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X. Feng1,*, L. Zhang2,*, Y. Hou1,*, W. Ma1, J. Ma1, X. Chang2, L. Yang2


1. Shanxi University of Traditional Chinese Medicine, Xianyang, China; 2. Xian Hospital of Traditional Chinese Medicine, Xi’an, China; * These authors contributed equally to this work.

Corresponding Author: Lin Yang, Xian Hospital of Traditional Chinese Medicine, Xi’an, China,

J Prev Alz Dis 2024;
Published online January 30, 2024,



Alzheimer’s disease and its comorbidities pose a heavy disease burden globally, and its treatment remains a major challenge. Identifying the protective and risk factors for Alzheimer’s disease, as well as its possible underlying molecular processes, can facilitate the development of interventions that can slow its progression. Observational studies and randomized controlled trials have provided some evidence regarding potential risk factors for Alzheimer’s disease; however, the results of these studies vary. Mendelian randomization is a novel epidemiological methodology primarily used to infer causal relationships between exposures and outcomes. Many Mendelian randomization studies have identified potential causal relationships between Alzheimer’s disease and certain diseases, lifestyle habits, and biological exposures, thus providing valuable data for further mechanistic studies and the development and implementation of clinical prevention strategies. However, the results and data from Mendelian randomization studies must be interpreted based on comprehensive evidence. Moreover, the existing Mendelian randomization studies on the epidemiology of Alzheimer’s disease have some limitations that are worth exploring. Therefore, the aim of this review was to summarize the available evidence on the potential protective and risk factors for Alzheimer’s disease by assessing published Mendelian randomization studies on Alzheimer’s disease, and to provide new perspectives on the etiology of Alzheimer’s disease.

Key words: Alzheimer’s disease, causality, Mendelian randomization, genome-wide association studies, genetic variation.

Abbreviations: IGAP: the International Genomics of Alzheimer’s Project; FinnGen R4: the Fin-nGen consortium release 4; CHARGE: the Cohorts for Heart and Aging Research in Genomic Epidemiology; UKBB(UKB): the UK Biobank; CGPS: Copenhagen General Population Study; CCHS: Copenhagen City Heart Study; HGI: the COVID-19 Host Genetics Initiative; PGC-ALZ: the Psychiatric Genomic Consortium Alzheimer’s Disease Workgroup; ADSP: the Alzheimer’s Disease Sequencing Project; ADGC: the Alzheimer Disease Genetics Consortium; EADI: the European Alzheimer’s Disease Initiative; GERAD/PERADES: the Genetic and Environmental Risk in AD/ Polygenic and Environmental Risk for Alzheimer’s Disease Consortium; Andro: androsterone sulfate; DHEAS: dehydroepiandrosterone sulfate; E2: estradio; Tot T: total testosterone; LDL-c: Low-density lipoprotein cholesterol; TG: triglycerides; TC: total cholesterol; MFH-UKBB: maternal UKBB family history of AD; PFH-UKBB: paternal UKBB family history of AD; CGA: Chlorogenic Acid; ApoB: Apolipoprotein B; Ca: serum Calcium; VD: Vitamin D; UA: Uric Acid; Hcy: plasma Homocysteine; MCI: mitochondrial complex I; AHMS: AHMS gene (antihypertensive drug targets); sTREM1: soluble triggering receptor expressed on myeloid cells 1; GDF-15: growth differentiation factor 15; glycoprotein acetyls: GlycA.


