Abstract

Coffee and caffeine consumption has global popularity. However, evidence for the potential of these dietary constituents to influence energy intake, gut physiology, and appetite perceptions remains unclear. The purpose of this review was to examine the evidence regarding coffee and caffeine's influence on energy intake and appetite control. The literature was examined for studies that assessed the effects of caffeine and coffee on energy intake, gastric emptying, appetite-related hormones, and perceptual measures of appetite. The literature review indicated that coffee administered 3-4.5 h before a meal had minimal influence on food and macronutrient intake, while caffeine ingested 0.5-4 h before a meal may suppress acute energy intake. Evidence regarding the influence of caffeine and coffee on gastric emptying, appetite hormones, and appetite perceptions was equivocal. The influence of covariates such as genetics of caffeine metabolism and bitter taste phenotype remain unknown; longer controlled studies are needed.
Caffeine, coffee, and appetite control: a review
Matthew M. Schubert1, Christopher Irwin2, Rebekah F. Seay1, Holly E. Clarke1, Deanne Allegro1,
and Ben Desbrow2.
1 = Department of Kinesiology
Auburn University at Montgomery
Montgomery, AL, USA
2 = School of Allied Health Sciences
Menzies Health Institute Queensland
Griffith University
Gold Coast, QLD, Australia
Corresponding author: Matthew M. Schubert, PhD
Department of Kinesiology
Auburn University at Montgomery
Montgomery, AL, USA
mschuber@aum.edu
Schubert and Allegro are assistant professors and Seay and Clarke are graduate students at
Auburn University at Montgomery. Irwin is a senior lecturer and Desbrow is an associate
professor at Griffith University.
Caffeine, coffee, and appetite control: a review
Coffee and caffeine consumption has global popularity. However, evidence for the potential of
these dietary constituents to influence energy intake, gut physiology, and appetite perceptions
remains unclear. The purpose of this review was to examine the evidence regarding coffee and
caffeine’s influence on energy intake and appetite control. Literature was examined for studies
that assessed the effects of caffeine and coffee on energy intake, gastric emptying, appetite-
related hormones, and perceptual measures of appetite. The literature review indicated that
coffee administered 3-4.5 h before a meal had minimal influence on food and macronutrient
intake, while caffeine ingested 0.5-4 h before a meal may suppress acute energy intake.
Evidence regarding the influence of caffeine and coffee on gastric emptying, appetite hormones,
and appetite perceptions was equivocal. The influence of covariates such as genetics of caffeine
metabolism and bitter taste phenotype remain unknown; longer controlled studies are needed.
Key words: coffee; caffeine; energy intake; appetite
Introduction
Caffeine is the most widely consumed psychoactive substance in the world, with
documented use dating back to the early Paleolithic period (Barone and Roberts 1996). For the
majority of the population, coffee is the major vehicle for the delivery of caffeine, followed by
tea and cola beverages (Mitchell et al. 2015; Mitchell et al. 2014). Given its popularity, there is
interest in the effect of caffeine and caffeine-containing dietary products to influence population
health.
Decreased physical activity and increased energy intake (largely exacerbated by the
availability of inexpensive, nutrient-dense foods) has led to increasing weight gain with global
obesity rates nearly doubling since 1980 (Ogden et al. 2014). Many weight-loss ‘supplements’
marketed to the general public include caffeine, often describing it as an ‘appetite suppressant’
and ‘thermogenic aid’. There is some evidence to suggest these supplements can be effective for
weight loss (Liu et al. 2013); however, these products often contain other ingredients, such as
ephedrine, which makes an independent analysis on the effects of caffeine on weight loss
difficult.
An additional concern is that caffeinated beverages can vary widely in both their caffeine
(Desbrow et al. 2007; Ludwig et al. 2014) and energy content. For example, 250 ml of instant
coffee has ~16 kJ of energy and ~80 mg of caffeine, while an equivalent serving of a cola
beverage has ~440 kJ of energy and ~25 mg of caffeine (Foodworks; Xyris Software, Australia).
High calorie caffeinated beverages (e.g. carbonated soda beverages and energy drinks) are
unanimously discouraged by health authorities due to their refined sugar content (U.S.
Department of HHS and U.S. Department of Agriculture 2015-2020 Dietary Guidelines for
Americans, available at http://health.gov/dietaryguidelines/2015/guidelines/). The caloric density
of these products means they are less likely to contribute positively to healthy weight
management. Finally, coffee and caffeinated beverages are often consumed in social
environments and paired with meals or snacks, which can influence food choice and/or food
liking (Collins, Freeman and Palmer 2012; Freeman, Collins and Palmer 2012).
It has been reported that dietary caffeine intake (Lopez-Garcia, van Dam, Rajpathak, et
al. 2006) and coffee and tea consumption (Pan et al. 2013) have been associated with lower
levels of long term weight gain in cohort studies. However, there are few studies that have
examined if caffeinated coffee consumption reduces appetite and energy intake, and even they
are contradictory in their results (Arciero et al. 1995; Astrup et al. 1990; Bracco et al. 1995;
Dulloo et al. 1989; Gavrieli et al. 2011; Greenberg and Geliebter 2012). Additionally, it has been
reported that caffeine and coffee may influence the rate of gastric emptying and secretion of gut
hormones (Greenberg and Geliebter 2012; Johnston, Clifford and Morgan 2003), which play
roles in appetite control (Horner et al. 2011; Horner et al. 2015).
To date, little attention has been given to whether caffeine and caffeinated beverages
influence energy intake and appetite control. Therefore, the aim of this review was to provide an
overview of how caffeine and coffee may influence energy intake and its determinants
(perceptions of appetite, gastric emptying, and gut hormones). As long-term effects on these
variables have not been examined in the literature, the current review focuses on acute effects.
This information will clarify understanding of the role caffeinated beverages can play in energy
regulation.
Methods
Objectives
The primary objectives of this review were to address the following questions:
1) Does de/caffeinated coffee consumption influence acute (≤ 24 h) ad libitum energy intake
or macronutrient composition in humans?
2) Does caffeine (anhydrous; pure) consumption influence acute (≤ 24 h) ad libitum energy
intake or macronutrient composition in humans?
3) Does caffeine or de/caffeinated coffee influence acute gut physiology via changes in
gastric emptying and secretion of gut hormones?
4) Does caffeine or de/caffeinated coffee influence perceptual measures of appetite?
The secondary objective was to determine gaps in the literature and directions for future
research.
Study eligibility
This systematic literature review was conducted in accordance with the Preferred
Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (Liberati et al.
2009). Journal articles, abstracts, and published dissertations or theses were eligible for
inclusion in this review if they reported data for energy and/or macronutrient intake when a bolus
of caffeine or coffee was administered before participants were offered food or began a period of
observation. As such, studies were limited to acute trials generally conducted in a laboratory.
Treatments permitted included caffeinated coffee (referred to henceforth as coffee) and caffeine
(anhydrous/tablets). Studies needed to utilize a placebo (decaffeinated coffee or placebo tablets)
or controlled (decaffeinated coffee or water) design. Studies on cola and sugar-sweetened
beverages were excluded, generally because these studies (though utilizing similar design and
outcome measures) have neglected to report or account for the caffeine content of beverages.
Iced coffee beverages or milk-based coffee beverages were also excluded for this reason,
although the literature search revealed no studies on these products. It has also been reported that
fortifying cola beverages with caffeine requires an increase in sucrose content to maintain
sweetness and counteract the bitterness of caffeine (Keast et al. 2011), which would confound
results. Studies were included if they had been conducted in apparently healthy adult humans.
Studies were deemed eligible using the PICOS approach (Liberati et al. 2009), and data
on treatment and control conditions, participant characteristics, caffeine dose, beverage volume,
and other outcome measures were recorded in an Excel spreadsheet (Microsoft Excel, Microsoft
Corporation).
Sources of information and search strategy
Research databases (PubMed and Google Scholar) were initially searched independently
by two authors in through September 2016 using an identical keyword search strategy.
Keywords utilized included the following combinations and searched the title, abstract, and
keywords of each paper: coffee, caffeine, appetite, energy intake, gastric emptying, ghrelin, PYY,
GLP-1. Titles and abstracts were examined initially and full papers were retrieved if studies met
the inclusion criteria. A detailed search strategy is included as supplementary material.
