Biological mechanisms of aging predict age-related disease co-occurrence in patients

Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity

Does Culture Shape Our Understanding of Others’ Thoughts and Emotions? An Investigation Across 12 Countries

Q2Q2Measures of social cognition have now become central in neuropsychology, being essential for early and differential diagnoses, follow-up, and rehabilitation in a wide range of conditions. With the scientific world becoming increasingly interconnected, international neuropsychological and medical collaborations are burgeoning to tackle the global challenges that are mental health conditions. These initiatives commonly merge data across a diversity of populations and countries, while ignoring their specificity. Objective: In this context, we aimed to estimate the influence of participants’ nationality on social cognition evaluation. This issue is of particular importance as most cognitive tasks are developed in highly specific contexts, not representative of that encountered by the world’s population. Method: Through a large international study across 18 sites, neuropsychologists assessed core aspects of social cognition in 587 participants from 12 countries using traditional and widely used tasks. Results: Age, gender, and education were found to impact measures of mentalizing and emotion recognition. After controlling for these factors, differences between countries accounted for more than 20% of the variance on both measures. Importantly, it was possible to isolate participants’ nationality from potential translation issues, which classically constitute a major limitation. Conclusions: Overall, these findings highlight the need for important methodological shifts to better represent social cognition in both fundamental research and clinical practice, especially within emerging international networks and consortia.https://orcid.org/0000-0001-9422-3579https://orcid.org/0000-0001-6529-7077Revista Internacional - IndexadaA2N

Psychological and Cognitive Markers of Behavioral Variant Frontotemporal Dementia-A Clinical Neuropsychologist's View on Diagnostic Criteria and Beyond.

International audienceBehavioral variant frontotemporal dementia (bvFTD) is the second leading cognitive disorder caused by neurodegeneration in patients under 65 years of age. Characterized by frontal, insular, and/or temporal brain atrophy, patients present with heterogeneous constellations of behavioral and psychological symptoms among which progressive changes in social conduct, lack of empathy, apathy, disinhibited behaviors, and cognitive impairments are frequently observed. Since the histopathology of the disease is heterogeneous and identified genetic mutations only account for ~30% of cases, there are no reliable biomarkers for the diagnosis of bvFTD available in clinical routine as yet. Early detection of bvFTD thus relies on correct application of clinical diagnostic criteria. Their evaluation however, requires expertise and in-depth assessments of cognitive functions, history taking, clinical observations as well as caregiver reports on behavioral and psychological symptoms and their respective changes. With this review, we aim for a critical appraisal of common methods to access the behavioral and psychological symptoms as well as the cognitive alterations presented in the diagnostic criteria for bvFTD. We highlight both, practical difficulties as well as current controversies regarding an overlap of symptoms and particularly cognitive impairments with other neurodegenerative and primary psychiatric diseases. We then review more recent developments and evidence on cognitive, behavioral and psychological symptoms of bvFTD beyond the diagnostic criteria which may prospectively enhance the early detection and differential diagnosis in clinical routine. In particular, evidence on specific impairments in social and emotional processing, praxis abilities as well as interoceptive processing in bvFTD is summarized and potential links with behavior and classic cognitive domains are discussed. We finally outline both, future opportunities and major challenges with regard to the role of clinical neuropsychology in detecting bvFTD and related neurocognitive disorders

Data Fusion and Artificial Neural Networks for Modelling Crop Disease Severity

This paper analyzes the possibility of applying data fusion combined with artificial neural networks (ANN) on a dataset combining hard and soft data for prediction of one of the most devastating crop diseases of winter wheat, i.e., Septoria Tritici (Zymoseptoria tritici). In advanced decision support systems for crop protection choices, disease models form a major component.They reproduce the biophysical processes of disease development and temporal spread as a set of rules or processes to predict disease risk value. However, the adaptation of these rules or processes to incorporate the effects of climate change is complex and requires extensive rework. To remedy this issue, statistical machine learning techniques have been introduced to model disease severity percentage for some diseases.However, the use of artificial neural networks has been limited (mainly to image data) and is unexplored for Septoria Tritici.This paper explores the use of Feed Forward neural networks on fused tabular data for the task of disease severity modelling. First, ten years of trial data ranging from 2008 to 2018 across Europe is used for the creation of the new tabular dataset with a fusion of all important data sources baring impact on disease development: Field-specific data, weather data, crop growth stages, and disease severity observation made by human trial operators (response variable). %Correlation and regression analyses and domain expert knowledge were used for the selection of useful predictor variables from these data sources. Next, two implementation architectures of Feed Forward neural networks on tabular data are employed: a) standard architecture with backpropagation, drop out regularization, and batch normalization and b) advanced architecture with improvements such as cyclic learning rate and cosine annealing.%A comparison of generic two layer feed forward network vs the same with the incorporation of the latest advances to improve the performance of the architecture is presented. The advanced architecture is able to better model the data and make estimations of disease severity with a difference of +-10\% giving a better quantifiable estimate of disease stress. For better outreach to farmers, a technique to incorporate such modelling techniques into the well established Decision Support Systems is also presented.ISBN för värdpublikation: 978-0-578-64709-8, 978-1-7281-6830-2</p

