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Published online before print December 13, 2006, 10.1097/01.psy.0000249732.96753.8f
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Psychosomatic Medicine 69:61-67 (2007)
© 2007 American Psychosomatic Society

ORIGINAL ARTICLES

Socioeconomic Disparities in Insulin Resistance: Results From the Princeton School District Study

Elizabeth Goodman, MD, Stephen R. Daniels, MD, PhD and Lawrence M. Dolan, MD

From the Department of Pediatrics, Tufts-New England Medical Center and Tufts University School of Medicine, Boston, MA (E.G.); Department of Pediatrics, Denver Children’s Hospital, and the University of Colorado School of Medicine, Denver, CO (S.R.D.); and the Division of Endocrinology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH (L.M.D.).

Address correspondence and reprint requests to Elizabeth Goodman, MD, Department of Pediatrics, Tufts-New England Medical Center, NEMC Box 351, 750 Washington Street, Boston, MA 02111. E-mail: egoodman{at}tufts-nemc.org


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Objectives: The objectives of this study were to determine whether lower socioeconomic status (SES) is associated with changes in insulin resistance in adolescents over a 3-year period and explore moderators of this effect.

Methods: A total of 1167 healthy non-Hispanic black and white participants in the Princeton School District Study, a longitudinal study of fifth to 12th graders in a suburban Midwestern public school district were included in this study. Inclusion criteria were a) physical examination and fasting morning blood draw at baseline and 3 years later, b) younger than 20 years old at follow up, and c) information available on SES provided by a parent. The influence of SES on insulin resistance and change in insulin resistance over time was examined using general linear models adjusting for multiple covariates. Models also assessed if race or baseline weight status changed the SES–insulin resistance relationship and explored the role of perceived stress.

Results: Blacks and lower SES youth had higher body mass index z score and increased insulin resistance (p < .001). In multivariable models, lower parent education, but not household income, was associated with higher baseline insulin resistance (F = 7.84, p < .001) and worsening insulin resistance over time (F = 18.86, p < .001). Parent education’s effect on change in insulin resistance was more pronounced for obese youth compared with nonobese (F interaction = 10.12, p < .001) even with adjustment for multiple covariates. Perceived stress did not alter these relationships.

Conclusions: Lower parent education appears to be related to increased insulin resistance both cross-sectionally and over time in black and white adolescents. Worsening insulin resistance is especially problematic for obese adolescents from families with low parent education.

Key Words: obesity • insulin resistance • disparities • race • SES

Abbreviations: BMI = body mass index; CDC = Centers for Disease Control and Prevention; CV = coefficient of variation; HDL-C = high-density lipoprotein cholesterol; HOMA = homeostasis model assessment score; PSD = Princeton School District; SES = socioeconomic status.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Insulin resistance is strongly associated with obesity and numerous other morbidities (1). There has been increasing concern over the past decade that rising rates of obesity will lead to a marked increase in the frequency of type 2 diabetes, especially among non-Hispanic black adolescents. Prevalence of obesity is higher among non-Hispanic black youth compared with non-Hispanic white youth (2), and non-Hispanic black adolescents are more insulin-resistant (3). In addition to these physiological differences between non-Hispanic black and white youth, there are also social differences that place non-Hispanic black youth at increased risk. Non-Hispanic black adolescents are more likely to be from less well educated and lower income families than their non-Hispanic white counterparts. These differences in social circumstances can also have deleterious effects on diabetes risk, because coming from a lower socioeconomic status family has been associated with both obesity and insulin resistance in adolescence (4–6) and with obesity and type 2 diabetes in adulthood (7–9).

Adolescence may be a particularly critical developmental period vis-à-vis the intersections between obesity, insulin resistance, and social factors. Pubertal changes in insulin secretion and sensitivity occur (10). Obesity in adolescence is generally felt to predispose to obesity in adulthood (11–13), but the relationships between obesity and insulin resistance in adolescence and the tracking of these factors into adulthood is less clear. Studies suggest that racial differences in body mass index (BMI) tracking exist (14) as well as insulin secretion and clearance during adolescence (15). However, the role of socioeconomic status (SES) in these associations remains unexplored. Given the correlation between minority race/ethnicity and low SES, this is an important gap in the literature.

