Skip to main content
PMC home page
The Journal of Clinical Endocrinology and Metabolism logoLink to The Journal of Clinical Endocrinology and Metabolism
. 2009 Feb 10;94(5):1595–1601. doi: 10.1210/jc.2008-1829

Post-Challenge Hyperglycemia in Older Adults Is Associated with Increased Cardiovascular Risk Profile

Jill P Crandall 1, Harry Shamoon 1, Hillel W Cohen 1, Migdalia Reid 1, Srikanth Gajavelli 1, Georgeta Trandafirescu 1, Vafa Tabatabaie 1, Nir Barzilai 1
PMCID: PMC2684470  PMID: 19208733

Abstract

Context: Post-challenge hyperglycemia (PCH) is common in older adults and is associated with increased cardiovascular disease (CVD) risk and total mortality. However, PCH is rarely recognized in clinical settings, and the glycemic exposure and CVD risk profile of elderly individuals with PCH has not been defined.

Objective: The aim of the study was to characterize metabolic and CVD risk profile of elderly subjects with PCH and to determine the effect of acute postprandial metabolic changes on vascular biomarkers.

Design: We conducted a cross-sectional study with a standard meal challenge protocol.

Participants: Older adults with normal glucose tolerance (n = 30) or PCH (fasting glucose <126 mg/dl and 2-h glucose ≥170 mg/dl; n = 28) participated in the study.

Main Outcome Measures: We assessed fasting and postprandial levels of glucose, insulin, lipids, high sensitivity C-reactive protein, plasminogen activator inhibitor-1, and adiponectin and endothelial function using reactive hyperemia peripheral arterial tonometry.

Results: Normal glucose tolerance and PCH subjects were matched for age, sex, body mass index, and ethnicity. Fasting glucose (102 ± 3 vs. 93 ± 2 mg/dl; P < 0.001) and glycosylated hemoglobin (5.7 vs. 5.4%; P = 0.01) were modestly higher in the PCH group, which was also more insulin resistant (homeostasis model assessment for insulin resistance, 7.0 ± 1.3 vs. 4.1 ± 0.6; P = 0.03). Fasting high sensitivity C-reactive protein was higher (2.6 ± 0.5 vs. 1.3 ± 0.2 mg/dl; P = 0.05), and adiponectin was lower (11.6 ± 1.6 vs. 14.0 ± 1.3 μg/ml; P = 0.03) in subjects with PCH. Peak and 6-h postprandial area under the curve glucose, insulin, and lipids were higher in PCH subjects, who also had higher fasting and postprandial levels of plasminogen activator inhibitor-1. Reactive hyperemia peripheral arterial tonometry declined postprandially only in PCH.

Conclusions: Older adults with PCH experience significant fasting and postprandial metabolic dysregulation, which is accompanied by a proatherosclerotic and prothrombotic vascular profile.

Older adults with post-challenge hyperglycemia experience significant postprandial metabolic dysregulation, which is accompanied by an unfavorable vascular risk profile.

Impaired glucose regulation is common in older adults due to age-related metabolic changes that result in defects in both insulin secretion and insulin action. According to recent estimates, the prevalence of diabetes exceeds 20% in people age 60 and above, and a similar number of older adults have impaired glucose tolerance (IGT) and/or impaired fasting glucose (IFG) (1,2). Incident diabetes and prediabetes in the elderly characteristically manifest as post-challenge, rather than fasting, hyperglycemia. Although mild hyperglycemia, including IGT and isolated post-challenge hyperglycemia (PCH), defined by oral glucose tolerance testing, is common in older adults, it is generally unrecognized and untreated. IFG, IGT, and isolated PCH may all progress to overt diabetes and its attendant risk of microvascular complications, but there is evidence that PCH may be uniquely associated with increased risk for cardiovascular disease (CVD) and total mortality (3,4).

PCH may promote CVD by inducing expression of peptides that promote thrombosis and inflammation. These peptides [such as plasminogen activator inhibitor-1 (PAI-1)] and cytokines (such as IL) are transcriptionally induced by nutrients in fat, endothelial cells, and other tissues. Furthermore, PCH may induce oxidant stress and lead to impaired endothelial cell function, which is known to be an early event in the development of atherosclerosis. In this study, we sought to characterize the metabolic status and CVD risk profile of elderly subjects with PCH and to determine the effect of acute postprandial metabolic changes on vascular biomarkers, including endothelial function. Our hypothesis was that older adults with PCH determined by formal oral glucose tolerance testing (OGTT) also experience significant postmeal metabolic dysregulation that is accompanied by proatherothrombotic vascular changes that may contribute to CVD risk.

