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Erschienen in: BMC Psychiatry 1/2023

Open Access 01.12.2023 | Research

CYP2C19-rs4986893 confers risk to major depressive disorder and bipolar disorder in the Han Chinese population whereas ABCB1-rs1045642 acts as a protective factor

verfasst von: Ting Zhang, Qingmin Rao, Kangguang Lin, Yongyin He, Jintai Cai, Mengxin Yang, Ying Xu, Le Hou, Yulong Lin, Haiying Liu

Erschienen in: BMC Psychiatry | Ausgabe 1/2023

Abstract

Background

Genetic risks may predispose individuals to major mood disorders differently. This study investigated the gene polymorphisms of previously reported candidate genes for major depressive disorder (MDD) and bipolar disorder (BPD) in the Han Chinese population.

Methods

Twenty loci of 13 candidate genes were detected by MALDI-TOF mass spectrometry in 439 patients with MDD, 600 patients with BPD, and 464 healthy controls. The distribution of genotypes in alleles, Hardy-Weinberg equilibrium, and genetic association were analyzed using the PLINK software. The linkage of disequilibrium and haplotype analyses were performed using the Haploview software.

Results

Out of the 20 loci analyzed, CYP2C19-rs4986893, ABCB1-rs1045642, and SCN2A-rs17183814 passed Bonferroni correction; their statistical powers were > 55%. The minor allele frequencies (MAF) of CYP2C19-rs4986893 in the MDD group (0.0547) and BPD group (0.0533) were higher than that of the control group (0.0259, P < 0.05), leading to the odds ratios (ORs) of MDD (2.178) and BPD (2.122), respectively. In contrast, the lower MAFs of ABCB1-rs1045642 were observed in both MDD (0.3599, OR = 0.726) and BPD (0.3700, OR = 0.758) groups than controls (0.4364, P < 0.05). The MDD group had a higher MAF of SCN2A-rs17183814 than controls (0.1743 vs. 0.1207, OR = 1.538, P < 0.05). Moreover, a G-A haplotype composed by CYP2C19-rs4986893 and -rs4244285 was associated with BPD (OR = 1.361, P < 0.01), and the A-G haplotype increased the risks to both MDD (OR = 2.306, P < 0.01) and BPD (OR = 2.332, P < 0.001). The CYP2C19 intermediate metabolizer and poor metabolizer (IM&PM) status was related to the raised risk of both MDD (OR = 1.547, P < 0.01) and BPD (OR = 1.808, P < 0.001).

Conclusion

Our data indicate that the impaired CYP2C19 metabolism caused by the haplotypes integrated by CYP2C19 alleles might confer the risk to MDD and BPD, whereas the ABCB1-rs1045642 T allele serves as a protective factor.
Hinweise
Yulong Lin and Haiying Liu contributed equally to this work and share correspondence authorship.

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Background

Major depressive disorder (MDD) and bipolar disorder (BPD) are chronic and recurrent mood disorders affecting approximately 13% of the world’s population [1, 2]. Due to the substantial increase in healthcare expenditure and increasing suicide rate from MDD and BPD, these mood disorders form a tremendous socioeconomic burden on families and society. MDD is characterized by significant and persistent depressed mood, waned interest, slowed thinking, and cognitive impairment [3], whereas BPD is characterized by extreme mood state swing between mania and depression [4]. Previous studies have suggested that these two mood disorders do not only have overlapping symptoms but also share mechanisms [5], including metabolic dysregulation, insulin resistance, immune disorders, and neural signal transduction pathway malfunction. Several genes closely related to pathological mechanisms have been identified in previous studies. For example, polymorphisms of CYP2C19, CYP2C9, NAT2, UGT1A9, and ABCB1 related to the activation or detoxification of drugs and endogenous substances have emerged as major genetic factors in several psychiatric disorders [68].
Since genetic factors with accumulative multiple variants clearly play a critical role in the etiology and pathology of polygenic mood disorders [9], characterization of the genetic features involved in etiological mechanism is particularly required. However, the potential genetic associations remain unclear, and the results of genome-wide association studies (GWAS) on mood disorders are rarely repeatable [9]. Furthermore, several studies failed to identify gene-disease correlations in patients with mood disorders [10, 11]. One probable reason for the unsuccessful generation of repeatable results to demonstrate the main effects of these genes on these diseases is that allelic frequencies may vary in different racial and ethnic backgrounds. The results from previous studies on particular genetic backgrounds cannot be applied to other populations. In this study, we investigated the candidate genes in the Han Chinese population. An effective method was developed to simultaneously analyze the pathogenic effect of these specific genes for constructing a custom single nucleotide polymorphism (SNP) detection package covering loci selected based on current assumptions and proofs from previous studies. Twenty loci from LEPR, SCN2A, SCN1A, UGT1A9, GSK3B, HLA-B, ABCB1, NAT2, CYP2C19, CYP2C9, ANKK1, SH2B1, and INSR were present in the SNP detection array.
In terms of the study on genetic risk could be pathogenesis support and diagnostic reference for psychiatric diseases, this study verifies the association between SNPs in 13 candidate genes and the risk of mood disorders, including MDD and BPD, in the Han Chinese population via MALDI-TOF mass spectrometry. Additionally, the effect of haplotypes and metabolism statuses were analyzed.

