|Year : 2019 | Volume
| Issue : 5 | Page : 236-242
Association between Cardiovascular Health Metrics and Frailty in a Taiwanese Population
Ying-Chuan Wang1, Chun-Hsiang Chiu2, Chih-Chia Wang1, Wei-Liang Chen3, Hui-Fang Yang4, Tao-Chun Peng4
1 Division of Family Medicine, Department of Family and Community Medicine, Taipei, Taiwan
2 Division of Infectious Disease and Tropical Medicine, Department of Internal Medicine, Taipei, Taiwan
3 Division of Family Medicine, Department of Family and Community Medicine; Division of Geriatric Medicine, Department of Family and Community Medicine, Tri-Service General Hospital; Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
4 Division of Family Medicine, Department of Family and Community Medicine; Division of Geriatric Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
|Date of Submission||23-Jan-2019|
|Date of Decision||03-Mar-2019|
|Date of Acceptance||22-Mar-2019|
|Date of Web Publication||06-May-2019|
Dr. Tao-Chun Peng
Department of Family and Community Medicine, National Defense Medical Center, Tri-Service General Hospital, Number 325, Section 2, Chang-gong Road, Nei-Hu District, 114
Source of Support: None, Conflict of Interest: None
Introduction: Frailty is known as a reduced response to stressors and an increased vulnerability to adverse health events. Early intervention for individuals with risk factors for frailty will improve their outcomes. Despite the growing amount of evidence indicating that cardiovascular disease is associated with frailty, the evidence for primary prevention is poor. Materials and Methods: The data were drawn from the MJ Health Management Institution and were cross-sectional design. The participants were general population. Cardiovascular health (CVH) metrics included smoking status, body mass index, healthy diet score, physical activity, total cholesterol, blood pressure, and blood glucose. The definition of frailty was modified from the Study of Osteoporotic Fractures Frailty Index. Multiple logistic regression analysis was used to examine the associations among CVH metrics, individual components of frailty, and frailty itself. Results: The mean age of 37,843 participants was 62.15 ± 7.66 years. Overall, 19.2% of the participants were defined as having frailty. When those with 0–2 ideal CVH metrics were used as the reference group, the odds ratios for frailty were 0.68 (95% confidence interval [CI], 0.56–0.82) for those with 3–4 ideal CVH metrics and 0.36 (95% CI, 0.23–0.55) for those with 5–7 ideal CVH metrics after adjustment for potential confounders. Conclusions: Our results suggested a significantly lower risk of frailty among older individuals with more ideal CVH metrics. These findings suggest that attaining ideal CVH metrics has the potential to reduce the burden of frailty.
Keywords: Cardiovascular health metrics, frailty, weight loss, age
|How to cite this article:|
Wang YC, Chiu CH, Wang CC, Chen WL, Yang HF, Peng TC. Association between Cardiovascular Health Metrics and Frailty in a Taiwanese Population. J Med Sci 2019;39:236-42
|How to cite this URL:|
Wang YC, Chiu CH, Wang CC, Chen WL, Yang HF, Peng TC. Association between Cardiovascular Health Metrics and Frailty in a Taiwanese Population. J Med Sci [serial online] 2019 [cited 2020 Aug 3];39:236-42. Available from: http://www.jmedscindmc.com/text.asp?2019/39/5/236/257757
| Introduction|| |
Frailty is considered a condition in which an individual has an increased risk of adverse health events when exposed to even a common stressor. Although the concept of frailty is fairly well known, definitions of frailty are not universally shared and are complicated. There are two dominant definitions, including the frailty phenotype  and the Frailty Index. Many epidemiological studies have shown that frailty is associated with many adverse health outcomes, lower postoperative survival, greater health-care use, and mortality. Early intervention for individuals with risk factors for frailty will improve their outcomes. Therefore, it is essential to elucidate potential risk factors for frailty in older adults.
