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Prevalence of diabetes and hypertension and their interaction effects on cardio-cerebrovascular diseases: a cross-sectional study

Prevalence of diabetes and hypertension and their interaction effects on cardio-cerebrovascular... Background: Hypertension and diabetes mellitus are two of the major risk factors for cardio-cerebrovascular diseases (CVDs). Although prior studies have confirmed that the coexistence of the two can markedly increase the risk of CVDs, few studies investigated whether potential interaction effects of hypertension and diabetes can result in greater cardio-cerebrovascular damage. We aimed to investigate the prevalence of hypertension and diabetes and whether they both affect synergistically the risk of CVDs. Methods: A cross-sectional study was conducted by using a multistage stratified random sampling among communities in Changsha City, Hunan Province. Study participants aged > = 18 years were asked to complete questionnaires and physical examinations. Multivariate logistic regression models were performed to evaluate the association of diabetes, hypertension, and their multiplicative interaction with CVDs with adjustment for potential confounders. We also evaluated additive interaction with the relative excess risk ratio (RERI), attribution percentage (AP), synergy index (SI). Results: A total of 14,422 participants aged 18–98 years were collected (men = 5827, 40.7%). The prevalence was 22.7% for hypertension, 7.0% for diabetes, and 3.8% for diabetes with hypertension complication, respectively. Older age, women, higher educational level, unmarried status, obesity (central obesity) were associated with increased risk of hypertension and diabetes. We did not find significant multiplicative interaction of diabetes and hypertension on CVDs, but observed a synergistic additive interaction on coronary heart disease (SI, 1.43; 95% CI, 1.03–1.97; RERI, 1.94; 95% CI, 0.05–3.83; AP, 0.26; 95% CI, 0.06–0.46). Conclusions: Diabetes and hypertension were found to be associated with a significantly increased risk of CVDs and a significant synergistic additive interaction of diabetes and hypertension on coronary heart disease was observed. Participants who were old, women, highly educated, unmarried, obese (central obese) had increased risk of diabetes and hypertension. Keywords: Diabetes, Hypertension, Stroke, Coronary heart disease, Interaction * Correspondence: yangtbcsu@163.com Department of Epidemiology and Health Statistics, XiangYa School of Public Health, Central South University, Changsha, Hunan Province, China Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wang et al. BMC Public Health (2021) 21:1224 Page 2 of 9 Background the five geographical locations (East, West, South, North, Hypertension (HT) and Diabetes mellitus (DM) have and Middle) in the 5 selected districts. In the third stage, been confirmed as two of the major risk factors for 2 communities/villages were randomly selected from cardio-cerebrovascular diseases (CVDs) [1, 2]. It has each of the selected subregions. In the final stage, 150 been found that individuals with both DM and HT have households were randomly selected from each selected a greater risk of cardio-cerebrovascular disease than community/village and one household resident with those with only one condition [3]. However, few studies age > = 18 was selected as the study participants. There- investigated the interaction of DM and HT on the risk fore, a total of 50 subregions, 100 communities/villages, of CVDs [4]. It is not yet clear whether the increased and 15,000 household/participants were selected. Of risk resulting from the coexistence of DM and HT could those selected, 14,422 (96.1%) participants completed be attributed to a simple combination or their the baseline survey and were included in the current interaction. analysis. The study was submitted to the Ethics Commit- DM and HT share common comorbidities [5–7]. Their tee of Xiangya School of Public Health, Central South frequent coexistence is not a coincidence but due to University and was granted a waiver beacause this study some shared pathogenic mechanism. Diabetic patients used the data from 2012 national chronic disease man- are twice as likely to have HT as non-diabetic patients. agement project and did not involve the human trial or Similarly, patients with HT are more likely to develop personal information. Informed consent was obtained diabetes than those with normotensive people. In China, from the participants before the investigation. More de- nearly 100 million people have HT, nearly 30 million tails of the study design were previously published [10]. people have DM, and approximately 15 million people The baseline in-person interview was conducted by have both HT and DM [8].. The major outcome of this trained interviewers (Additional file 1: Appendix 1). comorbidity is cardio-cerebrovascular diseases, which Smoking status was categorized as never-smoking, account for nearly half of the death causes. current smoking (at least one cigarette per day), and In general, physicians pay more attention to the asso- former smoking (quit smoking more than 12 months). ciations between risk factors and outcomes. Thus, the Current drinkers were those who drank once or more in interaction is often neglected. The term “interaction” re- the recent month. Regular exercisers were those who fers to the fact that at different levels of a risk factor, an- exercised at least three times a week. The disease histor- other risk factor has different effects on the disease, and ies included the status of diabetes, hypertension, coron- vice versa [9]. Additive interaction scale and multiplica- ary heart disease, and stroke. tive interaction scale are usually used to measure the in- teractions between factors. Additive interaction refers to Physical examination and disease definition the effect of two (or more) factors acting on a disease is Height and weight measurement: the subjects took off greater (synergism) or less (antagonism) than the sum of their coats, shoes, and hats, stood on the automatic the independent effects of these factors. Multiplicative measuring instrument with feet 45 degrees apart, and interaction refers to the effect of two (or more) factors heels close to the instrument as far as possible. The in- acting on a disease is greater (positive) or less (negative) strument automatically reads the measurement results of than the product of the independent effects of these height and weight. BMI = weight (kg) / height (m) . BMI factors. ≧28.0 was defined as obesity according to the Chinese Given the high prevalence of DM and HT and their standard [11]. strong association with CVDs, our study aimed to deter- Waist circumference measurement: the subjects take mine whether HT and DM act synergistically towards off their coats, loosen their belts, stand naturally on both cardio-cerebrovascular diseases in a general population. legs and keep calm breathing. The medical staff used a tape measure to horizontally circle the abdomen of the Methods subjects to measure the waist circumference. Central Study participants and baseline survey obesity was defined as a WC > 90 cm for men and > 85 This cross-sectional study was based on National cm for women [12]. Chronic Disease Management Survey conducted in Blood pressure measurement: the subjects sit in the Changsha, the capital of Hunan Province, in 2012. A room for 5 min, keep calm and then start to measure. multistage stratified random sampling method was used The medical staff used an automatic blood pressure to select participants from the community population. meter to measure. During the measurement, the subjects In the first stage, 5 districts were randomly selected kept the arm position at heart level, and the instrument (Liuyang, Ningxiang, Kaifu, Tianxin, Furong) from a automatically read the blood pressure value. According total of 9 districts in Changsha. In the second stage, 2 to the Chinese hypertension guidelines, hypertension subregions were selected (streets/towns) from each of was defined as systolic BP (SBP) ≥ 140 mmHg, diastolic Wang et al. BMC Public Health (2021) 21:1224 Page 3 of 9 BP (DBP) ≥90 mmHg, or a history of diagnosed hyper- significantly higher in CHD patients than normal people tension [13]. (P < 0.001). Similar results were found in the other four Blood glucose measurement: After fasting for 12 h, a diseases except for stroke (P < 0.05). We found that only trained blood collector took fasting blood from each the mean values of age and percentages of former subject. The fasting blood glucose was detected by hexo- smokers, non-current drinkers, regular exercisers, and kinase method within 2 h of venous blood extraction. central obesity were significantly higher in stroke pa- According to the American Diabetes Association [14], tients than normal people (P < 0.05). Diabetes was defined as blood glucose ≥7.0 mmol/L or a history of type 1 diabetes or type 2 diabetes. Prevalence of DM and HT The history of CVDs, including CHD and stroke, was Table 3 showed the prevalence of DM and HT by gen- self-reported. CHD included ischemic coronary heart der and age. The overall prevalence was 22.7% for HT, disease, myocardial infarction, angina pectoris, coronary 7.0% for DM, and 3.8% for DM with HT complication, artery bypass grafting, percutaneous coronary thromb- respectively. The prevalences were positively correlated olysis, and coronary