Being Admired or Being Liked: Classroom Social Status and Depressive Problems in Early Adolescent Girls and Boys

This study investigates associations between depressive problems and classroom social status in a large population cohort of Dutch early adolescents (N = 1046, age 13.52 ± 0.51, 52.4% girls). Depressive problems were assessed by parent and self-reports and classroom status by peer nominations. We assessed peer status with respect to both achievement-related (being a good learner, being good at sports, being good-looking) and affection-related (being liked, being disliked, being best friend) areas. In boys, depressive problems were most strongly associated with not being good at sports, while in girls the association was strongest for not being liked. The risk of a low status in one area could largely be compensated by a high status in another area.

Depressive problems and low social status have been associated since ancient times. In fact, the word depression is derived from the Latin term deprimere, which means being brought down in status or fortune (Jackson, 1986). The association between depression and social status has been confirmed empirically in numerous studies, in both animals and humans (e.g., Blanchard & Blanchard, 1990; Eaton & Harrison, 2000; Gilbert, Allan, & Trent, 1995; Hecht, Inderbitzen, & Bukowski, 1998).
Social status can be defined in at least two ways: in terms of achievements (being admired) and in terms of affection (being liked). These two definitions are related, but by no means interchangeable, and could be differentially linked to depressive problems. The former contains an element of competition (being better than others), while the latter relates to being accepted or rejected by the social group. We will compare the relative importance of both of them with respect to depressive problems in early adolescent girls and boys.
Adolescence is a developmental context in which peer status is particularly salient (Connell & Dishion, 2006). Adolescents spend considerably more time in peer interactions than younger children (Larson & Richards, 1991), and use peers as primary sources for social comparison and self-appraisal (Prinstein & Aikins, 2004). Adolescence is also a highly interesting developmental stage for studying depressive problems, because the prevalence of these problems increases substantially in this phase of life, especially in girls (e.g., Hankin et al., 1998; Oldehinkel, Wittchen, & Schuster, 1999).
Achievement- and affection-related social status
Achievement-related operationalizations of social status are usually based on Price’s Social Ranking Theory (Gilbert & Allan, 1998; Price, 1972). Briefly, this theory postulates that in pursuing scarce resources (e.g., territories, sexual opportunities), animals will have to fight others aiming at the same resources. To prevent harm, animals who are likely to lose these conflicts need inhibitory mechanisms that force them to submit (withdraw, run away) and downgrade their aspirations so that they do not keep engaging in fights they cannot win. These inhibitory strategies may result in behavioral and physiological phenomena that closely resemble depressive symptoms in humans (e.g., Blanchard & Blanchard, 1990; Sapolsky, 1990). Hence, according to this theory, depression is an involuntary strategy to accept defeat in (ritual) agonistic encounters and to accommodate what would otherwise be unacceptably low social rank (Price, Sloman, Gardiner, Gilbert, & Rohde, 1994).
Human social hierarchy does not need actual (ritual) fighting to be established, but can also be formed by imagining what would happen in case of such an encounter. Just knowing the strength of your opponent may be enough to experience a sense of inferiority (Rohde, 2001), which in turn can boost depressive problems (Brown, Harris, & Hepworth, 1995; Gilbert & Allan, 1998). Internal hierarchical positions are also fed by the amount of social attention directed by others (Gilbert, 1989). As opposed to most other species, humans base (internal) hierarchies not just on physical strength, but on other capacities related to the availability of resources, such as intelligence and physical attractiveness (e.g., Anderson, John, Keltner, & Kring, 2001; Nettle, 2003), as well.
Whereas Price’s Social Ranking Theory is achievement-oriented and implies (possibly internal) social comparison and grading, peer status can also be defined in terms of affection, that is, being liked or disliked (rejected). While achievement-related status mainly refers to the hierarchical position within the group, affection-related status may also have implications for group membership in itself. Throughout human history, being banished from the group has been similar to a death sentence, so it is not surprising that being accepted by other people represents a core motive (e.g., Baumeister & Leary, 1995; Fiske, 2004). Hence, social rejection is a significant stressor (Coie, 1990), which can bring about feelings of loneliness (Asher & Wheeler, 1985) and low self-esteem (O’Brien & Bierman, 1988), and may contribute to the development of depressive problems (Boivin, Poulin, & Vitaro, 1994; Monroe & Hadjiyannakis, 2002).
Social ranking and social rejection theories offer sound theoretical frameworks to predict depressive problems in those with a low status, but are less explicit regarding consequences of having a high status. Although there is a social gradient of (mental) ill-health, that is, health improves with each step one moves up the socioeconomic ladder (e.g., Siegrist & Marmot, 2004), this may not be translated directly to the association between peer status and depressive problems in adolescence. In fact, animal research suggest that both high- and low-status individuals can suffer from distress, among other things depending on their personality and the stability of the hierarchy (Sapolsky, 2005). Hence, high and low peer status may not be two sides of the same coin, and better be investigated separately.
Though adolescents’ social status is multidimensional (Rohde, 2001), the achievement-related and the affection-related approaches to social status have evolved independently from each other and have, to the best of our knowledge, never been studied in concert. Hence, it is not known how the two are interrelated and how they jointly affect depressive problems. Multiple hierarchies exist within each of the two status domains as well: achievement includes specific areas such as sports and school performance; and affection may relate to being disliked or being someone’s best friend. This raises the question if and how a low status in one of the areas can be compensated by a high status in another area, or vice versa. Being caught in a (degrading) situation where no escape seems possible is a particularly potent trigger of depressive problems (Brown et al., 1995). A position at the bottom of the status hierarchy may be experienced as such, but this is considerably less likely in adolescents who are highly regarded in other respects, since people tend to value most the hierarchy in which they rank highest (Sapolsky, 2005). We therefore expected that the effects of low status could be compensated by high status in another area.
Gender differences
Until now, only a limited number of studies have focused on gender differences in associations between social status in particular domains and depressive problems. With respect to achievement-related status, this is at least partly due to the fact that there are several well-documented strategies to induce achievement-related social stress in males (e.g., defeat in aggressive encounters), but hardly any for females, among other things because it is hard to obtain strong dominance relationships in female animals (Haller, Fuchs, Halász, & Makara, 1998). Rather than winning or losing in competitive encounters, females seem to be sensitive to situations which disturb their relationships with other animals in the group, such as social isolation (Holston, Scallet, Ali, & Turner, 1991) and instability (Haller et al., 1998). There is some evidence for gender differences in humans as well. In a study where men and women were subjected to a series of different stress conditions, men appeared physiologically more reactive to achievement challenges, and women to social rejection challenges (Stroud, Salovey, & Epel, 2002). Consistent with this, work problems and downward social mobility (i.e., lower achievement-related status) have been found to be associated with men’s mental health in particular, while women are more sensitive to problems in getting along with dear ones and peer rejection (e.g., Brendgen, Wanner, Morin, & Vitaro, 2005; Kendler, Thornton, & Prescott, 2001; Tiffin, Pearce, & Parker, 2005). Taken together, these results suggest gender differences in the salience of specific domains of social status. We propose that autonomy and dominance are more important sources of well-being for males than for females, so that boys will be especially sensitive to achievement-related life events and status loss (Price et al., 1994; Rose & Rudolph, 2006). Girls’ depressive problems, on the other hand, are expected to be more strongly associated to affection-related peer nominations. Compared to boys, adolescent girls exhibit greater affiliative needs, increasing their sensitivity to conflict and rejection within interpersonal relationships (Cyranowski, Frank, Young, & Shear, 2000; Prinstein & Aikins, 2004).
Present study
We have studied the relationship between early adolescents’ depressive problems and social status in peers comprising an everyday social network. We chose to study classroom social status as measured by peer nominations, as this is a social environment in which adolescents spend a large part of the day, and are engaged in comparable activities. The fact that school attendance is compulsory in early adolescence, hence the situation is inescapable, is also relevant in this respect, because entrapment has been found to be a particularly potent elicitor of depressive(-like) emotions and behaviors (Brown et al., 1995; Gilbert & Allan, 1998). Peer status was assessed by sociometric nominations, which are generally considered the most reliable and valid indices of peer status (Coie & Dodge, 1983). Because the association between depression and peer status might be confounded by comorbid conduct problems (Connell & Dishion, 2006; Kiesner, 2002), we adjusted for comorbidity in our analyses.
We hypothesized that low peer status in the achievement-related domain would be most relevant for depressive problems in boys, whereas girls’ depressive problems would be more strongly associated with low affection-related peer status. A further hypothesis was that high peer status in a particular area, although not necessarily associated with the probability of depressive problems in itself, would reduce the association between depressive problems and low status in another status area.
Method
Sample
The TRacking Adolescents’ Individual Lives Survey (TRAILS) is a prospective cohort study of Dutch (pre)adolescents, with the aim to chart and explain the development of mental health from preadolescence into adulthood, which started in 2001.
Sample selection involved two steps. First, five municipalities in the North of the Netherlands, including both urban and rural areas, were requested to give names and addresses of all inhabitants born between 10‒01‒1989 and 09‒30‒1990 (first two municipalities) or 10‒01‒1990 and 09‒30‒1991 (last three municipalities), yielding 3483 names. Simultaneously, primary schools (including schools for special education) within these municipalities were approached with the request to participate in TRAILS. School participation was a prerequisite for eligible children and their parents to be approached by the TRAILS staff. Of the 135 primary schools within the municipalities, 122 (90.4% of the schools accommodating 90.3% of the children) agreed to participate in the study.
