Advanced Search

CN 34-1304/RISSN 1674-3679

Volume 28 Issue 3
Mar.  2024
Turn off MathJax
Article Contents
CHEN Quan, DU Jinling, HONG Xin. Association between fasting blood glucose trajectories and new-onset cardiovascular diseases in the elderly health check-up population in Nanjing[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(3): 277-283. doi: 10.16462/j.cnki.zhjbkz.2024.03.005
Citation: CHEN Quan, DU Jinling, HONG Xin. Association between fasting blood glucose trajectories and new-onset cardiovascular diseases in the elderly health check-up population in Nanjing[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(3): 277-283. doi: 10.16462/j.cnki.zhjbkz.2024.03.005

Association between fasting blood glucose trajectories and new-onset cardiovascular diseases in the elderly health check-up population in Nanjing

doi: 10.16462/j.cnki.zhjbkz.2024.03.005
Funds:

Jiangsu Provincial Health and Health Commission 2022 Medical Research Projects M2022028

More Information
  • Corresponding author: HONG Xin, E-mail: nj_hongxin@126.com
  • Received Date: 2023-07-04
  • Rev Recd Date: 2023-09-29
  • Available Online: 2024-04-08
  • Publish Date: 2024-03-10
  •   Objective  To investigate the association between fasting blood glucose trajectory and the incidence of cardiovascular disease among the elderly population aged 65 years and above in Nanjing.  Methods  A total of 7 079 participants were included from the Nanjing elderly health checkup cohort who met the inclusion criteria. The trajectory of fasting plasma glucose(FPG) index (FPG was taken as its logarithm to make it obey normal distribution) was constructed by group-based trajectory model (GBTM) over the years from 2018 to 2020. And we followed up the incidence of cardiovascular disease in 2021. A Cox regression model was used to analyze the association between distinct trajectory groups and the incidence of cardiovascular disease.  Results  The FPG trajectory was finally divided into low-level group (5 635, 79.6%), medium-level group (1 201, 17.0%) and high-level group (243, 3.4%). A total of 70 cases of cardiovascular disease were newly reported during the follow-up period, with the low, medium, and high level groups accounting for 0.83%, 1.42%, and 2.47% of the new cases in the group, respectively. The incidence of cardiovascular disease increased with increasing FPG levels (trend χ2 =8.750, P=0.003), and the difference was statistically significant (χ2 =9.050, P=0.011). The Cox regression model showed that the risk of cardiovascular disease in the high-level group was 2.96 times higher than that in the low-level group (95% CI: 1.27-6.92).  Conclusions  Long-term high levels of FPG increase the risk of cardiovascular disease in the elderly, so timely intervention and early prevention are warranted.
  • loading
  • [1]
    胡盛寿, 高润霖, 刘力生, 等. 《中国心血管病报告2018》概要[J]. 中国循环杂志, 2019, 34(3): 209-220. DOI: 10.3969/j.issn.1000-3614.2019.03.001.

    Hu SS, Gao RL, Liu LS, et al. Summary of the 2018 report on cardiovascular diseases in China[J]. Chin Circ J, 2019, 34(3): 209-220. DOI: 10.3969/j.issn.1000-3614.2019.03.001.
    [2]
    中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2021概要[J]. 中国循环杂志, 2022, 37(6): 553-578. DOI: 10.3969/j.issn.1000-3614.2022.06.001.

