Skip to main content
  • More from ADA
    • Diabetes
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care
  • Subscribe
  • Log in
  • My Cart
  • Follow ada on Twitter
  • RSS
  • Visit ada on Facebook
Diabetes Care

Advanced Search

Main menu

  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • Special Article Collections
    • ADA Standards of Medical Care
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • Special Article Collections
    • ADA Standards of Medical Care
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcasts
    • Diabetes Core Update
    • Special Podcast Series: Therapeutic Inertia
    • Special Podcast Series: Influenza Podcasts
    • Special Podcast Series: SGLT2 Inhibitors
    • Special Podcast Series: COVID-19
  • Submit
    • Submit a Manuscript
    • Journal Policies
    • Instructions for Authors
    • ADA Peer Review
  • More from ADA
    • Diabetes
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care

User menu

  • Subscribe
  • Log in
  • My Cart

Search

  • Advanced search
Diabetes Care
  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • Special Article Collections
    • ADA Standards of Medical Care
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • Special Article Collections
    • ADA Standards of Medical Care
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcasts
    • Diabetes Core Update
    • Special Podcast Series: Therapeutic Inertia
    • Special Podcast Series: Influenza Podcasts
    • Special Podcast Series: SGLT2 Inhibitors
    • Special Podcast Series: COVID-19
  • Submit
    • Submit a Manuscript
    • Journal Policies
    • Instructions for Authors
    • ADA Peer Review
Cardiovascular and Metabolic Risk

Impact of Glucose Level on Micro- and Macrovascular Disease in the General Population: A Mendelian Randomization Study

  1. Frida Emanuelsson1,2,3,
  2. Sarah Marott1,2,3,
  3. Anne Tybjærg-Hansen1,2,3,4,
  4. Børge G. Nordestgaard2,3,4,5 and
  5. Marianne Benn1,2,3⇑
  1. 1Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
  2. 2Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
  3. 3The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
  4. 4The Copenhagen City Heart Study, Frederiksberg and Bispebjerg Hospital, Copenhagen University Hospital, Frederiksberg, Denmark
  5. 5Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
  1. Corresponding author: Marianne Benn, marianne.benn{at}regionh.dk
Diabetes Care 2020 Apr; 43(4): 894-902. https://doi.org/10.2337/dc19-1850
PreviousNext
  • Article
  • Figures & Tables
  • Suppl Material
  • Info & Metrics
  • PDF
Loading

Abstract

OBJECTIVE To evaluate whether high glucose levels in the normoglycemic range and higher have a causal genetic effect on risk of retinopathy, neuropathy, nephropathy, chronic kidney disease (CKD), peripheral arterial disease (PAD), and myocardial infarction (MI; positive control) in the general population.

RESEARCH DESIGN AND METHODS This study applied observational and one-sample Mendelian randomization (MR) analyses to individual-level data from 117,193 Danish individuals, and validation by two-sample MR analyses on summary-level data from 133,010 individuals from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), 117,165 from the CKDGen Consortium, and 452,264 from the UK Biobank.

RESULTS Observationally, glucose levels in the normoglycemic range and higher were associated with high risks of retinopathy, neuropathy, diabetic nephropathy, PAD, and MI (all P for trend <0.001). In genetic causal analyses, the risk ratio for a 1 mmol/L higher glucose level was 2.01 (95% CI 1.18–3.41) for retinopathy, 2.15 (1.38–3.35) for neuropathy, 1.58 (1.04–2.40) for diabetic nephropathy, 0.97 (0.84–1.12) for estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, 1.19 (0.90–1.58) for PAD, and 1.49 (1.02–2.17) for MI. Summary-level data from the MAGIC, the CKDGen Consortium, and the UK Biobank gave a genetic risk ratio of 4.55 (95% CI 2.26–9.15) for retinopathy, 1.48 (0.83–2.66) for peripheral neuropathy, 0.98 (0.94–1.01) for eGFR <60 mL/min/1.73 m2, and 1.23 (0.57–2.67) for PAD per 1 mmol/L higher glucose level.

CONCLUSIONS Glucose levels in the normoglycemic range and higher were prospectively associated with a high risk of retinopathy, neuropathy, diabetic nephropathy, eGFR <60 mL/min/1.73 m2, PAD, and MI. These associations were confirmed in genetic causal analyses for retinopathy, neuropathy, diabetic nephropathy, and MI, but they could not be confirmed for PAD and seemed to be refuted for eGFR <60 mL/min/1.73 m2.

Introduction

High glucose levels are observationally and causally associated with a high risk of ischemic heart disease in individuals with and those without diabetes (1,2). Similarly, hyperglycemia is a well-known risk factor for microvascular disease in individuals with diabetes, and treatment with glucose-lowering drugs reduces the risk of microvascular complications in such individuals (3,4). Whether high glucose levels increase the risk of a spectrum of peripheral micro- and macrovascular diseases in the general population, and whether the magnitude of such risk varies between vascular compartments, is less clear. Also uncertain is whether the effect of glucose-lowering drugs on vascular disease is caused by glucose lowering per se or whether other mechanisms also are involved, as most glucose-lowering drugs have pleiotropic effects, including improvements in concomitant obesity, hyperlipidemia, and hypertension (5,6).