Results
General characteristics of studies and participants
Initial search results yielded a total of 4,087 unique results that were pared on the basis of
title and abstract down to 50. In sum, 12 studies met inclusion criteria. Five studies identified
assessed energy intake in response to coffee and caffeine. Another five studies identified
assessed gastric emptying responses to coffee and caffeine, 4 studies examined gut hormone
responses, and 5 reported measures of appetite perceptions. As studies were too few in each
domain for meta-analyses, studies are summarized and their results presented as median and
range. Table 1 summarizes all studies included in this review.
Question 1coffee and energy intake
Five studies assessed single meal ad libitum energy intake, a median of 3 h after
treatment ingestion (0.5-4 h). Three of these studies utilized a test meal of varying macronutrient
composition, thus providing data on macronutrient intake (Gavrieli, Karfopoulou, et al. 2013;
Gavrieli et al. 2011; Tremblay et al. 1988) while the remaining two studies provided single-item
pasta meals (Belza, Toubro and Astrup 2009; Schubert et al. 2014). Two studies extended
recording of energy and macronutrient intake for 24 h outside the lab using self-reported food
records (Gavrieli, Karfopoulou, et al. 2013; Gavrieli et al. 2011). Three studies provided a meal
concomitantly with treatment ingestion, with a median energy content of 594 kJ (594-1675 kJ);
the macronutrient composition of this meal was 62.5 % CHO (48-62.5 %), 31 % FAT (31-37 %),
and 6.5 % PRO (6.5-15 %) (Gavrieli, Karfopoulou, et al. 2013; Gavrieli et al. 2011; Schubert et
al. 2014). One study provided treatments in two boluses (4.5 and 2.5 h before lunch) (Schubert
et al. 2014), while the other studies administered treatment in a single dose.
Median dose of caffeine administered in the coffee conditions was 262 mg (192-526 mg).
Median volume of coffee provided was 200 mL (200-450 mL).
Results of single meal ad libitum energy and macronutrient intake are summarized in
Table 2. Median energy intake with coffee consumption was 3144 kJ compared to 3164 kJ for
the placebo or water control condition (three studies; 6 comparisons). For the two studies (5
comparisons) (Gavrieli, Karfopoulou, et al. 2013; Gavrieli et al. 2011) that evaluated energy
intake over the entire experimental day, median intake with coffee consumption was 9772 kJ
(8164-13500 kJ) compared to 10011 kJ (9198-13800 kJ) within the placebo or water control
condition.
Single-meal macronutrient intake for carbohydrate was equivalent to a median of 85 g
(72-251 g) with coffee consumption, compared to 80 g (73-260 g) in the placebo/control
condition. Median protein intake was 37 g (34-73 g) in both conditions and median fat intake
was 32 g (31-55 g) with coffee compared to 34 g (33-55 g) with placebo/control.
Daily macronutrient intake was equivalent to 250 g (198-399 g) carbohydrate, 84 g (71-
128 g) protein, and 99 g (78-115 g) fat with coffee consumption. For the placebo/control
condition, 255 g (234-426 g) of carbohydrate, 108 g (98-117 g) of fat, and 99 g (90-124 g) of
protein were consumed.
Question 2 – Caffeine and energy intake
The three studies (four comparisons) (Belza, Toubro and Astrup 2009; Schubert et al.
2014; Tremblay et al. 1988) reporting energy intake after caffeine consumption yielded a median
of 3017 kJ (2016-4708 kJ) compared to 3446 kJ (2118-4859 kJ) in the control condition. For
macronutrient intake (one study, two comparisons) (Tremblay et al. 1988), a median of 125 g
carbohydrate (103-146 g), 20 g (18-21 g) of protein, and 18 g (17-19 g) of fat were consumed in
the caffeine condition compared to 139 g (102-175 g) carbohydrate, 22 g (16-27 g) protein, and
23 g (17-28 g) fat in the placebo condition.
The median caffeine dose administered in these conditions was 281 mg (50-300 mg).
Median volume of water provided with treatment in these studies was 313 mL (175-450 mL).
Question 3 – Caffeine, coffee, and gut physiology
Results of the 5 studies examining gastric emptying responses and 4 studies examining
gut hormone responses are displayed in Table 3 (Akimoto et al. 2009; Beaudoin, Robinson and
Graham 2011; Boekema et al. 2000; Franke et al. 2008; Gavrieli et al. 2011; Greenberg and
Geliebter 2012; Lien et al. 1995; Schubert et al. 2014). For gastric emptying half-time in
response to coffee ingestion, the median was 106 minutes for caffeine (36-179 minutes)
compared to 122 minutes (45-182 minutes) for control. The only study using caffeine in this
group reported that half-time for caffeine was 154 minutes compared to 182 minutes for the
control condition.
Due to the various testing protocols, synthesis of the data for gut hormones was not
considered possible. Instead, these data are summed according to the authors’ conclusions in
Table 3. Two studies (out of 3 measuring) reported increased GLP-1 concentrations in response
to caffeinated and/or decaffeinated coffee (Beaudoin, Robinson and Graham 2011; Johnston,
Clifford and Morgan 2003). Two studies that assess ghrelin responses reported no changes due
to coffee, caffeine, or decaffeinated coffee; this was also observed with leptin levels (Gavrieli et
al. 2011; Greenberg and Geliebter 2012). Finally, results for peptide YY were divergent, with
one study reporting no treatment effects and another reporting increased concentrations only
after decaffeinated coffee (Gavrieli et al. 2011; Greenberg and Geliebter 2012).
Question 4 – Caffeine, coffee, and appetite
Five studies reported results for appetite perceptions and these results are summed in
Table 4 (Belza, Toubro and Astrup 2009; Gavrieli, Karfopoulou, et al. 2013; Gavrieli et al. 2011;
Greenberg and Geliebter 2012; Schubert et al. 2014). As with gut hormones, synthesis was not
possible as only 2 studies reported data for area under the concentration-time curve. Trends
favoring caffeinated or decaffeinated coffee were observed in 3 studies (Gavrieli, Karfopoulou,
et al. 2013; Greenberg and Geliebter 2012; Schubert et al. 2014). The studies that reported
changes had larger volumes (200-500 mL) and caffeine doses (250-526 mg) than the studies that
did not observe alterations in appetite perceptions (Belza, Toubro and Astrup 2009; Gavrieli,
Karfopoulou, et al. 2013; Gavrieli et al. 2011; Greenberg and Geliebter 2012; Schubert et al.
2014).
Discussion
Daily consumption of coffee is an extremely common behavior, yet its influence on
energy and macronutrient intake and appetite regulation remains largely unknown. The results of
this review indicate that coffee has no significant impact on single meal energy intake or
macronutrient selection. However, there appears to be a small decrease (-230 kJ) in daily energy
intake with coffee consumption. On the other hand, caffeine consumption may decrease single
meal energy intake (-430 kJ), but there is no evidence regarding its influence on free-living daily
energy intake. Additionally, there is no clear evidence to suggest caffeine alters gastric
emptying, gut hormone secretion, or appetite perceptions in a manner that could influence energy
intake.
Most of the studies examined varied both in dose of caffeine and coffee as well as
volume of the treatment beverage. Caffeine doses varied considerably (50-500+ mg), with
volume also varying between studies (20-500 mL). Due to the small number of studies and large
discrepancies between them, no clear pattern of dose-response could be determined. It is worth
noting that European coffees, particularly espresso, vary significantly in volume and caffeine
content compared to American coffee; Italian espresso typically is ~20 mL in volume with ~104
mg per serving of caffeine, while some of the beverages served at coffee shops in the United
States, such as a short Flat White, are ~240 mL in volume with 130 mg per serving of caffeine
(Ludwig et al. 2014). These authors and others also have noted considerable variation in caffeine
content of coffee purchased at coffee shops, suggesting that even when a standard protocol
exists, variations are likely to occur (Desbrow, Henry and Scheelings 2012; Desbrow et al. 2007;
Ludwig et al. 2014). It would be of interest in future studies to manipulate both the volume and
caffeine content of administered beverages to examine their independent and combined effects
on energy intake and appetite regulation.(Desbrow, Henry and Scheelings 2012; Desbrow et al.
