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Original Investigation |

Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease FREE

JAMA. 2014;311(12):1225-1233. doi:10.1001/jama.2014.1873.
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Published online

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.

Figures in this Article

To help achieve reductions in diabetes-specific microvascular complications, guidelines recommend screening people for diabetes mellitus by assessing glycemia measures, such as fasting blood glucose levels and levels of glycated hemoglobin (HbA1c), a measure of glucose exposure over the previous 2 to 3 months.1,2 Furthermore, because higher levels of glycemia measures have also been associated with higher cardiovascular disease (CVD) incidence,3,4 it has been proposed that including information on glycemia measures in algorithms used to predict the risk of CVD might be associated with improvements in the ability to predict CVD.57

The 2010 American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines concluded that measurement of HbA1c levels may be reasonable for CVD risk assessment in asymptomatic adults without a diagnosis of diabetes.8 In 2012 the Canadian Cardiovascular Society suggested that measurement of levels of fasting glucose, HbA1c, or both might be of value for CVD risk stratification.9 The Reynolds Risk Score for prediction of CVD risk incorporates information on HbA1c, although only for use in people known to have diabetes.10 However, measurement of glycemia measures was not recommended in the 2013 American College of Cardiology/American Heart Association Guideline on the Assessment of Cardiovascular Risk.11

The current study aimed to determine whether adding information on HbA1c levels to prognostic models containing conventional cardiovascular risk factors is associated with improvements in the prediction of first-onset CVD outcomes in middle-aged and older adults without a known history of diabetes. Additionally, we compared HbA1c measurement with assessment of other frequently used glycemia measures, ie, fasting, random, or postload glucose levels.

Study Design

Details of the Emerging Risk Factors Collaboration have been published.1214 The present study was designed and conducted by the collaboration’s independent coordinating center and approved by the Cambridgeshire ethics review committee. Prospective cohort studies were included if they met all the following criteria: assayed HbA1c, or fasting, random, or postload glucose level; had recorded baseline information for each participant on age, sex, smoking status, history of diabetes, systolic blood pressure, and levels of total and high-density lipoprotein (HDL) cholesterol (ie, conventional risk factors included in standard clinical risk scores8); were approximately population-based (ie, did not select participants on the basis of having previous disease); recorded cause-specific mortality, cardiovascular morbidity (nonfatal myocardial infarction or stroke), or both during follow-up using well-defined criteria; and recorded more than 1 year of follow-up. eTables 1-6 in Supplement and eAppendix 1 in Supplement provide study details, including criteria used in each study to define history of diabetes at the initial examination (ie based on self-reported information, medication usage, and/or on glycemia measures [eTable 1 in Supplement]), assay methods, acronyms, and study references. In registering fatal outcomes, the majority of contributing studies used International Classification of Diseases coding to at least 3 digits, and ascertainment was based on death certificates, with 42 of 73 studies also involving medical records, autopsy findings, and other supplementary sources. Studies used a definition of myocardial infarction based on World Health Organization (or similar) criteria and a definition of stroke based on clinical and brain imaging features.

Statistical Analysis

Analyses excluded people with a known history of diabetes or CVD at baseline, as defined by each study. The primary outcome was first-onset CVD, defined as fatal or nonfatal coronary heart disease event or any stroke. Analyses involved a 2-stage approach, with estimates of association calculated separately within each study before pooling across studies by random-effects meta-analysis (in which the random effects concerned between-study variations in the associations of the exposure variables analyzed and CVD risk). Hazard ratios were calculated using Cox proportional hazard regression models stratified by sex, censoring deaths from non-CVD causes. The proportional hazards assumptions were tested as previously described and were satisfied.15 Participants contributed only their first outcome (whether nonfatal or death) recorded at 40 years or older (ie, deaths preceded by nonfatal coronary heart disease event or stroke were not included). To characterize shapes of associations, study-specific hazard ratios were calculated by overall predefined categories of each baseline glycemia measure, pooled on the log scale by multivariable random-effects meta-analysis and plotted against pooled mean levels within each category.15 Glycemia measurements were categorized using predefined groups approximately corresponding to 1-SD increments (HbA1c: <4.5%, 4.5% to <5%, 5% to <5.5%, 5.5% to <6%, 6% to <6.5%, and ≥6.5%; fasting glucose [to convert mg/dL to mmol/L, multiply by 0.0555]: <76, 76 to <90, 90 to <105, 105 to <119, 119 to <133, and ≥133 mg/dL; random glucose: <68, 68 to <90, 90 to <112, 112 to <133, 133 to <155, and ≥155 mg/dL; postload glucose: <68, 68 to <108, 108 to <148, 148 to <187, 187 to <227, and ≥227 mg/dL). Confidence intervals (95%) were estimated using “floated” variances that assign an appropriate 95% CI to the log hazard ratio in every group, including the reference group, and enable valid comparisons to be made between any 2 exposure groups.16 Supplementary analyses used clinical cut points for glycemia measures defined by the American Diabetes Association.1