Subjects and Methods

Inclusion

Subjects age 65 and older were screened with a 75-g OGTT. Those with PCH [fasting plasma glucose (FPG) < 126 mg/dl and 2-h OGTT glucose ≥ 170 mg/dl; n = 28) and a control group with normal glucose tolerance (NGT) (FPG < 100 mg/dl and 2-h glucose < 140 mg/dl; n = 30) were recruited for the study. All subjects provided written informed consent in accordance with the procedures of the Committee on Clinical Investigation of the Albert Einstein College of Medicine. Exclusion criteria were: treated diabetes; recent (within 6 months) vascular event (myocardial infarction, stroke, coronary intervention); severe systemic illness, such as cancer, renal insufficiency, active liver disease; and inflammatory conditions, such as rheumatoid arthritis or systemic lupus. Also excluded were current treatment with statins, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, systemic estrogen therapy (because of the known effect of these agents on inflammatory markers and endothelial function), and treatment with drugs known to alter glucose metabolism. Smokers were also excluded.

Standard meal test protocol

After the screening OGTT, eligible subjects returned on a separate day for a standard meal test. They were instructed to eat a designated meal on the night before the test and to fast after 2100 h. Any prescribed medications were taken as usual, with the exception of blood pressure medication, which was held until after the postmeal endothelial function test. On the morning of the standard meal test, subjects were required to rest for 30 min in a quiet room, after which fasting endothelial function tests (see below) were performed, followed by blood sampling through an indwelling iv line. Subjects were then fed a standard mixed meal (110 g carbohydrate, 20 g protein, 20 g fat) to simulate breakfast, and blood was sampled again at 1, 2, and 3 h after the meal. A second meal with identical nutrient composition, to simulate lunch, was consumed 3.5 h after the first meal, and blood sampling continued at 1, 2, and 3 h after this meal. Subjects remained at rest in a chair or bed for the duration of the test and were allowed free access to water or other noncaloric, noncaffeinated beverages.

Endothelial function test

Endothelial function was assessed using reactive hyperemia peripheral arterial tonometry (RH-PAT). This noninvasive technique is used to assess peripheral microvascular endothelial function by measuring changes in digital pulse volume during reactive hyperemia. Pulse volume is measured by a finger plethysmographic device that allows isolated detection of pulsatile arterial volume changes, which are sensed by a pressure transducer and transferred to a computer where the signal is amplified, displayed, and stored (EndoPAT; Itamar Medical, Caesarea, Israel). Tests are conducted during fasting and 90 min after the standard breakfast meal. Studies are performed with the patient at rest, in a comfortable, thermoneutral environment. Fingertip probes are placed on the index fingers of both hands, and 5 min of baseline recording are obtained. Blood flow is then occluded in one arm for 5 min, using a standard blood pressure cuff. Recording continues in both fingers during occlusion and for 5 min after release of the cuff. The RH-PAT index is calculated as the ratio of the average pulse amplitude in the posthyperemic phase divided by the average baseline amplitude, with normalization to the signal in the control arm to compensate for any systemic changes. Test-retest repeatability testing among healthy controls in our lab resulted in a coefficient of variability of 15.2% for tests performed 2 h apart. RH-PAT testing was added to the study protocol after the first year of recruitment and was performed on 22 NGT and 22 PCH subjects.

Assays

Plasma glucose, fasting and pooled postprandial triglycerides, lipoproteins, and free fatty acids were measured using standard laboratory techniques in the core laboratory of the Einstein General Clinical Research Center. High sensitivity-C-reactive protein (hsCRP) was measured by latex-enhanced turbitimetric assay (CRP Ultra Wide Range Reagent Kit; Equal Diagnostics, Exton, PA). glycosylated hemoglobin (HbA1c) was assayed by HPLC; serum creatinine and other clinical tests were performed using standard techniques. PAI-1 antigen was measured using Lincoplex human serum adipokine panel (Linco Research, St. Charles, MO), adiponectin by RIA (Linco), and insulin by RIA in the Einstein Diabetes Research and Training Center Hormone Assay Core.

Other measurements

Homeostasis model assessment for insulin resistance (HOMA-IR) was calculated based on fasting variables: insulin (mU/ml) × glucose (mmol/liter)/22.5 (Ref. 5). Insulin secretion was calculated using the corrected insulin response at 30 min (CIR30): insulin30min (μU/ml)/glucose30min (mg/dl) × [glucose30min (mg/dl) − 70]. Percentage body fat was measured by bioimpedance analysis (RJL Systems, Clinton Township, MI) (6). Waist circumference was measured in the standing position midway between the highest point of the iliac crest and the lowest point of the costal margin in the midaxillary line.

Statistical analysis

Results are presented as mean (se). Insulin and glucose area under the curve (AUC) was calculated using the trapezoidal method. Differences between the NGT and PCH groups in fasting and AUC parameters and RH-PAT score were analyzed using an unpaired t test, or by the Mann-Whitney test for data that were not normally distributed. PAI-1 levels were log-transformed due to skewed data, and differences in fasting and postmeal PAI-1 levels within groups were analyzed by ANOVA and by paired t test using the Bonferonni correction. Correlations between RH-PAT score and metabolic variables were tested using Spearman’s correlation. A P value of ≤ 0.05 was considered significant.

