Evidence map›Paper›PMID 36992941›Full record

ArticlePeerJ2023

Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity.

Ali Oghabian, Birgitta W van der Kolk, Pekka Marttinen, Armand Valsesia, Dominique Langin, W H Saris, Arne Astrup, Ellen E Blaak, Kirsi H Pietiläinen

Open access · goldFull text readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 36% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. 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

9 authors at 7 institutions in 5 countries.

Ali Oghabian *Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Birgitta W van der Kolk *Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Pekka MarttinenHelsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
Armand ValsesiaNestlé Institute of Health Sciences, Lausanne, Switzerland.
Dominique LanginDepartment of Biochemistry, Toulouse University Hospitals, Toulouse, France.
W H SarisDepartment of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.
Arne AstrupHealthy Weight Center, Novo Nordisk Fonden, Copenhagen, Denmark.
Ellen E BlaakDepartment of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.
Kirsi H PietiläinenObesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Maastricht University · NLUniversity of Helsinki · FIHelsinki Institute for Information Technology · FIHelsinki University Hospital · FINestlé (Switzerland) · CHNovo Nordisk Foundation · DKUniversité Toulouse III - Paul Sabatier · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Weight loss effectively reduces cardiometabolic health risks among people with overweight and obesity, but inter-individual variability in weight loss maintenance is large. Here we studied whether baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss success. Methods: Within the 8-month multicenter dietary intervention study DiOGenes, we classified a low weight-losers (low-WL) group and a high-WL group based on median weight loss percentage (9.9%) from 281 individuals. Using RNA sequencing, we identified the significantly differentially expressed genes between high-WL and low-WL at baseline and their enriched pathways. We used this information together with support vector machines with linear kernel to build classifier models that predict the weight loss classes. Results: Prediction models based on a selection of genes that are associated with the discovered pathways 'lipid metabolism' (max AUC = 0.74, 95% CI [0.62-0.86]) and 'response to virus' (max AUC = 0.72, 95% CI [0.61-0.83]) predicted the weight-loss classes high-WL/low-WL significantly better than models based on randomly selected genes (

Indexed as

Diet, ReducingObesityGene ExpressionHumansLipidsSubcutaneous FatWeight LossLipidsBioinformaticsClassificationGene expressionMachine learningObesityPredictionRNA sequencingSubcutaneous adipose tissueTranscriptomicsWeight loss

Identifiers

PMID36992941
PMCPMC10042157
OpenAlexW4360860756

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read44
table measurements read7
identifiers read17
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.