Obesity and type 2 diabetes have been associated with a high-fat diet (HFD) and reduced mitochondrial mass and function. We hypothesized a HFD may affect expression of genes involved in mitochondrial function and biogenesis. To test this hypothesis, we fed 10 insulin-sensitive males an isoenergetic HFD for 3 days with muscle biopsies before and after intervention. Oligonucleotide microarray analysis revealed 297 genes were differentially regulated by the HFD (Bonferonni adjusted P < 0.001). Six genes involved in oxidative phosphorylation (OXPHOS) decreased. Four were members of mitochondrial complex I: NDUFB3, NDUFB5, NDUFS1, and NDUFV1; one was SDHB in complex II and a mitochondrial carrier protein SLC25A12. Peroxisome proliferator-activated receptor gamma coactivator-1 (PGC1) alpha and PGC1beta mRNA were decreased by -20%, P < 0.01, and -25%, P < 0.01, respectively. In a separate experiment, we fed C57Bl/6J mice a HFD for 3 weeks and found that the same OXPHOS and PGC1 mRNAs were downregulated by approximately 90%, cytochrome C and PGC1alpha protein by approximately 40%. Combined, these results suggest a mechanism whereby HFD downregulates genes necessary for OXPHOS and mitochondrial biogenesis. These changes mimic those observed in diabetes and insulin resistance and, if sustained, may result in mitochondrial dysfunction in the prediabetic/insulin-resistant state.

The Diabetes Complications and Control Trial (DCCT) established glycosylated hemoglobin (A1C) as the gold standard of glycemic control, with levels </=7% deemed appropriate for reducing the risk of vascular complications. Yet, even when A1Cs were comparable between intensively treated subjects and their conventionally treated counterparts, the latter group experienced a markedly higher risk of progression to retinopathy over time. Our speculative explanation, based on the discovery that hyperglycemia-induced oxidative stress is the chief underlying mechanism of glucose-mediated vascular damage, was that glycemic excursions were of greater frequency and magnitude among conventionally treated patients, who received fewer insulin injections. Subsequent studies correlating the magnitude of oxidative stress with fluctuating levels of glycemia support the hypothesis that glucose variability, considered in combination with A1C, may be a more reliable indicator of blood glucose control and the risk for long-term complications than mean A1C alone.

BACKGROUND: Tight perioperative control of blood glucose improves the outcome of diabetic patients undergoing cardiac surgery. Because stress response and cardiopulmonary bypass can induce profound hyperglycemia, intraoperative glycemic control may become difficult. The authors undertook a prospective cohort study to determine whether poor intraoperative glycemic control is associated with increased intrahospital morbidity. METHODS: Two hundred consecutive diabetic patients undergoing on-pump heart surgery were enrolled. A standard insulin protocol based on subcutaneous intermediary insulin was given the morning of the surgery. Intravenous insulin therapy was initiated intraoperatively from blood glucose concentrations of 180 mg/dl or greater and titrated according to a predefined protocol. Poor intraoperative glycemic control was defined as four consecutive blood glucose concentrations greater than 200 mg/dl without any decrease in despite insulin therapy. Postoperative blood glucose concentrations were maintained below 140 mg/dl by using aggressive insulin therapy. The main endpoints were severe cardiovascular, respiratory, infectious, neurologic, and renal in-hospital morbidity. RESULTS: Insulin therapy was required intraoperatively in 36% of patients, and poor intraoperative glycemic control was observed in 18% of patients. Poor intraoperative glycemic control was significantly more frequent in patients with severe postoperative morbidity (37% vs. 10%; P < 0.001). The adjusted odds ratio for severe postoperative morbidity among patients with a poor intraoperative glycemic control as compared with patients without was 7.2 (95% confidence interval, 2.7-19.0). CONCLUSION: Poor intraoperative control of blood glucose concentrations in diabetic patients undergoing cardiac surgery is associated with a worsened hospital outcome after surgery.

