基于血清代谢组学的布鲁氏菌病特异性代谢标志物筛选与鉴别诊断模型构建

doi:10.3969/j.issn.1674-8115.2026.07.012

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (7): 938-945.doi: 10.3969/j.issn.1674-8115.2026.07.012

• 论著 · 临床研究 • 上一篇

基于血清代谢组学的布鲁氏菌病特异性代谢标志物筛选与鉴别诊断模型构建

周金萍¹, 何晓燕², 宋文³, 刘玉梅³, 黄琳⁴(), 马秀敏¹()

^1.新疆医科大学附属肿瘤医院医学检验中心，乌鲁木齐 830011
^2.新疆维吾尔自治区伊宁市第二人民医院检验科，伊宁 835000
^3.新疆医科大学附属中医医院临床检验中心，乌鲁木齐 830054
^4.上海市胸科医院/上海交通大学医学院附属胸科医院检验科，上海 200030

收稿日期:2025-12-10 接受日期:2026-04-27 出版日期:2026-07-07 发布日期:2026-07-07
通讯作者: 马秀敏，教授，博士；电子信箱：maxiumin1210@sohu.com。
黄　琳，研究员，博士；电子信箱：linhuang@shsmu.edu.cn。
作者简介:第一联系人：马秀敏，黄　琳为共同第一作者（co-first authors）。
基金资助:
上海交通大学医学院“双百人”项目(20221714);中央引导地方科技发展资金(ZYYD2024CG06);省部共建中亚高发病成因与防治国家重点实验室开放课题(SKL-HIDCA-2024-10);新疆维吾尔自治区自然科学基金(2025D01C211)

Screening of specific metabolic biomarkers for brucellosis and construction of a differential diagnostic model based on serum metabolomics

Zhou Jinping¹, He Xiaoyan², Song Wen³, Liu Yumei³, Huang Lin⁴(), Ma Xiumin¹()

^1.Medical Laboratory Center, Cancer Hospital Affiliated to Xinjiang Medical University, Urumqi 830011, China
^2.Department of Laboratory Medicine, Second People′s Hospital of Yining, Xinjiang Uyghur Autonomous Region, Yining 835000, China
^3.Clinical Laboratory Center, Affiliated Hospital of Traditional Chinese Medicine, Xinjiang Medical University, Urumqi 830054, China
^4.Department of Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China

Received:2025-12-10 Accepted:2026-04-27 Online:2026-07-07 Published:2026-07-07
Contact: Ma Xiumin, E-mail: maxiumin1210@sohu.com.
Huang Lin, E-mail: linhuang@shsmu.edu.cn.
Supported by:
“Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20221714);Central Government Guidance Fund for Local Science and Technology Development(ZYYD2024CG06);State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia Fund(SKL-HIDCA-2024-10);Xinjiang Uyghur Autonomous Region Natural Science Foundation(2025D01C211)

摘要/Abstract

摘要：

目的·分析布鲁氏菌病与类风湿关节炎患者差异代谢特征与临床指标相关性，筛选具有鉴别诊断潜力的生物标志物。方法·收集70例布鲁氏菌病患者和75例类风湿关节炎患者临床资料，比较2组患者基线资料及临床实验室指标。利用纳米颗粒增强激光解吸/电离质谱（nanoparticle-enhanced laser desorption/ionization mass spectrometry，NPELDI-MS）技术获取血清代谢图谱，筛选组间差异代谢物。通过Spearman秩相关分析，筛选布鲁氏菌病组中与临床指标显著相关的特异性代谢物，对其进行通路富集分析，并基于神经网络算法构建诊断模型。结果·与类风湿关节炎组相比，布鲁氏菌病组患者的红细胞沉降率、中性粒细胞计数和血肌酐水平均较低，而红细胞计数、血红蛋白以及反映肝脏功能的相关指标（天冬氨酸氨基转移酶、丙氨酸氨基转移酶、碱性磷酸酶和总蛋白）均较高，差异均有统计学意义（均P<0.05）。在NPELDI-MS检测获得的273个代谢特征中，筛选出组间差异显著的133个代谢特征。对133个差异代谢特征与组间差异显著的临床指标进行相关性分析，通过系统性筛选，鉴定出9个布鲁氏菌病特异性代谢物，包括牛磺酸、焦谷氨酸、尿酸、L-天冬氨酸、谷氨酰胺、苯丙氨酸、尿刊酸、磷酸二羟丙酮和焦磷酸盐。这些代谢物与肝功能指标（天冬氨酸氨基转移酶、丙氨酸氨基转移酶、碱性磷酸酶）及炎症指标（中性粒细胞）均呈显著负相关（均P<0.05）。通路富集分析结果显示，上述代谢物显著富集于组氨酸代谢，丙氨酸、天冬氨酸和谷氨酸代谢，苯丙氨酸、酪氨酸和色氨酸生物合成，苯丙氨酸代谢，以及牛磺酸与亚牛磺酸代谢等关键通路。基于9个代谢物联合构建的诊断模型在训练集中的受试者操作特征曲线下面积达0.852，在验证集中为0.860。结论·该研究建立的9种代谢物联合诊断模型在鉴别布鲁氏菌病与类风湿关节炎方面具有良好的诊断效能，可作为潜在的辅助诊断标志物组合。

