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Non-alcoholic fatty liver disease (NAFLD) is characterized by dysbiosis. The bidirectional effects between intestinal microbiota (IM) and bile acids (BA) suggest that dysbiosis may be accompanied by an altered bile acid (BA) homeostasis, which in turn can contribute to the metabolic dysregulation seen in NAFLD. This study sought to examine BA homeostasis in patients with NAFLD and to relate that with IM data. This was a prospective, cross-sectional study of adults with biopsy-confirmed NAFLD (non-alcoholic fatty liver: NAFL or non-alcoholic steatohepatitis: NASH) and healthy controls (HC). Clinical and laboratory data, stool samples and 7-day food records were collected. Fecal BA profiles, serum markers of BA synthesis 7-alpha-hydroxy-4-cholesten-3-one (C4) and intestinal BA signalling, as well as IM composition were assessed. 53 subjects were included: 25 HC, 12 NAFL and 16 NASH. Levels of total fecal BA, cholic acid (CA), chenodeoxycholic acid (CDCA) and BA synthesis were higher in patients with NASH compared to HC (p<0.05 for all comparisons). The primary to secondary BA ratio was higher in NASH compared to HC (p = 0.004), but ratio of conjugated to unconjugated BAs was not different between the groups. Bacteroidetes and Clostridium leptum counts were decreased in in a subset of 16 patients with NASH compared to 25 HC, after adjusting for body mass index and weight-adjusted calorie intake (p = 0.028 and p = 0.030, respectively). C. leptum was positively correlated with fecal unconjugated lithocholic acid (LCA) (r = 0.526, p = 0.003) and inversely with unconjugated CA (r = -0.669, p<0.0001) and unconjugated CDCA (r = - 0.630, p<0.0001). FGF19 levels were not different between the groups (p = 0.114). In adults with NAFLD, dysbiosis is associated with altered BA homeostasis, which renders them at increased risk of hepatic injury.

This study aimed to determine if there is an association between dysbiosis and nonalcoholic fatty liver disease (NAFLD) independent of obesity and insulin resistance (IR). This is a prospective cross-sectional study assessing the intestinal microbiome (IM) of 39 adults with biopsy-proven NAFLD (15 simple steatosis [SS]; 24 nonalcoholic steatohepatitis [NASH]) and 28 healthy controls (HC). IM composition (llumina MiSeq Platform) in NAFLD patients compared to HC were identified by two statistical methods (Metastats, Wilcoxon). Selected taxa was validated using quantitative PCR (qPCR). Metabolites in feces and serum were also analyzed. In NAFLD, 8 operational taxonomic units, 6 genera, 6 families and 2 phyla (Bacteroidetes, Firmicutes) were less abundant and; 1 genus ( Lactobacillus ) and 1 family (Lactobacillaceae) were more abundant compared to HC. Lower abundance in both NASH and SS patients compared to HC were confirmed by qPCR for Ruminococcus, Faecalibacterium prausnitzii and Coprococcus . No difference was found between NASH and SS. This lower abundance in NAFLD (NASH+SS) was independent of BMI and IR. NAFLD patients had higher concentrations of fecal propionate and isobutyric acid and serum 2-hydroxybutyrate and L-lactic acid. These findings suggest a potential role for a specific IM community and functional profile in the pathogenesis of NAFLD.

