Evidence map›Paper›PMID 41427027›Full record

ArticleHuman mutation2025

Metabolic and Immune Adaptations in Preterm Neonates at Early Postnatal Period: Integrated Analysis of Key Metabolites and Pathways.

Xiaofan Li, Yue Gan, Lan Tan, Yuxi Lin, Pengxi Zhou, Jue Wang, Bing Yang, Quan Tang

Abstract read
In one paragraph

Article in Human mutation, 2025. 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

The trial behind it

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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

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

8 authors.

Xiaofan LiCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China, szu.edu.cn.ORCID 0009-0009-6303-9221
Yue GanCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China, szu.edu.cn.
Lan TanCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China, szu.edu.cn.
Yuxi LinCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China, szu.edu.cn.
Pengxi ZhouCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China, szu.edu.cn.
Jue WangShenzhen Institute for Drug Control, Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China.
Bing YangDepartment of Cell Biology, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China, tijmu.edu.cn.ORCID 0000-0002-0408-4518
Quan TangResearch Laboratory, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.ORCID 0009-0002-3870-8082

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study was aimed at clarifying the unique metabolic alterations in preterm neonates, distinct from full-term neonates, between the first 24 and 48 h postnatally. Methods: A cohort of 60 preterm and 60 full-term neonates was analyzed. The metabolomic profiles of plasma samples were determined using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Multivariate statistical analyses were employed to discern metabolic differences. Multiple machine learning models were constructed to further select key metabolites. Spearman's correlation analysis was performed to assess the correlation between neonatal immune cell subsets and key metabolites. Results: The study revealed 70 specific metabolic alterations in preterm neonates during the early postnatal period. Then, 32 of these metabolites were jointly selected by the Top 5 machine learning models, which exhibited high predictive performance with an AUC > 0.9. Subsequent analyses including multivariable linear regression and ROC curve revealed 12 key metabolites significantly associated with gestational age. Correlation analyses exposed significant associations between immune cells and these metabolites. Integrated pathway analysis identified 10 key metabolic pathways involved in preterm neonates. NMR-based validation confirmed two of the 12 prioritized metabolites and six additional metabolites from the broader panel, supporting the robustness of our findings. Conclusion: Our findings provide insights into the metabolic and immune adaptation processes in preterm neonates during the early life stage. The correlations between immune cell subsets and the key metabolites highlight a potential effect of metabolism on immune adaptation in preterm neonates. These key metabolites and pathways could serve as potential biomarkers for early diagnosis and therapeutic strategies to enhance immune function and health outcomes in preterm infants.

Indexed as

Adaptation, PhysiologicalInfant, PrematureMetabolic Networks and PathwaysMetabolomeMetabolomicsBiomarkersFemaleGestational AgeHumansInfant, NewbornMachine LearningMaleBiomarkersfull-term neonatesimmunological adaptationsmetabolomic profilingpostnatal adaptationpreterm neonates

Identifiers

PMID41427027
PMCPMC12714169

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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.