Background: Metabolic disorders caused by intestinal microbial dysregulation are considered to be important causes of gestational diabetes mellitus (GDM). Increasing evidence suggests that the diversity and composition of gut microbes are altered in disease states, yet the critical microbes and mechanisms of disease regulation remain unidentified.Methods: PubMed^® (National Library of Medicine, Bethesda, MD, USA), Embase^® (Elsevier, Amsterdam, the Netherlands), the Web of Science™ (Clarivate™, Philadelphia, PA, USA), and the Cochrane Library databases were searched to identify articles published between 7 July 2012 and 7 July 2022 reporting on case–control and controlled studies that analyzed differences in enterobacteria between patients with GDM and healthy individuals. Information on the relative abundance of enterobacteria was collected for comparative diversity comparison, and enterobacterial differences were analyzed using random effects to calculate standardized mean differences at a p-value of 5%.Results: A total of 22 studies were included in this review, involving a total of 965 GDM patients and 1,508 healthy control participants. Alpha diversity did not differ between the participant groups, but beta diversity was significantly different. Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were the dominant bacteria, but there was no significant difference between the two groups. Qualitative analysis showed differences between the groups in the Firmicutes/Bacteroidetes ratio, Blautia, and Collinsella, but these differences were not statistically different.Conclusion: Enterobacterial profiles were significantly different between the GDM and non-GDM populations. Alpha diversity in patients with GDM is similar to that in healthy people, but beta diversity is significantly different. Firmicutes/Bacteroidetes ratios were significantly increased in GDM, and this, as well as changes in the abundance of species of Blautia and Collinsella, may be responsible for changes in microbiota diversity. Although the results of our meta-analysis are encouraging, more well-conducted studies are needed to clarify the role of the gut microbiome in GDM. The systematic review was registered with PROSPERO (https://www.crd.york.ac.uk/prospero/) as CRD42022357391.

Background: Comprehensive smoke-free legislation has been implemented in many countries. The current study quantitatively examined the reduction in risk of acute myocardial infarction (MI) occurrence following the legislations and the relationship with the corresponding smoking prevalence decrease.Methods: PubMed, EMBASE, and Google Scholar databases and bibliographies of relevant studies and reviews were searched for potential original studies published from January 1, 2004, through October 31, 2011. Meta-analysis was performed using a random effect model to estimate the overall effects of the smoking-free legislations. Meta-regression was used to investigate possible causes of heterogeneity in risk estimates.Results: A total of 18 eligible studies with 44 estimates of effect size were used in this study. Meta-analysis produced a pooled estimate of the relative risk of 0.87 (95% confidence interval (CI): 0.84 to 0.91). There was significant heterogeneity in the risk estimates (overall I²= 96.03%, p<0.001). In meta-regression analysis, studies with greater smoking prevalence decrease produced larger relative risk (adjusted coefficient −0.027, 95% CI: -0.049 to −0.006, p=0.014).Conclusion: Smoke-free legislations in public and work places were associated with significant reduction in acute MI risk, which might be partly attributable to reduced smoking prevalence.

Background: The “gut–skin axis” has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.Methods: The 16S rRNA dataset, after applying the centered log-ratio transformation, was analyzed using five different machine learning models: random forest, light gradient boosting machine, extreme gradient boosting, support vector machine with radial kernel, and logistic regression. Interpretable machine learning methods, such as SHAP values, were used to identify significant features associated with atopic dermatitis.Results: Random forest performed better than the other “tree” models in the validation partitions. The SHAP global dependency plot indicated that Bifidobacterium ranked as the strongest predictive factor across all prediction horizons, although the SHAP values for some features were still higher in support vector machine and logistic regression models. The SHAP partial dependency plot for “tree” models showed that the best segmentation point for Bifidobacterium was further from the origin compared to other features in the respective models, quantitatively reflecting differences in gut microbiota.Conclusion: Machine learning models combined with SHAP could be used to quantitatively screen key gut flora in atopic dermatitis patients, providing doctors with an intuitive understanding of 16S rRNA sequencing data to support precision medicine in care and recovery.

