ArticlePDF AvailableLiterature Review

The Thermic Effect of Food: A Review

Authors:
  • Physicians Committee for Responsible Medicine

Abstract and Figures

Two-thirds of U.S. adults are overweight. There is an urgent need for effective methods for weight management. A potentially modifiable component of energy expenditure is the thermic effect of food (TEF), the increase in the metabolic rate that occurs after a meal. Evidence suggests that TEF is increased by larger meal sizes (as opposed to frequent small meals), intake of carbohydrate and protein (as opposed to dietary fat), and low-fat plant-based diets. Age and physical activity may also play roles in TEF. The effects of habitual diet, meal timing, and other factors remain to be clarified. Further research into the factors that affect TEF may lead to better treatment methods for improved weight management. • Key teaching points • Measurement of the thermic effect of food. • Physiological determinants of the thermic effect of food. • The effects of meal variations on postprandial thermogenesis. • Effect of age and physical activity on the thermic effect of food.
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=uacn20
Journal of the American College of Nutrition
ISSN: 0731-5724 (Print) 1541-1087 (Online) Journal homepage: https://www.tandfonline.com/loi/uacn20
The Thermic Effect of Food: A Review
Manuel Calcagno, Hana Kahleova, Jihad Alwarith, Nora N. Burgess, Rosendo
A. Flores, Melissa L. Busta & Neal D. Barnard
To cite this article: Manuel Calcagno, Hana Kahleova, Jihad Alwarith, Nora N. Burgess, Rosendo
A. Flores, Melissa L. Busta & Neal D. Barnard (2019): The Thermic Effect of Food: A Review,
Journal of the American College of Nutrition, DOI: 10.1080/07315724.2018.1552544
To link to this article: https://doi.org/10.1080/07315724.2018.1552544
Published online: 25 Apr 2019.
Submit your article to this journal
View Crossmark data
The Thermic Effect of Food: A Review
Manuel Calcagno
a
, Hana Kahleova
a
, Jihad Alwarith
a
, Nora N. Burgess
a
, Rosendo A. Flores
a
, Melissa L. Busta
a
,
and Neal D. Barnard
a,b
a
Clinical Research, Physicians Committee for Responsible Medicine, Washington, DC, USA;
b
Adjunct Faculty, George Washington University
School of Medicine and Health Sciences, Washington, DC, USA
ABSTRACT
Two-thirds of U.S. adults are overweight. There is an urgent need for effective methods for weight
management. A potentially modifiable component of energy expenditure is the thermic effect of
food (TEF), the increase in the metabolic rate that occurs after a meal. Evidence suggests that TEF
is increased by larger meal sizes (as opposed to frequent small meals), intake of carbohydrate and
protein (as opposed to dietary fat), and low-fat plant-based diets. Age and physical activity may
also play roles in TEF. The effects of habitual diet, meal timing, and other factors remain to be
clarified. Further research into the factors that affect TEF may lead to better treatment methods
for improved weight management.
KEY TEACHING POINTS
Measurement of the thermic effect of food.
Physiological determinants of the thermic effect of food.
The effects of meal variations on postprandial thermogenesis.
Effect of age and physical activity on the thermic effect of food.
ARTICLE HISTORY
Received 28 August 2018
Accepted 22 November 2018
KEYWORDS
Thermic effect of food;
energy expenditure;
thermogenesis; meta-
bolic rate
Introduction
Two-thirds of U.S. adults are overweight, and weight prob-
lems are increasingly common in much of the rest of the
world (1). There is an urgent need for effective methods for
prevention and treatment. Because obesity develops over
time as energy intake exceeds output (2), factors that influ-
ence energy expenditure, even modestly, may be clinically
important over time.
Total energy expenditure has several components. Basal
metabolism is energy expended at rest and accounts for
approximately 60% of total daily energy expenditure. The
thermic effect of food (TEF), also called specific dynamic
action or dietary induced thermogenesis, is the increase in
metabolism after a meal and accounts for approximately
10% of total energy expenditure. It represents the energy
expenditure of processing and storing food, as well as the
metabolic effects of the influx of nutrients. Intentional (e.g.,
sports-related) exercise accounts for between 0% and 10% of
total energy expenditure (3). Non-exercise activity thermo-
genesis (e.g., daily living activities, fidgeting, maintenance of
posture) accounts for the remaining roughly 20% of total
energy expenditure (4).
Evidence suggests that it may be possible to alter TEF as
a weight-loss tool in both research and clinical settings. This
article describes the factors that influence TEF and outlines
potential areas for further research.
Measurement of the thermic effect of food
Body metabolism is measured by several methods with vary-
ing degrees of accuracy and cost-effectiveness (Table 1)(5).
In the doubly labeled water method, nonradioactive hydro-
gen and oxygen isotopes are measured in body fluids (e.g.,
urine), allowing for extended measurements under free-liv-
ing conditions (3). Direct calorimetry measures the loss of
body heat using an isothermal system, a heat sink (adia-
batic) system, or a convection system. It is accurate, but
expensive to build and maintain (3). Indirect calorimetry,
the most widely used method, measures oxygen consump-
tion and carbon dioxide production (6,7). Other methods
are used less frequently due to expense or impracticality.
TEF is typically reported as the area between the energy
expenditure and basal metabolic curves (3).
Physiological determinants of the thermic effect
of food
In order for food to contribute to energy expenditure, it
must be digested and absorbed and its components (e.g.,
glucose) must enter cells to be metabolized. When cells are
insulin resistant, glucose is less able to enter muscle and
liver cells. Insulin sensitivity and, to a lesser extent, abdom-
inal adiposity appear to be the principal factors regulating
TEF (8).
CONTACT Hana Kahleova, MD, PhD hkahleova@pcrm.org Director of Clinical Research, Physicians Committee for Responsible Medicine, 5100 Wisconsin
Ave. NW, Suite 200, Washington, DC 20016, USA.
ß2018 American College of Nutrition
JOURNAL OF THE AMERICAN COLLEGE OF NUTRITION
https://doi.org/10.1080/07315724.2018.1552544
A 1984 study (n¼15) monitored the rate of glucose stor-
age in lean and obese individuals at a constant rate of glu-
cose uptake (0.624 g/min). The obese group displayed
delayed glucose metabolism compared with the lean group
(25 mg/kg-min and 7 mg/kg-min, respectively). At 3 mg/kg-
min, glucose oxidation is saturated, and glucose storage con-
tinues to rise. Thus, approximately 4 mg/kg-min is allocated
to glucose storage in lean subjects; in obese individuals only
a small amount of glucose is stored as glycogen through the
energy-requiring process, conserving the remaining
energy (9).
A 1992 study compared 24 moderately obese women who
first underwent a weight-reduction program until they
reached normal body weight versus 24 never-obese women,
matched for body weight, fat mass, and age. TEF was 1.6%
lower in the formerly obese group (8.2%) when compared to
never-obese participants (9.8%) (p¼0.043), but the lower
TEF in the formerly obese group remained relatively
unchanged even after weight loss (8.7%) (p¼0.341).
