Author + information
- Received August 3, 2015
- Revision received November 5, 2015
- Accepted November 13, 2015
- Published online April 1, 2016.
- Kentaro Kamiya, PT, PhDa,∗ (, )
- Takashi Masuda, MD, PhDb,c,
- Yuya Matsue, MD, PhDd,
- Takayuki Inomata, MD, PhDe,
- Nobuaki Hamazaki, PT, MSca,b,
- Ryota Matsuzawa, PT, PhDa,
- Shinya Tanaka, PT, MScb,
- Kohei Nozaki, PT, MSca,
- Emi Maekawa, MD, PhDe,
- Chiharu Noda, MD, PhDe,
- Minako Yamaoka-Tojo, MD, PhDb,c,
- Atsuhiko Matsunaga, PT, PhDb,c,
- Tohru Izumi, MD, PhDe and
- Junya Ako, MD, PhDe
- aDepartment of Rehabilitation, Kitasato University Hospital, Sagamihara, Japan
- bDepartment of Cardiovascular Medicine, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan
- cDepartment of Rehabilitation, School of Allied Health Sciences, Kitasato University, Sagamihara, Japan
- dDepartment of Cardiology, Kameda Medical Center, Chiba, Japan
- eDepartment of Cardiovascular Medicine, Kitasato University School of Medicine, Sagamihara, Japan
- ↵∗Reprint requests and correspondence:
Dr. Kentaro Kamiya, Kitasato University Hospital, Department of Rehabilitation, 1-15-1 Kitasato, Minami-ku, Sagamihara, Kanagawa 252-0375, Japan.
Objectives This study was performed to investigate the complementary role of arm circumference to body mass index (BMI) in risk stratification of patients with heart failure (HF).
Background High BMI is associated with improved survival in patients with HF. However, it does not discriminate between fat and lean muscle as a predominant factor.
Methods BMI, waist circumference (WC), and mid-upper arm circumference (MUAC) were evaluated in 570 consecutive Japanese patients with HF (mean age 67.4 ± 14.0 years). Patients were stratified into low and high groups according to BMI, WC, and MUAC and combined into low- or high-BMI and low- or high-WC groups or low- or high-BMI and low- or high-MUAC groups. The endpoint was all-cause mortality.
Results Seventy deaths occurred over a median follow-up period of 1.5 years (interquartile range: 0.7 to 2.8 years). After adjusting for several pre-existing prognostic factors, including Seattle Heart Failure Score and exercise capacity, BMI (hazard ratio [HR]: 0.68; p = 0.016), WC (HR: 0.76; p = 0.044), and MUAC (HR: 0.52; p < 0.001) were all inversely associated with prognosis. Compared with the high-BMI/high-WC group, both the low-BMI/high-WC and low-BMI/low-WC groups showed comparably poorer prognosis. However, the low-BMI/low-MUAC group but not the low-BMI/high-MUAC group showed poorer prognosis than the high-BMI/high-MUAC group. Adding MUAC to BMI (0.70 vs. 0.63, p = 0.012) but not WC to BMI (0.64 vs. 0.63, p = 0.763) significantly increased the area under the curve on receiver-operating characteristic curve analysis.
Conclusions MUAC, but not WC, plays a complementary role to BMI in predicting prognosis in patients with HF.
Both overweight and obesity increase the risk for cardiovascular disease, including heart failure (HF) (1,2). However, despite the adverse effects of obesity on risk factors for HF, many studies have shown that overweight and obesity are associated with better prognosis after the establishment of HF, which is known as the obesity paradox (3,4). Most studies describing the obesity paradox used body mass index (BMI) as a surrogate maker for obesity (5,6). However, BMI cannot discriminate between fat and lean muscle, and this may explain the obesity paradox. Indeed, several recent studies underlined the importance of evaluating body composition not only by BMI but in combination with fat mass or lean body mass to understand the true prognostic significance of BMI in patients with established cardiovascular disease (7–10). However, limited data are available on the impact of body fat and muscle mass on the prognosis in patients with HF. Therefore, we hypothesized that measuring fat mass and lean body mass might provide useful information in addition to BMI, and we examined the complementary role of evaluating body fat and muscle mass in addition to BMI in such cases.