Alzheimer’s disease (AD) is a chronic degenerative lesion of the central nervous system. It is one of the most common irreversible neurodegenerative diseases and the most common cause of dementia (1). Approximately 50 million people worldwide have dementia, and this number is expected to double by 2050 owing to aging of the global population, which increases disease burden and healthcare costs (2). AD is a complex multifactorial disease that occurs as a result of interactions between genetic susceptibility and environmental factors.
Causal inference is a key goal of epidemiological and clinical investigations. Prospective cohort studies and randomized controlled trials have provided high-quality evidence on the causal relationships between different exposures and outcomes (3). Epidemiological studies have identified potential risk and protective factors associated with AD (4). These studies have demonstrated that age (5), sex (6), education (7), high cholesterol level (8), depression (9), and hypertension (10) are strongly associated with AD. However, these identified risk factors differ to varying degrees between observational studies and clinical trials owing to differences in key aspects of the studies, such as sample size, follow-up duration, study methodology, and quantification criteria, resulting in variable qualities of evidence to support the conclusions and unclear causal relationships. Therefore, further in-depth evaluation of the etiology and risk factors for AD and the interactions between genetic and environmental risk factors are crucial for the prevention of AD and early implementation of interventions against the disease.
As the number of genome-wide association studies continues to grow exponentially, the emergence of Mendelian randomization (MR) offers a significant new opportunity for determining the risk factors for AD. Epidemiologists have long sought an ‘ideal’ method to establish clear causal relationships and determine their directions while eliminating potential confounders. MR studies draw upon Mendel’s principles of segregation and independent assortment (11), yielding results akin to those of randomized controlled trials, while effectively avoiding reverse causality and the effects of confounding factors. This allows for a more precise inference of the causal relationship between the exposure and outcome. In addition to its ethically acceptable nature, MR has advantages over traditional randomized controlled trials, including cost efficiency and the ability to track long-term risk factors and assess intervention effects (11, 12). However, the existing Mendelian randomization studies on the epidemiology of Alzheimer’s disease have some limitations that are worth exploring. Therefore, the aim of this review was to assess published MR studies on AD, summarize the potential risk and protective factors for AD, and assess the potential challenges and future prospects of MR research on AD.


Mendelian randomization

MR is an approach used to study the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with specific exposures. MR research involves searching for genetic variants, such as single-nucleotide polymorphisms (SNPs), associated with the exposure and determining the causal effects of the variants on outcomes (13). In MR analyses, the relationship between the exposure and outcome typically relies on one or more SNPs. However, not all SNPs are valid instrumental variables (IVs) that can be used for assessing causal effects. Therefore, MR studies must fulfill three core assumptions (Figure 1) (14).

Figure 1. The fundamental principles and core assumptions of Mendelian randomization

Assumption 1: SNPs are associated with exposures with the genome wide significance; Assumption 2: SNPs are not associated with either known or unknown confounders; Assumption 3: SNPs should influence risk of the outcome through the exposure, not through other pathways.


Common types of MR

The common types of MR are one-sample and two-sample MR. In the pre-genomic association research era, most applied MR studies were conducted using the one-sample method. One-sample MR involves the use of a single dataset of genetic variants, exposures, and outcomes of interest from the same sample. The principle of one-sample MR is to use two-stage least-squares regression to estimate the causal effect of exposures on the outcomes. However, this method is associated with the risks of weak instrumental bias and the winner’s curse (15). Two-sample MR analysis involves the use of two different datasets to estimate the SNPs for the exposure and outcome separately. This type of MR is conducted using larger sample sets such as datasets form genome-wide association studies and UK Biobank, involves more flexible sensitivity analyses, and has stronger test validity that allows for more accurate conclusions (11). In addition, the two-sample MR method has several derivative forms, such as two-step MR (16, 17), bidirectional MR (18), gene-environment interaction MR (19), drug-target MR (20), and network MR (21), which expand the application domains of MR. MR research methods include inverse variance weighting, MR-Egger regression, weighted median, and pattern-based estimation (22–28). Inverse variance weighting is important for causality judgement and has become the most commonly used method for evaluating the final causal effect. MR-Egger and weighted median allow for the presence of confounders or outliers in the data, which reduces bias and allows for the acquisition of more robust results (29, 30). Cochran Q, Rucker’s Q, leave-one-out analysis, and MR-PRESSO are mainly used to detect heterogeneity and pleiotropy in MR studies (23, 24). This helps to ensure the scientific validity and credibility of the results obtained.