We developed CVD risk prediction models containing several conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, and total and HDL cholesterol) without or with a glycemia measure, and calculated improvements in predictive ability using measures of risk discrimination and reclassification.17,18 We used a 2-stage approach that allowed for the examination of between-study heterogeneity through calculation of the C-index, a measure of risk discrimination, and changes therein within each individual study before pooling results.18 Studies were weighted by numbers of CVD outcomes contributed. Supplementary analyses excluded individuals with baseline diabetes defined according to glycemia measurements. Glycemia measurements were modeled using predefined categories as described above. Between-study heterogeneity in the risk discrimination measures and their changes was quantified using the I2 statistic.19 χ2 Tests were used to test for differences in changes in discrimination measures across subgroups, typically involving 2 to 4 categories. For participants in studies with at least 10 years of follow-up, we calculated measures of reclassification, which quantify the extent to which individuals are more appropriately classified into risk categories using a new vs old risk prediction model, using a 1-stage approach.18 We constructed reclassification tables using data from studies that had recorded both fatal and nonfatal CVD outcomes to examine movement of participants between 3 predicted 10-year CVD risk categories (low [<5%]; intermediate [5% to <7.5%]; and high [≥7.5%])20 on addition of a glycemia measure to conventional risk factors. Results were summarized using the net reclassification improvement, which is the sum of the percentage of events that move up and the percentage of nonevents that move down through the risk categories when using the new model.17,18 In further analyses, we also used reclassification measures not dependent on clinical risk categories (eg, integrated discrimination index, a measure that reflects the average improvement in predicted probabilities with the new vs old model, summed across events and nonevents).21

Analyses were performed using Stata version 12.0 (StataCorp), 2-sided P values, and 95% CIs.

Data were available for 294 998 participants without a known history of diabetes or CVD at baseline in 73 prospective cohorts. Overall, the mean age of participants at baseline was 58 (SD, 9) years, 49% were women, and 86% lived in Europe or North America (Table and eTables 1-6 in Supplement). Baseline glycemia measures were distributed similarly across the contributing cohorts (eFigure 1 in Supplement). Mean levels were 5.37% (SD, 0.54) for HbA1c, 96 (SD, 14) mg/dL for fasting glucose, 99 (SD, 21) mg/dL for random glucose, and 125 (SD, 41) mg/dL for postload glucose. Age- and sex-adjusted partial correlation coefficients with HbA1c were 0.42 for fasting glucose, 0.32 for random glucose, and 0.35 for postload glucose (eFigure 2 in Supplement). In an analysis of serial measurements (median interval, 4 years) in up to 72 314 participants without a known history of diabetes or CVD at baseline, the age- and sex-adjusted regression dilution ratios were 0.65 (95% CI, 0.57-0.73) for HbA1c, 0.70 (95% CI, 0.64-0.75) for fasting glucose, 0.46 (95% CI, 0.33-0.59) for random glucose, and 0.67 (95% CI, 0.61-0.72) for postload glucose (eFigure 3 in Supplement).

Table Graphic Jump LocationTable.  Hazard Ratios for Incident Cardiovascular Disease (CVD) With Measured Baseline Levels of Risk Factors
Associations With CVD Outcomes

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 CHD and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional CVD risk factors, there were approximately J-shaped associations between each glycemia measure we studied and CVD risk (Figure 1). Findings were similar in analyses that used fractional polynomials (eFigure 4 in Supplement). Hazard ratios for CVD changed only slightly after adjustment for total cholesterol levels, triglyceride levels, or estimated glomerular filtration rate, but attenuated somewhat after adjustment for HDL cholesterol levels or C-reactive protein concentrations (eTables 7-8 in Supplement). Although there was suggestive evidence of effect modification in some clinically relevant subgroups, cautious interpretation is required given the large number of comparisons made (eFigures 5-8 in Supplement). There was some evidence of heterogeneity according to assay characteristics for HbA1c (with some evidence of higher hazard ratios in studies using values aligned to the Diabetes Control and Complications Trial; P = .004), but no effect modification was observed according to year of baseline survey or duration of follow-up (Figure 2 and eFigures 5-8 in Supplement). Results qualitatively similar to those described above were observed in analyses that were limited to participants with concomitant data on at least 2 glycemia measures; used fixed-effects models; used competing risk models; excluded the initial 5 years of follow-up; included fatal outcomes without censoring after previous nonfatal outcomes; and considered coronary heart disease and stroke separately.

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Figure 1.
Hazard Ratios for Incident Cardiovascular Disease (CVD) Outcomes by Baseline Levels of Glycemia Measures

Analyses were adjusted for age, smoking status, systolic blood pressure, total cholesterol level, and high-density lipoprotein cholesterol level and stratified by sex and trial group where appropriate. Participants were classified into groups of (1) HbA1c% (mmol/mol): <4.5 (<25.7), 4.5 to <5 (25.7-<31.1), 5 to <5.5 (31.1-<36.3) [reference category], 5.5 to <6 (36.6-<42.1), 6 to <6.5 (42.1-<48.0), and ≥6.5 (≥48.0); (2) fasting glucose (mg/dL): <76, 76 to <90, 90 to <105 [reference category], 105 to <119, 119 to <133, ≥133; (3) random glucose (mg/dL) <68, 68 to <90, 90 to <112 [reference category], 112 to <133, 133 to <155, ≥155; (4) Postload glucose (mg/dL): <68, 68 to <108, 108 to <148 [reference category], 148 to <187, 187 to <227, ≥227. These categories approximately correspond to 1-SD increments for each factor. Incident CVD outcomes refer to first-onset CVD cases, defined as fatal or nonfatal coronary heart disease or any stroke. SI conversion factors: To convert glucose values to mmol/L, multiply by 0.0555. Sizes of boxes are proportional to the inverse of the variance.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Hazard Ratios for Incident Cardiovascular Disease for Glycemia Measures by Selected Study-Level Characteristics

Participants with levels of glycemia measures below the mean were excluded. Baseline SD was used to calculate per-SD hazard ratio (HR). Analyses were conducted using studies with information across all levels of each subgroup variable. DCCT indicates Diabetes Control and Complications Trial; HPLC, high-performance liquid chromatography; ITA, immunoturbidimetric assay. A full list of the characteristics examined for heterogeneity is provided in eFigures 5 through 8 in Supplement.

