LOW GLYCEMIC
PHARMACEUTICALS

QUANTIFICATION OF
PROTOCOL PERIMETERS

RESEARCH DESIGN
and METHODS



There are two separate clinical protocols involved in analyzing and identifying products that fit the criteria for “Low Glycemic Pharmaceuticals.” Though the testing Protocols vary, the pathophysiology is identical in its resulting elevation of glycemic factors in humans.

Wheras the Glycemic Research Institute ® has developed, over a 25-year period, FDA legal claims guidelines for Pharmaceutical products, including Board Approved Human In Vivo Clinical Trials, backed by a US government Certification Mark, said trials include the following perimeters.

Glycemic excursions in humans are evidenced in relation to:

1.
Oral ingestion of foods, beverages, Pharmaceutical and Nutraceutical agents: Post-Prandial Glycemic Index and Load
2.
Oral stimulation of Cephalic-Phase-Insulin-Release (CPIR)
Cephalic Response: Oral BRIX Load wherein swallowing and digestion is not required


FACTOR NUMBER 1: GLYCEMIC INDEX & LOAD

The glycemic index is a numerical classification based on Human In Vivo clinical trials designed to quantify the relative blood glucose response to foods, drinks, Nutraceuticals, Pharmaceuticals, and any edible agent.
The glycemic index (GI) of a particular food is determined by calculating the incremental area under the blood glucose response curves (iAUC) for that food compared with a standard control of white bread (utilizing the trapezoid rule).

GLYCEMIC RESPONSE/IMPACT
Refers to the effects elicited by oral ingestion of any edible agent (not just carbohydrate foods) on blood glucose concentration and insulin levels during the digestion process.

Glycemic Index (GI) alone is unable to predict the glycemic response/impact when different amounts of carbohydrates are eaten. Glycemic Load must be utilized in conjunction with GI to differentiate the acute impact on blood glucose and insulin responses induced by Test Foods.

GLYCEMIC LOAD (GL)
Glycemic Load is based on a specific quantity and carbohydrate content of the test food. GL is calculated by multiplying the weighted mean of the dietary glycemic index by the percentage of total energy from the test food.

When the test food contains quantifiable carbohydrates, the Glycemic Load equals GI (%) x grams of carbohydrate per serving. One unit of GL approximates the glycemic effect of 1 gram of glucose. Typical diets contain from 60-180 GL units per day.

A HIGH GLYCEMIC LOAD diet is defined as: 60% carbohydrate, 20% protein, 20% fat (glycemic load 116 g/1000 kcal).

A LOW GLYCEMIC LOAD diet is defined as: 40% carbohydrate, 30% protein, 30% fat, (glycemic load 45 g/1000 kcal).

GLUCOSE SCALE
Results presented in final Test Food reports are based on the glucose scale. Glycemic index and glycemic load values are converted to the glucose = 100 scale by multiplication with the factor 0.7.

METHODS
All blood work and analytical calculations are conducted in-house in Real-Time. Utilizing standardized Glycemic Research Institute (GRI) Board-Approved clinical protocols, accommodations are made for low-end or high-end carbohydrate Test Foods.

Ten to thirty pre-screened human subjects are typically used for each product (Test Food) tested. Larger subject pools are utilized when variables are high.

White bread is used as the standard. Each subject is fed a minimum of three bread standards for comparison to the products tested. Calculations are made using the area under the curve (AUC) as compared to bread standards (converted to the glucose scale). AUC is calculated by GRL statisticians using standard GRI Laboratory protocols.

Fasting blood glucose measurements are made, and at 15-minute intervals throughout the trial, for 2-4 hours, or until blood glucose levels stabilize.

Capillary blood is preferred: the results for capillary blood glucose (BG) are less variable than that of venous plasma glucose. Additionally, elevations in BG are greater in capillary blood than venous plasma, and the differences in Test Foods and bread standards are easier to detect statistically using capillary blood glucose.

CALCULATIONS & STASTICAL ANALYSIS
GI (%) = ∑(carbohydrate content of each food item (g) × GI)/total amount of carbohydrate in meal (g); GL (g) = ∑(carbohydrate content of each food item (g) × GI)/100.

Area beneath baseline is not utilized. Serum glucose and insulin postprandial responses are assessed using incremental (iAUC) and total area under the curve (tAUC) at 2 h, 5 h and between 2–5 h. Serum FFA and plasma glucagon postprandial responses are assessed using the tAUC at 2 h, 5 h and between 2–5 h. iAUC and tAUC are geometrically calculated using the trapezoidal method.

FACTOR NUMBER 2:
CEPHALIC PHASE INSULIN RESPONSE (CPIR)

Glycemic Response and Cephalic Response are defined and analyzed differently.

Cephalic Response occurs in a much shorter time-span than that of Glycemic testing.

The Cephalic Insulin Response (CPIR) to oral sweet-taste stimulation in humans is dependent on both cholinergic and noncholinergic mechanisms and is important for postprandial glycemia.

