Expression profile of miR-214, miR-204, miR-25, miR-15a, IL-33, and plasma level of Malondialdehyde might serve as potential biomarkers for Alzheimer’s disease

Expression profile of miR-214, miR-204, miR-25, miR-15a, IL-33, and plasma level of Malondialdehyde might serve as potential biomarkers for Alzheimer’s disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Expression profile of miR-214, miR-204, miR-25, miR-15a, IL-33, and plasma level of Malondialdehyde might serve as potential biomarkers for Alzheimer’s disease Haydar Sahib Almawashee, Mohammad Khalaj-Kondori, Mohammad Ali Hoseinpour Feizi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4500729/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alzheimer’s disease (AD) is a late-of-onset neurodegenerative disease that affects elder people. Despite immense research on deciphering the pathophysiology of AD, the precise etiology of AD remains still elusive. Deregulations of miRNAs play essential roles in AD pathogenesis and as a result, they might be potential biomarkers for AD development and diagnosis. This study was aimed to assess the expression of miR-214, miR-204, miR-15a, miR-25, and investigate their correlations with the expression of IL-33, plasma level of Malondialdehyde (MDA) and Mini-Mental State Examination (MMSE) score of the AD patients. Blood samples were obtained from125 participants including 75 AD patients and 50 healthy controls. Plasma and Blood leukocytes were isolated and used for subsequent analysis. Results showed that the plasma level of MDA was significantly higher in the AD patients. Besides, IL-33, miR-15a and miR-25 were downregulated in the patients’ group but miR-214 and miR-204 expressions were upregulated. Plasma MDA level showed a negative correlation with the MMSE and a positive correlation with the IL-33 expression. We also observed a statistically meaningful negative correlation between miR-15a and IL-33 expressions. Correlations between the studied miRNAs and MDA were all non-significant. Furthermore, none of the miRNAs or IL-33 expressions were correlated with the MMSE scores. ROC curve analysis revealed that expressions of the studied miRNAs, IL-33, and the plasma level of MDA could differentiate AD patients from healthy controls. In conclusion, our results showed that expressions of miR-214, miR-204, miR-25, miR-15a, IL33, and plasma level of MDA might be considered as potential biomarkers for AD development and diagnosis. Gene expression Alzheimer’s disease miRNA Malondialdehyde IL-33 Cytokine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Alzheimer's disease (AD) is a late-of-onset neurodegenerative disease characterized by memory impairment, progressive cognitive decline, and behavioral changes, posing a significant public health challenge worldwide. It was estimated that 6.7 million Americans age 65 and older were suffering from Alzheimer's disease in 2023 (Alzheimer’s Association 2023). It is a heterogenous and multifactorial disease that is governed by genetic and environmental factors (Fotuhi et al. 2020 ; Wei et al. 2020 ). The main pathobiological findings of AD are neurofibrillary tangles and amyloid plaques, which are linked with hyperphosphorylated tau protein and aberrant beta-amyloid (Aβ) metabolism, respectively (Fotuhi et al. 2020 ; Wei et al. 2020 ; Fotuhi et al. 2024). Despite decades of research, the precise etiology of AD remains elusive, reflecting its multifactorial nature and complex interplay of genetic, environmental, and lifestyle factors. With the global population aging rapidly, the prevalence of AD is projected to escalate dramatically in the coming decades, highlighting the urgent need for innovative diagnostic tools, effective therapeutic strategies, and preventive measures. Oxidative stress, resulting from an imbalance between ROS (reactive oxygen species) production and antioxidant defenses, has emerged as a pivotal contributor to AD pathogenesis (Huang 2016; Cassidy et al. 2020 ; Ionescu-Tucker et al. 2021). When oxidative damage accumulates to the macromolecules such as lipids, proteins, and nucleic acids in the brain, it can exacerbate neuronal dysfunction and promotes neurodegeneration. Malondialdehyde (MDA), a biomarker of lipid peroxidation, has been implicated in AD-related oxidative stress, with elevated levels detected in brain tissues and biological fluids of affected individuals (Bradley-Whitman et al. 2015; Rani et al. 2017 ; Arslan et al. 2020 ; Rao et al. 2021 ). However, the precise mechanisms by which oxidative stress contributes to AD progression and its potential as a therapeutic target warrant further elucidation. In addition to oxidative stress, chronic neuroinflammation has emerged as a hallmark feature of AD pathology (Sobue et al. 2023 ). Activation of microglia and astrocytes in response to pathological insults leads to producing pro-inflammatory cytokines and chemokines, perpetuating neuroinflammatory responses and exacerbating neuronal damage (Lecca et al. 2022 ; Wong-Guerra et al. 2023 ). Interleukin-33 (IL-33), a pleiotropic cytokine involved in immune regulation and tissue repair, has been implicated in modulating neuroinflammation in AD (Fu et al. 2016 ; Carlock et al. 2017 ; Liang et al. 2020 ; Saresella et al. 2020 ). Understanding the intricate crosstalk between neuroinflammation and AD pathogenesis may offer novel insights into disease mechanisms and therapeutic targets. Furthermore, dysregulation of microRNAs (miRNAs), small non-coding RNAs with 19–25 nucleotides in length, has been implicated in AD pathophysiology (Liu et al. 2022 ; Peña-Bautista et al. 2022 ). Altered expression profiles of specific miRNAs in AD brains and blood contribute to the dysregulation of key molecular pathways involved in amyloid-beta processing, tau phosphorylation, synaptic dysfunction, and neuroinflammation (Angelucci et al. 2019 ; Biglari et al. 2022 ; Khodayi-Shahrak et al. 2022 ; Abidin et al. 2023 ; Han et al. 2024 ). Notably, miR-214, miR-15a, miR-204, and miR-25 have been identified as potential players in AD-related neurodegeneration (Moncini et al. 2017 ; Duan et al. 2019; Tao et al. 2021 ; Hu et al. 2022 ). Investigating the roles of miRNAs in AD pathogenesis may offer important clues into disease mechanisms and facilitate the development of miRNA-based therapeutic and diagnostic approaches (Duan et al. 2019; Khodayi-Shahrak et al. 2022 ; Peña-Bautista et al. 2022 ). By elucidating the molecular mechanisms governing AD onset and progression, researchers aim to develop approaches for precision medicine, ultimately improving diagnostic accuracy, treatment efficacy, and patient outcomes. Integrative approaches that combine molecular, cellular, and clinical investigations are essential for deciphering the complex pathophysiology of AD and identifying novel therapeutic targets. Notably, in recent years, there has been a growing emphasis on elucidating the intricate interplay between oxidative stress, neuroinflammation, and miRNA dysregulation in AD. This study investigated the blood levels of Malondialdehyde (MDA) as an indicator of oxidative stress and IL-33 as a neuroinflammation modulator in AD patients. Furthermore, correlations between their blood levels and expression levels of miR-214, miR-204, miR-15a, and miR-25 in the blood cells as well as with the clinical findings of the subjects were assessed. Materials and Methods Study samples A total of 125 subjects including 75 AD patients and 50 healthy controls without any symptoms of neurological diseases and matched for age and gender were included in this study. The subjects were diagnosed with neuroscience specialists based on the Diagnostic and Statistical Manual of the American Psychiatric Association (DSM-V) criteria (Vahia 2013 ). All participants or their related caregivers signed the informed written consent and the study was authorized with the research ethics committee of Tabriz University of Medical Sciences. Subjects with age 65 or higher and without any neurologic and psychiatric diseases other than AD were included in the study. Subjects having other neurological conditions such as Parkinson’s disease, multiple sclerosis, autism, frontal lobe dementia, encephalitis, traumatic brain injury, Lewy body dementia, spastic lesions, diabetes, thyroid disease, liver and renal diseases, metabolic disorders, cancer, inflammatory diseases or taking anti-inflammatory medicines and alcoholism were excluded from both groups. Both groups were evaluated with the Mini-Mental State Examination (MMSE) test. A 6 mL peripheral blood was taken from each participant and used for subsequent analysis. Assessment of Malondialdehyde Malondialdehyde (MDA) level of the plasma samples from both AD patients and healthy controls was measured using the Malondialdehyde ELISA kit (ZellBio, Germany) based on the kit’s manual. Briefly, 50 µL from each sample was added to each well of the microplate and treated with 50 µL Biotin-labeled Antibody for 45 min at 37 ºC. Then, the solution was discarded, washed three times with washing buffer, and treated with 0.1 mL HRP-Streptavidin Conjugate solution for 30 min at 37 ºC. The solution was discarded, and the wells treated