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BackgroundInformation from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future.MethodsWe conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis.ResultsOf 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer’s disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological assessment battery, Alzheimer’s Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment.ConclusionThe predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.

BackgroundEvidence on preventing Alzheimer’s disease (AD) is challenging to interpret due to varying study designs with heterogeneous endpoints and credibility. We completed a systematic review and meta-analysis of current evidence with prospective designs to propose evidence-based suggestions on AD prevention.MethodsElectronic databases and relevant websites were searched from inception to 1 March 2019. Both observational prospective studies (OPSs) and randomised controlled trials (RCTs) were included. The multivariable-adjusted effect estimates were pooled by random-effects models, with credibility assessment according to its risk of bias, inconsistency and imprecision. Levels of evidence and classes of suggestions were summarised.ResultsA total of 44 676 reports were identified, and 243 OPSs and 153 RCTs were eligible for analysis after exclusion based on pre-decided criteria, from which 104 modifiable factors and 11 interventions were included in the meta-analyses. Twenty-one suggestions are proposed based on the consolidated evidence, with Class I suggestions targeting 19 factors: 10 with Level A strong evidence (education, cognitive activity, high body mass index in latelife, hyperhomocysteinaemia, depression, stress, diabetes, head trauma, hypertension in midlife and orthostatic hypotension) and 9 with Level B weaker evidence (obesity in midlife, weight loss in late life, physical exercise, smoking, sleep, cerebrovascular disease, frailty, atrial fibrillation and vitamin C). In contrast, two interventions are not recommended: oestrogen replacement therapy (Level A2) and acetylcholinesterase inhibitors (Level B).InterpretationEvidence-based suggestions are proposed, offering clinicians and stakeholders current guidance for the prevention of AD.

IntroductionCognitive impairment is a debilitating manifestation in Parkinson’s disease (PD). We sought to investigate predictors of PD-CI (PD with cognitive impairment).MethodsWe systematically searched PubMed and Cochrane Library for prospective cohort studies and pooled estimates via random-effects models. Primary analyses for all types of cognitive impairments and subgroup analyses by separate outcomes were conducted.ResultsA total of 28,009 studies were identified, of which 57 studies with 31 factors were included in the meta-analysis. In the primary analysis, 13 factors were associated with PD-CI, comprising advanced age [relative risk (RR) = 1.07, 95% confidence interval (CI) = 1.03–1.12], age at onset (RR = 4.43, 95% CI = 1.87–10.54), postural-instability-gait disorder (RR = 3.76, 95% CI = 1.36–10.40), higher Hoehn and Yahr stage (RR = 1.83, 95% CI = 1.35–2.47), higher UPDRS III score (RR = 1.04, 95% CI = 1.01–1.08), rapid eye movement sleep behavior disorder (RR = 3.72, 95% CI = 1.20–11.54), hallucinations (RR = 3.09, 95% CI = 1.61–5.93), orthostatic hypotension (RR = 2.98, 95% CI = 1.41–6.28), anxiety (RR = 2.59, 95% CI = 1.18–5.68), APOE ε2 (RR = 6.47, 95% CI = 1.29–32.53), APOE ε4 (RR = 3.04, 95% CI = 1.88–4.91), electroencephalogram theta power > median (RR = 2.93, 95% CI = 1.61–5.33), and alpha power < median (RR = 1.77, 95% CI = 1.07–2.92). In the subgroup analysis, MAPT H1/H1 genotype increased the risk of dementia in PD. Sixty-four studies were included in the systematic review, of which 12 factors were additionally correlated with PD-CI using single studies.ConclusionsAdvanced age, genetic variation in APOE and MAPT, gait disturbance, motor assessments, non-motor symptoms, and electroencephalogram may be promising predictors for PD-CI.

