A Clinical Guide to Pediatric Weight Management and Obesity, 1st Edition


Pathophysiology of Obesity

Obesity has become a central theme in pediatric medicine. The impact of the obesity epidemic is felt in both the number of children affected and the rising rates of obesity-related comorbidities. This chapter reviews some highlights of obesity pathophysiology so the reader can begin to develop an increased understanding of pediatric obesity and effective prevention, early intervention, and treatment strategies.


Obesity in childhood is a heterogeneous disease with unique weight gain trajectories, environmental triggers, and pathophysiologic responses.

Every obese child and family has individual genetic, behavioral, relational, environmental, and physiologic characteristics that contribute to the development and maintenance of excess adiposity.


There may also be critical periods of susceptibility to obesity during the child's growth that make them especially vulnerable to obesity-promoting influences. Understanding the factors contributing to the cause of obesity may shed light on effective methods of treatment and enable the practitioner to help children and families make necessary lifestyle changes aimed toward weight loss.

Obesity is clearly a result of gene-environment interaction. The epidemic can be seen as an ever-worsening interplay of environmental factors overlying a continuum of genetic responsiveness. This interplay is even more complex when the impact of the intrauterine environment is taken into consideration. For instance, the risk for obesity and diabetes increases for children born to mothers who had diabetes during pregnancy (1).

In a study of middle-aged adults, Barker (2,3) found that adults who were small for gestational age newborns were at increased risk for obesity, diabetes, and hypertension. This finding points to the long-standing effect of environmental change when it occurs during a susceptible developmental period. The term “developmental


plasticity” has been used to describe “the ability of a single genotype to produce more than one type of structure, physiologic state, or behavior in response to biologic conditions” (4). Infants who were exposed to cigarette smoke while in utero, one of the causes of intrauterine growth restriction, are at increased risk for obesity as well (5), the supposition being that the altered intrauterine environment also altered their susceptibility to later weight gain. A maternal diet high in carbohydrate early in pregnancy and low in protein late in pregnancy has also been associated with reduced placental weight and birth weight (6). Maternal hypertension, a risk for lower birth weight, can also reduce the ability of the placenta to transfer energy to the fetus (7), possibly adding to the infant's cumulative risk for later obesity.

The theory that altering the “programming” (8) of a fetus in the intrauterine environment may result in later changes in how the child responds to the environment represents a fundamental shift in thinking about what constitutes predisposition for later obesity. The mechanisms affecting the intrauterine environment and subsequent changes in individual response are not completely understood. It is hypothesized that an infant exposed to an energy-restricted intrauterine environment alters mechanisms of energy balance in preparation for a similarly restricted extrauterine environment. For example, under conditions of energy restriction in utero, fetal protein breakdown and amino acid utilization increase to maintain energy homeostasis at the expense of reducing energy required for growth (9). After birth, exposure to an energy-rich environment of “plenty” results in a “mismatch” between genetic programming for an environment of scarcity and the actual environmental experience of the infant. This mismatch leads to the accumulation of adipose tissue (10,11).

Intrauterine effects of diabetes or impaired glucose tolerance can also influence the future risk of obesity and diabetes. Increased exposure of the fetus to glucose, as occurs when the pregnant woman is diabetic, results in greater insulin production by the fetus and greater deposition of adipose tissue (1). Parental obesity, also a risk for later obesity, may reflect both genetic and environmental influences as well as psychobehavioral effects on eating and activity. Whitaker et al. (12) has shown that the risk of obesity when one or both parents are obese increases markedly as childhood advances.

Shared predisposition and shared environment mandate that prevention, intervention, and treatment efforts are targeted toward family-based change.


Energy Intake

Energy intake can be affected by multiple factors that represent the complex interaction between the external environment and the internal interplay of energy stores, central nervous system feedback, and psychological predisposition. The interface between the individual and the environment when food is readily available is modulated by hunger and satiety (Table 3.1).



TABLE 3.1. Influence on energy intake


Psychological states

Visual cues



Social expectations



Social situations

Food availability

Reaction to stress






Hunger is the internal experience of a desire to eat. The child's expression of hunger is often the driving force behind parent-child interactions involving food. A variety of internal and external cues may trigger hunger (13), and awareness of these triggers may allow parents to respond with a solution to the underlying cause rather than with food.