Materials and methods

Study participants

All participants were Han Chinese living in Guangdong Province, Southern China. The case group included 439 patients with MDD (158 males and 281 females) and 600 patients with BPD (258 males and 342 females) hospitalized at the Affiliated Brain Hospital of Guangzhou Medical University from February 2020 to September 2021. The diagnosis for each patient was strictly based on the DSM-V criteria [12, 13] for MDD and BPD, and was agreed by at least two independent and experienced psychiatrists. Patients were excluded if they were diagnosed with primary or comorbid physical diseases or other mental illnesses, such as schizoaffective disorder, schizophrenia, dementia, alcohol or drug addiction, post-traumatic stress disorder, obsessive-compulsive disorder, panic disorder, and anxiety disorder. The control group consisted of 464 adults (196 males and 268 females) who underwent annual physical examinations, and those with personal or family history of major psychiatric disorders were excluded. The corresponding mean ages of the control, MDD, and BPD groups were 30.7 ± 12.6 years, 29.8 ± 14.9 years, and 30.5 ± 14.7 years, respectively. Age and gender were matched between the case and control groups (P > 0.05). Demographic and clinical data of MDD and BPD cases are listed in Table 1.
Table 1
Demographic and clinical data in all groups
Characteristic
Control (N = 464)
MDD (N = 439)
BPD (N = 600)
χ2 or F
P value
Age (Mean ± SD)
30.7 ± 12.6
29.8 ± 14.9
30.5 ± 14.7
F = 0.431
0.650
Gender (N (%) female)
268 (57.8)
281 (64.0)
342 (57.0)
χ2 = 5.804
0.055
Number of hospitalizations (Mean ± SD)
----
1.5 ± 1.4
1.9 ± 1.8
----
----
Family history of mental illness (N (%))
----
72 (16.4)
122 (20.3)
----
----
Age at onset (Mean ± SD)
----
22.1 ± 13.6
23.1 ± 11.4
----
----
Education experience (N (%))
   
F = 5.630
0.466
 Primary school
33 (7.1)
35 (8.0)
44 (7.3)
  
 Junior school
134 (28.9)
124 (28.2)
146 (24.3)
  
 Senior school
130 (28.0)
135 (30.8)
177 (29.5)
  
 College/university
167 (36.0)
145 (33.0)
233 (38.8)
  
Marriage status (N (%))
   
χ2 = 0.964
0.617
 Married
148 (31.9)
131 (29.8)
175 (29.2)
  
 Single
316 (68.1)
308 (70.2)
425 (70.8)
  
Employment status (N (%))
   
χ2 = 3.512
0.173
 Employed
205 (44.2)
167 (38.0)
248 (41.3)
  
 Unemployed/Retired
259 (55.8)
272 (62.0)
352 (58.7)
  
