Association between Parity, Carotid Plaques, and Intima Media Thickness in Northern Chinese Women

YAO Yan LIU Hua Min FENG Xia LI Dong ZHOU Yong ZHANG Zhi Hui

YAO Yan, LIU Hua Min, FENG Xia, LI Dong, ZHOU Yong, ZHANG Zhi Hui. Association between Parity, Carotid Plaques, and Intima Media Thickness in Northern Chinese Women[J]. Biomedical and Environmental Sciences, 2021, 34(5): 416-420. doi: 10.3967/bes2021.056
Citation: YAO Yan, LIU Hua Min, FENG Xia, LI Dong, ZHOU Yong, ZHANG Zhi Hui. Association between Parity, Carotid Plaques, and Intima Media Thickness in Northern Chinese Women[J]. Biomedical and Environmental Sciences, 2021, 34(5): 416-420. doi: 10.3967/bes2021.056

doi: 10.3967/bes2021.056

Association between Parity, Carotid Plaques, and Intima Media Thickness in Northern Chinese Women

Funds: This work was supported by grants from the National Natural Science Foundation of China [No. 81670294, 82070332, and 81870303] and by the Scientific Research Key Program of Beijing Municipal Commission of Education [KZ202110025033]
More Information
    Author Bio:

    YAO Yan, female, born in 1981, Associate Professor, majoring in clinical intervention and basic study on cardiovascular diseases

    Corresponding author: ZHANG Zhi Hui, Tel: 86-731-88618156, E-mail: zhangzhihui0869@126.com
  • Figure  1.  Association between parity and carotid artery plaques. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives

    S1.  Association between parity and carotid artery plaques. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives

    S1.   Parity distribution in this study

    ParityFrequencyPercentageCumulative frequencyCumulative percentage
    0230.67230.67
    12,49873.282,52173.95
    263318.573,15492.52
    31865.463,34097.98
    4531.553,39399.53
    5130.383,40699.91
    620.063,40899.97
    710.033,409100.00
    下载: 导出CSV

    Table  1.   Baseline characteristics of participants stratified by parity

    CharacteristicsOverallParity groupP value
    123≥ 4
    Number, n (%)3,3862,49863318669
    Age, year52.5 ± 8.949.2 ± 6.359.4 ± 7.366.5 ± 6.971.9 ± 6.9< 0.001
    Age at menopause, year58.4 ± 7.454.5 ± 5.461.4 ± 5.666.3 ± 5.972.3 ± 6.2< 0.001
    BMI, kg/m224.5 ± 3.424.2 ± 3.325.2 ± 3.525.3 ± 3.725.2 ± 3.4< 0.001
    Current smoking, n (%)92 (2.7)57 (2.3)20 (3.2)10 (5.4)5 (7.3)0.006
    Alcohol use, n (%)84 (2.5)71 (2.8)13 (2.1)000.025
    Physical activity, n (%)< 0.001
     Inactive1,106 (32.7)831 (33.3)196 (31.0)62 (33.3)17 (24.6)
     Moderate active650 (19.2)535 (21.4)74 (11.7)29 (15.6)12 (17.4)
     Active1,630 (48.1)1,132 (45.3)363 (57.4)95 (51.1)40 (58.0)
    Hypertension, n (%)1,210 (35.7)720 (28.8)331 (52.3)115 (61.8)44 (63.8)< 0.001
    Diabetes mellitus, n (%)304 (9.0)168 (6.7)92 (14.5)32 (17.2)12 (17.4)< 0.001
    Dyslipidemia, n (%)1434 (42.4)960 (38.4)320 (50.6)115 (61.8)39 (56.5)< 0.001
    Antihypertensive medication, n (%)592 (27.8)301 (21.1)189 (37.1)69 (48.3)33 (67.4)< 0.001
    Insulin or oral hypoglycemic drug, n (%)178 (9.5)91 (7.1)61 (13.4)17 (16.0)9 (31.0)< 0.001
    Antilipemic agent, n (%)72 (2.1)41 (1.6)17 (2.7)12 (6.5)2 (2.9)< 0.001
    Oral contraceptives, n (%)119 (7.4)67 (5.3)32 (15.2)11 (12.1)9 (17.3)< 0.001
    Estrogen replacement therapy, n (%)33 (1.7)26 (2.2)5 (1.0)2 (1.2)00.361
    Education level, n (%)< 0.001
     Illiteracy/primary school280 (8.3)76 (3.0)129 (20.4)46 (24.7)29 (42.0)
     Middle/high school2,187 (64.6)1,588 (63.6)444 (70.1)121 (65.1)34 (49.3)
     College or above919 (27.1)834 (33.4)60 (9.5)19 (10.2)6 (8.7)
    Income, ¥/month, n (%)< 0.001
     ≤ ¥3,0002,444 (72.2)1721 (68.9)511 (80.7)155 (83.3)57 (82.6)
     ¥3,001–5,000813 (24.0)670 (26.8)105 (16.6)27 (14.5)11 (15.9)
     > ¥5,000129 (3.8)107 (4.3)17 (2.7)4 (2.2)1 (1.5)
    Plaques, n (%)853 (25.2)484 (19.4)215 (34.0)103 (55.4)51 (73.9)< 0.001
    Mean common carotid IMT, mm0.72 ± 0.130.70 ± 0.110.76 ± 0.130.84 ± 0.140.86 ± 0.13< 0.001
    Mean internal carotid IMT, mm0.65 ± 0.090.63 ± 0.080.67 ± 0.100.69 ± 0.100.73 ± 0.10< 0.001
    下载: 导出CSV

