Flow is a psychological concept, first proposed in the 1970s by the American psychologist Csikszentmihalyi and refers to the psychological experience of a person when they have a strong interest in an activity and is fully immersed in it. Csikszentmihalyi interviewed chess players, rock climbers, dancers, artists and others who emphasized enjoyment as the key reason for pursuing an activity. They all reported an optimal experience when engaging in activities where they met just-manageable challenges by tackling clear goals and continuously adjusted their actions through immediate feedback. Further study by Csikszentmihalyi confirmed the universality of flow. Ordinary people may enter the flow state in their daily learning or work activities. Many different perspectives on the feature dimensions of flow state have been reported in recent years. Csikszentmihalyi and others summarized the following nine features of flow state: The merging of action and awareness, concentration on the task in hand, loss of self-consciousness, sense of control, unambiguous feedback, autotelic experience, challenge-skill balance, clear goals, and distorted sense of time.
Flow state is thought to be relevant to positive psychology, and is also considered an indicator of subjective well-being.[2,4,5] It helps individuals gain pleasant emotions, and increases life satisfaction as well as other positive emotions. Several twin and family studies show that genetic factors may account for as much as 30% to 40% of the variance in subjective well-being. This suggests that flow may also have a stable genetic basis. Recent studies have identified several single nucleotide polymorphisms (SNPs) shared by subjective well-being and common mental illnesses, such as depression. It is possible that SNPs associated with mental disorders can also affect flow. Based on this assumption, we performed genetic studies to determine association between candidate SNPs and flow.
Flow has been extensively studied by psychologists but few genetic studies about flow have been reported, and its physiological mechanism is not known. We performed a genetic study of flow based on data from questionnaires of 800 1st year students (freshman) from Jining Medical University, Shandong Province, China. The primary focus of this study was to analyze the association between flow and several candidate gene loci. We compared the different allele frequencies of the selected SNPs to investigate the possible biological mechanism of flow.
Participants and methods
We recruited 896 1st year students of Jining Medical University. After questionnaire quality control, the subjects consisted of 329 males and 541 females and the male–female ratio was 0.61. The average age was 20.6 ± 0.84 years. The students completed a questionnaire about subjective happiness. This study did not involve any drugs or major illness-related samples.
The questionnaire contained the Dispositional Flow Scale-2, which was developed by Jackson and Eklund in 2002. Based on Csikszentmihalyi nine dimensional theory of flow, 34 item scales in Dispositional Flow Scale-2 are designed to measure flow experiences at both dispositional and state level. We used the sum formula in WPS Excel (Version 188.8.131.5288, Jinshan Office Software Co., Ltd., Beijing, China) to calculate the total flow score of 34 items and we separately calculated the total score of nine features of flow in Dispositional Flow Scale-2 for each individual.
The procedures were clearly explained to all the subjects and informed consent was provided by the participants. All the processes of this study were carried out under the approval of the Ethics Committee of Jining Medical University (approval No. JNMC-2016-KY-001) on June 1, 2016. The DNA used for sequencing was extracted from blood samples using a standard phenol-chloroform protocol.
We selected five candidate SNPs for this association study: rs35936514, rs11191454, rs2535629, rs12415800, and rs1024582. They are located in genes relevant to several common psychiatric disorders, including autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia. Basic information, including gene location of the five SNPs and chromosome position and are provided in the National Center for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov).
A matrix-assisted laser desorption/ionization time-of-flight (MALDI TOF) mass spectrometer using the MassARRAY® Analyzer 4 platform (Sequenom, CA, USA) was used to assay the Genotyping of the 5 candidate SNPs (rs35936514, rs11191454, rs2535629, rs12415800, and rs1024582).
R software (Version 0.99.467, 2009–2015 RStudio, Inc., Boston, MA) was used to analyze Hardy–Weinberg equilibrium, allele and SNP genotype frequency. The correlation between candidate SNPs and individual total flow score was assessed using the “SNPassoc” packages (Juan R González, Lluís Armengol, Elisabet Guinóetl., Boston, MA, USA). Pearson P value test and Bonferroni correction were also performed by R software (https://www.r-project.org/) to indicate whether the difference is significant and correct P values. Statistic Packages for Social Sciences (SPSS; Version 17.0; IBM Watson Studio, New York, NY, USA) was used to perform multivariate analysis of variance (MANOVA) to compare the different SNP genotypes within the nine features of flow with gender and age as covariates. The scores were treated as a continuous variable in all association studies. The significance level was set at P < 0.05 for all of the above analyses.
