Carb counting for bolus insulin dosing: Consider fat and protein content of diet as well
Presenters: Becky Sulik, RDN, CDCES, LD, Kirstine Bell, APD, CDE, PhD, Holly Willis, PhD, RDN, LD, and Bruce A. Buckingham, MD
Carbohydrate counting is an approach used for bolus insulin decision-making. It can be complex and burdensome. The utility of carbohydrate counting with evolving insulin delivery technologies was the subject of a clinical nutrition symposium.
The advanced carbohydrate counting concept uses the carbohydrate content and premeal blood glucose level to determine the bolus insulin dose. To accomplish this task accurately, patients must be able to identify carbohydrate foods, understand and evaluate portion size, estimate grams of carbohydrates, consider premeal blood glucose and target glucose, and calculate insulin dose based on individualized ratios, said Becky Sulik, RDN, CDCES, LD, director of education, Rocky Mountain Diabetes Center, Idaho Falls, Idaho.
Barriers to using carbohydrate counting include errors in reading labels and estimating portion sizes, the need for numeracy skills, and forgetting to give premeal insulin. Most patients underestimate the amount of carbohydrates in a meal,1 she said. Smartphone apps can be helpful in providing bolus calculators to reduce carbohydrate counting burden and reduce the fear of hypoglycemia.
Insulin dosing should not be based solely on carbohydrate counting when eating a mixed meal containing carbohydrates and a high amount of fat and/or protein, as dietary fat and protein impact early and delayed postprandial glycemia, said Kirstine Bell, APD, CDE, PhD, principal research fellow, University of Sydney, Australia.
Adding fat and protein to a meal significantly raises blood glucose levels.2 To achieve target glucose control, 65% more insulin is needed with a high-fat/high-protein meal, best given as a “dual wave,” (30/70% split) over 2.5 hours, “to make sure that more insulin is coming through in that later postprandial period . . . rather than upfront when there’s already a reduced glucose response,” she said.
Fat and protein have an additive effect on blood glucose levels.3 Dietary fat alone influences glycemia, with a reduction in the early postprandial glucose rise (first 2 to 3 hours) and a delay in the peak glucose level, leading to late postprandial hyperglycemia.4 The effect of dietary fat and protein on blood glucose level is reduced without a carbohydrate in the meal. “At the moment, there’s no evidence to suggest that we need to adjust doses for fat or protein type,” she said.
Carbohydrates with a low glycemic index (GI) and high fiber meals reduce the glucose response and insulin needs compared with high GI meals, but the impact of GI is masked with meals high in fat and protein.
Inter- and intra-individual variability in the glucose response to meals is substantial, so insulin dosing needs to be individualized. “If it’s a day when someone is really active, it may be that you don’t need to increase doses for their high-fat intake that evening because they’re going to be more insulin sensitive from their activity,” she said. Also, some people are more sensitive to fat and protein, as well as carbohydrates, than others.
In managing glucose, carbohydrate content is but one piece of a big puzzle with evolving technologies such as continuous glucose monitoring (CGM), said Holly Willis, PhD, RDN, LD, senior research dietitian, International Diabetes Center, Health Partners Institute, Minneapolis.
Using CGM data, carbohydrate-matched meals were found to produce different glucose responses depending on the source of carbohydrate (ie, white rice, high-protein pasta, regular pasta).5
CGM offers the most comprehensive and personalized view of glucose response “and allows people to engage with their data in ways that they couldn’t do with single blood glucose test points,” said Dr. Willis. The key to using CGM data to optimize glucose management lies in knowing glucose targets, which is 70 to 180 mg/dL in most patients with diabetes; observing the body’s response to foods and circumstances; and developing a date-driven, highly personalized eating pattern that achieves that target glucose range.6 A 5% increase in the time in the target range is clinically meaningful, which corresponds only to an additional 1.2 hours per day in range. “Thinking in small increments can help,” she said.
Be careful to avoid diabetes “tunnel vision” in which types of foods are not considered when trying to achieve target glucose, she advised. A few essential pillars can help to define the optimal diet. These pillars include choosing whole foods, emphasizing non-starchy vegetables, limiting sugar and refined grains, eliminating sugar-sweetened beverages, and personalizing eating patterns to the eater, taking into account patient preferences, culture, socioeconomic status, and health goals. “We owe it to our patients to encourage the highest-quality foods possible,” she said.
CGM can help define the best eating pattern for an individual, she believes. Eat-to-target experiments using the aforementioned pillars of food choices over 2 to 4 days and making adjustments based on the glycemic effect. “Try the manipulations for a few days and analyze for trends and patterns, which usually would be done by using the daily profiles,” she suggested. Consider the premeal glucose level and aim for postprandial glucose targets (≤180 mg/dL). In one survey of patients with type 1 and type 2 diabetes, 87% indicated that their food choices changed after using CGM and 88% noticed how different food choices affected glucose levels.7
The present and future of diabetes nutrition management using evolving technologies was reviewed by Bruce A. Buckingham, MD, professor of pediatrics, Stanford University, Palo Alto, Calif.
He showed individual patient glucose profiles to show that with automated correction bolus insulin doses, current closed loop systems can achieve A1c levels <7.5%, even with some missed or late meal boluses, and time in range can be as high as 90%.
Fully automated insulin delivery using faster acting insulins, multihormone therapy, automatic detection of eating, and algorithms to adapt to meal consumption is coming that may feature no carbohydrate counting and no timing of meal bolus.
References
- Roversi C, Vettoretti M, Del Favero S, Facchinetti A, Sparacino G. Modeling carbohydrate counting error in type 1 diabetes management. Diabetes Technol Ther 2020;22:749-759.
- Bell KJ, Toschi E, Steil GM, Wolpert HA. Optimized mealtime insulin dosing for fat and protein in type 1 diabetes: application of a model-based approach to derive insulin doses for open-loop diabetes management. Diabetes Care 2016;39:1631-1634.
- Smart CEM, Evans M, O’Connell SM, et al. Both dietary protein and fat increase postprandial glucose excursions in children with type 1 diabetes, and the effect is additive. Diabetes Care 2013;36:3897-3902.
- Bell KJ, Smart CE, Steil GM, Brand-Miller JC, King B, Wolpert HA. Impact of fat, protein, and glycemic index on postprandial glucose control in type 1 diabetes: implications for intensive diabetes management in the continuous glucose monitoring era. Diabetes Care 2015;38:1008-1015.
- Zavitsanou S, Massa J, Deshpande S, et al. The effect of two types of pasta versus white rice on postprandial blood glucose levels in adults with type 1 diabetes: a randomized crossover trial. Diabetes Technol Ther 2019;21:485-492.
- Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on Time in Range. Diabetes Care 2019;42:1593-1603.
- Ehrhardt N, Zaghal EA. Continuous glucose monitoring as a behavior modification tool. Clin Diabetes 2020;38:126-131s
Disclosures
Dr. Sulik reports financial relationships with Lilly Diabetes, Insulet Corporation, Medtronic, and Tandem Diabetes Care.
Dr. Bell reports a financial relationship with Aktivolans Pte. Ltd.
Dr. Willis reports financial relationships with Abbott Diabetes and Sanofi.
Dr. Buckingham reports financial relationships with Medtronic, Tolerion, Inc., Beta Bionics, Inc., Insulet Corporation, Medtronic, and Tandem Diabetes Care.