Current Diabetes Reviews - Volume 6, Issue 1, 2010

Individuals with type 2 diabetes have a two to four fold increased risk for developing cardiovascular disease than persons without diabetes. The presence of traditional and nontraditional risk factors that frequently coexist with type 2 diabetes are associated with this higher cardiovascular risk. Diabetes itself has been considered a cardiovascular disease equivalent. Nevertheless, the American Diabetes Association has recognized that absolute risk for cardiovascular disease varies among individuals with diabetes and has recommended the use of designed models and algorithms to estimate risk, especially in younger patients (<40 years). Cardiovascular risk is best evaluated with an estimation that takes into account the individual's characteristics and risk factors. The algorithms and models that have been designed specifically for the assessment of cardiovascular risk in individuals with diabetes will be the subject of this review. Specifically, the DECODE (Diabetes Epidemiology: Collaborative analysis of Diagnostic criteria in Europe) equation has been shown to have discriminative capacity of 0.67; the UKPDS Risk Engine model is reported to have a sensitivity of around 90% and specificity of 33%; and the Diabetes Personal Health Decisions (PHD) in our study had a sensitivity of 67% and specificity of 41%. In this review we will discuss the pros and cons of each model, their use in clinical practice and the application of the UKPDS risk engine and PHD model in a Mexican population.

Cystic fibrosis (CF) is the commonest autosomal recessive condition among Caucasian populations, affecting 1 in 2500 live births. The median age of survival has dramatically improved and will reach 40 years for children born in the 1990s. Complications such as cystic fibrosis related diabetes mellitus (CFRD) develop with age, and the median age at diagnosis is 21 years. The prevalence of CFRD progressively increases from 9% below the age of 10 years to 43% above the age of 30 years, with reported annual age-dependent incidence rates ranging from 4% to 9%. The onset of CFRD is insidious and annual screening using the standard oral glucose tolerance test (OGTT) from the age of 10 years has been recommended. However, OGTT thresholds for the diagnosis of impaired glucose tolerance and CFRD were derived from epidemiological studies of non-CF patients and it is uncertain whether they are appropriate for patients with CF. The ability of OGTT alone to detect early abnormalities in blood glucose regulation has been questioned. Continuous glucose monitoring systems (CGMS) may augment the screening and diagnosis of CFRD, as well as guide and monitor its management. Subcutaneous insulin therapy is currently recommended for the treatment of CFRD. Early use of insulin therapy might improve weight gain and lung function of CF patients, including those with normal OGTT results. It is still not clear when insulin therapy should be started, possibly reflecting the difficulties in detecting early but clinically relevant abnormalities in blood glucose metabolism among CF patients. We review the current evidence which highlight these diagnostic and management challenges in CFRD.

The measurement of glycaemic variation (GV) is conceived to be of clinical significance in determining diabetes outcomes. The debate as to the importance of GV has been complicated by studies using various meetrics of GV in qualitatively different datasets. The purpose of this review is to discuss the properties of 8 of the more commonly used metrics (M-value, MAGE, “J”-index, CONGA, BG rate of change, ADRR, Lability/HYPO score and GRADE). Comparable metrics that can be used to measure continuous glycaemic measurements (CGM) (SDBGL, “J”-index, MAGE, CONGA, GRADE) were then compared in assessing diabetic and non-diabetic datasets. In non-diabetic conditions there was very close correlation (correlation coefficients >0.92) between SDBGL, MAGE and CONGA, however under diabetic conditions the correlation coefficients of the GV metrics diminished significantly. The varying GV metrics have varying inherent properties depending upon the purpose for which they were designed and should not be seen as being interchangeable. Investigators therefore need to be clear about the nature of their enquiry of GV and choose an appropriate metric.

