AD is a progressive degenerative disease of the central nervous system, characterized by cognitive impairment, reduced functional capacity for daily living, and behavioral changes. It can be divided into two types: early-onset AD (EOAD, age of onset ≤65 years) and late-onset AD (LOAD, age of onset >65 years); the proportion of LOAD in patients with AD is approximately 95%, with LOAD having a stronger genetic predisposition than EOAD (Godyń et al., 2016; Ransohoff and El Khoury, 2015; Zou et al., 2014). According to the latest data from the World Health Organization (WHO), the population with AD is currently over 50 million worldwide and is expected to rise to 115 million by 2050 (GBD and Dementia Forecasting Collaborators, 2019; Anonymous, 2022). With the increasing aging population, the incidence of AD continues to rise, making AD the fifth leading cause of death worldwide. Given that AD is a chronic complex disorder involving multiple pathophysiological changes, it is likely caused by the joint action of various factors in a multifaceted pathological process, and this intricate nature of AD contributes to the current challenges in its diagnosis and treatment, such as low consultation rates, high rates of misdiagnosis at initial consultations, and low rates of long-term standardized treatment (World Alzheimer Report, 2022), thereby making AD one of the most perplexing diseases. Consequently, examining the pathogenic mechanisms of AD, identifying its risk factors, and conducting timely and effective early screening and diagnosis are of utmost importance.

Traditional epidemiological studies have reported common risk factors for AD. Some metabolic co-morbidities are highly associated with AD, such as cardiovascular disease (Falsetti et al., 2018; Femminella et al., 2018), obesity (Pegueroles et al., 2018; Anstey et al., 2013), and diabetes (Jayaraman and Pike, 2014; Vagelatos and Eslick, 2013). Serological parameters such as C-reactive protein (Cooper et al., 2023), lipids (Xu et al., 2020; Zhu et al., 2022), and vitamin levels Lopes da Silva et al., 2014; Yu et al., 2020; Douaud et al., 2013 have been previously reported as potential biomarkers for AD. In addition, some factors related to lifestyle, family history, education, economic level, and environment correlate with AD (Kivipelto et al., 2018; Wei et al., 2015; Livingston et al., 2017; Livingston et al., 2020). However, most epidemiological studies are insufficient to draw definitive conclusions on causal association due to the potential for reverse causality and confounding bias.

MR analysis is an emerging method to explore the causal association between AD and various factors (Hu et al., 2022; Williams et al., 2020; Cui et al., 2022). MR analysis reduces confounding and reverse causality due to the segregation and independent assortment of genes passed from parents to offspring (Hingorani and Humphries, 2005). In the absence of pleiotropy (that is, genetic variation related to a disease via other pathways) and demographic stratification, MR can present a clear estimate of risk of the disease (Emdin et al., 2017; Davey Smith and Hemani, 2014). MR analysis is increasingly used to determine a causal relationship between potentially modifiable risk factors and outcomes (Davies et al., 2018). These advantages make MR a valuable tool to better elucidate the potential risk or protective factors for AD.

Chen et al., 2023 used MR analysis to reveal the causal relationship between AD and factors including sociodemographic and early life status. However, the study revealed they are restricted by the available variables from the UKB database, which leads to variables such as air pollution, blood glucose measures, and so on were not included. And also, due to the high degree of heterogeneity present in AD subtypes, EOAD and LOAD have different biological and genetic characteristics. Our study uses the MRC IEU OpenGWAS database as the sample source for MR analysis to address the aforementioned limitations. The MRC IEU OpenGWAS database, the largest open GWAS database globally, has compiled 42,335 GWAS summary datasets from sources such as the UK Biobank, FinnGen Biobank, and Biobank Japan. Analyzing large-scale datasets will break new ground for MR research on AD.

MR requires a combination of background knowledge in biology, computer science, software studies, and statistics, which often leads to a dilemma where biologists are not well-versed in computer and statistical fields, while computer science experts struggle to adopt a medical biology mindset. Consequently, the vast majority of available GWAS data have not been effectively utilized through MR. Therefore, the construction of a multi-level data platform specifically for AD based on MR analysis of massive GWAS data is of great strategic significance, and it will facilitate researchers and clinicians worldwide to conveniently and rapidly obtain risk factors that are causally associated with AD.

In summary, in this work we attempt to identify risk or protective factors causally associated with AD from a holistic and systematic perspective, thereby providing new ideas for understanding the AD pathogenesis, achieving early diagnosis, and developing clinical drugs. In the first place, this study uses a hypothesis-free data mining approach to studying the possible etiology of Alzheimer’s disease based on MR, with specific attention to different AD subtypes (EOAD and LOAD). Based on this, we developed an online open integrated platform, MRAD (Mendelian randomization for Alzheimer’s disease, https://gwasmrad.com/mrad/). Moreover, the platform was further enriched by including related targets’ information such as functions and pathways retrieved from the public database Uniprot. The platform is the first multi-dimensional, integrated, shared, and interactive comprehensive platform for AD MR research to date.

Despite decades of research on AD, controversy still remains regarding which factors play an important in its pathogenesis. This study carried out hypothesis-free Mendelian randomization analysis for Alzheimer’s disease, which provided a thorough and comprehensive evaluation with regard to risk or protective factors for AD. This MR study covers most exposure traits that are causally associated with AD outcome traits, including diseases, medical laboratory science items, imaging items, anthropometric items, treatments, molecular traits, gut microbiota, past histories, family histories, and lifestyle traits, and reveals the causal associations between these exposure traits and different AD subtypes.

Based on this, for the convenience of display and operation, a user-friendly prediction platform was built online called MRAD. The MRAD provides a one-stop online analysis service for researchers worldwide, including data retrieval → visualization → personalized analysis → data download. Users can obtain analysis results of different MR models (the main IVW model and six sensitivity analysis models) on 18,097 exposure traits and 16 AD outcome traits, totaling 400,274 records, and are allowed to set personalized parameters to meet different analysis needs. Additionally, the MRAD provides interactive visualization interfaces and download functions for the above results.

MRAD platform provides a unique resource for systematically identifying risk or protective factors of AD, which facilitates early identification, diagnosis, prevention, and treatment, with significant clinical and social value. It could have several strengths: (i) The current methods for identifying AD mainly rely on assessment scales, cerebrospinal fluid (CSF) examinations, and brain PET/MRI. However, assessment scales can be biased by factors such as the anxiety and nervousness of the subjects. CSF examinations require an invasive lumbar puncture, leading to low patient acceptance. PET/MRI scans are expensive and have limited equipment accessibility. These limitations restrict early AD identification. Thus, there is a pressing clinical need for readily available, time- and cost-effective, and accurate detection methods. In this study, the Medical laboratory science and Molecular trait used could be less expensive, faster to detect, easier to operate, and more accessible for widespread adoption. They hold great value for early AD identification and have the potential to become crucial tools for identifying AD in the future. (ii) Imaging acts as a powerful assistive tool for diagnosing Alzheimer’s disease. Traditional imaging examinations mainly depict changes in the brain’s macroscopic structure, while research on microstructural changes in disease-related areas is relatively limited. Studies have demonstrated that microstructural neurodegenerative processes are extensive and pronounced during AD progression. Our study results cover traditional macroscopic neuroimaging results and reveal numerous potential causal relationships between brain microstructure and AD. The combination of macroscopic and microstructural insights will provide more valuable information for clinical diagnosis. (iii) Clarifying the patient’s disease, past history, and family history can aid in preventing AD at an early stage, and prevention of AD could be attained through monitoring anthropometric indicators, improving gut microbiota, and adjusting lifestyle traits. (iv) Currently, the development of new drugs for AD is mainly underscored by Aβ, Tau, and other inhibitors. Since 2000, global pharmaceutical companies have invested hundreds of billions of dollars in the development of new drugs for AD, and these drugs have not yielded successful results. AD drug development has thus been perceived as having the highest failure rate of all drug research, reaching 99.6%. Hence, further research on molecular traits to find new targets and develop new drugs for these targets will provide new pathways for AD treatment.

