Review Special Issues

The biological pathways of Alzheimer disease: a review

  • Received: 24 August 2020 Accepted: 23 November 2020 Published: 16 December 2020
  • Alzheimer disease is a progressive neurodegenerative disorder, mainly affecting older people, which severely impairs patients' quality of life. In the recent years, the number of affected individuals has seen a rapid increase. It is estimated that up to 107 million subjects will be affected by 2050 worldwide. Research in this area has revealed a lot about the biological and environmental underpinnings of Alzheimer, especially its correlation with β-Amyloid and Tau related mechanics; however, the precise molecular events and biological pathways behind the disease are yet to be discovered. In this review, we focus our attention on the biological mechanics that may lie behind Alzheimer development. In particular, we briefly describe the genetic elements and discuss about specific biological processes potentially associated with the disease.

    Citation: Marco Calabrò, Carmela Rinaldi, Giuseppe Santoro, Concetta Crisafulli. The biological pathways of Alzheimer disease: a review[J]. AIMS Neuroscience, 2021, 8(1): 86-132. doi: 10.3934/Neuroscience.2021005

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  • Alzheimer disease is a progressive neurodegenerative disorder, mainly affecting older people, which severely impairs patients' quality of life. In the recent years, the number of affected individuals has seen a rapid increase. It is estimated that up to 107 million subjects will be affected by 2050 worldwide. Research in this area has revealed a lot about the biological and environmental underpinnings of Alzheimer, especially its correlation with β-Amyloid and Tau related mechanics; however, the precise molecular events and biological pathways behind the disease are yet to be discovered. In this review, we focus our attention on the biological mechanics that may lie behind Alzheimer development. In particular, we briefly describe the genetic elements and discuss about specific biological processes potentially associated with the disease.


    Change in the global climate is having extreme impacts on the environment and human systems [1,2]. Farmers face substantial risks due to climate change, for example, variable precipitation patterns during planting seasons and intense weather phenomena [3,4]. The rise in risks and vulnerabilities may have adverse effects on the livelihood of rural farmers; hence there is urgent need for adaptation measures to manage risks and vulnerabilities resulting from adverse weather and climate phenomena [5]. The climate change and variability discourse have occupied center stage, globally, in recent times, due to the associated rising risks, dangers, and universality of its impacts [6]. Climate change is majorly characterized by prevalence of severe weather and temperature events, and varying rainfall patterns [7]. Efforts to deal with the current impacts of climate change, will require adaptation and mitigation responses [2,8]. Climate adaptation refers to a system’s capacity to accommodate changes in the climate, together with variability and extremes, to limit possible damage, to exploit the opportunities, and or deal with the outcomes [1,9].

    Developing countries, especially in Africa, face substantial risks from climate change due to increased exposure and inadequate adaptive potential [10]. Agricultural sector, being climate-sensitive, dominates economic activities in these countries, hence increasing the risks faced by these countries. Other factors increasing include underdeveloped education and health institutions, high incidence of poverty, unsustainable growth in population, and inadequate infrastructure [11]. Following the literature on the susceptibility of African countries to climate change impacts, this study focuses on Nigeria.

    Recently, adaptation to climate change has clearly become an important domain of practice and research. Adaptation in agricultural systems can be grouped into two broad areas; planned and autonomous adaptation. Planned adaptation includes measures and strategies carried out consciously, to foster the system’s capacity to adapt. Under planned adaptation, for example, farmers adopt purposive selection and distribution of crops across various agro ecological zones and replacing old crops with new crop varieties. Autonomous adaptation, on the other hand, is reactionary in nature. Variable rainfall patterns that result in changes in planting dates by farmers, hence reactionary, can be regarded as autonomous adaption [12,13].

    Our knowledge of adaptation is little; despite an increasing number of studies suggesting various assessment and adaptation measures, not many studies have systematically evaluated existing adaptation measures, quantitative and qualitatively, as well as adaptation measures’ contribution to sustainability and resilience, specifically at the national level [14]. Is adaptation occurring? What adaptation measures are in place? Does adaptation contribute to resilience? There are different views and frameworks of what constitutes resilience building to climate change. However, the crucial factors amongst these frameworks focus attention on buffer capacity, participatory processes and knowledge co-production, stakeholder and decision makers’ involvement [15].

    Nigeria has already witnessed increased air temperatures in the recent past (1971–2000). During this time, in Nigeria, minimum temperatures showed a faster increase of +0.8 ℃, which is more than the maximum temperatures which rose by +0.5 ℃ [16]. This situation is further exacerbated in the context of global warming, which is forecast to reach 1.5 degrees Celsius between 2030 and 2052 [17] under two different scenarios—A2 and B1. A2 and B1 are scenarios for future climate projections downscaled from the Global Circulation Models and used by scientists from the Climate Systems Analysis Group at the University of Cape Town South Africa to predict the future impacts of climate change on Nigeria’s economy under two scenarios [16]. The first scenario, A2, assumes that the world will consider more regional economic development in the future while the second scenario, B1, assumes that there would be dominance of environmental factors and global considerations in the future. See Abiodun et al. for more details and explanations of these scenarios [16].

    Figure 1 presents information on the annual predicted minimum and maximum temperature changes during the periods: 2046–2065 and 2081–2100, using different scenarios for Nigeria. The deviations are calculated with reference to the mean of present-day climate. The thick line represents the models' average, while the shaded area represents the area of one standard deviation away from the mean. In Figure 1, projected trends for Nigeria also show increased warming. This may likely occurrence of heat waves that will increase the rates of evaporation [16].

    Figure 1.  Observed and predicted future minimum and maximum temperature changes in Nigeria.

    Figure 2 presents information on the annual predicted changes in rainfall (mm/day) during the periods: 2046–2065 and 2081–2100, using different scenarios for Nigeria. The deviations are calculated with reference to the mean of present-day climate. The thick line represents the models’ average, while the shaded area represents the area of one standard deviation away from the mean. The figure shows no specific trend in future rainfall deviations [16].

    Figure 2.  Observed and predicted future rainfall changes in Nigeria.

    The Nigerian economy is largely agriculture-based. Agriculture accounts on average about 30 to 40 percent of the nominal GDP, and employs about 65 to 75 percent of the labor force, while providing various ecosystem services [18,19]. Agriculture and rural development are vital to the Nigerian economy; like in most developing countries, Nigerian agricultural systems depend mainly on rainfall. The future, including current, projected variations in the climate during different seasons makes Nigeria’s food production extremely susceptible [16,20]. Agricultural production consists mainly of cereals and tubers; in 2013, both cereals and tubers production contributed approximately 70 percent of total output in the agricultural sector. The production of cassava (a tuber crop) is a very important crop, due to ease of adjustment in its planting decision and high drought-tolerance. On the other hand, rice (a cereal crop) is planted in all eco-zones of Nigeria. Together, these crops have significant impacts on food security in Nigeria [21].

    Agricultural productivity in Nigeria has recently experienced declines [21]. Smallholder rural farmers dominate the farming system in Nigeria, accounting for about 80 to 90 percent of producers. However, productivity is hampered by insufficient capacity to acquire necessary farm inputs such enhanced or improved crop varieties, fertilizers, irrigation and other production inputs. Also, agricultural productivity declines have been linked to climatic and weather variability or change, hence facing problems with food productivity arising from dependence on rain-fed farming worsened by low inputs [22,23]. Farmers should therefore adopt climate resilient adaptation measures to cope with, or reduce climate vulnerability [21].

    Several climate adaptation practices exist; however, academic literature is scarce on the effectiveness, sustainability and contribution to resilience of these adaptation practices, especially in Sub-Saharan Africa [24]. Adverse climate events in the form of variable rainfall, increased drought, intense heat in the northern arid region, and increased erosion in the southern rainforest parts of the country are reported to persist in Nigeria [25,26] thus requiring adaptation practices aimed at enhancing resilience. Farmers adopt different measures to cope with a changing climate; however, often times, the adopted measures may have negative impacts on the environment, especially on the biophysical, social and economic dimensions, hence not contributing to resilience and sustainability [27].

    A review of the current literature is required to create knowledge on where adaptation is focused and areas requiring attention. This study also adds value by providing detailed information on the steps adopted for reviewed [28], which is missing in most review studies. Current systematic reviews have focused on other sectors, for example energy [29] and other countries and regions [30]. To the best of our knowledge, this study is the first to attempt a review of climate resilience of adaptation practices in Nigeria’s agricultural sector. This study identifies and classifies farmers’ adaptation practices across Nigeria. Publically available information—peer-reviewed, reports or documentation—will be used to analyze adaptation in Nigeria.

    This study extends the existing literature by identifying and analyzing many of the recent studies, both gray and peer-reviewed literature on planned and autonomous adaptation to climate change. It covered important areas of climate change adaptation research and practices, with focus on assessing resilience-improving practices in the agricultural sector, and suggests which areas may need more attention. From the foregoing, this study will conduct an online search and summarize current studies on Nigerian farmers’ adaptation to climate change. The specific objectives include to:

    1. ascertain which agricultural sectors, such as crop, fish, and livestock (including livestock) farming, have climate change adaption practices been focused or concentrated on in Nigeria;

    2. determine the agro-ecological zones the current studies on climate change adaptation focused on in Nigeria;

    3. categorize the resilience status of these identified adaptation practices in Nigeria.

    The rest of this report is as follows: We present the methodological framework used for the literature search and review in section two. In section three, we present the systematic review and synthesis of the current research, following from our methodological framework, on Nigeria farmers’ practices aimed toward climate change adaptation. In section four, we present a detailed resilience check of adaptation measures. Section five of this paper presents a brief discussion and way forward. In addition, we provide appendices with relevant, supplementary information.

    We search and select studies for review, as well as use a resilience-check as a general framework to investigate how adaptation is taking place in Nigeria and contribution to climate resilience of these measures. This study improves on the current approaches to meta-analysis and enables a critical examination of how adaptation is taking place in Nigeria.

    We extend the current literature by focusing on resilient adaptation measures geared toward coping with climate change-related risks in agricultural ecosystems. For our purpose, resilience to climate change implies an individual’s, a social group’s or a socio-ecological systems ability to cope with disruptions resulting from climate extremes, while maintaining its basic form or method of functioning, ability for self-organization and the ability to learn and adapt to changes [31,32]., These are the 3 major dimensions of resilience. This definition cuts across sustainability in agricultural systems facing climate change impacts. In agricultural systems, sustainability implies the long-term ability to conserve or boost natural resources, quality of the environment, productivity, economic viability and be socially beneficial [32,33].

    Following [15,32], we link the concept of resilience to study of livelihoods, focusing on the agricultural sector. We refer to livelihood as including the abilities, assets and tasks essential for a living. Dorward et al define livelihood functions as the welfare contributed by livelihoods, for example, food, earnings, insurance and poverty reduction [34]. With reference to livelihoods, resilience is dependent on one’s capabilities, social and natural conditions.

    Assessing the climate resilience of adaptive measures brings up the subject of context-specificity, since social-ecological circumstances have spatial and temporal dimensions [9]. The toolkit we adopt for this study provides no explicit index for classifying adaptation practices, leaving the researchers room to objectively utilize it as a general framework for this paper. Hence, our study assesses the basic characteristics of adaptation measures based on existing literature, and objectively classify them according to the component through which it contributes to climate resilience.

    From Figure 3, resilience can be divided into three components. These components are buffer capacity, self-organization and capacity for learning and adaptive management.

    Figure 3.  Resilient adaptation check toolkit.

    Buffer capacity: Within a livelihood context, this refers to the ability to withstand change, while taking advantage of the resulting opportunities to realize more desirable livelihood results such as poverty reduction. Self-organization: In self-organization, systems are assessed to see if they offer the opportunity for farmers to self-organize. Self-organization enables cooperation and networks among farmers with the advantage of reducing reliance on external parties for information, innovations, and financing. Farmers’ dependence on own talent and farm reserves cause for less reliance on external parties and quick decision making at the farm level. Capacity for learning and adaptive management: Implies an approach to management and openness to learning by the farmers. We briefly consider, where appropriate, the components social-ecological systems (SES) dimensions of these components. Considering the dynamism of SES, farmers continually alter their farm activities, while acquiring knowledge from peers on how to sustain and boost production. Adaptive management is important because it emphasizes the importance interpreting signals from the social and ecological systems and their management. For more details, see [15].

    To foster clarity and reproducibility, this study adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework. The PRISMA framework systematically guides researchers in the criteria for obtaining resources for a systematic review, inclusion and exclusion criteria, stepwise review process, data abstraction and analysis. The PRISMA framework has been used in many climate change systematic reviews (for example, see [30,35]). We conducted online literature search on relevant English language-published, peer-reviewed and gray literature, using databases such as Google Scholar (GS), Web of Science (WoS), JSTOR, Nigerian Higher Education-based Journals, Professional Association-based journals, and Government-owned Non-Governmental Organization-owned Repositories. We focus Open Access publications or documents.

    We restrict our study period to the 2010 to 2019 period. Furthermore, this study considers literature on farmers’ adaptation practices aimed at coping with climate change-related impacts. We also account for various agricultural sectors, for example, crop farming, livestock farming, and fishery. This study further extends the adaptation criteria to account for sustainability in the form of resilience, hence, using several keyword combinations to obtain our resources for the systematic review. These keywords include: “Climate change (accounting for shocks, weather)”, “adaptation (accounting for resilience, vulnerability, and risk)”, “agriculture (accounting for crop, livestock, and fishery)”, and “sub-Saharan Africa”, “Nigeria”.

    We identified 248 studies from the accessed databases during our initial search, consisting of peer-reviewed and gray literature (for example, working papers, project reports and conference proceedings). Table 1 present our literature selection criteria:

    Table 1.  Inclusion and exclusion criteria.
    Search checkpoints Acceptance criteria Rejection criteria
    Initial search Studies published in English Studies published in other languages
    National level studies on climate change studies Non-national level studies on climate change
    Climate change adaptation in the agricultural sector Climate change adaptation in the non-agricultural sector
    Studies focused on Nigeria Other countries/regions
    Distinct/single studies Non-distinct/duplicates
    Title and abstract screening Strictly focused on agricultural systems (crop, livestock, poultry and fish production) Non-agricultural systems (crop, livestock, poultry and fish production)
    Focus on smallholder farmers Large scale farmers
    Focus on vulnerability associated with climate risks Focus on vulnerability associated with non-climate risks
    Final step for review Non-systematic review studies Literature review or discourse analysis
    Livelihoods for rural farmers households Livelihoods for urban farmers households
    Adaptation studies focused on agricultural systems / productivity Adaptation studies focused on non- agricultural systems / productivity

     | Show Table
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    Figure 4 presents a schema of our literature search and selection procedure, leading to 90 studies.

    Figure 4.  Flow chart of literature search and selection based on PRISMA framework.

    A total of 248 studies were obtained from our literature search. Our selection criteria resulted in a final sample of 90 final studies reviewed—85 (94%) were peer-reviewed studies and 5 studies (6%) were gray literatures. According to Singh et al., the value in reviewing relevant gray literature on climate change studies lies on their ability to provide useful, area-specific information, policy-relevant responses, and practices which be ignored by peer-reviewed literature [37]. Our results show an increase in climate change adaptation studies since 2010, with the highest number of studies occurring in 2012, a year after the National Adaptation Strategy and Plan of Action on Climate Change for Nigeria (NASPA-CCN) and the Agricultural Transformation Agenda (ATA) were approved by the Nigeria’s Federal Government.

    The final studies for the review are broadly analyzed based on farm-level adaptation and institutional or policy-level adaptation measures. Furthermore, the paper identifies a total of 13 distinct broad themes of adaptation, from 58 sub-themes. Table 2 presents the farm-level agro-economic sectors and the adaptation measures. Table 2 shows that 83.3% of the total papers analyzed reported practices under soil and land management in the crop sector as adaptation strategy to climate risk management. Crop-specific innovation (77.8% of the papers) was the second most common adaptation strategy for climate risk management reported by the papers. Water-linked management practices were reported 54.4% of the papers analyzed. In the livestock sub-sector, improved livestock management systems, improved breeding strategies and sustainable health management were the common broad strategies for climate change adaptation reported in the literature in Nigeria. Improved fishery management and improved fishing infrastructure featured prominently in the papers analyzed.

    Table 2.  Nigerian farmers’ adaptation practices classified by agricultural sector.
    Agricultural sector Adaptation (Broad Theme) Frequency/Number of papers reporting measures under the broad theme (Percentage) Adaptation (Sub-Theme)
    Crop farming Soil and land management 75 (83.3%) Mulching, make ridges across slopes, plant cover crops, cross-slope, sand filling; Traditional tillage; Hand weeding farmland weeding and the control of pests; Use of organic and inorganic fertilizers, Herbicide use, Organic farming Integrated soil fertility enhancement using organic and chemical fertilizers; Integrated farming/mixed farming; Agroforestry; Conventional tillage integrated soil nutrient management, and slow-forming terraces; Zero/Minimum tillage minimum tillage, Conservation tillage; Family-supplied labor on farm land; Mechanization; Change to new farm land/shifting cultivation, Increase farm size; Cultural pest control; Retention of crop residues in fields; Family labor
    Crop-specific Innovation 70 (77.8%) Intercropping/mixed cropping or intercropping practices, crop diversification; Plant indigenous crop varieties; Crop rotation; Improved crop varieties Drought-resistant crop varieties, Disease- and heat-resistant crops, Early-maturing crops; Adjusting planting/harvesting time; Ecological pest management, seed and grain storage; Innovative crop development: early-maturing and higher yielding crop species; Farmers also use pest or disease-resistant crop varieties; Use of Nursery; Indigenous grains
    Water-linked management practices 49 (54.4%) Improved irrigation; Water harvesting technologies
    Climate information services and education 19 (21.1%) Climate information systems/Weather forecasting; Government climate education/extension services, Participation in trainings
    Access to finance 10 (11.1%) Access to credit facilities; Access to insurance services
    Livelihood diversification 35 (38.9%) Other income-generating opportunities; Shift to marketing/processing of agricultural produce; Out migration
    Livestock farming Improved livestock management systems 6 (6.7%) Tree planting for wind break; Reduction in flock size; Pen infrastructural reinforcement; Adequate ventilation and sanitation; Proper treatment of water, constant water to regulate body temperature, water harvesting; High nutrient feed/proper feed formulation
    Improved breeding strategies 6 (6.7%) Rearing heat-resistant animals & disease resistant breed, selective breeding, keeping of resistant varieties, cross breeding with exotic birds
    Sustainable health management 6 (6.7%) Quarantine services and veterinary services vaccines and antibiotics; introduction of anti-stress medications; Access to training and climate information
    Mixed farming 5 (5.6%) Keeping multiple livestock animals; Mixed farming
    Fish farming Improved fishery management 5 (5.6%) Raising quick maturing fish species; Water harvesting; Introduction of organic material; Adding of lime to reduce acidity; Building embankment to prevent flood water; Usage of weather and water-monitoring kits
    Diversification measures 4 (4.4%) Diversification in non-fishing portfolios; Migration; Acquiring information about climate change
    Improved fishing infrastructure 5 (5.6%) Specialized fishing gear, digging wells or boreholes to supply water during dry period, siting ponds close to steady water sources; Cover over ponds in dry seasons; Use of indoor fish production facilities
    Note: Multiple responses reported.