BACKGROUND: Lifestyle modification can forestall diabetes in high-risk people, but the long-term cost-effectiveness is uncertain. OBJECTIVE: To estimate the effects of the lifestyle modification program used in the Diabetes Prevention Program (DPP) on health and economic outcomes. DESIGN: Cost-effectiveness analysis using the Archimedes model. DATA SOURCES: Published basic and epidemiologic studies, clinical trials, and Kaiser Permanente administrative data. TARGET POPULATION: Adults at high risk for diabetes (body mass index >24 kg/m2, fasting plasma glucose level of 5.2725 to 6.9375 mmol/L [95 to 125 mg/dL], 2-hour glucose tolerance test result of 7.77 to 11.0445 mmol/L [140 to 199 mg/dL]). TIME HORIZON: 5 to 30 years. PERSPECTIVE: Patient, health plan, and societal. INTERVENTIONS: No prevention, DPP's lifestyle modification program, lifestyle modification begun after a person develops diabetes, and metformin. MEASUREMENTS: Diagnosis and complications of diabetes. RESULTS OF BASE-CASE ANALYSIS: Compared with no prevention program, the DPP lifestyle program would reduce a high-risk person's 30-year chances of getting diabetes from about 72% to 61%, the chances of a serious complication from about 38% to 30%, and the chances of dying of a complication of diabetes from about 13.5% to 11.2%. Metformin would deliver about one third the long-term health benefits achievable by immediate lifestyle modification. Compared with not implementing any prevention program, the expected 30-year cost/quality-adjusted life-year (QALY) of the DPP lifestyle intervention from the health plan's perspective would be about 143,000 dollars. From a societal perspective, the cost/QALY of the lifestyle intervention compared with doing nothing would be about 62,600 dollars. Either using metformin or delaying the lifestyle intervention until after a person develops diabetes would be more cost-effective, costing about 35,400 dollars or 24,500 dollars per QALY gained, respectively, compared with no program. Compared with delaying the lifestyle program until after diabetes is diagnosed, the marginal cost-effectiveness of beginning the DPP lifestyle program immediately would be about 201,800 dollars. Results of Sensitivity Analysis: Variability and uncertainty deriving from the structure of the model were tested by comparing the model's results with the results of real clinical trials of diabetes and its complications. The most critical element of uncertainty is the effectiveness of the lifestyle program, as expressed by the 95% CI of the DPP study. The most important potentially controllable factor is the cost of the lifestyle program. Compared with no program, lifestyle modification for high-risk people can be made cost-saving over 30 years if the annual cost of the intervention can be reduced to about 100 dollars. LIMITATIONS: Results depend on the accuracy of the model. CONCLUSIONS: Lifestyle modification is likely to have important effects on the morbidity and mortality of diabetes and should be recommended to all high-risk people. The program used in the DPP study may be too expensive for health plans or a national program to implement. Less expensive methods are needed to achieve the degree of weight loss seen in the DPP.

OBJECTIVES: We have developed an Internet-based, interactive computer model to determine the long-term health outcomes and economic consequences of implementing different treatment policies or interventions in type 1 and type 2 diabetes mellitus. The model projects outcomes for populations, taking into account baseline cohort characteristics and past history of complications, current and future diabetes management and concomitant medications, screening strategies and changes in physiological parameters over time. The development of complications, life expectancy, quality-adjusted life expectancy and total costs within populations can be calculated. METHODS: The model is based on a series of sub-models that simulate important complications of diabetes (cardiovascular disease, eye disease, hypoglycaemia, nephropathy, neuropathy, foot ulcer, amputation, stroke, ketoacidosis, lactic acidosis and mortality). Each sub-model is a Markov model using Monte Carlo simulation incorporating time, state, time-in state, and diabetes type-dependent probabilities derived from published sources. Analyses can be performed on cohorts with type 1 or type 2 diabetes. Cohorts, defined in terms of age, gender, baseline risk factors and pre-existing complications, can be modified or new cohorts defined by the user. Economic and clinical data in the model can be edited, thus ensuring adaptability by allowing the inclusion of new data as they become available; creation of country- or provider-specific versions of the model; and allowing the investigation of new hypotheses. CONCLUSIONS: The CORE Diabetes Model allows the calculation of long-term outcomes, based on the best data currently available. Diabetes management strategies can be compared in different patient populations in a variety of realistic clinical settings, allowing the identification of efficient diabetes management strategies.

