TOX and ADIPOQ Gene Polymorphisms Are Associated with Antipsychotic-Induced Weight Gain in Han Chinese.

To find the genetic markers related to the antipsychotic-induced weight gain (AIWG), we analyzed associations among candidate gene single-nucleotide polymorphisms (SNPs) and quantitative traits of weight changes and lipid profiles in a Chinese Han population. A total of 339 schizophrenic patients, including 86 first-episode patients (FEPs), meeting the entry criteria were collected. All patients received atypical antipsychotic drug monotherapy and hospitalization and were followed for 12 weeks. Forty-three SNPs in 23 candidate genes were calculated for quantitative genetic association with AIWG, performed by PLINK. The TOX gene SNP rs11777927 (P = 0.009) and the ADIPOQ gene SNP rs182052 (P = 0.019) were associated with AIWG (in body mass index, BMI). In addition, the BDNF SNP rs6265 (P = 0.002), BDAF SNP rs11030104 SNP (P = 0.001), and ADIPOQ SNPs rs822396 (P = 0.003) were significantly associated with the change of waist-to-hip ratio (WHR) induced by atypical antipsychotics. These results were still significant after age and gender adjustments. These findings provide preliminary evidence supporting the role of TOX, ADIPOQ and BDNF in weight and WHR gain induced by atypical antipsychotics.

Schizophrenia is a complex and devastating psychiatric disorder that occurs worldwide, and for decades it was generally thought to have a uniform lifetime risk of approximately 1% across time, geography and sex1. Obesity and overweight affect 30–70% of patients with schizophrenia spectrum disorders. They have a 2.8–3.5 increased likelihood of being obese2. One of the many reasons that psychiatric patients gain weight and become overweight and obese is atypical antipsychotic drugs (AAPDs) which may affect the recovery process and quality of life34.

AAPDs have become the first-line medication treatment for patients with schizophrenic and schizoaffective disorders. Although AAPDs appear to be a better choice for the treatment of schizophrenic patients, they are often associated with some common and debilitating side effects, such as accelerated weight gain, insulin resistance, diabetes, hyperlipidemia, and coronary heart disease567. These metabolic alterations can develop as quickly as six months after the initiation of pharmacotherapy8. Antipsychotic-induced weight gain (AIWG) is quite commonly associated with AAPDs; more-so than typical antipsychotic drugs (TAPDs)9.

Although the phenomenon of antipsychotic-induced weight gain (AIWG) is well recognized, some individuals actually lose weight or their weight remains unchanged during single AAPD treatment, which shows that not all individuals are equally prone to this adverse side effect10. Although many variables contribute to the heterogeneity of AIWG, genetic factors may plan a significant role. Twin and sibling genetic studies have demonstrated similar degrees in weight gain profiles upon receiving AAPD1112, and the heritability (h2) is 0.6–0.813.

With the rapid development of molecular genetics, numerous pharmacogenetics studies have focused on identifying specific gene variants contributing to AIWG. Most studies have been based on the candidate gene approach, such as HTR2C14, MC4R15, Leptin16, FTO17 and BDNF18. So far, three genome-wide association studies (GWAS) of AIWG were carried out, associations were found on pro-melanin-concentrating (PMCH) and MC4R gene polymorphisms1920. A recent GWAS for AIWG found that PTPRD polymorphisms might modulate AIWG21. Replications of these findings need to be carried out in independent cohorts. Compared with the GWAS design that requires very large sample sizes to achieve sufficient power, the candidate gene approach allowed us to perform an association study with increased statistical power for prioritized genes22.

Previous studies are mainly categorical correlation analyses. Patients with clinically significant weight gain (7% increases from baseline) were in the case group, and the others were controls. With such a research design, it is difficult to dynamically observe the quantitative traits related to AIWG. In this study, we aimed to find AIWG candidate genes in Han Chinese schizophrenia patients by studying quantitative associations between single-nucleotide polymorphisms (SNPs) of candidate genes and AIWG and other antipsychotic-treatment-related phenotypes.

Results

Sample demographics

Demographic characteristics of the 339 individuals, including 86 first-episode patients (FEPs), from this study are provided in Table 1. Patients received 12 weeks of treatment with single AAPDs: olanzapine (OLZ), risperidone (RIS), clozapine, quetiapine, aripiprazole, or ziprasidone.

