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Magnesium, CoQ10, Milk thistle, Cognitive function, and Carnosine

January 2019


The effects of magnesium nanovesicle formulations on spatial memory performance in mice.

AIM: The study investigates the effects of magnesium nanovesicles on the memory processes performance in mice. MATERIALS AND METHODS: L-a-phosphatidylcholine was used to obtain nano formulations as lipid vesicles systems stabilized thereafter with chitosan. The experiment was carried out on white Swiss mice, divided into 3 groups of 7 animals each, treated orally 7 consecutive days: Group I (Control): 0.1 mL/10g distilled water; Group II (Mg): 1 mmol/kbw magnesium chloride; Group III (Mg-vesicles): 1 mmol/kbw magnesium nanovesicles. The spatial memory performance was assessed by recording spontaneous alternation behavior in Y-maze test. Each animal was placed at the end of one arm and allowed to move freely through the maze during a single 8 min session. Alternation was defined as a consecutive entry in three different arms. The alternation percentage was computed according to the formula: (number of alternations/total number of arm visits--2) x 100. Data were analyzed using SPSS 17.0 software. Experimental protocols were implemented according to the recommendations of the University Committee for Research and Ethical Issues. RESULTS: New carrier formulations entrapping magnesium chloride were designed: their mean size was 129.56 nm and the mean Zeta potential was +36.1 mV, indicating a moderate stability of the solution. Oral administration of magnesium vesicles resulted in a significant increase of spontaneous alternation percent in Y-maze test (p < 0.01), which suggests an improvement of short-term memory. CONCLUSIONS: Using magnesium chloride entrapped in lipid vesicles induced an enhancement of cognitive functions in mice especially by facilitation of learning extinction.

Rev Med Chir Soc Med Nat Iasi. 2014 Jul-Sep; 118(3):847-53.

Efficacy and Safety of MMFS-01, a Synapse Density Enhancer, for Treating Cognitive Impairment in Older Adults: A Randomized, Double-Blind, Placebo-Controlled Trial.

BACKGROUND: Cognitive impairment is a major problem in elderly, affecting quality of life. Pre-clinical studies show that MMFS-01, a synapse density enhancer, is effective at reversing cognitive decline in aging rodents. OBJECTIVE: Since brain atrophy during aging is strongly associated with both cognitive decline and sleep disorder, we evaluated the efficacy of MMFS-01 in its ability to reverse cognitive impairment and improve sleep. METHODS: We conducted a randomized, double-blind, placebo-controlled, parallel-designed trial in older adult subjects (age 50-70) with cognitive impairment. Subjects were treated with MMFS-01 (n = 23) or placebo (n = 21) for 12 weeks and cognitive ability, sleep quality, and emotion were evaluated. Overall cognitive ability was determined by a composite score of tests in four major cognitive domains. RESULTS: With MMFS-01 treatment, overall cognitive ability improved significantly relative to placebo (p = 0.003; Cohen’s d = 0.91). Cognitive fluctuation was also reduced. The study population had more severe executive function deficits than age-matched controls from normative data and MMFS-01 treatment nearly restored their impaired executive function, demonstrating that MMFS-01 may be clinically significant. Due to the strong placebo effects on sleep and anxiety, the effects of MMFS-01 on sleep and anxiety could not be determined. CONCLUSIONS: The current study demonstrates the potential of MMFS-01 for treating cognitive impairment in older adults.

J Alzheimers Dis. 2016;49(4):971-90. doi: 10.3233/JAD-150538.

Risk factors for behavioral abnormalities in mild cognitive impairment and mild Alzheimer’s disease.

BACKGROUND: Behavioral symptoms are common in both mild cognitive impairment (MCI) and Alzheimer’s disease (AD). METHODS: We analyzed the Neuropsychiatric Inventory Questionnaire data of 3,456 MCI and 2,641 mild AD National Alzheimer’s Coordinating Center database participants. Using factor analysis and logistic regression we estimated the effects of age, sex, race, education, Mini-Mental State Examination, functional impairment, marital status and family history on the presence of behavioral symptoms. We also compared the observed prevalence of behavioral symptoms between amnestic and nonamnestic MCI. RESULTS: Four factors were identified: affective behaviors (depression, apathy and anxiety); distress/tension behaviors (irritability and agitation); impulse control behaviors (disinhibition, elation and aberrant motor behavior), and psychotic behaviors (delusions and hallucinations). Male gender was significantly associated with all factors. Younger age was associated with a higher prevalence of distress/tension, impulse control and psychotic behaviors. Being married was protective against psychotic behaviors. Lower education was associated with the presence of distress/tension behaviors. Caucasians showed a higher prevalence of affective behaviors. Functional impairment was strongly associated with all behavioral abnormalities. Amnestic MCI patients had more elation and agitation relative to nonamnestic MCI patients. CONCLUSIONS: Younger age, male gender and greater functional impairment were associated with higher overall presence of behavioral abnormalities in MCI and mild AD. Marital status, lower education and race had an effect on selected behaviors.

