Introduction.
Type 1 diabetes mellitus (T1D) is growing worldwide (1) with the incidence peaking at puberty.(2) Current population projections indicate that the numbers will nearly triple by 2050 with the prevalence primarily among minority racial/ethnic groups.(3) Despite this trend, only a limited number of studies of adolescents with T1D have included minorities. Future diabetes-related morbidity, mortality and health care expenditures associated with poor cardiovascular outcomes are concerning. (4) In adolescents with T1D, heart rate variability (HRV), a measure of cardiovascular autonomic function, is noted to be lower when compared with healthy control subjects and is an early marker of future cardiovascular dysautonomia.(5)
Cardiovascular autonomic neuropathy is one of the most overlooked of all serious complications of diabetes, resulting from microvascular damage to parasympathetic and sympathetic fibers (6). Evidence indicates that heart rate variability, particularly parasympathetic modulation, is associated with better cardiovascular fitness in healthy adolescents, (7) adults without diabetes or cardiorespiratory diseases, (8) and adults with T1D, (9) but studies examining this association in adolescents with T1D are not available.
Cardiovascular autonomic neuropathy occurs in approximately 17% of adult patients with T1D. (10) Seminal work by Ewing and colleagues (11) provided evidence of the predictive relationship between cardiovascular autonomic neuropathy and mortality in adults with T1D. In adolescents with T1D, the SEARCH for Diabetes in Youth Study found the prevalence of cardiovascular autonomic neuropathy to be 12%.(12) The main driver of this subclinical abnormality detected by decreased HRV is hyperglycemia. (5)
Along with hyperglycemia, HRV can be affected by lifestyle and linked to predictors of future cardiovascular disease. Available research on lifestyle interventions (13) (14) supports strategies for increasing physical activity in adolescents with T1D. Despite this, there is evidence that adolescents still do not meet American Diabetes Association guidelines of 60 minutes of daily moderate-to-vigorous intensity activity.(15) Decreased levels of physical activity are known to be associated with lower HRV measures as well as lower fitness levels.
Although poorer glucose control reflected by high HbA1c (the average of glucose over the past 3 months) has been linked to lower CR fitness (16) and heart rate variability (HRV) in adolescents with T1D, (5) research has not addressed racial variation in youth. A T1D Exchange Clinic Network study found that only 14% of Non-Hispanic Black (NHB) versus 34% Non-Hispanic White (NHW) youth met the American Diabetes Association (17) and International Society of Pediatric and Adolescent Diabetes (18) HbA1c targets of < 7.5%, potentially increasing the odds of poorer outcomes in NHB youth. (19) Although there is no clear evidence of disparities in coronary artery disease (CAD) between NHW and NHB adults with diabetes,(20) there are disparities in traditional risk factors for CAD, with NHBs being more likely than NHWs to have hypertension(21) and worse glycemic control.(22)
The American Heart Association’s (AHA) Scientific Statement on Cardiovascular Disease Risk Factors in Youth with Diabetes Mellitus (23) provided a summary of the current state of evidence regarding cardiovascular disease risks in youth with T1D from leading professional organizations, the American Diabetes Association,(24) the American Heart Association,(25) the American Academy of Pediatrics,(26) the International Society of Pediatric and Adolescent Diabetes,(27) and the Pediatric Cardiovascular Risk Reduction Initiative.(28) Future directions in research to improve cardiovascular health and manage risk factors in this population are outlined. Glycemic control and optimal HbA1c is stressed as a major influence on preventing the development of cardiovascular disease risks and future disease.
