Elevation of Peripheral BDNF Promoter Methylation Predicts Conversion from Amnestic Mild Cognitive Impairment to Alzheimer’s Disease: A 5-Year Longitudinal Study

Abstract

Epigenetic aberrations have been identified as biomarkers to predict the risk of Alzheimer’s disease (AD). This study aimed to evaluate whether altered DNA methylation status of BDNF promoter could be used as potential epigenetic biomarkers for predicting the progression from amnestic mild cognitive impairment (aMCI) to AD. A total of 506 aMCI patients and 728 cognitively normal controls were recruited in the cross-sectional analyses. Patients (n = 458) from aMCI cohort were classified into two groups after 5-year follow-up: aMCI-stable group (n = 330) and AD-conversion group (n = 128). DNA methylation of BDNF promoter was detected by bisulfite-PCR amplification and pyrosequencing. The DNA methylation levels of CpG1 and CpG2 in promoter I and CpG5 and CpG6 in promoter IV of BDNF gene were significantly higher in the aMCI group than in the control group at baseline and also were increased in the conversion group compared with the non-conversion group at 5-year follow up time point. CpG5 in BDNF promoter IV had the highest AUC of 0.910 (95% CI: 0.817–0.983, p < 0.05). Kaplan-Meier analysis showed a significant AD conversion propensity for aMCI patients with high methylation levels of CpG5 (HR = 1.96, 95% CI: 1.07–2.98, p < 0.001). Multivariate Cox regression analysis revealed elevated methylation status of CpG5 was a significant independent predictor for AD conversion (HR = 3.51, p = 0.013). These results suggest that elevation of peripheral BDNF promoter methylation might be used as potential epigenetic biomarkers for predicting the conversion from aMCI to AD.

Keywords

Alzheimer’s disease amnestic mild cognitive impairment BDNF promoter DNA methylation follow-up study

INTRODUCTION

Alzheimer’s disease (AD), the leading cause of dementia, has reached epidemic proportions, with major social, medical, and economical burdens [1]. As the global aging of the population increases and no efficient treatment has been found so far, AD is now recognized as a public health crisis Wortmann, 2012 [2]. Mild cognitive impairment (MCI) is viewed as a state between normal cognitive functioning and dementia [3]. MCI is classified into two clinical subtypes: amnestic MCI (aMCI) and non-amnestic MCI (naMCI). Particularly, aMCI has increasingly been accepted as a prodrome or significant risk factor for AD in clinical settings [4]. People with aMCI tend to progress to probable AD at a rate of approximately 10% to 15% per year [5]. Thus, aMCI represents a key prognostic and therapeutic target in the management of AD [6]. Cerebrospinal fluid (CSF) markers, neuroimaging data, genetic testing, and neuropsychological testing could potentially predict the conversion to AD [7 –9]. However, there are no reliable biomarkers for the prediction of AD progression. There is a need to identify novel and non-invasive prognostic markers to predict the conversion from aMCI to AD, which might be potential therapeutic targets for the prevention and management of AD.

AD is a complex multifactorial disease influenced by both genetic and environmental factors [10, 11]. Epigenetic biomarkers have already generated broad interest as novel monitors or indicators in many diseases, such as malignant glioma and gastrointestinal cancer [12, 13]. DNA methylation is a major epigenetic mechanism that plays important roles in the fundamental biological processes, including cell differentiation and development [14]. It was reported that altered DNA methylation/demethylation patterns in vulnerable brain regions were observed prior to the onset of clinical symptoms in AD, suggesting a link between the epigenetic changes and the progression of AD [15]. DNA methylation has the potential ability to either up- or down-regulate the transcription of certain genes involved in AD development, suggesting that aberrant methylated loci may be a valuable source for AD biomarkers [16, 17]. Brain-derived neurotrophic factor (BDNF) plays a key role in the growth, development, differentiation, and regeneration of various types of neurons in the central nervous system [18]. DNA methylation of the BDNF gene is associated with AD in several studies [19, 20]. To our knowledge, DNA methylation of the BDNF gene locus as a prognostic biomarker in the process of conversion from aMCI to AD has not been well demonstrated.

