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Investigating CR1 as an indicated Gene for mild cognitive impairment in type 2 diabetes mellitus

Diabetology & Metabolic Syndrome volume 16, Article number: 206 (2024) Cite this article

Abstract

Objective

Type 2 diabetes mellitus (T2DM) has beenis known as an important risk factor for cognitive impairment. Meanwhile, the liver plays a central role in the development of T2DM and insulin resistance. The present study attempted to identify and validate marker genes for mild cognitive impairment (MCI) in patients with T2DM.

Methods

In this study, insulin resistance-related differentially expressed genes were identified from the liver tissues of individuals with T2DM and those with normal glucose tolerance using the Gene Expression Omnibus database and MCI-associated genes were identified using the GeneCards database. Next, enrichment analysis was performed with overlapping T2DM and MCI genes, followed by the identification of specific genes using the LASSO logistic regression and SVM-RFE algorithms. An important experiment involved the implementation of clinical and in vitro validation using real-time quantitative polymerase chain reaction (RT-qPCR). Finally, multiple linear regression, binary logistic regression, and receiver operating characteristic curve analyses were performed to investigate the relationship between the key gene and cognitive function in these patients.

Result

The present study identified 40 overlapping genes between MCI and T2DM, with subsequent enrichment analysis revealing their significant association with the roles of neuronal and glial projections. The marker gene complement receptor 1(CR1) was identified for both diseases using two regression algorithms. Based on RT-qPCR validation in 65 T2DM patients with MCI (MCI group) and 65 T2DM patients without MCI (NC group), a significant upregulation of CR1 mRNA in peripheral blood mononuclear cells was observed in the MCI group (P < 0.001). Furthermore, the CR1 gene level was significantly negatively associated with MoCA and MMSE scores, which reflect the overall cognitive function, and positively correlated with TMTB scores, which indicate the executive function. Finally, elevated CR1 mRNA levels were identified as an independent risk factor for MCI (OR = 1.481, P < 0.001).

Conclusion

These findings suggest that CR1 is an important predictor of MCI in patients with T2DM. Thus, CR1 has potential clinical significance, which may offer new ideas and directions for the management and treatment of T2DM. The identification and clinical validation of dysregulated marker genes in both T2DM and MCI can offer valuable insights into the intrinsic association between these two conditions. The current study insights may inspire the development of novel strategies for addressing the complicated issues related to cognitive impairment associated with diabetes.

Introduction

Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia [1]. The predominant type (90%) is type 2 diabetes mellitus (T2DM), which is a well-established risk factor for mild cognitive impairment (MCI) and Alzheimer’s disease (AD) [2]. It has been reported that T2DM is associated with a 20% increased risk of MCI and a 37% increased risk of dementia [3]. MCI, considered as the prodromal phase of AD, signifies the transition from typical aging to dementia in this disorder [4]. Thus, the early detection and treatment of MCI could halt disease progression and even reinstate regular cognitive function [5].

Insulin resistance (IR), a key characteristic of T2DM, has been increasingly gaining attention as a potential risk factor of AD and related dementias [6]. The primary pathway through which T2DM may impact AD involves central IR, which diminishes insulin sensitivity in the brain and leads to hyperinsulinemia, compromised insulin receptor signaling, and glucose toxicity [7]. Previous research has revealed that intrahippocampal insulin infusion in a T2DM rat model mitigated cognitive decline and that nasally delivered insulin in diabetic mice enhanced diabetes-related cognition, which suggests that targeting IR has potential therapeutic benefits in AD [8]. IR can predispose individuals to AD development partly because of its negative impacts on the regional cerebral glucose metabolic rate [9], which impairs glucose utilization in brain regions, observed concurrently in AD and T2DM [10]. The processing of both is involved in disordered insulin and insulin signaling pathways, and AD has been referred to as “type 3 diabetes” or “IR of the brain” [11, 12]. At present, there is no effective remedy for AD, and more studies are needed to determine a screening index capable of detecting diabetic cognitive dysfunction. However, it is still difficult to diagnose and manage cognitive dysfunction in T2DM. As one of the major hallmarks of T2DM pathogenesis and etiology, IR is involved in the entire diabetes process.