OBJECTIVES: The aim of this study was to assess the validity of the CORE Diabetes Model by comparing results from model simulations with observed outcomes from published epidemiological and clinical studies in type 1 and type 2 diabetes. METHODS: A total of 66 second-(internal) and third-(external) order validation analyses were performed across a range of complications and outcomes simulated by the CORE Diabetes Model (amputation, cataract, hypoglycaemia, ketoacidosis, macular oedema, myocardial infarction, nephropathy, neuropathy, retinopathy, stroke and mortality). Published studies were reproduced in the model by recreating cohorts in terms of demographics, baseline risk factors and complications, treatment patterns and patient management strategies, and simulating the progress of the cohort to an equivalent time horizon. RESULTS: Correlation analysis on results from 66 validation simulations produced an R2 value of 0.9224 (perfect fit = 1). A correlation plot of published study data versus values simulated by the CORE Diabetes Model had a trend line with a gradient of 1.0187 (perfect fit = 1). Validation analyses in type 1 and type 2 diabetes were associated with R2 values of 0.9778 and 0.8861 respectively. Correlation of second-order validation analyses (model predictions versus observed outcomes in studies used to construct the model) produced an R2 value of 0.9574, and the value for third-order analyses (model predictions versus observed outcomes in studies not used to construct the model) was 0.9023. CONCLUSIONS: The CORE Diabetes Model provides an accurate representation of patient outcomes when compared to 66 studies of diabetes and its complications. Model flexibility ensures it can be used to compare diabetes management strategies in different cohorts across a variety of clinical settings.

BACKGROUND: Emerging evidence indicates that patients with mental health conditions (MHCs) may receive less intensive medical care. Diabetes serves as a useful condition in which to test for MHC-related disparities in care. We examined whether quality measures for diabetes care are worse for patients with or without MHCs. METHODS: This national, cross-sectional study included 313 586 noninstitutionalized Veterans Health Administration patients with diabetes (identified from diagnostic codes and prescriptions) whose Veterans Health Administration facility transmitted laboratory data to a central database; 76 799 (25%) had MHCs (based on diagnostic codes for depressed mood, anxiety, psychosis, manic symptoms, substance use disorders, personality disorders, and other categories). National data from Veterans Health Administration records, Medicare claims, and a national survey were linked to characterize 1999 diabetes care. RESULTS: Failure to meet diabetes performance measures was more common in patients with MHCs: unadjusted odds ratio (95% confidence interval) was 1.24 (1.22-1.27) for no hemoglobin A(1c) testing, 1.25 (1.23-1.28) for no low-density lipoprotein cholesterol testing, 1.05 (1.03-1.07) for no eye examination, 1.32 (1.30-1.35) for poor glycemic control, and 1.17 (1.15-1.20) for poor lipemic control. Disparities persisted after case mix adjustment and were more pronounced with specific MHCs (psychotic, manic, substance use, and personality disorders). The percentage not meeting diabetes care standards increased with increasing number of MHCs. CONCLUSION: Patients with mental illness merit special attention in national diabetes quality improvement efforts.