The original sample contained 896 individuals, and the final dataset consisted of 870 individuals after questionnaire quality control. The number of valid individuals accounted for 97% of the total. As shown in Figure 1, we filtered subject data with a quality-control factor.
The distributions of the 5 SNPs were all in Hardy–Weinberg equilibrium (Table 1). There were 5 association studies for 5 candidate SNPs (rs11191454, rs2535629, rs1024582, rs12415800, and rs35936514) in our study. “SNPassoc” packages in R showed the Pearson P value for the 5 tests: 0.020, 1.000, 0.475, 1.000, 0.390, rs2535629, rs1024582, rs12415800, and rs35936514 failed to show significant association. Then Bonferroni correction was used on the five tests. And the P value for rs11191454 was 0.03 after Bonferroni correction. rs11191454 appeared to be associated with flow in codominant (P = 0.004), recessive (P = 0.008) and over-dominant models (P = 0.013), and the association was still statistically significant after adjusting for age and gender. Table 2 presents the genotype frequencies of rs11191454, along with the P value before and after adjusting for age and gender in five genetic models.
The association of rs11191454 with flow, prompted us to performed multivariate analysis of variance to determine how rs11191454 influences flow. The three genotypes were divided into two groups: AA+AG and GG, and they were used as independent variables and the total score of nine features of flow were used as the dependent variables. Gender and age were set as covariables because they showed significant difference among genotypes (P < 0.05). The AA+AG genotype was significantly different from the GG genotype in the following four feature dimensions: the merging of action and awareness (P = 0.007), challenge-skill balance (P = 0.001), sense of control (P = 0.018), and clear goals (P = 0.027; Table 3). We also found that subjects with an A allele had higher scores in the above four feature dimensions of flow.
There has been growing interest in flow, but most recent studies have been psychological investigations. Here, we performed a genetic study of flow that demonstrates a positive association between rs11191454 and flow. The multivariate analysis of variance analysis showed rs11191454 can affect flow through 4 different aspects: merging of action and awareness, challenge-skill balance, sense of control, and clear goals.
Rs11191454 is located at Chr10:102900247, in the intron of arsenite methyltransferase (AS3MT). AS3MT has been associated with autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, schizophrenia, and other major psychiatric disorders.[9–13] The AS3MT gene encodes an enzyme that plays an important role in arsenic metabolism. It catalyzes transfer of a methyl group from S-adenosyl-L-methionine to trivalent arsenic. The metabolic action of AS3MT is related to the accumulation of various arsenic compounds in the body and is, therefore, involved in susceptibility to arsenic toxicity. Arsenic exposure can cause memory disturbance, developmental delay and impaired intelligence in children. Furthermore, arsenic-exposed rats display decreased locomotor activity and behavioral disorders. Based on the theory that arsenic neurotoxicity promotes hippocampal neuronal damage causing intellectual and cognitive impairments,[17,18] we predicted that haplotypes of rs11191454 may lead to abnormal gene function, resulting in neurotoxicity caused by defective arsenic metabolism, which will eventually result in intellectual and cognitive disabilities.
One of the manifestations of intellectual disability is attention deficit. rs11191454 is a risk locus in deficit-hyperactivity disorder. The connection between intelligence and flow may help us understand how rs11191454 exerts an influence on flow. Specifically, individuals must identify and accept progressively more complex challenges to continue experiencing flow, which requires developing greater levels of skill. The other feature of flow, the sense of control, means a sense that one can control an action because one knows how to respond to what happens next at the cognitive level. Therefore, intelligence disabilities may be explained the challenge-skill balance and the sense of control dimensions. According to Csikszentmihalyi, attention plays a key role in entering and staying in flow. Intense concentration allows participants to enter a state where consciousness and action are highly merged. The self becomes organized around goals that are set by an attention-oriented guide. Thereby, the other two feature dimensions, the merge of action and awareness and clear goals, can be partly explained by the attention factor.
Our study reveals a genetic association with flow, which will help determine the biological mechanism of flow and might provide a possible target for treating depression. Many phenotypes are highly polygenic and susceptible to genetic interactions; flow may also be like this. Our research provides a clue to the genetic basis of flow, and this mental phenotype is undoubtedly important and worth studying.
One of the limitations of our study was the lack of sample size. The statistical power (P value: 0.04) of the positive rs11191454 association was relatively weak. Further genetic studies are required with larger sample sizes to verify our results. Another limitation was that only a few candidate SNPs were studied. In addition, the SNPs in our study were all intron variants; they were related to disease but not necessarily the pathogenic site. Functional genetic loci should be investigated in future research.
