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).

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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

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