Mendelian randomization studies on the etiology of Alzheimer’s disease

Lifestyle and behavioral habits

Coffee consumption

Preclinical studies have confirmed that caffeine has anti-inflammatory, antioxidant, and apoptosis-promoting effects on neuronal cells. Wu et al. and Gelder et al. observed a «j-shaped» relationship between coffee consumption and cognitive impairment in their studies (31, 32). However, the results of the two studies were inconsistent with regard to the quantification of coffee consumption. In their MR study, Zhang et al. found that high coffee intake is associated with an increased risk of AD based on genetic prediction. Notably, further multivariable MR (MVMR) analysis conducted in the study showed that the causal effect between coffee intake and AD attenuated after adjusting for weekly alcohol consumption (33). Similarly, Zheng et al. found that high coffee intake has no significant effect on cerebral white matter hyperintensities, microhemorrhages, total brain volume, white matter volume, and hippocampal volume, but reduces the volume of cerebral gray matter (34). In addition, Susanna et al. found that plasma caffeine levels are negatively correlated with AD (35). On the other hand, Nordestgaard et al. found that self-reported moderate coffee consumption (2-4 cups) is negatively associated with all and non-Alzheimer’s dementia. The authors conducted a one-sample MR analysis that demonstrated that genetically predicted high coffee intake was not significantly associated with the risk of AD (36). The findings of these studies suggest that there is a threshold for the effect of coffee intake on AD, with high coffee consumption increasing the risk of AD and low coffee consumption decreasing the risk of the disease. Notably, the differences in the findings of the abovementioned studies may be related to differences in conceptualization and quantification criteria, coffee brands, coffee consumption, and plasma caffeine levels. (Application and results of MR analyses in AD research are shown in Figure 2)

Figure 2. A brief overview of the Mendelian randomization study in Alzheimer’s disease

The green font indicates a protective causal link between exposure and AD, the red font indicates a hazardous causal connection. The blue font implies heterogeneity in the results that requires further investigation.

Table 1. MR studies of related factors associated with AD

Physical activity

The findings of previous on the relationship between physical activity and AD are inconsistent (37, 38). A previous study demonstrated that physical activity during leisure time is particularly protective against AD (39), whereas the effect of work-related physical activity on AD is less pronounced. On the other hand, another study showed that occupationally demanding physical activity increases the risk of AD (40). However, Wu et al. reported that their MR analysis revealed no significant causal relationship between physical activity and AD (41–43). This may be related to the fact that the exposure factors analyzed in the study are non-strongly correlated IVs (41). Therefore, physical activity during leisure may reduce the risk of AD. The influence of exercise duration and intensity on the results should be considered when selecting IVs in future studies.

Educational attainment

Evidence from observational studies supports the protective effect of educational attainment on AD. Changes in brain structure induced by higher educational attainment compensate for damages caused by age- or disease-related neurodegeneration and raise the threshold for clinical dementia episodes (2, 44–46). Anderson et al. demonstrated a bidirectional causal relationship between intelligence and educational attainment using MR and found that years of education and increased intelligence are negatively associated with AD. However, when both intelligence and education were included in the MVMR model, the effect of education level on the risk of AD became weaker once intelligence was taken into account (47). Another MR study based on European ancestry confirmed the relationship between educational attainment and AD (48). Further analyses conducted in the study showed bidirectional causality between educational attainment and several cortical macro- and microstructural indices, including surface area, volume, and intrinsic curvature. These results indicate that increasing the number of years of education may be an effective public health strategy for reducing the incidence of AD.

