The accuracy of determining speeding directly from mass crash data and using the NSW Centre for Road Safety method

Exceeding the posted speed limit, or speeding, is generally accepted as a major cause of road crashes and in particular fatal crashes. However, the actual proportion of crashes in which one or more vehicles was speeding is not easily determined. The exact travelling speed of a vehicle prior to a crash can only be determined by detailed crash reconstruction. Such a reconstruction is considered beyond the scope of regular traffic police who record the majority of the crash data that makes up the mass crash databases such as the South Australian Traffic Accident Reporting System (TARS). It is therefore thought that speeding is underreported in the mass crash data. A method was developed by NSW to identify, from mass data, crashes that involved speeding as a factor. This method was subsequently used by other states, including South Australia. The Centre for Automotive Safety Research conducts the crash reconstructions required to determine speed as part of its at-scene in-depth crash investigation work. This paper compares the actual proportion of speeding crashes in the most recent set of at-scene in-depth crash investigation cases with that found by using the mass data and the method developed by the NSW Centre for Road Safety. It was found that the error ‘excessive speed’ recorded in the TARS database is not accurate in identifying crashes where a vehicle was speeding. The NSW Centre for Road Safety method of determining speeding in crashes was also found to lack accuracy, though it was more accurate than simply relying on the error ‘excessive speed’ in the TARS database.Doecke, S, Kloeden, C.N.http://acrs.org.au/events/acrs-past-conferences/2013-a-safe-system-the-road-safety-discussion

Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity which may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.Comment: Accepted to Biometrics January 202

A conformational variant of p53 (U-p53AZ) as blood-based biomarker for the prediction of the onset of symptomatic Alzheimer\u27s disease

BACKGROUND: Ongoing research seeks to identify blood-based biomarkers able to predict onset and progression of Alzheimer\u27s disease (AD). OBJECTIVE: The unfolded conformational variant of p53 (U-p53AZ), previously observed in AD individuals, was evaluated in plasma samples from individuals participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) cohort for diagnostic and prognostic assessment, validated on a neuropsychological-based diagnosis, over the course of six years. DESIGN: Retrospective Longitudinal Prognostic biomarker study. SETTING: Single-center study based on the AIBL cohort. PARTICIPANTS: 482 participants of the AIBL cohort, aged 60-85 years, without uncontrolled diabetes, vascular disease, severe depression or psychiatric illnesses. MEASUREMENTS: The AlzoSure® Predict test, consisting of immunoprecipitation (IP) followed by liquid chromatography (LC) tandem mass spectrometry (MS/MS), was performed to quantify the AZ 284® peptide as readout of U-p53AZ and compared with an independent neuropsychological diagnosis. The amyloid load via amyloid β-positron emission tomography (Aβ-PET) and supporting clinical information were included where possible. RESULTS: U-p53AZ diagnostic and prognostic performance was assessed in both time-independent and time-dependent (36, 72 and 90 months following initial sampling) analyses. Prognostic performance of Aβ-PET and survival analyses with different risk factors (gender, Aβ-PET and APOE ε4 allele status) were also performed. U-p53AZ differentiated neuropsychologically graded AD from non-AD samples, and its detection at intermediate/high levels precisely identified present and future symptomatic AD. In both time-independent and time-dependent prognostic analyses U-p53AZ achieved area under the curve (AUC) \u3e98%, significantly higher than Aβ-PET AUCs (between 84% and 93%, P respectively \u3c0.0001 and \u3c0.001). As single factor, U-p53AZ could clearly determine the risk of AD neuropsychological diagnosis over time (low versus intermediate/high U-p53AZ hazard ratio=2.99). Proportional hazards regression analysis identified U-p53AZ levels as a major independent predictor of AD onset. CONCLUSIONS: These findings support use of U-p53AZ as blood-based biomarker predicting whether individuals would reach neuropsychologically-defined AD within six years prior to AD diagnosis. Integration of U-p53AZ in screening processes could support refined participant stratification for interventional studies

The effect of an interactive tutorial on the prescribing performance of senior medical students

Objectives: To evaluate the effectiveness of small group tutorials in teaching senior medical students the requirements of prescription writing. Design: Random allocation to interactive tutorial or didactic lecture with blinded evaluation. Subjects: All 1999 6th year medical students, the University of Adelaide. Results: The Tutorial Attenders (mean 13.3, SD 2.6) performed significantly better than the Lecture Group (mean12.2, SD 3.0) p=0.041 and the Non-attenders (mean10.7, SD 3.1) p=<0.001. The 13 individual OSCE items formed four logical subgroups, and the Tutorial Attenders performed significantly better in Prescription Writing in all comparisons. Conclusion: A single, one-hour interactive tutorial is likely to be the minimum amount of intervention that will be effective in improving prescribing skills.Anne L Tonkin, David Taverner, Jenny Latte, Christopher Doeck

