Accelerometer-based sleep metrics and gut microbiota during adolescence: Association findings from a Brazilian population-based birth cohort.

BACKGROUND: Sleep and gut microbiota are emerging putative risk factors for several physical, mental, and cognitive conditions. Sleep deprivation has been shown to be linked with unhealthy microbiome environments in animal studies. However, in humans, the results are mixed. Epidemiological studies evaluating the effect of accelerometer-based sleep measures on gut microbiome are scarce. This study aims to explore the relationship between sleep duration and efficiency with the gut microbiota in adolescence. METHODS: A subsample of 352 participants from the 2004 Pelotas (Brazil) Birth Cohort Study with sleep and fecal microbiota data available were included in the study. Sleep duration and sleep efficiency were obtained from actigraphy information at 11 years old whereas microbiota information from fecal samples was collected at 12 years. The fecal microbiota was analyzed via Illumina MiSeq (16S rRNA V3-V4 region) and the UNOISE pipeline. Alpha was assessed in QIIME2. Association measures for sleep variables and microbial α-diversity, and bacterial relative abundance were assessed through generalized models (linear and logistic regression), adjusting for maternal and child variables confounders. RESULTS: Adjusted models showed that sleep duration was positively associated with Simpson index of α-diversity (ß = 0.003; CI95 %: 0.00004; 0.01). Both sleep duration (OR = 0.43; CI95 % 0.25; 0.74) and efficiency (OR = 0.55; CI95 % 0.38; 0.78) were associated with lower Bacteroidetes abundance. CONCLUSION: Our results suggest that sleep duration and efficiency are linked to gut microbiota diversity and composition even with 1-2 years gap from exposure to outcome. The findings support the role of sleep in the gut-brain axis as well as provide insights on how to improve microbiota health.

A novel systematic pipeline for increased predictability and explainability of growth patterns in children using trajectory features.

OBJECTIVE: Longitudinal patterns of growth in early childhood are associated with health conditions throughout life. Knowledge of such patterns and the ability to predict them can lead to better prevention and improved health promotion in adulthood. However, growth analyses are characterized by significant variability, and pattern detection is affected by the method applied. Moreover, pattern labelling is typically performed based on ad hoc methods, such as visualizations or clinical experience. Here, we propose a novel pipeline using features extracted from growth trajectories using mathematical, statistical and machine-learning approaches to predict growth patterns and label them in a systematic and unequivocal manner. METHODS: We extracted mathematical and clinical features from 9577 children growth trajectories embedded with machine-learning predictions of the growth patterns. We experimented with two sets of features (CAnonical Time-series Characteristics and trajectory features specific to growth), developmental periods and six machine-learning classifiers. Clinical experts provided labels for the detected patterns and decision rules were created to associate the features with the labelled patterns. The predictive capacity of the extracted features was validated on two heterogenous populations (The Applied Research Group for Kids and the 2004 Pelotas Birth Cohort, based in Canada and Brazil, respectively). RESULTS: Features predictive ability measured by accuracy and F1 score was ≥ 80% and ≥ 0.76 respectively in both cohorts. A small number of features (n = 74) was sufficient to distinguish between growth patterns in both cohorts. Slope, intercept of the trajectory, age at peak value, start value and change of the growth measure were among the top identified features. CONCLUSION: Growth features can be reliably used as predictors of growth patterns and provide an unbiased understanding of growth patterns. They can be used as tool to reduce the effort to repeat analysis and variability concerning anthropometric measures, time points and analytical methods, in the context of the same or similar populations.

A novel shape-based approach to identify gestational age-adjusted growth patterns from birth to 11 years of age.

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.

Dietary Changes and Gut Dysbiosis in Children With Type 1 Diabetes.

OBJECTIVE: Gut dysbiosis in type 1 diabetes (T1D), characterized by high Bacteroides proportion, tends to reverse as T1D progresses, without reaching full recovery. Since diet influences microbiota structure, the aim was to evaluate the impact of dietary changes on Bacteroides proportion the first year of T1D evolution. METHODS: Dietary intake was assessed by 24-hour recalls and Bacteroides proportion by quantitative polymerase chain reaction, in 10 Mexican children (11.6 ± 1.92 years) with T1D at baseline and 3, 6 and 9 months' follow-up. Repeated measures analysis of variance and multiple linear regression were performed to compare ingested nutrients in relation with Bacteroides proportion. Effects over time were evaluated by mixed regression models. RESULTS: Patients with T1D decreased their energy (2621.89 to 1867.85 kcal, p = 0.028), protein (83.06 to 75.17 g, p = 0.012), and saturated fat consumption (40.83 to 25.23 g, p = 0.031) from baseline to 3 months, without posterior changes. Bacteroides proportion increased in the first months and tended to decrease at around 9 months (p > 0.05) and was positively correlated with saturated fat (ß = 3.70, p = 0.009) and total carbohydrates (ß = 0.73, p = 0.005) at 3 months. Carbohydrate consumption was related to decreased Bacteroides abundance over time (ß = -14.9, p = 0.004), after adjusting for glycosylated hemoglobin. CONCLUSIONS: Besides autoimmunity, diet appears to have a central role determining the T1D-associated dysbiosis evolution.