  • The pleasure of eating
  • Emotional states of anxiety
  • Depression
  • Stress
  • Social cues
  • Sensory cues
  • Boredom
  • Environmental cues
  • Timing

Appetite, on the other hand, has been defined as “the internal driving force for the search, choice and ingestion of food” (13). Appetite can also be thought of as the external expression of hunger. Blundell (14) breaks appetite down into an interaction between psychological events and behavior, peripheral physiology, and the central nervous system (CNS).

These three systems operate in the equation of energy balance and dysfunction and at any level can alter energy intake. Studies have shown that food intake increases as taste becomes more pleasant (15). In studies with rats, greater availability of highly palatable foods increases intake, resulting in what Tordorff calls “obesity by choice” (16). Pleasant taste and aversion to tastes have been linked to the amygdala and orbital frontal cortex (17).

Feedback from the gastrointestinal tract can also affect the experience of hunger and appetite. Ghrelin, a 28-amino-acid acylated peptide, is the endogenous ligand for the growth hormone secretagogue receptor (GHS-R). The GHS-R is expressed in the arcuate nucleus (Arc), along with neuropeptide Y (NPY) neurons. Injections of ghrelin into the arcuate and paraventricular nuclei of the hypothalamus increased food intake in male and female rats (18).

When administered to humans, ghrelin stimulates appetite and food intake, including increased preprandial hunger and meal initiation. Produced by the stomach and duodenum (19), ghrelin increases with fasting and is suppressed with refeeding. Increased ghrelin levels have also been noted in patients with anorexia nervosa and those with Prader-Willi syndrome (20). Ghrelin acts as a stimulator of growth hormone secretion in both animals and humans and has been shown to have


a more potent action than that of growth hormone–releasing hormone (21). Ghrelin may also affect energy utilization; it has also been shown in rodents that peripheral daily administration of ghrelin causes weight gain by reducing fat utilization without a significant change in food intake (22).


Satiety has been associated with increased activity in the prefrontal cortex (23). This area exerts inhibitory effects on responses to internal and external stimuli. Patients with damage to the prefrontal cortex have hyperphagia (24,25).

The effects of food in the prefrontal cortex have also been found to mimic that of drugs (26). Food is used to self-medicate for distress (27), and sweet foods have been shown to have an analgesic effect (28).

Excessive food intake can induce downregulation, sensitization, and withdrawal and result in choosing the immediate reward of eating even in the face of negative long-term consequences (25).


The prefrontal cortex is involved in the system necessary for decision making and choosing among options for action. In particular, its critical function in this process is to activate feelings or emotional states, which help focus decision making, from “thoughts” about rewarding or punishing events that are not currently present in one's immediate environment (29,30). Davis et al. (25) found that decision-making deficits were greater in women with higher body mass indexes (BMIs) than in normal weight women and suggested that cortical functions that inhibit short-term rewards in the face of long-term negative consequences may be impaired.

A direct and inverse relationship exists between gastric distension and satiety (13). Increasing the food volume but not the energy content of food infused into the stomach has been found to reduce hunger ratings and food intake in both normal weight and obese women (31). Gastrointestinal hormones such as cholecystokinin (CCK) and glucagon-like peptide 1 (GLP-1) can regulate satiety. Sensitivity to the short-term signals produced by CCK and GLP-1 is modulated by leptin, insulin, and ghrelin, which are involved with long-term energy regulation, thereby linking these two systems (32). GLP-1 is a gastrointestinal peptide produced in the ileum in response to ingested carbohydrates and fat. It stimulates the islet cells in the pancreas to secrete insulin and has been shown to reduce appetite and body weight (33).

CCK delays gastric emptying and depends on a distended stomach for effect. CCK signals travel via the vagus nerve, which transmits neuronal signals to the nucleus tractus solitarius and from there to the hypothalamus (34). CCK is released into the blood as a result of the presence of fat or protein in the duodenum. Different types of foods may cause different CCK responses, with foods containing longer chain fatty acids causing a higher release of CCK than those with shorter chain fatty acids (35).



Energy Utilization

Regulation of energy utilization is less well understood than that of energy intake. An observational population-based study of children concluded that correlations within groups of spontaneous physical activity suggested the presence of an “activity stat,” indicating that activity was centrally and biologically regulated (36).

