Interaction effects between variants in TOMM40 and PVRL2 with plasma amyloid-β and Alzheimer's disease among Chinese older adults: a population-based study

doi:10.21203/rs.3.rs-832373/v1

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Interaction effects between variants in TOMM40 and PVRL2 with plasma amyloid-β and Alzheimer's disease among Chinese older adults: a population-based study

https://doi.org/10.21203/rs.3.rs-832373/v1

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Background

Emerging evidence has linked TOMM40 and PVRL2 polymorphisms with Alzheimer’s disease (AD). We examined their associations with AD and plasma AD biomarkers and interaction on AD risk among Chinese older adults.

Methods

This population-based study included 4876 participants (age ≥ 65 years, 57.2% women) from MIND-China. TOMM40(rs2075650) and PVRL2(rs6859) polymorphisms were detected using multiple-polymerase chain reaction amplification. Plasma Aβ40, Aβ42, and t-tau were measured using SIMOA in a subsample (n = 1257). Data was analyzed using multiple logistic and general linear regression models.

Results

AD was diagnosed in 182 participants. The multi-adjusted odds ratio of AD was 6.24(95%CI 1.73–22.48) for TOMM40GG (vs. AA), 1.47(0.89–2.42) for PVRL2AA (vs. GG), and 12.87(3.97–41.73) for having both risk alleles (P_interaction=0.0003). In the plasma biomarker subsample, TOMM40GG was significantly associated with lower plasma Aβ42 and the Aβ42-to-Aβ40 ratio (p < 0.05).

Conclusions

TOMM40 and PVRL2 genes could interact to substantially increase the likelihood of AD, possibly through influencing Aβ metabolism.

Neurology

Cognitive Neuroscience

Alzheimer’s disease

TOMM40

PVRL2

gene-gene interaction

plasma biomarkers

population-based study

Alzheimer’s disease (AD) is the most common neurodegenerative disease among elderly people[1]. The etiology and pathogenesis of AD remain poorly understood. The heritability of late-onset AD was estimated to be approximately 56%-79%, suggesting that genetic components play an important role in the pathogenesis[2]. Thus, exploring genetic factors may help increase our understanding of the etiopathogenesis of AD. Indeed, the genome-wide association studies (GWAS) have identified a large number of susceptibility loci, of which, the chromosome 19q13 region has been the strongest genetic loci for AD[3]. In addition to the well-known APOE gene within this region, translocase of outer mitochondrial membrane 40 homolog (TOMM40) gene and poliovirus receptor-related 2 (PVRL2) gene have been identified to be associated with AD[4, 5].

TOMM40, an APOE nearby gene[6], encodes a subunit of the translocase of the outer mitochondrial membrane complex, which is involved in AD-related pathology[7]. Evidence has shown that multiple single nucleotide polymorphisms (SNPs) within TOMM40 are associated with AD risk, of which, the SNP rs2075650 has been frequently reported in various ethnic populations[8–11]. PVRL2, which locates near the TOMM40, also serves as a risk variant that is involved in AD pathology[12]. In addition, TOMM40 and PVRL2 genes have been implicated in common biological pathways, such as lipid metabolism[13] and immunity[14, 15]. Thus, it is biologically plausible to hypothesize that TOMM40 and PVRL2 genes may confer an interaction effect in the pathogenesis of AD. However, this hypothesis has yet to be explored.

Although GWAS studies have identified the associations of TOMM40 and PVRL2 with AD risk, the neuropathological pathways underlying their associations remain unclear. Aggregation of the β-amyloid (Aβ) peptide and neurofibrillary tangles in the brain are the neuropathological hallmarks of AD[16]. Data from the Framingham Heart Study showed that lower plasma Aβ and higher plasma total tau (t-tau) proteins were associated with an increased risk of AD[17, 18] However, whether TOMM40 and PVRL2 are associated with AD via influencing plasma Aβ and tau proteins remains largely unknown.

Therefore, in this population-based study of rural-dwelling Chinese older adults, we aimed to investigate the associations of TOMM40 rs2075650 and PVRL2 rs6859 as well as their interactions with AD risk, and to further explore the associations of these two genes with plasma AD biomarkers (Aβ and t-tau proteins).

