Novel prediction equation for appendicular skeletal muscle mass estimation in patients with heart failure: Potential application in daily clinical practice

Katano, Satoshi; Yano, Toshiyuki; Ohori, Katsuhiko; Nagano, Nobutaka; Honma, Suguru; Shimomura, Kanako; Ishigo, Tomoyuki; Watanabe, Ayako; Honma, Remi; Fujito, Takefumi; Koyama, Masayuki; Kouzu, Hidemichi; Hashimoto, Akiyoshi; Katayose, Masaki; Miura, Tetsuji

doi:10.1177/2047487320904236

Cited by 16 publications

(31 citation statements)

References 9 publications

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“…However, the results of detailed analyses showed that the effect of hydration level and fat mass on predicted ASM by use of CC and MAC were not completely eliminated. 8 We agree with Safer et al that measurements of CC and MAC are not ideal methods for muscle mass estimation, particularly in patients with CHF. Therefore, the use of CC and MAC for the diagnosis of sarcopenia should be avoided as the statements for diagnosis of sarcopenia indicated.…”

Section: Dear Editorsupporting

confidence: 91%

“…8 Our equation for ASM estimation using CC and MAC predicted DEXA-measured ASM in patients with CHF with reasonable accuracy. 8 Indeed, application of the prediction equation to patients with CHF in the present study demonstrated that the predicted ASMI was closely correlated with DEXA-measured ASMI (Fig. 1).…”

Section: Dear Editormentioning

confidence: 71%

“…7 We recently developed a prediction equation for anthropometric estimation of ASM using stepwise multiple regression analyses in which DEXA-measured ASM served as a dependent variable. 8 The prediction equation is as follows: ASM (kg) = 0.214 × weight (kg) + 0.217 × CC (cm) -0.189 × MAC (cm) + 1.098 (male = 1, female = −1) + 0.576. 8 Our equation for ASM estimation using CC and MAC predicted DEXA-measured ASM in patients with CHF with reasonable accuracy.…”

Section: Dear Editormentioning

confidence: 99%

“…8 The prediction equation is as follows: ASM (kg) = 0.214 × weight (kg) + 0.217 × CC (cm) -0.189 × MAC (cm) + 1.098 (male = 1, female = −1) + 0.576. 8 Our equation for ASM estimation using CC and MAC predicted DEXA-measured ASM in patients with CHF with reasonable accuracy. 8 Indeed, application of the prediction equation to patients with CHF in the present study demonstrated that the predicted ASMI was closely correlated with DEXA-measured ASMI (Fig.…”

Section: Dear Editormentioning

confidence: 99%

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Reply to the comments on “Utility of the sarcopenia index for assessment of muscle mass and nutritional status in patients with chronic heart failure: Comparison with anthropometric parameters”

Abe

Yano

Katano

et al. 2020

Geriatrics Gerontology Int

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Section: Dear Editorsupporting

confidence: 91%

Section: Dear Editormentioning

confidence: 71%

Section: Dear Editormentioning

confidence: 99%

Section: Dear Editormentioning

confidence: 99%

See 2 more Smart Citations

Reply to the comments on “Utility of the sarcopenia index for assessment of muscle mass and nutritional status in patients with chronic heart failure: Comparison with anthropometric parameters”

Abe

Yano

Katano

et al. 2020

Geriatrics Gerontology Int

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“…Decreased muscle mass is often evaluated using anthropometric measurements in clinical practice during nutritional assessment. Several ASM estimating formulas using anthropometric measurements [12][13][14][15][16][17][18][19][20] have already been reported, but only few studies have focused on older adults. [17][18][19][20] In addition, given that paralyzed limbs have reduced muscle mass 21 and sarcopenia in paralyzed patients is a poor factor for ADL improvement, 22 the presence of paralysis is also an important factor for ASM estimation.…”

Section: Introductionmentioning

confidence: 99%

Estimating appendicular muscle mass in older adults with consideration on paralysis