Spontaneous physical activity, such as fidgeting and time spent moving or standing, may be affected by stimulation of the lateral hypothalamus. Orexins, neuropeptides located in the lateral hypothalamus, may be one link between feeding and spontaneous physical activity (37). In animals, centrally administered orexin increases food intake, waking time, motor activity, and metabolic rate, along with heart rate and blood pressure (38).

Energy Balance

Energy balance is maintained through a complex relationship between energy stores (adipose tissue), energy intake via feedback from the gastrointestinal tract, and energy utilization, all integrated at the level of the hypothalamus. The complex system regulating energy balance is centered in the hypothalamus (Fig. 3.1).


FIG. 3.1. Input to the hypothalamus from energy stores and hunger signals are integrated with efferent output regarding feeding behavior, insulin secretion, and autonomic regulation of adipokines.



Leptin, produced in adipocytes, is correlated to the body's total fat stores. Leptin regulates long-term energy balance in favor of conservation of fat mass (39). Melanocyte–stimulating hormone interacts at the level of the hypothalamus with a melanocortin receptor, MC4-R, to decrease food intake and increase energy expenditure (39). Appetite-stimulating (orexigenic) peptides such as agouti-related protein and neuropeptide Y are also expressed in the hypothalamus as a component of the CNS control of energy balance (39). Gut hormones such as CCK, ghrelin, and peptide YY and vagal nerve signals also input information at the hypothalamic level to regulate hunger and satiety (39).


Adipose tissue itself is a major factor in dynamic energy regulation.


Far from being simply a storage compartment for fat as an energy reserve, the adipose tissue system is an active endocrine organ system. Adipose tissue is composed of brown and white adipose tissue. White adipose tissue is the predominant tissue found in excess in obesity. Brown adipose tissue is present in the newborn and increases in states of cold exposure and starvation. White adipose tissue is made up of an array of cellular types: adipocytes; multipotent stem cells capable of differentiating into muscle, cartilage, adipose tissue, and bone; vascular endothelial cells; stromal cells; and macrophages (Fig. 3.2).

The adipocyte is of particular importance in energy balance. In addition to storing fuel, the adipocyte produces cytokines; participates in hormonal regulation, particularly glucose homeostasis; and is involved in energy regulation/signaling at the level of the CNS and peripheral tissues. A sampling of cytokines produced by the adipocyte is shown in Table 3.2.

Leptin and Energy Regulation

The identification of leptin provided the initial insight into the complex communication between peripheral energy stores and the central nervous system. Leptin is an adipocyte-produced product of the OB gene. Leptin administration to leptin-deficient obese mice reduces weight (40,41). Leptin's major site of production is white adipose tissue, but brown adipose tissue, stomach, placenta, mammary gland, ovarian follicles, and fetal organs have also been shown to produce it (42). Leptin


receptors have been detected in most tissues, and the hypothalamic nuclei involved in energy regulation are a major target for leptin action.

TABLE 3.2. Some cytokines produced by the adipocyte


Tumor necrosis factor-a

Retinol binding protein





Plasminogen activator inhibitor 1

Acylation stimulating protein


FIG. 3.2. Adipocytes, with centrally stored fat and peripheral nuclei, as well as vascular stromal elements.

Leptin also affects expression of the proopiomelanocortin (POMC) gene by neurons of the hypothalamic arcuate nucleus (43). As a result α-, β-, and γ-melanocyte–stimulating hormones are produced. These peptides signal target neurons in the lateral hypothalamus that express the melanocortin receptors MC3-R and


MC4-R, which results in decreased food intake and increased energy expenditure (44). Patients with a complete deficiency of POMC due to homozygous or compound heterozygous loss-of-function mutations exhibit a characteristic syndrome marked by childhood-onset severe obesity, red hair, and hypocortisolism (45). The influence of common polymorphisms in POMC, resulting in partial loss of function, on obesity phenotypes in less extreme individuals is unclear (46).

Links to Obesity-Related Comorbidities

Alterations in insulin sensitivity are hallmarks of the metabolic derangements due to obesity and the development of the major comorbidities of type 2 diabetes, dyslipidemia, and cardiovascular disease. Insulin-mediated glucose disposal by muscle varies almost 10-fold in healthy individuals, possibly explaining the variability of the impact of obesity on insulin resistance in each individual. The more insulin sensitive the muscle, the less insulin needs to be secreted to maintain normal glucose homeostasis. The more insulin resistant, the greater the degree of compensatory hyperinsulinemia, and the more likely the individual is to develop disease.

