The Associations between Muscle Ultrasound Measurements, Calf Circumference, Mid-upper Arm Circumference, and Body Mass Index

doi:10.21203/rs.3.rs-3770246/v1

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The Associations between Muscle Ultrasound Measurements, Calf Circumference, Mid-upper Arm Circumference, and Body Mass Index

https://doi.org/10.21203/rs.3.rs-3770246/v1

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Purpose

Muscle ultrasound (US) is a trending method for sarcopenia diagnosis. In this study, we aimed to show the changes in rectus femoris (RF) muscle thickness, RF cross-sectional area, gastrocnemius medialis (GC) muscle thickness, calf circumference (CC), and mid-upper arm circumference (MAC) throughout different body mass index (BMI) categories. Therefore, we planned to find out their associations with probable sarcopenia regardless of age, sex, and BMI.

Methods

This cross-sectional study was conducted in geriatric outpatients (≥ 65 years). Waist circumference (WC), hip circumference (HC), MAC, CC, BMI, handgrip strength (HGS), fat-free mass index (FFMI), fat%, and muscle US measurements were obtained.

Results

A total of 227 patients, 62.1% female, were included. Partial correlation, controlled for age, and sex, revealed that BMI had a positive strong correlation with WC (r = 0.727), HC (r = 0.793), MAC (r = 0.637), FFMI (r = 0.632), and fat% (r = 0.620). Therefore, BMI had a positive moderate correlation with CC (r = 0.545), and a positive weak correlation with GC muscle thickness (r = 0.283). RF muscle thickness (OR 0.781, 95% CI;0.691–0.882, p < 0.001), RF cross-sectional area (OR 0.601, 95% CI; 0.480–0.753, p < 0.001), and GC muscle thickness (OR 0.869, 95% CI; 0.783–0.964), p < 0.008) were independently associated with probable sarcopenia when adjusted for age, sex, and BMI categories. There were no independent associations between BMI, CC, and MAC.

Conclusion

Muscle US measurements are independently associated with probable sarcopenia, regardless of age, sex, and BMI. Future prospective studies are needed to support the importance of muscle US to predict sarcopenia in patients with obesity.

Muscle ultrasound

obesity

body mass index

sarcopenia

Aim: To explore the relationship between muscle ultrasound parameters, anthropometric measurements and body mass index categories.

Findings: Rectus femoris (RF) muscle thickness, RF cross-sectional area, and gastrocnemius medialis muscle thickness are independently associated with probable sarcopenia regardless of age, sex, and BMI.

Message: Muscle mass assessment methods may cause underestimation of sarcopenia in patients with obesity. Muscle US may reflect the more accurate results.

Sarcopenia is a geriatric syndrome, defined as a progressive muscle disorder characterized by loss of muscle mass and function, and is related to increased morbidity and mortality. It has a close relationship with falls, frailty, and osteoporosis [1–3]. Low handgrip strength (HGS) indicates probable sarcopenia and confirmation of sarcopenia needs muscle mass assessment [4]. Muscle ultrasound (US) is a trending method in sarcopenia assessment, and it is safe, cheap, accurate, and noninvasive. Therefore, its portability is a big advantage, especially for hospitalized patients [5].

On the other hand, sarcopenia may be underestimated especially for patients with obesity. Although obesity may be protective for older patients, sarcopenic obesity may cause devastating results. In this field, the more accurate assessment of muscle mass in patients with obesity is the cornerstone. The last European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) published a consensus statement. They supported using dual-energy x-ray absorptiometry (DXA), or bioelectrical impedance analysis (BIA) as an alternative second choice. Computerized tomography (CT) should be used when possible, especially in oncology clinics. However, the advantages and disadvantages of DXA, BIA, or CT are debatable. There are some concerns about availability, accuracy, cost-effectivity, and portability. Using BIA in individuals with BMI > 34 kg/m² may lead to an underestimation of fat mass and an overestimation of fat-free mass [6]. Recently, it is recommended to adjust appendicular lean mass and skeletal muscle mass for body mass index (BMI) or weight to prevent underestimation of sarcopenic obesity [7, 8].

Anthropometric measurements including calf circumference (CC) and mid-arm circumference (MAC) are widely used, especially in screening tests for malnutrition and sarcopenia. However, they are affected by excess subcutaneous fat or fluid accumulation, which is common in obesity. And they may delay the diagnosis [9, 10].