In order to determine whether a risk factor is causally related to a disease, several lines of evidence should be considered, including mechanistic studies, longitudinal epidemiological studies, genetic Mendelian randomization (MR) studies, and randomized controlled trials (7,8). MR is an epidemiological approach to assessing causality that uses the random assortment of alleles at conception to largely circumvent confounding and reverse causation (8). Applying the principles of MR in a general population, we tested the hypothesis that high glucose levels within the normal range and higher causally associate with high risks of retinopathy, peripheral neuropathy, and diabetic nephropathy, representing microvascular disease, or with high risks of chronic kidney disease (CKD) and peripheral arterial disease (PAD), representing a mix of micro- and macrovascular PAD. Myocardial infarction (MI) was included as a positive control for the genetic instrument, that is, as a confirmation of the association between the genetic instrument and the risk of MI.

In two cohorts from the Danish general population—participants in the Copenhagen City Heart Study (CCHS) and those in the Copenhagen General Population Study (CGPS)—we first tested whether random plasma glucose levels at baseline were prospectively associated with risk of disease. Seven variants of the genes GCP62/ABCB11, GCK, DGKB, ADCY5, CDKN2A/B, and TCF7L2 have been shown to affect glucose levels (1,9,10); thus, second, we tested whether they also did so in our populations. Third, we tested whether the genetic variants associated with high glucose levels also associated, as an indication of causality, with risk of disease. Fourth, we analyzed instrumental variables to obtain causal risk estimates per 1 mmol/L (18 mg/dL) higher glucose levels. Fifth and last, we validated our results using a two-sample MR design with summary-level data based on 26 genetic variants associated with fasting glucose levels in individuals without diabetes from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), and end point data from the UK Biobank and the CKDGen Consortium (11–14).

Research Design and Methods

Study Population

We included 117,193 individuals from two similar studies of the Danish general population: the CCHS and the CGPS (1). Participants were white and of Danish descent, and none were included in more than one study. For detailed descriptions of the cohorts, see Supplementary Appendix 1. Both studies were approved by institutional boards at Herlev and Gentofte Hospitals and the Danish National Committee on Health Research Ethics, Copenhagen, Denmark (KF-100.2039/91, KF-01–144/01, H-KF-01–144/01), and they were conducted according to the Declaration of Helsinki. Written informed consent was obtained from all individuals.

Plasma Glucose

Nonfasting plasma glucose was measured by using a colorimetric assay (Konelab; Boehringer, Mannheim, Germany). Blood samples were taken at random, irrespective of time since and content of the last meal. Time (hours) since the last meal was self-reported. In order to examine the observational association between glucose levels and disease, individuals without diabetes were categorized, on the basis of glucose level at baseline, into five groups that reflect stepwise increases in glucose level, ranging from ≥4.0 mmol/L to above the diabetes threshold of 11.1 mmol/L (15). Individuals with levels between 4.0 and 5.4 mmol/L were defined as the reference group.

Genotypes

An ABI PRISM 7900HT Sequence Detection System (Applied Biosystems Inc., Foster City, CA) and TaqMan-based assays were used in order to genotype seven variants of the genes GCP62/ABCB1 (rs560887), GCK (rs4607517), DGKB (rs2191349), ADCY5 (rs11708067), CDKN2A/B (rs10811661 and rs2383206), and TCF7L2 (rs7903146), which have been shown to be associated with high glucose levels (1,9,10). The variants were combined into a weighted allele score, which accounted for the effect of each allele on random plasma glucose and the frequency of each allele in the CCHS and CGPS populations (16). In order to compare the effect of the same genetic variants on fasting glucose levels, the allele score was also weighted on the basis of its effect on fasting glucose levels in MAGIC participants. The weighted allele scores were divided into five categories, cut at the 25th, 50th, 75th, and 95th percentiles, in order to reflect stepwise increases in glucose levels from below the median to extremely high values.

End Points

End points were based on diagnoses of retinopathy, peripheral neuropathy, diabetic nephropathy, PAD, and MI according to the codes from the World Health Organization’s ICD-8 and ICD-10. (For specific ICD codes, see Supplementary Table 1.) Relevant data were collected from 1 January 1977 through 10 April 2018 by reviewing diagnoses from all hospital admissions and outpatient clinic visits in the national Danish Patient Registry and the national Danish Registry of Causes of Death. CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, corresponding to the KDIGO definition of mildly to moderately decreased (stage G3) kidney function or worse (17). eGFR was calculated from creatinine by using the CKD-EPI creatinine equation (18). For detailed descriptions of end points and covariates, see Supplementary Appendix 2.

Statistical Analyses

We used Stata SE 14.2 to analyze the data. Deviation from Hardy-Weinberg equilibrium was tested by using the Pearson χ2 test. To test for trend across ordered categories of glucose levels, genotypes, and weighted allele scores, we used the nonparametric Cuzick extension of a Wilcoxon rank sum test.

First, the observational associations between glucose level and disease in individuals without diabetes were examined for predefined categories of glucose level (which increases with each category) by using Cox proportional hazards regression with age as the time scale and with delayed entry (left-truncation), excluding individuals with a glucose level <4.0 mmol/L (<72 mg/dL) because of nonlinearity (Supplementary Fig. 2). In the observational analyses, follow-up began at the first inclusion in a study and ended with censoring at the date of death, of an event, or of emigration (n = 610), or on 10 April 2018 (corresponding to the end of follow-up for the least updated registry)—whichever came first. We did not lose track of any individual, as all individuals living in Denmark have a Danish Civil Registration System number, which provides information, updated daily, on emigration and death. The models were adjusted for sex, birth year, current smoking, pack-years smoked, BMI, hypertension, LDL cholesterol, time since last meal, and menopausal status (for women). Missing values for covariates (0–2.4% missing) were imputed from age, sex, and cohort (i.e., whether the individual participated in CCHS or CGPS) by using multivariate imputation. Cohort was adjusted in order to accommodate for the time difference between the initiation of the two cohorts (1976 for the CCHS and 2003 for the CGPS), and for potential associated changes in patterns of risk behavior, diagnoses, and treatment options. We also examined the observational associations between glucose level (on a continuous scale) and disease by using restricted cubic splines with seven knots incorporated into Cox proportional hazards models including all individuals (those with and those without diabetes); the population-wide median glucose level was the reference value.

