Evidence map›Paper›PMID 32634859›Full record

ArticleDiabetic medicine : a journal of the British Diabetic Association2020

Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes.

A L J Carr, D J Perry, A L Lynam, S Chamala, C S Flaxman, S A Sharp, L A Ferrat, A G Jones, M L Beery, L M Jacobsen and 10 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetic medicine : a journal of the British Diabetic Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Actionability of Genetic Variants in Diabetes: Core Aspects and Applied Examples.Diabetes spectrum : a publication of the American Diabetes Association · 2025
    Article
  2. Article
  3. Diabetes Study of Children of Diverse Ethnicity and Race: Study design.Diabetes/metabolism research and reviews · 2024
    Observational
  4. Review
  5. Review
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  7. Article
  8. Article
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  10. Article
  11. Type 1 diabetes mellitus: much progress, many opportunities.The Journal of clinical investigation · 2021
    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

20 authors at 2 institutions in 2 countries.

A L J CarrInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
D J PerryDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
A L LynamInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
S ChamalaDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
C S FlaxmanInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
S A SharpInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
L A FerratInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
A G JonesInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.ORCID 0000-0002-0883-7599
M L BeeryDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
L M JacobsenDepartment of Pediatrics, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
C H WasserfallDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
M L Campbell-ThompsonDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
I KusmartsevaDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
A PosgaiDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
D A SchatzDepartment of Pediatrics, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
M A AtkinsonDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
T M BruskoDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
S J RichardsonInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
B M ShieldsInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
R A OramInstitute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.ORCID 0000-0003-3581-8980
University of Florida · USUniversity of Exeter · GB

Funding

Project 3P01AI042288 · NIAID · UNIVERSITY OF FLORIDA · PI Todd Michael Brusko · 1997 to 2026
$32.9M
Diabetes UK 16/0005480Diabetes UK 16/0005529Helmsley Charitable Trust #2018PG-T1D053JDRF Career Development 5-CDA-2014-221-A-NNIAID NIH HHS P01 AI042288NIH HHS AI-422388NIHR 17/0005624
6 · The paper itself

Abstract

aimsMisclassification of diabetes is common due to an overlap in the clinical features of type 1 and type 2 diabetes. Combined diagnostic models incorporating clinical and biomarker information have recently been developed that can aid classification, but they have not been validated using pancreatic pathology. We evaluated a clinical diagnostic model against histologically defined type 1 diabetes.

methodsWe classified cases from the Network for Pancreatic Organ donors with Diabetes (nPOD) biobank as type 1 (n = 111) or non-type 1 (n = 42) diabetes using histopathology. Type 1 diabetes was defined by lobular loss of insulin-containing islets along with multiple insulin-deficient islets. We assessed the discriminative performance of previously described type 1 diabetes diagnostic models, based on clinical features (age at diagnosis, BMI) and biomarker data [autoantibodies, type 1 diabetes genetic risk score (T1D-GRS)], and singular features for identifying type 1 diabetes by the area under the curve of the receiver operator characteristic (AUC-ROC).

resultsDiagnostic models validated well against histologically defined type 1 diabetes. The model combining clinical features, islet autoantibodies and T1D-GRS was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P = 0.03). Histological classification of type 1 diabetes was concordant with serum C-peptide [median < 17 pmol/l (limit of detection) vs. 1037 pmol/l in non-type 1 diabetes; P < 0.0001].

conclusionsOur study provides robust histological evidence that a clinical diagnostic model, combining clinical features and biomarkers, could improve diabetes classification. Our study also provides reassurance that a C-peptide-based definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. Parts of this study were presented in abstract form at the Network for Pancreatic Organ Donors Conference, Florida, USA, 19-22 February 2019 and Diabetes UK Professional Conference, Liverpool, UK, 6-8 March 2019.

Indexed as

AdultAge of OnsetAutoantibodiesBody Mass IndexC-PeptideDiabetes MellitusDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Diagnosis, DifferentialFemaleGenetic Predisposition to DiseaseHumansInsulinIslets of LangerhansMaleMiddle Agedanti-GAD65 autoantibodyAutoantibodiesC-PeptideICA512 autoantibodyInsulinSLC30A8 protein, humanZinc Transporter 8

Identifiers

PMID32634859
PMCPMC8086995
OpenAlexW3041311455

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.