Long‐term effects of adolescent sport experience, DRD2 and COMT genes, and their interaction on sport participation in adulthood

1 INTRODUCTION

One of the most effective ways to increase regular physical activity is to participate in sporting activities. Sports participation invariably involves physical activity and inherently includes various enjoyable aspects, such as personal challenge, social interaction, goal achievement, and competition. The International Society for Physical Activity and Health also suggests that sports participation is “an investment that works” to increase physical activity and improve health (Trost et al., 2014). Performing physical activity has been shown to be associated with a lower risk of developing various chronic diseases, such as hypertension, various types of cancer, coronary heart disease, stroke, osteoporosis, and Type 2 diabetes mellitus (U.S. Department of Health & Human Services, 2020). Engaging in physical activity also promotes psychological well-being by reducing psychological distress and enhancing self-esteem (U.S. Department of Health & Human Services, 2020). Despite these well-known benefits of physical activity, the sport participation rate has been shown to peak in late adolescence and decrease throughout adulthood in many countries including the United States (Australian Bureau of Statistics, 2012; Birchwood et al., 2008; Eime et al., 2016; Lee et al., 2020; Maia et al., 2010). Therefore, the development of efficient programs or policies that can help increase and maintain sport participation rate throughout adulthood is becoming critically important. However, numerous studies have been carried out on the modification and maintenance of physical activity-related behaviors in the adult population in the past decades and the results of these studies were unsatisfactory (Marcus et al., 2006; Müller-Riemenschneider et al., 2008). These results imply that there needs to be a new approach to understand sport participation behavior in adulthood.

Individual genomic variations have recently got momentous attention in addressing physical activity-related behaviors among adults. There is a growing body of studies emphasizing the role of dopamine-related genes in physical activity-related behaviors because the neurotransmitter dopamine affects the responsivity of the brain reward system and the way humans learn (Wise, 2004). Simonen et al. examined the relationship between dopamine receptor D2 (DRD2) gene and physical activity among black and white adults. They found out that DRD2 TT homozygotes were significantly less likely to participate in sports and physical activity than DRD2 CT heterozygotes and CC homozygotes in White women (Simonen et al., 2003). Similarly, Flack et al. showed that carrying the A1 allele of DRD2 gene is associated with a lower RRVexercise (relative reinforcing value of exercise) among 178 adults (127 were female) (Flack et al., 2019). Lee et al. also found out that the more women possess the DRD2 A1 allele, the more likely they are not to participate in sports throughout adolescence and young adulthood (Lee et al., 2020). Since A1 allele and T allele of DRD2 gene have been shown to be associated with several additive behaviors (Blum et al., 2017; Deng et al., 2015; Meyers et al., 2013; Ohmoto et al., 2013; Wang et al., 2016), women who carry these alleles may engage in addictive behaviors rather than sports in order to satisfy themselves. Among men, on the contrary, the more they carry the DRD2 A1 allele, the more likely they are to participate in sports throughout adolescence and young adulthood (Lee et al., 2020), indicating that exercise and sport participation can become addictive in the male population (Villella et al., 2011). The catechol-O-methyltransferase (COMT) gene is also one of the strongest candidate genes for physical activity-related behaviors and has been shown to be associated with several addictive behaviors (Kauhanen et al., 2000; Munafò et al., 2008; Schellekens et al., 2013). Rosso et al. examined whether intervention-induced changes in physical activity differ according to dopamine-related genes including the COMT gene. The differences in physical activity between intervention and control groups were greater for COMT methionine (Met) homozygotes than for those with at least one COMT valine (Val) allele (Rosso et al., 2018). Van der Mee et al. found out that externally paced exercise behavior was positively associated with COMT Met allele among adult population. They also found out that this association was even stronger among participants who regularly participate in exercise and sporting activities (Van der Mee et al., 2018). The results of these studies strongly suggest that current/previous exercise and sport experiences may act as a trigger for increasing or maintaining voluntary exercise and sport participation in later life among individuals with DRD2 A1 allele and/or COMT Met allele. On the other hand, there are studies showing that there is no significant association of DRD2 and COMT genes with physical activity-related behaviors in adults (De Moor et al., 2009; Flack et al., 2019; Huppertz et al., 2014; Jozkow et al., 2013). However, these studies did not consider current/previous exercise and sport experiences, did not analyze separately by gender, and were cross-sectional studies that tested the effects of genes on physical activity at some point in time.

