Diabetes Care
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Social Bookmarking
Add to CiteULike   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?
Diabetes Care 27:2836-2842, 2004
© 2004 by the American Diabetes Association, Inc.

Epidemiology/Health Services/Psychosocial Research
Original Article

Blood Glucose and Risk of Cardiovascular Disease in the Asia Pacific Region

Asia Pacific Cohort Studies Collaboration*,

From the Asia Pacific Cohort Studies Collaboration, Clinical Trials Research Unit, the University of Auckland, Auckland, New Zealand

Address correspondence and reprint requests to Anthony Rodgers, Asia Pacific Cohort Studies Collaboration, Clinical Trials Research Unit, the University of Auckland, Private Bag 92019, Auckland, New Zealand. E-mail: a.rodgers{at}ctru.auckland.ac.nz


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
 APPENDIX
 References
 
OBJECTIVE—To assess the shape and strength of the association between usual blood glucose and cardiovascular disease (CVD) in Asian and Australasian cohorts and to determine the impact of adjusting for other determinants of CVD risk and excluding people with diabetes.

RESEARCH DESIGN AND METHODS—Relative risk estimates and 95% CIs were calculated from Cox models, stratified by sex and cohort, and adjusted for age at risk on individual participant data from 17 cohort studies. Repeat measurements of blood glucose were used to adjust for regression dilution bias.

RESULTS—Fasting blood glucose data were available for 237,468 participants, and during ~1.2 million person-years of follow-up, there were 1,661 stroke and 816 ischemic heart disease (IHD) events. Data were also available on 27,996 participants with nonfasting glucose measurements. Continuous positive associations were demonstrated between usual fasting glucose and the risks of CVD down to at least 4.9 mmol/l. Overall, each 1 mmol/l lower usual fasting glucose was associated with a 21% (95% CI 18–24%) lower risk of total stroke and a 23% (19–27%) lower risk of total IHD. The associations were similar in men and women, across age-groups, and in Asian compared with Australasian (Australia and New Zealand) populations. Adjusting for potential confounders or removing those with diabetes as baseline did not substantially affect the associations. Associations for nonfasting glucose were weaker than those with fasting glucose.

CONCLUSIONS—Fasting blood glucose is an important determinant of CVD burden, with considerable potential benefit of usual blood glucose lowering down to levels of at least 4.9 mmol/l.

Abbreviations: APCSC, Asia Pacific Cohort Studies Collaboration • CVD, cardiovascular disease • IHD, ischemic heart disease • OGTT, oral glucose tolerance test


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
 APPENDIX
 References
 
The risk of cardiovascular disease (CVD) in type 2 diabetic subjects is about two- to fourfold greater than in people without diabetes (1–3) and appears to be independent of other risk factors including age, smoking, raised serum cholesterol, and blood pressure (4). Much of the research has been conducted in Western populations, but recent evidence demonstrated similarly increased risks in Asian populations (5).

Glucose cutoffs for defining diabetes are based on what has been interpreted as a threshold for microvascular disease such as retinopathy (6). However, there is uncertainty about the relationship between blood glucose and macrovascular complications such as CVD, which account for much of the diabetic morbidity and mortality. There has been some indication of a continuous association for CVD risk (7–9) as is seen for many other CVD determinants such as blood pressure, cholesterol, and BMI (10–15).

Large cohort studies with data across a range of blood glucose levels are necessary to reliably assess the shape and strength of the relationship between blood glucose and CVD risk. One recent overview observed a continuous association between glucose and CVD in nondiabetic subjects that was maintained when those with the highest glucose levels were removed from the analysis and extended below the usual "diabetic threshold" (8). Many smaller prospective studies concur that there is an increased risk of CVD at the upper end of the fasting blood glucose distribution, but the stated threshold levels range between 5.3 to >7.0 mmol/l (16).

Establishing the shape of associations between risk determinants and disease is important to gauge the potential for prevention (10,17). Review of the glucose-CVD association has particular relevance to non-Western parts of the world where there is an increasing burden of noncommunicable diseases such as diabetes and CVD. Data from the Asia Pacific Cohort Studies Collaboration (APCSC) presents an ideal opportunity to explore the shape and strength of this association, to assess the impact of adjusting for other determinants of CVD risk, and to determine whether the patterns differ between populations from Asia and those from Australasia (Australia and New Zealand).


   RESEARCH DESIGN AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
RESULTS
 CONCLUSIONS
 APPENDIX
 References
 
APCSC is an individual participant data overview (meta-analysis) of prospective cohort studies. The methods have been reported in detail elsewhere (18). In brief, prospective cohort studies were eligible for inclusion if they contained study populations from the Asia Pacific region and had at least 5,000 person-years of follow-up recorded or planned. At a minimum, they had to have recorded data on date of birth or age, sex, blood pressure at baseline, and date or age at death. Additional data sought included date of baseline survey, glucose, fasting status, blood cholesterol, height, weight, smoking, and any data on repeat measures of risk factors. Outcome data included nonfatal stroke and ischemic heart disease (IHD) and cause of death (18).

