Impact of Glycemic and Blood Pressure Variability on Surrogate Measures of Cardiovascular Outcomes in Type 2 Diabetic Patients

OBJECTIVE—The effect of glycemic variability (GV) on cardiovascular risk has not been fully clarified in type 2 diabetes. We evaluated the effect of GV, blood pressure (BP), and oxidative stress on intima-media thickness (IMT), left ventricular mass index (LVMI), flow-mediated dilation (FMD), and sympathovagal balance (low frequency [LF]/high frequency [HF] ratio) in 26 type 2 diabetic patients (diabetes duration 4.41 6 4.81 years; HbA1c 6.70 6 1.25%) receiving diet and/or metformin treatment, with no hypotensive treatment or complications. RESEARCH DESIGN AND METHODS—Continuous glucose monitoring (CGM) data were used to calculate mean amplitude of glycemic excursion (MAGE), continuous overall net glycemic action (CONGA)-2, mean blood glucose (MBG), mean postprandial glucose excursion (MPPGE), and incremental area under the curve (IAUC). Blood pressure (BP), circadian rhythm, and urinary 15-F2t-isoprostane (8-iso-prostaglandin F2a [PGF2a]) were also evaluated. Subjects were divided into dipper (D) and nondipper (ND) groups according to DBP. RESULTS—IMT and LVMIwere increased inNDversusD(0.7760.08 vs. 0.6860.13 [P=0.04] and 67 6 14 vs. 55 6 11 [P = 0.03], respectively). MBG, MAGE, and IAUC were significantly associated with LF/HF ratio at night (r = 0.50, P = 0.01; r = 0.40, P = 0.04; r = 0.41, P = 0.04, respectively), MPPGE was negatively associated with FMD (r =20.45, P = 0.02), andCONGA-2was positively associatedwith LVMI (r=0.55, P=0.006).TheDsystolic BP was negatively associated with IMT (r =20.43, P = 0.03) andwith LVMI (r =20.52, P = 0.01). Urinary 8-iso-PGF2a was positively associated with LVMI (r = 0.68 P , 0.001). CONCLUSIONS—An impaired GV and BP variability is associated with endothelial and cardiovascular damage in short-term diabetic patients with optimal metabolic control. Oxidative stress is the only independent predictor of increased LV mass and correlates with glucose and BP variability

The need for identifying standardized indices for measuring glucose variability.

We read the article by Siegelaar and colleagues,1 which clearly suggests the lack of association between glycemic variability (GV) and oxidative stress estimated by 8-iso-prostaglandin F2α (8-iso-PGF2α) excretion rate in patients with type 2 diabetes mellitus (T2DM) with good metabolic control by oral glucose lowering agents. We have, however, some concerns. As an index of GV, mean amplitude of glycemic excursions (MAGE), one of the most widely used indexes for measuring GV, was chosen; although a gold-standard procedure is still lacking. In our article,2 we observed a positive correlation between 8-iso-PGF2α and GV, measured as continuous overall net glycemic action (CONGA-2), in diabetic patients with short-term disease and optimal metabolic control. However, we were unable to show a significant correlation between MAGE and 8-iso-PGF2α. The observation of a different behavior between CONGA-2 and MAGE, in terms of association with oxidative stress, is possibly due to the metabolic characteristics of our patients. In fact, CONGA-2 is known to detect small glycemic swings, occurring over short-time intervals,3 thus appropriately describing the glycemic fluctuations of patients in optimal metabolic balance, without peaks and valleys. On the other side, MAGE displays several limitations, the most important being the arbitrary definition of significant peaks and nadirs in units of standard deviations. Moreover, the raw glycemic data, obtained by continuous glucose monitoring, are usually asymmetric (hypoglycemic is much narrower than hyperglycemic range). Hence we believe that because MAGE analysis is based on the standard deviation value, as a consequence, we can predict that MAGE will preferentially look at hyperglycemic peaks and will be relatively insensitive to hypoglycemic nadirs.3 Therefore, we suggest applying different indices for the measurement of GV, depending on the aim of the study and the metabolic characteristics of the studied population. We should also consider the possible confounding effect of insulin secretagogues and of the various drugs used in the population studied by Siegelaar and colleagues1 on GV and on oxidative stress parameters.4 In our study, in order to avoid these important confounding factors, we selected patients treated only by either diet alone or diet plus metformin. On the other side, it should be noted that, in agreement with our data, the population studied by Siegelaar and associates1 showed an optimal glycemic control, thus excluding the possible interference of glucotoxicity5 on the results observed

Flavonoids and insulin-resistance: from molecular evidences to clinical trials

Insulin-resistance is one of the main factors responsible for the onset and progression of Metabolic Syndrome (MetS). Among all polyphenols, the effects of flavonoids and their main food sources on insulin sensitivity have been widely evaluated in molecular and clinical studies. The aim of this review is to analyse the data observed in vitro, in vivo and in clinical trials concerning the effects of flavonoids on insulin resistance and to determine the molecular mechanisms with which flavonoids interact with insulin signaling

Impact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives.

Aims: The aim of this review is to focus on intra-day glucose variability (GV), specifically reviewing its correlation with HbA1c, the methods currently available to measure it, and finally the relationship between GV and cardiovascular outcomes, in type 1 and type 2 diabetic patients, and in the non-diabetic population. Data synthesis: The term GV has been used in the literature to express many different concepts; in the present review, we focus our attention on intra-day GV. In particular, we try to assess whether GV provides additional information on glycemic control beyond HbA1c, since GV seems to be incompletely expressed by HbA1c, particularly in patients with good metabolic control. Many different indexes have been proposed to measure GV, however at the moment no “gold standard” procedure is available. Evidence in vitro, in experimental settings and in animal studies, shows that fluctuating glucose levels display a more deleterious effect than constantly high glucose exposure. However, these findings are not completely reproducible in human settings. Moreover, the relationship between GV and cardiovascular events is still controversial. Conclusions: The term GV should be reserved to indicate intra-day variability and different indexes of GV should be used, depending on the metabolic profile of the population studied and the specific issue to be investigated. Self glucose monitoring or continuous glucose monitoring should be used for assessing glucose variability

Glycemic Status Assessment by the Latest Glucose Monitoring Technologies

The advanced and performing technologies of glucose monitoring systems provide a large amount of glucose data that needs to be properly read and interpreted by the diabetology team in order to make therapeutic decisions as close as possible to the patient's metabolic needs. For this purpose, new parameters have been developed, to allow a more integrated reading and interpretation of data by clinical professionals. The new challenge for the diabetes community consists of promoting an integrated and homogeneous reading, as well as interpretation of glucose monitoring data also by the patient himself. The purpose of this review is to offer an overview of the glycemic status assessment, opened by the current data management provided by latest glucose monitoring technologies. Furthermore, the applicability and personalization of the different glycemic monitoring devices used in specific insulin-treated diabetes mellitus patient populations will be evaluated