Schizophrenia and risk of dementia: a meta-analysis study

Cai, Laisheng; Huang, Jing-Wei

doi:10.2147/ndt.s172933

Cited by 108 publications

(74 citation statements)

References 68 publications

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“…For example, the higher prevalence of paranoid schizophrenia among cases in which benzodiazepine may be used for the treatment may lead to a higher benzodiazepine prevalence and consequent ROR overestimation. 36 Although we adjusted the comorbidities in the outcome model, the imbalance of confounders between cases and non-cases may have resulted in ROR overestimations. Cognitive decline and dementia can be clinically different conditions.…”

Section: Discussionmentioning

confidence: 99%

Benzodiazepine-Related Cognitive Impairment or Dementia: A Signal Detection Study Using a Case/Non-Case Approach

Baek

Kim

Bae

et al. 2020

Psychiatry Investig

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Objective The association between benzodiazepine use and the risk of cognitive impairment or dementia has been controversial. Our study aims to detect this association through a case/non-case method using the Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD) between 2007 and 2016.Methods Cases were adverse event (AE)-pairs with suspected cognitive impairment or dementia. 10 non-cases were matched to each case on age and sex. Exposure was defined as use of benzodiazepines, including long-, intermediate-, and short-acting benzodiazepine. We conducted multivariable logistic regression analyses to estimate reporting odds ratios (ROR) and 95% confidence intervals (CI).Results Of the 1,086,584 AE-pairs, 887 cases were suspected AE-pairs of cognitive impairment or dementia, and 775,444 non-cases were selected. Benzodiazepine use was associated with increased AE-pairs of cognitive impairment or dementia when assessed using those with certain, probable, and/or possible in causality assessments (ROR=2.69, 95% CI=1.66–4.38). Higher ROR estimates were shown in female (2.33, 1.48–3.67) and in those with polypharmacy (2.20, 1.35–3.57). Dementia safety profiles were inconsistent across individual benzodiazepine components.Conclusion These results suggest the potentially increased association between benzodiazepine use and cognitive impairment or dementia in female and those with polypharmacy. Inconsistent safety profiles of benzodiazepine components should be further investigated.

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Section: Discussionmentioning

confidence: 99%

Benzodiazepine-Related Cognitive Impairment or Dementia: A Signal Detection Study Using a Case/Non-Case Approach

Baek

Kim

Bae

et al. 2020

Psychiatry Investig

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“…Patients with schizophrenia (SZ) are at risk for elevated aging-related functional and neurological decline, termed accelerated aging (Kirkpatrick et al, 2008;Ito and Barnes, 2009;Jeste et al, 2011;Kochunov et al, 2013bKochunov et al, , 2016b. They are at significantly higher risks (1.5 to 5 times) for developing cognitive deficits and dementia before age (Cai and Huang, 2018;Chen et al, 2015;Diniz et al, 2017;Ribe et al, 2015). Previous studies demonstrated that the integrity of the cerebral white matter as measured by fractional anisotropy (FA) in diffusion weighted imaging (DTI), declined in SZ patients at nearly twice the aging rate of normal controls (Mori et al, 2007;Friedman et al, 2008;Kochunov et al, 2013aKochunov et al, , 2013bWright et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

Wang

Kochunov

Sampath

et al. 2020

Preprint

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We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age >30 group in patients (p < 0.001) but not in age ≤ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = -0.176, p = 0.007) and processing speed (β = -0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from middle life stage; the increase of Δage in SZs is associated with the decline neurocognitive performance.

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“…Thus, the prescription of Zotepine may indicate early AD symptoms and, consequently, an increasing likelihood of incident AD. Alternatively, some studies indicate that individuals with schizophrenia may have an increased risk for the development of dementia 32 . Given this, it might be possible that incident AD is high in individuals with schizophrenic symptoms to whom Zotepine is prescribed.…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

et al. 2020

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Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals' history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer's disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: "definite AD" with diagnostic codes and dementia medication (n = 614) and "probable AD" with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

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Schizophrenia and risk of dementia: a meta-analysis study

Cited by 108 publications

References 68 publications

Benzodiazepine-Related Cognitive Impairment or Dementia: A Signal Detection Study Using a Case/Non-Case Approach

Benzodiazepine-Related Cognitive Impairment or Dementia: A Signal Detection Study Using a Case/Non-Case Approach

White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

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