Increased Serum miR-206 Level Predicts Conversion from Amnestic Mild Cognitive Impairment to Alzheimer’s Disease: A 5-Year Follow-up Study

Abstract

Evidence suggests that individuals with amnestic mild cognitive impairment (aMCI) tend to progress to probable Alzheimer’s disease (AD) with aging. This study was performed to examine whether circulating miRNAs could be potential predictors for the progression of aMCI to AD. A total of 458 patients with aMCI were included in this study, and the clinical data were collected at two time points: the baseline and the follow-up assessment. These aMCI patients were classified into two groups after 5 years: aMCI-stable group (n = 330) and AD-conversion group (n = 128). The expression of miR-206 and miR-132 and the levels of BDNF and SIRT1 in serum were detected using a quantitative real-time RT-PCR (qPCR) and the ELISA method, respectively. Kaplan-Meier method (Log-rank test) was used for univariate survival analysis. Cox proportional hazard model was used to estimate the prognostic value of miRNAs in conversion from aMCI to AD. At the baseline, serum levels of miR-206 in aMCI-AD group were significantly elevated compared to aMCI-aMCI group and the same trend was found at 5-year follow-up time point as well. There were no significant differences in serum levels of miR-132 between the conversion and non-conversion group at both time points. Kaplan-Meier analysis showed significant correlation between AD conversion and higher serum levels of miR-206 for aMCI patients (HR = 3.60, 95% CI: 2.51– 5.36, p < 0.001). Multivariate Cox regression analysis revealed that serum miR-206 and its target BDNF were significant independent predictors for AD conversion (HR = 4.22, p < 0.001). These results suggested that increased serum miR-206 level might be a potential predictor of conversion from aMCI to AD.

Keywords

Alzheimer’s disease amnestic mild cognitive impairment circulating microRNA follow-up study predictor

INTRODUCTION

Mild cognitive impairment (MCI) is an intermediate clinical state of cognitive function between normal aging and dementia [1]. According to the clinical presentation in patients, MCI is classified into two subtypes: amnestic MCI (aMCI) and non-amnestic MCI (naMCI). Particularly, aMCI has increasingly been accepted as a prodrome or significant risk factor for Alzheimer’s disease (AD) in clinical settings [2]. Studies have revealed that peoples with aMCI tend to progress to probable AD at a rate of approximately 10% to 15% per year [3, 4]. Thus, aMCI represents a key prognostic and therapeutic target in the management of AD. Several recent studies have shown that cerebrospinal fluid (CSF) markers, neuroimaging data, genetic testing, and neuropsychological testing could predict the conversion to AD [5 –7]. However, the subjective, invasive, and inconvenient procedure with associated complications limited their uses in clinic. Therefore, there is a need to identify some novel objective and non-invasive prognostic markers to predict the conversion from aMCI to AD, which might be the potential therapeutic targets for the management of AD as well.

MicroRNAs (miRNAs) are single-stranded small non-coding RNA molecules with 18– 24 nt in length that primarily function at the post-transcriptional level by interacting with the 3’-untranslated region (UTR) of specific target mRNAs [8]. Previous studies have demonstrated that miRNAs were abundant in human serum and plasma (circulating miRNA) and that their expression profile responded to changes under different physiological and pathological conditions [9 –11]. Therefore, circulating miRNAs could be potential independent predictive biomarkers for evaluation of disease progression. Recently, the circulating miRNAs have attracted considerable attention in the diagnosis of AD because of their unique merits (i.e., stable, easily quantitative detected, potentially disease-specific, and non-invasive) [12 –14]. However, to our knowledge, very few studies have investigated the prognostic roles of circulating miRNAs in the process of conversion from aMCI to AD.

In our previous study, we have demonstrated that oxidative stress altered the miRNA expression profile in hippocampal neurons, and the deregulated miRNAs might play potential roles in the pathogenesis of AD [15]. Using miRNA microarray analysis, we have found that miR-206, miR-132, miR-193b, miR-130b, miR-20a, miR-296, and miR-329 were deregulated in AD mouse model (SAMP8) [16]. Then, these deregulated miRNAs were detected in serum samples from MCI patients and normal controls and the results indicated that circulating miR-206 and miR-132 were potential biomarkers for diagnosis of MCI [17]. However, it is not clear whether serum miR-206 and miR-132 could be the predictor of conversion from aMCI to AD for the elderly.

In the present 5-year follow-up study, we detected the serum levels of miR-206 and miR-132 in an elderly cohort using the quantitative real-time PCR (qRT-PCR), and the expression of their target genes in serum were examined further via an enzyme-linked immunosorbent assay (ELISA) method. The main purpose of this study was to evaluate the prognostic value of serum miR-206 and miR-132 in predicting the conversion from aMCI to AD and, accordingly, to determine the cut-off points of each miRNAs.