The liver plays an important role in maintaining glucose homeostasis and may contribute to the development of T2DM and IR through the production of hepatokines [13, 14]. Thus, screening for liver-based blood markers may provide a relatively low-cost, noninvasive means for identifying increased risk for T2DM development as well as offer a rationale for examining the potential benefits of interventions directed at improving diabetes-related cognitive dysfunction. By employing bioinformatic analyses coupled with in vitro experiments, this study aimed to establish a shared biological foundation between MCI and T2DM to help mitigate the risk of progression from MCI to AD.

Materials and methods

Study design

The research process is elucidated by illustrating the workflow of this study in Fig. 1.

Fig. 1
figure 1

The whole analysis flow for this study

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Candidate targets screening

Dataset preparation and MCI-related gene set

The liver gene expression profile in individuals with or without IR was obtained from the NCBI-Gene Expression Omnibus (GEO) database [15]. In particular, the GSE23343 dataset [16] for the human liver tissue (T2DM, 10 cases; normal glucose tolerance, 7 cases) was analyzed using the GPL570 platform. The raw data from this dataset were processed using R (version 3.6.3) with the packages “GEOquery” and “APFY”. The corresponding annotation file was utilized to match the probe ID with the gene symbol through “Bioconductor”. Subsequently, clustering analysis was performed using “pheatmap”, and differentially expressed genes (DEGs) related to IR were identified in the dataset employing “LIMMA” (P < 0.05 and |logFC| > 0.585 served as cutoff criteria). Comparisons between groups were visualized using a volcano plot, which effectively illustrates differential gene expression. Meanwhile, MCI-associated genes were extracted from the GeneCards database. Genes with a relevance score of > 0.5836 (median) were considered MCI-associated genes, and 951 targets related to MCI were obtained. Venn diagrams were plotted using R package “VennDiagram” to display overlapping genes between the two discovery datasets.

Functional and pathway enrichment analysis

The clusterProfiler package was utilized to perform enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to facilitate functional annotation and pathway prediction using R software. The “ggplot2” package in R was used to visualize the obtained enrichment results.

Screening of candidate diagnostic biomarkers

The least absolute shrinkage and selection operator (LASSO) logistic regression using the “glmnet” package [17] and the support vector machine-recursive feature elimination (SVM-RFE) utilizing the “e1071” package [18] were employed to shrink mRNA candidates. Venn diagram was used to illustrate the intersection of the outcomes from both algorithms.

In vitro validation of identified genes

A total of 130 T2DM patients, aged between 40 and 74 years, were recruited from the Department of Endocrinology at the Affiliated Zhongda Hospital of Southeast University between August 2022 and April 2023. Among them, there were 65 individuals with MCI and 65 without MCI.

The inclusion criteria for the study were as follows: (1) Participants had a minimum 6-year educational background, and (2) T2DM duration reached a threshold of three years. The exclusion criteria were defined as follows: (1) Acute complications (diabetic ketoacidosis, severe hypoglycemia, or cardiovascular crises); (2) Neurological pathologies and substance misuse, including alcohol and drugs; (3) Other prevalent illnesses, such as thyroid malfunction, significant infection, history of surgical procedure, or malignant tumor posing potential risks to cognitive function and neuropsychometric test outcomes; (4) Deafness/visual impairments impairing test integrity. MoCA scores were used for assessing global cognitive functioning according to published methodology. When a participant’s education years were less than 12 years, an additional point was added to their MoCA total score if it was below 30 points [19]. T2DM patients were categorized into MCI group (MoCA ≤ 26, with subjective memory impairment or subjective cognitive impairment) or NC group(MoCA > 26, without such impairments), approved by our institutional Research Ethics Committee (approval no. of ethics committee: 2023ZDSYLL435-P01).

Whole blood samples collected from the participants were utilized for isolating human peripheral blood mononuclear cells (PBMCs) via Ficoll–Paque density gradient separation (density 1.077 g/dl; TBD Science, China, LTS1077). TRIzol reagent was employed to extract total RNA from the isolated PBMC samples as per the manufacturer’s instructions, and the quantity of the extracted RNA was determined using a NanoDrop spectrophotometer (Thermo Scientific Nanodrop 2000). mRNA reverse transcription was conducted utilizing the FastQuant cDNA synthesis kit (RR036B, TaKaRa, Japan). Real-time qPCR analysis was performed with TB GreenTM Premix Ex Taq IITM (RR820A, TaKaRa ) on a Step ONE Plus Real time PCR system (Applied Biosystems). Sequences of the primers used for the analyses were shown in Table 1. β-Actin was used as the internal reference, and gene mRNA expressions were calculated by 2-ΔΔCt method [20].

