Page not availableMedically reviewed on May 4, by L. Androgens and anabolic steroids include the endogenous male sex hormone testosterone and dihydrotestosterone, and other agents that behave like these sex hormones. Androgens stimulate the development of male sexual characteristics such as deepening of the voice and beard growth and development of male sex organs. Stegoids steroids stimulate growth in many other types of anabolic steroids articles 2012, especially anabolix and muscle. Anabolic effects also include anabolic steroids articles 2012 production of red blood cells.
Anabolic Steroids - Abuse, Side Effects and Safety
A national household survey. Individuals aged 15—64 years in Sweden. AAS use and potential correlates of AAS use, including demographic data, financial situation, physical training, and substance use. AAS use was most strongly associated with a lifetime history of illicit drug use and the misuse of prescription drugs.
When controlling for substance use, AAS was associated with physical training and lower education. In this general population survey in men, lifetime use of AAS appears to share common characteristics with illicit substance use. Both substance use variables and physical training remained associated with AAS use when controlling for one another.
The use of anabolic androgenic steroids AAS for performance-enhancing purposes among athletes has been reported since the s [ 1 ], whereas in the s, the first reports revealed that AAS had gained popularity among young males for purposes related to muscle size and physical appearance [ 2 , 3 ]. The use of AAS has been associated with severe medical consequences, including cardiovascular complications [ 4 ], endocrine complications, and psychiatric complications such as depressed mood and a possible link to violent behavior [ 1 , 5 ].
In literature focusing on the purposes of AAS use, data show that apart from the performance-enhancing use of bodybuilders and other athletes, many AAS users report a desire to improve their physical appearance or to strengthen their self-esteem [ 2 , 6 , 7 ]. Several studies have described the characteristics of AAS users. However, while few surveys have been performed in the general population, there are several surveys conducted in specific subgroups with elevated risk of AAS use [ 8 ], such as adolescents [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], criminal justice clients [ 17 , 18 , 19 ], health club attendees [ 20 ], or clients in substance abuse treatment [ 7 , 21 ].
The paper by Yesalis et al. The US national household survey [ 22 ] reported that estimates of lifetime prevalence of AAS use were 0. In younger subjects, higher estimates have been reported, generally ranging between 1. A link between AAS use and other substance use has been demonstrated in several studies in different settings, including the younger age groups of the US general population [ 22 ], high school students, college students and adolescents [ 9 , 11 , 12 , 13 , 14 , 16 , 24 , 25 , 27 ], criminals [ 17 ], and in treated AAS users [ 21 ].
Other possible correlates of AAS use may be negative school experiences, lower level of education, a disadvantageous childhood, and a more unstable current social situation [ 28 ]. Also, a link between AAS use and criminal behavior has been described [ 29 ]. Intuitively, while the use of AAS appears to be more likely to occur in some risk groups and settings, it is relevant to examine individuals who have been offered to use AAS without actually using, and how users differ from these nonusers.
This has been studied in one paper in the international literature [ 15 ], demonstrating that subject who reported actually using AAS were more likely to report cannabis use. Thus, while the correlates of AAS use have been examined in specific risk groups such as adolescents, a significant proportion of lifetime AAS users are above their age [ 22 , 23 ], and there is relatively little knowledge about the clinical correlates of AAS use in the general population.
Therefore, the present study aims to analyse possible correlates of AAS use in the general population, compared to all nonusers and to nonusers who have been offered AAS, and to study, in a multivariate model, the associations of potential risk factors such as substance use, level of education, socioeconomic status and physical training, when controlling for one another.
The present analysis is based on a national household survey assessing the extent and characteristics of drug use in the Swedish general population. From the Swedish general population of individuals aged between 15 and 64 years, a sample of 58, individuals was randomly selected, although with an oversampling of groups suspected of being at higher risk of drug use and higher risk of a low response rate male gender, younger age, lower level of education and larger town of residence , in order to allow for a more extensive dataset of subjects with a history of illicit drug use [ 30 ].
