Abstract
Introduction
AXIN1 (axis inhibition protein 1), as a rate-limiting component of canonical Wingless-type mouse mammary tumor virus integration site (Wnt)/β-catenin signaling pathway, may influence midbrain dopaminergic neurons. A recent genome-wide association study identified AXIN1 as a candidate gene for Parkinson’s disease (PD). Our study aimed to investigate the potential relevance of AXIN1 single nucleotide polymorphisms (rs13337493 and rs9921222) in the risk, clinical characteristics, and pathology of PD.
Methods
Data were collected from the Northern Han Chinese and Parkinson’s Progression Markers Initiative (PPMI) cohorts. Associations between AXIN1 variants, PD-related biomarkers, and clinical manifestations were analyzed.
Results
Both loci were identified as risk factors in the Northern Han Chinese population, and the A allele of rs13337493 [odds ratio (OR) 1.320, 95% confidence interval (CI) 1.052, 1.653, Pc = 0.036] and the T allele of rs9921222 (OR 1.351, 95% CI 1.045, 1.747, Pc = 0.042) showed increased susceptibility to PD. The risk effect of rs9921222 was predominant in the male cohort (OR 1.504, 95% CI 1.058, 2.139, Pc = 0.044). Rs13337493 was related to worse motor function in white individuals, which was represented by the Hoehn & Yahr stage (OR 2.775, 95% CI 1.195, 6.447, Pc = 0.036). It also correlated with compensatory elevation of cerebrospinal fluid (CSF) 3,4-dihydroxyphenylalanine (DOPA, β = 0.040, 95% CI 0.007, 0.073, Pc = 0.038).
Conclusion
Our findings support a gatekeeper role for AXIN1; its polymorphisms contribute to increased PD susceptibility and accelerated motor progression, yet may also trigger a compensatory presynaptic response, as evidenced by elevated CSF DOPA levels, to counteract neurodegeneration. Future studies should include larger sample sizes, more diverse ethnic populations, and protein-level investigations.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40120-025-00864-1.
Keywords: Parkinson’s disease, AXIN1 gene, Single nucleotide polymorphism, East Asian population, White population
Key Summary Points
| Why we carry out this study? |
| Dysregulation of the Wingless-type mouse mammary tumor virus integration site (Wnt)/β-catenin signaling pathway, crucial for the survival of midbrain dopaminergic neurons, has been implicated in the pathogenesis of Parkinson’s disease (PD). AXIN1 (axis inhibition protein 1), a core regulator of Wnt pathway, was recently identified as a candidate PD risk gene through Genome-Wide association study, yet the role of its specific polymorphisms remains largely unknown. |
| There is a lack of data regarding the specific effects of AXIN1 genetic variants on PD susceptibility, clinical presentation, and pathological progression across different ethnicities. |
| We carried out this study to specifically investigate the association between two AXIN1 single nucleotide polymorphisms (rs13337493 and rs9921222) and the risk of developing PD, as well as its clinical characteristics and pathology in both Northern Han Chinese and white populations. |
| What was learned from the study? |
| A principal finding of our study was the identification of both AXIN1 polymorphisms (rs13337493 and rs9921222) as novel genetic risk factors for PD in the Northern Han Chinese and white populations. Subgroup analysis further pointed that the rs9921222 remained a predominant significant risk factor in male patients, suggesting a sex-specific genetic influence. |
| AXIN1 rs13337493 was associated with worse motor function in white populations, as measured by the Hoehn&Yahr stage. Furthermore, the correlation of rs13337493 with an elevation of CSF DOPA suggested that the AXIN1 might influence PD pathology through modulating the integrity and function of dopaminergic neurons. |
Introduction
Parkinson’s disease (PD) is an age-dependent neurodegenerative disease characterized by the progressive death of midbrain dopaminergic neurons and widespread formation of misfolded α-synuclein [1]. Exceeding 50% degeneration of dopaminergic neurons in the substantia nigra striatum may provoke motor impairment, including progressive asymmetric bradykinesia, rigidity, and tremor [2]. Apart from these typical motor symptoms, most patients also present with hyposmia, REM sleep behavior disorder (RBD), depression, and other nonmotor defects [3]. PD is a complex multifactorial disease, and the majority (95%) of PD cases arise from the interplay between common genetic variants and environmental factors [4]. These common risk variants collectively explain 16–36% of the heritable component of PD [5]. However, owing to differences in minor allele frequencies (MAF), linkage disequilibrium patterns (LD), environmental influences, and dietary habits, the genetic architecture of PD varies among different ethnicities [6]. For example, the hereditary proportion of PD in the Chinese population is approximately 18%, which is slightly lower than that of the white population (22%) [7]. These disparities underscore the necessity of a comprehensive understanding of PD-associated genetic factors across diverse populations.
Among the various signal pathways implicated in PD, the Wingless-type mouse mammary tumor virus integration site (Wnt)/β-catenin signaling pathway exerts critical effects on neural development and synaptic stability [8, 9]. In experimental models of PD, the activated Wnt/β-catenin pathway has been shown to directly reverse neurodegeneration [10]. Recently, axis inhibition protein 1 (AXIN1), a negative regulator of canonical Wnt/β-catenin signaling cascades, has been identified as a novel candidate gene for PD based on a genome-wide association studies [11–13]. Consistent with its genetic role in Wnt pathway, subsequent evidence has confirmed the upregulation of AXIN1 protein in PD and linked it to dopaminergic neuronal damage [14]. Notably, the study by Saeed identified rs13337493 as an expression quantitative trait locus (eQTL) for AXIN1 expression in the substantia nigra, directly connecting the rs13337493 variant to altered gene regulation in PD [12]. Furthermore, the AXIN1 locus rs9921222, which has been implicated in bone mineral density and osteoporosis [15], has not yet been evaluated in this context. Considering the reported link between osteoporosis and PD [16], we speculated that investigating rs9921222 of AXIN1 in PD might provide insights into the shared pathophysiological mechanisms. Furthermore, the potential role of AXIN1 polymorphisms in the common clinical features of PD (e.g., motor, cognition, and imaging biomarkers) remains poorly understood.
