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Published in final edited form as: Neurobiol Aging. 2014 Dec 24;36(3):1600.e1–1600.e4. doi: 10.1016/j.neurobiolaging.2014.12.023

Blood methylomic signatures of pre-symptomatic dementia in elderly subjects with Type 2 Diabetes Mellitus

Katie Lunnon 1,*, Rebecca G Smith 2,*, Itzik Cooper 3, Lior Greenbaum 3,4, Jonathan Mill1 2,*, Michal Schnaider Beeri 3,5,*
PMCID: PMC5747361  NIHMSID: NIHMS642355  PMID: 25585531

Abstract

Due to an aging population, the incidence of dementia is steadily rising. The ability to identify early markers in blood, which appear before the onset of clinical symptoms is of considerable interest to allow early intervention, particularly in “high risk” groups such as those with Type 2 Diabetes (T2D). Here we present a longitudinal study of genome-wide DNA methylation in whole blood from 18 elderly individuals with T2D who developed pre-symptomatic dementia within an 18 month period following baseline assessment and 18 age, sex and education matched controls who maintained normal cognitive function. We identified a significant overlap in methylomic differences between groups at baseline and follow-up, with ten CpG sites, several in the vicinity of loci previously implicated in neurodegenerative processes, being consistently differentially methylated above our nominal significance threshold prior to symptoms at baseline and at 18 month follow up, after a diagnosis of pre-symptomatic dementia. Finally we report a significant overlap between DNA methylation differences identified in converters, only after they develop symptoms of dementia, with differences at the same loci in blood samples from patients with clinically-diagnosed Alzheimer’s disease compared to unaffected controls.

Keywords: DNA methylation, biomarker, epigenetics, Alzheimer’s disease (AD), blood, type 2 diabetes, dementia

1. Introduction

Alzheimer’s disease (AD) is a chronic, currently incurable, neurodegenerative disorder with more than 26 million cases worldwide, and accounting for ~60% of dementia cases (Brookmeyer, et al., 2007). With an increasingly aging population, new estimates for dementia incidence predict >115 million cases worldwide by 2050 (Prince, et al., 2013). AD is characterised by the accumulation of extracellular amyloid plaques, and intracellular neurofibrillary tangles, leading to selective neuronal cell loss, behavioural and personality changes and ultimately death after many years of suffering. Although much progress has been made in understanding the molecular pathology of AD, the treatments currently available only temporarily alleviate some symptoms and do not modify the underlying pathology. By the time an individual becomes symptomatic there is already considerable neuronal cell loss, plaque deposition and neurofibrillary tangle burden within the brain, which can appear years before a clinical diagnosis is made (Jack, et al., 2010). Although the majority of AD cases are sporadic, occur later in life (age>65 years) and have no known cause, the disease is associated with several vascular risk factors, especially type 2 diabetes mellitus (T2D) (Arvanitakis, et al., 2004,Stewart and Liolitsa, 1999), which more than doubles the risk of developing AD (Schnaider Beeri, et al., 2004).

A number of studies have aimed to identify blood-based biomarkers for AD. These have identified changes in the abundance of plasma or serum proteins (Hye, et al., 2006,Hye, et al., 2014,O'Bryant, et al., 2010,O'Bryant, et al., 2011,Ray, et al., 2007,Thambisetty, et al., 2008) and specific gene expression signatures (Booij, et al., 2011,Fehlbaum-Beurdeley, et al., 2012,Lunnon, et al., 2013,Rye, et al., 2011), some of which differentiate AD, and even persons with mild cognitive impairment (MCI), from elderly controls with normal cognition. Recently we have started to examine whether epigenetic (DNA methylation) changes are seen in white blood cells in AD sufferers compared to elderly non-demented control subjects (Lunnon, et al., 2014).

The ability to identify early peripheral molecular signatures associated with the onset of dementia in “high risk” groups is of particular importance for the development of preventive interventions. To this end the aim of the current study was to identify DNA methylation differences in whole blood obtained from a longitudinal analysis of T2DM patients developing pre-symptomatic dementia symptoms over an 18 month period, compared to those remaining cognitively normal.

2. Methods

2.1 Subjects and Samples

This study builds on the longitudinal Israel Diabetes and Cognitive Decline (IDCD) study, which investigates the effects of long-term T2D-related characteristics on cognitive decline (Ravona-Springer, et al., 2013). The IDCD study design and subject selection is described in detail in the Supplementary Methods. The Clinical Dementia Rating (CDR) scale and neurological and psychiatric assessments were used to define intact cognition (CDR = 0) at study entry. For the purposes of this study, we chose the first 18 subjects whose cognition at 18-months follow-up declined to CDR=0.5 (i.e. pre-symptomatic dementia; converters) confirmed by a multidisciplinary diagnostic consensus conference. Eighteen control subjects (nonconverters), i.e. individuals whose normal cognition was maintained at follow up based on a CDR=0 and confirmed in consensus conference, were matched to the converters for age, sex, and number of years of education. Subject characteristics are summarised in Supplementary Table 1.

