Risk Factors for Cardiovascular Disease and Their Clustering among Adults in Jilin (China)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Ethics Statement
2.3. Data Collection and Measurement
2.4. Assessment Criteria
2.5. Statistical Analyses
3. Results
Variable | All (n = 16834) | Female (n = 9104) | Male (n = 7730) | t | p value |
---|---|---|---|---|---|
Age (year) | 42.67 ± 14.49 | 43.03 ± 14.55 | 42.33 ± 14.43 | 1.864 | 0.062 |
BMI (kg/m2) | 24.04 ± 3.81 | 23.74 ± 3.80 | 24.32 ± 3.81 | −6.184 | <0.001 |
SBP (mmHg) | 128.49 ± 20.18 | 124.59 ± 21.23 | 132.17 ± 18.41 | −18.976 | <0.001 |
DBP (mmHg) | 78.72 ± 11.58 | 76.4 ± 11.28 | 80.91 ± 11.44 | −17.151 | <0.001 |
TG (mmol/L) | 1.88 ± 1.82 | 1.61 ± 1.39 | 2.13 ± 2.13 | −14.406 | <0.001 |
TC (mmol/L) | 4.76 ± 1.07 | 4.72 ± 1.08 | 4.79 ± 1.05 | −2.688 | <0.001 |
LDL-C (mmol/L) | 2.83 ± 0.87 | 2.82 ± 0.89 | 2.84 ± 0.86 | −0.797 | 0.426 |
HDL-C (mmol/L) | 1.37 ± 0.38 | 1.43 ± 0.36 | 1.32 ± 0.38 | 13.105 | <0.001 |
FBG (mmol/L) | 5.39 ± 1.66 | 5.27 ± 1.63 | 5.53 ± 1.69 | −10.114 | <0.001 |
Category | Subcategory | Hypertension % (95%CI) | Diabetes % (95%CI) | Dyslipidemia % (95%CI) | Overweight % (95%CI) | Smoking % (95%CI) |
---|---|---|---|---|---|---|
Risk Factor | — | 31.0 (30.1, 31.9) | 8.2 (7.8, 8.7) | 36.8 (35.8, 37.8) | 47.3 (46.3, 48.4) | 31.0 (30.0, 32.0) |
Gender | Female | 26.7 (25.6, 27.9) | 7.4 (6.8, 8.0) | 30.4 (29.1, 31.7) | 43.6 (42.1, 45.1) | 9.1 (8.4, 9.9) |
Male | 35.0 (33.7, 36.4) | 9.0 (8.3, 9.8) | 42.9 (41.4, 44.3) | 50.8 (49.3, 52.4) | 51.6 (50.1, 53.1) | |
p value | <0.001 | 0.001 | <0.001 | <0.001 | <0.001 | |
Residence | Rural | 31.5 (30.2, 32.9) | 8.3 (7.6, 9.0) | 36.6 (35.1, 38.2) | 46.4 (44.7, 48.0) | 32.0 (30.5, 33.5) |
Town | 30.6 (29.4, 31.8) | 8.2 (7.5, 8.8) | 37.0 (35.7, 38.3) | 48.1 (46.7, 49.5) | 30.2 (28.9, 31.5) | |
p value | 0.324 | 0.801 | 0.714 | 0.112 | 0.075 | |
Age | 18– | 7.8 (5.5, 11.0) | 0.6 (0.2, 1.5) | 16.9 (13.6, 20.8) | 21.2 (17.6, 25.4) | 27.2 (23.3, 31.5) |
25– | 12.7 (10.8, 14.7) | 2.7 (1.8, 4.0) | 30.6 (28.1, 33.2) | 44.7 (42.0, 47.4) | 31.6 (29.1, 34.2) | |
35– | 25.5 (23.9, 27.1) | 5.3 (4.5, 6.3) | 37.2 (35.4, 39.0) | 50.0 (48.2, 51.8) | 32.3 (30.6, 34.0) | |
45– | 41.6 (40.1, 43.1) | 11.5 (10.6, 12.6) | 44.4(42.8, 45.9) | 56.1 (54.6, 57.6) | 33.9 (32.4, 35.4) | |
55– | 53.5 (51.7, 55.3) | 17.2 (15.9, 18.6) | 48.4(46.6, 50.2) | 56.6 (54.8, 58.3) | 30.3 (28.7, 32.0) | |