Adipose tissue in obesity becomes refractory to insulin's suppression of fat mobilization, and the resulting insulin resistance increases release of free fatty acids from the adipocytes. Elevated free fatty acid concentrations are linked to the onset of peripheral muscle and hepatic insulin resistance. Hyperinsulinemia stimulates fatty acid synthesis while inhibiting the oxidation of fatty acids and compromising triglyceride transport out of the liver. This results in net accumulation of fat in the hepatocytes and nonalcoholic fatty liver disease (NAFLD). Muscle is also affected; accumulated triacylglycerol inhibits insulin signaling and leads to a reduction in insulin-stimulated muscle glucose transport as well as in muscle glycogen synthesis and glycolysis (47).

Physical activity can decrease insulin resistance even without weight loss. In a study of obese adolescent girls attending a 12-week fitness program, fat-free mass increased, as did insulin sensitivity despite no change in weight or percent body fat (48).

Links between adipokines and cardiovascular disease may arise via adiponectin and its effects on inflammation. A cytokine produced in abundance by adipocytes, adiponectin, varies inversely with adiposity.

Plasma adiponectin concentration is lower in obese individuals than in normal weight persons. Adiponectin is inversely related to insulin levels, inflammatory markers, and fat mass. In adults, reduced adiponectin concentrations have been linked to cardiovascular disease. Physical activity has been found to increase adiponectin levels. In a 3-month physical activity/dietary intervention with adolescents, adiponectin increased by 34%, and fat mass, insulin resistance, and inflammatory factors were reduced despite no significant weight loss (49).

In addition to decreased expression of adiponectin, which has anti-inflammatory action, the secretion of proinflammatory cytokines tumor necrosis factor-α (TNF-α), interleukin-6, prothrombotic factors, and acute phase serum amyloid is increased in obesity (50). These cytokines stimulate macrophage migration into adipose tissue.



Adipocyte-secreted TNF-α stimulates preadipocytes and endothelial cells to produce monocyte chemoattractant protein–1. Increased leptin and decreased adiponectin also stimulate transport of macrophages to adipose tissue, creating a low-grade inflammatory state (51).

A study investigating the effect of lifestyle intervention on inflammatory markers showed an increase in adiponectin, a decrease in C-reactive protein and interleukin (IL)-6, and reduced inflammation in adipose tissue in response to diet and exercise (52). These inflammatory changes may link obesity with cardiovascular disease comorbidity in an as yet unexplained fashion.


LJ is a 9-year-old African American girl who comes to your office for a “check-up.” Her mother and grandmother accompany her. You notice that LJ is tall and looks older than her age. Her weight is 130 lb (>95th percentile), she is 4 ft 11 in. tall (>95th percentile), and her BMI is 26.3 (>95th percentile). Her blood pressure is 115/68 mm Hg. Over the past year, since your last visit with her, she has gained 25 lb. In catching up with the family, you ask how they are doing and the mother notes that she and LJ have moved back in with the grandmother while the mother is going back to school. You ask if they have been concerned about LJ's weight and the mother says she is. The grandmother says that LJ just “likes her food.” You review LJ's past history and note that she was a small for dates baby with a maternal history of hypertension and toxemia. Her old growth charts show that although starting out underweight she had rapid “catch-up growth.”

LJ's family history is positive for hypertension in the mother and maternal great aunt, for diabetes in the paternal grandfather, and for obesity in the maternal grandmother. Her review of systems yields essentially negative findings.

On physical examination you note that she is Tanner 2 and has mild acanthosis nigricans. You ask about LJ's diet, and the mother says she sometimes skips breakfast, sometimes eats at school. LJ buys lunch at school, and the mother is surprised to find out that the grandmother has been giving LJ extra money for ice cream. After school, the grandmother frequently makes cookies or a special treat for LJ. The family eats dinner together, served family style. Sometimes LJ and her grandmother have a snack together in the evening when watching television. During this review, the mother frequently expresses surprise at the amount LJ is snacking, noting how different this is from the time before they moved back with the grandmother.