Clinical determination of CPIR must be documented during a specific protocol. Swallow versus non-swallow protocols are utilized for accuracy, as digestion of dietary carbohydrates starts in the mouth, where salivary a-amylase initiates starch degradation.

The characteristic of CPIR is that plasma insulin secretion occurs before the rise of the plasma glucose level. Sweetness information conducted by human oral taste nerves provides essential information for eliciting CPIR.

In the central nervous system, neuronal circuits play a critical role in orchestrating the control of glucose and energy homeostasis. Glucose, besides being a nutrient, is also a signal detected by several glucose-sensing units that are located at different anatomical sites and converge to the hypothalamus to cooperate with leptin and insulin in controlling the melanocortin pathway.

These homeostatic processes rely on properly coordinated function of several organs: the liver, white and brown adipose tissues, muscle, and the brain. The brain processes CPIR data as provided by taste nerves and, in response to sweetness levels, disperses insulin.

In the mouth (oral cavity), glucose stimulates nervous reflexes, in part initiated by activation of taste receptors and of their afferent fibers, which project to the brain stem and are in relation to the nucleus of the tractus solitarius (NTS), the reticular formation, the parabrachial nucleus (PBN), and the dorsal motor nucleus of the vagus (DMNX). Activation of this reflex is responsible for the cephalic phase of insulin secretion, which plays an essential role in glucose tolerance.

In order to identify CPIR, acute insulin response (AIR) is tracked (in-house) with specially designed laboratory equipment, as CPIR occurs in humans. The swallowing process is obsolete in identifying CPIR.

Sugars and sweeteners, despite the caloric or carbohydrate content, are capable of high glycemic reactions on blood glucose and insulin levels. Sweeteners previously believed to have a glycemic response of zero have recently been proven to have definite glycemic properties. Most sugars/sweeteners trigger both CPIR and glycemic responses.

In the case of sweeteners, the Test Food is prepared per instructions and confirmed by Brix refractometry.

Sugar alcohols and herbal sweeteners also effect glycemic responses. Doses as low as 1 gram of Stevia elicit a glycemic index in clinical trials. As doses of Stevia increase, so does the glycemic index.

CONCLUSIONS

Identifying glycemic factors, as serum glucose and as brain-release of insulin (CPIR) create a total clinical profile that identifies oral edible agents for their potential in exacerbating type 2 diabetes, obesity, and insulin resistance.

The American Diabetes Association (ADA) and the American Association of Clinical Endocrinologists (AACE) recommend specific target goals in achieving blood glucose control (Table I).

Calculating perimeters in the control of diabetes requires identification of blood glucose and insulin elevation in response to orally ingested foods, Nutraceuticals, and Pharmaceutical agents.

Clinical trials as described herein are required to determine if Pharmaceutical agents qualify to utilize the “LOW GLYCEMIC PHARMACEUTICALS” Seal/Mark. Said Human In Vivo Clinical Trials accurately determines and identifies Pharmaceutical agents that elevate homeostatic glycemic factors versus agents that do not over-elevate glycemic (glycemia) perimeters in humans.



Table I
GLYCEMIC CONTROL
TARGETS in DIABETES

The American Diabetes Association (ADA)

American Association of Clinical Endocrinologists (AACE)


Measurement

Normal ADA Goal AACE Goal
Plasma glucose (mg/dL)
Preprandial
2h postprandial
< 100
< 140
90-130
< 180
< 100
< 140
A1C (%) < 6 < 7 < 6.5


GLYCEMIC RESEARCH INSTITUTE
601 Pennsylvania Avenue
Suite 900
Washington, D.C.
33701


MATHEMATIC MODELING METHODS


The following references represent Glycemic Research Institute’s review and adoption of protocols and methods utilized in “Low Glycemic Pharmaceuticals” testing and data analysis.

These include mathematical models used in the clinical identification of specific aspects of blood glucose, insulin, diabetes, insulin resistance, and other related metabolic perimeters. Various deterministic and stochastic tools are available, both simple and comprehensive, in evaluating trial data, which include partial differential equations, integral equations, matrix analysis, optimal control theory, differential equations, and computer algorithms.

Mari A. Mathematical modeling in glucose metabolism and insulin secretion. Current Opinion Clinical Nutrition Metabolism Care. 2002;5:495–501. doi: 10.1097/00075197-200209000-00007

Boutayeb A, Twizell EH, Achouyab K, Chetouani A. A mathematical model for the burden of diabetes and its complications. Biomedical Engineering Online. 2004;3:20. doi: 10.1186/1475-925X-3-20.

Boutayeb A, Chetouani A, Achouyab K, Twizell EH. A non-linear population model of diabetes mellitus. Journal of Applied Mathematics and Computing. 2006;21:127–139.

T. J. Orchard et al. Modeling Chronic Glycemic Exposure Variables as Correlates and Predictors of Microvascular Complications of Diabetes: Response to Dyck et al; Diabetes Care, February 1, 2007; 30(2): 448 - 448.

