Specific patterns of behavior put people at risk of developing obesity. Selection of particular types of foods are one of the key factors involved with the development of obesity. Two distinct groups, high-fat and low-fat phenotypes, eat primarily different foods and have different profiles. These differences suggest that HF and LF can be used as a tool to investigate the relationship between biology and the environment (diet) in the control of bodyweight. In this installment of The Practical Scientist we take a look at a paper that further explores the unique characteristics of these two phenotypes.
Key Points from High-fat and Low-fat (behavioral) phenotypes: biology or environment? John E. Blundell and John Cooling
“Although obesity is much more prevalent among HF than LF, some HF are lean with BMI well within the normal range.
It is known that food intake records cannot be regarded as a reliable estimate of food actually consumed (Macdiarmid & Blundell, 1998). For example, using energy intake, BMR is not an acceptable indicator of daily food diary records, and has been shown to be unacceptably low in 39% of women and 29% of men. Moreover, among the obese, under-reporting reached levels of 60% in men and 70% in women. It has now been demonstrated that a defect in one particular gene can lead to obesity through a dramatic increase in food intake characterized as a form of hyperphagia. One characteristic of the food supply (which has been identified as a likely promoter of high energy intake and a positive energy balance) is the prevalence of high-fat foods. However, although the presence of high-fat foods has been identified as a major environmental “risk factor” for weight gain, it is clear that the relationship between a high-fat diet and high BMI is not a biological inevitability (Blundell & Macdiarmid, 1998). Some individuals who habitually consume a high-fat diet do not appear to be gaining weight and are not obese. The present review will throw further light on this issue.
Most researchers adhere to the notion that a person’s metabolic profile plays a major factor in one’s proclivity for weight gain and obesity. However, as one becomes obese, one’s metabolic characteristics change so that one’s state of obesity itself is associated with a different metabolic profile than that which initially instigated the weight gain.
|Factors associated with obesity||Factors predicting weight gain|
|Relative metabolic rate||normal or high||low|
|Energy cost of physical activity||normal||low|
|Fat oxidation||normal or high||low|
|Sympathetic nervous system activity||high||low|
|Relative plasma leptin concentration||high||low|
The phenomenon of hyperphagia may be brought about through different processes which themselves constitute the risk factors which lead to hyperphagia or “over consumption” (high energy intake leading to a positive energy balance).
Proposed interactions between biologically-based behavioral risk factors and environmental features, leading to increases in energy intake
|Biological vulnerability (behavioral risk factor)||Environmental influence||Effect on food intake|
|Fat preference||abundance of high-fat foods||^ fat intake|
|Weak satiation (end of meal signals)||large portion sizes||^ meal size|
|Oro-sensory responsiveness||availability of high-palatability foods with specific sensory-nutrient combinations||^ amount eaten|
|High level of hunger||ready availability of foods||^ persistent drive to seek and eat food|
The combination of biological dispositions (behavioral risk factors) and the presence of a conducive food environment will lead to particular patterns of consumption characterized by the size of eating episodes, the frequency of eating, or by the intake of particular macronutrients.
In the case of HF and LF phenotypes, individuals are classified according to their habitually consumed diet. These measurable differences in the types of selected foods pose the following questions: are these choices biologically driven (by particular tissue needs)? Are they physiological requirements or neuro-sensory characteristics)? Or are they incidentally picked up from the food environment? In either case, it will be necessary for the physiological system to adapt to the ingestion of very large amounts of specific macronutrients.
Initially, HF displayed higher initial hunger levels, with a much sharper decline in hunger in response to meals or nutrient loads (Cooling & Blundell, 1998a). After eating, hunger recovered more rapidly in HF compared with LF. In addition, the size of a test meal consumed was closely related to the suppression of hunger in HF. In contrast, the appetite response system in LF appeared to be somewhat insensitive and damped. This relationship between habitual fat intake and hunger is reminiscent of a previous finding. French et al., 1996 found that during two weeks of high-fat overfeeding to normal-weight subjects, which caused a significant gain in weight, subjects displayed a progressive increase in hunger and a decrease in fullness before a test meal. Taken together, these findings may indicate that eating a high-fat diet may facilitate feelings of hunger.
A further feature of these behavior studies was that HF and LF differed in the control over meal size when offered an unlimited range of either high-fat or high-carbohydrate foods. HF consumed a similar weight of food on both diets, and therefore took in a much higher amount of energy with the high-fat (high-energy-dense) foods. By contrast, LF consumed a much smaller amount of the high-fat foods, and consequently took in similar amount of energy on both diets. These findings suggest that signaling systems for meal termination (satiation) and post-meal inhibition of appetite (satiety) operate with differing strengths in HF and LF. This finding may not be surprising in view of the fact that the gastrointestinal tract has adapted to dealing with different dietary components, and this factor will have exerted a priming effect on specific satiety signals.
The possibility of other physiological differences was investigated using indirect calorimetry to measure BMR, RQ and dietary-induced thermogenic responses to specific fat and carbohydrate loads.
The results indicated that HF had lower RQ than LF. This finding confirmed that fat oxidation was higher in HF, as would be expected due to the habitual high intake of fat-containing foods. However, an unexpected finding was a significantly higher BMR in HF than LF, together with different profiles of ’thermogenic’ responses to the high-fat and high-carbohydrate loads. A further important finding was that HF had higher plasma leptin levels than LF (Cooling et al. 1998), despite having similar levels of body fat.
