The risk of Alzheimer’s disease according to dynamic changes in metabolic health and obesity: a nationwide population-based cohort study

Cho, Yun Kyung; Lee, Jiwoo; Kim, Hwi Seung; Park, Joong‐Yeol; Lee, Woo Je; Kim, Ye‐Jee; Jung, Chang Hee

doi:10.18632/aging.203255

Cited by 9 publications

(4 citation statements)

References 63 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, more recent publications report that being overweight or obese at old age protects against Alzheimer’s disease [ 120 , 121 , 122 , 123 , 124 ]. According to our nationwide population-based cohort study, the risk of AD is considerably reduced among people with MHO [ 125 ], which is consistent with some earlier findings [ 126 , 127 ]. In addition, we analyzed the risk of developing AD based on the changes in metabolic phenotype.…”

Section: Clinical Outcomes Of Mho and Possible Mechanismssupporting

confidence: 90%

Pathogenesis, Murine Models, and Clinical Implications of Metabolically Healthy Obesity

Cho

Lee

Jung

2022

IJMS

Self Cite

View full text Add to dashboard Cite

Although obesity is commonly associated with numerous cardiometabolic pathologies, some people with obesity are resistant to detrimental effects of excess body fat, which constitutes a condition called “metabolically healthy obesity” (MHO). Metabolic features of MHO that distinguish it from metabolically unhealthy obesity (MUO) include differences in the fat distribution, adipokine types, and levels of chronic inflammation. Murine models are available that mimic the phenotype of human MHO, with increased adiposity but preserved insulin sensitivity. Clinically, there is no established definition of MHO yet. Despite the lack of a uniform definition, most studies describe MHO as a particular case of obesity with no or only one metabolic syndrome components and lower levels of insulin resistance or inflammatory markers. Another clinical viewpoint is the dynamic and changing nature of MHO, which substantially impacts the clinical outcome. In this review, we explore the pathophysiology and some murine models of MHO. The definition, variability, and clinical implications of the MHO phenotype are also discussed. Understanding the characteristics that differentiate people with MHO from those with MUO can lead to new insights into the mechanisms behind obesity-related metabolic derangements and diseases.

show abstract

Section: Clinical Outcomes Of Mho and Possible Mechanismssupporting

confidence: 90%

Pathogenesis, Murine Models, and Clinical Implications of Metabolically Healthy Obesity

Cho

Lee

Jung

2022

IJMS

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the pathophysiological features of AD was impaired cerebral glucose metabolism, which occurred long before cognitive impairment and pathological changed, and this prodromal period could last up to 10 years ( 48 – 50 ). Research confirmed diabetes can increase the risk of AD, and even the use of hypoglycemic drugs cannot reduce the risk ( 51 ), which can be increased two to threefold by previous study, which is not related to the risk of vascular dementia ( 52 – 55 ). Recombinant interferon γ the metabolic enhancement of treatment reversed the glycolytic metabolism and inflammatory functional defects of microglia, thereby alleviating the AD pathology of 5xfad mice ( 56 ).…”

Section: Discussionmentioning

confidence: 85%

Identification of diagnostic genes for both Alzheimer’s disease and Metabolic syndrome by the machine learning algorithm