angioplasty. Stroke included ische- with age in general, with the peak value of 66–75 years mic stroke and hemorrhagic stroke. old, and then decreased. Of note, the prevalences were relatively high in the young participants aged from 18 to Statistical analysis 25. Stratified by sex, we found that women had a higher Means and standard deviations were reported for con- prevalence of hypertension than men in age groups be- tinuous variables and frequencies and percentages were fore 36–40 years old (P < 0.001). reported for categorical variables. The chi-square test or The association of demographic factors and obesity t-test was used to compare the differences in demo- with DM and HT was shown in Table 4. In general, graphic characteristics between patients and normal older age, women, education with college or above, un- people. The status of DM and HT can be divided into married status, obesity, and central obesity were risk fac- four categories: a) non-DM & non-HT group: normo- tors of all three diseases (P < 0.05). In addition, tensive and normoglycemic participants; b) DM & non- education in middle school was associated with in- HT group: participants with diabetes only; c) non-DM & creased risks of DM with HT complication (P = 0.013), HT group: participants with hypertension only; d) DM and divorced or widowed status was associated with de- & HT group: participants with both diabetes and hyper- creased risks of DM (P = 0.006). tension. The multiplicative interaction between HT and DM on CVDs was evaluated using logistic regression models with adjustment for potential confounders deter- Prevalence of CVDs by DM and HT mined with Directed Acyclic Graphs (DAGs) [15]: The Table 5 showed the prevalence of CVDs by DM and HT. additive interaction between DM and HT on CVDs was The overall self-reported prevalence was 1.4% for stroke, evaluated with relative excess risk ratio (RERI), attribu- 7.5% for CHD, and 8.3% for total CVDs. The prevalence tion percentage (AP), and synergy index (SI). The confi- of total CVDs was 3.7% for non-DM & non-HT group, dence intervals (CIs) of the above indexes were 12.1% for DM & non-HT group, 21.2% for non-DM & estimated [16]. P < 0.05 (two-sided) was considered sta- HT group, and 31.4% for DM & HT group. The differ- tistically significant. Data with missing values were de- ence in prevalence among the four groups was highly leted. All statistical analyses were performed with SPSS significant (P < 0.001). A similar difference was observed for Windows 18.0 (SPSS Inc., Chicago, IL, USA). for stroke and CHD (P < 0.001). Results Demographics Association of DM and HT with CVDs The demographic characteristics of the participants clas- Table 6 showed the associations of CVDs with DM sified by disease status were summarized in Tables 1 and HT after adjusting for potential confounding fac- and 2. Of the 14,422 participants, 40.7% were male with tors (gender, age, education level, marital status, a mean age of 53.84 years and the range of 18–98 years. smoking, drinking, regular exercise, obesity, and cen- The proportions of participants who were current tral obesity for CHD and total CVDs; age, marital sta- smokers, current drinkers, having regular exercise, hav- tus, smoking, drinking, regular exercise, and central ing obesity and central obesity were 25.8, 20.1, 33.2, 8.6, obesity for stroke). Comparing with the non-DM & and 26.0%, respectively. The mean values of age, the pro- non-HT group, an significantly increased risk of portion of females, education level of primary school stroke, CHD, and total CVDs was observed among and below, former smokers, non-current drinkers, regu- the DM & non-HT group, the non-DM & HT group, lar exercisers, obese and central obese people were and the DM & HT group. Wang et al. BMC Public Health (2021) 21:1224 Page 4 of 9 Table 1 Basic demographic characteristics of participants by cardio-cerebrovascular diseases (CVD) status (n = 14,422) Variables Total CHD Stroke Total CVDs Patients Non- P Patients Non- P Patients Non- P patients patients patients Age (year) 53.84 ± 64.71 ± 52.96 ± < 0.001 64.52 ± 53.68 ± < 0.001 64.74 ± 52.85 ± < 0.001 14.88 12.28 14.73 13.46 14.85 12.23 14.71 Gender, n (%) Men 5927 (41.1) 381 (35.4) 5546 (41.6) < 0.001 95 (45.5) 5832 (41.0) 0.197 444 (37.0) 5483 (41.5) 0.002 Women 8495 (58.9) 696 (64.6) 7799 (58.4) 114 (54.5) 8381 (59.0) 757 (63.0) 7738 (58.5) Educational level , n (%) Primary school or 5909 (41.1) 657 (61.3) 5252 (39.5) < 0.001 93 (44.7) 5816 (41.0) 0.412 707 (59.2) 5202 (39.4) < 0.001 below Middle school 7090 (49.3) 337 (31.4) 6753 (50.7) 93 (44.7) 6997 (49.4) 400 (33.5) 6690 (50.7) College or above 1384 (9.6) 78 (7.3) 1306 (9.8) 22 (10.6) 1362 (9.6) 88 (7.4) 1296 (9.8) Marital status , n (%) Unmarried 702 (4.9) 26 (2.4) 676 (5.1) < 0.001 4 (1.9) 698 (4.9) 0.007 28 (2.3) 674 (5.1) < 0.001 Married 11,944 807 (75.2) 11,137 (83.7) 167 (80.3) 11,777 (83.1) 905 (75.6) 11,039 (83.9) (83.1) Divorced or widowed 1730 (12.0) 240 (22.4) 1490 (11.2) 37 (17.8) 1693 (11.9) 264 (22.1) 1466 (11.1) Smoking status , n (%) Never 10,054 770 (71.8) 9284 (69.9) < 0.001 148 (71.2) 9906 (70.0) < 0.001 857 (71.6) 9197 (69.9) < 0.001 (70.1) Former 600 (4.2) 94 (8.8) 506 (3.8) 19 (9.1) 581 (4.1) 104 (8.7) 496 (3.8) Current 3696 (25.8) 209 (19.5) 3487 (26.3) 41 (19.7) 3655 (25.8) 236 (19.7) 3460 (26.3) Current drinking , n (%) Yes 2887 (20.1) 157 (14.6) 2730 (20.5) < 0.001 26 (12.4) 2861 (20.2) 0.006 173 (14.4) 2714 (20.6) < 0.001 No 11,507 918 (85.4) 10,589 (79.5) 183 (87.6) 11,324 (79.8) 1026 (85.6) 10,481 (79.4) (79.9) Regular exercise , n (%) Yes 3678 (33.2) 380 (41.4) 3298 (32.5) < 0.001 83 (59.3) 3595 (32.9) < 0.001 419 (42.5) 3259 (32.3) < 0.001 No 7397 (66.8) 537 (58.6) 6860 (67.5) 57 (40.7) 7340 (67.1) 568 (57.5) 6829 (67.7) Obesity , n (%) Yes 1242 (8.6) 130 (12.1) 1112 (8.3) < 0.001 17 (8.1) 1225 (8.6) 0.800 140 (11.7) 1102 (8.3) < 0.001 No 13,164 946 (87.9) 12,218 (91.7) 192 (91.9) 12,972 (91.4) 1060 (88.3) 12,104 (91.7) (91.4) Central obesity , n (%) Yes 3731 (26.0) 420 (39.1) 3311 (24.9) < 0.001 74 (35.4) 3657 (25.8) 0.002 462 (38.6) 3269 (24.8) < 0.001 No 10,644 653 (60.9) 9991 (75.1) 135 (64.6) 10,509 (74.2) 735 (61.4) 9909 (75.2) (74.0) The variable contained missing values. Interaction effects of DM and HT on CVDs Discussion We added the interaction term (DM × HT) into logistic Main finding models and found that the multiplicative interaction of The prevalence was 22.7% for hypertension, 7.0% for dia- DM and HT on CVDs was not found (Table 6). We fur- betes, both were similar to the National population survey ther evaluated the additive interaction of DM and HT [17, 18]. Meanwhile, the prevalence of coronary heart dis- (Table 7) and found the additive interaction was statisti- ease, stroke, and total cardio-cerebrovascular diseases was cally significant for CHD (SI = 1.43, 95 CI, 1.03–1.97; 7.5, 1.4, and 8.3%, respectively. They were all lower than RERI = 1.94; 95% CI, 0.05–3.83; AP = 0.26; 95% CI, 0.06– the previously reported prevalence especially stroke [19]. 0.46) while the additive interaction on stroke was not Older age, women, higher educational level, unmarried significant. status, and obesity (central obesity) were risk factors of Wang et al. BMC Public Health (2021) 21:1224 Page 5 of 9 Table 2 Demographic description of participants by the status of HT or DM (n = 14,422) Variables DM HT DM complicated with HT Patients Non-patients P Patients Non-patients P Patients Non-patients P Age (year) 60.19 ± 12.92 53.37 ± 14.91 < 0.001 61.34 ± 12.85 51.65 ± 14.72 < 0.001 61.73 ± 13.35 53.53 ± 14.85 < 0.001 Gender, n (%) Men 345 (34.4) 5582 (41.6) < 0.001 1241 (38.1) 4686 (42.0) < 0.001 178 (32.5) 5749 (41.4) < 0.001 Women 658 (65.6) 7873 (58.4) 2017 (61.9) 6478 (58.0) 370 (67.5) 8125 (58.6) Educational level, n (%) Primary school or below 478 (47.7) 5431 (40.6) < 0.001 1658 (51.0) 4251 (38.2) < 0.001 254 (46.4) 5655 (40.9) 0.018 Middle school 442 (44.1) 6648 (49.7) 1332 (41.0) 5758 (51.7) 238 (43.4) 6852 (49.5) College or above 82 (8.2) 1302 (9.7) 261 (8.0) 1123 (10.1) 56 (10.2) 1328 (9.6) Marital status, n (%) Unmarried 37 (3.7) 665 (5.0) 0.001 102 (3.1) 600 (5.4) < 0.001 25 (4.6) 677 (4.9) < 0.001 Married 807 (80.8) 11,137 (83.3) 2556 (78.7) 9388 (84.4) 423 (77.5) 11,521 (83.3) Divorced or widowed 155 (15.5) 1575 (11.8) 590 (18.2) 1140 (10.2) 99 (17.9) 1632 (11.8) Smoking status, n (%) Never 752 (75.1) 9302 (69.7) < 0.001 2336 (71.9) 7718 (69.5) < 0.001 424 (77.5) 9630 (69.8) < 0.001 Former 52 (5.2) 548 (4.1) 217 (6.7) 383 (3.5) 32 (5.9) 568 (4.1) Current 197 (19.7) 3499 (26.2) 697 (21.4) 2999 (27.0) 91 (16.6) 3605 (26.1) Current drinking, n (%) Yes 122 (12.2) 2765 (20.6) < 0.001 503 (15.5) 2384 (21.4) < 0.001 63 (11.5) 2824 (20.4) < 0.001 No 880 (87.8) 10,627 (79.4) 2748 (84.5) 8759 (78.6) 485 (88.5) 11,022 (79.6) Regular exercise, n (%) Yes 381 (49.3) 3279 (32.0) < 0.001 1033 (40.4) 2645 (31.0) < 0.001 219 (53.2) 3459 (42.4) < 0.001 No 392 (50.7) 7005 (68.0) 1523 (59.6) 5874 (69.0) 193 (46.8) 7204 (67.6) Obesity, n (%) Yes 858 (85.7) 12,306 (91.8) < 0.001 2808 (86.3) 10,356 (92.9) < 0.001 452 (82.8) 12,712 (91.7) < 0.001 No 143 (14.3) 1099 (8.2) 446 (13.7) 796 (7.1) 94 (17.2) 1148 (8.3) Central obesity, n (%) Yes 569 (56.8) 10,075 (75.3) < 0.001 1953 (60.1) 8691 (78.1) < 0.001 274 (50.1) 10,370 (75.0) < 0.001 No 432 (43.2) 3299 (24.7) 1296 (39.9) 2435 (21.9) 273 (49.9) 3458 (25.0) diabetes and hypertension. Participants with both diabetes As pointed out by Rothan [28], the additive