Table 1 Sequences of the primer pairs used to analyze the genes
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Statistical analysis

Statistical charts were generated using GraphPad Prism 8.0, while statistical analyses were performed using SPSS 24.0 (Chicago, USA) and R software (version 4.2.1). Sample size calculations were conducted using PASS 15 (NCSS, LLC, UTAH) with an expected medium effect size of Cohen’s d = 0.60, alpha level set at 0.05 and beta level at 0.2 (80% power), indicating a minimum sample size requirement of 64 per group. The results of the molecular experiments and clinical data are presented as mean ± standard deviation (SD). The Student’s t-test was employed to analyze variables that exhibited a normal distribution. The nonparametric Mann-Whitney U test was conducted for variables with asymmetric distributions. The Chi-squared test was utilized to examine binary variables. Spearman and partial correlation analyses were conducted with or without adjustment for confounding factors. Furthermore, multiple linear regression was employed to assess the impact of these factors on global cognitive function. Binary logistic analysis was also utilized to identify risk factors for MCI, while ROC curve analysis was performed to evaluate diagnostic accuracy. Two-tailed P < 0.05 was considered statistically significant.

Results

Identification of intersecting genes in T2DM with MCI

The study flow chart is depicted in Fig. 1. The gene expression profiles from the GSE23343 dataset were acquired via the online GEO database, comprising data from 10 individuals diagnosed with T2DM and 7 subjects with normal glucose tolerance. A total of 1082 DEGs were identified based on the criteria of |logFC| ≥ 0.585 and P-value < 0.05, including 585 (54.1%) upregulated and 497 (45.9%) downregulated genes. The heat map and volcano plot of DEGs are presented in Fig. 2A and B, respectively. Utilizing the GeneCards database, 951 MCI-related genes were identified. Between these two sets, 40 genes overlapped (Fig. 2C). For subsequent investigations exploring the mechanisms linking T2DM with MCI, this overlapping gene set was analyzed further. GO and KEGG pathway enrichment analyses revealed that these overlapping genes were primarily enriched in calcium-mediated signaling, positive regulation of ion transport, second messenger-mediated signaling, neuron projection, glial cell projection, and cytokine–cytokine receptor interaction pathways (Fig. 2D).

Fig. 2
figure 2

Analysis of intersecting genes in T2DM with MCI. (A) Heat map illustrating the distribution of DEGs related IR in liver tissue from the GEO dataset (GSE23343). (B) Volcano map of DEGs. (C) Venn diagram revealed a total of 40 overlapping targets between T2DM (GSE23343) and MCI. (D) GO and KEGG analyses of 40 DEGs. “BP” stands for “biological process”, “CC” stands for “cellular component” and “MF” stands for “molecular function”

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Identification of the specific genes in T2DM with MCI

It was hypothesized that the abovementioned 40 overlapping genes are differentially expressed in patients with T2DM and that they increase susceptibility to MCI. With the gene expression matrix derived from the GSE23343 dataset as the primary resource for subsequent investigation, eight specific genes (DCD, NR4A1, SNAP91, CALB2, IL18, IFNB1, CHKB, and CR1) were identified from the overlapping genes using the LASSO logistic regression algorithm (Fig. 3A, B). Moreover, the SVM-RFE algorithm was used to filter these genes, identifying eight additional specific genes (HMGCR, HCRT, CCR6, IL33, CXCL9, PTPRC, CHKB, and CR1; Fig. 3C). Subsequently, a combined analysis utilizing both algorithms determined that CR1 and CHKB were the two genes of interest (Fig. 3D).

