Absence of association between specific common variants of the obesity-related FTO gene and psychological and behavioral eating disorder phenotypes.

Extensive population-based genome-wide association studies have identified an association between the FTO gene and BMI; however, the mechanism of action is still unknown. To determine whether FTO may influence weight regulation through psychological and behavioral factors, seven single-nucleotide polymorphisms (SNPs) of the FTO gene were genotyped in 1,085 individuals with anorexia nervosa (AN) and 677 healthy weight controls from the international Price Foundation Genetic Studies of Eating Disorders. Each SNP was tested in association with eating disorder phenotypes and measures that have previously been associated with eating behavior pathology: trait anxiety, harm-avoidance, novelty seeking, impulsivity, obsessionality, compulsivity, and concern over mistakes. After appropriate correction for multiple comparisons, no significant associations between individual FTO gene SNPs and eating disorder phenotypes or related eating behavior pathology were identified in cases or controls. Thus, this study found no evidence that FTO gene variants associated with weight regulation in the general population are associated with eating disorder phenotypes in AN participants or matched controls. © 2011 Wiley-Liss, Inc.

Decreased glucose-induced thermogenesis after weight loss in obese subjects: a predisposing factor for relapse of obesity?

Glucose-induced thermogenesis (GIT) after a 100-g oral glucose load was measured by continuous indirect calorimetry in 32 nondiabetic and diabetic obese subjects and compared to 17 young and 13 middle aged control subjects. The obese subjects were divided into three groups: A (n = 12) normal glucose tolerance, B (n = 13) impaired glucose tolerance, and C (n = 7) diabetics, and were studied before and after a body weight loss ranging from 9.6 to 33.5 kg consecutive to a 4 to 6 months hypocaloric diet. GIT, measured over 3 h and expressed as percentage of the energy content of the load, was significantly reduced in obese groups A and C (6.2 +/- 0.6, and 3.8 +/- 0.7%, respectively) when compared to their age-matched control groups: 8.6 +/- 0.7 (young) and 5.8 +/- 0.3% (middle aged). Obese group B had a GIT of 6.1 +/- 0.6% which was lower than that of the young control group but not different from the middle-aged control group. After weight loss, GIT in the obese was further reduced in groups A and B than before weight loss: ie, 3.4 +/- 0.6 (p less than 0.001), 3.7 +/- 0.5 (p less than 0.01) respectively, whereas in group C, weight loss induced no further diminution in GIT (3.8 +/- 0.6%). These results support the concept of a thermogenic defect after glucose ingestion in obese individuals which is not the consequence of their excess body weight but may be one of the factors favoring the relapse of obesity after weight loss.

The protective effect of the obesity-associated rs9939609 A variant in fat mass- and obesity-associated gene on depression.

Candidate gene and genome-wide association studies have not identified common variants, which are reliably associated with depression. The recent identification of obesity predisposing genes that are highly expressed in the brain raises the possibility of their genetic contribution to depression. As variation in the intron 1 of the fat mass- and obesity-associated (FTO) gene contributes to polygenic obesity, we assessed the possibility that FTO gene may contribute to depression in a cross-sectional multi-ethnic sample of 6561 depression cases and 21 932 controls selected from the EpiDREAM, INTERHEART, DeCC (depression case-control study) and Cohorte Lausannoise (CoLaus) studies. Major depression was defined according to DSM IV diagnostic criteria. Association analyses were performed under the additive genetic model. A meta-analysis of the four studies showed a significant inverse association between the obesity risk FTO rs9939609 A variant and depression (odds ratio=0.92 (0.89, 0.97), P=3 × 10(-4)) adjusted for age, sex, ethnicity/population structure and body-mass index (BMI) with no significant between-study heterogeneity (I(2)=0%, P=0.63). The FTO rs9939609 A variant was also associated with increased BMI in the four studies (β 0.30 (0.08, 0.51), P=0.0064) adjusted for age, sex and ethnicity/population structure. In conclusion, we provide the first evidence that the FTO rs9939609 A variant may be associated with a lower risk of depression independently of its effect on BMI. This study highlights the potential importance of obesity predisposing genes on depression.

The effect of obesity, age, puberty and gender on resting metabolic rate in children and adolescents.

