New Results

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

Basavaraj Vastrad, Chanabasayya Vastrad
doi: https://doi.org/10.1101/2021.11.24.469869

Abstract

Gestational diabetes mellitus (GDM) is a metabolic disorder during pregnancy. Numerous biomarkers have been identified that are linked with the occurrence and development of GDM. The aim of this investigation was to identify differentially expressed genes (DEGs) in GDM using a bioinformatics approach to elucidate their molecular pathogenesis. GDM associated expression profiling by high throughput sequencing dataset (GSE154377) was obtained from Gene Expression Omnibus (GEO) database including 28 normal pregnancy samples and 33 GDM samples. DEGs were identified using DESeq2. The gene ontology (GO) and REACTOME pathway enrichments of DEGs were performed by g:Profiler. Protein-protein interaction (PPI) networks were assembled with Cytoscape software and separated into modules using the PEWCC1 algorithm. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were performed with the miRNet database and NetworkAnalyst database. Receiver Operating Characteristic (ROC) analyses was conducted to validate the hub genes. A total of 953 DEGs were identified, of which 478 DEGs were up regulated and 475 DEGs were down regulated. Furthermore, GO and REACTOME pathway enrichment analysis demonstrated that these DEGs were mainly enriched in multicellular organismal process, cell activation, formation of the cornified envelope and hemostasis. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB were identified as key genes in the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB in GDM were validated using ROC analysis. This investigation provides further insights into the molecular pathogenesis of GDM, which might facilitate the diagnosis and treatment of GDM.

Introduction

Gestational diabetes mellitus (GDM) has emerged as most common medical complications of pregnancy, but current treatments are still suboptimal [1]. The number of cases GDM is rising worldwide and it has become a key health concern [2]. There are several important risk factors for GDM, such as advanced maternal age [3], family history of diabetes [4], obesity [5], cigarette smoking [6], genetics factors [7], hypertension and type 2 diabetes [8], and cardiovascular diseases [9]. However, GDM is a complex disorder and its biology remains poorly understood. Therefore, mining key biomarkers linked with GDM occurrence and advancement is essential for GDM early diagnosis, prognosis and treatment.

The GDM is an extremely complicated pathophysiological process and controlled by various genes and signaling pathways. Genes such as PVT1 [10], IL-6, IL-10, and TNF-α [11], CAPN10 [12], ADCYAP1 [13] and ABHD5 [14] are associated with GDM. Signaling pathways such as PI3K/AKT signaling pathway [15], AMPK signaling pathway [16], vitamin D signaling pathways [17], TLR4/NF-kappaB Signaling Pathway [18] and AGEs-RAGE signaling pathway [19] are linked with GDM. Recently, next-generation sequencing (NGS) technology is extensively applied in clinical investigation, with the marked gain relying on its capacity to together determine the expression data of massive genes at a time [20]. The public database Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) [21] are powerful tools used to screen the differentially expressed genes (DEGs) generated from NGS data corresponding to the progression of GDM [22]. Therefore, there are still many related genes to be identified, which will help us better understand the molecular pathogenesis of GDM and facilitate the discovery of new diagnostic biomarkers or therapeutic target.

In the current investigation, we tested integrated bioinformatics analysis for the identification of DEGs as potential biomarkers of GDM based on the expression profiling by high throughput sequencing from the GEO database. These analysis might provide possible perspectives for the progression and development of GDM as well as identification potential candidate genes for GDM diagnose or treatment.

Materials and methods

Data resources

The NGS data used in this investigation were acquired from GEO database, which is a public repository containing the expression profiling by high throughput sequencing data submitted by research institutions around the world. We chose the expression profiling by high throughput sequencing data GSE154377 [23] from the GEO database. The NGS data of GSE154377 was consisted with 28 normal pregnancy samples and 33 GDM samples based on the platform of GPL20301 Illumina HiSeq 4000 (Homo sapiens).

Identification of DEGs

DESeq2 [24] packages of R Bioconductor (http://www.bioconductor.org/) was applied to significance analysis of DEGs between normal pregnancy samples and GDM samples. The log-fold change (FC) in expression and adjusted P-values (adj. P) were resolved. The criterion of statistical significance was adj. P value <0.05, |Log2 (FC)|> 1.73 for up regulated genes and |Log2 (FC)|> -0.69 for down regulated genes. A heatmap of the DEGs was produced using gplots in R software. All significant DEGs were shown in a volcano plot generated using ggplot2 in R software.

Gene Ontology (GO) and REACTOME pathway enrichment analysis of DEGs

Gene Ontology (GO) (http://www.geneontology.org) [25] is a suitable technique to classify gene expression and its properties, including biological process (BP), cellular component (CC) and molecular function (MF), which provide extensive functional annotation tools for researchers to integrate key genes with a specific function. REACTOME (https://reactome.org/) [26] is an online pathway database that collects information on genomic, biochemical, and enzymatic pathways. The DEGs were mapped to the REACTOME pathway database and the significantly related pathways were screened. We could use g:Profiler (http://biit.cs.ut.ee/gprofiler/) [27] is an online functional annotation tool to visualize the DEGs enrichment of BP, MF, CC and pathways (P□<□0.05).

Construction of the PPI network and module analysis

IID interactome (Integrated Interactions Database) (http://iid.ophid.utoronto.ca/) [28] was used to construct PPI network. The identified DEGs were input into IID interactome to unravel a potential PPI network. Afterwards, a PPI network was constructed and visualized by Cytoscape software (version 3.8.2, http://www.cytoscape.org/) [29]. Subsequently, the topological properties including node degree [30], betweenness centrality [31], stress centrality [32] and closeness centrality [33] of the DEGs in the PPI network were calculated by Network Analyzer plug-in of Cytoscape to further analyze the candidate hub genes from the PPI network. The PEWCC1 plug-in [34] was used to search sub-networks of the PPI network and the default parameters (Degree cutoff ≥ 10, node score cutoff ≥ 0.4, K-core ≥ 4, and max depth=100.) were set in the functional interface of Cytoscape software.

MiRNA-hub gene regulatory network construction

The miRNA-hub gene interactions were predicted with miRNet database (https://www.mirnet.ca/) [35], which involves 14 predicted algorithms (TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0). The Cytoscape 3.8.2 software [29] was used to visualize the regulatory network.

TF-hub gene regulatory network construction

The TF-hub gene interactions were predicted with NetworkAnalyst database (https://www.networkanalyst.ca/) [36], which involves 1 predicted algorithm (ChEA). The Cytoscape 3.8.2 software [29] was used to visualize the regulatory network.

Receiver operating characteristic curve (ROC) analysis

pROC package in R statistical software [37] was used to operate ROC curve analysis and to resolve the specificity, sensitivity, positive and negative predictive values for all the feasible thresholds of the ROC curve. The value of the hub genes were predicted based on the ROC curve analysis.

Results

Identification of DEGs

DEGs were identified by DESeq2 in the R package. In total, 953 DEGs (478 up and 475 down regulated genes) were selected based on the criteria of adj. P value <0.05, |Log2 (FC)|> 1.73 for up regulated genes and |Log2 (FC)|> -0.69 for down regulated genes. Heatmap of DEGs is presented in Fig. 1, and volcano plot is presented in Fig. 2. The up and down regulated DEGs is listed in Table 1.

Fig. 1.
Fig. 1.

Heat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A28= Normal Pregnancy Samples; B1 – B33= Gestational diabetes Samples)

Fig. 2.
Fig. 2.

Volcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.

View this table:
Table 1

The statistical metrics for key differentially expressed genes (DEGs)

Gene Ontology (GO) and REACTOME pathway enrichment analysis of DEGs

All 953 DEGs were analyzed by g:Profiler software and the results of GO enrichment analysis indicated that 1) for BP, up regulated genes were particularly enriched in multicellular organismal process and developmental process, and down regulated genes in cell activation and immune system process; 2) for CC, up regulated genes were enriched in cell periphery and intrinsic component of membrane, and down regulated genes in endomembrane system and cytoplasm; 3) for GO MF, up regulated genes were significantly enriched in the transporter activity and channel activity, and down regulated geness in enzyme binding and protein binding (Table 2). REACTOME pathway enrichment analysis results were shown in Table 3 which demonstrated that up regulated genes were particularly enriched in formation of the cornified envelope and cell-cell junction organization, while down regulated genes were particularly enriched in hemostasis and neutrophil degranulation (P_<_0.05).

View this table:
Table 2

The enriched GO terms of the up and down regulated differentially expressed genes

View this table:
Table 3

The enriched pathway terms of the up and down regulated differentially expressed genes

Construction of the PPI network and module analysis

The PPI network of DEGs was constructed with 8472 nodes and 20624 edges (Fig.3). The node degree, betweenness centrality, stress centrality and closeness centrality methods were performed to calculate the top hub genes and are listed in Table 4. The results revealed 5 up and 5 down regulated genes identified as hub genes, including TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB. PEWCC1 was used to identify the significant modules in the PPI network and the two significant modules were selected. Module 1 consisted of 95 nodes and 103 edges (Fig.4A) and module 2 consisted of 79 nodes and 131 edges (Fig.4B). Following GO and pathway enrichment analysis, the module 1 was revealed to be associated with multicellular organismal process, developmental process and intrinsic component of membrane, and module 2 was revealed to be associated with hemostasis, immune system, enzyme binding, cytokine signaling in immune system and protein binding.

Fig. 3.
Fig. 3.

PPI network of DEGs. Up regulated genes are marked in green; down regulated genes are marked in red

Fig. 4.
Fig. 4.

Modules of isolated form PPI of DEGs. (A) The most significant module was obtained from PPI network with 95 nodes and 103 edges for up regulated genes (B) The most significant module was obtained from PPI network with 79 nodes and 131 edges for down regulated genes. Up regulated genes are marked in green; down regulated genes are marked in red

View this table:
Table 4

Topology table for up and down regulated genes.

MiRNA-hub gene regulatory network construction

To investigate interaction correlations between the hub genes and miRNAs, a miRNA-hub gene interaction network was constructed, which has 2598 nodes (hub genes:307; miRNAs: 2291) and 17035 edges. Results from the current investigation demonstrated that one target gene might be regulated by several miRNAs (Fig. 5) and are listed in Table 5. Up regulated hub genes including, ERRFI1 targeted by 70 miRNAs (ex; hsa-mir-198); CCNE1 targeted by 50 miRNAs (ex; hsa-mir-181b-5p); ELAVL2 targeted by 43 miRNAs (ex; hsa-mir-766-3p); ANTXR1 targeted by 36 miRNAs (ex; hsa-mir-624-5p); ZBTB8A targeted by 34 miRNAs (ex; hsa-mir-10b-5p). Similarly, down regulated hub genes including, SRRM2 targeted by 250 miRNAs (ex; hsa-mir-582-5p); CDKN1A targeted by 187 miRNAs (ex; hsa-mir-657); MYH9 targeted by 126 miRNAs (ex; hsa-mir-1231); APP targeted by 125 miRNAs (ex; hsa-mir-509-3-5p); ACTB targeted by 122 miRNAs (ex; hsa-mir-192-3p).

Fig. 5.
Fig. 5.

Target gene - miRNA regulatory network between target genes. The blue color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red.

View this table:
Table 5

miRNA - target gene and TF - target gene interaction

TF-hub gene regulatory network construction

To investigate interaction correlations between the hub genes and TFs, a TF-hub gene interaction network was constructed, which has 503 nodes (hub genes:309; miRNAs: 194) and 7686 edges. Results from the current investigation demonstrated that one target gene might be regulated by several TFs, (Fig. 6) and are listed in Table 5. Up regulated hub genes including, ERRFI1 targeted by 43 TFs (ex; CREM); ELAVL2 targeted by 41 TFs (ex; BMI1); KIT targeted by 34 TFs (ex; DMRT1); ZBTB8A targeted by 29 TFs (ex; GATA4); PPARGC1A targeted by 27 TFs (ex; FOXA2). Similarly, down regulated hub genes including, SMAD3 targeted by 58 TFs (ex; EP300); CDKN1A targeted by 57 TFs (ex; STAT3); ACTB targeted by 56 TFs (ex; HOXC9); PKM targeted by 55 TFs (ex; FOXP1); MYH9 targeted by 53 TFs (ex; ATF3).

Fig. 6.
Fig. 6.

Target gene - TF regulatory network between target genes. The purple color triangle nodes represent the key TFs; up regulated genes are marked in green; down regulated genes are marked in red.

Receiver operating characteristic curve (ROC) analysis

Based on the data of GSE154377, the patients were divided into the normal pregnancy group and GDM group. The ROC curves were analyzed by pROC package in R statistical software to evaluate the diagnostic accuracy of hub genes for GDM. As shown in Fig. 7, TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB achieved an AUC value of >0.90, demonstrating that these genes have high sensitivity and specificity for GDM diagnosis. Therefore, the ten hub genes identified, TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB might be considered as new biomarkers for GDM and require further investigation to validate these preliminary findings.

Fig. 7.
Fig. 7.

ROC curve analyses of hub genes. A) TRIM54 B) ELAVL2 C) PTN D) KIT E) BMPR1B F) APP G) SRC H) ITGA4 I) RPA1 J) ACTB

Discussion

A number of pre-clinical and clinical investigations have been conducted to reveal the underlying mechanisms of GDM in the past decades; however, the incidence of GDM remains high. This is primarily due to the majority of the investigation focusing genetic risk factor [38]. In the current investigation, expression profiling by high throughput sequencing dataset was analyzed and 953 DEGs (478 up and 475 down regulated genes) were identified. Existing evidences have reported that HNF4G [39], SLC15A1 [40], CXCL5 [41] and MALAT1 [42] promotes obesity, but these genes might be novel target for GDM. NPHP3-AS1 [43], LTA (lymphotoxin alpha) [44], CXCL5 [45] and MALAT1 [46] are found to be expressed in cardiovascular diseases, but these genes might be novel targets for GDM. A recent study demonstrated that XIST (X inactive specific transcript) [47] and MALAT1 [48] serves a crucial role in GDM progression. LTA (lymphotoxin alpha) [49], CXCL5 [50] and MALAT1 [51] are associated with prognosis of diabetes mellitus, but these genes might be novel targets for GDM. LTA (lymphotoxin alpha) [52], CXCL5 [53], MALAT1 [54] and TMEM70 [55] have been demonstrated to function in hypertension, but these genes might be novel targets for GDM. CXCL5 has been shown to be activated abortion [56].

GO and REACTOME pathway enrichment analyses were used to investigate the interactions of DEGs. Pathways include hemostasis [57], neutrophil degranulation [58], immune system [59] and cytokine signaling in immune system [60] are responsible for progression of GDM. LGR5 [61], GREM1 [62], GLRA3 [63], NEUROD4 [64], CYP2J2 [65], KCNH6 [66], LBP (lipopolysaccharide binding protein) [67], CXCL14 [68], RGN (regucalcin) [69], NPY2R [70], SERPINB13 [71], WNT5A [72], EDA (ectodysplasin A) [73], HSD11B2 [74], ACVR1C [75], NEUROD1 [76], SLIT2 [77], PPARGC1A [78], IGF1 [79], OSR1 [80], CYP46A1 [81], TLR3 [82], BMP7 [83], SELP (selectin P) [84], HLA-A [85], NOTCH2 [86], LRP1 [87], CLU (clusterin) [88], FCN1 [89], CDKN1A [90], SMAD3 [91], HLA-E [92], PTPRC (protein tyrosine phosphatase receptor type C) [93], MYH9 [94], JAK3 [95], IL6R [96], TIMP1 [97], DOCK8 [98], TNFRSF1B [99], ITGAL (integrin subunit alpha L) [100], CD47 [101], RARA (retinoic acid receptor alpha) [102], DGKD (diacylglycerol kinase delta) [103], PLEK (pleckstrin) [104], PREX1 [105], BSCL2 [106], PANX1 [107], IRF7 [108], NOTCH1 [109], STIM1 [110], TRIM13 [111], LRBA (LPS responsive beige-like anchor protein) [112], CXCR4 [113], MDM4 [114], MYO9B [115] and PDE5A [116] were revealed to be expressed in diabetes mellitus, but these genes might be novel targets for GDM. SIX1 [117], GREM1 [118], GHRHR (growth hormone releasing hormone receptor) [119], GPR37L1 [120], CYP2J2 [121], AQP4 [122], ROS1 [123], LBP (lipopolysaccharide binding protein) [124], SGCD (sarcoglycan delta) [125], CXCL14 [126], RGN (regucalcin) [127], F9 [128], KCND2 [129], AZGP1 [130], HAS2 [131], CNTN4 [132], WNT5A [133], PTHLH [134], FAP (fibroblast activation protein alpha) [135], THSD7A [136], SHROOM3 [137], ETV1 [138], CYP24A1 [139], SLIT2 [140], GJC1 [141], PPARGC1A [142], TRPM3 [143], IGF1 [144], TRPV6 [145], TLR3 [146], BMP7 [147], DSG2 [148], POSTN (periostin) [149], ENOX1 [150], KCNA7 [151], ITGB3 [152], THBS1 [153], TRPC6 [154], SELP (selectin P) [155], VCL (vinculin) [156], P2RY1 [157], NOTCH2 [158], F2RL3 [159], PSAP (prosaposin) [160], LRP1 [161], CLU (clusterin) [162], FCN1 [163], CDKN1A [164], SMAD3 [165], CAPN1 [166], BAK1 [167], FERMT3 [168], HLA-E [169], ADA2 [170], PTPRC (protein tyrosine phosphatase receptor type C) [171], PEAR1 [172], TGFB1 [173], JAK3 [174], ALOX12 [175], ITGB2 [176], SELPLG (selectin P ligand) [177], TNFSF4 [178], BCL11B [179], IL17RA [180], CST3 [181], BCL3 [182], TNFRSF1B [183], SERPINA1 [184], CD47 [185], CD74 [186], STXBP2 [187], LEF1 [188], F13A1 [189], BSCL2 [190], VCAN (versican) [191], SUN2 [192], PPM1L [193], IRF7 [194], NOTCH1 [195], EHD3 [196], PCSK6 [197], STIM1 [198], CXCR4 [199], ATP2A2 [200], CALM3 [201], CSF1R [202], CLPP (caseinolytic mitochondrial matrix peptidase proteolytic subunit) [203], PIK3IP1 [204], CDK9 [205], USP22 [206], MDM4 [207], TUBB1 [208], PDE5A [209] and GNB5 [210] plays important roles in cardiovascular diseases progression, but these genes might be novel targets for GDM. Polonikov et al [211], Chida et al [212], Yamada et al [213], Zhang et al [214], Kawarazaki et al [215], Ueda et al [216], Bao et al [217], Leng et al [218], Taghvaei et al [219], Norling et al [220], Brown et al [221], Singh et al [222], Liu et al [223], Nie et al [224], Liu et al [225], Sharma et al [226], Liu et al [227], Jain et al [228], Novoyatleva et al [229], Yu et al [230], Sahoo et al [231], Gamboa et al [232], Zeng et al [233], Zabini et al [234], Olivi et al [235], Liu et al [236], Nabrdalik et al [237], Katayose et al [238], Topchieva et al [239], Onal et al [240], Speirs et al [241], Novelli et al [242], Le Hiress et al [243], Chang et al [244], Deng et al [245], Wang et al [246], Dhande et al [247], Yu et al [248] and Li et al [249] showed that CYP2J2, BMPR1B, ROS1, SCN7A, WNT5A, HSD11B2, CYP24A1, WNK3, PPARGC1A, IGF1, OSR1, TLR3, BMP7, POSTN (periostin), ITGB3, THBS1, FLNA (filamin A), TRPC6, SELP (selectin P), VCL (vinculin), NOTCH2, F2RL3, LRP1, CLU (clusterin), SMAD3, PEAR1, MYH9, TGFB1, PDGFA (platelet derived growth factor subunit A), IL6R, TIMP1, TNFRSF1B, CD47, CD74, VCAN (versican), IRF7, NOTCH1, STIM1, CXCR4 and PDE5A expression can be associated with hypertension progression, but these genes might be novel targets for GDM. ROS1 [250], WNT2 [251], PHLDA2 [252], WNT5A [253], HSD11B2 [254], CLDN1 [255], CYP24A1 [256], HRG (histidine rich glycoprotein) [257], TLR3 [258], THBS1 [259], TRPC6 [260], SELP (selectin P) [261], CLU (clusterin) [262], CDKN1A [263], ITGB1 [264], HLA-E [265], IL6R [266], TIMP1 [267], SERPINA1 [268], CD74 [269], PANX1 [270], STIM1 [271] and CXCR4 [272] were revealed to be correlated with disease outcome in patients with preeclampsia. Previously reported studies have shown that the expression of LBP (lipopolysaccharide binding protein) [273], MC1R [274], CXCL14 [275], RGN (regucalcin) [276], NPY2R [277], MFAP5 [278], WNT5A [279], EDA (ectodysplasin A) [280], THSD7A [281], NEUROD1 [282], SLIT2 [283], PPARGC1A [219], IGF1 [144], OSR1 [284], TLR3 [285], BMP7 [286], POSTN (periostin) [287], LRP1B [288], THBS1 [289], NOTCH2 [290]. LRP1 [291], CLU (clusterin) [292], SMAD3 [91], TGFB1 [293], APP (amyloid beta precursor protein) [294], ITGB2 [295], IL6R [296], TIMP1 [297], CD47 [298], CD74 [299], RARA (retinoic acid receptor alpha) [300], DOCK2 [301], F13A1 [302], IRF7 [303], STIM1 [304], CXCR4 [305], MGLL (monoglyceride lipase) [306], M6PR [307], USP22 [308] and CASP2 [309] were mainly involved in progression of obesity, but these genes might be novel targets for GDM. Recent studies have shown that GLP1R [310], NEUROD1 [311], PPARGC1A [312], IGF1 [313], LRP1B [314], NOTCH2 [315], BAK1 [316], TLR2 [317], IL6R [318], TIMP1 [319], PIK3CD [320], PRKCA (protein kinase C alpha) [321], CXCR4 [322], RAB8A [323] and M6PR [324] are associated with GDM. Abnormal expression PDE5A was linked with abortion [325].

