Fermat's Library | An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods annotated/explained version.

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 11â€”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 â€”¿0.27,NS, n 36) and protein (r â€”¿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:l264â€”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 (3â€”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 (9â€”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

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 180Â°Cfor 6 mm, and served warm

Prepared according to manufacturer's directions, stored at

4 Â°Cup to 2 d before serving at room temperature

Prepared by supermarket from standard recipe, defrosted

overnight, reheated at 180 Â°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 Â°C,served cold

Stored frozen and served cold

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 Â°C,

served cold

Poached the day before serving, stored at 4 Â°Covernight,

reheatedin microwaveovenfor 1.5mmimmediately

before serving

Prepared in bulk according to recipe, stored at 4 Â°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 Â°Covernight; reheated in

microwave oven for 2 mm immediately before serving

Steamed the day before serving, stored at 4 Â°Covernight,

cut into bite-sized pieces, and reheated in microwave

oven for 2 miii immediately before serving

Served fresh and plain at room temperature

Boiled 12 miii and stored overnight at 4 Â°C,reheated in

microwave oven for 1.5 mm immediately before serving

Boiled 12 mm and stored overnight at 4 Â°C,reheated in

microwave oven for 1.5 mm immediately before serving

Boiled8 mmand storedovernightat 4 Â°C

Reheated in microwave oven for 1.5 mm immediately

before serving

Peeled,boiledfor 20 mm,and storedat 4 Â°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,

Australiaâ€”Special

KToasted

flakes made from wheat and rice flour, high in

protein; Kellogg's Australia PlyLtdâ€”HoneysmacksPuffed

whole-wheat grains with a honey-based coating;

Kellogg's Australia PlyLtdâ€”SustainA

mixture of wheat, corn, and rice flakes; rolled oats;

dried fruit; and flaked almonds; Kellogg's Australia

PtyLtdâ€”All-BranA

high-fiber cereal made from wheat bran; Kellogg's

Australia PtyLtdâ€”Natural

muesliBased on raw rolled oats, wheat bran, dried fruit, nuts,

and sunflower seeds; Uncle Toby's Company Ltd.

Wahgunyah,Australiaâ€”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 (20â€”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 = 11â€”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 Â±0.4 (range: 19â€”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

Â±3.922.9

Â±1.4Bakery

products (n = 6 F, 6 M)22.2

Â±3.723.1 Â±2.7Snacks

andconfectionery(n = 5 F, 7 M)21.0

Â±1.222.9 Â±3.5Protein-rich

foods (n 5 F, 6 M)22.4

Â±2.824.3 Â±3.1Carbohydrate-rich

foods (n = 5 F, 8 M)21.0

Â±1.923.0 Â±1.9Breakfast

cereals (n = 5 F, 6 M)22.8

Â±3.922.8

Â±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'

â€˜¿I Â± 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 (1.5â€”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 â€”¿20Â°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'

BreakfastcerealsWhitebread156Â±2113557Â±1756108Â±19295Â±38144Â±19100Â±0100Â±0All-Bran59

Â±94299 Â±61287 Â±1599 Â±1425

Â±340 Â±732 Â±4Porridge80

Â±95093 Â±49374 Â±11139

Â±1313

Â±I60 Â±1240 Â±4Muesli65Â±126034Â±813118Â±18163Â±2234Â±543Â±746Â±5Special

K106 Â±148038 Â±63595 Â±14195 Â±1547 Â±470

Â±966 Â±5Honeysmacks91Â±109102Â±1506108Â±12189Â±3153Â±960Â±767Â±6Sustain93Â±88938Â±757102Â±9209Â±1853Â±466Â±671Â±6Cornflakes1