Ueshima

Maeda

Murotani

et al. 2020

Geriatrics Gerontology Int

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Aim This study aimed to develop appendicular skeletal muscle mass (ASM) estimating formulas that also consider the presence of paralysis for older adults and people with disabilities. Methods This retrospective study analyzed 315 consecutive patients, post‐stroke, aged ≥65 years, in a rehabilitation hospital. Six different ASM estimating formulas were developed using a five‐fold cross‐validation method and compared with the measured ASM obtained from bioelectrical impedance analysis. These formulas included age, gender, height, weight, arm circumference, triceps skinfold, calf circumference and presence of paralysis. Using Pearson's correlation coefficients (r) and intraclass correlation coefficient (ICC), we examined the correlation between the formulas and the measured ASM. The accuracy of the ASM estimating formula for detecting decreased muscle mass was evaluated using the F‐value and Matthew's correlation coefficient. Results Patients' mean ± SD age was 79.0 ± 8.1 years, and 51.4% of them were men. The mean ± SD bioelectrical impedance analysis‐measured ASM was 13.7 ± 4.3 kg. Furthermore, 241 (76.5%) patients had decreased measured ASM. The mean adjusted R2 of the developed six formulas was 0.861–0.871. In all formulas, the r and ICC of the estimated ASM for the measured ASM were strong (r = 0.936–0.930 and ICC = 0.928–0.934). These formulas revealed excellent sensitivity (86.0–88.2%), specificity (72.5–81.1%), accuracy (0.838–0.870), F‐value (0.899–0.918) and Matthew's correlation coefficient (0.509–0.612) for measured ASM depletion. Conclusions We successfully developed ASM estimating formulas using anthropometric measurements considering the presence of paralysis. Thus, these formulas are beneficial for diagnosing sarcopenia in older adults, without requiring any special equipment. Geriatr Gerontol Int 2020; 20: 1145–1150.

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Independent link between peripheral artery disease and muscle wasting in patients with heart failure

et al. 2020

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Aims A high prevalence of muscle wasting, that is, reduction in muscle mass, in patients with peripheral artery disease (PAD) and heart failure (HF) has been reported. However, whether the association between PAD and muscle wasting is independent of shared risk factors such as diabetes mellitus has not been examined. Methods and results We retrospectively enrolled 440 HF patients (mean age, 74 years; inter-quartile range, 64-82 years; 52% male). Muscle wasting was defined as an appendicular skeletal muscle mass index (ASMI) of <7.0 kg/m 2 in men and <5.4 kg/m 2 in women. PAD was defined as an ankle brachial index (ABI) of <0.9 in either leg. The prevalence of PAD in HF patients was 21%. ASMI was positively correlated with ABI in HF patients. In multivariate logistic regression analysis, ASMI and muscle wasting were selected as independent explanatory factors of the presence of PAD after adjustment for age, sex, diabetes mellitus, hypertension, dyslipidaemia, estimated glomerular filtration rate, and smoking status, established risk factors of atherosclerosis. In propensity score-matched analysis, frequency of muscle wasting was higher in patients with PAD than in patients with an ABI of ≧1.1 (72.1% vs. 52.5%, P = 0.04). Conclusions The results suggest that there is an independent link between PAD and muscle wasting in HF patients.

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Novel prediction equation for appendicular skeletal muscle mass estimation in patients with heart failure: Potential application in daily clinical practice

Cited by 16 publications

References 9 publications

Reply to the comments on “Utility of the sarcopenia index for assessment of muscle mass and nutritional status in patients with chronic heart failure: Comparison with anthropometric parameters”

Reply to the comments on “Utility of the sarcopenia index for assessment of muscle mass and nutritional status in patients with chronic heart failure: Comparison with anthropometric parameters”

Estimating appendicular muscle mass in older adults with consideration on paralysis

Independent link between peripheral artery disease and muscle wasting in patients with heart failure

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