Physical fitness can independently lower the risk of cardiovascular disease (CVD). We explored the independent and combined associations of physical fitness, measured using the seated at rest heart rate (RHR), and the metabolic syndrome (MS), with CVD risk, as described by an elevated pulse wave velocity (PWV) in older Chinese. Data from 1,996 participants were drawn from the Guangzhou Biobank Cohort Study-Cardiovascular Disease Subcohort. Analysis of variance and logistic regression analysis were used to establish the independent and combined associations of the RHR and the MS with PWV. The RHR was independently associated with an elevated PWV (odds ratio [OR] 1.63, 95% confidence interval [CI] 1.22 to 2.18), as was the MS (OR 2.36, 95% CI 1.76 to 3.17). The participants with a high RHR, but without the MS, had an adjusted OR of 1.63 (95% CI 1.15 to 2.30) for the presence of the CVD proxy. Those with a low RHR and the MS had an adjusted OR of 2.35 (95% CI 1.66 to 3.33). The risk of an elevated PWV increased almost fourfold with both a high RHR and a diagnosis of the MS (OR 3.87, 95% CI 2.39 to 6.28, p = 0.52 for interaction). In conclusion, physical fitness, measured using the RHR, and the MS are independently associated with an elevated PWV, a surrogate marker for CVD. The strength of this association was further increased in the presence of both. These findings confirm the beneficial effects of physical fitness on attenuating the risk of CVD among older Chinese.
The risk factors for cardiovascular disease (CVD) have been attributed to a number of medical disorders, and their clustering is known as the metabolic syndrome (MS). The MS is largely the result of altered modifiable lifestyle factors, including physical fitness. Several studies have indicated poor physical fitness is associated with the MS, exacerbating an unhealthy CVD risk profile and promoting all-cause mortality. However, the current contributions of the heart rate at rest (RHR) and the MS toward the risk of CVD remain underexplored, particularly among Chinese populations. Therefore, in the present study, we evaluated the independent and combined associations of physical fitness, as measured by the seated RHR, and the MS, with CVD risk, as described by the pulse wave velocity (PWV) in older Chinese residents of Guangzhou, China. We specifically hypothesized that poor physical fitness would be coupled with a greater prevalence of CVD risk and the risk of CVD would additionally increase in the background of the MS, thus forewarning of a major developing health burden in a rapidly modernizing and burgeoning older Chinese population.
Methods
Data were drawn from 1,996 older Chinese volunteers (>50 years) as a part of the Guangzhou Biobank Cohort Study-Cardiovascular Disease Subcohort (GBCS-CVD). This subcohort enabled a thorough evaluation of volunteers’ current CVD status through the measurement of a range of surrogate markers of vascular disease or risk, allowing for the development of testable hypotheses. Our older volunteers were permanent residents of Guangzhou, belonging to a community and welfare association “The Guangzhou Health and Happiness Association for Respectable Elders,” which represents a homogenous Cantonese group, who has retained many traditional and cultural norms, despite previous turmoil and extensive economic transitions. This population formed a part of an ideal setting to assess, both cross-sectionally and longitudinally, the effect of rapid economic development on health outcomes. A more comprehensive description of this subcohort and its multidisciplinary measures is detailed by Jiang et al. The medical ethics committee of the Guangzhou Medical Association approved the study, and written, informed consent was obtained from all volunteers.
The volunteers were classified according to their physical fitness status as indicated by the seated RHR and the diagnosis of the MS. The seated RHR was measured 3 times (Omron 705CP, Omron Healthcare, Inc., Bannockburn, Illinois); 1 minute apart, after a 3-minute rest, with the average calculated from the second and third measurements. The volunteers were classified as having a high seated RHR if their heart rate was ≥83 beats/min, determined by the 75th percentile cutpoint. The presence of 3 of the 5 factors from the following criteria were used to identify those with the MS: (1) waist circumference ≥90 cm in men and ≥80 cm in women; (2) elevated blood pressure (≥130/85 mm Hg) or receiving hypertension treatment; (3) increased fasting plasma glucose (≥5.6 mmol/L or ≥100 mg/dl) or previously diagnosed type 2 diabetes; (4) increased plasma triglycerides (≥1.7 mmol/L or ≥150 mg/dl); and (5) reduced high-density lipoprotein cholesterol (<1.03 mmol/L or <40 mg/dl in men and <1.29 mmol/L or <50 mg/dl in women). The volunteers with a high-sensitivity C-reactive protein level ≥11 mg/L (4.8%) were excluded because of a suspected inflammatory or infectious condition, leaving 1,902 participants available for the analyses.