with 90 µL TMB for 20 min, and the reactions were stopped by 50 µL stop solution. The results were assessed by ELISA reader at 450 nm. RNA extraction and qRT-PCR Blood leukocytes were collected by red blood cell lysis buffer (RBC lysis) (Heng, Ruan and Gan 2018 ) and used for total RNA extraction from the samples. Total RNA was purified using RiboEx ™ (GeneAll) based on the manufacture’s instructions. The extracted total RNA samples were assessed by a NanoDrop (Thermo Fisher Scientific USA) instrument, and the quantity of the RNA samples was determined. The SMO BIO-kit (Taiwan) was applied for complementary DNA (cDNA) synthesis. The resulting cDNA was diluted 1:4 and assessed by a Rotor-gene-6000 system (Corbett-Australia) using SYBR-Amplicon (RealQ Plus 2x Master Mix Green). For each sample, qPCR reactions were done in a final volume of 20 µL and in a duplicate format. The U6 and GAPDH genes were used as internal normalizers for expression levels of the miRNAs and IL-33, respectively. The relative expressions of miRNAs and IL-33 in the case and control groups were expressed as 2 -Δct . Table 1 presents the primers used for qRT-PCR reactions. Table 1 Sequences of the primers used for amplification of studied genes. Gene name Forward primer Reverse primer miR-15a 5′- GCAGCACATAATGGTTTG − 3 5′- GAACATGTCTGCGTATCTC − 3′ miR-214 5′- TGCCTGTCTACACTTGC-3′ 5′- GAACATGTCTGCGTATCTC − 3′ miR-204 5′- CCCTTTGTCATCCTATGCC-3′ 5′- GAACATGTCTGCGTATCTC − 3′ miR-25 5′-CGGAGACTTGGGCAATT-3′ 5′- GAACATGTCTGCGTATCTC − 3′ IL-33 5′GCCTGTCAACAGCAGTCTACTG-3′ 5′- AGACATGCAGTGTTTCCCC-3′ U6 5′-GCTTCGGCAGCACATATACTAAAAT-3′ 5′-CGCTTCACGAATTTGCGTGTCAT-3′ GAPDH 5′- ATGGGGAAGGTGAAGGTCG − 3′ 5′-GGGGTCATTGATGGCAACAATA-3′ Statistical analysis We used SPSS version 26 and GraphPad Prism 9 for data analysis. Data normality was analyzed with the Shapiro-Wilk test, and for non-normal data, the Mann-Whitney test was employed. For normally distributed data, the Student's t-test was applied. To investigate the relationships between qualitative variables, the chi-square test was employed. The Pearson correlation coefficient and regression analysis were used to examine the associations between variables. Significance levels of tests were considered to be less than 5%. Results 3.1 Clinicopathological features of the subjects Seventy-five AD patients and fifty healthy controls were assessed in the present study. There was no significant difference between the mean age of control and patient groups (p = 0.235). In the patient group, 58.7% were female and 41.3% were male. Regarding the gender parameter, the difference between two groups was not statistically significant (Table 2 ). Table 2 Characteristics of the case and control groups Variable AD (n = 75) Control (n = 50) P value Age (years) (mean ± SD) 75.9 ± 7.5 77.5 ± 6.9 0.235 Sex (male/female) 31/44, (41.3/58.7%) 18/32, (36.0/64.0%) 0.549 MMSE : normal - 50 (100%) > 20 (mild) 33 (44%) 12 months 22 (29.33%) < 12 months 53 (70.66%) 3.2 Plasma level of Malondialdehyde Plasma level of Malondialdehyde (MDA), as a biomarker of lipid peroxidation, was compared between the AD cases and controls. The results indicated that the MDA plasma level in patients was 17.07 ± 7.06 ng/mL, while it was 5.93 ± 3.16 ng/mL (mean ± SD) in the healthy controls. Statistical analysis revealed that the MDA level was significantly higher in the Alzheimer’s patients than in the healthy controls (p < 0.0001) (Fig. 1 A). Detailed comparison data for the MDA level is provided in Table 3 . Table 3 Comparison of the plasma level of MDA between AD patients and Healthy controls Group Min Max Mean SD P-value Patients 0.095 2.957 1.176 0.671 t = -9.94 P = 0.0001** Controls 0.463 10.412 4.749 2.481 TOTAL 0.095 10.412 2.605 2.406 3.3 Expression levels of the miRNAs and IL-33 in blood cells Expression levels of miRNAs in the blood cells of the AD cases and healthy controls were quantified by qRT-PCR and compared between the two groups (Fig. 1 B-F). We observed that the relative expressions of miR-15a, miR-25, and IL-33 were significantly less in the AD cases compared to the control group (p < 0.0001). However, the relative expression of miR-214 and miR-204 were significantly higher in the AD cases than in the control group (p < 0.0001). Detailed comparisons data for the miRNAs and IL-33 expressions are provided in the Table 4 . Table 4 Comparison of miRNAs and IL-33 relative expression levels between the AD patients and healthy controls. miRNA/Gene Group Min Max Mean SD t-test P-value miR-15a Patient 0.106 2.169 0.738 0.507 t = -11.41 P < 0.0001 Control 0.851 4.351 2.330 0.895 miR-214 Patient 1.220 12.445 7.342 3.364 t = 14.38 P < 0.0001 Control 0.424 3.254 1.589 0.674 miR-204 Patient 1.124 9.841 4.984 2.551 t = 10.47 P < 0.0001 Control 0.119 4.021 1.507 1.082 miR-25 Patient 0.029 4.101 1.322 0.916 t = -3.81 P < 0.0001 Control 0.029 7.190 2.607 2.268 IL-33 Patient 0.095 2.957 1.176 0.671 t = -9.94 P < 0.0001 Control 0.463 10.412 4.749 2.481 3.4 Correlation analysis Correlations between the variables, including MDA, MMSE, IL-33, and miRNAs’ expressions in the patient group were investigated using Pearson correlation analysis. Figure 2 illustrates the correlations between MMSE score of the patients with the expressions of the miRNAs, IL-33 and the plasma level of MDA. The correlations between MMSE score and expressions of the studied miRNAs as well as the expression of IL-33 were all statistically non-significant (Fig. 2 A-D). As Fig. 2 F indicates, the MMSE score was negatively correlated with the plasma MDA level, and this correlation was statistically significant (R = -0.2607, P = 0.039). The analysis also revealed a significant positive correlation between MDA plasma level and expression of IL-33 in patients (R = 0.2733, P = 0.0185). Correlations of IL-33 with miRNAs’ expressions and plasma level of MDA are outlined in Fig. 3 . As the Fig. 3 shows, IL-33 expression was negatively correlated with the expression of miR-15a (p = 0.013), while it showed a positive correlation with the MDA level (p = 0.018). Correlations between IL-33 and other miRNAs were non-significant (p > 0.05). Correlations of MDA plasma level with expressions of the miRNAs are also outlined in Fig. 4 . MDA did not show a significant correlation with any of the studied miRNAs (p > 0.05) while as stated above in Fig. 3 E, MDA level was positively correlated with the IL-33 expression (p = 0.018). Details of these correlation analysis data are outlined in Table 5 . Table 5 Details of the correlation analysis between different studied variables. MMSE mir-15a miR-214 mir-204 mir-25 IL-33 MDA (ng/mL) MDA (ng/mL) Pearson Correlation − .261 * − .182 .124 − .011 .043 .273* 1 Sig. (2-tailed) .039 .120 .292 .927 .721 .185 MMSE Pearson Correlation 1 .061 − .230 .084 .208 .137 − .261 * Sig. (2-tailed) .637 .070 .515 .103 .284 .039 mir-15a Pearson Correlation .061 1 − .002 − .015 .016 − .285 * − .182 Sig. (2-tailed) .637 .987 .896 .889 .013 .120 mir-214 Pearson Correlation − .230 − .002 1 − .049 .046 .081 .124 Sig. (2-tailed) .070 .987 .675 .695 .491 .292 mir-204 Pearson Correlation .084 − .015 − .049 1 − .157 .025 − .011 Sig. (2-tailed) .515 .896 .675 .081 .829 .927 mir-25 Pearson Correlation .208 .016 .046 − .157 1 .152 .043 Sig. (2-tailed) .103 .889 .695 .081 .194 .721 IL-33 Pearson Correlation .137 − .285 * .081 .025 .152 1 − .273* Sig. (2-tailed) .284 .013 .491 .829 .194 .019 MDA (ng/mL) Pearson Correlation − .261 * − .182 .124 − .011 .043 − .273* 1 Sig. (2-tailed) .039 .120 .292 .927 .721 .019 3.5 Biomarker potency of the miRNAs, IL-33, and MDA for diagnosis of AD patients To understand whether MDA plasma level and relative expressions of miRNAs and IL-33 could differentiate AD patients from healthy controls, we conducted ROC curve analysis. Figure 5 outlines the results obtained from this analysis. We found that the relative expression of IL-33 may differentiate patients from healthy controls with an Area under the curve (AUC) of 0.911, and specificity and sensitivity of 80% and 84%, respectively (CI; 0.852–0.970, p < 0.0001) (Fig. 5 A). The results of ROC curve analysis for relative expressions of miRNAs including miR-15a (AUC = 0.946, CI; 0.912–0.980, p < 0.0001), miR-214 (AUC = 0.942, CI; 0.903–0.981, p < 0.0001), miR-204 (AUC = 0.914, CI; 0.860–0.961, p < 0.0001), and miR-25 (AUC = 0.696, CI; 0.543–0.749, p = 0.006) showed that all may differentiate AD patients from healthy controls, but, because of relatively low AUC (0.696), miR-25 can not be considered as a very good biomarker for AD diagnosis (Fig. 5 B- 5 E). ROC curve analysis for the plasma level of MDA resulted in an AUC of o.929, and specificity and sensitivity of 92% and 88%, respectively (CI; 0.882–0.977, p < 0.0001). Details of ROC curve analysis data are provided in Table 6 . Table 6 Details of the results obtained from ROC curve analysis for miRNAs, IL-33 and MDA. Sp%; specificity percent, Se%; sensitivity percent. Variable AUC Std. Error 95% CI P value Cut off Sp% Se% miR-15a 0.946 0.0173 0.912–0.980 < 0.0001 < 1.26 81.3 90 miR-214 0.942 0.0119 0.903–0.981 2.08 90.7 78 miR-204 0.914 0.0244 0.860–0.961 2.32 81.3 78 miR-25 0.696 0.0526 0.543–0.749 0.0057 < 1.21 