Does Autonomic Function Link Social Position to Coronary Risk;
http://www.100md.com
《循环学杂志》
the International Centre for Health and Society, Department of Epidemiology and Public Health
University College London Medical School (H.H., M.S., E.B., A.B., M. Marmot)
Cardiological Sciences, Medical School, St George’s Hospital, (M. Malik), London, UK.
Abstract
Background— Laboratory and clinical studies suggest that the autonomic nervous system responds to chronic behavioral and psychosocial stressors with adverse metabolic consequences and that this may explain the relation between low social position and high coronary risk. We sought to test this hypothesis in a healthy occupational cohort.
Methods and Results— This study comprised 2197 male civil servants 45 to 68 years of age in the Whitehall II study who were undergoing standardized assessments of social position (employment grade) and the psychosocial, behavioral, and metabolic risk factors for coronary disease previously found to be associated with low social position. Five-minute recordings of heart rate variability (HRV) were used to assess cardiac parasympathetic function (SD of N-N intervals and high-frequency power [0.15 to 0.40 Hz]) and the influence of sympathetic and parasympathetic function (low-frequency power [0.04 to 0.15 Hz]). Low employment grade was associated with low HRV (age-adjusted trend for each modality, P0.02). Adverse behavioral factors (smoking, exercise, alcohol, and diet) and psychosocial factors (job control) showed age-adjusted associations with low HRV (P<0.03). The age-adjusted mean low-frequency power was 319 ms2 among those participants in the bottom tertile of job control compared with 379 ms2 in the other participants (P=0.004). HRV showed strong (P<0.001) linear associations with components of the metabolic syndrome (waist circumference, systolic blood pressure, HDL cholesterol, triglycerides, and fasting and 2-hour postload glucose). The social gradient in prevalence of metabolic syndrome was explained statistically by adjustment for low-frequency power, behavioral factors, and job control.
Conclusions— Chronically impaired autonomic function may link social position to different components of coronary risk in the general population.
Key Words: disparities ; metabolism ; psychosocial factors ; social factors ; stress
Introduction
Lower position in the social hierarchy is associated with a higher incidence of coronary disease.1,2 Part of this effect is explained by behavioral factors such as smoking, with an additional contribution made by psychosocial factors.3,4 In the context of a working population, low job control predicts coronary disease incidence5 and may mediate the relationship between low social position and coronary disease.6 Laboratory and clinical studies suggest that the autonomic nervous system may link such distal influences to metabolic and physiological processes more proximal to coronary disease.7,8 Among nonhuman primates, the accelerated atherosclerosis of social stressors is prevented by ;-blockade.9 Among healthy humans subjected to acute laboratory stressors, those with low social position demonstrate impaired recovery of heart rate variability (HRV).10 High heart rate and low HRV, both measures of cardiac autonomic status, predict coronary disease incidence11 and prognosis.12
See p 3022
However, the relation between sympathetic and parasympathetic function and social position in the healthy populations is uncertain. Educational level may be associated with heart rate (inversely)13,14 and HRV (directly),11 but the extent to which this is explained by behavioral or psychosocial factors has not been addressed.15 Low social position is associated with an increased prevalence of the metabolic syndrome,16 components of which explain much of the social gradient in coronary events.17 In a case-control study, 30 men with the metabolic syndrome had lower HRV than control subjects.8 Autonomic dysfunction may lead to cases of insulin resistance,18–23 but the relations of HRV to central obesity, postload glucose, blood pressure, HDL cholesterol, and triglycerides across the continuous range of values are not known.
We therefore sought to determine the extent to which (1) HRV differs by social, behavioral, and psychosocial factors; (2) components of the metabolic syndrome differ by HRV; and (3) social differences in the metabolic syndrome are explained by these behavioral, psychosocial, and autonomic factors. The Whitehall II Study offers a unique opportunity to address this question with its precise measure of social hierarchy (civil service employment grade), range of psychosocial factors, oral glucose tolerance tests, and power spectral measures of HRV.
Methods
Participants
All nonindustrial civil servants 35 to 55 years of age working in the London offices of 20 departments were invited to participate in this study; 10 308 (6895 men) were recruited between 1985 and 1988. At phase 5 of the study (1997 to 1999), all participants known to be alive and in the country were invited to the screening clinic; 4646 men (67% of original sample) attended. HRV recordings were performed on 2344 men. Because of the availability of clinic staff, no HRV recordings were made on any men on 69 days during screening, which accounted for most of the missing HRV data. To address potential confounding by ethnicity, we confined all analyses to the 2197 white Europeans with HRV measurements. The University College London ethics committee approved the study, and written informed consent was obtained from each participant.
Employment Grade and Psychosocial Factors
Participants completed a questionnaire detailing job title and behavioral and psychosocial factors, as described previously.24 Using salary and work role, the civil service defines a hierarchy of employment grades, which we analyzed in 3 levels: unified grades 1 to 7 (high), executive officers (medium), and clerical and support staff (low). There were 1220 men in the high, 871 in the medium, and 106 in the low grades. Minor psychiatric morbidity was assessed with the 30-item General Health Questionnaire and a 4-item depression subscale identified on the basis of factor analysis and comparison with the items of the depression subscale of the 28-item General Health Questionnaire. Social networks were measured with a 4-item scale of frequency and number of contacts with friends, relatives, and participation in a social group. Job control was measured with a 15-item scale.
Behavioral Factors
Smoking, exercise, diet, and alcohol were assessed by questionnaire. Participants were asked how often they took part in vigorous exercise; those undertaking <1 hour per week were defined as getting little or no exercise. In addition, MET hours per week of total physical activity was derived from a 20-item questionnaire on the amount of time spent walking and cycling, in sports, in gardening activities, on housework, and on house maintenance.25 Diet was assessed by 3 items: frequency of fruit and vegetable consumption (8 levels ranging from never to 2 per day), the type of bread (3 levels: white, wheat meal, whole meal), and type of milk (3 levels: whole milk, semiskimmed, skimmed). Food frequency data were found to have acceptable validity.26 Repeatability (weighted ) for the 3 diet items, assessed in a subset (n=286) of participants who repeated the questionnaire, ranged from 0.57 to 0.82. 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, and fruit or vegetables were eaten less often than daily. Alcohol consumption in the last week was expressed in units of alcohol (1 U=8 g); >28 U/wk was categorized as high on the basis of UK government recommendations.
HRV Assessment
HRV measurement was carried out in accordance with current standards27 and is described in detail elsewhere.25,28 Five minutes of beat-to-beat heart rate data were sampled at 500-Hz frequency with a dedicated personal computer and software (Kardiosis) to obtain digitized recording of R waves. HRV was analyzed both in the time domain (SD of all intervals between R waves with normal-to-normal conduction [SDNN]) and in the frequency domain with the autoregressive method. Frequency domain components were computed by integrating the power spectrum within 2 frequency bands: low frequency (LF), 0.04 to 0.15 Hz (in ms2), and high frequency (HF), 0.15 to 0.4 Hz (in ms2). The LF power reflects both parasympathetic and sympathetic modulations; the HF component is a function of the variation in parasympathetic tone.27 Heart rate was estimated from standard resting ECG among those participants who did not undergo HRV recordings.
