A simple predictive model for sarcopenia based on the calf muscle maximum circumference

doi:10.21203/rs.3.rs-4809242/v1

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A simple predictive model for sarcopenia based on the calf muscle maximum circumference

https://doi.org/10.21203/rs.3.rs-4809242/v1

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Background: The correlation between calf circumference and sarcopenia has been demonstrated, but the correlation between the calf muscle maximum circumference and sarcopenia has not been studied. This study aimed to explore the relationship between the two and to construct a simple model to predict sarcopenia in hospitalized elderly patients.

Methods: This was a retrospective controlled study of patients >60 years of age hospitalized in the geriatric department of Hunan Provincial People's Hospital. The patients were thoroughly evaluated by questionnaires, laboratory, and ultrasound examinations, including measuring muscle thickness and calf muscle maximum circumference using ultrasound. Patients were categorized into sarcopenia and non-sarcopenia groups according to the consensus for diagnosis of sarcopenia recommended by the Asian Working Group on Sarcopenia 2019 (AWGS2). Independent predictors of sarcopenia were identified by univariate and multivariate logistic regression analyses, and a predictive model was developed and simplified. The prediction performance of the models was assessed using sensitivity, specificity, and area under the curve (AUC) and compared with independent predictors.

Results: We found that patient age, albumin level(ALB), brachioradialis muscle thickness (BRMT), gastrocnemius lateral head muscle thickness(Glh MT), and calf muscle maximal circumference(CMMC) were independent predictors of sarcopenia in hospitalized elderly patients. A predictive model was developed and simplified as Logistic P = - 4.5 + 1.4 × Age + 1.3 × ALB + 1.6 × BR MT + 3.7 × CMMC + 1.8 × Glh MT, and the diagnostic optimal cutoff value of the equation was 0.485. The sensitivity, specificity, and AUC of the model were 0.884(0.807-0.962), 0.837(0.762-0.911), and 0.927(0.890-0.963), respectively, which were significantly higher than those of the independent predictors.

Conclusion: We constructed a simple predictive model for sarcopenia including five variables: age, ABL level, BR MT, Glh MT, and CMMC. The AUC of the model is 0.927, which can help clinicians predict less muscle disease in patients with senile inpatients quickly.

sarcopenia

simple predictive model

calf muscle maximum circumference

hospitalized elderly patients

Sarcopenia, first introduced by Irwin Rosenberg in 1989, is a progressive age-related systemic disorder characterized by a progressive loss of muscle mass, weight, and function, which is strongly associated with adverse outcomes such as falls, fractures, disability, and death[1-3]. The global prevalence of sarcopenia has risen to 10%-27% with the accelerated aging of the world population[4]. In China, the prevalence of sarcopenia in the community-based elderly population is approximately 17%, and the prevalence in long-term hospitalized patients could be as high as 33%[5]. By 2050, the prevalence of sarcopenia is estimated to reach 500 million people[6]. Sarcopenia has also been reported to affect young people and has a prevalence of 8% to 36% in young adults[7-9]. The physical decline and adverse events caused by sarcopenia have a significant economic impact on individuals, families, and society. The direct medical costs associated with sarcopenia were estimated by the US Department of Health and Social Care in 2000 to be approximately $18.5 billion[6]. Given that sarcopenia could be effectively treated with low-cost, non-invasive methods, early, proactive, and effective interventions become essential to avoid adverse outcomes of sarcopenia[10]. However, in many countries, sarcopenia is not routinely diagnosed due to the lack of practical clinical diagnostic tools[6]. Computed tomography (CT), magnetic resonance imaging (MRI), and dual-energy X-ray absorptiometry (DXA) have been recommended by the European Working Group on Sarcopenia in the Elderly (EWGSOP) as the gold standard for determining muscle mass. Still, the equipment is expensive, and the CT and X-rays are also radioactive, which is not conducive to routine screening. Measurement of muscle mass with Bioelectrical impedance analysis (BIA) may be superior to DXA but is also influenced by the hydration status of the patient[10].

