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Lack of association between cathepsin D C224T polymorphism and Alzheimer’s disease risk: an update meta-analysis

Contributed equally
BMC Neurology201414:13

DOI: 10.1186/1471-2377-14-13

Received: 24 August 2013

Accepted: 27 November 2013

Published: 15 January 2014

Abstract

Background

Cathepsin D C224T polymorphism has been reported to associate with AD susceptibility. But the results were inconsistent. This study aimed to assess the relationship between C224T polymorphism and AD risk.

Methods

The relevant studies were identified by searching PubMed, Embase, Web of Science, Google Scholar and Wan fang electronic databases updated on July 2013. The relationship between Cathepsin D C224T polymorphism and AD risk was evaluated by ORs and 95% CIs.

Results

A total of 25 case-control studies including 5,602 cases and 11,049 controls were included in the meta-analysis. There was no association between C224T polymorphism and AD risk with all the studies were pooled in the meta-analysis (CT vs. CC: OR = 1.125, 95% CI = 0.974-1.299, P = 0.109; CT + TT vs. CC: OR = 1.136, 95% CI = 0.978-1.320, P = 0.094). Furthermore, when stratified by ethnicity, age of onset and APOEϵ4 status, significant association did not found in all subgroups.

Conclusion

The present meta-analysis suggested that the Cathepsin D C224T polymorphism was not associated with AD susceptibility.

Keywords

Cathepsin D AD Polymorphism Meta-analysis

Background

The neurodegenerative disorder Alzheimer’s disease (AD) caused the most of dementia in the elderly [1]. Previous findings indicated that the incidence increased from 1% in 65–69 year-olds to about 50% in 85–95 year-olds [2]. Many genetic and environmental risk factors contribute to the degenerative progress of AD, such as family history, low income and education, exposure to aluminium in drinking water, dietary habits, smoking, physical activity, hypertension, diabetes and genetic variations [3]. Molecular genetics researches have shown that AD was a class of complex polygenic diseases with genetic heterogeneity. Several genes have been reported to associate with AD. Beta-amyloid precursor protein (APP) and presenilin 2 played major role in early-onset familial AD [4, 5]. The death-associated protein kinase 1(DAPK1) [6] and ATP-binding cassette subfamily A member 7 (ABCA7) [7] have been mainly implicated with late-onset AD. The ϵ4 allele of apolipoprotein E (APOEϵ4) was the only verified risk factor for sporadic AD [8]. However, the presence of variants for these genes and the APOEϵ4 allele was neither necessary nor sufficient for AD development. About 50% of AD patients did not have mutations in the genes mentioned above or carry the APOEϵ4 allele, and not everyone who has the mutations of the genes will acquire AD [9], suggesting that it is necessary to identify additional genetic or non-genetic factors which modulate the AD susceptibility.

The main histopathologic features of AD are neurofibrillary tangles and Neuritic plaques which consist of hyperphosphorylated tau protein and amyloid peptides, respectively. Cathepsin D (CTSD), an intracellular acid protease, contributed to the proteolytic cleavage of APP and the clearance of the β-amyloid (Aβ) from the central nervous system [10, 11]. As such, CTSD might involve in the pathogenesis of AD. Variants of CTSD gene might impede the functions of proteolytic degradation, thus increasing the risk of AD. A CTSD C224T polymorphism(C-to-T) in exon 2 can bring about amino acid change (Ala38-to-Val), increase pro-CTSD secretion and alter intracellular maturation [12]. It has been proved that this polymorphism was significantly associated with the general intelligence of healthy elderly [13].

Recently, numerous studies have focused on the correlation between the CTSD C224T polymorphism and AD risk [1436]. Unfortunately, the results of these studies were contradictory. Five previous studies reported that the T allele of the CTSD-C/T polymorphism was a high-risk factor for developing AD [1418]; however, other relevant studies yielded contradictory results [1936]. Furthermore, the results of previous meta-analysis which research the association between the CTSD polymorphism and AD risk were contradictory as well. Bertram et al. [37] and Ntais et al. [38] did not find any significant association, whereas Schuur [18] reported that T allele increased the risk of AD in Caucasians. Possible reasons for these contradictory results include the small sample size of the Ntais study; the absence of an Asian population in the Schuur study; and the fact that the Bertram study only compared alleles T and C. Considering that those factors could contribute to bias in the final result, we updated the present meta-analysis which included a larger sample size to provide a more reliable correlation between CTSD C224T and AD.

Methods

Search strategy

The relevant studies were identified by searching PubMed, Embase, Web of Science, Google Scholar and Wan fang electronic databases in July 2013 for all the articles regarding the correlation between CTSD C224T polymorphism and AD risk. The key words of search strategy as follow: “Alzheimer’s disease or AD”, “CTSD or cathepsin D”, and “polymorphism, mutation or variant”. References listed in reviews and retrieved articles were also screened. There were no language or country restrictions. When multiple articles researched the same cohort, the one with the largest population was included. When a publication reported more than one subpopulation, we regarded every subpopulation as a separate study.

Selection criteria

The eligible studies were requested to agree with the inclusion criteria: (1) a case–control study; (2) research of the correlation between CTSD C224T polymorphism and AD susceptibility; (3)inclusion of the sample size and distribution of alleles and genotypes; (4) AD diagnosed according to the criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA), or the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Exclusion criteria of our study were followed as: (1) duplicated literature, reviews, or animal studies; (2) genotype frequency and distribution were not included; (3) not enough information for data extraction.