58.7 64 IL-33 0.911 0.0301 0.852–0.970 < 0.0001 < 1.76 80 84 MDA 0.929 0.0242 0.882–0.977 11.5 92 88 Discussion This study has evaluated the expression of microRNAs including miR-15a, miR-214, miR-204, and miR-25 in the blood cells of AD cases and assessed their relation with the MMSE and expression of IL-33 and plasma level of MDA. The results showed that the MDA plasma level in AD cases was significantly higher than in the healthy controls (Fig. 1 A). ROC cure analysis pointed that its level could differentiate AD cases from healthy people with an AUC of 0.929, indicating MDA as a very good biomarker in diagnosis of AD patients. Its level was also negatively correlated with the MMSE score of the AD patients (Fig. 2 D), meaning that the MDA level is correlated with the disease severity. Although MDA is not a specific determinant for AD, it might be considered as an informative factor in AD development and diagnosis. In agreement with our findings, elevated level of MDA or TBA-MDA adducts in plasma or serum of AD patients were also reported by several researchers (Casado et al. 2008 ; Gustaw-Rothenberg et al. 2010 ; Padurariu et al. 2010 ; Torres et al. 2011 ). In contrast, other studies reported no difference between plasma MDA levels of AD (Polidori et al. 2004 ) or MCI (Martín-Aragón et al. 2009 ) patients and healthy controls. In a meta-analysis of plasma and serum levels of MDA, Schrag et al. showed a significant elevation in probable AD subjects (n = 1098) compared to the control group (n = 1094) (Schrag et al. 2013 ). IL-33 primarily functions in immune defense but it plays critical roles in healing of injuries in CNS (central nervous system) and other disease (Carlock et al. 2017 ). Genetic and transcriptome and studies identified IL-33 as a candidate gene for AD (Chapuis et al. 2009 ). Our results showed that IL-33 was significantly downregulated in the AD cases compared to the healthy controls (Fig. 1 E). ROC curve analysis revealed that the relative expression of IL-33 could significantly differentiate AD from healthy subjects, indicating its potential as a very good biomarker for AD development and diagnosis. However, its expression was not correlated with the MMSE score of the AD patients (Fig. 2 E). In agreement with our findings, Saresella et al. reported that IL-33 was significantly decreased in CSF and serum of the MCI and AD patients (Saresella et al. 2020 ). Furthermore, Chapuis et al. reported that expression of IL-33 was lower in the brain samples of patients with AD compared to the healthy controls (Chapuis et al. 2009 ). Interestingly, Liang et al. showed that expression of IL-33 may preserve cognitive function of the AD patients (Liang et al. 2020 ). Besides, IL-33 reduced memory deficit and AD progression and stirred microglia polarization in an anti-inflammatory direction (Fu et al. 2016 ). MicroRNAs play important roles in the pathophysiology of AD and have been considered as valuable biomarkers for AD development and diagnosis (Angelucci et al. 2019 ; Liu et al. 2022 ; Abidin et al. 2023 ; Han et al. 2024 ). In a murine model of AD, Zhang et al. showed that miR-204 is upregulated, and silencing of the miR-204 could diminish the Aβ1-42-induced mitochondrial damage, mitochondrial autophagy and production of ROS via upregulation of the TRPML1 expression and activation of STAT3 pathway both in vivo and in vitro (Zhang et al. 2021 ). Consistent with this report, we observed that expression of miR-204 was upregulated in AD cases compared to the healthy controls (Fig. 1 C). Besides, ROC curve analysis revealed that expression of miR-204 could differentiate AD and healthy people with an AUC of 0.914 (Fig. 5 D). Correlation analysis also indicated that the relative expression of miR-204 was not correlated with each of the MMSE, plasma MDA level, expression of IL-33, and expression of other studied miRNAs in the patients’ group. In contrast, Tao et al. showed that miR-204-3p was downregulated in hippocampus of a 6-months APP/PS1 AD mouse model and overexpression of miR-204-3p could ameliorate oxidative stress and memory deficits by targeting Nox4 (Tao et al. 2021 ). In line with this report, Taşdelen et al. observed that miR-204 level in the exosomes isolated from serum of mild and moderate AD patients was significantly lower than that of control group (Taşdelen et al. 2022 ). We quantified the expression of miR-15a in the patient and control groups. The results showed a significant downregulation in the AD cases compared to the healthy controls (Fig. 1 F). ROC curve analysis also showed that the expression level of miR-15a could differentiate AD cases from healthy people with an AUC of 0.946 which indicates miR-25a might be considered as a very good biomarker for AD development and diagnosis (Fig. 5 B). Furthermore, its expression was negatively correlated with the expression of IL-33, while it did not show significant correlations with MMSE score, plasma MDA level, and relative expression of other studied miRNAs. Consistent with these findings, Satoh et al. reported downregulation of miR15-a in blood samples of 28 AD and 22 healthy controls by analyzing miRNA-seq data (Satoh et al. 2015 ). Its downregulation in White matter (Wang et al. 2011 ) and cortex (Hébert et al. 2010 ; Nunez-Iglesias et al. 2010 ) of AD patients was also reported. In contrast to these reports, Sorensen et al. reported miR-15a upregulation in CSF samples of AD patients (Sørensen et al. 2016 ). Brett et al. has reported that knock down of miR-25 could reduce proliferation of neural stem/progenitor cells, while ectopic expression of the miR-25 boosts their proliferation (Brett et al. 2011 ). miR-25 has several potential mRNA targets in IGF (insulin/insulin-like growth factor-1) signaling pathway which is implicated in aging. It binds to FoxO3, a transcription factor which involves in maintenance of adult stem cells, thereby playing important roles in the hemostasis of neural stem cells pool during aging (Brett et al. 2011 ). Furthermore, Yu et al. reported that miR-25 plays a role in the regulation of neuronal migration and differentiation (Yu et al. 2015 ). Besides, Guo et al. reported that miR-25 expression was lower in the spinal cord injury mice model, and overexpression of miR-25 protected PC-12 neuroblastoma cells from H 2 0 2 -induced oxidative damage, leading to a decreased ROS (reactive oxygen species) level, significant suppression of apoptosis, and increased cell viability (Guo and Niu 2018 ). We also found that expression of miR-25 in AD patients was significantly lower than in controls, but ROC curve analysis showed a reletively weak potential for miR-25 expression level as a biomarker for AD. Nevertheless, miR-25 expression did not show any correlation with the MMSE score, plasma MDA level, relative expression of IL-33, or other studied miRNAs in the patient group. Opposite to these findings, Duan and Si reported that miR-25 aggravates the hippocampal neuron injuries induced by Aβ1–42 via downregulation of KLF2 in Alzheimer’s disease (Duan and Si 2019 ). Relative expression of miR-214 in blood cells was also quantified and compared between the patient and control groups. The results showed a significant upregulation of miR-214 in patient group, but there was no correlation between its expression level and MMSE score, plasma MDA level, IL-33, or the miRNAs expression. Opposite to these findings, He et al. reported downregulation of the miR-214-3p in the plasma samples of AD patients (He et al. 2020 ). BACE1-AS acts as sponge for miR-214-3p (He et al. 2020 ), and it can promote autophagy-mediated damages of the neurons via miR-214-3p/ATG5 signaling in AD (Zhou et al. 2021 ). Collectively, our results showed upregulation of miR-204 and miR-214 and downregulation of miR-25, miR-15a, and IL-33 in AD patients. The MDA plasma level was also significantly higher in AD cases than in healthy controls. Among miRNAs and IL-33 expressions, there was just a negative correlation between miR-15a and IL-33. The plasma level of MDA showed a positive correlation with IL-33 expression and a negative correlation with MMSE scores. Expression levels of the miR-214, miR204, miR-25, miR-15a, IL-33, and plasma level of MDA might be considered as potential biomarkers for AD development and diagnosis. Declarations Conflict of interest Authors declare that there is no conflict of interest. Funding Authors have not received any external funding regarding this research. Author Contribution HSA has done the research and prepared the primary manuscript. MKK conceptualized the research plan and edited the manuscript. MAHF analyzed the data and edited the manuscript. RS conceptualized the research and edited the manuscript. Acknowledgement Authors kindly appreciate all participants and their families for their consent in using their blood samples in this study. Data availability Data available upon rational request. 