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[1]刘建广,贾依丹,牛文彦.探索尿沉渣筛查糖尿病患者尿路感染最优截断值[J].天津医科大学学报,2026,32(03):260-264.[doi:10.20135/j.issn.1006-8147.2026.03.0260]
 LIU Jianguang,JIA Yidan,NIU Wenyan.Exploring the optimal cut-off value for urinalysis sediment screening of urinary tract infections in diabetic patients[J].Journal of Tianjin Medical University,2026,32(03):260-264.[doi:10.20135/j.issn.1006-8147.2026.03.0260]
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探索尿沉渣筛查糖尿病患者尿路感染最优截断值(PDF)

《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:
32卷
期数:
2026年03期
页码:
260-264
栏目:
临床医学
出版日期:
2026-05-20

文章信息/Info

Title:
Exploring the optimal cut-off value for urinalysis sediment screening of urinary tract infections in diabetic patients
文章编号:
1006-8147(2026)03-0260-05
作者:
刘建广1贾依丹1牛文彦12
1.天津医科大学朱宪彝纪念医院检验科,天津市内分泌研究所,国家卫健委激素与发育重点实验室,天津市代谢性疾病重点实验室,天津300134;2.天津医科大学基础医学院免疫系,天津300070
Author(s):
LIU Jianguang1JIA Yidan1NIU Wenyan12
1.Department of Clinical Laboratory ,NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Dis-eases, Chu Hsien-I Memorial Hospital&Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134,China;2.De-partment of Immunology, College of Basic Medical Sciences, Tianjin Medical University, Tianjin 300134,China
关键词:
尿路感染白细胞计数细菌计数截断值
Keywords:
urinary tract infection white blood cell count bacterial count cut-off value
分类号:
R446.12
DOI:
10.20135/j.issn.1006-8147.2026.03.0260
文献标志码:
A
摘要:
目的:分析糖尿病患者尿路感染的危险因素,探索尿沉渣白细胞与细菌计数用于诊断糖尿病患者尿路感染的最优截断值。方法:选取2023年10月至2024年1月做尿沉渣检测的糖尿病患者1303例为研究对象,同时进行传统的尿液细菌培养和尿沉渣分析。根据尿培养结果将研究对象分成尿培养阴性组(n=1058)与尿培养阳性组(n=146)。比较两组基线资料和实验结果,多因素Logistic回归筛选糖尿病患者尿路感染危险因素,绘制受试者工作特征(ROC)曲线评估危险因素的预测能力。并以尿沉渣白细胞和细菌计数结果绘制ROC曲线,寻找最优截断值。结果:年龄(OR=1.056,P<0.001)、女性(OR=0.091,P<0.001)、白细胞(OR=1.067,P<0.001)、细菌(OR=1.206,P<0.001)与糖尿病患者尿路感染风险增加相关。白细胞计数>12.1/μL[曲线下面积(AUC)=0.929]、细菌计数>281.8/μL(AUC=0.977)是男性糖尿病患者尿路感染筛查的最优截断值;白细胞计数>60.6/μL(AUC=0.796)、细菌计数>6245.0/μL(AUC=0.926)是女性糖尿病患者尿路感染的最优截断值。结论:年龄、女性、白细胞、细菌是糖尿病患者并发尿路感染的危险因素,尿沉渣白细胞计数与细菌计数结果可用于糖尿病患者尿路感染的快速筛查。细菌计数的诊断效能优于白细胞计数。
Abstract:
Objective: To analyze the risk factors for urinary tract infections in diabetic patients and to explore the optimal cut-off values of urine sediment white blood cell and bacterial counts for the diagnosis of urinary tract infections in diabetic patients. Meth-ods: A total of 1 303 diabetic patients undergoing urine sediment analysis between October 2023 and January 2024 were enrolled. Concurrent traditional urine bacterial culture and urine sediment analysis were performed. Subjects were categorised into urine culture-negative(n=1 058) and urine culture-positive(n=146) groups based on culture results. Baseline characteristics and test outcomes were compared between groups. Multivariate Logistic regression identified urinary tract infection risk factors in diabetic patients, with receiver operating characteristic(ROC) curves plotting predictive capability.ROC curves were also plotted for urinary sediment white blood cell and bacterial count to identify optimal cut-off values. Results: Age(OR=1.056, P<0.001) , female gender(OR=0.091, P<0.001),white blood cell count(OR=1.067,P<0.001)and bacterial count(OR=1.206,P<0.001) were associated with an increased risk of urinary tract infection in diabetic patients. The optimal cut-off values for male diabetic patients were white blood cell count >12.1/μL(AUC=0.929) and BACT>281.8/μL(AUC=0.977) for UTI screening; for female diabetic patients, the optimal cutoff values were white blood cell count >60.6/μL(AUC=0.796) and bacterial count > 6 245.0/μL(AUC=0.926). Conclusion: Age, female gender, white blood cell count and bacterial count are risk factors for urinary tract infections in patients with diabetes.. Urine sedi-ment white blood cell count and bacterial count results can be used for rapid screening of UTI in diabetic patients. The diagnostic ef-ficacy of bacterial count is superior to that of white blood cell count.

