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基于纵向健康体检数据的高尿酸血症发病风险预测模型(PDF)

《现代预防医学》[ISSN:1003-8507/CN:51-1365/R]

期数:
2021年23期
页码:
4408-4412
栏目:
临床与预防
出版日期:
2021-12-15

文章信息/Info

Title:
Risk prediction model of hyperuricemia based on longitudinal health examination data
作者:
于汉成12张霁娟12刘峰3丁荔洁1梁海伦4
1.山东大学健康医疗大数据研究院,山东 济南 250012;
2.华中科技大学同济医学院公共卫生学院;
3.北京市体检中心;
4.中国人民大学公共管理学院
Author(s):
YU Han-cheng* ZHANG Ji-juan LIU Feng DING Li-jie LIANG Hai-lun
*Health and Medical Research Institute, Shandong University, Jinan, Shandong 250012, China
关键词:
高尿酸血症风险预测模型健康体检历史性队列
Keywords:
Hyperuricemia Risk prediction model Health examination Retrospective cohort
分类号:
R589.7
DOI:
-
文献标识码:
A
摘要:
目的 建立18岁及以上人群的高尿酸血症发病风险预测模型。方法 基于纵向健康体检数据,选取2008年3月至2016年7月间于北京市体检中心体检的年龄≥18岁、无重要信息缺失、首次体检时未患高尿酸血症和至少具有2条体检记录且始末体检时间间隔大于3个月的个体作为研究对象,采用Cox比例风险回归模型作为发病风险预测模型,受试者工作特征曲线(ROC)用于模型的预测效果评价,利用十折交叉验证法对模型进行内部验证。结果 本研究一共纳入98128名研究对象,随访时间为0.25~8.75年(中位随访时间2.11年),高尿酸血症的发病密度为40.83/1000人年。模型共纳入年龄、性别、基线尿酸、高密度脂蛋白、高甘油三酯血症、超重或肥胖、糖调节受损或糖尿病、高血压8个变量。模型的ROC曲线下面积(AUC)为0.84(95%CI:0.83~0.85),十折交叉验证后AUC平均值为0.84。结论 所构建的模型在健康体检人群中具有较好的预测能力。
Abstract:
Objective To establish a risk prediction model for hyperuricemia in the population aged 18 and above . Methods Based on the longitudinal health examination data, individuals aged 18 and above who had physical examinations in Beijing Physical Examination Center between March 2008 and July 2016, with no important information missing, had at least 2 records and had not suffered from hyperuricemia at the first physical examination were included as research objects . The Cox proportional hazard regression model was used as the risk prediction model . The receiver operating characteristic curve (ROC) was used to evaluate the predictive effect of the model, and the model was internally verified by the ten - fold cross - validation . Results A total of 98 128 subjects were enrolled in this study . The median follow - up time was 2 . 11 years (0 . 25 ~ 8 . 75 years) . The incidence density of hyperuricemia was 40 . 83/1000 person- years . The model included 8 variables: age, sex, uric acid at baseline, high-density lipoprotein, hypertriglyceridemia, overweight or obesity, impaired glucose regulation or diabetes, and high systolic blood pressure . The area under the ROC(AUC) was 0 . 84 (95% CI 0 . 83 ~ 0 . 85), and the average AUC was also 0 . 84 after the ten - fold cross - validation . Conclusion The risk prediction model for hyperuricemia has good predictive ability in the population receiving medical examinations .

参考文献/References

[1] 中华医学会内分泌学分会 . 中国高尿酸血症与痛风诊疗指南 (2019)[J]. 中华内分泌代谢杂志 ,2020,36(1):1 - 2.

[2] Liu R, Han C, Wu D, et al. Prevalence of hyperuricemia and gout in mainland China from 2000 to 2014: a systematic review and Meta - Analysis[J]. BioMed Research International, 2015, (11): 762820.

[3] Son M, Seo J, Yang S. Association between dyslipidemia and serum uric acid levels in Korean adults: Korea National Health and Nutrition Examination Survey 2016 - 2017[J]. PLOS One, 2020, 15(2): e0228684.

[4] Stewart DJ, Langlois V, Noone D. Hyperuricemia and hypertension: links and risks [J]. Integrated Blood Pressure Control, 2019, 12(1): 43 - 62.

[5] Gagliardi AC, Miname MH, Santos RD. Uric acid: A marker of increased cardiovascular risk[J]. Atherosclerosis, 2009, 202(1): 11 - 17.

[6] Liu J, Tao L, Zhao Z, et al. Two - Year changes in hyperuricemia and risk of diabetes: a Five - Year prospective cohort study[J]. Journal of Diabetes Research, 2018, (12): 6905720.

[7] 徐晓菲 , 姜宝法 , 张源潮 , . 山东沿海地区人群血尿酸水平及其在痛风筛检中的意义 [J]. 山东医科大学学报 ,2000,38(1): 39 - 40, 43.

[8] 梁冰倩 , 黄志碧 , 赖银娟 , . 随机森林模型和 logistic 回归模型在高尿酸血症预测中的应用效果比较 [J]. 广西医学 ,2020,42 (6):729 - 733.

[9] 诸骏仁 , 高润霖 , 赵水平 , . 中国成人血脂异常防治指南 (2016 年修订版 )[J]. 中国循环杂志 ,2016,44(10):937 - 953.

[10] 中国肥胖问题工作组数据汇总分析协作组 . 我国成人体重指数和腰围对相关疾病危险因素异常的预测价值 : 适宜体重指数和腰围切点的研究 [J]. 中华流行病学杂志 ,2002,23(1):5 - 10.