10 Â±118768

Â±62388

Â±5189 Â±1352 Â±476

Â±1175

Â±8Groupmeanâ€”7183Â±35792Â±5169Â±839Â±259Â±357Â±3Carbohydrate-rich

foodsWhitebread120Â±1312882Â±1901112Â±15281Â±41137Â±20100Â±0100Â±0Whitepasta50Â±114456Â±453156Â±4891Â±922Â±246Â±1040Â±5Brown

pasta74 Â±74535 Â±57467 Â±1093 Â±1221 Â±368 Â±1040 Â±5Grainbread68Â±96659Â±837106Â±12166Â±2162Â±860Â±1256Â±6Brownrice113Â±136240Â±61658Â±5117Â±1142Â±4104Â±1862Â±11French

fries70

Â±117643

Â±713146

Â±29209

Â±1982 Â±871

Â±1674 Â±12Whiterice129Â±168143Â±68369Â±5145Â±1240Â±3110Â±1579Â±12Whole-meal

bread106 Â±141 1 203 Â±1420122 Â±20247 Â±311 11 Â±1497 Â±1796

Â±12Potatoes148Â±2413930Â±1467120Â±19284Â±3038Â±4141Â±35121Â±11Groupmeanâ€”8410Â±461106Â±8182Â±1062Â±588Â±674Â±8Protein-rich

foodsWhitebread121Â±1917438Â±3154177Â±35387Â±63185Â±33100Â±0100Â±0Eggs36

Â±114744 Â±1017135

Â±929340

Â±184530

Â±642

Â±1631

Â±6Cheese42

Â±105994

Â±1590268 Â±15364 257 Â±15013106

Â±2755

Â±1845 Â±13Beef18Â±67910Â±21931583Â±939â€”50Â±1421Â±851Â±16Lentils63

Â±179268

Â±2174307 Â±103325 Â±6837

Â±962 Â±2258

Â±12Fish29

Â±149350 Â±2055775 Â±502â€”28 Â±628 Â±1359

Â±18Bakedbeans110Â±1420106Â±3776183Â±44504Â±8757Â±11114Â±18120Â±19Group

meanâ€”9983 Â±1032585 Â±6118 607 Â±545653

Â±654

Â±761 Â±7FruitWhite

bread171

Â±1915 563 Â±1632105 Â±18339 Â±36166

Â±17100

Â±0100 Â±0Apples83Â±78919Â±910118Â±18152Â±1520Â±250Â±659Â±4Oranges66Â±119345Â±1074166Â±23185Â±2115Â±239Â±760Â±3Bananas133

Â±1212445 Â±1353108 Â±22224 Â±2445

Â±579 Â±1081 Â±5Grapes126Â±1412293Â±1190113Â±19216Â±2131Â±374Â±982Â±6Groupmeanâ€”10751Â±605124Â±10194Â±1128Â±261Â±571Â±3Snacks

andconfectioneryWhitebread159Â±2915592Â±2376104Â±24340Â±52166Â±25100Â±0100Â±0Peanuts20

Â±73047

Â±828214 Â±88564 Â±15380 Â±2212 Â±420 Â±5Popcorn71Â±126537Â±679109Â±32239Â±25139Â±1462Â±1654Â±9Potatochips77Â±158195Â±1577169Â±78367Â±71186Â±3652Â±961Â±14Ice

cream93

Â±1712 348 Â±1867172 Â±38479 Â±72103 Â±1670 Â±1989 Â±13Yogurt88Â±2315611Â±1808167Â±33415Â±4865Â±762Â±15115Â±13MarsBar98Â±1016682Â±1896218Â±65441Â±50309Â±3579Â±13122Â±15Jellybeans161Â±1822860Â±368133Â±27407Â±64260Â±41118Â±18160Â±16Groupmeanâ€”12183Â±994191Â±20416Â±30163Â±1465Â±689Â±7Bakery

productsWhitebread129Â±1517599Â±3058188Â±64383Â±67187Â±33100Â±0100Â±0Doughnuts78Â±1412445Â±2402113Â±21480Â±93191Â±3763Â±1274Â±9Croissants89Â±613097Â±2978483Â±244604Â±137215Â±4974Â±979Â±14Cake61

