Evidence map›Paper›PMID 40212731›Full record

ArticleJournal of inflammation research2025

Development and Validation of a Predictive Model Using Inflammatory Biomarkers for Active Tuberculosis Risk in Diabetic Patients.

Xuan Zhang, Haiyan Fu, Jie Li, Junfang Yan, Jingjing Huang, Zhaoyuan Xu, Mingwu Li, Mengni Qian, Lifeng Wang, Hongjuan Li and 1 more

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Correlation Between CD38Diabetes, metabolic syndrome and obesity : targets and therapy · 2025
    Article
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xuan Zhang *Cardiology Department, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.
Haiyan Fu *Yunnan Infectious Disease Clinical Medical Center, Kunming, Yunnan, People's Republic of China.
Jie LiCardiology Department, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.
Junfang YanCardiology Department, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.
Jingjing HuangMedical Record Department, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.
Zhaoyuan XuCardiology Department, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.
Mingwu LiYunnan Infectious Disease Clinical Medical Center, Kunming, Yunnan, People's Republic of China.
Mengni QianHospice Care Center, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.
Lifeng WangHospice Care Center, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.
Hongjuan LiYunnan Infectious Disease Clinical Medical Center, Kunming, Yunnan, People's Republic of China.
Yingrong DuCardiology Department, The 3rd People's Hospital of Kunming, Kunming, Yunnan, People's Republic of China.ORCID 0000-0001-5872-865X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: Exploring the value of inflammatory markers in diagnosing active pulmonary tuberculosis in diabetics. Patients and Methods: Routine clinical indicators and a range of inflammatory markers were assessed in 276 diabetic patients (DM) and 276 patients with diabetes mellitus combined with active tuberculosis (DM-PTB) from Kunming, Yunnan Province, China. Differences between indicators were compared between the two groups, and factors influencing the susceptibility of diabetic patients to active tuberculosis were analyzed. A novel predictive model was constructed by combining inflammatory and lipid markers using R-Studio in a pioneering manner, and the efficacy of the predictive model was assessed using Calibration Curve and other methods in a multifaceted manner. Results: Univariate analysis showed that clinical markers including triglycerides, leukocytes, neutrophils, lymphocytes, monocytes, and platelets; inflammatory markers including the neutrophil-to-lymphocyte ratio (NLR), neutrophil to high-density lipoprotein ratio (NHR), platelet-to-lymphocyte ratio (PLR), platelet-to-neutrophil ratio (PNR), platelet-to-monocyte ratio (PMR), monocyte to high-density lipoprotein ratio (MHR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate inflammation systemic index (AISI), neutrophil-to-monocyte ratio (NMR), and lymphocyte-to-monocyte ratio (LMR) showed significant differences. Specifically, triglyceride, PNR, PMR, MHR, and MLR are risk factors for the development of PTB in DM patients. The model for predicting DM-PTB using a combination of indicators has a high sensitivity (75.0%) and specificity (81.9%). Conclusion: Triglycerides, PNR, PMR, MHR, and MLR were identified as influential factors in the progression to PTB in diabetic patients. The combined application of these indicators provides an economical, convenient and direct method for early identification of diabetic patients susceptible to Mycobacterium tuberculosis infection.

Indexed as

active pulmonary tuberculosisdiabetes mellitusinflammatory markersinfluencing factorspredictive modeling

Identifiers

PMID40212731
PMCPMC11981873

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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 OpenQuestion graph.