The objective of this national, cross-sectional study was to provide insight into the care and treatment of type 2 diabetes (T2DM) in the Canadian primary care setting. Specifically, the study examines glycemic control, management and morbidity load among T2DM patients, and investigates the relationship of glycemic control and morbidity load with duration of diabetes. Participating primary care physicians (PCPs)(N=243) completed a chart audit for the first 10 patients with T2DM attending their clinics (2473 eligible patient records). The mean A1C was 7.3% with 49% of patients not at target (A1C>or=7.0%). Glycemic control eroded significantly with increasing duration of diabetes in spite of increasing therapeutic intervention. T2DM patients experienced a high morbidity load (hypertension 63%; dyslipidemia 59%; macrovascular complications 28%; microvascular complications 38%) each of which increased significantly with duration of diabetes. For 79% of patients not at target, PCPs identified lifestyle intervention as the strategy for achieving glycemic targets while more aggressive treatment plans were identified for only 56%. These results underscore the complexity of primary care management of T2DM and suggest that current treatment approaches are not intensive enough for a large proportion of patients especially those with longer duration of disease.

Diabetes represents a serious risk factor for the development of cardiovascular problems such as coronary heart disease, peripheral arterial disease, hypertension, stroke, cardiomyopathy, nephropathy and retinopathy. Identifying the pathogenesis of this increased risk provides a basis for secondary intervention to reduce morbidity and mortality in diabetic patients. Hyperglycemia and protein glycation, increased inflammation, a prothrombotic state and endothelial dysfunction have all been implicated as possible mechanisms for such complications. A linking element between many of these phenomena could possibly be, among other factors, increased production of reactive oxygen species. Vascular endothelial cells have several physiological actions that are essential for the normal function of the cardiovascular system. These include the production of nitric oxide (NO), which regulates vasodilatation, anticoagulation, leukocyte adhesion, smooth muscle proliferation and the antioxidative capacity of endothelial cells. However, under conditions of hyperglycemia, excessive amounts of superoxide radicals are produced inside vascular cells and this can interfere with NO production leading to the possible complications. This article aims at reviewing the links between reactive oxygen species, diabetes and vascular disease and whether or not antioxidants can alter the course of vascular complications in diabetic patients and animal models. A possible beneficial effect of antioxidants might present a new addition to the range of secondary preventive measures used in diabetic patients.

BACKGROUND: LDL modification by endogenous advanced glycation end products (AGEs) is thought to contribute to cardiovascular disease of diabetes. It remains unclear, however, whether exogenous (diet-derived) AGEs influence glycoxidation and endothelial cell toxicity of diabetic LDL. METHODS AND RESULTS: Twenty-four diabetic subjects were randomized to either a standard diet (here called high-AGE, HAGE) or a diet 5-fold lower in AGE (LAGE diet) for 6 weeks. LDL pooled from patients on HAGE diet (Db-HAGE-LDL) was more glycated than LDL from the LAGE diet group (Db-LAGE-LDL)(192 versus 92 AGE U/mg apolipoprotein B) and more oxidized (5.7 versus 1.5 nmol malondialdehyde/mg lipoprotein). When added to human endothelial cells (ECV 304 or human umbilical vein endothelial cells), Db-HAGE-LDL promoted marked ERK1/2 phosphorylation (pERK1/2)(5.5- to 10-fold of control) in a time- and dose-dependent manner compared with Db-LAGE-LDL or native LDL. In addition, Db-HAGE-LDL stimulated NF-kappaB activity significantly in ECV 304 and human umbilical vein endothelial cells (2.3-fold above baseline) in a manner inhibitable by a MEK inhibitor PD98059 (10 micromol/L), the antioxidant N-acetyl-l-cysteine, NAC (30 mmol/L), and the NADPH oxidase inhibitor DPI (20 micromol/L). In contrast to Db-LAGE-LD and native LDL, Db-HAGE-LDL induced significant soluble vascular cell adhesion molecule-1 production (2.3-fold), which was blocked by PD98059, NAC, and DPI. CONCLUSIONS: Exposure to daily dietary glycoxidants enhances LDL-induced vascular toxicity via redox-sensitive mitogen-activated protein kinase activation. This can be prevented by dietary AGE restriction.