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Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease worldwide and includes a broad spectrum of histologic phenotypes, ranging from simple hepatic steatosis or nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH). While liver biopsy is the reference gold standard for NAFLD diagnosis and staging, it has limitations due to its sampling variability, invasive nature, and high cost. Thus, there is a need for noninvasive biomarkers that are robust, reliable, and cost effective. In this study, we measured 540 lipids and amino acids in serum samples from biopsy-proven subjects with normal liver (NL), NAFL, and NASH. Using logistic regression analysis, we identified two panels of triglycerides that could first discriminate between NAFLD and NL and second between NASH and NAFL. These noninvasive tests were compared to blinded histology as a reference standard. We performed these tests in an original cohort of 467 patients with NAFLD (90 NL, 246 NAFL, and 131 NASH) that was subsequently validated in a separate cohort of 192 patients (7 NL, 109 NAFL, 76 NASH). The diagnostic performances of the validated tests showed an area under the receiver operating characteristic curve, sensitivity, and specificity of 0.88 ± 0.05, 0.94, and 0.57, respectively, for the discrimination between NAFLD and NL and 0.79 ± 0.04, 0.70, and 0.81, respectively, for the discrimination between NASH and NAFL. When the analysis was performed excluding patients with glucose levels >136 mg/dL, the area under the receiver operating characteristic curve for the discrimination between NASH and NAFL increased to 0.81 ± 0.04 with sensitivity and specificity of 0.73 and 0.80, respectively. Conclusion: The assessed noninvasive lipidomic serum tests distinguish between NAFLD and NL and between NASH and NAFL with high accuracy. (Hepatology Communications 2018;2:807-820).

Nutraceuticals obtained from sprouted wheat and oat grains and processing by-products (bran and hull, respectively) naturally containing antioxidant and anti-inflammatory compounds were evaluated. The objective of this study was the development of a cereal-based nutraceutical formula combining extracts from sprouts and by-products and the exploration for potential synergetic effects in their bioactive properties. The antioxidant and anti-inflammatory capacities, glycemic index, phytic acid, and β-glucan of individual wheat bran hydrolysate (EH-WB), sprouted wheat (SW), oat hull hydrolysate (EH-OH), sprouted oat (SO), and combined ingredients (CI 1, CI 2, and CI3) were used to tailor an optimal nutraceutical formula. The three blend ingredients (CI 1, CI2, and CI3) were formulated at different ratios (EH-WB:SW:EH-OH:SO; 1:1:1:1, 2:1:2:1, and 1:2:1:2, w:w:w:w, respectively). The resulting mixtures showed total phenol (TPs) content ranging from 412.93 to 2556.66 µmol GAE 100 g−1 and antioxidant capacity values from 808.14 to 22,152.54 µmol TE 100 g−1 (ORAC) and 1914.05 to 7261.32 µmol TE 100 g−1 (ABTS•+), with Fe3+ reducing ability from 734. 02 to 8674.51 mmol reduced Fe 100 g−1 (FRAP) for the individual ingredients produced from EH-WB and EH-OH, where high antioxidant activity was observed. However, the anti-inflammatory results exhibited an interesting behavior, with a potentially synergistic effect of the individual ingredients. This effect was observed in CI2 and CI3, resulting in a higher ability to inhibit IL-6 and TNF-α than expected based on the anti-inflammatory values of their individual ingredients. Similar to the antioxidant properties, oat-based ingredients significantly contributed more to the anti-inflammatory properties of the overall mixture. This contribution is likely associated with the β-glucans and avenanthramides present in oats. To ensure the bioaccessibility of these ingredients, further studies including simulated digestion protocols would be necessary. The ingredient formulated with a 2:1 hydrolysate-to-sprout ratio was the most effective combination, reaching higher biological characteristics. [ABSTRACT FROM AUTHOR]