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    In Table 3, we find that 72 studies specifically focused on the crop farming sector and account for about 80 percent of the total studies reviewed, the rest are: fish farming (n = 5, 5.56 percent), livestock farming (n = 6, 6.67 percent), and studies that considered multiple sectors at once (n = 7, 7.78 percent). The focus of climate change adaptation research on the crop sub-sector could due to the dependence of majority of farmers on this sector and the vulnerability of the sub-sector to climate change.

    Table 3.  Frequency distribution of the papers according to agricultural sub-sectors covered.
    Agricultural Sub-sector Frequency Percentage
    Crop production 72 80.00
    Fish farming 5 5.56
    Livestock production 6 6.67
    Multiple enterprises 7 7.78
    Total 90 100.00
    Note: Source: Authors’ computation.

     | Show Table
    DownLoad: CSV

    The geographical coverage of the studies reviewed is categorized according to agro-ecological zones, geopolitical zones and states in Nigeria. In Figure 5, 5 agro-ecological zones were identified; Guinea Savanna, Sudan Savanna, Rainforest Belt, Mangrove Forest and Sahel Savanna. About 41 percent of the studies focused on the Rainforest Zone, 37 percent of the studies focus on the Guinea Savanna Zone, 9 percent of the studies focus on the Sudan Savanna Zone, 9 percent of the studies focus on the Mangrove Forest Zone, 3 percent of the studies focused on the Sahel Savanna Zone, while 1 percent studied farmers’ adaptation in multiple zones.

    Figure 5.  Percentage of adaptation studies classified according to agro-ecological zones.

    In terms of the geopolitical zone coverage, all the 6 geopolitical zones in Nigeria were covered; Figure 6 reports that 39 percent of the studies focused on the South East region, 28 percent of the studies focus on the South West zone, 15 percent of the studies focus on the South South zone, 14 percent of the studies focus on the North Central zone, 2 percent focused on the North East zone and 1 percent focused on the North West zone.

    Figure 6.  Percentage of adaptation studies classified by geopolitical zones.

    The studies under review applied four distinct analytical approaches. These are the qualitative, quantitative, mixed method and participatory approaches. The studies using qualitative approaches explore the literature and apply simple measures of central tendency such as means and percentages, while quantitative approaches advance beyond descriptive statistics and utilize quantitative analysis and models. Mixed methods approaches combine qualitative and quantitative approaches. Figure 7 reports that 54 percent of the studies utilized quantitative approaches, 36 percent of the studies utilized qualitative approaches, 7 percent used mixed method and 3 percent used participatory method.

    Figure 7.  Percentage of adaptation studies according to methods used.

    Climate change adaptation research in Nigeria utilizes various research methods. The majority of studies used questionnaire surveys to elicit information from Nigerian farmers about their climate change adaptation practices. Few papers (3 papers) used a participatory approach to study climate change adaptation decisions of farmers. This paper presented the findings of these papers separately because of the grounded approach adopted in such studies. The researchers in these studies allowed themes to emerge from the locales instead of imposing their knowledge of adaptation on the people. Regarding quantitative analytical approach, our results show the probit and multivariate probit model, logit model and spatiotemporal trend analyses as the main analytical techniques.

    In this section, we analyze the reported adaptation measures employed by Nigerian farmers to address climate-related impacts within the 4 agricultural sectors under study, using the previously-defined resilience framework. In Table 4, in the appendix, we show the adaptation practices classified by agricultural sector and contribution to resilience.

    Farmers facing increased environmental change can use crop diversification measures such as mixed cropping or intercropping practices to diversify farm-related risks. Rusinamhodzi et al. showed that intercropping could prevent total loss in farm output arising due to climate induced drought conditions [38]. Mixed farming practices, under climate change situations, that utilize indigenous crop diversity can foster resilience; since the crops must have adapted to local climatic conditions over time. They will thrive well and ensure sustenance of productivity at farm level [39,40]. Such crop diversification practices can provide resilience by restraining pests and diseases, due to diverse crops responding dissimilarly to climatic impacts and retaining functional ability relative to non-diverse cropping systems. This has the potential to improve food security, while sustaining or improving incomes for farmers [41,42].

    Farmers also plant indigenous crop varieties that are well-suited for the immediate environment, where other varieties might fail [43,44]. Crop rotation maximizes the use of lands for the production of various crops, while reducing pests and diseases. Drought-resistant crop varieties: crop farmers in drought-prone areas adopt drought-resistant to guard against yield declines. Wheat planting in dry areas will thrive significantly better than dry season rice. Drought-resistant maize varieties cultivation has been found to increase productivity by 617 kg/ha and of 240 kg/ha compared to cultivation of non-drought-resistant maize varieties, in mild drought-prone areas [45]. Adjusting planting dates: variability in rainfall has been linked with largely responsible for poor productivity in Nigerian agricultural system [16]. To prevent crop production risks resulting from variability in rainfall, farmers vary planting dates whereby crops are planted before the start of rains, immediately after the first rains, and a few days after the rain. Staggering planting dates are done deliberately to pass around risk, by ensuring that any available rainwater will be utilized maximally by crops planted in dry fields [46,47]. Sustainable crop management practices such as crop diversification, new crop varieties, ecological pest management, seed and grain storage foster climate resilience through innovations in crop development. Considering innovative crop development activities, farm productivity is increased through the use of early-maturing and higher yielding crop species. Furthermore, farmers use drought-resistant crops as buffer against crop failure from the increased incidence of climate-induced droughts. Farmers also use pest or disease-resistant crop varieties to adapt to climate-related pest and disease attack. Adopting other crops for production, especially heat-resistant crops serve as a buffer against climate change-induced high temperatures and low precipitation [48,49].

    Practices that comprise sustainable soil and land management contribute to buffer capacity in diverse ways. For example, against soil erosion, reduction of organic content, condensation, and soil acidity are increasingly worsened by adverse climate and weather changes, like wind gusts and variation in precipitation rates [50]. Soil erosion results in the reduction of soil surfaces, organic matter and essential nutrient sources, leading to the crop supporting and production capacity of the soil. Sustainable soil and land management practices that contribute to sustenance of smallholder farmers’ livelihoods by controlling erosion through structural and vegetative barriers include tree planting, cover cropping, mulching, cross-slope [51]. Traditional tillage may be a useful measure for farmland weeding and the control of pests, however, it may not be useful in climate change-prone areas; it may disrupt the physical quality of the soil, resulting in increased soil erosion and deterioration [52].

    Another set of approaches that avoid the negative impacts of traditional tillage, providing low disruption of the soil layers, while maintaining or improving soil quality, is the minimum or zero tillage practices. According to Lal, zero or minimum practices enhance productive capacity, enable vulnerable lands to retain soil organic carbon and improve environmental sustainability [53]. On the other hand, zero or minimum tillage is also known to increase the use of pesticides on farmlands, which may hinder ecological sustainability [50]. Integrated soil fertility enhancement using organic and chemical fertilizers fall under this broad theme. Fertilizer use in agriculture contributes to income and financial capital of the farmers by increasing crop yields, and to soil management through fixation of nitrogen. According to Stavi et al., chemical and organic fertilizers utilization boosts the quality of the soil, water retention capacity and retention of soil organic carbon [54]. On the other hand, improper use chemical fertilizers may lead to increase in soil degradation resulting from increased excessive usage.

    Another form of sustainable soil and land management system is the integrated agricultural practices. Specific practices here include mixed farming and agroforestry. In rural smallholder farming, growing trees and forests are vital to livelihoods. These practices can contribute to increased productivity of the Nigerian smallholder farmers [55]. Sustainable farming systems help farmers to diversify their livelihoods. The integrated diversification, where farmers move from single cropping systems to diversified systems, such as in mixed crop-livestock-agroforestry system fosters livelihood diversification and security. This system contributes to economic sustainability through removing the “Single Point of Failure” problem; in the event of crop failure, income from the sale of livestock and tree (including fruits, fuel and fodder) products could serve as buffer to farmer incomes. In mixed farming systems, the crop residues which are wastes from crop production serve as feed for livestock. The manure from livestock, in turn, serves to improve soil fertility and improved crop productivity. This system provides opportunities for recycling and organic farming for farmers, thus contributing to ecological sustainability.

    Sustainable soil management practices such as integrated soil nutrient management, conservation tillage, and slow-forming terraces foster enhanced site specific knowledge. Some soil management practices help enhance environmental resilience and benefits in areas of intense rainfall. To adapt to the risk of soil erosion by improving the rainwater seep-through ability of the soil, while retaining water for plant life, Nigerian farmers apply practices such as minimum tillage and ridges, surface mulching and agroforestry [56]. Enhanced environmental benefits: Improved soil Management adaptation techniques foster improved soil health and are key for productive and sustainable agriculture. These practices include integrated soil nutrient management, zero/minimum tillage, slow forming terraces, mulching. Economic resilience can be assessed from the relationship between productivity and income; since incomes are directly related to the rate of productivity, practices that improve soil quality and consequently, productivity over time, will lead to increase in incomes for the farmers. Regions with high precipitation face increasing risk of soil erosion. Agroforestry, through trees planting on farmlands provide windbreaks, protects the soil and enhances soil water infiltration that checks soil erosion, sustains good soil organisms, thus improving soil fertility, and higher productivity.

    Furthermore, family-supplied labor services are beneficial in terms of improvement in human capital, where more knowledgeable household members transfer knowledge of farm practices to other members, and the preservation or improvement of financial capital, where household farm labor wages are retained by the household members, instead of being paid out. The associated input cost reduction is expected to sustain current income levels or increase profitability [57].

    Water-linked management practices include practices are vital adaptation strategies by smallholder farmers facing droughts. In regions facing drought and risk of crop failure, sustainable water management techniques, in the form of, rainwater harvesting or application of irrigation, which are aimed at reducing crop water, will boost crop productivity, while contributing to economic sustainability. Water management practices contribute to improved food security, poverty reduction and increase in farm productivity [58].

    Information through climate information systems enable farmers to make better decisions, for example, choice of crop varieties, mode of production, and adjustment of planting dates, and these can improve farm productivity [59]. Climate education services provide knowledge to farmers about potential avenues to cope better in the presence of climate change. This new knowledge has the potential to improve willingness to access to credit facilities and enable farmers to adopt better farm technologies that improve farm productivity. This has the potential to add to economic resilience by compensating farmers in the event that adverse weather events disrupt crop production [59].

    Access to credit service can improve household livelihoods security, and it also improves the ability to adapt to climate change by providing ease of acquiring means of diversification. Index-based Insurance services within agriculture also serve as incentives to farmers, to plan for climate-related disruptions. In northern Nigeria, Abraham, Fonta find that about 96 percent of the farmers are aware of, and are negatively impacted by climate change [60]. They also attribute their ability to adapt to credit availability, especially through microcredit or micro insurance. Availability of credit will enable farmers in northern Nigeria to meet other requirements for adapting to climate such as purchasing of improved crop varieties (heat-, drought, pest and disease-resistant). Access to finance will provide financial capital and also enable the acquisition of natural capital such as new farmland, which are essential for the sustenance and improvement of rural livelihoods [61].

    As climate change effects persist, the need to diversify the sources of livelihoods by farmers beyond agriculture intensifies. This is vital for poverty reduction among poor rural farmers in Nigeria. Off-farm diversification contributes to sustained or improved incomes and farmers may earn income to further invest in agriculture [62]. Furthermore, diversification through value chain activities is an important adaptation measure. This could be in the form of cassava farmers in southern Nigeria, and millet and groundnut farmers in Northern Nigeria processing their produce into value adding products, as well as engaging in the sales and marketing of these products. Other activities include snail farming and bee-keeping. These farmers are reported to record increased productivity and incomes from these extra activities [61,63].,

    Self-organization includes the use of indigenous resources, indigenous knowledge and ease of decision making by smallholder rural farmers. Indigenous knowledge about plant health, as well as the pest and disease incidence is required by the farmers to adopt appropriate adaptation measures. These measures include application of organic manure, crop residue management, and the use of animal droppings. Furthermore, rural households can improve their adaptive capacity by pooling their knowledge and labor endowment toward providing labor services to their own farms. The potential benefits accruing from the supply of family labor could be knowledge transfer from more knowledgeable household members at little or no cost [57].

    Forming farmer groups constitute a very important measure aimed at adapting to climate change. Through own-initiatives and concerted efforts, local farmer groups ensure that their members have access to knowledge, competence necessary for day-to-day livelihoods improvement. Lead farmers within farmer groups are usually nominated, on behalf of the group members, to meet with different key stakeholders, including government agencies, extension services, for the purpose of acquiring new knowledge and skills, to be shared with group members. Hence, forming groups could foster trust among members, while promoting cooperation, such as pooling financial resources to purchase farm machinery, continuous training of lead farmers and test or exhibition plots, to boost livelihoods [64].

    Our results show that most farmers are conversant with climate change events, especially unpredictable rainfall rates and extreme temperature, and the implication for their livelihoods. Through ownership of group-managed exhibition plots, farmers could acquire knowledge and improve their adaptive capacity by experimenting with new techniques or technology before implementing into individual farms. Farmers also benefit from appointments between extension services and lead farmers, where the lead farmers are trained on proper farming practices aimed at climate adaptation, and they transfer the knowledge to the rest of the group members. Our results show that few studies reported on self-organization or farmer groups, there may be little scope for exchange between key stakeholders, such as government agencies, extension services, and farmers. This has the potential to slow down the acquisition of new ideas and adoption of new farming technologies to help adopt climate change [64].

    The importance of animal health cannot be over-emphasized. Given the adverse effects of climate change, farmers adopted measures that ensure good livestock health, for them to be productive and profitable for the farmers [65]. Some of the measures include: administration of vaccines and antibiotics, introduction of anti-stress, planting trees to create shade around poultry pen, proper feed formulation, animal vaccination, constant water to regulate body temperature, proper treatment of water, veterinary services and quarantine services.

    In developing countries, especially in Nigeria, majority of the rural population depends on livestock as a means of livelihood. Given the livelihood implications of livestock production, increasing production sustainably is necessary. However, production is hampered by militating factors such as nutrient deficiency, poor genetic potential, inappropriate husbandry, shortage of appropriate feed, zoonotic and other emerging infectious diseases [66]. Currently, productivity in livestock subsystems of most developing countries has been hampered by climate-related phenomena. The adaptive measures identified include the use of high nutrient feed, use of nutrient-dense diet, cross-breeding of animals and improved grazing sites. Improved grazing pastures can be a source of nutrient for the livestock. Proper feeding increases weight gain and high chance of reproducing. The weight gain causes the livestock to produce meat, eggs, which can improve food security and generate income for the farmers. Cross breeding between animals that are tolerant to harsh weather conditions, heat and disease will lead to genetic improvement which the farmer in turn sells to make profit [67].

    The poultry produce is of great importance in developing countries, contributing to the nutritional needs of local communities [68,69,70,71]. Climate change has been linked with reduction in poultry production [72]. Furthermore, temperature, sunshine and relative humidity are some environmental conditions that affect the productivity and performance of birds [73]. Growing hybrid birds is used as a form of adaptation. In this practice, indigenous species are cross-bred with foreign, improved species, with the aim of boosting adaptability of livestock to changing environmental conditions. Cross-bred birds that are well-adapted can improve survivability and productivity, which also improve farmer income. Cross breeding with exotic birds provides opportunities for knowledge combination, promoted by existence of a variety of learning platforms. Proper feed formulation system was adopted as an adaptation strategy which increases the economic, ecology and social benefits [65].

    The low productivity of livestock to the economy can be attributed to high disease incidence, inadequate management, and low genetic potential of indigenous breeds, poor nutrition and reproductive performance [68,69]. At the same time farmers reported adverse climate-related impacts on livestock. To adapt, farmers plant trees to serve as wind breaker, build shade to reduce heat, reduce flock size for adequate ventilation and improve livestock housing system. Batima noted that the reduced number of productive animals kept in a particular environment will lead to efficient production and reduce greenhouse gases emission [70]. The quality of an environment plays important roles in the growth rate and performance of livestock. In the eastern part of Nigeria, during rainy season, the wind destroys properties and livestock due to lack of any form of wind breakers. In such cases, the livestock farmer will lose income. Therefore, planting of trees is a necessary means of protecting the livestock from such threatening event that even the farmers in the rural areas can afford.

    Capacity building

    It is important to create awareness of climate change impacts and ways to cope with them among smallholder livestock farmers [46]. Professional training and development programs for livestock farmers create an opportunity for knowledge and farm practice improvement. Ampaire, Rothschild found that trainings on animal management are usually desirable among farmers, since they seem eager to improve on their knowledge and practices [74]. It also creates an avenue for interaction and networking amongst themselves.

    Improved Fish Management system comprises of water harvesting, stocking of early-maturing fish species, introduction of organic materials, use of weather and water-monitoring kits, building embankment to prevent flood water and adding of lime which serves as acid subsiding element. Organic materials such as fertilizers are being induced on the fish which increases fertility and thus fish production. Supplementary feeds are being administered to the fish. Stocking of quick-maturing fish species improves farm economy by ensuring accelerated time-to-market, which in return, increases the farm turnover and income, as well as improving food security.

    In mild drought areas, fish farmers can channel and store any rainfall, for use during dry periods. Advantages derived from water harvesting include erosion control and groundwater replacement, which are vital for agricultural development and resource conservation. When exposed to environmental conditions (water quality and food availability), fish are vulnerable to diseases. Water Harvesting contributes at a high degree in the social aspect, its highly beneficial towards the ecology system and utilized (for irrigation, herd watering, machinery cleaning, filter backwashing, washing) in the farm. The presence of disease in the fish pond makes it difficult to identify and treat the affected fish. The fishes get sick, the farmer loses money as harvest is delayed and the economic sustainability is reduced. Also, water is a perfect agent for spreading disease especially from fish farm, as the affected water from the fishpond is drained thereby affecting the ecological (animal, plants) and social (environment) sustainability. In other words, water harvesting is a cost-effective measure to aid smallholder fish farmers during dry periods and thus boost livelihoods.

    This involves the use of indoor fish production facilities, wells and boreholes to supply water, erecting cover over ponds and upgraded traditional fishing gear can improve access to livelihood, especially in capture fishery. This is especially important in the context that cultivated land is not available for extended period, thus improved traditional fishing gear can help farmers increase their catch, while serving as an additional source of food and income [75]. Use of the listed fishing infrastructures throws in a high input in the economy, thereby giving raise to sustainability in the ecosystem. Specialized fishing gear, digging wells or boreholes to supply water during dry period, building ponds close to water sources contribute towards improving the right and access to livelihood resources. Acquisition of information on climate change issues, the use of weather and water monitoring kits, and migration contribute to adaptation and reduce vulnerability of fish farmers and fishers. These help in the forecasting analysis of fish farming system. The forecasting aspects could be an ecological factor which gives insight importance of the weather and directs the flow of activity in the farm thereby increasing human capital endowments. Migration, on the other hand, contributes to diversity of livelihood. According to Ficke et al., fish production, growth and migration are affected by rainfall, hydrobiology and temperature [76]. In this case, pattern of fish species abundance and availability is highly altered [77].

