Breakfast skipping, or intermittent fasting, has gained a lot of steam in the past year or two (“Big Breakfast Has Little Effect on Appetite“, “Extended Daily Fasting Overrides Harmful Effects of a High-Fat Diet“, “Skipping Breakfast Can Lead to Unhealthy Habits All Day Long“, “Could fasting be good for us?“, “Is breakfast making you fat?“, “The new appetite for fasting“). In f
act I put the principle to use myself, and so thought this would be a great topic for my pathology research assignment. This report might be dry reading for those who aren’t used to reading scientific literature or associated nomenclature, so I’ll start by quoting the most relevant part of this report – the conclusion:
“The key findings of this report are that breakfast appears to be clearly associated with weight gain in a dose-dependent relationship, though the direction of this relationship is not clearly established. Depending on the type of descriptive research method employed, different results are apparent, though the study by Schusdziarra et al. (2011) addressed some of these apparent contradictions.
In conclusion, when interpreting the data in light of the contradictory nature of the research, there appears to be little evidence that breakfast skipping significantly contributes to overweight and obesity in adults, though individual factors such as age, obesity, exercise history, TDEE and TDEI should be taken into account and may in fact play a large role in the interpretation of the associations and correlations investigated in fasting and meal-skipping studies.”
And there you have it, no real evidence for a causal relationship between breakfast skipping and weight gain, though the correlational data shouldn’t be dismissed out of hand and does provide us with some insight. Also, take into account that my critical analysis within this report only covers two pieces of primary literature, but that dozens of articles were accessed in the process. That said, I was forced to neglect the biochemical (grehlin, leptin, insulin, glucose, not to mention the interplay with lipid cells) physiological point of view somewhat in order to remain within the word limit for my report. If it’s the biochemistry of breakfast skipping that you’re after however, then I won’t reinvent the wheel – just head on over to Martin Berkhan’s blog “Leangains“, linked in the side panel, for more fasting biochemistry and references to primary literature than you can poke a stick at.
Without further delay, here’s the nuts and bolts!
(Feel free to comment at the bottom – what are your thoughts on breakfast consumption?)
Skipping breakfast: is it a significant contributor to overweight and obesity in adults?
Introduction
Overweight and obesity are growing health problems in Australia, with an increase from 57% of the population in 1995, to 61% in 2007-2008 (Health Risk Factors, 2012), having an enormous cost to the Australian government and society at an estimated $58 billion in 2008. Figure 1 shows data from the National Health Survey 2007-08, in which a larger proportion of overweight and obesity is prevalent amongst the aged adult population, particularly amongst males, with younger adults showing lower prevalence rates.
Figure 1: Proportion of Australian Adults Overweight or Obese (by age and sex). This figure illustrates increasing incidence of obesity with age (Health Risk Factors, 2012)
The importance of experimental investigation into the causal mechanisms of obesity becomes obvious in light of this data. This report sets out to examine associations between meal skipping (breakfast in particular), and overweight and obesity, and to challenge the long-held belief that breakfast is “the most important meal of the day” – a notion which has been picked up and discussed by the media in recent years (Roan, 2009) and mainstreamed by popular books such as Eat-Stop-Eat (Pilon, 2007).
This report will cover background information on obesity, including definitions, aetiology, pathogenesis, diagnosis and treatment. Two descriptive, observational studies will be analysed, summarised and critically evaluated in the context of other descriptive and experimental scientific research, and the strengths and weaknesses of each study and their separate contributions to this research question discussed. Finally, key-findings from the two studies will be restated and summarised.
Background Information
There are typically four different categories into which diseases may be placed; pathological, hereditary, physiological and deficiency disease. For the purposes of this report, obesity and overweight are considered from the context of their most prevalent cause, being physiological disease as a result of excessive nutrient intake and inadequate physical exercise (Obesity, 2012).
Bodyweight categories are grouped based on body mass index (BMI) measurements, with obese being in excess of 30kg/m2, overweight within the range of 25-29.9kg/m2, and a normal bodyweight falling in the range of 18.5-24.9kg/m2 (Overweight & Obesity, 2012).
