Inferring Alzheimer's Disease Pathologic Traits from Clinical Measures in Living Adults.

Background: Alzheimer's disease neuropathologic changes (AD-NC) are important to identify people with high risk for AD dementia (ADD) and subtyping ADD. Objective: Develop imputation models based on clinical measures to infer AD-NC. Methods: We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-ß, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD. Results: Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2 = (0.188,0.316,0.262) respectively for amyloid-ß, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC = (0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values < 1.4×10-10. Conclusions: Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

Inferring Alzheimer's disease pathologic traits from clinical measures in living adults.

Background: Alzheimer's disease neuropathologic changes (AD-NC) are important for identify people with high risk for AD dementia (ADD) and subtyping ADD. Objective: Develop imputation models based on clinical measures to infer AD-NC. Methods: We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-ß, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD. Results: Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2=(0.188,0.316,0.262) respectively for amyloid-ß, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC=(0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values<1.4 × 10-10. Conclusion: Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

Predation of Daurian redstarts offspring in nest boxes by the Oriental magpie-robin and tree sparrow.

Birds select suitable nest sites for breeding to ensure their own and offspring's survival; however, they inevitably suffer some potential predation risk. We studied the breeding ecology of Daurian redstarts (Phoenicurus auroreus) by providing nest boxes for their breeding from March to August of 2022. We recorded the predation of both Daurian redstarts eggs or nestlings by Oriental magpie-robins (Copsychus saularis) and tree sparrow (Passer montanus). Oriental magpie-robin were recorded attacking a feeding female adult and depredating nestlings. After the nestling predation event, the Daurian redstarts abandoned the nest. This video evidence provide a better understanding of the potential predators of cavity-nesting birds.

First description of injury feigning behavior in Godlewski's buntings.

Nest defense behavior helps ground-nesting birds improve their breeding success. Among such behaviors, injury feigning behavior (IFB) is one of the better ways to attract predators and protect birds' nests. IFB is generally associated with shorebirds in general and plovers in particular, however, through field observation, it has been found this behavior is also exhibited in bunting species when they facing the risk of predation. We attempt to explore the detailed description of this behavior in buntings and the factors that affect this behavior. Based on video-recordings of the nest defense behavior by the ground-nesting bird Godlewski's bunting (Emberiza godlewskii) against nest invaders, we explored the characteristic of IFB and differences in nest defense behavior during their brooding seasons. The results showed that female buntings had a distinct IFB: the wings of buntings incited within 60°-90° of the body, ran and called rapidly, pretending to be injured and unable to fly. The nest stage had significant and extremely significant effects on IFB and movement distance (MD), respectively. And IFB was more likely to occur in brooding (34.62%) than incubation period (16.42%). This indicates that there are significant differences in the IFB of buntings at different breeding stages.

Risk Models Based on Non-Cognitive Measures May Identify Presymptomatic Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive disorder without a cure. Develop risk prediction models for detecting presymptomatic AD using non-cognitive measures is necessary to enable early interventions. OBJECTIVE: Examine if non-cognitive metrics alone can be used to construct risk models to identify adults at risk for AD dementia and cognitive impairment. METHODS: Clinical data from older adults without dementia from the Memory and Aging Project (MAP, n = 1,179) and Religious Orders Study (ROS, n = 1,103) were analyzed using Cox proportional hazard models to develop risk prediction models for AD dementia and cognitive impairment. Models using only non-cognitive covariates were compared to models that added cognitive covariates. All models were trained in MAP, tested in ROS, and evaluated by the AUC of ROC curve. RESULTS: Models based on non-cognitive covariates alone achieved AUC (0.800,0.785) for predicting AD dementia (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.916,0.881). A model with a single covariate of composite cognition score achieved AUC (0.905,0.863). Models based on non-cognitive covariates alone achieved AUC (0.717,0.714) for predicting cognitive impairment (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.783,0.770). A model with a single covariate of composite cognition score achieved AUC (0.754,0.730). CONCLUSION: Risk models based on non-cognitive metrics predict both AD dementia and cognitive impairment. However, non-cognitive covariates do not provide incremental predictivity for models that include cognitive metrics in predicting AD dementia, but do in models predicting cognitive impairment. Further improved risk prediction models for cognitive impairment are needed.