MDD Major depressive disorder, BPD Bipolar disorder

DNA selection

Based on previous studies on risk variants contributing to psychiatry disorders, 13 genes containing 20 SNPs were selected for further analysis. CYP2C19 (rs12248560, rs4986893, rs4244285) was obtained from the findings of a 4-week prospective study by Strumila et al. [7]. CYP2C9 (rs1057910) was chosen from a case-control study in a European population [1416]. NAT2 (rs1041983, rs1801280, rs1799929, rs1799930, rs1208) was obtained from a feature review [6]. UGT1A9 (rs2741049) was selected based on a study by Cecil et al. [17]. ABCB1 (rs1045642) was chosen based on mice knockout and genetic association studies [8, 18]. LEPR (rs1137101), INSR (rs2396185), SH2B1 (rs3888190), and GSK3B were the candidate genes because of their involvement in the insulin resistance process [1925]. SCN1A (rs2298771, rs3812718) and SCN2A (rs17183814) were selected due to their relation to the role of the signaling pathway in emotional disorders [26, 27]. Since ANKK1 (rs1800497) is involved in dopaminergic pathway regulation, it might be a risk variant of MDD and BPD [28]. Since immune reaction is shaped by diverse human leukocyte antigen loci to some extent, HLA-B (rs2442736) is postulated to be a genetic risk factor for mood disorders [29].

DNA extraction and SNP genotyping

EDTA-K2 anticoagulant blood, 2 mL, was collected from all participants for SNP detection. DNA was extracted from 0.5 mL of blood using the Blood Genomic DNA Isolation Kit (Shanghai BaiO Technology Co. Ltd), following the manufacturer’s manual. The samples were kept at − 80℃ until further analysis.
DNA samples were diluted to 5 ng/uL and then used for amplification. After the multiplex PCRs were performed, the products were treated with shrimp alkaline phosphatase to remove excess dNTPs and used as templates for the primer extension reactions using iPLEX mixture. The final products were automatically spotted on the MassARRAY SpectroCHIP. The target panels were inserted into the MALDI-TOF mass spectrometer, and SNP data were auto-analyzed by this instrument. Shanghai Kangli Medical Research Institute assisted with SNP genotyping. Twenty loci from LEPR, SCN2A, SCN1A, UGT1A9, GSK3B, HLA-B, ABCB1, NAT2, CYP2C19, CYP2C9, ANKK1, SH2B1, and INSR genes were typed.

Statistical analysis

Age difference was compared using the student’s t-test, and gender and haplotype were analyzed with Pearson’s Chi-square test using the IBM SPSS (IBM, Armonk, NY) version 20. Hardy-Weinberg equilibrium analysis, genotype and allele frequencies, and association tests were conducted using the PLINK software version 1.9 (https://​www.​cog-genomics.​org/​plink) [30]. Exact test was used for Hardy-Weinberg equilibrium analysis in PLINK software. The linkage disequilibrium and haplotype analysis were performed using the Haploview software (Broad, Cambridge, MA) version 4.2 [31]. The P values of alleles were corrected by Bonferroni correction, in which the adjusted P values acquired were multiplied by SNP amount. Statistical power was calculated using the PS program on line (https://​statcomp2.​app.​vumc.​org/​ps/​).