Low handgrip strength is an inevitable part of sarcopenia diagnosis. There are emerging studies supporting the relationship between rectus femoris muscle and HGS [11–13]. In light of these data, we aimed to show the changes in rectus femoris (RF) muscle thickness, RF cross-sectional area, gastrocnemius medialis (GC) muscle thickness, calf circumference (CC), and mid-upper arm circumference (MAC) throughout different body mass index (BMI) categories. Therefore, we planned to find out their associations with probable sarcopenia regardless of age, sex, and BMI.

Study design and participants

This cross-sectional study was conducted in a geriatric outpatient clinic (≥ 65 years) of a university hospital. The study was approved by the local ethics committee. Verbal and written Informed consent was obtained. The study protocol was in line with the Declaration of Helsinki.

Inclusion criteria

-Subjects, who were 65 years and over and potentially eligible

Exclusion criteria

-Subjects having moderate or severe dementia

-Subjects who could not cooperate, and who could not stand for anthropometric measurements

-Subjects with clinical hypervolemia or congestive heart failure, end-stage renal disease, acute kidney disease, hematologic or oncologic malignancy, uncontrolled hypertension, or any other disease that may cause hypervolemia and affect the BIA results

-Subjects having a pacemaker, arm or leg amputation, hip or knee replacement

-Subjects having rheumatologic disease causing hand deformity.

Patient characteristics

Katz's activities of daily living (ADL) (score 0–6) and Lawton-Brody's instrumental activities of daily living (IADL) (score 0–8) were used for functional assessment. The higher scores were related to higher independence [14, 15]. Mini Nutritional Assessment short-form (MNA-SF) was used for malnutrition screening. It ranges from 0 to 14 and was categorized as malnutrition (score 0–7), at risk of malnutrition (score 8–11), and nutritionally normal (score 12–14) [16]. SARC-F questionnaire was used for sarcopenia screening, and a score of 4 and over indicates positive sarcopenia screening. It includes questions about assistance in walking, rising from a chair, climbing stairs, falls, and strength [17, 18]. Patients living with frailty were assessed on 9 points clinical frailty scale. The score ranges between 1(very fit) and 9 (terminally ill). A score of 4 is called pre-frail and a score ≥ 5 is considered to be frail [19, 20].

Anthropometric measurements

Anthropometric measurements were obtained before breakfast, and early in the morning. Height, weight, MAC, CC, waist circumference (WC), and hip circumference (HC) were recorded. Height (meter) and weight (kilogram) were measured while standing (TEM-BEKO 035×040 height and weight measuring machine, Istanbul, Turkey). BMI was calculated by dividing body weight in kilograms by height in meters squared (kg/m²). Mid-arm circumference, CC, and WC were measured with a plastic tape. Mid-arm circumference was measured within 90ᵒ flexion of the elbow. Calf circumference was measured at the widest part of the leg in a sitting position when knees were at 90ᵒ flexion. Waist and hip circumference were measured at the umbilicus level, and at the level of the greatest part of the buttocks, respectively.

Muscle strength measurement and body composition

A Takei digital grip strength dynamometer (Takei Scientific Instruments Co, Niigata, Japan) was used for HGS. Patients were seated with adduction of shoulders, 90ᵒ flexion of the elbow, and neutrally rotated forearm. Handgrip strength was performed three times by the dominant hand and the highest value was used for analysis. The thresholds of 16 kg for females and 27 kg for males were determined according to uropean Working Group on Sarcopenia in Older People (EWGSOP2) [4]. Probable sarcopenia was defined as the existence of low handgrip strength. Fat-free mass index (FFMI), and fat% values were recorded by a multifrequency and tetrapolar technique using a Body Stat Quadscan 4000 bioimpedance analyzer (BodyStat Ltd, Douglas, Isle of Man, British Isles). Measurements were taken in a lying and supine position after overnight fasting.

Muscle ultrasound measurements

Muscle US measurements including GC muscle thickness, RF muscle thickness, and RF cross-sectional area were measured by a 5-cm width linear transducer probe with 8–10 MHz (LOGIQ 200 PRO, General Electric Medical Systems, Chicago, US). An experienced physician took the measurement, at the right sight of patient, after resting for at least 15 min. An excess amount of gel with minimal pressure was used. The muscle thickness was defined as the distance between the superficial and deep fascia of the muscle. Gastrocnemius medialis muscle thickness was assessed at prone position with extended legs at the bulkiest area of muscle between the popliteal crease and the medial malleolus. The RF muscle thickness and RF cross-sectional area were recorded the supine position with extended and neutrally positioned hip and shoulder. The values were calculated at the midpoint between the spina iliaca anterior superior and the superior border of the patella. Rectus femoris cross-sectional area was determined as the area of the cross-section of a muscle perpendicular to its longitudinal axis.