Hub genes in the PPI network and modules are linked to the progression of GDM. TRIM54, ELAVL2, PTN (pleiotrophin), KIT (KIT proto-oncogene, receptor tyrosine kinase), SRC (SRC proto-oncogene, non-receptor tyrosine kinase), ITGA4, RPA1, ACTB (actin beta) and IGSF1 were might be the novel biomarkers for GDM.

At the same time, hub genes, miRNAs and TFs imlpicated with GDM, all of which were located in the core nodes in MiRNA-hub gene regulatory network and TF-hub gene regulatory network, meaning that these hub genes, miRNAs and TFs could be critical therapeutic targets to protect against GDM. hsa-mir-766-3p [326], hsa-mir-10b-5p [327], hsa-mir-657 [328], GATA4 [329], FOXA2 [330], STAT3 [331] and ATF3 [332] plays an important role in the progression of diabetes mellitus, but these miRNAs might be novel targets for GDM. hsa-mir-582-5p is closely related to obesity [333], but these miRNAs might be novel targets for GDM. Wang et al. [334] found that the altered expression of hsa-mir-657 in GDM. Improta Caria et al [335], Li et al. [336], Park et al. [337], Cheng et al. [338] and Davis, [339] demonstrated that hsa-mir-657, BMI1, GATA4, STAT3 and ATF3 played a pivotal role in the hypertension, but these miRNAs might be novel targets for GDM. Improta Caria et al [335], Patankar et al. [340], Bochkis et al. [341], Nishimura et al. [342], Su et al. [343] and Kim et al. [344] suggested that hsa-mir-657, GATA4, FOXA2, EP300, STAT3 and ATF3 could induce obesity, but these genes might be novel targets for GDM. Accumulating evidence has demonstrated that hsa-mir-657 [345], hsa-mir-1231 [346], CREM (cAMP responsive element modulator) [347], BMI1 [348], GATA4 [349], EP300 [350], STAT3 [351], FOXP1 [352] and ATF3 [353] appears to be constitutively activated in cardiovascular diseases, but these genes might be novel targets for GDM. Abnormal expression of FOXP1 was responsible for loss of pregnancy [354]. ERRFI1, CCNE1, ANTXR1, ZBTB8A, SRRM2, PKM (pyruvate kinase M1/2), hsa-mir-198, hsa-mir-181b-5p, hsa-mir-624-5p, hsa-mir-509-3-5p, hsa-mir-192-3p, DMRT1, HOXC9, were might be the novel biomarkers for GDM.

Conclusions

The findings from this bioinformatics analysis investigations identified signaling pathwayes and the essential hub genes that might contribute to GDM. These findings improve our understanding of the molecular pathogenesis of GDM. Our investigation has key clinical importance for the early diagnosis and treatment, as well as the prevention, of GDM and provides effective targets for the treatment of GDM. However, further validation experiments are required to confirm the function of the identified hub genes linked with GDM.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

No informed consent because this study does not contain human or animals participants.

Availability of data and materials

The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE154377) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE154377)]

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author Contributions

B. V. - Writing original draft, and review and editing

C. V. - Software and investigation

Acknowledgement

I thank Giorgia Del Vecchio, University of California Los Angeles, Los Angeles, USA, very much, the author who deposited their profiling by high throughput sequencing dataset GSE154377, into the public GEO database.

