Shared whole environmental etiology between Alzheimer's disease and Age-related macular degeneration

doi:10.21203/rs.3.rs-3869235/v1

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Shared whole environmental etiology between Alzheimer's disease and Age-related macular degeneration

https://doi.org/10.21203/rs.3.rs-3869235/v1

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published 05 Aug, 2024

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The comorbidity of Alzheimer's disease (AD) and age-related macular degeneration (AMD) has been established in clinical and genetic studies. There is growing interest in determining the shared environmental factors associated with both conditions. Recent advancements in record linkage techniques enable us to identify the contributing factors to AD and AMD from a wide range of variables. As such, we firstly constructed a knowledge graph based on the literature, which included all statistically significant risk factors for Alzheimer's disease AD and AMD. An Environment-Wide Association Study (EWAS) was conducted to assess the contribution of various environmental factors to the comorbidity of AD and AMD based on the UK biobank. Based on the conditional QQ plots and Bayesian algorithm, several shared environmental factors were identified, which could be categorized into the domains of health condition, biological sample parameters, body index, and attendance availability. Finally, we generated a shared etiology landscape for AD and AMD by combining existing knowledge with our novel findings.

Biological sciences/Genetics/Genome

Biological sciences/Genetics/Epigenetics

Alzheimer's disease

Age-related macular degeneration

Combidity

Environment-wide association studies

Alzheimer's disease (AD) and (AMD) are common neurodevelopmental disorders with an onset typically within the elderly populations which share numerous clinical and pathological features¹. While there are around 50 million people worldwide live with dementia, and this number is projected to increase to 152 million by 2050². AD, an irreversible neuron degenerative disease with high heritability (~ 60–80%), has gradually become the major type of dementia and a considerable public health concern³. AMD is also an irreversible neuron degenerative disease in the macular area and is the leading common cause of irreversible blindness worldwide with an estimated number of patients approaching 196 million in 2020 and 288 million in 2040⁴.

The global burden of both disorders on healthcare services presents considerable direct costs^{5, 6}. Genome-wide association studies (GWAS) have helped in the understanding of the genetic association between different diseases by revealing thousands of single nucleotide polymorphisms (SNPs) related to many diseases^{7, 8, 9, 10}. Recently, we have proven the genetic pleiotropy for AD and AMD and identified APOC1 and APOE as pleiotropic genes, based on GWAS data using a Bayesian algorithm. GWAS have substantially enhanced comprehension of the genetic underpinnings of multifaceted traits and have facilitated the identification of a multitude of loci linked with these traits¹¹. GWAS have introduced a high-throughput methodology for exhaustive evaluation of genetic variants across the entire genome. Nonetheless, this methodological approach is insufficient in its failure to account for the intricately heterogeneous and intricate milieu within which human beings operate throughout their lifespan¹².

There is broad consensus that AD and AMD are both complex diseases caused by genetic and environmental factors. Previous GWAS investigations have demonstrated that SNP heritability of AD is approximately 41%, when taking into account the most considerable heritability estimate, such as APOE4^{13, 14}. And the emergence of precursor lesions of AMD was found to be ascribable to 49% genetic factors and 51% environmental factors¹⁵. Despite the abundance of SNPs identified by GWAS in relation to AD and AMD, there is still much that remains unknown about their underlying heritability and mechanisms. It is highly probable that environmental exposure exerts a significant influence on the genetic and cellular systems involved in the two complex traits¹⁶. Similar to the way that complex traits are influenced by multiple SNPs, the environmental influence on diseases are also driven by numerous factors^{17, 18}.

Many epidemiological reports have been focusing on investigating the environmental variables on AD and AMD individually in which a single or limited number of exposures are analyzed in relation to a phenotype. For example, it is reported that low educational levels and air pollution, despite of lifestyle and comorbidities, are well-established risk factors for dementia and are considered to significantly influence the risk of AD^{19, 20, 21}. On the other hand, the environmental risk factors affecting disease’s phenotypes of AMD included smoking, unfavorable diet, reduced physical activity and metabolic and other risk factors^{22, 23}. In the event that environmental factors demonstrate similar characteristics to genetic factors in terms of being characterized by polyenvironicity and pleiotropy, a comprehensive representation of the entire environome is necessary to capture them. However, the systemic investigation of whole environmental etiology for AD and AMD is still missing, since traditional epidemiology studies cannot integrate all of the environmental factors in a standard bioassay to further detect their relationships with diseases. Therefore, investigating the comorbidity reason for AD and AMD at the whole environmental level is extremely important.