2.1 Study population

This population-based study involved the participants from the ongoing Multimodal Interventions to Delay Dementia and Disability in Rural China (MIND-China)[19–21], a participating project in the World-Wide FINGERS Network[22]. Briefly, all registered residents who were aged ≥ 60 years and living in 52 villages of Yanlou Town, Yanggu County, western Shandong Province, China, were eligible for the MIND-China study. From March to September 2018, 5765 participants were enrolled. Of these, 519 participants aged 60–64 years were excluded, because we aimed to investigate the late-onset sporadic AD. Of the 5246 participants who were aged ≥65 years, 370 were excluded due to missing genotyping information (n = 230), insufficient information for dementia diagnosis (n = 46), and other types of dementia than AD (n = 94). Thus, the analytical sample included 4876 participants. Compared with the excluded participants (n = 370), individuals included in the analytical sample (n = 4876) were younger (mean age, 71.6 vs. 73.6 years, p < 0.001) and more educated (mean years of education, 3.14 vs. 2.60, p < 0.001). However, the two groups did not differ significantly in sex distribution (female, 57.2% vs. 56.2%, p = 0.701).

In addition, data on plasma AD biomarkers were available in a subsample of 1257 individuals that were derived from the participants of MIND-China. This subsample was used for the analysis involving plasma AD biomarkers. Figure 1 shows a flowchart of the study participants.

The MIND-China project was approved by the Ethics Committee of Shandong Provincial Hospital affiliated to Shandong University, Jinan, Shandong. All participants provided written informed consent for both study enrollment and blood sample collection, or in case of persons with severe cognitive impairment, their informants provided informed consent. Research within MIND-China has been conducted in accordance with the ethical principles expressed in the Declaration of Helsinki as well as relevant national guidelines and regulations. MIND-China was registered in the Chinese Clinical Trial Registry (registration no.: ChiCTR1800017758).

2.2 Data collection and assessments

The procedure of baseline data collection was described elsewhere[19, 20]. Briefly, all data were collected by trained staff via face-to-face interviews, clinical examinations, cognitive testing, and laboratory tests. The in-person interview was performed following a structured questionnaire covering demographic data (e.g., age, sex, and education), lifestyle factors (e.g., smoking and alcohol drinking), health conditions (e.g., hypertension, diabetes, and hyperlipidemia), medications, and cognitive function. Alcohol consumption and smoking status were categorized as current, former, and never drinking or smoking, respectively. After a 5-minute rest, seated arterial blood pressure was measured on the right upper arm using an electronic sphygmomanometer (HEM-7127J, Omron Corporation, Kyoto, Japan). After an overnight fast, venous blood samples were collected and blood glucose, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured using an Automatic Biochemical Analyzer (CS-600B, DIRUI Corporation, Changchun, China). Hypertension was defined as arterial blood pressure ≥ 140/90 mmHg or taking antihypertensive agents. Diabetes was defined as the fasting blood glucose ≥ 7.0 mmol/L or current use of antidiabetic agents. Hyperlipoidemia was defined as TC ≥ 6.2 mmol/L or TG ≥ 2.3 mmol/L or LDL-C ≥ 4.1mmol/L or HDL-C < 1.0 mmol/L or taking hypolipidemic agents.

2.3 Genomic DNA extraction and genotyping

Genomic DNA was extracted from venous blood leukocytes using the TIANamp blood DNA kit (Tiangen, Beijing, China) according to manufacturer’s instructions. Then, DNA was quantified using Nanodrop 3300 spectrometry and a total amount of 100 ng genomic DNA per sample was used for the DNA sample preparation. Subsequently, sequencing libraries were generated using MultipSeqCustom Panel (iGeneTech, Beijing, China) following manufacturer’s recommendations and index codes were added to each sample. Finally, qualified libraries were subjected to next-generation sequencing (NGS) on a Novaseq system (Illumina), and raw data were filtered to remove low-quality reads using FastQC. Genotyping was conducted by an operator who was blinded to the clinical data.

In addition to the APOE genotype, two SNPs of interest, rs2075650 (TOMM40) and rs6859 (PVRL2), were detected using multiple-polymerase chain reaction amplification (iGeneTech Bioscience Co., Ltd., Beijing, China).