Second, to test whether genotypes and the weighted allele score were associated with increased risk of disease, we used unadjusted Cox proportional hazards regression (as genotypes generally have a constant effect throughout life and are largely unaffected by confounding factors), with age as the time scale. Because an individual’s genotype is constant throughout life, follow-up time began when the national Danish Patient Registry was established (1 January 1977) or on the individual’s 20th birthday, whichever came last; follow-up ended as described for the observational analyses. A critical assumption of the MR design is that the genetic instrument should influence the risk of disease only through the exposure of interest (i.e., plasma glucose). To test this, we used logistic regression to assess whether the potential confounders—age, sex, current smoking, BMI, LDL cholesterol, hypertension, alcohol intake, physical activity, education, and menopausal status (for women)—were associated with glucose levels and with the weighted allele score.

Third, instrumental variable analysis by two-stage least-squares regression (through the Stata ivreg2 and ivpois commands) was used in order to estimate the potential causal association between 1 mmol/L higher glucose levels and risk of disease (19,20). The strength of the genetic instrument (i.e., the association between genotypes and glucose levels) was confirmed with the F statistic for a weighted allele score of 98, explaining 0.3% of the variation in glucose levels (21).

Fourth, to validate and test the generalizability of the results—that is, how well they represent glucose levels per se and are able to be reproduced in another general population—we conducted two-sample MR analyses with a broader genetic instrument based on summary-level data of 26 genetic variants associated with glucose levels in the MAGIC participants (n = 133,010) (13). (For further descriptions of methods of the two-sample MR, see Supplementary Appendix 3.) Genetic variants with P values <1 × 10−8 were selected; variants in linkage disequilibrium, defined as an R2 value >0.6, were excluded. End point data for the variants were extracted from the UK Biobank (n = 452,264) (12) and the CKDGen Consortium (n = 133,413) (14). (For more details about the genetic variants used, see Supplementary Table 2.)

Finally, summary-level estimates of causal effects for the data from the CGPS, CCHS, and consortia populations were estimated for each end point through meta-analyses by using the metan command in Stata. Between-study heterogeneity was assessed by using the I2 statistic. If heterogeneity was <50%, the analyses were performed with a fixed effects model. If heterogeneity was >50%, a random effects model was chosen (22).

Results

A total of 117,193 individuals were included from the CCHS and the CGPS. Of these, 4,738 had a diagnosis of diabetes at baseline or a glucose level below 4.0 mmol/L, leaving 112,455 individuals for the observational analyses. Baseline characteristics for all individuals, and for individuals without diabetes by glucose category, are shown in Table 1. Mean glucose level as a function of time since the last meal is shown in Supplementary Fig. 1 and Supplementary Table 4. Genotype distributions did not deviate from Hardy-Weinberg expectations (all P > 0.05).

View this table:
  • View inline
  • View popup
Table 1

Baseline characteristics for all individuals and by random plasma glucose in categories for individuals without diabetes

Observationally High Glucose Levels and Risk of Disease

Figure 1 shows the prospective risk of vascular end points by categories of glucose level in individuals without diagnosed diabetes at baseline. Baseline glucose levels were 80% higher (mean ± SEM 8.5 ± 0.02 mmol/L) in the highest glucose category below the diabetes cutoff and 186% higher in the highest category above the cutoff (13.4 ± 0.01 mmol/L); the level in the reference category was 4.8 ± 0.01 mmol/L) (Fig. 1). Higher glucose levels were associated with stepwise higher risks of all end points, and higher risks also were observed for glucose levels within the normal range.

Figure 1
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1

Observational prospective risk of retinopathy, peripheral neuropathy, diabetic nephropathy, PAD, and MI as a function of increasing random plasma glucose (p-glucose) in individuals without diabetes at baseline. Individuals with an event before baseline or with a glucose level below 4.0 mmol/L (n = 2,103) were excluded from the analyses. ∆%, percentage difference in mean glucose level.

Multifactorial adjusted hazard ratios were 13 (95% CI 8.0–20) for retinopathy, 2.5 (1.9–3.2) for peripheral neuropathy, 12 (7.6–18) for diabetic nephropathy, and 1.3 (1.1–1.6) for PAD in individuals with nonfasting glucose levels between 8.0 and 11.0 mmol/L. The risks of all end points were even higher in individuals with a nonfasting glucose level above the diabetes cutoff (≥11.1 mmol/L) (P for trend <0.001 for all end points) (Fig. 1). The known stepwise association between higher glucose levels and MI is shown as a positive control of the study’s power (P < 0.001). Restricted cubic spline analyses of the associations between glucose levels (on a continuous scale) and risk of retinopathy, peripheral neuropathy, diabetic nephropathy, and PAD for all individuals (i.e., those with and those without diabetes) showed an increasing risk of disease with glucose levels increasing from 5.2 mmol/L (Supplementary Fig. 2).

