Evidence map›Paper›PMID 37891760›Full record

ArticleBrain sciences2023

The Connection between Chronic Liver Damage and Sporadic Alzheimer's Disease: Evidence and Insights from a Rat Model.

Ruchi Jakhmola Mani, Nitu Dogra, Deepshikha Pande Katare

Open access · goldAbstract read
In one paragraph

Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
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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 1 institution in 1 country.

Ruchi Jakhmola ManiProteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida 201301, India.ORCID 0000-0002-8404-9375
Nitu DograProteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida 201301, India.
Deepshikha Pande KatareProteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida 201301, India.ORCID 0000-0002-1881-3566
Amity University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Junk foods are typically low in essential nutrients, such as vitamins, minerals, and antioxidants. They are also loaded with trans fats and saturated fats, which can increase the level of triglycerides in the blood. High triglyceride levels can contribute to the development of non-alcoholic fatty liver disease (NAFLD), a condition where excess fat accumulates in the liver. A high intake of junk foods can lead to insulin resistance, a condition where the body's cells become less responsive to insulin. A diet lacking in nutrients and loaded with unwanted toxins can impair the liver's ability to detoxify harmful substances and damage its overall function. It is known that the regular consumption of junk food can be linked to memory impairment and cognitive decline. Several studies have shown that diets high in unhealthy fats, sugars, and processed foods can negatively impact brain health, including memory function. In this study, Wistar rats were used to model Late-Onset Alzheimer's Disease (LOAD), which was inspired by knowledge of the liver-brain axis's role in causing dementia. The model mimicked junk-food-induced liver-brain damage, and was developed by using the toxins d-galactosamine, ethanol and d-galactose. To begin with, the model rats demonstrated insulin resistance, a characteristic of LOAD patients. Glucose levels in both the brain and liver tissues were significantly elevated in the model, paralleling clinical findings in LOAD patients. High glucose levels in the brain lead to the increased production of advanced glycation end-products (AGEs), which, along with amyloid beta, harm neighbouring neurons. Histopathological analysis revealed deformed glial nodules, apoptotic neurons, and amyloid plaques in the brain section in the later stages of the disease. Simultaneously, the liver section displayed features of cirrhosis, including an effaced lobular architecture and the extravasation of red blood cells. Liver enzymes ALT, AST and ALP were consistently elevated with disease progression. Furthermore, immunohistochemistry confirmed the presence of amyloid precursor protein (APP) in the diseased brain. The positive expression of Hypoxia-Inducible Factor 3-Alpha (HIF3A) in the brain indicated hypoxic conditions, which is consistent with other LOAD studies. This model also exhibited damaged intestinal villi and excessive bowel and urinary incontinence, indicating malnutrition and a disturbed gut microbiome, which is also consistent with LOAD patients. Bioinformatics analysis on serum protein suggests a few affected molecular pathways, like the amyloid secretase pathway, androgen/oestrogen/progesterone biosynthesis, the apoptosis signalling pathway, the insulin/IGF pathway-protein kinase B signalling cascade, the Metabotropic glutamate receptor group I pathway, the Wnt signalling pathway, etc. Behavioural analysis confirmed memory decline and the loss of muscle strength with disease progression. Overall, this rat model of LOAD sheds valuable light on LOAD pathology and highlights the potential link between liver dysfunction, particularly induced by the excessive consumption of junk food, and LOAD. This study contributes to a deeper understanding of the complex molecular mechanisms involved in LOAD, paving the way for new possibilities in therapeutic interventions.

Indexed as

ALTAlzheimer’s diseaseAPPbrainjunk foodliver

Identifiers

PMID37891760
PMCPMC10605053
OpenAlexW4387187481

What OpenQuestion holds

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LicenceCC BY
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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.