Latent consequences of early-life lead (Pb) exposure and the future: Addressing the Pb crisis

Background. The lead (Pb) exposure crisis in Flint, Michigan has passed from well-publicized event to a footnote, while its biological and social impact will linger for lifetimes. Interest in the “water crisis” has dropped to pre-event levels, which is neither appropriate nor safe. Flint’s exposure was severe, but it was not unique. Problematic Pb levels have also been found in schools and daycares in 42 states in the USA. The enormity of Pb exposure via municipal water systems requires multiple responses. Herein, we focus on addressing a possible answer to long-term sequelae of Pb exposure. We propose “4R’s” (remediation, renovation, reallocation, and research) against the Pb crisis that goes beyond a short-term fix. Remediation for affected individuals must continue to provide clean water and deal with both short and long-term effects of Pb exposure. Renovation of current water delivery systems, at both system-wide and individual site levels, is necessary. Reallocation of resources is needed to ensure these two responses occur and to get communities ready for potential sequelae of Pb exposure. Finally, properly focused research can track exposed individuals and illuminate latent (presumably epigenetic) results of Pb exposure and inform further resource reallocation. Conclusion. Motivation to act by not only the general public but also by scientific and medical leaders must be maintained beyond initial news cycle spikes and an annual follow-up story. Environmental impact of Pb contamination of drinking water goes beyond one exposure incident in an impoverished and forgotten Michigan city. Population effects must be addressed long-term and nationwide

Report: North Dakota Alzheimer’s and dementia state plan: Current status of, and recommendations for, meeting the needs of individuals and families with Alzheimer’s disease and other dementias

North Dakota reports the fourth highest mortality rate for Alzheimer’s disease in the United States (U.S.) at 52.9 per 100,000 North Dakota residents. The rate for the U.S. is 37 per 100,000 residents. Although leading in mortality, North Dakota is one of a few states with a State Plan for Alzheimer’s and Dementia over 12 years old. This plan was written in July of 2007 and is less than two pages. Although it does mention Alzheimer’s and other other dementias, it does not specifically outline a plan to address services for those living with, or supporting those living with, dementia in North Dakota. This current state plan, developed in 2021, reviewed existing data, involved stakeholders and community members, and identified strengths in the current plan as well as opportunities to reach the following vision: Create an inclusive community and health system that understands, respects, and supports persons who are at-risk of or diagnosed with Alzheimer’s and other dementias, and their caregivers

Engagement With Air Quality Information: Stated Versus Revealed Preferences

Air pollution has a significant impact on health but is often invisible to the naked eye. Real-time air quality information can help people take action to protect their health. However, little is known on how to most effectively frame air quality information to promote public health. We conducted a field experiment to study people’s engagement with real-time air quality information provided through a smartphone application (app). We tested 12 different messaging strategies on both intent to engage with air quality information (through a survey), and actual engagement with air quality information tracked through the app in response to the messaging strategies. Our results, based on 835 survey respondents and 2,740 app users, show that intent to engage and actual engagement differ. Overall, users’ demographics were the most important predictor of engagement with messages. This research demonstrates the significance of testing messaging strategies through field experiments rather than through surveys, and the importance of targeted messages

Redesigning Systems of Care for Older Adults with Alzheimer' Disease

Best-practice models of dementia care have evolved from strategies focused on family caregivers to guidelines predicated on supporting the patient-caregiver dyad along the care continuum. These models have grown in complexity to encompass medical and team-based care that is designed to coordinate dementia care across settings and providers for a defined population of patients. Although there is evidence that the models can improve outcomes, they have not been widely adopted. Barriers to the models' increased adoption include workforce limitations, the cost of necessary practice redesign, and limited evidence of their potential cost-effectiveness. We summarize the origins, evidence base, and common components of best-practice models of dementia care, and we discuss barriers to their implementation. We conclude by describing two current efforts to implement such models on a broad scale, supported by the Center for Medicare and Medicaid Innovation. Taken together, these models seek to demonstrate improved dementia care quality and outcomes, accompanied by cost savings, in both community-based and institutional care settings

Drug development in Alzheimer’s disease: The path to 2025

The global impact of Alzheimer’s disease (AD) continues to increase, and focused efforts are needed to address this immense public health challenge. National leaders have set a goal to prevent or effectively treat AD by 2025. In this paper, we discuss the path to 2025, and what is feasible in this time frame given the realities and challenges of AD drug development, with a focus on disease-modifying therapies (DMTs). Under the current conditions, only drugs currently in late Phase 1 or later will have a chance of being approved by 2025. If pipeline attrition rates remain high, only a few compounds at best will meet this time frame. There is an opportunity to reduce the time and risk of AD drug development through an improvement in trial design; better trial infrastructure; disease registries of well-characterized participant cohorts to help with more rapid enrollment of appropriate study populations; validated biomarkers to better detect disease, determine risk and monitor disease progression as well as predict disease response; more sensitive clinical assessment tools; and faster regulatory review. To implement change requires efforts to build awareness, educate and foster engagement; increase funding for both basic and clinical research; reduce fragmented environments and systems; increase learning from successes and failures; promote data standardization and increase wider data sharing; understand AD at the basic biology level; and rapidly translate new knowledge into clinical development. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets. The opportunity for more than a few new therapies by 2025 is small. Accelerating research and clinical development efforts and bringing DMTs to market sooner would have a significant impact on the future societal burden of AD. As these steps are put in place and plans come to fruition, e.g., approval of a DMT, it can be predicted that momentum will build, the process will be self-sustaining, and the path to 2025, and beyond, becomes clearer

Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges

Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer

Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study.