interaction and hypertension had a significantly increased risk of model is closer to the nature of biological interaction cardio-cerebrovascular diseases as compared with partici- and has more relevant public health significance than pants with only one condition. A significant synergistic the multiplication model. Vandenbroucke et al [29] sug- additive interaction of diabetes and hypertension on cor- gested that both additive and multiplicative interaction onary heart disease was observed. should be reported when evaluating interactions. Even when two factors have no multiplicative interaction, they Comparisons with previous studies may have a positive interaction in the additive model Prior studies [20–22] have found that the comorbidity of [30]. This study reported both results of multiplicative hypertension and diabetes increased the risk of cardiovas- and additive models. In the multiplicative model, the cular diseases dramatically, but their interaction was not re- interaction items were not statistically significant but in ported [23–25]. Yun Ju Lai [26] found a synergism of the additive model, the interaction effects were positive. diabetes and hypertension only among elderly women aged over 65 years old. In another cross-sectional study with a Potential explanations small sample size (886 participants), Cai [27] and colleagues It was reported that DM and HT shared common risk found an interaction on the severity of stroke. However, all factors and pathophysiological pathways which were in- of those studies only evaluated multiplicative interaction. terconnected into a network and may even lead to a Wang et al. BMC Public Health (2021) 21:1224 Page 6 of 9 Table 3 Prevalence (%) of DM and HT in different age and gender group Age Number, DM HT DM with HT complication (year) n Male Female Total Male Female Total Male Female Total 18–25 495 3.9 4.5 4.2 10.5 9.7 10.1 2.2 3.4 2.8 26–30 557 1.6 3.0 2.3 8.2 4.0 5.9 1.2 0.7 0.9 31–35 640 2.2 3.6 3.0 8.7 6.3 7.3 1.4 3.0 2.3 36–40 861 1.2 3.2 2.4 6.3 9.1 8.0 0.3 1.5 1.0 41–45 1437 3.3 3.1 3.2 9.2 9.6 9.5 0.7 0.9 0.8 46–50 1820 3.9 4.6 4.3 13.0 17.3 15.7 1.5 2.3 2.0 51–55 1355 6.7 7.2 7.0 15.5 26.0 22.3 2.5 3.6 3.2 56–60 1689 8.5 9.9 9.3 21.1 27.7 25.0 4.4 4.7 4.6 61–65 1741 6.7 12.9 10.1 31.3 33.3 32.4 3.7 7.1 5.6 66–70 1311 8.8 12.6 11.1 31.1 37.4 34.8 4.8 7.6 6.4 71–75 904 8.3 12.9 11.0 41.2 42.9 42.1 6.2 9.3 8.0 76–80 619 8.0 10.8 9.5 37.8 41.1 39.6 4.2 7.2 5.8 > 80 360 9.7 8.7 9.2 33.9 43.1 38.9 5.5 8.2 6.9 Total 13,789 5.8 7.8 7.0 21.0 23.9 22.7 3.0 4.4 3.8 vicious cycle. Therefore, HT and DM are the main parts found that the combination of DM and HT has adverse of the metabolic process of metabolic syndrome and effects on left ventricular structure, myocardial dysfunc- they are prone to comorbidity [31]. Cardio- tion, and arterial stiffness. Cesare Russo [34] found that cerebrovascular diseases are multifactorial diseases. The HT and DM are independently associated with impaired risk of occurrence depends not only on the severity of a left ventricular diastolic function. Their coexistence re- certain determinant but also on the number of determi- sulted in the most severe effect on left ventricular dia- nants possessed by the individual [32]. Jonathan N [33] stolic mechanics and was associated with higher left Table 4 Associations of demographic factors and obesity with DM and HT Variables DM HT DM with HT complication OR (95% CI) P OR (95% CI) P OR (95% CI) P Age 1.04 (1.03, 1.05) < 0.001 1.06 (1.05, 1.06) 1.05 (1.04, 1.06) < 0.001 Gender Men Ref. Ref. Ref. Women 1.35 (1.17, 1.56) < 0.001 1.16 (1.07, 1.27) 0.001 1.47 (1.22,1.79) < 0.001 Educational level Primary school or below Ref. Ref. Ref. Middle school 1.11 (0.96, 1.29) 0.153 0.10 (0.91, 1.09) 0.931 1.28 (1.05, 1.55) 0.013 College or above 1.38 (1.06, 1.79) 0.016 1.48 (1.25, 1.75) < 0.001 2.10 (1.52, 2.90) < 0.001 Marital status Married Ref. Ref. Ref. Unmarried 1.67 (1.16, 2.40) 0.006 1.56 (1.23, 1.98) < 0.001 2.53 (1.62, 3.95) < 0.001 Divorced or widowed 0.76 (0.62, 0.92) 0.006 0.91 (0.80, 1.03) 0.133 0.83 (0.65, 1.06) 0.139 Obesity Yes 1.34 (1.10, 1.65) 0.005 1.57 (1.37, 1.81) < 0.001 1.53 (1.19,1.97) 0.001 No Ref. Ref. Ref. Central obesity Yes 2.05 (1.78, 2.37) < 0.001 2.13 (1.94, 2.35) < 0.001 2.57 (2.12, 3.10) < 0.001 No Ref. Ref. Ref. Wang et al. BMC Public Health (2021) 21:1224 Page 7 of 9 Table 5 Prevalence of CVDs by the status of DM and HT (n = 14,422) Prevalence (%) Total -DM & -HT(n = 10,709) +DM & -HT -DM & + HT +DM & + HT χ P (n = 548) (n = 2710) (n = 455) 1 2 3 stroke 1.4 (209) 0.6 (61) 2.4 (11) 3.9 (107) 5.5 (30) 241.685 < 0.001 CHD 7.5 (1077) 3.4 (361) 10.5 (48) 18.8 (510) 28.8 (358) 1133.675 < 0.001 Total CVD 8.3 (1201) 3.7 (399) 12.1 (55) 21.2 (575) 31.4 (172) 134.737 < 0.001 () Frequency in brackets ventricular filling pressures than patients with one con- However, the specific mechanism and degree of inter- dition alone. Both DM and HT are crime culprits for action remain unclear, thus further study is still atherosclerosis and are essential parts of the formation warranted. and aggravation of endothelial and smooth muscle func- tion [35]. The combination of DM and HT can promote Strengths and limitations endothelial cell dysfunction [36]. The dysfunction of This is the first community-based cross-sectional study endothelial cells may change in the early stage of athero- with a large sample size (14,422 participants) that inves- sclerosis. Both DM and HT can promote the generation tigated the interaction of diabetes and hypertension on of oxygen-derived free radicals, thus damaging endothe- cardio-cerebrovascular diseases. Both multiplicative and lial function. When the two coexist, endothelial cell additive interactions were evaluated, and the results were function further decreases, and smooth muscle function consistent in theory, which provided strong support for is also impaired [35]. Besides, the combination of DM the main conclusion. The multivariable logistical regres- and HT can promote monocyte adhesion to endothelial sion models in this study were adjusted for potential cells, thus increasing the production of vascular super- confounding factors according to the variable selection oxide and the expression of monocyte chemoattractant principle of DAG, which greatly improved the reliability protein-1 [37], leading to atherosclerosis and subsequent of the results. This study adopted a cross-sectional de- cardio-cerebrovascular diseases. In conclusion, recent sign, which precluded causal correlations, and the infor- studies show that there is a great biological possibility of mation about the disease was provided by the interaction between diabetes and hypertension. investigators themselves, thus recall bias cannot be avoided. The prevalence of stroke, CHD, and CVD are Table 6 Association of CVDs with DM & HT low so that the interaction effects could be underesti- CVD status OR 95% CI Wald χ P multiplicative multiplicative mated. In particular, this may be the reason for the ten- Model 1 Total CVD dency to null of the interaction on stroke to some extent, because the prevalence of stroke observed is only -DM & -HT Ref. Ref. 1.4%. More prospective cohort studies will be needed in + DM & -HT 2.53 (1.81, 3.55) the future to prove this correlation and adjusted more -DM & + HT 4.35 (3.72, 5.09) confounders such as disease types, degree, treatment, + DM & + HT 7.51 (5.86, 9.63) and control status. DM × HT 3.321 0.068 Model 2 Stroke Conclusions DM combined with HT significantly increased the risk -DM & -HT Ref. Ref. of cardio-cerebrovascular diseases and had a significant + DM & -HT 2.71 (1.20, 6.14) synergistic interaction effect on coronary heart disease. -DM & + HT 4.78 (3.20, 7.14) Participants who were old, women, highly educated, un- + DM & + HT 5.25 (2.93, 9.40) married, and obese (central obese) had a high risk of DM × HT 3.368 0.066 Model 3 Table 7 Additive interaction of DM and HT on cardio- CHD cerebrovascular diseases -DM & -HT Ref. Ref. CVDs RERI AP SI + DM & -HT 2.44 (1.72, 3.45) status estimate 95% CI estimate 95% CI estimate 95% CI -DM & + HT 4.12 (3.50, 4.84) Total 1.63 (−0.25, 0.22 (0.01, 1.33 (0.97, + DM & + HT 7.49 (5.82, 9.64) CVDs 3.51) 0.43) 1.83) DM × HT 1.815 0.178 CHD 1.94 (0.05, 0.26 (0.06, 1.43 (1.03, 3.83) 0.46) 1.97) Gender, age, education level, marital status, smoking, drinking, regular exercise, obesity, and central obesity were adjusted for in Model 1 and Model stroke −1.25 (−4.71, −0.24 (−0.97, 0.77 (0.38, 3; Age, marital status, smoking, drinking, regular exercise, and central obesity 2.22) 0.50) 1.60) were adjusted for in Model 2; Ref, reference. Wang et al. BMC Public Health (2021) 21:1224 Page 8 of 9 diabetes and hypertension that we should take interven- Received: 28 October 2020 Accepted: 24 May 2021 tions to prevent the occurrence of cardio- cerebrovascular diseases. Also, since this study is a References cross-sectional study at a single time point, causality 1. Alloubani A, Saleh A, Abdelhafiz I. Hypertension and diabetes mellitus as a cannot be confirmed. Therefore, more prospective co- predictive risk factor for stroke. Diab Metab Syndr. 2018;12(4):577–84. hort studies should be carried out in the future to con- https://doi.org/10.1016/j.dsx.2018.03.009. 2. Gutierrez J, Alloubani A, Mari M, et al. Cardiovascular Disease Risk Factors: firm this conclusion. Hypertension, Diabetes Mellitus and Obesity among Tabuk Citizens in Saudi Arabia. Open Cardiovasc Med J. 2018;12:41–9. Abbreviations 3. Sunkara N, Ahsan CH. Hypertension in diabetes and the risk of HT: Hypertension; DM: Diabetes; CHD: Coronary heart disease; CVDs: Cardio- cardiovascular disease. Cardiovasc Endocrinol. 