am j clin nutr 1996 reed 164 9

164 Am J Clin Nutr 1996;63:l64-9. Printed in USA. © l996American Society for Clinical Nutrition

Measuring the thermic effect of food�3

George W Reed and James 0 Hill

ABSTRACT The thermic effect of food (TEF), defined as the

increase in metabolic rate after ingestion of a meal, has been

studied extensively, but its role in body weight regulation is

controversial. We analyzed 13 1 TEF tests from a wide range of

subjects ingesting meals of varying sizes and compositions. Each

test lasted 6 h. Of the total 6-h TEF, 60% of the total had been

measured after 3 h, 78% after 4 h, and 91% after 5 h. We

developed a three-parameter curve to fit the data, which reduced

noise and gave additional information about the TEF. The area

under this parametric curve was positively correlated with fat-free

mass (FFM) and meal size (MS) and negatively correlated with

meal size squared (MS2) with an R2 of 0.35. The usual area under

a curve created by connecting the data points of a line was

correlated with the same factors but with an R2 of 0.28. The peak

of the parametric curve was positively correlated with FFM and

MS and negatively correlated with MS2. percent body fat, and

meal composition. The time at which the peak occurred correlated

positively with MS and percent fat in the meal. Our analysis

suggests that an inadequate measurement duration of the TEF

could lead to errors. In general, we recommend that the TEF be

measured for � 5 h. Am J Clin Nutr 1996;63: 164-9.

KEY WORDS Thermic effect of food, metabolic rate, en-

ergy expenditure, obesity

INTRODUCTION

In attempts to understand the regulation of energy balance,

investigators have studied all of the components of energy

expenditure (EE). EE can be divided into sleeping metabolic

rate (SMR), resting metabolic rate (RMR = SMR + arousal),

the thermic effect of exercise (TEE), and the thermic effect of

food (TEF) (1). The TEF, sometimes also called diet-induced

thermogenesis (DIT), is defined as the increase in RMR after

ingestion of a meal. Although this component of TEE may

account for a relatively small proportion of total EE (3-10%),

it could play a role in the development and/or maintenance of

obesity (2) because small differences in the TEF over long

periods of time could account for large differences among

individuals.

Although the TEF has been investigated quite extensively,

its role in energy balance in man is still controversial. For

example, D’Alessio et al (3) cite 15 papers reporting a reduced

TEF in obese subjects as compared with lean subjects and 12

papers reporting no difference in the TEF between the two

groups. Such discrepant results suggest that methodologic dif-

ferences between studies may be important. Except in the three

studies with 24-h measurements, the TEF was measured for

from 60 to 360 mm after the meal. Similarly, Kinabo and

Durnin (4) and Weststrate (5) point out numerous studies

showing discrepancies concerning the influence of various

factors (age, exercise, nutritional status, energy content of a

meal, and meal composition) on the TEF.

Many of these discrepancies may be the result of method-

ologic differences, particularly differences in the time taken to

measure TEF. Weststrate (5) suggests that the TEF can be

accurately assessed within 3 h for meals providing = 2508 kJ.

Kinabo and Durnin (4) showed that the TEF did not return to

baseline after 5 h for a 5016-Id meal and their data indicate that

the TEF did not return to baseline after 3 h for a 2508-lU meal.

Belko et al (6) showed that the TEF returned to baseline after

3 h for meals containing 15% of energy requirements but not

for meals containing 30% and 45% of energy requirements.

D’Alessio et al (3) indicated that the TEF could last as long as

8 h after meals providing � 6688 kJ and that the TEF did not

return to baseline after 4 h for meals providing 2090 kJ. Welle

et al (7) showed that the TEF did not return to baseline after

4 h for a 1672-id meal. Hill et al (8) showed that the TEF did

not return to baseline after 3 h for meals providing 2090, 4180,

and 6270 Id. Segal et al (9) showed that 70% of a 6-h TEF is

measured by 3 h for a 3010-id liquid meal and state, “. . .even

a 6-hour period may not be sufficient to quantify the total

thermic response to a meal in all individuals. .

