Vitamin D and white matter abnormalities in older adults: a cross-sectional neuroimaging study.

BACKGROUND AND PURPOSE: Morphological brain changes related to hypovitaminosis D have been poorly studied. In particular, the age-related decrease in vitamin D concentrations may explain the onset of white matter abnormalities (WMA) in older adults. Our objectives were (i) to investigate whether there was an association between serum 25-hydroxyvitamin D (25OHD) concentration and the grade of WMA in older adults and (ii) to determine whether the location of WMA was associated with 25OHD concentration. METHODS: One hundred and thirty-three Caucasian older community-dwellers with no clinical hydrocephalus (mean 71.6 ± 5.6 years; 43.6% female) received a blood test and a magnetic resonance imaging scan of the brain. The grades of total, periventricular and deep WMA were scored using semiquantitative visual rating scales from T2-weighted fluid-attenuated inversion recovery images. The association of WMA with as-measured and deseasonalized 25OHD concentrations was evaluated with the following covariates: age, gender, body mass index, use of anti-vascular drugs, number of comorbidities, impaired mobility, education level, Mini-Mental State Examination score, medial temporal lobe atrophy, serum concentrations of calcium, thyroid-stimulating hormone and vitamin B12, and estimated glomerular filtration rate. RESULTS: Both as-measured and deseasonalized serum 25OHD concentrations were found to be inversely associated with the grade of total WMA (adjusted β = -0.32, P = 0.027), specifically with periventricular WMA (adjusted β = -0.15, P = 0.009) but not with deep WMA (adjusted β = -0.12, P = 0.090). Similarly, participants with 25OHD concentration33% higher grade of periventricular WMA than those with 25OHD ≥75 nM (P = 0.024). No difference in average grade was found for deep WMA (P = 0.949). CONCLUSIONS: Lower serum 25OHD concentration was associated with higher grade of WMA, particularly periventricular WMA. These findings provide a scientific basis for vitamin D replacement trials

Alzheimer’s Biomarkers From Multiple Modalities Selectively Discriminate Clinical Status: Relative Importance of Salivary Metabolomics Panels, Genetic, Lifestyle, Cognitive, Functional Health and Demographic Risk Markers

Background: Among the neurodegenerative diseases of aging, sporadic Alzheimer’s disease (AD) is the most prevalent and perhaps the most feared. With virtually no success at finding pharmaceutical therapeutics for altering progressive AD after diagnosis, research attention is increasingly directed at discovering biological and other markers that detect AD risk in the long asymptomatic phase. Both early detection and precision preclinical intervention require systematic investigation of multiple modalities and combinations of AD-related biomarkers and risk factors. We extend recent unbiased metabolomics research that produced a set of metabolite biomarker panels tailored to the discrimination of cognitively normal (CN), cognitively impaired and AD patients. Specifically, we compare the prediction importance of these panels with five other sets of modifiable and non-modifiable AD risk factors (genetic, lifestyle, cognitive, functional health and bio-demographic) in three clinical groups.Method: The three groups were: CN (n = 35), mild cognitive impairment (MCI; n = 25), and AD (n = 22). In a series of three pairwise comparisons, we used machine learning technology random forest analysis (RFA) to test relative predictive importance of up to 19 risk biomarkers from the six AD risk domains.Results: The three RFA multimodal prediction analyses produced significant discriminating risk factors. First, discriminating AD from CN was the AD metabolite panel and two cognitive markers. Second, discriminating AD from MCI was the AD/MCI metabolite panel and two cognitive markers. Third, discriminating MCI from CN was the MCI metabolite panel and seven markers from four other risk modalities: genetic, lifestyle, cognition and functional health.Conclusions: Salivary metabolomics biomarker panels, supplemented by other risk markers, were robust predictors of: (1) clinical differences in impairment and dementia and even; (2) subtle differences between CN and MCI. For the latter, the metabolite panel was supplemented by biomarkers that were both modifiable (e.g., functional) and non-modifiable (e.g., genetic). Comparing, integrating and identifying important multi-modal predictors may lead to novel combinations of complex risk profiles potentially indicative of neuropathological changes in asymptomatic or preclinical AD

Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretation

BackgroundPersons with Parkinson’s disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.MethodParticipants were 48 well-characterized PD patients (Mbaseline age = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.ResultsAn excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.ConclusionOur data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains

Mobility and Cognition in Seniors. Report from the 2008 Institute of Aging (CIHR) Mobility and Cognition Workshop

Background The annual Scientific Meeting of the Canadian Association on Gerontology was held on October 24 and 25, 2008 in London, Ontario. Prior to the annual meeting, mobility and cognition experts met on October 23, 2008 to engage in a pre-conference workshop. Methods Discussions during the workshop addressed novel areas of research and knowledge and research gaps pertaining to the interaction between mobility and cognition in seniors. Results Workshop presenters moved from the neuromuscular, biomechanics, and neurology of gait impairments, and falls through the role of cognition and mood on mobility regulation to the whole person in the environment. Research gaps were identified. Conclusions Despite a consensus that mobility and cognition are increasingly correlated as people age, several gaps in our understanding of mechanisms and how to assess the interaction were recognized. The gaps originally identified in 2008 are still pertinent today. Common and standardized assessments for “mobility and cognition” are still not in place in current practice. Interventions that target mobility and cognitive decline as a single entity are still lacking

