Abstract
OBJECTIVE
Understanding the risk factors associated with progression and regression of dyslipidemia in youth with type 1 diabetes may guide treatments.
RESEARCH DESIGN AND METHODS
We studied 1,478 youth with type 1 diabetes (age 10.8 ± 3.9 years, 50% male, 77% non-Hispanic white, not on lipid-lowering medications) at baseline and at a mean follow-up of 7.1 ± 1.9 years in the SEARCH for Diabetes in Youth (SEARCH) study. Progression to dyslipidemia was defined as normal lipid concentrations at baseline and abnormal at follow-up (non–HDL-cholesterol [C] >130 mg/dL or HDL-C <35 mg/dL). Regression was defined as abnormal lipids at baseline and normal at follow-up. Multivariable logistic regression was used to evaluate factors associated with progression and regression compared with stable normal and stable abnormal, respectively. An area under the curve (AUC) variable was used for the time-varying covariates A1C and waist-to-height ratio (WHtR).
RESULTS
Non–HDL-C progressed, regressed, was stable normal, and stable abnormal in 19%, 5%, 69%, and 7% of youth with type 1 diabetes, respectively. Corresponding percentages for HDL-C were 3%, 3%, 94%, and 1%, respectively. Factors associated with non–HDL-C progression were higher A1C AUC and higher WHtR AUC in males. Non–HDL-C regression was associated with lower WHtR AUC, and HDL-C progression was associated with male sex and higher WHtR AUC. HDL-C regression was not modeled due to small numbers.
CONCLUSIONS
A1C and WHtR are modifiable risk factors associated with change in dyslipidemia over time in youth with type 1 diabetes.
Introduction
Cardiovascular disease is the leading cause of death in adults with type 1 diabetes (1,2). This process begins in youth (3,4), and dyslipidemia is a major contributing risk factor (4).
Dyslipidemia has been well documented among youth with type 1 diabetes (5–9). However, few longitudinal studies exist, and those that have been published are limited by their retrospective nature, small sample size, inclusion of nonfasting lipid measurements, and relatively short duration of follow-up (10–14).
Thus, using 7 years of follow-up data in a large cohort of youth with type 1 diabetes, we examined 1) how fasting lipid levels track over time and 2) factors that are associated with progression or regression of dyslipidemia over time. Identifying risk factors that are associated with progression and regression of dyslipidemia in youth with type 1 diabetes may guide treatments.
Research Design and Methods
Study Participants
Participants for this study were enrolled in the SEARCH for Diabetes in Youth (SEARCH) study, a multicenter study examining the prevalence, incidence, and complications for youth with all forms of diabetes. Extensive details of the SEARCH study have been published and are summarized in a recent publication by Hamman et al. (15). Youth included in this analysis were diagnosed with incident type 1 diabetes starting in 2002 (baseline study visit occurred 9.0 ± 6.1 months after diabetes diagnosis) and subsequently participated in a SEARCH study follow-up visit (all visits completed by 2015). At baseline all participants had type 1 diabetes, defined as diabetes autoantibody positivity (GAD, islet antigen 2 [IA2], or zinc transporter 8 [ZnT8]) or no diabetes autoantibodies with high insulin sensitivity, as previously described by Dabelea et al. (16).
There were 2,004 SEARCH participants who had a baseline and follow-up visit. We excluded participants if they did not have a fasting lipid profile at the baseline (n = 179) or follow-up (n = 205) visit, if they did not report being on insulin at the follow-up visit (n = 29), if they reported taking lipid-lowering drugs at either visit (n = 63) to evaluate change in lipids without the influence of medications, or if they were younger than 10 years old at the follow-up visit (n = 50). Therefore, this report includes 1,478 youth with type 1 diabetes. Of those, 1,356 had diabetes autoantibody positivity and 122 had a high insulin sensitivity score alone (16). The study was reviewed and approved by each of the local institutional review boards, and all participants and parents provided written informed assent and/or consent.
Anthropometric and Metabolic Measurements
Race/ethnicity was self-reported, and participants were categorized as non-Hispanic white (NHW), non-Hispanic black, Hispanic, or other racial/ethnic group (Asian, Pacific Islander, American Indian, or other). Participants completed standardized questionnaires for medical history and medications. BMI was calculated as weight (kg)/height (m2), and age- and sex-specific BMI z scores were derived (17). Waist circumference was measured using the National Health and Nutrition Examination Survey (NHANES) protocol (18) and divided by height in centimeters to calculate the waist-to-height ratio (WHtR). Measurements of hemoglobin A1c (A1C), total cholesterol (TC), triglycerides (TGs), and HDL-cholesterol (C) were performed as previously described (19). LDL-C was calculated by the Friedewald equation or measured by the beta quantification procedure if TGs were ≥400 mg/dL.