METHODS

Subjects

This study was based on the data derived from a community-based cohort study called Mild cognitive impairment and Alzheimer’s disease Study in Heibei province (MASHB), which was designed to assess the risk factors and occurrence of dementia for elders in 2010. Detailed descriptions of the study have been provided in prior literature [18]. In this study, a total of 506 patients with aMCI were recruited from MASHB at baseline. At the 5-year follow-up time point, 21 patients withdrew from the study, 8 could not be contacted, 11 had died, and 8 had moved, leaving 458 patients for this aMCI cohort study. All 458 patients were included in this study and the clinical data were collected at two time points: baseline and 5-year follow-up. All follow-ups were done between 56 months to 63 months after baseline (60.97±3.79). According to the assessment, patients were classified into two groups: (1) conversion group (aMCI-AD), subjects with aMCI who progressed to AD (n = 128); (2) non-conversion group (aMCI-aMCI), subjects with aMCI who retained this diagnosis at follow-up time point (n = 330).

Clinical assessment

The diagnosis of aMCI was made using the Petersen (Mayo Clinic) diagnostic criteria as follows [2]: (1) memory complaints by patient; (2) objective memory impairment, presenting as a logicalmemory score on the Wechsler Memory Scale Revised (WMS-R, Chinese version) [19], but not significant impairment in other cognitive domains; (3) generally preserved activities of daily living; (4) a Clinical Dementia Rating (CDR) score of 0.5; (5) normal general cognitive function; A: Mini-Mental State Examination (MMSE) [20] scores between 20 and 27 [cut-off points for illiterate (≤20), primary school (≤23) and secondary school and above (≤27)]; B: Montreal Cognitive Assessment (MoCA) [21] scores <26; (6) no dementia. We used these criteria as described in our previous study [18]. The diagnosis of AD was based on the criteria of National Institute of Neurological and Communication Disorders and Stroke/ Alzheimer’s disease and Related Disorders Association (NINCDS-ADRDA) [22]. Elders with severe psychiatric disorders, poor hearing and vision, nervous system diseases, and history of use of psychotropic medicines were already excluded from both baseline and follow-up by investigating past medical history. The clinical diagnosis of aMCI and AD were made by a professional group of neurologists and psychiatrists from the Institute of Mental Health, Hebei Medical University.

Ethic statement

This study was conducted according to the principles of the Declaration of Helsinki. The study protocols and informed consents were approved by the Ethics Committee of the First Hospital of Hebei Medical University. All subjects provided written informed consent before they entered the study.

Detection of circulating miRNAs

A 5 mL sample of venous blood was collected from each subject before breakfast in the morning after psychological testing. Blood samples were collected in BD Vacutainer SST tubes (BD, New Jersey, NJ, EEUU). The tubes were kept in vertical position for 30 min to allow clot formation and centrifuged at 3,000 rpm for 10 min at room temperature (1– 25°C). The supernatant was then transferred into a miRNase-free microcentrifuge tube and stored at – 80°C until measurement.

Total RNAs from serum were isolated using miRNeasy Serum/Plasma kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions for serum samples. Briefly, 100 μL of human serum was diluted with 500 μL QIAzol Lysis reagent, and 3.5 μL miRNeasy Serum/Plasma Spike-In Control (C. elegans miR-39 miRNA mimic) was added to each sample as a spike control. Next, phenol extraction and various filter cartridge steps were performed according to the manufacturer’s instructions. A NanoDrop Lite Spectrophotometer (Thermo, Germany) was used to measure the purity and concentration of RNAs in each serum sample.

To evaluate the expression of the selected miRNAs in the serum samples, we performed the 20 μL reverse transcription (RT) reactions by using miScript II RT Kit (Qiagen, Hilden, Germany) and the 25 μL qPCR reactions by using miScript SYBR Green PCR kits (Qiagen, Hilden, Germany). RT reactions were performed in a Bio-RAD T100 Thermal cycle at 37°C for 60 min and then 95°C for 5 min. The qPCR reactions were performed on an ABI 7500 Fast instrument (Applied Biosystems, Carlsbad, CA, USA) at the cycling conditions of 95°C for 15 min, followed by 40 cycles of 94°C for 15 s, 55°C for 30 s, and 70°C for 34 s. At the end of the 40 cycles, a melt-curve analysis was used to evaluate the PCR specificity. Further details have been described in our previous study [17].

Determination of serum BDNF and SIRT1 levels

Serum brain-derived neurotrophic factor (BDNF) and Sirtuin 1(SIRT1) levels were measured by the enzyme-linked immunosorbent assay (ELISA) method using the Human Free BDNF Quantikine ELISA Kit (Catalog no: DBD00, R&D System, Minneapolis, MN, USA) and human SIRT1 ELISA Kit (Catalog no: ELH-SIRT1, RayBio, Georgia, USA) according to the manufacturer’s instructions. The minimum detectable dose was 20 pg/mL for BDNF and 1.23 ng/mL for SIRT1. The intra-assay precisions were 5.0% for BDNF and 10.0% for SIRT1. The inter-assay precisions were 11.3% for BDNF, and 12.0% for SIRT1.

