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Holt SHA, Brand Miller JC, Petocz P. An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods. Am J Clin Nutr 66, 1264-1276


Abstract and Figures

The aim of this study was to systematically compare postprandial insulin responses to isoenergetic 1000-kJ (240-kcal) portions of several common foods. Correlations with nutrient content were determined. Thirty-eight foods separated into six food categories (fruit, bakery products, snacks, carbohydrate-rich foods, protein-rich foods, and breakfast cereals) were fed to groups of 11-13 healthy subjects. Finger-prick blood samples were obtained every 15 min over 120 min. An insulin score was calculated from the area under the insulin response curve for each food with use of white bread as the reference food (score = 100%). Significant differences in insulin score were found both within and among the food categories and also among foods containing a similar amount of carbohydrate. Overall, glucose and insulin scores were highly correlated (r = 0.70, P < 0.001, n = 38). However, protein-rich foods and bakery products (rich in fat and refined carbohydrate) elicited insulin responses that were disproportionately higher than their glycemic responses. Total carbohydrate (r = 0.39, P < 0.05, n = 36) and sugar (r = 0.36, P < 0.05, n = 36) contents were positively related to the mean insulin scores, whereas fat (r = -0.27, NS, n = 36) and protein (r = -0.24, NS, n = 38) contents were negatively related. Consideration of insulin scores may be relevant to the dietary management and pathogenesis of non-insulin-dependent diabetes mellitus and hyperlipidemia and may help increase the accuracy of estimating preprandial insulin requirements.
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ABSTRACT The aim of this study was to systematically
compare postprandial insulin responses to isoenergetic 1000-U
(240-kcal) portions of several common foods. Correlations with
nutrient content were determined. Thirty-eight foods separated into
six food categories (fruit, bakery products, snacks, carbohydrate
rich foods, protein-rich foods, and breakfast cereals) were fed to
groups of 11—13healthy subjects. Finger-prick blood samples were
obtained every 15 mm over 120 mm. An insulin score was calcu
lated from the area under the insulin response curve for each food
with use of white bread as the reference food (score = 100%).
Significant differences in insulin score were found both within and
among the food categories and also among foods containing a
similar amount of carbohydrate. Overall, glucose and insulin
scores were highly correlated (r = 0.70, P < 0.001, n = 38).
However, protein-rich foods and bakery products (rich in fat and
refined carbohydrate) elicited insulin responses that were dispro
portionately higher than their glycemic responses. Total carbohy
drate (r = 0.39, P < 0.05, n = 36) and sugar (r = 0.36, P < 0.05,
n = 36) contents were positively relatedto the mean insulin scores,
whereas fat (r —¿0.27,NS, n 36) and protein (r —¿0.24,NS,
n = 38) contents were negatively related. Consideration of insulin
scores may be relevant to the dietary management and pathogen
esis of non-insulin-dependent diabetes mellitus and hyperlipidemia
and may help increase the accuracy of estimating preprandial
insulin requirements. Am J Clin Nutr l997;66:l264—76.
KEY WORDS Insulin, glycemic index, NIDDM, non
insulin-dependent diabetes meffitus, diabetic diet, hyperlipid
emia, carbohydrate, insulin score, glucose score, area under the
curve, humans
The insulinemic effects of foods may be relevant to the
treatment and prevention of weight gain, non-insulin-depen
dent diabetes mellitus (NIDDM), and associated complications.
Recent studies have shown that carbohydrate-rich diets, which
result in high postprandial glucose and insulin responses, are
associated with undesirable lipid profiles (1, 2), greater body
fat (3—5),and the development of insulin resistance in rats (6)
and humans (7, 8). Both obesity and NJDDM are associated
with varying degrees of insulin resistance and fasting hyperin
sulinemia. Prolonged or high degrees of postprandial insuline
mia are thought to contribute to the development of insulin
resistance and associated diseases (9—17).Therefore, the clas
sification of the relative insulinemic effects of different foods
is of both theoretical and practical significance.
Postprandial blood glucose responses have been the focus of
much research because of their importance for glycemic con
trol in patients with diabetes. It is now well accepted that
different foods containing equal amounts of carbohydrate can
produce a wide range of blood glucose responses. The glyce
mic index (GI) method was developed to rank foods according
to the extent to which they increase blood glucose concentra
tions (18). Tables of GI values of common carbohydrate
containing foods are a useful guide to help people with diabetes
choose foods that produce smaller glycemic responses. How
ever, the GI concept does not consider concurrent insulin
responses and few studies have reported GI values and their
accompanying insulin responses.
The extent to which different dietary factors affect post
prandial insulinemia has not been well researched because
insulin secretion is largely assumed to be proportional to
postprandial glycemia. Furthermore, hyperglycemia is
thought to be more relevant to the secondary complications
of NIDDM because the abnormal insulin secretion or action
in people with diabetes is controlled with exogenous insulin
or medications that counteract insulin resistance. However,
knowledge of factors that influence both postprandial gly
cemia and insulin secretion in nondiabetic persons is re
quired to devise treatment strategies that will completely
normalize meal-related glycemia (19).
To explore the importance of dietary habits and postprandial
insulinemia in the etiology and treatment of NIDDM, we need
to be able to systematically rate insulin responses to common
foods. If we are to compare insulin responses to foods, what is
the best basis of comparison? Should we compare insulin
responses to portions of food representing a normal serving
size, portions containing an equal amount of carbohydrate, or
portions containing an equal amount of energy? 01 tables
represent the glycemic effects of equal-carbohydrate portions
I From the Human Nutrition Unit, Department of Biochemistry, The
University of Sydney; and the School of Mathematical Sciences, The
University of Technology, Sydney, Australia.
2 Supported by research grants from The University of Sydney and
Kellogg's Australia Pty Ltd.
3 Address reprint requests to JC Brand Miller, Human Nutrition Unit,
Department of Biochemistry 008, The University of Sydney, NSW 2006,
Received November 21, 1996.
Accepted for publication May 22, 1997.
1264 Am J Clin Nutr 1997;66:1264—76. Printed in USA. ©1997 American Society for Clinical Nutrition
An insulinindexof foods:the insulindemandgeneratedby
1000-kJ portions of common foods13
Susanne HA Holt, Janette C Brand Miller, and Peter Petocz
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Food Variety, manufacturer, or place of purchase Preparation
Descriptionandpreparationof thetest foods
Black grapes
Bakery products
Chocolate cake with
Doughnuts with cinnamon
Chocolate chip cookies
Water crackers
Snack foods and confectionery
Mars Bar
Ice cream
Jellybeans (assorted colors)
Potato chips
Protein-rich foods
Baked beans
White fish
Carbohydrate-rich foods
White bread
Whole-meal bread
Grain bread
White rice
Brown rice
White pasta
Brown pasta
Waltham cross
Fresh, stem removed, served whole
Fresh, unpeeled, cut into eight segments
Fresh, peeled, cut into eight segments
Fresh, peeled, cut into quarters
Defrosted, reheated at 180 °Cfor 6 mm, and served warm
Prepared according to manufacturer's directions, stored at
4 °Cup to 2 d before serving at room temperature
Prepared by supermarket from standard recipe, defrosted
overnight, reheated at 180 °Cfor 5 mm, and served
Served crisp at room temperature, stored in airtight
Served crisp at room temperature
Cut into four standard pieces and served at room
Stored at 4 °C,served cold
Stored frozen and served cold
Served at room temperature, stored in airtight container
Served at room temperature, stored in airtight container
Served from freshly opened packet
Prepared according to manufacturer's directions
immediately before serving
All servings cut from same large block, stored at 4 °C,
served cold
Poached the day before serving, stored at 4 °Covernight,
reheatedin microwaveovenfor 1.5 mmimmediately
before serving
Prepared in bulk according to recipe, stored at 4 °Cfor up
to 2 d, reheated in a microwave oven for 2 mm
Heated on stove for 5 mm immediately before serving
Grilled the day before serving, cut into standard bite-sized
pieces, and stored at 4 °Covernight; reheated in
microwave oven for 2 mm immediately before serving
Steamed the day before serving, stored at 4 °Covernight,
cut into bite-sized pieces, and reheated in microwave
oven for 2 miii immediately before serving
Served fresh and plain at room temperature
Served fresh and plain at room temperature
Served fresh and plain at room temperature
Boiled 12 miii and stored overnight at 4 °C,reheated in
microwave oven for 1.5 mm immediately before serving
Boiled 12 mm and stored overnight at 4 °C,reheated in
microwave oven for 1.5 mm immediately before serving
Boiled8 mm and storedovernightat 4 °C
Reheated in microwave oven for 1.5 mm immediately
before serving
Peeled,boiledfor 20 mm, and storedat 4 °Covernight;
reheated in a microwave oven for 2 mm immediately
before serving
Purchased in bulk from supermarket and stored frozen
White Wings Foods, Smithfield, Sydney, Australia
Purchased in bulk from supermarket and stored frozen
Anion's Biscuits Ltd. Homebush, Sydney, Australia
Grocery Wholesalers Ltd, Yennora, Australia
Mars Confectionary Australia, Ballarat, Australia
Strawberry fruit yogurt; Australian Co-operative
Foods,' Wetherill Park, Sydney, Australia
Vanilla ice cream; Dairy Bell, Camperdown, Sydney,
Grocery Wholesalers Ltd
Salted roasted peanuts; Grocery Wholesalers Ltd
Crinkle cut chips; Smith's Snackfood Company,
Chatswood, Sydney, Australia
Microwave cooked popcorn; Uncle Toby's Company
Ltd, Wahgunyah, Australia
Mature cheddar cheese; Grocery Wholesalers Ltd
Poached hens eggs
Served in tomato sauce2
Canned navy beans in tomato sauce; Franklins,
Chullora, Sydney, Australia
Lean topside beef fillets bought in bulk from
supermarket, trimmed and stored frozen
Ling fish ifilets bought in bulk from Sydney fish
markets, trimmed and stored frozen
Fresh sliced wheat-flour bread; Quality Bakers
Australia Ltd. Eastwood, Sydney, Australia
Fresh sliced bread made from whole-meal wheat flour;
Riga Bakeries, Moorebank, Sydney, Australia
Fresh sliced rye bread containing 47% kibbled rye; Tip
Cairose rice (Sunwhite), Ricegrowers' Co-operative
Ltd. Leeton, Australia
Calrose rice (Sunbrown), Ricegrowers' Co-operative
Whole-meal spirals; San Remo Pasta Company,
Auburn, Sydney, Australia
Russet potatoes
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FoodVariety, manufacturer, or place ofpurchasePreparationFrench
friesPrefried oven-baked French fries; McCain's Foods
(Australia), Castle Hill, Sydney, AustraliaStored frozen, cooked in conventional oven for 15 mm
immediately beforeservingBreakfast
cereals3CornflakesKellogg's Australia Pty Ltd, Pagewood, Sydney,
KToasted flakes made from wheat and rice flour, high in
protein; Kellogg's Australia PlyLtd—HoneysmacksPuffed whole-wheat grains with a honey-based coating;
Kellogg's Australia PlyLtd—SustainA mixture of wheat, corn, and rice flakes; rolled oats;
dried fruit; and flaked almonds; Kellogg's Australia
PtyLtd—All-BranA high-fiber cereal made from wheat bran; Kellogg's
Australia PtyLtd—Natural
muesliBased on raw rolled oats, wheat bran, dried fruit, nuts,
and sunflower seeds; Uncle Toby's Company Ltd.
