Associations between genetic loci related to lean mass and body composition in type <scp>2</scp> diabetes

Minohara, Tatsuro; Noso, Shinsuke; Babaya, Naru; Hiromine, Yoshihisa; Taketomo, Yasunori; Niwano, Fumimaru; Makutani, Yukako; Yoshida, Sawa; Yasutake, Sara; Imamura, Shuzo; Ikegami, Hiroshi

doi:10.1111/ggi.14259

Cited by 5 publications

(4 citation statements)

References 25 publications

(33 reference statements)

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“…In a large meta-analysis of genomewide association studies for lean mass, VCAN loci (rs2287926) was identified and successfully replicated for whole body lean mass, and was important for sarcopenia diagnosis [30]. Moreover, a significant correlation between the VCAN loci and body fat mass in non-elderly individuals with T2D was also successfully replicated in another cross-sectional study [31]. FCRL1, a newly identified co-receptor of B cell receptors (BCR), was passively recruited into B cell immunological synapses upon BCR engagement in the absence of FCRL1 cross-linking, thus regulating B cell activation and function [32].…”

Section: Discussionmentioning

confidence: 89%

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia

Yuan

Zhu

Yao

et al. 2023

BMC Pregnancy Childbirth

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Introduction Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M. Methods Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model. Results We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR < 0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (PHosmer-Lemeshow test = 0.897). Conclusion The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.

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Section: Discussionmentioning

confidence: 89%

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia

Yuan

Zhu

Yao

et al. 2023

BMC Pregnancy Childbirth

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“…Moreover, downregulation of ADAMTSL3 displayed poor prognosis in hepatocellular carcinoma, supporting its tumor-suppressive role (Zhou et al 2020). ADAMTSL3 was reported to be a contribution of genetic factors in type 2 diabetes patients (Minohara et al 2021).…”

Section: Discussionmentioning

confidence: 91%

Differential Expressions of ADAM28 and ADAMTSL3 in Gingival Tissue of Patients with Periodontitis

Zhu

Zhong

2023

Tohoku J. Exp. Med.

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The modulation of gene expression via DNA methylation modifications is relevant to the pathogenesis of periodontitis. This study aimed at identifying novel biomarkers in gingival tissues from periodontitis by integrally analyzing methylation profiles and gene expression data. Differential gene expressions (DGEs) of dataset GSE106090 were obtained from the Gene Expression Omnibus (GEO) database for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. DNA methylation DGEs (DM-DGEs) were analyzed from dataset GSE173082.After integrating these two datasets, expressions of common genes were validated in gingival tissues from healthy controls and periodontitis patients by real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. GO analysis of 748 upregulated and 847 downregulated DEGs from the GSE106090 dataset revealed that immune response-regulating signaling pathway, cell-cell junction and signaling receptor activator activity as the top enriched biological process (BP), cellular component (CC) and molecular function (MF), respectively. DEGs were mainly enriched in cytokinecytokine receptor interaction, Ras signaling pathway, and chemokine signaling pathway. There was one up-regulated mRNA with hypo-methylated gene [ADAM28 (a disintegrin and metalloproteinase 28)] and one down-regulated mRNA with hyper-methylated gene [ADAMTSL3 (a disintegrin-like and metalloprotease domain with thrombospondin type I motifs-like-3)] after integrating GSE106090 and GSE173082 datasets. Increased ADAM28 expression was validated in gingival tissues from periodontitis patients as compared to the healthy controls with decreased ADAMTSL3 expression, which were correlated with disease stage. ADAM28 and ADAMTSL3 as novel biomarkers in gingival tissues from periodontitis by a comprehensive analysis of bioinformatics methods and executed validation.

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“…3 Natural genetic variation analysis has also been used to explore the mechanism of diabetes and its complications and predict the disease risk. [4][5][6][7] An ideal T2DM animal model is an essential tool for studying the pathogenesis of diabetes and developing new drugs. The minipig T2DM model can simulate the pathogenesis of human T2DM better than other current models.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of genome‐wide association studies (GWAS) and single‐nucleotide polymorphism (SNP) analysis technology, hundreds of single‐nucleotide polymorphism (SNP) loci have been found to be closely related to human T2DM 3 . Natural genetic variation analysis has also been used to explore the mechanism of diabetes and its complications and predict the disease risk 4–7 …”

Section: Introductionmentioning

confidence: 99%

Whole‐genome sequencing study to identify candidate markers indicating susceptibility to type 2 diabetes in Bama miniature pigs

Niu

Zhao

Jia

et al. 2023

Anim Models and Exp Med

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BackgroundHundreds of single‐nucleotide polymorphism (SNP) sites have been found to be potential genetic markers of type 2 diabetes mellitus (T2DM). However, SNPs related to T2DM in minipigs have been less reported. This study aimed to screen the T2DM‐susceptible candidate SNP loci in Bama minipigs so as to improve the success rate of the minipig T2DM model.MethodsThe genomic DNAs of three Bama minipigs with T2DM, six sibling low‐susceptibility minipigs with T2DM, and three normal control minipigs were compared by whole‐genome sequencing. The T2DM Bama minipig‐specific loci were obtained, and their functions were annotated. Meanwhile, the Biomart software was used to perform homology alignment with T2DM‐related loci obtained from the human genome‐wide association study to screen candidate SNP markers for T2DM in Bama miniature pigs.ResultsWhole‐genome resequencing detected 6960 specific loci in the minipigs with T2DM, and 13 loci corresponding to 9 diabetes‐related genes were selected. Further, a set of 122 specific loci in 69 orthologous genes of human T2DM candidate genes were obtained in the pigs. Collectively, a batch of T2DM‐susceptible candidate SNP markers in Bama minipigs, covering 16 genes and 135 loci, was established.ConclusionsWhole‐genome sequencing and comparative genomics analysis of the orthologous genes in pigs that corresponded to the human T2DM‐related variant loci successfully screened out T2DM‐susceptible candidate markers in Bama miniature pigs. Using these loci to predict the susceptibility of the pigs before constructing an animal model of T2DM may help to establish an ideal animal model.

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Associations between genetic loci related to lean mass and body composition in type 2 diabetes

Cited by 5 publications

References 25 publications

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia

Differential Expressions of ADAM28 and ADAMTSL3 in Gingival Tissue of Patients with Periodontitis

Whole‐genome sequencing study to identify candidate markers indicating susceptibility to type 2 diabetes in Bama miniature pigs

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