Cardiovascular Risks in Adolescents With Diabetes From Vulnerable Populations

Abstract

Limited data exist on the predisposition for an early trajectory of cardiovascular (CV) disease in adolescents with diabetes. We explored the effects of types of diabetes and sociodemographic factors (i.e., race, gender, income level, family structure) on the following CV risks: glucose control (A1c), blood pressure (BP), and lipid profile. Adolescents with type 1 DM (T1DM) or type 2 DM (T2DM) participated: 109 with T1DM and 42 with T2DM. The general linear model was used to examine the influence of type of DM, sociodemographic factors, and the interaction of type of DM and the sociodemographic factors on CV risks. Systolic and diastolic BP were increased in youth with T2DM versus T1DM. Non-Hispanic Blacks had a higher A1c and resting diastolic BP than non-Hispanic Whites. Lower income was also associated with higher resting diastolic BP. Males with T1DM had higher A1c, whereas females with T2DM had higher A1c. With low income, individuals with T1DM had higher A1c values than those with T2DM; those with high income and T2DM had higher A1c than those with T1DM. Adolescents with T1DM from single, divorced, or separated families had higher average A1c values. In comparison, those with T2DM from married families had higher A1c values. Triglycerides were increased for those with T2DM, with the greatest amount for Hispanics as compared with non-Hispanic Blacks. In summary, minority status, lower income, and family structure may have a greater impact on vulnerability for poor outcomes in adolescents with DM, regardless of the type of the disease.

For adults with diabetes mellitus (DM), there are known health disparities related to risks for complications and poor outcomes for certain vulnerable groups. Cardiovascular (CV) disease is the leading cause of morbidity and mortality in adults with DM, with current mortality rates being highest for African Americans and Hispanics (Kung, Hoyert, Xu, & Murphy, 2008). Although adult women with diabetes tend to have lower morbidity and mortality rates than their male counterparts, the risks for CV disease are greater (Natarajan, Liao, Cao, Lipsitz, & McGee, 2003; Natarajan et al., 2005). In review of research on the effects of diabetes on CV disease in women, Howard et al. (1998) reported that in 7 out of 10 population-based surveys comparing coronary heart disease (CHD) mortality in persons with and without diabetes, the relative risk of CHD due to diabetes was greater in women than in men. Elevated blood pressure, low high-density lipoprotein (HDL) cholesterol, and high triglycerides contribute to higher diabetes-related CHD risks in women (Juutilainen et al., 2004).

Given the known CV risk disparities in adults with DM, it is important to understand the early trajectory of these differences in youth to target interventions for those who are most vulnerable. Type 1 DM (T1DM) remains the more common type of diabetes in youth with a prevalence rate of 2.28 per 1,000 in all ethnic groups except American Indians, in whom type 2 DM (T2DM) is more common (Liese et al., 2006). The prevalence rate for T2DM in this age group for all ethnic groups remains much lower at 0.42 per 1,000 (Liese et al., 2006). With increasing numbers of youth diagnosed with DM, more recent studies have examined CV risks in youth with T1DM or T2DM (Jarvisalo et al., 2004; Margeirsdottir, Larsen, Brunborg, Overby, & Dahl-Jorgensen, 2008; Rodriguez et al., 2006). However, research has not focused on vulnerable groups of youth with DM, who may have an added burden for developing CV risks and later CV disease. Vulnerability conveys the notion of susceptibility to damage or harm and includes socioeconomic, biophysical, cultural, historical, and political characteristics (Eakin & Luers, 2006). There is some thought that vulnerabilities for poor outcomes related to chronic illness, such as DM, are related to environmental stressors and resources for managing such stressors (Davis, Cook, & Cohen, 2005). The public health burden of diabetes across all ages is far more common in ethnic minorities and individuals from low education and income levels (Cowie & Eberhardt, 1995). In adults with diabetes, CV disease prevalence is reported to be higher in persons who were not high school graduates compared with college graduates (Dray-Spira, Gary, & Brancati, 2008).

