Branched-Chain and Aromatic Amino Acids Related to Visceral Adipose Tissue Impact Metabolic Health Risk Markers.

Context: Visceral (VAT) and subcutaneous adipose tissue (SAT) function as endocrine organs capable of influencing metabolic health across adiposity levels.

Objective: We aimed to investigate whether metabolites associated with VAT and SAT impact metabolic health through metabolite concentrations.

Methods: Analyses are based on 1790 participants from the population-based Rhineland Study.

Serum amino acid concentrations are modified by age, insulin resistance, and BCAT2 rs11548193 and BCKDH rs45500792 polymorphisms in subjects with obesity.

Background & Aims: The amino acid profile of young adults is modified by sex, body mass index (BMI) and insulin resistance (IR). However, we do not know if age or the presence of specific polymorphisms in the genes of BCAT2 and BCKDH contribute to changes in the amino acid profile, especially in subjects with obesity. Therefore, we have evaluated the effect of age, the presence of IR and the polymorphisms of BCAT2 rs11548193 and BCKDH rs45500792 on the concentration of amino acids in subjects with obesity.

FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI.

Purpose: Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdominal region on Dixon MRI scans.

Methods: FatSegNet is composed of three stages: (a) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (b) Segmentation of adipose tissue on three views by independent CDFNets, and (c) View aggregation. FatSegNet is validated by: (1) comparison of segmentation accuracy (sixfold cross-validation), (2) test-retest reliability, (3) generalizability to randomly selected manually re-edited cases, and (4) replication of age and sex effects in the Rhineland Study-a large prospective population cohort.

Development and validation of new predictive equation for resting energy expenditure in adults with overweight and obesity.

Background & Aims: Accurate predictive equations of resting energy expenditure (REE) are crucial in devising nutritional strategies to manage overweight/obesity, especially in countries where these are highly prevalent. REE is the most common measurement used to estimate energy requirements in the nutritional context; the most accurate method of measuring REE is indirect calorimetry (IC). However, this method is costly and often rarely feasible in many clinical settings.

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.