Dads at the dinner table. A cross-sectional study of Australian fathers' child feeding perceptions and practices

Maternal perceptions and practices regarding child feeding have been extensively studied in the context of childhood overweight and obesity. To date, there is scant evidence on the role of fathers in child feeding. This cross-sectional study aimed to identify whether characteristics of fathers and their concerns about their children’s risk of overweight were associated with child feeding perceptions and practices. Questionnaires were used to collect data from 436 Australian fathers (mean age = 37 years, SD = 6) of a child (53% boys) aged between 2-5 years (M = 3.5 years, SD = 0.9). These data included a range of demographic variables and selected subscales from the Child Feeding Questionnaire on concern about child weight, perceived responsibility for child feeding and controlling practices (pressure to eat and restriction). Multivariable linear regression was used to examine associations between demographic variables and fathers’ feeding perceptions and practices. Results indicated that fathers’ who were more concerned about their child becoming overweight reported higher perceived responsibility for child feeding and were more controlling of what and how much their child eats. Greater time commitment to paid work, possessing a health care card (indicative of socioeconomic disadvantage) and younger child age were associated with fathers’ perceiving less responsibility for feeding. Factors such as paternal BMI and education level, as well as child gender were not associated with feeding perceptions or practices. This study contributes to the extant literature on fathers’ role in child feeding, revealing several implications for research and interventions in the child feeding field

“I have to rest all the time because you are not allowed to play”: Exploring children’s perceptions of autonomy during sleep-time in long day care services

Daytime sleep is a significant part of the daily routine for children attending early childhood education and care (ECEC) services in Australia and many other countries. The practice of sleep-time can account for a substantial portion of the day in ECEC and often involves a mandated sleep/rest period for all children, including older preschool-aged children. Yet, there is evidence that children have a reduced need for daytime sleep as they approach school entry age and that continuation of mandated sleep-time in ECEC for preschool-aged children may have a negative impact on their health, development, learning and well-being. Mandated sleep-time practices also go against current quality expectations for services to support children’s agency and autonomy in ECEC. This study documents children’s reports of their experiences of sleep-time in ECEC. Semi-structured interviews were conducted with 54 preschool-aged children (44–63 months) across four long day ECEC services that employed a range of sleep-time practices. Findings provide a snapshot of children’s views and experiences of sleep-time and perceptions of autonomy-supportive practices. These provide a unique platform to support critical reflection on sleep-time policies and practices, with a view to continuous quality improvement in ECEC. This study forms part of a programme of work from the Sleep in Early Childhood research group. Our work examines sleep practices in ECEC, the subsequent staff, parent and child experiences and impacts on family and child learning and development outcomes

Erratum: Autism-related dietary preferences mediate autism-gut microbiome associations (Cell (2021) 184(24) (5916–5931.e17), (S0092867421012319), (10.1016/j.cell.2021.10.015))