Most studies of the TEF have used small numbers of sub-

jects, which likely contributes to the controversy because

Weststrate (5) noted that the high intraindividual variation in

the measurement of TEF allows for small power in studies with

sample sizes of < 10 subjects.

It appears that differences in the duration of the TEF mea-

surement and the amount of variation in measurements are

contributing factors to the controversy regarding the impor-

tanceof differences in the TEF to body weight regulation. In

the present study we used an extensive database of TEF tests to

evaluate the effect of the duration of measurement on the TEF.

A second goal was to develop a model of the TEF to reduce

I From the Clinical Nutrition Research Unit, Vanderbilt University,

Nashville, TN, and the Center for Human Nutrition, University of Cob-

rado. Denver, CO 80262.

2 Supported by NIH grants DK42549 and DK26615.

3 Address reprint requests to GW Reed, Department of Preventive Mcd-

icine, Vanderbilt University School of Medicine, A- 1 124 Medical Center

North, Nashville, TN 37232-2637.

Received February 6, 1995.

Accepted for publication September 21, 1995.

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MEA�URINOTH� TK�MIC � O� P0011 1�some of the measurement variation and thus improve our

understanding of the factors that influence TEF.

SUBJECTS AND METHODS

The data consist of 131 independent baseline meal tests

conducted in the energy metabolism laboratory at Vanderbilt

University over 5 y (1988-1992). The TEF was measured

during several series of studies (10-17), although the TEF was

only reported in two studies (10, 14). Subjects participated invarious studies, with various interventions, but all tests used in

these analyses were baseline tests, before any intervention. If a

subject was in two separate studies, only the first baseline mealtest was used so that the 131 tests represent 131 separate

individuals.All subjects came to the laboratory early in the morning

between 0700 and 0800 after an overnight fast. Subjects layquietly for 45 mm, after which their RMR was determined for

30 mm with a ventilated-hood indirect calorimetry system(SensorMedics 2900; SensorMedics Corp. Anaheim, CA).Subjects then consumed the test meal within 15 mm. The testmeal size and composition varied for different studies. In some

studies, a fixed meal size and composition were given [eg,

4180 Id and 40% of energy as fat (10)] and in others the size

of the meal was related to the subject’s usual intake based ondiet records [25% of usual intake (14)]. All meals contained15% of energy as protein. RMR was then measured for 10 mm

every 30 mm for the subsequent 6 h, during which time thesubject remained lying down but awake. Energy expenditure

was calculated from the amount of oxygen consumed and

carbon dioxide produced (18). The TEF was calculated as thearea under the response curve minus the 6-h RMR (extrapo-

lated from the baseline measurement). Figure 1 illustrates asample TEF curve that was adjusted for RMR by subtractingthe RMR from the energy expenditure measured. The TEF isthe area under this curve, calculated by summing each trape-zoidal region formed with a base at zero.

There were 77 females (59%) and 54 males (41 %). Table 1

I 1 1 1 I J I

0 1 2 3 4 5 6

Time in hours

FIGURE 1. A sample of a TEF (thermic effect of food) curve that was

adjusted for resting metabolic rate (RMR) by subtracting the RMR from

the energy expenditure measured at each 30-ruin time point. The trapezoid

area of the TEF was measured as the area under this curve.

TABLE 1Subject and meal characteristics’

Value

Weight (kg) 88.6 (49.8-130.2)

Fat-free mass (kg) 56.5 (37.6-82.8)

Percent body fat (%) 35.4 (8.7-5 1.3)

Age (y) 38.1 (18-65)

Meal size (U) 3953 (2721-5831)

Meal composition(% of energy as fat) 36.6 (24-65)

(% of energy as carbohydrate) 48.4 (20-69)

(% of energy as protein)2 15

, j; range in parentheses. n = 54 males and 77 females.2 Fixed amount for all studies.

lists the subject and meal characteristics and the range ofvalues. Body composition was estimated from body density by

using underwater weighing to determine body volume (19).Residual lung volume was determined simultaneously with the

underwater weight by using the closed-circuit nitrogen-dilution

technique (19). Percent body fat was estimated from bodydensity by using the revised equation of Brozek Ct al (20).