Statistical analysis

SPSS software, version 25 was used for the statistical analysis. visual (histograms, probability plots) and analytical (Kolmogorov-Simirnow test) methods were used for normality of tests. Descriptive analyses were presented as percentages for categorical variables, means and standard deviations for normally distributed variables, and medians and interquartile range (25th–75th percentile) for non-normally distributed and ordinal variables. Subjects were categorized according to BMI quartiles. The differences between these four groups were tested by using Kruskal-Wallis test for non-normally distributed variables, by using independent samples T test for normally distributed variables. The Mann‐Whitney U test was performed to test the significance of pairwise differences using the Bonferroni correction to adjust for multiple comparisons. A one‐way analysis of variance was used to compare normally distributed variables. The Levene test was used to assess the homogeneity of the variances. When overall significance was observed, pairwise post hoc tests were performed. The chi‐square test or Fisher exact test, where appropriate, was used to compare proportions between groups. Partial correlation was performed to test the relationship between BMI and parameters associated with muscle strength and mass, controlling for age and sex. As HGS was non-normally distributed, logarithmic transformation (log10) of HGS was used.

The associations of CC, MAC, GC muscle thickness, RF muscle thickness, and RF cross-sectional area with probable sarcopenia were determined using multivariable logistic regression analyses with odds ratio (OR) and 95% CI. All variables were adjusted for age, sex and BMI in multivariable analysis. Hosmer Lemeshow goodness of fit statistics were used to assess model fit. A 5% type-1 error level was considered statistically significant.

A total of 227 patients, 62.1% (n = 141) female, were included in the study. The median (range) age was 72 (65–93). The median (range) BMI value was 29.7 (17.3–51.9) kg/m². The subjects were divided in to groups according to BMI quartiles. Q1 (< 26.7 kg/m²), Q2 (26.7–29.7 kg/m²), Q3(29.7–33.3 kg/m²), and Q4 (> 33.3 kg/m²) consisted of 24.7%, 24.7%, 16.3%, and 34.4% of subjects respectively. Baseline characteristics of 4 group were given in Table 1. The median age, ADL, IADL, CFS, SARC-F, and MNA-SF scores were not different between groups. The rate of female sex was lower in group 1 (p < 0.001).

Table 1

Geriatric assessment of patients according to BMI categories
	Group 1 (n = 56)	Group 2 (n = 56)	Group 3 (n = 37)	Group 4 (n = 78)	p
Age, median (IQR)	73.0(68.0–81.0)	73.0(68.0–79.0)	71.0(66.0–75.0)	72.0(68.0–76.0)	0.050
Sex, female, n(%)	22(39.3)	33(58.9)	25(67.6)	61(78.2)	< 0.001
ADL, median(IQR)	6.0(5.0–6.0)	6.0(6.0–6.0)	6.0(5.5-6.0)	6.0(5.0–6.0)	0.505
IADL, median(IQR)	8.0(6.0–8.0)	8.0(7.0–8.0)	8.0(7.0–8.0)	8.0(6.0–8.0)	0.585
CFS, median(IQR)	4.0(3.0–5.0)	3.0(3.0–4.0)	4.0(3.0–4.0)	4.0(3.0–5.0)	0.275
SARC-F, median(IQR)	3.0(0.2–4.7)	1.0(0.0–4.0)	1.0(0.5-4.0)	2.0(0.7-5.0)	0.688
MNA-SF, median(IQR)	11.0(8.0–13.0)	12.0(10.0–14.0)	13.0(10.0–14.0)	12.0(10.0–14.0)	0.076
ADL, activities of daily living; CFS, clinical frailty scale; IADL, Instrumental activities of daily living; MNA-SF, Mini Nutritional Assessment short form

Anthropometric measurements, BIA parameters, and muscle US measurements according to BMI quartiles were given in Table 2. The differences between groups were given for each sex separately. Waist circumference, HC, CC, MAC, FFMI, and fat% were significantly different between groups for each sex(p < 0.001). Handgrip strength, RF CSA, RF muscle thickness were not different between groups for each sex. GC muscle thicknesses for females (p = 0.003) and males (p = 0.002) were lower from others in group 1.