The PWV was measured using a volume-plethysmographic apparatus with an automatic waveform analyzer (Colin VP1000, Colin Medical Technology, Komaki, Japan). Measurements were taken with the volunteers lying in a supine position after 5 minutes of rest. Occlusion and monitoring cuffs were placed around both sites of the lower legs and upper arms. The pressure waveforms of the brachial and tibial arteries were then recorded simultaneously using semiconductor plethysmographic and oscillometric pressure sensors. These waveforms allow the determination of the interval between the initial increase in the brachial and tibial pressure waveforms (T). The path length from the suprasternal notch to the elbow (La) and from the suprasternal notch to the ankle (Lb) were automatically statistically determined from the patient’s height. The PWV was calculated using the formula PWV ¼(Lb − La)/T. Measurements of the left and right brachial-to-ankle PWV were obtained for an average of 10 seconds. The method’s validity has been reported previously, with an interobserver coefficient of variation of 8.4% and intraobserver coefficient of variation of 10.0%. The average of the left and right brachial-to-ankle PWV was used for the subsequent analyses.
The independent effects of the seated RHR and the MS on CVD status were examined first. Subsequently, these parameters were used to stratify the participants into 4 groups: 1, low RHR/no MS; 2, high RHR/no MS; 3, low RHR/MS; and 4, high RHR/MS. Comparisons among the groups were performed using analysis of variance with a Bonferroni post hoc test for continuous parameters, and chi-square test with P for the linear-by-linear test for categorical variables. All variables with a non-normal distribution were logarithmically transformed and their geometric mean (with 95% confidence intervals [CI]) are presented. Otherwise, the data are presented as the mean ± SD for continuous parameters or prevalence for categorical variables.
The associations were tested using 3 hierarchical models derived from multivariate logistic regression analysis. Model 1 included age (continuous) and gender as covariates. Model 2 was further adjusted for lifestyle factors, including smoking (never, ever), alcohol (never, ever), physical activity (MET minutes/week), and personal history of CVD (yes, no), which volunteers reported if a physician had ever told them they had experienced hypertension, dyslipidemia, valvular heart disease, coronary heart disease, stroke, angina, myocardial infarction, peripheral heart disease, rheumatic heart disease, or other CVD-related events. Finally, model 3 was also adjusted for socioeconomic measures, including education level (primary or below, middle, or college or greater) and personal income (<Y10,000, ≥Y10,000 to <Y15,000 and ≥Y15,000). For the purpose of our analyses, PWV was dichotomized into low- and high-risk groups using a 75% cutpoint (16.98 m/s). For the comparisons, we also explored the relation across each of the stratified groups using the median cutpoint for the PWV (14.80 m/s). All statistical tests were 2-tailed, and statistical significance was defined as p <0.05. The analyses were conducted using the Statistical Package for Social Sciences, version 18 (SPSS, Chicago, Illinois).
Results
The demographic characteristics are presented in Table 1 . The prevalence of those with a high RHR was 24.9% (n = 473), and 21.2% (n = 403) were classed as having the MS. Of these participants with the MS, 114 (28.3%) had a high RHR and 289 (71.2%) had a low RHR ( Table 1 ). The physical activity levels were greatest in those with a low RHR without the presence of the MS (p = 0.006). Between-group differences were observed for waist circumference and body mass index (p <0.001). Several parameters, including heart rate, PWV ( Figure 1 ), blood pressure, fasting glucose, glycosylated hemoglobin, lipids and high-sensitivity C-reactive protein were significantly greater in those with a high RHR and the MS and the high-density lipoprotein cholesterol level was significantly lower ( Table 1 ). No interaction effect was observed between RHR and the MS (p = 0.52).
