Changes in Heart Rate and Heart Rate Variability Over Time in Middle Aged Men and Women in the General Population (From the Whitehall II Cohort Study)

Abstract

Previous cross-sectional studies have shown that heart rate (HR) and heart rate variability (HRV) are influenced by several behavioral, biological and psychosocial factors. There are very few longitudinal studies that enable analyses of changes in HRV over time at an individual level. This study aims to describe changes in HR and HRV in a general population setting and to determine predictors of HR and HRV at follow-up. Between 1997 and 2004, 1,999 participants (29% women) in the UK Whitehall II Cohort Study had two measures of cardiac autonomic function (mean time between measures 5.47 years; range 4.07 to 6.93 yrs). Mean age at first measure was 55.6 years (SD 6.00). At baseline, men showed higher low frequency power than women, suggesting that they may have higher sympathetic activity. Conversely women had higher high frequency power, indicating higher parasympathetic tone. Over the 5 year follow-up, both men and women had declines in their HR. Men had reductions to their HRV in both time and frequency dimensions, whilst women showed increases in HRV. The probability of being in the adverse quartile of HRV function and HR at follow-up was related to baseline exercise, body mass index, cholesterol and blood pressure.

INTRODUCTION

Heart rate (HR) and heart rate variability (HRV) are largely under the control of the autonomic nervous system. Cardiac autonomic function is associated with the prognosis of coronary patients; high resting HRs and low HRV are associated with an increased risk of all-cause mortality and sudden death.^1,2,3 While much of the initial research focused on patients with established coronary disease and neuropathies, more recent investigation of cardiac autonomic function has extended into the general population. We now provide data from a large, longitudinal dataset with 2 measures of HRV taken 5 years apart in a middle-aged cohort. We sought to determine (1) how measures of resting HR and HRV change over time at an individual level and (2) whether HR and HRV at follow-up are associated with baseline behavioral, biological and social/psychosocial factors.

METHODS

The Whitehall II study was established in 1985 as a longitudinal study to examine the socioeconomic gradient in health and disease among 10,308 civil servants (6,895 men and 3,413 women).⁴ All non-industrial civil servants aged 35–55 years working in the London offices of 20 departments were invited to participate in this study, and recruitment took place between 1985–8. Phases of data collection have alternated between postal questionnaire alone and postal questionnaire accompanied by a clinical examination. Since baseline, 7 phases of data collection have been completed.⁵ The University College London ethics committee approved the study.

HRV recordings were made at the fifth (1997–1999) and seventh (2002–2004) data collection phases. The data presented here are based on the 1,999 participants (1,417 men and 582 women) for whom we have data at both phases, enabling analyses on individual changes over time.

5-minute supine resting 12-lead electrocardiograms were obtained using SEER MC recorders (GE Medical Systems, Milwaukee, Wisconsin, USA). The recorders were programmed to capture individual 10 second electrocardiograms every 10 seconds. Thus the signal obtained from the recorders was continuous since no electrocardiogram samples were lost between adjacent 10 second elementary electrocardiogram recordings. The tachograms representing the sequences of individual RR intervals were exported from each SEERMC.dat file. Five minutes of beat to beat heart rate data were re-sampled at 500 Hz frequency in order to obtain digitised recording of R waves. HRV was analysed both in the time domain (standard deviation of all intervals between R waves with Normal to Normal conduction – SDNN) and in the frequency domain using the autoregressive method (Blackman-Tukey algorithm). Frequency domain components were computed by integrating the power spectrum within two frequency bands; 0.04–0.15Hz (Low Frequency power LF, in ms²) and 0.15–0.4Hz (High Frequency power HF, in ms²). All the calculations were performed using in-house written software in Watcom C++ compiler version 9.5.

Smoking, exercise, diet and alcohol were assessed by self-completed questionnaire. Participants were asked how often they took part in vigorous exercise and those undertaking less than 1 hour per week were defined as little or none. Diet was assessed by three items, frequency of fruit and vegetable consumption (8 levels ranging from never to 2 or more per day), the type of bread (3 levels: white, wheatmeal, wholemeal), and type of milk (3 levels: whole milk, semi-skimmed, skimmed). A summary index of poor diet was defined if 2 or all of the following applied: most frequently used bread was white, usually used milk was whole, fruit or vegetables were eaten less often than daily. Alcohol consumption in the last week was expressed in units of alcohol (1 unit = 8g) and ≥ 21 units per week in men or ≥ 14 units per week in women were categorised as high.

Participants completed a questionnaire detailing job title, behavioral and psychosocial factors, as described previously.⁴ Based on salary and work role, the civil service defines a hierarchy of employment grades which we analysed in 3 levels: unified grades 1–7 (high), executive officers (medium), and clerical and support staff (low). Minor psychiatric morbidity was assessed using the 30 item General Health Questionnaire and a four item depression subscale identified on the basis of factor analysis and comparison with the items of the depression sub-scale of the 28 item General Health Questionnaire. Social networks were measured with a 4 item scale of frequency and number of contacts with friends and relatives. Job control was measured with a 15 item scale.

Blood pressure was measured twice in the sitting position after 5 minutes rest. Body mass index was calculated as weight/height². Total cholesterol concentration was determined from fasting serum samples and an indicator of high cholesterol ≥ 6.0 mmol/l was created. The use of antihypertensive medications in the last 14 days was ascertained from the questionnaires. Hypertensives were defined as those having a systolic blood pressure ≥160 mmHg or a diastolic blood pressure ≥ 90 mmHg or on antihypertensive medication.

SDNN, LF, and HF were transformed by natural logarithm because their distributions were skewed and are expressed as geometric means. The mean change in HR per 5 years between the first and second measures was defined as HR at time 2 minus HR at time 1, scaled to a time difference of 5 years. For HRV measures these changes were performed using the log transformed values and percentage changes per 5 years are presented. Four variables were created to indicate those subjects who were in the adverse, sex specific, quarter of the distribution for HR and for each of the three HRV measures. Logistic regression was used to estimate the age and sex adjusted odds ratio and 95% confidence interval between baseline (Phase 5) behavioural, biological and social/psychosocial factors and the adverse HRV indicators. Mutually adjusted odds ratios were also estimated for those factors that showed significant associations for the majority of outcomes in these analyses.

RESULTS

On average HR decreased significantly over the 5 year follow-up period by 1.4 beats per minute in men and 2.5 beats per minute in women. Decreases in HRV measures were seen for men in both time and frequency dimensions, with the biggest change in the LF component. Conversely, small increases were seen in all HRV measures over the 5 year period among women, although only SDNN changes reached statistical significance (table 1).

Table 1.

Age adjusted heart rate and heart rate variability means^* (95% CI) at Time 1 and Time 2 and changes per 5 years of follow up in men and women

	Men (N=1417)	Women (N=582)
Heart Rate (beats per minute)
Time 1	68.5 (68.0, 69.1)	70.7 (69.9, 71.5)
Time 2	67.1 (66.5, 67.7)	68.1 (67.2, 68.9)
Mean change per 5 years	−1.39 (−1.81, −0.95)	−2.49 (−3.24, −1.74)
SDNN (ms)
Time 1	34.5 (33.7, 35.3)	32.4 (31.3, 33.6)
Time 2	33.6 (32.7, 34.4)	33.7 (32.5, 35.0)
% Change per 5 years	−2.3% (−4.6%, 0.0%)	3.9% (0.3%, 7.7%)
LF power (ms²)
Time 1	332 (315, 350)	246 (228, 265)
Time 2	290 (274, 307)	256 (236, 279)
% Change per 5 years	−11.3% (−15.7%, −6.7%)	4.1% (−3.4%, 12.2%)
HF power (ms²)
Time 1	118 (111, 125)	136 (125, 149)
Time 2	110 (103, 117)	141 (129, 155)
% Change per 5 years	−6.0% (−11.3%, −0.3%)	3.6% (−4.9%, 12.8%)

^*Geometric means are shown for HRV

Table 2 shows the relations between baseline behavioral, social/psychosocial and biological factors and risk of being in the adverse quartile of HR (most increased) and HRV (most decreased) changes. As there was no evidence for any interactions of these effects by sex, we have presented the age and sex adjusted associations. There was some evidence to suggest that smoking, taking little or no exercise and having a poor diet were associated with higher HR and low HRV at follow-up. The social and psychosocial factors (employment grade, depression, low social networks, and low job control) were related to HR and HRV in the expected direction, but did not reach statistical significance. Having a high body mass index, high cholesterol or being hypertensive at baseline were related to poorest HR and HRV function at follow-up.

