Georgetown Food Studies

Following an introduction to this series posted previously, this is the fifth of many detailed examinations of lifestyle practices, physiological and clinical pathologies, and inherited factors. @DrTomSherman

MIROMOSOME by Luisa Lente

4. Paternal Epigenetic Influence ………………………………………affecting calories-in

Although mothers have born the predominance of the expectations and pressures of bearing healthy children, evidence is accumulating that make it more clear that a father’s health and lifestyle choices have a measurable impact on the health of his future children. The health of their yet-to-be-born children can be affected by how much men eat, what toxins or traumas they are exposed to, their socioeconomic status, and a father’s age at the time of conception. Not only do these life experiences direct biological influence over their children, these children can pass these traces to their children. This, of course, describes epigenetic inheritance, a mode of inheritance whereby methylation patters switch on or off select genes directed by three environmental cues: what we ingest (food, drink, air, toxins), what we experience (stress, trauma), and how long we live. The ability of a father to influence the health and body weights of their offspring has been demonstrated in both animal studies and humans. More than a decade ago, three Swedish researchers dug up records from Overkalix going back to 1799 in order to correlate its children’s health data with records of regional harvests and other documents showing when food was and was not available. What the researchers learned was extremely interesting. They found that when boys ate badly during the three years prior to the onset of puberty (ages of 9 and 12), a period known as the slow growth period, their sons, as adults, had lower than normal rates of heart disease. When boys ate all too well during that period, their grandsons had higher rates of diabetes. (Eur J Human Genetics 10: 682 (2002))

Figure 1. Metabolic Risk Can Be Conferred via the Paternal Lineage
(B) Males with a prior history of intrauterine exposure to maternal caloric restriction, even with normal postnatal nutrition (C), can result in increased metabolic risk in offspring. Despite different stages of exposure (in utero, influencing primordial germ cells, or postweaning, influencing the spermatogonial and subsequent stages) such paternal-lineage risk must be conferred via sperm, potentially via alterations in DNA methylation, chromatin properties, or small noncoding RNAs. In turn, alterations in gene expression and metabolic risk in offspring indicate either the possible persistence of epigenetic marks or effects on early postimplantation embryos, modulating developmental trajectories. Cell Metabolism 13: 115 (2011)

A decade later, animal studies confirm that a male mammal’s nutritional past has a surprisingly strong effect on his offspring. In a recent study, control female rats were bred with males with high-fat-diet-induced obesity. Female offspring of these obese males had glucose intolerance, reduced insulin secretion, and altered pancreatic islet gene expression, potentially linked to reduced methylation near the gene IL13ra2. In another study, male mice were fed a reduced-protein or chow diet and bred with chow-fed females; both male and female offspring of low-protein males demonstrated increased hepatic expression of lipid and cholesterol synthesis genes. Overall DNA methylation in offspring was unchanged; however, modestly increased methylation was observed in an intergenic CpG island between the PPARa and Wnt7b genes. In yet another study, male rats that are starved before they’re mated produce offspring with less blood sugar and altered levels of corticosterone (which protects against stress) and insulin-like growth factor 1 (which helps babies develop).

Following an introduction to this series posted previously, this is the fourth of many detailed examinations of lifestyle practices, physiological and clinical pathologies, and inherited factors. @DrTomSherman

4. Genetics and Obesogenic Polymorphisms ………………………affecting calories-in

Estimates of the heritability of BMI range from 30% to 70%. Adoption studies, for example, demonstrate clearly that the body weight of adoptees much more closely mirror the BMIs of their biological parents than of their adoptive parents (see below).