Statistical analysis

The relative expression levels of selected miRNAs in serum were calculated using the comparative cycle threshold (CT) (2^–ΔCT) method, and the data were normalized using spike-in C. elegans miR-39 (cel-miR-39) as the normalized external control. Differences of miRNA expression levels between conversion and non-conversion group were assessed using the Mann-Whitney U test. Quantitative data were compared with t-test between two groups. Chi-square tests were used for categorical data analysis. Two-way mixed model analysis of variance (Two-way ANOVA) was conducted in order to examine the interaction between group and time with respect to the changes in serum miRNA levels throughout the follow-up period. To minimize the potential confounding effects, the following covariates were entered into adjusted model: age, gender, years of education, and cognitive function scores. Kaplan-Meier method (Log-rank test) was used for univariate survival analysis. Cutoff Finder [23] (http://molpath.charite.de/cutoff/) based on time-independent survival ROC was used to find the optimal cut-off points of each miRNA in order to translate a continuous variable into a categorical variable and to stratify patients into distinct groups. Cox proportional hazard model analysis adjusted for age, gender, years of education, and cognitive function scores was used to estimate the hazard ratios (HRs) and 95% confidence interval (CI) of the conversion from aMCI to AD.

Statistical analyses were performed by SPSS software version 16.0 (SPSS, Inc., Chicago, IL, USA) and R software version 3.2.3 (AT&T, now Lucent Technologies, Vienna, Austria). Graphs were generated using GraphPad Prism version 5.0 (GraphPad Software, Inc., San Diego, CA, USA). All tests were two-sided, and p < 0.05 was considered to be statistically significant.

RESULTS

Study characteristics

During the period of 5-year follow-up, there were 128 subjects (27.9%) with aMCI who progressed to AD (aMCI-AD) and 330 subjects (72.1%) who maintained an aMCI (aMCI- aMCI) diagnosis. The study characteristics of all the subjects are summarized in Table 1.

At baseline, patients in the conversion group were significantly older (76.04±4.82, p < 0.001), were less educated (2.98±0.86, p < 0.001), had lower scores on cognitive function tests (MMSE: 23.86±2.06, p = 0.009; MoCA: 19.11±3.48, p < 0.001), had higher serum levels of miR-206 (lg2 ^–ΔCt: – 0.99, p = 0.035), and had lower serum levels of SIRT1 (1.96±0.29, p = 0.004). There were no significant differences found in gender or in serum levels of miR-132 and BDNF between the conversion and non-conversiongroup.

On the other hand, at the follow-up time point, individuals from the conversion group exhibited significantly lower MMSE scores (21.07±1.98, p < 0.001) and MoCA scores (17.33±4.55, p < 0.001), higher serum levels of miR-206 (lg2 ^–ΔCt: – 2.33, p < 0.001), and lower serum levels of BDNF (24.07±2.37 ng/mL, p < 0.001) and SIRT1 (1.32±0.28 ng/mL, p < 0.001).

Interaction between group and time on changes in serum miRNA levels

For serum levels of miR-206, a two-way ANOVA indicated significant main effects of group (F = 9.053, p = 0.003), time (F = 24.939, p < 0.001), and the interaction between group and time (F = 4.716, p = 0.014) (Fig. 1A). For serum levels of miR-132, the main effects of group (F = 5.865, p = 0.249), time (F = 1.150, p = 0.478), and the interaction between the group and time (F = 2.180, p = 0.143) were not significant (Fig. 1B).

For serum levels of BDNF, a mixed-model ANOVA indicated significant main effects of group (F = 9.260, p = 0.003), time (F = 20.369, p < 0.001), and the interaction between group and time (F = 12.723, p = 0.001) (Fig. 1C). For serum levels of SIRT1, the main effects of group (F = 0.678, p = 0.413), time (F = 2.681, p = 0.055), and the interaction between the group and time (F = 2.037, p = 0.160) were not significant (Fig. 1D).

Correlation between serum levels of miRNAs and the cognitive function at the follow-up time point

Figure 2 showed scatter plots of the relative expression levels of miR-206 and miR-132 in serum at baseline and the cognitive function tested at the 5-year follow-up time point. Both MMSE and MoCA scores at the 5-year follow-up time point were significantly correlated with the serum levels of miR-206 at baseline (MMSE: r = 0.403, p < 0.001; MoCA: r = 0.375, p < 0.001) (Fig. 2A, B). Additionally, the serum levels of miR-132 at baseline were also correlated to MMSE (r = 0.268, p = 0.010) (Fig. 2C) and MoCA (r = 0.254, p = 0.015) (Fig. 2D) scores at the 5-year follow-up time point.

Prognostic value of miRNAs in conversion from aMCI to AD

In order to translate continuous variables (miR-206, miR-132, BDNF, and SIRT1) into dichotomous variables, the optimal cutoff points were determined by survival ROC analysis.