Genetically High Glucose Levels and Risk of Disease

The selected genetic variants of GCP62/ABCB11, GCK, DGKB, ADCY5, CDKN2A/B, and TCF7L2 were separately associated with stepwise higher glucose levels, as was the weighted allele score obtained when those variants were combined: the mean glucose level was 5% higher in individuals with an allele score above the 95th percentile (mean ± SEM 5.56 ± 0.02 mmol/L) than in individuals with an allele score below the 26th percentile (mean ± SEM 5.28 ± 0.01 mmol/L) (Fig. 2 and Supplementary Fig. 3). The effect of allele frequency and the direction and magnitude of the effect of each genetic variant on random plasma glucose in the individuals from CCHS and from CGPS were largely comparable to the frequency, direction, and magnitude of their effects on fasting plasma glucose in the MAGIC participants (Supplementary Table 5). Categories of genetically higher glucose levels were associated with stepwise higher risks of retinopathy (P for trend 0.002), peripheral neuropathy (P = 0.001), and diabetic nephropathy (P = 0.007) (Fig. 2). The risk of PAD was higher for individuals in the highest allele score category than for those in the lowest (HR 1.19 [95% CI 1.05–1.35]). Estimates for the known association between genetically higher glucose levels and MI is shown as a positive control of the genetic instrument.

Figure 2
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2

Mean random plasma glucose (p-glucose) level as a function of weighted allele score categories, and the prospective risk of retinopathy, peripheral neuropathy, diabetic nephropathy, PAD, and MI as a function of allele score (by categories of increasing score). Risk of MI is included as a positive control of the genetic instrument. ∆%, percentage difference in mean glucose level.

Confounding Factors

We tested whether potentially confounding factors were associated with glucose level and the combined genetic variants. Age, sex, menopausal status (for women), BMI, hypertension, current smoking status, alcohol intake, physical activity, and education level were associated with glucose level but not with the combined genetic variants, indicating that pleiotropic effects are unlikely through any of the aforementioned factors (Supplementary Figs. 4 and 5).

Causal Association Between High Glucose Level and Disease

Multifactorial adjusted observational analyses and causal analyses of instrumental variables for risk estimates of disease per a 1 mmol/L higher glucose level showed observational and causal associations between high glucose levels and risk of retinopathy (observational risk ratio [RR] 1.32 [95% CI 1.29–1.35]; causal RR 2.01 [95% CI 1.18–3.41]), peripheral neuropathy (observational RR 1.16 [1.14–1.19]; causal RR 2.15 [1.38–3.35]), and diabetic nephropathy (observational RR 1.29 [1.25–1.32]; causal RR 1.58 [1.04–2.40]) (Fig. 3). For PAD, a 1 mmol/L higher glucose level had an observational RR of 1.06 (1.04–1.09) and a causal RR of 1.19 (0.90–1.58). To investigate whether the causal association between high glucose level and diabetic nephropathy also was valid for measured reduced kidney function, we performed the analyses for eGFR <60 mL/min/1.73 m2 and found an observational RR of 1.04 (1.02–1.06) and a causal RR of 0.97 (0.84–1.12) per 1 mmol/L higher glucose level. Sensitivity analyses of the instrumental variables gave similar results when using the seven genetic variants in an allele score weighted for their effects on fasting glucose level in participants in the MAGIC instead of their effect on random plasma glucose in participants in the CCHS or the CGPS (Supplementary Fig. 6).

Figure 3
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3

Risk of retinopathy, peripheral neuropathy, diabetic nephropathy, CKD (defined as eGFR <60 mL/min/1.73 m2), PAD, and MI per 1 mmol/L (18 mg/dL) higher observational and causal glucose levels. Hazard ratios for a 1 mmol/L higher observational glucose level were calculated by using Cox regression in individuals without diabetes at baseline, and RRs for genetically higher glucose levels were derived from instrumental variable analyses (IVAs) of all individuals with available genotypes. Risk was also estimated by using summary-level data from the UK Biobank and the CKDGen Consortium on genetic variants tested in the MAGIC by using MR IVW estimates. The causal estimates from the CCHS and CGPS cohorts and the validation cohorts were combined through meta-analysis. The I2 values denote the percentages of between-study heterogeneity in the meta-analyses. N, number; p-glucose, plasma glucose.

On the basis of summary-level data for the 26 genetic variants associated with high fasting glucose levels in individuals without diabetes in the MAGIC, combined with end point data from the UK Biobank participants into a causal estimate by two-sample MR regression, the inverse-variance weighted (IVW) estimates were similar to the results from the CCHS and the CGPS combined, that is, an RR of 4.55 (95% CI 2.26–9.15) for retinopathy, 1.48 (0.83–2.66) for peripheral neuropathy, and 1.23 (0.57–2.67) for PAD (Fig. 3). We did not have access to diagnosis codes for diabetic nephropathy or eGFR measurements from the UK Biobank. Using summary-level data for the same 26 genetic variants from the MAGIC combined with end point data for reduced kidney function from the CKDGen Consortium, we found results similar to those in the CCHS and CGPS combined, that is, no evidence of causal association between high glucose level and eGFR <60 mL/min/1.73 m2 (RR 0.98 [95% CI 0.95–1.01]). Combining the causal estimates from the CCHS and CGPS with the IWV estimates from the UK Biobank and CKDGen Consortium by using meta-analyses, a 1 mmol/L higher glucose level was associated with a causal RR of 2.93 (95% CI 1.32–6.50) for retinopathy, 1.87 (1.32–2.67) for peripheral neuropathy, 0.98 (0.95–1.01) for eGFR <60 mL/min/1.73 m2, and 1.19 (0.90–1.56) for PAD. The corresponding two-sample MR estimates using MR Egger regression and weighted median regression showed similar results for all end points, and we found no indication of pleiotropy (all P values ≥0.10 for the MR Egger intercept) or bias due to invalid instruments (Supplementary Fig. 7). Estimates for the known observational and causal association between high glucose level and MI is shown for the CCHS and CGPS combined, the UK Biobank cohort, and the CARDIoGRAMplusC4D Consortium as a positive control of study power and of the genetic instruments (Fig. 3 and Supplementary Fig. 7).

