is a major risk factor in cardiovascular disease (CVD), it is critical to understand the changes in cardiac structure and function during the aging process. The phenotypes and molecular mechanisms of cardiac aging include several factors. An increase in oxidative stress is a major player in cardiac aging. Reactive oxygen species (ROS) production is an important mechanism for maintaining physiological processes; its generation is regulated by a system of antioxidant enzymes. Oxidative stress occurs from an imbalance between ROS production and antioxidant defenses resulting in the accumulation of free radicals. In the heart, ROS activate signaling pathways involved in myocyte hypertrophy, interstitial fibrosis, contractile dysfunction, and inflammation thereby affecting cell structure and function, and contributing to cardiac damage and remodeling. In this manuscript, we review recent published research on cardiac aging. We summarize the aging heart biology, highlighting key molecular pathways and cellular processes that underlie the redox signaling changes during aging. Main ROS sources, antioxidant defenses, and the role of dysfunctional mitochondria in the aging heart are addressed. As metabolism changes contribute to cardiac aging, we also comment on the most prevalent metabolic alterations. This review will help us to understand the mechanisms involved in the heart aging process and will provide a background for attractive molecular targets to prevent age-driven pathology of the heart. A greater understanding of the processes involved in cardiac aging may facilitate our ability to mitigate the escalating burden of CVD in older individuals and promote healthy cardiac aging.
Collapse
|
14
|
Kersten J, Hackenbroch C, Bouly M, Tyl B, Bernhardt P. What Is Normal for an Aging Heart?: A Prospective CMR Cohort Study. J Cardiovasc Imaging 2022; 30:202-211. [PMID: 35879256 PMCID: PMC9314228 DOI: 10.4250/jcvi.2022.0021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/30/2022] [Accepted: 05/02/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND This study aims to investigate normal changes throughout aging of the heart in cardiac magnetic resonance (CMR) imaging in healthy volunteers. While type 2 diabetes mellitus is a frequent finding in the elderly population, also the influence of this circumstance in otherwise healthy persons is part of our study. METHODS In this prospective single-center trial, 75 healthy subjects in distinct age groups and 10 otherwise healthy diabetics were enrolled. All subjects underwent functional, flow sensitive, native T2- and T1-mapping in a 1.5T CMR scanner. RESULTS No differences in right and left ventricular ejection fractions were observed between aging healthy groups. Bi-ventricular volumes lowered significantly (p<0.001) between the age groups. There was also a significant decrease in myocardial T1 values, aortic distensibility, and left ventricular peak diastolic strain rates. There were no differences in T2 mapping and the other deformation parameters. Patients with type 2 diabetes mellitus had lower end-diastolic volume indexes; all the other measurements were comparable. CONCLUSIONS Aging processes in the healthy heart involve a decrease in ventricular volumes, with ejection fractions remaining normal. Stiffening of the myocardium and aorta and a decrease in T1 values are potential indications of age-related remodeling. Type 2 diabetes mellitus seems to have no major influence on aging processes of the heart. Trial Registration EudraCT Identifier: EudraCT 2017-000045-42
Collapse
Affiliation(s)
| | | | - Muriel Bouly
- Cardiovascular & Metabolic Disease Center for Therapeutic Innovation, Institut de Recherches Internationales Servier, Suresnes, France
| | - Benoit Tyl
- Cardiovascular & Metabolic Disease Center for Therapeutic Innovation, Institut de Recherches Internationales Servier, Suresnes, France
| | | |
Collapse
|
15
|
Chang S. Cardiac Magnetic Resonance in the Aging Heart. J Cardiovasc Imaging 2022; 30:212-213. [PMID: 35879257 PMCID: PMC9314223 DOI: 10.4250/jcvi.2022.0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 06/24/2022] [Indexed: 11/22/2022] Open
Affiliation(s)
- Suyon Chang
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| |
Collapse
|
16
|
Barbosa MF, Fusco DR, Gaiolla RD, Werys K, Tanni SE, Fernandes RA, Ribeiro SM, Szarf G. Characterization of subclinical diastolic dysfunction by cardiac magnetic resonance feature-tracking in adult survivors of non-Hodgkin lymphoma treated with anthracyclines. BMC Cardiovasc Disord 2021; 21:170. [PMID: 33845778 PMCID: PMC8040217 DOI: 10.1186/s12872-021-01996-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/07/2021] [Indexed: 01/19/2023] Open
Abstract
Background The use of anthracycline-based chemotherapy is associated with the development of heart failure, even years after the end of treatment. Early detection of cardiac dysfunction could identify a high-risk subset of survivors who would eventually benefit from early intervention. Cardiac magnetic resonance feature-tracking (CMR-FT) analysis offers a practical and rapid method to calculate systolic and diastolic strains from routinely acquired cine images. While early changes in systolic function have been described, less data are available about late effects of chemotherapy in diastolic parameters by CMR-FT. The main goal of this study was to determine whether left ventricular (LV) early diastolic strain rates (GDSR-E) by CMR-FT are impaired in long-term adult survivors of non-Hodgkin lymphoma (NHL). Our secondary objective was to analyze associations between GDSR-E with cumulative anthracycline dose, systolic function parameters and myocardial tissue characteristics.
Methods This is a single center cross-sectional observational study of asymptomatic patients in remission of NHL who previously received anthracycline therapy. All participants underwent their CMR examination on a 3.0-T scanner, including cines, T2 mapping, T1 mapping and late gadolinium enhancement imaging. Derived myocardial extracellular volume fraction was obtained from pre- and post-contrast T1 maps. CMR-FT analysis was performed using Trufi Strain software. The data obtained were compared between anthracycline group and volunteers without cardiovascular disease or neoplasia. Results A total of 18 adult survivors of NHL, 14 (77.8%) males, at mean age of 57.6 (± 14.7) years-old, were studied 88.2 (± 52.1) months after exposure to anthracycline therapy (median 400 mg/m2). Compared with controls, anthracycline group showed impaired LV global early diastolic circumferential strain rate (GCSR-E) [53.5%/s ± 19.3 vs 72.2%/s ± 26.7, p = 0.022], early diastolic longitudinal strain rate (GLSR-E) [40.4%/s ± 13.0 vs 55.9%/s ± 17.8, p = 0.006] and early diastolic radial strain rate (GRSR-E) [− 114.4%/s ± 37.1 vs − 170.5%/s ± 48.0, p < 0.001]. Impaired LV GCSR-E, GLSR-E and GRSR-E correlated with increased anthracycline dose and decreased systolic function. There were no correlations between GDSR-E and myocardial tissue characteristics. Conclusions Left ventricular early diastolic strain rates by CMR-FT are impaired late after anthracycline chemotherapy in adult survivors of non-Hodgkin lymphoma.
