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Duration of Diabetes and Carotid Wall Thickness

The Insulin Resistance Atherosclerosis Study (IRAS)
Originally publishedhttps://doi.org/10.1161/01.STR.28.5.999Stroke. 1997;28:999–1005

    Abstract

    Background and Purpose Diabetes is a major risk factor for morbidity and mortality from cardiovascular disease. However, the role of the primary metabolic abnormality of diabetes (chronic hyperglycemia) in this disease process has not been fully elucidated.

    Methods A cross-sectional analysis was conducted among 489 persons with non–insulin-dependent diabetes mellitus; 299 were established diabetics (diagnosed previously) and 190 were newly diagnosed at the time of the Insulin Resistance Atherosclerosis Study (IRAS) examination. These men and women, of three different ethnic groups, were participants in IRAS. Established diabetes (versus newly diagnosed diabetes) and mean fasting glucose level were used as measures of hyperglycemic burden. Intimal-medial wall thickness (IMT) of the internal (ICA) and common (CCA) carotid arteries were used as indices of atherosclerosis.

    Results The mean duration of disease among established diabetics was 7 years. The mean CCA IMT and ICA IMT were 872 and 946 μm, respectively. Established diabetes and mean fasting glucose level were positively associated with increased CCA IMT (P<.05) but not ICA IMT, even after adjustment for numerous cardiovascular disease risk factors. CCA IMT was increased by 70 μm in established diabetics (versus newly diagnosed diabetics) and by 26 μm per 1 SD of fasting glucose. Among established diabetics, however, duration of known diabetes (number of years) was not significantly related to IMT.

    Conclusions Among diabetics in IRAS, established diabetes and fasting glucose level were each independently associated with CCA IMT, suggesting that chronic hyperglycemia or its associated metabolic abnormalities may lead to increased risk of atherosclerosis.

    Diabetes is a major risk factor for morbidity and mortality from CVD.12 The twofold to fourfold increased risk of CVD among diabetics indicates the presence of more frequent and/or severe CVD risk factors. The major biochemical abnormality that is characteristic of diabetes, chronic hyperglycemia, has been hypothesized as a causal factor. This hypothesis would also be supported by the finding of a dose-response relationship among diabetics, with poorly controlled diabetics or diabetics of long duration having higher rates of cardiovascular complications.

    Several prospective studies of persons with NIDDM support such a hypothesis. Cardiovascular mortality was increased among diabetics with increased fasting blood glucose34 and glycated hemoglobin levels.45 The mean follow-up in these studies ranged from 5 to 8 years. Other prospective studies of CHD events have reported increased risk among elderly diabetics with increased glycated hemoglobin levels and a longer duration of diabetes6 as well as among middle-aged women with increased duration of diabetes.7

    Other data do not support a relationship between chronic hyperglycemia and CHD.8 Fifteen-year follow-up mortality data from the Whitehall Study9 and ischemic heart disease data from the WHO Multinational Study of Vascular Disease in Diabetes10 showed no relationship between duration of disease or severity of hyperglycemia and outcome. The Whitehall Study specifically found no difference in the relative risk of CHD or CVD between 56 newly diagnosed diabetic men and 121 previously diagnosed diabetic men. It has been suggested, therefore, that hyperglycemia is not a determinant of CVD in NIDDM but that both conditions may share common antecedents such as insulin resistance, hyperinsulinemia, or genetic susceptibility.81112

    A limitation of many previous mortality studies of CVD among diabetics is the potential for ascertainment bias. CVD is widely known to be associated with diabetes, which may exaggerate CVD death rates among diabetics of long duration. Studies in which an objective marker of disease is used may yield more valid results. In this study the relationship between atherosclerosis and diabetes was explored with an objective marker of subclinical atherosclerosis, B-mode ultrasound of the IMT of the carotid artery. Herein, we report on the cross-sectional relationship between level of glycemia and duration of diabetes with IMT in 489 diabetics examined in IRAS. We hypothesize that fasting blood glucose and duration of diabetes will be positively associated with IMT, independent of known CVD risk factors.