Although T1D is more common in NHWs, NHB youth with T1D are prone to poorer glucose control,(29) placing them at greater risk for complications. Despite the evidence of negative disease sequalae in NHB youth, NHB participants in epidemiological trials comprise approximately 2–6% of the samples, limiting the ability to examine racial variation in cardiovascular risks and related health outcomes.(30) A SEARCH cardiovascular study of adolescents with T1D indicated that none of the participants (n = 190) met the American Heart Association’s (AHA) ideal cardiovascular heath (ICH) metrics (i.e., not smoking; being physically active; having normal weight, blood pressure, blood glucose, and total cholesterol levels; and eating a healthy diet) for prevention of cardiovascular risk factors.(31). In summary, overall ICH prevalence was low in these adolescents with approximately 6.8% of the study sample represented by NHBs. This evidence supports a substantial gap in what is known regarding cardiovascular health disparities in NHB adolescents with T1D. Despite greater scientific evidence of cardiovascular risks in adolescents with T1D, the state of this knowledge is lacking related to racial/ethnic disparities and the best evidence for early detection and personalization of preventive treatment plans for optimal cardiovascular health.
Early identification of adolescents with the greatest risks for future health disparities is key for prevention of later cardiovascular morbidity and mortality. Therefore, the purpose of this study was to explore early markers for future cardiovascular disease, specifically in those with T1D. We sought to assess the association of sex and race with heart rate variability (HRV) and cardiorespiratory (CR) fitness in NHB and NHW adolescents with T1D. The association between the HRV and CR outcomes with glucose control was also examined.
Methods.
A secondary analysis of an existing parent dataset of adolescents with either T1D or T2D (PHS R01 NR07719) was conducted on data that were originally collected using a descriptive, correlational design. Differences in HRV and CR fitness based upon sex and associations among measures of HRV with CR fitness were also explored. Participants were recruited from a large, metropolitan pediatric diabetes clinic affiliated with a major academic medical center in the Midwestern United States. Institutional review board approval was obtained. Parental consent and youth assent were received prior to enrollment in the study. Adolescents were eligible for inclusion in the study if they had a diagnosis of T1D for at least one year and were between 13 and 18 years of age. Adolescents were excluded if they had developed diabetes as a secondary condition to treatment for another chronic condition (e.g., cancer) or had a known cardiac defect. Data were collected at the clinical research center of the affiliated academic medical center.
Sex and age-adjusted body mass index (BMI) and BMI z-scores were calculated based upon syntax files provided by the Centers for Disease Control (https://www.cdc.gov/growthcharts/percentile_data_files.htm). Both recent and average HbA1c (values averaged over 1 year) were collected. Recent HbA1c values were established at the time of data collection and analyzed using the Abbott IMx® assay (Abbott Park, IL, USA). Average HbA1c values were computed from those available on existing chart records.
Heart Rate Variability.
Twenty-four hour heart rate variability (HRV) was measured by use of the 3-channel SpaceLabs Burdick Model 92510 digital Holter Recorder (Deerfield, WI, USA). Quality assurance data provided by SpaceLabs Burdick demonstrated that calculations and statistics of the HRV program were verified from laboratory measures of predictable test signals. Kleiger et al.(32) previously established the stability of both time and frequency domain HRV measures using 24-hour Holter monitoring, reporting correlation coefficients > .80.
Measurement and analysis of heart rate variability are classified into frequency and time domain analyses. Power spectral analysis of heart rate variability offers a highly sensitive source of data related to autonomic function by quantifying and discriminating between sympathetic and parasympathetic autonomic function through frequency of R-R variation. Abnormalities detected by power spectral analysis are more sensitive indicators of autonomic dysfunction, particularly in patients with diabetes who have diminished 24-hour R-R interval variability. (33)
The SpaceLabs Vision Premier™ ECG Analysis and Editing software system utilizes the Fast Fourier Method of spectral analysis to calculate the frequency domain. Use of Fast Fourier Transformations (FFT) provides a mathematical representation of the spectrum of frequency, called Hertz (Hz) or power. Frequency domain measures of total Hertz (0.01–1.00 Hz), low Hertz (0.04–0.15) and high Hertz (0.15–0.40) are converted to log transformation by the computer software to correct for skewness. Total Hertz is the entire area under the curve in a power spectrum plot and represents the variance of all R-R intervals in the entire Holter recording. (34) Low Hertz (primarily sympathetic with some parasympathetic innervation) predominates during waking hours and high Hertz (parasympathetic innervation) predominates during sleep.(35)
Time domain analysis is computed on differing computations of the standard deviation of the beat-to-beat change in heart rate, based on sinus R-R intervals over time. (36) The time domain analysis of heart rate variability can be further divided into two categories. One category is derived from the R-R intervals, using means and standard deviations of the intervals measured in milliseconds. Measures in this category include the SDNN and SDANN. The SDNN is the standard deviation of all R-R intervals during a 24-hour period. In adults, SDNN values that are less than 50 milliseconds are associated with sudden cardiac death. (37) The SDANN is the standard deviation of the means of R-R intervals found in successive 5-minute time periods over 24 hours. The calculation for SDANN makes it the most resistant heart rate variability measure for QRS labeling errors, and the best measure for circadian fluctuation in heart rate. (36)
The second category of time domain variables is derived from differences between adjacent R-R intervals and includes indices that are independent of circadian rhythms. Measures in this category are the pNN50 and the rMSSD. The pNN50 represents the proportion of the total R-R intervals that have differences of successive R-R intervals greater than 50 milliseconds. The rMSSD represents the square root of the mean squared differences of successive R-R intervals. (36)
Cardiovascular Fitness.