    Table  2.   Association between parity and carotid artery IMT

    ParityModel 1Model 2Model 3Model 4
    β (SE)P valueβ (SE)P valueβ (SE)P valueβ (SE)P value
    Common carotid IMT
     10000
     20.062 (0.005)< 0.0010.017 (0.006)0.0030.010 (0.006)0.0860.005 (0.006)0.393
     30.136 (0.009)< 0.0010.056 (0.010)< 0.0010.046 (0.010)< 0.0010.039 (0.010)< 0.001
     ≥ 40.160 (0.015)< 0.0010.072 (0.015)< 0.0010.065 (0.015)< 0.0010.055 (0.015)< 0.001
     Per birth0.061 (0.003)< 0.0010.024 (0.004)< 0.0010.020 (0.004)< 0.0010.016 (0.004)< 0.001
    Internal carotid IMT
     10000
     20.037 (0.004)< 0.0010.019 (0.005)< 0.0010.016 (0.005)< 0.0010.010 (0.005)0.043
     30.059 (0.008)< 0.0010.028 (0.009)0.0020.024 (0.009)0.0060.018 (0.009)0.041
     ≥ 40.092 (0.014)< 0.0010.057 (0.015)< 0.0010.053 (0.015)< 0.0010.044 (0.015)0.003
     Per birth0.032 (0.003)< 0.0010.017 (0.003)< 0.0010.015 (0.003)< 0.0010.011 (0.003)< 0.001
      Note. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives. IMT, intima-media thickness.
    下载: 导出CSV

    S2.   Baseline characteristics of participants stratified by parity

    CharacteristicsOverallParity groupsP value
    0123≥ 4
    Number, n (%)3,409232,49863318669
    Age, year52.5 ± 8.946.4 ± 5.149.2 ± 6.3459.4 ± 7.366.5 ± 6.971.9 ± 6.9< 0.001
    Age at menopause, year58.4 ± 7.452.9 ± 5.354.5 ± 5.461.4 ± 5.666.3 ± 5.772.3 ± 6.2< 0.001
    BMI, kg/m224.5 ± 3.422.7 ± 2.224.2 ± 3.325.2 ± 3.525.3 ± 3.725.2 ± 3.4< 0.001
    Current smoking, n (%)93 (2.7)1 (4.4)57 (2.3)20 (3.2)10 (5.4)5 (7.2)0.013
    Alcohol use, n (%)*86 (2.5)2 (8.7)71 (2.8)13 (2.1)000.011
    Physical activity, n (%)< 0.001
     Inactive1,115 (32.7)9 (39.1)831 (33.3)196 (31.0)62 (33.3)17 (24.6)
     Moderate active652 (19.1)2 (8.7)535 (21.4)74 (11.7)29 (15.6)12 (17.4)
     Active1,642 (48.2)12 (52.2)1,132 (45.3)363 (57.4)95 (51.1)40 (58.0)
    Hypertension, n (%)1,216 (35.7)6 (26.1)720 (28.8)331 (52.3)115 (61.8)44 (63.8)< 0.001
    Diabetes mellitus, n (%)304 (8.9)0168 (6.7)92 (14.5)32 (17.2)12 (17.4)< 0.001
    Dyslipidemia, n (%)1,440 (42.2)6 (26.1)960 (38.4)320 (50.6)115 (61.8)39 (56.5)< 0.001
    Antihypertensive medication, n (%)592 (17.42)2 (8.7)301 (12.1)189 (29.9)69 (37.1)33 (47.8)< 0.001
    Insulin or oral hypoglycemic drug, n (%)178 (5.22)091 (3.6)61 (9.6)17 (9.1)9 (13.0)< 0.001
    Antilipemic agent, n (%)*72 (2.11)041 (1.6)17 (2.7)12 (6.5)2 (2.9)0.002
    Oral contraceptives, n (%)119 (3.5)067 (2.7)32 (5.1)11 (5.9)9 (13.0)< 0.001
    Estrogen replacement therapy, n (%)*33 (1.7)026 (2.2)5 (1.0)2 (1.2)00.415
    Education level, n (%)< 0.001
     Illiteracy/primary school281 (8.2)1 (4.4)76 (3.0)129 (20.4)46 (24.7)29 (42.0)
     Middle/high school2,195 (64.4)8 (34.8)1,588 (63.6)444 (70.1)121 (65.1)34 (49.3)
     College or above933 (27.4)14 (60.9)834 (33.4)60 (9.5)19 (10.2)6 (8.7)
    Income, ¥/month, n (%)< 0.001
     ≤ ¥3,0002,454 (72.0)10 (43.5)1,721 (68.9)511 (80.7)155 (83.3)57 (82.6)
     ¥3,001–5,000824 (24.2)11 (47.8)670 (26.8)105 (16.6)27 (14.5)11 (15.9)
     > ¥5,000131 (3.8)2 (8.7)107 (4.3)17 (2.7)4 (2.2)1 (1.5)
    Plaques, n (%)854 (25.1)1 (4.4)484 (19.4)215 (34.0)103 (55.4)51 (73.9)< 0.001
    Mean common carotid IMT, mm0.72 ± 0.130.66 ± 0.070.70 ± 0.110.76 ± 0.130.84 ± 0.140.86 ± 0.13< 0.001
    Mean internal carotid IMT, mm0.65 ± 0.090.62 ± 0.080.63 ± 0.080.67 ± 0.100.69 ± 0.100.73 ± 0.10< 0.001
      Note. *Use Fisher's exact test.
    下载: 导出CSV