In this study, we aimed to determine the genetic association between AXIN1 loci (rs13337493 and rs9921222) and susceptibility to PD in different ethnicities. In addition, we determined the correlation between AXIN1 polymorphisms and risk, clinical characteristics, and pathological biomarkers of PD in white populations. Collectively, this study will elucidate the pathogenic mechanisms of AXIN1 variants, thereby informing its potential as a clinically relevant biomarker.
Materials and Methods
Subjects
After exclusion of participants younger than 30 years of age, a total of 940 Northern Han Chinese subjects without consanguinity, including 479 patients with PD and 461 healthy-control subjects (HCs), were recruited from the Affiliated Hospital of Qingdao University. The diagnostic criteria for PD followed those of the Movement Disorder Society (MDS) and United Kingdom Parkinson’s Disease Society Brain Bank (UKPDSBB). All enrolled patients with PD were required to meet a definitive diagnosis of PD by at least two movement disorder specialists. Individuals with a diagnosis of other parkinsonian syndromes (e.g., progressive supranuclear palsy, multiple system atrophy, or secondary parkinsonism), other neurological diseases (e.g., Alzheimer’s disease, epilepsy), or psychiatric disorders (e.g., schizophrenia) were excluded. HCs were age- and sex-matched volunteers from the same geographic region with no clinical signs of parkinsonism or other neurological disorders. HCs were also required to be free of any major psychiatric illnesses, systemic diseases, or conditions that could affect motor or cognitive function, or impair activities of daily living. This retrospective chart review study, involving human participants, was conducted in accordance with the ethical standards of the institutional and national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the ethics committee of the Affiliated Hospital of Qingdao University (QYFY-WZLL-28612).
We additionally selected 803 participants from the PPMI database, who were mostly from White populations. Participants were required to be at least 30 years of age. Inclusion and exclusion criteria were as follows: Patients with PD must have been diagnosed as asymmetric resting tremor or bradykinesia, and with the DAT single-photon emission computed tomography (DAT-SPECT) deficit. HCs were required to have no history of neurological disorders, no first-degree relatives with PD, and normal DAT-SPECT imaging. Striatal binding ratios (SBR) of DAT-SPECT can be used to evaluate the activity of dopaminergic neurons [17]. Dopamine (DA), 3,4-dihydroxyphenylalanine (DOPA), 3,4-dihydroxyphenylacetic acid (DOPAC), 3-methoxytyrosine (3-MT), and homovanillic acid (HVA) levels in the cerebrospinal fluid (CSF) reflect DA metabolism level [18]. The Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) comprehensively evaluates PD manifestations: Part I, non-motor daily living experiences; Part II, motor-related daily activities; and Part III, clinical motor symptom quantification [19]. Cognition was assessed using the Montreal Cognitive Assessment (MoCA). Neurobehavioral function was measured using the Geriatric Depression Scale (GDS), which assesses depression and anxiety. Sleep disorders were evaluated using the Epworth Sleepiness Scale (ESS) and REM Sleep Behavior Disorder Questionnaire (RBDSQ). All the PPMI data in this study were approved by the institutional review board of each PPMI site.
SNP Selection
To ensure adequate statistical power for detecting associations, we selected single nucleotide polymorphisms (SNPs) with a MAF greater than 0.10, as low-frequency variants typically have insufficient power for association studies [20]. According to the 1000 Genomes Project, two SNPs, rs13337493 and rs9921222, are located within the AXIN1 gene region and above the set MAF threshold in both East Asian and white populations. Also, the two loci obeyed the Hardy–Weinberg equilibrium (HWE) in the control group to avoid population selection bias. Importantly, rs9921222 and rs13337493 did not show significant LD in either cohort (white population, R2 = 0.044; Northern Han Chinese population, R2 < 0.001) and were therefore analyzed as independent genetic variants.
DNA Genotyping
Genotype data from the PPMI cohort were extracted directly from whole-genome sequencing data. The SNPs of the clinically recruited participants were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) method. A TIANamp Genomic DNA Extraction Kit (TIANGEN Biotech Co., Ltd., Beijing, China) was used to extract genomic DNA from 3 ml peripheral venous blood. For SNPs rs13337493 and rs9921222, PCR primers were designed using NCBI Primer-BLAST and subsequently produced by RiboBio (China). All primers were specific to the PCR template and exclusively amplified the target sequence fragments. Restriction nucleic acid endonuclease was selected using NEBcutter V3.0 and produced by New England Biolabs (NEB). Figure 1 and Table S1 list the specific details, including primer sequences, restriction endonucleases, and product sizes. The specific PCR protocol was the same as that used in our previous studies [21]. The PCR-amplified product was digested with restriction endonuclease at 37 °C for at least 30 min. Genotypes were distinguished by 2.5% gel electrophoresis of the digestion product.
Fig. 1.
Polymorphism for AXIN1 rs13337493 and rs9921222 were genotyped by PCR-RFLP. PCR-RFLP polymerase chain reaction-restriction fragment length polymorphism
Statistical Analysis
Quantitative data, including age, scale scores, DAT-SPECT SBR, and CSF levels, are expressed as mean ± standard deviation (SD). Qualitative data are expressed as proportions, including sex, genotype distribution, cohort, and other binary response variables. The independent samples t test and chi-square test were used to examine demographic characteristics and compare variables between the different groups. Genotypic associations were further examined using three conventional genetic models: dominant, recessive, and additive [22]. Under the assumption that carrying allele D increases disease risk, three genetic models are commonly used: In the dominant model, individuals with either one or two copies of the D allele (ND or DD genotypes) are classified as having elevated risk; In the recessive model, risk is significantly increased only in those carrying two copies of the D allele (DD genotype); In the additive model, risk increases proportionally with the number of D alleles carried, reflecting a gene dosage effect [23]. This study applied logistic and linear regression modeling to analyze the correlation of AXIN1 polymorphisms with clinical phenotypes and biomarkers of PD, with age and sex as covariates. Before performing linear regression analysis, we log-transformed the data to follow a normal distribution. In addition, we used HWE in the control group to avoid population selection bias.
