A biomarker framework for cardiac aging: the Aging Biomarker Consortium consensus statement

Aging Biomarker Consortium; Weiwei Zhang, Yang Che, Xiaoqiang Tang, Siqi Chen, Moshi Song, Li Wang, Ai-Jun Sun, Hou-Zao Chen, Ming Xu, Miao Wang, Jun Pu, Zijian Li, Junjie Xiao, Chun-Mei Cao, Yan Zhang, Yao Lu, Yingxin Zhao, Yan-Jiang Wang, Cuntai Zhang, Tao Shen, Weiqi Zhang, Ling Tao, Jing Qu, Yi-Da Tang, Guang-Hui Liu, Gang Pei, Jian Li, Feng Cao

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Life Medicine ›› 2023, Vol. 2 ›› Issue (5) : 3. DOI: 10.1093/lifemedi/lnad035
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A biomarker framework for cardiac aging: the Aging Biomarker Consortium consensus statement

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Abstract

Cardiac aging constitutes a significant risk factor for cardiovascular diseases prevalent among the elderly population. Urgent attention is required to prioritize preventive and management strategies for age-related cardiovascular conditions to safeguard the well-being of elderly individuals. In response to this critical challenge, the Aging Biomarker Consortium (ABC) of China has formulated an expert consensus on cardiac aging biomarkers. This consensus draws upon the latest scientific literature and clinical expertise to provide a comprehensive assessment of biomarkers associated with cardiac aging. Furthermore, it presents a standardized methodology for characterizing biomarkers across three dimensions: functional, structural, and humoral. The functional dimension encompasses a broad spectrum of markers that reflect diastolic and systolic functions, sinus node pacing, neuroendocrine secretion, coronary micro-circulation, and cardiac metabolism. The structural domain emphasizes imaging markers relevant to concentric cardiac remodeling, coronary artery calcification, and epicardial fat deposition. The humoral aspect underscores various systemic (N) and heart-specific (X) markers, including endocrine hormones, cytokines, and other plasma metabolites. The ABC’s primary objective is to establish a robust foundation for assessing cardiac aging, thereby furnishing a dependable reference for clinical applications and future research endeavors. This aims to contribute significantly to the enhancement of cardiovascular health and overall well-being among elderly individuals.

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Aging Biomarker Consortium; Weiwei Zhang, Yang Che, Xiaoqiang Tang, Siqi Chen, Moshi Song, Li Wang, Ai-Jun Sun, Hou-Zao Chen, Ming Xu, Miao Wang, Jun Pu, Zijian Li, Junjie Xiao, Chun-Mei Cao, Yan Zhang, Yao Lu, Yingxin Zhao, Yan-Jiang Wang, Cuntai Zhang, Tao Shen, Weiqi Zhang, Ling Tao, Jing Qu, Yi-Da Tang, Guang-Hui Liu, Gang Pei, Jian Li, Feng Cao. A biomarker framework for cardiac aging: the Aging Biomarker Consortium consensus statement. Life Medicine, 2023, 2(5): 3 https://doi.org/10.1093/lifemedi/lnad035

References

[1]
Diseases NCfC. Report on Cardiovascular Health and Diseases in China 2022. Beijing, China: Peking Union Medical College Press, 2023
[2]
Peng Y, Ding L, Song M et al. Acting on ethics and governance of aging research. Trends Mol Med 2023;29:419–21
CrossRef Google scholar
[3]
Cai Y, Song W, Li J et al. The landscape of aging. Sci China Life Sci 2022;65:2354–454
CrossRef Google scholar
[4]
Ren J et al. The aging biomarker consortium represents a new era for aging research in China. Nat Med 2023;29:2162–5