To briefly demonstrate the performance of MRAD, we explored the IVW model-identified exposure traits that had significantl consistentl effect across all the three main outcome traits of AD.

The association of lipids and lipoproteins, C-reactive protein, family histories, neurological disorders, glutamine, and education level with AD has been widely reported (Hu et al., 2022; Picard et al., 2022; Caramelli et al., 1999; Kuo et al., 1998; Raygani et al., 2006; Takechi et al., 2009; Takechi et al., 2010; Liu et al., 2020; Wu et al., 2019; Wang et al., 2020; Zhou et al., 2020; Nordestgaard et al., 2022; Wingo et al., 2022; Hartmann, 2001; Tynkkynen et al., 2018; Hashemi et al., 2022; Treiber-Held et al., 2003; Zarrouk et al., 2018; Zuin et al., 2021; Tong et al., 2022; Button et al., 2019; Cannon-Albright et al., 2019; Wang et al., 2019; Letenneur et al., 1994; Stern et al., 1994) and is consistent with the results of this study. Moreover, given that the prevalence of LOAD is about 95% in patients with AD and that LOAD has a stronger genetic predisposition than EOAD (Godyń et al., 2016; Ransohoff and El Khoury, 2015; Zou et al., 2014), identifying new risk genes for LOAD is crucial for understanding its potential etiology. Therefore, this study further explored the relationships between these traits and different AD subtypes, leading to the following findings: (i) apolipoprotein B, cholesterol, total, LDL cholesterol, Low-density lipoprotein cholesterol levels, total cholesterol in LDL, total cholesterol in medium LDL, cholesterol to total lipids ratio in large LDL, free cholesterol in large LDL, free cholesterol in LDL, phospholipids in small LDL, parental or family history of AD, parental longevity (mother’s attained age), dementia, vascular dementia, dementia with Lewy bodies, other degenerative diseases of the nervous system, and organic, including symptomatic, mental disorders were all positively associated with LOAD; (ii) apolipoprotein A-I, phospholipids to total lipids ratio in chylomicrons and extremely large VLDL, C-reactive protein, parental longevity (both parents in top 10%), and qualifications: A levels/AS levels or equivalent were all negatively associated with LOAD. These findings suggest that the above traits may have critical impacts on LOAD.

Moreover, some novel potential therapeutic targets of AD were identified as follows: CD33 on Monocytic Myeloid-Derived Suppressor Cells, CD33 on CD33 + HLA DR +CD14 dim, CD33 on CD33 + HLA DR+, CD33 on CD33 + HLA DR + CD14-, CD33 on CD33dim HLA DR -, CD33 on CD33dim HLA DR + CD11b-, and Myeloid cell surface antigen CD33 were positively associated with all the three main outcome traits of AD and the risk of LOAD. It has been reported that CD33 is a 67 kDa glycosylated transmembrane protein, a member of the sialic acid-binding immunoglobulin-like lectins family (SIGLECS family), which is an important receptor for cell growth and survival, as well as a critical receptor for the clathrin-independent endocytosis pathway and the innate and adaptive immune system functions. CD33 is mainly expressed in microglia, which are a type of glial cells in the central nervous system (Villegas-Llerena et al., 2016). Meanwhile, the splicing efficiency of CD33 affects microglia activation (Malik et al., 2013). Several genome-wide association studies have demonstrated that CD33 is a high-risk gene for AD (Hollingworth et al., 2011; Gu et al., 2022). In animal models, knockdown of CD33 significantly reduced amyloid plaque levels and knockout mice did not exhibit other health defects. Sialylated glycoproteins and glycolipids on amyloid plaques bind to CD33, which is most likely the cause of the amyloid ‘immune escape’ (Griciuc et al., 2013). Furthermore, polymorphisms in CD33 can increase the risk of AD by causing neuronal degeneration in the hippocampal and parahippocampal regions of the brain (Wang et al., 2017). Downregulation of the sialic acid-binding domain of CD33 can reduce the risk of developing AD. Therefore, inhibiting CD33 is an effective approach to inhibiting the development of AD, and the sialic acid-binding site on CD33 is a promising pharmacophore (Miles et al., 2019).

Tubulin-specific chaperone A (TBCA) was negatively associated with all the three main outcome traits of AD, as well as EOAD and LOAD (pval is significant at the Bonferroni threshold). TBCA is an important member of the tubulin-specific chaperones (TBCs) family. Nolasco et al., 2005 demonstrated that TBCA can regulate the proportion of α and β-tubulin, enabling them to correctly aggregate into cellular microtubules. Cellular microtubules play important roles in many biological functions, especially in cell movement, cell division, intracellular transport, and cell structure. After silencing TBCA, abnormal microtubule aggregation occurs in mammalian cells, and the cells cannot grow and divide normally, ultimately leading to apoptosis (Gaitanos et al., 2004; Cormier et al., 2008). Moreover, studies have shown that TBCA plays a crucial role in correct β-tubulin folding and α/β-tubulin heterodimer formation (Bellmunt et al., 2009). Protein misfolding can lead to many diseases, such as neurodegenerative diseases. Additionally, higher levels of TBCA are significantly associated with lower AD risk (Hillary et al., 2022). These findings suggest that TBCA may serve as a potential protective factor against AD.

Vacuolar protein sorting-associated protein 29 (VPS29) was negatively associated with all the three main outcome traits of AD, as well as EOAD and LOAD (pval is significant at the Bonferroni threshold). VPS29 is a component of the retromer complex and is highly expressed in the brain, heart, and kidneys, playing an essential role in retromer functions such as synaptic transmission, survival, and movement (Ye et al., 2020). Retromer mainly consists of the VPS26-VPS29-VPS35 trimer and Sorting Nexins (SNXs), and its defects are closely related to various human diseases, including neurodegenerative diseases (Ye et al., 2020). Studies have reported that VPS29 knockdown leads to reduced levels of VPS35 and VPS26 (Fuse et al., 2015; Jimenez-Orgaz et al., 2018), which regulates the localization of retromer within neurons and is essential for the aging nervous system (Ye et al., 2020). The retromer complex has been found to regulate the transport of a variety of substances, including amyloid precursor protein (APP), β-secretase, and phagocytic receptors on microglia. The retromer complex regulates the production of amyloid-β (Aβ) by regulating the transport of relevant carrier proteins, thus playing a role in AD (Zhang et al., 2016). When the retromer complex malfunctions, the pathway for the reverse transport of APP and β-secretase to the trans-Golgi network is disrupted, resulting in an increase in the production of Aβ, which accelerates the pathological process of AD (Seaman, 2021). Meanwhile, the reduction of phagocytic receptors on the surface of microglia weakens the clearance and protective functions of microglia. Recent studies have shown that stabilizing the retromer complex through chaperone proteins can limit the amyloid processing of APP to reduce the production of Aβ (Zhang et al., 2016). These findings suggest that the retromer complex can serve as a new therapeutic target to intervene in the pathological progression of AD.