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Neurochem Res 45(10):2324–2335 Hébert SS, Papadopoulou AS, Smith P, Galas M-C, Planel E, Silahtaroglu AN, Sergeant N, Buée L, De Strooper B (2010) Genetic ablation of Dicer in adult forebrain neurons results in abnormal tau hyperphosphorylation and neurodegeneration. Hum Mol Genet 19(20):3959–3969 Heng Z, Ruan L, Gan R (2018) Three Methods to Purify Leukocytes and RNA Quality Assessment. Biopreserv Biobank 16(6):434–438 Hu G, Shi Z, Shao W, Xu B (2022) MicroRNA-214-5p involves in the protection effect of Dexmedetomidine against neurological injury in Alzheimer's disease via targeting the suppressor of zest 12. Brain Res Bull 178:164–172 Huang WJ, Zhang X, Chen WW (2016) Role of oxidative stress in Alzheimer's disease. Biomed Rep 4(5):519–522 Ionescu-Tucker A, Cotman CW (2021) Emerging roles of oxidative stress in brain aging and Alzheimer's disease. Neurobiol Aging 107:86–95 Khodayi-Shahrak M, Khalaj-Kondori M, Hosseinpour Feizi MA, Talebi M (2022) Insights into the mechanisms of non-coding RNAs' implication in the pathogenesis of Alzheimer's disease. Exclij 21:921–940 Lecca D, Jung YJ, Scerba MT, Hwang I, Kim YK, Kim S et al (2022) Role of chronic neuroinflammation in neuroplasticity and cognitive function: A hypothesis. Alzheimer's Dement 18(11):2327–2340 Liang C-S, Su K-P, Tsai C-L, Lee J-T, Chu C-S, Yeh TC et al (2020) The role of interleukin-33 in patients with mild cognitive impairment and Alzheimer’s disease. Alzheimers Res Ther 12(1):86 Liu S, Fan M, Zheng Q, Hao S, Yang L, Xia Q, Qi C, Ge J (2022) MicroRNAs in Alzheimer's disease: Potential diagnostic markers and therapeutic targets. Biomed Pharmacother 148:112681 Martín-Aragón S, Bermejo-Bescós P, Benedí J, Felici E, Gil P, Ribera JM, Villar AM (2009) Metalloproteinase’s activity and oxidative stress in mild cognitive impairment and Alzheimer’s disease. Neurochem Res 34:373–378 Moncini S, Lunghi M, Valmadre A, Grasso M, Del Vescovo V, Riva P, Denti MA, Venturin M (2017) The miR-15/107 Family of microRNA Genes Regulates CDK5R1/p35 with Implications for Alzheimer's Disease Pathogenesis. Mol Neurobiol 54(6):4329–4342 Nunez-Iglesias J, Liu CC, Morgan TE, Finch CE, Zhou XJ (2010) Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer's disease cortex reveals altered miRNA regulation. PLoS ONE 5(2):e8898 Padurariu M, Ciobica A, Hritcu L, Stoica B, Bild W, Stefanescu C (2010) Changes of some oxidative stress markers in the serum of patients with mild cognitive impairment and Alzheimer's disease. Neurosci Lett 469:16–10 Peña-Bautista C, Tarazona-Sánchez A, Braza-Boils A, BalaguerA et al (2022) Plasma microRNAs as potential biomarkers in early Alzheimer disease expression. Sci Rep 12(1):15589 Polidori MC, Mattioli P, Aldred S, Cecchetti R, Stahl W, Griffiths H et al (2004) Plasma antioxidant status, immunoglobulin g oxidation and lipid peroxidation in demented patients: relevance to Alzheimer disease and vascular dementia. Dement Geriatr Cogn Disord 18(3–4):265–270 Rani P, Krishnan S, Rani Cathrine C (2017) Study on Analysis of Peripheral Biomarkers for Alzheimer’s Disease Diagnosis. Front Neurol 8 Rao YL, Ganaraja B, Marathe A, Manjrekar PA, Joy T, Ullal S (2021) Comparison of malondialdehyde levels and superoxide dismutase activity in resveratrol and resveratrol/donepezil combination treatment groups in Alzheimer's disease induced rat model. Biotech 11(7):329 Saresella M, Marventano I, Piancone F, La Rosa F, Galimberti D, Fenoglio C, Scarpini E, Clerici M (2020) IL-33 and its decoy sST2 in patients with Alzheimer's disease and mild cognitive impairment. J Neuroinflammation 17(1):174 Satoh J, Kino Y, Niida S (2015) MicroRNA-Seq Data Analysis Pipeline to Identify Blood Biomarkers for Alzheimer's Disease from Public Data. Biomark Insights 10:21–31 Schrag M, Mueller C, Zabel M, Crofton A, Kirsch W, Ghribi O, Squitti R, Perry G (2013) Oxidative stress in blood in Alzheimer's disease and mild cognitive impairment: a meta-analysis. Neurobiol Dis 59:100–110 Sobue A, Komine O, Yamanaka K (2023) Neuroinflammation in Alzheimer’s disease: microglial signature and their relevance to disease. Inflamm Regeneration 43(1):26 Sørensen SS, Nygaard AB, Christensen T (2016) miRNA expression profiles in cerebrospinal fluid and blood of patients with Alzheimer's disease and other types of dementia - an exploratory study. Transl Neurodegener 5:6 Tao W, Yu L, Shu S, Liu Y, Zhuang Z, Xu S et al (2021) miR-204-3p/Nox4 Mediates Memory Deficits in a Mouse Model of Alzheimer's Disease. Mol Ther 29(1):396–408 Taşdelen E, Özel Kızıl ET, Tezcan S, Yalap E, Bingöl AP, Kutlay NY (2022) Determination of miR-373 and miR-204 levels in neuronal exosomes in Alzheimer's disease. Turk J Med Sci 52(5):1458–1467 Torres LL, Quaglio NB, de Souza GT, Garcia RT, Dati LMM, Moreira WL et al (2011) Peripheral oxidative stress biomarkers in mild cognitive impairment and Alzheimer's disease. J Alzheimers Dis 26(1):59–68 Vahia VN (2013) Diagnostic and statistical manual of mental disorders 5: A quick glance. Indian J Psychiatry 55(3):220–223 Wang WX, Huang Q, Hu Y, Stromberg AJ, Nelson PT (2011) Patterns of microRNA expression in normal and early Alzheimer's disease human temporal cortex: white matter versus gray matter. Acta Neuropathol 121(2):193–205 Wei W, Wang ZY, Ma LN, Zhang TT, Cao Y, Li H (2020) MicroRNAs in Alzheimer's Disease: Function and Potential Applications as Diagnostic Biomarkers. Front Mol Neurosci 13:160 Wong-Guerra M, Calfio C, Maccioni RB, Rojo LE (2023) Revisiting the neuroinflammation hypothesis in Alzheimer’s disease: a focus on the druggability of current targets. Front Pharmacol 14 Yu Y, Lü X, Ding F (2015) microRNA regulatory mechanism by which PLLA aligned nanofibers influence PC12 cell differentiation. J Neural Eng 12(4):046010 Zhang L, Fang Y, Zhao X, Zheng Y, Ma Y, Li S, Huang Z, Li L (2021) miR-204 silencing reduces mitochondrial autophagy and ROS production in a murine AD model via the TRPML1-activated STAT3 pathway. Mol Ther Nucleic Acids 24:822–831 Zhou Y, Ge Y, Liu Q, Li YX, Chao X, Guan JJ, Diwu YC, Zhang Q (2021) LncRNA BACE1-AS Promotes Autophagy-Mediated Neuronal Damage Through The miR-214-3p/ATG5 Signalling Axis In Alzheimer's Disease. Neuroscience 455:52–64 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4500729","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312228098,"identity":"7b34f706-593c-4206-99db-72cf6d231d6a","order_by":0,"name":"Haydar Sahib Almawashee","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Haydar","middleName":"Sahib","lastName":"Almawashee","suffix":""},{"id":312228099,"identity":"70bef1e4-36ae-4d72-b719-e11756cfb257","order_by":1,"name":"Mohammad Khalaj-Kondori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3OvQrCMBSG4S90DXQ9xVJvIe7+3Ioi6OLg5CiFQlwqrgVvQu8goaCbroIOirOgWwUHqw5ujW6CeadDOM8hgM32gxGBKfBqPgHq9cZCE8k3eYe+JUif5LO8abRXmb8eutPxQV8lym7oyHMRKfkLoWO+JdothfIkKoliUVJEAmoKxXOCTQeqIsFmYFHhxwLqnvWNr6j8IC2JhpGUqCdSzhWJB9ESLSPxkl4/9Xnbm+8W0OGK2klqILTpzg+nuO4GW+lcskG1NhmNjoXkGYvfJwDHDPKyj7ZsNpvtX7sDHx9J+4c/k0IAAAAASUVORK5CYII=","orcid":"","institution":"University of Tabriz","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Khalaj-Kondori","suffix":""},{"id":312228102,"identity":"3d65beab-a505-4439-b5dc-634b1bc78261","order_by":2,"name":"Mohammad Ali Hoseinpour Feizi","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Ali Hoseinpour","lastName":"Feizi","suffix":""},{"id":312228104,"identity":"f80a9215-2629-4bc3-9e15-f9e65aa8ab2c","order_by":3,"name":"Reza Safaralizadeh","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Safaralizadeh","suffix":""}],"badges":[],"createdAt":"2024-05-30 06:21:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4500729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4500729/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59121407,"identity":"f7fd174a-65e8-4fff-9fbc-b217dcc4ed1f","added_by":"auto","created_at":"2024-06-26 14:55:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109063,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the A) plasma level of MDA, B) relative expression of miR-214, C) relative expression of miR-204, D) relative expression of miR-25, E) relative expression of IL-33 and F) relative expression of miR-15a between the AD and healthy control groups. \u0026nbsp;The **** represents P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4500729/v1/1737b226b472e053571fbb09.jpeg"},{"id":59121412,"identity":"b61c1ede-7bfc-4862-9da5-ab89e73e8c6c","added_by":"auto","created_at":"2024-06-26 14:55:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254636,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs illustrating correlations between A) MMSE and miR-15a expression B) MMSE and miR-214 expression, C) MMSE and miR-204 expression, D) MMSE and miR-25 expression, E) MMSE and IL-33 expression, F) MMSE and MDA plasma level in the patient group.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4500729/v1/dd39d2c8e573635d79ab2c9d.jpeg"},{"id":59121413,"identity":"dc91b612-c13f-44c4-afbc-4255e455a1ae","added_by":"auto","created_at":"2024-06-26 14:55:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":222603,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs illustrating correlations between A) IL-33 and miR-15a relative expressions B) IL-33 and miR-214 relative expressions, C) IL-33 and miR-204 relative expressions, D) IL-33 and miR-25 relative expressions, and E) relative expression of IL-33 and MDA plasma level in the patient group.