    S3.   Association between parity and carotid artery IMT

    ParityModel 1Model 2Model 3Model 4
    β (SE)P valueβ (SE)P valueβ (SE)P valueβ (SE)P value
    common carotid IMT
     0−0.043 (0.025)0.084−0.042 (0.024)0.083−0.035 (0.023)0.136−0.026 (0.023)0.272
     10000
     20.062 (0.005)< 0.0010.017 (0.006)0.0030.010 (0.006)0.0770.005 (0.006)0.373
     30.136 (0.009)< 0.0010.056 (0.010)< 0.0010.046 (0.010)< 0.0010.040 (0.010)< 0.001
     ≥ 40.160 (0.015)< 0.0010.072 (0.015)< 0.0010.065 (0.015)< 0.0010.055 (0.015)< 0.001
     Per birth0.058 (0.003)< 0.0010.023 (0.004)< 0.0010.019 (0.003)< 0.0010.016 (0.003)< 0.001
    internal carotid IMT
     0−0.013 (0.019)0.489−0.013 (0.019)0.503−0.010 (0.019)0.588−0.002 (0.019)0.924
     10000
     20.037 (0.004)< 0.0010.019 (0.005)< 0.0010.016 (0.005)< 0.0010.010 (0.005)0.046
     30.059 (0.008)< 0.0010.028 (0.009)0.0020.024 (0.009)0.0060.018 (0.009)0.043
     ≥ 40.092 (0.014)< 0.0010.056 (0.015)< 0.0010.053 (0.015)< 0.0010.044 (0.015)0.003
     Per birth0.030 (0.003)< 0.0010.016 (0.003)< 0.0010.014 (0.003)< 0.0010.010 (0.003)0.001
      Note. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives.
    下载: 导出CSV
  • [1] Fraser A, Nelson SM, Macdonald-Wallis C, et al. Associations of pregnancy complications with calculated cardiovascular disease risk and cardiovascular risk factors in middle age: The Avon Longitudinal Study of Parents and Children. Circulation, 2012; 125, 1367−80. doi:  10.1161/CIRCULATIONAHA.111.044784
    [2] Humphries KH, Westendorp IC, Bots ML, et al. Parity and carotid artery atherosclerosis in elderly women: the Rotterdam Study. Stroke, 2001; 32, 2259−64. doi:  10.1161/hs1001.097224
    [3] Wolff B, Volzke H, Robinson D, et al. Relation of parity with common carotid intima-media thickness among women of the Study of Health in Pomerania. Stroke, 2005; 36, 938−43. doi:  10.1161/01.STR.0000162712.27799.20
    [4] Lawlor DA, Emberson JR, Ebrahim S, et al. Is the association between parity and coronary heart disease due to biological effects of pregnancy or adverse lifestyle risk factors associated with child-rearing? Circulation, 2003; 107, 1260−4. doi:  10.1161/01.CIR.0000053441.43495.1A
    [5] Kharazmi E, Moilanen L, Fallah M, et al. Reproductive history and carotid intima-media thickness. Acta Obstet Gynecol Scand, 2007; 86, 995−1002. doi:  10.1080/00016340701464374
    [6] Stöckl D, Peters A, Thorand B, et al. Reproductive factors, intima media thickness and carotid plaques in a cross-sectional study of postmenopausal women enrolled in the population-based KORA F4 study. BMC Womens Health, 2014; 14, 17. doi:  10.1186/1472-6874-14-17
    [7] Skilton MR, Serusclat A, Begg LM, et al. Parity and carotid atherosclerosis in men and women: insights into the roles of childbearing and child-rearing. Stroke, 2009; 40, 1152−7. doi:  10.1161/STROKEAHA.108.535807
    [8] Sanghavi M, Kulinski J, Ayers CR, et al. Association between number of live births and markers of subclinical atherosclerosis: The Dallas Heart Study. Eur J Prev Cardiol, 2016; 23, 391−9. doi:  10.1177/2047487315571891
    [9] Mathieu RAt, Powell-Wiley TM, Ayers CR, et al. Physical activity participation, health perceptions, and cardiovascular disease mortality in a multiethnic population: The Dallas Heart Study. Am Heart J, 2012; 163, 1037−40. doi:  10.1016/j.ahj.2012.03.005
  • [1] CHEN Hong Yu, ZHENG Ming Yang, CHENG Qing Li, ZHAO Jia Hui, ZHENG Yan Song.  An Investigation of the Association between Metabolic Syndrome and Osteoporosis Based on Chinese Health Examination Data . Biomedical and Environmental Sciences, 2024, 37(): 1-13. doi: 10.3967/bes2024.097
    [2] WANG Yu Tong, HU Kui Ru, ZHAO Jian, AI Fei Ling, SHI Yu Lin, WANG Xue Wei, YANG Wen Yi, WANG Jing Xin, AI Li Mei, WAN Xia.  The Association between Exposure to Second-Hand Smoke and Disease in the Chinese Population: A Systematic Review and Meta-Analysis . Biomedical and Environmental Sciences, 2023, 36(1): 24-37. doi: 10.3967/bes2023.003
    [3] ZHAO Lei, JIA Ya Ning, LIU Qi Si Jing, LIU Zi Quan, LIN Hui Shu, SHUI Xin Ying, GUO Li Qiong, HOU Shi Ke.  Association between Mitochondrial DNA Methylation and Hypertension Risk: A Cross-sectional Study in Chinese Northern Population . Biomedical and Environmental Sciences, 2023, 36(10): 972-978. doi: 10.3967/bes2023.122
    [4] WANG Jia Lu, CAO Qiu Yu, XIN Zhuo Jun, LIU Shan Shan, XU Min, WANG Tian Ge, LU Jie Li, CHEN Yu Hong, WANG Shuang Yuan, ZHAO Zhi Yun, XU Yu, NING Guang, WANG Wei Qing, BI Yu Fang, LI Mian.  Association between the Neutrophil-to-lymphocyte Ratio and New-onset Subclinical Macrovascular and Microvascular Diseases in the Chinese Population . Biomedical and Environmental Sciences, 2022, 35(1): 4-12. doi: 10.3967/bes2022.002
    [5] SUN Hua Lei, LONG Shao Rong, FU San Xian, CHEN Gai Yun, WANG Ya Juan, LIANG Rui, WANG Su Fan, ZHANG Li Ke, ZHOU Li Wei, LU Quan Jun, LI Wen Jie.  Association between Vitamin D Levels and the Risk of Metabolic Syndrome in a Rural Chinese Population . Biomedical and Environmental Sciences, 2021, 34(4): 330-333. doi: 10.3967/bes2021.043
