Diet in the first years of life is a key determinant of lifelong disease risk and is highly affected by socio-economic status (SES). However, the specific relation between SES and food consumption in toddlers and preschoolers is poorly understood. This study assesses SES-related differences in food consumption in 1- to 5-year-olds in Germany using weighed food records (3 + 1 d) of a subsample of 887 children from the cross-sectional Children’s Nutrition Survey to Record Food Consumption (KiESEL) undertaken between 2014 and 2017. Children were categorised as having a low, medium or high SES depending on parental income, education and occupation. A two-step generalised linear model corrected for age and sex was applied to assess differences in food consumption, using bootstrapping to address unequal group sizes. Differences between SES groups were found for unfavourable foods (and the subgroups sugar-sweetened beverages and confectionary/desserts), fruit, bread/cereals and fats/oils (P_Boot < 0·05). Mean daily consumption in the low-SES group as compared with the high-SES group was 84 g lower for total fruit, 22 g lower for bread/cereals and 3 g lower for fats/oils, while being 123 g higher for sugar-sweetened beverages and 158 g higher for unfavourable foods in total (based on bootstrap 95 % CI). In conclusion, this study suggests a social gradient in the diet of German toddlers and preschoolers, with lower SES linked to lower diet quality. To prevent adverse health trajectories, public health measures to improve early life nutrition should address all children, prioritising those of lower SES.

Background/Objectives: Nutritional medicine remains underrepresented in medical education despite its relevance across specialties. Online learning offers a resource-efficient option to address this gap, yet evidence on the effectiveness and acceptability of online learning modules (OLMs) is limited. Methods: In this exploratory randomized controlled single post-test trial, medical students were assigned to either an OLM or an in-person lecture (IPL) on nutritional medicine (n = 91, no a priori sample size calculation performed). After course completion, students took a knowledge test and completed a questionnaire on their learning experience. Group differences were analyzed using permutation Welch t-tests, Wilcoxon–Mann–Whitney tests, or Fisher’s exact tests, depending on variable characteristics, with α = 0.05. Results: OLM students achieved significantly higher test scores than IPL students (mean difference: 2.4 points on a 0–40 scale), resulting in differences in grade classification (p < 0.05). OLM was further rated more favorably regarding content delivery, overall course evaluation, and exam preparation (all p < 0.05), while self-reported attention, concentration, and involvement did not differ between groups. Flexibility, time savings, and convenience were the most frequently reported advantages of OLM over IPL. Conclusions: This study suggests that OLM in nutritional medicine may be associated with higher test performance and more favorable student evaluations compared to IPL. These findings highlight the potential of online learning as a scalable, resource-efficient approach that may help address persistent gaps in nutritional medicine education. Building on this evidence, future work should examine how such modules can be optimally integrated into medical curricula to complement existing teaching structures.

Background/Objectives: Although discrepancies between bioelectrical impedance analysis (BIA) devices are well documented, their clinical relevance in vulnerable populations remains unclear. This study aims to assess the impact of device choice on muscle mass classification criteria in patients with cancer or obesity and to identify modifiers of device variability. Methods: BIA data from 224 adults (85 with cancer, 139 with obesity) measured with two segmental multi-frequency devices (seca mBCA 515 and InBody 970) were analyzed. Device differences were assessed using the Wilcoxon signed-rank test and agreement analyses. Differences in classification of body composition cut-offs cited in the GLIM criteria for malnutrition and the ESPEN and EASO criteria for sarcopenic obesity were evaluated using McNemar’s test. The impact of disease type, sex, and age on device differences was examined through multivariable models. Results: Significant device differences were found for all parameters (all p ≤ 0.005). Discrepancies were largest for skeletal muscle mass (kg and %), with effect sizes r > 0.8 and poor agreement (Lin’s CCC < 0.90). A significant impact of device choice on muscle mass classification was observed for both cancer and obesity patients (p < 0.001), with seca classifying more patients as having low fat-free mass (50% vs. 20%) and as having a body composition consistent with sarcopenic obesity (90% vs. 50%) than InBody. Discrepancies were more pronounced in cancer patients and females. Conclusions: Muscle mass assessment by BIA is highly dependent on device choice, potentially leading to clinically relevant discrepancies in classification when rigid cut-offs are applied. An individualized interpretation of BIA data and further validation of prediction equations in disease-specific subpopulations is warranted.