    The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Summary of china cardiovascular health and disease report 2021[J]. Chin Circ J, 2022, 37(6): 553-578. DOI: 10.3969/j.issn.1000-3614.2022.06.001.
    [3]
    GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet, 2020, 396(10258): 1204-1222. DOI: 10.1016/S0140-6736(20)30925-9.
    [4]
    Zhao D, Liu J, Wang M, et al. Epidemiology of cardiovascular disease in China: current features and implications[J]. Nat Rev Cardiol, 2019, 16(4): 203-212. DOI: 10.1038/s41569-018-0119-4.
    [5]
    Roth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study[J]. J Am Coll Cardiol, 2020, 76(25): 2982-3021. DOI: 10.1016/j.jacc.2020.11.010.
    [6]
    Teo KK, Rafiq T. Cardiovascular risk factors and prevention: a perspective from developing countries[J]. Can J Cardiol, 2021, 37(5): 733-743. DOI: 10.1016/j.cjca.2021.02.009.
    [7]
    Liu S, Li Y, Zeng X, et al. Burden of cardiovascular diseases in China, 1990-2016: findings from the 2016 global burden of disease study[J]. JAMA Cardiol, 2019, 4(4): 342-352. DOI: 10.1001/jamacardio.2019.0295.
    [8]
    Goldsborough E 3rd, Osuji N, Blaha MJ. Assessment of cardiovascular disease risk: a 2022 update[J]. Endocrinol Metab Clin North Am, 2022, 51(3): 483-509. DOI: 10.1016/j.ecl.2022.02.005.
    [9]
    Han C, Liu F, Yang X, et al. Ideal cardiovascular health and incidence of atherosclerotic cardiovascular disease among Chinese adults: the China-PAR project[J]. Sci China Life Sci, 2018, 61(5): 504-514. DOI: 10.1007/s11427-018-9281-6.
    [10]
    北京高血压防治协会, 北京糖尿病防治协会, 北京慢性病防治与健康教育研究会, 等. 基层心血管病综合管理实践指南2020[J]. 中国医学前沿杂志(电子版), 2020, 12(8): 1-73. https://www.cnki.com.cn/Article/CJFDTOTAL-YXQY202008002.htm

    Beijing Hypertension Association, Beijing Diabetes Prevention and Treatment Association, Beijing Research for Chronic Diseases Control and Health Education, et al. Practice guidelines for comprehensive management of primary cardiovascular disease 2020[J]. Chinese Journal of Frontiers in Medicine (Electronic Version), 2020, 12(8): 1-73. https://www.cnki.com.cn/Article/CJFDTOTAL-YXQY202008002.htm
    [11]
    Wang L, Peng W, Zhao Z, et al. Prevalence and treatment of diabetes in China, 2013-2018[J]. JAMA, 2021, 326(24): 2498-2506. DOI: 10.1001/jama.2021.22208.
    [12]
    Zang E, Max JT. Bayesian estimation and model selection in group-based trajectory models[J]. Psychol Methods, 2022, 27(3): 347-372. DOI: 10.1037/met0000359.
    [13]
    Benjamin EJ, Virani SS, Callaway CW, et al. American heart association council on epidemiology and prevention statistics committee and stroke statistics subcommittee. Heart disease and stroke statistics-2018 update: a report from the American heart association[J]. Circulation, 2018, 137: e67-e492.
    [14]
    中国预防医学科学院, 中国吸烟与健康协会, 卫生部疾病控制司, 等. 1996年全国吸烟行为的流行病学调查[M]. 北京: 中国科学技术出版社, 1997: 155-158.
    [15]
    Jones BL, Nagin D, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories[J]. Sociol Methods Res, 2001, 29(3): 374-393. DOI: 10.1177/0049124101029003005.
    [16]
    Nina V, Mendes AG, Sevdalis N, et al. Applicability of the disruptions in surgery index in the cardiovascular management scenarios - a marker for developing functionally efficient teams[J]. Braz J Cardiovasc Surg, 2021, 36(4): 445-452. DOI: 10.21470/1678-9741-2020-0685.
    [17]
    徐文华, 刘晋, 王增武, 等. 江苏省成年人高血压患病现状及影响因素分析[J]. 中国心血管病研究, 2020, 18(9): 775-782. DOI: 10.3969/j.issn.1672-5301.2020.09.002.

    Xu WH, Liu J, Wang ZW, et al. Analysis of the current prevalence and influencing factors of hypertension among adults in Jiangsu Province[J]. Chin J Cardiovasc Res, 2020, 18(9): 775-782. DOI: 10.3969/j.issn.1672-5301.2020.09.002.
    [18]
    Falkner B, Gidding S. Life-course implications of pediatric risk factors for cardiovascular disease[J]. Can J Cardiol, 2021, 37(5): 766-775. DOI: 10.1016/j.cjca.2021.02.001.
    [19]
    Francula-Zaninovic S, Nola IA. Management of measurable variable cardiovascular disease' risk factors[J]. Curr Cardiol Rev, 2018, 14(3): 153-163. DOI: 10.2174/1573403X14666180222102312.
    [20]
    Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the american college of cardiology/american heart association task force on clinical practice guidelines[J]. Circulation, 2019, 140(11): e596-e646. DOI: 10.1161/CIR.0000000000000678.
    [21]
    Li Y, Teng D, Shi X, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American diabetes association: national cross sectional study[J]. BMJ, 2020, 369: m997. DOI: 10.1136/bmj.m997.
    [22]
    赵越, 张梅, 李伟, 等. 血糖控制不佳的2型糖尿病患者心血管病风险分析[J]. 心血管病学进展, 2020, 41(5): 551-555. DOI: 10.16806/j.cnki.issn.1004-3934.2020.05.026.