References

  1. 1.↵
    Chiefari E, Arcidiacono B, Foti D, Brunetti A. Gestational diabetes mellitus: an updated overview. J Endocrinol Invest. 2017;40(9):899–909. doi:10.1007/s40618-016-0607-5
    OpenUrlCrossRefPubMed
  2. 2.↵
    Lende M, Rijhsinghani A. Gestational Diabetes: Overview with Emphasis on Medical Management. Int J Environ Res Public Health. 2020;17(24):9573.. doi:10.3390/ijerph17249573
    OpenUrlCrossRef
  3. 3.↵
    Brown J, Ceysens G, Boulvain M. Exercise for pregnant women with gestational diabetes for improving maternal and fetal outcomes. Cochrane Database Syst Rev. 2017;6(6):CD012202. doi:10.1002/14651858.CD012202.pub2
  4. 4.↵
    Moon JH, Kwak SH, Jang HC. Prevention of type 2 diabetes mellitus in women with previous gestational diabetes mellitus. Korean J Intern Med. 2017;32(1):26–41. doi:10.3904/kjim.2016.203
    OpenUrlCrossRef
  5. 5.↵
    Callaway LK, McIntyre HD, Barrett HL, Foxcroft K, Tremellen A, Lingwood BE, Tobin JM, Wilkinson S, Kothari A, Morrison M, et al. Probiotics for the Prevention of Gestational Diabetes Mellitus in Overweight and Obese Women: Findings From the SPRING Double-Blind Randomized Controlled Trial. Diabetes Care. 2019;42(3):364–371. doi:10.2337/dc18-2248
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    Wang JW, Cao SS, Hu RY, Wang M. Association between cigarette smoking during pregnancy and gestational diabetes mellitus: a meta-analysis. J Matern Fetal Neonatal Med. 2020;33(5):758–767. doi:10.1080/14767058.2018.1500547
    OpenUrlCrossRef
  7. 7.↵
    Dalfrà MG, Burlina S, Del Vescovo GG, Lapolla A. Genetics and Epigenetics: New Insight on Gestational Diabetes Mellitus. Front Endocrinol (Lausanne). 2020;11:602477. doi:10.3389/fendo.2020.602477
    OpenUrlCrossRef
  8. 8.↵
    Daly B, Toulis KA, Thomas N, Gokhale K, Martin J, Webber J, Keerthy D, Jolly K, Saravanan P, Nirantharakumar K. Increased risk of ischemic heart disease, hypertension, and type 2 diabetes in women with previous gestational diabetes mellitus, a target group in general practice for preventive interventions: A population-based cohort study. PLoS Med. 2018;15(1):e1002488. doi:10.1371/journal.pmed.1002488
    OpenUrlCrossRefPubMed
  9. 9.↵
    Lorenzo-Almorós A, Hang T, Peiró C, Soriano-Guillén L, Egido J, Tuñón J, Lorenzo Ó. Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases. Cardiovasc Diabetol. 2019;18(1):140. doi:10.1186/s12933-019-0935-9
    OpenUrlCrossRef
  10. 10.↵
    Wang Q, Lu X, Li C, Zhang W, Lv Y, Wang L, Wu L, Meng L, Fan Y, Ding H, et al. Down-regulated long non-coding RNA PVT1 contributes to gestational diabetes mellitus and preeclampsia via regulation of human trophoblast cells. Biomed Pharmacother. 2019;120:109501. doi:10.1016/j.biopha.2019.109501
    OpenUrlCrossRef
  11. 11.↵
    Wei Q, Chen X, Chen H. Association of Single Nucleotide Polymorphisms of the IL-6, IL-10, and TNF-α Genes with Susceptibility to Gestational Diabetes Mellitus. Genet Test Mol Biomarkers. 2020;24(7):390–398. doi:10.1089/gtmb.2020.0069
    OpenUrlCrossRef
  12. 12.↵
    Zhang X, Shi C, Wei L, Sun F, Ji L. The Association Between the rs2975760 and rs3792267 Single Nucleotide Polymorphisms of Calpain 10 (CAPN10) and Gestational Diabetes Mellitus. Med Sci Monit. 2019;25:5137–5142. doi:10.12659/MSM.914930
    OpenUrlCrossRef
  13. 13.↵
    Li X, Yang W, Fang Y. Epigenetic Profiles Reveal That ADCYAP1 Serves as Key Molecule in Gestational Diabetes Mellitus. Comput Math Methods Med. 2019;2019:6936175. doi:10.1155/2019/6936175
    OpenUrlCrossRef
  14. 14.↵
    Peng HY, Li HP, Li MQ. Downregulated ABHD5 Aggravates Insulin Resistance of Trophoblast Cells During Gestational Diabetes Mellitus. Reprod Sci. 2020;27(1):233–245. doi:10.1007/s43032-019-00010-x
    OpenUrlCrossRef
  15. 15.↵
    Chen SH, Liu XN, Peng Y. MicroRNA-351 eases insulin resistance and liver gluconeogenesis via the PI3K/AKT pathway by inhibiting FLOT2 in mice of gestational diabetes mellitus. J Cell Mol Med. 2019;23(9):5895–5906. doi:10.1111/jcmm.14079
    OpenUrlCrossRef
  16. 16.↵
    Zhang Z, Zhao H, Wang A. Oleuropein alleviates gestational diabetes mellitus by activating AMPK signaling. Endocr Connect. 2021;10(1):45–53. doi:10.1530/EC-20-0466
    OpenUrlCrossRef
  17. 17.↵
    Shi A, Wen J, Liu G, Liu H, Fu Z, Zhou J, Zhu Y, Liu Y, Guo X, Xu J. Genetic variants in vitamin D signaling pathways and risk of gestational diabetes mellitus. Oncotarget. 2016;7(42):67788–67795. doi:10.18632/oncotarget.11984
    OpenUrlCrossRef
  18. 18.↵
    Liu T, Zheng W, Wang L, Wang L, Zhang Y. TLR4/NF-κ Signaling Pathway Participates in the Protective Effects of Apocynin on Gestational Diabetes Mellitus Induced Placental Oxidative Stress and Inflammation. Reprod Sci. 2020;27(2):722–730. doi:10.1007/s43032-019-00078-5
    OpenUrlCrossRef
  19. 19.↵
    Dai S, Meng X, Cai X, Yuan C, Zhao Z, Zhong L, Shi Y, Yin F. Therapeutic effect of ursolic acid on fetal development in pregnant rats with gestational diabetes mellitus via AGEs-RAGE signaling pathway. J Food Biochem. 2021;45(4):e13651. doi:10.1111/jfbc.13651
    OpenUrlCrossRef
  20. 20.↵
    Stenson PD, Mort M, Ball EV, Evans K, Hayden M, Heywood S, Hussain M, Phillips AD, Cooper DN. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genet. 2017;136(6):665–677. doi:10.1007/s00439-017-1779-6
    OpenUrlCrossRefPubMed
  21. 21.↵
    Clough E, Barrett T. The Gene Expression Omnibus Database. Methods Mol Biol. 2016;1418:93–110. doi:10.1007/978-1-4939-3578-9_5
    OpenUrlCrossRefPubMed
  22. 22.↵
    Yan L, Feng J, Cheng F, Cui X, Gao L, Chen Y, Wang F, Zhong T, Li Y, Liu L. Circular RNA expression profiles in placental villi from women with gestational diabetes mellitus. Biochem Biophys Res Commun. 2018;498(4):743–750. doi:10.1016/j.bbrc.2018.03.051
    OpenUrlCrossRef
  23. 23.↵
    Del Vecchio G, Li Q, Li W, Thamotharan S, Tosevska A, Morselli M, Sung K, Janzen C, Zhou X, Pellegrini M, et al. Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes. Epigenetics. 2021;16(6):642–661. doi:10.1080/15592294.2020.1816774
    OpenUrlCrossRef
  24. 24.↵
    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi:10.1186/s13059-014-0550-8
    OpenUrlCrossRefPubMed
  25. 25.↵
    Thomas PD. The Gene Ontology and the Meaning of Biological Function. Methods Mol Biol. 2017;1446:15_ doi:10.1007/978-1-4939-3743-1_2
    OpenUrlCrossRef
  26. 26.↵
    Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649–D655. doi:10.1093/nar/gkx1132
    OpenUrlCrossRefPubMed
  27. 27.↵
    Reimand J, Kull M, Peterson H, Hansen J, Vilo J. g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007;35(Web Server issue):W193–W200. doi:10.1093/nar/gkm226
    OpenUrlCrossRefPubMedWeb of Science
  28. 28.↵
    Pastrello C, Kotlyar M, Jurisica I. Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID). Methods Mol Biol. 2020;2074:125–134. doi:10.1007/978-1-4939-9873-9_10
    OpenUrlCrossRef
  29. 29.↵
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13(11):2498–2504. doi:10.1101/gr.1239303
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    Przulj N, Wigle DA, Jurisica I. Functional topology in a network of protein interactions. Bioinformatics. 2004;20(3):340–348. doi:10.1093/bioinformatics/btg415
    OpenUrlCrossRefPubMedWeb of Science
  31. 31.↵
    Nguyen TP, Liu WC, Jordán F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179. doi:10.1186/1752-0509-5-179
    OpenUrlCrossRefPubMed
  32. 32.↵
    Shi Z, Zhang B. Fast network centrality analysis using GPUs. BMC Bioinformatics. 2011;12:149. doi:10.1186/1471-2105-12-149
    OpenUrlCrossRefPubMed
  33. 33.↵
    Fadhal E, Gamieldien J, Mwambene EC. Protein interaction networks as metric spaces: a novel perspective on distribution of hubs. BMC Syst Biol. 2014;8:6. doi:10.1186/1752-0509-8-6
    OpenUrlCrossRef
  34. 34.↵
    Zaki N, Efimov D, Berengueres J. Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics. 2013;14:163. doi:10.1186/1471-2105-14
    OpenUrlCrossRef
  35. 35.↵
    Fan Y, Xia J (2018) miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol Biol 1819:215–233. doi:10.1007/978-1-4939-8618-7_10
    OpenUrlCrossRef
  36. 36.↵
    Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47:W234–W241. doi:10.1093/nar/gkz240
    OpenUrlCrossRefPubMed
  37. 37.↵
    Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77. doi:10.1186/1471-2105-12-77
    OpenUrlCrossRefPubMed
  38. 38.↵
    Rosik J, Szostak B, Machaj F, Pawlik A. The role of genetics and epigenetics in the pathogenesis of gestational diabetes mellitus. Ann Hum Genet. 2020;84(2):114–124. doi:10.1111/ahg.12356
    OpenUrlCrossRef
  39. 39.↵
    Ayari S, Gil-Iturbe E, le Gléau L, Osinski C, Kapel N, Soula HA, Leturque A, Andreelli F, Clément K, Serradas P, et al. HNF4g invalidation prevents diet-induced obesity via intestinal lipid malabsorption. J Endocrinol. 2021;JOE-21-0092.R1. doi:10.1530/JOE-21-0092
    OpenUrlCrossRef
  40. 40.↵
    Wang CY, Liu S, Xie XN, Luo ZY, Yang L, Tan ZR. Association between polymorphisms in SLC15A1 and PLA2G16 genes and development of obesity in Chinese subjects. Diabetes Metab Syndr Obes. 2018;11:439–446. doi:10.2147/DMSO.S161808
    OpenUrlCrossRef
  41. 41.↵
    Sepuru KM, Poluri KM, Rajarathnam K. Solution structure of CXCL5--a novel chemokine and adipokine implicated in inflammation and obesity. PLoS One. 2014;9(4):e93228. doi:10.1371/journal.pone.0093228
    OpenUrlCrossRefPubMed
  42. 42.↵
    Ebrahimi R, Toolabi K, Jannat Ali Pour N, Mohassel Azadi S, Bahiraee A, Zamani-Garmsiri F, Emamgholipour S. Adipose tissue gene expression of long non-coding RNAs; MALAT1, TUG1 in obesity: is it associated with metabolic profile and lipid homeostasis-related genes expression?. Diabetol Metab Syndr. 2020;12:36. doi:10.1186/s13098-020-00544-0
    OpenUrlCrossRef
  43. 43.↵
    Zhao Z, Liu G, Zhang H, Ruan P, Ge J, Liu Q. BIRC5, GAJ5, and lncRNA NPHP3-AS1 Are Correlated with the Development of Atrial Fibrillation-Valvular Heart Disease. Int Heart J. 2021;62(1):153–161. doi:10.1536/ihj.20-238
    OpenUrlCrossRef
  44. 44.↵
    Palikhe A, Lokki ML, Pussinen PJ, Paju S, Ahlberg J, Asikainen S, Seppänen M, Valtonen V, Nieminen MS, Sinisalo J. Lymphotoxin alpha LTA+496C allele is a risk factor for periodontitis in patients with coronary artery disease. Tissue Antigens. 2008;71(6):530–537. doi:10.1111/j.1399-0039.2008.01038.x
    OpenUrlCrossRefPubMed
  45. 45.↵
    Ravi S, Schuck RN, Hilliard E, Lee CR, Dai X, Lenhart K, Willis MS, Jensen BC, Stouffer GA, Patterson C, et al. Clinical Evidence Supports a Protective Role for CXCL5 in Coronary Artery Disease. Am J Pathol. 2017;187(12):2895–2911. doi:10.1016/j.ajpath.2017.08.006
    OpenUrlCrossRef
  46. 46.↵
    Yan Y, Song D, Song X, Song C. The role of lncRNA MALAT1 in cardiovascular disease. IUBMB Life. 2020;72(3):334–342. doi:10.1002/iub.2210
    OpenUrlCrossRef
  47. 47.↵
    Li Y, Yuan X, Shi Z, Wang H, Ren D, Zhang Y, Fan Y, Liu Y, Cui Z. LncRNA XIST serves as a diagnostic biomarker in gestational diabetes mellitus and its regulatory effect on trophoblast cell via miR-497-5p/FOXO1 axis. Cardiovasc Diagn Ther. 2021;11(3):716–725. doi:10.21037/cdt-21-110
    OpenUrlCrossRef
  48. 48.↵
    Zhang Y, Qu L, Ni H, Wang Y, Li L, Yang X, Wang X, Hou Y. Expression and function of lncRNA MALAT1 in gestational diabetes mellitus. Adv Clin Exp Med. 2020;29(8):903–910. doi:10.17219/acem/121524
    OpenUrlCrossRef
  49. 49.↵
    Kaidonis G, Craig JE, Gillies MC, Abhary S, Essex RW, Chang JH, Pal B, Pefkianaki M, Daniell M, Lake S, et al. Promoter polymorphism at the tumour necrosis factor/lymphotoxin-alpha locus is associated with type of diabetes but not with susceptibility to sight-threatening diabetic retinopathy. Diab Vasc Dis Res. 2016;13(2):164–167. doi:10.1177/1479164115616902
    OpenUrlCrossRefPubMed
  50. 50.↵
    Hasani Ranjbar S, Amiri P, Zineh I, Langaee TY, Namakchian M, Heshmet R, Sajadi M, Mirzaee M, Rezazadeh E, Balaei P, et al. CXCL5 gene polymorphism association with diabetes mellitus. Mol Diagn Ther. 2008;12(6):391–394. doi:10.1007/BF03256304
    OpenUrlCrossRefPubMed
  51. 51.↵
    Alfaifi M, Ali Beg MM, Alshahrani MY, Ahmad I, Alkhathami AG, Joshi PC, Alshehri OM, Alamri AM, Verma AK. Circulating long non-coding RNAs NKILA, NEAT1, MALAT1, and MIAT expression and their association in type 2 diabetes mellitus. BMJ Open Diabetes Res Care. 2021;9(1):e001821. doi:10.1136/bmjdrc-2020-001821
    OpenUrlAbstract/FREE Full Text
  52. 52.↵
    Nakayama T, Soma M, Sato N, Haketa A, Kosuge K, Aoi N, Sato M, Izumi Y, Matsumoto K, Kanmatsuse K, et al. An association study in essential hypertension using functional polymorphisms in lymphotoxin-alpha gene. Am J Hypertens. 2004;17(11 Pt 1):1045–1049. doi:10.1016/j.amjhyper.2004.07.010
    OpenUrlCrossRefPubMed
  53. 53.↵
    Beitelshees AL, Aquilante CL, Allayee H, Langaee TY, Welder GJ, Schofield RS, Zineh I. CXCL5 polymorphisms are associated with variable blood pressure in cardiovascular disease-free adults. Hum Genomics. 2012;6(1):9. doi:10.1186/1479-7364-6-9
    OpenUrlCrossRef
  54. 54.↵
    Ou M, Zhao H, Ji G, Zhao X, Zhang Q. Long noncoding RNA MALAT1 contributes to pregnancy-induced hypertension development by enhancing oxidative stress and inflammation through the regulation of the miR-150-5p/ET-1 axis. FASEB J. 2020;34(5):6070–6085. doi:10.1096/fj.201902280R
    OpenUrlCrossRef
  55. 55.↵
    Catteruccia M, Verrigni D, Martinelli D, Torraco A, Agovino T, Bonafé L, D’Amico A, Donati MA, Adorisio R, Santorelli FM, et al. Persistent pulmonary arterial hypertension in the newborn (PPHN): a frequent manifestation of TMEM70 defective patients. Mol Genet Metab. 2014;111(3):353–359. doi:10.1016/j.ymgme.2014.01.001
    OpenUrlCrossRefPubMed
  56. 56.↵
    Zhang S, Ding J, Wang J, Yin T, Zhang Y, Yang J. CXCL5 Downregulation in Villous Tissue Is Correlated With Recurrent Spontaneous Abortion. Front Immunol. 2021;12:717483. doi:10.3389/fimmu.2021.717483
    OpenUrlCrossRef
  57. 57.↵
    Abdel Gader AG, Khashoggi TY, Habib F, Awadallah SB. Haemostatic and cytokine changes in gestational diabetes mellitus. Gynecol Endocrinol. 2011;27(5):356–360. doi:10.3109/09513590.2010.495241
    OpenUrlCrossRefPubMed
  58. 58.↵
    Shen D, Lu Y, Li G, Hu M, Li S, Ju H, Zhang M, Wang X. Mechanism of neutrophil extracellular traps generation and their role in trophoblast apoptosis in gestational diabetes mellitus. Cell Signal. 2021;110168. doi:10.1016/j.cellsig.2021.110168
    OpenUrlCrossRef
  59. 59.↵
    Lin HC, Su CT, Wang PC. An application of artificial immune recognition system for prediction of diabetes following gestational diabetes. J Med Syst. 2011;35(3):283–289. doi:10.1007/s10916-009-9364-8
    OpenUrlCrossRefPubMed
  60. 60.↵
    Kuzmicki M, Telejko B, Wawrusiewicz-Kurylonek N, Citko A, Lipinska D, Pliszka J, Wilk J, Kalejta K, Lemancewicz A, Grabiec M, et al. The expression of suppressor of cytokine signaling 1 and 3 in fat and placental tissue from women with gestational diabetes. Gynecol Endocrinol. 2012;28(11):841–844. doi:10.3109/09513590.2012.683055
    OpenUrlCrossRefPubMed
  61. 61.↵
    Alharbi KK, Ali Khan I, Syed R, Alharbi FK, Mohammed AK, Vinodson B, Al-Daghri NM. Association of JAZF1 and TSPAN8/LGR5 variants in relation to type 2 diabetes mellitus in a Saudi population. Diabetol Metab Syndr. 2015;7:92. doi:10.1186/s13098-015-0091-7
    OpenUrlCrossRef
  62. 62.↵
    Thameem F, Puppala S, He X, Arar NH, Stern MP, Blangero J, Duggirala R, Abboud HE. Evaluation of gremlin 1 (GREM1) as a candidate susceptibility gene for albuminuria-related traits in Mexican Americans with type 2 diabetes mellitus. Metabolism. 2009;58(10):1496–1502. doi:10.1016/j.metabol.2009.04.039
