IMR Press / FBL / Volume 27 / Issue 11 / DOI: 10.31083/j.fbl2711309
Open Access Original Research
Adiponectin. rs266729 Polymorphism and Nicotine Dependence Interaction: Genetic Investigations on the Anxiety Susceptibility
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1 Huzhou Third Municipal Hospital, The Affiliated Hospital of Wenzhou Medical University, 313000 Huzhou, Zhejiang, China
2 Laboratory of Translational Medicine, Affiliated Cixi Hospital, Wenzhou Medical University, 315300 Ningbo, Zhejiang, China
3 School of Mental Health, Wenzhou Medical University, 325035 Wenzhou, Zhejiang, China
4 School of Pharmacy, Wenzhou Medical University, 325035 Wenzhou, Zhejiang, China
5 Beijing Hui-Long-Guan Hospital, Peking University, 100096 Beijing, China
6 Key Laboratory of Psychosomatic Medicine, Inner Mongolia Medical University, 010110 Hohhot, Inner Mongolia, China
7 The Affiliated Kangning Hospital, Wenzhou Medical University, 325035 Wenzhou, Zhejiang, China
*Correspondence: (Yanlong Liu); (Fan Wang); (Li Chen)
These authors contributed equally.
Academic Editor: Maria Pina Concas
Front. Biosci. (Landmark Ed) 2022, 27(11), 309;
Submitted: 11 August 2022 | Revised: 25 October 2022 | Accepted: 26 October 2022 | Published: 17 November 2022
(This article belongs to the Special Issue Genetics of Psychiatric Disorders)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background and Aims: Nicotine dependence (ND)-induced anxiety might be modulated by genetic polymorphisms. The gene-by-environment interaction can be fitted into the diathesis-stress and differential susceptibility models. Nevertheless, knowledge of the interaction between adiponectin (ADPN) polymorphisms and ND on the incident mental disorder is currently scarce. This study aims to understand the role of ADPN rs266729 on anxiety in patients with ND while elucidating the psychology model and the various reactions across genotypes. Methods: We included 315 Chinese males with confirmed ND, measured using the Fagerstrom test for nicotine dependence (FTND). Anxiety was assessed using the Self-rating Anxiety Scale. Genomic DNA was extracted and genotyped from peripheral blood. Hierarchical regression models were used to test the interactions. Results: There was a significant interaction between ADPN rs266729 and ND (β = –0.19, p < 0.05). The CC homozygote was more likely to be affected by ND-induced anxiety (β = 0.14, t = 4.43, p < 0.01). Re-parameterized regression models revealed that the interaction between ADPN rs266729 and ND could fit the strong differential susceptibility model (R2 = 0.05, p < 0.001). Conclusions: ADPN rs266729 was correlated with susceptibility to anxiety symptoms among male adults with ND and could fit the differential susceptibility model. The CC homozygote of rs266729 was a plasticity factor that increased anxiety symptoms in individuals with ND.

nicotine dependence (ND)
differential susceptibility
1. Introduction

Tobacco abuse is a global health problem associated with nicotine dependence (ND) [1]. A 2012–2014 study found that the prevalence of cigarette smoking was >66.1% in Chinese males aged 15 years, and 11.8% of these were highly-nicotine dependent [2]. A common feature of any drug of abuse is the increased risk of developing other addictions and neuropsychiatric problems [3, 4, 5, 6, 7]. Many experts believe smoking relieves depression and anxiety; however, recent studies have shown that smoking exposure is associated with late depression/anxiety [8]. Preclinical studies showed that long-term exposure to nicotine might cause upregulation of neuropeptide corticotropin-releasing factor (CRF) and neuronal circuit alteration, which triggers severe anxiety disorder in rats [9]. A growing body of evidence suggests that ND-induced anxiety is related to genetic factors. One study found that ND in twins was significantly associated with nicotine withdrawal-induced symptoms of anxiety and depression; monozygotic twins more strongly predicted outcomes than dizygotic twins, suggesting that genetic factors contribute to the risk of developing anxiety disorder [10]. Nevertheless, the interaction of ND and specific genetic polymorphisms on the onset of anxiety is less studied.

Adiponectin (ADPN) is a plasma protein secreted by adipocytes; it is connected with ND or anxiety. Previous studies suggested that nicotine administration decreased serum ADPN levels [11, 12, 13]. Regarding the relationship between adiponectin and anxiety, decreased ADPN was associated with negative emotion-related behaviors in chronic social defeat model mice. This reduction can be reversed by rosiglitazone (a PPARγ agonist), and the anxiolytic effects of rosiglitazone were abolished in ADPN knockout mice [14]. In another study, ADPN-haploinsufficient mice showed a decreased percentage of open/total arm time, entries into an elevated plus maze test and increased latency to the light side in the light/dark box test, effects that were reversed by ADPN intervention; this study also showed that ADPN modulates anxiety-related behavior through AdipoR1 [15]. These findings suggest a potential link between the ADPN gene and anxiety. Rs266729, an ADPN polymorphism, has been studied in coronary heart disease (CHD), Alzheimer’s disease (AD), and diabetes [16, 17, 18]. However, there are no studies on the effect of ND and ADPN on anxiety.

