Associations Between Plasma, Imaging, and Cerebrospinal Fluid Biomarkers with Driving Behavior and Cognitive Tests: Implications for Biomarker Usefulness.

Background: Declines in instrumental activities of daily living like driving are hallmarks sequelae of Alzheimer's disease (AD). Although driving has been shown to be associated with traditional imaging and cerebrospinal fluid (CSF) biomarkers, it is possible that some biomarkers have stronger associations with specific aspects of driving behavior. Furthermore, associations between newer plasma biomarkers and driving behaviors are unknown. Objective: This study assessed the extent to which individual plasma, imaging, and CSF biomarkers are related to specific driving behaviors and cognitive functions among cognitively normal older adults. Methods: We analyzed naturalistic driving behavior from cognitively healthy older drivers (N = 167, 47% female, mean age = 73.3 years). All participants had driving, clinical, and demographic data and completed biomarker testing, including imaging, CSF, and/or plasma, within two years of study commencement. Results: AD biomarkers were associated with different characteristics of driving and cognitive functioning within the same individuals. Elevated levels of plasma Aß40 were associated with more speeding incidents, higher levels of CSF tau were related to shorter duration of trips, and higher CSF neurofilament light chain values were associated with traveling shorter distances, smaller radius of gyration, and fewer trips at night. We demonstrated that plasma, like CSF and imaging biomarkers, were helpful in predicting everyday driving behaviors. Conclusions: These findings suggest that different biomarkers offer complementary information with respect to driving behaviors. These distinct relationships may help in understanding how different biological changes that occur during the preclinical stage of AD can impact various sensorimotor and cognitive processes.

Cohort profile: radiotherapy dataset (RTDS) in England.

PURPOSE: The purpose of the Radiotherapy Dataset (RTDS) is to collect consistent and comparable data across all providers of National Health Service (NHS)-funded radiotherapy and to provide intelligence for service planning, commissioning, clinical practice and research. PARTICIPANTS: The RTDS is a mandated dataset requiring providers to collect and submit data monthly for patients treated in England. Data is available from 01 April 2009 to 2 months behind the calendar month.The National Disease Registration Service (NDRS) started receiving data from 01 April 2016. Prior to this, the National Clinical Analysis and Specialised Applications Team (NATCANSAT) were responsible for the RTDS. NDRS holds a copy of the NATCANSAT data for English NHS providers.The RTDS contains clinical information on the primary disease being treated, modality and intent of treatment, dose fractionation and hospital appointment details. Due to constraints in RTDS coding, linkage to the English National Cancer Registration dataset is beneficial. FINDINGS TO DATE: The RTDS has been linked to the English National Cancer Registration and Systemic Anti-Cancer Therapy (SACT) datasets and to Hospital Episode Statistics (HES) to provide a more complete picture of the patient cancer pathway. Findings include a study to compare outcomes for patients treated with radical radiotherapy, an investigation of factors influencing 30-day mortality, assessing sociodemographic variation in the use of treatment and a study to assess the service impact of the COVID-19 pandemic. A range of other studies have been completed or are ongoing currently. FUTURE PLANS: The RTDS can be used for a variety of functions including cancer epidemiological studies to investigate inequalities in treatment access; provide service planning intelligence; monitor clinical practice; and support clinical trial design and recruitment. Collection is to continue indefinitely, with regular updates to the data specification to enable capture of more detailed information on radiotherapy planning and delivery.

Everyday Driving and Plasma Biomarkers in Alzheimer's Disease: Leveraging Artificial Intelligence to Expand Our Diagnostic Toolkit.