65–79 | 64.3 (61.3, 67.2) | 18.6 (16.6, 20.8) | 45.4(42.5, 48.3) | 52.0 (49.0, 55.0) | 25.9 (23.0, 29.0) | |
p value | <0.001 | <0.001 | <0.001 | <0.001 | 0.001 | |
Education | Junior school | 40.3 (38.6, 42.0) | 12.6 (11.6, 13.7) | 38.5 (36.7, 40.3) | 49.6 (47.8, 51.5) | 32.8 (31.0, 34.7) |
Junior high school | 31.7 (30.1, 33.5) | 7.4 (6.7, 8.3) | 36.7 (34.8, 38.6) | 47.5 (45.5, 49.5) | 32.3 (30.4, 34.1) | |
High school | 29.5 (27.8, 31.3) | 7.9 (7.0, 8.9) | 38.6 (36.5, 40.7) | 47.2 (44.9, 49.4) | 31.9 (29.9, 34.0) | |
Undergraduate | 21.0 (19.0, 23.2) | 4.7 (3.9, 5.6) | 32.7 (30.4, 35.1) | 44.7 (42.2, 47.3) | 25.5 (23.4, 27.7) | |
p value | <0.001 | <0.001 | <0.001 | 0.039 | <0.001 | |
Family income (Chinese Yuan) | <500 | 38.7 (36.7, 40.7) | 11.2 (10, 12.4) | 38.5 (36.5, 40.6) | 50.1 (47.9, 52.3) | 31.4 (29.5, 33.4) |
500– | 34.6 (32.6, 36.8) | 9.5 (8.4, 10.7) | 38.2 (36.0, 40.5) | 47.6 (45.3, 49.9) | 29.3 (27.3, 31.4) | |
1000– | 30.4 (28.8, 32.1) | 8.1 (7.3, 9.0) | 36.3 (34.5, 38.2) | 46.7 (44.7, 48.7) | 30.0 (28.2, 31.8) | |
2000– | 27.4 (25.3, 29.5) | 6.2 (5.4, 7.3) | 37.1 (34.8, 39.6) | 49.2 (46.6, 51.8) | 33.7 (31.4, 36.2) | |
3000– | 25.9 (23.0, 29.0) | 5.8 (4.5, 7.4) | 33.7 (30.3, 37.2) | 46.0 (42.2, 49.9) | 32.7 (28.9, 36.7) | |
p value | <0.001 | <0.001 | 0.147 | 0.277 | 0.035 | |
Occupation | Manual labor | 29.3 (28.2, 30.4) | 7.1 (6.5, 7.7) | 35.2 (34.0, 36.5) | 46.9 (45.5, 48.3) | 37.2 (35.9, 38.5) |
Mental labor | 24.0 (22.1, 26.1) | 5.9 (5.1, 6.9) | 33.7 (31.5, 36.0) | 44.5 (42.0, 47.0) | 26.8 (24.7, 29.1) | |
Other * | 41.5 (39.3, 43.7) | 13 (11.8, 14.2) | 43.5 (41.2, 45.8) | 51.1 (48.7, 53.4) | 20.2 (18.4, 22.1) | |
p value | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
Category | Subcategory | The Number of CVD Risk Factors | χ2 | p value | |||
---|---|---|---|---|---|---|---|
0 (n = 3320) | 1 (n = 4697) | 2 (n = 4356) | ≥3 (n = 4466) | ||||
Gender | Female | 36.1 (34.5, 37.8) | 29.1 (27.7, 30.6) | 19.8 (18.8, 20.9) | 14.9 (14.1, 15.8) | 1517.202 | <0.001 |
Male | 13.3 (12.1, 14.5) | 27.3 (25.9, 28.7) | 27.9 (26.5, 29.2) | 31.6 (30.3, 32.9) | |||
Residence | Rural | 23.4 (21.6, 25.3) | 28.7 (27.2, 30.2) | 25.1 (23.8, 26.5) | 22.8 (21.7, 24.0) | 16.897 | 0.054 |
Town | 25.2 (23.9, 26.5) | 27.8 (26.5, 29.1) | 23.0 (21.9, 24.2) | 24.0 (22.9, 25.2) | |||
Age | 18– | 50.1 (44.9, 55.2) | 31.0 (26.6, 35.8) | 14.7 (11.6, 18.4) | 4.2 (3.1, 5.8) | 2303.899 | <0.001 |
25– | 33.9 (31.5, 36.4) | 29.3 (26.9, 31.8) | 21.1 (18.9, 23.5) | 15.6 (13.6, 17.9) | |||