The precise form of the interaction between the ND and ADPN rs266729 polymorphism has not been investigated. According to the literature on G × E interactions, the interaction between environment and gene polymorphism can be fitted into two models. The diathesis-stress model of environmental action [19] suggests that individuals with a risk gene are affected negatively by poor environments, whereas individuals with a different version of the same gene are relatively unaffected by environments. On the other hand, the differential susceptibility model suggests that individuals carrying “risk alleles” might be more susceptible to environmental changes [20]. Individuals with a putative high-risk allele would exhibit poorer outcomes in poor environments, similar to those with a low-risk allele in average environments.

Our study aims to elucidate the effect of ADPN rs266729 on anxiety in patients with ND, clarifying the psychological model and the varying reactions associated with different genotypes. Specifically, the form of G×E interaction based on two theoretical models (diathesis-stress or differential susceptibility) will be identified using confirmatory analytic approaches.

2. Materials and Methods
2.1 Participants

The 315 men in the study were drawn from our previous study [21], including 300 alcohol-dependent (AD) inpatients recruited from psychiatric hospitals in northern China and 15 healthy controls (HCs) from Inner Mongolia Autonomous Region were recruited from December 2009 to December 2012. All patients met the criteria for ND based on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV). The exclusion criteria were as follows: (1) other drug abuse or dependence, except alcohol; (2) severe cardiovascular disease, liver disease, or kidney disease; (3) participant, or a first-degree relative of the participant, with a severe mental illness.

The participants were asked to complete a series of questionnaires and provide a blood sample for DNA extraction. All staff involved in this study were trained before the study commenced. The Institutional Review Board of the Inner Mongolian Medical University approved the study (Ethic approval number: YKD2015003). All patients provided written informed consent and were told that the blood sample would be subjected to a gene assay.

2.2 Measures
2.2.1 Nicotine Dependence

The Fagerstrom test for nicotine dependence (FTND) was used to assess ND [22]. FTND is the most used scale for assessing ND. The FTND scores ranged from 0 to 10. The scale consists of six questions with scores at every answer related to the smoker’s level of ND. A score of 1–3 was categorized as low ND, 4–6 is medium ND, and 7–10 is high ND [23].

2.2.2 Assessment of Anxiety

The Self-rating Anxiety Scale (SAS) was used to assess each participant’s level of anxiety. Zung introduced the SAS [24] for measuring what may be described as state anxiety, a term referring to “a transitory emotional state or condition of the human organism that is characterized by subjective, consciously perceived feelings of tension and apprehension and heightened autonomic nervous system activity” [25]. The questionnaire uses a four-point Likert score, ranging from 1 (none or a small amount of time) to 4 (most or all of the time). SAS has satisfactory psychometric properties, Cronbach’s alpha = 0.82 [26].

2.2.3 Alcohol Dependence Level

Alcohol dependence level was measured using the Michigan Alcoholism Screening Test (MAST) [27]. The MAST is a questionnaire containing a 25-item self-report in which respondents rated the severity of a range of dependence-related alcohol use behaviors using a 4-point scale ranging from 1 (not at all) to 4 (very much). The scale has high internal consistency with Cronbach’s Alpha value of 0.90 [28].

2.3 Genotyping

Genomic DNA was extracted from 5 ml of peripheral blood of each participant using the salting-out method. Primers were designed using Assay Design 3.1 software from Sequenom (San Diego, CA, USA) and synthesized by Gene-Cloud Biotechnology Co., Ltd. (Beijing, China). PCR-specific and single-base extension primer sequences are provided as follows: 5-ACGTTGGATGACACCTTGGACTTTCTTGGC-3, 5-ACGTTGGATGATGTGTGGCTTGCAAGAACC-3, and single-base extension primer: 5-GCTCATGTTTTGTTTTTGAAG-3. Reactions were carried out according to the manufacturer’s protocol [29, 30]. All laboratory procedures were carried out in a manner blind to case-control status. The conditions of PCR were as follows: 50 °C for 2 min, 95 °C for 10 min, followed by 50 cycles of 95 °C for 15 s and 60 °C for 1 min. Ten percent of the DNA samples were duplicated randomly and tested, and no-fault genotyping was found.