Figure 3A and D showed the survival ROC curves of serum miR-206 and miR-132 (AUC_miR - 206 = 0.95, p < 0.05; AUC_miR - 132 = 0.83, p = 0.137). The optimal cutoff points of miR-206 and miR-132 were separately determined as – 2.01 (sensitivity: 95.3%, specificity: 77.8%) and – 1.985 (sensitivity: 76.6%, specificity: 85.2%) (Fig. 3B, E). Kaplan-Meier analysis showed a significant AD conversion trend for aMCI patients with high serum levels of miR-206 (HR = 3.60, 95% CI: 2.51– 5.36, p < 0.001) and miR-132 (HR = 1.34, 95% CI: 1.05– 1.77, p = 0.035) (Fig. 3C, F).

Figure 3G and J showed survival ROC curves of serum BDNF and SIRT1 (AUC_BDNF = 0.78, p = 0.069; AUC_SIRT1 = 0.66, p = 0.537). The optimal cutoff points of BDNF and SIRT1 were separately determined as 26.28 (sensitivity: 79.7%, specificity: 63.0%) and 1.385 (sensitivity: 71.9%, specificity: 59.3%) (Fig. 3H, K). Kaplan-Meier analysis showed a significant AD conversion outcome for aMCI patients with low serum BDNF levels (HR = 2.74, 95% CI: 1.69– 6.62, p = 0.013) (Fig. 3I), but no significant AD conversion trend was found for aMCI patients with low serum SIRT1 levels (HR = 1.26, 95% CI: 0.63– 2.77, p = 0.608) (Fig. 3L).

As shown in Table 2, multivariate Cox regression analysis revealed that serum levels of miR-206 and BDNF were significant independent predictors of AD conversion. Higher levels of serum miR-206 and lower levels of serum BDNF were significantly associated with higher risk of conversion from aMCI to AD (HR_miR - 206 = 4.22, p < 0.001; HR_BDNF = 1.26, p < 0.023).

DISCUSSION

AD is the most common cause of dementia, affecting more than 35 million people worldwide [24]. Currently there are no drugs to cure AD, although some medications are available that can temporarily reduce some symptoms. Thus, identification and validation of predictors for monitoring the progression of AD have been a main focus in recent years. Some predictors or biomarkers have been validated and studied extensively, including CSF Aβ₄₂ [25], tau protein, APOE genotype [26], ¹⁸F-FDG positron emission tomography [27], atrophy of hippocampus volume on magnetic resonance imaging [28], and amyloid deposition by imaging with positron emission tomography [29]. Although these factors may serve as predictors for AD conversion, the invasive and inconvenient features make their routine use unfeasible in the clinical setting. Recently, scientific interest has partially focused on another predictor source, such as the circulating miRNAs, especially in cancer and neurodegenerative diseases [30, 31]. In accordance with the criteria described by the National Institute on Aging, the use of circulating miRNAs as predictors has the following advantages [32]: (1) directly affect a fundamental feature of AD neuropathology; (2) sensitive enough to detect; (3) could be detected in the early course of the AD progression; (4) non-invasive, convenient and cost-effective. These advantages indicate that circulating miRNAs could potentially be good predictors for AD conversion.

In this 5-year follow-up study, we focused on serum miR-206 and miR-132 to evaluate their prognostic value on the conversion from aMCI to AD. Our results revealed that the serum levels of miR-206 were significant higher in the aMCI-AD group than in the aMCI-aMCI group at baseline. The factorial design ANOVA was used to analyze the interaction between conversion state and time. The results revealed that higher serum levels of miR-206 accelerated the progression from aMCI to AD. Moreover, higher expression levels of serum miR-206 were independent prognostic marker of conversion from aMCI to AD according to the multivariate Cox proportional hazard model.

Several lines of evidence have suggested that circulating miRNAs may be used as potential diagnostic biomarkers for screening and early detection of AD [14 , 33– 37]. Dong et al. [14] used Solexa sequencing and hydrolysis probe-based RT-qPCR to detect the expression profile of miRNAs in the serum of AD patients. The results demonstrated that miR-31, miR-93, miR-143, and miR-146a could serve as biomarkers in diagnosing AD. Wang et al. [33] demonstrated that miR-107 expression in plasma had a high capability to discriminate between patients with aMCI and healthy controls. Kiko et al. [34] provided a possibility that plasma miR-34a and miR-146a could serve as biomarkers for AD. In addition, mir-206, mir-132, miR-137, miR-181c, miR-29a, miR-125b, miR-132, and miR-134 were also reported in the diagnosis studies of AD [35 –37]. However, the above studies were conducted using a cross-sectional design which could not reveal the roles of serum miRNAs in monitoring the AD progression. In this study, we made up for the shortage and a follow-up study was conducted to investigate the relationship between serum miRNAs and the AD conversion. The results revealed that serum miR-206 was a predictor in the progression of aMCI to AD. To our knowledge, there are no relative reports yet published in this field.