    Subjects and Methods

    IRAS is a multicenter, epidemiological study that aimed to explore the relationships among levels of glucose and insulin, insulin resistance, CVD risk factors and risk behaviors, and clinical and subclinical CVD in a large multiethnic population. The study is the first large undertaking to directly measure both insulin resistance and subclinical atherosclerosis, as well as numerous CVD risk factors. The study was approved by the institutional review committees of each participating center, and all subjects gave informed consent.

    The cohort of 1625 men and women was enrolled at four sites: San Antonio, Tex; San Luis Valley, Colo; and Oakland and Los Angeles, Calif. Sampling was designed to identify potential participants across the range of age (40 to 69 years), ethnicity (non-Hispanic white, Hispanic, and black), and glucose tolerance strata (normal, impaired glucose tolerance, and NIDDM). To meet the recruitment goals for glucose tolerance strata, clinic staff oversampled from lists of persons known to have impaired glucose tolerance (by OGTT) or moderately elevated fasting glucose levels. Persons with diabetes were also recruited. Use of insulin in the previous 5 years was an exclusion criterion, which may have resulted in the recruitment of less severe diabetics. Recruitment strategies and results have been previously reported.13

    Participants attended two examinations, usually within a 2-week period. Age, sex, ethnicity, smoking status, and diabetes history and treatment were obtained by self-report. Insulin was measured in duplicate by the dextran-charcoal radioimmunoassay method.14 Total cholesterol, LDL-C, and HDL-C were measured in plasma by the β-quantification method, as described by the Lipid Research Clinics.15 TG were measured by enzymatic methods with the use of glycerol blanked assays on the Hitachi autoanalyzer. The externally measured coefficient of variation was 4% for LDL-C, HDL-C, and TG. Insulin sensitivity was assessed by the frequently sampled intravenous glucose tolerance test with minimal model analysis.1617 Albumin and creatinine were measured in a random urine sample; their ratio was used as a measure of microalbuminuria.

    A 75-g OGTT was administered to all participants; fasting and 2-hour postload blood samples were collected. Plasma glucose was measured by the glucose oxidase technique on an autoanalyzer (Yellow Springs Equipment Co); the externally measured coefficient of variation was 3%. Diabetes was defined according to WHO criteria18 as fasting glucose ≥7.8 mmol/L (≥140 mg/dL) or 2-hour postload glucose ≥11.1 mmol/L (≥200 mg/dL). Participants whose OGTT results met the above criteria but who did not report having been previously diagnosed with diabetes were considered newly diagnosed diabetics. Participants whose OGTT results met the above criteria and who reported a previous diagnosis of diabetes were considered established diabetics. Participants taking oral hypoglycemic medications were considered established diabetics, regardless of their OGTT results or previous report of a diagnosis. Fasting glucose measures from each of the two IRAS examination were averaged for the present analyses. Duration of diabetes was calculated for established diabetics as current age minus the reported age at the time of diagnosis.

    Anthropometric measures were made following a standardized protocol19 ; body mass index was calculated as weight (kilograms)/height (meters) squared. A minimum waist and maximal hip circumference were taken. Sitting blood pressure was measured after a 5-minute rest; the mean of the second and third measures was computed. Hypertension was defined as systolic blood pressure >140 mm Hg, diastolic blood pressure >90 mm Hg, or current use of antihypertensive medication.

    High-resolution B-mode carotid ultrasonography was performed with Toshiba SSA-270A imaging units (Toshiba America Medical Systems) to provide an index of atherosclerosis.202122 The scanning and reading protocol was identical to that used in the Cardiovascular Health Study.23 A bilateral assessment of the wall thickness was made in the ICA and the CCA. For the ICA the sonographer sought the site of maximal IMT in the region between the dilatation of the carotid bulb and the ICA 1 cm distal to the tip of the flow divider. Three images were obtained (bilaterally) at the site of maximal thickness at different interrogation angles (proximal, lateral, and anterior). For the CCA, bilateral images were obtained 1 cm proximal to the dilatation of the carotid bulb at a single (lateral) angle.