Components of Metabolic Syndrome
Systolic and diastolic blood pressures, waist circumference, fasting and 2-hour post–oral glucose load plasma glucose, HDL cholesterol, and triglyceride levels were determined as previously reported.16 Cases of metabolic syndrome were defined by use of the ATP-III definition.29
Statistical Analysis
SDNN, LF, and HF were transformed by natural logarithm because their distributions were skewed and are expressed as geometric means with 95% CIs. Age-adjusted proportions of participants with the metabolic syndrome were calculated with a Cochrane-Mantel-Haenszel test for trend across heart rate and HRV quartiles. For the continuous components of the metabolic syndrome, age-adjusted means and tests for trend were obtained from linear regression (proc GLM in SAS). Similarly, age-adjusted means and tests of differences were calculated for heart rate and HRV by behavioral and psychosocial factors.
To estimate how much of the employment grade gradient in metabolic syndrome was mediated by health-related behaviors, psychosocial factors, and HRV, we fitted linear regression models, assuming the grade effect to be linear across its 6 levels. Because previous studies suggest a link between the sympathetic nervous system and stressors related to social position, we chose to model LF power on a priori grounds as a marker of the balance between sympathetic and parasympathetic activity. We estimated the odds ratios of having the metabolic syndrome for low versus high employment grade by fitting logistic regression models, with employment grade as a single linear term. A higher percentage reduction in the employment grade coefficient on adjustment for 1 or a combination of these variables denotes stronger evidence that they play a mediating role. Adjustments were made for smoking (3 levels: never, ex-, current smoker), alcohol (6 levels), exercise (4 levels based on METS of vigorous exercise), diet score (4 levels), and job control (3 levels). Among the 1398 participants who were still in work and had a measure of job control and metabolic syndrome, data were missing on 1 covariates for 120 (9%). To examine whether working participants with complete data (n=1278) were representative of all working participants (n=1398), we carried out both a complete case analysis and an analysis using multiple imputation of missing values. Imputed data sets were generated with the NORM program. Five data sets were randomly selected, and because the analyses of these data sets gave very similar results, the mean is presented.30
Results
Employment Grade–HRV
Employment grade was inversely associated with heart rate and positively associated with HRV in men (Table 1). Thus, SDNN (P for trend=0.004), LF power (P for trend=0.02), and HF power (P for trend=0.002) were all lower among men in the low employment grades. The difference between low and high grades in mean heart rate was 3.2 bpm. These grade effects were independent of history of prevalent coronary heart disease. The participants who did not undergo HRV recordings did not differ in age, employment grade, educational level, job control, smoking status, marital status, history of coronary heart disease, body mass index, triglycerides, or 2-hour postload glucose compared with those who did undergo HRV recordings. There were small (<0.12 SD) differences in heart rate, waist circumference, HDL, and systolic blood pressure between these groups. Those participants without HRV measurements exhibited the same inverse grade–heart rate relationship; the mean heart rates for high-, medium-, and low-grade men were 65.6, 66.9, and 68.0 bpm (P for trend=0.002).
Behavioral and Psychosocial Factors–HRV
Adverse health-related behaviors were associated with adverse heart rate and HRV (Table 2). Thus, current smoking, little or no vigorous exercise, poor diet, and high alcohol consumption were associated with lower age-adjusted means of SDNN, LF power, and HF power. Participants in the low (adverse) tertile of job control had higher mean heart rates and lower SDNN, LF power, and HF power than participants in the middle and top tertiles. For LF power, the age-adjusted means were 319 ms2 (95% CI, 286 to 355) and 379 ms2 (95% CI, 360 to 399), respectively (P=0.004). This effect remained after further adjustment for employment grade (330 versus 376 ms2; P=0.05). The highest heart rates and lowest HRV were consistently found in those with depression and low social networks (P=0.03 to 0.30).
Metabolic Syndrome–HRV
There were strong linear associations between the mean values of each component of the metabolic syndrome and heart rate and HRV (Table 3) (P for trend <0.001 for all except HDL cholesterol). After exclusion of cases of metabolic syndrome, the association of systolic blood pressure, waist circumference, triglycerides, and 2-hour postload glucose with HRV remained in men. Based on correlation coefficients, the strongest association with HRV was observed for waist circumference. The age-adjusted prevalence of the metabolic syndrome across the quartiles of LF HRV was 5.2% (top quartile), 7.7%, 14.6%, and 21.5% (bottom quartile; P for linear trend <0.001). Among participants who did not undergo HRV recordings, the age-adjusted prevalence of metabolic syndrome across quartiles of heart rate was 5.5% (bottom quartile), 8.6%, 10.2%, and 16.5% (top quartile; P for linear trend <0.001).
Effects of Behavioral, Psychosocial, and Autonomic Factors on Social Differences in the Metabolic Syndrome
There was an employment grade gradient in the prevalence of the metabolic syndrome among the subset of participants with HRV and psychosocial measures (as in the whole cohort); for each grade level lower in the social hierarchy, the prevalence of metabolic syndrome increased (Table 4). The proportion of variation in job control explained (age adjusted r2) by employment grade was 0.29. For the imputed data sets, the age-adjusted odds ratio for having the metabolic syndrome in the low versus high employment grade was 1.71 (P=0.05). This age-adjusted grade gradient was reduced by adding LF power (40%), behavioral factors (52%), and job control (55%) separately to the models. Adding LF power to the job control or behavioral models removed virtually all the employment grade gradient in metabolic syndrome. These findings were consistent when the analysis was confined to participants with complete data for all covariates, with respective attenuations of 61%, 92%, and 61%. When analyses were carried on the working and nonworking population combined, the model including age, behavioral factors, and LF power reduced the grade effect by 65%.
Discussion
Lower social position in men was associated with higher heart rate and lower HRV. The high coronary risk of low social position is mediated by behavioral and psychosocial factors and components of the metabolic syndrome.3,4,16 Here, we demonstrate that each of these factors was associated with low HRV. Furthermore, the relationship between social position and metabolic syndrome16 was mediated by low LF power, behavioral factors, and low job control. This provides population evidence for the hypothesis that disturbances of the autonomic nervous system are involved in mediating the excess coronary risk associated with low social position.
Social Position
High heart rate31,32 and low SDNN,11,33–36 LF power,11,33,37 and HF power11,33,38 are associated with increased risk of coronary disease or all-cause mortality in healthy populations. We found that each measure of HRV was associated with low social position and found little evidence to support specific effects with LF power, which denotes sympathetic:parasympathetic balance, as opposed to markers of parasympathetic function (SDNN and HF power). The biological pathways linking the social, autonomic, and metabolic disturbances are not clear but may involve central serotonergic and hypothalamo-pituitary adrenal pathways. Reduced serotonergic activity is associated with individual components of the metabolic syndrome,39 autonomic function,40 and a diverse array of behaviors.41
Psychosocial Effects–Job Control
Autonomic responses to acute psychosocial stressors are well described, but associations with the chronic psychosocial stressors associated with risk of coronary disease have been less widely investigated.42–44 An important source of psychosocial stress is the workplace, and we found that low job control was associated with higher heart rate and low HRV independently of civil service employment grade. Other work characteristics were not associated (data not shown). Psychosocial work characteristics are associated with cardiovascular mortality45 and morbidity,3 but the biological mechanism by which such psychosocial exposures operate has been elusive. A previous small study found no association with job stress and HRV.46
Behavioral Effects on HRV
Smoking, exercise, diet, and alcohol were each associated with low HRV. Behavioral factors contributed to the grade differences in the metabolic syndrome. Although low job control and other psychosocial factors may lead to adverse behavioral responses,47 both behavioral and psychosocial factors independently contributed to explanations of grade differences in metabolic syndrome. The findings in relation to exercise have been reported more fully elsewhere.25 Few previous studies have reported on diet and alcohol in relation to HRV.48 Our results suggest a role for autonomic dysfunction among the biological mechanisms that mediate coronary risk of health-related behaviors.
Explaining Social Differences in Metabolic Syndrome
We found strong, linear associations between each component of the metabolic syndrome and low HRV. These relationships extended into the normal range, below the cutoff values used to define the syndrome. LF power and low job control each offered important explanatory power for social differences in metabolic syndrome. In broad terms, these findings are consistent with the proposal7 that autonomic dysfunction brought about by chronic stressors may in turn have adverse metabolic consequences.8,18–20 However, the finding that job control influenced social differences in metabolic syndrome independently of LF power suggests the importance of other pathways beyond cardiac autonomic function, including the hypothalamo-pituitary adrenal axis.49 The allostatic burden of low social position may involve the factors that cluster together as the metabolic syndrome. Increasingly recognized as a disease entity in its own right,29 the metabolic syndrome is more prevalent as the social hierarchy is descended.16 Low HRV predicts the onset of impaired fasting glucose,21 new diabetes,18 and hypertension.50 In the ARIC study, lower HF power, LF power, and SDNN was observed in the presence (compared with the absence) of hypertension and fasting measures of glucose tolerance.22
Metabolic Syndrome Explaining Social Differences in HRV
The causal pathway between autonomic function and metabolic disturbance may operate in both directions, with insulin resistance, an underlying disturbance in the metabolic syndrome, leading to sympathetic activation.51 Consistent with this, we found that components of the metabolic syndrome explained 27% of the social gradient in LF power. However, in a cross-sectional study, it is not possible to establish the relative importance of each causal direction.
Study Limitations
These findings are based on white, male civil servants. Further studies are required in other ethnic groups, women, nonworking populations, and people in developing countries. However, the results are consistent with findings of a study of the general population not selected on the basis of occupation in which low HRV was related to educational level in women and men.11 It is also possible that the subset of the Whitehall II population in whom HRV was assessed may be biased. However, evidence against this comes from the consistent relationships between heart rate and grade and between heart rate and metabolic syndrome among those without and with HRV assessments. We confined our analyses to men because women have higher heart rates and lower HRV than men48 and because the patterns of social and psychosocial stressors show gender differences.43 Although 5-minute HRV recordings are highly repeatable,52 24-hour recordings, especially involving naturalistic psychosocial exposures, may offer better characterization of these relationships.
Study Implications
Follow-up of the Whitehall II study, including women, is underway to test longitudinal associations between autonomic function and psychosocial, behavioral, and metabolic factors and ultimately to assess whether disturbances in autonomic function mediate the relation between social position and coronary events. If it does, prospective studies are required to identify interventions, for example, to improve control in the workplace.
Conclusions
In this occupational cohort, disturbances of autonomic function may link "distal" (behavioral and psychosocial) and more "proximal" (components of the metabolic syndrome) causes of social differences in coronary disease.
Acknowledgments
The study was supported by grants from the Medical Research Council, British Heart Foundation, National Heart Lung and Blood Institute (2RO1 HL–36310), Agency for Health Care Policy and Research (5 RO1 HS06516), National Institute on Aging (RO1 AG13196–02), Health and Safety Executive, Institute for Work and Health, Canada, Volvo Research Foundation, Sweden New England Medical Centre–Division of Health Improvement, Department of Health, and John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status. Harry Hemingway is supported by a National Career Scientist Award from the Department of Health. Martin Shipley is supported by the British Heart Foundation. Michael Marmot is supported by an MRC Research Professorship. We thank participating civil service departments and their welfare, personnel, and establishment officers; the Civil Service Occupational Health Service: and all participating civil servants, all members of the Whitehall II study team.
References
Marmot M. Inequalities in health. N Engl J Med. 2001; 345: 134–136.
Rozanski A, Blumenthal JA, Kaplan J. Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation. 1999; 99: 2192–2217.
Marmot M, Bosma H, Hemingway H, Brunner EJ, Stansfeld SA. Contribution of job control and other risk factors to social variations in coronary heart disease incidence: Whitehall II Study. Lancet. 1997; 350: 235–239.
Hemingway H, Marmot M. Psychosocial factors in the aetiology and prognosis of coronary heart disease: systematic review of prospective cohort studies. BMJ. 1999; 318: 1460–1467.
Bosma H, Marmot MG, Hemingway H, Nicholson A, Brunner EJ, Stansfeld S. Low job control and risk of coronary heart disease in the Whitehall II (prospective cohort) study. BMJ. 1997; 314: 558–565.
Marmot MG, Bosma H, Hemingway H, Brunner E, Stansfeld S. Contribution of job control and other risk factors to social variations in coronary heart disease. Lancet. 1997; 350: 235–240.
McEwen BS. Protective and damaging effects of stress mediators. N Engl J Med. 1998; 338: 171–179.
Brunner EJ, Hemingway H, Walker BR, Page M, Clarke P, Juneja M, Shipley MJ, Kumari M, Andrew R, Seckl JR, Papadopoulos A, Checkley S, Rumley A, Lowe GDO, Stansfeld SA, Marmot MG. Adrenocortical, autonomic, and inflammatory causes of the metabolic syndrome. Circulation. 2002; 106: 2659–2665.
Kaplan JR, Pettersson K, Manuck SB, Olsson G. Role of sympatho- adrenal medullary activation in the initiation and progression of atherosclerosis. Circulation. 1991; 84 (suppl VI): VI-23–VI-32.
Steptoe A, Marmot M. The role of psychobiological pathways in socio-economic inequalities in cardiovascular disease risk. Eur Heart J. 2002; 23: 13–25.
Liao D, Cai J, Rosamond WD, Barnes RW, Hutchinson RG, Whitsel EA, Rautaharju P, Heiss G. Cardiac autonomic function and incident coronary heart disease: a population-based case-cohort study: the ARIC Study: Atherosclerosis Risk in Communities Study. Am J Epidemiol. 1997; 145: 696–706.
Bigger JT, Fleiss JL, Rolnitzky LM, Steinman RC. The ability of several short-term measures of RR variability to predict mortality after myocardial infarction. Circulation. 1993; 88: 927–934.
Gillum RF. Epidemiology of resting pulse rate of persons ages 25–74: data from NHANES 1971–74. Public Health Rep. 1992; 107: 193–201.
Greenland P, Daviglus ML, Dyer AR, Liu K, Huang CF, Goldberger JJ, Stamler J. Resting heart rate is a risk factor for cardiovascular and noncardiovascular mortality: the Chicago Heart Association Detection Project in Industry. Am J Epidemiol. 1999; 149: 853–862.
Hemingway H, Malik M, Marmot M. Social and psychosocial influences on sudden cardiac death, ventricular arrhythmia and cardiac autonomic function. Eur Heart J. 2001; 22: 1082–1101.
Brunner EJ, Marmot MG, Nanchahal K, Shipley MJ, Stansfeld SA, Juneja M, Alberti KGMM. Social inequality in coronary risk: central obesity and the metabolic syndrome: evidence from the WII study. Diabetologia. 1997; 40: 1341–1349.
Lynch JW, Kaplan GA, Cohen RD, Tuomilehto J, Salonen JT. Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction; Am J Epidemiol. 1996; 144: 934–942.
Carnethon MR, Golden SH, Folsom AR, Haskell W, Liao D. Prospective investigation of autonomic nervous system function and the development of type 2 diabetes: the Atherosclerosis Risk In Communities study, 1987–1998. Circulation. 2003; 107: 2190–2195.
Brook RD, Julius S. Autonomic imbalance, hypertension, and cardiovascular risk. Am J Hypertens. 2000; 13: 112S–122S.
Nonogaki K. New insights into sympathetic regulation of glucose and fat metabolism. Diabetologia. 2000; 43: 533–549.
Singh JP, Larson MG, O’Donnell CJ, Wilson PF, Tsuji H, Lloyd-Jones DM, Levy D. Association of hyperglycemia with reduced heart rate variability (The Framingham Heart Study). Am J Cardiol. 2000; 86: 309–312.
Liao D, Sloan RP, Cascio WE, Folsom AR, Liese AD, Evans GW, Cai J, Sharrett AR. Multiple metabolic syndrome is associated with lower heart rate variability: the Atherosclerosis Risk in Communities Study. Diabetes Care. 1998; 21: 2116–2122.
Pikkujamsa SM, Huikuri HV, Airaksinen KE, Rantala AO, Kauma H, Lilja M, Savolainen MJ, Kesaniemi YA. Heart rate variability and baroreflex sensitivity in hypertensive subjects with and without metabolic features of insulin resistance syndrome. Am J Hypertens. 1998; 11: 523–531.
Marmot MG, Davey Smith G, Stansfeld SA, Patel C, North F, Head J, White I, Brunner EJ, Feeney A. Health inequalities among British civil servants: the Whitehall II study. Lancet. 1991; 337: 1387–1393.
Rennie KL, Hemingway H, Kumari M, Brunner E, Malik M, Marmot M. Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. Am J Epidemiol. 2003; 158: 135–143.
Brunner EJ, Stallone D, Juneja M, Bingham S, Marmot M. Dietary assessment in Whitehall II: comparison of 7 day diet diary and food frequency questionnaire and validity against biomarkers. Br J Nutr. 2001; 86: 405–414.
Heart rate variability: standards of measurement, physiological interpretation and clinical use: Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996; 93: 1043–1065.
Acar B, Savlieva I, Hemingway H, Malik M. Automated ectopic beat elimination in short-term heart rate variability measurement: Whitehall II Study. Comput Methods Programs Biomed. 2000; 63: 123–131.
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.
Schafer, JL. Analysis of Incomplete Multivariate Data. New York, NY: Chapman and Hall; 1997.
Shaper AG, Wannamethee G, Macfarlane PW, Walker M. Heart rate, ischaemic heart disease, and sudden cardiac death in middle-aged British men. Br Heart J. 1993; 70: 49–55.
Benetos A, Rudnichi A, Thomas F, Safar M, Guize L. Influence of heart rate on mortality in a French population: role of age, gender, and blood pressure. Hypertension. 1999; 33: 44–52.
Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, Levy D. Impact of reduced heart rate variability on risk for cardiac events: the Framingham Heart Study. Circulation. 1996; 94: 2850–2855.
Dekker JM, Schouten EG, Klootwijk P, Pool J, Swenne CA, Kromhout D. Heart rate variability from short electrocardiographic recordings predicts mortality from all causes in middle-aged and elderly men: the Zutphen Study. Am J Epidemiol. 1997; 145: 899–908.
de Bruyne MC, Hoes AW, Kors JA, Hofman A, van Bemmel JH, Grobbee DE. Prolonged QT interval predicts cardiac and all-cause mortality in the elderly: the Rotterdam Study. Eur Heart J. 1999; 20: 278–284.
Dekker JM, Crow RS, Folsom AR, Hannan PJ, Liao D, Swenne CA, Schouten EG. Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes: the ARIC Study: Atherosclerosis Risk in Communities. Circulation. 2000; 102: 1239–1244.
Huikuri HV, Makikallio TH, Airaksinen KE, Seppanen T, Puukka P, Raiha IJ, Sourander LB. Power-law relationship of heart rate variability as a predictor of mortality in the elderly. Circulation. 1998; 97: 2031–2036.
Carnethon MR, Liao D, Evans GW, Cascio WE, Chambless LE, Rosamond WD, Heiss G. Does the cardiac autonomic response to postural change predict incident coronary heart disease and mortality; The Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2002; 155: 48–56.
Muldoon MF, Mackey RH, Williams KV, Korytkowski MT, Flory JD, Manuck SB. Low central nervous system serotonergic responsivity is associated with the metabolic syndrome and physical inactivity. J Clin Endocrinol Metab. 2004; 89: 266–271.
Ramage AG. Central cardiovascular regulation and 5-hydroxytryptamine receptors. Brain Res Bull. 2001; 56: 425–439.
Lucki I. The spectrum of behaviors influenced by serotonin. Biol Psychiatry. 1998; 44: 151–162.
Kawachi I, Sparrow D, Vokonas PS, Weiss ST. Decreased heart rate variability in men with phobic anxiety (data from the Normative Aging Study). Am J Cardiol. 1995; 75: 882–885.
Horsten M, Ericson M, Perski A, Wamala SP, Schenck-Gustafsson K, Orth-Gomer K. Psychosocial factors and heart rate variability in healthy women. Psychosom Med. 1999; 61: 49–57.
Carney RM, Blumenthal JA, Stein PK, Watkins L, Catellier D, Berkman LF, Czajkowski SM, O’Connor C, Stone PH, Freedland KE. Depression, heart rate variability, and acute myocardial infarction. Circulation. 2001; 104: 2024–2028.
Kivimaki M, Leino-Arjas P, Luukkonen R, Riihimaki H, Vahtera J, Kirjonen J. Work stress and risk of cardiovascular mortality: prospective cohort study of industrial employees. BMJ. 2002; 325: 857–860.
Kageyama T, Nishikido N, Kobayashi T, Kurokawa Y, Kaneko T, Kabuto M. Self-reported sleep quality, job stress, and daytime autonomic activities assessed in terms of short-term heart rate variability among male white-collar workers. Ind Health. 1998; 36: 263–272.
Epel E, Jimenez S, Brownell K, Stroud L, Stoney C, Niaura R. Are stress eaters at risk for the metabolic syndrome; Ann N Y Acad Sci. 2004; 1032: 208–210.
Britton A, Hemingway H. Heart rate variability in healthy populations: correlates and consequences. In: Malik A, Camm A, eds. Dynamic Electrocardiography. Oxford, UK: Blackwell; 2004.
Phillips DIW, Barker DJP, Fall CHD, Seckl JR, Whorwood CB, Wood PJ, Walker BR. Elevated plasma cortisol concentrations: a link between low birth weight and insulin resistance syndrome; J Clin Endocrinol Metab. 1998; 83: 757–760.
Singh JP, Larson MG, Tsuji H, Evans JC, O’Donnell CJ, Levy D. Reduced heart rate variability and new-onset hypertension: insights into pathogenesis of hypertension: the Framingham Heart Study. Hypertension. 1998; 32: 293–297.
Reaven GM, Lithell H, Landsberg L. Hypertension and associated metabolic abnormalities: the role of insulin resistance and the sympathoadrenal system. N Engl J Med. 1996; 334: 374–381.