Table 1

Clinical characteristics of subjects.

Characteristic	N (%)/Mean ± SD
Gender
Male	135 (39.8)
Female	204 (60.2)
Age (years)	38.7 ± 11.53
Duration of illness (years)	9.31 ± 8.67
First-episode patients (FEPs)	86 (25.4)
BMI at baseline	23.42 ± 4.10
WHR at baseline	0.88 ± 0.06
Medication
Olanzapine	131 (38.6)
Risperidone	133 (39.2)
Clozapine	23 (6.8)
Quetiapine	27 (8.0)
Aripiprazole	14 (4.1)
Ziprasidone	11 (3.2)

Abbreviations: BMI, body mass index; WHR, Waist -Hip Ratio.

After 12-week AAPD intervention, the average weight gain was 4.21 kg in all patients. The 12-week BMI change in our subjects (ΔBMI12w) was 1.52 ± 1.51 kg/m2, ranged from −0.78–8.36 kg/m2. The 12-week change in waist-to-hip ratio (ΔWHR12w) was 0.04 ± 0.05, ranged from −0.15–0.18. The definition of a weight gain “case” was that a patient who gained 7% or more of baseline body weight in a short-term trial23. The percentage of all patients meeting this criterion was 41%; 57% in OLZ group; and 24% in the RIS group. OLZ-treated patients demonstrated a markedly different distribution, with the majority of patients experiencing extreme AIWG. RIS treatment showed moderate effects on weight gain (Supplemental Figure 1).

Quantitative association analysis

After HWE tests, SNP rs1526167 failed. In our candidate association study, the TOX SNP rs11777927 (P = 0.009) and the ADIPOQ rs182052 (P = 0.019) were significantly associated with AIWG. The CDKN2A/B SNPs rs3731245 (P = 0.04) and rs2811708 SNP (P = 0.039) were also associated with the AIWG; CDKN2A/B associations with AIWG remained significant in the FEP and RIS groups. The BDNF SNP rs6265 (P = 0.002), BDAF SNP rs11030104 SNP (P = 0.001), and ADIPOQ SNPs rs822396 (P = 0.003) and rs1501299 (P = 0.040) were associated with the change of WHR; consistent results were found for those SNPs in the OLZ group. These results were still significant after age adjustments (Table 2). The BDNF SNP rs6265 and the BDAF SNP rs11030104 associations remained significant after Bonferroni corrections.

Table 2

Quantitative association analysis of candidate genes and changes in phenotype at 12 weeks.

SNP	Gene	Risk allele	P	P (adjusteda)
All patients
ΔBMI
rs182052	ADIPOQ	A	0.019	0.019
rs11777927	TOX	A	0.009	0.009
rs351320	EPHA7	G	0.025	0.035
rs3731245	CDKN2A/B	A	0.040	0.040
rs2811708	CDKN2A/B	T	0.039	0.044
ΔWHR
rs822396	ADIPOQ	G	0.003	0.005
rs1501299	ADIPOQ	A	0.040	0.045
rs6265	BDNF	G	0.002	0.002
rs11030104	BDAF	G	0.001	0.002
FEP groupb
ΔBMI
rs279858	GABRA2	G	0.029	—
rs249496	PCAF	A	0.019	—
rs2811708	CDKN2A/B	T	0.028	—
rs6567160	MC4R	C	0.041	—
ΔWHR
rs35747495	PCAF	T	0.024	—
rs10811661	CDKN2A/B	C	0.031	—
rs489693	MC4R	A	0.011	—
OLZ groupb
ΔBMI
rs279858	GABRA2	G	0.021	—
rs10968576	LINGO2	G	0.020	—
ΔWHR
rs822396	ADIPOQ	G	0.047	—
rs6265	BDNF	G	0.020	—
rs11030104	BDAF	G	0.020	—
RIS groupb
ΔBMI
rs3776871	PAM	A	0.039	—
rs7799039	LEP	G	0.002	—
rs3731245	CDKN2A/B	A	0.006	—
rs2811708	CDKN2A/B	T	0.002	—
rs6567160	MC4R	C	0.006	—
rs489693	MC4R	A	0.018	—

ΔBMI, change in body mass index; ΔWHR, change in waist-to-hip ratio.

aAll the dependent variables were adjusted for age and gender.

bNo age and gender adjusted phenotypes were used in subgroups due to limited sample size.