    OpenUrlCrossRefPubMed
  63. 63.↵
    Sandholm N, Haukka JK, Toppila I, Valo E, Harjutsalo V, Forsblom C, Groop PH. Confirmation of GLRA3 as a susceptibility locus for albuminuria in Finnish patients with type 1 diabetes. Sci Rep. 2018;8(1):12408. doi:10.1038/s41598-018-29211-1
    OpenUrlCrossRefPubMed
  64. 64.↵
    Horikawa Y, Horikawa Y, Cox NJ, Iwasaki N, Ogata M, Iwamoto Y, Schwitzgebel V, German MS, Bell GI. beta-cell transcription factors and diabetes: no evidence for diabetes-associated mutations in the gene encoding the basic helix-loop-helix transcription factor neurogenic differentiation 4 (NEUROD4) in Japanese patients with MODY. Diabetes. 2000;49(11):1955–1957. doi:10.2337/diabetes.49.11.1955
    OpenUrlAbstract
  65. 65.↵
    Wang CP, Hung WC, Yu TH, Chiu CA, Lu LF, Chung FM, Hung CH, Shin SJ, Chen HJ, Lee YJ. Genetic variation in the G-50T polymorphism of the cytochrome P450 epoxygenase CYP2J2 gene and the risk of younger onset type 2 diabetes among Chinese population: potential interaction with body mass index and family history. Exp Clin Endocrinol Diabetes. 2010;118(6):346–352. doi:10.1055/s-0029-1243604
    OpenUrlCrossRefPubMed
  66. 66.↵
    Yang JK, Lu J, Yuan SS, Asan, Cao X, Qiu HY, Shi TT, Yang FY, Li Q, Liu CP, et al. From Hyper- to Hypoinsulinemia and Diabetes: Effect of KCNH6 on Insulin Secretion. Cell Rep. 2018;25(13):3800–3810.e6. doi:10.1016/j.celrep.2018.12.005
    OpenUrlCrossRef
  67. 67.↵
    Sakura T, Morioka T, Shioi A, Kakutani Y, Miki Y, Yamazaki Y, Motoyama K, Mori K, Fukumoto S, Shoji T, et al. Lipopolysaccharide-binding protein is associated with arterial stiffness in patients with type 2 diabetes: a cross-sectional study. Cardiovasc Diabetol. 2017;16(1):62. doi:10.1186/s12933-017-0545-3
    OpenUrlCrossRefPubMed
  68. 68.↵
    Matsushita Y, Hasegawa Y, Takebe N, Onodera K, Shozushima M, Oda T, Nagasawa K, Honma H, Nata K, Sasaki A, et al. Serum C-X-C motif chemokine ligand 14 levels are associated with serum C-peptide and fatty liver index in type 2 diabetes mellitus patients. J Diabetes Investig. 2021;12(6):1042–1049. doi:10.1111/jdi.13438
    OpenUrlCrossRef
  69. 69.↵
    Yamaguchi M, Murata T. Involvement of regucalcin in lipid metabolism and diabetes. Metabolism. 2013;62(8):1045–1051. doi:10.1016/j.metabol.2013.01.023
    OpenUrlCrossRef
  70. 70.↵
    Campbell CD, Lyon HN, Nemesh J, Drake JA, Tuomi T, Gaudet D, Zhu X, Cooper RS, Ardlie KG, Groop LC, et al. Association studies of BMI and type 2 diabetes in the neuropeptide Y pathway: a possible role for NPY2R as a candidate gene for type 2 diabetes in men. Diabetes. 2007;56(5):1460–1467. doi:10.2337/db06-1051
    OpenUrlAbstract/FREE Full Text
  71. 71.↵
    Kryvalap Y, Jiang ML, Kryvalap N, Hendrickson C, Czyzyk J. SerpinB13 antibodies promote β cell development and resistance to type 1 diabetes. Sci Transl Med. 2021;13(588):eabf1587. doi:10.1126/scitranslmed.abf1587
    OpenUrlAbstract/FREE Full Text
  72. 72.↵
    Bretón-Romero R, Feng B, Holbrook M, Farb MG, Fetterman JL, Linder EA, Berk BD, Masaki N, Weisbrod RM, Inagaki E, et al. Endothelial Dysfunction in Human Diabetes Is Mediated by Wnt5a-JNK Signaling. Arterioscler Thromb Vasc Biol. 2016;36(3):561–569. doi:10.1161/ATVBAHA.115.306578
    OpenUrlAbstract/FREE Full Text
  73. 73.↵
    Bayliss J, Ooi GJ, De Nardo W, Shah YJH, Montgomery MK, McLean C, Kemp W, Roberts SK, Brown WA, Burton PR, et al. Ectodysplasin A Is Increased in Non-Alcoholic Fatty Liver Disease, But Is Not Associated With Type 2 Diabetes. Front Endocrinol (Lausanne). 2021;12:642432. doi:10.3389/fendo.2021.642432
    OpenUrlCrossRef
  74. 74.↵
    Mune T, Suwa T, Morita H, Isomura Y, Takada N, Yamamoto Y, Hayashi M, Yamakita N, Sasaki A, Takeda N, et al. Longer HSD11B2 CA-repeat in impaired glucose tolerance and type 2 diabetes. Endocr J. 2013;60(5):671–678. doi:10.1507/endocrj.ej12-0108
    OpenUrlCrossRefPubMed
  75. 75.↵
    Emdin CA, Khera AV, Aragam K, Haas M, Chaffin M, Klarin D, Natarajan P, Bick A, Zekavat SM, Nomura A, et al. DNA Sequence Variation in ACVR1C Encoding the Activin Receptor-Like Kinase 7 Influences Body Fat Distribution and Protects Against Type 2 Diabetes. Diabetes. 2019;68(1):226–234. doi:10.2337/db18-0857
    OpenUrlAbstract/FREE Full Text
  76. 76.↵
    Demirbilek H, Hatipoglu N, Gul U, Tatli ZU, Ellard S, Flanagan SE, De Franco E, Kurtoglu S. Permanent neonatal diabetes mellitus and neurological abnormalities due to a novel homozygous missense mutation in NEUROD1. Pediatr Diabetes. 2018;19(5):898–904. doi:10.1111/pedi.12669
    OpenUrlCrossRef
  77. 77.↵
    Kang YE, Choung S, Lee JH, Kim HJ, Ku BJ. The Role of Circulating Slit2, the One of the Newly Batokines, in Human Diabetes Mellitus. Endocrinol Metab (Seoul). 2017;32(3):383–388. doi:10.3803/EnM.2017.32.3.383
    OpenUrlCrossRef
  78. 78.↵
    Zhu L, Huang Q, Xie Z, Kang M, Ding H, Chen B, Chen Y, Liu C, Wang Y, Tang W. PPARGC1A rs3736265 G>A polymorphism is associated with decreased risk of type 2 diabetes mellitus and fasting plasma glucose level. Oncotarget. 2017;8(23):37308–37320. doi:10.18632/oncotarget.16307
    OpenUrlCrossRefPubMed
  79. 79.↵
    van Dijk PR, Logtenberg SJ, Groenier KH, Kleefstra N, Bilo HJ, Arnqvist HJ. Effect of i.p. insulin administration on IGF1 and IGFBP1 in type 1 diabetes. Endocr Connect. 2014;3(1):17–23. doi:10.1530/EC-13-0089
    OpenUrlAbstract/FREE Full Text
  80. 80.↵
    Guo Q, Zhang Y, Jiang GR, Zhang C. Decreased KLHL3 expression is involved in the activation of WNK-OSR1/SPAK-NCC cascade in type 1 diabetic mice. Pflugers Arch. 2021;473(2):185–196. doi:10.1007/s00424-020-02509-8
    OpenUrlCrossRef
  81. 81.↵
    Shi J, Jia J, Tian S, Zhang H, An K, Zhu W, Cao W, Yuan Y, Wang S. Increased Plasma Level of 24S-Hydroxycholesterol and Polymorphism of CYP46A1 SNP (rs754203) Are Associated With Mild Cognitive Impairment in Patients With Type 2 Diabetes. Front Aging Neurosci. 2021;13:619916. doi:10.3389/fnagi.2021.619916
    OpenUrlCrossRef
  82. 82.↵
    Zhou Z, Zeng C, Nie L, Huang S, Guo C, Xiao D, Han Y, Ye X, Ou M, Huang C, et al. The effects of TLR3, TRIF and TRAF3 SNPs and interactions with environmental factors on type 2 diabetes mellitus and vascular complications in a Han Chinese population. Gene. 2017;626:41–47. doi:10.1016/j.gene.2017.05.011
    OpenUrlCrossRef
  83. 83.↵
    Chattopadhyay T, Singh RR, Gupta S, Surolia A. Bone morphogenetic protein-7 (BMP-7) augments insulin sensitivity in mice with type II diabetes mellitus by potentiating PI3K/AKT pathway. Biofactors. 2017;43(2):195–209. doi:10.1002/biof.1334
    OpenUrlCrossRef
  84. 84.↵
    Kaur R, Singh J, Kapoor R, Kaur M. Association of SELP Polymorphisms with Soluble P-Selectin Levels and Vascular Risk in Patients with Type 2 Diabetes Mellitus: A Case-Control Study. Biochem Genet. 2019;57(1):73–97. doi:10.1007/s10528-018-9881-6
    OpenUrlCrossRefPubMed
  85. 85.↵
    Howson JM, Walker NM, Clayton D, Todd JA; Type 1 Diabetes Genetics Consortium. Confirmation of HLA class II independent type 1 diabetes associations in the major histocompatibility complex including HLA-B and HLA-A. Diabetes Obes Metab. 2009;11 Suppl 1(Suppl 1):31–45. doi:10.1111/j.1463-1326.2008.01001.x
    OpenUrlCrossRefPubMedWeb of Science
  86. 86.↵
    Al-Awaida WJ, Hameed WS, Al Hassany HJ, Al-Dabet MM, Al-Bawareed O, Hadi NR. Evaluation of the Genetic Association and Expressions of Notch-2 /Jagged-1 in Patients with Type 2 Diabetes Mellitus. Med Arch. 2021;75(2):101–108. doi:10.5455/medarh.2021.75.101-108
    OpenUrlCrossRef
  87. 87.↵
    El-Horany HE, Watany MM, Hagag RY, El-Attar SH, Basiouny MA. Expression of LRP1 and CHOP genes associated with peripheral neuropathy in type 2 diabetes mellitus: Correlations with nerve conduction studies. Gene. 2019;702:114–122. doi:10.1016/j.gene.2019.02.105
    OpenUrlCrossRef
  88. 88.↵
    Cai R, Han J, Sun J, Huang R, Tian S, Shen Y, Dong X, Xia W, Wang S. Plasma Clusterin and the CLU Gene rs11136000 Variant Are Associated with Mild Cognitive Impairment in Type 2 Diabetic Patients. Front Aging Neurosci. 2016;8:179. doi:10.3389/fnagi.2016.00179
    OpenUrlCrossRef
  89. 89.↵
    Anjosa ZP, Santos MM, Rodrigues NJ, Lacerda GA, Araujo J, Silva JA, Tavares NA, Guimarães RL, Crovella S, Brandão LA. Polymorphism in ficolin-1 (FCN1) gene is associated with an earlier onset of type 1 diabetes mellitus in children and adolescents from northeast Brazil. J Genet. 2016;95(4):1031–1034. doi:10.1007/s12041-016-0719-x
    OpenUrlCrossRef
  90. 90.↵
    Muhammad SA, Qousain Naqvi ST, Nguyen T, Wu X, Munir F, Jamshed MB, Zhang Q. Cisplatin’s potential for type 2 diabetes repositioning by inhibiting CDKN1A, FAS, and SESN1. Comput Biol Med. 2021;135:104640. doi:10.1016/j.compbiomed.2021.104640
    OpenUrlCrossRef
  91. 91.↵
    Yadav H, Quijano C, Kamaraju AK, Gavrilova O, Malek R, Chen W, Zerfas P, Zhigang D, Wright EC, Stuelten C, et al. Protection from obesity and diabetes by blockade of TGF-β /Smad3 signaling. Cell Metab. 2011;14(1):67–79. doi:10.1016/j.cmet.2011.04.013
    OpenUrlCrossRefPubMedWeb of Science
  92. 92.↵
    Jiang H, Canfield SM, Gallagher MP, Jiang HH, Jiang Y, Zheng Z, Chess L. HLA-E-restricted regulatory CD8(+) T cells are involved in development and control of human autoimmune type 1 diabetes. J Clin Invest. 2010;120(10):3641–3650. doi:10.1172/JCI43522
    OpenUrlCrossRefPubMedWeb of Science
  93. 93.↵
    Thude H, Rosenhahn S, Hunger-Dathe W, Müller UA, Barz D. A transmembrane protein-tyrosine phosphatase receptor type C (CD45) exon A point mutation (77 C to G) is not associated with the development of type 1 diabetes mellitus in a German population. Eur J Immunogenet. 2004;31(6):245–247. doi:10.1111/j.1365-2370.2004.00479.x
    OpenUrlCrossRefPubMed
  94. 94.↵
    Freedman BI, Langefeld CD, Lu L, Divers J, Comeau ME, Kopp JB, Winkler CA, Nelson GW, Johnson RC, Palmer ND, et al. Differential effects of MYH9 and APOL1 risk variants on FRMD3 Association with Diabetic ESRD in African Americans. PLoS Genet. 2011;7(6):e1002150. doi:10.1371/journal.pgen.1002150
    OpenUrlCrossRefPubMed
  95. 95.↵
    Cetkovic-Cvrlje M, Dragt AL, Vassilev A, Liu XP, Uckun FM. Targeting JAK3 with JANEX-1 for prevention of autoimmune type 1 diabetes in NOD mice. Clin Immunol. 2003;106(3):213–225. doi:10.1016/s1521-6616(02)00049-9
    OpenUrlCrossRefPubMedWeb of Science
  96. 96.↵
    Wu X, Yu T, Ji N, Huang Y, Gao L, Shi W, Yan Y, Li H, Ma L, Wu K, Wu Z. IL6R inhibits viability and apoptosis of pancreatic beta-cells in type 2 diabetes mellitus via regulation by miR-22 of the JAK/STAT signaling pathway. Diabetes Metab Syndr Obes. 2019;12:1645–1657. doi:10.2147/DMSO.S211700
    OpenUrlCrossRef
  97. 97.↵
    Papazafiropoulou A, Perrea D, Moyssakis I, Kokkinos A, Katsilambros N, Tentolouris N. Plasma levels of MMP-2, MMP-9 and TIMP-1 are not associated with arterial stiffness in subjects with type 2 diabetes mellitus. J Diabetes Complications. 2010;24(1):20–27. doi:10.1016/j.jdiacomp.2008.10.004
    OpenUrlCrossRefPubMed
  98. 98.↵
    Arndt T, Wedekind D, Jörns A, Tsiavaliaris G, Cuppen E, Hedrich HJ, Lenzen S. A novel Dock8 gene mutation confers diabetogenic susceptibility in the LEW.1AR1/Ztm-iddm rat, an animal model of human type 1 diabetes. Diabetologia. 2015;58(12):2800–2809. doi:10.1007/s00125-015-3757-7
    OpenUrlCrossRefPubMed
  99. 99.↵
    Tabassum R, Mahajan A, Chauhan G, Dwivedi OP, Dubey H, Sharma V, Kundu B, Ghosh S, Tandon N, Bharadwaj D. No association of TNFRSF1B variants with type 2 diabetes in Indians of Indo-European origin. BMC Med Genet. 2011;12:110. doi:10.1186/1471-2350-12-110
    OpenUrlCrossRefPubMed
  100. 100.↵
    Glawe JD, Patrick DR, Huang M, Sharp CD, Barlow SC, Kevil CG. Genetic deficiency of Itgb2 or ItgaL prevents autoimmune diabetes through distinctly different mechanisms in NOD/LtJ mice. Diabetes. 2009;58(6):1292–1301. doi:10.2337/db08-0804
    OpenUrlAbstract/FREE Full Text
  101. 101.↵
    Roberts DD, Isenberg JS. CD47 and thrombospondin-1 regulation of mitochondria, metabolism, and diabetes. Am J Physiol Cell Physiol. 2021;321(2):C201–C213. doi:10.1152/ajpcell.00175.2021
    OpenUrlCrossRef
  102. 102.↵
    Zhang Y, Liu Y, Liu Y, Zhang Y, Su Z. Genetic Variants of Retinoic Acid Receptor-Related Orphan Receptor Alpha Determine Susceptibility to Type 2 Diabetes Mellitus in Han Chinese. Genes (Basel). 2016;7(8):54. doi:10.3390/genes7080054
    OpenUrlCrossRef
  103. 103.↵
    Chibalin AV, Leng Y, Vieira E, Krook A, Björnholm M, Long YC, Kotova O, Zhong Z, Sakane F, Steiler T, et al. Downregulation of diacylglycerol kinase delta contributes to hyperglycemia-induced insulin resistance. Cell. 2008;132(3):375–386. doi:10.1016/j.cell.2007.12.035
    OpenUrlCrossRefPubMedWeb of Science
  104. 104.↵
    Ding Y, Kantarci A, Badwey JA, Hasturk H, Malabanan A, Van Dyke TE. Phosphorylation of pleckstrin increases proinflammatory cytokine secretion by mononuclear phagocytes in diabetes mellitus. J Immunol. 2007;179(1):647–654. doi:10.4049/jimmunol.179.1.647
    OpenUrlAbstract/FREE Full Text
  105. 105.↵
    Lewis JP, Palmer ND, Ellington JB, Divers J, Ng MC, Lu L, Langefeld CD, Freedman BI, Bowden DW. Analysis of candidate genes on chromosome 20q12-13.1 reveals evidence for BMI mediated association of PREX1 with type 2 diabetes in European Americans. Genomics. 2010;96(4):211–219. doi:10.1016/j.ygeno.2010.07.006
    OpenUrlCrossRefPubMed
  106. 106.↵
    Jin J, Cao L, Zhao Z, Shen S, Kiess W, Zhi D, Ye R, Cheng R, Chen L, Yang Y, et al. Novel BSCL2 gene mutation E189X in Chinese congenital generalized lipodystrophy child with early onset diabetes mellitus. Eur J Endocrinol. 2007;157(6):783–787. doi:10.1530/EJE-07-0393
    OpenUrlAbstract/FREE Full Text
  107. 107.↵
    Seref-Ferlengez Z, Maung S, Schaffler MB, Spray DC, Suadicani SO, Thi MM. P2X7R-Panx1 Complex Impairs Bone Mechanosignaling under High Glucose Levels Associated with Type-1 Diabetes. PLoS One. 2016;11(5):e0155107. doi:10.1371/journal.pone.0155107
    OpenUrlCrossRef
  108. 108.↵
    Wang Z, Zheng Y, Hou C, Yang L, Li X, Lin J, Huang G, Lu Q, Wang CY, Zhou Z. DNA methylation impairs TLR9 induced Foxp3 expression by attenuating IRF-7 binding activity in fulminant type 1 diabetes. J Autoimmun. 2013;41:50–59. doi:10.1016/j.jaut.2013.01.009
    OpenUrlCrossRefPubMed
  109. 109.↵
    Wang Y, Wang S, Zhang W, Liu J, Yang Z, Liu C. Notch1 participates in the activation of autophagy in the hippocampus of type I diabetic mice. Neurochem Int. 2021;150:105156. doi:10.1016/j.neuint.2021.105156
    OpenUrlCrossRef
  110. 110.↵
    Jardín I, López JJ, Zbidi H, Bartegi A, Salido GM, Rosado JA. Attenuated store-operated divalent cation entry and association between STIM1, Orai1, hTRPC1 and hTRPC6 in platelets from type 2 diabetic patients. Blood Cells Mol Dis. 2011;46(3):252–260. doi:10.1016/j.bcmd.2010.12.008
    OpenUrlCrossRefPubMedWeb of Science
  111. 111.↵
    Qian Y, Lei G, Wen L. Brain-specific deletion of TRIM13 promotes metabolic stress-triggered insulin resistance, glucose intolerance, and neuroinflammation. Biochem Biophys Res Commun. 2020;527(1):138–145. doi:10.1016/j.bbrc.2020.03.076
    OpenUrlCrossRef
  112. 112.↵
    Kardelen AD, Kara M, Güller D, Ozturan EK, Abalı ZY, Ceylaner S, Kıykım A, Cantez S, Torun SH, Poyrazoglu S, et al. LRBA deficiency: a rare cause of type 1 diabetes, colitis, and severe immunodeficiency. Hormones (Athens). 2021;20(2):389–394. doi:10.1007/s42000-020-00257-z