Guanine nucleotide-binding protein G(k) subunit alpha (GNAI3) was negatively associated with the three main outcome traits of AD and the risk of LOAD. G proteins are a class of signal transduction proteins that can bind with guanosine diphosphate (GDP) and have guanosine triphosphate (GTP) hydrolysis activity; they have more than 40 types, consisting of alpha, beta, and gamma subunits with a total molecular weight of about 100 kDa, with the alpha subunit having the greatest variation and determining the specificity of the G proteins (Heese, 2013). G proteins are intracellular membrane proteins that shuttle between receptors and effector proteins, acting as signal transducers and playing an absolute dominant role in transmembrane cell signaling in the body. All cellular activities are related to signals, and signals are the initiating factors of all cell activities, while physiological responses are only the final results of signals acting on cells. After receiving external stimuli, cells respond by implementing signal transduction through a set of specific mechanisms to ultimately regulate the expression of specific genes, and the whole process is referred to as a cellular signaling pathway. In the pathogenesis of AD, the abnormal content and distribution of multiple signaling molecules, as well as the abnormality of signa transmission pathways, play an important role in AD pathological changes (Fowler et al., 1995), suggesting that gaining insights into signal transduction mechanisms may provide a potential new pathway to explore the pathogenesis of AD.

Proteasome activator complex subunit 1 (PSME1) was negatively associated with all the three main outcome traits of AD and the risk of LOAD. PSME1 is the encoding gene of the 11 s proteasome activator subunit (also known as PA28α) and is located on human chromosome 14q11.2. PA28α is an activator of the proteasome, which mainly increases the protein degradation activity of 20 S proteasome and participates in MHC-I (major histocompatibility complex I) restricted antigen presentation (Adelöf et al., 2018). Studies have shown that PA28α overexpression in the brain of female mice can effectively prevent protein aggregation in the hippocampus, thereby reducing depression-like behavior and enhancing learning and memory ability (Donggui, 2021). Related studies have shown that proteasome function and PA28α expression are inhibited in the brains of diabetic rats (Donggui, 2021). The PA28 expression in the diabetic brain has a certain regulatory effect on protein metabolism caused by oxidative damage (Donggui, 2021). As suggested above, PSME1 may be a new potential therapeutic target for AD and deserves further investigation.

In conclusion, this is one of the most comprehensive studies to provide important insight into the genetic etiology underlying AD based on hypothesis-free Mendelian randomization analysis. In the meantime, we developed the first MR platform for AD, of great clinical and scientific significance that provided a thorough and comprehensive evaluation with regard to risk or protective factors for AD. It also provided physicians and scientists with a very convenient, free as well as user-friendly tool for further scientific investigation. It is important to notice that we recognized CD33, TBCA, VPS29, GNAI3, and PSME1 as novel potential therapeutic targets for AD that deserve further investigation in more detail. However, in this study, since the GWAS datasets for both the exposure and the outcome traits (AD) selected were obtained from the public database (MRC IEU OpenGWAS), where the GWAS datasets for AD are only of European population, and since we use the TwoSampleMR, which requires that the populations for the exposure traits and the outcome traits be the same to satisfy the requirement for a control variable, this study currently has certain limitations in terms of population. We initiated a Mendelian randomization study on AD at clinical hospitals in China and are currently in the sample collection stage to address the limitations. In the future, we will integrate data from more populations and continuously update new advances in AD research to explore its potential differences in different populations.

Publicly available datasets were analyzed in this study. These data can be found here: MRC IEU OpenGWAS at https://gwas.mrcieu.ac.uk/, and UniProt at https://www.uniprot.org/, the database searches were completed on January 30, 2023. The MRAD platform can be freely accessed online at https://gwasmrad.com/mrad/. The main project development repository is at https://github.com/ZhaoTianyu-zty/MRAD (copy archived at Zhao, 2023).

1. 1. Adelöf J
   2. Andersson M
   3. Porritt M
   4. Petersen A
   5. Zetterberg M
   6. Wiseman J
   7. Hernebring M

   (2018) PA28αβ overexpression enhances learning and memory of female mice without inducing 20S proteasome activity

   BMC Neuroscience 19:70.

   https://doi.org/10.1186/s12868-018-0468-2

   • PubMed
   • Google Scholar
2. 1. Anonymous

   (2022) Alzheimer’s disease facts and figures

   Alzheimer’s & Dementia 18:700–789.

   https://doi.org/10.1002/alz.12638

   • Google Scholar
3. 1. Anstey KJ
   2. Cherbuin N
   3. Herath PM

   (2013) Development of a new method for assessing global risk of Alzheimer’s disease for use in population health approaches to prevention

   Prevention Science 14:411–421.

   https://doi.org/10.1007/s11121-012-0313-2

   • PubMed
   • Google Scholar
4. 1. Bellmunt J
   2. Théodore C
   3. Demkov T
   4. Komyakov B
   5. Sengelov L
   6. Daugaard G
   7. Caty A
   8. Carles J
   9. Jagiello-Gruszfeld A
   10. Karyakin O
   11. Delgado F-M
   12. Hurteloup P
   13. Winquist E
   14. Morsli N
   15. Salhi Y
   16. Culine S
   17. von der Maase H

   (2009) Phase III Trial of vinflunine plus best supportive care compared with best supportive care alone after a platinum-containing regimen in patients with advanced transitional cell carcinoma of the urothelial tract

   Journal of Clinical Oncology 27:4454–4461.

   https://doi.org/10.1200/JCO.2008.20.5534

   • Google Scholar
5. 1. Bowden J
   2. Davey Smith G
   3. Burgess S

   (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

   International Journal of Epidemiology 44:512–525.

   https://doi.org/10.1093/ije/dyv080

   • PubMed
   • Google Scholar
6. 1. Bowden J
   2. Davey Smith G
   3. Haycock PC
   4. Burgess S

   (2016) Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator

   Genetic Epidemiology 40:304–314.

   https://doi.org/10.1002/gepi.21965

   • Google Scholar
7. 1. Bowden J
   2. Del Greco M F
   3. Minelli C
   4. Davey Smith G
   5. Sheehan N
   6. Thompson J

   (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization

   Statistics in Medicine 36:1783–1802.

   https://doi.org/10.1002/sim.7221

   • PubMed
   • Google Scholar
8. 1. Burgess S
   2. Thompson SG

   (2017) Interpreting findings from mendelian randomization using the MR-egger method

   European Journal of Epidemiology 32:377–389.

   https://doi.org/10.1007/s10654-017-0255-x

   • PubMed
   • Google Scholar
9. 1. Button EB
   2. Boyce GK
   3. Wilkinson A
   4. Stukas S
   5. Hayat A
   6. Fan J
   7. Wadsworth BJ
   8. Robert J
   9. Martens KM
   10. Wellington CL

   (2019) ApoA-I deficiency increases cortical amyloid deposition, cerebral amyloid angiopathy, cortical and hippocampal astrogliosis, and amyloid-associated astrocyte reactivity in APP/PS1 mice

   Alzheimer’s Research & Therapy 11:44.

   https://doi.org/10.1186/s13195-019-0497-9

   • PubMed
   • Google Scholar
10. 1. Cannon-Albright LA
    2. Foster NL
    3. Schliep K
    4. Farnham JM
    5. Teerlink CC
    6. Kaddas H
    7. Tschanz J
    8. Corcoran C
    9. Kauwe JSK

    (2019) Relative risk for Alzheimer disease based on complete family history

    Neurology 92:e1745–e1753.