Portable ultrasounds are lightweight and easy to move to the community and other primary care units for screening. The latest version of the European Working Group on Sarcopenia in the Elderly Consensus (EWGSOP2) recommends ultrasound as a valid and reliable tool for measuring muscle mass[8]. Researchers have chosen muscle thickness, cross-sectional area, and echo intensity to assess muscle mass, but the optimal muscle group to reflect whole-body muscle mass has not been identified, and the diagnostic efficacy of the same parameter varies widely in different studies. The 2019 Asian Working Group for Sarcopenia (AWGS) recommends combining the SARC-F scale and calf circumference (CC) to form the SARC-calF score for sarcopenia screening[11]. The CC includes subcutaneous fat and other soft tissues, which makes it easy to miss the diagnosis of obese and edematous sarcopenia[12]. Direct measurement of calf muscle circumference by ultrasound can avoid the interference of subcutaneous fat, and theoretically can truly reflect the amount of muscle, which is convenient for more accurate screening of sarcopenia. However, no relevant research has been reported.

The purpose of this study is to explore the feasibility of ultrasound measurement of the maximum circumference of a calf muscle group, brachioradialis muscle, and lower limb muscle thickness combined with clinical basis, serum biochemistry, and living habits to predict sarcopenia, and to construct a simple prediction model. Our research hypothesis is that the predictive model incorporating the maximum circumference of the calf muscle group can better predict sarcopenia with good sensitivity and specificity.

2. 1 Study subjects

This study was a controlled observational study conducted in accordance with the Declaration of Helsinki. It was reviewed and approved by the Medical Ethics Committee of Hunan Provincial People's Hospital (approval number: LL-20250517-457). As this was not a prospective intervention study, no clinical study lot number was requested. All participants signed written informed consent.

Patients who were hospitalized in the Department of Geriatrics of our hospital from November 2021 to January 2023 and voluntarily underwent sarcopenia screening were selected. Inclusion criteria: (1) age ≥60 years old; (2) able to independently complete all motor function assessments required for sarcopenia screening; (3) be conscious and able to complete the questionnaire; Exclusion criteria: (1) Recent conditions that may cause drastic changes in body composition, such as acute systemic infection, electrolyte disturbance, massive blood loss, etc. (2) acute or chronic organ failure; (3) patients with stroke or hemiplegia cannot independently complete motor function assessment; (4) Unable to complete the questionnaire independently. After strict screening according to the inclusion and exclusion criteria, the AWGS (Asia Working Group for Sarcopenia) 2019 diagnostic consensus (AWGS2) was used as the “gold standard” [13], and all enrolled patients were divided into the sarcopenia group and the non-sarcopenia group.

2. 2 Equipment and Methods

2. 2. 1 Clinical data collection

All patients were informed about the content and methods of the study and signed an informed consent form before participating in the study. The same physician measured the height and weight of the patients on the day of hospitalization, recorded their age and other basic clinical information, and drew venous blood from the patients early in the morning before the start of the treatment to test the biochemical indexes: albumin(ALB), triglyceride(TG), and so on.

2. 2. 2 Basic Information Questionnaire

A questionnaire survey was conducted in the awake state of the patients, and the intake of eggs and meat, and the number and duration of weekly exercises were recorded. The questionnaire was developed by us according to the research needs (shown in supplemental material 1).

2. 2. 3 HGS ( hand grip strength ) measurement

The patient was instructed to stand in a position with the shoulders retracted, the upper arms close to the median axillary line, the elbows flexed at 90 degrees, the long axis of the upper arms perpendicular to the floor, and the long axis of the forearms parallel to the floor. The data were read when the patient held the force surface of the grip strength device (RL-PG-07 from Guangzhou, China) with the dominant hand and tightened the handle device to the maximum extent. A total of 3 measurements were made and the maximum value was recorded.

2. 2. 4 Gait speed (GS) measurement

Measure the length of 6 meters on a flat, clean, and unobstructed ground, mark the starting point and the endpoint, ask the patient to walk as fast as possible from the starting point to the endpoint without slowing down in the middle, and use a stopwatch to record the time needed, test twice in total and take the average value.