Environment-wide association studies (EWAS) could provide a comprehensive and unbiased approach to examining various environmental exposures in a high-throughput manner, analogous to the method used by GWAS in testing genetic effects, which has been used in type 2 diabetes²⁴, childhood obesity²⁵, age-related diseases²⁶ and so on. However, there is no EWAS study focusing on detecting the overlapped environmental factors for AD or AMD. In this study, the objective was to utilize the EWAS approach to thoroughly and systematically explore potential associations between specific physical environmental factors and AD and AMD on a broad scale in the UK biobank data. Additionally, we aimed to identify common environmental factors that act in a consistent direction for both diseases. We have explored how comorbidity science can draw insights from genomic research to enhance our comprehension and models of environmental influences.

2.1Knowledge graph

We firstly retrieved the published papers concerning the environmental factors for AD and AMD on PubMed. We selected the studies that used cox regression model to detect the association between environmental factors with AD or AMD. Next, we manually examined the abstracts of the obtained papers and extracted the exposure-phenotype relations and the effect size (Table 1). More than one exposure might be studied in a paper. Finally, 72 studies for AD and AMD were chosen as the data source for knowledge graph (Fig. 1).

2.2Data source

The data are publicly accessible via an open access repository located at https://www.ukbiobank.ac.uk/. The UK biobank is a large-scale cohort of approximately 500,000 participants aged 40 to 73 years recruited from March to June 2006 to July 2010 with longitudinal phenotypic data and median 10-year follow-up. Baseline assessments were conducted at 22 assessment centers across the UK²⁷. Use of the data was approved by the UK Biobank Ethics Advisory Committee. All study participants provided informed consent in accordance with the principles outlined in the Declaration of Helsinki. Moreover, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline was followed during the course of the study. The present study was conducted under application number 62443 of the UK Biobank resource.

AD phenotype was collected through hospital in-patient admission records and death registries. The identification of dementia was based on the International Classification of Diseases (ICD) code, including 290.0–290.4, 294.1, 331.0–331.2, and 331.5 in ICD-9 and A81.0, F00, F01, F02, F03, F05.1, F10.6, G30, G31.0, G31.1, and G31.8 in ICD-10, covering Alzheimer's Disease dementia, vascular dementia, and dementias of other causes.

AMD phenotype was defined using a combination of main and secondary ICD-10 (Field IDs 41202, 41204: Code H35.3) and ICD-9 (Field IDs 41203, 41205: Code 3625) diagnoses for macular degeneration, self-reported macular degeneration (Field ID 20002: Code 1528), and macular degeneration from the available general practice data.

2.3EWAS

The Environmental-Wide Association Study (EWAS) is a theoretical approach akin to GWAS, which takes into account a distinctive panel of environmental assays or "loci", quantified across both cases of diseases and controls, resulting in the identification of several environmental factors exhibiting significant associations with diseases while effectively accounting for multiple hypotheses²⁴. EWAS is a well-established methodology that is extensively utilized to investigate environmental factors linked to disease outcomes in large-scale populations and to uncover the underlying epidemiological pathways of disease risk. In this investigation, we undertook an EWAS analysis utilizing UK Biobank data to scrutinize 450 environmental factors. However, not all factors were observed in every cohort. To evaluate the odds ratio (OR) of these factors, we utilized Cox regression models. We established the statistical significance level at a p-value of 0.05.

2.4 Shared environmental analysis

Stratified quantile-quantile (QQ) plots with -log10(P value-exposure) as the x-axis and -log10(P value-outcome) as the y-axis were used to detect the overall trend between AD|AMD (AD to AMD), and AMD|AD (AMD to AD) in whole environment level²⁸. Distinct P-value cut-off thresholds were established for exposure in order to demarcate individual curves. The appraisal of the general trend was predicated upon the degree of leftward deviation from the null. This approach was utilized to examine the statistical significance of the association between the predictor variable and the outcome variable.