To our knowledge, no longitudinal study has examined socioeconomic disparities in insulin resistance and changes in insulin resistance over time in a diverse cohort of youth. The purpose of this study was twofold: first to determine if parent education and household income, two commonly used indicators of SES, influence both baseline insulin resistance and changes in insulin resistance over time and second, to assess if race/ethnicity or weight status altered the SES–insulin resistance relationship. In addition, because the stress mediation hypothesis is one of the leading theories on how social disadvantage becomes embodied to create health disparities (16–18), the role perceived stress may play in any demonstrated socioeconomic disparities in insulin resistance was explored.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Sample
The data for this study were drawn from the Princeton School District (PSD) Study, a school-based longitudinal study (19,20). The PSD study follows a cohort of students who were in the fifth to 12th grades (initial ages 12–19 years) in the 2001 to 2002 academic year. There were 4269 eligible students when we went into the field in September 2001. Of these students, 2501 (58.6%) participated in year 1 of the PSD Study, eight of whom were known to have diabetes. The cohort is 95% non-Hispanic black and white, which reflects the demographics of the school district. However, non-Hispanic whites were slightly more likely to participate than non-Hispanic blacks. This difference, although statistically significant, was small (60.3% non-Hispanic white versus 58.2% non-Hispanic black, p < .001). There was also higher participation among females (62.7% versus 54.9%, p < .01). Mean grade among participants was 9.1 ± 1.6 compared with 9.3 ± 1.6 among nonparticipants (p = nonsignificant). Thus, although there are some statistically significant differences in participation, the cohort is quite representative of the underlying population and brings a great deal of diversity. Parental consent and student assent was obtained from all participants as previously described (21). The study was approved by the Institutional Review Boards at both the local children’s hospital and the participating university.

These analyses are restricted to healthy non-Hispanic black and white participants, because sample sizes were too small for inclusion of other racial/ethnic groups. All healthy (nondiabetic) non-Hispanic black and white participants, hereafter referred to as black and white, who were a) seen at baseline and 3 years later for a follow-up visit; b) had data on insulin resistance, lipids, and weight status from both time points; c) had a parent provide information on parent education at baseline; and d) were less than 240.5 months of age at follow up were included in the analyses. The age restriction allowed us to assess the influence of change in BMI z score based on the Centers for Disease Control and Prevention (CDC) 2000 growth charts, which include information for derivation of BMI z scores only up through age 240.5 months. A total of 1167 PSD study participants fulfilled inclusion criteria. This is a larger, younger cohort than was included in an earlier cross-sectional study of social inequalities in multiple cardiovascular risks, including fasting insulin and glucose (5). The sample for the current analyses was 46.7% non-Hispanic black and 19.0% were obese at baseline. Mean time to follow up was 33.3 months with a standard deviation of 2.4 months.

Procedures
The study visits, which took place in the school setting or local children’s hospital after a verified overnight minimum 10-hour fast, included a physical examination and venipuncture. Participants who were in the seventh through 12th grades at baseline (N = 856) also completed a survey that assessed perceived stress.

Measures
Socioeconomic Status
SES was reported by a parent or guardian through a questionnaire distributed in the informed consent process. Completed parental surveys were brought by students to the baseline visit along with completed consent forms. If the participant did not have the parental survey, stamped self-addressed envelopes were sent home for the parent to return the survey by mail. Parent education for the reporting parent and his or her current spouse/partner was obtained in categories ranging from never attended school to professional training beyond a 4-year college or university. The highest level for either parent was used in analyses. Analysis categories were high school or less, some college or vocational training after high school, or college graduate or higher. Household income was reported in nine ordered categories ranging from less than $5000 to greater than $100,000. The midpoint of the range was used in analyses. Because 13.5% (N = 158) were missing data on household income, multiple imputation was used to impute the missing values in multivariable analyses.

Demographic Data
Date of birth, gender, and parent-identified race/ethnicity were available from school records.

Body Mass Index
BMI was calculated from measured height and weight according to the following equation: BMI = weight (kg)/height (m2). Height and weight were measured per a standardized protocol (19).

Adiposity Measures
BMI z score and a dichotomous variable representing obesity were used as adiposity measures. BMI z scores and percentiles were derived from CDC 2000 growth chart standards based on nationally representative data (22). Obesity was defined as a BMI-for-age greater at or above the 95% based on the CDC Growth Chart standards.

Stage of Pubertal Development
Pubertal status (prepubertal, pubertal, and postpubertal) was assessed using plasma estradiol concentration and the presence or absence of menarche for 2 years in females and plasma-free testosterone concentration and the stage of axillary hair in males per a validated protocol (20).