Definitions of Abnormal Lipids
The major outcomes for this analysis were changes in dyslipidemia status for non–HDL-C (computed as TC − HDL-C) and HDL-C over time. Non–HDL-C was selected because it accounts for the cholesterol carried by all particles containing apolipoprotein B and outperforms the individual lipid parameters (TC, TGs, and LDL-C) in predicting subclinical atherosclerosis and cardiovascular disease (20–22). Abnormal non–HDL-C was defined as >130 mg/dL, and abnormal HDL-C was defined as <35 mg/dL, thresholds based on current recommendations in adults and children with diabetes (23,24). For each of these two measures, we defined progression of dyslipidemia as normal lipid concentrations at baseline (non–HDL-C ≤130 mg/dL or HDL-C ≥35 mg/dL) and abnormal at final follow-up, and regression was defined as abnormal at baseline and normal at final follow-up. Stable normal was defined as normal at baseline and follow-up and stable abnormal as abnormal at both baseline and follow-up.
Statistical Analysis
Data are presented as mean ± SD or median (interquartile range) for continuous variables, or frequencies (and percentages) for categorical variables. Demographics, anthropometrics, and cardiovascular risk factors were compared across the four groups (stable normal, stable abnormal, progression, and regression) by one-way ANOVA for continuous variables and χ2 tests for categorical variables.
We used separate multivariable logistic regression models to examine factors associated with non–HDL-C and HDL-C progression compared with stable normal and those associated with non–HDL-C regression compared with stable abnormal. HDL-C regression was not modeled because of small numbers in the regression and stable abnormal groups. Model covariates included age at baseline visit (in years), race/ethnicity (NHW vs. other), sex (female vs. male), and duration of type 1 diabetes at baseline (in years).
A derived area under the curve (AUC) summary statistic (a continuous variable) for WHtR and A1C was also included in the models. AUC summarizes the longitudinal measures collected over time adjusting for the interval between each measure. WHtR was chosen over other measures of adiposity (BMI z score or waist circumference) because the former has been shown to be more strongly associated with adverse cardiovascular risk factors in children and adults (25,26). We also evaluated interaction terms (race/ethnicity or sex by WHtR) to determine whether the associations between WHtR and lipid progression and regression were different by race/ethnic group or sex. All models were also adjusted for clinic site, time interval between the visits, and season of the baseline visit. Variables with P values of <0.05 were considered statistically significant. Statistical analyses were performed using SAS 9.4 software (SAS Institute, Inc., Cary, NC).
Results
Characteristics of SEARCH participants with type 1 diabetes included in this analysis at baseline and follow-up are presented in Table 1. At baseline, the cohort was a mean age of 10.8 ± 3.9 years, the average disease duration was 0.75 ± 0.5 years, and mean A1C was 7.6 ± 1.5%. NHW comprised 77% of the cohort, and 50% were male.
Table 1.
Study cohort at baseline and follow-up
| Baseline |
Follow-up |
|||
|---|---|---|---|---|
| n | Mean ± SD or n (%) | n | Mean ± SD or n (%) | |
| Age (years) | 1,478 | 10.8 ± 3.9 | 1,478 | 17.9 ± 4.1 |
| Race/ethnicity | 1,477 | |||
| Non-Hispanic | ||||
| White | 1,141 (77.3) | – | ||
| Black | 140 (9.5) | – | ||
| Hispanic | 170 (11.5) | – | ||
| Other | 26 (1.8) | |||
| Male sex | 1,478 | 743 (50.3) | – | |
| BMI z score | 1,457 | 0.48 ± 1.04 | 1,473 | 0.59 ± 0.96 |
| WHtR | 1,358 | 0.48 ± 0.06 | 1,472 | 0.51 ± 0.08 |
| Type 1 diabetes duration (years) | 1,478 | 0.7 ± 0.5 | 1,478 | 7.8 ± 1.9 |
| A1C (%) | 1,472 | 7.6 ± 1.5 | 1,474 | 9.2 ± 1.8 |
| A1C (mmol/mol) | 1,472 | 59.8 ± 16.1 | 1,474 | 76.6 ± 19.9 |
| TC (mg/dL) | 1,478 | 159 ± 27 | 1,478 | 169 ± 34 |
| LDL-C (mg/dL) | 1,478 | 91 ± 22 | 1,478 | 96 ± 28 |
| HDL-C (mg/dL) | 1,478 | 56 ± 13 | 1,478 | 55 ± 13 |
| Non–HDL-C (mg/dL) | 1,478 | 103 ± 25 | 1,478 | 114 ± 35 |
| TGs (mg/dL), median (Q1, Q3) | 1,478 | 55 (42, 71) | 1,478 | 75 (56, 105) |
| Systolic blood pressure (mmHg) | 1,438 | 99 ± 12 | 1,475 | 106 ± 11 |
| Diastolic blood pressure (mmHg) | 1,436 | 63 ± 10 | 1,475 | 69 ± 9 |
Mean interval between visits 7.1 ± 1.9 years. Q, quartile.