In general, CV disease is a leading cause of morbidity and mortality in the United States (Kung et al., 2008). An increase of two to four times in the risk of heart disease is reported in persons with diabetes, regardless of whether they have T1DM or T2DM (U.S. Department of Health and Human Services, 2008). Longitudinal studies that explore the effects of diabetes diagnosed during youth on the development of CV disease are limited. The landmark Diabetes Control and Complications Trial Research Group (DCCT, 1993) and subsequent Epidemiology of Diabetes Interventions and Complications Study (Writing Group, 2003) provided longitudinal evidence that intensive glucose control (A1c) minimized microvascular complications in persons with T1DM, including a cohort of pubertal adolescents. In both the DCCT and Epidemiology of Diabetes Interventions and Complications studies, lower resting heart rate, an indicator of better heart rate variability, was associated with the intensive treatment group (Paterson, Rutledge, Cleary, Lachin, & Crow, 2007).

Research suggests that abnormal CV autonomic tests are associated with early stages of childhood diabetes (Barkai & Madacsy, 1995) and with those experiencing hypoglycemic episodes requiring outside assistance due to loss of consciousness or convulsions (Barkai, Madacsy, & Vamosi, 1991). CV reflex abnormality is reported to be as high as 29% of children with T1DM (Ringel et al., 1993). Additional research in youth with T1DM indicates that CV risks, such as atherogenic lipids and lipoproteins (total cholesterol, total triglycerides, and low-density lipoprotein [LDL]-cholesterol) are elevated for some individuals above levels recommended by the American Diabetes Association and the American Heart Association (Maahs et al., 2008). Youth with T1DM also have an increased incidence of hypertension, which is associated with the progression of nephropathy and atherosclerosis (Newkumet, Goble, Young, Kaplowitz, & Schieken, 1994).

Recent studies have indicated that youth with T2DM may have any even higher risks for CV disease than those with T1DM (Faulkner, Quinn, Rimmer, & Rich, 2005; Rodriguez et al., 2006; West et al., 2009). The SEARCH for Diabetes in Youth Study, a multicenter, population-based investigation of youth up to 19 years of age, reports that the prevalence of at least two CV risk factors was 7% in children 3 to 9 years and 25% in youth 10 to 19 years. In those with T2DM versus T1DM, 92% compared with 14% exhibited CV risk factors (Rodriguez et al., 2006). Compared with youth without DM, those with T2DM had a higher prevalence of elevated blood pressure, obesity, large waist circumference, low HDL cholesterol, high triglycerides, and high albumin-to-creatinine ratio (West et al., 2009). Adolescents with T2DM also have lower heart rate variability and CV fitness than those with T1DM, which are predictors of later CV risks in adulthood (Faulkner et al., 2005).

Although adult studies highlight disparities in DM outcomes related to gender, minority status, and socioeconomic position, fewer details are available for certain groups of youth with DM. Some reports indicate a disparity in glycemic control, with African American children and adolescents having higher A1c levels than Caucasians (Auslander, Thompson, Dreitzer, White, & Santiago, 1997; Chalew et al., 2000; Delamater, Albrecht, Postellon, & Gutai, 1991; Hanson, 1987). Hanson (1987) found that adolescent African American females were in poorer metabolic control than African American males and Caucasian children of both genders. Studies support the finding that adolescent females, in general, have poorer glycemic control and more episodes of ketoacidosis and hospitalizations than their male counterparts (Brink, 1997; Cohn, Cirillo, Wingard, Austin, & Roffers, 1997). Single-parent household status and lower adherence to treatment regimens appear to account for the poorer glycemic control in African American populations (Auslander et al., 1997). Thus, current evidence exists that female adolescents with DM, particularly from minority families and one-parent households, may have greater predisposition for difficulties with glucose control.

