Evidence mapPaperPMID 42345832Full record

ReviewBiology2026

The Programmable Microbiome: Integrative AI and Multi-Omics Frameworks for Precision T2DM Management.

Barlina Konwar, Kwang-Sun Kim

Abstract readReview
In one paragraph

Review in Biology, 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

2 authors.

Barlina KonwarDepartment of Chemistry and Chemistry Institute of Functional Materials, Pusan National University, Busan 46241, Republic of Korea.
Kwang-Sun KimDepartment of Chemistry and Chemistry Institute of Functional Materials, Pusan National University, Busan 46241, Republic of Korea.ORCID 0000-0003-3703-5461

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The gut microbiota is recognized as a programmable metabolic organ that governs systemic homeostasis. Recent advances (2023-2025) have pivoted Type 2 Diabetes Mellitus (T2DM) research from a host-centric perspective toward a failure of bidirectional host-microbe metabolic flux. This review evaluates the molecular mechanisms underpinning this shift, focusing on microbial metabolite signaling, virome-mediated modulation, and the emergence of drug-microbiome interactions as critical therapeutic variables. We highlight the transformative role of AI-guided mapping and digital twin simulations in modeling high-resolution metabolic flux and predicting the stability of engineered microbial consortia. By integrating meta-transcriptomics and epigenomics, we characterize the functional plasticity of the microbiome under therapeutic stress. We argue that framing the microbiota as a programmable infrastructure-integrated with AI analytics and metabolic engineering-enables adaptive, real-time interventions. This synthesis offers a blueprint for transitioning from correlative observations toward precision microbiome engineering to achieve sustained metabolic resilience.

Indexed as

artificial intelligencedigital twinmicrobiome engineeringpharmaco-microbiomicsprecision medicineType 2 Diabetes Mellitus

Identifiers

PMID42345832
PMCPMC13295491

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

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.