Sinnreich R, Kark JD, Friedlander Y, Sapoznikov D, Luria MH. Five minute recordings of heart rate variability for population studies: repeatability and age-sex characteristics. Heart. 1998; 80: 156–162.(Harry Hemingway, FRCP; Ma)
University College London Medical School (H.H., M.S., E.B., A.B., M. Marmot)
Cardiological Sciences, Medical School, St George’s Hospital, (M. Malik), London, UK.
Abstract
Background— Laboratory and clinical studies suggest that the autonomic nervous system responds to chronic behavioral and psychosocial stressors with adverse metabolic consequences and that this may explain the relation between low social position and high coronary risk. We sought to test this hypothesis in a healthy occupational cohort.
Methods and Results— This study comprised 2197 male civil servants 45 to 68 years of age in the Whitehall II study who were undergoing standardized assessments of social position (employment grade) and the psychosocial, behavioral, and metabolic risk factors for coronary disease previously found to be associated with low social position. Five-minute recordings of heart rate variability (HRV) were used to assess cardiac parasympathetic function (SD of N-N intervals and high-frequency power [0.15 to 0.40 Hz]) and the influence of sympathetic and parasympathetic function (low-frequency power [0.04 to 0.15 Hz]). Low employment grade was associated with low HRV (age-adjusted trend for each modality, P0.02). Adverse behavioral factors (smoking, exercise, alcohol, and diet) and psychosocial factors (job control) showed age-adjusted associations with low HRV (P<0.03). The age-adjusted mean low-frequency power was 319 ms2 among those participants in the bottom tertile of job control compared with 379 ms2 in the other participants (P=0.004). HRV showed strong (P<0.001) linear associations with components of the metabolic syndrome (waist circumference, systolic blood pressure, HDL cholesterol, triglycerides, and fasting and 2-hour postload glucose). The social gradient in prevalence of metabolic syndrome was explained statistically by adjustment for low-frequency power, behavioral factors, and job control.
Conclusions— Chronically impaired autonomic function may link social position to different components of coronary risk in the general population.
Key Words: disparities ; metabolism ; psychosocial factors ; social factors ; stress
Introduction
Lower position in the social hierarchy is associated with a higher incidence of coronary disease.1,2 Part of this effect is explained by behavioral factors such as smoking, with an additional contribution made by psychosocial factors.3,4 In the context of a working population, low job control predicts coronary disease incidence5 and may mediate the relationship between low social position and coronary disease.6 Laboratory and clinical studies suggest that the autonomic nervous system may link such distal influences to metabolic and physiological processes more proximal to coronary disease.7,8 Among nonhuman primates, the accelerated atherosclerosis of social stressors is prevented by ;-blockade.9 Among healthy humans subjected to acute laboratory stressors, those with low social position demonstrate impaired recovery of heart rate variability (HRV).10 High heart rate and low HRV, both measures of cardiac autonomic status, predict coronary disease incidence11 and prognosis.12
See p 3022
However, the relation between sympathetic and parasympathetic function and social position in the healthy populations is uncertain. Educational level may be associated with heart rate (inversely)13,14 and HRV (directly),11 but the extent to which this is explained by behavioral or psychosocial factors has not been addressed.15 Low social position is associated with an increased prevalence of the metabolic syndrome,16 components of which explain much of the social gradient in coronary events.17 In a case-control study, 30 men with the metabolic syndrome had lower HRV than control subjects.8 Autonomic dysfunction may lead to cases of insulin resistance,18–23 but the relations of HRV to central obesity, postload glucose, blood pressure, HDL cholesterol, and triglycerides across the continuous range of values are not known.
We therefore sought to determine the extent to which (1) HRV differs by social, behavioral, and psychosocial factors; (2) components of the metabolic syndrome differ by HRV; and (3) social differences in the metabolic syndrome are explained by these behavioral, psychosocial, and autonomic factors. The Whitehall II Study offers a unique opportunity to address this question with its precise measure of social hierarchy (civil service employment grade), range of psychosocial factors, oral glucose tolerance tests, and power spectral measures of HRV.
Methods
Participants
All nonindustrial civil servants 35 to 55 years of age working in the London offices of 20 departments were invited to participate in this study; 10 308 (6895 men) were recruited between 1985 and 1988. At phase 5 of the study (1997 to 1999), all participants known to be alive and in the country were invited to the screening clinic; 4646 men (67% of original sample) attended. HRV recordings were performed on 2344 men. Because of the availability of clinic staff, no HRV recordings were made on any men on 69 days during screening, which accounted for most of the missing HRV data. To address potential confounding by ethnicity, we confined all analyses to the 2197 white Europeans with HRV measurements. The University College London ethics committee approved the study, and written informed consent was obtained from each participant.
Employment Grade and Psychosocial Factors
Participants completed a questionnaire detailing job title and behavioral and psychosocial factors, as described previously.24 Using salary and work role, the civil service defines a hierarchy of employment grades, which we analyzed in 3 levels: unified grades 1 to 7 (high), executive officers (medium), and clerical and support staff (low). There were 1220 men in the high, 871 in the medium, and 106 in the low grades. Minor psychiatric morbidity was assessed with the 30-item General Health Questionnaire and a 4-item depression subscale identified on the basis of factor analysis and comparison with the items of the depression subscale of the 28-item General Health Questionnaire. Social networks were measured with a 4-item scale of frequency and number of contacts with friends, relatives, and participation in a social group. Job control was measured with a 15-item scale.
Behavioral Factors
Smoking, exercise, diet, and alcohol were assessed by questionnaire. Participants were asked how often they took part in vigorous exercise; those undertaking <1 hour per week were defined as getting little or no exercise. In addition, MET hours per week of total physical activity was derived from a 20-item questionnaire on the amount of time spent walking and cycling, in sports, in gardening activities, on housework, and on house maintenance.25 Diet was assessed by 3 items: frequency of fruit and vegetable consumption (8 levels ranging from never to 2 per day), the type of bread (3 levels: white, wheat meal, whole meal), and type of milk (3 levels: whole milk, semiskimmed, skimmed). Food frequency data were found to have acceptable validity.26 Repeatability (weighted ) for the 3 diet items, assessed in a subset (n=286) of participants who repeated the questionnaire, ranged from 0.57 to 0.82. 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, and fruit or vegetables were eaten less often than daily. Alcohol consumption in the last week was expressed in units of alcohol (1 U=8 g); >28 U/wk was categorized as high on the basis of UK government recommendations.
HRV Assessment
HRV measurement was carried out in accordance with current standards27 and is described in detail elsewhere.25,28 Five minutes of beat-to-beat heart rate data were sampled at 500-Hz frequency with a dedicated personal computer and software (Kardiosis) to obtain digitized recording of R waves. HRV was analyzed both in the time domain (SD of all intervals between R waves with normal-to-normal conduction [SDNN]) and in the frequency domain with the autoregressive method. Frequency domain components were computed by integrating the power spectrum within 2 frequency bands: low frequency (LF), 0.04 to 0.15 Hz (in ms2), and high frequency (HF), 0.15 to 0.4 Hz (in ms2). The LF power reflects both parasympathetic and sympathetic modulations; the HF component is a function of the variation in parasympathetic tone.27 Heart rate was estimated from standard resting ECG among those participants who did not undergo HRV recordings.
