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The definition of the glycemic index is often given as the area under curve (AUC) of their two-hour blood sugar response. However, it's essentially meant to be a measure of whether food causes a fast or slow rise in blood sugar level. Why is the two-hour AUC a good measure of the speed of the rise?
When ingesting the "same" amount of sugar, no matter the form, the AUC should be the same, right? The only explanation I can find is that the two-hour cutoff means some carbohydrates are metabolized beyond that cutoff, so their AUC and hence their GI is lower, is that what's happening?
The reason I'm a bit confused is that all of the images of blood sugar curves illustrating low & high GI seem to show two curves with the same AUC, both going to 0 before the two-hour cutoff.
The AUC of identical dosages of sugar depends on digestion speed because of the rate at which sugar is removed from the blood stream. If sugar exponentially decayed in a first order rate, then the AOC would be the same, but it's save to assume some 0 order mechanisms contributing. E.g. alcohol is mostly metabolized in a 0 order reaction rate: A fixed amount per hour.
Edit: If you take up sugar slowly, it might never reach high concentrations in the blood stream and lead to lower AUC then if taken up fast. Each molecule has a longer lifetime if taken up faster.
How to test normal blood glucose levels
Blood glucose levels vary, depending on a person’s health status and whether they have eaten. People without diabetes typically have between 72–140 milligrams of glucose per 1 deciliter of blood.
People who have diabetes tend to have slightly higher blood glucose, or sugar, levels at around 80–180 milligrams per deciliter (mg/dL).
The Centers for Disease Control and Prevention (CDC) recommend that monitoring blood glucose levels helps people stay within their target ranges. Keeping in a healthy range can prevent long-term complications of diabetes, such as vision loss, heart disease, and kidney disease.
In this article, we discuss normal ranges for blood sugar levels. We also cover how and why doctors test blood sugar levels.
Share on Pinterest A glucose monitor can help a person measure blood glucose levels.
Blood sugar levels change throughout the day. Typically, blood sugar levels are at their lowest first thing in the morning or after a period of fasting. Blood sugar levels increase during and after meals, as the body digests food.
The following chart outlines normal blood sugar ranges for people with and without diabetes, depending on the time of day:
|Time of day||Target blood sugar for people without diabetes||Target blood sugar for people with diabetes|
|Before meals or while fasting||72–99 mg/dL||80–130 mg/dL|
|2 hours after the start of a meal||less than 140 mg/dL||less than 180 mg/dL|
|A1C results: Average over a 3-month period||less than 5.7%||less than 7%|
Abnormal blood sugar levels
Abnormal blood sugar levels occur when there is either too much or little sugar in the blood. The blood sugar ranges for each are:
- Hypoglycemia. Known as low blood sugar: 70 mg/dL or less.
- Hyperglycemia. Known as high blood sugar: More than 180 mg/dL.
What is a blood glucose test?
There are two ways to measure blood glucose levels:
- Blood sugar test. This measures the current level of glucose in the blood.
- A1C test. This measures the average blood glucose level over the past 2–3 months. This test occurs in a laboratory.
People can measure their blood sugar levels with either a blood sugar meter or a continuous glucose monitor.
A continuous glucose monitor uses a sensor to measure blood sugar levels. A doctor inserts the sensor under the skin, usually in the abdomen or arm. The sensor transmits information to a monitor that displays glucose levels every few minutes.
A blood sugar meter measures the amount of glucose in a drop of blood, usually from the finger.
Follow these steps when using a blood sugar meter:
- Thoroughly wash the hands and disinfect the meter.
- Gather the meter, a test strip, a lancet, and an alcohol wipe.
- Rub the hands together to encourage blood flow to the fingertips.
- Turn the meter on and insert the test strip.
- Wipe the fingertip with the alcohol pad and let the alcohol evaporate.
- Prick the finger with the lancet.
- Gently squeeze at the base of the finger until a drop of blood forms on the fingertip.
- Place the blood droplet on the test strip.
- Wait for the meter to display the blood sugar measurement.
- Record the results, adding notes about anything that may have contributed to an abnormal reading, such as food or physical activity.
- Properly dispose of the wipe, lancet, and test strip.
The A1C test measures the percentage of glucose bound hemoglobin in a person’s blood.
According to The National Institutes of Health (NIH), this gives a general picture of a person’s blood glucose levels over the past 2–3 months.
Abnormal A1C test results do not necessarily mean a person has diabetes. A doctor will confirm these findings with another blood glucose test.
The doctor may recommend running more tests, such as blood work, to rule out other conditions that can affect blood sugar levels.
When should a person get a A1C blood sugar test?
The CDC recommend that people with diabetes get an A1C test at least twice a year.
Doctors use A1C results to monitor how well a person responds to a certain glucose management regime. They can also use A1C tests to diagnose prediabetes and diabetes.
Symptoms of diabetes that may prompt an A1C test
As mentioned by the NIH, a doctor may recommend an A1C test if a person shows signs of poor glucose control, diabetes, or prediabetes.
- increased thirst
- increased urination, especially at night
- increased hunger
- extreme fatigue
- recurring infections
- numbness or tingling in the hands or feet
- slow healing sores
Doctors may also recommend an A1C test for people who have the following risk factors for prediabetes:
- more than 45 years of age
- family history of diabetes
- history of gestational diabetes
- overweight or obesity
- sedentary lifestyle
- preexisting health conditions, such as high cholesterol levels or high blood pressure
- history of hormonal disorders, such as Cushing’s syndrome
- history of sleep apnea
- long-term use of glucocorticoids, antipsychotics, and certain medications for HIV
What happens during the test?
Most people can take an A1C test at any time without preparing beforehand. However, a doctor may sometimes request that a person avoids eating or drinking for 8 hours before the test.
Women who are pregnant may need to drink a sugary beverage 1 hour before the test.
A doctor or nurse will collect a blood sample, usually from a vein in the arm or hand. They will send the sample to a laboratory for analysis.
Risks of the test
A1C tests are safe and reliable methods of measuring a person’s blood sugar levels. These tests carry a low risk of complications.
However, people may experience temporary pain or bruising at the injection site. Using an unclean needle or lancet can lead to an infection.
A doctor can make dietary and lifestyle recommendations that address an individual’s needs. Doctors can also prescribe insulin and other medication that help stabilize blood sugar levels.
People can use the following tips to keep their blood glucose within a healthy range:
- closely monitor blood sugar levels
- maintain a healthy weight
- exercise regularly
- eat foods with a low glycemic index
- increase dietary fiber intake
- drink plenty of water
- eat regular times and do not skip meals
- restricting carbohydrate intake
- choosing complex carbs over simple carbs
- eating more whole grains, nonstarchy vegetables, and fruit
- controlling portion size
- exercising regularly
- getting plenty of sleep
- staying hydrated
People can raise low blood sugar by:
- consuming 15 grams of fast acting carbs, such as glucose tablets or 4 ounces of juice
- eating dried fruit or applesauce
- eating 1 tablespoon of peanut butter
- using a glucose injection as a doctor prescribes
Type 1 and type 2 diabetes can affect how the body produces and responds to insulin. When insulin is only available in smaller quantities, or cells no longer respond to it, sugar does not enter these cells and remains in the blood.
Type 1 diabetes: The beta cells that make insulin in the pancreas are damaged or destroyed, so sugar stays in the blood for longer.
Type 2 diabetes: The cells in the liver, muscles, and fat tissue no longer respond to insulin, and they release more sugar into the blood. The beta cells not producing enough insulin compounds in this situation.
People who have diabetes can experience high blood sugar levels, or hyperglycemia, because the cells in their body cannot absorb sugar from the blood.
Factors that can raise a person’s blood sugar level include:
- taking too little insulin
- eating too many carbs
- lack of physical activity
- certain medications
If people take too much insulin, exercise more than usual, or skip a meal, they may experience low blood sugar levels, or hypoglycemia.
Glycemic index and postprandial blood glucose response to Japanese strawberry jam in normal adults
We investigated in 30 healthy adults the glycemic index (GI) of five strawberry jams made from various sugar compositions. The jam containing the highest ratio of glucose showed a high GI, while that containing a high ratio of fructose, a jam made from polydextrose, showed a low GI. There was a high correlation (r=0.969, p=0.006) between the GI and the predicted GI calculated from the sugar composition of the jams. Moreover, the influence on postprandial blood glucose response after an intake of only 20 g of jam and one slice of bread with 20 g jam was measured in 8 healthy adults. The blood glucose level after an intake of 20 g of the high GI jam containing the high glucose ratio was higher than that of other jams at 15 min, but there was no significant difference after 30 min. Regardless of whether the GI was low or high, differences in the jams were not observed in the postprandial blood glucose level or the area under the curve after eating either one slice of bread (60 g) or one slice of bread with less than 20 g of jam.
Add the results of Step 3 together. In this example, add 38.18 and 24.84 to get the sum of 63.02, which is the total glycemic index of the meal.
Things You'll Need
Glycemic index table or database
It helps to record your calculations step-by-step to get as accurate a result as possible.
You should not rely solely on the glycemic index to determine how your meal will influence your blood sugar levels. Test your blood sugar according to instructions from your doctor or registered dietitian.
How to Calculate Glycemic Index
Knowing that low-GI foods are a healthful addition to any diet is the first step, but learning which foods fall on the low end of the index can be challenging.
Committing these foods to memory or referencing them in a cumbersome chart in the aisles of the grocery store can be a daunting task.
Then there’s the question of where processed foods, or those with multiple ingredients, fall.
With one simple equation you can calculate the GI from any food label. The math is simple.
To assess how a packaged food will affect your blood sugar, find the carbohydrate number in the label, subtract the grams of fiber and sugar alcohols. Your result will be the grams of digestible carbohydrates. The higher the number the bigger the effect on blood glucose.
Carbohydrate – grams of fiber (and sugar alcohols) =
Grams of digestible carbohydrates
The GI diet is not right for everyone, but it can be a health-supportive option for many. Ask your Nutrition Consultant if the GI diet is right for your bio-individual and optimal health.
