Associations between types and sources of dietary carbohydrates and liver fat: a UK Biobank study.

BACKGROUND AND AIMS: Excess energy intake can lead to metabolic dysfunction-associated steatotic liver disease (MASLD), but the relationship between dietary carbohydrate intake and liver fat content remains unclear. This study aimed to examine the associations between types and sources of dietary carbohydrates and liver fat content. METHODS: UK Biobank participants with no pre-existing diabetes, liver disease or cardiovascular disease reported dietary intake of types and sources of carbohydrates (total carbohydrates, free sugars, non-free sugars, starch from whole grains, starch from refined grains, and fibre) on at least two 24-h dietary assessments. In cross-sectional analyses, (n = 22,973), odds ratios (OR) of high liver fat content (defined as a score of ≥ 36 in the hepatic steatosis index) by quintiles of carbohydrate intakes were estimated using multivariable logistic regression models. In prospective analyses, a second sample (n = 9268) had liver proton density fat fraction (PDFF) measured by magnetic resonance imaging (2014-2020). Multivariable linear regression models estimated geometric means of PDFF (%) by quintiles of carbohydrate intakes. Models were adjusted for demographic and lifestyle confounders, including total energy intake. RESULTS: In the cross-sectional analyses, 6894 cases of high liver fat content were identified. Inverse associations between intakes of fibre (OR of highest vs. lowest quintile 0.46 [95% CI: 0.41-0.52]), non-free sugars (0.63 [0.57-0.70]) and starch from whole grains (0.52 [0.47-0.57]) with liver fat were observed. There were positive associations between starch from refined grains and liver fat (1.33 [1.21-1.46]), but no association with free sugars (p=0.61). In prospective analyses, inverse associations with PDFF (%) were observed for intakes of fibre (- 0.48 geometric mean difference between highest and lowest quintile of intake [- 0.60 to - 0.35]), non-free sugars (- 0.37 [- 0.49 to - 0.25]) and starch from whole grains (- 0.31 [- 0.42 to - 0.19]). Free sugars, but not starch from refined grains, were positively associated with PDFF (0.17 [0.05 to 0.28]). CONCLUSION: This study suggests that different carbohydrate types and sources have varying associations with liver fat, which may be important for MASLD prevention. Non-free sugars, fibre, and starch from whole grains could be protective, while associations with free sugars and starch from refined grains are less clear.

Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies.

BACKGROUND: In studies of the association of adiposity with disease risk, widely used anthropometric measures of adiposity (e.g. body-mass-index [BMI], waist circumference [WC], waist-hip ratio [WHR]) are simple and inexpensive to implement at scale. In contrast, imaging-based techniques (e.g. magnetic resonance imaging [MRI] and dual x-ray absorptiometry [DXA]) are expensive and labour intensive, but can provide more accurate quantification of body fat composition. There is, however, limited evidence about the relationship between conventional and imaging-derived measures of adiposity. METHODS: We searched Scopus and Web of Science for published reports in English of conventional versus imaging-derived measurements of adiposity. We identified 42 articles (MRI = 22; DXA = 20) that met selection criteria, involving 42,556 (MRI = 15,130; DXA = 27,426) individuals recruited from community or hospital settings. Study-specific correlation coefficients (r) were transformed using Fisher's Z transformation, and meta-analysed to yield weighted average correlations, both overall and by ancestry, sex and age, where feasible. Publication bias was investigated using funnel plots and Egger's test. RESULTS: Overall, 98% of participants were 18 + years old, 85% male and 95% White. BMI and WC were most strongly correlated with imaging-derived total abdominal (MRI-derived: r = 0.88-; DXA-derived: 0.50-0.86) and subcutaneous abdominal fat (MRI-derived: 0.83-0.85), but were less strongly correlated with visceral abdominal fat (MRI-derived: 0.76-0.79; DXA-derived: 0.80) and with DXA-derived %body fat (0.76). WHR was, at best, strongly correlated with imaging-derived total abdominal (MRI-derived: 0.60; DXA-derived: 0.13), and visceral abdominal fat (MRI-derived: 0.67; DXA-derived: 0.65), and moderately with subcutaneous abdominal (MRI-derived: 0.54), and with DXA-derived %body fat (0.58). All conventional adiposity measures were at best moderately correlated with hepatic fat (MRI-derived: 0.36-0.43). In general, correlations were stronger in women than in men, in Whites than in non-Whites, and in those aged 18 + years. CONCLUSIONS: In this meta-analysis, BMI and WC, but not WHR, were very strongly correlated with imaging-derived total and subcutaneous abdominal fat. By comparison, all three measures were moderately or strongly correlated with imaging-based visceral abdominal fat, with WC showing the greatest correlation. No anthropometric measure was substantially correlated with hepatic fat. Further larger studies are needed to compare these measures within the same study population, and to assess their relevance for disease risks in diverse populations.