Graphic Jump Location
Incremental CVD Prediction

Figure 3 and eTable 9 in Supplement show that there were small changes in the C-index and in the integrated discrimination index after adding information on levels of HbA1c, fasting glucose, random glucose, or postload glucose to CVD risk prediction models containing age, sex, smoking, systolic blood pressure, and levels of total and HDL cholesterol. However, adding information on these glycemia measures did not yield significant improvements in net reclassification (eTable 9 in Supplement). There were no major differences in risk discrimination according to sex or in other clinically relevant subgroups (eFigures 9-12 in Supplement). Again, although there was no strong evidence of heterogeneity according to year of baseline survey or duration of follow-up, some evidence of heterogeneity was found according to the assay standards used for HbA1c measurement (P = .001). In analyses limited to participants who had concomitant data on HbA1c values and at least 1 other glycemia measure, the change in the C-index when 2 markers were used was broadly similar to the change when either marker was used alone (eFigure 13 in Supplement). Results similar to those observed overall were also found in analyses that used clinical categories of dysglycemia (eFigure 14 in Supplement); omitted participants with diabetes defined using baseline glycemia measurements (eFigure 15 in Supplement); omitted participants known to be taking medications that lowered lipid levels or blood pressure at study entry; omitted extreme low levels of glycemia measures; or were restricted to studies with at least 10 years of follow-up. There was no good evidence for small study effects (eFigures 16-23 in Supplement).

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Figure 3.
Changes in Cardiovascular Disease Risk Discrimination After the Addition of Information on Glycemia Measures to Conventional Risk Factors

Incident cardiovascular disease outcomes refer to first-onset cardiovascular disease cases, defined as fatal or nonfatal coronary heart disease event or any stroke. Studies with missing self-reported diabetes information were excluded.aConventional risk factors include age, sex (stratified), smoking status, systolic blood pressure, and levels of total cholesterol and high-density lipoprotein cholesterol.bP < .05cP < .001.

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Contrary to recommendations in some guidelines, the current analysis of individual-participant data in almost 300 000 people without known diabetes and CVD at baseline indicates that measurement of HbA1c is not associated with clinically meaningful improvement in assessment of CVD risk. First, we found that adding information on levels of HbA1c to conventional CVD risk factors was associated with only slight improvement in risk discrimination, which aims to assess how well a statistical model can separate individuals who do and do not go on to develop CVD. Second, we found that adding information on HbA1c was not associated with significant improvement in reclassification of participants across clinical risk categories currently recommended to inform decisions about the initiation of preventive treatment.20

Third, our analysis provided a comparison of 4 glycemia measures, ie, HbA1c levels and fasting, random, or postload plasma glucose levels. In contrast to some previous findings,3,22,23 we observed approximately J-shaped associations between each of these glycemia measures and CVD risk. The consistency of this finding is notable because the different glycemia measures we analyzed were only moderately correlated with one another and had differing degrees of reproducibility across glycemic measures. Indeed, in an analysis of serial measurements in up to 72 000 participants, we observed that the long-term reproducibility of fasting glucose measurements was at least as high as that for HbA1c values and postload glucose levels. This result challenges suggestions that fasting glucose values are prone to greater long-term fluctuation than are these other glycemia measures.24 Our observation of consistent J-shaped associations between various glycemia measures and CVD incidence should encourage further studies to test whether very low glycemia levels are markers of ill health, such as that caused by hepatic dysfunction or other comorbidities.25,26 Regarding comparison of glycemia measures to predict first-onset CVD outcomes, our results suggest that the improvement provided by HbA1c assessment in prediction of CVD risk was at least equal to improvements estimated for assessment of fasting, random, or postload plasma glucose levels. This finding challenges suggestions that postload glucose levels predict CVD incidence more strongly than do other glycemia measures.27 However, it was not possible to evaluate the value of assessing several glycemia measures jointly, because few people in our study had the necessary concomitant data.

Because our study involved a large number of participants, it could provide precise estimates, even for analyses that involved categorization of glycemia measures. The generalizability of our findings was enhanced by inclusion of data from 73 prospective cohort studies in 20 countries and by the general consistency of the results across these studies. A further strength was the analysis of individual-participant data from studies with extended durations of follow-up. This feature enabled time-to-event analysis, analysis of subgroups, and a consistent approach to statistical analyses across the contributing studies. To further enhance the validity of risk estimates, we restricted analyses to people with information on a complete set of relevant risk factors.

It is important to note that our study did not address the value of assessing glycemia measures to screen for diabetes to reduce diabetes-specific microvascular complications, nor did it address etiologic and therapeutic questions. Our study had other limitations. We had incomplete information on medication use (eg, statins, antihypertensive drugs, or glucose-lowering drugs) during follow-up, which may have influenced our estimates of the effect of individual risk factors, or risk models, on outcomes. The reclassification measures used in our risk prediction analyses are intrinsically sensitive to choice of follow-up interval and clinical risk categories.21

In adults without a known history of diabetes or CVD, adding HbA1c to conventional CVD risk factors was associated with little improvement in the prediction of CVD risk.

