From August 2019 to July 2020, we continuously invited healthy adults (aged 18 years and older) who received routine health examinations in West China Hospital, Sichuan University, to participate in this study. The exclusion criteria were: (1) individuals who did not receive a chest CT scan; (2) individuals with chronic diseases, such as hypertension, diabetes, coronary heart disease, stroke, chronic respiratory diseases, liver diseases, gastrointestinal diseases, and any type of tumor; (3) individuals with low-quality CT images or had any anatomical distortion such as chest wall edema or loss of any muscle mass area on CT images; (4) individuals with planted pacemaker; and (5) individuals with visible edema. The clinical information collection, anthropometry measurements, and blood samplings were performed on the same day by trained nurses. The study protocol was approved by the Biomedical Ethics Committee of West China Hospital, Sichuan University. All participants signed the written informed consent.
Measurement of anthropometry and laboratory parameters
The following information was collected from the Electronic Health Record System: age, sex, smoking status, and alcohol drinking status. Waist circumference (WC) was measured at the mid-point between the costal inferior border and the iliac crest in a horizontal plane using a flexible rule to the nearest of 0.1 cm. Body height and weight were measured using an automatic body scale (Sonka Co., Ltd, Shenzhen, China) to the nearest of 0.1 cm and 0.1 kg, respectively. Body mass index (BMI) was calculated using the equation: BMI = weight (kg) / height2 (m2).
After at least 8 h fasting, early morning blood was drawn from an antecubital vein in the arm of each participant. Levels of fasting total cholesterol, triglycerides, high-density lipoprotein-cholesterol (HDL-C), and low-density lipoprotein-cholesterol (LDL-C) were tested using the enzymatic colorimetric method with the Toshiba 200FR Neo analyzer (Toshiba Medical System Co., Ltd., Tokyo, Japan).
Measurement of body composition based on CT images
Chest CT scans were completed on the same day of anthropometry measurements for each participant using a 16-slice spiral CT scanner (Brilliance; Philips Healthcare, Ohio, USA) with a 5-mm slice thickness. Acquisition parameters were as follows: 100–140 kV, variable mAs based on the patient’s body size, and detector collimation of 0.75–1.5 mm.
Unenhanced cross-sectional CT images at the T12 level were analyzed using a dedicated segmentation software (Mimics version 21.0; Materialise, Leuven, Belgium). On a single CT image, skeletal muscle area (SMA) was segmented according to the widely accepted muscle tissues thresholds (− 29 to 150 HU) , including erector spinae, latissimus dorsi, rectus abdominis, obliquus externus, internus abdominis, and internal and external intercostal muscles. The mean of skeletal muscle radiodensity (SMD) of T12 SMA was automatically calculated by Mimics software. The lower the SMD, the higher the degree of myosteatosis. Furthermore, IMAT was segmented according to the widely accepted fat tissue thresholds (− 30 to − 190 HU) . Myosteatosis severity increases with increased IMAT. A trained researcher (L.T.) who was blinded to the participants’ clinical information segmented all CT images, and another researcher (G.J.) reviewed the segmented images.
According to previous studies [16, 17], we divided SMA by body height squared (m2), body weight (kg), and BMI (kg/m2), respectively, to adjust for the impact of body size on SMA. The height-, weight-, and BMI-adjusted SMAs were collectively known as skeletal muscle indices (SMIs). As reported previously , IMAT/SMA ratio was also calculated using the equation: IMAT/SMA ratio = IMAT (cm2)/ SMA (cm2) × 100%.
Body composition measurements based on BIA
Whole-body skeletal muscle mass (BSM) and whole-body fat mass (BFM) were estimated by segmental multi-frequency BIA (InBody 770, Biospace Co., Ltd., Korea). After at least 8 h fasting, participants were asked to stand on the platform of the BIA device barefoot with their feet on the electrode and to grasp the handles of the device with their fingers directly contacting with the electrodes. Then, they were asked to stand still for 1 min with their elbows fully extended, and their shoulder abducted to approximately 30°. The device measured the bioimpedance of the participant’s body and estimated BSM and BFM automatically.
Continuous data are presented as mean and standard deviation (SD) or median and interquartile range where appropriate; whereas categorical data are presented as number and percentage. The differences between groups were compared using independent samples t-test or Mann–Whitney U test for the continuous variables with normal or abnormal distribution, respectively. The distributions of SMA, SMD, IMAT, and IMAT/SMA in men and women were presented in density plots.
Due to the significant differences in SMA, SMIs (SMA/height2, SMA/weight, and SMA/BMI), SMD, and IMAT between men and women, we stratified the data by sex. Pearson’s correlation coefficients (r) were calculated to explore the correlations of SMA and SMIs with BSM. Spearman’s rank correlation coefficients (ρ) were calculated to explore the correlations of SMD, IMAT, and IMAT/SMA with BFM. We also used scatter plots and linear models to examine the correlation between SMA and BSM and the correlations of SMD and IMAT with BFM. The correlation coefficients are considered as high, moderate, or low when r (or ρ) is > 0.5, 0.3–0.5, or < 0.3, respectively .
We defined the T-score for SMA and SMIs by calculating the difference between the individual’s measured SMA (or SMIs) and the corresponding means of healthy young adults (aged 18 to 40 years). The equation for T-score calculation is as follows: T-score = (individual’s value—young adults’ mean value)/young adults’ SD value. According to the EWGSOP2 , individuals with an SMA (or SMIs) less than the sex-specific mean values of SMA (or SMIs) at the point of T-score = − 2.0 in the young reference group were considered to have sarcopenia. As previously reported [17, 20], we also provided the sex-specific mean values of SMA (or SMIs) at the point of T-score = − 1.0 or − 2.5 in the young reference group as the alternative cut-off points of sarcopenia.
Because SMD, IMAT, and IMAT/SMA were of abnormal distribution, SMD-defined myosteatosis was determined when the individual’s SMD was less than the sex-specific fifth percentile (p5) cut-off points of SMD in the young reference group. The sex-specific first percentile (p1) and 10th percentile (p10) cut-off points of SMD were also reported. Similarly, IMAT-defined myosteatosis was determined when the individual’s IMAT was more than the sex-specific 95th percentile (p95) cut-off points of IMAT in the young reference group. The sex-specific 90th percentile (p90) and 99th percentile (p99) cut-off points of IMAT were also reported.
All statistical analyses were performed in SPSS software 26.0 (IBM SPSS Inc., New York, US) and R version 3.5.1(R Foundation for Statistical Computing, Vienna, Austria). A p value < 0.05 indicates statistical significance.