Changes of lipid profiles (high- and low-density lipoprotein and total cholesterol) after AAPD treatment were linked to a PKHD1 gene SNP rs9395706. The CDKN2A/B gene SNP rs10811661 was associated with the FPG changes induced by OLZ (P = 0.009). Results remained significant after age adjustments (Supplemental Table 1).

Gene-gene interaction analysis

Gene × gene interaction analyses showed significant epistasis between MTHFR and PCAF, EPB41L4A and LEPR, ADIPOQ and CDKN2A/B, ADIPOQ and NRXN3, TOX and PKHD1, TOX and RPTOR, and MC4R and COMT gene SNPs for AIWG (ΔBMI) in all patients (Table 3).

Table 3

Quantitative gene-gene interaction analysis of AIWG (ΔBMI).

Gene 1	SNP1	Gene 2	SNP2	P	P (adjusteda)
All patients
MTHFR	rs1801131	PCAF	rs1986917	4.79 × 10−3	5.71 × 10−4
LEPR	rs1137101	EPB41L4A	rs7732687	2.08 × 10−3	2.33 × 10−3
ADIPOQ	rs182052	NRXN3	rs11845632	7.29 × 10−3	7.53 × 10−3
ADIPOQ	rs822396	CDKN2A/B	rs3731245	8.29 × 10−3	3.09 × 10−3
ADIPOQ	rs7649121	CDKN2A/B	rs3731245	6.91 × 10−4	6.37 × 10−4
ADIPOQ	rs7649121	CDKN2A/B	rs2811708	4.19 × 10−4	3.46 × 10−4
GABRA2	rs279858	RPTOR	rs12940622	2.84 × 10−3	5.18 × 10−3
PKHD1	rs2894788	TOX	rs2726557	6.21 × 10−3	5.64 × 10−3
TOX	rs2726557	RPTOR	rs12940622	9.21 × 10−3	7.52 × 10−3
PTPRD	rs10977144	CDKN2A/B	rs10811661	8.31 × 10−3	8.33 × 10−3
MC4R	rs489693	COMT	rs4680	5.63 × 10−3	6.20 × 10−3
FEP groupb
MTHFR	rs1801131	EPB41L4A	rs7732687	7.98 × 10−3	—
LEPR	rs1137101	PCAF	rs1986917	3.74 × 10−3	—
LEPR	rs1137101	EPB41L4A	rs7732687	8.15 × 10−3	—
INSIG2	rs7566605	NRXN3	rs11845632	1.23 × 10−3	—
PCAF	rs1986917	ADIPOQ	rs7649121	7.13 × 10−4	—
PCAF	rs1986917	LINGO2	rs10968576	5.24 × 10−3	—
ADIPOQ	rs7649121	PKHD1	rs9296661	7.23 × 10−3	—
ADIPOQ	rs7649121	PKHD1	rs1326589	7.23 × 10−3	—
ADIPOQ	rs7649121	NRXN3	rs12891144	3.88 × 10−4	—
PAM	rs249496	NRXN3	rs12891144	6.37 × 10−3	—
PKHD1	rs2894788	PAPPA	rs10817871	6.54 × 10−3	—
CNR1	rs806368	EPHA7	rs351320	2.70 × 10−3	—
BDNF	rs6265	NRXN3	rs11845632	1.75 × 10−3	—
BDAF	rs11030104	NRXN3	rs11845632	3.13 × 10−3	—
OLZ groupb
INSIG2	rs7566605	TOX	rs11777927	9.38 × 10−3	—

aAll the dependent variables were adjusted for age and gender.

bNo age and gender adjusted phenotypes were used in subgroups due to limited sample size.

Genotypic association analysis

Compared with patients that carried the TOX rs11777927 T allele, the BMI of the patients with the TOX rs11777927 AA genotype increased significantly after AAPD treatment (P < 0.05); compared with patients that carried ADIPOQ rs182052 genotypes GG and AG, BMI of patients with the AA genotype increased significantly (P < 0.05) (Fig. 1).

Figure 1

Body changes (ΔBMI) of TOX rs11777927 genotypes and ADIPOQ rs182052 genotypes.