2.4 Measurements of Plasma Aβ42, Aβ40 and Tau

Blood samples were collected in EDTA-coated vacutainers, followed by centrifugation to obtain plasma. The samples were stored at -80℃ and thawed immediately before component quantification. Levels of plasma Aβ42, Aβ40, and t-tau were measured using a Human Neurology 3-Plex A assay (N3PA) Kit run on the fully automated single molecule array (SIMOA) instrument (Quanterix Corp, MA, USA) according to the manufacturer’s protocol. Two quality control samples were run in duplicates on each plate for each analyte. The upper and lower detection limits for quality sample of each index were within the range indicated in the manual, and the inter-assay coefficient of variability (CV) and inter-plate inter-assay CV were controlled within 13%.

2.5 Diagnosis of Alzheimer’s disease

We employed a 3-step procedure for the clinical diagnosis of dementia. In brief, clinicians and trained junior interviewers conducted comprehensive assessments for each participant following the structured questionnaires. The assessments included health-related factors, medical history, a neurocognitive assessment battery, the Chinese version of activities of daily living (ADLs), and the Clinical Dementia Rating Scale (CDR). Then, the neurologists specialized in dementia diagnosis and care reviewed all the data collected from clinical examination, in-person interviews, and neurocognitive testing, and made a preliminary judgement for participants who were suspected to have dementia. In addition, ~ 9.5% had incomplete data for the assessment of dementia status. Finally, all these persons were contacted by neurologists to conduct the second interview and reassess their medical history, cognitive status, ADLs, and whenever available, neuroimaging data. If the participants were not able to take the interview due to severe cognitive impairment or not available for the face-to-face interviews (~ 13%), the neurologists interviewed their family members, neighbors, or village doctors (primary care providers to local residents). Based on all the assessments, a diagnosis of dementia was made according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, criteria[23]. In case of uncertainty, a senior neurologist was consulted and discussed, and a consensus diagnosis of dementia was reached. The diagnosis of AD was made according to the National Institute on Aging-Alzheimer's Association criteria for probable AD[24] .

2.6 Statistical Analysis

Characteristics of study participants were present and compared using the non-parametric test for continuous variables or chi-square test for categorical variables. We used chi-square test to assess whether the allele frequencies agreed with expectations in Hardy-Weinberg equilibrium (HEW). The Haploview software was used to explore the stratified linkage disequilibrium (LD) patterns of SNPs[25]. Multivariable logistic regression models were used to calculate the odds ratio and 95% confidence interval (95% CI) of AD associated with TOMM40 and PVRL2 genotypes. Furthermore, we evaluated the interaction and joint exposures of TOMM40 and PVRL2 genotypes associated with AD risk. We reported the main results from two models: Model 1 was controlled for age, sex, and education, and Model 2 was additionally controlled for current smoking, current drinking, diabetes, hyperlipoidemia, hypertension, and APOE genotype. In the plasma biomarker subsample, we used the multiple general linear models to examine the associations of TOMM40 and PVRL2 genotypes with plasma AD biomarkers. The statistical significance was set to be two-tailed p < 0.05. All analyses were performed using IBM SPSS Statistics version 26.0 (IBM SPSS Inc., Chicago, Illinois, USA) and Haploview version 4.2 (Cambridge, MA02141, USA).

3.1 Characteristics of study participants (n = 4876)

Of the 4876 participants, the mean age was 71.6 (SD, 5.4) years, 57.2% were women, and the average years of formal schooling was 3.1 (SD, 3.4; 39.9% were illiterate) years. Among all the participants, 182 (3.7%) were ascertained to have AD. Compared with people without dementia, participants with AD were older, more likely to be women, and less educated (p < 0.001). The distribution of the TOMM40 and PVRL2 genotypes conformed to Hardy-Weinberg equilibrium (HWE). In addition, there was a significant difference in the distributions of current smoking, alcohol drinking, and TOMM40 and PVRL2 genotypes between the two groups. However, the two groups did not significantly differ in the distributions of diabetes, hyperlipoidemia, hypertension, and carrying APOE ε4 allele (Table 1).