If schools agreed to participate, parents (or guardians) and children received brochures with information about the study; and a TRAILS staff member visited the school to inform eligible children about the study. Shortly thereafter a TRAILS interviewer contacted parents by telephone to ask whether they and their child were willing to participate in the study. Respondents with an unlisted telephone number were requested by mail to pass on their number. If they reacted neither to that letter, nor to a reminder letter sent a few weeks later, staff members paid personal visits to their house. Parents who refused to participate were asked for permission to call back in about 2 months. If both parents and children agreed to participate, parental written informed consent was obtained after the procedures had been fully explained. Children were excluded from the study if they were incapable of participating due to mental retardation or a serious physical illness or handicap, or if no Dutch-speaking parent or parent surrogate was available and it was not feasible to administer any of the measurements in the parent’s language. Of all children approached for enrollment in the study (N = 3145), 6.7% were excluded because of mental or physical incapability or language problems. Of the remaining 2935 children, 76.0% (N = 2230, mean age = 11.09, SD = 0.56, 50.8% girls) were enrolled in the study (i.e., both child and parent agreed to participate). Responders and non-responders did not differ with respect to the prevalence of teacher-rated problem behavior, nor regarding associations between sociodemographic variables and mental health outcomes (De Winter et al., 2005).
The present study is based on data from the second assessment wave of TRAILS (T2), which was held 2 to 3 years after baseline assessment (mean number of months 29.44, SD = 5.37, range 16.69–48.06). During this wave, questionnaires were filled out by the adolescents, their parents, and their teachers. The adolescents filled out their questionnaires at school, in the classroom, under the supervision of one or more TRAILS assistants. In addition to the regular questionnaires, which were filled out by TRAILS participants only, the T2 assessment wave also included peer nominations, which were collected in both TRAILS participants and their classmates, at a separate occasion. Of the 2230 baseline participants, 96.4% (N = 2149, 51.0% girls) participated in the first follow-up assessment (T2). Mean age at T2 was 13.56, SD = 0.53.
Subsample with peer information
Peer nominations were assessed in classes with at least three regular TRAILS participants. In total, 150 school classes within 34 schools met this criterion and agreed to participate. Two schools dropped out because of planning difficulties, while one school did not give permission to assess peer nominations within the class. At this school, we collected nominations from TRAILS participants only, providing there were more than 10 participants per class. Schools provided the names of classmates of TRAILS participants. Subsequently, all eligible students received an information letter for themselves as well as their parents in which they were asked to participate in this part of the data collection. If students or their parents refrained from participation they were requested to send a reply card within 10 days. Passive informed consent was adequate for non-TRAILS participants because they were used as informants rather than being study subjects themselves. A total number of 98 students, 3 of whom were regular TRAILS participants, refused to participate. Peer nominations were assessed approximately 2 weeks after the information letter had been sent. The assessment of the peer nominations lasted about 15 min and took place during the regular lessons. Two participating classes were excluded due to too many recent changes in the composition of the class. In total, 3334 students, 1012 of whom were regular TRAILS participants, filled out the questionnaire and nominated their classmates (outdegree), whereas 3798 students, 1078 of whom TRAILS respondents, were nominated by others (indegree). These 1078 adolescents did not differ from the rest of the TRAILS participants regarding gender, χ2 (1, N = 2149) = 1.17, p = .28, or depressive problems, t(2125) = −1.15, p = .25, but were slightly younger (mean age 13.52, SD = 0.51, versus 13.60, SD = 0.54), t(2085) = 3.49, p < .001. Persons with missing or unreliable data on depressive problems were excluded, leaving 1046 cases for analysis.
Measures
Affection- and achievement related status
Respondents could nominate any of their classmates on a total of 18 questions, covering a wide range of issues and behaviors (e.g., gossiping, helping others, substance abuse). For the purpose of this study, we selected six questions, three of which referred to affection-related status (Which classmates do you like? Which classmates do you totally dislike? Which classmates are your best friends?), and three to achievement-related status (Who are good at sports? Who are good learners? Who are good-looking?). For each of these questions, we used the proportion of nominations (indegree) as a measure of peer status. Despite the fact that being good-looking is usually more due to inherited features than to personal accomplishments, it was classified as achievement-related, because it is a salient social-ranking dimension in adolescence, which has been found to contribute to a higher social ranking and influence through halo effects (beautiful is good) (Anderson et al., 2001; Eagly, Ashmore, Makhijani, & Longo, 1991; Feingold, 1992).
Depressive problems
Internalizing and externalizing problem behaviors were assessed by the Child Behavior Checklist (CBCL), one of the most commonly used questionnaires in current child and adolescent psychiatric research (Achenbach, 1991a; Verhulst & Achenbach, 1995). It contains a list of 120 behavioral and emotional problems, which parents can rate as 0 = not true, 1 = somewhat or sometimes true, or 2 = very or often true in the past 6 months. In addition to the CBCL, we administered the self-report version of this questionnaire, the Youth Self-Report (YSR, Achenbach, 1991b). The original, empirically derived, CBCL and YSR scales did not distinguish between anxiety and depressive problems. In order to improve the correspondence with clinical diagnostic categories, Achenbach, Dumenci, and Rescorla (2003) constructed DSM-IV scales for CBCL/YSR problem behaviors, based on international experts’ ratings. The resulting CBCL/YSR Depressive Problems scale consists of 13 items (Cronbach’s α CBCL = 0.73, YSR = 0.77) covering depressed mood, anhedonia, loss of energy, feelings of worthlessness and guilt, suicidal ideation, sleep problems, and eating problems. The scale has been found to correspond more closely to DSM-IV Major Depressive Disorder than the original CBCL/YSR scales (Van Lang, Ferdinand, Oldehinkel, Ormel, & Verhulst, 2005). Test-retest reliabilities of the DSM-IV scales are good, CBCL: r = 0.88; YSR: r = 0.79 (Achenbach et al., 2003). The mean item score of the CBCL-scale was 0.15 (SD = 0.19, range 0–1.15) and the mean item score of the YSR-scale 0.27 (SD = 0.26, range 0–1.72), while 6.1% (CBCL) and 16.9% (YSR) of the scores were greater than 0.5 (which implies that more than half of the symptoms were rated as at least somewhat true and/or more than 25% of the symptoms as very true).
The agreement between parent-reported and adolescent-reported depressive problems was moderate, r = .41. Because depressive problems rated as present by both parent and adolescent are assumed to be more severe (more generalized) than problems rated by only one informant, we used the mean of the standardized parent and adolescent scores as a measure of depressive symptoms in this study. When data of one informant were missing or unreliable (YSRs: n = 10, CBCLs: n = 81), the composite score was based on only one informant.
Disruptive behavior
Disruptive behavior was included to adjust for comorbidity. Disruptive behavior was assessed with the CBCL and the YSR, by combining the items for Oppositional Defiant Disorder and Conduct Disorder, which resulted in a scale of 22 (CBCL, Cronbach’s α = 0.86) or 20 (YSR, Cronbach’s α = 0.79) items.
Statistical analysis
First, means of and correlations between the variables used in the study were calculated and gender differences tested by means of, respectively, t-tests and z-tests.
Subsequently, depressive problems were predicted by peer-status variables in a linear regression model. Standard errors were adjusted for possibly dependent observations within classes, using the cluster option available in the statistical package of STATA (StataCorp., 2003). We adjusted for disruptive behavior by including it as an independent variable in all regression analyses. Continuous variables were standardized to mean 0 and standard deviation 1 to ease interpretation of the regression coefficients. In order to be able to distinguish between associations with low status and associations with high status, each of the peer-status variables were categorized into low status (below the 20th percentile), high status (above the 80th percentile), and intermediate status (between the 20th and 80th percentile). For being disliked, high status referred to the lowest 20% (few nominations) and low status to the highest 20% (many nominations). Status was included in the analyses as dummy variables, with intermediate status as the reference category. We analyzed both the effects of each status area individually, and the effects of all areas adjusted for each other. The analyses were stratified by gender. To establish if gender differences were statistically significant, we performed a two-step regression analysis in the total sample. In the first step, main effects of all peer-status variables were entered in the model, as well as gender and disruptive problems. Interactions between gender and each of the peer-status variables, if any, were entered in a second step, by forward stepwise selection with a criterion for entry of p < .10.
Finally, we examined whether low status in (at least) one area could be compensated by high status in one or more other areas. Peer-status variables that were significantly related to depressive problems (as established by correlations with the effects of gender and disruptive behavior partialled out) were used to create four combined status groups: 1 = low & high, referring to adolescents with a low status (i.e., below the 20th percentile) in at least one area and a high status (above the 80th percentile) in at least one area; 2 = only low, designating adolescents with a low status in at least one area and no high status; 3 = only high, representing adolescents with a high status in at least one area and no low status, and 4 = intermediate, that is, adolescents with an intermediate status in all areas. Depressive problems in the only-low, only-high, and intermediate group were compared to those in the low & high group (the reference category) by linear regression analysis, adjusting for disruptive behaviors and for dependent observations within classes. Gender differences in each of the associations were tested by interaction effects. A lower depression score in the low & high group than in the only-low group suggests that compensation of a low status by a high status in another area is possible; a lower depression score in the only-high group than in the low & high group suggests that a low status counteracts the benefits of a high status in another area. Comparison of the intermediate group with the low & high group provides insight in the differences between being extraordinary (in any direction) and being average. To take into account that the specific areas may represent the two broad domains of achievement-related and affection-related status disproportionately, this procedure was repeated for the strongest predictor of each.
For all analyses, a p-value smaller than .05 was considered statistically significant. Because we performed multiple statistical tests, the results may suffer from capitalization on chance: one would expect some 5% of the associations examined to be significant merely on the basis of chance, and the probability to find at least one significant effect increases with every additional statistical test. We therefore recommend to consider the general pattern of associations, rather than interpreting each individual effect as if it were the only one tested.
