Guan, Weihua

Abstract Background Although periodontitis and oral microbiota are linked to cardiometabolic diseases (CMD), it is unclear if they similarly predict CMD mortality. We compared the predictive ability of salivary microbiota and periodontal disease measures for CMD mortality in the National Health and Nutrition Examination Survey (NHANES). Methods We included 5,037 adults aged ≥30 years (mean age [± standard deviation (SD)]: 48[± 14]; 50% male) from the 2009–2010 and 2011–2012 NHANES cycles. We used 16S rRNA sequencing data from saliva to operationalize microbial composition and diversity. We calculated the relative abundance log‐ratio of Treponema (linked with periodontal disease) to Corynebacterium (linked with periodontal health) to compute the Microbial Indicator of Periodontitis (MIP). Interproximal periodontal probing depth and clinical attachment loss were measured from periodontal examinations. Mortality was ascertained through 2019. Survey‐weighted Cox models regressed mortality rates on MIP, microbial diversity, and periodontal measures to estimate hazard ratios and 95% confidence intervals (HR [95% CI]). Results Over 8.8 median follow‐up years, there were 81 CMD and 267 all‐cause deaths. After multivariable adjustment, MIP was associated with increased CMD mortality risk (HR per 1‐SD: 2.10 [1.30–3.38]). Neither microbial diversity nor periodontitis measures were associated with CMD mortality. MIP was associated with periodontitis in multivariable modeling (risk ratio per 1‐SD: 1.29 [1.22–1.39]). Conclusions In a nationally representative cohort, greater baseline salivary Treponema to Corynebacterium ratio predicted increased CMD mortality risk, while microbial diversity metrics and periodontal parameters were not significantly associated with CMD mortality. Longitudinal studies that further contextualize the oral microbiota are warranted. Plain Language Summary Bacteria in the mouth that cause gum disease are linked to cardiometabolic diseases (e.g., diabetes, cardiovascular disease, kidney disease). However, it is not well understood if bacteria of the mouth can predict the risk of death due to cardiometabolic diseases. We used data from a nationwide survey of US adults to explore whether bacteria from saliva, collected from a single time point, are associated with the future risk of cardiometabolic disease death. We found that people with higher levels of gum disease bacteria were more likely to die from cardiometabolic diseases. Future studies are needed to better understand the role of gum disease bacteria in the development of cardiometabolic diseases and the risk of death.

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) has become an important method for characterizing cellular heterogeneity, revealing more biological insights than the bulk RNA-seq. The surge in scRNA-seq data across multiple individuals calls for efficient and statistically powerful methods for differential expression (DE) analysis that addresses individual-level biological variability. Results We introduced DiSC, a method for conducting individual-level DE analysis by extracting multiple distributional characteristics, jointly testing their association with a variable of interest, and using a flexible permutation testing framework to control the false discovery rate (FDR). Our simulation studies demonstrated that DiSC effectively controlled the FDR across various settings and exhibited high statistical power in detecting different types of gene expression changes. Moreover, DiSC is computationally efficient and scalable to the rapidly increasing sample sizes in scRNA-seq studies. When applying DiSC to identify DE genes potentially associated with COVID-19 severity and Alzheimer’s disease across various types of peripheral blood mononuclear cells and neural cells, we found that our method was approximately 100 times faster than other state-of-the-art methods and the results were consistent and supported by existing literature. While DiSC was developed for scRNA-seq data, its robust testing framework can also be applied to other types of single-cell data. We applied DiSC to cytometry by time-of-flight data, DiSC identified significantly more DE markers than traditional methods. Availability and implementation The R software package “SingleCellStat” is freely available on CRAN (https://cran.r-project.org/web/packages/SingleCellStat/index.html) and GitHub (https://github.com/Lujun995/DiSC). The replication code for reproducing the analyses in this study is publicly accessible at https://github.com/Lujun995/DiSC_Replication_Code. The scRNA-seq expression matrix and metadata utilized in our simulations and analyses can be retrieved from https://cells.ucsc.edu/autism/rawMatrix.zip, https://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30, and https://covid19.cog.sanger.ac.uk/submissions/release1/haniffa21.processed.h5ad.