Zhang

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

BackgroundAlzheimer’s disease is the most common neurodegenerative disease worldwide. Metabolic syndrome is the most common metabolic and endocrine disease in the elderly. Some studies have suggested a possible association between MetS and AD, but few studied genes that have a co-diagnostic role in both diseases.MethodsThe microarray data of AD (GSE63060 and GSE63061 were merged after the batch effect was removed) and MetS (GSE98895) in the GEO database were downloaded. The WGCNA was used to identify the co-expression modules related to AD and MetS. RF and LASSO were used to identify the candidate genes. Machine learning XGBoost improves the diagnostic effect of hub gene in AD and MetS. The CIBERSORT algorithm was performed to assess immune cell infiltration MetS and AD samples and to investigate the relationship between biomarkers and infiltrating immune cells. The peripheral blood mononuclear cells (PBMCs) single-cell RNA (scRNA) sequencing data from patients with AD and normal individuals were visualized with the Seurat standard flow dimension reduction clustering the metabolic pathway activity changes each cell with ssGSEA.ResultsThe brown module was identified as the significant module with AD and MetS. GO analysis of shared genes showed that intracellular transport and establishment of localization in cell and organelle organization were enriched in the pathophysiology of AD and MetS. By using RF and Lasso learning methods, we finally obtained eight diagnostic genes, namely ARHGAP4, SNRPG, UQCRB, PSMA3, DPM1, MED6, RPL36AL and RPS27A. Their AUC were all greater than 0.7. Higher immune cell infiltrations expressions were found in the two diseases and were positively linked to the characteristic genes. The scRNA-seq datasets finally obtained seven cell clusters. Seven major cell types including CD8 T cell, monocytes, T cells, NK cell, B cells, dendritic cells and macrophages were clustered according to immune cell markers. The ssGSEA revealed that immune-related gene (SNRPG) was significantly regulated in the glycolysis-metabolic pathway.ConclusionWe identified genes with common diagnostic effects on both MetS and AD, and found genes involved in multiple metabolic pathways associated with various immune cells.

show abstract

“…Of these, 23 studies were excluded because the diagnosis did not meet the standardized criteria, for reasons described in Table S5. Finally, three research papers were included in this analysis [25][26][27].…”

Section: Literature Searchmentioning

confidence: 99%

“…A summary of the characteristics of the studies included is presented in Table 1 and Table S4 shows the sample sizes and incidence of dementia. The three studies were conducted in Asia and used a cohort design [25][26][27]. A total of 5,786,661 participants were included, with study population sizes from 1199 [27] to 5,669,488 [26].…”

Section: Study and Patient Characteristicsmentioning

confidence: 99%

Association between metabolically healthy obesity and risk of dementia: A systematic review and meta‐analysis

Su,

Li,

Ding

et al. 2024

Medicine Advances

View full text Add to dashboard Cite

BackgroundSome studies suggest that the metabolic alterations linked to obesity in humans may increase the likelihood of developing dementia. However, there are currently no existing meta‐analyses exploring the possible occurrence of dementia in patients with metabolically healthy obesity (MHO). In the present study, we aimed to investigate a potential correlation between individuals with MHO and the likelihood of developing dementia.MethodOur research focused on searching three databases to locate pertinent studies starting from January 10, 2023. We used sensitivity analysis, the Egger's test, and a visual funnel plot to separately assess the reliability of the research results and publication bias. We conducted this study according to a pre‐established protocol in the PROSPERO registry (CRD42023449459).ResultsWe incorporated three potential cohort investigations. Individuals with good metabolic health and obesity showed a decreased likelihood of developing dementia compared with those who were metabolically healthy but not obese (hazard ratio (HR) = 0.79; 95% confidence interval (CI) 0.68–0.92). In contrast, individuals who were metabolically unhealthy and nonobese had an increased likelihood of developing dementia (HR = 1.33; 95% CI 1.25–1.42), whereas the population with metabolically unhealthy obesity did not exhibit a notable association with dementia onset (HR = 1.06; 95% CI 0.83–1.37).ConclusionAccording to our meta‐analysis, patients with MHO had a reduced likelihood of developing general dementia and Alzheimer disease. Maintaining a well‐functioning metabolism can provide advantages in reducing the occurrence of dementia.

show abstract

The risk of Alzheimer’s disease according to dynamic changes in metabolic health and obesity: a nationwide population-based cohort study

Cited by 9 publications

References 63 publications

Pathogenesis, Murine Models, and Clinical Implications of Metabolically Healthy Obesity

Pathogenesis, Murine Models, and Clinical Implications of Metabolically Healthy Obesity

Identification of diagnostic genes for both Alzheimer’s disease and Metabolic syndrome by the machine learning algorithm

Association between metabolically healthy obesity and risk of dementia: A systematic review and meta‐analysis

Contact Info

Product

Resources

About