Obesity is a chronic disease in which excessive amounts of adipose tissue are accumulated over time, leading to potential systemic issues and increased likelihood of comorbid disease (Weiss & Elixhauser, 2006). A caloric surplus is required over an extensive period of time for obesity to develop, usually necessitating low levels of exercise and a surplus in nutritional intake in order for total daily energy intake (TDEI) to exceed total daily energy expenditure (TDEE). Though this is the predominating cause of obesity, there are rare genetic disorders in which leptin insensitivity may cause obesity from childhood (Farooqi et al., 2007).
The pathogenesis of obesity is complex and only partially understood, though it’s clear that it depends on the function of several key metabolic hormones, including glucagon, insulin, leptin and ghrelin (Carlson et al., 2007), with adipose tissue itself also producing important mediators for the metabolism of triglycerides to adipose, including adiponectin, cytokines, chemokines and steroid hormones (Stanley L Robbins, 2012). Leptin is known to play a key role in the suppression of appetite (Duntas & Biondi, 2012), with decreasing levels of leptin being secreted when fat is lost from adipocytes – making long term weight loss more difficult. Insulin and glucagon control the transport of glucose into and out of the cells respectively, with long term blood-glucose levels and excessive lipid intake being tightly correlated with insulin resistance and type 2 diabetes (Fu, Gilbert, & Li, 2012). In addition, the risk of other comorbid diseases such as cardiovascular disease, gall bladder disease, osteoarthritis, bowel cancer and diabetes are increased in the presence of obesity (Schienkiewitz, Mensink, & Scheidt-Nave, 2012).
As obesity is defined as a BMI above 30kg/m2, diagnosis is simply made by utilising the formula BMI = weight/height2. The drawbacks of the simplicity of this formula lie in the fact that there is no distinction made between fat and muscle mess, therefore an incredibly muscular bodybuilder might be considered “obese” if only the BMI were used as a measurement. Obviously BMI needs to be utilised in the context of other available information to ensure the wrong demographic of “overweight” individuals are not captured and misclassified.
Treatment for obesity typically lies in nutritional and exercise interventions, more specifically improving the quality of nutritional intake and decreasing the number of overall calories consumed along with implementation of an effective exercise program. Though this treatment is successful in a large number of obese subjects, particularly when delivered with cognitive behavioural therapy (Jakicic et al., 2012), those who suffer from hereditary causes and complications may require more medical intervention. Finally, some of the latest research by Mestdagh et al. (2012) involving intestinal bacteria and their role in the development of obesity opens interesting avenues for future exploration.
Annotated Bibliography & Critical Evaluation
Annotation 1
Huang, C. J., Hu, H. T., Fan, Y. C., Liao, Y. M., & Tsai, P. S. (2010). Associations of breakfast skipping with obesity and health-related quality of life: evidence from a national survey in Taiwan. International Journal of Obesity, 34(4), 720-725. doi: 10.1038/ijo.2009.285
This cross-sectional, descriptive study by Huang et al. used data from a National Health Interview survey in Taiwan which surveyed 15,340 individuals between the ages of 18 and 64. The study tested the association between breakfast skipping and obesity, as well as examining the possibility of a dose-dependent relationship between frequency of breakfast consumption and obesity.
The authors hypothesise that there exists an association between breakfast skipping and obesity, as well as a dose-dependent relationship in which an increased frequency of breakfast skipping is associated with an increased prevalence of obesity (amongst other behaviours deemed detrimental to health). The authors aimed to investigate these associations in adults as the majority of research conducted in this area in the past has focussed on a much younger demographic; primarily children and adolescents, making that research difficult to generalise to adults.
Although published in the International Journal of Obesity, the authors concede that the intended audience is specifically Taiwanese, and that the results are less applicable to other demographics, therefore these results are likely more relevant to Taiwanese health specialists and policy makers who are tasked with examination of potential causes and treatments for the growing obesity epidemic.
After careful analysis of the data, the authors conclude that breakfast skipping is associated with higher prevalence rates of obesity, in addition to being associated with other unhealthy behaviours such as smoking, alcohol consumption (though at what point this was considered detrimental to health was never addressed by the authors), and lack of exercise.
This study was chosen for analysis as it utilises a large segment of the adult population and is able to draw meaningful statistics due to this large sample size (n = 15,340).
