The effect of agar jelly on energy expenditure, appetite, gastric emptying and glycaemic response


Background and purpose

Agar contains a high amount of soluble fibre and has been shown to delay gastric emptying (GE) without impacting on glycaemic response (GR). The current study aimed to further the limited data on the effect of agar on metabolism by assessing the effects on GE and GR as well as appetite- and diet-induced thermogenesis (DIT).


In this randomized control trial, eleven healthy volunteers were tested on two occasions following an overnight fast. Following baseline and resting measurements, volunteers were either fed a fruit-flavoured drink (liquid) or consumed a fruit-flavoured jelly (jelly). The two were exactly the same in composition except the jelly contained 4 g of agar crystals. Both contained 50 g of available carbohydrate. DIT was measured using indirect calorimetry, GE using the 13C sodium acetate breath test, appetite using visual analogue scale and GR using finger prick blood samples.


The jelly significantly delayed GE across all time points—latency phase (p = 0.07), lag phase (p = 0.04), half-time (p < 0.0001), ascension time (p = 0.025). The jelly also increased all appetite parameters—hunger (p = 0.006), fullness (p = 0.035), desire to eat (p = 0.03) and prospective consumption (p = 0.011). However, there were no significant differences in either GR or postprandial DIT between the liquid and jelly.


Agar delays GE and increases appetite but does not change GR or DIT most probably due to the increase in viscosity caused by the agar jelly.


Agar is a red algal polysaccharide containing ~80 % soluble fibre commonly used in the Japanese diet. Agar forms a viscous gel when heated in water. The addition of soluble dietary fibres to foods increases the viscosity of that food which has implications for their digestion and absorption. Many other soluble fibre foods have been shown to reduce glycaemic response (GR) by delaying gastric emptying (GE) with GE accounting for 34 % of the variability in peak blood glucose responses after a 75-g glucose load [1]. For example, high molecular weight barley β-glucan increases the viscosity of a soup, which decreases its GE rate and reduces its GR [2]. For agar, only two previous studies on its effects on glycaemic control have been undertaken. The first indicated that a 12-week dietary intervention resulted in a decreased insulin response compared to a control group in diabetic patients but no difference in blood glucose [3]. The second by Sanaka et al. [4] found that following the ingestion of 2.5 g of agar, GE was delayed yet there were no decreases in postprandial blood glucose concentration. It is likely that the delay in GE observed was due to the increased viscosity of the agar. Previous research from our laboratory has also shown that GR and GE do not always correlate [5].

Soluble dietary fibre has been shown to have beneficial effects on glycaemic and insulin responses and cholesterol levels. It has also been shown to increase satiety that may encourage body weight maintenance [6, 7]. Several explanations are possible for this. One potential mechanism is that low GR foods have been shown to be more satiating via the glucostatic theory [8, 9]. The delayed GE is another explanation as the prolonged gastric distension due to the retention of food in the stomach causes an enhanced and elongated period of satiety [5, 10, 11]. However, there is potential that satiety may be altered by gut hormones as previous research has indicated that dietary fibre can increase GLP-1 and decrease ghrelin [12, 13]. To date, no studies have examined the effect of agar on appetite, even though it has been shown to influence GE [4] and cause significant body weight reduction [3].

In order to ascertain the true potential benefit of agar as a functional food in the management of metabolic diseases, it is necessary to ascertain the effect that the food can have on both energy expenditure and appetite. Diet-induced thermogenesis (DIT) is the amount of energy required for absorption and metabolism of food and represents ~10 % of total daily energy expenditure [14]. Although DIT consists of only a small proportion of total energy expenditure, if a food that has the potential to cause a rise in DIT is eaten repeatedly over time, it could prove beneficial in controlling the development of obesity. Soluble fibre has been shown to increase appetite; however, there is also limited evidence, suggesting that fibre may decrease DIT [2, 15, 16] with suggestions that this may be due to decreased palatability causing a reduced cephalic response (early initiation of digestion prior to ingestion that results in fast release of insulin that peaks between 1 and 4 min). If agar is to be a viable food ingredient for weight maintenance or loss, it is important to ascertain the degree to which agar may decrease DIT and the influence of this on energy balance.

The objective of the current study was to further the limited data on the effect of agar on metabolism as a potential functional ingredient to aid weight loss. The aims are to measure the effect of agar on GR, GE, appetite and DIT.



Twelve healthy subjects were recruited for the study by means of advertisements and personal communications. One volunteer discontinued his participation for personal reasons leaving 11 volunteers in total (Table 1).

