Does the Cardiac Autonomic Response to Postural Change Predict Incident Coronary Heart Disease and Mortality?

The Atherosclerosis Risk in Communities Study

Mercedes R. Carnethon1, Duanping Liao2, Gregory W. Evans3, Wayne E. Cascio4, Lloyd E. Chambless5, Wayne D. Rosamond6 and Gerardo Heiss6

1 Stanford Center for Research in Disease Prevention, Stanford University School of Medicine, Palo Alto, CA.
2 Department of Health Evaluation Sciences, Pennsylvania State Medical College, Hershey,PA.
3 Department of Public Health Sciences, Wake Forest University, Winston-Salem, NC.
4 Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC.
5 Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
6 Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
This study evaluated whether small shifts in cardiac autonomic balance with standing, as measured by heart rate variability (HRV), were prospectively associated with incident coronary heart disease (CHD) and mortality. Both Black and White men and women aged 45–64 years from the Atherosclerosis Risk in Communities Study (n = 9,267) were followed from 1987 to 1997 for myocardial infarction (n = 296), fatal CHD (n = 63), and non-CHD mortality (n = 533). HRV indices and mean R-R interval length (inverse of heart rate) were measured in the supine and standing positions for 2 minutes each; HRV shift was calculated as the difference between positions. After adjustment for demographic characteristics and medication use, HRV in each position was significantly inversely related to events in Cox proportional hazards models. With the exception of R-R interval length shift and myocardial infarction (hazard ratio = 1.42, 95% confidence interval: 1.02, 1.98 for the smallest vs. the largest quartile), there was no association between HRV shift and the other events. Despite clinical research suggesting that HRV shift with standing is a more sensitive measure of autonomic balance than is HRV in one position, simple measures such as heart rate change and supine and standing HRV were better predictors of events.

coronary disease; heart rate; mortality; nervous system, autonomic; risk factors

Abbreviations: ARIC, Atherosclerosis Risk in Communities Study; CHD, coronary heart disease; HF, high-frequency power; HRV, heart rate variability; MI, myocardial infarction; SDNN, standard deviation of normal R-R intervals


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Supine heart rate variability (HRV) is an estimate of parasympathetic modulation of the autonomic nervous system that is inversely associated with incident coronary heart disease (CHD) (1GoGo–3Go) and all-cause mortality (4Go, 5Go) in the population. However, the basis of normal cardiac autonomic functioning is the shift from parasympathetic to sympathetic modulation in response to environmental or physiologic stimuli. Postural change from supine to standing is one such stimulus used in clinical practice to elicit this shift in cardiac autonomic balance and correlate it with prevalent disease (6GoGoGoGo–10Go). The HRV response to postural change is hypothesized to be a more sensitive measure of cardiac autonomic modulation than is supine HRV because cardiac damage is thought to occur primarily through sympathetic impairment, which can only be estimated with a stimulus (11Go, 12Go).

Using HRV measures with a postural change, our primary objective was to determine whether small changes in parasympathetic autonomic modulation and heart rate or large decreases in overall autonomic modulation (indicating an absent reciprocal increase in sympathetic modulation) predict nonfatal myocardial infarction (MI), fatal MI or fatal CHD, and non-CHD mortality. Additionally, we investigated whether standing HRV demonstrated the same inverse relation with incident events as supine HRV did. Further, because recent population-based research found that HRV was related only to incident CHD and mortality among diabetics (13Go), we tested diabetes as an effect modifier of the relation between HRV and incident events.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study population
This was an ancillary investigation to the Atherosclerosis Risk in Communities (ARIC) Study. A biracial probability sample of men and women aged 45–64 years were recruited from the Minneapolis suburbs, Minnesota; Forsyth County, North Carolina; and Washington County, Maryland. Black adults were sampled exclusively in a fourth community, Jackson, Mississippi, and oversampled in Forsyth County, North Carolina. Between 1987 and 1989, 15,792 eligible persons (46 percent in Jackson, Mississippi, and 66 percent in the other communities) were interviewed and examined. A detailed description of the study design and methods is published (14Go).