Fig. 3
figure 3

Machine learning algorithms for finding characteristic genes. (A) The optimal parameter was determined through screening, with lambda.1se identified as the best lambda value for drawing vertical lines. (B) LASSO logistic regression algorithm was employed to select 8 genes. (C) SVM-RFE algorithm was used to identify another set of 8 genes. (D) The final selection of 2 target genes resulted from the intersection of the two algorithms

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Validation of specific genes in clinical samples

Comparison of clinical characteristics in T2DM patients with and without MCI

In the current study, the clinical data of individuals who provided blood samples are shown in Table 2. Compared with the NC group, patients in the MCI group were older and had a significantly longer duration of diabetes, higher HbA1c level, increased hypertension prevalence, and lower educational level (all P < 0.05). However, there was no significant difference in BMI, FBG, TG, TC, HDL, LDL, Cr, BUN, UA, ALT, and AST between the two groups (all P > 0.05). Meanwhile, the results demonstrated a significant decline in the scores of MoCA, MMSE, DST, VFT, CDT, AVLT-IR, AVLT-DR, and LMT, accompanied by prolonged completion times for TMTA and TMTB in the MCI group (all P < 0.05).

Table 2 Clinical Data of Validated individuals
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Comparison and association between CR1-RNA levels and cognitive preference in T2DM patients

The target mRNA expression was quantified using RT-qPCR. Compared with the NC group, the MCI group showed a tendency toward increased CR1 mRNA expression (P < 0.001) and decreased CHKB mRNA expression (P = 0.057; Fig. 4). The mRNA expression patterns were consistent with those obtained from bioinformatic analyses. Of note, only CR1 exhibited a significant difference in the mRNA expression level, which necessitated a correlation analysis between CR1 and neuropsychometric test. Spearman correlation analysis revealed a significant negative association between CR1 mRNA expression and MoCA (R = − 0.459, P < 0.001) as well as with MMSE (R = − 0.397, P < 0.001; Fig. 5).

Given the age, education, T2DM duration, hypertension prevalence, and HbA1c level differences between patients with and without MCI, partial association analyses were performed. Both the unadjusted and adjusted models revealed that CR1 levels were negatively associated with MoCA, MMSE, and CDT scores and positively associated with TMTB scores (all P < 0.05). While MMSE and MoCA served as the measures of global cognition function, CDT assessed praxis and planning abilities and TMTB determined executive function. Meanwhile, CR1 levels were significantly associated with VFT, TMTA, AVLT-DR, and LMT only in the unadjusted model (shown in Supplementary Table 1). Next, stepwise multivariable linear regression was performed with MoCA and MMSE as the dependent variables to identify the factor predicting cognitive function. The potential confounding factors were adjusted for three sequential models with the increasing levels of covariate adjustment for all analyses. Model 1 was unadjusted, whereas models 2 and 3 were gradually adjusted using age, education, T2DM duration, hypertension prevalence, and HbA1c level. The results revealed that CR1 mRNA independently contributed to the risk of MoCA and MMSE (P < 0.05; Table 3).

Fig. 4
figure 4

Validation of CR1 and CHKB using RT-PCR

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Fig. 5
figure 5

Scatter plot for correlation analysis between CR1-mRNA and Cognitive Function

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Table 3 Multiple linear regression analysis of the association between CR1 and MoCA/MMSE in patients with T2DM
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Analysis and evaluation of CR1 diagnosis value for MCI in T2DM patients

To determine whether elevated CR1 levels serve as a risk factor for MCI in patients with T2DM, binary logistic regression analysis was performed. The results indicated that CR1 mRNA levels were indeed a risk factor for MCI in patients with T2DM (odds ratio [OR] = 1.456, P < 0.001). In addition, after adjusting for age, education, T2DM duration, hypertension prevalence, and HbA1c level, elevated CR1 mRNA levels remained an independent risk factor for MCI in these patients (OR = 1.481, P < 0.001; Table 4). Considering these results, a detailed investigation was performed to ascertain the diagnostic utility of CR1 mRNA levels. The results revealed that the area under the receiver operating characteristic curve (AUC) analysis was 0.748 (Fig. 6).

