Evidence map›Paper›PMID 39695671›Full record

ArticleNutrition & metabolism2024

An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern.

Xiaoli Hu, Qingjun Xu, Xuan Ma, Lin Li, Yongning Wu, Feifei Sun

RetractedAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in Nutrition & metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Xiaoli HuAnimal-Derived Food Safety Innovation Team, College of Veterinary Medicine, Anhui Agricultural University, Hefei, 230036, People's Republic of China.
Qingjun XuAnimal-Derived Food Safety Innovation Team, College of Veterinary Medicine, Anhui Agricultural University, Hefei, 230036, People's Republic of China.
Xuan MaAnimal-Derived Food Safety Innovation Team, College of Veterinary Medicine, Anhui Agricultural University, Hefei, 230036, People's Republic of China.
Lin LiAnimal-Derived Food Safety Innovation Team, College of Veterinary Medicine, Anhui Agricultural University, Hefei, 230036, People's Republic of China. lilin@ahau.edu.cn.
Yongning WuNHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100017, China.
Feifei SunAnimal-Derived Food Safety Innovation Team, College of Veterinary Medicine, Anhui Agricultural University, Hefei, 230036, People's Republic of China. 13141212481@163.com.

Funding

Animal-Derived Food Safety Innovation Team ANRC2021040National Natural Science Foundation of China No. 2240061359, 22193064Natural Science Foundation of Anhui Province No. 2408085MC067Natural Science Research Project of Anhui Educational Committee No. 2024AH050461
6 · The paper itself

Abstract

Intermittent fasting is currently a highly sought-after dietary pattern. To explore the potential biomarkers of intermittent fasting, untargeted metabolomics analysis of fecal metabolites in two groups of mice, intermittent fasting and normal feeding, was conducted using UPLC-HRMS. The data was further analyzed through interpretable machine learning (ML) to data mine the biomarkers for two dietary patterns. We developed five machine learning models and results showed that under three-fold cross-validation, Random Forest model was the most suitable for distinguishing the two dietary patterns. Finally, Shapely Additive exPlanations (SHAP) were explored to perform a weighted explanatory analysis on the Random Forest model, and the contribution of each metabolite to the model was calculated. Results indicated that Ganoderenic Acid C is the potential biomarkers to distinguish the two dietary patterns. Our work provides new insights for metabolic biomarker analysis and lays a theoretical foundation for the selection of a healthieir dietary lifestyle.

Indexed as

BiomarkersIntermittent fastingMachine learningUntargeted metabolomics

Identifiers

PMID39695671
PMCPMC11658198

What OpenQuestion 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 OpenQuestion graph.