Evidence map›Paper›PMID 36091252›Full record

ArticleFrontiers in nutrition2022

Analysis of serum fatty acid, amino acid, and organic acid profiles in gestational hypertension and gestational diabetes mellitus

Xiangju Kong, Qiushuang Zhu, Yuanjie Dong, Yuqiao Li, Jinxiao Liu, Qingna Yan, Mingli Huang, Yucun Niu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 6% of its field
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Observational
  6. Review
  7. Article
  8. Observational
  9. Article
  10. Coagulation indices for predicting hypertensive pregnancy disorders.American journal of translational research · 2025
    Article
  11. Article
  12. Review
  13. Article
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 at 1 institution in 1 country.

Xiangju KongDepartment of Gynaecology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Qiushuang ZhuDepartment of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China.
Yuanjie DongDepartment of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China.
Yuqiao LiDepartment of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China.
Jinxiao LiuDepartment of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China.
Qingna YanDepartment of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China.
Mingli HuangDepartment of Gynaecology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Yucun NiuDepartment of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to characterize metabolite differences and correlations between hypertensive disorders of pregnancy (HP) and gestational diabetes mellitus (GDM) using univariate, multivariate analyses, RF, and pathway analyses in a cross-sectional study. Dietary surveys were collected and targeted metabolomics was applied to measure levels of serum fatty acids, amino acids, and organic acids in 90 pregnant women at 24-28 weeks gestation at the First Affiliated Hospital of Harbin Medical University. Principal components analysis (PCA) and partial least squares-discriminatory analysis (PLS-DA) models were established to distinguish HP, GDM, and healthy, pregnant control individuals. Univariate and multivariate statistical analyses and Random Forest (RF) were used to identify and map co-metabolites to corresponding pathways in the disease states. Finally, risk factors for the disease were assessed by receiver operating characteristics (ROC) analysis. Dietary survey results showed that HP and GDM patients consumed a high-energy diet and the latter also consumed a high-carbohydrate and high-fat diet. Univariate analysis of clinical indices revealed HP and GDM patients had glycolipid disorders, with the former possessing more severe organ dysfunction. Subsequently, co-areas with significant differences identified by basic discriminant analyses and RF revealed lower levels of pyroglutamic acid and higher levels of 2-hydroxybutyric acid and glutamic acid in the GDM group. The number of metabolites increased in the HP group as compared to the healthy pregnant control group, including pyroglutamic acid, γ-aminobutyric acid (GABA), glutamic acid, oleic acid (C18:1), and palmitic acid (C16:0). ROC curves indicated that area under curve (AUC) for pyroglutamic acid in the GDM group was 0.962 (95% CI, 0.920-1.000), and the AUC of joint indicators, including pyroglutamic acid and GABA, in the HP group was 0.972 (95% CI, 0.938-1.000). Collectively, these results show that both GDM and HP patients at mid-gestation possessed dysregulated glucose and lipid metabolism, which may trigger oxidative stress

Indexed as

amino acidfatty acidgestational diabetes mellitushypertensive disorders of pregnancymetabolomicsorganic acid

Identifiers

PMID36091252
PMCPMC9458889
OpenAlexW4293224404

What OpenQuestion holds

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LicenceCC BY
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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.