35– | 25.3 (23.8, 26.8) | 29.7 (28.1, 31.3) | 22.4 (20.9, 23.9) | 22.7 (21.2, 24.3) | |||
45– | 13.8 (12.8, 14.9) | 27.9 (26.6, 29.3) | 26.7 (25.4, 28.1) | 31.6 (30.1, 33.1) | |||
55– | 9.7 (8.7, 10.9) | 23.6 (22.2, 25.2) | 30.5 (28.9, 32.2) | 36.1 (34.4, 37.8) | |||
65–79 | 7.4 (6.2, 8.9) | 25.1 (22.4, 28.0) | 32.4 (29.8, 35.1) | 35.1 (32.2, 38.1) | |||
Education | Junior school | 15.6 (14.1, 17.2) | 30.2 (28.5, 31.9) | 27.5 (25.9, 29.1) | 26.7 (25.2, 28.3) | 364.427 | <0.001 |
Junior high school | 23.5 (21.6, 25.4) | 28.8 (26.9, 30.8) | 24.3 (22.8, 25.9) | 23.4 (21.9, 24.9) | |||
High school | 25.9 (23.5, 28.5) | 25.8 (24.1, 27.6) | 23.4 (21.7, 25.2) | 24.8 (23.2, 26.6) | |||
Undergraduate | 33.7 (31.2, 36.2) | 28.1 (25.8, 30.6) | 20.1 (18.1, 22.1) | 18.1 (16.4, 20.0) | |||
Family Income (Chinese Yuan) | <500 | 18.0 (15.9, 20.3) | 29.4 (27.5, 31.4) | 25.9 (24.2, 27.7) | 26.7 (25.0, 28.5) | 219.416 | <0.001 |
500– | 22.5 (20.3, 24.9) | 27.8 (25.8, 30.0) | 25.4 (23.5, 27.4) | 24.3 (22.5, 26.2) | |||
1000– | 25.5 (23.5, 27.6) | 28.4 (26.7, 30.3) | 22.7 (21.2, 24.3) | 23.3 (21.9, 24.8) | |||
2000– | 24.6 (22.4, 26.9) | 28.1 (25.8, 30.5) | 24.3 (22.1, 26.5) | 23.1 (21.2, 25.1) | |||
3000– | 28.5 (24.9, 32.5) | 26.6 (23.2, 30.2) | 23.8 (20.7, 27.2) | 21.1 (18.3, 24.2) | |||
Occupation | Manual labor | 22.8 (21.5, 24.1) | 29.1 (27.9, 30.3) | 25.1 (23.9, 26.3) | 23.0 (22.0, 24.1) | 90.579 | <0.001 |
Mental labor | 32.0 (29.3, 34.8) | 28.5 (26.2, 30.9) | 19.6 (17.9, 21.5) | 19.9 (18.3, 21.7) | |||
Other * | 20.9 (18.7, 23.3) | 25.7 (23.6, 28.0) | 25.4 (23.6, 27.2) | 28.0 (26.2, 29.9) |
Category | Subcategory | The Number of CVD Risk Factors | |||
---|---|---|---|---|---|
0 | ≥1 | ≥2 | ≥3 | ||
Gender | Female | 36.1 (34.5, 37.8) | 63.9 (62.2, 65.5) | 34.7 (33.4, 36.1) | 14.9 (14.1, 15.8) |
Male | 13.3 (12.1, 14.5) | 86.7 (85.5, 87.9) | 59.5 (57.9, 61.0) | 31.6 (30.3, 32.9) | |
p value | — | <0.001 | <0.001 | <0.001 | |
Residence | Rural | 23.4 (21.6, 25.3) | 76.6 (74.7, 78.4) | 48.0 (46.3, 49.6) | 22.8 (21.7, 24.0) |
Town | 25.2 (23.9, 26.5) | 74.8 (73.5, 76.1) | 47.1 (45.7, 48.5) | 24.0 (22.9, 25.2) | |
p value | — | 0.123 | 0.226 | 0.041 | |
Age | 18– | 50.1 (44.9, 55.2) | 49.9 (44.8, 55.1) | 18.9 (15.6, 22.8) | 4.2 (3.1, 5.8) |
25– | 33.9 (31.5, 36.4) | 66.1 (63.6, 68.5) | 36.7 (34.1, 39.5) | 15.6 (13.6, 17.9) | |
35– | 25.3 (23.8, 26.8) | 74.7 (73.2, 76.2) | 45.1 (43.3, 46.9) | 22.7 (21.2, 24.3) | |