Bergman RN, Finegood DT, Ader M. Assessment of Insulin Sensitivity in Vivo. Endocrine Reviews. 1985;6:45–86

Bergman, RN. The minimal model of glucose regulation: a biography. In: Novotny, Green, Boston., editor. Mathematical Modeling in Nutrition and Health. Kluwer Academic/Plenum; 2001

Bergman, RN. The minimal model: yesterday, today and tomorrow. In: Bergman RN, Lovejoy JC., editor. The minimal model Approach and Determination of Glucose Tolerance. Vol. 7. Boston: Louisiana State University Press; 1997. pp. 3–50

Nucci G, Cobelli C. Models of subcatuneous insulin kinetics: a critical review. Computer Methods and Programs in Biomedicine. 2000;62:249–257. doi: 10.1016/S0169-2607(00)00071-7

Bellazzi R et al. The Subcutaneous Route to Insulin Dependent Diabetes Therapy: Closed-Loop and Partially Closed-Loop Control Strategies for insulin Delivery and Measuring Glucose Concentration. IEEE Engrg Medicine Biol. 2001;20:54–64. doi: 10.1109/51.897828

The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 20:1183–1197, 1997

Makroglou A, Li J, Kuang Y. Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: an overview. Applied Numerical Mathematics. 2006;56:559–573. doi: 10.1016/j.apnum.2005.04.023.

Parker RS et al. The Intraveneous Route to Blood Glucose Control: A Review of Control Algorithms for Noninvasive Monitoring and Regulation in Type 1 Diabetic Patients. IEEE Engineering in Medicine and Biology.. 2001;20:65–73. doi: 10.1109/51.897829.

Koschinsky T, Heinemann . Sensors for glucose monitoring: technical and clinical aspects. Diabetes/Metabolism Research and Reviews. 2001;17:113–123. doi: 10.1002/dmrr.188.

Della C, Romano MR, Voehhelin MR, Seriam E. On a mathematical model for the analysis of the glucose tolerance curve. Diabetes. 1970;19:145–148.

Bolie VW. Coefficients of normal blood glucose regulation. J Appl Physiol. 1961;16:783–788.]

Serge G, Turcogl M, Varcellone G. Modelling blood glucose and insulin kinetics in normal diabetic and obese subjects. Diabetes. 1973;22:94–97.

Mukhopadhyay A, De Gaetano A, Arino O. Modelling the intra-venous glucose tolerance test: A global study for single-distributed-delay model. Discrete and Continous Dynamical Systems Series B. 2004;4:407–417.

Ackerman E, Gatewood LC, Rosevear JW, Molnar GD. Model studies of blood glucose regulation. Bull Math Biophys. 1965;27:21–24.

Srinivasan R, Kadish AH, Sridhar R. A mathematical model for the control mechanism of free-fatty acid and glucose metabolism in normal humans. Comp Biomed Res. 1970;3:146–149. doi: 10.1016/0010-4809(70)90021-2.

Brownlee M: Biochemistry and molecular cell biology of diabetic complications. Nature 414:813–820, 2001

Bergman RN, Ider YZ, Bowden CR, Cobelli C. Quantitative Estimation of Insulin Sensitivity. Am J Physiol. 1979;23:E667–E677.

Cobelli C, Mari A. Validation of mathematical models complex endocrine-metabolism systems. A case study on a model of glucose regulation. Med & Biot Eng & Comput. 1983;21:390–399.

Orchard TJ, Forrest KY, Ellis D, Becker DJ: Cumulative glycemic exposure and microvascular complications in insulin-dependent diabetes mellitus: the glycemic threshold revisited. Arch Intern Med 157:1851–1856, 1997

DCCT Research Group: The Diabetes Control and Complications Trial (DCCT): design and methodologic considerations for the feasibility phase. Diabetes 35:530–545, 1986

Copyright ® 2008
Glycemic Research Institute®










Copyright © 2008-2011 Glycemic Research Institute®

glycemic index testing, glycemic index, Glycemic Index Testing, Glycemic Index, glycemic testing, Glycemic Research Institute, Glycemic Research Laboratories, Inulin, Glycemic Load, Low Cephalic Index, Clinical Studies, Human In Vivo Clinical Studies, Glycemic Testing, Glycemic Load, Clinical Studies, Cephalic Research Institute, Dr. Ann de Wees Allen, Cephalic, Cephalic Response, Nano Certified, Certification program, Seal, Diabetic Testing, Diabetic Friendly, Diabetes, GI Testing, GI Clinical Studies, GI Seal, Glycaemic, Blood Glucose, Insulin, Nano Certification, Nanotechnology, NanoFoods, Certified Nano Technology Seal, diet sodas artificial sweeteners, Katie Couric, CBS news, diet sodas fattening, agave, dangers of agave, Glycemic Seal, Diabetic Testing, Diabetic Friendly, Diabetes, GI Testing, GI Clinical Studies, GI Testing Laboratory, GI Seal, Glycaemic