Results
Descriptive statistics
Table 1 presents descriptive statistics of the variables used in this study. Girls had more nominations for being good learners and good-looking, while boys were nominated more often as being good at sports. Affection-related status domains showed no significant gender differences. Girls had more depressive problems, and boys more disruptive behavior problems.Table 1Mean (standard deviation) of peer status and mental health, by genderMean (SD)Girls (N = 549)Boys (N = 497)Difference t-testbEffect sizecProportion nominations for beingLiked0.56 (0.21)0.55 (0.20)t = −0.26, p = .800.05Disliked0.10 (0.13)0.12 (0.15)t = 1.61, p = .11−0.14Best friend0.20 (0.12)0.21 (0.13)t = 1.05, p = .30−0.04Good at sports0.22 (0.21)0.40 (0.29)t = 11.11, p < .001−0.71A good learner0.33 (0.26)0.28 (0.25)t = −2.60, p = .010.20Good-looking0.27 (0.22)0.12 (0.13)t = −13.87, p < .0010.83Mental healthDepressive Problemsa0.24 (0.21)0.19 (0.18)t = −3.57, p <.0010.26Disruptive Behaviora0.21 (0.15)0.25 (0.18)t = 3.26, p = .001−0.24aFor descriptive purposes, the means are based on mean unstandardized scores of parent and adolescent reports. The tests are based on mean standardized scores.bEffects are adjusted for possible dependence of observations within classes.cCohen’s d = (M1−M2)/σpooled, where M1 is the mean of the first group, M2 is the mean of the second group, and σpooled is the square root of the mean variance of the two groups ().
Bivariate associations
Correlations between the peer nominations were generally moderate to high (Table 2). Being a good learner was not significantly related to being good-looking, and in girls neither to being liked and being someone’s best friend. Strongest associations were found among the affection-related variables (being liked, being disliked, and being someone’s best friend). In girls, depressive problems were most strongly associated with being disliked, in boys the correlation was highest for being good at sports.Table 2Correlations between peer status and mental health, in girls (above the diagonal) and boys (below the diagonal)DepressiveDisruptiveLikedDislikedBest friendGood at sportsGood learnerGood-lookingproblemsbehaviorLiked−.62  .4 9  .34  .06  .55−.17−.07Disliked−.68−.34−.23−.18−.36  .21  .14Best Friend  .49−.36  .34  .08  .46−.06  .11Good at sports  .38−.25  .34  .21  .44−.15−.04A good learner  .14−.16  .15  .15  .07−.14−.21Good-looking  .42−.28  .30  .51  .09−.10  .01Depressive Problemsa−.16  .16−.11−.19−.09−.12  .52Disruptive Behaviora−.09  .20−.02  .03−.20−.04  .48Note. Bold: p < .05, italics: significant gender difference.aStandardized scores, based on mean standardized scores of parent and adolescent reports..
Hierarchy of status areas
To distinguish between associations with low and high status, each of the peer-status variables was categorized into low status (below the 20th percentile), high status (above the 80th percentile), and intermediate status (between the 20th and 80th percentile). For being disliked, high status referred to the lowest 20% (few nominations) and low status to the highest 20% (many nominations). The distribution of the peer nominations precluded that the high-status group of being disliked contained approximately 20% of the cases: 32.0% of the adolescents were nominated by no one and hence classified as having high disliked status.
Table 3 shows gender-specific regression coefficients for each of the low- and high-status groups, vis-à-vis the intermediate group, both without and with adjustment for the other peer-status dimensions. All effects were adjusted for disruptive behavior. Together, the peer-status variables explained 6.4% of the variance in boys, and 7.7% in girls.Table 3Prediction of depressive problems by low and high peer-status in several areas, adjusted for disruptive behavior, stratified by genderB (p)Unadjusted for other areasAdjusted for other areasStatus areaStatusGirlsBoysGirlsBoysAffectionLikedLow  0.41 (<.001)  0.16 (.05)  0.33 (.02)  0.02 (.85)High−0.07 (.48)−0.21 (.008)−0.03 (.80)−0.10 (.28)DislikedLowa  0.21 (.08)  0.00 (.95)  0.05 (.72)−0.11 (.33)Higha−0.13 (.11)−0.22 (.002)−0.06 (.47)−0.14 (.05)Best friendLow  0.24 (.02)  0.22 (.02)  0.08 (.47)  0.13 (.19)High−0.14 (.15)−0.15 (.046)−0.07 (.47)−0.05 (.49)AchievementGood at sportsLow  0.27 (.004)  0.44 (<.001)  0.19 (.08)  0.38 (.005)High−0.12 (.34)−0.17 (.02)−0.07 (.57)−0.10 (.17)Good learnerLow  0.12 (.35)  0.08 (.39)  0.05 (.68)  0.02 (.79)High−0.02 (.83)−0.01 (.91)  0.01 (.88)  0.04 (.68)Good-lookingLow  0.12 (.40)  0.17 (.04)−0.10 (.52)  0.01 (.92)High−0.11 (.13)−0.13 (.19)  0.06 (.45)−0.04 (.67)Note. Effects of low and high status are with regard to intermediate status. Depressive problems were standardized to mean 0 and standard deviation 1. Effects are adjusted for possible dependence of observations within classes.aAs opposed to other status areas, a low status at being disliked reflects many nominations, and a high status few.Bold: p < .05.
It is interesting to note that in girls, only low status appeared associated with depressive problems, while in boys, high status tended to be relevant as well, especially in the affection domain. Both without and with adjustment for other status areas, girls’ regression coefficients were highest for a low status in being liked, and boys’ coefficients for a low status in sports, indicating that girls and boys with few nominations regarding these areas had the highest number of estimated depressive problems. When all status areas were analyzed jointly, these two associations were the only significant ones. Without adjustment for disruptive problems, the effects remained largely the same, except that the effect of a high status for being disliked (reflecting no nominations) became significant in boys, B = −0.20, p = .02.1
Gender differences in the associations between peer status and depressive problems were tested by interaction effects of gender and each of the peer-status variables in the total sample, using a forward stepwise selection procedure (with criterion for entry p < .10) after all main effects had been entered in the model. Gender interactions with a low status on being liked, B = 0.32, p = .04, and a low status on being good at sports, B = −0.26, p = .09, were both selected by this procedure, but the latter only showed a trend and did not reach the significance criterion of p < .05. Other status variables did not the meet the entry criterion.
Across-area compensation
With the effects of gender and disruptive behavior partialled out, all peer-status variables were significantly associated with depressive problems, except being a good learner, r = −0.02, p = .60. This variable was excluded from further analyses, hence the index describing the combined statuses was based on five areas. Of the total sample, 17.5% fell in the low & high group, 32.0% in the only-low group, 40.8% in the only-high group, and 9.7% in the intermediate group. This distribution was approximately equal for girls and boys, χ2(3, N = 1046) = 2.23, p = .53. The regression coefficients for the only-low, only-high and intermediate group, in relation to the high & low group, are presented in Table 4.Table 4Prediction of depressive problems by cross-area peer status groups, adjusted for disruptive behaviorStatus groupnB (p)Low & high (reference category)183–Only low335  0.30 (.001)Only high427−0.11 (0.12)Intermediate101  0.02 (0.87)Note. Low & High: low status in at least one area and high status in at least one area; Only Low: low status in at least one area and no high status; Only High: high status in at least one area and no low status, Intermediate: intermediate status in all areas. Effects are adjusted for possible dependence of observations within classes.Bold: p < .05.
The coefficients indicate that the only-low group had more depressive problems than the low & high group, while the only-high and intermediate group did not differ significantly from the low & high group. Thus, these results suggest that the risk associated with low status in one area can be compensated by a high status in another area, while the opposite (i.e., counteraction of the beneficial effects of a high status by a low status in another area) is not true, at least not to the same degree. None of the associations showed gender differences (all p-values > .42).
To test the robustness of these findings, we also constructed a combined status index (with the categories only low, only high, low & high, and intermediate as defined in the method section) based on only the strongest predictors from each of the two broad domains of achievement-related and affection-related status, that is, being liked and being good at sports respectively. The use of this alternative status measure yielded similar results: more depressive problems in the only-low group than in the low & high group, B = 0.33, p = .004; no significant differences between the low & high group and, respectively, the only-high group, B = −0.10, p = .33, and the intermediate group, B = 0.07, p = .55; and no gender differences (all p-values > .31).
Discussion
Striving for status has been proposed as a universal human motive, and a wealth of evidence has indicated that persons’ status within their group influences their psychological and physiological well-being (e.g., Anderson et al., 2001). For adolescents, one of the most salient social groups is made up by their classmates. In this study on associations between classroom peer status and depressive problems in young adolescents from the general population, the peer status variables explained about 7% of the variance in depressive problems (unadjusted for measurement error, hence underestimating the true variance), which is a modest, yet noteworthy amount.
We hypothesized that peer status in the achievement-related domain would be most relevant for depressive problems in boys, whereas girls’ depressive problems would be more strongly associated to their affection-related peer nominations. A further hypothesis was that high alternative hierarchical positions would (wholly or partly) counteract the association between depressive problems and low status in a particular status area. These hypotheses were largely supported by the data, although it should be noted that the expected patterns were not found in all status areas investigated. Compared with other status areas, not being good at sports, hence a low achievement-related status, was most strongly associated with depressive problems in boys; while not being liked, hence a low status on an affection-related area, had the strongest association with depressive problems in girls. However, our data suggest that not being good at sports may affect girls’ well-being as well, and the gender difference regarding this status domain, although showing a trend, was not statistically significant. Not being liked, on the other hand, appeared to be related to depressive problems in girls only. Hence, girls seem to be more sensitive than boys to affection-related status, particularly to a lack of peers that like them.
Low status versus high status
In general, the risk associated with a low group status was stronger than the resilience associated with a high status, especially in girls, and when the effects of all status areas were adjusted for each other, the only variables that remained significantly associated with depressive problems reflected a low status. Low peer status may lead to depressive problems through a variety of pathways, such as a reduced opportunity to make friends, controlling or dominating behavior by others, and expressions of disapproval or contempt (Deater-Deckard, 2001). All of these may undermine self-esteem and invoke feelings of loneliness (Asher & Wheeler, 1985; O’Brien & Bierman, 1988), which are known risk factors for depressive problems (e.g., Monroe & Hadjiyannakis, 2002; Pelkonen, Marttunen, & Aro, 2003).
Although a high peer status does not seem to be very effective in preventing depressive problems in itself, this may be different for those with a low status in another area: a high status appeared to have the potential to compensate the risk associated with a low status. Adolescents with a low status in at least one area and no high status to counterbalance this had most depressive problems, which supports the idea that depression is particularly likely when no escape from the degrading situation seems possible (Brown et al., 1995), and underscores once more the importance of emphasizing and stimulating adolescents’ strengths and talents, particularly for adolescents with weaknesses in other areas.