To account for multiple testing of the two independent SNPs, Bonferroni correction was applied. The corrected significance threshold was set at Pc < 0.05, which corresponded to a raw P value threshold of 0.025. Odds ratios (OR) and regression coefficients (β) were used to assess the relevance of qualitative and quantitative data, respectively. A 95% confidence interval (95% CI) was used to assess the statistical significance of the values. All statistical analyses were performed using the IBM SPSS Statistics software (version 26.0) and RStudio (4.3.3). The statistical power for this study was calculated using the online Genetic Association Study (GAS) Power Calculator.
Results
Participant Characteristics
Tables S2 and S3 list the basic characteristics of Northern Han Chinese and white participants. A total of 479 patients with PD and 461 HCs from the ethnic Northern Han Chinese population, corresponding to age and sex, were included in our study. Patients with PD (60.7 ± 9.9 years; 52% male) and HCs (60.2 ± 10.7 years; 55.3% male) were well matched in terms of age and sex (P = 0.460 and P = 0.327, respectively). In addition, 803 white participants from the PPMI database were analyzed, including 606 patients with PD and 197 HCs. No significant differences were observed in age (P = 0.296) or sex distribution (P = 0.329) between the two groups. Compared with healthy cohorts, patients with PD displayed a decrease in DAT-SBR in the bilateral caudate (P < 0.001), putamen (P < 0.001), and anterior putamen regions (P < 0.001). Patients with PD showed worse motor and nonmotor functions (P < 0.001) on these scales (MDS-UPDRS and MoCA).
Association of AXIN1 Polymorphisms with the Risk of PD in Northern Han Chinese Population
Neither loci deviated from HWE (Prs13337493 = 0.750, Prs9921222 = 0.250). Both loci showed distinct allele and genotype distributions in PD and control groups (Table 1). The frequency of the A allele of rs13337493 was significantly higher in patients compared to controls (OR 1.320, 95% CI 1.052, 1.653, Pc = 0.036), suggesting an association with elevated PD risk. The genetic variant rs9921222 was associated with an increased risk of PD in the allelic models. The minor allele T conferred higher susceptibility to the disease (OR 1.351, 95% CI 1.045, 1.747, Pc = 0.042).
Table 1.
Association of AXIN1 polymorphisms with PD risk under different genetic models in Northern Han Chinese
| SNPs | PD | HC | OR (95% CI) | P | Pc | Statistical ower |
|---|---|---|---|---|---|---|
| rs13337493 (G > A) | N = 479 | N = 461 | ||||
| Allele frequency, n (%) | ||||||
| Major allele G | 744 (77.7) | 757 (82.1) | Reference | |||
| Minor allele A | 214 (22.3) | 165 (17.9) | 1.320 (1.052, 1.653) | 0.018 | 0.036* | 0.658 |
| Genotype frequency, n (%) | ||||||
| Dominant | ||||||
| GG | 288 (60.1) | 309 (67.0) | Reference | |||
| GA + AA | 191 (39.9) | 152 (33.0) | 1.350 (1.030, 1.760) | 0.028 | 0.056 | 0.394 |
| Recessive | ||||||
| GG + GA | 456 (95.2) | 448 (97.2) | Reference | |||
| AA | 23 (4.8) | 13 (2.8) | 1.780 (0.890, 3.560) | 0.113 | 0.226 | 0.229 |
| Additive | ||||||
| GG | 288 (60.1) | 309 (67) | Reference | |||
| GA | 168 (35.1) | 139 (30.2) | 1.290 (0.980, 1.710) | 0.065 | 0.130 | 0.570 |
| AA | 23 (4.8) | 13 (2.8) | 1.900 (0.944, 3.818) | 0.068 | 0.136 | 0.999 |
| HWE | P = 0.750 | |||||
| rs9921222 (C > T) | N = 479 | N = 382 | ||||
| Allele frequency, n (%) | ||||||
| Major allele C | 776 (81.0) | 651 (85.2) | Reference | |||
| Minor allele T | 182 (19.0) | 113 (14.8) | 1.351 (1.045, 1.747) | 0.021 | 0.042* | 0.686 |
| Genotype frequency, n (%) | ||||||
| Dominant | ||||||
| CC | 311 (64.9) | 275 (72.0) | Reference | |||
| CT + TT | 168 (35.1) | 107 (28.0) | 1.390 (1.040, 1.860) | 0.027 | 0.054 | 0.532 |
| Recessive | ||||||
| CC + CT | 465 (97.1) | 376 (98.4) | Reference | |||
| TT | 14 (2.9) | 6 (1.6) | 1.870 (0.710, 4.910) | 0.082 | 0.164 | 0.188 |
| Additive | ||||||
| CC | 311 (64.9) | 275 (72.0) | Reference | |||
| CT | 154 (32.2) | 101 (26.4) | 1.350 (1.000, 1.820) | 0.039 | 0.078 | 0.684 |
| TT | 14 (2.9) | 6 (1.6) | 2.040 (0.770, 5.390) | 0.061 | 0.122 | 1.000 |
| HWE | P = 0.420 | |||||
Categorical variables were compared using Pearson’s χ2 test; * Pc < 0.05, statistical significance
PD Parkinson’s disease, HC healthy control, OR odds ratio, 95% CI 95% confidence interval, HWE Hardy–Weinberg equilibrium, PC Bonferroni correction
Stratified analysis revealed that the T allele of rs9921222 was a risk factor for PD in the male cohort (OR 1.504, 95% CI 1.058, 2.139, Pc = 0.044) (Table 2).
Table 2.