45– | 13.8 (12.8, 14.9) | 86.2 (85.1, 87.2) | 58.3 (56.8, 59.8) | 31.6 (30.1, 33.1) | |
55– | 9.7 (8.7, 10.9) | 90.3 (89.1, 91.3) | 66.6 (64.9, 68.3) | 36.1 (34.4, 37.8) | |
65–79 | 7.4 (6.2, 8.9) | 92.6 (91.1, 93.8) | 67.5 (64.5, 70.3) | 35.1 (32.2, 38.1) | |
p value | — | <0.001 | <0.001 | <0.001 | |
Education | Junior school | 15.6 (14.1, 17.2) | 84.4 (82.8, 85.9) | 54.2 (52.3, 56.0) | 26.7 (25.2 ,28.3) |
Junior high school | 23.5 (21.6, 25.4) | 76.5 (74.6, 78.4) | 47.7 (45.7, 49.7) | 23.4 (21.9, 24.9) | |
High school | 25.9 (23.5, 28.5) | 74.1 (71.5, 76.5) | 48.2 (46.0, 50.5) | 24.8 (23.2, 26.6) | |
Undergraduate | 33.7 (31.2, 36.2) | 66.3 (63.8, 68.8) | 38.2 (35.8, 40.7) | 18.1 (16.4, 20.0) | |
p value | — | <0.001 | <0.001 | <0.001 | |
Family Income (Chinese Yuan) | <500 | 18.0 (15.9, 20.3) | 82.0 (79.7, 84.1) | 52.6 (50.3, 54.8) | 26.7 (25.0, 28.5) |
500– | 22.5 (20.3, 24.9) | 77.5 (75.1, 79.7) | 49.7 (47.3, 52.0) | 24.3 (22.5, 26.2) | |
1000– | 25.5 (23.5, 27.6) | 74.5 (72.4, 76.5) | 46.1 (44.1, 48.0) | 23.3 (21.9, 24.8) | |
2000– | 24.6 (22.4, 26.9) | 75.4 (73.1, 77.6) | 47.3 (44.8, 49.9) | 23.1 (21.2, 25.1) | |
3000– | 28.5 (24.9, 32.5) | 71.5 (67.5, 75.1) | 44.9 (41.1, 48.8) | 21.1 (18.3, 24.2) | |
5000– | 26.6 (19.3, 35.5) | 73.4 (64.5, 80.7) | 45.3 (37.9, 52.9) | 22.5 (17.0, 29.2) | |
p value | — | <0.001 | <0.001 | <0.001 | |
Occupation | Manual labor | 22.8 (21.5, 24.1) | 77.2 (75.9, 78.5) | 48.1 (46.7, 49.5) | 23.0 (22.0,24.1) |
Mental labor | 32.0 (29.3, 34.8) | 68.0 (65.2, 70.7) | 39.5 (37.2, 41.9) | 19.9 (18.3,21.7) | |
Other * | 20.9 (18.7, 23.3) | 79.1 (76.7, 81.3) | 53.4 (51.0, 55.8) | 28.0 (26.2,29.9) | |
p value | — | <0.001 | <0.001 | <0.001 |
Category | Subcategory | The CVD Risk Factors and Adjusted OR (95%CI) | ||
---|---|---|---|---|
≥1 | ≥2 | ≥3 | ||
Gender | Female | 1.00 | 1.00 | 1.00 |
Male | 3.70 (3.26,4.20) | 4.66 (4.09, 5.31) | 5.76 (5.01, 6.63) | |
Age | 18– | 1.00 | 1.00 | 1.00 |
25– | 1.95 (1.55, 2.47) | 2.86 (2.15, 3.82) | 5.44 (3.66, 8.08) | |
35– | 2.97 (2.38, 3.70) | 4.72 (3.59, 6.20) | 10.61 (7.32, 15.37) | |
45– | 6.28 (5.01, 7.86) | 11.19 (8.51, 14.72) | 27.06 (18.7, 39.16) | |
55– | 9.30 (7.32, 11.82) | 18.10 (13.59, 24.10) | 43.77 (29.97, 63.94) | |
65–79 | 12.49 (9.41, 16.59) | 24.01 (17.35, 33.22) | 55.71 (36.81, 84.3) | |
Education | Junior school | 1.00 | 1.00 | 1.00 |
Junior high school | 0.60 (0.52, 0.71) | 0.59 (0.50, 0.69) | 0.58 (0.49, 0.69) | |
High school | 0.53 (0.45, 0.63) | 0.54 (0.45, 0.64) | 0.56 (0.46, 0.68) | |