    OpenUrlCrossRef
  113. 113.↵
    Zhang H, Luan S, Xiao X, Lin L, Zhao X, Liu X. Silenced microRNA-222 suppresses inflammatory response in gestational diabetes mellitus mice by promoting CXCR4. Life Sci. 2021;266:118850. doi:10.1016/j.lfs.2020.118850
    OpenUrlCrossRef
  114. 114.↵
    Pellegrino M, Traversi G, Arena A, Cappa M, Rosado MM, Andreani M, Delfino DV, Moretti F, Fierabracci A. Effect of p53 activation through targeting MDM2/MDM4 heterodimer on T regulatory and effector cells in the peripheral blood of Type 1 diabetes patients. PLoS One. 2020;15(1):e0228296. doi:10.1371/journal.pone.0228296
    OpenUrlCrossRef
  115. 115.↵
    Santiago JL, Martínez A, Núñez C, de la Calle H, Fernández-Arquero M, de la Concha EG, Urcelay E. Association of MYO9B haplotype with type 1 diabetes. Hum Immunol. 2008;69(2):112–115. doi:10.1016/j.humimm.2008.01.003
    OpenUrlCrossRefPubMed
  116. 116.↵
    Mátyás C, Németh BT, Oláh A, Török M, Ruppert M, Kellermayer D, Barta BA, Szabó G, Kökény G, Horváth EM, et al. Prevention of the development of heart failure with preserved ejection fraction by the phosphodiesterase-5A inhibitor vardenafil in rats with type 2 diabetes. Eur J Heart Fail. 2017;19(3):326–336. doi:10.1002/ejhf.711
    OpenUrlCrossRef
  117. 117.↵
    Gao W, Guo N, Zhao S, Chen Z, Zhang W, Yan F, Liao H, Chi K. FBXW7 promotes pathological cardiac hypertrophy by targeting EZH2-SIX1 signaling. Exp Cell Res. 2020;393(1):112059. doi:10.1016/j.yexcr.2020.112059
    OpenUrlCrossRef
  118. 118.↵
    Müller II, Müller KA, Karathanos A, Schönleber H, Rath D, Vogel S, Chatterjee M, Schmid M, Haas M, Seizer P, et al. Impact of counterbalance between macrophage migration inhibitory factor and its inhibitor Gremlin-1 in patients with coronary artery disease. Atherosclerosis. 2014;237(2):426–432. doi:10.1016/j.atherosclerosis.2014.09.010
    OpenUrlCrossRef
  119. 119.↵
    Kanashiro-Takeuchi RM, Takeuchi LM, Rick FG, Dulce R, Treuer AV, Florea V, Rodrigues CO, Paulino EC, Hatzistergos KE, Selem SM, et al. Activation of growth hormone releasing hormone (GHRH) receptor stimulates cardiac reverse remodeling after myocardial infarction (MI). Proc Natl Acad Sci U S A. 2012;109(2):559–563. doi:10.1073/pnas.1119203109
    OpenUrlAbstract/FREE Full Text
  120. 120.↵
    Mouat MA, Coleman JLJ, Wu J, Dos Remedios CG, Feneley MP, Graham RM, Smith NJ. Involvement of GPR37L1 in murine blood pressure regulation and human cardiac disease pathophysiology. Am J Physiol Heart Circ Physiol. 2021;321(4):H807–H817. doi:10.1152/ajpheart.00198.2021
    OpenUrlCrossRef
  121. 121.↵
    Aliwarga T, Evangelista EA, Sotoodehnia N, Lemaitre RN, Totah RA. Regulation of CYP2J2 and EET Levels in Cardiac Disease and Diabetes. Int J Mol Sci. 2018;19(7):1916. doi:10.3390/ijms19071916
    OpenUrlCrossRef
  122. 122.↵
    Jiang Q, Dong X, Hu D, Chen L, Luo Y. Aquaporin 4 Inhibition Alleviates Myocardial Ischemia-Reperfusion Injury by Restraining Cardiomyocyte Pyroptosis. Bioengineered. 2021;doi:10.1080/21655979.2021.1992332. doi:10.1080/21655979.2021.1992332
    OpenUrlCrossRef
  123. 123.↵
    Theodoraki EV, Nikopensius T, Suhorutsenko J, Papamikos V, Kolovou GD, Peppes V, Panagiotakos D, Limberi S, Zakopoulos N, Metspalu A, et al. ROS1 Asp2213Asn polymorphism is not associated with coronary artery disease in a Greek case-control study. Clin Chem Lab Med. 2009;47(12):1471–1473. doi:10.1515/CCLM.2009.155
    OpenUrlCrossRefPubMed
  124. 124.↵
    Asada M, Oishi E, Sakata S, Hata J, Yoshida D, Honda T, Furuta Y, Shibata M, Suzuki K, Watanabe H, et al.Serum Lipopolysaccharide-Binding Protein Levels and the Incidence of Cardiovascular Disease in a General Japanese Population: The Hisayama Study. J Am Heart Assoc. 2019;8(21):e013628. doi:10.1161/JAHA.119.013628
    OpenUrlCrossRef
  125. 125.↵
    Terranegra A, Arcidiacono T, Macrina L, Brasacchio C, Pivari F, Mingione A, Tomei S, Mezzavilla M, Silcock L, Cozzolino M, et al. Glucagon-like peptide-1 receptor and sarcoglycan delta genetic variants can affect cardiovascular risk in chronic kidney disease patients under hemodialysis. Clin Kidney J. 2020;13(4):666–673. doi:10.1093/ckj/sfz182
    OpenUrlCrossRef
  126. 126.↵
    Liu D, Qiao C, Luo H. MicroRNA-1278 ameliorates the inflammation of cardiomyocytes during myocardial ischemia by targeting both IL-22 and CXCL14. Life Sci. 2021;269:118817. doi:10.1016/j.lfs.2020.118817
    OpenUrlCrossRef
  127. 127.↵
    Yamaguchi M. Regulatory role of regucalcin in heart calcium signaling: Insight into cardiac failure (Review). Biomed Rep. 2014;2(3):303–308. doi:10.3892/br.2014.245
    OpenUrlCrossRef
  128. 128.↵
    Ameri A, Kurachi S, Sueishi K, Kuwahara M, Kurachi K. Myocardial fibrosis in mice with overexpression of human blood coagulation factor IX. Blood. 2003;101(5):1871–1873. doi:10.1182/blood-2002-05-1581
    OpenUrlAbstract/FREE Full Text
  129. 129.↵
    Perrin MJ, Adler A, Green S, Al-Zoughool F, Doroshenko P, Orr N, Uppal S, Healey JS, Birnie D, Sanatani S, et al. Evaluation of genes encoding for the transient outward current (Ito) identifies the KCND2 gene as a cause of J-wave syndrome associated with sudden cardiac death. Circ Cardiovasc Genet. 2014;7(6):782–789. doi:10.1161/CIRCGENETICS.114.000623
    OpenUrlAbstract/FREE Full Text
  130. 130.↵
    Huscher D, Ebert N, Soerensen-Zender I, Mielke N, Schaeffner E, Schmitt R. Development of a prediction model for mortality and cardiovascular outcomes in older adults taking into account AZGP1. Sci Rep. 2021;11(1):11792. doi:10.1038/s41598-021-91169-4
    OpenUrlCrossRef
  131. 131.↵
    Msheik A, Kaspar C, Mailhac A, Hoballah JJ, Tamim H, Dakik HA. Performance of the AUB-HAS2 Cardiovascular Risk Index in vascular surgery patients. Vasc Med. 2021;26(5):535–541. doi:10.1177/1358863X21996806
    OpenUrlCrossRef
  132. 132.↵
    McCarthy NS, Vangjeli C, Surendran P, Treumann A, Rooney C, Ho E, Sever P, Thom S, Hughes AD, Munroe PB, et al. Genetic variants in PPARGC1B and CNTN4 are associated with thromboxane A2 formation and with cardiovascular event free survival in the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT). Atherosclerosis. 2018;269:42–49. doi:10.1016/j.atherosclerosis.2017.12.013
    OpenUrlCrossRef
  133. 133.↵
    Skaria T, Bachli E, Schoedon G. Transcriptional Regulation of Drug Metabolizing CYP Enzymes by Proinflammatory Wnt5A Signaling in Human Coronary Artery Endothelial Cells. Front Pharmacol. 2021;12:619588. doi:10.3389/fphar.2021.619588
    OpenUrlCrossRef
  134. 134.↵
    Horne BD, Rasmusson KD, Alharethi R, Budge D, Brunisholz KD, Metz T, Carlquist JF, Connolly JJ, Porter TF, Lappé DL, et al. Genome-wide significance and replication of the chromosome 12p11.22 locus near the PTHLH gene for peripartum cardiomyopathy. Circ Cardiovasc Genet. 2011;4(4):359–366. doi:10.1161/CIRCGENETICS.110.959205
    OpenUrlAbstract/FREE Full Text
  135. 135.↵
    Tillmanns J, Hoffmann D, Habbaba Y, Schmitto JD, Sedding D, Fraccarollo D, Galuppo P, Bauersachs J. Fibroblast activation protein alpha expression identifies activated fibroblasts after myocardial infarction. J Mol Cell Cardiol. 2015;87:194–203. doi:10.1016/j.yjmcc.2015.08.016
    OpenUrlCrossRef
  136. 136.↵
    Li Y, Wang DW, Chen Y, Chen C, Guo J, Zhang S, Sun Z, Ding H, Yao Y, Zhou L, et al. Genome-Wide Association and Functional Studies Identify SCML4 and THSD7A as Novel Susceptibility Genes for Coronary Artery Disease. Arterioscler Thromb Vasc Biol. 2018;38(4):964–975. doi:10.1161/ATVBAHA.117.310594
    OpenUrlAbstract/FREE Full Text
  137. 137.↵
    Durbin MD, O’Kane J, Lorentz S, Firulli AB, Ware SM. SHROOM3 is downstream of the planar cell polarity pathway and loss-of-function results in congenital heart defects. Dev Biol. 2020;464(2):124–136. doi:10.1016/j.ydbio.2020.05.013
    OpenUrlCrossRef
  138. 138.↵
    Rommel C, Rösner S, Lother A, Barg M, Schwaderer M, Gilsbach R, Bömicke T, Schnick T, Mayer S, Doll S, et al. The Transcription Factor ETV1 Induces Atrial Remodeling and Arrhythmia. Circ Res. 2018;123(5):550–563. doi:10.1161/CIRCRESAHA.118.313036
    OpenUrlAbstract/FREE Full Text
  139. 139.↵
    Qian P, Cao X, Xu X, Duan M, Zhang Q, Huang G. Contribution of CYP24A1 variants in coronary heart disease among the Chinese population. Lipids Health Dis. 2020;19(1):181. doi:10.1186/s12944-020-01356-x
    OpenUrlCrossRef
  140. 140.↵
    Liu JW, Liu HT, Chen L. The Therapeutic Role of Slit2 in Anti-fibrosis, Anti-inflammation and Anti-oxidative Stress in Rats with Coronary Heart Disease. Cardiovasc Toxicol. 2021;21(12):973–983. doi:10.1007/s12012-021-09688-5
    OpenUrlCrossRef
  141. 141.↵
    Li RG, Xu YJ, Ye WG, Li YJ, Chen H, Qiu XB, Yang YQ, Bai D. Connexin45 (GJC1) loss-of-function mutation contributes to familial atrial fibrillation and conduction disease. Heart Rhythm. 2021;18(5):684–693. doi:10.1016/j.hrthm.2020.12.033
    OpenUrlCrossRef
  142. 142.↵
    Nikitin AG, Chistiakov DA, Minushkina LO, Zateyshchikov DA, Nosikov VV. Association of the CYBA, PPARGC1A, PPARG3, and PPARD gene variants with coronary artery disease and metabolic risk factors of coronary atherosclerosis in a Russian population. Heart Vessels. 2010;25(3):229–236. doi:10.1007/s00380-009-1159-9
    OpenUrlCrossRefPubMed
  143. 143.↵
    Pan RY, Liu P, Zhou HT, Sun WX, Song J, Shu J, Cui GJ, Yang ZJ, Jia EZ. Circular RNAs promote TRPM3 expression by inhibiting hsa-miR-130a-3p in coronary artery disease patients. Oncotarget. 2017;8(36):60280–60290. doi:10.18632/oncotarget.19941
    OpenUrlCrossRef
  144. 144.↵
    AsghariHanjani N, Vafa M. The role of IGF-1 in obesity, cardiovascular disease, and cancer. Med J Islam Repub Iran. 2019;33:56. doi:10.34171/mjiri.33.56
    OpenUrlCrossRef
  145. 145.↵
    Zhang H, Ding P, Zheng L. TRPV6 Is Associated with Prognosis of ST-Elevation Acute Myocardial Infarction. Ann Clin Lab Sci. 2020;50(6):775–780.
    OpenUrlAbstract/FREE Full Text
  146. 146.↵
    Shao L, Zhang P, Zhang Y, Lu Q, Ma A. TLR3 and TLR4 as potential clinically biomarkers of cardiovascular risk in coronary artery disease (CAD) patients. Heart Vessels. 2014;29(5):690–698. doi:10.1007/s00380-013-0421-3
    OpenUrlCrossRef
  147. 147.↵
    Aluganti Narasimhulu C, Singla DK. The Role of Bone Morphogenetic Protein 7 (BMP-7) in Inflammation in Heart Diseases. Cells. 2020;9(2):280. doi:10.3390/cells9020280
    OpenUrlCrossRef
  148. 148.↵
    Zou Y, Zhang Q, Zhang J, Chen X, Zhou W, Yang Z, Yang Q, Yu H, Li L, He Y, et al. A common indel polymorphism of the Desmoglein-2 (DSG2) is associated with sudden cardiac death in Chinese populations. Forensic Sci Int. 2019;301:382–387. doi:10.1016/j.forsciint.2019.06.008
    OpenUrlCrossRef
  149. 149.↵
    Azharuddin M, Adil M, Ghosh P, Kapur P, Sharma M. Periostin as a novel biomarker of cardiovascular disease: A systematic evidence landscape of preclinical and clinical studies. J Evid Based Med. 2019;12(4):325–336. doi:10.1111/jebm.12368
    OpenUrlCrossRef
  150. 150.↵
    Zhang R, Ji Z, Cai J, Li Y, Ma G. Clinical Significance of Serum Kallistatin and ENOX1 Levels in Patients with Coronary Heart Disease. Med Princ Pract. 2021;30(4):339–346. doi:10.1159/000510427
    OpenUrlCrossRef
  151. 151.↵
    Bardien-Kruger S, Wulff H, Arieff Z, Brink P, Chandy KG, Corfield V. Characterisation of the human voltage-gated potassium channel gene, KCNA7, a candidate gene for inherited cardiac disorders, and its exclusion as cause of progressive familial heart block I (PFHBI). Eur J Hum Genet. 2002;10(1):36–43. doi:10.1038/sj.ejhg.5200739
    OpenUrlCrossRefPubMed
  152. 152.↵
    Khatami M, Heidari MM, Soheilyfar S. Common rs5918 (PlA1/A2) polymorphism in the ITGB3 gene and risk of coronary artery disease. Arch Med Sci Atheroscler Dis. 2016;1(1):e9–e15. doi:10.5114/amsad.2016.59587
    OpenUrlCrossRef
  153. 153.↵
    Vanhoutte D, Schips TG, Vo A, Grimes KM, Baldwin TA, Brody MJ, Accornero F, Sargent MA, Molkentin JD. Thbs1 induces lethal cardiac atrophy through PERK-ATF4 regulated autophagy. Nat Commun. 2021;12(1):3928. doi:10.1038/s41467-021-24215-4
    OpenUrlCrossRef
  154. 154.↵
    Seo K, Rainer PP, Shalkey Hahn V, Lee DI, Jo SH, Andersen A, Liu T, Xu X, Willette RN, Lepore JJ, et al. Combined TRPC3 and TRPC6 blockade by selective small-molecule or genetic deletion inhibits pathological cardiac hypertrophy. Proc Natl Acad Sci U S A. 2014;111(4):1551–1556. doi:10.1073/pnas.1308963111
    OpenUrlAbstract/FREE Full Text
  155. 155.↵
    Reiner AP, Carlson CS, Thyagarajan B, Rieder MJ, Polak JF, Siscovick DS, Nickerson DA, Jacobs DR Jr., Gross MD. Soluble P-selectin, SELP polymorphisms, and atherosclerotic risk in European-American and African-African young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Arterioscler Thromb Vasc Biol. 2008;28(8):1549–1555. doi:10.1161/ATVBAHA.108.169532
    OpenUrlAbstract/FREE Full Text
  156. 156.↵
    Hawley MH, Almontashiri N, Biesecker LG, Berger N, Chung WK, Garcia J, Grebe TA, Kelly MA, Lebo MS, Macaya D, et al. An assessment of the role of vinculin loss of function variants in inherited cardiomyopathy. Hum Mutat. 2020;41(9):1577–1587. doi:10.1002/humu.24061
    OpenUrlCrossRefPubMed
  157. 157.↵
    Ueda Y, Li RHL, Nguyen N, Ontiveros ES, Kovacs SL, Oldach MS, Vernau KM, Court MH, Stern JA. A genetic polymorphism in P2RY1 impacts response to clopidogrel in cats with hypertrophic cardiomyopathy. Sci Rep. 2021;11(1):12522. doi:10.1038/s41598-021-91372-3
    OpenUrlCrossRef
  158. 158.↵
    Geng H, Guan J. MiR-18a-5p inhibits endothelial-mesenchymal transition and cardiac fibrosis through the Notch2 pathway. Biochem Biophys Res Commun. 2017;491(2):329–336. doi:10.1016/j.bbrc.2017.07.101
    OpenUrlCrossRef
  159. 159.↵
    Gao BF, Shen ZC, Bian WS, Wu SX, Kang ZX, Gao Y. Correlation of hypertension and F2RL3 gene methylation with Prognosis of coronary heart disease. J Biol Regul Homeost Agents. 2018;32(6):1539–1544.
    OpenUrl
  160. 160.↵
    Nicholson AM, Finch NA, Almeida M, Perkerson RB, van Blitterswijk M, Wojtas A, Cenik B, Rotondo S, Inskeep V, Almasy L, et al. Prosaposin is a regulator of progranulin levels and oligomerization. Nat Commun. 2016;7:11992. doi:10.1038/ncomms1199
    OpenUrlCrossRef
  161. 161.↵
    Konaniah ES, Kuhel DG, Basford JE, Weintraub NL, Hui DY. Deficiency of LRP1 in Mature Adipocytes Promotes Diet-Induced Inflammation and Atherosclerosis-Brief Report. Arterioscler Thromb Vasc Biol. 2017;37(6):1046–1049. doi:10.1161/ATVBAHA.117.309414
    OpenUrlAbstract/FREE Full Text
  162. 162.↵
    Koller L, Richter B, Winter MP, Sulzgruber P, Potolidis C, Liebhart F, Mörtl D, Berger R, Goliasch G, Lang I, et al. Clusterin/apolipoprotein J is independently associated with survival in patients with chronic heart failure. J Clin Lipidol. 2017;11(1):178–184. doi:10.1016/j.jacl.2016.11.009
    OpenUrlCrossRef
  163. 163.↵
    Mo XG, Liu W, Yang Y, Imani S, Lu S, Dan G, Nie X, Yan J, Zhan R, Li X, et al. NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co-expression network analysis. J Cell Biochem. 2019;120(10):18219–18235. doi:10.1002/jcb.29128
    OpenUrlCrossRef
  164. 164.↵
    Rodríguez I, Coto E, Reguero JR, González P, Andrés V, Lozano I, Martín M, Alvarez V, Morís C. Role of the CDKN1A/p21, CDKN1C/p57, and CDKN2A/p16 genes in the risk of atherosclerosis and myocardial infarction. Cell Cycle. 2007;6(5):620–625. doi:10.4161/cc.6.5.3927
    OpenUrlCrossRefPubMedWeb of Science
  165. 165.↵