Data extraction

Two reviewers (Cuiju Mo and Jingzhe Sui) extracted the information independently. If there was a disagreement, the data was checked again, and a third reviewer (Xue Qin) was invited to check the data. Information collected from each eligible study was included: first author, year of publication, country, ethnicity, genotyping method, AD diagnosis, control sources, sample sizes, age of onset, and genotype distribution in cases and controls.

Statistical analysis

All analysis was conducted using Stata version 12.0 software (Stata Corp, College Station, TX). The association was assessed by pooled odds ratio (OR) together with the corresponding 95% confidence interval (CI). Only heterozygote comparison model (TC vs. CC) and the dominant genetic model (TT + TC vs. CC) were analysed. Furthermore, we evaluated the effect in different subgroup stratified by ethnicity (Asian vs. Caucasian) and age of onset. Early-onset AD (EOAD) was defined as age at onset <65 years, and age at onset ≥65 years was considered as late-onset AD (LOAD). To evaluate the interaction of the CTSD with the APOEϵ4 allele, we compared the dominant genetic model (TT + TC vs. CC) between case and control subjects stratified by the APOEϵ4 allele status. Similarly, the relationship of the APOEϵ4 allele with AD risk was investigated between the patients carrying the T allele or not.

The x2-test based Q-statistic and I2 statistic was used to evaluate the heterogeneity among the studies. The DerSimonian–Laird random-effects model was used to assess pooled OR when a significant heterogeneity (PQ < 0.1 or I2 ≥ 50%) was observed. Otherwise, the Mantel–Haenszel fixed-effects model was used. The publication bias was detected by funnel plot and Egger’s test. An Egger’s test P value <0.05 was considered as statistically significant. The genotype distribution of the control population was used to evaluate Hardy–Weinberg Equilibrium (HWE) by a goodness-of-fit Chi-square test. P <0.05 (two-side) was considered as statistically significant.

Results

Eligible studies

Figure 1 showed the screening process of our study. A total of 345 articles were identified from the database searching and references of review. 31 relevant articles were identified according to inclusion criteria. Then eight articles were excluded based on the full texts: one article was a meta-analysis [38], two articles did not provide sufficient data [39, 40], and five articles overlapped with other published studies [32, 4144]. Finally, 23 articles including 22 English papers and 1 Chinese paper [19] were included in our study. Two out of the including articles reported two subpopulations, and each subpopulation was considered as a separate study. Therefore, 25 case–control studies including 5,602 cases and 11,049 controls were included in the meta-analysis, encompassing 4 Asian and 21 Caucasian samples. All AD patients were diagnosed by NINCDS-ADRDA criteria, DSM-IV criteria, or autopsy confirmation in all eligible studies. The genotype frequencies of the control groups in two case–control studies deviated from the HWE [22, 29]. Ten of the eligible studies evaluated the interaction between the CTSD and the APOEϵ4 allele [1, 14, 15, 1821, 24, 28, 29, 34]. Six of the studies included early-onset and late-onset cases [20, 21, 28, 29, 33, 34]. The baseline data of each case–control study were presented in Table 1.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2377-14-13/MediaObjects/12883_2013_Article_944_Fig1_HTML.jpg
Figure 1

Flow chart of literature screening for this meta-analysis.

Table 1

The baseline data of all including study

First author

Year

Country

Ethnicity

Genotyping method

AD diagnostic

Control sources

HWE

Case (EOAD/LOAD)

Control

Sun

2005

China

Asian

PCR-RFLP

NINCDS-ADRDA and DSM-IV

PB

0.552

165

174

Li

2004

China

Asian

PCR-RFLP

NINCDS-ADRDA

PB

0.484

156(42/114)

183

Jhoo

2005

Korea

Asian

DASH

NINCDS-ADRDA

PB

0.701

107(36/71)

216

Matsui

2001

Japan

Asian

PCR-RFLP

NINCDS-ADRDA

PB

0.000

275

479

  

USA

Caucasian

PCR-RFLP

autopsy-confirmed

PB

0.191

69

50

Papassotiropoulos

1999

Germany

Caucasian

PCR-RFLP

NINCDS-ADRDA

PB

0.21

102

351

McIlroy

1999

Ireland

Caucasian

PCR-RFLP

DSM IV and NINCDS-ADRDA

PB

0.367

183

187

Papassotiropoulos

2000(b)

Germany

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.485

127

184

Bhojak

2000

USA

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.084

531

316

Crawford

2000

USA

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.319

210

120

  

Spain

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.101

79

112

Menzer

2001

Germany, Switzerland, Italy

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB and PB

0.988

324

302

Bertram

2001

USA

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.373

200

182

Emahazion

2001

Scotland

Caucasian

DASH

DSM-IV

Not clarified.

0.329

120

149

Bagnoli

2002

Italy

Caucasian

PCR-RFLP

DSM-IV

PB

0.616

197(33/33)

126

Mateo

2002

Spain

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.008

311(126/185)

346

Styczynska

2003

Polish

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.637

100

100

Ingegni

2003

Italy

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.914

142

120

Beryer

2005

Spain

Caucasian

PCR-RFLP

DSM-IV and NINCDS-ADRDA

Not clarified.