Corresponding Author: John Danesh, FRCP, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, United Kingdom (erfc@phpc.cam.ac.uk).

Authors/Members of The Emerging Risk Factors Collaboration: Emanuele Di Angelantonio, MD; Pei Gao, PhD; Hassan Khan, MD; Adam S. Butterworth, PhD; David Wormser, PhD; Stephen Kaptoge, PhD; Sreenivasa Rao Kondapally Seshasai, MD; Alex Thompson, PhD; Nadeem Sarwar, PhD; Peter Willeit, MD; Paul M Ridker, MD; Elizabeth L. M. Barr, PhD; Kay-Tee Khaw, FMedSci; Bruce M. Psaty, MD; Hermann Brenner, MD; Beverley Balkau, PhD; Jacqueline M. Dekker, PhD; Debbie A. Lawlor, PhD; Makoto Daimon, MD; Johann Willeit, MD; Inger Njølstad, MD; Aulikki Nissinen, MD; Eric J. Brunner, PhD; Lewis H. Kuller, MD; Jackie F. Price, MD; Johan Sundström, MD; Matthew W. Knuiman, PhD; Edith J. M. Feskens, PhD; W. M. M. Verschuren, PhD; Nicholas Wald, FRS; Stephan J. L. Bakker, MD; Peter H. Whincup, FRCP; Ian Ford, PhD; Uri Goldbourt, PhD; Agustín Gómez-de-la-Cámara, MD; John Gallacher, PhD; Leon A. Simons, MD; Annika Rosengren, MD; Susan E. Sutherland, PhD; Cecilia Björkelund, MD; Dan G. Blazer, MD; Sylvia Wassertheil-Smoller, PhD; Altan Onat, MD; Alejandro Marín Ibañez, MD; Edoardo Casiglia, MD; J. Wouter Jukema, MD; Lara M. Simpson, PhD; Simona Giampaoli, MD; Børge G. Nordestgaard, MD; Randi Selmer, PhD; Patrik Wennberg, MD; Jussi Kauhanen, MD; Jukka T. Salonen, MD; Rachel Dankner, MD; Elizabeth Barrett-Connor, MD; Maryam Kavousi, MD; Vilmundur Gudnason, MD; Denis Evans, MD; Robert B. Wallace, MD; Mary Cushman, MD; Ralph B. D’Agostino Sr, PhD; Jason G. Umans, MD; Yutaka Kiyohara, MD; Hidaeki Nakagawa, MD; Shinichi Sato, MD; Richard F. Gillum, MD; Aaron R. Folsom, MD; Yvonne T. van der Schouw, PhD; Karel G. Moons, PhD; Simon J. Griffin, DM; Naveed Sattar, FRCP; Nicholas J. Wareham, FRCP; Elizabeth Selvin, PhD; Simon G. Thompson, FMedSci; John Danesh, FRCP.

Affiliations of Authors/Members of The Emerging Risk Factors Collaboration: University of Cambridge, Cambridge, United Kingdom (Di Angelantonio, Gao, Khan, Butterworth, Wormser, Kaptoge, A. Thompson, Sarwar, P. Willeit, Khaw, Griffin, Wareham, S. G. Thompson, Danesh); St George's University of London, London, United Kingdom (Kondapally Seshasai, Whincup); Brigham and Women’s Hospital, Boston, Massachusetts (Ridker); Baker IDI Heart and Diabetes Institute, Victoria, Australia (Barr); University of Washington, Seattle (Psaty); Group Health Research Institute, Seattle, Washington (Psaty); German Cancer Research Center, Heidelberg, Germany (Brenner); Inserm, Villejuif, France (Balkau); University Paris-Sud, Villejuif, France (Balkau); Vrije Universiteit Medical Center, Amsterdam, the Netherlands (Dekker); University of Bristol, Bristol, United Kingdom (Lawlor); Yamagata University, Japan (Daimon); Medical University Innsbruck, Austria (J. Willeit); University of Tromsø, Tromsø, Norway (Njølstad); National Institute of Health and Welfare, Helsinki, Finland (Nissinen); University College London, London, United Kingdom (Brunner); University of Pittsburgh (Kuller); University of Edinburgh, Edinburgh, United Kingdom (Price); Uppsala University, Uppsala, Sweden (Sundström); University of Western Australia, Perth, Australia (Knuiman); Wageningen University, Wageningen, the Netherlands (Feskens); National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands (Verschuren); Wolfson Institute of Preventive Medicine, London, United Kingdom (Wald); University of Groningen, University Medical Center Groningen, the Netherlands (Bakker); University of Glasgow, Glasgow, United Kingdom (Ford, Sattar); Sheba Medical Center, Tel Hashomer, Israel (Goldbourt); Hospital 12 de Octubre, Madrid, Spain (Gómez-de-la-Cámara); Cardiff University, Cardiff, United Kingdom (Gallacher); University of New South Wales, Kensington, Australia (Simons); Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden (Rosengren); Medical University of South Carolina, Charleston (Sutherland); University of Gothenburg, Gothenburg, Sweden (Björkelund); Duke University Medical Center, Durham, North Carolina (Blazer); Albert Einstein College of Medicine, New York, New York (Wassertheil-Smoller); University of Istanbul, Istanbul, Turkey (Onat); San Jose Norte Health Centre, Zaragoza, Spain (Marín Ibañez); University of Padova, Padova, Italy (Casiglia); Leiden University Medical Center, Leiden, the Netherlands (Jukema); University of Texas School of Public Health, Houston (Simpson); Istituto Superiore di Sanità, Rome, Italy (Giampaoli); Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark (Nordestgaard); Norwegian Institute of Public Health, Oslo, Norway (Selmer); Umeå University, Umeå, Sweden (Wennberg); University of Eastern Finland, Kuopio, Finland (Kauhanen); University of Helsinki, Helsinki, Finland (Salonen); The Gertner Institute for Epidemiology and Health Policy Research, Tel Hashomer, Israel (Dankner); Tel Aviv University, Tel Aviv, Israel (Dankner); The Feinstein Institute for Medical Research, New York, New York (Dankner); University of California, San Diego (Barrett-Connor); Erasmus Medical Center, Rotterdam, the Netherlands (Kavousi); Icelandic Heart Association, Reyjavik, Iceland (Gudnason); University of Iceland, Reykjavik, Iceland (Gudnason); Rush University Medical Center, Chicago, Illinois (Evans); University of Iowa College of Public Health, Iowa City (Wallace); University of Vermont, Burlington (Cushman); Boston University, Boston, Massachusetts (D’Agostino); Georgetown University Medical Centre, Washington, DC (Umans); Kyushu University, Kyushu, Japan (Kiyohara); Kanazawa Medical University, Ishikawa, Japan (Nakagawa); Osaka Medical Center for Health Science and Promotion/Chiba Prefectural Institute of Public Health, Osaka, Japan (Sato); Howard University, Washington, DC (Gillum); University of Minnesota, Minneapolis (Folsom); Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands (van der Schouw, Moons); Johns Hopkins University, Baltimore, Maryland (Selvin).