2.4 Statistical Analysis

First, the Hardy-Weinberg equilibrium for genotype distributions of ADPN rs266729 was tested using the Chi-square test for goodness of fit. Then, Pearson correlations were examined between genetic polymorphisms, age, years of education, ND, and anxiety. Consistent with other studies [31, 32, 33], CG and GG genotypes were collapsed into a G allele group coded as one and the CC genotype coded as 0. Then, traditional linear regression was used to test the interaction between ND and the rs266729 polymorphism. When a significant interaction was found, region of significance (RoS) analysis was used to examine the form of the interaction [34].

Finally, a re-parameterized regression model was fitted to examine the specific pattern of gene × environment pattern [35] with the following form:

(1) Y = { Group: D = 0 B 0 + B 1 ( X - C ) + B 3 X 2 + B 4 X 3 + E Group: D = 1 B 0 + B 2 ( X - C ) + B 3 X 2 + B 4 X 3 + E

Diathesis-Stress model is generated by the re-parameterized regression equations. In this regression equation, independent variable (X) is FTND scores, dependent variable (Y) is anxiety, age and years of education are demographic covariates (X2, X3). All variables are continuous variables and model were fitted using the least-squares method [35]. Crossover point C is a point where the slopes of the different groups cross. In differential-susceptibility, crossover point C is estimated by regression equations. What distinguishes the diathesis-stress and differential susceptibility models is the estimate and interval estimate of crossover point C. If the estimate and interval estimate of crossover point C fall within the range of ND, the model is consistent with the differential susceptibility model. Otherwise, if crossover point C is over the maximum of ND, the model is consistent with the diathesis-stress model. Results of the final best-fit models arrived at via model comparison, including F test, Akaike information criterion (AIC) and Bayesian information criterion (BIC).

The diathesis-stress and differential susceptibility models can be further subdivided into strong and weak versions. Strong versions assume that non-risk/non-plasticity allele carriers are not susceptible to the environment. Weak versions assume both allele carriers are susceptible to the environment; however, non-risk/non-plasticity carriers are less susceptible to the environment than risk/plasticity carriers. These models are nested within one another. Therefore, the F-test was used to compare the models and identify a difference in the parameter estimates. For non-nested models, the Akaike information criterion and Bayesian information criterion were compared to evaluate which model was a better fit.

3. Results
3.1 Descriptive Statistics

Of the 315 male inpatients, 157 (49.8%) were CC homozygotes, 133 (42.2%) were CG heterozygotes, and 25 (7.9%) were GG homozygotes. The genotype distribution of ADPN rs266729 was consistent with Hardy-Weinberg equilibrium (χ2 = 0.19, p > 0.05, Table 1). Table 2 displays the demographic and clinical characteristics of the participants. The mean age, academic years, mean Nicotine Dependence (FTND scores), and Anxiety (SAS scores) are all listed separately with genotype CC and CG/GG in Table 3. The ADPN genotypes were dummy-coded into 0 = CC (CC homozygotes) and 1 = CG/GG (G allele carriers). A series of t-tests were then conducted to determine whether participants with and without ND and anxiety symptoms differed regarding the polymorphism ADPN rs266729. We found no significant differences (FTND: t = –0.43; SAS: t = 0.60, both p > 0.05, Table 3).

Table 1.Hardy-Weinberg equilibrium.
Genotype Number of people Percentage
CC 157 49.8%
CG 133 42.2%
GG 25 7.9%
χ2 0.19 p 0.67
Table 2.Total sample descriptive demographics.
Variables M ± SD
Age 45.22 ± 9.08
Educational Years 10.25 ± 2.73
Nicotine Dependence (FTND scores) 5.89 ± 2.53
Anxiety (SAS scores) 35.03 ± 8.56
M, mean; SD, standard deviation.
Table 3.Independent sample test.
rs266729 polymorphism Age Educational Years Nicotine Dependence (FTND) Anxiety (SAS)
CC 45.06 ± 9.10 10.28 ± 2.63 5.83 ± 2.47 35.32 ± 9.23
G 45.37 ± 9.07 10.22 ± 2.83 5.95 ± 2.61 34.75 ± 7.85
t –0.31 0.19 –0.43 0.60
p 0.76 0.85 0.67 0.55
CC, CC homozygote; G, G allele; t, independent t-test; p, p-value for t-test.
3.2 Correlation of FTND and SAS Scores

The descriptive statistics for each research variable are shown in Table 4. FTND scores were positively correlated with SAS scores (r = 0.15, p < 0.01). No significant correlation between polymorphisms of rs266729 and FTND (p > 0.05), as well as SAS scores (p > 0.05) were observed.