    [6] ZHAI Gang, LIN Zhong, WANG Feng Hua, WANG Yu, LI Dong, WEN Liang, DING Xiao Xia, JIANG Jing, FENG Ke Mi, LIANG Yuan Bo, XIE Cong.  Association between Mean Ocular Perfusion Pressure and Diabetic Retinopathy in a Northeastern Chinese Population . Biomedical and Environmental Sciences, 2020, 33(9): 701-707. doi: 10.3967/bes2020.091
    [7] HUANG Li Na, WANG Hui Jun, WANG Zhi Hong, DING Gang Qiang.  Association between Chinese Famine Exposure and the Risk of Overweight/Obesity and Abdominal Obesity in Laterlife: A Cross-sectional Study . Biomedical and Environmental Sciences, 2020, 33(2): 133-137. doi: 10.3967/bes2020.017
    [8] YAO Yan, LIU Hua Min, WANG Xian Wei, FENG Xia, GAO Li Jian, LI Dong, ZHOU Yong.  Effect of Body Mass Index on the Associations between Parity and Metabolic Syndrome and its Components among Northern Chinese Women . Biomedical and Environmental Sciences, 2020, 33(1): 11-18. doi: 10.3967/bes2020.002
    [9] WU Xue Yan, LIN Lin, Qi Hong Yan, DU Rui, HU Chun Yan, MA Li Na, PENG Kui, LI Mian, XU Yu, XU Min, CHEN Yu Hong, LU Jie Li, BI Yu Fang, WANG Wei Qing, NING Guang.  Association between Lipoprotein (a) Levels and Metabolic Syndrome in a Middle-aged and Elderly Chinese Cohort . Biomedical and Environmental Sciences, 2019, 32(7): 477-485. doi: 10.3967/bes2019.065
    [10] GAO Huai Quan, WANG Bang Xuan, SUN Li Li, LI Ting, WU Lu, FU Lian Guo, MA Jun.  The Mediating Effect of Body Dissatisfaction in Association between Obesity and Dietary Behavior Changes for Weight Loss in Chinese Children . Biomedical and Environmental Sciences, 2019, 32(9): 639-646. doi: 10.3967/bes2019.083
    [11] YE Yi Cong, LIU Hua Min, ZHOU Yong, ZENG Yong.  Association between Serum Alkaline Phosphatase and Carotid Atherosclerosis in a Chinese Population: A Community-based Cross-sectional Study . Biomedical and Environmental Sciences, 2019, 32(6): 446-453. doi: 10.3967/bes2019.059
    [12] ZHOU Ping An, ZHANG Chen Huan, CHEN Yan Ru, LI Dong, SONG Dai Yu, LIU Hua Min, ZHOU Ming Yue, SONG Guo Shun, CHEN Sheng Yun.  Association between Metabolic Syndrome and Carotid Atherosclerosis: A Cross-sectional Study in Northern China . Biomedical and Environmental Sciences, 2019, 32(12): 914-921. doi: 10.3967/bes2019.114
    [13] ZHAO Zhong Yao, LIU Di, CAO Wei Jie, SUN Ming, SONG Man Shu, WANG Wei, WANG You Xin.  Association between IgG N-glycans and Nonalcoholic Fatty Liver Disease in Han Chinese . Biomedical and Environmental Sciences, 2018, 31(6): 454-458. doi: 10.3967/bes2018.059
    [14] DING Li Xiang, ZHANG Yan Hong, XU Xi Zhu, ZHANG Jie, SUN Ming, LIU Di, ZHAO Zhong Yao, ZHOU Yong, ZHANG Qun, WANG You Xin.  Association between Physical Activity and Telomere Length in a North Chinese Population: A China Suboptimal Health Cohort Study . Biomedical and Environmental Sciences, 2018, 31(5): 394-398. doi: 10.3967/bes2018.051
    [15] CHEN Lin, ZHAO Qiu Ni, QIAN Xiu Rong, ZHU Bao Li, DING En Min, WANG Bo Shen, ZHANG Heng Dong, YANG Hong.  Association between the HOTAIR Polymorphism and Susceptibility to Lead Poisoning in a Chinese Population . Biomedical and Environmental Sciences, 2018, 31(6): 473-478. doi: 10.3967/bes2018.063
    [16] XU Qing, GAO Zhi Ying, LI Li Ming, WANG Lu, ZHANG Qian, TENG Yue, ZHAO Xia, GE Sheng, JING Hong Jiang, YANG Yong Tao, LIU Xiao Jun, LYU Chun Jian, MAO Lun, YU Xiao Ming, LIU Ying Hua, KONG Ai Jing, YANG Xue Yan, LIU Zhao, ZHANG Yong, WANG Jin, ZHANG Xin Sheng, XUE Chang Yong, LU Yan Ping.  The Association of Maternal Body Composition and Dietary Intake with the Risk of Gestational Diabetes Mellitus during the Second Trimester in a Cohort of Chinese Pregnant Women . Biomedical and Environmental Sciences, 2016, 29(1): 1-11. doi: 10.3967/bes2016.001
    [17] ZHANG Lei, SHEN Yun, ZHOU Jian, PAN Jie Min, YU Hao Yong, CHEN Hai Bing, LI Qing, LI Ming, BAO Yu Qian, JIA Wei Ping.  Relationship between Waist Circumference and Elevation of Carotid Intima-media Thickness in Newly-diagnosed Diabetic Patients . Biomedical and Environmental Sciences, 2014, 27(5): 335-342. doi: 10.3967/bes2014.058
    [18] SHEN Yun, ZHANG Lei, ZONG Wen Hong, WANG Zheng, ZHANG Yin, YANG Man Jing, MA Xiao Jing.  Correlation between Waist Circumference and Carotid Intima-Media Thickness in Women from Shanghai, China . Biomedical and Environmental Sciences, 2013, 26(7): 531-538. doi: 10.3967/0895-3988.2013.07.003
    [19] WANG Ou, NIE Min, HU Ying Ying, ZHANG Kui, LI Wei, PING Fan, LIU Jun Tao, CHEN Li Meng, XING Xiao Ping.  Association between Vitamin D Insufficiency and the Risk for Gestational Diabetes Mellitus in Pregnant Chinese Women . Biomedical and Environmental Sciences, 2012, 25(4): 399-406. doi: 10.3967/0895-3988.2012.04.004
    [20] WEI-YAN ZHAO, JIAN-FENG HUANG, LAI-YUAN WANG, HONG-FAN LI, PENG-HUA ZHANG, QI ZHAO, SHU-FENG CHEN, DONG-FENG GU.  Association of the Apolipoprotein B Gene Polymorphisms With Essential Hypertension in Northern Chinese Han Population . Biomedical and Environmental Sciences, 2007, 20(3): 260-264.
  • 加载中
图(2) / 表ll (5)
计量
  • 文章访问数:  638
  • HTML全文浏览量:  416
  • PDF下载量:  28
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-06-26
  • 录用日期:  2021-01-21
  • 刊出日期:  2021-05-20

