Taste in mild cognitive impairment and Alzheimer’s disease

In this prospective study we investigated the quantitative and qualitative taste function of patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). 29 healthy, elderly subjects, 29 MCI and 30 AD patients were tested using a validated taste test, the “taste strips”. Additionally, odor identification, odor discrimination, odor threshold, the mini-mental state examination (MMSE) and Apo E epsilon 4 status were examined. Regarding taste, there was a significant reduction of total taste scores and also the score for individual tastes on either side of the tongue between controls and MCI/AD patients. There was no significant difference in the taste scores between MCI and AD patients. A taste test may be a useful procedure for differentiating between healthy subjects and patients with MCI/AD in a clinical context. For diagnosing MCI versus AD, further tests such as smell test, MMSE, Apo E epsilon 4 status, FDG-PET and MRI appear to be useful.     

To treat or not to treat Alzheimer’s disease by the ketogenic diet? That is the question

Alzheimer’s disease (AD) and current treatments: AD is a serious neurological disorder worldwide that affects about 26 million people,and whose prevalence has been calculated to quadruple by 2050,thus reaching over 1% of the total population,with the highest prevalence occurring in both adults and elderly (Pluta et al.,2018).Neurodegenerative processes of the sporadic form of AD probably start 20 years before the clinical onset of the disorder (Pluta et al.,2018).This disease is the most important cause of dementia in world aged society (~75%).AD is a disorder that affects not only patients but also their caregivers.The social and economic burden associated with AD was calculated as an example in the United States alone;600 billion dollars annually is spent on caring for AD patients (Pluta et al.,2018).AD is the one of the great health-care challenges of the 21st century.The incidence of AD,a chronic and progressive neurodegenerative disorder,is increasing,as well as the need for efficient methods of diagnosis,prevention and treatment (Pluta et al.,2018).The characteristic clinical and neuropathological hallmarks of AD are: dementia as the main clinical symptom and in post-mortem neuropathological examination,the presence of amyloid plaques as well as neurofibrillary tangles and loss of neurons in the brain of AD patients.The role of amyloid and tau protein is questioned in the etiology of AD and other causes such as ischemic etiology are being considered (Pluta and U?amek-Kozio?,2019).There are several treatments that are not causal but symptomatic that are not effective,especially for advanced disease.To date,only a few drugs are approved,such as acetylcholinesterase inhibitors and memantine.Drugs that regulate partly the activity of neurotransmitters and partly alleviate behavioral symptoms.Other treatment options include active and passive immunization,anti-aggregation specifics,and secretase inhibitors.The road to clarity AD etiology,early final ante mortem diagnosis and treatment has been one fraught with a wide range of complications and numerous revisions with a lack of a final solution.Research has recently been launched to identify new mechanisms underlying AD that could be the target of new prevention strategies (Pluta and U?amek-Kozio?,2019).Therefore,other treatment options can be recommended,and the ketogenic diet seems to be an interesting last resort solution at the moment (Rusek et al.,2019).The diet contains large amounts of fat and low carbohydrates with vitamin supplementation.New scientific articles suggest that a low-carbohydrate and high-fat ketogenic diet may help alleviate the brain damage in AD (Ota et al.,2019;Rusek et al.,2019).A ketogenic diet can alleviate the effects of impaired glucose metabolism in AD by providing ketones as an additional source of energy.Here,based on new data,we have presented that a ketogenic diet can be effective in preventing and treating AD,but both ketone bodies production and carbohydrate reduction are needed to achieve this.     

Genetic risk score predicting accelerated progression from mild cognitive impairment to Alzheimer’s disease

Aside from APOE, the genetic factors that influence the progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) remain largely unknown. We assessed whether a genetic risk score (GRS), based on eight non-APOE genetic variants previously associated with AD risk in genome-wide association studies, is associated with either risk of conversion or with rapid progression from MCI to AD. Among 288 subjects with MCI, follow-up (mean 26.3 months) identified 118 MCI-converters to AD and 170 MCI-nonconverters. We genotyped ABCA7 rs3764650, BIN1 rs744373, CD2AP rs9296559, CLU rs1113600, CR1 rs1408077, MS4A4E rs670139, MS4A6A rs610932, and PICALM rs3851179. For each subject we calculated a cumulative GRS, defined as the number of risk alleles (range 0–16) with each allele weighted by the AD risk odds ratio. GRS was not associated with risk of conversion from MCI to AD. However, MCI-converters to AD harboring six or more risk alleles (second and third GRS tertiles) progressed twofold more rapidly to AD when compared with those with less than six risk alleles (first GRS tertile). Our GRS is a first step toward development of prediction models for conversion from MCI to AD that incorporate aggregate genetic factors.     

