Evidence map›Paper›PMID 42840472›Full record

ReviewFrontiers in medicine2026

From microbiome to outcome: the cascading effects of combining acid suppressants with anti-tuberculosis therapy.

Shiyu Fang, Xiaoman Yang, Fengjun Liu

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Shiyu FangDepartment of Infectious Diseases, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Xiaoman YangDepartment of Infectious Diseases, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Fengjun LiuDepartment of Infectious Diseases, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastrointestinal adverse reactions occur in up to 71.8% of patients receiving anti-tuberculosis drugs (ATDs), and acid-suppressing agents are frequently co-prescribed to alleviate these symptoms. Direct evidence from human studies demonstrates that ATDs alone reduce gut microbial diversity, deplete short-chain fatty acid (SCFA)-producing bacteria, and promote opportunistic pathogen overgrowth. Concurrently, acid suppressants, particularly proton pump inhibitors (PPIs), raise intragastric pH and facilitate oral-to-gut translocation of bacteria, independently disrupting microbiota composition and metabolic pathways. In this review, we systematically examine the distinct effects of ATDs and acid suppressants on the gut microbiota, the role of the gut-lung axis in tuberculosis immunity, and, on the basis of mechanistic evidence, propose a theoretical cascade under dual exposure: synergistic microbiota depletion, dual metabolic pathway inhibition, immune homeostasis imbalance, and potentially worsened clinical outcomes. This cascade represents a hypothesis-generating framework derived from preclinical and mechanistic studies, rather than an established clinical pathway, and the critical links from dysbiosis to delayed sputum conversion, poor lesion resolution, or increased drug-induced liver injury currently lack direct human confirmation. On the basis of the available evidence, we recommend that clinicians strictly follow indications for acid suppressants, prefer H₂ receptor antagonists(H

Indexed as

acid suppressantsanti-tuberculosis therapygut-lung axisgut microbiotatuberculosis

Identifiers

PMID42840472
PMCPMC13639264

What OpenQuestion 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 OpenQuestion graph.