It is known that miR-132 is one of the best-characterized miRNAs in the nervous system, and the roles of miR-132 in synaptic function and dendrite morphology are currently being studied [38]. Hancock et al. identified that miR-132 as a positive regulator of developing axon extension, acted locally within the axon through repression of Rasa1 mRNA (a novel target in neuronal function) [39]. Lambert et al. demonstrated that miR-132 could regulate short-term plasticity in neurons [40]. In our previous study, it had been found that serum miR-132 was significantly upregulated in aMCI patients compared with control group [17]. However, at the 5-year follow-up time point, the serum levels of miR-132 had no significant difference between the conversion and non-conversion group. Although Kaplan-Meier analysis showed a significant AD conversion trend for aMCI patients with higher serum levels of miR-132 at baseline, but no significant difference was found between the conversion and non-conversion group after adjusting for confounding factors in multivariate Cox regression analysis. In addition, it have been found that both MMSE and MoCA scores at the 5-year follow-up time point were significantly correlated with the serum levels of miR-132 at baseline. However, miR-132 levels were not significantly different between the conversion and non-conversion group at baseline. This discrepancy indicates that miR-132 might play a role in aMCI, but has no effect on the progression of aMCI to AD.

According to the results of previous bioinformatics analysis [17], miR-206 potentially participates in the following biological progresses: learning or memory, nerve development, neuron recognition, and the regulation of neuron differentiation which are closely related to the development of AD. It has been reported that miR-206 was elevated in the brains of AD patients and animal models and that it contributed to cognitive decline by suppressing BDNF expression in the brains. Inhibition of miR-206 prevented the detrimental effects of Aβ₄₂ on BDNF and dendritic spine degeneration in Tg2576 neurons. Injection of AM206 (a neutralizing inhibitor of miR-206) into the cerebral ventricles of AD mice increased the brain levels of BDNF and enhanced the hippocampal synaptic density as well as neurogenesis [41]. MiR-206 is a member of the miR-1 gene family, which includes miR-1-1, miR-1-2, miR-206, miR-133a-1, miR-133a-2, and miR-133b. Among them, miR-1 was reported to be downregulated in adult-onset Drosophila AD model [42]. Ma et al. found that knockdown of miR-1 by hippocampal stereotaxic injection of an anti-miR-1 oligonucleotide fragment carried by a lentivirus vector led to upregulation of BDNF expression and prevented the reduction in cognitive performance in the transgenic mouse [43].

We used TargetScan to predict the target genes of miR-206 and miR-132 in earlier studies, and found that BDNF and SIRT1 were target genes of the two miRNAs [17]. A previous study investigated the mechanism of BDNF suppression that occurred during myogenic differentiation and suggested that miR-206 might play a role in regulating retrograde signaling of BDNF at the neuromuscular junction [44]. A microarray study found that miR-132 could regulate BDNF levels and might influence the development and function of midbrain dopaminergic neurons [45]. It has been reported that the miR-132/212-mediated action of GnRH involved in a posttranscriptional decrease of sirtuin 1 (SIRT1) deacetylase [46]. Some studies have found that the downregulation of BDNF and SIRT1 gene expression in brains maybe due to the upregulation of miR-206 and miR-132 [41 , 48]. Laske et al. [49] demonstrated that serum BDNF levels were significantly decreased in AD patients with fast cognitive decline compared to those with slow cognitive decline and showed a significant correlation with the rate of cognitive decline during a 1-year follow-up. In present study, we also measured serum levels of BDNF and SIRT1 in patients with aMCI and found their levels were obviously decreased in the aMCI-AD group at the 5-year follow-up time point, which was in accordance with the upregulation of miR-206. These results further supported the idea that miR-206 might become a significant predictor for AD conversion.

The levels of the predictive biomarkers (miR-206 and miR-132) were measured as continuous variables in this study. In order to translate a continuous variable into a clinical decision, it is necessary to determine a cut-off point and to stratify patients into two groups that require a different kind of treatment. Currently, there is no standard method or standard software for biomarker cut-off determination. Methods for cut-off point determination vary among published studies and the underlying algorithms remain obscure in many instances. Most studies determined the cut-off points based on ROC method [50, 51]. Some presented the cut-off points of miRNA levels using the second tertile [52, 53]. However, the main problems of the above cut-off point methods were the overestimated significance and effect size of the optimal cut-off points. Therefore, the survival ROC method, which can help to improve the quality of biomarker studies, was applied to determine the optimal cut-off point in this study. Cutoff Finder, a bundle of optimization and visualization tool based on time-independent survival ROC, was used in our study. The optimal cut-off points of serum miR-206 and miR-132 in this study were separately determined as – 2.01 and – 1.985.

The main strengths of this study were summed up as follows: the population based on a cohort study, the 5-year follow-up design, the candidate serum miRNAs strategy, and the reasonable cut-off point finding. However, some limitations should be noted. Firstly, the clinical diagnosis of probable AD has an accuracy of 70– 90% relative to the pathological diagnosis. The implication of this is that the miRNAs as biomarkers to predict progression from aMCI to clinically diagnosed AD could only be as accurate as the clinical diagnosis itself. Secondly, there was only one cohort of aMCI measurement reported in this study. MCI has been subdivided into two major forms: aMCI and other domain cognitive impairment no dementia (oCIND). Normal and oCIND cohorts should be considered in future study.

In conclusion, our results suggest that circulating miR-206 might serve as a new non-invasive prognostic biomarker for AD progression. A future research direction is to develop a multivariable prognostic model for predicting the conversion from aMCI to AD by using clinical, imaging, genetic, and fluid biomarker data.