Gender differences
The gender differences found in this study, notably the fact that a direct association between affection-related status and depressive problems was found only in girls, add evidence to the notion that interpersonal stressors are more depressogenic for adolescent girls than for boys (e.g., Brendgen et al., 2005; Shih, Eberhart, Hammen, & Brennan, 2006). Associations with depressive problems were adjusted for disruptive behaviors to take into account comorbidity of the two. Without adjustment for disruptive behaviors, being disliked was significantly related to boys’ depressive problems, which may indicate that disliked boys tend to show disruptive behavior rather than (solely) depressive problems. Achievement-related status was most strongly related to depressive problems in boys. Compared to girls, boys focus more on agentic goals, including their own dominance on the peer group (e.g., Rose & Rudolph, 2006), which apparently makes them more vulnerable to low status in relevant areas. However, we found some indications that sports-related achievements may be relevant for girls too. This finding could hint at a dwindling of traditional socio-cultural gender differences; but it could as well be due to the fact that not being good at sports reflects not only a low probability of winning in ritual fights, but also lack of physical energy and activity, which is likely to occur in both depressed boys and depressed girls. The issue of reverse causality is discussed more extensively hereafter.
Differences between specific status areas
Within the achievement-related domain, sporting performance was more strongly associated with depressive problems than being a good learner and being good-looking. Sports, more than theoretical subjects and outward appearances, reflect ritual agonistic behavior, with the end being signified by the loser yielding and signals of superiority by the winner (Rohde, 2001). In other words, sports are much more closely related to social defeat than the other two achievement domains. But other factors may account for the relatively exceptional position of sports as well. To start with, adolescents who are good at sports may do relatively more physical exercise, which can help to reduce depressive problems (e.g., Nabkasorn et al., 2006), so there may be a direct effect of sports on mental health, on top of the effect through peer status. Furthermore, being good at sports increases the likelihood of team membership, which in turn may result into admiration and new friendships. The latter could be especially relevant for boys, who tend to have multiple activity-related friendships rather than a few close friends (e.g., Moller, Hymel, & Rubin, 1992). The association between being a good learner and having a high peer status is probably more equivocal than that between being good at sports and peer popularity. Although some studies found positive correlations between grade point average and popularity among peers (e.g., Cauce, 1987; Green, Forehand, Beck, & Vosk, 1980), others suggested that intelligence is not associated with peer status (Luthar & McMahon, 1996), and that overachievers do not have a secure position in their peer group (Hartup, 1970). In fact, Bukowski, Sippola, and Newcomb (2000) showed that good classroom behavior starts to loose its attraction for peers during early adolescence, in favor of more aggressive behaviors. As concerning the role of being considered good-looking, our results seem to contradict earlier findings that physically unattractive adolescents are likely to receive more negative feedback (Burns & Farina, 1992; Cash, 1995), and that body dissatisfaction is an independent risk factor for (girls’) depressive problems in adolescence (e.g., Stice, Hayward, Cameron, Killen, & Taylor, 2000). That peer nominations for being good-looking did not independently contribute to adolescents’ (lack of) depressive problems in our study could be due to the fact that peer judgments have little to do with one’s own body image (Rosenblum & Lewis, 1999). Furthermore, not being good-looking is not necessarily equivalent to being ugly; in other words, the question ‘Who is unattractive?’ might have been a stronger correlate of depressive problems than the question ‘Who is good-looking?.’
Within the affection-related domain, which appeared to be relevant to girls in particular, not being liked was the most salient factor in relation to depressive problems. In other words, it does not seem to matter much whether or not some people dislike you, as long as there are still others that like you. A lack of sympathizers is a threat to mental health. From an evolutionary point of view, this makes good sense, because being liked by others can offer social support and protection against being expelled from the group, and the ‘need to belong’ is a fundamental motivation (Baumeister & Leary, 1995; Fiske, 2004).
Limitations, strengths, and recommendations for future research
Our study has some limitations. To start with, the CBCL/YSR Depressive Problems scale was not developed to assess depressive problems according to DSM-IV criteria, but constructed on the basis of expert ratings of the original, empirically derived, CBCL and YSR scale items. Consequently, the items do not represent one-to-one counterparts of all DSM-IV criteria, and inferences about associations between peer status and DSM-IV Major Depression are putative. Furthermore, we performed multiple statistical tests, which increases the probability of chance findings and warrants replications in other samples. A final limitation of the study is the cross-sectional and non-experimental nature of our data, which does not allow causal interpretation of the associations found (Kraemer et al., 1997). Most likely, however, the association between peer status and depressive problems is bidirectional: not only does low peer status induce depressive problems, depressed children and adolescents tend to display maladaptive interpersonal behavior as well (e.g., Altman & Gotlib, 1988; Baker, Milich, & Manolis, 1996). A bidirectional association is especially plausible with respect to sporting performance. Being looked down on because you are bad at sports may reduce self confidence and through this incite the development of depressive problems; but it is just as likely that depressive problems, which often include lack of energy and motivation, have a negative impact on adolescents’ sporting achievements. Similarly, one could argue that the association between depressive problems and not being liked is a two-way one. Having no allies in a peer group can be devastating for your self-worth and hence trigger depression, but depressed behavior may also fend off others (e.g., Prinstein, Borelli, Cheah, Simon, & Aikins, 2005). It seems plausible, however, that if depression were a cause of not being liked, we would expect that to happen in both boys and girls and not in girls only. We thus assume that not being liked is actually a risk factor for depression, at least in girls.
Compared to the vast literature on peer processes predicting conduct problems, research on peer processes in depression has been relatively rare (Connell & Dishion, 2006; Deater-Deckard, 2001). To the best of our knowledge, our study is one of the first to examine the association between status-related peer nominations and depressive problems in a large population cohort of early adolescents. In addition to the size and representativeness of our sample, important assets of this study relate to the use of multiple informants, multiple aspects of peer status, and multiple outcomes, which allowed adjusting for comorbid disruptive behavior. Hence, we feel that it is an excellent starting point for further research. We propose three lines of extension. Firstly, the cross-sectional associations should be extended to (bidirectional) patterns of associations across time, to be better able to distinguish between concomitants and risk factors (Kraemer et al., 1997). Secondly, the study could be extended to include other outcomes, not only within the domain of psychopathology, but also regarding somatic health. Thirdly, it is highly relevant from a public-health point of view to (further) explore to what extent and how peer processes can be manipulated in order to prevent aversive outcomes.

adolescent
depressive symptoms
social hierarchy
gender


Association analysis of the IGF1 gene with childhood growth, IGF-1 concentrations and type 1 diabetes

Aims/hypothesis Insulin-like growth factor-1 is a major childhood growth factor and promotes pancreatic islet cell survival and growth in vitro. We hypothesised that genetic variation in IGF1 might be associated with childhood growth, glucose metabolism and type 1 diabetes risk. We therefore examined the association between common genetic variation in IGF1 and predisposition to type 1 diabetes, childhood growth and metabolism.
Introduction
Type 1 diabetes is a common autoimmune disorder that arises by an interaction between genes and the environment. To date, ten loci have been identified including the HLA class II genes, the cytotoxic T-lymphocyte associated protein 4 (CTLA4) locus, protein tyrosine phosphatase, non-receptor type 22 (lymphoid) (PTPN22), IL-2 receptor, alpha (IL2RA), interferon induced with helicase C domain 1 (IFIH1) and four novel loci identified by a genome-wide association study [1].
The protein products of these genes play important roles in antigen presentation and the cellular immune response, highlighting the importance of the immune system in the pathogenesis of type 1 diabetes. However, not all patients with evidence of islet autoimmunity develop complete beta cell failure [2]. Given the evidence to suggest that early weight gain and growth in childhood are associated with type 1 diabetes [3], it is possible that factors influencing insulin action (body weight and fat mass) and islet function may predispose to or influence the presentation of type 1 diabetes.
Lower IGF-1 concentrations predict the development of glucose intolerance in adults [4]. George et al. reported that islet overexpression of IGF1 in diabetic mice enabled islet regeneration and gradual correction of hyperglycaemia and hypoinsulinaemia [5]. A microsatellite in the region 5′ to IGF1 has been associated with adult height, cardiovascular risk, osteoporosis and type 2 diabetes in some [6] but not all [7] studies.
Since common variation in IGF1 could alter circulating IGF-1 concentrations and therefore body habitus, insulin action and beta cell secretion, we sought to determine the associations, if any, between IGF1 and circulating IGF-1 concentrations, size at birth, both fasting and postprandial insulin concentrations, and type 1 diabetes.
Materials and methods
Polymorphism identification
DNA samples from 32 randomly selected type 1 diabetic probands were amplified using specifically designed forward and reverse primers. This provided 88% probability of detecting single nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) of 0.033, 96% probability for MAF 0.05 and 99.8% for MAF 0.10 [8]. Resequencing included the 3 kb region 5′ to the gene, all exons and exon–intron boundaries and 3′ untranslated region. Four 1 kb segments in the second intron, ~10 kb intervals apart, were also resequenced.
Tag-SNP selection
The resequencing genotype data were used to select SNP subsets that predicted the genotypes of the remainder, using a coefficient of determination, R2, which measures the ability to predict each known SNP genotype by linear regression on the tag-SNP genotypes [9]. We considered only SNPs with a MAF ≥ 0.10 in the type 1 diabetes collection and ≥0.05 in the Avon Longitudinal Study of Parents And Children (ALSPAC) cohort (for details see below and Electronic supplementary material [ESM]), using a minimum R2 of 0.8.
Genotyping
All genotyping data were double-scored to minimise error. All genotypes were in Hardy–Weinberg equilibrium (p > 0.05).
Populations studied
All DNA samples were collected after ethics approval and informed consent had been obtained.
Type 1 diabetes family collection Type 1 diabetes families were of white European descent, with two parents and at least one affected child. The populations studied have been described previously [8].