An OxPLORE Initiative Evaluating Children's Surgery Resources Worldwide: A Cross-sectional Implementation of the OReCS Document.

BACKGROUND: The Global Initiative for Children's Surgery (GICS) group produced the Optimal Resources for Children's Surgery (OReCS) document in 2019, listing standards of children's surgical care by level of healthcare facilities within low resource settings. We have previously created and piloted an audit tool based on the OReCS criteria in a high-income setting. In this study, we aimed to validate its use in identifying gaps in children's surgery provision worldwide. METHODS: Our OReCS audit tool was implemented in 10 hospitals providing children's surgery across eight countries. Collaborators were recruited via the Oxford Paediatrics Linking Our Research with Electives (OxPLORE) international network of medical students and trainees. The audit tool measured a hospital's current capacity for children's surgery. Data were analysed firstly to express the percentage of 'essential' criteria met for each specialty. Secondly, the 'OxPLORE method' was used to allocate each hospital specialty a level based on procedures performed and resources available. A User Evaluation Tool (UET) was developed to obtain feedback on the ease of use of the tool. RESULTS: The percentage of essential criteria met within each category varied widely between hospitals. The level given to hospitals for subspecialties based on OReCS criteria often did not reflect their self-defined level. The UET indicated the audit tool was practicable across multiple settings. CONCLUSIONS: We recommend the use of the OReCS criteria to identify areas for local hospital improvement and inform national children's surgical plans. We have made informed suggestions to increase usability of the OReCS audit tool.

Sleep disruption in medicine students and its relationship with impaired academic performance: A systematic review and meta-analysis.

Sleep disruption severely impairs learning ability, affecting academic performance in students. This systematic review and meta-analysis aimed at assessing the prevalence of sleep disruption in medical students and its relationship with academic performance. PubMed, Web of Sciences, EBSCO and SciELO databases searches allowed to retrieve 41 papers with data about the prevalence of sleep deprivation, 20 of which also contained data on its association with academic performance. Poor sleep quality was reported by 5646 out of 14,170 students in 29 studies (39.8%, 95% confidence interval = 39.0-40.6%), insufficient sleep duration by 3762/12,906 students in 28 studies (29.1%, 23.3-29.9%) and excessive diurnal sleepiness by 1324/3688 students in 13 studies (35.9%, 34.3-37.4). Academic grades correlated significantly with sleep quality scores (r, 95% CI = 0.15, 0.05-0.26, random-effects model; p = 0.002, n = 10,420 subjects, k = 15 studies) and diurnal sleepiness (r = -0.12, -0.19/-0.06 under the fixed effects model, p < 0.001, n = 1539, k = 6), but not with sleep duration (r = 0.03, -0.12/0.17 under the random-effects model, p = 0.132, n = 2469, k = 9). These findings advocate for an urgent intervention aiming at improving sleep quality among medical students as a way of increasing academic achievements and, ultimately, the quality of health care.