According to the American College of Sports Medicine, (38) the criterion measure of CR fitness is the direct measure of maximal oxygen uptake or VO2max. (i.e., ml•kg−1•min−1). Exercise testing using cycle ergometer procedures for the measurement of CR fitness was used in this investigation and reliability has previously been successfully established in adolescents, including diverse ethnic groups.(39)
The SensorMedics® VMAX29 cardiopulmonary metabolic cart and cycle ergometer (SensorMedics, Yorba Linda, CA) were used for exercise testing in this investigation. Blood glucose measurement by fingerstick using an Accu-Chek™ Advantage monitor (Roche, Indianapolis, IN) occurred immediately prior to exercise testing. No episodes of hypoglycemia (blood glucose < 90 mg/dl) were detected. If blood glucose levels were above 250 mg/dl, participants were rescheduled to have all data collected on another day.
Each subject performed a cycle ergometer test, specifically the McMaster Cycle Test (38) to measure CR fitness. The protocol is based on the height and sex of the adolescent. Total exercise time to the point of subjective exhaustion is optimally 8 to 12 minutes. The objective criteria for reaching VO2max are as follows: reaching an oxygen plateau, a respiratory exchange ratio (RER) of >1.0, and heart rate > 200 beats/minute. (40) However, because youths often fail to meet all of the objective criteria for VO2max, we used VO2peak, a commonly acceptable, alternative measure. (38) VO2peak is measured the same as VO2max, except fewer criteria are used to establish fitness. Specifically, the subject’s indication of exhaustion and a RER > 1.0 were used to determine VO2peak since an oxygen plateau was not always noted. Termination of exercise was based upon the subject’s request due to fatigue indicated by the Borg Scale. Before, during, and immediately after exercise testing, heart rate was continuously monitored via the metabolic cart from a 12-lead electrocardiogram (ECG). Blood pressure was taken at baseline and every 2 minutes while the subject was cycling.
Statistical Analysis.
Exploratory data analysis included frequency distributions for categorical variables and measures of central tendency and dispersion for continuous variables. Pearson’s correlation coefficient was used to quantify associations between glucose control (HbA1c) and study outcomes. General linear models (GLM) were used to assess associations among sex, race, and HRV and CR outcomes. The GLM approach for modeling of the HRV and CR outcomes with the two factors of race and sex is equivalent to a two-way ANOVA. The overall F-test result tested for any difference between sex and/or race means. If any pairwise difference was found, then significant sex and/or race effects were noted. In addition to these analyses, we also examined race as a moderator of the relationship between glucose control and HRV and CR outcomes. This was examined with the inclusion of an interaction term in the GLMs and sex as a covariate. The SAS Software System, Version 9.4, was used for statistical analysis, and the level of significance was set at .05.
Results.
Table 1 displays summary statistics for study variables. Count and percentage were reported for categorical study variables and the mean and standard deviation used for summarizing continuous study variables. The study sample included 95 adolescents with T1D. Fifty-four (56.8%) of the study sample was male and ages ranged from 13 to 18 years. The race distribution included 29 (30.5%) NHB and 66 (69.5%) NHW.
