Evidence mapPaperPMID 38162016Full record

ArticleJournal of the Endocrine Society2023

Transcriptome Changes and Metabolic Outcomes After Bariatric Surgery in Adults With Obesity and Type 2 Diabetes.

Mamoon Rashid, Ali Al Qarni, Saeed Al Mahri, Sameer Mohammad, Altaf Khan, Mashan L Abdullah, Cynthia Lehe, Reem Al Amoudi, Omar Aldibasi, Abderrezak Bouchama

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Journal of the Endocrine Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Article
  3. Unraveling Obesity: A Five-Year Integrative Review of Transcriptomic Data.International journal of molecular sciences · 2025
    Review
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Mamoon RashidDepartment of AI and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 11426, Saudi Arabia.ORCID https://orcid.org/0000-0003-1457-477X
Ali Al QarniEndocrinology and Metabolism, Department of Medicine, King Abdulaziz Hospital, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Al Ahsa 31982, Saudi Arabia.ORCID https://orcid.org/0000-0002-2413-6637
Saeed Al MahriExperimental Medicine Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 11426, Saudi Arabia.ORCID https://orcid.org/0000-0002-9602-3623
Sameer MohammadExperimental Medicine Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 11426, Saudi Arabia.ORCID https://orcid.org/0000-0002-1129-7685
Altaf KhanDepartment of Biostatistics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
Mashan L AbdullahExperimental Medicine Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 11426, Saudi Arabia.ORCID https://orcid.org/0000-0002-4628-1536
Cynthia LeheExperimental Medicine Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 11426, Saudi Arabia.ORCID https://orcid.org/0000-0002-5536-929X
Reem Al AmoudiEndocrinology and Metabolism, Department of Medicine, King Abdulaziz Hospital, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Al Ahsa 31982, Saudi Arabia.
Omar AldibasiDepartment of Biostatistics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0002-4120-577X
Abderrezak BouchamaExperimental Medicine Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 11426, Saudi Arabia.ORCID https://orcid.org/0000-0003-3089-5936
King Saud bin Abdulaziz University for Health Sciences · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Context: Bariatric surgery has been shown to be effective in inducing complete remission of type 2 diabetes in adults with obesity. However, its efficacy in achieving complete diabetes remission remains variable and difficult to predict before surgery. Objective: We aimed to characterize bariatric surgery-induced transcriptome changes associated with diabetes remission and the predictive role of the baseline transcriptome. Methods: We performed a whole-genome microarray in peripheral mononuclear cells at baseline (before surgery) and 2 and 12 months after bariatric surgery in a prospective cohort of 26 adults with obesity and type 2 diabetes. We applied machine learning to the baseline transcriptome to identify genes that predict metabolic outcomes. We validated the microarray expression profile using a real-time polymerase chain reaction. Results: Sixteen patients entered diabetes remission at 12 months and 10 did not. The gene-expression analysis showed similarities and differences between responders and nonresponders. The difference included the expression of critical genes ( Conclusion: The changes in the transcriptome after bariatric surgery distinguish patients in whom diabetes enters complete remission from those who do not. The baseline transcriptome can contribute to the prediction of bariatric surgery-induced diabetes remission preoperatively.

Indexed as

bariatric surgerygenetic predictorsmicroarrayobesitytranscriptomicstype 2 diabetes

Identifiers

PMID38162016
PMCPMC10755185
OpenAlexW4390032522

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.