Results

Subject characteristics

Characteristics of the PCH and NGT subjects are shown in Table 1. The two groups were well matched for age, sex, body mass index (BMI), percentage body fat, and waist circumference. Due to selection criteria, 2-h glucose levels were higher in the PCH group (193 ± 4.6 vs. 105 ± 3.3 mg/dl; P < 0.001), as were fasting glucose (102 ± 3 vs. 93 ± 2 mg/dl; P < 0.001) and HbA1c (5.7 vs. 5.4%; P = 0.01). Twenty-one subjects in the PCH group had fasting glucose of at least 100 mg/dl and thus also met criteria for IFG. Eight subjects in the PCH group had 2-h glucose levels of at least 200 mg/dl (mean ± sd, 223 ± 10 mg/dl), consistent with diagnostic criteria for diabetes. These subjects with diabetic OGTT were more likely to be female (88 vs. 55%; P < 0.001) and tended to have greater percentage body fat (39.9 ± 3.9 vs. 32.7 ± 2.7; P = 0.16) and slightly lower FPG (102 ± 3 vs. 109 ± 3 mg/dl; P = 0.19), but did not differ from the IGT subjects in other relevant variables, including age, BMI, lipid profile, or HbA1c (supplemental Table 1, published as supplemental data on The Endocrine Society’s Journals online web site at http://jcem.endojournals.org). The PCH group was more insulin resistant (HOMA-IR, 7.0 ± 1.3 vs. 4.1 ± 0.6; P = 0.03) and had lower insulin secretion (CIR30, 0.8 ± 0.1 vs. 1.7 ± 0.3; P = 0.02) than the NGT group. Fasting levels of hsCRP were higher (2.6 ± 0.5 vs. 1.3 ± 0.2 mg/dl; P = 0.05), and adiponectin levels were lower (11.6 ± 1.6 vs. 14.0 ± 1.3 μg/ml; P = 0.03) in the PCH group compared with the NGT group. Fasting lipoprotein levels did not differ between the two groups.

Table 1.

Subject characteristics

PCH (n = 28) NGT (n = 30) P
Age (yr) 71.5 (1) 71.4 (1) 0.99
Sex (% female) 64 67 0.80
BMI (kg/m2) 30.3 (0.9) 28.5 (1.0) 0.20
Percent body fat (BIA) 34.8 (2.3) 32.2 (2.5) 0.56
Waist circumference (cm) 101 (3) 98 (3) 0.72
Hypertension (%) 46 27 0.20
FPG (mg/dl) 102 (3) 93 (2) 0.0001
2-h (OGTT) glucose (mg/dl) 193 (4.6) 104 (3.3) 0.0001
HbA1c (%) 5.7 (0.1) 5.4 (0.1) 0.01
HOMA-IR 6.9 (1.3) 4.1 (0.6) 0.03
CIR30 0.9 (0.1) 1.8 (0.3) 0.02
HDL cholesterol (mg/dl) 55 (3) 57 (2) 0.23
LDL cholesterol (mg/dl) 115 (5) 115 (7) 0.99
Triglycerides (mg/dl) 119 (14) 94 (8) 0.52
hsCRP (mg/dl) 2.6 (0.5) 1.3 (0.2) 0.05
Adiponectin (μg/ml) 11.6 (1.6) 13.7 (1.3) 0.03
Open in a new tab

Results are expressed as mean (sem); P value by unpaired t test or Mann-Whitney test if not normally distributed; χ 2 for categorical values. Hypertension is treated hypertension or clinic blood pressure of at least 140/85 mm Hg. Among NGT subjects, seven used beta blockers, four used calcium channel blockers, and three used thiazide diuretics. Among PCH subjects, five used beta blockers, eight used calcium blockers, and three used thiazide diuretics. BIA, Bioelectrical impedance analysis; HDL, high-density lipoprotein; LDL, low-density lipoprotein. 

Standard meal challenges

Levels of metabolites during the standard meal challenges are shown in Fig. 1. Consistent with the PCH detected on OGTT, peak (187 ± 6 vs.142 ± 6 mg/dl; P < 0.0001) and 6-h AUC glucose (910 ± 23 vs. 747 ± 18 mg/dl · h; P < 0.0001) were higher in the PCH group. Although the nutrient compositions of both meals were identical, the postmeal glucose excursion was attenuated after the second meal in each group. Peak (270 ± 21 vs. 183 ± 22 μU/ml; P = 0.006) and AUC (944 ± 86 vs. 730 ± 97 μU/ml · h; P = 0.02) insulin levels were also higher in the PCH subjects, and there was a trend for higher pooled postmeal triglyceride levels, although this difference was not statistically significant (Fig. 2A). The postmeal decline in free fatty acid levels was similar in both groups (Fig. 2B).

Figure 1.

Figure 1

Open in a new tab

Fasting and postmeal metabolites during standard meal test. A, NGT vs. PCH peak (P < 0.001) and AUC (P < 0.001) plasma glucose. B, NGT vs. PCH peak (P = 0.006) and AUC (P = 0.02) plasma insulin levels. pc, Postmeal.

Figure 2.

