top of page

Obesity - theories and mysteries: Are we really what we eat?

Considering the extent to which obesity has highjacked the status quo – by increasing the risk of co-morbidities like cardiovascular diseases and stroke, raising healthcare expenditure and decreasing productivity, the mechanisms by which obesity occurs and thus the ways to prevent, or at least mitigate, this complex disease has been areas of intense research.

[Check https://www.snowhillscience.com/post/body-composition-parameters-and-mortality for how body composition correlate with and predict mortality risk.]


Different models, e.g., the energy balance model (EBM) and carbohydrate-insulin model (CIM), that attempt to describe and predict fat change have been proposed. Are all calories equal? In fact, fat changes may involve not only our mouths and guts, but also our brains. Most researchers agree that fat changes are intimately linked with the brain. With emerging evidence, we are still in the infancy stage of integrating neuroscience with obesity. Notwithstanding the limits of current understanding, this article presents the epidemiology and significance of obesity, two popular models – EBM and CIM – for describing the fat change, and neurohormonal control of fat change.


Obesity: What is it? How does one (not) become obese?

Obesity is a complex disease characterised by an excessive accumulation of fat in the body. The global obesity rate has tripled in almost 50 years despite being a preventable disease, affecting more than 650 million people – 13% of adults worldwide – since 2016 [1]. Obesity is diagnosed clinically as having a body-mass index (BMI) (body weight (kg) divided by height (m) squared) greater than or equal to 30; nevertheless, it is noteworthy that BMI does not account for fat mass specifically and is a product of total weight – yet, this classification is an effective preliminary guide for normal subjects who otherwise do not have confounders, e.g., high muscle mass, that lead to high BMI. Whilst this method of defining obesity is useful at a population level, for a specific individual, you may want to dig deeper. Nowadays, obtaining one’s precise body composition data is common, e.g., through DEXA scans, which are accessible and affordable for predicting disease and diagnosis. [Check https://www.snowhillscience.com/post/a-case-for-dexa-scans-in-clinical-measurement-of-body-composition for arguments for popularising DEXA scans in the clinic.] Arguably facilitated by the increasingly obesogenic Western food environment, obesity has sadly become a public health epidemic.


The changing food landscape of the West has certainly not gone unmentioned by the scientific literature. In particular, models pertaining to the intake and storage of energy from our diet have been proposed to describe the mechanisms by which we become obese. While the golden rule for weight change remains true and undisputed – the “calorie-in-calorie-out” principle states that when caloric input is larger than caloric output, weight is gained, and vice versa; a biological application of the physical principle of energy balance – that energy cannot be destroyed, and can only be gained, lost, or stored by an organism, there is increasing research to suggest that not all calories are equal and the amount of fat stored or metabolised is a function of not just energy content, but also diet composition. Two models that have gained attention are the EBM and CIM.


Models of obesity – Energy Balance Model (EBM) vs Carbohydrate-Insulin Model (CIM): Are all calories equal?


In a nutshell, the EBM puts forth that the brain is the major organ that influences body weight, by integrating external signals from the food environment and internal signals from peripheral organs to regulate appetite and hormonal concentration. This integration is done below conscious awareness by energy homeostasis through short-term signals, e.g., ghrelin and incretin hormones, to control feeding, and long-term signals, e.g., leptin, to attune short-term weight regulation systems. Comparing with the CIM, the EBM is a much broader and more encompassing model. The CIM posits the uncontested importance of carbohydrates and insulin in leading to fat change. It specifies a narrow pathway that leads to obesity – obesity is a consequence of high dietary carbohydrate content leading to excess insulin secretion causing adipose tissue to accumulate and trap fat, thereby starving the nonadipose tissues of fuel.


Energy-based model

The EBM approaches obesity with a holistic perspective owing to the brain’s control over the whole body. Complex endocrine, metabolic and nervous system signals are controlled by certain brain circuits, e.g., those of the dopaminergic basal ganglia and hypothalamus, to modulate food intake below conscious perception, responding to the body’s energy demand and environmental factors (Watts et al, 2022) [2]. Besides, our will to eat is influenced by cognition and enhanced by rewarding – much like Pavlov’s classical salivation conditioning, we have conditioned ourselves to certain diets. Industrialised and expansively marketed foods contain high amounts of nutrients and additives, e.g., processed sugars, trans fat, and artificial flavour, that induce appetite, leading to a positive feedback that drives consumption (Hall, 2018) [3]. Foods of high-calorie density are favoured by subconscious reinforcement. The conventional postulate – that conscious preference for taste and aroma are the drivers for overconsumption in individuals – was shown inadequate to explain eating behaviour by Araujo, Schatzker & Small (2020), who showed that 1. obese individuals report the same liking of food as their healthy counterparts, 2. food palatability has no influence on the amount of food eaten, instead palatability affected which types of food are preferred, and 3. low-road neural circuits beneath the brain cortex reinforce the behaviour of eating energy-dense foods, independent of any conscious preference or avoidance [4]. This subconscious motivation is further enhanced in obese individuals. Thus, the motivation for how much one eats is subconscious and subject to food energy density.