The 2010 American Heart Association (AHA) Guidelines developed the concept of ideal cardiovascular health (CVH) with seven cardiovascular disease risk factors or behaviors, which include smoking status, body mass index (BMI), healthy diet, physical activity, total cholesterol, blood pressure, and fasting serum glucose. The prevalence of ideal levels of all seven CVH factors has been reported to be only 0.1% in the US community population. In addition, increased numbers of ideal CVH metrics were associated with a lower risk of cardiovascular disease incidence, diabetes, and nonalcoholic fatty liver disease. Despite the growing amount of evidence indicating that cardiovascular disease is associated with frailty, the evidence for primary prevention is poor.
Graciani et al. revealed that the number of ideal cardiovascular metrics increases with a reduced risk of frailty based on the Fried criteria. However, due to the relatively small number of study populations, most subgroup analyses have shown no significant results. Wong et al. also found that ideal CVH was inversely correlated with a higher risk of frailty based on the Frailty Index. Another study using two large cohorts by Atkins et al. also had similar results. There are no data available on the healthy diet metric, which is an important risk factor for frailty, in both Wong's study and Atkins' study. In addition, these three studies were conducted in the Western population. To further explore the above questions, we aimed to undertake a detailed examination of differences in ideal CVH across groups defined by frailty status. In addition, we hypothesized that increased numbers of ideal CVH components would be related to a lower frailty rate in our Asian population.
| Methods|| |
Study population and study design
The data for the present study were drawn from the MJ Health Management Institution and were collected between 2000 and 2016. The MJ Health Management Institution, which has four branches (Taipei, Taoyuan, Taichung, and Kaohsiung) across Taiwan, is a private institute providing periodic health examinations. All branches used the same screening protocols and test machines. The original sample size was around 70,000/year. This study was based on the cross-sectional design. The participants were general population. The participants over the age of 48 years were selected. To evaluate the primary preventive effect of CVH metrics, only those without cardiovascular disease were included in this analysis. All the participants completed physical examinations (height, weight, and blood pressure), blood tests (glucose and total cholesterol), and self-administered questionnaires (lifestyle, medical history, and medications). All the participants provided written informed consent for the use of anonymous personal data for research purposes. The Institutional Review Board of the Tri-Service General Hospital approved this study.
Cardiovascular health metrics
CVH metrics included smoking status, BMI, healthy diet score, physical activity, total cholesterol, blood pressure, and blood glucose based on a definition modified from that presented in the AHA Guidelines. We categorized the CVH metrics into ideal, intermediate, and poor. Ideal, intermediate, and poor smoking statuses were defined as never smoker, former smoker, and current smoker. Physical activity in leisure time was calculated into min/week using physical activity questionnaires. Ideal, intermediate, and poor physical activities were defined as ≥210 min/week, 60–210 min/week, and <60 min/week, respectively. BMI was calculated using measured weight (in kg) divided by height squared (in m 2). Ideal, intermediate, and poor BMIs were defined as lower than 25 kg/m 2, 25–29.99 kg/m 2, and ≥30 kg/m 2, respectively. The diet metric was composed of five items using the following components: (1) ≥300 g of vegetables and fresh fruits per day, (2) ≥200 g of fish per week, (3) ≥two 30 g equivalent servings of whole grains per day, (4) ≤1500 mg of sodium per day, and (5) ≥240 g of milk per day. Ideal, intermediate, and poor diet metrics were defined as 4–5 components, 2–3 components, and 0–1 components, respectively. Ideal, intermediate, and poor blood pressures were defined as systolic blood pressure (SBP) <120 and diastolic blood pressure (DBP) <80 mmHg, SBP 120–139 or DBP 80–89 mmHg, and SBP ≥140 or DBP ≥90 mmHg, respectively. Ideal, intermediate, and poor fasting serum glucose were defined as <100 mg/dl, 100–125 mg/dl, and ≥126 mg/dl, respectively. Ideal, intermediate, and poor total cholesterol were defined as <200 mg/dl, 200–239 mg/dl, and ≥240 mg/dl, respectively. Each CVH metric categorized into ideal status would get one point. It was healthier in CVH with higher scores.
