Evidence map›Paper›PMID 37538198›Full record

ReviewJournal of the Indian Institute of Science2023

Precision Medicine in Type 1 Diabetes.

Dominika A Michalek, Suna Onengut-Gumuscu, David R Repaske, Stephen S Rich

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of the Indian Institute of Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

9 citing papers in PubMed, 12 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Dominika A MichalekCenter for Public Health Genomics, University of Virginia, Charlottesville, VA USA.ORCID 0000-0002-0634-0319
Suna Onengut-GumuscuCenter for Public Health Genomics, University of Virginia, Charlottesville, VA USA.ORCID 0000-0002-6563-8334
David R RepaskeDivision of Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA USA.ORCID 0000-0002-5417-0967
Stephen S RichCenter for Public Health Genomics, University of Virginia, Charlottesville, VA USA.ORCID 0000-0003-3872-7793
University of Virginia · US

Funding

Functional Mechanisms of T1D Risk Variants and their Target Genes using 3D Epigenomics and Single Cell ApproachesR01DK122586 · NIDDK · UNIVERSITY OF VIRGINIA · PI GRANT, STRUAN F A, RICH, STEPHEN S. · 2019 to 2022
$4.8M
NIDDK NIH HHS R01 DK122586
6 · The paper itself

Abstract

Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.

Indexed as

Precision diagnosticsPrecision medicinePrecision monitoringPrecision preventionPrecision prognosticsPrecision therapeuticsType 1 diabetes

Identifiers

PMID37538198
PMCPMC10393845
OpenAlexW4323364611

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.