Micronutrient Information Center
In the past, carbohydrates were classified as simple or complex based on the number of simple sugars in the molecule. Carbohydrates composed of one or two simple sugars like fructose or sucrose (table sugar a disaccharide composed of one molecule of glucose and one molecule of fructose) were labeled simple, while starchy foods were labeled complex because starch is composed of long chains of the simple sugar, glucose. Advice to eat less simple and more complex carbohydrates (i.e., polysaccharides) was based on the assumption that consuming starchy foods would lead to smaller increases in blood glucose than sugary foods (1). This assumption turned out to be too simplistic since the blood glucose (glycemic) response to complex carbohydrates has been found to vary considerably. The concept of glycemic index (GI) has thus been developed in order to rank dietary carbohydrates based on their overall effect on postprandial blood glucose concentration relative to a referent carbohydrate, generally pure glucose (2). The GI is meant to represent the relative quality of a carbohydrate-containing food. Foods containing carbohydrates that are easily digested, absorbed, and metabolized have a high GI (GI≥70 on the glucose scale), while low-GI foods (GI≤55 on the glucose scale) have slowly digestible carbohydrates that elicit a reduced postprandial glucose response. Intermediate-GI foods have a GI between 56 and 69 (3). The GI of selected carbohydrate-containing foods can be found in Table 1.
Measuring the glycemic index of foods
To determine the glycemic index (GI) of a food, healthy volunteers are typically given a test food that provides 50 grams (g) of carbohydrate and a control food (white, wheat bread or pure glucose) that provides the same amount of carbohydrate, on different days (4). Blood samples for the determination of glucose concentrations are taken prior to eating, and at regular intervals for a few hours after eating. The changes in blood glucose concentration over time are plotted as a curve. The GI is calculated as the incremental area under the glucose curve (iAUC) after the test food is eaten, divided by the corresponding iAUC after the control food (pure glucose) is eaten. The value is multiplied by 100 to represent a percentage of the control food (5):
GI = (iAUCtest food/iAUCglucose) x 100
For example, a boiled white potato has an average GI of 82 relative to glucose and 116 relative to white bread, which means that the blood glucose response to the carbohydrate in a baked potato is 82% of the blood glucose response to the same amount of carbohydrate in pure glucose and 116% of the blood glucose response to the same amount of carbohydrate in white bread. In contrast, cooked brown rice has an average GI of 50 relative to glucose and 69 relative to white bread. In the traditional system of classifying carbohydrates, both brown rice and potato would be classified as complex carbohydrates despite the difference in their effects on blood glucose concentrations.
While the GI should preferably be expressed relative to glucose, other reference foods (e.g., white bread) can be used for practical reasons as long as their preparation has been standardized and they have been calibrated against glucose (2). Additional recommendations have been suggested to improve the reliability of GI values for research, public health, and commercial application purposes (2, 6).
Physiological responses to high- versus low-glycemic index foods
By definition, the consumption of high-GI foods results in higher and more rapid increases in blood glucose concentrations than the consumption of low-GI foods. Rapid increases in blood glucose (resulting in hyperglycemia) are potent signals to the β-cells of the pancreas to increase insulin secretion (7). Over the next few hours, the increase in blood insulin concentration (hyperinsulinemia) induced by the consumption of high-GI foods may cause a sharp decrease in the concentration of glucose in blood (resulting in hypoglycemia). In contrast, the consumption of low-GI foods results in lower but more sustained increases in blood glucose and lower insulin demands on pancreatic β-cells (8).
Glycemic index of a mixed meal or diet
Many observational studies have examined the association between GI and risk of chronic disease, relying on published GI values of individual foods and using the following formula to calculate meal (or diet) GI (9):
Meal GI = [(GI x amount of available carbohydrate)Food A + (GI x amount of available carbohydrate)Food B +…]/ total amount of available carbohydrate
Yet, the use of published GI values of individual foods to estimate the average GI value of a meal or diet may be inappropriate because factors such as food variety, ripeness, processing, and cooking are known to modify GI values. In a study by Dodd et al., the estimation of meal GIs using published GI values of individual foods was overestimated by 22 to 50% compared to direct measures of meal GIs (9).
Besides the GI of individual foods, various food factors are known to influence the postprandial glucose and insulin responses to a carbohydrate-containing mixed diet. A recent cross-over, randomized trial in 14 subjects with type 2 diabetes mellitus examined the acute effects of four types of breakfasts with high- or low-GI and high- or low-fiber content on postprandial glucose concentrations. Plasma glucose was found to be significantly higher following consumption of a high-GI and low-fiber breakfast than following a low-GI and high-fiber breakfast. However, there was no significant difference in postprandial glycemic responses between high-GI and low-GI breakfasts of similar fiber content (10). In this study, meal GI values (derived from published data) failed to correctly predict postprandial glucose response, which appeared to be essentially influenced by the fiber content of meals. Since the amounts and types of carbohydrate, fat, protein, and other dietary factors in a mixed meal modify the glycemic impact of carbohydrate GI values, the GI of a mixed meal calculated using the above-mentioned formula is unlikely to accurately predict the postprandial glucose response to this meal (3). Moreover, the GI is a property of a given food carbohydrate such that it does not take into account individuals’ characteristics like ethnicity, metabolic status, or eating habits (e.g., the degree to which we masticate) which might, to a limited extent, also influence the glycemic response to a given carbohydrate-containing meal (11-14).
Using direct measures of meal GIs in future trials — rather than estimates derived from GI tables — would increase the accuracy and predictive value of the GI method (2, 6). In addition, in a recent meta-analysis of 28 studies examining the effect of low- versus high-GI diets on serum lipids, Goff et al. indicated that the mean GI of low-GI diets varied from 21 to 57 across studies, while the mean GI of high-GI diets ranged from 51 to 75 (15). Therefore, a stricter use of GI cutoff values may also be warranted to provide more reliable information about carbohydrate-containing foods.
The glycemic index (GI) compares the potential of foods containing the same amount of carbohydrate to raise blood glucose. However, the amount of carbohydrate contained in a food serving also affects blood glucose concentrations and insulin responses. For example, the mean GI of watermelon is 76, which is as high as the GI of a doughnut (see Table 1). Yet, one serving of watermelon provides 11 g of available carbohydrate, while a medium doughnut provides 23 g of available carbohydrate.
The concept of glycemic load (GL) was developed by scientists to simultaneously describe the quality (GI) and quantity of carbohydrate in a food serving, meal, or diet. The GL of a single food is calculated by multiplying the GI by the amount of carbohydrate in grams (g) provided by a food serving and then dividing the total by 100 (4):
GLFood = (GIFood x amount (g) of available carbohydrateFood per serving)/100
For a typical serving of a food, GL would be considered high with GL≥20, intermediate with GL of 11-19, and low with GL≤10. Using the above-mentioned example, despite similar GIs, one serving of watermelon has a GL of 8, while a medium-sized doughnut has a GL of 17. Dietary GL is the sum of the GLs for all foods consumed in the diet.
It should be noted that while healthy food choices generally include low-GI foods, this is not always the case. For example, intermediate-to-high-GI foods like parsnip, watermelon, banana, and pineapple, have low-to-intermediate GLs (see Table 1).
Type 2 diabetes mellitus
The consumption of high-GI and -GL diets for several years might result in higher postprandial blood glucose concentration and excessive insulin secretion. This might contribute to the loss of the insulin-secreting function of pancreatic β-cells and lead to irreversible type 2 diabetes mellitus (16).
A US ecologic study of national data from 1909 to 1997 found that the increased consumption of refined carbohydrates in the form of corn syrup, coupled with the declining intake of dietary fiber, has paralleled the increased prevalence of type 2 diabetes (17). In addition, high-GI and -GL diets have been associated with an increased risk of type 2 diabetes in several large prospective cohort studies. A recent updated analysis of three large US cohorts indicated consumption of foods with the highest versus lowest GI was associated with a risk of developing type 2 diabetes that was increased by 44% in the Nurses’ Health Study (NHS) I, 20% in the NHS II, and 30% in the Health Professionals Follow-up Study (HPFS). High-GL diets were associated with an increased risk of type 2 diabetes (+18%) only in the NHS I and in the pooled analysis of the three studies (+10%) (18). Additionally, the consumption of high-GI foods that are low in cereal fiber was associated with a 59% increase in diabetes risk compared to low-GI and high-cereal-fiber foods. High-GL and low-cereal-fiber diets were associated with a 47% increase in risk compared to low-GL and high-cereal-fiber diets. Moreover, obese participants who consumed foods with high-GI or -GL values had a risk of developing type 2 diabetes that was more than 10-fold greater than lean subjects consuming low-GI or -GL diets (18).
However, a number of prospective cohort studies have reported a lack of association between GI or GL and type 2 diabetes (19-24). The use of GI food classification tables based predominantly on Australian and American food products might be a source of GI value misassignment and partly explain null associations reported in many prospective studies of European and Asian cohorts.
Nevertheless, conclusions from several recent meta-analyses of prospective studies (including the above-mentioned studies) suggest that low-GI and -GL diets might have a modest but significant effect in the prevention of type 2 diabetes (18, 25, 26). Organizations like Diabetes UK (27) and the European Association for the Study of Diabetes (28) have included the use of diets of low GI/GL and high in dietary fiber and whole grains in their recommendations for diabetes prevention in high-risk individuals. The use of GI and GL is currently not implemented in US dietary guidelines (29).
Numerous observational studies have examined the relationship between dietary GI/GL and the incidence of cardiovascular events, especially coronary heart disease (CHD) and stroke. A meta-analysis of 14 prospective cohort studies (229,213 participants mean follow-up of 11.5 years) found a 13% and 23% increased risk of cardiovascular disease (CVD) with high versus low dietary GI and GL, respectively (30). Three independent meta-analyses of prospective studies also reported that higher GI or GL was associated with increased risk of CHD in women but not in men (31-33). A recent analysis of the European Prospective Investigation into Cancer and Nutrition (EPIC) study in 20,275 Greek participants, followed for a median of 10.4 years, showed a significant increase in CHD incidence and mortality with high dietary GL specifically in those with high BMI (≥28 kg/m 2 ) (34). This is in line with earlier findings in the Nurses’ Health Study (NHS) showing that a high dietary GL was associated with a doubling of the risk of CHD over 10 years in women with higher (≥23 kg/m 2 ) vs. lower BMI (35). A similar finding was reported in a cohort of middle-aged Dutch women followed for nine years (36).