Researchers concluded that a reduction in TEF is a contri-
buting factor to obesity rather than a consequence of obes-
ity (10).
The effects of variations in body composition on TEF
have not been fully characterized, but multiple factors that
are found in overweight individuals (e.g., less physical activ-
ity, insulin resistance, and differences in meal composition)
are likely to reduce TEF in this group, compared with lean
individuals.
A 1992 study (n¼32) documented the independent
effects of obesity and insulin resistance on postprandial
thermogenesis. After a euglycemic hyperinsulinemic clamp
was administered, insulin-resistant individuals (both lean
and obese) displayed reduced glucose storage compared with
their insulin-sensitive counterparts. A positive correlation
(r¼0.5) between thermogenesis and the rate of glucose
storage was observed (p<0.01) (11).
In summary, insulin sensitivity seems to play a role in
metabolism, particularly affecting TEF. Individuals with
excessive body weight tend to have a higher risk of develop-
ing insulin resistance, thus increasing their chances of
having decreased TEF. It is not yet clear how much it actu-
ally affects TEF, and more research is needed in this area.
Factors influencing the thermic effect of food
Age, physical activity, and meal size, composition, frequency,
and timing all influence the thermic effect of food and are
described next.
a. Age: TEF may decrease with age. A 2014 Mayo Clinic
study comparing 123 older (6088 years) adults to 86
younger (1835 years) adults found that, expressed as
a percentage of meal size, TEF was lower in older adults
(6.4% versus 7.3%, p¼0.02). The difference remained after
adjustment for fat-free mass, fat mass, and subcutaneous
fat (12). Two smaller studies made similar observations
(13,14). The result of reduced TEF, along with decreased
physical activity, may be an increased fat storage with age.
However, the observed fall in TEF may not reflect the aging
process per se; it may reflect other changes in metabolism
occurring over time (e.g., those resulting from meal compos-
ition, described in the preceding).
b. Physical activity: A study (n¼36) comparing active
and sedentary men in both younger and older age groups
found TEF to be 45% higher in the active, young
group (323.42 kJ) and 31% higher in the active, older
group (292.04 kJ), compared with their respective sedentary
groups (222.17 kJ and 215.47 kJ, respectively) (p<0.01).
Table 1. Methods Used to Measure the Thermic Effect of Food (TEF).
Method Complexity Cost Measurement time (hours) Accuracy Reliability
Indirect calorimetry
1. Confinement system High Moderate/high 148 80% 100%
2. Closed circuit system
Respiratory chamber High Moderate/high 1100 90% 75%
Spirometry Moderate Moderate 0.3250% 25%
3. Total collection system
Flexible Moderate Moderate 0.3260% 25%
Rigid Moderate Moderate 0.52 40% 25%
4. Open circuit system
Ventilated hood/canopy Moderate Low/moderate 0.26 80% 75%
Ventilated chamber Moderate/high Low/moderate 148 80% 75%
Expiratory collecting system Low Moderate 0.348 50% 25%
5. Isotope dilution Low High 48240 70% 75%
Direct calorimetry
1. Isothermal system Moderate/high Moderate/high 0.31.5 100% 100%
2. Heat-sink/Adiabetic system
Chamber High Moderate/high 248 90% 100%
Suite Moderate/high Moderate/high 248 70% 50%
3. Convection system High Moderate/high 248 80% 75%
Noncalorimetric methods
1. Physiologic measurements
Heart rate Low Moderate 172 10% 10%
Electromyography Moderate Moderate 124 10% 10%
Pulmonary ventilation Moderate/high Moderate 124 10% 10%
2. Physiologic observations Not applicable Low N/A 10% 10%
Multiple readings.

Highly variable.
2 M. CALCAGNO ET AL.
The researchers concluded that, regardless of age or body
composition, physical activity increases TEF (15).
c. Energy content of meals: A 1990 study (n¼16)
reported that a higher energy intake, regardless of meal
composition, results in increased TEF (p<0.001) (16). One
study compared three different meals of 2092 kJ, 4184 kJ,
and 6276 kJ. The corresponding TEF values were <10%,
21%, and 33.5% from baseline, respectively (17). A meta-
analysis of 27 studies showed a significant increase in TEF
of 1.11.2 kJ/h for every 100 kJ of energy intake (p<0.001)
(18). A similar study (n¼10) compared a low-energy, high-
fat meal (818 kJ) with a high-energy, low-fat meal (2,929 kJ),
finding higher TEF values with the high-energy meal (19).
d. Meal composition: Three studies have compared the
effects of high-carbohydrate versus high-fat meals. Two of
these (n¼12, n¼24), providing similar caloric content
meals, found TEF to be 96% (26.8 kJ/h) (20) and 16% (8 kJ/
h) (21) higher on the high-carbohydrate meal, compared
with the high/moderate fat group, respectively, but did not
provide data on statistical significance. A 2005 study of lean
young men (n¼14) also found TEF to be 32% higher on
the high-carbohydrate meal (43.1 kJ/h), compared with a
high-fat meal (32.6 kJ/h) with isoenergetic content (3,255 kJ)
(p<0.05) (22).
In contrast, a crossover study (n¼19) compared isoener-
getic high-protein, high-fat, and high-carbohydrate meals,
finding no difference between the high-carbohydrate
(39.2 kJ/h) and high-fat (39.2 kJ/h) meals, while TEF was
17% higher on the high-protein group (45.9 kJ/h)
(p<0.01) (23).
The type of dietary fat may make a difference. A 2013
study (n¼7) provided isoenergetic meals with similar
macronutrient composition with either medium-chain (20 g)
or long-chain (18.4 g) triglyceride and found TEF to be 34%
higher (7.5 kJ/h) in the medium-chain triglyceride meal
group (p<0.005) (24). Another study (n¼8) similarly
found TEF to be 132% higher with meals containing
medium-chain triglyceride alone (11.1 kJ/h higher) (p<0.01)
and 110% higher with both medium-chain and long-chain
triglyceride (9.3 kJ/h) (p<0.01) as opposed to long-chain tri-
glyceride alone (25).
Additionally, a study (n¼29) comparing meals contain-
ing polyunsaturated, monounsaturated, and saturated fat
reported thermogenesis of 37.2 kJ/h, 36.8 kJ/h, and 30.0 kJ/h,
respectively (p<0.05) (26). In contrast, a previous small
(n¼14) study comparing MUFA from extra virgin olive oil
versus saturated fat from cream found no significant differ-
ence in thermogenesis between groups (27).
Vegetables, fruits, whole grains, and legumes have higher
fiber content, compared with refined grains or animal-
derived products, and may require more energy to digest. A
2005 randomized, controlled study (n¼64) compared a
low-fat vegan diet to a control diet (National Cholesterol
Education Program guidelines) in overweight, postmeno-
pausal women. Researchers measured TEF after consump-
tion of a 720-calorie test meal, then asked participants
randomized to the vegan diet to follow the diet for 14 weeks.