During puberty fat-free mass (FFM) and fat mass (FM) change quickly and these changes are influenced by sex and obesity. Since it is not completely known how these changes affect resting metabolic rate (RMR), the aim of the present study was to investigate the effect of body composition, age, sex and pubertal development of postabsorptive RMR in 9.5- to 16.5- year-old obese and non-obese children. Postabsorptive RMR was measured in a sample of 371 pre- and postpubertal children comprising 193 males (116 non-obese and 77 obese) and 178 females (119 non-obese and 59 obese). RMR was assessed by indirect calorimetry using a ventilated hood system for 45 min after an overnight fast. Body composition (FFM and FM) was estimated from skinfold measurements. The mean (+/- SD) RMR was significantly (P < 0.001) lower in non-obese (males: 5600 +/- 972 kJ/24 h; females: 5112 +/- 632 kJ/24 h) than in obese (males: 7223 +/- 1220 kJ/24 h; females: 6665 +/- 1106 kJ/24 h) children. This difference became non-significant when RMR was adjusted for body composition (FFM+FM). However, the difference between the genders still remained significant (control male: 6118 +/- 507, control female: 5652 +/- 507, P < 0.001; obese male: 6256 +/- 507, obese female: 5818 +/- 507 kJ/24 h, P < 0.001). The main determinant of RMR was FFM. In the whole cohort. FFM explained 79.8% of the variation in RMR, followed by age, gender and FM adding further 3.8%, 1.1% and 0.8% to the predictability of RMR, respectively. No significant contribution for study group (obese, non-obese), pubertal stage, or fat distribution was found in the regression for RMR. The adjusted value of RMR (for FFM and FM) slightly, but significantly (P < 0.01) decreased between the age of 10-16 years, demonstrating the important effect of age on RMR. CONCLUSIONS: The resting metabolic rate of obese and control children is not different when adjusted for body composition. The main determinant of RMR is the fat-free mass, however, age, gender and fat mass are also significant factors. Pubertal development and fat distribution do not influence RMR independently from the changes in body composition.

Association between Inflammatory and Obesity Markers in a Swiss Population-Based Sample (CoLaus Study).

Objective: To assess the associations between obesity markers (BMI, waist circumference and %body fat) and inflammatory markers (interleukin-1β (IL-1β); interleukin-6 (IL-6); tumor necrosis factor-α (TNF-α) and high-sensitivity C-reactive protein (hs-CRP)). Methods: Population sample of 2,884 men and 3,201 women aged 35-75 years. Associations were assessed using ridge regression adjusting for age, leisure-time physical activity, and smoking. Results: No differences were found in IL-1β levels between participants with increased obesity markers and healthy counterparts; multivariate regression showed %body fat to be negatively associated with IL-1β. Participants with high %body fat or abdominal obesity had higher IL-6 levels, but no independent association between IL-6 levels and obesity markers was found on multivariate regression. Participants with abdominal obesity had higher TNF-α levels, and positive associations were found between TNF-α levels and waist circumference in men and between TNF-α levels and BMI in women. Obese participants had higher hs-CRP levels, and these differences persisted after multivariate adjustment; similarly, positive associations were found between hs-CRP levels and all obesity markers studied. Conclusion: Obesity markers are differentially associated with cytokine levels. %Body fat is negatively associated with IL-1β; BMI (in women) and waist circumference (in men) are associated with TNF-α; all obesity markers are positively associated with hs-CRP. Copyright © 2012 S. Karger GmbH, Freiburg.

Neuroendocrine deregulation of food intake, adipose tissue and the gastrointestinal system in obesity and metabolic syndrome.

Obesity is an excess of fat mass. Fat mass is an energy depot but also an endocrine organ. A deregulation of the sympathetic nervous system (SNS) might produce obesity. Stress exaggerates diet-induced obesity. After stress, SNS fibers release neuropeptide Y (NPY) which directly increases visceral fat mass producing a metabolic syndrome (MbS)-like phenotype. Adrenergic receptors are the main regulators of lipolysis. In severe obesity, we demonstrated that the adrenergic receptor subtypes are differentially expressed in different fat depots. Liver and visceral fat share a common sympathetic pathway, which might explain the low-grade inflammation which simultaneously occurs in liver and fat of the obese with MbS. The neuroendocrine melanocortinergic system and gastric ghrelin are also greatly deregulated in obesity. A specific mutation in the type 4 melanocortin receptor induces early obesity onset, hyperphagia and insulin-resistance. Nonetheless, it was recently discovered that a mutation in the prohormone convertase 1/3 simultaneously produces severe gastrointestinal dysfunctions and obesity.

Body fat levels in children and adolescents: effects on the prevalence of obesity

