10reactive vs. proactive diabetes care Actions predeterminedMinimal to no flexibility: RIGIDOutcomes don’t immediately affect long term actionsEasy to teach/learnLess time neededFavors “concrete” thinkingLess motivation neededActions are dependent on situation/circumstanceFlexible and adaptableOutcomes influence subsequent actionsTraining needed, plus ongoing reinforcementMore time intensiveFavors problem-solvingRequires motivation
12(Glucose production – Glucose disposal) = FLUX Here is a picture of FLUX
13To manage fluxEverything becomes a TOOL to understand, use, and masterFoodInsulinExerciseTimingDevices, etc….
14If insulin keeps us alive, as does food, then why should one get more attention than the other?
15Because… Most doctors are not nutrition specialists Diagnosing and prescribing are what we’re trained to doOur health care system downplays the role of RD’s by not always paying for those servicesPlus WE think we’re all food experts anyway!
16New paradigm: “Insulin keeps us alive while food helps keep us in control”
17“A well trained mind is the greatest weapon against diabetes”
18Diabetes care is not an action, it’s a process…like a recipe
19Why does diabetes seem so slippery? It’s like the weatherBut like weather, it can be predicted and prepared forIn the end, it’s a self managed conditionAnd outcomes are largely driven by choices
20Point of diminishing returns? “The good is the enemy of the perfect”Point of diminishing returns?
23Meters are commodity items “a commodity is the generic term for any marketable item produced to satisfy wants or needs”The best BG meter is the one you’ll use$10.41/50 stripsChanges aheadKetone meter
24Don’t pass up an opportunity to correct a high (or low) BG Choose what you consider “actionable”?BG above or below chosen thresholdsConsider recent and impending actionsCheck your results with BG levelsRepeat as necessary
25Check your targets often Make sure you hit your target “zone” sugar (± 30 mg/dl)Rapid-acting insulin results are best examined at 2-3 hoursResults should feedback to the next attempt“Practice makes better”
26Curb your liver! The liver makes as well as stores sugar A proper insulin level “calms down” the liverAim for an in-range sugar level (<120 mg/dl) upon waking up each day
27Why do lows happen at night? Hormonal patternsLower insulin needInsulin peaks?Post-exercise effectSnacking stacking?Lower overnight insulin/add snack
28D-teens count carbs POORLY 23%TitleThe carbohydrate counting in adolescents with type 1 diabetes (CCAT) study.AuthorsBishop, F. K.; Maahs, D. M.; Spiegel, G.; Owen, D.; Klingensmith, G. J.; Bortsov, A.; Thomas, J.; Mayer-Davis, E. J.Journal Diabetes Spectrum 2009 Vol. 22 No. 1 pp ISSN DOI /diaspect URLhttp://spectrum.diabetesjournals.org/cgi/content/a...This article reports pilot study results evaluating the accuracy of carbohydrate counting among adolescents with type 1 diabetes. This cross-sectional observational study included 48 adolescents ages years (mean 15.2±1.8 years) with type 1 diabetes of >1 year in duration (mean A1C 8.0±1.0%) who used insulin:carbohydrate (I:C) ratios for at least one meal per day. The adolescents were asked to assess the amount of carbohydrate in 32 foods commonly consumed by youths. Foods were presented either as food models or as actual food, with some items presented as standard serving sizes and some self-served by study participants. T-tests were used to assess the significance of over- or underestimation of carbohydrate content. For each meal, accuracy was categorized as accurate (within 10 grams), overestimated (by >10 grams), or underestimated (by >10 grams) based on the commonly used I:C ratio of 1 unit of insulin per 10 grams of carbohydrate. Only 23% of adolescents estimated daily carbohydrate within 10 grams of the true amount despite selection of common meals. For dinner meals, individuals with accurate estimation of carbohydrate grams had the lowest A1C values (7.69±0.82%, P=0.04). The pilot study provides preliminary evidence that adolescents with type 1 diabetes do not accurately count carbohydrates. Further data are needed on carbohydrate counting accuracy and other factors that affect glycemic control.
29clinical dietitian (n.) A person specializing in medical nutrition therapy.An underappreciated and underpaid member of the diabetes team.Someone who can help your left brain
30We have > 60,000 thoughts daily Eat at homeGroups of thoughts comprise decisionsThe typical non-D person makes ~ 250 decisions a day about foodHow many more food choices does a PWD/CWD make?“What are we doing for dinner, dear?”
31“You can delegate authority but you can’t delegate responsibility”
33“Assuming a good working knowledge of the system, diabetes control is generally proportional to the time and attention directed towards it.”