Statistical methods
Associations between glucose and disease.
All analyses are restricted to participants aged ≥20 years who had blood glucose and fasting status recorded at baseline. Analyses reported here excluded individuals with a baseline glucose value of ≥20 mmol/l. Data on fasting and nonfasting glucose were analyzed separately. Stratified Cox proportional-hazards analyses (19) were used to regress time until first event against baseline glucose with individual participant data collected on all cohorts. All analyses were stratified by sex and cohort to control for confounding and reduce statistical heterogeneity. Age was treated as an external time-dependent covariate (20) to assess change in hazards as an individual’s age increases. These "age at risk" analyses account for the fact that cohorts had different start and follow-up times.

Age-specific analyses included age at risk categories of <60, 60–69, and ≥70 years (analyses were also conducted by sex) and region (Asia vs. Australasia and comparison of regions within Asia). Where possible, analyses were undertaken for total (fatal and nonfatal) and fatal (death occurring within 28 days of event) CVD outcomes. Further sensitivity analyses included restricting analyses to those participants who had no diagnosis of diabetes at baseline and/or adjusting for other potential confounders, such as systolic blood pressure (mmHg), serum total cholesterol (mmol/l), smoking (current vs. not current), and BMI (kg/m2), for those cohorts who had recorded these variables at baseline. Insufficient data were available for ethnic-specific analysis. Effect modification was assessed with the use of statistical interaction terms for sex and region in the Cox model.

Estimation of usual glucose.
A single baseline measure is subject to random fluctuations of fasting glucose, due partly to the measurement process and partly to any real but temporary deviations at the baseline visit from the usual blood glucose level (21). The distribution of single baseline glucose measures is therefore wider than the distribution of true usual glucose values due to regression to the mean. The term for the resulting underestimation of an exposure with an end point that occurs with one-off baseline measures is "regression dilution bias" (21). Usual glucose values are estimates that have been corrected for regression dilution bias and are therefore closer to true glucose values. These usual estimates were made using repeated glucose measurement data from three cohort studies. A mixed-model approach was used to calculate the regression dilution ratio ({lambda}) that took into account the varying time intervals between the glucose measurements. Several other methods of correction (22), including the method of MacMahon et al. (21), have been used in previous analyses of APCSC data, and each yielded very similar regression dilution ratios (11). To adjust for regression dilution bias, the regression coefficient (ß*) was calculated by multiplying the uncorrected regression coefficient (ß) by {lambda}–1 (where {lambda}–1 = 1.6). A similar approach was used to adjust the standard errors.

The hazard ratios and corresponding 95% CIs were estimated for a 1-mmol/l reduction in usual glucose. In the nonparametric analyses, participants were divided into quartiles according to baseline glucose (<5, 5–5.9, 6–6.9, and ≥7 mmol/l), and the hazard ratios were plotted against "usual" glucose rather than baseline glucose. The 95% CIs for each exposure group were estimated by treating the hazard ratios as "floating variances." This approach does not affect the hazard ratios but enables the comparison between pairs of exposure groups rather than the comparison of a single exposure group with an arbitrary reference group (23,24).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
CONCLUSIONS
 APPENDIX
 References
 
Data availability and study population characteristics
Baseline glucose data were available in 17 of the 43 cohort studies (Table 1). Of these cohorts, fasting glucose measurements were recorded in 13 studies, including 237,468 participants with 1,194,320 person-years of follow-up, and nonfasting glucose measurements were recorded in 6 studies, including 27,996 participants with 185,324 person-years of follow-up (the Melbourne and Akabane cohorts included a mixture of fasting and nonfasting glucose data). During a mean follow-up of 5 years, there were 1,661 stroke and 816 IHD events in the fasting glucose studies, and during a mean follow-up of 6.5 years, there were 144 stroke and 240 IHD events in the nonfasting glucose studies.


View this table:
[in this window]
[in a new window]
  		Table 1— Characteristics of study populations

 
Levels of other risk factors (with the exception of smoking prevalence) tended to be lower in the lowest two quartiles (<5 and 5.0–5.9 mmol/l) compared with the top two glucose quartiles (6.0–6.9 and ≥7mmol/l). For example, in those studies with fasting glucose data, the mean systolic blood pressure levels were 120–127 vs. 135 mmHg, mean total cholesterol was 4.9–5.2 vs. 5.4–5.5 mmol/l, and mean BMI was 22.8–24.1 vs. 25.0–25.5 kg/m2.

Glucose and risk of cardiovascular end points
There was a positive log-linear association between usual fasting glucose and the risk of total stroke and IHD (Fig. 1). For both end points, this association was continuous down to at least 4.9 mmol/l, with no evidence of a threshold level. Overall, a 1 mmol/l lower usual fasting glucose level was associated with a 21% (95% CI 18–24%) lower risk of total stroke and a 23% (19–27%) lower risk of total IHD. Associations were of similar magnitude for both stroke and IHD mortality (fatal events) and incidence (total events).