2007; Ludwig et al. 2014)
Coffee consumption has been linked with numerous health benefits, including decreased
mortality (Freedman et al. 2012; Lopez-Garcia et al. 2008), decreased risk of heart disease
(Lopez-Garcia, van Dam, Willett, et al. 2006), lower levels of inflammation and endothelial
dysfunction (Andersen et al. 2006; Lopez-Garcia, van Dam, Qi, et al. 2006), reduced incidence
of diabetes (Ding et al. 2014; Huxley et al. 2009; van Dam and Hu 2005), decreased risk of some
cancers (Floegel et al. 2012; Malerba et al. 2013; Wang et al. 2016), and improved mental health
and well-being (Arab et al. 2011; Arab, Khan and Lam 2013; Ruusunen et al. 2010; van Gelder
et al. 2007). However, the mechanisms needed to establish causation for many of these health
benefits remain unclear. Additionally, coffee and caffeine consumption have been linked with
less weight gain in data from cohort studies (Greenberg et al. 2005; Greenberg, Boozer and
Geliebter 2006; Lopez-Garcia, van Dam, Rajpathak, et al. 2006; Pan et al. 2013). The results of
the current review suggest the reasons for these associations remain to be elucidated.
Despite the lack of evidence supporting (or refuting) coffee and caffeine’s roles in body
weight regulation, there are some potential mechanisms by which they may influence body
weight. The thermic effect of caffeine is well-established (Hursel et al. 2011), and the increases
in energy expenditure with caffeinated coffee consumption (~430 kJ over 24 h) (Hursel et al.
2011) may be adequate in some individuals for weight maintenance. Furthermore, evidence
suggests coffee consumption may modify the gastrointestinal tract in such a way that lipid and
glucose absorption are attenuated (Cha et al. 2012; Jaquet et al. 2009; Johnston, Clifford and
Morgan 2003). It has recently been reported that consuming 4 cups of coffee per day before and
during a 5-d high-fructose diet attenuated a diet-induced increase in hepatic insulin resistance;
but this occurred without changes in body weight and despite the diet causing increased lipid
deposition (Lecoultre et al. 2014). However, as coffee is a bioactive compound containing over
1000 ingredients, the precise mechanisms remain unknown. Caffeine, chlorogenic acids, and
other biologically active compounds may all contribute to coffee’s effect(s) on energy intake and
eating behavior.
Most long-term research on caffeine and weight control has focused on combined
supplements or use of caffeine in conjunction with a low calorie diet, and not caffeine alone.
Thus, the data on controlled caffeine supplementation for weight control is minimal. Two short-
term studies evaluated the effect of controlled caffeine supplementation (2 x 2.5 mg/kg per day)
over 4 days (Judice, Magalhaes, et al. 2013; Judice, Matias, et al. 2013) on energy expenditure
and voluntary activity. Neither study reported changes in body weight over the observation
period; however, both reported a non-significant decrease in energy intake of 460-880 kJ during
the caffeine trials, which could have implications for body weight over the long term (Judice,
Magalhaes, et al. 2013; Judice, Matias, et al. 2013).
This review found insufficient evidence to determine coffee or caffeine’s role in
influencing appetite perceptions. However, caffeine has been implicated in appetite and feeding
control in specific brain regions. Caffeine has been shown to decrease whole-brain cerebral
blood flow and oxygen metabolism (Perthen et al. 2008; Vidyasagar et al. 2013; Wu et al. 2014),
although this is dependent upon habituation to caffeine (Addicott et al. 2009; Kennedy and
Haskell 2011). Caffeine has also been shown to influence neurotransmitter release via its
inhibition of adenosine receptors, particularly the increased release of dopamine and serotonin
(Fredholm et al. 1999). Increased dopamine and serotonin transmission could theoretically
explain caffeine’s influence on mood, which itself has been shown to influence energy intake and
feeding behavior (Macht 2008; Mela 2006). The influence of caffeine or coffee on the neural
responses to food cues therefore require further investigation to determine if central effects of
these substances may influence feeding behavior and food choice, and how neurotransmitters
such as dopamine and serotonin may contribute.
The relationship(s) between genetics and coffee and caffeine consumption also warrant
further attention. To date, no study examining coffee/caffeine and appetite has accounted for
genetic variation such as CYP1A2 genotype (the liver enzyme that metabolizes the majority of
caffeine) or the PROP-6 taste receptor phenotype. For example, ‘super-tasters’ have a lower
threshold for bitter taste, and may be more likely to avoid non-sweetened coffee (Ly and
Drewnowski 2001).
The influence of additives in coffee (milk, sugar, non-nutritive sweetener, etc.) also
deserves attention, but evidence is again lacking in the current literature. Since instant/black
coffee is a beverage of minimal caloric value, adding milk or sugar increases the calorie content;
the effect (if any) this would have on subsequent energy intake is unknown. Analysis of cohort
data reported that additive use may explain some of the variation in body composition results
among coffee consumers (Bouchard, Ross and Janssen 2010), but this area still requires further
study. In addition, some observational evidence from Australian coffee shops highlighted the
divergence of coffee products available to consumers, with those selecting a blended coffee (i.e.
Frappuccino), requesting full cream milk, and purchasing the largest drink size were associated
with increased energy consumption (Collins, Freeman and Palmer 2012; Freeman, Collins and
Palmer 2012).
It has been reported that body composition can influence caffeine concentrations
(Gavrieli, Fragopoulou, et al. 2013; Skinner et al. 2013), as well as modify glycemic and
insulinaemic responses postprandially when a meal is co-ingested with caffeine (Gavrieli,
Karfopoulou, et al. 2013). Only one of the studies in this review examined differences between
lean and overweight/obese individuals (Gavrieli, Karfopoulou, et al. 2013). This study reported
that appetite and energy intake were suppressed in obese individuals when they consumed 6
mg/kg of caffeinated coffee, but not 3 mg/kg or at either dose in lean individuals (Gavrieli,
Karfopoulou, et al. 2013). It is possible that this dose, and these individuals’ larger fat mass, led
to elevated caffeine concentrations (Skinner et al. 2013). Whether a threshold dose of caffeine
and/or other coffee compounds is necessary to elicit changes in energy intake and appetite
requires more study. An additional concern is that only one of the reviewed studies was
conducted in obese individuals, with the remaining studies recruiting individuals with healthy
BMIs; thus, future research needs to incorporate a participant group with a wider range of body
compositions. This is important as it is well-known that obese individuals have different energy
intake and appetite hormone responses compared to healthy controls, which suggests they may
respond to coffee and caffeine intake differently than their lean counterparts (Brennan et al.
2012; Clamp et al. 2015).
Given the sparsity of research investigating the impact of coffee on appetite, it is
pertinent to discuss recommendations for future studies. Controlled laboratory trials provide one
option. Ideally, these studies should incorporate a pre-trial standardization and screening
protocol, including phenotype and genotype screening, rigorous dietary and physical activity
control procedures, and a supervised environment. Previous researchers have indicated that water
with quinine can serve as an effective placebo (Hodgson, Randell and Jeukendrup 2013) to
mimic coffee without complicating the design by using decaffeinated coffee, since decaf still
contains active ingredients. It is paramount volume be standardized between conditions, unless
this is the dependent variable of interest – in this case, care must be taken to match other
variables and confounders such as caffeine intake and chlorogenic acid concentrations. If the
research team seeks to investigate coffee additives or other coffee-based beverages, again, care
should be taken to match volume and caffeine levels so any “true” effects can be identified.
Studies conducted in the lab are useful for examining the mechanisms related to coffee, caffeine,
and appetite regulation, but results are not always applicable to real world settings.