However, while high energy-density motivates eating behaviour, EBM proposes that macronutrient composition also regulates nutrient metabolism, and that energy imbalances manifest as body fat imbalances. The high heritability of obesity allows us to use genetics to see which organs or physiological circuits affect obesity (Elks et al, 2012) [5]. In agreement with the EBM, genome-wide association studies have found that the genetic inter-individual variability in the fat composition is explained primarily by variation in genes highly expressed in the brain (Locke et al, 2015; Finucane et al, 2018) [6,7]; moreover, all known monogenic obesity-causing disorders involve genes that are fundamental to hypothalamic energy homeostasis [8]. Additionally, a recently identified two-way circuit between the hypothalamus and dopaminergic midbrain is altered when exposed to a high-fat diet (Alhadeff et al, 2019) [9]. A longitudinal study found that high fat-diet alters this circuitry, responsible for food reward, and encourages food intake and decreases appetite for low-fat diet, accelerating obesity development (Mazzone et al, 2020) [10]. Thus far, EBM is consistent with findings that suggest diet composition, along with caloric content, are important influencers of food intake through modulating brain activity.


Carbohydrate-insulin model

Unlike EBM, CIM narrows down the main driver for obesity to carbohydrates and the resultant insulin release. While its proponents may not have intended as such, CIM led many to believe carbohydrates are inherently fattening and that low carb diets would avoid fat gain. But is this true? what if one eats a low-carbohydrate-high-fat diet while living a sedentary lifestyle? CIM’s simplicity and specificity has therefore attracted much criticism from many scientists. Is CIM supported by evidence?


The contemporary version of CIM was formalised in part by science journalist Taubes in his 2007 book – “Good calories, bad calories: challenging the conventional wisdom on diet, weight control, and disease” [11]. Taubes had justified the CIM by claiming that “by the mid-1960s four facts had been established beyond reasonable doubt: 1) carbohydrates are singularly responsible for prompting insulin secretion; 2) insulin is singularly responsible for inducing fat accumulation; 3) dietary carbohydrates are required for excess fat accumulation; and 4) both type 2 diabetics and the obese have abnormally elevated concentrations of circulating insulin”. If these were true, dietary carbohydrate-driven insulin variability would be sufficient to explain inter-individual variability in obesity; furthermore, global obesity would merely be a result of higher carbohydrate content in modern diet.


Unfortunately, it appears the 4 premises on which the CIM is built upon have increasingly been thrown into doubt. Firstly, fat storage – lipogenesis – can take place independent of dietary carbohydrate or higher-than-basal insulin concentrations. For example, while insulin is a long-known stimulator of lipogenesis, other hormones are increasingly recognised for their roles in lipogenesis that are not known to involve the insulin pathway. For example, the hormone leptin induces the expression of a gene for fat cell synthesis independent of insulin (Kersten, 2001) [12]. Furthermore, even if we posit that “insulin is singularly responsible for inducing fat accumulation”, it is not true that insulin secretion is determined only by dietary carbohydrates – e.g. high insulin level even during fasting state – basal hyperinsulinaemia – is maintained by other substances, e.g., reactive oxygen species (Corkey, Deeney & Merrins, 2021). At best, it can only be said that it is uncertain if carbohydrates and insulin are the only factors driving lipogenesis, if it is not already clear enough that there are other pathways that contribute to fat production. It would therefore be wrong to summarise the complexity of obesity as “by stimulating insulin secretion, carbohydrates make us fat and cause obesity”. More likely, insulin probably has a role among others in lipogenesis and obesity.


Evaluating the evidence for CIM, it becomes more obvious that it fails as a model to describe obesity and is a narrow application of EBM with a particular focus on the effect of carbohydrates in the diet. For example, intake of high carbohydrate-content foods does not necessarily lead to weight gain. Meta-analyses and many long-term studies have found that adherence to healthy foods in the diet leads to weight loss, despite being high in carbohydrate content (He et al, 2004) [13] – are carbohydrates really the culprit if eating more potato chips and soft drinks lead to weight gain while eating more whole grains and nuts in the diet, which are also high in carbohydrate content, leads to weight loss (Maki et al, 2019) [14]? or have carbohydrates been wrongly accused and become the nutritional industry’s scapegoat?


Which model is better?

Comparing the two models, it seems EBM is more applicable model; its robustness has stood the test of many studies. Nonetheless, the precise physiological pathways and the factors contributing to the “optimal diet”, if one even exists, are to be determined. The precise mechanisms for fat change that will soon, hopefully, be discovered will allow refinement of the EBM. Until then, we should keep in mind that no matter what kind of diet you eat, losing weight can be ensured with physical certitude when caloric expenditure is larger than caloric intake – this is a matter of physics; dietary composition however seems to influence how much fat mass is made.