Participants were excluded sequentially from the cohort for the following reasons: HRV records collected prior to establishing the final protocol (n = 804); prevalent or missing CHD information (n = 1,037); unusable HRV records in the supine (n = 2,106) and standing (n = 4,046) positions (total = 4,586); age younger than 45 years (n = 32); race other than Black or White, or Black race in Minnesota or Maryland (n = 27); and antiarrhythmic medication use (n = 39). This report includes 9,267 participants.

HRV measurement
All measurements took place between 8:30 and 11:30 a.m. (within 45 minutes to 2 hours of a caffeine-free, light snack) in examination rooms that were maintained at a comfortable temperature (70°F (21.1°C)), with dim light and with limited distractions. The procedure was described to participants in advance, and they were given specific instructions about the postural change maneuver and asked to lie quietly during the recording.

Three standard silver/silver chloride electrodes were attached to the epigastriums of participants, who rested for at least 20 minutes during a carotid B-mode ultrasound measurement. A dedicated desktop computer and software were used for continuous detection of R waves from the electrocardiogram at a sampling frequency of 1,000 Hz. After a 2-minute recording in the supine position, participants were instructed to hold the electrocardiograph harness, place their feet on the floor, and stand upright as quickly, as safely, and in as smooth a motion as possible. The study technician indicated the beginning of the postural change maneuver on the computer as well as when participants achieved the standing position. R-R intervals were recorded during the postural change maneuver and for approximately 2 minutes in the standing position. Stored R-R intervals were converted into beat-to-beat heart rate, including a record of the real time of each beat (15Go).

A single trained operator used a filter program to identify and label any visually apparent artifacts in the heart rate record. Records were transferred to a dedicated computer with specialized variance-preserving imputation software (PREDICT II HRVECG, Arrhythmia Research Technology, Inc., Austin, Texas) for data imputation in segments with artifacts. Records were excluded when more than 20 percent of intervals were affected by artifacts and when the total record was shorter than 60 seconds or had less than 30 acceptable intervals. After imputation, heart rate data were converted back into R-R intervals for data processing and spectrum analysis.

Statistics were computed on the edited R-R interval records to describe the variability attributable to autonomic influence during the recording period. The standard deviation of the length of all normal R-R intervals (SDNN) quantifies cycle length variability and estimates overall modulation of cardiac autonomic tone. Mean R-R interval length (msec) is the inverse of heart rate.

A fast Fourier transform algorithm was performed on the edited R-R interval records to calculate the power spectral density curve. Power spectral density analysis is a function of the frequency of the cyclic components of variation in R-R interval length, equal to the square of the amplitude of the fast Fourier transform of the HRV signal divided by the length of sampling time. Cycle frequencies of 9.5–24 per minute (0.15–0.40 Hz) (high-frequency cycles (HF)) are regulated by the parasympathetic system. Cycle frequencies of 2.5–9.5 per minute (0.04–0.15 Hz) (low-frequency cycles) are jointly regulated by the parasympathetic and sympathetic divisions. Very low-frequency waves (0.00–0.04 Hz) are less well defined, but are most likely influenced by baroreceptors and were not measured in this study (16Go, 17Go). HF power was used to estimate parasympathetic modulation of overall variability.

Measures to estimate the shift in autonomic balance with postural change take the general form: supine HRV – standing HRV = {Delta}HRV. Standing HF should decline, and R-R intervals should be shorter to reflect the decrease in parasympathetic modulation and increased heart rate, relative to supine measures. Smaller {Delta} HF and {Delta} R-R intervals represent an attenuated parasympathetic response to postural change that we hypothesize to predict incident events. Conversely, the shift from parasympathetic to sympathetic modulation should cause little change in the overall modulation of cardiac autonomic balance as estimated by SDNN. Thus, we hypothesize that participants with a large {Delta} SDNN are at higher risk of incident events.

Event ascertainment
All events were identified and processed according to the ARIC protocol for event follow-up and surveillance (18Go). Potential events that occurred among cohort participants between the baseline clinic visit (1987–1989) and December 31, 1997, were identified annually by telephone interview with the participant (or next-of-kin of a decedent) and through community hospital surveillance. Hospitalizations or deaths with specific cardiac-related discharge or underlying cause-of-death codes (International Classification of Diseases, Ninth Revision, codes 402, 410–414, 427, 428, and 518.4) were investigated further for classification.