    Adding of lime to reduce acidity is used by farmers to make the environment of the fish to be more conducive and foster increased fish productivity. Farmers also erect covers over ponds during dry seasons and build embankments to prevent flood water. These adaptation practices have made use of resources from the farmers’ environments, and require farmers’ own initiatives and thus constitute self-organized adaptation.

    Access to information on climate change, weather and monitoring of water temperature are of great importance to fish farmers’ adaptation and resilience [78]. Infrastructural provision such as indoor fish production facilities add to opportunities for knowledge combination, promoted by existence of a variety of learning platforms also contributes towards the adaptive capacity. According to Huq, Reid, in order to understand and cope with climate risks, adaptive capacity of current knowledge and practice needs to be considered [79]. Planting of wind breakers, avoidance of pond linkages and regular change of water pond plays a more influential role in adaptive capacity which increases capacity to survive external shock and changes which increases production [78].

    We assessed the climate-resilience capacity of measures used by smallholder rural farmers in the crop, livestock (including poultry), and fishery (sub) ecosystems of the agricultural sector in Nigeria. Also, the aspects of resilience considered account for contribution to livelihoods and sustainability, as defined by [15]. Adaptation measures were determined through a review of existing studies on climate, agriculture and adaptation in Nigeria. Using the resilience definition and Resilience Check toolkit reference in this study, our findings show that resilient adaptation is happening: The resilience toolkit used on the 95 studies we reviewed show several examples measures that contribute to farm level climate-resilience, within the most recent decade (2010–2019), in multiple agro-ecological zones in Nigeria.

    Our results show that farmers are using climate-resilient adaptation measures. We also find that existing studies on climate change adaptation in Nigeria are largely targeted at crop production. The major agricultural ecosystems and the broad adaptation areas are: crop farming (improved soil and land management, crop-specific innovation, water management practices, climate information services and education, access to finance, and off-farm diversification), livestock farming (improved livestock management systems, improved breeding strategies, sustainable health improvement, proper feed formulation early maturing and heat-resistant bird varieties), and fish farming (water harvesting measures, organic material, quick-maturing varieties). We find that most adaptation studies, about 80 percent, in Nigeria’s farming system have focused on the rainforest (44 percent) and the guinea savanna (36 percent) agro-ecological zones.

    While our assessments based on the resilience check toolkit and reference to other literature show that adaptation measures by Nigerian farmers can be classified using the three attributes of resilience, it is however, not clear which practices are more resilient and such measures have led to sustainable agricultural systems. We also find more practices contributing to the buffer capacity of the crop sub-sector than any other sector. Our study set to assess climate resilience of adaptation measures using selected indicators. Furthermore, our results show that further studies are required to focus extensively on the broad resilience profiles of Nigerian farmers’ adaptation measures, taking into account all the pertinent indicators that make up the three major components of resilience: buffer capacity, self-organization and capacity for learning and adaptation.

    It is clear from the review that building resilience against climate change can be pursued systematically by analyzing strategies that show potentials to increasing buffer capacity of farmers and farming systems, enhancing their capacities for self-organization, and improving their abilities to learn and adapt iteratively. This can serve as a template or guideline by development partners, government agencies, private sector operators, civil society groups and communities in building resilience of agricultural systems.

    The authors declare no conflict of interest.

    Figure 8.  Number of adaptation studies by year.
    Figure 9.  Number of studies reporting adaptation measures.
    Table 4.  Nigerian farmers’ adaptation practices classified by agricultural sector and contribution to resilience.
    Agricultural sector Adaptation (Broad Theme) Resilience component Adaptation (Sub-Theme)
    Crop farming Soil and land management Buffer cpacity Mulching, make ridges across slopes, plant cover crops, cross-slope, sand filling; Traditional tillage; Hand weeding farmland weeding and the control of pests; Use of organic and inorganic fertilizers, Herbicide use, Organic farming Integrated soil fertility enhancement using organic and chemical fertilizers; Integrated farming/mixed farming; Agroforestry; Conventional tillage integrated soil nutrient management, and slow-forming terraces; Zero/Minimum tillage minimum tillage, Conservation tillage; Family-supplied labor on farm land; Mechanization; Change to new farm land/shifting cultivation, Increase farm size; Cultural pest control; Retention of crop residues in fields
    Self-oganization Family labor
    Crop-specific innovation Buffer cpacity Inter cropping/mixed cropping or intercropping practices, crop diversification; Plant indigenous crop varieties; Crop rotation; Improved crop varieties Drought-resistant crop varieties, Disease- and heat-resistant crops, Early-maturing crops; Adjusting planting / harvesting time; Ecological pest management, seed and grain storage; Innovative crop development: early-maturing and higher yielding crop species; Farmers also use pest or disease-resistant crop varieties; Use of Nursery
    Self-oganization Indigenous grains
    Water-linked management practices Buffer cpacity Improved irrigation; Water harvesting technologies
    Climate information services and education Adaptive cpacity Climate information systems/Weather forecasting; Government climate education/extension services, Participation in trainings
    Access to finance Buffer cpacity Access to credit facilities; Access to insurance services
    Livelihood diversification Buffer cpacity Other income-generating opportunities; Shift to marketing / processing of agricultural produce; Out migration
    Livestock farming Improved livestock management systems Buffer cpacity Tree planting for wind break; Reduction in flock size; Pen infrastructural reinforcement; Adequate ventilation and sanitation; Proper treatment of water, constant water to regulate body temperature, water harvesting; High nutrient feed / proper feed formulation
    Improved breeding strategies Buffer capacity Rearing heat-resistant animals & disease resistant breed, selective breeding, keeping of resistant varieties, cross breeding with exotic birds
    Sustainable health management Buffer capacity Quarantine services and veterinary services vaccines and antibiotics; introduction of anti-stress medications
    Self-organization Access to training and climate information
    Mixed farming Buffer capacity Keeping multiple livestock animals; Mixed farming
    Fish farming Improved fishery management Buffer capacity Raising quick maturing fish species; Water harvesting; Introduction of organic material
    Self-organization Adding of lime to reduce acidity; Building embankment to prevent flood water
    Adaptive capacity Usage of weather and water-monitoring kits
    Diversification measures Buffer capacity Diversification in non-fishing portfolios; Migration
    Adaptive capacity Acquiring information about climate change
    Improved fishing infrastructure Buffer capacity Specialized fishing gear, digging wells or boreholes to supply water during dry period, siting ponds close to steady water sources
    Self-organization Cover over ponds in dry seasons
    Adaptive capacity Use of indoor fish production facilities

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    Table 5.  Adaptation studies focused on crop farming sub-sector classified according to resilience component.
    Resilience component
    Buffer capacity Self-organization Adaptive capacity
    Authors [Reference] Regional focus Study design SLM CSI WMP FIN DIV SLM CSI CIS
    Abraham and Fonta (2018) [60] NC QN
    Achoja and Oguh (2018) [80] SS QN
    Agomuo et al. (2015) [81] SE QN
    Ajayi (2016) [82] SW QN
    Ajieh and Okoh (2012) [83] SS QL
    Akinbile et al. (2018) [84] SW QL
    Akinwalere (2017) [85] SW QN
    Anyoha et al. (2013) [86] SE QN
    Apata (2012) [87] SW QN
    Arimi (2014) [88] SW QL
    Asadu et al. (2018) [89] SE QL
    Ayanlade et al. (2017) [90] SW QL
    Ayoade (2012) [91] SW QL
    Chukwuone (2015) [92] SE QN
    Chukwuone et al. (2018) [93] SE QN
    Emodi and Bonjoru (2013) [94] NC QL
    Enete et al. (2011) [95] SE QN
    Enete et al. (2015) [96] SW QN
    Eregha (2014) [97] MZ QN
    Esan et al. (2018) [98] SW QL
    Ezeh and Eze (2016) [99] SE QL
    Ezike (2018) [100] SE QN
    Falola and Achem (2017) [101] NC QN
    Farauta et al. (2011) [102] NC MM
    Henri-Ukoha and Adesope (2018) [103] SS QN
    Ifeanyi-obi (2012) [104] SS QL
    Ifeanyi-Obi et al. (2014) [105] SE QL
    Igwe (2018) [106] SE QN
    Iheke and Agodike (2016) [107] SE QN
    Ihenacho et al. (2019) [108] SE QN
    Ikehi et al. (2014) [109] SE QL
    Kim et al. (2017) [110] NC QL
    Koyenikan and Anozie (2017) [111] SS QN
    Mbah et al. (2016) [112] NC QL
    Mustapha et al. (2012) [113] NE QL
    Mustapha et al. (2017) [114] NE QL
    Nnadi et al. (2012) [115] SE QL
    Nwaiwu et al. (2014) [116] SE QL
    Nwalieji and Onwubuya (2012) [117] SE QL
    Nwankwo et al. (2017) [118] SE QN
    Nzeadibe et al. (2011) [119] SE QN
    Obayelu (2014) [120] SW MM
    Ofuoku (2011) [121] SW QN
    Ogbodo et al. (2018) [122] SE QN
    Ogogo et al. (2019) [123] SS QL
    Okpe and Aye (2015) [124] NC QN
    Oluwatusin (2014) [125] SW QN
    Oluwole et al. (2016) [126] SW QN
    Onyeagocha et al. (2018) [127] SE QN
    Onyegbula and Oladeji (2017) [128] SE, SS, NC MM
    Onyekuru (2017) [129] MZ QN
    Onyeneke (2016) [130] SE QN
    Onyeneke (2018) [131] SE QN
    Onyeneke and Madukwe (2010) [132] SE QL
    Onyeneke et al. (2012) [133] SS QN
    Tarfa et al. (2019) [134] MZ QN
    Onyeneke et al. (2017) [135] SE QL
    Oriakhi et al. (2017) [136] SS QN
    Orowole et al. (2015) [137] SW QN
    Oruonye (2014) [138] NW QL
    Oselebe et al. (2016) [139] SE QL
    Oti et al. (2019) [140] SE QN
    Owombo et al. (2014) [141] SW QN
    Oyekale and Oladele (2012) [57] SW QN
    Ozor et al. (2012) [142] SE, SS, SW QL
    Sangotegbe et al. (2012) [143] SW QL
    Sanni (2019) [144] SW MM
    Solomon and Edet (2018) [145] SE QN
    Tanko and Muhsinat (2014) [146] NC QN
    Tanko and Muhsinat (2014) [146] NC QN
    Tanko and Muhsinat (2014) [146] NC QN
    Usman et al. (2016) [147] NC QN
    Uzokwe and Okonkwo (2012) [148] SS QN
    Weli and Bajie (2017) [149] SE MM
    Legend
    Region Design Adaptation Broad Theme
    NC = North Central QN = Quantitative SLM = Soil and Land Management
    NW = North West QL = Qualitative CSI = Crop-specific Innovation
    SE = South East MM = Mixed Methods WMP = Water-linked management practices
    SW = South West FIN = Access to Finance
    SS = South South DIV = Livelihood Diversification
    Multiple = Study conducted in more than 3 regions at one time CIS = Climate information services and education

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    Table 6.  Adaptation studies focused on livestock sub-sector classified according to resilience component.
    Resilience Component
    Buffer capacity Self-organization
    Authors [Reference] Regional Focus Study Design ILM IBS SHM MF SHM
    Adepoju and Osunbor (2018) [65] SW QU
    Chah et al. (2013) [150] SE MM
    Chah et al. (2018) [151] SE QN
    Ibrahim and Azemheta (2016) [152] NC QN
    Tologbonse et al. (2011) [153] MZ QL
    Ume et al. (2018) [154] SE QU
    Legend
    Region Design Adaptation Broad Theme
    NC = North Central QN = Quantitative ILM = Improved Livestock management systems
    SE = South East QL = Qualitative IBS = Improved breeding strategies
    SW = South West MM = Mixed Methods SHM = Sustainable health management
    MZ = Study conducted in more than 3 regions at one time MF = Mixed Farming

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    Table 7.  Adaptation studies focused on fish farming sub-sSector classified according to resilience component.
    Resilience Component
    Buffer capacity Self-organization Adaptive capacity
    Authors [Reference] Regional Focus Study Design IFM DIV IFI IFM IFM DIV
    Adebayo (2012) [78] SW QL
    Adeleke and Omoboyeje (2016) [155] SW QL
    Aphunu and Nwabeze (2012) [156] SS QN
    Nwabeze et al. (2012) [77] NC QL
    Owolabi and Olokor (2016) [157] NC QL
    Legend
    Region Design Adaptation Broad Theme
    NC = North Central QN = Quantitative IFM = Improved Fishery Management
    SW = South West QL = Qualitative DIV = Diversification Measures
    SS = South South IFI = Improved Fishing Infrastructure

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    Table 8.  Adaptation studies focused on multiple sub-sectors classified according to resilience component.
    Crop farming Livestock farming Fish farming
    Resilience component
    Buffer capacity Self-organization Adaptive capacity Buffer capacity Self-organization Buffer capacity Self-organization Adaptive capacity
    Authors [Reference] Region Design SLM CSI WMP FIN DIV SLM CSI CIS ILM IBS SHM MF SHM IFM DIV IFI IFM IFM DIV
    Amusa et al (2015) [158] SW QN
    BNRCC (2011) [61] MZ PA
    NEST, Woodley (2012) [159] SW PA
    BNRCC, Federal Ministry of Environment (2012) [160] MZ PA
    Nzegbule et al (2019) [63] MZ QL
    Oladipo (2010) [161] MZ QN
    Tijjani and Chikaire (2016) [162] SE QN
    Legend
    Region Design Adaptation Broad Theme
    NC = North Central QN = Quantitative SLM = Soil and Land Management
    NW = North West QL = Qualitative CSI = Crop-specific Innovation
    SE = South East MM = Mixed Methods WMP = Water-linked management practices
    SW = South West PA = Participatory Approach FIN = Access to Finance
    SS = South South DIV = Livelihood Diversification
    Legend
    MZ = Study conducted in more than 3 regions at one time CIS = Climate information services and education
    ILM = Improved Livestock management systems
    IBS = Improved breeding strategies
    SHM = Sustainable health management
    MF = Mixed Farming
    IFM = Improved Fishery Management
    DIV = Diversification Measures
    IFI = Improved Fishing Infrastructure

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    Conflicts of interest



    The authors declare there are no conflicts of interest.