Footnotes

ACKNOWLEDGMENTS

The authors sincerely thank all the subjects who participated in this study and the neurologist who helped us identify the studied subjects. This research was supported by National Natural Science Foundation of China (81570728, 81400887, and 81400884), Youth Natural Science Foundation of Hebei Province (C2014206380), and Hebei Province Health and Family Planning Committee Program (20120053 and ZD20140312).

Authors’ disclosures available online ().

References

Petersen

, Roberts

, Knopman

, Boeve

, Geda

, Ivnik

, Smith

, Jack

Jr (2009) Mild cognitive impairment: Ten years later. Arch Neurol 66, 1447–1455.

Petersen

, Smith

, Waring

, Ivnik

, Tangalos

, Kokmen

(1999) Mild cognitive impairment: Clinical characterization and outcome.. Arch Neurol 56, 303–308.

Grundman

, Petersen

, Ferris

, Thomas

, Aisen

, Bennett

, Foster

, Jack

Jr , Galasko

, Doody

, Kaye

, Sano

, Mohs

, Gauthier

, Kim

, Jin

, Schultz

, Schafer

, Mulnard

, van Dyck

, Mintzer

, Zamrini

, Cahn-Weiner

, Thal

, Alzheimer’s Disease Cooperative Study (2004) Mild cognitive impairment can be distinguished from alzheimer disease and normal aging for clinical trials. Arch Neurol 61, 59–66.

Petersen

(2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256, 183–194.

Suppa

, Hampel

, Kepp

, Lange

, Spies

, Fiebach

, Dubois

, Buchert

, Alzheimer’s Disease Neuroimaging Initiative (2016) Performance of hippocampus volumetry with FSL-first for prediction of Alzheimer’s disease dementia in at risk subjects with amnestic mild cognitive impairment. J Alzheimers Dis 51, 867–873.

Lacour

, Espinosa

, Louwersheimer

, Heilmann

, Hernández

, Wolfsgruber

, Fernández

, Wagner

, Rosende-Roca

, Mauleón

, Moreno-Grau

, Vargas

, Pijnenburg

, Koene

, Rodríguez-Gómez

, Ortega

, Ruiz

, Holstege

, Sotolongo-Grau

, Kornhuber

, Peters

, Frölich

, Hüll

, Rüther

, Wiltfang

, Scherer

, Riedel-Heller

, Alegret

, Nöthen

, Scheltens

, Wagner

, Tárraga

, Jessen

, Boada

, Maier

, van der Flier

, Becker

, Ramirez

, Ruiz

(2016) Genome-wide significant risk factors for alzheimer’s disease: Role in progression to dementia due to alzheimer’s disease among subjects with mild cognitive impairment. Mol Psychiatry. doi: 10.1038/mp.2016.18. [Epub ahead of print].

Korolev

, Symonds

, Bozoki

, Alzheimer’s Disease Neuroimaging Initiative (2016) Predicting progression from mild cognitive impairment to Alzheimer’s dementia using clinical, MRI, and plasma biomarkers via probabilistic pattern classification. PLoS One 11, e0138866.

Lugli

, Cohen

, Bennett

, Shah

, Fields

, Hernandez

, Smalheiser

(2015) Plasma exosomal miRNAs in persons with and without Alzheimer disease: Altered expression and prospects for biomarkers. PLoS One 10, e0139233.

Huang

, Liu

, Ma

, Yang

, Zhang

, Sun

, Zhang

, Xu

, Wang

(2016) Systematic review and meta-analysis: Circulating miRNAs for diagnosis of hepatocellular carcinoma. J Cell Physiol 231, 328–335.

10.

Mirra

, Raciti

, Nigro

, Fiory

, D’Esposito

, Formisano

, Beguinot

, Miele

(2015) Circulating miRNAs as intercellular messengers, potential biomarkers and therapeutic targets for type 2 diabetes. Epigenomics 7, 653–667.

11.

Cheng

(2015) Circulating miRNAs: Roles in cancer diagnosis, prognosis and therapy. Adv Drug Deliv Rev 81, 75–93.

12.

Tan

, Yu

, Tan

, Liu

, Wang

, Zhang

, Jiang

, Tan

(2014) Genome-wide serum microRNA expression profiling identifies serum biomarkers for Alzheimer’s disease. J Alzheimers Dis 40, 1017–1027.

13.

Galimberti

, Villa

, Fenoglio

, Serpente

, Ghezzi

, Cioffi

, Arighi

, Fumagalli

, Scarpini

(2014) Circulating miRNAs as potential biomarkers in Alzheimer’s disease. J Alzheimers Dis 42, 1261–1267.

14.

Dong

, Li

, Huang

, Chen

, Li

, Wang

, Hu

, Xu

, Zhang

, Zen

, Xiao

, Yan

, Wang

, Zhang

(2015) Serum microRNA profiles serve as novel biomarkers for the diagnosis of Alzheimer’s disease. Dis Markers 2015, 625659.

15.

