Altered Cardiac Autonomic Function May Precede Insulin Resistance in Metabolic Syndrome
Article Outline
Abstract
Background
Previous studies on the change of cardiac autonomic function and insulin resistance in metabolic syndrome recruited subjects with cardiovascular-related disease and defined metabolic abnormality with a more severe cutoff. We explored the alteration of cardiac autonomic function and insulin resistance in predisease community dwellers with different numbers of metabolic abnormalities.
Methods
A total of 1298 subjects were classified as none (n
=539), one (n=378), 2 (n=218), and 3 or more metabolic abnormalities (n=154). Insulin resistance was calculated by homeostatic model assessment. Cardiac autonomic function included 5-minute standard deviation of R-R interval, low- and high-frequency power spectrum, and low-/high-frequency power spectrum ratio, the ratio of the longest R-R interval around the 30th beat and the shortest R-R interval around the 15th beat after standing, and the ratio of the longest expiratory R-R interval to the shortest inspiratory R-R interval during deep breathing.
Results
Subjects with a single metabolic abnormality or more had a lower standard deviation of R-R interval and expiratory/inspiratory ratio than subjects without metabolic abnormality in multivariate analysis. Subjects with 3 or more metabolic abnormalities had a higher low-/high-frequency power spectrum ratio, but a lower high-frequency power. Insulin resistance was higher in groups with 2 metabolic abnormalities or more, but not in the group with one metabolic abnormality, than those without metabolic abnormality.
Conclusions
Cardiac autonomic function altered in predisease subjects with one or more metabolic abnormalities, while insulin resistance existed in subjects with 2 or more metabolic abnormalities. Thus, autonomic function change may precede insulin resistance in the initiation of metabolic syndrome.
Keywords: Cardiac autonomic function, Epidemiology, Heart rate variability, Insulin resistance, Metabolic abnormality
Metabolic syndrome presents a pro-inflammatory and prothrombotic state that includes hyperglycemia, obesity, elevated blood pressure, and dyslipidemia.1, 2 It has been shown to be associated with an increased risk of diabetes3, 4, 5 and cardiovascular disease.6, 7, 8 The most common definition of the metabolic syndrome is that of the United States National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III),1 while the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) recommends a lower fasting blood glucose cutoff from 6.1 to 5.6 mmol/L.2 For an Asian population, the diagnostic criteria of central obesity is a waist circumference of >90 cm in men and >80 cm in women.9 Insulin resistance has been suggested as the core pathophysiological feature of metabolic syndrome,10 and while the cause of insulin resistance remains unresolved, autonomic alteration has been proposed as one of the underlying causes.10 Insulin and sympathetic activity reciprocally reinforce each other,11 but which one exists first and triggers the vicious cycle of metabolic abnormality remains a chicken and egg question.
The direct measurements of sympathetic and parasympathetic activity by microneurography and vagus nerve are not feasible in a large epidemiological study. In contrast, heart rate variability is a useful measure of cardiac autonomic function.12 The high- and low-frequency spectral components indicate a parasympathetic tone and a major sympathetic drive with parasympathetic modulation, respectively, and the low-frequency power/high-frequency power ratio is an index of sympathovagal balance.12 Standard deviation of all normal-to-normal (SDNN) intervals or R-R intervals for a period, R-R interval variation during deep expiration and inspiration (E/I ratio), and the ratio between the longest R-R interval around the 30th beat and the shortest R-R interval around the 15th beat (30 max/15 min) after standing from a supine position, reflect the cardiac parasympathetic drive.12
Although some studies have shown an association between metabolic abnormality and sympathetic activation,13, 14, 15 the epidemiological study on both cardiac autonomic function and insulin resistance in subjects with metabolic abnormalities is rare.16 The Atherosclerosis Risk in Communities (ARIC) study has revealed that lower heart rate variability is associated with multiple metabolic syndrome, as defined by only 3 components, namely, diabetes mellitus, hypertension, and dyslipidemia,16 not the 5 predisease components of NCEP ATP III, which are central obesity, prediabetes, prehypertension, hypertriglyceridemia, and low high-density lipoprotein cholesterol.1 Furthermore, the ARIC study also included diseased subjects with hypertension, diabetes mellitus, and cardiovascular disease, and defined metabolic abnormality with more severe cutoff points than the NCEP ATP III criteria, and this may overestimate the relationship between cardiac autonomic function and metabolic abnormality. Therefore, we explored the alteration of cardiac autonomic function and insulin resistance in community dwellers with different numbers of metabolic abnormalities, as defined by NCEP ATP III and AHA/NHLBI from the epidemiological data collected in Taiwan.
