Evidence map›Paper›PMID 32899957›Full record

ReviewJournal of personalized medicine2020

The Other Side of Alzheimer's Disease: Influence of Metabolic Disorder Features for Novel Diagnostic Biomarkers.

Chiara Argentati, Ilaria Tortorella, Martina Bazzucchi, Carla Emiliani, Francesco Morena, Sabata Martino

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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. Article
  2. Observational
  3. Review
  4. Review
  5. Review
  6. Article
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.

Chiara ArgentatiDepartment of Chemistry, Biology and Biotechnologies, University of Perugia, 06123 Perugia, Italy.
Ilaria TortorellaDepartment of Chemistry, Biology and Biotechnologies, University of Perugia, 06123 Perugia, Italy.
Martina BazzucchiDepartment of Chemistry, Biology and Biotechnologies, University of Perugia, 06123 Perugia, Italy.
Carla EmilianiDepartment of Chemistry, Biology and Biotechnologies, University of Perugia, 06123 Perugia, Italy.
Francesco MorenaDepartment of Chemistry, Biology and Biotechnologies, University of Perugia, 06123 Perugia, Italy.
Sabata MartinoDepartment of Chemistry, Biology and Biotechnologies, University of Perugia, 06123 Perugia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, the amyloid cascade hypothesis is the dominant model to explain Alzheimer's disease (AD) pathogenesis. By this hypothesis, the inherited genetic form of AD is discriminated from the sporadic form of AD (SAD) that accounts for 85-90% of total patients. The cause of SAD is still unclear, but several studies have shed light on the involvement of environmental factors and multiple susceptibility genes, such as Apolipoprotein E and other genetic risk factors, which are key mediators in different metabolic pathways (e.g., glucose metabolism, lipid metabolism, energetic metabolism, and inflammation). Furthermore, growing clinical evidence in AD patients highlighted the presence of affected systemic organs and blood similarly to the brain. Collectively, these findings revise the canonical understating of AD pathogenesis and suggest that AD has metabolic disorder features. This review will focus on AD as a metabolic disorder and highlight the contribution of this novel understanding on the identification of new biomarkers for improving an early AD diagnosis.

Indexed as

adipose tissue dysfunctionamyloid cascade hypothesisenergetic metabolismglucose metabolismlysosomes dysfunctionneurodegenerationneuroinflammationType-3-Diabetes

Identifiers

PMID32899957
PMCPMC7563360

What OpenQuestion holds

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Registered trials

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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.