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Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease
Open accessAbstract
IMPORTANCE: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE: To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS: Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES: Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (≥ 7.5%) risk. RESULTS: During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20,840 incident fatal and nonfatal CVD outcomes (13,237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. CONCLUSIONS AND RELEVANCE: In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.
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“It should be noted that data from 73 studies on 294 998 individuals without known diabetes suggest that HbA1c is as good as or better than fasting, random, or post-load glucose levels for predicting CV risk.14”
From 2023 ESC guidelines for the management of cardiovascular disease in patients with diabetes. (2023) · cites this paper as [14]
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Citations
Cites
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
Self-citationDiagnosis and Classification of Diabetes Mellitus
American Diabetes Association (2010) · Diabetes Care · doi:10.2337/dc11-s062 · Cited by 14,094
2013 ACCF/AHA Guideline for the Management of Heart Failure
Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Drazner, M. H., Fonarow, G. C., Geraci, S. A., Horwich, T., Januzzi, J. L., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.05.019 · Cited by 12,536
Stone, N. J., Robinson, J. G., Lichtenstein, A. H., Bairey Merz, C. N., Blum, C. B., Eckel, R. H., Goldberg, A. C., Gordon, D., Levy, D., Lloyd-Jones, D. M., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437738.63853.7a · Cited by 7,081
The Emerging Risk Factors Collaboration (2010) · The Lancet · doi:10.1016/s0140-6736(10)60484-9 · Cited by 5,125
2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.005 · Cited by 4,945
Self-citationStone, N. J., Robinson, J. G., Lichtenstein, A. H., Bairey Merz, C. N., Blum, C. B., Eckel, R. H., Goldberg, A. C., Gordon, D., Levy, D., Lloyd-Jones, D. M., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.002 · Cited by 3,845
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437741.48606.98 · Cited by 3,697
Self-citationDiagnosis and classification of diabetes mellitus
American Diabetes Association (2013) · Diabetes Care · doi:10.2337/dc13-s067 · Cited by 2,245
Authors Task Force Members, Rydén, L., Grant, P. J., Anker, S. D., Berne, C., Cosentino, F., Danchin, N., Deaton, C., Escaned, J., Hammes, H., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht108 · Cited by 1,994
Self-citation
Cited by
Sorted by citations
2023 ESC guidelines for the management of cardiovascular disease in patients with diabetes.
Marx, N., Federici, M., Schütt, K., Müller-Wieland, D., Ajjan, R. A., Antunes, M. J., Christodorescu, R. M., Crawford, C., Di Angelantonio, E., Eliasson, B., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad192 · Cited by 1,642
Self-citationFox, C. S., Golden, S. H., Anderson, C. A. M., Bray, G. A., Burke, L. E., de Boer, I. H., DEEDWANIA, P. C., Eckel, R. H., Ershow, A. G., FRADKIN, J. E., et al. (2015) · Diabetes Care · doi:10.2337/dci15-0012 · Cited by 504
Self-citationImamura, F., Micha, R., Wu, J. H. Y., Otto, M. C. D. O., Otite, F. O., Abioye, A. I., & Mozaffarian, D. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1002087 · Cited by 499
Fox, C. S., Golden, S. H., Anderson, C., Bray, G. A., Burke, L. E., de Boer, I. H., Deedwania, P., Eckel, R. H., Ershow, A. G., Fradkin, J., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000230 · Cited by 386
Self-citationSchwingshackl, L., Lampousi, A.-M., Portillo, M. P., Romaguera, D., HOFFMANN, G. E., & Boeing, H. (2017) · Nutrition and Diabetes · doi:10.1038/nutd.2017.12 · Cited by 225
Ali, M. K., Bullard, K. M., Saydah, S., Imperatore, G., & Gregg, E. W. (2018) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(18)30027-5 · Cited by 219
Phenotypes of prediabetes and stratification of cardiometabolic risk
Stefan, N., Fritsche, A., Schick, F., & Häring, H.-U. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(16)00082-6 · Cited by 208
Warren, B., Pankow, J. S., Matsushita, K., Punjabi, N. M., Daya, N. R., Grams, M. E., Woodward, M., & Selvin, E. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(16)30321-7 · Cited by 203
Self-citationDanaei, G., Fahimi, S., Lu, Y., Zhou, B., Hajifathalian, K., Cesare, M. D., Lo, W., Reis-Santos, B., Cowan, M., Shaw, J. E., et al. (2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00129-1 · Cited by 201
Self-citationSex differences in the burden and complications of diabetes
Peters, S. A. E., & Woodward, M. (2018) · Current Diabetes Reports · doi:10.1007/s11892-018-1005-5 · Cited by 172
Lu, J., He, J., Li, M., Tang, X., Hu, R., Shi, L., Su, Q., Peng, K., Xu, M., Xu, Y., et al. (2019) · Diabetes Care · doi:10.2337/dc18-1390 · Cited by 180
Fructosamine and glycated albumin and the risk of cardiovascular outcomes and death
Selvin, E., Rawlings, A. M., Lutsey, P. L., Maruthur, N., Pankow, J. S., Steffes, M., & Coresh, J. (2015) · Circulation · doi:10.1161/circulationaha.115.015415 · Cited by 151
Self-citationZhou, Z., Macpherson, J., Gray, S. R., Gill, J. M. R., Welsh, P. I., Celis-Morales, C. A., Sattar, N. A., Pell, J. P., & Ho, F. K. W. (2021) · Diabetologia · doi:10.1007/s00125-021-05484-6 · Cited by 159
“However, HbA1c is a wellestablished measure to reflect the mean blood glucose level in the last 2–3months, and predicts relevant outcomes with similar efficacy to other blood glucose level measures [27].”