    Gong L, Zhu L, Yang T. Fendrr involves in the pathogenesis of cardiac fibrosis via regulating miR-106b/SMAD3 axis. Biochem Biophys Res Commun. 2020;524(1):169–177. doi:10.1016/j.bbrc.2020.01.062
    OpenUrlCrossRef
  166. 166.↵
    Chelko SP, Keceli G, Carpi A, Doti N, Agrimi J, Asimaki A, Beti CB, Miyamoto M, Amat-Codina N, Bedja D, et al. Exercise triggers CAPN1-mediated AIF truncation, inducing myocyte cell death in arrhythmogenic cardiomyopathy. Sci Transl Med. 2021;13(581):eabf0891. doi:10.1126/scitranslmed.abf0891
    OpenUrlAbstract/FREE Full Text
  167. 167.↵
    Bayoumi AS, Park KM, Wang Y, Teoh JP, Aonuma T, Tang Y, Su H, Weintraub NL, Kim IM. A carvedilol-responsive microRNA, miR-125b-5p protects the heart from acute myocardial infarction by repressing pro-apoptotic bak1 and klf13 in cardiomyocytes. J Mol Cell Cardiol. 2018;114:72–82. doi:10.1016/j.yjmcc.2017.11.003
    OpenUrlCrossRef
  168. 168.↵
    Oksala N, Pärssinen J, Seppälä I, Klopp N, Illig T, Laaksonen R, Levula M, Raitoharju E, Kholova I, Sioris T, et al. Kindlin 3 (FERMT3) is associated with unstable atherosclerotic plaques, anti-inflammatory type II macrophages and upregulation of beta-2 integrins in all major arterial beds. Atherosclerosis. 2015;242(1):145–154. doi:10.1016/j.atherosclerosis.2015.06.058
    OpenUrlCrossRef
  169. 169.↵
    Zidi I, Kharrat N, Abdelhedi R, Hassine AB, Laaribi AB, Yahia HB, Abdelmoula NB, Abid L, Rebai A, Rizzo R. Nonclassical human leukocyte antigen (HLA-G, HLA-E, and HLA-F) in coronary artery disease. Hum Immunol. 2016;77(4):325–329. doi:10.1016/j.humimm.2016.01.008
    OpenUrlCrossRef
  170. 170.↵
    Safranow K, Rzeuski R, Binczak-Kuleta A, Czyzycka E, Skowronek J, Jakubowska K, Wojtarowicz A, Loniewska B, et al. ADA*2 allele of the adenosine deaminase gene may protect against coronary artery disease. Cardiology. 2007;108(4):275–281. doi:10.1159/000099096
    OpenUrlCrossRefPubMedWeb of Science
  171. 171.↵
    Thude H, Gerlach K, Richartz B, Krack A, Brenke B, Pethig K, Figulla HR, Barz D. No association between transmembrane protein-tyrosine phosphatase receptor type C (CD45) exon A point mutation (77C>G) and idiopathic dilated cardiomyopathy. Hum Immunol. 2005;66(9):1008–1012. doi:10.1016/j.humimm.2005.07.004
    OpenUrlCrossRefPubMed
  172. 172.↵
    Ansari N, Najafi S, Shahrabi S, Saki N. PEAR1 polymorphisms as a prognostic factor in hemostasis and cardiovascular diseases. J Thromb Thrombolysis. 2021;51(1):89–95. doi:10.1007/s11239-020-02149-w
    OpenUrlCrossRef
  173. 173.↵
    Lu Y, Boer JM, Barsova RM, Favorova O, Goel A, Müller M, Feskens EJ. TGFB1 genetic polymorphisms and coronary heart disease risk: a meta-analysis. BMC Med Genet. 2012;13:39. doi:10.1186/1471-2350-13-39
    OpenUrlCrossRefPubMed
  174. 174.↵
    García-Bermúdez M, López-Mejías R, Genre F, Castañeda S, Corrales A, Llorca J, González-Juanatey C, Ubilla B, Miranda-Filloy JA, et al. Lack of association between JAK3 gene polymorphisms and cardiovascular disease in Spanish patients with rheumatoid arthritis. Biomed Res Int. 2015;2015:318364. doi:10.1155/2015/318364
    OpenUrlCrossRef
  175. 175.↵
    Kim JY, Choi BG, Jelinek J, Kim DH, Lee SH, Cho K, Rha SH, Lee YH, Jin HS, Choi DK, et al. Promoter methylation changes in ALOX12 and AIRE1: novel epigenetic markers for atherosclerosis. Clin Epigenetics. 2020;12(1):66. doi:10.1186/s13148-020-00846-0
    OpenUrlCrossRef
  176. 176.↵
    Liu L, Zhao Q, Kong M, Mao L, Yang Y, Xu Y. Myocardin-related transcription factor A (MRTF-A) regulates integrin beta 2 transcription to promote macrophage infiltration and cardiac hypertrophy in mice. Cardiovasc Res. 2021;cvab110. doi:10.1093/cvr/cvab110
    OpenUrlCrossRef
  177. 177.↵
    Tregouet DA, Barbaux S, Poirier O, Blankenberg S, Bickel C, Escolano S, Rupprecht HJ, Meyer J, Cambien F, Tiret L. SELPLG gene polymorphisms in relation to plasma SELPLG levels and coronary artery disease. Ann Hum Genet. 2003;67(Pt 6):504–511. doi:10.1046/j.1529-8817.2003.00053.x
    OpenUrlCrossRefPubMedWeb of Science
  178. 178.↵
    Liu S, Wang X, Yu S, Yan M, Peng Y, Zhang G, Xu Z. A Meta-Analysis on the Association Between TNFSF4 Polymorphisms (rs3861950 T > C and rs1234313 A > G) and Susceptibility to Coronary Artery Disease. Front Physiol. 2020;11:539288. doi:10.3389/fphys.2020.539288
    OpenUrlCrossRef
  179. 179.↵
    Mitchell GF, Verwoert GC, Tarasov KV, Isaacs A, Smith AV, Yasmin, Rietzschel ER, Tanaka T, Liu Y, Parsa A, et al. Common genetic variation in the 3’-BCL11B gene desert is associated with carotid-femoral pulse wave velocity and excess cardiovascular disease risk: the AortaGen Consortium. Circ Cardiovasc Genet. 2012;5(1):81–90. doi:10.1161/CIRCGENETICS.111.959817
    OpenUrlAbstract/FREE Full Text
  180. 180.↵
    Coto E, Pascual I, Avanzas P, Cuesta-Lavona E, Lorca R, Martín M, Vázquez-Coto D, Díaz-Corte C, Morís C, Rodríguez-Reguero J, et al. IL17RA in early-onset coronary artery disease: Total leukocyte transcript analysis and promoter polymorphism (rs4819554) association. Cytokine. 2020;136:155285. doi:10.1016/j.cyto.2020.155285
    OpenUrlCrossRef
  181. 181.↵
    Ding Y, Xu Z, Pan Y, Meng X, Xiang X, Li H, Liu L, Wang Y. Association Between CST3 Gene Polymorphisms and Large-Artery Atherosclerotic Stroke. Front Neurol. 2021;12:738148. doi:10.3389/fneur.2021.738148
    OpenUrlCrossRef
  182. 182.↵
    Prasongsukarn K, Dechkhajorn W, Benjathummarak S, Maneerat Y. TRPM2, PDLIM5, BCL3, CD14, GBA Genes as Feasible Markers for Premature Coronary Heart Disease Risk. Front Genet. 2021;12:598296. doi:10.3389/fgene.2021.598296
    OpenUrlCrossRef
  183. 183.↵
    Benjafield AV, Wang XL, Morris BJ. Tumor necrosis factor receptor 2 gene (TNFRSF1B) in genetic basis of coronary artery disease. J Mol Med (Berl). 2001;79(2-3):109–115. doi:10.1007/s001090000168
    OpenUrlCrossRefPubMedWeb of Science
  184. 184.↵
    Crawford AA, Bankier S, Altmaier E, Barnes CLK, Clark DW, Ermel R, Friedrich N, van der Harst P, Joshi PK, Karhunen V, et al. Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease. J Hum Genet. 2021;66(6):625–636. doi:10.1038/s10038-020-00895-6
    OpenUrlCrossRef
  185. 185.↵
    Cheng Q, Gu J, Adhikari BK, Sun L, Sun J. Is CD47 a potentially promising therapeutic target in cardiovascular diseases? - Role of CD47 in cardiovascular diseases. Life Sci. 2020;247:117426. doi:10.1016/j.lfs.2020.117426
    OpenUrlCrossRef
  186. 186.↵
    Cheng WL, Kao YH, Chen YC, Lin YK, Chen SA, Chen YJ. Macrophage migration inhibitory factor increases atrial arrhythmogenesis through CD74 signaling. Transl Res. 2020;216:43–56. doi:10.1016/j.trsl.2019.10.002
    OpenUrlCrossRef
  187. 187.↵
    Yamada Y, Sakuma J, Takeuchi I, Yasukochi Y, Kato K, Oguri M, Fujimaki T, Horibe H, Muramatsu M, Sawabe M, et al. Identification of STXBP2 as a novel susceptibility locus for myocardial infarction in Japanese individuals by an exome-wide association study. Oncotarget. 2017;8(20):33527–33535. doi:10.18632/oncotarget.16536
    OpenUrlCrossRef
  188. 188.↵
    Lai CH, Pandey S, Day CH, Ho TJ, Chen RJ, Chang RL, Pai PY, Padma VV, Kuo WW, Huang CY. β-catenin/LEF1/IGF-IIR Signaling Axis Galvanizes the Angiotensin-II-induced Cardiac Hypertrophy. Int J Mol Sci. 2019;20(17):4288. doi:10.3390/ijms20174288
    OpenUrlCrossRef
  189. 189.↵
    Carreras-Torres R, Athanasiadis G, Via M, Trenchs J, Gayà-Vidal M, Santamaria J, Esteban E, Moral P. Allele-allele interaction within the F13A1 gene: a risk factor for ischaemic heart disease in Spanish population. Thromb Res. 2010;126(3):e241–e245. doi:10.1016/j.thromres.2010.04.021
    OpenUrlCrossRefPubMed
  190. 190.↵
    Zhou H, Lei X, Yan Y, Lydic T, Li J, Weintraub NL, Su H, Chen W. Targeting ATGL to rescue BSCL2 lipodystrophy and its associated cardiomyopathy. JCI Insight. 2019;5(14):e129781. doi:10.1172/jci.insight.129781
    OpenUrlCrossRef
  191. 191.↵
    Chabior A, Gąsecka A, Marchel M, Gozdowska R, Makowska A, Maciak K, Góra M, Filipiak KJ, Burzyńska B, Opolski G. Expression of versican mRNA transcript to predict cardiac remodelling after myocardial infarction. Kardiol Pol. 2021;79(7-8):833–840. doi:10.33963/KP.a2021.0042
    OpenUrlCrossRef
  192. 192.↵
    Stewart RM, Rodriguez EC, King MC. Ablation of SUN2-containing LINC complexes drives cardiac hypertrophy without interstitial fibrosis. Mol Biol Cell. 2019;30(14):1664–1675. doi:10.1091/mbc.E18-07-0438
    OpenUrlCrossRefPubMed
  193. 193.↵
    Wang B, Zhou Q, Bi Y, Zhou W, Zeng Q, Liu Z, Liu X, Zhan Z. Phosphatase PPM1L Prevents Excessive Inflammatory Responses and Cardiac Dysfunction after Myocardial Infarction by Inhibiting IKKβ Activation. J Immunol. 2019;203(5):1338–1347. doi:10.4049/jimmunol.1900148
    OpenUrlAbstract/FREE Full Text
  194. 194.↵
    Jiang DS, Liu Y, Zhou H, Zhang Y, Zhang XD, Zhang XF, Chen K, Gao L, Peng J, Gong H, et al. Interferon regulatory factor 7 functions as a novel negative regulator of pathological cardiac hypertrophy. Hypertension. 2014;63(4):713–722. doi:10.1161/HYPERTENSIONAHA.113.02653
    OpenUrlCrossRef
  195. 195.↵
    Chen X, Ren L, Liu X, Sun X, Dong C, Jiang Y, Qin Y, Qu H, Jiao J, Wang S, et al. Ranolazine protects against diabetic cardiomyopathy by activating the NOTCH1/NRG1 pathway. Life Sci. 2020;261:118306. doi:10.1016/j.lfs.2020.118306
    OpenUrlCrossRef
  196. 196.↵
    Gudmundsson H, Curran J, Kashef F, Snyder JS, Smith SA, Vargas-Pinto P, Bonilla IM, Weiss RM, Anderson ME, Binkley P, et al. Differential regulation of EHD3 in human and mammalian heart failure. J Mol Cell Cardiol. 2012;52(5):1183–1190. doi:10.1016/j.yjmcc.2012.02.008
    OpenUrlCrossRefPubMed
  197. 197.↵
    Perisic L, Hedin E, Razuvaev A, Lengquist M, Osterholm C, Folkersen L, Gillgren P, Paulsson-Berne G, Ponten F, Odeberg J, Hedin U. Profiling of atherosclerotic lesions by gene and tissue microarrays reveals PCSK6 as a novel protease in unstable carotid atherosclerosis. Arterioscler Thromb Vasc Biol. 2013;33(10):2432–2443. doi:10.1161/ATVBAHA.113.301743
    OpenUrlAbstract/FREE Full Text
  198. 198.↵
    Correll RN, Goonasekera SA, van Berlo JH, Burr AR, Accornero F, Zhang H, Makarewich CA, York AJ, Sargent MA, Chen X, et al. STIM1 elevation in the heart results in aberrant Ca²□ handling and cardiomyopathy. J Mol Cell Cardiol. 2015;87:38–47. doi:10.1016/j.yjmcc.2015.07.032
    OpenUrlCrossRefPubMed
  199. 199.↵
    Weiberg D, Thackeray JT, Daum G, Sohns JM, Kropf S, Wester HJ, Ross TL, Bengel FM, Derlin T. Clinical Molecular Imaging of Chemokine Receptor CXCR4 Expression in Atherosclerotic Plaque Using 68Ga- Pentixafor PET: Correlation with Cardiovascular Risk Factors and Calcified Plaque Burden. J Nucl Med. 2018;59(2):266–272. doi:10.2967/jnumed.117.196485
    OpenUrlAbstract/FREE Full Text
  200. 200.↵
    Angrisano T, Schiattarella GG, Keller S, Pironti G, Florio E, Magliulo F, Bottino R, Pero R, Lembo F, Avvedimento EV, et al. Epigenetic switch at atp2a2 and myh7 gene promoters in pressure overload-induced heart failure. PLoS One. 2014;9(9):e106024. doi:10.1371/journal.pone.0106024
    OpenUrlCrossRefPubMed
  201. 201.↵
    Gomez-Hurtado N, Boczek NJ, Kryshtal DO, Johnson CN, Sun J, Nitu FR, Cornea RL, Chazin WJ, Calvert ML, Tester DJ, et al. Novel CPVT-Associated Calmodulin Mutation in CALM3 (CALM3-A103V) Activates Arrhythmogenic Ca Waves and Sparks. Circ Arrhythm Electrophysiol. 2016;9(8):doi:10.1161/CIRCEP.116.004161 e004161. doi:10.1161/CIRCEP.116.004161
    OpenUrlAbstract/FREE Full Text
  202. 202.↵
    Wei Y, Zhu M, Corbalán-Campos J, Heyll K, Weber C, Schober A. Regulation of Csf1r and Bcl6 in macrophages mediates the stage-specific effects of microRNA-155 on atherosclerosis. Arterioscler Thromb Vasc Biol. 2015;35(4):796–803. doi:10.1161/ATVBAHA.114.304723
    OpenUrlAbstract/FREE Full Text
  203. 203.↵
    Bhaskaran S, Pharaoh G, Ranjit R, Murphy A, Matsuzaki S, Nair BC, Forbes B, Gispert S, Auburger G, Humphries KM, et al. Loss of mitochondrial protease ClpP protects mice from diet-induced obesity and insulin resistance. EMBO Rep. 2018;19(3):e45009. doi:10.15252/embr.201745009
    OpenUrlAbstract/FREE Full Text
  204. 204.↵
    Song HK, Kim J, Lee JS, Nho KJ, Jeong HC, Kim J, Ahn Y, Park WJ, Kim DH. Pik3ip1 modulates cardiac hypertrophy by inhibiting PI3K pathway. PLoS One. 2015;10(3):e0122251. doi:10.1371/journal.pone.0122251
    OpenUrlCrossRef
  205. 205.↵
    Krystof V, Chamrád I, Jorda R, Kohoutek J. Pharmacological targeting of CDK9 in cardiac hypertrophy. Med Res Rev. 2010;30(4):646–666. doi:10.1002/med.20172
    OpenUrlCrossRefPubMed
  206. 206.↵
    Ma S, Sun L, Wu W, Wu J, Sun Z, Ren J. USP22 Protects Against Myocardial Ischemia-Reperfusion Injury via the SIRT1-p53/SLC7A11-Dependent Inhibition of Ferroptosis-Induced Cardiomyocyte Death. Front Physiol. 2020;11:551318. doi:10.3389/fphys.2020.551318
    OpenUrlCrossRef
  207. 207.↵
    Zhang H, Wang J, Du A, Li Y. MiR-483-3p inhibition ameliorates myocardial ischemia/reperfusion injury by targeting the MDM4/p53 pathway. Mol Immunol. 2020;125:9–14. doi:10.1016/j.molimm.2020.06.014
    OpenUrlCrossRef
  208. 208.↵
    Navarro-Núñez L, Roldán V, Lozano ML, Rivera J, Marin F, Vicente V, Martínez C. TUBB1 Q43P polymorphism does not protect against acute coronary syndrome and premature myocardial infarction. Thromb Haemost. 2008;100(6):1211–1213.
    OpenUrlPubMed
  209. 209.↵
    Maryam A, Khalid RR, Siddiqi AR, Ece A. E-pharmacophore based virtual screening for identification of dual specific PDE5A and PDE3A inhibitors as potential leads against cardiovascular diseases. J Biomol Struct Dyn. 2021;39(7):2302–2317. doi:10.1080/07391102.2020.1748718
    OpenUrlCrossRef
  210. 210.↵
    Sciacca FL, Ciaccio C, Fontana F, Strano C, Gilardoni F, Pantaleoni C, D’Arrigo S. Severe Phenotype in a Patient With Homozygous 15q21.2 Microdeletion Involving BCL2L10, GNB5, and MYO5C Genes, Resembling Infantile Developmental Disorder With Cardiac Arrhythmias (IDDCA). Front Genet. 2020;11:399. doi:10.3389/fgene.2020.00399
    OpenUrlCrossRefPubMed
  211. 211.↵
    Polonikov AV, Ponomarenko IV, Bykanova MA, Sirotina SS, Bocharova AV, Vagaytseva KV, Stepanov VA, Azarova IE, Churnosov MI, Solodilova MA. A comprehensive study revealed SNP-SNP interactions and a sex-dependent relationship between polymorphisms of the CYP2J2 gene and hypertension risk. Hypertens Res. 2019;42(2):257–272. doi:10.1038/s41440-018-0142-1
    OpenUrlCrossRef
  212. 212.↵
    Chida A, Shintani M, Nakayama T, Furutani Y, Hayama E, Inai K, Saji T, Nonoyama S, Nakanishi T. Missense mutations of the BMPR1B (ALK6) gene in childhood idiopathic pulmonary arterial hypertension. Circ J. 2012;76(6):1501–1508. doi:10.1253/circj.cj-11-1281
    OpenUrlCrossRefPubMed
  213. 213.↵
    Yamada Y, Kato K, Yoshida T, Yokoi K, Matsuo H, Watanabe S, Ichihara S, Metoki N, Yoshida H, Satoh K, et al. Association of polymorphisms of ABCA1 and ROS1 with hypertension in Japanese individuals. Int J Mol Med. 2008;21(1):83–89.
    OpenUrlPubMed
  214. 214.↵
    Zhang B, Li M, Wang L, Li C, Lou Y, Liu J, Liu Y, Wang Z, Wen S. The association between the polymorphisms in a sodium channel gene SCN7A and essential hypertension: a case-control study in the Northern Han Chinese. Ann Hum Genet. 2015;79(1):28–36. doi:10.1111/ahg.12085
    OpenUrlCrossRef
  215. 215.↵
    Kawarazaki W, Mizuno R, Nishimoto M, Ayuzawa N, Hirohama D, Ueda K, Kawakami-Mori F, Oba S, Marumo T, Fujita T. Salt causes aging-associated hypertension via vascular Wnt5a under Klotho deficiency. J Clin Invest. 2020;130(8):4152–4166. doi:10.1172/JCI134431
    OpenUrlCrossRef
  216. 216.↵