Â±1114

305 Â±3472178 Â±54467

Â±I13223 Â±5456 Â±1482 Â±12Crackers139

Â±2614 673 Â±2686331

Â±104354 Â±65253 Â±461 18 Â±2487 Â±12Cookies92Â±1215223Â±382200Â±57436Â±110298Â±7574Â±1192Â±15Groupmeanâ€”12681Â±1325261Â±56468Â±47236Â±2477Â±783Â±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 â€œ¿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 â€”¿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 Â±16%), eightfold higher than the lowest

IS (for peanuts: 20 Â±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

60J

â€˜¿I)

Breakfast

cereals

Carbohydrate- Bakery

rich foods products

Protein

rich foods

Fruit Snacksand

confectionery

FIGURE 1. Mean(Â±SEM)glucoseand insulinscoresfor each foodgroup.

Downloaded from https://academic.oup.com/ajcn/article-abstract/66/5/1264/4655967 by guest on 26 February 2019

...@White

breac.IPrthifr@c.

Doughnuts

Croissants

Cake

Crackers

PeanutsI=@lIIIâ€•;â€•t-1@PopcornI=â€•iiPotato

chipsI='=â€•iIcecreamlâ€•;lIYogurtI

. lip

INSULIN INDEX OF FOODS

1271

I@1

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

Jellybeans

100 200

Insulin score (%)

FIGURE 2. Mean (Â±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 â€˜¿. g 1) fruit (194 pmol . mm . L â€˜¿. g 1),

carbohydrate-rich foods (182 pmol . mm . L@ . g'), and

breakfast cereals ( 169 pmol . mm . L â€˜¿. g â€˜¿).When the

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1272

HOLT ET AL

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 â€˜¿. g 1), fol

lowed by snack foods (163 pmol . mm . L â€˜¿. g 1), carbo

hydrate-rich foods (62 pmol . mm . L â€˜¿. g 1), protein-rich

foods (53 pmol . mm . L â€˜¿. g 1), breakfast cereals (39

pmol . mm . L â€˜¿. g â€˜¿),and fruit (28 pmol . mm . L â€˜¿. 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.

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 â€”¿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 = â€”¿0.09,n = 30).

Fat content was negatively related to the mean IS (r = â€”¿0.27,

NS, n = 36). When expressed as a percentage of total energy,

fat (r = â€”¿0.27,NS, n = 36) and protein (r = â€”¿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 = â€”¿0.38,

P < 0.05, n = 36) and protein (r = â€”¿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 = â€”¿0.07,NS, n = 36). When expressed as a

percentageof total energy, fat (r = â€”¿0.38,P < 0.05, n = 36)

and protein (r = â€”¿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 â€”¿1.88 fat â€”¿0.103 water

+ 0.509 sugar â€”¿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 â€”¿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).

â€¢¿â€¢

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

-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 Â±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 20 40 60

Protein (glservlng)

100

S S

0 20 40

1274

HOLT ET AL

200

100

140

S 5

â€¢¿t,.. S

SIâ€¢

0 10 20

Fiber (9/serving)

200

100

@fS55

Starch (9/serving)

S S

200W

100

S 55

S S

1@b5 s

Fat (9/serving)

0 20 40 60

Total carbohydrate (9/serving)

0 i I

0 20 40 60

Sugar (9/serving)

140

100

S S

7c1

0 10 20 30

FIGURE 5. Relations between the nutrient contents of the test foods and the mean insulin scores. Fiber: r = â€”¿0.10,NS, n = 32; protein: r = â€”¿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 = â€”¿0.09,NS,n = 30; fat: r = â€”¿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, 30â€”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

10â€”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 (37â€”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. Protein is made of amino acids. This implies protein (or some forms of protein) can also cause an insulin spike. Most of the subjects were healthy lean people without diabetes 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.

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