Results

Hardy-Weinberg equilibrium analysis of 20 SNPs in all groups

Hardy-Weinberg equilibrium of 20 SNPs was tested (Table 2). The SNPs passed the Hardy-Weinberg equilibrium test in all groups, showed that sample sets were representative of the population. The GRCh38 human reference genome was used for genetic variant location. ID number and position of SNPs are shown in Table 2.
Table 2
Hardy-Weinberg equilibrium analysis of 20 SNPs in all groups
Gene
SNP ID
Position
P value
Control
(N = 464)
MDD
(N = 439)
BPD
(N = 600)
LEPR
rs1137101
chr1:65592830
0.330
0.999
0.430
SCN2A
rs17183814
chr2:165295879
0.829
0.510
0.999
SCN1A
rs2298771
chr2:166036278
0.768
0.188
0.803
SCN1A
rs3812718
chr2:166053034
0.699
0.620
0.307
UGT1A9
rs2741049
chr2:233673188
0.509
0.082
0.412
GSK3B
rs334558
chr3:120094435
0.999
0.999
0.728
HLA-B
rs2442736
chr6:31378844
0.615
0.999
0.642
ABCB1
rs1045642
chr7:87509329
0.059
0.605
0.335
NAT2
rs1041983
chr8:18400285
0.707
0.209
0.805
NAT2
rs1801280
chr8:18400344
0.065
0.606
0.241
NAT2
rs1799929
chr8:18400484
0.144
0.063
0.257
NAT2
rs1799930
chr8:18400593
0.526
0.224
0.217
NAT2
rs1208
chr8:18400806
0.354
0.239
0.257
CYP2C19
rs12248560
chr10:94761900
0.999
0.999
0.999
CYP2C19
rs4986893
chr10:94780653
0.999
0.373
0.402
CYP2C19
rs4244285
chr10:94781859
0.123
0.648
0.999
CYP2C9
rs1057910
chr10:94981296
0.999
0.999
0.999
ANKK1
rs1800497
chr11:113400106
0.104
0.377
0.867
SH2B1
rs3888190
chr16:28878165
0.188
0.156
0.082
INSR
rs2396185
chr19:7246650
0.054
0.545
0.576
The GRCh38 human reference genome was used for genetic variant location

Association analysis of genetic predisposition in MDD and BPD

The genotype distribution and minor allele frequencies (MAF) of each SNP are listed in Table 3. After Bonferroni correction, only CYP2C19-rs4986893, ABCB1-rs1045642, and SCN2A-rs17183814 were passed for subsequent analysis; their statistical powers were greater than 55%. The MAF of CYP2C19-rs4986893 in the MDD group (0.0547) and BPD group (0.0533) were higher than that of the control group (0.0259, P < 0.05). With the control group as reference, participants with the CYP2C19-rs4986893 A allele had odds ratios (ORs) of 2.178 and 2.122 for MDD and BPD, respectively. In contrast, both MDD (0.3599) and BPD (0.3700) groups had lower MAFs of ABCB1-rs1045642 than the control (0.4364, P < 0.05) group. Therefore, participants with the ABCB1-rs1045642 T allele had ORs of 0.726 and 0.758 for MDD and BPD, respectively. The MAF of the SCN2A-rs17183814 in patients with MDD (0.1743) was higher than that of the controls (0.1207, P < 0.05). Participants with the SCN2A-rs17183814 A allele had a 1.538-fold greater risk to suffer from MDD than those without it.
Table 3
Genotype distribution of SNPs in all groups
Gene
SNP ID
 