View larger version (12K):
[in this window]
[in a new window]
  		Figure 1— Usual fasting glucose and risk of cardiovascular end points. The hazard ratios for total (fatal and nonfatal) stroke and IHD and cardiovascular death events adjusted for age, sex, and cohort are plotted on a log scale against usual fasting glucose for each of the four groups defined by baseline fasting glucose (<5, 5–5.9, 6–6.9, ≥7 mmol/l). The x-axis coordinate for each group is the mean follow-up usual fasting glucose (not the mean baseline fasting glucose [see RESEARCH DESIGN AND METHODS]). For studies without follow-up measurements, weighted average x-axis values were calculated from other cohorts. The 95% CIs for the y-axis coordinate (hazard ratios) are calculated as floating variances, with the glucose group 5–5.9 mmol/l as the reference. The solid squares are larger where there are more events because their size is proportional to the inverse variance, and the vertical lines represent 95% CIs.

 
There was a strong continuous relationship between usual fasting glucose and the risk of CVD death (Fig. 1); a 1 mmol/l lower usual fasting glucose level was associated with a 19% (15–22%) lower risk of CVD death.

Consistency of associations in population subgroups
Figure 2 illustrates that while the 95% CIs became wider, the strength of the associations for usual fasting glucose and either CVD end point were not substantially changed when adjusting for confounders, excluding individuals with a diagnosis of diabetes or excluding those with a diagnosis of diabetes and adjusting for confounders. Overall, the associations for usual nonfasting glucose were weaker and less precise than those for usual fasting glucose. This was particularly evident for stroke.



View larger version (25K):
[in this window]
[in a new window]
  		Figure 2— Associations of 1 mmol/l reduction in usual glucose and risk of total stroke and IHD by subgroups. The hazard ratios and 95% CIs for fasting and nonfasting glucose are plotted separately for different subgroups. Analysis was restricted to cohort studies that contributed to all three age-groups and provided baseline variables on diagnosis of diabetes, systolic blood pressure, serum total cholesterol, smoking, and BMI (Table 1). Other conventions are as in Fig. 1. *Additionally adjusted for systolic blood pressure, total cholesterol, smoking, and BMI.

 
There was a suggestion of a slightly stronger association between usual fasting glucose for both total stroke and IHD in the youngest age-group, but the 95% CIs overlapped with those of older age-groups. There was no significant difference between the associations for males and females (e.g., a 1 mmol/l lower usual fasting glucose was associated with 21% [14–29%] and 23% [11–34%] lower risk of total IHD in males and females, respectively). The hazard ratio for stroke in Australasia appeared stronger than in Asia; however, the CIs of the hazard ratios overlapped, and Australasia estimates were only based on two Australian cohorts. Sensitivity analyses that excluded those with diabetes (at baseline) and adjusted for potential confounders diminished the regional differences. No significant differences were found between regions within Asia. Limited data were available by stroke subtype, but analyses indicated that a 1 mmol/l lower usual fasting glucose level was associated with a 20% (0–36%) lower risk of total hemorrhagic stroke (excluding subarachnoid hemorrhage) and a 17% (–1 to 32%) lower risk of total ischemic stroke.

Associations in individuals with and without diagnosed diabetes
Nine cohort studies included data on fasting glucose and diagnosis of diabetes at baseline. Figure 3 demonstrates that the continuous log-linear association was maintained, regardless of diabetes status at baseline. Overall, a 1 mmol/l lower usual fasting glucose was associated with a 20% (13–25%) reduction in total stroke risk and a 23% (18–29%) reduction in total IHD risk.



View larger version (17K):
[in this window]
[in a new window]
  		Figure 3— Usual fasting glucose and risk of total stroke and IHD by baseline diabetic status. The hazard ratios for total stroke and IHD events for those without diagnosed diabetes at baseline (glucose categories <5, 5–5.9, 6–6.9, ≥7 mmol/l) and those with diabetes at baseline (glucose categories <7, ≥7 mmol/l) are plotted on the same log scale against usual fasting glucose. Other conventions are as in Fig. 1.

 

   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
APPENDIX
 References
 
This analysis of cohorts from the Asia Pacific region clearly indicates a positive continuous association between usual blood glucose and CVD risk. This association extends down to about 4.9 mmol/l, well below the usual fasting glucose cutoff levels for diagnosis of diabetes and impaired glucose tolerance. The positive association was maintained when individuals with diabetes at baseline were excluded and with adjustment for potential confounders. The associations were very similar for males and females and across age subgroups. There was no strong evidence of regional differences, but small numbers limited subgroup analysis. The overall associations for usual nonfasting glucose were weaker than those for usual fasting glucose; however, nonfasting measures are less robust because they are affected by the postprandial state.

