Bolus Estimation--Rethinking the Effect of Meal Fat Content.
BACKGROUND
Traditionally, insulin bolus calculations for managing postprandial glucose levels in individuals with type 1 diabetes rely solely on the carbohydrate content of a meal. However, recent studies have reported that other macronutrients in a meal can alter the insulin required for good postprandial control. Specifically, studies have shown that high-fat (HF) meals require more insulin than low-fat (LF) meals with identical carbohydrate content. Our objective was to assess the mechanisms underlying the higher insulin requirement observed in one of these studies.


METHODS
We used a combination of previously validated metabolic models to fit data from a study comparing HF and LF dinners with identical carbohydrate content in seven subjects with type 1 diabetes. For each subject and dinner type, we estimated the model parameters representing the time of peak meal-glucose appearance (τ(m)), insulin sensitivity (S(I)), the net hepatic glucose balance, and the glucose effect at zero insulin in four time windows (dinner, early night, late night, and breakfast) and assessed the differences in model parameters via paired Wilcoxon signed-rank tests.


RESULTS
During the HF meal, the τ(m) was significantly delayed (mean and standard error [SE]: 102 [14] min vs. 71 [4] min; P = 0.02), and S(I) was significantly lower (7.25 × 10(-4) [1.29 × 10(-4)] mL/μU/min vs. 8.72 × 10(-4) [1.08 × 10(-4)] mL/μU/min; P = 0.02).


CONCLUSIONS
In addition to considering the putative delay in gastric emptying associated with HF meals, we suggest that clinicians reviewing patient records consider that the fat content of these meals may alter S(I).
1 Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center , U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland.
Srinivas
S
Laxminarayan
1 Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center , U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland.
Jaques
J
Reifman
2 Joslin Diabetes Center , Boston, Massachusetts.
Stephanie S
SS
Edwards
2 Joslin Diabetes Center , Boston, Massachusetts.|3 Harvard Medical School , Boston, Massachusetts.
Howard
H
Wolpert
3 Harvard Medical School , Boston, Massachusetts.|4 Children's Hospital , Boston, Massachusetts.
Garry M
GM
Steil
D000328:Adult; D000368:Aged; D003131:Combined Modality Therapy; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000178:diet therapy / Q000188:drug therapy* / Q000378:metabolism; D003927:Diet, Diabetic; D018752:Diet, Fat-Restricted; D059305:Diet, High-Fat; D004041:Dietary Fats / Q000008:administration & dosage* / Q000378:metabolism; D004063:Digestion; D054796:Drug Dosage Calculations; D005260:Female; D005746:Gastric Emptying; D006801:Humans; D006943:Hyperglycemia / Q000517:prevention & control; D007003:Hypoglycemia / Q000139:chemically induced / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000627:therapeutic use; D007328:Insulin / Q000008:administration & dosage* / Q000627:therapeutic use; D007333:Insulin Resistance; D008297:Male; D016555:Matched-Pair Analysis; D062407:Meals; D008875:Middle Aged
D004041:Dietary Fats; D007004:Hypoglycemic Agents; D007328:Insulin