Genotype distribution
MAF
χ2
P
OR
95%CI
Statistical power
LEPR
rs1137101
 
AA
AG
GG
      
  
Control
11
105
348
0.1369
     
  
MDD
5
86
348
0.1093
3.156
0.076
0.774
0.584–1.027
28%
  
BPD
10
120
470
0.1167
1.943
0.163
0.833
0.644–1.077
18%
SCN2A
rs17183814
 
GG
AG
AA
      
  
Control
359
98
7
0.1207
     
  
MDD
297
131
11
0.1743
10.34
0.001
1.538
1.181–2.001
62%
  
BPD
416
168
16
0.1667
8.841
0.003
1.457
1.136–1.869
56%
SCN1A
rs2298771
 
AA
AG
GG
      
  
Control
387
73
4
0.0873
     
  
MDD
357
75
7
0.1014
1.049
0.306
1.18
0.860–1.618
10%
  
BPD
498
97
5
0.0892
0.023
0.879
1.024
0.757–1.385
12%
SCN1A
rs3812718
 
GG
AG
AA
      
  
Control
72
228
164
0.4009
     
  
MDD
74
206
159
0.4032
0.01
0.92
1.01
0.837–1.219
1%
  
BPD
100
275
225
0.3958
0.055
0.814
0.979
0.822–1.166
5%
UGT1A9
rs2741049
 
TT
CT
CC
      
  
Control
152
221
91
0.4343
     
  
MDD
149
198
92
0.4351
0.001
0.972
1.003
0.833–1.209
5%
  
BPD
167
309
124
0.4642
1.889
0.169
1.128
0.950–1.341
16%
GSK3B
rs334558
 
TT
CT
CC
      
  
Control
56
212
196
0.3491
     
  
MDD
54
200
185
0.3508
0.005
0.941
1.007
0.830–1.222
5%
  
BPD
83
287
230
0.3775
1.816
0.178
1.13
0.946–1.351
15%
HLA-B
rs2442736
 
GG
GC
CC
      
  
Control
422
42
0
0.0453
     
  
MDD
385
53
1
0.0626
2.682
0.102
1.41
0.933–2.130
23%
  
BPD
544
54
2
0.0483
0.111
0.74
1.071
0.713–1.609
6%
ABCB1
rs1045642
 
CC
CT
TT
      
  
Control
137
249
78
0.4364
     
  
MDD
177
208
54
0.3599
11.01
0.001
0.726
0.601–0.877
67%
  
BPD
244
268
88
0.3700
9.628
0.002
0.758
0.637–0.903
61%
NAT2
rs1041983
 
CC
CT
TT
      
  
Control
144
225
95
0.4472
     
  
MDD
130
230
79
0.4419
0.051
0.821
0.979
0.813–1.179
5%
  
BPD
182
300
118
0.4467
0.001
0.981
0.998
0.840–1.186
5%
NAT2
rs1801280
 
TT
CT
CC
      
  
Control
416
40
8
0.0603
     
  
MDD
400
38
1
0.0456
1.96
0.162
0.743
0.490–1.127
17%
  
BPD
554
44
2
0.0400
4.66
0.031
0.649
0.437–0.964
33%
NAT2
rs1799929
 
CC
CT
TT
      
  
Control
430
32
2
0.0388
     
  
MDD
390
45
4
0.0604
4.481
0.034
1.592
1.032–2.456
33%
  
BPD
553
45
2
0.0408
0.057
0.812
1.055
0.680–1.636
36%
NAT2
rs1799930
 
GG
AG
AA
      
  
Control
264
176
24
0.2414
     
  
MDD
231
182
26
0.2665
1.506
0.22
1.142
0.924–1.412
14%
  
BPD
312
250
38
0.2717
2.505
0.114
1.172
0.963–1.427
20%
NAT2
rs1208
 
AA
AG
GG
      
  
Control
416
44
4
0.0560
     
  
MDD
400
37
2
0.0467
0.805
0.37
0.825
0.542–1.256
11%
  
BPD
553
45
2
0.0408
2.675
0.102
0.717
0.481–1.070
27%
CYP2C19
rs12248560
 
CC
CT
TT
      
  
Control
460
4
0
0.0043
     
  
MDD
434
5
0
0.0057
0.174
0.676
1.323
0.354–4.943
----
  
BPD
594
6
0
0.0050
0.053
0.818
1.161
0.327–4.125
----
CYP2C19
rs4986893
 
GG
AG
AA
      
  
Control
440
24
0
0.0259
     
  
MDD
393
44
2
0.0547
9.781
0.002
2.178
1.323–3.588
66%
  
BPD
536
64
0
0.0533
9.962
0.002
2.122
1.317–3.420
67%
CYP2C19
rs4244285
 
GG
AG
AA
      
  
Control
256
168
40
0.2672
     
  
MDD
219
179
41
0.2973
2.009
0.156
1.16
0.945–1.424
17%
  
BPD
275
263
62
0.3225
7.633
0.006
1.305
1.080–1.577
50%
CYP2C9
rs1057910
 
AA
AC
CC
      
  
Control
433
31
0
0.0334
     
  
MDD
406
33
0
0.0376
0.231
0.631
1.13
0.686–1.862
6%
  
BPD
568
32
0
0.0267
0.827
0.363
0.793
0.480–1.309
10%
ANKK1
rs1800497
 
GG
AG
AA
      
  
Control
168
208
88
0.4138
     
  