TABLE 2.

Behavioural, social/psychosocial and biological factors as predictors of being in worst quartile of heart rate and heart rate variability at follow up: Men and women. Age and sex adjusted odds ratios (95% CI)

	N	Heart rate	SDNN	LF power	HF power
Behavioural factors at phase 5
Smoking habit
Non-smoker	1213	1	1	1	1
Ex-smoker	630	1.28 (1.03, 1.60)	1.24 (0.99, 1.56)	1.00 (0.79, 1.25)	1.21 (0.96, 1.51)
Current smoker	150	1.11 (0.75, 1.64)	1.49 (1.02, 2.19)	1.20 (0.81, 1.78)	1.35 (0.92, 1.98)
Vigorous exercise
Some	624	1	1	1	1
little/none	1219	1.41 (1.12, 1.78)	1.25 (0.99, 1.58)	1.29 (1.02, 1.64)	1.23 (0.97, 1.55)
High alcohol consumption
No	1497	1	1	1	1
Yes	453	1.16 (0.91, 1.47)	1.12 (0.87, 1.44)	1.03 (0.80, 1.33)	1.10 (0.85, 1.41)
Poor diet
No	1473	1	1	1	1
Yes	409	1.39 (1.09, 1.77)	1.23 (0.95, 1.58)	1.16 (0.90, 1.50)	1.16 (0.90, 1.50)
Social/Psychosocial factors
Employment grade
High	873	1	1	1	1
Medium	851	1.24 (0.99, 1.55)	1.12 (0.89, 1.42)	1.03 (0.82, 1.30)	1.00 (0.80, 1.26)
Low	264	1.14 (0.80, 1.61)	1.24 (0.87, 1.77)	1.23 (0.86, 1.75)	1.31 (0.93, 1.86)
Depression
No	1452	1	1	1	1
Yes	478	1.11 (0.88, 1.41)	1.00 (0.78, 1.29)	1.18 (0.92, 1.51)	1.02 (0.80, 1.30)
Low social networks
no	1381	1	1	1	1
Yes	498	1.05 (0.82, 1.33)	1.06 (0.83, 1.35)	1.17 (0.91, 1.50)	1.20 (0.94, 1.53)
Low job control*
No	966	1	1	1	1
Yes	310	1.15 (0.85, 1.56)	1.20 (0.87, 1.66)	1.25 (0.91, 1.73)	1.21 (0.88, 1.66)
Biological factors
Body mass index
< 25 kgm−2	764	1	1	1	1
25–30 kgm−2	811	1.52 (1.20, 1.93)	1.61 (1.27, 2.04)	1.68 (1.32, 2.14)	1.47 (1.16, 1.88)
> 30 kgm−2	254	2.44 (1.78, 3.33)	1.94 (1.39, 2.68)	2.23 (1.60, 3.09)	2.63 (1.91, 3.62)
High cholesterol
No	1110	1	1	1	1
Yes	873	1.29 (1.05, 1.59)	1.33 (1.08, 1.64)	1.13 (0.92, 1.39)	1.31 (1.07, 1.62)
Hypertensive
No	1577	1	1	1	1
Yes	411	1.07 (0.83, 1.38)	1.47 (1.15, 1.87)	1.58 (1.24, 2.01)	1.25 (0.98, 1.60)

^*Job control was estimated only in those participants still working when the first HRV measurements were taken (1997–9)

Excluding individuals who were diabetic or had prior heart disease had little effect on the results apart from reducing the effects among those who were obese or in the lowest employment grade (data not shown). Mutual adjustment for the effects of exercise, body mass index, high cholesterol and blood pressure reduced the effects of blood pressure on SDNN and LF power by about a quarter but removed the association with heart rate and HF power. The adjustments had little effect on the other factors (logistic regression coefficients reduced by <10%) (data not shown).