Conclusions

In this cohort from the Danish general population, we found observational and causal associations between elevated glucose level and high risk of retinopathy, peripheral neuropathy, and diabetic nephropathy. Observationally, we found positive associations between high random plasma glucose level and risk of PAD and CKD (defined as eGFR <60 mL/min/1.73 m2); however, a causal association could not be confirmed for PAD and seemed to be refuted for an eGFR <60 mL/min/1.73 m2. These findings were validated by similar results from a two-sample MR analysis using summary-level data for fasting glucose levels from participants in the MAGIC (13) and end point data from the UK Biobank (12) and the CKDGen Consortium (14). Our findings indicate that a high glucose level within the normal range is an important causal risk factor for microvascular disease. Furthermore, our study replicates previous findings showing that a high glucose level within the normal range is a causal risk factor for MI (1), suggesting that in a general population, elevated glucose levels over time may be important in the development of both micro- and macrovascular disease.

Previous observational studies have shown that retinopathy, neuropathy, and signs of microvascular dysfunction are prevalent in individuals with prediabetes (23–25). Observational findings may be confounded by concomitant cardiometabolic risk factors such as obesity, hyperlipidemia, and hypertension. Our finding of a stepwise increase in the risk of vascular disease with increasing glucose levels within the normoglycemic range or higher support the idea that an elevated glucose level per se has a causal role in the pathogenesis of microvascular disease, as do levels below the diabetes cutoff. This is in line with the general understanding of the natural history of type 2 diabetes as a continuous process of declining β-cell function and increasing relative insulin deficiency, leading to a continuous increase in glucose that is initiated years before the diabetes threshold is reached (26). Randomized controlled trials have shown that lifestyle changes and treatment with glucose-lowering drugs can reduce the progression from prediabetes to diabetes (27–29). Recent 30-year follow-up data from a study of 577 Chinese individuals showed that lifestyle interventions in individuals with prediabetes reduce long-term risks of diabetes, a composite of microvascular complications, cardiovascular disease, cardiovascular mortality, and all-cause mortality (30). The effects of lifestyle intervention are not likely to be due to glucose lowering alone but to several beneficial metabolic effects. Our findings highlight the importance of early detection of glycemia and screening for prediabetes in asymptomatic individuals through the use of risk assessment tools—such as the one currently provided by the American Diabetes Association (www.diabetes.org/are-you-at-risk/diabetes-risk-test/) (15)—in order to prevent glycemic deterioration as early as possible. In addition to being a risk factor for diabetes and cardiovascular disease, our data suggest that an elevated glucose level below the diabetes cutoff is an important risk factor for microvascular disease. Screening for retinopathy, neuropathy, diabetic nephropathy, and additional risk factors such as obesity, hyperlipidemia, and hypertension might be indicated in individuals with prediabetes.

We found observational and causal associations between glucose level and diabetic nephropathy, but no causal association between glucose level and CKD (defined as an eGFR <60 mL/min/1.73 m2). These findings may be explained by the fact that these two end points represent different etiologies of kidney disease: Diabetic nephropathy is a microvascular disease with characteristic histopathological changes induced by hyperglycemia and is initially characterized by albuminuria, glomerular hyperfiltration, and—though only in late stages—reductions in eGFR (17,31). In contrast, CKD covers a variety of kidney abnormalities of different etiologies, and eGFR is the gold standard but an unspecific measure of a decline in kidney function. In many individuals with prediabetes or type 2 diabetes, CKD is probably attributable to a mix of several pathological changes leading to reduced kidney function, including diabetic nephropathy, glomerulopathies, previous episodes of acute kidney disease, and aging-related nephropathy with micro- and macrovascular components (31); hyperglycemia may not be the predominant causal factor. In line with this, the renoprotective effects of sodium–glucose cotransporter 2 inhibitors seen in clinical intervention trials do not seem to be mediated by lowering glucose, but by other mechanisms that target common pathways of CKD progression, regardless of etiology (31,32).

The mechanisms by which glucose contributes to the pathogenesis of macrovascular disease such as MI and PAD are not completely known. Mechanistic studies suggest that metabolic changes induced by hyperglycemia, together with increased insulin resistance and free fatty acids, accelerate the atherosclerotic process through increased oxidative stress in arterial endothelial cells, the formation of advanced glycation end products, and nonenzymatic glycation of LDL, apolipoproteins, and clotting factors, collectively resulting in vasoconstriction, inflammation, and thrombosis (33,34). These mechanisms are likely to be similar in coronary arteries and large peripheral arteries, and even though in this study we could not confirm a causal association between glucose level and PAD (defined on the basis of ICD codes), a previous study of individuals without diabetes reported a causal association between fasting glucose level and carotid intima-media thickness (35).

A potential limitation of MR is that the selected genetic variants have pleiotropic effects on other risk factors of the diseases studied (21). However, we did not find any associations between the one-sample MR genetic instrument and age, sex, BMI, smoking, hypertension, LDL cholesterol, alcohol intake, physical activity, years of education, or, for women, menopause, and we found no indications of directional pleiotropy using MR Egger regression in the two-sample MR. Also, even though our aim was to study individuals from the general population, the confirmation cohorts used in the two-sample MR consisted of selected samples that may not be representative of the general population and may thus not be completely comparable (participation rates: CCHS, 61%; CGPS, 43%; UK Biobank, 5%) (36).