This study has several advantages over previous studies, such as the substantial size of the database, the availability of individual participant data across a range of glucose levels, and the ability to correct for regression dilution bias, exclude those with diabetes at baseline, and control for potential confounders. Unlike most other analyses, it also enabled the shape and strength of the dose-response relationship to be assessed in non-Western cohorts.

No data were available for oral glucose tolerance tests (OGTTs). Previous comparisons have concluded that 2-h post-load glucose from an OGTT is a superior test to fasting glucose for diabetes (25,26) and predicting cardiovascular and/or all-cause mortality (27,28). However, the OGTT is more difficult and expensive to perform than simpler fasting blood glucose measures (8,29). Fasting blood glucose measures are still useful (29) and are likely to be more reliable than the OGTT across several studies (8).

Many studies and overviews dating back to the 1970s and 1980s investigated whether a threshold relationship existed between glycemia and IHD risk (30) and investigated the effect of adjusting for classic risk factors (31). The overall results of the early studies were inconclusive and conflicting (30,31), and it was unclear whether glucose was a risk factor in the absence of diabetes (32,33). Subsequent analyses have found a positive association between the highest and lowest glucose centiles and CVD. The shape of the association has often been reported as nonlinear (e.g., J-shaped) (34–40). However, the categorization of glucose and small study size substantially limited the ability to explore the shape of the dose-response relationship across the whole range of glucose levels.

A recent overview of 20 studies concluded that there was a significant exponential association between glucose and CVD in nondiabetic individuals that extended below the usual "diabetic threshold" (8). Another prospective study demonstrated that HbA1c was continuously related to subsequent CVD and all-cause mortality through the whole population distribution, with lowest rates in individuals with HbA1c concentrations <5% (~4.5–5.0 mmol/l fasting glucose) (41).

Randomized controlled trials indicate to what extent the "epidemiologically expected" reductions in disease are realized. Trials have examined the effect of blood glucose lowering in type 2 diabetes (42–48), but several have had small sample size, less glycemic control than planned, and relatively short follow-up periods (49,50). Overall, the trials did demonstrate decreased microvascular complications and a nonsignificant trend toward decreasing macrovascular complications. The modest size of the effect on macrovascular outcomes is consistent with the predicted effects from this analysis and the relatively small change in achieved glucose differences. Further analysis showed a continuous association between glycemia and the risk of macrovascular complications below diabetic thresholds (45,49,50).

There are several implications for clinical and public health practice from this study. The continuous association suggests that a wider group of individuals may benefit from glucose lowering, not just those with diabetes or impaired glucose tolerance, but further trial evidence would confirm this. Risk prediction tools could be improved by including fasting glucose as a continuous risk factor, rather than diagnosis of diabetes. Finally, and perhaps most importantly, there are potential benefits from population-wide lowering of determinants of CVD risk. The most appropriate approach to cardiovascular prevention is often a coordinated effort to lower the risk profiles of entire populations coupled with a targeted approach to those at highest absolute risk of CVD. Previous analyses have demonstrated continuous associations between risk factors such as blood pressure, cholesterol, and BMI and CVD risk (11–15). Results from the current study support the view that this is also the case for blood glucose, and the blood glucose levels may be an important risk factor in the Asia Pacific region in individuals with and without diagnosed diabetes.


   APPENDIX
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
 APPENDIX
References
 
Asia Pacific Cohort Studies Collaboration
Writing committee.
C.M.M. Lawes, V. Parag, D.A. Bennett, I. Suh, T.H. Lam, G. Whitlock, F. Barzi, W.H. Pan, and A. Rodgers.

Statistical analyses.
V. Parag, D.A. Bennett, R.B. Lin, F. Barzi, and M. Woodward.

Executive committee.
D.F. Gu, T.H. Lam, C.M.M. Lawes, S. Macmahon, W.H. Pan, A. Rodgers, I. Suh, H. Ueshima, M. Woodward.