Occupational attainment

Occupational attainment is a marker of cognitive reserve. Different sociooccupational experiences lead to individual differences in the anatomical and structural characteristics of the brain, resulting in different states of cognitive functioning (49). Valenzuela et al. demonstrated that occupational attainment is associated with a reduced risk of dementia and slower rates of memory loss over the normal course of aging. In addition, the authors noted that individuals with higher occupational attainment have higher cognitive compensatory capacity and are better able to cope with the onset of AD than those with lower occupational attainment (50–52). In the MR study by Ko et al., the authors used classified job grades developed in the British Standard Occupational Classification system to define occupational attainment and showed that high occupational attainment is associated with a reduced risk of AD. In addition, further MVMR analyses conducted in the study revealed that occupational attainment has an independent effect on AD, even when educational attainment was taken into account (53).


Smoking is a risk factor for several diseases, including AD. Most previous studies, including those conducted using self-reported and genetic data, have shown that smoking increases the risk of AD. However, relevant studies have also demonstrated that low-dose nicotine exerts neuroprotective effects by promoting NAD+ synthesis via SIRT1 binding and NAMPT deacetylation (54, 55). In the MR study by Nordestgaard et al., which was conducted using a Danish population cohort, progressive cumulative smoking increased the risk of all dementias and AD when rs1051730 was chosen as an IV (36). Therefore, widespread smoking cessation campaigns may help reduce the risk of AD.


The coronavirus disease 2019 (COVID-19) may damage the central nervous system, potentially resulting in cognitive decline or other psychosomatic disorders (56–58). In an MR study conducted by Alfonsina et al., three distinct variants related to COVID-19 exposure (all cases, hospitalized cases, and severe cases) were examined (59). The study revealed a significant positive genetic correlation between COVID-19 hospitalization and AD, indicating that susceptibility to and severity of COVID-19 are associated with an increased risk of AD (60). Huang et al. investigated the causal relationship between AD and varicella, herpes zoster, oral herpes, and mononucleosis caused by herpes zoster virus, herpes simplex virus type 1, and Epstein-Barr virus infections. The authors confirmed that mononucleosis and herpes zoster are significantly associated with the risk of AD (61).
Heart Failure (HF) can contribute to the dysfunction of the neurovascular unit, causing abnormalities in neuronal energy metabolism, which lead to impaired clearance of beta-amyloid and hyperphosphorylation of tau proteins (62). The available neuroimaging evidence also supports an association between HF and structural brain abnormalities (63). However, a bidirectional MR study by Duan et al. showed no causal relationship between HF and AD (64). Similarly, the results of the study by Yibeltal et al. did not support a causal relationship between HF and AD (65). In addition, MR studies conducted using diseases such as ulcerative colitis, Crohn’s disease66, major psychiatric disorders (67), obstructive sleep apnea (68), gout (69), and stroke (70) as exposures did not reveal a significant causal relationship between AD and the abovementioned diseases.
Observational studies have consistently shown that lifelong major depression disorder is a significant risk factor for AD (71). A meta-analysis conducted by Mourao et al. provided compelling evidence that the presence of depressive symptoms, combined with mild cognitive impairment, increases the risk of developing AD (72). In addition, an MR study by Harerimana et al. established that depression promotes the development of AD. The authors further suggested that a causal relationship between the depression and AD may be associated with 46 brain transcripts and seven specific brain proteins, such as DDAH2, RAB27B, and B3GLCT) (73).
Preclinical research has indicated that sleep disorders and prolonged chronic arousal can lead to the deposition of Aβ amyloid in the brain (74–77). However, the MR study by Anderson et al. showed no evidence to support a causal relationship between various sleep characteristics and the risk of AD. Interestingly, their findings indicated that self-reported daytime napping may reduce the risk of AD (78). In contrast, the results risk of AD (79). The disparities in these findings indicate that results regarding the causal relationships between sleep disorders and AD should be interpreted with caution.

