Evidence map›Paper›PMID 39695396›Full record

ArticleClinical proteomics2024

Combined urine proteomics and metabolomics analysis for the diagnosis of pulmonary tuberculosis.

Jiajia Yu, Jinfeng Yuan, Zhidong Liu, Huan Ye, Minggui Lin, Liping Ma, Rongmei Liu, Weimin Ding, Li Li, Tianyu Ma and 2 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical proteomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Jiajia YuDepartment of Infectious Diseases and Clinical Microbiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China.
Jinfeng YuanDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Zhidong LiuDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Huan YeDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Minggui LinSchool of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, 102218, China.
Liping MaDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Rongmei LiuDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Weimin DingDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Li LiSchool of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, 102218, China.
Tianyu MaDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Shenjie TangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Yu PangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China. pangyupound@163.com.

Funding

Beijing Hospitals Authority Ascent Plan DFL20191601Beijing Hospitals Authority Clinical Medicine Development of Special Funding ZYLX202122Sponsored by Beijing Nova Program 20220484169&20230484295
6 · The paper itself

Abstract

backgroundTuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis.

methodsIn the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) patients and 40 healthy controls (HC) was collected. Biomarker panels were selected based on random forest (RF) analysis.

resultsA total of 3,868 proteins and 1,272 annotated metabolic features were detected using pairwise comparisons. Using AUC ≥ 0.80 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC for PTB/HC, PTB/CAP and PTB/LCA of 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570.

conclusionOur data demonstrate that metabolomic and proteomic analysis can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity. The receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of PTB through these urine biomarkers.

Indexed as

BiomarkersMetabolomicsProteomicsTuberculosisUrine

Identifiers

PMID39695396
PMCPMC11657435

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.