A limitation to our study is that in CCHS and CGPS, glucose was measured from samples obtained from participants in a nonfasted state, and oral glucose tolerance tests were not performed. Consequently we could not classify individuals according to impaired fasting glucose or impaired glucose tolerance status. In the observational analyses, estimates were adjusted for time since the last meal, and in the genetic analyses the results were validated by using summary data for fasting glucose levels. Another limitation is that eGFR was calculated from a single measurement of plasma creatinine, which may lead to some misclassification of kidney disease. However, the prevalence of eGFR <60 mL/min/1.73 m2 among the CCHS/CGPS population was ∼10%, which corresponds well with prevalence estimates from other general populations (37). Also, we did not have access to albuminuria measurements, which could have strengthened the validity of the nephropathy end point.

Strengths of the study include the examination of a large number of individuals from a genetically homogenous general population, access to highly valid data from individual participants, no losses to follow-up, and the use of MR, which allowed us to assess potential causal effects of high glucose levels on the risk of disease, minimizing residual confounding and reverse causation. In addition, the similar results found by using the two-sample MR approach increase the generalizability and validity of the results, making them reproducible with a different genetic instrument and in another population. The two-sample approach consisted of three separate analyses: the conventional IVW analysis unadjusted for pleiotropy; an analysis that used MR Egger regression adjusting for directional pleiotropic effects; and a weighted median regression accounting for up to 50% of information coming from invalid or weak instruments (38,39). Risk estimates from these three two-sample MR analyses were largely in the same direction and showed no indication of pleiotropy, confirming the validity of the instrument (38,39). It is still possible, however, that the genetic instruments influence the risk of disease through unknown glucose-independent pathways that we could not control for. An assumption of two-sample MR analyses is that the two samples represent similar but nonoverlapping populations. Summary data used for the two-sample MR analyses in this study were from the MAGIC and the CKDGen Consortium, both general population cohorts of European ancestry (13,14), and the overlap between two samples was low (<4% of individuals participating in both consortia). The UK Biobank is a general population cohort comprising participants of mainly white and British descent, and is not a part of the MAGIC (12,13). The CCHS and the CGPS were not part of the MAGIC or the CKDGen Consortium.

In conclusion, in this cohort from the Danish general population, random plasma glucose levels within the normal range and higher were causally associated with high risks of retinopathy, neuropathy, diabetic nephropathy, and MI. A causal association could not be confirmed for PAD and seemed to be refuted for eGFR <60 mL/min/1.73 m2. The findings were validated with similar results by using summary-level data for fasting glucose levels from the MAGIC and end point data from the UK Biobank and the CKDGen Consortium. These findings suggest that elevated glucose levels should be identified as an important risk factor for micro- and macrovascular disease in the general population and that screening for microvascular disease may be recommended, along with screening for additional cardiovascular risk factors, in individuals with prediabetes.

Article Information

Acknowledgments. The authors thank the staff and participants of the Copenhagen General Population Study and the Copenhagen City Heart Study, and the participants of the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), the CKDGen Consortium, the UK Biobank, and the CARDIoGRAMplusC4D Consortium for their generous participation. The authors also thank the consortia for making their data publicly available.

Funding. This study was supported by Det Frie Forskningsråd (the Danish Council for Independent Research) (#4183-00171B to M.B.).

The funder was not involved in the design, conduct, analyses of the study, or the interpretation of the results.

Duality of Interest. B.G.N. reports acting as a consultant to AstraZeneca, Sanofi, Regeneron, Akcea Therapeutics, Amgen, Kowa Company, Ltd., Denka Seiken Co., Ltd., Amarin Corporation, Novartis, Novo Nordisk, and Silence Therapeutics and has received lecture honoraria from Amgen, Kowa Company, Ltd., and Amarin Corporation. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. F.E. researched data, performed the analyses, and wrote the manuscript. S.M. reviewed and edited the manuscript. A.T.-H. and B.G.N. collected data and reviewed and edited the manuscript. M.B. performed the analyses and reviewed and edited the manuscript. All authors discussed the manuscript. M.B. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the 79th Scientific Sessions of the American Diabetes Association, San Francisco, CA, 7–11 June 2019.

Footnotes

  • This article contains Supplementary Data online at https://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc19-1850/-/DC1.

  • Received September 17, 2019.
  • Accepted January 23, 2020.
  • © 2020 by the American Diabetes Association.
https://www.diabetesjournals.org/content/license

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.