Brain imaging of mild cognitive impairment and Alzheimer’s disease

The rapidly increasing prevalence of cognitive impairment and Alzheimer’s disease has the potential to create a major worldwide healthcare crisis. Structural MRI studies in patients with Alzheimer’s disease and mild cognitive impairment are currently attracting considerable interest. It is extremely important to study early structural and metabolic changes, such as those in the hippocampus, entorhinal cortex, and gray matter structures in the medial temporal lobe, to allow the early detection of mild cognitive impairment and Alzheimer’s disease. The microstructural integrity of white matter can be studied with diffusion tensor imaging. Increased mean diffusivity and decreased fractional anisotropy are found in subjects with white matter damage. Functional imaging studies with positron emission tomography tracer compounds enable detection of amyloid plaques in the living brain in patients with Alzheimer’s disease. In this review, we will focus on key findings from brain imaging studies in mild cognitive impairment and Alzheimer’s disease, including structural brain changes studied with MRI and white matter changes seen with diffusion tensor imaging, and other specific imaging methodologies will also be discussed.     

Novel CSF biomarkers for Alzheimer’s disease and mild cognitive impairment

Altered levels of cerebrospinal fluid (CSF) peptides related to Alzheimer’s disease (AD) are associated with pathologic AD diagnosis, although cognitively normal subjects can also have abnormal levels of these AD biomarkers. To identify novel CSF biomarkers that distinguish pathologically confirmed AD from cognitively normal subjects and patients with other neurodegenerative disorders, we collected antemortem CSF samples from 66 AD patients and 25 patients with other neurodegenerative dementias followed longitudinally to neuropathologic confirmation, plus CSF from 33 cognitively normal subjects. We measured levels of 151 novel analytes via a targeted multiplex panel enriched in cytokines, chemokines and growth factors, as well as established AD CSF biomarkers (levels of Aβ42, tau and p-tau₁₈₁). Two categories of biomarkers were identified: (1) analytes that specifically distinguished AD (especially CSF Aβ42 levels) from cognitively normal subjects and other disorders; and (2) analytes altered in multiple diseases (NrCAM, PDGF, C3, IL-1α), but not in cognitively normal subjects. A multi-prong analytical approach showed AD patients were best distinguished from non-AD cases (including cognitively normal subjects and patients with other neurodegenerative disorders) by a combination of traditional AD biomarkers and novel multiplex biomarkers. Six novel biomarkers (C3, CgA, IL-1α, I-309, NrCAM and VEGF) were correlated with the severity of cognitive impairment at CSF collection, and altered levels of IL-1α and TECK associated with subsequent cognitive decline in 38 longitudinally followed subjects with mild cognitive impairment. In summary, our targeted proteomic screen revealed novel CSF biomarkers that can improve the distinction between AD and non-AD cases by established biomarkers alone.     

Abnormal visual search in mild cognitive impairment and Alzheimer’s disease

Our aim was to further characterize the clinical concept of mild cognitive impairment (MCI). We examined the status of visual attention-related processing in such patients in relation to healthy older adults and patients with Alzheimer’s disease (AD) by measuring performance on a computer-based visual search task. We tested 20 older adult control participants, 13 patients with amnestic mild cognitive impairment and 12 patients with AD. Patients with AD and with MCI exhibited a significant detriment in visual search performance compared to the older adult controls. The deficit in visual search was greater for the patients with AD than the patients with MCI. The pattern of results displayed by the MCI group indicates that patients who appear clinically to suffer only from a deficit in memory also display a deficit in visual attention-related processing, which although not as severe as those with AD, represents a significant detriment in such performance compared to that seen in healthy ageing.     