Author Contributions: Dr Di Angelantonio had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Di Angelantonio, Gao, Khan, and Butterworth contributed equally to the study.

Study concept and design: Di Angelantonio, Gao, Khan, Kondapally Seshasai, Sarwar, Khaw, Daimon, Goldbourt, Casiglia, Jukema, Sattar, Selvin, Danesh.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Di Angelantonio, Gao, Khan, Lawlor, Wennberg, Selvin, Danesh.

Critical revision of the manuscript for important intellectual content: Di Angelantonio, Gao, Khan, Butterworth, Wormser, Kaptoge, Kondapally Seshasai, A. Thompson, Sarwar, P. Willeit, Ridker, Barr, Khaw, Psaty, Brenner, Balkau, Dekker, Daimon, J. Willeit, Njølstad, Nissinen, Brunner, Kuller, Price, Sundström, Knuiman, Feskens, Verschuren, Wald, Bakker, Whincup, Ford, Goldbourt, Gómez-de-la-Cámara, Gallacher, Simons, Rosengren, Sutherland, Björkelund, Blazer, Wassertheil-Smoller, Onat, Marín Ibañez, Casiglia, Jukema, Simpson, Giampaoli, Nordestgaard, Selmer, Wennberg, Kauhanen, Salonen, Dankner, Barrett-Connor, Kavousi, Gudnason, Evans, Wallace, Cushman, D’Agostino, Umans, Kiyohara, Nakagawa, Sato, Gillum, Folsom, van der Schouw, Moons, Griffin, Sattar, Wareham, Selvin, S. G. Thompson, Danesh.

Statistical analysis: Di Angelantonio, Gao, Khan, Wormser, Kondapally Seshasai, P. Willeit, Goldbourt, Simpson, D’Agostino, Moons, Selvin, S. G. Thompson.

Obtained funding: Ridker, Khaw, Brenner, Balkau, Brunner, Feskens, Ford, Simons, Rosengren, Blazer, Wassertheil-Smoller, Casiglia, Nordestgaard, Salonen, Barrett-Connor, Wallace, Cushman, D’Agostino, Nakagawa, Wareham, S. G. Thompson, Danesh.

Administrative, technical, or material support: Khan, Barr, Khaw, Psaty, Brenner, Lawlor, J. Willeit, Kuller, Verschuren, Bakker, Sutherland, Björkelund, Blazer, Onat, Casiglia, Nordestgaard, Wennberg, Salonen, Kavousi, Gudnason, Evans, Wallace, Cushman, Umans, Gillum, Moons, Wareham, Selvin, Danesh.