Gait parameters and characteristics associated with increased risk of falls in people with dementia: a systematic review

Background: People with dementia fall twice as often and have more serious fall-related injuries than healthy older adults. While gait impairment as a generic term is understood as a fall risk factor in this population, a clear elaboration of the specific components of gait that are associated with falls risk is needed for knowledge translation to clinical practice and the development of fall prevention strategies for people with dementia. Objective: To review gait parameters and characteristics associated with falls in people with dementia. Methods: Electronic databases CINAHL, EMBASE, MedLine, PsycINFO, and PubMed were searched (from inception to April 2017) to identify prospective cohort studies evaluating the association between gait and falls in people with dementia. Results: Increased double support time variability, use of mobility aids, walking outdoors, higher scores on the Unified Parkinson’s Disease Rating Scale, and lower average walking bouts were associated with elevated risk of any fall. Increased double support time and step length variability were associated with recurrent falls. The reviewed articles do not support using the Performance Oriented Mobility Assessment and the Timed Up-and-Go tests to predict any fall in this population. There is limited research on the use of dual-task gait assessments for predicting falls in people with dementia. Conclusion: This systematic review shows the specific spatiotemporal gait parameters and features that are associated with falls in people with dementia. Future research is recommended to focus on developing specialized treatment methods for these specific gait impairments in this patient population

Dementia with Lewy bodies research consortia: A global perspective from the ISTAART Lewy Body Dementias Professional Interest Area working group

Dementia with Lewy bodies (DLB) research has seen a significant growth in international collaboration over the last three decades. However, researchers face a challenge in identifying large and diverse samples capable of powering longitudinal studies and clinical trials. The DLB research community has begun to focus efforts on supporting the development and harmonization of consortia, while also continuing to forge networks within which data and findings can be shared. This article describes the current state of DLB research collaborations on each continent. We discuss several established DLB cohorts, many of whom have adopted a common framework, and identify emerging collaborative initiatives that hold the potential to expand DLB networks and diversify research cohorts. Our findings identify geographical areas into which the global DLB networks should seek to expand, and we propose strategies, such as the creation of data-sharing platforms and the harmonization of protocols, which may further potentiate international collaboration.publishedVersio

Dual-tasking and gait in people with Mild Cognitive Impairment. The effect of working memory

<p>Abstract</p> <p>Background</p> <p>Cognition and mobility in older adults are closely associated and they decline together with aging. Studies evaluating associations between cognitive factors and gait performance in people with Mild Cognitive Impairment (MCI) are scarce. In this study, our aim was to determine whether specific cognitive factors have a more identifiable effect on gait velocity during dual-tasking in people with MCI.</p> <p>Methods</p> <p>Fifty-five participants, mean age 77.7 (SD = 5.9), 45% women, with MCI were evaluated for global cognition, working memory, executive function, and attention. Gait Velocity (GV) was measured under a single-task condition (single GV) and under two dual-task conditions: 1) while counting backwards (counting GV), 2) while naming animals (verbal GV). Multivariable linear regression analysis was used to examine associations with an alpha-level of 0.05.</p> <p>Results</p> <p>Participants experienced a reduction in GV while engaging in dual-task challenges (p < 0.005). Low executive function and working memory performances were associated with slow single GV (p = 0.038), slow counting GV (p = 0.017), and slow verbal GV (p = 0.031). After adjustments, working memory was the only cognitive factor which remained significantly associated with a slow GV.</p> <p>Conclusion</p> <p>In older adults with MCI, low working memory performance was associated with slow GV. Dual-task conditions showed the strongest associations with gait slowing. Our findings suggest that cortical control of gait is associated with decline in working memory in people with MCI.</p

Patterns of grey matter loss associated with motor subscores in early Parkinson's disease

Classical motor symptoms of Parkinson's disease (PD) such as tremor, rigidity, bradykinesia, and axial symptoms are graded in the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III. It is yet to be ascertained whether parkinsonian motor symptoms are associated with different anatomical patterns of neurodegeneration as reflected by brain grey matter (GM) alteration. This study aimed to investigate associations between motor subscores and brain GM at voxel level. High resolution structural MRI T1 scans from the Parkinson's Progression Markers Initiative (PPMI) repository were employed to estimate brain GM intensity of PD subjects. Correlations between GM intensity and total MDS-UPDRS III and its four subscores were computed. The total MDS-UPDRS III score was significantly negatively correlated bilaterally with putamen and caudate GM density. Lower anterior striatal GM intensity was significantly associated with higher rigidity subscores, whereas left-sided anterior striatal and precentral cortical GM reduction were correlated with severity of axial symptoms. No significant morphometric associations were demonstrated for tremor subscores. In conclusion, we provide evidence for neuroanatomical patterns underpinning motor symptoms in early PD