There are several population-based studies of aging, memory, and dementia being conducted worldwide. Of these, the Cache County Study on Memory, Health and Aging is noteworthy for its large number of "oldest-old" members. This study, which has been following an initial cohort of 5,092 seniors since 1995, has reported among its major findings the role of the Apolipoprotein E gene on modifying the risk for Alzheimer's disease (AD) in males and females and identifying pharmacologic compounds that may act to reduce AD risk. This article summarizes the major findings of the Cache County study to date, describes ongoing investigations, and reports preliminary analyses on the outcome of the oldest-old in this population, the subgroup of participants who were over age 84 at the study's inception.
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- This study investigates how Mild Cognitive Impairment (MCI), Mild Behavioral Impairment (MBI), and cognitive complaints (CC) can predict the risk of developing dementia or Alzheimer’s disease in older adults.
- Participants included 598 older adults without dementia at the start of the study, who were monitored for up to 12 years for any diagnosis of dementia.
- Results showed that combinations of MCI and CC significantly increased the risk of dementia, with the highest risk linked to those with MCI, CC, and MBI combined, while MBI only or CC only did not increase risk.
Predicting the onset of Alzheimer's disease and related dementia using electronic health records: findings from the cache county study on memory in aging (1995-2008).
BMC Med Inform Decis Mak
October 2024
Department of Family and Preventive Medicine, Division of Public Health, University of Utah Health, 375 Chipeta Way, Suite A, Salt Lake City, UT, 84108, USA.
Introduction: Clinical notes, biomarkers, and neuroimaging have proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict gold-standard, research-based diagnoses of dementia including Alzheimer's disease (AD) and/or Alzheimer's disease related dementias (ADRD), in addition to ICD-based AD and/or ADRD diagnoses, in a well-phenotyped, population-based cohort using a machine learning approach.
View Article and Find Full Text PDFProject RESPOND: Development of a New Capacity in Disaster Response.
Disaster Med Public Health Prep
September 2024
Department of Emergency Medicine, University of California-San Francisco, EMS/Disaster Medicine Fellowship, University of California, San Francisco, CA, USA.
Response to the coronavirus disease (COVID-19) pandemic revealed gaps in medical supply quality and personnel training and familiarity in San Francisco County, prompting the reexamination of county disaster supply caches and emergency medical services (EMS) system decompression protocols. Project RESPOND (Rapid Emergency Supplies for Prehospital Operations in Disaster) was developed to bridge the gap in patient care infrastructure during short- or no-warning disasters and enhance EMS system offloading by introducing a novel capacity for the safe treatment and discharge of patients with minor injuries from the scene of an event. This design, while scaled to the needs of a unique metropolitan population, can be used as a template for the reimagining of disaster response policy and development of disaster supply caches.
View Article and Find Full Text PDFIntroduction: Clinical notes, biomarkers, and neuroimaging have been proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict Alzheimer's disease (AD) and Alzheimer's disease related dementias (ADRD) in a well-phenotyped, population-based cohort using a machine learning approach.
View Article and Find Full Text PDFScheltens ratings, clinical white matter hyperintensities and executive functioning in the Cache County Memory Study.
Appl Neuropsychol Adult
December 2023
Departments of Psychiatry and Neurology, Duke University Medical Center, Durham, NC, USA.
Objective: Examine the association between neuropsychologically assessed executive function and clinically identifiable white matter burden from magnetic resonance imaging, using a visual rating system (Scheltens Rating System) applied to the Cache County Memory Study (CCMS) archival database.
Method: We used the Scheltens Ratings Scale to quantify white matter lesion burden in the CCMS sample and used this metric as a predictor of executive function. The sample included 60 individuals with dementia and 13 healthy controls.
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