Subjects and Methods
Subjects
The study subjects consisted of a stratified systematic cluster sample drawn from an epidemiological survey of chronic diseases in Tainan city, southern Taiwan. A 3-stage sampling scheme with stratified systemic cluster sampling was used to select subjects of 20 years of age or older from 7 administrative districts throughout the city. First, the city was classified into 7 strata according to the administrative districts. In each district, one area was selected from each stratum by adopting a probability proportional to the size of the areas within that specific stratum. Second, every fifth household of each of the 7 selected areas was systematically identified. Third, all the members of the selected households aged ≥20 years were invited to participate in the study. A total of 1638 subjects, representing a response rate of 67.8%, completed a health examination. The nonresponders were slightly younger and consisted of more men compared with the responders, but the differences were not statistically significant, and the details have been described elsewhere.17 For the final analysis, 1289 subjects were included after excluding 349 subjects with a documented history of hypertension, diabetes mellitus, coronary heart disease, and cerebrovascular disease, and taking medications known to influence blood pressure, plasma glucose and lipid profile, and cardiac autonomic function.
Clinical Examination and Definition of Metabolic Abnormality
Subjects were instructed to fast for at least 10 hours and avoid smoking, alcohol, coffee, and tea on the day of the examination. Demography, medical history, medication use, family history of diabetes, smoking, alcohol, and physical activity during the past year were assessed. Total physical activity, calculated from the sum of work, walking, and leisure-time exercise, was presented in metabolic equivalent-hours per week.18 A positive family history of diabetes was defined as when at least one of the subject's first-degree relatives, including parents, offspring, and siblings, had a documented history of diabetes. Subjects were measured for body weight and height when wearing light indoor clothes and no shoes. Waist circumference measurements were performed at the end of normal expiration, on bare skin at the midway between the lower rib margin and the iliac crest. Two seated readings of blood pressure were separately measured with a Dinamap vital sign monitor (Model 1846SX, Critikon Inc., Irvine, Calif) at least 5 minutes after the subject had been at rest for at least 15 minutes between 8:00 and 10:00 AM. The laboratory tests included blood biochemistry, insulin, urine examination, and electrocardiography. The 75-gm oral glucose tolerance test was performed in subjects without a history of diabetes, and a 2-hour postload blood sample was obtained. Fasting and 2-hour postload plasma glucose levels were determined using the glucose oxidase method (Synchron CX3, Beckman Coulter Inc., Brea, Calif). Fasting serum insulin concentrations were measured using a solid-phase radioimmunoassay method (Diagnostic Products Corporation, Los Angeles, Calif). Fasting triglyceride and high-density lipoprotein cholesterol concentrations were measured by enzymatic methods using the automated analyzer TBA-200FR (Toshiba Lab Medical, Tokyo, Japan).