As an alternative to laboratory studies, researchers could consider a pragmatic
investigation. Participants should still be screened for phenotype and genotype, as these
variables would be included in analyses as covariates or subgroups. The pragmatic approach
would allow researchers to enhance ecological validity. A protocol similar to recent work
examining the influence of coffee on hydration status (Killer, Blannin and Jeukendrup 2014)
could be employed. In this study, participants were provided enough coffee to consume 4 mg∙kg-1
per day over 3 days and consumed their coffee as four 200 mL boluses each day. A study of this
nature would provide more information on overall energy balance, physical activity, and eating
behavior instead of mechanisms. Ideally, the study would last several months and utilize cutting
edge technology to monitor energy balance, such as doubly-labeled water, multisensory activity
monitors (i.e. ActiHeart or Sensewear Pro), food diaries, and calculation of energy intake from
changes in body composition and total energy expenditure (Hall 2012). However, the logistics of
such a study present challenges, such as should coffee and caffeine consumption outside of what
is provided to participants be standardized, should the design be a parallel group or cross-over
study, and how many participants would be appropriate.
A limitation of the present review is that all included studies utilized instant coffee or
anhydrous caffeine compared to other forms of coffee or vehicles for caffeine consumption (i.e.
colas, energy drinks, etc.). It is also likely that many individuals consume coffee with other
components (i.e. milk, sugar). Additionally, we were only able to find one study assessing tea
(green, oolong, or black) consumed as a beverage (Reinbach et al. 2009). Other studies
examining tea extracts in capsule form or as part of a different beverage have been conducted but
did not have tea-only beverage conditions and therefore were not eligible for this review (Belza,
Toubro and Astrup 2009; Carter and Drewnowski 2012; Diepvens et al. 2007; Diepvens,
Westerterp and Westerterp-Plantenga 2007; Gregersen et al. 2009; Hsu et al. 2008; Kovacs et al.
2004). Studies examining other forms of coffee (i.e. espresso, latte, etc.) were also lacking. The
influence of these commonly consumed caffeinated beverages and beverage additives on energy
and macronutrient intake deserves further attention. Finally, the characteristics of study designs
(i.e. beverage volume, energy content of the beverages or test meals) complicated the ability to
conduct more formal statistical analyses.
Conclusion
The literature to date examining the effect of caffeine and coffee consumption on energy
intake is limited, and therefore mostly equivocal. Thus, evidence is relatively scarce at present
for how these dietary constituents influence appetite, gut physiology, and food intake. Based on
this review, coffee appears to some promise as a means of altering appetite and energy intake, but
whether this is due to the volume ingested or coffee’s ingredients specifically needs to be
elucidated. However, their influence(s) on energy and macronutrient intake over longer periods
of time remains unknown. Further controlled, well-designed, and adequately powered cross-over
trials are needed to determine the efficacy of caffeine and coffee to manipulate food intake,
feeding behavior, and appetite. In addition, covariates such as genetics related to taste perception
and caffeine metabolism provide novel areas of study to determine patterns of caffeine and
coffee consumption and susceptibility to their potential effects. Neuro-imaging studies
investigating the influence of caffeine and coffee consumption on appetite-regulating brain
regions may also provide a novel means for examining the potential mechanisms of these
compounds.
Acknowledgements
The authors thank Anna Gavrieli for sharing her data. No funding or sponsorship was received
as part of preparing this review. The authors report no conflicts of interest, aside from the
consumption of copious amounts of coffee and other caffeinated substances.
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Table 1: Description of included studies
Study Participants Caffeine Dose (mg) Administration
method
Assessments Notes
Tremblay et al. (1988)
(caffeine)
10 M
BMI: 22.8
300 Capsule EI Dose administered 30 min
before meal; habitual
caffeine consumption and
abstention not reported.
Tremblay et al. (1988)
(caffeine)
10 F
BMI: 19.9
300 Capsule EI As above
Lien et al. (1995) 93 (56 M 37 F)
BMI NR
~100 mg (4 g instant
coffee)
Coffee GE GE assessed via
scintigraphy. 500 mL
bolus + 420 kJ glucose.
Boekema et al. (2000) 12 M
BMI NR
180 mg Coffee GE GE assessed via applied
potential tomography. 280
mL bolus + 1675 kJ test
meal.
Johnston, Clifford and
Morgan (2003)
9 (4 M, 5 F)
BMI NR
NR Coffee H Glucose-dependent
insulinotropic polypeptide
(GIP) and glucagon-like
peptide-1(GLP-1)
measured. 400 mL bolus +
420 kJ glucose.
Franke et al. (2008) 10 M
BMI: 23.8
~300 mg (12 g Espresso) Coffee GE, A GE assessed via
ultrasound. 2419 kJ meal
+ 40 mL bolus.
Akimoto et al. (2009) 6 M
BMI: 21.5
NR Coffee GE GE assessed via 13C breath
testing. 19 mL bolus + 200
mL/820 kJ test meal.
Belza, Toubro and Astrup
(2009)
12 M
BMI: 22.4
50 Capsule EI, A Dose administered 4 h
before meal w/ 175 mL
H2O; habitual caffeine
consumption and
(caffeine) abstention not reported.
Beaudoin, Robinson and
Graham (2011)
11 M
BMI: 24.7
5 mg/kg: 395 Coffee H Given oral fat tolerance
test (1 g/kg lipid, ~2940
kJ); GIP, GLP-1
measured. 5 h later,
received coffee. At 6 h,
oral glucose tolerance test
(1260 kJ).
Gavrieli et al. (2011) 16 M
BMI: 25.5
3 mg/kg: 247 Coffee EI, H, A 200 mL coffee with 594 kJ
breakfast; test meal 3 h
later
Ghrelin, PYY, GLP-1
measured.
Greenberg and Geliebter
(2012) (coffee)
11 M
BMI: 23.6
6 mg/kg: 423 Coffee H, A ~500 ml bolus, 60 min
rest, oral glucose tolerance
test with 1260 kJ glucose.
Greenberg and Geliebter
(2012) (caffeine)
As above As above In water As above As above
Gavrieli, Karfopoulou, et
al. (2013) (lean)
16 (9 F)
BMI: 21.3
3 mg/kg: 192
6 mg/kg: 383
Coffee EI, A 200 mL coffee with 594 kJ
breakfast; test meal 3 h
later
Gavrieli, Karfopoulou, et
al. (2013)
(overweight/obese)
17 (8 F)
BMI: 30
3 mg/kg: 263
6 mg/kg: 526
Coffee As above As above
Schubert et al. (2014)
(coffee)
12 (9 F)
BMI: 22.7
4 mg/kg + 15 mg in 450
ml coffee: 277
Capsules with decaf
coffee
EI, A, GE 225 mL coffee with 1675
kJ breakfast; 225 mL
coffee 2 h later; test meal
2.5 h after 2nd coffee (4.5 h
post-breakfast). GE
assessed via 13C breath
testing
Schubert et al. (2014)
(caffeine)
As above 4 mg/kg: 262 Capsules EI, A, GE As above, but with H2O
instead of coffee
M, male F, female NR, not reported EI, energy intake A, appetite GE, gastric emptying H, appetite hormones
Table 2: Results of included studies for single meal energy and macronutrient intake
Study Treatment EI Control/Placebo
EI
CHO Intake Fat Intake Protein Intake
Tremblay et al.
(1988)
(caffeine - men)
3416±925 kJ* 4367±875 kJ Treatment: 146±39 g
(72 ± 19 %)
Control: 175±38 g
(67 ± 15 %)
Treatment: 19±9 g*
(21 ± 10 %)
Control: 28±10 g
(24 ± 9 %)
Treatment: 21±7 g
(10 ± 3 %)
Control: 27±7 g
(10 ± 3 %)
Tremblay et al.
(1988)
(caffeine – women)
2617±871 kJ 2525±858 kJ Treatment: 103±29 g
(66 ± 19 %)
Control: 102±42 g
(68 ± 28 %)
Treatment: 17±11 g
(25 ± 16 %)
Control: 17±8 g
(25 ± 12 %)
Treatment: 18±11 g
(12 ± 7 %)
Control: 16±8 g
(11 ± 5 %)
Belza, Toubro and
Astrup (2009)
(caffeine)
4708 ±1306 kJ 4859±1493 kJ NM NM NM
Gavrieli et al. (2011)
(coffee)
7300±1700 kJ 7300±1900 kJ Treatment: 251±14 g
(58 ± 3 %)
Control: 260±15 g
(60 ± 3 %)
Treatment: 55±4 g
(28 ± 2 %)
Control: 55±5 g
(28 ± 3 %)
Treatment: 73±4 g
(17 ± 1 %)
Control: 73±5 g
(17 ± 1 %)
Gavrieli,
Karfopoulou, et al.