Repeat TEF testing showed a 16% increase in TEF within
the vegan group (p<0.05) In a regression model, thermic
effect of food emerged as a significant predictor of weight
change (p<0.05) (28).
e. Processed versus unprocessed foods: Milling of grains
leads to a loss of dietary fiber (from bran) as well as a loss
of protein (from the germ). A crossover study (n¼17) com-
paring isocaloric meals consisting of sandwiches made with
either refined or unrefined grains showed a greater thermic
effect (46.8% higher) from the unrefined grain product
(p<0.001) (29), presumably related to changes in fiber and
macronutrient content.
f. Palatability: A 1985 study (n¼8) suggested that palat-
ability could possibly increase sympathetic activity, thus
increasing TEF (30). However, several subsequent studies
found no differences in TEF when comparing palatable to
unpalatable meals (p>0.05) (3133).
g. Meal frequency, regularity, and timing: Four studies
compared the effects on TEF of a single large meal versus
frequent, small meals with the same total energy density.
Two trials found TEF to be higher on the single, large meal,
as opposed to several frequent, small meals, during 3- to 5-
hour measurements (10.6 kJ/h, or 32%, higher after one large
vs. four smaller meals, and 13.3 kJ/h, or 38%, higher after
one large vs. six smaller meals, respectively, p<0.05 on
both) (34,35). One study did not detect any difference
between one large meal and two smaller meals, potentially
due to a relatively small difference between the interventions
(36). Another study showed a 30.3% higher TEF on a single,
large meal, compared with three smaller meals.
Unfortunately, the researchers have measured TEF for only
a short period of time, which may have resulted in insuffi-
cient power to detect significant differences in response to
changes in meal frequency (37). A 2016 meta-analysis that
standardized the units concluded that TEF was significantly
higher with a single large meal, compared with smaller fre-
quent meals (p¼0.02) (18).
A 2003 randomized crossover study (n¼9) compared a
regular meal plan (6 meals/day) to an irregular meal plan
(39 meals/day), with the number of meals being the same
throughout the week, resulting in a significant decrease in
TEF during the irregular meal plan (p¼0.003) (38).
The effect of meal timing was examined in a 1993 study
(n¼9) that found TEF to be higher in the morning as com-
pared to the afternoon (p¼0.02) and night (p¼0.002), and
higher in the afternoon compared to the night (p¼0.06)
(39). Later studies tested the effects of skipping meals. In a
crossover design with 17 participants, comparing a conven-
tional 3/day meal pattern to skipping breakfast or dinner,
TEF was higher when a meal had been skipped (þ41 kcal/
day with breakfast skipping and þ91 kcal/day for dinner
skipping) (p<0.01), while fat oxidation was only increased
when breakfast was skipped (p<0.001). It is also conceiv-
able that prolonged fasting could lead to a state of stress,
increasing adrenergic activity, lipolysis, and energy expend-
iture in those who skip a meal (40). However, a small 2014
study (n¼9) found no relationship between skipping break-
fast and TEF (41).
JOURNAL OF THE AMERICAN COLLEGE OF NUTRITION 3
h. Meal duration: Two studies (n¼21, n¼9) investi-
gated the effect of meal duration on TEF, but only one
reached statistical significance. One study (n¼21) recorded
meal duration and number of chews in men, showing that
slow eating was associated with a considerable increase in
TEF at 90 minutes (p<0.05), possibly due to postprandial
splanchnic circulation after the meal (42). The other study
(n¼9) showed that slow eating tended to increase TEF in
females by 32% (10.3 kJ/kg/h) at 180 minutes compared to
fast eating (p>0.05) (43).
In summary, TEF tends to decrease with age. In contrast,
physical activity, higher energy meals, high-carbohydrate
and high-protein meals as opposed to high-fat meals, and
single large meals tend to increase TEF. In addition, high
consumption of fruits, vegetables, and high-fiber-content
meals also seem to have a positive effect on TEF. Meal tim-
ing and meal duration might play a role but to what extent
is not yet clear, while palatability does not seem to have an
effect on TEF. More research with larger sample size would
be beneficial.
Conclusion
TEF is a significant part of energy expenditure and can be
to a certain degree increased by factors that are under indi-
vidual control, such as by eating larger meals and meals
high in carbohydrates and protein, and by increased physical
activity. Although the effects of such manipulations are
small, they may play an important role over the long term,
suggesting that they may have value as part of the manage-
ment of obesity and obesity-related conditions, such as type
2 diabetes (44).
The body of literature on TEF is limited. Many studies
are small in size, and methodology varies considerably
between studies. Nonetheless, to the extent that postprandial
energy expenditure can be increased, weight-control efforts
may be facilitated. More research studies with larger sample
sizes and appropriate controls are needed.
Funding
This work was funded by the Physicians Committee for
Responsible Medicine.
ORCID
Manuel Calcagno http://orcid.org/0000-0002-2419-6402
References
1. Adult Obesity Facts jOverweight & Obesity jCDC. [accessed
2017 Oct 16] Available from: https://www.cdc.gov/obesity/data/
adult.html.
2. Romieu I, Dossus L, Barquera S, Blotti
ere HM, Franks PW,
Gunter M, Hwalla N, Hursting SD, Leitzmann M, Margetts B,
et al. Energy balance and obesity: what are the main drivers?
Cancer Causes Control. 2017;28(3):247258. doi:10.1007/s10552-
017-0869-z.
3. Levine JA. Measurement of energy expenditure. Public Health
Nutr. 2005;8(7A):112332.
4. Levine JA. Non-exercise activity thermogenesis (NEAT). Best
Pract Res Clin Endocrinol Metab. 2002;16(4):679702.
5. Levine J, Morgan M. Measurement of energy expenditure in
man: a review of available methods. J Nutr Med. 1990;(1):
32543. doi:10.3109/13590849009097889.
6. Levine JA, Schleusner SJ, Jensen MD. Energy expenditure of
nonexercise activity. Am J Clin Nutr. 2000;72(6):14514.
7. Sun M, Reed GW, Hill JO. Modification of a whole room indir-
ect calorimeter for measurement of rapid changes in energy
expenditure. J Appl Physiol Bethesda Md 1985. 1994;76:
26862691. doi:10.1152/jappl.1994.76.6.2686.
8. Camastra S, Bonora E, Del Prato S, Rett K, Weck M, Ferrannini
E. Effect of obesity and insulin resistance on resting and glucose-
induced thermogenesis in man. EGIR (European Group for the
Study of Insulin Resistance). Int J Obes. 1999;23(12):130713.
doi:10.1038/sj.ijo.0801072.
9. Ravussin E, Acheson KJ, Vernet O, Danforth E, J
equier E.
Evidence that insulin resistance is responsible for the decreased
thermic effect of glucose in human obesity. J Clin Invest. 1985;
76(3):126873. doi:10.1172/JCI112083.
10. Nelson KM, Weinsier RL, James LD, Darnell B, Hunter G, Long
CL. Effect of weight reduction on resting energy expenditure,
substrate utilization, and the thermic effect of food in moderately
obese women. Am J Clin Nutr. 1992;55(5):92433. doi:10.1093/
ajcn/55.5.924.
