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Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD.
Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend).
Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance.
Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too.
Abstract
Objectives
To examine the association between third molars and orofacial pain. We hypothesized that impacted third molars are a cause of orofacial pain.
Methods
Magnetic resonance images of 1808 participants from two population‐based cohorts from Northeastern Germany were analysed to define the status of third molars according to the Pell and Gregory classification. A self‐reported questionnaire and a clinical dental examination were used to detect chronic and acute complaints of orofacial pain, masticatory muscle pain, migraine and other types of headache. Logistic regression models were used to analyse the associations between third molar status and orofacial pain.
Results
Individuals with impacted third molars in the maxilla had a higher chance of chronic orofacial pain than those with erupted third molars (odds ratio 2.19; 95% CI 1.19‐4.02). No such association was detected for third molars in the lower jaw. Third molars were not associated with masticatory muscle pain, migraine or other types of headache.
Conclusions
Impacted maxillary third molars might be a cause of chronic orofacial pain. Thus, physicians should consider the eruption/impaction status of third molars in their decision‐making process when treating patients who complain of orofacial pain.
ObjectiveWhole-body MRI (wb-MRI) is increasingly used in research and screening but little is known about the effects of incidental findings (IFs) on health service utilisation and costs. Such effects are particularly critical in an observational study. Our principal research question was therefore how participation in a wb-MRI examination with its resemblance to a population-based health screening is associated with outpatient service costs.DesignProspective cohort study.SettingGeneral population Mecklenburg-Vorpommern, Germany.ParticipantsAnalyses included 5019 participants of the Study of Health in Pomerania with statutory health insurance data. 2969 took part in a wb-MRI examination in addition to a clinical examination programme that was administered to all participants. MRI non-participants served as a quasi-experimental control group with propensity score weighting to account for baseline differences.Primary and secondary outcome measuresOutpatient costs (total healthcare usage, primary care, specialist care, laboratory tests, imaging) during 24 months after the examination were retrieved from claims data. Two-part models were used to compute treatment effects.ResultsIn total, 1366 potentially relevant IFs were disclosed to 948 MRI participants (32% of all participants); most concerned masses and lesions (769 participants, 81%). Costs for outpatient care during the 2-year observation period amounted to an average of €2547 (95% CI 2424 to 2671) for MRI non-participants and to €2839 (95% CI 2741 to 2936) for MRI participants, indicating an increase of €295 (95% CI 134 to 456) per participant which corresponds to 11.6% (95% CI 5.2% to 17.9%). The cost increase was sustained rather than being a short-term spike. Imaging and specialist care related costs were the main contributors to the increase in costs.ConclusionsCommunicated findings from population-based wb-MRI substantially impacted health service utilisation and costs. This introduced bias into the natural course of healthcare utilisation and should be taken care for in any longitudinal analyses.
The relationship between Alzheimer's-related brain atrophy patterns and sleep macro-architecture
(2022)
Introduction
Sleep is increasingly recognized as a major risk factor for neurodegenerative disorders such as Alzheimer's disease (AD).
Methods
Using an magnetic resonance imaging (MRI)–based AD score based on clinical data from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) case-control cohort, we investigated the associations between polysomnography-based sleep macro-architecture and AD-related brain atrophy patterns in 712 pre-symptomatic, healthy subjects from the population-based Study of Health in Pomerania.
Results
We identified a robust inverse association between slow-wave sleep and the AD marker (estimate: −0.019; 95% confidence interval: −0.03 to −0.0076; false discovery rate [FDR] = 0.0041), as well as with gray matter (GM) thicknesses in typical individual cortical AD-signature regions. No effects were identified regarding rapid eye movement or non–rapid eye movement (NREM) stage 2 sleep, and NREM stage 1 was positively associated with GM thickness, mainly in the prefrontal cortical regions.
Discussion
There is a cross-sectional relationship between AD-related neurodegenerative patterns and the proportion of sleep spent in slow-wave sleep.