Wahgunyah,Australia—PorridgeUncle Toby's Company Ltd. Wahgunyah, AustraliaRaw rolled oats cooked in a microwave oven according to
manufacturer's directions and served without sweetener
1 Now Dairy Farmer's.
2 Recipe: 15 mL olive oil, 350 g dried green lentils, 410 g canned tomatoes, 120 g onion, 1 clove garlic, and 1 tsp pepper.
3 All cereals were served fresh with 125 mL fat-reduced (1.5% fat) milk.
of foods.However,carbohydrateis not theonly stimulusfor
insulin secretion. Protein-rich foods or the addition of protein
to a carbohydrate-rich meal can stimulate a modest rise in
insulin secretion without increasing blood glucose concentra
tions, particularly in subjects with diabetes (20—22).Similarly,
adding a large amount of fat to a carbohydrate-rich meal
increases insulin secretion even though plasma glucose re
sponses are reduced (23, 24).
Thus, postprandial insulin responses are not always propor
tional to blood glucose concentrations or to a meal's total
carbohydrate content. Several insulinotropic factors are known
to potentiate the stimulatory effect of glucose and mediate
postprandial insulin secretion. These include fructose, certain
amino acids and fatty acids, and gastrointestinal hormones such
as gastric inhibitory peptide, glucagon, and cholecystokiin
(25, 26). Thus, protein- and fat-rich foods may induce substan
tial insulin secretion despite producing relatively small blood
glucose responses. We therefore decided that comparing the
insulinemic effects of foods on an isoenergetic basis was a
logical and practical approach.
The aim of this study was to systematically compare post
prandial insulin responses to isoenergetic portions of a range of
common foods. An insulin score (IS) was calculated for each
food on the basis of its insulinemic effect relative to a reference
food. Thirty-eight foods, categorized into six different food
groups, were studied to determine which foods within the same
food group were most insulinogenic. We hypothesized that
postprandial insulin responses are not closely related to the
carbohydrate content or glycemic effects of some foods.
Test foods
Thirty-eight foods were tested and were grouped into six
food categories: 1) fruit: grapes, bananas, apples, and oranges;
2) bakery products: croissants, chocolate cake with icing,
doughnuts with cinnamon sugar, chocolate chip cookies, and
water crackers; 3) snack foods and confectionery: Mars Bar
candy bar (Mars Confectionary Australia, Ballarat, Australia),
strawberry yogurt, vanilla ice cream, jellybeans, salted roasted
peanuts, plain potato chips, and plain popcorn; 4) protein-rich
foods: cheddar cheese, poached eggs, boiled lentils in a tomato
sauce, baked beans in a tomato sauce, grilled beef steak, and
steamed white fish; 5) carbohydrate-rich foods: white bread,
whole-meal bread, rye-grain bread, white rice, brown rice,
white pasta, brown pasta, boiled potatoes, and oven-baked
French fries; and 6) breakfast cereals: Cornflakes (Kellogg's
Australia Pty Ltd. Pagewood, Australia), Special K (Kellogg's
Australia Pty Ltd), Honeysmacks (Kellogg's Australia Pty
Ltd), Sustain (Kellogg's Australia Pty Ltd), All-Bran
(Kellogg's Australia Pty Ltd), natural muesli, and oatmeal
Each food was served plain as a 1000-U portion with 220
mL water. White bread was used as the reference food for each
food group. The foods were selected to represent a range of
natural and processed foods commonly eaten in industrialized
societies. Details of the foods and their preparation methods are
listed in Table 1. Foods were bought in bulk to minimize
variations in composition and were served in standard-sized
pieces. The nutritional composition ofeach food per 1000 U as
calculated from Australian food tables or manufacturers' data
is shown in Table 2.
Separate groups of healthy subjects (n = 11—13)were re
cruited to test each category of foods. Volunteers were ex
cluded if they were smokers or taking prescription medications,
had a family history of diabetes or obesity, were dieting, or had
irregular eating habits. In total, 41 subjects participated. One
subject consumed all of the test foods and 15 other subjects
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weight FatProtein SugarStarch FiberWater density
Nuthtional composition of the test foods per 1000-U serving as calculated from Australian food tables or manufacturers' data'
g g g g g g
I Mars Bar, Mars Confectionary Australia, Ballarat, Australia; Comfiakes, Special K, Honeysmacks, Sustain, and All-Bran: Kellogg's Australia Pty Ltd.
Pagewood, Australia.
2 Nutrient composition calculated from manufacturer's data.
completed two or more food categories. All of the subjects approved by the Medical Ethical Review Committee of the
were university students; relevant characteristics ofthe subjects University of Sydney.
are listed in Table 3. The mean body mass index (BMI, in
kg/m2) of the 41 subjects was 22.7 ±0.4 (range: 19—29).Three P1@Ot(WOl
subjects had a BMI > 25 but two of these were short, stocky Each subject first consumed a 1000-U portion of white bread
males who had excess muscle rather than fat. Female subjects (45.9 g carbohydrate) to confirm normal glucose tolerance.
did not participate during their menstrual period or if they White bread was also used as the reference food (IS = 100%)
experienced adverse premenstrual symptoms. Informed con- against which all other foods were compared, similar to the
sent was obtained from all of the subjects and the study was method used for calculating GI values of foods (18). The use of
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(n = 5 F, 6 M)22.9 ±3.922.9 ±1.4Bakery
products (n = 6 F, 6 M)22.2 ±3.723.1 ±2.7Snacks
andconfectionery(n = 5 F, 7 M)21.0 ±1.222.9 ±3.5Protein-rich
foods (n 5 F, 6 M)22.4 ±2.824.3 ±3.1Carbohydrate-richfoods (n = 5 F, 8 M)21.0 ±1.923.0 ±1.9Breakfast
cereals (n = 5 F, 6 M)22.8 ±3.922.8 ±1.4
tube radioimmunoassay kit (Coat-A-Count; Diagnostic Prod
ucts Corporation, Los Angeles). For both plasma glucose and
insulin analysis, all nine plasma samples for a particular sub
ject's test were analyzed within the same run to reduce any
error introducedby interassayvariation.When possible,all
plasma samples for a particular subject were analyzed for
insulin within the same run. For the insulin analysis, the mean
within-assay CV was 5% and the mean between-assay CV was
Statistical analysis
Cumulative changes in postprandial plasma glucose and
insulin responses for each food were quantified as the incre
mental area under the 120-mn response curve (AUC), which
was calculated by using the trapezoidal rule with fasting con
centrations as the baseline and truncated at zero. Any negative
areas tended to be small and were ignored. For each subject, an
IS (%) was calculated for each test food by dividing the insulin
AUC value for the test food by the insulin AUC value for white
bread (the reference food), and expressed as a percentage as
Area under the 120-mm insulin response
curve for 1000 U test food
Areaunderthe 120-mminsulinresponsecurve
for 1000 Id white bread
Characteristics of each group of subjects'
‘¿I ± SD.