The false discovery rate (FDR) is a statistical methodology utilized to correct for multiple hypothesis testing. Within the domain of pleiotropy analysis, FDR serves as a metric to capture the probability of an environmental factor exhibiting non-pleiotropic effects. Detection of pleiotropy between two diseases can be enhanced with Bayesian conditional FDR (cFDR), an extension of FDR²⁹. This statistical technique offers a refined approach which extended the unconditional FDR from an empirical Bayes perspective. Besides, a novel statistical technique termed conjunction-cFDR (ccFDR) was borrowing to identify environmental factors with polyenvironicity effect underlying the co-occurrence of AD and AMD²⁸. In this study, we calculate cFDR from AD to AMD and AMD to AD separately, then select the larger as ccFDR.

2.5 The construction of comorbidity landscape for AD and AMD

The identified shared environmental factors together with previously reported shared factors were mapped together on the interaction network to construct the comorbidity landscape for AD and AMD. Diseases and factors were represented by different size and shapes.

3.1Knowledge graph of environmental factors associated with AMD and AD.

In order to comprehensively elucidate the landscape of environmental factors that are associated with the two age-related diseases using the UK Biobank (UKB) dataset, an exhaustive literature review was conducted to identify all relevant studies investigating the role of environmental determinants in both the original National Health Service (NHS) cohort and its subsequent iterations. This rigorous approach enabled a comprehensive evaluation of the existing knowledge base in this domain and facilitated the identification of potential gaps and avenues for further research. Finally, we excavated 64 associated factors for AD/AMD, from 1,215 previously published papers. The relationships of these factors were presented as knowledge graph in Fig. 1. Among them, smoking, gout, cataract, cancer, Mediterranean diet and fruit and vegetable juices are found to be associated with AD and AMD.

3.2 EWAS for AD and AMD

For the first phase of EWAS, using the UK Biobank dataset we collected whole environmental factors associated with AMD or AD. We included 2943 AD and 7308 AMD patients (86 with both AD and AMD) in the analysis. The AD and AMD “Manhattan plot” analogous to the association results from a GWAS study of discovery and replication EWAS dataset were presented in Fig. 2A and Fig. 2B, respectively. The findings of the EWAS aimed at identifying potential environmental factors associated with AD revealed a total of 112 variables that exhibited statistically significant differences in their levels (p < 0.05, OR ≠ 1) between AD cases and controls, with 52 of these factors displaying a positive association with disease risk while 60 demonstrated a protective effect (Table S1). Similarly, the EWAS results for AMD identified a total of 132 environmental determinants that exhibited significant statistical differences in their levels (p < 0.05, OR ≠ 1) between cases and controls, with 78 factors being associated with an increased risk of AMD while 54 factors displayed a protective effect (Table S2). These findings offer novel insights into the potential modifiable risk factors that may contribute to the development of these debilitating age-related conditions.

3.3 Shared environmental factors between AD and AMD

By utilizing EWAS analytical methods to explore the shared environmental factors of AD and AMD, we identified a total of 29 environmental factors that co-acted in both diseases. Conditional Q-Q plots were plotted to identify the polyenvironicity between AD and AMD (Fig. 3). Several co-environmental factors with OR > 1.0 that were negatively associated with both diseases, including no Started insulin, receive attendance allowance and disability living allowance, no diabetes, logMAR (mean arterial pressure), receive attendance allowance, started insulin within one year diagnosis of diabetes, cystatin C level, poor health condition, glucose and glycated haemoglobin (HbA1c) (Fig. 4, Table S3). In contrast, biological factors with OR < 1.0 such as haematocrit percentage, cholesterol level, LDL level, haemoglobin concentration, 3mm asymmetry index for irregular astigmatism level (normal) and 3mm regularity index for irregular astigmatism level (normal) along with good health condition and diabetes, were positively associated with both AMD and AD (Fig. 3, Table S3). Furthermore, some factors exhibited opposite effects on the two diseases, including NO2 air pollution, weight, whole body fat-free mass, whole body water mass, trunk predicted mass, trunk fat-free mass, arm fat-free mass (left), arm predicted mass (left) and arm fat-free mass (right) (Fig. 3, Table S3).