(2013) (lean)
(3 mg/kg coffee)
3031±1595 kJ 3199±1072 kJ Treatment: 73±44 g
(40 ± 24 %)
Control: 80±36 g
(42 ± 19 %)
Treatment: 32±20 g
(40 ± 25 %)
Control: 33±10 g
(39 ± 12 %)
Treatment: 34±17 g
(19 ± 9 %)
Control: 37±11 g
(19 ± 6 %)
Gavrieli,
Karfopoulou, et al.
3257±1063 kJ 3199±1072 kJ Treatment: 85±39 g Treatment: 32±10 g Treatment: 37±10 g
(2013)(lean)
(6 mg/kg coffee)
(44 ± 16 %)
Control: 80±36 g
(42 ± 19 %)
(37 ± 12 %)
Control: 33±10 g
(39 ± 12 %)
(19 ± 5 %)
Control: 37±11 g
(19 ± 6 %)
Gavrieli,
Karfopoulou, et al.
(2013)
(overweight/obese)
(3 mg/kg coffee)
4103±1532 kJ* 3128±1629 kJ Treatment: 108±40 g
(44 ± 16 %)
Control: 73±40 g
(39 ± 21 %)
Treatment: 41±16 g
(38 ± 15 %)
Control: 34±21 g
(41 ± 25 %)
Treatment: 44±14 g
(18 ± 6 %)
Control: 37±19 g
(20 ± 10 %)
Gavrieli,
Karfopoulou, et al.
(2013)
(overweight/obese) (6
mg/kg coffee)
2927±1428 kJ 3128±1629 kJ Treatment: 72±48 g
(41 ± 28 %)
Control: 73±40 g
(39 ± 21 %)
Treatment: 31±17 g
(40 ± 22 %)
Control: 34±21 g
(41 ± 25 %)
Treatment: 34±17 g
(20 ± 10 %)
Control: 37±19 g
(20 ± 10 %)
Schubert et al. (2014)
(coffee)
2016±750 kJ 2118±663 kJ NM NM NM
Schubert et al. (2014)
(caffeine)
2287±648 kJ 2118±663 kJ NM NM NM
Data are means ± SDs (converted where applicable). NM = not measured NA = not applicable EE = energy expenditure
EI = Energy intake * = significant difference reported
Table 3 Results of included studies for gastric emptying and gut hormones
Study Gastric emptying Half time Gut Hormones
Lien et al. (1995) Control condition: 45.0±23.1 min
Coffee condition: 35.7±10.5 min*
NA
Boekema et al. (2000) Control condition: 83.4 min (median)
Coffee condition: 75.7 min (median)
NA
Johnston, Clifford and Morgan (2003) NA GIP AUC lower after caffeinated and decaffeinated
coffee
GLP AUC higher after decaffeinated coffee
Franke et al. (2008) Control condition: 123±5 min
Coffee condition: 125±9 min
NA
Akimoto et al. (2009) Control condition: 121.5 min (median)
Coffee condition: 105.7 min (median)*
NA
Beaudoin, Robinson and Graham (2011) NA Increased GLP-1in caffeinated coffee and
decaffeinated coffee trials over OFTT and OGTT
control trials
Increase GIP only in OFTT/Caf trial
Gavrieli, Karfopoulou, et al. (2013) NA Ghrelin, PYY, and GLP-1 revealed no effect of
treatment during testing or for AUC
Greenberg and Geliebter (2012) NA Ghrelin and leptin revealed no effect of treatment
during testing or for AUC
PYY significantly elevated 60-90 after decaf coffee;
AUC higher than caffeine or control
Schubert et al. (2014) Control condition: 182±34 min
Caffeine condition: 154±18 min
NA
Decaf condition: 177±25 min
Coffee condition: 179±61 min
Data are means ± SDs unless noted. Half time represents the length of time for half of the test meal/beverage to empty from the
stomach.
NA = not applicable *= significant difference reported AUC = Area under the concentration-time curve
OFTT = oral fat tolerance test OGTT = oral glucose tolerance test
Table 4 Results for included studies assessing appetite perceptions
Study Appetite results
Belza, Toubro and Astrup (2009) No differences for hunger, fullness, prospective food consumption, or
satiety between caffeine and placebo
Gavrieli et al. (2011) Hunger, desire to eat, satiety did not differ over time; hunger at 3 hours
was lower in caffeinated coffee than decaf or control; incremental AUC
trended towards reduced desire to eat compared to control
Gavrieli, Karfopoulou, et al. (2013) Only in overweight/obese participants, satiety was higher 15 and 60
minutes post low dose (3 mg/kg) coffee and 15 min post high dose coffee
(6 mg/kg) compared to water. No other appetite differences.
Greenberg and Geliebter (2012) Hunger AUC lowest in decaffeinated coffee, followed by caffeinated
coffee. Decaf was significantly different from placebo; caffeinated coffee
and caffeine were not.
Schubert et al. (2014) No effects on hunger. Main effects of trial observed for satiety, prospective
food consumption, and fullness; effects were in favor increased satiety and
fullness and decreased prospective food consumption such that coffee >
decaf > placebo > caffeine. Post-hoc comparisons were non-significant.
... Ingestão de cafeína, dessa forma, afeta processos discriminativos, de aprendizagem e de memória que possivelmente perpassam a resolução de problemas. Além disso, em diferentes dosagens a cafeína inibe o apetite (Gavrieli et al., 2013), entretanto este é um efeito que nem sempre ocorre, e os dados sobre como e quando a cafeína inibe o apetite de mamíferos ainda é inconclusivo (Schubert et al., 2017). Desta maneira, dado todos 4 RBTCC 24 (2022) seus efeitos, é esperado que a cafeína possa afetar a formação das relações de controle necessárias para a resolução de problemas. ...
... Estudos sobre o efeito da cafeína sobre a aquisição de pré-requisitos de uma situação de resolução de problemas são escassos (Schubert et al., 2017). Entretanto, em um estudo com o labirinto aquático de Morris, um procedimento de fuga (Angelucci et al., 1999(Angelucci et al., , 2002 e aprendizagem espacial, os dados obtidos indicaram que a administração de cafeína 30 mg/kg não teve efeito significativo na aquisição de comportamentos relevantes para a tarefa, mas teve o efeito de manter ou aprimorar o desempenho nesta tarefa em repetidas exposições, o que seria um efeito de aprimoramento de retenção de memória. ...
... Outro motivo pelo qual a administração aguda da cafeína pode ter dificultado a resolução do problema pode ter relação com o fato de que o problema a ser resolvido no experimento envolver comportamento motivado por alimento. Alguns estudos indicam que a cafeína pode exercer função inibidora de apetite, entretanto ainda não existem dados conclusivos sobre isso (Schubert et al., 2017). De qualquer forma, se este for o caso, estudos posteriores com este mesmo procedimento, e com a mesma administração de cafeína, porém com reforçadores não alimentares, podem produzir resultados distintos, bem como também fornecer mais dados sobre o eventual papel de inibidor de apetite da cafeína. ...
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Resolução de problemas por uma nova cadeia de respostas é uma forma abrangente de falar sobre recombinação de repertórios, entendida como a resolução de um problema a partir da reorganização de aprendizagens isoladas. Neste estudo, investigou-se a influência da cafeína no processo de recombinação em ratos. O procedimento consistiu em: 1) treino discriminativo, 2) campo aberto, 3) pré-teste, 4) treino dos repertórios pré-requisitos, 5) treino de recuperação, 6) teste de recombinação, e 7) campo aberto (reexposição). Os animais foram divididos em três grupos de quatro animais cada: grupo de administração de cafeína crônica, grupo agudo e grupo controle (sem cafeína). Nenhum animal do grupo agudo resolveu o problema, enquanto que animais dos grupos crônico e controle resolveram. Os dados indicam que a ingestão aguda inibiu a recombinação, bem como indicam que o procedimento utilizado é candidato a ser um modelo animal de comportamento novo (criatividade) para estudo de variáveis farmacológicas.