MDD
143
224
72
0.4191
0.053
0.818
1.022
0.848–1.233
5%
  
BPD
202
290
108
0.4217
0.133
0.715
1.033
0.868–1.229
6%
SH2B1
rs3888190
 
CC
AC
AA
      
  
Control
396
63
5
0.0787
     
  
MDD
365
68
6
0.0911
0.902
0.342
1.174
0.843–1.636
10%
  
BPD
498
93
9
0.0925
1.268
0.26
1.194
0.877–1.625
12%
INSR
rs2396185
 
AA
AC
CC
      
  
Control
336
124
4
0.1422
     
  
MDD
328
101
10
0.1378
0.073
0.786
0.964
0.739–1.258
5%
  
BPD
408
171
21
0.1775
4.789
0.029
1.301
1.027–1.648
33%
χ2, P, OR, and 95%CI in this table indicate chi-square value, P value, odds ratio, and 95% confidence interval of minor alleles, respectively
MAF Minor allele frequency

Linkage disequilibrium analysis and haplotype analysis

Among the 20 SNPs, 13 SNPs were present in three chromosome (chr) blocks, including chr 2 (SCN2A-rs17183814, SCN1A-rs2298771, SCN1A-rs3812718, and UGT1A9-rs2741049), chr 8 (NAT2-rs1041983, -rs1801280, -rs1799929, -rs1799930, and -rs1208), and chr 10 (CYP2C19-rs12248560, -rs4986893, -rs4244285 and CYP2C9-rs1057910) (Fig. 1). The significant SNP (rs4986893) was located at the block in chr 10, and showed strong linkage disequilibrium with rs4244285 (Fig. 2). Therefore, the haplotypes of rs4986893 and rs4244285 were reconstructed, and a total of three haplotypes were observed in the studied population. The distributions of these haplotypes were significantly different between cases and controls. Moreover, the ORs of the haplotypes between the case and control groups were analyzed (Table 4). The G-A haplotype from rs4986893 and rs4244285 was related to the increased risk of BPD (χ2 = 10.068, OR = 1.361, P = 0.002), while the A-G haplotype raised both the risks of MDD and BPD (χ2 = 11.145, OR = 2.306, P = 0.001; χ2 = 12.549, OR = 2.332, P < 0.001).
Table 4
Haplotype analysis of rs4986893 and rs4244285
 
rs4986893
rs4244285
 
Control(n = 464)
MDD(n = 439)
BPD(n = 600)
Haplotype 1
G
G
 
656
569
750
Haplotype 2
G
A
 
248
261
386
Haplotype 3
A
G
 
24
48
64
 
MDD
BPD
 
χ2
P
OR (CI 95%)
χ2
P
OR (95% CI)
Overall
13.137
0.001
––
20.065
< 0.001
––
2 vs. 1
3.359
0.067
1.213 (0.987, 1.492)
10.068
0.002
1.361(1.125, 1.648)
3 vs. 1
11.145
0.001
2.306(1.395, 3.812)
12.549
< 0.001
2.332 (1.442, 3.772)

CYP2C19 metabolizer status distribution in controls and cases

When genotypes composed of rs4244285 and rs4986893 were translated into predicted CYP2C19 metabolism, it could be categorized as normal metabolizer (NM), intermediate metabolizer (IM), poor metabolizer (PM). NM was the subject carried none of these defective alleles, while IM was the subject had one defective allele and PM was the one had two defective alleles. The distributions of these CYP2C19 metabolizer statuses were significantly different between cases and controls. The frequencies of IM&PM status were higher in MDD (57.40%, OR = 1.547) and BPD (61.17%, OR = 1.808) cases than those in controls (46.55%, P < 0.05), showed in Table 5.
Table 5
CYP2C19 metabolizer status distribution in controls and cases with MDD and BPD
 
Control(n = 464)
MDD(n = 439)
BPD(n = 600)
   
NM
248 (53.45%)
187 (42.60%)
233 (38.83%)
   
IM
160 (34.48%)
195 (44.42%)
283 (47.17%)
   
PM
56 (12.07%)
57 (12.98%)
84 (14.00%)
   
IM&PM
216 (46.55%)
252 (57.40%)
367 (61.17%)
   