Table 2

Anthropometric measurements, BIA parameters, and muscle ultrasound measurements according to BMI quartiles
	Group 1 n = 56	Group 2 n = 56	Group 3 n = 37	Group 4 n = 78	p
Waist circumference, cm Female Male	88.8 ± 11.7 91.7 ± 10.0	93.0 ± 6.6 98.2 ± 7.6	98.2 ± 7.2 107.0 ± 5.4	109.5 ± 10.4 115.5 ± 10.0	< 0.001 < 0.001
Hip circumference, cm Female Male	96.4 ± 8.2 94.4 ± 9.0	102.0 ± 5.4 100.6 ± 5.2	107.4 ± 7.4 107.8 ± 5.9	117.8 ± 13.2 112.6 ± 6.8	< 0.001 < 0.001
Calf circumference, cm Female Male	30.8 ± 6.0 31.7 ± 2.5	32.7 ± 3.8 35.1 ± 4.0	35.6 ± 6.3 38.2 ± 6.8	38.8 ± 7.1 38.8 ± 6.1	< 0.001 < 0.001
Mid-upper arm circumference, cm Female Male	25.4 ± 3.6 25.7 ± 3.1	26.7 ± 2.8 28.0 ± 2.5	28.7 ± 3.8 29.1 ± 2.2	31.8 ± 4.7 32.2 ± 4.5	< 0.001 < 0.001
Handgrip strength, kg Female Male	15.1 ± 4.0 25.8 ± 8.4	14.9 ± 4.0 26.3 ± 8.7	16.8 ± 5.0 29.9 ± 6.7	17.8 ± 4.9 25.1 ± 7.1	0.026 0.768
Fat-free mass index, kg/m² Female Male	14.2 ± 2.1 16.9 ± 1.9	15.5 ± 1.3 19.7 ± 2.1	16.6 ± 1.3 20.4 ± 1.5	18.1 ± 2.0 21.0 ± 2.3	< 0.001 < 0.001
Fat%, Female male	39.9 ± 8.2 29.7 ± 6.2	44.0 ± 5.4 29.0 ± 9.5	46.2 ± 4.7 34.5 ± 4.8	50.6 ± 5.6 38.5 ± 9.1	< 0.001 < 0.001
GC muscle thickness Female Male	12.0 ± 3.0 13.2 ± 2.6	14.0 ± 2.6 15.7 ± 2.7	13.9 ± 3.3 14.4 ± 1.7	14.5 ± 3.2 15.8 ± 2.5	0.003 0.002
RF muscle thickness Female Male	11.7 ± 2.0 14.5 ± 3.2	13.9 ± 2.7 16.4 ± 3.0	14.4 ± 2.1 17.9 ± 2.3	13.5 ± 2.9 15.5 ± 3.8	0.008 0.339
RF CSA Female Male	4.3 ± 1.2 6.0 ± 2.0	5.0 ± 1.4 6.6 ± 2.0	5.5 ± 1.5 7.5 ± 1.3	5.2 ± 1.5 6.8 ± 1.8	0.013 0.195
CSA, cross-sectional area; GC, gastrocnemius muscle; RF, rectus femoris;

Table 3

Partial correlation (controlled for age, and sex) between BMI and parameters associated with muscle strength and mass
Variables	BMI
Variables	r	p
Waist circumference, cm	0.727	< 0.001
Hip circumference, cm	0.793	< 0.001
Calf circumference, cm	0.545	< 0.001
Mid-upper arm circumference, cm	0.637	< 0.001
Log-HGS, kg	-0.045	0.501
FFMI, kg/m²	0.632	< 0.001
Fat%,	0.620	< 0.001
GC muscle thickness, cm	0.283	< 0.001
RF muscle thickness, cm	0.059	0.400
RF cross-sectional area, cm²	0.101	0.149
FFMI, fat-free mass index; GC, gastrocnemius medialis; HGS, handgrip strength; RF, rectus femoris

Partial correlation between BMI and parameters associated with muscle strength and mass were conducted and controlled for age, and sex. BMI had positive strong correlation with WC (r = 0.727), HC (r = 0.793), MAC (r = 0.637), FFMI (r = 0.632), and fat% (r = 0.620). Therefore, BMI had positive moderate correlation with CC (r = 0.545), and positive weak correlation with GC muscle thickness (r = 0.283). There was no significant correlation between BMI, and HGS (r = 0.067), RF muscle thickness (r = 0.059) and RF cross-sectional area (r = 0.101).

Multivariate analyses for associations of RF muscle thickness, RF cross-sectional area, GC muscle thickness, CC, and MAC with probable sarcopenia were given in Table 4. RF muscle thickness (OR 0.781, 95% CI;0.691–0.882), p < 0.001), RF cross-sectional area (OR 0.601, 95% CI; 0.480–0.753, p < 0.001), and GC muscle thickness (OR 0.869, 95% CI; 0.783–0.964), p < 0.008) were independently associated with probable sarcopenia when adjusted for age, sex, and BMI categories. There were no independent associations between BMI, CC, and MAC.