In our previous study, we found that some oxidative stress-mediated miRNAs, including miR-206, miR-132, miR-193b, miR-130b, miR-20a, miR-296, and miR-329, were deregulated and associated with the pathogenesis of AD in SAMP8 mouse model [21, 22]. These deregulated miRNAs have been detected in serum samples from aMCI patients and normal controls, indicating that circulating miR-206 and miR-132 might be potential biomarkers for screening or diagnosis of aMCI in elderly population [23]. We identified target genes of miR-206 and miR-132 by using TargetScan, and the results demonstrated that BDNF was the target gene of the two miRNAs. BDNF levels in serum were consistently decreased in the aMCI group compared to the control group [23]. However, we have not found genetic association of BDNF gene with the pathogenesis of aMCI. It is postulated that some epigenetic factors might play important roles in the progression of aMCI to AD.

In present 5-year longitudinal study, we detected the levels of BDNF promoter methylation in an aMCI cohort using the Bisulfite-PCR amplification and pyrosequencing. The main goal of this study was to investigate whether aberrant DNA methylation status of BDNF gene could be used as an epigenetic biomarker for predicting the conversion from aMCI to AD.

METHODS

Subjects

This study was based on the database 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 from 2010 to 2015. Detailed descriptions of the study have been provided [24]. The current study was organized in two parts: cross-sectional analyses and longitudinal analyses. For cross-sectional analyses, the baseline data were collected in 2010–2011, including 506 aMCI patients and 728 cognitive normal controls. The cases and the controls were matched by age, gender, and education. Inclusion and exclusion criteria were the same as that in the MASHB study. For longitudinal analyses, 506 aMCI patients were invited to attend a follow-up examination in 2011–2015. After 5-year follow-up, 21 patients withdrew from the study, 8 could not be contacted, 11 had died, and 8 had moved, leaving 458 patients for the aMCI cohort in present study. All follow-up assessments were done between 56 months to 63 months after the baseline assessment (60.97±3.79). According to the assessment, 458 aMCI 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 [3]: (1) memory complaints by patient; (2) objective memory impairment, presenting as a logical memory score on the Wechsler Memory Scale Revised (WMS-R, Chinese version), but no 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 measured with A: Mini-Mental State Examination (MMSE) score between 20 and 27 [cutoff points for illiterate (≤20), primary school (≤23) and secondary school and above (≤27)]; or B: Montreal Cognitive Assessment (MoCA) score <26; (6) no dementia. We used these criteria as described in our previous study [23]. 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) [25]. 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 neurologists and psychiatrists in the Institute of Mental Health, Hebei Medical University.

Ethic statement

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

DNA extraction and bisulfite modification

Genomic DNA was extracted from whole-blood samples using a genomic DNA extraction kit (Promega, Wisconsin, USA). Bisulfite conversion of unmethylated cytosines to uracil was performed on 2 μg of DNA using the EpiTect Bisulfite Kit (Qiagen, Hilden, Germany) following the manufacturer’s suggested protocol. Elution was performed with 70 μL EB Buffer and 10 μL Top Elute Fluid.

Bisulfite-PCR amplification and pyrosequencing

BDNF promoter CpG sites were designated according to previous studies [26]. The DNA methylation locus of BDNF promoter region were 3 CpG sites in CpG-rich region of the promoter I and 4 CpG sites in CpG-rich region of the promoter IV. The BDNF promoter region for analyzing methylation status is presented in Fig. 1. These data have been deposited in GenBank (BDNF promoter I accession number: BankIt1959187 BDNF KX964616; BDNF promoter IV accession number: BankIt1959207 BDNF KX964617). The bisulfite-PCR reactions were performed in 50 μL final volume containing 2.0 μL bisulite-treated genomic DNA, 2.0 μL primers, 1.0 μL dNTP (10 mM), 5.0 μL Taq Buffer, 5 μL MgCl₂ (25 mM), 0.5 μL Hot Start Taq DNA polymerase (Qiagen), and 35.5 μL water. For BDNF promoter I, the PCR condition was as follows: 95°C for 3 min; and 35 cycles of 94°C for 25 s, 60°C for 25 s, 72°C for 25 s. For BDNF promoter IV, the PCR condition was as follows: 95°C for 3 min; and 35 cycles of 94°C for 25 s, 60°C for 20 s, 72°C for 20 s. The amplification primers to measure methylation levels in the 7 CpG sites within BDNF promoter were designed using MethPrimer software [27]. The primers used in these PCR amplifications are listed in Table 1. For pyrosequencing analyses, bisulfite-PCR products were sequenced with the PSQ 96MA instrument (Qiagen) according to the manufacturer’s protocol using the sequencing primers designated in Table 1. The methylation percentage at each CpG site was calculated using PSQ 96MA software.

