Excessive fructose intake is associated with increased de novo lipogenesis, blood triglycerides, and hepatic insulin resistance. We aimed to determine whether fructose elicits specific effects on lipid metabolism independently of excessive caloric intake. A total of 94 healthy men were studied in this double-blind, randomized trial. They were assigned to daily consumption of sugar-sweetened beverages (SSBs) containing moderate amounts of fructose, sucrose (fructose-glucose disaccharide) or glucose (80 g/day) in addition to their usual diet or SSB abstinence (control group) for 7 weeks. De novo fatty acid (FA) and triglyceride synthesis, lipolysis and plasma free FA (FFA) oxidation were assessed by tracer methodology. Daily intake of beverages sweetened with free fructose and fructose combined with glucose (sucrose) led to a 2-fold increase in basal hepatic fractional secretion rates (FSR) compared to control (median FSR %/day: sucrose 20.8 (p = 0.0015); fructose 19.7 (p = 0.013); control 9.1). Conversely, the same amounts of glucose did not change FSR (median of FSR %/day 11.0 (n.s.)). Fructose intake did not change basal secretion of newly synthesized VLDL-triglyceride, nor did it alter rates of peripheral lipolysis, nor total FA and plasma FFA oxidation. Total energy intake was similar across groups. Regular consumption of both fructose- and sucrose-sweetened beverages in moderate doses - associated with stable caloric intake - increases hepatic FA synthesis even in a basal state; this effect is not observed after glucose consumption. These findings provide evidence of an adaptative response to regular fructose exposure in the liver. This study investigated the metabolic effects of daily sugar-sweetened beverage consumption for several weeks in healthy lean men. It revealed that beverages sweetened with the sugars fructose and sucrose (glucose and fructose combined), but not glucose, increase the ability of the liver to produce lipids. This change may pave the way for further unfavorable effects on metabolic health. NCT01733563.

Non-invasive breath analysis with mobile health devices bears tremendous potential to guide therapeutic treatment and personalize lifestyle changes. Of particular interest is the breath volatile acetone, a biomarker for fat burning, that could help in understanding and treating metabolic diseases. Here, we report a hand-held (6 × 10 × 19.5 cm³), light-weight (490 g), and simple device for rapid acetone detection in breath. It comprises a tailor-made end-tidal breath sampling unit, connected to a sensor and a pump for on-demand breath sampling, all operated using a Raspberry Pi microcontroller connected with a HDMI touchscreen. Accurate acetone detection is enabled by introducing a catalytic filter and a separation column, which remove and separate undesired interferents from acetone upstream of the sensor. This way, acetone is detected selectively even in complex gas mixtures containing highly concentrated interferents. This device accurately tracks breath acetone concentrations in the exhaled breath of five volunteers during a ketogenic diet, being as high as 26.3 ppm. Most importantly, it can differentiate small acetone changes during a baseline visit as well as before and after an exercise stimulus, being as low as 0.5 ppm. It is stable for at least four months (122 days), and features excellent bias and precision of 0.03 and 0.6 ppm at concentrations below 5 ppm, as validated by proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). Hence, this detector is highly promising for simple-in-use, non-invasive, and routine monitoring of acetone to guide therapeutic treatment and track lifestyle changes.

Intermittent fasting (IF) is a popular nutritional strategy for weight control and improved metabolic health, however it is unclear which type of intermittent fasting is most effective. This randomized trial directly compared short-term alternate-day fasting (ADF) and time-restricted eating (TRE) with controls in adults with overweight or a high normal weight. The aim was to compare the effects of ADF and TRE versus controls regarding whole-body fat mass loss, weight control and cardiometabolic health. In this 4-week, parallel-arm, randomized clinical trial (February 2021-May 2022), participants aged 18-40 years with a body mass index between 23 and 30 kg/m² were assigned to ADF (alternating fasting and ad libitum eating days), to TRE (eating only between 12:00-20:00), or control (no change in eating times). The primary outcome was change in total fat volume (assessed by whole-body magnetic resonance imaging). Secondary outcomes were subcutaneous and visceral fat mass, body weight, resting metabolic rate, biochemical markers, energy intake, activity energy expenditure and health-related quality of life. Seventy-six participants (mean [standard deviation (SD)] age, 29.6 [5.6] years; body mass index, 25.8 [2.2] kg/m²; 34 [44 %] female) were randomized to ADF (n = 26), TRE (n = 26), or control (n = 24). Seventy-five participants completed the trial (25 in ADF, 26 in TRE, 24 in control). ADF led to a greater reduction in total fat volume than control (mean difference -1059.8 cm³, 95 % CI: -1380.0 cm³ to -739.6 cm³, p < 0.001) and TRE (-695.7 cm³, 95 % CI: -1013.9 to -377.6 cm³, p < 0.001). TRE also reduced total fat volume compared to control (-364.0 cm³, 95 % CI: -621.3 cm³ to -106.7 cm³, p = 0.007). Energy intake was reduced by 34 % [18 %] in ADF, 15 % [21 %] in TRE and 3 % [22 %] in control. ADF, but not TRE, reduced visceral fat mass, resting metabolic rate, triiodothyronine and non-HDL cholesterol compared to controls. Only ADF increased activity energy expenditure and health-related quality of life. No serious adverse events occurred. In this randomized clinical trial, ADF was more effective in reducing energy intake than TRE which has subsequent effects on fat mass and body weight. Only ADF improved several cardiometabolic risk factors. https://clinicaltrials.gov/study/NCT04732130; Unique identifier: NCT04732130.

Background: The aim of this paper is to develop a clinically useful benchmark signature of deep learning (DL)-based MRI segmentations and objectively predict user satisfaction based on a weighted combination of multiple performance metrics. Methods: This post hoc analysis of a prospective study analyzed MRI data from 68 patients (29.4 ± 5.6 years; 45% female) acquired during a randomized clinical trial. Fat fraction maps of axial Dixon MRI were used to segment six classes of the thigh. Two radiologists qualitatively scored DL-based segmentations using a 5-point Likert scale. A hybrid deep learning model (MobileNetV2 and DINOv2) was developed to predict Likert scores directly from MRI slices and segmentation maps. Additionally, a weighted combination of quantitative metrics (Dice score, Hausdorff distance, Jaccard index) was determined to best correlate with the Likert scores. Statistical analyses included Cohen’s kappa, Spearman correlation, and regression model performance (MAE, RMSE, ROC_AUC). Results: Interreader agreement for Likert scores was near-perfect (κ = 0.82). The DINOv2 model achieved lower MAE (0.2343) and RMSE (0.1129) than MobileNetV2 (0.3488 and 0.431, respectively) in predicting Likert scores. The weighted metric combination model, using Lasso regression, predicted Likert = 5 with 84.1% accuracy, 100% recall, and ROC_AUC of 0.824. Individual metrics showed weak-to-moderate correlation with Likert scores, with Dice and Jaccard indices for extensor intramuscular fat exhibiting the highest correlation (r = 0.37 and r = 0.34, respectively). Conclusions: The proposed DL-based model accurately predicted radiologist-assigned Likert five scores, while the weighted metric combination model aligned closely with user satisfaction, offering a practical alternative to manual validation for clinical adoption of automated segmentation tools.