Gut microbiota

The intricate crosstalk between the gut and central nervous system is facilitated by the gut microbiota. These interactions play pivotal roles in neurodegenerative diseases (80–82). In a study by Ning et al., IVs were selected using two distinct thresholds; the smaller threshold was used to enhance the comprehensiveness and sensitivity of IV acquisition. Exposure-associated SNPs with genome-wide significance (p < 5 × 10-8) were chosen for secondary analyses of IVs to increase specificity. The results revealed that certain bacteria in the gut microbiota, including Actinobacteria, Lactobacillaceae, Faecalibacterium, Ruminiclostridium, and Lachnoclostridium, are associated with an increased risk of AD. Interestingly, the gut microbial metabolite glutamine exhibits a protective effect against AD (83). In addition, a bidirectional MR study conducted by Zhuang et al. indicated that the gut microbial metabolite trimethylamine N-oxide and its precursors have no significant causal effect on AD (84). These findings suggest that targeted interventions such as appropriate gut microbiota transplantation could potentially mitigate the risk of AD.

Circulating metabolic markers

Sex hormones

The significant sex-specific differences in the incidence of AD may be attributed to variations in sex hormone levels between both sexes. Estradiol, an estrogenic steroid hormone, is known to exert neuroprotective effects against AD. In addition, it promotes the secretion of growth factors and reduces abnormal activation of astrocytes and microglia, among other beneficial effects (85). Testosterone exerts a protective effect against AD in men. Notably, patients with prostate cancer undergoing androgen deprivation therapy have an elevated risk of developing AD (86). Despite these findings, the relationship between steroids and AD remains a subject of ongoing debate and investigation (87, 88).
In a study by Cynthia et al., MR analyses revealed a negative correlation between higher concentrations of testosterone, dehydroepiandrosterone sulfate, and androstenedione sulfate in men and the risk of AD. These findings held true even when factors such as body mass index, triglyceride level, and hematopoietic cell type composition were incorporated into the MVMR model. This objective evidence provides a basis for understanding the differences in the prevalence of AD between men and women (89).

Lipid metabolism

Lipid metabolism plays a pivotal role in the development of AD. Altered lipid metabolism has been implicated in the pathophysiological processes of AD. However, the precise molecular mechanisms underlying the relationship between cholesterol and AD pathology remains debatable (90–92). Proitsi et al. assessed the associations between four blood lipid phenotypes (HDL-c, LDL-c, TG, and TC) and late-onset AD using genotype risk score. Their findings revealed that serum total cholesterol and triglyceride levels are not associated with an increased risk of late-onset AD (93). However, it is important to note that there is a potential concern regarding multiple comparisons when conducting MR analyses using multiple gene sets. This may increase the risk of obtaining spurious results if appropriate corrections are not made.
The MR study conducted by Yasutake et al. did not show any evidence supporting the hypothesis that polyunsaturated fatty acids reduce the risk of AD (94). Conversely, the study by Huang et al. revealed a positive association between circulating glycoprotein acetyl, APOE, low-density lipoprotein-cholesterol, and triglyceride levels and AD. Interestingly, the results also indicated that glutamine exerts protective effects against AD (95).

Non-lipid metabolism

Altered calcium homeostasis is one of the pathogenic mechanisms implicated in various neurodegenerative diseases, including cognitive impairment and AD. Observational findings regarding the relationship between serum calcium levels and the risk of AD are not consistent, possibly because of known confounders and reverse causality (96, 97). The MR studies conducted by He et al. and Shi et al. revealed trends suggesting a correlation between increase in serum calcium levels and reduction in the risk of AD (98, 99). Similarly, the MR studies by Wang et al. and Larsson et al. demonstrated that elevated circulating 25-hydroxyvitamin D level is negatively associated with AD (100, 101). In a meta-analysis by Du et al., hyperuricemia was associated with a reduced risk of AD (102). Long-term follow-up of patients with AD has revealed that individuals with high uric acid levels tend to exhibit better cognitive performance (103). However, Verhaaren et al. observed a strong correlation between hyperuricemia and cerebral atrophy in their MR study (104). In contrast, an MR analysis conducted by Ya-Nan et al. identified high serum uric acid level as a risk factor for AD (69). Given that hyperuricemia is a risk factor for several diseases, it is advisable to maintain uric acid levels within the normal range. Regarding the correlation between homocysteine and AD, the MR study by Hu et al. confirmed that elevated homocysteine level increases the risk of AD (105). Interventions targeting modifiable risk factors have emerged as effective therapeutic approaches for AD.


