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. We assessed plasma levels of methionine (Met), branched-chain amino acids (BCAA), aromatic amino acids (AAA), and their metabolic downstream metabolites with liquid chromatography-mass spectrometry. VAT and SAT volumes were assessed by magnetic resonance imaging (MRI). Metabolically healthy and unhealthy phenotypes were defined using Wildman criteria. RESULTS: Metabolically unhealthy participants had higher concentrations of BCAA than metabolically healthy participants (P < 0.001). In metabolically unhealthy participants, VAT volumes were significantly associated with levels of L-isoleucine, L-leucine, indole-3-lactic acid, and indole-3-propionic acid (in log SD units: ß = 0.16, P = 0.003; ß = 0.12, P = 0.038; ß = 0.11, P = 0.035 and ß = -0.16, P = 0.010, respectively). Higher concentrations of certain BCAA and AAA-downstream metabolites significantly increased the odds of cardiometabolic risk markers. The relation between VAT volume and cardiometabolic risk markers was mediated by BCAA (indirect effects 3.7%-11%, P = 0.02 to < 0.0001), while the effect of VAT on systemic inflammation was mediated through higher kynurenine concentrations (indirect effect 6.4%, P < 0.0001). CONCLUSION: Larger volumes of VAT in metabolically unhealthy individuals are associated with altered concentrations of circulating BCAA and AAA-downstream metabolites, increasing the odds of cardiometabolic risk markers. This suggests that these metabolites are involved in the mechanisms that underlie the relationship of abdominal VAT with metabolic health.

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. METHODS: This was a cross-sectional study conducted with 487 subjects with obesity. Participants underwent a physical examination in which their clinical history was obtained and a blood sample was taken for biochemical, hormonal, and DNA analysis. RESULTS: Adults <30 years old with obesity had higher levels of alanine, arginine, aspartate, histidine, leucine, lysine, methionine, phenylalanine, proline, serine and valine than adults ≥30 years old. Interestingly, regardless of age, we found that arginine, aspartate, serine decreased, while proline and tyrosine increased in the presence of IR; tyrosine and sum of branched-chain amino acids (∑BCAA) were the amino acids that increased in the presence of BCAT2 rs11548193 and BCKDH rs45500792 polymorphisms. CONCLUSIONS: We found that the amino acid profiles of subjects with obesity are differentially modified by age, the presence of IR, and the presence of the BCAT2 rs11548193 and BCKDH rs45500792 polymorphisms.

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. RESULTS: The CDFNet demonstrates increased accuracy and robustness compared to traditional deep learning networks. FatSegNet Dice score outperforms manual raters on VAT (0.850 vs. 0.788) and produces comparable results on SAT (0.975 vs. 0.982). The pipeline has excellent agreement for both test-retest (ICC VAT 0.998 and SAT 0.996) and manual re-editing (ICC VAT 0.999 and SAT 0.999). CONCLUSIONS: FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study and permits localized analysis of fat compartments. Furthermore, it can reliably analyze a 3D Dixon MRI in ∼1 minute, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study.

Metabolic Profiling of Human Plasma and Urine, Targeting Tryptophan, Tyrosine and Branched Chain Amino Acid Pathways.

Tryptophan and tyrosine metabolism has a major effect on human health, and disorders have been associated with the development of several pathologies. Recently, gut microbial metabolism was found to be important for maintaining correct physiology. Here, we describe the development and validation of a UHPLC-ESI-MS/MS method for targeted quantification of 39 metabolites related to tryptophan and tyrosine metabolism, branched chain amino acids and gut-derived metabolites in human plasma and urine. Extraction from plasma was optimised using 96-well plates, shown to be effective in removing phospholipids. Urine was filtered and diluted ten-fold. Metabolites were separated with reverse phase chromatography and detected using triple quadrupole MS. Linear ranges (from ppb to ppm) and correlation coefficients (r2 > 0.990) were established for both matrices independently and the method was shown to be linear for all tested metabolites. At medium spiked concentration, recovery was over 80% in both matrices, while analytical precision was excellent (CV < 15%). Matrix effects were minimal and retention time stability was excellent. The applicability of the methods was tested on biological samples, and metabolite concentrations were found to be in agreement with available data. The method allows the analysis of up to 96 samples per day and was demonstrated to be stable for up to three weeks from acquisition.

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. The objective of the present study was to develop and validate a new equation for predicting REE in adults with overweight and obesity. METHODS: This was a cross-sectional study including 410 men and women with overweight and obesity (20-60 y). Participants were randomly assigned; the development group included 200 subjects and the validation group 210 subjects. The new predictive equation was derived using stepwise multiple linear regression analysis. The accuracy of the new equation was compared to several existing predictive equations (PEs). The accuracy rate was calculated as the percentage of subjects whose REE-PE was within ±10% of the REE-IC. REE was measured by IC and anthropometric measurements. RESULTS: One predictive equation was developed (NEQ) in which weight was the strongest predictor of REE. Compared with others predicted equations already using, the new designed equation showed the less mean bias (Kj/day): NEQ: 25.7, Valencia:129, WHO/FAO/United Nations University: 270, Mifflin-St Jeor: 308, Owen: -808, Carrasco: -1097, Korth: -36.4, Johnstone: -375, Livingstone: -315, De Lorenzo: -28.3, Lazzer: -123, Muller: -145, Huang: -399 and Bernstein: -1335. CONCLUSIONS: The present equation had the highest predictive accuracy in subjects with overweight or obesity compared with the previous equations derived from different populations. Thus, these new equation can be used to assist the nutritional management of these subjects.

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.