BackgroundSelf-citationRoss, S. A., Gerstein, H. C., Eikelboom, J. W., Anand, S. S., Yusuf, S., & Paré, G. (2015) · European Heart Journal · doi:10.1093/eurheartj/ehv083 · Cited by 131
Guo, C., Zhang, J., Liu, Y., Xie, Y., Han, Z., & Yu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2981159 · Cited by 123
“To estimate its Sensitivity and Speci-ficity [24], [25], the classifier is then applied to each data set with the rhythm classification graphical structure as shown in the Figure.8 for arterial flutter has been analyzed whereas the dataset has been taken from administrative datasets of rom the…”
MethodRelationship Between Glycated Hemoglobin and Stroke Risk: A Systematic Review and Meta‐Analysis
Mitsios, J. P., Ekinci, E. I., Mitsios, G. P., Churilov, L. P., & Thijs, V. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.007858 · Cited by 127
Vallejo‐Vaz, A. J., Akram, A., Seshasai, S. R. K., Cole, D., Watts, G. F., Hovingh, G. K., Kastelein, J. J. P., Mata, P., Raal, F. J., Santos, R. D., et al. (2016) · Atherosclerosis Supplements · doi:10.1016/j.atherosclerosissup.2016.10.001 · Cited by 113
Self-citationStress Hyperglycemia and Outcome of Non-diabetic Patients After Acute Ischemic Stroke
Zhu, B., Pan, Y., Jing, J., Meng, X., Zhao, X., Liu, L., Wang, Y., Wang, Y., & Wang, Z. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01003 · Cited by 109
Glycated Hemoglobin, Prediabetes, and the Links to Cardiovascular Disease: Data From UK Biobank
Welsh, C. E., Welsh, P. I., Celis-Morales, C. A., Mark, P. B., Mackay, D., Ghouri, N., Ho, F. K. W., Ferguson, L. D., Brown, R., Lewsey, J., et al. (2019) · Diabetes Care · doi:10.2337/dc19-1683 · Cited by 112
Self-citationNovel metabolic biomarkers of cardiovascular disease
Jensen, M. K., Bertoia, M. L., Cahill, L. E., Agarwal, I., Rimm, E. B., & Mukamal, K. J. (2014) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2014.155 · Cited by 109
Pirro, M., Mannarino, M. R., Bianconi, V., Simental‐Mendía, L. E., Bagaglia, F., Mannarino, E., & Sahebkar, A. (2016) · Pharmacological Research · doi:10.1016/j.phrs.2016.04.021 · Cited by 104
Pan, B., Wu, Y., Yang, Q., Ge, L., Gao, C., Xun, Y., Tian, J., & Ding, G. (2019) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12312 · Cited by 99
Palladino, R., Tabak, A. G., Khunti, K., Valabhji, J., Majeed, A., Millett, C., & Vamos, E. P. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001061 · Cited by 98
Selvin, E. (2016) · Diabetes Care · doi:10.2337/dc16-0042 · Cited by 96
“Indeed, studies using modern HbA1c assays have now shown that HbA1c is more strongly associated with outcomes as compared with fasting glucose or 2-h postprandial glucose (19,54).”
