Evidence map›Paper›PMID 36407766›Full record

ReviewFrontiers in molecular neuroscience2022

RNA biomarkers for alcohol use disorder.

Laura B Ferguson, R Dayne Mayfield, Robert O Messing

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in molecular neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 38% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

3 authors at 1 institution in 1 country.

Laura B FergusonWaggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States.
R Dayne MayfieldWaggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States.
Robert O MessingWaggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States.
The University of Texas at Austin · US

Funding

GENE EXPRESSION IN THE HUMAN ALCOHOLIC BRAINR01AA012404 · NIAAA · UNIVERSITY OF TEXAS AUSTIN · PI MAYFIELD, ROY DAYNE · 2000 to 2025
$10.7M
Next Generation Sequencing of Human Alcoholic BrainU01AA020926 · NIAAA · UNIVERSITY OF TEXAS AT AUSTIN · PI Roy DAYNE MAYFIELD · 2011 to 2026
$6.7M
NIAAA NIH HHS R01 AA012404NIAAA NIH HHS U01 AA020926
6 · The paper itself

Abstract

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Indexed as

alcohol dependencealcoholismbiomarkermicroarrayRNA seqtranscriptome

Identifiers

PMID36407766
PMCPMC9673015
OpenAlexW4308204853

What OpenQuestion 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 OpenQuestion graph.