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• Glycemic Index and Load
• Cephalic Index
• Obesity Index: Adipose Tissue Fat Cell Storage & Replication
• Diabetes Index
• Child Safety Factors

Ingredients outside the perimeter of those identified by the Medical Advisory Board of the Glycemic Research Institute as Safe for Children include substances not considered appropriate for children under age 18.

Said ingredients are identified by their chemical composition and analysis, history of use in humans, published clinical trial data, FDA GRAS status, FDA published safe or unsafe use in humans, published scientific side effects and potential side effects, toxicity, Natural versus Synthetic or Non-Natural profile, history of use in children under age 18, questionable use in children under age 18, ingredients that do not meet the Critical Health Number Protocols, and other scientific data related to safe use in children (Unacceptable Substances).

TABLE OF CONTENTS

Adipose Tissue Fat Storage & Replication

Quantification of Perimeters for
"Kid Friendly" Protocols

Research Design and Methods

Analysis Directive

The Critical Health Number is a numerical index (created by the Glycemic Research Institute over a 25-year period) derived from Human In Vivo Clinical Trials designed to ascertain specific pathophysiological perimeters:

The clinical protocols involved in analyzing and identifying foods and beverages (Test Foods) that fit the criteria for “Kid Friendly” require specific Human In Vivo Clinical Trials and a resulting Critical Health Number that scores a Test Food’s ability to increase a child’s risk of obesity and diabetes.

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.

Taste, mouth-feel, gastrointestinal issues; nausea, flatulence, bloating, are recorded.

Results presented in the final Test Food report 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.

Specific Trial Perimeters are not included in this educational document and are the property of the Glycemic Research Institute.

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 the first 60 seconds after the subjects have mouth-contact with a Test Food, and for 30-second intervals thereafter. 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.

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, as Sugar Alcohols and herbal sweeteners also effect glycemic responses.

Specific Trial Perimeters are not included in this educational document and are the property of the Glycemic Research Institute®.

IDENTIFYING GLYCEMIC FACTORS

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, beverages, Nutraceuticals, and Pharmaceutical agents.

Table I

Insulin and glucose are the primary contributors to type 2 diabetes, weight gain, and obesity. Excess levels of insulin and/or glucose trigger adipose tissue fat storage via multiple pathways. Dietary-induced insulin/glucose elevation is caused by ingestion of high glycemic and/or high cephalic foods/beverages, thus triggering Adipose Tissue Fat Storage (ATFS) mechanisms.

Weight gain and obesity are a result of ATFS trigger-mechanisms, and include primary increase in adipose tissue fat cell size, and secondary increase in fat-cell-replication (FCR). Fat-cell-replication occurs primarily in childhood, beginning with the 9-month period in-womb, first year of life, and teen-age years.

Once fat-cell-replication has occurred during these childhood years, it cannot be reversed, even by liposuction intervention. This crucial childhood period determines the resulting adult body size, and attendant health problems associated with weight gain and obesity.

Both pregnancy and expanding fat cell size (overeating) are responsible for adult fat-cell-replication, as pregnancy hormones trigger increases in both fat cell size and replication as a fetus-protective mechanism, and overeating triggers fat cell size leading to the production of more fat cells.

PPARy plays a role in the induction of differentiation of pre-adipocytes into mature cells. As a key transcription factor in adipose tissue, PPARy is activated by fatty acids and their eicosanoid derivatives. Thus the eicosanoid-factor plays a significant role in future treatments for obesity.

PPARy expression is generated by insulin, which further identifies insulin as a primary component of adipogenesis and lipogenesis in humans, leading to weight gain, obesity, and type 2 diabetes. In clinical trials, patients taking synthetic PPARy gain weight whereas PPARy-blockage results in smaller fat stores, even on high fat diets.

The genes regulated by PPARy encode the adipocyte fatty acid binding protein, Lipoprotein Lipase (LPL), a key regulator in weight gain and obesity. Tracking LPL-driven mechanisms are mandatory in controlling obesity in humans, and are included in GRI’s clinical trials.

Glucose regulates expression of lipogenic genes via a carbohydrate response transcription factor (ChoRF), which can be tracked and quantified in Human In Vivo Clinical trials focusing on total glycemic response.