Follow-up data were obtained an average of 7.1 ± 1.9 years later, when participants were an average age of 17.9 ± 4.1 years and had an average disease duration of 7.8 ± 1.9 years. The mean A1C at follow-up was 9.2 ± 1.8%. Non–HDL-C progressed in 19%, regressed in 5%, and remained stable abnormal in 7% and stable normal in 69%. HDL-C progressed in 3%, regressed in 3%, and remained stable abnormal in 1% and stable normal in 94%.
Participants who had progression of non–HDL-C levels compared with those who remained stable normal (Table 2) were older, more likely to be female, had greater adiposity (measured by BMI z score or WHtR), a longer duration of type 1 diabetes, a higher A1C, and higher diastolic blood pressure (all P < 0.05). Participants who remained stable abnormal were more likely to be non–Hispanic black, Hispanic, or other race/ethnicity, female, have more adiposity, and have higher A1C than youth who were stable normal (all P < 0.05).
Table 2.
Characteristics of participants at follow-up visit: comparisons using non–HDL-C*
| Stable normal n = 1,020 | Stable abnormal n = 105 | Progression n = 285 | Regression n = 68 | P value* | P value* | P value* | P value* | |
|---|---|---|---|---|---|---|---|---|
| Mean ± SD or n (%) | Mean ± SD or n (%) | Mean ± SD or n (%) | Mean ± SD or n (%) | Overall among four groups | Progression vs. stable normal | Stable abnormal vs. stable normal | Regression vs. stable normal | |
| Age (years) | 17.68 ± 4.14 | 17.88 ± 4.15 | 18.53 ± 3.73 | 18.49 ± 4.58 | 0.0111 | 0.0019 | 0.6198 | 0.1121 |
| Race/ethnicity | 0.0225 | 0.0530 | 0.0224 | 0.5495 | ||||
| Non-Hispanic | ||||||||
| White | 806 (79.1) | 71 (67.6) | 206 (72.3) | 58 (85.3) | ||||
| Black | 88 (8.6) | 18 (17.1) | 30 (10.5) | 4 (5.9) | ||||
| Hispanic | 110 (10.8) | 14 (13.3) | 40 (14.0) | 6 (8.8) | ||||
| Other | 15 (1.5) | 2 (1.9) | 9 (3.2) | 0 (0) | ||||
| Male sex | 535 (52.5) | 42 (40.0) | 130 (45.6) | 36 (52.9) | 0.0288 | 0.0412 | 0.0151 | 0.9375 |
| BMI z score | 0.52 ± 0.93 | 1.01 ± 0.99 | 0.77 ± 0.92 | 0.32 ± 1.11 | <0.0001 | <0.0001 | <0.0001 | 0.0924 |
| WHtR | 0.49 ± 0.07 | 0.55 ± 0.10 | 0.53 ± 0.08 | 0.49 ± 0.08 | <0.0001 | <0.0001 | <0.0001 | 0.7894 |
| Type 1 diabetes duration (years) | 7.73 ± 1.85 | 7.97 ± 1.91 | 8.07 ± 1.93 | 8.14 ± 1.97 | 0.0184 | 0.0069 | 0.2082 | 0.0792 |
| A1C (%) | 8.90 ± 1.66 | 9.80 ± 1.96 | 9.92 ± 2.02 | 8.78 ± 1.97 | <0.0001 | <0.0001 | <0.0001 | 0.5943 |
| TC (mg/dL) | 153.54 ± 21.23 | 211.56 ± 29.96 | 209.54 ± 30.67 | 166.13 ± 18.94 | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
| LDL-C (mg/dL) | 82.57 ± 17.08 | 136.57 ± 26.32 | 128.26 ± 22.63 | 95.74 ± 12.09 | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
| HDL-C (mg/dL) | 56.32 ± 13.56 | 49.67 ± 13.64 | 52.36 ± 12.46 | 53.72 ± 12.68 | <0.0001 | <0.0001 | <0.0001 | 0.1198 |
| Non–HDL-C (mg/dL) | 97.22 ± 18.68 | 161.90 ± 29.54 | 157.18 ± 29.57 | 112.41 ± 12.15 | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
| TGs† (mg/dL), median (Q1, Q3) | 65.0 (51, 86) | 107.0 (76, 157) | 115.0 (89, 167) | 74.5 (57.5, 94.5) | <0.0001 | <0.0001 | <0.0001 | 0.0259 |
| Blood pressure | ||||||||
| Systolic (mmHg) | 106.02 ± 10.77 | 106.84 ± 9.84 | 106.32 ± 11.01 | 105.95 ± 12.39 | 0.8801 | ^ | ^ | ^ |
| Diastolic (mmHg) | 68.31 ± 8.55 | 69.93 ± 8.53 | 70.28 ± 9.29 | 67.49 ± 9.50 | 0.0022 | 0.0008 | 0.0707 | 0.4512 |
Q, quartile.