    [1] GBD 2015 Mortality and Causes of Death Collaborators (2016) Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388: 1459-1544.
    [2] APA (2020) 2020 Alzheimer's disease facts and figures. Alzheimers Dement in press.
    [3] Brookmeyer R, Johnson E, Ziegler-Graham K, et al. (2007) Forecasting the global burden of Alzheimer's disease. Alzheimers Dement 3: 186-191. doi: 10.1016/j.jalz.2007.04.381
    [4] Prince M, Bryce R, Albanese E, et al. (2013) The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement 9: 63-75. e2. doi: 10.1016/j.jalz.2012.11.007
    [5] Alzheimer's Association (2018) 2018 Alzheimer's disease facts and figures. Alzheimers Dement 14: 367-429.
    [6] Perl DP (2010) Neuropathology of Alzheimer's disease. Mt Sinai J Med 77: 32-42. doi: 10.1002/msj.20157
    [7] Kumar A, Tsao JW (2018)  Alzheimer Disease StatPearls: Treasure Island (FL).
    [8] Braak H, Braak E (1996) Development of Alzheimer-related neurofibrillary changes in the neocortex inversely recapitulates cortical myelogenesis. Acta Neuropathol 92: 197-201. doi: 10.1007/s004010050508
    [9] Cummings JL, Morstorf T, Zhong K (2014) Alzheimer's disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther 6: 37. doi: 10.1186/alzrt269
    [10] Hussein W, Saglik BN, Levent S, et al. (2018) Synthesis and biological evaluation of new cholinesterase inhibitors for Alzheimer's disease. Molecules 23: 2033. doi: 10.3390/molecules23082033
    [11] Leblhuber F, Steiner K, Schuetz B, et al. (2018) Probiotic supplementation in patients with Alzheimer's dementia—An explorative intervention study. Curr Alzheimer Res 15: 1106-1113. doi: 10.2174/1389200219666180813144834
    [12] Farlow MR, Salloway S, Tariot PN, et al. (2010) Effectiveness and tolerability of high-dose (23 mg/d) versus standard-dose (10 mg/d) donepezil in moderate to severe Alzheimer's disease: A 24-week, randomized, double-blind study. Clin Ther 32: 1234-1251. doi: 10.1016/j.clinthera.2010.06.019
    [13] Homma A, Atarashi H, Kubota N, et al. (2016) Efficacy and safety of sustained release donepezil high dose versus immediate release donepezil standard dose in Japanese patients with severe Alzheimer's disease: a randomized, double-blind trial. J Alzheimers Dis 52: 345-357. doi: 10.3233/JAD-151149
    [14] Winblad B, Kilander L, Eriksson S, et al. (2006) Donepezil in patients with severe Alzheimer's disease: double-blind, parallel-group, placebo-controlled study. Lancet 367: 1057-1065. doi: 10.1016/S0140-6736(06)68350-5
    [15] Feldman H, Gauthier S, Hecker J, et al. (2005) Efficacy and safety of donepezil in patients with more severe Alzheimer's disease: a subgroup analysis from a randomized, placebo-controlled trial. Int J Geriatr Psychiatry 20: 559-569. doi: 10.1002/gps.1325
    [16] Black SE, Doody R, Li H, et al. (2007) Donepezil preserves cognition and global function in patients with severe Alzheimer disease. Neurology 69: 459-469. doi: 10.1212/01.wnl.0000266627.96040.5a
    [17] Howard R, McShane R, Lindesay J, et al. (2015) Nursing home placement in the donepezil and memantine in moderate to severe Alzheimer's disease (DOMINO-AD) trial: secondary and post-hoc analyses. Lancet Neurol 14: 1171-1181. doi: 10.1016/S1474-4422(15)00258-6
    [18] Bond M, Rogers G, Peters J, et al. (2012) The effectiveness and cost-effectiveness of donepezil, galantamine, rivastigmine and memantine for the treatment of Alzheimer's disease (review of Technology Appraisal No. 111): a systematic review and economic model. Health Technol Assess 16: 1-470. doi: 10.3310/hta16210
    [19] Zhang N, Wei C, Du H, et al. (2015) The effect of memantine on cognitive function and behavioral and psychological symptoms in mild-to-moderate Alzheimer's disease patients. Dement Geriatr Cogn Disord 40: 85-93. doi: 10.1159/000430808
    [20] Molinuevo JL, Berthier ML, Rami L (2011) Donepezil provides greater benefits in mild compared to moderate Alzheimer's disease: implications for early diagnosis and treatment. Arch Gerontol Geriatr 52: 18-22. doi: 10.1016/j.archger.2009.11.004
    [21] Takeda A, Loveman E, Clegg A, et al. (2006) A systematic review of the clinical effectiveness of donepezil, rivastigmine and galantamine on cognition, quality of life and adverse events in Alzheimer's disease. Int J Geriatr Psychiatry 21: 17-28. doi: 10.1002/gps.1402
    [22] Lam B, Masellis M, Freedman M, et al. (2013) Clinical, imaging, and pathological heterogeneity of the Alzheimer's disease syndrome. Alzheimers Res Ther 5: 1-14. doi: 10.1186/alzrt155
    [23] Jack CR, Albert MS, Knopman DS, et al. (2011) Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7: 257-262. doi: 10.1016/j.jalz.2011.03.004
    [24] Albert MS, DeKosky ST, Dickson D, et al. (2011) The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7: 270-279. doi: 10.1016/j.jalz.2011.03.008
    [25] McKhann GM, Knopman DS, Chertkow H, et al. (2011) The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7: 263-269. doi: 10.1016/j.jalz.2011.03.005
    [26] Beason-Held LL, Goh JO, An Y, et al. (2013) Changes in brain function occur years before the onset of cognitive impairment. J Neurosci 33: 18008-18014. doi: 10.1523/JNEUROSCI.1402-13.2013
    [27] O'Brien RJ, Wong PC (2011) Amyloid precursor protein processing and Alzheimer's disease. Annu Rev Neurosci 34: 185-204. doi: 10.1146/annurev-neuro-061010-113613
    [28] Wilkins HM, Swerdlow RH (2017) Amyloid precursor protein processing and bioenergetics. Brain Res Bull 133: 71-79. doi: 10.1016/j.brainresbull.2016.08.009
    [29] Vassar R, Kovacs DM, Yan R, et al. (2009) The beta-secretase enzyme BACE in health and Alzheimer's disease: regulation, cell biology, function, and therapeutic potential. J Neurosci 29: 12787-12794. doi: 10.1523/JNEUROSCI.3657-09.2009
    [30] De Strooper B, Vassar R, Golde T (2010) The secretases: enzymes with therapeutic potential in Alzheimer disease. Nat Rev Neurol 6: 99-107. doi: 10.1038/nrneurol.2009.218
    [31] Walsh DM, Klyubin I, Fadeeva JV, et al. (2002) Naturally secreted oligomers of amyloid beta protein potently inhibit hippocampal long-term potentiation in vivo. Nature 416: 535-539. doi: 10.1038/416535a
    [32] Chen YY, Wang MC, Wang YN, et al. (2020) Redox signaling and Alzheimer's disease: from pathomechanism insights to biomarker discovery and therapy strategy. Biomark Res 8: 42. doi: 10.1186/s40364-020-00218-z
    [33] Alonso AC, Zaidi T, Grundke-Iqbal I, et al. (1994) Role of abnormally phosphorylated tau in the breakdown of microtubules in Alzheimer disease. Proc Natl Acad Sci U S A 91: 5562-5566. doi: 10.1073/pnas.91.12.5562
    [34] Alonso AC, Grundke-Iqbal I, Iqbal K (1996) Alzheimer's disease hyperphosphorylated tau sequesters normal tau into tangles of filaments and disassembles microtubules. Nat Med 2: 783-787. doi: 10.1038/nm0796-783
    [35] Braak H, Alafuzoff I, Arzberger T, et al. (2006) Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol 112: 389-404. doi: 10.1007/s00401-006-0127-z
    [36] Morrison BM, Hof PR, Morrison JH (1998) Determinants of neuronal vulnerability in neurodegenerative diseases. Ann Neurol 44: S32-44. doi: 10.1002/ana.410440706
    [37] Braak H, Braak E (1985) On areas of transition between entorhinal allocortex and temporal isocortex in the human brain. Normal morphology and lamina-specific pathology in Alzheimer's disease. Acta Neuropathol 68: 325-332. doi: 10.1007/BF00690836
    [38] Mesulam MM (1999) Neuroplasticity failure in Alzheimer's disease: bridging the gap between plaques and tangles. Neuron 24: 521-529. doi: 10.1016/S0896-6273(00)81109-5
    [39] Serrano-Pozo A, Frosch MP, Masliah E, et al. (2011) Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med 1: a006189. doi: 10.1101/cshperspect.a006189
    [40] Goldman JS, Hahn SE, Catania JW, et al. (2011) Genetic counseling and testing for Alzheimer disease: joint practice guidelines of the American College of Medical Genetics and the National Society of Genetic Counselors. Genet Med 13: 597-605. doi: 10.1097/GIM.0b013e31821d69b8
    [41] Bekris LM, Yu CE, Bird TD, et al. (2010) Genetics of Alzheimer disease. J Geriatr Psychiatry Neurol 23: 213-227. doi: 10.1177/0891988710383571
    [42] Brickell KL, Steinbart EJ, Rumbaugh M, et al. (2006) Early-onset Alzheimer disease in families with late-onset Alzheimer disease: a potential important subtype of familial Alzheimer disease. Arch Neurol 63: 1307-1311. doi: 10.1001/archneur.63.9.1307
    [43] Bird TD (2008) Genetic aspects of Alzheimer disease. Genet Med 10: 231-239. doi: 10.1097/GIM.0b013e31816b64dc
    [44] Serretti A, Artioli P, Quartesan R, et al. (2005) Genes involved in Alzheimer's disease, a survey of possible candidates. J Alzheimers Dis 7: 331-353. doi: 10.3233/JAD-2005-7410
    [45] Mahley RW (1988) Apolipoprotein E: cholesterol transport protein with expanding role in cell biology. Science 240: 622-630. doi: 10.1126/science.3283935
    [46] Puglielli L, Tanzi RE, Kovacs DM (2003) Alzheimer's disease: the cholesterol connection. Nat Neurosci 6: 345-351. doi: 10.1038/nn0403-345
    [47] Harold D, Abraham R, Hollingworth P, et al. (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease. Nat Genet 41: 1088-1093. doi: 10.1038/ng.440
    [48] Lambert JC, Heath S, Even G, et al. (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease. Nat Genet 41: 1094-1099. doi: 10.1038/ng.439
    [49] Saunders AM, Strittmatter WJ, Schmechel D, et al. (1993) Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer's disease. Neurology 43: 1467-1472. doi: 10.1212/WNL.43.8.1467
    [50] Farrer LA, Cupples LA, Haines JL, et al. (1997) Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA 278: 1349-1356. doi: 10.1001/jama.1997.03550160069041
    [51] Zlokovic BV (2013) Cerebrovascular effects of apolipoprotein E: implications for Alzheimer disease. JAMA Neurol 70: 440-444. doi: 10.1001/jamaneurol.2013.2152
    [52] Ghebremedhin E, Schultz C, Braak E, et al. (1998) High frequency of apolipoprotein E epsilon4 allele in young individuals with very mild Alzheimer's disease-related neurofibrillary changes. Exp Neurol 153: 152-155. doi: 10.1006/exnr.1998.6860
    [53] Shi Y, Yamada K, Liddelow SA, et al. (2017) ApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy. Nature 549: 523-527. doi: 10.1038/nature24016
    [54] Ulrich JD, Ulland TK, Mahan TE, et al. (2018) ApoE facilitates the microglial response to amyloid plaque pathology. J Exp Med 215: 1047-1058. doi: 10.1084/jem.20171265
    [55] Mahley RW (2016) Central nervous system lipoproteins: ApoE and regulation of cholesterol metabolism. Arterioscler Thromb Vasc Biol 36: 1305-1315. doi: 10.1161/ATVBAHA.116.307023
    [56] Yamazaki Y, Zhao N, Caulfield TR, et al. (2019) Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat Rev Neurol 15: 501-518. doi: 10.1038/s41582-019-0228-7
    [57] Verghese PB, Castellano JM, Garai K, et al. (2013) ApoE influences amyloid-beta (Abeta) clearance despite minimal apoE/Abeta association in physiological conditions. Proc Natl Acad Sci U S A 110: E1807-1816. doi: 10.1073/pnas.1220484110
    [58] Huang Y, Mahley RW (2014) Apolipoprotein E: structure and function in lipid metabolism, neurobiology, and Alzheimer's diseases. Neurobiol Dis 72: 3-12. doi: 10.1016/j.nbd.2014.08.025
    [59] Miyata M, Smith JD (1996) Apolipoprotein E allele-specific antioxidant activity and effects on cytotoxicity by oxidative insults and beta-amyloid peptides. Nat Genet 14: 55-61. doi: 10.1038/ng0996-55
    [60] Montine KS, Olson SJ, Amarnath V, et al. (1997) Immunohistochemical detection of 4-hydroxy-2-nonenal adducts in Alzheimer's disease is associated with inheritance of APOE4. Am J Pathol 150: 437-443.
    [61] Ramassamy C, Averill D, Beffert U, et al. (1999) Oxidative damage and protection by antioxidants in the frontal cortex of Alzheimer's disease is related to the apolipoprotein E genotype. Free Radic Biol Med 27: 544-553. doi: 10.1016/S0891-5849(99)00102-1
    [62] Chen Y, Zhang J, Zhang B, et al. (2016) Targeting insulin signaling for the treatment of Alzheimer's disease. Curr Top Med Chem 16: 485-492. doi: 10.2174/1568026615666150813142423
    [63] Mahley RW, Huang Y (2012) Apolipoprotein e sets the stage: response to injury triggers neuropathology. Neuron 76: 871-885. doi: 10.1016/j.neuron.2012.11.020
    [64] Zhong N, Weisgraber KH (2009) Understanding the association of apolipoprotein E4 with Alzheimer disease: clues from its structure. J Biol Chem 284: 6027-6031. doi: 10.1074/jbc.R800009200
    [65] Reiman EM, Caselli RJ, Chen K, et al. (2001) Declining brain activity in cognitively normal apolipoprotein E epsilon 4 heterozygotes: a foundation for using positron emission tomography to efficiently test treatments to prevent Alzheimer's disease. Proc Natl Acad Sci U S A 98: 3334-3339. doi: 10.1073/pnas.061509598
    [66] Reiman EM, Chen K, Alexander GE, et al. (2004) Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer's dementia. Proc Natl Acad Sci U S A 101: 284-289. doi: 10.1073/pnas.2635903100
    [67] Skotte N (2010) Genome-wide association studies identify new interesting loci for late-onset Alzheimer's disease. Clin Genet 77: 330-332. doi: 10.1111/j.1399-0004.2009.01366_3.x
    [68] Chouraki V, Seshadri S (2014) Genetics of Alzheimer's disease. Adv Genet 87: 245-294. doi: 10.1016/B978-0-12-800149-3.00005-6
    [69] Bertram L, McQueen MB, Mullin K, et al. (2007) Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet 39: 17-23. doi: 10.1038/ng1934
    [70] Goldgaber D, Lerman MI, McBride OW, et al. (1987) Characterization and chromosomal localization of a cDNA encoding brain amyloid of Alzheimer's disease. Science 235: 877-880. doi: 10.1126/science.3810169
    [71] Kang J, Lemaire HG, Unterbeck A, et al. (1987) The precursor of Alzheimer's disease amyloid A4 protein resembles a cell-surface receptor. Nature 325: 733-736. doi: 10.1038/325733a0
    [72] Robakis NK, Wisniewski HM, Jenkins EC, et al. (1987) Chromosome 21q21 sublocalisation of gene encoding beta-amyloid peptide in cerebral vessels and neuritic (senile) plaques of people with Alzheimer disease and Down syndrome. Lancet 1: 384-385. doi: 10.1016/S0140-6736(87)91754-5
    [73] Tanzi RE, Gusella JF, Watkins PC, et al. (1987) Amyloid beta protein gene: cDNA, mRNA distribution, and genetic linkage near the Alzheimer locus. Science 235: 880-884. doi: 10.1126/science.2949367
    [74] Sherrington R, Rogaev EI, Liang Y, et al. (1995) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer's disease. Nature 375: 754-760. doi: 10.1038/375754a0
    [75] Levy-Lahad E, Wasco W, Poorkaj P, et al. (1995) Candidate gene for the chromosome 1 familial Alzheimer's disease locus. Science 269: 973-977. doi: 10.1126/science.7638622
    [76] Zhao QF, Yu JT, Tan MS, et al. (2015) ABCA7 in Alzheimer's Disease. Mol Neurobiol 51: 1008-1016. doi: 10.1007/s12035-014-8759-9
    [77] Hollingworth P, Harold D, Sims R, et al. (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet 43: 429-435. doi: 10.1038/ng.803
    [78] Marcello E, Saraceno C, Musardo S, et al. (2013) Endocytosis of synaptic ADAM10 in neuronal plasticity and Alzheimer's disease. J Clin Invest 123: 2523-2538. doi: 10.1172/JCI65401
    [79] Corder EH, Saunders AM, Risch NJ, et al. (1994) Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat Genet 7: 180-184. doi: 10.1038/ng0694-180
    [80] Royston MC, Mann D, Pickering-Brown S, et al. (1994) Apolipoprotein E epsilon 2 allele promotes longevity and protects patients with Down's syndrome from dementia. Neuroreport 5: 2583-2585. doi: 10.1097/00001756-199412000-00044
    [81] Coimbra JRM, Marques DFF, Baptista SJ, et al. (2018) Highlights in BACE1 inhibitors for Alzheimer's disease treatment. Front Chem 6: 178. doi: 10.3389/fchem.2018.00178
    [82] Holsinger RM, Goense N, Bohorquez J, et al. (2013) Splice variants of the Alzheimer's disease beta-secretase, BACE1. Neurogenetics 14: 1-9. doi: 10.1007/s10048-012-0348-3
    [83] Tan MS, Yu JT, Tan L (2013) Bridging integrator 1 (BIN1): form, function, and Alzheimer's disease. Trends Mol Med 19: 594-603. doi: 10.1016/j.molmed.2013.06.004
    [84] Seshadri S, Fitzpatrick AL, Ikram MA, et al. (2010) Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 303: 1832-1840. doi: 10.1001/jama.2010.574
    [85] Liu G, Zhang S, Cai Z, et al. (2013) BIN1 gene rs744373 polymorphism contributes to Alzheimer's disease in East Asian population. Neurosci Lett 544: 47-51. doi: 10.1016/j.neulet.2013.02.075
    [86] Miyashita A, Koike A, Jun G, et al. (2013) SORL1 is genetically associated with late-onset Alzheimer's disease in Japanese, Koreans and Caucasians. PLoS One 8: e58618. doi: 10.1371/journal.pone.0058618
    [87] Harms M, Benitez BA, Cairns N, et al. (2013) C9orf72 hexanucleotide repeat expansions in clinical Alzheimer disease. JAMA Neurol 70: 736-741. doi: 10.1001/2013.jamaneurol.537
    [88] Jiang T, Yu JT, Hu N, et al. (2014) CD33 in Alzheimer's disease. Mol Neurobiol 49: 529-535. doi: 10.1007/s12035-013-8536-1