CrossRef Google scholar
[5]
Sousa-Uva M, Head SJ, Thielmann M et al. Methodology manual for European Association for Cardio-Thoracic Surgery (EACTS) clinical guidelines. Euro J Cardio-Thorac Surg 2015;48:809–16
CrossRef Google scholar
[6]
Lakatta EG, Levy D. Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises. Part II. the aging heart in health: links to heart disease. Circulation 2003;107:346–54
CrossRef Google scholar
[7]
Rodeheffer RJ, Gerstenblith G, Becker LC et al. Exercise cardiac output is maintained with advancing age in healthy human subjects: cardiac dilatation and increased stroke volume compensate for a diminished heart rate. Circulation 1984;69:203–13
CrossRef Google scholar
[8]
Lakatta EG. Age-associated cardiovascular changes in health: impact on cardiovascular disease in older persons. Heart Fail Rev 2002;7:29–49
CrossRef Google scholar
[9]
Obas V, Vasan RS. The aging heart. ClinSci (London, England: 1979) 2018;132:1367–82
CrossRef Google scholar
[10]
Hung CL, Gonçalves A, Shah AM et al. Age- and sex-related influences on left ventricular mechanics in elderly individuals free of prevalent heart failure: the ARIC Study (Atherosclerosis Risk in Communities). Circ Cardiovasc Imaging 2017;10:4510–8
CrossRef Google scholar
[11]
Gulati M, Shaw LJ, Thisted RA et al. Heart rate response to exercise stress testing in asymptomatic women: the St. James women take heart project. Circulation 2010;122:130–7
CrossRef Google scholar
[12]
Fleg JL, O’Connor F, Gerstenblith G et al. Impact of age on the car-diovascular response to dynamic upright exercise in healthy men and women. J Appl Physiol (1985) 1995;78:890–900
CrossRef Google scholar
[13]
Wohlfahrt P, Redfield MM, Melenovsky V et al. Impact of chronic changes in arterial compliance and resistance on left ventricular ageing in humans. Eur J Heart Fail 2015;17:27–34
CrossRef Google scholar
[14]
AlGhatrif M, Morrell CH, Becker LC et al. Longitudinal uncoupling of the heart and arteries with aging in a community-dwelling population. Geroscience 2021;43:551–61
CrossRef Google scholar
[15]
Steenman M, Lande G. Cardiac aging and heart disease in humans. Biophys Rev 2017;9:131–7
CrossRef Google scholar
[16]
Forman DE, de Lemos JA, Shaw LJ et al. Geriatric Cardiology Section Leadership Council. Cardiovascular biomarkers and imaging in older adults: JACC council perspectives. J Am Coll Cardiol 2020;76:1577–94
CrossRef Google scholar
[17]
Swinne CJ, Shapiro EP, Lima SD et al. Age-associated changes in left-ventricular diastolic performance during isometric-exercise in normal subjects. Am J Cardiol 1992;69:823–6
CrossRef Google scholar
[18]
Schulman SP, Lakatta EG, Fleg JL et al. Age-related decline in left ventricular filling at rest and exercise. Am J Physiol 1992;263:H1932–8
CrossRef Google scholar
[19]
Miller TR, Grossman SJ, Schectman KB et al. Left-ventricular diastolic filling and its association with age. Am J Cardiol 1986;58:531–5
CrossRef Google scholar
[20]
Benjamin EJ, Levy D, Anderson KM et al. Determinants of doppler indexes of left-ventricular diastolic function in normal subjects (the framingham heart-study). Am J Cardiol 1992;70:508–15
CrossRef Google scholar
[21]
Zhao L, Zierath R, Claggett B et al. Longitudinal changes in left ventricular diastolic function in late life: the ARIC study. JACC Cardiovasc Imaging 2023;16:1133–45
CrossRef Google scholar
[22]