The definition of frailty was modified from the Study of Osteoporotic Fractures (SOF) Frailty Index. There were three components of this Frailty Index. First, the participants were defined as having experienced weight loss if they answered “yes” to questions such as “Have you lost more than 4 kg in the past 3 months (irrespective of intent to lose weight)?” Second, the participants were defined as having reduced energy levels if they answered “completely absent” to questions such as “How much energy have you had in the past four weeks (1 month)?” Third, if both working strength and athletic strength were classified at the lowest strength level, the participant was defined as having a low lower-leg function. The presence of 0 of the above three components was classified as robust. The presence of 1–3 of the above three components was classified frailty.
The characteristics of participants were summarized as the mean (standard deviation) for continuous variables and frequency (%) for categorical variables. The characteristics of the study population according to the CVH status were compared using t-tests for continuous variables and Chi-square tests for categorical variables. Multiple logistic regression analysis was applied to examine the associations among CVH metrics, individual components of frailty, and frailty itself. Further adjustment models were constructed for age, sex, and education. All of the statistical tests were two-tailed with P < 0.05. All statistical analyses were performed using the Statistical Package for the Social Sciences version 18.0 (SPSS, Inc., Chicago, IL, USA).
| Results|| |
The characteristics of the participants in this study are provided in [Table 1]. The mean age of the participants was 62.15 ± 7.66 years. Overall, 19.2% of the participants were defined as having frailty. Age, sex, and other clinical conditions vary among individuals with different CVH statuses. The ideal smoking metric was the most prevalent (79.9%) in this population. The ideal physical activity metric (23.5%) and the ideal healthy diet score metric (4.9%) were the least prevalent in this population [Table 2]. Only 0.1% of older adults had all seven CVH metrics classified into the ideal status, and 9.1% of older adults had more than 5 CVH metrics in the ideal status. The prevalence of more than five ideal CVH metrics was gradually lower across the age groups (from 9.8% to 8%). In addition, the prevalence of more than five ideal CVH metrics was more prevalent in women than in men (10.9% and 7%, respectively) [Table 3].
|Table 1: Characteristics of the study participants according to the cardiovascular health status|
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|Table 3: Distribution of ideal cardiovascular health metrics in age and gender subgroups|
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When those with 0–2 ideal CVH metrics were used as the reference group, the odds ratios (ORs) for frailty were 0.76 (95% confidence interval [CI], 0.63–0.91) for those with 3–4 ideal CVH metrics and 0.37 (95% CI, 0.24–0.56) for those with 5–7 ideal CVH metrics. After adjustment for age, sex, and education, these relationships remained significant. For weight loss, increased risks were observed for those with 3–4 ideal CVH metrics and in those with 5–7 ideal CVH metrics (OR, 1.23; 95% CI, 1.08–1.39 and OR, 1.31; 95% CI, 1.06–1.61, respectively) [Table 4]. For lower leg weakness, decreased risks were observed for those with 3–4 ideal CVH metrics and in those with 5–7 ideal CVH metrics (OR, 0.63; 95% CI, 0.59–0.66 and OR, 0.27; 95% CI, 0.24–0.31, respectively) [Table 4]. For low energy, the relationship was not statistically significant.
|Table 4: Odds ratios of frailty to the number of ideal cardiovascular health metrics|
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| Discussion|| |
Our results indicated that the prevalence of ideal CVH metrics differs by age, sex, and individual CVH metrics. In addition, participating in a high number of ideal CVH metrics is related to a lower risk of frailty.
There is an inverse association between the number of ideal CVH metrics and frailty in our study. This result is consistent with those of several previous studies.,, Atkins et al. found that a higher number of ideal CVH metrics was related to a lower risk of frailty, as defined by both the Fried criteria and the Frailty Index. Graciani et al. and Wong et al. had the same conclusions using the Fried criteria and Frailty Index, respectively. Our study further confirmed the association between ideal CVH metrics and frailty in Asian populations. Moreover, we included seven complete CVH metrics defined by the AHA and sufficient sample size for the analysis.