Psychological and Cognitive Markers of Behavioral Variant Frontotemporal Dementia–A Clinical Neuropsychologist's View on Diagnostic Criteria and Beyond

Behavioral variant frontotemporal dementia (bvFTD) is the second leading cognitive disorder caused by neurodegeneration in patients under 65 years of age. Characterized by frontal, insular, and/or temporal brain atrophy, patients present with heterogeneous constellations of behavioral and psychological symptoms among which progressive changes in social conduct, lack of empathy, apathy, disinhibited behaviors, and cognitive impairments are frequently observed. Since the histopathology of the disease is heterogeneous and identified genetic mutations only account for ~30% of cases, there are no reliable biomarkers for the diagnosis of bvFTD available in clinical routine as yet. Early detection of bvFTD thus relies on correct application of clinical diagnostic criteria. Their evaluation however, requires expertise and in-depth assessments of cognitive functions, history taking, clinical observations as well as caregiver reports on behavioral and psychological symptoms and their respective changes. With this review, we aim for a critical appraisal of common methods to access the behavioral and psychological symptoms as well as the cognitive alterations presented in the diagnostic criteria for bvFTD. We highlight both, practical difficulties as well as current controversies regarding an overlap of symptoms and particularly cognitive impairments with other neurodegenerative and primary psychiatric diseases. We then review more recent developments and evidence on cognitive, behavioral and psychological symptoms of bvFTD beyond the diagnostic criteria which may prospectively enhance the early detection and differential diagnosis in clinical routine. In particular, evidence on specific impairments in social and emotional processing, praxis abilities as well as interoceptive processing in bvFTD is summarized and potential links with behavior and classic cognitive domains are discussed. We finally outline both, future opportunities and major challenges with regard to the role of clinical neuropsychology in detecting bvFTD and related neurocognitive disorders

Distinguishing neurocognitive deficits in adult patients with NP-C from early onset Alzheimer’s dementia

Abstract Background Niemann-Pick disease type C (NP-C) is a rare, progressive neurodegenerative disease caused by mutations in the NPC1 or the NPC2 gene. Neurocognitive deficits are common in NP-C, particularly in patients with the adolescent/adult-onset form. As a disease-specific therapy is available, it is important to distinguish clinically between the cognitive profiles in NP-C and primary dementia (e.g., early Alzheimer’s disease; eAD). Methods In a prospective observational study, we directly compared the neurocognitive profiles of patients with confirmed NP-C (n = 7) and eAD (n = 15). All patients underwent neurocognitive assessment using dementia screening tests (mini-mental status examination [MMSE] and frontal assessment battery [FAB]) and an extensive battery of tests assessing verbal memory, visuoconstructive abilities, visual memory, executive functions and verbal fluency. Results Overall cognitive impairment (MMSE) was significantly greater in eAD vs. NP-C (p = 0.010). The frequency of patients classified as cognitively ‘impaired’ was also significantly greater in eAD vs. NP-C (p = 0.025). Patients with NP-C showed relatively preserved verbal memory, but frequent impairment in visual memory, visuoconstruction, executive functions and in particular, verbal fluency. In the eAD group, a wider profile of more frequent and more severe neurocognitive deficits was seen, primarily featuring severe verbal and visual memory deficits along with major executive impairment. Delayed verbal memory recall was a particularly strong distinguishing factor between the two groups. Conclusion A combination of detailed yet easy-to-apply neurocognitive tests assessing verbal memory, executive functions and verbal fluency may help distinguish NP-C cases from those with primary dementia due to eAD

Früh beginnende Demenzen

Background!#!Due to the demographic change dementia is a common and dramatically increasing reason for medical presentations. In approximately 8% of cases dementia occurs before the age of 65 years. The psychosocial and economic consequences are often severe, particularly in younger patients. Clinicians face major diagnostic challenges. A rapid diagnosis is crucial for patient counselling and management.!##!Objective!#!This review article presents the special features of dementia in younger people, the most important underlying diseases and a rational clinical diagnostic approach.!##!Methods!#!Narrative review. The literature search was carried out in PubMed.!##!Results!#!The differential diagnostic spectrum of dementia in younger people under the age of 65 years is very broad. The most common causes are Alzheimer's disease with typical or atypical clinical presentations and frontotemporal lobar degeneration. The younger the age of onset, the higher the proportion of treatable and potentially reversible causes of dementia.!##!Conclusion!#!The diagnostics of primary neurodegenerative diseases have continuously improved, especially due to the availability of an increasing number of clinical, molecular and imaging biomarkers. Nevertheless, in order to avoid unnecessary and burdensome examinations, the diagnostic work-up of young onset dementia must be hypothesis-driven, i.e. following a precise clinical syndromic classification of the symptoms

Optimizing network propagation for multi-omics data integration

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a 'topology bias' caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand