{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:02:20Z","timestamp":1789390940429,"version":"build-2803163510"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":79,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Cardiovasc Disord"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality among aging populations. Single-time-point risk assessments may not reflect dynamic cardiometabolic changes. The cardiometabolic index (CMI), integrating triglyceride-to-HDL cholesterol ratio and waist-to-height ratio, reflects both atherogenic dyslipidemia and central adiposity, but its longitudinal prognostic value remains unclear. Thus, this study aims to evaluate the associations of baseline CMI, longitudinal change patterns, cumulative exposure, and absolute change (\u0394CMI) with incident CVD among Chinese adults aged\u2009\u2265\u200945 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from the China Health and Retirement Longitudinal Study (CHARLS). Baseline analyses included 9120 participants. Longitudinal analyses included 6164 participants. CVD (including heart diseases and stroke) was ascertained during follow-up. Multivariable Cox proportional hazard models were fitted with adjustment for confounding factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Higher baseline CMI was independently associated with increased CVD risk with a non-linear association and an inflection point at 0.42. Compared with stable low CMI, participants with persistently high CMI showed the highest CVD risk (HR\u2009=\u20091.31; 95%CI: 1.14, 1.50), followed by progression from low-to-high CMI (HR\u2009=\u20091.23; 95%CI: 1.04, 1.46), whereas improvement from high-to-low was not associated with excess risk. Among participants with low baseline CMI, higher cumulative CMI and less favorable \u0394CMI were associated with increased CVD risk. Incorporating longitudinal CMI changes modestly but significantly improved risk discrimination and reclassification beyond baseline assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CMI is a dynamic, threshold-dependent predictor of CVD, independent of LDL-C and hsCRP. Longitudinal monitoring enhances risk stratification beyond single measurements.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12872-026-06101-3","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T10:25:46Z","timestamp":1782555946000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal changes and cumulative exposure of cardiometabolic index predict cardiovascular disease risk in middle-aged and older adults: a national prospective cohort study"],"prefix":"10.1186","volume":"26","author":[{"given":"Xiaoyun","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"issue":"19","key":"6101_CR1","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1097\/CM9.0000000000003270","volume":"137","author":"H Liu","year":"2024","unstructured":"Liu H, Yin P, Qi J, Zhou M. Burden of non-communicable diseases in China and its provinces, 1990\u20132021: Results from the Global Burden of Disease Study 2021. Chin Med J. 2024;137(19):2325\u201333.","journal-title":"Chin Med J"},{"issue":"7","key":"6101_CR2","first-page":"577","volume":"22","author":"L Ming-bo","year":"2024","unstructured":"Ming-bo L, Zeng-wu W, Jing F, Sheng-shou H. on WCotIoR, 2023 CHaDiC. Key points of Report on Cardiovascular Health and Diseases in China 2023. Chin J Cardiovasc Res. 2024;22(7):577\u201393.","journal-title":"Chin J Cardiovasc Res"},{"issue":"11","key":"6101_CR3","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1093\/eurjpc\/zwae281","volume":"32","author":"B Chong","year":"2025","unstructured":"Chong B, Jayabaskaran J, Jauhari SM, Chan SP, Goh R, Kueh MTW, et al. Global burden of cardiovascular diseases: projections from 2025 to 2050. Eur J Prev Cardiol. 2025;32(11):1001\u201315.","journal-title":"Eur J Prev Cardiol"},{"issue":"25","key":"6101_CR4","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","volume":"76","author":"GA Roth","year":"2020","unstructured":"Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u20132019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982\u20133021.","journal-title":"J Am Coll Cardiol"},{"key":"6101_CR5","doi-asserted-by":"crossref","unstructured":"Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zu\u00f1iga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122.","DOI":"10.1186\/s12933-018-0762-4"},{"issue":"5","key":"6101_CR6","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.cmet.2011.07.015","volume":"14","author":"KE Bornfeldt","year":"2011","unstructured":"Bornfeldt KE, Tabas I. Insulin resistance, hyperglycemia, and atherosclerosis. Cell Metab. 2011;14(5):575\u201385.","journal-title":"Cell Metab"},{"key":"6101_CR7","doi-asserted-by":"crossref","unstructured":"Che B, Zhong C, Zhang R, Pu L, Zhao T, Zhang Y, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22(1):34.","DOI":"10.1186\/s12933-023-01762-2"},{"key":"6101_CR8","doi-asserted-by":"crossref","unstructured":"Yeh W-C, Tsao Y-C, Li W-C, Tzeng IS, Chen L-S, Chen J-Y. Elevated triglyceride-to-HDL cholesterol ratio is an indicator for insulin resistance in middle-aged and elderly Taiwanese population: a cross-sectional study. Lipids Health Dis. 2019;18(1):176.","DOI":"10.1186\/s12944-019-1123-3"},{"key":"6101_CR9","unstructured":"Federation WO. World Obesity Atlas 2025 London: World Obesity Federation. 2025. https:\/\/data.worldobesity.org\/publications\/?cat=23."},{"key":"6101_CR10","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-031-63657-8_14","volume-title":"Obesity and Lipotoxicity","author":"D Yaz\u0131c\u0131","year":"2024","unstructured":"Yaz\u0131c\u0131 D, Demir S\u00c7, Sezer H. Insulin Resistance, Obesity, and Lipotoxicity. In: Engin AB, Engin A, editors. Obesity and Lipotoxicity. Cham: Springer International Publishing; 2024. pp. 391\u2013430."},{"issue":"6","key":"6101_CR11","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1016\/j.ajcnut.2024.03.018","volume":"119","author":"Q Feng","year":"2024","unstructured":"Feng Q, Be\u0161evi\u0107 J, Conroy M, Omiyale W, Woodward M, Lacey B, et al. Waist-to-height ratio and body fat percentage as risk factors for ischemic cardiovascular disease: a prospective cohort study from UK Biobank. Am J Clin Nutr. 2024;119(6):1386\u201396.","journal-title":"Am J Clin Nutr"},{"key":"6101_CR12","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.cca.2014.08.042","volume":"438","author":"I Wakabayashi","year":"2015","unstructured":"Wakabayashi I, Daimon T. The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274\u20138.","journal-title":"Clin Chim Acta"},{"issue":"8","key":"6101_CR13","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1111\/jdi.13805","volume":"13","author":"Y Qiu","year":"2022","unstructured":"Qiu Y, Yi Q, Li S, Sun W, Ren Z, Shen Y, et al. Transition of cardiometabolic status and the risk of type 2 diabetes mellitus among middle-aged and older Chinese: A national cohort study. J Diabetes Invest. 2022;13(8):1426\u201337.","journal-title":"J Diabetes Invest"},{"issue":"12","key":"6101_CR14","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.5551\/jat.62730","volume":"28","author":"A Higashiyama","year":"2021","unstructured":"Higashiyama A, Wakabayashi I, Okamura T, Kokubo Y, Watanabe M, Takegami M, et al. The Risk of Fasting Triglycerides and its Related Indices for Ischemic Cardiovascular Diseases in Japanese Community Dwellers: the Suita Study. J Atheroscler Thromb. 