BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort

The recent advancement of single-cell RNA sequencing (scRNA-seq) technologies facilitates the study of cell lineages in developmental processes and cancer.In this study,we developed a computational method,called redPATH,to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm.Besides,we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights.We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets,as well as other single-cell transcriptome data.In particular,we identified a stem cell-like subpopulation in malig-nant glioma cells.These cells express known proliferative markers,such as GFAP,ATP1A2,IGFBPL1,and ALDOC,and remain silenced for quiescent markers such as ID3.Furthermore,we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogram-ming macrophages to control tumor growth.In conclusion,redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time.redPATH is available at https://github.com/tinglabs/redPATH.

Identification of significant biological relationships or patterns is central to many metagenomic studies.Methods that estimate association networks have been proposed for this pur-pose;however,they assume that associations are static,neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors(EFs),which can result in inaccurate estimations.Therefore,in this study,we propose a computational model,called the k-Lognormal-Dirichlet-Multinomial(kLDM)model,which estimates multiple association networks that correspond to specific environmental conditions,and simultaneously infers microbe-microbe and EF-microbe associations for each network.The effectiveness of the kLDM model was demonstrated on synthetic data,a colorectal cancer(CRC)dataset,the Tara Oceans dataset,and the American Gut Project dataset.The results revealed that the widely-used Spearman's rank correlation coefficient method performed much worse than the other methods,indicating the importance of separating samples by environmental conditions.Cancer fecal samples were then compared with cancer-free samples,and the estimation achieved by kLDM exhibited fewer associations among microbes but stronger associations between specific bacteria,especially five CRC-associated operational taxonomic units,indicating gut microbe translocation in cancer patients.Some EF-dependent associations were then found within a marine eukaryotic community.Finally,the gut microbial heterogeneity of inflammatory bowel disease patients was detected.These results demonstrate that kLDM can elucidate the complex associations within microbial ecosys-tems.The kLDM program,R,and Python scripts,together with all experimental datasets,are accessible at https://github.com/tinglab/kLDM.git.