Table 4

Multivariate analyses for associations of muscle ultrasound parameters, calf circumference, and mid-upper arm circumference with probable sarcopenia
		Probable Sarcopenia
		В	OR (9 5%CI)	P
MODEL1 (R² 0.259)	Age	0.079	1.083(1.029–1.138)	0.002
	Sex	0.338	1.403(0.698–2.818)	0.342
	BMI Q1 ref Q2 Q3 Q4	0.457 0.015 -0.247	1.580(0.664–3.762) 1.015(0.377–2.730) 0.769(0.338–1.750)	0.355 0.301 0.977 0.532
	RF muscle thickness	-0.247	0.781(0.691–0.882)	< 0.001
MODEL2 (R² 0.283)	Age	0.070	1.073(1.020–1.129)	0.007
	Sex	0.572	1.772(0.843–3.728)	0.131
	BMI Q1 ref Q2 Q3 Q4	0.283 -0.150 -0.215	1.327(0.556–3.163) 0.861(0.320–2.312) 0.806(0.351–1.853)	0.642 0.524 0.766 0.612
	RF CSA	-0.509	0.601(0.480–0.753)	< 0.001
MODEL3 (R² 0.206)	Age	0.099	1.104(1.052–1.158)	< 0.001
	Sex	-0.092	0.912(0.485–1.713)	0.774
	BMI Q1 ref Q2 Q3 Q4	0.291 -0.347 -0.141	1.338(0.571–3.137) 0.707(0.274–1.826) 0.808(0.354–1.841)	0.484 0.503 0.474 0.611
	GC muscle thickness	-0.141	0.869(0.783–0.964)	0.008
MODEL4 (R² 0.169)	Age	0.107	1.113(1.061–1.167)	< 0.001
	Sex	-0.258	0.772(0.417–1.429)	0.410
	BMI Q1 ref Q2 Q3 Q4	0.069 -0.401 -0.342	1.072(0.471–2.439) 0.670(0.254–1.766) 0.711(0.293–1.723)	0.671 0.868 0.418 0.450
	Calf circumference	-0.016	0.984(0.933–1.038)	0.552
MODEL5 (R² 0.168)	Age	0.106	1.112(1.060–1.166)	< 0.001
	Sex	-0.246	0.782(0.424–1.441)	0.430
	BMI Q1 ref Q2 Q3 Q4	0.029 -0.448 -0.350	1.029(0.454–2.333) 0.639(0.245–1.665) 0.705(0.281–1.768)	0.680 0.945 0.360 0.456
	Mid-upper arm circumference	-7.159	0.976(0.904–1.054)	0.537

In the present study, we showed the changes in RF muscle thickness, RF cross-sectional area, GC muscle thickness, CC, and MAC throughout different BMI categories. Therefore, we found out that RF muscle thickness, RF cross-sectional area, and GC muscle thickness measured by the US are independently associated with probable sarcopenia, regardless of age, sex, and BMI. This is the first study, that shows the independent association of muscle US parameters with probable sarcopenia. Low HGS is defined as probable sarcopenia by the European Working Group on Sarcopenia in Older People (EWGSOP2) [4]. It is an easy and inexpensive way of assessing muscle strength. The most important thing is that low HGS is an indicator of poor health outcomes like longer hospital stays, increased functional disabilities, poor life quality and death. It is recommended to use in daily hospital practice and in community healthcare. [4, 21, 22]. To diagnose confirmed sarcopenia, muscle mass assessment is required by magnetic resonance imaging (MRI), computed tomography (CT), dual-energy X-ray absorptiometry (DXA), or bioelectrical impedance analysis (BIA) according to EWGSOP2. [4, 23, 24] They have some advantages and disadvantages. Bioelectrical impedance analysis is a widely available method. It does not measure muscle mass directly. It derives an estimate of muscle mass based on whole-body electrical conductivity, and there are several BIA prediction models. Age, ethnicity, and specific disease conditions need specific cut-off values for different devices. In addition, BIA measurements may be influenced by hydration status of the patient.[25, 26] Muscle US is widely used technique to define muscle wasting and muscle quality, and it is getting to be used especially at bed sight.

