Comparison of Accuracy of Diabetes Risk Score and Components of the Metabolic Syndrome in Assessing Risk of Incident Type 2 Diabetes in Inter99 Cohort

BACKGROUND: Given the increasing worldwide incidence of diabetes, methods to assess diabetes risk which would identify those at highest risk are needed. We compared two risk-stratification approaches for incident type 2 diabetes mellitus (T2DM); factors of metabolic syndrome (MetS) and a previously developed diabetes risk score, PreDx® Diabetes Risk Score (DRS). DRS assesses 5 yr risk of incident T2DM based on the measurement of 7 biomarkers in fasting blood. METHODOLOGY/PRINCIPAL FINDINGS: DRS was evaluated in baseline serum samples from 4,128 non-diabetic subjects in the Inter99 cohort (Danes aged 30-60) for whom diabetes outcomes at 5 years were known. Subjects were classified as having MetS based on the presence of at least 3 MetS risk factors in baseline clinical data. The sensitivity and false positive rate for predicting diabetes using MetS was compared to DRS. When the sensitivity was fixed to match MetS, DRS had a significantly lower false positive rate. Similarly, when the false positive rate was fixed to match MetS, DRS had a significantly higher specificity. In further analyses, subjects were classified by presence of 0-2, 3 or 4-5 risk factors with matching proportions of subjects distributed among three DRS groups. Comparison between the two risk stratification schemes, MetS risk factors and DRS, were evaluated using Net Reclassification Improvement (NRI). Comparing risk stratification by DRS to MetS factors in the total population, the NRI was 0.146 (p = 0.008) demonstrating DRS provides significantly improved stratification. Additionally, the relative risk of T2DM differed by 15 fold between the low and high DRS risk groups, but only 8-fold between the low and high risk MetS groups. CONCLUSIONS/SIGNIFICANCE: DRS provides a more accurate assessment of risk for diabetes than MetS. This improved performance may allow clinicians to focus preventive strategies on those most in need of urgent intervention

Associations Between Features of Glucose Exposure and A1C: The A1C-Derived Average Glucose (ADAG) Study

OBJECTIVE: Various methods are used to quantify postprandial glycemia or glucose variability, but few have been compared and none are standardized. Our objective was to examine the relationship among common indexes of postprandial glycemia, overall hyperglycemia, glucose variability, and A1C using detailed glucose measures obtained during everyday life and to study which blood glucose values of the day provide the strongest prediction of A1C. RESEARCH DESIGN AND METHODS: In the A1C-Derived Average Glucose (ADAG) study, glucose levels were monitored in 507 participants (268 type 1 diabetic, 159 type 2 diabetic, and 80 nondiabetic subjects) with continuous glucose monitoring (CGM) and frequent self-monitoring of blood glucose (SMBG) during 16 weeks. We calculated several indexes of glycemia and analyzed their intercorrelations. The association between glucose measurements at different times of the day (pre- and postprandial) and A1C was examined using multiple linear regression. RESULTS: Indexes of glucose variability showed strong intercorrelation. Among postprandial indexes, the area under the glucose curve calculated from CGM 2 h after a meal correlated well with the 90-min SMBG postprandial measurements. Fasting blood glucose (FBG) levels were only moderately correlated with indexes of hyperglycemia and average or postprandial glucose levels. Indexes derived with SMBG strongly correlated with those from CGM. Some SMBG time points had a stronger association with A1C than others. Overall, preprandial glucose values had a stronger association with A1C than postprandial values for both diabetes types, particularly for type 2 diabetes. CONCLUSIONS: Indexes of glucose variability and average and postprandial glycemia intercorrelate strongly within each category. Variability indexes are weakly correlated with the other categories, indicating that these measures convey different information. FBG is not a clear indicator of general glycemia. Preprandial glucose values have a larger impact on A1C levels than postprandial values

Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study

Abstract AIMS/HYPOTHESIS: Real-life glycaemic profiles of healthy individuals are poorly studied. Our aim was to analyse to what extent individuals without diabetes exceed OGTT thresholds for impaired glucose tolerance (IGT) and diabetes. METHODS: In the A1C-Derived Average Glucose (ADAG) study, 80 participants without diabetes completed an intensive glucose monitoring period of 12 weeks. From these data, we calculated the average 24 h glucose exposure as time spent above different plasma glucose thresholds. We also derived indices of postprandial glucose levels, glucose variability and HbA(1c). RESULTS: We found that 93% of participants reached glucose concentrations above the IGT threshold of 7.8 mmol/l and spent a median of 26 min/day above this level during continuous glucose monitoring. Eight individuals (10%) spent more than 2 h in the IGT range. They had higher HbA(1c), fasting plasma glucose (FPG), age and BMI than those who did not. Seven participants (9%) reached glucose concentrations above 11.1 mmol/l during monitoring. CONCLUSIONS/INTERPRETATION: Even though the non-diabetic individuals monitored in the ADAG study were selected on the basis of a very low level of baseline FPG, 10% of these spent a considerable amount of time at glucose levels considered to be 'prediabetic' or indicating IGT. This highlights the fact that exposure to moderately elevated glucose levels remains under-appreciated when individuals are classified on the basis of isolated glucose measurements

Tissue-Specific Remodeling of the Mitochondrial Proteome in Type 1 Diabetic Akita Mice

ObjectiveTo elucidate the molecular basis for mitochondrial dysfunction, which has been implicated in the pathogenesis of diabetes complications.Research design and methodsMitochondrial matrix and membrane fractions were generated from liver, brain, heart, and kidney of wild-type and type 1 diabetic Akita mice. Comparative proteomics was performed using label-free proteome expression analysis. Mitochondrial state 3 respirations and ATP synthesis were measured, and mitochondrial morphology was evaluated by electron microscopy. Expression of genes that regulate mitochondrial biogenesis, substrate utilization, and oxidative phosphorylation (OXPHOS) were determined.ResultsIn diabetic mice, fatty acid oxidation (FAO) proteins were less abundant in liver mitochondria, whereas FAO protein content was induced in mitochondria from all other tissues. Kidney mitochondria showed coordinate induction of tricarboxylic acid (TCA) cycle enzymes, whereas TCA cycle proteins were repressed in cardiac mitochondria. Levels of OXPHOS subunits were coordinately increased in liver mitochondria, whereas mitochondria of other tissues were unaffected. Mitochondrial respiration, ATP synthesis, and morphology were unaffected in liver and kidney mitochondria. In contrast, state 3 respirations, ATP synthesis, and mitochondrial cristae density were decreased in cardiac mitochondria and were accompanied by coordinate repression of OXPHOS and peroxisome proliferator-activated receptor (PPAR)-gamma coactivator (PGC)-1alpha transcripts.ConclusionsType 1 diabetes causes tissue-specific remodeling of the mitochondrial proteome. Preservation of mitochondrial function in kidney, brain, and liver, versus mitochondrial dysfunction in the heart, supports a central role for mitochondrial dysfunction in diabetic cardiomyopathy

Microalbuminuria and Cardiovascular Autonomic Dysfunction Are Independently Associated With Cardiovascular Mortality: Evidence for Distinct Pathways: The Hoorn Study

) or microalbuminuria (1.76 [1.05-2.94]), respectively. CONCLUSIONS: Both microalbuminuria and C-AD are independently associated with cardiovascular mortality, and the excess mortality attributable to microalbuminuria cannot be explained by C-A