The cross-sectional data were collected from a community-based study used to identify factors that contribute to non-communicable chronic diseases. Artificial intelligence methods were applied to examine the impact of health promotions. The data include anthropometric and biochemical parameters, heart function, lifestyle, illnesses, family medical history, and mental health. An electronic health promotion system was used to collect data on an annual basis for these dwellers.
Residents were offered the chance to participate in the investigation through six community health service centers located three cities in Anhui province ( Bengbu, Chuzhou, and Hefei). The total number of participants aged 18 or older was 4529. They were surveyed from June 2018 to January 2020. Three exclusion criteria were applied: 1896 individuals not aged 60 or older were excluded, 59 participants without baPWV data were excluded, and 63 participants without HGS data were excluded. This left 2511 participants with sufficient data, including 1475 females and 1036 males. The mean age of the participants was 65.88 (SD 5.81). Each participant was asked to provide written, informed consent for the use of their data for this research. The Ethics Committee of Bengbu Medical College approved the study protocol (Anhui, China; no. 2018045).
Three anthropometric measures were taken for each participant: height, weight, and waist circumference (WC). Height and WC were measured using a steel measuring tape, and weight was assessed with a bioelectric impedance analyzer (BIA, Model BX-BCA-100; Institute of Intelligent Machines, Hefei, China). Weight and height were used to calculate BMI.
Body composition measurements
Participants fasted from food and water in the 3 hours prior to measurement. They were told to remove their shoes and socks and position themselves on the BIA machine. Electrodes were applied to their extremities and the participants were told to lift both arms and touch the electrodes. Skeletal muscle mass (SMM) was calculated according to the formula SMM (kg)=0.566 * FFM . where FFM denotes the fat free mass computed by the BIA. Appendicular muscle mass index (AMI) was calculated using the formula AMI = (ASM)/height2 , where ASM is appendicular skeletal muscle mass.
HGS (defined here as the maximal HGS of the dominant hand) was measured using a spring-type dynamometer (TSN100/200-WL, Ti Shi Neng Sports Technology company, Beijing, China). While standing and extending their arms at their sides, participants were told to squeeze the dynamometer as hard as possible for up to 3 seconds. They were asked to do this three times with 30-seconds rests between each attempt.
Measurement of baPWV and definition of high PWV
An IIM-AS-100 system (Institute of Intelligent Machines) was used to record baPWV (m/s). It uses oscillation to record bilateral brachial and posterior tibial-artery pressure waveforms with cuffs applied to the participants’ arms and ankles. The baPWV for each arterial segment is computed according to the formula baPWV = path length/time.
The Japanese Guidelines for Noninvasive Vascular Function Test considers a baPWV >14 m/s as unhealthy and suggestive of an elevated risk of high blood pressure in untreated normotensive individuals .
Definition of sarcopenia
Based on the AWGS criteria (2019) , sarcopenia is characterized as the incidence of low muscle mass, low muscle strength, and/or low physical performance. Here, low muscle mass and low muscle strength were used. Low muscle mass was defined as an AMI <7.0 kg/m2 in males and <5.7 kg/m2 in females. Low muscle strength was defined as a HGS <26 kg for males and <18 kg for females.
All physical examinations were performed by trained medical staff or medical postgraduate students according to standardized procedures. Participants were questioned regarding health-related behaviors including cigarette and alcohol consumption and physical activity levels. For cigarette consumption, total smoking during the subject’s lifetime was calculated based on the quantity of cigarettes that were smoked and the weekly frequency; this was extended to consumption before quitting for former smokers. The amounts of alcohol in one bottle of the most popular alcoholic beverages in Anhui province are as follows: beer (500 ml, 3.2% alcohol), 17.5 g; white liquor (450 ml, 42% alcohol), 210 g; and wine (750 ml, 13.5–14% alcohol), 97.5 g. Daily alcohol consumption was calculated using these values. When data for cigarette and alcohol consumption were missing, a value of zero was assigned. Subjects were questioned about the types of physical activities in which they engaged, the duration of activity (minutes), and the frequency (per week). According to activity codes and metabolic equivalent (MET) intensities in the Compendium of Physical Activities, physical activity time was determined as minutes/MET/day, and missing values were assigned the median value. The remaining data were collected through self-report questionnaires, and missing values were assigned a value of zero.
SPSS v23.0 software (IBM, Armonk, NY, USA) was utilized for data analyses. All continuous variables are expressed as mean±SD. The significance of differences in baseline characteristics between participants with and without sarcopenia were examined using Student’s t-tests for continuuus variable and Pearson’s chi-squared tests for discontinuous variable. Significance test calculations were stratified by gender. Student’s t-tests were also used to assess differences in sarcopenia rates among participants with normal and high baPWV.
HGS/AMI and baPWV were investigated with Pearson’s correlations. Whether Age and prevalence of sarcopenia independently correlated with high PWV were determined using binary logistic regression models that were adjusted for smoking and drinking levels, sleep issues, kidney/heart disease, high blood pressure, and physical activity time.