2021;28(12):1275\u201388.","journal-title":"J Atheroscler Thromb"},{"issue":"4","key":"6101_CR15","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1016\/j.clnu.2017.06.018","volume":"37","author":"D Lin","year":"2018","unstructured":"Lin D, Qi Y, Huang C, Wu M, Wang C, Li F, et al. Associations of lipid parameters with insulin resistance and diabetes: A population-based study. Clin Nutr. 2018;37(4):1423\u20139.","journal-title":"Clin Nutr"},{"issue":"9","key":"6101_CR16","doi-asserted-by":"publisher","first-page":"2644","DOI":"10.1016\/j.numecd.2021.05.026","volume":"31","author":"Y Lu","year":"2021","unstructured":"Lu Y, Liu S, Qiao Y, Li G, Wu Y, Ke C. Waist-to-height ratio, waist circumference, body mass index, waist divided by height0.5 and the risk of cardiometabolic multimorbidity: A national longitudinal cohort study. Nutr Metabolism Cardiovasc Dis. 2021;31(9):2644\u201351.","journal-title":"Nutr Metabolism Cardiovasc Dis"},{"issue":"8","key":"6101_CR17","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1080\/00325481.2017.1375714","volume":"129","author":"H Wang","year":"2017","unstructured":"Wang H, Chen Y, Guo X, Chang Y, Sun Y. Usefulness of cardiometabolic index for the estimation of ischemic stroke risk among general population in rural China. Postgrad Med. 2017;129(8):834\u201341.","journal-title":"Postgrad Med"},{"key":"6101_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/4914791","volume":"2022","author":"X Cai","year":"2022","unstructured":"Cai X, Hu J, Wen W, Wang J, Wang M, Liu S, et al. Associations of the Cardiometabolic Index with the Risk of Cardiovascular Disease in Patients with Hypertension and Obstructive Sleep Apnea: Results of a Longitudinal Cohort Study. Oxidative Med Cell Longev. 2022;2022:1\u201315.","journal-title":"Oxidative Med Cell Longev"},{"key":"6101_CR19","doi-asserted-by":"crossref","unstructured":"Zha F, Cao C, Hong M, Hou H, Zhang Q, Tang B, et al. The nonlinear correlation between the cardiometabolic index and the risk of diabetes: A retrospective Japanese cohort study. Front Endocrinol. 2023;14:1120277.","DOI":"10.3389\/fendo.2023.1120277"},{"issue":"1","key":"6101_CR20","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1093\/ije\/dys203","volume":"43","author":"Y Zhao","year":"2012","unstructured":"Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2012;43(1):61\u20138.","journal-title":"Int J Epidemiol"},{"issue":"11","key":"6101_CR21","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1093\/aje\/kwz170","volume":"188","author":"X Chen","year":"2019","unstructured":"Chen X, Crimmins E, Hu P, Kim JK, Meng Q, Strauss J, et al. Venous Blood-based Biomarkers in the China Health and Retirement Longitudinal Study: Rationale, Design, and Results of the 2015 Wave. Am J Epidemiol. 2019;188(11):1871\u20137.","journal-title":"Am J Epidemiol"},{"issue":"1","key":"6101_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.26599\/1671-5411.2025.01.008","volume":"22","author":"J-G Wang","year":"2025","unstructured":"Wang J-G. Chinese Guidelines for the Prevention and Treatment of Hypertension (2024 revision). J Geriatric Cardiol. 2025;22(1):1\u2013149.","journal-title":"J Geriatric Cardiol"},{"issue":"4","key":"6101_CR23","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1002\/sim.4067","volume":"30","author":"IR White","year":"2011","unstructured":"White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011;30(4):377\u201399.","journal-title":"Stat Med"},{"key":"6101_CR24","doi-asserted-by":"crossref","unstructured":"Hewage N, Wijesekara U, Perera R. Effectiveness of cardiometabolic index, lipid accumulation product, visceral adiposity index and waist-to-thigh ratio for the early diagnosis of diabetes in young females at childbearing age who were obese or non-obese. Clin Epidemiol Global Health. 2024;30:101783.","DOI":"10.1016\/j.cegh.2024.101783"},{"key":"6101_CR25","doi-asserted-by":"crossref","unstructured":"Shi W-R, Wang H-Y, Chen S, Guo X-F, Li Z, Sun Y-X. Estimate of prevalent diabetes from cardiometabolic index in general Chinese population: a community-based study. Lipids Health Dis. 2018;17(1):236.","DOI":"10.1186\/s12944-018-0886-2"},{"key":"6101_CR26","doi-asserted-by":"crossref","unstructured":"Torres-Orozco AK, De Le\u00f3n LG, Ortiz-Rodr\u00edguez B, Candia-Luj\u00e1n R, Wakabayashi. & Daimon cardiometabolic index as an indicator to assess risk in adults. A systematic review. Atenci\u00f3n Primaria. 