Table 1.
Summary statistics for all participants (n=95).
| Count (%) | |
|---|---|
| Sex | |
| Male | 54 (56.8) |
| Female | 41 (43.2) |
| Race | |
| Non-Hispanic black | 29 (30.5) |
| Non-Hispanic white | 66 (69.5) |
| Mean (SD)* | |
| Age | 15.4 (1.9) |
| Body mass index | 23.3 (3.9) |
| Body mass index z-score | 0.7 (0.8) |
| Duration of diabetes (years) | 6.1 (3.7) |
| Recent HbA1c | 8.7 (1.6) |
| Average HbA1c | 8.8 (1.6) |
| Resting diastolic blood pressure | 64.7 (8.8) |
| Resting systolic blood pressure | 113.3 (11.3) |
SD=standard deviation
Summary statistics grouped by race and by sex are presented in Table 2. Body mass index was similar between NHB and NHW adolescents. Although not statistically significant, duration of diabetes was slightly higher for NHW as compared to NHB adolescents. Age and systolic and diastolic blood pressure were similar across the sex and race groups. Race differences were found for recent HbA1c and average HbA1c, with higher values indicating poorer glucose control for NHB adolescents. The HRV measures of total Hertz, low Hertz, SDNN, and cardiorespiratory fitness were significantly lower in NHB compared to NHW adolescents. Females were also found to have significantly lower HRV measures, including total Hertz, low Hertz, SDNN, and cardiorespiratory fitness.
Table 2.
Summary statistics grouped by race and sex (n=95).*
| Non-Hispanic Black n = 29 Mean (SD) |
Non-Hispanic White n = 66 Mean (SD) |
Male n = 54 Mean (SD) |
Female n = 41 Mean (SD) |
Total n = 95 Mean (SD) |
|
|---|---|---|---|---|---|
| Age | 15.1 (1.8) | 15.6 (1.9) | 15.2 (1.8) | 15.7 (2.0) | 15.4 (1.9) |
| Body mass index | 22.7 (3.8) | 23.5 (3.9) | 22.7 (4.2) | 24.1 (3.3) | 23.3 (3.9) |
| Body mass index z-score | 0.6 (0.8) | 0.8 (0.8) | 0.6 (0.9) | 0.9 (0.5) | 0.7 (0.8) |
| Duration of diabetes (years) | 5.4 (3.4) | 6.4 (3.8) | 5.7 (3.6) | 6.6 (3.8) | 6.1 (3.7) |
| Recent HbA1cb | 9.7 (1.8) | 8.2 (1.2) | 8.5 (1.8) | 8.8 (1.3) | 8.7 (1.6) |
| Average HbA1cb | 9.9 (2.0) | 8.3 (1.2) | 8.4 (1.7) | 9.3 (1.5) | 8.8 (1.6) |
| Resting diastolic blood pressure | 67.5 (9.6) | 63.4 (8.2) | 63.7 (9.2) | 66.0 (8.3) | 64.7 (8.8) |
| Resting systolic blood pressure | 113.6 (9.2) | 113.2 (12.2) | 114.6 (12.9) | 111.6 (8.7) | 113.3 (11.3) |
| Average Heart Rateb | 88.3 (10.8) | 80.5 (9.1) | 81.3 (10.9) | 84.9 (9.0) | 82.8 (10.2) |
| Total Hertza,b | 8.0 (0.9) | 8.5 (0.6) | 8.5 (0.7) | 8.1 (0.7) | 8.4 (0.7) |
| High Hertz | 6.5 (1.1) | 6.8 (0.9) | 6.9 (0.9) | 6.6 (1.0) | 6.7 (0.9) |
| Low Hertza,b | 6.6 (0.9) | 7.3 (0.5) | 7.3 (0.7) | 6.9 (0.7) | 7.1 (0.7) |
| SDNNa,b | 130.3 (37.9) | 166.9 (35.2) | 169.4 (36.9) | 137.7 (36.2) | 155.7 (39.7) |
| SDANN | 108.9 (31.8) | 139.4 (33.8) | 141.5 (34.6) | 115.1 (32.3) | 130.1 (36.0) |
| rMSSD | 55.7 (30.6) | 62.2 (27.3) | 64.2 (26.2) | 55.0 (30.5) | 60.2 (28.4) |
| pNN50 | 18.4 (14.2) | 21.6 (12.3) | 23.0 (12.7) | 17. 6 (12.7) | 20.6 (12.9) |
| VO2peaka,b | 29.3 (6.1) | 37.1 (8.9) | 39.3 (8.2) | 28.7 (5.6) | 34.7 (8.9) |
Difference in group means for sex (p<.05)
Difference in group means for race (p<.05)
SD=standard deviation