Collapse
Affiliation(s)
- Maurício Fregonesi Barbosa
- Department of Diagnostic Imaging, Universidade Federal de São Paulo (UNIFESP), Rua Napoleão de Barros 800, Vila Clementino, São Paulo, 04024-002, Brazil. .,Department of Tropical Diseases and Diagnostic Imaging, Universidade Estadual Paulista (UNESP), Botucatu, Brazil.
| | - Daniéliso Renato Fusco
- Cardiology Division, Internal Medicine Department, Universidade Estadual Paulista (UNESP), Botucatu, Brazil
| | - Rafael Dezen Gaiolla
- Hematology Division, Internal Medicine Department, Universidade Estadual Paulista (UNESP), Botucatu, Brazil
| | - Konrad Werys
- University of Oxford Centre for Clinical Magnetic Resonance Research (OCMR), University of Oxford, Oxford, UK
| | - Suzana Erico Tanni
- Pneumology Division, Internal Medicine Department, Universidade Estadual Paulista (UNESP), Botucatu, Brazil
| | - Rômulo Araújo Fernandes
- Department of Physical Education, Universidade Estadual Paulista (UNESP), Presidente Prudente, Brazil
| | - Sergio Marrone Ribeiro
- Department of Tropical Diseases and Diagnostic Imaging, Universidade Estadual Paulista (UNESP), Botucatu, Brazil
| | - Gilberto Szarf
- Department of Diagnostic Imaging, Universidade Federal de São Paulo (UNIFESP), Rua Napoleão de Barros 800, Vila Clementino, São Paulo, 04024-002, Brazil.,Hospital Israelita Albert Einstein, São Paulo, Brazil
| |
Collapse
|
17
|
Philip C, Seifried R, Peterson PG, Liotta R, Steel K, Bittencourt MS, Hulten EA. Cardiac MRI for Patients with Increased Cardiometabolic Risk. Radiol Cardiothorac Imaging 2021; 3:e200575. [PMID: 33969314 DOI: 10.1148/ryct.2021200575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 02/07/2021] [Accepted: 02/12/2021] [Indexed: 11/11/2022]
Abstract
Cardiac MRI (CMR) has rich potential for future cardiovascular screening even though not approved clinically for routine screening for cardiovascular disease among patients with increased cardiometabolic risk. Patients with increased cardiometabolic risk include those with abnormal blood pressure, body mass, cholesterol level, or fasting glucose level, which may be related to dietary and exercise habits. However, CMR does accurately evaluate cardiac structure and function. CMR allows for effective tissue characterization with a variety of sequences that provide unique insights as to fibrosis, infiltration, inflammation, edema, presence of fat, strain, and other potential pathologic features that influence future cardiovascular risk. Ongoing epidemiologic and clinical research may demonstrate clinical benefit leading to increased future use. © RSNA, 2021.
Collapse
Affiliation(s)
- Cynthia Philip
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Rebecca Seifried
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - P Gabriel Peterson
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Robert Liotta
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Kevin Steel
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Marcio S Bittencourt
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Edward A Hulten
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| |
Collapse
|
18
|
Pezel T, Viallon M, Croisille P, Sebbag L, Bochaton T, Garot J, Lima JAC, Mewton N. Imaging Interstitial Fibrosis, Left Ventricular Remodeling, and Function in Stage A and B Heart Failure. JACC Cardiovasc Imaging 2020; 14:1038-1052. [PMID: 32828781 DOI: 10.1016/j.jcmg.2020.05.036] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 05/11/2020] [Accepted: 05/29/2020] [Indexed: 02/07/2023]
Abstract
Myocardial interstitial fibrosis is part of the advanced disease stage of most cardiovascular pathologies. It has been characterized histologically in various disease settings from hypertensive heart disease and diabetic cardiomyopathy to severe aortic stenosis. It is also involved in the process of aging. In cardiovascular medicine, myocardial interstitial fibrosis is associated with several adverse outcomes, especially heart failure (HF) and sudden cardiac death. Until recently, clinical measures of interstitial fibrosis could only be made by invasive myocardial biopsy. The availability of cardiac magnetic resonance (CMR) T1 mapping techniques allows for the indirect measurement of interstitial space characteristics and extracellular volume size, which is closely correlated with collagen content and interstitial infiltration by amyloid and other molecules. There has been significant improvement in the accuracy and reproducibility of T1 acquisition sequences in the last decade; however, the correct use of this technique requires a solid CMR expertise in daily imaging practice. CMR has become the gold standard to assess left ventricular (LV) remodeling and functional features associated with interstitial fibrosis. These features can be detected in the early stages of HF. The main objective of this paper is to review the relevant results of preclinical and clinical observational studies that demonstrate the prognostic impact of interstitial fibrosis assessed by T1 mapping, as well as adverse left ventricular remodeling, as determinants of HF. Therefore, this review focuses on the pathological mechanisms underlying LV remodeling and interstitial fibrosis, in addition to the technical considerations involved in the assessment of interstitial LV fibrosis by CMR. It provides a thorough review of clinical evidence that demonstrates the association of interstitial fibrosis and other-CMR derived LV phenotypes with Stages A and B HF.
