Evidence map›Paper›PMID 28922161›Full record

ArticleJournal of Alzheimer's disease : JAD2017

Comorbidity Analysis between Alzheimer's Disease and Type 2 Diabetes Mellitus (T2DM) Based on Shared Pathways and the Role of T2DM Drugs.

Reagon Karki, Alpha Tom Kodamullil, Martin Hofmann-Apitius

Open access · hybridFull text read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
5.9field-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

40 citing papers in PubMed, 69 citations in OpenAlex.

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  12. The Aβ42 Peptide and IAPP Physically Interact in a Yeast-Based Assay.International journal of molecular sciences · 2023
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  15. Protective effects ofPharmaceutical biology · 2022
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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

3 authors at 2 institutions in 1 country.

Reagon KarkiDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.
Alpha Tom KodamullilDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.
Martin Hofmann-ApitiusDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.
Bonn Aachen International Center for Information Technology · DEFraunhofer Institute for Algorithms and Scientific Computing · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVarious studies suggest a comorbid association between Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) indicating that there could be shared underlying pathophysiological mechanisms.

objectiveThis study aims to systematically model relevant knowledge at the molecular level to find a mechanistic rationale explaining the existing comorbid association between AD and T2DM.

methodWe have used a knowledge-based modeling approach to build two network models for AD and T2DM using Biological Expression Language (BEL), which is capable of capturing and representing causal and correlative relationships at both molecular and clinical levels from various knowledge resources.

resultsUsing comparative analysis, we have identified several putative "shared pathways". We demonstrate, at a mechanistic level, how the insulin signaling pathway is related to other significant AD pathways such as the neurotrophin signaling pathway, PI3K/AKT signaling, MTOR signaling, and MAPK signaling and how these pathways do cross-talk with each other both in AD and T2DM. In addition, we present a mechanistic hypothesis that explains both favorable and adverse effects of the anti-diabetic drug metformin in AD.

conclusionThe two computable models introduced here provide a powerful framework to identify plausible mechanistic links shared between AD and T2DM and thereby identify targeted pathways for new therapeutics. Our approach can also be used to provide mechanistic answers to the question of why some T2DM treatments seem to increase the risk of AD.

Indexed as

Alzheimer DiseaseComorbidityComputer SimulationDiabetes Mellitus, Type 2FemaleHumansHypoglycemic AgentsInsulinMaleMetabolic Networks and PathwaysMetforminModels, BiologicalSignal TransductionHypoglycemic AgentsInsulinMetforminAlzheimer’s diseasecomorbiditydisease mechanismsdisease modelingmetforminOpenBELtype 2 diabetes mellitus

Identifiers

PMID28922161
PMCPMC5611890
OpenAlexW2754782024

What OpenQuestion holds

Textfull text, public
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.