(a) ΔBMI at 12 weeks of TOX rs11777927 genotypes (AA, AT, TT) in all patients and by treatment group: olanzapine (OLZ) and risperidone (RIS). ΔBMI was compared across the three genotypes in each group (*P < 0.05, one-way ANOVA). (b) ΔBMI over a 12-week single AAPD treatment by TOX rs11777927 genotype (AA, AT, TT) in all patients. BMI was measured at baseline and at weeks 2, 4, 6, 8, and 12. ΔBMI was compared across the three genotypes at each time point (*P < 0.05, **P < 0.01, one-way-ANOVA). (c) ΔBMI at 12 weeks of ADIPOQ rs182052 genotypes (GG, AG, AA) in all patients and by treatment group: OLZ and RIS. ΔBMI was compared across the three genotypes in each group (*P < 0.05, one-way-ANOVA). (d) ΔBMI over a 12-week single AAPD treatment by ADIPOQ rs182052 genotype (GG, AG, AA) in all patients. BMI was measured at baseline and at weeks 2, 4, 6, 8, and 12. ΔBMI was compared across the three genotypes at each time point (*P < 0.05, one-way-ANOVA).

Compared with patients that carried CDKN2A/B rs3731245 genotypes CC and CT, BMI of patients with the TT genotype increased significantly (P < 0.05) (Supplemental Figure 2); compared with the patients that carried the CDKN2A/B rs2811708 G allele, the BMI of the patients with the CDKN2A/B rs2811708 TT genotype increased significantly after the treatment of AAPD (P < 0.05) (Supplemental Figure 3).

In binary association studies for BMI changes, the TOX gene SNP rs11777927 (P = 0.004), the ADIPOQ gene SNP rs1501299 (P = 0.016), the CDKN2A/B gene SNP rs3731245 (P = 0.032), rs2811708 (P = 0.017), the MC4R gene SNP rs6567160 (P = 0.016), and the NRXN3 gene SNP rs12891144 (P = 0.023) were associated with AIWG.

Discussion

With the development of molecular genetics during the past decade, intensive research has examined the influence of genetic variations on AIWG. However, as yet no genetic tests for AIWG are endorsed for clinical application. Although there are significant findings for AIWG associations with many other genes, the most consistently replicated findings are with HTR2C, MC4R, and leptin genes2425. A recent meta-analysis reported that 11 SNPs from 8 genes were associated with weight or BMI change, and 4 SNPs from 2 genes were significantly related to categorical weight or BMI increase14. Many previous studies have not been confirmed because of varying criteria, differences in frequencies across different populations, or poor statistical power. In addition, compared with the candidate-gene-based approach, GWAS of AIWG are relatively limited192021. Genes PTPRD, MC4R, and PMCH were discovered having the strongest associations with AIWG.

AIWG is thought to be multifactorial and polygenic, which may be different from simple obesity per se. In recent years research has pointed to a broader array of genes and pathways hypothesized to underpin AIWG. Attention has turned to genes and pathways harboring variants that might make the risk of energy unbalance increased by the influence of antipsychotics, leading to AIWG26. Thus, seeking AIWG genes in Han Chinese would promote the development of predictive genetic tests and help us better understand and guide treatment.

In this study, we analyzed 43 SNPs in 23 candidate genes for quantitative association with AIWG for the first time. Here, we decided to use the candidate gene approach rather than performing a GWAS to select a small set of variants for a more focused study, providing sufficient power for these selected variants given the small sample size. As candidate genes, we chose (a) genes mainly from a previous GWAS for Caucasian obesity-AIWG subjects and (b) genes associated with simple obesity, type 2 diabetes, microvascular complications of diabetes, and AIWG.

In the present study, SNPs in TOX and ADIPOQ yielded the most significant associations for AIWG, and CDKN2A/B showed light to strong associations. We did not replicate associations with several well-known AIWG genes, including FTO, MTHFR, and COMT, in our Han Chinese population. Potential reasons for this include genetic heterogeneity, less covered genes (i.e., too few SNPs were genotyped for certain candidate genes), entry criteria, and the relatively small sample size. Although we were unable to test genome-wide association for all genes, the spectrum of AIWG-associated genes differed between Han Chinese in our study and European populations in the previous study15161727.

