Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

Emerging Risk Factors; Emanuele Di Angelantonio; Pei Gao; Hassan Khan; Adam S. Butterworth; David Wormser; Stephen Kaptoge; Sreenivasa Rao Kondapally Seshasai; Alex Thompson; Nadeem Sarwar; Peter Willeit; Paul M. Ridker; Elizabeth L. M. Barr; Kay-Tee Khaw; Bruce M. Psaty; Hermann Brenner; Beverley Balkau; Jacqueline M. Dekker; Debbie A. Lawlor; Makoto Daimon; Johann Willeit; Inger Njolstad; Aulikki Nissinen; Eric J. Brunner; Lewis H. Kuller; Jackie F. Price; Johan Sundstrom; Matthew W. Knuiman; Edith J. M. Feskens; W. M. M. Verschuren; Nicholas Wald; Stephan J. L. Bakker; Peter H. Whincup; Ian Ford; Uri Goldbourt; Agustin Gomez-de-la-Camara; John Gallacher; Leon A. Simons; Annika Rosengren; Susan E. Sutherland; Cecilia Bjorkelund; Dan G. Blazer; Sylvia Wassertheil-Smoller; Altan Onat; Alejandro Marin Ibanez; Edoardo Casiglia; J. Wouter Jukema; Lara M. Simpson; Simona Giampaoli; Borge G. Nordestgaard; Randi Selmer

doi:10.1001/jama.2014.1873

Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

Emerging Risk Factors, Emanuele Di Angelantonio, Pei Gao, Hassan Khan, Adam S. Butterworth, David Wormser, Stephen Kaptoge, Sreenivasa Rao Kondapally Seshasai, Alex Thompson, Nadeem Sarwar, Peter Willeit, Paul M. Ridker, Elizabeth L. M. Barr, Kay-Tee Khaw, Bruce M. Psaty, Hermann Brenner, Beverley Balkau, Jacqueline M. Dekker, Debbie A. Lawlor, Makoto DaimonJohann Willeit, Inger Njolstad, Aulikki Nissinen, Eric J. Brunner, Lewis H. Kuller, Jackie F. Price, Johan Sundstrom, Matthew W. Knuiman, Edith J. M. Feskens, W. M. M. Verschuren, Nicholas Wald, Stephan J. L. Bakker, Peter H. Whincup, Ian Ford, Uri Goldbourt, Agustin Gomez-de-la-Camara, John Gallacher, Leon A. Simons, Annika Rosengren, Susan E. Sutherland, Cecilia Bjorkelund, Dan G. Blazer, Sylvia Wassertheil-Smoller, Altan Onat, Alejandro Marin Ibanez, Edoardo Casiglia, J. Wouter Jukema, Lara M. Simpson, Simona Giampaoli, Borge G. Nordestgaard, Randi Selmer

Research output: Contribution to journal › Article › peer-review

Abstract

IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA(1c)) for the prediction of first cardiovascular events is uncertain.

OBJECTIVE To determine whether adding information on HbA(1c) 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 (= 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 HbA(1c) values and CVD risk. The association between HbA(1c) 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 HbA(1c) 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 HbA(1c) 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 HbA(1c) values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.

Original language	English
Pages (from-to)	1225-1233
Number of pages	9
Journal	Journal of the American Medical Association
Volume	311
Issue number	12
DOIs	https://doi.org/10.1001/jama.2014.1873
Publication status	Published - 26 Mar 2014

Keywords

STATISTICAL-METHODS
DIABETES-MELLITUS
RISK SCORE
TASK-FORCE
GLUCOSE
GUIDELINES
METAANALYSIS
MORTALITY
ADULTS

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10.1001/jama.2014.1873

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Emerging Risk Factors, Di Angelantonio, E., Gao, P., Khan, H., Butterworth, A. S., Wormser, D., Kaptoge, S., Seshasai, S. R. K., 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., ... Selmer, R. (2014). Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease. Journal of the American Medical Association, 311(12), 1225-1233. https://doi.org/10.1001/jama.2014.1873

@article{f40b5dbc2bbd42b69dc4f954e2310f88,

title = "Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease",

abstract = "IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA(1c)) for the prediction of first cardiovascular events is uncertain.OBJECTIVE To determine whether adding information on HbA(1c) 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 (= 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 HbA(1c) values and CVD risk. The association between HbA(1c) 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 HbA(1c) 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 HbA(1c) 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 HbA(1c) values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.",

keywords = "STATISTICAL-METHODS, DIABETES-MELLITUS, RISK SCORE, TASK-FORCE, GLUCOSE, GUIDELINES, METAANALYSIS, MORTALITY, ADULTS",

author = "{Emerging Risk Factors} and {Di Angelantonio}, Emanuele and Pei Gao and Hassan Khan and Butterworth, {Adam S.} and David Wormser and Stephen Kaptoge and Seshasai, {Sreenivasa Rao Kondapally} and Alex Thompson and Nadeem Sarwar and Peter Willeit and Ridker, {Paul M.} and Barr, {Elizabeth L. M.} and Kay-Tee Khaw and Psaty, {Bruce M.} and Hermann Brenner and Beverley Balkau and Dekker, {Jacqueline M.} and Lawlor, {Debbie A.} and Makoto Daimon and Johann Willeit and Inger Njolstad and Aulikki Nissinen and Brunner, {Eric J.} and Kuller, {Lewis H.} and Price, {Jackie F.} and Johan Sundstrom and Knuiman, {Matthew W.} and Feskens, {Edith J. M.} and Verschuren, {W. M. M.} and Nicholas Wald and Bakker, {Stephan J. L.} and Whincup, {Peter H.} and Ian Ford and Uri Goldbourt and Agustin Gomez-de-la-Camara and John Gallacher and Simons, {Leon A.} and Annika Rosengren and Sutherland, {Susan E.} and Cecilia Bjorkelund and Blazer, {Dan G.} and Sylvia Wassertheil-Smoller and Altan Onat and Ibanez, {Alejandro Marin} and Edoardo Casiglia and Jukema, {J. Wouter} and Simpson, {Lara M.} and Simona Giampaoli and Nordestgaard, {Borge G.} and Randi Selmer",