 
MDD
BPD
 
χ2
P
OR (CI 95%)
χ2
P
OR (95% CI)
NM vs. IM vs. PM
11.330
0.003
––
23.215
 < 0.001
––
IM&PM vs. NM
10.639
0.001
1.547(1.190,2.012)
22.563
 < 0.001
1.808(1.415,2.311)
NM Normal metabolizer, IM Intermediate metabolizer, PM Poor metabolizer, IM&PM Intermediate metabolizers plus poor metabolizers

Discussion

Many etiopathogenetic mechanisms are involved in mood disorders, such as MDD and BPD. Due to the common symptoms and shared etiologies between these disorders, we sought to clarify whether correlations existed between candidate genetic variants in selected genes and susceptibility to MDD and BPD in the Han Chinese population. To the best of our knowledge, this study is the first Chinese study to examine the implication of 13 genes on both MDD and BPD risk, covering LEPR, SCN2A, SCN1A, UGT1A9, GSK3B, HLA-B, ABCB1, NAT2, CYP2C19, CYP2C9, ANKK1, SH2B1, and INSR. Our data suggested that SCN2A-rs17183814, ABCB1-rs1045642, and CYP2C19-rs4986893 had associations with MDD or BPD, providing evidence for genetic vulnerability to mood disorders, and provided a basis for understanding the etiology of these disorders for earlier prevention.
The neuronal voltage-gated sodium channel, which modulates neuron excitability and initial transduction, is encoded by the SCN2A gene expressed in the initial segment of the axon and plays a crucial part in neuronal pathfinding and neurite outgrowth [32]. Once neuronal voltage-gated sodium channels are deficient in mature neurons, action potential is back-propagated, dendrite excitability is reduced, and synaptic efficacy is damaged [33]. Diminished channel function interferes with the neural signal transduction pathway, resulting in the occurrence of MDD, BPD, and autism spectrum disorder [26, 34]. Our data could not confirm the association between SCN2A-rs17183814 and BPD in European and Chinese Han populations [26]. We thought the reasons for the discrepancy might be due to the heterogeneity in the cases, the differences in racial composition and the smaller sample sizes than Zhao’s study (1146 BPD cases and 1956 controls). Additionally, we found that the A allele of SCN2A-rs17183814 increased the odds of developing MDD by 1.583-fold, which was different from the contribution of the G allele to the prevalence of MDD (OR = 1.116) observed by Zhao [26]. The biological link between the locus and affective disorders needs further clarification.
ABCB1, which encodes a permeability glycoprotein that is highly expressed in the brain for exporting various hydrophobic compounds, plays a vital role in forming a protective physiological barrier and emerges as an active eliminator for xenobiotics and cellular metabolites [35]. The vulnerability to MDD can be predicted with ABCB1 by altering the activity of the hypothalamic-pituitary-adrenal axis [36]. The C allele of the ABCB1-rs1045642 polymorphism was connected with boosted interpersonal sensitivity among Japanese populations [8]; this allele has been generally accepted as one of the vulnerability factors for depression. Ozbey et al. [37] showed in a Turkish population that ABCB1-rs1045642 C allele and CC genotype were associated with susceptibility to the development of MDD. In addition, a study using a mouse model has shown that higher cortisol levels accumulated in the plasma and brain of ABCB1 -/- knockout mice [18]. Based on these findings, we assumed that the ABCB1-rs1045642 C allele over-expresses the permeability glycoprotein, restricting the entry of cortisol into the brain. This leads to a lower cortisol level in the brain and higher interpersonal sensitivity. Negative feedback from the lower cortisol level can lead to a hyperactive hypothalamic-pituitary-adrenal axis, which promotes the release of cortisol and probably causes mood disorders. Consequently, T allele carriers may have lower risks of MDD and BPD. T allele carriers of three ABCB1 loci, including rs1045642, have nearly 70% less risk of MDD among male Portuguese individuals [38]. A Chinese study showed that the TG haplotype of rs1045642–rs2032582 carriers reduced MDD risk by approximately 53% [39]. Our results were consistent with those of previous studies, where the T allele lowered the risks for MDD and BPD by 0.726- and 0.758-fold, respectively. However, the association between ABCB1-rs1045642 polymorphism and mood disorders has not been established. Some studies have suggested that ABCB1-rs1045642 T allele as the variant contributes to the predisposition of MDD [40, 41].