Figure 2

Open in a new tab

Fasting and postmeal variables during standard meal test. A, NGT vs. PCH free fatty acid levels fasting, 3 h after breakfast (3 h pcb), and 3 h after lunch (3 h pcl). B, NGT vs. PCH triglycerides fasting and 3 h pooled postbreakfast (pcb) and postlunch (pcl); 3-h pcb NGT vs. PCH, P = 0.39; 3 h pcl NGT vs. PCH, P = 0.19. Fasting vs. pcb vs. pcl, P < 0.0001 by ANOVA for NGT and PCH. C, NGT vs. PCH PAI-1 levels fasting, 3 h pcb and 3 h pcl. *, P = 0.03; **, P = 0.007.

Levels of the fat-derived peptide PAI-1 were highest in the fasting state and declined thereafter in both groups (Fig. 2C); however, the magnitude of decline was less and was not significant in the NGT group (P = 0.07 for NGT; P = 0.002 for PCH by ANOVA). PAI-1 levels were significantly higher in the PCH group while fasting (13.6 ± 1.7 vs. 8.1 ± 0.8 ng/ml; P = 0.03) and 3 h after the second meal (11.0 ± 1.4 vs. 6.7 ± 0.8 ng/ml; P = 0.007). Levels of adiponectin and hsCRP did not change after meals in either group (data not shown).

Endothelial function

Assessment of endothelial function using RH-PAT was performed during fasting and 90 min after the first meal. Fasting RH-PAT score was somewhat lower in the NGT group compared with PCH (2.48 ± 0.11 vs. 2.16 ± 0.09; P = 0.04). The RH-PAT score did not change after the meal in the NGT group. In contrast, RH-PAT score declined in the PCH group (postmeal Δ, −0.28 ± 0.01 vs. 0.04 ± 0.04, P = 0.05, in the PCH and NGT groups, respectively) (Fig. 3). This decline was similar in those PCH subjects with diabetes (postmeal Δ, −0.26) and IGT (postmeal Δ, −0.23). We also analyzed the RH-PAT scores (test to control arm ratio) by 30-sec intervals after cuff deflation (Fig. 4). In the PCH group, the postmeal decline was most apparent in the first 2 min postischemia, consistent with impairment in endothelial dependent [nitric oxide (NO)-mediated] vasodilation. There was no corresponding postmeal change in RH-PAT score in the NGT group. Because some antihypertensive medications may influence endothelial function, blood pressure medications were withheld on the morning of the RH-PAT tests. In addition, we repeated our analysis after excluding the seven NGT and 11 PCH subjects under treatment for hypertension. This did not alter the pattern of pre-post meal changes in RH-PAT scores (pre-post meal Δ, +0.06 in NGT; pre-post meal Δ, −0.34 in the PCH group).

Figure 3.

Figure 3

Open in a new tab

Pre-post meal change in RH-PAT score; RH-PAT measured fasting and 90 min after breakfast; Δ NGT (n = 22) vs. Δ PCH (n = 22). *, P = 0.05.

Figure 4.

Figure 4

Open in a new tab

RH-PAT score by 30-sec intervals after occlusion. A, PCH fasting vs. postmeal. B, NGT fasting vs. postmeal. *, P = 0.01; **, P = 0.002.

Baseline (fasting) RH-PAT score was correlated with BMI (r = −0.51, P = 0.02, and −0.38, P = 0.08, for the NGT and PCH groups, respectively), but not with fasting or postmeal metabolites (glucose, insulin, lipids). Pre-post meal change in RH-PAT showed borderline correlation with fasting glucose (r = −0.42; P = 0.07), 2-h glucose (−0.31; P = 0.18), fasting insulin (−0.40; P = 0.09), and 2-h insulin (−0.38; P = 0.15) in the NGT group only (Supplemental Table 2).

Discussion

In this study, we confirmed that older adults with normal or minimally elevated fasting glucose concentrations and HbA1c in the nondiabetic range may indeed display significant glucose intolerance when challenged with an oral glucose load. Furthermore, these older persons experience significant alterations in fasting as well as postprandial metabolites after a standard high-carbohydrate meal. These metabolic abnormalities are associated with unfavorable changes in biological markers of vascular risk, including a reduction in postprandial endothelial function. Together, these findings suggest that typical age-related glucose intolerance, although common and generally unrecognized clinically, may have important consequences. The two groups in our cohort were selected on the basis of 2-h glucose levels and had similar age, sex, adiposity, and general health status. It should be noted that the PCH group also had mildly elevated FPG and thus was exposed to both PCH and fasting hyperglycemia. It is not clear whether IFG confers equivalent CVD risk as PCH (7,8), but individuals with combined IFG and PCH may constitute a group with even greater cardiometabolic risk (9).

PCH has consistently been reported to be a better predictor of CVD and mortality than fasting glucose levels (10), and it is correlated with the presence of subclinical atherosclerosis and other CVD markers (11,12). Transient, acute hyperglycemia (such as occurs after an oral glucose load) has been shown to induce inflammation and oxidative stress in both diabetic and nondiabetic subjects (13,14), but such data are not available in older persons. In our study, the altered CVD risk profile of PCH subjects, including levels of hsCRP and adiponectin, was apparent in the fasting state and was further accompanied by postprandial metabolic dysregulation (elevated levels of glucose, insulin, triglycerides) and unfavorable changes in some vascular risk parameters.