Undergraduate | 0.37 (0.31, 0.43) | 0.33 (0.28, 0.39) | 0.32 (0.26, 0.38) | |
Family Income (Chinese Yuan) | <500 | 1.00 | 1.00 | 1.00 |
500– | 0.76 (0.62, 0.92) | 0.75 (0.61, 0.93) | 0.73 (0.58, 0.91) | |
1000– | 0.64 (0.53, 0.77) | 0.62 (0.51, 0.75) | 0.62 (0.50, 0.76) | |
2000– | 0.67 (0.55, 0.82) | 0.66 (0.54, 0.81) | 0.63 (0.51, 0.79) | |
3000– | 0.55 (0.43, 0.70) | 0.54 (0.42, 0.69) | 0.50 (0.38, 0.66) | |
Occupation | Other * | 1.00 | 1.00 | 1.00 |
Manual labor | 0.90 (0.76, 1.05) | 0.83 (0.70, 0.97) | 0.76 (0.64, 0.90) | |
Mental labor | 0.56 (0.47, 0.68) | 0.48 (0.40, 0.59) | 0.47 (0.38, 0.57) |
4. Discussion
Author | Hypertension | Diabetes | Dyslipidemia | Overweight | Smoking | Survey Time and Region |
---|---|---|---|---|---|---|
Our study | 37.3 | 8.2 | 36.8 | 47.3 | 31.0 | 2012, Jilin |
Gu et al. [16] | 26.1 | 5.2 | 53.6 | 28.2 | 34.5 | 2000–2001, China |
Zhang et al. [28] | 36.6 | 6.5 | 35.4 | 36.2 | 36.3 | 2007, Beijing |
Xu et al. [4] | 62.4 | 6.4 | 42.7 | 34.3 | 6.1 | 2011, Tibetan |
5. Conclusions
Supplementary Files
Supplementary File 1Acknowledgments
Author Contributions
Conflicts of Interest
References
- Gao, B.X.; Zhang, L.X.; Wang, H.Y.; The China National Survey of Chronic Kidney Disease Working Group. Clustering of major cardiovascular risk factors and the association with unhealthy lifestyles in the Chinese adult population. PLoS ONE 2013. [Google Scholar] [CrossRef]
- Ferket, B.S.; Colkesen, E.B.; Visser, J.J.; Spronk, S.; Kraaijenhagen, R.A.; Steyerberg, E.W.; Hunink, M.G.M. Systematic review of guidelines on cardiovascular risk assessment which recommendations should clinicians follow for a cardiovascular health check? Arch. Intern. Med. 2010, 170, 27–40. [Google Scholar] [CrossRef] [PubMed]
- Yang, Z.J.; Liu, J.; Ge, J.P.; Chen, L.; Zhao, Z.G.; Yang, W.Y.; Disorders, C.N.D.M. Prevalence of cardiovascular disease risk factor in the Chinese population: The 2007–2008 China National Diabetes and Metabolic Disorders Study. Eur. Heart J. 2012, 33, 213–220. [Google Scholar] [CrossRef] [PubMed]
- Xu, S.P.; Jiayong, Z.P.; Li, B.; Zhu, H.; Chang, H.; Shi, W.; Gao, Z.X.; Ning, X.J.; Wang, J.H. Prevalence and clustering of cardiovascular disease risk factors among Tibetan adults in China: A population-based study. PLoS ONE 2015. [Google Scholar] [CrossRef] [PubMed]
- Yu, D.H.; Huang, J.F.; Hu, D.S.; Chen, J.C.; Cao, J.; Li, J.X.; Gu, D.F. Association between prehypertension and clustering of cardiovascular disease risk factors among Chinese adults. J. Cardiovasc Pharm. 2009, 53, 388–400. [Google Scholar] [CrossRef] [PubMed]