*Comparisons among groups evaluated using one-way ANOVA (continuous) or χ2 tests (categorical);
^pairwise tests are not reported where the overall test across four groups is not statistically significant (P > 0.05);
†tested using log (TGs).
We constructed multivariable logistic regression models to examine factors associated with progression and regression of dyslipidemia compared with stable normal and stable abnormal, respectively, after adjusting for covariates (Table 3). Factors associated with non–HDL-C progression were higher A1C AUC and higher WHtR AUC. Non–HDL-C regression was associated with lower WHtR AUC. HDL-C progression was associated with male sex and higher WHtR AUC. HDL-C regression was not modeled because of small numbers in the regression (3%) and stable abnormal (1%) groups.
Table 3.
Multivariable logistic regression models for dyslipidemia progression and regression
| Non–HDL-C progression compared with stable normal n = 1,288 (281 events) | Non–HDL-C regression compared with stable abnormal n = 170 (67 events) | HDL progression compared with stable normal n = 1,405 (38 events) | ||||
|---|---|---|---|---|---|---|
| Variable | OR (95% CI) |
P value |
OR (95% CI) |
P value |
OR (95% CI) |
P value |
| Age at initial visit: 1 year increase | 1.03 (0.99, 1.07) | 0.1815 | 1.07 (0.98, 1.18) | 0.1205 | 1.02 (0.94, 1.11) | 0.6354 |
| Race/ethnicity: other vs. NHW | 1.22 (0.85, 1.75) | 0.2726 | 0.48 (0.19, 1.23) | 0.1274 | 1.10 (0.48, 2.53) | 0.8172 |
| Sex: female vs. male | 1.03 (0.77, 1.38) | 0.8199 | 0.71 (0.32, 1.54) | 0.3815 | 0.44 (0.22, 0.91) | 0.0257 |
| Type 1 diabetes duration at initial visit: 1 year increase | 0.98 (0.74, 1.31) | 0.8985 | 1.77 (0.93, 3.33) | 0.0800 | 1.48 (0.79, 2.76) | 0.2183 |
| A1C (AUC): 1% unit increase | 1.39 (1.25, 1.55) | <0.0001 | 0.84 (0.65, 1.08) | 0.1734 | 1.07 (0.83, 1.37) | 0.5894 |
| WHtR (AUC): 0.1 unit increase | 1.81 (1.42, 2.29) | <0.0001 | 0.49 (0.29, 0.84) | 0.0089 | 1.64 (1.03, 2.59) | 0.0353 |
Variables included in the models: age and type 1 diabetes duration at initial visit, race/ethnicity, sex, A1C AUC, and WHtR AUC. Each model also adjusted for clinical site, the time interval between the baseline and follow-up visit, and season at the baseline visit. Statistically significant covariates appear in boldface type. OR, odds ratio.
We evaluated the interactions between race or sex and WHtR for each of the outcomes. We found a significant sex-by-WHtR interaction (P = 0.0071) for non–HDL-C progression such that the association between WHtR and non–HDL-C progression was stronger for males (2.63; 95% CI 1.83, 3.77) than for females (1.38; 95% CI 1.02, 1.87).