2. 2. 5 Skeletal muscle mass was measured by Bioimpedance analysis（BIA）

The skeletal muscle mass of the patients was measured using a Korean Jevon IO1 human tissue composition analyzer. During the examination, patients removed their shoes and socks after urination, took off their metal jewelry, stood calmly, and held the handle of the instrument with both hands to ensure that the skin on the soles of the feet and palms of the hands was in direct contact with the electrodes of the feet and the handle. The patient's legs were slightly spread to ensure that the skin of the inner thighs did not come into direct contact with the electrodes, and the patient remained calm and did not move until the machine indicated that the measurement had been completed, obtaining the patient's body weight, fat mass, defatted mass, and muscle mass of the limbs.

Body Mass Index (BMI) and Fat-Free Mass Index (FMI) were calculated according to the formula BMI / FMI = body weight or fat-free mass (kg) / height (m)², and the unit was kg/m². Appendicular Skeletal Mass (ASM) was obtained by summing the muscle mass of the skeletal muscles of the extremities measured by BIA. Skeletal Muscle Mass Index (SMI) is obtained by dividing ASM (kg) by the square of height (m) in kg/m².

2. 2. 6 Ultrasonic measurement

Mindray M9 ultrasound instrument (Myriad Medical International Ltd, Shen Zhen, China) was used to measure the muscle thickness (MT) of bilateral brachioradialis (BR), vastus medialis(VM), gastrocnemius medial head(Gmh) and gastrocnemius lateral head(Glh) in the resting state of patients with L12-4 high-frequency superficial probe.

Muscle measurement methods

1. Brachioradialis(BR): The patient was placed in the supine position with the hands flat on either side of the body, palms naturally facing upward, and the probe was placed in the proximal 1/3 of the radius between the radial head and the condylar eminence of the carpal tunnel, with the long axis of the probe perpendicular to the long axis of the forearm. The MT of the BR was measured by scanning over an area of 6 centimeters above and below the BR, taking the largest cross-section of the BR(as show in Figure 1).

Figure 1: Measurement of BR MT (B: brachioradialis muscle, R: radius)

2. Vastus medialis (VM): the patient lies supine with the knee bent at 120° and the legs slightly separated. The probe was placed horizontally at the distal 1/3 of the inner thigh, and the scanning range was 6cm up and down. The MT of the VM was measured at the maximum cross-section(as show in Figure 2).

Figure 2: Measurement of VM MT (V:Vastus medialis muscle, F:Femur)

3. Gastrocnemius medial head(Gmh) and Gastrocnemius lateral head(Glh): the patient lies in the supine position with both knees bent at 120 degrees and legs slightly separated by 30 degrees—probe on the lateral and medial crus, popliteal fossa proximal 1/3, and tibial articular surface level. The longitudinal axis of the probe was perpendicular to the long axis of the calf. The scan range was 6 cm above and below, and MT was measured at the thickest point of the Glh and Gmh(as show in Figure 3).

Figure3: Measurement of Gmh MT ( G: gastrocnemius medial head, S: Soleus )

4. Calf muscle maximum circumference(CMMC): Select the C5-1 abdominal probe and extend it to the maximum imaging angle, with the probe perpendicular to the skin. Take the largest cross-section of the calf muscle group and track the trajectory along the outer edge of the calf muscle group to measure the CMMC(as show in Figure 4).

Figure 4: Measurement of CMMC. The white line was the trajectory depicted when the maximum circumference was measured around the periphery of the calf muscle using the trajectory method.

2. 2. 7 Quality control

1. All instruments are calibrated before use to ensure optimal functioning. 2. questionnaires were conducted and recorded by the same trained professional to ensure consistency. 3. all ultrasound images are captured and stored by a physician trained in musculoskeletal ultrasound with at least one year of experience in diagnostic ultrasound to ensure standardization of sections and clear imaging. 4. During the ultrasound procedure, a thick layer of coupling agent is applied between the probe and the skin to prevent external forces from interfering with the imaging. 5. Ultrasound data are averaged by the same sonographer after three repeated measurements on the stored images, retaining one decimal place.

2. 2. 8 Diagnostic criteria

The consensus for the diagnosis of Sarcopeniarecommended by the Asia Working Group for Sarcopenia in 2019 ( AWGS2) was used as the "gold standard" for this study. For details, see the figure:

(1) SMI ≤7.0 kg/m² in men and ≤5.7 kg/m² in women (measured by BIA);

(2) HGS < 28 kg in males and < 18 kg in females;

(3) GS < 1.0 m/s;

If it was 1+2/3, it could be judged as sarcopenia, and all patients were divided into the sarcopenia group and the non-sarcopenia group.