The survey was sent by mail, and could be answered either by mail or on the internet. The survey was sent along with an information letter and an envelope for the return of the survey. Reminders were sent after two weeks and, again, after four weeks. Statistics Sweden, the national agency for population statistics, performed the statistical design of the survey, and also completed survey data with register data regarding a number of demographic variables.
From this analysis, it was reported that no significant differences in drug use variables were seen between original responders and telephone responders. As part of the larger project, prevalence of substance use in the general population was calculated, and reported by the Swedish National Institute of Public Health in a report published online [ 30 ].
In the calculations of prevalence figures, data were weighted, consistent with the oversamplings carried out in the sample addressed. Here, in the calculation of statistical associations rather than prevalence rates, unweighted data are used. In a section addressing other drug use than alcohol, three questions in the survey assessed substances used for physical enhancement. In the first analysis, we dichotomized the data into a variable describing lifetime use of AAS versus no use, and in the second analysis, we used a dichotomous variables describing lifetime history of AAS use versus individuals who answered that they had been offered but had not used AAS.
As independent variables examined for their possible association with AAS use, the following variables were included: Consistent with previous data and the cut-off level usually applied in men [ 32 ], we defined risk drinking as an AUDIT score of eight or above.
Thus, subjects were considered not to be risk drinkers if their AUDIT scores were below eight, or if they reported to be never-drinkers subjects who answered, in the first AUDIT item, that they never drink, were told to leave out the remaining AUDIT items in the questionnaire.
Subjects who failed to answer the question about AAS experience 3. Thus, a total number of 14, males and 7, females were further assessed. Due to the low number of females reporting AAS use, the further statistical analysis is based on the male respondents only.
The analysis was performed as a hierarchical logistic regression analysis, with AAS use as the dichotomized dependent variable in the first analysis, AAS users vs.
All statistical analyses were conducted in the software SPSS version In the first logistic regression model, background factors were entered as independent variables age, country of birth, level of education, income and marital status.
In the second model, in addition, regular physical training was entered. In the third model, in addition, variables describing substance use and current situation were entered financial problems, bad general health, smoking, risk drinking, prescribed drug misuse and illicit drug use. Logistic regression analyses excluded clients for whom data were missing in any one of the variables entered into the model.
Thus, the number of male individuals assessed in each model was 13,, 13,, and 12,, respectively, in the first analysis. In the second analysis, the numbers of subjects included in each model were , , and , respectively. Any history of AAS use was reported by males 1. Another males 3. In the sample of male respondents, who were included in the further analysis, the mean age unweighted was In the hierarchical logistic regression analysis, in the first model, describing demographic background data, lifetime history of AAS use was significantly associated with older age, and negatively associated with higher education.
In the second model, regular training was significantly associated with AAS use, while the associations with age and education remained. In the third model, where information about current situation and substance use was included into the model, the association with older age disappeared, while AAS use remained negatively associated with higher education. Here, the association with physical training was strengthened, and a higher income became significantly associated with a history of AAS use.
Also, AAS use was significantly associated with recent financial problems, current tobacco smoking, and more strongly with use of illicit drugs and misuse of prescription drugs.
No association was seen between AAS use and risk alcohol drinking or general health. In the subgroup of subjects reporting they had ever been offered AAS, in the final logistic regression model, the actual use of AAS was associated with the misuse of prescription drugs, illicit drugs and with tobacco smoking.
In all three models, AAS use was associated with older age, whereas no association was seen with other variables table 3. The present study is one of the few studies analyzing risk factors of AAS use among men in a general population survey, and here, in this broader population survey, we included several potential risk factors of AAS use identified in other studies performed in adolescents, young adults or other subgroups at particularly high risk of AAS use.
Also, the present study is the first to report data from the general male population regarding how AAS users differ from nonusers who report that they have been offered to use AAS. As in many previous studies [ 34 ], prevalence figures among women are very low, and the statistical analysis of the present paper is therefore restricted to the male population.