PAI-1 levels and activity have been considered a CVD risk factor in insulin resistance and diabetes; thus, the increased plasma PAI-1 levels in the fasting PCH subjects were not unexpected. However, although there was a decrease in PAI-1 levels after the first meal, we observed a trend toward increased PAI-1 levels after the second meal, a U-shaped pattern that was not apparent in the subjects with NGT. We have previously noted that an acute increase in plasma levels of glucose and other nutrients leads to an increase in the expression and levels of PAI-1 in rodents (15) via activation of the nutrient-sensing hexosamine biosynthetic pathway (16). In the current study, the higher postmeal glucose levels in the PCH subjects may act on adipose tissue to increase expression of PAI-1, ultimately resulting in increased plasma levels. Diurnal variation in PAI-1 levels, with highest levels in the early morning, have previously been reported, but the biological mechanisms responsible for this pattern are not well understood. Our observations suggest that higher PAI-1 levels in the morning may be a result of nutrient fluxes during the previous day, with the daytime nadir reflecting the period of overnight fasting. Of note, at least one other fat-derived peptide, leptin, has been shown to be regulated by food ingestion rather than a true diurnal pattern (17).

Fasting levels of the fat-derived peptide adiponectin were lower in the PCH subjects, consistent with previous data linking hypoadiponectinemia with insulin resistance, type 2 diabetes, and CVD risk (18). Although there is some evidence that adiponectin levels tend to be higher overall with increasing age, lower adiponectin levels remain a predictor of incident diabetes even in older cohorts (19). Circulating adiponectin levels are closely and inversely correlated with fat mass and central (visceral) fat distribution, and it is notable that we observed lower adiponectin levels in the PCH subjects compared with NGT, despite comparable fat mass and waist circumference. This finding suggests that adiponectin levels reflect components of metabolic and vascular risk beyond adiposity alone. We have recently shown that centenarians have an increased frequency of a genotype in the 3′ untranslated region of the adiponectin gene, which is associated with high adiponectin levels and less metabolic syndrome, underscoring the role of adiponectin in aging and longevity (20). Although nutrient regulation of adiponectin has been reported (14), we and others have failed to detect any consistent postmeal changes in circulating adiponectin (21).

Inflammation has emerged as a key process in the pathogenesis of both atherosclerosis and diabetes. Circulating CRP may reflect inflammatory processes in various tissues, but a substantial contributor is adipose tissue, which produces IL-6, an important regulator of CRP synthesis in the liver. Therefore, it is notable that hsCRP levels were higher in the PCH subjects, again despite a similar degree of adiposity in the two groups. This greater inflammatory burden may reflect the presence of subclinical CVD in the PCH subjects and may also be a contributor to IGT.

Endothelial dysfunction is present in the earliest stages of atherosclerotic vascular disease and results in vasoconstriction, platelet aggregation, and monocyte adhesion, all of which contribute to development of atherosclerosis (22). Endothelial dysfunction in the coronary (23) and peripheral arteries (24) is predictive of future CVD events in patients with established vascular disease and in otherwise healthy older adults. Endothelial function may be impaired in normal aging (25), due to decreased production or increased degradation of NO (26,27), rather than fixed or structural changes within the vessel wall. Digital pulse wave amplitude during reactive hyperemia, as measured using RH-PAT, has been shown to be a NO-dependent phenomenon (28) and thus reflects the integrity of endothelial vasodilator function. RH-PAT scores are lower in subjects with documented coronary artery disease and have been correlated with coronary endothelial function (29,30,31). In a middle-aged cohort from the Framingham Third Generation Study, fasting RH-PAT scores showed modest correlation with traditional CVD risk factors (32), although in our cohort, fasting RH-PAT showed borderline correlation only with adiposity.

Endothelial function can be impaired by both acute (33) and chronic (34) hyperglycemia, possibly mediated by generation of reactive oxygen species and consequent inactivation of NO (35). Wascher et al. (36) reported reduction of endothelial function (by flow-mediated dilatation) after an oral sugar (saccharose) load in middle-aged subjects with IGT. This effect was attenuated by coadministration of the α-glucosidase inhibitor, acarbose, which blunted the rise in glucose and insulin. Elevations in circulating lipids, including free fatty acids, triglycerides, and remnant lipoproteins, may also contribute to impaired endothelial function in the postprandial period (37,38). In our study, we observed a decline in endothelial function in the postprandial state among subjects with PCH, but not those with NGT. This decline in endothelial function occurred coincident with postprandial increases in glucose, insulin, and triglycerides, which were greater in the PCH subjects. However, we did not find a strong or consistent correlation of change from baseline RH-PAT score with postmeal changes in glucose, insulin, or lipids. This suggests that other factors not measured in our study (such as reactive oxygen species or dicarbonyls) may play a role in postmeal endothelial dysfunction. We propose that our findings be interpreted with caution, however, considering the small sample size and the inherent (biological) variability of endothelial function. Additional studies, are needed to clarify the relationship between metabolic variables and RH-PAT changes.