11. Segal KR, Albu J, Chun A, Edano A, Legaspi B, Pi-Sunyer FX.
Independent effects of obesity and insulin resistance on post-
prandial thermogenesis in men. J Clin Invest. 1992;89(3):82433.
doi:10.1172/JCI115661.
12. Du S, Rajjo T, Santosa S, Jensen MD. The thermic effect of food
is reduced in older adults. Horm Metab Res. 2014;46:365369.
doi:10.1055/s-0033-1357205.
13. Schwartz RS, Jaeger LF, Veith RC. The thermic effect of
feeding in older men: The importance of the sympathetic
nervous system. Metabolism. 1990;39(7):7337. doi:10.1016/0026-
0495(90)90109-P.
14. Jones PP, Van Pelt RE, Johnson DG, Seals DR. Role of sympa-
thetic neural activation in age- and habitual exercise-related dif-
ferences in the thermic effect of food. J Clin Endocrinol Metab.
2004;89(10):513844. doi:10.1210/jc.2004-0101.
15. Poehlman ET, Melby CL, Badylak SF. Relation of age and phys-
ical exercise status on metabolic rate in younger and older
healthy men. J Gerontol. 1991;46(2):B5458.
16. Kinabo JL, Durnin JV. Thermic effect of food in man: effect of
meal composition, and energy content. Br J Nutr. 1990;64(1):
3744.
17. Hill JO, Heymsfield SB, McMannus C, DiGirolamo M. Meal size
and thermic response to food in male subjects as a function of
maximum aerobic capacity. Metabolism. 1984;33(8):7439. doi:
10.1016/0026-0495(84)90216-6.
18. Quatela A, Callister R, Patterson A, MacDonald-Wicks L. The
energy content and composition of meals consumed after an
overnight fast and their effects on diet induced thermogenesis: a
systematic review, meta-analyses and meta-regressions.
Nutrients. 2016;8(11):670. doi:10.3390/nu8110670.
19. Martin A, Normand S, Sothier M, Peyrat J, Louche-Pelissier C,
Laville M. Is advice for breakfast consumption justified? Results
from a short-term dietary and metabolic experiment in young
healthy men. Br J Nutr. 2000;84(3):33744.
20. Bowden VL, McMurray RG. Effects of training status on the
metabolic responses to high carbohydrate and high fat meals. Int
J Sport Nutr Exerc Metab. 2000;10(1):1627.
21. Thyfault JP, Richmond SR, Carper MJ, Potteiger JA, Hulver
MW. Postprandial metabolism in resistance-trained versus sed-
entary males. Med Sci Sports Exerc. 2004;36(4):709716. doi:
10.1249/01.MSS.0000121946.98885.F5.
22. Nagai N, Sakane N, Moritani T. Metabolic responses to high-fat
or low-fat meals and association with sympathetic nervous
4 M. CALCAGNO ET AL.
system activity in healthy young men. J Nutr Sci Vitaminol.
2005;51(5):35560.
23. Raben A, Agerholm-Larsen L, Flint A, Holst JJ, Astrup A. Meals
with similar energy densities but rich in protein, fat, carbohy-
drate, or alcohol have different effects on energy expenditure
and substrate metabolism but not on appetite and energy intake.
Am J Clin Nutr. 2003;77(1):91100. doi:10.1093/ajcn/77.1.91.
24. Clegg ME, Golsorkhi M, Henry CJ. Combined medium-chain
triglyceride and chilli feeding increases diet-induced thermogen-
esis in normal-weight humans. Eur J Nutr. 2013;52(6):157985.
doi:10.1007/s00394-012-0463-9.
25. Kasai M, Nosaka N, Maki H, Suzuki Y, Takeuchi H, Aoyama T,
Ohra A, Harada Y, Okazaki M, Kondo K. Comparison of diet-
induced thermogenesis of foods containing medium- versus
long-chain triacylglycerols. J Nutr Sci Vitaminol. 2002;48(6):
53640. doi:10.3177/jnsv.48.536.
26. Casas-Agustench P, L
opez-Uriarte P, Bull
o M, Ros E, G
omez-
Flores A, Salas-Salvad
o J. Acute effects of three high-fat meals
with different fat saturations on energy expenditure, substrate
oxidation and satiety. Clin Nutr Edinb Scotl. 2009;28(1):3945.
doi:10.1016/j.clnu.2008.10.008.
27. Piers LS, Walker KZ, Stoney RM, Soares MJ, ODea K. The
influence of the type of dietary fat on postprandial fat oxidation
rates: monounsaturated (olive oil) vs saturated fat (cream). Int J
Obes. 2002;26(6):814821. doi:10.1038/sj.ijo.0801993.
28. Barnard ND, Scialli AR, Turner-McGrievy G, Lanou AJ, Glass J.
The effects of a low-fat, plant-based dietary intervention on body
weight, metabolism, and insulin sensitivity. Am J Med. 2005;
118(9):9917. doi:10.1016/j.amjmed.2005.03.039.
29. Barr SB, Wright JC. Postprandial energy expenditure in whole-
food and processed-food meals: implications for daily energy
expenditure. Food Nutr. Res. 2010;54(1):5144. doi:10.3402/
fnr.v54i0.5144.
30. LeBlanc J, Brondel L. Role of palatability on meal-induced
thermogenesis in human subjects. Am J Physiol. 1985;248(3 Pt
1):E3336.
31. Weststrate JA, Dopheide T, Robroch L, Deurenberg P, Hautvast
JG. Does variation in palatability affect the postprandial response
in energy expenditure? Appetite. 1990;15(3):20919. doi:10.1016/
0195-6663(90)90021-Y.
32. Prat-Larquemin L, Oppert JM, Bellisle F, Guy-Grand B. Sweet
taste of aspartame and sucrose: effects on diet-induced thermo-
genesis. Appetite. 2000;34(3):24551. doi:10.1006/appe.1999.0310.
33. Sawaya AL, Fuss PJ, Dallal GE, Tsay R, McCrory MA, Young V,
Roberts SB. Meal palatability, substrate oxidation and blood glu-
cose in young and older men. Physiol Behav. 2001;72(12):512.
34. Allirot X, Saulais L, Seyssel K, Graeppi-Dulac J, Roth H, Charri
e
A, Drai J, Goudable J, Blond E, Disse E, Laville M. An isocaloric
increase of eating episodes in the morning contributes to
decrease energy intake at lunch in lean men. Physiol Behav.
2013;110111:169178. doi:10.1016/j.physbeh.2013.01.009.
35. Tai MM, Castillo P, Pi-Sunyer FX. Meal size and frequency:
effect on the thermic effect of food. Am J Clin Nutr. 1991;54(5):
783787.
36. Kinabo JL, Durnin JV. Effect of meal frequency on the thermic
effect of food in women. Eur J Clin Nutr. 1990;44(5):38995.