Non-alcoholic fatty liver disease (NAFLD), especially non-alcoholic steatohepatitis (NASH) is a chronic liver disease commonly associated with hepatic fibrosis. NASH patients have an increased risk for hepatocellular carcinoma (HCC). An altered retinol metabolism is one of the pathways involved in the process of hepatic fibrosis, and enzymes involved in retinol metabolism have been associated with HCC. We aimed to determine the association between plasma retinol levels and hepatic expression of genes related to retinol metabolism, as well as to assess the hepatic expression of transcription factors regulated by retinoic acid in patients with NAFLD. Cross-sectional study where hepatic gene expression (Illumina microarray) and plasma retinol levels (HPLC) were measured in 17 patients with simple steatosis (SS), 15 with NASH, and 22 living liver donors (LD) as controls. Plasma retinol levels were higher in SS (1.53 ± 0.44 μmol/L) and NASH (1.51 ± 0.56 μmol/L) compared to LD (1.21 ± 0.38 μmol/L; p<0.05). AKR1B10 was highly overexpressed in NASH compared to SS (+6.2-fold) and LD (+9.9-fold; p = 4.89E-11). Retinaldehyde dehydrogenase 1 family, member A2 (ALDH1A2) and retinaldehyde dehydrogenase 1 family, member A3 (ALDH1A3), key enzymes for retinoic acid synthesis, were underexpressed in SS (-1.48 and -2.3-fold, respectively) and NASH (-1.47 and -2.6-fold, respectively) versus LD. In NASH, hepatic ALDH1A2 and ALDH1A3 were underexpressed and inversely correlated with plasma retinol levels, which may reduce retinoic acid in the liver. This, in addition to changes in expression of other genes involved in retinol metabolism, suggests a role for altered retinol homeostasis in NASH.

BackgroundParents play a pivotal role in shaping their children’s food environment and eating behaviours. Involving parents in interventions designed to promote nutritional outcomes such as dietary intake in children has been shown to improve parental feeding practices. However, it remains unclear how such interventions influence children’s eating behaviour outcomes. This protocol describes the methods of a systematic review evaluating the effectiveness of interventions involving parents in improving the eating behaviours of healthy children aged 0–12 years.Methods and analysisElectronic databases including MEDLINE, EMBASE, CENTRAL, APA PsycINFO, CINAHL, Scopus and Web of Science will be searched from inception to September 2025. A search strategy is developed to identify randomised controlled trials directly involving parents and reporting eating behaviours in children as either primary or secondary outcomes. Two independent reviewers will screen identified records and extract data on study, participant and intervention characteristics. Study results relevant to our primary and secondary outcomes will also be extracted using a prepiloted standardised data extraction form. We will use the Revised Cochrane Risk of Bias tool (RoB2) and Grading of Recommendations Assessment, Development and Evaluation approach to assess risk of bias and certainty of evidence, respectively. Where possible, meta‐analysis using random‐effects models will be performed; otherwise a qualitative summary will be provided.Ethics and disseminationEthics approval is not required for this study as no primary data will be collected. The findings will provide valuable insights for stakeholders to inform and optimise public health policies and practices aimed at empowering families to promote healthy eating behaviours early in childhood. The results will be submitted for publication in a peer-reviewed journal.PROSPERO registration numberCRD420251076540.

Chronic low-grade inflammation has been identified as an underlying cause of many diseases including osteoporosis. Lipopolysaccharide (LPS) is a potent inducer of the inflammatory response that can negatively affect bone outcomes by upregulating bone resorption and inhibiting bone formation. The objective of this study was to assess the longitudinal response of trabecular and cortical bone structure and bone mineral density to LPS continuously administered for 12 weeks in male and female CD-1 mice. Mice were assigned to one of four LPS groups at 8-weeks of age: placebo (0.0 μg/d), low (0.9 μg/d), mid (3.6 μg/d) and high (14.4 μg/d) dose. Trabecular and cortical bone outcomes were measured at 8, 12, 16, and 20 weeks of age using in vivo micro-computed tomography. The anticipated serum LPS dose-dependent response was not observed. Therefore, the low, mid, and high LPS groups were combined for analysis. Compared to the placebo group, endpoint serum LPS was elevated in both males ( p < 0.05) and females ( p < 0.05) when all LPS treatment groups were combined. However, there was no significant change in trabecular or cortical bone outcomes in the combined LPS groups compared to the placebo following the 12-week LPS intervention for either sex. This suggests that although serum LPS was elevated following the 12-week LPS intervention, the dosages administered using the osmotic pumps was not sufficient to negatively impact trabecular or cortical bone outcomes in either male or female CD-1 mice.

