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.

Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition.

Children admitted to hospital with an acute illness and concurrent severe malnutrition [complicated severe malnutrition (CSM)] have a high risk of dying. The biological processes underlying their mortality are poorly understood. In this case-control study nested within a multicenter randomized controlled trial among children with CSM in Kenya and Malawi, we found that blood metabolomic and proteomic profiles robustly differentiated children who died (n = 92) from those who survived (n = 92). Fatalities were characterized by increased energetic substrates (tricarboxylic acid cycle metabolites), microbial metabolites (e.g., propionate and isobutyrate), acute phase proteins (e.g., calprotectin and C-reactive protein), and inflammatory markers (e.g., interleukin-8 and tumor necrosis factor-α). These perturbations indicated disruptions in mitochondria-related bioenergetic pathways and sepsis-like responses. This study identified specific biomolecular disturbances associated with CSM mortality, revealing that systemic inflammation and bioenergetic deficits are targetable pathophysiological processes for improving survival of this vulnerable population.

Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques.

BACKGROUND: Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection. METHODS: We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features. RESULTS: Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%-89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures. CONCLUSION: Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns.

Metabolomic Changes in Serum of Children with Different Clinical Diagnoses of Malnutrition.

BACKGROUND: Mortality in children with severe acute malnutrition (SAM) remains high despite standardized rehabilitation protocols. Two forms of SAM are classically distinguished: kwashiorkor and marasmus. Children with kwashiorkor have nutritional edema and metabolic disturbances, including hypoalbuminemia and hepatic steatosis, whereas marasmus is characterized by severe wasting. The metabolic changes underlying these phenotypes have been poorly characterized, and whether homeostasis is achieved during hospital stay is unclear. OBJECTIVES: We aimed to characterize metabolic differences between children with marasmus and kwashiorkor at hospital admission and after clinical stabilization and to compare them with stunted and nonstunted community controls. METHODS: We studied children aged 9-59 mo from Malawi who were hospitalized with SAM (n = 40; 21 with kwashiorkor and 19 with marasmus) or living in the community (n = 157; 78 stunted and 79 nonstunted). Serum from patients with SAM was obtained at hospital admission and 3 d after nutritional stabilization and from community controls. With the use of targeted metabolomics, 141 metabolites, including amino acids, biogenic amines, acylcarnitines, sphingomyelins, and phosphatidylcholines, were measured. RESULTS: At admission, most metabolites (128 of 141; 91%) were lower in children with kwashiorkor than in those with marasmus, with significant differences in several amino acids and biogenic amines, including those of the kynurenine-tryptophan pathway. Several phosphatidylcholines and some acylcarnitines also differed. Patients with SAM had profiles that were profoundly different from those of stunted and nonstunted controls, even after clinical stabilization. Amino acids and biogenic amines generally improved with nutritional rehabilitation, but most sphingomyelins and phosphatidylcholines did not. CONCLUSIONS: Children with kwashiorkor were metabolically distinct from those with marasmus, and were more prone to severe metabolic disruptions. Children with SAM showed metabolic profiles that were profoundly different from stunted and nonstunted controls, even after clinical stabilization. Therefore, metabolic recovery in children with SAM likely extends beyond discharge, which may explain the poor long-term outcomes in these children. This trial was registered at isrctn.org as ISRCTN13916953.

Mortality in children with complicated severe acute malnutrition is related to intestinal and systemic inflammation: an observational cohort study.

BACKGROUND: Diarrhea affects a large proportion of children with severe acute malnutrition (SAM). However, its etiology and clinical consequences remain unclear. OBJECTIVE: We investigated diarrhea, enteropathogens, and systemic and intestinal inflammation for their interrelation and their associations with mortality in children with SAM. DESIGN: Intestinal pathogens (n = 15), cytokines (n = 29), fecal calprotectin, and the short-chain fatty acids (SCFAs) butyrate and propionate were determined in children aged 6-59 mo (n = 79) hospitalized in Malawi for complicated SAM. The relation between variables, diarrhea, and death was assessed with partial least squares (PLS) path modeling. RESULTS: Fatal subjects (n = 14; 18%) were younger (mean ± SD age: 17 ± 11 compared with 25 ± 11 mo; P = 0.01) with higher prevalence of diarrhea (46% compared with 18%, P = 0.03). Intestinal pathogens Shigella (36%), Giardia (33%), and Campylobacter (30%) predominated, but their presence was not associated with death or diarrhea. Calprotectin was significantly higher in children who died [median (IQR): 1360 mg/kg feces (2443-535 mg/kg feces) compared with 698 mg/kg feces (1438-244 mg/kg feces), P = 0.03]. Butyrate [median (IQR): 31 ng/mL (112-22 ng/mL) compared with 2036 ng/mL (5800-149 ng/mL), P = 0.02] and propionate [median (IQR): 167 ng/mL (831-131 ng/mL) compared with 3174 ng/mL (5819-357 ng/mL), P = 0.04] were lower in those who died. Mortality was directly related to high systemic inflammation (path coefficient = 0.49), whereas diarrhea, high calprotectin, and low SCFA production related to death indirectly via their more direct association with systemic inflammation. CONCLUSIONS: Diarrhea, high intestinal inflammation, low concentrations of fecal SCFAs, and high systemic inflammation are significantly related to mortality in SAM. However, these relations were not mediated by the presence of intestinal pathogens. These findings offer an important understanding of inflammatory changes in SAM, which may lead to improved therapies. This trial was registered at www.controlled-trials.com as ISRCTN13916953.