Association of AXIN1 polymorphisms with PD risk in sex-based subgroups in Northern Han Chinese
| SNPs | Male | OR (95% CI) | P | Pc | Female | OR (95% CI) | P | Pc | ||
|---|---|---|---|---|---|---|---|---|---|---|
| rs13337493 (G > A) | PD (N = 249) | HC (N = 255) | PD (N = 230) | HC (N = 206) | ||||||
| Allele frequency, n (%) | ||||||||||
| Major allele G | 386 (77.5) | 418 (82.0) | Reference | 358 (77.8) | 339 (82.3) | Reference | ||||
| Minor allele A | 112 (22.5) | 92 (18.0) | 1.318 (0.968, 1.818) | 0.085 | 0.170 | 102 (22.2) | 73 (17.7) | 1.323 (0.946, 1.848) | 0.060 | 0.120 |
| Genotype frequency, n (%) | ||||||||||
| Dominant | ||||||||||
| GG | 151 (60.7) | 173 (67.8) | Reference | 137 (59.6) | 136 (66.0) | Reference | ||||
| GA + AA | 98 (39.3) | 82 (32.2) | 1.369 (0.950, 1.974) | 0.920 | 1.000 | 93 (40.4) | 70 (34.0) | 1.319 (0.893, 1.949) | 0.164 | 0.328 |
| Recessive | ||||||||||
| GG + GA | 235 (94.4) | 245 (96.1) | Reference | 221 (96.1) | 203 (98.5) | Reference | ||||
| AA | 14 (5.6) | 10 (3.9) | 1.345 (0.586, 3.085) | 0.483 | 0.966 | 9 (3.9) | 3 (1.5) | 2.756 (0.736, 10.320) | 0.203 | 0.406 |
| Additive | ||||||||||
| GG | 151 (60.7) | 173 (67.8) | Reference | 137 (59.6) | 136 (66.0) | Reference | ||||
| GA | 84 (33.7) | 72 (28.2) | 1.337 (0.911, 1.961) | 0.137 | 0.274 | 84 (36.5) | 67 (32.5) | 1.245 (0.835, 1.855) | 0.282 | 0.564 |
| AA | 14 (5.6) | 10 (4.0.) | 1.604 (0.692, 3.717) | 0.267 | 0.534 | 9 (3.9) | 3 (1.5) | 2.978 (0.789, 11.238) | 0.165 | 0.330 |
| HWE | P = 0.771 | P = 0.253 | ||||||||
| rs9921222 (C > T) | PD (N = 249) | HC (N = 219) | PD (N = 230) | HC (N = 163) | ||||||
| Allele frequency, n (%) | ||||||||||
| Major allele C | 402 (80.7) | 378 (86.3) |
Reference 1.635 (1.161–2.303) |
374 (81.3) | 273 (83.7) | Reference | ||||
| Minor allele T | 96 (19.3) | 60 (13.7) | 1.504 (1.058, 2.139) | 0.022 | 0.044* | 86 (18.7) | 53 (16.3) | 1.184 (0.813, 1.725) | 0.377 | 0.754 |
| Genotype frequency, n (%) | ||||||||||
| Dominant | ||||||||||
| CC | 162 (65.1) | 161 (73.5) | Reference | 149 (64.8) | 114 (69.9) | Reference | 0.284 | 0.568 | ||
| CT + TT | 87 (34.9) | 58 (26.5) | 1.491 (1.002, 2.218) | 0.048 | 0.096 | 81 (35.2) | 49 (30.1) | 1.265 (0.822, 1.945) | ||
| Recessive | ||||||||||
| CC + CT | 240 (96.4) | 217 (99.1) | Reference | 225 (97.8) | 159 (97.5) | Reference | 1.000 | 1.000 | ||
| TT | 9 (3.6) | 2 (0.9) | 4.744 (1.015, 22.179) | 0.062 | 0.124 | 5 (2.2) | 4 (2.5) | 0.883 (0.234, 3.341) | ||
| Additive | ||||||||||
| CC | 162 (65.1) | 161 (73.5) | Reference | 149 (64.8) | 114 (69.9) | Reference | ||||
| CT | 78 (31.3) | 56 (25.6) | 1.384 (0.922, 2.079) | 0.117 | 0.234 | 76 (33.0) | 45 (27.6) | 1.292 (0.831, 2.010) | 0.305 | 0.610 |
| TT | 9 (3.6) | 2 (0.9) | 4.472 (0.951, 21.021) | 0.079 | 0.158 | 5 (2.2) | 4 (2.5) | 0.956 (0.251, 3.642) | 1.000 | 1.000 |
| HWE | P = 0.486 | P = 0.984 | ||||||||
Categorical variables were compared using Pearson’s χ2 test; * Pc < 0.05, statistical significance
PD Parkinson’s disease, HC healthy control, OR odds ratio, 95% CI 95% confidence interval, HWE Hardy–Weinberg equilibrium, PC Bonferroni correction
Association of AXIN1 Polymorphisms with Clinical Characteristics of PD in Northern Han Chinese Population
To further evaluate the relationship between the two SNPs and clinical manifestations of PD, we assessed several non-motor and motor symptoms in some patients with PD. As shown in Table S4, rs13337493 was associated with a higher incidence of hyposmia (OR 2.071, 95% CI 1.083, 4.057, P = 0.030) and worse motor function (OR 1.932, 95% CI 1.041, 3.698, P = 0.041) in the PD. However, neither association remained statistically significant after the Bonferroni correction (Pc = 0.060; Pc = 0.082). Furthermore, rs9921222 showed no relationship with any of the clinical phenotypes.
Association of AXIN1 Polymorphisms with the Risk of PD in white Populations
The genotype distributions of rs13337493 and rs9921222 did not deviate from the HWE (Prs13337493 = 0.860, Prs9921222 = 0.112). As shown in Table 3, neither the genotypic nor allelic distributions of these loci differed significantly between the PD and HC groups. Similarly, the subgroup analysis showed no significant differences between the two groups (Table 4).
Table 3.