The metabolic abnormalities assessed in our study were as designated by the NCEP ATP III criteria,1 including central obesity with waist circumference >90 cm in males or 80 cm in females, according to ethnic-specific values;9 fasting plasma glucose ≥6.1 mmol/L; blood pressure ≥130/85 mm Hg; triglyceride ≥1.7 mmol/L; and high-density lipoprotein cholesterol <1.0 mmol/L in males or <1.3 mmol/L in females.1 Another definition of metabolic abnormality is the AHA/NHLBI criteria, which are similar to NCEP ATP III except for the lower fasting glucose cutoff of ≥5.6 mmol/L.2 Insulin resistance was calculated using homeostasis model assessment, that is, serum insulin (micro units/mL)×fasting glucose (mmol/L)/22.5.19
Measurement of Cardiac Autonomic Function
Cardiac autonomic function was determined by heart rate variability and was measured continuously by the beat-to-beat R-R intervals of the cardiac cycle with an electrocardiogram (ECG) monitor (CardiSuny α 800, Fukuda M-E Kogyo Inc., Tokyo, Japan) on a personal computer-based data-acquisition system. After the subjects rested in a supine position for at least 15 minutes, the cardiac autonomic function tests were performed in the following sequence: normal breathing for 5 minutes in the supine position, active standing from the supine position, and deep breathing at a rate of 6 breaths per minute while sitting after a 10-minute rest period. The analog signals from the ECG monitor were transmitted and converted via an analog-to-digital converter (DAQPad-6020E and SCB-68, National Instruments, Austin, Tex) for further processing in a personal computer. The ECG digital signals were processed and heart rate variability measures were calculated using the LabView 6.1 software program (National Instruments). A fast Fourier transform analysis was used to compute the area under the curve of the power spectrum in various frequency regions, including very low frequency (<0.04 Hz), low frequency (0.04-0.15 Hz), and high frequency (0.15-0.4 Hz).12 Low-frequency power/high-frequency power ratio and the SDNN also were calculated. The longest R-R interval around beat 30 and the shortest R-R interval around beat 15 after standing were identified and their ratio (30 max/15 min ratio) was computed. E/I ratio was defined as the longest R-R interval of expiration divided by the shortest R-R interval during inspiration of each breathing cycle, and the average of 6 E/I ratios was calculated. The cardiac autonomic function measures were summarized in Table 1.12
Table 1. Definition and Method of Selected Autonomic Function Tests Classified by Physiological Basis
| Parasympathetic | |
| SDNN (ms) | The standard deviation of R-R intervals in supine position for 5 minutes after a 15-minute rest period |
| High-frequency power (ms2) | High-frequency (0.15-0.40 Hz) power in supine position for 5 minutes after a 15-minute rest period |
| 30 max/15 min ratio | The ratio between the longest R-R interval around the 30th beat and the shortest R-R interval around the 15th beat after standing from a supine position |
| E/I ratio | The average of 6 ratios between the longest R-R interval during expiration and the shortest R-R interval during inspiration for each breathing cycle, while subjects were sitting and breathing deeply at a rate of 6 breaths per minute |
| Predominantly sympathetic with parasympathetic modulation | |
| Low-frequency power (ms2) | Low-frequency (0.04-0.15 Hz) power in supine position for 5 minutes after a 15-minute rest period |
| Sympathovagal balance | |
| Low-frequency/high-frequency power ratio | The ratio between low-frequency power and high-frequency power in supine position for 5 minutes after a 15-minute rest period |
Statistics
The subjects were divided into 4 groups, with 0, 1, 2, and ≥3 metabolic abnormalities according to the NCEP ATP III criteria. Because the low-frequency power/high-frequency power ratio presents an apparently right-skewed distribution, a square root transformation was used to make the values follow a normal distribution. In univariate analysis, one-way analysis of variance was used to compare continuous variables among groups with different numbers of metabolic abnormalities. The nonparametric Kruskal-Wallis test was used for comparison of the plasma triglyceride and physical activity level among these groups. Comparisons of categorical variables were made using the chi-squared or Fisher's exact test, where appropriate.
In multivariate analysis, analysis of covariance (ANCOVA) was used to compare the cardiac autonomic function and insulin resistance among different metabolic abnormality groups, as defined by NCEP ATP III, with adjustment for other confounders, namely age, sex, body mass index, physical activity, plasma cholesterol, current smoking, and alcohol use. We also examined the difference in cardiac autonomic function and insulin resistance among groups with different numbers of metabolic abnormalities as defined by the AHA/NHLBI criteria on the basis of ANCOVA. A P value <.05 was considered significant. All data analyses were conducted using the Statistical Package for Social Sciences 13.0 for Windows (SPSS Inc., Chicago, Ill).