Telomeres are located at the ends of eukaryotic chromosomes and play a central role in controlling cellular senescence and biological aging. Telomere dysfunction, due to a short telomere length or altered telomere structure, can lead to replicative cellular senescence and chromosomal instability (106). In an MR analysis, Kai et al. demonstrated that shorter telomere length is associated with a higher risk of AD (107). Another MR study related to telomeres showed similar results; however, sensitivity analyses suggested the presence of horizontal pleiotropy, hinting at a complex interaction between these factors (108). The MR analysis conducted by Wu et al. provided evidence of a strong correlation between elevated levels of growth differentiation factor-15 and an increased risk of AD (109).
Shi et al. conducted an MR study on AD and plasma Trem1 and the results suggested that elevated plasma Trem1 level is associated with an increased risk of AD (110). The core pathological mechanisms of AD involve Aβ amyloid deposition and isophosphorylation of the tau protein (111, 112). Surprisingly, an MR study by Yeung et al. did not find causal relationships between AD and plasma amyloid, cerebrospinal fluid total tau, or p-Tau181 levels (113). This discrepancy may be attributed to the population of the abovementioned study, sample size of the genetic dataset, and lack of neuroimaging analysis.

Drug-targeted therapy

Some studies have demonstrated that midlife hypertension is associated with an increased risk of AD, whereas late-life hypertension may exert a protective effect against AD (114, 115). In their MR study, Sproviero et al. identified that the protective effect of calcium channel blockers against AD may be linked to the AHMS gene (116). Pan et al. employed the MR approach to evaluate the causal relationship between six glycemic traits and AD, presenting evidence that elevated fasting glucose level and pancreatic β-cell dysfunction are correlated with an increased risk of AD (117). Zheng et al. conducted an MR study on metformin targets, and their results indicated that mitochondrial complex 1, one of the targets of metformin, and its related gene, NDUFA2, have a potent effect in reducing the risk of AD (118). Bowen et al. conducted an MR analysis and found that sulfonylurea targets may reduce the risk of AD (119). Dylan et al. analyzed lipid-lowering drug targets (HMGCR, PCSK9, NPC1L1, and APOB) using MR and discovered that PCSK9 inhibitors increase the risk of AD (120). It should be noted that it is essential to interpret these results with caution as they represent a significant departure from those of previous clinical studies.



MR is an effective approach to exclude confounding factors and address causal relationships between exposure factors and outcomes. However, MR studies have some drawbacks, and there are difficulties in interpreting negative results (e.g., bidirectional relationship of Aβ and Tau proteins with AD). Nevertheless, MR has great potential in studying complex diseases such as AD and may lead to individualized prediction and personalized medicine for most AD patients.

In conclusion, MR improves the study of AD and helps researchers to prevent or slow down the progression of AD. Similarly, the use of MR studies can better provide strong evidence showing causal inferences between environmental factors and AD, which may facilitate future drug development and clinical trials to improve the existing burden of AD.



Ethics statement: Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions: XF wrote the main manuscript text and prepared the figures. LZ and YH prepared the table. WM, XC and ZM revised the manuscript. LY provided the concepts and guided this study. All authors reviewed the manuscript. All authors contributed to the article and approved the submitted version.

Funding: This work was supported by a grant from the Shaanxi Provincial Department of Science and Technology (2023-YBSF-152). The funder had no role in the research process or in the writing of the paper.

Acknowledgments: None.

Conflict of interest: None.



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