BACKGROUND AND AIMS: There is little information regarding the effect of different definitions of obesity on nutritional epidemiology. The aim was thus to assess: (a) the values of percentage of body fat (%BF) by gender and age; (b) the prevalence of obesity according to different %BF cut-offs; and (c) the sensitivity and specificity of BMI according to different %BF cut-offs used to define obesity. METHODS: Cross-sectional study on 2494 boys and 2519 girls aged 10­18 years from the Lisbon area. %BF was measured using a hand-held device. In a sub sample of 211 boys and 724 girls %BF was assessed using skin folds. RESULTS: %BF levels were higher in girls and decreased with age in both genders. Prevalence of obesity varied considerably according to the %BF cut-off used: in boys, it ranged from 4.7% (age-specific 95th percentile) to 26.5% (fixed 25% cut-off), whereas by BMI it was 5.3%. In girls, prevalence of obesity ranged from 0.4% (age-specific BMI-derived %BF values) to 25.4% (fixed 30% cut-off), whereas by BMI it was 4.7%. The specificity of BMI criteria was over 95% irrespective of the %BF cut-off used; conversely, most sensitivities were below 40%. Sensitivities over 50% were obtained for the age-specific BMI-derived %BF values in boys and the age-specific 95th %BF percentile in both genders. Using %BF derived from the skin fold measurements leads to similar results. CONCLUSIONS: Prevalence of obesity varies considerably according to the %BF cut-off used. BMI cut-offs have a low sensitivity but a high specificity. Age- and gender-specific cut-offs for %BF should be used to define pediatric obesity.

Concept of fat balance in human obesity revisited with particular reference to de novo lipogenesis.

The measurement of fat balance (fat input minus fat output) involves the accurate estimation of both metabolizable fat intake and total fat oxidation. This is possible mostly under laboratory conditions and not yet in free-living conditions. In the latter situation, net fat retention/mobilization can be estimated based on precise and accurate sequential body composition measurements. In case of positive balance, lipids stored in adipose tissue can originate from dietary (exogenous) lipids or from nonlipid precursors, mainly from carbohydrates (CHOs) but also from ethanol, through a process known as de novo lipogenesis (DNL). Basic equations are provided in this review to facilitate the interpretation of the different subcomponents of fat balance (endogenous vs exogenous) under different nutritional circumstances. One difficulty is methodological: total DNL is difficult to measure quantitatively in man; for example, indirect calorimetry only tracks net DNL, not total DNL. Although the numerous factors (mostly exogenous) influencing DNL have been studied, in particular the effect of CHO overfeeding, there is little information on the rate of DNL in habitual conditions of life, that is, large day-to-day fluctuations of CHO intakes, different types of CHO ingested with different glycemic indexes, alcohol combined with excess CHO intakes, etc. Three issues, which are still controversial today, will be addressed: (1) Is the increase of fat mass induced by CHO overfeeding explained by DNL only, or by decreased endogenous fat oxidation, or both? (2) Is DNL different in overweight and obese individuals as compared to their lean counterparts? (3) Does DNL occur both in the liver and in adipose tissue? Recent studies have demonstrated that acute CHO overfeeding influences adipose tissue lipogenic gene expression and that CHO may stimulate DNL in skeletal muscles, at least in vitro. The role of DNL and its importance in health and disease remain to be further clarified, in particular the putative effect of DNL on the control of energy intake and energy expenditure, as well as the occurrence of DNL in other tissues (such as in myocytes) in addition to hepatocytes and adipocytes.

Maximal lipid oxidation during exercise: a target for individualizing endurance training in obesity and diabetes?

This review summarizes the rationale for personalized exercise training in obesity and diabetes, targeted at the level of maximal lipid oxidation as can be determined by exercise calorimetry. This measurement is reproducible and reflects muscles' ability to oxidize lipids. Targeted training at this level is well tolerated, increases the ability to oxidize lipids during exercise and improves body composition, lipid and inflammatory status, and glycated hemoglobin, thus representing a possible future strategy for exercise prescription in patients suffering from obesity and diabetes.

Energy gap in the aetiology of body weight gain and obesity: a challenging concept with a complex evaluation and pitfalls.

The concept of energy gap(s) is useful for understanding the consequence of a small daily, weekly, or monthly positive energy balance and the inconspicuous shift in weight gain ultimately leading to overweight and obesity. Energy gap is a dynamic concept: an initial positive energy gap incurred via an increase in energy intake (or a decrease in physical activity) is not constant, may fade out with time if the initial conditions are maintained, and depends on the 'efficiency' with which the readjustment of the energy imbalance gap occurs with time. The metabolic response to an energy imbalance gap and the magnitude of the energy gap(s) can be estimated by at least two methods, i.e. i) assessment by longitudinal overfeeding studies, imposing (by design) an initial positive energy imbalance gap; ii) retrospective assessment based on epidemiological surveys, whereby the accumulated endogenous energy storage per unit of time is calculated from the change in body weight and body composition. In order to illustrate the difficulty of accurately assessing an energy gap we have used, as an illustrative example, a recent epidemiological study which tracked changes in total energy intake (estimated by gross food availability) and body weight over 3 decades in the US, combined with total energy expenditure prediction from body weight using doubly labelled water data. At the population level, the study attempted to assess the cause of the energy gap purported to be entirely due to increased food intake. Based on an estimate of change in energy intake judged to be more reliable (i.e. in the same study population) and together with calculations of simple energetic indices, our analysis suggests that conclusions about the fundamental causes of obesity development in a population (excess intake vs. low physical activity or both) is clouded by a high level of uncertainty.