Both insulin and glucose affect fat-storage mechanisms, such as SREBP’s, which are transcription factors that regulate gene expression related to fatty acid and cholesterol metabolism, including SREBP-2, SREBP-1a and SREBP-1c.

Over-expression of SCREBP’s activates lipogenesis genes related to white adipose tissue. Various nutrients and hormones (variants) affect expression of lipogenic genes that are mediated by SREBP’s, thus indication avoidance and/or promulgation of said variants.

Insulin and glucose affect SREBP-1 transcriptional activity via several mechanisms. Insulin stimulates SREBP-1 mRNA expression in adipocytes and increases gene transcriptional activation by activating the proteolytic cleavage of membrane-bound SREBP-1.

Identification of these SCREBP’s variants can be key in avoiding obesity and type 2 diabetes. Continuing clinical trials will help identify these variants.

METABOLIC VARIANCES

Obese and type 2 diabetic individuals conserve calories more effectively than their leaner counterparts, resulting in deposition in adipocytes.

Obesity research has begun to focus on metabolic variances leading certain individuals (Somatotypes) to accumulate adipose tissue, whereas other Somatotypes maintain relatively normal adipocytes with equal food intake.

Uncoupling proteins have taken a spotlight in this field, as these molecules guide the passage of nutrients into the mitochondria, where nutrient energy is released. It has also been recently discovered that female mitochondrial DNA can “learn” in one generation, thus passing on fat genes in a very short period of genetic time.

Mechanisms in females involving mitochondrial differentiation process and lipolytic systems are due, in part, to the unfavourable adrenergic receptor balance for thermogenic activation.

GENDER-DEPENDENT FAT-STORAGE MECHANISMS

Fat burning mechanisms are different in females than their male counterparts, which is one reason women typically hold more adipose tissue fat than males, and have a harder time losing weight. Females conserve energy more efficiently, showing a higher resistance to weight loss and higher protection of vital organs mass. Caloric restriction (CR) studies have shown that females conserve energy more efficiently, showing a higher resistance to weight loss and higher protection of vital organs mass than males.

Gender-dependent inactivation of thermogenesis in brown adipose tissue (BAT) is one of the female energy conserving mechanisms. In females, gender-dependent inactivation of thermogenesis in brown adipose tissue (BAT) is one of the energy conserving mechanisms. Under ad libitum conditions, females have greater BAT recruitment and greater oxygen consumption than their male counterparts. Total and mitochondrial protein, as well as triglyceride and DNA content are more reduced in females than males.

Similarly, the levels of key BAT functional proteins, such as Lipoprotein Lipase (LPL), UCP1, HSL, and TFAM, are more reduced in females, whereas no changes are found in mitochondrial DNA levels (mtDNA) and OXPHOS activities in males and females.

Caloric restriction in females triggers Thrifty Gene-Activation (TGA). Females whose BAT thermogenic activity is higher in ad libitum conditions, is depressed during caloric restriction (CR). This inactivation involves the mitochondrial differentiation process and lipolytic system and is due, in part, to the unfavourable adrenergic receptor balance for thermogenic activation.

ADIPOSITY IN DIABETES

Adipose tissue lipolysis (ATL) is highly sensitive to insulin and is impaired in poorly controlled diabetes (both Non-Insulin Dependent and IDDM). In non-diabetics and normal persons, ATL results in added weight gain via increased size of fat cells and fat cell replication.

In type 1 diabetes, this imbalance results in reduced total body weight, including loss of muscle mass via catabolism, which is not the case in type 2 diabetes, in which increasing body weight (adipose tissue fat) exacerbates health problems and insulin issues.

Adipose tissue fat build-up is determined by the balance between lipogenesis and lipolysis/fatty acid oxidation. Lipogenesis, in normal and type 2 diabetic humans, and particularly in children under age 18, is stimulated by a high glycemic and/or high Cephalic diet, whereas it is inhibited by a low glycemic, low Cephalic diet.

These effects are partly mediated by hormones, which inhibit growth hormone or leptin, or stimulate (insulin) lipogenesis. Sterol regulatory element binding protein-1 is a critical intermediate in the pro- or anti-lipogenic action of several hormones and nutrients. Another transcription factor implicated in lipogenesis is the peroxisome proliferator activated receptor γ. Both transcription factors are attractive targets for dietary intervention of hypertriglyceridemia and obesity.