BackgroundSelf-citationUtility of different glycemic control metrics for optimizing management of diabetes
Kohnert, K. (2015) · World Journal of Diabetes · doi:10.4239/wjd.v6.i1.17 · Cited by 101
Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c
NCD Risk Factor Collaboration (NCD-RisC), Zhou, B., Sheffer, K. E., Bennett, J. E., Gregg, E. W., Danaei, G., Singleton, R. K., Shaw, J. E., Mishra, A., Lhoste, V. P. F., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02610-2 · Cited by 94
Hemoglobin A1c and the Progression of Coronary Artery Calcification Among Adults Without Diabetes
Carson, A. P., Steffes, M. W., Carr, J. J., Kim, Y., Gross, M. D., Carnethon, M. R., Reis, J. P., Loria, C. M., Jacobs, D. R., & Lewis, C. E. (2014) · Diabetes Care · doi:10.2337/dc14-0360 · Cited by 91
“A recent analysis of participants without diabetes from 73 prospective studies reported higher HbA1c was associated with an increased risk of clinical cardiovascular events, although the addition of HbA1c to risk prediction models resulted in marginal improvements in cardiovascular disease risk assessment beyond tradi…”
BackgroundWelsh, C. E., Celis-Morales, C. A., Ho, F. K., Brown, R., Mackay, D. F., Lyall, D. M., Anderson, J. J., Pell, J. P., Gill, J. M. R., Sattar, N., et al. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2019.12.032 · Cited by 85
Self-citationHerrington, W. G., Alegre-Díaz, J., Wade, R. L., Gnatiuc, L., Ramírez-Reyes, R., Hill, M., Solano-Sánchez, M., Baigent, C., Lewington, S., Collins, R., et al. (2018) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(18)30050-0 · Cited by 81
Association of 1,5-Anhydroglucitol With Cardiovascular Disease and Mortality
Selvin, E., Rawlings, A. M., Lutsey, P. L., Maruthur, N. M., Pankow, J. S., Steffes, M. W., & Coresh, J. (2015) · Diabetes · doi:10.2337/db15-0607 · Cited by 81
Self-citationKunutsor, S. K., Balasubramanian, V. G., Zaccardi, F., Gillies, C., Aroda, V. R., Seidu, S., & Khunti, K. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15511 · Cited by 94
“4,5 As a result, strategies to lower HbA1c levels have become a cornerstone of T2D management, aiming to prevent or delay the onset of both microvascular and macrovascular complications.”
BackgroundMadsbad, S. (2015) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2015.09.019 · Cited by 78
Kunutsor, S. K., Khan, H., & Laukkanen, J. A. (2015) · Diabetologia · doi:10.1007/s00125-015-3520-0 · Cited by 73
“Given that serum albumin is a simple, standardised, costeffective and scalable biomarker, we propose additional larger studies and formal risk analyses (such as those conducted by our group [33, 34]) to assess its role in type 2 diabetes risk prediction.”
BackgroundSelf-citationDiabetes duration and glycaemic control as predictors of cardiovascular disease and mortality
Li, F., Yang, H., Zhou, R., Zheng, J., Chen, G., Zou, M., Wu, X., & Wu, X. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14348 · Cited by 78
“HbA1c levels were used as a stable marker of glycaemic control as they reflect average blood glucose concentrations over the preceding 3 months.(26) All biochemical assays, including serum total cholesterol, HDL cholesterol (Beckman Coulter AU5400) and HbA1c (VARIANT II Turbo; Bio-Rad, Hercules, California) levels, we…”
MethodScicali, R., Giral, P., Gallo, A., Pino, A. D., Rabuazzo, A. M., Purrello, F., Cluzel, P., Redheuil, A. B., Bruckert, É., & Rosenbaum, D. H. (2016) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2016.11.003 · Cited by 73
Sinning, C., Makarova, N., Völzke, H., Schnabel, R. B., Ojeda, F., Dörr, M., Felix, S. B., Köenig, W., Peters, A., Rathmann, W., et al. (2021) · Cardiovascular Diabetology · doi:10.1186/s12933-021-01413-4 · Cited by 79
Self-citationSattar, N., Presslie, C., Rutter, M. K., & McGuire, D. K. (2024) · Diabetes Care · doi:10.2337/dci23-0041 · Cited by 70
Self-citationon behalf of the ESCARVAL STUDY GROUP, Navarro-Pérez, J., Orozco‐Beltrán, D., Gil-Guillén, V. F., Pallarés‐Carratalá, V., Valls, F., Fernández, A. G., Perez-Navarro, A. M., Sanchís, C., Dominguez-Lucas, A., et al. (2018) · BMC Cardiovascular Disorders · doi:10.1186/s12872-018-0914-1 · Cited by 68
An improved hawks optimizer based learning algorithms for cardiovascular disease prediction
Kumar, A. S., & Rekha, R. U. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104442 · Cited by 62
Ho, Y.-R., Wang, P., Lu, M.-C., Tseng, S.-T., Yang, C.-P., & Yan, Y.-H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177563 · Cited by 59
Li, J., Quan, K., Wang, Y., Zhao, X., Li, Z., Pan, Y., Li, H., Liu, L., & Wang, Y. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.576895 · Cited by 61
“Glycated hemoglobin (HbA1c) is a well-validated marker for glucose control over 2–3 months, reflecting the background glucose level before the event (5).”