Background: Hand, foot and mouth disease (HFMD) is an acute enterovirus-induced disease. Gut microbiota dysbiosis has been identified as a factor that plays an important role in enteral virus infection, but the gut microbiota profile in hand, foot and mouth disease has rarely been studied in a large population.Methods: A total of 749 children (HFMD: n = 262, healthy control: n = 487) aged 2 to 7 years were recruited from hospitals and communities in the period from May to July, 2017. Clinical and demographical information was collected by trained personnel, and fecal samples were collected and processed for 16S ribosomal RNA(rRNA) gene sequencing.Results: We observed a significant alteration in the microbiota profile of children with HFMD compared with that of control children. Patients with enteroviruses A71(EV71) positive had more dysbiotic gut microbiota than those with coxsackievirus A16 (CAV16) positive. We found that Prevotella and Streptococcus were enriched in children with HFMD, whereas beneficial bacteria, including Bifidobacterium and Faecalibacterium, were depleted. Children with synbiotics supplements had lower risk of HFMD and we observed that the gut microbiota of HFMD patients who were administered synbiotics exhibited potential resistance to the dysbiosis detected in HFMD.Conclusions: This study suggested that the gut microbiota of patients with hand, foot and mouth disease exhibits dysbiosis and that synbiotics supplements potentially helps maintain the homeostasis of the gut flora.

Background: Over time, there have been significant changes in the dietary patterns of the Chinese population with the emergence of ultra-processed foods (UPFs). Methods: To ascertain the changes in UPFs intake among adults in southern China, over the past two decades, the study included residents aged 18 years and older who participated in the China National Nutrition Surveys in Guangdong province in 2002, 2012, and 2022. Dietary intake data were collected via three-day 24 h dietary recalls and weighing household foods and condiments. The recorded dietary data were classified according to the NOVA classification system, and the percentage of total energy derived from each food group was calculated. Results: From 2002 to 2022, there was a notable increase in the consumption of UPFs among adults in Guangdong Province, with the percentage of UPFs intake rising from 0.88% to 8.52% (p-value < 0.001). This growth was especially pronounced among specific population groups, including the young, the better educated, and those living in highly urbanized areas. The largest increase in energy intake from UPFs was observed among students, from 0.42% to 17.26% (p-value < 0.001). The nutrient contents of UPFs in Guangdong in 2022 were found to contain a markedly higher calculated percentage of calories provided by carbohydrates in comparison to minimally processed foods (56.6% vs. 43.8%) as well as in sodium (749 mg/100 kcal vs. 29 mg/100 kcal). Conclusions: Given the increasing consumption of UPFs and the growing evidence linking these products to chronic diseases, it is important to promote healthy food intake and balanced diets through active nutritional education campaigns to prevent potential health risks that may arise.

The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based predictions, and the rapid advancements of machine learning techniques have introduced new opportunities for exploiting microbiome data. To predict various host nonhealthy conditions, this study proposed an integrated machine learning-based estimation pipeline of Gut Age Index (GAI) by establishing a health aging baseline with the gut microbiome data from healthy individuals. We assessed the performance of GAI pipeline on two extensive cohorts - the Guangdong Gut Microbiome Project (GGMP) and the American Gut Project (AGP). In the GGMP cohort, for 20 common chronic diseases such as metabolic syndrome, obesity, and cardiovascular diseases, the proposed GAI achieved a balanced accuracy, ranging from 66 to 75%, with the prediction performance for atherosclerosis being the highest. In the AGP cohort, the balanced accuracy of GAI ranged from 58 to 72% for 10 diseases. Based on the results from these two datasets, we conclude that our proposed approach in this study can be used to predict individual health status, which offers the potential for scalable, cost-effective, and personalized health insights.