Clinical, functional, behavioural and epigenomic biomarkers of obesity.

Overweight and obesity are highly prevalent conditions worldwide, linked to an increased risk for death, disability and disease due to metabolic and biochemical abnormalities affecting the biological human system throughout different domains. Biomarkers, defined as indicators of biological processes in health and disease, relevant for body mass excess management have been identified according to different criteria, including anthropometric and molecular indexes, as well as physiological and behavioural aspects. Analysing these different biomarkers, we identified their potential role in diagnosis, prognosis and treatment. Epigenetic biomarkers, cellular mediators of inflammation and factors related to microbiota-host interactions may be considered to have a theranostic value. Though, the molecular processes responsible for the biological phenomenology detected by the other analysed markers, is not clear yet. Nevertheless, these biomarkers possess valuable diagnostic and prognostic power. A new frontier for theranostic biomarkers can be foreseen in the exploitation of parameters defining behaviours and lifestyles linked to the risk of obesity, capable to describe the effects of interventions for obesity prevention and treatment which include also behaviour change strategies.

Automatic muscle and fat segmentation in the thigh from T1-Weighted MRI.

PURPOSE: To introduce and validate an automatic segmentation method for the discrimination of skeletal muscle (SM), and adipose tissue (AT) components (subcutaneous adipose tissue [SAT] and intermuscular adipose tissue [IMAT]) from T1-weighted (T1 -W) magnetic resonance imaging (MRI) images of the thigh. MATERIALS AND METHODS: Eighteen subjects underwent an MRI examination on a 1.5T Philips Achieva scanner. Acquisition was performed using a T1 -W sequence (TR = 550 msec, TE = 15 msec), pixel size between 0.81-1.28 mm, slice thickness of 6 mm. Bone, AT, and SM were discriminated using a fuzzy c-mean algorithm and morphologic operators. The muscle fascia that separates SAT from IMAT was detected by integrating a morphological-based segmentation with an active contour Snake. The method was validated on five young normal weight, five older normal weight, and five older obese females, comparing automatic with manual segmentations. RESULTS: We reported good performance in the extraction of SM, AT, and bone in each subject typology (mean sensitivity above 96%, mean relative area difference of 1.8%, 2.7%, and 2.5%, respectively). A mean distance between contours pairs of 0.81 mm and a mean percentage of contour points with distance smaller than 2 pixels of 86.2% were obtained in the muscle fascia identification. Significant correlation was also found between manual and automatic IMAT and SAT cross-sectional areas in all subject typologies (p < 0.001). CONCLUSION: The proposed automatic segmentation approach provides adequate thigh tissue segmentation and may be helpful in studies of regional composition.