    Ultrasound images were recorded on super VHS tape and sent weekly to a central reading facility. One reader at this facility was responsible for reading all ultrasound images from IRAS. For each of the eight available images, the maximal IMT was taken over a 1-cm segment of the arterial wall distant from the skin surface (far wall). (Because of the geometry of the artery and the physics of ultrasound assessment, measurements of the far wall are considered both more reliable and valid24 and will be the focus of these analyses.) Two summary measures were calculated: (1) the mean of the six ICA sites and (2) the mean of the two CCA sites. To allow equal weighting of the left and right arteries in the presence of missing data, the mean values of the available measures on the left ICA and the mean values of the available measures on the right ICA were calculated, and then the mean of these two means was used in analysis.

    A subset of 43 participants were rescanned to assess intrasonographer variability; the correlation coefficient between scans was .86 and .75 for CCA and ICA IMT, respectively. Likewise, a subset of 64 scans were reread to assess intrareader variability; the Pearson correlation coefficient between scans was .95 and .94 for CCA and ICA IMT, respectively.

    Statistical Methods

    Two variables were considered measures of hyperglycemic burden: the mean of two fasting glucose measures (a measure of glycemia) and established versus newly diagnosed diabetes (a measure of duration). Duration was then further explored by contrasting IMT among newly diagnosed diabetics with IMT among established diabetics with durations of 0.1 to 3.0 years, 3.1 to 7.5 years, and 7.6 to 31.0 years (tertiles). First, demographic variables and cardiovascular risk factors were compared, univariately, between the established and newly diagnosed diabetics. Statistical significance was assessed by means of t tests and χ2 tests. Multiple linear regression techniques (SAS version 6.08) were then used to model the two primary independent variables (fasting glucose and diabetes status) versus IMT. (Two-hour postload glucose was also examined but found to have a weaker relationship with IMT than fasting glucose.) Standard analytical techniques to examine the models for heteroscedasticity, linearity, and normality of residuals were used.25 The initial model was adjusted only for demographic variables (clinic, ethnicity, sex, age, and a clinic-by-ethnicity interaction term). Further modeling was also adjusted for LDL-C, HDL-C, TG, hypertension, smoking, body mass index, waist-hip ratio, and albumin-creatinine ratio. The statistical significance of contrasts between groups was determined from the multiple linear regression models. Current use of hypoglycemic medications was not included in the linear models because of its collinear relationship with the severity and duration of disease (see below). Additional possible interactions between ethnicity and sex with the independent variables of interest were evaluated; none met our criterion of significance (P<.10). Consequently, since these interactions were also not driven by previous scientific findings, they were eliminated from the final model.

    Results

    A total of 537 IRAS participants were classified as diabetic at the time of the baseline examination. This report includes only those 489 (91%) for whom ultrasound data were available. Missing ultrasound data occurred primarily because the equipment was unavailable. Of these 489, 299 (61%) had been previously diagnosed with diabetes (established diabetics) and 190 (39%) were first considered diabetic at the time of their IRAS examination (newly diagnosed diabetics) (Table 1). The mean age was 57 years, 54% were female, and the sample was split evenly across the three ethnic groups. Among established diabetics, the mean duration of disease was 7 years (±6.6 years). Established diabetics had higher mean glucose levels, higher TG levels, and lower HDL-C levels. Newly diagnosed diabetics had higher LDL-C levels. ICA IMT was significantly greater in the established diabetics than in the newly diagnosed diabetics (976 versus 898 μm; P<.05). ICA IMT measurements also yielded greater interindividual variability than CCA IMT measurements.