Statistical analysis

Quantitative data was compared with t-test between two groups. Chi-square tests were used for categorical data. 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 (Log-rank test) was used for univariate survival analysis. Cutoff Finder [28] (http://molpath.charite.de/cutoff/) based on time-independent survival ROC was used to find the optimal cut-off points of each BDNF promoter methylation 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 using 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

This cross-sectional cohort study was organized in two stages: at baseline and at the longitudinal assessment. At baseline, 506 patients with aMCI were significantly less educated (2.47±0.88, p < 0.001) and had lower scores on cognitive function tests (MMSE: 25.83±4.82, p < 0.001; MoCA: 21.49±3.24, p < 0.001) compared to cognitively normal controls. Of the 506 aMCI patients at baseline, 458 (90.5%) patients were followed up for a period of 5 years. During the period of follow-up, there were 128 subjects (27.9%) with aMCI progressed to AD (aMCI-AD) and 330 subjects (72.1%) remained with the aMCI (aMCI- aMCI) diagnosis. Individuals from the conversion group were significantly older (76.04±4.82, p < 0.001), less educated (2.28±0.86, p < 0.001), lower scores on cognitive function tests (MMSE: 21.07±1.98, p < 0.001; MoCA: 17.33±4.55, p < 0.001) compared to the non-conversion group. The study characteristics of all the subjects are summarized in Table 2.

Comparison of BDNF promoter methylation levels

The chromosomal locations and the number of BDNF promoter CpG sites examined in this study are shown in Fig. 1A. All PCR products were checked on the agarose gel to ensure successful specific amplification before proceeding to pyrosequencing (Fig. 1B). By means of pyrosequencing, we obtained signals successfully to analyze at promoter I and promoter IV (Fig. 1C). All examined CpG sites showed relatively low levels of DNA methylation, the average values were less than 20%.

At baseline, DNA methylation levels of CpG1 and CpG2 at promoter I and CpG5 and CpG6 at promoter IV were significantly increased in the aMCI group compared with the control group (CpG1: 8.58±3.58 versus 4.68±1.54, p < 0.001; CpG2: 8.37±3.47 versus 6.62±2.15, p < 0.001; CpG5: 18.48±5.49 versus 14.14±3.31, p < 0.001; CpG6:17.36±5.40 versus 16.50±5.24, p = 0.018; average methylation: 16.11±4.48 versus 14.85±4.07, p < 0.001) (Fig. 2). At the 5-year follow up time point, DNA methylation levels of CpG1 and CpG2 at promoter I and CpG5 and CpG6 at promoter IV were also significantly increased in the conversion group compared with the non-conversion group (CpG1: 12.39±4.15 versus 9.44±3.76, p < 0.001; CpG2: 9.90±3.82 versus 8.99±3.51, p < 0.001; CpG5: 24.63±6.77 versus 19.21±5.99, p < 0.001; CpG6: 20.01±5.11 versus 18.25±4.41, p < 0.001; average methylation: 19.92±5.89 versus 17.43±5.10, p < 0.001) (Fig. 2).

At the baseline, a further subgroup analysis by gender showed significant associations with aMCI for CpG1 (p < 0.001), CpG2 (p < 0.001), CpG4 (p < 0.001), and CpG5 (p < 0.001) in males and CpG1 (p < 0.001), CpG2 (p = 0.040), and CpG5 (p < 0.001) in females (Table 3). At the 5-year follow up time point, gender showed significant associations with AD conversion for CpG1 (p < 0.001), CpG2 (p < 0.001), CpG4 (p < 0.001), and CpG5 (p < 0.001) in males and CpG1 (p < 0.001) and CpG5 (p < 0.001) in females (Table 3).