2.2 Methylomic Profiling

Genomic DNA was isolated in individuals at baseline and follow up from whole blood stored in EDTA collection tubes using a standard phenol-chloroform extraction method and tested for degradation and purity prior to analysis. 500ng DNA from each sample was treated with sodium bisulfite using the Zymo EZ96 DNA methylation kit (Zymo Research, CA, USA) according to the manufacturer’s standard protocol. Samples were assessed using the Illumina Infinium HumanMethylation450K BeadChip (Illumina Inc., CA, USA) using the Illumina HiScan System (Illumina, CA, USA). All samples were grouped by individual with their age and sex-matched pair processed alongside. All samples were processed in a single batch of six BeadChips. Illumina Genome Studio software was used to extract the raw signal intensities of each probe (without background correction or normalization).

2.3 Data Analysis

All computations and statistical analyses were performed within the R statistical environment (version 2.15.3) (R Development Core Team, 2012) and Bioconductor 2.14 (Gentleman, et al., 2004). Signal intensities were imported into R using the methylumi package (Davis, et al., 2012). Initial quality control checks (QC) were performed to assess concordance between reported and genotyped gender. Non-CpG SNP probes on the array were used to confirm that longitudinal samples were sourced from the same individual. Data was pre-processed using wateRmelon (version 1.4.0) using the dasen function as previously described (Pidsley, et al., 2013), with two samples being excluded at baseline for failing QC (Supplementary Table 1). Non-CpG SNP probes, non-specific probes, and probes that have been reported to contain common (MAF > 5%) SNPs in the CpG position or single base extension position were flagged and removed from analyses, leaving 388,850 probes (Chen, et al., 2013).

We identified differentially methylated positions (DMPs) by comparing non-converters and converters at both baseline and 18 month follow-up using linear models at each time point separately, whilst controlling for the effects of age and sex. The Genomic Regions Enrichment of Annotations Tool (GREAT) (McLean, et al., 2010) was used to annotate probes with genes within 5000bp upstream, and 1000bp downstream. Similarities of DNA methylation differences across datasets was assessed using Pearson’s correlation. Raw microarray data are available in the online Gene Expression Omnibus (GEO) (accession number: GSE62003).

3. Results and Discussion

The top-ranked DMPs at baseline and 18-month follow-up are shown in Supplementary Tables 2 and 3. A particular interest was the identification of stable DNA methylation differences between converters and non-converters, detectable at both time points. In this regard, it is notable that DNA methylation differences for the 100 top-ranked DMPs at baseline are strongly correlated with DNA methylation differences at the same probes at 18-month follow-up (Figure 1A; r=0.652, P=1.69E-13). Similarly, DNA methylation changes for the 100 top-ranked DMPs at follow-up are strongly correlated with DNA methylation differences at the same probes at baseline (Figure 1B; r=0.828, P=1.57E-26). Furthermore, using a nominal p-value threshold (p≤0.005), we identified ten probes that were differentially methylated at both time points above our nominal threshold (Table 1; Supplementary Figure 1). This group of DMPs may represent early and consistent markers of cognitive change. Of these, six DMPs were hypermethylated in converters at both time points and four were hypomethylated at both time points. Several of these probes are located in close proximity to genes with putative roles in either dementia or cognitive decline. For example cg23630131 and cg09788352 lie downstream of KCDT7 and CLN6 respectively, with mutations in both these genes being associated with severe dementia in progressive myoclonus epilepsy (Kousi, et al., 2012) and ceroid-lipofuscinosis (Arsov, et al., 2011) respectively. Furthermore, cg01094683 lies upstream of TBC1D16, with decreased leukocyte expression of this gene having been previously associated with lower Mini Mental State Examination (MMSE) scores (Harries, et al., 2012). Although the other identified loci have not been previously associated with dementia, they could still represent novel biomarkers. We have previously shown that the most significant DMPs in blood between AD patients and non-demented control individuals are, as expected, very distinct from those seen in brain (Lunnon, et al., 2014), and it is plausible that novel DMPs identified in this study represent a peripheral response by leukocytes to early disease changes in the brain, rather than a direct reflection of neuropathological changes observed in the brain.

Figure 1. Correlation of the % methylation differences for the 100 top-ranked DMPs across both time points.