2017;6(1):33–8. https://doi. cerebrovascular diseases org/10.1097/XCE.0000000000000114. 4. Zhan YQ, Yu JM, Hu DY, et al. Interaction between fasting blood glucose and hypertension on cardiovascular and cerebrovascular diseases. Chin J Supplementary Information Cardiovasc Dis. 2012;(1):57–61. The online version contains supplementary material available at https://doi. 5. Channanath AM, Farran B, Behbehani K, et al. State of diabetes, org/10.1186/s12889-021-11122-y. hypertension, and comorbidity in Kuwait: showcasing the trends as seen in native versus expatriate populations. Diab Care. 2013;36(6):e75. Additional file 1: Appendix 1. Questionnaire -- extract 6. Okosun IS, Chandra KM, Choi S, et al. Hypertension and type 2 diabetes comorbidity in adults in the United States: risk of overall and regional adiposity. Obes Res. 2001;9(1):1–9. https://doi.org/10.1038/oby.2001.1. Acknowledgments 7. Wittchen HU, Krause P, Höfler M, et al. Diabetes mellitus und assoziierte We thank Changsha CDC for its support of this study and Professor Wen Erkrankungen in der Allgemeinarztpraxis. Grössenordnung und Indikatoren Wanqing, the epidemiologist and biostatistician at the Vanderbilt University der Belastung und der Versorgungsqualität [Hypertension, diabetes mellitus of the United States, for his help in polishing the language of this paper. and comorbidity in primary care]. Fortschr Med Orig. 2003;121(Suppl 1):19– Conflict of interest 8. Yu HM, Liu GZ. Relationship between hypertension, diabetes mellitus, and All authors declare that they have no financial relationships with any cardiovascular disease. Mol Cardiol China. 2004;4(1):52–5. organizations that might have an interest in the submitted work and no 9. Ali N, Akram R, Sheikh N, et al. Sex-specific prevalence, inequality and other relationships or activities that could appear to have influenced the associated predictors of hypertension, diabetes, and comorbidity among submitted work. Bangladeshi adults: results from a nationwide cross-sectional demographic and health survey. BMJ Open. 2019;9:e029364. Authors’ contributions 10. Fu H, Wang X, Wang T, et al. Risk factors for type 2 diabetes complicated ZW conceived the research, analyzed the data, wrote and revised the paper. with hypertension in adult residents in Liuyang. Zhong Nan Da Xue Xue TY conducted the survey and participated in the revision of the paper. HF Bao Yi Xue Ban. 2015;40(12):1384–90. participated in the revision of the paper. All authors have read and approved 11. Jia W. Obesity in China: its characteristics, diagnostic criteria, and the manuscript. implications. Front Med. 2015;9(2):129–33. https://doi.org/10.1007/s11684-01 5-0387-x. 12. Zhai Y, Fang HY, Yu WT, et al. [Epidemiological characteristics of waist Funding circumference and abdominal obesity among Chinese adults in 2010–2012]. Not applicable for the current study. Zhonghua Yu Fang Yi Xue Za Zhi. 2017; 51(6): 506–512. Chinese. 13. Liu LS. 2018 Chinese guidelines for the management of hypertension. Availability of data and materials Beijing: People’s Medical Publishing House (China), 2018. Not publically available except for reasonable requests by contacting the 14. American Diabetes Association. Diagnosis and classification of diabetes corresponding author. mellitus. Diab Care. 2013; 36(Suppl 1): S67–S74. 15. Evans D, Chaix B, Lobbedez T, et al. Combining directed acyclic graphs and Declarations the change-in-estimate procedure as a novel approach to adjustment- variable selection in epidemiology. BMC Med Res Methodol. 2012;12:156. Ethics approval and consent to participate 16. Andersson T, Alfredsson L, Källberg H, et al. Calculating measures of The study was submitted to the Ethics Committee of Xiangya School of biological interaction. Eur J Epidemiol. 2005;20(7):575–9. https://doi.org/10.1 Public Health, Central South University and was granted a waiver beacause 007/s10654-005-7835-x. this study used the data from 2012 National Chronic Disease Management 17. Zuo H, Shi Z, Hussain A. Prevalence, trends and risk factors for the diabetes Project and did not involve the human trial or personal information. epidemic in China: a systematic review and meta-analysis. Diabetes Res Clin Informed consent was obtained from the participants before the Pract. 2014;104(1):63–72. https://doi.org/10.1016/j.diabres.2014.01.002. investigation. 18. Yang ZJ, Liu J, Ge JP, et al. Prevalence of cardiovascular disease risk factor in the Chinese population: the 2007-2008 China National Diabetes and Consent for publication metabolic disorders study. Eur Heart J. 2012;33(2):213–20. https://doi.org/1 Not applicable because this study did not involve the disclosure of personal 0.1093/eurheartj/ehr205. privacy information. 19. Liu S, Li Y, Zeng X. Elt. The burden of cardiovascular diseases in China, 1990- 2016: findings from the 2016 global burden of disease study. JAMA Cardiol. Competing interests 2019;4(4):342–52. https://doi.org/10.1001/jamacardio.2019.0295. No conflict of interest between the study and other commercial institutions 20. Hu G, Sarti C, Jousilahti P. Elt. The impact of a history of hypertension and or individuals. type 2 diabetes at baseline on the incidence of stroke and stroke mortality. Stroke. 2005;36(12):2538–43. https://doi.org/10.1161/01.STR.0000190894.3 Author details 0964.75. Department of Epidemiology and Health Statistics, XiangYa School of Public 21. Zafari N, Asgari S, Lotfaliany M, et al. Impact Of Hypertension versus Health, Central South University, Changsha, Hunan Province, China. Hunan Diabetes on Cardiovascular and All-cause Mortality in Iranian Older Adults: Provincial Key Laboratory of Clinical Epidemiology, Changsha, China. Results of 14 Years of Follow-up. Sci Rep. 2017;7(1):14220. Department of Obstetrics and Gynecology, The First Affiliated Hospital of 22. Hu G, Jousilahti P, Tuomilehto J. Joint effects of a history of hypertension at Zhengzhou University, Zhengzhou 450052, China. baseline and type 2 diabetes at baseline and during follow-up on the risk of Wang et al. BMC Public Health (2021) 21:1224 Page 9 of 9 coronary heart disease. Eur Heart J. 2007;28(24):3059–66. https://doi.org/10.1 093/eurheartj/ehm501. 23. Sehestedt T, Hansen TW, Li Y. Elt. Are blood pressure and diabetes additive or synergistic risk factors-outcomes in 8494 subjects randomly recruited from 10 populations. Hypertens Res. 2011 Jun;34(6):714–21. https://doi.org/1 0.1038/hr.2011.6. 24. Zhang Y, Jiang X, Bo J. Elt. Risk of stroke and coronary heart disease among various levels of blood pressure in diabetic and nondiabetic Chinese patients. J Hypertens. 2018 Jan;36(1):93–100. https://doi.org/10.1097/HJH. 25. Lu S, Bao MY, Miao SM, et al. Prevalence of hypertension, diabetes, and dyslipidemia, and their additive effects on myocardial infarction and stroke: a cross-sectional study in Nanjing, China. Ann Transl Med. 2019; 7(18): 436. 26. Lai YJ, Chen HC, Chou P. sex Difference in the Interaction Effects of Diabetes and Hypertension on Stroke among the Elderly in the Shih-Pai Study, Taiwan. PLoS One. 2015; 10(8): e0136634. 27. Cai H, Liu XF. Effect of interaction between diabetes mellitus and hypertension on the severity of ischemic stroke. Proceedings of the 23rd neurology conference of six provinces and one city in East China and 2016 annual meeting of neurology of Zhejiang Province. 2016; (pp. 196-197). Ningbo, Zhejiang, China Chinese 28. KJ R. Epidemiology: an introduction. New York: Oxford University Press; 29. Vandenbroucke JP, von Elm E, Altman DG. Elt. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Int J Surg. 2014;12(12):1500–24. https://doi.org/10.1016/j.ijsu.2 014.07.014. 30. KJ R. Modern epidemiology. Lippincott: Williams& Wilkins press; 2008. 31. Petrie JR, Guzik TJ, Touyz RM. Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Can J Cardiol. 2018;34(5): 575–84. https://doi.org/10.1016/j.cjca.2017.12.005. 32. Zhao D, Liu J, Xie W. Elt. Cardiovascular risk assessment: a global perspective. Nat Rev Cardiol. 2015;12(5):301–11. https://doi.org/10.1038/nrca rdio.2015.28. 33. Bella JN, Devereux RB, Roman MJ, et al. Separate and joint effects of systemic hypertension and diabetes mellitus on left ventricular structure and function in American Indians (the strong heart study). Am J Cardiol. 2001;87(11):1260–5. https://doi.org/10.1016/S0002-9149(01)01516-8. 34. Russo C, Jin Z, Homma S, et al. Effect of diabetes and hypertension on left ventricular diastolic function in a high-risk population without evidence of heart disease. Eur J Heart Fail. 2010;12(5):454–61. https://doi.org/10.1093/ eurjhf/hfq022. 35. Ma L, Zhao S, Li J, et al. Interaction of hypertension and diabetes on impairment of endothelial function. Chin Med J(Engl). 2001;114(6):563–7. 36. Widlansky ME, Gokce N, Keaney JF Jr. Elt. The clinical implications of endothelial dysfunction. J Am Coll Cardiol. 2003;42(7):1149–60. https://doi. org/10.1016/S0735-1097(03)00994-X. 37. Tsao PS, Niebauer J, Buitrago R, et al. Interaction of diabetes and hypertension on determinants of endothelial adhesiveness. Arterioscler Thromb Vasc Biol. 1998;18(6):947–53. https://doi.org/10.1161/01.ATV.18.6.947. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Public Health Springer Journals

Prevalence of diabetes and hypertension and their interaction effects on cardio-cerebrovascular diseases: a cross-sectional study