Conclusion

In a way, we really are what we eat – we obtain raw material for our body composition through our diet, the components of which play an important role in regulating how much fat we store. Notwithstanding the current limitations in our knowledge, such as the full metabolic and hormonal pathways that mediate changes in appetite and metabolism, we can be assured that high-fat diets seem to do more harm than good if taken on the long-run, and that a healthy balance of carbohydrates, proteins and fats, without any excess or deficiency of any one of then, and regular exercise is a definite, albeit cliche, combination to stay fit for, hopefully, many years to come.

References

  1. Watts, A. G., Kanoski, S. E., Sanchez-Watts, G., & Langhans, W. (2022). The physiological control of eating: signals, neurons, and networks. Physiological reviews, 102(2), 689–813. https://doi.org/10.1152/physrev.00028.2020

  2. Hall K. D. (2018). Did the Food Environment Cause the Obesity Epidemic?. Obesity (Silver Spring, Md.), 26(1), 11–13. https://doi.org/10.1002/oby.22073

  3. de Araujo, I. E., Schatzker, M., & Small, D. M. (2020). Rethinking Food Reward. Annual review of psychology, 71, 139–164. https://doi.org/10.1146/annurev-psych-122216-011643

  4. Elks, C. E., den Hoed, M., Zhao, J. H., Sharp, S. J., Wareham, N. J., Loos, R. J., & Ong, K. K. (2012). Variability in the heritability of body mass index: a systematic review and meta-regression. Frontiers in endocrinology, 3, 29. https://doi.org/10.3389/fendo.2012.00029

  5. Locke, A. E., Kahali, B., Berndt, S. I., Justice, A. E., Pers, T. H., Day, F. R., Powell, C., Vedantam, S., Buchkovich, M. L., Yang, J., Croteau-Chonka, D. C., Esko, T., Fall, T., Ferreira, T., Gustafsson, S., Kutalik, Z., Luan, J., Mägi, R., Randall, J. C., Winkler, T. W., … Speliotes, E. K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197–206. https://doi.org/10.1038/nature14177

  6. Finucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P. R., Lareau, C., Shoresh, N., Genovese, G., Saunders, A., Macosko, E., Pollack, S., Brainstorm Consortium, Perry, J., Buenrostro, J. D., Bernstein, B. E., Raychaudhuri, S., McCarroll, S., … Price, A. L. (2018). Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nature genetics, 50(4), 621–629. https://doi.org/10.1038/s41588-018-0081-4

  7. Loos, R., & Yeo, G. (2022). The genetics of obesity: from discovery to biology. Nature reviews. Genetics, 23(2), 120–133. https://doi.org/10.1038/s41576-021-00414-z

  8. Alhadeff, A. L., Goldstein, N., Park, O., Klima, M. L., Vargas, A., & Betley, J. N. (2019). Natural and Drug Rewards Engage Distinct Pathways that Converge on Coordinated Hypothalamic and Reward Circuits. Neuron, 103(5), 891–908.e6. https://doi.org/10.1016/j.neuron.2019.05.050

  9. Mazzone, C. M., Liang-Guallpa, J., Li, C., Wolcott, N. S., Boone, M. H., Southern, M., Kobzar, N. P., Salgado, I. A., Reddy, D. M., Sun, F., Zhang, Y., Li, Y., Cui, G., & Krashes, M. J. (2020). High-fat food biases hypothalamic and mesolimbic expression of consummatory drives. Nature neuroscience, 23(10), 1253–1266. https://doi.org/10.1038/s41593-020-0684-9

  10. Taubes G. Good calories, bad calories: challenging the conventional wisdom on diet, weight control, and disease. New York: Alfred A. Knopf; 2007.

  11. Kersten S. (2001). Mechanisms of nutritional and hormonal regulation of lipogenesis. EMBO reports, 2(4), 282–286. https://doi.org/10.1093/embo-reports/kve071

  12. He, K., Hu, F. B., Colditz, G. A., Manson, J. E., Willett, W. C., & Liu, S. (2004). Changes in intake of fruits and vegetables in relation to risk of obesity and weight gain among middle-aged women. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity, 28(12), 1569–1574. https://doi.org/10.1038/sj.ijo.0802795

  13. Maki, K. C., Palacios, O. M., Koecher, K., Sawicki, C. M., Livingston, K. A., Bell, M., Nelson Cortes, H., & McKeown, N. M. (2019). The Relationship between Whole Grain Intake and Body Weight: Results of Meta-Analyses of Observational Studies and Randomized Controlled Trials. Nutrients, 11(6), 1245. https://doi.org/10.3390/nu11061245

24 views0 comments

Comments


bottom of page