    Ueda K, Nishimoto M, Hirohama D, Ayuzawa N, Kawarazaki W, Watanabe A, Shimosawa T, Loffing J, Zhang MZ, Marumo T, et al. Renal Dysfunction Induced by Kidney-Specific Gene Deletion of Hsd11b2 as a Primary Cause of Salt-Dependent Hypertension. Hypertension. 2017;70(1):111–118. doi:10.1161/HYPERTENSIONAHA.116.08966
    OpenUrlAbstract/FREE Full Text
  217. 217.↵
    Bao Q, Wang D, Zhang Y, Bao L, Jia H. The Impact of CYP24A1 Polymorphisms on Hypertension Susceptibility. Kidney Blood Press Res. 2020;45(1):28–37. doi:10.1159/000503925
    OpenUrlCrossRef
  218. 218.↵
    Leng Q, Kahle KT, Rinehart J, MacGregor GG, Wilson FH, Canessa CM, Lifton RP, Hebert SC. WNK3, a kinase related to genes mutated in hereditary hypertension with hyperkalaemia, regulates the K+ channel ROMK1 (Kir1.1). J Physiol. 2006;571(Pt 2):275–286. doi:10.1113/jphysiol.2005.102202
    OpenUrlCrossRefPubMed
  219. 219.↵
    Taghvaei S, Saremi L, Babaniamansour S. Computational Analysis of Gly482Ser Single-Nucleotide Polymorphism in PPARGC1A Gene Associated with CAD, NAFLD, T2DM, Obesity, Hypertension, and Metabolic Diseases. PPAR Res. 2021;2021:5544233. doi:10.1155/2021/5544233
    OpenUrlCrossRef
  220. 220.↵
    Norling AM, Gerstenecker AT, Buford TW, Khan B, Oparil S, Lazar RM. The role of exercise in the reversal of IGF-1 deficiencies in microvascular rarefaction and hypertension. Geroscience. 2020;42(1):141–158. doi:10.1007/s11357-019-00139-2
    OpenUrlCrossRef
  221. 221.↵
    Brown A, Meor Azlan NF, Wu Z, Zhang J. WNK-SPAK/OSR1-NCC kinase signaling pathway as a novel target for the treatment of salt-sensitive hypertension. Acta Pharmacol Sin. 2021;42(4):508–517. doi:10.1038/s41401-020-0474-7
    OpenUrlCrossRef
  222. 222.↵
    Singh MV, Cicha MZ, Nunez S, Meyerholz DK, Chapleau MW, Abboud FM. Angiotensin II-induced hypertension and cardiac hypertrophy are differentially mediated by TLR3- and TLR4-dependent pathways. Am J Physiol Heart Circ Physiol. 2019;316(5):H1027–H1038. doi:10.1152/ajpheart.00697.2018
    OpenUrlCrossRef
  223. 223.↵
    Liu D, Wu BX, Sun N, Yan Y, Yuan P, Qu JM, Jing ZC. Elevated Levels of Circulating Bone Morphogenetic Protein 7 Predict Mortality in Pulmonary Arterial Hypertension. Chest. 2016;150(2):367–373. doi:10.1016/j.chest.2016.03.007
    OpenUrlCrossRef
  224. 224.↵
    Nie X, Shen C, Tan J, Wu Z, Wang W, Chen Y, Dai Y, Yang X, Ye S, Chen J, et al. Periostin: A Potential Therapeutic Target For Pulmonary Hypertension?. Circ Res. 2020;127(9):1138–1152. doi:10.1161/CIRCRESAHA.120.316943
    OpenUrlCrossRef
  225. 225.↵
    Liu A, Liu Y, Li B, Yang M, Liu Y, Su J. Role of miR-223-3p in pulmonary arterial hypertension via targeting ITGB3 in the ECM pathway. Cell Prolif. 2019;52(2):e12550. doi:10.1111/cpr.12550
    OpenUrlCrossRef
  226. 226.↵
    Sharma K, Chanana N, Mohammad G, Thinlas T, Gupta M, Syed MA, Das RS, Pasha Q, Mishra A. Hypertensive Patients Exhibit Enhanced Thrombospondin-1 Levels at High-Altitude. Life (Basel). 2021;11(9):893. doi:10.3390/life11090893
    OpenUrlCrossRef
  227. 227.↵
    Liu C, Tang W, Zhao H, Yang S, Ren Z, Li J, Chen Y, Zhao X, Xu D, Zhao Y, et al. The variants at FLNA and FLNB contribute to the susceptibility of hypertension and stroke with differentially expressed mRNA. Pharmacogenomics J. 2021;21(4):458–466. doi:10.1038/s41397-021-00222-y
    OpenUrlCrossRef
  228. 228.↵
    Jain PP, Lai N, Xiong M, Chen J, Babicheva A, Zhao T, Parmisano S, Zhao M, Paquin C, Matti M, et al. TRPC6, a Therapeutic Target for Pulmonary Hypertension. Am J Physiol Lung Cell Mol Physiol. 2021;doi:10.1152/ajplung.00159.2021. doi:10.1152/ajplung.00159.2021
    OpenUrlCrossRef
  229. 229.↵
    Novoyatleva T, Kojonazarov B, Owczarek A, Veeroju S, Rai N, Henneke I, Böhm M, Grimminger F, Ghofrani HA, Seeger W, Weissmann N, et al. Evidence for the Fucoidan/P-Selectin Axis as a Therapeutic Target in Hypoxia-induced Pulmonary Hypertension. Am J Respir Crit Care Med. 2019;199(11):1407–1420. doi:10.1164/rccm.201806-1170OC
    OpenUrlCrossRef
  230. 230.↵
    Yu Q, Sun HP, Chen WQ, Chen XQ, Xu Y, He YH, Kong QP. Sex-specific association of rs4746172 of VCL gene with hypertension in two Han populations from Southern China. Sci Rep. 2015;5:15245. doi:10.1038/srep15245
    OpenUrlCrossRefPubMed
  231. 231.↵
    Sahoo S, Li Y, de Jesus D, Sembrat J, Rojas MM, Goncharova E, Cifuentes-Pagano E, Straub AC, Pagano PJ. Notch2 suppression mimicking changes in human pulmonary hypertension modulates Notch1 and promotes endothelial cell proliferation. Am J Physiol Heart Circ Physiol. 2021;321(3):H542–H557. doi:10.1152/ajpheart.00125.2021
    OpenUrlCrossRef
  232. 232.↵
    Gamboa R, Jaramillo-Estrella MJ, Martínez-Alvarado MDR, Soto ME, Torres-Paz YE, Gonzalo-Calvo D, Del Valle-Mondragón L, López-Marure R, Llorente-Cortés VC, Huesca-Gómez C. Monocyte Low-Density Lipoprotein Receptor-Related Protein 1 (LRP1) Expression Correlates with cIMT in Mexican Hypertensive Patients. Expressão de Proteína-1 Relacionada a Receptor de Lipoproteína de Baixa Densidade (LRP1) em Monócito em Correlação com EIMC em Pacientes Mexicanos Hipertensos. Arq Bras Cardiol. 2021;116(1):56–65. doi:10.36660/abc.20190535
    OpenUrlCrossRef
  233. 233.↵
    Zeng S, Han M, Jiang M, Liu F, Hu Y, Long Y, Zhu C, Zeng F, Gan Q, Ye W, et al. Serum complement proteomics reveal biomarkers for hypertension disorder of pregnancy and the potential role of Clusterin. Reprod Biol Endocrinol. 2021;19(1):56. doi:10.1186/s12958-021-00742-z
    OpenUrlCrossRef
  234. 234.↵
    Zabini D, Granton E, Hu Y, Miranda MZ, Weichelt U, Breuils Bonnet S, Bonnet S, Morrell NW, Connelly KA, et al. Loss of SMAD3 Promotes Vascular Remodeling in Pulmonary Arterial Hypertension via MRTF Disinhibition. Am J Respir Crit Care Med. 2018;197(2):244–260. doi:10.1164/rccm.201702-0386OC
    OpenUrlCrossRef
  235. 235.↵
    Olivi L, Vandenbriele C, Gu YM, Salvi E, Carpini SD, Liu YP, Jacobs L, Jin Y, Thijs L, Citterio L, et al. PEAR1 is not a human hypertension-susceptibility gene. Blood Press. 2015;24(1):61–64. doi:10.3109/08037051.2014.986928
    OpenUrlCrossRef
  236. 236.↵
    Liu L, Wang C, Mi Y, Liu D, Li L, Fan J, Nan L, Jia N, Du Y. Association of MYH9 Polymorphisms with Hypertension in Patients with Chronic Kidney Disease in China. Kidney Blood Press Res. 2016;41(6):956–965. doi:10.1159/000452597
    OpenUrlCrossRef
  237. 237.↵
    Nabrdalik K, Gumprecht J, Adamczyk P, Górczyńska-Kosiorz S, Zywiec J, Grzeszczak W. Association of rs1800471 polymorphism of TGFB1 gene with chronic kidney disease occurrence and progression and hypertension appearance. Arch Med Sci. 2013;9(2):230–237. doi:10.5114/aoms.2013.34418
    OpenUrlCrossRef
  238. 238.↵
    Katayose D, Ohe M, Yamauchi K, Ogata M, Shirato K, Fujita H, Shibahara S, Takishima T. Increased expression of PDGF A- and B-chain genes in rat lungs with hypoxic pulmonary hypertension. Am J Physiol. 1993;264(2 Pt 1):L100-L106. doi:10.1152/ajplung.1993.264.2.L100
    OpenUrlCrossRef
  239. 239.↵
    Topchieva LV, Korneva VA, Kurbatova IV. The relationship of the carriership of allelic variations in rs2228145 (A > C) of the IL6R gene with the levels of VCAM1 and ICAM1 gene transcripts in patients with essential hypertension. Vavilovskii Zhurnal Genet Selektsii. 2020;24(1):96–101. doi:10.18699/VJ20.600
    OpenUrlCrossRef
  240. 240.↵
    Onal IK, Altun B, Onal ED, Kirkpantur A, Gul Oz S, Turgan C. Serum levels of MMP-9 and TIMP-1 in primary hypertension and effect of antihypertensive treatment. Eur J Intern Med. 2009;20(4):369–372. doi:10.1016/j.ejim.2008.10.003
    OpenUrlCrossRefPubMed
  241. 241.↵
    Speirs HJ, Wang WY, Benjafield AV, Morris BJ. No association with hypertension of CLCNKB and TNFRSF1B polymorphisms at a hypertension locus on chromosome 1p36. J Hypertens. 2005;23(8):1491–1496. doi:10.1097/01.hjh.0000174300.73992.cc
    OpenUrlCrossRefPubMedWeb of Science
  242. 242.↵
    Novelli EM, Little-Ihrig L, Knupp HE, Rogers NM, Yao M, Baust JJ, Meijles D, St Croix CM, Ross MA, Pagano PJ, et al. Vascular TSP1-CD47 signaling promotes sickle cell-associated arterial vasculopathy and pulmonary hypertension in mice. Am J Physiol Lung Cell Mol Physiol. 2019;316(6):L1150–L1164. doi:10.1152/ajplung.00302.2018
    OpenUrlCrossRef
  243. 243.↵
    Le Hiress M, Tu L, Ricard N, Phan C, Thuillet R, Fadel E, Dorfmüller P, Montani D, de Man F, Humbert M, et al. Proinflammatory Signature of the Dysfunctional Endothelium in Pulmonary Hypertension. Role of the Macrophage Migration Inhibitory Factor/CD74 Complex. Am J Respir Crit Care Med. 2015;192(8):983–997. doi:10.1164/rccm.201402-0322OC
    OpenUrlCrossRefPubMed
  244. 244.↵
    Chang YT, Chan CK, Eriksson I, Johnson PY, Cao X, Westöö C, Norvik C, Andersson-Sjöland A, Westergren-Thorsson G, Johansson S, et al. Versican accumulates in vascular lesions in pulmonary arterial hypertension. Pulm Circ. 2016;6(3):347–359. doi:10.1086/686994
    OpenUrlCrossRef
  245. 245.↵
    Deng Y, Guo SL, Li JQ, Xie SS, Zhou YC, Wei B, Wang Q, Wang F. Interferon regulatory factor 7 inhibits rat vascular smooth muscle cell proliferation and inflammation in monocrotaline-induced pulmonary hypertension. Life Sci. 2021;264:118709. doi:10.1016/j.lfs.2020.118709
    OpenUrlCrossRef
  246. 246.↵
    Wang S, Zhu G, Jiang D, Rhen J, Li X, Liu H, Lyu Y, Tsai P, Rose Y, Nguyen T, et al. Reduced Notch1 Cleavage Promotes the Development of Pulmonary Hypertension. Hypertension. 2021;HYPERTENSIONAHA12016065. doi:10.1161/HYPERTENSIONAHA.120.16065
    OpenUrlCrossRef
  247. 247.↵
    Dhande IS, Zhu Y, Kneedler SC, Joshi AS, Hicks MJ, Wenderfer SE, Braun MC, Doris PA. Stim1 Polymorphism Disrupts Immune Signaling and Creates Renal Injury in Hypertension. J Am Heart Assoc. 2020;9(5):e014142. doi:10.1161/JAHA.119.014142
    OpenUrlCrossRef
  248. 248.↵
    Yu BB, Zhi H, Zhang XY, Liang JW, He J, Su C, Xia WH, Zhang GX, Tao J. Mitochondrial dysfunction-mediated decline in angiogenic capacity of endothelial progenitor cells is associated with capillary rarefaction in patients with hypertension via downregulation of CXCR4/JAK2/SIRT5 signaling. EBioMedicine. 2019;42:64–75. doi:10.1016/j.ebiom.2019.03.031
    OpenUrlCrossRef
  249. 249.↵
    Li ZK, Gao LF, Zhu XA, Xiang DK. LncRNA HOXA-AS3 Promotes the Progression of Pulmonary Arterial Hypertension through Mediation of miR-675-3p/PDE5A Axis. Biochem Genet. 2021;59(5):1158–1172. doi:10.1007/s10528-021-10053-y
    OpenUrlCrossRef
  250. 250.↵
    Li H, Yan X, Yang M, Liu M, Tian S, Yu M, Li WP, Zhang C. The Impact of PTPRK and ROS1 Polymorphisms on the Preeclampsia Risk in Han Chinese Women. Int J Hypertens. 2021;2021:3275081. doi:10.1155/2021/3275081
    OpenUrlCrossRef
  251. 251.↵
    Liu Y, Huang J, Yu N, Wei S, Liu Z. Involvement of WNT2 in trophoblast cell behavior in preeclampsia development. Cell Cycle. 2020;19(17):2207–2215. doi:10.1080/15384101.2020.1802913
    OpenUrlCrossRef
  252. 252.↵
    Ding L, Blitz MJ, Wing DA, Epstein AJ, Gjessing HK, Wilson ML. PHLDA2 gene polymorphisms and risk of HELLP syndrome and severe preeclampsia. Pregnancy Hypertens. 2020;19:190–194. doi:10.1016/j.preghy.2020.01.013
    OpenUrlCrossRef
  253. 253.↵
    Li G, Wei W, Suo L, Zhang C, Yu H, Liu H, Guo Q, Zhen X, Yu Y. Low-Dose Aspirin Prevents Kidney Damage in LPS-Induced Preeclampsia by Inhibiting the WNT5A and NF-κB Signaling Pathways. Front Endocrinol (Lausanne). 2021;12:639592. doi:10.3389/fendo.2021.639592
    OpenUrlCrossRef
  254. 254.↵
    Majchrzak-Celiń ka A, Kosicka K, Paczkowska J, Główka FK, Bręborowicz GH, Krzyścin M, Siemiątkowska A, Szaumkessel M, Baer-Dubowska W. HSD11B2, RUNX3, and LINE-1 Methylation in Placental DNA of Hypertensive Disorders of Pregnancy Patients. Reprod Sci. 2017;24(11):1520–1531. doi:10.1177/1933719117692043
    OpenUrlCrossRef
  255. 255.↵
    Zhang YC, Qin XL, Ma XL, Mo HQ, Qin S, Zhang CX, Wei XW, Liu XQ, Zhang Y, Tian FJ, et al. CLDN1 regulates trophoblast apoptosis and proliferation in preeclampsia. Reproduction. 2021;161(6):623–632. doi:10.1530/REP-20-0677
    OpenUrlCrossRef
  256. 256.↵
    Ma R, Gu Y, Zhao S, Sun J, Groome LJ, Wang Y. Expressions of vitamin D metabolic components VDBP, CYP2R1, CYP27B1, CYP24A1, and VDR in placentas from normal and preeclamptic pregnancies. Am J Physiol Endocrinol Metab. 2012;303(7):E928–E935. doi:10.1152/ajpendo.00279.2012
    OpenUrlCrossRefPubMedWeb of Science
  257. 257.↵
    Zhou C, Song C, Huang X, Chen S, Long Y, Zeng S, Yang H, Jiang M. Early Prediction Model of Gestational Hypertension by Multi-Biomarkers Before 20 Weeks Gestation. Diabetes Metab Syndr Obes. 2021;14:2441–2451. doi:10.2147/DMSO.S309725
    OpenUrlCrossRef
  258. 258.↵
    Chen A, Li C, Wang J, Sha H, Piao S, Liu S. Role of Toll-Like Receptor 3 Gene Polymorphisms in Preeclampsia. Cell Physiol Biochem. 2015;37(5):1927–1933. doi:10.1159/000438553
    OpenUrlCrossRef
  259. 259.↵
    Duan FM, Fu LJ, Wang YH, Adu-Gyamfi EA, Ruan LL, Xu ZW, Xiao SQ, Chen XM, Wang YX, Liu TH, et al. THBS1 regulates trophoblast fusion through a CD36-dependent inhibition of cAMP, and its upregulation participates in preeclampsia. Genes Dis. 2020;8(3):353–363. doi:10.1016/j.gendis.2020.05.007
    OpenUrlCrossRef
  260. 260.↵
    Yu Y, Zhang L, Xu G, Wu Z, Li Q, Gu Y, Niu J. Angiotensin II Type I Receptor Agonistic Autoantibody Induces Podocyte Injury via Activation of the TRPC6-Calcium/Calcineurin Pathway in Pre-Eclampsia. Kidney Blood Press Res. 2018;43(5):1666–1676. doi:10.1159/000494744
    OpenUrlCrossRef
  261. 261.↵
    Purwosunu Y, Sekizawa A, Farina A, Wibowo N, Okazaki S, Nakamura M, Samura O, Fujito N, Okai T. Cell-free mRNA concentrations of CRH, PLAC1, and selectin-P are increased in the plasma of pregnant women with preeclampsia. Prenat Diagn. 2007;27(8):772–777. doi:10.1002/pd.1780
    OpenUrlCrossRefPubMedWeb of Science
  262. 262.↵
    Zeng S, Pan Y, Liu F, Yin J, Jiang M, Long Y, Zhao X, Lash GE, Yang H. Role of clusterin in the regulation of trophoblast development and preeclampsia. Biochem Biophys Res Commun. 2021;583:128–134. doi:10.1016/j.bbrc.2021.10.064
    OpenUrlCrossRef
  263. 263.↵
    Kreis NN, Friemel A, Jennewein L, Hoock SC, Hentrich AE, Nowak T, Louwen F, Yuan J. Functional Analysis of p21Cip1/CDKN1A and Its Family Members in Trophoblastic Cells of the Placenta and Its Roles in Preeclampsia. Cells. 2021;10(9):2214. doi:10.3390/cells10092214
    OpenUrlCrossRef
  264. 264.↵
    Zou AX, Chen B, Li QX, Liang YC. MiR-134 inhibits infiltration of trophoblast cells in placenta of patients with preeclampsia by decreasing ITGB1 expression. Eur Rev Med Pharmacol Sci. 2018;22(8):2199–2206. doi:10.26355/eurrev_201804_14804
    OpenUrlCrossRef
  265. 265.↵
    Nilsson LL, Djurisic S, Andersen AM, Melbye M, Bjerre D, Ferrero-Miliani L, Hackmon R, Geraghty DE, Hviid TV. Distribution of HLA-G extended haplotypes and one HLA-E polymorphism in a large-scale study of mother-child dyads with and without severe preeclampsia and eclampsia. HLA. 2016;88(4):172–186. doi:10.1111/tan.12871
    OpenUrlCrossRef
  266. 266.↵
    Zhang Z, Gao Y, Zhang L, Jia L, Wang P, Zhang L, Li H. Alterations of IL-6, IL-6R and gp130 in early and late onset severe preeclampsia. Hypertens Pregnancy. 2013;32(3):270–280. doi:10.3109/10641955.2013.798332
    OpenUrlCrossRefPubMed
  267. 267.↵
    Hu G, Xia ZS, Guo X. Differential expression of serum GBP-28, NBP-Cyc 3 and TIMP-1 complicates pregnancy in hypertensive disorder pregnancy. J Reprod Immunol. 2021;144:103288. doi:10.1016/j.jri.2021.103288
    OpenUrlCrossRef
  268. 268.↵
