Understanding the Ups and Downs of Blood Glucose

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Presentation transcript:

Understanding the Ups and Downs of Blood Glucose Irl B. Hirsch, M.D. University of Washington

Why are the risks of PDR different? Question Who has the greatest risk of proliferative diabetic retinopathy (PDR) over the next 10 years A 55 y/o man with type 2 diabetes for 5 years, on oral agents, A1c = 9.0% An 18 y/o man with 5 years of type 1 diabetes, on BID NPH/R, A1c = 9.0% An 18 y/o man with 5 years of type 1 diabetes, on CSII, A1c = 9.0% Why are the risks of PDR different?

Don’t forget about the “ups” and “downs”! Postprandial hyperglycemia ≠ glycemic variability Don’t forget about the “ups” and “downs”!

“Oxidative Stress” What Should You Know? Oxygen is critical for life: respiration and energy Oxygen is also implicated in many disease processes, ranging from arthritis, cancer, Lou Gehrig’s disease as well as aging This dangerous form of oxygen is from the formation of “free radicals” or “reactive oxygen species”, or pro-oxidants Normally, pro-oxidants are neutralized by anti-oxidants

“Oxidative Stress”: What You Should Know Imbalance between pro-oxidants (free radicals, reactive oxygen species) and anti-oxidants

Oxidative Stress: Why is it Important? Free radicals (reactive oxygen species) are known to fuel diabetic vascular complications

OK, What Turns On Oxidative Stress, Free Radicals, and Reactive Oxygen Species High blood glucose Science is confirmed on this point Variability in blood glucose Science is highly suggestive on this point

How Does One Measure…? Oxidative Stress Glycemic variability Urinary isoprostanes: best marker of oxidative stress in total body “HbA1c of oxidative stress” Glycemic variability Mean Amplitude of Glycemic Excursions (MAGE) Standard deviation on SMBG meter download I Hirsch

Urinary 8-SO-PGF2 alpha Excretion Rates Correlation Between Urinary 8-iso-PGF2 alpha and MAGE in T2DM 1200 1000 800 600 400 200 0 20 40 60 80 100 120 140 160 Urinary 8-SO-PGF2 alpha Excretion Rates (pg/mg creatinine) MAGE (mg glucose/dL) R=0.86, p<0.0001 JAMA 295:1688-97, 2006 I. Hirsch

Why This Study is So Important Oxidative stress not related to A1c, fasting glucose, fasting insulin, mean blood glucose Stronger correlation of oxidative stress to MAGE than to postprandial glucose levels! MAGE = both the UPS and the DOWNS of blood glucose I. Hirsch

So What Is The Significance of the Understanding of GV? “…it suggests that different therapeutic strategies now in use should be evaluated for their potential to minimize glycemic excursion, as well as their ability to lower A1c.” “…wider use of real-time continuous glucose monitoring in clinical practice would provide the required monitoring tool to minimize glycemic variability and superoxide overproduction.” Brownlee M, Hirsch IB: JAMA: 295:1707, 2006 I. Hirsch

What About Long-Term Glycemic Variability? Pittsburgh Epidemiology of Diabetes Complications 16-year follow-up of childhood T1DM, N=408 Results: Risks of coronary disease over time related to A1c and variability of A1c! Diabetes 55 (Supp 1): A1, 2006

What We KNOW Risk of complications are related to Glycemic exposure as measured as A1c over time Proven Genetic risks Clearly true, but little understanding Glycemic variability Supported by most but not all studies

Conclusion 1 Glycemic variability may be an important mechanism increasing oxidative stress and vascular complications So how do we best measure glycemic variability in our patients with diabetes? I. Hirsch

What’s a better way to assess glycemic variability? Meter Downloads! I. Hirsch

Which Patient Has More Variable Fasting Glucose Data? Joe: HbA1c = 6.5%; on CSII with insulin aspart 60 54 148 146 70 203 165 132 110 79 185 68 210 138 144 252 75 77 Mary: HbA1c = 6.5%; on HS glargine and prandial lispro Mean = 123 mg% Mean = 123 mg% SD = 51 SD = 63 I. Hirsch

Standard Deviation A measurement of glycemic variability Can determine both overall and time specific SD Need sufficient data points Minimum 5 but prefer 10 I. Hirsch

Calculation To Determine SD Target SD X 2 < MEAN Ideally SD X 3 < mean, but extremely difficult with type 1 patients I. Hirsch

Significance of a High SD Insulin deficiency (especially good with fasting blood glucose) Poor matching of calories (especially carbohydrates) with insulin Gastroparesis Giving mealtime insulin late (or missing shots completely) Erratic snacking Poor matching of basal insulin, need for CSII? I. Hirsch

Other Significance of a High SD Increased Oxidative Stress! I. Hirsch

Caveats of the SD Need sufficient SMBG data Low or high averages makes the 2XSD<mean rule irrelevant I. Hirsch

Caveats of the SD: Low Mean 56 85 98 106 110 113 46 60 59 128 Mean = 81; SD = 29 I. Hirsch

Caveats of SD: High Mean 210 249 294 112 77 302 288 259 321 193 Mean = 217; SD = 82 I. Hirsch

Putting it all together Typical new patient visit to UW DCC 27 y/o woman on CSII for 5 years Testing 4 to 5 times daily, A1c=6.4% Major problems with hypoglycemia unawareness Poor understanding of basic concepts of insulin use despite seen by specialists for 20 years (last appointment with endocrinologist was no more than 12 min for her “new patient appointment”)

After thinking about glycemic variability and oxidative stress Question After thinking about glycemic variability and oxidative stress Who has the greatest risk of PDR over the next 10 years? A 55 y/o man with T2DM for 5 years, on oral agents, A1c = 9.0%; Mean/SD = 210/50; An 18 y/o man with 5 years of T1DM, on BID N/R, A1c = 9%; Mean/SD = 210/100; An 18 y/o man with 5 years of T1DM, on CSII, A1c = 9% Mean/SD = 210/75;

The Future of Glycemic Variability: Measurements For the Future SD: used with SMBG for over a decade with meter downloads; underutilized Interquartile ratio: the range where the middle 50% of the values in a distribution falls, calculated by subtracting the 25th from the 75th percentile Compared to SD, IQR not influenced by outliers MAGE: gold standard (?) but requires continuous glucose sensing. May be more useful as we move into the CGM era I. Hirsch

Data comparing these tools to markers of oxidative stress! What We Need Data comparing these tools to markers of oxidative stress! I. Hirsch

Conclusions Although there is no definitive proof from a randomized controlled trial, the data suggests that glycemic variability is a risk factor for microvascular complications We have the opportunity to quantitate GV now with meter downloads I. Hirsch

What You Should Take Away From This Discussion A1c is not the only factor contributing to the complications of diabetes