Additionally, high dietary GL (but not GI) was associated with a 19% increased risk of stroke in pooled analyses of prospective cohort studies (32, 37). A meta-analysis of seven prospective studies (242,132 participants 3,255 stroke cases) found that dietary GL was associated with an overall 23% increase in risk of stroke and a specific 35% increase in risk of ischemic stroke GL was not found to be related to hemorrhagic stroke (38).
Overall, observational studies have found that higher glycemic load diets are associated with increased risk of cardiovascular disease, especially in women and in those with higher BMIs.
GI/GL and cardiometabolic markers
The GI/GL of carbohydrate foods may modify cardiometabolic markers associated with CVD risk. A meta-analysis of 27 randomized controlled trials (published between 1991 and 2008) examining the effect of low-GI diets on serum lipid profile reported a significant reduction in total and LDL-cholesterol independent of weight loss (15). Yet, further analysis suggested significant reductions in serum lipids only with the consumption of low-GI diets with high fiber content. In a three-month, randomized controlled study, an increase in the values of flow-mediated dilation (FMD) of the brachial artery, a surrogate marker of vascular health, was observed following the consumption of a low- versus high-GI hypocaloric diet in obese subjects (39).
High dietary GLs have been associated with increased concentrations of markers of systemic inflammation, such as C-reactive protein (CRP), interleukin-6, and tumor necrosis factor-α (TNF-α) (40, 41). In a small 12-week dietary intervention study, the consumption of a Mediterranean-style, low-GL diet (without caloric restriction) significantly reduced waist circumference, insulin resistance, systolic blood pressure, as well as plasma fasting insulin, triglycerides, LDL-cholesterol, and TNF-α in women with metabolic syndrome. A reduction in the expression of the gene coding for 3-hydroxy-3-methylglutaryl (HMG)-CoA reductase, the rate-limiting enzyme in cholesterol synthesis, in blood cells further confirmed an effect for the low-GI diet on cholesterol homeostasis (42). Well-controlled, long-term intervention studies are needed to confirm the potential cardiometabolic benefits of low GI/GL diets in people at risk for CVD.
Evidence that high-GI or -GL diets are related to cancer is inconsistent. A recent meta-analysis of 32 case-control studies and 20 prospective cohort studies found modest and nonsignificant increased risks of hormone-related cancers (breast, prostate, ovarian, and endometrial cancers) and digestive tract cancers (esophageal, gastric, pancreas, and liver cancers) with high versus low dietary GI and GL (43). A significant positive association was found only between a high dietary GI and colorectal cancer (43). Yet, earlier meta-analyses of prospective cohort studies failed to find a link between high-GI or -GL diets and colorectal cancer (44-46). Another recent meta-analysis of prospective studies suggested a borderline increase in breast cancer risk with high dietary GI and GL. Adjustment for confounding factors across studies found no modification of menopausal status or BMI on the association (47). Further investigations are needed to verify whether GI and GL are associated with various cancers.
Results of two studies indicate GI and GL may be related to gallbladder disease: a higher dietary GI and GL were associated with significantly increased risks of developing gallstones in a cohort of men participating in the Health Professionals Follow-up Study (48) and in a cohort of women participating in the Nurses’ Health Study (49). However, more epidemiological research is needed to determine an association between dietary glycemic index/load and gallbladder disease.
Whether low-GI foods could improve overall blood glucose control in people with type 1 or type 2 diabetes mellitus has been investigated in a number of intervention studies. A meta-analysis of 19 randomized controlled trials that included 840 diabetic patients (191 with type 1 diabetes and 649 with type 2 diabetes) found that consumption of low-GI foods improved short-term and long-term control of blood glucose concentrations, reflected by significant decreases in fructosamine and glycated hemoglobin (HbA1c) levels (50). However, these results need to be cautiously interpreted because of significant heterogeneity among the included studies. The American Diabetes Association has rated poorly the current evidence supporting the substitution of low-GL foods for high-GL foods to improve glycemic control in adults with type 1 or type 2 diabetes (51, 52). Well-controlled studies are needed to further assess whether the use of low-GI/GL diets could significantly improve long-term glycemic control and the quality of life of subjects with diabetes.
A randomized controlled study in 92 pregnant women (20-32 weeks) diagnosed with gestational diabetes found no significant effects of a low-GI diet on maternal metabolic profile (e.g., blood concentrations of glucose, insulin, fructosamine, HbA1c insulin resistance) and pregnancy outcomes (i.e., maternal weight gain and neonatal anthropometric measures) compared to a conventional high-fiber, moderate-GI diet (53). The low-GI diet consumed during the pregnancy also failed to improve maternal glucose tolerance, insulin sensitivity, and other cardiovascular risk factors, or maternal and infant anthropometric data in a three-month postpartum follow-up study of 55 of the mother-infant pairs (54). In addition, another trial in 139 pregnant women (12-20 weeks’ gestation) at high risk for gestational diabetes showed no statistical differences regarding the diagnosis of gestational diabetes during the second and third trimester of pregnancy, the requirement for insulin therapy, and pregnancy outcomes and neonatal anthropometry whether women followed a low-GI diet or a high-fiber, moderate-GI diet (55). At present, there is no evidence that a low-GI diet provides benefits beyond those of a healthy, moderate-GI diet in women at high risk or affected by gestational diabetes.
Obesity is often associated with metabolic disorders, such as hyperglycemia, insulin resistance, dyslipidemia, and hypertension, which place individuals at increased risk for type 2 diabetes mellitus, cardiovascular disease, and early death (56, 57). Traditionally, weight-loss strategies have included energy-restricted, low-fat, high-carbohydrate diets with >50% of calories from carbohydrates, ≤30% from fat, and the remainder from protein. However, a recent meta-analysis of randomized controlled intervention studies (≥6 months’ duration) has reported that low- or moderate-carbohydrate diets (4%-45% carbohydrate) and low-fat diets (10%-30% fat) were equally effective at reducing body weight and waist circumference in overweight or obese subjects (58).
Low-GI/GL diet versus moderate-GI/GL, low-fat diet
Several dietary intervention studies have examined how low-GI/GL diets compared with conventional low-fat diets to promote weight loss. Lowering the GI of conventional energy-restricted, low-fat diets was proven to be more effective to reduce postpartum body weight and waist and hip circumferences and prevent type 2 diabetes mellitus in women with prior gestational diabetes mellitus (59). In a six-month dietary intervention study in 73 obese adults, no differences in weight loss were reported in subjects following either a low-GL diet (40% carbohydrate and 35% fat) or a low-fat diet (55% carbohydrate and 20% fat). Yet, the consumption of a low-GL diet increased HDL-cholesterol and decreased triglyceride concentrations significantly more than the low-fat diet, but LDL-cholesterol concentration was significantly more reduced with the low-fat than low-GI diet (60).
A one-year randomized controlled study of 202 individuals with a body mass index (BMI) ≥28 and at least another metabolic disorder compared the effect of two dietary counseling-based interventions advocating either for a low-GL diet (30%-35% of calories from low-GI carbohydrates) or a low-fat diet (<30% of calories from fat) (61). Weight loss with each diet was equivalent (
4 kg). Both interventions similarly reduced triglycerides, C-reactive protein (CRP), and fasting insulin, and increased HDL-cholesterol. Yet, the reduction in waist and hip circumferences was greater with the low-fat diet, while blood pressure was significantly more reduced with the low-GL diet (61). In the GLYNDIET study, a six-month randomized dietary intervention trial, the comparison of two moderate-carbohydrate diets (42% of calories from carbohydrates) with different GIs (GI of 34 or GI of 62) and a low-fat diet (30% of calories from fat GI of 65) on weight loss indicated that the low-GI diet reduced body weight more effectively than the low-fat diet. Additionally, the low-GI diet improved fasting insulin concentration, β-cell function, and insulin resistance better than the low-fat diet. None of the diets modulated hunger or satiety or affected biomarkers of endothelial function or inflammation. Finally, no significant differences were observed in low- compared to high-GL diets regarding weight loss and insulin metabolism (62).
Low-GI/GL diet versus high-GI/GL diet
In a meta-analysis of 14 randomized controlled trials published between 2005 and 2011, neither high- nor low-GI/GL dietary interventions conducted for 6 to 17 months had any significant effect on body weight and waist circumference in a total of 2,344 overweight and obese subjects (63). Low-GI/GL diets were found to significantly reduce C-reactive protein and fasting insulin but had no effect on blood lipid profile, fasting glucose concentration, or HbA1c concentration compared to high-GI/GL diets.
It has been suggested that the consumption of low-GI foods delayed the return of hunger, decreased subsequent food intake, and increased satiety when compared to high-GI foods (64). The effect of isocaloric low- and high-GI test meals on the activity of brain regions controlling appetite and eating behavior was evaluated in a small randomized, blinded, cross-over study in 12 overweight or obese men (65). During the postprandial period, blood glucose and insulin rose higher after the high-GI meal than after the low-GI meal. In addition, in response to the excess insulin secretion, blood glucose dropped below fasting concentrations three to five hours after high-GI meal consumption. Cerebral blood flow was significantly higher four hours after ingestion of the high-GI meal (compared to a low-GI meal) in a specific region of the striatum (right nucleus accumbens) associated with food intake reward and craving. If the data suggested that consuming low- rather than high-GI foods may help restrain overeating and protect against weight gain, this has not yet been confirmed in long-term randomized controlled trials. In the recent multicenter, randomized controlled Diet, Obesity, and Genes (DiOGenes) study in 256 overweight and obese individuals who lost ≥8% of body weight following an eight-week calorie-restricted diet, consumption of ad libitum diets with different protein and GI content for 12 months showed that only high-protein diets — regardless of their GI — could mitigate weight regain (66). However, the dietary interventions only achieved a modest difference in GI (
5 units) between high- and low-GI diets such that the effect of GI in weight maintenance remained unknown.
Lifestyle modification programs do not currently include the reduction of calories from carbohydrate as an alternative to standard prescription of low-fat diets, nor do they suggest the use of GI/GL as a guide to healthier dietary choices (67).