We retrospectively reviewed a cohort of 771 consecutive patients ages 18 years and older admitted to Kitasato University Hospital between August 2008 and July 2014 for acute HF, defined by the presence of volume overload and dyspnea at rest or with minimal activity. All patients with BMI measurement were included. We used waist circumference (WC) as a metric of fat mass and mid-upper arm circumference (MUAC) as a metric of lean body mass because MUAC was shown to be well correlated with lean body mass as determined by whole-body magnetic resonance imaging compared with other parameters of body composition, including BMI and WC (11). The exclusion criterion was BMI <18.5 kg/m2 (5,8,12). The study protocol was performed in accordance with the tenets of the Declaration of Helsinki and was approved by the Ethics Committee of Kitasato University Hospital.
Data on all variables were collected from electronic medical records. Clinical details of presentation (medication use, comorbidities) as well as demographic, echocardiographic, and biochemical data just before discharge from the hospital were recorded. BMI was calculated as body weight (kilograms) divided by the square of height (meters). A commercially available immunoradiometric assay was used to measure B-type natriuretic peptide (BNP) concentration (Shionogi, Osaka, Japan). Estimated glomerular filtration rate (eGFR) was defined according to the formula recommended by the Japanese Society of Nephrology as: 194 × (serum creatinine)1.094 × (age)0.287 in men and 194 × (serum creatinine)1.094 × (age)0.287 × 0.739 in women (13). Left ventricular ejection fraction was estimated using Simpson’s method on 2-dimensional echocardiograms. The Seattle Heart Failure Score (SHFS) was derived in each patient from 14 variables (age, sex, New York Heart Association functional classification, left ventricular ejection fraction, ischemic etiology, systolic blood pressure, diuretic agent dose, allopurinol use, statin use, lymphocyte percentage, serum sodium, cholesterol, hemoglobin, and uric acid) (14). The 6-min walking distance (6MWD) was measured according to standard guidelines at hospital discharge (15).
The endpoint of this study was all-cause mortality, as determined by a review of medical records, and the time for the endpoint was calculated as the number of days from the date of MUAC and WC measurement to the date of events.
MUAC and WC measurement
MUAC and WC were measured to the nearest 1 mm by trained physiotherapists or nurses using a plastic measuring tape. We measured MUAC at the point midway between the lateral projection of the acromion process and the lateral epicondyle of the humerus with the elbow fully extended. The mean of the left and right MUAC measurements was used for the analyses. WC was measured at the level of the umbilicus (16,17).
The results of normally distributed continuous variables are expressed as mean ± SD, and variables not normally distributed are presented as median (interquartile range). Categorical variables are expressed as numbers and percentages. Patients were divided into low-BMI (<23 kg/m2) and high-BMI (≥23 kg/m2) groups according to the Asian-specific BMI categorizations recommended by the World Health Organization (18) and low-WC (<85 cm in men and <90 cm in women) and high-WC (≥85 cm in men and ≥90 cm in women) groups according to the Japanese guidelines for metabolic syndrome (16,17). We also divided the whole cohort into low- and high-MUAC groups according to the median age- and sex-stratified values, which were obtained from the Japanese Anthropometric Reference Data 2001 (19). We also divided the whole cohort into 4 groups according to BMI and WC as follows: high-BMI/high-WC, high-BMI/low-WC, low-BMI/high-WC, and low-BMI/low-WC. We also divided patients into 4 groups according to BMI and MUAC as follows: high-BMI/high-MUAC, high-BMI/low-MUAC, low-BMI/high-MUAC, and low-BMI/low-MUAC.
Baseline characteristics were compared using Student t tests or one-way analysis of variance, chi-square tests, or Fisher exact tests as appropriate. Correlations among BMI, WC, and MUAC in the full sample and stratified into high- and low-BMI groups were assessed using Pearson’s correlation coefficients. Kaplan-Meier, log-rank test, and Cox regression analyses were performed to evaluate prognostic predictive capability. Multiple imputation generated 20 datasets of complemented missing values (20). In Cox regression analysis, we constructed 3 predictive models using pre-existing prognostic factors: model 1 used SHFS as an adjusting variable; model 2 included SHFS, log BNP, and eGFR; and model 3 included SHFS, log BNP, eGFR, and 6MWD.
In addition, to assess the potential effect modification on the association of WC and MUAC with mortality, we performed subgroup analysis of WC and MUAC in various subgroups relevant to HF prognosis, including BMI (stratified at 23 kg/m2), sex, age (stratified at 75 years), left ventricular ejection fraction (stratified at 50%), and 6MWD (stratified at 300 m) (21).
Finally, to examine whether WC and MUAC had complementary predictive capability to BMI, we constructed receiver-operating characteristic curves for all-cause mortality using 3 models: BMI only, BMI plus WC, and BMI plus MUAC. The areas under the curves (AUCs) were compared according to the method of DeLong et al (22). In addition, the AUCs were compared in high- versus low-BMI groups to confirm the complementary predictive capability in addition to BMI.
Analyses were performed using SPSS version 22.0 (IBM Corporation, Armonk, New York), Stata version 13.0 (StataCorp LP, College Station, Texas), and R version 3.1.2 (R Foundation for Statistical Computing, Vienna, Austria). A 2-tailed p value <0.05 was taken to indicate statistical significance.
After excluding 201 patients with BMIs <18.5 kg/m2, 570 patients were included in the study. Table 1 shows the baseline characteristics for all participants and for groups stratified by BMI, WC, and MUAC. The mean age of the study population was 67 ± 14 years, 70% of the patients were men, and 66% had reduced left ventricular ejection fractions. Eighty percent of the patients were prescribed beta-blockers at discharge, 89% received angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and 81% received diuretic agents. The mean BMI, WC, and MUAC were 23.2 kg/m2, 86.0 cm, and 25.9 cm, respectively. Relative to high-BMI patients, those with low BMIs were older and had more severe HF (e.g., higher BNP and SHFS) and had a lower prevalence of obesity-associated comorbidities, such as hypertension, diabetes, and dyslipidemia. Similar differences were seen in low-WC and low-MUAC groups.
Associations among BMI, WC, and MUAC
Correlations among BMI, WC, and MUAC in the full sample and stratified by high and low BMI are shown in Online Table 1. BMI had moderate to strong positive correlations with WC (r = 0.82, p < 0.001) and MUAC (r = 0.78, p < 0.001). The correlations between these variables were higher in the high-BMI group than the low-BMI group.
Associations of BMI, WC, and MUAC with all-cause mortality
Seventy deaths occurred over a median follow-up period of 1.5 years (interquartile range: 0.7 to 2.8 years). Kaplan-Meier curves followed by log-rank test showed that all-cause mortality increased significantly in low-BMI, low-WC, and low-MUAC groups (Figures 1A to 1C). Kaplan-Meier curves of each stratified group are shown in Figures 1D and 1E, and the results indicated significant differences in prognosis between both BMI- and WC-stratified groups and BMI- and MUAC-stratified groups. In groups stratified by BMI and WC, outcomes were worse in patients with low BMIs irrespective of WC. However, in groups stratified by BMI and MUAC, outcomes in the high-BMI/low-MUAC group were worse than those in the high-BMI/high-MUAC group, and the low-BMI/low-MUAC group showed poorer outcomes than the low-BMI/high-MUAC group.
Table 2 shows the results of Cox regression analysis for all-cause mortality. BMI, WC, and MUAC were all significant and independent predictors of mortality in our cohort, even after adjusting for any of the prognostic models.