... It has widespread popularity for improving physical or mental performance due to its well-known stimulant effects [4]. Ice coffee, iced tea, and energy shots are the most consumed forms of caffeinated soft beverages, with a global industry that targets not only the general population but also adolescents [5,6]. Moderate consumption of caffeine is usually safe. ...
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Background Caffeine is most used psychoactive substance as a central nervous system stimulant in the methylxanthine family. Purpose The objectives of this research paper was to develop a validated LC-MS/MS assay to measure the caffeine levels in soft beverages (iced tea, ice coffee, and energy drinks) available in Istanbul, Turkey. Methods An analytical assay using LC-MS/MS was conducted, while separation was undertaken using a Shim-Pack FC ODS (150 mm × 2 mm). The validation was carried out in accordance with the ISO/IEC 17025 standard. Results The linearity was characterized up to 1000 mg/L, using correlation coefficients (r² = 0.9997) for caffeine. The limit of quantification and limit of detection values of the LC-MS/MS method for caffeine analysis was 0.13 mg/L and 0.04 mg/L, respectively. Demonstrating that the method was accurate, the mean recovery value acquired throughout the spiking experiment was 96.76%, while the relative standard deviation and relative error were equal to 1.68 and 1.75%, respectively. The bias and percent relative standard deviation in caffeine concentration during the inter-day stability testing based on the control chart study were 0.95 and 2.23, respectively. Mean caffeine concentrations in iced tea, energy drinks, and ice coffee were quantified as 37.96 ± 7.46, 105.71 ± 58.48 and 469.10 ± 196.23 mg/L, respectively. Conclusion Of 13 energy drinks analyzed, two of them exceeded the acceptable caffeine concentration standardized by the Turkish Food Codex. Therefore, toxicological monitoring of caffeine levels in soft beverages should be undertaken with a stricter approach by public authorities.
... Caffeine (3,7-dihydro-13,7-trimethyl-1H-purine-2,6-dione) was first discovered in coffee in 1820 and has taken its place as one of the most consumed central nervous system stimulants in the world (Butt and Tauseef Sultan, 2011;Egawa et al., 2006;Quadra et al., 2020). The use of caffeine has become a habit since the past, and it has been used and continues to be used as an appetite suppressant to stay awake and even lose weight (Cappelletti et al., 2014;Schubert et al., 2017). Caffeine intake is thought to be at the highest level in Scandinavian countries, where coffee consumption is the highest in the world (Köksal et al., 2017). ...
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Background: Caffeine in the safe dose range has been associated with a reduction in the risk of chronic diseases. There is evidence that caffeine intake has both protective and negative effects on cardiovascular diseases. Aim: This study aimed to investigate caffeine intake in cardiovascular patients. Methods: The study sample was selected from individuals who applied to the Cardiology policlinic of Tekirdağ Namık Kemal University Hospital. A questionnaire was applied using face-to-face interview method to determine their demographic information, nutritional status and anthropometric measurements. Moreover, the nutritional status of the participants was determined by the Food Frequency Questionnaire and the type of cardiovascular disease was determined by a physician. The blood parameters of the sample for the last three months were questioned. The sample has been ninety people of whom fifty cardiovascular diseases (CVDs) were diagnosed and forty were non-diagnosed (ND). Results: The mean age of individuals (n = 90) was 43.2 ± 14.4. The BMI and waist circumference of the CVDs group were statistically significantly higher than the ND group (p < 0.001). While the total caffeine consumption of the ND group was 209.34 ± 143.85 mg/day, consumption of the CVDs group was 209.99 ± 196.76 mg/day. LDL cholesterol and total cholesterol did not show statistically significant difference between the two groups. However, HDL cholesterol was significantly higher in the ND group (p ≤ 0.001). Conclusion: Present results show that daily caffeine consumption may partially affect blood parameters associated with cardiovascular diseases, especially in the presence of coronary artery disease.
... Other studies have also shown that coffee consumption does not affect a person's intake, so it is very unlikely to affect a person's nutritional status [27]. Other studies have also shown that coffee consumption 3-4 h before eating has a low effect on macronutrient intake and coffee consumption ½-4 h can also suppress energy intake [28]. This is in line with a study which showed that consumption of instant coffee more than three times a day led to a 1.3 times higher risk of developing obesity, such as central obesity in adults [29]. ...
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Technology development causes easy access to various sectors, including ordering food online. Fast food is one of the foods that many people reviewed in online applications that are high in fat with a density of 40% of total calories. Meanwhile, the consumption of vegetables and fruits of Indonesia’s people is still inadequate; only 63.3% consume as recommended. These things will undoubtedly increase the body mass index (BMI) and increase the risk of overweight and obesity. This study aims to analyze the impact of online order development on fast food, vegetables, and fruits consumption behavior on students in Surabaya. This descriptive cross-sectional study enrolled 317 students in Surabaya City, East Java, Indonesia. The online survey collected data through online platforms, SurveyMonkey. Data were analyzed in statistical software SPSS 25.0 using multivariate binomial linear regression test. The significance level was set at p<0.05. Regression analysis shows that the habit of ordering boba drinks with a weekly frequency has a significant relationship with the incidence of overweight/obesity in respondents (p = 0.015; OR = 3.037; 95% CI (1.236-7.462)) when compared to respondents who have the habit of ordering and consuming boba drinks every month. Consumption habits of boba drink are associated with higher body mass index (BMI), increasing the risk of overweight and obesity. A policy from the government and related parties is needed to regulate boba consumption limits for the community.
... Less than half of the population carry the CYP1A2 gene associated with fast metabolism. Some evidence suggest that higher CA intakes increases the risk of insulin resistance [10], high blood pressure [11], and heart attacks [12] in intermediate and slow but not rapid caffeine metabolizers [8] with fast metabolizer seeing greater improvements in athletic performance [13,14] and reduced appetite [15,16]. ...
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Paraxanthine (PXN) is a metabolite of caffeine that has recently been reported to enhance cognition at a dose of 200 mg. Objective: To determine the acute and short-term (7-day) effects of varying doses of PXN on cognitive function and side effects. Methods: In a double blind, placebo-controlled, crossover, and counterbalanced manner, 12 healthy male and female volunteers (22.7 ± 4 years, 165 ± 7 cm, 66.5 ± 11 kg, 24.4 ± 3 kg/m2) ingested 200 mg of a placebo (PLA), 50 mg of PXN (ENFINITY™, Ingenious Ingredients, L.P.) + 150 mg PLA, 100 mg PXN + 100 mg PLA, or 200 mg of PXN. With each treatment experiment, participants completed side effect questionnaires and donated a fasting blood sample. Participants then performed a series of tests assessing cognition, executive function, memory, and reaction time. Participants then ingested one capsule of PLA or PXN treatments. Participants then completed side effects and cognitive function tests after 1, 2, 3, 4, 5, and 6 h of treatment ingestion. Participants continued ingesting one dose of the assigned treatment daily for 6-days and returned to the lab on day 7 to donate a fasting blood sample, assess side effects, and perform cognitive function tests. Participants repeated the experiment while ingesting remaining treatments in a counterbalanced manner after at least a 7-day washout period until all treatments were assessed. Results: The Sternberg Task Test (STT) 4-Letter Length Present Reaction Time tended to differ among groups (p = 0.06). Assessment of mean changes from baseline with 95% CI's revealed several significant differences among treatments in Berg-Wisconsin Card Sorting Correct Responses, Preservative Errors (PEBL), and Preservative Errors (PAR Rules). There was also evidence of significant differences among treatments in the Go/No-Go Task tests in Mean Accuracy as well as several time points of increasing complexity among STT variables. Finally, there was evidence from Psychomotor Vigilance Task Test assessment that response time improved over the series of 20 trials assessed as well as during the 6-h experiment in the PXN treatment. Acute and short-term benefits compared to PLA were seen with each dose studied but more consistent effects appeared to be at 100 mg and 200 mg doses. No significant differences were observed among treatments in clinical chemistry panels or the frequency or severity of reported side effects. Results provide evidence that acute ingestion of 100 mg and 200 mg of PXN may affect some measures of cognition, memory, reasoning, and response time as well as help sustain attention. Additionally, that acute and daily ingestion of PXN for 7 days is not associated with any clinically significant side effects. Conclusions: PXN may serve as an effective nootropic agent at doses as low as 50 mg.