Background Growth studies rely on longitudinal measurements, typically represented as trajectories. However, anthropometry is prone to errors that can generate outliers. While various methods are available for detecting outlier measurements, a gold standard has yet to be identified, and there is no established method for outlying trajectories. Thus, outlier types and their effects on growth pattern detection still need to be investigated. This work aimed to assess the performance of six methods at detecting different types of outliers, propose two novel methods for outlier trajectory detection and evaluate how outliers affect growth pattern detection. Methods We included 393 healthy infants from The Applied Research Group for Kids (TARGet Kids!) cohort and 1651 children with severe malnutrition from the co-trimoxazole prophylaxis clinical trial. We injected outliers of three types and six intensities and applied four outlier detection methods for measurements (model-based and World Health Organization cut-offs-based) and two for trajectories. We also assessed growth pattern detection before and after outlier injection using time series clustering and latent class mixed models. Error type, intensity, and population affected method performance. Results Model-based outlier detection methods performed best for measurements with precision between 5.72-99.89%, especially for low and moderate error intensities. The clustering-based outlier trajectory method had high precision of 14.93-99.12%. Combining methods improved the detection rate to 21.82% in outlier measurements. Finally, when comparing growth groups with and without outliers, the outliers were shown to alter group membership by 57.9 -79.04%. Conclusions World Health Organization cut-off-based techniques were shown to perform well in few very particular cases (extreme errors of high intensity), while model-based techniques performed well, especially for moderate errors of low intensity. Clustering-based outlier trajectory detection performed exceptionally well across all types and intensities of errors, indicating a potential strategic change in how outliers in growth data are viewed. Finally, the importance of detecting outliers was shown, given its impact on children growth studies, as demonstrated by comparing results of growth group detection.

Background Metabolic dysfunction-associated steatotic liver disease (MASLD) encompasses a range of histological findings from the generally benign simple steatosis to steatohepatitis (MASH) which can progress to fibrosis and cirrhosis. Several factors, including the microbiome, may contribute to disease progression. Results Here, we demonstrate links between the presence and abundance of specific bacteria in the adipose and liver tissues, inflammatory genes, immune cell responses, and disease severity. Overall, in MASLD patients, we observed a generalized obesity-induced translocation of gut bacteria to hepatic and adipose tissues. We identified microbial patterns unique to more severely diseased tissues. Specifically, Enterococcus , Granulicatella , and Morganellaceae abundance is positively correlated with immune cell counts and inflammatory gene expression levels, and both genera are significantly enriched in MASH patients. Brevibacterium is enriched in adipose tissues of patients with liver fibrosis. Conclusion Together, these results provide further insight into the microbial factors that may be driving disease severity. 5gz9zWLTQ4ZzRXZ3CTt_7q Video Abstract

Child growth patterns assessment is critical to design public health interventions. However, current analytical approaches may overlook population heterogeneity. To overcome this limitation, we developed a growth trajectories clustering pipeline that incorporates a shape-respecting distance, baseline centering (i.e., birth-size normalized trajectories) and Gestational Age (GA)-correction to characterize shape-based child growth patterns. We used data from 3945 children (461 preterm) in the 2004 Pelotas Birth Cohort with at least 3 measurements between birth (included) and 11 years of age. Sex-adjusted weight-, length/height- and body mass index-for-age z-scores were derived at birth, 3 months, and at 1, 2, 4, 6 and 11 years of age (INTERGROWTH-21st and WHO growth standards). Growth trajectories clustering was conducted for each anthropometric index using k-means and a shape-respecting distance, accounting or not for birth size and/or GA-correction. We identified 3 trajectory patterns for each anthropometric index: increasing ( High ), stable ( Middle ) and decreasing ( Low ). Baseline centering resulted in pattern classification that considered early life growth traits. GA-correction increased the intercepts of preterm-born children trajectories, impacting their pattern classification. Incorporating shape-based clustering, baseline centering and GA-correction in growth patterns analysis improves the identification of subgroups meaningful for public health interventions.