Table 1

Characteristics of the study participants
Characteristics	Total sample (n = 4876)	Non-dementia (n = 4694)	AD (n = 182)	P-value
Age, y, mean (SD)	71.6(5.4)	71.4(5.1)	77.5 (7.2)	< 0.001
Female, n (%)	2791(57.2)	2653(56.5)	138(75.8)	< 0.001
Education, y, mean (SD)	3.1(3.4)	3.2(3.4)	1.2(2.3)	< 0.001
Current smoking, n (%)	729(15.0)	711(15.2)	18(9.9)	< 0.001
Current drinking, n (%)	1416(29.0)	1392(29.7)	23(12.6)	< 0.001
Diabetes, n (%)	678(13.9)	649(13.8)	29(15.9)	0.420
Hyperlipoidemia, n (%)	1133(23.2)	1085(23.1)	48(26.4)	0.307
Hypertension, n (%)	3251(67.7)	3134(66.8)	117(64.3)	0.700
APOE ε4 allele, n (%)	781(16.0)	747(15.9)	34(18.7)	0.318
TOMM40(rs2075650), n (%)
AA	4076(83.6)	3930(83.7)	146(80.2)	0.006
AG	763(15.6)	732(15.6)	31(17.0)
GG	37(0.8)	32(0.7)	5(2.8)
P(HWE)	0.98
PVRL2(rs6859), n (%)
GG	2379(48.8)	2287(48.7)	92(50.6)	0.024
AG	2070(42.4)	2005(42.7)	65(35.7)
AA	427(8.8)	402(8.6)	25(13.7)
P(HWE)	0.31
The number of participants with missing values was 39 for hypertension. As a covariate in the subsequence analysis, a dummy variable was created for hypertension to represent these with missing values.
Abbreviations: AD, Alzheimer’s disease; SD, standard deviation; APOE, apolipoprotein E gene; HWE, Hardy-Weinberg equilibrium; TOMM40, Translocase of outer mitochondrial membrane 40 homolog gene; PVRL2, poliovirus receptor-related 2 gene.

3.2 Associations of TOMM40 and PVRL2 SNPs with AD risk (n = 4876)

To dissect AD-associated genetic structure among TOMM40 rs2075650 and PVRL2 rs6859, we used Haploview software to investigate stratified LD patterns of the two SNPs among AD cases and non-demented subjects respectively(Figure S1). People with AD manifested a diverse genomic structure relative to people who were free of dementia, represented by a stronger D’ value among rs2075650 and rs6859 variants (0.91 vs. 0.79).

Controlling for age, sex, and education, TOMM40 GG genotype was significantly associated with an increased likelihood of AD (Table 2, Model 1). When additionally adjusting for smoking, drinking, diabetes, hyperlipoidemia, hypertension, and APOE genotype, the association between TOMM40 GG and AD remained significant (Table 2, Model 2). There was no significant association of PVRL2 genotype with AD, although the PVRL2 AA genotype (vs. GG genotype) was associated with a non-significantly increased likelihood of AD (odds ratio = 1.56) (Table 2).

Table 2

Associations of *TOMM40* and *PVRL2* SNPs and their interaction with Alzheimer’s disease risk
Genotype		No. of subjects	No. of cases	Odds ratio (95% confidence interval)
Genotype		No. of subjects	No. of cases	Model 1^*	Model 2^*
TOMM40 rs2075650
AA		4076	146	1.00 (Reference)	1.00 (Reference)
AG		763	31	1.25(0.83–1.89)	1.34(0.60–3.01)
GG		37	5	5.77(2.03–16.44)^a	6.24(1.73–22.48)^a
PVRL2 rs6859
GG		2379	92	1.00 (Reference)	1.00 (Reference)
AG		2070	65	0.83(0.60–1.17)	0.79(0.56–1.12)
AA		427	25	1.56(0.96–2.51)	1.47(0.89–2.42)
TOMM40 rs2075650	PVRL2 rs6859
AA + AG	GG + AG	4433	157	1.00 (Reference)	1.00 (Reference)
GG	GG + AG	16	0	-	-
AA + AG	AA	406	20	1.36(0.83–2.24)	1.37(0.83–2.28)
GG	AA	21	5	14.01(4.56–42.99)^a	12.87(3.97–41.73)^a
^Model 1 was adjusted for age, sex, and education; Model 2 was additionally adjusted for smoking, alcohol consumption, hypertension, diabetes, hyperlipoidemia, and APOE* genotype.
^ap<0.05.
Abbreviations: TOMM40, Translocase of outer mitochondrial membrane 40 homolog gene; PVRL2, poliovirus receptor-related 2 gene.

