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Satiation And Satiety: An Overview And Clinical Calculator

Satiation And Satiety: An Overview And Clinical Calculator

Satiation and satiety are often confused and used interchangeably, though both are physiologically and behaviorally different sensations. Satiation describes the series of processes that cause one to stop eating. Governed by hormones and gastric stretch receptors, satiation occurs during an eating episode and is frequently associated with meal size (g or kcal). Satiety is described as a state of not eating, characterized by the physical feeling of fullness. Ideally, satiety is associated with measures of the inter-meal period (kcal). 

This article summarizes the most commonly used as well as novel methods for quantifying satiation and satiety, and will introduce the benefits of satiety-specific clinical calculators.

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Measuring Satiation 

Ad libitum meal is used to measure satiation, wherein total energy or the amount of food consumed to fullness is recorded and compared to control food. Ad libitum food intake is also used to capture food choice, eating patterns during the meal, temporal changes in appetite feelings, and the reasons for voluntary cessation of eating. 

Measuring Satiety 

Satiety or between-meals Satiety captures the intensity and duration of the feeling of fullness between meals and the timing and extent of calorie intake as primary outcomes. 

The measurement of satiety can be achieved by i) tracking changes in hunger, fullness, and desire to eat over time or ii) measuring the duration between the treatment and the next meal. Studies on satiety are complicated by socio-environmental factors, food palatability, and cognitive pressures. Since these factors jointly affect an individual’s eating behavior, multiple methods are used to study satiety.  

Free-living vs. Laboratory studies 

Human eating behavior is complex and multifaceted. Therefore, in appetite research, authors are likely to make compromises about the requirements for internal and external validity. Controlled laboratory studies, in general, offer a high level of sensitivity and control over external factors and outcome measures. Hence, they have high internal validity.  On the other hand, while free-living studies have a theoretically high level of external validity, their internal validity is limited by several methodological issues. Data collection errors or biases are high, particularly when habitual dietary intake assessment under free-living conditions uses self-reported data.  Furthermore, free-living studies are not subject to the same rigorous control as laboratory studies, which makes it difficult to interpret the effects of dietary components and the environment and yield meaningful results. 

Though laboratory studies cannot replace studies in free-living circumstances, they can provide vital data to complement them. Therefore, there is a lot of scope for developing overlapping protocols that help circumvent the problems inherent in both approaches and bridge the gap between them. 

Preload method 

The preload meal paradigm is one of the most influential methods used to study the short-term regulation of food intake and appetite. This study design uses preloads that vary along one dimension (e.g. ingredients, nutrients, energy content, weight, or volume) given on different occasions. A test meal is given to the subject ad libitum following the preload, and energy intake is measured after a variable time delay. In many of the preload studies, the subjects self-report their food intake for the remainder of the day. Several physiological mechanisms contribute to satiety during the sensory, cognitive, post-ingestive, and post-absorptive phases of the satiety cascade. Therefore, the time interval between the preload and the test meal is crucial to the study outcomes. 

One of the major limitations of preload studies is that they are designed to minimize learning about the post-ingestive effects of eating. Another problem with these studies is that they are prone to type 2 errors, and evidence of the sensitivity of the preload study design should always be provided.  Several extensions and adaptations to the preload studies have been made. Some studies have even manipulated the composition of both preload and test meals such that the effects of both on subsequent food intake can be measured.  

Self-reported measures 

VAS lines

Numerous self-reported scales are used to address feelings of hunger, fullness, satiety, somatic sensations, prospective consumption, etc.  In general, these measures are completed before and after consumption of the test meal, and then at regular time intervals, usually for 4-5 hours, or until the start of the next meal. A good example is the Visual Analogue Scale (VAS), a 100-or 150-mm line with the extremes of a question being asked. For example, VAS for hunger may be labeled with “Not hungry at all” or “Never been more hungry”. Subjects are asked to mark across the line corresponding to their hunger at that time.  Though the VAS line scale is cheap, easy to use, and simple to interpret, there are mixed conclusions as to the ability of the VAS to predict food intake.  Rolls et al. and Kissileff reported that line scales do not accurately predict satiety or provide a sufficient means for food differentiation. A meta-analysis by Holt et al. concluded that self-reported appetite ratings do not predict energy intake accurately and emphasized the need for caution in interpreting later food intake from appetite ratings alone.  