BMC Public Health , Volume 21 (1) – Jun 25, 2021

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10.1186/s12889-021-11122-y
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Abstract

Background: Hypertension and diabetes mellitus are two of the major risk factors for cardio-cerebrovascular diseases (CVDs). Although prior studies have confirmed that the coexistence of the two can markedly increase the risk of CVDs, few studies investigated whether potential interaction effects of hypertension and diabetes can result in greater cardio-cerebrovascular damage. We aimed to investigate the prevalence of hypertension and diabetes and whether they both affect synergistically the risk of CVDs. Methods: A cross-sectional study was conducted by using a multistage stratified random sampling among communities in Changsha City, Hunan Province. Study participants aged > = 18 years were asked to complete questionnaires and physical examinations. Multivariate logistic regression models were performed to evaluate the association of diabetes, hypertension, and their multiplicative interaction with CVDs with adjustment for potential confounders. We also evaluated additive interaction with the relative excess risk ratio (RERI), attribution percentage (AP), synergy index (SI). Results: A total of 14,422 participants aged 18–98 years were collected (men = 5827, 40.7%). The prevalence was 22.7% for hypertension, 7.0% for diabetes, and 3.8% for diabetes with hypertension complication, respectively. Older age, women, higher educational level, unmarried status, obesity (central obesity) were associated with increased risk of hypertension and diabetes. We did not find significant multiplicative interaction of diabetes and hypertension on CVDs, but observed a synergistic additive interaction on coronary heart disease (SI, 1.43; 95% CI, 1.03–1.97; RERI, 1.94; 95% CI, 0.05–3.83; AP, 0.26; 95% CI, 0.06–0.46). Conclusions: Diabetes and hypertension were found to be associated with a significantly increased risk of CVDs and a significant synergistic additive interaction of diabetes and hypertension on coronary heart disease was observed. Participants who were old, women, highly educated, unmarried, obese (central obese) had increased risk of diabetes and hypertension. Keywords: Diabetes, Hypertension, Stroke, Coronary heart disease, Interaction * Correspondence: yangtbcsu@163.com Department of Epidemiology and Health Statistics, XiangYa School of Public Health, Central South University, Changsha, Hunan Province, China Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wang et al. BMC Public Health (2021) 21:1224 Page 2 of 9 Background the five geographical locations (East, West, South, North, Hypertension (HT) and Diabetes mellitus (DM) have and Middle) in the 5 selected districts. In the third stage, been confirmed as two of the major risk factors for 2 communities/villages were randomly selected from cardio-cerebrovascular diseases (CVDs) [1, 2]. It has each of the selected subregions. In the final stage, 150 been found that individuals with both DM and HT have households were randomly selected from each selected a greater risk of cardio-cerebrovascular disease than community/village and one household resident with those with only one condition [3]. However, few studies age > = 18 was selected as the study participants. There- investigated the interaction of DM and HT on the risk fore, a total of 50 subregions, 100 communities/villages, of CVDs [4]. It is not yet clear whether the increased and 15,000 household/participants were selected. Of risk resulting from the coexistence of DM and HT could those selected, 14,422 (96.1%) participants completed be attributed to a simple combination or their the baseline survey and were included in the current interaction. analysis. The study was submitted to the Ethics Commit- DM and HT share common comorbidities [5–7]. Their tee of Xiangya School of Public Health, Central South frequent coexistence is not a coincidence but due to University and was granted a waiver beacause this study some shared pathogenic mechanism. Diabetic patients used the data from 2012 national chronic disease man- are twice as likely to have HT as non-diabetic patients. agement project and did not involve the human trial or Similarly, patients with HT are more likely to develop personal information. Informed consent was obtained diabetes than those with normotensive people. In China, from the participants before the investigation. More de- nearly 100 million people have HT, nearly 30 million tails of the study design were previously published [10]. people have DM, and approximately 15 million people The baseline in-person interview was conducted by have both HT and DM [8].. The major outcome of this trained interviewers (Additional file 1: Appendix 1). comorbidity is cardio-cerebrovascular diseases, which Smoking status was categorized as never-smoking, account for nearly half of the death causes. current smoking (at least one cigarette per day), and In general, physicians pay more attention to the asso- former smoking (quit smoking more than 12 months). ciations between risk factors and outcomes. Thus, the Current drinkers were those who drank once or more in interaction is often neglected. The term “interaction” re- the recent month. Regular exercisers were those who fers to the fact that at different levels of a risk factor, an- exercised at least three times a week. The disease histor- other risk factor has different effects on the disease, and ies included the status of diabetes, hypertension, coron- vice versa [9]. Additive interaction scale and multiplica- ary heart disease, and stroke. tive interaction scale are usually used to measure the in- teractions between factors. Additive interaction refers to Physical examination and disease definition the effect of two (or more) factors acting on a disease is Height and weight measurement: the subjects took off greater (synergism) or less (antagonism) than the sum of their coats, shoes, and hats, stood on the automatic the independent effects of these factors. Multiplicative measuring instrument with feet 45 degrees apart, and interaction refers to the effect of two (or more) factors heels close to the instrument as far as possible. The in- acting on a disease is greater (positive) or less (negative) strument automatically reads the measurement results of than the product of the independent effects of these height and weight. BMI = weight (kg) / height (m) . BMI factors. ≧28.0 was defined as obesity according to the Chinese Given the high prevalence of DM and HT and their standard [11]. strong association with CVDs, our study aimed to deter- Waist circumference measurement: the subjects take mine whether HT and DM act synergistically towards off their coats, loosen their belts, stand naturally on both cardio-cerebrovascular diseases in a general population. legs and keep calm breathing. The medical staff used a tape measure to horizontally circle the abdomen of the Methods subjects to measure the waist circumference. Central Study participants and baseline survey obesity was defined as a WC > 90 cm for men and > 85 This cross-sectional study was based on National cm for women [12]. Chronic Disease Management Survey conducted in Blood pressure measurement: the subjects sit in the Changsha, the capital of Hunan Province, in 2012. A room for 5 min, keep calm and then start to measure. multistage stratified random sampling method was used The medical staff used an automatic blood pressure to select participants from the community population. meter to measure. During the measurement, the subjects In the first stage, 5 districts were randomly selected kept the arm position at heart level, and the instrument (Liuyang, Ningxiang, Kaifu, Tianxin, Furong) from a automatically read the blood pressure value. According total of 9 districts in Changsha. In the second stage, 2 to the Chinese hypertension guidelines, hypertension subregions were selected (streets/towns) from each of was defined as systolic BP (SBP) ≥ 140 mmHg, diastolic Wang et al. BMC Public Health (2021) 21:1224 Page 3 of 9 BP (DBP) ≥90 mmHg, or a history of diagnosed hyper- significantly higher in CHD patients than normal people tension [13]. (P < 0.001). Similar results were found in the other four Blood glucose measurement: After fasting for 12 h, a diseases except for stroke (P < 0.05). We found that only trained blood collector took fasting blood from each the mean values of age and percentages of former subject. The fasting blood glucose was detected by hexo- smokers, non-current drinkers, regular exercisers, and kinase method within 2 h of venous blood extraction. central obesity were significantly higher in stroke pa- According to the American Diabetes Association [14], tients than normal people (P < 0.05). Diabetes was defined as blood glucose ≥7.0 mmol/L or a history of type 1 diabetes or type 2 diabetes. Prevalence of DM and HT The history of CVDs, including CHD and stroke, was Table 3 showed the prevalence of DM and HT by gen- self-reported. CHD included ischemic coronary heart der and age. The overall prevalence was 22.7% for HT, disease, myocardial infarction, angina pectoris, coronary 7.0% for DM, and 3.8% for DM with HT complication, artery bypass grafting, percutaneous coronary thromb- respectively. The prevalences were positively correlated olysis, and coronary angioplasty. Stroke included ische- with age in general, with the