We used cross-sectional data obtained from a community-based study which in order to identify factors that influence non-communicable chronic diseases and investigate the effects of health promotion through artificial intelligence. Data (anthropometric and biochemical parameters, cardiovascular function, lifestyle, disease status, family history of disease, and mental health) were collected each year using an e-health promotion system.
We invited local residents to participate in the study through 6 community health service centers located in Anhui province (Hefei, Bengbu, and Chuzhou). A total of 4529 participants aged >18 years were surveyed between June 2018 and January 2020. Exclusion criteria were age <40 years (n=800); ankle-brachial index >1.4 or <0.89; cardiovascular diseases (n=350); insufficient data for baPWV (n=28) or body composition analysis (n=132). A total of 3219 subjects (1951 women and 1264 men; mean age±SD, 61.32±9.81 years) were ultimately included in the analysis. All subjects provided written, informed consent for their data to be used in the study, and the study protocol was approved by the Ethics Committee of Bengbu Medical College (Anhui, China; no. 2018045).
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 amount of physical activity. 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 in the case of 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 type 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. Data on sleep disorder, kidney disease, diabetes, dietary salt preferences, and dietary fat content were collected through self-report questionnaires, and missing values were assigned a value of zero.
Anthropometric measurements including body height, weight, and WC were obtained while subjects were standing and wearing light clothing. Height was measured with steel tape, and weight was measured with a bioelectric impedance analyzer (Model BX-BCA-100; Institute of Intelligent Machines, Hefei, China). WC was measured above the iliac crest and below the lowest rib margin at minimum respiration using flexible leather tape as subjects were in the standing position. After obtaining the measurements, BMI and WHtR were calculated as the ratio of weight (kg)/height (m)2 and WC (cm)/height (cm), respectively. There were no missing values in the anthropometric data.
Body composition measurements
Body composition parameters were measured using bioelectric impedance analyzer. The participants refrained from eating and drinking 3 h before measurements were performed, and were instructed to remove their socks and stand on the machine; electrodes were placed on both hands and feet, and the subjects were instructed to lift both arms upright and touch the electrodes with their hands. Fat-free mass(FFM), including lean tissue mass, total body water, were derived from the impedance data, and fat-free tissue index (FFTI; FFM/height2), fat tissue index (FTI; FM/height2), FFTI/FTI, and ratio of trunk fat to free mass (RTFFM; trunk FFM/trunk weight) were calculated.
Measurement of baPWV and definition of high PWV
BaPWV (m/s) was measured using an IIM-AS-100 system (Institute of Intelligent Machines), which recorded bilateral brachial and posterior tibial-artery pressure waveforms by an oscillometric method by means of cuffs placed on subjects’ arms and ankles. baPWV was calculated automatically for each arterial segment as the path length divided by the corresponding time interval.
High PWV was defined according to the Japanese Guidelines for Noninvasive Vascular Function Test, which recommend lifestyle modifications for a baPWV value >14 m/s on one side, which indicates a high risk of hypertension onset in untreated normotensive individuals .
Data were analyzed using SPSS v23.0 software (IBM, Armonk, NY, USA). Continuous variables are expressed as mean±SD. The Student’s t test for independent samples and Pearson’s chi-squared test were used to assess the significance of differences in baseline characteristics between groups according to baPWV level stratified by sex and age.
Partial correlations (adjusted for age) between obesity-related parameters and baPWV were examined. Binary logistic regression models were used to identify obesity-related parameters that were independently associated with high PWV after adjusting for age, heart rate, systolic blood pressure (SBP), cigarette consumption, physical activity, and diabetes status.
Receiver operating characteristic (ROC) curves were analyzed to identify the optimal cutoff points and assess the predictive capacity of obesity-related parameters for occurrence of high PWV by age (40–59 years and ≥60 years) and sex, with sensitivity and specificity values reported. Optimal cutoff points for the parameters were determined according to the largest Youden’s index value (sensitivity+specificity−1).