Results
Following the NCEP ATP III definition of metabolic abnormality, the subjects were classified as having none (n=539), one (n=378), 2 (n=218), and 3 or more (n=154) metabolic abnormalities. There were significant differences in age, sex, body mass index, waist circumference, seated systolic and diastolic blood pressure, fasting plasma glucose, cholesterol, triglyceride, and high-density lipoprotein cholesterol levels, and the prevalence of central obesity, blood pressure ≥130/85 mm Hg, fasting glucose ≥6.1 mmol/L, high triglyceridemia, and low high-density lipoprotein cholesterol, current smoking, and alcohol use among these 4 groups (Table 2). However, the physical activity level and the prevalence of family history of diabetes were not significantly different among groups.
Table 2. Comparisons of Clinical Variables among Subjects with Different Numbers of Metabolic Abnormalities, as Defined by NCEP ATP III Criteria
| Number of Metabolic Abnormalities | P Value | ||||
|---|---|---|---|---|---|
| 0(n=539) | 1(n=378) | 2(n=218) | ≥3(n=154) | ||
| Age, years | 36.4(12.4) | 39.8(14.1) | 46.2(13.7) | 48.1(13.8) | <.001 |
| Male, % | 229(42.5) | 174(46.0) | 113(51.8) | 85(55.2) | .014 |
| Body mass index, kg/m2 | 21.7(2.4) | 23.0(3.1) | 25.9(3.6) | 27.1(3.4) | <.001 |
| Waist circumference, cm | 72.1(9.9) | 76.2(12.7) | 85.0(14.3) | 90.2(8.3) | <.001 |
| Seated SBP, mm Hg | 107.9(9.4) | 112.5(14.7) | 120.3(15.1) | 129.6(17.5) | <.001 |
| Seated DBP, mm Hg | 66.4(7.1) | 68.7(8.3) | 73.7(8.2) | 77.5(10.3) | <.001 |
| Fasting glucose, mmo/L | 4.9(0.5) | 5.0(0.5) | 5.3(1.2) | 5.9(1.6) | <.001 |
| Cholesterol, mmol/L | 4.8(0.9) | 4.8(1.1) | 5.1(1.0) | 5.2(1.2) | <.001 |
| Triglyceride, mmol/L⁎ | 0.9(0.3) | 1.2(0.5) | 1.8(1.0) | 2.6(3.1) | <.001 |
| HDL-C, mmol/L | 1.5(0.3) | 1.3(0.3) | 1.2(0.3) | 1.0(0.3) | <.001 |
| Physical activity, met-h/week⁎ | 63.4(82.0) | 60.5(104.4) | 62.0(66.8) | 60.5(92.6) | .186 |
| Central obesity, %† | 0 | 61(16.1) | 114(52.3) | 127(82.5) | <.001 |
| BP ≥130/85 mm Hg, % | 0 | 44(11.6) | 53(24.3) | 92(59.7) | <.001 |
| Fasting glucose ≥6.1 mmol/L, % | 0 | 5(1.3) | 17(7.8) | 47(30.5) | <.001 |
| Triglyceride ≥1.7 mmol/L, % | 0 | 46(12.2) | 114(52.3) | 116(75.3) | <.001 |
| Low HDL-C, %‡ | 0 | 222(58.7) | 138(63.3) | 122(79.2) | <.001 |
| Family history of diabetes, % | 94(17.4) | 76(20.1) | 48(22.0) | 37(24.0) | .206 |
| Current alcohol use, % | 57(10.6) | 43(11.4) | 39(17.9) | 28(18.2) | .007 |
| Current smoking, % | 99(18.4) | 69(18.3) | 62(28.4) | 38(24.7) | .006 |
⁎Kruskal-Wallis test. |
†Waist circumference >90 cm in males or 80 cm in females. |
‡HDL-C <1.0 mmol/L in male or <1.3 mmol/L in female. |
Figure 1 presents the comparisons of cardiac autonomic function among subjects with different numbers of metabolic abnormalities defined by NCEP ATP III on the basis of ANCOVA, which adjusted for other variables, including age, sex, body mass index, physical activity, plasma cholesterol, current smoking, and alcohol use. When compared with subjects without metabolic abnormality, not only subjects with 3 or more metabolic abnormalities, but also subjects with 1 or 2 metabolic abnormalities had a lower parasympathetic drive, as shown by the SDNN and E/I ratio. Subjects with 3 or more metabolic abnormalities also had a lower high-frequency power than those without metabolic abnormality. However, the 30 max/15 min ratio was not different among groups after adjusting for other confounders. With regard to predominantly sympathetic drive with partial parasympathetic modulation of the heart, there was no apparent difference in low-frequency power among these 4 groups. In the sympathovagal balance, only subjects with 3 or more metabolic abnormalities presented a higher square root of low-frequency power/high-frequency power ratio than subjects without metabolic abnormality.