Bai X, Han L, Liu Q et al. Evaluation of biological aging process—a population-based study of healthy people in China. Gerontology 2010;56:129–40
CrossRef Google scholar
[23]
Wang JN, Olsen NT, Taraldsen IA et al. Whole-cycle analysis of echocardiographic tissue doppler velocities as a marker of biological age. Front Cardiovasc Med 2023;9:1040647
CrossRef Google scholar
[24]
Jones SA. Ageing to arrhythmias: conundrums of connections in the ageing heart. J Pharm Pharmacol 2010;58:1571–6
CrossRef Google scholar
[25]
Peters CH, Sharpe EJ, Proenza C. Cardiac pacemaker activity and aging. Annu Rev Physiol 2020;82:21–43
CrossRef Google scholar
[26]
Fleg JL, Lakatta EG. Normal aging of the cardiovascular system. In: Cardiovascular Disease in the Elderly. Florida, USA: CRC Press; 2008
CrossRef Google scholar
[27]
Keller KM, Howlett SE. Sex differences in the biology and pathology of the aging heart. Can J Cardiol 2016;32:1065–73
CrossRef Google scholar
[28]
Strait JB, Lakatta EG. Aging-associated cardiovascular changes and their relationship to heart failure. Heart Fail Clin 2012;8:143–64
CrossRef Google scholar
[29]
Abou R, Leung M, Tonsbeek AM et al. Effect of aging on left atrial compliance and electromechanical properties in subjects without structural heart disease. Am J Cardiol 2017;120:140–7
CrossRef Google scholar
[30]
Aging Biomarker Consortium et al. Biomarkers of aging. Sci China Life Sci 2023;66:893–1066
CrossRef Google scholar
[31]
Lakatta EG, Gerstenblith G, Angell CS et al. Prolonged contraction duration in aged myocardium. J Clin Invest 1975;55:61–8
CrossRef Google scholar
[32]
Lopez-Otin C, Blasco MA, Partridge L et al. The hallmarks of aging. Cell 2013;153:1194–217
CrossRef Google scholar
[33]
Ma TK, Kam KKH, Yan BP et al. Renin-angiotensin-aldosterone system blockade for cardiovascular diseases: current status. Br J Pharmacol 2010;160:1273–92
CrossRef Google scholar
[34]
Estorch M, Carrió I, Berná L et al. Myocardial iodine-labeled metaiodobenzylguanidine 123 uptake relates to age. J Nucl Cardiol 1995;2:126–32
CrossRef Google scholar
[35]
Esler MD, Turner AG, Kaye DM et al. Aging effects on human sympathetic neuronal function. Am J Physiol 1995;268:R278–85
CrossRef Google scholar
[36]
Lei J, Wang S, Kang W et al. FOXO3-engineered human mesenchymal progenitor cells efficiently promote cardiac repair after myocardial infarction. Protein Cell 2021;12:145–51
CrossRef Google scholar
[37]
Reeson P, Choi K, Brown CE. VEGF signaling regulates the fate of obstructed capillaries in mouse cortex. Elife 2018;7:e33670
CrossRef Google scholar
[38]
Ramandika E, Kurisu S, Nitta K et al. Effects of aging on coronary flow reserve in patients with no evidence of myocardial perfusion abnormality. Heart Vessels 2020;35:1633–9
CrossRef Google scholar
[39]
Galderisi M, Rigo F, Gherardi S et al. The impact of aging and athero-sclerotic risk factors on transthoracic coronary flow reserve in subjects with normal coronary angiography. Cardiovasc Ultrasound 2012;10:20
CrossRef Google scholar
[40]
Uren NG, Camici PG, Melin JA et al. Effect of aging on myocardial perfusion reserve. J Nucl Med 1995;36:2032–6
[41]
Lee SH, Choi KH, Lee JM et al. Physiologic characteristics and clinical outcomes of patients with discordance between FFR and iFR. Jacc Cardiovasc Interv 2019;12:2018–31
CrossRef Google scholar
[42]
van de Hoef TP, Echavarria-Pinto M, Meuwissen M et al. Contribution of age-related microvascular dysfunction to abnormal coronary: hemodynamics in patients with ischemic heart disease. JACC Cardiovasc Interv 2020;13:20–9