Association of AXIN1 polymorphisms with PD risk under different genetic models in white populations
| SNPs | PD | HC | OR (95% CI) | P | Pc | Statistical power |
|---|---|---|---|---|---|---|
| N = 606 | N = 197 | |||||
| rs13337493 (G > A) | ||||||
| Allele frequency, n (%) | ||||||
| Major allele G | 906 (74.8) | 288 (73.1) | Reference | 0.513 | 1.000 | 0.059 |
| Minor allele A | 306 (25.2) | 106 (26.9) | 0.918 (0.709, 1.187) | |||
| Genotype frequency, n (%) | ||||||
| Dominant | ||||||
| GG | 338 (55.8) | 106 (53.8) | Reference | |||
| GA + AA | 268 (44.2) | 91 (46.2) | 0.920 (0.670, 1.280) | 0.630 | 1.000 | 0.042 |
| Recessive | ||||||
| GG + GA | 568 (93.7) | 182 (92.4) | Reference | |||
| AA | 38 (6.3) | 15 (7.6) | 0.810 (0.440, 1.510) | 0.520 | 1.000 | 0.044 |
| Additive | ||||||
| GG | 338 (55.8) | 106 (53.8) | Reference | |||
| GA | 230 (37.9) | 76 (38.6) | 0.950 (0.680, 1.330) | 0.770 | 1.000 | 0.037 |
| AA | 38 (6.3) | 15 (7.6) | 0.790 (0.420, 1.500) | 0.478 | 0.956 | 0.362 |
| HWE | P = 0.860 | |||||
| rs9921222 (C > T) | ||||||
| Allele frequency, n (%) | ||||||
| Major allele C | 712 (58.7) | 230 (58.4) | Reference | |||
| Minor allele T | 500 (41.3) | 164 (41.6) | 0.985 (0.782, 1.240) | 0.897 | 1.000 | 0.028 |
| Genotype frequency, n (%) | ||||||
| Dominant | ||||||
| CC | 217 (35.8) | 60 (30.5) | Reference | |||
| CT + TT | 389 (64.2) | 137 (69.5) | 0.785 (0.556, 1.110) | 0.170 | 0.340 | 0.197 |
| Recessive | ||||||
| CC + CT | 495 (81.7) | 170 (86.3) | Reference | |||
| TT | 111 (18.3) | 27 (13.7) | 1.412 (0.896, 2.226) | 0.136 | 0.272 | 0.072 |
| Additive | ||||||
| CC | 217 (35.8) | 60 (30.5) | Reference | |||
| CT | 278 (45.9) | 110 (55.8) | 0.699 (0.487, 1.003) | 0.051 | 0.102 | 0.664 |
| TT | 111 (18.3) | 27 (13.7) | 1.137 (0.684, 1.890) | 0.621 | 1.000 | 0.104 |
| HWE | P = 0.112 | |||||
Categorical variables were compared using Pearson’s χ2 test; Pc < 0.05, statistical significance
PD Parkinson’s disease, HC healthy control, OR odds ratio, 95% CI 95% confidence interval, HWE Hardy–Weinberg equilibrium, PC Bonferroni correction
Table 4.
Association of AXIN1 polymorphisms with PD risk in sex-based subgroups in white populations
| SNPs | Male | OR (95% CI) | P | Pc | Female | OR (95% CI) | P | Pc | ||
|---|---|---|---|---|---|---|---|---|---|---|
| PD (N = 365) | HC (N = 127) | PD (N = 241) | HC (N = 70) | |||||||
| rs13337493 (G > A) | ||||||||||
| Allele frequency, n (%) | ||||||||||
| Major allele G | 551 (75.5) | 184 (72.4) | Reference | 355 (73.7) | 104 (74.3) | Reference | ||||
| Minor allele A | 179 (24.5) | 70 (27.6) | 0.854 (0.618, 1.179) | 0.337 | 0.674 | 127 (26.3) | 36 (25.7) | 1.033 (0.672, 1.588) | 0.881 | 1.000 |
| Genotype frequency, n (%) | ||||||||||
| Dominant | ||||||||||
| GG | 208 (57.0) | 68 (53.5) | Reference | 130 (53.9) | 38 (54.3) | Reference | ||||
| GA + AA | 157 (43.0) | 59 (46.5) | 0.870 (0.580, 1.305) | 0.501 | 1.000 | 111 (46.1) | 32 (45.7) | 1.014 (0.594, 1.730) | 1.000 | 1.000 |
| Recessive | ||||||||||
| GG + GA | 343 (94.0) | 116 (91.3) | Reference | 225 (93.4 | 66 (94.3) | Reference | ||||
| AA | 22 (6.0) | 11 (8.7) | 0.676 (0.318, 1.437) | 0.307 | 0.614 | 16 (6.6) | 4 (5.7) | 1.173 (0.379, 3.630) | 0.999 | 1.000 |
| Additive | ||||||||||
| GG | 208 (57.0) | 68 (53.5) | Reference | 130 (54.0) | 38 (54.3) | Reference | ||||
| GA | 135 (37.0) | 48 (37.8) | 0.919 (0.599, 1.411) | 0.701 | 1.000 | 95 (39.4) | 28 (40.0) | 0.992 (0.569, 1.728) | 1.000 | 1.000 |
| AA | 22 (6.0) | 11 (8.7) | 0.654 (0.302, 1.418) | 0.279 | 0.558 | 16 (6.6) | 4 (5.7) | 1.169 (0.369, 3.706) | 0.790 | 1.000 |
| HWE | P = 0.834 | P = 0.926 | ||||||||
| rs9921222 (C > T) | ||||||||||
| Allele frequency, n (%) | ||||||||||
| Major allele C | 434 (59.5) | 143 (56.3) | Reference | 278 (57.7) | 87 (62.1) | Reference | ||||
| Minor allele T | 296 (40.5) | 111 (43.7) | 0.887 (0.664, 1.184) | 0.415 | 0.830 | 204 (42.3) | 53 (37.9) | 1.205 (0.819, 1.773) | 0.345 | 0.690 |
| Genotype frequency, n (%) | ||||||||||
| Dominant | ||||||||||
| CC | 131 (35.9) | 35 (27.5) | Reference | 86 (35.7) | 25 (35.7) | Reference | ||||
| CT + TT | 234 (64.1) | 92 (72.5) | 0.680 (0.436, 1.060) | 0.087 | 0.174 | 155 (64.3) | 45 (64.3) | 1.001 (0.575, 1.745) | 0.996 | 1.000 |
| Recessive | ||||||||||
| CC + CT | 303 (83.0) | 108 (85.0) | Reference | 192 (79.7) | 62 (88.6) | Reference | ||||
| TT | 62 (17.0) | 19 (15.0) | 1.163 (0.665, 2.034) | 0.596 | 1.000 | 49 (20.3) | 8 (11.4) | 1.978 (0.888, 4.403) | 0.090 | 0.180 |
| Additive | ||||||||||
| CC | 131(35.9) | 35 (27.5) | Reference | 86 (35.7) | 25 (35.7) | Reference | ||||
| CT | 172 (47.1) | 73 (57.5) | 0.630 (0.396, 1.000) | 0.049 | 0.098 | 106 (44.0) | 37 (52.9) | 0.833 (0.465, 1.490) | 0.537 | 1.000 |