Table 1 Volunteer characteristics

Before inclusion in the study, potential participants were briefed on all aspects of the experiment and were given the opportunity to ask questions. This was followed by a health assessment, which included anthropometric measurements and a health questionnaire (giving details of food allergies/intolerances, metabolic diseases, special dietary needs and smoking habits). Those who fulfilled all the acceptable criteria (age 18–60 years; BMI < 30 kg/m2; blood pressure 110–120/75–85 mmHg; fasting blood glucose <6 mmol/l; not on prescription medication; no self-declared genetic or metabolic diseases) were included in the study. On the day before each test, subjects were asked to restrict their intake of alcohol and caffeine-containing drinks and to refrain from strenuous physical activity.

The study was conducted at the Functional Food Centre at Oxford Brookes University. All participants gave written informed consent before starting, and the study was initiated after the approval by the Oxford Brookes University Research Ethics Committee according to the guidelines laid down in the Declaration of Helsinki. On each test day, subjects came to the Functional Food Centre between 7 and 9 a.m. in the morning after an overnight fasting (10–12 h before testing time). Subjects were instructed to keep all physical exertion in the morning of testing to a minimum.

Study design

Volunteers participated in a randomized, controlled crossover study where they consumed either a jelly or a liquid on separate days in a random order. On the day prior to testing, volunteers were asked to record their food and repeat it prior to the subsequent test.

Energy expenditure

On arrival in the laboratory, volunteers were asked to rest for 30 min in a supine position on a bed before baseline measurements of EE were taken. Resting metabolic rate (RMR) was determined in the morning between 7 and 9 am. RMR was measured at 1-min intervals for 30 min under the ventilated hood indirect calorimetry system (Deltatrac™ II Metabolic Monitor, Datex-Ohmeda Inc., Finland). The analyser was calibrated on each test day with standardized gases containing 5 % CO2 and 95 % O2.

DIT was determined for 15 min in every 30 min for 180 min after test meal ingestion [17]. The first 5 min of every 15-min time period was discarded to allow for stabilization within the Deltatrac hood, and the average of the remaining 10 min was used. This time period was recommended to be appropriate to measure the thermic effect of foods [17]. Energy expenditure was calculated using the equations of Lusk [18]. DIT was calculated as the increase in energy expenditure/min above pre-meal values for 3 h after meal intake. The incremental area under the curve (iAUC) was then calculated using the trapezoidal rule.


One hundred millimetre continuous line visual analogue scales (VAS) was utilized to measure subjective feelings of hunger, fullness, desire to eat and prospective food consumption, at baseline (0 min) and then every 15 min for the first hour and every 30 min for the following 3 h after the commencement of eating the test food [19]. The VAS ratings were quantified by measuring in millimetres, the distance between the left end of the scale and the point marked by the participant. The ‘change in the subjective feeling’ was calculated by computing the difference between the response at a time point and the baseline value (at 0 min). Using the ‘change in subjective feeling’ data, temporal curves were constructed for each of the four VAS questions for the testing time. The iAUC (using the trapezoidal rule) was then calculated for each of these curves.

Blood glucose measurements

The protocol used to measure the blood glucose response was adopted from that described by Brouns et al. [20] and is in line with procedures recommended by the Food and Agricultural Organization (FAO)/World Health Organization (WHO) [21]. Blood was obtained by finger prick using the Unistik 3 single-use lancing device (Owen Mumford, Woodstock, UK). Before a finger prick, subjects were encouraged to warm their hand to increase blood flow. To minimize plasma dilution, fingertips were not squeezed to extract blood but were instead gently massaged starting from the base of the hand moving towards the tips. The first 2 drops of expressed blood were discarded, and the next drop was used for testing.

Blood glucose was measured using the HemoCue 201+ Glucose analyzer (HemoCue Ltd, Dronfield, UK). The HemoCue is a reliable method of blood glucose analysis [22]. Fasting blood samples were taken at −5 and 0 min, and the test food was consumed immediately afterwards. The participants consumed the test food and the water at a comfortable pace, within 15 min. Further blood samples were then taken at 15, 30, 45 60, 90, 120, 150 and 180 min after consuming the test meal.

The GR data were converted to ‘the change in GR’ values. The change in GR was calculated by computing the difference between the blood glucose concentration at a time point and mean baseline blood glucose concentration (based on 2 baseline values taken 5 min apart). The total blood glucose response was expressed as the iAUC ignoring the area beneath the baseline and was calculated geometrically using the trapezoidal rule [20, 23, 24]. It was this change in GR that was used for all further analyses, including iAUC calculations, blood glucose response curve construction and statistics.