    Fasting glucose and established diabetes (versus newly diagnosed diabetes) were each associated with increased CCA IMT but not ICA IMT after adjustment for demographic characteristics (Table 2). Adjustment for CVD risk factors did not attenuate either relationship: established diabetes (versus newly diagnosed diabetes) was associated with a 70-μm thicker CCA IMT (P=.003), and a 1 SD difference in fasting glucose (3.2 mmol/L or 57.6 mg/dL) was associated with a 26-μm thicker CCA IMT (P=.03). While no association between these independent variables and the ICA IMT was observed, the effect sizes were similar for the CCA and the ICA.

    Other independent correlates of CCA IMT, in addition to established diabetes, included older age (P<.0001), male sex (P=.02), higher LDL-C (P=.006), current cigarette smoking (P=.02), and IRAS clinical center (P=.006) (not shown). Correlates of ICA IMT included older age (P=.0004) and current cigarette smoking (P=.008).

    After adjustment for demographic characteristics and CVD risk factors, no differences in mean CCA IMT were observed between the three categories of duration among established diabetics (Figure); however, CCA IMT for each of the three categories of duration among the established diabetics was significantly higher than the CCA IMT of the newly diagnosed diabetics (P<.05). No significant differences were observed between the categories of duration for ICA IMT (not shown). Duration of diabetes (years) was explored further in a linear model that included only the established diabetics. After adjustment for demographics and CVD risk factors, CCA IMT increased, nonsignificantly, by 3.8 μm per year of duration (P=.12). No association with the ICA was observed.

    The current use of hypoglycemic medications (74% of established diabetics; none of the newly diagnosed diabetics, by definition) was investigated as a possible modifier of these findings. Among established diabetics, medication use was associated with a longer duration of diabetes (7.2 versus 5.4 years; P=.02), higher mean fasting glucose (10.9 versus 9.3 mmol/L, or 196 versus 168 mg/dL, P<.0001), higher albumin-creatinine ratio (5.3 versus 2.6 mg/mmol; P=.006), and greater CCA IMT (909 versus 817 μm; P=.002). For the CCA, the relationship between duration of disease and IMT did not differ between persons currently taking and not taking medications (interaction P=.18). However, for the ICA the relationship differed (interaction P=.04). After adjustment for demographic factors, ICA IMT increased by 13.8 μm per year of duration (P=.12) in those not on medication (n=77) but decreased by −7.7 μm per year of duration (P=.14) in those on medication (n=216). Thus, an increasing duration of diabetes had a more potent effect on increasing ICA IMT in those not currently taking hypoglycemic medication.

    Discussion

    Fasting glucose and established diabetes were each independently associated with CCA IMT, a subclinical measure of atherosclerosis. The magnitude of the effect, approximately 70 μm for established diabetes compared with newly diagnosed diabetes, is similar to the effect of prevalent, clinically defined CVD compared with disease-free individuals.26 Among established diabetics, however, there was insufficient statistical evidence (P=.12) to support the hypothesis that duration of diabetes (number of years) was positively associated with IMT. The degree of glycemia, measured as the mean of two fasting glucose values, was also associated with CCA IMT in multivariate models with an effect size of 26 μm per 1 SD of glucose. These effects were independent of several potentially important metabolic factors such as lipids and coagulation factors.

    A threshold effect of duration on CCA IMT is suggested in these data, with IMT levels increased in those with minimal duration of diabetes by approximately 70 μm above the levels of newly diagnosed diabetics. This is in sharp contrast to the graded relationship observed between duration of diabetes and microvascular complications, particularly retinopathy.27 This is not wholly unexpected, however, since the WHO definition of diabetes was based on the association between glucose and risk of microvascular complications.28 Manson et al7 also reported an early deleterious effect of diabetes on the risk of CVD. The relative risk of combined nonfatal myocardial infarction and fatal CHD was markedly elevated even in women with previously diagnosed diabetes of short duration (<4 years) compared with nondiabetic women. The authors also reported a threshold effect after approximately 15 years’ duration, with another marked increase in risk. They suggest that the absence of a duration effect in previous studies of diabetics may be related to the small number of subjects with sufficiently long duration. Although this does not explain the lack of a difference between the risk of CVD between newly and previously diagnosed diabetics in the Whitehall Study,9 it may in part explain the lack of a duration (dose-response) effect among the 121 previously diagnosed diabetics whose mean duration of disease was 4 to 5 years. The implication of a threshold effect for epidemiological studies is that newly diagnosed diabetics should not be pooled with established diabetics in assessment of CVD risk.