Prognostic value of BDNF promoter methylation in conversion from aMCI to AD

In order to translate continuous variables (BDNF promoter methylation levels) into dichotomous variables, the optimal cutoff points were determined by survival ROC analysis. Figure 3 showed the survival ROC curves of the four significant altered BDNF promoter sites. The results suggested that CpG5 in BDNF promoter IV had the highest AUC of 0.910 (95% CI: 0.817–0.983, p < 0.05). The optimal cutoff point of methylation levels of CpG5 in BDNF promoter IV was determined as 16.68, with 91.3% sensitivity, 96.2% specificity, 98.3% +PV, and 82.1% –PV (Table 4). There were no significant diagnosis values were found in the methylation levels of CpG1, CpG2, CpG6, and the average CpG (Table 4). Kaplan-Meier analysis showed asignificant AD conversion propensity for aMCI patients with high methylation levels of CpG5 (HR = 1.96, 95% CI: 1.07–2.98, p < 0.001) (Fig. 4).

Multivariate Cox regression analysis revealed that methylation levels of CpG5 were significant independent predictors of AD conversion (Table 5). Higher levels of CpG5 were significantly associated with higher risk of conversion from aMCI to AD (HR = 3.51, p = 0.013).

DISCUSSION

A biomarker is a molecular target analyzed in a qualitative or quantitative manner to diagnose the presence of a disease, or to predict the outcome and the response to a specific treatment allowing personalized tailoring of patient management [29]. Biomarkers include proteins, DNA, RNA, or lipids. Protein biomarkers have been extensively studied and used, notably in blood-based protein quantification tests or immunohistochemistry [29, 30]. DNA methylation has also been identified as a new type of biomarkers with great potential for manyapplications [31, 32]. This stable and heritable covalent modification mostly affects cytosines in the context of a CpG dinucleotide in humans. In some cases, DNA methylation-based biomarkers have been proven to be more specific and sensitive than commonly used protein biomarkers, which could clearly justify their use in clinics [33, 34]. However, very few of DNA methylation-based biomarkers have been identified to predict the conversion from aMCI to AD.

In this 5-year longitudinal study, we focused on DNA methylation levels of BDNF promoter to evaluate its prognostic value on the conversion from aMCI to AD. Our results revealed that the DNA methylation levels of CpG5 in BDNF promoter IV were significant higher in the aMCI-AD group than that in the aMCI-aMCI group. The survival ROC curve analysis showed that DNA methylation levels of CpG5 yielded the highest AUC (0.910). The optimal cutoff point of CpG5 was determined as 16.68, with 91.3% sensitivity, 96.2% specificity, 98.3% +PV, and 82.1% –PV. Moreover, higher DNA methylation levels at CpG5 site were an independent prognostic marker of conversion from aMCI to AD according to the multivariate Cox proportional hazard model. The results indicated that higher DNA methylation levels at CpG5 might accelerate the progression from aMCI to AD.

BDNF, a neurotrophic factor, is widely expressed throughout the human brain, including the cerebral cortex, hippocampus, basal forebrain, striatum, hypothalamus, brainstem, and cerebellum [35]. Altered expression and/or function of the BDNF protein, which promotes nerve cell survival, generation, and function, have been implicated in several neurodegenerative conditions, including AD. A meta-analysis result strengthen the clinical evidence that AD or MCI is accompanied by reduced peripheral blood BDNF levels, supporting an link between the decreased levels of BDNF and the progression of AD [36]. Laske et al. found a significant decrease of BDNF serum concentration in AD and in contrast to serum, CSF seems to be not a useful source to determine BDNF in AD patients because of too low concentration [37]. Alterations of DNA methylation in the BDNF promoter region (evaluated using postmortem or peripheral blood samples) have been reported in patients with neuropsychiatric disorders [26 , 38–40]. BDNF promoter region was hypermethylated in postmortem AD brain tissues, resulting in a reduction in the mRNA or protein levels in certain brain areas (hippocampus or frontal cortex) [41]. Several lines of evidence have suggested that DNA methylation levels of BDNF may be used as potential diagnostic biomarkers for early screening and diagnosis of AD [19, 40]. Nagata et al. investigated the correlation between the DNA methylation levels of BDNF gene and the clinical presentation of AD [19]. Chang et al. have demonstrated that peripheral BDNF promoter methylation might be a diagnostic marker of AD risk [36]. However, the above studies were conducted using a cross-sectional design which could not reveal the roles of DNA methylation level of BDNF gene in monitoring the progression of AD. In this study, we made up for this shortage and performed a 5-year longitudinal study to investigate the relationship between BDNF promoter methylation and the conversion from aMCI to AD. We found that DNA methylation level of CpG5 in the BDNF gene predicted the progression of aMCI to AD. To our knowledge, there are no related reports in this field.