37. Vaz M, Turner A, Kingwell B, Chin J, Koff E, Cox H, Jennings
G, Esler M. Postprandial sympatho-adrenal activity: its relation
to metabolic and cardiovascular events and to changes in meal
frequency. Clin Sci. 1995;89:349357. doi:10.1042/cs0890349.
38. Farshchi HR, Taylor MA, Macdonald IA. Decreased thermic
effect of food after an irregular compared with a regular meal
pattern in healthy lean women. Int J Obes. 2004;28(5):65360.
doi:10.1038/sj.ijo.0802616.
39. Romon M, Edme JL, Boulenguez C, Lescroart JL, Frimat P.
Circadian variation of diet-induced thermogenesis. Am J Clin
Nutr. 1993;57(4):47680.
40. Nas A, Mirza N, H
agele F, Kahlh
ofer J, Keller J, Rising R, Kufer
TA, Bosy-Westphal A. Impact of breakfast skipping compared
with dinner skipping on regulation of energy balance and meta-
bolic risk. Am J Clin Nutr. 2017;105:135161. doi:10.3945/
ajcn.116.151332.
41. Kobayashi F, Ogata H, Omi N, Nagasaka S, Yamaguchi S, Hibi
M, Tokuyama K. Effect of breakfast skipping on diurnal vari-
ation of energy metabolism and blood glucose. Obes Res Clin
Pract. 2014;8:e20198. doi:10.1016/j.orcp.2013.01.001.
42. Hamada Y, Kashima H, Hayashi N. The number of chews and
meal duration affect diet-induced thermogenesis and splanchnic
circulation. Obes Silver Spring Md. 2014;22(5):E629. doi:
10.1002/oby.20715.
43. Toyama K, Zhao X, Kuranuki S, Oguri Y, Kashiwa Kato E,
Yoshitake Y, Nakamura T. The effect of fast eating on the ther-
mic effect of food in young Japanese women. Int J Food Sci
Nutr. 2015;66(2):1407. doi:10.3109/09637486.2014.986069.
44. Schrauwen P, van Marken Lichtenbelt WD. Combatting type 2
diabetes by turning up the heat. Diabetologia. 2016;59(11):
226979. doi:10.1007/s00125-016-4068-3.
JOURNAL OF THE AMERICAN COLLEGE OF NUTRITION 5
... Thermic effect of food Thermic effect of food (TEF), sometimes also called post prandial energy expenditure (PPEE), is estimated to contribute up to 10% of total energy expenditure (33). Factors that increase TEF are meals with large meals with high caloric content, high carbohydrate-or protein content (39). It decreases with age. ...
... It decreases with age. Although a small part of total energy expenditure it has been proposed as a possible strategy to influence energy expenditure and prevent longterm weight gain (39). It can be measured after ingestion of food with indirect calorimetry as with an open-circuit ventilated hood. ...
... Although further research is always needed, a comparison of different dietary patterns has shown that a well-designed dietary pattern should primarily be based on unprocessed or minimally processed plant-based food [44,67]. The mechanisms of action are known and described in great detail elsewhere [60,[68][69][70][71][72][73][74][75]. Regardless, it is necessary to be aware that a sustainable diet can only be built on a multidisciplinary approach, as due to various objec-tive challenges/limitations in society (e.g., sociological, physiological, ethnic-traditional, geographical and economic), an easy shift to a stricter plant-based dietary pattern, therefore, cannot be a "one-way street" [76]. ...
Article
Full-text available
Background: Monitoring nutritional status data in the adult population is extremely important to mediate their health status. Unfortunately, for Slovenia (2.1 million European Union citizens), data on the body composition status of the general adult population are currently rare or nonexistent in scientific journals. Furthermore, dietary intake was last assessed several years before the COVID-19 epidemic period. Methods: We randomly recruited 844 adult Slovenes from all regions of Slovenia. The primary aim of the cross-sectional study was to examine body composition status (using a medically approved electrical bioimpedance monitor) during the post-COVID-19 epidemic period. In addition, we assessed dietary intake (using a standardized food frequency questionnaire) and compared the obesity propensity for both sexes separately using the body mass index (BMI) and body fat percentage (FAT%) obesity classification of the World Health Organization. Results: Regarding BMI classification, 43% of the whole sample was overweight (28%) or obese (15%), and there were more older adults than adults (64% vs. 42%, p < 0.001). The average FAT% of adult females and males was 26.9% and 19.5% (p < 0.001), respectively, while for older adult females and males, it was 32.7% and 23% (p < 0.001). In addition, a comparison of the proportions of obese people between the two cut-off obesity classifications (BMI vs. FAT%) showed a significantly underestimated proportion of obese female participants based on BMI classification (13% vs. 17%, p = 0.005). In terms of the dietary intake of the assessed nutrients in comparison with the national dietary reference values for energy and nutrient intake, the participants, on average, had lower intake than the recommended values for carbohydrates, fiber, vitamins C, D and E (for males) and calcium, and higher intake than the recommended values for total fat, saturated fatty acids, cholesterol, sodium and chloride (for males). Conclusions: The results urgently call for the need to not only improve the overall national nutritional status but also for regular national monitoring of body composition and dietary intake statuses.
... To address this gap in the literature, we designed the current study based on the understanding of SCIinduced physiological changes and the effects of a lowcarbohydrate/high-protein diet (LC/HP) on several risk factors for metabolic disorders observed in non-SCI populations (Conlon & Bird, 2014;Yu et al., 2016). A higher protein diet confers its benefits in several ways: (1) dietary protein has the highest thermic effect of feeding among the macronutrients, which improves energy balance for weight control purposes (Calcagno et al., 2019); (2) a meal higher in protein with concomitantly reduced carbohydrate content elicits lower postprandial glucose responses (Gannon & Nuttall, 2004;Nuttall & Gannon, 1991). Postprandial glucose response is not only the main determinant of the overall glucose control (i.e., hemoglobin A1c), but also an independent risk factor for the development of cardiovascular diseases (Mann et al., 2019;Monnier & Colette, 2006); and (3) a meal with higher protein and reduced carbohydrate content can improve blood lipid profiles by limiting substrate availability (i.e., carbohydrate) for de novo lipogenesis in the liver (Sanders & Griffin, 2016). ...
Article
Full-text available
Abstract We explored the impact of a low‐carbohydrate/high‐protein diet (LC/HP, ~30% energy from protein, 40% energy from carbohydrate) on indices of metabolic function and body composition in individuals with chronic spinal cord injury (SCI). Adults with SCI (≥3 years post‐injury, C4‐L2, AIS A‐D) and insulin resistance or pre‐diabetes were randomly assigned to an 8‐week iso‐caloric LC/HP diet group (n = 11) or control group (n = 14). All LC/HP meals were delivered weekly to participants' homes, and participants in the control group consumed their habitual diet. Each participant underwent an oral glucose tolerance test (OGTT) to assess glucose tolerance, insulin, area under the curve (AUC) for glucose and insulin, Matsuda Index, glucose‐stimulated insulin secretion (GSIS), disposition index, and hepatic insulin extraction (HIE). Fasting blood lipid and inflammation were assessed, and body composition was estimated using dual‐energy x‐ray absorptiometry. A linear mixed model was used to evaluate the main effect of diet, time, and their interaction. Compared to the control group, participants in the LC/HP group had reduced total body fat mass (LC/HP: −5.9%, Control: 0.7%), visceral fat mass (LC/HP: −16.2%, Control: 5.2%), total‐ (LC/HP: −20.1, Control: 3.7 mg/dl), and LDL‐cholesterol (LC/HP: −13.9, Control: 3.1 mg/dl) (pdiet*time
... Studies in the past reported that the increase in MR was due to the thermic effect of food. [7][8][9][10][11] Similar findings were also observed in this study. The increase in VO2 post-meal was 1.49 ± 0.95 ml/Kg/min compared to baseline. ...