2 In kg/rn2.
a reference food controls for inherent differences between
individuals that affect insulin sensitivity, such as body weight
and activity levels.
Subjects were fed 1000-U portions of the test foods in a
random order on separate mornings after a 10-h overnight
fast. Within each food group, each subject acted as his or her
own control, being tested at the same time of day and under
as similar conditions as possible. Subjects were asked to
refrain from unusual activity and food intake patterns, to
abstain from alcohol and legumes the day before a test, and
to eat a similar meal the night before each test. When
subjects arrived at the lab in the morning, they completed a
short questionnaire assessing recent food intake and activity
patterns. A fasting finger-prick blood sample was collected
and subjects were then given a test food and 220 mL water
(0 mm). When possible, foods were presented under a large
opaque plastic hood with a hole through which volunteers
pulled out pieces of the test food one at a time. This was an
attempt to minimize between-subject variation in cephalic
phase insulin secretion arising from the sensory stimulation
associated with the anticipation and act of eating (27).
However, this was not feasible for the liquid foods (yogurt
and ice cream), foods served in a sauce (baked beans and
lentils), or with milk (all of the breakfast cereals), which
were presented in standard bowls without the hood.
Subjects were asked to eat and drink at a comfortable rate.
Immediately after finishing the test food, subjects recorded the
time taken to eat the food and completed a questionnaire
assessing various appetite responses and the food's palatability.
[These results are reported in a separate paper (28).] Subjects
remained seated at tables in a quiet environment and were not
permitted to eat or drink until the end of the session (120 mm).
Finger-prick blood samples (1.5—2.5mL) were collected
from warmed hands immediately before the meal (0 mm) and
15, 30, 45, 60, 75, 90, 105, and 120 mm after the start of the
meal (into plastic tubes that had been kept on ice) with use of
an automatic lancet device (Autoclix; Boehringer Mannheim
Australia, Castle Hill, Australia). Blood samples were centri
fuged immediately after collection (1 miii at 12 500 X g at
room temperature) and plasma was pipetted into chilled tubes
and immediately stored at —¿20°Cuntil analyzed (< 1 mo).
Plasma glucose concentrations were analyzed in duplicate with
a Cobas Fara automatic spectrophotometric analyzer (Roche
Diagnostica, Basel, Switzerland) and the glucose hexokinase
enzymatic assay. The mean within-assay and between-assay
precisions (CVs) were both < 6%. Plasma insulin concentra
tions were measured in duplicate by using an antibody-coated
X100 (1)
This equation is similar to that developed by Wolever and
Jenkins (29) for calculating GI values. A glucose score (GS)
(not the same as a GI score, which is based on a 50-g carbo
hydrate portion) for each food was also calculated by using the
same equation with the corresponding plasma glucose results.
Analysis of variance (ANOVA) and Fisher's probable least
significant-difference test for multiple comparisons were used to
determine statistical differences among the foods within each food
mc, Berkley, CA). Linear-regressionanalysis was used to test
associations between glucose and insulin responses and nutritional
7.0; Minitab Inc, State College, PA). Test foods not containing a
particular nutrient were excluded from these analyses. Therefore,
sample sizes for the correlations between individual nutrients and
the mean GSs and ISs varied from 32 to 36. Mean results for white
bread for each food group were included in some statistical anal
yses, so these correlations were made with 43 values. One subject
from the protein-rich food group did not complete the fish test and
one subject from the breakfast cereal group did not complete the
Sustain test. Therefore, in total, 503 indiVidUal tests were fully
Stepwise-multiple-regression analysis was used to examine
the extent to which the different macronutrients and GSs ac
counted for the variability of the ISs (MINITAB DATA
ANALYSIS SOFTWARE). For this analysis, the individual
white bread OS and IS results were included for the carbohy
drate-rich food group only; therefore, this analysis was per
formed with 446 individual observations for 38 foods. Includ
ing the white bread results for each food group (n = 503)
suggests that independent repeat tests were done for white
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Food Glucose AUCInsulin AUCInsulin AUC:
glucose AUCInsulin AUC per g
carbohydrateInsulin AUC per g
serving weightGlucose scoreInsulinscoremol-miniLpmolmin/LpmolminL'g'pmolminL'g'%%
Areas under the 120-mm plasma glucose and insulin response curves (AUCs), ratio of insulin AUC to glucose AUC, the insulin AUC per g
carbohydrate and per g serving weight, and mean glucose and insulin scores'
±94299 ±61287 ±1599 ±1425 ±340 ±732 ±4Porridge80 ±95093 ±49374 ±11139 ±1313 ±I60 ±1240 ±4Muesli65±126034±813118±18163±2234±543±746±5Special
K106 ±148038 ±63595 ±14195 ±1547 ±470 ±966 ±5Honeysmacks91±109102±1506108±12189±3153±960±767±6Sustain93±88938±757102±9209±1853±466±671±6Cornflakes1
10 ±118768 ±62388 ±5189 ±1352 ±476 ±1175 ±8Groupmean—7183±35792±5169±839±259±357±3Carbohydrate-rich
pasta74 ±74535 ±57467 ±1093 ±1221 ±368 ±1040 ±5Grainbread68±96659±837106±12166±2162±860±1256±6Brownrice113±136240±61658±5117±1142±4104±1862±11French
fries70 ±117643 ±713146 ±29209 ±1982 ±871 ±1674 ±12Whiterice129±168143±68369±5145±1240±3110±1579±12Whole-meal
bread106 ±141 1 203 ±1420122 ±20247 ±311 11 ±1497 ±1796 ±12Potatoes148±2413930±1467120±19284±3038±4141±35121±11Groupmean—8410±461106±8182±1062±588±674±8Protein-rich
±114744 ±1017135 ±929340 ±184530 ±642 ±1631 ±6Cheese42 ±105994 ±1590268 ±15364 257 ±15013106 ±2755 ±1845 ±13Beef18±67910±21931583±939—50±1421±851±16Lentils63
±179268 ±2174307 ±103325 ±6837 ±962 ±2258 ±12Fish29 ±149350 ±2055775 ±502—28 ±628 ±1359 ±18Bakedbeans110±1420106±3776183±44504±8757±11114±18120±19Group
mean—9983 ±1032585 ±6118 607 ±545653 ±654 ±761 ±7FruitWhite
bread171 ±1915 563 ±1632105 ±18339 ±36166 ±17100 ±0100 ±0Apples83±78919±910118±18152±1520±250±659±4Oranges66±119345±1074166±23185±2115±239±760±3Bananas133
±1212445 ±1353108 ±22224 ±2445 ±579 ±1081 ±5Grapes126±1412293±1190113±19216±2131±374±982±6Groupmean—10751±605124±10194±1128±261±571±3Snacks
±73047 ±828214 ±88564 ±15380 ±2212 ±420 ±5Popcorn71±126537±679109±32239±25139±1462±1654±9Potatochips77±158195±1577169±78367±71186±3652±961±14Ice
cream93 ±1712 348 ±1867172 ±38479 ±72103 ±1670 ±1989 ±13Yogurt88±2315611±1808167±33415±4865±762±15115±13MarsBar98±1016682±1896218±65441±50309±3579±13122±15Jellybeans161±1822860±368133±27407±64260±41118±18160±16Groupmean—12183±994191±20416±30163±1465±689±7Bakery
±1114 305 ±3472178 ±54467 ±I13223 ±5456 ±1482 ±12Crackers139 ±2614 673 ±2686331 ±104354 ±65253 ±461 18 ±2487 ±12Cookies92±1215223±382200±57436±110298±7574±1192±15Groupmean—12681±1325261±56468±47236±2477±783±5
I1@ SEM. Mars Bar, Mars Confectionary Australia, Ballarat, Australia; All-Bran, Special K, Honeysmacks, Sustain, and Cornflakes: Kellogg's
Australia Pty Ltd. Pagewood, Australia.
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bread, which artificially increases the accuracy of any calcu
lation involving white bread.
Fasting glucose and insulin concentrations
Within each food group, the subjects' average fasting
plasma glucose and insulin concentrations were not signif
icantly different among the foods. Mean fasting plasma
glucose concentrations did not vary significantly among the
six food groups, whereas mean fasting insulin concentra
tions were more variable, ranging from “¿42to 120 pmol/L.
Fasting insulin concentrations were not more variable in
females than in males and there were no significant differ
ences at various stages of the menstrual cycle. A significant
correlation was found between mean fasting insulin concen
trations and mean BMI values for the six groups of subjects
(r —¿0.81,P < 0.05,n 6).
Postprandial glucoseand insulin responses
As with any biological response, there was between-subject
variation in the glucose and insulin responses to the same food.