| Parameter | Low RHR/no MS (n = 1,140) | High RHR/no MS (n = 359) | Low RHR/MS (n = 289) | High RHR/MS (n = 114) | p Value ⁎ |
|---|---|---|---|---|---|
| Age (years) | 59.0 ± 6.8 | 58.5 ± 6.9 | 60.8 ± 6.9 ⁎ † | 60.6 ± 7.0 | <0.001 |
| Men | 48.8% | 51.0% | 53.6% | 43.9% | 0.689 |
| Heart rate (beats/min) | 71 ± 7 | 91 ± 7 † | 72 ± 7 ‡ | 92 ± 7 † § | <0.001 |
| Pulse wave velocity (m/s) | 14.8 ± 2.8 | 15.6 ± 2.7 † | 16.6 ± 3.0 † ‡ | 17.4 ± 3.1 † ‡ | <0.001 |
| Waist circumference (cm) | 76.2 ± 7.9 | 76.8 ± 7.8 | 86.0 ± 8.0 † ‡ | 86.2 ± 9.0 † ‡ | <0.001 |
| Body mass index (kg/m 2 ) | 23.0 ± 2.7 | 23.0 ± 2.7 | 26.1 ± 2.6 † ‡ | 25.9 ± 2.9 † ‡ | <0.001 |
| Fasting glucose (mg/dl) | 94.22 (93.67–94.93) | 97.82 (95.83–99.62) † | 108.44 (106.10–110.97) † ‡ | 116.91 (110.79–123.40) † ‡ § | <0.001 |
| Glycosylated hemoglobin (%) | 5.9 | 6.0 | 6.3 † ‡ | 6.7 † ‡ § | <0.001 |
| Impaired fasting glucose | 16.6% | 27.3% | 59.5% | 61.4% | <0.001 |
| Type 2 diabetes mellitus | 2.1% | 3.6% | 12.5% | 16.7% | <0.001 |
| Total cholesterol (mg/dl) | 223.89 ± 40.60 | 224.28 ± 41.37 | 231.24 ± 43.31 ⁎ | 234.72 ± 45.63 ⁎ | 0.006 |
| Low-density lipoprotein cholesterol (mg/dl) | 129.15 ± 25.52 | 129.54 ± 26.29 | 133.02 ± 25.90 | 136.89 ± 28.61 ⁎ | 0.003 |
| High-density lipoprotein cholesterol (mg/dl) | 64.19 ± 14.69 | 62.64 ± 15.08 | 51.43 ± 12.37 ⁎ † | 50.65 ± 12.37 ⁎ † | <0.001 |
| Triglycerides (mg/dl) | 116.03 (113.37–119.57) | 129.88 (103.87–130.20) | 221.43 (208.14–235.60) ⁎ † | 237.37 (214.34–263.06) ⁎ † | <0.001 |
| Dyslipidemia | 41.8% | 44.6% | 70.2% | 73.7% | <0.001 |
| Systolic blood pressure (mm Hg) | 123 ± 20 | 126 ± 18 | 142 ± 20 ⁎ † | 142 ± 19 ⁎ † | <0.001 |
| Diastolic blood pressure (mm Hg) | 72 ± 10 | 75 ± 10 ⁎ | 81 ± 10 ⁎ † | 81 ± 10 ⁎ † | <0.001 |
| Arterial hypertension | 27.0% | 31.5% | 74.7% | 69.3% | <0.001 |
| Cardiovascular disease history | 35.2% | 34.0% | 58.2% | 50.9% | <0.001 |
| High-sensitivity C-reactive protein (mg/L) | 1.13 (1.06–1.19) | 1.34 (1.20–1.50) ⁎ | 1.92 (1.72–2.15) ⁎ † | 2.09 (1.75–2.49) ⁎ † | <0.001 |
| Current smoker | 39.8% | 42.3% | 43.7% | 38.1% | 0.518 |
| Current alcohol user | 59.4% | 52.9% | 58.5% | 60.2% | 0.635 |
| Physical activity (MET min/week) | 3,167 (2,973–3,373) | 2,741 (2,224–2,886) ⁎ | 2,784 (2,455–3,157) | 3,128 (2,643–3,702) | 0.006 |
| Education | 0.059 | ||||
| College or above | 12.9% | 10.6% | 14.9% | 14.0% | |
| Middle | 61.4% | 61.8% | 54.9% | 46.5% | |
| Primary or below | 25.7% | 27.6% | 30.2% | 39.5% | |
| Personal income (Yuan/USD) | 0.088 | ||||
| ≥15,000/2,292 | 14.7% | 16.6% | 14.3% | 14.7% | |
| ≥10,000–<15,000/≥1,528–<2,292 | 63.6% | 62.7% | 60.5% | 51.4% | |
| <10,000/<1,528 | 21.7% | 20.7% | 25.2% | 33.9% |
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