CrossRef Google scholar
[43]
Iwatsuka R, Matsue Y, Yonetsu T et al. Arterial inflammation measured by (18)F-FDG-PET-CT to predict coronary events in older subjects. Atherosclerosis 2018;268:49–54
CrossRef Google scholar
[44]
Chen MS, Lee RT, Garbern JC. Senescence mechanisms and targets in the heart. Cardiovasc Res 2022;118:1173–87
CrossRef Google scholar
[45]
Ruiz-Meana M, Bou-Teen D, Ferdinandy P et al. Cardiomyocyte ageing and cardioprotection: consensus document from the ESC working groups cell biology of the heart and myocardial function. Cardiovasc Res 2020;116:1835–49
CrossRef Google scholar
[46]
Kates AM, Herrero P, Dence C et al. Impact of aging on substrate metabolism by the human heart. J Am Coll Cardiol 2003;41:293–9
CrossRef Google scholar
[47]
Yoneyama K, Venkatesh BA, Bluemke DA et al. Cardiovascular magnetic resonance in an adult human population: serial observations from the multi-ethnic study of atherosclerosis. J Cardiovasc Magn Reson 2017;19:52
CrossRef Google scholar
[48]
Zhang Z, Ma Q, Gao Y et al. Biventricular morphology and function reference values derived from a large sample of healthy Chinese adults by magnetic resonance imaging. Front Cardiovasc Med 2021;8:697481
CrossRef Google scholar
[49]
McManus DD, Xanthakis V, Sullivan LM et al. Longitudinal tracking of left atrial diameter over the adult life course: clinical correlates in the community. Circulation 2010;121:667–74
CrossRef Google scholar
[50]
Liu CY, Liu Y-C, Wu C et al. Evaluation of age-related interstitial myocardial fibrosis with cardiac magnetic resonance contrast-enhanced T1 mapping: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol 2013;62:1280–7
CrossRef Google scholar
[51]
Zhuang B, Li S, Xu J et al. Age- and sex-specific reference values for atrial and ventricular structures in the validated normal Chinese population: a comprehensive measurement by cardiac mri. J Magn Reson Imaging 2020;52:1031–43
CrossRef Google scholar
[52]
Bai W, Suzuki H, Huang J et al. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat Med 2020;26:1654–62
CrossRef Google scholar
[53]
Chen Z, Liu Y, Li M et al. Paeoniflorin relieves arterial stiffness induced by a high-fat/high-sugar diet by disrupting the YAP-PPM1B interaction. Life Med 2023;2
CrossRef Google scholar
[54]
Consortium AB et al. A framework of biomarkers for vascular aging: a consensus statement by the aging biomarker consortium. Life Med 2023;2.
CrossRef Google scholar
[55]
Tesauro M, Mauriello A, Rovella V et al. Arterial ageing: from endothelial dysfunction to vascular calcification. J Intern Med 2017;281:471–82
CrossRef Google scholar
[56]
Nasir K, Cainzos-Achirica M. Role of coronary artery calcium score in the primary prevention of cardiovascular disease. BMJ 2021;373:n776
CrossRef Google scholar
[57]
Kim M, Lee S-P, Kwak S et al. Impact of age on coronary artery plaque progression and clinical outcome: a paradigm substudy. J Cardiovasc Comput Tomogr 2021;15:232–9
CrossRef Google scholar
[58]
Sun T, Wang Y, Wang X et al. Effect of long-term intensive cholesterol control on the plaque progression in elderly based on CTA cohort study. Eur Radiol 2022;32:4374–83
CrossRef Google scholar
[59]
Iacobellis G. Aging effects on epicardial adipose tissue. Front Aging 2021;2:666260
CrossRef Google scholar
[60]