    https://doi.org/10.1212/WNL.0000000000007231

    • PubMed
    • Google Scholar
11. 1. Caramelli P
    2. Nitrini R
    3. Maranhão R
    4. Lourenço ACG
    5. Damasceno MC
    6. Vinagre C
    7. Caramelli B

    (1999) Increased apolipoprotein B serum concentration in Alzheimer’s disease

    Acta Neurologica Scandinavica 100:61–63.

    https://doi.org/10.1111/j.1600-0404.1999.tb00724.x

    • Google Scholar
12. 1. Chen T
    2. Zhang H
    3. Liu Y
    4. Liu Y-X
    5. Huang L

    (2021) EVenn: Easy to create repeatable and editable Venn diagrams and Venn networks online

    Journal of Genetics and Genomics 48:863–866.

    https://doi.org/10.1016/j.jgg.2021.07.007

    • Google Scholar
13. 1. Chen SD
    2. Zhang W
    3. Li YZ
    4. Yang L
    5. Huang YY
    6. Deng YT
    7. Wu BS
    8. Suckling J
    9. Rolls ET
    10. Feng JF
    11. Cheng W
    12. Dong Q
    13. Yu JT

    (2023) A phenome-wide association and mendelian randomization study for alzheimer’s disease: a prospective cohort study of 502,493 participants from the UK biobank

    Biological Psychiatry 93:790–801.

    https://doi.org/10.1016/j.biopsych.2022.08.002

    • PubMed
    • Google Scholar
14. 1. Cooper J
    2. Pastorello Y
    3. Slevin M

    (2023) A meta-analysis investigating the relationship between inflammation in autoimmune disease, elevated CRP, and the risk of dementia

    Frontiers in Immunology 14:1087571.

    https://doi.org/10.3389/fimmu.2023.1087571

    • PubMed
    • Google Scholar
15. 1. Cormier A
    2. Marchand M
    3. Ravelli RBG
    4. Knossow M
    5. Gigant B

    (2008) Structural insight into the inhibition of tubulin by vinca domain peptide ligands

    EMBO Reports 9:1101–1106.

    https://doi.org/10.1038/embor.2008.171

    • PubMed
    • Google Scholar
16. 1. Cui G
    2. Li S
    3. Ye H
    4. Yang Y
    5. Huang Q
    6. Chu Y
    7. Shi Z
    8. Zhang X

    (2022) Are neurodegenerative diseases associated with an increased risk of inflammatory bowel disease? A two-sample Mendelian randomization study

    Frontiers in Immunology 13:956005.

    https://doi.org/10.3389/fimmu.2022.956005

    • PubMed
    • Google Scholar
17. 1. Davey Smith G
    2. Hemani G

    (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies

    Human Molecular Genetics 23:R89–R98.

    https://doi.org/10.1093/hmg/ddu328

    • Google Scholar
18. 1. Davies NM
    2. Holmes MV
    3. Davey Smith G

    (2018) Reading mendelian randomisation studies: a guide, glossary, and checklist for clinicians

    BMJ 362:k601.

    https://doi.org/10.1136/bmj.k601

    • PubMed
    • Google Scholar
19. Book

    1. Donggui WU

    (2021)

    Study on the role of PA28 in protein metabolic pathway of oxidative damage in the brain of diabetic rats

    Dali University.

    • Google Scholar
20. 1. Douaud G
    2. Refsum H
    3. de Jager CA
    4. Jacoby R
    5. E. Nichols T
    6. Smith SM
    7. Smith AD

    (2013) Preventing Alzheimer’s disease-related gray matter atrophy by B-vitamin treatment

    PNAS 110:9523–9528.

    https://doi.org/10.1073/pnas.1301816110

    • Google Scholar
21. Preprint

    1. Elsworth B
    2. Lyon M
    3. Alexander T
    4. Liu Y
    5. Matthews P
    6. Hallett J
    7. Bates P
    8. Palmer T
    9. Haberland V
    10. Smith GD
    11. Zheng J
    12. Haycock P
    13. Gaunt TR
    14. Hemani G

    (2020) The MRC IEU OpenGWAS Data Infrastructure

    bioRxiv.

    https://doi.org/10.1101/2020.08.10.244293

    • Google Scholar
22. 1. Emdin CA
    2. Khera AV
    3. Kathiresan S

    (2017) Mendelian randomization

    JAMA 318:1925–1926.

    https://doi.org/10.1001/jama.2017.17219

    • PubMed
    • Google Scholar
23. 1. Falsetti L
    2. Viticchi G
    3. Buratti L
    4. Grigioni F
    5. Capucci A
    6. Silvestrini M

    (2018) Interactions between atrial fibrillation, cardiovascular risk factors, and apoe genotype in promoting cognitive decline in patients with alzheimer’s disease: a prospective cohort study

    Journal of Alzheimer’s Disease 62:713–725.

    https://doi.org/10.3233/JAD-170544

    • Google Scholar
24. 1. Femminella GD
    2. Taylor-Davies G
    3. Scott J
    4. Edison P
    5. the Alzheimer’s Disease Neuroimaging Initiative

    (2018) Do cardiometabolic risk factors influence amyloid, tau, and neuronal function in APOE4 carriers and non-carriers in Alzheimer’s Disease trajectory?

    Journal of Alzheimer’s Disease 64:981–993.

    https://doi.org/10.3233/JAD-180365

    • Google Scholar
25. 1. Fowler CJ
    2. Cowburn RF
    3. Garlind A
    4. Winblad B
    5. O’Neill C

    (1995) Disturbances in signal transduction mechanisms in Alzheimer’s disease

    Molecular and Cellular Biochemistry 149–150:287–292.

    https://doi.org/10.1007/BF01076590

    • Google Scholar
26. 1. Fuse A
    2. Furuya N
    3. Kakuta S
    4. Inose A
    5. Sato M
    6. Koike M
    7. Saiki S
    8. Hattori N

    (2015) VPS29–VPS35 intermediate of retromer is stable and may be involved in the retromer complex assembly process

    FEBS Letters 589:1430–1436.

    https://doi.org/10.1016/j.febslet.2015.04.040

    • Google Scholar
27. 1. Gaitanos TN
    2. Buey RM
    3. Díaz JF
    4. Northcote PT
    5. Teesdale-Spittle P
    6. Andreu JM
    7. Miller JH

    (2004) Peloruside a does not bind to the taxoid site on β-tubulin and retains its activity in multidrug-resistant cell lines

    Cancer Research 64:5063–5067.

    https://doi.org/10.1158/0008-5472.CAN-04-0771

    • Google Scholar
28. 1. GBD
    2. Dementia Forecasting Collaborators

    (2019)

    Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019

    The Lancet. Public Health 7:e105–e125.