Laboratory Assays
Plasma insulin concentration was measured by radioimmunoassay using an antiinsulin serum raised in guinea pigs, 125I labeled insulin (Linco, St. Louis, MO) as a standard and a double antibody method to separate bound from free tracer. The sensitivity is 2 pM with intra- and interassay coefficients of variation (CVs) of 5% and 8%, respectively. Glucose was measured by an enzymatic method. Intra and interassay CVs are 1.2% and 1.6%, respectively. Results from the glucose and insulin assays were used to derive insulin resistance measured by the homeostasis model assessment scores (HOMA) model (23). HOMA is calculated as [fasting insulin (mIU) x fasting glucose (mM/L)]/22.5. Lipid profiles were performed on the Hitachi 704. National Cholesterol Education Program performance criteria for accuracy and precision are followed. Direct measurement of high-density lipoprotein cholesterol (HDL-C) was made using the HDL C-plus kit from Roche (Boehringer Mannheim) The intraassay CV is 1.3% and the interassay CV is 2.6%. Triglycerides were measured using a single reagent system from Roche-BMD. The intra- and interassay CVs were approximately 4%.

Stress
Stress was assessed with the Perceived Stress Scale, a measure of global stress (24). This 14-item scale, which measures a person’s appraisal of how stressful his or her life was during the past month, has been shown to be reliable and valid in adolescents (24). Responses are provided on a 5-point Likert scale ranging from never to very often and scores can range from 0 to 56. Cronbach alpha in the PSD cohort was 0.65.

Statistical Analyses
Because some variables were not normally distributed, nonparametric tests were used in bivariate analyses. {chi}2 test was performed for categorical variables, and Mann-Whitney U tests were used for continuous variables. Correlations were assessed with Spearman’s rho. Multivariable analyses were performed using general linear models. Baseline HOMA and triglycerides were log transformed to improve normality of the distribution before inclusion in the multivariable models. The multivariable analyses occurred in two phases. Inclusion of variables in the model in both phases was theoretically driven; automated stepwise selection procedures were not used. The first phase assessed the influence of parent education and household income on baseline HOMA adjusting for factors known to be associated with insulin resistance (age, gender, race, baseline HDL-C, triglycerides, pubertal stage, and weight status). Both BMI z score and the dichotomous variable representing obesity were included. The former assessed a linear relationship between general adiposity and HOMA and the latter assessed a threshold effect. In phase 2, change in HOMA was regressed on these same factors. In addition, the model adjusted for change in BMI z score and baseline HOMA. In both phase 1 and phase 2, two-way and three-way interactions were explored with a particular interest in interactions among SES measures, race, and weight status. In addition, we ran a parallel set of analyses that used fasting insulin as the dependent variable and included adjustment for fasting glucose. Results were nearly identical as those for models that used HOMA as the dependent variable. Because HOMA is the widely used estimate of insulin resistance, we report results of the HOMA models. Last, perceived stress was added to the regression models to determine if addition of this variable altered any demonstrated SES–insulin resistance relationships. Parameter estimates (B) and their standard errors (SE) are reported from the general linear models.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 NOTES
 REFERENCES
 
Descriptive Analyses
Table 1 provides a description of the demographic and clinical characteristics of the sample. Although there were no racial differences in age, gender, or pubertal status, black-white differences were present for all other characteristics. Blacks had lower parent education, lower household income, were more likely to be obese at both baseline and follow up, and had higher BMI z score and HOMA at both time points and were more stressed at baseline than whites. However, blacks also had better lipid profiles at baseline with lower triglycerides and higher HDL-C. Similarly, obese youth differed from nonobese youth in all characteristics except age, gender, and perceived stress. Baseline obese youth had lower parent education, lower household income, were more likely to be postpubertal, were more insulin-resistant at both time points, and had worse lipid profiles than nonobese youth. Cumulative incidence of obesity was 7%, whereas 21.2% of obese youth had become nonobese by follow up. SES measures were strongly correlated with each other (rho = 0.57, p < .001) and weakly correlated with perceived stress (parent education rho = –0.18, p < .001; income rho = –0.17, p < .001). Perceived stress was also weakly correlated with baseline HOMA (rho = 0.11, p = .001) but was not associated with change in HOMA (rho = 0.02, p = .49).


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  		TABLE 1. Sociodemographic and Clinical Characteristics of the Study Sample

 

Socioeconomic Status Effects on Baseline Insulin Resistance
Although household income was not associated with baseline HOMA, parental education had a strong effect on baseline insulin resistance (Table 2). Lower parental education was associated with progressively increased HOMA. Estimated marginal means in HOMA from the multivariable models for the parent education categories were 4.35 for high school or less parent education, 4.06 for more than high school but less than college, and 3.60 for college graduate or higher. The regression model, which accounted for 35% of the variance in baseline HOMA, also demonstrates that females, blacks, obese subjects, those who are pubertal, or have higher triglycerides are more insulin-resistant. No interactions were demonstrated. Addition of perceived stress to the model did not alter these results, and perceived stress was not associated with HOMA (Bstress = 0.005, SE = 0.003, p = .14).