2, 3 Statistical methods

SPSS 26.0 software was utilized for conducting statistical analysis. The Shapiro-Wilk test was employed to assess the normality of the data distribution. In cases where the data followed a normal distribution, intergroup comparisons were performed using an independent t-test. For non-normally distributed data, non-parametric tests were applied for comparison purposes. Frequency was used to express count data, and group comparisons were analyzed using the chi-square test. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of sarcopenia in hospitalized elderly patients, leading to the development of a predictive model. The predictive performance of individual predictors and the model was evaluated through receiver operating characteristic (ROC) curves analysis. Statistical significance was defined as P<0.05.

Finally, 167 patients completed all examinations and were included in this study. The mean age was 70.9±8.1 years, including 91 males (70.9±8.2 years) and 76 females (70.9±7.9 years). Using AWGS2 as the “gold standard”, 69 cases of sarcopenia and 98 cases of non-sarcopenia were diagnosed. The prevalence of sarcopenia was 41.3% (male: 49.5%, female: 31.5%).

3.1 Comparison of clinical basis, serum biochemical, and ultrasound measurements between the sarcopenia and non-sarcopenia groups.

The comparison of clinical basis, serum biochemical, and ultrasound measurements between the sarcopenia and non-sarcopenia groups is shown in Table 1. There were significant differences in gender, age, ALB, CMMC, BR MT, Glh MT, and Gmh MT between the two groups, but there were no significant differences in other variables between the two groups. The sarcopenia group had a higher proportion of males, older age, and lower ALB levels. The BR MT, Glh MT, Gmh MT, and the CMMC in the sarcopenia group were lower.

The ROC curve was drawn for the measurement data with statistical differences, and the maximum cut-off value was obtained according to the maximum Youden index, and the measurement variables were converted into binary data according to the cut-off value, as shown in Table 2. The age cutoff was 70 years, and the prevalence of sarcopenia was 51.1% (46/90) in patients >70 years and 29.9% (23/77) in patients ≤70 years. The ALB cutoff was < 40 g/L and the prevalence of sarcopenia in patients below the cutoff was 66.7% (48/72), and above the cutoff the prevalence was only 22.1% (21/95).

The cutoff value for BR MT was 15 mm. The prevalence of sarcopenia in hospitalized elderly patients with BR MT < 15 mm and ≥ 15 mm was 58.0% (47/81 cases), and 25.6% (22/86 cases), respectively. The cutoff value for Glh MT was 15 mm, and the prevalence of sarcopenia in patients with Glh MT < 15 mm and ≥ 15 mm was 61.4% (51/83 cases) and 21.4% (18/84 cases), respectively. The cutoff value for Gmh MT was also 15 mm, and the prevalence of sarcopenia in patients with Glh MT < 15 mm and ≥ 15 mm was 60.5% (52/86 cases) and 21.0% (17/81 cases). The cutoff value of CMMC was 24 cm, and the incidence in patients with CMMC < 24 cm and ≥ 24 cm was 90.7% (39/43 cases) and 24.2% (30/124 cases).

3.2 Comparison of lifestyle habits between sarcopenia and non-sarcopenia groups

The comparison of lifestyle habits between the sarcopenia and non-sarcopenia groups is shown in Table 3. There were differences in meat intake per meal and weekly exercise time between the two groups. The proportion of meat intake per meal and weekly exercise duration in the non-sarcopenia group was higher than in the sarcopenia group. There were no significant differences in the number of weekly exercises, calcium supplementation, and one egg intake per day between the two groups.

3. 3 Construction of sarcopenia prediction models

The binary data with statistical differences were included in the multivariate binary logistic regression analysis, and the results are shown in Table 4. Finally, age, ALB level, BR MT, Glh MT, and CMMC were independent predictors of sarcopenia. The diagnostic model was constructed as follows: Logistic P = - 4.449 + 1.373×Age + 1.291× ALB + 1.572× BR MT + 3.655× CMMC + 1.809 × Glh MT. When the Youden index was 0.721, the best cut-off value of the equation was 0.485, the diagnostic sensitivity was 0.884 (0.807-0.962) and the specificity was 0.837 (0.762-0.911). For the convenience of clinical use, the model was simplified as follows: Logistic P = - 4.5 + 1.4 × Age + 1.3× ALB + 1.6 × BR MT + 3.7×CMMC + 1.8×Glh MT. Its cut-off value, sensitivity, and specificity did not change.