BackgroundBlood Sugar Regulation for Cardiovascular Health Promotion and Disease Prevention
Schwarz, P. E. H., Timpel, P., Harst, L., Greaves, C. J., Ali, M. K., Lambert, J. D., Weber, M. B., Almedawar, M. M., & Morawietz, H. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.07.081 · Cited by 66
Effect of Testosterone on Progression From Prediabetes to Diabetes in Men With Hypogonadism
Bhasin, S., Lincoff, A. M., Nissen, S. E., Wannemuehler, K. A., McDonnell, M. E., Peters, A. L., Khan, N., Snabes, M. C., Li, X., Li, G., et al. (2024) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.7862 · Cited by 61
Mendelian Randomization Analysis of Hemoglobin A1c as a Risk Factor for Coronary Artery Disease
Leong, A., Ji, C. F., Wheeler, E., Hivert, M., Liu, C., Merino, J., Dupuis, J., Tai, E. S., Rotter, J. I., Florez, J. C., et al. (2019) · Diabetes Care · doi:10.2337/dc18-1712 · Cited by 52
Chattopadhyay, S., George, A., John, J., & Sathyapalan, T. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy233 · Cited by 53
Chen, Y.-Y., Lin, Y.-J., Chong, E., Chen, P.-C., Chao, T.-F., Chen, S.-A., & Chien, K.-L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123116 · Cited by 56
Continued efforts to translate diabetes cardiovascular outcome trials into clinical practice
Avogaro, A., Fadini, G. P., Sesti, G., Bonora, E., & Prato, S. D. (2016) · Cardiovascular Diabetology · doi:10.1186/s12933-016-0431-4 · Cited by 53
Schwingshackl, L., Zähringer, J., Nitschke, K., Torbahn, G., Lohner, S., Kühn, T., Fontana, L., Veronese, N., Schmucker, C., & Meerpohl, J. J. (2021) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2020.1757616 · Cited by 53
“…and mendelian randomization studies have shown that these risk factors were strongly related to cardiovascular disease (Holmes et al. 2015; Ettehad et al. 2016; Di Angelantonio et al. 2014) including the use of the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system.”
BackgroundLi, S., Wang, Y., Wang, W., Zhang, Q., Wang, A., & Zhao, X. (2022) · BMC Neurology · doi:10.1186/s12883-022-02760-9 · Cited by 52
“Glycated hemoglobin (HbA1c) is a reliable measure of the mean levels of glucose concentration over 2–3 months before the onset of the acute illness [15, 16].”
BackgroundMeirow, Y., & Baniyash, M. (2017) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-017-2035-6 · Cited by 54
“Presently, most biomarkers used for evaluation of diabetes severity and progression, rely on metabolic parameters and are limited in improving risk stratification regarding disease-related outcomes [74].”
BackgroundMechanisms of Risk Reduction in the Clinical Practice of Alzheimer’s Disease Prevention
Schelke, M. W., Attia, P., Palenchar, D. J., Kaplan, B., Mureb, M., Ganzer, C. A., Scheyer, O., Rahman, A., Kachko, R., Krikorian, R., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00096 · Cited by 52
Kok, M. M., von Birgelen, C., Sattar, N. A., Zocca, P., Löwik, M. M., Danse, P. W., Schotborgh, C. E., Scholte, M., Hartmann, M., Kant, G. D., et al. (2018) · EuroIntervention · doi:10.4244/eij-d-17-01067 · Cited by 49
Self-citationEastwood, S. V., Tillin, T., Sattar, N. A., Forouhi, N. G., Hughes, A. D., & Chaturvedi, N. (2015) · Diabetes Care · doi:10.2337/dc15-1078 · Cited by 44
Self-citationon behalf of the CHANCES group, Schöttker, B., Rathmann, W., Herder, C., Thorand, B., Wilsgaard, T., Njølstad, I., Siganos, G., Mathiesen, E. B., Saum, K.-U., et al. (2016) · BMC Medicine · doi:10.1186/s12916-016-0570-1 · Cited by 47
“The latter contrasts with the meta-analysis of the ERFC that observed a significantly increased cardiovascular risk in subjects with an HbA1c <4.5 % (<26 mmol/mol) (HR 1.23 [1.02; 1.50]) compared with subjects with an HbA1c of 5 to <5.5 % (31 to <37 mmol/mol).”
BackgroundInfluentialSelf-citationZheng, X., Zhang, W., Wan, X., Lv, X., Lin, P., Si, S., Xue, F., Wang, A., & Cao, Y. (2024) · BMC Nutrition · doi:10.1186/s40795-024-00836-y · Cited by 44
Zhang, H.-W., Zhao, X., Guo, Y.-L., Gao, Y., Zhu, C.-G., Wu, N. Q., & Li, J.-J. (2018) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2018.05.010 · Cited by 43
Kunutsor, S. K., Zaccardi, F., Balasubramanian, V. G., Gillies, C., Aroda, V. R., Seidu, S., Davies, M. J., & Khunti, K. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15500 · Cited by 45
Chang, C., Chang, Y., Lin, J., Chen, S., Chuang, L., & Lai, M. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-2443 · Cited by 41
Diabetes mellitus: Possible risk and promoting factors of cholangiocarcinoma
Saengboonmee, C., Seubwai, W., Wongkham, C., & Wongkham, S. (2015) · Cancer Epidemiology · doi:10.1016/j.canep.2015.04.002 · Cited by 43
“complications, but is also the risk factor for other noncommunicable diseases including cardiovascular disease and cancer [3–5].”
BackgroundHemoglobin A1c Levels and the Risk of Cardiovascular Disease in People Without Known Diabetes
Goto, A., Noda, M., Matsushita, Y., Goto, M., Kato, M., Isogawa, A., Takahashi, Y., Kurotani, K., Oba, S., Nanri, A., et al. (2015) · Medicine · doi:10.1097/md.0000000000000785 · Cited by 39
Myocardial glucotoxicity: Mechanisms and potential therapeutic targets
Battault, S., Renguet, E., Steenbergen, A. V., Horman, S., Beauloye, C., & Bertrand, L. (2020) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2020.06.006 · Cited by 43
The role of hemoglobin A1c in the assessment of diabetes and cardiovascular risk
Sandler, C. N., & McDonnell, M. E. (2016) · Cleveland Clinic Journal of Medicine · doi:10.3949/ccjm.83.s1.02 · Cited by 41
Giannella, A., Ceolotto, G., Radu, C. M., Cattelan, A. M., Iori, E., Benetti, A., Fabris, F., Simioni, P., Avogaro, A., & de Kreutzenberg, S. V. (2021) · Cardiovascular Diabetology · doi:10.1186/s12933-021-01267-w · Cited by 37
Lexis, C. P. H., van der Horst‐Schrivers, A. N. A., Lipšic, E., Valente, M. A. E., Kobold, A. C. M., de Boer, R. A., van Veldhuisen, D. J., van der Harst, P., & van der Horst, I. C. C. (2015) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2015-000090 · Cited by 34
Liao, H., Saver, J. L., Yeh, H., Chen, C.-H. S., Wu, Y., Lee, M., & Ovbiagele, B. I. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-026010 · Cited by 31
Climie, R. E., van Sloten, T. T., Perier, M., Tafflet, M., Fayossé, A., Dugravot, A., Singh‐Manoux, A., & Empana, J. P. (2019) · Diabetes Care · doi:10.2337/dc19-0379 · Cited by 31
Guo, L., Zhang, J., An, R., Wang, W., Fen, J., Wu, Y., & Wang, Y. (2025) · BMC Medicine · doi:10.1186/s12916-025-04064-4 · Cited by 30
Zhang, L., Wang, M., Zhang, R., Zhong, Y., Fan, H., Wang, M., & Wang, J. (2020) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2020.107673 · Cited by 30
Butalia, S., Chu, L. M., Dover, D. C., Lau, D., Yeung, R. O., Eurich, D. T., Senior, P. A., & Kaul, P. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.123.031095 · Cited by 33
“27 The Emerging Risk Factors Collaboration pooled analysis excluded patients with a known history of diabetes or CVD at baseline and categorized 101 280 participants according to 0.5% increments of HbA1c (from 4.5% to ≥6.5%).”
InfluentialImpact of Diabetes Mellitus on Stroke and Survival in Patients With Atrial Fibrillation
Patlolla, S. H., Lee, H.-C., Noseworthy, P. A., Wysokiński, W. E., Hodge, D. O., Greene, E. L., Gersh, B. J., & Melduni, R. M. (2020) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2020.06.049 · Cited by 30
Goldberg, R. B. (2022) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2022.02.011 · Cited by 31
Eldor, R., Klieger, Y., Sade-Feldman, M., Vaknin, I., Varfolomeev, I., Fuchs, C., & Baniyash, M. (2014) · Diabetes Care · doi:10.2337/dc14-1544 · Cited by 29
“Likewise, a recent study showed that assessment of A1C values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk (16).”
BackgroundInfluentialUnwin, N. C., Howitt, C., Rose, A. M., Samuels, T. A., Hennis, A. J. M., & Hambleton, I. R. (2017) · Journal of Global Health · doi:10.7189/jogh.07.020407 · Cited by 29
Gajos, G., Konieczyńska, M., Zalewski, J., & Undas, A. (2015) · Cardiovascular Diabetology · doi:10.1186/s12933-015-0207-2 · Cited by 27
Recomendaciones preventivas cardiovasculares. Actualización PAPPS 2018
Cuixart, C. B., Sánchez, J. J. A., Banegas, J. R., León, C. F., Lobos-Bejarano, J. M., Rioboó, E. M., Pérez, J. N., Orozco‐Beltrán, D., & Álvarez, F. V. (2018) · Atención Primaria · doi:10.1016/s0212-6567(18)30360-3 · Cited by 29
Davidson, M. B. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109219 · Cited by 34
Mohamed, H., Abbas, A. M., Huneif, M. A., Alqahtani, S. M., Ahmed, A. M., Babker, A. M. A., Elagab, E. A. M., & Haris, P. I. (2021) · Open Access Macedonian Journal of Medical Sciences · doi:10.3889/oamjms.2021.6084 · Cited by 27
“2% reduction in HbA1c significantly reduces the risk of mortality by 10% [25].”