Study supervision: Di Angelantonio, Kaptoge, Ridker, Brenner, Whincup, Simons, Marín Ibañez, Casiglia, Jukema, Giampaoli, Kauhanen, Umans, Wareham, Selvin, S. G. Thompson, Danesh.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Di Angelantonio reported serving as a consultant for the Lead-up Medical Network and Merck/Merck Sharp & Dohme; receiving payment for lectures from John Wiley & Sons; receiving royalties from Elsevier (France); receiving travel expenses from Pfizer; and that his instituton has received grants from the European Union, National Health Service Blood and Transplant, and the Medical Research Council. Dr Kondapally Seshasai reported serving as a consultant for Amgen. Dr A. Thompson reported holding stocks/stock options from Roche. Dr Sarwar reported employment by Pfizer. Dr Ridker reported serving as a consultant for Merck, ISIS, BostonHeart, Vascular Biogenics, Genzyme, and Sanofi; receiving grants from AstraZeneca, Novartis, and Amgen; being listed as a coinventor on patents related to use of inflammatory biomarkers in cardiovascular disease and diabetes; and that his institution has received grants from the Reynolds Foundation, the National Heart, Lung, and Blood Institute, and the National Cancer Institute. Dr Barr reported that her institution has received grants from the National Health and Medical Research Council. Dr Khaw reported that her institution has received grants from the Medical Research Council UK and Cancer UK. Dr Psaty reported serving on the data and safety monitoring board for a clinical trial funded by Zoll Life Cor. Dr Brenner reported that his institution has received a grant from the German Federal Ministry of Education and Research. Dr Balkau reported seving as a board member for Bristol-Myers Squibb, receiving payment for lectures from Lilly, and that his institution has received a grant from Servier. Dr Dekker reported receiving an institutional grant from Novo Nordisk. Dr Lawlor reported receiving grants from several funders. Dr Daimon reported that his institution has received grants from the Government of Japan. Dr Nissinen reported receiving an instutional grant from the Finnish Academy of Science. Dr Sundström reported serving on an advisory board for a weight loss company.Dr Gómez de la Cámera reported that his institution has received a grant from the National Health Institute Research Fund Carlos III. Dr Wassertheil-Smoller reported receiving honoraria from the Fred Hutchinson Cancer Center; receiving royalties; and that her institution has received grants or other support from the National Institutes of Health. Dr Salonen reported receiving an institutional grant from the Academy of Finland. Dr Evans reported that his institution has received a grant from the National Institutes of Health. Dr Cushman reported that her institution has received a grant from the National Institutes of Health. Dr D’Agostino reported that his institution has received a grant from the National Heart, Lung, and Blood Institute. Dr Umans reported that his institution has received a grant from the National Institutes of Health. Dr Moons reported receiving travel expenses from the Royal Academy of Statistics and Bayer and that his institution has received grants from the Netherlands Organization of Scientific Research, the Dutch Heart Foundation, GlaxoSmithKline, Bayer, and Boehringer. Dr Griffin reported serving as a consultant for Eli Lilly and that his institution has received a grant from the Medical Research Council. Dr Selvin reported that her institution has received a grant from the National Kidney Foundation. Dr Danesh reported serving as a board member for Merck Sharp & Dohme, Novartis, Pfizer, and Sanofi; receiving travel expenses from Merck, Novartis, Pfizer, and Sanofi; and that his institution has received grants froma number of entities. No other authors reported disclosures.

Funding/Support: The study was funded by the British Heart Foundation, the UK Medical Research Council, UK National Institute of Health Research, and the Cambridge Biomedical Research Centre. The collaboration’s website (http://www.phpc.cam.ac.uk/ceu/research/erfc/studies/) has compiled a list provided by investigators of some of the funders of the component studies in this analysis.

Role of the Sponsors: None of the funding organizations or sponsors had any role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