Karadag B, Ozulu B, Ozturk FY et al. Comparison of epicardial adipose tissue (EAT) thickness and anthropometric measurements in metabolic syndrome (MS) cases above and under the age of 65. Arch Gerontol Geriatr 2011;52:e79–84
CrossRef Google scholar
[61]
Homsi R, Thomas D, Gieseke J et al. Epicardial fat volume and aortic stiffness in healthy individuals: a quantitative cardiac magnetic resonance study. Rofo 2016;188:853–8
CrossRef Google scholar
[62]
McClain J, Hsu F, Brown E et al. Pericardial adipose tissue and coronary artery calcification in the multi-ethnic study of atherosclerosis (MESA). Obesity (Silver Spring) 2013;21:1056–63
CrossRef Google scholar
[63]
Molnar AA, Pasztor D, Merkely B. Cellular senescence, aging and non-aging processes in calcified aortic valve stenosis: from benchside to bedside. Cells 2022;11:3389
CrossRef Google scholar
[64]
Kanwar A, Thaden JJ, Nkomo VT. Management of patients with aortic valve stenosis. Mayo Clin Proc 2018;93:488–508
CrossRef Google scholar
[65]
VanAuker MD. Age-related changes in hemodynamics affecting valve performance. Am J Geriatr Cardiol 2006;15:277–83; quiz 284
CrossRef Google scholar
[66]
Zhang Y, Zheng Y, Wang S et al. Single-nucleus transcriptomics reveals a gatekeeper role for FOXP1 in primate cardiac aging. Protein Cell 2023;14:279–93
CrossRef Google scholar
[67]
Zhang F, Qiu H, Dong X et al. Single-cell atlas of multilineage cardiac organoids derived from human induced pluripotent stem cells. Life Med 2022;1:179–95
CrossRef Google scholar
[68]
Meloni A, Nicola M, Positano V et al. Myocardial T2 values at 1.5 T by a segmental approach with healthy aging and gender. Eur Radiol 2022;32:2962–75
CrossRef Google scholar
[69]
Bonner F, Janzarik N, Jacoby C et al. Myocardial T2 mapping reveals age- and sex-related differences in volunteers. J Cardiovasc Magn Reson 2015;17:9
CrossRef Google scholar
[70]
Song W, Zhang X, He SK et al. (68)Ga-FAPI PET visualize heart failure: from mechanism to clinic. Eur J Nucl Med Mol Imaging 2023;50:475–85
CrossRef Google scholar
[71]
Lyu Z, Han W, Zhao H et al. A clinical study on relationship between visualization of cardiac fibroblast activation protein activity by Al(18)FNOTA-FAPI-04 positron emission tomography and cardiovascular disease. Front Cardiovasc Med 2022;9:921724
CrossRef Google scholar
[72]
Zhang B, Yan H, Liu X et al. SenoIndex: S100A8/S100A9 as a novel aging biomarker. Life Med 2023;2
CrossRef Google scholar
[73]
Wu Z, Lu M, Liu D et al. m6A epitranscriptomic regulation of tissue homeostasis during primate aging. Nat Aging 2023;3:705–21
CrossRef Google scholar
[74]
Liu X, Liu Z, Wu Z et al. Resurrection of endogenous retroviruses during aging reinforces senescence. Cell 2023;186:287–304.e26
CrossRef Google scholar
[75]
Forrester SJ, Booz GW, Sigmund CD et al. Angiotensin II signal transduction: an update on mechanisms of physiology and pathophysiology. Physiol Rev 2018;98:1627–738
CrossRef Google scholar
[76]
Tracy E, Rowe G, LeBlanc AJ. Cardiac tissue remodeling in healthy aging: the road to pathology. Am J Physiol Cell Physiol 2020;319:C166–82
CrossRef Google scholar
[77]
Evangelou K, Vasileiou PVS, Papaspyropoulos A et al. Cellular senescence and cardiovascular diseases: moving to the “heart” of the problem. Physiol Rev 2023;103:609–47
CrossRef Google scholar
[78]
Soares AA, Freitas WM, Japiassú AVT et al. Enhanced parathyroid hormone levels are associated with left ventricle hypertrophy in very elderly men and women. J Am Soc Hypertens 2015;9:697–704