(Cell 184, 5916–5931; November 24, 2021) Our paper reported evidence that autism-related dietary preferences mediate autism-microbiome associations. Since publication, we have become aware of an error in our paper that we are now correcting. Specifically, in the code we wrote and used to transform the microbiome count matrices in our variance component analysis, we inadvertently missed a matrix transposition, which affected their centered-log-ratio (clr) transformation and affected variance estimates in Figure 2 and Table S1 (listed in detail below). By missing the matrix transposition, we incorrectly calculated the geometric mean per-taxa rather than per-individual. However, the error does not affect the conclusions of the paper because the per-taxa and per-individual geometric means are similar, and so the resulting clr transformed matrices are similar as well (note that the clr transform should take the quotient of a microbiome/taxa quantity by the geometric mean of microbiome quantities across the sample/individual). To show that this is the case, we compared the correctly (geometric mean calculated per-individual) and incorrectly (geometric mean calculated per-taxa) clr transformed matrices by taking the nth column of both matrices (representing each of 247 individuals’ microbiome data) and calculating the Pearson's correlation coefficient between them. The median Pearson's correlation coefficient ranged from 0.90–0.94 for the common species, rare species, common genes, and rare genes matrices. As the correctly and incorrectly transformed matrices are highly correlated, this error has negligible impact on the variance component analysis results and does not change the overall conclusions of our work. The code error did not affect which microbiome features were identified as being differentially abundant, as the method used for this analysis (ANCOMv2.1) takes un-transformed count data as input. However, the data visualization for this analysis was affected with respect to the x-axes of Figures 3A–3C, 3E, 3F, and S4, which reflect the degree and directionality of differential abundance. In the updated plots, all the significant or near-significant microbiome features have identical directions of effect to the original plots as well as similar magnitudes of effect. We can confirm that the other instances of clr transformation were performed correctly; namely, in generating the dietary PCs, CD4+ T cells, and the PCA plot (Figure 5A). We have updated the following: (1) Figure 2 has been amended with the updated data: • Under age, species_common has changed from 33 to 35, transporter(TCDB)_common from 42 to 36, pathway(MetaCyc)_common from 40 to 39, and food(AES) from 33 to 46.• Under BMI, species, transporter(TCDB)_common has changed from 1 to 4, pathway(MetaCyc)_common from 0 to 1, genes(Microba)_common from 7 to 10, and food(AES) from 12 to 22.• Under ASD, genes(Microba)_rare has changed from 7 to 9.• Under IQ_DQ, species_common has changed from 3 to 5, genes(Microba)_common from 7 to 14, and food(AES) from 3 to 14.• Under Sleep, species_common has changed from 10 to 11, and genes(Microba)_common has changed from 0 to 6.• Under rBSC, species_common has changed from 5 to 6, species_rare from 41 to 40, transporter(TCDB)_common from 3 to 4, and genes(Microba)_common from 49 to 50.• Under dietary_PC1, enzyme(ECL4)_common has changed from 48 to 46, pathway(MetaCyc)_common from 25 to 24, and genes(Microba)_common from 48 to 47.• Under dietary_PC2, species_rare has changed from 1 to 0, genes(Microba)_common from 7 to 8, and genes(Microba)_rare from 3 to 0.• Under dietary_PC3, species_common has changed from 4 to 6, species_rare from 1 to 0, transporter(TCDB)_common from 21 to 17, pathway(MetaCyc)_common from 11 to 10, and genes(Microba)_common from 27 to 28.• Under diet_diversity, species_rare has changed from 20 to 14, transporter(TCDB)_common from 26 to 23, and genes(Microba)_common from 26 to 23.• We have also taken this opportunity to switch the y-axis order for “species_rare” and “enzyme(ECL4)_common” to better separate the taxonomic and functional datasets.(2) Table S1, which contains the raw data presented in Figure 2, has been amended with the updated OREML results.(3) In the main text, the third to fifth paragraphs of the section titled “Negligible variance in ASD diagnostic status is associated with the microbiome compared to age, stool and dietary traits” has been amended: • The age common species b2 estimate and standard error has changed from 33% (SE = 8%) to 35% (SE = 7%).• The p value for the BMI common species analysis has changed from p = 3.5e-2 (not FDR significant) to 1.8e-2 (FDR-significant).• With reference to the age gene-level ORM analyses, the range of standard errors has been changed from 13%–17% to 14%–17%.• The BMI rare genes b2 estimate has changed from 46% to 47%, and the p value has changed from 8.4e-3 to 1.1e-2.• The ASD rare genes b2 estimate has changed from 7% to 9%, and the p value has changed from 0.33 to 0.29.• The IQ-DQ common species b2 estimate has changed from 7% (SE = 13%, p = 0.39) to 5% (SE = 6%, p = 0.20).• The sleep problems common species b2 estimate has changed from 10% to 11%, and the p value has changed from 0.17 to 8.2e-2.• The stool consistency rare species b2 estimate has changed from 41% to 40%, and the p value has changed from 8.7e-6 to 2.8e-5.• The stool consistency rare genes standard error has changed from 20% to 21%, and the p value has changed from 2.5e-5 to 5.8e-5.• We have corrected an error where the dietary PC1 common genes b2 estimate (b2 = 48%, SE = 15%, p = 3.8e-4) was mislabeled as the rare genes analysis. We have also updated the common genes b2 estimate from 48% to 47% and updated the p value from 3.8e-4 to 4.5e-5.(4) Figure S1, which visualizes the diagonals and off-diagonals of the omics relationship matrix (ORM; which, in turn, is based on the centered-log-ratio transformed microbiome matrices) has been amended with the updated OREML results.(5) Figure S2, which draws upon ORMs using rare microbiome features to compare the effects of prior clr transformation versus binary coding as a sensitivity analysis, has been amended with the updated OREML results.(6) Figure S3, which provides a variety of OREML estimates to support Figure 2 (including the impact of estimating b2 with a combination of multiple ORMs and collapsing taxonomic microbiome data into higher levels of hierarchy), has been amended with the updated OREML results.(7) Methods S1, which provides results from extensive sensitivity analyses to support the main results, has also been amended with the updated OREML results. We have also updated the section “Estimating the upper limit of predictivity using non-additive models,” for which we used adaboost as a sensitivity analysis for a method that does not assume additivity. In this analysis, the mean prediction accuracy for ASD changed from 53% (SD = 7%) to 53% (SD = 8%), and the prediction accuracy for age changed from 62% (SD = 7%) to 63% (SD = 9%).(8) Figures 3A–3C, 3E, and 3F, which visualize differentially abundant microbiome features, now have updated x-axes.(9) Figure S4, which supports Figure 3 by providing results from sensitivity analyses for differential abundance, also has updated x-axes.(10) Tables S2.1, S2.3, S2.8, S2.13, and S2.14, which provide data (including x-axis coordinates) for Figures 3A–3C, 3E, and 3F, have been updated.(11) Unrelated to the clr transformation error, we have also updated the heading of the upper plot in Figure 4I to read “Diet ∼ Sensory score” rather than “Taxa ∼ Sensory score.”(12) The accompanying Zenodo code has been updated, and the link has been changed from https://zenodo.org/records/5558047 to https://zenodo.org/records/5558046. The specific code updates can be viewed on the linked GitHub page.These errors have now been corrected in the online version of the paper. We apologize for any inconvenience that this may have caused the readers.[Formula</p