Copies of the electrocardiograms were sent to the University of Minnesota and classified according to the Minnesota code (19Go). A combination of symptoms, cardiac enzymes, and electrocardiogram evidence was used to determine a diagnosis, and physician reviewers on the ARIC Morbidity and Mortality Classification Committee validated and classified all events (18Go). Fatal CHD was defined as death within 4 weeks of hospitalization for an MI, death preceded by chest pains within 72 hours, a history of chronic ischemic heart disease, or death certificate codes consistent with the underlying cause of death related to CHD. Death during the follow-up period in the absence of an MI or fatal CHD was classified as non-CHD mortality on the basis of death certificate information and/or annual follow-up information.

Covariates
Demographic characteristics and CHD risk factors were measured according to standardized protocols common to all ARIC study sites and were subject to regular quality-control checks (20Go, 21Go). Prevalent CHD was defined as a history of coronary artery bypass surgery, balloon angioplasty, or MI based on electrocardiograph or self-report. Age, race/ethnicity, gender, education level, and smoking history were identified on the basis of self-report. Education was dichotomized to compare participants with less than a high school education to those with a high school education or greater. Smoking status (current, former, or never) was dichotomized to compare current with never or former smokers. Medication use was identified and defined by coding all reported medications, vitamins, and supplements used in the 2 weeks prior to the clinic examination. Heart rhythm control medications included beta-blockers, angiotensin-converting enzyme inhibitors, calcium channel blockers, antianginals, antihypertensives (excluding diuretics), vasodilators, and digoxin.

After a 12-hour fast, blood was drawn from the antecubital vein of seated participants by using a butterfly needle and vacutainers and shipped to a central laboratory for assay. Glucose was measured by a hexokinase/glucose-6-phosphate dehydrogenase method on a Coulter DACOS device (Beckman Coulter, Inc., Fullerton, California). Diabetes was defined as fasting serum glucose of 126 mg/dl or more, nonfasting glucose of 200 mg/dl or more, or self-reported current use of medications for diabetes or a self-reported previous diagnosis. Insulin was measured by radioimmunoassay using an Insulin Kit (Cambridge Medical Diagnosis, Inc., Billerica, Massachusetts). Sitting blood pressure was measured three times by using a random zero sphygmomanometer after a 5-minute rest; the average of the last two measurements was used in this study. Hypertension was defined as any of the following: 1) systolic blood pressure of 140 mmHg or more; 2) diastolic blood pressure of 90 mmHg or more; or 3) reported use of hypertension-lowering medications during the 2 weeks preceding the clinic examination.

Body mass index was calculated as the ratio of weight (km) to standing height (m)2; participants with a body mass index of 30 or more were classified as obese. The modified Baecke questionnaire was used to assess physical activity by deriving a score based on the frequency of overall sport and exercise participation and the duration and intensity of up to four activities (22Go).

Statistical methods
The distribution of covariates and the cumulative incidence of events were compared between the sample of ARIC participants with complete HRV data and those excluded from analysis by using a {chi}2 test of proportions and t tests for means. Multivariable Cox proportional hazards models were used to test whether exclusion status was significantly related to the incidence of events after adjustment for established risk factors. HRV means were adjusted to nonmedication use and compared by event with F tests using analysis of variance.

After confirming previous findings that the relation between HRV and events is not linear (1GoGoGoGo–5Go) (data not shown), we created indicator variables to correspond to the quartiles of the distribution of HRV, using the highest quartile as the referent for each index. Multivariable Cox proportional hazards models were used to test the association between HRV and the time to event while accounting for varying follow-up time between the baseline examination and the event or censoring date. All two-way interactions between the main exposure and other covariates in the model were evaluated as a set by using a likelihood ratio test. If the likelihood ratio test was significant (p < 0.05), individual interaction terms were examined by using stepwise regression procedures. Log-log survival plots and time/covariate interaction terms were used to evaluate the proportional hazards assumption. No covariates violated this assumption.

