Obesity is a global issue and it has been suggested that an addiction to certain foods could be a factor contributing to overeating and subsequent obesity. Only one tool, the Yale Food Addiction Scale (YFAS) has been developed to specifically assess food addiction. This review aimed to determine the prevalence of food addiction diagnosis and symptom scores, as assessed by the YFAS. Published studies to July 2014 were included if they reported the YFAS diagnosis or symptom score and were published in the English language. Twenty-five studies were identified including a total of 196,211 predominantly female, overweight/obese participants (60%). Using meta-analysis, the weighted mean prevalence of YFAS food addiction diagnosis was 19.9%. Food addiction (FA) diagnosis was found to be higher in adults aged >35 years, females, and overweight/obese participants. Additionally, YFAS diagnosis and symptom score was higher in clinical samples compared to non-clinical counterparts. YFAS outcomes were related to a range of other eating behavior measures and anthropometrics. Further research is required to explore YFAS outcomes across a broader spectrum of ages, other types of eating disorders and in conjunction with weight loss interventions to confirm the efficacy of the tool to assess for the presence of FA. OPEN ACCESSNutrients 2014, 6 4553

Emerging evidence from recent neuroimaging studies suggests that specific food-related behaviors contribute to the development of obesity. The aim of this review was to report the neural responses to visual food cues, as assessed by functional magnetic resonance imaging (fMRI), in humans of differing weight status. Published studies to 2014 were retrieved and included if they used visual food cues, studied humans >18 years old, reported weight status, and included fMRI outcomes. Sixty studies were identified that investigated the neural responses of healthy weight participants (n = 26), healthy weight compared to obese participants (n = 17), and weight-loss interventions (n = 12). High-calorie food images were used in the majority of studies (n = 36), however, image selection justification was only provided in 19 studies. Obese individuals had increased activation of reward-related brain areas including the insula and orbitofrontal cortex in response to visual food cues compared to healthy weight individuals, and this was particularly evident in response to energy dense cues. Additionally, obese individuals were more responsive to food images when satiated. Meta-analysis of changes in neural activation post-weight loss revealed small areas of convergence across studies in brain areas related to emotion, memory, and learning, including the cingulate gyrus, lentiform nucleus, and precuneus. Differential activation patterns to visual food cues were observed between obese, healthy weight, and weight-loss populations. Future studies require standardization of nutrition variables and fMRI outcomes to enable more direct comparisons between studies.

BackgroundWeb-based approaches are an effective and convenient medium to deliver eHealth interventions. However, few studies have attempted to evaluate the accuracy of online self-reported weight, and only one has assessed the accuracy of online self-reported height and body mass index (BMI).ObjectiveThis study aimed to validate online self-reported height, weight, and calculated BMI against objectively measured data in young Australian adults.MethodsParticipants aged 18-35 years were recruited via advertisements on social media sites and reported their current height and weight as part of an online survey. They then subsequently had the same measures objectively assessed by a trained researcher.ResultsSelf-reported height was significantly overestimated by a mean of 1.36 cm (SD 1.93; P<.001), while self-reported weight was significantly underestimated by –0.55 kg (SD 2.03; P<.001). Calculated BMI was also underestimated by –0.56 kg/m2 (SD 0.08; P<.001). The discrepancy in reporting resulted in the misclassification of the BMI category of three participants. Measured and self-reported data were strongly positively correlated (height: r=.98, weight: r=.99, BMI: r=.99; P<.001). When accuracy was evaluated by BMI category and gender, weight remained significantly underreported by females (P=.002) and overweight/obese participants (P=.02).ConclusionsThere was moderate to high agreement between self-reported and measured anthropometric data. Findings suggest that online self-reported height and weight can be a valid method of collecting anthropometric data.

Diffusion-weighted imaging provides a novel contrast mechanism in magnetic resonance (MR) imaging and has a high sensitivity in the detection of changes in the local biologic environment. A significant advantage of diffusion-weighted MR imaging over conventional contrast material-enhanced MR imaging is its high sensitivity to change in the microscopic cellular environment without the need for intravenous contrast material injection. Approaches to the assessment of diffusion-weighted breast imaging findings include assessment of these data alone and interpretation of the data in conjunction with T2-weighted imaging findings. In addition, the analysis of apparent diffusion coefficient (ADC) value can be undertaken either in isolation or in combination with diffusion-weighted and T2-weighted imaging. Most previous studies have evaluated ADC value alone; however, overlap in the ADC values of malignant and benign disease has been observed. This overlap may be partly due to selection of b value, which can influence the concomitant effect of perfusion and emphasize the contribution of multicomponent model influences. The simultaneous assessment of diffusion-weighted and T2-weighted imaging data and ADC value has the potential to improve specificity. In addition, the use of diffusion-weighted imaging in a standard breast MR imaging protocol may heighten sensitivity and thereby improve diagnostic accuracy. Standardization of diffusion-weighted imaging parameters is needed to allow comparison of multicenter studies and assessment of the clinical utility of diffusion-weighted imaging and ADC values in breast evaluation.

Our understanding of the roles that the amino acids glutamate (Glu) and glutamine (Gln) play in the mammalian central nervous system has increased rapidly in recent times. Many conditions are known to exhibit a disturbance in Glu-Gln equilibrium and the exact relationship between these changed conditions and these amino acids are not fully understood. This has led to increased interest in Glu/Gln quantitation in the human brain in an array of conditions (e.g. mental illness, tumor, neuro-degeneration) as well as in normal brain function. Accordingly, this review has been undertaken to describe the increasing number of in vivo techniques available to study Glu and Gln separately, or pooled as ‘Glx’. The present range of magnetic resonance spectroscopy (MRS) methods used to assess Glu and Gln, vary in approach, complexity and outcome, thus the focus of this review is on a description of MRS acquisition approaches, and an indication of relative utility of each technique rather than brain pathologies associated to Glu and/or Gln perturbation. Consequently, this review focuses particularly on (1) one-dimensional (1D) 1H MRS, (2) two-dimensional (2D) 1H MRS, and (3) 1D 13C MRS techniques.