Table 4 Univariate and Multivariate logistic regression analysis for MCI risk in T2DM patients
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Fig. 6
figure 6

ROC curve of CR1 for MCI in T2DM

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Discussion

Type 2 diabetes mellitus adversely impacts a significant portion of the population and is closely associated with cognitive impairment [21]. At present, there is an escalating concern regarding the numerous acute and chronic complications associated with diabetes, with a specific emphasis on the attention received by the link between T2DM and cognitive impairment. The role of insulin and insulin-like growth factor I (IGF-I) in the pathogenesis of AD is well established, with a robust association with glucose metabolism. The brain of individuals with AD is characterized by diminished levels of insulin, IGF-I, and related elements such as insulin receptor substrate, juxtaposed with elevated levels of amyloid-precursor protein [22]. IR, characterized by an inadequate responsiveness to insulin in target tissues, is a causative factor in pre-diabetes and T2DM and it heightens the susceptibility to AD development [23]. MCI, which signifies a stage of cognitive decline antecedent to the clinical symptoms of dementia and AD, represents a pre-dementia state associated with a tenfold increased risk of progression to dementia [24, 25]. At present, no therapies exist to prevent or decelerate AD progression. Therefore, interventions during MCI stages are crucial for mitigating AD progression. Consequently, the identification of high-risk populations with MCI in T2DM and pursuit of interventions targeting innovative therapeutic approaches have emerged as prominent focal points in this field of research. The cognitive effects associated with T2DM involve systemic IR, which can result in both brain IR and subsequent brain dysfunction [6]. However, the mechanism of cognitive dysfunction in diabetes has not yet been clarified, and effective drugs to prevent and treat cognitive dysfunction are still lacking. Meanwhile, evidence has indicated that inflammation causes IR [26]. Hepatic inflammation, coupled with a heightened release of inflammatory cytokines from adipose tissue, can induce IR [27, 28]. This chronic metabolic inflammation significantly contributes to the development of IR [29]. The liver serves as the central hub of glucose metabolism [30], which orchestrates the dynamic equilibrium of systemic metabolism [31].

The present study findings may enhance the comprehension of mechanisms underlying cognitive impairment in T2DM via the analysis of human liver transcriptomics. Among the 40 dysregulated genes identified, the enrichment analysis of GO and KEGG pathways revealed predominant involvement in neuron projection and glial cell projection, which are known for their significant contribution to cognitive impairment. Investigating these specific genes holds paramount importance for advancing the diagnosis and treatment of MCI in T2DM. Meanwhile, the machine learning algorithm identified two targets (CR1 and CHKB). In the clinical validation experiments, CR1 was significantly enriched in the MCI group of patients with T2DM, whereas CHKB was downregulated, albeit without statistical significance. Complement receptor 1 (CR1), also referred to as CD35, resides on chromosome 1 at the 1q32 locus within a genetic cluster called the regulators of complement activation (RCA), which consists of proteins belonging to the RCA family [32]. Prior studies have indicated that CR1 significantly impacts AD pathology, primarily influencing Aβ deposition, immune response modulation, neuroinflammation, brain structure changes, and glucose metabolism throughout AD progression [33,34,35,36]. GWAS studies identified CR1 as a novel locus for CSF Aβ42 levels [37]. At present, the association between CR1 and late-onset AD has been validated across diverse regions and populations [38,39,40,41,42,43,44]. However, limited research has been conducted on the early diagnosis of CR1 in individuals with MCI, prompting this investigation in T2DM populations characterized by a high prevalence of MCI. In particular, CR1 mRNA levels were significantly upregulated in the PBMCs of patients with T2DM and MCI. In the correlation analysis, both the unadjusted and the adjusted models revealed that CR1 mRNA levels were significantly correlated with both global cognitive function (MoCA and MMSE) and executive function (TMTB). Furthermore, the diagnostic values of CR1 in MCI were analyzed, aside from its relationship with cognitive function. The findings showed that the inhibition of CR1 mRNA expression in patients with T2DM may ameliorate cognitive impairment. These findings as well as the results of the correlation analysis aligned with the observation that the AD group exhibited an elevated blood soluble CR1 level compared with the control group [45]. CR1 serves as a receptor for C3 (C4b2a) and C5 (4b2a3b) invertases in both the classical and bypass pathways of the complement system. Astrocyte activation by C3a can exert a neuroprotective effect, thereby interfering with neuronal death [46, 47]. Moreover, neocortical plaques in patients with AD demonstrate C3b activation [47], which has been shown to enhance clearance and confer various beneficial effects. Given its involvement in regulating C3 transformation, it is plausible that CR1 may contribute to AD pathology by modulating the complement system via multiple pathways. The results of the current study showed that CR1 was enriched considerably in the MCI group of patients with T2DM and that it was an independent risk factor of MCI in these patients. Therefore, CR1 may serve as a promising therapeutic candidate for the early diagnosis and treatment of cognitive dysfunction in patients with T2DM.