3.4 The landscape of comorbidity reason for AD and AMD

We mapped the shared environmental factors and genes (APOE and APOC1) on the same network to construct the comorbidity landscape for AD and AMD (Fig. 5). Our EWAS results demonstrate a more comprehensive capture of the environmental factors associated with AD and AMD than prior knowledge graphs. Furthermore, they reveal novel associations between many factors and the diseases of interest.

The present study applied EWAS analytical method to UK biobank data in order to examine shared environmental factors existing in the AD and AMD. First, a multiple of environmental factors associating with emerging AD and AMD are listed from the existing literature. Second, using this environment-wide association approach, we identified 112 and 132 environmental factors that were associated with AD and AMD respectively. These factors cluster in the following domains: health condition, biological samples’ parameters, body index and attendance availability. Moreover, an examination of the co-environmental factors that influence AD and AMD reveals potential association of AD and AMD.

It remains a matter of debate whether AMD and AD are closely associated. A recent meta-analysis reported no significant association between AMD and incident dementia or AD³⁰. On the contrary, another meta-analysis mainly enrolled case-controlled and cross-sectional studies documented the increased risk of AD in patients with AMD³¹. Based on GWAS data from 690,000 participants included in this study from nine multi-omics datasets, our unpublished results showed us that AD and AMD share common genetic factors such as APOC1 and APOE, which play a substantial role in the etiology. However, environmental exposures may also have a major impact on molecular and cellular systems for many diseases.

Referring to the constructed knowledge graph, most previous research has focused on testing the effect of one or two environmental influences that stemmed from one or two sources. Yet EWAS could provide a practical method to test a variety of exposures in human environment in an unbiased manner, similar to GWAS tests for genetic effects³².

After conducting EWAS analyses to explore shared environmental items between AD and AMD, we identified several common risk and protective factors. Factors including "no Started insulin," "Receive Attendance allowance and Disability living allowance," "Diabetes," "logMAR (mean arterial pressure)," "Started insulin within one year diagnosis of diabetes," "Cystatin C level," "Poor health condition," "Glucose," and "Glycated haemoglobin (HbA1c)" were found to be common risk factors for both diseases. On the other hand, "Haematocrit percentage," "Cholesterol level," "LDL level," "Haemoglobin concentration," "3mm asymmetry index for irregular astigmatism level (normal)," "3mm regularity index for irregular astigmatism level (normal)," "Good health condition," and "no Diabetes" were common protective factors for AD.

We found that the risk factors for AD and AMD include "no Started insulin," "Diabetes," "Started insulin within one year diagnosis of diabetes," "Glucose," and "Glycated haemoglobin (HbA1c)." These findings are consistent with previous research. A few of studies have highlighted the substantial similarities between AD and diabetes, including common metabolic alterations and genetic underpinnings. As such, AD has been referred to as "type 3 diabetes" (T3DM)^{33, 34, 35, 36, 37}. Retinopathy is one of common and feared complications of diabetes, several studies have shown a relationship between glucose disturbance (GD) and AMD³⁸. Additionally, a growing body of literature has identified diabetes as a risk factor for AMD ^{39, 40, 41}.

Recent investigations have highlighted the correlation between variations in Cystatin C level and various disorders, such as AD and retinal inflammation, the latter being a prominent characteristic of AMD^{42, 43, 44}. Ten years ago, several studies provided evidence that hypertension is associated with an increased risk of developing AD and AMD^{45, 46, 47}. Epidemiological studies show a positive association between long-term exposure to nitrogen dioxide (NO₂) air pollution and the risk of cognitive decline in older adults^{48, 49}. Similar trends were also observed in AMD^{50, 51}.