Participating studies and principal collaborators.
Aito Town: A. Okayama, H. Ueshima, H. Maegawa; Akabane: N. Aoki, M. Nakamura, N. Kubo, T. Yamada; Anzhen02: Z.S. Wu; Anzhen: C.H. Yao, Z.S. Wu; Australian Longitudinal Study of Ageing: G. Andrews; Australian National Heart Foundation: T.A. Welborn; Beijing Ageing: Z. Tang; Beijing Steelworkers: L.S. Liu, J.X. Xie; Blood Donors’ Health: R. Norton, S. Ameratunga, S. MacMahon, G. Whitlock; Busselton: M.W. Knuiman; Canberra-Queanbeyan: H. Christensen; Capital Iron and Steel Company: X.G. Wu; CISCH: J. Zhou, X.H. Yu; Civil Service Workers: A. Tamakoshi; CVDFACTS: W.H. Pan; East Beijing: Z.L. Wu, L.Q. Chen, G.L. Shan; Electricity Generating Authority of Thailand (EGAT): P. Sritara; Fangshan: D.F. Gu, X.F. Duan; Fletcher Challenge: S. MacMahon, R. Norton, G. Whitlock, R. Jackson; Guangzhou: Y.H. Li; Guangzhou Occupational: T.H. Lam, C.Q. Jiang; Hisayama: M. Fujishima, Y. Kiyohara, H. Iwamoto; Hong Kong: J. Woo, S.C. Ho; Huashan: Z. Hong, M.S. Huang, B. Zhou; Kinmen: J.L. Fuh; Konan: H. Ueshima, Y. Kita, S.R. Choudhury; KMIC: I. Suh, S.H. Jee, I.S. Kim; Melbourne: G.G. Giles; Miyama: T. Hashimoto, K. Sakata; Newcastle: A. Dobson; Ohasama: Y. Imai, T. Ohkubo, A. Hozawa; Perth: K. Jamrozik, M. Hobbs, R. Broadhurst; Saitama: K. Nakachi; Seven Cities: X.H. Fang, S.C. Li, Q.D. Yang; Shanghai Factory Workers: Z.M. Chen; Shibata: H. Tanaka; Shigaraki Town: Y. Kita, A. Nozaki, H. Ueshima; Shirakawa: H. Horibe, Y. Matsutani, M. Kagaya; Singapore Heart: K. Hughes, J. Lee; Singapore NHS92: D. Heng, S.K. Chew; Six Cohorts: B.F. Zhou, H.Y. Zhang; Tanno/Soubetsu: K. Shimamoto, S. Saitoh; Tianjin: Z.Z. Li, H.Y. Zhang; Western Australia AAA Screenees: P. Norman, K. Jamrozik; Xi’an: Y. He, T.H. Lam; Yunnan: S.X. Yao. (Note: The italicized studies provided data used in this article.)


   Acknowledgments
 
This project was supported by a grant from the Health Research Council of New Zealand, the National Institute on Aging Grant PO1-AG17625, the National Health and Medical Research Council of Australia, and an unrestricted educational grant from Pfizer. The sponsors had no influence on design, analysis, or interpretation of results.


   Footnotes
 
* A complete list of the writing committee and collaboration members is available in the APPENDIX. Back

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

Received for publication April 19, 2004. Accepted for publication August 28, 2004.


   References
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
 APPENDIX
 References
 