The cross-sectional study design does not allow us to determine causality, and thus we cannot predict CVD event outcomes. However, our finding of an adverse CVD risk profile in subjects with PCH is consistent with several reports linking PCH with increased CVD and total mortality in middle-aged individuals. Most of the PCH subjects also had mildly elevated fasting glucose (IFG), a metabolic profile that has been associated with greater cardiometabolic risk than IGT alone (9). Although it would have been ideal to enroll only subjects with isolated PCH (who had FPG identical to the NGT group), this glycemic pattern was rare among the over 300 individuals that were screened for this study. Therefore, we cannot determine the relative contributions of IFG vs. PCH to CVD risk parameters, including endothelial function. Finally, our study population was generally healthy, and the exclusion of subjects under treatment for coronary artery disease (statins, angiotensin-converting enzyme inhibitors) resulted in a highly selected cohort that may not be representative of the larger group of older adults. On the other hand, this selection of a healthy “survivor” population (without diabetes or clinical evidence of CVD) is perhaps more convincing in demonstrating clinically meaningful differences between groups defined by post-challenge glucose levels and also reduced potential confounding by medication use.

In conclusion, older adults with mild fasting and post-challenge hyperglycemia have a proatherosclerotic and prothrombotic vascular risk profile, along with evidence of significant postprandial metabolic dysregulation. Routine glucose screening of the elderly, including OGTT, would allow identification of these high-risk individuals, but whether treatment aimed at reducing fasting and/or postprandial hyperglycemia will improve vascular risk in this population has not been established. Consequently, other interventions designed to reduce CVD risk, including the use of statins and aspirin, should be strongly considered for older adults with IFG and PCH.

Supplementary Material

[Supplemental Data]
jc.2008-1829_index.html (924B, html)

Acknowledgments

The authors thank Dr. Sylvia Wassertheil-Smoller and the Women’s Health Initiative and the late Dr. Ivan Kahn for valuable assistance with recruitment.

Footnotes

This work was supported by the National Institute on Aging (1 P01 AG021654-01, to N.B.), by a General Clinical Research Center grant from the National Institutes of Health (NIH) (M01-RR12248), and by the NIH-funded Diabetes Research and Training Center (5P60DK20541).

Disclosure Summary: The authors have nothing to declare.

First Published Online February 10, 2009

Abbreviations: AUC, Area under the curve; BMI, body mass index; CIR30, corrected insulin response at 30 min; CVD, cardiovascular disease; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; HOMA-IR, homeostasis model assessment for insulin resistance; hsCRP, high sensitivity C-reactive protein; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; NGT, normal glucose tolerance; NO, nitric oxide; OGTT, oral glucose tolerance testing; PAI-1, plasminogen activator inhibitor-1; PCH, post-challenge hyperglycemia; RH-PAT, reactive hyperemia peripheral arterial tonometry.