We detected a statistical interaction of TOMM40 and PVRL2 genotypes on the likelihood of AD (p for interactions = 0.0003). Further analysis stratified by both genotypes showed that TOMM40 GG genotype in combination with PVRL2 AA genotype was significantly associated with a substantially increased likelihood of AD compared with TOMM40 AA + AG genotype in combination with PVRL2 GG + AG genotype (Table 2, Model 1). Further adjusting for additional confounders, including APOE genotype, slightly decreased the association with AD (Table 2, Model 2).

3.3 Associations of TOMM40 and PVRL2 SNPs with plasma AD biomarkers (n = 1257)

Multiple general linear regression analysis showed that TOMM40 AG and GG genotypes (vs. AA genotype) were significantly associated with a lower level of plasma Aβ42 after controlling for age, sex, and education (Table 3, Model 1). When additionally adjusting for smoking, drinking, diabetes, hyperlipoidemia, hypertension, and APOE genotype, the association between TOMM40 GG genotype and lower plasma Aβ42 was still significant (Table 3, Model 2). The associations of TOMM40 genotype with the Aβ42/Aβ40 ratio were similar to those with plasma Aβ42 (Table 3). There were no significant associations of the PVRL2 genotype with either plasma Aβ42 or the Aβ42/Aβ40 ratio.

Table 3

Associations of *TOMM40* and *PVRL2* polymorphisms with plasma Alzheimer’s disease biomarkers
Genotype	No. of participants	β-coefficient (95% confidence interval)
Genotype	No. of participants	Model 1^*	Model 2^*
Plasma Aβ42 (pg/ml)
TOMM40 rs2075650
AA	1054	0.000(Reference)	0.000(Reference)
AG	194	-0.553(-1.004–0.102)^a	-0.060(-0.897-0.776)
GG	9	-2.733(-4.666–0.801)^a	-2.196(-4.264–0.128)^a
PVRL2 rs6859
GG	633	0.000(Reference)	0.000(Reference)
AG	514	-0.013(-0.356-0.331)	0.159(-0.197-0.515)
AA	110	0.266(-0.333-0.865)	0.565(-0.053-1.183)
Plasma Aβ40 (pg/ml)
TOMM40 rs2075650
AA	1054	0.000(Reference)	0.000(Reference)
AG	194	1.378(-5.924-8.680)	-3.070(-16.595-10.455)
GG	9	6.000(-25.274-37.274)	2.997(-30.438-36.431)
PVRL2 rs6859
GG	633	0.000(Reference)	0.000(Reference)
AG	514	-0.735(-6.267-4.796)	-1.235(-6.981-4.510)
AA	110	8.895(-0.744-18.534)	8.357(-1.618-18.331)
Plasma t-tau (pg/ml)
TOMM40 rs2075650
AA	1054	0.000(Reference)	0.000(Reference)
AG	194	-0.013(-0.162-0.135)	-0.008(-0.283-0.267)
GG	9	0.016(-0.620-0.652)	0.075(-0.605-0.755)
PVRL2 rs6859
GG	633	0.000(Reference)	0.000(Reference)
AG	514	-0.013(-0.126-0.099)	-0.010(-0.126-0.107)
AA	110	-0.126(-0.322-0.070)	-0.136(-0.339-0.067)
Plasma Aβ42-to-Aβ40 ratio
TOMM40 rs2075650
AA	1054	0.000(Reference)	0.000(Reference)
AG	194	-0.004(-0.0061–0.001)^a	0.001(-0.004-0.005)
GG	9	-0.017(-0.028–0.006)^a	-0.013(-0.024–0.001)^a
PVRL2 rs6859
GG	633	0.000(Reference)	0.000(Reference)
AG	514	0.000(-0.002-0.002)	0.001(-0.001-0.003)
AA	110	-0.002(-0.005-0.002)	0.000(-0.003-0.004)
^Model 1 was adjusted for age, sex, and education; Model 2 was additionally adjusted for smoking, drinking, hypertension, diabetes, hyperlipoidemia, and APOE* genotype.
^ap<0.05.
Abbreviations: Aβ, amyloid-β; t-tau, total tau protein; TOMM40, Translocase of outer mitochondrial membrane 40 homolog gene; PVRL2, poliovirus receptor-related 2 gene.