Lowering Dietary Glycemic Load
Some strategies for lowering dietary GL include:
• Increasing the consumption of whole grains, nuts, legumes, fruit, and non-starchy vegetables
• Decreasing the consumption of starchy, moderate- and high-GI foods like potatoes, white rice, and white bread
• Decreasing the consumption of sugary foods like cookies, cakes, candy, and soft drinks
Table 1 includes GI and GL values of selected foods relative to pure glucose (68). Foods are ranked in descending order of their GI values, with high-GI foods (GI≥70) at the top and foods with low-GI values (≤55) at the bottom of the table. To look up the GI values for other foods, visit the University of Sydney’s GI website.
Measures of glucose variability
The M-value of Schlichtkrull (18,19) has proven to be a durable nyctohemeral measurement of glycemic behavior. It was the mean of the logarithmic transformation of the deviation from a reference value of six blood sugar (BS) measurements taken over a 24-h period plus an amplitude correction factor (Table 1). The latter is the difference between maximum and minimum BS values for the 24-h period divided by 20 (W/20). In the following formula, PG is plasma glucose.
The formula gives greater emphasis to hypoglycemia than hyperglycemia. The choice of 120 mg/dL as the reference value is somewhat puzzling since the intent of the creators of the M-value was to determine “the difference between the observed blood sugar and normal blood sugar” (18), which was 95 mg/dL in their reference group of normal patients (20). A plausible explanation is that the M-value was generated initially from data of persons with diabetes and a margin of safety was permitted. Fidelity to the original intent of the M-value warrants using a reference value consonant with basal glycemia in normal subjects, e.g., 80 for whole blood (21) and 90 for plasma measurements of glucose (22) (Table 1). When this principle is applied, comparisons among various studies can be done as long as the reference value for each study in question uses the normal basal glucose value as determined by local methodology. When 25 or more glucose values are obtained over a 24-h period, the amplitude correction factor can be eliminated (23). Unfortunately, the M-value is not an indicator solely of glucose variability but is a hybrid measure of both variability and mean glycemia.
Mean amplitude of glycemic excursions.
The development of continuous in vivo blood glucose (BG) analysis in the 1960s eliminated the shortcomings of intermittent discrete BG sampling (24). Application of this methodology was pursued in only a few centers worldwide and often only for descriptive purposes (25,26). In contrast, G.D. Molnar of the Mayo Clinic dedicated this tool to the furtherance of his longstanding interest in the quantification of “brittle diabetes” (17,27). Since the ultimate goal in the treatment of diabetes is the restoration of glycemia to that of persons without diabetes, the Mayo group argued that the generation of a metric of glycemic excursions should begin with an examination of the profiles of nondiabetic individuals. Furthermore, such a measure should be simple in concept and faithful to the physiological basis for the glucose swings. To do otherwise would condemn the endeavor to the fruitless task of bringing order from the chaos of the glucose profiles characteristic of type 1 diabetes and risk failure to establish biological relevance.
Because interest lay in the amplitude of glycemic swings and not in the dispersion of all the glucose data, SD was considered to be unsuitable. Because glycemic excursions in normal subjects occurred solely in response to food ingestion (Fig. 1), their recognition required a criterion exclusive to the meal-related glycemic responses. Use of an absolute value of BG such as 25 mg/dL or 50 mg/dL as a criterion for a glucose swing was abandoned because each failed to account for all of the meal-related nondiabetic glucose excursions. Upon reflection, an absolute value of BG was an ill-conceived benchmark because it failed to recognize that even among normal subjects the responses to identical food-related perturbations may result in differing glucose elevations. The criterion, which did recognize all of the meal-related glucose excursions for all of the normal subjects, was the SD of the mean BG for each 24-h period of study (288 values taken q5min from the continuous record) for each individual (Fig. 1). In contrast, 0.5 SD and 1.5 SD were less inclusive/exclusive. Although the numerical value of 1 SD will perforce differ in absolute value from person to person, it nevertheless acts as an individualized standard. By convention, a glycemic excursion (both trough-to-peak and peak-to-trough) must exceed 1 SD of the respective 24-h BG profile. For continuous recordings exceeding 24 h, the use of 1 SD calculated for the whole period of study may result in the inclusion of the same excursions as use of the separate 24-h SDs, since SDs from successive days do not differ by much (even in type 1 diabetic patients as long as therapy has not changed during the period of monitoring) (Fig. 2). Only one limb of the excursion, ascending or descending, determined by the initial excursion (which is not always an inflection especially in type 1 diabetic patients) is used for calculation of subsequent excursions.
Continuous BG analysis for 48 h in an ambulatory fed normal subject. The timing and frequency of food ingestion matches that of the type 1 diabetic patient in Fig. 2. Note that each glucose excursion occurs in response to food ingestion and that each limb, ascending and descending, exceeds 1 SD of the 288 data points/24 h taken every 5 min from the 48-h tracing. Note the small difference in SD between days 1 and 2. Mean BG was 84 and 82 mg/dL and MAGE 41 and 48 for days 1 and 2, respectively. M, meal Sn, snack.
Continuous BG analysis for 48 h in a patient with type 1 diabetes. The qualifying excursions are shown as pairs of solid and stippled yellow beginning with the leftmost deflection, 333 to 208 mg/dL. The inflection component of that excursion is 208 to 432 mg/dL, which incorporates an intermediary excursion. The latter fails to qualify as an excursion on its own because one limb (322 to 287 mg/dL) fails to exceed 1 SD for that 24-h period. Note the small difference in SD from day 1 to day 2. Whether MAGE is calculated from the descending (184 mg/dL) or ascending (171 mg/dL) limbs, the values are similar. M, meal Sn, snack.
Glycemic excursions of the same magnitude may qualify for one subject but not for another should the SD of the latter be larger than that of the former. The excluded excursion is not lost, however, but is incorporated into a larger one of which it is a part. Whether this is problematic is unknown. Should the subsumed excursion be of a magnitude observed for normal subjects its exclusion may be inconsequential relevant to the risk for the development of microvascular complications of diabetes. The arithmetic mean of the glycemic excursions for the period of study (24 h, 48 h, or longer) is the value of mean amplitude of glycemic excursions (MAGE) (21).
An automated algorithm has been created for the calculation of MAGE (28). Although created for determination from continuous BG analysis, MAGE has been applied to intermittent (7- and 22-point sampling/24 h) measurements (6,29) as well as continuous interstitial glucose monitoring (30).
SD is a commonly reported expression of glucose variability. Its ease of calculation and possible concern that its absence would impugn authors’ commitment to a comprehensive assessment of variability drives its inclusion in virtually all articles on this topic. SD is not a fall-back measure by any means it does have vigorous support (31). Unfortunately its utility is hampered by the lack of Gaussianness of glucose profile data (Fig. 3) and the potential for widely different glycemic curves having the identical numerical value of SD (32).
Frequency distribution of the 576 glucose values/48 h from Fig. 1 plotted per 24-h period showing a lack of normal distribution.
The J-index perpetuates the inclusion of SD into the measurement of glycemic variability. Originally derived from intermittent BG determinations, it has been adapted to continuous monitoring data. Its proponent recommends it as a measure of both the mean level and variability of glycemia (33). This parameter has not been widely used. In the following formula, MBG is mean BG.
Mean absolute difference, mean absolute glucose, and continuous overall net glycemic action n.
Three parameters based on the analyses of sequential BG values have been proposed as measures of glycemic variability. The mean absolute difference (MAD) of consecutive BG values was derived from self-monitored BG data performed five times per 24 h (34). The authors have acknowledged that MAD has no advantage over SD as an estimate of glycemic variability.
Mean absolute glucose (MAG) is the summed differences between sequential 7-point self-measured BG profiles per 24 h divided by the time in hours between the first and last BG measurement (35). A limitation to MAG is that two excursions of identical extent but of different duration have different values.
Continuous overall net glycemic action (CONGA) n, was conceived for continuous interstitial glucose monitoring. Analysis requires a complete tracing, i.e., 288 values per 24 h. For each glucose datum after the first n hours of observations, the difference between the current glucose and the glucose n hours previous is determined. n can vary from 1 to 8 h. For instance, for n = 1 and 24-h period of monitoring beginning at 0800, the calculations would begin as follows: BG at 0900 minus BG at 0800 BG at 0905 minus BG at 00805 BG at 0910 minus BG at 0810 and so on until BG 0800 (the next day) minus BG at 0700 (Fig. 4). The period of analysis is 24 h minus n. CONGA is expressed as the SD of the differences despite their lack of normal distribution (Fig. 4) (36).
CONGA 1 analysis for the breakfast meal day 1 from Fig. 1. For illustration purposes only 4 h are shown. For this period, the mean of hourly differences determined at 5-min intervals is −5.5 mg/dL with an SD of 22.2, which is the actual CONGA value. The insert shows the frequency distribution of the sequential glucose differences, which clearly does not have a normal distribution.
For none of these parameters—MAD, MAG and CONGA n—has a rationale been promulgated to support its use. Since each was based on examinations of tracings from patients with diabetes rather than normal subjects, it is difficult to assign any biological relevance to them. Reliance solely on mathematical manipulations to the exclusion of relevance is analogous to the feckless statistician who drowned wading across a river whose average depth he calculated to be 4 feet: failure to appreciate the relevance of the variation in water depth from shore to shore was his undoing.
Inclusion of all data points fails to discriminate glycemia directly related to excursions from that which might be considered as noise. Furthermore, it is difficult to identify a biorhythm with periodicities of 1, 2, 3, or more hours implicit in the generation of CONGA n.
For postprandial hyperglycemia to play a role in the development of diabetes complications, its influence must exceed its contribution to mean glycemia. Otherwise the effect of improved mean glycemia is amenable to study with techniques less arduous than the task of controlling postprandial hyperglycemia (37). Implicit in the putative special role for postprandial glucose is the assumption of unique properties associated with the meal-related glucose excursion not attendant upon hyperglycemia of a similar degree in the interprandial state (10,11). A clinical trial designed to assess the effect of postprandial glucose on the development of diabetes complications must ensure no difference in HbA1c or mean glycemia while generating a difference in postprandial glycemia. To achieve these goals, the interprandial glucose would of necessity have to increase, thereby resulting in reduced glucose excursions (38). When measured in this context postprandial glucose therefore takes on the mantle of a surrogate for glycemic variability.