Since human behaviors are most likely to be influenced by the joint impact of environmental and genetic factors (Halldorsdottir & Binder, 2017; Young-Wolff et al., 2011), gene-by-environment interactions should be considered when predicting sport participation. In previous literature, there is some evidence showing that participating in physical activity in adolescence increases the probability of participation in physical activity in adulthood (Batista et al., 2019). Moreover, the association between adolescence and adulthood physical activity is greater if the type of physical activity is related to sports (Bélanger et al., 2015; Kjønniksen et al., 2008). Using the National Longitudinal Study of Adolescent Health (Add Health) data, the present study investigated the joint impact of adolescent sport experience and dopamine-related genes (i.e., DRD2 and COMT genes) on sport participation in adulthood because the results of previous genetic studies imply that there are potential interactions between current/previous exercise and sport experiences and dopamine-related genes in the prediction of voluntary exercise and sport participation.

2 METHODS 2.1 Data

The present study used the Add Health data. It is a prospective longitudinal study that followed up a nationally representative sample of middle and high school students in the United States. All high schools in the United States with more than 30 enrolled students were included in the primary sampling frame for Add Health. Using systematic random sampling, a total of 80 high schools were selected after stratification by school type, size, ethnic mix, region, and urbanicity. Approximately 70% of these 80 high schools were recruited. Middle schools in the United States that sent graduates to recruited high schools were also recruited. The final sample consisted of 134 middle and high schools in the United States. Students were randomly selected from each recruited school after stratification by age and grade. The wave 1 survey was conducted in 1995 and a total of 20,745 adolescents were surveyed in their homes (79% response rate). The wave 2 follow-up survey was conducted 1 year after wave 1 (89% response rate). The wave 3 (77% response rate) and wave 4 (80% response rate) follow-up surveys were conducted 6 and 13 years after wave 1, respectively. More information about the survey design of Add Health can be found elsewhere (Harris et al., 2019). The participants for the present study were those who provided their genetic information (i.e., COMT and DRD2 genes) at wave 4. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

2.2 Measures

Adolescent sport experience was measured by asking respondents to indicate the number of times per week they played an active sport, such as softball, basketball, baseball, football, swimming, or soccer in waves 1 and 2. Respondents who responded “more than 4 days per week” in wave 1 or 2 were defined as a person who actively engaged in sports during adolescence. Sport participation in young adulthood was assessed by asking respondents to indicate the number of times per week they participated in individual (e.g., cycle racing, running, wrestling, cross-country skiing, swimming, or martial arts) or strenuous team sports (e.g., field hockey, football, soccer, lacrosse, basketball, rugby, or ice hockey) in waves 3 and 4. Respondents who participated in individual or team sports more than 4 days per week in each wave were categorized as active participators.

Using a salivary DNA collection device (OrageneTM DNA genotek, Ottawa, Ontario, Canada), participants provided 2 ml of saliva in wave 4. In order to extract DNA, saliva was packed in the container (SafTpak #STP-210, Edmonton, Alberta, Canada) and shipped to the University of Colorado, Institute for Behavioural Genetics. Nine (0.06%) were empty and 24 (0.15%) were damaged or leaking during shipping. Further information about Add Health wave 4 candidate gene data is reported in more detail elsewhere (Smolen et al., 2013).

Single nucleotide polymorphisms (SNPs) were genotyped on either an Applied Biosystems TaqMan® OpenArray® (archived samples) or an Illumina BeadXpress® GoldenGate® (non-archived samples) platform for both DRD2 Taq1A SNP rs1800497 in the 30 UTR and COMT val158met SNP rs4680. The DRD2 Taq1A (rs1800497) assay and COMT val158met (rs4680) assay were carried out using a fluorogenic 5′nuclease (Taqman®, ABI, Foster City, CA) method (Haberstick & Smolen, 2004). These were done on an ABI Prism® 7000 Sequence Detection System using the allelic discrimination mode (Livak, 1999). Since the A1 allele of the Taq1A polymorphism (rs1800497) and Met allele of the val158met polymorphism (rs4680) have been associated with exercise and sports participation in adults, we used the number of DRD2 A1 alleles (0 to 2) and COMT Met alleles (0 to 2) to predict sport participation in young adulthood.