Despite the growing emphasis about CV risks in youth with DM (Wadwa, 2006), there remains a paucity of information regarding vulnerabilities associated with youth-onset T1DM or T2DM. Determining variations in risk factors and the influence of key variables on the development of these risk factors is necessary for planning individualized interventions that promote positive health outcomes, ultimately control escalating costs for diabetes care, and minimize adverse effects on health status and well-being. Clearly, adolescents with DM are predisposed to CV risks factors related to lifestyle behaviors, such as dietary practices, physical activity, smoking, and hormonal contraceptive use, similar to adolescents without DM. However, understanding the influences of nonmodifiable CV risks associated with minority status, gender, and socioeconomic position will aid in further delineating approaches to lifestyle interventions for vulnerable groups of youth with DM. Therefore, the purpose of the study was to explore the effects of specific sociodemographic factors (i.e., race, gender, socioeconomic status [income level], and family structure) on the following CV risks: glucose control (A1c), blood pressure (BP), and lipid profile. Both the main effects of each independent variable and the interaction of the sociodemographic factors with type of DM on CV risks are reported here.

Method

A cross-sectional, descriptive design was used to examine key demographic variables and the outcome variables of glucose control (A1c), resting BP (systolic and diastolic), and lipid profile (total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides) for adolescents diagnosed with either T1DM or T2DM. Recruitment occurred at two large, Midwestern pediatric diabetes clinics in a metropolitan community. Both clinics were served by a comprehensive, multidisciplinary team of health care providers.

The University of Illinois at Chicago provided institutional board approval for the protection of human subjects. Parental permission (consent) and child assent was obtained for adolescents younger than 18 years of age. Adolescents who were 18 years gave their own consent for participation and they and the parents also signed the Heath Information Protection and Accountability authorization for access to retrieve protected health information from their medical chart. The following inclusion and exclusion criteria were used for subject recruitment:

Inclusion criteria

1. Adolescents from 13 through 18 years of age with a diagnosis of T1DM or T2DM.

2. Adolescents and parents or legal guardians who are able to read and speak English.

Exclusion criteria

1. Individuals whose school grade was not appropriate to age within 2 years. This controlled for overt delays in cognitive, psychological, or behavioral functioning.

2. Adolescents who had developed diabetes as a secondary condition to treatment for another chronic condition (e.g., cancer) or other chronic illnesses, except for asthma that was controlled with medications and Hashimoto’s thyroiditis.

3. Adolescents with known cardiac defects.

4. Adolescents who were pregnant.

Data collection occurred at the Clinical Research Center of the University of Illinois at Chicago. Subjects were scheduled to come to the Clinical Research Center after an overnight fast to have laboratory assays for lipid profile and glycosylated hemoglobin (A1c) drawn. The Beckman Synchron CX-7 Analyzer (Beckman-Coulter, Inc., Brea, CA) was used to determine total cholesterol (cholesterol oxidase), triglycerides (glycerophosphate oxidase), and HDL-cholesterol (direct, immunoinhibition). LDL-cholesterol was calculated using the Friedewald equation (Friedewald, Levy, & Fredrickson, 1972). Glycosylated hemogobin (A1c) was measured using ethylenediaminetetraacetic acid whole blood analysis with high performance liquid chromatography (BioRad Laboratories, Hercules, CA). Laboratory procedures included quality control measurements by routine calibration of all assay equipment.

Sitting BP was measured using an appropriately sized cuff on the right arm after the subject had rested quietly for 5 minutes. Three readings of BP were obtained and the average was recorded (National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents, 2004). Height and weight were recorded to the nearest 0.1 cm and 0.1 kg, respectively. Body mass index (BMI) was calculated as kg/m². Gender and age-adjusted BMI percentile was determined based on syntax files provided by the U.S. Centers for Disease Control and Prevention (2000).