| TT | 62 (17.0) | 19 (15.0) | 0.872 (0.462, 1.645) | 0.672 | 1.000 | 49 (20.3) | 8 (11.4) | 1.781 (0.760, 4.250) | 0.190 | 0.380 |
| HWE | P = 0.166 | P = 0.587 | ||||||||
Categorical variables were compared using Pearson’s χ2 test; Pc < 0.05, statistical significance
PD Parkinson’s disease, HC healthy control, OR odds ratio, 95% CI 95% confidence interval, HWE Hardy–Weinberg equilibrium, PC Bonferroni correction
Association of AXIN1 Polymorphisms with PD Clinical Features and Biomarkers in white Populations
As shown in Table 5, we investigated the associations between both SNPs and various clinical features of PD. The rs13337493 variant was associated with worse motor function in white individuals, as assessed by the reduced scores in Hoehn & Yahr stage 3 (OR 2.775, 95% CI 1.195, 6.447, Pc = 0.036). In addition, analysis of CSF biomarkers revealed that rs13337493 might be correlated with increased DOPA levels (β = 0.040, 95% CI 0.007, 0.073, Pc = 0.038). Unfortunately, neither SNP was associated with cognitive impairment, RBD, olfactory disorders, or depression.
Table 5.
Association of AXIN1 polymorphisms with clinical manifestations and biomarkers in white populations
| Phenotype | rs13337493 | rs9921222 | ||||
|---|---|---|---|---|---|---|
| β/OR (95% CI)a | P | Pc | β/OR (95% CI) | P | Pc | |
| DATSCAN | ||||||
| Right caudate | 0.083 (− 0.007, 0.173) | 0.070 | 0.140 | − 0.039 (− 0.119, 0.041) | 0.343 | 0.686 |
| Left caudate | 0.053 (− 0.036, 0.142) | 0.246 | 0.492 | − 0.013 (− 0.093, 0.066) | 0.746 | 1.000 |
| Right putamen | 0.070 (− 0.015, 0.154) | 0.106 | 0.212 | − 0.015 (− 0.090, 0.061) | 0.702 | 1.000 |
| Left putamen | 0.048 (− 0.036, 0.131) | 0.265 | 0.530 | 0.002 (− 0.072, 0.077) | 0.951 | 1.000 |
| Right anterior putamen | 0.090 (2.000 × 10−4, 0.180) | 0.049 | 0.098 | − 0.015 (− 0.095, 0.066) | 0.720 | 1.000 |
| Left anterior putamen | 0.046 (− 0.042, 0.135) | 0.305 | 0.610 | 0.009 (− 0.070, 0.088) | 0.824 | 1.000 |
| Motor function | ||||||
| H&Y stage 1 | 0.918 (0.678, 1.242) | 0.578 | 1.000 | 1.007 (0.772, 1.315) | 0.957 | 1.000 |
| H&Y stage 2 | 0.948 (0.718, 1.252) | 0.706 | 1.000 | 1.162 (0.908, 1.485) | 0.232 | 0.464 |
| H&Y stage 3 | 2.775 (1.195, 6.447) | 0.018 | 0.036* | 0.299 (0.100, 0.893) | 0.031 | 0.061 |
| UPDRS PART I | − 0.212 (− 0.419, 0.006) | 0.044 | 0.088 | − 0.035 (− 0.218, 0.149) | 0.711 | 1.000 |
| UPDRS PART II | − 0.176 (− 0.736, 0.383) | 0.536 | 1.000 | − 0.090 (− 0.586, 0.406) | 0.722 | 1.000 |
| UPDRS PART III | − 0.466 (− 1.806, 0.873) | 0.495 | 0.990 | 0.240 (− 0.947, 1.428) | 0.691 | 1.000 |
| Nonmotor function | ||||||
| MoCA | − 0.052 (− 0.352, 0249) | 0.736 | 1.000 | 0.180 (− 0.086, 0.446) | 0.185 | 0.370 |
| RBD | − 0.029 (− 0.318, 0.260) | 0.844 | 1.000 | 0.053 (− 0.203, 0.309) | 0.686 | 1.000 |
| ESS | − 0.126 (0.559, 0.308) | 0.569 | 1.000 | − 0.028 (− 0.412, 0.356) | 0.886 | 1.000 |
| Olfactory disorders | 1.116 (0.423, 2.966) | 0.822 | 1.000 | 0.381 (0.131, 0.958) | 0.053 | 0.106 |
| Depression | − 0.114 (− 0.417, 0.190) | 0.463 | 0.926 | − 0.182 (0.450, 0.087) | 0.184 | 0.368 |
| CSF level | ||||||
| α-Synuclein | − 0.029 (− 0.010, 0.069) | 0.138 | 0.276 | 4.000 × 10−4 (− 0.035, 0.036) | 0.981 | 1.000 |
| Dopamine | 0.120 ( − 0.011, 0.250) | 0.072 | 0.144 | − 0.010 (− 0.127, 0.108) | 0.872 | 1.000 |
| DOPA | 0.040 (0.007, 0.073) | 0.019 | 0.038* | − 0.016 (− 0.046, 0.015) | 0.311 | 0.622 |
| DOPAC | 0.053 (0.005, 0.101) | 0.030 | 0.060 | − 0.015 (0.058, 0.029) | 0.508 | 1.000 |
| HVA | 0.02 (− 0.033, 0.073) | 0.456 | 0.912 | − 0.047 (− 0.094, − 0.001) | 0.046 | 0.092 |
| 3-MT | 0.017 (− 0.017, 0.051) | 0.319 | 0.638 | − 0.007 (− 0.037, 0.023) | 0.656 | 1.000 |
| Aβ | 0.007 (− 0.057, 0.071) | 0.826 | 1.000 | 0.065 (0.005, 0.126) | 0.035 | 0.070 |
| Aβ1–42 | 0.022 (− 0.001, 0.045) | 0.066 | 0.132 | 0.010 (− 0.010, 0.031) | 0.333 | 0.666 |
| P-tau | 0.001(− 0.017, 0.019) | 0.878 | 1.000 | − 0.001 (− 0.017, 0.015) | 0.872 | 1.000 |
| T-tau | − 0.001 (− 0.015, 0.015) | 0.991 | 1.000 | 0.004 (− 0.013, 0.022) | 0.627 | 1.000 |
Categorical variables were assessed by logistic regression analysis with age and sex as covariate; Continuous variable were evaluated by linear regression with age and sex as covariate
DATSCAN SBR striatal binding ratios of dopamine transporter scan, H&Y Hoehn&Yahr, UPDRS Unified Parkinson’s Disease Rating Scale, Part I non-motor experiences of daily living, Part II motor experiences of daily living, Part III motor examination, MoCA Montreal Cognitive Assessment, ESS Epworth Sleepiness Scale, RBD rapid eye movement behavior disorder, DOPA dihydroxyphenylalanine, DOPAC dihydroxyphenylacetic acid, HVA homovanillic acid, 3-MT 3-methoxytyrosine, Aβ beta-amyloid, Aβ1–42 beta-amyloid1–42, P-tau total tau, T-tau phosphorylated at threonine, OR odds ratio, 95% CI 95% confidence interval, Pc Bonferroni correction