Application of the NIA-AA research framework: Towards a biological definition of Alzheimer’s disease using cerebrospinal fluid biomarkers in the AIBL study

BACKGROUND: The National Institute on Aging and Alzheimer’s Association (NIA-AA) have proposed a new Research Framework: Towards a biological definition of Alzheimer’s disease, which uses a three-biomarker construct: Aß-amyloid, tau and neurodegeneration AT(N), to generate a biomarker based definition of Alzheimer’s disease. OBJECTIVES: To stratify AIBL participants using the new NIA-AA Research Framework using cerebrospinal fluid (CSF) biomarkers. To evaluate the clinical and cognitive profiles of the different groups resultant from the AT(N) stratification. To compare the findings to those that result from stratification using two-biomarker construct criteria (AT and/or A(N)). DESIGN: Individuals were classified as being positive or negative for each of the A, T, and (N) categories and then assigned to the appropriate AT(N) combinatorial group: A-T-(N)-; A+T-(N)-; A+T+(N)-; A+T-(N)+; A+T+(N)+; A-T+(N)-; A-T-(N)+; A-T+(N)+. In line with the NIA-AA research framework, these eight AT(N) groups were then collapsed into four main groups of interest (normal AD biomarkers, AD pathologic change, AD and non-AD pathologic change) and the respective clinical and cognitive trajectories over 4.5 years for each group were assessed. In two sensitivity analyses the methods were replicated after assigning individuals to four groups based on being positive or negative for AT biomarkers as well as A(N) biomarkers. SETTING: Two study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited MCI individuals and individuals with AD from primary care physicians or tertiary memory disorder clinics. Cognitively healthy, elderly NCs were recruited through advertisement or via spouses of participants in the study. PARTICIPANTS: One-hundred and forty NC, 33 MCI participants, and 27 participants with AD from the AIBL study who had undergone CSF evaluation using Elecsys® assays. INTERVENTION (if any): Not applicable. MEASUREMENTS: Three CSF biomarkers, namely amyloid β1-42, phosphorylated tau181, and total tau, were measured to provide the AT(N) classifications. Clinical and cognitive trajectories were evaluated using the AIBL Preclinical Alzheimer Cognitive Composite (AIBL-PACC), a verbal episodic memory composite, an executive function composite, California Verbal Learning Test – Second Edition; Long-Delay Free Recall, Mini-Mental State Examination, and Clinical Dementia Rating Sum of Boxes scores. RESULTS: Thirty-eight percent of the elderly NCs had no evidence of abnormal AD biomarkers, whereas 33% had biomarker levels consistent with AD or AD pathologic change, and 29% had evidence of non-AD biomarker change. Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on cognitive outcome assessments than other biomarker groups. Approximately three in four participants with MCI or AD had biomarker levels consistent with the research framework’s definition of AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; and increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures. CONCLUSIONS: Increasing biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of biomarker classifications could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to clinically progress, for their inclusion in future therapeutic trials

Core Alzheimer’s disease cerebrospinal fluid biomarker assays are not affected by aspiration or gravity drip extraction methods

Background CSF biomarkers are well-established for routine clinical use, yet a paucity of comparative assessment exists regarding CSF extraction methods during lumbar puncture. Here, we compare in detail biomarker profiles in CSF extracted using either gravity drip or aspiration. Methods Biomarkers for β-amyloidopathy (Aβ1–42, Aβ1–40), tauopathy (total tau), or synapse pathology (BACE1, Neurogranin Trunc-p75, α-synuclein) were assessed between gravity or aspiration extraction methods in a sub-population of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study (cognitively normal, N = 36; mild cognitive impairment, N = 8; Alzheimer’s disease, N = 6). Results High biomarker concordance between extraction methods was seen (concordance correlation > 0.85). Passing Bablock regression defined low beta coefficients indicating high scalability. Conclusions Levels of these commonly assessed CSF biomarkers are not influenced by extraction method. Results of this study should be incorporated into new consensus guidelines for CSF collection, storage, and analysis of biomarkers

Assessment of a polygenic hazard score for the onset of pre-clinical Alzheimer’s disease