The joint significance of the three indicator variables as a group were tested in relation to each event with a Wald {chi}2 test. Next, we calculated the hazards of each event by comparing each individual quartile (quartiles 1–3) of HRV with the uppermost quartile (quartile 4) for each index. For evaluation of effect modification by diabetes, quartiles were collapsed to compare the lowest quartile (quartile 1) with the upper three (quartiles 2–4) of each HRV index. The significance of an interaction term between diabetes and each index was tested in proportional hazards models. Proportional hazards models were then stratified by diabetes. All analyses were conducted using the SAS system version 6.12 (SAS Institute, Inc., Cary, North Carolina).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Over an average of 8.9 years of follow-up, 297 (3.2 percent) cases of incident MI, 63 (0.7 percent) cases of fatal CHD, and 540 (5.9 percent) deaths due to causes other than CHD were identified. On average, participants included in this study were younger, less likely to be male, and more often of Black race than the remainder of ARIC participants (table 1). Despite these demographic differences, the distribution of covariates did not differ between participants in this study and the total cohort. The cumulative incidence of events was significantly higher in the ARIC participants who were not included in this sample. After controlling for age, race, gender, and the cardiovascular disease risk factors listed in table 1, there was no significant association between exclusion from the HRV cohort and incident MI ({chi}2 = 0.66, p = 0.42) or non-CHD mortality ({chi}2 = 0.01, p = 0.98). However, the relation between exclusion from the HRV cohort and fatal CHD remained marginally significant ({chi}2 = 4.02, p = 0.05).


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TABLE 1. Distribution of baseline covariates (1987–1989) and incident events, stratified by study inclusion status, Atherosclerosis Risk in Communities Study, 1987–1989

 
HRV indices stratified by incident events
Supine and standing mean HF did not differ between participants who experienced MI or fatal CHD and those who were event free, but HF was significantly lower in both positions among participants who died of other causes (table 2). SDNN was significantly lower in both positions among participants who experienced any of the three events compared with those who remained event free, while supine and standing mean R-R interval lengths were shorter only for fatal CHD and non-CHD mortality. Despite differences in supine and standing HRV by event status, mean {Delta} HRV differed only between those who experienced fatal CHD and those who were event free; {Delta} R-R interval length was smaller among participants who died from CHD.


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TABLE 2. Adjusted{dagger} means and standard errors of supine, standing, and {Delta} heart rate variability stratified by incident events, Atherosclerosis Risk in Communities Study, 1987–1997

 
Proportional hazards modeling
After adjustment for age, race, gender, medication use, and supine heart rate, quartiles of supine and standing HRV were inversely associated with the study outcomes (table 3). Participants in the lowest quartile (quartile 1) of supine and standing SDNN were at a significantly increased risk of incident MI and non-CHD mortality. Similarly, the lowest quartile of supine R-R intervals was inversely associated with incident MI, and the risk of non-CHD mortality was elevated in quartiles 1 and 2 in both positions. HF power in the standing position predicted fatal CHD, and the lowest quartile of supine and standing HF power was inversely associated with non-CHD mortality.


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TABLE 3. Adjusted{dagger} hazard ratios and 95% confidence intervals of incident events by quartiles of supine, standing, and supine–standing ({Delta}) heart rate variability, Atherosclerosis Risk in Communities Study, 1987–1997

 
With the exception of the relation between {Delta} HF and non-CHD mortality, global Wald tests to evaluate the quartiles as a group in relation to the events failed to detect differences for {Delta} HRV; multivariable modeling comparing each quartile individually largely supported those findings. While the risk of non-CHD mortality was elevated among participants in the second quartile of {Delta} HF, the smallest quartile of {Delta} HF was not associated with mortality. The risk of MI was significantly elevated for participants in the smallest quartile (quartile 1) of {Delta} R-R interval lengths compared with the largest (quartile 4). Preliminary analysis detected a relation between the smallest quartile of {Delta} R-R intervals and fatal CHD before adjustment for medication use (hazard ratio = 1.98, 95 percent confidence interval: 1.04, 3.76), but this attenuated to nonsignificance in fully adjusted models.