Interactions between the lipidome and genetic and environmental factors in autism

Autism omics research has historically been reductionist and diagnosis centric, with little attention paid to common co-occurring conditions (for example, sleep and feeding disorders) and the complex interplay between molecular profiles and neurodevelopment, genetics, environmental factors and health. Here we explored the plasma lipidome (783 lipid species) in 765 children (485 diagnosed with autism spectrum disorder (ASD)) within the Australian Autism Biobank. We identified lipids associated with ASD diagnosis (n = 8), sleep disturbances (n = 20) and cognitive function (n = 8) and found that long-chain polyunsaturated fatty acids may causally contribute to sleep disturbances mediated by the FADS gene cluster. We explored the interplay of environmental factors with neurodevelopment and the lipidome, finding that sleep disturbances and unhealthy diet have a convergent lipidome profile (with potential mediation by the microbiome) that is also independently associated with poorer adaptive function. In contrast, ASD lipidome differences were accounted for by dietary differences and sleep disturbances. We identified a large chr19p13.2 copy number variant genetic deletion spanning the LDLR gene and two high-confidence ASD genes (ELAVL3 and SMARCA4) in one child with an ASD diagnosis and widespread low-density lipoprotein-related lipidome derangements. Lipidomics captures the complexity of neurodevelopment, as well as the biological effects of conditions that commonly affect quality of life among autistic people.</p

Autism-related dietary preferences mediate autism-gut microbiome associations

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.</p