Two-way ANOVA was used to examine the ranking of each
subject's responses to the different test foods within a food
group (ie, interindividual variation). There were significant
differences among the subjects in the rank order of their glu
cose AUC responses except within the fruit and protein-rich
food groups. There were also significant differences among the
subjects' rank order of insulin AUC responses within all food
groups. However, individual subjects within each food group
consistently produced relatively low, medium, or high insulin
responses. Furthermore, subjects produced their lowest insulin
responses for the least insulinogenic foods and their highest
insulin responses for the most insulinogenic foods within each
food group.
There were large differences in mean glycemic and insulin
responses to the foods, both within and between food groups.
Mean glucose and insulin AUC results, mean GSs and ISs, and
the mean ratios of insulin to glucose AUCs (the amount of
insulin secretion in relation to the blood glucose response) are
listed in Table 4. Mean GSs and ISs were calculated for each
food group by averaging the scores for all test foods within the
food group. On average, the snack food group produced the
highest food group IS (89%), followed by bakery products
(83%), carbohydrate-rich foods (74%), fruit (71%), protein
rich foods (61%), and breakfast cereals (57%). Average GSs
for the food groups did not follow the same rank order (Figure
1). The carbohydrate-rich food group produced the highest
average GS (88%), followed by bakery products (77%), snack
foods (65%), fruit (6 1%), breakfast cereals (59%), and protein
rich foods (54%). Interestingly, the GS rank order is not pro
portional to the average total carbohydrate content of each food
group, which highlights the influence of other food factors (eg,
fiber and processing) in determining the rate of carbohydrate
digestion and absorption.
Overall, among the 38 test foods, jellybeans produced the
highest mean IS (160 ±16%), eightfold higher than the lowest
IS (for peanuts: 20 ±5%) (Figure 2). White bread, the stan
dard food, consistently produced one of the highest glucose and
insulin responses (peak and AUC) and had a higher IS than
most of the other foods (84%). All of the breakfast cereals were
significantly less insulinogenic than white bread (P < 0.001).
All-Bran and porridge both produced a significantly lower IS
than the other cereals (P < 0.001), except muesli. Despite
containing more carbohydrate than porridge and muesli, All
Bran produced the lowest GS. Baked beans, which contain
considerably more carbohydrate than the other protein-rich
foods, produced a significantly higher GS and IS (P < 0.001).
On average, fish elicited twice as much insulin secretion as did
the equivalent portion of eggs. Within the fruit group, oranges
and apples produced a significantly lower GS and IS than
0 Glucosescore
. Insulinscore
cereals Carbohydrate- Bakery
rich foods products
rich foods Fruit Snacksand
FIGURE 1. Mean (±SEM) glucoseand insulinscoresfor each foodgroup.
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. lip
Brown pasta
White pasta
Grain bread
White rice
Whole-meal bread
100 200
Insulin score (%)
FIGURE 2. Mean (±SEM) insulin scores for 1000-Id portions of the test foods. White bread was the reference food (insulin score = 100%). All-Bran
cereal, Special K cereal, Honeysmacks cereal, Sustain cereal, and Cornflakes, Kellogg's Australia Pty Ltd. Pagewood, Australia; Mars Bar candy bar, Mars
Confectionary Australia, Ballarat, Australia.
grapes and bananas (P < 0.05 to P < 0.001), despite contain
ing a similar amount of carbohydrate.
Potatoes produced significantly higher GSs and ISs than all
of the other carbohydrate-rich foods. White bread produced a
higher GS and IS than grain bread (P < 0.05 and P < 0.001
respectively), but whole-meal bread and white bread had sim
ilar scores. White and brown rice had similar GSs and ISs, as
did white and brown pasta. Among the bakery products, crack
ers produced a significantly higher GS than the other test foods,
but there were no significant differences in ISs within this
group (all tended to be high). Among the snack foods, jelly
beans produced a significantly higher GS and IS than the other
foods in this group. Despite containing similar amounts of
carbohydrate, jellybeans induced twice as much insulin secre
tion as any of the four fruits. The candy bar and yogurt, which
both contained large amounts of sugar in combination with
fat or protein, produced relatively high ISs. Popcorn and potato
chips elicited twice as much insulin secretion as peanuts
(P < 0.05 and P < 0.01, respectively).
Significant differences were found both within and among
the food groups when the insulin AUC responses were
examined as a function of the food's carbohydrate content
(Table 4). On average, protein-rich foods produced the
highest insulin secretion per gram of carbohydrate (food
group mean: 18 607 pmol . mm . L' . g@1) (because of
their mostly low carbohydrate contents), followed by bakery
products (468 pmol . mm . L@ . g1), snack foods (416
pmol . mm . L ‘¿. g 1) fruit (194 pmol . mm . L ‘¿. g 1),
carbohydrate-rich foods (182 pmol . mm . L@ . g'), and
breakfast cereals ( 169 pmol . mm . L ‘¿. g ‘¿).When the
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insulin AUC response was examined in relation to the food's
serving size (g), the bakery products were the most insuli
nogenic (food group mean: 236 pmol . mm . L ‘¿. g 1), fol
lowed by snack foods (163 pmol . mm . L ‘¿. g 1), carbo
hydrate-rich foods (62 pmol . mm . L ‘¿. g 1), protein-rich
foods (53 pmol . mm . L ‘¿. g 1), breakfast cereals (39
pmol . mm . L ‘¿. g ‘¿),and fruit (28 pmol . mm . L ‘¿. g 1).
These results reflect the insulinogenic effects of protein and
Insulin responses in relation to glucose responses
Overall, mean glucose and insulin AUC values were posi
tively correlated (r = 0.67, P < 0.001, n = 43), as were the
peak glucose and insulin values (r = 0.57, P < 0.001, n = 43).
Hence, the mean GSs and ISs were highly correlated (r = 0.70,
P < 0.001, n = 38) (Figure 3). The peak glucose concentration
(change from fasting) correlated positively with glucose AUC
values (r = 0.74, P < 0.001, n = 43) and peak insulin
concentrations were proportional to the insulin AUC values
(r 0.95, P < 0.001, n 43). In addition, the observed GSs
for 1000-U portions of the foods correlated with previously
published GI values based on portions of foods containing 50 g
carbohydrate (r = 0.65, P < 0.001, n = 32). Six test foods
(chocolate chip cookies, eggs, cheese, beef, fish, and Hon
eysmackscereal)werenotincludedin this analysisbecauseGI
values were not available.
Insulin AUC values were divided by glucose AUC values to
determine which foods were markedly insulinogenic relative to
their glycemic effect (Table 4 and Figure 4). On average, the
protein-richfoodsstimulateda largeamountof insulinsecre
tion relative to their glycemic response, followed by the bakery
products, snack foods, fruit, carbohydrate-rich foods, and
breakfast cereals.
FIGURE3. Relationbetweenthemeanglucoseandinsulinscores(r =
0.74, P < 0.001, n = 38).
Relations betweenmetabolic responsesand nutrient
contents of the foods
Correlations between the macronutrient compositions of the
test foods and the mean ISs are shown in Figure 5. The portion
size (energy density: kJ/g), water, and fiber contents of the
foods were not significantly related to the mean ISs. The
relation between protein contents and ISs was negative but not
significant (r —¿0.24,n = 38). The mean ISs were positively
related to total carbohydrate content (r = 0.39, P < 0.05, n =
36) and sugar content (r = 0.36, P < 0.05, n = 36), but were
not significantly related to starch content (r = —¿0.09,n = 30).
Fat content was negatively related to the mean IS (r = —¿0.27,
NS, n = 36). When expressed as a percentage of total energy,
fat (r = —¿0.27,NS, n = 36) and protein (r = —¿0.24,NS, n =
38) were negatively associated with the mean IS, whereas total
carbohydrate was positively related (r = 0.37, P < 0.05, n
Relations between the GSs and the nutrients largely followed
the same directions as the IS correlations. Mean GSs were not
significantly related to the foods' serving sizes or water or fiber
contents. Mean GSs correlated negatively with fat (r = —¿0.38,
P < 0.05, n = 36) and protein (r = —¿0.38,P < 0.05, n = 38)
contents, and positively with total carbohydrate content (r =
0.32, NS, n = 36). Unlike the ISs, the GSs were significantly
related to starch content (r = 0.43, P < 0.05, n = 30) but not
sugar content (r = —¿0.07,NS, n = 36). When expressed as a
percentageof total energy, fat (r = —¿0.38,P < 0.05, n = 36)
and protein (r = —¿0.39,P < 0.05, n = 38) were negatively
associated with mean GSs, whereas total carbohydrate content
was positively related (r = 0.46, P < 0.01, n = 36).