Association between Parity, Carotid Plaques, and Intima Media Thickness in Northern Chinese Women

doi: 10.3967/bes2021.056
    基金项目:  This work was supported by grants from the National Natural Science Foundation of China [No. 81670294, 82070332, and 81870303] and by the Scientific Research Key Program of Beijing Municipal Commission of Education [KZ202110025033]
    作者简介:

    YAO Yan, female, born in 1981, Associate Professor, majoring in clinical intervention and basic study on cardiovascular diseases

    通讯作者: ZHANG Zhi Hui, Tel: 86-731-88618156, E-mail: zhangzhihui0869@126.com

English Abstract

YAO Yan, LIU Hua Min, FENG Xia, LI Dong, ZHOU Yong, ZHANG Zhi Hui. Association between Parity, Carotid Plaques, and Intima Media Thickness in Northern Chinese Women[J]. Biomedical and Environmental Sciences, 2021, 34(5): 416-420. doi: 10.3967/bes2021.056
Citation: YAO Yan, LIU Hua Min, FENG Xia, LI Dong, ZHOU Yong, ZHANG Zhi Hui. Association between Parity, Carotid Plaques, and Intima Media Thickness in Northern Chinese Women[J]. Biomedical and Environmental Sciences, 2021, 34(5): 416-420. doi: 10.3967/bes2021.056
  • Pregnancy is always associated with several important physiological changes in women, including sex hormone levels, glycolipid metabolism, and oxidative stress. These changes may have short-term and long-term effects on their cardiovascular system. Parity is the number of times a woman has given birth. Several studies have investigated the association between parity and risk of cardiovascular diseases in women. Prospective studies have shown a fairly low incidence of cardiovascular endpoints for parous women[1]. Therefore, it would be meaningful to assess the relationship between parity and other surrogate markers.

    Carotid atherosclerosis is widely recognized as a good marker of systemic atherosclerotic burden, including the presence of carotid plaques and the measurement of carotid intima-media thickness (IMT). It is well known that carotid atherosclerosis is a significant predictor of subsequent cardiovascular diseases such as myocardial infarction and ischemic stroke. Although the association between parity and carotid atherosclerosis has been assessed in various ethnic populations, the results are conflicting. A progressive increasing tendency was found in elderly women from the Netherlands[2], a U-shaped association was found in women from Germany[3], and a J-shaped association was observed in British women[4]. However, no significant association was found between parity and either IMT or presence of plaques in women from Finland and southern Germany[5,6]. To the best of our knowledge, the findings related to this topic have not been reported in Chinese population. Therefore, we performed a cohort study of women aged ≥ 40 years in Northern China with an aim to assess the association between parity and carotid atherosclerosis in this cross-sectional analysis.

    Participants were recruited from Jidong and Kailuan communities (Tangshan City, Hebei, Northern China) in 2010 to 2014. A total of 3,386 participants remained in the statistical analysis after excluding 8,025 males, 2,002 females aged < 40 years, and 1,082 females with missing information on parity and carotid plaques. Twenty-three nulliparous women were also excluded from the current analysis because of small sample size. The study was conducted according to the guidelines of Helsinki Declaration and was approved by the Ethics Committee of Jidong Oilfield Inc. Medical Center and Kailuan General Hospital. Written informed consent was obtained from all the participants.

    A standardized, structured questionnaire was administered to collect information on subjects’ demographic characteristics, socioeconomic status, cardiovascular risk factors, and medical history by well-trained interviewers. Parity was classified into four categories: one, two, three, and four or more (Supplementary Table S1 available in www.besjournal.com).

    Table S1.  Parity distribution in this study

    ParityFrequencyPercentageCumulative frequencyCumulative percentage
    0230.67230.67
    12,49873.282,52173.95
    263318.573,15492.52
    31865.463,34097.98
    4531.553,39399.53
    5130.383,40699.91
    620.063,40899.97
    710.033,409100.00

    The carotid artery was assessed by certified sonographers blinded to participants’ clinical characteristics. High-resolution B-mode ultrasound (Philips iU22 ultrasound system, Philips Medical Systems, Bothell, WA, USA) with a 5–12 MHz linear array transducer was used to detect plaques bilaterally on three segments: the common carotid artery, the carotid artery bifurcation, and the internal carotid artery. IMT was defined as the distance from the leading edge of the lumen-intima interface to the leading edge of the media-adventitia interface on a longitudinal image of each carotid artery. Carotid plaque was defined as a focal structure encroaching into the arterial lumen of at least 0.5 mm or 50% of the surrounding IMT value or a thickness > 1.5 mm as measured from the media-adventitia interface to the intima-lumen interface. Carotid atherosclerosis was considered as the presence of plaques at one or more sites. IMTs of the bilateral common carotid artery and the internal carotid artery were then averaged to obtain a mean IMT value for each subject. IMTs were measured twice by the same technician for subsequent assessment of interpreter reproducibility. The results were reviewed by two independent sonographers. Discrepancies between their evaluations were resolved by consensus.