Associations of WC and MUAC with mortality in various subgroups
A higher MUAC was consistently associated with a favorable prognosis across various subgroups, even after adjusting for BMI and SHFS, except for patients with 6MWD ≥300 m (Figure 2). The favorable effect of higher MUAC was comparable between high- and low-BMI groups (BMI <23 kg/m2, hazard ratio: 0.44; 95% confidence interval [CI]: 0.26 to 0.72; BMI ≥23 kg/m2, hazard ratio: 0.37; 95% CI: 0.17 to 0.81; p for interaction = 0.22). In contrast, WC was consistently not associated with prognosis in any of the subgroups.
Complementary prognostic predictive capabilities of WC and MUAC to BMI
Receiver-operating characteristic curve analysis was performed for the logistic regression models of BMI only, BMI plus WC, and BMI plus MUAC (Figure 3). The AUCs on receiver-operating characteristic curve analysis were 0.63 (95% CI: 0.57 to 0.70) for BMI only, 0.64 (95% CI: 0.57 to 0.70) for BMI plus WC, and 0.70 (95% CI: 0.64 to 0.76) for BMI plus MUAC. There was no statistically significant difference between the AUCs of BMI and BMI plus WC (p = 0.763). However, the AUC was significantly better for BMI plus MUAC than for both BMI only (p = 0.012) and BMI plus WC (p = 0.022). In subgroup analysis stratified by BMI status (Online Figure 1), BMI plus MUAC showed the highest AUC and significantly better predictive capability compared with BMI only in patients in the low-BMI group. In the high-BMI group, BMI plus MUAC showed a numerically high AUC compared with both BMI only and BMI plus WC, but the differences were not significant.
The primary findings of our study were as follows: 1) BMI, WC, and MUAC were independently associated with outcomes in patients with HF; 2) when adjusted by BMI, a higher MUAC was consistently associated with favorable prognosis both in the entire cohort and across the selected subgroups; however, WC lost its significance after adjustment for BMI; and 3) MUAC but not WC showed complementary prognostic predictive capability to BMI in patients with HF. These results suggest that MUAC, which is an inexpensive and easily measurable metric, can be used as a risk stratification tool over BMI in patients with HF. Moreover, the results of the present study indicate that muscle rather than fat mass is an important complementary prognostic factor for BMI in patients with HF.
Most previous studies on obesity in patients with HF did not analyze body composition and did not address whether fat or another body component was associated with the prognosis of patients with HF. Although recent studies have shown that the association between high BMI and good prognosis in HF could be explained by muscle mass and nutritional status (10,23), these studies did not incorporate measurements of exercise capacity. Several studies have suggested that exercise capacity alters the relationship between adiposity and prognosis in both cardiovascular disease and HF (6,12,24,25). Our results showed that high MUAC had a protective effect for survival even after adjusting for several prognostic factors, including exercise capacity and SHFS. These findings suggest that muscle mass could partially explain the obesity paradox in HF and stratify the risk both in patients with high BMIs and in those with low BMIs.
A consistent protective effect of muscle mass or strength was found in community-dwelling subjects and patients with cardiovascular disease, including HF (9,23–29). With regard to the relationship between muscle mass and prognosis, Heitmann and Frederiksen (30) reported that smaller thigh circumference was associated with the development of cardiovascular morbidity and early mortality even after adjustment for WC, life-style, and cardiovascular risk factors in healthy subjects. In addition, those investigators reported that outcomes were more closely related to thigh circumference than to WC. The possible underlying mechanisms of low MUAC and mortality are frailty or cachexia and sarcopenia. In patients with HF, both skeletal muscle mass and muscle strength constitute significant determinants of physical fitness and capacity and are protective against cardiovascular disease and HF (31–33). On subgroup analysis, higher MUAC was significantly associated with better prognosis in HF patients with low but not high 6MWDs in our study. This finding suggests that exercise capacity may alter the relationship between body composition and prognosis in patients with HF, which was supported by a recent study by Lavie et al. (12) in 2,066 patients indicating that low BMI was associated with poorer prognosis in patients with HF with poor exercise capacity but not in those with good exercise capacity. Another possible mechanism involves the anti-inflammatory effect of skeletal muscle. Pre-clinical studies demonstrated that healthy skeletal muscle secretes an array of anti-inflammatory and cardioprotective cytokines (34). It is possible that the association between the loss of muscle mass and mortality is in part mediated by the loss of healthy skeletal muscle, which has beneficial anti-inflammatory or other effects (35). Recently, Araki et al. (36) reported that skeletal muscle growth attenuated cardiac remodeling and dysfunction in a mouse myocardial infraction model. This study also demonstrated that skeletal muscle growth led to increased myocardial capillary density, decreased myocardial interstitial fibrosis, reduction in cardiac fibrosis, and decreased BNP. These findings support our hypothesis regarding the impact of MUAC on prognosis over BMI.