ALSPAC Details of this birth cohort are available on the ALSPAC website (www.alspac.bris.ac.uk). The children in this study are from a 10% ‘Children in Focus’ sub-cohort (1,335 full-term singleton infants) randomly selected from the last 6 months of recruitment for more detailed measurements of growth. Birthweight was noted from hospital records, and length and head circumference were measured after birth. At age 7 years (mean age 7.5 ± 0.1 years), body weight and height were measured. Body composition was assessed at age 9 years by whole-body dual-energy X-ray absorptiometry. Internal SD scores were calculated for all parameters of growth to adjust for age and sex. IGF-1 concentrations were measured in cord blood samples (birth) and in venous blood (7 or 8 years of age). Fasting insulin sensitivity was assessed by the homeostatic model assessment index (www.dtu.ox.ac.uk/homa/) and insulin secretion at 30 min post oral glucose by the insulinogenic index at age 8 years.
Statistical analysis
All statistical analyses to test the association between IGF1 and type 1 diabetes were performed in either Stata (www.stata.com/) or R (www.r-project.org/) statistical systems. Additional routines may be downloaded (www-gene.cimr.cam.ac.uk/clayton/software/). Missing tag-SNP genotypes were imputed under the null hypothesis and were analysed using a multilocus test [9]. The microsatellite, 5′ IGF1 CA repeat, was analysed using TRANSMIT [10]. The global effect of the IGF1 tag-SNP set on each outcome variable was entered into a multi-locus regression model. R2 change was taken as the contribution to the total variation in each outcome. Associations of IGF1 SNPs with growth and metabolic phenotypes were analysed using univariate ANOVA (general linear models). The association of the 5′ IGF1 CA repeat was analysed by comparing the wild-type allele (192 bp) to all other alleles [6, 7].
For further details on Materials and methods section, see ESM.
Results
Genetic variation in IGF1
Resequencing identified 27 novel polymorphisms consisting of 24 SNPs, one rare non-synonymous SNP in exon 3 (Ala to Thr) and two deletion/insertion polymorphisms. Eleven SNPs had a MAF > 0.05. Four tag-SNPs (Table 1) were selected and genotyped in the type 1 diabetes families studied. In ALSPAC, seven tag-SNPs were selected (ESM Table 1).
Table 1A summary of IGF1 tag-SNPs genotyped in 2,396 familiesParameterTag-SNPrs35140968ars6214rs3730220s6219SequenceAAATAACT[-/AT]CTCAAATAGACTTAAC[A/G]TGTTTTCTCAGGTTGG[A/T]CTCAAACTAACCTCAA[A/G]CTGTCTACAllele coding, 1 (minor)/2Ins/DelA/GT/AA/GMAF in parents0.2770.4430.4930.0769Minor allele transmissionsTransmitted8151309915446Untransmittedb7901386942446Genotype 2/2Transmitted1,150 (53.1)947 (33.3)798 (33.0)2,501 (84.8)Untransmittedb3,478 (53.6)2,724 (31.9)2,352 (32.47,489 (84.6)Genotype 2/1Transmitted849 (39.2)1,382 (48.5)885 (36.6)423 (14.3)Untransmittedb2,541 (39.1)4,226 (49.5)2,685 (37.0)1,297 (14.7)Genotype 1/1Transmitted166 (7.7)519 (18.2)737 (30.5)27 (0.91)Untransmittedb476 (7.3)1,594 (18.7)2,223 (30.6)67 (0.8)OR for minor allele (95% CI)1.03 (0.93–1.14)0.94 (0.87–1.01)0.97 (0.89–1.06)1.00 (0.88–1.14)aDeletion/insertion polymorphismbUntransmitted (pseudocontrol) genotypes are estimated as previously described [8]Multilocus test p = 0.425
IGF1 and type 1 diabetes
The selected tag-SNPs were genotyped in 2,439 families (3,121 parent–child trios). The multilocus test of the IGF1 tag-SNPs provided no evidence of an association with type 1 diabetes (χ42 = 4.4; p = 0.356; Table 1). The microsatellite, 5′ IGF1 CA repeat, was genotyped in 2,109 families and analysed using TRANSMIT [10]. There was no evidence of association (p = 0.358) (ESM Table 2).
Wellcome Trust Case Control Consortium data
We used data from the Wellcome Trust Case Control Consortium (WTCCC) genome-wide association study [1] to test association of the extended IGF1 region with type 1 diabetes. The region contained 22 SNPs. When the WTCCC data were analysed using a logistic regression model adjusted for variation in allele frequencies across Great Britain, no evidence of association of these SNPs within the region was found (ESM Table 3).
Association with IGF-1 concentrations
In the ALSPAC children, multilocus regression models showed that the IGF1 tag-SNP set was associated with IGF-1 protein levels at birth (R2 = 0.063; p = 0.029) and weakly associated at age 7 to 8 years (R2 = 0.030; p = 0.055; ESM Table 4). Results of IGF-1 protein level associations with individual SNPs are shown in Table 2. However, the multilocus IGF1 tag-SNP set did not associate with any other childhood growth or metabolic phenotype (ESM Table 4). No associations were observed between the IGF1 CA repeat and IGF-1 concentrations or any growth or metabolic phenotype (ESM Table 5).
Table 2IGF-1 protein levels (ng/ml) at birth and at age 7 or 8 years by genotypes of seven IGF1 tag-SNPs in a representative birth cohortParameternGenotype (%)p valuers35140968 I/D (58)I/I (35)D/D (7)  At birth31588.1 (2.8)91.1 (3.6)105.0 (8.9)0.179 7 or 8 years547147.8 (2.9)147.3 (3.9)157.4 (9.1)0.581rs6214 G/G (34)G/A (50)A/A (16)  At birth30588.6 (3.6)87.7 (3.2)102.43 (5.30)0.048 7 or 8 years556144.1 (4.0)148.9 (3.1)155.83 (5.70)0.239rs3730220 A/A (38)A/T (54)T/T (9)  Birth29994.1 (3.8)88.6 (2.9)89.6 (7.4)0.509 7 or 8 years548152.0 (3.8)145.7 (3.0)141.8 (8.1)0.331rs6219 G/G (84)*/A (16)  Birth30091.0 (2.4)85.0 (5.7)0.329 7 or 8 years547150.1 (2.5)138.8 (5.8)0.072rs2946831 T/T (89)*/G (11)  Birth29490.0 (2.3)95.2 (7.2)0.501 7 or 8 years541148.0 (2.4)153.9 (7.1)0.432rs3730204 T/T (97)*/C (3)  Birth294111.3 (13.5)90.3 (2.3)0.126 7 or 8 years548173.8 (12.9)147.5 (2.3)0.046rs12579108 C/C (97)*/A (3)  Birth30190.0 (2.2)101.4 (12.1)0.358 7 or 8 years554147.6 (2.3)176.8 (15.9)0.017Values are mean (SE) for IGF-1 concentration and per cent for genotype*Additive models, adjusted for age and sex
Discussion
We used a UK birth cohort, ALSPAC, and a large family collection of type 1 diabetes to explore association with a subset of SNPs generated by in-depth resequencing of the IGF1 locus. A subset of SNPs that effectively predicted the genotype of the remainder was subsequently genotyped in these cohorts [9]. We report no evidence to support the hypothesis that genetic variation in IGF1 is associated with major susceptibility to type 1 diabetes. However, genetic variation in this locus may modestly influence circulating IGF-1 concentrations, at least at birth and during childhood.
Prior studies examining the role of genetic variation in IGF1 in predisposition to common human disease have focused on a microsatellite in the region 5′ to IGF1. This variant has been inconsistently associated with adult height and type 2 diabetes [6, 7]. Perplexingly, the same allele has been associated with high [6] and low [7] IGF-1 concentrations. It is possible that other common genetic variants, in different degrees of linkage disequilibrium with this variant, alter IGF-1 concentrations and explain the discrepancy in the published studies.
We found no association between this microsatellite and circulating IGF-1 concentrations, growth or type 1 diabetes. To exclude the possibility that other polymorphisms in the gene predispose to disease, we undertook a systematic analysis of IGF1 gene variation using tagging SNPs [8]. Our relatively large white type 1 diabetes family collection of European ancestry provided ~84% power to detect a causal allele for type 1 diabetes predisposition with MAF = 0.1 and OR of 1.2 at the 5% significance level. The study was therefore adequately powered to detect weak predisposition to type 1 diabetes conferred by IGF1. We did not directly examine whether these variants alter the age at diagnosis of type 1 diabetes, since there was no primary evidence of association.
The possibility remains that variation in IGF1 could alter insulin secretion or action as well as childhood growth. Therefore, to examine these associations, we adopted the same strategy in an established birth cohort. While we demonstrate that common genetic variation in IGF1 modestly influences circulating IGF-1 concentrations at birth and during childhood, we did not find a major contribution of IGF1 to birthweight, growth or response to oral glucose. It remains possible that maternal IGF1 genotypes that influence maternal metabolism might alter birth size, but they are unlikely to significantly impact on childhood growth.
In summary, this large systematic study shows that common IGF1 variants modestly influence circulating IGF-1 concentrations at birth and during childhood. However, these variants are not associated with birthweight, childhood growth and insulin secretion or action. Moreover they do not alter type 1 diabetes risk.