    [89] Deng YL, Liu LH, Wang Y, et al. (2012) The prevalence of CD33 and MS4A6A variant in Chinese Han population with Alzheimer's disease. Hum Genet 131: 1245-1249. doi: 10.1007/s00439-012-1154-6
    [90] Tan L, Yu JT, Zhang W, et al. (2013) Association of GWAS-linked loci with late-onset Alzheimer's disease in a northern Han Chinese population. Alzheimers Dement 9: 546-553. doi: 10.1016/j.jalz.2012.08.007
    [91] Liu G, Wang H, Liu J, et al. (2014) The CLU gene rs11136000 variant is significantly associated with Alzheimer's disease in Caucasian and Asian populations. Neuromolecular Med 16: 52-60. doi: 10.1007/s12017-013-8250-1
    [92] Komatsu M, Shibata N, Kuerban B, et al. (2011) Genetic association between clusterin polymorphisms and Alzheimer's disease in a Japanese population. Psychogeriatrics 11: 14-18. doi: 10.1111/j.1479-8301.2010.00346.x
    [93] Yu JT, Ma XY, Wang YL, et al. (2013) Genetic variation in clusterin gene and Alzheimer's disease risk in Han Chinese. Neurobiol Aging 34: 1921. e17-23. doi: 10.1016/j.neurobiolaging.2013.01.010
    [94] Jin C, Li W, Yuan J, et al. (2012) Association of the CR1 polymorphism with late-onset Alzheimer's disease in Chinese Han populations: a meta-analysis. Neurosci Lett 527: 46-49. doi: 10.1016/j.neulet.2012.08.032
    [95] Crehan H, Holton P, Wray S, et al. (2012) Complement receptor 1 (CR1) and Alzheimer's disease. Immunobiology 217: 244-250. doi: 10.1016/j.imbio.2011.07.017
    [96] Nacmias B, Piaceri I, Bagnoli S, et al. (2014) Genetics of Alzheimer's disease and frontotemporal dementia. Curr Mol Med 14: 993-1000. doi: 10.2174/1566524014666141010152143
    [97] Perry DC, Lehmann M, Yokoyama JS, et al. (2013) Progranulin mutations as risk factors for Alzheimer disease. JAMA Neurol 70: 774-778. doi: 10.1001/2013.jamaneurol.393
    [98] Li HL, Lu SJ, Sun YM, et al. (2013) The LRRK2 R1628P variant plays a protective role in Han Chinese population with Alzheimer's disease. CNS Neurosci Ther 19: 207-215. doi: 10.1111/cns.12062
    [99] Zhao Y, Ho P, Yih Y, et al. (2011) LRRK2 variant associated with Alzheimer's disease. Neurobiol Aging 32: 1990-1993. doi: 10.1016/j.neurobiolaging.2009.11.019
    [100] Parikh I, Fardo DW, Estus S (2014) Genetics of PICALM expression and Alzheimer's disease. PLoS One 9: e91242. doi: 10.1371/journal.pone.0091242
    [101] Chung SJ, Lee JH, Kim SY, et al. (2013) Association of GWAS top hits with late-onset Alzheimer disease in Korean population. Alzheimer Dis Assoc Disord 27: 250-257. doi: 10.1097/WAD.0b013e31826d7281
    [102] Liu G, Zhang S, Cai Z, et al. (2013) PICALM gene rs3851179 polymorphism contributes to Alzheimer's disease in an Asian population. Neuromolecular Med 15: 384-388. doi: 10.1007/s12017-013-8225-2
    [103] Wang Q, Tian Q, Song X, et al. (2016) SNCA gene polymorphism may contribute to an increased risk of Alzheimer's disease. J Clin Lab Anal 30: 1092-1099. doi: 10.1002/jcla.21986
    [104] Linnertz C, Lutz MW, Ervin JF, et al. (2014) The genetic contributions of SNCA and LRRK2 genes to Lewy body pathology in Alzheimer's disease. Hum Mol Genet 23: 4814-4821. doi: 10.1093/hmg/ddu196
    [105] Vardarajan BN, Zhang Y, Lee JH, et al. (2015) Coding mutations in SORL1 and Alzheimer disease. Ann Neurol 77: 215-227. doi: 10.1002/ana.24305
    [106] Yin RH, Yu JT, Tan L (2015) The role of SORL1 in Alzheimer's disease. Mol Neurobiol 51: 909-918. doi: 10.1007/s12035-014-8742-5
    [107] Rogaeva E, Meng Y, Lee JH, et al. (2007) The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease. Nat Genet 39: 168-177. doi: 10.1038/ng1943
    [108] Lambert JC, Ibrahim-Verbaas CA, Harold D, et al. (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet 45: 1452-1458. doi: 10.1038/ng.2802
    [109] Allen M, Kachadoorian M, Quicksall Z, et al. (2014) Association of MAPT haplotypes with Alzheimer's disease risk and MAPT brain gene expression levels. Alzheimers Res Ther 6: 39. doi: 10.1186/alzrt268
    [110] Zhang N, Yu JT, Yang Y, et al. (2011) Association analysis of GSK3B and MAPT polymorphisms with Alzheimer's disease in Han Chinese. Brain Res 1391: 147-153. doi: 10.1016/j.brainres.2011.03.052
    [111] Bloom GS (2014) Amyloid-beta and tau: the trigger and bullet in Alzheimer disease pathogenesis. JAMA Neurol 71: 505-508. doi: 10.1001/jamaneurol.2013.5847
    [112] James BD, Wilson RS, Boyle PA, et al. (2016) TDP-43 stage, mixed pathologies, and clinical Alzheimer's-type dementia. Brain 139: 2983-2993. doi: 10.1093/brain/aww224
    [113] Brouwers N, Bettens K, Gijselinck I, et al. (2010) Contribution of TARDBP to Alzheimer's disease genetic etiology. J Alzheimers Dis 21: 423-430. doi: 10.3233/JAD-2010-100198
    [114] Guerreiro R, Wojtas A, Bras J, et al. (2013) TREM2 variants in Alzheimer's disease. N Engl J Med 368: 117-127. doi: 10.1056/NEJMoa1211851
    [115] Lu Y, Liu W, Wang X (2015) TREM2 variants and risk of Alzheimer's disease: a meta-analysis. Neurol Sci 36: 1881-1888. doi: 10.1007/s10072-015-2274-2
    [116] Naj AC, Jun G, Beecham GW, et al. (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat Genet 43: 436-441. doi: 10.1038/ng.801
    [117] Wijsman EM, Pankratz ND, Choi Y, et al. (2011) Genome-wide association of familial late-onset Alzheimer's disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE. PLoS Genet 7: e1001308. doi: 10.1371/journal.pgen.1001308
    [118] Boada M, Antunez C, Ramirez-Lorca R, et al. (2014) ATP5H/KCTD2 locus is associated with Alzheimer's disease risk. Mol Psychiatry 19: 682-687. doi: 10.1038/mp.2013.86
    [119] Beecham GW, Naj AC, Gilbert JR, et al. (2010) PCDH11X variation is not associated with late-onset Alzheimer disease susceptibility. Psychiatr Genet 20: 321-324. doi: 10.1097/YPG.0b013e32833b635d
    [120] Miar A, Alvarez V, Corao AI, et al. (2011) Lack of association between protocadherin 11-X/Y (PCDH11X and PCDH11Y) polymorphisms and late onset Alzheimer's disease. Brain Res 1383: 252-256. doi: 10.1016/j.brainres.2011.01.054
    [121] Jiang T, Yu JT, Wang YL, et al. (2014) The genetic variation of ARRB2 is associated with late-onset Alzheimer's disease in Han Chinese. Curr Alzheimer Res 11: 408-412. doi: 10.2174/1567205011666140317095014
    [122] Gorski DH, Walsh K (2003) Control of vascular cell differentiation by homeobox transcription factors. Trends Cardiovasc Med 13: 213-220. doi: 10.1016/S1050-1738(03)00081-1
    [123] Rovelet-Lecrux A, Legallic S, Wallon D, et al. (2012) A genome-wide study reveals rare CNVs exclusive to extreme phenotypes of Alzheimer disease. Eur J Hum Genet 20: 613-617. doi: 10.1038/ejhg.2011.225
    [124] Wu Z, Guo H, Chow N, et al. (2005) Role of the MEOX2 homeobox gene in neurovascular dysfunction in Alzheimer disease. Nat Med 11: 959-965. doi: 10.1038/nm1287
    [125] Mielke MM, Vemuri P, Rocca WA (2014) Clinical epidemiology of Alzheimer's disease: assessing sex and gender differences. Clin Epidemiol 6: 37-48. doi: 10.2147/CLEP.S37929
    [126] Fjell AM, McEvoy L, Holland D, et al. (2014) What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus. Prog Neurobiol 117: 20-40. doi: 10.1016/j.pneurobio.2014.02.004
    [127] Lee JY, Chang SM, Jang HS, et al. (2008) Illiteracy and the incidence of Alzheimer's disease in the Yonchon County survey, Korea. Int Psychogeriatr 20: 976-985.
    [128] Durazzo TC, Mattsson N, Weiner MW, et al. (2014) Smoking and increased Alzheimer's disease risk: a review of potential mechanisms. Alzheimers Dement 10: S122-145. doi: 10.1016/j.jalz.2014.04.009
    [129] Barnard ND, Bush AI, Ceccarelli A, et al. (2014) Dietary and lifestyle guidelines for the prevention of Alzheimer's disease. Neurobiol Aging 35: S74-78. doi: 10.1016/j.neurobiolaging.2014.03.033
    [130] Kawahara M, Kato-Negishi M, Tanaka K (2017) Cross talk between neurometals and amyloidogenic proteins at the synapse and the pathogenesis of neurodegenerative diseases. Metallomics 9: 619-633. doi: 10.1039/C7MT00046D
    [131] Zheng W, Aschner M, Ghersi-Egea JF (2003) Brain barrier systems: a new frontier in metal neurotoxicological research. Toxicol Appl Pharmacol 192: 1-11. doi: 10.1016/S0041-008X(03)00251-5
    [132] Heneka MT, Carson MJ, El Khoury J, et al. (2015) Neuroinflammation in Alzheimer's disease. Lancet Neurol 14: 388-405. doi: 10.1016/S1474-4422(15)70016-5
    [133] Wang X, Wang W, Li L, et al. (2014) Oxidative stress and mitochondrial dysfunction in Alzheimer's disease. Biochim Biophys Acta 1842: 1240-1247. doi: 10.1016/j.bbadis.2013.10.015
    [134] Chen Z, Zhong C (2014) Oxidative stress in Alzheimer's disease. Neurosci Bull 30: 271-281. doi: 10.1007/s12264-013-1423-y
    [135] DuBoff B, Feany M, Gotz J (2013) Why size matters-balancing mitochondrial dynamics in Alzheimer's disease. Trends Neurosci 36: 325-335. doi: 10.1016/j.tins.2013.03.002
    [136] Dik MG, Jonker C, Comijs HC, et al. (2007) Contribution of metabolic syndrome components to cognition in older individuals. Diabetes Care 30: 2655-2660. doi: 10.2337/dc06-1190
    [137] Campos-Pena V, Toral-Rios D, Becerril-Perez F, et al. (2017) Metabolic syndrome as a risk factor for Alzheimer's disease: is abeta a crucial factor in both pathologies? Antioxid Redox Signal 26: 542-560. doi: 10.1089/ars.2016.6768
    [138] Whyte LS, Lau AA, Hemsley KM, et al. (2017) Endo-lysosomal and autophagic dysfunction: a driving factor in Alzheimer's disease? J Neurochem 140: 703-717. doi: 10.1111/jnc.13935
    [139] Ferreira ST, Clarke JR, Bomfim TR, et al. (2014) Inflammation, defective insulin signaling, and neuronal dysfunction in Alzheimer's disease. Alzheimers Dement 10: S76-83. doi: 10.1016/j.jalz.2013.12.010
    [140] Kinney JW, Bemiller SM, Murtishaw AS, et al. (2018) Inflammation as a central mechanism in Alzheimer's disease. Alzheimers Dement 4: 575-590. doi: 10.1016/j.trci.2018.06.014
    [141] Rubio-Perez JM, Morillas-Ruiz JM (2012) A review: inflammatory process in Alzheimer's disease, role of cytokines. Sci World J 2012: 756357.
    [142] Mengel-From J, Christensen K, McGue M, et al. (2011) Genetic variations in the CLU and PICALM genes are associated with cognitive function in the oldest old. Neurobiol Aging 32: 554. e7-11. doi: 10.1016/j.neurobiolaging.2010.07.016
    [143] Jonsson T, Stefansson H, Steinberg S, et al. (2013) Variant of TREM2 associated with the risk of Alzheimer's disease. N Engl J Med 368: 107-116. doi: 10.1056/NEJMoa1211103
    [144] Cunningham C (2013) Microglia and neurodegeneration: the role of systemic inflammation. Glia 61: 71-90. doi: 10.1002/glia.22350
    [145] Bodea LG, Wang Y, Linnartz-Gerlach B, et al. (2014) Neurodegeneration by activation of the microglial complement-phagosome pathway. J Neurosci 34: 8546-8556. doi: 10.1523/JNEUROSCI.5002-13.2014
    [146] Mhatre SD, Tsai CA, Rubin AJ, et al. (2015) Microglial malfunction: the third rail in the development of Alzheimer's disease. Trends Neurosci 38: 621-636. doi: 10.1016/j.tins.2015.08.006
    [147] Zotova E, Nicoll JA, Kalaria R, et al. (2010) Inflammation in Alzheimer's disease: relevance to pathogenesis and therapy. Alzheimers Res Ther 2: 1. doi: 10.1186/alzrt24
    [148] Musicco C, Capelli V, Pesce V, et al. (2009) Accumulation of overoxidized peroxiredoxin III in aged rat liver mitochondria. Biochim Biophys Acta 1787: 890-896. doi: 10.1016/j.bbabio.2009.03.002
    [149] Sutinen EM, Pirttila T, Anderson G, et al. (2012) Pro-inflammatory interleukin-18 increases Alzheimer's disease-associated amyloid-beta production in human neuron-like cells. J Neuroinflammation 9: 199. doi: 10.1186/1742-2094-9-199
    [150] Sutinen EM, Korolainen MA, Hayrinen J, et al. (2014) Interleukin-18 alters protein expressions of neurodegenerative diseases-linked proteins in human SH-SY5Y neuron-like cells. Front Cell Neurosci 8: 214. doi: 10.3389/fncel.2014.00214
    [151] Oakley R, Tharakan B (2014) Vascular hyperpermeability and aging. Aging Dis 5: 114-125.
    [152] McColl BW, Rose N, Robson FH, et al. (2010) Increased brain microvascular MMP-9 and incidence of haemorrhagic transformation in obese mice after experimental stroke. J Cereb Blood Flow Metab 30: 267-272. doi: 10.1038/jcbfm.2009.217
    [153] Ojala JO, Sutinen EM, Salminen A, et al. (2008) Interleukin-18 increases expression of kinases involved in tau phosphorylation in SH-SY5Y neuroblastoma cells. J Neuroimmunol 205: 86-93. doi: 10.1016/j.jneuroim.2008.09.012
    [154] Alvarez A, Toro R, Caceres A, et al. (1999) Inhibition of tau phosphorylating protein kinase cdk5 prevents beta-amyloid-induced neuronal death. FEBS Lett 459: 421-426. doi: 10.1016/S0014-5793(99)01279-X
    [155] Alvarez A, Munoz JP, Maccioni RB (2001) A Cdk5-p35 stable complex is involved in the beta-amyloid-induced deregulation of Cdk5 activity in hippocampal neurons. Exp Cell Res 264: 266-274. doi: 10.1006/excr.2001.5152
    [156] Seo J, Kritskiy O, Watson LA, et al. (2017) Inhibition of p25/Cdk5 attenuates tauopathy in mouse and iPSC models of frontotemporal dementia. J Neurosci 37: 9917-9924. doi: 10.1523/JNEUROSCI.0621-17.2017
    [157] Morgan BP (2018) Complement in the pathogenesis of Alzheimer's disease. Semin Immunopathol 40: 113-124. doi: 10.1007/s00281-017-0662-9
    [158] Bonham LW, Desikan RS, Yokoyama JS, et al. (2016) The relationship between complement factor C3, APOE epsilon4, amyloid and tau in Alzheimer's disease. Acta Neuropathol Commun 4: 65. doi: 10.1186/s40478-016-0339-y
    [159] Bonham LW, Geier EG, Fan CC, et al. (2016) Age-dependent effects of APOE epsilon4 in preclinical Alzheimer's disease. Ann Clin Transl Neurol 3: 668-677. doi: 10.1002/acn3.333
    [160] Fritzinger DC, Benjamin DE (2016) The complement system in neuropathic and postoperative pain. Open Pain J 9: 26-37. doi: 10.2174/1876386301609010026
    [161] Sheedy FJ, Grebe A, Rayner KJ, et al. (2013) CD36 coordinates NLRP3 inflammasome activation by facilitating intracellular nucleation of soluble ligands into particulate ligands in sterile inflammation. Nat Immunol 14: 812-820. doi: 10.1038/ni.2639
    [162] Devanand DP (2018) Viral hypothesis and antiviral treatment in Alzheimer's disease. Curr Neurol Neurosci Rep 18: 55. doi: 10.1007/s11910-018-0863-1
    [163] Duggan MR, Torkzaban B, Ahooyi TM, et al. (2020) Potential role for herpesviruses in Alzheimer's disease. J Alzheimers Dis 1-15. (Preprint).
    [164] Wang JH, Cheng XR, Zhang XR, et al. (2016) Neuroendocrine immunomodulation network dysfunction in SAMP8 mice and PrP-hAbetaPPswe/PS1DeltaE9 mice: potential mechanism underlying cognitive impairment. Oncotarget 7: 22988-3005. doi: 10.18632/oncotarget.8453
    [165] Lopategui Cabezas I, Herrera Batista A, Penton Rol G (2014) The role of glial cells in Alzheimer disease: potential therapeutic implications. Neurologia 29: 305-309. doi: 10.1016/j.nrl.2012.10.006
    [166] Birch AM (2014) The contribution of astrocytes to Alzheimer's disease. Biochem Soc Trans 42: 1316-1320. doi: 10.1042/BST20140171
    [167] Takata K, Kitamura Y, Saeki M, et al. (2010) Galantamine-induced amyloid-β clearance mediated via stimulation of microglial nicotinic acetylcholine receptors. J Biol Chem 285: 40180-40191. doi: 10.1074/jbc.M110.142356
    [168] Koenigsknecht-Talboo J, Landreth GE (2005) Microglial phagocytosis induced by fibrillar beta-amyloid and IgGs are differentially regulated by proinflammatory cytokines. J Neurosci 25: 8240-8249. doi: 10.1523/JNEUROSCI.1808-05.2005
    [169] Xie J, Wang H, Lin T, et al. (2017) Microglia-Synapse pathways: promising therapeutic strategy for Alzheimer's disease. Biomed Res Int 2017: 2986460.
    [170] Qi Y, Klyubin I, Harney SC, et al. (2014) Longitudinal testing of hippocampal plasticity reveals the onset and maintenance of endogenous human Ass-induced synaptic dysfunction in individual freely behaving pre-plaque transgenic rats: rapid reversal by anti-Ass agents. Acta Neuropathol Commun 2: 175. doi: 10.1186/s40478-014-0175-x
    [171] Lo AC, Iscru E, Blum D, et al. (2013) Amyloid and tau neuropathology differentially affect prefrontal synaptic plasticity and cognitive performance in mouse models of Alzheimer's disease. J Alzheimers Dis 37: 109-125. doi: 10.3233/JAD-122296
    [172] Giulian D, Baker TJ (1986) Characterization of ameboid microglia isolated from developing mammalian brain. J Neurosci 6: 2163-2178. doi: 10.1523/JNEUROSCI.06-08-02163.1986
    [173] Kreutzberg GW (1996) Microglia: a sensor for pathological events in the CNS. Trends Neurosci 19: 312-318. doi: 10.1016/0166-2236(96)10049-7
    [174] Mecha M, Carrillo-Salinas FJ, Feliu A, et al. (2016) Microglia activation states and cannabinoid system: therapeutic implications. Pharmacol Ther 166: 40-55. doi: 10.1016/j.pharmthera.2016.06.011
    [175] Henkel JS, Beers DR, Zhao W, et al. (2009) Microglia in ALS: the good, the bad, and the resting. J Neuroimmune Pharmacol 4: 389-398. doi: 10.1007/s11481-009-9171-5
    [176] Stansley B, Post J, Hensley K (2012) A comparative review of cell culture systems for the study of microglial biology in Alzheimer's disease. J Neuroinflammation 9: 115. doi: 10.1186/1742-2094-9-115