aORs are used for categorical outcomes, including NHY stage; β are reported for other continuous variables; * Pc < 0.05, statistical significance
Discussion
This study innovatively revealed the potential correlation of AXIN1 genetic polymorphisms (rs13337493 and rs9921222) with PD in Northern Han Chinese and white populations and further explored their associations with PD movement disorders and biomarkers. First, we confirmed that the two AXIN1 loci might serve as genetic risk factors for PD in the Northern Han Chinese . Subgroup analysis further showed that the T allele of rs9921222 remained a significant risk factor in male patients, suggesting gender-related genetic influence. Second, although these two loci showed no significant association with PD risk in white populations, rs13337493 was associated with severe motor symptoms and enhanced CSF DOPA levels.
AXIN1 polymorphisms (rs13337493 and rs9921222) are associated with increased PD susceptibility in Northern Han Chinese, and with disease severity in the white populations. The underlying mechanism may involve the dysregulation of AXIN1-mediated negative control of the Wnt/β-catenin signaling pathway [24]. The Wnt/β-catenin pathway itself exerts neuroprotective effects and plays a crucial role in sustaining the survival of dopaminergic neurons, synaptic function, and mitochondrial homeostasis [9, 25]. Consequently, enhanced AXIN1 function leading to downregulation of this pathway would directly increase neuronal vulnerability [26]. Recent studies have substantiated the increased AXIN1 protein expression in PD models [27]. Our findings now provide direct genetic evidence supporting this link: First, we identified that the T allele of rs9921222 was significantly related to increased PD risk in the Northern Han Chinese. The intronic SNP rs9921222 and its high-LD proxy variants significantly influenced the expression of AXIN1 in multiple brain regions, particularly in the cerebellum and the cerebellar hemisphere (Table S5) [28]. To date, studies have reported that the TT genotype of rs9921222 might contribute to reduced bone mineral density and an increased osteoporosis risk [29]. As lower bone mineral density and osteoporosis play important roles in the risk of PD [16], these findings suggest that rs9921222 may act as a shared risk variant, influencing both bone and brain vulnerability through a common regulatory mechanism. Second, regarding rs13337493, we found that the A allele was associated with increased PD risk in the Northern Han Chinese. Interestingly, this seemed to contrast with a Taiwanese cohort in which rs13337493 was not significant, but two SNPs in high LD with it (rs758033 and rs2361988) exhibited a protective effect on PD [30]. The underlying reasons for such discrepancies may be attributed to multiple factors, including differences in genetic backgrounds, environmental exposures, epigenetic modifications, and gene–gene or gene–environment interactions. Moreover, variations in linkage disequilibrium patterns across ethnic groups may result in distinct functional consequences of the same polymorphism [6]. The potential mechanisms underlying this conflicting effect in various ethnic populations remain to be elucidated.
Male sex is a well-established risk factor for PD, while endogenous estrogen is thought to exert a protective effect on dopaminergic neurons [31]. To investigate whether the genetic effects of AXIN1 are modulated by sex-specific factors, we performed sex-stratified analyses. Notably, the association between rs9921222 and PD risk exhibited a potential sex-specific effect, which appeared to be more prominent in the male cohort. This potential mechanism may be attributed to the role of rs9921222 as a binding site for GATA4 and estrogen receptor-α (ERα) [15]. Furthermore, recent studies have shown that ERα can suppress AXIN1 expression while increasing β-catenin levels in ER-positive cancer cells [32], further supporting the notion that rs9921222 modulates AXIN1 transcription through its effects on ERα binding. ERα mediates the neuroprotective effects of estrogen in PD. These effects include prevention of striatal DA loss and nigral dopaminergic cell death [33]. Physiologically, circulating estradiol (E2) levels in adult men are markedly lower than those in premenopausal women, potentially resulting in the reduced ligand-dependent activation of ERα in men [31]. It is plausible that in men, substantially lower estrogen levels attenuate ERα-mediated repression of AXIN1, thereby enhancing its inhibitory effect on the Wnt/β-catenin pathway and ultimately amplifying the pathogenic impact of rs9921222 in male PD.