Stepwise-multiple-regression analysis of the 446 individual
results for the 38 foods was performed to determine the extent
to which the macronutrients and GSs accounted for the van
ability of the ISs. Unfortunately, it was not possible to generate
a single multiple-regression equation that included all of the
macronutrients because some pairs of nutrients were highly
correlated (eg, fat and protein, fiber and water, total carbohy
drate and sugar or starch, and sugar and starch). The regression
equation that included all of the macronutrients had unaccept
ably high variance inflation factors. Therefore, two separate
regression equations were generated that were limited to the
factors that were measured and not interdependent. Equation 2
includes fat but not protein, whereas equation 3 includes pro
tein but not fat:
IS = 72.4 + 0.383 GS —¿1.88 fat —¿0.103 water
+ 0.509 sugar —¿0.421 starch (2)
for whichSD= 37.34,R2= 33.1%,andadjustedR2= 32.4%.
P values (significance found in the linear-regression analysis
for the associations between the individual nutrients and the IS)
are as follows: OS and water (P < 0.000), fat (P < 0.001),
sugar (P < 0.005), and starch (P < 0.036).
IS = 23.2 + 0.383 05 + 0.785 protein —¿0.098 water
+ 1.29 sugar + 0.377 starch (3)
for which SD = 37.42, R2 = 32.8%, and adjusted R2 = 32.1%.
P values are as follows: GS, water, and sugar (P < 0.000);
protein (P < 0.003); and starch (P < 0.02).
Glucose score (%)
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Special K
White rice
Whole-meal bread
White pasta
0500 1000 1500 2000 2500 3000
FIGURE 4. Ratio of insulin area under the curve(AUC) to glucose AUC responses.I ±SEM. All-Brancereal, Special K cereal, Honeysmackscereal,
Linear-regression analysis of the individual OS and IS re
sults had an R2 value of 23%. Therefore, the glycemic response
was a significant predictor of the insulin response, but it
accounted for only 23% of the variability in insulinemia. The
macronutrients (protein or fat, water, sugar, and starch) were
also significant predictors but together accounted for only
another 10% of the variability of the insulin responses. Thus,
we can explain only 33% of the variation of the insulin re
sponses to the 38 foods studied.
The results of this study confirm and also challenge some of
our basic assumptions about the relation between food intake
and insulinemia. Within each food group, there was a wide
range of insulin responses, despite similarities in nutrient corn
position. The important Western staples, bread and potato,
were among the most insulinogenic foods. Similarly, the highly
refined bakery products and snack foods induced substantially
more insulin secretion per kilojoule or per gram of food than
did the other test foods. In contrast, pasta, oatmeal porridge,
and All-Bran cereal produced relatively low insulin responses,
despite their high carbohydrate contents. Carbohydrate was
quantitatively the major macronutrient for most foods. Thus, it
is not surprising that we observed a strong correlation between
GSs and ISs (r = 0.70, P < 0.001). However, some protein
and fat-rich foods (eggs, beef, fish, lentils, cheese, cake, and
doughnuts) induced as much insulin secretion as did some
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0 20 40 60
0 20 40 60
SS 5
•¿t,.. S
0 10 20
Fiber (9/serving)
Starch (9/serving)
@S S
S 55
1@b5 s
Fat (9/serving)
0 20 40 60
Total carbohydrate (9/serving)
0 i I
0 20 40 60
Sugar (9/serving)
00 10 20 30
FIGURE 5. Relations between the nutrient contents of the test foods and the mean insulin scores. Fiber: r = —¿0.10,NS, n = 32; protein: r = —¿0.24,
NS,n = 38;totalcarbohydrate:r = 0.39,P < 0.05,n = 36; sugar:r = 0.36,P < 0.05,n 36; starch:r = —¿0.09,NS,n = 30;fat: r = —¿0.27,P <
0.05, n = 36.
carbohydrate-rich foods (eg, beef was equal to brown rice and
fish was equal to grain bread). As hypothesized, several foods
with similar GSs had disparate ISs (eg, ice cream and yogurt,
brown rice and baked beans, cake and apples, and doughnuts
and brown pasta). Overall, the fiber content did not predict the
magnitude of the insulin response. Similar ISs were observed
for white and brown pasta, white and brown rice, and white and
whole-meal bread. All of these foods are relatively refined
compared with their traditional counterparts. Collectively, the
fmdings imply that typical Western diets are likely to be
significantly more insulinogenic than more traditional diets
based on less refined foods.
In this study, we choseto test isoenergeticportions of foods
rather than equal-carbohydrate servings to determine the insu
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lin response to all of the nutrients in the foods as normally
consumed. A standard portion size of 1000 kJ was chosen
because this resulted in realistic serving sizes for most of the
foods except apples, oranges, fish, and potatoes. Although
some of the protein-rich foods may normally be eaten in
smaller quantities, fish, beef, cheese, and eggs still had larger
insulin responses per gram than did many of the foods consist
ing predominantly of carbohydrate. As observed in previous
studies, consumption of protein or fat with carbohydrate in
creases insulin secretion compared with the insulinogenic ef
fect of these nutrients alone (22, 30—32). This may partly
explain the markedly high insulin response to baked beans.
Dried hancot beans, which are soaked and boiled, are likely to
have a lower IS than commercial baked beans, which are more
readily digestible.
The results confirm that increased insulin secretion does not
account for the low glycemic responses produced by low-GI
foods such as pasta, porridge, and All-Bran cereal (33). Fur
thermore, equal-carbohydrate servings of foods do not neces
sarily stimulate insulin secretion to the same extent. For exam
ple, isoenergetic servings of pasta and potatoes both contained
=,%50g carbohydrate, yet the IS for potatoes was three times
greater than that for pasta. Similarly, porridge and yogurt, and
whole-grain bread and baked beans, produced disparate ISs
despite their similar carbohydrate contents. These findings, like
others, challenge the scientific basis of carbohydrate exchange
tables, which assume that portions of different foods containing
10—15g carbohydrate will have equal physiologic effects and
will require equal amounts of exogenous insulin to be metab
olized. It is possible that preprandial insulin doses for patients
with NIDDM could be more scientifically estimated or
matched on the basis of a meal's average insulinemic effect in
healthy individuals, rather than on the basis of the meal's
carbohydrate content or 01. Further research is required to test
this hypothesis. The advent of intensive insulin therapy and the
added risk of hypoglycemia increases the urgency of this
research (34).
Our study was undertaken to test the hypothesis that the
postprandial insulin response was not necessarily proportional
to the blood glucose response and that nutrients other than
carbohydrate influence the overall level of insulinemia. Multi
pIe-regression analysis of the individual results showed that the
glycemic response was a significant predictor of the insulin
response, but it accounted for only 23% of the variability in
insulinemia. The macronutrients (protein or fat, water, sugar,
and starch) were also significant predictors, but together ac
counted for only another 10% of the variability of the insulin
responses. Thus, we can explain only 33% of the variation of
the insulin responses to the 38 foods under examination. The
low R2 value indicates that the macronutrient composition of
foods has relatively limited power for predicting the extent of
postprandial insulinemia. The rate of starch digestion, the
amount of rapidly available glucose and resistant starch, the
degree ofosmolality, the viscosity ofthe gut's contents, and the
rate of gastric emptying must be other important factors influ
encing the degree of postprandial insulin secretion. Further
research is required to examine the relation between postpran
dial insulinemia, food form, and various digestive factors for a
much larger range of foods to produce a regression equation
with greater predictive value.
Dietary guidelines for healthy people and persons with
NIDDM have undergone considerable change and will con
tinue to be modified as our understanding of the relations
between dietary patterns and disease improves. There is con
cern that high-carbohydrate diets may increase triacylglycerol
concentrations and reduce high-density lipoprotein concentra
tions (35, 36). The use of diets high in monounsaturated fat is
an attempt to overcome the undesirable effects of some high
carbohydrate diets on plasma lipids (37—39).However, diets
high in monounsaturated fat are unlikely to facilitate weight
loss. A low-fat diet based on less-refined, carbohydrate-rich
foods with relatively low ISs may help enhance satiety and aid
weight loss as well as improve blood glucose and lipid control
The results of this study are preliminary but we hope they
stimulate discussion and further research. Additional studies are
needed to determine whether the IS concept is useful, reproducible
around the world, predictable in a mixed-meal context, and cliii
ically useful in the treatment of diabetes mellitus, hyperlipidemia,
and overweight. Studies examining the relation between postpran
dial insulinemia and the storage and oxidation of fat, protein, and
carbohydrate may provide further insight into the relation between
fuel metabolism and satiety, and establish whether low-insuline
mic diets can facilitate greater body fat loss than isoenergetic
high-insWinemic diets.
We thankEfi Farmakalidisfor her assistancein the planningof this
study and Natasha Porter for her technical assistance with the experimental
work for the carbohydrate-rich food group.
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... The energy content of each of the food preparations were measured with Bomb Calorimeter (e2k) and serving weight was calculated for each food is have a total energy of 1000kJ [14]. Nutrient analysis All the food samples were analyzed for moisture, fat, protein, ash and total dietary fiber contents [15]. ...
... Area Under Curves (AUC) for glucose and insulin (gAUC and iAUC) were calculated, based on Simpson's Rule, where fasting concentrations were used as the baseline and truncated at zero. Descriptive data are expressed as mean ± standard error mean [14]. Insulin score IS (%) was calculated for each test food by dividing the insulin AUC value for the test food by the insulin AUC value for glucose (reference food), and expressed as a percentage. ...