    Several other studies have investigated the relationship between duration of diabetes, degree of glycemia, and objective measures of atherosclerosis. Kawamori et al29 reported a cross-sectional relationship between duration of diabetes (but not HbA1c) and carotid IMT in 275 NIDDM subjects. The partial regression coefficient for duration of diabetes was 5.6 μm/y (P=.03), somewhat larger than our (nonsignificant) finding in previously diagnosed diabetics (3.8 μm/y), perhaps as a result of the longer mean duration of diabetes in their sample (13 years). Pujia et al30 reported a correlation (r=.33, P<.03) between duration of diabetes (but not HbA1c or glucose) and CCA IMT in 54 patients with NIDDM. However, the relationship did not persist in multiple regression analysis. In an elderly population of 84 men and women with NIDDM, neither fasting glucose nor HbA1c was associated with carotid IMT.31 Instead, the main determinants of IMT were postglucose 1-hour plasma insulin, serum LDL TG, and apolipoprotein B concentrations. In a case-control study of nearly 1000 diabetics with angiographically defined coronary artery disease, severity of diabetes (as defined by treatment modality) but not duration of diabetes was associated with coronary artery disease.32 In summary, only one report has shown an independent relationship between duration of diabetes and/or glycemia and an objective measure of atherosclerosis.29

    Together, these findings suggest either a direct effect of chronic hyperglycemia on atherosclerosis or an indirect effect due to the multiple metabolic abnormalities associated with diabetes. Several hypotheses regarding the direct effect of glucose have been postulated. Hyperglycemia leads to the nonenzymatic glycosylation of proteins in the arterial wall.33 These advanced glycosylation end products have been shown to contribute to the microvascular3435 as well as the macrovascular complications of diabetes.3637 Another mechanism by which hyperglycemia can theoretically contribute to atherosclerosis is through stimulating proliferation of endothelial, mesangial, and smooth muscle cells.38 Hyperglycemia can also accelerate oxidation of lipoproteins,39 thereby enhancing the atherogenicity of the particle. Other metabolic abnormalities associated with chronic hyperglycemia may be the causal factor associated with increased IMT in established diabetics. Dyslipidemia, particularly hypertriglyceridemia and low levels of HDL, is common in diabetics and predicts cardiovascular mortality.4 Numerous hemostatic abnormalities have also been described. A clustering of these risk factors and their association with hyperinsulinemia and insulin resistance are observed in persons with NIDDM.40 Consequently, disentangling the effects is extremely difficult.

    The correlates of IMT as reported here are consistent with other reports in nondiabetics2241 and diabetics.29 Another finding consistent with the literature is that IMT/risk factor relationships are different across carotid sites.42 For example, we observed a statistically significant relationship between established diabetes and CCA IMT but not ICA IMT. It has been suggested that site-specific relationships are indicators of focal plaque as opposed to systemic atherosclerotic thickening.43 Regardless, measures of both the ICA and CCA relate well to major risk factors for atherosclerosis as well as to existing coronary and atherosclerotic disease.44

    This analysis and the study sample are limited in several ways. First, and common to most other related studies, is the measurement of chronic hyperglycemia by the mean of two fasting glucose measurements. Glycemia, measured over a period of years, would be a more valid assessment. Likewise, duration of diabetes, a possible surrogate measure of chronic hyperglycemia, is difficult to define with reasonable accuracy, particularly in NIDDM, in which the prodromal period can be as long as 20 years. Furthermore, a worsening of CVD risk factors is reported to occur throughout this period.45 A second limitation was the exclusion of insulin-taking diabetics, a requirement for a study of insulin resistance in which the frequently sampled intravenous glucose tolerance test methodology was used.1617 The likely truncation of the upper end of disease severity and hence duration may have led to an underestimate of the effect of glycemia and/or duration on IMT. On the other hand, the exclusion of insulin-taking diabetics may have been beneficial in that it resulted in a study sample that was more homogeneous with regard to treatment, and it eliminated a possible direct atherogenic effect of exogenously administered insulin.46

