Evidence map›Paper›PMID 34910252›Full record

ReviewHandbook of experimental pharmacology2022

Precision Medicine and Obesity.

Daniel Sacoto, Maria Daniela Hurtado, Andres Acosta

Open access · greenAbstract readReview
In one paragraph

Review in Handbook of experimental pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
7.4field-weighted citation impact, top 3% 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

14 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Precision Medicine for Obesity Treatment.Journal of the Endocrine Society · 2025
    Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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

3 authors at 1 institution in 1 country.

Daniel SacotoDivision of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Maria Daniela HurtadoMayo Clinic Alix School of Medicine, Rochester, MN, USA.
Andres AcostaDivision of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA. acosta.andres@mayo.edu.
Mayo Clinic in Arizona · US

Funding

Mechanisms of Enteroendocrine L-Cell Dysfunction and Bile Adaptations to Weight LossK23DK114460 · NIDDK · MAYO CLINIC ROCHESTER · PI ACOSTA, ANDRES J · 2018 to 2022
$980k
NIDDK NIH HHS K23 DK114460
6 · The paper itself

Abstract

Obesity is a chronic, relapsing, and multifactorial disease, with a rising prevalence and an associated high economic burden. Achieving successful and sustained weight loss outcomes with current interventions is challenging. This is due, at least in part, to the disease's heterogenous pathophysiology that is yet to be completely understood. Technological advances and greater capabilities for the extraction and storage of information have facilitated the application of precision medicine. Several precision medicine initiatives have been proposed to improve obesity outcomes. Most of these initiatives are based on -omics technologies. Although the data generated from these technologies have led to developing hypotheses that may explain the underpinnings of obesity, their applicability to the clinical practice is yet to be determined. There are other initiatives that have identified quantitative or qualitative physiologic traits that can be targeted and that could have a more immediate clinical impact. This review aims to provide a perspective of current initiatives for precision medicine for obesity.

Indexed as

ObesityPrecision MedicineHumansPhenotypeWeight LossObesityPhenotypePrecision medicine

Identifiers

PMID34910252
PMCPMC9200903
OpenAlexW4205164185

What OpenQuestion holds

Textmetadata
LicenceTDM
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.