3. 4 ROC analysis of the independent predictors and the models

The ROC analysis of single independent predictors of sarcopenia and the models are shown in Table 5 and Figure 5. The area under the ROC curve (AUC) of the simplified model and the original model was larger than that of the single predictor, and the simplified model was slightly higher than the original model ( AUC = 0.927 vs.0.925 ).

Sarcopenia is a muscle failure rooted in adverse muscle changes[14]. It is common in the elderly but can occur early in life[8, 10]. In our study, we showed that hospitalized patients ≥70 years of age had a 4-fold increased risk of sarcopenia compared to patients <70 years of age, due to progressive muscle failure with age[15, 16]. The unfavorable outcomes resulting from sarcopenia can increase the personal, social, and economic burdens of patients by eventually losing their independence and requiring long-term care[17, 18]. 2018 EWGSOP2 suggested that early and effective interventions have the potential to delay or even reverse sarcopenia[10]. However, the initial symptoms of sarcopenia are not obvious and the clinical presentation varies and has been overlooked, therefore effective recognition of sarcopenia is essential for prevention[1]. MRI and CT are the gold standard for noninvasive assessment of muscle number and mass, but their widespread use is limited by the high cost of equipment and unclear diagnostic cutoffs. DXA is susceptible to the patient's body size and hydration status, and measurements are inconsistent across brands[10, 19]. BIA is used to estimate muscle mass by whole-body conductivity, but the results are still influenced by the person and the device. The 2019 revision of EWGSOP2 recommends using ultrasound to assess muscle condition[10], with good intra- and inter-observer agreement[20]. In contrast, the most convenient way to predict sarcopenia in older adults is CC <31 cm[14]. However, the increased insulin resistance caused by sarcopenia may induce obesity and thickening of subcutaneous fat, and the thickened subcutaneous fat of obese sarcopenic patients may lead to missed diagnosis due to the normal range of CC values. Theoretically, the use of ultrasound-measured CMMC instead of CC can avoid the underdiagnosis of obesity due to the thickening of subcutaneous fat[8].

Our study found that CMMC along with Glh MT, Gmh MT, and BR MT were independent risk factors for sarcopenia in the elderly. The probability of developing sarcopenia was significantly increased when the patient's age was > 70 years, ALB < 40g/L, Glh MT, Gmh MT, BR MT < 15mm, and CMMC < 24cm. We constructed a predictive model based on this and simplified it, the simple model had good predictive efficacy with sensitivity and specificity of 88.4% and 83.7%, respectively, and an AUC of 0.927.

The nursing team of Xiangya Hospital, China, developed a predictive nomograph for sarcopenia in a community-based population in Hunan Province, China. It predicts sarcopenia from multiple dimensions such as diet, exercise habits, and marital status through questionnaires, but there are no indicators that can directly measure skeletal muscle[15]. We performed a direct quantitative analysis of skeletal muscle by portable ultrasound. Since there is no consensus on the optimal muscles, parameters, and their thresholds for predicting sarcopenia, and most of the data are from Western populations, our study incorporated multiple metrics from BR MT, VM MT, Ghl MT, Gml MT, and CMMC[16, 21-23]. Our findings that BR MT is an independent predictor of sarcopenia are consistent with previous findings on the association of forearm muscle groups with grip strength and were included in the predictive model[24]. Our model also included Glh MT and CMMC, which reflect lower limb muscle strength. The risk of myasthenia gravis increased to 6-fold when Ghl MT < 15 mm and 4.8-fold when Ghl MT < 15 mm. The risk of myasthenia gravis increased 38-fold when CMMC < 24 cm.

In addition to ultrasound measurements, we also compared the lifestyle habits of the two groups of patients in our study. We found that the probability of developing sarcopenia was lower in those who consumed meat at every meal and exercised ≥90 min per week, and the difference was statistically significant. Those who did not eat meat at every meal had a 1.8-fold increased risk of developing sarcopenia, and those who exercised ≤90 min per week had an approximately 1.3-fold increased risk of developing sarcopenia. Current research has confirmed that exercise and nutritional supplementation are effective in preventing sarcopenia and suggests that prevention of sarcopenia requires exercising 2-3 times per week for 1 hour[25-28], but this is physically demanding for older adults. We found that 30 minutes of exercise 3 times a week also produced a significant preventive effect.