Table 4.Descriptive statistics and correlations among study variables
rs266729 Age Educational Years Nicotine Dependence (FTND) Anxiety (SAS)
rs266729 1
Age 0.02 1
Educational Years –0.02 –0.28*** 1
Nicotine Dependence (FTND) 0.03 0.03 –0.10 1
Anxiety (SAS) –0.04 –0.3 –0.09 0.15** 1
M (–) 45.22 10.25 5.89 35.03
SD (–) 9.08 2.73 2.53 8.56
M, mean; SD, standard deviation. Note: **p < 0.01; ***p < 0.001.
3.3 Effect of Interactions between ADPN Genotyping and ND on Anxiety

Next, hierarchical regression models were used to predict anxiety from ND and test the interaction between ND and rs266729 polymorphism on anxiety, with age and years of education as covariates. Table 5 shows that ND had a significant main effect on anxiety symptoms (β = 0.14, p < 0.05); however, there was no main effect of genotype (β = 0.04, p = 0.51). The interaction between ADPN rs266729 and ND was significant (β = –0.19, p < 0.05). Then, the RoS test was used to examine the interaction effect. As shown in Fig. 1, the slopes for ND on anxiety were as follows: CC homozygotes, β = 0.14, t = 4.43, p < 0.01; G allele carriers, β = –0.05, t = 1.07, p < 0.01. Relative to G allele carriers, CC homozygotes were more likely to be affected by ND, causing anxiety.

Table 5.Interaction between rs266729 and Nicotine Dependence on Anxiety.
Variables Anxiety (SAS)
ΔR2 B (SE) β t p 95% CI
Age 0.01 0.01 (0.01) 0.06 1.04 0.30 [–0.01, 0.02]
Educational Years 0.04 (0.02) 0.10 1.74 0.08 [–0.01, 0.08]
Nicotine Dependence (FTND) 0.02 0.14 (0.06) 0.14 2.49 0.01 [0.03, 0.25]
rs266729 0.07 (0.11) 0.04 0.66 0.51 [–0.15, 0.29]
Nicotine Dependence×rs266729 0.02 –0.26 (0.11) –0.19 2.31 0.02 [–0.48, –0.04]
ΔR2, Proportion of explained variance of anxiety; B, unstandardized regression coefficient; β, standardized regression coefficient; SE, standard errors; 95% CI, 95% confidence interval.
Fig. 1.

Region of significance (RoS) test on anxiety from nicotine dependence severity in rs266729 allelic groups. Note: Anxiety and nicotine dependence: z scores (unit: standard deviations [SDs]); Simple slope at CC homozygote: 0.14, t = 4.43, p < 0.001; Simple slope at G allele: –0.05, t = 1.07, p = 0.287; Lower threshold for RoS with respect to nicotine dependence = –0.115; Upper threshold for RoS with respect to nicotine dependence = 0.610; Crossover point on predictor = 0.211.

3.4 No Interactive Effect of Alcohol Dependence and ADPN rs266729 on Anxiety

In addition, we re-analyzed the data to explain how alcohol use influences these results. As shown in Supplementary Table 1, MAST scores were positively correlated with SAS scores (r = 0.36, p < 0.001), suggesting that anxiety was positively correlated with alcohol dependence, as our previous study reported [36]. However, there is no significant correlation between polymorphisms of rs266729 and MAST scores (r = 0.003, p > 0.05). In addition, as shown in Supplementary Table 2, alcohol dependence significantly affected anxiety symptoms (β = 0.35, p < 0.001). However, there was no significant interaction between alcohol dependence and ADPN rs266729 on anxiety (β = –0.22, p > 0.05). Thus, although anxiety was positively correlated with alcohol dependence, there was no significant interactive effect of alcohol dependence and ADPN rs266729 on anxiety.

3.5 Re-Parameterized Regression

To improve the accuracy and robustness of the interaction results, a re-parameterized regression model was fitted to examine the specific pattern of G × E. The fit of the strong differential susceptibility model, Model A, yielded a significant R2 = 0.05, p < 0.001 (Table 6), explaining a significant amount of variance in anxiety, in which the slope for the G allele group was significant (B2 = 0.28, p < 0.001). The estimated crossover point C and 95% confidence interval (CI) of C both fell within the range of ND, C = 0.27 (SE = 0.41), 95% CI = [–0.53, 1.07]. We analyzed variance and compared the AIC and BIC values to confirm if Model A is the best model. As shown in Table 6, compared with Model A, Model B had one more parameter, but the explained variance was not significantly increased (ΔR2 <0.001, p > 0.05). Model C explained a significantly lower variance (ΔR2 = 0.03, p < 0.05) than Model A. The AIC and BIC values were smaller than Model D. These findings suggest that Model A provides strong support for the strong differential susceptibility model, indicating that the G allele carriers with ND are less susceptible to anxiety than CC homozygotes.