Plasma lipids metabolism in mild cognitive impairment and Alzheimer’s disease

Objectives: Expression of phospholipids and related molecules could provide panels of multiple biomarkers searching for the signature of Alzheimer’s disease (AD). The aim of the present study was to quantify ten phospholipids and simultaneously determine phospholipase A₂ (PLA₂) activity in blood of mild cognitive impairment (MCI) and AD patients.

Methods: Thirty-four AD, 20?MCI and 25 controls were enrolled. The phospholipids where analysed using the AbsoluteIDQ^® p180 Kit. PLA₂ activities were accessed in platelets by a radio-enzymatic assay.

Results: The study failed to fix the ten phospholipids as a panel to predict AD; the levels of PCaaC36:6, PCaaC40:6 and C16:1-OH were lower in MCI than in controls (P?=?0.041, P?=?0.012, P?=?0.044 respectively). PCaaC40:2 levels were lower in MCI than in AD (P?=?0.041). The converters MCI-AD showed at baseline lower levels of PCaaC40:2 (P?=?0.050) and PCaaC40:6 (P?=?0.037) than controls. iPLA₂ activity was reduced in AD and MCI than in controls (P?2 (r?=?0.680; P?=?0.001) and sPLA₂ (r?=?0.601; P?=?0.004); PCaaC40:1 and iPLA₂ (r?=?0.503; P?=?0.020); PCaaC40:6 and tPLA₂ (r?=?0.532; P?=?0.013) and sPLA₂ (r?=?0.523; P?=?0.015).

Conclusions: Lipids metabolites in plasma might indirectly indicate changes in neuronal membrane and this deregulation can outline the transition between healthy and diseased brains.     

Free and cued selective reminding test predicts progression to Alzheimer’s disease in people with mild cognitive impairment

Introduction

To assess the diagnostic accuracy of the free and cued selective reminding test (FCSRT) for the development of Alzheimer’s disease (AD) in people with mild cognitive impairment (MCI).

Methods

We enrolled 187 consecutive MCI outpatients from a memory clinic that were evaluated at baseline and every 6 to 12 months through an extensive clinical and neuropsychological protocol. For each test, measures of diagnostic accuracy were obtained. To improve the overall specificity of the neuropsychological battery, we also used the diagnostic tests in parallel combination. The association between FCSRT indexes and AD was tested through proportional hazard regression models with other dementia subtypes as competing event. Laplace regression was used to model time-to-AD diagnosis as a function of FCSRT indexes.

Results

The area under the curve of the FCSRT indexes ranged from 0.69 (95% CI: 0.62–0.76) to 0.76 (95% CI: 0.70–0.82). The specificity peaked up to 100% when we combined the category fluency test with the delayed total recall index of the FCSRT. Participants who tested positive at the FCSRT, as compared with those with negative tests, presented a twofold to fivefold higher risk of developing AD (median follow-up time 2.5 years; p?<?0.001) and were diagnosed with AD 2–3 years earlier (p?<?0.001).

Discussion

The FCSRT assessment suite shows the best predictive performance in detecting AD in people with MCI. These findings might help to reliably and timely identify people at higher risk of AD that is crucial both for properly selecting participants to clinical trials and to fine tune an effective and patient-centered care.

     

Heterogenous mechanisms of mild cognitive impairment in Parkinson’s disease

Mild cognitive impairment in Parkinson disease (PD-MCI) shows heterogeneity in the clinical presentation, neuropsychology, neuroimaging, and neuropathology, suggesting abnormal metabolic network activities involving several cortical and subcortical systems. Prospective studies using specific biomarkers, including amyloid imaging and CSF biomarkers are important for the diagnosis and prognostic assessment of early cognitive deficits in PD patients.     

Is all cognitive impairment in Parkinson’s disease “mild cognitive impairment”?

