Evidence map›Paper›PMID 41245891›Full record

ReviewComputational and structural biotechnology journal2025

Metabolic phenotypes: Molecular bridges between health homeostasis and disease imbalance.

Qiang Yang, Ying Cai, Yu Guan, Zhibo Wang, Sifan Guo, Shi Qiu, Aihua Zhang

Abstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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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

7 authors.

Qiang YangGAP Center and Graduate School, College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China.
Ying CaiGAP Center and Graduate School, College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China.
Yu GuanInternational Advanced Functional Omics Platform, School of Chinese Medicine, Scientific Experiment Center, Hainan Medical University, Xueyuan Road 3, Haikou 571199, China.
Zhibo WangGAP Center and Graduate School, College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China.
Sifan GuoGAP Center and Graduate School, College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China.
Shi QiuInternational Advanced Functional Omics Platform, School of Chinese Medicine, Scientific Experiment Center, Hainan Medical University, Xueyuan Road 3, Haikou 571199, China.
Aihua ZhangGAP Center and Graduate School, College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic phenotypes represent the overall characterization of an individual's metabolites at a specific point in time. They precisely reflect the complex interactions among genetic background, environmental factors, lifestyle, and gut microbiome, thereby serving as a key molecular link between healthy homeostasis and disease-related metabolic disruption. In recent years, high-throughput metabolomics strategies have enabled the systematic analysis of small molecule metabolites in physiological and pathological processes. These metabolites not only serve as biomarkers for disease diagnosis, prognosis assessment, and treatment response prediction, but also elucidate novel mechanistic pathways in disease progression. The high-coverage, high-sensitivity detection of metabolites afforded by mass spectrometry and NMR-based metabolomics enables advances in precision medicine, facilitating biomarker discovery, pharmacokinetic studies, and the assessment of nutritional interventions. This review uses several common metabolic diseases, such as obesity, diabetes, cardiovascular diseases, and cancer, to explore the key role of metabolic phenotypes in disease risk stratification and precise prediction. Future phenotypic research will shift toward integrating artificial intelligence, big data mining, and multi-omics with the goal of revealing the complete network through which metabolic phenotypes regulate diseases. This research is expected to advance early diagnosis, precise prevention, and targeted treatment, contributing to a medical paradigm shift from disease treatment to health maintenance.

Indexed as

Biomarker discoveryrHealth maintenanceHealthy homeostasisMetabolic disruptionMetabolic phenotype

Identifiers

PMID41245891
PMCPMC12615335

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.