Conclusions
We report the natural evolution of dyslipidemia over 7 years in a large cohort of youth with type 1 diabetes. After adjusting for covariates, we identified two modifiable risk factors, WHtR and A1C burden over time, that were independent predictors of unfavorable changes in lipids or of stable abnormal levels over time.
The prevalence of dyslipidemia in youth with type 1 diabetes has been well documented in two large multicenter cross-sectional studies, the SEARCH for Diabetes in Youth study and the German prospective documentation and quality management system (DPV) study (5–7), as well several smaller cross-sectional studies (8,9,27). Although a few longitudinal studies exist, these studies are retrospective, have small sample sizes, include nonfasting lipid measurements, and are of relatively short follow-up duration (10–14). In 2007, Maahs et al. (11) retrospectively examined lipids over time in 360 youth with type 1 diabetes (age range 2–21 years) with a mean follow-up of 2.9 years. Using the thresholds for non–HDL-C and HDL-C of ≥130 mg/dL and <35 mg/dL as abnormal, they reported 27.8 and 3.3%, respectively, of youth with type 1 diabetes had sustained dyslipidemia over time. In addition, they found that higher A1C was positively associated with non–HDL-C levels and that a higher BMI z score was inversely related to HDL-C levels (11). Using similar criteria, Edge et al. (10) reported the frequency of dyslipidemia in 229 youth with type 1 diabetes in the U.K. as 4.3% for non–HDL-C and 0% for HDL-C. Furthermore, they showed that a higher non–HDL-C concentration was associated with higher A1C and longer duration of type 1 diabetes but lacked measures of adiposity to evaluate associations with lipids over time. Marcovecchio et al. (12) did find that sustained non–HDL-C abnormalities were related to older age, longer duration of type 1 diabetes, and higher BMI and A1C levels. However, with loss of greater than 75% of their cohort at the end of 2.3 years, this precluded definitive conclusions. In contrast, Reh et al. (13) reported longitudinal lipid levels in a cohort of 46 adolescents and young adults with type 1 diabetes in the U.S. (age range 12–25 years) during 3 years of follow-up and found that 0% of the cohort had sustained abnormal HDL-C (defined as <40 mg/dL) over time; non–HDL-C was not reported.
Here, we report prospective lipid data in youth with type 1 diabetes over a mean follow-up of ∼7 years, the longest follow-up published in this population to date. We show that 19% of the cohort progressed to abnormal non–HDL-C concentrations during this time. Also concerning is that 7% of youth had sustained abnormal non–HDL-C over time, but only 5% had regressed. This stable abnormal frequency is somewhat lower than previously reported by Maahs et al. (11), where 27.8% of their adolescent cohort with type 1 diabetes had sustained elevation in non–HDL-C. The lower frequency of dyslipidemia reported here may be explained by our exclusion of those on lipid-lowering medication. Differences may also be explained by lower baseline BMI and A1C in our cohort (11). Progression to abnormal HDL-C was 3% and sustained abnormal HDL-C was 1% in this study, consistent with previous reports (10,11,13).
We used the WHtR AUC to explore the association between burden of adiposity over time and dyslipidemia, which has not been assessed in longitudinal studies of youth with type 1 diabetes to date. We show that although a higher WHtR AUC is independently associated with non–HDL-C and HDL-C progression, a lower WHtR AUC ratio is associated with higher odds of non–HDL-C regression. Furthermore, we show that the association between WHtR AUC and non–HDL-C progression is stronger for males compared with females. These data suggest that similar to youth without diabetes (28), adiposity is an important independent risk factor for dyslipidemia among youth with type 1 diabetes. Future work is needed to determine whether reductions in abdominal adiposity improve lipid levels over time in youth with type 1 diabetes and whether these effects are more pronounced in males.
We show that glycemic control over time is another important modifiable risk factor that is associated with higher odds of non–HDL-C progression. These findings are consistent with prior cross-sectional and longitudinal studies in youth with type 1 diabetes (5,7,8,11) as well as data from adults who participated in the Diabetes Control and Complications Trial (DCCT) (29). Although worse glycemic control over time appears to adversely affect lipid levels, lowering of A1C through intensive insulin therapy has been shown to negatively affect weight (30), although not in all studies (31). These results point to a delicate balance between achieving glycemic control and maintaining body weight that affects lipids and remains to be elucidated.
