Supplemental Training and Injury Prevention in the Sport of Ultra-Running

By definition, ultra-marathon running is a sport that covers any distance above the 26.2-mile marathon distance in a single race. The mountains of Virginia host many ultra-marathon races that attract endurance runners from all over the nation to participate. Training is an essential component of running an ultra. Although there is not one specific way to train for an ultra, there is research that correlates the success of the top ultra-runners and their training preferences regarding supplemental training and injury prevention. To gain a more complete understanding of how to prepare for a successful ultra-marathon, an online survey was created and sent to all runners who participated in four local ultra-marathons. The findings were compared and contrasted to published literature on the subject matter

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

The formation of the advisory group on risk evaluation education for dementia

BackgroundWhen and how to communicate effectively the results of genetic and biomarker based prediction, detection, and quantification of the brain substrates of dementia involve important ethical and legal issues critical for precision medicine. The urgency of the issue has increased as People Living with Dementia (PLwD) and with Risk for Dementia (PwRD) can access direct to consumer genetic testing, amyloid targeting drugs, and clinical amyloid PET scans. To address the need for effective dissemination and consultation, an advisory group was convened that welcomes all interested members.MethodMembers attend two meetings monthly via phone/computer/WebEx. One meeting is a targeted working group that focuses on the following: 1. Symptomatic (PLwD), 2. Asymptomatic (PwRD), 3. Research, 4. Ethics/Healthcare Law, 5. Trainee/Mentorship. These discussion groups hear from and present to stakeholders (PLwD/PwRD/caregivers, professional organizations, companies) to solicit feedback on the efficacy of their efforts. Members also attend a monthly - all hands- meeting where they receive updates from other groups and hear presentations on emerging research and resources.ResultThe advisory group is composed of 104 members who represent advocacy/stakeholders (21%, e.g. professional organization representatives, (PLwD/PwRD/caregivers, FDA), academia (78%, e.g. university, funders, foundations), and healthcare law (1%). Professions include geneticists, genetic counsellors, researchers, clinicians, ethicists, and lawyers. Motivations for joining include improving communication in research and clinical contexts, mitigating potential negative impacts (e.g.emotional distress or discrimination), and protecting rights to know. Topics have included DTC genomics, the impact of APOE disclosure, genetics and personalized medicine, ecological momentary assessment of response to disclosure, and ethical issues in national and international research registries (EPAD). Activities included a survey on disclosure practices in NIA funded ADCs and collaborations with ADEAR. Stakeholders varied in concerns ranging from a need to protect patients from disclosure to a need to protect the right of access.ConclusionMembership is increasing and is engaging diverse specialties and stakeholders who provide education and consultation around communication and use of genetic and biomarkers related to dementia. The group structure and inclusion of members from multiple organizations supports open and free collaboration. Future efforts will be developing structured education for stakeholders and publications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163965/1/alz045562.pd

Predicting missing biomarker data in a longitudinal study of Alzheimer disease

Objective:To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).Methods:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.Results:CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.Conclusion:The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD

Predicting missing biomarker data in a longitudinal study of Alzheimer disease

Objective:To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).Methods:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.Results:CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.Conclusion:The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD

A blood-based signature of cerebrospinal fluid A beta(1-42) status

It is increasingly recognized that Alzheimer's disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid beta(1-42) (A beta(1-42)) may be an earlier indicator of Alzheimer's disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual's CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF A beta(1-42) levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOE epsilon 4 carrier status and four plasma analytes (CGA, A beta(1-42), Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A beta(1-42) levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF A beta(1-42) levels and that the resulting model also validates reasonably across PET A beta(1-42) status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOE epsilon 4 carrier status, is able to predict CSF A beta(1-42) status, the earliest risk indicator for AD, with high accuracy