Assessment of postprandial glycemia poses not just a difficult but a virtually impossible task when limited to one after-meal determination: a static measurement in a dynamic situation. In persons without diabetes, glucose responses to food ingestion are influenced by the size, composition, and time of day of the meal (39,40). The responses in patients with diabetes are more variable (41). Even in the situation of complete ascertainment from continuous glucose monitoring, reliance on peak postprandial glucose as a measure of variability is fraught with potential error because it represents only the north end of the meal-related excursion without the south end there is no actual excursion. Without documentation of the starting point of an excursion its size cannot be known.
Low BG index, high BG index, and glycemic risk assessment diabetes equation.
Two quantifications of risk for hypoglycemia and hyperglycemia have been reported under the rubric of glucose variability (42,43). High BG index (HBGI) and low BG index (LBGI) are generated from a correction of the skewness of glycemia (narrow hypoglycemic vs. broad hyperglycemic range) through a symmetrization process around zero (equivalent to glucose 112.5 mg/dL) by expanding the hypoglycemic range and reducing the hyperglycemic range (42).
The rationale for this maneuver is not stated nor is it readily inferred since risks associated with hypoglycemia are different from those associated with hyperglycemia in type, timing, and predictability, and they have no interaction. Larger values of LBGI and HBGI indicate higher risk for hypoglycemia and hyperglycemia, respectively. Although originally developed from self-monitored BG data, these parameters have been adapted to continuous interstitial glucose monitoring (44). Correlations between LBGI and subsequent hypoglycemia and between HBGI and HbA1c have been reported.
The glycemic risk assessment diabetes equation (GRADE) score was created to summarize the degree of risk associated with a glucose profile (43). Qualitative risk scoring for a wide range of glucose levels inclusive of marked hypoglycemia and hyperglycemia was generated by a committee of diabetes practitioners. The nature of the risk was not specified. In the determination of GRADE, glucose values are transformed to yield a continuous curvilinear response with a nadir of 90 mg/dL and high adverse weighting to hyperglycemia and hypoglycemia.
Since a high GRADE score may be generated from either hyperglycemia or hypoglycemia, the range of glucose contributing to the score is reported as percentages: <70 mg/dL (hypoglycemia), 70–140 mg/dL (euglycemia), and >140 mg/dL (hyperglycemia).
Neither LBGI/HBGI nor GRADE measures glucose fluctuations directly. Since both manipulations use all of the available glucose data, the highly derivatized results appear to be an expression of quasi mean glycemia (LBGI/HBGI) or a frequency distribution (GRADE). Unfortunately, the term “risk” may not serve these parameters well, especially in the context of predicting future events. An undesirable value of LBGI, HBGI, or GRADE should lead to an immediate change in therapy for the purpose of mitigating future adverse events rather than act as predictors in the face of persistent flawed treatment.
This substance has been proposed as a surrogate marker for glycemic excursions (45). Once circulating glucose levels exceed the renal threshold for glucosuria plasma, levels of 1,5-anhydroglucitol (all because its renal reabsorption is competitively inhibited by glucose. Distinction between chronic and intermittent hyperglycemia, both of which are characterized by low concentrations of 1,5-anhydroglucitol, is governed by the HbA1c level, which when normal or near-so suggests intermittent forays of glycemia into the hyperglycemic range, i.e., large glycemic excursions. There are several limitations of 1,5-anhydroglucitol as a measure of glycemic variability. It does not measure glucose fluctuations directly and therefore cannot determine their size and frequency or those occurring below a BG ∼180 mg/dL and is not useful when the HbA1c level is elevated.
How is the glycemic index computed from a blood sugar curve? - Biology
When you look at a food label you’re presented with a few different things. Fat, sugar, carbohydrates and nutrients are all clearly labeled, with either a percentage or a measurement of how much that food item contains. These guides can help us make informed decisions about the foods we eat, which can be crucial if we need to enjoy a special diet.
For example, diabetics must be mindful of the amount of sugar they consume. Yet, even though you may pay extra attention to sugar, carbohydrates may actually be a more accurate factor when it comes to spikes in blood sugar.
You see, carbohydrates turn into sugar – a fact that is often overlooked. We tend to see the word sugar and then ignore the fact that the amount of carbohydrates in the food we consume can also increase our blood sugar levels.
So whether you’re diabetic or not, if you’re looking to maintain healthy blood sugar, it’s wiser for you to know a food’s glycemic index and glycemic load (GI and GL).
What is the glycemic index?
The home of the glycemic index comes from the University of Sydney in Australia. They provide an international database of glycemic indexes for a variety of foods. They are also responsible for providing up-to-date information for consumers to help them make informed decisions when choosing what foods to eat.
But what exactly is the glycemic index? Well, as mentioned, carbohydrates can affect blood sugar levels. The glycemic index ranks those carbohydrates between zero and 100 based on their ability to raise blood sugar. Foods that are digested quickly tend to have a higher glycemic index because they raise blood sugar rapidly. On the other hand, foods that are digested slowly fall on the lower side of the scale.
When it comes to diabetes, it is recommended that those with the illness stick with foods with a low glycemic index. This is because they respond better to insulin and can aid in weight management as well as feeling fuller longer – all important aspects of managing diabetes.
Both the World Health Organization (WHO) and Harvard School of Public Health have made recommendations through research suggesting that people should try to eat foods with a low glycemic index as foods with a high one have been linked to numerous illnesses, such as diabetes and heart disease. They found that by eating low glycemic index foods people could prevent such illnesses.
How to determine a food’s glycemic index
Now that we have a better understanding of glycemic index, let’s explore how it’s formed.
Portions of food are set aside, which contain at least 50 mg of carbohydrates. If a food naturally has lower carbohydrates, then the portion sample will only contain 25 mg. With the help of 10 healthy people, they are served the food item after they have fasted overnight. Once consumed, blood samples are taken every 15 to 30 minutes over the span of two hours.
A number is then calculated, referred to as the incremental area under the curve (iAUC), which shows the total increase in blood sugar post-meal. The iAUC from the test food is then divided by the iAUC from the reference food and multiplied by 100. The average is calculated from all 10 participants and that becomes the glycemic index.
Glycemic index foods that are considered low have a score of 55 or less. Medium glycemic index foods are 56 to 69 and high glycemic index foods rank 70 or more.
Currently, it is not mandatory that food companies include the glycemic index on their product labels. Some countries, like Australia, do indicate the glycemic index to help consumers. The lack of information is largely in part due to the fact that testing for GI is not widely available, so it can take quite some time to retrieve accurate information.
What is glycemic load?
On the other side of monitoring blood sugar is glycemic load. Where glycemic index measures how much a food can raise blood sugar, glycemic load is how much carbohydrates affect insulin. This can help people control their portions and understand how the amount they eat can affect their blood sugar.
Glycemic load is calculated by multiplying the glycemic index by the amount of carbohydrates a food item has and dividing it by 100 (GL = GI x carbohydrates / 100).
Although the glycemic index is ranked from zero to 100, the glycemic load ranks a little differently. A low glycemic load is classified as being between zero and 10. A medium glycemic load is 11 to 19 and a high glycemic load is anything over 20.
Knowing the glycemic load is useful for anyone, but it is particularly of high significance for those with diabetes. Similar to why a diabetic should know glycemic index, glycemic load can further help a diabetic eat well without creating spikes in their blood sugar.
50 common foods with their GI and GL values
In North America, it isn’t as common to see a glycemic index or glycemic load value on food items. With the help of an online search it may be a bit easier to obtain such information.
Below you will find 50 of the most common foods, along with their glycemic index and glycemic loads. This way you can have a better baseline understanding of how food affects your body. Furthermore, you can use the information as a basis to create a glycemic index and glycemic load diet to help manage blood sugar.
Glycemic index and glycemic load foods chart
Eating a glycemic index and glycemic load diet
Now that you are familiar with 50 of the most common foods and their GI and GL, you can begin to be more mindful about incorporating them into your diet. By eating right and avoiding spikes in blood sugar you can ward off illness and manage your diabetes more effectively. Of course, other aspects like proper sleep and physical activity can also impact healthy living and should be a priority.
Whether you’re diabetic or not, being aware of glycemic index and glycemic load can be beneficial to overall good health. Preventing diabetes, especially if it runs in your family, is as easy as eating right, and that is where GI and GL can come in handy. Although both measurements may not be as well-known now, hopefully food companies will move in a direction that will provide us with such information so that we become better informed and conscious consumers.
How is the glycemic index computed from a blood sugar curve? - Biology
Graduate School of Life Science, Showa Women&aposs University
Department of Health and Nutrition, University of Human Arts and Sciences
Graduate School of Life Science, Showa Women&aposs University
2016 Volume 22 Issue 1 Pages 117-126
- Published: 2016 Received: July 25, 2015 Released on J-STAGE: March 01, 2016 Accepted: October 21, 2015 Advance online publication: - Revised: -
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This study investigated how different combinations of xanthan gum and rice affect blood sugar levels after rice consumption. The addition of ≥1.0% xanthan gum during rice cooking (XGP-added groups) suppressed blood sugar levels 15 and 30 min after rice consumption. The glycemic index (GI) was significantly lower in all XGP-added groups than in the standard rice group. In all groups where xanthan gum sol was mixed with the cooked rice (XGS-mixed groups), blood sugar levels at 15 – 60 min were significantly lower and GI was lower than those in the standard rice group. Suppression of blood sugar levels by xanthan gum sol was more effective when consumed concurrently with rice than when consumed before or after consumption. The above findings revealed that blood sugar levels after rice consumption are suppressed most effectively when the rice is coated in xanthan gum sol.
According to the 2012 National Health and Nutrition Examination Survey (Ministry of Health and Welfare, 2014), 20.5 million Japanese people currently have or are believed to have diabetes, largely due to lifestyle diseases prevalent in modern society. Prolonged high blood sugar levels are known to cause vascular complications. Because diabetes is closely connected to eating habits and because calorie dense meals high in sugar or fat elevate blood sugar levels, diabetes has become a social challenge in Japan. Cooked rice is a staple food in Japan, and is one that is high in starch. Therefore, cooked rice is considered one of the foods that increase postprandial blood sugar levels. In recent years, the nutritional and physiological effects of dietary fiber ingestion have become widely known. These effects include enhanced digestive juice secretion and peristalsis, increased stool volume, shortened digestive tract time, and various effects on glucose and lipid metabolism (Takahashi, 2011 Juliet, 2006).
