High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram

Ye, Sheng; Pan, Huiqing; Li, Weijia; Wang, Bing; Xing, Jingjing; Xu, Li

doi:10.3389/fneur.2022.972771

Cited by 4 publications

(5 citation statements)

References 65 publications

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“…The subject cohort size ranged from 72 to 22,950. The diagnostic criteria used in the included studies were Mini-Mental State Examination (MMSE) ( 33 ), Montreal Cognitive Assessment (MoCA) ( 34 ) ( n = 5) ( 20 , 21 , 28 , 29 , 31 ), MMSE ( n = 6) ( 12–14 , 18 , 24 , 32 ), MoCA ( n = 6) ( 17 , 19 , 22 , 25–27 ), Center of Cancelation (CoC) ( 35 ) ( n = 1) ( 15 ), Global Deterioration Scale (GDS) ( 36 ) ( n = 1) ( 16 ), Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) ( 37 ) ( n = 1) ( 23 ), and vascular dementia criteria of the AHA/ASA scientific statement ( 38 ) ( n = 1) ( 30 ). The duration of follow-up was predominantly 3 to 12 months, and was 36 years in only one study ( 23 ).…”

Section: Resultsmentioning

confidence: 99%

“…Of the 21 eligible studies, 10 were conducted in China (12,13,17,19,21,25,26,28,29,32), 2 in Norway (14,27), 1 in Australia (20), 2 in German (15, 16), 2 in the Republic of Korea (18,30), 1 in the Netherlands (14), 1 in France (23), 1 in the UK (24), and 1 in Singapore (31). These studies were published between 2015 and 2023, predominantly in 2021-2023 (n = 17) (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)32).…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

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Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

Chen

Jiao

et al. 2023

Front. Neurol.

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ObjectiveCognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.MethodsRelevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.ResultsA total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77–0.87), 0.77 (95% CI 0.72–0.80), and 0.80 (95% CI 0.71–0.86) in the training set, and 0.82 (95% CI 0.77–0.87), 0.82 (95% CI 0.70–0.90), and 0.80 (95% CI 0.68–0.82) in the validation set, respectively.ConclusionML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

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Section: Resultsmentioning

confidence: 99%

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

Chen

Jiao

et al. 2023

Front. Neurol.

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“… 23 Abebe et al 24 assessed cognitive function in patients with BC. The MMSE has been widely applied in cognitive studies of Chinese patients 25 - 27 and its reliability and validity have been verified previously. 28 , 29 MMSE scores ≤26 were considered to indicate cognitive impairment, while scores of 27 to 30 were considered normal.…”

Section: Methodsmentioning

confidence: 95%

The Managing Cancer and Living Meaningfully (CALM) Intervention Alleviates Chemotherapy-Related Cognitive Impairment in Patients with Breast Cancer by Modulating Pan-Immune-Inflammation Values

et al. 2022

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Background: The number of patients with breast cancer is increasing worldwide, resulting in a growing number of patients with chemotherapy-related cognitive impairment (CRCI), which seriously affects their quality of life. CRCI is associated with inflammatory factors and systemic inflammatory markers such as pan-immune-inflammation value (PIV) and monocyte-to-lymphocyte ratio (MLR), which can reflect the level of inflammation in the body. While the Managing Cancer and Living Meaningfully (CALM) intervention has been demonstrated to alleviate CRCI in patients with breast cancer, the specific mechanism remains unclear. Objective: This study evaluated the impact of the CALM intervention on systemic inflammation. Methods: Ninety patients with breast cancer with CRCI were enrolled and randomized into care as usual (CAU) and CALM intervention groups. All patients were assessed using the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog), Mini-Mental State Exam (MMSE), and Functional Assessment of Cancer Therapy-Breast (FACT-B) before and after the CAU/CALM intervention. The blood levels of inflammatory markers were also analyzed before and after the intervention. Results: Compared to the CAU group, the CALM group showed significantly improved cognitive function and significantly decreased PIV ( P < .05). PIV was significantly negatively correlated with FACT-Cog ( P < .05). The levels of other inflammatory markers, including MLR, neutrophil-to-lymphocyte ratio (NLR), granulocyte-to-lymphocyte ratio (GLR), and systemic immune-inflammation index (SII), were also reduced in the CALM group. Conclusion: PIV is an important marker of inflammation. The CALM intervention may improve the cognitive function of patients by regulating the systemic inflammation marker PIV through the neuroimmune axis.