Figure 1. Mean Body-Mass Index of Parents of Four Weight Classes of Adoptees. Note the increase in mean body-mass index of biologic parents with the increase in weight class of the adoptees. No such increase was found with adopted parents. Bars represent 1 SEM. BF denotes biologic fathers, BM biologic mother, AF adoptive fathers, and AM adoptive mothers.
NEJM 314: 193 (1986)

Studies of monozygotic and dizygotic twins reared together or apart yield similar data. Although the contributions of environmental factors are far from insignificant, one’s genetic background is a very important factor to body weight. A significant part of this genetic background is the polymorphisms in genes controlling appetite and metabolism that predispose one to obesity under certain dietary conditions. More than 41 sites on the human genome have been linked to the development of significant weight gain when a favorable environment is present. For example, the FTO gene located on chromosome 16 encodes Fat mass and obesity-associated protein in humans. Polymorphisms in FTO are strongly associated with body fat estimates in populations of different ethnic backgrounds or ages. It was estimated that each copy of the FTO rs9939609 minor allele (ie, A allele) corresponds to approximately 1.5-kg heavier body weight (figure at top right). The prevalence of this risk allele in white individuals is around 40%. Interestingly, adolescents meeting the daily physical activity recommendations were able to overcome the weight gain effect of the FTO polymorphism:

Fig. 3. BMI as a function of age, sex, and rs7566605 genotype. Unadjusted mean values for BMI comparing individuals homozygous for the minor allele (CC) with those that are either het- erozygous or homozygous for the major allele (CG or GG) are shown. Data are for 923 related offspring and combine all the measurements from the first five offspring exams. Panels are for the pooled sample and for sex-specific analysis. Data are shown as mean T SEM for each age category. Data from individuals over age 60 are omitted because few of those individuals have the CC genotype.
Science 312: 279 (2006)

Another common obesity-predisposing genotype near the INSIG2 gene is present in 10% of individuals:

Figure 1. Association between the FTO polymorphism rs9939609 and mean body mass index (calculated as weight in kilograms divided by height in meters squared), body fat percentage, and waist circumference. Error bars indicate 95% confidence intervals. Values are adjusted for center, sex, and age.
Arch Pediatr Adolesc Med. 164(4):328-333 (2010)

Although each polymorphism contributes only a modest amount of weight gain, it is unknown how multiple poly-morphisms in any one person interact. It is possible that certain polymorphism combinations may contribute to significant weight gain and a risk for obesity.

Following an introduction to this series posted previously, this is the third of many detailed examinations of lifestyle practices, physiological and clinical pathologies, and inherited factors. @DrTomSherman

3. Hypothalamic Obesity……………………………………………………….affecting calories-in

A very rare “organic” form of obesity is seen in patients who suffer damage to an area at the base of the brain called the hypothalamus. This area, the size of a fingernail, is home to the primary function of regulating homeostasis, including the control of energy balance. Damage to this area has long been known to promote excessive eating (hyperphagia) and weight gain, termed “hypothalamic obesity.” This form of weight gain is not responsive to diet and exercise, and victims of this form of obesity continue to gain weight despite their best efforts.

Hypothalamic obesity is an unfortunate complication in some survivors of brain tumors, especially those diagnosed in childhood. It is estimated that as many as one-third of all survivors of craniopharyngiomas develop severe obesity after diagnosis and treatment. In a review of the BMI curves of 148 children who survived brain tumors for at least five years, correlations were identified between the development of obesity and: 1) tumor location (hypothalamus), 2) tumor histology (craniopharyngioma and hypothalamic astrocytoma), 3) having had surgery (either a biopsy and/or a gross total resection), 4) the amount of radiation directed at the hypothalamus (greater than 51 Gy), and 5) the presence of hypothalamic hormone disturbances. Thus, we conclude that obesity in this setting is due directly to damage to the hypothalamus, whether it is from the tumor itself, or the surgery or radiation treatments thereof.

A rat model of hypothalamic obesity has been available for approximately 50 years; experimental damage to an area of the brain known as the ventromedial hypothalamus (VMH) triggers non-stop eating and weight gain. Even in the face of severe caloric restriction, animals with VMH lesions continue to gain weight, because their metabolism is geared toward energy storage instead of energy burning. Prior to the development of severe obesity, it has been shown that VMH-damaged rats manifest increased insulin; however, investigators have also found that cutting the nerve that leads to and from the brain to the pancreas (the vagus nerve) in rats prevents the hyperphagia, insulin rise, and weight gain. Thus, it has been proposed that the VMH may control energy balance by influencing insulin secretion through the vagus nerve. These data also suggest a potential target for therapy.