Collapse
Affiliation(s)
- Theo Pezel
- Department of Cardiology, Paris University, Lariboisiere Hospital, AP-HP, INSERM, UMRS 942, Paris, France; Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Magalie Viallon
- University Lyon, UJM-Saint-Etienne, INSA, CNRS UMR 5520, INSERM U1206, CREATIS, Saint-Etienne, France
| | - Pierre Croisille
- University Lyon, UJM-Saint-Etienne, INSA, CNRS UMR 5520, INSERM U1206, CREATIS, Saint-Etienne, France
| | - Laurent Sebbag
- Heart Failure and Transplant Department, Hospices Civils de Lyon, Hôpital Louis Pradel, Bron, France
| | - Thomas Bochaton
- Hospices Civils de Lyon, Hôpital Louis Pradel, Cardiac Intensive Care Unit, Bron, France
| | - Jerome Garot
- Institut Cardiovasculaire Paris Sud, Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques Cartier, Ramsay-Générale de Santé, Massy, France
| | - Joao A C Lima
- Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Nathan Mewton
- Cardiovascular Hospital Louis Pradel, Clinical Investigation Center and Heart Failure Department, INSERM 1407, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Lyon, France.
| |
Collapse
|
19
|
Moussavi A, Mietsch M, Drummer C, Behr R, Mylius J, Boretius S. Cardiac MRI in common marmosets revealing age-dependency of cardiac function. Sci Rep 2020; 10:10221. [PMID: 32576909 PMCID: PMC7311402 DOI: 10.1038/s41598-020-67157-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 05/26/2020] [Indexed: 11/09/2022] Open
Abstract
The aim of this study was to establish a feasible and robust magnetic resonance imaging protocol for the quantitative assessment of cardiac function in marmosets and to present normal values of cardiac function across different ages from young adult, middle-aged, to very old clinically healthy animals. Cardiac MRI of 33 anesthetized marmosets at the age of 2-15 years was performed at 9.4 T using IntraGate-FLASH that operates without any ECG-triggering and breath holding. Normalized to post-mortem heart weight, the left ventricular end-diastolic volume (LV-EDV) was significantly reduced in older marmosets. The LV end-systolic volume (LV-ESV) and the LV stroke volume (LV-SV) showed a similar trend while the LV ejection fraction (LV-EF) and wall thickening remained unchanged. Similar observations were made for the right ventricle. Moreover, the total ventricular myocardial volume was lower in older monkeys while no significant difference in heart weight was found. In conclusion, IntraGate-FLASH allowed for quantification of left ventricular cardiac function but seems to underestimate the volumes of the right ventricle. Although less strong and without significant sex differences, the observed age related changes were similar to previously reported findings in humans supporting marmosets as a model system for age related cardiovascular human diseases.
Collapse
Affiliation(s)
- Amir Moussavi
- Functional Imaging Laboratory, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany. .,DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany.
| | - Matthias Mietsch
- DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany.,Unit of Infection Models, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany.,Department of Laboratory Animal Science, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Charis Drummer
- DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany.,Platform Degenerative Diseases, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Rüdiger Behr
- DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany.,Platform Degenerative Diseases, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Judith Mylius
- Functional Imaging Laboratory, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Susann Boretius
- Functional Imaging Laboratory, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany.,DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| |
Collapse
|
20
|
Maimaitiaili R, Teliewubai J, Zhao S, Tang J, Chi C, Zhang Y, Xu Y. Relationship Between Vascular Aging and Left Ventricular Concentric Geometry in Community-Dwelling Elderly: The Northern Shanghai Study. Clin Interv Aging 2020; 15:853-863. [PMID: 32606625 PMCID: PMC7283487 DOI: 10.2147/cia.s248816] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 05/05/2020] [Indexed: 01/19/2023] Open
Abstract
Purpose There have been few recent studies regarding vascular aging and its relationship with left ventricular (LV) geometry. Moreover, the association of abnormal LV geometry with various kinds of vascular aging has not yet been systematically analyzed. Thus, this study aimed to further elucidate this relationship. Materials and Methods In this study, 3363 older participants (43.6% male, aged 71.1±5.9 years; 56.4% female, aged 71.1±6.1 years) derived from the Northern Shanghai Study were enrolled. Vascular aging criteria included arteriosclerosis, defined as carotid-femoral pulse wave velocity >10 m/s or brachial-ankle pulse wave velocity >1800 cm/s, and peripheral atherosclerosis, defined as ankle-brachial index <0.9, carotid artery intima-media thickness (cIMT) greater than 0.9 mm, or carotid plaque indicating carotid artery abnormality. Micro-albuminuria was defined as urinary albumin-to-creatinine ratio >30 mg/g. Decreased estimated glomerular filtration rate (eGFR) was defined as eGFR <60 mL/min/1.73 m2. Results When vascular aging parameters were respectively adjusted for age and sex, arteriosclerosis, micro-albuminuria, and peripheral atherosclerosis were significantly associated with concentric remodeling, eccentric LV hypertrophy (LVH), and concentric LVH (P<0.045) but not with decreased eGFR or abnormal cIMT and presence of plaque. Peripheral atherosclerosis was strongly associated with LV concentric geometry (LVCG) when considering other covariates (risk factors, diseases, and treatments) (P<0.012). Conclusion Vascular aging parameters such as arteriosclerosis, micro-albuminuria, and peripheral atherosclerosis are significantly and independently associated with LVCG in community-dwelling older Chinese population, suggesting the importance of vascular aging during early clinical assessment of abnormal LV geometry change and serious cardiovascular events.