BACKGROUND: Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR). METHODS AND FINDINGS: We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the International Genomics of Alzheimer's Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62-0.91]; p = 3.4 × 10(-3)). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10(-8)). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51-0.89]; p = 6.5 × 10(-3)), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses. CONCLUSIONS: Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure--or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications--may reduce AD risk.We thank the International Genomics of Alzheimer's Project (IGAP) for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i–Select chips were funded by the French National Foundation on Alzheimer's disease and related disorders. EADI was supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. GERAD was supported by the Medical Research Council (Grant n° 503480), Alzheimer's Research UK (Grant n° 503176), the Wellcome Trust (Grant n° 082604/2/07/Z) and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant n° 01GI0102, 01GI0711, 01GI0420. CHARGE was partly supported by the NIH/NIA grant R01 AG033193 and the NIA AG081220 and AGES contract N01–AG–12100, the NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by the NIH/NIA grants: U01 AG032984, U24 AG021886, U01 AG016976, and the Alzheimer's Association grant ADGC–10–196728.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pmed.100184

Alzheimer's Disease-Related Dementias Summit 2022: National Research Priorities for the Investigation of Post-Traumatic Brain Injury Alzheimer's Disease and Related Dementias

Traumatic Brain Injury (TBI) is a risk factor for Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD) and otherwise classified post-traumatic neurodegeneration (PTND). Targeted research is needed to elucidate the circumstances and mechanisms through which TBI contributes to the initiation, development, and progression of AD/ADRD pathologies including multiple etiology dementia (MED). The National Institutes of Health hosts triennial ADRD summits to inform a national research agenda, and TBI was included for a second time in 2022. A multidisciplinary expert panel of TBI and dementia researchers was convened to re-evaluate the 2019 research recommendations for understanding TBI as an AD/ADRD risk factor and to assess current progress and research gaps in understanding post-TBI AD/ADRD. Refined and new recommendations were presented during the MED special topic session at the virtual ADRD Summit in March 2022. Final research recommendations incorporating broad stakeholder input are organized into four priority areas as follows: (1) Promote interdisciplinary collaboration and data harmonization to accelerate progress of rigorous, clinically meaningful research; (2) Characterize clinical and biological phenotypes of PTND associated with varied lifetime TBI histories in diverse populations to validate multimodal biomarkers; (3) Establish and enrich infrastructure to support multimodal longitudinal studies of individuals with varied TBI exposure histories and standardized methods including common data elements (CDEs) for ante-mortem and post-mortem clinical and neuropathological characterization; and (4) Support basic and translational research to elucidate mechanistic pathways, development, progression, and clinical manifestations of post-TBI AD/ADRDs. Recommendations conceptualize TBI as a contributor to MED and emphasize the unique opportunity to study AD/ADRD following known exposure, to inform disease mechanisms and treatment targets for shared common AD/ADRD pathways

Degenerative diseases early stages biomarker detection using a silicon carbide (SiC) based biosensor

Alzheimer’s disease (AD) is a neurodegenerative disease with only late stage detection; thus, diagnosis is made when it is no longer possible to treat the disease, only its symptoms. Consequently, this often leads to caregivers who are the patient’s relatives, which adversely impacts the workforce along with severely diminishing the quality of life for all involved. It is, therefore, highly desirable to develop a fast, effective and reliable sensor to enable early-stage detection in an attempt to reverse disease progression. This research validates the detection of amyloid-beta 42 (Aβ42) using a Silicon Carbide (SiC) electrode, a fact that is unprecedented in the literature. Aβ42 is considered a reliable biomarker for AD detection, as reported in previous studies. To validate the detection with a SiC based electrochemical sensor, a gold (Au) electrode-based electrochemical sensor was used as a control. The same cleaning, functionalization and Aβ1–28 antibody immobilization steps were used on both electrodes. Sensor validation was carried out by means of Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) aiming to detect an 0.5 µg·mL−1 Aβ42 concentration in 0.1 M buffer solution as a proof of concept. A repeatable peak directly related to the presence of Aβ42 was observed, indicating that a fast SiC-based electrochemical sensor was constructed and may prove to be a useful approach for the early detection of AD.MaestríaMagister en Ingeniería Electrónic