One interpretation of the findings so far is that the habitual consumption of a high-fat diet (generating a high energy intake) leads to physiological adaptations in the form of a raised BMR. However, the converse could also be possible. This interpretation would imply that individuals with a naturally (genetically conferred) high BMR select a high-energy diet.The two arguments can be formulated as an environmentally-driven diet selection leading to protective physiological adaptations, or a biologically-driven energy expenditure leading to an appropriate diet selection.
It has previously been noted that the metabolic predictors of weight gain include a low relative BMR, a high RQ, and relatively low plasma leptin levels. Interestingly, all three variables characterize LF when compared with HF. Consequently, the habitual consumption of a low-fat diet, usually regarded as being an optimal dietary strategy to prevent obesity, is associated with a metabolic profile indicating a susceptibility to weight gain. Is this feasible? On the other hand, in this group of subjects (young adult males) a habitual high-fat diet was associated with metabolic variables that were apparently protective against weight gain.
Whether it be genetic determination, physiological adaptation, or nutrient-gene reactions, some individuals will be better equipped physiologically to deal with a high-fat high-energy diet. Indeed, although we maintain that a diet replete with fatty foods remains the single most prominent food-related risk factor, it is clear that some individuals can remain lean on such a diet, at least for a certain period of time.”
When looking at the paper, notice the chart Metabolic factors related to obesity itself, or to the development of obesity. Most of the information listed is probably not a surprise to readers, but insulin sensitivity as a predictor of weight gain may surprise some readers. The bodybuilding community has misconstrued the role of insulin on the comprehensive metabolism. You know the popular sayings: Insulin resistance makes you fat; insulin increases protein synthesis; the key to weight loss is controlling insulin; and it’s good to be insulin sensitive. The same people who promote insulin as being the only thing that matters (concerning body composition) have obviously not looked into the primary research concerning insulin and do not have an inadequate understanding of insulin’s complexity. When attempting to gain or lose weight, regardless of insulin levels, calorie intake and energy expenditure must be considered.
Excerpt from Knowledge and Nonsense: the science of nutrition and exercise (Hale 2007):
“Many factors influence hunger, appetite, and subsequent food intake. Macronutrient content of the diet is one, and it may not be most important. Neurochemical factors (e.g., serotonin, endorphins, dopamine, hypothalamic neuropeptide transmitters), gastric signals (e.g., peptides, stomach distention), hedonistic qualities of food (e.g., taste, texture, smell), genetic, environmental (e.g., food availability, cost, cultural norms) and emotional factors (e.g., eating when bored, depressed, stressed, happy) must be considered. These parameters influence appetite primarily on a meal to meal basis. However, long-term body weight regulation seems to be controlled by hormonal signals from the endocrine pancreas and adipose tissue, I.e., insulin and leptin (my comment: other gut hormones that influence food intake include peptide yy, ghrelin, pancreatic polypeptide, glucagon-like peptide 1, oxymtomodulin, and cholecystokinin). Because insulin secretion and leptin production are influenced by the macronutrient content of the diet, effects of different diets on these long-term regulators of energy balance also need to be considered when investigating hunger and appetite.
Dietary compliance is likely a function of psychological issues (e.g., frequency of dietary counseling, coping with emotional eating, group support) rather than macronutrient composition, per se. Ogden notes that successful weight loss and maintenance may be predicted by an individual’s belief system (e.g., that obesity is perceived as a problem that can be modified, and modification brings changes in the short-term that are valued by the individual).”
A couple of things that surprised me about the paper was the higher BMR found in the HF group, and the higher Leptin levels. I think the HF group naturally had higher BMR rates. Higher leptin levels have also been shown to increase energy expenditure, thus contributing to higher BMR in HF group. It was very surprising to see higher Leptin levels in HF group. Most studies indicate that high fat dieters actually have lower levels of Leptin. Heightened Leptin may be a adaptive response generated in HF phenotypes.
Excerpt from Brain and Hunger Hormones by Jamie Hale :
“Your leptin levels change in response to body fat levels. They also change in response to short term over and underfeeding. When energy deficits occur circulating Leptin concentrations decrease while levels increase when overfeeding occurs. Postprandial (after a meal) Leptin concentrations are dependent on meal profile, with high carb low fat meals producing higher Leptin concentrations as compared with high-fat, low carb meals. Twenty-four hour circulating Leptin levels are also reduced in women consuming high-fat, low-carb diets compared with those consuming a high- carb, low fat diet. Research also indicates high fat feeding in rats result in Leptin resistance. Regarding other dietary macronutrients, neither protein nor fiber intake seem to impact circulating Leptin concentrations. Generally speaking when on a diet your leptin levels can drop up to 50% in one week. A properly designed overfeed can heighten leptin levels quickly.”
Individuals vary greatly in their response to higher fat or carbohydrate intake. This response can be influenced by numerous factors. I have noticed that people who respond well on high carbohydrate diets generally don’t do well on high fat diets and vice versa. Although under certain conditions your response to high or lowfat may be different. It is also important to consider the protein content of diet as it can influence various physiological and psychological responses.
Blundell JE, Cooling J. High-fat and low-fat (behavioural) phenotypes: biology or environment? BioPsychology Group, Dept of Psychology, Univ of Leeds, Leeds LS2 9JT, UK.
Hale J. Brain and Hunger Hormones.
Hale J. (2007). Knowledge and Nonsense: the science of nutrition and exercise. MaxCondition Publishing.