General population surveys are few [ 22 , 23 ], and many studies instead have been conducted among adolescents. The data published by Yesalis et al. However, subjects in other risk groups such as athletes or criminals may often be older [ 17 , 20 , 28 ], and since the use of AAS in nonathletes has been a problem since the s, lifetime history of AAS is now also reported among adults.
This group of older men who report lifetime use of AAS is likely to differ from younger users, and weighted data on AAS users here showed that users of illicit drugs were younger, and clients reporting misuse of prescription drugs were older data not shown , but such estimates on a small number of AAS users must be interpreted with great caution, and go beyond the scope of the present study. Several previous studies have documented an association between AAS use and other substance use, lower level of education, and physical exercise, but mainly in risk groups, including adolescents.
In the present analysis in the general male population, where older subjects were also assessed, the same associations were seen. The association between AAS use and the misuse of prescription drugs [ 12 ] or illicit drugs is consistent with previous findings in the general US population [ 22 ], and in different subgroups such as among adolescents [ 9 , 13 , 14 ]. Also, in weight lifters, AAS dependence has been reported to be associated with opiod use disorders [ 35 ], and a link between AAS and opioid abuse has been supported by observations in animals and humans [ 36 ].
The present findings demonstrate that male users of AAS may share similar characteristics with individuals who use illicit drugs or who misuse sedatives or analgesics. On the other hand, risk drinking was not related to AAS use in the present survey. Although weighted prevalence data should be interpreted with caution, the proportion of risk drinkers was high among AAS users compared to previous population data from the Swedish male general population [ 37 ].
The proportion of risk drinkers was also higher than among nonusers, whereas no statistical association was seen between risk drinking and AAS use with illicit drugs and prescription drugs included in the model. Previously, an association has been described between alcohol use and AAS in adolescent samples [ 9 , 11 , 13 , 14 ], and in the general population [ 22 ].
In the present study, the time frame considered was different for AAS use lifetime and alcohol use, as most items in the instrument chosen measure the present or the past 12 months. It can not be excluded that an association would have been seen if a lifetime measure of risky alcohol behavior would have been used.
Interestingly, when analyzing only individuals who had been offered to use or who actually used AAS, drug-related variables were the only variables associated with the actual use of AAS. Here, no association was seen with physical training. This may indicate that involvement in sports or other physical training is crucial in many cases for an individual to get into contact with AAS use. Also, the association with substance use variables here may indicate that regardless of setting and whether the individual is regularly training or not, males who are more likely to use drugs in their lifetime are also more likely to be those who actually use AAS when it is offered.
The present study does not permit conclusions to be drawn about the temporality of AAS and other substance use. The results demonstrate a relatively strong association between AAS use and illicit drug use or misuse of prescription drugs, whereas substance use may have occurred before or after the use of AAS. A gateway hypothesis has been discussed in the literature, meaning that the use of AAS may increase the risk of subsequent illicit drug use [ 38 ], although there is also data showing that in many individuals with both types of substance use, the use of illicit drugs preceded the use of AAS [ 17 ].
Although temporality can not be thoroughly analyzed from the present study, where first-time use was not assessed, we conclude that AAS use and illicit substance use had a relatively strong correlation in this male general population study, and regardless of whether illicit drugs use or AAS occurred first, these variables appear to be associated and may share common characteristics.
Importantly, the association with AAS use was seen both for regular physical training and substance use variables, when controlling for one another. The association with physical training is consistent with previous literature in other studies [ 9 , 23 ] and not surprising. Here, this association was even strengthened when substance use variables were included in the analysis.
Although the association between the use of AAS and other drugs was strong, involvement in physical training in males appears to have an independent association with AAS use, independent of substance use variables. The negative association between AAS use and higher education is consistent with previous data [ 28 ].
Here, this association remained stable throughout the hierarchical regression analysis, also when physical training and substance use variables were included in the model. Also, the analysis demonstrated an association of AAS use with past-year financial problems, which may be seen as an expected finding, considering previous data on socioeconomic and educational problems among AAS users [ 28 ].