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This study aims to examine the antecedents of consequences of healthiness in the café business context. Additionally, this study attests to the moderating effect of one's concern for health (health concern) between healthiness and attitude. To attain a more vivid response, this research selected Starbucks coffeehouse as a case study. Hygiene, healthiness, and nutritional disclosure are the determinants of healthiness in the café business area. The consequences of healthiness are attitude and purchase intention for café products. Health concern is the moderating variable between healthiness and attitude in the context of café businesses. In order to test the association between attributes, a survey was used. Amazon Mechanical Turk was chosen to recruit survey participants. The valid observation for data analysis was 455 participants. For hypothesis testing, a structural equation model was implemented. Regarding the results, health concern is positively influenced by hygiene and organicness, but healthiness is negatively affected by nutritional disclosure. Moreover, it was found that health concern significantly moderates the relationship between healthiness and attitude, and attitude exerts a positive effect on purchase intention.
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Background: Both catechin polyphenols and caffeine have been shown to have beneficial effects on weight control in the adult population. However, the influence of tea or coffee supplementation on body weight in adolescents has never been tested. The aim of the present study was to investigate the effect of tea and coffee consumption on body weight and body fat in adolescents with obesity. Methods: Randomized clinical trial comparing three weight-loss interventions composed of similar family-based counseling sessions on nutritional education with coffee (2 cups per day, total amount 160 mg caffeine), green tea (3 cups per day, total amount 252 mg catechin and 96 mg caffeine), or herbal tea (as placebo, 3 cups per day). Nutritional intake, BMI, and fat percentage, as measured by bioelectrical impedance, were compared between the groups at 3 and 6 months. Results: Forty-eight children were included in the final analysis: 18 in the coffee arm, 17 in the green tea arm, and 13 in the placebo arm. Nineteen (39.6%) children were males, with a median (interquartile range) age of 13 (11-14) years. There were no significant group differences in age, sex, and BMI (absolute number and percent of the 95th percentile) upon study entry. Comparison between the three interventions in total change in BMI from baseline revealed a significant advantage for coffee consumption compared with green tea and placebo (-9.2% change in BMI in the coffee group compared with -2.3% and 0.76% in the green tea and placebo group, respectively, p = 0.002). Conclusions: Dietary recommendations combined with coffee intake and, to a lesser extent, tea catechins may be associated with reduced weight and adiposity among adolescents. Clinical trial registration number: NCT05181176.
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The objective of the present research was to review the state of the art on the consequences of drinking coffee at the different levels of the gastrointestinal tract. At some steps of the digestive process, the effects of coffee consumption seem rather clear. This is the case for the stimulation of gastric acid secretion, the stimulation of biliary and pancreatic secretion, the reduction of gallstone risk, the stimulation of colic motility, and changes in the composition of gut microbiota. Other aspects are still controversial, such as the possibility for coffee to affect gastro-esophageal reflux, peptic ulcers, and intestinal inflammatory diseases. This review also includes a brief summary on the lack of association between coffee consumption and cancer of the different digestive organs, and points to the powerful protective effect of coffee against the risk of hepatocellular carcinoma. This review reports the available evidence on different topics and identifies the areas that would most benefit from additional studies.
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Introduction: Many consumers use dietary supplements in the hopes of increasing energy and burning more calories, which if sustained over time may help accelerate weight loss. The purpose of this clinical trial was to investigate the effects of an over-the-counter thermogenic supplement called Burn-XT™ (BXT) on metabolic rate, substrate oxidation, and various psychometric indices of affect that impact weight management. Methods: Using a double-blind, placebo-controlled, cross-over design, 16 women and 10 men (29.3 ± 7.3 yr, 169.4 ± 8.6 cm, 75.5 ± 14.3 kg) underwent two testing sessions: placebo (PL) and BXT. Seated metabolic rate and substrate oxidation, vital signs, and anchored visual analogue scale (VAS) assessments of energy, mood, motivation, focus, fatigue, concentration, and appetite were made before supplementation and hourly for three hours post-ingestion. Two-factor (2x4) factorial ANOVAs and paired sample t-tests (corrected for multiple comparisons) were used for analyses. Results: Significant increases in metabolic rate (oxygen consumption) were noted at 60 minutes in BXT (+11.9 mL O2/min) vs. PL (-2.5 mL O2/min), p = 0.004, d = -0.74. Only BXT increased metabolic rate compared to baseline at 60 minutes (+11.9 mL O2/min, p = 0.021, d = -0.53) and 120 minutes (+12.1 mL O2/min, p = 0.019, d = -0.54). The AUC for resting energy expenditure increased more in BXT vs. PL (p = 0.007, d = -0.57). VAS detected significant improvements in energy, mood, focus, and concentration for BXT vs. PL at 120 and 180 minutes (all p < 0.05, d = -0.58 to -0.68). In all cases, within-group changes from baseline for these VAS parameters were significant (all p < 0.05, d = -0.76 to -1.38) in BXT but not in PL. No within or between group differences in appetite, substrate oxidation, or heart rate were noted. Small (~3-4 mm Hg), but statistically significant (p < 0.05, d = -0.51 to -0.69) increases in diastolic blood pressure were noted in BXT at 60, 120, and 180 min vs. PL; and in systolic blood pressure at 60 min vs. PL. In all cases, values remained within normal clinical hemodynamic ranges. Conclusions: A single dose of BXT safely increased metabolic rate, energy, mood, focus, and concentration. Given that these factors are known to favorably impact weight management, future studies should determine whether daily supplementation with BXT reduces body weight and improves body composition.
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Meta-analyses on coffee and cancer incidence mainly restricted to limited cancers. We carried out a more comprehensive meta-analysis of cohort studies to explore association between coffee and most cancer types. We conducted comprehensive search and summarized relative risk (RR) and 95% confidence intervals for the highest versus lowest coffee intake and cancer using STATA12. We conducted dose-analysis if result suggested significant association. The publication bias was evaluated with begg's and egger's test. Finally, 105 individual prospective studies were included. Inverse associations were observed on oral, pharyngeal, colon, liver, prostate, endometrial cancer and melanoma, with RR 0.69 (95% CI?=?0.48-0.99, I(2)?=?73.4%, P?=?0.044), 0.87 (95% CI?=?0.78-0.96, I(2)?=?28.4%, P?=?0.007), 0.46 (95% CI?=?0.37-0.57, I(2)?=?0%, P?=?0), 0.89 (95% CI?=?0.84-0.93, I(2)?=?30.3%, P?=?0.003), 0.73 (95% CI?=?0.67-0.80, I(2) ?=?0%, P?=?0) and 0.89 (95% CI?=?0.80-0.99, I(2) ?=?0%, P?=?0.031) respectively. However, the relative risk for lung cancer is 2.18 (95% CI?=?1.26-3.75, I(2) ?=?63.3%, P?=?0.005). The summary relative risk for increment of 2 cups of coffee were RR?=?0.73, 95% CI?=?0.67-0.79 for liver cancer, RR?=?0.97, 95% CI?=?0.96-0.98 for prostate cancer and RR?=?0.88, 95% CI?=?0.85-0.92 for endometrial cancer. Accordingly, coffee intake was associated with reduced risk of oral, pharynx, liver, colon, prostate, endometrial cancer and melanoma and increased lung cancer risk.
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This study aimed to characterise lean and obese phenotypes according to diet and body composition, and to compare fasting and postprandial appetite and metabolic profiles following a high-fat test meal. A total of ten lean (BMI40 and 30 kg/m2) high-fat consumers (OHF; >40 % energy from fat) were recruited. Before and following the test meal (4727 kJ (1130 kcal), 77 % fat, 20 % carbohydrate (CHO) and 3 % protein), fasting plasma glucose, insulin, leptin, ghrelin, peptide YY (PYY), RER, RMR and subjective appetite ratings (AR) were measured for 6 h. Thereafter, subjects consumed a self-selected portion of a standardised post-test meal (40 % fat, 45 % CHO and 15 % protein) and reported AR. Fasting (P=0·01) and postprandial (P<0·001) fat oxidation was significantly higher in LHF than in LLF but was not different between LHF and OHF. Although similar between the lean groups, fasting and postprandial energy expenditures were significantly higher in OHF compared with LHF (P<0·01). Despite similar AR across groups, LLF consumed a relatively greater quantity of the post-test meal than did LHF (7·87 (sd 2·96) v. 7·23 (sd 2·67) g/kg, P=0·013). The lean groups showed appropriate changes in plasma ghrelin and PYY following the test meal, whereas the OHF group showed a blunted response. In conclusion, the LHF phenotype had a greater capacity for fat oxidation, which may be protective against weight gain. OHF individuals had a blunted appetite hormone response to the high-fat test meal, which may subsequently increase energy intake, driving further weight gain.