In this study, WC as an indicator of central obesity was an independent predictor of prognosis but did not have complementary predictive capability to BMI. Several studies showed that percentage body fat and higher WC were associated with better survival in both patients with HF and those with coronary artery disease (8,9,37). In contrast, Gastelurrutia et al. (10) reported that body fat was not associated with survival in patients with HF. Further studies are needed to confirm the complementary role of WC or body fat to BMI on risk stratification in HF.
MUAC can be obtained easily in routine clinical practice using a simple measuring tape, without any cost. In addition, there are no constraints regarding standing problems or spinal deformities and a negligible influence of fluid retention, all of which are common in older patients with HF (10,23). The reproducibility of MUAC measurements has been shown to be exceptionally good, with intraclass correlation coefficients of 0.98 for between-observer variation and 0.99 for within-observer variation, with the patient in either the sitting or standing position (38). Another strength of this study was that our conclusions held even after adjusting for a broad range of potential confounders. SHFS was developed and validated in a large population of HF patients and considers a number of pre-existing prognostic factors. Because SHFS does not include BMI, BNP, eGFR, and 6MWD, all of which are well-established risk factors in HF, we included these variables as potential confounders in multiple adjustments in Cox models.
First, this was a relatively small retrospective study with limited follow-up. Recent studies have demonstrated that the protective effects of overweight and obesity in coronary artery disease disappeared after 5-year follow-up (39,40). Therefore, the long-term protective effect of high MUAC is unclear in this population.
Second, we did not perform direct measurement of muscle mass and body fat (e.g., using magnetic resonance imaging or computed tomography). However, this was also a strength of our study, because we demonstrated the utility of a metric, MUAC, that can be measured easily in daily clinical practice. In addition, MUAC was more strongly correlated with WC in the high-BMI group than the low-BMI group (Online Table 1), suggesting the possibility that MUAC captures more variation in fat mass than lean mass among overweight and obese patients.
Third, multiple testing was conducted in this study. Such multiple testing may increase the false positive (type I error) rate.
Finally, the mean BMI of our cohort was lower than those of previous studies in Western populations. Therefore, it is unclear whether our study results are readily applicable to Westerners with higher BMI.
BMI, WC, and MUAC are independent predictors of prognosis in patients with HF. However, only MUAC added prognostic information to BMI in patients with HF. Because MUAC can be measured both rapidly and easily in clinical practice, this should be used as a complementary prognostic predictor in addition to BMI to achieve more accurate prognostic information in patients with HF.
COMPETENCY IN MEDICAL KNOWLEDGE: MUAC could substantially explain the obesity paradox in HF. Evaluation of MUAC could be used in addition to BMI for prediction of mortality.
TRANSLATIONAL OUTLOOK: Further research is needed to confirm the independent associations of MUAC and WC with mortality in different ethnic populations.
This study was supported by the Grant for Clinical and Epidemiologic Research of the Joint Project of Japan Heart Foundation and the Japanese Society of Cardiovascular Disease Prevention Sponsored by AstraZeneca. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- area under the curve
- body mass index
- B-type natriuretic peptide
- confidence interval
- estimated glomerular filtration rate
- heart failure
- mid-upper arm circumference
- Seattle Heart Failure Score
- 6-min walking distance
- waist circumference
- Received August 3, 2015.
- Revision received November 5, 2015.
- Accepted November 13, 2015.
- American College of Cardiology Foundation
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