    • Google Scholar
29. 1. Godyń J
    2. Jończyk J
    3. Panek D
    4. Malawska B

    (2016) Therapeutic strategies for Alzheimer’s disease in clinical trials

    Pharmacological Reports 68:127–138.

    https://doi.org/10.1016/j.pharep.2015.07.006

    • Google Scholar
30. 1. Griciuc A
    2. Serrano-Pozo A
    3. Parrado AR
    4. Lesinski AN
    5. Asselin CN
    6. Mullin K
    7. Hooli B
    8. Choi SH
    9. Hyman BT
    10. Tanzi RE

    (2013) Alzheimer’s disease risk gene cD33 inhibits microglial uptake of amyloid beta

    Neuron 78:631–643.

    https://doi.org/10.1016/j.neuron.2013.04.014

    • Google Scholar
31. 1. Gu X
    2. Dou M
    3. Cao B
    4. Jiang Z
    5. Chen Y

    (2022) Peripheral level of CD33 and Alzheimer’s disease: a bidirectional two-sample Mendelian randomization study

    Translational Psychiatry 12:427.

    https://doi.org/10.1038/s41398-022-02205-4

    • PubMed
    • Google Scholar
32. 1. Hartmann T

    (2001) Cholesterol, Aβ and Alzheimer’s disease

    Trends in Neurosciences 24:S45–S48.

    https://doi.org/10.1016/S0166-2236(00)01990-1

    • Google Scholar
33. 1. Hashemi M
    2. Banerjee S
    3. Lyubchenko Y

    (2022) Free cholesterol accelerates Aβ self-assembly on membranes at physiological concentration

    International Journal of Molecular Sciences 23:2803.

    https://doi.org/10.3390/ijms23052803

    • PubMed
    • Google Scholar
34. 1. Heese K

    (2013) G Proteins, p60TRP, and neurodegenerative diseases

    Molecular Neurobiology 47:1103–1111.

    https://doi.org/10.1007/s12035-013-8410-1

    • Google Scholar
35. 1. Hemani G
    2. Zheng J
    3. Elsworth B
    4. Wade KH
    5. Haberland V
    6. Baird D
    7. Laurin C
    8. Burgess S
    9. Bowden J
    10. Langdon R
    11. Tan VY
    12. Yarmolinsky J
    13. Shihab HA
    14. Timpson NJ
    15. Evans DM
    16. Relton C
    17. Martin RM
    18. Davey Smith G
    19. Gaunt TR
    20. Haycock PC

    (2018) The MR-Base platform supports systematic causal inference across the human phenome

    eLife 7:e34408.

    https://doi.org/10.7554/eLife.34408

    • PubMed
    • Google Scholar
36. 1. Higgins JPT
    2. Thompson SG
    3. Deeks JJ
    4. Altman DG

    (2003) Measuring inconsistency in meta-analyses

    BMJ 327:557–560.

    https://doi.org/10.1136/bmj.327.7414.557

    • PubMed
    • Google Scholar
37. 1. Hillary RF
    2. Gadd DA
    3. McCartney DL
    4. Shi L
    5. Campbell A
    6. Walker RM
    7. Ritchie CW
    8. Deary IJ
    9. Evans KL
    10. Nevado‐Holgado AJ
    11. Hayward C
    12. Porteous DJ
    13. McIntosh AM
    14. Lovestone S
    15. Robinson MR
    16. Marioni RE

    (2022) Genome‐ and epigenome‐wide studies of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk

    Alzheimer’s & Dementia 14:12280.

    https://doi.org/10.1002/dad2.12280

    • Google Scholar
38. 1. Hingorani A
    2. Humphries S

    (2005) Nature’s randomised trials

    The Lancet 366:1906–1908.

    https://doi.org/10.1016/S0140-6736(05)67767-7

    • Google Scholar
39. 1. Hollingworth P
    2. Harold D
    3. Sims R
    4. Gerrish A
    5. Lambert J-C
    6. Carrasquillo MM
    7. Abraham R
    8. Hamshere ML
    9. Pahwa JS
    10. Moskvina V
    11. Dowzell K
    12. Jones N
    13. Stretton A
    14. Thomas C
    15. Richards A
    16. Ivanov D
    17. Widdowson C
    18. Chapman J
    19. Lovestone S
    20. Powell J
    21. Proitsi P
    22. Lupton MK
    23. Brayne C
    24. Rubinsztein DC
    25. Gill M
    26. Lawlor B
    27. Lynch A
    28. Brown KS
    29. Passmore PA
    30. Craig D
    31. McGuinness B
    32. Todd S
    33. Holmes C
    34. Mann D
    35. Smith AD
    36. Beaumont H
    37. Warden D
    38. Wilcock G
    39. Love S
    40. Kehoe PG
    41. Hooper NM
    42. Vardy ERLC
    43. Hardy J
    44. Mead S
    45. Fox NC
    46. Rossor M
    47. Collinge J
    48. Maier W
    49. Jessen F
    50. Rüther E
    51. Schürmann B
    52. Heun R
    53. Kölsch H
    54. van den Bussche H
    55. Heuser I
    56. Kornhuber J
    57. Wiltfang J
    58. Dichgans M
    59. Frölich L
    60. Hampel H
    61. Gallacher J
    62. Hüll M
    63. Rujescu D
    64. Giegling I
    65. Goate AM
    66. Kauwe JSK
    67. Cruchaga C
    68. Nowotny P
    69. Morris JC
    70. Mayo K
    71. Sleegers K
    72. Bettens K
    73. Engelborghs S
    74. De Deyn PP
    75. Van Broeckhoven C
    76. Livingston G
    77. Bass NJ
    78. Gurling H
    79. McQuillin A
    80. Gwilliam R
    81. Deloukas P
    82. Al-Chalabi A
    83. Shaw CE
    84. Tsolaki M
    85. Singleton AB
    86. Guerreiro R
    87. Mühleisen TW
    88. Nöthen MM
    89. Moebus S
    90. Jöckel K-H
    91. Klopp N
    92. Wichmann H-E
    93. Pankratz VS
    94. Sando SB
    95. Aasly JO
    96. Barcikowska M
    97. Wszolek ZK
    98. Dickson DW
    99. Graff-Radford NR
    100. Petersen RC
    101. van Duijn CM
    102. Breteler MMB
    103. Ikram MA
    104. DeStefano AL
    105. Fitzpatrick AL
    106. Lopez O
    107. Launer LJ
    108. Seshadri S
    109. Berr C
    110. Campion D
    111. Epelbaum J
    112. Dartigues J-F
    113. Tzourio C
    114. Alpérovitch A
    115. Lathrop M
    116. Feulner TM
    117. Friedrich P
    118. Riehle C
    119. Krawczak M
    120. Schreiber S
    121. Mayhaus M
    122. Nicolhaus S
    123. Wagenpfeil S
    124. Steinberg S
    125. Stefansson H
    126. Stefansson K
    127. Snaedal J
    128. Björnsson S
    129. Jonsson PV
    130. Chouraki V
    131. Genier-Boley B
    132. Hiltunen M
    133. Soininen H
    134. Combarros O
    135. Zelenika D
    136. Delepine M
    137. Bullido MJ
    138. Pasquier F
    139. Mateo I
    140. Frank-Garcia A
    141. Porcellini E
    142. Hanon O
    143. Coto E
    144. Alvarez V
    145. Bosco P
    146. Siciliano G
    147. Mancuso M
    148. Panza F
    149. Solfrizzi V
    150. Nacmias B
    151. Sorbi S
    152. Bossù P
    153. Piccardi P
    154. Arosio B
    155. Annoni G
    156. Seripa D
    157. Pilotto A
    158. Scarpini E
    159. Galimberti D
    160. Brice A
    161. Hannequin D
    162. Licastro F
    163. Jones L
    164. Holmans PA
    165. Jonsson T
    166. Riemenschneider M
    167. Morgan K
    168. Younkin SG
    169. Owen MJ
    170. O’Donovan M
    171. Amouyel P
    172. Williams J
    173. Alzheimer’s Disease Neuroimaging Initiative
    174. CHARGE consortium
    175. EADI1 consortium

    (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease

    Nature Genetics 43:429–435.