Figure 1.
Comparisons of cardiac autonomic function among subjects with different numbers of metabolic abnormalities, as defined by US National Cholesterol Education Program Adult Treatment Panel III criteria, based on the analysis of covariance, which adjusted for age, sex, body mass index, physical activity, plasma cholesterol, current smoking, and alcohol use. HF
=high frequency; LF=low frequency; LF/HF=low-frequency power/high-frequency power. Data are expressed as adjusted mean±standard error of mean.
We further made a multivariate analysis in the comparisons of cardiac autonomic function among subjects with different numbers of metabolic abnormalities defined by AHA/NHLBI criteria (data not shown), and the results were similar to those derived from the NCEP ATP III criteria. Subjects with one or more metabolic abnormalities had a lower parasympathetic drive, as shown by the SDNN (P <.05) and E/I ratio (P <.05), when compared with those without metabolic abnormality. Only subjects with 3 or more metabolic abnormalities had a higher square root of low-frequency power/high-frequency power ratio (P <.001), but a lower high-frequency power than those without metabolic abnormality (P <.01).
Figure 2 displays the comparison of insulin resistance among subjects with different numbers of metabolic abnormalities, as defined by NCEP ATP III. After adjusting for other variables, subjects with 2 and 3 or more metabolic abnormalities had a higher insulin resistance than the subjects without metabolic abnormality. However, there was no significant difference in insulin resistance between subjects without metabolic abnormality and subjects with one metabolic abnormality. When metabolic abnormality was defined by the AHA/NHLBI criteria, the results of the differences in insulin resistance among groups were similar to those with the NCEP ATP III criteria. Subjects with 2 (P <.001) and 3 or more metabolic abnormalities (P <.001) had a higher insulin resistance than subjects without metabolic abnormality, but the insulin resistance was not significantly different between subjects with one and without metabolic abnormality (P=.493, data not shown).
Figure 2.
Comparisons of homeostasis model assessment of insulin resistance (HOMA-IR) among subjects with different numbers of metabolic abnormalities, as defined by US National Cholesterol Education Program Adult Treatment Panel III criteria, based on the analysis of covariance, which adjusted for age, sex, body mass index, physical activity, plasma cholesterol, current smoking and alcohol use. Data are expressed as adjusted mean
±standard error of mean.
Discussion
Our study provides the epidemiological finding that altered cardiac autonomic function existed even in subjects with 1 or 2 metabolic abnormalities, in addition to subjects with 3 or more metabolic abnormalities. However, the insulin resistance was not significantly different between subjects with one and without metabolic abnormality, although subjects with 2 or more metabolic abnormalities exhibited a higher insulin resistance than subjects without metabolic abnormality. This may be related to a mild degree of insulin resistance in our subjects with one metabolic abnormality, because we excluded those with a more severe insulin resistance, including patients with hypertension, diabetes mellitus, coronary heart disease, and cerebrovascular disease, and taking medications known to influence blood pressure, cardiac autonomic function, plasma glucose, and lipid profile. Due to an altered cardiac autonomic function, but not insulin resistance in subjects with one metabolic abnormality, it is conceivable that altered cardiac autonomic function precedes the presence of insulin resistance in metabolic syndrome. Although this study does not indicate that cardiac autonomic function alteration causes insulin resistance, our results are consistent with 2 follow-up studies, which have shown that sympathetic hyperactivity, shown by plasma norepinephrine, has been shown to precede hyperinsulinemia in young, nonobese Japanese subjects,20 and norepinephrine response to cold pressor test also has been found to be a positive predictor of future insulin resistance in Norwegian men.21