Components of Metabolic Syndrome
Systolic and diastolic blood pressures, waist circumference, fasting and 2-hour post–oral glucose load plasma glucose, HDL cholesterol, and triglyceride levels were determined as previously reported.16 Cases of metabolic syndrome were defined by use of the ATP-III definition.29
Statistical Analysis
SDNN, LF, and HF were transformed by natural logarithm because their distributions were skewed and are expressed as geometric means with 95% CIs. Age-adjusted proportions of participants with the metabolic syndrome were calculated with a Cochrane-Mantel-Haenszel test for trend across heart rate and HRV quartiles. For the continuous components of the metabolic syndrome, age-adjusted means and tests for trend were obtained from linear regression (proc GLM in SAS). Similarly, age-adjusted means and tests of differences were calculated for heart rate and HRV by behavioral and psychosocial factors.
To estimate how much of the employment grade gradient in metabolic syndrome was mediated by health-related behaviors, psychosocial factors, and HRV, we fitted linear regression models, assuming the grade effect to be linear across its 6 levels. Because previous studies suggest a link between the sympathetic nervous system and stressors related to social position, we chose to model LF power on a priori grounds as a marker of the balance between sympathetic and parasympathetic activity. We estimated the odds ratios of having the metabolic syndrome for low versus high employment grade by fitting logistic regression models, with employment grade as a single linear term. A higher percentage reduction in the employment grade coefficient on adjustment for 1 or a combination of these variables denotes stronger evidence that they play a mediating role. Adjustments were made for smoking (3 levels: never, ex-, current smoker), alcohol (6 levels), exercise (4 levels based on METS of vigorous exercise), diet score (4 levels), and job control (3 levels). Among the 1398 participants who were still in work and had a measure of job control and metabolic syndrome, data were missing on 1 covariates for 120 (9%). To examine whether working participants with complete data (n=1278) were representative of all working participants (n=1398), we carried out both a complete case analysis and an analysis using multiple imputation of missing values. Imputed data sets were generated with the NORM program. Five data sets were randomly selected, and because the analyses of these data sets gave very similar results, the mean is presented.30
Results
Employment Grade–HRV
Employment grade was inversely associated with heart rate and positively associated with HRV in men (Table 1). Thus, SDNN (P for trend=0.004), LF power (P for trend=0.02), and HF power (P for trend=0.002) were all lower among men in the low employment grades. The difference between low and high grades in mean heart rate was 3.2 bpm. These grade effects were independent of history of prevalent coronary heart disease. The participants who did not undergo HRV recordings did not differ in age, employment grade, educational level, job control, smoking status, marital status, history of coronary heart disease, body mass index, triglycerides, or 2-hour postload glucose compared with those who did undergo HRV recordings. There were small (<0.12 SD) differences in heart rate, waist circumference, HDL, and systolic blood pressure between these groups. Those participants without HRV measurements exhibited the same inverse grade–heart rate relationship; the mean heart rates for high-, medium-, and low-grade men were 65.6, 66.9, and 68.0 bpm (P for trend=0.002).
Behavioral and Psychosocial Factors–HRV
Adverse health-related behaviors were associated with adverse heart rate and HRV (Table 2). Thus, current smoking, little or no vigorous exercise, poor diet, and high alcohol consumption were associated with lower age-adjusted means of SDNN, LF power, and HF power. Participants in the low (adverse) tertile of job control had higher mean heart rates and lower SDNN, LF power, and HF power than participants in the middle and top tertiles. For LF power, the age-adjusted means were 319 ms2 (95% CI, 286 to 355) and 379 ms2 (95% CI, 360 to 399), respectively (P=0.004). This effect remained after further adjustment for employment grade (330 versus 376 ms2; P=0.05). The highest heart rates and lowest HRV were consistently found in those with depression and low social networks (P=0.03 to 0.30).
Metabolic Syndrome–HRV
There were strong linear associations between the mean values of each component of the metabolic syndrome and heart rate and HRV (Table 3) (P for trend <0.001 for all except HDL cholesterol). After exclusion of cases of metabolic syndrome, the association of systolic blood pressure, waist circumference, triglycerides, and 2-hour postload glucose with HRV remained in men. Based on correlation coefficients, the strongest association with HRV was observed for waist circumference. The age-adjusted prevalence of the metabolic syndrome across the quartiles of LF HRV was 5.2% (top quartile), 7.7%, 14.6%, and 21.5% (bottom quartile; P for linear trend <0.001). Among participants who did not undergo HRV recordings, the age-adjusted prevalence of metabolic syndrome across quartiles of heart rate was 5.5% (bottom quartile), 8.6%, 10.2%, and 16.5% (top quartile; P for linear trend <0.001).
Effects of Behavioral, Psychosocial, and Autonomic Factors on Social Differences in the Metabolic Syndrome
There was an employment grade gradient in the prevalence of the metabolic syndrome among the subset of participants with HRV and psychosocial measures (as in the whole cohort); for each grade level lower in the social hierarchy, the prevalence of metabolic syndrome increased (Table 4). The proportion of variation in job control explained (age adjusted r2) by employment grade was 0.29. For the imputed data sets, the age-adjusted odds ratio for having the metabolic syndrome in the low versus high employment grade was 1.71 (P=0.05). This age-adjusted grade gradient was reduced by adding LF power (40%), behavioral factors (52%), and job control (55%) separately to the models. Adding LF power to the job control or behavioral models removed virtually all the employment grade gradient in metabolic syndrome. These findings were consistent when the analysis was confined to participants with complete data for all covariates, with respective attenuations of 61%, 92%, and 61%. When analyses were carried on the working and nonworking population combined, the model including age, behavioral factors, and LF power reduced the grade effect by 65%.
Discussion
Lower social position in men was associated with higher heart rate and lower HRV. The high coronary risk of low social position is mediated by behavioral and psychosocial factors and components of the metabolic syndrome.3,4,16 Here, we demonstrate that each of these factors was associated with low HRV. Furthermore, the relationship between social position and metabolic syndrome16 was mediated by low LF power, behavioral factors, and low job control. This provides population evidence for the hypothesis that disturbances of the autonomic nervous system are involved in mediating the excess coronary risk associated with low social position.
Social Position
High heart rate31,32 and low SDNN,11,33–36 LF power,11,33,37 and HF power11,33,38 are associated with increased risk of coronary disease or all-cause mortality in healthy populations. We found that each measure of HRV was associated with low social position and found little evidence to support specific effects with LF power, which denotes sympathetic:parasympathetic balance, as opposed to markers of parasympathetic function (SDNN and HF power). The biological pathways linking the social, autonomic, and metabolic disturbances are not clear but may involve central serotonergic and hypothalamo-pituitary adrenal pathways. Reduced serotonergic activity is associated with individual components of the metabolic syndrome,39 autonomic function,40 and a diverse array of behaviors.41
Psychosocial Effects–Job Control
Autonomic responses to acute psychosocial stressors are well described, but associations with the chronic psychosocial stressors associated with risk of coronary disease have been less widely investigated.42–44 An important source of psychosocial stress is the workplace, and we found that low job control was associated with higher heart rate and low HRV independently of civil service employment grade. Other work characteristics were not associated (data not shown). Psychosocial work characteristics are associated with cardiovascular mortality45 and morbidity,3 but the biological mechanism by which such psychosocial exposures operate has been elusive. A previous small study found no association with job stress and HRV.46
Behavioral Effects on HRV
Smoking, exercise, diet, and alcohol were each associated with low HRV. Behavioral factors contributed to the grade differences in the metabolic syndrome. Although low job control and other psychosocial factors may lead to adverse behavioral responses,47 both behavioral and psychosocial factors independently contributed to explanations of grade differences in metabolic syndrome. The findings in relation to exercise have been reported more fully elsewhere.25 Few previous studies have reported on diet and alcohol in relation to HRV.48 Our results suggest a role for autonomic dysfunction among the biological mechanisms that mediate coronary risk of health-related behaviors.