Collapse
Affiliation(s)
- Rusitanmujiang Maimaitiaili
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Jiadela Teliewubai
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Song Zhao
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Jiamin Tang
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chen Chi
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yi Zhang
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yawei Xu
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| |
Collapse
|
21
|
Pirruccello JP, Bick A, Wang M, Chaffin M, Friedman S, Yao J, Guo X, Venkatesh BA, Taylor KD, Post WS, Rich S, Lima JAC, Rotter JI, Philippakis A, Lubitz SA, Ellinor PT, Khera AV, Kathiresan S, Aragam KG. Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nat Commun 2020; 11:2254. [PMID: 32382064 PMCID: PMC7206184 DOI: 10.1038/s41467-020-15823-7] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 03/18/2020] [Indexed: 01/09/2023] Open
Abstract
Dilated cardiomyopathy (DCM) is an important cause of heart failure and the leading indication for heart transplantation. Many rare genetic variants have been associated with DCM, but common variant studies of the disease have yielded few associated loci. As structural changes in the heart are a defining feature of DCM, we report a genome-wide association study of cardiac magnetic resonance imaging (MRI)-derived left ventricular measurements in 36,041 UK Biobank participants, with replication in 2184 participants from the Multi-Ethnic Study of Atherosclerosis. We identify 45 previously unreported loci associated with cardiac structure and function, many near well-established genes for Mendelian cardiomyopathies. A polygenic score of MRI-derived left ventricular end systolic volume strongly associates with incident DCM in the general population. Even among carriers of TTN truncating mutations, this polygenic score influences the size and function of the human heart. These results further implicate common genetic polymorphisms in the pathogenesis of DCM.
Collapse
Affiliation(s)
- James P Pirruccello
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Alexander Bick
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Minxian Wang
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Mark Chaffin
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | | | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wendy S Post
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MA, USA
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MA, USA
| | - Stephen Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Joao A C Lima
- Department of Radiology, Johns Hopkins University, Baltimore, MA, USA
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute, Cambridge, MA, USA
- GV, Mountain View, CA, USA
| | - Steven A Lubitz
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Patrick T Ellinor
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Amit V Khera
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sekar Kathiresan
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Verve Therapeutics, Cambridge, MA, USA
| | - Krishna G Aragam
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
22
|
Image quality of late gadolinium enhancement in cardiac magnetic resonance with different doses of contrast material in patients with chronic myocardial infarction. Eur Radiol Exp 2020; 4:21. [PMID: 32242266 PMCID: PMC7118177 DOI: 10.1186/s41747-020-00149-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 02/21/2020] [Indexed: 01/26/2023] Open
Abstract
Background Contrast-enhanced cardiac magnetic resonance (CMR) is pivotal for evaluating chronic myocardial infarction (CMI). Concerns about safety of gadolinium-based contrast agents favour dose reduction. We assessed image quality of scar tissue in CMRs performed with different doses of gadobutrol in CMI patients. Methods Informed consent was waived for this Ethics Committee-approved single-centre retrospective study. Consecutive contrast-enhanced CMRs from CMI patients were retrospectively analysed according to the administered gadobutrol dose (group A, 0.10 mmol/kg; group B, 0.15 mmol/kg; group C, 0.20 mmol/kg). We calculated the signal-to-noise ratio for scar tissue (SNRscar) and contrast-to-noise ratio between scar and either remote myocardium (CNRscar-rem) or blood (CNRscar-blood). Results Of 79 CMRs from 79 patients, 22 belonged to group A, 26 to group B, and 31 to group C. The groups were homogeneous for age, sex, left ventricular morpho-functional parameters, and percentage of scar tissue over whole myocardium (p ≥ 0.300). SNRscar was lower in group A (46.4; 40.3–65.1) than in group B (70.1; 52.2–111.5) (p = 0.013) and group C (72.1; 59.4–100.0) (p = 0.002), CNRscar-rem was lower in group A (62.9; 52.2–87.4) than in group B (96.5; 73.1–152.8) (p = 0.008) and in group C (103.9; 83.9–132.0) (p = 0.001). No other significant differences were found (p ≥ 0.335). Conclusions Gadobutrol at 0.10 mmol/kg provides inferior scar image quality of CMI than 0.15 and 0.20 mmol/kg; the last two dosages seem to provide similar LGE. Thus, for CMR of CMI, 0.15 mmol/kg of gadobutrol can be suggested instead of 0.20 mmol/kg, with no hindrance to scar visualisation. Dose reduction would not impact on diagnostic utility of CMR examinations.
Collapse
|
23
|
CMR in the Evaluation of Diastolic Dysfunction and Phenotyping of HFpEF. JACC Cardiovasc Imaging 2020; 13:283-296. [DOI: 10.1016/j.jcmg.2019.02.031] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 02/13/2019] [Accepted: 02/14/2019] [Indexed: 01/20/2023]
|
24
|
Manning WJ. Journal of Cardiovascular Magnetic Resonance: 2017/2018 in review. J Cardiovasc Magn Reson 2019; 21:79. [PMID: 31884956 PMCID: PMC6936125 DOI: 10.1186/s12968-019-0594-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 12/14/2022] Open
Abstract
There were 89 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2017, including 76 original research papers, 4 reviews, 5 technical notes, 1 guideline, and 3 corrections. The volume was down slightly from 2017 with a corresponding 15% decrease in manuscript submissions from 405 to 346 and thus reflects a slight increase in the acceptance rate from 25 to 26%. The decrease in submissions for the year followed the initiation of the increased author processing charge (APC) for Society for Cardiovascular Magnetic Resonance (SCMR) members for manuscripts submitted after June 30, 2018. The quality of the submissions continues to be high. The 2018 JCMR Impact Factor (which is published in June 2019) was slightly lower at 5.1 (vs. 5.46 for 2017; as published in June 2018. The 2018 impact factor means that on average, each JCMR published in 2016 and 2017 was cited 5.1 times in 2018. Our 5 year impact factor was 5.82.In accordance with Open-Access publishing guidelines of BMC, the JCMR articles are published on-line in a continuus fashion in the chronologic order of acceptance, with no collating of the articles into sections or special thematic issues. For this reason, over the years, the Editors have felt that it is useful for the JCMR audience to annually summarize the publications into broad areas of interest or themes, so that readers can view areas of interest in a single article in relation to each other and contemporaneous JCMR publications. In this publication, the manuscripts are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought within the journal. In addition, as in the past two years, I have used this publication to also convey information regarding the editorial process and as a "State of our JCMR."This is the 12th year of JCMR as an open-access publication with BMC (formerly known as Biomed Central). The timing of the JCMR transition to the open access platform was "ahead of the curve" and a tribute to the vision of Dr. Matthias Friedrich, the SCMR Publications Committee Chair and Dr. Dudley Pennell, the JCMR editor-in-chief at the time. The open-access system has dramatically increased the reading and citation of JCMR publications and I hope that you, our authors, will continue to send your very best, high quality manuscripts to JCMR for consideration. It takes a village to run a journal and I thank our very dedicated Associate Editors, Guest Editors, Reviewers for their efforts to ensure that the review process occurs in a timely and responsible manner. These efforts have allowed the JCMR to continue as the premier journal of our field. This entire process would also not be possible without the dedication and efforts of our managing editor, Diana Gethers. Finally, I thank you for entrusting me with the editorship of the JCMR as I begin my 4th year as your editor-in-chief. It has been a tremendous experience for me and the opportunity to review manuscripts that reflect the best in our field remains a great joy and highlight of my week!