It turns out that LJ's level of activity is also low; by the time she finishes her homework, dinner is ready, after which it is dark outside. You calculate with the mother and grandmother that LJ is watching about 4 hours of television per day.

After hearing about LJ's weight gain and your description of her increased risk for heart disease and diabetes, the mother and grandmother decide it is “time to do something.” They propose to limit the night-time snacking and let LJ go outdoors after school and finish her homework after dinner. The grandmother also says she will save LJ's school snack money and buy her something special (not food), as well as try to come up with a healthier snack after school.



You order laboratory studies to evaluate LJ's risk for metabolic syndrome, nonalcoholic steatohepatitis (NASH), and dyslipidemia and schedule a return visit for 1 month.

One month later, LJ comes back to your office with her grandmother. Her mother is at school but wrote a note saying that the family has been trying hard to work on LJ's weight. In reviewing the results from the laboratory studies you just received, you see that LJ has an increased triglyceride level of 175 mg/dL and a high fasting insulin level of 40 µU/mL, with all other studies within normal limits. You weigh LJ and she has lost 1 lb. The grandmother says the family is “doing well,” but about 2 weeks after the last visit, a great aunt passed away and this resulted in more eating away from home and decreased exercise. You encourage the family to continue in their efforts and to complete a diet and activity record. You schedule them to return to your office in 1 month. You ask the grandmother to call and check in with your nurse in 1 to 2 weeks to let you know how they are doing.

On the next visit, you find that LJ has lost 3 lb, she is spending time outdoors every day, and the grandmother has been trying to walk with her about 3 days per week. You review diet records and note the family has been choosing fruit or salad for snacks and LJ has begun to pack her lunch 1 or 2 times per week. You encourage them to continue and arrange to see LJ again in 6 weeks.


  1. Weiss PAM, Scholz HS, Haas J, Tamusinno KF, Seissler J, Borkenstein MH. Long term follow up of infants of mothers with type I diabetes; evidence for hereditary and non hereditary transmission of diabetes and precursors. Diabetes Care.2000;23(7):905–911.
  2. Hales CN, Barker DJ, Clark PM, Cox LJ, Fall C, Osmond C, Winter PD. Fetal and infant growth and impaired glucose tolerance at age 64. BMJ.1991;30:1019–1022.
  3. Law CM, Barker DJ, Osmond C, Fall CH, Simmonds SJ. Early growth and abdominal fatness in adult life. J Epidemiol Community Health.1992;46(3):184–186.
  4. McMillen IC, Robinson JS. Developmental origins of the metabolic syndrome: predication, plasticity, programming. Physiol Rev.2005;85(2):571–633.
  5. Toschke AM, Montgomery SM, Pfeiffer U, von Kries R. Early intrauterine exposure to tobacco-inhaled products and obesity. Am J Epidemiol.2003;158:1068–1074.
  6. Godfrey KM, Robinson JS, Barker DJ, et al. Maternal nutrition in early and late pregnancy in relation to placental and fetal growth.Bone Miner J.1996;312:410–414.
  7. Barker DJ. The intrauterine origins of cardiovascular disease. Acta Paediatr.1993;82(Suppl 391):93–99.
  8. Lucas A. Programming by early nutrition in man. Ciba Found Symp.1991;156:38–50.
  9. Hay WW. Recent observations on the regulation of fetal metabolism by glucose. J Physiol.2006;572(Pt 1):17–24.
  10. Bateson P, Barker D, Clutton-Brock T, Deb D, D'Udine B, Foley RA, Gluckman P, Godfrey K, Kirkwood T, Lahr MM, McNamara J, Metcalfe NB, Monaghan P, Spencer HG, Sultan SE. Developmental plasticity and human health. Nature.2004;430:419–422.
  11. Hales CN, Barker DJ. The thrifty phenotype hypothesis. Br Med Bull.2001;60:5–20.
  12. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med.1997;337:869–873.
  13. de Graaf C, Blom WA Smeets PA, stafleu A, Hendriks HF. Biomarkers of satiation and satiety. Am J Clin Nutr.2004;79:946–961.
  14. Blundell JE, Lawton CL, Cotton JR, Macdiamid JI. Control of human appetite: implications for the intake of dietary fat. Annu Rev Nutr.1996;16:285–319.
  15. de Graaf C, DeJong LS, Lambers AC. Palatability affects satiation but not satiety. Physiol Behav.1999;66(4):681–688.
  16. Tordorff MG. Obesity by choice the powerful influence of nutrient availability on nutrient intake. Am J Physiol Regul Integr Comp Physiol.2002;282:R1536–1539.