0.871

205

181

Blomqvist2

2006

Switzerland

Caucasian

DASH

NINCDS-ADRDA

HB and PB

0.372

385

173

Mariani

2006

Italy

Caucasian

PCR-RFLP

NINCDS-ADRDA

PB

0.355

100

136

Davidson

2006

UK

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.168

560(317/243)

767

Capurso

2008

Italy

Caucasian

PCR-RFLP

NINCDS-ADRDA

PB

0.205

242(57/185)

421

Albayrak

2010

Germany

Caucasian

PCR-RFLP

NINCDS-ADRDA

HB

0.143

219

215

M. Schuur

2011

Netherland

Caucasian

Taqman assay

NINCDS-ADRDA

PB

0.631

493

5619

PCR–RFLP, Polymerase chain reaction-restriction fragment length polymorphism; DASH, dynamic allele specific hybridization; PB, Population–based; HB, Hospital–based; HWE, hardy-Weinberg equilibrium; EOAD, early-onset AD; LOAD, late-onset AD.

Results of meta-analysis

The present finding of this meta-analysis revealed that the C224T polymorphism was not associated with AD risk. The heterogeneities of CT vs. CC and the dominant CT + TT vs. CC models were assessed in the overall population, and the PQ values were 0.023 and 0.007, respectively. Thus, random-effects model was chose to analyse the CT vs. CC model (OR = 1.125, 95% CI = 0.974–1.299, P = 0.109, Table 2, Figure 2A) and the dominant CT + TT vs. CC model (OR = 1.136, 95% CI = 0.978–1.320, P = 0.094, Table 2, Figure 2B) in the overall population. The control genotypes of two case-control studies [22, 29] deviated from the HWE. The summary ORs were slightly elevated in the CT vs. CC (OR = 1.127, 95% CI = 0.965-1.317, P = 0.132) and dominant CT + TT vs. CC models (OR = 1.149, 95% CI = 0.978-1.35, P = 0.09) without a statistical significance, when we excluded those two studies.
Table 2

Results of the association between CTSD C224T polymorphism and AD risk in the meta-analysis

Comparison

Population

No. of studies

Test of association

Mode

Test of heterogeneity

   

OR

95% CI

P Value

 

x 2

PQValue

I2

 CT vs. CC

Overall

25

1.125

0.974–1.299

0.109

R

39.65

0.023

39.5

 CT + TT vs. CC

Overall

25

1.136

0.978–1.320

0.094

R

44.23

0.007

45.7

Subgroup analysis

Ethnicity

         

 CT vs. CC

Asian

4

0.971

0.626–1.506

0.895

F

2.04

0.565

0.0

 

Caucasian

21

1.139

0.974–1.331

0.102

R

37.20

0.011

46.2

 CT + TT vs. CC

Asian

4

0.954

0.616–1.477

0.833

F

2.04

0.565

0.0

 

Caucasian

21

1.154

0.982–1.357

0.082

R

41.54

0.003

51.8

EOAD

         

 CT vs. CC

Overall

6

0.937

0.706–1.245

0.654

F

2.87

0.719

0.0

 CT + TT vs. CC

Overall

6

0.93

0.704–1.229

0.612

F

2.68

0.749

0.0

LOAD

         

 CT vs. CC

Overall

6

0.935

0.724–1.207

0.606

F

3.86

0.57

0.0

 CT + TT vs. CC

Overall

6

0.931

0.726–1.195

0.575

F

3.88

0.567

0.0

OR, odds ratio; CI, confidence intervals; R, random effects model; F, fixed effects model; EOAD, early-onset AD; LOAD ,late-onset AD.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2377-14-13/MediaObjects/12883_2013_Article_944_Fig2_HTML.jpg
Figure 2

Forest plots of CTSD C224T polymorphism and AD risk (A, CT vs. CC model; B, TT + CT vs. CC model) in all analysis using random-effect model.

In subgroup analyses stratified by ethnicity, we failed to find any significant associations between the CTSD C224T polymorphism and AD risk in the Asian (CT vs. CC: OR = 0.971, 95% CI = 0.626-1.506, P = 0.895; CT + TT vs. CC: OR = 0.954, 95% CI = 0.616-1.477, P = 0.833, Table 2) and Caucasian(CT vs. CC: OR = 1.139, 95% CI = 0.974-1.331, P = 0.102; CT + TT vs. CC: OR = 1.154, 95% CI = 0.982-1.357, P = 0.082, Table 2) populations. After excluding two studies [22, 29] which deviated from the HWE,no significant associations were found between the CTSD C224T polymorphism and AD risk in the Asian (CT + TT vs. CC: OR = 0.968, 95% CI =0.605-1.548, P = 0.891) and Caucasian(CT + TT vs. CC: OR = 1.165, 95% CI =0.981-1.383, P = 0.081). Similarly, we found non-significant associations in the EOAD (CT vs. CC: OR = 0.937, 95% CI = 0.706-1.245, P = 0.654; CT + TT vs. CC: OR = 0.930, 95% CI = 0.704-1.229, P = 0.612) and LOAD (CT vs. CC: OR = 0.935, 95% CI = 0.724-1.207, P = 0.606; CT + TT vs. CC: OR = 0.931, 95% CI = 0.726-1.195, P = 0.575)subgroups in any of the comparisons (Table 2).