American Diabetes Association.  Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(suppl 1):S67-S74.
PubMed   |  Link to Article
Rydén  L, Grant  PJ, Anker  SD,  et al; Authors/Task Force Members; ESC Committee for Practice Guidelines (CPG); Document Reviewers.  ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J. 2013;34(39):3035-3087.
PubMed   |  Link to Article
Selvin  E, Steffes  MW, Zhu  H,  et al.  Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med. 2010;362(9):800-811.
PubMed   |  Link to Article
Sarwar  N, Gao  P, Seshasai  SR,  et al; Emerging Risk Factors Collaboration.  Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies [published correction appears in Lancet. 2010;376(9745):958]. Lancet. 2010;375(9733):2215-2222.
PubMed   |  Link to Article
US Preventive Services Task Force.  Using nontraditional risk factors in coronary heart disease risk assessment: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2009;151(7):474-482.
PubMed   |  Link to Article
Schöttker  B, Müller  H, Rothenbacher  D, Brenner  H.  Fasting plasma glucose and HbA1c in cardiovascular risk prediction: a sex-specific comparison in individuals without diabetes mellitus. Diabetologia. 2013;56(1):92-100.
PubMed   |  Link to Article
Simmons  RK, Sharp  S, Boekholdt  SM,  et al.  Evaluation of the Framingham risk score in the European Prospective Investigation of Cancer-Norfolk cohort: does adding glycated hemoglobin improve the prediction of coronary heart disease events? Arch Intern Med. 2008;168(11):1209-1216.
PubMed   |  Link to Article
Greenland  P, Alpert  JS, Beller  GA,  et al; American College of Cardiology Foundation; American Heart Association.  2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56(25):e50-e103.
PubMed   |  Link to Article
Anderson  TJ, Grégoire  J, Hegele  RA,  et al.  2012 update of the Canadian Cardiovascular Society guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol. 2013;29(2):151-167.
PubMed   |  Link to Article
Ridker  PM, Buring  JE, Rifai  N, Cook  NR.  Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611-619.
PubMed   |  Link to Article
Goff  DC, Lloyd-Jones  DM, Bennett  G,  et al.  ACC/AHA Guideline on the Assessment of Cardiovascular Risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines [published online November 12, 2013]. Circulation. 2013. doi:10.1161/01.cir.0000437741.48606.98.
Danesh  J, Erqou  S, Walker  M,  et al; Emerging Risk Factors Collaboration.  The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. Eur J Epidemiol. 2007;22(12):839-869.
PubMed   |  Link to Article
Di Angelantonio  E, Gao  P, Pennells  L,  et al; Emerging Risk Factors Collaboration.  Lipid-related markers and cardiovascular disease prediction. JAMA. 2012;307(23):2499-2506.
PubMed
Kaptoge  S, Di Angelantonio  E, Pennells  L,  et al; Emerging Risk Factors Collaboration.  C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012;367(14):1310-1320.
PubMed   |  Link to Article
Thompson  S, Kaptoge  S, White  I, Wood  A, Perry  P, Danesh  J; Emerging Risk Factors Collaboration.  Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. Int J Epidemiol. 2010;39(5):1345-1359.
PubMed   |  Link to Article
Easton  DF, Peto  J, Babiker  AG.  Floating absolute risk: an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group. Stat Med. 1991;10(7):1025-1035.
PubMed   |  Link to Article
Fibrinogen Studies Collaboration.  Measures to assess the prognostic ability of the stratified Cox proportional hazards model. Stat Med. 2009;28(3):389-411.
PubMed   |  Link to Article
Pennells  L, Kaptoge  S, White  IR, Thompson  SG, Wood  AM.  Assessing risk prediction models using individual participant data from multiple studies [published online December 22, 2013]. Am J Epidemiol. doi:10.1093/aje/kwt298.
Higgins  JP, Thompson  SG.  Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539-1558.
PubMed   |  Link to Article
Stone  NJ, Robinson  J, Lichtenstein  AH,  et al.  ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines [published online November 12, 2013]. Circulation. 2013. doi:10.1161/01.cir.0000437738.63853.7a.
Pencina  MJ, D’Agostino  RB, Vasan  RS.  Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med. 2010;48(12):1703-1711.
PubMed   |  Link to Article
Pfister  R, Sharp  SJ, Luben  R, Khaw  KT, Wareham  NJ.  No evidence of an increased mortality risk associated with low levels of glycated haemoglobin in a non-diabetic UK population. Diabetologia. 2011;54(8):2025-2032.
PubMed   |  Link to Article
Barr  ELM, Boyko  EJ, Zimmet  PZ, Wolfe  R, Tonkin  AM, Shaw  JE.  Continuous relationships between non-diabetic hyperglycaemia and both cardiovascular disease and all-cause mortality: the Australian Diabetes, Obesity, and Lifestyle (AusDiab) study. Diabetologia. 2009;52(3):415-424.
PubMed   |  Link to Article
Sacks  DB.  A1C versus glucose testing: a comparison. Diabetes Care. 2011;34(2):518-523.
PubMed   |  Link to Article
Christman  AL, Lazo  M, Clark  JM, Selvin  E.  Low glycated hemoglobin and liver disease in the U.S. population. Diabetes Care. 2011;34(12):2548-2550.
PubMed   |  Link to Article
Rutter  MK.  Low HbA1c and mortality: causation and confounding. Diabetologia. 2012;55(9):2307-2311.
PubMed   |  Link to Article
DECODE Study Group, the European Diabetes Epidemiology Group. Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med. 2001;161(3):397-405.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Hazard Ratios for Incident Cardiovascular Disease (CVD) Outcomes by Baseline Levels of Glycemia Measures

Analyses were adjusted for age, smoking status, systolic blood pressure, total cholesterol level, and high-density lipoprotein cholesterol level and stratified by sex and trial group where appropriate. Participants were classified into groups of (1) HbA1c% (mmol/mol): <4.5 (<25.7), 4.5 to <5 (25.7-<31.1), 5 to <5.5 (31.1-<36.3) [reference category], 5.5 to <6 (36.6-<42.1), 6 to <6.5 (42.1-<48.0), and ≥6.5 (≥48.0); (2) fasting glucose (mg/dL): <76, 76 to <90, 90 to <105 [reference category], 105 to <119, 119 to <133, ≥133; (3) random glucose (mg/dL) <68, 68 to <90, 90 to <112 [reference category], 112 to <133, 133 to <155, ≥155; (4) Postload glucose (mg/dL): <68, 68 to <108, 108 to <148 [reference category], 148 to <187, 187 to <227, ≥227. These categories approximately correspond to 1-SD increments for each factor. Incident CVD outcomes refer to first-onset CVD cases, defined as fatal or nonfatal coronary heart disease or any stroke. SI conversion factors: To convert glucose values to mmol/L, multiply by 0.0555. Sizes of boxes are proportional to the inverse of the variance.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Hazard Ratios for Incident Cardiovascular Disease for Glycemia Measures by Selected Study-Level Characteristics

Participants with levels of glycemia measures below the mean were excluded. Baseline SD was used to calculate per-SD hazard ratio (HR). Analyses were conducted using studies with information across all levels of each subgroup variable. DCCT indicates Diabetes Control and Complications Trial; HPLC, high-performance liquid chromatography; ITA, immunoturbidimetric assay. A full list of the characteristics examined for heterogeneity is provided in eFigures 5 through 8 in Supplement.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.
Changes in Cardiovascular Disease Risk Discrimination After the Addition of Information on Glycemia Measures to Conventional Risk Factors

Incident cardiovascular disease outcomes refer to first-onset cardiovascular disease cases, defined as fatal or nonfatal coronary heart disease event or any stroke. Studies with missing self-reported diabetes information were excluded.aConventional risk factors include age, sex (stratified), smoking status, systolic blood pressure, and levels of total cholesterol and high-density lipoprotein cholesterol.bP < .05cP < .001.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable.  Hazard Ratios for Incident Cardiovascular Disease (CVD) With Measured Baseline Levels of Risk Factors