    [177] Acosta C, Anderson HD, Anderson CM (2017) Astrocyte dysfunction in Alzheimer disease. J Neurosci Res 95: 2430-2447. doi: 10.1002/jnr.24075
    [178] Sofroniew MV (2009) Molecular dissection of reactive astrogliosis and glial scar formation. Trends Neurosci 32: 638-647. doi: 10.1016/j.tins.2009.08.002
    [179] Ricci G, Volpi L, Pasquali L, et al. (2009) Astrocyte-neuron interactions in neurological disorders. J Biol Phys 35: 317-336. doi: 10.1007/s10867-009-9157-9
    [180] Mattsson N, Tosun D, Insel PS, et al. (2014) Association of brain amyloid-beta with cerebral perfusion and structure in Alzheimer's disease and mild cognitive impairment. Brain 137: 1550-1561. doi: 10.1093/brain/awu043
    [181] Huang KL, Lin KJ, Ho MY, et al. (2012) Amyloid deposition after cerebral hypoperfusion: evidenced on [(18)F]AV-45 positron emission tomography. J Neurol Sci 319: 124-129. doi: 10.1016/j.jns.2012.04.014
    [182] Okamoto Y, Yamamoto T, Kalaria RN, et al. (2012) Cerebral hypoperfusion accelerates cerebral amyloid angiopathy and promotes cortical microinfarcts. Acta Neuropathol 123: 381-394. doi: 10.1007/s00401-011-0925-9
    [183] Pietronigro EC, Della Bianca V, Zenaro E, et al. (2017) NETosis in Alzheimer's disease. Front Immunol 8: 211. doi: 10.3389/fimmu.2017.00211
    [184] Mantovani A, Cassatella MA, Costantini C, et al. (2011) Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol 11: 519-531. doi: 10.1038/nri3024
    [185] Tillack K, Breiden P, Martin R, et al. (2012) T lymphocyte priming by neutrophil extracellular traps links innate and adaptive immune responses. J Immunol 188: 3150-3159. doi: 10.4049/jimmunol.1103414
    [186] Llanos-Gonzalez E, Henares-Chavarino AA, Pedrero-Prieto CM, et al. (2019) Interplay between mitochondrial oxidative disorders and proteostasis in Alzheimer's disease. Front Neurosci 13: 1444. doi: 10.3389/fnins.2019.01444
    [187] Franco R, Vargas MR (2018) Redox biology in neurological function, dysfunction, and aging. Antioxid Redox Signal 28: 1583-1586. doi: 10.1089/ars.2018.7509
    [188] Miller VM, Lawrence DA, Mondal TK, et al. (2009) Reduced glutathione is highly expressed in white matter and neurons in the unperturbed mouse brain—implications for oxidative stress associated with neurodegeneration. Brain Res 1276: 22-30. doi: 10.1016/j.brainres.2009.04.029
    [189] Munoz P, Ardiles AO, Perez-Espinosa B, et al. (2020) Redox modifications in synaptic components as biomarkers of cognitive status, in brain aging and disease. Mech Ageing Dev 189: 111250. doi: 10.1016/j.mad.2020.111250
    [190] Kumar A, Yegla B, Foster TC (2018) Redox signaling in neurotransmission and cognition during aging. Antioxid Redox Signal 28: 1724-1745. doi: 10.1089/ars.2017.7111
    [191] Sbodio JI, Snyder SH, Paul BD (2019) Redox mechanisms in neurodegeneration: from disease outcomes to therapeutic opportunities. Antioxid Redox Signal 30: 1450-1499. doi: 10.1089/ars.2017.7321
    [192] Arias-Cavieres A, Adasme T, Sanchez G, et al. (2017) Aging impairs hippocampal-dependent recognition memory and LTP and prevents the associated RyR Up-regulation. Front Aging Neurosci 9: 111. doi: 10.3389/fnagi.2017.00111
    [193] Wilson C, Gonzalez-Billault C (2015) Regulation of cytoskeletal dynamics by redox signaling and oxidative stress: implications for neuronal development and trafficking. Front Cell Neurosci 9: 381. doi: 10.3389/fncel.2015.00381
    [194] Quintanilla RA, Orellana JA, von Bernhardi R (2012) Understanding risk factors for Alzheimer's disease: interplay of neuroinflammation, connexin-based communication and oxidative stress. Arch Med Res 43: 632-644. doi: 10.1016/j.arcmed.2012.10.016
    [195] Butterfield DA, Bader Lange ML, Sultana R (2010) Involvements of the lipid peroxidation product, HNE, in the pathogenesis and progression of Alzheimer's disease. Biochim Biophys Acta 1801: 924-929. doi: 10.1016/j.bbalip.2010.02.005
    [196] Chen YY, Yu XY, Chen L, et al. (2019) Redox signaling in aging kidney and opportunity for therapeutic intervention through natural products. Free Radic Biol Med 141: 141-149. doi: 10.1016/j.freeradbiomed.2019.06.012
    [197] Bruce-Keller AJ, Gupta S, Knight AG, et al. (2011) Cognitive impairment in humanized APPxPS1 mice is linked to Abeta(1-42) and NOX activation. Neurobiol Dis 44: 317-326. doi: 10.1016/j.nbd.2011.07.012
    [198] Kothari V, Luo Y, Tornabene T, et al. (2017) High fat diet induces brain insulin resistance and cognitive impairment in mice. Biochim Biophys Acta 1863: 499-508. doi: 10.1016/j.bbadis.2016.10.006
    [199] Wilkinson BL, Landreth GE (2006) The microglial NADPH oxidase complex as a source of oxidative stress in Alzheimer's disease. J Neuroinflammation 3: 30. doi: 10.1186/1742-2094-3-30
    [200] Wong KY, Roy J, Fung ML, et al. (2020) Relationships between mitochondrial dysfunction and neurotransmission failure in Alzheimer's disease. Aging Dis 11: 1291-1316. doi: 10.14336/AD.2019.1125
    [201] Bissette G, Seidler FJ, Nemeroff CB, et al. (1996) High affinity choline transporter status in Alzheimer's disease tissue from rapid autopsy. Ann N Y Acad Sci 777: 197-204. doi: 10.1111/j.1749-6632.1996.tb34419.x
    [202] Campanucci VA, Krishnaswamy A, Cooper E (2008) Mitochondrial reactive oxygen species inactivate neuronal nicotinic acetylcholine receptors and induce long-term depression of fast nicotinic synaptic transmission. J Neurosci 28: 1733-1744. doi: 10.1523/JNEUROSCI.5130-07.2008
    [203] Jomova K, Vondrakova D, Lawson M, et al. (2010) Metals, oxidative stress and neurodegenerative disorders. Mol Cell Biochem 345: 91-104. doi: 10.1007/s11010-010-0563-x
    [204] Swerdlow RH, Khan SM (2004) A “mitochondrial cascade hypothesis” for sporadic Alzheimer's disease. Med Hypotheses 63: 8-20. doi: 10.1016/j.mehy.2003.12.045
    [205] Kim GW, Gasche Y, Grzeschik S, et al. (2003) Neurodegeneration in striatum induced by the mitochondrial toxin 3-nitropropionic acid: role of matrix metalloproteinase-9 in early blood-brain barrier disruption? J Neurosci 23: 8733-8742. doi: 10.1523/JNEUROSCI.23-25-08733.2003
    [206] Ridnour LA, Dhanapal S, Hoos M, et al. (2012) Nitric oxide-mediated regulation of beta-amyloid clearance via alterations of MMP-9/TIMP-1. J Neurochem 123: 736-749. doi: 10.1111/jnc.12028
    [207] Liu Y, Liu F, Iqbal K, et al. (2008) Decreased glucose transporters correlate to abnormal hyperphosphorylation of tau in Alzheimer disease. FEBS Lett 582: 359-364. doi: 10.1016/j.febslet.2007.12.035
    [208] Nikinmaa M, Pursiheimo S, Soitamo AJ (2004) Redox state regulates HIF-1alpha and its DNA binding and phosphorylation in salmonid cells. J Cell Sci 117: 3201-3206. doi: 10.1242/jcs.01192
    [209] Morris G, Walder K, Puri BK, et al. (2016) The deleterious effects of oxidative and nitrosative stress on palmitoylation, membrane lipid rafts and lipid-based cellular signalling: new drug targets in neuroimmune disorders. Mol Neurobiol 53: 4638-4658. doi: 10.1007/s12035-015-9392-y
    [210] Zou Y, Watters A, Cheng N, et al. (2019) Polyunsaturated fatty acids from astrocytes activate PPARgamma signaling in cancer cells to promote brain metastasis. Cancer Discov 9: 1720-1735. doi: 10.1158/2159-8290.CD-19-0270
    [211] Marnett LJ (1999) Lipid peroxidation-DNA damage by malondialdehyde. Mutat Res 424: 83-95. doi: 10.1016/S0027-5107(99)00010-X
    [212] Lu Y, Ren J, Cui S, et al. (2016) Cerebral glucose metabolism assessment in rat models of Alzheimer's disease: an 18F-FDG-PET study. Am J Alzheimers Dis Other Demen 31: 333-340. doi: 10.1177/1533317515617725
    [213] Jeong DU, Oh JH, Lee JE, et al. (2016) Basal forebrain cholinergic deficits reduce glucose metabolism and function of cholinergic and GABAergic systems in the cingulate cortex. Yonsei Med J 57: 165-172. doi: 10.3349/ymj.2016.57.1.165
    [214] Braak H, Del Tredici K (2015) The preclinical phase of the pathological process underlying sporadic Alzheimer's disease. Brain 138: 2814-2833. doi: 10.1093/brain/awv236
    [215] Morrison BM, Lee Y, Rothstein JD (2013) Oligodendroglia: metabolic supporters of axons. Trends Cell Biol 23: 644-651. doi: 10.1016/j.tcb.2013.07.007
    [216] Grant CM, Quinn KA, Dawes IW (1999) Differential protein S-thiolation of glyceraldehyde-3-phosphate dehydrogenase isoenzymes influences sensitivity to oxidative stress. Mol Cell Biol 19: 2650-2656. doi: 10.1128/MCB.19.4.2650
    [217] Avery SV (2011) Molecular targets of oxidative stress. Biochem J 434: 201-210. doi: 10.1042/BJ20101695
    [218] Bubber P, Haroutunian V, Fisch G, et al. (2005) Mitochondrial abnormalities in Alzheimer brain: mechanistic implications. Ann Neurol 57: 695-703. doi: 10.1002/ana.20474
    [219] Gibson GE, Blass JP, Beal MF, et al. (2005) The alpha-ketoglutarate-dehydrogenase complex: a mediator between mitochondria and oxidative stress in neurodegeneration. Mol Neurobiol 31: 43-63. doi: 10.1385/MN:31:1-3:043
    [220] Long J, Liu C, Sun L, et al. (2009) Neuronal mitochondrial toxicity of malondialdehyde: inhibitory effects on respiratory function and enzyme activities in rat brain mitochondria. Neurochem Res 34: 786-794. doi: 10.1007/s11064-008-9882-7
    [221] Martin E, Rosenthal RE, Fiskum G (2005) Pyruvate dehydrogenase complex: metabolic link to ischemic brain injury and target of oxidative stress. J Neurosci Res 79: 240-247. doi: 10.1002/jnr.20293
    [222] Salminen A, Haapasalo A, Kauppinen A, et al. (2015) Impaired mitochondrial energy metabolism in Alzheimer's disease: impact on pathogenesis via disturbed epigenetic regulation of chromatin landscape. Prog Neurobiol 131: 1-20. doi: 10.1016/j.pneurobio.2015.05.001
    [223] Liguori C, Chiaravalloti A, Sancesario G, et al. (2016) Cerebrospinal fluid lactate levels and brain [18F]FDG PET hypometabolism within the default mode network in Alzheimer's disease. Eur J Nucl Med Mol Imaging 43: 2040-2049. doi: 10.1007/s00259-016-3417-2
    [224] Isopi E, Granzotto A, Corona C, et al. (2015) Pyruvate prevents the development of age-dependent cognitive deficits in a mouse model of Alzheimer's disease without reducing amyloid and tau pathology. Neurobiol Dis 81: 214-224. doi: 10.1016/j.nbd.2014.11.013
    [225] Talbot K, Wang HY, Kazi H, et al. (2012) Demonstrated brain insulin resistance in Alzheimer's disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J Clin Invest 122: 1316-1338. doi: 10.1172/JCI59903
    [226] Morales-Corraliza J, Wong H, Mazzella MJ, et al. (2016) Brain-Wide insulin resistance, Tau phosphorylation changes, and hippocampal neprilysin and Amyloid-beta alterations in a monkey model of Type 1 diabetes. J Neurosci 36: 4248-4258. doi: 10.1523/JNEUROSCI.4640-14.2016
    [227] Yamamoto N, Ishikuro R, Tanida M, et al. (2018) Insulin-signaling pathway regulates the degradation of amyloid beta-protein via astrocytes. Neuroscience 385: 227-236. doi: 10.1016/j.neuroscience.2018.06.018
    [228] Han X, Yang L, Du H, et al. (2016) Insulin attenuates beta-amyloid-associated Insulin/Akt/EAAT signaling perturbations in human astrocytes. Cell Mol Neurobiol 36: 851-864. doi: 10.1007/s10571-015-0268-5
    [229] Ng RC, Chan KH (2017) Potential neuroprotective effects of adiponectin in Alzheimer's disease. Int J Mol Sci 18: 592. doi: 10.3390/ijms18030592
    [230] Pei JJ, Khatoon S, An WL, et al. (2003) Role of protein kinase B in Alzheimer's neurofibrillary pathology. Acta Neuropathol 105: 381-392. doi: 10.1007/s00401-002-0657-y
    [231] Anderson NJ, King MR, Delbruck L, et al. (2014) Role of insulin signaling impairment, adiponectin and dyslipidemia in peripheral and central neuropathy in mice. Dis Model Mech 7: 625-633. doi: 10.1242/dmm.015750
    [232] Garcia-Casares N, Jorge RE, Garcia-Arnes JA, et al. (2014) Cognitive dysfunctions in middle-aged type 2 diabetic patients and neuroimaging correlations: a cross-sectional study. J Alzheimers Dis 42: 1337-1346. doi: 10.3233/JAD-140702
    [233] Purnell C, Gao S, Callahan CM, et al. (2009) Cardiovascular risk factors and incident Alzheimer disease: a systematic review of the literature. Alzheimer Dis Assoc Disord 23: 1-10. doi: 10.1097/WAD.0b013e318187541c
    [234] de la Torre JC (2012) Cardiovascular risk factors promote brain hypoperfusion leading to cognitive decline and dementia. Cardiovasc Psychiatry Neurol 2012: 367516. doi: 10.1155/2012/367516
    [235] Klohs J (2019) An integrated view on vascular dysfunction in Alzheimer's disease. Neurodegener Dis 19: 109-127. doi: 10.1159/000505625
    [236] Popovic M, Laumonnier Y, Burysek L, et al. (2008) Thrombin-induced expression of endothelial CX3CL1 potentiates monocyte CCL2 production and transendothelial migration. J Leukoc Biol 84: 215-223. doi: 10.1189/jlb.0907652
    [237] Sole M, Esteban-Lopez M, Taltavull B, et al. (2019) Blood-brain barrier dysfunction underlying Alzheimer's disease is induced by an SSAO/VAP-1-dependent cerebrovascular activation with enhanced Abeta deposition. Biochim Biophys Acta Mol Basis Dis 1865: 2189-2202. doi: 10.1016/j.bbadis.2019.04.016
    [238] Zlokovic BV (2011) Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders. Nat Rev Neurosci 12: 723-738. doi: 10.1038/nrn3114
    [239] Ramanathan A, Nelson AR, Sagare AP, et al. (2015) Impaired vascular-mediated clearance of brain amyloid beta in Alzheimer's disease: the role, regulation and restoration of LRP1. Front Aging Neurosci 7: 136. doi: 10.3389/fnagi.2015.00136
    [240] Montagne A, Pa J, Zlokovic BV (2015) Vascular plasticity and cognition during normal aging and dementia. JAMA Neurol 72: 495-496. doi: 10.1001/jamaneurol.2014.4636
    [241] Montine TJ, Koroshetz WJ, Babcock D, et al. (2014) Recommendations of the Alzheimer's disease-related dementias conference. Neurology 83: 851-860. doi: 10.1212/WNL.0000000000000733
    [242] Sweeney MD, Sagare AP, Zlokovic BV (2015) Cerebrospinal fluid biomarkers of neurovascular dysfunction in mild dementia and Alzheimer's disease. J Cereb Blood Flow Metab 35: 1055-1068. doi: 10.1038/jcbfm.2015.76
    [243] Yan SD, Chen X, Fu J, et al. (1996) RAGE and amyloid-beta peptide neurotoxicity in Alzheimer's disease. Nature 382: 685-691. doi: 10.1038/382685a0
    [244] Miller MC, Tavares R, Johanson CE, et al. (2008) Hippocampal RAGE immunoreactivity in early and advanced Alzheimer's disease. Brain Res 1230: 273-280. doi: 10.1016/j.brainres.2008.06.124
    [245] Lue LF, Walker DG, Brachova L, et al. (2001) Involvement of microglial receptor for advanced glycation endproducts (RAGE) in Alzheimer's disease: identification of a cellular activation mechanism. Exp Neurol 171: 29-45. doi: 10.1006/exnr.2001.7732
    [246] Carnevale D, Mascio G, D'Andrea I, et al. (2012) Hypertension induces brain beta-amyloid accumulation, cognitive impairment, and memory deterioration through activation of receptor for advanced glycation end products in brain vasculature. Hypertension 60: 188-197. doi: 10.1161/HYPERTENSIONAHA.112.195511
    [247] Srikanth V, Maczurek A, Phan T, et al. (2011) Advanced glycation endproducts and their receptor RAGE in Alzheimer's disease. Neurobiol Aging 32: 763-777. doi: 10.1016/j.neurobiolaging.2009.04.016
    [248] de la Torre J (2018) The vascular hypothesis of Alzheimer's disease: a key to preclinical prediction of dementia using neuroimaging. J Alzheimers Dis 63: 35-52. doi: 10.3233/JAD-180004
    [249] Wierenga CE, Hays CC, Zlatar ZZ (2014) Cerebral blood flow measured by arterial spin labeling MRI as a preclinical marker of Alzheimer's disease. J Alzheimers Dis 42: S411-419. doi: 10.3233/JAD-141467
    [250] Chuang YF, Breitner JCS, Chiu YL, et al. (2014) Use of diuretics is associated with reduced risk of Alzheimer's disease: the cache county study. Neurobiol Aging 35: 2429-2435. doi: 10.1016/j.neurobiolaging.2014.05.002
    [251] Ashby EL, Kehoe PG (2013) Current status of renin-aldosterone angiotensin system-targeting anti-hypertensive drugs as therapeutic options for Alzheimer's disease. Expert Opin Investig Drugs 22: 1229-1242. doi: 10.1517/13543784.2013.812631
    [252] Yasar S, Xia J, Yao W, et al. (2013) Antihypertensive drugs decrease risk of Alzheimer disease: ginkgo evaluation of memory study. Neurology 81: 896-903. doi: 10.1212/WNL.0b013e3182a35228
    [253] Thomas T, Miners S, Love S (2015) Post-mortem assessment of hypoperfusion of cerebral cortex in Alzheimer's disease and vascular dementia. Brain 138: 1059-1069. doi: 10.1093/brain/awv025
    [254] Zhao Y, Gong CX (2015) From chronic cerebral hypoperfusion to Alzheimer-like brain pathology and neurodegeneration. Cell Mol Neurobiol 35: 101-110. doi: 10.1007/s10571-014-0127-9
    [255] Glodzik L, Rusinek H, Pirraglia E, et al. (2014) Blood pressure decrease correlates with tau pathology and memory decline in hypertensive elderly. Neurobiol Aging 35: 64-71. doi: 10.1016/j.neurobiolaging.2013.06.011
    [256] Hong S, Beja-Glasser VF, Nfonoyim BM, et al. (2016) Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352: 712-716. doi: 10.1126/science.aad8373