The current study has inherent constraints and limitations that need to be considered. Firstly, our focus was primarily on mRNA expression changes. Therefore, future studies should incorporate proteomic analyses to confirm the functional significance of the mRNA alterations documented in this study. Secondly, the cross-sectional nature of our clinical data limits our ability to establish a causal relationship between CR1 and cognitive decline. Moreover, the sample size may not fully represent the broader diabetic population, highlighting the need for larger, multicenter, prospective studies to validate our findings. These studies should aim to track the progression of cognitive changes over time and examine the long-term effects of CR1 expression on cognitive health. Thirdly, our analysis lacked neuroimaging data and cognitive biomarkers such as tau proteins and Aβ40/Aβ42, which could provide a more comprehensive understanding of the pathophysiological mechanisms linking T2DM with MCI. Incorporating these biomarkers in future studies could enhance our understanding of the underlying molecular mechanisms.Finally, while we have demonstrated the potential role of CR1 in cognitive impairment, the exact molecular mechanisms by which CR1 influences MCI development in T2DM remain unclear. Detailed molecular and cellular studies are required to elucidate how CR1 regulates cognitive function and contributes to the pathophysiology of cognitive decline in diabetes. We advocate for comprehensive research including longitudinal clinical trials and advanced bioinformatics analyses to dissect the impact of CR1 on cognitive functions. This approach will not only confirm the role of CR1 but also explore its utility as a predictive biomarker and therapeutic target.In summary, the identification of potential biomarkers opens up promising avenues for improving the understanding of MCI in T2DM. There is a critical need for extensive research to fully elucidate the molecular basis of this association. Insights gained from such studies could be pivotal in developing innovative strategies to prevent and treat cognitive impairment in diabetes, ultimately enhancing patient outcomes.

Conclusion

In the current study, the GEO and GeneCards databases were used to confirm the identification of a key target gene, CR1. In general, the evidence suggested that CR1 mRNA is linked to MCI in patients with T2DM. Elevated CR1 levels may represent a potential risk factor of MCI in patients with T2DM, specifically affecting praxis and planning abilities as well as executive function. The study results showed that CR1 was enriched considerably in the MCI group of patients with T2DM and that it was an independent risk factor of MCI in these patients. However, further investigation is warranted to explore the pathological mechanisms underlying MCI in patients with T2DM induced by CR1. The study results may enhance the comprehension of the mechanisms involved in cognitive disorders in diabetes and their treatment. However, the validation of these results and exploration of potential therapeutic applications require further research. Taken together, the identification of potential biomarkers offers a promising avenue for further exploration into the molecular mechanisms of MCI in diabetes, providing a foundation for the early diagnosis and treatment of MCI in patients with T2DM.

Data availability

The data used and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This study was supported by the National Natural Science Foundation of China (grant number: 81870568, Shaohua Wang).

Author information

Authors and Affiliations

  1. School of Medicine, Southeast University, Nanjing, China

    Xueling Zhou, Shaohua Wang, Dandan Yu & Tong Niu

  2. Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China

    Xueling Zhou, Shaohua Wang, Dandan Yu & Tong Niu

Authors
  1. Xueling Zhou
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  2. Shaohua Wang
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  3. Dandan Yu
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  4. Tong Niu
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Contributions

XZ and SW desinged the study. NT and DY examined the blood samples and collected the clinical data. XZ analyzed the data, wrote the manuscript and prepared all the Figures and Tables. SW and DY interpretated the data and revised the manuscript. All authors have made an intellectual contribution to the manuscript and approved the final submission.

Corresponding author

Correspondence to Shaohua Wang.

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Ethical approval

All participants were informed about the course of this experiment and received a handwritten letter signature on informed consent before the experiment. This study was conducted according to Declaration of Helsinki and approved by the Research Ethics Committee of ZhongDa Affiliated Hospital Southeastern University(approval no. of ethics committee: 2023ZDSYLL435-P01).

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The authors declare no competing interests.

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Zhou, X., Wang, S., Yu, D. et al. Investigating CR1 as an indicated Gene for mild cognitive impairment in type 2 diabetes mellitus. Diabetol Metab Syndr 16, 206 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01449-y

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01449-y

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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