References

  1. 2005 Centers for Disease Control and Prevention National Diabetes Fact Sheet. General information and national estimates on diabetes in the United States, 2005. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention [Google Scholar]
  2. Cowie CC, Rust KF, Byrd-Holt DD, Eberhardt MS, Flegal KM, Engelgau MM, Saydah SH, Williams DE, Geiss LS, Gregg EW 2006 Prevalence of diabetes and impaired fasting glucose in adults in the U.S. population: National Health and Nutrition Examination Survey 1999–2002. Diabetes Care 29:1263–1268 [DOI] [PubMed] [Google Scholar]
  3. Barrett-Connor E, Ferrara A 1998 Isolated postchallenge hyperglycemia and the risk of fatal cardiovascular disease in older women and men. The Rancho Bernardo Study. Diabetes Care 21:1236–1239 [DOI] [PubMed] [Google Scholar]
  4. The DECODE Study Group 2001 Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med 161:397–405 [DOI] [PubMed] [Google Scholar]
  5. Hanson RL, Pratley RE, Bogardus C, Narayan KM, Roumain JM, Imperatore G, Fagot-Campagna A, Pettitt DJ, Bennett PH, Knowler WC 2000 Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemiologic studies. Am J Epidemiol 151:190–198 [DOI] [PubMed] [Google Scholar]
  6. Segal KR, Van LM, Fitzgerald PI, Hodgdon JA, Van Itallie TB 1988 Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation study. Am J Clin Nutr 47:7–14 [DOI] [PubMed] [Google Scholar]
  7. Blake DR, Meigs JB, Muller DC, Najjar SS, Andres R, Nathan DM 2004 Impaired glucose tolerance, but not impaired fasting glucose, is associated with increased levels of coronary heart disease risk factors: results from the Baltimore Longitudinal Study on Aging. Diabetes 53:2095–2100 [DOI] [PubMed] [Google Scholar]
  8. Resnick HE, Shorr RI, Kuller L, Franse L, Harris TB 2001 Prevalence and clinical implications of American Diabetes Association-defined diabetes and other categories of glucose dysregulation in older adults: the health, aging and body composition study. J Clin Epidemiol 54:869–876 [DOI] [PubMed] [Google Scholar]
  9. Novoa FJ, Boronat M, Saavedra P, Díaz-Cremades JM, Varillas VF, La Roche F, Alberiche MP, Carrillo A 2005 Differences in cardiovascular risk factors, insulin resistance, and insulin secretion in individuals with normal glucose tolerance and in subjects with impaired glucose regulation: the Telde Study. Diabetes Care 28:2388–2393 [DOI] [PubMed] [Google Scholar]
  10. Saydah SH, Miret M, Sung J, Varas C, Gause D, Brancati FL 2001 Postchallenge hyperglycemia and mortality in a national sample of U.S. adults. Diabetes Care 24:1397–1402 [DOI] [PubMed] [Google Scholar]
  11. Hanefeld M, Koehler C, Henkel E, Fuecker K, Schaper F, Temelkova-Kurktschiev T 2000 Post-challenge hyperglycaemia relates more strongly than fasting hyperglycaemia with carotid intima-media thickness: the RIAD Study. Risk Factors in Impaired Glucose Tolerance for Atherosclerosis and Diabetes. Diabet Med 17:835–840 [DOI] [PubMed] [Google Scholar]
  12. Ohshita K, Yamane K, Ishida K, Watanabe H, Okubo M, Kohno N 2004 Post-challenge hyperglycaemia is an independent risk factor for arterial stiffness in Japanese men. Diabet Med 21:636–639 [DOI] [PubMed] [Google Scholar]
  13. Esposito K, Nappo F, Marfella R, Giugliano G, Giugliano F, Ciotola M, Quagliaro L, Ceriello A, Giugliano D 2002 Inflammatory cytokine concentrations are acutely increased by hyperglycemia in humans: role of oxidative stress. Circulation 106:2067–2072 [DOI] [PubMed] [Google Scholar]
  14. Esposito K, Nappo F, Giugliano F, Di Palo C, Ciotola M, Barbieri M, Paolisso G, Giugliano D 2003 Meal modulation of circulating interleukin 18 and adiponectin concentrations in healthy subjects and in patients with type 2 diabetes mellitus. Am J Clin Nutr 78:1135–1140 [DOI] [PubMed] [Google Scholar]
  15. Einstein FH, Atzmon G, Yang XM, Ma XH, Rincon M, Rudin E, Muzumdar R, Barzilai N 2005 Differential responses of visceral and subcutaneous fat depots to nutrients. Diabetes 54:672–678 [DOI] [PubMed] [Google Scholar]
  16. Gabriely I, Yang XM, Cases JA, Ma XH, Rossetti L, Barzilai N 2002 Hyperglycemia induces PAI-1 gene expression in adipose tissue by activation of the hexosamine biosynthetic pathway. Atherosclerosis 160:115–122 [DOI] [PubMed] [Google Scholar]
  17. Schoeller DA, Cella LK, Sinha MK, Caro JF 1997 Entrainment of the diurnal rhythm of plasma leptin to meal timing. J Clin Invest 100:1882–1887 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kadowaki T, Yamauchi T, Kubota N, Hara K, Ueki K, Tobe K 2006 Adiponectin and adiponectin receptors in insulin resistance, diabetes, and the metabolic syndrome. J Clin Invest 116:1784–1792 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Snijder MB, Heine RJ, Seidell JC, Bouter LM, Stehouwer CD, Nijpels G, Funahashi T, Matsuzawa Y, Shimomura I, Dekker JM 2006 Associations of adiponectin levels with incident impaired glucose metabolism and type 2 diabetes in older men and women: the hoorn study. Diabetes Care 29:2498–2503 [DOI] [PubMed] [Google Scholar]