    https://doi.org/10.1038/ng.803

    • PubMed
    • Google Scholar
40. 1. Hu Y
    2. Zhang Y
    3. Zhang H
    4. Gao S
    5. Wang L
    6. Wang T
    7. Han Z
    8. Liu G
    9. International Genomics of Alzheimer’s Project

    (2022) Mendelian randomization highlights causal association between genetically increased C‐reactive protein levels and reduced Alzheimer’s disease risk

    Alzheimer’s & Dementia 18:2003–2006.

    https://doi.org/10.1002/alz.12687

    • Google Scholar
41. 1. Jayaraman A
    2. Pike CJ

    (2014) Alzheimer’s disease and type 2 diabetes: multiple mechanisms contribute to interactions

    Current Diabetes Reports 14:476.

    https://doi.org/10.1007/s11892-014-0476-2

    • PubMed
    • Google Scholar
42. 1. Jimenez-Orgaz A
    2. Kvainickas A
    3. Nägele H
    4. Denner J
    5. Eimer S
    6. Dengjel J
    7. Steinberg F

    (2018) Control of RAB7 activity and localization through the retromer-TBC1D5 complex enables RAB7-dependent mitophagy

    The EMBO Journal 37:235–254.

    https://doi.org/10.15252/embj.201797128

    • PubMed
    • Google Scholar
43. 1. Kivipelto M
    2. Mangialasche F
    3. Ngandu T

    (2018) Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease

    Nature Reviews Neurology 14:653–666.

    https://doi.org/10.1038/s41582-018-0070-3

    • Google Scholar
44. 1. Kuo Y-M
    2. Emmerling MR
    3. Bisgaier CL
    4. Essenburg AD
    5. Lampert HC
    6. Drumm D
    7. Roher AE

    (1998) Elevated low-density lipoprotein in Alzheimer’s disease correlates with brain Aβ 1–42 levels

    Biochemical and Biophysical Research Communications 252:711–715.

    https://doi.org/10.1006/bbrc.1998.9652

    • Google Scholar
45. 1. Letenneur L
    2. Commenges D
    3. Dartigues JF
    4. Barberger-gateau P

    (1994) Incidence of Dementia and Alzheimer’s disease in elderly community residents of South-Western France

    International Journal of Epidemiology 23:1256–1261.

    https://doi.org/10.1093/ije/23.6.1256

    • Google Scholar
46. 1. Liu Y
    2. Zhong X
    3. Shen J
    4. Jiao L
    5. Tong J
    6. Zhao W
    7. Du K
    8. Gong S
    9. Liu M
    10. Wei M

    (2020) Elevated serum TC and LDL-C levels in Alzheimer’s disease and mild cognitive impairment: A meta-analysis study

    Brain Research 1727:146554.

    https://doi.org/10.1016/j.brainres.2019.146554

    • Google Scholar
47. 1. Livingston G
    2. Sommerlad A
    3. Orgeta V
    4. Costafreda SG
    5. Huntley J
    6. Ames D
    7. Ballard C
    8. Banerjee S
    9. Burns A
    10. Cohen-Mansfield J
    11. Cooper C
    12. Fox N
    13. Gitlin LN
    14. Howard R
    15. Kales HC
    16. Larson EB
    17. Ritchie K
    18. Rockwood K
    19. Sampson EL
    20. Samus Q
    21. Schneider LS
    22. Selbæk G
    23. Teri L
    24. Mukadam N

    (2017) Dementia prevention, intervention, and care

    The Lancet 390:2673–2734.

    https://doi.org/10.1016/S0140-6736(17)31363-6

    • Google Scholar
48. 1. Livingston G
    2. Huntley J
    3. Sommerlad A
    4. Ames D
    5. Ballard C
    6. Banerjee S
    7. Brayne C
    8. Burns A
    9. Cohen-Mansfield J
    10. Cooper C
    11. Costafreda SG
    12. Dias A
    13. Fox N
    14. Gitlin LN
    15. Howard R
    16. Kales HC
    17. Kivimäki M
    18. Larson EB
    19. Ogunniyi A
    20. Orgeta V
    21. Ritchie K
    22. Rockwood K
    23. Sampson EL
    24. Samus Q
    25. Schneider LS
    26. Selbæk G
    27. Teri L
    28. Mukadam N

    (2020) Dementia prevention, intervention, and care: 2020 report of the Lancet Commission

    The Lancet 396:413–446.

    https://doi.org/10.1016/S0140-6736(20)30367-6

    • Google Scholar
49. 1. Lopes da Silva S
    2. Vellas B
    3. Elemans S
    4. Luchsinger J
    5. Kamphuis P
    6. Yaffe K
    7. Sijben J
    8. Groenendijk M
    9. Stijnen T

    (2014) Plasma nutrient status of patients with Alzheimer’s disease: Systematic review and meta‐analysis

    Alzheimer’s & Dementia 10:485–502.

    https://doi.org/10.1016/j.jalz.2013.05.1771

    • Google Scholar
50. 1. Malik M
    2. Simpson JF
    3. Parikh I
    4. Wilfred BR
    5. Fardo DW
    6. Nelson PT
    7. Estus S

    (2013) CD33 Alzheimer’s risk-altering polymorphism, CD33 expression, and exon 2 splicing

    The Journal of Neuroscience 33:13320–13325.

    https://doi.org/10.1523/JNEUROSCI.1224-13.2013

    • Google Scholar
51. 1. Miles LA
    2. Hermans SJ
    3. Crespi GAN
    4. Gooi JH
    5. Doughty L
    6. Nero TL
    7. Markulić J
    8. Ebneth A
    9. Wroblowski B
    10. Oehlrich D
    11. Trabanco AA
    12. Rives M-L
    13. Royaux I
    14. Hancock NC
    15. Parker MW

    (2019) Small molecule binding to Alzheimer risk factor CD33 Promotes Aβ phagocytosis

    iScience 19:110–118.

    https://doi.org/10.1016/j.isci.2019.07.023

    • PubMed
    • Google Scholar
52. 1. Nolasco S
    2. Bellido J
    3. Gonçalves J
    4. Zabala JC
    5. Soares H

    (2005) Tubulin cofactor A gene silencing in mammalian cells induces changes in microtubule cytoskeleton, cell cycle arrest and cell death

    FEBS Letters 579:3515–3524.

    https://doi.org/10.1016/j.febslet.2005.05.022

    • Google Scholar
53. 1. Nordestgaard LT
    2. Christoffersen M
    3. Frikke-Schmidt R

    (2022) Shared risk factors between dementia and atherosclerotic cardiovascular disease

    International Journal of Molecular Sciences 23:9777.

    https://doi.org/10.3390/ijms23179777

    • Google Scholar
54. 1. Pegueroles J
    2. Jiménez A
    3. Vilaplana E
    4. Montal V
    5. Carmona-Iragui M
    6. Pané A
    7. Alcolea D
    8. Videla L
    9. Casajoana A
    10. Clarimón J
    11. Ortega E
    12. Vidal J
    13. Blesa R
    14. Lleó A
    15. Fortea J

    (2018) Obesity and Alzheimer’s disease, does the obesity paradox really exist? A magnetic resonance imaging study

    Oncotarget 9:34691–34698.

    https://doi.org/10.18632/oncotarget.26162

    • PubMed
    • Google Scholar
55. 1. Picard C
    2. Nilsson N
    3. Labonté A
    4. Auld D
    5. Rosa-Neto P
    6. Ashton NJ
    7. Zetterberg H
    8. Blennow K
    9. Breitner JCB
    10. Villeneuve S
    11. Poirier J
    12. PREVENT-AD research group