As in the individual frailty component, the risk of low energy is not related to ideal CVH metrics. This result was consistent with those of the study by Graciani et al. In that study, exhaustion, which is one of the components of the Fried criteria, was not associated with ideal CVH metrics. Since the question is somewhat ambiguous and general, this finding might be attributed to the insufficient ability of the question to detect frailty. Moreover, we thought that the population age might be the main cause. In one previous meta-regression analysis, the authors found that the protection effect of achieving more ideal CVH metrics was attenuated with older age. Therefore, if our study population were younger, the relationship between ideal CVH metrics and low energy might be more prominent. In addition, the other individual frailty component, weight loss, was more prevalent in the participants with more ideal CVH metrics. This result is unexpected because weight loss is a risk factor for poor outcomes in older adults., The main reason for this problem might be that we could not distinguish between unintentional weight loss and intentional weight loss. Locher et al. found that unintentional weight loss would increase mortality, whereas intentional weight loss would not. Since our participants regularly received health examinations, weight control might be more popular in this group, suggesting that most weight loss in our population is intentional weight loss.
A systematic review  showed that the prevalence of 6–7 ideal CVH metrics extended from 0.5% to 12% in the US studies and from 0.3% to 15% in non-US studies. The prevalence of 6–7 ideal CVH metrics was only 1.4% in our study. This low prevalence might be attributable to the sample of older participants in our study. This result is consistent with those of the study by Gaye et al., which was composed of older adults. However, the participants in our study had higher socioeconomic status and were healthier. Therefore, the prevalence might be lower among older adults in community settings in Taiwan.
In addition, the low prevalence of ideal individual CVH metrics was observed, mainly through the healthy diet scores and physical activity. In contrast, the prevalence of ideal smoking metrics was relatively high. The distribution of ideal CVH metrics was slightly different from that found in the young population. Folsom et al. studied patients aged 45–64 years and found that the prevalence of ideal fasting serum glucose and ideal blood pressure was higher than that in our population. Older people usually had multiple chronic diseases and took more medications for treating hypertension and hyperglycemia compared to the young adults. Therefore, the prevalence of ideal CVH metrics for fasting serum glucose and blood pressure is low. The information summarized above indicates that preventive interventions and health education focused on older adults should stress healthy dietary habits and regular physical activity. These two lifestyle habits are also prominently emphasized the guidelines for preventing hypertension and dyslipidemia.
We acknowledge several limitations of our study. First, the healthy diet score and physical activity score were not standardized as published by the AHA. Second, although the current mainstream definition of frailty is the Fried criteria or Frailty Index, the definition of frailty in this study was derived from the modified SOF criteria. However, this definition is simple for clinical practice and is currently used by the Health Promotion Administration in Taiwan. Third, given the greater socioeconomic and health status of our population, the results may not be generalizable to the general population. Fourth, because of the cross-sectional design of this study, the temporal relationship cannot be confirmed.
| Conclusions|| |
Our results suggest a significantly lower risk of frailty among individuals in the older population with more ideal CVH metrics. An improvement in ideal CVH metrics could prevent not only cardiovascular-related diseases but also frailty. More well-designed, prospective, and long-term studies will provide greater perspective on the frailty process and will promote healthy aging.
The study was supported by the National Defense Medical Center and Tri-Service General Hospital (TSGH-C107-164). All of the data used in this research were authorized by and received from the MJ Health Research Foundation (Authorization Code: MJHRF2018001A). Any interpretation or conclusion described in this paper does not represent the views of the MJ Health Research Foundation.
Financial support and sponsorship
The study was supported by the National Defense Medical Center and Tri-Service General Hospital (TSGH-C107-164).
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]