  1. Gerstein HC, Yusuf S: Dysglycaemia and risk of cardiovascular disease. Lancet 347:949–950, 1996[Medline]
  2. Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Bonadonna R, Muggeo M: Plasma glucose within the normal range is not associated with carotid atherosclerosis: prospective results in subjects with normal glucose tolerance from the Bruneck Study. Diabetes Care 22:1339–1346, 1999[Abstract]
  3. Harris MI, Eastman RC: Is there a glycemic threshold for mortality risk? Diabetes Care 21:331–333, 1998[Medline]
  4. Donahue RP, Orchard TJ: Diabetes mellitus and macrovascular complications: an epidemiological perspective. Diabetes Care 15:1141–1155, 1992[Abstract]
  5. Asia Pacific Cohort Studies Collaboration: The effects of diabetes on the risks of major cardiovascular diseases and death in the Asia Pacific region. Diabetes Care 26:360–366, 2003[Abstract/Free Full Text]
  6. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 26 (Suppl. 1):S5–S20, 2003
  7. Perry RC, Baron AD: Impaired glucose tolerance: why is it not a disease? Diabetes Care 22:883–885, 1999[Medline]
  8. Coutinho M, Gerstein HC, Wang Y, Yusuf S: The relationship between glucose and incident cardiovascular events: a metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 year. Diabetes Care 22:233–240, 1999[Abstract/Free Full Text]
  9. Nakagami T, the DECODA Study Group: Hyperglycaemia and mortality from all causes and from cardiovascular disease in five populations of Asian origin. Diabetologia 47:385–394, 2004[Medline]
  10. Law MR, Wald NJ: Risk factor thresholds: their existence under scrutiny. BMJ 324:1570–1576, 2002[Free Full Text]
  11. Asia Pacific Cohort Studies Collaboration: Blood pressure and cardiovascular disease in the Asia Pacific region. J Hypertens 21:707–716, 2003[Medline]
  12. Asia Pacific Cohort Studies Collaboration: Cholesterol, coronary heart disease and stroke in the Asia Pacific region. Int J Epidemiol 32:563–572, 2003[Abstract/Free Full Text]
  13. Asia Pacific Cohort Studies Collaboration: Body mass index and cardiovascular disease in the Asia-Pacific Region: an overview of 33 cohorts involving 305,000 participants. Int J Epidemiol 33:1–8, 2004[Free Full Text]
  14. Neaton JD, Wentworth D: Serum cholesterol, blood pressure, cigarette smoking, and death from coronary heart disease: overall findings and differences by age for 316,099 white men: Multiple Risk Factor Intervention Trial Research Group. Arch Intern Med 152:56–64, 1992[Abstract]
  15. Prospective Studies Collaboration: Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 360:1903–1913, 2002[Medline]
  16. Jarrett RJ: The cardiovascular risk associated with impaired glucose tolerance. Diabet Med 13:S15–S19, 1996[Medline]
  17. Rose G: The Strategy of Preventive Medicine. Oxford, U.K., Oxford University Press, 1992
  18. Asia Pacific Cohort Studies Collaboration: Determinants of cardiovascular disease in the Asia Pacific region: protocol for a collaborative overview of cohort studies. CVD Prevention 2:281–289, 1999
  19. Cox DR: Regression models and life tables (with discussion). Journal of the Royal Statistical Society 34:187–220, 1972
  20. Collett D: Modelling Survival Data in Medical Research. London, Chapman and Hall, 1994
  21. MacMahon S, Peto R, Cutler J, Collins R, Sorlie P, Neaton J, Abbott R, Godwin J, Dyer A, Stamler J: Blood pressure, stroke, and coronary heart disease. I. Prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet 335:765–774, 1990[Medline]
  22. Caroll R, Stefanski L: Measurement error, instrumental variables and corrections for attenuation with applications to meta-analyses. Stat Med 3:1265–1282, 1994
  23. 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 10:1025–1035, 1991[Medline]
  24. Prospective Studies Collaboration: Cholesterol, diastolic blood pressure, and stroke: 13,000 strokes in 45,000 people in 45 prospective cohorts. Lancet 346:1647–1653, 1995[Medline]
  25. The DECODE-Study Group, the European Diabetes Epidemiology Group: Is fasting glucose sufficient to define diabetes? Epidemiological data from 20 European studies: Diabetes Epidemiology: Collaborative analysis of Diagnostic Criteria in Europe. Diabetologia 42:647–654, 1999[Medline]
  26. Qiao Q, Nakagami T, Tuomilehto J, Borch-Johnsen K, Balkau B, Iwamoto Y, Tajima N, International Diabetes Epidemiology Group, DECODA Study Group: Comparison of the fasting and the 2-h glucose criteria for diabetes in different Asian cohorts. Diabetologia 43:1470–1475, 2000[Medline]
  27. DECODE Study Group, the European Diabetes Epidemiology Group: Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med 161:397–405, 2001[Abstract/Free Full Text]
  28. Qiao Q, Pyorala K, Pyorala M, Nissinen A, Lindstrom J, Tilvis R, Tuomilehto J: Two-hour glucose is a better risk predictor for incident coronary heart disease and cardiovascular mortality than fasting glucose. Eur Heart J 23:1267–1275, 2002[Abstract/Free Full Text]
  29. Gerstein HC: Fasting versus postload glucose levels: why the controversy? Diabetes Care 24:1855–1857, 2001[Free Full Text]
  30. Anonymous: Asymptomatic hyperglycemia and coronary heart disease: a series of papers by the International Collaborative Group, based on studies in fifteen populations. J Chronic Dis 32:681–837, 1979[Medline]
  31. Epstein FH: Hyperglycaemia as a risk factor for coronary heart disease. Monographs on Atherosclerosis 13:92–97, 1985[Medline]
  32. Barrett-Connor E: Does hyperglycemia really cause coronary heart disease? Diabetes Care 20:1620–1623, 1997[Medline]
  33. Stern MP: Glycemia and cardiovascular risk. Diabetes Care 20:1501–1502, 1997[Medline]
  34. Perry IJ, Wannamethee SG, Whincup PH, Shaper AG: Asymptomatic hyperglycaemia and major ischaemic heart disease events in Britain. J Epidemiol Community Health 48:538–542, 1994[Abstract]
  35. Wannamethee SG, Shaper AG, Whincup PH, Walker M: Role of risk factors for major coronary heart disease events with increasing length of follow up. Heart 81:374–379, 1999[Abstract/Free Full Text]
  36. Tunstall-Pedoe H, Woodward M, Tavendale R, A’Brook R, McCluskey MK: Comparison of the prediction by 27 different factors of coronary heart disease and death in men and women of the Scottish Heart Health Study: cohort study. BMJ 315:722–729, 1997 (erratum appears in BMJ 316:1881, 1998)[Abstract/Free Full Text]
  37. DECODE Study the Group, the European Diabetes Epidemiology Group: Is the current definition for diabetes relevant to mortality risk from all causes and cardiovascular and noncardiovascular diseases? Diabetes Care 26:688–696, 2003[Abstract/Free Full Text]
  38. Balkau B, Shipley M, Jarrett RJ, Pyorala K, Pyorala M, Forhan A, Eschwege E: High blood glucose concentration is a risk factor for mortality in middle-aged nondiabetic men: 20-year follow-up in the Whitehall Study, the Paris Prospective Study, and the Helsinki Policemen Study. Diabetes Care 21:360–367, 1998[Abstract]
  39. Balkau B, Bertrais S, Ducimetiere P, Eschwege E: Is there a glycemic threshold for mortality risk? Diabetes Care 22:696–699, 1999[Abstract/Free Full Text]
  40. Bjornholt JV, Erikssen G, Aaser E, Sandvik L, Nitter-Hauge S, Jervell J, Erikssen J, Thaulow E: Fasting blood glucose: an underestimated risk factor for cardiovascular death: results from a 22-year follow-up of healthy nondiabetic men. Diabetes Care 22:45–49, 1999[Abstract/Free Full Text]
  41. Khaw KT, Wareham N, Luben R, Bingham S, Oakes S, Welch A, Day N: Gly-cated haemoglobin, diabetes, and mortality in men in Norfolk cohort of European prospective investigation of cancer and nutrition (EPIC-Norfolk). BMJ 322:15–18, 2001[Abstract/Free Full Text]
  42. University Group Diabetes Program: A study of the effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. Diabetes 19 (Suppl. 2):747–830, 1970
  43. UK Prospective Diabetes Study Group: Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ 317:703–713, 1998[Abstract/Free Full Text]
  44. UK Prospective Diabetes Study Group: Efficacy of atenolol and captopril in reducing risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 39. BMJ 317:713–720, 1998[Abstract/Free Full Text]
  45. UK Prospective Diabetes Study (UKPDS) Group: Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352:837–853, 1998[Medline]
  46. UK Prospective Diabetes Study (UKPDS) Group: Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet 352:854–865, 1998[Medline]
  47. Ohkubo Y, Kishikawa H, Araki E, Miyata T, Isami S, Motoyoshi S, Kojima Y, Furuyoshi N, Shichiri M: Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study. Diabetes Res Clin Pract 28:103–117, 1995[Medline]
  48. Abraira C, Colwell J, Nuttall F, Sawin CT, Henderson W, Comstock JP, Emanuele NV, Levin SR, Pacold I, Lee HS: Cardiovascular events and correlates in the Veterans Affairs Diabetes Feasibility Trial: Veterans Affairs Cooperative Study on Glycemic Control and Complications in Type II Diabetes. Arch Intern Med 157:181–188, 1997[Abstract]
  49. Bouldin MJ, Low AK, Blackston JW, Duddleston DN, Holman HE, Hicks GS, Brown CA: Quality of care in diabetes: understanding the guidelines. Am J Med Sci 324:196–206, 2002[Medline]
  50. American Diabetes Association: Implications of the United kingdom Prospective Diabetes Study (Position Statement). Diabetes Care 26 (Suppl. 1):S28–S32, 2003