We have previously reported the inhibitory effect of agar added during rice cooking on blood sugar levels (Moritaka et al., 2012). Glucose response curves taken for 120 min after rice consumption revealed that the addition of agar slowed increases in blood sugar levels, and the maximum level was lower than that in the control group receiving no agar. In addition, the glycemic index (GI) decreased as the concentration of agar increased. We also previously reported that ≥1.0% glucomannan added during rice cooking significantly suppressed GI and the release of glucose (Fuwa et al., 2013).
Furthermore, our previous study showed that κ-carrageenan added at rice cooking effectively suppressed the elevation of blood sugar levels (Fuwa et al., 2014). When added to rice in a gel form and without CaCl2, κ-carrageenan suppressed the elevation of blood sugar levels similarly, but this suppression was not observed in the gel with CaCl2. The reason for suppressed blood sugar levels by the κ-carrageenan gel without CaCl2 was thought to be that because a portion of the gel was converted into a sol form in the oral cavity, the rice may be absorbed into the structure of the κ-carrageenan sol, thus potentially hindering the activity of digestive enzymes and decreasing glucose absorption.
Some studies have also reported on the relationship between blood sugar levels and other dietary fibers (Ebihara et al., 1981 Ebihara and Kiriyama, 1982 Jenkins et al., 1977 Mochizuki et al., 1995 Vuksan et al., 1999 Maeda et al., 2005 Dumelod et al., 1999). Some dietary fibers, such as xanthan gum, guar gum, and gum arabic, generate sol forms. Several studies have reported the suppression of blood sugar levels by xanthan gum in particular (Edwards et al., 1987 Cameron-Smith et al., 1994 Shiyi et al., 2001 Sagawa et al., 2013). Structurally, xanthan gum is comprised of a main chain and a side chain the main chain consists of glucose-linked β-1,4 bonds, and the side chain is composed of one molecule of glucuronic acid and two molecules of mannose. One side chain is attached to every two molecules of glucose in the main chain. In this study, we investigated the effects of xanthan gum added during rice cooking (XGP-added rice) or xanthan gum sol mixed with cooked rice (XGS-mixed rice) on blood sugar levels after cooked rice consumption.
Materials The rice used in this study was Japonica ‘Koshiibuki’ rice (yield rate of polished rice, 89% ratio of water to rice edible portion, 14.1% ratio of protein to rice edible portion, 6.5% ratio of amylose to starch, 19.4% ratio of amylopectin to starch, 80.6%) cultivated in Niigata Prefecture, Japan. Xanthan gum (Lot. 130730-01 purity, 100%) was provided by San-Ei Gen F.F.I. (Osaka, Japan).
Standard rice was prepared by soaking 180 g of rice in 1.4-fold (by wt.) deionized water at room temperature for 1 h and then cooking and steaming in a rice cooker for 30 min (SR-CL05P Panasonic, Osaka, Japan). Xanthan gum added during rice cooking (XGP-added rice) was prepared by adding xanthan gum to raw rice grains just before cooking and steaming. The concentrations of xanthan gum were 0.5, 1.0, 1.5, 2.0, and 2.5% of raw rice weight. The cooked rice ratio of increase was calculated by dividing the weight of cooked rice by the raw rice grain weight.
The concentration of xanthan gum sol was 2.0%. The sol form of xanthan gum was first produced by dispersing xanthan gum held in deionized water overnight at room temperature using a magnetic stirrer. Xanthan gum sol was then heated at 98°C for 60 min with stirring. To prepare xanthan gum sol mixed with cooked rice (XGS-mixed rice), the xanthan gum sol was mixed with standard cooked rice just prior to experiments. Concentrations of xanthan gum in XGS-mixed rice were the same as for the XGP-added rice.
Cooked rice samples for texture measurements and sensory evaluation were always taken from the center of the rice cooker.
Blood sugar levels Subjects were comprised of 11 healthy female students aged 19 – 39 years who had never been diagnosed with diabetes. Mean fasting blood sugar level of subjects was 78.3 ± 6.1 mg/dL, and all subjects were determined to be normal type based on the diagnostic criteria for diabetes. The measurement of blood sugar level and the sensory evaluation were approved by the Ethics Committee of Showa Women's University (Approval #13-12). In accordance with the principles of the Declaration of Helsinki, subjects were fully informed of the main purpose of the study, the safety of test samples, and how to measure blood sugar levels and perform sensory evaluation before written consent was obtained.
Test samples were the standard rice, 0.5, 1.0, and 1.5% XGP-added rice, and 0.5, 1.0, and 2.5% XGS-mixed rice. In this study, we used the rice grain rather than commercially available packaged rice recommended in the Unified Protocol for the Study of Glycemic Index by the Japanese Association for the Study of Glycemic Index (Tanaka et al., 2011). The rice heated by a rice cooker was employed as a common sample in this experiment, because a rice heated with xanthan gum was needed as a test sample. In all experiments, the total weight of carbohydrate included in each test sample was 50 g. The total weight of the test sample and water was 250 g. In the test using XGS-mixed rice, the weight of the provided drinking water was determined by subtracting the weight of water used in the sol preparation.
Blood sugar levels were measured after more than 3 days from the previous measurement, but not during menstruation. Subjects were instructed not to engage in strenuous exercise on the day before an experiment. Only cold or warm water was allowed from 12 to 4 h before an experiment. Intake of all food and drink was prohibited from 4 h before an experiment. Test samples were consumed within 10 min, and subjects consumed rice and water alternately. One bite-size portion was approximately 10 g, which was chewed 30 times. After consumption of test samples, subjects rested until the completion of the 120-min blood sugar level measurement. In addition, to investigate how different timings of XGS consumption affect blood sugar levels, the xanthan gum sol was consumed 10 min earlier than the consumption of the standard rice (XGS-before rice), concurrently with the standard rice (XGS-mixed rice), or 10 min later than consumption of the standard rice (XGS-after rice).
Using a small blood sugar level measurement device (Glu-Test-Ace-R Sanwa Kagaku Kenkyusho Co., Nagoya, Japan), subjects measured blood sugar levels before (0 min) and 15, 30, 45, 60, 90, and 120 min after the consumption of the test sample. A finger-prick blood sampling was performed using the Auto-Lancet-II (Sanwa Kagaku Kenkyusho Co.). Puncture needle devices were prepared for each individual subject.
According to the method recommended by the Food and Agriculture Organization of the United Nations and the World Health Organization, the glycemic index (GI) of each test sample containing 50 g of carbohydrate was calculated by dividing the area under the blood sugar response curve by the area under the response curve (set as 100) to 50 g/250 mL glucose solution.
Measurement for the standard rice was performed once per subject, and the validity of the data was verified by comparing the pattern of blood sugar response curve to patterns obtained previously in our laboratory or other laboratories on our campus.
Glucose release of in-vitro digestion experiments Mastication and digestion were reproduced in vitro according to the methods reported by Kumai et al. (2007) and Nakanishi (2011). A meat grinder with a disc having 6-mm holes (MUM4435JP BOSCH, Germany) was used to simulate the mastication of the standard rice, XGP-added rice, and XGS-mixed rice. Before measurement, pancreatin and invertase were added to rice samples and shaken for 20 min at 37°C.
Textural properties Examination of textural properties was performed as reported previously using a rheometer (RE-33005 Yamaden Co., Tokyo, Japan) with a 20-mm acrylic cylindrical plunger. One grain of rice was placed on the sample plate at 25°C and compressed twice by the plunger at a speed of 0.5 mm/s. Each sample was compressed to 80% of its height, after which the plunger then rose 5 mm higher than the surface of the sample and the second compression was performed.
Using the accompanying automatic texture profiling software (Ver.2.0A Yamaden Co.), texture curves were analyzed to evaluate the hardness and adhesiveness of samples. Hardness was determined by dividing the first peak of the texture curve by the contact area of the rice and plunger.
Before compression, the flat surface of a rice grain was placed parallel to the sample plate, with the germ facing the right or left side, and the maximum horizontal plane passing through the dorsal and ventral side of the rice was used as a contact area. In each group, measurement was repeated 30 times and the mean contact area was calculated. Because the contact area of rice and plunger changes depending on compression force, hardness in this study was expressed as conventional stress rather than true stress. In order to prevent movement of the rice grains, rice grain was solid with double-sided tape on the sample plate. Adhesiveness was calculated using the peak area under the baseline that appeared after the first peak on the texture curve. In each sample group, measurement was repeated 30 times.
Morphological evaluation of rice grains The lengths of the long and short axes were determined in the standard rice and in the 0.5, 1.0, and 1.5% XGP-added rice. The condition of the rice immediately after cooking was photographed.
Sensory evaluation Based on the Guidelines for Taste Testing issued by the Food Agency, sensory evaluation was performed by 13 female students with normal vision, taste and flavor sensing abilities.
The appearance, flavor, taste, glutinousness, hardness, and overall quality of 0.5, 1.0, 1.5, and 2.0% XGP-added rice were compared with those of standard rice according to the sensory evaluation method recommended by the Japan Grain Inspection Association (Kawabata et al., 2009), which evaluates cooked rice according to a seven-point interval scale. Appearance, taste, and overall quality were expressed in terms of high-to-low, flavor and glutinousness in terms of strong-to-weak, and hardness in terms of hard-to-soft. “Rather (±3)” was selected when the difference was clear after the first bite. “A little (±2)” or “barely (±1)” was selected when the difference was somehow clear after the first bite or only after the second bite, respectively. “The same (0)” was selected when no difference was observed even after the second bite.
Statistical analysis Statistical analysis was performed using SPSS Statistics 21 (IBM, Tokyo, Japan). Blood sugar level, GI, and released glucose were analyzed using a two-way analysis of variance and Tukey's multiple comparison test. Texture profiles and sensory evaluation data were analyzed using a one-way analysis of variance followed by Tukey's test. In all analyses, statistical significance was set at 5% (p < 0.05).