SAS software, version 6.03 (21), on a digital vaxstation 3100

with a VMS (a standard operating system) operating systemwas used for all statistical analyses and nonlinear curve fittingdescribed in the results. A nonparametric sign test was used to

test whether medians were different from zero; t tests were

used to test whether means were different from zero. Multiple-

regression analysis was used to compare the TEF area and

model parameters with subject and meal characteristics.

RESULTS

How long should the TEF be measured?

To determine the optimum time that it takes to measure the

TEF it was necessary to ask two questions. First, was there apositive component to TEF between N hours and 6 h, where

N = 3, 4, and 5? And second, what was the correlation betweentheTEFat6handtheTEFat3,4,and5h?

EE at the end of the TEF test (ie, at 6 h) was compared with

RMR (measured before the test meal). The mean differencewas 15.9 kJ/h and the median difference was 12.5 U/h; both

differences were significantly different from zero, P < 0.0001.Seventy percent of the subjects had positive differences at 6 h.

Next, the TEF was computed at 3, 4, 5, and 6 h andexpressed as a percentage of the meal size. The distributions ofthe difference between the TEF over 6 h and the TEF over 3,

4, and 5 h are illustrated in Figure 2. The distribution meansare all significantly different from zero (P < 0.0001) and thevariation becomes smaller as the time approaches 6 h. Aone-sample sign test in each case indicates that the mediandifference is significantly different from zero (P < 0.0001).Table 2 lists the median differences and the 25th percentiles of

the difference distributions to indicate that even between 5 and

6 h > 75% of the subjects had a positive component of theTEF. Assuming that the TEF at 6 h indicates the total TEF, we

computed the percentage of the total TEF that had not beenmeasured if the measurement were stopped at 3, 4, or 5 h. Themedian percentage of TEF “missed” at each stopping point is


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REED AND HILL

TEF Difference: 6 hrs - 3 hrs

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0.0 0.02 0.04 0.06 0.08 0.10

TEF over 4 hours

I-0.02 0.0 0.02 0.04 0.06 0.08 0.10 0.12

TEF over 5 hours

FIGURE 3. Regression line for TEF6 (thermic effect of food measured

for 6 h) compared with TEF3, TEF4, and TEF5 with the surrounding lines

showing the 95% CIs for individual measures. The variance explained (R2

value) is noted in the corner of each graph.

-I-0.02 0.0 0.02 0.04 0.06

Difference

FIGURE 2. Histograms of the difference in the TEF (thermic effect of

food) measured for 6 h and measured at 3, 4, and 5 h. The variation

decreases but the mean and median are significantly greater than zero even

at 5 h.

A measure of the TEF

listed in Table 2. At 4 h, > 20% of the TEF is not measured for

one-half of the subjects.

The TEF is related to meal size, but even for meals providing� 3344 Id (n = 39) the mean and median differences in theTEF at 6 and 5 h are significantly positive and the 25th

percentile is positive. For these smaller meals � 10% of the

total TEF was not measured at 4 h.

Figure 3 illustrates the correlation between the TEF at 6 hand the TEF at 3, 4, and 5 h. The variance explained is noted

on each graph. At 4 h 90% of the variance of TEF6 is ex-plained. There is still variation and hence information about

differences among individuals in the TEF from 4 to 6 h.

TABLE 2Thermic effect of food (TEF) missed by short-term measurements’

Median 25th Percentile Median percent of TEF6

TEF63 0.0252 0.0125 40.0

TEF64 0.0128 0.0057 22.5

TEF65 0.0054 0.0021 9.1

‘ The median and 25th percentile of the difference between TEF mea-

sured at 6 h and that at 3 (TEF63), 4 (TEF64), and 5 (TEF65) h. Thedifference is also expressed as a percent of TEF at 6 h. TEF is measured

as a proportion of meal size.