Existing chart data at the clinic site where the adolescent obtained diabetes care was used to obtain information on A1c values over the prior year to reflect average glucose control. The date of diagnosis of DM was also recorded. Each adolescent’s parent completed a demographic data collection form to gather descriptive information. Variables on this form included the adolescent’s birth date, gender, and race; parental income and marital status, used to reflect family structure, were also collected.

Data Analysis

To describe the influence of sociodemographic factors on CV risks in adolescents with T1DM versus T2DM, the general linear model was used (SAS Institute, Inc., Cary, NC, Version 9.2). In the model, the dependent variables were the CV risks of glucose control, BP, and lipid profile. The independent variables were the sociodemographic factors (SDF), the type of diabetes (TYPE, T1DM versus T2DM), and the interaction of the sociodemographic factor and type of diabetes (SDF × TYPE). Thus, the general linear model tested was as follows: dependent variable (DV) = SDF + TYPE + SDF × TYPE. When the sociodemographic factor was a categorical variable like race or marital status, the general linear model was an analysis of variance model. When the sociodemographic component was a continuous variable like age or income, the general linear model was an analysis of covariance type model.

Model testing proceeded as follows: First, the full model was tested including SDF, TYPE, and SDF × TYPE. If the interaction was significant, that was the final model. If the interaction was not significant, it was removed from the model and just the influence of SDF and TYPE on the DV was assessed. If TYPE was significant, that was the final model. If TYPE was not significant, it was removed from the model and just the influence of SDF on the DV was assessed.

Results

A sample of 151 adolescents with either T1DM or T2DM participated in the study. Table 1 presents the race, gender, age, onset age, duration of diabetes, BMI, and gender and age-adjusted BMI, and Tanner stage of sexual maturity for the two groups. Consistent with the typical age of onset, which is higher for those with T2DM, a similar pattern emerged with the data presented here. Thus, the duration of diabetes was significantly less for those with T2DM. Those with T2DM had significantly higher mean values for BMI and BMI percentile. The median Tanner stage was the same for the groups (Stage 5, the highest stage). Adolescents with T2DM had a significantly higher Tanner score (or had significantly higher sexual maturity) because 71% of them were in Stage 5 compared with 52% of the adolescents with T1DM. Results were obtained using the Kruskal–Wallis test for comparison of median values (χ² = 6.4, df = 1, p = .01).

Table 1.

Sample Demographics

	Type 1 Diabetes (n = 109)	Type 2 Diabetes (n = 42)
Race
Non-Hispanic Black	30	30
Hispanic	6	11
Non-Hispanic White	73	0
Asian	0	1
Gender
Male	60	19
Female	49	23
Age in years (mean ± SD)	15.29 ± 1.87	15.86 ± 1.82
Onset age in years (mean ± SD)**	9.11 ± 3.39	13.49 ± 2.25
Duration in years (mean ± SD)**	6.18 ± 3.69	2.40 ± 2.02
Body mass index (BMI; mean ± SD)**	23.3 ± 4.0	33.7 ± 9.0
BMI percentile (mean ± SD)**	72 ± 23	96 ± 6
Median Tanner stage*, n (%)	5 (52)	5 (71)

Source: Authors.

^*p < .05.

^**p < .001.

Glucose Control

Glucose control (A1c) was measured by both recent values (drawn at the time of data collection) and average values over the past year. As noted in Table 2, there was a significant race effect for glucose control. Tukey’s post hoc test revealed a significant difference between Non-Hispanic Blacks and Whites, with Blacks having higher recent and average A1c levels. Although no differences in A1c values based on type of DM, a significant interaction was noted between race and type of DM. Table 3 shows the differences in A1c values adjusted for race. These differences are likely due to the higher proportion of non-Hispanic Blacks in the T2DM versus the T1DM group.

Table 2.