The TOX quantitative association with AIWG was first discovered in this study. TOX (thymocyte selection-associated HMG box) is a member of a novel gene family and encodes a novel nuclear DNA-binding protein belonging to a large superfamily of HMG (high-mobility-group) proteins2829. TOX may play a role in regulating expression of genes involved in cell cycle progression, such as the cell division cycle gene and oncogenes30. In recent years, emerging studies have found that TOX is aberrantly expressed or mutated in various diseases, such as leukemia29 or cardiovascular diseases31. A previous study by our group first found the TOX gene association with type 2 diabetes32. In the present study, we tested 3 SNPs of the TOX gene, rs1526167, rs2726557, and rs11777927 (Supplemental Figure 4). Unfortunately, the SNP rs1526167, which was located in a separate haplotype block, not in linkage disequilibrium with SNPs in the TOX gene coding region and introns, failed the HWE test.

Our results showed that the TOX SNP rs11777927 was significantly associated with AIWG (P = 0.009) and that the A allele contributed to the increased risk for AIWG. And gene × gene interaction analyses showed significant epistasis between TOX and PKHD1 and between TOX and RPTOR for AIWG (ΔBMI) in all patients. We found some associations of TOX SNPs with obesity and metabolic-syndrome-related phenotypes. It is stated by Cox et al. in a published US patent application (US 2006/0177847 A1, August 10, 2006), that they found the TOX polymorphism and other 27 DNA sequence variations to be related to OLZ-treatment-emergent weight gain and “metabolic syndrome” in a 1.7 million SNP genome association study.

However, no rs11777927 association has been reported for AIWG. To the best of our knowledge, only one association study of rs11777927 and disease—intracranial aneurysm— has been reported33. The biological connections between TOX and AIWG are poorly understood, perhaps owing to the inflammatory responses mediated by immune cells, developmentally regulated by TOX gene, contributing to AIWG.

The ADIPOQ locus has been shown to be the only major gene for plasma adiponectin, which is exclusively expressed in adipose tissue34. The ADIPOQ SNP rs1501299 has been reported to be associated with the risk of obesity3536 and cardiovascular diseases373839. Four candidate SNPs in ADIPOQ in this study were selected from four different haplotype blocks: rs182052, rs822396, rs7649121, and rs1501299 (Supplemental Figure 5). The results showed that the ADIPOQ SNP rs182052 (P = 0.019) was significantly associated with AIWG, and that the ADIPOQ SNPs rs822396 (P = 0.003) and rs1501299 (P = 0.040) were associated with changes in WHR.

Other studies have not delivered consistent findings for ADIPOQ and AIWG. An association of rs1501299 with significant weight gain (>7% of the baseline weight) was reported in Chinese patients40. Another study reported genotypic or allelic association of 6 ADIPOQ variants with AIWG in European patients41. However, the latest two studies did not support a major role of ADIPOQ rs1501299 in the regulation of AIWG4243, and the association of rs1501299 with AIWG was not present in Japanese patients44. The SNP rs1501299 was a hot spot that had attracted many researchers’ interest. Interestingly, our results showed rs1501299 was associated with changes in WHR but not in AIWG, and that rs182052 and rs822396 were significantly associated with AIWG and changes in WHR induced by AAPD, which haven’t previously been reported. ADIPOQ is most likely closely related to AIWG, which needs further research.

BDNF can encode the BDNF precursor protein located in the chromosome 11p13 region45. And BDNF crossing the blood-brain barrier is the most abundant neurotrophin that modulates synaptic transmission and neuroplasticity in the central nervous system4647. Previous researches suggested that the BDNF rs6265 was associated with many eating disorders4849. In addition, several SNPs in the BDNF and BDAF were strongly associated with obesity in GWAS studies5051.

In the field of the genetics of AIWG, the recent two studies showed BDNF rs6265 was associated with the increased BMI in the psychiatric patients receiving AAPD1852. Moreover, the BDNF haplotypes including SNP rs6265 were associated with AIWG5354. However, a study reported that BDNF rs6265 was not associated with AIWG performed by Tsai et al.55. In the present study, we failed to find the association between BDNF rs6265 and AIWG, which was consisted with the results of Tsai’s. Interestingly, we found the BDNF SNP rs6265 (P = 0.002) and BDAF SNP rs11030104 SNP (P = 0.001) were significantly associated with the change of WHR induced by AAPD. The WHR is a alternative measure which have been found to be superior to BMI to reflect abdominal obesity in the World Health Organization (WHO) guidelines56.