Article
Full-text available
Objectives Heat stress is one of the major stressors in military aviation with the potential to adversely affect the efficiency of the aircrew and hence flight safety. Metabolic rate (MR) increases on exposure to heat and metabolic gases are considered as a proxy for MR. This study examined the influence of heat stress on metabolic gas kinetics in healthy Indian males and assessed the duration of attaining normal baseline values of metabolic gases post-exposure. Materials and Methods In 16 healthy male volunteers, cardiorespiratory variables, including metabolic gases (oxygen uptake [VO2], carbon dioxide output [VCO2], minute ventilation [VE], breathing frequency [BF], and heart rate [HR]), were recorded before and approximately 2 h after a standard meal. The subjects were then exposed to a simulated temperature of 40°C with a relative humidity of 70% for 1 h in the environmental chamber. Same physiological parameters were recorded at the end of 30 min and 60 min during heat exposure and up to 90 min following exposure to heat stress at an interval of 30 min. Results A significant increase ( P < 0.001) in mean VO2 (ml/Kg/min) was observed post-meal (1.49 ± 0.95) as well as at 30 min (1.17 ± 0.96) and at 60 min (2.14 ± 1.19) of heat exposure. A similar increase ( P < 0.05) in mean VCO2 was observed post-meal and following heat exposure. VE (L/min) increased by 12.12% post-meal ( P = 0.01), 16.16% ( P < 0.001) at 30 min, and 19.65% ( P < 0.001) at 60 min of heat exposure. There was a significant increase in mean BF (per min) during heat exposure (2.31 ± 1.24 at 30 min and 3.53 ± 1.05 at 60 min) and till 60 min of the recovery period compared to baseline ( P < 0.001). HR (bpm) increased by 14 bpm at 30 min and 17 bpm at 60 min of exposure and till 30 min after elimination of heat stress ( P < 0.001). Conclusion A statistically significant increase was observed in VO2, VCO2, VE, BF, and HR on exposure to heat stress. Optimal recovery was observed after 30 min of eliminating the heat stress for VO2 and VCO2. Similar recovery was observed after 60 min of eliminating the heat stress for HR and following 90 min for VE and BF. Hence, if the crew is required to continue to operate in the heat stress environment, a minimum period of 90 min of a break in between the sorties must be ensured in a relatively cooler environment.
... electrical signals from force sensors) with different parameters for O2 and CO2 (Labussière et al., 2013). The last component of total HP is the TEF which is also referred to as dietary induced thermogenesis and is commonly split into components, i.e. the short-term and the long-term TEF (Rothwell and Stock, 1981;van Milgen and Noblet, 2003;Armbruszt and Garami, 2014;Calcagno et al., 2019). In modelling energy partitioning, short-term TEF is assumed to represent the dynamic variations in HP after meal ingestion that are related to the act of ingestion, the digestion and absorption of food, and a part of intermediary metabolism . ...
... Moreover, as data on whole-body metabolism are lacking, we are unable to describe potential effects resulting from thermic effects, which may differ between carbohydrate-and fat-rich meals. 34 Finally, experiments were not designed to evaluate the possible effects of somatostatin, which is linked with glucagon secretion in a reciprocal feedback cycle and represents a key regulator of insulin secretion. 35 In conclusion, in our systematically designed attempt to bring dietary studies back to the islet and hormonal control, we show that rodent islets may serve as a relevant model to study nutrient-elicited regulation of metabolic control in humans. ...
Article
Full-text available
Aim: The influence of dietary carbohydrates and fats on weight gain is inconclusively understood. We studied the acute impact of these nutrients on the overall metabolic state utilising the insulin:glucagon ratio (IGR). Methods: Following in vitro glucose and palmitate treatment, insulin and glucagon secretion from islets isolated from C57Bl/6J mice was measured. Our human in vivo study included 21 normoglycemic (mean age 51.9 ± 16.5 years, BMI 23.9 ± 3.5 kg/m2 and HbA1c 36.9 ±3.3 mmol/mol) and 20 type 2 diabetes (T2D) diagnosed individuals (duration 12±7 years, mean age 63.6 ± 4.5 years, BMI 29.1±2.4 kg/m2 , HbA1c 52.3±9.5 mmol/mol). Individuals consumed a carbohydrate-rich or fat-rich meal (600 kcal) in a cross-over design. Plasma insulin and glucagon levels were measured at -30, -5 and 0 min, and every 30 min until 240 min after meal ingestion. Results: The IGR measured from mouse islets was determined solely by glucose levels. The palmitate-stimulated hormone secretion was largely glucose-independent in analysed mouse islets. The acute meal tolerance test demonstrated that insulin and glucagon secretion is dependent on glycaemic status and meal composition, whereas the IGR was dependent upon meal composition. The relative reduction in IGR elicited by the fat-rich meal was more pronounced in obese individuals. This effect was blunted in T2D individuals with elevated HbA1c levels. Conclusion: Metabolic state in normoglycemic individuals and T2D diagnosed individuals is regulated by glucose. We demonstrate that consumption of a low carbohydrate diet, eliciting a catabolic state, may be beneficial for weight loss, particularly in obese individuals.
... accessed 5 July 2022). where TEA is the thermic effect of activity, RMR is the resting metabolism ratio, both measured using the values provided by the ZEPP Life ® app [19], and TEF is the thermic effect of food, referring to the energy expenditure related to food consumption [20] (i.e., digestion, absorption, assimilation, and storage), dependent on the amount and type of food consumed, which accounts for about 10% [21] of TEE and is estimated from food data through the following formula: ...
Article
Full-text available
Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism. Here, we developed a data-driven model of end-user metabolism, the Personalized Metabolic Avatar (PMA), to estimate its personalized reactions to diets. PMA consists of a gated recurrent unit (GRU) deep learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. The model can perform simulations and evaluation of diet plans, allowing the definition of tailored goals for achieving ideal weight. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim to achieve long-lasting results in pursuing a healthy lifestyle.