References

  1. ↵
    1. Benn M,
    2. Tybjaerg-Hansen A,
    3. McCarthy MI,
    4. Jensen GB,
    5. Grande P,
    6. Nordestgaard BG
    . Nonfasting glucose, ischemic heart disease, and myocardial infarction: a Mendelian randomization study. J Am Coll Cardiol 2012;59:2356–2365
    OpenUrlFREE Full Text
  2. ↵
    1. Sarwar N,
    2. Gao P,
    3. 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;376:958]. Lancet 2010;375:2215–2222
    OpenUrlCrossRefPubMedWeb of Science
  3. ↵
    1. Bonadonna R,
    2. Cucinotta D,
    3. Fedele D,
    4. Riccardi G,
    5. Tiengo A; Metascreen Writing Committee
    . The metabolic syndrome is a risk indicator of microvascular and macrovascular complications in diabetes: results from Metascreen, a multicenter diabetes clinic-based survey. Diabetes Care 2006;29:2701–2707
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Zoungas S,
    2. Arima H,
    3. Gerstein HC, et al.; Collaborators on Trials of Lowering Glucose (CONTROL) group
    . Effects of intensive glucose control on microvascular outcomes in patients with type 2 diabetes: a meta-analysis of individual participant data from randomised controlled trials. Lancet Diabetes Endocrinol 2017;5:431–437
    OpenUrl
  5. ↵
    1. Davies MJ,
    2. D’Alessio DA,
    3. Fradkin J, et al
    . Management of hyperglycaemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia 2018;61:2461–2498
    OpenUrl
  6. ↵
    1. Stehouwer CDA
    . Microvascular dysfunction and hyperglycemia: a vicious cycle with widespread consequences. Diabetes 2018;67:1729–1741
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Fedak KM,
    2. Bernal A,
    3. Capshaw ZA,
    4. Gross S
    . Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. Emerg Themes Epidemiol 2015;12:14
    OpenUrlPubMed
  8. ↵
    1. Lawlor DA,
    2. Harbord RM,
    3. Sterne JAC,
    4. Timpson N,
    5. Davey Smith G
    . Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008;27:1133–1163
    OpenUrlCrossRefPubMed
  9. ↵
    1. Florez JC,
    2. Jablonski KA,
    3. Bayley N, et al.; Diabetes Prevention Program Research Group
    . TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med 2006;355:241–250
    OpenUrlCrossRefPubMedWeb of Science
  10. ↵
    1. Hribal ML,
    2. Presta I,
    3. Procopio T, et al.; EUGENE2 Consortium
    . Glucose tolerance, insulin sensitivity and insulin release in European non-diabetic carriers of a polymorphism upstream of CDKN2A and CDKN2B. Diabetologia 2011;54:795–802
    OpenUrl
  11. ↵
    1. UK Biobank
    . UK Biobank: protocol for a large-scale prospective epidemiological resource [Internet], 21 March 2007. Available from www.ukbiobank.ac.uk/wp-content/uploads/2011/11/UK-Biobank-Protocol.pdf. Accessed 9 February 2020
  12. ↵
    1. Canela-Xandri O,
    2. Rawlik K,
    3. Tenesa A
    . An atlas of genetic associations in UK Biobank. Nat Genet 2018;50:1593–1599
    OpenUrlCrossRefPubMed
  13. ↵
    1. Scott RA,
    2. Lagou V,
    3. Welch RP, et al.; DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium
    . Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 2012;44:991–1005
    OpenUrlCrossRefPubMed
  14. ↵
    1. Pattaro C,
    2. Teumer A,
    3. Gorski M, et al.; ICBP Consortium; AGEN Consortium; CARDIOGRAM; CHARGe-Heart Failure Group; ECHOGen Consortium
    . Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun 2016;7:10023
    OpenUrlCrossRefPubMed
  15. ↵
    1. American Diabetes Association
    . 2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2018. Diabetes Care 2018;41(Suppl. 1):S13–S27
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Burgess S,
    2. Thompson SG
    . Use of allele scores as instrumental variables for Mendelian randomization. Int J Epidemiol 2013;42:1134–1144
    OpenUrlCrossRefPubMedWeb of Science
  17. ↵
    1. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group
    . KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013;3:1–150
    OpenUrlCrossRef
  18. ↵
    1. Levey AS,
    2. Stevens LA,
    3. Schmid CH, et al.; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
    . A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–612
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    1. Baum C,
    2. Schaffer M,
    3. Stillman S
    . Instrumental variables and GMM: estimation and testing. Stata J 2003;3:1–31
    OpenUrl
  20. ↵
    1. Nichols A.
    IVPOIS: stata module to estimate an instrumental variables Poisson regression via GMM [article online], 2008. Available from https://EconPapers.repec.org/RePEc:boc:bocode:s456890. Accessed 9 February 2020
  21. ↵
    1. Benn M,
    2. Nordestgaard BG
    . From genome-wide association studies to Mendelian randomization: novel opportunities for understanding cardiovascular disease causality, pathogenesis, prevention, and treatment. Cardiovasc Res 2018;114:1192–1208
    OpenUrlPubMed
  22. ↵
    1. Higgins JPT,
    2. Thompson SG,
    3. Deeks JJ,
    4. Altman DG
    . Measuring inconsistency in meta-analyses. BMJ 2003;327:557–560
    OpenUrlFREE Full Text
  23. ↵
    1. Abdul-Ghani M,
    2. DeFronzo RA,
    3. Jayyousi A
    . Prediabetes and risk of diabetes and associated complications: impaired fasting glucose versus impaired glucose tolerance: does it matter? Curr Opin Clin Nutr Metab Care 2016;19:394–399
    OpenUrl
    1. Sörensen BM,
    2. Houben AJHM,
    3. Berendschot TTJM, et al
    . Prediabetes and type 2 diabetes are associated with generalized microvascular dysfunction: the Maastricht Study. Circulation 2016;134:1339–1352
    OpenUrlAbstract/FREE Full Text
  24. ↵
    1. Azmi S,
    2. Ferdousi M,
    3. Petropoulos IN, et al
    . Corneal confocal microscopy identifies small-fiber neuropathy in subjects with impaired glucose tolerance who develop type 2 diabetes. Diabetes Care 2015;38:1502–1508
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Phillips LS,
    2. Ratner RE,
    3. Buse JB,
    4. Kahn SE
    . We can change the natural history of type 2 diabetes. Diabetes Care 2014;37:2668–2676
    OpenUrlAbstract/FREE Full Text
  26. ↵
    1. Knowler WC,
    2. Fowler SE,
    3. Hamman RF, et al.; Diabetes Prevention Program Research Group
    . 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet 2009;374:1677–1686
    OpenUrlCrossRefPubMedWeb of Science
    1. Lindström J,
    2. Ilanne-Parikka P,
    3. Peltonen M, et al.; Finnish Diabetes Prevention Study Group
    . Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet 2006;368:1673–1679
    OpenUrlCrossRefPubMedWeb of Science
  27. ↵
    1. Li G,
    2. Zhang P,
    3. Wang J, et al
    . The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet 2008;371:1783–1789
    OpenUrlCrossRefPubMedWeb of Science
  28. ↵
    1. Gong Q,
    2. Zhang P,
    3. Wang J, et al.; Da Qing Diabetes Prevention Study Group
    . Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. Lancet Diabetes Endocrinol 2019;7:452–461
    OpenUrl
  29. ↵
    1. Anders H-J,
    2. Huber TB,
    3. Isermann B,
    4. Schiffer M
    . CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol 2018;14:361–377
    OpenUrlCrossRefPubMed
  30. ↵
    1. Perkovic V,
    2. Jardine MJ,
    3. Neal B, et al.; CREDENCE Trial Investigators
    . Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med 2019;380:2295–2306
    OpenUrl
  31. ↵
    1. Shah MS,
    2. Brownlee M
    . Molecular and cellular mechanisms of cardiovascular disorders in diabetes. Circ Res 2016;118:1808–1829
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Soran H,
    2. Durrington PN
    . Susceptibility of LDL and its subfractions to glycation. Curr Opin Lipidol 2011;22:254–261
    OpenUrlCrossRefPubMed
  33. ↵
    1. Rasmussen-Torvik LJ,
    2. Li M,
    3. Kao WH, et al
    . Association of a fasting glucose genetic risk score with subclinical atherosclerosis: the Atherosclerosis Risk in Communities (ARIC) study. Diabetes 2011;60:331–335
    OpenUrlAbstract/FREE Full Text
  34. ↵
    1. Munafò MR,
    2. Tilling K,
    3. Taylor AE,
    4. Evans DM,
    5. Davey Smith G
    . Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol 2018;47:226–235
    OpenUrlCrossRefPubMed
  35. ↵
    1. Hill NR,
    2. Fatoba ST,
    3. Oke JL, et al
    . Global prevalence of chronic kidney disease - a systematic review and meta-analysis. PLoS One 2016;11:e0158765
    OpenUrlCrossRefPubMed
  36. ↵
    1. Bowden J,
    2. Davey Smith G,
    3. Burgess S
    . Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015;44:512–525
    OpenUrlCrossRefPubMed
  37. ↵
    1. Burgess S,
    2. Butterworth A,
    3. Thompson SG
    . Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013;37:658–665
    OpenUrlCrossRefPubMed
PreviousNext
Back to top
Diabetes Care: 43 (4)