- Murakami, Y.; Okamura, T.; Nakamura, K.; Miura, K.; Ueshima, H. The clustering of cardiovascular disease risk factors and their impacts on annual medical expenditure in Japan: Community-based cost analysis using gamma regression models. BMJ Open. 2013. [Google Scholar] [CrossRef] [PubMed]
- Cooper, R.S.; Ordunez, P.; Ferrer, M.D.I.; Munoz, J.L.B.; Espinosa-Brito, A. Cardiovascular disease and associated risk factors in Cuba: Prospects for prevention and control. Am. J. Public Health 2006, 96, 94–101. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Ning, X.; Yang, L.; Lu, H.; Tu, J.; Jin, W.; Zhang, W.; Su, T.C. Trends of hypertension prevalence, awareness, treatment and control in rural areas of Northern China during 1991–2011. J. Hum. Hypertens 2014, 28, 25–31. [Google Scholar] [CrossRef] [PubMed]
- Reynolds, K.; Gu, D.F.; Whelton, P.K.; Wu, X.G.; Duan, X.F.; Mo, J.P.; He, J.; Grp, I.C. Prevalence and risk factors of overweight and obesity in China. Obesity 2007, 15, 10–18. [Google Scholar] [CrossRef] [PubMed]
- He, J.; Gu, D.F.; Reynolds, K.; Wu, X.G.; Muntner, P.; Zhao, J.G.; Chen, J.; Liu, D.H.; Mo, J.P.; Whelton, P.K.; et al. Serum total and lipoprotein cholesterol levels and awareness, treatment, and control of hypercholesterolemia in China. Circulation 2004, 110, 405–411. [Google Scholar] [CrossRef] [PubMed]
- Shan, X.Y.; Xi, B.; Cheng, H.; Hou, D.Q.; Wang, Y.F.; Mi, J. Prevalence and behavioral risk factors of overweight and obesity among children aged 2–18 in Beijing, China. Int J. Pediatr. Obes. 2010, 5, 383–389. [Google Scholar] [CrossRef] [PubMed]
- de Simone, G.; Olsen, M.H.; Wachtell, K.; Hille, D.A.; Dahlof, B.; Ibsen, H.; Kjeldsen, S.E.; Lyle, P.A.; Devereux, R.B. Clusters of metabolic risk factors predict cardiovascular events in hypertension with target-organ damage: The life study. J. Hum. Hypertens 2007, 21, 625–632. [Google Scholar] [CrossRef] [PubMed]
- Andegiorgish, A.K.; Wang, J.H.; Zhang, X.; Liu, X.M.; Zhu, H. Prevalence of overweight, obesity, and associated risk factors among school children and adolescents in Tianjin, China. Eur. J. Pediatr 2012, 171, 697–703. [Google Scholar] [CrossRef] [PubMed]
- Xue, H.; Liu, Y.; Zhou, X.; Duan, R.; Cheng, G. The overview of prevalence of overweight/obesity among children and its risk factors in China. Ann. Nutr. Metab. 2013, 63, 1163–1163. [Google Scholar]