Although weight gain via hypothalamic damage is very rare, and offers little practical advice for people seeking information on weight loss, the hope is that mechanisms of weight gain learned from these patients will aid future researchers and physicians.

Following an introduction to this series posted previously, this is the second of many detailed examinations of lifestyle practices, physiological and clinical pathologies, and inherited factors. @DrTomSherman

2. Imperception of Liquid Calories……………………………………….affecting calories-in

Until recently, the consumption of only one food category was statistically associated with excessive weight gain: sugar sweetened beverages (SSB). There are clear, obvious, and negative health implications for SSBs as an abundant source of sugar and empty calories. In another context, however, there is a hypothesis that SSBs represent a significant source of calories that are not accurately registered as such: adults and children fail to adequately adjust food intake to compensate for the consumption of liquid calories. As intriguing and attractive as this phenomenon may be, however, it remains far from proven; the studies purporting to demonstrated this effect are often too small and utilize study designs that make comparisons too difficult. That is not to say that there have not been many studies that have concluded that liquid calories are perceived less well than solid calories, including several well-cited examples such as those described below:

Left: International Journal of Obesity 24: 794 (2000); right: Journal of Pediatrics 142: 604 (2003)

On the left are the results from a crossover design study in which 15 subjects were provided with an extra 450 kcal/day for 4 weeks in the form of either jellybeans or a SSB (after a 4 week washout period, the groups were switched). Free-feeding intakes were measured throughout the day. For those consuming jellybeans, free-feeding intakes were significantly lower than baseline, resulting in dietary compensation of -17% for the jellybeans. No decrease in intake was observed for those drinking the extra calories, resulting in a positive energy balance.

On the right are similar results from a study in children in which the intake of solid food was measured across an increasing range of SSB consumption. SSB intake contributed to an increase in total daily calories that was not compensated by a commensurate decrease in solid food calories, again resulting in a positive energy balance.

These data are undoubtedly intriguing, but unfortunately, as I mentioned above, too often we are comparing apples to oranges when we attempt to quantitate the relative impact of liquid or solid calorie preloading on subsequent caloric intake. I think the weight of evidence indicates that liquid calories are more poorly perceived than solid food calories – rather than not perceived at all – thereby contributing to a net increase in calories consumed.

Although SSB represent a significant source of high-fructose corn syrup, which contribute additional risks associated with increased circulating triglycerides and insulin resistance, it is the simple observation that SSB represent pure added calories that factors most prominently in its association with weight gain and obesity in children.

Following an introduction to this series posted previously, this is the first of many detailed examinations of lifestyle practices, physiological and clinical pathologies, and inherited factors. @DrTomSherman

1. Simple Obesity……………………………………………………………………..affecting calories-in

In lieu of a better term, simple obesity is the descriptor for excessive weight gain as predicted by the basic thermodynamic model: excessive calories-in. In a series of National Health and Nutrition Examination Surveys (NHANES) conducted by the CDC for the three decades following 1971, American men experienced a 7% increase in caloric intake and American women a whopping 22% increase:

CDC Morbidity and Mortality Weekly Report, Feb 05, 2004
Adapted from: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5304a3.htm

The rates of caloric increase slowed with the 2001–2002 and 2003–2004 surveys, as shown in Table 2 below (Table 2 expresses energy intake in kilojoules; to convert to kilocalories (1 kilocalorie = 1 food Calorie), divide kJ by 4.184).

Public Health Nutrition 17: 256 (2013)

The six NHANES surveys between 1971 and 2004 overlap with the low-fat era: when USDA dietary advice was to decrease dietary fat intake. Calories from fat did decrease about 3% during this period, whereas, dietary carbohydrate intake increased more than 5%. An obvious result of excess calories-in is the average American woman now weights as much as the average 1960s man:

https://www.washingtonpost.com/news/wonk/wp/2015/06/12/look-at-how-much-weight-weve-gained-since-the-1960s/