    Starodubtseva N, Nizyaeva N, Baev O, Bugrova A, Gapaeva M, Muminova K, Kononikhin A, Frankevich V, Nikolaev E, Sukhikh G. SERPINA1 Peptides in Urine as A Potential Marker of Preeclampsia Severity. Int J Mol Sci. 2020;21(3):914. doi:10.3390/ijms21030914
    OpenUrlCrossRef
  269. 269.↵
    Przybyl L, Haase N, Golic M, Rugor J, Solano ME, Arck PC, Gauster M, Huppertz B, Emontzpohl C, Stoppe C, et al. CD74-Downregulation of Placental Macrophage-Trophoblastic Interactions in Preeclampsia. Circ Res. 2016;119(1):55–68. doi:10.1161/CIRCRESAHA.116.308304
    OpenUrlAbstract/FREE Full Text
  270. 270.↵
    Meems LM, Mahmud H, Buikema H, Tost J, Michel S, Takens J, Verkaik-Schakel RN, Vreeswijk-Baudoin I, Mateo-Leach IV, van der Harst P, et al. Parental vitamin D deficiency during pregnancy is associated with increased blood pressure in offspring via Panx1 hypermethylation. Am J Physiol Heart Circ Physiol. 2016;311(6):H1459–H1469. doi:10.1152/ajpheart.00141.2016
    OpenUrlCrossRefPubMed
  271. 271.↵
    Shinya K, Nakayama T, Nakayama T, Yamamoto T. A case-control study between the STIM1 gene and hypertensive disorders of pregnancy. Hypertens Res. 2018;41(1):39–44. doi:10.1038/hr.2017.84
    OpenUrlCrossRef
  272. 272.↵
    Lei GQ, Wu ZY, Jiang WB, Luo J, Xu H, Luo SF, Peng ZY, Wang W, Chen M, Yu LL. Effect of CXCL12/CXCR4 on migration of decidua-derived mesenchymal stem cells from pregnancies with preeclampsia. Am J Reprod Immunol. 2019;82(5):e13180. doi:10.1111/aji.13180
    OpenUrlCrossRef
  273. 273.↵
    Liu X, Lu L, Yao P, Ma Y, Wang F, Jin Q, Ye X, Li H, Hu FB, Sun L, et al. Lipopolysaccharide binding protein, obesity status and incidence of metabolic syndrome: a prospective study among middle-aged and older Chinese. Diabetologia. 2014;57(9):1834–1841. doi:10.1007/s00125-014-3288-7
    OpenUrlCrossRefPubMed
  274. 274.↵
    Neocleous V, Shammas C, Phelan MM, Fanis P, Pantelidou M, Skordis N, Mantzoros C, Phylactou LA, Toumba M. A novel MC4R deletion coexisting with FTO and MC1R gene variants, causes severe early onset obesity. Hormones (Athens). 2016;15(3):445–452. doi:10.14310/horm.2002.1686
    OpenUrlCrossRef
  275. 275.↵
    Cereijo R, Quesada-López T, Gavaldà-Navarro A, Tarascó J, Pellitero S, Reyes M, Puig-Domingo M, Giralt M, Sánchez-Infantes D, Villarroya F. The chemokine CXCL14 is negatively associated with obesity and concomitant type-2 diabetes in humans. Int J Obes (Lond). 2021;45(3):706–710. doi:10.1038/s41366-020-00732-y
    OpenUrlCrossRef
  276. 276.↵
    Yamaguchi M, Murata T. Involvement of regucalcin gene promoter region-related protein-p117, a transcription factor, in human obesity. Biomed Rep. 2017;6(4):374–378. doi:10.3892/br.2017.874
    OpenUrlCrossRef
  277. 277.↵
    Lear S, Pflimlin E, Zhou Z, Huang D, Weng S, Nguyen-Tran V, Joseph SB, Roller S, Peterson S, Li J, et al. Engineering of a Potent, Long-Acting NPY2R Agonist for Combination with a GLP-1R Agonist as a Multi-Hormonal Treatment for Obesity. J Med Chem. 2020;63(17):9660–9671. doi:10.1021/acs.jmedchem.0c00740
    OpenUrlCrossRef
  278. 278.↵
    Vaittinen M, Kolehmainen M, Rydén M, Eskelinen M, Wabitsch M, Pihlajamäki J, Uusitupa M, Pulkkinen L. et al. MFAP5 is related to obesity-associated adipose tissue and extracellular matrix remodeling and inflammation. Obesity (Silver Spring). 2015;23(7):1371–1378. doi:10.1002/oby.21103
    OpenUrlCrossRef
  279. 279.↵
    Koutaki D, Michos A, Bacopoulou F, Charmandari E. The Emerging Role of Sfrp5 and Wnt5a in the Pathogenesis of Obesity: Implications for a Healthy Diet and Lifestyle. Nutrients. 2021;13(7):2459. doi:10.3390/nu13072459
    OpenUrlCrossRef
  280. 280.↵
    Awazawa M, Gabel P, Tsaousidou E, Nolte H, Krüger M, Schmitz J, Ackermann PJ, Brandt C, Altmüller J, Motameny S, et al. A microRNA screen reveals that elevated hepatic ectodysplasin A expression contributes to obesity-induced insulin resistance in skeletal muscle. Nat Med. 2017;23(12):1466–1473. doi:10.1038/nm.4420
    OpenUrlCrossRefPubMed
  281. 281.↵
    Ahmad M, Nongmaithem SS, Krishnaveni GV, Fall CH, Yajnik CS, Chandak GR. Lack of replication of association of THSD7A with obesity. Int J Obes (Lond). 2016;40(4):725–726. doi:10.1038/ijo.2016.15
    OpenUrlCrossRef
  282. 282.↵
    Gonsorcíková L, Prů ová S, Cinek O, Ek J, Pelikánová T, Jørgensen T, Eiberg H, Pedersen O, Hansen T, Lebl J. Autosomal inheritance of diabetes in two families characterized by obesity and a novel H241Q mutation in NEUROD1. Pediatr Diabetes. 2008;9(4 Pt 2):367-372. doi:10.1111/j.1399-5448.2008.00379.x
    OpenUrlCrossRefPubMed
  283. 283.↵
    Lim R, Lappas M. Slit2 exerts anti-inflammatory actions in human placenta and is decreased with maternal obesity. Am J Reprod Immunol. 2015;73(1):66–78. doi:10.1111/aji.12334
    OpenUrlCrossRefPubMed
  284. 284.↵
    Davies M, Fraser SA, Galic S, Choy SW, Katerelos M, Gleich K, Kemp BE, Mount PF, Power DA. Novel mechanisms of Na+ retention in obesity: phosphorylation of NKCC2 and regulation of SPAK/OSR1 by AMPK. Am J Physiol Renal Physiol. 2014;307(1):F96–F106. doi:10.1152/ajprenal.00524.2013
    OpenUrlCrossRefPubMedWeb of Science
  285. 285.↵
    Fabre O, Breuker C, Amouzou C, Salehzada T, Kitzmann M, Mercier J, Bisbal C. Defects in TLR3 expression and RNase L activation lead to decreased MnSOD expression and insulin resistance in muscle cells of obese people. Cell Death Dis. 2014;5(3):e1136. doi:10.1038/cddis.2014.104
    OpenUrlCrossRef
  286. 286.↵
    Boon MR, van den Berg SA, Wang Y, van den Bossche J, Karkampouna S, Bauwens M, De Saint-Hubert M, van der Horst G, Vukicevic S, de Winther MP, et al. BMP7 activates brown adipose tissue and reduces diet-induced obesity only at subthermoneutrality. PLoS One. 2013;8(9):e74083. doi:10.1371/journal.pone.0074083
    OpenUrlCrossRefPubMed
  287. 287.↵
    Yang Y, Zhang Y, Zhou X, Chen D, Ouyang G, Liu Y, Cui D. Periostin deficiency attenuates lipopolysaccharide- and obesity-induced adipose tissue fibrosis. FEBS Lett. 2021;595(16):2099–2112. doi:10.1002/1873-3468.14154
    OpenUrlCrossRef
  288. 288.↵
    Lee S. The genetic and epigenetic association of LDL Receptor Related Protein 1B (LRP1B) gene with childhood obesity. Sci Rep. 2019;9(1):1815. Published 2019 Feb 12. doi:10.1038/s41598-019-38538-2
    OpenUrlCrossRef
  289. 289.↵
    Matsuo Y, Tanaka M, Yamakage H, Sasaki Y, Muranaka K, Hata H, Ikai I, Shimatsu A, Inoue M, Chun TH, et al. Thrombospondin 1 as a novel biological marker of obesity and metabolic syndrome. Metabolism. 2015;64(11):1490–1499. doi:10.1016/j.metabol.2015.07.016
    OpenUrlCrossRefPubMed
  290. 290.↵
    Grarup N, Andersen G, Krarup NT, Albrechtsen A, Schmitz O, Jørgensen T, Borch-Johnsen K, Hansen T, Pedersen O. Association testing of novel type 2 diabetes risk alleles in the JAZF1, CDC123/CAMK1D, TSPAN8, THADA, ADAMTS9, and NOTCH2 loci with insulin release, insulin sensitivity, and obesity in a population-based sample of 4,516 glucose-tolerant middle-aged Danes. Diabetes. 2008;57(9):2534–2540. doi:10.2337/db08-0436
    OpenUrlAbstract/FREE Full Text
  291. 291.↵
    Masson O, Chavey C, Dray C, Meulle A, Daviaud D, Quilliot D, Muller C, Valet P, Liaudet-Coopman E. LRP1 receptor controls adipogenesis and is up-regulated in human and mouse obese adipose tissue. PLoS One. 2009;4(10):e7422. doi:10.1371/journal.pone.0007422.
    OpenUrlCrossRefPubMed
  292. 292.↵
    Park JS, Lee WK, Kim HS, Seo JA, Kim DH, Han HC, Min BH. C Clusterin overexpression protects against western diet-induced obesity and NAFLD. Sci Rep. 2020;10(1):17484. doi:10.1038/s41598-020-73927-y
    OpenUrlCrossRef
  293. 293.↵
    Collet C, Laplanche JL, de Vernejoul MC. Camurati-Engelmann disease with obesity in a newly identified family carrying a missense p.Arg156Cys mutation in the TGFB1 gene. Am J Med Genet A. 2013;161A(8):2074–2077. doi:10.1002/ajmg.a.36022
    OpenUrlCrossRef
  294. 294.↵
    An YA, Crewe C, Asterholm IW, Sun K, Chen S, Zhang F, Shao M, Funcke JB, Zhang Z, Straub L, et al. Dysregulation of Amyloid Precursor Protein Impairs Adipose Tissue Mitochondrial Function and Promotes Obesity. Nat Metab. 2019;1(12):1243–1257. doi:10.1038/s42255-019-0149-1
    OpenUrlCrossRef
  295. 295.↵
    Awaya T, Yokosaki Y, Yamane K, Usui H, Kohno N, Eboshida A. Gene-environment association of an ITGB2 sequence variant with obesity in ethnic Japanese. Obesity (Silver Spring). 2008;16(6):1463–1466. doi:10.1038/oby.2008.68
    OpenUrlCrossRefPubMed
  296. 296.↵
    Sindhu S, Thomas R, Shihab P, Sriraman D, Behbehani K, Ahmad R. Obesity Is a Positive Modulator of IL-6R and IL-6 Expression in the Subcutaneous Adipose Tissue: Significance for Metabolic Inflammation. PLoS One. 2015;10(7):e0133494. doi:10.1371/journal.pone.0133494
    OpenUrlCrossRef
  297. 297.↵
    Andrade VL, Petruceli E, Belo VA, Andrade-Fernandes CM, Caetano Russi CV, Bosco AA, Tanus-Santos JE, Sandrim VC. Evaluation of plasmatic MMP-8, MMP-9, TIMP-1 and MPO levels in obese and lean women. Clin Biochem. 2012;45(6):412–415. doi:10.1016/j.clinbiochem.2012.01.008
    OpenUrlCrossRefPubMed
  298. 298.↵
    Li D, Gwag T, Wang S. Absence of CD47 maintains brown fat thermogenic capacity and protects mice from aging-related obesity and metabolic disorder. Biochem Biophys Res Commun. 2021;575:14–19. doi:10.1016/j.bbrc.2021.08.062
    OpenUrlCrossRef
  299. 299.↵
    Chan PC, Wu TN, Chen YC, Lu CH, Wabitsch M, Tian YF, Hsieh PS. Targetted inhibition of CD74 attenuates adipose COX-2-MIF-mediated M1 macrophage polarization and retards obesity-related adipose tissue inflammation and insulin resistance. Clin Sci (Lond). 2018;132(14):1581–1596. doi:10.1042/CS20180041
    OpenUrlAbstract/FREE Full Text
  300. 300.↵
    Redonnet A, Bonilla S, Noël-Suberville C, Pallet V, Dabadie H, Gin H, Higueret P. Relationship between peroxisome proliferator-activated receptor gamma and retinoic acid receptor alpha gene expression in obese human adipose tissue. Int J Obes Relat Metab Disord. 2002;26(7):920–927. doi:10.1038/sj.ijo.0802025
    OpenUrlCrossRefPubMedWeb of Science
  301. 301.↵
    Qian G, Adeyanju O, Sunil C, Huang SK, Chen SY, Tucker TA, Idell S, Guo X. Dedicator of cytokinesis 2 (DOCK2) deficiency attenuates lung injury associated with chronic high-fat and high-fructose diet-induced obesity. Am J Pathol. 2021;S0002-9440(21)00474-0. doi:10.1016/j.ajpath.2021.10.011
    OpenUrlCrossRef
  302. 302.↵
    Kaartinen MT, Arora M, Heinonen S, Rissanen A, Kaprio J, Pietiläinen KH. Transglutaminases and Obesity in Humans: Association of F13A1 to Adipocyte Hypertrophy and Adipose Tissue Immune Response. Int J Mol Sci. 2020;21(21):8289. doi:10.3390/ijms21218289
    OpenUrlCrossRef
  303. 303.↵
    Kuroda M, Nishiguchi M, Ugawa N, Ishikawa E, Kawabata Y, Okamoto S, Sasaki W, Miyatake Y, Sebe M, Masumoto S, et al. Interferon regulatory factor 7 mediates obesity-associated MCP-1 transcription. PLoS One. 2020;15(5):e0233390. doi:10.1371/journal.pone.0233390
    OpenUrlCrossRef
  304. 304.↵
    Chen Z, Pan S, Yin K, Zhang Y, Yuan X, Wang S, Yang S, Shen Q, Tang Y, Li J, et al. Deficiency of ER Ca2+ sensor STIM1 in AgRP neurons confers protection against dietary obesity. Cell Rep. 2021;37(3):109868. doi:10.1016/j.celrep.2021.109868
    OpenUrlCrossRef
  305. 305.↵
    Yao L, Heuser-Baker J, Herlea-Pana O, Zhang N, Szweda LI, Griffin TM, Barlic-Dicen J. Deficiency in adipocyte chemokine receptor CXCR4 exacerbates obesity and compromises thermoregulatory responses of brown adipose tissue in a mouse model of diet-induced obesity. FASEB J. 2014;28(10):4534–4550. doi:10.1096/fj.14-249797
    OpenUrlCrossRefPubMedWeb of Science
  306. 306.↵
    Yoshida K, Kita Y, Tokuoka SM, Hamano F, Yamazaki M, Sakimura K, Kano M, Shimizu T. Monoacylglycerol lipase deficiency affects diet-induced obesity, fat absorption, and feeding behavior in CB1 cannabinoid receptor-deficient mice. FASEB J. 2019;33(2):2484–2497. doi:10.1096/fj.201801203R
    OpenUrlCrossRef
  307. 307.↵
    Jeyaratnaganthan N, Højlund K, Kroustrup JP, Larsen JF, Bjerre M, Levin K, Beck-Nielsen H, Frago S, Hassan AB, Flyvbjerg A, et al. Circulating levels of insulin-like growth factor-II/mannose-6-phosphate receptor in obesity and type 2 diabetes. Growth Horm IGF Res. 2010;20(3):185–191. doi:10.1016/j.ghir.2009.12.005
    OpenUrlCrossRefPubMed
  308. 308.↵
    Kim TH, Yang YM, Han CY, Koo JH, Oh H, Kim SS, You BH, Choi YH, Park TS, Lee CH, et al. Gα12 ablation exacerbates liver steatosis and obesity by suppressing USP22/SIRT1-regulated mitochondrial respiration. J Clin Invest. 2018;128(12):5587–5602. doi:10.1172/JCI97831
    OpenUrlCrossRef
  309. 309.↵
    Wilson CH, Nikolic A, Kentish SJ, Keller M, Hatzinikolas G, Dorstyn L, Page AJ, Kumar S. Caspase-2 deficiency enhances whole-body carbohydrate utilisation and prevents high-fat diet-induced obesity. Cell Death Dis. 2017;8(10):e3136. doi:10.1038/cddis.2017.518
    OpenUrlCrossRef
  310. 310.↵
    Foghsgaard S, Vedtofte L, Mathiesen ER, Svare JA, Gluud LL, Holst JJ, Damm P, Knop FK, Vilsbøll T. The effect of a glucagon-like peptide-1 receptor agonist on glucose tolerance in women with previous gestational diabetes mellitus: protocol for an investigator-initiated, randomised, placebo-controlled, double-blinded, parallel intervention trial. BMJ Open. 2013;3(10):e003834. doi:10.1136/bmjopen-2013-003834
    OpenUrlAbstract/FREE Full Text
  311. 311.↵
    Sagen JV, Baumann ME, Salvesen HB, Molven A, Søvik O, Njølstad PR. Diagnostic screening of NEUROD1 (MODY6) in subjects with MODY or gestational diabetes mellitus. Diabet Med. 2005;22(8):1012–1015. doi:10.1111/j.1464-5491.2005.01565.x
    OpenUrlCrossRefPubMed
  312. 312.↵
    Kelstrup L, Hjort L, Houshmand-Oeregaard A, Clausen TD, Hansen NS, Broholm C, Borch-Johnsen L, Mathiesen ER, Vaag AA, Damm P. Gene Expression and DNA Methylation of PPARGC1A in Muscle and Adipose Tissue From Adult Offspring of Women With Diabetes in Pregnancy. Diabetes. 2016;65(10):2900–2910. doi:10.2337/db16-0227
    OpenUrlAbstract/FREE Full Text
  313. 313.↵
    Balachandiran M, Bobby Z, Dorairajan G, Gladwin V, Vinayagam V, Packirisamy RM. Decreased maternal serum adiponectin and increased insulin-like growth factor-1 levels along with increased placental glucose transporter-1 expression in gestational diabetes mellitus: Possible role in fetal overgrowth. Placenta. 2021;104:71–80. doi:10.1016/j.placenta.2020.11.008
    OpenUrlCrossRef
  314. 314.↵
    Houde AA, Ruchat SM, Allard C, Baillargeon JP, St-Pierre J, Perron P, Gaudet D, Brisson D, Hivert MF, Bouchard L. LRP1B, BRD2 and CACNA1D: new candidate genes in fetal metabolic programming of newborns exposed to maternal hyperglycemia. Epigenomics. 2015;7(7):1111–1122. doi:10.2217/epi.15.72
    OpenUrlCrossRef
  315. 315.↵
    Tarnowski M, Malinowski D, Safranow K, Dziedziejko V, Pawlik A. HNF1B, TSPAN8 and NOTCH2 gene polymorphisms in women with gestational diabetes. J Matern Fetal Neonatal Med. 2018;31(7):837–842. doi:10.1080/14767058.2017.1297793
    OpenUrlCrossRef
  316. 316.↵
    Li Y, Zhuang J. miR-345-3p serves a protective role during gestational diabetes mellitus by targeting BAK1. Exp Ther Med. 2021;21(1):2. doi:10.3892/etm.2020.9434
    OpenUrlCrossRef
  317. 317.↵
    Li Q, Pereira TJ, Moyce BL, Mahood TH, Doucette CA, Rempel J, Dolinsky VW. In utero exposure to gestational diabetes mellitus conditions TLR4 and TLR2 activated IL-1beta responses in spleen cells from rat offspring. Biochim Biophys Acta. 2016;1862(11):2137–2146. doi:10.1016/j.bbadis.2016.08.004
    OpenUrlCrossRef
  318. 318.↵
    Kuźmicki M, Telejko B, Lipińska D, Pliszka J, Wilk J, Wawrusiewicz-Kurylonek N, Zielińska A, Sobota A,