Additional covariates were age, gender (male and female), education (high school or less, college, and graduate school or more). Since addictive behaviors have been shown to be associated with DRD2 and COMT genes, smoking (current smoker), binge drinking (5 or more drinks in a row), and marijuana use (1 or more times) were also used as a covariate when predicting sport participation in adulthood.

2.3 Statistical analysis

The hierarchical multivariable logistic regression models for predicting sport participation in wave 3 (around 20 years of age) and wave 4 (around 30 years of age) were conducted separately by gender (male and female) and gene (DRD2 and COMT genes). In model 1, the effect of gene on adult sport participation was examined after controlling for age, education, and addictive behaviors. In model 2, adolescent sport experience was included in model 1 to see if there is any change in the effect of gene on sport participation. In model 3, the interaction between adolescent sport experience and gene was included in model 2 to examine the joint impact of adolescent sport experience and gene on adult sport participation. Wave 3 Sport participation was included as a covariate in all models predicting wave 4 sport participation. Other covariates (i.e., age, education, and addictive behaviors) were assessed at the same wave as dependent variables. Deviance statistics (-2 log likelihood) were used to compare nested models. The p-values lower than .05 were considered as significant. Analysis of variance (ANOVA) and Chi-square test were used to compare between active and inactive participators in sports. All the analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC).

3 RESULTS 3.1 Descriptive statistics

Table 1 presents characteristics of female and male participants by sport participation in wave 3. The mean age of male participants at wave 3 was 22.03 years. Male participants with higher levels of education were more likely to be an active participator in sports. Current smokers and marijuana users were less likely to be an active participator in sports. Adolescent sport experience was positively associated with wave 3 sport participation in male participants. The number of DRD2 A1 alleles was also positively associated with wave 3 sport participation. The mean age of female participants at wave 3 was 21.83 years. Unlike male participants, binge drinkers were more likely to be active participators in sports among female participants. Marijuana use and the number of DRD2 A1 alleles were not associated with sport participation among female participants in wave 3.

TABLE 1. Characteristics of participants by sport participation in wave 3 (n= 15,699) Sport participation in wave 3 Characteristics Total sample Active participator Inactive participator p-Value Male (n = 7,352) Mean age (SD) 22.03 (1.76) 21.62 (1.79) 22.09 (1.75) <.0001 Education (%) .0032 High school or less 2900 (48.87) 330 (43.19) 2570 (49.71) College 2939 (49.53) 419 (54.84) 2520 (48.74) Graduate school or more 95 (1.60) 15 (1.96) 80 (1.55) Smoking (%) <.0001 Current smoker 2045 (34.57) 192 (25.26) 1853 (35.94) Non-smoker 3871 (65.43) 568 (74.74) 3303 (64.06) Binge drinking (%) .1917 Binge drinker 3360 (56.80) 415 (54.61) 2945 (57.12) Non-binge drinker 2556 (43.20) 345 (45.39) 2211 (42.88) Marijuana use (%) .0001 Marijuana user 1594 (26.98) 161 (21.24) 1433 (27.82) Non-marijuana user 4315 (73.02) 597 (78.76) 3718 (72.18) Adolescent sport participation (%) <.0001 Active participator 2643 (53.23) 499 (73.71) 2144 (50.00) Inactive participator 2322 (46.77) 178 (26.29) 2144 (50.00) DRD2 (%) .0467 A1 (-) group 2985 (53.35) 371 (51.39) 2614 (53.64) A1 (+) group 2199 (39.30) 282 (39.06) 1917 (39.34) A1 (++) group 411 (7.35) 69 (9.56) 342 (7.02) COMT (%) .7382 Met (-) group 1745 (32.43) 230 (33.00) 1515 (32.35) Met (+) group 2565 (47.68) 336 (48.21) 2229 (47.60) Met (++) group 1070 (19.89) 131 (18.79) 939 (20.05) Total (%) 5938 764 (12.87) 5174 (87.13) Female (n = 8,347) Mean age (SD) 21.83 (1.75) 333 (21.44) 6732 (21.84) <.0001 Education (%) <.0001 High school or less 3009 (42.60) 100 (30.12) 2909 (43.21) College 3911 (55.37) 218 (65.66) 3693 (54.86) Graduate school or more 144 (2.04) 14 (4.22) 130 (1.93) Smoking (%) .0003 Current smoker 2037 (28.90) 67 (20.12) 1970 (29.33) Non-smoker 5012 (71.10) 266 (79.88) 4746 (70.67) Binge drinking (%) .0011 Binge drinker 2781 (39.49) 160 (48.05) 2,621 (39.07) Non-binge drinker 4261 (60.51) 173 (51.95) 4088 (60.93) Marijuana use (%) .8409 Marijuana user 1214 (17.22) 56 (16.82) 1158 (17.24) Non-marijuana user 5835 (82.78) 277 (83.18) 5558 (82.76) Adolescent sport participation (%) <.0001 Active participator 1605 (28.18) 158 (52.84) 1447 (26.82) Inactive participator 4090 (71.82) 141 (47.16) 3949 (73.18) DRD2 (%) .3989 A1 (-) group 3658 (54.77) 183 (57.55) 3475 (54.63) A1 (+) group 2508 (37.55) 116 (36.48) 2392 (37.60) A1 (++) group 513 (7.68) 19 (5.97) 494 (7.77) COMT (%) .6405 Met (-) group 2014 (31.28) 98 (31.31) 1916 (31.28) Met (+) group 3040 (47.22) 154 (49.20) 2886 (47.12) Met (++) group 1384 (21.50) 61 (19.49) 1323 (21.60) Total (%) 7067 333 (4.71) 6734 (95.29) Abbreviations: DRD2 , dopamine receptor D2; COMT , catechol-O-methyltransferase; SD, standard deviation. Missing data were excluded in calculating the percentage.