Decreased protein metabolism is also one of the important factors contributing to sarcopenia[29]. In our study, we found that the threshold value of ALB to predict sarcopenia was 40 g/L, which overlapped with the normal range of ALB (35 to 50 g/L), and that patients with albumin < 40 g/L had a 3.6-fold increased risk of sarcopenia. This is close to the significantly increased probability of disability due to sarcopenia in older people over 65 years of age with ALB < 40 g/L reported in Japan [30]. This elevates the need for older adults to consume adequate high-quality proteins to ensure serum ALB levels greater than 40 g/l[31, 32]. The proportion of meat intake per meal was higher in the non-muscle-losing group than in the muscle-losing group in our study. In contrast, egg intake was not statistically different between the two groups. Whether this suggests that meat protein intake is more beneficial in the prevention of sarcopenia needs to be further confirmed by expanding the scope of the study. However, the need to ensure food variety for the effective prevention of sarcopenia in the elderly is indisputable.

The final predictive model we constructed incorporated five variables: age, ABL, and BF MT(related to GS), as well as GlhMT and CMMC(related to lower limb muscle strength)[24, 33]. The Yoden's index of the model was 0.721, indicating its predictive efficacy. Moreover, the AUC of the model was 0.925, surpassing that of a single variable alone[22]. Notably, the sensitivity of the model, while maintaining 83.7% specificity, was significantly improved over previously reported methods (88.4% vs. 50%, 68.1%)[15, 34]. Although the cutoff value, sensitivity, and specificity remained unchanged from the simple model in our original study design; however, there was a slight improvement in AUC to 0.927.

There are some limitations to our study. First, the data came from a small sample in one hospital, and it remains to be verified whether they apply to different populations in other healthcare organizations; second, there are additional factors that may affect sarcopenia that were not considered, such as smoking and alcohol consumption. Third, this study required patients to complete a physical fitness test and questionnaire, which was relatively time-consuming, and some patients were excluded because they did not complete all of them for a variety of reasons, so there was a case selection bias. Finally, external validation of the model validity has not yet been performed, which will be done in a follow-up study.

In summary, binary logistic regression analysis revealed that age > 70 years, albumin level less than 40 g/L, thickness of brachioradialis and gastrocnemius < 15 mm, and maximal circumference of calf muscle group < 24 cm were independent predictors of sarcopenia, and a simple predictive modelwas established, which provides a simple and practical method for healthcare professionals to predict sarcopenia. It was also found that the probability of sarcopenia was lower in the elderly hospitalized population who consumed meat at every meal and exercised > 90 minutes per week, which is of great significance for the clinical guidance of the prevention of sarcopenia in the elderly hospitalized population.

ALB Albumin

ASM Appendicular Skeletal Mass

AWGS Asia Working Group for Sarcopenia

BIA Bioimpedance analysis

BMI Body Mass Index

BR Brachioradialis

CI Confidence interval

CC Calf circumference

CMMC Calf muscle maximum circumference

CSA Cross-sectional area

CT Computed tomography

DXA Dual-energy X-ray absorptiometry

EWGSOP the European Working Group on Sarcopenia in the Elderly

FMI Fat-Free Mass Index

GLB Globulin

GS Gait speed

Glh Gastrocnemius lateral head

Gmh Gastrocnemius medial head

HDL High density lipoprotein

HGS Hand grip strength

LDL Low density lipoprotein

MRI Magnetic resonance imaging

MT Muscle thickness

ROC Receiver operating characteristic

SMI Skeletal Muscle Mass Index

TC Total cholesterol

TG Triglyceride

TP Total protein

VLDL Very low density lipoprotein

VM Vastus medialis

Ethics approval and consent to participate

Consent for publication

The images included in this publication are presented anonymously and all individuals with person’s data contained made the consent to publish of this work.

Availability of data and material

The raw data supporting the conclusions of this article will be made available by the first author without reservation, with the consent of all the authors.

