Overfeeding polyunsaturated fat compared with saturated fat does not differentially influence lean tissue accumulation in individuals with overweight: a randomized controlled trial.

BACKGROUND: Fatty acids may influence lean tissue volume and skeletal muscle function. We previously reported in young lean participants that overfeeding PUFA compared with SFA induced greater lean tissue accumulation despite similar weight gain. OBJECTIVES: In a double-blind randomized controlled trial, we aimed to investigate if the differential effects of overfeeding SFA and PUFA on lean tissue accumulation could be replicated in individuals with overweight and identify potential determinants. Further, using substitution models, we investigated associations between SFA and PUFA concentrations with lean tissue volume in a large population-based sample (UK Biobank). METHODS: Sixty-one males and females with overweight [BMI (kg/m2): 27.3 (interquartile range (IQR), 25.4-29.3); age: 43 (IQR, 36-48)] were overfed SFA (palm oil) or n-6 (ω-6) PUFA (sunflower oil) for 8 wk. Lean tissue was assessed by MRI. We had access to n = 13,849 participants with data on diet, covariates, and MRI measurements of lean tissue, as well as 9119 participants with data on circulating fatty acids in the UK Biobank. RESULTS: Body weight gain mean (SD) was similar in PUFA (2.01 ± 1.90 kg) and SFA (2.31 ± 1.38 kg) groups. Lean tissue increased to a similar extent [0.54 ± 0.93 L and 0.67 ± 1.21 L for PUFA and SFA groups, respectively, with a difference between groups of 0.07 (-0.21, 0.35)]. We observed no differential effects on circulating amino acids, myostatin, or IL-15 and no clear determinants of lean tissue accumulation. Similar nonsignificant results for SFA and PUFA were observed in UK Biobank, but circulating fatty acids demonstrated ambiguous and sex-dependent associations. CONCLUSIONS: Overfeeding SFA or PUFA does not differentially affect lean tissue accumulation during 8 wk in individuals with overweight. A lack of dietary fat type-specific effects on lean tissue is supported by specified substitution models in a large population-based cohort consuming their habitual diet. This trial was registered at clinicaltrials.gov identifier as NCT02211612.

MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans.

UK Biobank (UKB) has recruited more than 500 000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. This retrospective cross-validation study includes data from 38 916 participants (52% female; mean age, 64 years) to capture baseline characteristics, such as age, height, weight, and sex, as well as measurements of body composition, organ volumes, and abstract properties, such as grip strength, pulse rate, and type 2 diabetes status. Prediction intervals for each end point were generated based on uncertainty quantification. On a subsequent release of UKB data, the proposed method predicted 12 body composition metrics with a 3% median error and yielded mostly well-calibrated individual prediction intervals. The processing of MRI scans from 1000 participants required 10 minutes. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. Keywords: MRI, Adipose Tissue, Obesity, Metabolic Disorders, Volume Analysis, Whole-Body Imaging, Quantification, Supervised Learning, Convolutional Neural Network (CNN) © RSNA, 2022.

Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.

Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed up the progression of novel candidate molecules. Herein, we have investigated the question if multiple in vitro clearance endpoints could be accurately predicted from image-based molecular representations. Thus, compound measurements for four commonly investigated clearance endpoints were curated from AstraZeneca internal sources, providing a sound basis for building multi-task convolutional neural network models. Application of several increasingly challenging data splitting strategies confirmed that convolutional neural network models were successful at capturing implicit chemical relationships contained in training and test data, similar to what is commonly observed for structural fingerprints. Furthermore, model benchmarking against state-of-the-art machine learning methods, including deep neural networks and graph convolutional neural networks, trained with structure- and graph-based representations, respectively, revealed on par or increased accuracy of convolutional neural networks with clear benefit of multi-task learning across all clearance endpoints. Our findings indicate that image-based molecular representations can be applied to predict multiple clearance endpoints, suggesting a potential follow-up to investigate model interpretability from molecular images.