Table 6.Results for re-parameterized regression model for anxiety.
Differential susceptibility Diathesis-stress
Parameter Strong: Model A Weak: Model B Strong: Model C Weak: Model D
B0 –0.61 (0.41) –0.60 (0.42) –0.55 (0.42) –0.36 (0.43)
B1 (–) 0.02 (0.07) (–) 0.10 (0.06)
C 0.27 (0.41) 0.29 (0.45) 1.93 (–) 1.93 (–)
95% CI of C [–0.53, 1.07] [–0.59, 1.17] (–) (–)
B2 0.28*** (0.08) 0.28*** (0.08) 0.12* (0.05) 0.19** (0.06)
B3 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)
B4 0.03 (0.02) 0.03 (0.02) 0.04 (0.02) 0.03 (0.02)
0.05 0.05 0.03 0.04
F (df) 3.85** (4,310) 3.86** (5,309) 3.15* (3,311) 3.07* (4,310)
F vs.A (df) (–) 0.06 5.80* (–)
F vs.B (df) 0.06 (–) 2.92 3.03
AIC 889.66 891.60 893.51 892.68
BIC 912.18 917.87 912.27 915.20
Note: *p < 0.05; **p < 0.01; ***p < 0.001. Model: Anxiety = (rs266729 = CG/GG)(B0 + B1 (XMAST-C)) + (rs266729 = CC)(B0 + B2 (XMAST-C)) + B3 × Age + B4 × Educational years; F versus a stand for F tests of the difference in R2 for a given model versus the robust differential susceptibility model; a Parameter fixed at reported value; SE is not applicable, so is listed as (–); C, cross point; 95% CI of C, 95% confidence interval of C; AIC, Akaike Information Criterion; BIC, Bayesian Information Criteria. df: The number of independent variables that can freely estimate in the regression equation, sample size minus the number of independent variables minus 1.
4. Discussion

Our study aimed to explore the interactive effect of ND and ADPN rs266729 on anxiety. First, our results show that ND positively correlates with anxiety, consistent with previous reports [9, 21, 37, 38]. Although previous studies remain mixed regarding the direction of the association between smoking and mental illness, more and more studies support the view that nicotine exposure is associated with the later onset of depression or anxiety [8, 39]. Second, our results revealed that ADPN rs266729 CC homozygous carriers were more likely to suffer from anxiety among ND patients than G allele carriers. In other words, the CC genotype is a high-risk allele or plasticity factor, while the G allele is a low-risk allele or non-plasticity factor. Further, the psychology model was confirmed to fit the robust differential susceptibility model.

ADPN is also associated with anxiety. Peripheral ADPN levels appear inversely associated with anxiety and other diseases and may be a promising biomarker for diagnosis and disease monitoring [8]. Serum ADPN levels throughout pregnancy were inversely associated with antenatal anxiety [40]. Evidence suggests that ADPN can cross the blood-brain barrier and act on specific neuronal populations through its receptors, AdipoR1, and AdipoR2. While AdipoR1 is widely expressed in the brain, AdipoR2 expression is restricted to a few brain regions, including the hippocampus and hypothalamus. ADPN decreases the activity of the Ventral tegmental area (VTA) dopamine neurons and induces anxiolytic responses through direct activation of AdipoR1 [15]. AdipoR1 and AdipoR2 are also closely associated with anxiety by modulating the hypothalamic-pituitary axis [41]. Nicotine is an agonist of the alpha-4 beta-2 nicotine receptor that induces dopamine secretion in the mesolimbic pathway and improves motivation; subjects with high anxiety tendencies are a potentially high-risk group susceptible to developing ND [42]. One review suggested that current smokers have reduced ADPN levels and that this reduction can be reversed by quitting smoking [12]. Another study suggested that tobacco use was significantly associated with a low ADPN level in community-dwelling young males [43]. The present study showed a significant interaction between ADPN rs266729 and ND. Compared to the G allele, ADPN rs266729 CC homozygous carriers were more likely to be affected by anxiety in ND patients. This result suggests that the GG genotype is a protective gene in anxiety induced by ND. These findings provide some guidance regarding preventing anxiety in people with ND. In the case of high-risk individuals (CC homozygous), we can intervene in advance to prevent exacerbations; for individuals with the G allele, we can enhance their ability to adjust themselves when facing adverse environments.

