Serum ferritin as a risk factor for type 2 diabetes mellitus, regulated by liver transferrin receptor 2

Zhang, Rui; Huang, Xinmei; Li, Yue; Yu, Zhiyan; Wu, Yueyue; Zha, Bingbing; Ding, Hu; Zang, Shufei; Liu, Jun

doi:10.1530/ec-21-0316

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…Previous studies have established a significant link between excessive iron accumulation in the body and the risk of developing T2DM (50). High iron levels can be a risk factor for T2DM and its complications (16,(51)(52)(53)(54)(55)(56). Notably, diabetes has also been associated with increased iron accumulation in the brain through multiple mechanisms (16).…”

Section: Discussionmentioning

confidence: 99%

Quantitative susceptibility mapping for iron monitoring of multiple subcortical nuclei in type 2 diabetes mellitus: a systematic review and meta-analysis

Mohammadi,

Ghaderi,

Sayehmiri

et al. 2024

Front. Endocrinol.

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IntroductionIron accumulation in the brain has been linked to diabetes, but its role in subcortical structures involved in motor and cognitive functions remains unclear. Quantitative susceptibility mapping (QSM) allows the non-invasive quantification of iron deposition in the brain. This systematic review and meta-analysis examined magnetic susceptibility measured by QSM in the subcortical nuclei of patients with type 2 diabetes mellitus (T2DM) compared with controls.MethodsPubMed, Scopus, and Web of Science databases were systematically searched [following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines] for studies reporting QSM values in the deep gray matter (DGM) regions of patients with T2DM and controls. Pooled standardized mean differences (SMDs) for susceptibility were calculated using fixed-effects meta-analysis models, and heterogeneity was assessed using I2. Sensitivity analyses were conducted, and publication bias was evaluated using Begg’s and Egger’s tests.ResultsSix studies including 192 patients with T2DM and 245 controls were included. This study found a significant increase in iron deposition in the subcortical nuclei of patients with T2DM compared to the control group. The study found moderate increases in the putamen (SMD = 0.53, 95% CI 0.33 to 0.72, p = 0.00) and dentate nucleus (SMD = 0.56, 95% CI 0.27 to 0.85, p = 0.00) but weak associations between increased iron levels in the caudate nucleus (SMD = 0.32, 95% CI 0.13 to 0.52, p = 0.00) and red nucleus (SMD = 0.22, 95% CI 0.00 0.44, p = 0.05). No statistical significance was found for iron deposition alterations in the globus pallidus (SMD = 0.19; 95% CI −0.01 to 0.38; p = 0.06) and substantia nigra (SMD = 0.12, 95% CI −0.10, 0.34, p = 0.29). Sensitivity analysis showed that the findings remained unaffected by individual studies, and consistent increases were observed in multiple subcortical areas.DiscussionQSM revealed an increase in iron in the DGM/subcortical nuclei in T2DM patients versus controls, particularly in the motor and cognitive nuclei, including the putamen, dentate nucleus, caudate nucleus, and red nucleus. Thus, QSM may serve as a potential biomarker for iron accumulation in T2DM patients. However, further research is needed to validate these findings.

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Section: Discussionmentioning

confidence: 99%

Quantitative susceptibility mapping for iron monitoring of multiple subcortical nuclei in type 2 diabetes mellitus: a systematic review and meta-analysis

Mohammadi,

Ghaderi,

Sayehmiri

et al. 2024

Front. Endocrinol.

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“…Iron plays an important role in the functioning of human cells and any disturbances in its homeostasis will result in iron overload and deficiencies [ 15 ]. The serum concentrations of ferritin were significantly higher among T2DM patients as compared with the control group (227 (140-352) vs 203.5 (130.5-312) ng/mL, p < 0.05) in a Chinese study [ 16 ]. These results were in concordance with the results of the present study.…”

Section: Discussionmentioning

confidence: 99%

Serum Activities of Ferritin Among Controlled and Uncontrolled Type 2 Diabetes Mellitus Patients

Tummalacharla

Pavuluri

Maram³

et al. 2022

Cureus

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“…S100A12 [324], CDH1 [325], S100A9 [326], ANK1 [327], KCNH2 [328], HP (haptoglobin) [329], FRMD4A [330], SNCA (synuclein alpha) [331], FBXO7 [332], PINK1 [333], B2M [334], KCNH3 [335], CA2 [336], FUZ (fuzzy planar cell polarity protein) [337], UBB (ubiquitin B) [338], C5AR1 [339], CBS (cystathionine beta-synthase) [340], F12 [341], TSPAN5 [342], NQO2 [343], NDUFA1 [344], SRXN1 [345], BASP1 [346], GPX1 [347], OLIG2 [348], EFHC2 [349], FOXA1 [350], PAX4 [351], BGN (biglycan) [352], IL33 [353], LRIG3 [354], GJA1 [355], SHANK3 [356], ARC (activity regulated cytoskeleton associated protein) [357], GFAP (glial fibrillary acidic protein) [358], UNC13A [359], MSTN (myostatin) [360], HSPG2 [361], TERT (telomerase reverse transcriptase) [362], GNB3 [363], L1CAM [364], CALB1 [365], RYR2 [366], MYO15A [367], MYH11 [368], GDF15 [369], CAV1 [370], KIRREL3 [371], COBL (cordon-bleu WH2 repeat protein) [372], LOX (lysyl oxidase) [373], SPARCL1 [374], FLNC (filamin C) [375], TMPRSS4 [376], VWA2 [377], OGDHL (oxoglutarate dehydrogenase L) [378], CYP3A5 [379] and FBXO40 [380] were revealed to be associated with cognitive dysfunction. S100A12 [381], SLC6A19 [382], TXN (thioredoxin) [383], TLR9 [384], S100P [385], S100A9 [386], ANK1 [387], CA1 [388], HP (haptoglobin) [389], ARG1 [390], SNCA (synuclein alpha) [391], STARD10 [392], PINK1 [393], TFR2 [394], B2M [395...…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

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Serum ferritin as a risk factor for type 2 diabetes mellitus, regulated by liver transferrin receptor 2

Cited by 6 publications

References 27 publications

Quantitative susceptibility mapping for iron monitoring of multiple subcortical nuclei in type 2 diabetes mellitus: a systematic review and meta-analysis

Quantitative susceptibility mapping for iron monitoring of multiple subcortical nuclei in type 2 diabetes mellitus: a systematic review and meta-analysis

Serum Activities of Ferritin Among Controlled and Uncontrolled Type 2 Diabetes Mellitus Patients

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

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