In this population-based study of rural-dwelling Chinese older adults, we found that TOMM40 GG genotype was associated with an increased likelihood of AD. Whereas, there was a marginal association of PVRL2 AA genotype with AD. An interaction between TOMM40 and PVRL2 on the likelihood of AD was detected such that carrying both TOMM40 GG and PVRL2 AA genotypes was associated with an over 12-fold increased likelihood of AD. In our subsample, the TOMM40 GG genotype, but not PVRL2 AA genotype, was associated with a reduced level of plasma Aβ42. Taken together, these results suggest that TOMM40 and PVRL2 risk alleles may act interactively to increase the likelihood of AD, possibly through influencing Aβ metabolism.

The GWAS analyses suggested that TOMM40 SNPs may play some role in AD[14, 26]. In addition, case-control studies from Italy[9], Colombia[10], and China[11] also revealed that the TOMM40 G allele serves as a risk variant for AD. In line with previous studies, our community-based study confirmed that the TOMM40 G allele could confer a substantial risk to AD. However, the mechanism underlying the association of TOMM40 with AD is not well established. The neuroimaging and neuropathological data showed that the TOMM40 was associated with amyloid burden in the brain parenchyma and vessels[27]. Coincidentally, reports from ADNI database demonstrated that TOMM40 was a susceptible putative locus associated with cerebrospinal fluid AD biomarkers (e.g., Aβ42, p-tau181p/Aβ42 ratio, and t-tau/Aβ42 ratio)[28, 29]. In the current population-based study, we found that TOMM40 GG genotype was associated with a lower level of plasma Aβ42 and the Aβ42/Aβ40 ratio. Taken together, these results suggested that TOMM40 might be involved in the Aβ metabolism, which in turn may be linked with AD.

The GWAS analyses among Chinese or European populations showed that PVRL2 rs6859 was associated with AD, with OR ranging from 1.46 to 1.61[4, 5, 26]. In line with these GWAS analyses, our community-based study showed that PVRL2 AA genotype was associated with a 56% increased likelihood of AD after controlling for demographic factors (OR = 1.56; 95% CI 0.96–2.51), although the association was not statistically evident, possibly owing to limited statistical power. Thus, large-scale population-based studies are warranted to replicate the association of PVRL2 genotype with AD risk.

Our study revealed a synergistic interaction between TOMM40 and PVRL2 variants on AD, such that having both TOMM40 GG and PVRL2 AA genotypes was associated with an over 12-fold likelihood of AD. To the best of our knowledge, this is the first population-based study that identified the interaction between TOMM40 and PVRL2 genes on AD risk. The neurobiological mechanisms underlying their interaction effect on AD are not clear. The expression quantitative trait loci (eQTL) analysis indicated that the TOMM40 rs2075650 G allele could increase the binding affinity of transcription factors (TFs), thus, increasing PVRL2 gene expression[15]. In this context, the synergistic interaction of TOMM40 GG and PVRL2 AA genotypes with AD may partly attribute to the mechanism that TOMM40 G allele might enhance the effect of PVRL2 risk allele. Alternatively, the synergistic effects of TOMM40 and PVRL2 risk variants on AD might be resulted from their shared molecular pathways involved in AD pathological mechanisms such as Aβ metabolism[30, 31] and inflammation[14, 15].

Previous studies reported that TOMM40 and PVRL2 might be indirectly involved in the accumulation of Aβ[32, 33]. Our results showed that TOMM40 GG carriers had the lower level of plasma Aβ42 than carriers of other genotypes, supporting a possible link of TOMM40 with metabolism of Aβ42, but not Aβ40. However, we did not find associations between PVRL2 genotype with any of the three plasma AD biomarkers (Aβ40, Aβ42, and t-tau), which is in line with the lack of evident association between PVRL2 genotype and AD risk. In addition, the lack of associations between plasma t-tau and TOMM40 or PVRL2 genotype may be due partly to the facts that plasma t-tau is not associated with dementia and AD[34]. Alternatively, TOMM40 and PVRL2 genes may be not involved in the process of tau pathology.

Our study was based on a large-scale sample of rural-dwelling Chinese older adults, where epidemiological, clinical, neuropsychological, and genetic data were integrated with plasma AD biomarkers. Thus, we are able to explore the associations and interaction effects of TOMM40 and PVRL2 genes with AD risk as well as the possible neuropathological mechanisms.