Critical Evaluation
This study is relevant to this report as it directly investigates the association between frequency of breakfast skipping and prevalence of obesity, though there are advantages and disadvantages to their method of approach. One advantage is the fact that the authors utilised survey data, which allows a large breadth of information and amount of data to be acquired, however a major drawback of this approach is the inability to control confounding variables or tailor the data collected to include information such as macronutrient and micronutrient content and meal timing.
This study contributed to this report by confirming initial impressions that the majority of research is not applicable to the general adult population, as most studies examining the association between skipping breakfast (or fasting) and obesity are performed on a much younger demographic. This study also introduced the concept of a dose-dependent relationship, and more specifically that as breakfast-skipping increases so too does the prevalence of obesity. It also introduced the fact that breakfast skipping is associated with other unhealthy behaviours, including smoking, alcohol consumption, and lack of exercise. Finally, the associations found between breakfast skipping and lower scores of general health, vitality, mental health and social functioning helped to inform this report.
The authors addressed the research question by categorising the existing data into two groups: breakfast skippers and breakfast eaters (with breakfast skippers being those who ate breakfast once a week or not at all). The authors then analysed the percentage and P-value for each group, and subcategorised them into obese, smokers, alcohol drinkers, physically active, level of education, monthly income, marital status, and gender. The data was finally analysed using odds ratios.
This study gives direction for ongoing research into the links between breakfast dose (weekly frequency) and obesity, and helps to provide a basis for comparison of the results with other cross-sectional data as well as comparison with experimental, randomised controlled trials to compare and contrast the similarities and differences in outcomes.
Annotation 2
Schusdziarra, V., Hausmann, M., Wittke, C., Mittermeier, J., Kellner, M., Naumann, A., . . . Erdmann, J. (2011). Impact of breakfast on daily energy intake–an analysis of absolute versus relative breakfast calories. Nutr J, 10, 5. doi: 10.1186/1475-2891-10-5
This study sets out to investigate whether the reduction of caloric intake at breakfast can reduce the total daily caloric intake in a free-living and cross-sectional segment of the population, without compensatory eating behaviour. The study utilised 380 participants, 280 of which were obese and 100 of which were normal weight. Macronutrient and micronutrient intake were not analysed, though full nutrient details were gathered and recorded by each patient in the form of a food diary. This study focussed on the ratio of calories consumed at breakfast compared with those consumed throughout the entire day.
The authors introduce background information that shows seemingly contradictory studies and research around the effects of skipping breakfast or reducing caloric intake at breakfast and the prevalence of overweight and obesity. It’s proposed that these contradictory results between studies are a likely result of the different methodologies employed to manipulate the variables and analyse the data, particularly with regard to cross-sectional data vs. intraindividual analysis. The aim of this study was to compare two separate testing methodologies (cross-sectional data of a free living population and intraindividual analysis) on one data set and compare the results, helping to bridge the gap between studies utilising these separate methodologies.
This article was published in the Nutrition Journal, with the intended audience being those who are interested in nutritional research and have an understanding and appreciation of the application of the scientific process. This might include doctors, dieticians, exercise physiologists and other specialists with a nutritional interest, with repercussions for all segments of the healthy, overweight or obese populations.
The primary conclusion drawn by the authors is that a lower caloric intake at breakfast decreases the total daily caloric intake (with no compensatory eating behaviour), though this is put in the context of equivocal and at times contradictory research. This text was chosen for evaluation in part due to this balanced perspective of the available and contradictory studies, and the intent by the authors to resolve some of these differences.
Critical Evaluation
This study is relevant to this report as it puts into context the apparently conflicting research. It increases understanding by showing that a higher energy intake at breakfast is associated with an increased TDEI, but that this should be put into context of the energy expended during the day – particularly the amount of physical exercise engaged in. It also demonstrates the complexity of the issue and the difficulty of drawing clear conclusions on the issue of breakfast skipping or breakfast size and its association with overweight and obesity.
The authors address the research question by utilising patients from a hospital outpatient department, having them keep accurate logs of all food that was consumed and at what time of the day it was consumed. Both intra-individual data and ratio of daily energy intake were analysed from records taken over a two week period.
This study can be used for ongoing research into the differences in ratio between breakfast and total daily energy intake, and how this relates to obesity in studies that utilise different methodologies (preferably randomised controlled trials).