Explaining Social Differences in Metabolic Syndrome
We found strong, linear associations between each component of the metabolic syndrome and low HRV. These relationships extended into the normal range, below the cutoff values used to define the syndrome. LF power and low job control each offered important explanatory power for social differences in metabolic syndrome. In broad terms, these findings are consistent with the proposal7 that autonomic dysfunction brought about by chronic stressors may in turn have adverse metabolic consequences.8,18–20 However, the finding that job control influenced social differences in metabolic syndrome independently of LF power suggests the importance of other pathways beyond cardiac autonomic function, including the hypothalamo-pituitary adrenal axis.49 The allostatic burden of low social position may involve the factors that cluster together as the metabolic syndrome. Increasingly recognized as a disease entity in its own right,29 the metabolic syndrome is more prevalent as the social hierarchy is descended.16 Low HRV predicts the onset of impaired fasting glucose,21 new diabetes,18 and hypertension.50 In the ARIC study, lower HF power, LF power, and SDNN was observed in the presence (compared with the absence) of hypertension and fasting measures of glucose tolerance.22
Metabolic Syndrome Explaining Social Differences in HRV
The causal pathway between autonomic function and metabolic disturbance may operate in both directions, with insulin resistance, an underlying disturbance in the metabolic syndrome, leading to sympathetic activation.51 Consistent with this, we found that components of the metabolic syndrome explained 27% of the social gradient in LF power. However, in a cross-sectional study, it is not possible to establish the relative importance of each causal direction.
Study Limitations
These findings are based on white, male civil servants. Further studies are required in other ethnic groups, women, nonworking populations, and people in developing countries. However, the results are consistent with findings of a study of the general population not selected on the basis of occupation in which low HRV was related to educational level in women and men.11 It is also possible that the subset of the Whitehall II population in whom HRV was assessed may be biased. However, evidence against this comes from the consistent relationships between heart rate and grade and between heart rate and metabolic syndrome among those without and with HRV assessments. We confined our analyses to men because women have higher heart rates and lower HRV than men48 and because the patterns of social and psychosocial stressors show gender differences.43 Although 5-minute HRV recordings are highly repeatable,52 24-hour recordings, especially involving naturalistic psychosocial exposures, may offer better characterization of these relationships.
Study Implications
Follow-up of the Whitehall II study, including women, is underway to test longitudinal associations between autonomic function and psychosocial, behavioral, and metabolic factors and ultimately to assess whether disturbances in autonomic function mediate the relation between social position and coronary events. If it does, prospective studies are required to identify interventions, for example, to improve control in the workplace.
Conclusions
In this occupational cohort, disturbances of autonomic function may link "distal" (behavioral and psychosocial) and more "proximal" (components of the metabolic syndrome) causes of social differences in coronary disease.
Acknowledgments
The study was supported by grants from the Medical Research Council, British Heart Foundation, National Heart Lung and Blood Institute (2RO1 HL–36310), Agency for Health Care Policy and Research (5 RO1 HS06516), National Institute on Aging (RO1 AG13196–02), Health and Safety Executive, Institute for Work and Health, Canada, Volvo Research Foundation, Sweden New England Medical Centre–Division of Health Improvement, Department of Health, and John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status. Harry Hemingway is supported by a National Career Scientist Award from the Department of Health. Martin Shipley is supported by the British Heart Foundation. Michael Marmot is supported by an MRC Research Professorship. We thank participating civil service departments and their welfare, personnel, and establishment officers; the Civil Service Occupational Health Service: and all participating civil servants, all members of the Whitehall II study team.
References
Marmot M. Inequalities in health. N Engl J Med. 2001; 345: 134–136.
Rozanski A, Blumenthal JA, Kaplan J. Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation. 1999; 99: 2192–2217.
Marmot M, Bosma H, Hemingway H, Brunner EJ, Stansfeld SA. Contribution of job control and other risk factors to social variations in coronary heart disease incidence: Whitehall II Study. Lancet. 1997; 350: 235–239.
Hemingway H, Marmot M. Psychosocial factors in the aetiology and prognosis of coronary heart disease: systematic review of prospective cohort studies. BMJ. 1999; 318: 1460–1467.
Bosma H, Marmot MG, Hemingway H, Nicholson A, Brunner EJ, Stansfeld S. Low job control and risk of coronary heart disease in the Whitehall II (prospective cohort) study. BMJ. 1997; 314: 558–565.
Marmot MG, Bosma H, Hemingway H, Brunner E, Stansfeld S. Contribution of job control and other risk factors to social variations in coronary heart disease. Lancet. 1997; 350: 235–240.
McEwen BS. Protective and damaging effects of stress mediators. N Engl J Med. 1998; 338: 171–179.
Brunner EJ, Hemingway H, Walker BR, Page M, Clarke P, Juneja M, Shipley MJ, Kumari M, Andrew R, Seckl JR, Papadopoulos A, Checkley S, Rumley A, Lowe GDO, Stansfeld SA, Marmot MG. Adrenocortical, autonomic, and inflammatory causes of the metabolic syndrome. Circulation. 2002; 106: 2659–2665.
Kaplan JR, Pettersson K, Manuck SB, Olsson G. Role of sympatho- adrenal medullary activation in the initiation and progression of atherosclerosis. Circulation. 1991; 84 (suppl VI): VI-23–VI-32.
Steptoe A, Marmot M. The role of psychobiological pathways in socio-economic inequalities in cardiovascular disease risk. Eur Heart J. 2002; 23: 13–25.
Liao D, Cai J, Rosamond WD, Barnes RW, Hutchinson RG, Whitsel EA, Rautaharju P, Heiss G. Cardiac autonomic function and incident coronary heart disease: a population-based case-cohort study: the ARIC Study: Atherosclerosis Risk in Communities Study. Am J Epidemiol. 1997; 145: 696–706.
Bigger JT, Fleiss JL, Rolnitzky LM, Steinman RC. The ability of several short-term measures of RR variability to predict mortality after myocardial infarction. Circulation. 1993; 88: 927–934.
Gillum RF. Epidemiology of resting pulse rate of persons ages 25–74: data from NHANES 1971–74. Public Health Rep. 1992; 107: 193–201.
Greenland P, Daviglus ML, Dyer AR, Liu K, Huang CF, Goldberger JJ, Stamler J. Resting heart rate is a risk factor for cardiovascular and noncardiovascular mortality: the Chicago Heart Association Detection Project in Industry. Am J Epidemiol. 1999; 149: 853–862.
Hemingway H, Malik M, Marmot M. Social and psychosocial influences on sudden cardiac death, ventricular arrhythmia and cardiac autonomic function. Eur Heart J. 2001; 22: 1082–1101.
Brunner EJ, Marmot MG, Nanchahal K, Shipley MJ, Stansfeld SA, Juneja M, Alberti KGMM. Social inequality in coronary risk: central obesity and the metabolic syndrome: evidence from the WII study. Diabetologia. 1997; 40: 1341–1349.
Lynch JW, Kaplan GA, Cohen RD, Tuomilehto J, Salonen JT. Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction; Am J Epidemiol. 1996; 144: 934–942.