Add to CiteULike CiteULike   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 Arch Intern MedHome page
H. C. Gerstein, K. Swedberg, J. Carlsson, J. J. V. McMurray, E. L. Michelson, B. Olofsson, M. A. Pfeffer, S. Yusuf, and for the CHARM Program Investigators
The Hemoglobin A1c Level as a Progressive Risk Factor for Cardiovascular Death, Hospitalization for Heart Failure, or Death in Patients With Chronic Heart Failure: An Analysis of the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) Program
Arch Intern Med, August 11, 2008; 168(15): 1699 - 1704.
[Abstract] [Full Text] [PDF]

Home page
 ScienceHome page
N. Bouatia-Naji, G. Rocheleau, L. Van Lommel, K. Lemaire, F. Schuit, C. Cavalcanti-Proenca, M. Marchand, A.-L. Hartikainen, U. Sovio, F. De Graeve, et al.
A Polymorphism Within the G6PC2 Gene Is Associated with Fasting Plasma Glucose Levels
Science, May 23, 2008; 320(5879): 1085 - 1088.
[Abstract] [Full Text] [PDF]

Home page
 Diabetes CareHome page
The DREAM Trial Investigators
Effects of Ramipril and Rosiglitazone on Cardiovascular and Renal Outcomes in People With Impaired Glucose Tolerance or Impaired Fasting Glucose: Results of the Diabetes REduction Assessment with ramipril and rosiglitazone Medication (DREAM) trial
Diabetes Care, May 1, 2008; 31(5): 1007 - 1014.
[Abstract] [Full Text] [PDF]

Home page
 Int J EpidemiolHome page
P. E Norman, L. Flicker, O. P Almeida, G. J Hankey, Z. Hyde, and K. Jamrozik
Cohort Profile: The Health In Men Study (HIMS)
Int. J. Epidemiol., March 3, 2008; (2008) dyn041v1.
[Full Text] [PDF]

Home page
 Diabetes CareHome page
C. Bianchi, R. Miccoli, G. Penno, and S. Del Prato
Primary Prevention of Cardiovascular Disease in People With Dysglycemia
Diabetes Care, February 1, 2008; 31(Supplement_2): S208 - S214.
[Abstract] [Full Text] [PDF]