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4500729/v1/222fb6b410669bc289007136.jpeg"},{"id":59121409,"identity":"561332d5-fbfb-4632-9d1d-7a7d494bd39d","added_by":"auto","created_at":"2024-06-26 14:55:12","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144845,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs illustrating correlations between A) MDA plasma level and miR-15a relative expression, B) MDA plasma level and miR-214 expression, C) MDA plasma level and miR-204 expression, and D) MDA plasma level and miR-25 expression in the patient group.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4500729/v1/620f9adc45b1e2ece3a788cc.jpeg"},{"id":59122159,"identity":"600e7c28-8508-4f32-bc28-1979b71b1877","added_by":"auto","created_at":"2024-06-26 15:03:12","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":242767,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrations of the ROC curve analysis. A) relative expression of IL-33, B) relative expression of miR-15a, C) relative expression of miR-214, D) relative expression of miR-204, E) relative expression of miR-25, and F) plasma level of MDA.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4500729/v1/39ef7b50daa41c8f04e80151.jpeg"},{"id":69984721,"identity":"438338fb-87fd-41bb-b73e-1a0d52a4a7a8","added_by":"auto","created_at":"2024-11-27 08:24:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1709660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4500729/v1/96b5c8a9-38cf-48a6-8134-d26812470991.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression profile of miR-214, miR-204, miR-25, miR-15a, IL-33, and plasma level of Malondialdehyde might serve as potential biomarkers for Alzheimer’s disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) is a late-of-onset neurodegenerative disease characterized by memory impairment, progressive cognitive decline, and behavioral changes, posing a significant public health challenge worldwide. It was estimated that 6.7\u0026nbsp;million Americans age 65 and older were suffering from Alzheimer's disease in 2023 (Alzheimer\u0026rsquo;s Association 2023). It is a heterogenous and multifactorial disease that is governed by genetic and environmental factors (Fotuhi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The main pathobiological findings of AD are neurofibrillary tangles and amyloid plaques, which are linked with hyperphosphorylated tau protein and aberrant beta-amyloid (Aβ) metabolism, respectively (Fotuhi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fotuhi et al. 2024). Despite decades of research, the precise etiology of AD remains elusive, reflecting its multifactorial nature and complex interplay of genetic, environmental, and lifestyle factors. With the global population aging rapidly, the prevalence of AD is projected to escalate dramatically in the coming decades, highlighting the urgent need for innovative diagnostic tools, effective therapeutic strategies, and preventive measures.\u003c/p\u003e \u003cp\u003eOxidative stress, resulting from an imbalance between ROS (reactive oxygen species) production and antioxidant defenses, has emerged as a pivotal contributor to AD pathogenesis (Huang 2016; Cassidy et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ionescu-Tucker et al. 2021). When oxidative damage accumulates to the macromolecules such as lipids, proteins, and nucleic acids in the brain, it can exacerbate neuronal dysfunction and promotes neurodegeneration. Malondialdehyde (MDA), a biomarker of lipid peroxidation, has been implicated in AD-related oxidative stress, with elevated levels detected in brain tissues and biological fluids of affected individuals (Bradley-Whitman et al. 2015; Rani et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Arslan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the precise mechanisms by which oxidative stress contributes to AD progression and its potential as a therapeutic target warrant further elucidation.\u003c/p\u003e \u003cp\u003eIn addition to oxidative stress, chronic neuroinflammation has emerged as a hallmark feature of AD pathology (Sobue et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Activation of microglia and astrocytes in response to pathological insults leads to producing pro-inflammatory cytokines and chemokines, perpetuating neuroinflammatory responses and exacerbating neuronal damage (Lecca et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wong-Guerra et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Interleukin-33 (IL-33), a pleiotropic cytokine involved in immune regulation and tissue repair, has been implicated in modulating neuroinflammation in AD (Fu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Carlock et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Saresella et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding the intricate crosstalk between neuroinflammation and AD pathogenesis may offer novel insights into disease mechanisms and therapeutic targets.\u003c/p\u003e \u003cp\u003eFurthermore, dysregulation of microRNAs (miRNAs), small non-coding RNAs with 19\u0026ndash;25 nucleotides in length, has been implicated in AD pathophysiology (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pe\u0026ntilde;a-Bautista et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Altered expression profiles of specific miRNAs in AD brains and blood contribute to the dysregulation of key molecular pathways involved in amyloid-beta processing, tau phosphorylation, synaptic dysfunction, and neuroinflammation (Angelucci et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Biglari et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khodayi-Shahrak et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Abidin et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, miR-214, miR-15a, miR-204, and miR-25 have been identified as potential players in AD-related neurodegeneration (Moncini et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Duan et al. 2019; Tao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Investigating the roles of miRNAs in AD pathogenesis may offer important clues into disease mechanisms and facilitate the development of miRNA-based therapeutic and diagnostic approaches (Duan et al. 2019; Khodayi-Shahrak et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pe\u0026ntilde;a-Bautista et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By elucidating the molecular mechanisms governing AD onset and progression, researchers aim to develop approaches for precision medicine, ultimately improving diagnostic accuracy, treatment efficacy, and patient outcomes.\u003c/p\u003e \u003cp\u003eIntegrative approaches that combine molecular, cellular, and clinical investigations are essential for deciphering the complex pathophysiology of AD and identifying novel therapeutic targets. Notably, in recent years, there has been a growing emphasis on elucidating the intricate interplay between oxidative stress, neuroinflammation, and miRNA dysregulation in AD. This study investigated the blood levels of Malondialdehyde (MDA) as an indicator of oxidative stress and IL-33 as a neuroinflammation modulator in AD patients. Furthermore, correlations between their blood levels and expression levels of miR-214, miR-204, miR-15a, and miR-25 in the blood cells as well as with the clinical findings of the subjects were assessed.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy samples\u003c/h2\u003e \u003cp\u003eA total of 125 subjects including 75 AD patients and 50 healthy controls without any symptoms of neurological diseases and matched for age and gender were included in this study. The subjects were diagnosed with neuroscience specialists based on the Diagnostic and Statistical Manual of the American Psychiatric Association (DSM-V) criteria (Vahia \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). All participants or their related caregivers signed the informed written consent and the study was authorized with the research ethics committee of Tabriz University of Medical Sciences. Subjects with age 65 or higher and without any neurologic and psychiatric diseases other than AD were included in the study. Subjects having other neurological conditions such as Parkinson\u0026rsquo;s disease, multiple sclerosis, autism, frontal lobe dementia, encephalitis, traumatic brain injury, Lewy body dementia, spastic lesions, diabetes, thyroid disease, liver and renal diseases, metabolic disorders, cancer, inflammatory diseases or taking anti-inflammatory medicines and alcoholism were excluded from both groups. Both groups were evaluated with the Mini-Mental State Examination (MMSE) test. A 6 mL peripheral blood was taken from each participant and used for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Malondialdehyde\u003c/h2\u003e \u003cp\u003eMalondialdehyde (MDA) level of the plasma samples from both AD patients and healthy controls was measured using the Malondialdehyde ELISA kit (ZellBio, Germany) based on the kit\u0026rsquo;s manual. Briefly, 50 \u0026micro;L from each sample was added to each well of the microplate and treated with 50 \u0026micro;L Biotin-labeled Antibody for 45 min