There was little evidence of an elevated risk of events among diabetics in this cohort (table 4). More frequently, risks were elevated in both strata or were elevated only among the larger strata of nondiabetics, such as the twofold increased risk between R-R intervals and fatal CHD. The only exceptions included the relations between HF, MI, and fatal CHD and between R-R intervals and MI. MI risk was elevated among diabetics compared with nondiabetics in the lowest quartile of supine and standing HF; similarly, the risk of fatal CHD was elevated among diabetics in the lowest quartile of standing HF. The association between {Delta} HF and MI contradicted expectations; a small {Delta} HF protected against MI among diabetics. Risk of MI was elevated among diabetics in the lowest quartile of supine R-R intervals and the lowest quartile of {Delta} R-R intervals.


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TABLE 4. Adjusted{dagger} hazard ratios and 95% confidence intervals of incident events comparing participants in the lowest quartile (quartile 1) of heart rate variability with those in the upper three quartiles (quartiles 2–173;4), stratified by diabetes{ddagger}, Atherosclerosis Risk in Communities Study, 1987–173;1997

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
To our knowledge, this is the largest cohort study of healthy men and women to collect noninvasive measures of the autonomic response to postural change and the longest follow-up for incident events. In this cohort, supine and standing HRV were inversely associated with incident events and, compared with the supine position, the magnitude of effect between standing HRV and events was stronger. However, there was little evidence to support our hypothesis that a smaller HRV response to standing was associated with incident events, and prevalent diabetes did not modify this finding.

Both sympathetic and parasympathetic nerve fibers are affected by neuropathy, but parasympathetic dysfunction reportedly precedes sympathetic dysfunction by approximately 5 years (23GoGoGo–26Go). Bigger et al. (27Go) recommend using records of at least 1 minute in duration to capture HF oscillations and 2.5 minutes for low-frequency oscillations. With record lengths of 1.5–2 minutes in each position in this study, we limited our presentation to HF power and were restricted to making conclusions about parasympathetic withdrawal and overall variability. Further, we did not report results using normalized HF power (HF power/total power), commonly recommended to represent the relative balance between parasympathetic input and total power (16Go) because an accurate measure of low-frequency power is required for the calculation. Given these limitations, we cannot address the relation between the sympathetic component and disease, which is theorized to cause the most detrimental changes in cardiac function by lowering the threshold for fibrillation (28Go) and increasing arterial vasoconstriction (29GoGo–31Go) and thrombosis (32GoGoGo–35Go). Had our low-frequency measures been sufficiently lengthy, this pathway could have been studied more precisely.

The absolute change in any value may depend on the baseline value, thus presenting potential confounding if it is related to both the change and the outcome under study. However, including both the change and the supine measure in a regression model is problematic because the two measures are not independent and introduce near collinearity that can bias coefficient variances. To address this problem, Oldham (36Go) proposed including the average or the sum of the two measures (supine and standing) in the regression model with the change value. We included the average of supine and standing HRV as a covariate in proportional hazards models with HRV change as the primary predictor and found that the magnitude and direction of the hazard ratios did not change markedly. For example, the hazard ratio for the relation between R-R and MI decreased from 1.42 to 1.35, a change of 5 percent. Thus, we concluded that the average change had little appreciable effect on the estimates and was not needed in final models.

Our secondary hypothesis that diabetics with depressed autonomic responses would have an increased risk of events was based on the epidemiology of diabetic autonomic neuropathy and on earlier results based on this cohort (37GoGo–39Go). Liao et al. (13Go) identified an increased risk of CHD and non-CHD mortality among diabetic persons in a different sample of the same cohort with reduced supine parasympathetic tone. This was confirmed in relation to MI in this cohort (table 4), but not for the other events. The smaller sample size in this study, 1,009 diabetic and 8,177 nondiabetic participants, and the correspondingly lower number of events might have reduced the power to detect those differences in this study.

The absence of a consistent finding for effect modification by diabetes could reflect the inability to account for the duration and severity of diabetes in this sample. If most participants had diabetes for a short time, responses attributable to sympathetic involvement may not have been detectable. Further, the definition of diabetes in this study, namely fasting glucose of 126 mg/dl or more or the reported use of hypoglycemic agents, was more sensitive than specific, introducing heterogeneity by disease severity that may have obscured the expected association. Autonomic impairment might have been more easily detectable in a population of diabetics with a longer disease duration or poor glycemic control.