We show that the SNP, rs11191454, is associated with flow in a sample of 870 1st year students of Jining Medical University. This SNP might affect arsenic metabolism, which in turn, might affect flow in four feature dimensions, which mainly involved intelligence and attention. Together with previous research into rs11191454 and the AS3MT gene, this study provides a reference for the physiological mechanism of flow.
ZC contributed to data analysis and manuscript drafting. CL, LA, NZ, DR, FY, RY, YB, QS, LJ, ZG, GM, FX, LS, FY, LD, LZ, YX, and BB contributed to data collection. LH, TY, XL, and GH conceived the study, data analysis, and manuscript drafting. All authors approved the final version of the paper.
Institutional review board statement
This study was approved by the Ethics Committee of Jining Medical University, China (approval No. JNMC-2016-KY-001) on June 1, 2016.
Declaration of participant consent
The authors certify that they have obtained the participant consent forms. In the forms, participants have given their consent for their images and other clinical information to be reported in the journal. The participants understand that their names and initials will not be published and due efforts will be made to conceal their identity.
Conflicts of interest
The authors declare that they have no conflicts of interest.
1. Csikszentmihalyi M. Beyond Boredom and Anxiety. San Francisca, USA:Jossey-Bass; 1975.
2. Csikszentmihalyi M, Csikszentmihalyi IS. Optimal Experience: Psychological Studies of Flow
in Consciousness. New York, NY, USA:Cambridge University Press; 1988.
3. Jackson SA, Marsh HW. Development and validation of a scale to measure optimal experience: the flow
state scale. J Sport Exerc Psychol
4. Diener E. Subjective well-being. Psychol Bull
5. Csikszentmihalyi M. Happiness and creativity: going with the flow
6. Csikszentmihalyi M, Hunter J. Happiness in everyday life: the uses of experience sampling. J Happiness Stud
7. Rietveld CA, Cesarini D, Benjamin DJ, et al. Molecular genetics and subjective well-being. Proc Natl Acad Sci U S A
8. Okbay A, Baselmans BM, De Neve JE, et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet
9. Cross-Disorder Group of the Psychiatric Genomics ConsortiumIdentification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet
10. Lara DR, Souza DO. Schizophrenia: a purinergic hypothesis. Med Hypotheses
11. Aberg KA, Liu Y, Bukszar J, et al. A comprehensive family-based replication study of schizophrenia genes. JAMA Psychiatry
12. Duarte RRR, Troakes C, Nolan M, et al. Genome-wide significant schizophrenia risk variation on chromosome 10q24 is associated with altered cis-regulation of BORCS7, AS3MT
, and NT5C2 in the human brain. Am J Med Genet B Neuropsychiatr Genet
13. Li M, Jaffe AE, Straub RE, et al. A human-specific AS3MT
isoform and BORCS7 are molecular risk factors in the 10q24.32 schizophrenia-associated locus. Nat Med
14. Agusa T, Fujihara J, Takeshita H, et al. Individual variations in inorganic arsenic metabolism
associated with AS3MT
genetic polymorphisms. Int J Mol Sci
15. Hsieh RL, Su CT, Shiue HS, et al. Relation of polymorphism of arsenic metabolism
genes to arsenic methylation capacity and developmental delay in preschool children in Taiwan. Toxicol Appl Pharmacol
16. Rodriguez VM, Carrizales L, Jimenez-Capdeville ME, et al. The effects of sodium arsenite exposure on behavioral parameters in the rat. Brain Res Bull
17. Pandey R, Rai V, Mishra J, et al. From the cover: arsenic induces hippocampal neuronal apoptosis and cognitive impairments via an up-regulated BMP2/Smad-dependent reduced BDNF/TrkB signaling in rats. Toxicol Sci
18. Du X, Tian M, Wang X, et al. Cortex and hippocampus DNA epigenetic response to a long-term arsenic exposure via drinking water. Environ Pollut
19. Park S, Park JE, Yoo HJ, et al. Family-based association study
of the arsenite methyltransferase gene (AS3MT
, rs11191454) in Korean children with attention-deficit hyperactivity disorder. Psychiatr Genet
20. Csikszentmihalyi M. Flow
and the Foundations of Positive Psychology. Dordrecht, the Netherlands:Springer; 2014.
21. Houle D. Colloquium papers: Numbering the hairs on our heads: the shared challenge and promise of phenomics. Proc Natl Acad Sci U S A
2010; 107 (Suppl 1):1793–1799.
Keywords:Copyright © 2019 The Chinese Medical Association. Published by Wolters Kluwer Health, Inc.
arsenic metabolism; AS3MT; association study; flow; single nucleotide polymorphism