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  		TABLE 2. Determinants of Baseline Homeostasis Model Assessment Score From Multivariable General Linear Model Analyses

 

Socioeconomic Status Effects on Change in Insulin Resistance Over 3 Years
Results of longitudinal analyses to assess predictors of change in HOMA are presented in Table 3. Because results of the longitudinal models were nearly identical when log transformed or untransformed HOMA was used, we present results of the model using the difference in untransformed HOMA, which is more straightforward. Overall, the regression model accounted for 36% of the variance in change in HOMA. Lower parental education, but not lower household income, was associated with worse insulin resistance (F = 18.86, p < .001). This was especially true for obese youth as evidenced by the baseline obesity by parent education interaction (F = 10.12, p < .001). The effect was most pronounced for those who came from families in which the highest level of parental education was high school or less (B interaction = 3.66, p < .001). No interaction was noted between race/ethnicity and parent education, suggesting that the influence of parent education is not different between non-Hispanic black and white teens. No three-way interactions were noted. Like in the baseline analyses, addition of perceived stress did not alter these relationships (Bstress = 0.016, SE = 0.024, p = .49).


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  		TABLE 3. Determinants of Change in Homeostasis Model Assessment Score (HOMA) During 3-Year Follow-Up Period in 1167 Princeton School District Study Participants Based on General Linear Model Analyses

 

Figure 1 illustrates the effect of parent education and the interaction with baseline obesity using data derived from the multivariable longitudinal model. At all levels of parent education, insulin resistance was higher for obese youth for whom the slope of the line for parent education is much steeper than for the nonobese. Over the 3 years, on average, insulin resistance decreased among all nonobese adolescents as evidenced by the negative estimated change in HOMA for at each level of parent education. This is likely the result of resolution of pubertal insulin resistance. The gradient effect is shown by the increasingly negative magnitude of the estimated change as parent education increased. Thus, lower education appears to be associated with less of a decline in insulin resistance than expected during the late pubertal and early postpubertal years. In contrast, for obese youth, insulin resistance decreased only for those from the most highly educated families. For obese youth from less well educated families, the average change in insulin resistance was positive reflecting worsening insulin resistance. The increase among obese youth from parents with high school or less education was especially marked.


Figure 111
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  		Figure 1. Influence of parent education on change in homeostasis model assessment score (HOMA) over 3 years. From multivariable general linear model adjusting for age, gender, race, months of follow up, baseline pubertal stage, baseline household income, baseline high-density lipoprotein cholesterol and triglycerides, baseline body mass index z score, baseline HOMA, and change in body mass index z score. Closed circles = nonobese; open circles = obese.

 

The longitudinal model has a number of other notable findings. Baseline HOMA was an important determinant of change in insulin resistance over time. The negative sign of this parameter estimate reflects the fall in insulin resistance, which occurs as young people transition through puberty. However, this decrease is mitigated by increasing adiposity as demonstrated by the interaction between baseline BMI z score and baseline HOMA. Baseline BMI z score itself was not predictive of change in HOMA. This lack of significance may be because the dichotomous variable representing baseline obesity captured most of the effect of baseline BMI z score. In addition, although baseline BMI z score was not significant, change in BMI z score was strongly associated with change in HOMA. The positive sign of the parameter estimate indicates that loss of adiposity was related to decreasing insulin resistance, whereas gain in adiposity was related to worsening insulin resistance independent of baseline values.

There were some other notable similarities and differences between the baseline and longitudinal models. Like in the baseline model, higher baseline triglycerides and black race were related to worsening HOMA. However, age, female gender, and puberty were not predictive of change in HOMA. The direction of the pubertal effect was in the correct direction (baseline pubertal youth had decreasing insulin resistance as they aged consistent with resolving pubertal insulin resistance). However, the estimate did not reach statistical significance. This was also true for the relationship of lower HDL-C to higher insulin resistance. The parameter estimates did not reach statistical significance in either baseline (p = .07) or longitudinal (p = .051) models.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
NOTES
 REFERENCES
 
This study has two major findings. First, among non-Hispanic black and white teens, lower parent education, but not household income, was related to increased insulin resistance both cross-sectionally and over time; these effects were independent of race/ethnicity. Second, the influence of lower parent education was especially marked for obese youth. For those whose parent(s) had a high school degree or less education, estimated mean change in HOMA was fourfold higher for obese youth compared with nonobese youth. For those whose parent(s) had between a high school and college degree, there was approximately a 1.5-fold difference. These data suggest that the influence of parent education on insulin resistance is profound and sustained, especially because lower parent education has been associated with obesity in adolescence (4,25). In addition, this study provides preliminary evidence that the influence of parent education on insulin resistance is not mediated through the conscious appraisal of the environment as stressful. However, because perceived stress does not correlate well with physiological stress, chronic adaptation to a more challenging environment and the resultant wear and tear on regulatory systems may still play a role in the development of these disparities (17).