Sarcopenic obesity is another important problem as it causes more devastating results than both sarcopenia and obesity. Diagnosing sarcopenic obesity needs altered skeletal muscle functional parameters, and altered body composition including increased fat mass and reduced muscle mass assessed as appendicular lean mass/weight by DXA or as skeletal muscle mass/weight by BIA after screening. [7] Recently, appendicular lean mass and skeletal muscle mass are recommended to be adjusted for weight, BMI, or height square. [27] Therefore, BMI is strongly advised as the ideal representative of the body size for these adjustments. [8] Bahat et al also showed that skeletal muscle mass adjusted for BMI was highly related to functionality, physical performance, and frailty, compared to adjustments made for height squared or weight. [28] Another important issue is that BIA may cause underestimation of fat mass and overestimation of fat-free mass index in patients with obesity. [6]

Obesity, sarcopenic obesity, and sarcopenia are different terms, and it is crucial to classify them. There is a fact like the obesity paradox. There are studies reporting the protective effects of obesity in older patients. [29] In fact, it is associated with higher muscle mass and strength. [30] Besides, current BMI cutoff values may not represent the risk of morbidity and mortality in older subjects compared with younger adults. [31] As we consider all the above data, using muscle US for the diagnosis of both sarcopenia and sarcopenic obesity is rational. In this study, we presented the independent association of muscle US parameters with low HGS regardless of age, sex, and BMI. We examined data according to BMI quartiles for each sex. The higher the BMI, the higher the WC, HC, CC, MAC, fat%, and FFMI in both females and males. Therefore, BMI had a strong positive partial correlation with WC, HC, MAC, fat%, and FFMI, and a moderate positive partial correlation with CC after controlling age and sex. Although there were weak and positive partial correlation between BMI and GC muscle thickness; there was no statistically significant correlation between BMI, RF muscle thickness, and RF cross-sectional area.

Age-related muscle loss is regional and muscles rich in type 2 fibers like the anterior thigh undergo atrophy earlier. And when we measure total muscle mass, we may overlook the sarcopenia. The second striking point is that previous studies have shown higher correlations of rectus femoris muscle measurements with muscle strength when compared with other muscle mass measurements. [11, 32, 33]

A recently published systematic review and meta-analysis revealed the diagnostic test accuracy of ultrasound for sarcopenia diagnosis. The meta-analysis of the five studies for RF muscle thickness demonstrated the pooled sensitivity as 72% (95% CI 62–81%) and the pooled specificity as 72% (95%CI 64–79%). Therefore, the meta-analysis of the five studies for gastrocnemius muscle thickness showed the pooled sensitivity and specificity as 82% (95% CI 71–90%)and 64% (95% CI 48–77%), respectively. Lower extremity muscles were reported to be commonly studied and muscle thickness was defined to be the most widely assessed parameter, followed by CSA. [5] In a study conducted in critical care, rectus femoris muscle thickness measured by US detected the loss of muscle mass in a short time more sensitively than the anthropometric measurements. [34] In our study, although the higher BMI, the higher MAC, CC, WC, and HC, we did not observe the same relationship with HGS, RF muscle thickness, and RF cross-sectional area. Therefore, ın multivariate analysis, we presented the independent association of RF muscle thickness, RF cross-sectional area, and GC muscle thickness with HGS. Calf circumference and MAC was not independently associated with probable sarcopenia regardless of ag, sex and BMI.

Strength and limitations

This is the first study that show the clinic importance of muscle ultrasound for probable sarcopenia regardless of age, sex and BMI in geriatric outpatients. This data has an important role to diagnose sarcopenia especially for patients with obesity, and it will keep light to new studies. Therefore, future studies for standardized cut-off values are needed. On the other hand, a cross-sectional design is a limitation of this study. Besides, we included only cooperated geriatric outpatients without acute ilnessess.

Rectus femoris muscle thickness, RF cross- sectional area, and GC muscle thickness measured by US are independently associated with probable sarcopenia, regardless of age, sex, and BMI. Although, WC, HC, CC, MAC, fat%, and FFMI are significantly correlated to BMI; RF muscle thickness and RF cross-sectional area had no correlation when controlled for age and sex. The importance of muscle US for sarcopenic obesity diagnosis will attract attention, but future prospective studies to support this data are needed.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of interest

No conflict of interest to declare.

Author contributions

Yelda Ozturk, Burcu Balam Dogu, Mustafa Cankurtaran and Meltem Halil equally contributed to the conception and design of the research; Serdar Ceylan, Merve Guner, Meltem Koca, and Arzu Okyar Bas equally contributed to acquisition of data; Serdar Ceylan, Merve Guner, Meltem Koca, Mert Esme and Cafer Balci equally contributed to the analysis and interpretation of data; Arzu Okyar Bas, Mert Esme, Cafer Balci, Burcu Balam Dogu, Mustafa Cankurtaran, and Meltem Halil contributed to drafting the article or revising it critically; Yelda Ozturk and Meltem Halil completed the final approval of the version to be submitted. All authors agree to be fully accountable for ensuring the integrity and accuracy of the work and read and approved the final manuscript.