  1. O'Dorherty J, Rolls ET, Francis S, Bowtell R, McGlone F. Representation of pleasant and aversive tastes in the human brain. J Neurophysiol.2001;85:1315–1321.
  2. Currie PJ, Mirza A, Fuld R, Park D, Vasselli JR. Ghrelin is an anorexigenic and metabolic signaling peptide in the arcuate and paraventricular nucleus. Am J Physiol Regul Integr Comp Physiol.2005;289:R353–358.
  3. Kojima M, Hosoda H, Date Y, Nakazato M, Matsuo H, Kangawa K. Ghrelin is a growth-hormone-releasing acylated peptide from stomach. Nature.1999;402:656–660.
  4. Cummings DE, Clement K, Purnell JQ, Vaisse C, Foster KE, Frayo RS, Schwartz MW, Basdevant A, Weigle DS. Elevated plasma ghrelin levels in Prader Willi syndrome. Nat Med.2002;8:643–644.
  5. Ariyasu H, Takaya K, Tagami T, Ogawa Y, Hosoda K, Akamizu T, Suda M, Koh T, Natsui K, Toyooka S, Shirakami G, Usui T, Shimatsu A, Doi K, Hosoda H, Kojima M, Kangawa K, Nakao K. Stomach is a major source of circulating ghrelin, and feeding state determines plasma ghrelin-like immunoreactivity levels in humans. J Clin Endocrinol Metab.2001;86:4753–4758.
  6. Tschöp M, Smiley DL, Heiman ML. Ghrelin induces adiposity in rodents. Nature.2000;407: 908–913.
  7. Tataranni PA, Gautier JF, Chen K, Uecker A, Bandy D, Salbe AD, Pratley RD, Lawson M, Reimen EM, Ravssun E. Neuroanatomic correlates of hunger and satiation in humans using positron emission tomography. Proc Natl Acad Sci U S A.1999;9:4569–4574.
  8. Graff-Radford NR, Russell JW, Rezai K. Frontal degenerative dementia and neuroimaging. Adv Neurol.1995;66:37–47.
  9. Davis C, Levitan RD, Muglia P, Bewell C, Kennedy JL. Decision-making deficits and overeating; a risk model for obesity. Obes Res.2004;12:929–935.
  10. Schroeder BE, Binzak JM, Kelley AE. A common profile of prefrontal cortical activation following exposure to nicotine- or chocolate-associated contextual cues. Neuroscience.2001;105:535–545.
  11. Wallis DJ, Hetherington MM. Stress and eating; the effects of ego-threat and cognitive demand on food intake in restrained and emotional eaters. Appetite.2004;4391:39–46.
  12. Mercer ME, Holder MD. Antinociceptive effects of palatable sweet ingesta on human responsivity to pressure pain. Physiol Behav.1997;61:311–318.
  13. Bechara A, Damasio H, Damasio AR, Lee GP. Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. J Neurosci.1999;19:5473–5481.
  14. Bechara A, Damasio AR, Damasio H. Emotion, decision-making, and the orbitofrontal cortex. Cereb Cortex.2000;10:295–307.
  15. Rolls BJ, Roe LS. Effect of the volume of liquid food infused intragastrically on satiety in women. Physiol Behav.2002;76(4-5):623–631.
  16. Havel PJ. Peripheral signals conveying metabolic information to the brain: short term and long term regulation of food intake and energy homeostasis. Exp Biol Med (Maywood).2001;226(11):963–977.
  17. Zander M, Madsbad S, Madson JL, Holst JJ. Effect of 6 week course of glucagon like peptide 1 on glycemic control, insulin sensitivity, and beta cell function in type 2 diabetes: a parallel group study. Lancet.2002;359:824–830.
  18. Kisseleff HR, Carretta JC, Geleibter A, Pi Sunyer FX. Cholecystokinin and stomach distension combine to reduce food intake in humans. Am J Physiol Regul Integr Comp Physiol.2003;285: R992–998.
  19. Matzinger D, Degen L, Drewe J, Meuli J, Duebendorfer R, Ruckstuhl N, D'Amato M, Rovati L, Berlinger C. The role of long chain fatty acids in regulating food intake and cholecystokinin release in humans. Gut.2000;46:688–693.
  20. Wilkin TJ, Mallam KM, Metcalf BS, Jeffery AN, Voss LD. Variation in physical activity lies with the child, not his environment: evidence for an ‘activitystat’ in young children (EarlyBird 16). Int J Obes. (Lond)2006;30(7):1050–1055.
  21. Kotz CM. Integration of feeding and spontaneous physical activity: role for orexin. Physiol Behav.2006;88(3):294–301.
  22. Sakurai T. Orexin: a link between energy homeostasis and adaptive behaviour. Curr Opin Clin Nutr Metab Care.2003;6940:353–360.
  23. Korner J, Leibel RL. To eat or not to eat—how the gut talks to the brain. N Engl J Med.2003;349: 926–927.
  24. Maffei M, Fei H, Lee GH, Dani C, Leroy P, Zahang Y, Proenca R, Negrel R, Aihaud G, Friedman JM. Increased expression in adipocytes of ob RNA in mice with lesions of the hypothalamus and with mutations at the db locus. Proc Natl Acad Sci U S A.1995;92(15):6957–6960.
  25. Halaas JL, Gajiwala KS, Maffei M, Cohen SL, Chait BT, Rabinowitz D, Lallone RL, Burley SK, Friedman JM. Weight-reducing effects of the plasma protein encoded by the obese gene. Science.1995;269:543–546.