Category scales

Feelings of hunger, fullness, or satiety can be measured with a 9-point category scale. Similar to VAS, category scales may go from the absence of a factor to the extremes of it. For example, if participants are asked about their hunger at a particular time, they can mark ‘1’ on a category scale to indicate ‘Not hungry at all’ or ‘9’ for ‘Extremely hungry’. As with VAS, category scales have interpretation issues. Furthermore, the distance between units 1 and 2 on a VAS line or category scale may not necessarily be perpetually equivalent to the distance between units 3 and 4. 

Satiety-labeled intensity magnitude (SLIM) scale

 Cardello et al. (2005) proposed the SLIM scale, a 100-mm bidirectional hunger-fullness scale to assess the satiety level reported by individuals following a meal. The SLIM offers good accuracy and enables better discrimination in comparison with line or VAS. As with other bipolar scales, SLIM does not allow for times when an individual could feel both slightly hungry and/or slightly full at the same time. 

Measuring food intake 

Measuring food intake is probably the most difficult aspect of nutritional assessment since even the same individual eats different foods prepared with different methods, at different places and times. Mattes et al. reported that psychosocial or environmental factors can cause a loss of appetite in some people.  Furthermore, eating in the absence of hunger, consuming palatable foods when satiated, or stress eating complicates the accuracy of food intake data. 

Given the net effect of these sources of variability, and the dubious accuracy, most satiety studies are done under laboratory conditions.  The rate of eating either solids or liquids can be measured using a universal eating monitor (UEM). 

Quantifying satiety 

Several procedures have been proposed to calculate the potency of foods to induce satiety. These include: 

Satiating efficiency

It uses a preloading method with many preloads given on different occasions, followed by an ad libitum test meal. The negative slope of the intake-preload equation represents satiating efficiency. The satiating efficiency provides a means to compare the ability of different foods to induce satiety, along any dimension (e.g. food composition, nutrition levels, energy, weight, or volume). 

Satiety index (SI)

The satiety index (SI), designed by Holt et al. (1995), involves giving specific food to be measured as a preload, then obtaining satiety ratings every 15 minutes over the next 2 hours. After that, the subjects are free to eat ad libitum from a standard range of foods and drinks. The SI score calculated reflects the total amount of fullness produced by the test foods over two hours, i.e., short-term satiety. 

Satiety quotient (SQ): Green et al. (1997) developed a satiety quotient (SQ) to assess the satiating effect of food or an eating episode. It is calculated by dividing the change in subjective appetite sensations in response to a meal by the weight or energy content of the food. 

Confounders in satiety research 

Different individuals have different responses to dietary manipulation, and these responses vary according to physiological and behavioral confounders.  

Physiological confounders 

Bodyweight: The measurement of an appetite response or energy test may vary according to the body weight or overall BMI of an individual. A study published in the International Journal of Obesity reported blunt responses to dietary manipulations in obese subjects as compared with lean ones. Barkeling et al. showed that obese females show similar hunger and fullness profiles in response to fixed-size meals when compared with lean women. There is an ongoing debate about the merit of using preloads or test meals as a function of body weight. 

Gender and Age: Males, both adults and adolescents, experience hunger in a physical manner, while females experience it in a more diffuse and cerebral manner, says the literature. In extreme hunger, both adult men and women reported more intense sensations than adolescents. In Monello & Mayer’s research, adolescents described satiety as a gastric sensation, and they continued to feel mild hunger for longer after a meal than adults. Leahy et al. found that children adjusted for caloric density by eating lower energy-dense foods, while adults preferred a consistent volume of food with variable energy density. 

Behavioral/psychometric confounders 

Diet, alcohol, caffeine, and physical activity may be involved in boredom or satiation. The Three Factor Eating Questionnaire (TFEQ) or the Dutch Eating Behavior Questionnaire (DEBQ) is often used as a screening tool in appetite research.  There is evidence that eating restraint levels can influence the outcomes of appetite studies. Lluch et al. reported that individuals stratified by their restraint scores showed different hunger responses when subjected to an exercise intervention. 

The physiological state of the individuals, especially energy balance and physical activity, are potentially important confounders in satiety studies.  

It has recently been demonstrated that allelic variation and individual differences can generate a wide range of responses. 

Conclusion

In recent years, there has been an increased focus on the satiety index of foods and developing clinical calculators to establish enhanced satiety as a benefit to the ever increasing health-conscious market. And accurately measuring satiety and satiation is critical to understanding eating behavior, energy selection and intake.

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