BACKGROUND: Driving behavior as a digital marker and recent developments in blood-based biomarkers show promise as a widespread solution for the early identification of Alzheimer's disease (AD). OBJECTIVE: This study used artificial intelligence methods to evaluate the association between naturalistic driving behavior and blood-based biomarkers of AD. METHODS: We employed an artificial neural network (ANN) to examine the relationship between everyday driving behavior and plasma biomarker of AD. The primary outcome was plasma Aß42/Aß40, where Aß42/Aß40 < 0.1013 was used to define amyloid positivity. Two ANN models were trained and tested for predicting the outcome. The first model architecture only includes driving variables as input, whereas the second architecture includes the combination of age, APOE ɛ4 status, and driving variables. RESULTS: All 142 participants (mean [SD] age 73.9 [5.2] years; 76 [53.5%] men; 80 participants [56.3% ] with amyloid positivity based on plasma Aß42/Aß40) were cognitively normal. The six driving features, included in the ANN models, were the number of trips during rush hour, the median and standard deviation of jerk, the number of hard braking incidents and night trips, and the standard deviation of speed. The F1 score of the model with driving variables alone was 0.75 [0.023] for predicting plasma Aß42/Aß40. Incorporating age and APOE ɛ4 carrier status improved the diagnostic performance of the model to 0.80 [>0.051]. CONCLUSION: Blood-based AD biomarkers offer a novel opportunity to establish the efficacy of naturalistic driving as an accessible digital marker for AD pathology in driving research.

Driving assessment in preclinical Alzheimer's disease: progress to date and the path forward.

BACKGROUND: Changes in driving behaviour may start at the preclinical stage of Alzheimer's disease (AD), where the underlying AD biological process has begun in the presence of cognitive normality. Here, we summarize the emerging evidence suggesting that preclinical AD may impact everyday driving behaviour. MAIN: Increasing evidence links driving performance and behaviour with AD biomarkers in cognitively intact older adults. These studies have found subtle yet detectable differences in driving associated with AD biomarker status among cognitively intact older adults. CONCLUSION: Recent studies suggest that changes in driving, a highly complex activity, are linked to, and can indicate the presence of, neuropathological AD. Future research must now examine the internal and external validity of driving for widespread use in identifying biological AD.

Improving Our Understanding of Driving Changes in Preclinical and Early Symptomatic Alzheimer's Disease: The Role of Naturalistic Driving Studies.

Research on how preclinical and early symptomatic Alzheimer's disease (AD) impacts driving behavior is in its infancy, with several important research areas yet to be explored. This paper identifies research gaps and suggests priorities for driving studies over the next few years among those at the earliest stages of AD. These priorities include how individual differences in demographic and biomarker measures of AD pathology, as well as differences in the in-vehicle and external driving environment, affect driving behavior. Understanding these differences is important to developing future interventions to increase driving safety among those at the earliest stages of AD.

Driving, Social Distancing, Protective, and Coping Behaviors of Older Adults Before and During COVID-19.

A thorough understanding of individual characteristics of older adults during the COVID-19 pandemic is critical for managing the ongoing pandemic course and planning for the future pandemics. Here, we explore the impact of the COVID-19 pandemic on driving, social distancing, protective, and coping behaviors of older adults. This study reports data on participants aged above 65 whose driving behaviors are being monitored using Global Positioning System (GPS) devices. Participants completed a COVID-19 survey in May 2020. We found that older adults decreased their number of days driving, number of trips per day, as well as average driving speed, and had fewer speeding incidents following COVID-19 onset. We also show that female and African American older adults engaged in more positive coping and cleaning behaviors, and had greater decreases in the number of days driving during the pandemic. The findings highlight the importance of considering older adults' individual characteristics for an equitable response to the COVID-19 pandemic.

Naturalistic driving measures of route selection associate with resting state networks in older adults.

Our objective was to identify functional brain changes that associate with driving behaviors in older adults. Within a cohort of 64 cognitively normal adults (age 60+), we compared naturalistic driving behavior with resting state functional connectivity using machine learning. Functional networks associated with the ability to interpret and respond to external sensory stimuli and the ability to multi-task were associated with measures of route selection. Maintenance of these networks may be important for continued preservation of driving abilities.

