Background: Previous studies indicated that blood-based biomarkers could predict cognitive decline in Alzheimer's disease (AD) continuum.Method: Two hundred and fifty-one participants with amnestic mild cognitive impairment (aMCI) from the Shanghai Memory Study were followed up for a maximum of 8 years. Baseline blood biomarkers were measured with the single-molecule array (Simoa) platform. Multipoint clinical diagnosis and domain-specific cognitive functions were assessed to investigate the longitudinal relationship between blood biomarkers and clinical AD progression.Result: Participants with high-risk plasma Aβ42/Aβ40 (A) and p-tau181 (T) level demonstrated the highest probability of incident AD (HR 5.54, 95% CI 2.99-10.27), and the most dramatic decline in global cognition, attention, executive function, visuospatial function, and language. Comparing to young-old participants, the old-old ones with low-and moderate-risk AT showed higher AD risks (HR 3.70, HR 3.15,, and faster cognitive deterioration. Conclusion:The results supported the use of plasma Aβ42/Aβ40 and p-tau181 as accessible and feasible indicators of AD progression and the long-term cognitive deterioration, especially in patients with older age.
Background Neuropsychiatric symptoms (NPS) of dementia are a common issue in dementia patients. It generally presents as three disparate symptom clusters—agitation, psychosis, and mood disorders which have different biological and psychosocial triggers. Therefore, a “one size fits all” solution may not exist. Pharmacological treatments are its treatment of choice, but there is controversy about which agent should be used. We aimed to update and expand our previous work to compare and rank drugs for the treatment of neuropsychiatric symptoms of dementia. Methods We included all randomized controlled trials reported as double‐blind and comparing one active drug with another or with placebo. Studies searched Cochrane Central Register of Controlled Trials, PubMed, MEDLINE, EMBASE, from database inception to Jun 28, 2019. Our primary outcome was change in overall neuropsychiatric symptoms measured with standardised rating scales. And the secondary outcomes were change in aggressive behavior, psychosis, apathy, and depressive symptoms. Effect size measures were standardised mean differences (SMD) with 95% credible intervals (CrIs). We ranked the comparative effects of all drugs against placebo with surface under the cumulative ranking probabilities. We conducted subgroup analyses to assess the robustness of our findings. Confidence in the evidence was assessed using CINeMA. The study protocol is registered with PROSPERO, number CRD42019132231. Results We identified 8175 citations and of these included 114 trials comprising nearly thirty thousand participants. Effect size estimates suggested galantamine was more effective than placebo for reducing overall neuropsychiatric symptoms. SMD compared with placebo for reduction of aggressive behavior found that aripiprazole and risperidone appeared to be more efficacious, citalopram and methylphenidate for reduction of apathy symptoms, memantine and donepezil for reduction of psychosis. While the certainty of evidence was low to very low. Conclusion This systematic review synthesized the available evidence on the comparative efficacy of different pharmacological approaches in the management of overall NPS, agitation, psychosis, apathy and depressive symptoms in dementia patients. These results may serve evidence‐based practice and inform patients, physicians, guideline developers. Especially, clinicians should differentiate these disparate symptom clusters since the appreciation of such complexity is vital to determine the appropriate treatment.
Background Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer’s disease (AD) with high risk of conversion. It is of great challenge to construct reliable biomarkers for predicting conversion from MCI to AD, while the underlying mechanism is still not fully explored. Inter‐dataset generalizability is a prerequisite for clinical use of biomarkers and always a shortage of neuroimaging‐based studies. Method In this study, we propose a novel framework by integrating structural MRI (sMRI) and both static and dynamic resting‐state functional MRI (fMRI) measurements to investigate the differences between MCI converters (MCI_C) and non‐converters (MCI_NC), and then utilized support vector machine (SVM) to construct the prediction models based on selected features. A total of 186 MCI patients with both MRI and three‐year outcome data were selected from two independent cohorts: Shanghai Memory Study (SMS) cohort for selection of MRI predictors and internal cross‐validation, and ADNI cohort for external validation on the generalizability of these MRI predictors. Result In comparison with MCI_NC, the MRI converters were mainly characterized by alterations of medial temporal lobe (MTL) with atrophy extending to lateral temporal and regional hyperactivity and instability, posterior parietal cortex (PPC) with atrophy and inter‐regional hypo‐connectivity and connectional instability, and occipital cortex with functional instability. All of the imaging‐based prediction models achieved an AUC above 0.7 and ACC above 70% in both SMS and ADNI cohorts. The combination of static and dynamic fMRI features resulted in overall good performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features into the fMRI model. In both cohorts, the best imaging model was the multi‐modality MRI model which provided excellent performance with AUC above 0.85 and average ACC/sensitivity/specificity around 80%. Conclusion This inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion and paves a way for eventual clinical use of MRI biomarkers.
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