Dataset Information

Which Severe Mental Illnesses Most Increase the Risk of Developing Dementia? Comparing the Risk of Dementia in Patients with Schizophrenia, Major Depressive Disorder and Bipolar Disorder.

ABSTRACT:

Objective

Previous studies have shown that certain severe mental illnesses (SMIs) increase the risk of dementia, but those that increase the risk to a greater degree in comparison with other SMIs are unknown. Furthermore, physical illnesses may alter the risk of developing dementia, but these cannot be well-controlled.

Methods

Using the Taiwan National Health Insurance Research Database, patients with schizophrenia, bipolar disorder and major depressive disorder (MDD) were recruited. We also recruited normal healthy subjects as the control group. All subjects were aged over 60 years, and the duration of follow-up was from 2008 to 2015. Multiple confounders were adjusted, including physical illnesses and other variables. Use of medications, especially benzodiazepines, was analyzed in a sensitivity analysis.

Results

36,029 subjects (MDD: 23,371, bipolar disorder: 4,883, schizophrenia: 7,775) and 108,084 control subjects were recruited after matching according to age and sex. The results showed that bipolar disorder had the highest hazard ratio (HR) (HR: 2.14, 95% confidence interval [CI]: 1.99-2.30), followed by schizophrenia (HR: 2.06, 95% CI: 1.93-2.19) and MDD (HR: 1.60, 95% CI: 1.51-1.69). The results remained robust after adjusting for covariates, and sensitivity analysis showed similar results. Anxiolytics use did not increase the risk of dementia in any of the three groups of SMI patients.

Conclusion

SMIs increase the risk of dementia, and among them, bipolar disorder confers the greatest risk of developing dementia. Anxiolytics may not increase the risk of developing dementia in patients with an SMI, but still need to be used with caution in clinical practices.

SUBMITTER: Chang WH

PROVIDER: S-EPMC10335904 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Which Severe Mental Illnesses Most Increase the Risk of Developing Dementia? Comparing the Risk of Dementia in Patients with Schizophrenia, Major Depressive Disorder and Bipolar Disorder.

Chang Wei Hung WH Su Chien-Chou CC Chen Kao Chin KC Hsiao Yin Ying YY Chen Po See PS Yang Yen Kuang YK

Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology 20230801 3

<h4>Objective</h4>Previous studies have shown that certain severe mental illnesses (SMIs) increase the risk of dementia, but those that increase the risk to a greater degree in comparison with other SMIs are unknown. Furthermore, physical illnesses may alter the risk of developing dementia, but these cannot be well-controlled.<h4>Methods</h4>Using the Taiwan National Health Insurance Research Database, patients with schizophrenia, bipolar disorder and major depressive disorder (MDD) were recruit ...[more]

PMID: 37424416

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Project description:Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

Project description:BACKGROUND:A challenge of understanding the mechanisms underlying cognition including neurodevelopmental and neuropsychiatric disorders is mainly given by the potential severity of cognitive disorders for the quality of life and their prevalence. However, the field has been focused predominantly on protein coding variation until recently. Given the importance of tightly controlled gene expression for normal brain function, the goal of the study was to assess the functional variation including non-coding variation in human genome that is likely to play an important role in cognitive functions. To this end, we organized and utilized available genome-wide datasets from genomic, transcriptomic and association studies into a comprehensive data corpus. We focused on genomic regions that are enriched in regulatory activity-overlapping transcriptional factor binding regions and repurpose our data collection especially for identification of the regulatory SNPs (rSNPs) that showed associations both with allele-specific binding and allele-specific expression. We matched these rSNPs to the nearby and distant targeted genes and then selected the variants that could implicate the etiology of cognitive disorders according to Genome-Wide Association Studies (GWAS). Next, we use DeSeq 2.0 package to test the differences in the expression of the certain targeted genes between the controls and the patients that were diagnosed bipolar affective disorder and schizophrenia. Finally, we assess the potential biological role for identified drivers of cognition using DAVID and GeneMANIA. RESULTS:As a result, we selected fourteen regulatory SNPs locating within the loci, implicated from GWAS for cognitive disorders with six of the variants unreported previously. Grouping of the targeted genes according to biological functions revealed the involvement of processes such as 'posttranscriptional regulation of gene expression', 'neuron differentiation', 'neuron projection development', 'regulation of cell cycle process' and 'protein catabolic processes'. We identified four rSNP-targeted genes that showed differential expression between patient and control groups depending on brain region: NRAS-in schizophrenia cohort, CDC25B, DDX21 and NUCKS1-in bipolar disorder cohort. CONCLUSIONS:Overall, our findings are likely to provide the keys for unraveling the mechanisms that underlie cognitive functions including major depressive disorder, bipolar disorder and schizophrenia etiopathogenesis.

Dataset Information

Which Severe Mental Illnesses Most Increase the Risk of Developing Dementia? Comparing the Risk of Dementia in Patients with Schizophrenia, Major Depressive Disorder and Bipolar Disorder.

Objective

Methods

Results

Conclusion

Publications

Which Severe Mental Illnesses Most Increase the Risk of Developing Dementia? Comparing the Risk of Dementia in Patients with Schizophrenia, Major Depressive Disorder and Bipolar Disorder.

Similar Datasets

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