Evidence map›Paper›PMID 36829165›Full record

ArticleJournal of translational medicine2023

Modeling the enigma of complex disease etiology.

Lynn M Schriml, Richard Lichenstein, Katharine Bisordi, Cynthia Bearer, J Allen Baron, Carol Greene

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

7 citing papers in PubMed, 17 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

6 authors at 2 institutions in 1 country.

Lynn M SchrimlUniversity of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA. lschriml@som.umaryland.edu.ORCID 0000-0001-8910-9851
Richard LichensteinUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Katharine BisordiUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Cynthia BearerCase Western Reserve University, Cleveland, OH, USA.
J Allen BaronUniversity of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA.
Carol GreeneUniversity of Maryland School of Medicine, Baltimore, MD, USA.
University of Maryland, Baltimore · USCase Western Reserve University · US

Funding

The Human Disease Ontology: An integrated, mechanistic knowledge resource for biomedical research.U24HG012557 · NHGRI · UNIVERSITY OF MARYLAND BALTIMORE · PI Lynn Marie Schriml · 2022 to 2026
$3.7M
The Disease Ontology Project: mechanistic profiles of human disease for biomedical and clinical researchU41HG008735 · NHGRI · UNIVERSITY OF MARYLAND BALTIMORE · PI SCHRIML, LYNN MARIE · 2017 to 2021
$3.4M
NHGRI NIH HHS HG008735-01A1NHGRI NIH HHS U24 HG012557NHGRI NIH HHS U41 HG008735
6 · The paper itself

Abstract

backgroundComplex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors.

methodsHere we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model.

resultsWe provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves.

conclusionsThe Human Disease Ontology's Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.

Indexed as

AsthmaDiabetes MellitusFetal Alcohol Spectrum DisordersCausalityFemaleHumansPregnancyAsthmaDiabetesDisease etiologyEnvironmental driversFetal alcohol syndromeGeneticsPathophysiology

Identifiers

PMID36829165
PMCPMC9957692
OpenAlexW4321768804

What Socratic 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 Socratic graph.