In addition, some protective factors have also been validated in the literatures. A recent prospective cohort study of 313,448 participants demonstrated a U-shaped association of hematocrit percentage and hemoglobin (HGB) concentration with dementia risk and concluded that HGB was causally associated with AD⁵². Plenty of epidemiological, genetic, and biochemical evidence suggests that cholesterol is a risk factor for AD ^{53, 54} and the receptor-binding domains of apolipoproteins comprise low-density lipoprotein (LDL) receptors that aid in the transport of amyloid peptides across the blood-brain barrier (BBB), thereby clearing them. Enhancing the expression of this receptor could be a promising therapeutic strategy for AD^{55, 56}.

Our study identified 18 shared environmental factors associated with both AD and AMD, exhibiting a consistent trend across the comorbidity landscape. The majority of these factors have been previously validated. This finding may illustrate that there are complex shared environmental risk factors contributing to the pathogenesis of AD and AMD.

EWAS enable capturing the environome across levels, dimensions, and time in unprecedented depth and detail. This EWAS analysis helped us visualize the shared environmental factors between AMD and AD. However, our study has a few limitations that should be considered. Although assessment technologies have rapidly improved in recent years, capturing even one individual’s environome in its totality remains impossible to date.

In summary, this study utilized record linkage from UK Biobank data and employed EWAS methodology to investigate the role of environmental factors in the comorbidity etiology of AD and AMD. Our findings indicate that AD and AMD exhibit overlapping environmental factors, providing valuable insights for AD patients to receive appropriate clinical recommendations for regular mental and cognitive assessments, aiding in the early detection and treatment of AMD.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of Data and Material

The UK biobank database (https://www.ukbiobank.ac.uk/) provided epidemiological data concerning the patients situation.

Competing interests

The authors declare no conflict of interest.

Funding

The present work was supported by the National Natural Science Foundation of China (32200545), and the GDPH Supporting Fund for Talent Program (KJ012020633, KJ012019530).

Author Contributions:

Siting Ye: Data curation, Formal analysis, Validation, Writing–original draft. ShuoMa: Data curation, Formal analysis, Validation, Writing–original draft. Huang Yu: Writing–review & editing. Dantong Li: Writing–review & editing. Shunming Liu: Visualization. TingSu: Writing–original draft. Min Li: Visualization. Jing Luo: Writing–review & editing. Chi Zhang: Data curation. Danli Shi: Writing–review & editing.Lianting Hu: Writing–review & editing. Lei Zhang: Writing–review & editing. Honghua Yu: Supervision, Writing–review & editing. Mingguang He: Conceptualization, Supervision, Writing–review & editing. Xianwen Shang: Data curation,Methodology. Conceptualization, Supervision, Writing–review & editing. Xueli Zhang: Conceptualization, Visualization.

Acknowledgements

This research was conducted, in part, using the UK Biobank Resource under application number 62443. We are grateful to UK Biobank for making the data available and to all UK Biobank study participants, who generously donated their time to make this resource possible.