To reduce the noise and further describe the TEF, a three-parameter curve was used to describe each TEF curve over the

6-h period. Figure 4 illustrates the parametrized TEF model

curve. Each TEF curve was assumed to be of this form, the

equation for the curve being

A + Bte � t/C (1)

where t is the time in hours. The three parameters have the

following interpretation: A is the intercept, a term used for

adjustment for error in the measurement of RMR; BCe � is the

maximum value or peak value on the curve; and C is themaximum time or the time at which the peak value occurs.

For a given subject’ s TEF values, the parametric curve was

fit as illustrated in Figure 5. SAS proc NLIN was used to fiteach curve. Because the fit of the curve in some cases could be

sensitive to the initial values, a matrix of initial values was

considered, and the set that minimized the mean squared error

was used. (A was set at 0, B went from 0 to 40 in steps of 5,and C went from 0.5 to 4.5 in steps of 1.) This automated

curve-fitting method was used on all TEF curves. Of the 131

subject curves, 128 were fit by the automated program. The

three curves that did not converge or converged to a degenerate

curve were essentially flat curves. When the initial values were

adjusted and the program was rerun these curves could be fit

but this was felt to bias the fitting process. The analysis was


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MEASURING THE THERMIC EFFECT OF FOOD

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illustrates whether the relation was positive or negative. The

rc�u1t�forbothtlictrapezoidarcaan� the�niooth�dareaw�r�similar. The TEF was related to meal size in a quadratic fashionand was related to the subject’s FFM; however, the R2 value

was � 25% higher for the model area. This indicates a reduc-

tion in noise when the TEF model is used.

Table 4 shows the characteristics that were significantlyrelated to the parameters of the smooth curve that describes

TEF. The intercept was not correlated with any of the factors.

This was expected because the intercept represents an adjust-

ment for error in the initial RMR measurement. The peak EE

was related to meal size and FFM just as the smoothed area was

related to these factors; however, EE also correlated negatively

with the subject’s percentage body fat. There was a trend

(P = 0.06) for the peak to decrease with increases in therelative fat content of meals. The time of the peak was related

to meal size in a positive linear fashion and to amount of body

- I I I I I I I fat (kg) of the subject in a positive fashion. Figure 6 illustrates0 1 2 3 4 5 6 how the TEF curve shifts depending on the meal and subject

characteristics.Time in hours

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FIGURE 5. An example of one subject’s actual TEF (thermic effect of

food) data and the corresponding TEF curve that was fit to the data.

Equation of the TEF curve: - 10.28 + 175.9 X T X e T/I.3, where T

is time and e is the base of the natural logarithm. RMR, resting metabolic

rate.

0 1 2 3 4 5 6

FIGURE 4. An example of the form of the parametrized curve used to fit

the TEF(thermic effect offood)data. Equation: 175.9 X T X e TII 3 where

T is time and e is the base of the natural logarithm. RMR, resting metabolic

rate.

done using the 128 fitted curves. The average mean squared

error of the fitted curves was 336. The curve depicted in Figure

S has a mean squared error of 546.

Using the fitted curves, TEF could be computed by finding

the area under the smooth curve (smoothed area). The

smoothed area, the trapezoid area (original TEF), and the

smooth-curve parameters were compared with subject (FFM,

fat mass, percentage fat, age, and sex) and meal characteristics

(size and content) by using multiple-regression analysis.

Table 3 shows the characteristics that were significantlyrelated to the trapezoid area and the smoothed area. It also

DISCUSSION

Analysis of this database of meal tests strongly indicates that

the TEF lasts beyond 6 h for the majority of subjects. The TEF

is commonly measured for periods as short as 3 h. A 3-h

TEF measurement can miss > 40% of the total TEF, and a

4-h TEF measurement can miss 22.5% of the total TEF. Even

for meals providing 3344 kJ, 10% of the 6-h TEF is left

between 4 and 6 h. Most studies indicate that a positive

component exists beyond 3 h. Segal et al (9) indicate that

60-70% of the measured TEF is sufficient for comparison of

the TEF across subjects. We feel that there is important infor-

mation contained in the remaining unmeasured TEF. Although

the 3-h and 4-h TEFs are significantly correlated with the total

6-h TEF, our curve fitting indicates that many factors can

produce differences in the shape of the TEF curve. Thus, by not

measuring to � 5 h it is possible that important effects could

either be masked or misinterpreted. For example, our new

summarization of the TEF indicates that nonobese subjects

show an earlier and higher peak TEF than do obese subjects.

