Background Infertility has troubled millions of people worldwide while always being an ignored issue. The high cost of treatment or lack of services placed a barrier to the alleviation of infertility status. Governments play a significant role to promote infertility-related policies for better access to infertility services and comprehensive supports for infertile people. Methods Data of infertility status indicators and infertility-related policies in ten representative countries were collected. An infertility-related policy system was established, then classification and quantification were processed according to specific criteria, and different policy implementation patterns were identified. The effectiveness of specific infertility-related policy and various patterns on infertility prevalence relief between 1990 and 2017 were evaluated via generalized linear models and analyses of covariance for the first time. Results Economic support policies would be less prioritized compared with social security policies, while economic support policy had a significant positive role in the decline of female infertility prevalence (β = -2·16, p = 0·042). In detail, insurance coverage and economic reward policies were crucial (β = -3·31, p = 0·031; β = -4·10, p = 0·025) with adjusted with covariates. The effect of economic support-oriented pattern was relatively better than other patterns for both male and female infertility prevalence relief. Nevertheless, the effectiveness of gradual-promotion pattern seemed preferable for male infertility prevalence relief while was similar with simultaneous-promotion pattern for females. Conclusions Our data-driven analysis revealed that insurance coverage and economic reward policies played the pivotal role in moderation of female infertility status. Economic support-oriented pattern and gradual-promotion pattern were preferable when promoting infertility-related policies.
The gut microbiota alternations are associated with gestational anemia (GA); however, limited predictive value for the subsequent incidence of anemia in normal gestational women has been obtained. We sought to rigorously characterise gut dysbiosis in subjects with GA and explored the potential predictive value of novel microbial signatures for the risk of developing GA. A prospective cohort of subjects with GA (n = 156) and healthy control (n = 402), all of whom were free of GA in the second trimester, by 16S rRNA gene sequencing was conducted. Microbial signatures altered dramatically in GA compared with healthy control in the second trimester. Megamonas, Veillonella, and Haemophilus were confirmed to show differential abundances in GA after adjusting for covariates. On the contrary, Lachnospiraceae and Blautia were enriched in control. Microbial co-abundance group (CAG) network was constructed. Prospectively, CAG network relatively accurately predicted upcoming GA in normal pregnant women with an AUC of 0.7738 (95%CI: 0.7171, 0.8306) and the performance was further validated in Validation set (0.8223, 95%CI: 0.7573, 0.8874). Overall, our study demonstrated that alterations in the gut microbial community were associated with anemia in pregnancy and microbial signatures could accurately predict the subsequent incidence of anemia in normal pregnant women. Our findings provided new insights into understanding the role of gut microbiota in GA, identifying high-risk individuals, and modulating gut microbiota as a therapeutic target, thus improving quality of life and well-being of women and children.
The composition of the gut microbiome was previously found to be associated with clinical responses to dyslipidemia, but there is limited consensus on the dynamic change of the gut microbiota during pregnancy and the specific microbiome characteristics linked to dyslipidemia in pregnant women. We collected fecal samples from 513 pregnant women at multiple time points during pregnancy in a prospective cohort. Taxonomic composition and functional annotations were determined by 16S rRNA amplicon sequencing and shotgun metagenomic sequencing. The predictive potential of gut microbiota on the risk of dyslipidemia was determined. The gut microbiome underwent dynamic changes during pregnancy, with significantly lower alpha diversity observed in dyslipidemic patients compared to their healthy counterparts. Several genera, including Bacteroides, Paraprevotella, Alistipes, Christensenellaceae R7 group, Clostridia UCG-014, and UCG-002 were negatively associated with lipid profiles and dyslipidemia. Further metagenomic analysis recognized a common set of pathways involved in gastrointestinal inflammation, where disease-specific microbes played an important role. Machine learning analysis confirmed the link between the microbiome and its progression to dyslipidemia, with a micro-averaged AUC of 0.824 (95% CI: 0.782-0.855) combined with blood biochemical data. Overall, the human gut microbiome, including Alistipes and Bacteroides, was associated with the lipid profile and maternal dyslipidemia during pregnancy by perturbing inflammatory functional pathways. Gut microbiota combined with blood biochemical data at the mid-pregnancy stage could predict the risk of dyslipidemia in late pregnancy. Therefore, the gut microbiota may represent a potential noninvasive diagnostic and therapeutic strategy for preventing dyslipidemia in pregnancy.
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