2024;56(5):102846.","DOI":"10.1016\/j.aprim.2023.102846"},{"key":"6101_CR27","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ijcard.2023.04.017","volume":"384","author":"M Miao","year":"2023","unstructured":"Miao M, Deng X, Wang Z, Jiang D, Lai S, Yu S, et al. Cardiometabolic index is associated with urinary albumin excretion and renal function in aged person over 60: Data from NHANES 2011\u20132018. Int J Cardiol. 2023;384:76\u201381.","journal-title":"Int J Cardiol"},{"key":"6101_CR28","doi-asserted-by":"crossref","unstructured":"Xu B, Wu Q, La R, Lu L, Abdu FA, Yin G, et al. Is systemic inflammation a missing link between cardiometabolic index with mortality? Evidence from a large population-based study. Cardiovasc Diabetol. 2024;23(1):212.","DOI":"10.1186\/s12933-024-02251-w"},{"issue":"7","key":"6101_CR29","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/S0009-9120(01)00263-6","volume":"34","author":"M Dobi\u00e1sov\u00e1","year":"2001","unstructured":"Dobi\u00e1sov\u00e1 M, Frohlich J. The plasma parameter log (TG\/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34(7):583\u20138.","journal-title":"Clin Biochem"},{"issue":"8","key":"6101_CR30","doi-asserted-by":"publisher","first-page":"2520","DOI":"10.1161\/01.CIR.96.8.2520","volume":"96","author":"JM Gaziano","year":"1997","unstructured":"Gaziano JM, Hennekens CH, O\u2019Donnell CJ, Breslow JL, Buring JE. Fasting triglycerides, high-density lipoprotein, and risk of myocardial infarction. Circulation. 1997;96(8):2520\u20135.","journal-title":"Circulation"},{"key":"6101_CR31","doi-asserted-by":"crossref","unstructured":"Kosmas CE, Rodriguez Polanco S, Bousvarou MD, Papakonstantinou EJ, Pe\u00f1a Genao E, Guzman E, et al. The Triglyceride\/High-Density Lipoprotein Cholesterol (TG\/HDL-C) Ratio as a Risk Marker for Metabolic Syndrome and Cardiovascular Disease. Diagnostics (Basel Switzerland). 2023;13(5):929.","DOI":"10.3390\/diagnostics13050929"},{"issue":"3","key":"6101_CR32","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1111\/j.1467-789X.2011.00952.x","volume":"13","author":"M Ashwell","year":"2012","unstructured":"Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes reviews: official J Int Association Study Obes. 2012;13(3):275\u201386.","journal-title":"Obes reviews: official J Int Association Study Obes"},{"key":"6101_CR33","doi-asserted-by":"crossref","unstructured":"Zhong P, Tan S, Zhu Z, Zhu Z, Liang Y, Huang W, et al. Normal-weight central obesity and risk of cardiovascular and microvascular events in adults with prediabetes or diabetes: Chinese and British cohorts. Diab\/Metab Res Rev. 2023;39(8):e3707.","DOI":"10.1002\/dmrr.3707"},{"key":"6101_CR34","doi-asserted-by":"publisher","first-page":"736073","DOI":"10.3389\/fcvm.2021.736073","volume":"8","author":"Y Liu","year":"2021","unstructured":"Liu Y, Liu X, Guan H, Zhang S, Zhu Q, Fu X, et al. Body Roundness Index Is a Superior Obesity Index in Predicting Diabetes Risk Among Hypertensive Patients: A Prospective Cohort Study in China. Front Cardiovasc Med. 2021;8:736073.","journal-title":"Front Cardiovasc Med"},{"issue":"13","key":"6101_CR35","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1161\/CIRCULATIONAHA.117.029617","volume":"137","author":"IJ Neeland","year":"2018","unstructured":"Neeland IJ, Poirier P, Despr\u00e9s J-P. Cardiovascular and Metabolic Heterogeneity of Obesity. Circulation. 