... The GI helps to know the type of foods which are useful to control the blood glucose level. Insulin index which is less well-known index is more important than the measures of glycemic response [14]. Therefore this study on glycemic index and insulin index was under taken to measure it GI and IS of the palmyrah based edible products. ...
Full-text available
The objective of this study was to determine the insulin index and glucose index to isoenergetic (1000-kJ) portions of palmyrah based foods commonly consumed in Jaffna and their correlations between nutrient and phytochemical contents. Subjects were selected those having fasting blood sugar less than 100 mg/dl. Glucose (reference food) and four test foods such as pinnatu, Jaggery, odiyalpittu and pullukodiyal flour snak were administrated to four groups of 24 subjects and serving weight were calculated based on the energy content. Subjects were fasted for12h before the administration of foods then venous blood samples were collected at 0 time and every 30 min for two hours after either feeding with glucose and test foods. An insulin score and glycemic score were calculated from the area under the insulin/glucose response curve for each food with the use of reference food. Among the test foods pinattu has low glycemic (52.9%) and low insulin index (36.47%).Carbohydrate, ash and Total Dietary Fiber (TDF) present in the those products were negatively correlated with glycemic index. Insulin index was negatively correlated with TDF, protein and ash contents. Not only the nutrient contents but also the phytochemicals such as total phenols and flavonoids showed negative correlation. All the tested products showed less plasma insulin, although high plasma insulin causes coronary heart disease. Therefore palmyrah products could be used in order to develop new value added foods, which could have more beneficial health properties regarding glucose and insulin metabolism.
... Thus, elevations in basal and stimulated insulin levels may constitute an important early marker of metabolic dysfunction that could be monitored in both apparently healthy and at-risk individuals. The theoretical and practical importance of postprandial insulin levels have previously been emphasized by proposing the use of a food insulin index that ranks foods based on their ability to elevate postprandial insulin [12,13]. Although potentially useful, a general food insulin index based on the postprandial blood insulin responses to isolated foods in healthy volunteers [12] would not take into account the inter-individual variability in insulin secretion and/or responses to mixed meals nor would it be able to account for expected differences in postprandial insulin levels between individuals with different levels of insulin resistance. ...
... The theoretical and practical importance of postprandial insulin levels have previously been emphasized by proposing the use of a food insulin index that ranks foods based on their ability to elevate postprandial insulin [12,13]. Although potentially useful, a general food insulin index based on the postprandial blood insulin responses to isolated foods in healthy volunteers [12] would not take into account the inter-individual variability in insulin secretion and/or responses to mixed meals nor would it be able to account for expected differences in postprandial insulin levels between individuals with different levels of insulin resistance. From a clinical and monitoring perspective, measuring postprandial insulin levels would be highly valuable but presents several challenges, including the requirement for repeated blood sampling. ...
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Developing non-invasive alternatives to monitor insulin levels in humans holds potential practical value for identifying individuals with, or at risk of developing, insulin resistance. The aims of this study were: (1) to determine if saliva insulin can be used to delineate between low and high postprandial insulin levels following the ingestion of mixed breakfast meals; and (2) to determine if expected differences in postprandial hyperinsulinemia between young lean and young overweight/obese participants could be detected in saliva. Sixteen individuals (n = 8 classified as normal weight (NW); BMI 20.0–24.9 kg/m2, and n = 8 classified as overweight/obese (OO); BMI ≥ 28.0 kg/m2) completed two isocaloric mixed-meal tolerance tests following an overnight fast, consisting of a low-carbohydrate (LC) breakfast or a high-carbohydrate (HC) breakfast. Blood and saliva samples were collected at regular intervals for two hours postprandially. In both groups, plasma and saliva insulin total area under the curve (AUC) and incremental AUC (iAUC) were significantly higher after the HC as compared to the LC meal (all p ≤ 0.005). Insulin AUC and iAUC in both plasma and saliva were higher in OO than in NW after the HC meal (all p ≤ 0.02) but only plasma and saliva total AUC were higher in OO after the LC meal (both p ≤ 0.01). Plasma insulin AUC was significantly correlated with salivary insulin AUC in LC (r = 0.821; p < 0.001) and HC (r = 0.882; p < 0.001). These findings indicate that saliva could potentially be used to delineate between low and high insulin levels following mixed breakfast meals.
... More Westernized South American Indians and Pacific Islanders show high rates of acne (though not quite as high as the white population) (Fleischer, Feldman, and Bradham 1994;Freyre et al. 1998). Cordain et al. (2002) hypothesize that acne in Western pop- ulations is caused by diet-induced hyperinsulinemia, and this could explain the relationship between milk and acne: Despite its low glycemic load, dairy triggers a large insulin response (Holt, Brand Miller, and Petocz 1997;Hoyt, Hickey, and Cordain 2005; € Ostman, Liljeberg Elmsta hl, and Bj€ orck 2001). ...
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According to the Academy of Nutrition and Dietetics’ influential position statement on vegetarianism, meat and seafood can be replaced with milk, soy/legumes, and eggs without any negative effects in children. The United States Department of Agriculture endorses a similar view. The present paper argues that the Academy of Nutrition and Dietetics ignores or gives short shrift to direct and indirect evidence that vegetarianism may be associated with serious risks for brain and body development in fetuses and children. Regular supplementation with iron, zinc, and B12 will not mitigate all of these risks. Consequently, we cannot say decisively that vegetarianism or veganism is safe for children.
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Glycaemic index (GI) is used as an indicator to guide consumers in making healthier food choices. We compared the GI, insulin index (II), and the area under the curve for blood glucose and insulin as glucose (GR) and insulin responses (IR) of a newly developed liquid nutritional formula with one commercially available liquid product with different types of carbohydrates. We then evaluated the glucose and insulin responses of two test foods with comparable energy density and protein percentage but presented in different food forms (liquid vs. solid). Fourteen healthy women participated in the study. GI, II, GR, and IR were assessed after (independent) consumption of two liquid products and a solid breakfast meal. The two liquid foods showed comparable GI, whilst the liquid form appeared to produce lower median GI (25 vs. 54), and II (52 vs. 98) values compared to the solid breakfast (p < 0.02). The median GR and IR for solid breakfast were respectively 44% and 45% higher compared to the liquid product (p < 0.02). Liquid formulas with different carbohydrate qualities produced comparable glucose responses, while foods with comparable energy density and protein percentage but different food form elicited differential effects on GI, II, GR, and IR. Nutrient quality and food form need to be taken into consideration when developing low GI products to manage glycaemic responses.
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Dietary guidelines indicate that complex carbohydrates should provide around half of the calories in a balanced diet, while sugars (i.e., simple carbohydrates) should be limited to no more than 5–10% of total energy intake. To achieve this public health goal a collective effort from different entities including governments, food & beverage industries and consumers is required. Some food companies have committed to continually reduce sugars in their products. Different solutions can be used to replace sugars in food products but it is important to ensure that these solutions are more healthful than the sugars they replace. The objectives of this paper are, (1) to identify carbohydrates and carbohydrates sources to promote and those to limit for dietary intake and food product development, based on current knowledge about the impact of carbohydrates on the development of dental caries, obesity and cardio-metabolic disorders (2) to evaluate the impact of food processing on the quality of carbohydrates and (3) to highlight the challenges of developing healthier products due to the limitations and gaps in food regulations, science & technology and consumer education.