peak value of 66–75 years mic stroke and hemorrhagic stroke. old, and then decreased. Of note, the prevalences were relatively high in the young participants aged from 18 to Statistical analysis 25. Stratified by sex, we found that women had a higher Means and standard deviations were reported for con- prevalence of hypertension than men in age groups be- tinuous variables and frequencies and percentages were fore 36–40 years old (P < 0.001). reported for categorical variables. The chi-square test or The association of demographic factors and obesity t-test was used to compare the differences in demo- with DM and HT was shown in Table 4. In general, graphic characteristics between patients and normal older age, women, education with college or above, un- people. The status of DM and HT can be divided into married status, obesity, and central obesity were risk fac- four categories: a) non-DM & non-HT group: normo- tors of all three diseases (P < 0.05). In addition, tensive and normoglycemic participants; b) DM & non- education in middle school was associated with in- HT group: participants with diabetes only; c) non-DM & creased risks of DM with HT complication (P = 0.013), HT group: participants with hypertension only; d) DM and divorced or widowed status was associated with de- & HT group: participants with both diabetes and hyper- creased risks of DM (P = 0.006). tension. The multiplicative interaction between HT and DM on CVDs was evaluated using logistic regression models with adjustment for potential confounders deter- Prevalence of CVDs by DM and HT mined with Directed Acyclic Graphs (DAGs) [15]: The Table 5 showed the prevalence of CVDs by DM and HT. additive interaction between DM and HT on CVDs was The overall self-reported prevalence was 1.4% for stroke, evaluated with relative excess risk ratio (RERI), attribu- 7.5% for CHD, and 8.3% for total CVDs. The prevalence tion percentage (AP), and synergy index (SI). The confi- of total CVDs was 3.7% for non-DM & non-HT group, dence intervals (CIs) of the above indexes were 12.1% for DM & non-HT group, 21.2% for non-DM & estimated [16]. P < 0.05 (two-sided) was considered sta- HT group, and 31.4% for DM & HT group. The differ- tistically significant. Data with missing values were de- ence in prevalence among the four groups was highly leted. All statistical analyses were performed with SPSS significant (P < 0.001). A similar difference was observed for Windows 18.0 (SPSS Inc., Chicago, IL, USA). for stroke and CHD (P < 0.001). Results Demographics Association of DM and HT with CVDs The demographic characteristics of the participants clas- Table 6 showed the associations of CVDs with DM sified by disease status were summarized in Tables 1 and HT after adjusting for potential confounding fac- and 2. Of the 14,422 participants, 40.7% were male with tors (gender, age, education level, marital status, a mean age of 53.84 years and the range of 18–98 years. smoking, drinking, regular exercise, obesity, and cen- The proportions of participants who were current tral obesity for CHD and total CVDs; age, marital sta- smokers, current drinkers, having regular exercise, hav- tus, smoking, drinking, regular exercise, and central ing obesity and central obesity were 25.8, 20.1, 33.2, 8.6, obesity for stroke). Comparing with the non-DM & and 26.0%, respectively. The mean values of age, the pro- non-HT group, an significantly increased risk of portion of females, education level of primary school stroke, CHD, and total CVDs was observed among and below, former smokers, non-current drinkers, regu- the DM & non-HT group, the non-DM & HT group, lar exercisers, obese and central obese people were and the DM & HT group. Wang et al. BMC Public Health (2021) 21:1224 Page 4 of 9 Table 1 Basic demographic characteristics of participants by cardio-cerebrovascular diseases (CVD) status (n = 14,422) Variables Total CHD Stroke Total CVDs Patients Non- P Patients Non- P Patients Non- P patients patients patients Age (year) 53.84 ± 64.71 ± 52.96 ± < 0.001 64.52 ± 53.68 ± < 0.001 64.74 ± 52.85 ± < 0.001 14.88 12.28 14.73 13.46 14.85 12.23 14.71 Gender, n (%) Men 5927 (41.1) 381 (35.4) 5546 (41.6) < 0.001 95 (45.5) 5832 (41.0) 0.197 444 (37.0) 5483 (41.5) 0.002 Women 8495 (58.9) 696 (64.6) 7799 (58.4) 114 (54.5) 8381 (59.0) 757 (63.0) 7738 (58.5) Educational level , n (%) Primary school or 5909 (41.1) 657 (61.3) 5252 (39.5) < 0.001 93 (44.7) 5816 (41.0) 0.412 707 (59.2) 5202 (39.4) < 0.001 below Middle school 7090 (49.3) 337 (31.4) 6753 (50.7) 93 (44.7) 6997 (49.4) 400 (33.5) 6690 (50.7) College or above 1384 (9.6) 78 (7.3) 1306 (9.8) 22 (10.6) 1362 (9.6) 88 (7.4) 1296 (9.8) Marital status , n (%) Unmarried 702 (4.9) 26 (2.4) 676 (5.1) < 0.001 4 (1.9) 698 (4.9) 0.007 28 (2.3) 674 (5.1) < 0.001 Married 11,944 807 (75.2) 11,137 (83.7) 167 (80.3) 11,777 (83.1) 905 (75.6) 11,039 (83.9) (83.1) Divorced or widowed 1730 (12.0) 240 (22.4) 1490 (11.2) 37 (17.8) 1693 (11.9) 264 (22.1) 1466 (11.1) Smoking status , n (%) Never 10,054 770 (71.8) 9284 (69.9) < 0.001 148 (71.2) 9906 (70.0) < 0.001 857 (71.6) 9197 (69.9) < 0.001 (70.1) Former 600 (4.2) 94 (8.8) 506 (3.8) 19 (9.1) 581 (4.1) 104 (8.7) 496 (3.8) Current 3696 (25.8) 209 (19.5) 3487 (26.3) 41 (19.7) 3655 (25.8) 236 (19.7) 3460 (26.3) Current drinking , n (%) Yes 2887 (20.1) 157 (14.6) 2730 (20.5) < 0.001 26 (12.4) 2861 (20.2) 0.006 173 (14.4) 2714 (20.6) < 0.001 No 11,507 918 (85.4) 10,589 (79.5) 183 (87.6) 11,324 (79.8) 1026 (85.6) 10,481 (79.4) (79.9) Regular exercise , n (%) Yes 3678 (33.2) 380 (41.4) 3298 (32.5) < 0.001 83 (59.3) 3595 (32.9) < 0.001 419 (42.5) 3259 (32.3) < 0.001 No 7397 (66.8) 537 (58.6) 6860 (67.5) 57 (40.7) 7340 (67.1) 568 (57.5) 6829 (67.7) Obesity , n (%) Yes 1242 (8.6) 130 (12.1) 1112 (8.3) < 0.001 17 (8.1) 1225 (8.6) 0.800 140 (11.7) 1102 (8.3) < 0.001 No 13,164 946 (87.9) 12,218 (91.7) 192 (91.9) 12,972 (91.4) 1060 (88.3) 12,104 (91.7) (91.4) Central obesity , n (%) Yes 3731 (26.0) 420 (39.1) 3311 (24.9) < 0.001 74 (35.4) 3657 (25.8) 0.002 462 (38.6) 3269 (24.8) < 0.001 No 10,644 653 (60.9) 9991 (75.1) 135 (64.6) 10,509 (74.2) 735 (61.4) 9909 (75.2) (74.0) The variable contained missing values. Interaction effects of DM and HT on CVDs Discussion We added the interaction term (DM × HT) into logistic Main finding models and found that the multiplicative interaction of The prevalence was 22.7% for hypertension, 7.0% for dia- DM and HT on CVDs was not found (Table 6). We fur- betes, both were similar to the National population survey ther evaluated the additive interaction of DM and HT [17, 18]. Meanwhile, the prevalence of coronary heart dis- (Table 7) and found the additive interaction was statisti- ease, stroke, and total cardio-cerebrovascular diseases was cally significant for CHD (SI = 1.43, 95 CI, 1.03–1.97; 7.5, 1.4, and 8.3%, respectively. They were all lower than RERI = 1.94; 95% CI, 0.05–3.83; AP = 0.26; 95% CI, 0.06– the previously reported prevalence especially stroke [19]. 0.46) while the additive interaction on stroke was not Older age, women, higher educational level, unmarried significant. status, and obesity (central obesity) were risk factors of Wang et al. BMC Public Health (2021) 21:1224 Page 5 of 9 Table 2 Demographic description of participants by the status of HT or DM (n = 14,422) Variables DM HT DM complicated with HT Patients Non-patients P Patients Non-patients P Patients Non-patients P Age (year) 60.19 ± 12.92 53.37 ± 14.91 < 0.001 61.34 ± 12.85 51.65 ± 14.72 < 0.001 61.73 ± 13.35 53.53 ± 14.85 < 0.001 Gender, n (%) Men 345 (34.4) 5582 (41.6) < 0.001 1241 (38.1) 4686 (42.0) < 0.001 178 (32.5) 5749 (41.4) < 0.001 Women 658 (65.6) 7873 (58.4) 2017 (61.9) 6478 (58.0) 370 (67.5) 8125 (58.6) Educational level, n (%) Primary school or below 478 (47.7) 5431 (40.6) < 0.001 1658 (51.0) 4251 (38.2) < 0.001 254 (46.4) 5655 (40.9) 0.018 Middle school 442 (44.1) 6648 (49.7) 1332 (41.0) 5758 (51.7) 238 (43.4) 6852 (49.5) College or above 82 (8.2) 1302 (9.7) 261 (8.0) 1123 (10.1) 56 (10.2) 1328 (9.6) Marital status, n (%) Unmarried 37 (3.7) 665 (5.0) 0.001 102 (3.1) 600 (5.4) < 0.001 25 (4.6) 677 (4.9) < 0.001 Married 807 (80.8) 11,137 (83.3) 2556 (78.7) 9388 (84.4) 423 (77.5) 11,521 (83.3) Divorced or widowed 155 (15.5) 1575 (11.8) 590 (18.2) 1140 (10.2) 99 (17.9) 1632 (11.8) Smoking status, n (%) Never 752 (75.1) 9302 (69.7) < 0.001 2336 (71.9) 7718 (69.5) < 0.001 424 (77.5) 9630 (69.8) < 0.001 Former 52 (5.2) 548 (4.1) 217 (6.7) 383 (3.5) 32 (5.9) 568 (4.1) Current 197 (19.7) 3499 (26.2) 697 (21.4) 2999 (27.0) 91 (16.6) 3605 (26.1) Current drinking, n (%) Yes 122 (12.2) 2765 (20.6) < 0.001 503 (15.5) 2384 (21.4) < 0.001 63 (11.5) 2824 (20.4) < 0.001 No 880 (87.8) 10,627 (79.4) 2748 (84.5) 8759 (78.6) 485 (88.5) 11,022 (79.6) Regular exercise, n (%) Yes 381 (49.3) 3279 (32.0) < 0.001 1033 (40.4) 2645 (31.0) < 0.001 219 (53.2) 3459 (42.4) < 0.001 No 392 (50.7) 7005 (68.0) 1523 (59.6) 5874 (69.0) 193 (46.8) 7204 (67.6) Obesity, n (%) Yes 858 (85.7) 12,306 (91.8) < 0.001 2808 (86.3) 10,356 (92.9) < 0.001 452 (82.8) 12,712 (91.7) < 0.001 No 143 (14.3) 1099 (8.2) 446 (13.7) 796 (7.1) 94 (17.2) 1148 (8.3) Central obesity, n (%) Yes 569 (56.8) 10,075 (75.3) < 0.001 1953 (60.1) 8691 (78.1) < 0.001 274 (50.1) 10,370 (75.0) < 0.001 No 432 (43.2) 3299 (24.7) 1296 (39.9) 2435 (21.9) 273 (49.9) 3458 (25.0) diabetes and hypertension. Participants with both diabetes As pointed out by Rothan [28], the additive interaction and hypertension had a significantly increased risk of