Kaarniranta K, Salminen A, Haapasalo A, Soininen H, Hiltunen M. Age-related macular degeneration (AMD): Alzheimer's disease in the eye? J Alzheimers Dis 24, 615–631 (2011).
Patterson C. World alzheimer report 2018. (2018).
Gatz M, et al. Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry 63, 168–174 (2006).
Zou M, et al. Variations and trends in global disease burden of age-related macular degeneration: 1990–2017. Acta Ophthalmol 99, e330-e335 (2021).
Jia L, et al. Dementia in China: epidemiology, clinical management, and research advances. Lancet Neurol 19, 81–92 (2020).
Marques AP, et al. The economics of vision impairment and its leading causes: A systematic review. EClinicalMedicine 46, 101354 (2022).
Yao X, et al. Genome-wide analysis of genetic pleiotropy and causal genes across three age-related ocular disorders. Hum Genet 142, 507–522 (2023).
Bellenguez C, et al. New insights into the genetic etiology of Alzheimer's disease and related dementias. Nat Genet 54, 412–436 (2022).
Winkler TW, et al. Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Med Genomics 13, 120 (2020).
Rämö JT, et al. Overlap of Genetic Loci for Central Serous Chorioretinopathy With Age-Related Macular Degeneration. JAMA Ophthalmol, (2023).
Hindorff LA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106, 9362–9367 (2009).
Hall MA, et al. Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield Personalized Medicine Research Project Biobank. Pac Symp Biocomput, 200–211 (2014).
Karlsson IK, et al. Measuring heritable contributions to Alzheimer's disease: polygenic risk score analysis with twins. Brain Commun 4, fcab308 (2022).
Schramm C, Wallon D, Nicolas G, Charbonnier C. What contribution can genetics make to predict the risk of Alzheimer's disease? Rev Neurol (Paris) 178, 414–421 (2022).
Belmouhand M, et al. Heritability and Risk Factors of Incident Small and Large Drusen in the Copenhagen Twin Cohort Eye Study: A 20-Year Follow-Up. Ophthalmologica 245, 421–430 (2022).
Maher B. Personal genomes: The case of the missing heritability. Nature 456, 18–21 (2008).
Boardman JD, Daw J, Freese J. Defining the environment in gene-environment research: lessons from social epidemiology. Am J Public Health 103 Suppl 1, S64-72 (2013).
Jensen SKG, Berens AE, Nelson CA, 3rd. Effects of poverty on interacting biological systems underlying child development. Lancet Child Adolesc Health 1, 225–239 (2017).
Livingston G, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396, 413–446 (2020).
Shin S, et al. Effects of ambient air pollution on incident Parkinson’s disease in Ontario, 2001 to 2013: a population-based cohort study. International journal of epidemiology 47, 2038–2048 (2018).
Hayes-Larson E, et al. Association of Education With Dementia Incidence Stratified by Ethnicity and Nativity in a Cohort of Older Asian American Individuals. JAMA Netw Open 6, e231661 (2023).
Rim TH, Cheng CY, Kim DW, Kim SS, Wong TY. A nationwide cohort study of cigarette smoking and risk of neovascular age-related macular degeneration in East Asian men. Br J Ophthalmol 101, 1367–1373 (2017).
Mauschitz MM, et al. Physical Activity, Incidence, and Progression of Age-Related Macular Degeneration: A Multicohort Study. Am J Ophthalmol 236, 99–106 (2022).
Patel CJ, Bhattacharya J, Butte AJ. An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS One 5, e10746 (2010).
Vrijheid M, et al. Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach. Environ Health Perspect 128, 67009 (2020).
Ding E, Wang Y, Liu J, Tang S, Shi X. A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases. Hum Genomics 16, 54 (2022).
Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12, e1001779 (2015).
Andreassen OA, et al. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am J Hum Genet 92, 197–209 (2013).
Andreassen OA, et al. Shared common variants in prostate cancer and blood lipids. Int J Epidemiol 43, 1205–1214 (2014).
Jiang L, Li JC, Tang BS, Guo JF, Shen L. Lack of bidirectional association between age-related macular degeneration and Alzheimer's disease: A Mendelian randomization study. Alzheimers Dement 18, 2725–2729 (2022).
Rong SS, et al. Comorbidity of dementia and age-related macular degeneration calls for clinical awareness: a meta-analysis. Br J Ophthalmol 103, 1777–1783 (2019).
von Stumm S, d'Apice K. From Genome-Wide to Environment-Wide: Capturing the Environome. Perspect Psychol Sci 17, 30–40 (2022).
Hölscher C. Diabetes as a risk factor for Alzheimer's disease: insulin signalling impairment in the brain as an alternative model of Alzheimer's disease. Biochem Soc Trans 39, 891–897 (2011).