Current evidence supports a central role of inflammation in the pathogenesis of atherosclerosis and diabetes. Type 2 diabetes is an inflammatory atherothrombotic condition associated with a high prevalence of cardiovascular disease. In patients with type 2 diabetes, low grade inflammation is reflected by increased plasma levels of several biomarkers of inflammation such as C-reactive protein (CRP). Small increases in CRP predict the likelihood of developing cardiovascular events both in diabetic and nondiabetic populations. In addition, in apparently healthy subjects, increased levels of CRP predict the risk of developing type 2 diabetes. There is some evidence that CRP, besides its predictive role in determining cardiovascular risk, may represent an active participant in atherogenesis. CRP is expressed in human atherosclerotic plaques and both vascular cells and monocytes/macrophages appear to represent a significant source of CRP in the inflammatory vessel wall. By activating the main cell types present in the atherosclerotic lesions, CRP generated within the coronary plaques may contribute to the development and progression of atherosclerosis. Data on vascular CRP regulation are scarce. Current evidence suggests that inflammatory and metabolic factors associated with diabetes, such as high glucose, adipokines, modified lipoproteins and free fatty acids may trigger CRP production by endothelial cells, smooth muscle cells and monocytes/macrophages. These data suggest that local CRP concentration in diabetic atherosclerotic plaques could be higher than in nondiabetic ones. Given the possible correlation between local CRP production and the degree of severity of coronary artery disease or the nature of the lesion, such alteration may contribute to the accelerated development of vascular disease in patients with type 2 diabetes.

Endothelial tight junctions (TJs) in the retina are potential therapeutic targets for diabetic complications such as retinopathy. TJs primarily determine the endothelial barrier, regulating vascular permeability to maintain tightly closed circulating homeostasis. Our recent study has demonstrated that glial cell-derived cytokines limit vascular permeability by modulating the TJ function of retinal capillary endothelium and eventually attenuate the breakdown of vascular integrity in diabetic microangiopathy. In addition, suppression of deregulated protease activity in lens cells results in dramatic inhibition of the development of murine diabetic cataracts. We believe that pharmacological modulation of the ocular tissue microenvironment such as that regulated by endothelial TJs may be a feasible strategy for reducing the development of diabetic complications in the eye.

The worldwide epidemic scale of Diabetes mellitus (DM) has been underestimated for a long time. Currently every 10 seconds one patient dies of diabetes-related pathologies. Given the high risk and prevalence of secondary complications as well as individual predisposition to target organ injury, DM is one of the best examples for the application of predictive diagnostics aimed at preventive measures and personalized treatment. Generally, there are three levels desirable for pre- and Diabetes care: 1st level: prediction of the predisposition early in childhood 2nd level: prediction of early/premature aging and pre-stages of Diabetes 3rd level: prediction of Diabetes-related complications - cardiovascular, neurodegenerative and cancer diseases frequently developed in Diabetics. Predictive diagnosis is considered as the basis for targeted preventive measures and consequent development of individualized treatment approaches. Communication among the professionals' e.g. healthcare providers, policy-makers, educators, obligatorily involved in the overall process to improve (pre)Diabetes care is of paramount importance.

Zinc concentrates in islet cells and is related to insulin secretion. Islet cells act as a unit within islets and hormone secretion in the islets is profoundly influenced by paracrine and autocrine regulation. Zinc has been recognised as a candidate paracrine inhibitor of glucagon secretion in α-cells. Further zinc fluxes may contribute to regulation of cell mass, Zn2+ may be cytotoxic and Zn2+ depletion by itself can cause cell death induced by oxidative stress. Recently, both free zinc ions and a number of zinc transporters have been localized in α-cells. These include zinc importers, ZIP1, ZIP10, and ZIP14 of the SLC39A family and zinc exporters, ZnT1, and ZnT4-8 of the SLC30A family. Furthermore, the redox state of thiol groups and Voltage Gated Ca2+ Channels (VGCC) add to the maintenance of a tight cytoplasmatic zinc homeostasis in the α-cells. The ZnT8 protein has emerged as particularly interesting since this zinc transporter has been identified as a genetic risk factor for the development of both type 1 and type 2 diabetes in which both α- and β-cell functions are affected. Recent data discussed here suggest specific effects of Zn2+ on glucagon secretion and other α-cell functions.