    Comparisons between the parity groups were tested using one-way ANOVA for continuous variables or chi-square test for categorical variables. Ordinal variables and continuous variables with skewed distribution were compared using the nonparametric Kruskal-Wallis test. The association between parity and carotid artery plaques was examined by logistic regression models, while the association of parity with carotid artery IMT was analyzed by multivariate linear regression models. Results of logistic regression models are presented as odds ratio (OR) with 95% confidence interval (CI), and results of linear regression models are presented as unstandardized β-coefficient and standard error (SE). Potential confounders, including age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, body mass index (BMI), physical activity, antihypertensive medication, use of insulin or oral hypoglycemic drugs, use of antilipidemic agents, and oral contraceptives, were adjusted in these models. The parity of one was considered as the reference in all models. A P value of < 0.05 was considered to be statistically significant. All statistical analyses were performed with SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

    Parity in this population ranged from one to a maximum of seven births (Supplementary Table S1). Women with one birth (2,498 participants) constituted the majority (73.8%) of the population. Table 1 shows the baseline characteristics of the participants according to the parity status. Both age and age at menopause increased with increasing parity. The incidence of current smoking increased with increasing parity, while alcohol usage decreased with increasing parity. Physical activity showed a significant difference across the parity groups. Higher prevalence of hypertension, diabetes mellitus, and dyslipidemia was associated with increasing parity (all with P < 0.001). There was also a significant trend toward the usage of more antihypertensive, antidiabetic, antihyperlipidemic, and contraceptive drugs with parity (all with P < 0.001), except estrogen replacement therapy (P = 0.36). Socioeconomic status, measured by education level and mean equivalent household income, showed a statistical difference among the parity groups (all with P < 0.001). In Supplementary Table S2 (available  in  www.besjournal.com), it adds the parity group of zero.

    Table 1.  Baseline characteristics of participants stratified by parity

    CharacteristicsOverallParity groupP value
    123≥ 4
    Number, n (%)3,3862,49863318669
    Age, year52.5 ± 8.949.2 ± 6.359.4 ± 7.366.5 ± 6.971.9 ± 6.9< 0.001
    Age at menopause, year58.4 ± 7.454.5 ± 5.461.4 ± 5.666.3 ± 5.972.3 ± 6.2< 0.001
    BMI, kg/m224.5 ± 3.424.2 ± 3.325.2 ± 3.525.3 ± 3.725.2 ± 3.4< 0.001
    Current smoking, n (%)92 (2.7)57 (2.3)20 (3.2)10 (5.4)5 (7.3)0.006
    Alcohol use, n (%)84 (2.5)71 (2.8)13 (2.1)000.025
    Physical activity, n (%)< 0.001
     Inactive1,106 (32.7)831 (33.3)196 (31.0)62 (33.3)17 (24.6)
     Moderate active650 (19.2)535 (21.4)74 (11.7)29 (15.6)12 (17.4)
     Active1,630 (48.1)1,132 (45.3)363 (57.4)95 (51.1)40 (58.0)
    Hypertension, n (%)1,210 (35.7)720 (28.8)331 (52.3)115 (61.8)44 (63.8)< 0.001
    Diabetes mellitus, n (%)304 (9.0)168 (6.7)92 (14.5)32 (17.2)12 (17.4)< 0.001
    Dyslipidemia, n (%)1434 (42.4)960 (38.4)320 (50.6)115 (61.8)39 (56.5)< 0.001
    Antihypertensive medication, n (%)592 (27.8)301 (21.1)189 (37.1)69 (48.3)33 (67.4)< 0.001
    Insulin or oral hypoglycemic drug, n (%)178 (9.5)91 (7.1)61 (13.4)17 (16.0)9 (31.0)< 0.001
    Antilipemic agent, n (%)72 (2.1)41 (1.6)17 (2.7)12 (6.5)2 (2.9)< 0.001
    Oral contraceptives, n (%)119 (7.4)67 (5.3)32 (15.2)11 (12.1)9 (17.3)< 0.001
    Estrogen replacement therapy, n (%)33 (1.7)26 (2.2)5 (1.0)2 (1.2)00.361
    Education level, n (%)< 0.001
     Illiteracy/primary school280 (8.3)76 (3.0)129 (20.4)46 (24.7)29 (42.0)
     Middle/high school2,187 (64.6)1,588 (63.6)444 (70.1)121 (65.1)34 (49.3)
     College or above919 (27.1)834 (33.4)60 (9.5)19 (10.2)6 (8.7)
    Income, ¥/month, n (%)< 0.001
     ≤ ¥3,0002,444 (72.2)1721 (68.9)511 (80.7)155 (83.3)57 (82.6)
     ¥3,001–5,000813 (24.0)670 (26.8)105 (16.6)27 (14.5)11 (15.9)
     > ¥5,000129 (3.8)107 (4.3)17 (2.7)4 (2.2)1 (1.5)
    Plaques, n (%)853 (25.2)484 (19.4)215 (34.0)103 (55.4)51 (73.9)< 0.001
    Mean common carotid IMT, mm0.72 ± 0.130.70 ± 0.110.76 ± 0.130.84 ± 0.140.86 ± 0.13< 0.001
    Mean internal carotid IMT, mm0.65 ± 0.090.63 ± 0.080.67 ± 0.100.69 ± 0.100.73 ± 0.10< 0.001

    Figure 1 shows the association between parity and carotid artery plaques. Women with more births tended to have higher risk of carotid artery plaques than those with only one birth. Logistic regression analysis yielded an age-adjusted OR of 1.61 (95% CI 1.41 to 1.84) per birth. Additional adjustment for hypertension, diabetes mellitus, and dyslipidemia did not alter the association (OR 1.55, 95% CI 1.35 to 1.77). In the multivariate adjusted model, the risk of carotid artery plaques increased by 49% (95% CI 29% to 72%) per birth after full adjustment for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, use of insulin or oral hypoglycemic drugs, use of antilipidemic agents, and oral contraceptives. Supplementary Figure S1 (available  in  www.besjournal.com) shows the results of parity group of zero.