Park SA. A common pathogenic mechanism linking type-2 diabetes and Alzheimer's disease: evidence from animal models. J Clin Neurol 7, 10–18 (2011).
Błaszczyk JW. Energy Metabolism Decline in the Aging Brain-Pathogenesis of Neurodegenerative Disorders. Metabolites 10, (2020).
Lyall DM, et al. Alzheimer disease genetic risk factor APOE e4 and cognitive abilities in 111,739 UK Biobank participants. Age Ageing 45, 511–517 (2016).
Kopp KO, Glotfelty EJ, Li Y, Greig NH. Glucagon-like peptide-1 (GLP-1) receptor agonists and neuroinflammation: Implications for neurodegenerative disease treatment. Pharmacol Res 186, 106550 (2022).
Budnik A, et al. Elevated Plasma Levels of C1qTNF1 Protein in Patients with Age-Related Macular Degeneration and Glucose Disturbances. J Clin Med 11, (2022).
Nitsch D, Douglas I, Smeeth L, Fletcher A. Age-related macular degeneration and complement activation-related diseases: a population-based case-control study. Ophthalmology 115, 1904–1910 (2008).
Topouzis F, et al. Association of diabetes with age-related macular degeneration in the EUREYE study. Br J Ophthalmol 93, 1037–1041 (2009).
Chen X, et al. Diabetes mellitus and risk of age-related macular degeneration: a systematic review and meta-analysis. PLoS One 9, e108196 (2014).
Duan J, Marcellus KA, Qin X, Wang Y, Paudel HK. Cystatin C promotes tau protein phosphorylation and causes microtubule instability by inhibiting intracellular turnover of GSK3β in neurons. Mol Cell Neurosci 89, 1–8 (2018).
Paraoan L, Hiscott P, Gosden C, Grierson I. Cystatin C in macular and neuronal degenerations: implications for mechanism(s) of age-related macular degeneration. Vision Res 50, 737–742 (2010).
Liu L, Jiang Y, Steinle JJ. Loss of cystatin C regulates permeability and inflammatory pathways in retina. Microvasc Res 148, 104510 (2023).
Glodzik L, et al. Alzheimer's disease markers, hypertension, and gray matter damage in normal elderly. Neurobiol Aging 33, 1215–1227 (2012).
Nelson L, Gard P, Tabet N. Hypertension and inflammation in Alzheimer's disease: close partners in disease development and progression! J Alzheimers Dis 41, 331–343 (2014).
Flammer J, Konieczka K, Bruno RM, Virdis A, Flammer AJ, Taddei S. The eye and the heart. Eur Heart J 34, 1270–1278 (2013).
Kulick ER, et al. Long-term exposure to air pollution and trajectories of cognitive decline among older adults. Neurology 94, e1782-e1792 (2020).
Kulick ER, et al. Long-term exposure to ambient air pollution, APOE-ε4 status, and cognitive decline in a cohort of older adults in northern Manhattan. Environ Int 136, 105440 (2020).
Ju MJ, Kim J, Park SK, Kim DH, Choi YH. Long-term exposure to ambient air pollutants and age-related macular degeneration in middle-aged and older adults. Environ Res 204, 111953 (2022).
Chua SYL, et al. Association of ambient air pollution with age-related macular degeneration and retinal thickness in UK Biobank. Br J Ophthalmol 106, 705–711 (2022).
Qiang YX, et al. Associations of blood cell indices and anemia with risk of incident dementia: A prospective cohort study of 313,448 participants. Alzheimers Dement, (2023).
Castello MA, Soriano S. Rational heterodoxy: cholesterol reformation of the amyloid doctrine. Ageing Res Rev 12, 282–288 (2013).
Dai L, Zou L, Meng L, Qiang G, Yan M, Zhang Z. Cholesterol Metabolism in Neurodegenerative Diseases: Molecular Mechanisms and Therapeutic Targets. Mol Neurobiol 58, 2183–2201 (2021).
Shibata M, et al. Clearance of Alzheimer's amyloid-ss(1–40) peptide from brain by LDL receptor-related protein-1 at the blood-brain barrier. J Clin Invest 106, 1489–1499 (2000).
Zhao N, Liu C-C, Qiao W, Bu G. Apolipoprotein E, receptors, and modulation of Alzheimer’s disease. Biological psychiatry 83, 347–357 (2018).

Table 1 is available in the Supplementary Files section.

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Shared whole environmental etiology between Alzheimer's disease and Age-related macular degeneration

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Abstract

Figures

1. Introduction

2. Methods

2.1Knowledge graph

2.2Data source

2.3EWAS

2.4 Shared environmental analysis

2.5 The construction of comorbidity landscape for AD and AMD

3. Result

3.1Knowledge graph of environmental factors associated with AMD and AD.

3.2 EWAS for AD and AMD

3.3 Shared environmental factors between AD and AMD

3.4 The landscape of comorbidity reason for AD and AMD

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

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