34Why do some PWD/CWD’s seem to have it “easier” Why do some PWD/CWD’s seem to have it “easier”? It depends on your point of view“Honeymoon”Type 2MODY?Other?Residual insulin…honeymoon. Early type 2 and weight loss lowers resistance…wrong diagnosis…MODY
36The pancreas has an “off” switch for insulin …and it’s triggered by exercise
37Kinetic versus Dynamic Insulin Kinetic: how fast insulin gets in and outDynamic: time that insulin lowers sugarGlucose infusion rate(mg/kg/minute)Time in hours
38Current insulin pump therapy… Early Insulin PumpsCurrent insulin pump therapy…Multi-dose insulin therapy“Think of insulin as a tool”Different tools for different jobsLantusLevemirHumalogNovologNPHGet my point?70/30
39The “3 dimensions” of insulin What is the 4th dimension? peakonsetduration
40And the 4th dimension is: “consistency” The final product6 h12 h18 h24 h
41The 2013 “insulin arsenal” Long (Lantus, Levemir) Intermediate (NPH) Fast (Regular)Rapid (Humalog, Novolog, Apidra)Premixed (75/25 and 70/30)Ultra-rapid? (in development)Ultra-long? (Degludec and others)
43basal insulins are not very precise Figure 2. Within-subject variability of insulin detemir, NPH insulin, and insulin glargine are graphically shown by the width of a prediction interval containing 95% of the predicted values. The prediction intervals illustrating day-to-day variability in the pharmacodynamic response are exemplified for a subject with the same mean response with any given treatment (insulin detemir, NPH insulin, or insulin glargine). A: A subject with a mean GIR over 24 h of 1 mg · kg-1 · min-1 has a probability to experience an effect of less than half the usual effect (i.e., <0.5 mg · kg-1 · min-1) of 0.5% using insulin detemir, 16% with NPH insulin, and 7% with insulin glargine. B: Similarly, for a subject with a maximum effect of 2 mg · kg-1 · min-1, the probability of experiencing a maximum effect of more than twice the usual level (i.e., >4 mg · kg-1 · min-1) will be 0.1% if the subject uses insulin detemir, 6% with NPH insulin, and 3% with insulin glargine. Note: a linear scale has been used in this figure to improve readability of values, and therefore the prediction intervals are not distributed symmetrically around the mean.
44Levemir variability in 9 subjects Figure 1. Individual time-action profiles (glucose infusion rates over time) of the first nine patients randomized to insulin detemir (A), NPH insulin (B), or insulin glargine (C). The four clamps in one subject are summarized in one plot. A low within-subject variability is indicated by the four lines in one plot being close to each other (e.g., subject no. 204), whereas major deviations between the time-action profiles in one subject (e.g., subject no. 224) shows a high within-subject variability.
45Lantus variability in 9 subjects Figure 1. Individual time-action profiles (glucose infusion rates over time) of the first nine patients randomized to insulin detemir (A), NPH insulin (B), or insulin glargine (C). The four clamps in one subject are summarized in one plot. A low within-subject variability is indicated by the four lines in one plot being close to each other (e.g., subject no. 204), whereas major deviations between the time-action profiles in one subject (e.g., subject no. 224) shows a high within-subject variability.
46Insulin Pens Discreet Different needle sizes ½ unit increments DisposableDurable unitsMore popular today
49Timing of Bolus Insulin (humalog/novolog/apidra)High GIModerate GILow GIBG Above Target Range30-40 min. prior15-20 min. prior0-5 min. priorBG Within Target Range15-20 min. afterBG Below Target Range30-40 min. after
50Why timing matters… Note: Carbs estimated w/pre-meal insulin. Carbs known with post-meal insulin.Source: Clinical Therapeutics 2004; 26:
51Why timing matters… CGMS data Bolusing with meal CGMS data Bolusing pre-meal
52Highs after meals depend on… Size of the bolusHow early bolus is givenHow many carbs eatenActivity level after mealFood’s glycemic index
60How does a “basal” insulin work? Turns off or tones down sugar coming out of the liverAllows a reasonable amount of sugar to enter cellsKeeps sugar levels steady or in balance between meals and snacks.Picture of a complex machine with many working parts capable of failing
62Exercise is the wild card since… It can occur suddenly or unexpectedlyIt can last for different periods of timeIntensity can shift up or downIt’s hard to measureIt’s impact on blood sugar can vary
63Tools you have seen today… The concept of FLUXInsulin onset, peak, duration, amountMacronutrientsFast, medium and slow carbohydrate effectsThe volatile role of exerciseRole of amount, timing and consistencyIncreasing your assessment and analysis frequencyThe role of choice and persistence
64“Good” control of diabetes is all about the journey, not the destination. Diabetes control exists largely “in the moment”