In the APOEϵ4 stratified analyses, the results did not show significant associations between the C224T polymorphism and AD risk in APOEϵ4 carriers and non-carriers. However, the pooled OR were higher in APOEϵ4 carriers (CT + TT vs. CC: OR = 1.267, 95% CI = 0.979-1.641, P = 0.072, Table 3) than in non-carriers (CT + TT vs. CC: OR = 1.139, 95% CI = 0.844-1.539, P = 0.395, Table 3). Furthermore, among the T allele carriers, APOEϵ4 allele increased the risk of AD 4.5-fold (OR = 4.532, 95% CI = 2.755-7.455, P = 0.000, Table 3) accompanied by heterogeneity (P = 0.033). Among the subjects without the T allele, APOEϵ4 increased the risk of AD 4.2-fold (OR = 4.193, 95% CI =3.096-5.679, P = 0.000, Table 3) with significant between-study heterogeneity (P = 0.000). Extensive overlap existed between the two estimates; however, the ORs were greater in the T allele carriers.
Table 3

Meta-analysis the association of CTSD C224T polymorphism with APOEϵ4 carrier in AD

Comparison

Population

No. of studies

Test of association

Mode

Test of heterogeneity

   

OR

95% CI

P Value

 

x 2

PQ Value

I2

APOEϵ4 noncarriers

CT + TT vs. CC

Overall

10

1.139

0.844–1.539

0.395

R

19.28

0.023

53.3

 

Asian

3

0.73

0.390–1.365

0.324

F

5.81

0.055

65.5

 

Caucasian

7

1.212

0.998–1.472

0.052

F

11.86

0.065

49.4

APOEϵ4 carriers

CT + TT vs. CC

Overall

10

1.267

0.979–1.641

0.072

F

10.89

0.283

17.4

 

Asian

3

1.273

0.511–3.184

0.604

F

0.01

0.995

0.0

T carriers

Caucasian

7

1.267

0.979–1.641

0.085

F

10.88

0.092

44.9

APOEϵ4(+) vs. APOEϵ4(–)

Overall

10

4.532

2.755–7.455

0.000

R

18.16

0.033

50.4

 

Asian

3

7.913

2.632–23.785

0.000

F

0.20

0.904

0.0

 

Caucasian

7

4.134

2.338–7.310

0.000

R

15.58

0.016

61.5

T noncarriers

         

APOEϵ4(+) vs. APOEϵ4(–)

Overall

10

4.193

3.096–5.679

0.000

R

43.54

0.000

79.3

 

Asian

3

4.217

2.333–7.620

0.000

R

6.88

0.032

70.9

 

Caucasian

7

4.195

2.888–6.093

0.000

R

35.89

0.000

83.3

OR, odds ratio; CI, confidence intervals; R, random effects model; F, fixed effects model.

Publication bias

There was no visible publication bias among the studies because of the shape of the Begg’s funnel plots revealed symmetry in the CT vs. CC and CT + TT vs. CC comparative genetic models (Figure 3). Statistical evidence of funnel plot symmetry was provided by Egger’s test. The results also showed no publication bias in the C224T polymorphism (t = -0.19, P = 0.853 for CT vs. CC; t = -0.34, P = 0.736 for CT + TT vs. CC).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2377-14-13/MediaObjects/12883_2013_Article_944_Fig3_HTML.jpg
Figure 3

Funnel plot for publication bias of all eligible studies (A, CT vs. CC; B, CT + TT vs. CC).

Discussion

The effects of genetic sequence variants in complex human traits are not readily detectable in population samples. However, meta-analysis that accumulates published data from small single research is a valuable tool in identifying disease genes. The functions of CTSD are to hydrolyse APP protein and clear Aβ from the central nervous system [10, 11]. In AD patients, CTSD was expressed in the core of neuritic plaques [45], and cellular and cerebrospinal levels are elevated [46]. The variants of this gene might impede the proteolytic cleavage of APP and the degradation and clearance of Aβ, the synthesis of which is a supposed pivotal event in the pathogenesis of AD. Therefore, our motivation for the present study was to determine the association between CTSD polymorphism and AD risk from abundant data over 16,651 genotype cases and controls.

As far as we know, the present meta-analysis involving 5602 cases and 11,049 healthy controls was the most comprehensive to date to investigate the relation between the CTSD C224T polymorphism and AD susceptibility. Our finding indicated that the C224T polymorphism was not associated with the AD risk both in Asian and Caucasian populations, which were in accord with the results of the previous meta-analysis [38] and inconsistent with Schuur’s results [18]. Compared to the previous study, our meta-analysis has some particular strength. First, we had the largest sample size; we added four Asian population studies, the absence of which in the Schuur study might have caused a deviation in the final result; and ten new case–control studies were added compared to the Ntais study, which might have effectively altered the overall results. Second, because nearly half of the eligible studies did not detect the homozygous TT polymorphism, and the proportion of TT was very small, as is usual in common polymorphisms, heterozygote might be responsible for the significant difference in frequency; therefore, we only compared the CT vs. CC and the dominant CT + TT vs. CC models. Lastly, no significant publication bias was observed in any of the studies analyzing by Egger’s test and Begg’s funnel plot. Thus, based on the above factors, the results of our meta-analysis were more reliable than those of previous studies.

Our results from the CT vs. CC and dominant CT + TT vs. CC comparison models suggested that no significant correlation was existed between the CTSD C224T polymorphism and AD risk. Given that the control genotypes of two case-control studies [22, 29] were out of HWE, they might have contributed some bias to our summary OR. When we excluded those two studies, the summary OR was not effectively altered, showing that our result was reliable. A great degree of heterogeneity among studies was identified for CT vs. CC (x2 = 39.65, PQ = 0.023) and CT + TT vs. CC (x2 = 44.23, PQ = 0.007) in the overall populations. Several factors might contribute to the heterogeneity. First, AD is a complicated and multi-genetic disease. Second, clinical heterogeneity, such as gender, age of onset, and diagnosis criteria, were factors. The different studied populations, such as ethnicity, might also explain the discrepancy. In subgroup analysis stratified by ethnicity and age of onset, heterogeneity only existed in the Caucasian subgroup, indicating that age was the major contributor to the existence of all heterogeneity.