Dement Geriatr Cogn Disord. 2014;37(5-6): 315-26.

Prognosis of mild cognitive impairment in general practice: results of the German AgeCoDe study.

PURPOSE: The concept of mild cognitive impairment (MCI) has recently been introduced into the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) as mild neurocognitive disorder, making it a formal diagnosis. We investigated the prognostic value of such a diagnosis and analyzed the determinants of the future course of MCI in the AgeCoDe study (German Study on Ageing, Cognition, and Dementia in Primary Care Patients). METHODS: We recruited 357 patients with MCI aged 75 years or older from primary care practices and conducted follow-up with interviews for 3 years. Depending on the course of impairment over time, the patients were retrospectively split into 4 groups representing remittent, fluctuating, stable, and progressive courses of MCI. We performed ordinal logistic regression analysis and classification and regression tree (CART) analysis. RESULTS: Overall, 41.5% of the patients had remission of symptoms with normal cognitive function 1.5 and 3 years later, 21.3% showed a fluctuating course, 14.8% had stable symptoms, and 22.4% had progression to dementia. Patients were at higher risk for advancing from one course to the next along this spectrum if they had symptoms of depression, impairment in more than 1 cognitive domain, or more severe cognitive impairment, or were older. The result on a test of the ability to learn and reproduce new material 10 minutes later was the best indicator at baseline for differentiating between remittent and progressive MCI. Symptoms of depression modified the prognosis. CONCLUSIONS: In primary care, about one-quarter of patients with MCI have progression to dementia within the next 3 years. Assessments of memory function and depressive symptoms are helpful in predicting a progressive vs a remittent course. When transferring the concept of MCI into clinical diagnostic algorithms (eg, DSM-5), however, we should not forget that three-quarters of patients with MCI stayed cognitively stable or even improved within 3 years. They should not be alarmed unnecessarily by receiving such a diagnosis.

Ann Fam Med. 2014 Mar-Apr;12(2):158-65.

Prediction of brain age suggests accelerated atrophy after traumatic brain injury.

OBJECTIVE: The long-term effects of traumatic brain injury (TBI) can resemble observed in normal ageing, suggesting that TBI may accelerate the ageing process. We investigate this using a neuroimaging model that predicts brain age in healthy individuals and then apply it to TBI patients. We define individuals’ differences in chronological and predicted structural “brain age,” and test whether TBI produces progressive atrophy and how this relates to cognitive function. METHODS: A predictive model of normal ageing was defined using machine learning in 1,537 healthy individuals, based on magnetic resonance imaging-derived estimates of gray matter (GM) and white matter (WM). This ageing model was then applied to test 99 TBI patients and 113 healthy controls to estimate brain age. RESULTS: The initial model accurately predicted age in healthy individuals (r = 0.92). TBI brains were estimated to be “older,” with a mean predicted age difference (PAD) between chronological and estimated brain age of 4.66 years (±10.8) for GM and 5.97 years (±11.22) for WM. This PAD predicted cognitive impairment and correlated strongly with the time since TBI, indicating that brain tissue loss increases throughout the chronic postinjury phase. INTERPRETATION: TBI patients’ brains were estimated to be older than their chronological age. This discrepancy increases with time since injury, suggesting that TBI accelerates the rate of brain atrophy. This may be an important factor in the increased susceptibility in TBI patients for dementia and other age-associated conditions, motivating further research into the age-like effects of brain injury and other neurological diseases.

Ann Neurol. 2015 Apr;77(4):571-81. doi: 10.1002/ana.24367.

Predicting brain-age from multimodal imaging data captures cognitive impairment.

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N=2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N=475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.

Neuroimage. 2017 Mar 1;148:179-188.

Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners.

Normal aging is known to be accompanied by loss of brain substance. The present study was designed to examine whether the practice of meditation is associated with a reduced brain age. Specific focus was directed at age fifty and beyond, as mid-life is a time when aging processes are known to become more prominent. We applied a recently developed machine learning algorithm trained to identify anatomical correlates of age in the brain translating those into one single score: the BrainAGE index (in years). Using this validated approach based on high-dimensional pattern recognition, we re-analyzed a large sample of 50 long-term meditators and 50 control subjects estimating and comparing their brain ages. We observed that, at age fifty, brains of meditators were estimated to be 7.5 years younger than those of controls. In addition, we examined if the brain age estimates change with increasing age. While brain age estimates varied only little in controls, significant changes were detected in meditators: for every additional year over fifty, meditators’ brains were estimated to be an additional 1month and 22 days younger than their chronological age. Altogether, these findings seem to suggest that meditation is beneficial for brain preservation, effectively protecting against age-related atrophy with a consistently slower rate of brain aging throughout life.

Neuroimage. 2016 Jul 1;134:508-513.