Type 2 Diabetes Biomarkers of Human Gut Microbiota Selected via Iterative Sure Independent Screening Method

• Published in: PloS one 2015-10, Vol.10 (10), p.e0140827-e0140827
• Main Authors: Cai, Lihua, Wu, Honglong, Li, Dongfang, Zhou, Ke, Zou, Fuhao
• Format: Article
• Language: English
• Subjects: Aged; Analysis; Bioindicators; Bioinformatics; Biological markers; Biomarkers; Care and treatment; Data analysis; Data mining; Data processing; Datasets; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - genetics; Diabetes Mellitus, Type 2 - microbiology; Diagnosis; Diagnostic systems; Dimensional analysis; DNA methylation; Female; Gastrointestinal Microbiome - genetics; Gene expression; Gene sequencing; Genetic Markers - genetics; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Humans; Intestinal microflora; Iterative methods; Laboratories; Male; Medical screening; Microbiota; Microbiota (Symbiotic organisms); Middle Aged; Predictions; Regularization methods; Researchers; Risk factors; Science; Signal transduction; Sparsity; Statistics; Type 2 diabetes
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0140827

Type 2 diabetes, which is a complex metabolic disease influenced by genetic and environment, has become a worldwide problem. Previous published results focused on genetic components through genome-wide association studies that just interpret this disease to some extent. Recently, two research groups published metagenome-wide association studies (MGWAS) result that found meta-biomarkers related with type 2 diabetes. However, One key problem of analyzing genomic data is that how to deal with the ultra-high dimensionality of features. From a statistical viewpoint it is challenging to filter true factors in high dimensional data. Various methods and techniques have been proposed on this issue, which can only achieve limited prediction performance and poor interpretability. New statistical procedure with higher performance and clear interpretability is appealing in analyzing high dimensional data. To address this problem, we apply an excellent statistical variable selection procedure called iterative sure independence screening to gene profiles that obtained from metagenome sequencing, and 48/24 meta-markers were selected in Chinese/European cohorts as predictors with 0.97/0.99 accuracy in AUC (area under the curve), which showed a better performance than other model selection methods, respectively. These results demonstrate the power and utility of data mining technologies within the large-scale and ultra-high dimensional genomic-related dataset for diagnostic and predictive markers identifying.