1. Study Subjects
The data were consecutively collected from hospitalized people of Chinese Han population 60 years old and older, who were examined using DXA (Hologic Discovery A; Hologic, Bedford, MA, USA) and measured both muscle strength (grip strength) or/and physical performance (6-m usual gait speed) between 2015 and 2020. All of the information was collected from a large general hospital in Chongqing, a city with a population of 5.4 million in the southwest of China. And most of them were hospitalized because of either acute medical problems or elective admission.
Exclusion criteria for all participants were as follows: 1) acute changes of body composition such as severe edema and dehydration; 2) having a fever or an acute infection; 3) having an end-stage heart failure, renal disease;4) missing data for clinical laboratory measures, including LP(a) and any body composition measure. All the exclusion criteria were based on medical history, physical examinations, and the laboratory data of participants. Ultimately, 528 subjects were involved in the study and all entered in the final result analysis.
2. Laboratory measurements
All blood samples were obtained in the morning between 7–9 o’clock, after a 12–14h overnight fast. Specimens were collected about 5ml in tubes without anticoagulant. After 30 minutes’ standing, samples were centrifuged at 3000 rpm for 15 minutes. Serum samples were divided into aliquots and immediately stored at-80°C for subsequent assays. Serum Lp(a) levels were determined enzymatically using a chemistry analyzer (Hitachi 7020; Tokyo, Japan).
3. Body-composition, grip strength, 6-m usual gait speed measurements
Body compositional analysis was performed using dual energy X-ray absorptiometry (DXA). A whole-body DXA scan was performed for each patient to measure total and regional lean mass (g), total body fat (g), visceral adipose tissue (VAT) (g) and total body fat percentage (%). Appendicular skeletal muscle mass (kg) was defined as the sum of the lean soft tissue masses of the arms and legs .ASM (kg) and ASM/height2 (Kg/m2) were obtained . We assessed hand grip strength using a hydraulic jamar dynamometer (Sammons Preston Rplyan, 4 Sammons Court Bolingbrook, IL, 60440). We measured walking speed by having the participant walk at his usual pace over a 6-m course.
4. The definition of sarcopenia and obesity
In the study, sarcopenia was defined by using height-adjusted skeletal muscle mass (the ASM/height2 index) and gait speed or handgrip strength based on the definition of sarcopenia for Asian population in 2019 .For the ASM/height2 index, the cut-off values are 7.0 kg/m2 for men and 5.4 kg/m2 for women. The cut-off values of handgrip strength are 26 kg for men and 18 kg for women, while gait speed are 0.8 m/s. The ASM/height2 index is correlated with body mass index (BMI) .BMI is used as a current criterion for obesity, which could have limited applications for underestimating obese subjects with sarcopenia. In our study, obesity was defined as values greater than 27% for men and 38% for women, based on the New Mexico Aging Process Study (NMAPS) and the New Mexico Elder Health Survey (NMEHS) .We classified the subjects as the following: sarcopenia without obesity, without sarcopenia obesity, obesity without sarcopenia, and sarcopenic obesity, according to the definitions above.
5. Statistical Analysis
The numerical data were expressed as mean and standard deviation (SD). The normally distributed of examined variables were verified by Kolmogorov-Smirnov test. If the variables were normally distributed, we performed statistical comparisons applying student’s t-test. If not, we applied nonparametric test. The correlation coefficient test was applied in order to assess the existence of significant interdependence between ASM/height2 and Lp(a), as well as total body fat percentage and Lp(a). In addition, we used multiple binary logistic regression models to evaluate the relationship between sarcopenia, as well as obesity, as the dependent variable, and Lp(a). The regression models were adjusted for age, sex, and diseases. P <0.05 was considered to be statistically significant. Statistical analysis was performed using the SPSS program, version 19.0.