Why should you read this paper? Glucose and insulin are two of the ...
Postprandial = after the meal. This is usually measured within two ...
Glucose can be measured today using a CGM (continuous glucose monit...
Since an increase in glucose levels usually results in insulin secr...
This was the most interesting insight of this paper in my opinion. ...
Protein is made of amino acids. This implies protein (or some forms...
Most of the subjects were healthy lean people without diabetes
The convention is to use white bread or sugar when computing glycem...
This is the best way to calculate an integral =) https://www.wik...
Glucose is very much affected by the food you ate, but also from th...
Ouch
There is not much difference between white or brown rice. White bre...
AUC = Area Under the Curve
They add it as a meaningless comment but there may be something muc...
Don't eat jollybeans LOL!
These equations give the fit between glucose and insulin scores. Do...
This graph is great and while it shows that there is a great correl...
This is a great figure. Together with the previous learnings we can...
Glucose is great but is still only a limited proxy. That being said...
NOT QUESTION
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 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 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
INTRODUCTION
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,
Australia.
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
INSULIN INDEX OF FOODS
1265
TABLE!
Descriptionand preparationof the test foods
Fruit
Black grapes
Apples
Oranges
Bananas
Bakery products
Croissants
Chocolate cake with
frosting
Doughnuts with cinnamon
sugar
Chocolate chip cookies
Water crackers
Snack foods and confectionery
Mars Bar
Yogurt
Ice cream
Jellybeans (assorted colors)
Peanuts
Potato chips
Popcorn
Protein-rich foods
Cheese
Eggs
Lentils
Baked beans
Beefsteak
White fish
Carbohydrate-rich foods
White bread
Whole-meal bread
Grain bread
White rice
Brown rice
White pasta
Brown pasta
Potatoes
Waltham cross
Reddelicious
Navel
Cavendish
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
warm
Served crisp at room temperature, stored in airtight
container
Served crisp at room temperature
Cut into four standard pieces and served at room
temperature
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 1.5mmimmediately
before serving
Prepared in bulk according to recipe, stored at 4 °Cfor up
to 2 d, reheated in a microwave oven for 2 mm
immediatelybeforeserving
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 mmand 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,
Australia
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
TopBakeries,Chatswood,Sydney,Australia
Cairose rice (Sunwhite), Ricegrowers' Co-operative
Ltd. Leeton, Australia
Calrose rice (Sunbrown), Ricegrowers' Co-operative
Ltd
Spirals
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,
Australia—Special
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
1266
HOLT ET AL
TABLE 1
Continued
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 the only 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.
SUBJECTS AND METHODS
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
porridge.
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.
Subjects
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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ServingCarbohydrateEnergyFood
weight FatProtein
SugarStarch
FiberWater
density
INSULIN INDEX OF FOODS
1267
TABLE 2
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
k@I/gFruitGrapes3950.43.256.90.03.6317.02.5Bananas2790.34.747.28.46.1210.13.6Apples4350.01.356.52.29.1360.92.3Oranges6250.66.950.60.012.5539.41.6Bakery
productsCroissants6114.46.13.118.61.813.516.4Cake26411.94.320.110.50.710.715.6Doughnuts6513.44.38.917.01.416.115.4Cookies25110.92.418.716.21.02.119.6Crackers585.45.81.340.21.62.217.2Snacks
andconfectioneryMars
Barr549.42.936.71.11.73.518.5Yogurt@2415.311.837.60.00.5187.04.2Ice
cream12013.45.225.80.00.074.28.3Jellybeans880.05.344.611.50.012.211.4Peanuts3820.19.61.73.72.40.626.3Potatochips24416.22.70.222.12.41.122.7Popcorn24713.04.62.125.36.21.721.3Protein-rich
foodsCheese5920.015.00.10.00.020.916.9Eggs15917.919.60.50.00.0119.46.3Lentils2534.619.44.224.91
1.4222.03.9Baked
beans3511.716.116.123.216.8267.12.8Beef
steak1587.742.00.00.00.0104.36.3Fish3331.056.30.00.00.0250.03.0Carbohydrate-rich
foodsWhite
bread2942.18.51.844.13.336.110.6Whole-meal
bread21012.67.61.743.76.640.39.9Grain
bread21085.49.42.437.66.541.49.3White
rice22030.55.00.156.00.4140.04.9Brown
rice21482.15.20.552.61.493.96.8White
pasta2010.87.82.047.13.5134.85.0Brown
pasta22181.611.30.747.810.9132.64.6Potatoes3681.010.03.145.99.2290.82.7French
fries2938.73.91.135.43.533.810.7Breakfast
cerealsCornflakes21702.18.410.236.11.5110.95.9Special
K21722.115.314.027.21.4111.25.8Honeysmacks21722.28.731.117.02.61
15.05.8Sustain21683.19.713.729.13.2119.15.9Muesli21756.110.717.119.86.61
14.15.7Pomdge23836.210.97.529.04.7333.72.6All-Bran21742.911.713.929.414.1111.05.7
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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FoodgroupAgeBMI2yFruit
(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-rich
foods (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
1268
HOLT ET AL
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
7%.