CrossRef Google scholar
[79]
Rakov H, De Angelis M, Renko K et al. Aging is associated with low thyroid state and organ-specific sensitivity to thyroxine. Thyroid 2019;29:1723–33
CrossRef Google scholar
[80]
Barbesino G. Thyroid function changes in the elderly and their relationship to cardiovascular health: A mini-review. Gerontology 2019;65:1–8
CrossRef Google scholar
[81]
Jabbar A, Pingitore A, Pearce SHS et al. Thyroid hormones and car-diovascular disease. Nat Rev Cardiol 2017;14:39–55
CrossRef Google scholar
[82]
Higashikuni Y, Liu W, Numata G et al. NLRP3 inflammasome activation through heart-brain interaction initiates cardiac inflammation and hypertrophy during pressure overload. Circulation 2023;147:338–55
CrossRef Google scholar
[83]
Xiao H, Li H, Wang J-J et al. IL-18 cleavage triggers cardiac inflammation and fibrosis upon β-adrenergic insult. Eur Heart J 2018;39:60–9
CrossRef Google scholar
[84]
Liberale L, Montecucco F, Tardif J-C et al. Inflamm-ageing: the role of inflammation in age-dependent cardiovascular disease. Eur Heart J 2020;41:2974–82
CrossRef Google scholar
[85]
Ridker PM, Everett BM, Thuren T et al. CANTOS Trial Group. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med 2017;377:1119–31
CrossRef Google scholar
[86]
Consortium AB et al. A framework of biomarkers for brain aging: a consensus statement by the aging biomarker consortium. Life Med 2023;2
CrossRef Google scholar
[87]
Mehdizadeh M, Aguilar M, Thorin E et al. The role of cellular senescence in cardiac disease: basic biology and clinical relevance. Nat Rev Cardiol 2022;19:250–64
CrossRef Google scholar
[88]
Lyu G, Guan Y, Zhang C et al. TGF-β signaling alters H4K20me3 status via miR-29 and contributes to cellular senescence and cardiac aging. Nat Commun 2018;9:2560
CrossRef Google scholar
[89]
Roh JD, Hobson R, Chaudhari V et al. Activin type II receptor signaling in cardiac aging and heart failure. Sci Transl Med 2019;11:eaau8680
CrossRef Google scholar
[90]
Jankowich M, Choudhary G. Endothelin-1 levels and cardiovascular events. Trends Cardiovasc Med 2020;30:1–8
CrossRef Google scholar
[91]
Leary PJ, Jenny NS, Bluemke DA et al. Endothelin-1, cardiac morphology, and heart failure: the MESA angiogenesis study. J H Lung Transplant 2020;39:45–52
CrossRef Google scholar
[92]
Feng J, Liang L, Chen Y et al. Big endothelin-1 as a predictor of reverse remodeling and prognosis in dilated cardiomyopathy. J Clin Med 2023;12:1363
CrossRef Google scholar
[93]
Kato ET, Morrow DA, Guo J et al. Growth differentiation factor 15 and cardiovascular risk: individual patient meta-analysis. Eur Heart J 2023;44:293–300
CrossRef Google scholar
[94]
Meessen JMTA, Cesaroni G, Mureddu GF et al. PREDICTOR Investigators. IGFBP7 and GDF-15, but not P1NP, are associated with cardiac alterations and 10-year outcome in an elderly community-based study. BMC Cardiovasc Disord 2021;21:328
CrossRef Google scholar
[95]
Tang Y, Fung E, Xu A et al. C-reactive protein and ageing. Clin Exp Pharmacol Physiol 2017;44:9–14
CrossRef Google scholar
[96]
Zhang Y, Tang X, Wang Z et al. The chemokine CCL17 is a novel therapeutic target for cardiovascular aging. Signal Transduct Target Ther 2023;8:157
CrossRef Google scholar
[97]
Zhang Y, Ye Y, Tang X et al. CCL17 acts as a novel therapeutic target in pathological cardiac hypertrophy and heart failure. J Exp Med 2022;219:e20200418
CrossRef Google scholar
[98]