Recent and Average Glucose Control by Race

Race/Ethnicity	Recent A1c (%)	Average A1c (%)
Non-Hispanic Black	9.22 ± 0.23*	9.33 ± 0.25**
Hispanic	8.23 ± 0.45	8.75 ± 0.53
Non-Hispanic White	7.78 ± 0.30*	7.75 ± 0.32**

Source: Authors.

Note: Data reported as mean ± standard error. Comparisons made between racial groups.

^*p ≤ .05.

^**p ≤ .01.

Table 3.

Glucose Control Adjusted by Race/Ethnicity

Type of DM	Raw Recent A1c (%)	Adjusted Recent A1c (%)	Raw Average A1c (%)	Adjusted Average A1c (%)
T1DM	8.71 ± 0.18	8.91 ± 0.24*	8.92 ± 0. l 7	9.29 ± 0.26**
T2DM	8.51 ± 0.30	7.91 ± 0.32*	8.54 ± 0.44	7.93 ± 0.36**

Source: Authors.

Note: DM = diabetes mellitus; T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus. Data reported as mean ± standard error.

^*p ≤ .05.

^**p ≤ .01.

There was a significant gender by diabetes type interaction. As shown in the Table 4, male individuals with T1DM had higher recent A1c levels, whereas for females, individuals with T2DM had higher A1c levels. However, this finding was not significant for the average A1c levels.

Table 4.

Gender × Type of Diabetes Interaction on Recent Glucose Control^*

Gender	Group	Recent A1c (%)
Male	T1DM	8.62 ± 0.24
	T2DM	7.57 ± 0.44
Female	T1DM	8.8l ± 0.26
	T2DM	9.20 ± 0.38

Source: Authors.

Note: T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus. Data reported as mean ± standard error.

^*p ≤ .05.

There was a significant income by diabetes type interaction. As shown in Figure 1, for adolescents with T1DM there was a negative relationship between recent A1c and income, whereas for those with T2DM, there was a slightly positive relationship. For adolescents from families with low income, those with T1DM had higher A1c values than those with T2DM. Whereas, for adolescents from families with high income, those with Type T2DM had higher A1c than those with T1DM. The relationships were consistent for average A1c and income as well. However, there was a stronger positive relationship between average A1c and income for those with T2DM.

Figure 1. Linear regression of income × type of diabetes interaction.

Note: T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus.

There was a significant marital status by diabetes type interaction for glucose control over the past year. As shown in Table 5, for adolescents with T1DM, those from single, divorced, and separated families had higher average A1c levels; whereas, adolescents with T2DM from married families had higher average A1c levels. Although a significant difference in recent A1c was found for adolescents from single versus married families (p < .05), there was not an interaction with type of diabetes.

Table 5.

Marital Status × Type of Diabetes Interaction on Average Glucose Control^*

Type of Diabetes	Marital Status	Average A1c (%)
T1DM	Single	10.28 ± 0.46
	Married	8.55 ± 0.22
	Divorced/separated	9.17 ± 0.45
T2DM	Single	8.11 ± 0.66
	Married	8.97 ± 0.45
	Divorced/separated	8.39 ± 0.70

Source: Authors.

Note: T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus. Data reported as mean ± standard error.

^*p ≤ .05.

Blood Pressure

Adolescents with T2DM had significantly higher resting systolic and diastolic BP than adolescent with T1DM (see Table 6). For resting systolic blood pressure, there was a significant gender effect. Males had higher BP than females (p < .05). For resting diastolic blood pressure, there was a significant race effect. Tukey’s post hoc test revealed a significant difference between non-Hispanic Whites and non-Hispanic Blacks, with non-Hispanic Blacks having higher resting diastolic BP (see Table 7). There was a significant negative relationship between income and resting diastolic BP (r = −.18, p < .05); higher income was associated with lower resting diastolic blood pressure. Marital status of families had no effect on BP in the adolescents.

Table 6.

