CARDIOVASCULAR JOURNAL OF AFRICA • Volume 34, No 4, September/October 2023 226 AFRICA or other autoimmune rheumatic diseases or connective tissue diseases. This study was reviewed and approved by the ethics committee of the First Affiliated Hospital of Hebei North University. All the subjects were informed of this study and personally signed the informed consent. Blood pressure was measured using an automatic 24-hour ambulatory blood pressure monitor (Spacelabs, USA). The monitor was fixed on the patient’s right upper arm by professionals, and the upper arm was kept relatively static during automatic measurement for 24 hours from 8:00 to 8:00 the next day (6:00 to 22:00 in the daytime, automatically measured every 30 minutes; 22:00 to 6:00 the next day in the night-time, automatically measured every 60 minutes). The monitoring indices included 24-hour heart rate, daytime and night-time systolic blood pressure, daytime and night-time diastolic blood pressure, 24-hour mean systolic blood pressure and 24-hour mean diastolic blood pressure. The measurement results were valid if the valid data accounted for more than 80% throughout the day. The blood pressure rhythm was calculated: (daytime mean blood pressure–night-time mean blood pressure)/daytime mean blood pressure × 100%. The blood pressure rhythm of 10–20% and < 10% indicated dipper and non-dipper blood pressure, respectively. With blood pressure circadian rhythm and heart rate as evaluation indices, the subjects were divided into a low-risk group (24-hour heart rate ≤ 70 bpm, dipper blood pressure), a medium-risk group (24-hour heart rate < 70 bpm, non-dipper blood pressure; or 24-hour heart rate > 70 bpm, dipper blood pressure) and a high-risk group (24-hour heart rate ≥ 70 bpm, non-dipper blood pressure) according to the subjects’ 24-hour heart rate and whether blood pressure was dipper pattern or not.12 Fasting venous blood was drawn in the morning, centrifuged at 3 000 rpm for 10 minutes and stored at –20°C. A biochemical analyser was used to measure levels of fasting blood glucose, total cholesterol, triglycerides, high- and low-density lipoprotein cholesterol, uric acid and serum creatinine through spectrophotometry. The electrocardiographic indices were monitored for 24 hours using a TLC5000 dynamic electrocardiograph (Shanghai Lixin Instrument Co, Ltd). After the interference signals such as ectopic cardiac rhythm were eliminated, the standard deviation of the normal-to-normal intervals and standard deviation of the averages of normal-tonormal intervals in all standard deviation of sequential fiveminute normal-to-normal intervals were calculated using the computer system. Blood pressure variability was expressed as blood pressure standard deviation and coefficient of variation. Moreover, daytime systolic pressure standard deviation, nighttime systolic and diastolic standard deviation, 24-hour systolic and diastolic standard deviation, 24-hour systolic and diastolic blood pressure coefficient of variation, daytime systolic and diastolic blood pressure coefficient of variation, night-time systolic and diastolic blood pressure coefficient of variation were calculated. The diagnostic criteria for essential hypertension were based on the Chinese Guidelines for the Diagnosis and Treatment of Hypertension 2013,13 which is daytime systolic arterial pressure ≥ 140 mmHg or/and diastolic arterial pressure ≥ 90 mmHg in three or more detections, and patients with secondary hypertension were excluded. Drinking was defined as alcohol consumption ≥ 50 ml/time, once or more per week on average for one year. Statistical analysis IBM SPSS 19.0 software was used for statistical analysis. The normality test was conducted for the measurement of data. Normally distributed measurement data are expressed as mean ± standard deviation, while abnormally distributed measurement data are expressed as median. Comparison was made with the independent-samples t-test between two groups, and with one-way analysis of variance among the groups. The SNK test was conducted for pairwise comparison. Enumeration data were compared with the chi-squared test between two groups, and with the Kruskal–Wallis rank sum test among the groups. Spearman rank correlation analysis was performed between salt sensitivity and blood pressure variability, the risk factors for cardiovascular events in patients with essential hypertension were analysed through Cox multivariate regression analysis, and their predictive value for cardiovascular events was detected using receiver operating characteristic (ROC) curves. A p < 0.05 was considered statistically significant. Results All of the 730 subjects were subjected to salt-sensitivity risk stratification according to 24-hour ambulatory blood pressure monitoring and blood pressure circadian rhythm. There were 141 cases (19.32%) in the low-risk group, 410 cases (56.16%) in the medium-risk group and 179 cases (24.52%) in the high-risk group. The baseline data were compared among the three groups. The results showed that among the three groups, the patient’s age and family history of cardiovascular diseases had significant differences (p < 0.05), but gender, body mass index, course of hypertension, history of smoking and drinking, and history of diabetes had no significant differences (p > 0.05) (Table 1). The clinical detection indices were compared among the three groups. The results revealed that among groups with different grades of salt-sensitivity risk, low-density lipoprotein cholesterol and standard deviation of sequential five-minute normal-to-normal interval had significant differences (p < 0.05), while fasting blood glucose, total cholesterol, triglycerides, highdensity lipoprotein cholesterol, uric acid, serum creatinine and standard deviation of the normal-to-normal intervals had no significant differences (p > 0.05) (Table 2). Table 1. Baseline data of patients with different grades of salt-sensitivity risk Baseline data Low-risk group (n = 141) Medium-risk group (n = 410) High-risk group (n = 179) Male/female 69/72 210/200 92/87 Age (year) 53.1 ± 11.8 55.4 ± 13.2a 57.3 ± 12.5a,b Body mass index (kg/m2) 25.9 ± 3.7 26.2 ± 4.3 27.8 ± 4.6 Course of hypertension (year) 10.5 ± 3.1 9.6 ± 4.7 9.3 ± 5.9 History of smoking, n (%) 21 (14.89) 78 (19.02) 39 (21.79) History of drinking, n (%) 18 (12.77) 66 (16.10) 27 (15.08) History of diabetes, n (%) 28 (19.86) 103 (25.12) 37 (20.67) Family history of cardiovascular diseases, n (%) 21 (14.89) 62 (15.12)a 58 (32.40)a,b ap < 0.05 vs low-risk group, bp < 0.05 vs medium-risk group.
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