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
follows:
IS (%)
Area under the 120-mm insulin response
curve for 1000 U test food
Areaunderthe 120-mminsulinresponsecurve
for 1000 Id white bread
TABLE 3
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
group (STATVIEW STUDENT SOFFWARE; Abacus Concepts
mc, Berkley, CA). Linear-regressionanalysis was used to test
associations between glucose and insulin responses and nutritional
indexes (MINITAB DATA ANALYSIS SOFFWARE, version
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
completed.
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'%%
INSULIN INDEX OF FOODS
1269
TABLE 4
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'
BreakfastcerealsWhitebread156±2113557±1756108±19295±38144±19100±0100±0All-Bran59
±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
foodsWhitebread120±1312882±1901112±15281±41137±20100±0100±0Whitepasta50±114456±453156±4891±922±246±1040±5Brown
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
foodsWhitebread121±1917438±3154177±35387±63185±33100±0100±0Eggs36
±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
andconfectioneryWhitebread159±2915592±2376104±24340±52166±25100±0100±0Peanuts20
±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
productsWhitebread129±1517599±3058188±64383±67187±33100±0100±0Doughnuts78±1412445±2402113±21480±93191±3763±1274±9Croissants89±613097±2978483±244604±137215±4974±979±14Cake61
±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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1270
HOLT ET AL
bread, which artificially increases the accuracy of any calcu
lation involving white bread.
RESULTS
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.
100
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
80
60J
40
20
0
4)
0
U
‘¿I)
C
4)
4)
E
0.
0
0
Breakfast
cereals
Carbohydrate- Bakery
rich foods products
Protein
rich foods
Fruit Snacksand
confectionery
FIGURE 1. Mean(±SEM)glucoseand insulinscoresfor each foodgroup.
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...@White
breac.IPrthifr@c.
Doughnuts
Croissants
Cake
Crackers
Cookies
PeanutsI=@lIII―;―t-1@PopcornI=―iiPotato
chipsI='=―iIcecreaml―;lIYogurtI
. lip
INSULIN INDEX OF FOODS
1271
I@1
ft
All-Bran
Porridge
Muesli
OUSL4I(J
Eg@
Cheese
Beef
Lentils
Fish
Beans
Apples
Oranges
Bananas
Grapes
Brown pasta
White pasta
Grain bread
Brownrice
Frenchfries
White rice
Whole-meal bread
-i
.1
1=1
Jellybeans
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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1272
HOLT ET AL
S
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
fat.
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 thisanalysisbecauseGI
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.
4)
1@
0
U
4)
C
4)
C
200
FIGURE3. Relationbetweenthemeanglucoseandinsulinscores(r =
0.74, P < 0.001, n = 38).
Relationsbetweenmetabolic 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
36).
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).
.
S
•¿•
S
0
0
100
Glucose score (%)
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INSULIN INDEX OF FOODS 1273
Porridge
All-Bran
Comfiakes
Special K
Sustain
Honeysmacks
Muesli
Brownrice
Brownpasta
White rice
Grain bread
Whitebread
Potatoes
Whole-meal bread
Frenchfries
White pasta
Bananas
Grapes
Apples
Oranges
Popcorn
Jellybeans
Yogurt
Chips
Icecream
Peanuts
MarsBar
Doughnuts
Cake
Cooides
Crackers
Croissant
Eggs
Beans
Cheese
Lentils
Fish
Be@
I..
I-,
I-i
@1
-I
-t
0 500 1000 1500 2000
2500
3000
InsulinAUC/GiucoseAUC
FIGURE 4. Ratio of insulin area under the curve (AUC) to glucose AUC responses. I ±SEM. All-Bran cereal, Special K cereal, Honeysmacks cereal,
Sustaincereal,andCornflakes,Kellogg'sAustraliaPtyLtd.Pagewood,Australia;MarsBarcandybar,MarsConfectionaryAustralia,Ballarat,Australia.
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.
DISCUSSION
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
C
U)
C
0 20 40 60
Protein (glservlng)
100
S S
0 20 40
60
1274
HOLT ET AL
200
100
0
140
S
S 5
S
S
S
S
S.