Collapse
Affiliation(s)
- Warren J Manning
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
| |
Collapse
|
25
|
Mauger C, Gilbert K, Lee AM, Sanghvi MM, Aung N, Fung K, Carapella V, Piechnik SK, Neubauer S, Petersen SE, Suinesiaputra A, Young AA. Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank. J Cardiovasc Magn Reson 2019; 21:41. [PMID: 31315625 PMCID: PMC6637624 DOI: 10.1186/s12968-019-0551-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 06/14/2019] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND The associations between cardiovascular disease (CVD) risk factors and the biventricular geometry of the right ventricle (RV) and left ventricle (LV) have been difficult to assess, due to subtle and complex shape changes. We sought to quantify reference RV morphology as well as biventricular variations associated with common cardiovascular risk factors. METHODS A biventricular shape atlas was automatically constructed using contours and landmarks from 4329 UK Biobank cardiovascular magnetic resonance (CMR) studies. A subdivision surface geometric mesh was customized to the contours using a diffeomorphic registration algorithm, with automatic correction of slice shifts due to differences in breath-hold position. A reference sub-cohort was identified consisting of 630 participants with no CVD risk factors. Morphometric scores were computed using linear regression to quantify shape variations associated with four risk factors (high cholesterol, high blood pressure, obesity and smoking) and three disease factors (diabetes, previous myocardial infarction and angina). RESULTS The atlas construction led to an accurate representation of 3D shapes at end-diastole and end-systole, with acceptable fitting errors between surfaces and contours (average error less than 1.5 mm). Atlas shape features had stronger associations than traditional mass and volume measures for all factors (p < 0.005 for each). High blood pressure was associated with outward displacement of the LV free walls, but inward displacement of the RV free wall and thickening of the septum. Smoking was associated with a rounder RV with inward displacement of the RV free wall and increased relative wall thickness. CONCLUSION Morphometric relationships between biventricular shape and cardiovascular risk factors in a large cohort show complex interactions between RV and LV morphology. These can be quantified by z-scores, which can be used to study the morphological correlates of disease.
Collapse
Affiliation(s)
- Charlène Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Kathleen Gilbert
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Mihir M. Sanghvi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Kenneth Fung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Valentina Carapella
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan K. Piechnik
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King’s College London, London, UK
| |
Collapse
|
26
|
Fries JA, Varma P, Chen VS, Xiao K, Tejeda H, Saha P, Dunnmon J, Chubb H, Maskatia S, Fiterau M, Delp S, Ashley E, Ré C, Priest JR. Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences. Nat Commun 2019; 10:3111. [PMID: 31308376 PMCID: PMC6629670 DOI: 10.1038/s41467-019-11012-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 06/13/2019] [Indexed: 11/23/2022] Open
Abstract
Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.
Collapse
Affiliation(s)
- Jason A Fries
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
- Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA, 94305, USA.
| | - Paroma Varma
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, 94305, USA
| | - Vincent S Chen
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Ke Xiao
- Department of Pediatrics, Stanford University, Stanford, CA, 94304, USA
| | - Heliodoro Tejeda
- Department of Pediatrics, Stanford University, Stanford, CA, 94304, USA
| | - Priyanka Saha
- Department of Pediatrics, Stanford University, Stanford, CA, 94304, USA
| | - Jared Dunnmon
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Henry Chubb
- Department of Pediatrics, Stanford University, Stanford, CA, 94304, USA
| | - Shiraz Maskatia
- Department of Pediatrics, Stanford University, Stanford, CA, 94304, USA
| | - Madalina Fiterau
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Scott Delp
- Department of Bioengineering, Stanford University, Palo Alto, CA, 94305, USA
| | - Euan Ashley
- Department of Medicine, Stanford University, Stanford, CA, 94304, USA
- Chan Zuckerberg BioHub, San Francisco, CA, 94158, USA
| | - Christopher Ré
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
- Chan Zuckerberg BioHub, San Francisco, CA, 94158, USA
| | - James R Priest
- Department of Pediatrics, Stanford University, Stanford, CA, 94304, USA
- Chan Zuckerberg BioHub, San Francisco, CA, 94158, USA
| |
Collapse
|
27
|
Milotta G, Ginami G, Cruz G, Neji R, Prieto C, Botnar RM. Simultaneous 3D whole-heart bright-blood and black blood imaging for cardiovascular anatomy and wall assessment with interleaved T 2 prep-IR. Magn Reson Med 2019; 82:312-325. [PMID: 30896049 DOI: 10.1002/mrm.27734] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 02/19/2019] [Accepted: 02/20/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To develop a motion-corrected 3D flow-insensitive imaging approach interleaved T2 prepared-inversion recovery (iT2 prep-IR) for simultaneous lumen and wall visualization of the great thoracic vessels and cardiac structures. METHODS A 3D flow-insensitive approach for simultaneous cardiovascular lumen and wall visualization (iT2 prep) has been previously proposed. This approach requires subject-dependent weighted subtraction to completely null the arterial blood signal in the black-blood volume. Here, we propose an (T2 prep-IR) approach to improve wall visualization and remove need for weighted subtraction. The proposed sequence is based on the acquisition and direct subtraction of 2 interleaved 3D whole-heart data sets acquired with and without T2 prep-IR preparation. Image navigators are acquired before data acquisition to enable 2D translational and 3D non-rigid motion correction allowing 100% respiratory scan efficiency. The proposed approach was evaluated in 10 healthy subjects and compared with the conventional 2D double inversion recovery (DIR) sequence and the 3D iT2 prep sequence. Additionally, 5 patients with congenital heart disease were acquired to test the clinical feasibility of the proposed approach. RESULTS The proposed iT2 prep-IR sequence showed improved blood nulling compared to both DIR and iT2 prep techniques in terms of SNR (SNRblood = 6.9, 12.2, and 18.2, respectively) and contrast-to-noise-ratio (CNRmyoc-blood = 28.4, 15.4, and 15.3, respectively). No statistical difference was observed between iT2 prep-IR, iT2 prep and DIR atrial and ventricular wall thickness quantification. CONCLUSION The proposed interleaved T2 prep-IR sequence enables the simultaneous lumen and wall visualization of cardiac structures and shows promising results in terms of SNR, CNR, and wall thickness measurement.