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Background: Although hyperinsulinemia is hypothesised to be involved in colorectal carcinogenesis, it remains unclear whether a diet inducing an elevated insulin response influences colorectal cancer (CRC) survival. Methods: We examined the association of post-diagnosis dietary insulin scores with survival among 2006 patients from two large prospective cohorts who were diagnosed with CRC from 1976 to 2010. Dietary insulin load was calculated as a function of the food insulin index. Dietary insulin index was calculated by dividing insulin load by total energy intake. Cox proportional hazards models were used to calculate hazard ratios (HRs) for CRC-specific mortality and overall mortality, adjusted for other risk factors for cancer survival. Results: The adjusted HRs for CRC-specific mortality comparing the highest to the lowest quintiles were 1.82 (95% CI: 1.20-2.75, Ptrend=0.006) for dietary insulin load and 1.66 (95% CI: 1.10-2.50, Ptrend=0.004) for dietary insulin index. We also observed an increased risk for overall mortality, with adjusted HRs of 1.33 (95% CI: 1.03-1.72, Ptrend=0.03) for dietary insulin load and 1.32 (95% CI: 1.02-1.71, Ptrend=0.02) for dietary insulin index, comparing extreme quintiles. The increase in CRC-specific mortality associated with higher dietary insulin scores was more apparent among patients with body mass index (BMI)⩾25 kg m(-2) than BMI<25 kg m(-2) (Pinteraction=0.01). Conclusions: Higher dietary insulin scores after CRC diagnosis were associated with a statistically significant increase in CRC-specific and overall mortality.British Journal of Cancer advance online publication, 17 August 2017; doi:10.1038/bjc.2017.272
Background: Australians have used the glycemic index (GI) since 1995; however, there are no data on changes in carbohydrate quality over time.Objectives: The aim was to compare average dietary GI and glycemic load (GL), and contributing carbohydrate foods, in the 2 most recent national dietary surveys.Design: Dietary data from adult participants of national nutrition surveys conducted in 1995 (the 1995 Australian National Nutrition Survey; n = 8703) and 2012 (the 2011-2012 National Nutrition and Physical Activity Survey; n = 6278), collected by a single 24-h recall, were analyzed. The differences in mean dietary GI and GL between surveys were compared by using 1-factor ANOVA. The main sources of dietary GL in the 2 surveys were also assessed. Multiple linear regression was performed to examine the contributions of the food groups to interindividual variations in dietary GI and GL.Results: Overall, dietary GI and GL decreased by 5% and 12%, respectively, from 1995 to 2012 (GI on glucose standard: 56.5 ± 6.2 compared with 53.9 ± 6.8, respectively; GL: 153.3 ± 62.1 compared with 135.4 ± 58.5, respectively; both P < 0.001). Breads were the main contributor to GL at both time points. Potatoes and sweetened beverages contributed less, whereas cereal-based dishes contributed more in 2012 than in 1995. The top 20 GL-contributing food groups explained less interindividual variation in dietary GI (R(2): 0.376 compared with 0.290) and GL (R(2): 0.825 compared with 0.770) in 2012 than in 1995.Conclusion: Although the average dietary GI and GL declined between 1995 and 2012, trends in specific carbohydrate foods suggest that Australians are avoiding potatoes and sugary beverages in favor of a greater variety of carbohydrate foods, particularly cereal products.
Accumulating evidence suggests that post-diagnostic insulin levels may influence colorectal cancer (CRC) survival. Yet, no previous study has examined CRC survival in relation to a post-diagnostic diet rich in foods that increase post-prandial insulin levels. We hypothesized that glycemic and insulin scores (index or load; derived from food frequency questionnaire data) may be associated with survival from specific CRC subtypes sensitive to the insulin signaling pathway. We prospectively followed 1,160 CRC patients from the Nurses' Health Study (1980-2012) and Health Professionals Follow-Up Study (1986-2012), resulting in 266 CRC deaths in 10,235 person-years. CRC subtypes were defined by seven tumor biomarkers (KRAS, BRAF, PIK3CA mutations, and IRS1, IRS2, FASN, and CTNNB1 expression) implicated in the insulin signaling pathway. For overall CRC and each subtype, hazard ratio (HR) and 95% confidence interval (95% CI) for an increase of one standard deviation in each of glycemic and insulin scores were estimated using time-dependent Cox proportional hazards model. We found that insulin scores, but not glycemic scores, were positively associated with CRC mortality (HR=1.19, 95% CI=1.02 to 1.38 for index; HR=1.23, 95% CI=1.04 to 1.47 for load). The significant positive associations appeared more pronounced among PIK3CA wild-type cases and FASN-negative cases, with HR ranging from 1.36 to 1.60 across insulin scores. However, we did not observe statistically significant interactions of insulin scores with PIK3CA, FASN, or any other tumor marker (P interaction > .12). While additional studies are needed for definitive evidence, a high-insulinogenic diet after CRC diagnosis may contribute to worse CRC survival. This article is protected by copyright. All rights reserved.
Orange pomace (OP), a fiber-rich byproduct of juice production, has the potential for being formulated into a variety of food products. We hypothesized that OP would diminish postprandial glycemic responses to a high carbohydrate/fat breakfast and lunch. We conducted an acute, randomized, placebo-controlled, double blind, crossover trial with 34 overweight men who consumed either a 255 g placebo (PLA), a low (35% OP (LOP)), or a high (77% (HOP)) dose OP beverage with breakfast. Blood was collected at 0, 10, 20, 30, and 45 min and at 1, 1.5, 2, 3, 4, 5, 5.5, 6, 6.5, 7, and 8 h. Lunch was consumed after the 5.5-h blood draw. OP delayed the time (Tmax1) to the maximum concentration (Cmax1) of serum glucose during the 2-h period post breakfast by ≥36% from 33 (PLA) to 45 (HOP) and 47 (LOP) min (p = 0.055 and 0.013, respectively). OP decreased post-breakfast insulin Cmax1 by ≥10% and LOP delayed the Tmax1 by 14 min, compared to PLA at 46 min (p ≤ 0.05). HOP reduced the first 2-h insulin area under concentration time curve (AUC) by 23% compared to PLA. Thus, OP diminishes postprandial glycemic responses to a high carbohydrate/fat breakfast and the second meal in overweight men.
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Aims/hypothesis: The gut incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) have a major role in the pathophysiology of type 2 diabetes. Specific genetic and dietary factors have been found to influence the release and action of incretins. We examined the effect of interactions between seven incretin-related genetic variants in GIPR, KCNQ1, TCF7L2 and WFS1 and dietary components (whey-containing dairy, cereal fibre, coffee and olive oil) on the risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study. Methods: The current case-cohort study included 8086 incident type 2 diabetes cases and a representative subcohort of 11,035 participants (median follow-up: 12.5 years). Prentice-weighted Cox proportional hazard regression models were used to investigate the associations and interactions between the dietary factors and genes in relation to the risk of type 2 diabetes. Results: An interaction (p = 0.048) between TCF7L2 variants and coffee intake was apparent, with an inverse association between coffee and type 2 diabetes present among carriers of the diabetes risk allele (T) in rs12255372 (GG: HR 0.99 [95% CI 0.97, 1.02] per cup of coffee; GT: HR 0.96 [95% CI 0.93, 0.98]); and TT: HR 0.93 [95% CI 0.88, 0.98]). In addition, an interaction (p = 0.005) between an incretin-specific genetic risk score and coffee was observed, again with a stronger inverse association with coffee in carriers with more risk alleles (0-3 risk alleles: HR 0.99 [95% CI 0.94, 1.04]; 7-10 risk alleles: HR 0.95 [95% CI 0.90, 0.99]). None of these associations were statistically significant after correction for multiple testing. Conclusions/interpretation: Our large-scale case-cohort study provides some evidence for a possible interaction of TCF7L2 variants and an incretin-specific genetic risk score with coffee consumption in relation to the risk of type 2 diabetes. Further large-scale studies and/or meta-analyses are needed to confirm these interactions in other populations.
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To determine the relations of diet with risk of clinical noninsulin-dependent diabetes, we analyzed data from a prospective cohort of 84360 US women. During 6 y of follow-up we identified 702 definite incident cases. Because body mass index (BMI) is a powerful risk factor for diabetes, we examined the relations of fat (including type), fiber, sucrose, and other components of diet to risk of diabetes, among women with BMIs (in kg/m2) less than 29 kg/m2. After controlling for body mass index, previous weight change, and alcohol intake, we observed no associations between intakes of energy, protein, sucrose, carbohydrate, or fiber and risk of diabetes. Compared with women in the lowest quintile of energy-adjusted intake, and relative risks (and tests for trend) for those in the highest quintile were 0.61 (P trend = 0.03) for vegetable fat, 0.62 (P trend = 0.008) for potassium, 0.70 (P trend = 0.005) for calcium, and 0.68 (P trend = 0.02) for magnesium. These inverse associations were attenuated among obese women (BMIs greater than or equal to 29).