We also explored the effects of the two AXIN1 variants on PD in a white populations. Although no significant association with PD susceptibility was observed, our results indicated that the rs13337493 variant was linked to motor severity in white populations. This indirectly strengthens the biological credibility of our finding that the two AXIN1 loci confer increased PD vulnerability in the Northern Han Chinese. Mechanistically, the association of AXIN1 with PD motor function might be explained by the pivotal role of AXIN1 in modulating the neuro-regeneration and differentiation of neural stem cells [34]. First, AXIN1 reduces neuronal apoptosis and promotes neuronal survival. Studies have demonstrated that AXIN1 protein expression levels are increased in PD, and that downregulation of AXIN1 could alleviate dopaminergic neuronal apoptosis [35]. Upregulation of AXIN1 has been observed in the penumbra region after photothrombotic infarction, suggesting that it played a regulatory role in neuronal survival and apoptosis [36]. Second, AXIN1 promoted neural stem cell proliferation and hippocampal neurogenesis [37]. Importantly, these processes can contribute to the generation of dopaminergic neurons, whose progressive loss disrupts DA signal projections to the basal ganglia, ultimately leading to motor dysfunction [38, 39]. Thus, the regulatory functions of AXIN1 in neuronal survival and neurogenesis provide a plausible link between AXIN1 genetic variation and motor severity in PD.
CSF DOPA, an immediate precursor of DA, reflects the presynaptic capacity of dopaminergic neurons in the nigrostriatal pathway [40]. Its reduction in patients with PD directly mirrors the progressive degeneration of these vulnerable neurons, which is the core pathological basis of the motor symptoms [40]. Interestingly, we observed that the AXIN1 variant rs13337493 was correlated with an increase in CSF DOPA levels, which appeared to contradict its association with accelerated motor progression. We hypothesized that elevated CSF DOPA may represent a partial compensatory mechanism involving the remaining dopaminergic neurons to counteract ongoing neurodegeneration. Nevertheless, this compensatory increase appears to be inadequate for fully preserving the normal motor control. Thus, AXIN1 may influence motor function by modulating the functional integrity of dopaminergic neurons, and its polymorphisms may serve as potential genetic markers for predicting motor progression or dopaminergic dysfunction in PD.
However, this study has several limitations. First, the sample size of the discovery cohort was relatively small. Future research should expand the sample size and include more diverse ethnic populations. Second, although our findings reinforce the pivotal role of AXIN1 in modulating dopaminergic neuronal function, we did not investigate its expression levels or mechanistic role in PD. Therefore, further studies are warranted to explore the transcription and expression patterns of AXIN1 as well as its regulatory effects on dopaminergic neurons in PD. This should involve a combination of in vitro and in vivo approaches, including the modulation of AXIN1 expression (e.g., knockdown or overexpression) and the introduction of patient-derived risk variants (e.g., knock-in), to definitively link AXIN1 dysregulation to impaired neuronal integrity via its impact on Wnt/β-catenin signaling and dopaminergic neuron survival.
Conclusion
In general, our study highlighted the association of AXIN1 polymorphisms rs13337493 and rs9921222 with PD in the Northern Han Chinese and white populations. Our findings suggested a gatekeeper role for AXIN1: its polymorphisms contribute to increased susceptibility to PD and accelerated motor progression, yet may concurrently trigger a compensatory presynaptic response, as evidenced by elevated CSF DOPA levels, to counteract neurodegeneration. Further studies employing larger multicenter cohorts and functional genetic analyses are warranted to fully elucidate this dual mechanism and its population-specific nuances.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We sincerely appreciate the Parkinson’s Progression Markers Initiative (PPMI) database for providing the invaluable data that made this study possible. We also sincerely thank all the subjects for their kind approval to participate in this study. Their participation is fundamental in advancing our understanding of Parkinson’s.
Author Contributions
Zhen Kong: Conceptualization, Methodology, Investigation, Formal Analysis, Writing—Original Draft. Ran Yu: Methodology, Investigation, Formal Analysis, Critical Revision, data curation. Chengqian Li: Methodology, Investigation, Writing—Review & Editing. Qiqing He: Methodology, Investigation, Writing—Review & Editing. Yuting Zhou: Methodology, Investigation, Study Design, Critical Revision. Xue Zhang: Argumentation Framing, Investigation, Funding Acquisition. Yaqing Li: Writing—Review & Editing, Funding Acquisition, Supervision. Anmu Xie: Conceptualization, Supervision, Funding Acquisition. Binghui Hou: Conceptualization, Supervision. All authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.
Funding
This work was funded by the National Natural Science Foundation of China (Grant Nos. 82471459 and 82501680), China Postdoctoral Science Foundation (No. 2023M741857 and No. 2023TQ0165), and Shandong Postdoctoral Science Foundation (No. SDBX2023050 and No. SDCX-ZG-202400038), Qingdao Postdoctoral Science Foundation (no. QDBSH20230202039), and Natural Science Foundation of Qingdao Municipality (23-2-1-130-zyyd-jch). The rapid service fee for this journal will be covered by the aforementioned grant.
Data Availability
Data derived from the Northern Han Chinese participants supporting this study’s findings cannot be provided because of privacy or ethical restrictions. White population data supporting the findings of this study are available from the Parkinson’s Progression Markers Initiative dataset (https://www.ppmi-info.org).
Declarations
Conflict of Interest
Zhen Kong, Ran Yu, Chengqian Li, Qiqing He, Yuting Zhou, Xue Zhang, Yaqing Li, Anmu Xie and Binghui Hou have no conflict of interest.
Ethical Approval and Informed Consent
This retrospective chart review study, involving human participants, was conducted in accordance with the ethical standards of the institutional and national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the ethics committee of the Affiliated Hospital of Qingdao University (QYFY-WZLL-28612). All the PPMI data in this study were approved by the institutional review board of each PPMI site. Informed consent was obtained from all participants.
Contributor Information
Yaqing Li, Email: LiYaqingqy@126.com.
Anmu Xie, Email: xieanmu@163.com.
Binghui Hou, Email: drhoubh@163.com, Email: drhoubh@qdu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data derived from the Northern Han Chinese participants supporting this study’s findings cannot be provided because of privacy or ethical restrictions. White population data supporting the findings of this study are available from the Parkinson’s Progression Markers Initiative dataset (https://www.ppmi-info.org).