at 37 \u0026ordm;C. Then, the solution was discarded, washed three times with washing buffer, and treated with 0.1 mL HRP-Streptavidin Conjugate solution for 30 min at 37 \u0026ordm;C. The solution was discarded, and the wells treated with 90 \u0026micro;L TMB for 20 min, and the reactions were stopped by 50 \u0026micro;L stop solution. The results were assessed by ELISA reader at 450 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and qRT-PCR\u003c/h2\u003e \u003cp\u003eBlood leukocytes were collected by red blood cell lysis buffer (RBC lysis) (Heng, Ruan and Gan \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and used for total RNA extraction from the samples. Total RNA was purified using RiboEx\u003csup\u003e\u0026trade;\u003c/sup\u003e (GeneAll) based on the manufacture\u0026rsquo;s instructions. The extracted total RNA samples were assessed by a NanoDrop (Thermo Fisher Scientific USA) instrument, and the quantity of the RNA samples was determined. The SMO BIO-kit (Taiwan) was applied for complementary DNA (cDNA) synthesis. The resulting cDNA was diluted 1:4 and assessed by a Rotor-gene-6000 system (Corbett-Australia) using SYBR-Amplicon (RealQ Plus 2x Master Mix Green). For each sample, qPCR reactions were done in a final volume of 20 \u0026micro;L and in a duplicate format. The U6 and GAPDH genes were used as internal normalizers for expression levels of the miRNAs and IL-33, respectively. The relative expressions of miRNAs and IL-33 in the case and control groups were expressed as 2\u003csup\u003e-Δct\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the primers used for qRT-PCR reactions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of the primers used for amplification of studied genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward primer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse primer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-15a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026prime;- GCAGCACATAATGGTTTG \u0026minus;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026prime;- GAACATGTCTGCGTATCTC \u0026minus;\u0026thinsp;3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-214\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026prime;- TGCCTGTCTACACTTGC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026prime;- GAACATGTCTGCGTATCTC \u0026minus;\u0026thinsp;3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-204\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026prime;- CCCTTTGTCATCCTATGCC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026prime;- GAACATGTCTGCGTATCTC \u0026minus;\u0026thinsp;3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026prime;-CGGAGACTTGGGCAATT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026prime;- GAACATGTCTGCGTATCTC \u0026minus;\u0026thinsp;3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026prime;GCCTGTCAACAGCAGTCTACTG-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026prime;- AGACATGCAGTGTTTCCCC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eU6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026prime;-GCTTCGGCAGCACATATACTAAAAT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026prime;-CGCTTCACGAATTTGCGTGTCAT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAPDH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026prime;- ATGGGGAAGGTGAAGGTCG \u0026minus;\u0026thinsp;3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026prime;-GGGGTCATTGATGGCAACAATA-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used SPSS version 26 and GraphPad Prism 9 for data analysis. Data normality was analyzed with the Shapiro-Wilk test, and for non-normal data, the Mann-Whitney test was employed. For normally distributed data, the Student's t-test was applied. To investigate the relationships between qualitative variables, the chi-square test was employed. The Pearson correlation coefficient and regression analysis were used to examine the associations between variables. Significance levels of tests were considered to be less than 5%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinicopathological features of the subjects\u003c/h2\u003e \u003cp\u003eSeventy-five AD patients and fifty healthy controls were assessed in the present study. There was no significant difference between the mean age of control and patient groups (p\u0026thinsp;=\u0026thinsp;0.235). In the patient group, 58.7% were female and 41.3% were male. Regarding the gender parameter, the difference between two groups was not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the case and control groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (male/female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/44, (41.3/58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18/32, (36.0/64.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e: normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 (mild)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 (moderate-to-severe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease duration (month);\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (29.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (70.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Plasma level of Malondialdehyde\u003c/h2\u003e \u003cp\u003ePlasma level of Malondialdehyde (MDA), as a biomarker of lipid peroxidation, was compared between the AD cases and controls. The results indicated that the MDA plasma level in patients was 17.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.06 ng/mL, while it was 5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16 ng/mL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) in the healthy controls. Statistical analysis revealed that the MDA level was significantly higher in the Alzheimer\u0026rsquo;s patients than in the healthy controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Detailed comparison data for the MDA level is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the plasma level of MDA between AD patients and Healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et = -9.94\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControls\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Expression levels of the miRNAs and IL-33 in blood cells\u003c/h2\u003e \u003cp\u003eExpression levels of miRNAs in the blood cells of the AD cases and healthy controls were quantified by qRT-PCR and compared between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-F). We observed that the relative expressions of miR-15a, miR-25, and IL-33 were significantly less in the AD cases compared to the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). However, the relative expression of miR-214 and miR-204 were significantly higher in the AD cases than in the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Detailed comparisons data for the miRNAs and IL-33 expressions are provided in the Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of miRNAs and IL-33 relative expression levels between the AD patients and healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA/Gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003emiR-15a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et = -11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003emiR-214\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;14.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003emiR-204\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;10.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003emiR-25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et = -3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eIL-33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et = -9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation analysis\u003c/h2\u003e \u003cp\u003eCorrelations between the variables, including MDA, MMSE, IL-33, and miRNAs\u0026rsquo; expressions in the patient group were investigated using Pearson correlation analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the correlations between MMSE score of the patients with the expressions of the miRNAs, IL-33 and the plasma level of MDA. The correlations between MMSE score and expressions of the studied miRNAs as well as the expression of IL-33 were all statistically non-significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D). As Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF indicates, the MMSE score was negatively correlated with the plasma MDA level, and this correlation was statistically significant (R = -0.2607, P\u0026thinsp;=\u0026thinsp;0.039). The analysis also revealed a significant positive correlation between MDA plasma level and expression of IL-33 in patients (R\u0026thinsp;=\u0026thinsp;0.2733, P\u0026thinsp;=\u0026thinsp;0.0185).