The CYP2C19 enzyme plays a critical role in metabolizing not only drugs or xenobiotics that affect therapeutic outcomes but also endogenous substrates containing steroid hormones, vitamin D, eicosanoids, arachidonic acids, and cholesterol that could also confer susceptibility to many diseases [42, 43]. Recent studies have suggested that impaired CYP2C19 metabolizers had higher self-rated Beck Depression Inventory-II scores than normal metabolizers. Damaged CYP2C19 enzyme activity was associated with more severe MDD, despite CYP2C19-metabolized medication treatment and treatment discrepancy status [7]. CYP2C19 polymorphism has been demonstrated to affect the conversion and degradation of endogenous compounds, including psychoactive steroid hormones (e.g. estrone, estradiol, progesterone, and testosterone) in in vitro studies [44]. Our study findings suggest that the A allele of CYP2C19-rs4986893 had a 2.178-fold higher prevalence of MDD and 2.122-fold increased possibility of BPD occurrence. The haplotypes of rs4986893-G and rs4244285-A might increase the risk for BPD, while the rs4986893-A and rs4244285-G haplotype soared both the risks of MDD and BPD. Additionally, the frequencies of IM&PM status were higher in MDD and BPD cases than those in controls, which also meant defective allele (rs4986893 or rs4244285) was related to the raised risk of both MDD and BPD. Our hypothesis is that the A allele of CYP2C19-rs4986893, as a variant, encodes impaired CYP2C19 enzyme, promoting steroid hormone disequilibrium, and resulting in a change in hypothalamic-pituitary-adrenal axis activity and mood disorder development. The hypothesis could be verified by the G-A and A-G haplotype found in the current study, which carried genetic variant and induced impaired metabolic enzyme activity. Nevertheless, contrasting results indicated that elevated CYP2C19 expression is related to depressive symptoms [45, 46]. These deviations can be explained by inter-study discrepancies in CYP2C19 frequency or study methods.
This study had several limitations. First, the channel function caused by SCN2A-rs17183814 mutation was not examined, and the substrate concentrations due to ABCB1-rs1045642 and CYP2C19-rs4986893 polymorphisms were not measured. These would have helped to characterize the physiological mechanisms. Second, the location of 20 candidate loci in various chromosomes make analysis of the effect of haplotypes on diseases difficult. Third, this study did not include controversial risk genes for depression, such as SLC6A4 and 5-HTTLPR; therefore, further investigations are needed [10, 14].
In conclusion, we have provided additional evidence for genetic association, confirming that the CYP2C19-rs4986893 A allele is a risk factor and ABCB1-rs1045642 T allele is protective for MDD. For the first time, we showed that these two variants have a similar effect on BPD. Additionally, the SCN2A-rs17183814 A allele was found to increase the morbidity of MDD. The haplotype integrated by CYP2C19 alleles, and the CYP2C19 metabolizer status which was categorized as IM or PM might contribute to the risk of developing mood disorder.

Acknowledgements

The authors are extremely grateful to Botao Liu who provide valuable comments and technology supports.

Declarations

All participants provided informed consent. This study has been performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University (No.2022026).
Not Applicable.

Competing interests

The authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results or discussions reported in this paper.
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Metadaten
Titel
CYP2C19-rs4986893 confers risk to major depressive disorder and bipolar disorder in the Han Chinese population whereas ABCB1-rs1045642 acts as a protective factor
verfasst von
Ting Zhang
Qingmin Rao
Kangguang Lin
Yongyin He
Jintai Cai
Mengxin Yang
Ying Xu
Le Hou
Yulong Lin
Haiying Liu
Publikationsdatum
01.12.2023
Verlag
BioMed Central
Erschienen in
BMC Psychiatry / Ausgabe 1/2023
Elektronische ISSN: 1471-244X
DOI
https://doi.org/10.1186/s12888-022-04514-w

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