, Zhang

, Niu

, Cui

, Xie

, Zhang

, Lu

, Yu

, Wang

, Zhang

(2012) Oxidative stress mediated-alterations of the microRNA expression profile in mouse hippocampal neurons. Int J Mol Sci 13, 16945–16960.

16.

Zhang

, Zhang

, Niu

, Lu

, Xie

, Cui

, Xu

(2014) Screening of microRNAs associated with Alzheimer’s disease using oxidative stress cell model and different strains of senescence accelerated mice. J Neurol Sci 338, 57–64.

17.

Xie

, Zhou

, Zhang

, Song

, Yu

, Wang

, Liu

, Zhang

, Cui

, Wang

, Xu

(2015) Serum mir-206 and mir-132 as potential circulating biomarkers for mild cognitive impairment. J Alzheimers Dis 45, 721–731.

18.

, Xie

, Song

, Yu

, Wang

, An

, Zhu

, Han

, Zhao

, Zhang

, Dong

, Chai

, Gao

, Zhang

, Wang

(2014) High prevalence of mild cognitive impairment in the elderly: A community-based study in four cities of the Hebei province, China. Neuroepidemiology 42, 123–130.

19.

Dai

, Ryan

, Paolo

, Harrington

(1990) Factor analysis of the mainland Chinese version of the Wechsler Adult Intelligence Scale (WAIS-RC) in a brain-damaged sample. Int J Neurosci 55, 107–111.

20.

Folstein

, Folstein

, McHugh

(1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189–198.

21.

Nasreddine

, Phillips

, Bédirian

, Charbonneau

, Whitehead

, Collin

, Cummings

, Chertkow

(2005) The Montreal Cognitive Assessment, MOCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53, 695–699.

22.

McKhann

, Drachman

, Folstein

, Katzman

, Price

, Stadlan

(1984) Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology 34, 939–944.

23.

Budczies

, Klauschen

, Sinn

, Győrffy

, Schmitt

, Darb-Esfahani

, Denkert

(2012) Cutoff finder: A comprehensive and straightforward web application enabling rapid biomarker cutoff optimization. PloS One 7, e51862.

24.

Agostinho

, Pliássova

, Oliveira

, Cunha

(2015) Localization and trafficking of amyloid-beta protein precursor and secretases: Impact on Alzheimer’s disease. J Alzheimers Dis 45, 329–347.

25.

Hansson

, Zetterberg

, Buchhave

, Londos

, Blennow

, Minthon

(2006) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: A follow-up study. Lancet Neurol 5, 228–234.

26.

, Caracciolo

, Wang

, Santoni

, Winblad

, Fratiglioni

(2013) Accelerated progression from mild cognitive impairment to dementia among APOE epsilon4 carriers. J Alzheimers Dis 33, 507–515.

27.

Choo

, Ni

, Scholl

, Wall

, Almkvist

, Nordberg

(2013) Combination of 18F-FDG PET and cerebrospinal fluid biomarkers as a better predictor of the progression to Alzheimer’s disease in mild cognitive impairment patients. J Alzheimers Dis 33, 929–939.

28.

Jack

CR Jr

, Petersen

, Xu

, O’Brien

, Smith

, Ivnik

, Boeve

, Waring

, Tangalos

, Kokmen

(1999) Prediction of ad with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52, 1397–1403.

29.

Grimmer

, Wutz

, Alexopoulos

, Drzezga

, Förster

, Förstl

, Goldhardt

, Ortner

, Sorg

, Kurz

(2016) Visual versus fully automated analyses of 18F-FDG and amyloid PET for prediction of dementia due to Alzheimer disease in mild cognitive impairment. J Nucl Med 57, 204–207.

30.

Fleming

, Zhong

, da Silva

, Vega-Saenz de Miera

, Brady

, Han

, Hanniford

, Wang

, Shapiro

, Hernando

, Osman

(2015) Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients. Cancer 121, 51–59.

31.

Zhao

, Liu

, Shi

, Zhang

, Jia

, Ling

(2015) Role of serum miRNAs in the prediction of ovarian hyperstimulation syndrome in polycystic ovarian syndrome patients. Cell Physiol Biochem 35, 1086–1094.

32.

Geekiyanage

, Jicha

, Nelson

, Chan

(2012) Blood serum miRNA: Non-invasive biomarkers for Alzheimer’s disease. Exp Neurol 235, 491–496.

33.

Wang

, Chen

, Li

, Dong

, Su

, Liu

, Cheng

, Dai

, Yang

, Xiao

(2015) The feasibility of utilizing plasma mirna107 and bace1 messenger RNA gene expression for clinical diagnosis of amnestic mild cognitive impairment. J Clin Psychiatry 76, 135–141.

34.

Kiko

, Nakagawa

, Tsuduki

, Furukawa

, Arai

, Miyazawa

(2014) MicroRNAs in plasma and cerebrospinal fluid as potential markers for Alzheimer’s disease. J Alzheimers Dis 39, 253–259.

35.