\u003c/p\u003e \u003cp\u003eCorrelations of IL-33 with miRNAs\u0026rsquo; expressions and plasma level of MDA are outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows, IL-33 expression was negatively correlated with the expression of miR-15a (p\u0026thinsp;=\u0026thinsp;0.013), while it showed a positive correlation with the MDA level (p\u0026thinsp;=\u0026thinsp;0.018). Correlations between IL-33 and other miRNAs were non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCorrelations of MDA plasma level with expressions of the miRNAs are also outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. MDA did not show a significant correlation with any of the studied miRNAs (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) while as stated above in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, MDA level was positively correlated with the IL-33 expression (p\u0026thinsp;=\u0026thinsp;0.018). Details of these correlation analysis data are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the correlation analysis between different studied variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emir-15a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emiR-214\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emir-204\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003emir-25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-33\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMDA (ng/mL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMDA (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.261\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.273*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.261\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emir-15a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.285\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emir-214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emir-204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emir-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIL-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.285\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.273*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMDA (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.261\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.273*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Biomarker potency of the miRNAs, IL-33, and MDA for diagnosis of AD patients\u003c/h2\u003e \u003cp\u003eTo understand whether MDA plasma level and relative expressions of miRNAs and IL-33 could differentiate AD patients from healthy controls, we conducted ROC curve analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e outlines the results obtained from this analysis. We found that the relative expression of IL-33 may differentiate patients from healthy controls with an Area under the curve (AUC) of 0.911, and specificity and sensitivity of 80% and 84%, respectively (CI; 0.852\u0026ndash;0.970, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe results of ROC curve analysis for relative expressions of miRNAs including miR-15a (AUC\u0026thinsp;=\u0026thinsp;0.946, CI; 0.912\u0026ndash;0.980, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), miR-214 (AUC\u0026thinsp;=\u0026thinsp;0.942, CI; 0.903\u0026ndash;0.981, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), miR-204 (AUC\u0026thinsp;=\u0026thinsp;0.914, CI; 0.860\u0026ndash;0.961, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and miR-25 (AUC\u0026thinsp;=\u0026thinsp;0.696, CI; 0.543\u0026ndash;0.749, p\u0026thinsp;=\u0026thinsp;0.006) showed that all may differentiate AD patients from healthy controls, but, because of relatively low AUC (0.696), miR-25 can not be considered as a very good biomarker for AD diagnosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). ROC curve analysis for the plasma level of MDA resulted in an AUC of o.929, and specificity and sensitivity of 92% and 88%, respectively (CI; 0.882\u0026ndash;0.977, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Details of ROC curve analysis data are provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the results obtained from ROC curve analysis for miRNAs, IL-33 and MDA. Sp%; specificity percent, Se%; sensitivity percent.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSp%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSe%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-15a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912\u0026ndash;0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-214\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.903\u0026ndash;0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-204\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.860\u0026ndash;0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.543\u0026ndash;0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.852\u0026ndash;0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMDA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.882\u0026ndash;0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study has evaluated the expression of microRNAs including miR-15a, miR-214, miR-204, and miR-25 in the blood cells of AD cases and assessed their relation with the MMSE and expression of IL-33 and plasma level of MDA. The results showed that the MDA plasma level in AD cases was significantly higher than in the healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). ROC cure analysis pointed that its level could differentiate AD cases from healthy people with an AUC of 0.929, indicating MDA as a very good biomarker in diagnosis of AD patients. Its level was also negatively correlated with the MMSE score of the AD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), meaning that the MDA level is correlated with the disease severity. Although MDA is not a specific determinant for AD, it might be considered as an informative factor in AD development and diagnosis. In agreement with our findings, elevated level of MDA or TBA-MDA adducts in plasma or serum of AD patients were also reported by several researchers (Casado et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gustaw-Rothenberg et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Padurariu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Torres et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast, other studies reported no difference between plasma MDA levels of AD (Polidori et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) or MCI (Mart\u0026iacute;n-Arag\u0026oacute;n et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) patients and healthy controls. In a meta-analysis of plasma and serum levels of MDA, Schrag et al. showed a significant elevation in probable AD subjects (n\u0026thinsp;=\u0026thinsp;1098) compared to the control group (n\u0026thinsp;=\u0026thinsp;1094) (Schrag et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIL-33 primarily functions in immune defense but it plays critical roles in healing of injuries in CNS (central nervous system) and other disease (Carlock et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Genetic and transcriptome and studies identified IL-33 as a candidate gene for AD (Chapuis et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our results showed that IL-33 was significantly downregulated in the AD cases compared to the healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). ROC curve analysis revealed that the relative expression of IL-33 could significantly differentiate AD from healthy subjects, indicating its potential as a very good biomarker for AD development and diagnosis. However, its expression was not correlated with the MMSE score of the AD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). In agreement with our findings, Saresella et al. reported that IL-33 was significantly decreased in CSF and serum of the MCI and AD patients (Saresella et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, Chapuis et al. reported that expression of IL-33 was lower in the brain samples of patients with AD compared to the healthy controls (Chapuis et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Interestingly, Liang et al. showed that expression of IL-33 may preserve cognitive function of the AD patients (Liang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Besides, IL-33 reduced memory deficit and AD progression and stirred microglia polarization in an anti-inflammatory direction (Fu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicroRNAs play important roles in the pathophysiology of AD and have been considered as valuable biomarkers for AD development and diagnosis (Angelucci et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Abidin et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In a murine model of AD, Zhang et al. showed that miR-204 is upregulated, and silencing of the miR-204 could diminish the Aβ1-42-induced mitochondrial damage, mitochondrial autophagy and production of ROS via upregulation of the TRPML1 expression and activation of STAT3 pathway both in vivo and in vitro (Zhang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consistent with this report, we observed that expression of miR-204 was upregulated in AD cases compared to the healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Besides, ROC curve analysis revealed that expression of miR-204 could differentiate AD and healthy people with an AUC of 0.914 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Correlation analysis also indicated that the relative expression of miR-204 was not correlated with each of the MMSE, plasma MDA level, expression