    [257] Gottfries CG, Bartfai T, Carlsson A, et al. (1986) Multiple biochemical deficits in both gray and white matter of Alzheimer brains. Prog Neuropsychopharmacol Biol Psychiatry 10: 405-413. doi: 10.1016/0278-5846(86)90014-X
    [258] Storga D, Vrecko K, Birkmayer JG, et al. (1996) Monoaminergic neurotransmitters, their precursors and metabolites in brains of Alzheimer patients. Neurosci Lett 203: 29-32. doi: 10.1016/0304-3940(95)12256-7
    [259] Arai H, Ichimiya Y, Kosaka K, et al. (1992) Neurotransmitter changes in early- and late-onset Alzheimer-type dementia. Prog Neuropsychopharmacol Biol Psychiatry 16: 883-890. doi: 10.1016/0278-5846(92)90106-O
    [260] Lanari A, Amenta F, Silvestrelli G, et al. (2006) Neurotransmitter deficits in behavioural and psychological symptoms of Alzheimer's disease. Mech Ageing Dev 127: 158-165. doi: 10.1016/j.mad.2005.09.016
    [261] Nobili A, Latagliata EC, Viscomi MT, et al. (2017) Dopamine neuronal loss contributes to memory and reward dysfunction in a model of Alzheimer's disease. Nat Commun 8: 14727. doi: 10.1038/ncomms14727
    [262] Chalermpalanupap T, Kinkead B, Hu WT, et al. (2013) Targeting norepinephrine in mild cognitive impairment and Alzheimer's disease. Alzheimers Res Ther 5: 21. doi: 10.1186/alzrt175
    [263] Woolf NJ, Butcher LL (2011) Cholinergic systems mediate action from movement to higher consciousness. Behav Brain Res 221: 488-498. doi: 10.1016/j.bbr.2009.12.046
    [264] Baker-Nigh A, Vahedi S, Davis EG, et al. (2015) Neuronal amyloid-beta accumulation within cholinergic basal forebrain in ageing and Alzheimer's disease. Brain 138: 1722-1737. doi: 10.1093/brain/awv024
    [265] Bracco L, Bessi V, Padiglioni S, et al. (2014) Do cholinesterase inhibitors act primarily on attention deficit? A naturalistic study in Alzheimer's disease patients. J Alzheimers Dis 40: 737-742. doi: 10.3233/JAD-131154
    [266] Stephan AH, Barres BA, Stevens B (2012) The complement system: an unexpected role in synaptic pruning during development and disease. Annu Rev Neurosci 35: 369-389. doi: 10.1146/annurev-neuro-061010-113810
    [267] Bialas AR, Stevens B (2013) TGF-beta signaling regulates neuronal C1q expression and developmental synaptic refinement. Nat Neurosci 16: 1773-1782. doi: 10.1038/nn.3560
    [268] Nakajima K, Tohyama Y, Maeda S, et al. (2007) Neuronal regulation by which microglia enhance the production of neurotrophic factors for GABAergic, catecholaminergic, and cholinergic neurons. Neurochem Int 50: 807-820. doi: 10.1016/j.neuint.2007.02.006
    [269] Sierra A, Gottfried-Blackmore AC, McEwen BS, et al. (2007) Microglia derived from aging mice exhibit an altered inflammatory profile. Glia 55: 412-424. doi: 10.1002/glia.20468
    [270] Welser-Alves JV, Milner R (2013) Microglia are the major source of TNF-alpha and TGF-beta1 in postnatal glial cultures; regulation by cytokines, lipopolysaccharide, and vitronectin. Neurochem Int 63: 47-53. doi: 10.1016/j.neuint.2013.04.007
    [271] Brucato FH, Benjamin DE (2020) Synaptic pruning in Alzheimer's disease: role of the complement system. Glob J Med Res 20.
    [272] Gajardo I, Salazar CS, Lopez-Espindola D, et al. (2018) Lack of pannexin 1 alters synaptic GluN2 subunit composition and spatial reversal learning in mice. Front Mol Neurosci 11: 114. doi: 10.3389/fnmol.2018.00114
    [273] Flores-Munoz C, Gomez B, Mery E, et al. (2020) Acute pannexin 1 blockade mitigates early synaptic plasticity defects in a mouse model of Alzheimer's disease. Front Cell Neurosci 14: 46. doi: 10.3389/fncel.2020.00046
    [274] Liu J, Chang L, Song Y, et al. (2019) The role of NMDA receptors in Alzheimer's disease. Front Neurosci 13: 43. doi: 10.3389/fnins.2019.00043
    [275] Hidalgo C, Arias-Cavieres A (2016) Calcium, reactive oxygen species, and synaptic plasticity. Physiology 31: 201-215. doi: 10.1152/physiol.00038.2015
    [276] Lu YF, Hawkins RD (2002) Ryanodine receptors contribute to cGMP-induced late-phase LTP and CREB phosphorylation in the hippocampus. J Neurophysiol 88: 1270-1278. doi: 10.1152/jn.2002.88.3.1270
    [277] Del Prete D, Checler F, Chami M (2014) Ryanodine receptors: physiological function and deregulation in Alzheimer disease. Mol Neurodegener 9: 21. doi: 10.1186/1750-1326-9-21
    [278] Oules B, Del Prete D, Greco B, et al. (2012) Ryanodine receptor blockade reduces amyloid-beta load and memory impairments in Tg2576 mouse model of Alzheimer disease. J Neurosci 32: 11820-11834. doi: 10.1523/JNEUROSCI.0875-12.2012
    [279] SanMartin CD, Veloso P, Adasme T, et al. (2017) RyR2-Mediated Ca(2+) release and mitochondrial ros generation partake in the synaptic dysfunction caused by amyloid beta peptide oligomers. Front Mol Neurosci 10: 115. doi: 10.3389/fnmol.2017.00115
    [280] Munoz Y, Paula-Lima AC, Nunez MT (2018) Reactive oxygen species released from astrocytes treated with amyloid beta oligomers elicit neuronal calcium signals that decrease phospho-Ser727-STAT3 nuclear content. Free Radic Biol Med 117: 132-144. doi: 10.1016/j.freeradbiomed.2018.01.006
    [281] Bitzer-Quintero OK, Gonzalez-Burgos I (2012) Immune system in the brain: a modulatory role on dendritic spine morphophysiology? Neural Plast 2012: 348642. doi: 10.1155/2012/348642
    [282] Fonseca MI, Chu SH, Hernandez MX, et al. (2017) Cell-specific deletion of C1qa identifies microglia as the dominant source of C1q in mouse brain. J Neuroinflammation 14: 48. doi: 10.1186/s12974-017-0814-9
    [283] Schafer DP, Lehrman EK, Kautzman AG, et al. (2012) Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron 74: 691-705. doi: 10.1016/j.neuron.2012.03.026
    [284] Gasque P (2004) Complement: a unique innate immune sensor for danger signals. Mol Immunol 41: 1089-1098. doi: 10.1016/j.molimm.2004.06.011
    [285] Azevedo EP, Ledo JH, Barbosa G, et al. (2013) Activated microglia mediate synapse loss and short-term memory deficits in a mouse model of transthyretin-related oculoleptomeningeal amyloidosis. Cell Death Dis 4: e789. doi: 10.1038/cddis.2013.325
    [286] Wang WY, Tan MS, Yu JT, et al. (2015) Role of pro-inflammatory cytokines released from microglia in Alzheimer's disease. Ann Transl Med 3: 136.
    [287] Liddelow SA, Guttenplan KA, Clarke LE, et al. (2017) Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541: 481-487. doi: 10.1038/nature21029
    [288] Stevens B, Allen NJ, Vazquez LE, et al. (2007) The classical complement cascade mediates CNS synapse elimination. Cell 131: 1164-1178. doi: 10.1016/j.cell.2007.10.036
    [289] Stephan AH, Madison DV, Mateos JM, et al. (2013) A dramatic increase of C1q protein in the CNS during normal aging. J Neurosci 33: 13460-13474. doi: 10.1523/JNEUROSCI.1333-13.2013
    [290] Shi Q, Colodner KJ, Matousek SB, et al. (2015) Complement C3-Deficient mice fail to display age-related hippocampal decline. J Neurosci 35: 13029-13042. doi: 10.1523/JNEUROSCI.1698-15.2015
    [291] Shi Q, Chowdhury S, Ma R, et al. (2017) Complement C3 deficiency protects against neurodegeneration in aged plaque-rich APP/PS1 mice. Sci Transl Med 9: eaaf6295. doi: 10.1126/scitranslmed.aaf6295
    [292] Reichwald J, Danner S, Wiederhold KH, et al. (2009) Expression of complement system components during aging and amyloid deposition in APP transgenic mice. J Neuroinflammation 6: 35. doi: 10.1186/1742-2094-6-35
    [293] Paolicelli RC, Jawaid A, Henstridge CM, et al. (2017) TDP-43 depletion in microglia promotes amyloid clearance but also induces synapse loss. Neuron 95: 297-308. e6. doi: 10.1016/j.neuron.2017.05.037
    [294] Orellana JA, Shoji KF, Abudara V, et al. (2011) Amyloid beta-induced death in neurons involves glial and neuronal hemichannels. J Neurosci 31: 4962-4977. doi: 10.1523/JNEUROSCI.6417-10.2011
    [295] Pena-Oyarzun D, Bravo-Sagua R, Diaz-Vega A, et al. (2018) Autophagy and oxidative stress in non-communicable diseases: a matter of the inflammatory state? Free Radic Biol Med 124: 61-78. doi: 10.1016/j.freeradbiomed.2018.05.084
    [296] Mizushima N (2009) Physiological functions of autophagy. Curr Top Microbiol Immunol 335: 71-84.
    [297] Alirezaei M, Kiosses WB, Flynn CT, et al. (2008) Disruption of neuronal autophagy by infected microglia results in neurodegeneration. PLoS One 3: e2906. doi: 10.1371/journal.pone.0002906
    [298] Komatsu M, Waguri S, Chiba T, et al. (2006) Loss of autophagy in the central nervous system causes neurodegeneration in mice. Nature 441: 880-884. doi: 10.1038/nature04723
    [299] Ji ZS, Mullendorff K, Cheng IH, et al. (2006) Reactivity of apolipoprotein E4 and amyloid beta peptide: lysosomal stability and neurodegeneration. J Biol Chem 281: 2683-2692. doi: 10.1074/jbc.M506646200
    [300] Belinson H, Lev D, Masliah E, et al. (2008) Activation of the amyloid cascade in apolipoprotein E4 transgenic mice induces lysosomal activation and neurodegeneration resulting in marked cognitive deficits. J Neurosci 28: 4690-4701. doi: 10.1523/JNEUROSCI.5633-07.2008
    [301] Friedman LG, Qureshi YH, Yu WH (2015) Promoting autophagic clearance: viable therapeutic targets in Alzheimer's disease. Neurotherapeutics 12: 94-108. doi: 10.1007/s13311-014-0320-z
    [302] Yu WH, Cuervo AM, Kumar A, et al. (2005) Macroautophagy—a novel Beta-amyloid peptide-generating pathway activated in Alzheimer's disease. J Cell Biol 171: 87-98. doi: 10.1083/jcb.200505082
    [303] Nixon RA (2007) Autophagy, amyloidogenesis and Alzheimer disease. J Cell Sci 120: 4081-4091. doi: 10.1242/jcs.019265
    [304] Klionsky DJ, Elazar Z, Seglen PO, et al. (2008) Does bafilomycin A1 block the fusion of autophagosomes with lysosomes? Autophagy 4: 849-850. doi: 10.4161/auto.6845
    [305] Nixon RA, Wegiel J, Kumar A, et al. (2005) Extensive involvement of autophagy in Alzheimer disease: an immuno-electron microscopy study. J Neuropathol Exp Neurol 64: 113-122. doi: 10.1093/jnen/64.2.113
    [306] Funderburk SF, Marcellino BK, Yue Z (2010) Cell “self-eating” (autophagy) mechanism in Alzheimer's disease. Mt Sinai J Med 77: 59-68. doi: 10.1002/msj.20161
    [307] Silva DF, Esteves AR, Oliveira CR, et al. (2011) Mitochondria: the common upstream driver of amyloid-beta and tau pathology in Alzheimer's disease. Curr Alzheimer Res 8: 563-572. doi: 10.2174/156720511796391872
    [308] Yu WH, Kumar A, Peterhoff C, et al. (2004) Autophagic vacuoles are enriched in amyloid precursor protein-secretase activities: implications for beta-amyloid peptide over-production and localization in Alzheimer's disease. Int J Biochem Cell Biol 36: 2531-2540. doi: 10.1016/j.biocel.2004.05.010
    [309] Mizushima N (2005) A(beta) generation in autophagic vacuoles. J Cell Biol 171: 15-17. doi: 10.1083/jcb.200508097
    [310] Boland B, Kumar A, Lee S, et al. (2008) Autophagy induction and autophagosome clearance in neurons: relationship to autophagic pathology in Alzheimer's disease. J Neurosci 28: 6926-6937. doi: 10.1523/JNEUROSCI.0800-08.2008
    [311] Lautrup S, Lou G, Aman Y, et al. (2019) Microglial mitophagy mitigates neuroinflammation in Alzheimer's disease. Neurochem Int 129: 104469. doi: 10.1016/j.neuint.2019.104469
    [312] Reddy PH, Oliver DM (2019) Amyloid Beta and phosphorylated Tau-Induced defective autophagy and mitophagy in Alzheimer's disease. Cells 8: 488. doi: 10.3390/cells8050488
    [313] Kerr JS, Adriaanse BA, Greig NH, et al. (2017) Mitophagy and Alzheimer's disease: cellular and molecular mechanisms. Trends Neurosci 40: 151-166. doi: 10.1016/j.tins.2017.01.002
    [314] Hu Y, Li XC, Wang ZH, et al. (2016) Tau accumulation impairs mitophagy via increasing mitochondrial membrane potential and reducing mitochondrial Parkin. Oncotarget 7: 17356-17368. doi: 10.18632/oncotarget.7861
    [315] Ellisdon AM, Bottomley SP (2004) The role of protein misfolding in the pathogenesis of human diseases. IUBMB Life 56: 119-123. doi: 10.1080/15216540410001674003
    [316] Vingtdeux V, Sergeant N, Buee L (2012) Potential contribution of exosomes to the prion-like propagation of lesions in Alzheimer's disease. Front Physiol 3: 229. doi: 10.3389/fphys.2012.00229
    [317] Uddin MS, Tewari D, Sharma G, et al. (2020) Molecular mechanisms of ER stress and UPR in the pathogenesis of Alzheimer's disease. Mol Neurobiol 57: 2902-2919. doi: 10.1007/s12035-020-01929-y
    [318] Schiera G, Di Liegro CM, Di Liegro I (2015) Extracellular membrane vesicles as vehicles for brain cell-to-cell interactions in physiological as well as pathological conditions. Biomed Res Int 2015: 152926. doi: 10.1155/2015/152926
    [319] Christianson JC, Ye Y (2014) Cleaning up in the endoplasmic reticulum: ubiquitin in charge. Nat Struct Mol Biol 21: 325-335. doi: 10.1038/nsmb.2793
    [320] Diehl JA, Fuchs SY, Koumenis C (2011) The cell biology of the unfolded protein response. Gastroenterology 141: 38-41. e2. doi: 10.1053/j.gastro.2011.05.018
    [321] Dhakal S, Macreadie I (2020) Protein homeostasis networks and the use of yeast to guide interventions in Alzheimer's disease. Int J Mol Sci 21: 8014. doi: 10.3390/ijms21218014
    [322] Kabir MT, Uddin MS, Zaman S, et al. (2020) Molecular mechanisms of metal toxicity in the pathogenesis of Alzheimer's disease. Mol Neurobiol 1-20.
    [323] Cristovao JS, Santos R, Gomes CM (2016) Metals and neuronal metal binding proteins implicated in Alzheimer's disease. Oxid Med Cell Longev 2016: 9812178. doi: 10.1155/2016/9812178
    [324] Gerber H, Wu F, Dimitrov M, et al. (2017) Zinc and copper differentially modulate amyloid precursor protein processing by gamma-secretase and amyloid-beta peptide production. J Biol Chem 292: 3751-3767. doi: 10.1074/jbc.M116.754101
    [325] Savelieff MG, Lee S, Liu Y, et al. (2013) Untangling amyloid-beta, tau, and metals in Alzheimer's disease. ACS Chem Biol 8: 856-865. doi: 10.1021/cb400080f
    [326] Dahms SO, Konnig I, Roeser D, et al. (2012) Metal binding dictates conformation and function of the amyloid precursor protein (APP) E2 domain. J Mol Biol 416: 438-452. doi: 10.1016/j.jmb.2011.12.057
    [327] Flaten TP (2001) Aluminium as a risk factor in Alzheimer's disease, with emphasis on drinking water. Brain Res Bull 55: 187-196. doi: 10.1016/S0361-9230(01)00459-2
    [328] Ward RJ, Zucca FA, Duyn JH, et al. (2014) The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol 13: 1045-1060. doi: 10.1016/S1474-4422(14)70117-6
    [329] Lane DJR, Ayton S, Bush AI (2018) Iron and Alzheimer's disease: an update on emerging mechanisms. J Alzheimers Dis 64: S379-S395. doi: 10.3233/JAD-179944
    [330] Tamano H, Takeda A (2011) Dynamic action of neurometals at the synapse. Metallomics 3: 656-661. doi: 10.1039/c1mt00008j
    [331] Ashraf A, Clark M, So PW (2018) The aging of iron man. Front Aging Neurosci 10: 65. doi: 10.3389/fnagi.2018.00065
    [332] Ojala JO, Sutinen EM (2017) The role of interleukin-18, oxidative stress and metabolic syndrome in Alzheimer's disease. J Clin Med 6: 55. doi: 10.3390/jcm6050055
    [333] Arrigoni F, Rizza F, Tisi R, et al. (2020) On the propagation of the OH radical produced by Cu-amyloid beta peptide model complexes. Insight from molecular modelling. Metallomics 12: 1765-1780. doi: 10.1039/D0MT00113A
    [334] Colvin RA, Jin Q, Lai B, et al. (2016) Visualizing metal content and intracellular distribution in primary hippocampal neurons with synchrotron X-ray fluorescence. PLoS One 11: e0159582. doi: 10.1371/journal.pone.0159582
    [335] Acevedo KM, Hung YH, Dalziel AH, et al. (2011) Copper promotes the trafficking of the amyloid precursor protein. J Biol Chem 286: 8252-8262. doi: 10.1074/jbc.M110.128512
    [336] Hickey JL, James JL, Henderson CA, et al. (2015) Intracellular distribution of fluorescent copper and zinc bis(thiosemicarbazonato) complexes measured with fluorescence lifetime spectroscopy. Inorg Chem 54: 9556-9567. doi: 10.1021/acs.inorgchem.5b01599
    [337] Alaverdashvili M, Hackett MJ, Pickering IJ, et al. (2014) Laminar-specific distribution of zinc: evidence for presence of layer IV in forelimb motor cortex in the rat. Neuroimage 103: 502-510. doi: 10.1016/j.neuroimage.2014.08.046
    [338] Ciccotosto GD, James SA, Altissimo M, et al. (2014) Quantitation and localization of intracellular redox active metals by X-ray fluorescence microscopy in cortical neurons derived from APP and APLP2 knockout tissue. Metallomics 6: 1894-1904. doi: 10.1039/C4MT00176A
    [339] Craddock TJ, Tuszynski JA, Chopra D, et al. (2012) The zinc dyshomeostasis hypothesis of Alzheimer's disease. PLoS One 7: e33552. doi: 10.1371/journal.pone.0033552
    [340] Christensen MK, Geneser FA (1995) Distribution of neurons of origin of zinc-containing projections in the amygdala of the rat. Anat Embryol 191: 227-237. doi: 10.1007/BF00187821
    [341] Jiang L, Dong H, Cao H, et al. (2019) Exosomes in pathogenesis, diagnosis, and treatment of Alzheimer's disease. Med Sci Monit 25: 3329-3335. doi: 10.12659/MSM.914027
    [342] Song Z, Xu Y, Deng W, et al. (2020) Brain derived exosomes are a double-edged sword in Alzheimer's disease. Front Mol Neurosci 13: 79. doi: 10.3389/fnmol.2020.00079