Considering the impact on the summary OR of different ethnicities, we further performed subgroup analysis based on ethnicity. Those results indicated no significant association between the CTSD C224T polymorphism and AD risk either in Asian or in Caucasian population, which was inconsistent with the previous meta-analysis[18]. Similarly, the results did not change when the two studies that violated HWE [22, 29] were excluded. The number of samples in the Asian subgroup was dramatically less than those in the Caucasian subgroup, which may weaken the conclusions. Our results also differed from the Schurr study after excluding the Mateo study in Caucasian population. While after excluding Albayrak [39] and Mateo [33] study, a significant association was found in the dominant CT + TT vs. CC genetic model(OR = 1.201, 95% CI = 1.004-1.436, P = 0.045). The principal cause for the difference with our results was the inclusion of the Albayrak [39] study. The Albayrak study reported that the CTSD C224T polymorphism increased AD risk in men only which might cause the false-negative result. As no study has clarified gender-specific differences regarding lysosomes or its components and the characteristic lesions in AD, therefore, future study with larger samples to investigate the gender-specific is necessary. When stratified by age of onset, we found no significant differences both in EOAD and LOAD subsets. Possible explanations for these findings might be the small sample sizes for analysis; the same control source, without strict age matching, and missing age information in some studies. Given these factors may affect the statistical power. Further research is required to assess the gene effects and validate our findings.

To evaluate the interaction of CTSD polymorphism and APOEϵ4 allele on AD, ten studies which provided genotype distribution data of APOEϵ4 status were chosen for further study, and of which only four showed evidence of an association [14, 15, 20, 25]. The results of our study showed non-significant relation between the C224T polymorphism and AD risk in APOEϵ4 carriers and non-carriers. The association of CTSD T allele with AD risk between APOEϵ4 carriers and non-carriers in Caucasians was quite similar, contrary to the Schuur result. Due to the lack of an Asian population in the Schuur study, sample size and ethnicity might have contributed to some bias in the final result. While the ORs of APOEϵ4 were greater in the T allele carriers group than the subjects without the T allele. Because of the extensive overlap in two effect sizes and the remarkably small group of subjects who carry both the APOEϵ4 and CTSD T alleles, the association between the CTSD T and APOEϵ4 alleles should be interpreted cautiously.

There were some limitations that merit attention. First, some of the eligible studies lacked sufficient information for detailed and deep analysis. In some studies, the controls were not uniformly defined as matched by age and gender; and it may lead to some negative correlation. Second, we mainly focused on the C224T polymorphism, discounted the potential linkage disequilibrium with another mutation of this gene, and ignored the interactions between gene and gene or gene and environment. Third, the data of our meta-analysis was unadjusted; the suspected factors could be analysed, such as, gender, diet, lifestyle habit, and environmental factors. Fourth, we included the English or Chinese publications only; the lack of unpublished data and data published in other languages might contribute some bias. There were only four articles in the Asian subgroup, with small sample size, which may cause low statistical power.

Conclusions

The finding of our present study revealed that the CTSD C224T polymorphism was not associated with AD risk both in the overall populations and the subgroups stratified by ethnicity and age of onset. In addition, we found no statistically significant differences between the CTSD C224T genotypes and AD stratified by APOEϵ4 allele status. Our data did not suggest that the CTSD C224T polymorphism was a possible susceptibility factor for AD. Future studies will require much larger sample sizes and will need to analyse the impact of this polymorphism in other populations.

Notes

Abbreviations

AD: 

Alzheimer’s disease

CTSD: 

Cathepsin D

APOE: 

Apollipoprotein E

EOAD: 

Early-onset AD

LOAD: 

Late-onset AD.