    (2022) Apolipoprotein B is a novel marker for early tau pathology in Alzheimer’s disease

    Alzheimer’s & Dementia 18:875–887.

    https://doi.org/10.1002/alz.12442

    • PubMed
    • Google Scholar
56. 1. Ransohoff RM
    2. El Khoury J

    (2015) Microglia in health and disease

    Cold Spring Harbor Perspectives in Biology 8:a020560.

    https://doi.org/10.1101/cshperspect.a020560

    • PubMed
    • Google Scholar
57. 1. Raygani AV
    2. Rahimi Z
    3. Kharazi H
    4. Tavilani H
    5. Pourmotabbed T

    (2006) Association between apolipoprotein E polymorphism and serum lipid and apolipoprotein levels with Alzheimer’s disease

    Neuroscience Letters 408:68–72.

    https://doi.org/10.1016/j.neulet.2006.08.048

    • PubMed
    • Google Scholar
58. Software

    1. R Development Core Team

    (2021) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.r-project.org/
59. 1. Seaman MNJ

    (2021) The retromer complex: from genesis to revelations

    Trends in Biochemical Sciences 46:608–620.

    https://doi.org/10.1016/j.tibs.2020.12.009

    • PubMed
    • Google Scholar
60. Software

    1. Shiny

    (2024) EB/OL, version 0.1

    Shiny for R.

    https://shiny.rstudio.com/
61. 1. Slob EA
    2. Groenen PJ
    3. Thurik AR
    4. Rietveld CA

    (2017) A note on the use of egger regression in mendelian randomization studies

    International Journal of Epidemiology 46:2094–2097.

    https://doi.org/10.1093/ije/dyx191

    • Google Scholar
62. 1. Stern Y
    2. Gurland B
    3. Tatemichi TK
    4. Tang MX
    5. Wilder D
    6. Mayeux R

    (1994) Influence of education and occupation on the incidence of Alzheimer’s disease

    JAMA 271:1004–1010.

    https://doi.org/10.1001/jama.272.18.1405c

    • PubMed
    • Google Scholar
63. 1. Takechi R
    2. Galloway S
    3. Pallebage-Gamarallage M
    4. Wellington C
    5. Johnsen R
    6. Mamo JC

    (2009) Three-dimensional colocalization analysis of plasma-derived apolipoprotein B with amyloid plaques in APP/PS1 transgenic mice

    Histochemistry and Cell Biology 131:661–666.

    https://doi.org/10.1007/s00418-009-0567-3

    • Google Scholar
64. 1. Takechi R
    2. Galloway S
    3. Pallebage-Gamarallage MMS
    4. Wellington CL
    5. Johnsen RD
    6. Dhaliwal SS
    7. Mamo JCL

    (2010) Differential effects of dietary fatty acids on the cerebral distribution of plasma-derived apo B lipoproteins with amyloid-β

    British Journal of Nutrition 103:652–662.

    https://doi.org/10.1017/S0007114509992194

    • Google Scholar
65. 1. Tong J-H
    2. Gong S-Q
    3. Zhang Y-S
    4. Dong J-R
    5. Zhong X
    6. Wei M-J
    7. Liu M-Y

    (2022) Association of circulating apolipoprotein ai levels in patients with Alzheimer’s disease: a systematic review and meta-analysis

    Frontiers in Aging Neuroscience 14:899175.

    https://doi.org/10.3389/fnagi.2022.899175

    • PubMed
    • Google Scholar
66. 1. Treiber-Held S
    2. Distl R
    3. Meske V
    4. Albert F
    5. Ohm TG

    (2003) Spatial and temporal distribution of intracellular free cholesterol in brains of a Niemann-Pick type C mouse model showing hyperphosphorylated tau protein. Implications for Alzheimer’s disease

    The Journal of Pathology 200:95–103.

    https://doi.org/10.1002/path.1345

    • PubMed
    • Google Scholar
67. 1. Tynkkynen J
    2. Chouraki V
    3. van der Lee SJ
    4. Hernesniemi J
    5. Yang Q
    6. Li S
    7. Beiser A
    8. Larson MG
    9. Sääksjärvi K
    10. Shipley MJ
    11. Singh‐Manoux A
    12. Gerszten RE
    13. Wang TJ
    14. Havulinna AS
    15. Würtz P
    16. Fischer K
    17. Demirkan A
    18. Ikram MA
    19. Amin N
    20. Lehtimäki T
    21. Kähönen M
    22. Perola M
    23. Metspalu A
    24. Kangas AJ
    25. Soininen P
    26. Ala‐Korpela M
    27. Vasan RS
    28. Kivimäki M
    29. van Duijn CM
    30. Seshadri S
    31. Salomaa V

    (2018) Association of branched‐chain amino acids and other circulating metabolites with risk of incident dementia and Alzheimer’s disease: A prospective study in eight cohorts

    Alzheimer’s & Dementia 14:723–733.

    https://doi.org/10.1016/j.jalz.2018.01.003

    • Google Scholar
68. 1. UniProt Consortium T

    (2018) UniProt: the universal protein knowledgebase

    Nucleic Acids Research 46:2699.

    https://doi.org/10.1093/nar/gky092

    • PubMed
    • Google Scholar
69. 1. Vagelatos NT
    2. Eslick GD

    (2013) Type 2 diabetes as a risk factor for alzheimer’s disease: the confounders, interactions, and neuropathology associated with this relationship

    Epidemiologic Reviews 35:152–160.

    https://doi.org/10.1093/epirev/mxs012

    • Google Scholar
70. 1. Villegas-Llerena C
    2. Phillips A
    3. Garcia-Reitboeck P
    4. Hardy J
    5. Pocock JM

    (2016) Microglial genes regulating neuroinflammation in the progression of Alzheimer’s disease

    Current Opinion in Neurobiology 36:74–81.

    https://doi.org/10.1016/j.conb.2015.10.004

    • Google Scholar
71. 1. Wang W-Y
    2. Liu Y
    3. Wang H-F
    4. Tan L
    5. Sun F-R
    6. Tan M-S
    7. Tan C-C
    8. Jiang T
    9. Tan L
    10. Yu J-T
    11. Alzheimer’s Disease Neuroimaging Initiative

    (2017) Impacts of CD33 genetic variations on the atrophy rates of hippocampus and parahippocampal gyrus in normal aging and mild cognitive impairment

    Molecular Neurobiology 54:1111–1118.

    https://doi.org/10.1007/s12035-016-9718-4

    • Google Scholar
72. 1. Wang Y
    2. Wang Q
    3. Li J
    4. Lu G
    5. Liu Z

    (2019) Glutamine improves oxidative stress through the Wnt3a/β-catenin signaling pathway in alzheimer’s disease in vitro and in vivo

    BioMed Research International 2019:4690280.

    https://doi.org/10.1155/2019/4690280

    • PubMed
    • Google Scholar
73. 1. Wang P
    2. Zhang H
    3. Wang Y
    4. Zhang M
    5. Zhou Y

    (2020) Plasma cholesterol in Alzheimer’s disease and frontotemporal dementia

    Translational Neuroscience 11:116–123.

    https://doi.org/10.1515/tnsci-2020-0098

    • PubMed
    • Google Scholar
74. 1. Wei XU
    2. Lan TAN
    3. Hui Fu W

    (2015)

    Meta-analysis of modifiable risk factors for Alzheimer’s disease

    J. Neurol. Neurosurg. Psychiatry 86:1299–1306.

    • Google Scholar
75. 1. Williams DM
    2. Finan C
    3. Schmidt AF
    4. Burgess S
    5. Hingorani AD

    (2020) Lipid lowering and Alzheimer disease risk: a mendelian randomization study

    Annals of Neurology 87:30–39.

    https://doi.org/10.1002/ana.25642

    • Google Scholar
76. 1. Wingo AP
    2. Vattathil SM
    3. Liu J
    4. Fan W
    5. Cutler DJ
    6. Levey AI
    7. Schneider JA
    8. Bennett DA
    9. Wingo TS

    (2022) LDL cholesterol is associated with higher AD neuropathology burden independent of APOE

    Journal of Neurology, Neurosurgery & Psychiatry 93:930–938.

    https://doi.org/10.1136/jnnp-2021-328164

    • Google Scholar
77. Report

    1. World Alzheimer Report

    (2022)

    Life after diagnosis: navigating treatment, care and support

    Alzheimer’s Disease International.