Noninvasive tests of autonomic function are relatively easy to implement, but can be difficult to interpret (40Go). This single postural change maneuver is expected to represent a person's habitual response to postural change. Without repeatability measures of this response in our sample, we are unable to quantify the potential variability or to confirm whether the short recordings in this study represent the cardiac autonomic response that is characteristic of an exam-inee. However, recent research suggests that short HRV recordings in a nonlaboratory setting are stable over months and therefore are characteristic of an individual (41Go).

The postural change protocol in this study is comparable with those in previous research (16Go, 37Go, 42Go). Using pharmacologic autonomic blockade, previous work has confirmed the shift in autonomic balance from parasympathetic to sympathetic control during postural change. While most laboratory-based studies use motorized tilt tables to bring volunteers to the upright position to standardize the procedure and limit muscular engagement, we relied on active postural change. Invasive measurements of specific cardiovascular responses suggest that active and passive changes in posture evoke different initial (first 30 seconds) cardiovascular responses.

The muscular effort required by active standing compresses vessels in the contracting muscles of the leg, resulting in an immediate translocation of blood toward the heart and a faster cardiac output response. Because cardiac output cannot fully compensate for the drop in total peripheral resistance associated with active postural change, an additional fall in systemic blood pressure is associated with active change (43Go). Accordingly, we excluded the first 30 seconds of R-R intervals after the postural change to avoid artifacts associated with motion and muscle activity. Thus, the comparability between active postural change and passive tilt reported by Bloomfield et al. (44Go) is likely to be met by this study.

Study generalizability is constrained by the large number records excluded from the analyses. The most serious concern applies to participants excluded because of artifacts in the R-R interval record that precluded data conversion into HRV indices. Twice as many participants in the standing position (n = 4,046) compared with supine position (n = 2,106) were affected. Our data suggest that small, statistically significant differences in the distribution of age, gender, and race and significant differences in cumulative incidence of events over follow-up exist between the group with HRV data and those without (table 1). However, once established CHD risk factors were statistically controlled, neither incident MI nor non-CHD mortality was related to inclusion/exclusion status. The marginal association between exclusion status and fatal CHD is likely to reflect the role of fatal arrhythmias suggested by our exclusion criteria of the use of antiarrhythmic agents and the presence of artifacts in the HRV record that can be attributable to arrhythmias during the baseline examination. Thus, we believe that the use of these exclusions is appropriate and has no appreciable effect in biasing the results.

Other effect estimates in this sample could be biased toward the null if participants with missing data were more likely to have attenuated or absent cardiac autonomic balance adjustments to postural challenge. The current exposure groups, based on quartiles of the distribution, have similar survival probabilities. Introducing observations with a higher incidence of disease and worse HRV values could increase the contrasts between groups and raise the relative risks. Alternatively, if the relation between missing data and cardiac autonomic balance were opposite, then the effect estimates in this study would be inflated. Given the demographic profiles of persons with missing data and the association between older age and male gender with postural changes in cardiac autonomic balance (10GoGo–12Go, 45Go, 46Go), the former scenario is more likely. Thus, the estimates in this study are likely to be conservative estimates of the true risk.

The cardiac autonomic response to postural change, as measured by short HRV records, generally did not predict incident events at the population level. The HRV response to postural change may not confer any additional predictive ability for incident cardiac and noncardiac events beyond measures in the supine or standing position when short (<5 minutes) HRV records are used. A simple measure, such as the change in heart rate with standing ({Delta} R-R intervals) that reflects autonomic input and additional control mechanisms, appears to be a more sensitive predictor of incident events.


    ACKNOWLEDGMENTS
 
Supported by National Heart, Lung, and Blood Institute ARIC contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, and HRV grant 5 R01 HL55669.

The work for this paper was completed while the lead author (M. R. C.) was a predoctoral fellow in the Cardiovascular Disease Epidemiology Training Program at the University of North Carolina, Chapel Hill, and was supported by National Institutes of Health, National Heart, Lung, and Blood Institute, National Research Service Awards grant 5T32HL07055.


    NOTES
 
Reprint requests to Dr. Gerardo Heiss, Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Bank of America Center, 137 E. Franklin St., Suite 306, Chapel Hill, NC 27514 (e-mail: gerardo_heiss{at}unc.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication December 22, 2000. Accepted for publication July 19, 2001.