(1) Background: The aim was to validate an AI-based system compared to the classic method of reading ultrasound images of the rectus femur (RF) muscle in a real cohort of patients with disease-related malnutrition. (2) Methods: One hundred adult patients with DRM aged 18 to 85 years were enrolled. The risk of DRM was assessed by the Global Leadership Initiative on Malnutrition (GLIM). The variation, reproducibility, and reliability of measurements for the RF subcutaneous fat thickness (SFT), muscle thickness (MT), and cross-sectional area (CSA), were measured conventionally with the incorporated tools of a portable ultrasound imaging device (method A) and compared with the automated quantification of the ultrasound imaging system (method B). (3) Results: Measurements obtained using method A (i.e., conventionally) and method B (i.e., raw images analyzed by AI), showed similar values with no significant differences in absolute values and coefficients of variation, 58.39–57.68% for SFT, 30.50–28.36% for MT, and 36.50–36.91% for CSA, respectively. The Intraclass Correlation Coefficient (ICC) for reliability and consistency analysis between methods A and B showed correlations of 0.912 and 95% CI [0.872–0.940] for SFT, 0.960 and 95% CI [0.941–0.973] for MT, and 0.995 and 95% CI [0.993–0.997] for CSA; the Bland–Altman Analysis shows that the spread of points is quite uniform around the bias lines with no evidence of strong bias for any variable. (4) Conclusions: The study demonstrated the consistency and reliability of this new automatic system based on machine learning and AI for the quantification of ultrasound imaging of the muscle architecture parameters of the rectus femoris muscle compared with the conventional method of measurement. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories. [ABSTRACT FROM AUTHOR]

Background: the single nucleotide polymorphism (SNP) (rs3138167) is a polymorphism that has been associated with metabolic disorder in obese subjects and its effect on the metabolic response after a dietary intervention has not been evaluated. Objective: our aim was to analyze the effects of the rs3138167 on metabolic changes secondary to weight loss with a hypocaloric diet with a Mediterranean pattern. Method: one thousand and eight Caucasian obese patients were evaluated. Before and after 12 weeks on a hypocaloric diet with Mediterranean pattern, an anthropometric evaluation and a biochemical analysis were performed. The statistical analysis was performed as a dominant model (CC vs. CT + TT). Results: the values of insulin, HOMA-IR and resistin were higher in T allele carriers than non-T allele carriers in pre- and post-intervention time. In non-T allele carriers, resistin, insulin, HOMA-IR, triglycerides and C-reactive protein levels decreased. The improvement was statistically superior in non-T allele carriers; resistin (-1.2 ± 0.2 ng/dl; p = 0.02), triglycerides (-18.3 ± 4.3 mg/dl; p = 0.02), C-reactive protein (-2.6 ± 0.3 mg/dl; p = 0.02), insulin -4.4 ± 1.9 mUI/l; p = 0.02) and HOMA-IR (-2.1 ± 0.7; p = 0.03). Conclusion: we report an association of rs3138167 with a worse metabolic response (insulin, HOMA-IR, triglyceride and C-reactive protein) in T allele carriers after weight loss with a hypocaloric diet with Mediterranean pattern. [ABSTRACT FROM AUTHOR]

Aim: The effects of weight loss with a partial or total meal replacement programme (MRP) on atherosclerotic cardiovascular disease (ASCVD) risk factors are not fully understood, in particular in people at higher CV risk. In the 52‐week randomized controlled OPTIWIN study in men and women with obesity, meal replacement programme (total for first 26 weeks, partial for the ensuing 26 weeks) with OPTIFAST (OP) resulted in significantly greater weight loss compared with a low‐calorie food‐based (FB) dietary plan, both as part of a comprehensive lifestyle intervention [OP (n = 135)/FB (n = 138) week 26: −12.4%/−6.0%, p <.001; week 52: −10.5%/−5.5%, p <.001]. Here, we examined effects on ASCVD risk factors and 10‐year ASCVD risk. Materials and Methods: Participants with body mass index 30‐55 kg/m2 and age 18‐70 years, and not on anti‐obesity medications, were recruited. The effects on systolic and diastolic blood pressure (SBP, DBP), lipid parameters and 10‐year ASCVD risk were analysed as changes over time using linear mixed models. Subgroup analyses were conducted for changes in SBP, DBP and ASCVD risk by categories of age (<40, 40‐59, ≥60 years), baseline SBP (