参考文献/References:

[1] DUNCAN B B, MAGLIANO D J, BOYKO E J. IDF Diabetes atlas 11th edition 2025: global prevalence and projections for 2050 [J]. Nephrol Dial Transplant, 2025, 41(1): 7-9.
[2] HIRJI I, GUO Z, ANDERSSON S W, et al. Incidence of urinary tract infection among patients with type 2 diabetes in the UK Gen-eral Practice Research Database(GPRD) [J]. J Diabetes Complica-tions, 2012, 26(6): 513-516.
[3] SORESCU T, COSNITA A, BRAHA A, et al. Predictive factors for urinary tract infections in patients with type 2 diabetes [J]. J Clin Med, 2024, 13(24):7628.
[4] LJUNGBERG C, KRISTENSEN F P B, DALAGER-PEDERSEN M, et al. Risk of urogenital infections in people with type 2 diabetes initiating SGLT2is versus GLP-1RAs in routine clinical care: a Danish cohort study [J]. Diabetes Care, 2025, 48(6): 945-954.
[5] PISHDAD R, AUWAERTER P G, KALYANI R R. Diabetes, SGLT-2 inhibitors, and urinary tract infection: a review [J]. Curr Diab Rep, 2024, 24(5): 108-117.
[6] MOHANTY S, KAMOLVIT W, SCHEFFSCHICK A, et al. Diabetes downregulates the antimicrobial peptide psoriasin and increases E. coli burden in the urinary bladder [J]. Nat Commun, 2022, 13(1):4983.
[7] AAMIR A H, RAJA U Y, ASGHAR A, et al. Asymptomatic urinary tract infections and associated risk factors in Pakistani Muslim type 2 diabetic patients [J]. BMC Infect Dis, 2021, 21(1): 388.
[8] PARI B, GALLUCCI M, GHIGO A, et al. Insight on infections in diabetic setting [J]. Biomedicines, 2023, 11(3): 971.
[9] GOND D P, SINGH S, AGRAWAL N K. Testing an association be-tween TLR4 and CXCR1 gene polymorphisms with susceptibility to urinary tract infection in type 2 diabetes in north Indian population [J]. Gene, 2018, 641: 196-202.
[10] FESTA R A, OPEL M, MATHUR M, et al. Quantitative multiplex polymerase chain reaction in copies ml-1 linearly correlates with standard urine culture in colonies ml-1 for urinary tract infection (UTI)pathogens [J]. Lett Appl Microbiol, 2023, 76(8):ovad085.
[11] PAPP S B, CHRISTIE A L, ZIMMERN P E. Characteristics of na-tionwide urinary tract infection(UTI) visits by age and typeⅡdi-abetes status in women [J]. Cureus, 2023, 15(9): e46000.
[12] 薛笑楠,郑伟坤,官雯娟,等. 2型糖尿病并发尿路感染病原学和耐药性及其危险因素[J].中华医院感染学杂志, 2024, 34(5):703-706.
[13] 冯聪,杨博,梁琳琅.老年糖尿病肾病患者尿路感染的危险因素及预测模型构建[J].国际老年医学杂志, 2025, 46(1): 52-57.
[14] 胡晖,李婉媚,卢超翰.连续5年2型糖尿病合并尿路感染者病原菌分析[J].检验医学与临床, 2022, 19(10): 1400-1403.
[15] JIANG W, WANG J, SHEN X, et al. Establishment and validation of a risk prediction model for early diabetic kidney disease based on a systematic review and meta-analysis of 20 cohorts[J]. Diabetes Care, 2020, 43(4): 925-933.
[16] TOLEDO H, PUNZ魷N S G, MART魱N-GUTIéRREZ G, et al. Use-fulness of UF-5000 automatic screening system in UTI diagnosis[J]. Braz J Microbiol, 2023, 54(3): 1803-1808.
[17] 林花,路明亮,李俊虹.新型全自动尿沉渣分析仪UF-5000的诊断性能分析[J].昆明医科大学学报, 2020, 41(8): 68-71.
[18] JIM魪NEZ-GUERRA G, HERAS-CA譙AS V, VALERA-ARCAS M D, et al. Comparison between urine culture profile and morphology classification using fluorescence parameters of the sysmex UF-1000i urine flow cytometer [J]. J Appl Microbiol, 2017, 122(2): 473-480.
[19] CHEN Y, ZHANG Z, DIAO Y, et al. Combination of UC-3500 and UF-5000 as a quick and effective method to exclude bacterial uri-nary tract infection[J]. J Infect Chemother, 2023, 29(7): 667-672.
[20] 杨博,冯聪,梁琳琅.住院2型糖尿病患者合并尿路感染的危险因素及预测模型构建[J].成都医学院学报, 2024, 19(6): 985-988,992.

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备注/Memo

备注/Memo:
基金项目 国家自然科学基金资助项目(82270856);天津市医学重点学科建设项目(TJYXZDXK-3-007B)
作者简介 刘建广(1987-),男,主管技师,学士,研究方向:糖尿病肾病;通信作者:牛文彦,E-mail:wniu@tmu.edu.cn。
(2025-12-19收稿)
更新日期/Last Update: 2026-05-25