    Figure 1.  Association between parity and carotid artery plaques. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives

    As shown in Table 2, a positive association was found between parity and mean carotid artery IMT. Linear regression analysis showed an age-adjusted β of 0.024 (SE = 0.004) for common carotid IMT and 0.017 (SE = 0.003) for internal carotid IMT. In the fully adjusted model, this association remained statistically significant between parity and common carotid IMT [β (SE) = 0.016 (0.004)] and between parity and internal carotid IMT [β (SE) = 0.011 (0.003)]. Interestingly, higher parity was associated with greater IMT values for both the common carotid artery and the internal carotid artery in all the adjusted models, although this association appeared to be weaker with internal carotid IMT than with common carotid IMT for women with the same parity. If added the parity group of zero, it seemed no influences on these associations (Supplementary Table S3 available  in  www.besjournal.com).

    Table 2.  Association between parity and carotid artery IMT

    ParityModel 1Model 2Model 3Model 4
    β (SE)P valueβ (SE)P valueβ (SE)P valueβ (SE)P value
    Common carotid IMT
     10000
     20.062 (0.005)< 0.0010.017 (0.006)0.0030.010 (0.006)0.0860.005 (0.006)0.393
     30.136 (0.009)< 0.0010.056 (0.010)< 0.0010.046 (0.010)< 0.0010.039 (0.010)< 0.001
     ≥ 40.160 (0.015)< 0.0010.072 (0.015)< 0.0010.065 (0.015)< 0.0010.055 (0.015)< 0.001
     Per birth0.061 (0.003)< 0.0010.024 (0.004)< 0.0010.020 (0.004)< 0.0010.016 (0.004)< 0.001
    Internal carotid IMT
     10000
     20.037 (0.004)< 0.0010.019 (0.005)< 0.0010.016 (0.005)< 0.0010.010 (0.005)0.043
     30.059 (0.008)< 0.0010.028 (0.009)0.0020.024 (0.009)0.0060.018 (0.009)0.041
     ≥ 40.092 (0.014)< 0.0010.057 (0.015)< 0.0010.053 (0.015)< 0.0010.044 (0.015)0.003
     Per birth0.032 (0.003)< 0.0010.017 (0.003)< 0.0010.015 (0.003)< 0.0010.011 (0.003)< 0.001
      Note. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives. IMT, intima-media thickness.

    We assessed for the first time the association between parity and carotid atherosclerosis in Chinese women. We found that women with more births had increased risk of carotid plaques. A positive association was observed between parity and carotid artery IMT, and increased parity was associated with higher IMT values for both the common carotid artery and the internal carotid artery.

    Several previous studies have reported inconsistent relationships between parity and carotid atherosclerosis. Humphries et al. found a progressive increase between parity and risk of carotid artery plaques in Dutch women aged ≥ 55 years[2]. Skilton et al observed a linear association between parity and risk of carotid artery plaques in French women aged > 45 years[7]. Wolff et al. found a U-shaped association between parity and mean and maximum common carotid IMT among women aged 45–79 years in northeast Germany[3]. Sanghavi et al. reported a U-shaped relationship between parity and the risk of subclinical coronary and aortic atherosclerosis in US women aged 30–65 years[8]. In British women aged 60–79 years, a J-shaped association was found between parity and the risk of coronary heart disease[4]. However, in Finnish women aged 45–74 years and in women aged 50–81 years from southern Germany, no significant associations were observed between parity and carotid plaques or IMT[5,6]. In the present study, we found positive associations between parity and carotid artery plaques and between parity and carotid IMT. Women with more births tended to have higher risk of carotid artery plaques than those with only one birth. Higher parity was associated with greater IMT values for both the common carotid artery and the internal carotid artery. Our results agreed with the findings of Humphries in women from the Netherlands[2].

    Several reasons might explain the inconsistencies among these studies. First, the study populations were from different countries with different ethnicities, and consequently, they had specific sociodemographic characteristics, which might lead to confounding results related to the associations[9]. Second, women were enrolled at different age groups. Several studies included only elderly women, whereas some other studies included both young and old women. In addition, the composition of study population was distinct from each other. Nulliparous women constituted 21.5% of the population in the Netherlands[2], but there were 8.5% and 18.0% nulliparous women in cohorts of Germany and France[3,7], respectively. In our cohort, the majority were women with parity of one (73.8%), which might be because of specific one-child birth control policies in China.

    The present study has several limitations. First, the study participants comprised women from northern China and had specific sociodemographic characteristics; hence, the findings might not be generalized to other districts and countries. Second, there were particular family planning policies in China during the study period, such as one-child policy; therefore, the composition of study population was different from other cohorts, which might cause unavoidable bias. In addition, reproductive factors, including miscarriage, abortions, breastfeeding, pre-eclampsia, and gestational hypertension, were not assessed in the present study. Finally, the cross-sectional study design might overlook some ongoing changes in carotid IMT.

    In conclusion, we found there were positive associations between parity and carotid atherosclerosis in Chinese women. Increased parity conferred more risk of developing carotid plaques and higher values of carotid IMT, suggesting that multiparous women may experience more atherosclerotic burden.