  1. Green ED, Maffei M, Braden VV, Proenca R, DeSilva U, Zhang Y, Chua SC, Leibel RL, Weissenbach J, Friedman JM. The human obese (OB) gene: RNA expression pattern and mapping on the physical, cytogenetic and genetic maps of chromosome 7. Genome Res.1995;5(1):5–12.
  2. Baker M, Gaukrodger N, Mayosi BM, Imrie H, Farrall M, Watkins H, Connell JM, Avery PJ, Keavney B. Association between common polymorphisms of the proopiomelanocortin gene and body fat distribution: a family study. Diabetes.2005;54:2492–2499.
  3. Yeo GS, Farooqi IS, Challis BG, Jackson RS, O'Rahilly S. The role of melanocortin signaling in the control of body weight: evidence from human and murine genetic models. Q J Med.2000;9391: 7–14.
  4. Krude H, Biebermann H, Luck W, Horn R, Brabant G, Gruters A. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat Genet.1998;19:155–157.
  5. Krude H, Biebermann H, Gruters A. Mutations in the human proopiomelanocortin gene. Ann N Y Acad Sci.2003;994:233–239.
  6. Kovacs P, Stumvoll M. Fatty acids and insulin resistance in muscle and liver. Best Pract Res Clin Endocrinol Metab.2005;19:625–635.
  7. Nassis GP, Papantakou K, Skenderi K, Triandafillopoulou M, Kavouras SA, Yannakoulia M, Chrousos GP, Sidossis LS. Aerobic exercise training improves insulin sensitivity without changes in body weight, body fat, adiponectin, and inflammatory markers in overweight and obese girls. Metabolism.2005;54:1472–1479.
  8. Balagopal P, George D, Yarandi H, Funanage V, Bayne E. Reversal of obesity related hypoadiponectinemia by lifestyle intervention: a controlled, randomized study in obese adolescents. J Clin Endocrinol Metab.2005;90:6192–6197.
  9. Yang RZ, Lee MJ, Hu H, Pollin TI, Ryan AS, Nicklas BJ, Snitker S, Horenstein RB, Hull K, Goldberg NH, Goldberg AP, Shuldiner AR, Fried SK, Gong DW. Acute phase serum amyloid A: an inflammatory adipokine and potential link between obesity and its metabolic complications. PLoS Med.2006;3(6):e287.
  10. Wellen KI, Hotamisligil GS. Obesity-induced inflammatory changes in adipose tissue. J Clin Invest.2003;112:1785–1788.
  11. Bruun JM, Helge JW, Richelsen B, Stallknecht B. Diet and exercise reduce low grade inflammation and macrophage infiltration in adipose tissue but not in skeletal muscle in severely obese subjects. Am J Physiol Endocrinol Metab.2006;290:E961–967.