[11] 中华医学会糖尿病学分会 . 中国 2 型糖尿病防治指南 (2017 年版 )[J]. 中国实用内科杂志 ,2018,38(4):292 - 344.

[12] 中国高血压防治指南修订委员会 , 高血压联盟 , 中华医学会心血管病学分会 , . 中国高血压防治指南 (2018 年修订版 ) [ J]. 中国心血管杂志 ,2019,24(1):24 - 56.

[13] 孙苑潆 , 杨亚超 , 曲明苓 , . 健康管理人群代谢综合征发病风 险预测模型[J]. 山东大学学报:医学版,2017,55(6):87 - 92.

[14] Ford ES, Li CY, Cook S, et al. Serum concentrations of uric acid and the metabolic syndrome among US children and adolescents [J]. Circulation, 2007, 115(19): 2526 - 2532.

[15] Zitt E, Fischer A, Lhotta K, et al. Sex - and age - specific variations, temporal trends and metabolic determinants of serum uric acid concentrations in a large population - based Austrian cohort[J]. Scientific Reports, 2020, 10(1): 7578.

[16] Kim Y, Kang J, Kim GT. Prevalence of hyperuricemia and its associated factors in the general Korean population: an analysis of a population - based nationally representative sample [ J]. Clinical Rheumatology, 2018, 37(9): 2529 - 2538.

[17] Huang XB, Zhang WQ, Tang WW, et al. Prevalence and associated factors of hyperuricemia among urban adults aged 35 - 79 years in southwestern China: a community - based cross - sectional study[J]. Scientific Reports, 2020, 10(1): 15683.

[18] Zhang X, Meng Q, Feng J, et al. The prevalence of hyperuricemia and its correlates in Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China[J]. Lipids in Health and Disease, 2018, 17(1): 235.

[19] 唐小芬 , 仇小强 , 曾小云 , . 广西 35 ~ 74 岁壮族居民高尿酸血症患病情况及其影响因素分析 [ J]. 广西医科大学学报 ,2021, 38(3):583 - 590.

[20] Mcadams - Demarco MA, Law A, Maynard JW, et al. Risk factors for incident hyperuricemia during mid - adulthood in African American and white men and women enrolled in the ARIC cohort study[J]. BMC Musculoskeletal Disorders, 2013, 14: 347.

[21] Xu J, Peng H, Ma QH, et al. Associations of non - high density lipoprotein cholesterol and traditional blood lipid profiles with hyperuricemia among middle - aged and elderly Chinese People: a community - based cross - sectional study [ J]. Lipids in Health and Disease, 2014, 13: 117.

[22] Trifio G, Morabito P, Cavagna L, et al. Epidemiology of gout and hyperuricaemia in Italy during the years 2005 - 2009: a nationwide population - based study[ J]. Annals of the Rheumatic Diseases, 2013, 72(5): 694 - 700.

[23] 彭文慧 , 赖石凤 , 陈悦 , . 中山市高尿酸血症的流行概况及其危险因素分析 [J]. 现代预防医学 ,2020,47(18):3418 - 3421, 3426.

[24] 许敏锐 , 强德仁 , 周义红 , . 常州农村社区 35 ~ 70 岁人群代谢综合征及其组分与高尿酸血症的相关性研究 [ J]. 现代预防医 ,2020,47(9):1607 - 1611.

[25] 林春蕾 . 广西南宁地区 40 岁以上人群高尿酸血症与 2 型糖尿病患病?病关系的队列研究 [D]. 南宁 : 广西医科大学 ,2019.

[26] 付佐娣 , 赵子厚 , 王连英 , . 北京社区人群高尿酸血症患病率与肥胖关系的研究 [J]. 中国糖尿病杂志 ,2021,29(1):30 - 34.

[27] Chang HY, Pan WH, Yeh WT, et al. Hyperuricemia and gout in Taiwan: results from the Nutritional and Health Survey in Taiwan (1993 - 96) [ J]. The Journal of Rheumatology, 2001, 28 (7): 1640 - 1646.

[28] Li C, Hsieh MC, Chang SJ. Metabolic syndrome, diabetes, and hyperuricemia[ J]. Current Opinion in Rheumatology, 2013, 25 (2): 210 - 216.

[29] 孙萌潞 , 徐锦江 , 祝春梅 , . 基于倾向性评分分析高尿酸血症对糖尿病发病风险的影响 [ J]. 现代预防医学 ,2021,48 (15): 2866 - 2869.

[30] Li Q, Yang Z, Lu B, et al. Serum uric acid level and its association with metabolic syndrome and carotid atherosclerosis in patients with type 2 diabetes [J]. Cardiovascular Diabetology, 2011, 10: 72.

[31] Tuomilehto J, Zimmet P, Wolf E, et al. Plasma uric acid level and its association with diabetes mellitus and some biologic parameters in a biracial population of Fiji [J]. American Journal of Epidemiology, 1988, 127(2): 321 - 336.

[32] Herman JB, Medalie JH, Goldbourt UD. Prediabetes and uricaemia[J]. Diabetologia, 1976, 12(1): 47 - 52.

[33] Zhang Y, Zhang M, Yu X, et al. Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8 - year prospective cohort study [J]. Journal of Translational Medicine, 2020, 18(1): 409.


备注/Memo

备注/Memo:
基金项目:北京市社会科学基金(18GLC063)
作者简介:于汉成(1998—),男,硕士在读,研究方向:慢性病流行病学
通讯作者:梁海伦,E-mail: hliang@ruc.edu.cn
更新日期/Last Update: 2021-12-13