All the above findings, including ours, indicate that the SNPs of BDNF have a significant impact on the obesity schizophrenic patients induced by AAPD. The associated genetic markers from BDNF may have different effects, and accumulated mutations may provide a whole contribution to the obesity induced by AAPD. The SNP BDNF rs6265 may play an important role in this process. Hence, the following study about the function of the genetic variants will be necessary to elucidate the mechanism how the genetic variants in BDNF medicate signaling pathway and lead to obesity.

CDKN2A/B is located in the chromosome 9p21 region, has been highlighted as the strongest genetic susceptibility locus for cardiovascular disease5758 and type 2 diabetes5859. CDKN2A/B encodes the CDK inhibitor proteins involved in cell cycle regulation, aging, senescence, and apoptosis. In the present study, we selected 3 SNPs of CDKN2A/B: rs3731245, rs2811708 and rs10811661 (Supplemental Figure 6); rs3731245 and rs2811708 are in a haplotype block. SNPs rs3731245 (P = 0.04) and rs2811708 SNP (P = 0.039) were associated with the AIWG but with low statistical power. We found no association between rs10811661 and AIWG, but this SNP was associated with the FPG changes that induced by OLZ (P = 0.009). These results suggest that glucose and lipid metabolism abnormalities and weight gain are influenced by AAPD through different pathways. Previous studies have found that AAPD may not affect glucose and lipid metabolism directly through weight gain1060. The mechanism by which the CDKN2A/B gene affects susceptibility for AIWG remains to be investigated.

In the future, we will further substantiate our gene results and explore more genetic factors underlying AIWG. However, the heterogeneity of medications and psychiatric disorder factors still exist. We analyzed the genetic association in FEPs, OLZ-treated patients, and RIS-treated patients, but the subgroups were too small for strong statistical power. For this reason, a better designed trial with different AAPDs in larger samples and different populations should also be implemented to validate the genes associated with AIWG.

Given the sample size of our study, we have moderate power to detect major AIWG associations. We performed power calculations by GPC (Genetic Power Calculator61), given a total QTL (quantitative trait locus) variance = 0.05, QTL increaser allele frequency = 0.25, marker allele frequency = 0.25, dominant model, total LD (linkage disequilibrium, D’ = 0.95), case trait threshold (0–4 standard deviations), control threshold (0 to −1.5), type I error α = 0.05, we have more than 80% (84%) power.

We also performed binary association studies for AIWG, the discrete analyses showed similar associations with quantitative BMI changes. Replication is essential for association studies, although GWASs for AIWG were relatively limited. We compared our associations with published GWAS for body weight related traits (GWASdb, v2. http://jjwanglab.org/gwasdb), ADIPOQ (rs182052), BDNF (rs6265), and BDAF (rs11030104) gene SNPs all yielded genome-wide associations.

In our study, we used quantitative associations between SNPs of candidate genes and AIWG and other antipsychotic-related phenotypes to find AIWG candidate genes in Han Chinese schizophrenia patients. We measured not only weight but also the WHR and lipid and glycemic profiles, which are a common index of metabolic syndrome. In addition, for the purpose of excluding environmental factors that may affected AIWG62, we selected a sample of inpatients with lifestyle, exercise, and diet under unified management by the hospital in order to reduce the heterogeneity. Our comprehensive study of TOX and ADIPOQ is an important contribution to understanding the biology of AIWG.

Patients and Materials

Study participants and design

A total of 339 patients hospitalized with schizophrenia or schizoaffective disorders were recruited. All the subjects were unrelated Han Chinese collected from the Tianjin Metal Health Centre, which is Asia’s largest independent psychiatric hospital. The inclusion criteria for this study were as follows: (a) Clinical diagnoses were independently confirmed by two psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision. (b) All patients were first-episode drug-naive patients or without any antipsychotic drugs at least 4 weeks before enrollment. (c) The patients were physically healthy with normal hematological and biochemical parameters. (d) All patients were 18–60 years old. Subjects with neurological disorders, eating disorders, and thyroid diseases were excluded from the study. Clinical characteristics of patients were shown in Table 1. All subjects provided written informed consent prior for this study, and the protocol was approved by the Committee on Studies Involving Human Beings at Tianjin Medical University. All experiments were performed in accordance with relevant guidelines and regulations.