Total Hertz [total frequency variation of sympathetic and parasympathetic innervations (ln ms2)]
High Hertz [primarily parasympathetic innervation (ln ms2)]
Low Hertz [primarily sympathetic with some parasympathetic innervation (ln ms2)]
SDNN [standard deviation of all R-R intervals]
SDANN [standard deviation of all the means of R-R intervals of each 5-minute block]
rMSSD [square root of the mean of the sum of squares of differences between adjacent R-R intervals]
pNN50 [percent of adjacent R-R intervals with ≥ 50 ms difference]
VO2peak [cardiorespiratory fitness]
Correlation results for examining associations between glucose control and study outcomes for HRV and CR fitness are displayed in Table 3. Pearson’s correlation coefficient (r) was used. Moderate associations were found between average HbA1c (values averaged over 1 year) and HRV and CR fitness outcomes. None of the statistical tests for a moderating effect of race on the relationship between glucose control and study outcomes were statistically significant.
Table 3.
Correlations of HbA1c measures among heart rate variability and cardiorespiratory fitness measures.
| Recent HbA1c | Average HbA1c | |
|---|---|---|
| Average Heart Rate | 0.29* | 0.41* |
| Total Hertz | −0.29* | −0.34* |
| High Hertz | −0.17 | −0.21 |
| Low Hertz | −0.32* | −0.41* |
| SDNN | −0.22* | −0.28* |
| SDANN | −0.17 | −0.26* |
| rMSSD | −0.06 | −0.06 |
| pNN50 | −0.14 | −0.21* |
| VO2peak | −0.31* | −0.46* |
| Recent HbA1c | 0.84* |
p < .05
Total Hertz (total frequency variation of sympathetic and parasympathetic innervations (ln ms2)),
High Hertz (parasympathetic innervation),
Low Hertz (primarily sympathetic with some parasympathetic innervation (ln ms2)),
SDNN (standard deviation of all R-R intervals),
SDANN (standard deviation of all the means of R-R intervals of each 5-minute block),
rMSSD (square root of the mean of the sum of squares of differences between adjacent R-R intervals),
pNN50 (percent of adjacent R-R intervals with ≥ 50 ms difference),
VO2peak (cardiorespiratory fitness)
Discussion and Conclusion.
A major contribution of this investigation is the finding of significantly lower values for both HRV and CR fitness in NHB versus NHW adolescents with T1D. To our knowledge, this is the first investigation that examined racial variation in these established markers for future CVD. Although other recent research on HRV outcomes in youth populations with T1D included larger samples, the majority were NHW (87%) with other racial groups combined (NHB, Hispanics and American Indian/Pacific Islanders, 13%) and variation based on racial differences were not reported. (41) However, poorer glucose control as a modifiable risk factor for decreased HRV is a consistent finding in earlier studies of adolescents with T1D, (12, 42) and is supported by this study. Both higher recent and average yearly HbA1c values were associated with lower HRV. The stronger associations with the average values over the past year is a warning sign about the importance of stability in glucose control over time versus taking a “snapshot” view of only interpreting a recent measure of HbA1c.
