Evidence map›Paper›PMID 39452938›Full record

ArticleMetabolites2024

Causal Metabolomic and Lipidomic Analysis of Circulating Plasma Metabolites in Autism: A Comprehensive Mendelian Randomization Study with Independent Cohort Validation.

Zhifan Li, Yanrong Li, Xinrong Tang, Abao Xing, Jianlin Lin, Junrong Li, Junjun Ji, Tiantian Cai, Ke Zheng, Sai Sachin Lingampelly and 1 more

Abstract read
In one paragraph

Article in Metabolites, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Sphingolipids in Emotional Well-Being.Journal of neurochemistry · 2026
    Review
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

11 authors.

Zhifan LiBig Data and Internet of Things Program, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.ORCID 0009-0001-1852-4318
Yanrong LiCenter for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Xinrong TangYantai Special Education School, Yantai 264001, China.
Abao XingCenter for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Jianlin LinCenter for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Junrong LiBig Data and Internet of Things Program, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Junjun JiCenter for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Tiantian CaiBig Data and Internet of Things Program, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Ke ZhengBig Data and Internet of Things Program, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Sai Sachin LingampellyDepartment of Medicine, University of California, San Diego School of Medicine, San Diego, CA 92103-8467, USA.
Kefeng LiCenter for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.ORCID 0000-0002-7233-4347

Funding

Macao Polytechnic University RP/FCA-14/2023The Science and Technology Development Funds (FDCT) of Macao 0033/2023/RIB2
6 · The paper itself

Abstract

backgroundThe increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear.

methodsBidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets-comprising 1091 metabolites, 309 ratios, and 179 lipids-and three European autism datasets (PGC 2015:

resultsHigher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024-1.245;

conclusionUtilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology.

Indexed as

autism spectrum disordercausal inferencecohort validationmachine learningMendelian randomizationmetabolites

Identifiers

PMID39452938
PMCPMC11509474

What Socratic holds

Textmetadata
LicenceCC BY
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.