Lehallier B, Gate D, Schaum N et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 2019;25:1843–50
CrossRef Google scholar
[99]
Ye Y, Yang X, Zhao X et al. Serum chemokine CCL17/thymus activation and regulated chemokine is correlated with coronary artery diseases. Atherosclerosis 2015;238:365–9
CrossRef Google scholar
[100]
Zhang L, Smyth D, Al-Khalaf M et al. Insulin-like growth factor-binding protein-7 (IGFBP7) links senescence to heart failure. Nat Cardiovasc Res 2022;1:1195–214
CrossRef Google scholar
[101]
Choi RH, Tatum SM, Symons JD et al. Ceramides and other sphingolipids as drivers of cardiovascular disease. Nat Rev Cardiol 2021;18:701–11
CrossRef Google scholar
[102]
Tang WHW, Wang Z, Levison BS et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med 2013;368:1575–84
CrossRef Google scholar
[103]
Hilvo M, Meikle PJ, Pedersen ER et al. Development and validation of a ceramide- and phospholipid-based cardiovascular risk estimation score for coronary artery disease patients. Eur Heart J 2020;41:371–80
CrossRef Google scholar
[104]
Katajamäki TT, Koivula M-K, Hilvo M et al. Ceramides and phosphatidylcholines associate with cardiovascular diseases in the elderly. Clin Chem 2022;68:1502–8
CrossRef Google scholar
[105]
Huang R, Yan L, Lei Y. The gut microbial-derived metabolite trimethylamine n-oxide and atrial fibrillation: relationships, mechanisms, and therapeutic strategies. Clin Interv Aging 2021;16:1975–86
CrossRef Google scholar
[106]
Schuett K, Kleber ME, Scharnagl H et al. Trimethylamine-n-oxide and heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol 2017;70:3202–4
CrossRef Google scholar
[107]
Muscari A, Bianchi G, Forti P et al. Pianoro Study Group. N-terminal pro B-type natriuretic peptide (NT-proBNP): a possible surrogate of biological age in the elderly people. GeroScience 2021;43:845–57
CrossRef Google scholar
[108]
Vergaro G, Januzzi JL, Cohen Solal A et al. NT-proBNP prognostic value is maintained in elderly and very elderly patients with chronic systolic heart failure. Int J Cardiol 2018;271:324–30
CrossRef Google scholar
[109]
Zhao H, Yang K, Zhang Y et al. APOE-mediated suppression of the lncRNA MEG3 protects human cardiovascular cells from chronic inflammation. Protein Cell 2023
CrossRef Google scholar
[110]
Zhang W, Song M, Qu J et al. Epigenetic modifications in cardiovascular aging and diseases. Circ Res 2018;123:773–86
CrossRef Google scholar
[111]
Dolcini J, Wu H, Nwanaji-Enwerem JC et al. Mitochondria and aging in older individuals: an analysis of DNA methylation age metrics, leukocyte telomere length, and mitochondrial DNA copy number in the VA normative aging study. Aging (Albany NY) 2020;12:2070–83
CrossRef Google scholar
[112]
Zhang W, Zhang S, Yan P et al. A single-cell transcriptomic landscape of primate arterial aging. Nat Commun 2020;11:2202
CrossRef Google scholar
[113]
Sánchez-Cabo F, Fuster V, Silla-Castro JC et al. Subclinical atherosclerosis and accelerated epigenetic age mediated by inflammation: a multi-omics study. Eur Heart J 2023;44:2698–709
CrossRef Google scholar
[114]
Song M, Franco A, Fleischer JA et al. Abrogating mitochondrial dynamics in mouse hearts accelerates mitochondrial senescence. Cell Metab 2017;26:872–883.e5
CrossRef Google scholar
[115]
Nidadavolu LS, Feger D, Chen D et al. Associatifondrial DNA, inflammatory markers, and cognitive and physical outcomes in community dwelling older adults. Immun Ageing 2023;20:24
CrossRef Google scholar
[116]