Background: With a growing number of loci associated with late-onset (sporadic) Alzheimer’s disease (AD), the polygenic contribution to AD is now well established. The development of polygenic risk score approaches have shown promising results for identifying individuals at higher risk of developing AD, thereby facilitating the development of preventative and therapeutic strategies. A polygenic hazard score (PHS) has been proposed to quantify age-specific genetic risk for AD. In this study, we assessed the predictive power and transferability of this PHS in an independent cohort, to support its clinical utility. Results: Using genotype and imaging data from 780 individuals enrolled in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, we investigated associations between the PHS and several AD-related traits, including 1) cross-sectional Aβ-amyloid (Aβ) deposition, 2) longitudinal brain atrophy, 3) longitudinal cognitive decline, 4) age of onset. Except in the cognitive domain, we obtained results that were consistent with previously published findings. The PHS was associated with increased Aβ burden, faster regional brain atrophy and an earlier age of onset. Conclusion: Overall, the results support the predictive power of a PHS, however, with only marginal improvement compared to apolipoprotein E alone

Validation of a priori candidate Alzheimer’s disease SNPs with brain amyloid-beta deposition

The accumulation of brain amyloid β (Aβ) is one of the main pathological hallmarks of Alzheimer’s disease (AD). However, the role of brain amyloid deposition in the development of AD and the genetic variants associated with this process remain unclear. In this study, we sought to identify associations between Aβ deposition and an a priori evidence based set of 1610 genetic markers, genotyped from 505 unrelated individuals (258 Aβ+ and 247 Aβ−) enrolled in the Australian Imaging, Biomarker & Lifestyle (AIBL) study. We found statistically significant associations for 6 markers located within intronic regions of 6 genes, including AC103796.1-BDNF, PPP3R1, NGFR, KL, ABCA7 & CALHM1. Although functional studies are required to elucidate the role of these genes in the accumulation of Aβ and their potential implication in AD pathophysiology, our findings are consistent with results obtained in previous GWAS efforts

Glutamine repeat variants in human RUNX2 associated with decreased femoral neck BMD, broadband ultrasound attenuation and target gene transactivation

RUNX2 is an essential transcription factor required for skeletal development and cartilage formation. Haploinsufficiency of RUNX2 leads to cleidocranial displaysia (CCD) a skeletal disorder characterised by gross dysgenesis of bones particularly those derived from intramembranous bone formation. A notable feature of the RUNX2 protein is the polyglutamine and polyalanine (23Q/17A) domain coded by a repeat sequence. Since none of the known mutations causing CCD characterised to date map in the glutamine repeat region, we hypothesised that Q-repeat mutations may be related to a more subtle bone phenotype. We screened subjects derived from four normal populations for Q-repeat variants. A total of 22 subjects were identified who were heterozygous for a wild type allele and a Q-repeat variant allele: (15Q, 16Q, 18Q and 30Q). Although not every subject had data for all measures, Q-repeat variants had a significant deficit in BMD with an average decrease of 0.7SD measured over 12 BMD-related parameters (p = 0.005). Femoral neck BMD was measured in all subjects (&minus;0.6SD, p = 0.0007). The transactivation function of RUNX2 was determined for 16Q and 30Q alleles using a reporter gene assay. 16Q and 30Q alleles displayed significantly lower transactivation function compared to wild type (23Q). Our analysis has identified novel Q-repeat mutations that occur at a collective frequency of about 0.4%. These mutations significantly alter BMD and display impaired transactivation function, introducing a new class of functionally relevant RUNX2 mutants.<br /

A blood-based biomarker panel indicates IL-10 and IL-12/23p40 are jointly associated as predictors of β-amyloid load in an AD cohort

Alzheimer’s Disease (AD) is the most common form of dementia, characterised by extracellular amyloid deposition as plaques and intracellular neurofibrillary tangles of tau protein. As no current clinical test can diagnose individuals at risk of developing AD, the aim of this project is to evaluate a blood-based biomarker panel to identify individuals who carry this risk. We analysed the levels of 22 biomarkers in clinically classified healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer’s participants from the well characterised Australian Imaging, Biomarker and Lifestyle (AIBL) study of aging. High levels of IL-10 and IL-12/23p40 were significantly associated with amyloid deposition in HC, suggesting that these two biomarkers might be used to detect at risk individuals. Additionally, other biomarkers (Eotaxin-3, Leptin, PYY) exhibited altered levels in AD participants possessing the APOE ε4 allele. This suggests that the physiology of some potential biomarkers may be altered in AD due to the APOE ε4 allele, a major risk factor for AD. Taken together, these data highlight several potential biomarkers that can be used in a blood-based panel to allow earlier identification of individuals at risk of developing AD and/or early stage AD for which current therapies may be more beneficial