Background Cholinesterase inhibitors and memantine have been approved for management of Alzheimer’s disease (AD), but there has been no consensus about the choice of various types and doses of drugs at different stages. Hence, we compared and ranked the efficacy and tolerability of these available drugs. Methods We searched PubMed, the Cochrane Central Register of Controlled Trials, and Embase for randomized controlled trials (RCTs) published from database inception to July 21, 2017. The primary outcomes were the mean overall changes in cognitive function and responders who had any adverse events. We conducted a random-effects network meta-analysis. Results Forty-one RCTs were included in this study. Compared with placebo, galantamine 32 mg daily (standardized mean difference – 0.51, 95% credible interval – 0.67 to − 0.35), galantamine 24 mg daily (− 0.50, − 0.61 to − 0.40), and donepezil 10 mg daily (− 0.40, − 0.51 to − 0.29) were probably the most effective agents on cognition for mild to moderate AD, and memantine 20 mg combined with donepezil 10 mg (0.76, 0.39 to 1.11) was recommended for moderate to severe patients. Memantine showed the best profile of acceptability. Rivastigmine transdermal 15-cm 2 patch was the best optional treatment both in function and global changes. None of the medicines was likely to improve neuropsychiatric symptoms through this analysis. Conclusions Pharmacological interventions have beneficial effects on cognition, function, and global changes, but not on neuropsychiatric symptoms, through current network meta-analysis. The choice of drugs may mainly depend on the disease severity and clinical symptoms.

Background Early prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features. Methods A total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction. Results During the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91). Conclusions The risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

Individual variation in brain structure influences deterioration due to disease and comprehensive profiling of the associated proteomic signature advances mechanistic understanding. Here, using data from 4997 UK Biobank participants, we analyzed the associations between 2920 plasma proteins and 272 neuroimaging-derived brain structure measures. We identified 5358 associations between 1143 proteins and 256 brain structure measures, with NCAN and LEP proteins showing the most associations. Functional enrichment implicated these proteins in neurogenesis, immune/apoptotic processes and neurons. Furthermore, bidirectional Mendelian randomization revealed 33 associations between 32 proteins and 23 brain structure measures, and 21 associations between nine brain structure associated proteins and ten brain disorders. Moreover, the significant associations between the identified proteins and mental health were mediated by brain volume and surface area. In summary, this study generates a comprehensive atlas mapping the patterns of association between proteome and brain structure, highlighting their potential value for studying brain disorders. The connection between plasma proteomic and brain structure remains unclear. Here, the authors establish a comprehensive atlas of the patterns of associations between microscale proteome and brain structure, and demonstrate their potential value for studying brain disorders.

The bridging integrator 1 (BIN1) gene is the second most important susceptibility gene for late-onset Alzheimer’s disease (LOAD) after apolipoprotein E (APOE) gene. To explore whether the BIN1 methylation in peripheral blood changed in the early stage of LOAD, we included 814 participants (484 cognitively normal participants [CN] and 330 participants with subjective cognitive decline [SCD]) from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database. Then we tested associations of methylation of BIN1 promoter in peripheral blood with the susceptibility for preclinical AD or early changes of cerebrospinal fluid (CSF) AD-related biomarkers. Results showed that SCD participants with significant AD biological characteristics had lower methylation levels of BIN1 promoter, even after correcting for covariates. Hypomethylation of BIN1 promoter were associated with decreased CSF Aβ42 (p = 0.0008), as well as increased p-tau/Aβ42 (p = 0.0001) and t-tau/Aβ42 (p < 0.0001) in total participants. Subgroup analysis showed that the above associations only remained in the SCD subgroup. In addition, hypomethylation of BIN1 promoter was also accompanied by increased CSF p-tau (p = 0.0028) and t-tau (p = 0.0130) in the SCD subgroup, which was independent of CSF Aβ42. Finally, above associations were still significant after correcting single nucleotide polymorphic sites (SNPs) and interaction of APOE ɛ4 status. Our study is the first to find a robust association between hypomethylation of BIN1 promoter in peripheral blood and preclinical AD. This provides new evidence for the involvement of BIN1 in AD, and may contribute to the discovery of new therapeutic targets for AD.