BackgroundHellgren, M. I., Daka, B., Jansson, P., Lindblad, U., & Larsson, C. (2014) · European Journal of Preventive Cardiology · doi:10.1177/2047487314537917 · Cited by 27
Naeem, M. T., Khattak, R. M., Rehman, M. U., & Khattak, M. N. K. (2016) · South East Asia Journal of Public Health · doi:10.3329/seajph.v5i2.28310 · Cited by 28
“Growing evidence supports the finding that HbA1c level is an independent risk factor for cardiovascular events, regardless of the diagnosis of diabetes.(17-20)”
Hyperglycemia: a bad signature on the vascular system.
Costantino, S., Paneni, F., & Cosentino, F. (2015) · PubMed · doi:10.3978/j.issn.2223-3652.2015.05.02 · Cited by 28
“Interestingly, a very recent study conducted on individuals without known CVD or diabetes, additional assessment of HbA1c values provided incremental benefit for prediction of CVD risk (5).”
BackgroundCai, A., Li, G., Chen, J., Li, X., Wei, X., Li, L., & Zhou, Y. (2014) · Lipids in Health and Disease · doi:10.1186/1476-511x-13-181 · Cited by 27
“Nonetheless, data from the Emerging Risk Factors Collaboration revealed that HbA1c merely added little incremental benefit for ASCVD risk prediction in patients without known ASCVD and diabetes [12].”
MethodLiang, M., McEvoy, J. W., Chen, Y., Sharrett, A. R., & Selvin, E. (2016) · Diabetes Care · doi:10.2337/dc16-0840 · Cited by 27
Self-citationCooking Classes: A Diabetes Self-Management Support Intervention Enhancing Clinical Values
Byrne, C., Kurmas, N., Burant, C. J., Utech, A. E., Steiber, A. L., & Julius, M. (2017) · The Science of Diabetes Self-Management and Care · doi:10.1177/0145721717737741 · Cited by 25
“7% (8 mmol/mol) represents a significant improvement in control.(16,17) The results of this study indicate that DSMS HTK participants are able to improve glycemic control regardless of the number of classes they attend, and they may experience an additional reduction in A1C with more frequent HTK participation.”
BackgroundBuccheri, E., Dell'Aquila, D., & Russo, M. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108722 · Cited by 24
Davidson, M. B., & Kahn, R. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-1370 · Cited by 23
Luo, S., Yeung, S. L. A., & Schooling, C. M. (2021) · Diabetologia · doi:10.1007/s00125-021-05537-w · Cited by 23
“The Emerging Risk Factors Collaboration study suggested a J-shaped association of HbA1c with incident CVD [3], whilst another study suggests a threshold relationship of HbA1c (≥53 mmol/mol, 7.0”
BackgroundInfluentialSerum cystatin C for risk stratification of prediabetes and diabetes populations
Xiong, K., Zhang, S., Zhong, P., Zhu, Z., Chen, Y., Huang, W., & Wang, W. (2023) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2023.102882 · Cited by 22
Xu, R., Kong, X., Xu, B. P., Song, Y., Ji, M., Zhao, M., Huang, X. B., Li, P., Cheng, X., Chen, F., et al. (2017) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.116.145656 · Cited by 22
Is Corneal Arcus Independently Associated With Incident Cardiovascular Disease in Asians?
Wong, M. Y. Z., Man, R. E. K., Gupta, P., Lim, S. H., Lim, B., Tham, Y. C., Sabanayagam, C., Wong, T. Y., Cheng, C., & Lamoureux, E. L. (2017) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2017.09.002 · Cited by 21
Sattar, N. A., & Preiss, D. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(16)00085-1 · Cited by 20
Self-citationBrutsaert, E. F., Shitole, S. G., Biggs, M. L., Mukamal, K. J., DeBoer, I., Thacker, E. L., Barzilay, J. I., Djoussé, L., Ix, J. H., Smith, N. L., et al. (2015) · The Journals of Gerontology Series A · doi:10.1093/gerona/glv106 · Cited by 22
“This notion, however, was not supported by a recent systematic review (6), where associations and risk prediction for CVD were similar for HbA1c, FPG, and 2hPG, although joint evaluation of these measures was not possible.”