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“…While different pathological ATN profiles appear to characterize different CSVD subtypes, they similarly relate to poor global cognition. Additive Aβ co-pathology has been associated with accelerated longitudinal cognitive decline in lacunar stroke, CAA and CSVD with preexisting cognitive impairment [16, 21, 42]. One may speculate that this particular CSVD cohort could also benefit from Aβ modifying immunotherapy.…”

Section: Discussionmentioning

confidence: 99%

“…The [16,21,42]. One may speculate that this particular CSVD cohort could also benefit from Aβ modifying immunotherapy.…”

Section: Non-ad Pathological Changesmentioning

confidence: 97%

Alzheimer’s disease and neurodegeneration in symptomatic cerebral small vessel disease

Arndt,

Pfister,

Perosa

et al. 2024

Preprint

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Background: Co-occurrence of cerebral small vessel disease (CSVD) is common in aging and Alzheimer disease (AD) dementia, but, in symptomatic CSVD prevalence and role of AD and neurodegenerative co-pathologies have been less explored. Methods: In vivo determination of prevalence, predictors and relevance for cognition of AD and neurodegenerative co-pathologies in symptomatic CSVD, including deep perforator arteriopathy (DPA) and cerebral amyloid angiopathy (CAA), utilizing cerebrospinal fluid (CSF) biomarkers. Cross-sectional study from October 2010 to September 2021 of participants with magnetic resonance imaging (MRI) and CSF biomarkers (amyloid-beta 42/40 ratio, phosphorylated-tau, total-tau, neurofilament light). Biomarker levels were compared among groups; prevalence of ATN classification subtypes was estimated and related to clinical phenotype, CSVD MRI markers and global cognition. Results: The study comprised 229 individuals (median age 74 years; 47% females), 70 with AD dementia, 79 with probable CAA, 62 with DPA patients and 18 healthy controls. Participants were categorized based on the ATN classification: normal biomarkers (A-T-N-), AD pathology continuum (A+), and non-AD pathological changes, including primary age-related tauopathy (PART, A-T+N+) or isolated neurodegeneration (A-T-N+). Of 141 CSVD patients, 43 (30%) were A-T-N-, 39 (28%) A+, with lower prevalence in DPA than CAA (15% vs. 38%, p = .003), 18 (13%) A-T+N+, and 41 (29%) A-T-N+, with higher prevalence in DPA than CAA (42% vs. 19%, p = .002). A+ was associated with increasing age, female sex, lobar hemorrhages and low burden of deep white matter hyperintensities and lacunes. A-T-N+ was related to younger age, symptomatic stroke and lacunes. A-T+N+ had no specific predictors, except advanced age. Each pathological ATN profile was independently related to lower Mini Mental State Examination scores (A+: B = -2.7, p = .013; A-T+N+: B = -4.6, p = .002; A-T-N+: B = -2.3, p = .034), accounting for demographics, clinical phenotype and MRI CSVD severity. Conclusions: Using biomarkers, this study confirms in vivo that CSVD frequently co-occurs with AD or neurodegenerative pathologies, exerting independent effects on cognitive health. As disease-modifying therapies emerge, integrating interacting biomarkers will be crucial for the selection of patients with the greatest benefit.

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High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram

Cited by 4 publications

References 65 publications

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

The Managing Cancer and Living Meaningfully (CALM) Intervention Alleviates Chemotherapy-Related Cognitive Impairment in Patients with Breast Cancer by Modulating Pan-Immune-Inflammation Values

Alzheimer’s disease and neurodegeneration in symptomatic cerebral small vessel disease

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