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
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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
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Glycemic Research Institute® |
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