All patients received single AAPD intervention on the basis of clinical treatment need. Only trihexyphenidyl for extrapyramidal symptoms and lorazepam for insomnia or agitation were allowed as needed during the study period as concomitant medications.

Patients were followed up during the 12-week treatment course. All patients were measured for body weight and for waist, abdominal, and hip circumferences at baseline and at weeks 2, 4, 6, 8 and 12 after treatment initiation. Body mass index (BMI) and waist-to-hip ratio (WHR) were calculated. Before and after treatment for 4, 8, and 12 weeks, triglyceride, high- and low-density lipoprotein, total cholesterol, total protein, albumin, fasting plasma glucose (FPG), blood creatinine, urea nitrogen (urea), urea/creatinine ratio, and serum C reactive protein were measured for all patients.

Candidate gene and SNP selection

Candidate genes mainly came from a comparative GWAS for Caucasian obesity-AIWG subjects performed by our project team members and colleagues. Genes associated with simple obesity, type 2 diabetes, or microvascular complications of diabetes in our previous studies were also included3263, in a GWAS of simple obesity, which was the largest sample size by far (340,000 persons)51, and in other previous candidate gene associations for AIWG that gave inconsistent or controversial results1516171827. Forty-three (43) SNPs in 23 candidate genes were selected in our study (Table 4).

Table 4

Candidate genes and SNPs genotyped in this study.

Chr	SNP	Base-Pair Position	Gene	HWE (P)	MAF CHB*	Location
1	rs1801133	11856378	MTHFR	0.165	0.49 (C)	Intragenic
1	rs1801131	11854476	MTHFR	0.390	0.20 (C)	Intragenic
1	rs1137101	66058513	LEPR	0.171	0.11 (A)	Intron
2	rs7566605	118836025	INSIG2	0.287	0.36 (G)	Intergenic
3	rs2929402	20096110	PCAF	0.348	0.49 (A)	Intergenic
3	rs35747495	20077262	PCAF	0.273	0.32 (T)	Intron
3	rs1986917	20118522	PCAF	0.723	0.43 (C)	Intron
3	rs1501299	186571123	ADIPOQ	0.053	0.33 (A)	Intron
3	rs182052	186560782	ADIPOQ	0.338	0.47 (A)	Intron
3	rs822396	186566877	ADIPOQ	0.407	0.11 (G)	Intron
3	rs7649121	186568785	ADIPOQ	0.216	0.24 (T)	Intron
4	rs279858	46314593	GABRA2	0.120	0.48 (G)	Intragenic
5	rs3776871	102292211	PAM	0.689	0.39 (A)	Intron
5	rs249496	102201590	PAM	0.725	0.29 (G)	UTR 5
5	rs7732687	111571642	EPB41L4A	0.643	0.47 (C)	Intron
6	rs351320	94019904	EPHA7	0.057	0.4 (G)	Intron
6	rs164547	94071318	EPHA7	0.113	0.44 (T)	Intron
6	rs1326589	51674161	PKHD1	0.052	0.25 (T)	Intron
6	rs2894788	51643268	PKHD1	0.191	0.22 (C)	Intron
6	rs9296661	51673439	PKHD1	0.052	0.23 (G)	Intron
6	rs9395706	51544360	PKHD1	0.477	0.41 (G)	Intron
6	rs806368	88850100	CNR1	0.105	0.49 (C)	UTR 3
7	rs7799039	127878783	LEP	0.057	0.23 (G)	Intergenic
8	rs1526167	59702355	TOX	0.000	0.48 (G)	Intergenic
8	rs2726557	59792800	TOX	0.808	0.49 (G)	Intron
8	rs11777927	59881039	TOX	0.286	0.38 (A)	Intron
9	rs10968576	28414339	LINGO2	0.557	0.22 (G)	Intron
9	rs10811661	22134094	CDKN2A/B	0.466	0.43 (C)	Intergenic
9	rs2811708	21973422	CDKN2A/B	0.246	0.22 (T)	Intron
9	rs3731245	21972445	CDKN2A/B	0.072	0.20 (A)	Intron
9	rs10977144	8474233	PTPRD	0.741	0.11 (T)	Intron
11	rs11030104	27684517	BDAF	0.370	0.41 (G)	Intron
11	rs6265	27679916	BDNF	0.400	0.37 (G)	Unknown
14	rs12891144	79945552	NRXN3	0.458	0.17 (C)	Intron
14	rs11845632	79953814	NRXN3	0.827	0.34 (A)	Intron
14	rs7141420	79899454	NRXN3	0.900	0.36 (T)	Intron
16	rs9939609	53820527	FTO	0.072	0.12 (T)	Intron
16	rs1558902	53803574	FTO	0.058	0.12 (A)	Intron
17	rs12940622	78615571	RPTOR	0.330	0.34 (A)	Intron
18	rs6567160	57829135	MC4R	0.821	0.14 (C)	Intergenic
18	rs489693	57882787	MC4R	0.100	0.19 (A)	Intergenic
19	rs10423928	46182304	GIPR	0.471	0.21 (A)	Intron
22	rs4680	19951271	COMT	0.773	0.31 (A)	Intragenic