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Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement--a reporting guideline published in 1999--there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.
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Recent reports on caffeine intakes in the United States have highlighted the importance of obtaining accurate and valid measures of caffeine exposure. The objective of this study is to compare two methods of assigning caffeine values to beverages: brand-specific values versus an aggregate single value representing a broader range of products within a beverage category (i.e., category-specific). The two methods yielded some small, but statistically significant differences in the estimation of caffeine intake from coffee, tea, and carbonated soft drinks (CSDs) for all ages combined and within several of the adult age groups (i.e., 35-49, 50-64, and ≥65 years). These differences, while small, suggest that detailed brand-specific data, particularly for CSDs, commercially pre-packaged or bottled teas, coffee, and specialty coffee drinks, provide more accurate estimates of caffeine exposure for some age groups. Despite these differences, these data provide some assurance that studies using a single aggregate caffeine value provide reasonable measures of caffeine exposure, particularly for studies conducted over a decade ago when there were fewer caffeinated products and brand-specific data available. As the caffeinated beverage marketplace continues to evolve, the use of more detailed, brand-specific data will likely strengthen the assessment of caffeine exposure in the United States. Copyright © 2015. Published by Elsevier Ltd.
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Background: Gastric emptying (GE) could influence exercise-induced changes in appetite and energy intake. GE also could contribute to changes in gastric symptoms and the availability of nutrients during exercise, which will subsequently affect performance. Objective: The objective of this review was to determine the effects of acute exercise on GE using a systematic review and meta-analysis. The most common parameters to determine GE were selected, consisting of half-emptying time and volume emptied. Oral-caecal transit time (OCTT) was also examined. Data sources: Research databases (PubMed, Scopus, Google Scholar, EBSCOhost, SPORTDiscus) were searched through November 2013 for original studies, abstracts, theses and dissertations that examined the influence of acute exercise on GE. Study selection: Studies were included if they evaluated GE or OCTT during and/or after exercise and involved a resting control trial. Study appraisal and synthesis: Initially, 195 studies were identified. After evaluation of study characteristics and quality and validity, data from 20 studies (35 trials) involving 221 participants (157 men; 52 women; 12 unknown) were extracted for meta-analysis. Random-effects meta-analyses were utilised for the three main outcome variables, and effect sizes (ES) are reported as Hedge's g due to numerous small sample sizes. Results: Random-effects modelling revealed non-significant and small/null main effect sizes for volume emptied (ES = 0.195; 95% CI -0.25 to 0.64), half-time (ES = -0.109, 95% CI -0.66 to 0.44) and OCTT (ES = 0.089; 95% CI -0.64 to 0.82). All analyses exhibited significant heterogeneity and numerous variables moderated the results. There was a dose response of exercise intensity; at lower intensities GE was faster, and at high exercise intensities GE was slower. Walking was associated with faster GE and cycling with slower GE. Greater volume of meal/fluid ingested, higher osmolality of beverage and longer exercise duration were also associated with slower GE with exercise. Limitations: The major limitation is that the majority of studies utilised a liquid bolus administered pre-exercise to determine GE; the relationship to post-exercise appetite and energy intake remains unknown. Study populations were also generally active or trained individuals. Furthermore, our review was limited to English language studies and studies that utilised resting control conditions. Conclusions: These results suggest that exercise intensity, mode, duration and the nature of meal/fluid ingested all influence GE during and after acute exercise. The relationship of GE parameters with appetite regulation after exercise remains largely unexplored. Further integrative studies combining GE and alterations in gut hormones, as well as in populations such as overweight and obese individuals are needed.
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Coffee is one of the most widely consumed beverages in the world and has a number of potential health benefits. Coffee may influence energy expenditure and energy intake, which in turn may affect body weight. However, the influence of coffee and its constituents – particularly caffeine – on appetite remains largely unexplored. The objective of this study was to examine the impact of coffee consumption (with and without caffeine) on appetite sensations, energy intake, gastric emptying, and plasma glucose between breakfast and lunch meals. In a double-blind, randomised crossover design. Participants (n = 12, 9 women; Mean ± SD age and BMI: 26.3 ± 6.3 y and 22.7 ± 2.2 kg•m−2) completed 4 trials: placebo (PLA), decaffeinated coffee (DECAF), caffeine (CAF), and caffeine with decaffeinated coffee (COF). Participants were given a standardised breakfast labelled with 13C-octanoic acid and 225 mL of treatment beverage and a capsule containing either caffeine or placebo. Two hours later, another 225 mL of the treatment beverage and capsule was administered. Four and a half hours after breakfast, participants were given access to an ad libitum meal for determination of energy intake. Between meals, participants provided exhaled breath samples for determination of gastric emptying; venous blood and appetite sensations. Energy intake was not significantly different between the trials (Means ± SD, p > 0.05; Placebo: 2118 ± 663 kJ; Decaf: 2128 ± 739 kJ; Caffeine: 2287 ± 649 kJ; Coffee: 2016 ± 750 kJ); Other than main effects of time (p < 0.05), no significant differences were detected for appetite sensations or plasma glucose between treatments (p > 0.05). Gastric emptying was not significantly different across trials (p > 0.05). No significant effects of decaffeinated coffee, caffeine or their combination were detected. However, the consumption of caffeine and/or coffee for regulation of energy balance over longer periods of time warrant further investigation.
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The effect of roasting of coffee beans and the extraction of ground coffee with different volumes of hot pressurised water on the caffeine and the total caffeoylquinic acids (CQAs) content of the resultant beverages was investigated. While caffeine was stable higher roasting temperatures resulted in a loss of CQAs so that the caffeine/CQA ratio was a good marker of the degree of roasting. The caffeine and CQA content and volume was determined for 104 espresso coffees obtained from coffee shops in Scotland, Italy and Spain, limited numbers of cappuccino coffees from commercial outlets and several instant coffees. The caffeine content ranged from 48-317 mg per serving and CQAs from 6-188 mg. It is evident that the ingestion of 200 mg of caffeine per day can be readily and unwittingly exceeded by regular coffee drinkers. This is the upper limit of caffeine intake from all sources recommended by US and UK health agencies for pregnant women. In view of the variable volume of serving sizes, it is also clear that the term "one cup of coffee" is not a reproducible measurement for consumption, yet it is the prevailing unit used in epidemiology to assess coffee consumption and to link the potential effects of the beverage and its components on the outcome of diseases. More accurate measurement of the intake of coffee and its potentially bioactive components are required if epidemiological studies are to produce more reliable information.
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A cross-section of Australian “Espresso/short black” coffee and coffee-flavoured milk samples were purchased and analysed for their caffeine content using micellar electrokinetic capillary chromatography (MEKC). Coffees were collected using convenience cluster sampling across four major cities. Packaged coffee-flavoured milks were collected from national grocery distributors. In all, 131 espresso samples and 20 coffee-flavoured milks were analysed. The mean (±SD) quantity of caffeine from espresso coffee was 107 ± 37 mg/serving with a concentration of 2550 ± 1030 mg/L. The mean (±SD) quantity of caffeine from coffee-flavoured milk was 99 ± 50 mg/carton with a concentration of 193 ± 90 mg/L. There was considerable variation in caffeine content across both categories and within the same espresso brand purchased at different locations. In total, 42 samples (27.5%) contained ≥120 mg per serving of caffeine, and 20 samples (13.1%) exceeded 165 mg per serving.The expanded caffeine data supports our original findings which indicated that the probability of consumer exposure to high caffeine doses from popular coffee beverages in Australia is greater than previously reported.