Background: Steatosis hepatis is of outstanding clinical importance. The aim of this study was to apply the new parametric method “Ultrasound Derived Fat Fraction” (UDFF) to monitor changes of liver fat in obese patients undergoing weight loss measures. Methods: In 53 obese patients BMI, absolute body fat, waist circumference and UDFF were measured before and after a three-month weight loss intervention. UDFF measurements were performed using a Siemens ultrasound system (DAX, 1.0–3.5 Mz). We analysed the correlation of change in BMI (≥−1 vs < −1), body fat (≥−2 kg vs < −2 kg) and waist circumference (≥−10 cm vs < −10 cm) with the change in UDFF (≥−30% vs < −30%) (significance level: p < 0.0167). Results: At baseline, mean BMI was 39.2 (range: 29.8–63.6), body fat 51.3 kg (range: 29.9–111.7), waist circumference 120.2 cm (range: 95–164) and UDFF 26% (range: 7–41%). 54.7% of patients lost at least 2 kg of body fat. These patients were significantly more likely to have a reduction in UDFF of at least 30% (≥2 kg fat: 58.6% vs. < 2 kg fat: 20.8%; p = 0.011). For BMI and waist circumference changes no significant correlation with UDFF was observed. Conclusion: This study underlines that UDFF could be a promising, non-invasive tool to monitor changes in liver fat.

Background/Objectives: Nutritional therapy is an essential part of oncologic care, yet patients with brain tumors—especially those with glioblastoma—remain underserved by disease-specific dietary guidelines. This cross-sectional study compares energy and macronutrient intake, as well as body composition, between brain tumor patients, including a glioblastoma subgroup, and patients with gastrointestinal (GI) cancer. Methods: A total of 95 brain tumor patients and 109 GI cancer patients completed standardized three-day estimated food records and underwent bioelectrical impedance analysis and hand-grip strength measurements. Anthropometric parameters, as well as energy and macronutrient intake, were compared between groups. Results: Energy intake was lower in brain tumor patients compared with GI cancer patients (22.8 kcal/kg/day vs. 31.2 kcal/kg/day), as were protein (1.01 g/kg/day vs. 1.34 g/kg/day) and carbohydrates (182.8 g/day vs. 246.8 g/day; all p < 0.01). Despite the lower intake, brain tumor patients exhibited higher BMI scores, body fat percentages, and visceral fat levels (p < 0.05), while fat-free mass and skeletal muscle mass were comparable between groups. The phase angle was higher in brain tumor patients (p = 0.002), whereas the ECW/TBW ratio was lower (p = 0.003). In the glioblastoma subgroup, protein intake did not differ significantly compared with the GI cancer group. However, carbohydrate intake (190.9 g/day vs. 246.8 g/day; p = 0.01), as well as energy intake (25.7 kcal/kg/day vs. 31.2 kcal/kg/day; p = 0.05), remained significantly lower. Conclusions: Brain tumor patients were found to have energy intake levels below ESPEN recommendations for cancer patients (25–30 kcal/kg/day), and their protein intake was at the lower ESPEN threshold (1.0 g/kg/day), coupled with increased adipose tissue. The observed caloric deficit was accompanied by reduced carbohydrate intake, particularly in the glioblastoma subgroup. These findings highlight the need for individualized nutritional approaches in neuro-oncology. Until disease-specific recommendations are available, general dietary guidelines such as those by ESPEN offer a pragmatic interim orientation.