The characteristics of male and female participants by sport participation in wave 4 are presented in Table 2. The mean age of male and female participants at wave 4 were 28.59 and 28.38 years, respectively. Individuals with higher levels of education were more likely to participate in sports among both male and female participants. Current smokers were less likely to participate in sports among both genders. Binge drinkers were more likely to be an active participator in sports among female participants only. Adolescent sport experience was positively associated with wave 4 sport participation in both genders. Both DRD2 and COMT genes were not associated with wave 4 sport participation among both genders.

TABLE 2. Characteristics of participants by sport participation in wave 4 (n =15,699) Sport participation in wave 4 Characteristics Total sample Active participator Inactive participator p-Value Male (n = 7352) Mean age (SD) 28.59 (1.77) 28.49 (1.75) 28.60 (1.78) .1126 Education (%) <.0001 High school or less 2870 (39.10) 210 (29.58) 2660 (40.11) College 3791 (51.64) 408 (57.46) 3383 (51.02) Graduate school or more 680 (9.26) 92 (12.96) 588 (8.87) Smoking (%) <.0001 Current smoker 2595 (35.46) 195 (27.50) 2400 (36.31) Non-smoker 4724 (64.54) 514 (72.50) 4210 (63.69) Binge drinking (%) .6890 Binge drinker 3989 (54.61) 380 (53.90) 3609 (54.69) Non-binge drinker 3315 (45.39) 325 (46.10) 2990 (45.31) Marijuana use (%) .3500 Marijuana user 1494 (20.38) 135 (19.04) 1359 (20.53) Non-marijuana user 5835 (79.62) 574 (80.96) 5261 (79.47) Adolescent sport participation (%) <.0001 Active participator 3239 (53.84) 420 (67.96) 2819 (52.22) Inactive participator 2777 (46.16) 198 (32.04) 2579 (47.78) DRD2 (%) .3659 A1 (-) group 3740 (54.50) 345 (52.51) 3395 (54.71) A1 (+) group 2627 (38.28) 257 (39.12) 2370 (38.20) A1 (++) group 495 (7.21) 55 (8.37) 440 (7.09) COMT (%) .1282 Met (-) group 2125 (32.19) 222 (35.81) 1903 (31.82) Met (+) group 3171 (48.04) 283 (45.65) 2888 (48.29) Met (++) group 1305 (19.77) 115 (18.55) 1190 (19.90) Total (%) 7343 710 (9.67) 6633 (90.33) Female (n = 8,347) Mean age (SD) 28.38 (1.77) 28.21 (1.82) 28.39 (1.76) .0619 Education (%) <.0001 High school or less 2490 (29.86) 65 (18.62) 2425 (30.35) College 4624 (55.45) 204 (58.45) 4420 (55.32) Graduate school or more 1225 (14.69) 80 (22.92) 1145 (14.33) Smoking (%) .0265 Current smoker 2342 (28.15) 80 (22.92) 2262 (28.38)

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