Figure 1

Figure 1

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DNA methylation differences for the 100 most significant DMPs at baseline (shown in Supplementary Table 2) (X-axis) are significantly correlated (r=0.652, P=1.69E-13) with DNA methylation differences in the same probes at 18 month follow up (Y-axis) (A). Similarly DNA methylation differences for the 100 most significant DMPs at 18 month follow-up (shown in Supplementary Table 3) (Y-axis) are significantly correlated (r=0.828, P=1.57E-26) with DNA methylation differences in the same probes at baseline (X-axis) (B).

Table 1. CpG sites consistently differentially methylated at baseline and 18 month follow up representing robust markers of conversion.

Probes were selected that had a difference in methylation between converters and non-converters at both time points (p<0.005). Shown for each DMP is chromosomal location (Hg19), Illumina gene annotation, up-/downstream genes (from GREAT annotation), P-value from linear model at baseline and at follow up and normalised mean group methylation values and difference between groups after controlling for the effect of age and sex (Δ).

Baseline 18 Month Follow Up
Probe Position CpG Density Illumina Annotation Genes Upstream Genes Downstream P value % NC % C Δ P value % NC % C Δ
cg26426080 1:3039210 Shore PRDM16 ARHGEF16(-331936) PRDM16(+53469) 2.42E-03 86.29 75.81 -10.48 2.68E-03 87.53 76.96 -10.57
cg00388673 1:99729460 Shore LPPR4 LPPR4(-387) - 4.40E-04 79.32 83.29 3.97 2.96E-03 78.48 82.17 3.68
cg23630131 7:65973040 Shore - KCTD7(-120827) TPST1(+302782) 3.17E-03 74.19 76.86 2.67 1.34E-03 76.65 79.4 2.75
cg00841151 7:100612136 Shelf MUC12 MUC12(-767) - 2.20E-03 22.67 18.85 -3.82 7.70E-04 25.05 20.83 -4.22
cg14797402 11:58673453 Shore - GLYATL2(-61457) GLYATL1(-37268) 3.79E-03 15 17.43 2.44 2.33E-04 15.43 19.09 3.67
cg09788352 15:68522666 Shore CLN6 CLN6(-587) - 9.04E-04 22.16 19.31 -2.85 2.19E-03 23.51 21.44 -2.07
cg08317738 16:89034292 Island CBFA2T3 PABPN1L(-101225) CBFA2T3(+9211) 4.34E-03 43.22 34.33 -8.89 7.31E-04 50.63 39 -11.62
cg26999431 17:67903789 - - KCNJ16(-167636) MAP2K6(+492952) 1.19E-03 82.32 83.61 1.29 4.62E-03 83.16 84.46 1.3
cg01094683 17:77984346 Island TBC1D16 CBX4(-171134) TBC1D16(+25300) 2.58E-03 83.89 85.5 1.6 9.29E-04 82.8 84.67 1.87
cg16488953 18:77638446 Island KCNG2 KCNG2(+14779) PQLC1(+73206) 3.88E-03 95.25 95.69 0.44 3.60E-03 94.99 95.36 0.36
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Data is organized in genomic by location. Abbreviations: DMP (Differentially methylated position), % NC (Percentage methylation in non-converters) % C (Percentage methylation in converters), Δ (Difference between groups).

Having previously identified a number of CpG sites that are differentially methylated in whole blood in AD patients compared to elderly non-demented control subjects (Lunnon, et al., 2014), we were interested in investigating whether any of the DMPs identified in the current study overlapped with loci differentially methylated in clinically-diagnosed AD patients. We found no significant correlation between DNA methylation differences at the 100 top-ranked converter-associated DMPs at baseline with our previously reported AD-associated DMPs (Supplementary Figure 2; r=0.142, P=0.158). Interestingly, however, there was a significant correlation between DNA methylation differences at the top-ranked DMPs identified postconversion with differences seen at the same CpG sites between control and clinically-defined AD subjects (Supplementary Figure 3; r=0.40, P=3.16E-5). This indicates that the differences seen in converters at follow-up, after they display symptoms of pre-symptomatic dementia, reflect differences identified in clinically-presenting AD patients. Differences between converters and non-converters at baseline, however, are not seen in clinically-recognized AD patients. This reinforces the hypothesis that epigenetic differences identified in the blood from AD patients most likely reflect peripheral responses to the disorder, rather than causally-related variation. Such changes are, however, potentially useful as biomarkers of underlying neuropathology.