However, neither NHANES, nor other national data demonstrate an increase in caloric intake among children and young adolescents since the 1999–2000 survey (Third Report on Nutrition Monitoring in the United States, Vol 2); and yet, obesity rates continued to increase for children (although there is some evidence for a plateau):

Trends in Intake of Energy and Macronutrients in Children and Adolescents From 1999–2000 Through 2009–2010
http://www.cdc.gov/nchs/data/databriefs/db113.htm

From these data, simple obesity would seem to be simple: excessive calories-in leads to weight gain. Anecdotally, however, we all know those who can seemingly eat anything and remain stick thin, and those who gain weight by only thinking about food. And indeed, there are many studies that illustrate how non-linear the relationship between consumptive behaviors and body weight can be. These are exactly the types of studies that critics of the thermodynamic model point to – understandable so. Overfeeding studies, for example, illustrate this disconnect well. In a typical study,

Obesity 14: 690 (2006)

as shown in figure 2 above, 16 lean healthy sedentary males and females were overfed by 1000 kcal/d for 8 weeks. After ingesting an additional 56,000 kcal, the subjects gained anywhere from ~3 lbs to more than 15 lbs, with total weight gained evenly distributed between these two extremes; thus, it is difficult to predict, and impossible to calculate, body weight changes purely as a function of total calories entering the mouth. Many of the mechanisms that impact what happens to these calories are described in near-future posts.

While making the final edits on this post, it was of great interest – relevant to the topic of this section – that this systematic review was published in The Cochrane Collaboration database:

Portion, package or tableware size for changing selection and consumption of food, alcohol and tobacco (Review)

Cochrane Database of Systematic Reviews 2015, Issue 9. Art. No.: CD011045. DOI: 10.1002/14651858.CD011045.pub2

Authors’ conclusions: This review found that people consistently consume more food and drink when offered larger-sized portions, packages or tableware than when offered smaller-sized versions. This suggests that policies and practices that successfully reduce the size, availability and appeal of larger-sized portions, packages, individual units and tableware can contribute to meaningful reductions in the quantities of food (including non-alcoholic beverages) people select and consume in the immediate and short term. However, it is uncertain whether reducing portions at the smaller end of the size range can be as effective in reducing food consumption as reductions at the larger end of the range. We are unable to highlight clear implications for tobacco or alcohol policy due to identified gaps in the current evidence base.

I encourage you to review an earlier blog post that helps explain the behaviors behind this: Examining New York City’s Ban on Sugar Sweetened Fountain Drink Sizes Greater Than 16 Ounces

A question from a medical student last year prompted me to begin assembling a list of lifestyle practices, physiological and clinical pathologies, and inherited factors that are associated with, or directly promote, weight gain. Once assembled, it was of great interest to me to discover that the vast majority of these practices, pathologies and factors increased the total number of calories ingested and/or absorbed. Inadvertently, therefore, this list became a defense of the thermodynamic model of body weight regulation. Secondly, I argue that the difficulty defending the thermodynamic model is not because the model is wrong, but because it is difficult to properly quantitate the calories-in side of the equation. I discuss my reasoning in this post. @DrTomSherman

Our challenge in understanding weight gain in general, and childhood obesity in particular, is not only identifying mechanisms and practices that result in weight gain, but that do so at a rate that explains how, over the course of only 2-4 generations, greater than 30% of the population can now be clinically diagnosed as obese. Undoubtedly, the causes for excessive weight gain are multifold, and include, for example, underlying structural practices that distort the true cost of inexpensive food, and food company marketing practices; but it is also recognized that Western civilization has generated more and varied mechanisms for weight gain, many of which seemingly hijack and corrupt our intrinsic homeostatic mechanisms of body weight control. In this post, I list those practices, pathologies and factors that make significant contributions to adult and childhood obesity, and will detail the state of the evidence for each of these in a series of subsequent posts.