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM Table 1
IGF1 SNPs genotyped in ALSPAC children (PDF 16.2 KB)
ESM Table 2
TRANSMIT analysis for association of the microsatellite in the 5′ region of IGF1 with type 1 diabetes (PDF 62.5 KB)
ESM Table 3
Summary of 23 WTCCC SNPs in the extended IGF1 region (PDF 28.6 KB)
ESM Table 4
Multilocus test for associations with IGF-1 protein levels, childhood growth and glucose metabolism in a representative birth cohort (PDF 17.1 KB)
ESM Table 5
Association testing for the 5′ IGF1 CA repeat with childhood growth and glucose metabolism in a representative birth cohort (PDF 72.3 KB)
ESM Table 6
ESM Text (PDF 81.3 KB)

growth
igf-1
type 1 diabetes
insulin-like growth factor-1
genetic variation
microsatellite
single nucleotide polymorphism
snp
tag-snps
children


An on-line post-column detection system for the detection of reactive-oxygen-species-producing compounds and antioxidants in mixtures

Reactive oxygen species (ROS) can damage proteins, cause lipid peroxidation, and react with DNA, ultimately resulting in harmful effects. Antioxidants constitute one of the defense systems used to neutralize pro-oxidants. Since pro-oxidants and antioxidants are found ubiquitously in nature, pro-and antioxidant effects of individual compounds and of mixtures receive much attention in scientific research. A major bottleneck in these studies, however, is the identification of the individual pro-oxidants and antioxidants in mixtures. Here, we describe the development and validation of an on-line post-column biochemical detection system for ROS-producing compounds and antioxidants in mixtures. Inclusion of cytochrome P450s and cytochrome P450 reductase also permitted the screening of compounds that need bioactivation to exert their ROS-producing properties. This pro-oxidant and antioxidant detection system was integrated on-line with gradient HPLC. The resulting high-resolution screening technology was able to separate mixtures of ROS-producing compounds and antioxidants, allowing each species to be characterized rapidly and sensitively.
Introduction
The potentially toxic and beneficial properties of pro-oxidants and antioxidants have made them the focus of many studies. Pro-oxidants may represent a threat to health, whereas antioxidants may counteract these effects by scavenging pro-oxidants [1, 2]. Antioxidants are very important in industrial processes as well as in biological systems. They are known to possess anti-inflammatory [3], anti-cardiovascular disease [4], antineurogenerative [5], and anticancer properties [6]. Imbalances between pro-oxidants and antioxidants in favor of the pro-oxidants may result in oxidative stress, which in turn may result in oxidative damage [7] of cellular components in the form of lipid peroxidation, protein denaturation or DNA conjugation [8]. Oxidative stress has been associated with many diseases like cancer [6], post-ischemic and neural degradation [2], Parkinson’s and Alzheimer disease [5], AIDS [9], and aging and cardiovascular diseases [4]. The metabolism of pro-oxidants by cytochrome P450s is another important process that can result in the formation of reactive oxygen species (ROS) [10, 11].
Because antioxidants may neutralize the potentially harmful effects of direct pro-oxidants or pro-oxidants formed upon bioactivation, much of the work done in this field has been directed at synthesizing antioxidants, in some cases organ- or tissue-specific ones [12–14]. In addition, the food industry pays a great deal of attention to antioxidants in foods. Phenols in tea [15], fish oils [16], curcumin in curry [17] and flavanoids in plants [18] are only a few examples of the large number of natural compounds that have been and are being studied for their positive, antioxidant-based, effects. When the pro-oxidant or antioxidant effects of natural extracts or synthetic compounds are investigated, batch assay formats are normally utilized [19–21]. On-line assays that measure the pro-oxidant or antioxidant effects of compounds are also described [22–24]. However, when individual compounds in mixtures need be analyzed for their pro-oxidant or antioxidant properties using these techniques, cumbersome gradient HPLC separations are required before the purified compounds can be characterized. Moreover, care must be taken to ensure that the purified compounds are not oxidized or degraded in air before analysis. One strategy that could be employed to circumvent the need for this cumbersome purification and subsequent screening process is high-resolution screening (HRS), which stands for “on-line post-column biodetection after HPLC separation.” HRS methodologies, which screen individual compounds in mixtures for affinity, have been developed for receptors (e.g., the estrogen receptor [25]), enzymes (e.g., cathepsin B [26], cytochrome P450s [27]), and antibodies (e.g., digoxin antibodies [28]). These HRS strategies have proven to be very useful for the rapid profiling and identifying of individual ligands in active mixtures, especially when HRS systems are run simultaneously with MS [29]. Moreover, the use of these fast HRS strategies can result in a reduced risk of the oxidation or degradation of sample components before analysis.
This paper presents the development and validation of a HRS-based on-line post-column detection system for the detection of ROS-producing compounds as well as antioxidants in mixtures. This so-called pro-oxidant and antioxidant detection (PAD) system is based on the oxidation of 4-hydroxyphenylacetic acid (4HPAA) by H2O2 in the presence of horseradish peroxidase (HRP) to a highly fluorescent dimer [30, 31]. Scheme 1 shows the general principles of the assay. H2O2 may be present as such or it may be formed from superoxide anion radicals (resulting from ROS-producing compounds) in the presence of superoxide dismutase (SOD). After optimization, the on-line detection system was validated with the well-known ROS-producing compounds paraquat, menadione and duroquinone, and the antioxidants L-ascorbic acid and glutathione, in flow injection analysis (FIA) mode. Finally, the on-line PAD system was coupled to gradient HPLC and thus used in HRS mode to screen individual compounds in mixtures for their ROS-producing and/or antioxidant properties.
Scheme 1Schematic representation of the principles of the biochemical assay
Experimental section
Materials
L-ascorbic acid, L-glutathione (reduced; GSH) and 4-hydroxyphenylacetic acid (4-HPAA) were purchased from Aldrich (Zwijndrecht, The Netherlands). Tween 20, menadione, polyethyleneglycol 6000 (PEG6000), polyethyleneglycol 3350 (PEG3350), methylviologen (paraquat dichloride), peroxidase (horseradish, type I; HRP), and superoxide dismutase (from bovine erythrocytes; SOD) were purchased from Sigma (Zwijndrecht, The Netherlands). β-Nicotinamide adenine dinucleotide phosphate tetra sodium salt (NADPH) was from Applichem (Lokeren, Belgium). Methanol (MeOH) and isopropanol (IPA) were purchased from Riedel de Haën (Seelze, Germany). Acetonitrile (ACN) was from Baker (Deventer, The Netherlands). The MeOH, ACN and IPA were of HPLC reagent grade. Rat liver microsomes (β-NF induced) were prepared as described elsewhere [32]. In short: livers from β-NF induced rats were homogenized at 4 °C in two volumes of 50 mM potassium phosphate buffer (pH 7.4) with 0.9% sodium chloride using a Potter-Elvehjem (Sigma) homogenizer. The homogenate was centrifuged for 20 min at 12.000×g, and the supernatant obtained was further centrifuged for 60 min at 100.000×g. The resulting pellet was washed twice and subsequently resuspended in 50 mM potassium phosphate buffer (pH 7.4), 0.9% sodium chloride, and 25% glycerol, and stored at −80 °C. The protein concentration in the microsomes was determined as 13.1 mg/ml. Protein concentrations were determined with the standard Bio-Rad (Hercules, CA, USA) protein assay based on the method of Bradford.
Instrumentation
A Gilson 234 auto injector (Villiers-le-Bel, France) equipped with a Rheodyne (Bensheim, Germany) six-port injection valve (injection loop, 50 μl) was used for sample injections. A Knauer K-500 HPLC pump (Berlin, Germany) was used to deliver the injected samples into the on-line PAD system in FIA mode. Two Knauer K-500 HPLC pumps were used to deliver the cofactors, substrate and enzymes by means of superloops (SL-A and SL-B) (50 ml, Pharmacia, Peapack, NJ, USA), which were kept on ice, into the PAD system. Prior to detection via an Agilent 1100 (Waldbronn, Germany) series fluorescence detector (λex 320 nm; λem 409 nm), a knitted reaction coil (0.25 mm i.d.; 1.59 mm o.d.; internal volume of 75 μl) positioned in a Shimadzu CTO-10AC column oven (Duisburg, Germany), was used to perform the enzymatic reaction on-line. To reduce pump pulsing, flow restrictors were inserted between the pumps and the superloops. The flow restrictors were made in a similar way to the ones used by Kool et al. [27].
Pro-oxidant and antioxidant assay optimization
The initial optimization of the biochemical assay for the PAD system was performed off-line on a Shimadzu RF-1501 spectrofluorometer (λex 320 nm; λem 409 nm), before the biochemical assay was transferred to the on-line format for further optimization. All measurements were performed in triplicate at 37 °C in quartz cuvettes with total volumes of 2 ml. Different concentrations of enzymes (rat liver microsomal cytochrome P450s and cytochrome P450 reductase, SOD and HRP), cofactor NADPH, and 4-HPAA were tested. Blocking reagents and detergents (that can improve the resolution of the PAD system) [27] and organic modifiers (that are necessary when the PAD system is operated in on-line gradient HPLC mode) were investigated as well. Potassium phosphate buffer (50 mM; pH 7.8) was used in all experiments. Initial conditions were rat liver microsomes (50 μg/ml), NADPH (40 μM), 4-HPAA (1 mM), SOD (10 U/ml), and HRP (10 U/ml). Reactions were started after 5 min of pre-incubation with the addition of paraquat (70 μM). The different parameters were optimized in the abovementioned order. When the optimal (or best compromise) concentration of every parameter was subsequently used in the optimization of the next parameter. After the optimization process, the optimized conditions were used in the PAD system in FIA and HPLC mode.
Pro-oxidant and antioxidant detection system in flow-injection analysis mode
A schematic view of a similar on-line detection system to the PAD system used in FIA mode is shown and described elsewhere [27]. The main difference between the PAD system used in FIA mode and that used in HPLC mode is the replacement of a carrier solution used in FIA mode with a gradient reversed-phase HPLC system. The general scheme for a PAD system coupled on-line to gradient reversed-phase HPLC (described in the next paragraph) is shown in Fig. 1, which also shows the schematics of the PAD system in general. The continuous mixing of enzymes (cytochrome P450s and cytochrome P450 reductase, HRP and SOD) from SL-A and cofactors/substrate (NADPH/4-HPAA) from SL-B with a carrier solution in a reaction coil is the basic principle of the PAD system (when used in FIA mode). After mixing in a knitted reaction coil, ROS formed from pro-oxidants (after cytochrome P450/cytochrome P450 reductase-mediated bioactivation) are converted by SOD to (relatively stable) H2O2. The subsequent H2O2-dependent conversion of non-fluorescent 4-HPAA to its fluorescent dimer by HRP yields a spectroscopic handle for the efficient measurement of ROS formation. Thus, the ROS formed by eluting compounds give rise to a temporary increase in the formation of fluorescent product, which is seen in the PAD system as a peak. When SL-B also contains a ROS-producing compound, continuous oxidation of the 4-HPAA results in an elevated fluorescent baseline. In this situation, injection of antioxidants causes a temporary decrease in 4-HPAA oxidation, resulting in a negative peak in the PAD system. Thus, the system is sensitive to both ROS-producing compounds and antioxidants.