    [343] van Niel G, Porto-Carreiro I, Simoes S, et al. (2006) Exosomes: a common pathway for a specialized function. J Biochem 140: 13-21. doi: 10.1093/jb/mvj128
    [344] Eitan E, Suire C, Zhang S, et al. (2016) Impact of lysosome status on extracellular vesicle content and release. Ageing Res Rev 32: 65-74. doi: 10.1016/j.arr.2016.05.001
    [345] Asai H, Ikezu S, Tsunoda S, et al. (2015) Depletion of microglia and inhibition of exosome synthesis halt tau propagation. Nat Neurosci 18: 1584-1593. doi: 10.1038/nn.4132
    [346] Dinkins MB, Enasko J, Hernandez C, et al. (2016) Neutral sphingomyelinase-2 deficiency ameliorates Alzheimer's disease pathology and improves cognition in the 5XFAD mouse. J Neurosci 36: 8653-8667. doi: 10.1523/JNEUROSCI.1429-16.2016
    [347] Xiao T, Zhang W, Jiao B, et al. (2017) The role of exosomes in the pathogenesis of Alzheimer' disease. Transl Neurodegener 6: 3. doi: 10.1186/s40035-017-0072-x
    [348] Yuyama K, Igarashi Y (2017) Exosomes as carriers of Alzheimer's amyloid-ss. Front Neurosci 11: 229. doi: 10.3389/fnins.2017.00229
    [349] Cataldo AM, Barnett JL, Pieroni C, et al. (1997) Increased neuronal endocytosis and protease delivery to early endosomes in sporadic Alzheimer's disease: neuropathologic evidence for a mechanism of increased beta-amyloidogenesis. J Neurosci 17: 6142-6151. doi: 10.1523/JNEUROSCI.17-16-06142.1997
    [350] Cataldo A, Rebeck GW, Ghetri B, et al. (2001) Endocytic disturbances distinguish among subtypes of Alzheimer's disease and related disorders. Ann Neurol 50: 661-665. doi: 10.1002/ana.1254
    [351] Cataldo AM, Petanceska S, Terio NB, et al. (2004) Abeta localization in abnormal endosomes: association with earliest Abeta elevations in AD and Down syndrome. Neurobiol Aging 25: 1263-1272. doi: 10.1016/j.neurobiolaging.2004.02.027
    [352] Takahashi RH, Milner TA, Li F, et al. (2002) Intraneuronal Alzheimer abeta42 accumulates in multivesicular bodies and is associated with synaptic pathology. Am J Pathol 161: 1869-1879. doi: 10.1016/S0002-9440(10)64463-X
    [353] Langui D, Girardot N, El Hachimi KH, et al. (2004) Subcellular topography of neuronal Abeta peptide in APPxPS1 transgenic mice. Am J Pathol 165: 1465-1477. doi: 10.1016/S0002-9440(10)63405-0
    [354] Fiandaca MS, Kapogiannis D, Mapstone M, et al. (2015) Identification of preclinical Alzheimer's disease by a profile of pathogenic proteins in neurally derived blood exosomes: a case-control study. Alzheimers Dement 11: 600-607. e1. doi: 10.1016/j.jalz.2014.06.008
    [355] Wang G, Dinkins M, He Q, et al. (2012) Astrocytes secrete exosomes enriched with proapoptotic ceramide and prostate apoptosis response 4 (PAR-4): potential mechanism of apoptosis induction in Alzheimer disease (AD). J Biol Chem 287: 21384-21395. doi: 10.1074/jbc.M112.340513
    [356] Bindea G, Mlecnik B, Hackl H, et al. (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25: 1091-1093. doi: 10.1093/bioinformatics/btp101
    [357] Mosconi L, Berti V, Swerdlow RH, et al. (2010) Maternal transmission of Alzheimer's disease: prodromal metabolic phenotype and the search for genes. Hum Genomics 4: 170-193. doi: 10.1186/1479-7364-4-3-170
    [358] Birky CW (1995) Uniparental inheritance of mitochondrial and chloroplast genes: mechanisms and evolution. Proc Natl Acad Sci U S A 92: 11331-11338. doi: 10.1073/pnas.92.25.11331
    [359] Luo SM, Ge ZJ, Wang ZW, et al. (2013) Unique insights into maternal mitochondrial inheritance in mice. Proc Natl Acad Sci U S A 110: 13038-13043. doi: 10.1073/pnas.1303231110
    [360] Yu Z, O'Farrell PH, Yakubovich N, et al. (2017) The mitochondrial DNA polymerase promotes elimination of paternal mitochondrial genomes. Curr Biol 27: 1033-1039. doi: 10.1016/j.cub.2017.02.014
    [361] Boumezbeur F, Mason GF, de Graaf RA, et al. (2010) Altered brain mitochondrial metabolism in healthy aging as assessed by in vivo magnetic resonance spectroscopy. J Cereb Blood Flow Metab 30: 211-221. doi: 10.1038/jcbfm.2009.197
    [362] Rhein V, Song X, Wiesner A, et al. (2009) Amyloid-beta and tau synergistically impair the oxidative phosphorylation system in triple transgenic Alzheimer's disease mice. Proc Natl Acad Sci U S A 106: 20057-20062. doi: 10.1073/pnas.0905529106
    [363] Larosa V, Remacle C (2018) Insights into the respiratory chain and oxidative stress. Biosci Rep 38. doi: 10.1042/BSR20171492
    [364] Carden T, Singh B, Mooga V, et al. (2017) Epigenetic modification of miR-663 controls mitochondria-to-nucleus retrograde signaling and tumor progression. J Biol Chem 292: 20694-20706. doi: 10.1074/jbc.M117.797001
    [365] Spuch C, Ortolano S, Navarro C (2012) New insights in the amyloid-Beta interaction with mitochondria. J Aging Res 2012: 324968. doi: 10.1155/2012/324968
    [366] Yan SD, Stern DM (2005) Mitochondrial dysfunction and Alzheimer's disease: role of amyloid-beta peptide alcohol dehydrogenase (ABAD). Int J Exp Pathol 86: 161-171. doi: 10.1111/j.0959-9673.2005.00427.x
    [367] Cho DH, Nakamura T, Fang J, et al. (2009) S-nitrosylation of Drp1 mediates beta-amyloid-related mitochondrial fission and neuronal injury. Science 324: 102-105. doi: 10.1126/science.1171091
    [368] Area-Gomez E, Schon EA (2017) On the pathogenesis of Alzheimer's disease: the MAM Hypothesis. FASEB J 31: 864-867. doi: 10.1096/fj.201601309
    [369] Snowden SG, Ebshiana AA, Hye A, et al. (2017) Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: a nontargeted metabolomic study. PLoS Med 14: e1002266. doi: 10.1371/journal.pmed.1002266
    [370] Kao YC, Ho PC, Tu YK, et al. (2020) Lipids and Alzheimer's disease. Int J Mol Sci 21: 1505. doi: 10.3390/ijms21041505
    [371] Czubowicz K, Jesko H, Wencel P, et al. (2019) The role of ceramide and Sphingosine-1-Phosphate in Alzheimer's disease and other neurodegenerative disorders. Mol Neurobiol 56: 5436-5455. doi: 10.1007/s12035-018-1448-3
    [372] Popugaeva E, Pchitskaya E, Bezprozvanny I (2018) Dysregulation of intracellular calcium signaling in Alzheimer's disease. Antioxid Redox Signal 29: 1176-1188. doi: 10.1089/ars.2018.7506
    [373] Ruiz A, Matute C, Alberdi E (2009) Endoplasmic reticulum Ca(2+) release through ryanodine and IP(3) receptors contributes to neuronal excitotoxicity. Cell Calcium 46: 273-281. doi: 10.1016/j.ceca.2009.08.005
    [374] Bezprozvanny I (2009) Calcium signaling and neurodegenerative diseases. Trends Mol Med 15: 89-100. doi: 10.1016/j.molmed.2009.01.001
    [375] Tong BC, Wu AJ, Li M, et al. (2018) Calcium signaling in Alzheimer's disease & therapies. Biochim Biophys Acta Mol Cell Res 1865: 1745-1760. doi: 10.1016/j.bbamcr.2018.07.018
    [376] Etcheberrigaray R, Hirashima N, Nee L, et al. (1998) Calcium responses in fibroblasts from asymptomatic members of Alzheimer's disease families. Neurobiol Dis 5: 37-45. doi: 10.1006/nbdi.1998.0176
    [377] Berridge MJ (2009) Inositol trisphosphate and calcium signalling mechanisms. Biochim Biophys Acta 1793: 933-940. doi: 10.1016/j.bbamcr.2008.10.005
    [378] Huang WJ, Zhang X, Chen WW (2016) Role of oxidative stress in Alzheimer's disease. Biomed Rep 4: 519-522. doi: 10.3892/br.2016.630
    [379] Mauvezin C, Neufeld TP (2015) Bafilomycin A1 disrupts autophagic flux by inhibiting both V-ATPase-dependent acidification and Ca-P60A/SERCA-dependent autophagosome-lysosome fusion. Autophagy 11: 1437-1438. doi: 10.1080/15548627.2015.1066957
    [380] Medina DL, Di Paola S, Peluso I, et al. (2015) Lysosomal calcium signalling regulates autophagy through calcineurin and TFEB. Nat Cell Biol 17: 288-299. doi: 10.1038/ncb3114
    [381] Mondal AC (2019) Role of GPCR signaling and calcium dysregulation in Alzheimer's disease. Mol Cell Neurosci 101: 103414. doi: 10.1016/j.mcn.2019.103414
    [382] Fernandez-Fernandez D, Rosenbrock H, Kroker KS (2015) Inhibition of PDE2A, but not PDE9A, modulates presynaptic short-term plasticity measured by paired-pulse facilitation in the CA1 region of the hippocampus. Synapse 69: 484-496. doi: 10.1002/syn.21840
    [383] Zhang G, Stackman RW (2015) The role of serotonin 5-HT2A receptors in memory and cognition. Front Pharmacol 6: 225.
    [384] Raote I, Bhattacharya A, Panicker MM (2007) Serotonin 2A (5-HT2A) receptor function: ligand-dependent mechanisms and pathways. Serotonin Receptors in Neurobiology Boca Raton (FL): (Frontiers in Neuroscience), 105-132.
    [385] Chang L, Karin M (2001) Mammalian MAP kinase signalling cascades. Nature 410: 37-40. doi: 10.1038/35065000
    [386] Hullinger R, O'Riordan K, Burger C (2015) Environmental enrichment improves learning and memory and long-term potentiation in young adult rats through a mechanism requiring mGluR5 signaling and sustained activation of p70s6k. Neurobiol Learn Mem 125: 126-134. doi: 10.1016/j.nlm.2015.08.006
    [387] Allen KD, Gourov AV, Harte C, et al. (2014) Nucleolar integrity is required for the maintenance of long-term synaptic plasticity. PLoS One 9: e104364. doi: 10.1371/journal.pone.0104364
    [388] Borroto-Escuela DO, Tarakanov AO, Guidolin D, et al. (2011) Moonlighting characteristics of G protein-coupled receptors: focus on receptor heteromers and relevance for neurodegeneration. IUBMB Life 63: 463-472. doi: 10.1002/iub.473
    [389] Spilman P, Podlutskaya N, Hart MJ, et al. (2010) Inhibition of mTOR by rapamycin abolishes cognitive deficits and reduces amyloid-beta levels in a mouse model of Alzheimer's disease. PLoS One 5: e9979. doi: 10.1371/journal.pone.0009979
    [390] Caccamo A, Maldonado MA, Majumder S, et al. (2011) Naturally secreted amyloid-beta increases mammalian target of rapamycin (mTOR) activity via a PRAS40-mediated mechanism. J Biol Chem 286: 8924-8932. doi: 10.1074/jbc.M110.180638
    [391] Lipton JO, Sahin M (2014) The neurology of mTOR. Neuron 84: 275-291. doi: 10.1016/j.neuron.2014.09.034
    [392] Oddo S (2012) The role of mTOR signaling in Alzheimer disease. Front Biosci 4: 941-952. doi: 10.2741/s310
    [393] Caccamo A, De Pinto V, Messina A, et al. (2014) Genetic reduction of mammalian target of rapamycin ameliorates Alzheimer's disease-like cognitive and pathological deficits by restoring hippocampal gene expression signature. J Neurosci 34: 7988-7998. doi: 10.1523/JNEUROSCI.0777-14.2014
    [394] Hodges SL, Reynolds CD, Smith GD, et al. (2018) Molecular interplay between hyperactive mammalian target of rapamycin signaling and Alzheimer's disease neuropathology in the NS-Pten knockout mouse model. Neuroreport 29: 1109-1113. doi: 10.1097/WNR.0000000000001081
    [395] Gabbouj S, Ryhanen S, Marttinen M, et al. (2019) Altered insulin signaling in Alzheimer's disease brain—special emphasis on PI3K-Akt Pathway. Front Neurosci 13: 629. doi: 10.3389/fnins.2019.00629
    [396] Magri L, Cambiaghi M, Cominelli M, et al. (2011) Sustained activation of mTOR pathway in embryonic neural stem cells leads to development of tuberous sclerosis complex-associated lesions. Cell Stem Cell 9: 447-462. doi: 10.1016/j.stem.2011.09.008
    [397] Li YH, Werner H, Puschel AW (2008) Rheb and mTOR regulate neuronal polarity through Rap1B. J Biol Chem 283: 33784-33792. doi: 10.1074/jbc.M802431200
    [398] Urbanska M, Gozdz A, Swiech LJ, et al. (2012) Mammalian target of rapamycin complex 1 (mTORC1) and 2 (mTORC2) control the dendritic arbor morphology of hippocampal neurons. J Biol Chem 287: 30240-30256. doi: 10.1074/jbc.M112.374405
    [399] Franco R, Martinez-Pinilla E, Navarro G, et al. (2017) Potential of GPCRs to modulate MAPK and mTOR pathways in Alzheimer's disease. Prog Neurobiol 149: 21-38. doi: 10.1016/j.pneurobio.2017.01.004
    [400] Perluigi M, Di Domenico F, Butterfield DA (2015) mTOR signaling in aging and neurodegeneration: at the crossroad between metabolism dysfunction and impairment of autophagy. Neurobiol Dis 84: 39-49. doi: 10.1016/j.nbd.2015.03.014
    [401] Ma T, Hoeffer CA, Capetillo-Zarate E, et al. (2010) Dysregulation of the mTOR pathway mediates impairment of synaptic plasticity in a mouse model of Alzheimer's disease. PLoS One 5: e12845. doi: 10.1371/journal.pone.0012845
    [402] Kudo W, Lee HP, Smith MA, et al. (2012) Inhibition of Bax protects neuronal cells from oligomeric Abeta neurotoxicity. Cell Death Dis 3: e309. doi: 10.1038/cddis.2012.43
    [403] Tait SW, Green DR (2010) Mitochondria and cell death: outer membrane permeabilization and beyond. Nat Rev Mol Cell Biol 11: 621-632. doi: 10.1038/nrm2952
    [404] Gross A, McDonnell JM, Korsmeyer SJ (1999) BCL-2 family members and the mitochondria in apoptosis. Genes Dev 13: 1899-1911. doi: 10.1101/gad.13.15.1899
    [405] Putcha GV, Deshmukh M, Johnson EM (1999) BAX translocation is a critical event in neuronal apoptosis: regulation by neuroprotectants, BCL-2, and caspases. J Neurosci 19: 7476-7485. doi: 10.1523/JNEUROSCI.19-17-07476.1999
    [406] Edlich F, Banerjee S, Suzuki M, et al. (2011) Bcl-x(L) retrotranslocates Bax from the mitochondria into the cytosol. Cell 145: 104-116. doi: 10.1016/j.cell.2011.02.034
    [407] Su Y, Ryder J, Li B, et al. (2004) Lithium, a common drug for bipolar disorder treatment, regulates amyloid-beta precursor protein processing. Biochemistry 43: 6899-6908. doi: 10.1021/bi035627j
    [408] Xu X, Zhang A, Zhu Y, et al. (2018) MFG-E8 reverses microglial-induced neurotoxic astrocyte (A1) via NF-kappaB and PI3K-Akt pathways. J Cell Physiol 234: 904-914. doi: 10.1002/jcp.26918
    [409] Jimenez S, Torres M, Vizuete M, et al. (2011) Age-dependent accumulation of soluble amyloid beta (Abeta) oligomers reverses the neuroprotective effect of soluble amyloid precursor protein-alpha (sAPP(alpha)) by modulating phosphatidylinositol 3-kinase (PI3K)/Akt-GSK-3beta pathway in Alzheimer mouse model. J Biol Chem 286: 18414-18425. doi: 10.1074/jbc.M110.209718
    [410] Cho SJ, Yun SM, Jo C, et al. (2019) Altered expression of Notch1 in Alzheimer's disease. PLoS One 14: e0224941. doi: 10.1371/journal.pone.0224941
    [411] Taylor KL, Henderson AM, Hughes CC (2002) Notch activation during endothelial cell network formation in vitro targets the basic HLH transcription factor HESR-1 and downregulates VEGFR-2/KDR expression. Microvasc Res 64: 372-383. doi: 10.1006/mvre.2002.2443
    [412] Yoon KJ, Lee HR, Jo YS, et al. (2012) Mind bomb-1 is an essential modulator of long-term memory and synaptic plasticity via the Notch signaling pathway. Mol Brain 5: 40. doi: 10.1186/1756-6606-5-40
    [413] Basak O, Giachino C, Fiorini E, et al. (2012) Neurogenic subventricular zone stem/progenitor cells are Notch1-dependent in their active but not quiescent state. J Neurosci 32: 5654-5666. doi: 10.1523/JNEUROSCI.0455-12.2012
    [414] Brai E, Alina Raio N, Alberi L (2016) Notch1 hallmarks fibrillary depositions in sporadic Alzheimer's disease. Acta Neuropathol Commun 4: 64. doi: 10.1186/s40478-016-0327-2
    [415] Caricasole A, Copani A, Caraci F, et al. (2004) Induction of Dickkopf-1, a negative modulator of the Wnt pathway, is associated with neuronal degeneration in Alzheimer's brain. J Neurosci 24: 6021-6027. doi: 10.1523/JNEUROSCI.1381-04.2004
    [416] Rosi MC, Luccarini I, Grossi C, et al. (2010) Increased Dickkopf-1 expression in transgenic mouse models of neurodegenerative disease. J Neurochem 112: 1539-1551. doi: 10.1111/j.1471-4159.2009.06566.x
    [417] Cerpa W, Godoy JA, Alfaro I, et al. (2008) Wnt-7a modulates the synaptic vesicle cycle and synaptic transmission in hippocampal neurons. J Biol Chem 283: 5918-5927. doi: 10.1074/jbc.M705943200
    [418] Farias GG, Valles AS, Colombres M, et al. (2007) Wnt-7a induces presynaptic colocalization of alpha 7-nicotinic acetylcholine receptors and adenomatous polyposis coli in hippocampal neurons. J Neurosci 27: 5313-5325. doi: 10.1523/JNEUROSCI.3934-06.2007
    [419] Tapia-Rojas C, Inestrosa NC (2018) Loss of canonical Wnt signaling is involved in the pathogenesis of Alzheimer's disease. Neural Regen Res 13: 1705-1710. doi: 10.4103/1673-5374.238606
    [420] Elliott C, Rojo AI, Ribe E, et al. (2018) A role for APP in Wnt signalling links synapse loss with beta-amyloid production. Transl Psychiatry 8: 179. doi: 10.1038/s41398-018-0231-6
    [421] Aso E, Ferrer I (2014) Cannabinoids for treatment of Alzheimer's disease: moving toward the clinic. Front Pharmacol 5: 37.
    [422] Solas M, Francis PT, Franco R, et al. (2013) CB2 receptor and amyloid pathology in frontal cortex of Alzheimer's disease patients. Neurobiol Aging 34: 805-808. doi: 10.1016/j.neurobiolaging.2012.06.005
    [423] Barnado A, Crofford LJ, Oates JC (2016) At the Bedside: Neutrophil extracellular traps (NETs) as targets for biomarkers and therapies in autoimmune diseases. J Leukoc Biol 99: 265-278. doi: 10.1189/jlb.5BT0615-234R
    [424] Hamilton A, Esseltine JL, DeVries RA, et al. (2014) Metabotropic glutamate receptor 5 knockout reduces cognitive impairment and pathogenesis in a mouse model of Alzheimer's disease. Mol Brain 7: 40. doi: 10.1186/1756-6606-7-40
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