Objective We aimed to investigate the associations between healthy lifestyles and Alzheimer’s disease (AD) biomarkers in cerebrospinal fluid (CSF). Methods A total of 1108 cognitively intact individuals from Chinese Alzheimer’s Biomarker and LifestylE (CABLE) study were examined to evaluate the associations of AD biomarkers with healthy lifestyle factors, including no current smoking, no harmful drinking, absence of social isolation, and regular physical activity. The participants were categorized into groups of favorable, intermediate, and unfavorable lifestyles according to the lifestyle factors. The associations between overall lifestyle and CSF biomarkers were also analyzed. Results Among cognitively intact older adults, those having more social engagement had lower CSF tau ( p  = 0.009) and p-tau ( p  < 0.001) than those who had social isolation. Regular physical activity was associated with higher CSF Aβ42 ( p  = 0.013) and lower levels of CSF tau ( p  = 0.036) and p-tau ( p  = 0.007). However, no significant associations were found of smoking status or alcohol intake with CSF biomarkers. When the overall lifestyle of the participants was evaluated by all the four lifestyle factors, favorable lifestyle profiles were related to lower levels of CSF tau ( p  < 0.001) and p-tau ( p  < 0.001). Conclusions These findings suggest that healthy lifestyles had a beneficial effect on AD pathology among cognitively intact elders.

Background Anaemia has been reported to be associated with cognitive decline and Alzheimer’s disease (AD), but the associations between anaemia and cerebrospinal fluid (CSF) AD biomarkers are still unknown. This study aimed to investigate the associations between anaemia and CSF AD biomarkers. Methods Participants were included from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) study. The associations of anaemia and its severity with CSF AD biomarkers including β-amyloid 1–42 (Aβ42), total tau (t-tau) and phosphorylated tau (p-tau) were analysed by multiple linear regression models. Adjusted for age, gender, educational levels, APOE ε4 alleles, comorbidities (history of coronary heart disease, history of stroke, hypertension, diabetes mellitus, dyslipidaemia) and glomerular filtration rate. Results A total of 646 cognitively normal older adults, consisting of 117 anaemia patients and 529 non-anaemia individuals, were included in this study. Anaemia patients had lower levels of CSF Aβ42 than individuals without anaemia ( p  = 0.035). Besides, participants with more severe anaemia had lower CSF Aβ42 levels ( p  = 0.045). No significant association of anaemia with CSF t-tau and p-tau levels was found. Conclusion Cross-sectionally, anaemia was associated with lower CSF Aβ42 levels. These findings consolidated the causal close relationship between anaemia and AD.

Background Inflammation plays an important role in atherosclerosis but the contribution of neutrophils to this process is unclear. We sought to assess whether neutrophil count is associated with intracranial atherosclerotic stenosis (ICAS). Methods A total of 2847 individuals were included in our study, including 1363 with acute ischemic stroke and 1484 normal controls without stroke. The presence of ICAS was confirmed by magnetic resonance angiography. The association between neutrophil count and ICAS was evaluated by multivariable logistic regression analysis. Results Among 2847 individuals included in this study, individuals with ICAS had higher neutrophil counts than those without ICAS in groups with and without stroke ( P  <  0.0001 for stroke group, P  = 0.0097 for group without stroke). The multivariable logistic regression analysis showed that the third and fourth quartiles were independent predictors of ICAS in all the subjects (Q3: OR 1.81, 95% CI 1.39–2.37, Q4: OR 2.29, 95% CI 1.70–3.10) and patients in the fourth quartile had a higher risk for the occurrence of ICAS in stroke group (Q4: OR 2.82, 95% CI 1.79–4.48). However, there was no significant association between neutrophil count and ICAS in the group without stroke. Conclusions The levels of circulating neutrophils were associated with the presence of ICAS. Our findings suggest that neutrophils may play a role in the pathogenesis of stroke related to ICAS and emphasize the need to develop proper strategies to control neutrophil response for the treatment of ICAS.