    In summary, established NIDDM and fasting glucose levels are important correlates of increased CCA IMT in IRAS participants. However, although the relationship between the duration of known disease to CCA IMT was positive, it did not reach statistical significance (P=.12), perhaps because of the truncated distribution of duration in this sample. Our observation of increased CCA IMT levels in the diabetics with even minimal duration above that of newly diagnosed diabetics suggests a threshold effect, not the graded effect observed between duration of diabetes and microvascular complications. These results add to a small body of evidence that severity and duration of diabetes and/or its associated metabolic abnormalities increase the risk of atherosclerosis. Primary and secondary prevention trials, now under way, will be instrumental in determining whether delaying the onset of diabetes and/or controlling blood glucose levels will reduce atherosclerotic disease risk.

    Selected Abbreviations and Acronyms

    CCA=common carotid artery
    CHD=coronary heart disease
    CVD=cardiovascular disease
    HDL-C=HDL cholesterol
    ICA=internal carotid artery
    IMT=intimal-medial wall thickness
    IRAS=Insulin Resistance Atherosclerosis Study
    LDL-C=LDL cholesterol
    NIDDM=non–insulin-dependent diabetes mellitus
    OGTT=oral glucose tolerance test
    TG=triglycerides
    WHO=World Health Organization

    Appendix A1

    Participating Institutions and Principal Staff

    Field Centers

    Oakland, Calif—Kaiser Permanente Division of Research and Stanford University: Dr Joseph Selby, Dr Gerald Reaven, Dr Ami Laws, Pat McCoy, Barbara Anglin

    Los Angeles, Calif—University of Southern California and Kaiser Permanente: Dr Mohammed Saad, Dr Gerald Borok, Dr David Blumfield, Dr Mara Vitolins, Dr Wagdy Kades, Dr Maged Ayad, Dr Victor Wassily

    San Antonio, Tex—University of Texas Health Science Center at San Antonio: Dr Steven Haffner, Dr Leena Mykkänen, Maria Montez

    San Luis Valley, Colo—University of Colorado Health Sciences Center: Dr Marian Rewers, Dr Richard Hamman, Dr William Hiatt, Dr Eugene Wolfel, Judith Baxter, Carolyn Swenson

    Coordinating Center

    Winston-Salem, NC—Bowman Gray School of Medicine of Wake Forest University: Dr George Howard, Dr Lynne Wagenknecht, Dr Gregory Burke, Dr Ralph D’Agostino, Jr, Dr Elizabeth Mayer, Leora Henkin

    Blood Analysis Laboratories and Reading Centers

    Central Blood Analysis Center, University of Southern California School of Medicine, Los Angeles: Dr Richard Bergman, Dr Herbert Meiselman, Richard Watanabe

    Other participating laboratories: Medlantic Research Foundation, Washington, DC: Dr Barbara Howard; University of Vermont, Colchester: Dr Russell Tracy

    ECG Reading Center, Bowman Gray School of Medicine of Wake Forest University: Dr Pentti Rautaharju

    Ultrasound Reading Center, Geisinger Institute, Danville, Pa: Dr Daniel O’Leary

    National Institutes of Health Project Office

    National Heart, Lung, and Blood Institute Project Office, Bethesda, Md: Dr Peter Savage, Phyliss Sholinsky