References

American Diabetes Association.  Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(suppl 1):S67-S74.
PubMed   |  Link to Article
Rydén  L, Grant  PJ, Anker  SD,  et al; Authors/Task Force Members; ESC Committee for Practice Guidelines (CPG); Document Reviewers.  ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J. 2013;34(39):3035-3087.
PubMed   |  Link to Article
Selvin  E, Steffes  MW, Zhu  H,  et al.  Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med. 2010;362(9):800-811.
PubMed   |  Link to Article
Sarwar  N, Gao  P, Seshasai  SR,  et al; Emerging Risk Factors Collaboration.  Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies [published correction appears in Lancet. 2010;376(9745):958]. Lancet. 2010;375(9733):2215-2222.
PubMed   |  Link to Article
US Preventive Services Task Force.  Using nontraditional risk factors in coronary heart disease risk assessment: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2009;151(7):474-482.
PubMed   |  Link to Article
Schöttker  B, Müller  H, Rothenbacher  D, Brenner  H.  Fasting plasma glucose and HbA1c in cardiovascular risk prediction: a sex-specific comparison in individuals without diabetes mellitus. Diabetologia. 2013;56(1):92-100.
PubMed   |  Link to Article
Simmons  RK, Sharp  S, Boekholdt  SM,  et al.  Evaluation of the Framingham risk score in the European Prospective Investigation of Cancer-Norfolk cohort: does adding glycated hemoglobin improve the prediction of coronary heart disease events? Arch Intern Med. 2008;168(11):1209-1216.
PubMed   |  Link to Article
Greenland  P, Alpert  JS, Beller  GA,  et al; American College of Cardiology Foundation; American Heart Association.  2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56(25):e50-e103.
PubMed   |  Link to Article
Anderson  TJ, Grégoire  J, Hegele  RA,  et al.  2012 update of the Canadian Cardiovascular Society guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol. 2013;29(2):151-167.
PubMed   |  Link to Article
Ridker  PM, Buring  JE, Rifai  N, Cook  NR.  Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611-619.
PubMed   |  Link to Article
Goff  DC, Lloyd-Jones  DM, Bennett  G,  et al.  ACC/AHA Guideline on the Assessment of Cardiovascular Risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines [published online November 12, 2013]. Circulation. 2013. doi:10.1161/01.cir.0000437741.48606.98.
Danesh  J, Erqou  S, Walker  M,  et al; Emerging Risk Factors Collaboration.  The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. Eur J Epidemiol. 2007;22(12):839-869.
PubMed   |  Link to Article
Di Angelantonio  E, Gao  P, Pennells  L,  et al; Emerging Risk Factors Collaboration.  Lipid-related markers and cardiovascular disease prediction. JAMA. 2012;307(23):2499-2506.
PubMed
Kaptoge  S, Di Angelantonio  E, Pennells  L,  et al; Emerging Risk Factors Collaboration.  C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012;367(14):1310-1320.
PubMed   |  Link to Article
Thompson  S, Kaptoge  S, White  I, Wood  A, Perry  P, Danesh  J; Emerging Risk Factors Collaboration.  Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. Int J Epidemiol. 2010;39(5):1345-1359.
PubMed   |  Link to Article
Easton  DF, Peto  J, Babiker  AG.  Floating absolute risk: an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group. Stat Med. 1991;10(7):1025-1035.
PubMed   |  Link to Article
Fibrinogen Studies Collaboration.  Measures to assess the prognostic ability of the stratified Cox proportional hazards model. Stat Med. 2009;28(3):389-411.
PubMed   |  Link to Article
Pennells  L, Kaptoge  S, White  IR, Thompson  SG, Wood  AM.  Assessing risk prediction models using individual participant data from multiple studies [published online December 22, 2013]. Am J Epidemiol. doi:10.1093/aje/kwt298.
Higgins  JP, Thompson  SG.  Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539-1558.
PubMed   |  Link to Article
Stone  NJ, Robinson  J, Lichtenstein  AH,  et al.  ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines [published online November 12, 2013]. Circulation. 2013. doi:10.1161/01.cir.0000437738.63853.7a.
Pencina  MJ, D’Agostino  RB, Vasan  RS.  Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med. 2010;48(12):1703-1711.
PubMed   |  Link to Article
Pfister  R, Sharp  SJ, Luben  R, Khaw  KT, Wareham  NJ.  No evidence of an increased mortality risk associated with low levels of glycated haemoglobin in a non-diabetic UK population. Diabetologia. 2011;54(8):2025-2032.
PubMed   |  Link to Article
Barr  ELM, Boyko  EJ, Zimmet  PZ, Wolfe  R, Tonkin  AM, Shaw  JE.  Continuous relationships between non-diabetic hyperglycaemia and both cardiovascular disease and all-cause mortality: the Australian Diabetes, Obesity, and Lifestyle (AusDiab) study. Diabetologia. 2009;52(3):415-424.
PubMed   |  Link to Article
Sacks  DB.  A1C versus glucose testing: a comparison. Diabetes Care. 2011;34(2):518-523.
PubMed   |  Link to Article
Christman  AL, Lazo  M, Clark  JM, Selvin  E.  Low glycated hemoglobin and liver disease in the U.S. population. Diabetes Care. 2011;34(12):2548-2550.
PubMed   |  Link to Article
Rutter  MK.  Low HbA1c and mortality: causation and confounding. Diabetologia. 2012;55(9):2307-2311.
PubMed   |  Link to Article
DECODE Study Group, the European Diabetes Epidemiology Group. Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med. 2001;161(3):397-405.
PubMed   |  Link to Article
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