The Diabetes Control and Complications Trial has demonstrated that intensive diabetes treatment delays the onset and slows the progression of diabetic complications in subjects with insulin-dependent diabetes mellitus from 13 to 39 years of age. We examined whether the effects of such treatment also occurred in the subset of young diabetic subjects (13 to 17 years of age at entry) in the Diabetes Control and Complications Trial. One hundred twenty-five adolescent subjects with insulin-dependent diabetes mellitus but with no retinopathy at baseline (primary prevention cohort) and 70 adolescent subjects with mild retinopathy (secondary intervention cohort) were randomly assigned to receive either (1) intensive therapy with an external insulin pump or at least three daily insulin injections, together with frequent daily blood-glucose monitoring, or (2) conventional therapy with one or two daily insulin injections and once-daily monitoring. Subjects were followed for a mean of 7.4 years (4 to 9 years). In the primary prevention cohort, intensive therapy decreased the risk of having retinopathy by 53% (95% confidence interval: 1% to 78%; p = 0.048) in comparison with conventional therapy. In the secondary intervention cohort, intensive therapy decreased the risk of retinopathy progression by 70% (95% confidence interval: 25% to 88%; p = 0.010) and the occurrence of microalbuminuria by 55% (95% confidence interval: 3% to 79%; p = 0.042). Motor and sensory nerve conduction velocities were faster in intensively treated subjects. The major adverse event with intensive therapy was a nearly threefold increase of severe hypoglycemia. We conclude that intensive therapy effectively delays the onset and slows the progression of diabetic retinopathy and nephropathy when initiated in adolescent subjects; the benefits outweigh the increased risk of hypoglycemia that accompanies such treatment. (J PEDIATR 1994;125:177-88)
Objective. —To study effects of variation in carbohydrate content of diet on glycemia and plasma lipoproteins in patients with non—insulin-dependent diabetes mellitus (NIDDM).Design. —A four-center randomized crossover trial.Setting. —Outpatient and inpatient evaluation in metabolic units.Patients. —Forty-two NIDDM patients receiving glipizide therapy.Interventions. —A high-carbohydrate diet containing 55% of the total energy as carbohydrates and 30% as fats was compared with a high—monounsaturated-fat diet containing 40% carbohydrates and 45% fats. The amounts of saturated fats, polyunsaturated fats, cholesterol, sucrose, and protein were similar. The study diets, prepared in metabolic kitchens, were provided as the sole nutrients to subjects for 6 weeks each. To assess longer-term effects, a subgroup of 21 patients continued the diet they received second for an additional 8 weeks.Main Outcome Measures. —Fasting plasma glucose, insulin, lipoproteins, and glycosylated hemoglobin concentrations. Twenty-four-hour profiles of glucose, insulin, and triglyceride levels.Results. —The site of study as well as the diet order did not affect the results. Compared with the high—monounsaturated-fat diet, the high-carbohydrate diet increased fasting plasma triglyceride levels and very low-density lipoprotein cholesterol levels by 24% (P<.0001) and 23% (P=.0001), respectively, and increased daylong plasma triglyceride, glucose, and insulin values by 10% (P=.03), 12% (P<.0001), and 9% (P=.02), respectively. Plasma total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol levels remained unchanged. The effects of both diets on plasma glucose, insulin, and triglyceride levels persisted for 14 weeks.Conclusions. —In NIDDM patients, high-carbohydrate diets compared with high—monounsaturated-fat diets caused persistent deterioration of glycemic control and accentuation of hyperinsulinemia, as well as increased plasma triglyceride and very-low-density lipoprotein cholesterol levels, which may not be desirable.(JAMA. 1994;271:1421-1428)
Low glycaemic index diets reduce blood glucose and lipid levels in humans but glycaemic index values are only available for a small number of foods. Thus, we determined the glycaemic index of 102 complex carbohydrate foods in patients with diabetes. The values varied from 37 for bean thread noodles to 127 for Rice Chex cereal (p<0.001). There were no significant differences between the values of 14 different commercial leavened, wheat breads, which had a mean glycaemic index of 97. This supports the validity of using white bread as the standard food with an arbitrary glycaemic index of 100. There were significant differences between the glycaemic index values of individual foods in the following groups: rye breads, cakes, corn products, cookies, crackers, grains, pasta, potato, soups, legumes and breakfast cereals. Legumes and pasta tended to have low glycaemic index values. The glycaemic index values of the foods were weakly negatively related to their protein (r=−0.407; p<0.001) and dietary fibre (r=0.322; p<0.001) content but not fat (r=−0.054, ns). Thus, there are sufficient differences between the glycaemic responses of complex carbohydrate foods to make the glycaemic index classification a useful supplement to food tables in planning diets for patients with metabolic disorders such as diabetes or hyperlipidaemia.
Previous studies indicate that diets rich in digestible carbohydrates improve glucose tolerance in nondiabetic individuals, but may worsen glycemic control in NIDDM patients with moderately severe hyperglycemia. The effects of such high-carbohydrate diets on glucose metabolism in patients with mild NIDDM have not been studied adequately. This study compares responses to an isocaloric high-carbohydrate diet (60% of total energy from carbohydrates) and a low-carbohydrate diet (35% of total energy from carbohydrates) in 8 men with mild NIDDM. Both diets were low in saturated fatty acids, whereas the low-carbohydrate diet was rich in monounsaturated fatty acids. The two diets were matched for dietary fiber content (25 g/day). All patients were randomly assigned to receive first one and then the other diet, each for a period of 21 days, in a metabolic ward. Compared with the low-carbohydrate diet, the high-carbohydrate diet caused a 27.5% increase in plasma triglycerides and a similar increase in VLDL-cholesterol levels; it also reduced levels of HDL cholesterol by 11%. Plasma glucose and insulin responses to identical standard breakfast meals were studied on days 4 and 21 of each period, and these did not differ significantly between the two diets. At the end of each period, a euglycemic hyperinsulinemic glucose clamp study with simultaneous infusion of [3-3H]glucose revealed no significant changes in hepatic insulin sensitivity; and peripheral insulin-mediated glucose disposal remained unchanged (14.7 +/- 1.4 vs. 16.5 +/- 2.3 on the high-carbohydrate and low-carbohydrate diets, respectively).(ABSTRACT TRUNCATED AT 250 WORDS)
Non-insulin-dependent diabetes mellitus (NIDDM) results from an imbalance between insulin sensitivity and insulin secretion. Both longitudinal and cross-sectional studies have demonstrated that the earliest detectable abnormality in NIDDM is an impairment in the body's ability to respond to insulin. Because the pancreas is able to appropriately augment its secretion of insulin to offset the insulin resistance, glucose tolerance remains normal. With time, however, the beta-cell fails to maintain its high rate of insulin secretion and the relative insulinopenia (i.e., relative to the degree of insulin resistance) leads to the development of impaired glucose tolerance and eventually overt diabetes mellitus. The cause of pancreatic "exhaustion" remains unknown but may be related to the effect of glucose toxicity in a genetically predisposed beta-cell. Information concerning the loss of first-phase insulin secretion, altered pulsatility of insulin release, and enhanced proinsulin-insulin secretory ratio is discussed as it pertains to altered beta-cell function in NIDDM. Insulin resistance in NIDDM involves both hepatic and peripheral, muscle, tissues. In the postabsorptive state hepatic glucose output is normal or increased, despite the presence of fasting hyperinsulinemia, whereas the efficiency of tissue glucose uptake is reduced. In response to both endogenously secreted or exogenously administered insulin, hepatic glucose production fails to suppress normally and muscle glucose uptake is diminished. The accelerated rate of hepatic glucose output is due entirely to augmented gluconeogenesis. In muscle many cellular defects in insulin action have been described including impaired insulin-receptor tyrosine kinase activity, diminished glucose transport, and reduced glycogen synthase and pyruvate dehydrogenase. The abnormalities account for disturbances in the two major intracellular pathways of glucose disposal, glycogen synthesis, and glucose oxidation. In the earliest stages of NIDDM, the major defect involves the inability of insulin to promote glucose uptake and storage as glycogen. Other potential mechanisms that have been put forward to explain the insulin resistance, include increased lipid oxidation, altered skeletal muscle capillary density/fiber type/blood flow, impaired insulin transport across the vascular endothelium, increased amylin, calcitonin gene-related peptide levels, and glucose toxicity.
Many studies have shown that hyperinsulinemia and/or insulin resistance are related to various metabolic and physiological disorders including hypertension, dyslipidemia, and non-insulin-dependent diabetes mellitus. This syndrome has been termed Syndrome X. An important limitation of previous studies has been that they all have been cross sectional, and thus the presence of insulin resistance could be a consequence of the underlying metabolic disorders rather than its cause. We examined the relationship of fasting insulin concentration (as an indicator of insulin resistance) to the incidence of multiple metabolic abnormalities in the 8-yr follow-up of the cohort enrolled in the San Antonio Heart Study, a population-based study of diabetes and cardiovascular disease in Mexican Americans and non-Hispanic whites. In univariate analyses, fasting insulin was related to the incidence of the following conditions: hypertension, decreased high-density lipoprotein cholesterol concentration, increased triglyceride concentration, and non-insulin-dependent diabetes mellitus. Hyperinsulinemia was not related to increased low-density lipoprotein or total cholesterol concentration. In multivariate analyses, after adjustment for obesity and body fat distribution, fasting insulin continued to be significantly related to the incidence of decreased high-density lipoprotein cholesterol and increased triglyceride concentrations and to the incidence of non-insulin-dependent diabetes mellitus. Baseline insulin concentrations were higher in subjects who subsequently developed multiple metabolic disorders. These results were not attributable to differences in baseline obesity and were similar in Mexican Americans and non-Hispanic whites. These results support the existence of a metabolic syndrome and the relationship of that syndrome to multiple metabolic disorders by showing that elevations of insulin concentration precede the development of numerous metabolic disorders.
There is controversy regarding the clinical utility of classifying foods according to their glycemic responses by using the glycemic index (GI). Part of the controversy is due to methodologic variables that can markedly affect the interpretation of glycemic responses and the GI values obtained. Recent studies support the clinical utility of the GI. Within limits determined by the expected GI difference and by the day-to-day variation of glycemic responses, the GI predicts the ranking of the glycemic potential of different meals in individual subjects. In long-term trials, low-GI diets result in modest improvements in overall blood glucose control in patients with insulin-dependent and non-insulin-dependent diabetes. Of perhaps greater therapeutic importance is the ability of low-GI diets to reduce insulin secretion and lower blood lipid concentrations in patients with hypertriglyceridemia.