With regard to autonomic function in the mechanism underlying insulin resistance, altered cardiac autonomic function may contribute to the pathogenesis of insulin resistance in several ways.10 First, sympathetic activation inhibits insulin secretion from the pancreatic beta cells, resulting in an impaired glucose transport to the peripheral tissue.22 Second, sympathetic vasoconstriction could decrease glucose uptake of skeletal muscle by lessening blood flow, reducing the number of open capillaries, and increasing the traveling distance of insulin to cell membranes.10, 23, 24, 25, 26 Finally, sympathetic activation increases adipose tissue lipolysis with an increase of circulating free fatty acid, contributing to a further insulin resistance and, reciprocally, hyperinsulinemia in the insulin-resistant state stimulates central sympathetic outflow, hence establishing a vicious cycle.9, 27
Some reports have shown that metabolic syndrome with 3 or more metabolic abnormalities is characterized by sympathetic predominance.13, 14, 15 In our study, subjects with one metabolic abnormality presented a reduced parasympathetic modulation of the heart, as shown by the SDNN and E/I ratio. For subjects with 2 or more metabolic abnormalities, the cardiac autonomic function alteration is both a decrease in parasympathetic drive and a sympathovagal imbalance with a shift toward sympathetic predominance. The explanation is that individuals with metabolic abnormalities tend to have obesity, glucose intolerance, atherogenic dyslipidemia, and prehypertension, all of which are associated with decreased parasympathetic or increased sympathetic tone.28, 29, 30, 31, 32
Strength and Limitation
The ARIC study has suggested that multiple metabolic syndrome is associated with a lower heart rate variability,16 but central obesity was not included as a component of metabolic abnormality in their research. Furthermore, they defined metabolic abnormality with more severe cutoff points, such as blood pressure ≥140/90 mm Hg, fasting plasma glucose ≥7.8 mmol/L, triglyceride ≥2.26 mmol/L, and high-density lipoprotein cholesterol <1.16 mmol/L for females and <0.91 mmol/L for males.16 This may result in an overestimation of the association between cardiac autonomic function and metabolic abnormality. Our study defined metabolic abnormality by the NCEP ATP III criteria, the components of which are predisease states and less severe than those in the ARIC study. Furthermore, subjects with cardiometabolic disorders and medications known to influence metabolic abnormality and cardiac autonomic function were excluded from the analysis, and thus, the classification bias in disease severity and the confounding effects of medications are avoided in our study. One limitation of our study is that the design is cross-sectional and the effect of altered cardiac autonomic function on both insulin resistance and metabolic abnormality in our results needs to be confirmed in a longitudinal study. In addition, the use of short-term (5 minutes), not long-term (24 hours) heart rate variability recordings on the evaluation of cardiac autonomic function, is another limitation. Finally, our study subjects were all drawn from a Chinese population, and further studies are needed in other groups.
Conclusions
Cardiac autonomic function is already altered, even in subjects with one or 2 metabolic abnormalities, in addition to subjects with 3 or more metabolic abnormalities. It is conceivable that altered cardiac autonomic function precedes the presence of insulin resistance in the initiation of metabolic syndrome, because subjects with one metabolic abnormality exhibit an altered cardiac autonomic function, but not insulin resistance.
Acknowledgement
This study was supported by grants from the National Science Council, Taiwan, R.O.C. (NSC 87-2314-B-006-084, NSC 89-2314-B-006-043, and NSC 92-2314-B-006-117).