year = "2014",

month = mar,

day = "26",

doi = "10.1001/jama.2014.1873",

language = "English",

volume = "311",

pages = "1225--1233",

journal = "Journal of the American Medical Association",

issn = "0098-7484",

publisher = "American Medical Association",

number = "12",

}

Emerging Risk Factors, Di Angelantonio, E, Gao, P, Khan, H, Butterworth, AS, Wormser, D, Kaptoge, S, Seshasai, SRK, Thompson, A, Sarwar, N, Willeit, P, Ridker, PM, Barr, ELM, Khaw, K-T, Psaty, BM, Brenner, H, Balkau, B, Dekker, JM, Lawlor, DA, Daimon, M, Willeit, J, Njolstad, I, Nissinen, A, Brunner, EJ, Kuller, LH, Price, JF, Sundstrom, J, Knuiman, MW, Feskens, EJM, Verschuren, WMM, Wald, N, Bakker, SJL, Whincup, PH, Ford, I, Goldbourt, U, Gomez-de-la-Camara, A, Gallacher, J, Simons, LA, Rosengren, A, Sutherland, SE, Bjorkelund, C, Blazer, DG, Wassertheil-Smoller, S, Onat, A, Ibanez, AM, Casiglia, E, Jukema, JW, Simpson, LM, Giampaoli, S, Nordestgaard, BG & Selmer, R 2014, 'Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease', Journal of the American Medical Association, vol. 311, no. 12, pp. 1225-1233. https://doi.org/10.1001/jama.2014.1873

TY - JOUR

T1 - Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

AU - Emerging Risk Factors

AU - Di Angelantonio, Emanuele

AU - Gao, Pei

AU - Khan, Hassan

AU - Butterworth, Adam S.

AU - Wormser, David

AU - Kaptoge, Stephen

AU - Seshasai, Sreenivasa Rao Kondapally

AU - Thompson, Alex

AU - Sarwar, Nadeem

AU - Willeit, Peter

AU - Ridker, Paul M.

AU - Barr, Elizabeth L. M.

AU - Khaw, Kay-Tee

AU - Psaty, Bruce M.

AU - Brenner, Hermann

AU - Balkau, Beverley

AU - Dekker, Jacqueline M.

AU - Lawlor, Debbie A.

AU - Daimon, Makoto

AU - Willeit, Johann

AU - Njolstad, Inger

AU - Nissinen, Aulikki

AU - Brunner, Eric J.

AU - Kuller, Lewis H.

AU - Price, Jackie F.

AU - Sundstrom, Johan

AU - Knuiman, Matthew W.

AU - Feskens, Edith J. M.

AU - Verschuren, W. M. M.

AU - Wald, Nicholas

AU - Bakker, Stephan J. L.

AU - Whincup, Peter H.

AU - Ford, Ian

AU - Goldbourt, Uri

AU - Gomez-de-la-Camara, Agustin

AU - Gallacher, John

AU - Simons, Leon A.

AU - Rosengren, Annika

AU - Sutherland, Susan E.

AU - Bjorkelund, Cecilia

AU - Blazer, Dan G.

AU - Wassertheil-Smoller, Sylvia

AU - Onat, Altan

AU - Ibanez, Alejandro Marin

AU - Casiglia, Edoardo

AU - Jukema, J. Wouter

AU - Simpson, Lara M.

AU - Giampaoli, Simona

AU - Nordestgaard, Borge G.

AU - Selmer, Randi

PY - 2014/3/26

Y1 - 2014/3/26

N2 - IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA(1c)) for the prediction of first cardiovascular events is uncertain.OBJECTIVE To determine whether adding information on HbA(1c) 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 (= 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 HbA(1c) values and CVD risk. The association between HbA(1c) 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 HbA(1c) 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 HbA(1c) 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 HbA(1c) values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.

AB - IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA(1c)) for the prediction of first cardiovascular events is uncertain.OBJECTIVE To determine whether adding information on HbA(1c) 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 (= 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 HbA(1c) values and CVD risk. The association between HbA(1c) 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 HbA(1c) 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 HbA(1c) 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 HbA(1c) values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.

KW - STATISTICAL-METHODS

KW - DIABETES-MELLITUS

KW - RISK SCORE

KW - TASK-FORCE

KW - GLUCOSE

KW - GUIDELINES

KW - METAANALYSIS

KW - MORTALITY

KW - ADULTS

U2 - 10.1001/jama.2014.1873

DO - 10.1001/jama.2014.1873

M3 - Article

VL - 311

SP - 1225

EP - 1233

JO - Journal of the American Medical Association

JF - Journal of the American Medical Association

SN - 0098-7484

IS - 12

ER -

Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

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Keywords

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