Chapter
The cellular metabolism produces CO2 in the body and circulatory system conveys to the lungs, expelled by the lungs, and forwarded by the breathing system. Therefore, it is believed that the expired CO2 alternation may provide the information about the airway passage blockages, circulation, and metabolism, breathing respiration, or breathing system. There are several reasons that cause increase in CO2, which are administration of blood, shivering, convulsions, and many others. Capnography is noninvasive method, measures human expired CO2, and has various applications to know the status of circulatory system. The CO2 waveform produced is known as capnogram. In this book, CO2 waveform is also referred as CO2 signal. The term CO2 waveform, CO2 signal and capnogram are used interchangeably throughout the book. The capnogram and associated features are also useful to know the metabolic CO2 status from the exhaled gas continuously. Capnography also displays the spontaneous respiratory rate in nonintubated patients breathing that allows the detection of airway obstruction and apnea. In this chapter and chapters coming, we focus on the pattern of CO2 signal for asthmatic condition.
Article
Background This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) is intended to provide clinicians an overview of 30 common obesity myths, misunderstandings, and/or oversimplifications. Methods The scientific support for this CPS is based upon published citations, clinical perspectives of OMA authors, and peer review by the Obesity Medicine Association leadership. Results This CPS discusses 30 common obesity myths, misunderstandings, and/or oversimplifications, utilizing referenced scientific publications such as the integrative use of other published OMA CPSs to help explain the applicable physiology/pathophysiology. Conclusions This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on 30 common obesity myths, misunderstandings, and/or oversimplifications is one of a series of OMA CPSs designed to assist clinicians in the care of patients with the disease of obesity. Knowledge of the underlying science may assist the obesity medicine clinician improve the care of their patients with obesity.
Article
Full-text available
The increasingly popular plant-based diet presents a challenging change for many in today’s modern lifestyle – both in terms of introducing it and maintaining it in the long term. There are several motives for such a diet, usually associated with weight management and health benefits or ethical reasons. People who decide on changing their diet face many challenges. Some of these challenges are related to the (i) disapproval of whole-food, plant-based diet by the “profession”, associated with the (ii) characteristics of a whole-food, plant-based diet, some with the (iii) need for acquiring new skills and some with a (iv) much-needed supportive environment. Here, a comprehensive ongoing support system can play a very important role, since it can offer a personalized and proven process of change for every individual. Such a model makes it easier for an individual to change a dietary behaviour into a new habit, make sense of it and live a healthy and active lifestyle in a tolerant manner to people with different dietary patterns. A well-planned whole-food, plant-based diet enables people an efficient control over their appetite, which is one of the main reasons for unsuccessful weight loss with popular weight-loss diets. Today, a comprehensive approach to a whole-food, plant-based diet is a well-founded and proven model. The majority of energy should be invested in efficient methods of informing and raising awareness about the benefits, potential risks and, consequently, the responsibility for a proper implementation of a plant-based diet and finding sustainable business models that are available to a broader audience.
Article
Full-text available
PurposeThe aim of this paper is to review the evidence of the association between energy balance and obesity. Methods In December 2015, the International Agency for Research on Cancer (IARC), Lyon, France convened a Working Group of international experts to review the evidence regarding energy balance and obesity, with a focus on Low and Middle Income Countries (LMIC). ResultsThe global epidemic of obesity and the double burden, in LMICs, of malnutrition (coexistence of undernutrition and overnutrition) are both related to poor quality diet and unbalanced energy intake. Dietary patterns consistent with a traditional Mediterranean diet and other measures of diet quality can contribute to long-term weight control. Limiting consumption of sugar-sweetened beverages has a particularly important role in weight control. Genetic factors alone cannot explain the global epidemic of obesity. However, genetic, epigenetic factors and the microbiota could influence individual responses to diet and physical activity. Conclusion Energy intake that exceeds energy expenditure is the main driver of weight gain. The quality of the diet may exert its effect on energy balance through complex hormonal and neurological pathways that influence satiety and possibly through other mechanisms. The food environment, marketing of unhealthy foods and urbanization, and reduction in sedentary behaviors and physical activity play important roles. Most of the evidence comes from High Income Countries and more research is needed in LMICs.
Article
Full-text available
This systematic review investigated the effects of differing energy intakes, macronutrient compositions, and eating patterns of meals consumed after an overnight fast on Diet Induced Thermogenesis (DIT). The initial search identified 2482 records; 26 papers remained once duplicates were removed and inclusion criteria were applied. Studies (n = 27) in the analyses were randomized crossover designs comparing the effects of two or more eating events on DIT. Higher energy intake increased DIT; in a mixed model meta-regression, for every 100 kJ increase in energy intake, DIT increased by 1.1 kJ/h (p < 0.001). Meals with a high protein or carbohydrate content had a higher DIT than high fat, although this effect was not always significant. Meals with medium chain triglycerides had a significantly higher DIT than long chain triglycerides (meta-analysis, p = 0.002). Consuming the same meal as a single bolus eating event compared to multiple small meals or snacks was associated with a significantly higher DIT (meta-analysis, p = 0.02). Unclear or inconsistent findings were found by comparing the consumption of meals quickly or slowly, and palatability was not significantly associated with DIT. These findings indicate that the magnitude of the increase in DIT is influenced by the energy intake, macronutrient composition, and eating pattern of the meal.
Article
Full-text available
In our westernised society, the level of physical activity is low. Interventions that increase energy expenditure are generally associated with an improvement in metabolic health. Exercise and exercise training increase energy metabolism and are considered to be among the best strategies for prevention of type 2 diabetes mellitus. More recently, cold exposure has been suggested to have a therapeutic value in type 2 diabetes. At a cellular level, there is evidence that increasing the turnover of cellular substrates such as fatty acids is associated with preventive effects against lipid-induced insulin resistance. Cellular energy sensors may underlie the effects linking energy turnover with metabolic health effects. Here we review data supporting the hypothesis that increasing energy and substrate turnover has beneficial effects on insulin sensitivity and should be considered a target for the prevention and treatment of type 2 diabetes. Electronic supplementary material The online version of this article (doi:10.1007/s00125-016-4068-3) contains a slideset of the figures for download, which is available to authorised users.
Article
Background: Meal skipping has become an increasing trend of the modern lifestyle that may lead to obesity and type 2 diabetes. Objective: We investigated whether the timing of meal skipping impacts these risks by affecting circadian regulation of energy balance, glucose metabolism, and postprandial inflammatory responses. Design: In a randomized controlled crossover trial, 17 participants [body mass index (in kg/m²): 23.7 ± 4.6] underwent 3 isocaloric 24-h interventions (55%, 30%, and 15% carbohydrate, fat, and protein, respectively): a breakfast skipping day (BSD) and a dinner skipping day (DSD) separated by a conventional 3-meal-structure day (control). Energy and macronutrient balance was measured in a respiration chamber. Postprandial glucose, insulin, and inflammatory responses in leukocytes as well as 24-h glycemia and insulin secretion were analyzed. Results: When compared with the 3-meal control, 24-h energy expenditure was higher on both skipping days (BSD: +41 kcal/d; DSD: +91 kcal/d; both P < 0.01), whereas fat oxidation increased on the BSD only (+16 g/d; P < 0.001). Spontaneous physical activity, 24-h glycemia, and 24-h insulin secretion did not differ between intervention days. The postprandial homeostasis model assessment index (+54%) and glucose concentrations after lunch (+46%) were, however, higher on the BSD than on the DSD (both P < 0.05). Concomitantly, a longer fasting period with breakfast skipping also increased the inflammatory potential of peripheral blood cells after lunch. Conclusions: Compared with 3 meals/d, meal skipping increased energy expenditure. In contrast, higher postprandial insulin concentrations and increased fat oxidation with breakfast skipping suggest the development of metabolic inflexibility in response to prolonged fasting that may in the long term lead to low-grade inflammation and impaired glucose homeostasis. This trial was registered at clinicaltrials.gov as NCT02635139.