Moon

, Lee

, Kong

, Byun

, Sunwoo

, Shin

, Shim

, Park

, Jeon

, Jung

, Kim

, Lee

, Kim

, Chu

(2016) Early diagnosis of Alzheimer’s disease from elevated olfactory mucosal mir-206 level. Sci Rep 6, 20364.

36.

Hébert

, Wang

, Zhu

, Nelson

(2013) A study of small RNAs from cerebral neocortex of pathology-verified Alzheimer’s disease, dementia with Lewy bodies, hippocampal sclerosis, frontotemporal lobar dementia, and non-demented human controls. J Alzheimers Dis 35, 335–348.

37.

Tan

, Yu

, Liu

, Tan

, Zhang

, Hu

, Wang

, Sun

, Jiang

, Tan

(2014) Circulating mir-125b as a biomarker of Alzheimer’s disease. J Neurol Sci 336, 52–56.

38.

Kawashima

, Numakawa

, Kumamaru

, Adachi

, Mizuno

, Ninomiya

, Kunugi

, Hashido

(2010) Glucocorticoid attenuates brain-derived neurotrophic factor-dependent upregulation of glutamate receptors via the suppression of microrna-132 expression. Neuroscience 165, 1301–1311.

39.

Hancock

, Preitner

, Quan

, Flanagan

(2014) MicroRNA-132 is enriched in developing axons, locally regulates rasa1 mRNAa, and promotes extension. J Neurosci 34, 66–78.

40.

Lambert

, Storm

, Sullivan

(2010) MicroRNA132 modulates short-term synaptic plasticity but not basal release probability in hippocampal neurons. PloS One 5, e15182.

41.

Lee

, Chu

, Jung

, Kim

, Huh

, Yoon

, Park

, Lim

, Kim

, Jeon

, Ryu

, Lee

, Kim

, Roh

(2012) Mir-206 regulates brain-derived neurotrophic factor in Alzheimer disease model. Ann Neurol 72, 269–277.

42.

Kong

, Wu

, Yuan

(2014) MicroRNA expression analysis of adult-onset drosophila Alzheimer’s disease model. Curr Alzheimer Res 11, 882–891.

43.

, Duan

, Sun

, Yan

, Liu

, Wang

, Liu

, Wang

, Kang

, Pei

, Zong

, Chen

, Wang

, Ai

(2015) Cardiac over-expression of microrna-1 induces impairment of cognition in mice. Neuroscience 299, 66–78.

44.

Miura

, Amirouche

, Clow

, Bélanger

, Jasmin

(2012) Brain-derived neurotrophic factor expression is repressed during myogenic differentiation by mir-206. J Neurochem 120, 230–238.

45.

Lungu

, Stoica

, Ambrus

(2013) MicroRNA profiling and the role of microrna-132 in neurodegeneration using a rat model. Neurosci Lett 553, 153–158.

46.

Lannes

, L’Hôte

, Garrel

, Laverrière

, Cohen-Tannoudji

, Quérat

(2015) Rapid communication: A microrna-132/212 pathway mediates GNRH activation of FSH expression. Mol Endocrinol 29, 364–372.

47.

Di Emidio

, Falone

, Vitti

, D’Alessandro

, Vento

, Di Pietro

, Amicarelli

, Tatone

(2014) Sirt1 signalling protects mouse oocytes against oxidative stress and is deregulated during aging. Hum Reprod 29, 2006–2017.

48.

Tian

, Cao

, Zhang

(2014) Mir-206 decreases brain-derived neurotrophic factor levels in a transgenic mouse model of Alzheimer’s disease. Neurosci Bull 30, 191–197.

49.

Laske

, Stellos

, Hoffmann

, Stransky

, Straten

, Eschweiler

, Leyhe

(2011) Higher BDNF serum levels predict slower cognitive decline in Alzheimer’s disease patients. Int J Neuropsychopharmacol 14, 399–404.

50.

van Remoortel

, Camillo

, Langer

, Hornikx

, Demeyer

, Burtin

, Decramer

, Gosselink

, Janssens

, Troosters

(2013) Moderate intense physical activity depends on selected metabolic equivalent of task (met) cut-off and type of data analysis. PloS One 8, e84365.

51.

Moghtaderi

, Niazi

, Alavi-Naini

, Yaghoobi

, Narouie

(2010) Comparative analysis of cerebrospinal fluid adenosine deaminase in tuberculous and non-tuberculous meningitis. Clin Neurol Neurosurg 112, 459–462.

52.

Schulte

, Molz

, Appelbaum

, Karakas

, Ojeda

, Lau

, Hartmann

, Lackner

, Westermann

, Schnabel

, Blankenberg

, Zeller

(2015) miRNA-197 and miRNA-223 predict cardiovascular death in a cohort of patients with symptomatic coronary artery disease. PloS One 10, e0145930.

53.

Ravaglia

, Forti

, Maioli

, Martelli

, Servadei

, Brunetti

, Pantieri

, Mariani

(2006) Conversion of mild cognitive impairment to dementia: Predictive role of mild cognitive impairment subtypes and vascular risk factors. Dement Geriatr Cogn Disord 21, 51–58.