    Table 1. Characteristics of Study Population

    Newly Diagnosed DiabeticsEstablished DiabeticsAll
    n190299489
    Clinical center, %1
    San Antonio, Tex231619
    San Luis Valley, Colo133325
    Oakland, Calif382027
    Los Angeles, Calif2530228
    Age, y57.7 (8.2)56.9 (8.2)57.2 (8.2)
    Sex, % female585254
    Ethnicity, %
    Non-Hispanic white353535
    Hispanic283532
    Black373033
    Body mass index, kg/m231.9 (5.8)31.1 (6.0)31.4 (6.0)
    Waist-hip ratio0.97 (0.060)0.98 (0.067)0.98 (0.064)
    Hypertensive, %565052
    Current smokers, %181516
    Past smokers, %384643
    TG, mmol/L1.93 (1.28)2.24 (2.20)32.12 (1.91)
    LDL-C, mmol/L3.73 (0.94)3.56 (0.88)33.63 (0.91)
    HDL-C, mmol/L1.13 (0.33)1.01 (0.29)21.06 (0.31)
    Fasting insulin, pmol/L152 (113)130 (78)3139 (94)
    Insulin sensitivity, 10–4 min–1/μU/mL0.59 (0.84)0.55 (0.82)0.57 (0.83)
    Albumin-creatinine ratio, mg/mmol3.6 (8.5)4.3 (10.9)4.0 (10.0)
    Duration of diabetes, yNA7.0 (6.6)NA
    Fasting glucose, mmol/L7.8 (2.5)10.4 (3.1)29.4 (3.2)
    Hypoglycemic medication use, %NA74NA
    CCA IMT, μm851 (205)885 (269)872 (246)
    ICA IMT, μm898 (382)976 (463)3946 (435)

    NA indicates not applicable. Values are mean (SD) unless otherwise noted. Fasting insulin and insulin sensitivity were log transformed for statistical testing purposes. Missing values do not exceed 6 for any variable except LDL-C (24 missing), insulin sensitivity (45 missing), and albumin-creatinine ratio (19 missing).

    1Proportions do not add to 100% as a result of rounding.

    2P<.0001,

    3P<.05 for difference between newly diagnosed and established diabetics.

    Table 2. Multiple Linear Regression Coefficients of Fasting Glucose and Diabetes Status on CCA IMT and ICA IMT

    IMT
    CCAICA
    AdjustmentFasting Glucose, mmol/LEstablished vs Newly DiagnosedFasting Glucose, mmol/LEstablished vs Newly Diagnosed
    Demographics1 (n=489)
    β-Coefficient8.561.29.841.1
    P.01.007.11.32
    r 2.12.12.08.08
    Demographics and CVD factors1 (n=404)
    β-Coefficient8.070.07.656.0
    P.03.003.39.21
    r 2.16.17.11.11

    1Fasting glucose is not included in the diabetes status model and vice versa. Demographic variables include clinic, ethnic, clinic/ethnic interaction, sex, and age. CVD factors include LDL-C, HDL-C, TG, hypertension, smoking status, body mass index, waist-hip ratio, and albumin-creatinine ratio. The correlation coefficient comes from the multiple linear regression model.

    
          Figure 1.

    Figure 1. CCA IMT (mean and 95% confidence interval) by duration of diabetes. Numbers in bars are means adjusted for demographic characteristics and CVD risk factors. Sample sizes are 191, 94, 106, and 99. All pairwise comparisons were made: newly diagnosed diabetics vs duration of 0.1 to 3.0 years, P=.02; newly diagnosed diabetics vs duration of 3.1 to 7.5 years, P=.03; newly diagnosed diabetics vs duration of 7.6 to 31.0 years, P=.02. No other contrasts reached statistical significance.

    This study was supported by National Heart, Lung, and Blood Institute contracts U01-HL47887, U01-HL47889, U01-HL47890, U01-HL47892, U01-HL47902, and DK-29867.

    Footnotes

    Correspondence to Dr Lynne E. Wagenknecht, Department of Public Health Sciences, Bowman Gray School of Medicine of Wake Forest University, Medical Center Blvd, Winston-Salem, NC 27157. E-mail

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