model is closer to the nature of biological interaction cardio-cerebrovascular diseases as compared with partici- and has more relevant public health significance than pants with only one condition. A significant synergistic the multiplication model. Vandenbroucke et al [29] sug- additive interaction of diabetes and hypertension on cor- gested that both additive and multiplicative interaction onary heart disease was observed. should be reported when evaluating interactions. Even when two factors have no multiplicative interaction, they Comparisons with previous studies may have a positive interaction in the additive model Prior studies [20–22] have found that the comorbidity of [30]. This study reported both results of multiplicative hypertension and diabetes increased the risk of cardiovas- and additive models. In the multiplicative model, the cular diseases dramatically, but their interaction was not re- interaction items were not statistically significant but in ported [23–25]. Yun Ju Lai [26] found a synergism of the additive model, the interaction effects were positive. diabetes and hypertension only among elderly women aged over 65 years old. In another cross-sectional study with a Potential explanations small sample size (886 participants), Cai [27] and colleagues It was reported that DM and HT shared common risk found an interaction on the severity of stroke. However, all factors and pathophysiological pathways which were in- of those studies only evaluated multiplicative interaction. terconnected into a network and may even lead to a Wang et al. BMC Public Health (2021) 21:1224 Page 6 of 9 Table 3 Prevalence (%) of DM and HT in different age and gender group Age Number, DM HT DM with HT complication (year) n Male Female Total Male Female Total Male Female Total 18–25 495 3.9 4.5 4.2 10.5 9.7 10.1 2.2 3.4 2.8 26–30 557 1.6 3.0 2.3 8.2 4.0 5.9 1.2 0.7 0.9 31–35 640 2.2 3.6 3.0 8.7 6.3 7.3 1.4 3.0 2.3 36–40 861 1.2 3.2 2.4 6.3 9.1 8.0 0.3 1.5 1.0 41–45 1437 3.3 3.1 3.2 9.2 9.6 9.5 0.7 0.9 0.8 46–50 1820 3.9 4.6 4.3 13.0 17.3 15.7 1.5 2.3 2.0 51–55 1355 6.7 7.2 7.0 15.5 26.0 22.3 2.5 3.6 3.2 56–60 1689 8.5 9.9 9.3 21.1 27.7 25.0 4.4 4.7 4.6 61–65 1741 6.7 12.9 10.1 31.3 33.3 32.4 3.7 7.1 5.6 66–70 1311 8.8 12.6 11.1 31.1 37.4 34.8 4.8 7.6 6.4 71–75 904 8.3 12.9 11.0 41.2 42.9 42.1 6.2 9.3 8.0 76–80 619 8.0 10.8 9.5 37.8 41.1 39.6 4.2 7.2 5.8 > 80 360 9.7 8.7 9.2 33.9 43.1 38.9 5.5 8.2 6.9 Total 13,789 5.8 7.8 7.0 21.0 23.9 22.7 3.0 4.4 3.8 vicious cycle. Therefore, HT and DM are the main parts found that the combination of DM and HT has adverse of the metabolic process of metabolic syndrome and effects on left ventricular structure, myocardial dysfunc- they are prone to comorbidity [31]. Cardio- tion, and arterial stiffness. Cesare Russo [34] found that cerebrovascular diseases are multifactorial diseases. The HT and DM are independently associated with impaired risk of occurrence depends not only on the severity of a left ventricular diastolic function. Their coexistence re- certain determinant but also on the number of determi- sulted in the most severe effect on left ventricular dia- nants possessed by the individual [32]. Jonathan N [33] stolic mechanics and was associated with higher left Table 4 Associations of demographic factors and obesity with DM and HT Variables DM HT DM with HT complication OR (95% CI) P OR (95% CI) P OR (95% CI) P Age 1.04 (1.03, 1.05) < 0.001 1.06 (1.05, 1.06) 1.05 (1.04, 1.06) < 0.001 Gender Men Ref. Ref. Ref. Women 1.35 (1.17, 1.56) < 0.001 1.16 (1.07, 1.27) 0.001 1.47 (1.22,1.79) < 0.001 Educational level Primary school or below Ref. Ref. Ref. Middle school 1.11 (0.96, 1.29) 0.153 0.10 (0.91, 1.09) 0.931 1.28 (1.05, 1.55) 0.013 College or above 1.38 (1.06, 1.79) 0.016 1.48 (1.25, 1.75) < 0.001 2.10 (1.52, 2.90) < 0.001 Marital status Married Ref. Ref. Ref. Unmarried 1.67 (1.16, 2.40) 0.006 1.56 (1.23, 1.98) < 0.001 2.53 (1.62, 3.95) < 0.001 Divorced or widowed 0.76 (0.62, 0.92) 0.006 0.91 (0.80, 1.03) 0.133 0.83 (0.65, 1.06) 0.139 Obesity Yes 1.34 (1.10, 1.65) 0.005 1.57 (1.37, 1.81) < 0.001 1.53 (1.19,1.97) 0.001 No Ref. Ref. Ref. Central obesity Yes 2.05 (1.78, 2.37) < 0.001 2.13 (1.94, 2.35) < 0.001 2.57 (2.12, 3.10) < 0.001 No Ref. Ref. Ref. Wang et al. BMC Public Health (2021) 21:1224 Page 7 of 9 Table 5 Prevalence of CVDs by the status of DM and HT (n = 14,422) Prevalence (%) Total -DM & -HT(n = 10,709) +DM & -HT -DM & + HT +DM & + HT χ P (n = 548) (n = 2710) (n = 455) 1 2 3 stroke 1.4 (209) 0.6 (61) 2.4 (11) 3.9 (107) 5.5 (30) 241.685 < 0.001 CHD 7.5 (1077) 3.4 (361) 10.5 (48) 18.8 (510) 28.8 (358) 1133.675 < 0.001 Total CVD 8.3 (1201) 3.7 (399) 12.1 (55) 21.2 (575) 31.4 (172) 134.737 < 0.001 () Frequency in brackets ventricular filling pressures than patients with one con- However, the specific mechanism and degree of inter- dition alone. Both DM and HT are crime culprits for action remain unclear, thus further study is still atherosclerosis and are essential parts of the formation warranted. and aggravation of endothelial and smooth muscle func- tion [35]. The combination of DM and HT can promote Strengths and limitations endothelial cell dysfunction [36]. The dysfunction of This is the first community-based cross-sectional study endothelial cells may change in the early stage of athero- with a large sample size (14,422 participants) that inves- sclerosis. Both DM and HT can promote the generation tigated the interaction of diabetes and hypertension on of oxygen-derived free radicals, thus damaging endothe- cardio-cerebrovascular diseases. Both multiplicative and lial function. When the two coexist, endothelial cell additive interactions were evaluated, and the results were function further decreases, and smooth muscle function consistent in theory, which provided strong support for is also impaired [35]. Besides, the combination of DM the main conclusion. The multivariable logistical regres- and HT can promote monocyte adhesion to endothelial sion models in this study were adjusted for potential cells, thus increasing the production of vascular super- confounding factors according to the variable selection oxide and the expression of monocyte chemoattractant principle of DAG, which greatly improved the reliability protein-1 [37], leading to atherosclerosis and subsequent of the results. This study adopted a cross-sectional de- cardio-cerebrovascular diseases. In conclusion, recent sign, which precluded causal correlations, and the infor- studies show that there is a great biological possibility of mation about the disease was provided by the interaction between diabetes and hypertension. investigators themselves, thus recall bias cannot be avoided. The prevalence of stroke, CHD, and CVD are Table 6 Association of CVDs with DM & HT low so that the interaction effects could be underesti- CVD status OR 95% CI Wald χ P multiplicative multiplicative mated. In particular, this may be the reason for the ten- Model 1 Total CVD dency to null of the interaction on stroke to some extent, because the prevalence of stroke observed is only -DM & -HT Ref. Ref. 1.4%. More prospective cohort studies will be needed in + DM & -HT 2.53 (1.81, 3.55) the future to prove this correlation and adjusted more -DM & + HT 4.35 (3.72, 5.09) confounders such as disease types, degree, treatment, + DM & + HT 7.51 (5.86, 9.63) and control status. DM × HT 3.321 0.068 Model 2 Stroke Conclusions DM combined with HT significantly increased the risk -DM & -HT Ref. Ref. of cardio-cerebrovascular diseases and had a significant + DM & -HT 2.71 (1.20, 6.14) synergistic interaction effect on coronary heart disease. -DM & + HT 4.78 (3.20, 7.14) Participants who were old, women, highly educated, un- + DM & + HT 5.25 (2.93, 9.40) married, and obese (central obese) had a high risk of DM × HT 3.368 0.066 Model 3 Table 7 Additive interaction of DM and HT on cardio- CHD cerebrovascular diseases -DM & -HT Ref. Ref. CVDs RERI AP SI + DM & -HT 2.44 (1.72, 3.45) status estimate 95% CI estimate 95% CI estimate 95% CI -DM & + HT 4.12 (3.50, 4.84) Total 1.63 (−0.25, 0.22 (0.01, 1.33 (0.97, + DM & + HT 7.49 (5.82, 9.64) CVDs 3.51) 0.43) 1.83) DM × HT 1.815 0.178 CHD 1.94 (0.05, 0.26 (0.06, 1.43 (1.03, 3.83) 0.46) 1.97) Gender, age, education level, marital status, smoking, drinking, regular exercise, obesity, and central obesity were adjusted for in Model 1 and Model stroke −1.25 (−4.71, −0.24 (−0.97, 0.77 (0.38, 3; Age, marital status, smoking, drinking, regular exercise, and central obesity 2.22) 0.50) 1.60) were adjusted for in Model 2; Ref, reference. Wang et al. BMC Public Health (2021) 21:1224 Page 8 of 9 diabetes and hypertension that we should take interven- Received: 28 October 2020 Accepted: 24 May 2021 tions to prevent the occurrence of cardio- cerebrovascular diseases. Also, since this study is a References cross-sectional study at a single time point, causality 1. Alloubani A, Saleh A, Abdelhafiz I. Hypertension and diabetes mellitus as a cannot be confirmed. Therefore, more prospective co- predictive risk factor for stroke. Diab Metab Syndr. 2018;12(4):577–84. hort studies should be carried out in the future to con- https://doi.org/10.1016/j.dsx.2018.03.009. 2. Gutierrez J, Alloubani A, Mari M, et al. Cardiovascular Disease Risk Factors: firm this conclusion. Hypertension, Diabetes Mellitus and Obesity among Tabuk Citizens in Saudi Arabia. Open Cardiovasc Med J. 2018;12:41–9. Abbreviations 3. Sunkara N, Ahsan CH. Hypertension in diabetes and the risk of HT: Hypertension; DM: Diabetes; CHD: Coronary heart disease; CVDs: Cardio- cardiovascular disease. Cardiovasc Endocrinol. 