Our analysis suggests that the shorter the duration of measure-ment, the more likely it is that the total TEF will differ between

TABLE 3Regression analysis of trapezoid area and smoothed area TEF’

Variable P Relation

Trapezoid area (R2 = 0.28)

Meal size 0.02 +

Meal size squared 0.03 -

FFM 0.0001 +

Smoothed area (R2 = 0.35)

Meal size 0.0001 +


FFM 0.0001 +

‘ Results of multiple-regression analysis using the usual trapezoid area

measure of the TEF (thermic effect of food) and the area under the

smoothed curve. The R2 values indicate that the relation of the smoothed-

area TEF is stronger with increased meal size and fat-free mass (FFM) than

the usual trapezoid area TEF measure.


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‘ Meal and subject characteristics were significantly related to the pa-

rameters of the TEF (thermic effect of food) curve, by multiple-regression

analysis.

FFM

Meal Size

‘1’Meal Size

Fat

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Time in hours

168 REED AND HILL

TABLE 4Characteristics related to peak energy expenditure and time of peak’

Variable P Relation

Peak energy expenditure (maximum; R2 = 0.39)

Meal size 0.0001 +


FFM 0.001 +

Percentage body fat 0.004 -

Meal composition 0.06 -

Time of peak (maximum time; R2 = 0.09)

Meal size 0.003 +

Fat 0.004 +

obese and nonobese subjects. A measurement lasting an addi-

tional 2 h may wipe out that difference. It is of interest to note

that if the smooth curve for the TEF is projected to 8 h, the totalTEF would increase an average of 7% and that the increase is

correlated with meal size (r = 0.28, P < 0.002).We used our database to show how a three-parameter

curve fit to TEF data will give a more detailed summary of

these data points. The curve is not intended to predict the

individual TEF based on subject and meal characteristics

anymore so than subject and meal characteristics can predict

TEF measured by the trapezoid area under the curve. Eachindividual with different meal tests would have different

parameters in the same way that they would have different

areas. The purpose of the curve fitting is to give a more

detailed description of the measure of the TEF. The usual

area under the curve can be thought of as a single-parameter

description of the TEF. The new curve can be thought of as

I I I I I I I

FIGURE 6. An illustration of how the TEF (thermic effect of food)

curve shifts depending on subject and meal characteristics. Meal size and

fat-free mass (FFM) tend to increase the peak; subject’s body fat and meal

size squared (MS2) tend to lower the peak; meal size and subject’s body fat

tend to move the time of the peak further out. There is some evidence that

increased fat in the meal may decrease the peak. Illustrated curve equation:

175.9 x T X e T/I.3 where T is time and e is the base of the natural

logarithm. RMR, resting metabolic rate.

distilling the curve to two parameters (plus one parameter

for noise reduction). We believe two individuals can havethe same area under the curve, but drastically different TEF

curves. The usual trapezoid area method of measuring the

TEF would not pick this up. The new method can pick thisup. The data illustrate that the shape of the TEF curve may

differ between lean and obese individuals. Ifthe shape of the curvediffers, but the total TEF does not, this might explain why some

studies find differences in the total TEF between lean and obese

individuals and others do not. When the TEF is measured to 3 h,

there may be differences in the TEF curve between lean and obese

subjects; however, when the TEF is measured to 6 h, these

differences may disappear.

Our parametric curve fitting reduces some of the variationin TEF measurements and opens a new avenue of investi-

gation of the TEF. Our method demonstrates the same

relation between factors such as FFM and meal size and TEF

as the conventional measurement, but the R2 value increases.