Carnethon MR, Golden SH, Folsom AR, Haskell W, Liao D. Prospective investigation of autonomic nervous system function and the development of type 2 diabetes: the Atherosclerosis Risk In Communities study, 1987–1998. Circulation. 2003; 107: 2190–2195.
Brook RD, Julius S. Autonomic imbalance, hypertension, and cardiovascular risk. Am J Hypertens. 2000; 13: 112S–122S.
Nonogaki K. New insights into sympathetic regulation of glucose and fat metabolism. Diabetologia. 2000; 43: 533–549.
Singh JP, Larson MG, O’Donnell CJ, Wilson PF, Tsuji H, Lloyd-Jones DM, Levy D. Association of hyperglycemia with reduced heart rate variability (The Framingham Heart Study). Am J Cardiol. 2000; 86: 309–312.
Liao D, Sloan RP, Cascio WE, Folsom AR, Liese AD, Evans GW, Cai J, Sharrett AR. Multiple metabolic syndrome is associated with lower heart rate variability: the Atherosclerosis Risk in Communities Study. Diabetes Care. 1998; 21: 2116–2122.
Pikkujamsa SM, Huikuri HV, Airaksinen KE, Rantala AO, Kauma H, Lilja M, Savolainen MJ, Kesaniemi YA. Heart rate variability and baroreflex sensitivity in hypertensive subjects with and without metabolic features of insulin resistance syndrome. Am J Hypertens. 1998; 11: 523–531.
Marmot MG, Davey Smith G, Stansfeld SA, Patel C, North F, Head J, White I, Brunner EJ, Feeney A. Health inequalities among British civil servants: the Whitehall II study. Lancet. 1991; 337: 1387–1393.
Rennie KL, Hemingway H, Kumari M, Brunner E, Malik M, Marmot M. Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. Am J Epidemiol. 2003; 158: 135–143.
Brunner EJ, Stallone D, Juneja M, Bingham S, Marmot M. Dietary assessment in Whitehall II: comparison of 7 day diet diary and food frequency questionnaire and validity against biomarkers. Br J Nutr. 2001; 86: 405–414.
Heart rate variability: standards of measurement, physiological interpretation and clinical use: Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996; 93: 1043–1065.
Acar B, Savlieva I, Hemingway H, Malik M. Automated ectopic beat elimination in short-term heart rate variability measurement: Whitehall II Study. Comput Methods Programs Biomed. 2000; 63: 123–131.
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.
Schafer, JL. Analysis of Incomplete Multivariate Data. New York, NY: Chapman and Hall; 1997.
Shaper AG, Wannamethee G, Macfarlane PW, Walker M. Heart rate, ischaemic heart disease, and sudden cardiac death in middle-aged British men. Br Heart J. 1993; 70: 49–55.
Benetos A, Rudnichi A, Thomas F, Safar M, Guize L. Influence of heart rate on mortality in a French population: role of age, gender, and blood pressure. Hypertension. 1999; 33: 44–52.
Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, Levy D. Impact of reduced heart rate variability on risk for cardiac events: the Framingham Heart Study. Circulation. 1996; 94: 2850–2855.
Dekker JM, Schouten EG, Klootwijk P, Pool J, Swenne CA, Kromhout D. Heart rate variability from short electrocardiographic recordings predicts mortality from all causes in middle-aged and elderly men: the Zutphen Study. Am J Epidemiol. 1997; 145: 899–908.
de Bruyne MC, Hoes AW, Kors JA, Hofman A, van Bemmel JH, Grobbee DE. Prolonged QT interval predicts cardiac and all-cause mortality in the elderly: the Rotterdam Study. Eur Heart J. 1999; 20: 278–284.
Dekker JM, Crow RS, Folsom AR, Hannan PJ, Liao D, Swenne CA, Schouten EG. Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes: the ARIC Study: Atherosclerosis Risk in Communities. Circulation. 2000; 102: 1239–1244.
Huikuri HV, Makikallio TH, Airaksinen KE, Seppanen T, Puukka P, Raiha IJ, Sourander LB. Power-law relationship of heart rate variability as a predictor of mortality in the elderly. Circulation. 1998; 97: 2031–2036.
Carnethon MR, Liao D, Evans GW, Cascio WE, Chambless LE, Rosamond WD, Heiss G. Does the cardiac autonomic response to postural change predict incident coronary heart disease and mortality; The Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2002; 155: 48–56.
Muldoon MF, Mackey RH, Williams KV, Korytkowski MT, Flory JD, Manuck SB. Low central nervous system serotonergic responsivity is associated with the metabolic syndrome and physical inactivity. J Clin Endocrinol Metab. 2004; 89: 266–271.
Ramage AG. Central cardiovascular regulation and 5-hydroxytryptamine receptors. Brain Res Bull. 2001; 56: 425–439.
Lucki I. The spectrum of behaviors influenced by serotonin. Biol Psychiatry. 1998; 44: 151–162.
Kawachi I, Sparrow D, Vokonas PS, Weiss ST. Decreased heart rate variability in men with phobic anxiety (data from the Normative Aging Study). Am J Cardiol. 1995; 75: 882–885.
Horsten M, Ericson M, Perski A, Wamala SP, Schenck-Gustafsson K, Orth-Gomer K. Psychosocial factors and heart rate variability in healthy women. Psychosom Med. 1999; 61: 49–57.
Carney RM, Blumenthal JA, Stein PK, Watkins L, Catellier D, Berkman LF, Czajkowski SM, O’Connor C, Stone PH, Freedland KE. Depression, heart rate variability, and acute myocardial infarction. Circulation. 2001; 104: 2024–2028.
Kivimaki M, Leino-Arjas P, Luukkonen R, Riihimaki H, Vahtera J, Kirjonen J. Work stress and risk of cardiovascular mortality: prospective cohort study of industrial employees. BMJ. 2002; 325: 857–860.
Kageyama T, Nishikido N, Kobayashi T, Kurokawa Y, Kaneko T, Kabuto M. Self-reported sleep quality, job stress, and daytime autonomic activities assessed in terms of short-term heart rate variability among male white-collar workers. Ind Health. 1998; 36: 263–272.
Epel E, Jimenez S, Brownell K, Stroud L, Stoney C, Niaura R. Are stress eaters at risk for the metabolic syndrome; Ann N Y Acad Sci. 2004; 1032: 208–210.
Britton A, Hemingway H. Heart rate variability in healthy populations: correlates and consequences. In: Malik A, Camm A, eds. Dynamic Electrocardiography. Oxford, UK: Blackwell; 2004.
Phillips DIW, Barker DJP, Fall CHD, Seckl JR, Whorwood CB, Wood PJ, Walker BR. Elevated plasma cortisol concentrations: a link between low birth weight and insulin resistance syndrome; J Clin Endocrinol Metab. 1998; 83: 757–760.
Singh JP, Larson MG, Tsuji H, Evans JC, O’Donnell CJ, Levy D. Reduced heart rate variability and new-onset hypertension: insights into pathogenesis of hypertension: the Framingham Heart Study. Hypertension. 1998; 32: 293–297.
Reaven GM, Lithell H, Landsberg L. Hypertension and associated metabolic abnormalities: the role of insulin resistance and the sympathoadrenal system. N Engl J Med. 1996; 334: 374–381.
Sinnreich R, Kark JD, Friedlander Y, Sapoznikov D, Luria MH. Five minute recordings of heart rate variability for population studies: repeatability and age-sex characteristics. Heart. 1998; 80: 156–162.(Harry Hemingway, FRCP; Ma)