4. Conclusions

Given the predicted increase in dementia incidence, the identification of early and robust markers of disease, which are detectable prior to the emergence of clinical symptoms, is of upmost importance, particularly in ‘high risk’ groups. This study identified a number of DMPs in blood samples from T2D patients after they had developed pre-symptomatic dementia, which are also altered in white blood cells from AD patients, and could thus represent early markers of dementia. This study also demonstrated robust alterations at several CpG sites in blood samples from T2D patients at baseline who developed presymptomatic dementia. These loci were altered prior to the emergence of clinical symptoms, and remained altered after conversion. Interestingly two of these CpG sites reside close to genes previously associated with dementia, and another CpG site is located close to a gene whose expression in blood has been previously associated with lower MMSE. Although the other identified CpG sites have not been associated before with dementia, they still could represent part of an early peripheral response to dementia and serve as potential biomarkers for early cognitive changes. Although the changes reported in this study do not reach genome-wide significance, this is not surprising given the relatively small number of samples. An optimum level of significance for epigenome-wide association studies (EWAS) has yet to be established, but given the non-independence of DNA methylation across CpG sites and the non-variable nature of most probes on the 450K array (Mill and Heijmans, 2013), it is likely that a Bonferroni correction is overly stringent. The intra-individual longitudinal repeated-measure design used in this study is relatively robust and controls for many potential confounders in epigenetic epidemiology. Although we were able to validate DMPs at two independent time points and in an independent study comparing non-demented control and clinically-diagnosed AD patients, future research is needed to validate the findings from this pilot study in larger independent sample cohorts, and to determine the exact specificity and timing of these changes.

Supplementary Material

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10. Supplementary Figure 1: Corrected methylation levels in non-converters and converters at baseline and 18 month follow-up for consistently differentially methylated CpGs.

(A) cg26426080, (B) cg00388673, (C) cg23630131, (D) cg00841151, (E) cg14797402, (F) cg09788352, (G) cg08317738, (H) cg26999431, (I) cg01094683, (J) cg16488953.

11. Supplementary Figure 2: Correlation of the % methylation differences for the 100 top-ranked DMPs at baseline between non-converters and converters with % methylation differences in blood between control and clinically-recognised AD subjects.

DNA methylation differences for the 100 most significant DMPs at baseline (shown in Supplementary Table 2) (X-axis) are not significantly correlated (r=0.142, P=0.158) with DNA methylation differences in the same probes in our previous analysis of control and clinically-recognised AD blood samples (Lunnon, et al., 2014)

12. Supplementary Figure 3: Correlation of the % methylation differences for the 100 top-ranked DMPs at 18 month follow-up between non-converters and converters with % methylation differences in blood between control and clinically-recognised AD subjects.

DNA methylation differences for the 100 most significant DMPs at 18 month follow-up (shown in Supplementary Table 3) (X-axis) are significantly correlated (r=0.40, P=3.16E-5) with DNA methylation differences in the same probes in our previous analysis of control and clinically-recognised AD blood samples (Lunnon, et al., 2014).

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Highlights.

  • We analysed blood methylomic levels in T2D persons developing pre-symptomatic dementia

  • We compared these to T2D persons remaining cognitively normal

  • We identified ten robust CpG sites showing consistent differential methylation

  • The top-ranked differences in converters were also seen in Alzheimer’s disease blood

Acknowledgments

This work was funded by NIH grant AG036039 to JM, NIH grant R01AG034087 to MB and the Helen Bader Foundation, the Leroy Schecter Foundation, and the BrightFocus foundation. We thank the Israel Diabetes and Cognitive Decline study subjects who made this research possible.

Footnotes

Disclosure Statement

The authors declare that they have no conflicts of interest in regard to this work.

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Associated Data

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Supplementary Materials

1
NIHMS642355-supplement-1.docx (12.9KB, docx)
10. Supplementary Figure 1: Corrected methylation levels in non-converters and converters at baseline and 18 month follow-up for consistently differentially methylated CpGs.

(A) cg26426080, (B) cg00388673, (C) cg23630131, (D) cg00841151, (E) cg14797402, (F) cg09788352, (G) cg08317738, (H) cg26999431, (I) cg01094683, (J) cg16488953.

NIHMS642355-supplement-10.tiff (675.1KB, tiff)
11. Supplementary Figure 2: Correlation of the % methylation differences for the 100 top-ranked DMPs at baseline between non-converters and converters with % methylation differences in blood between control and clinically-recognised AD subjects.

DNA methylation differences for the 100 most significant DMPs at baseline (shown in Supplementary Table 2) (X-axis) are not significantly correlated (r=0.142, P=0.158) with DNA methylation differences in the same probes in our previous analysis of control and clinically-recognised AD blood samples (Lunnon, et al., 2014)

NIHMS642355-supplement-11.tiff (675.1KB, tiff)
12. Supplementary Figure 3: Correlation of the % methylation differences for the 100 top-ranked DMPs at 18 month follow-up between non-converters and converters with % methylation differences in blood between control and clinically-recognised AD subjects.

DNA methylation differences for the 100 most significant DMPs at 18 month follow-up (shown in Supplementary Table 3) (X-axis) are significantly correlated (r=0.40, P=3.16E-5) with DNA methylation differences in the same probes in our previous analysis of control and clinically-recognised AD blood samples (Lunnon, et al., 2014).

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