Evidence map›Paper›PMID 39400834›Full record

ArticleAdvances in experimental medicine and biology2024

Relationship Between Dementia and Systemic Metabolic Disorders.

Kaoru Sakatani

Abstract read
PubMed Publisher
In one paragraph

Article in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Kaoru SakataniInstitute of Gerontology, The University of Tokyo, Tokyo, Japan. k.sakatani@edu.k.u-tokyo.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent research has linked systemic metabolic disorders to cognitive decline and dementia risk, including Alzheimer's. This is suspected to be due to lifestyle-related vascular impairments from atherosclerosis and other factors, such as malnutrition and anaemia. Applying deep learning using 2897 cases from a rehabilitation hospital and health screenings, we trained a model to predict cognitive function [mini-mental state examination (MMSE) scores] and brain atrophy [Brain Healthcare Quotient (BHQ) scores] from basic blood tests and age. The deep learning model accurately estimated MMSE and BHQ from these inputs, with age, nutritional information, and organ function indicators being top predictors. These findings highlight the relationship of dementia with systemic metabolic disorders and suggest the potential of using routine blood tests for dementia risk assessment. Furthermore, personalised dietary interventions could be tailored based on blood test anomalies. This holistic view mirrors traditional Chinese medicine, which considers brain disorders systemic, that is related to vital organs but not the brain itself.

Indexed as

DementiaMetabolic DiseasesAgedAged, 80 and overAtrophyBrainCognitionCognitive DysfunctionDeep LearningFemaleHumansMaleMental Status and Dementia TestsMiddle AgedRisk FactorsAlzheimer’s diseaseAtherosclerosisBlood testsCognitive declineDeep learning

Identifiers

What OpenQuestion holds

Textmetadata
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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.