Collapse
Affiliation(s)
- Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Giulia Ginami
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
28
|
Manning WJ. Journal of Cardiovascular Magnetic Resonance 2017. J Cardiovasc Magn Reson 2018; 20:89. [PMID: 30593280 PMCID: PMC6309095 DOI: 10.1186/s12968-018-0518-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 12/06/2018] [Indexed: 02/07/2023] Open
Abstract
There were 106 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2017, including 92 original research papers, 3 reviews, 9 technical notes, and 1 Position paper, 1 erratum and 1 correction. The volume was similar to 2016 despite an increase in manuscript submissions to 405 and thus reflects a slight decrease in the acceptance rate to 26.7%. The quality of the submissions continues to be high. The 2017 JCMR Impact Factor (which is published in June 2018) was minimally lower at 5.46 (vs. 5.71 for 2016; as published in June 2017), which is the second highest impact factor ever recorded for JCMR. The 2017 impact factor means that an average, each JCMR paper that were published in 2015 and 2016 was cited 5.46 times in 2017.In accordance with Open-Access publishing of Biomed Central, the JCMR articles are published on-line in continuus fashion and in the chronologic order of acceptance, with no collating of the articles into sections or special thematic issues. For this reason, over the years, the Editors have felt that it is useful to annually summarize the publications into broad areas of interest or theme, so that readers can view areas of interest in a single article in relation to each other and other contemporary JCMR articles. In this publication, the manuscripts are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought within the journal. In addition, I have elected to use this format to convey information regarding the editorial process to the readership.I hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your very best, high quality manuscripts to JCMR for consideration. I thank our very dedicated Associate Editors, Guest Editors, and Reviewers for their efforts to ensure that the review process occurs in a timely and responsible manner and that the JCMR continues to be recognized as the forefront journal of our field. And finally, I thank you for entrusting me with the editorship of the JCMR as I begin my 3rd year as your editor-in-chief. It has been a tremendous learning experience for me and the opportunity to review manuscripts that reflect the best in our field remains a great joy and highlight of my week!
Collapse
Affiliation(s)
- Warren J Manning
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA.
| |
Collapse
|
29
|
Dogdus M, Simsek E, Cinar CS. 3D-speckle tracking echocardiography for assessment of coronary artery disease severity in stable angina pectoris. Echocardiography 2018; 36:320-327. [PMID: 30515893 DOI: 10.1111/echo.14214] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/28/2018] [Accepted: 11/01/2018] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND AIM Stable angina pectoris is a common disease that may cause disability. Some noninvasive new methods can be useful for the detection of early-stage coronary artery disease. The relationship between coronary artery disease (CAD) severity and resting 3-dimensional-speckle tracking echocardiography (3D-STE) in stable angina pectoris patients was evaluated in this study. METHODS One hundred and twenty consecutive patients between 18-80 years of age and without a history of CAD to whom elective coronary angiography was planned after positive stress test or myocardial perfusion scintigraphy were enrolled in the study. 3D-STE was performed and global longitudinal strain (GLS), global circumferential strain (GCS), global radial strain (GRS), and global area strain (GAS) were measured before coronary angiography. A Gensini score of ≥20 was accepted as critical CAD. Correlation between Gensini scores and 3D-STE results were evaluated. RESULTS Mean age was 60.7 ± 10.01 years, and 55% of the patient population were male. There were not any significant differences between critical CAD and noncritical CAD groups for age, gender, history of hypertension, diabetes mellitus, hyperlipidemia, and Left Ventricular Ejection Fraction. Mean GLS was -12, GCS was -18.8, GRS was 33.4, GAS was -28.9, and mean Gensini score was 18.8. GLS and all other strain parameters were significantly worse in patients with critical CAD group compared with noncritical CAD group and also positive linear correlation was observed between Gensini score and all measured strain parameters (r = 0.568, P < 0.001 for Gensini score and GLS; r = 0.617, P < 0.001 for Gensini score and GAS). A GLS value of >-10 has 88.9% sensitivity and 92.9% specificity; A GAS value of >-21 has 97.2% sensitivity and 88.1% specificity to detect critical CAD. CONCLUSIONS 3D-STE is a noninvasive and handy parameter to detect subclinical left ventricular dysfunction and global strain values were significantly correlated with CAD severity. GAS has the sensitivity of 97.2% and specificity of 88.1% to detect critical CAD. Adding 3D strain echocardiography to exercise test or myocardial perfusion scintigraphy might increase sensitivity to detect critical CAD in clinical practice.