- Li, N.F.; Wang, H.M.; Yan, Z.T.; Yao, X.G.; Hong, J.; Zhou, L. Ethnic disparities in the clustering of risk factors for cardiovascular disease among the Kazakh, Uygur, Mongolian and Jan populations of Xinjiang: A cross-sectional study. BMC Public Health 2012. [Google Scholar] [CrossRef]
- Gu, D.F.; Gupta, A.; Muntner, P.; Hu, S.S.; Duan, X.F.; Chen, J.C.; Reynolds, R.F.; Whelton, P.K.; He, J. Prevalence of cardiovascular disease risk factor clustering among the adult population of China—Results from the international collaborative study of cardiovascular disease in Asia (Interasia). Circulation 2005, 112, 658–665. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.; Yu, Y.; Zhang, X.; Li, Y.; Kou, C.; Li, B.; Tao, Y.; Zhen, Q.; He, H.; Kanu, J.S.; et al. Awareness, treatment, control of diabetes mellitus and the risk factors: Survey results from Northeast China. PLoS ONE 2014. [Google Scholar] [CrossRef] [PubMed]
- Pan, S.; He, C.H.; Ma, Y.T.; Yang, Y.N.; Ma, X.; Fu, Z.Y.; Li, X.M.; Xie, X.; Yu, Z.X.; Chen, Y.; et al. Serum uric acid levels are associated with high blood pressure in Chinese children and adolescents aged 10–15 years. J. Hypertens. 2014, 32, 998–1003. [Google Scholar] [CrossRef] [PubMed]
- Xie, X.; Ma, Y.T.; Yang, Y.N.; Fu, Z.Y.; Li, X.M.; Zheng, Y.Y.; Huang, D.; Ma, X.; Chen, B.D.; Liu, F. Polymorphisms in the SAA1 gene are associated with ankle-to-brachial index in Han Chinese healthy subjects. Blood Press. 2011, 20, 232–238. [Google Scholar] [CrossRef] [PubMed]
- Ezzati, M.; Vander Hoorn, S.; Rodgers, A. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet 2003, 362, 271–280. [Google Scholar] [CrossRef]
- Dong, X.L.; Liu, Y.; Yang, J.; Sun, Y.; Chen, L. Efficiency of anthropometric indicators of obesity for identifying cardiovascular risk factors in a Chinese population. Postgrad Med. J. 2011, 87, 251–256. [Google Scholar] [CrossRef] [PubMed]
- Yip, G.W.; Li, A.M.; So, H.K.; Choi, K.C.; Leung, L.C.; Fong, N.C.; Lee, K.W.; Li, S.P.; Wong, S.N.; Sung, R.Y. Oscillometric 24-h ambulatory blood pressure reference values in Hong Kong Chinese children and adolescents. J. Hypertens. 2014, 32, 606–619. [Google Scholar] [CrossRef] [PubMed]
- Alberti, K.G.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A.; Fruchart, J.C.; James, W.P.; Loria, C.M.; Smith, S.C., Jr. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009, 120, 1640–1645. [Google Scholar] [PubMed]