Re-parameterized regression models revealed that the interaction between ADPN rs266729 and ND fit the robust differential susceptibility model. In favorable environments, individuals with a putative high-risk allele show superior outcomes to individuals with the low-risk allele. In other words, individuals with “risk alleles” are sensitive to environmental conditions and may benefit from supportive environments; conversely, they may exhibit worse outcomes in unfavorable environments. The present study revealed that the robust differential susceptibility model was the best fit. This finding suggests that ADPN rs266729 CC homozygote carriers were more likely to be affected by ND-induced anxiety and might be a protective factor against anxiety in non-smoking people. Individual genetic variation and the interactions between genes and external factors may characterize neural circuits and neurochemical functions, representing adaptable individuals’ psychological strength. Stress-related events can increase an individual’s susceptibility to severe psychiatric problems such as anxiety. Our findings might be explained by the fact that individuals with genetic variation respond differently to different degrees of ND. Given that genetic factors contribute to recovery, it is critical to identify candidate genetic variations to explain genetic patterns.

Many reports of increased anxiety levels and smoking rates during the COVID-19 pandemic. Higher anxiety correlated with higher ND among university students during the pandemic [44]. These findings suggest that intervention with anxiety symptoms can reduce ND, reducing harm during the pandemic. Mental health prevention and intervention programs may reduce the risk for ND in teen smokers with and without symptoms of depression and anxiety [45]. One study showed that dependent smoking was positively associated with current anxiety/mood disorders; greater clinical attention could be directed toward the role of anxiety in smoking cessation [37]. Social interaction anxiety can also affect nicotine dependence via negative metacognitions about smoking [46]. In the long run, the probability of recurrence of ND can be reduced by improving anxiety symptoms.

This study has some limitations which should be considered. First, we investigated only one polymorphism; therefore, it is likely that differences across smoking groups could be related to internalizing outcomes in many ways other than their genotype at this ADPN variant. Other polymorphisms in the ADPN gene or other genes are likely to be involved. Second, the study is cross-sectional; therefore, causality cannot be inferred. Third, the baseline values of participants’ anxiety symptoms were not measured before admission and the diagnosis of nicotine dependence, which greatly limited the intensity of our study. Fourth, we did not consider dietary habits, physical activity, socioeconomic status, location of residence, or type of family. Finally, our findings may not be generalizable to other ethnic groups.

5. Conclusions

There was an interaction between ADPN rs266729 and ND, suggesting that rs266729 might correlate with anxiety symptoms among male adults with ND. These findings support the differential susceptibility model, in which the CC homozygote of rs266729 is a plasticity factor rather than a factor that only increases anxiety symptoms of individuals during ND. These empirical findings have important implications for understanding the genetic moderation of ND and its effect on individual differences in anxiety symptoms. Further work is required to explore the underlying mechanisms of anxiety modulation at the molecular level and functional studies of neural systems in a larger sample.

Availability of Data and Materials

The datasets presented in this article are not readily available because data use sharing agreements would be necessary. Requests to access the datasets should be directed to

Author Contributions

Conceptualization—LC, YL and FWang; methodology—XZ, FWu, GS, SY, YH and YW; software—KX; validation—LZ; formal analysis—WW; writing - original draft preparation—XZ; writing - review and editing—XS and LC; funding acquisition—XZ and FWang. All authors have read and agreed to the published version of the manuscript.

Ethics Approval and Consent to Participate

The study was approved by the Ethics Committee of Peking University Health Science Center. Written informed consent has been obtained from the patients to publish this paper (Ethic approval number: YKD2015003).


We thank all the participants in our study for their time and cooperation.


This research was funded by Natural Science Foundation of Xinjiang Uyghur Autonomous Region (2018D01C239), Project of Health Department in Zhejiang Province (2019KY681) and Huzhou Municipal Science and Tech Commission (2018GY20).

Conflict of Interest

The authors declare no conflict of interest.