One potential mechanism linking adiposity, glycemic control, and dyslipidemia in type 1 diabetes may be insulin resistance. Although insulin resistance among those with type 1 diabetes appears counterintuitive, because they are by definition insulin deficient, prior work has shown that youth with type 1 diabetes exhibit insulin resistance (32,33). The etiology of insulin resistance in type 1 diabetes is not clear, but adiposity, physical inactivity, and/or chronic exogenous insulin use may all play a role. Therefore, determining the optimal level of insulin needed to achieve glycemic control while avoiding weight gain appears critical to decreasing the progression of dyslipidemia in youth with type 1 diabetes. Unfortunately, we were not able to assess or estimate insulin resistance or sensitivity in this study. Prior SEARCH studies have used an equation that incorporates A1C, waist circumference, and TGs (32) to estimate insulin sensitivity, but the current study used insulin sensitivity to define the cohort, included A1C and WHtR AUC in the models, and TGs are included in the outcome non–HDL-C.
We found that male sex was associated with higher odds of HDL-C progression. Longitudinal data in healthy children, including work from the Bogalusa Heart Study, have shown that HDL-C levels, particularly for NHW males, decline at age 14 years and continue to drop until age 26 years, unlike NHW females, who have little decrease in HDL-C (34). Therefore, it is unclear whether the higher odds of HDL-C progression observed in this cohort of predominantly NHW males is a result of type 1 diabetes or normal tracking of lipids through adolescence.
Strengths of this study include a large cohort of youth with type 1 diabetes, standardized lipid measurements, follow-up data over 7 years, and the ability to evaluate the associations between burden of risk factors and lipids over time. Limitations of the study include a lack of more frequent lipid assessments during the 7 years of follow-up, relatively small numbers of participants in each category that limited our ability to explore HDL-C regression, and lack of some variables, including thyroid status, family history of hyper/dyslipidemia, and pubertal status, each of which is known to influence lipids. In addition, physical activity, diet history, and smoking status were not obtained on all participants at the baseline visit and thus could not be included as covariates to evaluate change in lipids overtime, although it is possible physical activity and diet may be reflected by changes in adiposity. Future studies should include these variables.
In conclusion, we demonstrate approximately one-quarter of youth with type 1 diabetes has progression of dyslipidemia or abnormal lipids that persists over time. Risk factors that influence progression include both increased abdominal adiposity and worse glycemic control over time. Until the complex interplay between adiposity and glycemic control on lipids is elucidated, our data suggest both risk factors are important and influence lipids in youth with type 1 diabetes and are potential opportunities for intervention.
Article Information
Acknowledgments. The SEARCH for Diabetes in Youth Study is indebted to the many youth and their families, and their health care providers, whose participation made this study possible.
Funding. The SEARCH for Diabetes in Youth Cohort Study (1UC4DK108173-01) is funded by the National Institutes of Health (NIH) and National Institute of Diabetes and Digestive and Kidney Diseases and supported by the Centers for Disease Control and Prevention.
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention and the National Institute of Diabetes and Digestive and Kidney Diseases.
Each clinical site has also received funding: Kaiser Permanente Southern California (U18-DP-006133, U48/CCU919219, U01-DP-000246, and U18-DP-002714), University of Colorado Denver (U18-DP-006139, U48/CCU819241-3, U01-DP-000247, and U18-DP-000247-06A1), Cincinnati Children’s Hospital Medical Center (U18-DP-006134, U48/CCU519239, U01-DP-000248, and 1U18-DP-002709), University of North Carolina at Chapel Hill (U18-DP-006138, U48/CCU419249, U01-DP-000254, and U18-DP-002708), Seattle Children’s Hospital (U18-DP-006136, U58/CCU019235-4, U01-DP-000244, and U18-DP-002710-01), and Wake Forest University School of Medicine (U18-DP-006131, U48/CCU919219, U01-DP-000250, and 200-2010-35171).
The authors acknowledge the involvement of the South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina, NIH/National Center for Advancing Translational Sciences (NCATS) grant number UL1-TR-001450; Seattle Children’s Hospital and the University of Washington, NIH/NCATS grant number UL1-TR-00423; University of Colorado Pediatric Clinical and Translational Research Center, NIH/NCATS grant number UL1-TR-000154; the Barbara Davis Center at the University of Colorado Denver, Diabetes Endocrinology Research Centers NIH grant number P30-DK-57516; the University of Cincinnati, NIH/NCATS grant number UL1-TR-001425; and the Children with Medical Handicaps program managed by the Department of Health.
Duality of Interest. No potential conflicts of interest relevant to this article were reported.