Adverse driving behaviors are associated with sleep apnea severity and age in cognitively normal older adults at risk for Alzheimer's disease.

Alzheimer's disease (AD) pathology accumulates for decades before the onset of cognitive decline. Cognitively normal individuals with biomarker evidence of AD brain pathology (i.e. biomarker + or preclinical AD) can be differentiated from individuals without AD brain pathology based on naturalistic driving data, such as hard acceleration or braking and speeding, measured using in-vehicle dataloggers. Older adults are at increased risk of injury and death from motor vehicle crashes and driving cessation is also linked to negative health outcomes. Identifying potentially modifiable risk factors that increase driving risk may prolong safe driving in old age. Sleep apnea is associated with adverse driving behaviors across the age span. In this study, we hypothesized that high-risk driving behaviors would be associated with increased sleep apnea severity and AD pathology. We found that higher sleep apnea severity measured by a home sleep apnea test was associated with a higher incidence of adverse driving behaviors even after controlling for multiple confounders (ß = 0.24 ±â€…0.09, p < 0.01). This association was independent of AD biomarker positivity (i.e. increased t-tau/Aß 42 ratio). Increasing age was associated with a higher likelihood of high-risk driving behaviors in individuals with AD brain pathology (ß = 0.12 ±â€…0.04, p < 0.01), but a lower likelihood in individuals without AD brain pathology (ß = -0.06 ±â€…0.03, p < 0.05). These findings suggest that adverse driving behaviors linked to a higher rate of traffic crashes in older adults are associated with sleep apnea severity and AD pathology even in cognitively unimpaired individuals. Further studies are needed to determine if treatment of sleep apnea decreases high-risk driving behaviors and therefore motor vehicle crashes.

Longitudinal Changes in Anger, Anxiety, and Fatigue Are Associated with Cerebrospinal Fluid Biomarkers of Alzheimer's Disease.

Alzheimer's disease (AD) studies in cognitively normal (CN) older adults age≥65 suggest depression is associated with molecular biomarkers (imaging and cerebrospinal fluid [CSF]). This study used linear mixed models (covariance pattern model) to assess whether baseline CSF biomarkers (Aß42/Aß40, t-Tau/Aß42, p-Tau/Aß42) predicted changes in non-depressed mood states in CN older adults (N = 248), with an average of three follow-up years. Participants with higher levels of CSF biomarkers developed more anger, anxiety, and fatigue over time compared to those with more normal levels. Non-depressed mood states in preclinical AD may be a prodrome for neuropsychiatric symptoms in symptomatic AD.

The complex relationship between depression and progression to incident cognitive impairment across race and ethnicity.

INTRODUCTION: We examined baseline differences in depression and antidepressant use among cognitively normal older adults in five ethnoracial groups and assessed whether depression predicted a faster progression to incident cognitive impairment across groups. METHODS: Data from the National Alzheimer's Coordinating Center (n = 8168) were used to examine differences between non-Hispanic Whites (nHW), African Americans (AA), Hispanics, Asians, and American Indian and Alaskan Natives in cross-sectional and longitudinal models. RESULTS: AA had a lower risk of depression compared to nHW at baseline. No statistical interactions were noted between ethnoracial groups and depression. However, depression independently predicted a faster progression to incident cognitive impairment. Hispanics and Asian participants had a higher hazard for progression compared to nHW. DISCUSSION: Previously established risk factors between depression and dementia were not found among AA and nHW participants. The relationship between depression and ethnoracial groups is complex and suggests differential effects on progression from cognitive normality to impairment.

Limited Longitudinal Change in Self-reported Spatial Navigation Ability in Preclinical Alzheimer Disease.