Publisher’s Note: IMR Press stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Prochaska JJ, Benowitz NL. Current advances in research in treatment and recovery: Nicotine addiction. Science Advances. 2019; 5: eaay9763.
Ma Y, Wen L, Cui W, Yuan W, Yang Z, Jiang K, et al. Prevalence of Cigarette Smoking and Nicotine Dependence in Men and Women Residing in Two Provinces in China. Frontiers in Psychiatry. 2017; 8: 254.
Dani JA, Harris RA. Nicotine addiction and comorbidity with alcohol abuse and mental illness. Nature Neuroscience. 2005; 8: 1465–1470.
Cannizzaro E, Lavanco G, Castelli V, Cirrincione L, Di Majo D, Martines F, et al. Alcohol and Nicotine Use among Adolescents: An Observational Study in a Sicilian Cohort of High School Students. International Journal of Environmental Research and Public Health. 2022; 19: 6152.
Lavanco G, Castelli V, Brancato A, Tringali G, Plescia F, Cannizzaro C. The endocannabinoid-alcohol crosstalk: Recent advances on a bi-faceted target. Clinical and Experimental Pharmacology & Physiology. 2018. (online ahead of print)
Agrawal A, Madden PA, Bucholz KK, Heath AC, Lynskey MT. Transitions to regular smoking and to nicotine dependence in women using cannabis. Drug and Alcohol Dependence. 2008; 95: 107–114.
Castelli V, Plescia F, Maniaci G, Lavanco G, Pizzolanti G, Brancato A, et al. Alcohol binge drinking in adolescence and psychological profile: Can the preclinical model crack the chicken-or-egg question? Front in Psychiatry. 2022; 13: 996965.
Fluharty M, Taylor AE, Grabski M, Munafò MR. The Association of Cigarette Smoking with Depression and Anxiety: a Systematic Review. Nicotine & Tobacco Research. 2017; 19: 3–13.
Molas S, DeGroot SR, Zhao-Shea R, Tapper AR. Anxiety and Nicotine Dependence: Emerging Role of the Habenulo-Interpeduncular Axis. Trends in Pharmacological Sciences. 2017; 38: 169–180.
Edwards AC, Kendler KS. Nicotine withdrawal-induced negative affect is a function of nicotine dependence and not liability to depression or anxiety. Nicotine & Tobacco Research. 2011; 13: 677–685.
Fan LH, He Y, Xu W, Tian HY, Zhou Y, Liang Q, et al. Adiponectin may be a biomarker of early atherosclerosis of smokers and decreased by nicotine through KATP channel in adipocytes. Nutrition. 2015; 31: 955–958.
Kotani K, Hazama A, Hagimoto A, Saika K, Shigeta M, Katanoda K, et al. Adiponectin and Smoking Status: a Systematic Review. Journal of Atherosclerosis and Thrombosis. 2012; 19: 787–794.
Won W, Lee C, Chae J, Kim J, Lee C, Kim D. Changes of Plasma Adiponectin Levels after Smoking Cessation. Psychiatry Investigation. 2014; 11: 173.
Guo M, Li C, Lei Y, Xu S, Zhao D, Lu X. Role of the adipose PPARγ-adiponectin axis in susceptibility to stress and depression/anxiety-related behaviors. Molecular Psychiatry. 2017; 22: 1056–1068.
Sun F, Lei Y, You J, Li C, Sun L, Garza J, et al. Adiponectin modulates ventral tegmental area dopamine neuron activity and anxiety-related behavior through AdipoR1. Molecular Psychiatry. 2019; 24: 126–144.
Zhang H, Mo X, Hao Y, Gu D. Association between polymorphisms in the adiponectin gene and cardiovascular disease: a meta-analysis. BMC Medical Genetics. 2012; 13: 40.
Li W, Yu Z, Hou D, Zhou L, Deng Y, Tian M, et al. Relationship between Adiponectin Gene Polymorphisms and Late-Onset Alzheimer’s Disease. PLoS ONE. 2015; 10: e0125186.
Bai Y, Tang L, Li L, Li L. The roles of ADIPOQ rs266729 and MTNR1B rs10830963 polymorphisms in patients with gestational diabetes mellitus: a meta-analysis. Gene. 2020; 730: 144302.
Burmeister M, McInnis MG, Zöllner S. Psychiatric genetics: progress amid controversy. Nature Reviews Genetics. 2008; 9: 527–540.
Belsky J, Pluess M. Beyond diathesis stress: differential susceptibility to environmental influences. Psychological Bulletin. 2009; 135: 885–908.
Yang L, Wang F, Wang M, Han M, Hu L, Zheng M, et al. Association between oxytocin and receptor genetic polymorphisms and aggression in a northern Chinese Han population with alcohol dependence. Neuroscience Letters. 2017; 636: 140–144.
Jamal M, Willem Van der Does AJ, Cuijpers P, Penninx BW. Association of smoking and nicotine dependence with severity and course of symptoms in patients with depressive or anxiety disorder. Drug and Alcohol Dependence. 2012; 126: 138–146.
Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom K. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Addiction. 1991; 86: 1119–1127.
Zung WWK. A Rating Instrument for Anxiety Disorders. Psychosomatics. 1971; 12: 371–379.