Cruz-Jentoft AJ, Sayer AA, Sarcopenia (2019) Lancet 393(10191):2636–2646 Epub 20190603. doi: 10.1016/s0140-6736(19)31138-9. PubMed PMID: 31171417
Tiftik T, Kara M, Koyuncu EG et al (2023) The relationship between sarcopenia-related measurements and osteoporosis: The SARCOP study. Osteoporos Int. ;34(1):53 – 8. Epub 20221004. 10.1007/s00198-022-06563-z. PubMed PMID: 36194277
Yuan L, Chang M, Wang J (2021) Abdominal obesity, body mass index and the risk of frailty in community-dwelling older adults: a systematic review and meta-analysis. Age Ageing 50(4):1118–1128. 10.1093/ageing/afab039PubMed PMID: 33693472
Cruz-Jentoft AJ, Bahat G, Bauer J et al (2019) Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48(1):16–31. 10.1093/ageing/afy169PubMed PMID: 30312372; PubMed Central PMCID: PMCPMC6322506
Fu H, Wang L, Zhang W et al (2023) Diagnostic test accuracy of ultrasound for sarcopenia diagnosis: A systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 14(1):57–70 PubMed PMID: 36513380; PubMed Central PMCID: PMCPMC9891970. 10.1002/jcsm.13149
Becroft L, Ooi G, Forsyth A et al (2019) Validity of multi-frequency bioelectric impedance methods to measure body composition in obese patients: a systematic review. Int J Obes (Lond) 43(8):1497–1507 PubMed PMID: 30568268. 10.1038/s41366-018-0285-9
Donini LM, Busetto L, Bischoff SC et al (2022) Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement. Clin Nutr 41(4):990–1000 PubMed PMID: 35227529
Bahat G, Ozkok S (2023) How to adjust muscle mass while defining sarcopenia component of sarcopenic obesity: is body weight sufficient enough to represent body size? Aging Clin Exp Res 35(3):723–724 PubMed PMID: 36622546. 10.1007/s40520-022-02326-2
Vasileiou AM, Bull R, Kitou D et al (2011) Oedema in obesity; role of structural lymphatic abnormalities. Int J Obes (Lond) 35(9):1247–1250 PubMed PMID: 21266949. 10.1038/ijo.2010.273
Ishida Y, Maeda K, Nonogaki T et al (2019) Impact of edema on length of calf circumference in older adults. Geriatr Gerontol Int 19(10):993–998 PubMed PMID: 31397070. 10.1111/ggi.13756
Deniz O, Cruz-Jentoft A, Sengul Aycicek G et al (2020) Role of Ultrasonography in Estimating Muscle Mass in Sarcopenic Obesity. JPEN J Parenter Enteral Nutr 44(8):1398–1406 PubMed PMID: 32342544. 10.1002/jpen.1830
Kara M, Frontera WR, Özçakar L (2021) Measure What Matters Most in Sarcopenia: Regional vs. Appendicular Muscle Mass? J Am Med Dir Assoc 22(4):883–884 PubMed PMID: 33705744. 10.1016/j.jamda.2021.02.006
Kara M, Kaymak B, Frontera W et al (2021) Diagnosing sarcopenia: Functional perspectives and a new algorithm from the ISarcoPRM. J Rehabil Med 53(6):jrm00209 Epub 20210621. doi: 10.2340/16501977-2851. PubMed PMID: 34121127; PubMed Central PMCID: PMCPMC8814891
Katz S, Ford AB, Moskowitz RW, STUDIES OF ILLNESS IN THE AGED. THE INDEX OF ADL: A STANDARDIZED MEASURE OF BIOLOGICAL AND PSYCHOSOCIAL FUNCTION et al (1963) JAMA 185:914–919. 10.1001/jama.1963.03060120024016PubMed PMID: 14044222
Lawton MP, Brody EM (1969) Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9(3):179–186 PubMed PMID: 5349366
Rubenstein LZ, Harker JO, Salvà A et al (2001) Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci 56(6):M366–M372. 10.1093/gerona/56.6.m366PubMed PMID: 11382797
Malmstrom TK, Morley JE (2013) SARC-F: a simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc 14(8):531–532 PubMed PMID: 23810110. 10.1016/j.jamda.2013.05.018
Bahat G, Yilmaz O, Kılıç C et al (2018) Performance of SARC-F in Regard to Sarcopenia Definitions, Muscle Mass and Functional Measures. J Nutr Health Aging 22(8):898–903. 10.1007/s12603-018-1067-8PubMed PMID: 30272090
Özsürekci C, Balcı C, Kızılarslanoğlu MC et al (2020) An important problem in an aging country: identifying the frailty via 9 Point Clinical Frailty Scale. Acta Clin Belg 75(3):200–204 PubMed PMID: 30919742. 10.1080/17843286.2019.1597457