Subtle changes in objective spatial navigation ability have been observed in the preclinical stage of Alzheimer disease (AD) cross-sectionally and have been found to predict clinical progression. However, longitudinal change in self-reported spatial navigation ability in preclinical AD has yet to be examined. The current study examined whether AD biomarkers suggestive of preclinical AD at baseline spatial navigation assessment and APOE genotype predicted decline in self-reported spatial navigation ability and whether APOE genotype moderated the association of AD biomarkers with change in self-reported spatial navigation. Clinically normal (Clinical Dementia Rating Scale=0) adults aged 56 to 90 completed the Santa Barbara Sense of Direction Scale (SBSOD) annually for an average of 2.73 years. Biomarker data was collected within +/-2 years of baseline (ie, cerebrospinal fluid Aß42, p-tau181, p-tau181/Aß42 ratio, positron emission tomography imaging with Florbetapir or Pittsburgh Compound-B, and hippocampal volume). APOE genotyping was obtained for all participants. SBSOD demonstrated a nonsignificant trend toward a decline over time (P=0.082). AD biomarkers did not predict change in self-reported spatial navigation (all Ps>0.163). APOE genotype did not moderate the relationship between AD biomarkers and self-reported spatial navigation in planned analyses (all Ps>0.222). Results suggest that self-reported spatial navigation ability, as estimated with the SBSOD, may be limited as a measure of subtle cognitive change in the preclinical stage of AD.

GPS driving: a digital biomarker for preclinical Alzheimer disease.

BACKGROUND: Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods. METHODS: We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD. RESULTS: The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96. CONCLUSION: The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults.

The ideological divide in confidence in science and participation in medical research.

In the United States, the wide ideological divergence in public confidence in science poses a potentially significant problem for the scientific enterprise. We examine the behavioral consequences of this ideological divide for Americans' contributions to medical research. Based on a mass survey of American adults, we find that engagement in a wide range of medical research activities is a function of a latent propensity to participate. The propensity is systematically higher among liberals than among conservatives. A substantial part of this ideological divide is due to conservative Americans' lower confidence in science. These findings raise important issues for the recruitment of subjects for medical studies and the generalizability of results from such studies.

Lack of association between acute stroke, post-stroke dementia, race, and ß-amyloid status.

INTRODUCTION: Stroke and Alzheimer disease share risk factors and often co-occur, and both have been reported to have a higher prevalence in African Americans as compared to non-Hispanic whites. However, their interaction has not been established. The objective of this study was to determine if preclinical Alzheimer disease is a risk factor for stroke and post-stroke dementia and whether racial differences moderate this relationship. METHODS: This case-control study was analyzed in 2019 using retrospective data from 2007 to 2013. Participants were adults age 65 and older with and without acute ischemic stroke. Recruitment included word of mouth and referrals in Saint Louis, MO, with stroke participants recruited from acutely hospitalized patients and non-stroke participants from community living older adults who were research volunteers. Our assessment included radiologic reads of infarcts, microbleeds, and white matter hyperintensitites (WMH); a Pittsburgh Compound B PET measure of cortical ß-amyloid binding; quantitative measures of hippocampal and WMH volume; longitudinal Mini Mental State Examination (MMSE) scores; and Clinical Dementia Rating (CDR) 1 year post-stroke. RESULTS: A total of 243 participants were enrolled, 81 of which had a recent ischemic stroke. Participants had a mean age of 75, 57% were women, and 52% were African American. Cortical amyloid did not differ significantly by race, stroke status, or CDR post-stroke. There were racial differences in MMSE scores at baseline (mean 26.8 for African Americans, 27.9 for non-Hispanic whites, p = 0.03), but not longitudinally. African Americans were more likely to have microbleeds (32.8% vs 22.6%, p = 0.04), and within the acute stroke group, African Americans were more likely to have small infarcts (75.6% vs 56.8%, p = 0.049). CONCLUSION: Preclinical Alzheimer disease did not show evidence of being a risk factor for stroke nor predictive of post-stroke dementia. We did not observe racial differences in ß-amyloid levels. However, even after controlling for several vascular risk factors, African Americans with clinical stroke presentations had greater levels of vascular pathology on MRI.