•¿t,.. S
SI•
S
0 10 20
Fiber (9/serving)
200
100
S
S
S
U)
C
a,
C
S
U)
C
U)
C
S
@fS55
I
Starch (9/serving)
S S
200W
100
0
@S
S
S 55
55
S S
1@b5 s
S
Fat (9/serving)
S
0 20 40 60
Total carbohydrate (9/serving)
0 i I
0 20 40 60
Sugar (9/serving)
140
S
100
S
S
S
0
S
S
S
S S
60
7c1
0
0 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 chose to test isoenergetic portions of foods
rather than equal-carbohydrate servings to determine the insu
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INSULIN INDEX OF FOODS
1275
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
(4).
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 thank Efi 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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Discussion

Since an increase in glucose levels usually results in insulin secretion, there is a strong correlation between foods that are high in carbs (carbs can be easily converted to glucose and thus often cause a glucose spike) and foods that had high insulin spikes. The convention is to use white bread or sugar when computing glycemic index (this means that a food that has GI of 100% has the same spike as bread. Saying it has the same spike doesn't mean it has the same curve, the technical way to do it is to calculate the area under the glucose curve). So in this case they do the same with the insulin curve. Most of the subjects were healthy lean people without diabetes Protein is made of amino acids. This implies protein (or some forms of protein) can also cause an insulin spike. This is the best way to calculate an integral =) https://www.wikiwand.com/en/Trapezoidal_rule They add it as a meaningless comment but there may be something much deeper here. AUC = Area Under the Curve Don't eat jollybeans LOL! These equations give the fit between glucose and insulin scores. Don't forget that there are real exceptions there (see the graph on the left). Ouch Glucose is very much affected by the food you ate, but also from the quality of sleep, exercise and stress levels. So it's common to try and clear potential noises by measuring the response to a meal after they fast enough (10 hours) at night and eat the meals immediately in the morning. This graph is great and while it shows that there is a great correlation between glucose and insulin scores (thus - glucose is an excellent proxy to insulin) we see that it is not a perfect fit and there are several exceptions. The exceptions are fat & carb reach foods (e.g. donuts and croissants) and various meats. Glucose can be measured today using a CGM (continuous glucose monitor) that measures the glucose levels every few minutes. However, insulin is trickier and we must draw blood in order to measure it. So the people who participated in this study drew blood 8 times after each meal they ate! Glucose is great but is still only a limited proxy. That being said, if one is living with diabetes managing glucose can have very meaningful health implications (so the conclusion here should be taken with a grain of salt). There is not much difference between white or brown rice. White bread is practically evil, even worse than doughnuts. If you must eat cereals, make it All-Bran. Cheese and peanuts are great. NOT QUESTION I AM DIABETIC There must be some published work on the relationship between insulin sensitivity (which is essentially mean fasting insulin) and BMI. Why should you read this paper? Glucose and insulin are two of the most important elements of metabolism. In an oversimplified nutshell - when we consume food our blood glucose usually increases. Consequently, the pancreas secretes insulin which lowers the glucose levels back to normal. Insulin is a hormone and is often considered to be the principal regulator of fat storage in the body (this is a bit controversial, but is mostly considered correct. Read the book "Why we get fat" by Gary Taubes for more info). Therefore knowing how the foods we eat spike our insulin levels can be helpful in determining what should be eaten for weight loss. In glucose levels, but there is a big difference in insulin levels... This is a great figure. Together with the previous learnings we can conclude: - Glucose is an excellent proxy to insulin (but it is not a perfect proxy as we saw there are exceptions) - Carbs are the best proxy for glucose (again, far from being perfect). Another good proxy is sugars. However, concluding that carbs => insulin spikes is a huge leap as we accumulate the errors there. Moreover, another thing that was not discussed here is the individual responses people seem to have for meals. So this paper can just give us a rule of thumbs to work with, but without measuring your own body you can never know what foods would be best for you (at least in respect to glucose or insulin management). This was the most interesting insight of this paper in my opinion. There are foods that are relatively low in carbs (meats) and foods that are high in carbs (donuts and the like) that still cause an insulin spike despite not causing a significant glucose spike. The reason this is so interesting is that it shows the fundamental limitation glucose monitoring has. On one hand, glucose is the most interesting biomarker we can probably measure continuously today as it is directly related to our metabolism. It is especially interesting because it is the best proxy we have for insulin in the body. On the other hand, this result shows that this proxy is not perfect and has limitations. Postprandial = after the meal. This is usually measured within two hours after the meal. A word of caution: this paper averages postprandial insulin response over multiple subjects. However, in reality, different people react differently to different foods, so while the average values provided here can give you a general idea of what they usually do to people, it doesn't necessarily mean you will respond the same to those foods.