Declarations

Authors’ Affiliations

(1)
Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University

References

  1. Caracciolo B, Palmer K, Monastero R, Winblad B, Backman L, Fratiglioni L: Occurrence of cognitive impairment and dementia in the community: a 9-year-long prospective study. Neurology. 2008, 70 (19 Pt 2): 1778-1785.View ArticlePubMedGoogle Scholar
  2. Gao S, Hendrie HC, Hall KS, Hui S: The relationships between age, sex, and the incidence of dementia and Alzheimer disease: a meta-analysis. Arch Gen Psychiatry. 1998, 55 (9): 809-815. 10.1001/archpsyc.55.9.809.View ArticlePubMedGoogle Scholar
  3. Launer LJ, Andersen K, Dewey ME, Letenneur L, Ott A, Amaducci LA, Brayne C, Copeland JR, Dartigues JF, Kragh-Sorensen P, et al: Rates and risk factors for dementia and Alzheimer’s disease: results from EURODEM pooled analyses. EURODEM Incidence Research Group and Work Groups. European Studies of Dementia. Neurology. 1999, 52 (1): 78-84. 10.1212/WNL.52.1.78.View ArticlePubMedGoogle Scholar
  4. Campion D, Dumanchin C, Hannequin D, Dubois B, Belliard S, Puel M, Thomas-Anterion C, Michon A, Martin C, Charbonnier F, et al: Early-onset autosomal dominant Alzheimer disease: prevalence, genetic heterogeneity, and mutation spectrum. Am J Hum Genet. 1999, 65 (3): 664-670. 10.1086/302553.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Rocca WA, Hofman A, Brayne C, Breteler MM, Clarke M, Copeland JR, Dartigues JF, Engedal K, Hagnell O, Heeren TJ, et al: Frequency and distribution of Alzheimer’s disease in Europe: a collaborative study of 1980-1990 prevalence findings. The EURODEM-Prevalence Research Group. Ann Neurol. 1991, 30 (3): 381-390. 10.1002/ana.410300310.View ArticlePubMedGoogle Scholar
  6. Li Y, Grupe A, Rowland C, Nowotny P, Kauwe JS, Smemo S, Hinrichs A, Tacey K, Toombs TA, Kwok S, et al: DAPK1 variants are associated with Alzheimer’s disease and allele-specific expression. Hum Mol Genet. 2006, 15 (17): 2560-2568. 10.1093/hmg/ddl178.View ArticlePubMedGoogle Scholar
  7. Reitz C, Jun G, Naj A, Rajbhandary R, Vardarajan BN, Wang LS, Valladares O, Lin CF, Larson EB, Graff-Radford NR, et al: Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein E 4, and the risk of late-onset Alzheimer disease in African Americans. JAMA. 2013, 309 (14): 1483-1492. 10.1001/jama.2013.2973.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Coon KD, Myers AJ, Craig DW, Webster JA, Pearson JV, Lince DH, Zismann VL, Beach TG, Leung D, Bryden L, et al: A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer’s disease. J Clin Psychiatry. 2007, 68 (4): 613-618. 10.4088/JCP.v68n0419.View ArticlePubMedGoogle Scholar
  9. Carrasquillo MM, Belbin O, Hunter TA, Ma L, Bisceglio GD, Zou F, Crook JE, Pankratz VS, Sando SB, Aasly JO, et al: Replication of BIN1 association with Alzheimer’s disease and evaluation of genetic interactions. J Alzheimers Dis. 2011, 24 (4): 751-758.PubMedPubMed CentralGoogle Scholar
  10. Sadik G, Kaji H, Takeda K, Yamagata F, Kameoka Y, Hashimoto K, Miyanaga K, Shinoda T: In vitro processing of amyloid precursor protein by cathepsin D. Int J Biochem Cell Biol. 1999, 31 (11): 1327-1337. 10.1016/S1357-2725(99)00053-9.View ArticlePubMedGoogle Scholar
  11. Wischik CM, Novak M, Thogersen HC, Edwards PC, Runswick MJ, Jakes R, Walker JE, Milstein C, Roth M, Klug A: Isolation of a fragment of tau derived from the core of the paired helical filament of Alzheimer disease. Proc Natl Acad Sci USA. 1988, 85 (12): 4506-4510. 10.1073/pnas.85.12.4506.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Touitou I, Capony F, Brouillet JP, Rochefort H: Missense polymorphism (C/T224) in the human cathepsin D pro-fragment determined by polymerase chain reaction–single strand conformational polymorphism analysis and possible consequences in cancer cells. Eur J Cancer. 1994, 30A (3): 390-394.View ArticlePubMedGoogle Scholar
  13. Payton A, Holland F, Diggle P, Rabbitt P, Horan M, Davidson Y, Gibbons L, Worthington J, Ollier WE, Pendleton N: Cathepsin D exon 2 polymorphism associated with general intelligence in a healthy older population. Mol Psychiatry. 2003, 8 (1): 14-18. 10.1038/sj.mp.4001239.View ArticlePubMedGoogle Scholar
  14. Papassotiropoulos A, Bagli M, Feder O, Jessen F, Maier W, Rao ML, Ludwig M, Schwab SG, Heun R: Genetic polymorphism of cathepsin D is strongly associated with the risk for developing sporadic Alzheimer’s disease. Neurosci Lett. 1999, 262 (3): 171-174. 10.1016/S0304-3940(99)00071-3.View ArticlePubMedGoogle Scholar
  15. Papassotiropoulos A, Bagli M, Kurz A, Kornhuber J, Forstl H, Maier W, Pauls J, Lautenschlager N, Heun R: A genetic variation of cathepsin D is a major risk factor for Alzheimer’s disease. Ann Neurol. 2000, 47 (3): 399-403. 10.1002/1531-8249(200003)47:3<399::AID-ANA22>3.0.CO;2-5.View ArticlePubMedGoogle Scholar