References
- . 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) final report. Circulation. 2002;106:3143–3421
- Diagnosis and management of the metabolic syndrome (An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement). Circulation. 2005;112:2735–2752
- Features of the metabolic syndrome predict higher risk of diabetes and impaired glucose tolerance: a prospective study in Mauritius. Diabetes Care. 2000;23:1242–1248
- The metabolic syndrome as predictor of type 2 diabetes: the San Antonio Heart Study. Diabetes Care. 2003;26:3153–3159
- The metabolic syndrome defined by factor analysis and incident type 2 diabetes in a Chinese population with high postprandial glucose. Diabetes Care. 2004;27:2429–2437
- National Cholesterol Education Program versus World Health Organization metabolic syndrome in relation to all-cause and cardiovascular mortality in the San Antonio Heart Study. Circulation. 2004;110:1251–1257
- Cardiovascular disease in U.S. patients with metabolic syndrome, diabetes, and elevated C-reactive protein. Diabetes Care. 2005;28:690–693
- The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care. 2005;28:385–390
- Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians?. Diabetes Care. 2004;27:1182–1186
- The sympathetic nervous system and the metabolic syndrome. J Hypertens. 2007;25:909–920
- Mechanisms of sympathetic activation in obesity-related hypertension. Hypertension. 2006;48:787–796
- . Heart rate variability standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;93:1043–1065
- . Sympathetic neural activation in nondiabetic metabolic syndrome and its further augmentation by hypertension. Hypertension. 2004;44:847–852
- Neuroadrenergic and reflex abnormalities in patients with metabolic syndrome. Diabetologia. 2005;48:1359–1365
- Adrenocortical, autonomic, and inflammatory causes of the metabolic syndrome: nested case-control study. Circulation. 2002;106:2659–2665
- Multiple metabolic syndrome is associated with lower heart rate variability (The Atherosclerosis Risk in Communities Study). Diabetes Care. 1998;21:2116–2122
- A population-based study of the prevalence and associated factors of diabetes mellitus in southern Taiwan. Diabet Med. 1998;15:564–572
- . Measurement of physical activity to assess health effects in free-living populations. Med Sci Sports Exerc. 1993;25:60–70
- Homeostasis model assessment: insulin resistance and b-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–419
- . Sympathetic nerve hyperactivity precedes hyperinsulinemia and blood pressure elevation in a young, nonobese Japanese population. Am J Hypertens. 1997;10:77–83
- . Increased sympathetic reactivity may predict insulin resistance: an 18-year follow-up study. Metabolism. 2008;57:1422–1427
- The association among autonomic nervous system function, incident diabetes, and intervention arm in the Diabetes Prevention Program. Diabetes Care. 2006;29:914–919
- Reflex sympathetic activation induces acute insulin resistance in the human forearm. Hypertension. 1993;21:618–623
- . Neurohumoral, hemodynamic and microvascular changes as mechanisms of insulin resistance in hypertension: a provocative but partial picture. Int J Obes. 1991;15:133–139
- Reduced postprandial skeletal muscle blood flow contributes to glucose intolerance in human obesity. J Clin Endocrinol Metab. 1997;70:1525–1533
- . The interconnection between sympathetics, microcirculation and insulin resistance in hypertension. Blood Press. 1992;1:9–19
- Excess portal venous long-chain fatty acids induce syndrome X via HPA axis and sympathetic activation. Am J Physiol-Endocrinol Metab. 2000;279:E1286–E1293
- . Vasoactive mediators affect the clearance of lipids from emulsion models of plasma lipoproteins in rats. J Cardiovasc Pharmacol. 1996;27:447–454
- Epidemiological study on the effect of pre-hypertension and family history of hypertension on cardiac autonomic function. J Am Coll Cardiol. 2008;51:1896–1901
- Epidemiological evidence of altered cardiac autonomic function in overweight, but not underweight subjects. Int J Obes Relat Metab Disord. 2008;32:788–794
- Epidemiological evidence of altered cardiac autonomic function in subjects with impaired glucose tolerance but not isolated impaired fasting glucose. J Clin Endocrinol Metab. 2007;92:3885–3889
- . Metabolic syndrome: a clinical and molecular perspective. Annu Rev Med. 2005;56:45–62
Funding: This study was supported by grants from the National Science Council, Taiwan, R.O.C. (NSC 87-2314-B-006-084, NSC 89-2314-B-006-043, and NSC92-2314-B-006-117).
Conflict of Interest: There are no conflicts of interest.
Authorship: All authors had access to the data and a role in writing the article. C.-J. Chang and Y.-C. Yang contributed equally to this work.
PII: S0002-9343(09)01057-2
doi:10.1016/j.amjmed.2009.07.031
© 2010 Elsevier Inc. All rights reserved.