In this Issue

April 2020, 43(4)
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by Author
  • Masthead (PDF)
Sign up to receive current issue alerts
View Selected Citations (0)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word about Diabetes Care.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Impact of Glucose Level on Micro- and Macrovascular Disease in the General Population: A Mendelian Randomization Study
(Your Name) has forwarded a page to you from Diabetes Care
(Your Name) thought you would like to see this page from the Diabetes Care web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Impact of Glucose Level on Micro- and Macrovascular Disease in the General Population: A Mendelian Randomization Study
Frida Emanuelsson, Sarah Marott, Anne Tybjærg-Hansen, Børge G. Nordestgaard, Marianne Benn
Diabetes Care Apr 2020, 43 (4) 894-902; DOI: 10.2337/dc19-1850

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Add to Selected Citations
Share

Impact of Glucose Level on Micro- and Macrovascular Disease in the General Population: A Mendelian Randomization Study
Frida Emanuelsson, Sarah Marott, Anne Tybjærg-Hansen, Børge G. Nordestgaard, Marianne Benn
Diabetes Care Apr 2020, 43 (4) 894-902; DOI: 10.2337/dc19-1850
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Research Design and Methods
    • Results
    • Conclusions
    • Article Information
    • Footnotes
    • References
  • Figures & Tables
  • Suppl Material
  • Info & Metrics
  • PDF

Related Articles

Cited By...

More in this TOC Section

  • Association of Objectively Measured Timing of Physical Activity Bouts With Cardiovascular Health in Type 2 Diabetes
  • Changes in Albuminuria Predict Cardiovascular and Renal Outcomes in Type 2 Diabetes: A Post Hoc Analysis of the LEADER Trial
  • Heart Rate and Heart Rate Variability Changes Are Not Related to Future Cardiovascular Disease and Death in People With and Without Dysglycemia: A Downfall of Risk Markers? The Whitehall II Cohort Study
Show more Cardiovascular and Metabolic Risk

Similar Articles

Subjects

  • Epidemiology-Cardiovascular Disease

Navigate

  • Current Issue
  • Standards of Care Guidelines
  • Online Ahead of Print
  • Archives
  • Submit
  • Subscribe
  • Email Alerts
  • RSS Feeds

More Information

  • About the Journal
  • Instructions for Authors
  • Journal Policies
  • Reprints and Permissions
  • Advertising
  • Privacy Policy: ADA Journals
  • Copyright Notice/Public Access Policy
  • Contact Us

Other ADA Resources

  • Diabetes
  • Clinical Diabetes
  • Diabetes Spectrum
  • Scientific Sessions Abstracts
  • Standards of Medical Care in Diabetes
  • BMJ Open - Diabetes Research & Care
  • Professional Books
  • Diabetes Forecast

 

  • DiabetesJournals.org
  • Diabetes Core Update
  • ADA's DiabetesPro
  • ADA Member Directory
  • Diabetes.org

© 2021 by the American Diabetes Association. Diabetes Care Print ISSN: 0149-5992, Online ISSN: 1935-5548.