Article
Abstract The relationship between eating speed and the thermic effect of food (TEF) remains unclear. We investigated the difference in the TEF when meals containing the same amount of energy were eaten in 5 min (fast eating) or 15 min (regular eating). Subjects were nine non-obese young women. Following a 350 kcal (1464 kJ) meal, energy expenditure and autonomic nervous system activity were measured. The frequency of mastication was also calculated. The TEF for the 15-min period after the start of eating with fast eating was significantly lower than with regular eating (p < 0.01). There was a significant positive correlation between the low-frequency/high-frequency ratio and TEF at 5-min intervals up to 20 min after the start of eating and between total mastication frequency and TEF during ingestion. Fast eating may reduce the TEF, potentially because a decrease in mastication frequency decreases sympathetic nervous system activity.
Article
Epidemiological studies suggest an association between breakfast skipping and body weight gain, insulin resistance or type 2 diabetes. Time when meal is consumed affects postprandial increase in energy expenditure and blood glucose, and breakfast skipping may reduce 24 h energy expenditure and elevate blood glucose level. The present study evaluated the effect of breakfast skipping on diurnal variation of energy metabolism and blood glucose. The skipped breakfast was compensated by following big meals at lunch and supper. In a randomized repeated-measure design with or without breakfast, eight males stayed twice in a room-size respiratory chamber. Blood glucose was recorded with a continuous glucose monitoring system. Breakfast skipping did not affect 24 h energy expenditure, fat oxidation and thermic effect of food, but increased overall 24 h average of blood glucose (83 ± 3 vs 89 ± 2 mg/dl, P < 0.05). Unlike 24 h glucose level, 24 h energy expenditure was robust when challenged by breakfast skipping. These observations suggest that changes in glucose homeostasis precede that of energy balance, in the potential sequence caused by breakfast skipping, if this dietary habit has any effect on energy balance.:
Article
Objective: To determine the effects of the number of chews and meal duration on diet-induced thermogenesis (DIT) and splanchnic blood flow (BF). Methods: Healthy normal-weight subjects (11 subjects in the 100-kcal test and 10 subjects in the 300-kcal test) participated in two trials: a rapid-eating trial and a slow-eating trial. The meal duration and the number of chews were recorded. DIT was calculated from oxygen uptake and body mass, and splanchnic BF was calculated from the diameters of and blood velocities in the celiac artery and superior mesenteric artery, which were recorded until 90 min after consuming the food samples. Results: For the 100-kcal and 300-kcal food samples, DIT and postprandial splanchnic BF in both the celiac artery and superior mesenteric artery were significantly larger in the slow-eating trial than in the rapid-eating trial. There were significant correlations among meal duration, the number of chews, DIT, and postprandial splanchnic BF, with the exception of the relationship between DIT and splanchnic BF in the 300-kcal trial. Conclusions: These results suggest that fewer chews and/or shorter meal duration decreases DIT and the postprandial splanchnic BF, and that the increased DIT is at least partially due to the postprandial splanchnic circulation.
Article
The thermic effect of food accounts for ̴ 10% of daily energy expenditure. A reduction in the thermic effect of food, which has been variably observed in the older adults, could predispose to fat gain. We tested whether the thermic effect of food is reduced in older adults compared with young adults by analyzing our database of standardized studies conducted at the Mayo Clinic between 1999 and 2009. Data were available from 136 older adult volunteers aged 60-88 (56 females) and 141 young adults aged 18-35 years (67 females). Basal energy expenditure was measured by indirect calorimetry to assess basal metabolic rate. Body fat, fat free mass, and visceral fat were measured using a combination of dual energy X-ray absorptiometry and an abdominal CT scan. The thermic effect of food and postprandial insulinemia were measured in 123 older adults (52 females) and 86 young adults (38 females) of these volunteers. Basal metabolic rate adjusted for fat-free mass was less in older adults (p=0.01) and the thermic effect of food was ̴ 1% (p=0.02) less in the older adults. After controlling for meal size and fat-free mass, body fat and fat distribution did not predict the thermic effect of food. Both basal metabolic rate and the thermic effect of food are less in older adults than young adults, even when they have similar amounts of lean tissue and consume a similar size meal. These factors contribute to lower daily energy expenditure in the older adults.
Article
Measurement of energy expenditure in man is required to assess metabolic needs and fuel utilization. Indirect and direct calorimetric and non-calorimetric methods for measuring energy expenditure are reviewed and their relative value for measurement in the clinical setting assessed. Where high accuracy is required and sufficient resources are available, chamber-based indirect and direct calorimeters are optimal. If less accurate measurements are acceptable, or resources are limited, flexible total collection systems or canopy ventilated, flow-over, indirect calorimetry systems provide assessments of acceptable accuracy.
Article
Objective: To assess the impact of obesity and insulin sensitivity on resting (REE) and glucose-induced thermogenesis (GIT). Design: Data from 322 studies carried out in non-diabetic subjects of either gender, covering a wide range of age (18-80y) and body mass index (BMI, 18-50 kg/m2). Measurements: Insulin sensitivity and thermogenesis were measured by combining the euglycaemic insulin clamp technique with indirect calorimetry. Results: REE was inversely related to age (P = 0.001) and the respiratory quotient (P = 0.03), and positively related to BMI, lean body mass (LBM), fat mass, and percentage fat mass (all P<0.0001). In a multiple regression model, LBM-adjusted REE was estimated to decline by 9% between 18 and 80 y, independently of obesity and insulin sensitivity. In contrast, GIT was strongly associated with insulin sensitivity (P<0.0001) but not with gender, age or BMI. By multiple regression analysis, GIT was linearly related to insulin sensitivity after controlling for gender, age, BMI and steady-state plasma insulin levels. Furthermore, both of the main components of insulin-mediated glucose disposal (glucose oxidation and glycogen synthesis) correlated with GIT independently of one another. In the subset of subjects (n = 89) in whom waist-to-hip ratio (WHR) measurements were available, GIT was inversely associated with WHR (P<0.001 after adjustment by gender, age, BMI, insulin sensitivity and steady-state plasma insulin concentration). In this model, a significant interaction between WHR and gender indicated a stronger adverse effect on GIT of a high WHR in women than in men. Conclusions: In healthy humans, age, lean mass and respiratory quotient are the main independent determinants of resting thermogenesis. In contrast, insulin sensitivity and, to a lesser extent, abdominal obesity are the principal factors controlling glucose-induced thermogenesis.