2018;137(13):1391\u2013406.","journal-title":"Circulation"},{"issue":"7121","key":"6101_CR36","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1038\/nature05487","volume":"444","author":"LF Van Gaal","year":"2006","unstructured":"Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875\u201380.","journal-title":"Nature"},{"key":"6101_CR37","doi-asserted-by":"crossref","unstructured":"Lazzer S, D\u2019Alleva M, Isola M, De Martino M, Caroli D, Bondesan A, et al. Cardiometabolic Index (CMI) and Visceral Adiposity Index (VAI) Highlight a Higher Risk of Metabolic Syndrome in Women with Severe Obesity. J Clin Med. 2023;12(9):3055.","DOI":"10.3390\/jcm12093055"},{"key":"6101_CR38","doi-asserted-by":"crossref","unstructured":"Kurt B, Reugels M, Schneider KM, Spiesshoefer J, Milzi A, Gombert A, et al. C-reactive protein and cardiovascular risk in the general population. Eur Heart J. 2025;47(15):1799\u2013810.","DOI":"10.1093\/eurheartj\/ehaf937"},{"issue":"42","key":"6101_CR39","doi-asserted-by":"publisher","first-page":"4359","DOI":"10.1093\/eurheartj\/ehaf190","volume":"46","author":"F Mach","year":"2025","unstructured":"Mach F, Koskinas KC, Roeters van Lennep JE, Tokg\u00f6zo\u011flu L, Badimon L, Baigent C, et al. 2025 Focused Update of the 2019 ESC\/EAS Guidelines for the management of dyslipidaemias. Eur Heart J. 2025;46(42):4359\u201378.","journal-title":"Eur Heart J"},{"issue":"9729","key":"6101_CR40","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1016\/S0140-6736(10)60656-3","volume":"375","author":"M Jun","year":"2010","unstructured":"Jun M, Foote C, Lv J, Neal B, Patel A, Nicholls SJ, et al. Effects of fibrates on cardiovascular outcomes: a systematic review and meta-analysis. Lancet (London England). 2010;375(9729):1875\u201384.","journal-title":"Lancet (London England)"},{"issue":"17","key":"6101_CR41","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1056\/NEJMoa1001282","volume":"362","author":"HN Ginsberg","year":"2010","unstructured":"Ginsberg HN, Elam MB, Lovato LC, Crouse JR 3rd, Leiter LA, Linz P, et al. Effects of combination lipid therapy in type 2 diabetes mellitus. N Engl J Med. 2010;362(17):1563\u201374.","journal-title":"N Engl J Med"},{"issue":"21","key":"6101_CR42","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1056\/NEJMoa2210645","volume":"387","author":"A Das Pradhan","year":"2022","unstructured":"Das Pradhan A, Glynn RJ, Fruchart JC, MacFadyen JG, Zaharris ES, Everett BM, et al. Triglyceride Lowering with Pemafibrate to Reduce Cardiovascular Risk. N Engl J Med. 2022;387(21):1923\u201334.","journal-title":"N Engl J Med"},{"key":"6101_CR43","doi-asserted-by":"crossref","unstructured":"Mensah GA, Arnold N, Prabhu SD, Ridker PM, Welty FK. Inflammation and Cardiovascular Disease: 2025 ACC Scientific Statement. J Am Coll Cardiol. 2025;87(11):1381\u2013404.","DOI":"10.1016\/j.jacc.2025.08.047"},{"issue":"1","key":"6101_CR44","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1093\/clinchem\/hvaa275","volume":"67","author":"P Libby","year":"2021","unstructured":"Libby P. Inflammation in Atherosclerosis\u2014No Longer a Theory. Clin Chem. 2021;67(1):131\u201342.","journal-title":"Clin Chem"},{"issue":"12","key":"6101_CR45","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1093\/eurheartj\/ehad885","volume":"45","author":"D He","year":"2024","unstructured":"He D, Wang Z, Li J, Yu K, He Y, He X, et al. Changes in frailty and incident cardiovascular disease in three prospective cohorts. Eur Heart J. 2024;45(12):1058\u201368.","journal-title":"Eur Heart J"}],"container-title":["BMC Cardiovascular Disorders"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12872-026-06101-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12872-026-06101-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12872-026-06101-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:02:46Z","timestamp":1789387366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12872-026-06101-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["6101"],"URL":"https:\/\/doi.org\/10.1186\/s12872-026-06101-3","relation":{},"ISSN":["1471-2261"],"issn-type":[{"value":"1471-2261","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]},"assertion":[{"value":"4 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The studies involving human participants were reviewed and approved by the Ethics Review Committee of Peking University. The participants provided their written informed consent to participate in this study.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"794"}}