In our previous study, the combined detection levels of circulating miR-206 and miR-132 ever served as a biomarker for aMCI with high diagnostic performance (AUC = 0.981) [23]. TargetScan was used to predict the target genes of miR-206 and miR-132, and the bioinformatics results revealed that BDNF was the target gene of the two miRNAs. Downregulation of BDNF expression in the serum of aMCI patients was correlated with upregulation of miR-206 and miR-132. In the present study, we found DNA methylation levels of BDNF promoters in patients with aMCI were increased in the aMCI-AD group at the 5-year follow-up point, which is consistent with the upregulation of miR-206 and downregulation of BDNF in serum. These results further support the idea that the DNA methylation level of BDNF gene might predict AD conversion from aMCI.

The levels of the potential predictive biomarker (DNA methylation) were measured as continuous variable 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 required a different kind of treatment. Currently, there are no standard methods or standard software for determination of biomarker cut-off. Methods for determination of cut-off point vary among published studies and the underlying algorithms remain obscure in many instances. Most studies determined the cut-off points based on ROC method [42, 43]. Some reports presented the cut-off points using the second tertile. However, the main problems of the above cut-off point methods were the overestimated significance and effect size of the optimal cut-off points [44, 45]. 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 tools based on time-independent survival ROC, showed that the optimal cut-off points of the BDNF gene methylation levels in sites of CpG1, CpG2, CpG5, CpG6, and average CpG were 7.29, 7.73, 16.68, 17.12, and 15.39, respectively.

The main strengths of this study were a population based on a cohort study, the 5-year follow-up design, the candidate predictor 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 limitation was that the accuracy of BDNF promoter methylation 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 cognitive impairment no dementia (oCIND). Normal cohort and oCIND cohort should be compared in future studies.

In summary, the levels of BDNF promoter methylation in an aMCI cohort were detected in this 5-year longitudinal study and our results demonstrate that elevation of peripheral BDNF promoter methylation might be used as epigenetic biomarkers for predicting the conversion from aMCI to AD. Future research directions are to develop a multivariable prognostic model for predicting the conversion from aMCI to AD by using clinical, imaging, genetic, and fluid biomarkers data.

Footnotes

ACKNOWLEDGMENTS

The authors sincerely thank all the subjects who participated in this study and the neurologist who helped us identify the study subjects. This research was supported by National Natural Science Foundation of China (81400887, 81570728, and 81400884), Natural Science Foundation of Hebei Province (C2014206380 and H2014206326) and Hebei Province Health and Family Planning Committee Program (20120053 and ZD20140312).

Authors’ disclosures available online ().

References

Sindi

, Mangialasche

, Kivipelto

(2015) Advances in the prevention of Alzheimer’s Disease. F1000Prime Rep 7, 50.

Wortmann

(2012) Dementia: A global health priority - highlights from an ADI and World Health Organization report. Alzheimers Res Ther 4, 40.

Petersen

, Roberts

, Knopman

, Boeve

, Geda

, Ivnik

, Smith

, Jack

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

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

(2000) Mild cognitive impairment: Transition between aging and Alzheimer’s disease. Neurologia 15, 93–101.

Janoutova

, Sery

, Hosak

, Janout

(2015) Is mild cognitive impairment a precursor of Alzheimer’s disease? Short review. Cent Eur J Public Health 23, 365–367.