  16. Beyer K, Lao JI, Latorre P, Ariza A: Age at onset: an essential variable for the definition of genetic risk factors for sporadic Alzheimer’s disease. Ann N Y Acad Sci. 2005, 1057: 260-278. 10.1196/annals.1322.021.View ArticlePubMedGoogle Scholar
  17. Mariani E, Seripa D, Ingegni T, Nocentini G, Mangialasche F, Ercolani S, Cherubini A, Metastasio A, Pilotto A, Senin U, et al: Interaction of CTSD and A2M polymorphisms in the risk for Alzheimer’s disease. J Neurol Sci. 2006, 247 (2): 187-191. 10.1016/j.jns.2006.05.043.View ArticlePubMedGoogle Scholar
  18. Schuur M, Ikram MA, van Swieten JC, Isaacs A, Vergeer-Drop JM, Hofman A, Oostra BA, Breteler MM, van Duijn CM: Cathepsin D gene and the risk of Alzheimer’s disease: a population-based study and meta-analysis. Neurobiol Aging. 2011, 32 (9): 1607-1614. 10.1016/j.neurobiolaging.2009.10.011.View ArticlePubMedGoogle Scholar
  19. Sun Y, Shi JJ, Zhang SZ, Tang MN, Han HY, Guo YB, Ma C, Liu XH, Li T: [The C224T polymorphism in the cathepsin D gene is not associated with sporadic Alzheimer’s disease in Chinese]. Yi Chuan. 2005, 27 (2): 190-194.PubMedGoogle Scholar
  20. Li XQ, Chen D, Zhang ZX, Qu QM, Zhang JW: Association between cathepsin D polymorphism and Alzheimer’s disease in a Chinese Han population. Dement Geriatr Cogn Disord. 2004, 18 (2): 115-119. 10.1159/000079189.View ArticlePubMedGoogle Scholar
  21. Jhoo JH, Park WY, Kim KW, Lee KH, Lee DY, Youn JC, Choo IH, Seo JS, Woo JI: Lack of association of cathepsin D genetic polymorphism with Alzheimer’s disease in Koreans. Arch Gerontol Geriatr. 2005, 41 (2): 121-127. 10.1016/j.archger.2004.12.003.View ArticlePubMedGoogle Scholar
  22. Matsui T, Morikawa Y, Tojo M, Okamura N, Maruyama M, Hirai H, Chiba H, Matsushita S, Higuchi S, Arai H, et al: Cathepsin D polymorphism not associated with Alzheimer’s disease in Japanese. Ann Neurol. 2001, 49 (4): 544-545.View ArticlePubMedGoogle Scholar
  23. McIlroy SP, Dynan KB, McGleenon BM, Lawson JT, Passmore AP: Cathepsin D gene exon 2 polymorphism and sporadic Alzheimer’s disease. Neurosci Lett. 1999, 273 (2): 140-141. 10.1016/S0304-3940(99)00635-7.View ArticlePubMedGoogle Scholar
  24. Bhojak TJ, DeKosky ST, Ganguli M, Kamboh MI: Genetic polymorphisms in the cathespin D and interleukin-6 genes and the risk of Alzheimer’s disease. Neurosci Lett. 2000, 288 (1): 21-24. 10.1016/S0304-3940(00)01185-X.View ArticlePubMedGoogle Scholar
  25. Menzer G, Muller-Thomsen T, Meins W, Alberici A, Binetti G, Hock C, Nitsch RM, Stoppe G, Reiss J, Finckh U: Non-replication of association between cathepsin D genotype and late onset Alzheimer disease. Am J Med Genet. 2001, 105 (2): 179-182. 10.1002/ajmg.1204.View ArticlePubMedGoogle Scholar
  26. Bertram L, Guenette S, Jones J, Keeney D, Mullin K, Crystal A, Basu S, Yhu S, Deng A, Rebeck GW, et al: No evidence for genetic association or linkage of the cathepsin D (CTSD) exon 2 polymorphism and Alzheimer disease. Ann Neurol. 2001, 49 (1): 114-116. 10.1002/1531-8249(200101)49:1<114::AID-ANA18>3.0.CO;2-M.View ArticlePubMedGoogle Scholar
  27. Emahazion T, Feuk L, Jobs M, Sawyer SL, Fredman D, St Clair D, Prince JA, Brookes AJ: SNP association studies in Alzheimer’s disease highlight problems for complex disease analysis. Trends Genet. 2001, 17 (7): 407-413. 10.1016/S0168-9525(01)02342-3.View ArticlePubMedGoogle Scholar
  28. Bagnoli S, Nacmias B, Tedde A, Guarnieri BM, Cellini E, Ciantelli M, Petruzzi C, Bartoli A, Ortenzi L, Serio A, et al: Cathepsin D polymorphism in Italian sporadic and familial Alzheimer’s disease. Neurosci Lett. 2002, 328 (3): 273-276. 10.1016/S0304-3940(02)00547-5.View ArticlePubMedGoogle Scholar
  29. Mateo I, Sanchez-Guerra M, Combarros O, Llorca J, Infante J, Gonzalez-Garcia J, del Molino JP, Berciano J: Lack of association between cathepsin D genetic polymorphism and Alzheimer disease in a Spanish sample. Am J Med Genet. 2002, 114 (1): 31-33. 10.1002/ajmg.1623.View ArticlePubMedGoogle Scholar
  30. Styczynska M, Religa D, Pfeffer A, Luczywek E, Wasiak B, Styczynski G, Peplonska B, Gabryelewicz T, Golebiowski M, Kobrys M, et al: Simultaneous analysis of five genetic risk factors in Polish patients with Alzheimer’s disease. Neurosci Lett. 2003, 344 (2): 99-102. 10.1016/S0304-3940(03)00438-5.View ArticlePubMedGoogle Scholar
  31. Ingegni T, Nocentini G, Mariani E, Spazzafumo L, Polidori MC, Cherubini A, Catani M, Cadini D, Senin U, Mecocci P: Cathepsin D polymorphism in Italian elderly subjects with sporadic late-onset Alzheimer’s disease. Dement Geriatr Cogn Disord. 2003, 16 (3): 151-155. 10.1159/000071003.View ArticlePubMedGoogle Scholar