关键词: 布鲁氏菌病, 类风湿关节炎, 鉴别诊断, 代谢组学

Abstract:

Objective ·To analyze the correlations between differential metabolic features and clinical indicators in patients with brucellosis and rheumatoid arthritis, and to identify biomarkers with potential for differential diagnosis. Methods ·Clinical data were collected from 70 patients with brucellosis and 75 patients with rheumatoid arthritis, and baseline characteristics and clinical laboratory indicators were compared between the two groups. Serum metabolomic profiles were obtained using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to identity differentially expressed metabolites between the groups. Furthermore, Spearman′s rank correlation analysis was performed to identify brucellosis-specific metabolites significantly correlated with clinical indicators in the brucellosis group, followed by pathway enrichment analysis. A neural network-based diagnostic model was constructed based on these metabolites. Results ·Compared with the rheumatoid arthritis group, the brucellosis group demonstrated significantly lower erythrocyte sedimentation rate, neutrophil count, and serum creatinine levels (all P<0.05). In contrast, red blood cell count, hemoglobin level, and liver function-related indicators (aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, and total protein) were all significantly higher in the brucellosis group (all P<0.05). Among the 273 metabolic features obtained by NPELDI-MS, 133 showed significant differences between the two groups. Correlation analysis of these 133 differential metabolic features with the significantly different clinical indicators identified nine brucellosis-specific metabolites: taurine, pyroglutamic acid, uric acid, L-aspartic acid, glutamine, phenylalanine, urocanic acid, dihydroxyacetone phosphate, and pyrophosphate. These metabolites showed significant negative correlations with liver function-related indicators (aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase) and the inflammatory indicator (neutrophil count) (all P<0.05). Pathway enrichment analysis demonstrated that these metabolites were significantly enriched in several key metabolic pathways, including histidine metabolism; alanine, aspartate, and glutamate metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; and taurine and hypotaurine metabolism. The diagnostic model constructed based on the combination of the nine metabolites achieved an area under the receiver operating characteristic curve of 0.852 in the training set and 0.860 in the validation set. Conclusion ·The nine‑metabolite combined diagnostic model shows favorable diagnostic efficacy for differentiating brucellosis from rheumatoid arthritis, providing a potential auxiliary biomarker panel for clinical application.

Key words: brucellosis, rheumatoid arthritis, differential diagnosis, metabolomics

中图分类号:

R378.5

周金萍, 何晓燕, 宋文, 刘玉梅, 黄琳, 马秀敏. 基于血清代谢组学的布鲁氏菌病特异性代谢标志物筛选与鉴别诊断模型构建[J]. 上海交通大学学报（医学版）, 2026, 46(7): 938-945.

Zhou Jinping, He Xiaoyan, Song Wen, Liu Yumei, Huang Lin, Ma Xiumin. Screening of specific metabolic biomarkers for brucellosis and construction of a differential diagnostic model based on serum metabolomics[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(7): 938-945.

图/表 6

参考文献 25

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Variable	Brucellosis (n=70)	Rheumatoid arthritis (n=75)	P value
ESR/(mm·h^-1)	20.500 (11.750, 33.000)	34.000 (17.000, 54.000)	0.002
CRP/(mg·L^-1)	11.500 (3.400, 28.000)	10.270 (2.690, 25.640)	0.789
WBC/(×10⁹·L^-1)	5.640 (4.325, 6.913)	6.200 (5.240, 7.920)	0.060
NEUT/(×10⁹·L^-1)	2.985 (2.168, 4.080)	3.740 (2.700, 5.240)	0.006
LYM/(×10⁹·L^-1)	1.925 (1.338, 2.508)	1.740 (1.180, 2.290)	0.227
RBC/(×10¹²·L^-1)	4.682±0.059	4.294±0.056	<0.001
Hb/(g·L^-1)	130.600±1.760	125.133±2.000	0.043
PLT/(×10⁹·L^-1)	256.000 (213.000, 303.500)	261.000 (223.000, 318.000)	0.420
AST/(U·L^-1)	21.950 (17.700, 32.600)	19.100 (15.300, 23.200)	0.004
ALT/(U·L^-1)	26.500 (17.630, 47.450)	16.800 (12.300, 24.400)	<0.001
ALP/(U·L^-1)	109.350 (89.550, 140.950)	78.200 (61.400, 101.100)	<0.001
TP/(g·L^-1)	73.201±0.930	67.943±0.632	<0.001
SCr/(μmol·L^-1)	54.300 (46.975, 62.100)	62.270 (55.540, 71.780)	<0.001
BUN/(mmol·L^-1)	5.090±0.199	5.306±0.153	0.389