2017;6(1):33–8. https://doi. cerebrovascular diseases org/10.1097/XCE.0000000000000114. 4. Zhan YQ, Yu JM, Hu DY, et al. Interaction between fasting blood glucose and hypertension on cardiovascular and cerebrovascular diseases. Chin J Supplementary Information Cardiovasc Dis. 2012;(1):57–61. The online version contains supplementary material available at https://doi. 5. Channanath AM, Farran B, Behbehani K, et al. State of diabetes, org/10.1186/s12889-021-11122-y. hypertension, and comorbidity in Kuwait: showcasing the trends as seen in native versus expatriate populations. Diab Care. 2013;36(6):e75. Additional file 1: Appendix 1. Questionnaire -- extract 6. Okosun IS, Chandra KM, Choi S, et al. Hypertension and type 2 diabetes comorbidity in adults in the United States: risk of overall and regional adiposity. Obes Res. 2001;9(1):1–9. https://doi.org/10.1038/oby.2001.1. Acknowledgments 7. Wittchen HU, Krause P, Höfler M, et al. Diabetes mellitus und assoziierte We thank Changsha CDC for its support of this study and Professor Wen Erkrankungen in der Allgemeinarztpraxis. Grössenordnung und Indikatoren Wanqing, the epidemiologist and biostatistician at the Vanderbilt University der Belastung und der Versorgungsqualität [Hypertension, diabetes mellitus of the United States, for his help in polishing the language of this paper. and comorbidity in primary care]. Fortschr Med Orig. 2003;121(Suppl 1):19– Conflict of interest 8. Yu HM, Liu GZ. Relationship between hypertension, diabetes mellitus, and All authors declare that they have no financial relationships with any cardiovascular disease. Mol Cardiol China. 2004;4(1):52–5. organizations that might have an interest in the submitted work and no 9. Ali N, Akram R, Sheikh N, et al. Sex-specific prevalence, inequality and other relationships or activities that could appear to have influenced the associated predictors of hypertension, diabetes, and comorbidity among submitted work. Bangladeshi adults: results from a nationwide cross-sectional demographic and health survey. BMJ Open. 2019;9:e029364. Authors’ contributions 10. Fu H, Wang X, Wang T, et al. Risk factors for type 2 diabetes complicated ZW conceived the research, analyzed the data, wrote and revised the paper. with hypertension in adult residents in Liuyang. Zhong Nan Da Xue Xue TY conducted the survey and participated in the revision of the paper. HF Bao Yi Xue Ban. 2015;40(12):1384–90. participated in the revision of the paper. All authors have read and approved 11. Jia W. Obesity in China: its characteristics, diagnostic criteria, and the manuscript. implications. Front Med. 2015;9(2):129–33. https://doi.org/10.1007/s11684-01 5-0387-x. 12. Zhai Y, Fang HY, Yu WT, et al. [Epidemiological characteristics of waist Funding circumference and abdominal obesity among Chinese adults in 2010–2012]. Not applicable for the current study. Zhonghua Yu Fang Yi Xue Za Zhi. 2017; 51(6): 506–512. Chinese. 13. Liu LS. 2018 Chinese guidelines for the management of hypertension. Availability of data and materials Beijing: People’s Medical Publishing House (China), 2018. Not publically available except for reasonable requests by contacting the 14. American Diabetes Association. Diagnosis and classification of diabetes corresponding author. mellitus. Diab Care. 2013; 36(Suppl 1): S67–S74. 15. Evans D, Chaix B, Lobbedez T, et al. Combining directed acyclic graphs and Declarations the change-in-estimate procedure as a novel approach to adjustment- variable selection in epidemiology. BMC Med Res Methodol. 2012;12:156. Ethics approval and consent to participate 16. Andersson T, Alfredsson L, Källberg H, et al. Calculating measures of The study was submitted to the Ethics Committee of Xiangya School of biological interaction. Eur J Epidemiol. 2005;20(7):575–9. https://doi.org/10.1 Public Health, Central South University and was granted a waiver beacause 007/s10654-005-7835-x. this study used the data from 2012 National Chronic Disease Management 17. Zuo H, Shi Z, Hussain A. Prevalence, trends and risk factors for the diabetes Project and did not involve the human trial or personal information. epidemic in China: a systematic review and meta-analysis. Diabetes Res Clin Informed consent was obtained from the participants before the Pract. 2014;104(1):63–72. https://doi.org/10.1016/j.diabres.2014.01.002. investigation. 18. Yang ZJ, Liu J, Ge JP, et al. Prevalence of cardiovascular disease risk factor in the Chinese population: the 2007-2008 China National Diabetes and Consent for publication metabolic disorders study. Eur Heart J. 2012;33(2):213–20. https://doi.org/1 Not applicable because this study did not involve the disclosure of personal 0.1093/eurheartj/ehr205. privacy information. 19. Liu S, Li Y, Zeng X. Elt. The burden of cardiovascular diseases in China, 1990- 2016: findings from the 2016 global burden of disease study. JAMA Cardiol. Competing interests 2019;4(4):342–52. https://doi.org/10.1001/jamacardio.2019.0295. No conflict of interest between the study and other commercial institutions 20. Hu G, Sarti C, Jousilahti P. Elt. The impact of a history of hypertension and or individuals. type 2 diabetes at baseline on the incidence of stroke and stroke mortality. Stroke. 2005;36(12):2538–43. https://doi.org/10.1161/01.STR.0000190894.3 Author details 0964.75. Department of Epidemiology and Health Statistics, XiangYa School of Public 21. Zafari N, Asgari S, Lotfaliany M, et al. Impact Of Hypertension versus Health, Central South University, Changsha, Hunan Province, China. Hunan Diabetes on Cardiovascular and All-cause Mortality in Iranian Older Adults: Provincial Key Laboratory of Clinical Epidemiology, Changsha, China. Results of 14 Years of Follow-up. Sci Rep. 2017;7(1):14220. Department of Obstetrics and Gynecology, The First Affiliated Hospital of 22. Hu G, Jousilahti P, Tuomilehto J. Joint effects of a history of hypertension at Zhengzhou University, Zhengzhou 450052, China. baseline and type 2 diabetes at baseline and during follow-up on the risk of Wang et al. BMC Public Health (2021) 21:1224 Page 9 of 9 coronary heart disease. Eur Heart J. 2007;28(24):3059–66. https://doi.org/10.1 093/eurheartj/ehm501. 23. Sehestedt T, Hansen TW, Li Y. Elt. Are blood pressure and diabetes additive or synergistic risk factors-outcomes in 8494 subjects randomly recruited from 10 populations. Hypertens Res. 2011 Jun;34(6):714–21. https://doi.org/1 0.1038/hr.2011.6. 24. Zhang Y, Jiang X, Bo J. Elt. Risk of stroke and coronary heart disease among various levels of blood pressure in diabetic and nondiabetic Chinese patients. J Hypertens. 2018 Jan;36(1):93–100. https://doi.org/10.1097/HJH. 25. Lu S, Bao MY, Miao SM, et al. Prevalence of hypertension, diabetes, and dyslipidemia, and their additive effects on myocardial infarction and stroke: a cross-sectional study in Nanjing, China. Ann Transl Med. 2019; 7(18): 436. 26. Lai YJ, Chen HC, Chou P. sex Difference in the Interaction Effects of Diabetes and Hypertension on Stroke among the Elderly in the Shih-Pai Study, Taiwan. PLoS One. 2015; 10(8): e0136634. 27. Cai H, Liu XF. Effect of interaction between diabetes mellitus and hypertension on the severity of ischemic stroke. Proceedings of the 23rd neurology conference of six provinces and one city in East China and 2016 annual meeting of neurology of Zhejiang Province. 2016; (pp. 196-197). Ningbo, Zhejiang, China Chinese 28. KJ R. Epidemiology: an introduction. New York: Oxford University Press; 29. Vandenbroucke JP, von Elm E, Altman DG. Elt. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Int J Surg. 2014;12(12):1500–24. https://doi.org/10.1016/j.ijsu.2 014.07.014. 30. KJ R. Modern epidemiology. Lippincott: Williams& Wilkins press; 2008. 31. Petrie JR, Guzik TJ, Touyz RM. Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Can J Cardiol. 2018;34(5): 575–84. https://doi.org/10.1016/j.cjca.2017.12.005. 32. Zhao D, Liu J, Xie W. Elt. Cardiovascular risk assessment: a global perspective. Nat Rev Cardiol. 2015;12(5):301–11. https://doi.org/10.1038/nrca rdio.2015.28. 33. Bella JN, Devereux RB, Roman MJ, et al. Separate and joint effects of systemic hypertension and diabetes mellitus on left ventricular structure and function in American Indians (the strong heart study). Am J Cardiol. 2001;87(11):1260–5. https://doi.org/10.1016/S0002-9149(01)01516-8. 34. Russo C, Jin Z, Homma S, et al. Effect of diabetes and hypertension on left ventricular diastolic function in a high-risk population without evidence of heart disease. Eur J Heart Fail. 2010;12(5):454–61. https://doi.org/10.1093/ eurjhf/hfq022. 35. Ma L, Zhao S, Li J, et al. Interaction of hypertension and diabetes on impairment of endothelial function. Chin Med J(Engl). 2001;114(6):563–7. 36. Widlansky ME, Gokce N, Keaney JF Jr. Elt. The clinical implications of endothelial dysfunction. J Am Coll Cardiol. 2003;42(7):1149–60. https://doi. org/10.1016/S0735-1097(03)00994-X. 37. Tsao PS, Niebauer J, Buitrago R, et al. Interaction of diabetes and hypertension on determinants of endothelial adhesiveness. Arterioscler Thromb Vasc Biol. 1998;18(6):947–53. https://doi.org/10.1161/01.ATV.18.6.947. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Journal

BMC Public HealthSpringer Journals

Published: Jun 25, 2021

Keywords: Diabetes; Hypertension; Stroke; Coronary heart disease; Interaction

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