Author Contributions. A.S.S. developed the idea and wrote the manuscript. D.M.M. developed the idea and edited the manuscript. J.M.S. performed the statistical analysis and edited the manuscript. L.M.D., G.I., R.A.B., C.P., and S.M. designed the study and edited the manuscript. W.L. oversaw the statistical analysis. A.D.L. and K.R. edited the manuscript. R.B.D designed the study, oversaw the statistical analyses, and edited the manuscript. D.D. designed the study, developed the idea, and edited the manuscript. A.S.S. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
References
- 1.The Diabetes Control and Complications Trial (DCCT) Research Group Effect of intensive diabetes management on macrovascular events and risk factors in the Diabetes Control and Complications Trial. Am J Cardiol 1995;75:894–903 [DOI] [PubMed] [Google Scholar]
- 2.Lawson ML, Gerstein HC, Tsui E, Zinman B. Effect of intensive therapy on early macrovascular disease in young individuals with type 1 diabetes. A systematic review and meta-analysis. Diabetes Care 1999;22(Suppl. 2):B35–B39 [PubMed] [Google Scholar]
- 3.Relationship of atherosclerosis in young men to serum lipoprotein cholesterol concentrations and smoking. A preliminary report from the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) Research Group. JAMA 1990;264:3018–3024 [DOI] [PubMed] [Google Scholar]
- 4.Berenson GS, Srinivasan SR, Bao W, Newman WP 3rd, Tracy RE, Wattigney WA. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study. N Engl J Med 1998;338:1650–1656 [DOI] [PubMed] [Google Scholar]
- 5.Kershnar AK, Daniels SR, Imperatore G, et al. . Lipid abnormalities are prevalent in youth with type 1 and type 2 diabetes: the SEARCH for Diabetes in Youth Study. J Pediatr 2006;149:314–319 [DOI] [PubMed] [Google Scholar]
- 6.Schwab KO, Doerfer J, Hecker W, et al.; DPV Initiative of the German Working Group for Pediatric Diabetology . Spectrum and prevalence of atherogenic risk factors in 27,358 children, adolescents, and young adults with type 1 diabetes: cross-sectional data from the German diabetes documentation and quality management system (DPV). Diabetes Care 2006;29:218–225 [DOI] [PubMed] [Google Scholar]
- 7.Guy J, Ogden L, Wadwa RP, et al. . Lipid and lipoprotein profiles in youth with and without type 1 diabetes: the SEARCH for Diabetes in Youth case-control study. Diabetes Care 2009;32:416–420 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Maahs DM, Maniatis AK, Nadeau K, Wadwa RP, McFann K, Klingensmith GJ. Total cholesterol and high-density lipoprotein levels in pediatric subjects with type 1 diabetes mellitus. J Pediatr 2005;147:544–546 [DOI] [PubMed] [Google Scholar]
- 9.Polak M, Souchon PF, Benali K, Tubiana-Rufi N, Czernichow P. Type 1 diabetic children have abnormal lipid profiles during pubertal years. Pediatr Diabetes 2000;1:74–81 [DOI] [PubMed] [Google Scholar]
- 10.Edge JA, James T, Shine B. Longitudinal screening of serum lipids in children and adolescents with type 1 diabetes in a UK clinic population. Diabet Med 2008;25:942–948 [DOI] [PubMed] [Google Scholar]
- 11.Maahs DM, Wadwa RP, McFann K, et al. Longitudinal lipid screening and use of lipid-lowering medications in pediatric type 1 diabetes. J Pediatr 2007;150:146–150, 150.e1–e2 [DOI] [PubMed]
- 12.Marcovecchio ML, Dalton RN, Prevost AT, et al. . Prevalence of abnormal lipid profiles and the relationship with the development of microalbuminuria in adolescents with type 1 diabetes. Diabetes Care 2009;32:658–663 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Reh CM, Mittelman SD, Wee CP, Shah AC, Kaufman FR, Wood JR. A longitudinal assessment of lipids in youth with type 1 diabetes. Pediatr Diabetes 2011;12:365–371 [DOI] [PubMed] [Google Scholar]
- 14.Lopes-Virella MF, Wohltmann HJ, Mayfield RK, Loadholt CB, Colwell JA. Effect of metabolic control on lipid, lipoprotein, and apolipoprotein levels in 55 insulin-dependent diabetic patients. A longitudinal study. Diabetes 1983;32:20–25 [DOI] [PubMed] [Google Scholar]
