Physical Activity Is Prospectively Associated With Adolescent Nonalcoholic Fatty Liver Disease.

OBJECTIVES: The aim of the present study was to assess whether objectively measured physical activity at mean ages 12 and 14 years are prospectively associated with ultrasound scan liver fat and stiffness (alanine aminotransferase, aspartate aminotransferase [AST], and γ-glutamyl transferase [GGT]) assessed at mean age 17.8 years. METHODS: Participants were from the Avon Longitudinal Study of Parents and Children. Total physical activity (counts per minute) and minutes of moderate to vigorous physical activity (MVPA) were measured using ActiGraph accelerometers at mean ages 12 and 14 years. RESULTS: Greater total physical activity and MVPA at ages 12 and 14 years were associated with lower odds of liver fat and lower GGT levels at mean age 17.8 years, such as per 15-minute increase in daily MVPA at age 12 years, the confounder adjusted odds ratio of liver fat was 0.47 (95% confidence interval [CI] 0.27-0.84). Associations attenuated after additional adjustment for fat mass as a potential confounder (eg, per 15-minute increase in daily MVPA at age 12 years, the odds ratio of liver fat attenuated to 0.65 [95% CI 0.35-1.21]) or a potential mediator (eg, per 15-minute increase in daily MVPA at age 12 years the odds ratio of liver fat attenuated to 0.59 [95% CI 0.32-1.09]). Results did not further attenuate after additional adjustment for insulin resistance. There was some evidence that greater total physical activity and MVPA at age 12 years were associated with the higher AST levels. CONCLUSIONS: Adolescents who were more active in childhood have lower odds of fatty liver and lower GGT levels. These findings are likely to be, at least in part, explained by adiposity.

Childhood energy intake is associated with nonalcoholic fatty liver disease in adolescents.

BACKGROUND: Greater adiposity is an important risk factor for nonalcoholic fatty liver disease (NAFLD). Thus, it is likely that dietary intake is involved in the development of the disease. Prospective studies assessing the relation between childhood dietary intake and risk of NAFLD are lacking. OBJECTIVE: This study was designed to explore associations between energy, carbohydrate, sugar, starch, protein, monounsaturated fat, polyunsaturated fat, saturated fat, and total fat intake by youth at ages 3, 7, and 13 y and subsequent (mean age: 17.8 y) ultrasound scan (USS)-measured liver fat and stiffness and serum alanine aminotransferase, aspartate aminotransferase, and γ-glutamyltransferase. We assessed whether observed associations were mediated through fat mass at the time of outcome assessment. METHODS: Participants were from the Avon Longitudinal Study of Parents and Children. Trajectories of energy and macronutrient intake from ages 3-13 y were obtained with linear-spline multilevel models. Linear and logistic regression models examined whether energy intake and absolute and energy-adjusted macronutrient intake at ages 3, 7, and 13 y were associated with liver outcomes. RESULTS: Energy intake at all ages was positively associated with liver outcomes; for example, the odds of having a USS-measured liver fat per 100 kcal increase in energy intake at age 3 y were 1.79 (95% CI: 1.14, 2.79). Associations between absolute macronutrient intake and liver outcomes were inconsistent and attenuated to the null after adjustment for total energy intake. The majority of associations attenuated to the null after adjustment for fat mass at the time liver outcomes were assessed. CONCLUSION: Higher childhood and early adolescent energy intake is associated with greater NAFLD risk, and the macronutrients from which energy intake is derived are less important. These associations appear to be mediated, at least in part, by fat mass at the time of outcome assessment.

Weight trajectories through infancy and childhood and risk of non-alcoholic fatty liver disease in adolescence: the ALSPAC study.

BACKGROUND & AIMS: Adiposity is a key risk factor for NAFLD. Few studies have examined prospective associations of infant and childhood adiposity with subsequent NAFLD risk. We examined associations of weight-for-height trajectories from birth to age 10 with liver outcomes in adolescence, and assessed the extent to which associations are mediated through fat mass at the time of outcome assessment. METHODS: Individual trajectories of weight and height were estimated for participants in the Avon Longitudinal Study of Parents and Children using random-effects linear-spline models. Associations of birthweight (adjusted for birth length) and weight change (adjusted for length/height change) from 0-3 months, 3 months-1 y, 1-3 y, 3-7 y, and 7-10 y with ultrasound scan (USS) determined liver fat and stiffness, and serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) at mean age 17.8 y were assessed with linear and logistic regressions. Mediation by concurrent fat mass was assessed with adjustment for fat mass at mean age 17.8 y. RESULTS: Birth weight was positively associated with liver stiffness and negatively with ALT and AST. Weight change from birth to 1 y was not associated with outcomes. Weight change from 1-3 y, 3-7 y, and 7-10 y was consistently positively associated with USS and blood-based liver outcomes. Adjusting for fat mass at mean age 17.8 y attenuated associations toward the null, suggesting associations are largely mediated by concurrent body fatness. CONCLUSIONS: Greater rates of weight-for-height change between 1 y and 10 y are consistently associated with adverse liver outcomes in adolescence. These associations are largely mediated through concurrent fatness.

Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of surgical pathology in the abdominopelvic cavity: a systematic review.

OBJECTIVES: There is emerging use of artificial intelligence (AI) models to aid diagnostic imaging. This review examined and critically appraised the application of AI models to identify surgical pathology from radiological images of the abdominopelvic cavity, to identify current limitations and inform future research. DESIGN: Systematic review. DATA SOURCES: Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) were performed. Date limitations (January 2012 to July 2021) were applied. ELIGIBILITY CRITERIA: Primary research studies were considered for eligibility using the PIRT (participants, index test(s), reference standard and target condition) framework. Only publications in the English language were eligible for inclusion in the review. DATA EXTRACTION AND SYNTHESIS: Study characteristics, descriptions of AI models and outcomes assessing diagnostic performance were extracted by independent reviewers. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines. Risk of bias was assessed (Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2)). RESULTS: Fifteen retrospective studies were included. Studies were diverse in surgical specialty, the intention of the AI applications and the models used. AI training and test sets comprised a median of 130 (range: 5-2440) and 37 (range: 10-1045) patients, respectively. Diagnostic performance of models varied (range: 70%-95% sensitivity, 53%-98% specificity). Only four studies compared the AI model with human performance. Reporting of studies was unstandardised and often lacking in detail. Most studies (n=14) were judged as having overall high risk of bias with concerns regarding applicability. CONCLUSIONS: AI application in this field is diverse. Adherence to reporting guidelines is warranted. With finite healthcare resources, future endeavours may benefit from targeting areas where radiological expertise is in high demand to provide greater efficiency in clinical care. Translation to clinical practice and adoption of a multidisciplinary approach should be of high priority. PROSPERO REGISTRATION NUMBER: CRD42021237249.

Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review.

INTRODUCTION: The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research. METHODS AND ANALYSIS: A systematic search will be conducted on Medline, EMBASE and the Cochrane Central Register of Controlled Trials to identify relevant studies. Primary studies where AI-based technologies have been used as a diagnostic aid in cross-sectional radiological images of the abdominopelvic cavity will be included. Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. Risk of bias of included studies will be assessed using the QUADAS-2 tool. Findings will be reported according to the Synthesis Without Meta-analysis guidelines. ETHICS AND DISSEMINATION: No ethical approval is required as primary data will not be collected. The results will inform further research studies in this field. Findings will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42021237249.

Pancreatic volume is reduced in adult patients with recently diagnosed type 1 diabetes.

CONTEXT: Pancreatic atrophy is common in longstanding type 1 diabetes, but there are limited data concerning pancreas size at diagnosis. OBJECTIVE: Our objective was to determine whether pancreatic size was reduced in patients with recently diagnosed type 1 diabetes and assess whether pancreatic volume was related to residual ß-cell function or islet autoantibodies. DESIGN AND SETTING: We conducted a controlled cohort study with strict inclusion criteria, recruiting from hospital diabetes clinics between 2007 and 2010. PATIENTS AND HEALTHY CONTROLS: Participants included 20 male adult patients (median age 27 yr) with recent-onset type 1 diabetes (median duration 3.8 months) and 24 male healthy controls (median age 27 yr). INTERVENTION: Interventions included noninvasive magnetic resonance imaging, collection of fasting blood samples, and glucagon stimulation testing in patients. MAIN OUTCOME MEASURES: We compared pancreatic volume estimates between patients with recent-onset type 1 diabetes and healthy controls as planned a priori. RESULTS: Scans were analyzed by an experienced radiologist blinded to diabetes status. Pancreatic volume correlated with body weight in patients and controls (P = 0.007). After adjustment for body weight, mean pancreatic volume index was 26% less in patients (1.19 ml/kg, se 0.07 ml/kg) than in controls (1.61 ml/kg, se 0.08 ml/kg) (P = 0.001). No correlation was seen between pancreatic volume index in patients and diabetes duration, glucose or C-peptide levels, glycated hemoglobin, and islet autoantibodies. CONCLUSIONS: Pancreatic volume is reduced by 26% in patients with type 1 diabetes within months of diagnosis, suggesting that atrophy begins years before the onset of clinical disease. Pancreatic atrophy within individuals is therefore a potential clinical marker of disease progression.