Tang X, Li PH, Chen HZ. Cardiomyocyte senescence and cellular communications within myocardial microenvironments. Front Endocrinol 2020;11:280
CrossRef Google scholar
[117]
Li J, Xiong M, Fu X-H et al. Determining a multimodal aging clock in a cohort of Chinese women. Med 2023
[118]
Guzman-Vilca WC, Quispe-Villegas GA, Carrillo-Larco RM. Predicted heart age profile across 41 countries: a cross-sectional study of nationally representative surveys in six world regions. Eclinicalmedicine 2022;52:101688
CrossRef Google scholar
[119]
Organization WHHEARTS Technical Package for Cardiovascular Disease Management in Primary Health Care. Avenue Apia 20, Geneva, Switzerland: World Health Organization.2020
[120]
Visseren FLJ, Mach F, Smulders YM et al. ESC National Cardiac Societies. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J 2021;42:3227–337
CrossRef Google scholar
[121]
Wessler BS, Lai Yh L, Kramer W et al. Clinical prediction models for cardiovascular disease: tufts predictive analytics and comparative effectiveness clinical prediction model database. Circ Cardiovasc Qual Outcomes 2015;8:368–75
CrossRef Google scholar
[122]
Ball RL, Feiveson AH, Schlegel TT et al. Predicting “heart age” using electrocardiography. J Pers Med 2014;4:65–78
CrossRef Google scholar
[123]
Le Goallec A et al. Dissecting heart age using cardiac magnetic resonance videos, electrocardiograms, biobanks, and deep learning. Circulation 2021;144:A12758
CrossRef Google scholar
[124]
Lima EM, Ribeiro AH, Paixão GMM et al. Deep neural network-estimated electrocardiographic age as a mortality predictor. Nat Commun 2021;12:5117–26
CrossRef Google scholar
[125]
Baek YS, Lee D-H, Jo Y et al. Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes. Front Cardiov Med 2023;10:1137892
CrossRef Google scholar
[126]
Salih AM, Pujadas ER, Campello VM et al. Image-based biological heart age estimation reveals differential aging patterns across cardiac chambers. J Magn Reson Imaging 2023
CrossRef Google scholar
[127]
Lindow T, Palencia-Lamela I, Schlegel TT et al. Heart age estimated using explainable advanced electrocardiography. Sci Rep 2022;12:9840
CrossRef Google scholar
[128]
Raghunath S, Cerna AEU, Jing L et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med 2020;26:886–91
CrossRef Google scholar
[129]
Kim YJ, Saqlian M, Lee JY. Deep learning–based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining. Pers Ubiquitous Comput 2022;26:259–67
CrossRef Google scholar
[130]
Pathan F, Sivaraj E, Negishi K et al. Use of atrial strain to predict atrial fibrillation after cerebral ischemia. JACC Cardiovasc Imaging 2018;11:1557–65
CrossRef Google scholar
[131]
Frizzell JD, Liang Li, Schulte PJ et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol 2017;2:204–9
CrossRef Google scholar
[132]
Ladejobi AO, Medina-Inojosa JR, Shelly Cohen M et al. The 12-lead electrocardiogram as a biomarker of biological age. Eur Heart J Digit Health 2021;2:379–89
CrossRef Google scholar
[133]
Chang CH, Lin C-S, Luo Y-S et al. Electrocardiogram-based heart age estimation by a deep learning model provides more information on the incidence of cardiovascular disorders. Front Cardiovasc Med 2022;9:754909
CrossRef Google scholar
[134]
Attia ZI, Friedman PA, Noseworthy PA et al. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circ Arrhythm Electrophysiol 2019;12:e007284
CrossRef Google scholar

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