Gastric emptying

Sodium acetate labelled with 1-13C was used in this study to measure GE as acetate is hydrophilic, poorly absorbed in the stomach and rapidly metabolized after absorption. Sodium [1-13C] acetate is considered a reliable and valid method for identifying changes in GE of semi-solids [25]. Breath samples were collected by blowing gently into a 10-ml Exetainer (Labco, Buckinghamshire, UK) with a drinking straw and replacing the cap just before the end of exhalation. Breath samples were analysed using isotope ratio mass spectrometry (ABCA, Sercon, Crewe, UK), and the results were expressed relative to V-PDB, an international standard for known 13C composition. 13CO2 values were expressed as the excess amount in the breath above baseline and converted into moles. Data are then displayed as percentage of 13CO2 dose recovered per hour and cumulative percentage 13CO2 recovered over time. CO2 production was assumed to be 300 mmol/m2 body surface area per hour. Body surface area was calculated using a validated weight–height formula [26]. This was then fitted to a GE model developed by Ghoos et al. [27]. For all the data, r 2 coefficient between the modelled and raw data was calculated and r 2 was <0.95 for all tests. From this model, several parameters were measured. Lag phase and half-time were calculated using the formulae derived by Ghoos et al. [27]. Lag phase is the time taken to maximal rate of 13CO2 excretion [28] and is equivalent to the time of the inflection point [29]. Half-time is the time it takes 50 % of the 13C dose to be excreted [28]. Latency phase [29] is the point of intersection of the tangent at the inflection point of the 13CO2 excretion curve representing an initial delay in the excretion curve. Ascension time [29] is the time course between the latency phase and half-time, representing a period of high 13CO2 excretion rates.

Test meal

The test meal consisted of either a liquid or a jelly. The liquid contained 150 ml of apple and mango juice (Tesco, Cheshunt, Hertfordshire, UK) and 35 g glucose powder (Lloyds, Coventry, UK). The jelly contained 150 ml of apple and mango juice (Tesco, Cheshunt, Hertfordshire, UK), 35 g glucose powder (Lloyds, Coventry, UK) and 4 g agar (80.9 % fibre) (Clearspring Ltd, London, UK). The jelly was made by heating the juice and agar in a saucepan until it was boiling, adding in the glucose powder and 13C sodium acetate and then simmering and stirring for 5 min at which point the agar was dissolved. The jelly was then cooled and allowed to set. The same procedure was repeated for the liquid to control for evaporation between the two test meals. Both meals were served with 150 ml of drinking water. Available carbohydrates of the meals were calculated using the FAO/WHO procedure [21] according to the nutrition information available from the food manufacturers. The test meals were calculated to provide 50 g of available carbohydrates.

Statistical analysis

Studies of the analysis of GR in humans have been based on 10 subjects, as reviewed by the FAO/WHO [21] to take into account the inter-individual variations, and this number is similar to that used in the previous study on this topic [4]. Hence, a sample size of 11 was considered adequate for the current study. Statistical analysis was performed using Statistical Package for the Social Sciences (version 20.0; SPSS, Chicago, IL, USA), and data and figures were processed in Microsoft Excel spreadsheet (2006, Reading, UK).

Differences in the iAUC for DIT, GR and satiety as well as the GE parameters were compared using paired sample t test. A Kolmogorov–Smirnov test before analysis indicated that all the data sets were normally distributed. Significance was set at p < 0.05. Values are presented as mean ± standard deviation.


Gastric emptying

There were significant differences in GE for all time points between the two meals—latency phase (p = 0.007), lag phase (p = 0.04), half-time (p < 0.0001) and ascension time (p = 0.025) with the jelly causing a delayed GE in comparison with the liquid (Table 2). The differences in all time points indicate that GE was delayed both for initial emptying and for later emptying following the jelly (Fig. 1).

Table 2 Indicates volunteer’s gastric emptying (GE) half-time, lag phase, latency phase and ascension time of each of the meals—jelly and liquid
Fig. 1

Indicates volunteer’s gastric emptying following both of the meals—jelly and liquid. Data are given as mean ± SD (n = 11) *p < 0.05


There were significant differences in appetite AUC for all appetite parameters—hunger (p = 0.006), fullness (p = 0.035), desire to eat (p = 0.03) and prospective consumption (p = 0.011) between the two meals with the jelly causing an increase in appetite in comparison with the liquids (Table 3).

Table 3 Indicates volunteer’s (n = 11) area under the appetite curve from visual analogue scales for hunger, fullness, desire to eat and prospective consumption of each of the meals—jelly and liquid

Glycaemic response

There was no significant difference in GR AUC between the two meals (liquid 161 ± 54, jelly 168 ± 45 mmol min/l; p = 0.738; Fig. 2). There was no significant correlation between GE and GR (p > 0.05).

Fig. 2

Indicates volunteer’s glycaemic response following both of the meals—jelly and liquid. Data are given as mean ± SD (n = 11)

Energy expenditure

There were no significant differences in baseline energy expenditure between the two test days (liquid 0.98 ± 0.20; jelly 0.99 ± 0.21 kcal/min). There was no significant difference in total DIT between the two meals (liquid 7.8 ± 4.0, jelly 6.0 ± 4.7 kcal; p = 0.436; Fig. 3).