  32. Blomqvist ME, Reynolds C, Katzov H, Feuk L, Andreasen N, Bogdanovic N, Blennow K, Brookes AJ, Prince JA: Towards compendia of negative genetic association studies: an example for Alzheimer disease. Hum Genet. 2006, 119 (1–2): 29-37.View ArticlePubMedGoogle Scholar
  33. Davidson Y, Gibbons L, Pritchard A, Hardicre J, Wren J, Tian J, Shi J, Stopford C, Julien C, Thompson J, et al: Genetic associations between cathepsin D exon 2 C–> T polymorphism and Alzheimer’s disease, and pathological correlations with genotype. J Neurol Neurosurg Psychiatry. 2006, 77 (4): 515-517. 10.1136/jnnp.2005.063917.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Capurso C, Solfrizzi V, D’Introno A, Colacicco AM, Capurso SA, Bifaro L, Menga R, Santamato A, Seripa D, Pilotto A, et al: Short arm of chromosome 11 and sporadic Alzheimer’s disease: catalase and cathepsin D gene polymorphisms. Neurosci Lett. 2008, 432 (3): 237-242. 10.1016/j.neulet.2007.12.026.View ArticlePubMedGoogle Scholar
  35. Albayrak O, Tirniceriu A, Riemenschneider M, Kurz A, Scherag A, Egensperger R: The cathepsin D (224C/T) polymorphism confers an increased risk to develop Alzheimer’s disease in men. J Gerontol A Biol Sci Med Sci. 2010, 65 (3): 219-224.View ArticlePubMedGoogle Scholar
  36. Crawford FC, Freeman MJ, Schinka J, Abdullah LI, Richards D, Sevush S, Duara R, Mullan MJ: The genetic association between Cathepsin D and Alzheimer’s disease. Neurosci Lett. 2000, 289 (1): 61-65. 10.1016/S0304-3940(00)01260-X.View ArticlePubMedGoogle Scholar
  37. Bertram L, McQueen MB, Mullin K, Blacker D, Tanzi RE: Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet. 2007, 39 (1): 17-23. 10.1038/ng1934.View ArticlePubMedGoogle Scholar
  38. Ntais C, Polycarpou A, Ioannidis JP: Meta-analysis of the association of the cathepsin D Ala224Val gene polymorphism with the risk of Alzheimer’s disease: a HuGE gene-disease association review. Am J Epidemiol. 2004, 159 (6): 527-536. 10.1093/aje/kwh069.View ArticlePubMedGoogle Scholar
  39. Papassotiropoulos A, Lewis HD, Bagli M, Jessen F, Ptok U, Schulte A, Shearman MS, Heun R: Cerebrospinal fluid levels of beta-amyloid(42) in patients with Alzheimer’s disease are related to the exon 2 polymorphism of the cathepsin D gene. Neuroreport. 2002, 13 (10): 1291-1294. 10.1097/00001756-200207190-00015.View ArticlePubMedGoogle Scholar
  40. Corder EH, Huang R, Cathcart HM, Lanham IS, Parker GR, Cheng D, Smith S, Poduslo SE: Membership in genetic groups predicts Alzheimer disease. Rejuvenation Res. 2006, 9 (1): 89-93. 10.1089/rej.2006.9.89.View ArticlePubMedGoogle Scholar
  41. Papassotiropoulos A, Bagli M, Jessen F, Maier W, Forstl H, Kurz A, Heun R: Interaction of two genes possibly involved in the regulation of the amyloid precursor protein (APP) processing. Mol Psychiatry. 2000, 5 (3): 240-241. 10.1038/sj.mp.4000699.View ArticlePubMedGoogle Scholar
  42. Kolsch H, Ptok U, Majores M, Schmitz S, Rao ML, Maier W, Heun R: Putative association of polymorphism in the mannose 6-phosphate receptor gene with major depression and Alzheimer’s disease. Psychiatr Genet. 2004, 14 (2): 97-100. 10.1097/01.ypg.0000129204.58574.c2.View ArticlePubMedGoogle Scholar
  43. Capurso C, Solfrizzi V, D’Introno A, Colacicco AM, Capurso SA, Mastroianni F, Liaci M, Vendemiale G, Capurso A, Panza F: The cathepsin D gene exon 2 (C224T) polymorphism and sporadic Alzheimer’s disease in European populations. J Gerontol A Biol Sci Med Sci. 2005, 60 (8): 991-996. 10.1093/gerona/60.8.991.View ArticlePubMedGoogle Scholar
  44. Prince JA, Feuk L, Sawyer SL, Gottfries J, Ricksten A, Nagga K, Bogdanovic N, Blennow K, Brookes AJ: Lack of replication of association findings in complex disease: an analysis of 15 polymorphisms in prior candidate genes for sporadic Alzheimer’s disease. Eur J Hum Genet. 2001, 9 (6): 437-444. 10.1038/sj.ejhg.5200651.View ArticlePubMedGoogle Scholar
  45. Bernstein HG, Bruszis S, Schmidt D, Wiederanders B, Dorn A: Immunodetection of cathepsin D in neuritic plaques found in brains of patients with dementia of Alzheimer type. J Hirnforsch. 1989, 30 (5): 613-618.PubMedGoogle Scholar
  46. Cataldo AM, Barnett JL, Berman SA, Li J, Quarless S, Bursztajn S, Lippa C, Nixon RA: Gene expression and cellular content of cathepsin D in Alzheimer’s disease brain: evidence for early up-regulation of the endosomal-lysosomal system. Neuron. 1995, 14 (3): 671-680. 10.1016/0896-6273(95)90324-0.View ArticlePubMedGoogle Scholar
  47. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2377/14/13/prepub

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