Almeida

, Schultz

, Austin

, Boots

, Dowling

, Gleason

, Bendlin

, Sager

, Hermann

, Zetterberg

, Carlsson

, Johnson

, Asthana

, Okonkwo

(2015) Effect of cognitive reserve on age-related changes in cerebrospinal fluid biomarkers of Alzheimer disease. JAMA Neurol 72, 699–706.

Lacour

, Espinosa

, Louwersheimer

, Heilmann

, Hernandez

, Wolfsgruber

, Fernandez

, Wagner

, Rosende-Roca

, Mauleon

, Moreno-Grau

, Vargas

, Pijnenburg

, Koene

, Rodriguez-Gomez

, Ortega

, Ruiz

, Holstege

, Sotolongo-Grau

, Kornhuber

, Peters

, Frolich

, Hull

, Ruther

, Wiltfang

, Scherer

, Riedel-Heller

, Alegret

, Nothen

, Scheltens

, Wagner

, Tarraga

, 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 22, 153–160.

Peters

, Villeneuve

, Belleville

(2014) Predicting progression to dementia in elderly subjects with mild cognitive impairment using both cognitive and neuroimaging predictors. J Alzheimers Dis 38, 307–318.

10.

Bufill

, Bartes

, Moral

, Casadevall

, Codinachs

, Zapater

, Carles Rovira

, Roura

, Oliva

, Blesa

(2009) [Genetic and environmental factors that may influence in the senile form of Alzheimer’s disease: Nested case control studies]. Neurologia 24, 108–112.

11.

Bellou

, Belbasis

, Tzoulaki

, Middleton

, Ioannidis

, Evangelou

(2016) Systematic evaluation of the associations between environmental risk factors and dementia: An umbrella review of systematic reviews and meta-analyses. Alzheimers Dement. doi: 10.1016/j.jalz.2016.07.152 [Epub ahead of print].

12.

Jeon

, Song

, Yun

, Kang

, Kim

, Han

, Kim

(2016) Aberrant epigenetic modifications of LPHN2 function as a potential cisplatin-specific biomarker for human gastrointestinal cancer. Cancer Res Treat 48, 676–686.

13.

Choudhury

, Cui

, Milton

, Li

, Irudayaraj

(2015) Selective increase in subtelomeric DNA methylation: An epigenetic biomarker for malignant glioma. Clin Epigenetics 7, 107.

14.

Srinageshwar

, Maiti

, Dunbar

, Rossignol

(2016) Role of epigenetics in stem cell proliferation and differentiation: Implications for treating neurodegenerative diseases.pii: E. Int J Mol Sci 17, 199–.

15.

Bradley-Whitman

, Lovell

(2013) Epigenetic changes in the progression of Alzheimer’s disease. Mech Ageing Dev 134, 486–495.

16.

, Chibnik

, Srivastava

, Pochet

, Yang

, Xu

, Kozubek

, Obholzer

, Leurgans

, Schneider

, Meissner

, De Jager

, Bennett

(2015) Association of brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 with pathological diagnosis of Alzheimer disease. JAMA Neurol 72, 15–24.

17.

Brohede

, Rinde

, Winblad

, Graff

(2010) A DNA methylation study of the amyloid precursor protein gene in several brain regions from patients with familial Alzheimer disease. J Neurogenet 24, 179–181.

18.

Benarroch

(2015) Brain-derived neurotrophic factor: Regulation, effects, and potential clinical relevance. Neurology 84, 1693–1704.

19.

Nagata

, Kobayashi

, Ishii

, Shinagawa

, Nakayama

, Shibata

, Kuerban

, Ohnuma

, Kondo

, Arai

, Yamada

, Nakayama

(2015) Association between DNA methylation of the BDNF promoter region and clinical presentation in Alzheimer’s disease. Dement Geriatr Cogn Dis Extra 5, 64–73.

20.

, Zhou

, Ji

, Luo

, Liu

, Li

, Wang

, Duan

(2015) Population difference in the association of BDNF promoter methylation with mild cognitive impairment in the Xinjiang Uygur and Han populations. Psychiatry Res 229, 926–932.