To determine whether insulin regulation of lipolysis is abnormal in subjects with poorly controlled insulin-dependent diabetes mellitus (IDDM), free-fatty acid flux ([1-14C]palmitate) can be measured under conditions ranging from complete insulin withdrawal to hyperinsulinemia.

In one particular trial, seven non-diabetic and seven IDDM subjects were studied with the pancreatic clamp technique to control plasma insulin, growth hormone, and glucagon concentrations at the desired levels.

Preliminary studies were performed to validate the experimental design. The palmitate flux response to insulin withdrawal (2.5 +/- 0.2 vs. 2.5 +/- 0.2 mumol.kg-1.min-1) and maximally antilipolytic insulin concentrations (0.17 +/- 0.02 vs. 0.23 +/- 0.03 mumol.kg-1.min-1) were not different in non-diabetic and IDDM subjects, respectively.

In contrast, IDDM subjects required significantly greater plasma free-insulin concentrations to result in equivalent suppression of palmitate flux compared with non-diabetic subjects.

Lipolysis was found to be very sensitive to insulin in non-diabetic humans, with half-maximal suppression occurring at plasma free-insulin concentrations of approximately 12 pM (less than 2 microU/ml).

HORMONAL REGULATION OF LIPOGENESIS

Insulin is the most important hormonal factor influencing lipogenesis. Secondly, glucose induces the expression of lipogenic genes. Thirdly, glucose increases lipogenesis by stimulating the release of insulin and inhibiting the release of glucagon from the pancreas.

Insulin stimulates lipogenesis by increasing the uptake of glucose in the adipose cell via recruitment of glucose transporters to the plasma membrane, as well as activating lipogenic and glycolytic enzymes via covalent modification.

This mechanism is achieved by the binding of insulin to the insulin receptor at the cell surface, thus activating its tyrosine kinase activity and inducing a plethora of downstream effects, including the long-term effects on expression of lipogenic genes.

GROWTH HORMONE

Growth hormone (GH) also has a significant role in lipogenesis. GH is abundant in youth and steadily decreases, starting at age 23 in humans. Both diabetes and obesity are linked to growth hormone levels. As GH levels decline, risk of type 2 diabetes and obesity increases.

Reinstation of GH via synthetic chemical intervention is contraindicated, and results in shortened lifespan, brain cancer, and bone deformities. GH can be resinstated in humans via oral ingestion of large doses of the free form amino acid, L-Arginine (10, 000 mg).

Growth Hormone dramatically reduces adipose tissue lipogenesis, which results in significant fat loss, and concomitant gains in muscle mass via anabolic activity. GH decreases insulin sensitivity, which down-regulates fatty acid synthase expression in adipose tissue, and decreases lipogenesis by phosphorylating transcription factors Stat5a and 5b. Stat5a and 5b decreases fat accumulation in adipose tissue.

LEPTIN

Leptin limits adipose tissue fat storage by blunting excess food intake and by inhibiting lipogenesis. Leptin also moderates lipogenesis by down-regulating gene expression involved in triglyceride and fatty acid synthesis.

LONG-TERM SOLUTIONS

Food plans, diets, snacks, foods and beverages, as well as fast-food, can be naturally modified for children under age 18, and directed at avoiding inclusion of ingredients that trigger Adipose Tissue Fat Storage (ATFS) and replication.

Avoidance of high glycemic and high Cephalic ingredients is key in the formulation and identification of Kid Friendly foods.

In adults, inhibition of Thrifty Gene-Activation (TGA), particularly in the female overweight and/or type 2 diabetic population is key in controlling and preventing the obesity epidemic.

The metabolic relationship between adipose tissue fat-storage and ingested food (hereinafter “Test Foods”) can be tracked and documented In Vivo. Test Foods that increase total Lipoprotein Lipase (LPL) activity, both secreted and cell-associated, promote adipose fat storage in humans.

Lipoprotein lipase (LPL) is a key enzyme regulating the disposal of fuels in the body. LPL is expressed in a number of peripheral tissues including adipose tissue, skeletal and cardiac muscle and mammary gland. In white adipose tissue, LPL is activated in the fed state and suppressed during fasting. The reverse is true in muscle. LPL is the definitive metabolic gatekeeper for fat storage in the fat cell.