ResultSelf-citationAssessing Cardiovascular Risk and Testing in Type 2 Diabetes
Saeed, A., & Ballantyne, C. M. (2017) · Current Cardiology Reports · doi:10.1007/s11886-017-0831-4 · Cited by 20
Cardiometabolic and renal phenotypes and transitions in the United States population
Lhoste, V. P. F., Zhou, B., Mishra, A., Bennett, J. E., Filippi, S., Asaria, P., Gregg, E. W., Danaei, G., & Ezzati, M. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00391-y · Cited by 20
Recomendaciones preventivas cardiovasculares. Actualización PAPPS 2020
Orozco‐Beltrán, D., Cuixart, C. B., Sánchez, J. J. A., Banegas, J. R., Cuenca, A. M. C., Gil-Guillén, V. F., Rioboó, E. M., & Pérez, J. N. (2020) · Atención Primaria · doi:10.1016/j.aprim.2020.08.002 · Cited by 24
New and Emerging Biomarkers in Cardiovascular Disease
Cahill, L. E., Bertoia, M. L., Aroner, S. A., Mukamal, K. J., & Jensen, M. K. (2015) · Current Diabetes Reports · doi:10.1007/s11892-015-0661-y · Cited by 20
Rebnord, E. W., Pedersen, E. R., Strand, E., Svingen, G. F. T., Meyer, K., Schartum-Hansen, H., Løland, K. H., Seifert, R., Ueland, P. M., Nilsen, D. W. T., et al. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.02.053 · Cited by 19
Biomarkers, Biosensors and Biomedicine
de Oliveira, W. F., Silva, P. M. D. S., Coelho, L. C. B. B., & Correia, M. T. D. S. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867326666190124103125 · Cited by 19
Jarmul, J. A., Pignone, M. P., & Pletcher, M. J. (2015) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.115.001639 · Cited by 20
Stevens, S., McManus, R. J., & Stevens, R. (2018) · Journal of Hypertension · doi:10.1097/hjh.0000000000001923 · Cited by 17
Recomendaciones preventivas cardiovasculares. Actualización PAPPS 2022
Orozco‐Beltrán, D., Cuixart, C. B., Banegas, J. R., Gil-Guillén, V. F., Cuenca, A. M. C., Rioboó, E. M., Baldó, A. J., Vicuña, J., & Pérez, J. N. (2022) · Atención Primaria · doi:10.1016/j.aprim.2022.102444 · Cited by 64
Metadata
- Authors
- Di Angelantonio, E., Gao, P., Khan, H., Butterworth, A. S., Wormser, D., Kaptoge, S., Kondapally Seshasai, S. R., Thompson, A., Sarwar, N., Willeit, P., Ridker, P. M., Barr, E. L. M., Khaw, K.-T., Psaty, B. M., Brenner, H., Balkau, B., Dekker, J. M., Lawlor, D. A., Daimon, M., Willeit, J., Njølstad, I., Nissinen, A., Brunner, E. J., Kuller, L. H., Price, J. F., Sundström, J., Knuiman, M. W., Feskens, E. J. M., Verschuren, W. M. M., Wald, N., Bakker, S. J. L., Whincup, P. H., Ford, I., Goldbourt, U., Gómez-de-la-Cámara, A., Gallacher, J., Simons, L. A., Rosengren, A., Sutherland, S. E., Björkelund, C., Blazer, D. G., Wassertheil-Smoller, S., Onat, A., Marín Ibañez, A., Casiglia, E., Jukema, J. W., Simpson, L. M., Giampaoli, S., Nordestgaard, B. G., Selmer, R., Wennberg, P., Kauhanen, J., Salonen, J. T., Dankner, R., Barrett-Connor, E., Kavousi, M., Gudnason, V., Evans, D., Wallace, R. B., Cushman, M., D’Agostino, R. B., Umans, J. G., Kiyohara, Y., Nakagawa, H., Sato, S., Gillum, R. F., Folsom, A. R., van der Schouw, Y. T., Moons, K. G., Griffin, S. J., Sattar, N., Wareham, N. J., Selvin, E., Thompson, S. G., & Danesh, J.