Collapse
Affiliation(s)
- Mustafa Dogdus
- Department of Cardiology, Ege University School of Medicine, Izmir, Turkey
| | - Evrim Simsek
- Department of Cardiology, Ege University School of Medicine, Izmir, Turkey
| | | |
Collapse
|
30
|
Leng S, Tan RS, Zhao X, Allen JC, Koh AS, Zhong L. Validation of a rapid semi-automated method to assess left atrial longitudinal phasic strains on cine cardiovascular magnetic resonance imaging. J Cardiovasc Magn Reson 2018; 20:71. [PMID: 30396356 PMCID: PMC6219067 DOI: 10.1186/s12968-018-0496-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 10/09/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Abnormal left atrial (LA) function is a marker of cardiac dysfunction and adverse cardiovascular outcome, but is difficult to assess, and hence not, routinely quantified. We aimed to determine the feasibility and effectiveness of a fast method to measure long-axis LA strain and strain rate (SR) with standard cardiovascular magnetic resonance (CMR) compared to conventional feature tracking (FT) derived longitudinal strain. METHODS We studied 50 normal controls, 30 patients with hypertrophic cardiomyopathy, and 100 heart failure (HF) patients, including 40 with reduced ejection fraction (HFrEF), 30 mid-range ejection fraction (HFmrEF) and 30 preserved ejection fraction (HFpEF). LA longitudinal strain and SR parameters were derived by tracking the distance between the left atrioventricular junction and a user-defined point at the mid posterior LA wall on standard cine CMR two- and four-chamber views. LA performance was analyzed at three distinct cardiac phases: reservoir function (reservoir strain εs and strain rate SRs), conduit function (conduit strain εe and strain rate SRe) and booster pump function (booster strain εa and strain rate SRa). RESULTS There was good agreement between LA longitudinal strain and SR assessed using the fast and conventional FT-CMR approaches (r = 0.89 to 0.99, p < 0.001). The fast strain and SRs showed a better intra- and inter-observer reproducibility and a 55% reduction in evaluation time (85 ± 10 vs. 190 ± 12 s, p < 0.001) compared to FT-CMR. Fast LA measurements in normal controls were 35.3 ± 5.2% for εs, 18.1 ± 4.3% for εe, 17.2 ± 3.5% for εa, and 1.8 ± 0.4, - 2.0 ± 0.5, - 2.3 ± 0.6 s- 1 for the respective phasic SRs. Significantly reduced LA strains and SRs were observed in all patient groups compared to normal controls. Patients with HFpEF and HFmrEF had significantly smaller εs, SRs, εe and SRe than hypertrophic cardiomyopathy, and HFmrEF had significantly impaired LA reservoir and booster function compared to HFpEF. The fast LA strains and SRs were similar to FT-CMR for discriminating patients from controls (area under the curve (AUC) = 0.79 to 0.96 vs. 0.76 to 0.93, p = NS). CONCLUSIONS Novel quantitative LA strain and SR derived from conventional cine CMR images are fast assessable parameters for LA phasic function analysis.
Collapse
Affiliation(s)
- Shuang Leng
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609 Singapore
| | - Ru-San Tan
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609 Singapore
- Duke-NUS Medical School, 8 College Road, Singapore, 169857 Singapore
| | - Xiaodan Zhao
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609 Singapore
| | - John C. Allen
- Duke-NUS Medical School, 8 College Road, Singapore, 169857 Singapore
| | - Angela S. Koh
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609 Singapore
- Duke-NUS Medical School, 8 College Road, Singapore, 169857 Singapore
| | - Liang Zhong
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609 Singapore
- Duke-NUS Medical School, 8 College Road, Singapore, 169857 Singapore
| |
Collapse
|
31
|
Yoneyama K, Venkatesh BA, Wu CO, Mewton N, Gjesdal O, Kishi S, McClelland RL, Bluemke DA, Lima JAC. Diabetes mellitus and insulin resistance associate with left ventricular shape and torsion by cardiovascular magnetic resonance imaging in asymptomatic individuals from the multi-ethnic study of atherosclerosis. J Cardiovasc Magn Reson 2018; 20:53. [PMID: 30064457 PMCID: PMC6069876 DOI: 10.1186/s12968-018-0472-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 06/20/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although diabetes mellitus (DM) and insulin resistance associate with adverse cardiac events, the associations of left ventricular (LV) remodeling and function with compromised glucose metabolism have not been fully evaluated in a general population. We used cardiovascular magnetic resonance (CMR) to evaluate how CMR indices are associated with DM or insulin resistance among participants before developing cardiac events. METHODS We studied 1476 participants who were free of clinical cardiovascular disease and who underwent tagged CMR in the Multi-Ethnic Study of Atherosclerosis (MESA). LV shape and longitudinal myocardial shortening and torsion were assessed by CMR. A higher sphericity index represents a more spherical LV shape. Multivariable linear regression was used to evaluate the associations of DM or homeostasis model assessment-estimated insulin resistance (HOMA-IR) with CMR indices. RESULTS In multiple linear regression, longitudinal shortening was lower in impaired fasting glucose than normal fasting glucose (NFG) (0.36% lower vs. NFG, p < 0.05); torsion was greater in treated DM (0.24 °/cm greater vs. NFG, p < 0.05) after full adjustments. Among participants without DM, greater log-HOMA-IR was correlated with greater LV mass (3.92 g/index, p < 0.05) and LV mass-to-volume ratio (0.05 /index, p < 0.01), and lower sphericity index (- 1.26/index, p < 0.01). Greater log-HOMA IR was associated with lower longitudinal shortening (- 0.26%/index, p < 0.05) and circumferential shortening (- 0.30%/index, p < 0.05). Torsion was positively correlated with log-HOMA-IR until 1.5 of log-HOMA-IR (0.16 °/cm/index, p = 0.030).), and tended to fall once above 1.5 of log-HOMA-IR (- 0.50 °/cm/index, p = 0.203). The sphericity index was associated negatively with LV mass-to-volume ratio (- 0.02/%, p < 0.001) and torsion (- 0.03°/cm/%, p < 0.001). CONCLUSIONS Glucose metabolism disorders are associated with LV concentric remodeling, less spherical shape, and reduced systolic myocardial shortening in the general population. Although torsion is higher in participants who are treated for DM and impaired insulin resistance, myocardial shortening was progressively decreased with higher HOMA-IR and torsion was increased only with less severe insulin resistance. CLINICAL TRIAL REGISTRATION Multi-Ethnic Study of Atherosclerosis (MESA): A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org/ . Study Start Date: January 1999 ( NCT00005487 ).