*MAF, minor allele frequencies, taken from dbSNP; CHB, Han Chinese.

For some genes, tagging SNPs were selected using the HapMap database (phase2 + phase3, release #28, CEU population, Build36; www.hapmap.org) and Tagger in Haploview64. Minor allele frequencies of Han Chinese were taken from dbSNP (http://www.ncbi.nlm.nih.gov/snp/). For previously reported associations, we selected SNPs with the most significant association rather than genotyping the whole gene. For less studied genes, multiple SNPs were chosen based on the LD pattern of the gene (r2 > 0.8).

Genotyping and quality control

Genomic DNA samples were extracted from 5 ml of peripheral whole blood samples using the high-salt method. Samples were stored and processed by the Center for Molecular and Population Genetics at Tianjin Medical University. Genotyping was performed by primer extension of multiplex products with detection by matrix-assisted laser desorption time-of-flight mass spectrometry. For quality control, 10% of the sample was randomly re-genotyped, with a 100% concordant rate. All genotyping was done blind to knowledge of subjects’ clinical data.

Statistical analysis

The Hardy-Weinberg equilibrium (HWE) test was carried out before the association analysis (Table 4). All phenotypes were documented in a Filemaker Pro database. Statistical analyses for phenotypes were performed by SPSS software (version 20.0). We tested for recessive effects for three significant SNPs by comparing the three genotypes or different subgroups using ANOVA, with change in BMI across the 12-week trial as the dependent measure. The level of statistical significance for the above tests was set a priori at P < 0.05.

The linear regression model in the PLINK65 was used to test the association between the chosen SNPs and several phenotypes, where the changes of weight, BMI, WHR, lipid and glycemic profiles were used as phenotypes for the quantitative trait locus analyses. Linear regressions were performed for each quantitative trait against age within sexes, standardized residuals were saved to make mean = 0 and standard deviation = 1 for each phenotype. Outliers (more than 4 standard deviations) were deleted from this study. As 43 SNPs in 23 candidate genes were analyzed, we used Bonferroni corrections for multiple testing, and the nominal P-value should be at least 0.002 to be significant after multiple test corrections.

Pairwise gene-gene interaction analyses (epistasis) were carried out by PLINK65 among candidate gene SNPs in all patients, the FEP group, the OLZ treatment group, and the RIS treatment group, Bonferroni corrections were also employed for multiple testing.

In addition, we also performed a discrete association study for AIWG by PLINK. A weight gain “case” was defined as a patient who gained 7% or more of his or her baseline body weight in a 12-week trial.

Conclusions

In Conclusion, our findings suggest the role of TOX, ADIPOQ and BDNF in weight and WHR gain induced by atypical antipsychotics in schizophrenia subjects. This study provides a better understanding of genetic factors predisposing individuals to AAPD-induced obesity, dyslipidemia, and abnormal glucose metabolism that may help guide clinical medication intervention and also reveal the pathogenesis of AIWG.

Additional Information

How to cite this article: Li, S. et al. TOX and ADIPOQ Gene Polymorphisms Are Associated with Antipsychotic-Induced Weight Gain in Han Chinese. Sci. Rep. 7, 45203; doi: 10.1038/srep45203 (2017).

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