- Affiliations
- University of Cambridge, Cambridge, United Kingdom
- University of Cambridge (GB)
- St George's University of London, London, United Kingdom
- Brigham and Women's Hospital (US)
- Baker Heart and Diabetes Institute (AU)
- University of Washington (US)
- German Cancer Research Center (DE)
- Inserm (FR)
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
- University of Bristol (GB)
- Yamagata University (JP)
- Innsbruck Medical University (AT)
- UiT The Arctic University of Norway (NO)
- Finnish Institute for Health and Welfare (FI)
- University College London (GB)
- University of Pittsburgh (US)
- University of Edinburgh (GB)
- Uppsala University (SE)
- The University of Western Australia (AU)
- Wageningen University & Research (NL)
- National Institute for Public Health and the Environment (NL)
- Wolfson Institute of Preventive Medicine, London, United Kingdom
- University Medical Center Groningen (NL)
- St George's, University of London (GB)
- University of Glasgow (GB)
- Sheba Medical Center (IL)
- Hospital 12 de Octubre, Madrid, Spain
- Cardiff University (GB)
- UNSW Sydney (AU)
- University of Gothenburg (SE)
- Medical University of South Carolina (US)
- Duke Medical Center (US)
- Albert Einstein College of Medicine, New York, New York
- Istanbul University (TR)
- San Jose Norte Health Centre, Zaragoza, Spain
- University of Padua (IT)
- Leiden University Medical Center (NL)
- University of Texas School of Public Health, Houston
- Istituto Superiore di Sanità (IT)
- University of Copenhagen (DK)
- Norwegian Institute of Public Health (NO)
- Umeå University (SE)
- University of Eastern Finland (FI)
- University of Helsinki (FI)
- Feinstein Institute for Medical Research (US)
- University of California San Diego (US)
- Erasmus MC (NL)
- University of Iceland (IS)
- Rush University Medical Center (US)
- University of Iowa (US)
- University of Vermont (US)
- Boston University (US)
- Georgetown University (US)
- Kyushu University (JP)
- Kanazawa Medical University (JP)
- Chiba Prefectural Institute of Public Health (JP)
- Howard University (US)
- University of Minnesota (US)
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
- University Medical Center Utrecht (NL)
- Johns Hopkins University (US)
- Corresponding author
- Danesh, John
- ORCID
- Gao, Pei
- Khan, Hassan
- Butterworth, Adam S.
- Kaptoge, Stephen
- Thompson, Alex
- Sarwar, Nadeem
- Willeit, Peter
- Ridker, Paul M
- Barr, Elizabeth L. M.
- Khaw, Kay-Tee
- Psaty, Bruce M.
- Brenner, Hermann
- Balkau, Beverley
- Lawlor, Debbie A.
- Daimon, Makoto
- Willeit, Johann
- Njølstad, Inger
- Brunner, Eric J.
- Kuller, Lewis H.
- Price, Jackie F.
- Sundström, Johan
- Knuiman, Matthew W.
- Feskens, Edith J. M.
- Verschuren, W. M. M.
- Wald, Nicholas
- Bakker, Stephan J. L.
- Whincup, Peter H.
- Ford, Ian
- Gallacher, John
- Simons, Leon A.
- Rosengren, Annika
- Sutherland, Susan E.
- Blazer, Dan G.
- Onat, Altan
- Casiglia, Edoardo
- Jukema, J. Wouter
- Giampaoli, Simona
- Nordestgaard, Børge G.
- Selmer, Randi
- Wennberg, Patrik
- Dankner, Rachel
- Barrett-Connor, Elizabeth
- Kavousi, Maryam
- Gudnason, Vilmundur
- Evans, Denis
- Wallace, Robert B.
- Cushman, Mary
- D’Agostino, Ralph B.
- Umans, Jason G.
- Sato, Shinichi
- Gillum, Richard F.
- Folsom, Aaron R.
- Moons, Karel G.
- Griffin, Simon J.
- Sattar, Naveed
- Wareham, Nicholas J.
- Selvin, Elizabeth
- Thompson, Simon G.
- Danesh, John
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2014-03-26
- Volume
- 311
- Issue
- 12
- Pages
- 1225
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC4386007
- OpenAlex
- W2114008139
- Cited by
- 231
- References
- 32
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 17 · 2023: 12 · 2022: 20 · 2021: 14 · 2020: 22 · 2019: 13 · 2018: 20 · 2017: 22 · 2016: 23 · 2015: 34 · 2014: 12
- FWCI
- 24.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Rivm Repository (Netherlands National Institute for Public Health and the Environment) (submittedVersion)
- STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- National Institute for Health and Care Research (NF-SI-0512-10165, NF-SI-0512-10114)
- British Heart Foundation (RG/08/013/25942, RG/13/16/30528, PG/13/66/30442, RG/08/014/24067)
- National Institutes of Health (UL1TR000062)
- Medical Research Council (MC_UU_12015/4, MR/K013351/1, MR/L003120/1)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Keywords
- HbA1c, cardiovascular risk prediction, coronary artery disease, stroke, risk discrimination, net reclassification improvement, cardiovascular risk factors, J-shaped association, fasting blood glucose, post-load glucose, C-reactive protein, HDL cholesterol, estimated glomerular filtration rate, non-diabetic adults
- MeSH terms
- Aged, C-Reactive Protein, Coronary Disease, Diabetes Mellitus, Female, Glycated Hemoglobin, Humans, Cholesterol, HDL, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Risk Assessment, Stroke
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