Rockwood K, Song X, MacKnight C et al (2005) A global clinical measure of fitness and frailty in elderly people. CMAJ 173(5):489–495 doi: 10.1503/cmaj.050051. PubMed PMID: 16129869; PubMed Central PMCID: PMCPMC1188185
Ibrahim K, May C, Patel HP et al (2016) A feasibility study of implementing grip strength measurement into routine hospital practice (GRImP): study protocol. Pilot Feasibility Stud 2:27 PubMed PMID: 27965846; PubMed Central PMCID: PMCPMC5154137. 10.1186/s40814-016-0067-x
Leong DP, Teo KK, Rangarajan S et al (2015) Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet 386(9990):266–273 PubMed PMID: 25982160. 10.1016/s0140-6736(14)62000-6
Beaudart C, McCloskey E, Bruyère O et al (2016) Sarcopenia in daily practice: assessment and management. BMC Geriatr 16(1):170 Epub 20161005. doi: 10.1186/s12877-016-0349-4. PubMed PMID: 27716195; PubMed Central PMCID: PMCPMC5052976
Buckinx F, Landi F, Cesari M et al (2018) Pitfalls in the measurement of muscle mass: a need for a reference standard. J Cachexia Sarcopenia Muscle 9(2):269–278 PubMed PMID: 29349935; PubMed Central PMCID: PMCPMC5879987. 10.1002/jcsm.12268
Gonzalez MC, Heymsfield SB (2017) Bioelectrical impedance analysis for diagnosing sarcopenia and cachexia: what are we really estimating? J Cachexia Sarcopenia Muscle 8(2):187–189 Epub 20170131. doi: 10.1002/jcsm.12159. PubMed PMID: 28145079; PubMed Central PMCID: PMCPMC5377383
Sergi G, De Rui M, Veronese N et al (2015) Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free-living Caucasian older adults. Clin Nutr 34(4):667–673 PubMed PMID: 25103151. 10.1016/j.clnu.2014.07.010
Bahat G, Tufan A, Kilic C et al (2019) Cut-off points for weight and body mass index adjusted bioimpedance analysis measurements of muscle mass. Aging Clin Exp Res 31(7):935–942 PubMed PMID: 30267332. 10.1007/s40520-018-1042-6
Bahat G, Kilic C, Ilhan B et al (2019) Association of different bioimpedanciometry estimations of muscle mass with functional measures. Geriatr Gerontol Int 19(7):593–597 PubMed PMID: 31006968. 10.1111/ggi.13668
Simati S, Kokkinos A, Dalamaga M et al (2023) Obesity Paradox: Fact or Fiction? Curr Obes Rep 12(2):75–85 PubMed PMID: 36808566. 10.1007/s13679-023-00497-1
Liu C, Wong PY, Chung YL et al (2023) Deciphering the obesity paradox in the elderly: A systematic review and meta-analysis of sarcopenic obesity. Obes Rev 24(2):e13534 PubMed PMID: 36443946. 10.1111/obr.13534
Malandrino N, Bhat SZ, Alfaraidhy M et al (2023) Obesity and Aging. Endocrinol Metab Clin North Am 52(2):317–339 PubMed PMID: 36948782. 10.1016/j.ecl.2022.10.001
Tsukasaki K, Matsui Y, Arai H et al (2020) Association of Muscle Strength and Gait Speed with Cross-Sectional Muscle Area Determined by Mid-Thigh Computed Tomography - A Comparison with Skeletal Muscle Mass Measured by Dual-Energy X-Ray Absorptiometry. J Frailty Aging 9(2):82–89. 10.14283/jfa.2020.16PubMed PMID: 32259181
Sengul Aycicek G, Ozsurekci C, Caliskan H et al (2021) Ultrasonography versus bioelectrical impedance analysis: which predicts muscle strength better? Acta Clin Belg 76(3):204–208 PubMed PMID: 31847723
Mendes J, Rodrigues IG, Arcoverde G et al (2023) Evaluation of muscle loss by ultrasonography in critically ill patients. Nutr Clin Pract 38(3):664–671 PubMed PMID: 36566358. 10.1002/ncp.10945

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The Associations between Muscle Ultrasound Measurements, Calf Circumference, Mid-upper Arm Circumference, and Body Mass Index

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Purpose

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Introduction

Methods

Study design and participants

Patient characteristics

Anthropometric measurements

Muscle strength measurement and body composition

Muscle ultrasound measurements

Statistical analysis

Results

Discussion

Conclusion

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