Reaction to a Pandemic: Social Distancing and Driving Among Older Adults During COVID-19.

Coronavirus disease 2019 (COVID-19) has produced considerable morbidity and mortality worldwide, and older adults are at especially high risk for developing severe COVID-19. A cohort study of driving behavior from January 1, 2019, to April 25, 2019, and January 1, 2020, to April 25, 2020, was conducted. We hypothesized that older adults would reduce the number of days driving and number of trips/day they make after COVID-19 case acceleration. Data from 214 adults aged 66.5 to 92.8 years were used. Women comprised 47.6% of the sample and 15.4% were African American. Participants reduced the proportion of days driven during the pandemic (.673 vs. .382 [p < .001]) compared with same period the year before (.695 vs. .749). Trips/day showed a similar decline (p < .001). Participants also took shorter trips (p = .02), drove slower (p < .001), had fewer speeding incidents (p < .001), and had different trip destinations (p < .001). These results indicate that older adults reduce their driving behavior when faced with a pandemic.

The Importance of Advancing Research on Aging and Driving.

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The impact of the COVID-19 pandemic on radiotherapy services in England, UK: a population-based study.

BACKGROUND: The indirect impact of the COVID-19 pandemic on cancer outcomes is of increasing concern. However, the extent to which key treatment modalities have been affected is unclear. We aimed to assess the impact of the pandemic on radiotherapy activity in England. METHODS: In this population-based study, data relating to all radiotherapy delivered for cancer in the English NHS, between Feb 4, 2019, and June 28, 2020, were extracted from the National Radiotherapy Dataset. Changes in mean weekly radiotherapy courses, attendances (reflecting fractions), and fractionation patterns following the start of the UK lockdown were compared with corresponding months in 2019 overall, for specific diagnoses, and across age groups. The significance of changes in radiotherapy activity during lockdown was examined using interrupted time-series (ITS) analysis. FINDINGS: In 2020, mean weekly radiotherapy courses fell by 19·9% in April, 6·2% in May, and 11·6% in June compared with corresponding months in 2019. A relatively greater fall was observed for attendances (29·1% in April, 31·4% in May, and 31·5% in June). These changes were significant on ITS analysis (p<0·0001). A greater reduction in treatment courses between 2019 and 2020 was seen for patients aged 70 years or older compared with those aged younger than 70 years (34·4% vs 7·3% in April). By diagnosis, the largest reduction from 2019 to 2020 in treatment courses was for prostate cancer (77·0% in April) and non-melanoma skin cancer (72·4% in April). Conversely, radiotherapy courses in April, 2020, compared with April, 2019, increased by 41·2% in oesophageal cancer, 64·2% in bladder cancer, and 36·3% in rectal cancer. Increased use of ultra-hypofractionated (26 Gy in five fractions) breast radiotherapy as a percentage of all courses (0·2% in April, 2019, to 60·6% in April, 2020; ITS p<0·0001) contributed to the substantial reduction in attendances. INTERPRETATION: Radiotherapy activity fell significantly, but use of hypofractionated regimens rapidly increased in the English NHS during the first peak of the COVID-19 pandemic. An increase in treatments for some cancers suggests that radiotherapy compensated for reduced surgical activity. These data will assist health-care providers in understanding the indirect consequences of the pandemic and the role of radiotherapy services in minimising these consequences. FUNDING: None.

Rosai-Dorfman disease of the pancreas: Cytologic analysis of three cases presenting as pancreatic masses.