    Zhao Y, Zhang M, Li W, et al. Analysis of cardiovascular disease risk in patients with type 2 diabetes mellitus with poor glycemic control[J]. Adv Cardiovasc Dis, 2020, 41(5): 551-555. DOI: 10.16806/j.cnki.issn.1004-3934.2020.05.026.
    [23]
    Xu Y, Bi Y, Li M, et al. Significant coronary stenosis in asymptomatic Chinese with different glycemic status[J]. Diabetes Care, 2013, 36(6): 1687-1694. DOI: 10.2337/dc12-0977.
    [24]
    冯国双, 于石成, 刘世炜. 轨迹分析模型在追踪数据分析中的应用[J]. 中国预防医学杂志, 2014, 15(3): 292-295. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGYC201403036.htm

    Feng GS, Yu SC, Liu SW. Application of trajectory analysis model in tracking data analysis[J]. Chin Prev Med, 2014, 15(3): 292-295. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGYC201403036.htm
    [25]
    雷芳, 宋桂荣, 刘启贵, 等. 潜变量增长混合模型在学龄儿童体质指数变化轨迹分析中的应用[J]. 中国卫生统计, 2021, 38(4): 519-522, 527. DOI: 10.3969/j.issn.1002-3674.2021.04.009.

    Lei F, Song GR, Liu QG, et al. Application of latent variable growth mixed model in the analysis of trajectory of change in body mass index of school-aged children[J]. Chinese Journal of Health Statistics, 2021, 38(4): 519-522, 527. DOI: 10.3969/j.issn.1002-3674.2021.04.009.
    [26]
    喻嘉宏, 陈小娜, 郜艳晖, 等. 潜变量增长混合模型在医学研究中的应用[J]. 中国卫生统计, 2018, 35(4): 496-499. DOI: 10.3969/j.issn.1671-3710.2007.03.023.

    Yu JH, Chen XN, Gao YH, et al. Application of latent variable growth mixture model in medical research[J]. Chinese Journal of Health Statistics, 2018, 35(4): 496-499, DOI: 10.3969/j.issn.1671-3710.2007.03.023.
    [27]
    Kim BJ, Cho YJ, Hong KS, et al. Trajectory groups of 24-hour systolic blood pressure after acute ischemic stroke and recurrent vascular events[J]. Stroke, 2018, 49(8): 1836-1842. DOI: 10.1161/STROKEAHA.118.021117.
    [28]
    Lennon H, Kelly S, Sperrin M, et al. Framework to construct and interpret latent class trajectory modelling[J]. BMJ Open, 2018, 8(7): e020683. DOI: 10.1136/bmjopen-2017-020683.
    [29]
    王燕逍翔, 白建军, 宇传华. 基于全球视角的中国心血管病疾病负担现状及趋势[J]. 公共卫生与预防医学, 2021, 32(6): 6-11. DOI: 10.3969/j.issn.1006-2483.2021.06.002.

    Wang YYX, Bai JJ, Yu CH. Current status and trends of cardiovascular disease burden in China based on a global perspective[J]. J Public Health Prev Med, 2021, 32(6): 6-11. DOI: 10.3969/j.issn.1006-2483.2021.06.002.
    [30]
    Teo KK, Rafiq T. Cardiovascular risk factors and prevention: a perspective from developing countries[J]. Can J Cardiol, 2021, 37(5): 733-743. DOI: 10.1016/j.cjca.2021.02.009.
    [31]
    Li JJ, Liu HH, Li S. Landscape of cardiometabolic risk factors in Chinese population: a narrative review[J]. Cardiovasc Diabetol, 2022, 21(1): 113. DOI: 10.1186/s12933-022-01551-3.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)  / Tables(5)

    Article Metrics

    Article views (267) PDF downloads(58) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return