Cognitive impairment can be demonstrated in Parkinson’s disease (PD) from the very beginning of the disease. Clinical manifestations range from slight deficits, only demonstrable by means of neuropsychological testing, up to dementia. If a linear involution is supposed for the cognitive worsening in PD, then the relatively subtle cognitive defects should be taken as the earliest signs of dementia implying that PD-MCI concept would be thoroughly equivalent to that used for the early prediction of other dementias among healthy population. Cognitive defects in PD, however, may not follow a normal distribution. While fronto-striatal deficits, such as working memory, set-shifting and free-recall verbal memory appear altered in most patients during long periods of time, certain functions depending on more posterior-cortical regions, such as copying or naming, usually characterize patients with dementia. Fronto-striatal and posterior-cortical cognitive defects may have a different pathophysiological substrates, evolution and prognosis. While fronto-striatal defects appear more related to dopaminergic defects, posterior-cortical defects may obey multiple neurotransmitter failure. Designing criteria to accurately diagnose PD-MCI is highly relevant for clinical treatment, research, care-giving and decision-making. Besides quantitative defects, an operative definition of MCI in PD should clearly distinguish a “risky cognitive profile” among the broad cognitive defects intrinsic to PD. Thus, along with other possible biological markers, from a neuropsychological point of view, posterior-cortical defects probably represent the very syndrome of MCI in PD.     

Elman neural network for the early identification of cognitive impairment in Alzheimer’s disease

Early detection of dementia can be useful to delay progression of the disease and to raise awareness of the condition. Alterations in temporal and spatial EEG markers have been found in patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Herein, we propose an automatic recognition method of cognitive impairment evaluation based on EEG analysis using an artificial neural network (ANN) combined with a genetic algorithm (GA). The EEGs of 43 AD and MCI patients (aged between 62 and 88 years) were recorded, analyzed and correlated with their MMSE scores. Quantitative EEGs were calculated using discrete wavelet transform. The data obtained were analyzed by the means of the combined use of ANN and GA to determine the degree of cognitive impairment. The good recognition rate of ANN fed with these inputs suggests that the combined GA/ANN approach may be useful for early detection of AD and could be a valuable tool to support physicians in clinical practice.     

Increased EEG gamma band activity in Alzheimer’s disease and mild cognitive impairment

High frequency (30–70 Hz) gamma band oscillations in the human electro-encephalogram (EEG) are thought to reflect perceptual and cognitive processes. It is therefore interesting to study these measures in cognitive impairment and dementia. To evaluate gamma band oscillations as a diagnostic biomarker in Alzheimer’s disease (AD) and mild cognitive impairment (MCI), 15 psychoactive drug naïve AD patients, 20 MCI patients and 20 healthy controls participated in this study. Gamma band power (GBP) was measured in four conditions viz. resting state, music listening, story listening and visual stimulation. To evaluate test–retest reliability (TRR), subjects underwent a similar assessment one week after the first. The overall TRR was high. Elevated GBP was observed in AD when compared to MCI and control subjects in all conditions. The results suggest that elevated GBP is a reproducible and sensitive measure for cognitive dysfunction in AD in comparison with MCI and controls.     

Predicting progression to Alzheimer’s disease in subjects with amnestic mild cognitive impairment using performance on recall and recognition tests

Journal of Neurology - The research of reliable procedures for predicting cognitive decline or stability in persons with amnestic mild cognitive impairment (a-MCI) is a major goal for the early...     

Contribution of auditory P300 test to the diagnosis of mild cognitive impairment in Parkinson’s disease

Abnormalities in auditory P300 test have been observed in patients with Parkinson’s disease (PD). We aimed to investigate whether or not additional electrophysiological tests assist in making the clinical diagnosis of mild cognitive impairment in Parkinson’s disease (PD-MCI), and we evaluated P300 changes in patients with non-demented PD and analyzed the correlation between the cognitive features and P300 changes. Twenty patients with PD who had been diagnosed with mild cognitive impairment (PD-MCI group) according to the Movement Disorder Society (MDS) 2012 PD-MCI level II criteria, 21 patients with PD without cognitive impairment (PD-Normal group), and 20 control subjects (control group) who were neurologically normal were examined by the standard auditory oddball paradigm. The N100, P200, N200, and P300 latencies and N100-P200, P200-N200, and N200-P300 amplitudes were measured and analyzed. P300 latencies recorded from Fz, Cz, and Pz and N200 latency recorded from Fz were significantly longer in the PD-MCI group than in the PD-Normal and the control group (respectively p < 0.001, p = 0.041). P300 amplitude recorded from Fz was significantly lower in PD-MCI group than those in the other groups (p = 0.038). While P300 was obtained in all patients in the PD-Normal and the control group, it was lost in 35% of PD-MCI patients. The results show that P300 provides a diagnostic tool for detecting PDMCI. We suggest that P300 prolongation and loss of P300 potential could be used as supportive parameter in the diagnosis of PD-MCI.     