IntroductionThe gut microbiota interacts with diet to affect body health throughout the life cycle. Critical periods of growth, such as infancy and puberty, are characterised by microbiota remodelling and changes in dietary habits. While the relationship between gut microbiota and growth in early life has been studied, our understanding of this relationship during puberty remains limited. Here, we describe the MIcrobiota, GROWth and Diet in peripubertal children (The MiGrowD) study, which aims to assess the tripartite growth-gut microbiota-diet relationship at puberty.Methods and analysisThe MiGrowD study will be a cross-sectional, community-based study involving children 8–12 years participating in the TARGet Kids! cohort. TARGet Kids! is a primary healthcare practice-based research network in Canada. Children will be asked to provide a stool sample, complete two non-consecutive 24-hour dietary recalls and a pubertal self-assessment based on Tanner Stages. Anthropometry will also be conducted. The primary outcome is the association between gut microbiota composition and longitudinal growth from birth until entry into the study. Anthropometrics data from birth will be from the data collected prospectively through TARGet Kids!. Body mass index z-scores will be calculated according to WHO. The secondary outcome is the association between gut microbiota, diet and pubertal stage.Ethics and disseminationEthics approval has been obtained by the Hospital for Sick Children and St. Michael’s Hospital—Unity Health, and the University of Toronto. Results will be disseminated in the public and academic sector, including participants, TARGet Kids! primary healthcare physicians teams, scientists via participation in the TARGet Kids! science and physician meetings, conferences and publications in peer-reviewed journals. The MiGrowD study results will help researchers understand the relationships underlying growth, gut microbiota and pubertal maturation in children.

Background Periodontal disease is a chronic state of inflammation that can destroy the supporting tissues around the teeth, leading to the resorption of alveolar bone. The initial strategy for treating periodontal disease is non-surgical sanative therapy (ST). Periodontal disease can also induce dysbiosis in the gut microbiota and contribute to low-grade systemic inflammation. Prebiotic fibers such as inulin can selectively alter the intestinal microbiota and support homeostasis by improving gut barrier functions and preventing inflammation. Providing an inulin supplement prior to and post-ST may influence periodontal health while providing insight into the complex relationship between periodontal disease and the gut microbiota. The primary objective is to determine if inulin is more effective than the placebo at improving clinical periodontal outcomes including probing depth (PD) and bleeding on probing (BOP). Secondary objectives include determining the effects of inulin supplementation pre- and post-ST on salivary markers of inflammation and periodontal-associated pathogens, as these outcomes reflect more rapid changes that can occur. Methods We will employ a single-center, randomized, double-blind, placebo-controlled study design and recruit and randomize 170 participants who are receiving ST to manage the periodontal disease to the intervention (inulin) or placebo (maltodextrin) group. A pilot study will be embedded within the randomized controlled trial using the first 48 participants to test the feasibility for the larger, powered trial. The intervention period will begin 4 weeks before ST through to their follow-up appointment at 10 weeks post-ST. Clinical outcomes of periodontal disease including the number of sites with PD ≥ 4 mm and the presence of BOP will be measured at baseline and post-ST. Salivary markers of inflammation, periodontal-associated pathogens, body mass index, and diet will be measured at baseline, pre-ST (after 4 weeks of intervention), and post-ST (after 14 weeks of intervention). Discussion We expect that inulin will enhance the positive effect of ST on the management of periodontal disease. The results of the study will provide guidance regarding the use of prebiotics prior to and as a supportive adjunct to ST for periodontal health. Trial registration ClinicalTrials.gov NCT04670133 . Registered on 17 December 2020.