    • Google Scholar
78. 1. Wu Y
    2. Wang Z
    3. Jia X
    4. Zhang H
    5. Zhang H
    6. Li J
    7. Zhang K

    (2019) Prediction of Alzheimer’s disease with serum lipid levels in Asian individuals: a meta-analysis

    Biomarkers 24:341–351.

    https://doi.org/10.1080/1354750X.2019.1571633

    • Google Scholar
79. 1. Xu Q
    2. Zhang Y
    3. Zhang X
    4. Liu L
    5. Zhou B
    6. Mo R
    7. Li Y
    8. Li H
    9. Li F
    10. Tao Y
    11. Liu Y
    12. Xue C

    (2020) Medium-chain triglycerides improved cognition and lipid metabolomics in mild to moderate Alzheimer’s disease patients with APOE4−/−: A double-blind, randomized, placebo-controlled crossover trial

    Clinical Nutrition 39:2092–2105.

    https://doi.org/10.1016/j.clnu.2019.10.017

    • Google Scholar
80. 1. Xue H
    2. Shen X
    3. Pan W

    (2021) Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects

    American Journal of Human Genetics 108:1251–1269.

    https://doi.org/10.1016/j.ajhg.2021.05.014

    • PubMed
    • Google Scholar
81. 1. Ye H
    2. Ojelade SA
    3. Li-Kroeger D
    4. Zuo Z
    5. Wang L
    6. Li Y
    7. Gu JY
    8. Tepass U
    9. Rodal AA
    10. Bellen HJ
    11. Shulman JM

    (2020) Retromer subunit, VPS29, regulates synaptic transmission and is required for endolysosomal function in the aging brain

    eLife 9:e51977.

    https://doi.org/10.7554/eLife.51977

    • PubMed
    • Google Scholar
82. 1. Yu J-T
    2. Xu W
    3. Tan C-C
    4. Andrieu S
    5. Suckling J
    6. Evangelou E
    7. Pan A
    8. Zhang C
    9. Jia J
    10. Feng L
    11. Kua E-H
    12. Wang Y-J
    13. Wang H-F
    14. Tan M-S
    15. Li J-Q
    16. Hou X-H
    17. Wan Y
    18. Tan L
    19. Mok V
    20. Tan L
    21. Dong Q
    22. Touchon J
    23. Gauthier S
    24. Aisen PS
    25. Vellas B

    (2020) Evidence-based prevention of Alzheimer’s disease: systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials

    Journal of Neurology, Neurosurgery & Psychiatry 91:1201–1209.

    https://doi.org/10.1136/jnnp-2019-321913

    • Google Scholar
83. 1. Zarrouk A
    2. Debbabi M
    3. Bezine M
    4. Karym EM
    5. Badreddine A
    6. Rouaud O
    7. Moreau T
    8. Cherkaoui-Malki M
    9. El Ayeb M
    10. Nasser B
    11. Hammami M
    12. Lizard G

    (2018) Lipid biomarkers in Alzheimer’s Disease

    Current Alzheimer Research 15:303–312.

    https://doi.org/10.2174/1567205014666170505101426

    • Google Scholar
84. 1. Zhang Q-Y
    2. Tan M-S
    3. Yu J-T
    4. Tan L

    (2016) The role of retromer in Alzheimer’s disease

    Molecular Neurobiology 53:4201–4209.

    https://doi.org/10.1007/s12035-015-9366-0

    • Google Scholar
85. Software

    1. Zhao T

    (2023) MRAD, version swh:1:rev:14d31a3c70890fa315fd5f18f6523a2601682e3a

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:00252224a62c9dc41be3dee1e51e950fc4ba752e;origin=https://github.com/ZhaoTianyu-zty/MRAD;visit=swh:1:snp:0178b11881fbb8220f7fba5df991dd68f23b235d;anchor=swh:1:rev:14d31a3c70890fa315fd5f18f6523a2601682e3a
86. 1. Zhou Z
    2. Liang Y
    3. Zhang X
    4. Xu J
    5. Lin J
    6. Zhang R
    7. Kang K
    8. Liu C
    9. Zhao C
    10. Zhao M

    (2020) Low-density lipoprotein cholesterol and alzheimer’s disease: a systematic review and meta-analysis

    Frontiers in Aging Neuroscience 12:5.

    https://doi.org/10.3389/fnagi.2020.00005

    • PubMed
    • Google Scholar
87. 1. Zhu Y
    2. Liu X
    3. Zhu R
    4. Zhao J
    5. Wang Q

    (2022) Lipid levels and the risk of dementia: A dose-response meta-analysis of prospective cohort studies

    Annals of Clinical and Translational Neurology 9:296–311.

    https://doi.org/10.1002/acn3.51516

    • PubMed
    • Google Scholar
88. 1. Zou Z
    2. Liu C
    3. Che C
    4. Huang H

    (2014) Clinical genetics of Alzheimer’s disease

    BioMed Research International 2014:291862.

    https://doi.org/10.1155/2014/291862

    • PubMed
    • Google Scholar
89. 1. Zuin M
    2. Cervellati C
    3. Trentini A
    4. Passaro A
    5. Rosta V
    6. Zimetti F
    7. Zuliani G

    (2021) Association between serum concentrations of apolipoprotein A-I (ApoA-I) and Alzheimer’s Disease: systematic review and meta-analysis

    Diagnostics 11:984.

    https://doi.org/10.3390/diagnostics11060984

    • PubMed
    • Google Scholar

Author details

1. Tianyu Zhao

   Department of Pharmacology, College of Basic Medical Sciences, Jilin University, Changchun, China

   Contribution

   Data curation, Formal analysis, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0000-6835-6482

2. Hui Li

   1. Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
   2. Neurology and Intracranial Hypertension & Cerebral Venous Disease Center National Health Commission of China, Xuanwu Hospital, Capital Medical University, Beijing, China

   Contribution

   Software

   Competing interests

   No competing interests declared

3. Meishuang Zhang

   School of Nursing, Jilin University, Changchun, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

4. Yang Xu

   Department of Pharmacology, College of Basic Medical Sciences, Jilin University, Changchun, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

5. Ming Zhang

   Department of Pharmacology, College of Basic Medical Sciences, Jilin University, Changchun, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

6. Li Chen

   Department of Pharmacology, College of Basic Medical Sciences, Jilin University, Changchun, China

   Contribution

   Conceptualization, Supervision

   For correspondence

   chenl@jlu.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9601-4903

• 282

  views

• 20

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.