Competing interests

The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This study was supported by the Natural Science Foundation of Hunan Province(No.2021JJ70020), Hunan Provincial Department of Finance Project (No.2020CZT03) and the Science and Technology Department of Hunan Province(No.2021SK50924)

Authors' contributions

The conception and design of the study were performed by An Wei. The acquisition of data was performed by Zhen-Hua Tang, Feng Guo and Yan Zhou. The analysis and interpretation of data was performed by An Wei and Yan Zou . An Wei has drafted the article and revised it critically for important intellectual content. All authors made the final approval of the version to be submitted.

Acknowledgements

Thanks to Xu-Yan Liu and Yi-Xuan Chen of Hunan Provincial People's Hospital for providing suggestions for the writing of this paper.

Author :An Wei^1*, Yan Zou^2†, Zhen-Hua Tang^1†, Feng Guo^1†, Yan Zhou^3†

¹Department of Ultrasound, Hunan Provincial People's Hospital/The First Affiliated Hospital of Hunan Normal University, Changsha, HuNan, China

²Department of International Medicine, Hunan Provincial People's Hospital/The First Affiliated Hospital of Hunan Normal University, Changsha, HuNan, China

³Department of Geriatrics, Hunan Provincial People's Hospital/The First Affiliated Hospital of Hunan Normal University, Changsha, HuNan, China

*Corresponding author(s). E-mail(s): [email protected];

Contributing authors: [email protected]; [email protected]; [email protected]; [email protected]

†These authors contributed equally to this work.

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Table 1 Comparison of clinical basis, serological biochemical and ultrasound measurements

variables		Sarcopenia （n=69）	Non-sarcopenia （n=98）	Z/X²	P
Gender	male(n=91)	45(49.5%)	46(50.5%)	5.4555	0.020
Gender	female(n=76)	24(31.5%)	53(68.4%)	5.4555	0.020
Age（year）		73.4±8.7	69.1±7.2	-3.106	0.002
Weight（kg）		62.26±11.03	64.60±10.22	-0.761	0.447
Height（cm）		162.7±6.8	162.9±8.8	-0.111	0.912
BMI		23.34±2.46	24.24±2.68	-1.745	0.081
GS(m/s)		6.68±2.83	7.04±3.69	-0.34	0.734
TG (mmol/L)		1.63±1.03	1.77±1.22	-0.816	0.415
VLDL(mmol/L)		0.83±0.35	0.80±0.30	-0.124	0.902
LDL(mmol/L)		2.71±0.72	2.70±1.02	-0.874	0.382
HDL(mmol/L)		1.23±0.35	1.17±0.29	-0.478	0.633
TC(mmol/L)		4.68±0.98	4.59±1.29	-0.926	0.354
GLB（g/L）		27.22±3.63	27.22±3.61	-0.059	0.953
ALB（g/L）		39.14±2.85	41.77±2.85	-4.954	＜0.001
TP（g/L）		68.32±5.13	67.52±9.12	-0.221	0.825
CMMC（cm）		24.93±3.74	28.4±2.66	-5.613	＜0.001
Glh MT（mm）		13.45±2.55	15.86±2.07	-6.083	＜0.001
Gmh MT（mm）		13.42±2.55	15.84±2.08	-6.092	＜0.001
VM MT（mm）		25.29±6.00	25.14±5.10	-0.137	0.891
BR MT（mm）		13.67±2.80	15.42±2.20	-3.972	＜0.001

BMI:Body mass index GS: glucose TG:Triglyceride VLDL : very low density lipoprotein LDL: low density lipoprotein

HDL: high density lipoprotein TC total cholesterol GLB: globulin ALB: albumin TP: total protein

CMMC: Calf muscle maximum circumference MT: muscle thickness Glh: gastrocnemius lateral head