Collapse
Affiliation(s)
- Kihei Yoneyama
- Department of Cardiology, Johns Hopkins University, Baltimore, MD USA
- St. Marianna University School of Medicine, Kawasaki, Japan
| | | | - Colin O. Wu
- Offices of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, MD USA
| | - Nathan Mewton
- Department of Cardiology, Johns Hopkins University, Baltimore, MD USA
| | - Ola Gjesdal
- Department of Cardiology, Johns Hopkins University, Baltimore, MD USA
| | - Satoru Kishi
- Department of Cardiology, Johns Hopkins University, Baltimore, MD USA
| | | | - David A. Bluemke
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health Clinical Center, Bethesda, MD USA
| | - João A. C. Lima
- Department of Cardiology, Johns Hopkins University, Baltimore, MD USA
- Radiology and Epidemiology, Johns Hopkins University, Blalock 524D1, Johns Hopkins Hospital, 600 North Wolfe Street, Baltimore, MD 21287 USA
| |
Collapse
|
32
|
The aging heart. Clin Sci (Lond) 2018; 132:1367-1382. [PMID: 29986877 DOI: 10.1042/cs20171156] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2018] [Revised: 06/10/2018] [Accepted: 06/13/2018] [Indexed: 12/19/2022]
Abstract
As the elderly segment of the world population increases, it is critical to understand the changes in cardiac structure and function during the normal aging process. In this review, we outline the key molecular pathways and cellular processes that underlie the phenotypic changes in the heart and vasculature that accompany aging. Reduced autophagy, increased mitochondrial oxidative stress, telomere attrition, altered signaling in insulin-like growth factor, growth differentiation factor 11, and 5'- AMP-activated protein kinase pathways are among the key molecular mechanisms underlying cardiac aging. Aging promotes structural and functional changes in the atria, ventricles, valves, myocardium, pericardium, the cardiac conduction system, and the vasculature. We highlight the factors known to accelerate and attenuate the intrinsic aging of the heart and vessels in addition to potential preventive and therapeutic avenues. A greater understanding of the processes involved in cardiac aging may facilitate our ability to mitigate the escalating burden of CVD in older individuals and promote healthy cardiac aging.
Collapse
|
33
|
Dilated cardiomyopathy with re-worsening left ventricular ejection fraction. Heart Vessels 2018; 34:95-103. [PMID: 29942977 DOI: 10.1007/s00380-018-1214-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Accepted: 06/22/2018] [Indexed: 10/28/2022]
Abstract
Re-worsening left ventricular ejection fraction (LVEF) is observed in some patients with dilated cardiomyopathy (DCM) despite initial improvements in LVEF. We analyzed cardiac outcomes and clinical variables associated with this re-worsening LVEF. A total of 180 newly diagnosed DCM patients who received only pharmacotherapy were enrolled. Echocardiography was performed after 6, 12, 24, and 36 months after initiation of pharmacotherapy. Patients were divided into three groups: (1) Improved: (n = 113, 63%), defined as those > 10% increase in LVEF after 12 months and no decrease (> 10%) between 12 and 36 months; (2) Re-worse: (n = 12, 7%), those with > 10% increase in LVEF after 12 months but with decrease (> 10%) between 12 and 36 months; and (3) Not-improved: (n = 55: 30%), those with no increase in LVEF (> 10%) after 12 months. Patients with re-worse group were older (P = 0.04) and had higher brain natriuretic peptide (BNP) levels after 12 months (P = 0.002) than those in the Improved group. Major cardiac events (sudden death, implantation of a ventricular assist device, and death due to heart failure,) were observed in 13 (7%) patients after 36 months of pharmacotherapy. Multivariate analysis revealed that the Re-worse group had a higher risk for cardiac events (hazard ratio 11.7, 95% confidence interval 1.9-90.7, P = 0.01) than the Improved group, but had a similar risk compared with the Not-improved group. Re-worsening LVEF was associated with poor cardiac outcomes in newly diagnosed DCM patients. Age and persistently high-BNP levels after improvement in LVEF were significantly associated with re-worsening LVEF.
Collapse
|
34
|
Yoneyama K, Kitanaka Y, Tanaka O, Akashi YJ. Cardiovascular magnetic resonance imaging in heart failure. Expert Rev Cardiovasc Ther 2018; 16:237-248. [PMID: 29478345 DOI: 10.1080/14779072.2018.1445525] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Heart failure is a complex clinical syndrome resulting from heart structural remodeling and impaired function in ejecting blood; its incidence is increasing markedly worldwide. The observed variations in the structure and function of the heart are attributable to differences in etiology of heart failure. Cardiac magnetic resonance imaging (CMR) can characterize myocardial tissue, assess myocardial viability, and help diagnose specific cardiomyopathies. The emergence of T1 mapping techniques further improves our knowledge and the clinical assessment of myocardial diffuse fibrosis. Physicians, therefore, must identify the variations using CMR to improve patient's symptoms, survival, and quality of life. Area covered: Current reports regarding CMR and the evidence for heart failure diagnosis and therapy as a potential marker of therapeutic response, including low- and high-risk patients, were reviewed. Literature search was performed using PubMed and Google Scholar for literature relevant to CMR, late gadolinium enhancement, T1 mapping, assessment of fibrosis and remodeling, coronary artery, myocardial infarction, heart failure, and its outcomes. Expert commentary: The authors review current evidence and discuss the potential ability of CMR to guide, diagnose, plan risk strategies, and treat patients with heart failure.
Collapse
Affiliation(s)
- Kihei Yoneyama
- a Division of Cardiology, Department of Internal Medicine , St. Marianna University School of Medicine , Kawasaki , Japan.,b Heart Disease Center , St. Marianna University School of Medicine Toyoko hospital , Kawasaki , Japan
| | - Yuki Kitanaka
- c Department of Radiology , St. Marianna University School of Medicine Toyoko hospital , Kawasaki , Japan
| | - Osamu Tanaka
- b Heart Disease Center , St. Marianna University School of Medicine Toyoko hospital , Kawasaki , Japan
| | - Yoshihiro J Akashi
- a Division of Cardiology, Department of Internal Medicine , St. Marianna University School of Medicine , Kawasaki , Japan
| |
Collapse
|