If the new model was not a closer approximation to TEF, it

is possible that the variation could increase. Thus, our

smooth curve increases the power to identify factors that

influence the TEF. Both the trapezoid area and smoothed

area are positively related to a subject’s FFM and to meal

size. However, the smoothed area identifies a negative qua-

dratic relation between TEF and meal size. This positive

linear and negative quadratic effect was reported previously

(6, 8, 22) and could be an indication of a meal-size thresholdeffect. Once the meal reaches a certain size, no increase in

meal size will increase the TEF. It is also possible that this

resulted because the TEF was only measured for 6 h and that

the total TEF was underestimated for large meals as

suggested by D’Alessio et al (3). However, the small posi-

tive difference between the TEF at 6 h and RMR was not

correlated with meal size (P - 0.43, r = 0.07) whereas the

positive difference between the TEF at 5 h and the RMR

was significantly correlated with meal size (P = 0.002,

r 0.26).

We present an alternative to the standard way of assessing

the area under the TEF curve. Our three-parameter curve

summarizes other information in addition to total TEF and

thus provides a means of better assessing the ways in which

characteristics of the subject and of the diet effect the TEF.

For example, we found that whereas total TEF may not beinfluenced by the subject’s body fat content, the shape of the

TEF curve may differ by the subject’s body fat content. Asseen in Figure 6, the peak of the TEF curve moves up as

meal size and the subject’s FFM increase. The peak is

related positively to meal size and negatively to meal size

squared in the same way as for the total TEF. This nonlinear

relation to meal size indicates that the peak increases with

meal size, then reaches a saturation level. The new factor

here is that peak TEF is inversely related to percent body fat

of the subject.

Using this three-parameter curve, we can begin to under-

stand how characteristics of the meal and of the subject influ-

ence the TEF. As meal size increases, the peak TEF increases

and occurs at a later time after meal ingestion, and total TEF

increases. The peak (and possibly the total TEF) appears to

reach a threshold once the meal reaches a certain size. There is

a direct positive relation between the amount of FFM of the

subject and both the peak and total TEF. The body fat content


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I Lean

L- Obese

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MEASURING THE THERMIC EFFECT OF FOOD 169

0 1 2 3 4 5 6

Time in hours

FIGURE 7. Predicted TEF (thermic effect of food) curves for a lean

individual with a higher peak and a shorter time of occurrence of peak

(dotted line) and for an obese individual (solid line). Equation for lean

subjects: 175.9 X T X e 771.3; Equation for obese subjects: 113.6x T X e_T/I85 where T is time and e is the base of the natural logarithm.

RMR, resting metabolic rate.

of the subjects (either amount or percent) appears to effect the

shape of the TEF response but not the total amount of the TEF.

As the subject’s body fat content increases, the peak TEF is

lowered and occurs at a later time after the meal. Figure 7

illustrates the possible difference in the TEF due to differences

in body fat content. The dotted line shows the response of the

theoretical lean individual, who will have a high early peak that

then drops off. Conversely, the theoretical obese individual

(solid line) will have a lower flat curve. The total area under

both curves would be the same.Measuring the TEF for 6 h and using the parametric curve

will not resolve all differences. For example, Segal et al (9)measured the TEF for 6 h and found differences in the TEF

between lean and obese subjects. In general, we did not find

such a difference across a variety of conditions. However, com-

pared with our study, Segal et al used meals that were lower in fat,

meals that had a lower energy content, and a liquid diet.

It is likely that the pattern of the TEF is more complicated

than is the three-parameter curve presented here; however, ahigher-order model is more difficult to fit given 12 data points.

Our studies show that a three-parameter curve reduces noisebetter than does a two-parameter curve (excluding the interceptterm) and that a four-parameter curve fits fewer TEF curves

(101 of 131).In summary, our results indicate that the TEF is a response

lasting � 6 h in most people and we recommend that measure-

ments last � 5 h. Additionally, we demonstrated that variation in

the TEF can be reduced by fining the data to a three-parameter

curve. This alternative summary ofTEF data provides information

not only about total TEF but also about the time course of the TEF

and will be useful in understanding how characteristics of the diet

and of the subject influence the TEF. U

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