- 15.Hamman RF, Bell RA, Dabelea D, et al.; SEARCH for Diabetes in Youth Study Group . The SEARCH for Diabetes in Youth study: rationale, findings, and future directions. Diabetes Care 2014;37:3336–3344 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Dabelea D, Pihoker C, Talton JW, et al.; SEARCH for Diabetes in Youth Study . Etiological approach to characterization of diabetes type: the SEARCH for Diabetes in Youth Study. Diabetes Care 2011;34:1628–1633 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kuczmarski RJ, Ogden CL, Guo SS, et al. . 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11 2002;2002:1–190 [PubMed] [Google Scholar]
- 18.National Health and Nutrition Examination Survey (NHANES). Anthropometry Procedures Manual. Atlanta, GA, Centers for Disease Control and Prevention, 2000, p. 3–31
- 19.Shah AS, Dolan LM, Dabelea D, et al.; SEARCH for Diabetes in Youth Study . Change in adiposity minimally affects the lipid profile in youth with recent onset type 1 diabetes. Pediatr Diabetes 2015;16:280–286 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Blaha MJ, Blumenthal RS, Brinton EA, Jacobson TA; National Lipid Association Taskforce on Non-HDL Cholesterol . The importance of non-HDL cholesterol reporting in lipid management. J Clin Lipidol 2008;2:267–273 [DOI] [PubMed] [Google Scholar]
- 21.Grundy SM, Cleeman JI, Merz CN, et al.; National Heart, Lung, and Blood Institute; American College of Cardiology Foundation; American Heart Association . Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation 2004;110:227–239 [DOI] [PubMed] [Google Scholar]
- 22.van Deventer HE, Miller WG, Myers GL, et al. . Non-HDL cholesterol shows improved accuracy for cardiovascular risk score classification compared to direct or calculated LDL cholesterol in a dyslipidemic population. Clin Chem 2011;57:490–501 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Executive summary of the Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486–2497 [DOI] [PubMed] [Google Scholar]
- 24.American Diabetes Association Management of dyslipidemia in children and adolescents with diabetes. Diabetes Care 2003;26:2194–2197 [DOI] [PubMed] [Google Scholar]
- 25.Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 2012;13:275–286 [DOI] [PubMed] [Google Scholar]
- 26.Kahn HS, Imperatore G, Cheng YJ. A population-based comparison of BMI percentiles and waist-to-height ratio for identifying cardiovascular risk in youth. J Pediatr 2005;146:482–488 [DOI] [PubMed] [Google Scholar]
- 27.Kuryan RE, Jacobson MS, Frank GR. Non-HDL-cholesterol in an adolescent diabetes population. J Clin Lipidol 2014;8:194–198 [DOI] [PubMed] [Google Scholar]
- 28.Dai S, Fulton JE, Harrist RB, Grunbaum JA, Steffen LM, Labarthe DR. Blood lipids in children: age-related patterns and association with body-fat indices: Project HeartBeat! Am J Prev Med 2009;37(Suppl.):S56–S64 [DOI] [PubMed] [Google Scholar]
- 29.The DCCT Research Group Lipid and lipoprotein levels in patients with IDDM diabetes control and complication. Trial experience. Diabetes Care 1992;15:886–894 [DOI] [PubMed] [Google Scholar]
- 30.Purnell JQ, Hokanson JE, Marcovina SM, Steffes MW, Cleary PA, Brunzell JD. Effect of excessive weight gain with intensive therapy of type 1 diabetes on lipid levels and blood pressure: results from the DCCT. Diabetes Control and Complications Trial. JAMA 1998;280:140–146 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Brown RJ, Wijewickrama RC, Harlan DM, Rother KI. Uncoupling intensive insulin therapy from weight gain and hypoglycemia in type 1 diabetes. Diabetes Technol Ther 2011;13:457–460 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Dabelea D, D’Agostino RB Jr, Mason CC, et al. . Development, validation and use of an insulin sensitivity score in youths with diabetes: the SEARCH for Diabetes in Youth study. Diabetologia 2011;54:78–86 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Nadeau KJ, Regensteiner JG, Bauer TA, et al. . Insulin resistance in adolescents with type 1 diabetes and its relationship to cardiovascular function. J Clin Endocrinol Metab 2010;95:513–521 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Webber LS, Srinivasan SR, Wattigney WA, Berenson GS. Tracking of serum lipids and lipoproteins from childhood to adulthood. The Bogalusa Heart Study. Am J Epidemiol 1991;133:884–899 [DOI] [PubMed] [Google Scholar]