Blood sugar level, GI, and released glucose of XGP-added rice Figure 1a shows blood sugar levels in subjects who consumed standard rice or XGP-added rice. In subjects who consumed standard rice, the 15-min postprandial blood sugar level was increased significantly compared with the 0-min pre-prandial level, and the increase was even more significant at 30 min. Although no significant difference in blood sugar levels was observed between 30 and 45 min or between 45 and 60 min, the 60-min postprandial blood sugar level was significantly lower than the 30-min level. The 90-min postprandial blood sugar level was decreased significantly compared with the 60-min postprandial level however, the 120-min postprandial blood sugar level was still significantly higher than the 0-min pre-prandial level.
Blood sugar response curve and glycemic index after consuming XGP-added rice, and concentration of released glucose from XGP-added rice.
a) Blood sugar response curve after consuming XGP-added rice.
○, Cooked rice alone (0% XGP) △, 0.5% XGP-added rice □, 1.0% XGP-added rice ◊, 1.5% XGP-added rice
0% XGP: (0 min < 15, 30, 45, 60, 90 and 120 min), (15 min < 30 min), (15 min > 90 and 120 min), (30 min > 60, 90 and 120 min), (45 and 60 min > 90 and 120 min)
0.5% XGP-added rice: (0 min < 15, 30, 45, 60 and 90 min), (15, 45 and 60 min > 120 min), (30 min > 90 and 120 min)
1.0% XGP-added rice: (0 min < 15, 30, 45, 60, 90 and 120 min), (30, 45 and 60 min > 120 min)
1.5% XGP-added rice: (0 min < 30, 45, 60, 90 and 120 min)
15, 30 min: (0% XGP > 1.0, 1.5% XGP-added rice), (0.5% XGP-added rice > 1.5% XGP-added rice)
45 min: (0, 0.5% XGP-added rice > 1.5% XGP-added rice)
b) Glycemic index after consuming XGP-added rice.
c) Concentration of released glucose from XGP-added rice.
Each value represents the mean ± SD.
Means indicated by different letters are significant at p < 0.05.
In subjects who consumed 0.5% or 1.0% XGP-added rice, the 15-min postprandial blood sugar level was increased significantly compared with the 0-min pre-prandial level. However, no significant difference was observed between the 15 – 60-min blood sugar levels in the 0.5% XGP-added group and between the 15 – 90-min levels in the 1.0% XGP-added group. In the 0.5% XGP-added group, the 90-min postprandial level was significantly lower than the 30-min level, whereas the 120-min level was significantly lower than the 15 – 60-min levels, with no significant difference from the 0-min pre-prandial level. In the 1.0% XGP-added group, the 120-min postprandial level was significantly lower than the 60-min level, but significantly higher than the 0-min pre-prandial level. In the 1.5% XGP-added group, compared with the 0-min pre-prandial level, the 30-min postprandial blood sugar level increased significantly, with no significant change after 30 min. Nevertheless, the 120-min level was significantly higher than the 0-min pre-prandial level.
In subjects who consumed ≥1.0% XGP-added rice, the 15- and 30-min postprandial blood sugar levels were significantly lower than those in the standard rice group. In addition, in the 1.5% XGP-added group, the 45-min level was significantly lower than that in the standard group, with no significant difference in the 60-min or later levels between the two groups.
Figure 1b shows GI in subjects who consumed XGP-added rice. Compared with the standard group, GI was significantly lower in all XGP-added groups, with no significant difference due to the concentration of xanthan gum. The amount of released glucose was significantly lower in the 0.5 – 2.5% XGP-added groups than the standard group (Fig. 1c).
Textural properties of XGP-added rice The hardness and adhesiveness of XGP-added rice are shown in Figure 2. While 0.5% XGP-added rice was significantly harder than the standard rice, hardness did not differ significantly between the standard rice and 1.0% XGP-added rice. Interestingly, the hardness of ≥1.5% XGP-added rice was significantly reduced in a concentration-dependent manner (Fig. 2a). Adhesiveness was significantly higher in 0.5% XGP-added rice than in the standard rice, whereas no difference was observed between the standard rice and 1.0% or 1.5% XGP-added rice. Adhesiveness was significantly lower in 2.5% XGP-added rice (Fig. 2b).
Textural properties of XGP-added rice.
a) Hardness, b) Adhesiveness
Each value represents the mean ± SD.
Means indicated by different letters are significant at p < 0.05.
Structural organization of XGP-added rice Figure 3 shows the length of the long and short axes in XGP-added rice. In the standard group, the length increased along the long axis during cooking. In comparison, increase in the length along the long axis was significantly suppressed in all XGP-added groups, and there was a significant increase in length along the short axis in ≥1.0% XGP-added groups compared with the standard group.
Lengths of long and short axes of XGP-added rice grain.
Each value represents the mean ± SD.
Means indicated by different letters are significant at p < 0.05.
The condition of rice immediately after cooking is shown in Figure 4. The standard rice grains showed low adhesion to each other and were easily separated (Fig. 4a). However, it became difficult to separate the rice as the concentration of xanthan gum increased (Fig. 4c, d). In the XGP-added groups, rice was coated with a thin layer of xanthan gum, and as the concentration of xanthan gum increased, many thread- or cotton-shaped xanthan gum filaments were observed between the rice grains and the thin coating of xanthan gum.
Concentration of xanthan gum: a) 0%, b) 0.5%, c) 1.0%, and d) 1.5%
Weight change after cooking of XGP-added rice Table 1 shows the weight change of rice after cooking. After cooking, the weight of rice increased significantly in all XGP-added groups compared with the standard group. Compared with the 0.5% XGP-added group, the 1.0% XGP-added group showed a significant increase in weight however, there was no significant difference between the 1.0% and 1.5% XGP-added groups.
|Sample||Weight ratio (%)|
|0% cooked rice||217 ±0.7 a|
|0.5% rice cooked with xanthan gum||222 ±0.6 b|
|1.0% rice cooked with xanthan gum||234 ±0.6 c|
|1.5% rice cooked with xanthan gum||235 ±0.5 c|
Each value represents the mean ± SD.
Means indicated by different letters are significant at p < 0.05.
Sensory evaluation of XGP-added rice Figure 5 shows the sensory evaluation of XGP-added rice. The appearance of the 0.5% and 1.0% XGP-added rice declined in a concentration-dependent manner (Fig. 5a), but no significant difference was observed between the ≥1.0% XGP-added groups. The flavor of rice was significantly weaker in the ≥1.5% XGP-added groups, with no significant difference between the 1.0% – 2.0% XGP-added groups (Fig. 5b). When comparisons were made between the standard and 0.5% XGP-added groups, between the 0.5% and 1.0% XGP-added groups, and between the 1.0% and 2.0% XGP-added groups, taste was determined to be significantly lower as the concentration of xanthan gum increased (Fig. 5c).
Sensory evaluation of XGP-added rice.
a) Appearance, b) Flavor, c) Taste, d) Glutinousness, e) Hardness, f) Overall quality
Each value represents the mean ± SD.
Means indicated by different letters are significant at p < 0.05.
Compared with the standard group, glutinousness was significantly weaker in the ≥1.5% XGP-added groups, with no significant difference among the 0.5% – 2.0% XGP-added groups (Fig. 5d). Hardness did not differ significantly between the standard group and any of the XGP-added groups (Fig. 5e). Overall quality was significantly worse in the 0.5% XGP-added group than in the standard group, and the quality declined in a concentration-dependent manner in the 1.0% and 1.5% XGP-added groups, with no difference between the 1.5% and 2.0% XGP-added groups (Fig. 5f).
Blood sugar level, GI, and released glucose of XGS-mixed rice Figure 6a shows the blood sugar levels of subjects who consumed XGS-mixed rice. In the 0.5% and 1.0% XGS-mixed groups, postprandial blood sugar levels were significantly higher at 15 min than at 0 min, with no significant difference between the 15 – 90-min levels. This result differed from that of the standard group, in which the 30-min postprandial level was significantly higher than the 15-min level. In the 0.5% XGS-mixed group, the 120-min postprandial level was significantly lower than the 30-min level, but not the 0-min level. In the 1.0% XGS-mixed group, the 120- min postprandial level was significantly lower than the 30- and 45- min levels, with no significant difference between the 60-min postprandial and 0-min pre-prandial levels. In the 2.5% XGS-mixed group, although the 30-min postprandial level increased significantly, there was no significant change between the 0-min pre-prandial and 15-min postprandial levels or among the 30 – 60-min postprandial levels. The 120-min postprandial level did not differ significantly from the 0-min pre-prandial level. Compared with the standard group, the 15 – 60-min postprandial blood sugar levels were reduced in all XGS-mixed groups, with no significant differences among the groups.
Blood sugar response curve and glycemic index after consuming XGS-mixed rice, and concentration of released glucose from XGS-mixed rice.
a) Blood sugar response curve after consuming XGS-mixed rice.
○, Cooked rice alone (0% XGS) △, 0.5% XGS-mixed rice □, 1.0% XGS-mixed rice ◊, 2.5% XGS-mixed rice
0% XGS: (0 min < 15, 30, 45, 60, 90 and 120 min), (15 min < 30 min), (15 min > 90 and 120 min), (30 min > 60, 90 and 120 min), (45 and 60 min > 90 and 120 min)
0.5% XGS-mixed rice: (0 min < 15, 30, 45 and 60 min), (30 min > 120 min)
1.0% XGS-mixed rice: (0 min < 15, 30, 45, 60 and 90 min), (30 and 45 min > 120 min)
2.5% XGS-mixed rice: (0 min < 30, 45 and 60 min)
15, 30, 45 and 60 min: (0% XGS > 0.5, 1.0 and 2.5% XGS-mixed rice)
b) Glycemic index after consuming XGS-mixed rice.
c) Concentration of released glucose from XGS-mixed rice.
Each value represents the mean ± SD.
Means indicated by different letters are significant at p < 0.05.
In all XGS-mixed groups, GI was significantly lower than that in the standard group in a concentration-independent manner (Fig. 6b).