Differences in Blood Pressure for Type of Diabetes

Type of Diabetes	Systolic Blood Pressure*	Diastolic Blood Pressure*
T1DM	113.5 ± 11.4	64.7 ± 9.0
T2DM	118.3 ± 12.2	68.6 ± 9.1

Source: Authors.

Note: T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus. Data reported as mean ± standard deviation.

^*p ≤ .05.

Table 7.

Differences in Diastolic Blood Pressure for Race/Ethnicity

Race	Diastolic Blood Pressure (mmHg)
Non-Hispanic White	63.2 ± 8.5**
Non-Hispanic Black	68.4 ± 9.6**
Hispanic	68.7 ± 6.1

Source: Authors.

Note: Data reported as mean ± standard deviation.

^**p ≤ .01.

Lipid Profiles

For lipid profiles, males had significantly lower HDL values than females (p < .05). There was a significant race by diabetes type interaction for HDL and triglycerides. As can be seen in the Table 8, although HDL was always lower and triglycerides were always higher for those with T2DM, this difference was larger for Hispanics than for non-Hispanic Blacks.

Table 8.

Race × Type of Diabetes Interaction on Lipids

Type of Diabetes		High-Density Lipoprotein**	Triglycerides*
T1DM	Non-Hispanic White	51.24 ± 1.44	64.65 ± 6.0
	Non-Hispanic Black	53.00 ± 2.23	50.03 ± 9.25
	Hispanic	62.50 ± 4.98	53.17 ± 20.68
T2DM	Non-Hispanic Whitea
	Non-Hispanic Black	44.57 ± 2.23	93.07 ± 9.25
	Hispanic	35.82 ± 3.68	165.80 ± 16.02

Source: Authors.

Note: T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus. Data reported as mean ± standard error.

^aThere were no non-Hispanic Whites in the T2 DM group.

^*p ≤ .05 .

^**p ≤ .01.

Discussion

The sample of adolescents with T1DM or T2DM in this study was reflective of the demographic characteristics that are typically associated with these two types of DM. Youth with T1DM are diagnosed most commonly in late childhood to early adolescence, whereas those with T2DM are most often diagnosed in middle adolescence. Although non-Hispanic White adolescents can develop T2DM, minority populations are disproportionately affected. A total of 72% of the T2DM group was non-Hispanic Black, with 26% Hispanic and 2% Asian. The higher BMI of the T2DM group reflects the known risks of obesity with the onset of this particular type of diabetes.

When examining glucose control, the finding of an interaction between race and type of diabetes raises possible implications in the interpretation of A1c measures for minority youth. Studies have reported that minority adolescents, particularly non-Hispanic Blacks, tend to have poorer glucose control than non-Hispanic Whites (Auslander et al., 1997; Chalew et al., 2000; Delamater et al., 1991; Hanson, 1987). Thus, the interpretation for these youth all too often is that they are not as compliant with diabetes self-management as they should be or that they are more vulnerable for poor glucose control because of a lack of resources. In fact, these findings shed light on the need for clinicians to be more cognizant of the higher A1c values in African American and Mexican American nondiabetic children from the Third National Health and Nutrition Examination Survey (Eldeirawi & Lipton, 2003). The National Health and Nutrition Examination Survey report found that the normal A1c values for these minority children were significantly higher than non-Hispanic Whites after controlling for age, gender, BMI, and poverty income ratio. Therefore, the predominance of minority youth in the T2DM group could explain the interaction between race and type of diabetes on glucose control. However, despite higher normative values for A1c in minority youth, longitudinal research evidence indicates that hyperglycemia leading to diabetic ketoacidosis is a significant cause of death for young persons from minorities with childhood-onset diabetes (Burnet, Cooper, Drum, & Lipton, 2007; Kung et al., 2008).