Gmh:gastrocnemius medial head VM: vastus medialis BR: brachioradialis

Table 2 Best cut-off values of each variable

Variable	Youden index	Best cut-off value	Layer value (1=,else=0)	Sensitivity ( % )	Specificity (% )	AUC
Age（year）	0.218	69.5	＞70	66.7	55.1	0.641 ( 0.557-0.725 )
ALB（g/L）	0.463	39.8	＜40	79.6	66.7	0.725 ( 0.644-0.806 )
Gmh MT（mm）	0.436	15.2	＜15	65.3	78.3	0.777 ( 0.705-0.849 )
Glh MT（mm）	0.436	15.2	＜15	65.3	78.3	0.777 ( 0.705-0.849 )
BR MT（mm）	0.401	15.2	＜15	63.3	76.8	0.681 ( 0.594-0.768 )
CMMC（cm）	0.524	24.3	＜24	95.9	56.5	0.755 ( 0.675-0.836 )

ALB: albumin MT: muscle thickness Gmh:gastrocnemius medial head Glh: gastrocnemius lateral head BR: brachioradialis CMMC: Calf muscle maximum circumference

Table 3 Comparison of lifestyle habits between the two groups

Variable		Sarcopenia n（%）	non-Sarcopenia n（%）	X²	P value
Meat intake per meal	Y n=71	19（27.5）	52（72.5）	10.794	0.001
Meat intake per meal	N n=96	50（53.1）	46（46.9）	10.794	0.001
Weekly exercise duration＜90min	Y n=43	27（62.8）	16（37.2）	1.013	0.001
Weekly exercise duration＜90min	N n=124	42（33.9）	82（66.1）	1.013	0.001
Weekly exercise ≥3 times	Y n=123	47（38.2）	76（61.8）	1.857	0.173
Weekly exercise ≥3 times	N n=44	22（50.0）	22（50.0）	1.857	0.173
Calcium supplements≥600mg	Y n=33	13（39.4）	20（60.6）	0.063	0.082
Calcium supplements≥600mg	N n=134	56（41.8）	78（58.2）	0.063	0.082
One egg intake per day	Y n=100	42（42.0）	58（58.0）	0.048	0.827
One egg intake per day	N n=67	27（40.3）	40（59.3）	0.048	0.827

Table 4 Binary logistic regression analysis of statistically significant variables
Variable		Univariate analysis			Multifactor analysis
Variable		P value	OR	95% CI	P value	OR	95% CI
Gender		0.020	2.120	1.124-3.998	0.354	1.585	0.598~4.197
Age > 70 years old		0.006	2.455	1.295~4.653	0.008	3.949	1.430~10.902
ALB＜40g/L		＜0.001	7.048	3.538~14.039	0.008	3.637	1.392~9.505
BRMT＜15mm		＜0.001	4.021	2.088~7.744	0.002	4.818	1.761~13.187
CNNC＜24cm		＜0.001	30.550	10.088~92.519	＜0.001	38.659	10.135~147.457
Glh MT＜15mm		＜0.001	5.844	2.951~11.574	0.001	6.102	2.100~17.731
Gmh MT＜15mm		＜0.001	5.758	2.895~11.452	0.979	1.061	0.013~88.396
Not eating meat at every meal		0.001	2.975	1.537~5.759	0.264	1.790	0.644~4.973
Weekly exercise duration＜90min		0.001	3.295	1.601~6.779	0.571	1.343	0.484~3.726
Constant quantity	-4.449				＜0.001	0.012

VLDL : very low density lipoprotein LDL: low density lipoprotein HDL: high density lipoprotein TC: total cholesterol

GLB: globulin ALB: albumin TP: total protein CMMC: Calf muscle maximum circumference MT: muscle thickness

Glh: gastrocnemius lateral head Gmh: gastrocnemius medial head VM: vastus medialis BR: brachioradialis

Table 5 ROC analysis of individual predictors and models
Variable	AUC	P value	95% CI
Variable	AUC	P value	Lower limit	upper limit
simplified model	0.927	＜0.001	0.890	0.963
original model	0.925	＜0.001	0.888	0.962
Age	0.609	0.017	0.522	0.695
Glh MT	0.706	＜0.001	0.626	0.787
CMMC	0.762	＜0.001	0.683	0.842
BR MT	0.667	＜0.001	0.583	0.751
ALB	0.725	＜0.001	0.645	0.805

Note: the independent variables in this table are binary variables based on the optimal truncation value

ALB: albumin CMMC: Calf muscle maximum circumference MT: muscle thickness Glh: gastrocnemius lateral head BR: brachioradialis

No competing interests reported.

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A simple predictive model for sarcopenia based on the calf muscle maximum circumference

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