The amount of released glucose due to in vitro digestion of the standard rice and XGS-mixed rice is shown in Figure 6c. Compared with the standard group, the amount of released glucose was significantly reduced in the 0.5% XGS-mixed group, and the reduction was significantly greater in the 1.0% XGS-mixed group, with no significant difference among 1.0% – 2.5% XGS-mixed groups.
Changes in blood sugar level due to the consumption of xanthan gum in the sol state Based on the results of the previous section, in which xanthan gum consumed concurrently with rice suppressed postprandial blood sugar levels, we investigated the suppression of postprandial blood sugar level by the sol form of xanthan gum consumed before or after rice intake.
Figure 7a shows blood sugar levels in the XGS-before and -after groups as well as in the standard and XGS-mixed groups as a reference. Compared with the standard group, the 15-min postprandial blood sugar levels were significantly lower in the XGS-mixed and XGS-before groups. The 30-min postprandial level was significantly lower in the XGS-mixed group, but not in the XGS-before or -after groups. The 45- and 60-min postprandial blood sugar levels in the XGS-mixed group were significantly lower than those in the standard. The 90-min postprandial blood sugar levels did not differ significantly among the groups.
Blood sugar response curve and glycemic index after consuming XGS-mixed rice, XGS-before rice and XGS-after rice.
a) Blood sugar response curve after consuming XGS-mixed rice, XGS-before rice and XGS-after rice.
○, Cooked rice alone (0% XGS) △, XGS-before rice □, XGS-after rice ◊, XGS-mixed rice
0% XGS: (0 min < 15, 30, 45, 60, 90 and 120 min), (15 min < 30 min), (15 min > 90 and 120 min), (30 min > 60, 90 and 120 min), (45 and 60 min > 90 and 120 min)
XGS-before rice: (0 min < 15, 30, 45, 60, 90 and 120 min), (15 min < 30, 45 and 60 min), (30 min > 120 min), (45 min > 90 and 120 min), (60 min > 120 min)
XGS-after rice: (0 min < 15, 30, 45, 60 min), (15 min > 90 and 120 min), (30 min > 60, 90 and 120 min), (45 min > 90 and 120 min), (60 min > 120 min)
XGS-mixed rice: (0 min < 15, 30, 45, 60 and 90 min), (30 and 45 min > 120 min)
15 min: (0% XGS, XGS-after rice > XGS-mixed rice, XGS-before rice)
30 min: (0% XGS, XGS-after rice > XGS-mixed rice)
45 and 60 min: (0% XGS, XGS-before rice > XGS-mixed rice)
b) Glycemic index after consuming XGS-mixed rice, XGS-before rice and XGS-after rice
Each value represents the mean ± SD.
Means indicated by different letters are significant at p < 0.05.
GI in the XGS-before and -after groups is shown in Figure 7b. GI in the standard and XGS-mixed groups are also shown as a reference. Compared with the standard group, GI was significantly lower in the XGS-mixed group but not in the XGS-before or -after groups.
This study investigated how postprandial blood sugar levels after consumption of rice are affected by simultaneous consumption of a specific form of xanthan gum and by the timing of the consumption of xanthan gum sol.
The addition of ≥1.0% xanthan gum to rice significantly reduced postprandial blood sugar levels at 15 and 30 min. The level was still significantly low at 45 min in subjects who had consumed 1.5% XGP-added rice (Fig. 1a). In addition, GI and the amount of released glucose were significantly reduced in all XGP-added groups compared with the standard group (Fig. 1b, c). Thus, all experiments verified that xanthan gum suppresses postprandial blood sugar levels. The length of the long axis was significantly shorter (≥0.5%) in XGP-added rice than in the standard rice (Fig. 3). This suggests that the addition of xanthan gum during rice cooking suppressed the absorption of water to rice during cooking, reducing the gelatinization of rice.
Although 0.5% XGP-added rice was significantly harder than the standard rice, no significant difference was observed between the standard rice and 1.0% XGP-added rice. In contrast, ≥1.5% XGP-added rice was significantly softer than the standard rice in a concentration-dependent manner (Fig. 2a). In subjective assessment, hardness did not differ significantly between the standard rice and XGP-added rice, regardless of the concentration of xanthan gum (Fig. 5e). However, the rice grain in the XGP-added rice was surrounded by the xanthan gum (Fig. 4), and the length of the short axis increased (Fig. 3). Although xanthan gum appeared to suppress the gelatinization of rice more strongly at higher concentrations, it concurrently increased the amount of the sol that adhered to the rice (Figs. 3, 4). Therefore, the hardness of rice reflects the physical property of not only the rice, but also the sol-state xanthan gum. Furthermore, as the concentration of xanthan gum increases, the contribution of XGS on the textural hardness of rice increases. We presume that the changes in rice hardness due to the addition of xanthan gum effectively cancelled each other out, indicated by the lack of significance in the 1.0% XGP-added rice group. When the concentration of xanthan gum exceeds 1.0%, it appears that the contribution of the sol form of xanthan gum to hardness overcomes the hardness due to the suppression of gelatinization, making rice softer. Therefore, we propose that changes in hardness do not contradict the suppression of rice gelatinization by xanthan gum (Fig. 2a).
Compared with the standard rice, adhesiveness increased in 0.5%, but not 1.0% or 1.5% XGP-added rice, whereas adhesiveness significantly decreased in 2.0% and 2.5% XGP-added rice (Fig. 2b). These changes were thought to be attributable to the sol form of xanthan gum adhering to the surface of the rice. Immediately after cooking, rice grains became increasingly difficult to separate as the concentration of xanthan gum increased. Moreover, they were surrounded by a thin coating of xanthan gum, with many thread- or cotton-like xanthan gum filaments between the rice grains (Fig. 4). This is presumably because xanthan gum absorbs rice into the sol structure during cooking. However, adhesiveness was measured using individual rice grains with broken sol structure. Furthermore, the addition of xanthan gum resulted in a significant increase in the weight of cooked rice (Table 1). Although not shown in the figure, the apparent viscosity of xanthan gum sol was significantly higher than that of water at a high temperature, indicating that the flow of water during cooking was hampered, reducing the evaporation of water and increasing the weight of rice during cooking. This is also inferred to be one of the reasons for the reduced adhesion of rice grains.
An increase in the amount of xanthan gum sol adhering to the rice surface and the suppression of gelatinization are thought to be the reasons for the poor subjective assessment of appearance, flavor, taste, and overall quality of rice containing xanthan gum in sensory evaluation. Our previous studies suggest that the addition of glucomannan (Fuwa et al., 2013) or κ-carrageenan (Fuwa et al., 2014) in rice cooking suppresses postprandial blood sugar levels mainly through the suppression of gelatinization rather than through the adhesion of the water-soluble dietary fibers to rice. However, in the XGP-added rice, the length of the short axis increased (Fig. 3), and the addition of xanthan gum resulted in a significant increase in the weight of the cooked rice (Table 1). Figure 4 illustrates that the cooked rice was surrounded by a thin coating of xanthan gum. These results suggest that xanthan gum adhered to the exterior of the rice grains. Therefore, when xanthan gum was added during rice cooking, the postprandial blood sugar level was thought to be suppressed due to the adhesion of xanthan gum, in addition to the suppression of gelatinization.
In subjects who had consumed XGS-mixed rice, the 15 – 60-min postprandial blood sugar levels were lower compared to the standard, and the rate of blood sugar elevation was reduced compared with XGP-added rice (Fig. 6a). In addition, no significant difference was observed between the 120-min postprandial and 0-min pre-prandial levels in the XGS-mixed groups. In contrast, the 120-min postprandial level was significantly higher than the 0-min pre-prandial level in the standard group, demonstrating different results and suggesting that the coexistence of the sol form facilitates decreases in the blood sugar level. Compared with the standard group, postprandial blood sugar levels were reduced in all XGS-mixed groups. Furthermore, compared with the standard group, GI was significantly lower in all XGS-mixed groups (Fig. 6b), and the amount of released glucose was significantly lower in the ≥0.5% XGS-mixed groups (Fig. 6c). The amount of xanthan gum and rice intake was the same between experiments where xanthan gum was added during rice cooking and those where xanthan gum in the sol state was consumed with rice. However, xanthan gum suppressed blood sugar levels after the consumption of rice differently, demonstrating that xanthan gum in the sol form suppresses postprandial blood sugar levels more effectively when consumed concurrently with rice. This supports our previous study investigating the effect of κ-carrageenan gel consumed concurrently with rice, showing that the blood sugar level was suppressed more effectively when the gel was partly converted into the sol form (Fuwa et al., 2014). Sasaki and Kohyama (2011, 2012) investigated the effect of water-soluble polysaccharide polymers on starch digestion. They reported that xanthan gum with high apparent viscosity greatly suppresses not only the dispersion of glucose, but also the digestion of glucose. Furthermore, Fabek et al. (2014) reported in their study using a digestive tract model that the sol structure of xanthan gum is maintained in the presence of digestive enzymes and under an acidic digestive environment. These properties of xanthan gum appear to be one reason for the regulation of blood sugar response to the consumption of glucose. On the other hand, for XGS-mixed rice, the concentration dependence of xanthan gum was not observed in the suppression of postprandial blood sugar levels (Fig. 6a, b). One possible explanation might be that a sufficient amount of xanthan gum is available to fully surround the rice grains, even at a xanthan gum concentration of 0.5% in the XGS-mixed rice. For this reason, even if the concentration of xanthan gum increases, the suppression of blood sugar level may not increase.
We also investigated whether the sol form of xanthan gum affects blood sugar levels similarly between XGS consumed concurrently with rice and XGS consumed before or after rice. Compared with the standard group, the 15-min postprandial blood sugar levels were significantly reduced in subjects who had consumed the sol form of xanthan gum before rice. Such a reduction was not observed at other time points in the XGS-before group or any time point in the XGS-after group (Fig. 7a). No significant difference in GI was observed between the standard group and the XGS-before or -after groups (Fig. 7b), suggesting that to effectively suppress blood sugar levels after the consumption of rice, it is necessary to consume the sol form of xanthan gum concurrently with rice. Based on these findings, to suppress blood sugar levels, it is very important to disperse and coat the rice with xanthan gum sol.
Acknowledgment This study was supported by funded of Showa Women's University.