Fig. 3

Indicates volunteer’s diet-induced thermogenesis following both of the meals—jelly and liquid. Data are given as mean ± SD (n = 11)


The current study demonstrated the ability of 4 g of agar to delay GE and increase appetite but without having a significant effect on either GR or DIT. Although other studies have shown a significant effect of soluble fibre on GR [30, 31], the current study was unable to see any difference in GR when agar was added to a liquid. This result is counterintuitive as it would be expected that a delay in GE would result in a decreased availability of glucose for absorption, and hence a lower GR. However, the result of the current study is in keeping with the one other previous study on the effect of agar on GR [4]. Sanaka et al. [4] in their discussion highlight that their findings imply there may be a tendency towards a reduced GR following the agar. They believe this difference may have been masked by their low subject numbers. Although subject numbers in the current study were the same as the previous one, the data clearly indicate no difference or tendency towards a difference in GR with the addition of agar. This is especially true given that the quantity of agar used in the current was larger than that used in Sanaka et al. [4]. Following an intervention consisting of 180 g of agar per day for 12 weeks in diabetic individuals, Maeda et al. [3] found a difference in the baseline HbA1C and a decrease in insulin AUC, yet no difference in GR. However, in the study, no agar was added to the test meal itself. Together, these data suggest that even very large amounts of agar do not change GR.

An interesting result from the current study is that GE was significantly delayed throughout the emptying period, yet this did not result in a lower GR and the two parameters were not correlated with each other. It has previously been shown that the delivery of nutrients into the duodenum for absorption plays a significant role in the blood glucose response [1]. In previous work by our group, we have shown that soups have the ability to delay GE yet still significantly increase GR, indicating that the two may not always be related. However, the soup data may be easily explained by the increased viscosity of the soup causing a delay in GE and the increased bioavailability of the starch for digestion and absorption causing an amplified GR. In the current study, it is not as clear. It is possible that the initial rapid emptying of the liquid may have induced an immediate and large insulinaemic response that may have blunted the GR to the extent that the jelly and liquid GR could not be differentiated. Unfortunately, the insulinaemic responses were not measured in the current study.

The current study indicated that the agar has the ability to increase appetite significantly in comparison with the control liquid. This is first study to the author’s knowledge that has assessed the appetite effect of agar. Food form plays a large role in appetite with an increased viscosity being known to induce appetite [32, 33]. It is probable that the delayed GE may have resulted in this decreased appetite due to the prolonged distension of the stomach and the delayed delivery of nutrients into the small intestine. It has previously been shown that the gastric distension results in a reduction in food intake via neural reflex arcs. Similarly, gastric distension has been found to augment the reduction in nutrient intake affected by intravenous CCK-8 [34]. By maintaining the food in the stomach for a longer period of time, the stomach remains full and distended for longer prolonging the period of satiety. However, recent research by Cassidy et al. [35] has indicated that a cognitive effect and the implied satiating properties of solid or viscous food can induce sensations of appetite. Whether the effect of agar on appetite is due to cognition or food form, or a combination of these, its role has implications for its use in the control of food intake and weight maintenance.

This increase in appetite is considerably significant given that the DIT was the same following the two meals. Previous research on fibre and DIT has shown that fibre can moderately decrease DIT [15, 16]. However, in the current study, there was no discernible difference between the jelly with agar and the liquid. This is significant as it implies that any reduction in food intake due to the agar would not be compensated for by a decrease in DIT as has been found with other high-fibre meals. However, it is important to note that this potential energy deficit would need to be confirmed using a measurement technique that can quantify appetite such as ad libitum food intake, as opposed to the cognitive method of visual analogue scales used in the current study. In the current study, quantification was not physically possible due to the measurement of DIT and GE requiring 3 h minimum for testing. The jelly and liquid induced similar levels of appetite at 3 h due to both groups being extremely hungry; hence, if measurements of ad libitum food intake had been taken, it is unlikely any differences would have been detected.

The current study highlights some interesting findings in terms of the implications for agar as a food ingredient that has the potential to increase appetite most likely due to the delayed emptying of food from the stomach. It would be expected that a delay in GE would result in a reduced rate of delivery of nutrients and hence a reduced GR; however, no difference in GR was observed even though GE was delayed. This requires further investigation. Further research into the insulinaemic response and ad libitum food intake in response to agar is warranted to support these findings and understand how agar is exerting its effects.


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Correspondence to Miriam E. Clegg.

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Clegg, M.E., Shafat, A. The effect of agar jelly on energy expenditure, appetite, gastric emptying and glycaemic response. Eur J Nutr 53, 533–539 (2014).

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  • Jelly
  • Appetite
  • Gastric emptying
  • Glycaemic response