DISCUSSION

As people age, we might expect to see a degeneration in cardiac autonomic function, with increases in HR and decreases in HRV over time. Our results therefore, particularly for women, are unexpected. There are very few other studies that have looked at individual change in HRV over time to compare our findings. Using data from The Atherosclerosis Risk in Communities Study (ARIC), Schroeder et al. looked at changes in HRV on > 6,000 middle aged men and women over 9 years. They reported a mean decrease in SDNN and an increase in the beat-to-beat interval,^6,7 but unfortunately they did not report HRV frequency measures. In a small study of repeated HRV measures on 15 elderly men and women taken 15 years apart HRs increased while both time and frequency measures of HRV decreased (except no change in high frequency).⁸ Finally, Jokinen and colleagues looked at changes over a 16 year period among 41 older subjects (mean age 69 years) and found no changes in SDNN or HF, but declines in LF.⁹ So the picture from longitudinal studies is mixed.

We can use data from previous cross-sectional studies in an attempt to clarify the relation between HRV and age. One study suggests that SDNN decreased gradually with age, reaching 60% of baseline by 90 years.¹⁰ In another study HF declined from age 20 years, whereas LF declines were not observed until 40 years of age.¹¹ The magnitude of the decline was estimated to be around 15% in HF and LF power for every 10 year increase in age.¹² The different components of HRV appear to decline at different rates, suggesting a shift in balance between the parasympathetic and sympathetic pathways in autonomic control. Age and disease progression may have a similar influence on HR control, but before middle age, disease is unlikely to explain the entire decline in HRV measures. Most existing studies removed people with clinically manifest disease from the analyses and still observed HRV declines with age. However, it is possible that HRV declines are due to sub-clinical disease.

In our middle-aged cohort, we see some evidence of gender differences in HRV. Men showed higher LF power than women, suggesting that they may have higher sympathetic activity. Conversely women had higher HF power, indicating higher parasympathetic tone. Other studies suggest that gender differences diminish at older ages. For example, in a comparison of two age groups, 26–43 year olds and 64–76 year olds, power spectral analyses revealed gender differences only among the younger cohort and not the older group.¹³ Likewise, in a study of 276 healthy volunteers aged 18–71 years there were no gender differences in power spectral analyses after the age of 40 years.¹⁴ Others reported that the gender differences diminished later at 60 years and above.¹⁵ Gender differences in the time components of HRV also weakened with age, perhaps as young as 30 years.¹⁰ Our finding that HRV declined with age in men but not in women warrants further study and corroboration with other longitudinal studies.

We looked at a variety of psychosocial measures (depression, low social networks and low job control) and found that those with adverse responses at baseline were more likely to have high HR and low HRV at follow-up, although the relationships failed to reach statistical significance. There are very few existing studies with which to compare our findings.^16,17 In a population-based cross-sectional study of healthy women, social isolation was associated with low HRV (SDNN, LF and LF/HF ratio, but not HF), an inability to relieve anger by talking to others was associated with decreases in SDNN and LF, whilst depressive symptoms were unrelated to HRV.¹⁸ In a study of 70 office workers, the need for control was associated with a decrease in HF power. However, effort and rewards and effort-reward imbalance were not associated with HRV.¹⁹

Our study has a number of limitations. The findings are based on mainly white, middle-aged civil servants. Further studies are required in other ethnic groups, non-working populations and people in developing countries. Although 5 minute HRV recordings are highly repeatable,²⁰ 24-hour recordings may offer better characterization of these relations and allow for circadian rhythms. Although we were able to look at change in autonomic function over time using the 2 measurements of HRV, five years may not be long enough and additional measures are important in order to analyze trajectories of change and disease etiology. Follow-up of the Whitehall II study is planned to obtain a third measure of HRV.