Meta-analysis of the relationship of peripheral retinal nerve fiber layer thickness to Alzheimer’s disease and mild cognitive impairment

Background

Previous studies report that the thickness of the peripheral retinal nerve fiber layer (RNFL) in individuals with Alzheimer''s disease (AD) and mild cognitive impairment (MCI) is significantly thinner than in normal controls (NC), but RNFL thickness in different quadrants of the optic nerve remains unclear.

Aim

Conduct a systematic review of studies that assess peripheral RNFL thickness in AD and MCI.

Methods

Based on pre-defined criteria, studies in English or Chinese were identified from PubMed, Embase, ISI web of knowledge, Ovid/Medline, Science Direct, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Chongqing VIP database, WANFANG DATA, and the China BioMedical Literature Service System (SinoMed). Review Manager 5.3 was used for analysis.

Results

The 19 cross-sectional studies identified had a pooled sample of 1455 individuals. There was substantial heterogeneity between studies that compared RNFL in AD or MCI to normal controls, but this heterogeneity was primarily restricted to low-quality studies. Combining 6 high-quality studies (n=578) indicated that total RNFL thickness and the thickness of superior and inferior RNFL quadrants in AD were significantly thinner than in normal controls. Similarly, combining 5 high-quality studies (n=541) indicated significantly thinner total RNFL thickness in MCI than in controls. Six studies (n=589) found thinner RNFL in the superior and inferior quadrants in MCI than in controls;and 6 studies (n=487) found thinner RNFL in the temporal quadrant in MCI than in controls. Finally, 7 studies (n=432) indicated that total RNFL was thinner in AD than in MCI, and 6 studies (n=364) indicated thinner RNFL in the superior and inferior quadrants in AD than in MCI.

Conclusion

Much of the heterogeneity in results from previous studies may be due to poor methodology. Peripheral RNFL thicknesses, particularly in the superior and inferior quadrants, becomes progressively thinner as cognitive function declines, so this could be a candidate biomarker for early identification of AD. Methodologically rigorous studies in large population-based cohort studies that follow elderly individuals over time and that simultaneously collect information on potential mediating factors (such as blood pressure, blood glucose, and lipid levels) are needed to confirm or disprove the potential predictive value of RNFL.     

Quantitative assessment of finger tapping characteristics in mild cognitive impairment,Alzheimer’s disease,and Parkinson’s disease

Background

Fine motor impairments are common in neurodegenerative disorders, yet standardized, quantitative measurements of motor abilities are uncommonly used in neurological practice. Thus, understanding and comparing fine motor abilities across disorders have been limited.

Objectives

The current study compared differences in finger tapping, inter-tap interval, and variability in Alzheimer’s disease (AD), Parkinson’s disease (PD), mild cognitive impairment (MCI), and healthy older adults (HOA).

Methods

Finger tapping was measured using a highly sensitive light-diode finger tapper. Total number of finger taps, inter-tap interval, and intra-individual variability (IIV) of finger tapping was measured and compared in AD (n?=?131), PD (n?=?63), MCI (n?=?46), and HOA (n?=?62), controlling for age and sex.

Results

All patient groups had fine motor impairments relative to HOA. AD and MCI groups produced fewer taps with longer inter-tap interval and higher IIV compared to HOA. The PD group, however, produced more taps with shorter inter-tap interval and higher IIV compared to HOA.

Conclusions

Disease-specific changes in fine motor function occur in the most common neurodegenerative diseases. The findings suggest that alterations in finger tapping patterns are common in AD, MCI, and PD. In addition, the present results underscore the importance of motor dysfunction even in neurodegenerative disorders without primary motor symptoms.

     

Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment

The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid β (Aβ42) in two sets of longitudinally followed MCI patients (n?=?217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.     

Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease

Alzheimer’s disease (AD) is the most common type of dementia, comprising an estimated 60–80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.