Variable	Brucellosis (n=70)	Rheumatoid arthritis (n=75)	P value
ESR/(mm·h^-1)	20.500 (11.750, 33.000)	34.000 (17.000, 54.000)	0.002
CRP/(mg·L^-1)	11.500 (3.400, 28.000)	10.270 (2.690, 25.640)	0.789
WBC/(×10⁹·L^-1)	5.640 (4.325, 6.913)	6.200 (5.240, 7.920)	0.060
NEUT/(×10⁹·L^-1)	2.985 (2.168, 4.080)	3.740 (2.700, 5.240)	0.006
LYM/(×10⁹·L^-1)	1.925 (1.338, 2.508)	1.740 (1.180, 2.290)	0.227
RBC/(×10¹²·L^-1)	4.682±0.059	4.294±0.056	<0.001
Hb/(g·L^-1)	130.600±1.760	125.133±2.000	0.043
PLT/(×10⁹·L^-1)	256.000 (213.000, 303.500)	261.000 (223.000, 318.000)	0.420
AST/(U·L^-1)	21.950 (17.700, 32.600)	19.100 (15.300, 23.200)	0.004
ALT/(U·L^-1)	26.500 (17.630, 47.450)	16.800 (12.300, 24.400)	<0.001
ALP/(U·L^-1)	109.350 (89.550, 140.950)	78.200 (61.400, 101.100)	<0.001
TP/(g·L^-1)	73.201±0.930	67.943±0.632	<0.001
SCr/(μmol·L^-1)	54.300 (46.975, 62.100)	62.270 (55.540, 71.780)	<0.001
BUN/(mmol·L^-1)	5.090±0.199	5.306±0.153	0.389

No.	Variable	Metabolite	Brucellosis		Rheumatoid arthritis
No.	Variable	Metabolite	r	P value (FDR-adjusted)	r	P value (FDR-adjusted)
1	NEUT	PPi	-0.346	0.021	-0.213	0.163
2	NEUT	Gln	-0.380	0.010	-0.078	0.664
3	NEUT	UA	-0.467	0.001	-0.191	0.221
4	AST	L-Asp	-0.374	0.012	-0.065	0.716
5	AST	Tau	-0.394	0.007	0.193	0.218
6	AST	pGlu	-0.403	0.006	-0.106	0.526
7	ALT	pGlu	-0.321	0.033	-0.014	0.943
8	ALT	DHAP	-0.322	0.033	0.043	0.822
9	ALT	UA	-0.332	0.027	-0.086	0.624
10	ALT	Tau	-0.425	0.003	0.130	0.431
11	ALP	UCA	-0.343	0.023	-0.134	0.414
12	ALP	Phe	-0.360	0.016	-0.044	0.813
13	ALP	pGlu	-0.362	0.015	-0.112	0.504
14	ALP	Tau	-0.384	0.009	-0.004	0.982

No.	Variable	Metabolite	Brucellosis		Rheumatoid arthritis
No.	Variable	Metabolite	r	P value (FDR-adjusted)	r	P value (FDR-adjusted)
1	NEUT	PPi	-0.346	0.021	-0.213	0.163
2	NEUT	Gln	-0.380	0.010	-0.078	0.664
3	NEUT	UA	-0.467	0.001	-0.191	0.221
4	AST	L-Asp	-0.374	0.012	-0.065	0.716
5	AST	Tau	-0.394	0.007	0.193	0.218
6	AST	pGlu	-0.403	0.006	-0.106	0.526
7	ALT	pGlu	-0.321	0.033	-0.014	0.943
8	ALT	DHAP	-0.322	0.033	0.043	0.822
9	ALT	UA	-0.332	0.027	-0.086	0.624
10	ALT	Tau	-0.425	0.003	0.130	0.431
11	ALP	UCA	-0.343	0.023	-0.134	0.414
12	ALP	Phe	-0.360	0.016	-0.044	0.813
13	ALP	pGlu	-0.362	0.015	-0.112	0.504
14	ALP	Tau	-0.384	0.009	-0.004	0.982

基于血清代谢组学的布鲁氏菌病特异性代谢标志物筛选与鉴别诊断模型构建

Screening of specific metabolic biomarkers for brucellosis and construction of a differential diagnostic model based on serum metabolomics

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