Conclusion
The key findings of this report are that breakfast appears to be clearly associated with weight gain in a dose-dependent relationship, though the direction of this relationship is not clearly established. Depending on the type of descriptive research method employed, different results are apparent, though the study by Schusdziarra et al. (2011) addressed some of these apparent contradictions.
In conclusion, when interpreting the data in light of the contradictory nature of the research, there appears to be little evidence that breakfast skipping significantly contributes to overweight and obesity in adults, though individual factors such as age, obesity, exercise history, TDEE and TDEI should be taken into account and may in fact play a large role in the interpretation of the associations and correlations investigated in fasting and meal-skipping studies.
References
Carlson, O., Martin, B., Stote, K. S., Golden, E., Maudsley, S., Najjar, S. S., . . . Mattson, M. P. (2007). Impact of reduced meal frequency without caloric restriction on glucose regulation in healthy, normal-weight middle-aged men and women. Metabolism, 56(12), 1729-1734. doi: 10.1016/j.metabol.2007.07.018
Duntas, L. H., & Biondi, B. Md. (2012). The interconnections between obesity, thyroid function, and autoimmunity: the multifold role of leptin. Thyroid. doi: 10.1089/thy.2011.0499
Farooqi, I. S., Wangensteen, T., Collins, S., Kimber, W., Matarese, G., Keogh, J. M., . . . O’Rahilly, S. (2007). Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor. N Engl J Med, 356(3), 237-247. doi: 10.1056/NEJMoa063988
Fu, Z., Gilbert, E. R., & Li, D. (2012). Regulation of Insulin Synthesis and Secretion and Pancreatic Beta-Cell Dysfunction in Diabetes. Curr Diabetes Rev.
Health Risk Factors. (2012). Australian Bureau of Statistics, Retrieved 16/09/12, from http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/1301.0~2012~Main%20Features~Health%20risk%20factors~233.
Jakicic, J. M., Tate, D. F., Lang, W., Davis, K. K., Polzien, K., Rickman, A. D., . . . Finkelstein, E. A. (2012). Effect of a stepped-care intervention approach on weight loss in adults: a randomized clinical trial. JAMA, 307(24), 2617-2626. doi: 10.1001/jama.2012.6866
Mestdagh, R., Dumas, M. E., Rezzi, S., Kochhar, S., Holmes, E., Claus, S. P., & Nicholson, J. K. (2012). Gut microbiota modulate the metabolism of brown adipose tissue in mice. J Proteome Res, 11(2), 620-630. doi: 10.1021/pr200938v
Obesity. (2012). Medline Plus, Retrieved 18/09/12, from http://www.nlm.nih.gov/medlineplus/obesity.html.
Overweight & Obesity. (2012). Centers for Disease Control and Prevention, Retrieved 16/09/12, from http://www.cdc.gov/obesity/adult/defining.html.
Pilon, Brad. (2007). Eat Stop Eat. Ontario, Canada: Strength Works Inc.
Roan, Shari. (2009). The new appetite for fasting, Sydney Morning Herald. Retrieved 25/08/12, from http://www.smh.com.au/lifestyle/diet-and-fitness/the-new-appetite-for-fasting-20090403-9muo.html
Schienkiewitz, A., Mensink, G. B., & Scheidt-Nave, C. (2012). Comorbidity of overweight and obesity in a nationally representative sample of German adults aged 18-79 years. BMC Public Health, 12(1), 658. doi: 10.1186/1471-2458-12-658
Schusdziarra, V., Hausmann, M., Wittke, C., Mittermeier, J., Kellner, M., Naumann, A., . . . Erdmann, J. (2011). Impact of breakfast on daily energy intake–an analysis of absolute versus relative breakfast calories. Nutr J, 10, 5. doi: 10.1186/1475-2891-10-5
Stanley L Robbins, Vinay Kumar, Ramzi S. Cotran (2012). Robbins Basic Pathology (NINTH EDITION ed.).
Weiss, A. J., & Elixhauser, A. (2006). Obesity-Related Hospitalizations, 2004 versus 2009: Statistical Brief #137 Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville MD.
Skeptifit 
I have ways wondered about this question. Thanks for bringing the science!
No problem – I’m glad you found it helpful.