- Cai, L.; Liu, A.P.; Zhang, Y.M.; Wang, P.Y. Waist-to-height ratio and cardiovascular risk factors among Chinese adults in Beijing. PLoS ONE 2013. [Google Scholar] [CrossRef] [PubMed]
- Syamlal, G.; Mazurek, J.M.; Dube, S.R. Gender differences in smoking among U.S.Working adults. Am. J. Prevent. Med. 2014, 47, 467–475. [Google Scholar] [CrossRef] [PubMed]
- Zhang, L.; Wang, F.; Wang, L.; Wang, W.; Liu, B.; Liu, J.; Chen, M.; He, Q.; Liao, Y.; Yu, X.; et al. Prevalence of chronic kidney disease in China: A cross-sectional survey. Lancet 2012, 379, 815–822. [Google Scholar] [CrossRef]
- Gao, B.; Xu, Q.T.; Li, Y.B. Dynamic change and analysis of driving factors of carbon emissions from traffic and transportation energy consumption in Jilin Province. Appl. Mech. Mater. 2014, 472, 851–855. [Google Scholar] [CrossRef]
- Zhang, L.; Gui, H.; Liu, A.; Hu, D.; Wang, P. Risk factors for cardiovascular disease and its clustering among middle-aged and old suburban people in rural-urban fringes of Beijing. Chin. J. Prevent. Control. Chronic Non-Commun. Dis. 2010, 18, 238–240. [Google Scholar]
- Mao, X.Q.; Ait-Aissa, K.; Lagrange, J.; Youcef, G.; Louis, H. Hypertension, hypercoagulability and the metabolic syndrome: A cluster of risk factors for cardiovascular disease. Bio-Med. Mater. Eng. 2012, 22, 35–48. [Google Scholar]
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Yu, J.; Ma, Y.; Yang, S.; Pang, K.; Yu, Y.; Tao, Y.; Jin, L. Risk Factors for Cardiovascular Disease and Their Clustering among Adults in Jilin (China). Int. J. Environ. Res. Public Health 2016, 13, 70. https://doi.org/10.3390/ijerph13010070
Yu J, Ma Y, Yang S, Pang K, Yu Y, Tao Y, Jin L. Risk Factors for Cardiovascular Disease and Their Clustering among Adults in Jilin (China). International Journal of Environmental Research and Public Health. 2016; 13(1):70. https://doi.org/10.3390/ijerph13010070
Chicago/Turabian StyleYu, Jianxing, Yonghui Ma, Sen Yang, Kai Pang, Yaqin Yu, Yuchun Tao, and Lina Jin. 2016. "Risk Factors for Cardiovascular Disease and Their Clustering among Adults in Jilin (China)" International Journal of Environmental Research and Public Health 13, no. 1: 70. https://doi.org/10.3390/ijerph13010070
APA StyleYu, J., Ma, Y., Yang, S., Pang, K., Yu, Y., Tao, Y., & Jin, L. (2016). Risk Factors for Cardiovascular Disease and Their Clustering among Adults in Jilin (China). International Journal of Environmental Research and Public Health, 13(1), 70. https://doi.org/10.3390/ijerph13010070