of IL-33, and expression of other studied miRNAs in the patients\u0026rsquo; group. In contrast, Tao et al. showed that miR-204-3p was downregulated in hippocampus of a 6-months APP/PS1 AD mouse model and overexpression of miR-204-3p could ameliorate oxidative stress and memory deficits by targeting Nox4 (Tao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In line with this report, Taşdelen et al. observed that miR-204 level in the exosomes isolated from serum of mild and moderate AD patients was significantly lower than that of control group (Taşdelen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe quantified the expression of miR-15a in the patient and control groups. The results showed a significant downregulation in the AD cases compared to the healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). ROC curve analysis also showed that the expression level of miR-15a could differentiate AD cases from healthy people with an AUC of 0.946 which indicates miR-25a might be considered as a very good biomarker for AD development and diagnosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Furthermore, its expression was negatively correlated with the expression of IL-33, while it did not show significant correlations with MMSE score, plasma MDA level, and relative expression of other studied miRNAs. Consistent with these findings, Satoh et al. reported downregulation of miR15-a in blood samples of 28 AD and 22 healthy controls by analyzing miRNA-seq data (Satoh et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Its downregulation in White matter (Wang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and cortex (H\u0026eacute;bert et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nunez-Iglesias et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) of AD patients was also reported. In contrast to these reports, Sorensen et al. reported miR-15a upregulation in CSF samples of AD patients (S\u0026oslash;rensen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBrett et al. has reported that knock down of miR-25 could reduce proliferation of neural stem/progenitor cells, while ectopic expression of the miR-25 boosts their proliferation (Brett et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). miR-25 has several potential mRNA targets in IGF (insulin/insulin-like growth factor-1) signaling pathway which is implicated in aging. It binds to FoxO3, a transcription factor which involves in maintenance of adult stem cells, thereby playing important roles in the hemostasis of neural stem cells pool during aging (Brett et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, Yu et al. reported that miR-25 plays a role in the regulation of neuronal migration and differentiation (Yu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Besides, Guo et al. reported that miR-25 expression was lower in the spinal cord injury mice model, and overexpression of miR-25 protected PC-12 neuroblastoma cells from H\u003csub\u003e2\u003c/sub\u003e0\u003csub\u003e2\u003c/sub\u003e-induced oxidative damage, leading to a decreased ROS (reactive oxygen species) level, significant suppression of apoptosis, and increased cell viability (Guo and Niu \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We also found that expression of miR-25 in AD patients was significantly lower than in controls, but ROC curve analysis showed a reletively weak potential for miR-25 expression level as a biomarker for AD. Nevertheless, miR-25 expression did not show any correlation with the MMSE score, plasma MDA level, relative expression of IL-33, or other studied miRNAs in the patient group. Opposite to these findings, Duan and Si reported that miR-25 aggravates the hippocampal neuron injuries induced by Aβ1\u0026ndash;42 via downregulation of KLF2 in Alzheimer\u0026rsquo;s disease (Duan and Si \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRelative expression of miR-214 in blood cells was also quantified and compared between the patient and control groups. The results showed a significant upregulation of miR-214 in patient group, but there was no correlation between its expression level and MMSE score, plasma MDA level, IL-33, or the miRNAs expression. Opposite to these findings, He et al. reported downregulation of the miR-214-3p in the plasma samples of AD patients (He et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). BACE1-AS acts as sponge for miR-214-3p (He et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and it can promote autophagy-mediated damages of the neurons via miR-214-3p/ATG5 signaling in AD (Zhou et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCollectively, our results showed upregulation of miR-204 and miR-214 and downregulation of miR-25, miR-15a, and IL-33 in AD patients. The MDA plasma level was also significantly higher in AD cases than in healthy controls. Among miRNAs and IL-33 expressions, there was just a negative correlation between miR-15a and IL-33. The plasma level of MDA showed a positive correlation with IL-33 expression and a negative correlation with MMSE scores. Expression levels of the miR-214, miR204, miR-25, miR-15a, IL-33, and plasma level of MDA might be considered as potential biomarkers for AD development and diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eAuthors declare that there is no conflict of interest.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eAuthors have not received any external funding regarding this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHSA has done the research and prepared the primary manuscript. MKK conceptualized the research plan and edited the manuscript. MAHF analyzed the data and edited the manuscript. RS conceptualized the research and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAuthors kindly appreciate all participants and their families for their consent in using their blood samples in this study.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData available upon rational request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbidin SZ, Pauzi NA, Mansor NI, Mohd NI, Isa, Hamid AA (2023) A new perspective on Alzheimer's disease: microRNAs and circular RNAs. Front Genet 14:1231486\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngelucci F, Cechova K, Valis M, Kuca K, ZhangB, Hort J (2019) MicroRNAs in Alzheimer\u0026rsquo;s Disease: Diagnostic Markers or Therapeutic Agents? 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Neuroscience 455:52\u0026ndash;64\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gene expression, Alzheimer’s disease, miRNA, Malondialdehyde, IL-33, Cytokine","lastPublishedDoi":"10.21203/rs.3.rs-4500729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4500729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a late-of-onset neurodegenerative disease that affects elder people. Despite immense research on deciphering the pathophysiology of AD, the precise etiology of AD remains still elusive. Deregulations of miRNAs play essential roles in AD pathogenesis and as a result, they might be potential biomarkers for AD development and diagnosis. This study was aimed to assess the expression of miR-214, miR-204, miR-15a, miR-25, and investigate their correlations with the expression of IL-33, plasma level of Malondialdehyde (MDA) and Mini-Mental State Examination (MMSE) score of the AD patients. Blood samples were obtained from125 participants including 75 AD patients and 50 healthy controls. Plasma and Blood leukocytes were isolated and used for subsequent analysis. Results showed that the plasma level of MDA was significantly higher in the AD patients. Besides, IL-33, miR-15a and miR-25 were downregulated in the patients\u0026rsquo; group but miR-214 and miR-204 expressions were upregulated. Plasma MDA level showed a negative correlation with the MMSE and a positive correlation with the IL-33 expression. We also observed a statistically meaningful negative correlation between miR-15a and IL-33 expressions. Correlations between the studied miRNAs and MDA were all non-significant. Furthermore, none of the miRNAs or IL-33 expressions were correlated with the MMSE scores. ROC curve analysis revealed that expressions of the studied miRNAs, IL-33, and the plasma level of MDA could differentiate AD patients from healthy controls. In conclusion, our results showed that expressions of miR-214, miR-204, miR-25, miR-15a, IL33, and plasma level of MDA might be considered as potential biomarkers for AD development and diagnosis.\u003c/p\u003e","manuscriptTitle":"Expression profile of miR-214, miR-204, miR-25, miR-15a, IL-33, and plasma level of Malondialdehyde might serve as potential biomarkers for Alzheimer’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 14:55:08","doi":"10.21203/rs.3.rs-4500729/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2611235-6283-4413-8a09-d29268b34549","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-27T08:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-26 14:55:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4500729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4500729","identity":"rs-4500729","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}