Fig. 1Schematic view of the PAD system used in HPLC mode. Superloop A (SL-A) and superloop B (SL-B) are used to deliver enzymes and substrates to the reaction coil, respectively. ROS-producing pro-oxidants and antioxidants are introduced into the system by a gradient reversed-phase HPLC system. Antioxidants and ROS-producing compounds temporarily alter the amount of fluorescent product formed, and this change is detected with a fluorescence (FLD) detector. After HPLC, the make-up pumps produce a counteracting gradient, resulting in a PAD-compatible constant organic modifier concentration. The effluent is then split 1:9 (90% to UV detection and 10% to CYP EAD). AS, autosampler
Liquid chromatography coupled to the pro-oxidant and antioxidant detection system
A general scheme for the PAD system coupled on-line to gradient reversed-phase HPLC is shown in Fig. 1. Gradient HPLC separations were performed using a 30 mm length × 2 mm i.d. stainless steel column (Luna 3 μ C18(2), Phenomenex, Torrance, CA, USA). When the PAD system was operated in HPLC mode, two pumps were used to control the LC gradient and two pumps were used directly after the HPLC column to compensate for the increased concentration of organic modifier (during the gradient) before the delivery of the effluent to the PAD system. The following gradient was used for the HPLC separations: an initial flow rate of 300 μl/min for 3 min at H2O:MeOH (95:5); a decreasie in the flow rate gradient for 6 min to 150 μl/min H2O:MeOH (5:95); 14 min with H2O:MeOH (5:95) at a flow rate of 150 μl/min. Thereafter, the column was re-equilibrated to the starting conditions in 5 min. To maintain a constant concentration of MeOH after the HPLC column, a second gradient with an increasing flow-rate was included in the system, after HPLC separation, with an initial flow-rate of 700 μl/min H2O:MeOH (4:1) for 4.5 min. An increase in the flow-rate gradient for 6 min to 850 μl/min H2O:MeOH (100:0) was then followed by a post-gradient flow-rate of 850 μl/min H2O:MeOH (100:0) for 14 min. Finally, re-equilibration to starting conditions was performed in 5 min. The H2O and MeOH of the increasing flow-rate gradient contained 100 mg/L Tween 20. The final constant flow-rate was 1000 μl/min, with a MeOH concentration of 15% in H2O. This flow was connected to a T-piece and split 1/9 with a flow splitter. 90% was directed to the UV detector, while10% was pumped into the PAD system. For HPLC analysis, all tested compounds were dissolved in 30% MeOH in water.
Results and discussion
The primary aim of this study was to develop and validate a HRS-based on-line post-column detection system for the detection of ROS-producing compounds and antioxidants in mixtures. The biochemical assay is based on the oxidation of 4-hydroxyphenylacetic acid (4HPAA) to a highly fluorescent dimer (see Scheme 1). After optimization, the on-line detection system was validated using the well-known ROS-producing compounds paraquat, menadione, and duroquinone, as well as the antioxidants L-ascorbic acid and glutathione, in flow injection analysis (FIA) mode. The on-line PAD system was then coupled to gradient HPLC and this was used in HRS mode to screen for individual compounds in mixtures based on their ROS-producing and/or antioxidant properties.
Optimization of pro-oxidant and antioxidant detection assay
The optimization of the biochemical assay for the PAD system was conducted first in an off-line batch format before the optimized biochemical assay was transferred to the PAD system in FIA and HPLC modes. Paraquat was used as a model ROS-producing compound. The following parameters were optimized: enzyme concentrations (rat liver microsomes, SOD, and HRP), cofactor NADPH and substrate 4-HPAA, blocking reagents PEG3350 and PEG6000, the detergent Tween 20 (which can improve the resolution of the on-line PAD system) and organic modifiers (that are necessary when the on-line PAD system is operated on-line in gradient HPLC mode). All optimizations were performed without the presence of paraquat as the continuous ROS-producing compound in SL-B. However, paraquat (0.036 mM) was added to the SL-B in the final optimized system in order to permit measurements of both pro-oxidants and antioxidants using the on-line PAD system.
First, the concentration of cytochrome P450s/cytochrome P450 reductase-containing rat liver microsomes was evaluated. Insertion of this important, mainly hepatic, biotransformation system permits the reductive bioactivation of compounds that need to be bioactivated before they can exhibit their ROS-producing effects (e.g., paraquat [33]). For the liver microsomes, it was found that higher concentrations resulted in an increase in fluorescence signals up to at least 150 μg/ml (microsomal protein concentration). This is due to increased cytochrome P450s/cytochrome P450 reductase-mediated redox cycling [33]. Since the use of high concentrations of rat liver microsomes increases the risk that on-line biochemical detection systems will become clogged [27], a concentration of 50 μg/ml rat liver microsomes was used in subsequent experiments. For SOD, concentrations higher than 14 U/ml did not increase the fluorescence signal significantly. Therefore, this SOD concentration was used in subsequent experiments. For HRP, the same effect was observed at a concentration of 18 U/ml, and so this concentration was employed from then on. Increasing the concentration of 4-HPAA led to the increased formation of fluorescent dimer, up to a concentration of 1.2 mM, after which the additional increase in sensitivity was counteracted by an increase in noise. A 4-HPAA concentration of 1.2 mM was therefore used in later experiments. An NADPH concentration of greater than 44 μM did not increase the assay performance significantly and so this concentration was adopted. For the polymers PEG3350 and PEG6000, concentrations of up to 5 mg/ml did not produce significant differences in assay performance. Since a PEG6000 concentration of 1 mg/ml is sufficient to reduce possible peak broadening in on-line biodetection systems [27], this concentration was in subsequent experiments. Detergents, such as Tween 20, can be used to prevent (membrane-bound) enzymes from adhering to reaction coils in on-line biodetection systems, thereby resulting in reduced peak broadening [27]. Solubilization of membrane-bound enzymes, such as cytochrome P450s, can occur at high detergent concentrations and will inactivate the microsomes under the present conditions. A Tween 20 concentration of 100–200 mg/l showed minimal (5–20%) enzyme denaturation. These Tween 20 concentrations are known to efficiently reduce peak broadening when used in cytochrome-P450-containing on-line biodetection systems [27]. A Tween 20 concentration of 100 mg/l was therefore used from then on. Organic modifiers may be useful for preventing enzymes and lipophilic compounds from adhering to the walls of reaction coils, thus preventing peak broadening [27]. Moreover, when on-line biodetection systems are operated in the HPLC mode, organic modifiers are automatically introduced via the HPLC gradient. When testing MeOH, ACN, and IPA in the off-line biochemical batch assay format, they were tolerated up to concentrations of 10%, resulting in approximately half the fluorescence signal compared to that obtained when organic modifiers were not used.
Optimized conditions were derived from the abovementioned experiments performed in the off-line batch assay format and subsequently translated to the on-line PAD system in FIA mode. These final conditions were: a carrier solution consisting of 10% MeOH and 100 mg/l Tween 20 at a flow rate of 100 μl/min; SL-A containing potassium phosphate buffer (50 mM; pH 7.8), rat liver microsomes (50 μg/ml), HRP (18 U/ml), and SOD (14 U/ml), and SL-B with the same buffer containing PEG6000 (1 mg/ml), NADPH (44 μM), and 4-HPAA (1.2 mM). For continuous ROS production (resulting in a stable fluorescent baseline), paraquat was present in the optimized system in SL-B at a concentration of 0.036 mM. Both superloops had a flow-rate of 100 μl/min.
PAD system in flow-injection analysis mode
Before coupling the on-line PAD system to gradient HPLC, it was first evaluated and validated in FIA mode. Evaluation of the PAD system was done with three well-known model ROS-producing compounds (i.e., paraquat, menadione, and duroquinone), and two well-known model antioxidants (L-ascorbic acid and glutathione). First, paraquat was injected in triplicate at different concentrations (1.0, 0.25, 0.06, 0.015, 0.004, 0.001, 0 mM) into the PAD system in FIA mode. Figure 2a shows the resulting signals. The highest concentration of paraquat resulted in fluorescence quenching. An analogous FIA trace for the redox cycling compound menadione is depicted in Fig. 2b.
Fig. 2Triplicate injections of a series of dilutions (dilution factor of 4) of different compounds into the PAD system used in FIA mode. a The pro-oxidant paraquat (starting with 1.0 mM). b The pro-oxidant menadione (starting with 0.15 mM). c The antioxidant ascorbic acid (starting with 1.0 mM). d The antioxidant glutathione (starting with 25 mM)
Figure 2c and d show the resulting PAD traces when L-ascorbic acid and glutathione (well-known antioxidants) were injected in triplicate in a dose-response manner, respectively. It was shown that ascorbic acid acted as a pro-oxidant compound at low concentrations (Fig. 2c). Pro-oxidant effects of ascorbic acid at low concentrations were previously demonstrated by Abudu et al. [34], who stated that ascorbic acid can undergo one-electron reduction to form an ascorbyl radical. Ascorbic acid can also switch from being an antioxidant to being a pro-oxidant in the presence of transition metals [35].
The direct antioxidant effect of L-ascorbic was also shown using the PAD system (without paraquat in SL-B) by first injecting paraquat and then injecting a mixture of paraquat and L-ascorbic acid. Figure 3 shows that the addition of L-ascorbic acid neutralizes the ROS formed from paraquat. The same effect is shown for menadione (Fig. 3).
