Landscape of the gut mycobiome dynamics during pregnancy and its relationship with host metabolism and pregnancy health.

OBJECTIVE: The remodelling of gut mycobiome (ie, fungi) during pregnancy and its potential influence on host metabolism and pregnancy health remains largely unexplored. Here, we aim to examine the characteristics of gut fungi in pregnant women, and reveal the associations between gut mycobiome, host metabolome and pregnancy health. DESIGN: Based on a prospective birth cohort in central China (2017 to 2020): Tongji-Huaxi-Shuangliu Birth Cohort, we included 4800 participants who had available ITS2 sequencing data, dietary information and clinical records during their pregnancy. Additionally, we established a subcohort of 1059 participants, which included 514 women who gave birth to preterm, low birthweight or macrosomia infants, as well as 545 randomly selected controls. In this subcohort, a total of 750, 748 and 709 participants had ITS2 sequencing data, 16S sequencing data and serum metabolome data available, respectively, across all trimesters. RESULTS: The composition of gut fungi changes dramatically from early to late pregnancy, exhibiting a greater degree of variability and individuality compared with changes observed in gut bacteria. The multiomics data provide a landscape of the networks among gut mycobiome, biological functionality, serum metabolites and pregnancy health, pinpointing the link between Mucor and adverse pregnancy outcomes. The prepregnancy overweight status is a key factor influencing both gut mycobiome compositional alteration and the pattern of metabolic remodelling during pregnancy. CONCLUSION: This study provides a landscape of gut mycobiome dynamics during pregnancy and its relationship with host metabolism and pregnancy health, which lays the foundation of the future gut mycobiome investigation for healthy pregnancy.

Association between Biomarkers of Phthalate Exposure and Serum Folate Concentrations in Children: A Population-Based Cross-Sectional Study of the NHANES from 2011 to 2016.

BACKGROUND: Although adverse health effects of phthalates have been reported, very few studies have assessed the associations between biomarkers of phthalate exposure and serum folate concentrations in children. OBJECTIVES: We aimed to examine the association between urinary phthalate metabolites, as biomarkers of exposure to phthalates, and total serum folate concentrations in children using national data from the United States. METHODS: We conducted cross-sectional analyses of 2100 individuals aged 6-18 y enrolled in the National Health and Nutrition Examination Survey, 2011-2016. Multivariable linear regression was applied to examine the relationship between natural logarithm (ln)-transformed urinary phthalate metabolites and serum folate concentrations. The quantile-based g-computation was used to assess the association of urinary phthalate metabolite mixture with serum folate levels. Subgroup analyses were conducted by sex, age, and race/ethnicity, and the interactions were assessed by adding interaction terms of these stratifying variables and phthalates and modeling through the Wald test. RESULTS: In multiple linear regression models, for participants in the highest tertile of MEHHP, MEOHP, DEHP, MCPP, and MCOP, total serum folate concentrations were 1.566 [ß: -1.566; 95% confidence interval: -2.935, -0.196], 1.423 (-1.423; -2.689, -0.157), 1.309 (-1.309; -2.573, -0.044), 1.530 (-1.530; -2.918, -0.142), and 1.381 (-1.381; -2.641, -0.122) ng/mL lower than those in the lowest tertile. The inverse associations were consistent in different subgroups by sex, age, and race/ethnicity (P for interaction ≥0.083 for all). In addition, the phthalate mixture showed a strong inverse correlation with serum folate; a quartile increase in the phthalate mixture on the ln scale was associated with 0.888 (-0.888; -1.677, -0.099) ng/mL decrease in the serum folate. CONCLUSIONS: Higher concentrations of urinary phthalate metabolites were associated with lower serum folate concentrations in children. Although our findings should be validated through additional population and mechanistic studies, they support a potential adverse effect of phthalate exposure on folate metabolism in children.

Mapping multimorbidity progression among 190 diseases.

BACKGROUND: Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them. METHODS: In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations. RESULTS: We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes. CONCLUSION: Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Mapping out clusters of diseases is crucial to addressing the rising challenge of co-occurrence of multiple diseases, known as multimorbidity. However, the current way of grouping diseases based on their associations isn't enough to understand how they develop over time. We've come up with a new approach to map out how groups of diseases progress together based on the strength of their causal relationships. By looking at how each disease affects the development of others, we can get a better understanding of how they form clusters. Our research goes beyond just showing which diseases occur together, and it's a step toward improving how we prevent and manage multiple health conditions in the future.