Jegede RO. Psychometric attributes of the Self-Rating Anxiety Scale. Psychological Reports. 1977; 40: 303–306.
Dunstan DA, Scott N. Norms for Zung’s Self-rating Anxiety Scale. BMC Psychiatry. 2020; 20: 90.
Storgaard H, Nielsen SD, Gluud C. The validity of the Michigan Alcoholism Screening Test (MAST). Alcohol and Alcoholism. 1994; 29: 493–502.
Skinner HA, Allen BA. Alcohol dependence syndrome: measurement and validation. Journal of Abnormal Psychology. 1982; 91: 199–209.
Beltcheva O, Boyadzhieva M, Angelova O, Mitev V, Kaneva R, Atanasova I. The rs266729 single-nucleotide polymorphism in the adiponectin gene shows association with gestational diabetes. Archives of Gynecology and Obstetrics. 2014; 289: 743–748.
Hsiao TJ, Lin E. A Validation Study of Adiponectin rs266729 Gene Variant with Type 2 Diabetes, Obesity, and Metabolic Phenotypes in a Taiwanese Population. Biochemical Genetics. 2016; 54: 830–841.
de Luis D, Primo D, Izaola O, Aller R. Adiponectin Gene Variant rs266729 Interacts with Different Macronutrient Distribution of Two Different Hypocaloric Diets. Lifestyle Genomics. 2020; 13: 20–27.
de Luis DA, Izaola O, Primo D, Aller R. Relation of a variant in adiponectin gene (rs266729) with metabolic syndrome and diabetes mellitus type 2 in adult obese subjects. European Review for Medical and Pharmacological Sciences. 2020; 24: 10646–10652.
de Luis DA, Primo D, Izaola O, Gomez Hoyos E, Lopez Gomez JJ, Ortola A, et al. Role of the variant in adiponectin gene rs266729 on weight loss and cardiovascular risk factors after a hypocaloric diet with the Mediterranean pattern. Nutrition. 2019; 60: 1–5.
Roisman GI, Newman DA, Fraley RC, Haltigan JD, Groh AM, Haydon KC. Distinguishing differential susceptibility from diathesis–stress: Recommendations for evaluating interaction effects. Development and Psychopathology. 2012; 24: 389–409.
Belsky J, Pluess M, Widaman KF. Confirmatory and competitive evaluation of alternative gene-environment interaction hypotheses. Journal of Child Psychology and Psychiatry. 2013; 54: 1135–1143.
Hong L, Wen L, Niculescu M, Zhou F, Zou Y, Shen G, et al. The Interaction Between POMC rs2071345 Polymorphism and Alcohol Dependence in Anxiety Symptoms Among Chinese Male Problem Drinkers. Frontiers in Psychiatry. 2022; 13: 878960.
Grover KW, Goodwin RD, Zvolensky MJ. Does current versus former smoking play a role in the relationship between anxiety and mood disorders and nicotine dependence? Addictive Behaviors. 2012; 37: 682–685.
Moylan S, Jacka FN, Pasco JA, Berk M. Cigarette smoking, nicotine dependence and anxiety disorders: a systematic review of population-based, epidemiological studies. BMC Medicine. 2012; 10: 123.
Du X, Wu R, Kang L, Zhao L, Li C. Tobacco smoking and depressive symptoms in Chinese middle-aged and older adults: Handling missing values in panel data with multiple imputation. Frontiers in Public Health. 2022; 10: 913636.
Rebelo F, de Jesus Pereira Pinto T, Franco-Sena AB, Lepsch J, Benaim C, Struchiner CJ, et al. Plasma adiponectin is inversely associated with antenatal anxiety: Results from a Brazilian cohort. Psychoneuroendocrinology. 2015; 51: 92–100.
Wagner EN, Strippoli MF, Ajdacic-Gross V, Gholam-Rezaee M, Glaus J, Vandeleur C, et al. Generalized Anxiety Disorder is Prospectively Associated with Decreased Levels of Interleukin-6 and Adiponectin among Individuals from the Community. Journal of Affective Disorders. 2020; 270: 114–117.
Pietras T, Witusik A, Panek M, Szemraj J, Górski P. Anxiety, depression and methods of stress coping in patients with nicotine dependence syndrome. Medical Science Monitor. 2011; 17: CR272–CR276.
Ahmad S, Shah M, Ahmed J, Khan A, Hussain H, McVey M, et al. Association of hypoadiponectemia with smokeless/dipping tobacco use in young men. BMC Public Health. 2015; 15: 1072.
Ayran G, Köse S, Küçükoğlu S, Aytekin Özdemir A. The effect of anxiety on nicotine dependence among university students during the COVID-19 pandemic. Perspectives in Psychiatric Care. 2022; 58: 114–123.
McKenzie M, Olsson CA, Jorm AF, Romaniuk H, Patton GC. Association of adolescent symptoms of depression and anxiety with daily smoking and nicotine dependence in young adulthood: findings from a 10-year longitudinal study. Addiction. 2010; 105: 1652–1659.
Izadpanah M, Najafi M, Khosravani V. Anxiety in social interactions and nicotine dependence in nicotine-dependent men: The role of metacognitions about smoking. Addictive Behaviors. 2021; 112: 106656.
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