Oral consumption of foods, drinks, Nutraceuticals, and Pharmaceuticals elicit distinct responses in humans. These metabolic responses include:

Test Foods that activate deposition in human adipose tissue fat cells can be identified and classified as to their Fat-Storage capacity. Test Foods that trigger Lipoprotein Lipase (LPL) cause a net effect as adipocytes fill up and reach maximum storage capacity. As this occurs, new adipose tissue fat cells are created to fulfill storage needs. This situation also leads to fat deposition in other tissues. Accumulation of triacylglycerol in skeletal muscles and in liver is associated with insulin resistance.

Obese humans present 70-80% greater body fat than the lean humans, exhibited elevated levels of leptin and insulin and increased activity of Lipoprotein Lipase in adipose tissue (aLPL), with no change in muscle LPL.

Common characteristics of obese humans include hyperphagia, elevated circulating levels of triglycerides (TG), nonesterified fatty acids (NEFA) and glucose, and a significant increase in beta-hydroxyacyl-CoA dehydrogenase (HADH) activity in muscle, reflecting its greater capacity to metabolize fat. This is typically accompanied by a significant increase in expression of the peptide, galanin (GAL), in the paraventricular nucleus (PVN), as measured by in situ hybridization and real-time quantitative PCR, and also in GAL peptide immunoreactivity.

Specific characteristics of obesity, including expression of hypothalamic peptides, are dependent upon diet composition, thus the precise composition of a Test Food determines its fat-storage proclivity.

Whereas obesity on an HFD is associated with hyperphagia and elevated lipids, fat metabolism in muscle, and fat-stimulated peptides such as GAL, obesity on an HCD with a similar increase in body fat shows none of these characteristics and instead exhibits a metabolic pattern in muscle that favors carbohydrate over fat oxidation.

The existence of multiple forms of obesity, with different underlying mechanisms, are primarily diet dependent.

Glycemic Research Institute Adipose Tissue Fat (ATF) Studies focus on identification of the proclivity and ability of a Test Food to stimulate fat-storage in fat cells via stimulation of human fat-storing enzymes and mechanisms. Test Foods are clinically analyzed In Vivo to determine their metabolic fat-storing properties with optional specific focus on insulin-resistance disorders and adipose tissue fat-storage via LPL.

Test Foods that pass the GRI protocols for ATF will have met specific criteria in clinical studies that determine the Test Foods acceptability for use by non-diabetic and/or diabetic persons, overweight and obese persons, normal persons, hypoglycemics, and persons with Insulin Resistance and other known Metabolic Syndromes.

Identifying and controlling the fat-stimulating properties of Test Foods allows for better control over food-driven fat-storage, insulin stimulation, reactive hypoglycemia, as well as exacerbation and development of obesity, Metabolic Syndrome, Insulin-Resistance, and type 2 diabetes.

ANTHROPOLOGIC

The adipose tissue fat-storage (ATFS) value of Test Foods is not obvious by the ingredients, or by the percent of carbohydrate/protein/fat. Rice cakes rate “Very High”, though they do not possess high-fat or high-calories. Rice cakes stimulate adipose tissue fat-storage (ATFS) via LPL mechanisms in humans that defy logic, while following anthropologic (the logic of Anthropology in human development). Bread, a typically high-fat-storage food, has already been engineered to avoid ATFS (2007).

Adipose Tissue Rating Protocols

Test Foods undergoing clinical trials for KID FRIENDLY
protocols will be rated as follows:

QUALIFICATION FOR ADIPOSE TISSUE PROTOCOLS

Test Foods that qualify rate from Level 1 to Level 2.

Test Foods that rate over Level 2 do not qualify.

The following references represent Glycemic Research Institute’s review and adoption of protocols and methods utilized in Glycemic Genomics Testing.

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 modelling 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 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

Client fills in the Application Form and returns the form as instructed.

The Client will be assigned to a Clinical Studies Coordinator (CSC), and will be contacted by the CSC.

The Client will be advised by the CSC if the Test Food submitted will be accepted for Clinical Trials (per the GRI Medical Advisory Board decision). Test Foods submitted for Kid Friendly Clinical Trials (KFCP) must be considered Safe for Children per the GRI KFCP.

The Client will be advised of the specific costs for the Kid Friendly Protocol.

If the Client decides to proceed with the GRI Kid Friendly Clinical Trials as specified by the Kid Friendly Protocols, the CSC will coordinate with the Client one-on-one throughout the entire process.