Our study also has limitations. First, due to the cross-sectional nature of the study design, the observed associations might be subject to selective survival bias, which usually leads to an underestimation of the true association. Furthermore, the study participants were recruited from a single rural area in northern China, which should be kept in mind when generalizing our findings to other populations.

In conclusion, our study confirms the association of TOMM40 GG genotype with AD among rural-dwelling Chinese older adults and further reveals a potential genetic interaction between TOMM40 and PVRL2 risk alleles on AD risk and the association of TOMM40 GG genotype with low plasma Aβ42. These results increase our understanding of the roles of TOMM40 and PVRL2 genes in AD and the potential neuropathological mechanisms.

AD, Alzheimer’s disease; SNPs, single nucleotide polymorphisms; TOMM40, translocase of outer mitochondrial membrane 40 homolog gene; PVRL2, poliovirus receptor-related 2 gene; APOE, apolipoprotein E; Aβ, amyloid-β; t-tau, total tau protein; HWE, Hardy-Weinberg equilibrium; LD, linkage disequilibrium; MIND-China, Multimodal Intervention to Delay Dementia and Disability in Rural China; OR, odds ratio; SD, standard deviation.

Ethics approval and consent to participate

The MIND-China project was approved by the Ethics Committee of Shandong Provincial Hospital affiliated to Shandong University, Jinan, Shandong. All participants provided written informed consent for both study enrollment and blood sample collection, or in case of persons with severe cognitive impairment, their informants provided informed consent. Research within MIND-China has been conducted in accordance with the ethical principles expressed in the Declaration of Helsinki as well as relevant guidelines and regulations. MIND-China was registered in the Chinese Clinical Trial Registry (registration no.: ChiCTR1800017758).

Consent for publication

Not applicable.

Availability of data and materials

The datasets used for the analysis are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

MIND-China was supported by the grant from the National Key R&D Program of China (grant no.: 2017YFC1310100) and by additional grants from the National Nature Science Foundation of China (grants no.: 81861138008 and 82011530139), the Academic Promotion Program of Shandong First Medical University (2019QL020), and the Taishan Scholar Program of Shandong Province, China. C Qiu received grants from the Swedish Research Council (grants no.: 2017-00740, 2017-05819, and 2020-01574), the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) (grant no.: CH2019-8320) for the Joint China-Sweden Mobility program, and the Karolinska Institutet, Stockholm, Sweden. The funding agency had no role in the study design, data collection and analysis, the writing of this manuscript, and in the decision to submit the work for publication.

Author’s contributions

X.L., T.H., Y.D., and C.Q. designed the study. X.L., T.H., K.L., C.L., Y.W., R.L., W.F., N.T., Y.C., and N.W. contributed to data collection and assessment. X.L., T.H., and K.L. analyzed the data. X.L. drafted the manuscript. Y.D. and C.Q. supervised the study. All authors made critical comments on the manuscript and approved the final manuscript.

Acknowledgements

We would like to thank all participants of the MIND-China project as well as our research staff for their collaboration in data collection and management.

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FigureS1.docx
Figure S1. Stratified LD patterns of two SNPs between individuals with Alzheimer’s disease and those without dementia. The red map corresponds to D’ value.
Abbreviations: LD, linkage disequilibrium; SNPs, single nucleotide polymorphisms; AD, Alzheimer’s disease.

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Interaction effects between variants in TOMM40 and PVRL2 with plasma amyloid-β and Alzheimer's disease among Chinese older adults: a population-based study

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Abstract

Background

Methods

Results

Conclusions

Figures

1. Background

2. Methods

2.1 Study population

2.2 Data collection and assessments

2.3 Genomic DNA extraction and genotyping

2.4 Measurements of Plasma Aβ42, Aβ40 and Tau

2.5 Diagnosis of Alzheimer’s disease

2.6 Statistical Analysis

3. Results

3.1 Characteristics of study participants (n = 4876)

3.2 Associations of TOMM40 and PVRL2 SNPs with AD risk (n = 4876)

3.3 Associations of TOMM40 and PVRL2 SNPs with plasma AD biomarkers (n = 1257)

4. Discussion

5. Limitation

6. Conclusions

Abbreviations

Declarations

References

Supplementary Files

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