The strong relationship between parent education and insulin resistance provides a striking contrast to the lack of association between household income and insulin resistance. Cross-sectional studies of Danish youth have also documented the lack of association between income and insulin resistance in multivariable models, including parent education, which itself, has a robust effect (6). These differential effects of income and education highlight the need to carefully consider the mechanisms underlying social inequalities in health. Parent education, which is likely to be a stable factor in the lives of youth, may exert its effect though influences through psychosocial factors such as the ability to adapt to one’s environment (26). Those from less well-educated families may have fewer psychological reserves to cope with the difficulties inherent in their environments and thus, may experience more physiological stress as a result of living in a more challenging setting (17,27). Recently, lower parent education has been associated with decreased optimism among youth, and this decreased optimism partially mediated the influence of lower parent education on perceived stress (28). Because insulin is sensitive to signals along the hypothalamic-pituitary-adrenal axis, processive stress-related responses secondary to living in a lower status environment may explain increased insulin resistance among youth from less well-educated families (5,29). In contrast, income’s effect may relate more to material goods, which may not have direct links to insulin signaling pathways. Testing these potential mechanisms and others such as intergenerational effects and the role of relevant health-related behaviors (i.e., physical activity) is beyond the scope of the current study.

There are some limitations to this study. First, because this was a large epidemiological study, insulin resistance was measured using the HOMA model rather than euglycemic clamp. However, HOMA, which is widely used in epidemiologic studies of insulin resistance among youth, has been shown to adequately estimate clamp studies (30). Second, we could not assess whether regional differences exist in the pattern of social inequalities, as has been suggested by Lawlor et al. (6), because our data were derived from a single school district. Balancing these limitations are the strengths of this study—its nearly equal representation of non-Hispanic black and white youth from a wide range of socioeconomic backgrounds, its prospective design, parental report of SES, the careful measurement of multiple physiological parameters, and use of BMI z score, which accounts for normal growth and development as a measure of adiposity rather than BMI.

Although insulin resistance is associated with type 2 diabetes, metabolic syndrome, and multiple other morbidities, little attention has been paid to the socioeconomic patterning of this hormonal risk, especially in adolescence. Cross-sectional studies of adolescents have differed on the direction of this relationship. In both the United States and Denmark, lower SES has been associated increased insulin resistance, whereas higher SES has been associated increased insulin in Estonia and Portugal (5,6). To our knowledge, this is the first prospective study to explore whether the influence of socioeconomic status on insulin resistance. This study identifies insulin as a potential key hormone mediating the development of health disparities, thereby adding to a growing literature, which suggests that physiological mechanisms may underlie social inequalities in health (5,31–33).

Although the socioeconomic patterning of insulin resistance has not been well studied, racial/ethnic differences in insulin resistance are well described. Non-Hispanic blacks are generally considered to be more insulin-resistant than non-Hispanic whites (3,34–37). However, the intersections between race and SES in regard to insulin regulation have received little attention. In this study, both non-Hispanic black race/ethnicity and lower parent education were independently associated with increased insulin resistance at baseline, whereas only parent education influenced change in insulin resistance over time. This highlights the importance of longitudinal studies. These data suggest that, given the same level of insulin resistance at baseline, black and white adolescents do not differ in the degree of change over time, whereas those with lower parent education, especially obese youth with less educated parent(s), fared worse than youth from better educated families. These findings have important implications for understanding risk trajectories among youth. They suggest that interventions aimed at obese adolescents from poorly educated families may be the most effective in changing risk for morbidities associated with sustained or worsening insulin resistance.

We thank the students, parents, teachers, administration, and staff of the Princeton City School district and the PSD study staff.


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
REFERENCES
 
Received for publication April 25, 2006; revision received July 24, 2006.

This work was supported by NIH grants HD41527, DK59183, and M01 RR 08084.

This work was presented, in part, at the Pediatric Academic Societies Annual Meeting, April 30, 2006, San Francisco, CA.

DOI:10.1097/01.psy.0000249732.96753.8f


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 

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Behavioral Medicine Review: Strategic Management of Diabetes Risk
American Journal of Lifestyle Medicine, October 1, 2007; 1(5): 351 - 355.
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