21.

, 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.

22.

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.

23.

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.

24.

, 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.

25.

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.

26.

Ikegame

, Bundo

, Sunaga

, Asai

, Nishimura

, Yoshikawa

, Kawamura

, Hibino

, Tochigi

, Kakiuchi

, Sasaki

, Kato

, Kasai

, Iwamoto

(2013) DNA methylation analysis of BDNF gene promoters in peripheral blood cells of schizophrenia patients. Neurosci Res 77, 208–214.

27.

, Dahiya

(2002) MethPrimer: Designing primers for methylation PCRs. Bioinformatics 18, 1427–1431.

28.

Budczies

, Klauschen

, Sinn

, Gyorffy

, Schmitt

, Darb-Esfahani

, Denkert

(2012) Cutoff Finder: A comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization. PLoS One 7, e51862.

29.

How Kit

, Nielsen

, Tost

(2012) DNA methylation based biomarkers: Practical considerations and applications. Biochimie 94, 2314–2337.

30.

Barbieri

, Chinnaiyan

, Lerner

, Swanton

, Rubin

(2016) The emergence of precision urologic oncology: A collaborative review on biomarker-driven therapeutics. Eur Urol 71, 237–246.

31.

Lam

, Pan

, Linnekamp

, Medema

, Kandimalla

(2016) DNA methylation based biomarkers in colorectal cancer: A systematic review. Biochim Biophys Acta 1866, 106–120.

32.

, Cao

, Wu

(2016) New progress of epigenetic biomarkers in urological cancer. Dis Marker 2016, 9864047.

33.

, Shen

, Guo

, Feng

, Tang

(2012) DNA methylation as a biomarker for neuropsychiatric diseases. Int J Neurosci 122, 165–176.

34.

Board

, Knight

, Greystoke

, Blackhall

, Hughes

, Dive

, Ranson

(2008) DNA methylation in circulating tumour DNA as a biomarker for cancer. Biomark Insights 2, 307–319.

35.

Murer

, Yan

, Raisman-Vozari

(2001) Brain-derived neurotrophic factor in the control human brain, and in Alzheimer’s disease and Parkinson’s disease. Prog Neurobiol 63, 71–124.

36.

Qin

, Cao

, Cawley

, Liu

, Yuan

, Loh

, Cheng

(2016) Decreased peripheral brain-derived neurotrophic factor levels in Alzheimer’s disease: A meta-analysis study (N=7277). Mol Psychiatry. doi: 10.1038/mp.2016.62 [Epub ahead of print].

37.

Laske

, Stransky

, Leyhe

, Eschweiler

, Maetzler

, Wittorf

, Soekadar

, Richartz

, Koehler

, Bartels

, Buchkremer

, Schott

(2007) BDNF serum and CSF concentrations in Alzheimer’s disease, normal pressure hydrocephalus and healthy controls. J Psychiatr Res 41, 387–394.

38.

Kordi-Tamandani

, Sahranavard

, Torkamanzehi

(2012) DNA methylation and expression profiles of the brain-derived neurotrophic factor (BDNF) and dopamine transporter (DAT1) genes in patients with schizophrenia. Mol Biol Rep 39, 10889–10893.

39.

Zheleznyakova

, Cao

, Schioth

(2016) BDNF DNA methylation changes as a biomarker of psychiatric disorders: Literature review and open access database analysis. Behav Brain Funct 12, 17.

40.

Chang

, Wang

, Ji

, Dai

, Xu

, Jiang

, Hong

, Ye

, Zhang

, Zhou

, Liu

, Li

, Chen

, Li

, Zhou

, Zhuo

, Zhang

, Yin

, Mao

, Duan

, Wang

(2014) Elevation of peripheral BDNF promoter methylation links to the risk of Alzheimer’s disease. PLoS One 9, e110773.

41.

Rao

, Keleshian

, Klein

, Rapoport

(2012) Epigenetic modifications in frontal cortex from Alzheimer’s disease and bipolar disorder patients. Transl Psychiatry 2, e132.

42.

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.

43.

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.

44.

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.

45.

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.