Introduction: The Regulation of Body Weight                                                         Affecting:

1. Simple Obesity……………………………………………………………………………………Calories-In
2. Imperception of Liquid Calories…………………………………………………………..Calories-In
3. Hypothalamic Obesity…………………………………………………………………………Calories-In
4. Genetics and Obesogenic Polymorphisms……………………………………………..Calories-In
5. Paternal Epigenetic Influences……………………………………………………………..Calories-In
6. The Gut Biome……………………………………………………………………………………Calories-In
7. Antibiotics (and the Gut Biome)……………………………………………………………Calories-In
8. Breastfeeding (and the Gut Biome)……………………………………………………….Calories-In
9. Artificial Sweeteners (and the Gut Biome)……………………………………………..Calories-In
10. Side-Effects of Prescription Medication…………………………………………………Calories-In
11. Placental Leptin Reprogramming………………………………………………………….Calories-In
12. Infectobesity: Adenovirus-36 Infection………………………………………………….Calories-In
13. Insufficient Sleep…………………………………………………………………………………Calories-In
14. Circadian Rhythm Disruption……………………………………………………………….Calories-In
15. Exercise (exercise?!)…………………………………………………………………………….Calories-In
16. Eating as an addictive behavior……………………………………………………………..Calories-In
17. Brown Fat………………………………………………………………………………………….Calories-Out
18. Total Daily Energy Expenditure:
    Basal Metabolic Rate (BMR) & Glycemic Index………………………………………Calories-In
    Diet-Induced Thermogenesis & Processed Foods……………………………………Calories-In

Introduction – The Regulation of Body Weight

Animal studies have clearly demonstrated that body weights tend to be relatively stable over time. The figures below, for example, illustrate that forced overfed rats (gavage fed) will gain weight (Panel A), but will return to their normal weight once unrestricted (ad libitum) feeding is restarted. Similarly, as shown in the figure below right, rats will lose weight when caloric restricted, but regain their normal weight once ad libitum feeding begins again.

Daily body weight (A) and weight of chow (B) consumed spontaneously during 8 h (B) in a group of 7 gavage overfed (open symbols) and 5 control (closed symbols) rats. Solid bar represents the period of gavage overfeeding. Am J Physiol Regul Integr Comp Physiol 259:R1148-R1155, 1990

Data such as these lend support to the hypothesis that body weight is regulated around a set point using homeostatic mechanisms, much like our body temperature is regulated around the set point of 37°C. In this simple model for a body weight set point, shown below, body weight gravitates to a stable weight that balances energy in (EI) and energy out (EO): above the stable weight, control systems kick in to decrease appetite (EI) and increase energy expenditure (EO). This creates a net negative energy that draws the body back to the stable weight. The opposite will be true when below the set point:

Adapted from: http://hms.harvard.edu/news/energy-equation-proposes-patterns-weight-gain-and-loss-1-9-09

This model is referred to variously as the thermodynamic model, energy balance model, or calories-in calories-out (CICO) model for body weight regulation, for they all emphasize that obesity develops only when energy intake, in the form of feeding, chronically exceeds total body expenditure (physical activity, basal metabolic rate, and adaptive thermogenesis).

Unfortunately, the scientific community and writers/bloggers are passionately divided on the merits of the thermodynamic model, with critics arguing that excessive weight gain is the result of, typically, (1) eating too much of a certain food group (i.e. carbohydrates, sugars, or fat) or (2) decreased physical activity. Much of this criticism results from a wide assortment of studies interpreted, for example, as demonstrating the failure of caloric restriction to result in significant weight loss, or that the regulation of human body weight is far too complex to be adequately modeled by a simple calories-in vs calories-out equation. I will definitely grant that the regulation of body weight is very complex, but I will also argue that these criticisms are often accepted not because the basic premise of the thermodynamic model is wrong, but because there are many factors that govern and influence total calories-in or total calories-out – several of which we have little or no conscious control over – that make it difficult or impossible to accurately write a balanced thermodynamic equation. Thus, the failure is not with the thermodynamic model, but in our ability to properly quantitate its components. Many of the most significant of these mechanisms and factors will be described below.

Similarly problematic is the obvious conclusion that the homeostatic mechanisms responsible for regulating human body weight are not working; how else to explain the unprecedented increase in body weights over the past 25 years as shown in the following figure that charts obesity trends among U.S. adults as part of the CDC Behavioral Risk Factor Surveillance Study (BRFSS):