The gender difference for the different types of DM was unexpected. Male adolescents with T1DM and female adolescents in the T2DM group had higher A1c values than their gender counterparts. Studies have tended to show that female adolescents have poorer glucose control than their male counterparts (Brink, 1997; Cohn et al., 1997). However, research exploring this difference has included youth with T1DM, with limited focus on gender-specific risks for poor glucose control in those with T2DM (Burnet et al., 2007).

For adolescents with T1DM, the risk for poorer glucose control for those whose family had less income is consistent with existing literature on other poor health outcomes related to financial constraints, such as depression in youth (Van Voorhees et al., 2008). However, the finding of poorer glucose control in adolescents with T2DM from families with higher income was counterintuitive. Again, because the T2DM group included all minorities, the higher A1c values may have been in females from higher income families.

Similar to prior research on family structure (Thompson, Auslander, & White, 2001), being from a single, divorced, or separated family, was a factor associated with poor glucose control in adolescents with T1DM. The association of higher A1c in youth with T2DM from married families was also an unexpected finding. However, this is most likely confounded with the similar finding with higher income discussed earlier.

Although the finding that males had a higher systolic BP and lower HDL than females was not unexpected (Daniels & Greer, 2008; National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents, 2004), other findings suggest potential vulnerabilities in adolescents with DM. Non-Hispanic Blacks and those with T2DM have a higher predisposition for hypertension. Additionally, environmental stressors related to fewer financial resources may also predispose these adolescents to increased BP. The higher triglycerides and lower HDL in adolescents with T2DM have been reported previously (Rodriguez et al., 2006). However, Hispanic youth with T2DM may be most vulnerable for dyslipidemia.

Conclusions

As the numbers of youth with DM continue to increase, the vulnerabilities for CV risks once thought to be associated with poor outcomes in adulthood are presenting much earlier. As younger adolescents diagnosed with T2DM are seen predominantly in minorities, there is a major concern that this type of diabetes may exhibit a more aggressive course than the adult-onset disease (Burnet et al., 2007). Because evaluation of common risk factors for CV disease such as BP and lipids is not readily conducted in youth, there is a need for more community-based screenings for CV risks in these vulnerable groups. Even for adolescents who routinely attend clinic appointments for diabetes care, research has shown that a majority do not have their BP taken (Wood, O’Riordan, Vogt, & Palmert, 2006). Lipid screenings should begin at puberty for youth with T1DM and at diagnosis for those with T2DM (American Diabetes Association, 2008; Silverstein et al., 2005).

Clinicians need to be aware of nonmodifiable risk factors for adolescents who exhibit poor glucose control and to include home-based interventions to address modifiable risk factors, such as diet, exercise, and stress reduction, in this population. To further understand the complexities of CV risks in youth with DM, there is a need to increase community-based intervention research on the social determinants (income, life stressors) of CV disease (Liburd, Jack, Williams, & Tucker, 2005). Early screening and intervention for adolescents with DM who demonstrate the greatest vulnerabilities for CV disease has the potential of drastically improving their life quality, minimizing lost productivity, and preventing early morbidity and mortality.

Biographies

Melissa Spezia Faulkner, DSN, RN, FAAN, is the Gladys E. Sorensen Endowed Professor at the University of Arizona, College of Nursing. She has expertise in pediatric diabetes. Her research focuses on improving health outcomes for youth with diabetes and preventing future complications from the disease.

Cynthia Fritschi, PhD, RN, Clinical Instructor at the University of Illinois at Chicago, is a certified diabetes educator experienced in the care of children and adults with diabetes. Her research focuses on physical activity and symptom management in persons with diabetes.

Lauretta Quinn, PhD, RN, is a Clinical Associate Professor at the University of Illinois at Chicago and certified diabetes educator with extensive background in diabetes management. Her research focuses on risk reduction in vulnerable populations with diabetes.

Joseph T. Hepworth, PhD, is a methodologist and statistician for the University of Arizona, College of Nursing. He has assisted nurse researchers with the design, analysis and interpretation of research studies for over 25 years.