  20. Atzmon G, Pollin TI, Crandall J, Tanner K, Schechter CB, Scherer PE, Rincon M, Siegel G, Katz M, Lipton RB, Shuldiner AR, Barzilai N 2008 Adiponectin levels and genotype: a potential regulator of life span in humans. J Gerontol A Biol Sci Med Sci 63:447–453 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Shimabukuro M, Chinen I, Higa N, Takasu N, Yamakawa K, Ueda S 2007 Effects of dietary composition on postprandial endothelial function and adiponectin concentrations in healthy humans: a crossover controlled study. Am J Clin Nutr 86:923–928 [DOI] [PubMed] [Google Scholar]
  22. Deanfield JE, Halcox JP, Rabelink TJ 2007 Endothelial function and dysfunction: testing and clinical relevance. Circulation 115:1285–1295 [DOI] [PubMed] [Google Scholar]
  23. Suwaidi JA, Hamasaki S, Higano ST, Nishimura RA, Holmes Jr DR, Lerman A 2000 Long-term follow-up of patients with mild coronary artery disease and endothelial dysfunction. Circulation 101:948–954 [DOI] [PubMed] [Google Scholar]
  24. Yeboah J, Crouse JR, Hsu FC, Burke GL, Herrington DM 2007 Brachial flow-mediated dilation predicts incident cardiovascular events in older adults: the Cardiovascular Health Study. Circulation 115:2390–2397 [DOI] [PubMed] [Google Scholar]
  25. Andrawis N, Jones DS, Abernethy DR 2000 Aging is associated with endothelial dysfunction in the human forearm vasculature. J Am Geriatr Soc 48:193–198 [PubMed] [Google Scholar]
  26. Gates PE, Boucher ML, Silver AE, Monahan KD, Seals DR 2007 Impaired flow-mediated dilation with age is not explained by L-arginine bioavailability or endothelial asymmetric dimethylarginine protein expression. J Appl Physiol 102:63–71 [DOI] [PubMed] [Google Scholar]
  27. Gerhard M, Roddy MA, Creager SJ, Creager MA 1996 Aging progressively impairs endothelium-dependent vasodilation in forearm resistance vessels of humans. Hypertension 27:849–853 [DOI] [PubMed] [Google Scholar]
  28. Nohria A, Gerhard-Herman M, Creager MA, Hurley S, Mitra D, Ganz P 2006 Role of nitric oxide in the regulation of digital pulse volume amplitude in humans. J Appl Physiol 101:545–548 [DOI] [PubMed] [Google Scholar]
  29. Bonetti PO, Pumper GM, Higano ST, Holmes Jr DR, Kuvin JT, Lerman A 2004 Noninvasive identification of patients with early coronary atherosclerosis by assessment of digital reactive hyperemia. J Am Coll Cardiol 44:2137–2141 [DOI] [PubMed] [Google Scholar]
  30. Kuvin JT, Patel AR, Sliney KA, Pandian NG, Sheffy J, Schnall RP, Karas RH, Udelson JE 2003 Assessment of peripheral vascular endothelial function with finger arterial pulse wave amplitude. Am Heart J 146:168–174 [DOI] [PubMed] [Google Scholar]
  31. Kuvin JT, Mammen A, Mooney P, Sheikh-Ali AA, Karas RH 2007 Assessment of peripheral vascular endothelial function in the ambulatory setting. Vasc Med 12:13–16 [DOI] [PubMed] [Google Scholar]
  32. Hamburg NM, Keyes MJ, Larson MG, Vasan RS, Schnabel R, Pryde MM, Mitchell GF, Sheffy J, Vita JA, Benjamin EJ 2008 Cross-sectional relations of digital vascular function to cardiovascular risk factors in the Framingham Heart Study. Circulation 117:2467–2474 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Williams SB, Goldfine AB, Timimi FK, Ting HH, Roddy MA, Simonson DC, Creager MA 1998 Acute hyperglycemia attenuates endothelium-dependent vasodilation in humans in vivo. Circulation 97:1695–1701 [DOI] [PubMed] [Google Scholar]
  34. Johnstone MT, Creager SJ, Scales KM, Cusco JA, Lee BK, Creager MA 1993 Impaired endothelium-dependent vasodilation in patients with insulin-dependent diabetes mellitus. Circulation 88:2510–2516 [DOI] [PubMed] [Google Scholar]
  35. Neri S, Signorelli SS, Torrisi B, Pulvirenti D, Mauceri B, Abate G, Ignaccolo L, Bordonaro F, Cilio D, Calvagno S, Leotta C 2005 Effects of antioxidant supplementation on postprandial oxidative stress and endothelial dysfunction: a single-blind, 15-day clinical trial in patients with untreated type 2 diabetes, subjects with impaired glucose tolerance, and healthy controls. Clin Ther 27:1764–1773 [DOI] [PubMed] [Google Scholar]
  36. Wascher TC, Schmoelzer I, Wiegratz A, Stuehlinger M, Mueller-Wieland D, Kotzka J, Enderle M 2005 Reduction of postchallenge hyperglycaemia prevents acute endothelial dysfunction in subjects with impaired glucose tolerance. Eur J Clin Invest 35:551–557 [DOI] [PubMed] [Google Scholar]
  37. Lundman P, Eriksson M, Schenck-Gustafsson K, Karpe F, Tornvall P 1997 Transient triglyceridemia decreases vascular reactivity in young, healthy men without risk factors for coronary heart disease. Circulation 96:3266–3268 [DOI] [PubMed] [Google Scholar]
  38. Tushuizen ME, Nieuwland R, Scheffer PG, Sturk A, Heine RJ, Diamant M 2006 Two consecutive high-fat meals affect endothelial-dependent vasodilation, oxidative stress and cellular microparticles in healthy men. J Thromb Haemost 4:1003–1010 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

[Supplemental Data]
jc.2008-1829_index.html (924B, html)
jc.2008-1829_1.pdf (74.4KB, pdf)

Articles from The Journal of Clinical Endocrinology and Metabolism are provided here courtesy of The Endocrine Society

RESOURCES