Evidence map›Paper›PMID 42572598›Full record

ArticleiScience2026

Second trimester serum lipidomics predicts large-for-gestational-age infants in apparently metabolically healthy pregnancies.

Jing Wang, Yixin Gong, Tong Yue, Xianming Li, Qin Wang, Tian Wei, Likun Yang, Xueying Zheng, Sihui Luo, Yu Ding and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

13 authors.

Jing WangDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Yixin GongDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Tong YueDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Xianming LiDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Qin WangDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Tian WeiAnhui Provincial Key Laboratory of Metabolic Health and Panvascular Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Likun YangAnhui Provincial Key Laboratory of Metabolic Health and Panvascular Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Xueying ZhengDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Sihui LuoDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Yu DingDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Hongbo ChenDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Jianping WengDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
Yujie LiuDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large-for-gestational-age (LGA) births occur in many pregnancies with an apparently metabolically healthy phenotype, limiting risk identification based on conventional clinical characteristics alone. We explored the association between second-trimester maternal serum lipidomic profiles and LGA risk in this apparently healthy population using a nested case-control design within an ongoing prospective pregnancy cohort. The study included a derivation cohort of 135 participants and an independent temporal validation cohort of 66 participants. Lipidomic profiles were analyzed by using liquid chromatography and high-resolution mass spectrometry. Multivariate modeling identified 11 circulating lipid biomarkers (seven glycerophospholipids, two glycerolipids, and two sphingolipids) associated with LGA risk. Integrating these lipid biomarkers with routine clinical factors substantially improved predictive performance compared with clinical variables alone. These findings suggest that metabolic alterations are detectable before clinical manifestations become apparent and support serum lipidomic profiling as a complementary approach for early risk stratification in pregnancies traditionally considered low risk.

Indexed as

large-for-gestational-agelipidomicspregnancy

Identifiers

PMID42572598
PMCPMC13453007

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.