Home page
 J Am Coll Cardiol ImgHome page
R. S. Vasan
What is an Abnormal Blood Glucose Level?: Combining Lessons From Epidemiology and In Vivo Plaque Imaging
J. Am. Coll. Cardiol. Img., January 1, 2008; 1(1): 46 - 48.
[Full Text] [PDF]

Home page
 Diabetes CareHome page
M. Janghorbani, F. B. Hu, W. C. Willett, T. Y. Li, J. E. Manson, G. Logroscino, and K. M. Rexrode
Prospective Study of Type 1 and Type 2 Diabetes and Risk of Stroke Subtypes: The Nurses' Health Study
Diabetes Care, July 1, 2007; 30(7): 1730 - 1735.
[Abstract] [Full Text] [PDF]

Home page
 Diabetes CareHome page
G. Hu, P. Jousilahti, S. Bidel, R. Antikainen, and J. Tuomilehto
Type 2 Diabetes and the Risk of Parkinson's Disease
Diabetes Care, April 1, 2007; 30(4): 842 - 847.
[Abstract] [Full Text] [PDF]

Home page
 CirculationHome page
C. Held, H.C. Gerstein, S. Yusuf, F. Zhao, L. Hilbrich, C. Anderson, P. Sleight, K. Teo, and for the ONTARGET/TRANSCEND Investigators
Glucose Levels Predict Hospitalization for Congestive Heart Failure in Patients at High Cardiovascular Risk
Circulation, March 20, 2007; 115(11): 1371 - 1375.
[Abstract] [Full Text] [PDF]

Home page
 Diabetes CareHome page
H. C. Gerstein
Point: If It Is Important to Prevent Type 2 Diabetes, It Is Important to Consider All Proven Therapies Within a Comprehensive Approach
Diabetes Care, February 1, 2007; 30(2): 432 - 434.
[Full Text] [PDF]

Home page
 Cancer Epidemiol. Biomarkers Prev.Home page
A. Ansary-Moghaddam, R. Huxley, F. Barzi, C. Lawes, T. Ohkubo, X. Fang, S. H. Jee, M. Woodward, and Asia Pacific Cohort Studies Collaboration
The Effect of Modifiable Risk Factors on Pancreatic Cancer Mortality in Populations of the Asia-Pacific Region
Cancer Epidemiol. Biomarkers Prev., December 1, 2006; 15(12): 2435 - 2440.
[Abstract] [Full Text] [PDF]

Home page
 Int J EpidemiolHome page
M Woodward, F Barzi, A Martiniuk, X Fang, D. Gu, Y Imai, T. Lam, W. Pan, A Rodgers, I Suh, et al.
Cohort profile: the Asia Pacific Cohort Studies Collaboration
Int. J. Epidemiol., December 1, 2006; 35(6): 1412 - 1416.
[Full Text] [PDF]

Home page
 StrokeHome page
G. J. Hankey
Potential New Risk Factors for Ischemic Stroke: What Is Their Potential?
Stroke, August 1, 2006; 37(8): 2181 - 2188.
[Abstract] [Full Text] [PDF]

Home page
 J Am Coll CardiolHome page
S. H. Kim, L. Chunawala, R. Linde, and G. M. Reaven
Comparison of the 1997 and 2003 American Diabetes Association Classification of Impaired Fasting Glucose: Impact on Prevalence of Impaired Fasting Glucose, Coronary Heart Disease Risk Factors, and Coronary Heart Disease in a Community-Based Medical Practice
J. Am. Coll. Cardiol., July 18, 2006; 48(2): 293 - 297.
[Abstract] [Full Text] [PDF]

Home page
 StrokeHome page
G. Hu, C. Sarti, P. Jousilahti, M. Peltonen, Q. Qiao, R. Antikainen, and J. Tuomilehto
The Impact of History of Hypertension and Type 2 Diabetes at Baseline on the Incidence of Stroke and Stroke Mortality
Stroke, December 1, 2005; 36(12): 2538 - 2543.
[Abstract] [Full Text] [PDF]

Home page
 Diabetes CareHome page
C. P. Wen, T. Y. D. Cheng, S. P. Tsai, H. L. Hsu, and S. L. Wang
Increased Mortality Risks of Pre-Diabetes (Impaired Fasting Glucose) in Taiwan
Diabetes Care, November 1, 2005; 28(11): 2756 - 2761.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Physiol. Endocrinol. Metab.Home page
A. Scuteri, S. S. Najjar, D. Muller, R. Andres, C. H. Morrell, A. B. Zonderman, and E. G. Lakatta
apoE4 allele and the natural history of cardiovascular risk factors
Am J Physiol Endocrinol Metab, August 1, 2005; 289(2): E322 - E327.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Social Bookmarking