    Table S2.  Baseline characteristics of participants stratified by parity

    CharacteristicsOverallParity groupsP value
    0123≥ 4
    Number, n (%)3,409232,49863318669
    Age, year52.5 ± 8.946.4 ± 5.149.2 ± 6.3459.4 ± 7.366.5 ± 6.971.9 ± 6.9< 0.001
    Age at menopause, year58.4 ± 7.452.9 ± 5.354.5 ± 5.461.4 ± 5.666.3 ± 5.772.3 ± 6.2< 0.001
    BMI, kg/m224.5 ± 3.422.7 ± 2.224.2 ± 3.325.2 ± 3.525.3 ± 3.725.2 ± 3.4< 0.001
    Current smoking, n (%)93 (2.7)1 (4.4)57 (2.3)20 (3.2)10 (5.4)5 (7.2)0.013
    Alcohol use, n (%)*86 (2.5)2 (8.7)71 (2.8)13 (2.1)000.011
    Physical activity, n (%)< 0.001
     Inactive1,115 (32.7)9 (39.1)831 (33.3)196 (31.0)62 (33.3)17 (24.6)
     Moderate active652 (19.1)2 (8.7)535 (21.4)74 (11.7)29 (15.6)12 (17.4)
     Active1,642 (48.2)12 (52.2)1,132 (45.3)363 (57.4)95 (51.1)40 (58.0)
    Hypertension, n (%)1,216 (35.7)6 (26.1)720 (28.8)331 (52.3)115 (61.8)44 (63.8)< 0.001
    Diabetes mellitus, n (%)304 (8.9)0168 (6.7)92 (14.5)32 (17.2)12 (17.4)< 0.001
    Dyslipidemia, n (%)1,440 (42.2)6 (26.1)960 (38.4)320 (50.6)115 (61.8)39 (56.5)< 0.001
    Antihypertensive medication, n (%)592 (17.42)2 (8.7)301 (12.1)189 (29.9)69 (37.1)33 (47.8)< 0.001
    Insulin or oral hypoglycemic drug, n (%)178 (5.22)091 (3.6)61 (9.6)17 (9.1)9 (13.0)< 0.001
    Antilipemic agent, n (%)*72 (2.11)041 (1.6)17 (2.7)12 (6.5)2 (2.9)0.002
    Oral contraceptives, n (%)119 (3.5)067 (2.7)32 (5.1)11 (5.9)9 (13.0)< 0.001
    Estrogen replacement therapy, n (%)*33 (1.7)026 (2.2)5 (1.0)2 (1.2)00.415
    Education level, n (%)< 0.001
     Illiteracy/primary school281 (8.2)1 (4.4)76 (3.0)129 (20.4)46 (24.7)29 (42.0)
     Middle/high school2,195 (64.4)8 (34.8)1,588 (63.6)444 (70.1)121 (65.1)34 (49.3)
     College or above933 (27.4)14 (60.9)834 (33.4)60 (9.5)19 (10.2)6 (8.7)
    Income, ¥/month, n (%)< 0.001
     ≤ ¥3,0002,454 (72.0)10 (43.5)1,721 (68.9)511 (80.7)155 (83.3)57 (82.6)
     ¥3,001–5,000824 (24.2)11 (47.8)670 (26.8)105 (16.6)27 (14.5)11 (15.9)
     > ¥5,000131 (3.8)2 (8.7)107 (4.3)17 (2.7)4 (2.2)1 (1.5)
    Plaques, n (%)854 (25.1)1 (4.4)484 (19.4)215 (34.0)103 (55.4)51 (73.9)< 0.001
    Mean common carotid IMT, mm0.72 ± 0.130.66 ± 0.070.70 ± 0.110.76 ± 0.130.84 ± 0.140.86 ± 0.13< 0.001
    Mean internal carotid IMT, mm0.65 ± 0.090.62 ± 0.080.63 ± 0.080.67 ± 0.100.69 ± 0.100.73 ± 0.10< 0.001
      Note. *Use Fisher's exact test.

    Table S3.  Association between parity and carotid artery IMT

    ParityModel 1Model 2Model 3Model 4
    β (SE)P valueβ (SE)P valueβ (SE)P valueβ (SE)P value
    common carotid IMT
     0−0.043 (0.025)0.084−0.042 (0.024)0.083−0.035 (0.023)0.136−0.026 (0.023)0.272
     10000
     20.062 (0.005)< 0.0010.017 (0.006)0.0030.010 (0.006)0.0770.005 (0.006)0.373
     30.136 (0.009)< 0.0010.056 (0.010)< 0.0010.046 (0.010)< 0.0010.040 (0.010)< 0.001
     ≥ 40.160 (0.015)< 0.0010.072 (0.015)< 0.0010.065 (0.015)< 0.0010.055 (0.015)< 0.001
     Per birth0.058 (0.003)< 0.0010.023 (0.004)< 0.0010.019 (0.003)< 0.0010.016 (0.003)< 0.001
    internal carotid IMT
     0−0.013 (0.019)0.489−0.013 (0.019)0.503−0.010 (0.019)0.588−0.002 (0.019)0.924
     10000
     20.037 (0.004)< 0.0010.019 (0.005)< 0.0010.016 (0.005)< 0.0010.010 (0.005)0.046
     30.059 (0.008)< 0.0010.028 (0.009)0.0020.024 (0.009)0.0060.018 (0.009)0.043
     ≥ 40.092 (0.014)< 0.0010.056 (0.015)< 0.0010.053 (0.015)< 0.0010.044 (0.015)0.003
     Per birth0.030 (0.003)< 0.0010.016 (0.003)< 0.0010.014 (0.003)< 0.0010.010 (0.003)0.001
      Note. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives.

    Figure S1.  Association between parity and carotid artery plaques. Model 1: unadjusted; Model 2: adjusted for age; Model 3: adjusted for age, hypertension, diabetes mellitus and dyslipidemia; Model 4: adjusted for age, hypertension, diabetes mellitus, dyslipidemia, education level, income, current smoking, alcohol use, BMI, physical activity, antihypertensive medication, insulin or oral hypoglycemic drug, antilipemic agent, and oral contraceptives

  • Acknowledgments We thank all enrolled participants and their family members. We also thank Dr. Honghuang Lin (Boston University School of Medicine) for the critical review of the manuscript.

参考文献 (9)

目录

    /

    返回文章
    返回