Rosai-Dorfman Disease (RDD), or sinus histiocytosis with massive lymphadenopathy, is a rare entity characterized by proliferating S100-positive histiocytes. It is most commonly found in lymph nodes with extranodal involvement usually occurring in the head and neck. Pancreatic involvement is extremely rare. The pathology department archives were searched for fine needle aspirations and pancreatic resections showing evidence of RDD. Clinicopathologic features, cytologic smears, cell blocks, immunocytochemical stains and surgical resections were reviewed. Three cases were identified. They were all females, aged 65, 69 and 75, with involvement of the pancreatic tail or head by solid masses of median size 2.3 cm (range 2.1-4.5 cm). Cytologic findings on smears included multiple histiocyte clusters resembling loosely cohesive epithelioid granulomas, singly dispersed histiocytes with moderate to marked nuclear atypia and characteristic emperipolesis. These atypical histiocytes stained positively for CD68, CD163 and S100. Smear background contained variable mixed inflammatory cells, necrotic debris and stromal fragments. The RDD diagnosis was further confirmed on pancreatic resection in two patients and core biopsy in one. The latter patient required three separate procedures before a definitive diagnosis was made. RDD of pancreas is a rare benign inflammatory condition that is diagnostically challenging on cytology. This can cause delays in cytologic diagnosis and/or misdiagnosis. Identification of characteristic cytologic features, primarily histiocytes with emperipolesis, and matching immunocytochemical profile can ensure accurate diagnosis and distinction from mimics.

Identifying Preclinical Alzheimer's Disease Using Everyday Driving Behavior: Proof of Concept.

We examined whether driving behavior can predict preclinical Alzheimer's disease (AD). Data from 131 cognitively normal older adults with cerebrospinal fluid (CSF) and/or positron emission tomography (PET) biomarkers were examined with naturalistic driving behavior. Receiver operating characteristic curves were used to predict the highest 10%, 25%, and 50% of values for CSF tau/Aß42, ptau181/Aß42, or amyloid PET. Six in vivo driving variables alone yielded area under the curves (AUC) from 0.64-0.82. Addition of age, Apolipoprotein ɛ4, and neuropsychological measures to the models improved the AUC (0.81 to 0.90). Driving can be used as novel neurobehavioral marker to identify presence of preclinical AD.

Evaluation of Naturalistic Driving Behavior Using In-Vehicle Monitoring Technology in Preclinical and Early Alzheimer's Disease.

Cognitive impairment is a significant risk factor for hazardous driving among older drivers with Alzheimer's dementia, but little is known about how the driving behavior of mildly symptomatic compares with those in the preclinical, asymptomatic phase of Alzheimer's disease (AD). This study utilized two in-car technologies to characterize driving behavior in symptomatic and preclinical AD. The goals of this pilot study were to (1) describe unsafe driving behaviors in individuals with symptomatic early AD using G-force triggered video capture and (2) compare the driving habits of these symptomatic AD drivers to two groups of cognitively normal drivers, those with and those without evidence of cerebral amyloidosis (CN/A+ and CN/A-) using a global positioning system (GPS) datalogger. Thirty-three drivers (aged 60+ years) were studied over 3 months. G-force triggered video events captured instances of near-misses/collisions, traffic violations, risky driver conduct, and driving fundamentals. GPS data were sampled every 30 s and all instances of speeding, hard braking, and sudden acceleration were recorded. For the early AD participants, video capture identified driving unbelted, late response, driving too fast for conditions, traffic violations, poor judgment, and not scanning intersections as the most frequently occurring safety errors. When evaluating driving using the GPS datalogger, hard breaking events occurred most frequently on a per trip basis across all three groups. The CN/A+ group had the lowest event rate across all three event types with lower instances of speeding. Slower psychomotor speed (Trail Making Part A) was associated with fewer speeding events, more hard acceleration events, and more overall events. GPS tracked instances of speeding were correlated with total number of video-captured near-collisions/collisions and driving fundamentals. Results demonstrate the utility of electronic monitoring to identify potentially unsafe driving events in symptomatic and preclinical AD. Results suggest that drivers with preclinical AD may compensate for early, subtle cognitive changes by driving more slowly and cautiously than healthy older drivers or those with cognitive impairment. Self-regulatory changes in driving behavior appear to occur in the preclinical phase of AD, but safety concerns may not arise until symptoms of cognitive impairment emerge and the ability to self-monitor declines.