Fig. 3Injections (triplicates) of different compounds into the PAD system in FIA mode (without the continuous addition of a pro-oxidant): 1) paraquat (0.05 mM); 2) paraquat (0.05 mM) and ascorbic acid (0.1 mM); 3) menadione (0.03 mM); 4) menadione (0.03 mM) and ascorbic acid (0.1 mM)
To determine the effects of the test compounds paraquat, menadione, L-ascorbic acid, and glutathione on the fluorescence baseline, injections of different concentrations of the test compounds into the PAD system were performed without HRP and SOD in SL-A. Menadione and duroquinone caused fluorescence signals when injected in concentrations larger then 0.8 and 0.4 mM, respectively. This implies that these compounds can only be measured efficiently at these or higher concentrations if a parallel signal measuring their auto-fluorescence is employed, so that this can be subtracted from the PAD signal. In order to determine the relative pro-oxidant or antioxidant effects of the compounds, and to compare the off-line batch assay and the on-line assay (in FIA and HPLC mode) with each other, the fluorescence signals per mole of the test compound were determined from the data obtained in the off-line batch assay and the PAD system in FIA and HPLC modes (Table 1). When comparing the relative fluorescence signals per mole of test compound obtained using the off-line batch and both of the on-line systems, the relative fluorescence signals appeared to differ significantly for some compounds. This difference might be explained by the pre-incubation step, which was only performed for the off-line batch assays and not for the on-line PAD measurements. In contrast, for the off-line batch assay and both of the on-line PAD system formats, the measured pro-oxidant or antioxidant effects were comparable for all test compounds in terms of determinations of their individual pro-oxidant or antioxidant effects. Although the results obtained using the off-line batch format and both on-line formats were not similar, the comparable results still allowed the off-line batch assay to be transformed into both on-line assay formats. We can expect these relatively complex assay systems to give slightly different assay characteristics when performed under different assay conditions, like off-line batch or on-line assay conditions. Therefore, we can conclude that the off-line batch assay format can be transferred to the present PAD system used to screen for antioxidants and ROS-producing compounds.
Table 1Initial relative increases or decreases (in fluorescence units, FU) of different ROS-producing pro-oxidant compounds and antioxidants compared to paraquat for the PAD system used in FIA mode, in gradient HPLC mode and for a traditional batch assayPro-oxidant/AntioxidantFIA PAD system (FU/mol±SEM)Detection limit for the FIA PAD system (nmol)HPLC PAD system (FU/mol±SEM)Detection limit for the HPLC PAD system (nmol)Batch assay set-up (FU/mol±SEM)Paraquat1.00 ± 0.080.071.00 ± 0.330.91.00 ± 0.05Menadione1.55 ± 0.200.010.56 ± 0.010.41.60 ± 0.33Duroquinone0.13 ± 0.020.040.36 ± 0.191.30.16 ± 0.07Glutathione-2.20 ± 0.031.9-0.67 ± 0.188.0-1.01 ± 0.04Ascorbic acid-4.58 ± 0.220.1-1.35 ± 0.130.2-1.22 ± 0.12The detection limits of the PAD system used in the FIA and HPLC modes are also given
Interday and intra-day variabilities were determined in the on-line PAD system in FIA mode as follows: intraday variability was determined by injecting paraquat (0.2 mM) in triplicate at 3.5-hour time intervals into the PAD system. The intraday variability was determined without changing the contents of the superloops. For the interday variability, paraquat (0.2 mM) was injected daily in triplicate for three days with fresh solutions in the superloops each day. Intraday variability was 3.3%±1.1% and interday variability 4.1%±0.6%, which are both within the ranges exhibited by bioanalytical screening methods [27, 29]. Detection limits (Table 1) were determined by triplicate injections of a series of dilutions of every test compound. The detection limit was defined as the concentration of test compound that gave an average signal of three times the noise (S/N ratio = 3).
The sensitivities obtained for the different compounds, which are also intrinsically determined by their pro-oxidant or antioxidant potencies, are also indicated in Table 1. These sensitivities were of the same order as those obtained with the off-line batch assay format (data not shown). Thus, the present PAD system provided a useful novel rapid screening tool for pro-oxidant and antioxidant activities of individual compounds in mixtures in this respect too.
On-line coupling of the PAD system to gradient HPLC
The PAD system in gradient HPLC mode was evaluated by analyzing the five test compounds after HPLC separation with a decreasing flow-rate gradient. The advantage of this decreasing flow-rate gradient lies with the initially high flow-rate through the column at low concentrations of organic modifier, which results in better eluting compound resolution at the start of the gradient. At higher concentrations of organic modifier, the flow rates are obviously gradually decreased (and the post-column counteracting flow rates are gradually increased) in order to obtain a continuous flow rate (1 ml/min) and concentration of organic modifier (of 10%) after mixing in the post-column counteracting gradient. This results in a constant flow rate and organic modifier concentration (after the 1:9 split) when entering the on-line PAD. The added value of this approach is that alterations in the chromatographic method can be made without much effect on the on-line PAD assay.
First, the individual test compounds were analyzed. The test compounds were injected (in triplicate) in five different concentrations prepared by serial dilution of 100 μl of stock solution with 300 μl MeOH (30% v/v). Typical superimposed chromatograms of menadione and L-ascorbic acid are shown in Fig. 4a and b, respectively. At high concentrations of L-ascorbic acid, two peaks were seen. This was the result of overloading the analytical column, as additional mass spectrometry data showed that both peaks were from L-ascorbic acid (data not shown). The relative pro-oxidant or antioxidant response of every test compound in the PAD system used in gradient HPLC mode is shown in Table 1. The sensitivities obtained for the test compounds are also depicted in Table 1. When comparing the PAD system used in HPLC mode with the PAD system used in FIA mode and the off-line batch assays, it is seen that all three assay formats allow the detection of both pro-oxidants and antioxidants. The differences between the three assay formats probably result from different factors, such as the pre-incubation step applied in the off-line batch assay format and the peak broadening of compounds during the chromatographic separation. Another possible cause derives from minor pro-oxidant or antioxidant impurities in the test compounds. As these impurities would be separated from the test compounds in the on-line PAD system used in HPLC mode, they would only influence the results obtained in the off-line batch format and the on-line PAD system used in FIA mode. However, comparable results are still obtained (no more than threefold differences in measured pro-oxidant or antioxidant effects were obtained per mole of test compound) in the different experimental set-ups. Therefore, it can be concluded that the PAD system used in gradient HPLC mode can be easily applied to and is a sensitive method for the screening of individual compounds for their ROS-producing capacity and antioxidant potentials.
Fig. 4a Superimposed PAD traces of menadione injected in different amounts into the PAD system in HPLC mode (12.5, 50 and 200 μM from bottom to top chromatogram, respectively). b Superimposed PAD traces of ascorbic acid injected in different amounts into the PAD system in HPLC mode (50, 200 and 800 μM from top to bottom chromatogram, respectively)
The PAD system used in HPLC mode was also applied to the detection of individual antioxidants and ROS-producing compounds in mixtures. Typical PAD traces of two different mixtures that were injected and separated on HPLC are shown in Fig. 5a and b, respectively. Figure 5a shows that all three ROS-producing compounds—paraquat, menadione and duroquinone—were individually identified as oxidant species. The compounds in the second mixture, which contained two antioxidants and two ROS-producing compounds, were individually identified as antioxidants (L-ascorbic acid and glutathione) and pro-oxidants (menadione and duroquinone). Thus, mixtures in which ROS-producing compounds are present together with antioxidants could effectively be measured individually with the present PAD system in gradient HPLC mode. When analyzing such mixtures with traditional off-line batch assay formats, antioxidants can counteract the effects of pro-oxidants thereby reducing or even totally removing ability to detect the pro-oxidants. The relatively low resolutions obtained in the PAD assay directly reflect the resolutions resulting from the chromatographic part of the total system. Since this study was a proof-of-principle study, we did not perform a thorough optimization of the chromatographic part of the system. The resolutions that can be obtained with the PAD part of the system (used in FIA mode) are shown in Fig. 2 and reflect the performance of the assay. Connecting the PAD part to a chromatographic separation system with higher resolution will obviously result in higher resolution, which may be needed when screening real life samples. The present PAD system in HPLC mode therefore opens up new avenues to the efficient and rapid screening of complex mixtures for individual pro-oxidant and antioxidant components.
Fig. 5a PAD trace for a mixture of three pro-oxidants injected into the PAD system in HPLC mode. Injected compounds are: paraquat (0.12 mM; 3.5 min), menadion (0.05 mM; 17.5 min), and duroquinone (0.17 mM; 18.5 min). b PAD trace of a mixture of two pro-oxidants and two antioxidants injected into the PAD system in HPLC mode. Iinjected compounds are: ascorbic acid (0.14 mM; 5 min), glutathione (0.4 mM; 8 min), menadion (0.05 mM; 17.5 min), and duroquinone (0.17 mM; 18.5 min)
Conclusion
This paper presents the development and validation of a HRS-based on-line post-column detection system for the detection of ROS-producing compounds as well as antioxidants in mixtures. Different parameters, such as substrate (4-HPAA) and enzyme concentrations, reaction time, temperature, additives, and organic modifier concentrations were first optimized for the PAD system used in FIA mode. Several ROS-producing compounds as well as antioxidants were successfully measured with the optimized system. The intraday and interday variabilities of the PAD system used in FIA mode were determined and found to be lower than 5%. Good sensitivities, at least comparable with similar off-line batch assay formats for individual compounds, were obtained. On-line coupling of the novel PAD system to gradient HPLC permitted the screening of individual compounds in mixtures for ROS-producing and antioxidant properties. It should be noted, however, that compounds that show fluorescence quenching or intrinsic fluorescence may interfere with the methodology. However, it may be possible to adjust the system so that another split directs some of the flow to a second on-line assay that measures these artifacts with a “negative control PAD system” in order to detect such interferences. This PAD system used in gradient HPLC mode is potentially of great value to drug discovery and toxicology and food research.

on-line
ros
antioxidant
pro-oxidant
biochemical detection
bioactivation