Longitudinal measures of glucose control reflective of persistent hyperglycemia provide evidence of reduced HRV. Jaiswal and colleagues noted that over a 6-year time span, for every 1% increase in the average HbA1c, the SDNN and rMSSD time domain measures declined by 5–7% (P = 0.02) and were independent of demographic and traditional CVD risk factors. (5) The finding of lower HRV measures with poorer glycemic control in adolescents provides evidence that there may be development of early cardiovascular autonomic alterations. Average glucose control in both NHWs and NHBs in this study were higher than recommended and reflective of poor control in adolescents. (19) The higher HbA1c values in NHBs are recognized as markers for long-term complications and subsequently a poor prognosis (43), which may place them at a higher risk for cardiovascular dysautonomia. Although it is important to point out that recent evidence indicates that mean glucose values reflect higher HbA1c in NHBs than in NHWs at similar glucose levels, (44) the association with an elevated average 24-hour heart rate and lower measures of HRV and CR fitness as potential indicators for future CVD warrant further inquiry.
Moderate to more vigorous exercise has been shown to improve both fitness (7, 45) and HRV(7). Current research (13, 14) indicates a level of physical activity for adolescents with T1D that does not meet American Diabetes Association guidelines of 60 minutes of moderate-to-vigorous aerobic intensity exercise per day, (46) leading to potential decreased levels of physical fitness and poorer glucose control. In one of the few studies to explore the relationships between CR fitness and HRV, the German Diabetes Study included young adults with T1D and found that lower HRV measures correlated with lower VO2max (r values > 0.34), (9) lending support to the findings reported here.
In examining sex variation, females having lower values of HRV than males is consistent with previous research of adolescents with T1D. (41) In particular, SDNN, a measure of overall HRV, is consistently noted to be lower in females from this population,(41) as it was in the current investigation. Compared to males, lower CR fitness levels for females in this study are consistent with lower aerobic capacity in healthy adolescent girls in general, (47) and in those with T1D. (9)
In summary, the findings reported here support the importance of early identification of cardiovascular health disparities in adolescents with T1D, particularly variation experienced in NHB and female youth. Limitations of this investigation include the the use of a secondary analysis with unequal sample sizes and the smaller sample of NHB versus NHW adolescents whose data were available for analysis. However, the proportion of NHB to NHW in the sample is reflective of the racial distribution of T1D in youth.(1) With the increasing number of youth being diagnosed with T1D across all diverse ethnic and racial groups, exploration of CVD risks must be teased out for those who may have greater vulnerabilities for future complications. Despite the smaller sample of NHB in this study, the significant findings of higher recent and average HbA1c, and lower HRV and CR fitness measures should sound an alarm for increased vigilance of these early markers of future CVD in both practice and inquiry.
Table 4.
Correlations of study outcome variables for heart rate variability and cardiorespiratory fitness.
| Cardiorespiratory fitness (VO2peak) | |
|---|---|
| Average Heart Rate | −0.33* |
| Total Hertz | 0.36* |
| High Hertz | 0.21* |
| Low Hertz | 0.39* |
| SDNN | 0.47* |
| SDANN | 0.42* |
| rMSSD | 0.18 |
| pNN50 | 0.21* |
p<.05
Total Hertz (total frequency variation of sympathetic and parasympathetic innervations (ln ms2)),
High Hertz (parasympathetic innervation),
Low Hertz (primarily sympathetic with some parasympathetic innervation (ln ms2)),
SDNN (standard deviation of all R-R intervals),
SDANN (standard deviation of all the means of R-R intervals of each 5-minute block),
rMSSD (square root of the mean of the sum of squares of differences between adjacent R-R intervals)
pNN50 (percent of adjacent R-R intervals with ≥ 50 ms difference),
VO2peak (cardiorespiratory fitness)
Acknowledgments.
The authors extend their appreciation to the adolescents that participated in this investigation and provided an opportunity to gain valuable knowledge about risks for future cardiovascular disease inherent in living with T1D. The authors report no conflicts of interest. The results of this study are presented clearly, honestly, and without fabrication, falsification, or data manipulation. The findings reported in this paper were supported by NIH Grant R01 NR07719 (Faulkner, PI).
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